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- Responses to the AI grant flood must prioritize fairness as part of excellence

- ‘Alternative COP’ must drive real, cooperative change in climate action

- European funder must increase capacity to meet the ambition of scientists

EDITORIAL 05 May 2026

Responses to the AI grant flood must prioritize fairness as part of excellence

Research funding agencies are battling a wave of AI-assisted applications. Countermeasures should not entrench existing power structures.

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The agencies that disburse research funds must have clear rationales for rejecting grant proposals amid a surge in applications.Credit: Matt Cardy/Getty

Last month, the European Research Council (ERC) announced a policy change for some of its grants: it extended the period in which some unsuccessful applicants would not be able to reapply. The ERC, Europe’s premier research funder with more than €16 billion (US$19 billion) to disburse in 2021–27, was responding to a surge in applications, which appear to be driven partly by the use of artificial-intelligence tools.

Last week, however, the funder adjusted that change following an outcry from researchers. Many said that it was unfair, too sudden, too blunt, that it would discourage bold proposals and make researchers less able to respond to new advances. The council was right to rethink — and in the process it showed others how to listen to the concerns of the community. But the problem of how to handle AI in grant funding remains. Solutions must have fairness at their core.

As neuroscientist Geraint Rees and social scientist James Wilsdon wrote in Nature last week, funding bodies from Australia to the United Kingdom have seen a sharp rise in applications since 2022 (G. Rees and J. Wilsdon Nature 652, 1119–1121; 2026). This coincides with the advent of OpenAI’s ChatGPT, the first AI chatbot to be publicly available worldwide. And there is good evidence to suggest that many of these increases are AI-driven. Researchers are using AI tools not just to scan the literature or summarize studies, but for proposing ideas for projects, drafting the text of grant applications and refining applications on the basis of predictions of how grant review panels might react.

Current guidelines from some of the world’s key research funders allow limited use of generative AI in grant applications. In such cases, the guidelines state that it must be acknowledged and declared, be done responsibly and in line with ethical and legal requirements. By contrast, those who peer review grant proposals for funders are prohibited from uploading them to generative AI tools for the purpose of producing reviews. This is partly to maintain confidentiality, and because funders want peer reviewers to exercise their own judgement and not rely on a machine.

In practice, these policies are not always followed. If anything, the research world has ended up with a situation in which the increased ease of writing and reviewing grant applications has not been matched by improvements in ways to verify the degree of AI use.

Researchers are starting to show how such verification could happen. Pangram Labs, a firm in New York City, has developed tools to detect AI-generated text, which are being tested. Separately, researchers at Northwestern University in Evanston, Illinois, used a different method to compare evidence of AI use in grant applications to US federal agencies from two universities. A team led by computational social scientists Dashun Wang and Yifan Qian accessed publicly available grant abstracts from a database of US federally funded grants spanning 2021–25 (Y. Qian et al. Preprint at arXiv https://doi.org/q435; 2026). To spot AI-tool use, they got an AI model to rewrite the human-written abstracts from 2021 (before ChatGPT’s release), and then compared the human and AI versions of the same text. This enabled them to learn the telltale signs that distinguish the two types.

Radical rethinking

Rees and Wilsdon are among those arguing that the arrival of AI needs grant-making systems to be radically rethought. They make the point that as the quality of AI-assisted applications increases over time, funders will find it harder to distinguish between which proposals to fund and which to reject. Because funders will always have finite resources, many proposals will still need to be rejected — but not having a clear rationale for decisions will put funder credibility with researchers at risk.

Various countermeasures are being suggested. These include using lotteries to distribute grants and getting grant applicants to review each other’s work. Such models are seen to be at least as fair as distributing grants through existing methods. There is also a case for more rebalancing of grants so that relatively more funds go to institutions as block grants, which they could spend according to their needs.

At the same time, it is crucial to evaluate the strengths and weaknesses of different responses before deciding where to land. Rees and Wilsdon, for example, call for “shifting the emphasis of evaluation away from written proposals, and towards the principal investigator, their research team and their previous and ongoing research programmes”. That is likely to benefit individuals and institutions with a strong track record, as well as researchers at research-intensive universities. As the authors themselves acknowledge, less-established researchers and laboratory groups, as well as research in emerging fields, would be at a disadvantage. Although they propose ring-fencing funding for these purposes, there are risks that focusing substantially on principal investigators could reverse the benefits that come from increased diversity in science and the quality of questions that come from diversity. As we have said before, it is unwise to be investing so much authority in principal investigators. In a world of team science, power and responsibility need to be more equally distributed.

AI is transforming science. Funding bodies, along with researchers, publishers and policymakers, are all having to adapt, and quickly. Everyone involved should consider what steps they need to take to ensure that AI is used responsibly and transparently. That, not necessarily radical or disruptive change, is what is needed so that the funding system can continue to support proposals of the highest quality with fairness and equity.

Nature 653, 7 (2026)

doi: https://doi.org/10.1038/d41586-026-01422-x

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EDITORIAL 06 May 2026

‘Alternative COP’ must drive real, cooperative change in climate action

A new climate coalition to steer the transition away from fossil fuels is good news, but it must avoid undermining existing global scientific structures.

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Colombia’s environment minister Irene Vélez Torres (left) with her Netherlands counterpart Stientje van Veldhoven in Santa Marta, Colombia.Credit: Ivan Valencia/AP Photo/Alamy

An important international climate-change initiative was launched last week: more than 50 countries gathered in Santa Marta, Colombia, for the First Conference on Transitioning Away from Fossil Fuels. Co-chaired by the governments of Colombia and the Netherlands, the initiative was born at least partly out of frustration with the official United Nations Conference of the Parties (COP) process, in which all UN member states attempt to work together on their climate-action policies. The Santa Marta meeting’s organizers said that their meeting was a forum to discuss practical steps to move away from fossil fuels. It is intended to complement the COP process, not replace it.

The new initiative’s strong focus on having scientists lay out a path towards a fossil-free future deserves much applause. But it must not inadvertently undermine the scientific structures that inform climate policies all over the world — most notably the Intergovernmental Panel on Climate Change (IPCC).

Many climate scientists are disheartened by the COPs’ slow pace in addressing climate change. Scientific consensus leaves little doubt that, without more urgent action, a less stable, less prosperous future awaits humanity. First held in 1995, the annual COP events have failed to prioritize the creation of a road map for a just and equitable plan to phase out fossil-fuel use. This is despite overwhelming evidence that the world must do this to stay within 2 ºC of warming above pre-industrial levels and avert the most devastating effects of climate change.

As Nature’s news team reported last week, the formation of a panel of researchers representing different subdisciplines of climate science was also announced at the Santa Marta meeting (Nature https://doi.org/q5jd; 2026). Among other things, the Science Panel for the Global Energy Transition (SPGET) will help countries with their plans to phase out fossil fuels and provide benchmarks for progress.

It’s heartening to see climate scientists at the centre of the initiative, providing advice to the governments that convened the meeting. At the same time, it’s important that SPGET’s leadership states explicitly that its science advice will be within the IPCC consensus.

SPGET and the IPCC have different roles. The IPCC is the UN body that assesses the latest climate research — but it does not issue climate-action recommendations to countries. SPGET, by contrast, will use scientific expertise and the best available evidence to advise countries on how to meet their goals to wind down fossil-fuel use.

It is important for SPGET’s leadership to clarify the distinction between the two and to reaffirm support for the IPCC to protect global collaboration in climate science from the swirling geopolitical winds. The United States is in the process of withdrawing from the IPCC, and there is a real risk that other countries could follow. The US withdrawal is undoubtedly a blow — not only because of the loss of funding, but also because, in the future, IPCC reviewers will not have access to climate data that the country currently contributes. If the IPCC edifice starts to crumble, it risks diminishing the quality of global climate science.

There are climate scientists who would like the IPCC’s reports to be more prescriptive. However, that arises from a misunderstanding of the panel’s mandate, and the source of its credibility: its role is to enable scientists representing UN member states to agree on the facts, leaving it to the COP process and individual nations to decide how to act. Scientists from nearly every member country are in the room when the IPCC reports are written. As would be expected, plenty of arguments happen during that process — but, by the end, after the reports are signed off, every country must own the result, meaning they cannot deny its validity.

There is plenty of space for an action-focused science-advisory body such as SPGET. Scientists preparing to take up positions on the panel can have a useful, complementary role to those on the IPCC. They could issue more explicit recommendations on the basis of the available evidence, help to write road maps and agree on metrics of progress.

A climate-action refresh

SPGET could also help the new coalition of countries to broaden its impact. The major oil-producing states — including the United States and Saudi Arabia — were not invited to Santa Marta. Other countries that have advanced plans for decarbonization, such as China, were also not present. SPGET could include researchers from these and other countries that did not attend, and offer to advise their governments, too.

Researchers who spoke to Nature for our news report said that the atmosphere of the Santa Marta conference felt “refreshing”, because they were free to make ambitious, science-based recommendations, rather than the messy compromises driven by politics that are the characteristic outcome of the COPs. Gilberto Jannuzzi, an energy-transition specialist at the State University of Campinas in São Paulo, Brazil, said: “At the end, I think we found a smaller audience, but an audience that considers that we have something relevant to them.” That’s encouraging to hear. The real work now is to broaden that audience and coordinate efforts so as to effect the speedy, widespread change that the world needs.

Nature 653, 8 (2026)

doi: https://doi.org/10.1038/d41586-026-01423-w

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WORLD VIEW 01 May 2026

European funder must increase capacity to meet the ambition of scientists

To avoid sabotaging science, the European Research Council needs more funding and structural change.

By

João Conde

The European Research Council (ERC) — an important funder that is investing €16 billion (US$18.7 billion) in research between 2021 and 2027 — announced changes to its eligibility rules for grant applicants on 16 April. In a letter to researchers, ERC president Maria Leptin explained that the restrictions were necessary to respond to the “sharp increase in applications”.

On 29 April, the ERC partially reverted some of these changes. This is welcome, and the ERC deserves credit for listening to the concerns of the research community. But as a researcher from Portugal who held an ERC grant until last year, I am still deeply concerned by the approach Europe is taking.

The ERC has admitted that it cannot comfortably handle the number of grant applications that it receives. And, in response, it intends to limit how many grants researchers can apply for, and how long unsuccessful candidates have to wait before they can re-apply. These restrictions narrow access to the very scheme that is meant to reward scientific ambition. Instead of expanding its capacity, the ERC is raising barriers that will keep people out.

The ERC’s argument is that the peer-review process must be protected. Of course it must. No scientist wants exhausted panels of reviewers rubber-stamping complex proposals. In the 16 April letter, Leptin states that panels that used to handle between 50 and 150 proposals now handle some 250, and the ERC expects this number to increase.

But if Europe’s prestigious, investigator-driven programme is overwhelmed by demand, it shouldn’t choose to filter researchers out. This trend indicates that the system lacks the capacity to handle the scale of European scientific ambition. The situation calls for more structural investment, not for gatekeeping. The ERC has already taken some steps to improve capacity, streamlining procedures and increasing the number of people on peer-review panels — but there are limits to how large a panel can become and how many proposals can be discussed during review sessions, which are only one week long.

There is also a deeper problem. The ERC was built to back frontier research across all fields, and this type of research is messy. Big ideas often go through at least one failed articulation before they can secure funding. Blocking applicants from submitting proposals for several years because their applications were not strong enough in the previous round might reduce panel workload, but it also discourages scientific iteration. Furthermore, it privileges those who know the ERC system well and punishes people who are still learning how to describe the originality of their work in the specific language preferred by the scheme. That is not how a research ecosystem remains dynamic.

Furthermore, restrictions such as those in the ERC’s first announcement will not hinder institutions and individuals evenly. Well-resourced institutions that provide grant-writing assistance, offer internal mock panels and have researchers with experience applying to ERC schemes will adapt more easily. And researchers at institutions with fewer resources or in nations that already have lower success rates when applying for ERC grants will be hit harder. Early-career investigators with tight timelines will be disadvantaged, as will scientists who are changing fields, have followed non-linear career trajectories or are working on unconventional ideas.

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Nature 653, 9 (2026)

doi: https://doi.org/10.1038/d41586-026-01411-0

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Competing Interests

J.C. is a co-founder and shareholder of TargTex, Targeted Therapeutics in Torres Vedras, Portugal. J.C. is a member of the Global Burden of Disease consortium at the Institute for Health Metrics and Evaluation in Seattle, Washington, and is on the scientific advisory board of Vector Bioscience Cambridge, UK.

News in Focus

- Mitochondria can spawn new ‘organelles’ — hinting at how modern cells evolved

- How your heartbeat could keep cancer at bay

- Entire NSF science advisory board fired by Trump administration

- First detailed ‘smell maps’ reveal how noses track odours

- Did kraken-like octopuses rule Cretaceous seas? Massive jaw fossils offer clues

- How much for a fake authorship? Ad database reveals secrets of scientific fraud

- ‘The job description is changing’: mathematician Terence Tao on the rise of AI

- ‘World models’ are AI’s latest sensation: what are they and what can they do?

- Are attention spans really shrinking? What the science says

- Testosterone therapy is trending. Who really needs it, and why?

NEWS 27 April 2026

Mitochondria can spawn new ‘organelles’ — hinting at how modern cells evolved

Discovery bolsters theory that ancient mitochondria formed brand-new, specialized ‘sacs’ in cells by shedding their outer membranes.

By

Viviane Callier

Researchers are discovering that mitochondria (blue), known as cells’ power plants, also play parts in cells’ immune responses.Credit: David M. Phillips/Science Photo Library

When a parasite invades a cell, the cell’s mitochondria react by shedding their outer layers to form brand-new cellular compartments — or organelles — that digest molecular trash.

The team that made the discovery showed that when the mitochondria — best known as cells’ energy producers — form these organelles, it helps the parasite to proliferate, although it isn’t clear exactly how. Maybe the parasite ‘feeds’ off the degraded material inside the tiny compartments, says team leader Lena Pernas, an immunologist at the University of California, Los Angeles.

But one thing is certain, Pernas said when presenting the findings at a Keystone symposium on mitochondrial signalling, held in Colorado in February: “Mitochondria are able to give rise to new organelles during infection.”

The discovery, posted on 24 April to the preprint server bioRxiv[1] ahead of peer review, adds to a growing list of roles that researchers are uncovering for mitochondria in immunity, including surveilling pathogens[2] and coordinating immune signalling[3]. It also lends credence to the hypothesis that membrane sacs, or vesicles, shed by the earliest mitochondria gave rise to organelles in eukaryotes — a group of organisms, including plants and animals, whose cells have an enclosed nucleus and other intracellular compartments. If modern-day mitochondria can spawn new organelles, which have specialized jobs inside cells, it’s easy to imagine that their ancestors did, too.

Mitochondria manipulation

Pernas and her colleagues observed the formation of the organelles after infecting human cancer cells with the parasite Toxoplasma gondii, which can lurk in undercooked meat. A protein on the outer surface of the parasite latched onto a protein on the cells’ mitochondria, ‘pinning’ the tiny energy producers to T. gondii. These tethered mitochondria then began shedding their outer membranes, forming what are called structures positive for outer mitochondrial membrane (SPOTs). Remarkably, the SPOTs went on to engulf other organelles in the cells called lysosomes, which are acidified sacs that act as waste-disposal systems.

In this video, watch a large lysosome (red, near the centre of the screen) be enveloped by a SPOT (yellow).Credit: Dr. Xianhe Li

This envelopment of the lysosomes is quite the feat, says Shaeri Mukherjee, a cell biologist and immunologist at the University of California, San Francisco. “What is so cool and surprising is the ability of a pathogen to completely, not only manipulate the mitochondria, but use the mitochondria to generate an entire new organelle in the cell, with such precision,” she says.

The researchers confirmed that the SPOTs-engulfed lysosomes are a new type of organelle, different from lysosomes: the outer surfaces of the new organelles don’t bear any proteins typically expressed by lysosomes. They also demonstrated that to multiply, T. gondii needs the new organelles. When the researchers used a drug called a proton-pump inhibitor to prevent the SPOTs-engulfed lysosomes from acidifying — and therefore becoming waste-disposal systems — the parasite’s proliferation was impaired.

Interestingly, the SPOTs rarely engulf any other organelles inside cells other than acidified lysosomes. One reason the parasites might create the new organelles is to feed off their digested waste, Pernas says. Another could be that, by forcing the SPOTs to engulf lysosomes, T. gondii neutralizes the tiny waste disposers, which are harmful to pathogens, Pernas says.

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Nature 653, 11-12 (2026)

doi: https://doi.org/10.1038/d41586-026-01295-0

References

1. Pernas, L., Li, X., Sun, Y. & Delgado, J. M. Preprint at bioRxiv, https://doi.org/10.64898/2026.04.23.720395 (2026).

2. Dishart, J. G. et al. Sci. Adv. 10, eadn0014 (2024).

3. West. A. P. & Shadel, G. S. Nature Rev. Immunol. 17, 363–375 (2017).

4. Gould, S. B., Garg, S. G. & Martin, W. F. Trends Microbiol. 24, 525–534 (2016).

5. Sugiura, A., Mattie, S., Prudent, J. & McBride, H. M. Nature 542, 251–254 (2017).

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NEWS 23 April 2026

How your heartbeat could keep cancer at bay

Pressure on the heart as it pumps blood stops cancer cells from multiplying in mouse hearts.

By

Rachel Fieldhouse

Credit: zsv3207/Getty

The beating of the heart stops cancers from growing in this organ in mice, reports a study published today in Science[1]. This could explain why tumours affecting the heart are so rare in mammals, including in people.

Almost all organs and tissues in the body can develop tumours, but cancers that affect the heart are seldom observed. In people, primary cardiac tumours have been identified in fewer than 1% of autopsies, while secondary cancers, in which the primary tumour occurs in a different part of the body, have been found in up to 18% of autopsies.

Until now, there has not been a satisfactory explanation for why cardiac tumours are so uncommon, says James Chong, a cardiologist and researcher at the University of Sydney, Australia. This latest study puts forward a compelling case that mechanical strain on the heart could be an explanation, he says.

Serena Zacchigna, a clinician-scientist at the University of Trieste, Italy, and her team transplanted hearts onto the necks of genetically modified mice. These external hearts did not beat but did still receive a blood supply and were functional.

The team then injected cancer cells into the transplanted hearts on the necks of mice and into the ‘native’ hearts in the animals. Within two weeks, the cancer cells multiplied and replaced most of the healthy cells in the transplanted hearts. By contrast, about 20% of tissue in the native hearts was cancerous.

Engineered hearts

The team also grew engineered heart tissue from rat cells in a dish. The cells beat only if the researchers exposed the tissue to calcium — ions of which help to drive the beating of the heart inside the body.

The team then injected the heart tissue with lung cancer cells. It found that cancer cells grew in number and took up more space in the static tissue than they did in the beating tissue. Furthermore, the team found that cancer cells were distributed throughout the static tissue, but clustered only in the outer layers of the beating tissue.

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Nature 653, 12-13 (2026)

doi: https://doi.org/10.1038/d41586-026-01296-z

References

1. Ciucci, G. et al. Science 392, eads9412 (2026).

2. Suzuki, R. et al. J. Thorac. Cardiovasc. Surg. 133, 1051–1058 (2007).

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NEWS 26 April 2026 Update 28 April 2026 Update 28 April 2026

Entire NSF science advisory board fired by Trump administration

Members of the National Science Board, which the US Congress founded in 1950, were given no explanation for their termination.

By

Dan Garisto

Researchers serving on the National Science Board, which advises the US National Science Foundation, received a brief e-mail on Friday telling them that they had been dismissed.Credit: Briscoe Savoy for Nature

All 22 members of the advisory board that oversees the US National Science Foundation (NSF), a leading funder of fundamental science, were fired on 24 April without explanation. Every member of the NSF’s National Science Board (NSB) received an e-mail on Friday afternoon saying that “on behalf of President Donald J. Trump”, their positions were “terminated, effective immediately”.

Members of the NSB are appointed by the president and serve six-year terms that are staggered, avoiding complete turnover. Asked about the reason for the termination, a White House spokesperson said that the 2021 Supreme Court decision United States v. Arthrex, Inc. “raised constitutional questions about whether non-Senate confirmed appointees can exercise the authorities that Congress gave the National Science Board”. Members of the NSB were initially confirmed by the Senate, but have not been since 2012.

“This action to dismiss the NSB is unprecedented,” says Dan Reed, a computer scientist at the University of Utah in Salt Lake City and chair of the NSB from 2022 to 2024. “We need a vibrant, independent NSB, one representative of the broad science and engineering enterprise.”

Zoe Lofgren, a member of the US House of Representatives from California and the ranking Democrat on the House Committee on Science, Space, and Technology, criticized the move. “This is the latest stupid move made by a president who continues to harm science and American innovation,” she said in a statement. “It unfortunately is no surprise a president who has attacked NSF from day one would seek to destroy the board that helps guide the Foundation.”

But House science committee chairman Brian Babin, a Republican from Texas, said, “Every President expects advisors to serve in a manner consistent with executive and legislative priorities. I look forward to seeing whom President Trump selects to fill the NSB and refocus our science agencies on their core mission: pursuing science.”

This is not the first time the Trump administration has ousted federal science advisers en masse. Last year, the administration fired all 17 members of the Advisory Committee on Immunization Practices, which played a crucial part in US vaccine policy, and eliminated 14 advisory committees at the NSF. Also last year, Trump issued an order eliminating several advisory committees, including one on long COVID, to reduce government spending and “promote American freedom and innovation”.

Long history

The NSF and the NSB were established by Congress in 1950. The board meets five times a year and publishes reports on the state of US science and engineering that help to guide the president and Congress. Its next meeting was set for 5 May, and members say a report about the United States ceding scientific ground to China was set to be released.

“Where will advice come from?” asks Roger Beachy, a biologist at Washington University in St. Louis, Missouri. He was appointed to the NSB by US President Barack Obama in 2014 and reappointed by Trump in 2020 before being fired on Friday. “Who will help with what is the future of science in this nation?”

Keivan Stassun, an astrophysicist at Vanderbilt University in Nashville, Tennessee, says that the termination of NSB members fits into a pattern of the Trump administration’s approach to science advice, which is being “systematically either dissolved or eviscerated”, he says. “It felt like only a matter of time” before that happened to the NSB, he says.

Because the NSB was established by an act of Congress, the board can officially be dissolved only by Congress. Furthermore, its members are required to be ‘eminent’ in scientific fields, according to the founding legislation.

Tumultuous times

The firing of NSB members comes amid other turmoil at the NSF. The Trump administration proposed two years in a row to cut the NSF budget by more than half. (Congress declined to approve that proposal for the 2026 budget.) The agency has lost more than 30% of its staff since January 2025, and in December, it had to cede its headquarters to another federal agency. This year, new grants at the agency have been issued at a trickle, as the agency prepares major cuts to its divisions.

One of the NSB’s key statutory roles is to approve the NSF’s budget. But multiple NSB members say that the White House Office of Management and Budget (OMB), which oversees federal spending, told NSF leadership not to share details about the agency’s spending with board members.

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Nature 653, 13-14 (2026)

doi: https://doi.org/10.1038/d41586-026-01361-7

Updates & Corrections

- Update 28 April 2026: This story has been updated to include a comment from US Congressman Brian Babin.

- Update 28 April 2026: This story has been updated with a comment from the White House.

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NEWS 28 April 2026

First detailed ‘smell maps’ reveal how noses track odours

Detailed maps of smell receptors in the nose overturn textbook models of olfactory receptor organization in mice.

By

Chris Simms

A microscope cross-sectional image of a mouse nose, showing the anatomical structure of the nasal epithelium. Credit: Datta Lab

Olfactory receptors in the mouse nose have been mapped out in unprecedented detail — overturning researchers’ understanding of how noses build a sense of smell.

The research, published today in Cell[1], shows how around 1,100 olfactory receptors expressed on sensory neurons are organized in tightly regulated spatial locations in the epithelial tissue that lines the nasal cavity. A second study[2] provides a complementary atlas of olfactory receptor expression in the olfactory epithelium and their neural connections to the olfactory bulb in the brain.

“For 30 years, we’ve taught students that the mouse olfactory epithelium is divided into a handful of broad zones, within which receptor choice is essentially random,” says Johan Lundström, a psychologist and experimental neuroscientist at the Karolinska Institute in Stockholm.

“This is a landmark paper that overturns one of the foundational textbook models of olfactory organization,” he adds.

Smell stripes

In the study, researchers examined about five million neurons from hundreds of individual mice. They first used single-cell sequencing to identify which smell receptors were expressed by neurons in the nose, and then used spatial transcriptomics to map out where key genes were being expressed. This allowed them to pinpoint where the receptors are and show that they are always arranged in horizontal stripes running from the top of the nose to the bottom.

“Each receptor adopts a particular position in the nose. Since there are a thousand positions in the nose, each receptor is expressed basically in a stripe that overlaps with other receptor stripes, in a thousand overlapping stripes,” says study co-author Sandeep Robert Datta, a neurobiologist at Harvard Medical School in Boston, Massachusetts.

Datta and his colleagues propose that this spatial mapping is organized during development and is controlled by sets of genes. The authors found that a molecule called retinoic acid had a key role in this process. They discovered a gradient in the amount of retinoic acid present at different points in the nose. By tweaking how much this molecule was expressed, they showed that it helps to control gene activity, guiding each neuron to express the correct type of smell receptor for its location.

“There’s been a ton of back and forth in the field about how this is all mapped out, and this nails it. I think it really changes the way people think about the olfactory system and just solves a huge problem in the field about how the mapping happens,” says Joel Mainland, an olfactory neuroscientist at the Monell Chemical Senses Center in Philadelphia, Pennsylvania.

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Nature 653, 15 (2026)

doi: https://doi.org/10.1038/d41586-026-00894-1

References

1. Brann, D. H. et al. Cell https://doi.org/10.1016/j.cell.2026.03.051 (2026).

2. Bintu, B. et al. Cell https://doi.org/10.1016/j.cell.2026.03.053 (2026).

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NEWS 23 April 2026

Did kraken-like octopuses rule Cretaceous seas? Massive jaw fossils offer clues

Extinct cephalopods might have been up to 19 metres in length and probably dined high up the food chain in ancient oceans.

By

Ewen Callaway

Artist’s impression of an extinct Nanaimoteuthis species, often called krakens, which could have rivalled large marine reptiles in terms of size.Credit: Masato Hattori/Science Photo Library

In the age of the dinosaurs, while Tyrannosaurus rex terrorized the land, gargantuan octopuses might have been among the top predators in the sea.

Scientists have identified extinct octopuses — sometimes named krakens after the mythological monsters — that might have grown to nearly 19 metres in length. The estimate is based on fossilized jaws, which the researchers say show patterns of wear that came from devouring animals that had hard shells and skeletons.

Their study, published on 23 April in Science[1], challenges the idea that giant marine reptiles such as mosasaurs and other vertebrates exclusively dominated marine ecosystems in the Cretaceous period 143 million to 66 million years ago. But other scientists say the largest size estimates of the octopuses — around the length of an articulated lorry — and any firm conclusion about their role in ecosystems should be taken with a pinch of salt.

Deep-sea denizens

‘Krakens’ have been identified from chitinous jaw fossils from the late Cretaceous. But their size range, diet and role in ecosystems were unclear, says study co-author Yasuhiro Iba, a palaeontologist at Hokkaido University in Japan.

To plug such gaps, Iba, together with Hokkaido palaeontologist Shin Ikegami and their colleagues, reanalysed 15 large fossil octopus jaws. They also identified 12 new kraken fossils through an analysis of carbonate rock layers assisted by artificial intelligence, an approach Iba’s team used to discover an explosion in squid diversity 100 million years ago[2].

The analysis grouped the krakens into two species: Nanaimoteuthis jeletzkyi and N. haggarti, and discovered that they belong to the same evolutionary group as modern dumbo octopuses (Grimpoteuthis species).

On the basis of the anatomy of modern octopuses, the researchers estimate that the krakens’ mantles — the main, bag-like parts of their bodies — would have been between 67 and 443 centimetres long. Including the tentacles, N. jeletzkyi could have grown to between 2.8 and 7.7 metres in length, and N. haggarti from 6.6 metres to a whopping 18.6 metres.

“Some people will doubt that it really grew to 19 metres. I’m pretty sure about that,” says Christian Klug, a palaeobiologist at the University of Zurich in Switzerland, who was not involved in the research. The relationship between the mantle and tentacles of modern cephalopods is highly variable, so the lower or middle bounds of the size estimates could be more likely.

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Nature 653, 17-18 (2026)

doi: https://doi.org/10.1038/d41586-026-01342-w

References

1. Ikegami, S. et al. Science 392, aea628 (2026).

2. Ikegami, S., Takeda, Y., Mutterlose, J. & Iba, Y. Science 388, 1406–1409 (2025).

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NEWS 24 April 2026

How much for a fake authorship? Ad database reveals secrets of scientific fraud

An analysis of thousands of paper-mill adverts could help journals to crack down on misconduct.

By

Miryam Naddaf

The cost of authorship slots on studies produced by paper mills ranges from less than US$100 to more than $5,000, an analysis found. Credit: Ievgen Chabanov/Alamy

Researchers have amassed a data set of thousands of advertisements selling research-paper authorships online, shedding light on the global marketplace for academic fraud.

The collection — the largest of its kind — contains more than 18,700 adverts that were posted between March 2020 and early April 2026 by seven paper mills — businesses that produce fake or low-quality research and sell authorships. Together, the companies cater to academics in the Middle East, Central Asia, Eastern Europe and India.

An analysis of the advert data found that a first-author slot on an article sold by a paper mill costs a median value of nearly US$800, with prices ranging from $57 to more than $5,600. The work is described in a preprint posted to arXiv on 21 April[1].

Researchers, publishers and indexing services could use the list of adverts to screen their publications and audit which journals and research topics are most likely to be targeted, says study co-author Reese Richardson, a metascientist at Northwestern University in Evanston, Illinois.

“The preprint paints a valuable picture of the significant financial scale of these operations, underscoring the pressure put on researchers to publish in order to advance in their careers,” a spokesperson for the New Jersey-based publisher Wiley told Nature.

Global operations

Richardson and his colleagues archived 2,311 advertisements that were posted on the messaging app Telegram by three paper mills that seem to be based in India, Iraq and Uzbekistan. The team also identified 16,399 advertisements from the websites of four businesses thought to operate from Russia, Latvia, Kazakhstan and Ukraine.

“What we’re beginning to see here is a pattern of global operations and the platformization of social media and online websites to operate a global network of businesses and corporations that exist for the purposes of scientific and academic fraud,” says Sarah Eaton, who studies academic integrity and fraud at the University of Calgary in Canada. The data set “tells us an awful lot about the businesses, their marketing and some of their operations”.

All seven paper mills advertised authorships for research articles — sometimes giving the title of the article and the journal in which it would be published. But two companies also offered authorships on other types of publication, such as textbooks, as well as other services, including being named on patents and copyright registrations or receiving awards.

“Paper mills are really in a variety of different businesses to the extent that the phrase ‘paper mill’ doesn’t capture everything that’s going on,” says Richardson. “I like to think of them as businesses operating in the market for reputation manipulation.”

The study found that some of the paper mills even ran seasonal discounts and holiday sales.

Papers in the wild

There are signs that manuscripts sold by paper mills make their way into the published literature at least some of the time. Using the data set, Nature’s news team manually checked more than 600 adverts for some 400 research articles that included manuscript titles in English. The search identified 53 published papers with titles that matched those in adverts from three businesses. Only five of those papers have been retracted, and one was withdrawn while the article was in the press.

Out of the papers that have not been retracted, four were published in Springer Nature journals and five in Wiley journals. Twenty-three papers appeared in conference proceedings published by the Institute of Electrical and Electronics Engineers (IEEE). Nature’s analysis also identified a further three papers, one each in journals published by Elsevier, Frontiers and Taylor & Francis. Some of the papers appeared in different publishers to those named in the advert.

Nature’s news team shared these findings with all six publishers.

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Nature 653, 18 (2026)

doi: https://doi.org/10.1038/d41586-026-01340-y

References

1. Richardson, R., Hong, S. & Abalkina, A. Preprint at arXiv https://doi.org/10.48550/arXiv.2604.24576 (2026).

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NEWS Q&A 27 April 2026

‘The job description is changing’: mathematician Terence Tao on the rise of AI

The Fields medallist discusses how ever-evolving technology is transforming mathematicians’ work.

By

Davide Castelvecchi

Terence Tao has been exploring the intersection between maths and AI.Credit: David Esquivel/UCLA

Is mathematics being taken over by generative artificial intelligence? A spate of media reports has suggested that the field is being fundamentally changed by the technology. Many maths researchers say that AI’s actual capabilities are often hyped up, and that it’s not yet time to announce the death of their profession. Still, by many accounts, in the past year, AI has jumped from solving secondary-school-level problems to actually being useful in research mathematicians’ daily work.

Terence Tao, a mathematician at the University of California, Los Angeles, has been at the forefront of experimentation with large language models, including OpenAI’s GPT, Anthropic’s Claude and Google’s Gemini. In particular, he has contributed to a project to test the skills of AI systems on a collection of more than 1,000 problems, ranging from major conjectures to obscure factoids. The questions were accumulated by the late Hungarian mathematician Paul Erdős (1913–1996) over his lifetime.

Last month, Tao teamed up with Tanya Klowden, an art historian at the Courtauld Institute of Art in London, to explore the implications of AI for researchers and the world at large. They took mathematics as a test case, and urge society to adopt the technology but in a human-centric way. They posted a draft of their essay, due to be published in the forthcoming edition of The Blackwell Companion to the Philosophy of Mathematics, on arXiv[1]. Nature spoke to Tao about how the technology is transforming his profession.

Why do you think it is important to consider the impacts of rapidly evolving AI?

I feel like AI is not just another technology like the word processor or the web browser. It really is forcing us to rethink fundamental questions — what is a mathematical proof? What is a paper? What is the purpose of our profession? If we don’t ask these questions ourselves, then they will get answered for us by a technology company or decided by financial incentives. We have to get ahead of this.

Why has maths become ‘the next big thing’ for AI?

In almost any other application, the biggest Achilles heel of AI is that it makes unverifiable mistakes. But in mathematics, almost uniquely, you can automatically check the output — at least if the output is supposed to be the proof of a theorem, although that is not the only thing mathematicians do. So, AI companies have recognized that their most unambiguous successes — if they’re going to have any — are going to come from mathematics.

In my opinion, there are many use cases of AI that are risky and controversial. In mathematics, the downsides are much more limited

What will happen to mathematics as a field in the age of AI?

The job description is changing a lot. A graduate student who refuses to touch AI systems and just wants to prove things the way we’ve done in the past might find they have fewer opportunities, unfortunately. Those who understand maths traditionally but are also adept at using new tools can flourish.

I don’t think AI will replace mathematicians, but it will complement them. There could be a division of labour: we decide what to prove and what we think is interesting. We could get instant feedback from the AI. We could propose a definition, make some conjecture and AI could instantly evaluate it. But who knows, it’s all changing.

But we do have to somehow let go of conventional assumptions of what intellect is. I think we have a human-centric way of thinking about all types of intellectual task, and we have to accept that this is not the only perspective.

What are mathematicians’ attitudes? Are they embracing the use of AI?

It’s very much a spectrum. You see all the ‘five stages of grief’ play out — denial, anger, bargaining, depression and acceptance. And I think this is happening everywhere. But I think we’re beginning to see denial fade away.

How good are AI models at solving mathematical problems?

For a while, you could say they were just picking up proofs that were in the literature. Or that they solved an easy problem that nobody had looked at before. But recent progress has been increasingly impressive. We’re just beginning to see examples in which AI — maybe by luck — starts solving problems that people care about.

It’s still very occasional, and it still has a lot of weaknesses; it is not a replacement for what humans do. But it’s getting harder to deny that these tools can work.

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Nature 653, 16-17 (2026)

doi: https://doi.org/10.1038/d41586-026-01246-9

This interview has been edited for length and clarity.

Additional reporting by Elizabeth Gibney and Edward Chen.

References

1. Klowden, T. & Tao, T. Preprint at arXiv https://doi.org/10.48550/arXiv.2603.26524 (2026).

NEWS EXPLAINER 28 April 2026

‘World models’ are AI’s latest sensation: what are they and what can they do?

Training AI world models on data about physical environments could improve their real-world capabilities in technologies such as robotics.

By

Davide Castelvecchi

Nvidia’s Cosmos is one world model being trained on physics data about real-world environments.Credit: NVIDIA Corporation

An ongoing trend in artificial intelligence could have huge implications for how the technology is used in research.

Machine-learning systems such as large language models (LLMs), which turn prompts into text, images and video, are becoming increasingly sophisticated and continue to make astonishing progress, in sectors including science. But such ‘generative AI’ tools also have limitations. The approach does not always make accurate predictions about the physical world, and could fail at modelling correctly what would happen if a car were to go off the edge of a cliff, for example. This would have implications for developing effective and safe AI-powered robots and self-driving vehicles.

Some researchers, including the computer scientist and AI pioneer Yann LeCun, who founded the firm Advanced Machine Intelligence (AMI) Labs in Paris, have turned their attention to a different type of AI tool, developing systems known as ‘world models’ that are trained on real-world data and can embody virtual, interactive and 3D environments.

The approach is attracting huge investment and business interest. AMI Labs — which is taking a radical approach to world models — has raised more than US$1 billion, a record initial infusion of money for a European company. Technology giants such as Google and Nvidia are also developing world models, as are several other start-up companies.

What is a world model?

There are several definitions of what a world model is. In the broadest sense, any neural network trained on data about the real world (or even about some alternative universe) has some sort of model of a world embedded in it. But over the past two years or so, many researchers have begun to use the term to describe AI that can produce a consistent, explorable and often interactive world that is reminiscent of a first-person video game. A world model has to ‘know’ enough about physics to understand that if the user pushes an object off a table then the object will fall down.

World models also provide a more-interactive experience for a user than does generating images of video material from text prompts. For example, Google Deepmind’s world model Genie 3, which the company released in August 2025, uses simple text descriptions to generate photorealistic environments that can be explored in real time.

What sort of data are world models trained on?

The companies that build generative AI systems tend to guard their ‘secret sauce’ fiercely. What is known is that interactive world models are trained, in part, using thousands of hours of videos from the real world, as well as with accurate simulations of physical environments that are programmed to observe the laws of physics.

What AI capabilities could world models unlock?

“The more exciting version of a world model is one in which you can take actions,” says Jeff Clune, a computer scientist at the University of British Columbia in Vancouver, Canada. Such an environment could be a safe setting in which to train AI systems that control robots, and could offer a much faster means of training than letting robots learn by interacting with physical objects, says computer scientist Anastasis Germanidis, co-founder of Runway, a start-up company in New York City that released a world model called GWM-1 in December 2025.

How will world models benefit researchers?

A world model such as Genie 3 can provide vast numbers of ways to train the software that can power a robot or a self-driving car, says Clune, who contributed to the development of Genie during a contract with Google DeepMind.

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Nature 653, 14-15 (2026)

doi: https://doi.org/10.1038/d41586-026-00820-5

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NEWS FEATURE 06 May 2026

Are attention spans really shrinking? What the science says

Digital distractions are vying for people’s focus, but our underlying capacity to pay attention seems to be undiminished.

By

David Adam

Illustration: Karol Banach

A century before social-media bans and advice to disable device notifications, the inventor and science-fiction writer Hugo Gernsback proposed a more extreme way to avoid distraction: an isolating wooden helmet. Outside influences, he said, were “the greatest difficulty that the human mind has to contend with”. Gernsback’s isolator device — part diving suit, part monastic cell — did help him to work, he said, but it came with a risk of suffocation. He later installed an air supply.

Concerns that sustained thought is under assault have become even more acute in the digital era. Smartphones buzz, Internet tabs multiply and television episodes carry regular reminders to help people keep track of the plot. Surveys suggest that we feel less able to concentrate, teachers report distracted students and headlines declare that our attention spans are shrinking.

Inventor Hugo Gernsback wearing his ‘isolator’ wooden helmet.Credit: Bettmann/Getty

Research across psychology and neuroscience, however, has built up a more nuanced picture of what is happening to our attention spans. The results suggest that people do flit from one task to another more frequently than they did in previous decades, and that this switching is often detrimental to performance. But there is little evidence that the brain’s fundamental ability to concentrate has been impaired. This suggests that if we can shut down the distractions of our environment, it is possible to recover focus.

“I think there’s a huge disconnect between what we feel like is happening and what is actually happening,” says Monica Rosenberg, a psychologist at the University of Chicago in Illinois.

The attention-span confusion

“There is a whole flurry of people reporting that they feel like they can’t pay attention,” says Nilli Lavie, a cognitive neuroscientist at University College London. “They say they are constantly distracted, their attention jumps from one thing to another, and they can’t concentrate.”

In a 2021 survey of more than 2,000 UK adults, almost half said they felt their attention span was shorter than it used to be (see go.nature.com/4dfz8yc). And two-thirds thought that the attention span of young people has declined (see ‘Are attention spans waning?’). Teachers and schools around the world have responded to this perception with modular lessons that break topics into digestible pieces. Some students now study literary extracts rather than full novels. When the novelist Elif Shafak questioned why TED talks were becoming shorter, she said last year she was told that it was because “the world’s average attention span has shrunk”.

Source: KCL Policy Inst./Centre for Attention Studies

The idea of an average attention span carries intuitive appeal. But the way it’s discussed can tangle distinct concepts. Researchers distinguish between people’s capacity to pay attention, that is, their underlying ability to concentrate on a particular task, and their real-world behaviour, or what people actually focus on from moment to moment.

What’s more, the capacity to pay attention is the result of several processes in the brain. These include sustained attention, the ability to stay engaged with a task over time; selective attention, the ability to prioritize some information and ignore the rest; and executive control, the ability to steer attention in line with a goal rather than whatever happens to be more tempting.

Attention in the laboratory

Capacity is measured under controlled laboratory conditions that test performance on a task — often a tedious one — over time. To test sustained attention, volunteers might monitor a screen showing streams of letters and shapes and identify specific changes. The ‘d2’ task, for instance, displays rows of letters, such as d and p, sometimes with dashes drawn above or below them, and asks people to mark the letter d only if it has two lines underneath.

The ‘d2’ task used to test volunteers’ capacity for sustained attention in a laboratory. Nowadays, it is performed on a computer screen.Credit: Michael Szebor/Nature

Many lab studies have shown how performance on such tasks declines in about ten minutes, although the pattern of decline is not smooth: even apparently strong attention naturally fluctuates between bursts of good performance, lapses and recovery.

Further tests demonstrate how providing a distracting environment, such as playing sounds of babies crying and dogs barking, worsens people’s performance on cognitive tasks[1]. This provides a basis for understanding distractions in the real world. Analyses have demonstrated that, for instance, traffic accidents are more likely to occur if drivers are talking on their phones[2].

The lab studies haven’t shown evidence that — when free of distractions — people’s underlying capacity to pay attention has changed. But there are differences in how people perform. Those who say they frequently juggle several streams of media at once tend to perform worse on tests of selective attention, for example showing greater difficulty filtering out irrelevant information[3]. They also show differences in tests related to working memory and executive control[4].

But these correlations might only reflect that individuals with different attentional tendencies could be naturally drawn to switch focus more often; the observations can’t prove that their digital environment has causally altered their brains. And although attention deficit hyperactivity disorder (ADHD) diagnoses have increased in recent years, researchers generally attribute the rise to changes in awareness and access to assessment and diagnostic practice, rather than to an underlying change in people’s attention capacity.

Overall, there are no convincing data from controlled lab tests to support the idea that people have become less able to concentrate because the capacity of attention is being degraded over time. A 2024 meta-analysis of results from d2 tests performed by more than 21,000 people from 32 countries between 1990 and 2021 showed no differences in how children scored and, if anything, a slight improvement in adult performance[5].

“It’s not so much that human biology has changed, it’s more a change in habits. And the question is how reversible those habits are,” says Nelson Cowan, a psychologist at the University of Missouri in Columbia.

Real-world measurements

The strongest evidence for change when it comes to attention is not from laboratory tasks but from measures of real-world behaviour. For two decades, Gloria Mark, a psychologist at the University of California, Irvine, has monitored how office workers use computers. Her studies, based on direct observation and digital tracking, show that the average duration of attention to a single task has steadily declined. “We do know that attention spans on screens have measurably decreased,” she says.

Mark’s work does not seek to measure sustained focus towards a specific goal. Instead, she counted when and how often workers switch between tasks. Such switches don’t have to be towards trivial distractions that would annoy the boss. They include opening a new browser tab, checking an e-mail and moving between documents, as well as glancing at a phone. In the mid-2000s, she says, she observed that workers spent about two and a half minutes on average on a dedicated screen task before switching. By the 2010s, that was down to about 75 seconds, and in the early 2020s it was about 47 seconds, according to Mark’s 2023 book[6].

Often included in discussion of these results is a 2015 marketing report by Microsoft Canada, which stated that the average human attention span had fallen from 12 seconds in 2000 to 8 seconds in 2013. The report noted that this was shorter than a goldfish’s average attention span, which it reported as 9 seconds (see go.nature.com/4e88mh9). But the report’s findings, based on surveys, filmed behaviour and electroencephalogram (EEG) data — which uses spikes in brain activity to measure when people switch their focus — reflected changing digital habits rather than cognitive limits and even noted that people were becoming more efficient at processing information. (Also, goldfish are unfairly maligned; there is no evidence that they have particularly short attention spans, and studies show they retain some information for months).

Mark’s research shows that frequent switching of attention carries a cognitive cost. “When people switch, and especially when they switch fairly rapidly, which is what the data show, they tend to make more errors,” she says. “It takes them longer to accomplish any single task compared to if they were to do work sequentially, and stress goes up.” Constant switching also diverts the kind of mental effort used. “We’re not utilizing those skills of reflection, deliberation, working memory,” she says. This can lead to the familiar malaise of superficial busyness without seeming to make progress.

Every generation has a panic that new technology will undermine the ability to concentrate. “But now we are in the digital age, and I do argue it’s different,” Mark says: both the scale of information available and the speed with which we can access it has changed. Importantly, the nature of the competing pulls on our attention has changed, too. The modern environment does not simply impose distractions. It bombards us with alternatives that offer more immediate rewards. People are switching tasks so often and resetting their attention each time because they choose to do so, even if they don’t realize it.

“If the alternatives are really rewarding and high value, then it will be very hard to focus on something else that’s going to require more subjective effort,” says Michael Esterman, a neuroscientist at Boston University in Massachusetts.

That’s ‘high value’ as classed by a psychologist or neuroscientist — which is not necessarily the way a parent, teacher or corporate superior would see it. Notifications, messages and social-media feeds provide the brain with bursts of social validation, novelty and information.

Mark argues that these rewarding digital environments might be altering our attentional habits, including our tendency to drift even in the absence of obvious distraction. Her research suggests that the sources of interruptions are not solely external — such as the ping of an arriving message. “People are about as likely to self-interrupt as they are to be interrupted,” Mark says. And when external interruptions decrease, internal ones often increase — a pattern that suggests that distraction and switching can become habitual, she argues, and leave attention more fragmented.

Are our brains actually changing?

Real-world studies such as Mark’s are too messy to generate reliable data on specific aspects of brain performance. But Lavie also worries that this constant switching could relate to weaker executive control. She suggests that it could have long-term implications for the brain.

Her work shows that the ability to control attention is linked to structural differences in the brain, specifically the amount of grey matter in regions of the frontal cortex. Using magnetic resonance imaging (MRI) scans and behavioural tests, she has shown that individuals with greater grey-matter volume in these areas perform better on tasks that require maintaining focus and resisting distraction[7]. The grey-matter volume can be used to make accurate predictions about how people will perform, and might reflect a combination of genetic factors and long-term experience.

Cognitive neuroscientist Nilli Lavie instructs a volunteer on how to perform a task measuring attention; brain activity in this example is measured with an electroencephalogram (EEG) test. Credit: Attention and Cognitive Control laboratory, UCL Institute of Cognitive Neuroscience

In principle, such measurements might be used to detect changes in attention capacity over time or between cohorts of people. Lavie doesn’t have the data to show that, and no such pattern has shown up in controlled lab studies, but she argues that it could happen. “There is the possibility that you either exercise it and it’s got a good grey-matter volume,” she says, “or you don’t, and it shrinks.”

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Nature 653, 20-22 (2026)

doi: https://doi.org/10.1038/d41586-026-01407-w

References

1. Arruda, A., Mesquita, C., Couto, R., Sousa, V. & Mendonça, C. Noise Health 25, 247–256 (2023).

2. Redelmeier, D. A. & Tibshirani, R. J. N. Engl. J. Med. 336, 453–458 (1997).

3. Ophir, E., Nass, C. & Wagner, A. D. Proc. Natl Acad. Sci. USA 106, 15583–15587 (2009).

4. Murphy, K. & Creux, O. Comput. Hum. Behav. 114, 106532 (2021).

5. Andrzejewski, D., Zeilinger, E. L. & Pietschnig, J. Personalit. Individ. Differ. 216, 112417 (2024).

6. Mark, G. Attention Span (HarperCollins, 2023).

7. Eayrs, J. O. & Lavie, N. NeuroImage 202, 116148 (2019).

8. Rosenberg, M. D. et al. Proc. Natl Acad. Sci. USA 117, 3797–3807 (2020).

9. Ward, A. F., Duke, K., Gneezy, A. & Bos, M. W. J. Assoc. Consum. Res. 2, 140–154 (2017).

10. Massar, S. A. A., Lim, J., Sasmita, K. & Chee, M. W. L. Biol. Psychol. 120, 21–27 (2016).

11. Esterman, M., Noonan, S. K., Rosenberg, M. & DeGutis, J. Cereb. Cortex 23, 2712–2723 (2013).

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NEWS FEATURE 05 May 2026

Testosterone therapy is trending. Who really needs it, and why?

Some clinicians are pushing to broaden testosterone use, but there is debate about its benefits and risks.

By

Mariana Lenharo

Illustration: Gibson Kochanek; Images adapted from: Getty; Science Photo Library; Alamy

Is testosterone the next miracle drug? That seemed to be the consensus of an expert panel convened by the US Food and Drug Administration (FDA) in December. It argued for major changes in policy that would expand access to the hormone for people with a range of conditions. Committee members called testosterone replacement “a cornerstone of preventive health” and “a multibillion-dollar preventive-care opportunity”.

Testosterone is already available in the United States for people who have low levels of the hormone owing to a known medical issue, such as testicular damage. But evidence is growing that more men — and women — might benefit from the hormone, which is delivered through injections, patches, subcutaneous implants or gels. (This article uses ‘men’ and ‘women’ to reflect the language used by the panels and studies cited, while recognizing that trans, non-binary and intersex people are also affected by this issue.)

The panel’s recommendations intensify a debate that has been brewing about who might benefit from the treatment. Some clinicians say that most men with low testosterone, especially young ones with no medical issue contributing to the problem, don’t need supplemental treatment at all and should be able to raise their testosterone levels by adopting a healthier lifestyle and losing weight. Others argue that men with low testosterone who have symptoms such as low libido, fatigue and irritability could gain from the therapy.

More-enthusiastic proponents, including many members of the FDA panel at the December meeting, take a third view: that all cis men should be tested, and those with low testosterone levels should be treated even if they have no symptoms. “You could make a very strong argument that having a normal testosterone level is important for health and prevention of illness,” says Abraham Morgentaler, a urologist at Harvard Medical School in Boston, Massachusetts, who took part in the December panel.

Morgentaler and other panellists stressed at the meeting that testosterone is not just a ‘lifestyle drug’ that men take to build muscle and feel good. Yet it is increasingly being marketed that way. Podcasters such as Joe Rogan and his guests have sung the hormone’s praises. And scores of testosterone clinics are popping up around the world[1], promising fitter bodies and a boost in energy levels to people who might not even have low testosterone to begin with.

At high doses, testosterone use potentially comes with risks ranging from infertility to increased mortality. The drug is currently classified as a controlled substance with potential for abuse in the United States and several other countries, owing in part to doping scandals in the 1990s and 2000s. That classification is worth reconsidering according to statements made by FDA commissioner Marty Makary, who also voiced his enthusiasm for testosterone at the December panel.

So what is the evidence for the safety and benefits of testosterone replacement?

Safety record

Testosterone’s reputation has had its ups and downs since the hormone was first synthesized in the 1930s. After an initial golden period, in which it was described as “one of the most potent drugs recently introduced to medicine”[2], the therapy fell out of favour for fear that it could cause cancer. This idea originated from the work of urologist Charles Huggins who, in 1941, found that prostate cancer depends on testosterone and that lowering the hormone levels caused tumours to shrink[3]. It was a groundbreaking discovery for which he was awarded a share in the Nobel Prize in Physiology or Medicine in 1966.

Morgentaler says that when he was training as a urologist in the 1980s, there was a widespread belief, based on Huggins’s findings, that testosterone therapy could promote prostate cancer. Despite the presumed risks, he says he still thought that the hormone could help some of his patients who had low testosterone and sexual problems. So, he started treating them while monitoring them closely.

They didn’t get cancer, Morgentaler says, and they benefited from the treatment greatly. Some of his clinical findings — along with the revelation that Huggins’s most dire warnings about the hormone causing cancer were based on observations of a single person — helped to clear the way for renewed interest in testosterone therapy. Morgentaler is widely recognized for his testosterone-safety work, although some clinicians disagree with him on how much men stand to benefit from the hormone. Morgentaler notes he has consulted for companies that sell testosterone in the past, but says that he has no current financial interests.

Other potential risks cropped up over time. Two retrospective studies from 2013 and 2014 found an increased risk of heart attack in men on testosterone therapy[4],[5], which led the FDA to add a warning to testosterone product labels in 2015.

But a randomized clinical trial involving around 5,200 men — called TRAVERSE — found that middle-aged and older men with low testosterone and a high risk of cardiovascular disease who took the hormone did not have a higher incidence of severe cardiovascular events, including heart attack and stroke, than did those on placebo[6]. “This study only picked very sick people, the greatest-risk population, and nothing bad happened to them,” says co-author Mohit Khera, a urologist at the Baylor College of Medicine in Houston, Texas. He also participated in the FDA expert panel and says that he consults for testosterone companies.

On the basis of the TRAVERSE findings, the FDA removed the cardiovascular warning from testosterone products last year.

The safety evidence from TRAVERSE refers to men with confirmed low testosterone levels — below 300 nanograms per decilitre of blood serum — who are treated to bring their levels back to the normal range of 350–750 nanograms. Higher doses, however, that push levels well above the natural range, carry radically different risks.

At high doses, testosterone can thicken the heart’s muscles, impairing its ability to pump blood, a condition called cardiomyopathy. High testosterone levels can also cause infertility, shrunken testicles, reduced sperm count and erectile dysfunction, says Channa Jayasena, an endocrinologist at Imperial College London. There are also neuropsychiatric effects, including irritability and even psychosis, which might increase the risk of violent crimes and domestic abuse, says Jayasena, who advises companies that sell testosterone, but says that he declines payment to avoid a conflict of interest. “For reasons we don’t fully understand, very high doses of testosterone do something that therapeutic testosterone doesn’t do,” he says.

A Danish study[7] that followed some 500 men using high doses of anabolic steroids — which include medically approved testosterone products and other variations of the hormone — found that their mortality rate was three times higher than that of non-users over a period of around seven years, a risk that Jayasena says is similar to that of cocaine use. The study participants were caught by a doping control programme that inspects fitness centres in Denmark and tests people suspected of steroid abuse. The authors acknowledge that steroid use has been associated with risk-taking behaviours, which could explain part of the increased mortality.

This form of misuse is also addictive: about 30% of men on high doses become dependent. “They are flooding our clinics,” Jayasena adds.

Who can benefit?

Clinicians pushing for broader testosterone use often share striking anecdotes of the hormone transforming people’s lives. Morgentaler says that when he started treating people who had low testosterone levels in the late 1980s, they reported back things such as “My wife likes me again”, and “I’ve never had so much patience with my small children”. On a regular basis, they tell him that they finally feel like themselves again, with improved mood, vigour and libido. They go from feeling depleted, as if their fuel tank is empty, to regaining stamina and thriving at work, Morgentaler says.

These accounts are from people who stay on the therapy; many drop out. In the TRAVERSE trial, around 61% of participants receiving testosterone discontinued treatment. There is limited research into why men stop testosterone, but Morgentaler guesses that some don’t like how it’s administered and others just don’t feel the benefit (perhaps, he argues, because they are taking the wrong dose or have unrealistic expectations).

Clinical trials support only a modest portion of the benefits that testosterone fans tend to attribute to it.

The clearest effect is in sexual function. A subanalysis of the TRAVERSE trial, which included around 1,100 men with low libido, found that both the treatment and the placebo groups increased their sexual activity, but the increase was 25% greater in treated men[8] (see ‘Testosterone and sexual activity’). Sexual desire also improved with testosterone therapy, although erectile function did not (see ‘Testosterone and erectile dysfunction’).

Source: Ref. 8

A meta-analysis commissioned by the Endocrine Society, an organization based in Washington DC, concluded that testosterone was associated with a “small but statistically significant” improvement in sexual function, sexual satisfaction and libido in men with low testosterone[9]. It also found a small improvement in erectile function, contrary to the TRAVERSE trial. The review found no statistically significant difference in energy, mood or cognition.

Source: Ref. 8

Other studies have shown that testosterone treatment can effectively treat anaemia and that it improves bone density[10]. Surprisingly, men receiving testosterone in the TRAVERSE trial had more fractures than those on placebo[11]. “It must be that these inactive men were becoming more active”, which could be a good thing if that means they are getting more exercise, says Jayasena.

Smaller trials showed that testosterone is also associated with an increase in fat-free body mass and muscle strength, both in younger and older men[12].

Potential benefits for women

As testosterone use surges in some groups, many people who could benefit have mostly been left out of the debate, including trans men, for whom the hormone is recommended as a part of gender-affirming care, and postmenopausal women.

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Nature 653, 23-25 (2026)

doi: https://doi.org/10.1038/d41586-026-01408-9

References

1. Grant, B. et al. J. Clin. Endocrinol. Metab. https://doi.org/10.1210/clinem/dgaf689 (2026).

2. Aub, J. C. N. Engl. J. Med. 222, 877–881 (1940).

3. Huggins, C. & Hodges, C. V. Cancer Res. 1, 293–297 (1941).

4. Finkle, W. D. et al. PLoS ONE 9, e85805 (2014).

5. Vigen, R. et al. JAMA 310, 1829–1836 (2013).

6. Lincoff, A. M. et al. N. Engl. J. Med. 389, 107–117 (2023).

7. Horwitz, H., Andersen, J. T. & Dalhoff, K. P. J. Intern. Med. 285, 333–340 (2018).

8. Pencina, K. M. et al., J. Clin. Endocrinol. Metab. 109, 569–580 (2024).

9. Bhasin, S. et al. J. Clin. Endocrinol. Metab. 103, 1715–1744 (2018).

10. Snyder, P. J. et al. Endocr. Rev. 39, 369–386 (2018).

11. Snyder, P. J. et al. N. Engl. J. Med. 390, 203–211 (2024).

12. Bhasin, S. et al. J. Clin. Endocrinol. Metab. 90, 678–688 (2005).

13. Islam, R. M. et al. Lancet Diabet. Endocrinol. 7, 754–766 (2019).

14. Yeap, B. B. et al. J. Clin. Endocrinol. Metab. 94, 2353–2359 (2009).

15. Yeap, B. B. et al. Ann. Intern. Med. 177, 768–781 (2024).

16. Marriott, R. J. et al. Alzheimers Dement. 18, 1907–1918 (2022).

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Opinion

- How fertilizer shortages caused by the energy crisis threaten food security

COMMENT 05 May 2026

How fertilizer shortages caused by the energy crisis threaten food security

Unless governments treat fertilizer production as strategic infrastructure, the world will keep lurching from energy shock to harvest failure.

By

Meihua Yang,

Xing Wang,

Solmaz Fathololoumi,

Hiteshkumar B. Vasava,

Yuhong Zhou,

Henry W. Chau,

Huijie Li,

Bingcheng Si,

Hongyi Li &

…

Asim Biswas

Blockages at ports in China are leading to fertilizer-supply issues at a crucial time for agriculture. Credit: Shutterstock

Since March, war in the Middle East has disrupted global fertilizer markets. Urea prices jumped by nearly 46% in a month, as geopolitical and energy shocks hit nitrogen supply chains[1]. The disruptions caused by blocked maritime bottlenecks, including the Strait of Hormuz, limiting tanker movements and flows of oil and liquefied natural gas, underscore the coupled nature of global energy and food systems.

As a result of the crisis, the World Food Programme has warned that global food systems are under severe strain, with more than 360 million people facing acute food insecurity in 2026 and tens of millions at risk of famine (see go.nature.com/48jygpd).

These dynamics echo the fertilizer crisis of 2022, when Russia’s invasion of Ukraine decreased ammonia production across Europe — at times by more than half — and drove nitrogen fertilizer prices to record highs (see ‘Fertilizer shortfalls’). The recurrence of this pattern of an energy shock causing fertilizer disruption and food insecurity exposes a systemic vulnerability that must urgently be addressed.

Source: go.nature.com/3gha59s

Fertilizer production needs to be recognized as crucial infrastructure for ensuring food security. Meanwhile, the agricultural sector must reduce its dependence on volatile energy markets through precise nutrient management and diversified production technologies.

Food and energy links

Half of all food consumed globally depends on synthetic nitrogen fertilizers made using an industrial ammonia-producing method called the Haber–Bosch process[2]. This process consumes 1–2% of global energy and contributes a comparable share of carbon dioxide emissions. Natural gas serves as both a feedstock and the primary energy source, accounting for 70–80% of ammonia-production costs. This coupling means that disruptions in energy markets push up fertilizer prices rapidly.

The current fertilizer crisis is worse than the one in 2022 because more countries are affected. The Strait of Hormuz is a crucial passage for world trade — about 38% of global crude oil, 29% of liquefied petroleum gas, 19% of liquefied natural gas and 13% of chemicals, including fertilizers, normally pass through it[3].

Shipments have been all but halted by security threats and blockades. Exports from key suppliers, including Saudi Arabia, Qatar, Kuwait, Iran and the United Arab Emirates, to global nitrogen, sulfur and phosphate markets are constrained. This has raised nitrogen fertilizer prices by 30% and phosphates by 5–15% as buyers compete for limited supplies.

A cascading shock

The timing is particularly acute, because it coincides with the region’s spring planting season, when farmers are finalizing nutrient procurement and have limited capacity to adjust application strategies.

Energy price spikes generate immediate economic impacts and policy responses, whereas fertilizer shocks unfold on slower agricultural timescales. Farmers typically purchase fertilizer weeks to months before planting, and reduced applications translate into lower yields in subsequent growing seasons.

These production shocks then propagate through markets, reducing grain supplies and increasing food prices in subsequent seasons. Import-dependent regions, particularly in sub-Saharan Africa, South Asia and parts of Latin America, have limited capacity to absorb these delayed but persistent impacts[4].

The 2022 crisis highlights this dynamic. Because global urea prices rose from roughly US$250 to more than $800 per tonne, higher prices and reduced affordability led to decreased fertilizer application in several regions. The greatest impacts were seen in low-income economies, especially those in sub-Saharan Africa[4],[5].

A plant in Jordan that processes potash for use as a fertilizer.Credit: Bill Lyons/Alamy

India’s fertilizer subsidies amounted to about $30 billion in 2022–23, because the government absorbed global price shocks to shield domestic farmers from the full impact of soaring input costs[6]. Emergency subsidies are expensive to fund. Countries without such fiscal capacity had lower yields of staple crops, including maize (corn), rice and wheat. Over the longer term, the consequences contributed to restricting global grain supplies and higher food prices in subsequent years[7].

Crucially, the relationship between fertilizer and yield is not linear: even modest reductions in the quantities applied can produce disproportionately large decreases in output, particularly if minimal amounts are used already. The countries least able to afford fertilizer are therefore those where small reductions impose the greatest production losses.

Slow to react

Despite these lessons, responses remain reactive. When fertilizer prices surge, governments often impose export restrictions to protect domestic supplies, inadvertently exacerbating shortages for import-dependent nations. China, for instance, has periodically restricted exports of fertilizers such as phosphates and urea since 2021.

This reactive posture reflects a deeper gap: fertilizer is treated mainly as an industrial commodity rather than as a crucial part of the food system. Global food-security frameworks, including the Agricultural Market Information System, monitor grain stocks and trade flows but not fertilizer supply chains. There are no international fertilizer reserves analogous to strategic petroleum ones.

Few countries have policies that link energy resilience to agricultural production explicitly. Shipping networks that transport fertilizers remain vulnerable to geopolitical disruption, with rising war-risk insurance premiums increasing the cost and uncertainty of maritime trade. The result is a system that responds to fertilizer crises only after planting decisions have been made and yield losses are locked in.

Ending the cycle

Five areas of coordinated action could break this cycle.

First, governments and international institutions must treat fertilizer production and supply as strategic food-security infrastructure. This requires the development of nationally and internationally coordinated buffer mechanisms capable of mitigating short-term supply disruptions. These should be analogous to strategic petroleum reserves, even if they differ in design and governance.

Governments should incorporate ammonia-production capacity into energy-security planning, ensuring that fertilizer plants are prioritized to receive energy during crises. Multilateral platforms — including the G7 and G20 groups of the world’s biggest economies and the United Nations Food and Agriculture Organization — should integrate fertilizer supply chains into the mechanisms they use to coordinate responses to food and commodity crises.

The Agricultural Market Information System should be expanded to include real-time monitoring of fertilizer production, trade and inventories[8]. Industry and trade organizations, including the International Fertilizer Association and the World Trade Organization, can support this by promoting transparent reporting standards and strengthening norms that discourage unilateral export restrictions during periods of global market stress.

Second, the fertilizer industry must move away from using fossil-fuel feedstocks. Low-carbon ammonia production, using renewable electricity and electrolytic hydrogen, is no longer just theoretical. Small-scale and early commercial-scale projects are operating or under development in countries such as Norway, Australia, Chile and India.

Such green ammonia systems could reduce emissions, lower transport costs and decrease import dependency in low-income countries, particularly in those where renewable-energy resources are abundant. However, electrolytic ammonia production remains more expensive than conventional fossil-fuel-based methods, often by a factor of two or more depending on energy prices and system configuration. Closing this cost gap will require sustained policy support and long-term investment, similar to the coordinated public–private efforts that pushed prices of solar photovoltaic technologies and wind-energy systems down[8].

A fertilizer storage warehouse in Iowa.Credit: Amir Hamja/New York Times/Redux/eyevine

A dedicated global financing mechanism for green ammonia — supported by institutions such as the Green Climate Fund, the International Finance Corporation and the International Renewable Energy Agency — could provide the demand signal and the capital required to scale up production. For food-security purposes, priority should be given to decentralized, modular production systems that can be located near renewable-energy sources and agricultural areas, thus reducing exposure to disruptions in long-distance supply chains and geopolitical bottlenecks.

Third, the agricultural sector must reduce its exposure to volatility in fertilizer supplies and prices by improving how nutrients are managed in the field. The efficiency of nitrogen and phosphorus use remains suboptimal in most large cropping systems, particularly in low-income countries[9].

Precision agriculture technologies now enable fertilizers to be applied with much greater spatial and temporal accuracy than conventional uniform-rate methods. Variable-rate application systems, guided by soil sensing, remote imagery and data-driven decision tools, can reduce fertilizer inputs while maintaining or improving yields in many production systems, although outcomes remain site- and crop-specific.

Productivity benefits can be gained when nutrients are delivered in close alignment with crop demand in space and time. Enhanced-efficiency fertilizer formulations, including controlled-release products and nitrification inhibitors, increase nutrient retention and reduce losses. Affordable sensing technologies, including portable spectroscopic soil sensors and drone-based nutrient mapping, are becoming increasingly accessible.

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Nature 653, 26-29 (2026)

doi: https://doi.org/10.1038/d41586-026-01409-8

References

1. World Bank. Food and Nutrition Security Update (World Bank, 2026).

2. Erisman, J. W., Sutton, M. A., Galloway, J., Klimont, Z. & Winiwarter, W. Nature Geosci. 1, 636–639 (2008).

3. UN Trade and Development. Strait of Hormuz Disruptions: Implications for Global Trade and Development Report No. UNCTAD/OSG/TT/INF/2026/1 (UNCTAD, 2026).

4. Vos, R., Glauber, J., Hebebrand, C. & Rice, B. Food Policy 133, 102790 (2025).

5. Food and Agriculture Organization of the United Nations & World Trade Organization. Global Fertilizer Markets and Policies: A Joint FAO/WTO Mapping Exercise (FAO/WTO, 2022).

6. Gorain, S. & Dutta, S. Discov. Food 6, 129 (2026).

7. Food and Agriculture Organization of the United Nations. Food Outlook: Biannual Report on Global Food Markets (FAO, 2025).

8. International Energy Agency. World Energy Outlook 2023 (IEA, 2023).

9. Zhang, X. et al. Nature 528, 51–59 (2015).

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Competing Interests

The authors declare no competing interests.

Work

- The nine-to-five PhD: mere myth or an achievable goal?

- These powerful tools reveal the ‘control knobs’ of the genome

CAREER FEATURE 16 April 2026

The nine-to-five PhD: mere myth or an achievable goal?

Can you squeeze your graduate programme into a 40-hour working week? These 13 current and former PhD candidates reveal their top time-management tips.

By

Laura Woodrow

Find a new job

Illustrations: Lena Tokens

The nine-to-five PhD is an ever-elusive goal: many candidates aspire to it but few end up achieving such an impressive work–life balance. Nature’s 2025 global PhD survey found that 50% of respondents, who were self-selecting, identified a culture of long work hours at their university. This fuels dissatisfaction, and those who spent more than 60 hours a week on their PhDs were significantly more likely to report that they felt dissatisfied with their doctorates (21%) than were those who spent 30 hours or less (16%).

An analysis of more than 26,000 PhD candidates from 14 universities across the United Kingdom between 2006 and 2017, by online PhD information platform DiscoverPhDs.com, found that one in five fail to get their PhD, mostly owing to candidates leaving their programmes early. Another longitudinal study showed that time pressure was correlated with dropout rates[1].

It is not just the candidates who would benefit from making PhD study more sustainable. According to a review published in 2024, PhD holders are more likely to be employed in high-skilled jobs than are undergraduates, and having people with PhDs in teams boosts the productivity of their colleagues who do not have PhDs. PhD holders in industry also help to foster collaboration with academia and investment in research and development, particularly for smaller businesses[2].

Here, 13 past and current full-time PhD candidates, who say they spent or are spending an average of 40 hours a week or less on their doctorates, share their advice and observations.

You’re in charge, revel in the flexibility

“Be sure to take time off to make up for overtime: you often don’t have to work the same hours every day or the same hours as other people. And when you’re waiting for feedback from someone, take that as time off! Don’t be afraid to have a flexible schedule.” — Victoria Crozier, a fourth-year ecology PhD candidate at the University of Saskatchewan in Saskatoon, Canada

“Avoid working long hours continuously. My research project in health informatics involves structured phases — particularly during field data collection — which makes time planning more manageable. During proposal writing, I typically worked for three hours in the morning and three hours in the evening. During fieldwork, I usually spend seven hours per day at a health facility, conducting interviews and observating how people use health-management information systems. After each site visit, I spend an extra one to two hours reviewing notes and audio recordings. Although these periods are intensive, they are bounded in time and predictable, which helps prevent work from expanding uncontrollably.” — Bigten Kikoba, a fourth-year health-informatics PhD candidate at the University of Dodoma in Tanzania

“I typically work Monday to Friday from 9.30 a.m. to 7 p.m., but I take half a day off every week (typically on Wednesday morning). This way, I can work the whole week and not reach the weekend exhausted. I am able to organize my work better, because I can concentrate for longer and make sure to finish during my full days. I can also be more relaxed during my time off.” — Claudia Pisanti, a second-year physics PhD candidate at the University of Bologna in Italy

“The type of research I do — on a branch of functional analysis known as operator theory — greatly favours flexibility, so it’s easier for me to maintain a nine-to-five schedule than for people in a lot of other fields of study, in particular those that involve being in the laboratory. The only equipment I need are my computer plus some pens and paper or chalk and a board, so I can do research basically anywhere. In fact, I often work from home if I can’t go into the office.” — Julio Enciso, a second-year mathematics PhD candidate at the National Polytechnic Institute in Mexico City

“Many of my experiments span two weeks, so I plan exactly which days and times I will be in the lab before starting. This helps me work out the rest of my schedule, such as slots for meetings, without overstretching myself. The same principle applies to computer-based work: if I have multiple experiments planned, I intentionally lighten the load of tasks such as writing. Conversely, if I have a major deadline approaching, I avoid scheduling experiments during that period. There will be occasions when you need to push yourself hard, and others when things slow down and you have more time to breathe.” — Sarah McPhedran, a third-year immunology PhD candidate at the Deeley Research Centre in Victoria, Canada

You might work 40 hours a week, but not necessarily nine-to-five

“The eight-hour day and 40-hour week was created for factory work in which every day was the same, not academic work, for which each day can look wildly different. I don’t even remotely keep to a nine-to-five schedule. I’ve never been a morning person; my body prefers getting up later and working later. During my master’s, I tried to work nine-to-five, but I found myself wasting time and sitting at my desk pretending to work because I was always so tired.” — Victoria Crozier

“Fieldwork tends to defy normal working hours, especially when it comes to multi-day trips that you want to make as cost-effective as possible. Also, the conferences and science fairs at which you present your work do not always follow a standard schedule. Sometimes working outside the nine-to-five routine is a positive thing, especially when researching a fresh, interesting topic: I read paper upon paper out of sheer curiosity late into the night.” — Kateřina Bezányiová, a second-year zoology PhD candidate at Charles University in Prague

Know your limits

“Know when to stop working: avoid answering non-emergency e-mails late in the evening or at weekends. Instead, set aside dedicated time for these admin tasks. However, if there is something with a distant deadline and you have energy to spare, then you might as well start working on it. Making use of the half hour it takes to get the result of a PCR experiment in the lab can save you sleepless nights of feverish work before a deadline.” — Kateřina Bezányiová

“I like referring to my department’s policies regarding working hours. My department has explicit statements recommending a 40-hour work week. So, even if my peers are working more hours, I know that I am performing according to the department’s expectations.” — Karen Arevalo, a fifth-year kidney-cancer-biology PhD candidate at the University of Toronto in Canada

“Developing the ability to say no is an essential skill in graduate school. Setting firm boundaries not only protects your time and mental health, but also often earns respect from others. Consistently saying yes can lead to being overextended or taken advantage of.” — Sarah McPhedran

Be organized

“It all boils down to time management. If you walk into the lab without a plan, you will procrastinate and eventually find yourself working overtime to catch up. If you know something will take you the whole week to finish, plan accordingly. I use a Gantt chart to, as accurately as possible, plan out my PhD and give myself realistic deadlines. It helps me maintain progress by feeling like I’m ticking achievable things off a list.” — Luke Nel, a second-year palaeoecology candidate at Nelson Mandela University in Gqeberha, South Africa

“Set small milestones throughout your project. Finding a dedicated place at the university to carry out non-experimental tasks, such as writing and reviewing, helped me to cut down my hours. It also helped me to avoid taking work home.” — Leo Maia do Amaral, who holds a PhD in engineering and materials science from the University of São Paulo, Brazil

“Plan right from the start. Look at your submission deadlines and plan backwards from those. Every day, plan to write a certain amount of words, and try to stick to that figure.” — Sandra Kiessling, who holds a PhD in engineering from the University of Staffordshire in Stoke-on-Trent, UK

“Know your priorities and how long it takes, realistically, for you to finish your tasks. I often work with samples that easily degrade or get contaminated and so I need to carefully plan my work beforehand. I know what my top priority is and what it would be great to get done. But I’m prepared to wait if something takes longer than expected.” — Kateřina Bezányiová

“It might take time to optimize, but figure out what schedule works best for you. I do my most intensive work in the morning and early afternoon. After about 2 p.m., when I start to feel burnt out, I rest. I often return to work in the evening and use those evenings when I’m tired for mindless administrative work or routine data analysis.” — Sarah McPhedran

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Nature 653, 315-317 (2026)

doi: https://doi.org/10.1038/d41586-026-00509-9

Interviews have been edited for length and clarity.

Subscribe to Nature Briefing: Careers, an unmissable free weekly round-up of help and advice for working scientists.

References

1. Glorieux, A., Spruyt, B., Minnen, J. & van Tienoven, T. P. Stud. Continuing Educ. 47, 155–173 (2025).

2. Read, H., Pugh, A., Riley, B. & Bramley, G. A Review of the Economic and Social Value Produced through Funding PhD Students (National Civic Impact Accelerator, 2024).

TECHNOLOGY FEATURE 04 May 2026

These powerful tools reveal the ‘control knobs’ of the genome

By accelerating the identification of DNA sequences that control gene expression, assays are revealing the hidden grammar of the regulatory genome — and giving scientists the means to rewrite it.

By

Michael Eisenstein

Researchers are learning how to decode the DNA sequences that regulate gene expression. Credit: jxfzsy/iStock via Getty

For all that scientists talk of ‘decoding the genome’, the messy reality is that the genome isn’t even written in a single language. Scientists are fluent in the three-nucleotide codons that make up the protein-coding genes in DNA, but these represent only about 2% of the genomic text. The remainder is written in an entirely distinct language, which researchers are yet to untangle.

“Whenever we sequence a human individual, we get about 3.5 million variants, and only 0.6% of those will be in coding regions,” says Nadav Ahituv, a geneticist at the University of California, San Francisco. That fraction is relatively easy to interpret, Ahituv says, but for the rest, “we really don’t understand what it’s doing — we don’t have a regulatory code”.

But researchers are making progress on decoding the regulatory region of the genome, and learning the underlying grammar of the elements that govern when and where genes are turned on and off. To do so, they have turned to a suite of methods known as massively parallel reporter assays (MPRAs). These tools measure how millions of isolated genetic elements or sequence variants influence the expression of a hand-picked ‘reporter’ gene. That helps researchers to identify the genome’s control knobs and untangle their function without being overwhelmed by all the other parts of the genome. “It’s reducing it down to this synthetic system,” says Ryan Tewhey, a geneticist at the Jackson Laboratory in Bar Harbor, Maine. “But you’re still maintaining enough complexity to probe [genomic] space that we don’t fully understand, and that’s kind of the sweet spot.”

MPRAs could help to clarify the genetic foundations of disease, reveal the changes wrought by evolution and guide development of next-generation therapeutics. They could even be used to train artificial-intelligence systems to design genetic circuits, with applications in health care and other sectors. Customized regulatory elements could, for example, tighten the control of gene-based therapies and ensure that the treatments activate only in specific tissues and under particular conditions, minimizing the ever-present threat of off-target effects. “We’re really trying to engineer things that could be turned on very easily and simply, and even not by a drug,” says Ahituv.

The non-coding genome is not a complete black box, of course. Researchers have identified thousands of proteins, called transcription factors, that have established roles in gene expression and pinpointed the DNA sequences to which they bind.

By mapping these sequences across the genome, scientists can predict which ones initiate gene expression — these are known as promoters — and which act as volume knobs, or enhancers, to amplify that expression under specific conditions. They can also look for signatures of regulatory elements by probing genomic regions in which the DNA is exposed and ready for transcription. Chromosomal DNA is wrapped around proteins, forming material called chromatin. Elements in densely packed chromatin are generally inaccessible to regulatory proteins and thus inactive.

But validating these predictions has historically entailed a painstaking process of testing how different mutations in individual enhancers or promoters affect nearby genes. “I realized that if we wanted to ever computationally really analyse and predict enhancers, we would not need a handful of enhancers — we would need hundreds of thousands,” says Alexander Stark, a computational biologist at the Research Institute of Molecular Pathology in Vienna. Over the past 15 years or so, Stark and others have developed a range of methods, known generally as MPRAs, that enable such functional assessments at a previously unimaginable scale.

The core principles of these assays were established in 2009[1]. Jay Shendure and his colleagues at the University of Washington in Seattle used cloning to generate libraries of small, circular pieces of DNA known as plasmids in which a reporter gene was physically linked to hundreds of variations of a particular promoter. These variants represented every possible single-base mutation in that regulatory sequence, allowing the researchers to survey the role of each nucleotide individually. For identification purposes, each variant was linked to a unique DNA ‘barcode’. The researchers incubated their library in a test tube with the molecular components required for transcription and then sequenced the transcribed RNAs to determine the level of expression triggered by each promoter variant from the abundance of its associated barcode.

Today, MPRAs are typically performed in cultured cells. They are introduced using either a plasmid-based ‘episomal’ approach that never incorporates into the host genome, or lentiviruses, which integrate the libraries into random chromosomal sites. Tewhey prefers the episomal method because of its high efficiency. “Typically, you get a lot more copies in any one cell, so you can test a lot more constructs,” he says. But lentiviruses have their advantages — for example, they can infect cell types that tend to be resistant to plasmid delivery, such as stem cells.

Although essential to making MPRAs work, barcoding also presents one of the technology’s major challenges. Many of the regulatory variants tested in an assay differ by only a nucleotide or two, whereas a typical barcode spans 20 bases, potentially creating an even larger perturbation. “The barcode effect outweighs the variant effect,” explains Hyejung Won, a geneticist at the University of North Carolina at Chapel Hill. As a result, researchers typically use anywhere from 10 to 100 barcodes for each tested sequence. Furthermore, to interpret the results, researchers need to know which barcode is associated with each sequence variant. But a DNA-exchange process known as recombination can jumble these associations.

As a solution, Stark and his colleagues developed an alternative MPRA format, called STARR-seq. This approach takes advantage of the fact that enhancers are frequently located in gene sequences and thus incorporated into the transcribed RNA[2]. “You clone only a single fragment library, and that fragment library is then its own barcode,” says Stark, adding that this approach can reduce assay cost and complexity.

Putting the regulome on the map

Modern MPRAs can be performed on a vast scale, powering truly genome-wide surveys of the regulatory landscape. “The biggest MPRA that we’ve done was in total close to two billion fragments,” says Bas van Steensel, a genomics researcher at the Oncode Institute and the Netherlands Cancer Institute in Amsterdam.

Many of these experiments have focused on mapping enhancer locations throughout the genome and learning how they drive gene expression in specific tissues. Stark says that as his team began performing STARR-seq in fruit-fly and mammalian cells, it discovered many elements that would have been invisible with other methods because they are generally kept inactive in densely packed chromatin. But in the simplified context of a STARR-seq assay, they can be detected readily. “They can still work as naked DNA,” says Stark.

Last year, researchers led by Ahituv and Shendure reported a systematic analysis of almost every known regulatory sequence in the human genome. They tested how different combinations of promoters and enhancers affected reporter-gene activity in three human cell lines[3]. van Steensel’s team has used MPRAs to explore communication between genomic regulatory elements[4]. “Enhancers and promoters show a degree of compatibility — a degree of whether or not they can talk to each other,” he says. “It’s not a day and night thing — these are graded levels of compatibility.”

Regulatory elements in DNA (brown and orange) are often tightly wrapped around proteins.Credit: Mol* Image from the RCSB PDB (RCSB.org) of PDB ID 8XRJ (T. Kujirai et al./Nature Commun.)

His team often assembles libraries by simply breaking chromosomal DNA into small fragments, which allows the researchers to explore the natural genomic landscape. But when the goal is to learn why a given element works the way it does, it’s sometimes preferable to synthesize DNA that contains combinations of known regulatory sequences. Ahituv says his team starts with neutral sequences. “Then we start putting ‘words’ on them, like transcription factor-binding sites, and playing around with the spacing, the order, the amount, the orientation, and seeing what works and what doesn’t.” Alternatively, his group might systematically mutate a specific enhancer or promoter to determine the consequences.

But MPRAs can survey only so much of the genomic terrain. Synthetic DNA libraries become difficult and expensive to manufacture as they get longer, and there are limits to the size of DNA that can be packaged into a plasmid or lentivirus. van Steensel says that it is unusual for an MPRA library to contain sequences longer than 1,000 bases — an serious limitation, since natural genome regulatory regions can be much larger.

MPRAs thus provide a reductionist view of gene regulation. For example, there are open questions around how well different MPRA designs replicate the natural distribution of chromatin proteins on a given sequence in the genome itself, and the behaviour of genomic elements in MPRAs might not reflect how they work in their normal genomic context. “Reporter assays tell you what a sequence can do, not necessarily what it actually does in the genome,” says Tewhey. As such, MPRAs require considerable validation — which can be done, for instance, using CRISPR-based gene-editing strategies or transgenic animal models.

Alternative assay designs are emerging that can yield more biologically meaningful results. Last year, the Ahituv lab described a method called Capture-C, in which researchers first identify regulatory elements that interact physically and then use those as the building blocks for an MPRA experiment[5]. The method has proved especially effective at isolating silencer elements that repress rather than stimulate gene expression, says Ahituv. “We found over 1,000 silencers in our assay, which historically have been very hard to characterize.”

Making sense of mutations

Gene regulation is a matter of location, timing and environmental conditions, and an MPRA performed on a static cell culture will inevitably overlook elements that are active only under certain conditions. Accordingly, many researchers are developing experimental variants to document how different triggers affect regulatory activity.

Anat Kreimer, a computational biologist at Rutgers University in Piscataway, New Jersey, is using MPRAs to understand the regulatory pathways underlying normal and aberrant brain development. In one series of studies, she and her colleagues collected MPRA data over multiple time points to document changes in enhancer activity as embryonic stem cells develop into the progenitors of mature neurons. “We came up with a computational framework to understand which transcription factors are relevant for neural differentiation,” Kreimer says. She, Ahituv and their colleagues have used those data to reconstruct a regulatory blueprint for brain development and show how both timing and the cellular environment modulate gene activation or repression by different regulatory domains[6].

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Nature 653, 318-320 (2026)

doi: https://doi.org/10.1038/d41586-026-01410-1

References

1. Patwardhan, R. et al. Nature Biotechnol. 27, 1173–1175 (2009).

2. Arnold, C. D. et al. Science 339, 1074–1077 (2013).

3. Agarwal, V. et al. Nature 639, 411–420 (2025).

4. Martinez-Ara, M., Comoglio, F., van Arensbergen, J. & van Steensel, B. Mol. Cell 82, 2519–2531 (2022).

5. Arnould, C. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.06.11.658967 (2025).

6. Kreimer, A. et al. Nature Commun. 13, 1504 (2022).

7. Lee, S. et al. Cell 188, 1409–1424 (2025).

8. Weiss, C. V. et al. eLife 10, e63713 (2021).

9. Barbadilla-Martínez, L. et al. Nature 651, 1107–1116 (2026).

10. Gosai, S. J. et al. Nature 634, 1211–1220 (2024).

Research

- Quantum coherent manipulation and readout of superconducting vortex states

- Imaging the flat bands of magic-angle graphene reshaped by interactions

- Structural modifications in strain-engineered bilayer nickelate thin films

- Electronic origin of reorganization energy in interfacial electron transfer

- Decarboxylative alkylation of alkenes

- The evolutionary history and unique genetic diversity of Indigenous Americans

- Rapid concerted switching of the neural code in the inferotemporal cortex

- Adaptive evolution of gene regulatory networks in mammalian neocortex

- Single-cell spatiotemporal dissection of the human maternal–fetal interface

- Heart-nosed bat alphacoronaviruses use human CEACAM6 to enter cells

- A mechanism to initiate emergency type 2 myelopoiesis

- Cell-type-targeted mitochondrial transplantation rescues cell degeneration

- Synthetic super-enhancers enable precision viral immunotherapy

- Precancerous niche remodelling dictates nascent tumour persistence

- Early fibrotic niches establish tumour-permissive microenvironments

- A disease model resource reveals core principles of tissue-specific cancer evolution

- The molecular basis of force selectivity by PIEZO2

- Biosynthesis of cinchona alkaloids

Article Open access Published: 06 May 2026

Quantum coherent manipulation and readout of superconducting vortex states

Ameya Nambisan,

Simon Günzler,

Dennis Rieger,

Nicolas Gosling,

Simon Geisert,

Victor Carpentier,

Nicolas Zapata,

Mitchell Field,

Milorad V. Milošević,

Carlos A. Diaz Lopez,

Ciprian Padurariu,

Björn Kubala,

Joachim Ankerhold,

Wolfgang Wernsdorfer,

Martin Spiecker &

…

Ioan M. Pop

Nature volume 653, pages 63–67 (2026)

Abstract

A defining characteristic of superconductors is their tendency to expel magnetic fields, yet above a critical threshold, magnetic flux penetrates in discrete quanta carried by Abrikosov vortices[1]. The superconducting gap is completely suppressed at the vortex core, rendering them dissipative, semi-classical entities that impact applications from high-current-density wires to quantum devices. Material disorder can drive a crossover to vortices that preserve an energy gap at the core2,3,4, owing to intrinsic[5] or emergent granularity on the scale of the coherence length2,6. Although quantum vortex behaviour could emerge in this effective tunnel-junction regime[7], and signatures have been observed in diverse systems8,9,10, coherent manipulation of vortex states has remained elusive. Here we present evidence that vortices trapped in granular superconducting films can behave as two-level systems, exhibiting microsecond-range quantum coherence and energy relaxation times that reach fractions of a millisecond. Using the tools of circuit quantum electrodynamics[11], we perform coherent manipulation and quantum non-demolition readout of vortex states in granular aluminium microwave resonators, heralding future directions for quantum information processing, materials characterization and sensing.

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Main

From the moment of its discovery, the antagonistic relation between superconductivity and a magnetic field has provided a complex playground for experimentalists and theorists alike. The measurement of the critical field and the Meissner effect[12] have anchored phase-transition theories13,14, and the trapping of quantized flux inside superconductors has provided direct evidence for the existence of Cooper pairs[15]. A hallmark of type II superconductivity in a magnetic field is the formation of Abrikosov vortices: regions of local gap suppression that interact to form lattices[1]. Vortex dynamics is detrimental for a wide range of applications[15], causing heating, flux noise and magnetic hysteresis. However, pinned vortices enable quasiparticle trapping in their core, which enhances the critical current[16] of superconducting films, improves micro-cooler efficiency[17], boosts resonator quality factors[18] and improves qubit coherence19,20. In all these cases, owing to the normal state core, vortices can be understood within semi-classical models.

Gap suppression in the vortex core stems from the crowding of supercurrent at its centre, a consequence of continuity in the superconducting medium. Recent work[3] has proposed that in discretized systems, such as granular superconductors where non-superconducting regions separate superconducting islands, the vortex core can remain gapped and dissipationless; a closely related regime has also been predicted for strongly disordered superconductors, where emergent superconducting islands[2] host vortices with insulating cores[4]. Although quantum behaviour has been revealed by tunnelling of vortices in long Josephson junctions[8] and thin films[9], or via the zero-point motion of pinned vortices[10], direct evidence of coherent superconducting vortex states has yet to be observed.

Here we show that vortices trapped in a superconducting granular aluminium (grAl) microwave resonator form field-tunable two-level systems that behave like effective spins, strongly coupled to the resonator. They can therefore be regarded as quantum bits (qubits) that arise from vortex tunnelling in a field-modulated double-well potential formed between pinning sites. These vortex qubit (VQ) states exhibit microsecond coherence and energy relaxation times on the order of 10^2 μs, strikingly different from the dissipative dynamics of Abrikosov vortices. We find that VQs remain stable for weeks, enabling coherent control and quantum non-demolition readout within the framework of circuit quantum electrodynamics[11].

As schematized in Fig. 1, we use a grAl micro-stripline resonator, with resistivity ρ = 3,600 μΩ cm, chosen to be within a factor of 3 below the superconducting-to-insulating transition[21]. In this regime, the film consists of Al grains of 3–4-nm diameter separated by amorphous AlOx barriers, resulting in a coherence length ξ ≈ 7 nm and London penetration depth of λL ≈ 4 μm (refs. 5,22,23). The resonator is placed in a cylindrical copper waveguide (Supplementary Information section I) anchored to the 20-mK base plate of a dilution cryostat and measured in reflection. When cooled in zero magnetic field Bcd = 0 μT, the grAl resonator behaves as a weakly anharmonic oscillator[24], with a fundamental frequency fr = 7.572 GHz, set by its dimensions (3 μm wide, 400 μm long; Extended Data Fig. 1). Figure 1b shows the frequency decrease with perpendicular magnetic field B, as expected with the increase in kinetic inductance25,26.

Fig. 1: Field cooling introduces VQ states that couple to the grAl resonator.

The alternative text for this image may have been generated using AI.

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a, When cooled to 20 mK in perpendicular magnetic field Bcd = 0 μT, a λ/2 micro-stripline grAl resonator behaves as a quantum harmonic oscillator with resonant frequency ωr. The electric- and magnetic-field distributions are illustrated in blue and red, respectively. The grAl film has a thickness of t = 20 nm and a superconducting coherence length of ξ = 7 nm. b, Phase response arg(S_11) of the resonator measured in reflection, as a function of perpendicular magnetic field B applied after cooling. The measured parabolic suppression of the resonance is given by the increase in kinetic inductance owing to screening currents[25], and the field range is limited by the vortex penetration threshold[26]. c, When cooled in perpendicular magnetic field Bcd = 820 μT (see main text), vortices enter the grAl resonator and the system exhibits a behaviour akin to a flux qubit with a transition frequency ωq coupled to a readout resonator, as illustrated in d and e. d, The measured phase response of the resonator as a function of B reveals avoided level crossings, suggesting coupling to vortex states. The purple dashed line shows a fit to the asymmetric quantum Rabi model (equation (2)), yielding the coupling g/2π = 95 MHz. e, Extracted VQ frequency fq from two-tone spectroscopy (see inset) as a function of B. The green line corresponds to the joint fit of data in d and e to equation (2), and the purple dashed line marks the bare resonator frequency fr. Inset: two-tone spectroscopy in the vicinity of B_0 corresponding to the minimum frequency of the VQ. The colour scale indicates the measured phase response as a function of the frequency fd of the second drive.

Following field-cooling, sweeping B reveals avoided level crossings in the grAl resonator response as illustrated in Fig. 1d, which we interpret as evidence of strong coupling with g/2π = 95 MHz to vortex states. To extract the mode’s spectrum, we sweep a second microwave drive while probing the readout resonator (Fig. 1e). We observe a minimum vortex mode frequency fq = 2 GHz at the sweet spot B_0 = 128 μT (Fig. 1e, inset), with a slope of the hyperbolic field dispersion γ = 20 GHz mT−1, reminiscent of a flux qubit[27]. As the field approaches the sweet spot, the resonance narrows, pointing to magnetic-field fluctuations as dominant noise source[28]. From measured spectra across 32 field-cooling cycles in six different resonators, we extract values of g, fq, B_0 and γ that are of similar order of magnitude but vary between cycles (Supplementary Information section II), suggesting different underlying vortex configurations. Repeated resonator reflection coefficient S_11 measurements at the sweet spot reveal two distinct clusters in the quadrature plane (Fig. 2a), indicating that the vortex state has a lifetime well beyond the 1.2-μs integration time, thereby enabling single-shot state discrimination. As demonstrated in Fig. 2b, by driving at fq, we can calibrate a 20 ns π-pulse, which inverts its thermal population (see Supplementary Information section III for the Rabi oscillations). These signatures define the VQ states \(| {\rm{g}}\rangle \) (ground) and \(| {\rm{e}}\rangle \) (excited). From their steady-state populations, we extract a 74-mK effective temperature. The VQ–resonator interaction induces a state-dependent dispersive shift \(\chi /2{\rm{\pi }}={f}_{{\rm{r}},| {\rm{e}}\rangle }-\,{f}_{{\rm{r}},| {\rm{g}}\rangle }\). As shown in Fig. 2c, fitting the resonator’s phase response to the centres of in-phase and quadrature (IQ) clouds measured versus readout frequency yields χ/2π = −1.32 MHz (see Supplementary Information section IV for all measured IQ clouds).

Fig. 2: The asymmetric quantum Rabi model describes the VQ dispersively coupled to its resonator.

The alternative text for this image may have been generated using AI.

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a, Consecutive S_11 measurements at the sweet spot show two IQ clouds in the complex plane. The relative occurrence of points in the clouds corresponds to the population of the \(| {\rm{g}}\rangle \) (ground) and \(| {\rm{e}}\rangle \) (excited) states. The qubit excited state population Pq yields an effective qubit temperature Teff ≈ 74 mK. b, Measured IQ clouds following a 20-ns drive at fq calibrated to implement a π-pulse show a population inversion as expected for a two-level system. The black circles have a radius of 1.5 standard deviation. c, Resonator phase response arg(S_11), obtained from the centres of the IQ clouds, measured versus readout frequency fRO in the vicinity of fr. A fit to the data (black solid line) yields a dispersive shift of χ/2π = −1.32 MHz. The dark red (\(| {\rm{g}}\rangle \)) and light red (\(| {\rm{e}}\rangle \)) points correspond to the data in a at fRO = 7.5714 GHz (dashed line). d, Variation of χ with magnetic field B, shown as triangles, with the yellow triangle corresponding to the measurement in b. The dashed line indicates the expected values from the asymmetric quantum Rabi model equation (2) with gAQRM/2π = 92.5 MHz, and the dash-dotted line to the symmetric quantum Rabi model equation (1) with gSQRM/2π = 20 MHz. The solid green line represents the qubit frequency (right axis), similar to Fig. 1d.

For further insight into the nature of the VQ and its interaction with the grAl resonator, we measure χ versus field, as shown in Fig. 2d. We model it using the quantum Rabi model (QRM) for a spin S = 1/2 coupled via \({{\mathcal{H}}}_{{\rm{c}}}=\hbar g({\hat{a}}^{\dagger }+\hat{a}){\sigma }_{x}\) to a harmonic oscillator with frequency ωr and Hamiltonian \({{\mathcal{H}}}_{{\rm{r}}}=\hbar {\omega }_{{\rm{r}}}\left({\hat{a}}^{\dagger }\hat{a}+\frac{1}{2}\right)\) (Supplementary Information section V). Here \({\hat{a}}^{\dagger }\) and \(\hat{a}\) are the resonator bosonic operators, ħ = h/(2π) is the reduced Planck constant and σx is the Pauli matrix for a spin S = ħ/2σ. The interaction energy between the spin and the magnetic field is \(\gamma {\bf{S}}\cdot (\widetilde{{\bf{B}}}+{{\bf{B}}}^{{\prime} })\), where γ is the gyromagnetic ratio and the field consists of two contributions: a pseudo-field \(\widetilde{{\bf{B}}}\) that sets the VQ energy at the sweet spot, and the applied magnetic field \(| {{\bf{B}}}^{{\prime} }| =B-{B}_{0}\) measured from the sweet spot. We compare joint fits of the measured VQ and resonator frequencies in field (Fig. 1d,e), using the symmetric quantum Rabi model (SQRM)

$${{\mathcal{H}}}_{{\rm{S}}{\rm{Q}}{\rm{R}}{\rm{M}}}={{\mathcal{H}}}_{{\rm{r}}}+{{\mathcal{H}}}_{{\rm{c}}}+\frac{\hbar \gamma }{2}{{\sigma }}_{z}\sqrt{{\mathop{B}\limits^{ \sim }}^{2}+{B}^{{\prime} 2}},$$

(1)

and the asymmetric quantum Rabi model (AQRM)

$${{\mathcal{H}}}_{{\rm{A}}{\rm{Q}}{\rm{R}}{\rm{M}}}={{\mathcal{H}}}_{{\rm{r}}}+{{\mathcal{H}}}_{{\rm{c}}}+\frac{\hbar \gamma }{2}{{\sigma }}_{z}\mathop{B}\limits^{ \sim }-\frac{\hbar \gamma }{2}{{\sigma }}_{x}{B}^{{\prime} }\,.$$

(2)

Only the AQRM captures the non-monotonic dependence of χ with B. In contrast, the SQRM predicts a monotonically decreasing χ with detuning from the resonator. Moreover, using the coupling constant g from the joint fit in Fig. 1d,e, we obtain quantitative agreement for the measured χ, as shown in Fig. 2d. This suggests that the VQ, possibly consisting of persistent currents, arises from dynamics in a double-well potential, analogous to fluxon tunnelling through the Josephson junction of a flux qubit[27]. Within this model, the pseudo-field \(\widetilde{B}\) is given by the fluxon tunnelling amplitude[7].

We complete the characterization of the VQ with time-domain measurements at the sweet spot. As shown in Fig. 3a, the fitted energy relaxation time is T_1 = 186 μs, with values ranging from 40 μs to 300 μs across multiple VQ preparation cycles (Supplementary Information section VI). Relaxation times extracted from VQ quantum jumps (Supplementary Information section VI) fall within the temporal fluctuations observed in free decay, indicating a quantum non-demolition readout. Remarkably, the VQ exhibits quantum coherence, with a Ramsey time \({T}_{2}^{* }=440\,{\rm{n}}{\rm{s}}\), which extends to \({T}_{2}^{{\rm{e}}{\rm{c}}{\rm{h}}{\rm{o}}}=1.2\,{\rm{\mu }}{\rm{s}}\) in Hahn-echo measurements, which suppress the low-frequency noise (Fig. 3b,c). The Ramsey fringes exhibit a beating pattern, corresponding to a toggling of the VQ’s frequency between two values separated by 1.9 MHz. This feature is sometimes also observed in superconducting qubits[29], possibly indicative of charge noise or conductance channel fluctuations. The measured VQ lifetime T_1 is competitive with superconducting flux qubits30,31, whereas the coherence \({T}_{2}^{* },{T}_{2}^{\mathrm{echo}}\) remains more modest, in line with flux qubit devices realized entirely from disordered superconductors29,32. Away from the sweet spot, both \({T}_{2}^{* }\) and \({T}_{2}^{\mathrm{echo}}\) decrease (Supplementary Information section VI), consistent with flux-noise-limited dephasing in loop-based superconducting circuits and motivating a detailed comparison with established flux-noise mechanisms30,31,33. In future experiments, detailed noise characterization[31], environment polarizability[34], as well as susceptibility to in-plane magnetic[33] and electric fields[35] could shed light on the microscopic origin of the VQ and its environment.

Fig. 3: Measurement of low loss and coherence in the VQ.

The alternative text for this image may have been generated using AI.

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a, Free energy decay measured after a 20-ns π-pulse applied selectively to the VQ measured in the ground state \(| {\rm{g}}\rangle \). The readout pulse has a duration τm = 1.2 μs. The excited VQ population as a function of wait time t is fitted with an exponential corresponding to T_1 = 186 μs (solid line). b, Ramsey fringes exhibit a beating pattern, resulting from two frequencies separated by fbeat = 1.9 MHz. We extract \({T}_{2}^{* }\) Ramsey coherence times of 440 ns. c, Spin Hahn-echo measurement with extracted \({T}_{2}^{{\rm{e}}{\rm{c}}{\rm{h}}{\rm{o}}}=1.2\,{\rm{\mu }}{\rm{s}}\). For each panel, the corresponding pulse sequence is sketched at the top, and the insets show measured coherence times, with error bars indicating the standard deviation from the fit, over several hours.

To give a hypothesis for the origin of the double-well potential of the VQ, we consider the process of introducing vortices into the grAl resonator through field-cooling. Their formation and spatial arrangement depend on the value of the flux bias during cooling ϕ = Bcdw^2/Φ_0, where Φ_0 = h/2e is the magnetic flux quantum, e is the charge of an electron, and w is the width of the resonator. In the Pearl limit[36], where the thickness of the film t ≪ λL, the threshold for stable vortices is \({\phi }_{{\rm{S}}}=(2/{\rm{\pi }})\mathrm{ln}(2w/{\rm{\pi }}\xi )\) (refs. 37,38,39), corresponding to ϕS = 3.59 for our geometry (Supplementary Information section VII). The Gibbs energy for vortices threading the film40,41 is

$${G}_{1}(x)={\varepsilon }_{0}\mathrm{ln}\left(\frac{2w}{{\rm{\pi }}\xi }\sin \left(\frac{{\rm{\pi }}x}{w}\right)+1\right)-\frac{{\varPhi }_{0}(B-n{\varPhi }_{0})}{{\mu }_{0}\varLambda }x(w-x),$$

(3)

where \({\varepsilon }_{0}={\varPhi }_{0}^{2}/(2{\rm{\pi }}{\mu }_{0}\varLambda )\) sets the single-vortex energy scale, n is the density of vortices (n = 0 for the first vortex), \(\varLambda =2{\lambda }_{{\rm{L}}}^{2}/t\) is the Pearl length of the resonator, and x is the position of the vortex measured from the resonator edge. As B decreases from BS = ϕSΦ_0/w^2 to zero, the minimum of G_1(x) vanishes (Fig. 4, baseline), and in the absence of pinning the vortex would be expelled.

Fig. 4: Origin of the double-well potential.

The alternative text for this image may have been generated using AI.

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Gibbs free energy G_1 (equation (3), baseline) of a single vortex, shown with added pinning potentials modelled as Lorentzian dips, in units of \({\varepsilon }_{0}={\varPhi }_{0}^{2}/2{\rm{\pi }}{\mu }_{0}\varLambda \approx \text{2}\,\mathrm{THz}\). The vortex position is measured from the edge, as indicated by the coordinate axis. Colours represent different applied magnetic fields from BS = ϕSΦ_0/w^2 to −B_0. Top inset: example of a double-well potential formed by the energy landscape of adjacent pinning sites separated by δLR and offset in energy by ϵ. The localized wavefunctions correspond to the two vortex positions \(| {\rm{L}}\rangle \) and \(| {\rm{R}}\rangle \), coupled by tunnelling amplitude Δ, with an energy splitting of ħωq. Bottom inset: at the sweet spot (B_0, which can be higher or lower than BS; see Supplementary Information section II), the double well is degenerate, with VQ states forming symmetric and antisymmetric combinations of the localized wavefunctions, yielding ħωq = 2Δ.

To account for the measured stability of the VQ across magnetic-field sweeps (Fig. 1), we incorporate pinning potentials, presumably abundant given the disordered nature of grAl. They are modelled by adding Lorentzian dips \({V}_{{\rm{p}}{\rm{i}}{\rm{n}}}={V}_{i}{(1+{(x-{x}_{i})}^{2}/{\sigma }_{i}^{2})}^{-1}\) to G_1(x), at random positions xi, depth Vi and width σi, sketched as the coloured energy landscapes in Fig. 4. A vortex tunnelling between pinning sites forms a double-well potential (Fig. 4, top inset), in which B tunes the relative pinning depths according to equation (3). At B_0, the minima are degenerate and the vortex delocalizes, with \(| {\rm{g}}\rangle \) and \(| {\rm{e}}\rangle \) given by symmetric and antisymmetric superpositions of \(| {\rm{L}}\rangle \) and \(| {\rm{R}}\rangle \) wavefunctions (Fig. 4, bottom inset).

This hypothesis is supported by the fact that typically measured gyromagnetic ratios γ/2π = 3–25 GHz mT−1 are consistent with flux tunnelling between pinning sites separated by tens of nanometres (Supplementary Information section VII), reminiscent of tunnelling through grAl nanojunctions[29]. Moreover, to leading order, a kinetic-inductance-mediated VQ–resonator coupling g/ωr ≈ 0.1–1% (Supplementary Information section VIII) is consistent with the observed avoided level crossings. Although single-vortex pinning can account for the observed VQ, it is well established that multiple vortices simultaneously enter the resonator once the threshold for entry is reached[39], as illustrated by the set of Gibbs curves in the foreground of Fig. 4. We estimate the VQ–VQ interaction in the 10–100 MHz range (Supplementary Information section IX), suggesting that collective vortex dynamics is unlikely. Nevertheless, distinguishing between single- and multi-vortex dynamics, for instance, using imaging methods40,42,43,44,45, or by shaping the resonator width[18], remains an important avenue for future research.

In conclusion, field-cooling a grAl micro-stripline resonator reproducibly generates VQ states that couple dispersively to the resonator and can be coherently driven. Our results demonstrate that superconducting vortices can harbour quantum coherence on microsecond timescales. Remarkably, the VQ energy relaxation times are on the order of hundreds of microseconds, comparable to those of engineered superconducting qubits11,30, and qualitatively distinct from the dissipation expected for Abrikosov vortex dynamics. This supports a picture of grAl as a three-dimensional network of Josephson junctions, expected to host gapful-core vortices once the coherence length ξ becomes comparable to the intergrain spacing ℓ, with a growing minigap for ξ ≲ ℓ (refs. 2,3). The observed dispersive shifts and spectra are accurately captured by an asymmetric quantum Rabi model, consistent with a two-level system in a double-well potential. Microscopically, this may arise from vortex tunnelling between pinning sites, modulated by the magnetic-field dependence of the Gibbs energy. This hypothesis, although consistent with our measurements, remains to be confirmed by future experiments such as scanning tunnelling or scanning superconducting quantum interference device (SQUID) microscopy.

Looking ahead, the measurement of quantum coherence in vortex states, along with their relative technological simplicity, opens several exciting avenues in quantum science. Disordered superconductors beyond grAl46,47 or engineered two-dimensional networks of Josephson junctions[48] may host similar VQs, shedding light onto the complex physics in the vicinity of the superconductor-to-insulator transition49,50. Moreover, this would offer an embedded tool for material characterization at the microscopic level. In the same spirit, if the observed dynamics indeed stem from single-vortex tunnelling, VQs could be harnessed for nanoscale sensing. Ultimately, engineering the pinning landscape and device geometry, combined with noise spectroscopy and susceptibility measurements to magnetic and electric fields, will be crucial to enhance VQ coherence and possibly launch a vortex-based quantum information platform.

Methods

We fabricated the sample on a double-side polished c-plane sapphire substrate using electron-beam lithography and wet etching. The substrate was cleaned in a 50 °C acetone bath and rinsed in ethanol, before applying an Ar/O_2 ion descum process using a Kaufman ion source in a PreVac evaporation system. After performing titanium gettering, we deposited a 20-nm grAl film at room temperature by evaporating aluminium at 1 nm s−1 under dynamic oxidation. The resulting film had a sheet resistance of 1.5 kΩ □−1. To define the resonator geometry, we patterned a 300-nm-thick resist layer (ARN 7520.18) using a 50-keV e-beam writer. We developed the resist for 40 s in an AR 300-47:H_2O mixture (4:1), followed by wet etching in MF 319. Extended Data Fig. 1 shows microscopy of the resulting grAl resonator structures. The resonator had a uniform height of 24 nm with slanted edges.

Data availability

The data supporting this study are available via Zenodo at https://doi.org/10.5281/zenodo.18207758 (ref. [51]).

Code availability

The analysis scripts used in this study are are available via Zenodo at https://doi.org/10.5281/zenodo.18207758 (ref. [51]).

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Acknowledgements

We thank M. Feigel’man, L. Glazman, G. Kirchmair, N. Roch, A. Shnirman, U. Vool and W. Wulfhekel for discussions; and we acknowledge the technical support from L. Radtke and S. Diewald. We acknowledge funding from the Baden-Württemberg Stiftung under the QT-10 project (QEDHiNet). The KIT Nanostructure Service Laboratory provided support for the facilities used. We recognize the qKit measurement software framework. We acknowledge support by the state of Baden-Württemberg through the bwHPC. M.S. and N.G. acknowledge support from the Federal Ministry of Research, Technology, and Space (BMFTR) within the GEQCOS project (FKZ: 13N15683). N.Z. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG - German Research Foundation) under project number 450396347 (GeHoldeQED). S. Günzler, D.R. and W.W. acknowledge support from the Leibniz award WE 4458-5. C.P. and J.A. acknowledge support from the Carl-Zeiss-Stiftung via the QPhoton centre as well as through the Deutsche Forschungsgemeinschaft (DFG) via AN336/13-1. M.V.M. acknowledges support from the Research Foundation-Flanders (FWO).

Funding

Open access funding provided by Karlsruher Institut für Technologie (KIT).

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Authors and Affiliations

1. IQMT, Karlsruhe Institute of Technology, Karlsruhe, Germany

Ameya Nambisan, Simon Günzler, Dennis Rieger, Nicolas Gosling, Simon Geisert, Victor Carpentier, Nicolas Zapata, Mitchell Field, Wolfgang Wernsdorfer, Martin Spiecker & Ioan M. Pop

2. PHI, Karlsruhe Institute of Technology, Karlsruhe, Germany

Simon Günzler, Dennis Rieger, Wolfgang Wernsdorfer, Martin Spiecker & Ioan M. Pop

3. COMMIT, Department of Physics, University of Antwerp, Antwerp, Belgium

Milorad V. Milošević

4. Institute for Complex Quantum Systems and IQST, University of Ulm, Ulm, Germany

Carlos A. Diaz Lopez, Ciprian Padurariu, Björn Kubala & Joachim Ankerhold

5. German Aerospace Center (DLR), Institute of Quantum Technologies, Ulm, Germany

Björn Kubala

6. Physics Institute 1, Stuttgart University, Stuttgart, Germany

Ioan M. Pop

Contributions

A.N., S. Günzler, D.R., M.S. and I.M.P. conceived of and designed the experiment. A.N. and V.C. contributed to the device fabrication. A.N., S. Günzler, D.R., N.G., S. Geisert, V.C., N.Z., M.F. and M.S. participated in the measurements. A.N., S. Günzler., D.R., N.G. and M.S. analysed the data. A.N., S. Günzler and I.M.P. led the paper writing, and all authors contributed to the text. A.N., M.V.M., C.A.D.L., C.P., B.K., M.S. and I.M.P contributed to theoretical modelling. J.A., W.W., M.S. and I.M.P. supervised the project.

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Correspondence to Ioan M. Pop.

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Extended data figures and tables

Extended Data Fig. 1 Microscopy of the grAl resonator.

(a) Optical micrograph showing the 400 μm long resonator. (b) Scanning electron microscope (SEM) image of the resonator’s end (top) and center (bottom) reveals 250 nm wide slanted edges. (c) Atomic force microscopy (AFM) image acquired in tapping mode (tip radius < 8 nm). (d) The AFM line profile yields a film thickness of t = 24 nm.

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This Supplementary Information file has the following sections: I. Set-up and magnetic-field calibration; II. Repeated initialization of VQs; III. Rabi oscillation and active-state preparation; IV. Dispersive measurements; V. Asymmetric quantum Rabi model; VI. Extended time-domain characterization and quantum jumps of VQs; VII. Magnetic-field dispersion from a vortex tunnelling model; VIII. VQ–resonator interaction; IX. Vortex–vortex Interaction; and References.

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Nambisan, A., Günzler, S., Rieger, D. et al. Quantum coherent manipulation and readout of superconducting vortex states. Nature 653, 63–67 (2026). https://doi.org/10.1038/s41586-026-10441-7

- Received: 28 October 2025

- Accepted: 23 March 2026

- Published: 06 May 2026

- Version of record: 06 May 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10441-7

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Imaging the flat bands of magic-angle graphene reshaped by interactions

J. Xiao,

A. Inbar,

J. Birkbeck,

N. Gershon,

Y. Zamir,

Y. Vituri,

T. Taniguchi,

K. Watanabe,

E. Berg &

…

S. Ilani

Nature volume 653, pages 68–75 (2026)

Abstract

Electron interactions in quantum materials fundamentally shape their energy bands and, with them, the material’s most intriguing quantum phases. Magic-angle twisted bilayer graphene (MATBG)1,2,3 has emerged as a model system in which flat bands lead to a variety of such phases, yet the precise nature of these bands has remained elusive owing to the lack of high-resolution momentum-space probes. Here we use the quantum twisting microscope (QTM) to directly image the interacting energy bands of MATBG with unprecedented momentum and energy resolution. Away from the magic angle, the observed bands closely follow the single-particle theory. At the magic angle, however, we observe bands that are completely transformed by interactions, exhibiting light and heavy electronic character at different parts of momentum space. On doping, the interplay between these light and heavy components leads to a variety of notable phenomena, including interaction-induced bandwidth renormalization, Mott-like cascades of the heavy particles and Dirac revivals of the light particles. We also uncover a persistent low-energy excitation tied to the heavy sector, suggesting a new unaccounted degree of freedom. These results resolve the long-standing puzzle in MATBG—the dual nature of its electrons—by showing that it originates from electrons at different momenta within the same topological heavy-fermion-like flat bands. More broadly, our results establish the QTM as a powerful tool for high-resolution spectroscopic studies of quantum materials previously inaccessible to conventional techniques.

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Main

Since the discovery of MATBG1,2,3 a central challenge has been to determine whether its quantum states are best understood through a local framework or a topological one. On one hand, its correlated insulating states2,4,5,6,7,8 observed at integer moiré fillings resemble Mott insulators, which are naturally described by localized electrons. On the other hand, its competing Chern insulating states9,10,11,12,13,14,15,16 can only be explained through topological arguments. Perhaps the most puzzling observation, however, is that electrons in MATBG seem to exhibit contradictory behaviours even within the same electronic state: compressibility[17] and spectroscopic[18] measurements have revealed cascades of Dirac-like compressible states, characteristic of itinerant electrons. Yet, entropy measurements19,20 have uncovered a giant magnetic entropy that could only be explained if nearly all electrons behave as local moments. This noteworthy duality[19]—simultaneous signatures of both localized and extended behaviour—remains one of the most fundamental unresolved puzzles in the study of MATBG.

A range of theoretical approaches, including Hartree21,22, Hartree–Fock17,23,24,25,26, quantum Monte Carlo27,28, exact diagonalization29,30, density matrix renormalization group31,32,33 and dynamical mean-field theory34,35, showed that interactions can profoundly affect the flat bands predicted by the Bistritzer–MacDonald (BM) model[1]. A key insight, solidified with the development of the topological heavy fermion (THF)36,37,38 and Mott semimetal[39] frameworks, is that the topological nature of MATBG’s flat bands may lead to its electronic duality: although the electrons largely behave as heavy, localized ‘f-electrons’, topology mandates the existence of momentum-space regions in which they act instead as light, delocalized ‘c-electrons’. These two distinct electronic characters are thus predicted to coexist within the same flat bands, occupying different regions of momentum space. Although nano angle-resolved photoemission spectroscopy experiments have provided rough evidence for the presence of flat bands40,41,42,43,44, the absence of a technique capable of resolving their detailed momentum-space structure has, so far, left these fundamental puzzles unanswered.

In this work, we present high-resolution momentum-space images of the interacting energy bands of MATBG, obtained using quantum twisting microscopy at T = 4 K (ref. [45]). Away from the magic angle, the observed bands closely follow the BM prediction, showing Dirac points at the mini-Brillouin zone (BZ) corners with renormalized Fermi velocity. At the magic angle, however, the bands are substantially reshaped by interactions: across most of momentum space, the bands become extremely flat and largely gapped, hallmarks of heavy f-electron-like behaviour. By contrast, near the Γ point of the mini-BZ, the bands are dispersive and gapless, consistent with the presence of c-like light electrons. The evolution of the band structure with carrier density reveals Mott-like cascades of the heavy electrons and Dirac-like compressibility dominated by the light electrons. We directly observe how the reshuffling of charge between these light and heavy states underlies the previously reported Dirac revivals17,18. While resolving the long-standing dual-nature puzzle, our measurement also uncovers a new approximately 15 meV excitation, not captured by present models, hinting at an important, yet unaccounted for, degree of freedom in MATBG.

Experimental set-up

Our experiment examines momentum-resolved tunnelling across a two-dimensional junction formed between monolayer graphene (MLG) on the QTM tip (Fig. 1a, purple) and twisted bilayer graphene (TBG) on a flat substrate (Fig. 1a, red/blue). The TBG device includes a metallic back gate and is capped with a bilayer WSe_2 tunnelling barrier (Fig. 1a, green). The experiment has two relevant angles: θTBG, the twist angle of the TBG, and θQTM, the angle between the MLG on the tip and the top layer of the TBG. The QTM enables continuous tuning of θQTM during the experiment with millidegree precision. Although θTBG is fixed at any location in the sample, it naturally varies from one location to another as a result of inherent twist-angle disorder, allowing us to explore a range of θTBG values by performing experiments at different spatial locations.

Fig. 1: Measurement set-up and momentum-resolved spectroscopy of TBG with θTBG = 1.2°.

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a, Schematic of the QTM set-up (left) and cross-section (centre), featuring MLG (purple) on the QTM tip, separated by a bilayer WSe_2 tunnelling barrier (green) from back-gated TBG (top and bottom layers in red and blue). We measure differential tunnelling conductance, dI/dV, between MLG and TBG as a function of bias voltage, Vb. The top view (right) defines the TBG twist angle, θTBG, and the angle between MLG and the top TBG layer, θQTM. b, Momentum-space diagram showing BZs of the TBG’s top (red) and bottom (blue) layers, the resulting mini-BZ (black) and neighbouring mini-BZs (grey). Inset, when the tip is rotated with respect to the sample, the MLG Fermi surface (purple circle) traces an arc in momentum space—the ‘QTM trajectory’ (dashed purple line)—intersecting KT and KB and closely approaching the Γ points in adjacent mini-BZs. c, Calculated single-particle band structure using the non-interacting BM model[1] along the QTM trajectory for θTBG = 1.2°. The flat bands feature Dirac points at KT and KB with renormalized Fermi velocity, reaching maximum bandwidth at the ΓL points, at which they are minimally separated from remote bands by energy gaps. The colour scale indicates wavefunction weight on the top TBG layer. d, Measured dI/dV versus θQTM and Vb for θTBG = 1.2° (Methods section ‘Determine the larger twist angle of θTBG = 1.2°’). The top axis marks key momenta along the QTM trajectory: Γ, KT, M and KB. e, Same as d, plotted as normalized differential conductance, dI/dVnorm, in which, for each θQTM, the valence flat-band peak intensity is normalized to unity. f, Calculated momentum-resolved tunnelling conductance using the BM model[1], incorporating the MLG–TBG junction electrostatics and MLG probe Dirac dispersion (Methods sections ‘Electrostatics of the QTM junction’ and ‘The tunnelling matrix elements’). For this larger-than-magic-angle TBG, the single-particle model captures rather well the measured data. a.u., arbitrary units.

In momentum space (Fig. 1b), the energy bands of TBG reside within a mini-BZ (black hexagon) whose corners, KT and KB (red and blue), correspond to the K points of its top and bottom graphene layers. By rotating the QTM, we rotate the Dirac point of the MLG probe (purple circle) along an arc in momentum space (dashed purple line) that passes through the KT and KB points of the TBG and closely approaches the two Γ points in adjacent BZs, enabling us to map the TBG energy bands along the Γ–KT–M–KB–Γ trajectory (‘QTM trajectory’).

Figure 1c presents the canonical single-particle BM energy bands of TBG along the QTM trajectory for a twist angle slightly larger than the magic angle (θTBG = 1.2°). Theory predicts that the two low-energy flat bands will exhibit symmetry-protected Dirac points at KT and KB and reach the maximal width at the Γ points, at which energy gaps separate them from the higher-energy remote bands. The bands are coloured by the weight in momentum space projected on the top TBG layer, to which the electrons tunnel.

The energy bands at θ TBG = 1.2°

We begin with QTM measurements of TBG with θTBG = 1.2°. Figure 1d shows the tunnelling dI/dV measured as a function of θQTM and bias voltage, Vb, at moiré filling ν ≈ 0. Recalling45,46 that θQTM and Vb translate to momentum and energy through k = θQTMKD (KD is the Dirac momentum) and E = eVb, this measurement directly maps the energy bands of TBG along the QTM trajectory (the corresponding k-points are indicated on the top axis). The positions of the peaks in dI/dV along the Vb axis reveal the energy of the electronic bands, whereas the amplitudes of the peaks provide insight into the wavefunctions. To better trace the energies of the flat bands, for each θQTM, we normalize the valence flat-band peak to unity and plot the resulting dI/dVnorm in Fig. 1e. The very high resolution in the measurement (δk ≈ 0.1° × KD and δE ≤ 10 meV; Methods section ‘Energy and momentum resolution’) allows us to visualize these bands with unprecedented details. Specifically, we observe a Dirac dispersion with a crossing at KT but with a Fermi velocity that is seven times smaller than that in MLG. At Γ, the flat bands attain their maximum width of 70 meV, separated by gaps of 50 meV and 40 meV from the remote conduction and valence bands, respectively. Moreover, we can also identify van Hove singularities in the remote bands at energies of E = 174 and −171 meV.

Figure 1f presents a theoretical calculation of the momentum-resolved dI/dV, using the non-interacting BM bands at θTBG = 1.2°, considering the tunnelling matrix elements (Methods section ‘The tunnelling matrix elements’) as well as the electrostatics of the MLG–TBG junction and the Dirac dispersion of the MLG probe (Methods section ‘Electrostatics of the QTM junction’). Visibly, for this larger-than-magic-angle TBG, the single-particle model captures well both the band energies and the momentum-dependent tunnelling amplitudes.

The energy bands at the magic angle

We now turn to the central results of our paper—the energy bands of MATBG. The measurement is performed in a region of the sample in which atomic force microscopy (AFM) measurements taken at room temperature observed a regular moiré pattern with a periodicity of approximately 13 nm (Methods section ‘Fabrication and characterization of the QTM tip and van der Waals device’), corresponding to θTBG = 1.1°. Figure 2a shows dI/dV measured with the QTM as a function of θQTM and Vb, at a carrier density close to charge neutrality (ν = −0.2). Figure 2b presents the normalized dI/dV (dI/dVnorm), following the normalization described earlier.

Fig. 2: Momentum-resolved spectroscopy of MATBG.

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a, Measured dI/dV as a function of θQTM and Vb at a filling factor of ν = −0.2 for TBG with θTBG = 1.1°. Key momenta (ΓL, KT, M and KB) are marked on the top axis. b, Same data as in panel a but for each θQTM, we normalize the peak intensity of the valence flat band to unity, defining the quantity dI/dVnorm. c, Extracted flat-band dispersion from panel a (dots), in which we trace the centre of the flat-band peaks versus θQTM, with a guide to the eye (red). Error bar is obtained by the standard deviation of the peak centre within a 0.05° range. Grey lines plot the single-particle BM bands for θTBG = 1.1°. d, The intensity of the conduction flat band, given by integrating the area under its peak Ipeak = ∫dI/dV · dVb in panel a, plotted as a function of θQTM (dots). We compare this with the theoretically predicted momentum dependence of the intensity, given by A(k, ω) · Pt(k) · Ps(k), in which A(k, ω) is the spectral function, Pt(k) is the wavefunction projection on the top TBG layer and Ps(k) = ⟨σx + I⟩k is the sublattice projection. The calculation uses the wavefunctions of the BM model and the different lines correspond to different w_0/w_1 ratios in this model (Methods section ‘The tunnelling matrix elements’). We observe an excellent fit for w_0/w_1 = 0.6. a.u., arbitrary units.

The bands observed at θTBG = 1.1° are very different from those at θTBG = 1.2°. They do not show a dispersing Dirac point at KT but instead feature two extremely flat bands, separated by a large energy gap of approximately 35 meV. Notably, the gap appears at almost all momenta along the measured trajectory, except near the Γ point, at which the bands are gapless. At this specific filling, the conduction band seems wider than the valence band, but we note that this is because of further excitations (discussed with Fig. 4). Tracing the centre energy of the bands versus momentum (Fig. 2c, dots) shows that the bands are flat to within about 4 meV. Compared with the bands predicted by the non-interacting BM model for θTBG = 1.1° (grey lines), we see a substantial difference, demonstrating the central role of interactions in determining their shape.

As well as revealing band energies, the spectral function also carries crucial amplitude information. Notably, we observe a gradual reduction in the dI/dV amplitude of the flat bands along the momentum trajectory from KT to KB (Fig. 2a). To quantify the total spectral weight of a specific band—independent of its lifetime—we define the peak intensity, Ipeak = ∫dI/dV · dVb, obtained by integrating the area under its corresponding dI/dV peak. Plotting Ipeak versus θQTM for the conduction flat band (Fig. 2d, dots) reveals a sharp increase near the left Γ point, followed by a gradual decline towards the KT point and a steeper drop to near zero at KB. Beyond KB, the intensity remains negligible, falling below the measurement noise floor.

The magnitude of dI/dV encodes tunnelling matrix element information, offering further insights into the character of individual wavefunctions. Specifically, dI/dV is proportional to A(k, ω) · Pt(k) · Ps(k), in which A(k, ω) is the spectral function, Pt(k) is the projection operator onto the top TBG layer and Ps(k) = ⟨σx + I⟩k is the sublattice projection operator (Methods section ‘The tunnelling matrix elements’). At the magic angle, the BM model predicts that almost all wavefunctions in the mini-BZ have equal population on the top and bottom layers, Pt(k) ≈ 0.5 (Methods section ‘The tunnelling matrix elements’). However, the sublattice projection should exhibit strong momentum dependence, which is highly sensitive to the ratio w_0/w_1, which reflects the relative size of AA and AB stacking regions. Comparing our measurements with theoretical predictions[47] for various w_0/w_1 ratios (coloured traces), we find excellent agreement for w_0/w_1 = 0.6, whereas other values show substantial discrepancies. This value represents a notable deviation from the accepted value of 0.8 and could have marked implications on the physics predicted in MATBG33,48.

Evolution of the interacting bands with filling

Figure 3a presents the energy bands at all integer fillings from ν = −4 to ν = 4, as well as at ν = 4.6. At most integer fillings, two sharp and extremely flat bands are observed. At charge neutrality (ν = 0), these bands are symmetrically positioned about the Fermi energy, EF (Vb = 0 mV). On electron doping, the conduction flat band becomes pinned at EF from above, whereas the valence flat band lies ΔE ≈ 15 meV below EF . The opposite occurs for hole doping, in which the valence flat band becomes pinned to EF from below and the conduction flat band shifts to ΔE ≈ 15 meV above it. By ν = 4.6, EF has moved well into the remote bands.

Fig. 3: Evolution of the energy bands with filling.

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a, Momentum-resolved dI/dVnorm (normalized as in Figs. 1e and 2b) versus θQTM and Vb for all integer fillings ν = −4 to ν = 4 and for ν = 4.6 (ΓL and KT indicated on the top x-axis). At all fillings, the bands are flat throughout, except near the ΓL point. At neutrality, the two bands are symmetric around the Fermi level, EF (Vb = 0), and are separated by about 35 mV, except at ΓL, where they touch. At integer electron doping, the conduction flat band is pinned at EF from above, whereas the valence flat band lies ΔE ≈ 15 mV below EF (dashed purple line). At integer hole doping, the valence flat band pins to EF from below and the conduction flat band shifts to ΔE ≈ 15 meV above it (dashed purple line). At ν = 4.6, the flat bands are fully occupied and EF lies within the remote bands near ΓL. b, dI/dVnorm versus θQTM and Vb at fractional fillings between ν = −0.9 and ν = 0.9 (error bar δν ≈ 0.1). The dashed red line marks one band’s evolution from a high-energy smeared ‘plume’ at negative doping to a narrow energy level pinned above EF at positive doping. The dashed blue line marks the opposite evolution of another band. The dashed purple line marks two more energy bands that coexist with the former and remain fixed at Vb = ±15 mV. c, Linecuts of dI/dV versus Vb (black lines) at the same filling factor as panel b, averaged over the flat-band region (Methods section ‘Spectroscopy at different momenta: its Gaussians decomposition and spectral function peak and lifetime analysis versus quasiparticle energy’). The linecuts are decomposed into Gaussian contributions, marked by coloured peaks, revealing a multipeak structure with varying widths and amplitudes. Guiding lines from b are overlaid with the corresponding colour of the Gaussian peaks. The sum of Gaussian contributions in each panel (dashed red lines) shows a good fit. a.u., arbitrary units.

Although the bands shift in a mostly rigid manner—maintaining their flatness across most of momentum space—the behaviour near the Γ point is distinct. At ν = 0, the Γ-point states lie symmetrically between the flat bands, but on electron or hole doping, they undergo a pronounced, filling-dependent energy shift: a local peak at ν = −4 continuously transforms into a dip at ν = 4. This evolution—highlighted schematically in Extended Data Fig. 9—produces an effective ‘stretching’ of the bands around Γ, reaching energy shifts of about 42 meV at ν = 4 and about 54 meV at ν = −4.

This Γ-point stretching arises naturally once we consider how different momentum states respond to the Hartree potential that develops with carrier doping. The key point is that the topological nature of the flat bands dictates that wavefunctions near the Γ point are fundamentally different from those elsewhere in the mini-BZ—a feature already present in the single-particle BM model and which underlies the c/f decomposition used in THF models36,37,38,49,50. The flat-band states, which occupy most of the mini-BZ, are highly localized in real space, on the AA regions of the moiré cell. Conversely, the Γ-point states are extended and mostly avoid AA regions. As filling increases, electrons predominantly occupy these AA-localized states, producing a spatially varying Hartree potential that is strongly peaked at AA. Momentum states with large AA weight therefore experience large Hartree energy shifts, whereas the Γ-point states—with minimal AA overlap—shift only weakly. When energies are referenced to the Fermi level, this momentum-dependent Hartree response manifests precisely as the Γ-point stretching observed in Fig. 3a. Extended Data Fig. 9 and its accompanying discussion (Methods section ‘Hartree-driven band stretching and its connection to wavefunctions at different momenta within the flat band’) provide a step-by-step explanation of this ‘Hartree stretching’ mechanism.

Further insight into the evolution of the electronic structure emerges when we examine partial moiré fillings. Figure 3b plots this evolution from ν = −0.9 to ν = 0.9, with dashed lines following the progression of key spectral features. The dashed red line tracks a flat band that appears initially at ν = −0.9 at high energy as a diffuse ‘plume’—reflecting an incoherent band (spectral peak width comparable with its energy). As filling increases, this band gradually moves towards EF, increasing in spectral weight as it approaches. The dashed blue line follows a band with the opposite trend: it begins as a band with strong spectral weight just below EF and progressively shifts to large negative energies while becoming increasingly smeared. Notably, in both cases, just before the sharp band vanishes while crossing EF at ν ≈ 1 or ν ≈ −1, a new diffuse band appears at high energy. This behaviour is most clearly seen in Fig. 3c, which presents the energy spectrum, momentum-averaged over the flat part of the bands (excluding the region near Γ; Methods section ‘Spectroscopy at different momenta: its Gaussians decomposition and spectral function peak and lifetime analysis versus quasiparticle energy’), along with its decomposition into Gaussian contributions.

Filling-dependent spectroscopy at key momenta

Recent topological heavy fermion36,37,38 and Mott semimetal[39] theories predict that the dual-nature electrons in MATBG arises from the different character of the electronic states at different momenta within the flat bands. To investigate this, we use a key capability of the QTM—performing spectroscopy as a function of filling at a particular momentum.

Figure 4a presents dI/dV measured as a function of Vb and ν at momentum KT. At charge neutrality, a clear energy gap is observed at this momentum. The evolution of the energy bands with filling—previously shown in Fig. 3b—appears here in the familiar form of electronic cascades17,18: near each integer ν, a faint high-energy band emerges, which gradually shifts with filling to lower energies, becoming stronger and sharper the closer it approaches EF. This band crosses EF over a finite range of ν and, once fully occupied, a new incoherent high-energy band emerges, whereas the previous one persists below EF. This cascading behaviour recurs at all integer fillings and is more pronounced on the electron-doped side. Identical filling-dependent spectral evolution is observed at all other momenta along the flat part of the bands (Methods section ‘Spectroscopy at different momenta: its Gaussians decomposition and spectral function peak and lifetime analysis versus quasiparticle energy’). This explains why scanning tunnelling microscopy, which averages over all momenta, captures a similar pattern.

Fig. 4: Spectroscopy at fixed momentum points.

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a, dI/dV versus ν and Vb at KT (top inset indicates location on Fig. 2b). The data show clear cascading behaviour of the flat bands, identical across all momenta along the flat bands (Extended Data Fig. 5). Fixed-energy excitations appear at ΔE ≈ 15 meV for hole doping and ΔE ≈ −15 meV for electron doping (dashed purple lines). b, dI/dV versus ν and Vb at the Γ point (inset). Overlaid is −μ(ν) from compressibility measurements in ref. [17] (black line), showing surprising agreement between the filling dependence of the dI/dV peak at ΓL and the filling dependence of the total chemical potential. c, Correlated doping evolution of spectral features at KT (top) and Γ (middle), alongside inverse compressibility from ref. [17] (bottom). Top and middle panels are zoom-ins of a and b near charge neutrality. The flat band at KT (heavy electrons) shifts downward with increasing ν, reaching the Fermi level near ν ≈ 0.6, at which it begins to fill (dashed black lines). Simultaneously, the Dirac point at Γ (light electrons) also shifts downward and then reverses, moving up as the heavy band becomes populated. This reversal correlates with inverse compressibility becoming negative, signalling charge reshuffling from light to heavy electrons. d, Schematic toy model of Dirac revivals: light c-like electrons with Dirac dispersion (cyan) and heavy f-like electrons with flat bands (red). c–f hybridization is neglected. At ν = 0, the light-electron Dirac point lies at EF, with the heavy-electron Hubbard bands centred around it. At 0 < ν ≲ 0.6, light electrons fill and heavy-electron bands shift towards EF. For 0.6 ≲ ν < 1, the heavy band crosses EF and becomes populated, whereas the Dirac point of the light electrons moves back towards EF, driving the observed ‘Dirac revivals’[17]. At ν ≈ 1, a new heavy band appears at energy U above, restarting the cycle.

A markedly different behaviour is seen at the Γ point (Fig. 4b). Here there is no apparent gap at neutrality nor do we see any sign of cascading bands crossing through EF with filling. Instead, the dI/dV shows a peak at EF at ν ≈ 0, which gradually shifts to negative energies with filling. The shift is not strictly monotonic but exhibits wiggles that align with integer fillings, more prominently seen on the electron-doped side. Notably, when we overlay the negative of the chemical potential, −μ(ν), obtained from the compressibility measurements of ref. [17] (black line), we find that it closely follows this peak, even capturing its wiggles. Because that measurement was performed in a sample with a slightly larger twist angle (θTBG = 1.13°), we do not expect perfect correspondence.

Zooming in further (Fig. 4c), we uncover a strong correlation between the behaviour at the KT point, the Γ point and the inverse compressibility, dμ/dn(n), from ref. [17] (Fig. 4c top, centre and bottom). From ν ≈ 0 to ν ≈ 0.6, the peaks at both Γ and KT shift monotonically to lower energies with increasing filling. At ν ≈ 0.6, as the flat band at KT reaches EF, the peak at Γ reverses direction and begins to increase in energy. Notably, this turning point coincides with a sign change in the total inverse compressibility, which becomes negative. This behaviour continues until ν ≈ 1, at which the flat band at KT becomes fully occupied, the inverse compressibility sharply rises and the peak at Γ reverses direction again.

These observations naturally follow from the coexistence of two classes of electronic states that satisfy two basic conditions: they have light and heavy character and they couple differently to the Hartree potential. Although these conditions are naturally realized by the microscopic BM model ‘c’ and ‘f’ orbitals, they can be satisfied much more broadly by any two ‘c-like’ and ‘f-like’ components that retain these essential features. This is illustrated by a simple toy model (Fig. 4d and Methods section ‘Toy model to understand the essence of the Dirac revivals’), in which the light c-like electrons near Γ are described by a Dirac dispersion (cyan) and the heavy f-like electrons by flat bands (red). For simplicity, we neglect c–f hybridization, important in the actual bands but not essential for understanding the non-monotonic filling of the light electrons. In this picture, the measurement at Γ tracks the filling-dependent evolution of the Dirac point, whereas the measurement at KT follows the flat bands. At ν = 0, the flat bands lie away from EF and the Dirac point sits at EF. On doping, carriers initially populate the c-like bands, shifting their Dirac point downward in energy. Around ν ≈ 0.6, a flat band reaches EF and begins to fill, at which point the Dirac carriers start to depopulate and the Dirac point shifts upward towards EF. This behaviour is identical to ‘Dirac revivals’ of ref. [17], except that here the reshuffling of charge occurs between the light and heavy electrons rather than between flavours, as originally postulated[17]. The resulting evolution reflects a competition between a c–f Coulomb repulsion term, which drives the revivals and tends to depopulate the c-like electrons (producing upward wiggles), and Hartree terms, which lead to progressive population of the c-like electrons with filling (producing the overall downward trend). This competition naturally explains why the revivals do not fully reset to the Dirac point at integer fillings.

Although some key aspects of our experiments are captured by theoretical models of interacting electrons in MATBG23,24,27,28,34,35,38,39, one persistent feature remains unexplained: the excitation appearing at ΔE ≈ 15 meV for holes and ΔE ≈ −15 meV for electrons (purple lines in Figs. 3a and 4a). Similar features were previously observed in non-momentum-resolving scanning tunnelling microscopy measurements16,18, but our momentum-resolved experiments now show that this excitation exists only in the flat section of the bands (Fig. 4a) and not at the Γ point (Fig. 4b), suggesting its connection with the heavy electrons. Notably, its energy remains fixed with filling, in contrast to the strongly filling-dependent evolution of the heavy-electron bands themselves. The universality of this 15 meV scale—identical across widely separated regions in the sample and insensitive to local strain (Methods section ‘Spectroscopy of flat bands at different locations’)—may hint at the presence of an intrinsic excitation or collective mode whose microscopic origin remains unknown, but which may play an important role in the correlated and superconducting behaviour of MATBG.

Doping dependence of the light and heavy electrons

Finally, we present in Fig. 5 the zero-bias dI/dV, which reflects the spectral weight at EF. When plotted as a function of θQTM and ν, this quantity maps out where electronic states reside in momentum space as a function of doping. In this plot, the heavy electrons flat bands manifest as horizontal features extending over a wide range in momentum space. Their spectral weight oscillates with filling with a periodicity Δν ≈ 1, consistent with the cascading behaviour of the heavy electrons. The spectral weight decays near the Γ point owing to the Hartree stretching of the bands that pushes the light electrons below the Fermi energy. Visibly, the horizontal features get progressively shorter with filling (highlighted by the dashed white lines), indicating that the region of k-space occupied by heavy electrons shrinks, whereas the region occupied by light electrons expands.

Fig. 5: Doping evolution of the relative fraction of light and heavy electrons in momentum space.

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dI/dV versus θQTM and ν measured at Vb = 0 mV. This signal is proportional to the spectral weight at the Fermi energy and shows where electronic states reside in momentum space as a function of doping. The momenta ΓL, KT and M are labelled on the top x-axis and integer fillings are indicated by horizontal grey lines. We plot the raw dI/dV signal, whose magnitude drops sharply left to the ΓL point owing to tunnelling matrix elements. At charge neutrality (ν = 0), the spectral weight is concentrated near Γ and is absent elsewhere. With doping, horizontal features emerge across a wide momentum range, reflecting the spectral weight of the flat heavy-electron bands, appearing at the Fermi level. This weight oscillates with a periodicity Δν ≈ 1, consistent with the cascades of these bands. Near ΓL, the spectral weight disappears owing to the Hartree-induced bending that pushes the light-electron bands below the Fermi energy. The horizontal features get progressively shorter with increased filling, as highlighted by the dashed lines, tracing the momentum at which the dI/dV intensity falls to 80% of its value at each ν (Methods section ‘Fitting the transition between light and heavy electrons’). This boundary is mirrored about the Γ point; together, the two dashed lines delineate the region in momentum space occupied by light electrons, with the remaining area populated by heavy electrons.

Our measurements resolve a long-standing puzzle in the physics of MATBG: the dual nature of its electrons. By directly visualizing the energy bands, we show that this duality arises from distinct characteristics of the flat bands’ wavefunctions in different regions of momentum space. Near the Γ point, the bands are dispersive, consistent with c-like electron behaviour, whereas elsewhere, they remain extremely flat, characteristic of f-like electrons. We directly observe how this leads to stretching of the bands owing to Hartree potential, as well as to electronic cascades and ‘Dirac revivals’ owing to the reshuffling of charge between these two degrees of freedom. Qualitatively, our findings provide direct evidence for the THF and Mott semimetal descriptions of this system. However, we also uncover a new low-energy excitation, not accounted by any existing models, which may play an important role in the exotic physics of MATBG.

Methods

Cryogenic QTM

All measurements in this work were performed in a cryogenic QTM system operating at a temperature of 4 K, as described previously[45]. For conductance measurement, voltage biases are applied using a custom-built digital-to-analogue converter array, capable of supplying both d.c. and a.c. signals with 1 μV resolution. Currents were measured using a FEMTO current amplifier, followed by a National Instruments sampler.

Fabrication and characterization of the QTM tip and van der Waals device

The fabrication of both QTM tips and van der Waals devices on flat substrates were described previously45,46. Briefly, we use the standard dry transfer and polymer membrane transfer techniques to fabricate the TBG sample and the QTM tip, respectively. Extended Data Fig. 1a shows an optical image of the TBG heterostructure, comprising bilayer-WSe_2/TBG/hexagonal boron nitride (h-BN)/Pt on an approximately 100 μm width and 80 μm tall Si/SiO_2 pillar. Extended Data Fig. 1b shows an optical image of the MLG backed by h-BN and graphite placed on a QTM tip.

In Extended Data Fig. 1c, we characterize the TBG sample by conductive AFM scan of the region of magic twist angle carried out at room temperature (Bruker Icon). To compensate for the thermal drift during this AFM scan, we performed a small window scan at rate 10.9 Hz, such that the time for an entire scan is around 20 s. We apply an approximately 1 V d.c. voltage bias between the tip and sample and measure the tunnelling current. The scan image in Extended Data Fig. 1c shows both the moiré lattice and atomic defects in WSe_2 (bright spots). To accurately correct the thermal drift, we trace the position of three defects inside the scan window. We then obtain the drift velocity by following the shift of defects positions between consecutive scans and assuming that, within a single 2D scan, the drift velocity is constant. Extended Data Figure 1c,d shows the image and corresponding fast Fourier transform (FFT) after thermal drift correction. The three moiré lattice vectors obtained from this image are: 12.72 nm, 12.73 nm and 13.14 nm. This gives a twist angle of θTBG = 1.1° ± 0.02° and strain of ϵ = 0.03% ± 0.02%.

In Extended Data Fig. 1e, we characterize the QTM tip by in situ imaging of the tip contact area by an atomic defect in the WSe_2 barrier as a localized tunnelling channel. At large bias (Vb ≈ −900 mV, well outside the range used in the actual spectroscopy measurements), the defect level becomes energetically accessible and provides an extra, spatially localized current path. When the QTM tip passes over the defect, we observe a small but measurable increase in current. By scanning the tip across such defects and mapping this current enhancement, we obtain a real-space image of the tip’s contact footprint.

Extended Data Fig. 1e presents such a high-bias scan, showing several images of the tip footprint, each produced by a different defect, revealing a contact size of approximately 200 × 50 nm. These real-space dimensions set the momentum-space resolution through the Heisenberg uncertainty relation. The measured footprint corresponds to angular momentum resolutions (in angular units) of roughly δθQTM ≈ 0.05° and ≈ 0.2° along the two principal directions. In Extended Data Fig. 2, we show that the sharpest momentum features—obtained in the remote bands—exhibit a full width at half maximum (FWHM) of δθQTM ≈ 0.1°, consistent with the resolution expected from the measured tip dimensions.

Energy and momentum resolution

We determine the energy resolution of our QTM measurements by analysing a sharp spectral feature at ν = −0.7 in Fig. 4a (reproduced in Extended Data Fig. 2a). A linecut through this feature yields a FWHM of δE ≈ 10 meV by fitting a Lorentzian function. This is an upper bound to the energy resolution of the measurements and it may well be coming from the intrinsic lifetime of the energy bands of MATBG at this temperature. To determine the momentum resolution, we analyse a sharp spectral feature of the remote energy bands of MATBG measured under the same conditions as in Fig. 2a. Extended Data Fig. 2b plots a linecut of dI/dV along the dashed white line, cutting through the dispersive bands, yielding an angular FWHM of δθ = 0.1° by fitting a Lorentzian function. This translates into a δk ≈ 0.03 nm−1 momentum resolution. For the flat bands, we trace the dI/dV peak width versus θQTM at zero bias around the Γ point and identify the peak width δθ ≈ 0.5°. This peak width is composed of two peaks overlapping each other, such that the single peak broadening is δθ ≈ 0.25°.

Electrostatics of the QTM junction

The QTM junction is modelled as a three-plate capacitor consisting of: (1) the MLG on the QTM tip; (2) the TBG layer on the sample; and (3) the metallic back gate. Below, to stay consistent with our previous publication, we use in all of the formulas the notation ‘top layer’ (‘T’) and bottom layer (‘B’) to refer to the two layers between which the electrons perform the QTM’s momentum-resolved tunnelling, not the top and bottom layers of TBG. Namely, the top layer is the MLG layer on the QTM tip and the bottom layer is the TBG on the sample side.

The QTM tip and TBG are separated by two layers of WSe_2 (1.2 nm) and the TBG to the back gate by a 37 nm h-BN layer. The corresponding interlayer geometric capacitances are denoted as Cg (tip–TBG) and Cbg (TBG–back gate). A voltage bias Vb is applied between the tip and TBG sample layer. The gate voltage Vbg is applied between the TBG sample and the metal back gate. The electrostatic configuration of the measurements can be described by the following[46]:

$$-e{V}_{{\rm{b}}}={\mu }_{{\rm{B}}}-{\mu }_{{\rm{T}}}-\frac{{e}^{2}{n}_{{\rm{T}}}}{{C}_{{\rm{g}}}},$$

(1)

$$e{V}_{{\rm{bg}}}={\mu }_{{\rm{B}}}+\frac{{e}^{2}({n}_{{\rm{B}}}+{n}_{{\rm{T}}})}{{C}_{{\rm{bg}}}}$$

(2)

Note again that the notation ‘top’ (‘T’) refers to the MLG on the QTM tip and the notation ‘bottom’ (‘B’) refers to the TBG. μB and μT are the chemical potentials of the bottom TBG and top graphene probe, respectively, and nB and nT are their corresponding carrier densities. The back-gate capacitance was experimentally calibrated to be Cbg = 62.3 nF cm−2 based on the gate voltage required to reach filling factor ν = 4 in the magic-angle region (as verified by the conductive AFM measurements; Methods section ‘Fabrication and characterization of the QTM tip and van der Waals device’).

It is important to note that, under a finite voltage bias, TBG becomes doped as a result of the gating from the tip. This means that, at a fixed Vbg, the filling factor ν changes with the tip bias, Vb. To account for this and present our data as a function of the TBG’s filling factor, we determine the gating effect directly from the data. Extended Data Fig. 3 shows the raw measurement of QTM tunnelling conductance, dI/dVb (for simplicity called dI/dV throughout the paper), and the transconductance with respect to the back gate, dI/dVbg, as functions of Vb and Vbg. At integer fillings, and in particular at ν = ±4, dI/dVb shows a suppression and dI/dVbg shows a dipole-like feature. By tracing the ν = 4 line in the dI/dVbg measurements (through a polynomial fitting, overlaid on the data), we extract the trajectory of constant carrier density in the Vb–Vbg plane. We then ‘skew’ the raw data by plotting it as a function of ν that is determined by both Vbg and Vb. The skew-corrected data are shown in Figs. 3 and 4. For Figs. 1 and 2, the scans were taken at a fixed gate voltage without skew correction, so at high bias, the filling factor may deviate from the nominal one.

Along this skewed line, the TBG carrier density nB and chemical potential μB remain fixed. By using the graphene relation \({n}_{{\rm{T}}}={\mu }_{{\rm{T}}}^{2}/\pi {\nu }_{{\rm{f}}}^{2}\), the electrostatic equation can be further simplified to:

$$-e{V}_{{\rm{b}}}={\mu }_{{\rm{B}}}-e{V}_{{\rm{b}}{\rm{g}}}^{\ast }\frac{{C}_{{\rm{b}}{\rm{g}}}}{{C}_{{\rm{g}}}}-\sqrt{e{V}_{{\rm{b}}{\rm{g}}}^{\ast }\frac{{C}_{{\rm{b}}{\rm{g}}}\pi {\nu }_{{\rm{f}}}^{2}}{{e}^{2}}},$$

(3)

in which \({{eV}}_{{\rm{bg}}}^{\ast }={{eV}}_{{\rm{bg}}}-{\mu }_{{\rm{B}}}-{e}^{2}{n}_{{\rm{B}}}/{C}_{{\rm{bg}}}\), giving for every Vb the corresponding shift in Vbg to maintain fixed nB and μB. By fitting the skew lines with this relation, we derive the interlayer capacitance to be Cg = 2.3 μF cm−2, consistent with our previous tunnelling experiment using two layers of WSe_2 (ref. [46]).

The tunnelling matrix elements

The tunnelling current is modelled by Fermi’s golden rule assuming momentum-resolved tunnelling between the TBG sample and the graphene probe. As in the previous section, to stay consistent with our previous publication, in all of the formulas below, we use the notation ‘top layer’ (‘T’) and ‘bottom layer’ (‘B’) to refer to the two layers between which the electrons perform the QTM’s momentum-resolved tunnelling, not the top and bottom layers of TBG. Namely, the top layer is the MLG layer on the QTM tip and the bottom layer is the TBG on the sample side.

The calculation is described in detail in refs. 46,47 and here we briefly review the formula and tunnelling matrix elements:

$$I=\frac{2\pi e{g}_{{\rm{s}}}{g}_{{\rm{v}}}}{\hbar }\int {\rm{d}}\omega (\,{f}_{{\rm{B}}}(\omega )-{f}_{{\rm{T}}}(\omega +e\phi ))\sum _{n,n{\rm{{\prime} }}}\sum _{{{\bf{k}}}_{{\rm{T}}},{{\bf{k}}}_{{\rm{B}}}}{|\langle {u}_{{\rm{gr}},{{\bf{k}}}_{{\rm{T}}},n}|{T}^{{{\bf{G}}}_{{\rm{T}}},{{\bf{G}}}_{{\rm{B}}}}|{u}_{{\rm{tbg}},{{\bf{k}}}_{{\rm{B}}},n{\rm{{\prime} }}}\rangle |}^{2}{\delta }_{{{\bf{k}}}_{{\rm{T}}}+{{\bf{G}}}_{{\rm{T}}},{{\bf{k}}}_{{\rm{B}}}+{{\bf{G}}}_{{\rm{B}}}}{A}_{{{\bf{k}}}_{{\rm{B}}},\omega }{A}_{{{\bf{k}}}_{{\rm{T}}},\omega +e\phi }$$

(4)

gs/v is the spin/valley degeneracy, kB/T is the electron momentum of the bottom/top layer, n and n′ are the band indexes, fB/T(E) is the Fermi–Dirac distribution function and ϕ is the electrostatic potential that shifts the relative position of energy bands between TBG and graphene, derived from the electrostatic model in Methods section ‘Electrostatics of the QTM junction’. The chemical potential μB/T is defined relative to their charge neutrality (Dirac point of TBG and graphene probe). \({A}_{{{\bf{k}}}_{{\rm{B}}/{\rm{T}}},E}\) is the spectral function of the TBG and the graphene probe. \({T}^{{{\bf{G}}}_{{\rm{T}}},{{\bf{G}}}_{{\rm{B}}}}\) is the tunnelling matrix in sublattice space between the graphene probe wavefunction \(|{u}_{{\rm{gr}},{{\bf{k}}}_{{\rm{T}}}}\rangle \) and the TBG wavefunction \(|{u}_{{\rm{tbg}},{{\bf{k}}}_{{\rm{B}}}}\rangle \) projected on the top layer. GT and GB are the reciprocal lattice vectors of the graphene probe and the top layer graphene of TBG, at which it satisfied the relation kT + GT = kB + GB. We assume that the tunnelling happens within the first BZ of graphene probe and it limits the Bragg scattering process of {GT, GB} choices to be three. For example, when GT = GB = 0, the matrix is given by: \({T}^{{{\bf{G}}}_{{\rm{T}}}=0,{{\bf{G}}}_{{\rm{B}}}=0}=I+{\sigma }_{x}\), in which I is the unity matrix. The other two Bragg scattering processes are related by C_3. Here we focus on \({T}^{{{\bf{G}}}_{{\rm{T}}}=0,{{\bf{G}}}_{{\rm{B}}}=0}\) and the matrix elements are given by:

$$\begin{array}{c}{|\langle {u}_{{\rm{g}}{\rm{r}},{{\bf{k}}}_{{\rm{T}}}}|{T}^{{{\bf{G}}}_{{\rm{T}}}=0,{{\bf{G}}}_{{\rm{B}}}=0}|{u}_{{\rm{t}}{\rm{b}}{\rm{g}},{{\bf{k}}}_{{\rm{B}}}}\rangle |}^{2}\\ \,=\,{|{\langle ({u}_{{\rm{g}}{\rm{r}},{{\bf{k}}}_{{\rm{T}}},A}^{\ast }+{u}_{{\rm{g}}{\rm{r}},{{\bf{k}}}_{{\rm{T}}},B}^{\ast })\rangle }_{FS}({u}_{{\rm{t}}{\rm{b}}{\rm{g}},{{\bf{k}}}_{{\rm{B}}},A}+{u}_{{\rm{t}}{\rm{b}}{\rm{g}},{{\bf{k}}}_{{\rm{B}}},B})|}^{2}\\ \,\propto \,{|{u}_{{\rm{t}}{\rm{b}}{\rm{g}},{{\bf{k}}}_{{\rm{B}}},A}+{u}_{{\rm{t}}{\rm{b}}{\rm{g}},{{\bf{k}}}_{{\rm{B}}},B}|}^{2}=|{\langle I+{{\sigma }}_{x}\rangle }_{{u}_{{\rm{t}}{\rm{b}}{\rm{g}},{{\bf{k}}}_{{\rm{B}}}}}|,\end{array}$$

(5)

in which \({u}_{{{\rm{gr}},{\bf{k}}}_{{\rm{T}}},A/B}^{\ast }\) is the graphene probe wavefunction with the sublattice component A/B. The graphene probe wavefunction is averaged over the Fermi surface and we assume that, within the graphene Fermi surface range, the TBG wavefunctions \({u}_{{\rm{tbg}},{{\bf{k}}}_{{\rm{B}}},A/B}\) remain the same. The above equation shows that the tunnelling current is proportional to the \(|{\langle I+{\sigma }_{x}\rangle }_{{u}_{{\rm{tbg}},{{\bf{k}}}_{{\rm{B}}}}}|\), in which the \(|{u}_{{\rm{tbg}},{{\bf{k}}}_{{\rm{B}}}}\rangle \) contains both the layer polarization and sublattice information.

In Fig. 2d, we show the matching between the matrix element \(|{\langle I+{\sigma }_{x}\rangle }_{{u}_{{\rm{tbg}},{{\bf{k}}}_{{\rm{B}}}}}|\) and the dI/dV intensity in the flat-band region. It shows an excellent agreement at ratio w_0/w_1 = 0.6. Here we further show that the naive layer polarization quantity (Extended Data Fig. 4b,d) is inconsistent with the data, regardless of the w_0/w_1 ratios.

For the tunnelling current calculation in Fig. 1, we input the single-particle band structure of TBG at θQTM = 1.2°, the electrostatics from Methods section ‘Electrostatics of the QTM junction’ and calculate the tunnelling current based on equation (4).

Spectroscopy at different momenta: its Gaussians decomposition and spectral function peak and lifetime analysis versus quasiparticle energy

In Extended Data Fig. 5, we show the tunnelling spectroscopy at different momenta along the flat-band region. The momenta at which the spectroscopy images are measured are marked by dashed black lines on a zoom-in of the bands from Fig. 2b, shown in the top panel. The range of momenta covers angles from θQTM = −0.5° to 0.8°. Apart from an overall magnitude, the spectroscopy in all momenta looks very similar. In Fig. 3c, we use this invariance on momentum in the flat bands to make Gaussian fit to the averaged data over the flat-band region.

The Gaussian fitting procedure is performed as follows. We first identify the peak centres directly from the spectroscopy data by tracing the local maxima in the contour representation of Fig. 4a; these trajectories are shown as dashed black lines in Extended Data Fig. 6a. Specifically, we identify two classes of features: (1) the ‘Hubbard’ bands that exhibit the characteristic cascading behaviour and (2) further excitations whose energy (about ±15 meV) is independent of filling. For each filling, the number of peaks identified in this manner determines the number of Gaussians used in the corresponding spectral fit. The peak positions extracted from the dashed-line trajectories serve as initial guess for the fit, although we allow these positions to vary during the fitting. The free parameters in the fitting routine are the Gaussian centres, widths, amplitudes and the background offset.

In Figs. 3b and 4a, we observed many spectral features originating from the Hubbard bands of heavy electrons. The spectral features typically appear at high energy as smeared ‘plumes’ and become stronger the closer they approach the Fermi level.

In a simple lifetime-broadening picture, the spectral function peak corresponding to a quasiparticle at an energy E acquires a width inversely proportional to the lifetime and the peak amplitude of the spectral function decreases correspondingly. In the present experiment, several bands coexist within a narrow energy range, making it challenging to extract the linewidths of the bands quantitatively. Instead, we can reliably extract the peak height, Apeak, which is a robust experimental observable and reflects the combined effects of quasiparticle residue and lifetime. Inspection of Fig. 4 shows a clear and systematic trend: when a band lies far from the Fermi energy, its Apeak is small and this maximal spectral weight increases continuously as the band approaches the Fermi level.

To address this more quantitatively, in Extended Data Fig. 6b, we track the maximum of the spectral peak (dashed line) from the highest energy that we can reliably identify a peak to the point at which it reaches the Fermi energy, and the peak becomes less well defined. In panel c, we plot the one over the extracted peak height 1/Apeak versus peak energy E and compare it with two functional forms: \(\frac{1}{{A}_{{\rm{peak}}}}=a(E-{E}_{{\rm{F}}})+b\) and \(\frac{1}{{A}_{{\rm{peak}}}}=a{(E-{E}_{{\rm{F}}})}^{2}+b\). Overall, we see that a linear dependence of 1/Apeak on E − EF better fits the observations.

The height of a spectral-function peak reflects both the quasiparticle residue Z(E − EF) and the lifetime τ(E − EF). In principle, both quantities may evolve as a Hubbard band moves to higher energies. If, however, we interpret the dominant trend as arising mainly from lifetime effects, the observed scaling would imply that it is more likely that τ(E −E F) ∝ 1/(E − EF) and not the Fermi-liquid expectation τ(E − EF) ∝ 1/(E − EF)2. Such dependence is often seen in strongly correlated/Hubbard systems, but because we do not measure this quantity in separation, we cannot make a decisive claim.

Fitting the transition between light and heavy electrons

Figure 5 shows dI/dV at Vb = 0 mV as a function of θQTM and filling factor ν. To determine the boundary between heavy and light electrons, we plot horizontal linecuts of dI/dV versus θQTM for each filling and perform a fit of a sigmoid-type function:

$$\frac{{\rm{d}}I}{{\rm{d}}V}={c}_{1}+\frac{{c}_{2}-{c}_{1}}{1+{e}^{-\frac{{\theta }_{{\rm{QTM}}}-{\theta }_{0}}{\sigma }}}$$

(6)

On the basis of the fitting, when the heavy-electron spectral intensity decays by 80%, we mark the position θhl(ν) as a proxy for the transition between heavy and light electrons. The parameter σ characterizes the width of this transition. We then fit θhl(ν) to a third-order polynomial and plot this as the dashed white lines in Fig. 5.

Determine the larger twist angle of θ TBG = 1.2°

We determine the angle of larger-angle TBG from the measurement in Fig. 1d. By tracing the dI/dV intensity change at the conduction-band edge of flat bands around the Γ point, we performed a step function fitting of the form:

$$y=C+\frac{A}{2}(1+\tanh (B\ast (x-{x}_{0}))),$$

in which the x_0 marks the Γ point position, which is half the value of the tunnelling intensity decay. By measuring the angle distance between the Γ point to the KT point, at which the Dirac point crosses, we determine the twist angle to be θTBG = 1.2°.

Spectroscopy of flat bands at different locations

Extended Data Fig. 7a–f presents tunnelling spectroscopy at a momentum point within the flat part of the bands (KT) measured at several locations across the sample, separated by several microns. These separations are large enough such that each region effectively behaves as independent devices. In all cases, we observe excited states whose energy is independent of filling, similar to the measurement in Fig. 4a.

For each location, we use room-temperature conductive AFM to image the real-space moiré structure (Methods section ‘Fabrication and characterization of the QTM tip and van der Waals device’) and plot its corresponding FFT. From the latter, we extract the local twist angle, θTBG, and heterostrain, ϵTBG, of the moiré. From the spectroscopy, we determine the excitation energy, ΔE. These three locally measured quantities are indicated above each panel. The centre panel shows a real-space map of the measurement locations, with the colour of each point representing the corresponding ΔE (see colour bar).

Although we cannot definitively rule out heterostrain as the origin of the observed approximately 15 meV excitation, the data strongly indicate that strain alone cannot account for this feature, for several reasons:

1. 1.
Universality across widely separated regions of the same device. We observe nearly identical excitation energies (13–16 meV) at locations separated by several microns. It is highly unlikely that the same level of strain would persist uniformly over such large distances.

2. 2.
Universality across different samples. Similar excitation energies are measured in two distinct MATBG devices, measured in separate cooldowns, indicating that the feature is neither device-specific nor related to a particular thermal cycle.

3. 3.
Lack of correlation with independently measured strain. The heterostrain extracted from the moiré FFT varies by a factor of 3 across the measured positions (from ε = 0.02% to ε = 0.06%). Despite this substantial variation, the excitation energy remains essentially unchanged.

4. 4.
Quantitative inconsistency with theoretical expectations. For the least-strained region (ε = 0.02%), the observed excitation is about 15 meV. Existing theoretical estimates predict a strain-induced level splitting of about 10 meV per 0.1% strain[51], which would correspond to only about 2 meV splitting at ε = 0.02%, an order of magnitude smaller than what we observe.

QTM probing of the K and K′ valleys

In Extended Data Fig. 8, we explain why the time-reversal symmetry of the QTM probe dictates that it measures the combined contribution of the K and K′ valleys.

Panel a shows the inequivalent Fermi surfaces of the K and K′ valleys (red and blue) folded into the same mini-BZ. For illustration, we depict triangularly warped Fermi surfaces to emphasize that the two valleys can, in principle, be different.

To understand what the QTM actually probes, it is useful to unfold the picture back to the full BZ (panel b). There the K and K′ Fermi surfaces sit at opposite corners. In the experiment, the Dirac points of the probe (purple) move along a circular trajectory (dashed line) and current is detected whenever the Dirac point of the probe intersects a Fermi surface. Crucially, if both the probe and the sample obey time-reversal symmetry, the K and K′ valleys produce identical spectra as a function of the QTM angle θQTM (panel c). If we take the illustration in panel b as an example, although the two triangular Fermi surfaces are mirror reflections, the rotating Dirac points intersect them in exactly the same sequence—first the flat edge and then the triangle tip—and therefore yield identical spectroscopic measurement.

We might expect that spontaneous valley symmetry breaking (for example, a valley-polarized Chern insulator) would manifest as clear energy splitting between the K and K′ bands and that, consequently, it should be easy to identify flavour symmetry breaking from this band splitting. However, this expectation is incorrect—recent dynamical mean-field theory calculations performed in both the symmetric and flavour-symmetry-broken34,35 states show that very similar band splittings appear in both situations. These splittings originate primarily from the formation of Hubbard bands, which occur regardless of whether flavour symmetry is broken. As a result, the spectroscopic signatures of flavour symmetry breaking are subtle and cannot be identified simply by looking for energy-split bands.

Hartree-driven band stretching and its connection to wavefunctions at different momenta within the flat band

In this section, we provide a systematic explanation of the momentum-dependent band stretching observed near the Γ point. To highlight the effect, we plot in Extended Data Fig. 9h,i the measured bands from Fig. 3a alongside schematic guides to the eye marking the stretched areas indicated by arrows. Specifically, the stretching appears as a peak at Γ at ν = −4, which continuously evolves to a dip at Γ at ν = 4.

This stretching originates from the momentum-dependent response of the electronic wavefunctions to the Hartree potential that builds up with increasing carrier density. Early theoretical works (for example, refs. 21,22) already noted that, although moiré-scale electrostatic potentials are negligible at charge neutrality, a spatially varying Hartree potential VH(r) develops as electrons or holes are added. The energy of a state at momentum k then shifts according to its real-space overlap with this potential,

$$\delta {E}_{{\rm{H}}}(k)=\int {{\rm{d}}}^{2}r{V}_{{\rm{H}}}(r){|{\psi }_{k}(r)|}^{2},$$

and because different k-states have distinct real-space charge distributions, they experience different Hartree shifts. This naturally produces a stretching of the bands, even in the absence of strong correlations. Although indirect signatures of this effect have been reported previously (for example, through Landau-level spectroscopy in ref. [16]), Fig. 3 presents the first, to our knowledge, direct observation of this Hartree-driven band stretching.

Notably, our measurements do not only show the overall magnitude of the band stretching but they directly provide the momentum-dependent energy shifts δEk. From the expression above, it is evident that these shifts encode detailed information about the real-space structure of the wavefunctions at different k. This is especially valuable in MATBG, in which the flat bands are known to have strongly momentum-dependent wavefunctions arising from their nontrivial topology.

In fact, one of the most notable theoretical predictions for TBG, already present in the BM single-particle model, is that the flat-band wavefunctions change qualitatively across the mini-BZ. This effect does not need electronic correlation and happens also when the bands are not extremely flat—for example, for TBG with twist angles larger than the magic angle. In the BM continuum model, more than 90% of the momentum states in the band have a localized wavefunction in the AA regions of the moiré cell. However, near the Γ point, this structure is reversed: the states become more extended and feature a suppression of charge density at the AA sites.

Although this momentum-dependent real-space structure was used as the basis for formulating the c-electron and f-electron decomposition in the THF model, it is not specific to that framework. Rather, it is a robust and very general feature of the non-interacting flat bands themselves. Our ability to measure δEH(k) with momentum resolution therefore provides direct access to these underlying wavefunction characteristics, offering a powerful new experimental probe of the topology-driven structure of the MATBG bands.

Extended Data Fig. 9a–g provides a step-by-step illustration of how the observed Hartree stretching arises and how it is directly connected to the wavefunction structure of the flat bands. Panel a sketches the real-space Hartree potential VH(r) that develops on electron filling. Because more than 90% of the flat-band wavefunctions in the BM model reside on the AA sites and have an f-electron Wannier function, the added carriers generate a Hartree potential that is strongly peaked at these sites and therefore highly repulsive for f-electrons. We emphasize that here we are discussing single-particle wavefunctions, so the c/f decomposition is fully valid in this context. As shown in panel b, the shape of VH(r) remains fixed as a function of filling, whereas its overall amplitude grows approximately linearly with ν (ref. [21]).

Panel c plots the BM charge densities |Ψf(r)|2 (red) and |Ψc(r)|2 (blue). The f-electron density has far greater overlap with VH(r) than the c-electron density and thus experiences a substantially larger Hartree shift. This is captured schematically in panel d, in which both c-electron and f-electron energies increase linearly with ν but with a much steeper slope for the f-electrons.

Panel e translates these energy shifts into momentum space. States near the Γ point—dominated by c-character—shift only weakly with filling, whereas most of the states in the rest of the band—dominated by f-character—shift strongly. Because most of the flat-band states are f-like, the Fermi energy EF (dashed line) remains effectively pinned to the flatter regions of the band as ν varies.

Our experiment, however, measures energies relative to EF. Referencing to EF has the effect shown schematically in panel f: the strongly shifting f-electron states remain near zero energy, whereas the weakly shifting c-electron states acquire an apparent downward slope with filling. This behaviour matches precisely the trend observed in Fig. 4 (here we focus only on the Hartree component; further interaction terms responsible for Hubbard-like band splitting and cascades appear on top of this effect in the experiment).

Finally, panel g shows how the ν-dependent bands of panel e appear once referenced to EF: the bands exhibit a clear stretching near Γ—exactly as observed experimentally in Fig. 3a. This stretching provides compelling evidence for the distinct wavefunction character of states near Γ, a key prediction of the topological structure of the BM bands and a foundational ingredient of the THF model.

Toy model to understand the essence of the Dirac revivals

In Extended Data Fig. 10, we present a minimal toy model that separates the low-energy spectrum into two sectors: (1) light carriers with a Dirac-like dispersion (‘c-like’ electrons) and (2) heavy, nearly flat bands (‘f-like’ electrons). The aim is to capture, in a transparent way, the key experimental features, namely the cascade phenomenology and the Dirac revivals—without committing to microscopic details.

The components/assumption of the model are as follows:

1. 1.
We assume two decoupled electronic components, which we call ‘c-like’ and ‘f-like’. They are not necessarily the original c-electrons and f-electrons but they do carry their two important aspects: light/heavy, different Hartree couplings.

2. 2.
Specifically, we model the f-like electrons with a simplified quantum dot model, which assumes completely flat bands but with a phenomenological ‘lifetime’ broadening that gives a finite width to the energy bands. We compute the f-electron spectral function to capture the Hubbard bands filling evolution.

3. 3.
We assume that there is a finite Coulomb coupling between c-like and f-like electrons, W, and that W < U.

The f-electron quantum dot model Hamiltonian is: H = Unf(nf − 1)/2, in which U is the effective charging energy and nf is the f-electron occupation. This quantum dot has eight flavours such that 0 ≤ nf ≤ 8. We compute the spectral function given by this Hamiltonian: \(A(\omega )={Z}^{-1}{\sum }_{n,m}\delta (\omega -{\varepsilon }_{n}+{\varepsilon }_{m})({e}^{-\beta {\varepsilon }_{m}}+{e}^{-\beta {\varepsilon }_{n}}) < l|{c}_{\alpha ,{\rm{f}}}^{\dagger }{|m > |}^{2}\), in which Z = Tr(e−β(H − μn)) is the partition function, β = 1/kBT and εm is the energy of the |m> eigenstates. We compute the spectral function A(ω, nf) and then add smearing in energy to mimic the lifetime effect seeing in the measurement. From the smeared spectral function, we compute the relation μf(nf).

As well as the quantum dot, we add Dirac (c-like) electrons. Let nf and nc denote the f and c fillings (n = nf + nc is fixed externally). The interaction term can be written as Wnfnc = Wnf(n − nf). The total energy is:

$${E}_{{\rm{tot}}}({n}_{{\rm{f}}};\,n)={E}_{{\rm{f}}}({n}_{{\rm{f}}})+{E}_{{\rm{c}}}({n}_{{\rm{c}}})+W{n}_{{\rm{f}}}(n-{n}_{{\rm{f}}}),$$

and the stationarity condition

$$\frac{{\rm{d}}{E}_{{\rm{tot}}}}{{\rm{d}}{n}_{{\rm{f}}}}=0$$

gives a simple self-consistency equation:

$${\mu }_{{\rm{f}}}({n}_{{\rm{f}}})-{\mu }_{{\rm{c}}}({n}_{{\rm{c}}})+W[n-2{n}_{{\rm{f}}}]=0,$$

with μf/c ≡ dEf/c/dnf/c. We solve this equation graphically to obtain (nf, nc) at each total filling n and then compute the corresponding spectral functions in momentum space for both sectors.

Extended Data Fig. 10a,b presents the calculated spectral functions for doping range from n = 0 to n = 1.5. Panels a and b show the f and c spectral functions in momentum space versus filling, reproducing: (1) the Hubbard-band evolution of heavy f-electrons and (2) the Dirac revival of light c-electrons. Specifically, in panel b, we track the Dirac point evolution: it first shifts downward with increasing ν owing to the Hartree potential and then when f states start to populate, it shifts upward (solid white guideline); this is precisely the ‘Dirac revival’ phenomenology and, within this model, we can see that it is driven by the W Coulomb term. The schematic in Fig. 4 is based on this calculation.

The above model does not include c–f hybridization. To show that the Dirac revivals physics is robust and appears also in the case of strong hybridization (γ/U > 1), we solve a more realistic THF model that also includes hybridization (details in the next section). Extended Data Fig. 10c plots the calculated filling factor dependence of the spectral function. The black line traces the position of the c-electrons Dirac point, clearly demonstrating that the Dirac revivals phenomenology, observed in the experiment, appears also in the presence of hybridization.

THF model reproducing the Dirac revivals

We consider a THF model[38] that treats U, the on-site f–f interaction, non-perturbatively within the Hubbard-I approximation, while treating all other interactions within the self-consistent Hartree approximation. We use the following parameters for the calculations: v⋆ = −1 eV nm; v′⋆ = 366 meV nm; γ = −70 meV; M = 3.7 meV; U = 60 meV; W = 45 meV; Y = 45 meV.

To calculate the Green’s function of f-electrons and c-electrons within Hubbard-I, we first consider the non-hybridized f-electrons propagator:

$${G}_{{\rm{f}},\eta }^{0}({\bf{k}},\omega )=\left(\frac{\frac{1}{2}+\frac{{\nu }_{{\rm{f}}}}{N}}{\omega +\delta \mu +\frac{U}{2}+i{\tau }^{-1}}+\frac{\frac{1}{2}-\frac{{\nu }_{{\rm{f}}}}{N}}{\omega +\delta \mu -\frac{U}{2}+i{\tau }^{-1}}\right){I}_{2\times 2}.$$

νf = sign(ν)⌊|ν|⌋ is the integral filling of each f site relative to charge neutrality, within the non-hybridized theory, taken to be the total filling rounded towards zero. δμ = μf − ν · U sets the asymmetry between the Mott-band energies. N = 8 is the number of electronic states (flavours × orbitals) per f site and μf = μ − W · ⟨δnc⟩ is the effective electro-chemical potential felt by the f site owing to repulsion from c-electrons, with δnc measured relative to charge neutrality. τ is a finite quasiparticle lifetime introduced by hand for numerical stability. We take τ−1 = 1 meV.

The non-hybridized c-electrons propagator is given by

$${G}_{{\rm{c}},\eta }^{0}({\bf{k}},\omega )={[\omega +{\mu }_{{\rm{c}}}-{H}^{({\rm{c}},\eta )}({\bf{k}})+i{\tau }^{-1}]}^{-1},$$

in which H(c,η)(k) is the single-particle Hamiltonian of c-electrons in valley η as given by Song and Bernevig[38] and μc = μ − W · ⟨δnf⟩ − V · ⟨δnc⟩ is the effective electro-chemical potential felt by the c-electron, with δnf measured relative to charge neutrality. Finally, the Hubbard-I propagator is given by

$$\begin{array}{c}[\begin{array}{cc}{G}_{{\rm{c}},\eta } & {G}_{{\rm{cf}},\eta }\\ {G}_{{\rm{fc}},\eta } & {G}_{{\rm{f}},\eta }\end{array}]({\bf{k}},\omega )={[\begin{array}{cc}{({G}_{{\rm{c}},\eta }^{0}({\bf{k}},\omega ))}^{-1} & {({H}^{({\rm{fc}},\eta )}({\bf{k}}))}^{\dagger }\\ {H}^{({\rm{fc}},\eta )}({\bf{k}}) & {({G}_{{\rm{f}},\eta }^{0}({\bf{k}},\omega ))}^{-1}\end{array}]}^{-1},\end{array}$$

with H(fc,η)(k) the f–c single-particle hybridization term as given by Song and Bernevig[38].

The densities ⟨δnf⟩ and ⟨δnc⟩ are calculated by integrating the spectral functions over negative frequencies and subtracting their values at charge neutrality:

$$\langle \delta {n}_{\alpha }\rangle =4{\int }_{-\infty }^{0}{\rm{d}}\omega \int \frac{{{\rm{d}}}^{2}{\bf{k}}}{{A}_{\mathrm{BZ}}}{{\mathcal{A}}}_{\alpha }({\bf{k}},\omega )-{n}_{\alpha }^{0}.$$

The spectral function is given by

$${{\mathcal{A}}}_{\alpha }({\bf{k}},\omega )=-\frac{1}{\pi }{\rm{ImTr}}[{G}_{\alpha ,\eta }({\bf{k}},\omega )],$$

with α = f,c, \({n}_{\alpha }^{0}\) is the density at charge neutrality and the factor 4 corresponds to spin and valley degeneracy, for which we use the fact that the state is flavour symmetric by assumption. For each value of the total density, we solve for ⟨δnf⟩, ⟨δnc⟩ and the chemical potential μ self-consistently.

Comparing the energy spectrum at partially full and completely full flat bands

In Extended Data Fig. 11, we plot the spectroscopy data as in Fig. 4a and compare linecuts A and B, taken when the chemical potential lies inside and outside the flat bands, respectively (dashed lines in panel a). For partial filling of the flat bands, we observe a characteristic feature at ΔE = −15 meV, as indicated by the dashed black lines in linecut A at ν = 1.5. By contrast, when the flat bands are fully filled, we instead find a single broad peak, as shown in linecut B at ν = 4.3.

One possible interpretation of the ΔE feature is a single-particle splitting induced by strain. However, the strain in the regions in which we perform the measurements is generally small (Methods section ‘Spectroscopy of flat bands at different locations’), making it unlikely to account for the observed 15 meV splitting. Moreover, a single-particle splitting should also be present when the flat bands are fully filled. In panel c, we attempt to fit the broad peak in linecut B with two symmetric Gaussians constrained to have the same lifetime as at partial filling (FWHM of about 10 meV, comparable with the energy resolution) and a fixed splitting of 15 meV and obtain a poor fit. This further suggests that simple single-particle strain-induced splitting is unlikely to be the origin of the ΔE feature.

Discussions of possible ground states at integer fillings

At charge neutrality, we observed a semimetallic phase with states at Γ at the Fermi level. There are two physically distinct mechanisms to produce a semimetal: the thermally disordered Mott semimetal27,34,35,39 and the strain-induced or spontaneously C3 symmetry-broken semimetal29,52,53. The two states give very similar spectra, with Hubbard-like nearly flat bands away from the Γ point and a band touching at or near Γ. There should be small differences in the spectra between the two states—for example, the Dirac points of the strain-induced semimetal are generally not precisely at Γ and do not have to occur at the same energy—but we believe that these differences may be too small for us to observe within experimental resolution. The main factor limiting the resolution is probably the tip size, limiting the resolution in momentum space to discern the fine details of the dispersion near Γ.

At non-zero integer fillings, our experiment is not directly sensitive to symmetry breaking, as the tunnelling from the QTM tip is not sensitive to the electron spin and valley and we do not have real-space sensitivity that can identify the spatial modulation of the spectrum that occurs in intervalley coherent or Kekulé spiral states. Experiments do not show any obvious signatures of symmetry breaking in the momentum-resolved electronic spectrum. In particular, there is no clear gap in the electronic spectrum at the Fermi level at any density (Fig. 3a). Our results are incompatible with a gap-opening broken-symmetry state at integer fillings at the temperature of the experiment.

Transport experiments typically see quantum oscillations emanating from ν = 2 and −2. Our measurements, on the other hand, do not show any signatures of Fermi surface at these filling factors. It is important, however, to keep in mind the experimental conditions. Our measurements are performed at T = 4 K and zero magnetic field. By contrast, quantum oscillations are necessarily measured at finite magnetic fields and it is therefore difficult to exclude the possibility that the oscillatory features observed at finite field reflect a field-stabilized ground state that differs from the zero-field state examined here.

Data availability

The data shown in this paper are provided with the paper. Further data that support the plots and other analysis in this work are available from the corresponding author on request.

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Acknowledgements

We thank E. Andrei, L. Bascones, A. Bernevig, L. Glazman, E. Khalf, P. Guinea, P. Jarillo-Herrero, M. J. Calderon, A. MacDonald, P. Ledwith, F. von Oppen, A. Stern, R. Valenti, A. Vishwanath and A. Yazdani for fruitful discussions. This work was supported by the Leona M. and Harry B. Helmsley Charitable Trust grant, the Rosa and Emilio Segre Research Award, the ERC-AdG grant (QTM, no. 101097125), the Deutsche Forschungsgemeinschaft (DFG) funded project number 277101999 – CRC 183 (C02), SNF Sinergia project number CRSII_222792/1, BSF grant (2020260) and ISF Quantum grant (1621/24). A.I. was supported by the Azrieli Fellows Program. E.B. was supported by the European Research Council (ERC) under grant HQMAT (grant agreement no. 817799) and CRC 183 of the DFG (project C02).

Author information

Author notes

1. These authors contributed equally: J. Xiao, A. Inbar, J. Birkbeck

Authors and Affiliations

1. Department of Condensed Matter Physics, Weizmann Institute of Science, Rehovot, Israel

J. Xiao, A. Inbar, J. Birkbeck, N. Gershon, Y. Zamir, Y. Vituri, E. Berg & S. Ilani

2. National Institute for Materials Science, Tsukuba, Japan

T. Taniguchi & K. Watanabe

Contributions

J.X., A.I., J.B. and S.I. designed the experiment. J.X., A.I. and J.B. built the cryogenic QTM microscope, fabricated the devices with N.G. and Y.Z. and performed the experiments. J.X., A.I., J.B., Y.V., E.B. and S.I. analysed the data. K.W. and T.T. supplied the h-BN crystals. J.X., A.I., J.B. and S.I. wrote the manuscript, with input from the other authors.

Corresponding author

Correspondence to S. Ilani.

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Extended data figures and tables

Extended Data Fig. 1 MATBG device and QTM-tip characterization.

a, The TBG sample used in the experiments. The stack is composed of bilayer WSe_2 (blue dashed)/TBG (red dashed)/37-nm-thick h-BN (yellow dashed) placed on top of a metallic back gate split into two pads (green dashed). b, The QTM tip used in the experiments, composed of MLG placed over h-BN and graphite layers. Electrical contacts to a conducting line on the cantilever are made with a bridging graphite flake. c, Room-temperature conductive AFM image of the magic-angle region in the sample. This AFM image is obtained after the thermal drift correction by using the atomic defects in WSe_2 (bright spots), as described in Methods section ‘Fabrication and characterization of the QTM tip and van der Waals device’. d, FFT of this AFM image, from which we obtain the moiré lattice vectors to be 12.72 nm, 12.73 nm and 13.14 nm. It translates the twist angle and strain to be 1.10° ± 0.02° and 0.03% ± 0.02%. e, Tunnelling current map as a function of real-space coordinates x and y, acquired under a large bias Vb = −900 mV. When the QTM tip passes over atomic defects in the WSe_2 tunnelling barrier, the local tunnelling current increases, thereby revealing the spatial footprint of the tip–sample contact. The elongated region of enhanced current indicates a QTM tip footprint of approximately 50 × 200 nm, as marked on the image.

Extended Data Fig. 2 Energy and momentum resolution.

a, Zoom-in on the measurement of dI/dV versus Vb and ν in Fig. 4a. To estimate the energy resolution, we present in the bottom panel a linecut along the dashed white line at filling factor ν = −0.7. Along this cut, we see a narrow peak centred at around Vb ≈ −4 meV, whose width gives an upper bound on the energy resolution of our measurement. The FWHM of a Lorentzian fit to this peak (dashed red line) gives δE ≈ 10 meV. b, Measurement of dI/dV versus θ and Vb focusing on the remote dispersive bands at large negative energies. A linecut along the dashed white line cuts through a narrow peak as a function of θQTM (narrow in momentum). A Lorentzian fit to this peak (dashed red line) gives a FWHM of δθQTM ≈ 0.1°, corresponding to a momentum resolution of δk ≈ 0.03 nm−1.

Extended Data Fig. 3 Correcting for the doping by the tip at finite biases.

a,b, The spectroscopy, dI/dVb and the transconductance with respect to the back gate, dI/dVbg, measured as a function of Vb and Vbg with θQTM within the flat-band region. At each integer filling, dI/dVb shows a suppression of conduction and dI/dVbg shows a dipole-like feature. This feature is more prominent at ν = ±4 owing to the gaps to the remote bands. We can see that the position of these features in Vbg shift with increasing magnitude of Vb, making them follow an S-like trajectory. This shift reflects the fact that, because of the parallel capacitor formed by the QTM tip and TBG sample, a relative bias between them leads to doping of the TBG. To correct this, we trace the ν = 4 line by a polynomial fitting and overlay it at each integer filling (black lines). On the basis of this fitting line, we can skew the measurements such that their x-axis becomes the true filling factor (accounting for doping by the back gate as well as the tip) instead of just Vbg. The data presented in Figs. 3 and 4 have been skewed with the above method. The data in Figs. 1 and 2 are measured at a fixed back-gate voltage and can therefore have some deviations with respect to the nominal filling factor at large biases. Figure 5 is measured at zero bias and therefore it does not have any effect of doping by the tip.

Extended Data Fig. 4 The tunnelling wavefunction examined by QTM and comparison with theory.

a,c, The peak intensity, Ipeak = ∫dI/dV · dVb, obtained by integrating the area under the dI/dV peak in the conduction and valence flat band in Fig. 2a, plotted as a function of momenta (determined from θQTM), in which we overlay the tunnelling matrix elements Pk = Pt · Ps for different w_0/w_1 ratios at the top. w_0/w_1 = 0.6 gives the best fit, consistent for both conduction and valence band sides. b,d, The integrated Ipeak on the conduction and valence band sides, overlaid with the naive layer polarization Pt, which is inconsistent for different w_0/w_1 ratios.

Extended Data Fig. 5 Spectroscopy at different momenta along the flat-band region.

dI/dV spectroscopy versus voltage bias Vb and filling factor ν at different θQTM in the flat-band region (marked by the dashed black line in the top panel).

Extended Data Fig. 6 Gaussian peak fitting and spectral peak height versus quasiparticle energy.

a, Here we plot the contour representation of Fig. 4a. Peak centres are extracted by following local maxima in the contour lines. The resulting trajectories track both the Hubbard-like bands and excitations at energies of about ±15 meV, which are independent of doping, are marked by dashed black curves and used as initial guesses for the Gaussian fits in Fig. 3c. b, The zoom-in contour plot of the upper Hubbard band between ν = −1 and 1, which is traced by the dashed black line that follows the local maximum of the spectral peak. c, Along this trajectory, we extract the spectral amplitude Apeak as a function of quasiparticle energy E − EF and plot 1/Apeak versus E − EF. The data are compared with two functional forms, 1/Apeak ≈ a(E − EF) + b and 1/Apeak ≈ a(E − EF)2 + b. Linear dependence provides a noticeably better fit.

Extended Data Fig. 7 Spectroscopy of the flat bands at far-separated locations along the sample.

dI/dV spectroscopy versus voltage bias Vb and filling factor ν, measured at the KT momentum (flat-band region) at six widely separated locations across the sample. For each panel, the measurement position, the energy scale of the filling-dependent excitation ΔE, the local twist angle θtbg and heterostrain εtbg are indicated at the top. The last two quantities are extracted from FFT analysis of room-temperature conductive AFM measurements of the moiré lattice at the corresponding location, shown in the insets (bottom right of each panel). The energy ΔE is marked by a dashed black line in each spectrum. The central panel shows a real-space map of all measurement locations; the colour of each point corresponds to the locally extracted ΔE value (colour scale shown on the right).

Extended Data Fig. 8 How a time-reversal-symmetric QTM tip examines inequivalent K and K′ valleys.

a, Schematic of inequivalent TBG Fermi surfaces in the K valley (red) and K′ valley (blue) folded into the same mini-BZ. b, View in the full BZ, in which the two mini-BZs appear at opposite corners and are related by time-reversal symmetry. Also shown are the graphene–probe Fermi surfaces (purple) and the circular trajectory traced by the probe Dirac points as the tip is rotated (cyan). c, Expected QTM signal versus θQTM. When both the probe and the TBG sample preserve time-reversal symmetry, the K and K′ valleys yield identical spectral functions; the measured spectral function therefore represents their superposed contribution.

Extended Data Fig. 9 Illustration of how filling-dependent Hartree band stretching arises from the real-space structure of the wavefunctions at different momenta.

a, Schematic real-space Hartree potential VH(r) that develops on doping, with the AA stacking regions of the moiré unit cell indicated. b, Filling-dependent VH(r) plotted along a linecut through the centre of the moiré unit cell. The potential peaks at the AA sites; its shape remains approximately constant with filling, whereas its overall amplitude increases roughly linearly. c, Schematic charge densities of the c-electron and f-electron states near Γ and away from Γ, respectively. Because of their real-space structure, the f-electron states have much larger overlap with VH(r). d, Hartree energy shifts δEH for the c-electron and f-electron states as a function of filling. Both shift roughly linearly but the f-electron states shift more strongly owing to their larger spatial overlap with VH(r). e, Resulting momentum-space band evolution with filling. Dashed lines denote the Fermi energy at different fillings. f,g, Same as d,e, respectively but plotted relative to the Fermi energy. h, Experimental dispersions reproduced from Fig. 3a, showing the evolution of the flat-band structure as a function of filling. i, Schematic guides to the eye corresponding to the data in h, with arrows indicating the progressive stretching of the bands near the Γ point—from a pronounced peak at ν = −4 to a clear dip at ν = 4. This evolution reflects the momentum-dependent Hartree shifts discussed in the main text.

Extended Data Fig. 10 Spectral functions calculated with a toy model and a THF model with strong c–f hybridization, capturing the Dirac revival phenomenology.

The toy model includes a heavy-electrons sector, modelled by the energy spectrum of a quantum dot with phenomenological lifetime, a light-electrons sector, represented by Dirac dispersion, and a density–density repulsion between heavy and light carriers. For simplicity, the model neglects the hybridization between the two sectors. a, Spectral function of the flat (Hubbard-like) bands of heavy carriers, for total filling of ntot = 0 to 1.5. At charge neutrality, the Hubbard bands are separated by the effective charging energy in the model, U. With electron doping, one band is pushed towards Fermi level and another band is pushed away from it and gradually fades. b, Spectral function of the light carriers as a function of filling. The Dirac point, traced by the solid white line, shows that the Dirac electrons are initially populated and then slightly depopulated as one Hubbard band crosses the Fermi level. Once a Hubbard band is filled, the Dirac electrons continue to fill, demonstrating the reshuffling of charge between the light and heavy sectors that is at the heart of the observed Dirac revivals. c, Filling factor dependence of the spectral function calculated using a THF model that includes strong c–f hybridization. The black line traces the position of the c-electrons Dirac point, clearly demonstrating that the Dirac revivals phenomenology observed in the experiment also appears in the presence of hybridization.

Extended Data Fig. 11 Spectral feature ΔE = 15 meV inside and outside the flat bands.

a, dI/dV spectroscopy at the flat-band region (same dataset as Fig. 4a). b,c, Linecuts A and B at filling factors ν = 1.5 and 4.3. When the chemical potential lies inside the flat bands (linecut A), we see the ΔE = −15 meV feature, marked by the dashed black line relative to the Fermi energy. When the chemical potential lies outside the flat bands (linecut B), the spectrum shows a single broad peak. In panel c, we fit linecut B using a decomposition into two symmetric Gaussian peaks (labelled as peaks 1 and 2). We constrain the linewidth (FWHM ≈ 10 meV, roughly energy resolution) and the peak splitting to 15 meV. Under these constraints, we get a poor fit to the data, suggesting that a single particle splitting is less likely the origin of the ΔE = −15 meV feature.

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Xiao, J., Inbar, A., Birkbeck, J. et al. Imaging the flat bands of magic-angle graphene reshaped by interactions. Nature 653, 68–75 (2026). https://doi.org/10.1038/s41586-026-10378-x

- Received: 24 June 2025

- Accepted: 09 March 2026

- Published: 06 May 2026

- Version of record: 06 May 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10378-x

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Structural modifications in strain-engineered bilayer nickelate thin films

Lopa Bhatt,

Edgar Abarca Morales,

Abigail Y. Jiang,

Eun Kyo Ko,

Yi-Feng Zhao,

Noah Schnitzer,

Grace A. Pan,

Dan Ferenc Segedin,

Yidi Liu,

Yijun Yu,

Charles M. Brooks,

Antia S. Botana,

Harold Y. Hwang,

Julia A. Mundy,

David A. Muller &

…

Berit H. Goodge

Nature volume 653, pages 76–82 (2026)

Abstract

The discovery of high-temperature superconductivity in bulk La_3Ni_2O_7 under high hydrostatic pressure1,2,3,4 and biaxial compression in epitaxial thin films5,6,7,8 has generated substantial interest in understanding the interplay between atomic and electronic structure in these compounds. Subtle changes in the nickel–oxygen bonding environment are thought to be key drivers for stabilizing superconductivity, but specific details of which bonds and which modifications are most relevant remain unresolved so far. Although direct, atomic-scale structural characterization under hydrostatic pressure is beyond present experimental capabilities, static stabilization of strained La_3Ni_2O_7 films provides a platform well suited to investigation with new picometre-resolution electron microscopy methods. Here we use multislice electron ptychography (MEP)9,10 to directly measure the atomic-scale structural evolution of La_3Ni_2O_7 thin films across a wide range of biaxial strains tuned by substrate choice. By resolving both the cation and oxygen sublattices, we study the strain-dependent evolution of atomic bonds, providing the opportunity to isolate and disentangle the effects of specific structural motifs for stabilizing superconductivity. We identify the lifting of crystalline symmetry through modification of the nickel–oxygen octahedral distortions under compressive strain as a key structural ingredient for superconductivity and identify in-plane lattice compression as a common attribute between bulk and thin-film superconductivity. Building on the detailed structures obtained by MEP, we introduce a theoretical framework to disentangle coupled structural distortions in corner-sharing octahedra[11], which suggest that both known superconducting geometries of La_3Ni_2O_7 (hydrostatic pressure and compressive strain) suppress local t2g orbital mixing in the low-energy Ni bands by raising the octahedral symmetry.

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Main

Attempts to identify further high-temperature oxide superconductors have earned substantial experimental and theoretical efforts since the discovery of superconductivity in cuprates. As well as square-planar nickelates[12], high-temperature superconductivity surpassing the transition temperature (Tc) of 80 K has recently been observed in the layered n = 2 Ruddlesden–Popper nickelates, Lan+1NinO3n+1, under high hydrostatic pressure1,2,3,4 and Tc up to 40 K under compressive epitaxial strain5,6,7,8.

Although square-planar RNiO_2 (R = hole-doped rare earth) and related Nd_6Ni_5O_12 (ref. [13]) nickelate compounds seem in many ways cuprate analogues14,15, parallels to the Ruddlesden–Popper nickelates La_3Ni_2O_7 (Fig. 1a) and La_4Ni_3O_10 that superconduct under pressure are less obvious. Bilayer and trilayer Ruddlesden–Popper nickelates host formal Ni configurations of d7.5 and d7.33 rather than cuprate-like 3d^9 and the proposed Fermi surfaces contain prominent contributions from both the nickel \({d}_{{z}^{2}}\) and \({d}_{{x}^{2}-{y}^{2}}\) orbitals16,17,18,19,20,21,22. It has been suggested that the relevant role of different orbitals may lead to competition between different pairing symmetries from d-wave to s± (refs. 23,24). The emergence of superconductivity at low temperatures may also be linked to the concomitant suppression of a density-wave state[25]. So far, however, the relative roles of structural and electronic reconfiguration—and their interplay—for driving superconductivity in La_3Ni_2O_7 under pressure remain unclear. Although the high pressures required to achieve superconductivity in bulk limit accessible experimental probes, superconducting compressively strained La_3Ni_2O_7 thin films provide an experimental platform to isolate and disentangle specific structural or electronic contributions for superconductivity.

Fig. 1: Structural and electrical changes in La_3Ni_2O_7 thin films under compressive and tensile strain.

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a, Schematic model of high-pressure Fmmm La_3Ni_2O_7 with the unit cell marked by a grey box. The dashed box outlines a single bilayer of La_3Ni_2O_7. b, High-pressure and low-pressure bulk structures of one bilayer reported in ref. [1]. Dashed lines mark the Ni–apical O bond angles; Ni–planar O bond angles are highlighted by red and purple lines. c, Resistivity ρ(T) of La_3Ni_2O_7 thin films epitaxially strained on substrates SLAO (blue), LAO (cyan), NGO (yellow) and STO (red); chemical formulas are given in the text. The nominal (bold, εnom) and measured epitaxial (εexp) strains imparted on each film are given in parentheses. The film grown on SLAO shows a superconducting onset near 42 K (ref. [5]). d, Atomic structures of La_3Ni_2O_7 thin films under compressive (left) and tensile (right) strain measured experimentally by MEP with coloured circles identifying the La (green), Ni (grey), planar O (filled red) and outer apical O (open dashed red) atomic columns. Scale bars, 5 Å.

Here we use scanning transmission electron microscopy (STEM) to provide key insights into the atomic-scale structure of La_3Ni_2O_7 thin films with varying epitaxial strain. Using high-resolution STEM imaging and MEP9,10, we directly characterize structural parameters including octahedral Ni–O bond angles, bond lengths and lattice constants across a complete strain series to isolate common motifs in superconducting La_3Ni_2O_7. Our measurements reveal that the Ni–planar O bond coordination takes on specific configurations of higher (lower) symmetry under compressive (tensile) strain, pointing to the importance of raising crystalline symmetry for superconductivity in these materials. Compared with bulk crystals under hydrostatic pressure, we observe a distinct evolution of out-of-plane Ni–O bond lengths but a common trend of in-plane bond lengths in compressively strained thin films. With quantitative parameterization of atomic positions and lattice constants across the full strain series, these measurements provide accurate structural models for ab initio electronic structure calculations. We introduce a theoretical framework to disentangle coupled structural distortions in corner-sharing octahedra (for example, bond length changes, rotations) and their impact on electronic structure in model systems. This analysis reveals that both known superconducting geometries of La_3Ni_2O_7 under hydrostatic pressure and compressive strain are characterized by symmetry-driven expulsion of t2g orbital mixing in low-energy Ni bands. Our detailed structural study of La_3Ni_2O_7 thin films across a range of compressive and tensile strain sheds light on the complex relation between structural and electronic degrees of freedom in this new class of high-temperature superconductors.

Electrical properties of thin films

Epitaxially strained thin films of La_3Ni_2O7−δ are synthesized on substrates SrLaAlO_4 (SLAO), LaAlO_3 (LAO), NdGaO_3 (NGO) and SrTiO_3 (STO) to impart nominal epitaxial strains (εnom) of −2.0% (SLAO), −1.2% (LAO), +0.7% (NGO) and +1.9% (STO) (Methods). Experimentally measured biaxial strain values (εexp) show partial lattice relaxation in all films (Fig. 1c and Supplementary Table 1). Here negative (positive) values denote compressive (tensile) strain. For consistency, we use the lattice convention shown in Fig. 1a, in which c is the long axis along the layer stacking direction and refer to εnom throughout.

Electronically, the film under high compressive strain on SLAO shows a broad superconducting transition at ambient pressure with onset close to 42 K, reaching zero resistance around 2 K (ref. [5]) (Fig. 1c). Films grown on LAO and NGO show metallic behaviour, with LAO being more metallic than NGO. Films on STO are insulating, in agreement with previous studies5,26.

So far, superconductivity has been demonstrated in strained bilayer nickelate thin films grown by pulsed laser deposition (PLD)5,27,28,29,30,31,32, molecular beam epitaxy (MBE)7,33,34 and gigantic oxidative atomic layer-by-layer epitaxy[6]. In this work, the films on STO, NGO and LAO are grown by MBE, whereas the superconducting film on SLAO is grown by PLD. Although we cannot definitively exclude the possibility that properties such as superconductivity may be sensitive to the growth method or other details of film synthesis, comparison between electronic transport and atomic structure of MBE-grown and PLD-grown films on LAO finds good agreement between both techniques (Extended Data Fig. 1). These observations are in line with angle-resolved photoemission experiments, which found no distinction between the electronic structure measured in MBE-grown or PLD-grown superconducting films[33].

Strain-dependent Ni–O bond symmetry

The compression-induced straightening of the Ni–apical O bond angle in bulk La_3Ni_2O_7 (Fig. 1b) thought to modify Ni–O hybridization and Fermi surface structure correlates to a symmetry raising from orthorhombic Amam to an Fmmm or tetragonal I4/mmm structure1,35,36. The variability of crystal structures and symmetries reported so far highlights the experimental difficulty in distinguishing similar phases—especially in the presence of mixed structural domains or twins3,35,36,37—and emphasizes a need for direct, real-space measurements of octahedral structure (Supplementary Figs. 12 and 13).

Quantitative measurements of light element positions—especially in the presence of heavy ions—with high spatial resolution and precision is, however, experimentally challenging. Local cross-sectional imaging in the STEM provides a platform for directly measuring atomic structure in thin films but particular care must be taken for robust measurements of light elements such as oxygen. Although certain collection geometries such as annular bright field or integrated differential phase contrast can resolve light element positions in carefully prepared specimens, they are susceptible to artefacts based on the experimental set-up (such as crystalline projection alignment, aberrations in the imaging probe) and sample geometry (such as specimen thickness, electron dechannelling near defects), which hinder quantitative measurements38,39.

MEP is a recently implemented imaging technique enabling quantitative measurement of light atoms while eliminating many such artefacts. By solving the inverse problem of electron scattering to attain the sample potential, MEP provides deep sub-angstrom spatial resolution, depth-resolved information along the projection direction and interpretable contrast of both light and heavy atomic columns[9] (Supplementary Figs. 1 and 2). MEP therefore offers a unique capability to quantify subtle structural distortions in NiO_6 octahedra across the series of strained La_3Ni_2O_7 thin films.

Figure 1d shows MEP reconstructions of compressive and tensile strained La_3Ni_2O_7 thin films projected along the orthorhombic a-axis ([100]o). Lanthanum, nickel and planar oxygen atomic columns are clearly visible with high spatial resolution. Apical O are visible as faint tails of outer La columns, as indicated by dotted red circles (Fig. 1d). Qualitatively, the tails are aligned vertically with Ni columns in compressive films but tilted to alternating sides of the La sites in tensile strained films. This suggests that the Ni–apical O bond angles (dashed lines) are closer to 180° in compressive films than in tensile films. A similar trend is observed in density functional theory (DFT)-based structural relaxations40,41. Robust experimental quantification of precise vertical bond angles is, however, problematic owing to the projection nature of STEM imaging: although signatures of the O columns are clearly discernible in the orthorhombic projection shown here, their overlap with La columns makes precise measurement of atomic positions challenging. O and La columns are easily distinguished along the [110]o projection, but in this orientation, octahedral rotations in the low-symmetry space group are antiphase stacked, which results in an apparent ellipticity of O sites in incoherently distorted octahedra[10] (Supplementary Fig. 1).

We instead track the angle of distortion between Ni and planar O (red and purple lines in Fig. 1b,c), which also rearrange in response to pressure to fingerprint structural changes in thin films across varying strain. Here we focus on the projected octahedral distortion by measuring the Ni–planar O angle with respect to the horizontal Ni plane as illustrated in Supplementary Fig. 11, giving the O–Ni–Ni angle. Related Ni–O–Ni angles are illustrated and given in Supplementary Figs. 11 and 14.

Figure 2a–d, top shows MEP reconstructions of La_3Ni_2O_7 thin films spanning epitaxial strain from approximately −2% to +2%, each cropped to two bilayers for display purposes but representing the overall structure of each film (Methods and Supplementary Fig. 3). Maps of the individual bond angles overlaid on the MEP reconstructions are presented in the bottom row. Most notable is the qualitative difference between the symmetry of compressive and tensile strained films: across a single NiO_2 plane in each bilayer, Ni–planar O angles are aligned unidirectionally in compressive films but alternate in tensile films, as also clearly exhibited in O–Ni–Ni angle histograms separated between alternating sites (Supplementary Fig. 15). This change in symmetry is further visible through the presence of extra peaks in Fourier transforms of annular dark field (ADF)-STEM images of each sample (Fig. 2e and Supplementary Fig. 8). Further half-order peaks visible in tensile strained films (orange arrow in Fig. 2e) and lack thereof in compressive films indicate relatively lower and higher crystalline symmetries, respectively, in good agreement with expected patterns for structures reported for La_3Ni_2O_7 under low-pressure and high-pressure conditions1,35,40. Theoretical calculations also predict compressive stabilization of a higher-symmetry structure under compressive strain[40].

Fig. 2: Ni–O bond angle evolution across epitaxial strain in La_3Ni_2O_7 thin films.

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a–d, MEP reconstructions (top) and Ni–planar O bond angle maps (bottom) of La_3Ni_2O_7 thin films grown on SLAO, LAO, NGO and STO substrates. e, Fourier transforms (FT) of ADF images of thin films on NGO (upper right) and LAO (bottom left). Differences in symmetry between tensile and compressively strained films are apparent by additional superlattice peaks that indicate a lower crystalline symmetry. f, Ni–planar O (O–Ni–Ni) angles of all four thin films. The solid line is a Gaussian fit to the top layer angle distribution and the dashed line is a Gaussian fit to the bottom layer angle distribution. The total number of O–Ni–Ni angles analysed (N) are given for each film. g, Average Ni–planar O angle of the top and bottom layers for films under different epitaxial strains. Scale bars, 5 Å.

The octahedral distortions we observe in compressively strained films show a close match to the originally reported structure of bulk La_3Ni_2O_7 under high pressure at ambient temperatures[1], in which planar O shift unidirectionally away from the centre LaO plane towards the rock salt layer (Supplementary Fig. 13). This is distinct from the uniformly inward distortions reported[35] under high-pressure, low-temperature conditions (Supplementary Fig. 13). Whether superconducting thin films undergo a similar low-temperature inversion of O distortion direction remains an open question.

As well as this change in symmetry, MEP measurements also reveal a systematic trend in the magnitude of in-plane O–Ni–Ni angles across the strain series. Measuring several MEP reconstructions across each film, we find that the average O–Ni–Ni angle increases with increasing strain in both tensile and compressive directions (Fig. 2f,g). Specifically, superconducting film on SLAO has an average O–Ni–Ni angle larger than the metallic film on LAO with similar octahedral symmetry but smaller than high-pressure bulk La_3Ni_2O_7 refinements1,35 (Supplementary Fig. 13). These trends are reproduced by DFT relaxations of La_3Ni_2O_7 biaxially strained to SLAO and STO lattice constants (Supplementary Tables 2 and 3).

Lattice evolution across strain series

As well as understanding subtle changes in the structure of Ni–O bonds on compression, we also analyse in-plane and out-of-plane lattice constants examined both globally by X-ray diffraction (XRD) and locally at the atomic scale by ADF-STEM imaging. In-plane lattice constants measured using STEM and XRD decrease as expected with increasing compressive strain, similar to the decreasing trend measured in bulk crystals under hydrostatic pressure (Fig. 3a,d). The in-plane spacing of the superconducting film on SLAO closely matches that of bulk samples at the symmetry-raising structural transition and onset of superconductivity.

Fig. 3: Lattice parameter changes across epitaxial strain in La_3Ni_2O_7 thin films.

The alternative text for this image may have been generated using AI.

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a–c, In-plane lattice constant (a), c-axis lattice constant (b) and unit cell volume (c) of thin films as a function of epitaxial strain measured using XRD (circles) and ADF-STEM (squares). Dashed lines indicate the reported values of each parameter at the onset of superconductivity and structural transitions in bulk La_3Ni_2O_7. d–f, In-plane lattice constant (d), c-axis lattice constant (e) and unit cell volume (f) versus pressure in bulk La_3Ni_2O_7 from ref. [1].

Notably, this trend inverts for the out-of-plane lattice constant in thin films (Fig. 3b and Supplementary Fig. 17), in which the Poisson effect drives an expansion of the c-axis spacing under compression such that the superconducting film on SLAO has an out-of-plane lattice spacing furthest from bulk samples under high pressure (Fig. 3e). MEP further enables precise measurement of out-of-plane Ni–O bond lengths, which is more directly related to the corresponding orbital overlap. Extended Data Fig. 2 shows out-of-plane Ni–O spacing measured from MEP reconstructions along the [100]pc projection, distinguished by Ni to inner apical O (circles) and Ni to outer apical O (squares). A clear increasing trend in the out-of-plane Ni–O spacing is observed, consistent with c-axis expansion with increasing compressive strain. The ratio between the two bond lengths (Ni–outer apical O/Ni–inner apical O) remains relatively constant across the strain series. The unit cell volume of the strained films (Fig. 3c) decreases with compression as expected, although even at the highest compression in the superconducting film on SLAO, the volume is much larger than superconducting bulk samples under hydrostatic pressure (Fig. 3f), also in agreement with theoretical relaxations (Supplementary Tables 1–3).

So far, great emphasis has been given to compression of the c-axis in relation to superconductivity under pressure and related emergence of an extra Ni \({d}_{{z}^{2}}\) state near the Fermi level1,17,21,22. Our results, however, clearly show an increase in c-axis—and in particular out-of-plane Ni–O bond lengths—in the superconducting film, indicating that c-axis compression is not necessary to achieve superconductivity in La_3Ni_2O_7 thin films, thereby raising questions about relevant electronic bands. Hopping parameters calculated from our structural models of compressive (SLAO) and tensile (STO) strained films as well as for previously published structures of bulk phases under low-pressure and high-pressure conditions (Extended Data Tables 1 and 2) indicate that the ratio of interlayer \({d}_{{z}^{2}}\) hopping \({t}_{\perp }^{z}\) to intralayer \({d}_{{x}^{2}-{y}^{2}}\) hopping \({t}_{\parallel }^{x}\) (which has been previously discussed in the context of pairing symmetry21,23,41) decreases with both compressive strain in thin films as well as hydrostatic pressure in bulk, despite opposite evolutions of their out-of-plane bond lengths.

The combined parametrization of lattice structures in strained films and pressurized bulk crystals indicates two primary structural motifs common between the two superconducting geometries: in-plane lattice compression and higher octahedral symmetry, here with uniform outward Ni–O plane buckling. Experimentally, separating these contributions is challenging or impossible, as macroscopic manipulation of one (that is, hydrostatic pressure or strain) will induce a coupled modification to the other (lattice distortions). Instead, we develop and apply a DFT-based strain-decomposition method to isolate the effects of these structural motifs on the electronic bands systematically across different sample geometries.

Octahedral decomposition

To qualitatively disentangle the impact of structural parameters characterized in Figs. 2 and 3, we use a generalized theoretical framework for modelling distortions in two-dimensional systems of corner-sharing octahedra[11] (Supplementary Information Section IX). This model decomposes the total strain applied to corner-sharing octahedra into partial strain tensors corresponding to specific distortions: octahedral arm length changes (L), internal angle changes (G), rigid rotations (R) and their mixing into higher-order terms (Fig. 4a, Extended Data Fig. 3 and Supplementary Information Section IX). From these component matrices, we generate model octahedral geometries that embody each specific distortion to track their individual effects on the electronic structure in isolation.

Fig. 4: Octahedral-distortion-decomposed electronic structure calculations.

The alternative text for this image may have been generated using AI.

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a, Schematic representation of different distortions applied to a regular octahedron (I), including arm length changes (L), internal angle changes (G) and rigid rotations (R). The deformation of a system of corner-sharing octahedra can be decomposed into structures embodying the independent distortions. b–e, Low-energy band structures of La_3Ni_2O_7 projected to the local t2g manifold of Ni 3d states. Red boxes highlight the low-energy bands near Γ discussed in the text. b, 1.6-GPa bulk structure at ambient temperature reported in ref. [1]. c–e, Calculated band structures for atomic models with only L (leftmost column) and R (middle column) distortions obtained from decomposing the strain between the 1.6-GPa bulk La_3Ni_2O_7 in b and the structures for epitaxial tensile strain of 1.9% on STO (c); epitaxial compressive strain of −2.0% on SLAO (d); and the 29.5-GPa bulk structure at ambient temperature reported in ref. [1] (e). Band calculations for the full structures are in the rightmost column. Atomic models are depicted to the right of each row.

With this framework, we qualitatively study the evolution of electronic structure in La_3Ni_2O_7 across varying biaxial strain conditions and pressure. Taking the non-superconducting ambient-temperature structure of bulk La_3Ni_2O_7 at 1.6 GPa reported in ref. [1] (Fig. 4b) as reference, we examine three more geometries: (1) the non-superconducting La_3Ni_2O_7 film with εnom = 1.9% (Fig. 4c); (2) the superconducting film with εnom = −2.0% (Fig. 4d); (3) the superconducting bulk 29.5-GPa high-pressure structure at ambient temperature (Fig. 4e) (Methods and Supplementary Information Section IX). Atomic models and projected band-structure calculations for each of the considered structures are shown in the rightmost columns of Fig. 4, reproducing similar evolution reported previously1,42,43. Band structures for individual L and R distortions are shown in the left and middle columns; expanded energy ranges and G-distortion components are shown in Supplementary Fig. 21. Although the individual L, G and R structures are hypothetical, those in the rightmost column are, by definition, the DFT-relaxed structures of each sample geometry (Supplementary Table 3).

A clear distinction between the electronic structures of non-superconducting and superconducting sample geometries is observed on colouring the bands by fractional local t2g orbital character. Although the low-energy bands (red boxes in Fig. 4) are dominantly eg-like, we find small but non-zero t2g hybridization in both non-superconducting bulk and tensile strained thin-film structures (Fig. 4b,c). Residual t2g hybridization is considerably suppressed in the two nominally superconducting −2.0% compressive strain thin-film and high-pressure bulk structures (Fig. 4d,e). The importance of local t2g expulsion for superconductivity remains to be established, although previous work has shown a sensitive dependence of the leading superconducting instability symmetry on the Ni crystal field splitting[23].

Our strain decomposition reveals that ‘t2g-cleaning’ in low-energy bands is mainly driven by R distortions—that is, by the change in octahedral rotations—in combination with electron correlations phenomenologically considered in the Hubbard U (Supplementary Figs. 21 and 22). The role of octahedral rotation in modifying t2g-derived and eg-derived band hybridization for superconductivity has previously been discussed for Ruddlesden–Popper oxides Sr_2RuO_4 and Sr_3Ru_2O_7 (refs. 44,45). So far, these effects have been largely unexplored in the La_3Ni_2O_7 literature and warrant further consideration.

Bond length (L) and internal angle (G) distortion changes, on the other hand, preserve or even enhance few-percent t2g mixing in all structures (Fig. 4 and Supplementary Fig. 21). Comparing calculated band structures for full atomic models and those with L distortions only, we further identify that bond length changes predominantly drive energy shifts, whereas the energy levels of G and R distortion structures change only slightly from the near-ambient 1.6-GPa reference structure (Extended Data Figs. 4 and 5). This observation further highlights the need for extremely high-precision measurements of experimental atomic structures, as subtle differences in Ni–O bond lengths can greatly affect the position of low-energy bands. These considerations are particularly relevant for the interpretation of recent photoemission experiments33,46, in which discrepancies in band positions could arise from structural relaxation near defects or at the film surfaces.

Secondary phases and defects

Although STEM techniques cannot directly identify superconducting regions within the film, high-quality thin films7,29,30,31 have been found to exhibit predominantly bilayer structure and demonstrate bulk-like superconductivity[31], suggesting that the pristine bilayer regions examined in Figs. 2 and 3 and used for DFT calculations probably host superconductivity. Still, comprehensive local characterization of other existing inhomogeneities including strain relaxation and crystalline defects is crucial for understanding sources of further contributions to bulk-sensitive measurements (for example, electronic transport, X-ray techniques), as well as for synthesis optimization efforts. Large-field-of-view ADF-STEM images of the superconducting La_3Ni_2O_7 thin film on SLAO reveal a variety of defects and relaxation mechanisms, including strain-relieving dislocations, surface relaxation, step edges in the substrate leading to extended defects in the film and mixed Ruddlesden–Popper phases (Extended Data Fig. 6 and Supplementary Information Section III). Furthermore, investigation of oxygen occupancy—essential for superconductivity in bulk47,48,49 and thin films5,6—at mesoscopic length scales (Extended Data Fig. 6) by electron energy loss spectroscopy at the O K-edge shows local variability in oxygen stoichiometry within a single La_3Ni_2O_7 sample[10] (Supplementary Information Section III). It is likely that concentrated oxygen vacancies and proximity to defects may also modify the local octahedral coordination, as seen in Extended Data Fig. 6e. Together, these observations highlight the tremendous opportunity for improving La_3Ni_2O_7 thin-film quality with the hopes of achieving sharper and higher-temperature superconducting transitions7,8,28,31.

Conclusion and outlook

Through a systematic study of La_3Ni_2O_7 thin films grown on different substrates with varying epitaxial strain, we identify essential structural characteristics and their effect on electronic changes across the strain series. We find commonality between superconducting strained thin films on SLAO and bulk crystals under hydrostatic pressure in the compression of in-plane lattice constants. On the other hand, opposite evolution in out-of-plane Ni–O bond lengths between these geometries result in dissimilar positioning of the Ni \({d}_{{z}^{2}}\) band near Γ. Direct quantitative measurements of Ni–O bond angles using MEP reveal a lifting of crystalline symmetry under biaxial compressive strain, which mirrors that reported in bulk crystals under high-pressure conditions. Our method for structural analysis by geometric decomposition in strained octahedral systems enables us to decouple the effects of experimentally measured atomic distortions on electronic band structure, suggesting the possible importance of octahedral symmetry for eliminating t2g orbital hybridization in low-energy nickel bands in superconducting thin-film and bulk structures. The combined experimental and theoretical approaches here demonstrate the potential for high-precision analysis of subtle structural distortions and their impact on materials properties, generalizable across a wide range of functional materials.

Methods

Thin-film synthesis

Substrates SLAO, LAO, NGO and STO have pseudo-cubic in-plane lattice constants of 3.756 Å, 3.787 Å, 3.858 Å and 3.905 Å, respectively. Following εnom = (asub − abulk)/abulk, the changes of the in-plane lattice constant compared with ambient-temperature, ambient-pressure bulk La_3Ni_2O_7 (pseudo-tetragonal \({a}_{{\rm{pt}}}=\sqrt{{a}^{2}+{b}^{2}}/2=3.833\,\mathring{{\rm{A}}}\) (ref. [5])) impart nominal epitaxial strains (εnom) of −2.0% (SLAO), −1.2% (LAO), +0.7% (NGO) and +1.9% (STO).

The preparation of La_3Ni_2O_7 thin films on SLAO(001) substrates with PLD is detailed in ref. [5]. Epitaxial thin films of La_3Ni_2O_7 on LAO(100), NGO(110) and STO(001) were grown by means of ozone-assisted MBE following the preparation in refs. 13,50, with improved structural quality achieved by shuttering LaO and NiO_2 monolayers according to ref. [51]. The MBE-grown films were annealed in an OTF-1200X-S vacuum tube furnace (MTI Corporation) under 1 atm O_2 pressure for 3 h at 500 °C. All films are treated with post-growth annealing in molecular oxygen or ozone with the goal of obtaining oxygen occupancy close to stoichiometric La_3Ni_2O_7, although we note that the precise quantification of oxygen stoichiometry in both thin films and bulk crystals is experimentally challenging[10].

Bulk characterization

The structural and electronic characterization of La_3Ni_2O_7 thin films on SLAO(001) substrates are detailed in ref. [5]. For the thin films of La_3Ni_2O_7 on LAO(100), NGO(110) and STO(001), XRD was collected on a Malvern Panalytical Empyrean diffractometer with Cu Kα_1 radiation (λ = 1.5406 Å). c-axis lattice parameters were calculated with Nelson–Riley fitting[52] and in-plane parameters were obtained from the film peak centroid in reciprocal space maps collected with a PIXcel3D area detector. Electrical transport data were collected on a Quantum Design Physical Property Measurement System with ultrasonically wire-bonded aluminium–silicon contacts.

Electron microscopy

Cross-sectional STEM specimens were prepared by the standard focused ion beam lift-out method on a Thermo Fisher Scientific G4-UX FIB to projection thicknesses of <30 nm. For the experiments performed in this manuscript, we have taken extensive precautions to mitigate oxygen loss during our experiments and to try to account for any potential effect on the structural measurements reported here. The timing of our experiments is carefully coordinated such that all focused ion beam preparation and STEM measurements are performed within three days of oxygen annealing. By comparison, when measurements are not as carefully time-coordinated, we observe a complete absence of the O K-edge pre-peak across the sample and inconsistent non-uniform Ni–O angles, shown in Supplementary Fig. 16.

ADF-STEM images were acquired using a Cs-corrected Thermo Fisher Scientific Spectra 300 X-CFEG operating at 300 kV with a probe convergence angle of 30 mrad. Ptychographic 4D-STEM datasets were collected on the same tool equipped with an EMPAD-G2 detector[53]. The maximum likelihood algorithm implemented in the fold-slice package was used to perform iterative phase retrieval9,54,55, using position correction and multiple probe modes to account for partial coherence of the STEM probe56,57. A Bayesian optimization algorithm with data error as the objective function was used to optimize parameters such as defocus, convergence angle and rotation angle[58]. The typical ranges of optimized parameter values used in MEP reconstructions are given in Supplementary Tables 4 and 5.

The MEP bond angle and bond length analyses in this manuscript are based on 40 distinct datasets, with each dataset spanning a unique field of view of 10 × 10 nm, corresponding to a total of 2,082 (SLAO), 5,232 (LAO), 1,871 (NGO) and 825 (STO) bonds analysed, as indicated in Fig. 2f. ADF-STEM analysis of lattice spacing includes 23 unique datasets, each spanning a total of 30 × 30 nm field of view. All of the structural coordination analysis is based on atomic column positions determined by seven-parameter Gaussian fitting.

Electron energy loss spectroscopy at the O K-edge was measured on a second Thermo Fisher Scientific Spectra 300 X-CFEG equipped with a Gatan Continuum spectrometer and camera operating at 120 kV accelerating voltage. Probe currents were limited to <20 pA to reduce possible beam damage10,59.

First-principles calculations

We used DFT calculations as implemented in the Vienna Ab initio Simulation Package (VASP) to perform structural optimizations in La_3Ni_2O_7 under different strain levels (applied to both the Amam and Fmmm structures). To that end, we constrained the in-plane lattice constants to the appropriate biaxial strain level \({\epsilon }=\frac{{a}^{\ast }({b}^{\ast })}{a(b)}\) and relaxed both the out-of-plane lattice constant and the internal coordinates. The exchange-correlation functional chosen was the Perdew–Burke–Ernzerhof version of the generalized gradient approximation. An energy cut-off of 500 eV and a k-mesh of 8 × 8 × 3 were used for the different strain levels. We fully optimized both the internal coordinates and the c-axis until the Hellman–Feynman forces were lower than 1 meV Å−1. Crystal models are visualized using VESTA[60].

The structure of non-superconducting La_3Ni_2O_7 thin film with εnom = 1.9% was obtained by relaxing the internal coordinates provided in ref. [1] of bulk 1.6-GPa structure at ambient temperature with the in-plane lattice parameters of STO (Fig. 4c). The superconducting film structure with εnom = −2.0% was obtained by relaxing the internal coordinates of bulk 29.5-GPa high-pressure structure at ambient temperature from ref. [1] with the in-plane lattice parameters of SLAO (Fig. 4d). Supplementary Table 3 tabulates structural parameters for strained La_3Ni_2O_7 calculated from our DFT relaxations and projected for direct analogy to STEM measurements.

Octahedral distortion decomposition and calculations

The effect of crystalline distortions in strained La_3Ni_2O_7 was analysed using the 4-octahedra model introduced in ref. [11]. To apply this framework to La_3Ni_2O_7, the model was first generalized to account for non-collinear apical distortions, which are present in this material (Supplementary Information Section IX). Subsequently, the octahedral geometry of the initial and final La_3Ni_2O_7 unit cells was examined. The pair of two-dimensional layers forming each bilayer in the unit cell were independently parametrized, decomposed and reassembled to construct the L, G and R partial structures.

The La_3Ni_2O_7 partial structures were optimized in VASP using the same parameters described above but with the unit cell and octahedral geometry (that is, O positions) held fixed, whereas the La and Ni atomic positions were allowed to relax (Supplementary Information). Band-structure calculations were performed in Quantum ESPRESSO v6.4.1. The exchange-correlation functional used was the Perdew–Burke–Ernzerhof formulation of the generalized gradient approximation, with and without an on-site Hubbard U = 4 eV applied to the Ni sites (Supplementary Figs. 21 and 22). An energy cut-off of 60 Ry and a 5 × 5 × 2 k-mesh were used.

Data availability

The experimental datasets and crystal structures used in this study are available from Zenodo at https://doi.org/10.5281/zenodo.18917988 (ref. [61]). The source data for Figs. 2g and 3a–c are provided in Supplementary Information Section I. Further data and analysis that support the conclusions of this work are available on request to the authors.

Code availability

The codes for the ptychographic reconstructions are available from Zenodo at https://doi.org/10.5281/zenodo.13787851 (ref. [62]). Scripts for the octahedral decomposition are available at https://github.com/AMoralesEdgar/The-4-Octahedra-Model.

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Acknowledgements

L.B. dedicates this paper to L. F. Kourkoutis for her continuous guidance, support and inspiration. We thank M. R. Norman, B. Y. Wang and Y. Tarn for useful feedback. This work made use of the Cornell Center for Materials Research shared instrumentation facility. L.B. and D.A.M. acknowledge support by the National Science Foundation (NSF) Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials (PARADIM) under cooperative agreement no. DMR-2039380. The Thermo Fisher Scientific Spectra 300 X-CFEG was acquired with support from PARADIM, NSF Materials Innovation Platforms (MIP; DMR-2039380) and Cornell University. A.Y.J., G.A.P., D.F.S., C.M.B. and J.A.M. acknowledge support from the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, under award no. DE-SC0021925. A.Y.J., G.A.P. and D.F.S. acknowledge support from the NSF Graduate Research Fellowship. G.A.P. and A.Y.J. were also supported by the Paul & Daisy Soros Fellowships for New Americans and A.Y.J. by the Ford Foundation. J.A.M. acknowledges support from a Packard Fellowship and a Sloan Fellowship. E.K.K., Y.Y., Y.L. and H.Y.H. acknowledge support from the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering (contract no. DE-AC02-76SF00515) and the Gordon and Betty Moore Foundation’s Emergent Phenomena in Quantum Systems Initiative (grant no. GBMF9072, synthesis equipment). Part of the sample fabrication was conducted at the Stanford Nano Shared Facilities (SNSF), supported by the NSF under grant ECCS-1542152. Y.-F.Z. acknowledges support from NSF grant no. DMR-2323971. A.S.B. was supported by the Alfred P. Sloan Foundation (FG-2022-19086). E.A.M. and B.H.G. were supported by the Max Planck Society. B.H.G. was also supported by Schmidt Science Fellows in partnership with the Rhodes Trust.

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Open access funding provided by Max Planck Society.

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Authors and Affiliations

1. School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA

Lopa Bhatt & David A. Muller

2. Max Planck Institute for Chemical Physics of Solids, Dresden, Germany

Edgar Abarca Morales & Berit H. Goodge

3. Department of Physics, Harvard University, Cambridge, MA, USA

Abigail Y. Jiang, Grace A. Pan, Dan Ferenc Segedin, Charles M. Brooks & Julia A. Mundy

4. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

Abigail Y. Jiang & Julia A. Mundy

5. Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA

Eun Kyo Ko, Yidi Liu, Yijun Yu & Harold Y. Hwang

6. Department of Applied Physics, Stanford University, Stanford, CA, USA

Eun Kyo Ko, Yijun Yu & Harold Y. Hwang

7. Department of Physics, Arizona State University, Tempe, AZ, USA

Yi-Feng Zhao & Antia S. Botana

8. Department of Materials Science and Engineering, Cornell University, Ithaca, NY, USA

Noah Schnitzer

9. Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY, USA

Noah Schnitzer & David A. Muller

10. Department of Physics, Stanford University, Stanford, CA, USA

Yidi Liu

Contributions

The project was conceived by L.B., B.H.G. and J.A.M. Thin films on LAO, NGO and STO were synthesized by MBE by A.Y.J., G.A.P. and D.F.S., with assistance from C.M.B. and J.A.M. Thin films on SLAO and LAO were synthesized by PLD by E.K.K., Y.L. and Y.Y., with supervision from H.Y.H. The 4-octahedra framework was developed and implemented by E.A.M., with input from B.H.G. Structural relaxations and calculations by DFT were performed by E.A.M., Y.-F.Z. and A.S.B. Electron microscopy measurements were conducted by L.B., with input from N.S., B.H.G. and D.A.M. The manuscript was written by L.B., B.H.G., E.A.M., A.S.B. and J.A.M., with input from all authors.

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Correspondence to Lopa Bhatt or Berit H. Goodge.

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Extended data figures and tables

Extended Data Fig. 1 Electronic transport and Ni–O bond angle measurement of PLD-grown La[3]Ni[2]O[7] on LAO thin film.

a, Resistivity ρ(T) of La_3Ni_2O_7 thin films epitaxially strained on LAO grown using MBE (solid line) and PLD (dashed line) as described in Methods. The transport curves show similar behaviour between the films grown using MBE and PLD. b, Ptychographic reconstruction (left) and corresponding Ni–O bond angle map (right) of a PLD-grown La_3Ni_2O_7 film on LAO. The Ni–O coordination observed in MBE-grown La_3Ni_2O_7 films on LAO (Fig. 2b) are similar to the structure observed in the films grown by PLD.

Extended Data Fig. 2 Ni–apical O bond length change across epitaxial strain in La[3]Ni[2]O[7] thin films.

a, Experimental MEP reconstruction of La_3Ni_2O_7 thin film along pseudo-cubic projection. Ni–inner apical O and Ni–outer apical O bonds are highlighted. b, Average out-of-plane Ni–apical O bond lengths measured as a function of nominal epitaxial strain through MEP.

Extended Data Fig. 3 Partial strain tensors corresponding to L, G and R octahedral distortions.

a–c, Strain tensors are decomposed from the initial (I) 1.6-GPa structure reported in ref. [1] into three final structures: εnom = 1.9% obtained from relaxing I with the in-plane lattice parameters of STO (a); εnom = −2.0% obtained by relaxing the 29.5-GPa high-pressure structure from ref. [1] with the in-plane lattice parameters of SLAO (b); and the 29.5-GPa high-pressure structure (c). In each case, the first column shows the total strain tensor for the process, whereas the L, G and R columns show the tensors owing to octahedral bond length changes, internal angular changes and rigid rotations, respectively. Higher-order mixed terms are not shown. The component matrices do not individually correspond to experimentally measured values; comparisons can be derived from the complete octahedral parameters in Supplementary Fig. 19.

Extended Data Fig. 4 Low-energy band evolution for octahedral distortion-decomposed virtual structures.

Energy positions of the bonding γ band top (orange) and the anti-bonding γan band bottom (green) at the Γ point extracted from the electronic structure calculations of La_3Ni_2O_7 with U = 4 eV on the Ni site, the same calculations presented in Fig. 4. The band positions for the structures derived from octahedral bond length changes (L) closely match those of the full structures. By contrast, the structures associated with internal angle distortions (G) and rotational changes (R) exhibit an energy landscape more similar to that of the reference 1.6-GPa configuration.

Extended Data Fig. 5 Band splitting and densities of states for different structural conditions.

a, γ band splitting at Γ extracted from the band structures in Fig. 4. The splitting is defined as the smallest energy separation between nominally hole-like and electron-like bands. The negative splitting for εnom = +1.9% indicates a band crossing. b, Densities of states projected to the global t2g manifold of Ni 3d states. c, Detail of the densities of states around the Fermi level, showing the reduction (increase) of the residual t2g orbital content in the (non-)superconducting structures.

Extended Data Fig. 6 Impact of local inhomogeneities on lattice and oxygen structure.

a, Large-field-of-view ADF-STEM image of the La_3Ni_2O_7 film on SLAO. Dashed lines mark the interface between film and substrate. Yellow ⊤s mark crystalline dislocations. b,c, In-plane (b) and out-of-plane (c) La–La spacing measured by quantitative atom tracking in the region marked by the white box in a. Orange arrows mark relaxation near defects. Average in-plane and out-of-plane La–La spacings in clean regions of the film are marked by black arrows on the colour bar. d,e, O K-edge electron energy loss near edge structure (d) and Ni–O bond angle map (e) from three different regions of the same La_3Ni_2O_7 film on SLAO; ADF images of regions 1 and 2 are shown in Supplementary Fig. 7. Dotted pink line in d compares the O K-edge measured in bulk La_3Ni_2O7−δ with δ ≈ 0.04 from ref. [10].

Extended Data Table 1 Tight-binding parameters (eV) of bilayer La_3Ni_2O_7 for different conditions

Extended Data Table 2 Tight-binding parameters (meV) of bilayer La_3Ni_2O_7 for different conditions

Supplementary information

Supplementary Information (download PDF )

This file contains Supplementary Figs. 1–23 and Supplementary Tables 1–5; details on octahedral decomposition and generalization of the 4-octahedra model.

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Bhatt, L., Abarca Morales, E., Jiang, A.Y. et al. Structural modifications in strain-engineered bilayer nickelate thin films. Nature 653, 76–82 (2026). https://doi.org/10.1038/s41586-026-10446-2

- Received: 14 January 2025

- Accepted: 24 March 2026

- Published: 01 April 2026

- Version of record: 29 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10446-2

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Electronic origin of reorganization energy in interfacial electron transfer

Sonal Maroo,

Leonardo Coello Escalante,

Yizhe Wang,

Matthew P. Erodici,

Jonathon N. Nessralla,

Ayana Tabo,

Takashi Taniguchi,

Kenji Watanabe,

Ke Xu,

David T. Limmer &

…

D. Kwabena Bediako

Nature volume 653, pages 98–103 (2026)

Abstract

Electron transfer (ET) reactions underpin energy conversion and chemical transformations in both biological1,2 and abiological3,4,5 systems. The efficiency of any ET process relies on achieving a desired ET rate within an optimal driving force range. Marcus theory6,7 provides a microscopic framework for understanding the activation free energy—and therefore the rate—of ET in terms of a key parameter: the reorganization energy. For electrified solid–liquid interfaces, it has long been conventionally understood that only factors in the electrolyte phase are responsible for determining the reorganization energy and that the electronic density of states (DOS) of the electrode only serves to dictate the number of thermally accessible channels for ET5,8,9,10,11,12. Here we show instead that the electrode DOS plays a central role in governing the reorganization energy, far outweighing its conventionally assumed role. Using atomically layered heterostructures, we tune the DOS of graphene and measure outer-sphere ET kinetics. We find the ensuing variation in ET rate arises from strong modulation in a reorganization energy associated with image potential localization in the electrode. Here we redefine the traditional paradigm of heterogeneous ET kinetics, revealing a deeper role of the electrode electronic structure in interfacial reactivity.

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Main

In the initial formulation of Marcus theory for homogeneous electron transfer (ET) involving redox-active ions in solution, the activation free energy was explained in terms of a reorganization energy (λ) penalty required to distort the atomic configuration and solvation environment of the reactant species to resemble those of the product state6,7. Extensions in the so-called Marcus–Gerischer[8] and Marcus–Hush5,9 formalisms rationalized heterogeneous ET processes at electrode–electrolyte interfaces, specifically addressing the ET rate constant in the weak coupling limit. The quantum mechanical theory of ET—pioneered by Levich, Dogonadze, Chernenko and Kuznetsov11,12—similarly leads to Marcus–Hush-type rate expressions. The seminal adaptation of the Marcus–Hush model by Chidsey[10] explained the dependence of interfacial ET rates on driving force and temperature by incorporating the Fermi–Dirac distribution of occupied electronic states in the electrode. In all of these and later13,14 extensions that incorporated a non-uniform (energy-dependent) density of electronic states (DOS) profile of the electrode, the DOS of the electrode exclusively serves to dictate the number of thermally accessible channels for ET. Furthermore, consistent with the original framework, λ is presumed to arise largely from nuclear reconfigurations in the electrolyte phase (typically those of the solvent and in some cases the redox molecule itself).

Enhancements in interfacial charge transfer at electrodes and photoelectrodes due to electrostatic variations in carrier doping or defects (including vacancies, step edges and grain boundaries) are commonly explained as arising from increases in the electrode DOS near the Fermi level (EF), ostensibly due to increased number of thermally accessible channels for ET15,16,17. Yet the Marcus–Hush–Chidsey (MHC) or Marcus–Gerischer framework often fails to quantitatively predict interfacial ET rate constants, even for relatively simple electrode reactions—overestimating ET rates by an order of magnitude or more18,19,20. A recent example of these discrepancies is found in the interfacial ET behaviour of twisted-bilayer graphene and twisted-trilayer graphene14,21. In these moiré electrode systems, which display periodic spatial localization of the electronic charge density in moiré superlattice topological defects, models derived from the MHC framework are unable to account for the large variation in ET rate with twist angle despite modifications to account for a DOS profile that varies with energy[13] as well as quantum capacitance effects that lead to changes in DOS of the electrode following electrochemical polarization14,21.

What is missing from these frameworks to explain how electrode defects produce such strong local enhancements in interfacial ET is a consideration of the electrode’s contribution to λ. Yet, molecular dynamics simulations have long predicted that λ for interfacial ET would vary with the distance of a redox molecular ion from a metallic electrode, owing to image charge interactions19,22,23. First-principles calculations of ET rates at graphite electrodes also considered a contribution of the electrode dielectric response to λ, but the effect was presumed to be sufficiently small to be neglected[24]. Moreover, recent simulations of twisted-bilayer graphene have established a connection between the moiré twist angle and the screening of charge carriers within the electrode, identifying that a twist-angle-dependent reorganization energy can account for the interfacial ET behaviour observed at moiré graphene electrodes25,26. A unique aspect of these electrode systems is the ability to continuously change the density of states (DOS) at the Fermi energy, and correspondingly tune the ability of the electrode to screen electric fields. The Thomas–Fermi (TF) screening length (ℓTF) quantifies the length-scale over which charges are screened in imperfect metals. As ℓTF scales inversely with DOS, higher metallicity leads to sharper charge localization, whereas lower metallicity yields a more diffuse charge distribution. Such tunability offers a new avenue to investigate how electronic screening shapes interfacial ET[27].

Here we directly and systematically probe the DOS dependence of interfacial ET using van der Waals assembly of atomically thin crystals. Using solid-state dopant layers and hexagonal boron nitride (hBN) spacers, we electrostatically tune the doping levels in monolayer graphene (MLG) and measure the resulting variation in heterogeneous outer-sphere electrochemical ET rates of the [Ru(NH_3)6]3+/2+ couple. The strong variation in graphene charge density with changes to hBN spacer thickness is shown to be mediated by defects in the hBN crystals. A continuum model is leveraged to obtain a microscopic understanding of the dependence of interfacial ET rate on the electrode DOS. We find that the ensuing variation in ET rates with charge carrier density cannot be modelled in the Marcus framework when one only considers the change in the number of thermally accessible channels for ET. Instead, our measurements and simulations unveil the considerably more dominant DOS-dependent reorganization energy, which accurately captures the large experimental variation in interfacial ET rate with DOS. We observe that at low charge carrier densities—such as those found in many low-dimensional electrode materials, as well as bulk or nanocrystalline semiconductors—the reorganization energy penalty owing to the low electrode DOS can be of a magnitude comparable with that arising in the solvent at a metallic electrode. This systematic study of the DOS dependence of interfacial ET rates on well-defined electrode surfaces challenges the conventional paradigm that reorganization energy contributions predominantly arise from the electrolyte side of the electrode–electrolyte interface and establishes a general microscopic framework for understanding heterogeneous ET that explicitly accounts for the electronic properties of the electrode in governing the free energy of activation.

Measurements of interfacial ET as a function of DOS

Isolating two-dimensional (2D) crystals enables the assembly of van der Waals heterostructures with tailored electronic and chemical properties28,29. When 2D crystals with disparate work functions are interfaced, the resulting electric field leads to charge transfer and interfacial doping, analogous to the effect of an electrostatic gate in a field effect transistor; past work has shown that α-RuCl_3 and WSe_2 can thus be used to dope graphene with holes and electrons, respectively[30]. This approach provides a modular doping mechanism similar to vacancy or substitutional doping but avoids introducing chemical disorder into the active layer. Such heterostructures provide an exceptionally well-defined platform for examining how doping-induced DOS changes impact the rate of ET.

We fabricated mesoscopic electrochemical devices comprising MLG on RuCl_3. We also fabricated samples with hBN spacers placed between MLG and RuCl_3, and varied the hBN spacer thickness from 3 nm to 120 nm (Methods and Extended Data Fig. 1 for further details on sample fabrication and characterization). Electrochemical measurements were conducted using scanning electrochemical cell microscopy (SECCM), which enables nanoscale electrochemical measurements by positioning an electrolyte-filled nanopipette over the sample and forming a confined electrochemical cell following meniscus contact[31]. Here we used quartz nanopipettes approximately 600–800 nm in diameter (Extended Data Figs. 2 and 3) containing 2 mM hexaammineruthenium(III) chloride and 100 mM potassium chloride as supporting electrolytes.

Figure 1a,b presents schematics of the sample and measurement set-up alongside an optical micrograph of a representative MLG–hBN–RuCl_3 device in contact with a graphite flake, which serves as the electrical contact. Steady-state cyclic voltammograms of the [Ru(NH_3)6]3+/2+ couple at the basal plane of a MLG–10-nm-hBN–RuCl_3 heterostructures (Fig. 1c) reveal a shift in the half-wave potential E1/2 to more positive potentials compared with cyclic voltammograms of analogous samples without RuCl_3, consistent with enhanced DOS from RuCl_3-induced hole doping that facilitates the electroreduction of  [Ru(NH_3)6]3+. Notably, even with a 10 nm (25-layer) hBN spacer, MLG displays ET kinetics approaching those of graphite and has nearly reversible electrochemical behaviour at the basal plane. This enhanced kinetic behaviour can be understood as originating from a downward shift in the EF relative to the charge neutrality point (CNP), driven by the work function difference between RuCl_3 and MLG. To a first approximation, the associated increased DOS (hole doping) at EF would greatly expand the availability of states to mediate interfacial ET. In the Gerisher–Marcus framework, this increases the extent of energy overlap between states in Ru(NH_3)63+/(2+) and MLG.

Fig. 1: Electrochemistry of MLG–hBN–crystalline donor–acceptor heterostructures.

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a, Schematic illustrations of electrochemical measurement at MLG surfaces using SECCM. b, Optical micrograph of a device fabricated from an exfoliated MLG flake on hBN and RuCl_3. c, Representative steady-state voltammograms of 2 mM Ru(NH_3)63+, depicting the mean current from the forward and backward sweeps, in 0.1 M KCl solution obtained at gold, graphite and MLG–hBN heterostructures (with and without α-RuCl_3). Scan rate, 100 mV s−1. The current and limiting current are denoted by i and ilim, respectively. d, Dependence of the interfacial ET rate constant, k^0, on the thickness of hBN spacer between MLG and RuCl_3 (crystalline acceptor) or WSe_2 (crystalline donor). Each data point represents the mean of multiple measurements for samples with a given hBN thickness; error bars indicate the s.d. for each k^0, where n varies from 3 to 6. Scale bar, 10 μm (b).

Source data

Finite-element simulations were performed using COMSOL Multiphysics (v.5.4) to simulate voltammetric responses with the Butler–Volmer model and estimate standard electrochemical rate constants (k^0), as detailed in the Methods. Figure 1d illustrates the correlation between k^0 and hBN thickness for RuCl_3 (solid-state acceptor) and WSe_2 (solid-state donor), revealing a strong modulation in ET kinetics with varying hBN thickness. Even when a 50-nm-thick hBN spacer layer is used, RuCl_3 leads to a measurable increase in k^0 relative to the electrochemical response measured in the absence of RuCl_3. In the case of WSe_2, the effect diminishes beyond an hBN spacer thickness of 20 nm. In the absence of an hBN spacer, MLG–RuCl_3 achieves ET rates comparable with those of pristine bulk graphite.

Carrier density and fluorescence measurements

Raman spectroscopy and Hall measurements were used to quantify doping as a function of hBN thickness[30] (Methods and Extended Data Figs. 6 and 7). Figure 2a (top) compares the Raman G-peak positions of pristine MLG with those of MLG–RuCl_3, MLG–WSe_2 and MLG–hBN–RuCl_3 heterostructures across varying hBN thicknesses (Fig. 2a, bottom). The doping-induced shifts in the graphene G-peak position are stronger for RuCl_3 than WSe_2 and are seen to attenuate with increasing hBN thickness. Past work has shown a linear relationship between G peak position and charge carrier density[30], establishing a doping change of about 9 × 10^11 cm−2 carriers per wavenumber shift. This correlation was used to deduce the graphene charge carrier density induced by RuCl_3 or WSe_2 heterolayers as a function of hBN spacer thickness.

Fig. 2: RuCl_3-induced doping in MLG and quenching of hBN fluorescence.

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a, Top: Raman G-peak spectra of MLG–hBN, MLG–WSe_2 and MLG–α-RuCl_3 heterostructures. Bottom: G-peak spectra of MLG–hBN–α-RuCl_3 heterostructures with varying hBN thicknesses. Solid lines indicate Voigt fits from which peak positions are obtained. b, Hall resistance, Rxy, as a function of magnetic field at 1.8 K for three hBN thicknesses in MLG–hBN–α-RuCl_3 heterostructure devices, compared with undoped MLG. μ_0H represents the magnetic flux density, where μ_0 is the vacuum permeability and H is the magnetic field strength. ‘H||c, i||ab’ indicates that H is aligned parallel to the crystallographic c axis of graphene, and that current i is flowing in the a–b plane. c, Absolute carrier density in MLG, ∣n∣, as a function of hBN spacer thickness in MLG–α-RuCl_3, derived from Raman G-peak shifts and Hall measurements, compared with ∣n∣-predicted first-principles calculations (dashed black line[32]). A polynomial fit (solid red line) phenomenologically models the sub-20 nm regime in which enhanced doping deviates from classical screening, due to defect-mediated charge transfer. Error bars indicate the s.d. for each ∣n∣, where the number of data points for each ∣n∣ varies from 6 to 10. d, Schematic illustration of band alignment and interfacial charge transfer between graphene and α-RuCl_3, depicting EF shifts relative to its band structure and corresponding DOS modifications. WRedox denotes redox molecule probability distributions (WOx, oxidized; WRed, reduced). e denotes the elementary charge and E^0 denotes the standard reduction potential of the redox couple. e, Illustration of the experimental set-up for liquid-induced fluorescence measurements in hBN–α-RuCl_3 heterostructures. f, Normalized hBN emission intensity versus illumination time in regions with and without α-RuCl_3. The inset displays a wide-field fluorescence image (561 nm laser, ~5 kW cm−2, 6 ms exposure). Scale bar, 5 μm.

Source data

When MLG and RuCl_3 are separated by hBN crystals thicker than 20 nm, we are unable to resolve further shifts in the G-peak. Given that we observed modulations in ET kinetics beyond 20 nm (Fig. 1d), we fabricated mesoscopic Hall bar devices and performed electronic transport measurements to measure these low carrier densities (Methods). Figure 2b presents the Hall resistance (Rxy) at 1.8 K for MLG–hBN–RuCl_3 devices with hBN thicknesses of 22, 43 and 79 nm. The slope of Rxy as a function of magnetic field at low fields is inversely proportional to the carrier concentration, n, and indicates carrier type (positive, holes; negative, electrons), allowing for direct assessment of doping levels. Up to an hBN spacer thickness of 43 nm, RuCl_3 generates hole doping in MLG, whereas a negative slope is measured in the pristine MLG (no RuCl_3) region of the same device as well as in the MLG–hBN–RuCl_3 device comprising 79-nm-thick hBN, indicating electron doping.

Figure 2c plots |n| in MLG as a function of hBN thickness, derived from Raman G-peak shifts and Hall measurements. These experimental values are compared with the doping expected from first-principles calculations[32] (black dashed line), which account for both the work function difference between MLG and RuCl_3, and the dielectric constants of the spacers. Although the model aligns very well with experimental data for hBN thicknesses of over 20 nm, doping levels exceed those predicted by theory for thinner hBN spacer layers. The enhanced doping observed in ultrathin hBN may be a result of valence band alignment between hBN and RuCl_3, which activates a theoretically proposed defect-mediated charge transfer process[33]. To investigate this possibility of hBN-defect-mediated doping, we conducted fluorescence measurements on hBN–RuCl_3 heterostructures (Fig. 2e). Past work has shown that organic solvents enhance hBN defect fluorescence via solvent-defect charge transfer[34]. Using wide-field imaging (Methods), we observed that RuCl_3 strongly quenches the defect-based fluorescence in hBN (Fig. 2f). This result suggests that defect-mediated interactions may indeed contribute to the increased extent of MLG doping by RuCl_3, which is observed when hBN spacers less than 20-nm thick are used in MLG–hBN–RuCl_3 heterostructures.

Effect of metallicity on the reorganization energy

By relating the hBN-modulated carrier densities (Fig. 2c) to shifts in the chemical potential μ (that is, EF at equilibrium) of MLG relative to its band structure, Fig. 3a compares experimentally measured outer-sphere standard ET rate constants (symbols) as a function of μ to theoretical models (lines). In our modelling (Methods and Extended Data Figs. 8 and 9), rate constants are normalized to those at the undoped graphene (\({k}^{0{\prime} }\)), and are calculated at zero overpotential. Under these conditions, quantum capacitance effects on μ are negligible compared with the effects of solid-state doping.

Fig. 3: DOS-dependent electrode polarization and charge-transfer kinetics.

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a, Standard charge-transfer rate constants (k^0) as a function of μ, normalized to the standard rate constant at undoped graphene (\({k}^{0{\prime} }\)). Experimental data (symbols) are compared with the model using fixed λmetal (red dotted line) and λ(μ) derived from ℓTF (red solid line). Error bars indicate the s.d. for each k^0, where n varies from 3 to 6. b, Simulated polarization response of the electrode upon switching the charge state of a redox ion. The redox ion is positioned at a fixed distance of 5 Å above the electrode surface in the z-direction. Polarization magnitude is visualized using an exponential colour scale. In the colour scale bar labels, e is the elementary charge. c, Reorganization energy (λ) as a function of DOS and chemical potential (μ). Left inset: free energy surfaces G(ΔE) of ET for [Ru(NH_3)6]3+/2+ redox couple for μ = 0 eV (solid grey), 0.05 eV (dotted magenta) and 0.5 eV (dashed blue). Right inset: ℓTF versus μ, calculated from DOS.

Source data

First we consider an MHC model (red dotted line in Fig. 3a) in which the reorganization energy (λmetal) is assumed to be a constant λ = 0.82 eV, as measured for this redox pair on metal (gold modified by self-assembled monolayer thiols) electrodes[35]. This MHC model accounts for the doping-dependent DOS but captures only the effect on interfacial ET of increasing the number of thermally accessible states in the electrode. The result is a prediction of very modest rate enhancement with DOS relative to that at the undoped graphene (about five to tenfold at extreme doping) that completely fails to replicate the experimental scaling.

Instead we find that the strongest contribution to the ET rate enhancement with increasing DOS is derived from an attenuation in the reorganization energy. This DOS dependence of λ arises in response to changes in the dielectric screening within the electrode as a function of doping, and can be understood in terms of image charge interactions at the electrochemical interface. To explain this effect, Fig. 3b presents a series of simulation snapshots of the instantaneous discharging of an empty capacitor, displaying logarithmic maps of the induced charge distributions in response to a monovalent ion placed 5 Å away from the surface. The simulations were performed subject to a constant potential constraint, where electron–electron screening was modelled within TF theory27,36,37. The screening length, ℓTF, is inversely proportional to the Fermi level, μ, reflecting enhanced metallicity as the charge carrier density increases. As ∣μ∣ increases (corresponding to an increased metallicity and decrease in ℓTF), the induced charge density becomes sharply localized, whereas a decrease in doping, or equivalently a decrease in metallicity, leads to a more diffuse charge distribution.

Next, in Fig. 3c we account for this ℓTF to model a reorganization energy, λ(μ), which is a function of DOS and μ using non-local dielectric continuum theory. Our model predicts a strong attenuation of λ(μ) with increasing metallicity (increasing DOS), converging to a value of 0.82 eV, consistent with λmetal (ref. [35]). This trend arises from enhanced stabilization of the charge transfer transition state due to sharply localized polarization within the electrode. This manifests in the calculated free energy surfaces for the Ru3+–Ru2+ redox system at different values of μ (left inset of Fig. 3c). These calculations reveal a pronounced influence of electrode screening on ET kinetics: reduced μ monotonically increases the activation free energy (ΔG‡), corresponding to the intersection of the reactant and product free energy surfaces. The right inset of Fig. 3c plots ℓTF versus μ. The plot reveals a rapid decline in ℓTF as the system is doped away from the CNP (μ = 0), directly linking doping-induced Fermi level shifts to screening efficiency. By explicitly including λ(μ)—which decreases with rising DOS—our model achieves quantitative agreement with experimental rates (red line in Fig. 3a), establishing that electrode electronic structure governs λ and by extension the activation free energy landscape. Thus, the predominant contributor to increasing ET rates with increasing DOS is the decrease in λ, and not the change in number of thermally accessible electron donor–acceptor states.

Discussion and conclusions

Our experimental and theoretical results demonstrate the pivotal influence of the electrode electronic structure on interfacial ET kinetics, not only through the expected increase in the pre-exponential factor as DOS increases, but also through a significant impact on the reorganization energy itself. This paradigm shift challenges the traditional electrolyte-centric perspective of reorganization dynamics, compelling a revised activation free energy framework that explicitly integrates the influence of electrode electronic properties on λ. Our findings provide a more comprehensive mechanistic understanding of heterogeneous ET and establish foundational principles to guide the design and optimization of interfacial charge transfer in next-generation devices. We anticipate that this concept will be particularly relevant to photo-induced ET processes, which inherently involve semiconducting (thus low DOS) materials38,39,40. Future theoretical and experimental efforts should further unravel the interplay between electrode DOS, defect engineering and λ to fully harness the potential of low-DOS materials in quantum technologies and energy applications.

Methods

Chemicals and materials

Natural Kish graphite crystals (Grade 300, 99.99% purity) were procured from Graphene Supermarket. Hexagonal boron nitride crystals were provided by T. Taniguchi and K. Watanabe, and were used as received. Large, flat crystals of RuCl_3 were grown by chemical vapour transport following the procedure detailed in a previous study[41]. Briefly, commercial RuCl_3 powder (Alfa Aesar, anhydrous, Ru ≥ 47.7%) was loaded into a quartz ampoule in an argon glovebox, sealed under dynamic vacuum, and heated in a two-zone furnace with a temperature gradient and ramp rates of 1 K per min. The resulting crystals were collected from the cold end and stored in an argon-filled glovebox.

Si/SiO_2 wafers (0.5-mm-thick, 285 nm SiO_2) and polydimethylsiloxane stamps (PDMS) were obtained from NOVA Electronic Materials and MTI Corporation, respectively. Sn/In alloy (Custom Thermoelectric), poly(bisphenol-A carbonate), hexaammineruthenium(III) chloride (98%) and potassium chloride (>99%) were purchased from Sigma-Aldrich. Sulfuric acid (ACS grade, >95−98%, Thermo Fisher Scientific) was used as received. All aqueous electrolyte solutions were prepared using type I water (EMD Millipore, 18.2 MΩ cm). Solid KCl was added as a supporting electrolyte in Ru(NH_3)63+ solution to a final concentration of 100 mM.

Sample fabrication

Graphene and hBN flakes were mechanically exfoliated onto SiO_2 (285 nm)/Si substrates from bulk crystals using Scotch tape[42]. Exfoliated flakes on SiO_2/Si chips were identified by optical microscopy (Laxco LMC-5000). MLG flakes were distinguished by their approximately 7% optical contrast in the green channel14,43 and further verified by Raman spectroscopy (HORIBA LabRAM Evo). Extended Data Fig. 1 shows a representative optical contrast of around 7% in the green channel for MLG and about 14% for bilayer graphene. The thickness of hBN flakes was determined by atomic force microscopy (Park Systems NX10) (Extended Data Fig. 1c,d).

α-RuCl_3 crystals were exfoliated in an argon-filled glovebox onto SiO_2 (90 nm)/Si substrates to prevent degradation. Precise thickness control was not required, as even a single monolayer of α-RuCl_3 is sufficient to induce substantial hole doping in graphene30,41. Instead, emphasis was placed on selecting flakes smaller than the hBN to ensure complete encapsulation, and flatness was prioritized to minimize strain during stacking. Suitable flakes were identified with an optical microscope (Nikon) within the glovebox.

We selected the multilayer system comprising graphene, hBN, RuCl_3 and WSe_2 due to their complementary characteristics. Graphene offers a tunable and well-defined electronic platform, whereas hBN serves as an inert spacer that allows precise control of doping. The RuCl_3 and WSe_2 layers function as stable charge-transfer dopants, modulating graphene’s electronic properties without affecting its structural integrity. Together, these materials enable systematic tuning of interfacial doping while preserving the overall structural quality of the heterostructure. MLG–hBN–RuCl_3 heterostructures were assembled by a dry-transfer technique on a temperature controlled stage (Instec), equipped with an optical microscope (Mitutoyo FS70) and micromanipulator (MP-285, Sutter Instrument) in an argon glovebox. A poly(bisphenol-A carbonate) film on a PDMS stamp was used to pick up a RuCl_3 flake within 30 min of exfoliation to minimize moisture exposure, which could compromise its doping efficacy[41]. The picked RuCl_3 flake was then capped with an hBN flake (3–180 nm thick), followed by MLG, partially overlapping the RuCl_3 to leave a segment of graphene without RuCl_3. A thick graphite flake (10–100 nm) was finally transferred to partially overlap the graphene, providing electrical contact with the heterostructure. The poly(bisphenol-A carbonate) film was delaminated from the PDMS stamp and placed onto a clean SiO_2/Si chip. Electrical contacts with graphite were subsequently established using Sn/In microsoldering[14].

SECCM measurements

Single-channel SECCM nanopipettes were fabricated from quartz capillaries (0.7 mm inner diameter, 1 mm outer diameter; Sutter Instrument) using a laser puller (P-2000, Sutter Instrument) with the following parameters: heat = 700, filament = 4, velocity = 20, delay = 127, and pull = 140. This procedure yielded pipettes with orifice diameters of 600–800 nm, as confirmed by bright-field transmission electron microscopy (TEM; Extended Data Fig. 3). Each nanopipette was filled with an electrolyte solution containing the redox species of interest and equipped with a silver wire coated with AgCl, serving as a quasi-reference or counter electrode.

Scanning electrochemical cell microscopy experiments were performed using a Park NX10 SICM module. The nanopipette was positioned above the sample using an optical microscope and approached the surface at 100 nm s–1 until meniscus contact was detected by a current increase above 3 pA. During approach, a −0.5 V bias was applied to facilitate diffusion-limited reactions. Cyclic voltammograms were recorded at multiple locations by sweeping the potential at 100 mV s−1 between –0.6 V and 0 V, with the half-wave potential, E1/2, defined as the potential at which i = i∞/2, where i∞ represents the diffusion-limited current plateau. [Ru(NH_3)63+/2+] was chosen as the redox couple because it has well-characterized, reversible, outer-sphere ET with no detectable adsorption on graphite electrodes, as confirmed by in situ Raman spectroscopy14,21. This ensures that the measured kinetics are sensitive to the electronic properties of the electrode while avoiding complications from surface-specific reactions.

Measurements were conducted on multiple independently fabricated devices, each featuring a distinct hBN thickness and comprising regions of evaporated gold as well as MLG with and without RuCl_3. Notably, the thickness data for 0- and 3-nm-thick hBN were measured on the same device, as were the data for 77- and 93-nm-thick hBN. For devices without hBN, RuCl_3 and WSe_2 are sensitive to air exposure, so the entire MLG was used to encapsulate them and, consequently, no isolated MLG regions were available.

For each device, we recorded 1–2 voltammetric cycles at multiple spatially separated positions to ensure reproducibility and capture local variability. Voltammetric data from each MLG position, including multiple cycles, were binned and individually fitted to COMSOL simulations to extract k^0 values. The gold regions served as an internal reference, with their data fitted using a reversible rate constant of 0.5 cm s–1 to account for any variations E^0. This yielded multiple k^0 values per device. The values plotted in Fig. 1d represent averages across these measurements, with error bars indicating the standard deviation. All extracted k^0 values are provided in Extended Data Table 1. We find that the enhancements in k^0 observed here far exceed those predicted by MHC theory, consistent with other studies that have reported similar limitations of the MHC framework18,19,20,44,45,46.

Although our devices were measured on the same day as fabrication, a 4–5 h interval was required for device assembly—including stacking, making electrical contacts, and transfer to the SECCM measurement substrate—which may have contributed to the slower observed rates than reported in literature. In this context, having MLG regions without RuCl_3–WSe_2 on the same device provides a robust baseline to reliably study the relative enhancement in ET kinetics induced by these dopants.

Past contact angle studies on graphene report modest changes (from 105° to 90°) over several minutes[47], which is significantly longer than our measurement timescale (<10 s). Molecular dynamics simulations show that increasing surface charge reduces wettability[48], suggesting that electrowetting effects should be even weaker in doped graphene. Electrowetting experiments on highly oriented pyrolytic graphite in 0.1 M KF over a potential window of 0 to –2.0 V versus Ag/AgCl revealed negligible effects[49], consistent with our experimental conditions (0.1 M KCl, 0 to –0.7 V versus Ag/AgCl). Our cyclic voltammetry signals remained stable, and microscopy before and after testing confirmed no detectable morphological changes. These observations indicate that electrowetting does not significantly affect our measurements.

Finite-element simulations

Finite-element simulations of steady-state cyclic voltammograms were performed using COMSOL Multiphysics (v.5.6)[50], following a similar approach outlined in previous works14,21. The nanopipette geometry was modelled in a 2D axisymmetric configuration (Extended Data Fig. 2), with droplet radii assumed equal to the pipette aperture, consistent with past studies14,21,51. The pipette radius, as, and taper angle, θs, were determined from TEM images (Extended Data Fig. 3). A survey of multiple nanopipettes prepared under identical conditions revealed that the taper angles are highly consistent (14.1 ± 0. 3°), whereas the aperture sizes have a modest distribution ranging from 600 to 800 nm.

Mass transport of redox species was modelled using the ‘Transport of diluted species’ and ‘Electrostatics’ modules, solving the steady-state Nernst–Planck equation:

$$\begin{array}{c}{D}_{i}\,\left(\frac{{\partial }^{2}{c}_{i}}{\partial {r}^{2}}+\frac{1}{r}\frac{\partial {c}_{i}}{\partial r}+\frac{{\partial }^{2}{c}_{i}}{\partial {z}^{2}}\right)\\ \,=-\frac{{z}_{i}F{c}_{i}{D}_{i}}{RT}\left(\frac{{\partial }^{2}\phi }{\partial {r}^{2}}+\frac{1}{r}\frac{\partial \phi }{\partial r}+\frac{{\partial }^{2}\phi }{\partial {z}^{2}}\right);\,0 < r < {r}_{{\rm{s}}},0 < z < l\end{array}$$

(1)

where r and z represent the coordinates parallel and normal to the sample surface, respectively; F is the Faraday constant; and rs and l denote the width and height of the simulation space, respectively. The height l = 30 m was set to exceed the nanopipette aperture, ensuring boundary effects were negligible. The meniscus was modelled as a cylindrical droplet (height, h), consistent with the hydrophobic interaction of water on graphite (contact angle 90°)14,21. The electroactive radius, a, is set equal to the nanopipette radius as, in agreement with previous studies14,21. The variables ci, zi and Di represent the concentration, charge number and diffusion coefficient, respectively, of either the oxidized (cO) or the reduced (cR) form. The electric potential ϕ in solution is determined by solving the Poisson equation:

$$\frac{{\partial }^{2}\phi }{\partial {r}^{2}}+\frac{1}{r}\frac{\partial \phi }{\partial r}+\frac{{\partial }^{2}\phi }{\partial {z}^{2}}=-\frac{{\sum }_{i}{z}_{i}F{c}_{i}}{\varepsilon {\varepsilon }_{0}};\quad 0 < r < {r}_{{\rm{s}}},\,0 < z < l$$

(2)

where ε = 80 is the dielectric constant of the solvent (water), and ε_0 is the vacuum permittivity. The terms ci and zi in equation (2) include the ions of the supporting electrolyte (0.1 M KCl) in addition to the redox-active species cO and cR. The rate of the heterogeneous electron-transfer reaction is governed by the Butler–Volmer equations:

$${k}_{{\rm{red}}}={k}^{0}{e}^{-\alpha \frac{F}{RT}({V}_{{\rm{app}}}-{E}_{0})}$$

(3)

$${k}_{{\rm{ox}}}={k}^{0}{e}^{(1-\alpha )\frac{F}{RT}({V}_{{\rm{app}}}-{E}_{0})}$$

(4)

where k^0 is the standard rate constant, α is the transfer coefficient, E_0 is the standard potential and Vapp is the applied electrochemical potential. For the simulation of \({\rm{Ru}}{({{\rm{NH}}}_{3})}_{6}^{3+/2+}\), only the oxidized form (cO) is initially present in the solution. The flux is considered zero except at the contact surface. The general boundary conditions are given as follows:

$${c}_{{\rm{O}}}={c}_{{\rm{O}}}^{* },\,{c}_{{\rm{R}}}={c}_{{\rm{R}}}^{* }=0;\quad 0 < r\le {r}_{{\rm{s}}},\,z=l\,(\text{bulk})$$

(5)

$$\begin{array}{c}\frac{\partial {c}_{i}}{\partial n}=0;\,0 < z\le h,\,r={a}_{s};\\ \,h < z < l,\,r=a+(z-h)\,\tan ({\theta }_{p})\,(\mathrm{no}\,\mathrm{flux})\end{array}$$

(6)

$${J}_{{\rm{O}}}=-{J}_{{\rm{R}}}={k}_{\mathrm{red}}{c}_{{\rm{O}}}-{k}_{\mathrm{ox}}{c}_{{\rm{R}}};\quad 0 < r\le {a}_{s},\,z=0\,(\mathrm{sample}\,\mathrm{surface}\,)$$

(7)

where JO and JR represent the inward flux of the oxidized and reduced forms, respectively, and \({c}_{{\rm{O}}}^{* }\) and \({c}_{{\rm{R}}}^{* }\) are the bulk concentrations. The \(\frac{\partial {c}_{i}}{\partial n}\) term is the normal derivative of the concentration. The potential drop across the Helmholtz layer is implemented by defining the surface charge density, σ, at the sample surface:

$$\sigma =\frac{({V}_{\mathrm{dl}}-\phi ){\varepsilon }_{{\rm{H}}}{\varepsilon }_{0}}{{d}_{{\rm{H}}}};\quad 0 < r\le {a}_{s},\,z=0$$

(8)

where εH = 6 and dH = 0.5 nm are the dielectric constant and thickness of the Helmholtz layer, respectively, yielding the double-layer capacitance Cdl = 10 μF cm–2; Vdl is the corresponding double-layer potential relative to the charge neutrality point. The steady-state current was calculated by integrating the total flux of the reactants (JO) normal to the sample surface:

$$i=2{\rm{\pi }}F{\int }_{0}^{{a}_{s}}{J}_{{\rm{O}}}r\,dr$$

(9)

The diffusion coefficients DO and DR for the Ru(NH_3)63+/2+ couple were set to 8.43 × 10−6 cm^2 s–1 and 1.19 × 10−5 cm^2 s–1, respectively; α = 0.5 was used for all simulations, consistent with previous studies on graphene thin films14,21. Our observed rates for doped MLG are ≤0.02 cm s–1, and for graphite approximately 0.03 cm s–1, indicating that ET remains primarily kinetically controlled within the experimental window. E_0 was determined from electrochemically reversible voltammograms obtained on gold electrodes immediately before the experiments on graphene.

To extract the standard rate constant k^0 from experimental voltammograms, we performed finite-element simulations across a range of k^0 values and computed residuals between simulated and experimental data via sigmoidal fitting. For each simulation, the coefficient of determination (R^2) was calculated using least-squares minimization, with the optimal k^0 corresponding to the maximum R^2 (minimal residuals). This protocol is illustrated in Extended Data Fig. 4, where R^2 values for simulated rates are plotted alongside representative voltammograms.

Quantum capacitance

Quantum capacitance (Cq) is a material-specific capacitance that arises from the DOS at the EF in low-dimensional materials such as graphene14,52. When an electric potential (Vapp) is applied across a solid–solution interface, an electric double layer (EDL) forms at the surface to screen the excess charge5,53. In low-dimensional systems such as MLG, this EDL functions not only as a charge screening layer but also as an electrostatic ‘gate’, shifting the Fermi level and dynamically altering the material’s carrier concentration through electron or hole doping. In the case of MLG, applying Vapp results in two potential contributions: Vq, which is the potential change due to Cq, and represents the shift in the chemical potential; and Vdl, the potential drop across the double layer itself. These two components are related by:

$${V}_{\mathrm{app}}={V}_{{\rm{q}}}+{V}_{\mathrm{dl}}$$

(10)

The EDL capacitance, Cdl, in an aqueous solution, is estimated around 10 μF cm−2, assuming a compact layer capacitance with little dependence on ionic strength[54]. The diffuse-layer capacitance is >100 μF cm−2 in 0.1 M KCl solution[53] and can be neglected. The total capacitance Ctotal combines Cq and Cdl in series:

$$\frac{1}{{C}_{\mathrm{total}}}=\frac{1}{{C}_{{\rm{q}}}}+\frac{1}{{C}_{\mathrm{dl}}}$$

(11)

Calculating quantum capacitance for MLG

Quantum capacitance is fundamentally connected to the DOS at the Fermi level, which depends on the material’s band structure. For MLG, the quantum capacitance Cq can be expressed as:

$${C}_{{\rm{q}}}={e}^{2}\frac{dn}{d{V}_{{\rm{q}}}}$$

(12)

where e is the elementary charge, and \(\frac{dn}{d{V}_{{\rm{q}}}}\) represents the rate of change in carrier concentration n with respect to Vq. Under the two-dimensional free-electron gas model, considering graphene’s linear DOS near the Dirac point[55], this relation simplifies to

$${C}_{{\rm{q}}}=\frac{2{e}^{2}{k}_{{\rm{B}}}T}{{\rm{\pi }}{\hbar }^{2}{v}_{{\rm{F}}}^{2}}ln\left[2\left(1+\cosh \left(\frac{e{V}_{\mathrm{ch}}}{{k}_{{\rm{B}}}T}\right)\right)\right],$$

(13)

where ħ is the reduced Planck constant, kB is the Boltzmann constant, vF ≈ c/300 is the Fermi velocity of Dirac electrons and Vch = EF/e is the graphene potential. At the Dirac point, where carrier concentration n is minimal, Cq approaches zero. At T = 300 K, the channel potential can be written as:

$$e{V}_{{\rm{ch}}}=\mu +e{V}_{{\rm{app}}}.$$

(14)

Assuming constant charge, the relationship between Cq and Cdl is

$$\frac{{C}_{{\rm{q}}}}{{C}_{\mathrm{dl}}}=\frac{{V}_{\mathrm{dl}}}{{V}_{{\rm{q}}}}.$$

(15)

Substituting Cdl = 0.1 F m–2 gives

$${V}_{\mathrm{dl}}=10\,{C}_{{\rm{q}}}{V}_{{\rm{q}}}.$$

(16)

This leads to the relation between Vq and the applied potential Vapp:

$${V}_{\mathrm{app}}=(1+10\,{C}_{{\rm{q}}}){V}_{{\rm{q}}}.$$

(17)

Finally, substituting equation (13) into equation (17) yields

$$\frac{{V}_{{\rm{q}}}}{{V}_{\mathrm{app}}}=1+10\times \frac{2{e}^{2}{k}_{{\rm{B}}}T}{{\rm{\pi }}{\hbar }^{2}{v}_{{\rm{F}}}^{2}}ln\left[2\left(1+\cosh \left(\frac{e{V}_{\mathrm{ch}}}{{k}_{{\rm{B}}}T}\right)\right)\right].$$

(18)

This expression provides Vq/Vapp as a function of Vapp, from which Vdl(Vapp) is extracted for different values of μ and incorporated into our COMSOL simulations to systematically account for quantum capacitance effects. Extended Data Fig. 5 shows the ratio Vq/Vapp as a function of applied potential at 300 K. The inset presents the corresponding Cq as a function of Vapp.

Raman spectroscopy measurements

Sample preparation

The heterostructures used in this study were prepared using a dry transfer method in an argon-filled glovebox. A polymeric stamp consisting of poly(bisphenol-A carbonate) on PDMS was used to pick up a thin layer of hBN, thickness < 5 nm, which was then used to pick MLG and another hBN flake comprising steps of multiple thickness, ensuring that the multilayer hBN fully covered the MLG. The entire stamp was then placed onto freshly exfoliated RuCl_3 on a Si/SiO_2 substrate (90-nm-thick SiO_2), and the PDMS was gently lifted at 160 °C, leaving behind the poly(bisphenol-A carbonate). The resulting structure consisted of Si/SiO_2-RuCl_3-multilayer hBN–MLG-thin hBN–PC. The thin hBN layer—large enough to cover the entire heterostructure—served as a capping layer to protect the stack from solvents. The poly(bisphenol-A carbonate) was then dissolved in chloroform for 15 min, leaving the heterostructure device ready for Raman measurements. Notably, doping is localized to the graphene in contact with α-RuCl_3, forming an atomically sharp lateral junction30,41. During fabrication, defects such as gas bubbles trapped between layers or non-uniform strain distributions may modulate the coupling between the layers locally. This coupling controls charge transfer between graphene and α-RuCl_3, as seen in the distance dependence introduced by hBN spacers. It is therefore crucial to freshly exfoliate RuCl_3 just before stacking to avoid contamination, which could decouple the layers.

Spectra acquisition and analysis

Confocal Raman spectra were collected using a Horiba Multiline LabRam Evolution system with a 532 nm laser and 0.4–3 mW power, using either a 600- or 1,800-grooves-per-millimitre grating. Spectra were typically recorded with acquisition times of 5–10 s and 3–5 accumulations. The G peak position in the Raman spectra can be linearly correlated with the doping levels in graphene, particularly when modulated by the electric field effect30,41. In the MLG–RuCl_3 device, the G peak is blue-shifted by more than 25 cm−1 relative to the region without RuCl_3, indicating doping of approximately 2.5 × 10^13 holes per cm^2, consistent with previous studies30,41. Such studies have shown that the G and 2D peak shifts in graphene vary with doping induced by the electric field effect30,41, and the G peak shift has a quasi-linear dependence on doping. Averaging the slopes of the G peak shift versus carrier density across these studies yields a value of approximately 9 × 10^11 cm−2 carriers per wavenumber shift in the G peak position. Voigt profiles are used to fit the peaks, accounting for the Lorentzian nature of the phonons and the Gaussian instrumental resolution30,41. A constant background is subtracted from each spectrum before fitting the peaks. The 2D peak position provides a sensitive probe of strain and morphological changes. Extended Data Fig. 6 shows negligible shifts in the 2D peak for MLG with and without hBN/RuCl_3, across varying hBN thicknesses, thereby confirming the absence of significant geometric alteration.

First-principles modelling of doping in MLG/hBN/RuCl_3 heterostructures

The MLG/hBN/RuCl_3 heterostructure was modelled as a parallel-plate capacitor following Bokdam et al.[32], with the Fermi level shift given by:

$$\Delta {E}_{{\rm{F}}}({E}_{{\rm{ext}}})=\pm \frac{\sqrt{1+2{D}_{0}\alpha {\left(\frac{d}{\kappa }\right)}^{2}| e({E}_{{\rm{ext}}}+{E}_{0})| }-1}{{D}_{0}\alpha d/\kappa }$$

(19)

Here, \(\alpha =\frac{{e}^{2}}{{\varepsilon }_{0}A}=34.93\) eV Å–1 (where A = 5.18 Å^2 is the area of the graphene unit cell, and ε_0 is vacuum permittivity), D_0 = 0.102 eV−2 per unit cell (slope of MLG DOS), d is the dielectric spacer thickness, κ is relative permittivity and Eext is the external electric field. E_0 accounts for any built-in electric field or doping potential.

Experimental validation of defect-mediated doping

To resolve the anomalous doping in thin hBN heterostructures (<20 nm), we fabricated devices with alternating hBN-supported (MLG/hBN/RuCl_3) and suspended MLG regions using approximately 4-nm-thick hBN, as shown in Extended Data Fig. 7 (MLG/air/RuCl_3). Doping levels were calculated using equation (19) and measured via Raman G-peak shifts (Extended Data Table 2).

The suspended region shows good agreement between theory and experiment. In contrast, the hBN-supported region has 21% higher doping than predicted. This discrepancy, along with the thickness-dependent deviations shown in Fig. 2e, suggests that defect states in hBN do contribute further charge transfer beyond classical capacitive coupling.

Liquid-activated fluorescence measurements

The sample was mounted on a Nikon Ti-E inverted fluorescence microscope equipped with a 100× oil-immersion objective lens (CFI Plan Apochromat λ 100×, NA = 1.45). Intensities in Fig. 3c were captured under 561 nm laser excitation (OBIS 561LS, Coherent, 165 mW) with an exposure time of 6 ms. Emission was collected after a band-pass filter (ET605/70m, Chroma).

Nanofabrication of Hall measurement devices

All device fabrication was performed in the Marvell Nanofabrication Laboratory. Electron beam lithography (CRESTEC CABL-UH system), with an A6 950 poly(methyl methacrylate) resist, was used to define the electrode and contact regions. Reactive ion etching (SEMI RIE system) exposed the graphene edges in the hBN–graphene–RuCl_3 heterostructure through a sequence of plasma treatments: 15 s of O_2 plasma to remove surface residues; 40 s of SF_6/O_2 plasma to etch through hBN and reveal the graphene; and a final 15 s of O_2 plasma to eliminate etching by-products. Immediately following etching, Cr/Au (20/120 nm) was deposited by thermal evaporation (NRC Evaporator) at rates of 0.5 Å s–1 for Cr and 2–4 Å s–1 for Au to form electrical contacts and bonding pads. After lift-off, a second electron beam lithography step defined an etch mask for shaping the heterostructure into a Hall bar geometry, minimizing longitudinal and transverse resistance mixing. The poly(methyl methacrylate) mask was retained after fabrication to protect RuCl_3 from environmental degradation. Device integrity was verified by measuring resistance between electrical contacts using a lock-in amplifier (Stanford Research SR830) on a probe station. Functional devices were wire bonded (TPT HB05) to 16-pin ceramic dual inline packages for subsequent measurements in a Quantum Design PPMS.

Theoretical preliminaries

The [Ru(NH_3)6]2+/3+ redox couple is known to undergo ET through an outer-sphere mechanism, where the thermal fluctuations of long-ranged electrostatic interactions with the environment play a central role in mediating the dynamics[56]. In the weak-coupling (non-adiabatic) regime, the rate of interfacial reduction is well-described by the golden-rule expression6,57,

$${k}_{\mathrm{red}}({E}_{{\rm{F}}})=\frac{2{\rm{\pi }}}{\hbar }| V{| }^{2}{\int }_{-\infty }^{\infty }D(E){f}_{{E}_{{\rm{F}}}}(E){\langle \delta (\Delta E-E)\rangle }_{\mathrm{ox}}dE$$

(20)

$$=\,\frac{2{\rm{\pi }}}{\hbar }| V{| }^{2}{\int }_{-\infty }^{\infty }D(E){f}_{{E}_{{\rm{F}}}}(E){p}_{{E}_{{\rm{F}}}}^{{\rm{(ox)}}}(E)dE$$

(21)

Where V is the electronic coupling between the two redox states (assumed to be small); D(E) is the electrode’s DOS; \({f}_{{E}_{{\rm{F}}}}(E)\) is the Fermi–Dirac distribution, centred at the electrode’s Fermi level, EF;

$${f}_{{E}_{{\rm{F}}}}(E)=\frac{1}{1+{e}^{(E-{E}_{{\rm{F}}})/{k}_{{\rm{B}}}T}}$$

(22)

and \({p}_{{E}_{{\rm{F}}}}^{{\rm{(ox)}}}(\Delta E)\) is the equilibrium probability distribution of the vertical energy gap between the two charge transfer states, evaluated in the oxidized state. Under an assumption of linear dielectric response, the rate is appropriately computed with Marcus theory, where the energy gap obeys Gaussian statistics and the free energy surfaces of ET are parabolic6,7,

$$-{\rm{ln}}{p}_{{E}_{{\rm{F}}}}^{{\rm{(ox)}}}(E)=\frac{{(E-{E}_{{\rm{F}}}+\lambda )}^{2}}{4\lambda {k}_{{\rm{B}}}T}+\frac{1}{2}ln[4{\rm{\pi }}{k}_{{\rm{B}}}T]$$

(23)

An assumption of chemical equilibrium is made, such that the intersection of the Marcus curves is aligned with the electrode’s Fermi level[58]. The reorganization energy, λ—a critical determinant of the activation free energy—quantifies the reversible work required to deform the equilibrium solvation environment of a redox species into that of its counterpart without ET. Equivalently, λ represents the energy dissipated during a vertical transition, reflecting the solvent and electrode polarization response to instantaneous charge redistribution.

The effect of electrode metallicity on the reorganization energy

In outer sphere redox reactions, the electrostatic potential at the interface is critical in determining the ET rate59,60. As the Fermi level is shifted, the change in DOS renormalizes the material’s electrostatic interactions, reflecting variations in the material’s dielectric response function due to an altered number of charge carriers that can respond to external fields. Insight into this effect can be gained through simple models of electronic screening such as TF theory61,62 parametrized by a screening length, ℓTF, which sets the scale of exponential decay of electrostatic interactions in the material, and interpolates between a perfect metal (ℓTF = 0) and an insulating material (ℓTF → ∞). The screening length is closely related to a material’s low-energy electronic structure. In two dimensions, and for kBT < < EF, which is typically the case for the energy scale of valence electrons, ℓTF is[63]

$${{\ell }}_{\mathrm{TF}}=\frac{{{\epsilon }}^{(\mathrm{el})}}{2{\rm{\pi }}{e}^{2}D({E}_{{\rm{F}}})}$$

(24)

where n is the charge density of the material. Marcus derived dielectric continuum estimates of the reorganization energy for charge transfer with a perfect conductor ℓTF = 0 (ref. [7]),

$$\lambda =-\frac{\delta {q}^{2}}{4{z}_{0}}\left(\frac{1}{{{\epsilon }}_{\infty }^{({\rm{sol}})}}-\frac{1}{{{\epsilon }}^{({\rm{sol}})}}\right)+{\lambda }_{{\rm{B}}},$$

(25)

where δq is the amount of transferred charge, \({{\epsilon }}_{\infty }^{({\rm{sol}})}\) and ϵ(sol) are the electrolyte’s optical and static dielectric constants respectively, z_0 is the separation from the electrochemical interface, and λB is the bulk contribution to the reorganization energy, which dominates when far away from the interface. The \((1/{{\epsilon }}_{\infty }^{({\rm{sol}})}-1/{{\epsilon }}^{({\rm{sol}})})\) term, known as the Pekar factor[64], is a measure of the free energy difference between a charge exclusively solvated by the medium’s fast (electronic) degrees of freedom, and that of a charge fully stabilized by the polarization field’s nuclear and electronic degrees of freedom.

The electrostatic potential at dielectric discontinuities can be obtained by solving Poisson’s equation with appropriate boundary conditions at the interface. If the two media that make up the interface are complex materials with some degree of unbound charge, the dielectric constant is replaced by a dielectric response function ϵ(r), and the non-local Poisson equation reads,

$$\nabla \cdot \int \,{d}^{3}{{\bf{r}}}^{{\prime} }{{\epsilon }}_{\alpha }({\bf{r}}-{{\bf{r}}}^{{\prime} })\nabla \phi ({{\bf{r}}}^{{\prime} })=-{\rho }_{\alpha }({\bf{r}})$$

(26)

where α labels the medium, and ρα(r) is the charge density in medium α. The boundary conditions to be satisfied are the continuity of the potential and the electric displacement field across the interface. For a point charge near a perfect conductor, the potential energy due to the conductor’s polarization in response to the external field can be mapped to the interaction between the real charge and an opposite-sign ‘image’ charge placed symmetrically inside the metal. The electrode contribution to the reorganization energy in equation (25) directly corresponds to the electrostatic interaction of a point charge δq with its image, weighted by the Pekar factor. It provides reasonable estimates of the reorganization energy at electrodes that approach the behaviour of an ideal conductor, but becomes inaccurate away from this limit. We have recently developed a formalism[25]—building on previous developments65,66,67—that describes how the solvent reorganization energy changes as a function of the electrode’s metallicity in the context of TF theory. If we consider the electrostatic boundary-value problem of a charge q embedded in a dielectric in contact with a material with finite TF screening, Poisson’s equation can be solved making use of Fourier-Bessel transforms65,66. The end result is an expression that encodes the interaction of the point charge q with the induced charge density in the electrode, as well as the self energy of the induced charge density. Although the Fourier–Bessel transform of the potential cannot be analytically inverted, an analogy can be made to the method of images to write the electrostatic potential energy of this system as the effective interaction of q with a modified image charge at −z_0,

$$U({z}_{0},{{\ell }}_{\mathrm{TF}})=\frac{{q}^{2}{\xi }_{{{\ell }}_{\mathrm{TF}}}({z}_{0},{{\epsilon }}^{(\mathrm{sol})})}{4{{\epsilon }}^{(\mathrm{sol})}{z}_{0}}$$

(27)

where we have defined the image charge scaling function, \({\xi }_{{{\ell }}_{\mathrm{TF}}}({z}_{0},{{\epsilon }}^{(\mathrm{sol})})\), which informs on the value (with respect to q) of this fictitious image charge as a function of screening length ℓTF, and the dielectric constants of both media. It smoothly interpolates between the electrostatics at the boundary of an ideal conductor, and an insulator. We see that, at a fixed z_0, the TF screening length in the electrode takes the image charge from −q to ξ∞(z_0, ϵ(sol))q. The screening-dependent image potential results in a modified reorganization energy,

$$\lambda ({{\ell }}_{{\rm{TF}}})=\frac{\delta {q}^{2}}{4{z}_{0}}\left(\frac{{\xi }_{{{\ell }}_{{\rm{TF}}}}({z}_{0},{{\epsilon }}_{\infty }^{({\rm{sol}})})}{{{\epsilon }}_{\infty }^{({\rm{sol}})}}-\frac{{\xi }_{{{\ell }}_{{\rm{TF}}}}({z}_{0},{{\epsilon }}^{({\rm{sol}})})}{{{\epsilon }}^{({\rm{sol}})}}\right)+{\lambda }_{{\rm{B}}}$$

(28)

The term in parenthesis in equation (28) can be identified as a generalization of the Pekar factor, extended to describe the modulation of image interactions at the surface of a TF electrode.

Image interactions in hole-doped MLG

The low-energy band structure of graphene can be described analytically, obeying the well-known linear dispersion relation characteristic of massless Dirac fermions[68]. This in turn results in a linear electronic DOS,

$$D(E)=\frac{2}{{\rm{\pi }}{\hbar }^{2}{v}_{{\rm{F}}}^{2}}| E| $$

(29)

where vF ≈ 10^6 ms−1 is the Fermi velocity, and the electronic energy E is measured from the Dirac point. Under a low-temperature approximation, graphene’s charge density is[69],

$$\rho ({E}_{{\rm{F}}})={\rm{sgn}}({E}_{{\rm{F}}})\frac{{{E}_{{\rm{F}}}}^{2}}{{\rm{\pi }}{\hbar }^{2}{v}_{{\rm{F}}}^{2}}$$

(30)

leading to an explicit relationship between ℓTF and the Fermi level[70],

$${{\ell }}_{{\rm{TF}}}({E}_{{\rm{F}}})=\frac{{{\epsilon }}^{({\rm{el}})}{\hbar }^{2}{v}_{{\rm{F}}}^{2}}{4{e}^{2}| {E}_{{\rm{F}}}| }.$$

(31)

The separation of the redox ion from the electrode, z_0, must be chosen judiciously. Given the outer-sphere nature of the reaction, it must account for the structure of the interface, including an adlayer of water molecules on the surface of the electrode that are tightly bound and held together by a hydrogen-bonding network71,72, as well as the inner coordination environment and the outer solvation shell of the redox species. On the other hand, as the diabatic coupling term typically decays exponentially with separation \(V\approx {V}_{0}{e}^{-{z}_{0}/{z}_{\mathrm{ref}}}\), the rate will be dominated by the distance of closest approach to the electrode, so an estimated lower bound of the separation should always be chosen. A distance of z_0 = 6 Å was set on the basis of these considerations.

With an understanding of how the reorganization energy is modified in response to doping, the rate of electro-reduction may be estimated as:

$${k}_{{\rm{red}}}({E}_{{\rm{F}}})=\frac{2| V{| }^{2}}{{\hbar }^{3}{v}_{{\rm{F}}}^{2}\sqrt{{{\rm{\pi }}}^{3}\lambda ({E}_{{\rm{F}}}){k}_{{\rm{B}}}T}}\int \,\frac{| E| {e}^{-\frac{{(E-{E}_{{\rm{F}}}-\lambda ({E}_{{\rm{F}}}))}^{2}}{4{k}_{{\rm{B}}}T\lambda ({E}_{{\rm{F}}})}}}{1+{e}^{(E-{E}_{{\rm{F}}})/{k}_{{\rm{B}}}T}}dE.$$

(32)

In keeping with the non-adiabatic limit that makes this treatment valid, we assume that the electronic coupling ∣V∣ remains small regardless of the degree of doping. In fact, we take this factor to be roughly constant such that it approximately cancels when taking the ratio with respect to some reference, for instance the CNP, k(EF)/kCNP. This allows us to assess the behaviour of the rate as a function of doping without direct knowledge of ∣V∣.

As noted earlier, λ arises from solvation energy changes during instantaneous charge transfer between redox species and the electrode. These changes are stabilized exclusively by fast solvent polarization modes, quantified by the optical dielectric constant \({{\epsilon }}_{\infty }^{({\rm{sol}})}\). In water, the static dielectric constant vastly exceeds the optical dielectric constant, rendering the second term in equation (28) negligible compared with the first. Therefore, to a reasonable approximation, the behaviour of the reorganization energy will closely resemble the image potential of a charge interacting with the electrode only through the optical dielectric constant, close to 1 in water.

Adiabatic versus non-adiabatic outer-sphere ET in [Ru(NH_3)6]3+/2+

The question of whether outer-sphere ET in [Ru(NH_3)6]3+/2+ proceeds adiabatically or non-adiabatically remains an area of active debate in electrochemistry. We assume that interfacial ET for [Ru(NH_3)6]3+/2+ falls within the non-adiabatic regime, which contrasts previous work by Liu and co-workers[73], who proposed adiabatic ET for this redox couple at graphene electrodes. Several observations support our assumption.

First, we find that the rate is highly sensitive to electronic properties of the electrode, which is a distinctive characteristic of non-adiabatic ET. Furthermore, there is contrasting experimental evidence to that presented with regard to the adiabaticity of outer sphere ET in the [Ru(NH_3)6]3+/2+ redox couple at graphene electrodes[73]. The alluded work posits adiabatic ET on the basis of the assumption that increasing graphene layers equates to increasing tunnelling distance. However, this perspective may neglect the role of graphene’s intrinsic electronic states, which actively participate in ET, making the simplified tunnelling argument inadequate, as we have illustrated in this work. Conversely, studies using hBN as a true inert spacer on graphite electrodes indeed demonstrate an exponential decrease in ET rates with increasing spacer thickness, consistent with non-adiabatic tunnelling processes74,75. The pronounced dependence of ET kinetics on the electrode’s DOS observed here and in past studies further supports this interpretation14,21. Finally, ET for the [Ru(NH_3)6]3+/2+ system has been reported to occur near the outer Helmholtz plane, where the redox species is separated from the electrode surface by a structured solvent layer. Experimental evidence, including negative activation volumes, indicates ET through solvated species rather than direct electrode contact, implying weak electronic coupling[76]. Furthermore, the work of Nazmutdinov and colleagues supports our assumption[77]. They argue that ‘for all amine complexes residing outside of the compact layer, ET proceeds in a diabatic limit, which originates mostly from a strong localization of the molecular acceptor orbitals on the central atoms’; this directly aligns with our picture of weak electronic coupling mediated through the solvent layer. Collectively, these observations align with our assumption of non-adiabatic ET mediated by tunnelling through a solvent barrier. We have performed further calculations to clarify this question further, and our results are presented below.

A clear way to distinguish between adiabatic and non-adiabatic ET is by evaluating the rate dependence on the electronic coupling, V, between the two ET states. A rate that follows Fermi’s golden rule (and is therefore non-adiabatic) will increase with the coupling as ∣V∣2, whereas an adiabatic rate is expected to be weakly dependent on coupling up until ∣V∣ is large enough to induce barrier-lowering effects. Although we don’t have direct knowledge of the exact value of V in our system, we can make inferences by computing both non-adiabatic and adiabatic rates at different couplings, and evaluating which theory is in best agreement with experimental data.

The model that we use for adiabatic ET differs slightly from the impurity model in Schmickler’s formulation of the problem[78], which is the treatment adopted by Liu and colleagues[73]. We adopt an alternative, simpler model because we find it more amenable for a dielectric continuum description of the reorganization energy, and it allows us to implement the specific form of the DOS of MLG more easily[5]. In particular, we start with a 2 × 2 Hamiltonian describing the ET process between a discrete molecular state in the electrolyte and a specific electronic state k in the electrode:

$${{\mathbb{H}}}_{k}({\bf{q}})=\left(\begin{array}{cc}{H}_{1,k}({\bf{q}}) & {V}_{k}\\ {V}_{k}^{* } & {H}_{2,k}({\bf{q}})\end{array}\right)$$

(33)

where q denotes nuclear coordinates. Going forward, we will assume a ‘wide band approximation’, meaning,

$${V}_{k}=V={\rm{const}}\qquad \forall k$$

(34)

The classical non-adiabatic rate can be derived from this model by invoking Fermi’s golden rule and linear response, resulting in the Marcus expression:

$${k}_{1\to 2}({{\epsilon }}_{k})=\frac{| V{| }^{2}}{\hbar }\sqrt{\frac{{\rm{\pi }}}{{k}_{{\rm{B}}}T\lambda }}\exp \left[-\beta \frac{{(\lambda +\Delta {\epsilon }-{{\epsilon }}_{k})}^{2}}{4\lambda }\right]$$

(35)

The electrochemical rate is then given by averaging over electronic states in the electrode giving the well-known result:

$${k}_{1\to 2}=\int \,D({\epsilon })(1-f({\epsilon })){k}_{1\to 2}({\epsilon })d{\epsilon }$$

(36)

With electrode DOS D(ϵ), and Fermi–Dirac distribution, f(ϵ). An adiabatic rate can also be derived from the same model Hamiltonian in equation (33). We start by diagonalizing the 2 × 2 Hamiltonian, leading to an adiabatic Hamiltonian:

$${H}_{{\rm{ad}},k}({\bf{q}})=\frac{1}{2}({H}_{1,k}({\bf{q}})+{H}_{2,k}({\bf{q}}))-\frac{1}{2}\sqrt{{({H}_{1,k}({\bf{q}})-{H}_{2,k}({\bf{q}}))}^{2}+4|V{|}^{2}}$$

(37)

The adiabatic free energy surface associated with this Hamiltonian can be constructed from importance sampling in a molecular simulation. Alternatively, we can make the following simplifying assumption: within the linear response regime, we expect the adiabatic free energy surface to be given by a simple mixture of the corresponding diabatic (Marcus) free energy surfaces:

$${F}_{{\rm{ad}},k}(\Delta E)\approx \frac{1}{2}({F}_{1,k}(\Delta E)+{F}_{2,k}(\Delta E))-\frac{1}{2}\sqrt{\Delta {E}^{2}+4| V{| }^{2}}$$

(38)

where ΔE ≡ H2,k − H1,k = F2,k − F1,k is the vertical energy gap between the diabatic states, recognized in Marcus theory as the reaction coordinate, and F1,2 have the usual parabolic form:

$${F}_{1,k}(\Delta E)=\frac{{(\Delta E+\lambda +\Delta {\epsilon }-{{\epsilon }}_{k})}^{2}}{4\lambda }$$

(39)

$${F}_{2,k}(\Delta E)=\frac{{(\Delta E-\lambda +\Delta {\epsilon }-{{\epsilon }}_{k})}^{2}}{4\lambda }+\Delta {\epsilon }-{{\epsilon }}_{k}$$

(40)

Equation (38) describes a bistable free energy surface, depicted in Extended Data Fig. 8, with shape defined by the reorganization energy, driving force and electronic coupling. The rate of transition between these meta-stable wells can be calculated using standard approaches such as transition state theory or Kramers’ theory. The Kramers’ estimate for the rate is:

$${k}_{1\to 2}^{\mathrm{ad}}({{\epsilon }}_{k})=\frac{m{\omega }_{1}{\omega }_{b}}{2{\rm{\pi }}\gamma }{e}^{-\beta \Delta {F}_{\mathrm{ad}}^{\ddagger }}$$

(41)

where γ is the solvent friction and:

$${\omega }_{1}=\sqrt{\frac{1}{m}{\left(\frac{{\partial }^{2}{F}_{{\rm{ad}}}}{\partial \Delta {E}^{2}}\right)}_{\Delta E=\Delta {E}_{1}}}$$

(42)

$${\omega }_{b}=\sqrt{-\frac{1}{m}{\left(\frac{{\partial }^{2}{F}_{{\rm{ad}}}}{\partial \Delta {E}^{2}}\right)}_{\Delta E=\Delta {E}^{\ddagger }}}$$

(43)

$$\Delta {F}_{{\rm{ad}}}^{\ddagger }={F}_{{\rm{ad}}}(\Delta {E}^{\ddagger })-{F}_{{\rm{ad}}}(\Delta {E}_{1})$$

(44)

ΔE_1 is the location of the reactant minimum, and ΔE‡ is the location of the barrier. The dependence on the mass m cancels out when inserting equations (42) and (43) into equation (41), and the dependence on the solvent friction cancels when evaluating ratios of rates. All of these quantities depend on reorganization energy, driving and coupling. The net adiabatic rate can finally be estimated by summing over all thermally accessible reactive channels in the electrode:

$${k}_{1\to 2}^{{\rm{ad}}}(\lambda ,V)=\int \,D({\epsilon })(1-f({\epsilon })){k}_{1\to 2}^{{\rm{ad}}}({\epsilon };\lambda ,V)d{\epsilon }$$

(45)

$$=\,\frac{1}{2{\rm{\pi }}\gamma }\int \,D({\epsilon })(1-f({\epsilon })){\omega }_{1}({\epsilon };\lambda ,V){\omega }_{b}({\epsilon };\lambda ,V){e}^{-\beta \Delta {F}_{{\rm{a}}{\rm{d}}}^{\ddagger }({\epsilon };\lambda ,V)}d{\epsilon }$$

(46)

Note that every value of ϵ in the integrand defines a different Fad, with different stationary points and frequencies that need to be calculated at every point when evaluating the integral through quadrature.

We applied this model of adiabatic ET to calculate rates in doped MLG. Calculations using equation (45) were performed using both our model for a screening-dependent reorganization energy and a constant value of reorganization energy, and then compared with the corresponding non-adiabatic rate. The results can be found in Extended Data Fig. 9. As expected, the adiabatic rate approaches the non-adiabatic limit as ∣V∣ → 0, and the rate behaviour is even quite similar for a coupling of 5 kBT. As the coupling increases to larger values, we begin to see that the rate enhancement is less pronounced and deviates significantly from experimental measurements. We also note that accounting for doping-dependent reorganization energy in the adiabatic rates is also crucial to improve agreement with the experimental rate enhancement, as we see in the non-adiabatic calculation.

In summary, these results strongly indicate that the coupling in our system is small enough to warrant a non-adiabatic treatment, and adiabatic rates with stronger coupling cannot explain our experimental results. These calculations, in conjunction with the aforementioned arguments in support of non-adiabaticity, allow us to confidently assume non-adiabatic behaviour.

Data availability

Data supporting the findings of this study are available from the corresponding authors upon request. Source data are provided with this paper.

Code availability

Code generated during the study is available from the corresponding authors on reasonable request.

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Acknowledgements

This material is based on work supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, under award no. DE-SC0026007 (experimental studies by S.M., Y.W., M.P.E., J.N.N. and D.K.B.) and DEAC02-05-CH11231 in the Fundamentals of Semiconductor Nanowire Program (KCPY23) (theoretical work by L.C.E. and D.T.L.). Confocal Raman spectroscopy was supported by a Defense University Research Instrumentation Program grant through the Office of Naval Research under award no. N00014-20-1-2599 (D.K.B.). Fluorescence measurements by A.T. and K.X. were supported by STROBE, a National Science Foundation Science and Technology Center under grant no. DMR 1548924. K.W. and T.T. acknowledge support from the JSPS KAKENHI (grant nos. 21H05233 and 23H02052), the CREST (JPMJCR24A5), JST and World Premier International Research Center Initiative (WPI), MEXT, Japan.

Author information

Authors and Affiliations

1. Department of Chemistry, University of California, Berkeley, Berkeley, CA, USA

Sonal Maroo, Leonardo Coello Escalante, Yizhe Wang, Matthew P. Erodici, Jonathon N. Nessralla, Ayana Tabo, Ke Xu, David T. Limmer & D. Kwabena Bediako

2. Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan

Takashi Taniguchi

3. Research Center for Electronic and Optical Materials, National Institute for Materials Science, Tsukuba, Japan

Kenji Watanabe

4. Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

David T. Limmer & D. Kwabena Bediako

5. Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

David T. Limmer

6. Kavli Energy NanoScience Institute, Berkeley, CA, USA

David T. Limmer & D. Kwabena Bediako

Contributions

S.M. and D.K.B. conceived the study. S.M. and Y.W. fabricated the samples with assistance from M.P.E. J.N.N. grew the RuCl_3 crystals. S.M. and Y.W. performed the electrochemical measurements and COMSOL simulations. L.C.E. and D.T.L. performed the theoretical modelling. S.M. and M.P.E. performed the transport measurements. S.M. and A.T. performed the fluorescence measurements under the supervision of K.X. T.T. and K.W. provided the hBN crystals. S.M. and D.K.B. analysed the data. S.M., L.C.E., D.T.L. and D.K.B. wrote the paper.

Corresponding authors

Correspondence to David T. Limmer or D. Kwabena Bediako.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Optical and AFM characterization of exfoliated graphene and hBN.

(a) Optical image of a graphene flake comprising of monolayer, bilayer and trilayer graphene exfoliated on SiO_2/Si chips (SiO_2 thickness 285 nm). (b) Optical contrast (O.C.) line plot of red, green and blue channel intensities of panel a (along green arrow). (c,d) AFM line plot (along red arrow) of a multi-layer hBN exfoliated on SiO_2/Si chips (SiO_2 thickness 285 nm).

Source data

Extended Data Fig. 2 Model geometry for the COMSOL simulation.

Geometry of the simulation space and an example of mesh used for simulations (inset).

Extended Data Fig. 3 TEM bright field images of quartz pipettes.

Top: Survey of nanopipette geometries showing the taper angle and the orifice diameter distribution. Bottom: Representative TEM images of quartz pipettes used in SECCM.

Source data

Extended Data Fig. 4 Representative experimental and simulated voltammograms.

(a)–(i) Cyclic voltammograms of Ru3+/2+ on MLG/hBN/dopant electrodes (purple: RuCl_3 dopant, red: WSe_2 dopant) with associated COMSOL simulations assuming a potential-dependent Cq (black dashed line) and constant Cq (grey dashed line) at each hBN spacer thickness (Scan rate v = 100 mV/s). Inset: RMS error as a function of k^0 used in simulation.

Source data

Extended Data Fig. 5 Potential-dependent quantum capacitance of graphene.

Ratio of Vq/Vapp as a function of applied potential. Inset: Quantum capacitance, Cq/Vapp, versus applied potential.

Extended Data Fig. 6 Raman spectra of the graphene 2D peak.

Negligible shifts in graphene 2D peak in MLG/hBN/RuCl_3 heterostructures with varying hBN thickness are consistent with minimal strain or geometric alteration of the graphene lattice.

Source data

Extended Data Fig. 7 Spatial mapping and spectroscopic analysis of hBN-mediated doping in graphene heterostructures.

(Left) Optical micrograph of a patterned hBN heterostructure overlaid with a spatially resolved Raman G-peak map (colour scale: wavenumber shift, cm−1). (Right) Representative G-peak spectra for MLG/hBN (blue), MLG/hBN/RuCl_3 (red), and MLG/air/RuCl_3 (green), with dashed lines indicating Voigt fits from which peak positions are obtained.

Source data

Extended Data Fig. 8 Adiabatic free energy projected along the energy gap coordinate.

Left: Varying λ, Middle: Varying V, Right: Varying Δϵ.

Source data

Extended Data Fig. 9 Comparison of adiabatic, non-adiabatic and experimental rate at various values of electronic coupling.

Normalized rate of electro-reduction as a function of the location of the Fermi level at zero overpotential, shown on linear (left) and logarithmic (right) scales. Dashed lines correspond to the rate obtained from a constant reorganization energy, and solid lines doping-dependent reorganization energy. Error bars denote standard deviation for each experimental k^0 where n varies from 3 − 6.

Source data

Extended Data Table 1 k^0 (cm/s) as a function of hBN thickness for MLG regions with and without RuCl_3/WSe_2

Extended Data Table 2 Comparison of calculated and experimental Ef shifts in eV for d = 3.89 nm

Supplementary information

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Source Data Fig. 1 (download XLSX )

Source Data Fig. 2 (download XLSX )

Source Data Fig.3 (download XLSX )

Source Data Extended Data Fig. 1 (download XLSX )

Source Data Extended Data Fig. 3 (download XLSX )

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Source Data Extended Data Fig. 6 (download XLSX )

Source Data Extended Data Fig. 7 (download XLSX )

Source Data Extended Data Fig. 8 (download XLSX )

Source Data Extended Data Fig. 9 (download XLSX )

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Maroo, S., Coello Escalante, L., Wang, Y. et al. Electronic origin of reorganization energy in interfacial electron transfer. Nature 653, 98–103 (2026). https://doi.org/10.1038/s41586-026-10311-2

- Received: 23 May 2025

- Accepted: 20 February 2026

- Published: 22 April 2026

- Version of record: 22 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10311-2

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Decarboxylative alkylation of alkenes

Triptesh Kumar Roy,

Federico Maria Tamborini,

Roland Petzold,

Jianhan Fu,

Yiben Tang &

…

Tobias Ritter

Nature volume 653, pages 104–109 (2026)

Abstract

Alkenes are widely used functional groups in synthetic chemistry, important for producing polymers, detergents, agrochemicals and pharmaceuticals. When treated with electrophiles, alkenes typically undergo addition, not substitution, reactions[1]. As a consequence, the intuitive retrosynthetic disconnection to form a substituted alkene from the parent alkene does not exist in the toolbox of the chemist. For example, conversion of tri-substituted into tetra-substituted alkenes, or late-stage alkylation of complex alkenes, would provide access to molecules that are currently difficult to construct. Alkene cross-metathesis can formally alkylate appropriately substituted alkenes, but diastereoselectivity and alkene–alkyl combinations are restricted to specific cases[2], and several classes of alkenes, such as internal or cyclic alkenes, cannot be readily alkylated with known methods[3]. Here we report a formal regio- and diastereoselective C−H alkylation of alkenes with carboxylic acids as alkyl source, readily available in large diversity. Key to the development is a polar decarboxylative alkylation that deviates from the current model of radical-mediated C−C bond formation from carboxylic acid derivatives, enabled by a previously unappreciated access to persistent alkylzinc intermediates from redox-active esters. A Pd-catalysed cross-coupling of the alkylzinc species with alkenyl thianthrenium salts accessed from alkenes affords the substituted alkenes in high diastereoselectivity. The transformation offers alkylation of cyclic, acyclic, terminal, internal, mono-substituted, di-substituted and tri-substituted alkenes with diverse alkyl groups.

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Main

Arenes and alkenes share the presence of C(sp^2)–H bonds of similar bond dissociation energy, yet their reactivity differs fundamentally. Arenes undergo electrophilic substitution because of aromatic stabilization, for example, in Friedel–Crafts alkylation. By contrast, alkenes react by electrophilic addition (Fig. 1a), which prevents analogous substitution chemistry, so there is no alkene analogue to Friedel–Crafts alkylation, and the general alkylation of alkenes remains unknown. Current strategies for substituted alkene synthesis commonly rely on alkenation reactions such as the Wittig[4], Horner–Wadsworth–Emmons (HWE)5,6 and Julia protocols[7], which proceed from carbonyl compounds. Reductive alkylation of alkynes8,9,10 offers an alternative. Alkene cross-metathesis provides a formal alkylation pathway directly from alkenes by transalkenylidination, but the types of alkene suitable for cross-metathesis with one another must be carefully selected, and the reaction is restricted to alkene pairs of appropriate reactivity[11]. Moreover, E-selective cross-metathesis of 1-alkenes and 1,1-di-substituted alkenes remains unknown, and several alkene classes, such as tri-substituted and cyclic alkenes, cannot be alkylated with alkene metathesis at all due to the mechanism-based alkylidene transfer that breaks the C=C double bond before a new one is formed12,13,14. An alternative approach to alkene synthesis involves cross-coupling between alkenyl nucleophiles or electrophiles and appropriate alkyl donors. Examples include alkenyl organometallic reagents that react with alkyl (pseudo)halides or redox-active esters, or conversely, alkenyl (pseudo)halides that react in cross-coupling reactions with alkyl nucleophiles or electrophiles15,16,17. However, alkenyl nucleophiles such as Grignard reagents often require multistep preparation from alkenes, show low tolerance to functional groups, and are generally not readily available in large scope[18]. Alkenyl electrophiles such as halides or triflates are, while synthetically immensely versatile[19], similarly difficult to access directly from alkenes, typically using multistep sequences, such as dibromination followed by elimination, which can form multiple constitutional isomers that reduce practicality[20]. Alkylation of alkenylthianthrenium salts is known as demonstrated in our previous work, but so far limited to a few simple, available alkylzinc reagents and restricted to terminal 1-alkenes and vinyl groups only21,22,23. Alkylation of other alkenyl sulfonium salts is confined only to styrenes24,25. Efforts to alkylate alkenes have so far been limited to electronically activated alkenes, such as styrenes[26] or Michael acceptors[27], and the diversity of applicable alkyl groups is narrow[28]. Heck-type alkylations typically involve the addition of alkyl halides or equivalents under palladium catalysis[26] or through photo-induced radical pathways but are generally limited to styrenes or other activated alkenes29,30,31. Alkyl carboxylic acids are excellent alkyl sources because they offer a massive, structurally diverse pool of bench-stable resources. Alkenes and alkyl carboxylic acids are among the most abundant functional groups in organic synthesis, and a general method to use them in combination for C(sp^2)−C(sp^3) bond formation is hitherto unknown. Here we report a regio- and diastereoselective reaction to access alkylated alkenes that works across the various alkene substitution patterns and electronic environments with a large range of alkyl groups (Fig. 1b). The fundamental novelty reported herein is the recognition of a new retron for alkene alkylation, and the development of a polar decarboxylative alkylation distinct in mechanism from the established radical-mediated carbon–carbon bond-forming processes from activated carboxylic acids[32]. A combination of chemoselective alkylzinc formation from carboxylic acids through redox-active esters and diastereoselective alkene thianthrenation unlocks valuable chemical space and provides access to molecular architectures that are challenging to synthesize using existing methods (Fig. 1c).

Fig. 1: Development of polar decarboxylative strategy for selective alkene alkylation.

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a, Alkene reactivity for addition reaction and challenges with C−H alkylation of alkenes. b, Scope of alkene alkylation. c, Pd-catalysed formal decarboxylative alkylation of alkenes. d, Challenges with radical-mediated cross-coupling between two transient radicals formed from alkenyl thianthrenium salts and redox-active esters, and development of the polar decarboxylative C(sp^2)–C(sp^3) cross-coupling.

Alkene C−H bonds can be substituted through thianthrenation, which proceeds by an inverse-electron-demand hetero Diels–Alder cycloaddition between the thianthrenium dication and the alkene. The resulting bicyclic dicationic intermediate undergoes base-mediated elimination through an irreversible E1cB pathway to deliver the alkenyl thianthrenium salt20,33. This addition–elimination sequence effectively achieves vinylic C−H substitution in a single step to provide direct access to alkenyl electrophiles from simple alkenes. Carboxylic acids, on conversion to redox-active esters, can serve as versatile alkyl group donors[34]. However, all existing methods for decarboxylative alkylation proceed through single-electron pathways, which generate alkyl radicals34,35,36,37,38,39. Although alkenyl thianthrenium salts can also undergo single-electron reduction (Ep = −1.68 V compared with Ag/AgNO_3 in DMF), we recognized the potential difficulty to couple two non-persistent radicals[40] (Fig. 1d); selective radical–radical cross-coupling is challenging, and, to date, such an alkyl–alkenyl bond-forming transformation has not been disclosed.

In line with the analysis of challenging cross-coupling by conventional one-electron pathways, we observed predominant protodethianthrenation of the alkenyl electrophile and hydrodecarboxylation of the redox-active ester (Supplementary Information), when we evaluated reaction conditions akin to single-electron reduction of redox-active esters with common Ni- or Fe-based catalysts33,35,37,38 (Supplementary Table 1). These results underscore the challenge in chemoselectivity for a pathway that proceeds through C(sp^2)−C(sp^3) bond formation from carbon-based radicals, possibly due to competitive reduction of both electrophiles and the generation of two transient radical species. A polar, two-electron alternative through a Pd(0)/Pd(II) redox pathway with alkenylthianthrenium salts as electrophiles could improve this chemoselectivity, yet requires the conversion of redox-active esters to a carbanion synthon. A previous work[41] identified initial evidence for the conversion of alkyl redox-active esters to alkylzinc species, which has remained unexplored by the synthetic community since 2019. Based on an analysis in ref. [42], in which the reduction potential of Zn as a function of solvent was studied, we developed the alkylzinc formation from redox-active ester to ultimately result in successful formation of persistent alkylzinc species, suitable for efficient palladium-catalysed cross-coupling with alkenylthianthrenium salts (Fig. 1d). To our knowledge, this transformation represents the first example of a polar decarboxylative C(sp^2)−C(sp^3) cross-coupling, enabled by formation of a stable alkylzinc intermediate that replaces the commonly used fleeting carbon-based radical as the alkylating agent. Although formation of the alkylzinc species probably proceeds through single-electron transfer (SET) from zinc on account of the electronic configuration of zinc metal, this pathway is distinct from the radical pathway in other decarboxylative bond formation, in which the carbon radical is engaged in oxidative ligation to a transition metal complex, and does not share the same chemoselectivity constraints because persistent alkylzinc species are generated instead.

Reduction of redox-active esters with zinc in solvents such as acetonitrile (MeCN) and ethyl acetate have been used for the generation of alkyl radicals43,44. Yet, reaction of redox-active ester RAE-1 and Zn in DMF generated 52% of the corresponding alkylzinc species I-1 within the first 2 h (Fig. 2a); analogous reduction of redox-active esters with Zn in solvents that are commonly used in cross-coupling chemistry, such as THF and MeCN, did not lead to alkylzinc nucleophiles synthetically useful in this transformation45,46 (Supplementary Table 10). In-cage recombination of the incipient alkyl radical with Zn+ after the first single-electron transfer and stabilization of the zinc organometallic in the polar aprotic medium may be responsible for the distinct reduction chemistry. Addition of the catalyst and alkenyl thianthrenium salt after 2 h to the reaction mixture, resulted in optimal yields for the cross-coupling (Fig. 2b). Combination of all reagents at once resulted in predominant protodethianthrenation (Supplementary Table 6). This result indicates that alkylzinc formation is slower than oxidative addition of the alkenyl thianthrenium salt to Pd(0), and initial formation of alkylzinc is essential for efficient cross-coupling. The reaction can also proceed if the redox-active ester is prepared in situ from the corresponding carboxylic acid, albeit in lower yield; for example, compound 1 was prepared in 40% yield without and in 75% yield with previous isolation of the redox-active ester (Supplementary Information). Attempts to replace Zn with alternative reductants such as Mn, Mg and organic donors such as TDAE failed to produce a cross-coupled product (Supplementary Table 5).

Fig. 2: Reaction development for selective alkene alkylation.

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a, Alkylzinc formation from redox-active esters. b, Reaction conditions for two-step alkylation of alkene. Cross-coupling reactions were conducted on 0.2 mmol scale.

A large variety of different alkenes can be alkylated (Fig. 3). Because the palladium-catalysed cross-coupling from alkenyl thianthrenium salts proceeds stereospecifically with regard to alkene geometry, and thianthrenation generally proceeds with high E-selectivity (>20:1 for 1, 2, 4, 6, 8 and 12), products are produced with high stereochemical selectivity. Even 1,1-di-substituted alkenes deliver E-selective alkylation, for example, 10:1 for 23 and >20:1 for 20. At present, neither compound class can be accessed through alkene cross-metathesis because a general approach for E-selective alkene cross-metathesis is not yet known. The Wickens group reported a method for obtaining Z-selective thianthrenation[22]; in our protocol, Z-alkenyl thianthrenium salts can be converted to Z alkenes under identical reaction conditions as for the cross-coupling for E alkenes (Supplementary Information). Electron-rich alkenes can be selectively alkylated in the presence of electron-poor alkenes, such as α,β-unsaturated ketones (20, 23 and 24), because thianthrenation occurs preferentially at the most electron-rich double bond. The method is applicable to both simple feedstock alkenes and structurally complex substrates. Late-stage alkylation of terpenes such as rose oxide (18) and citronellol (21) could therefore be achieved. Aldehyde and ketone functionalities, such as those in carvone (20) and perillaldehyde (23), respectively, are well tolerated under the reaction conditions without the need for protecting groups. Terminal alkynes do not pose a chemoselectivity issue, and selective alkylation occurs exclusively at the alkene moiety without competing functionalization of the alkyne (4). Alkylation of tri-substituted alkenes remains synthetically challenging, with almost no precedent in the literature. The protocol here allows the selective installation of primary and secondary alkyl groups onto internal di- and tri-substituted alkenes, providing a general and predictable approach to highly substituted alkene motifs[47] (5, 10, 14, 21 and 22). The reaction accommodates diverse alkyl partners, including strained rings, saturated heterocycles and complex derivatives such as indomethacin (11) and baclofen (25). Oxyacids, such as 2,4-dichlorophenoxyacetic acid (17) and menthyloxyacetic acid (19), can also serve as alkyl sources. The scope is limited to primary and secondary alkyl groups because tertiary redox-active esters, although reduced by zinc, do not form organozinc intermediates effectively under these conditions.

Fig. 3: Scope for Pd-catalysed two-step alkylation of alkenes.

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Reactions for the thianthrenation of alkenes were conducted on a 0.5 mmol scale (see Supplementary Information for detailed reaction conditions). Cross-coupling reactions were conducted on a 0.2 mmol scale. General conditions for cross-coupling: alkenyl thianthrenium salt (0.20 mmol), redox-active ester (0.44 mmol), Zn dust (0.60 mmol, mesh size <60 µm), PdCl_2(amphos)2 (4.0 mol%) and DMF (0.125 M). TTO, thianthrene-S-oxide; TfOH, triflic acid; TFAA, trifluoroacetic anhydride.

Redox-active esters (Ep = −1.59 V compared with Ag/AgNO_3 in DMF) and alkenyl thianthrenium salts (Ep = −1.68 V compared with Ag/AgNO_3 in DMF) exhibit comparable reduction potentials (Supplementary Figs. 9 and 11). However, kinetic analysis showed that zinc reduces redox-active esters significantly faster than alkenyl thianthrenium salts (Fig. 4a,b). We observed an autocatalytic effect of soluble Zn(II) for the reaction of the redox-active ester with metallic zinc and established that addition of alkylzinc accelerates chemoselective reduction, possibly through coordination as Lewis acid to the redox-active ester[48] (Supplementary Figs. 14–16). Analysis of the zinc-mediated reduction of RAE-1 by 1H and 13C-NMR spectroscopy in DMF-d_7 suggests that a persistent alkylzinc species is formed (Supplementary Figs. 3 and 4). Electrospray ionization-mass spectrometry analysis detects species consistent with alkylzinc coordinated by two DMF molecules (Supplementary Fig. 7), which suggests that solvent coordination stabilizes the organozinc intermediate. Analysis of 1H NMR spectra of the zinc-mediated reduction of redox-active esters revealed three major side products: alkane formation by protodecarboxylation of the redox-active ester, the corresponding carboxylic acid and a homocoupling product, all of which suggesting that decarboxylation proceeds radically, during formation of alkylzinc as the key alkylating agent (Supplementary Information). By contrast, no spectroscopic evidence for alkylzinc formation was observed when the reduction was conducted in THF-d_8 at either 25 °C or 60 °C (Supplementary Fig. 6). To quantify the in situ-generated alkylzinc intermediate, iodine (I_2) was added to the reaction mixture of redox-active ester and zinc in DMF after 2 h, which resulted in 52% yield of the alkyl iodide derived from RAE-1 (Supplementary Information). Addition of 10 mol% I_2 to the alkylation reaction increased yield by 8% (Fig. 4c), consistent with in situ zinc activation. Replacement of the redox-active ester with an alkylzinc reagent directly prepared from alkyl iodide afforded cross-coupled product with 78% yield at 60 °C under otherwise identical conditions (Fig. 4d), which also supports in situ-formed alkylzinc reagent as the alkylating species for the cross-coupling reaction.

Fig. 4: Mechanistic studies.

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a, Validation and structure of the in situ-formed alkylzinc species from redox-active esters (asterisk indicates mass spectrometry experiments were conducted in non-deuterated DMF). b, Control experiment for zinc-mediated reduction of alkenyl thianthrenium salt at room temperature in DMF. c, Iodine quenching experiment for the quantification of alkylzinc species. d, Cross-coupling under standard conditions with alkyl iodide and alkenyl thianthrenium salt. e, Plausible catalytic cycle for the polar decarboxylative cross-coupling between alkenyl thianthrenium salt and redox-active ester. S, solvent (DMF).

Cross-electrophile coupling is an emerging area of research that offers access to a broader pool of structurally diverse precursors than traditional electrophile–nucleophile cross-couplings[16]. In particular, decarboxylative cross-coupling between redox-active esters with other electrophiles is attractive because carboxylic acids provide abundant, bench-stable alkyl sources. Over the past decade, numerous decarboxylative strategies for C(sp^2)−C(sp^3) and C(sp^3)−C(sp^3) bond formation have transformed synthetic planning and expanded the accessible chemical space[32]. To date, decarboxylative cross-coupling approaches to C−C bond formation have been guided predominantly by radical-based retrosynthetic logic. Here, we introduce a polar decarboxylative cross-coupling pathway that generates a persistent anionic intermediate from carboxylic acids, which enables the selective coupling of two otherwise incompatible transient radicals. The development of this polar decarboxylative manifold introduces a complementary, polarity-driven disconnection strategy for C−C bond formation and establishes a new retrosynthetic logic for the alkylation of alkenes.

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All data are available in the main text or the Supplementary Information.

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Acknowledgements

We thank S. Marcus, D. Margold, F. Köhler, N. Haupt and D. Kampen for mass spectrometry analysis and M. Kochius, M. Leutzsch, C. Fares, S. Tobegen and J. Jacob for NMR spectroscopy analysis. We thank P. Münstermann for her help with HPLC analysis. We thank G. Schoenn, Q. Sun, S. Müller and W. G. Whitehurst for their suggestions and discussions.

Funding

Open access funding provided by Max Planck Society.

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Authors and Affiliations

1. Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr, Germany

Triptesh Kumar Roy, Federico Maria Tamborini, Roland Petzold, Jianhan Fu, Yiben Tang & Tobias Ritter

2. Institute of Organic Chemistry, RWTH Aachen University, Aachen, Germany

Triptesh Kumar Roy, Federico Maria Tamborini & Yiben Tang

Contributions

T.K.R. designed the project, developed the reaction chemistry and investigated the mechanism. T.K.R., F.M.T., R.P., J.F. and Y.T. optimized and explored the substrate scope for alkene alkylation. T.K.R. and T.R. wrote the paper. T.R. directed the project.

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Correspondence to Tobias Ritter.

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Roy, T.K., Tamborini, F.M., Petzold, R. et al. Decarboxylative alkylation of alkenes. Nature 653, 104–109 (2026). https://doi.org/10.1038/s41586-026-10463-1

- Received: 09 December 2025

- Accepted: 30 March 2026

- Published: 29 April 2026

- Version of record: 29 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10463-1

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The evolutionary history and unique genetic diversity of Indigenous Americans

Marcos Araújo Castro e Silva,

Kelly Nunes,

Maíra R. Ribeiro,

Hemanoel Passarelli-Araujo,

Renan Barbosa Lemes,

Lilian Kimura,

Putira Sacuena,

Carlos Eduardo G. Amorim,

Maria Cátira Bortolini,

José Geraldo Mill,

João Farias Guerreiro,

Chiara Barbieri,

Diana Iraíz Hernández-Zaragoza,

Antonia Walter,

Trija Nag Chowdhury,

Daniela Macías-Herrera,

Julio César Lara-Riegos,

Oana Del Castillo-Chávez,

Camilo Zurita,

Ana María Tito-Álvarez,

Emilia Vásquez-Domínguez,

María Ermila Moo-Mezeta,

Julio César Torres-Romero,

Abraham Aguilar-Campos,

Ricardo Serrano-Osuna,

Maria Laura Parolín,

Claudio M. Bravi,

Virgínia Ramallo,

Graciela Bailliet,

Susana Revollo,

José R. Sandoval,

Ricardo Fujita,

Rodrigo Barquera,

Fabrício R. Santos,

David Comas &

…

Tábita Hünemeier

Nature volume 653, pages 134–145 (2026)

Abstract

Indigenous peoples of America represent the last principal expansion of humans across the globe[1], yet their genetic history remains one of the least explored[2]. Although these populations have inhabited the continent for thousands of years[3], their evolutionary history remains largely unresolved4,5, owing to the limited availability of genomic data. Here we present data on 128 high-coverage Indigenous American genomes and show they harbour extensive and previously uncharacterized genetic diversity, reflecting at least three dispersals into South America, followed by regional differentiation and long-term continuity. We identified widespread natural selection signals in genes associated with immunity, metabolism, reproduction and development, which were shaped by adaptation to diverse environmental conditions. Notably, several genomic regions exhibit a remarkable allele sharing with Australasian populations, probably originating from an ancient admixture event and partly maintained by selection for more than 10,000 years. We also detected distinct contributions from archaic humans with adaptive introgression affecting key biological functions. The limited overlap between the regions of Australasian affinity and archaic ancestry indicates independent evolutionary origins of these signals. These findings challenge simplified models of continental settlements and show a more dynamic and complex evolutionary history for the Indigenous peoples in America.

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Main

America was the final frontier of early human global dispersal[1], marking the last continent to be populated not only by Homo sapiens but also by any hominin species. The American continent and its populations have unique characteristics crucial for understanding human evolution. Human history on the American continent began at least 15,000 years ago[3]; however, the ancestry of Indigenous American populations traces back further in time, with their formation beginning about 25,000 years ago in and around the Beringian land bridge[6]. This period was crucial for the emergence of Indigenous American diversity[7], and after millennia of partial isolation in Beringia, the end of the Last Glacial Maximum triggered rapid and large-scale dispersal into America4,5.

As the only continent spanning both northern and southern global extremes, America encompasses a vast range of climates and biomes, further shaped by the most extensive mountain ranges in the world, including the two American cordilleras, the Andes in South America and the Rocky Mountains in North America. This environmental diversity imposed diverse selective pressures on human populations8,9,10. After the initial settlement, except for the northernmost regions, the first American inhabitants remained virtually isolated from the other continental groups for nearly 15,000 years, giving rise to diverse and numerous autochthonous populations after the initial wave of dispersal from Beringia and, in the case of South America, a few dispersals from North America4,5. Over the past five centuries, European-driven colonization has profoundly transformed the continent, leading to the widespread extermination of Indigenous populations and giving rise to a complex mosaic of diverse and multiethnic surviving Indigenous and admixed groups[11].

Despite this profound and impactful history, Indigenous populations from America remain the least studied from a genomic perspective among the principal human continental groups[2]. At present, only a few high-coverage genomes are available for evolutionary and biomedical research12,13. Over the past decade, studies of ancient and present-day individuals based primarily on genotyping arrays and, to a lesser extent, whole-genome sequencing have provided important insights into the initial peopling of the continent and the factors shaping genetic diversity among Indigenous populations3,4,5,11,14,15. However, inherent biases in genotyping arrays and the limited number of studied populations have left many gaps unfilled.

In this context, questions that have been extensively explored using high-quality genomic data from other continental populations remain largely unexamined in Indigenous Americans16,17. For instance, the full extent of genomic variability among Indigenous American populations, represented by diverse and geographically broad sampling, and its effect on health and evolution remain unknown. The fine-scale geographic structure and comprehensive picture of the relationships between present-day and ancient populations across the continent have yet to be thoroughly explored through genomic studies. Moreover, the effects of natural selection, the role of archaic introgressions—whether adaptive or not—and the influence of several ancient dispersals on shaping genetic diversity over time remain compelling and unresolved questions.

Here we generated a diverse and high-quality dataset of Indigenous American genomes to address these unresolved issues and shed light on how demographic history and natural selection have shaped the genetic diversity of Indigenous Americans, providing crucial insights into their unique genomic variability, history, relatedness, archaic introgressions and genetic adaptations.

Data overview

We present the Indigenous American Genomic Diversity Project, comprising 128 newly generated high-coverage (approximately 44 times) whole genomes from Indigenous individuals across 8 Latin American countries (Argentina, Bolivia, Brazil, Colombia, Ecuador, Mexico, Paraguay and Peru), representing 45 populations and 28 language families (Fig. 1a, Extended Data Fig. 1 and Supplementary Table 1). This dataset expands Indigenous representation in genomics, with emphasis on geographically and linguistically diverse populations, particularly from the South American lowlands. For most analyses, we integrated these data with published ancient and present-day Indigenous American genomes (Supplementary Tables 2 and 3) after quality filtering (Methods). The final dataset included 199 contemporary Indigenous individuals from 53 populations and 31 language families. Global ancestry inference (Methods) showed no detectable post-European admixture in most newly sequenced individuals (Supplementary Note 3). When required, we inferred local ancestry and masked non-Indigenous genomic segments (Methods and Supplementary Note 3).

Fig. 1: Genetic diversity of present-day Indigenous American populations.

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a, Geographic distribution of Indigenous American individuals included in the study. Each point represents a sampling location and its size is proportional to the number of individuals from that population. b, Stepwise filtering of SNVs to identify new variants. The UpSet plot shows the number of variants in different combinations of public databases: 1KGP + HGDP, gnomAD and dbSNP. Total variants = 12,493,650. c, Cumulative allele frequency spectrum: new variants (n = 1,426,511). The area plot displays the number of variants with allele frequency equal to or higher than a given threshold (greater than or equal to allele frequency (AF) in the x axis), separated by database overlap groups. d, Rarefaction curves showing the number of segregating SNPs as a function of the number of sampled genomes across African, European, East Asian and Indigenous American ancestry groups. e, Distribution of minor allele frequencies (MAFs) for SNPs in each population. Lines represent the proportion of SNPs with MAF greater than or equal to a given threshold.

All data were collected, generated and analysed following international ethical standards for research with Indigenous populations (Methods). The study was conducted in collaboration with Indigenous communities, with results returned in accessible formats. Community feedback was constructive and positive, emphasizing how genomic findings could complement traditional knowledge and historical narratives.

Global genomics miss Indigenous diversity

Owing to an evolutionary history marked by both ancient (out-of-Beringia[6]) and recent (European colonization[11]) demographic shifts, Indigenous populations from America have traditionally been considered and expected to be less genetically diverse than other continental human groups1,12,13. By analysing 128 high-coverage genomes against a dataset of more than 270,000 individuals from various genomic databases (1000 Genomes Project (1KGP)[18], Human Genome Diversity Project (HGDP)[13], Simons Genome Diversity Project (SGDP)[12], Genome Aggregation Database (gnomAD)[19] and Single Nucleotide Polymorphism Database (dbSNP)[20]), we identified 12,493,650 single-nucleotide variants (SNVs), of which 11.4% were new (1,426,511; Fig. 1b). Of these, 4.7% were polymorphic (allele frequency > 0.01) and 0.02% were common (allele frequency > 0.05; Fig. 1c–e). This corresponds to about 11,100 new variants per individual, fewer than reported for Africans (about 27,800 per individual in 180 genomes)[16] and similar to Oceanians (about 12,600 per individual in 159 genomes)[17], on the basis of comparable reference datasets.

Thus, despite lower overall diversity, Indigenous Americans carry many previously unreported variants. This reflects both substantial genetic variation and long-standing underrepresentation in genomic resources, underscoring the need for broader inclusion to improve evolutionary and biomedical inference and to ensure benefits for Indigenous and descendant communities.

Indigenous American genomic variation

We assessed present-day genetic structure in Indigenous Americans using principal component analysis (PCA) and ADMIXTURE (Methods). Global PCA showed no batch effects, with clustering by geography rather than dataset source (Supplementary Note 1). Analysis of 160 unrelated masked genomes showed clear differentiation between North and South America and substructure within regions (Fig. 2 and Extended Data Fig. 2). Because PCA and ADMIXTURE are sensitive to population-specific drift[21], several groups, including Karitiana, Suruí, Amahuaca and Yaminahua, show distinctive profiles. This pattern is partly explained by elevated inbreeding, reflected by increased runs of homozygosity (ROHs) and higher FROH, an ROH-based inbreeding coefficient (Fig. 2, Extended Data Fig. 3 and Supplementary Table 4). These results underscore the limitations of using the Suruí and Karitiana as representative proxies for lowland South American genetic diversity (a common practice in population genomic studies), given their pronounced genetic differentiation, even relative to geographically nearby populations.

Fig. 2: Genetic structure of present-day Indigenous American populations.

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a, The first two PCs inferred from linkage-disequilibrium-pruned masked genome-wide data. Individuals are colour-coded by genetic cluster, as defined in the analysis presented in Extended Data Fig. 2a. b, PCA visualization of the same data, colour-scaled by individual ROH-based inbreeding coefficients (FROH). c, Ancestry proportions inferred by means of unsupervised ADMIXTURE analysis at K = 12 (the highest K for which a consensus was reached), ordered by genetic cluster (as defined in Extended Data Fig. 2a) and plotted with PONG. d, Geographic distribution of population-averaged ancestry proportions at K = 12 overlaid on a partial map of America. Both analyses were restricted to the maximum set of individuals unrelated at first degree.

Genetic similarity mirrors geography when assessed with outgroup f_3 statistics, which are unbiased by population-specific drift. Four principal South American genetic clusters that closely follow geography were identified by applying a neighbour-joining tree to 1 − outgroup f_3 and multidimensional scaling to 1/outgroup f_3 (Methods, Extended Data Figs. 2 and 8a,b and Supplementary Table 5). These clusters are the Southern Cone, Eastern South America, Western South America (mostly lowlands) and Chaco. Southern Cone populations, including Mapuche-Tehuelche, were the most differentiated, yet Andean Quechua grouped within the same clade, indicating long-distance affinity. Chaco populations formed the sister branch, followed by the unclustered Tsimané, probably reflecting cultural and linguistic isolation[22]. The remaining split separates Eastern and Western lowland populations.

In North America, the earliest split separates Pima and Yaqui (both in northern Mexico), followed by divergence of the Nahua-Xochitlán group (central Mexico/Mexican Atlantic coast). Mixe, Mixtec and Zapotec then branch off (in southern Mexico), followed by Maya (on the Yucatán Peninsula) and finally the remaining continental diversity. This pattern mirrors the archaeological division between Aridoamerica and Mesoamerica and corresponding linguistic differences, with Pima and Yaqui belonging to Uto-Aztecan languages. On the basis of this concordance, we group these populations into two genetic clusters, Aridoamerica (northern Mexico) and Mesoamerica (central and southern Mexico, and Extended Data Figs. 2 and 8a). Although broadly consistent with previous studies11,23,24,25,26, our results show finer substructure and reinforce the link between genetic differentiation and geography, in line with PCA and ADMIXTURE results (Fig. 2).

Analysis of molecular variance showed that genetic clusters explained a modest proportion of genetic variation (9%; P = 0.04), whereas ethnolinguistic groupings explained none (P = 0.57; Supplementary Table 6). Genetic distances, estimated as 1 − outgroup f_3(Mbuti; X, Y), were correlated with geographic distance among Indigenous American populations, both continent-wide (Spearman’s r = 0.4401, P ≅ 0) and within South America (Spearman’s r = 0.148, P ≅ 0; Supplementary Table 7 and Supplementary Note 4). However, isolation by distance alone does not account for the observed structure. Estimated effective migration surface (EEMS) showed regions of greater-than-expected genetic differentiation under an isolation-by-distance model, including southeastern Peru, northern Bolivia and southwestern Amazonia (Extended Data Fig. 4), consistent with previous work[24]. Reduced migration was inferred in Aridoamerica, the northern Amazon, the Chaco and eastern South America, particularly north of the Amazon River. These patterns indicate that demographic history and geographic and cultural factors have shaped genetic structure. By contrast, elevated migration between Mesoamerica and South America indicates greater connectivity than expected from geographic distance alone, consistent with previous evidence of North to South America gene flow4,27.

Genomic legacies of colonization

Compared to other continental groups, Indigenous Americans show a distinctive ROH length distribution, with more segments in all categories except the shortest (less than 2 cM), where Oceanians exceed them (Extended Data Fig. 5), reflecting ancient bottlenecks and recent inbreeding. Mesoamerican and Southern Cone populations have the lowest ROH levels (Extended Data Figs. 3 and 5), indicating higher genetic diversity. By contrast, Moseten (Tsimané), Pano-Takana (Amahuaca, Yaminahua), Zamuco (Ayoreo) and Tupi (Sirionó, Suruí, Karitiana) speakers have the highest ROH counts and total length (Extended Data Figs. 3, 5 and Supplementary Table 4), with an excess of long segments (Extended Data Fig. 5) probably due to population collapse, fragmentation and isolation after European colonization11,28, driven by epidemics, enslavement, warfare, displacement, instigation of intergroup violence, habitat destruction and disruption of subsistence and traditional knowledge29,30.

Indigenous American ancestry was positively correlated with ROH number (Spearman’s r = 0.69; P = 3.16 × 10−22), whereas African (r = −0.45; P = 1.97 × 10−11) and European ancestries (r = −0.68; P = 4.20 × 10−22) were negatively correlated, highlighting elevated ROH in Indigenous Americans and corroborating previous studies[31]. The Arapium individual from Northeastern Brazilian Amazon showed low ROH consistent with their admixed profile (26.92% European, 11.25% African). This individual speaks Nheengatu, a Tupi-Guarani-derived creole historically used as a Brazilian lingua franca, reflecting increased contact and admixture with non-Indigenous populations.

Contrary to expectations under serial bottlenecks following entry into the Americas, population-averaged FROH values (Extended Data Fig. 3) showed no significant correlation with geographic distance from Alaska (r = 0.22; P = 0.1091), longitude (r = 0.16; P = 0.2506) or latitude (r = −0.25; P = 0.0734). Clusters of high FROH are observed in southern Amazonia, northern Bolivia and southeastern Peru, though uneven sampling could influence these patterns.

Identity by descent (IBD) analyses showed that segments greater than 8 cM were mostly shared within populations (Extended Data Fig. 6 and Supplementary Table 8), indicating limited postcolonial gene flow. Small, isolated populations probably experienced increased inbreeding, explaining the high prevalence of long ROHs (Extended Data Fig. 5) and reflecting population fragmentation and reduced intergroup contact following European colonization.

Effective population size (Ne) histories inferred from IBD (Methods) showed a widespread postcontact bottleneck across Indigenous American populations, with recent recovery in Chaco and western South America (Extended Data Fig. 2d). Precolonial expansions of roughly an order of magnitude (from 10^4 to 10^5) occurred in eastern and western South America (Extended Data Fig. 2d), consistent with Late Holocene growth32,33. Mesoamerican populations maintained high Ne, although eastern and western South America exceeded them about 1,500–500 years ago, with western South America and Chaco surpassing all in the most recent period. Aridoamerica had persistently low Ne and the strongest postcontact bottleneck (Extended Data Fig. 2d), matching the high number of long IBD segments in this cluster (Extended Data Fig. 6).

Cross-coalescence rate estimates (Methods) show that Indigenous American populations, grouped by genetic cluster, diverged from other continental groups between about 70,000 and 15,000 years ago (Extended Data Fig. 7), consistent with Out-of-Africa dispersal and the initial peopling of the Americas, when their ancestors were already genetically distinct from Asian ancestors4,5. Within the continent, differentiation among Indigenous American clusters increased during the Middle Holocene and became pronounced in the Late Holocene (Fig. 3c), coinciding with the emergence of distinct ancestral components and regionally structured genetic profiles (Fig. 3c). Estimates of cross-coalescence rates between South American clusters (regions) rise between 5,000 and 3,000 years ago for most pairs (Extended Data Fig. 7). These dates contrast with an independent study estimating divergence of principal South American groups (Amazonia, Andes, Chaco, Patagonia/Southern Cone) at 13,900–10,000 years ago[34]. Our results better match the timing of dispersals into South America, particularly the second main wave, which is inferred to have contributed most to present-day populations and to have started arriving around 9,000 years ago4,5. The difference probably reflects our broader, more representative sampling across ethnolinguistic and geographic diversity.

Fig. 3: Spatiotemporal patterns of genetic similarity among Indigenous Americans.

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a, Neighbour-joining tree constructed using genetic distances calculated as 1 − f_3(Mbuti; X, Y), where X and Y represent pairs of Indigenous American groups. Colour-coded clades define genetic clusters, with branch lengths omitted to emphasize topology, and clades encompassing individuals from the three proposed dispersals are highlighted by coloured circles. Present-day (black stroke) and ancient (white stroke) individuals are distinguished. b, The geographic distribution of genetic clusters shown in a, with labels for selected present-day (blue text) and ancient (black text) groups. c, Ancestry proportions inferred by unsupervised ADMIXTURE analysis at K = 10. For each genetic cluster (panels), individual ancestry estimates are shown along a temporal axis spanning the present to 15,000 years ago (x axis), with proportions ranging from 0 to 100% (y axis). The top bar indicates the earliest occurrence of individuals associated with the first (grey), second (red) and third (green) dispersals in South America and the Caribbean. The bottom bar denotes the geological timescale: Late Pleistocene (red) and Early (orange), Middle (gold) and Late Holocene (yellow).

Our inferred timeframe matches the diversification of several language families[35] and supports the increasing population structure during this period. The Middle Holocene brought major climatic shifts, weakening the South American monsoon and causing instability[36]. Combined with megafauna extinction[37], these changes disrupted ecosystems and reduced resources. Archaeological evidence indicates demographic decline and retreat into refugia, probably driving the genetic isolation and differentiation we observe[38]. Population recovery followed, aided by early plant cultivation and regionally structured societies[38].

We inferred the Ne history of genetic clusters from within-population coalescence rates (Methods) and found that over the past 10,000 years, Indigenous American effective population sizes declined by 40–90%, consistent with previous reports[34]. Historically, North American populations had slightly higher Ne than South American groups (Extended Data Fig. 2e), but in the past 1,000 years, South American lowland populations (Western and Eastern clusters) show marginally higher Ne, matching IBD-based inferences of recent expansion (Extended Data Fig. 2d,e). IBD-based methods capture recent events (past 50–100 generations) but miss deeper history, whereas coalescent approaches resolve ancient events but have limited recent resolution. Combining both provides a fuller view across timescales. These results highlight the complex demographic history of Indigenous Americans, from divergence from Asian ancestors to continental differentiation, and show the lasting effect of colonization on genetic diversity.

Continental dispersals and continuity

To assess shared ancestry across time and space, we estimated genetic affinities among all Indigenous Americans, including ancient genomes from the Allen Ancient DNA Resource[39] and Brazilian sambaqui mound builders[15] (Methods, Supplementary Note 3 and Supplementary Table 9). A neighbour-joining tree based on unbiased genetic distances showed a strong link between genetic similarity and geography, with some regions displaying long-term continuity (Fig. 3 and Extended Data Fig. 8c,d). Clustering mirrored ADMIXTURE results (Fig. 3 and Extended Data Figs. 8 and 9), forming geographically restricted clades indicative of at least partial continuity and limited mobility. Genetic and geographic distances were moderately correlated (Spearman’s r = 0.46; P ≅ 0), similar to when only present-day populations were considered (Supplementary Note 4). Genetic distance and temporal separation between individuals showed a weak but significant correlation (r = 0.29; P ≅ 0). Inferred correlations were stronger in North America and weaker in South America and across regions (Supplementary Note 4).

The neighbour-joining tree indicates at least three main dispersals into South America. The earliest includes Southern Native American (SNA) individuals older than about 9,000 years. They cluster together (Fig. 3 and Extended Data Fig. 8c,d) and show mixed ancestry components (Fig. 3 and Extended Data Fig. 9), reflecting an early undifferentiated SNA ancestry that later diverged into distinct lineages4,5. Key representatives include Anzick-1, Montana, USA (about 12,700 years; Clovis culture[40]); a Spirit Cave, Nevada, USA individual (about 11,000 years)4,5; a Los Rieles, Chile individual (about 12,000 years)[5]; and Sumidouro and Lapa do Santo, Lagoa Santa, Brazil individuals (more than 9,000 years)4,5.

About 9,000 years ago, a distinct genetic lineage spread through Central America into southern Mexico, Belize and Panama. This ancestry also appears in the Caribbean, especially in Archaic-period (preceramic) Cuban individuals (about 5,000–2,500 years ago) and in northern South America, including present-day Venezuela. We reference the archaeological period for Caribbean individuals because genetic patterns in the region align closely with shifts in material culture, as previously demonstrated[33] and as we further corroborated. This lineage extended into southern South America, where groups such as the Quechua in Peru and the Tehuelche in Argentina show continuity with it today. Genetic evidence supports this later dispersal and partial replacement of earlier populations4,5, consistent with ancient DNA studies showing a population turnover in the Americas beginning at least about 9,000 years ago.

Notably, nearly all present-day Indigenous South American and Caribbean individuals from the Ceramic period (about 2,500–500 years ago) show a distinct genetic affinity, showing a previously unrecognized third dispersal into South America. This ancestry peaks in the Ceramic-period Caribbean group (Extended Data Fig. 9;K = 5–8), and these populations form a separate branch in the neighbour-joining tree (Fig. 3 and Extended Data Fig. 8c,d). The dispersal probably occurred at least 1,300 years ago, on the basis of the earliest individuals in this cluster.

Our analysis of ancient and modern genomes improves resolution of population relationships, especially for the second dispersal. Archaic Caribbean and Middle Holocene sambaqui individuals from Brazil (Laranjal and Moraes) show high genetic similarity (Archaic Caribbean cluster; Fig. 3 and Extended Data Fig. 8c,d). Late Holocene sambaqui people are closer to ancient Caribbean Ceramic groups (Ancient Eastern South America cluster). The Archaic Caribbean cluster aligns with ancient Central Americans, whereas ancient Eastern South Americans group with Argentinean Pampas populations. Both diverge first from Southern Cone and then from Andes populations, indicating that the second dispersal might have moved south along the Pacific Coast before expanding north.

Several South American Indigenous groups cluster with ancient individuals from the second dispersal. The Quechua align with ancient Andean populations, whereas the Mapuche-Tehuelche and Tehuelche cluster with ancient Southern Cone individuals, reflecting continuity in their current regions. Indigenous North American populations are the most genetically distinct from South American groups. Aridoamerican populations diverged before all lineages that later expanded into South America, whereas Mesoamerican groups split after the initial dispersal but before the second-dispersal clusters formed (Fig. 3 and Extended Data Fig. 8c,d).

A Late Holocene third dispersal

We inferred population history by integrating genomic data from ancient and present-day Indigenous Americans. Representative individuals from all genetic clusters were selected (Fig. 3 and Extended Data Fig. 8c,d), focusing on the most differentiated ancestry components, and admixture graph modelling was applied (Methods). Late Pleistocene individuals were included to reconstruct Indigenous American ancestry. Starting from tree models without admixture, we progressively added admixture events using an unsupervised, genome-based approach.

Admixture graph analyses placed present-day Indigenous South Americans in a clade with Ceramic-period ancient Caribbeans (Extended Data Fig. 10a–c), whereas Central and South American ancient individuals, including Archaic-period Caribbeans, formed a separate clade, indicating distinct dispersals. This mirrors earlier genetic affinities (Fig. 3 and Extended Data Figs. 8 and 9), with present-day Indigenous South Americans closer to Ceramic-period Caribbeans and second-dispersal individuals more similar to each other. The 11,000-year-old Spirit Cave individual shows affinity to the first-dispersal clade (Fig. 3) but splits earliest in the second-dispersal clade (Extended Data Fig. 10a–c).

Single-admixture models identify the ancestral mixture forming Indigenous Americans from East Asians (Han) and Ancient North Siberians (Russia_MA1_HG.SG), with Ancient Beringians diverging first (Extended Data Fig. 10b). Including a second admixture into the model, third-dispersal individuals, including present-day Indigenous South Americans and Ceramic-period Caribbeans, derive from a mixture of branches related to Mixe and Sumidouro (Brazil_Sumidouro_10400BP.SG, and Extended Data Fig. 10c).

The third dispersal can be modelled as a mixture of early SNA and Mesoamerican ancestry, represented by Lagoa Santa (Brazil_Sumidouro_10400BP.SG) and Mixe. Late survival of Early SNA lineages in South America has been suggested for ancient individuals from Eastern South America[15]. The second and third dispersals remained genetically distinct, with no early admixture between the second dispersal and Mesoamericans. Over time, genetic affinity with Mesoamericans increases (f_3(Mbuti; Mixe, X), Spearman’s r = 0.7028, P < 2.2 × 10−16; Fig. 4b). A similar trend is seen with North American Pacific Coast groups (f_3(Mbuti; USA_CA_Early_SanNicolas.SG, X), Spearman’s r = 0.4198, P < 2.2 × 10−16), reflecting their phylogenetic proximity to Mesoamericans (Fig. 4a). Some later individuals in the second-dispersal clade (Fig. 3a and Extended Data Fig. 8c) also show increasing Mesoamerican affinity (Fig. 4b), indicating a gradual influx rather than rapid replacement. This progressive gene flow would have eventually reached a threshold sufficient to generate the genetic differentiation observed between the third- and second-dispersal populations.

Fig. 4: Population history modelling and genetic affinity to Mesoamericans.

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a, Admixture graph summarizing divergence and admixture events among ancient and contemporary populations. Models were inferred by means of an automated graph-search algorithm (‘find_graphs’ function from ‘admixtools’ R package), iteratively adding admixture events (0–5) to a base tree. Best-fit graphs (highest score and Extended Data Fig. 10) were refined manually, incorporating evidence from the literature, and then summarized and validated using ‘qpgraph’ (‘admixtools’ R package). Confidence intervals for drift lengths and admixture weights were calculated by means of SNP-block resampling. Analyses used transversions only to minimize biases. b, Genetic affinity of individuals to Mesoamericans (x axis) versus sample dates (y axis). Shaded backgrounds mark the earliest presence of individuals linked to the first (grey), second (red) and third (green) dispersals in South America and the Caribbean. The top bar denotes the geological timescale: Late Pleistocene (red) and Early (orange), Middle (gold) and Late Holocene (yellow).

Present-day Tehuelche and Quechua, showing strong continuity with second-dispersal ancestors (Fig. 3 and Extended Data Fig. 8c,d), were included in admixture graph models to assess ancestry. Without admixture, both clustered with other contemporary populations and Ceramic-period Caribbeans (third dispersal), whereas other Central and South Americans formed a separate second-dispersal branch (Extended Data Fig. 10d). Accounting for admixture, Tehuelche retain continuity with Southern Cone second-dispersal ancestors (Argentine Pampas) but derive most ancestry from a Chaco sister branch (third dispersal). Quechua mostly descend from an early-diverging third-dispersal branch while maintaining continuity with second-dispersal ancient Andeans (Extended Data Fig. 10f,g).

In automated models including Tehuelche and Quechua, the third dispersal is not modelled as early SNA-Mesoamerican admixture. The second-dispersal clade forms from admixture between early SNA lineages in South America (Brazil_Sumidouro_10400BP.SG) and North America (post-Anzick-1), indicating distinct early SNA contributions (Extended Data Fig. 10f,g).

We built a comprehensive admixture graph integrating previous knowledge about Indigenous American population history (Methods; Fig. 4). The summary model includes ancestry from an unsampled population A (PopA; Fig. 4a), contributing to present-day Mixe[4]. Gene flow into third-dispersal populations was modelled using both the Mixe and ancient California Channel Island individuals as a sister branch. We also modelled ancestry from an unsampled population Y (Ypikuéra, ‘ancestor’ in Tupi), explaining excess affinity between some Indigenous Americans and Australasians[41] (PopY; Fig. 4a). The model also accommodates PopA contributions to Mesoamericans and ancient Californians (Fig. 4a). Observed and predicted F-statistics show no significant deviation (maximum |Z | < 3; Fig. 4a). Adding Quechua and Tehuelche reduced model fit (maximum |Z | = 4.43), indicating that their ancestry is not fully captured.

We tested the minimum number of ancestral sources explaining genetic diversity in third-dispersal populations using qpWave, with first- and second-dispersal populations and ancient North American Pacific Coast individuals as candidate sources (right populations; Methods). Models with rank 0 and rank 1 were rejected (P = 0; P = 1.8 × 10−25), whereas rank 2 was not (P = 1; Supplementary Table 10). Thus, one or two ancestry streams are insufficient, and at least three distinct dispersal events from genetically differentiated sources are required to explain the observed patterns of genetic diversity.

Using qpAdm, we inferred the ancestral sources of present-day Indigenous Americans, initially excluding populations linked to the third dispersal (Methods). Most South American groups show substantial ancestry related to the North American Pacific Coast (Extended Data Fig. 11a and Supplementary Table 10). In the summary admixture graph (Fig. 4a), this source is modelled as sister to present-day Mesoamericans (Mixe), indicating gene flow from Mesoamerica into South America that probably shaped third-dispersal populations. Further ancestry derives from first-dispersal SNA groups and, to a lesser extent, second-dispersal populations, mainly ancient Eastern South Americans. These results are consistent across top-ranked models (Extended Data Fig. 11a and Supplementary Table 10).

Including all cluster representatives as sources (Methods) yields concordant patterns (Figs. 2–4), with most present-day Indigenous South American ancestry attributed to third-dispersal sources (Extended Data Fig. 11b and Supplementary Table 10). Limited continuity with the first and second dispersals is detected in a few groups, particularly Early SNA and ancient Eastern South American clusters. Tehuelche and Quechua are notable exceptions, deriving most ancestry from Early SNA or a mix of Early SNA and North American Pacific Coast sources, a pattern stable across high-probability models (Extended Data Fig. 11b and Supplementary Table 10).

Our results indicate that present-day Indigenous South Americans and Ceramic-period Caribbean populations were shaped by an unidentified third main dispersal, probably from Mesoamerican-related groups. This Late Holocene dispersal predates the arrival of ceramic-associated groups in the Caribbean and is at least 1,300 years old, on the basis of the earliest individuals assigned to it. Distinctive ancestry and affinity patterns (Fig. 3 and Extended Data Fig. 8c,d), inferred Mesoamerican-related contributions (Fig. 4a and Extended Data Figs. 10 and 11) and elevated Mesoamerican–South American affinity (Extended Data Fig. 5), together with increasing affinity through time (Fig. 4b), collectively support this model.

These findings are consistent with previously inferred Mesoamerican admixture in some Indigenous South American populations[4] and with a ‘non-Anzick’ SNA lineage related to ancient California Channel Islands individuals who largely replaced local South American groups, especially in the central Andes[5]. Our results further indicate that these components were introduced by the dispersal of closely related populations.

Affinity with Australasians and archaics

Some Indigenous American populations show elevated genetic affinity to present-day Australasians relative to other groups41,42, contradicting a single non-Arctic Indigenous American clade. This affinity is best explained by admixture between the ancestors of Indigenous Americans and an unsampled ancient Asian population, termed Ypykuéra41,42 (here referred to as Ypykuéra ancestry), partially related to a sister clade of present-day Australasians.

We assessed genetic affinity between ancient and modern Indigenous Americans and present-day Australasians, the closest living proxies for Ypykuéra ancestry (Methods). We applied F-statistics to modern Indigenous American pairwise comparisons (Supplementary Table 11) and to comparisons including ancient individuals (Supplementary Table 12).

Several Indigenous groups, including the Awajún, Ayoreo, Guarani, Karitiana, Sirionó, Suruí and Tsimané, show significant excess genetic affinity to Australasians relative to other present-day populations (Z > 3; Supplementary Table 11). These groups span eastern and western South America and the Chaco, with the strongest enrichment in the southwestern Amazon, where five of these seven populations are located.

A second analysis detected at least one individual with significant affinity in all examined clusters (Extended Data Fig. 12 and Supplementary Table 12), except Arctic and northern North American groups, which were excluded from this analysis because of partial or complete ancestry from independent Siberian dispersals43,44. The earliest signal occurs in the 10,400-year-old Sumidouro individual (Extended Data Fig. 12). Signals persist from the Early Holocene to the present, increasing in frequency during the Late Holocene, especially in the Andes, Pacific Coast and western South America (Extended Data Fig. 12). The partially discontinuous spatiotemporal pattern probably reflects variation in prevalence within and among populations[42]. Taken together, these findings indicate that this ancestry was present during the initial peopling of America and that it may have contributed more strongly to Late Holocene and present-day genetic diversity.

We tested whether Ypykuéra-related ancestry in Indigenous Americans reflects shared ancestry with Australasians by means of archaic hominins (Neanderthals and/or Denisovans). We compared D(Mbuti, Onge; Mixe, X) with D(Mbuti, Neanderthal or Denisova; Mixe, X), where X denotes Indigenous American groups (Supplementary Table 13). Mixe served as a Mesoamerican reference to match earlier studies reporting Australasian affinity41,42. No correlation was detected between Australasian and Neanderthal (Spearman’s r = −0.006, P = 0.971) or Denisovan affinity (r = −0.1002, P = 0.5372). By contrast, Neanderthal and Denisovan affinities were strongly correlated (r = 0.6572, P = 7.2 × 10−6), consistent with homogeneous archaic ancestry in the founding populations.

An alternative hypothesis proposes that Australasian affinity reflects retention of the Ypykuéra component in isolated groups with high internal genetic similarity[14]. Such populations and genomic regions, characterized by elevated ROH, would be less affected by admixture that could dilute signals of ancient Population Y ancestry. This hypothesis is not supported by our data, which show no correlation between Australasian affinity and inbreeding (FROH) (Spearman’s r = 0.2503; P = 0.1192). Moreover, ROH hotspots, defined as regions with ROH density greater than three standard deviations above the mean, show little overlap with loci of Australasian affinity, with only about 6% of such positions coinciding (Supplementary Note 5).

We tested whether Indigenous American affinity to present-day Australasians also includes ancient Hòabìnhian individuals, proposed ancestors of mainland Southeast Asian hunter-gatherers, including the Onge[45]. Using D(Mbuti, Y; Mixe, X), with Y as Onge or Hòabìnhian individuals (La368, La364) and X as Indigenous American populations, we evaluated correlations in affinity to Onge and Hòabìnhian individuals. La368 forms a sister branch to Onge, whereas La364 is modelled as Australasian-related plus Austronesian ancestry, sister to Ami[45]. We observed significant correlations for La368 (Spearman’s r = 0.6444; P = 1.1856 × 10−5) and La364 (Spearman’s r = 0.6208; P = 2.8848 × 10−5; Supplementary Table 14). These results support a shared ancestry component between Indigenous Americans and Australasians that extends deep into the past.

Genetic adaptation landscape

To investigate shared selective pressures shaping Indigenous American genomes, we performed a genome-wide scan for natural selection using complementary approaches based on population differentiation and extended haplotype homozygosity (Supplementary Note 7). We identified 12 candidate genomic regions (Fig. 5a), predominantly associated with immune response, cardiometabolic traits, fertility and anthropometric features (Supplementary Table 15).

Fig. 5: Genome-wide selection scan.

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a, Manhattan plot of the Fisher combined score of Indigenous Americans. The x axis shows the autosomal chromosomes, and the y axis represents the average of Fisher combined score values in a window of 200 SNPs, using a step size of 50 SNPs. The 99.5th and 99.9th percentiles of the empirical distribution are indicated by black dashed horizontal lines. b, Manhattan plot of PBS values and regions with excess genetic affinity to Australasians. Grey dots represent PBS values across the genome, and red dots mark positions with inferred excess genetic affinity to Australasians. The 95th and 99th percentiles of the empirical distribution are indicated by black dashed horizontal lines. c, Manhattan plot of PBS values and introgressed archaic alleles. Grey dots represent PBS values across the genome, and red dots highlight introgressed archaic alleles identified in Indigenous Americans. The 99.5th and 99.9th percentiles of the empirical distribution are indicated by black dashed horizontal lines.

Among the candidate genes, some have previously exhibited selection signals in Indigenous American or Latin American populations; however, these signals were reported to be restricted to specific ecoregions or populations rather than being broadly distributed across the American continent, as observed here. The strongest signal was in LINC00871, associated with the fertility rate46,47. Other signals were detected in GALNT13, involved in fetal growth[48] and malaria protection[49] and in IFH1, associated with viral responses and autoimmune diseases[50]. Signals in FADS (FADS2 and FADS3) and SLC25A17 genes[10] were also detected above the 99th percentile threshold.

Our analyses also identified new candidate genes under selection, including CMKLR1, a chemokine receptor involved in early immune response, adipocyte development and reproductive regulation[51]. CCDC134 (coiled-coil domain containing 134) encodes a secreted protein with cytokine-like activity that modulates CD8+ T cell activation[52]. DOCK2 (dedicator of cytokinesis 2), expressed in hematopoietic cells, regulates immune cell activation and has a central role in inflammatory responses[53]. Together, these candidate genes show diverse biological processes shaped by natural selection in Indigenous American populations, highlighting the evolutionary forces that have structured their genetic landscape (Supplementary Table 15).

Evolutionary effect of Ypykuéra ancestry

Ypykuéra ancestry in present-day Indigenous American populations remains consistently low (1–3%)41,42, comparable to estimates for the 10,400-year-old Sumidouro individual. Its persistence at similar levels over millennia raises the possibility that specific haplotypes were maintained by positive selection. To test this, we identified genomic regions in modern Indigenous Americans with excess affinity to present-day Australasians using two complementary approaches: a local ancestry deviation test and a genome-wide D-statistic in an overlapping sliding-window framework. Regions with Z-scores greater than 3 in both analyses were classified as candidate Ypykuéra ancestry regions. We then assessed evidence of selection using population branch statistics (PBS), evaluating whether these regions fall in the top 1% and 5% of the genome-wide PBS distribution (Supplementary Note 8).

This integrative approach identified 2,297 positions across 24 genomic regions spanning 17 chromosomes (Fig. 5b), of which 17 ranked in the 99th percentile of the PBS distribution (Supplementary Table 17). The strongest affinity to present-day Australasians mapped to LINC00871, associated with fertility[46], and was also the top hit in the genome-wide selection scan (Fig. 5a). Further prominent signals included ALX4, a transcription factor involved in craniofacial and hair follicle development[54], HMGA1, associated with immune response and insulin signalling[55], and MTDH, implicated in angiogenesis and cancer progression[56]. A complete list of candidate regions is provided in Supplementary Table 17.

Together, these findings indicate that several genomic regions sharing alleles with present-day Australasians were probably targets of positive selection. The candidate genes in these loci participate in critical biological processes that may have shaped the health and adaptive history of Indigenous American populations. However, positive selection seems to have acted on only a subset of Ypykuéra ancestry loci, whereas the remaining regions show no evidence of selection and are probably shaped by neutral evolutionary processes.

Adaptive introgression in America

Although archaic introgression from Neanderthals and Denisovans has been reported in the Americas13,57, its adaptive significance in Indigenous American populations remains largely unexplored. We therefore conducted a genome-wide scan using SPrime on local-ancestry-masked genomes from 128 newly sequenced Indigenous American individuals (Methods). After quality filtering (Supplementary Note 9), high-confidence archaic alleles comprised about 1.2% of the genome.

Overrepresentation analysis (ORA) on the basis of Gene Ontology categories identified significant enrichment for genes related to epidermal structure (P = 5.01 × 10−4), keratin filament (P = 1.19 × 10−4) and intermediate filament organization and keratinization (P = 6.07 × 10−8 and 2.22 × 10−6) (Supplementary Table 18). These findings support previous evidence that keratin-related Neanderthal alleles contributed to adaptation outside Africa58,59, consistent with an adaptive role in the Americas.

Similar patterns were observed for Denisovan-exclusive introgressed variants, with further enrichment in genes associated with calcium channel activity (P = 6.03 × 10−5) (Supplementary Table 19). By contrast, Neanderthal-exclusive introgressed variants were enriched for C–C chemokine receptor activity and structural constituents of the skin epidermis (P = 4.51 × 10−7 and 1.44 × 10−5). Notably, Neanderthal introgression of C–C chemokine receptor genes has been widely associated with immune responses and human health60,61,62,63.

Among the putative archaic alleles shared with Neanderthals and Denisovans and observed exclusively in Indigenous American populations, 488 alleles were identified across 84 genes. ORA showed functional enrichment for phenotypes associated with cyanosis (P = 5.68 × 10−4), limitation of joint mobility (P = 7.77 × 10−4) and agenesis of molars (P = 1.15 × 10−3) (Supplementary Table 21).

To identify adaptive archaic introgression in the Americas, we conducted a genome-wide PBS scan using Indigenous Americans as the focal population, Siberians as the sister group and East Asians as the outgroup (Fig. 5c). The list of introgressed archaic alleles in the 99.9th percentile is provided in Supplementary Table 22. Further selection signals were observed in functionally enriched loci such as IFIH1, PRDM16 and SCN9A (Supplementary Tables 21 and 22). Notably, several of these genes were also reported under selection in admixed Latin American populations, although previous analyses could not disentangle Indigenous American from European ancestry components[64].

Signals in PRKCZ, KBTBD2 and OR10G3 have not been reported in previous studies. PRKCZ encodes a serine/threonine kinase involved in immune regulation, memory formation and glucose metabolism[65]. KBTBD2 (BTB domain containing 2) has a key role in insulin signalling[66] and bone development[67], and OR10G3 encodes an olfactory receptor linked to fruit consumption[68]. Together, these findings highlight the contribution of introgressed archaic variants to immune function and energy metabolism in Indigenous American populations.

Finally, to determine whether the observed affinity between some Indigenous Americans and Australasians could be attributed to shared archaic ancestry, we examined the genomic overlap between regions with excess Ypykuéra ancestry and those with archaic introgression. This analysis showed a minimal overlap of 0.4%, corresponding to 11 genes/genomic regions (Supplementary Table 23). This minimal overlap indicates that the Ypykuéra ancestry is unlikely to have been derived from a known archaic hominin.

Conclusion

Our comprehensive genomic analysis showed a complex and dynamic demographic and evolutionary history that improved our understanding of human diversity and adaptation in the American continent. We present evidence of at least three principal population dispersals into South America, with subsequent regional differentiation and long-term genetic continuity in many areas, challenging oversimplified models of continental peopling. We demonstrated that Indigenous American genomes harbour extensive genetic diversity, highlighting the need to better represent these populations using global genomic datasets. This diversity reflects both deep-time evolutionary processes, such as the Beringian standstill and postglacial expansions, and more recent demographic shifts, including severe bottlenecks following European colonization and subsequent population isolation. Although structural variation was not assessed, these genomes probably contain substantial uncharacterized diversity, including copy number variation and insertions and deletions. Our analyses showed widespread signatures of natural selection across loci related to immune function, metabolism, fertility and development, indicating that Indigenous Americans underwent strong and diverse selective pressures shaped by the continent’s vast environmental heterogeneity. Particularly remarkable are genomic regions with excess allele sharing with Australasian populations, the Ypykuéra signal, many of which we show are probably targets of positive selection. This indicates that the persistence of this genetic signal, present at low but consistent levels for more than 10,000 years, is probably not just a signal of ancient admixture but may also reflect adaptive advantages in specific environmental contexts. Furthermore, we identified candidate regions of adaptive archaic introgression from Neanderthals and Denisovans that contribute to functions related to immunity, metabolism and epidermal integrity, thereby reinforcing the role of archaic alleles in shaping the evolutionary trajectory of non-African populations. Importantly, our data indicate minimal overlap between genomic regions with Australasian affinity and those introgressed from archaic hominins, supporting the interpretation that these signals represent distinct evolutionary phenomena. Together, these findings underscore the unique evolutionary history of Indigenous Americans, shaped by complex interactions between ancient population structure, migration, natural selection and introgression. They also emphasized the importance of expanding genomic research among underrepresented populations to capture the full scope of human genomic diversity.

Methods

Sample description and DNA sequencing

In this study, we generated new whole-genome sequencing (WGS) data from 128 Indigenous American individuals representing 45 populations and 28 language families across 8 Latin American countries (Extended Data Fig. 1 and Supplementary Note 1). Whole genomes were sequenced at the Beijing Genomics Institute (China) and Dasa Genômica (Brazil) using BGISEQ-500 and Illumina NovaSeq 6000, with an average sequencing depth of about 44×.

Ethical approval for sample collection was granted by local ethics committees in each country: Argentina (Puerto Madryn Zonal Hospital, resolution no. 009/2015; San Carlos de Bariloche Zonal Hospital, resolution no. 1510/2015), Brazil (CONEP, resolution nos. 763, 4599, 3828655, 7107656 and 8273857), Bolivia (Universidad Mayor de San Andrés), Ecuador (Universidad de Las Américas; Consejo Nacional de Ciencia y Tecnología—CONACyT, grant no. 69856; Instituto Nacional de Ciencias Médicas y de la Nutrición Salvador Zubirán, refs. 15,18), Mexico (CONACyT grant no. 69856; Instituto Nacional de Ciencias Médicas y de la Nutrición Salvador Zubirán, refs. 15,18; CNIC Salud 2013-01-201471; Committee of Ethics and Research, UADY, notice F-FENC-SAC-14/REV: 04, registry no. 09/17) and Peru (Universidad San Martín de Porres). Written informed consent was obtained from all participants before sample collection. Logistical support in Brazil was provided by the Fundação Nacional do Índio. All sampling adhered to the Declaration of Helsinki and the relevant national laws and regulations at the time (Supplementary Note 10).

Read mapping, variant calling and annotation

Whole-genome sequence data preprocessing, variant calling and annotation were performed using the Sarek v.3.5.0 pipeline[69]. Specifically, sequence data in FASTQ format were aligned to the GRCh38 reference genome and preprocessed according to the GATK Best Practices for germline variant discovery and joint variant calling. Variants in the joint cohort variant call format were then normalized and annotated using the Ensembl Variant Effect Predictor (VEP)[70] v.113, incorporating several annotation sources. Annotations from dbSNP, ClinVar and more custom annotations were retrieved using SnpSift and VEP plugins. Ancestral alleles were filtered using the VEP Ancestral Allele plugin to improve the specificity of downstream population genetic analyses. New SNVs were identified by comparing their positions and alleles with those of the variants in public databases (1KGP[18], HGDP[13], gnomAD[19] and dbSNP[20]). Variants absent from the dataset of more than 270,000 individuals were classified as new (Supplementary Note 2).

Site frequency spectra

We estimated the number of segregating single-nucleotide polymorphisms (SNPs) as a function of sample size using a rarefaction approach on the basis of the site frequency spectrum (SFS). Alternate allele counts were computed for biallelic sites and were used to construct the SFS using Scikit-Allel v.1.3.13. To account for different sample sizes and normalize the comparisons, the folded SFS was projected onto smaller sample sizes using a hypergeometric downsampling method.

Dataset assembly and quality control

Genomic coordinates of the newly sequenced individuals were mapped to the hg38 reference genome. The 128 newly generated genomes were merged with the following publicly available WGS databases (Supplementary Note 1): (1) 1KGP High Coverage, (2) HGDP and (3) SGDP. Sites and individuals with more than 5% missing data were eliminated, biallelic SNVs were selected, and positions with significant deviations (P < 10−8) from the Hardy–Weinberg equilibrium expectations were excluded. Ambiguous positions (A-T and C-G) were also removed. The resulting dataset comprised 199 Indigenous American individuals from 31 language families and 53 ethnic groups. It includes 5,308,880 biallelic SNPs and 3,710 individuals from 201 populations worldwide.

After the initial quality assessment, linkage disequilibrium pruning was performed with ‘SNPRelate’ v.1.28.0 R package[71] to exclude markers exhibiting a pairwise correlation greater than 20% (r^2 > 0.2) in a 50-kb sliding window, advancing in 10-kb steps. This procedure yielded an linkage-disequilibrium-pruned dataset for downstream analyses that required an independent set of markers (for example, PCA and ADMIXTURE).

PCA

PCA was performed using the ‘SNPRelate’ v.1.28.0 R package[71] on both the complete dataset and a subset comprising only local ancestry-masked Indigenous American populations to assess potential biases introduced during data merging and quality control, as well as to explore broad patterns of ancestry and genetic differentiation. In the case of the second analysis, positions with more than 10% missing data were eliminated, as well as those with a minor allele frequency below 5%.

Global ancestry inference

We analysed the Indigenous American genomes using the supervised mode of ADMIXTURE[72] v.1.3.0 and three putative ancestry components (K = 3) using a reference panel of diverse African (Bantu from Kenya, Bantu from South Africa, Biaka, Dinka, Khomani-San, Mandenka, Mbuti, San and Yoruba), European (Basque, Bergamo Italian, French, Orcadian, Sardinian and Tuscan) and Indigenous American (Karitiana, Surui, Colombian and Pima) populations without evidence of recent admixture with other continental groups, running 10 independent iterations with 100 bootstrap replicates per run. The consensus results of independent runs were obtained using CLUMPP[73] v.1.1.2.

We also investigated the genetic structure of present-day Indigenous Americans and their relationship with ancient Indigenous individuals through unsupervised ADMIXTURE analyses, considering 2 to 12 putative ancestry components (K = 2 to K = 12) and visualized the results using PONG[74] v.1.5. In the first analysis, we incorporated a reference panel of African, European and East Asian populations to model the non-Indigenous American ancestry. In the second analysis, we masked non-Indigenous American ancestry (following the approach detailed below) in present-day Indigenous American individuals and then combined them with ancient ones.

Relatedness analysis and sample selection

Subsequently, we performed kinship analysis to identify and remove closely related individuals from the dataset, minimizing the bias introduced by close relatives in downstream analyses. Using PLINK v.1.9 (ref. [75]), we estimated the IBD between all pairs of individuals, calculated as PI_HAT = P(IBD = 2) + 0.5 × P(IBD = 1). On the basis of these estimates, we identified the largest set of unrelated individuals by applying a first-degree kinship cut-off. Filtering was conducted using PRIMUS[76] v.1.9.0.

Haplotypic phase, local ancestry inference and masking

The haplotypic phase of the genomic data was statistically inferred using ShapeIT4 (ref. [77]) v.4.2.2, with the 1KGP dataset as the reference panel. The parameters were adjusted for sequencing data using the ‘--sequencing’ option, with the following settings: 15 burn-in iterations, 15 pruning iterations and 100 main iterations. Local ancestry inference was conducted using RFMix[78] v.1.5.4, applying a window size of 0.2 cM and a minimum of five reference haplotypes per tree node, using a reference panel of unadmixed (that is, with no evidence of recent admixture) Indigenous Americans, Sub-Saharan Africans and Western Europeans. We then used the inferred local ancestry tracts to mask genomic sites (code to perform local ancestry masking is available at https://github.com/macscastro/lamask), assigning segments with a posterior probability of being ancestrally Indigenous Americans below 99% as missing data (Supplementary Note 3).

Two further datasets were generated: (1) the first dataset was generated by combining the local ancestry-masked dataset with ten Indigenous Andamanese, of which six were Onge and four were Jarawa[79]; and (2) the second dataset was created by combining the local ancestry-masked dataset with the Allen Ancient DNA Resource[39] and ancient DNA data from sambaqui mound builders found in Brazil[15].

Effective migration surface modelling

EEMS modelling[80] (https://github.com/dipetkov/eems) was applied to subsets of 116 unadmixed and 160 ancestry-masked Indigenous Americans. The model used 1,200 demes and ran for 4 × 10^6 iterations with a 2 × 10^5 burn-in period (Supplementary Note 4). Migration and diversity rates were visualized using scripts from EEMS developers.

Patterns of allele sharing

Using the ‘admixtools’ v.2.0.10 R package, we calculated population pairwise f_3(Mbuti; X, Y) to investigate genetic similarity patterns among contemporary Indigenous American populations. These patterns were visualized using a neighbour-joining tree and multidimensional scaling. The same method was used to assess spatial and temporal genetic variation, although genetic similarity was estimated for all pairs of contemporary and ancient X and Y groups. Clusters with genetic similarities were identified as clades in a neighbour-joining tree. The 1 − outgroup f_3 distances were also used to infer the existence of a correlation with geographic distances, estimated as great circle distances with the R package ‘geosphere’ v.1.5.18.

Admixture graph and ancestry modelling

We used the ‘find_graphs’ function from the ‘admixtools’ v.2.0.10 R package to identify plausible population history models for contemporary Indigenous American populations (Supplementary Note 6). The algorithm ran ten times with 200 iterations for each inferred number of admixture events. The best-fitting admixture graph for each scenario (ranging from zero to five admixture events) was selected on the basis of its highest score. These graphs were constructed using f_2 statistics restricted to transversions, including one representative of each genetic cluster identified in the previous step.

To summarize our findings alongside existing evidence from the literature, we manually constructed admixture graphs and tested their fit to the data using the ‘qpgraph’ function from ‘admixtools’ v.2.0.10 R package. Confidence intervals for drift lengths and admixture weights were computed with the ‘qpgraph_resample_snps’ function, which fits the graph several times using random SNP subsets. The results were summarized using the ‘summarize_fits’ function to generate a data frame with parameter estimates. As in previous analyses, these models were inferred using only transversions, thus avoiding systematic errors in ancient DNA data caused by postmortem damage, which induces C-to-T transitions at methylated CpG sites.

We used the qpWave method to estimate the minimum number of independent ancestral sources contributing to third-dispersal populations and the rotating qpAdm approach to model the sources and their proportional ancestry contributions to present-day Indigenous South Americans (Supplementary Note 6), both from the ‘admixtools’ v.2.0.10 R package. Analyses were restricted to transversions with a maximum per-site missing rate of 10%. Two complementary analyses were performed: (1) including representatives from all genetic clusters and (2) focusing on individuals from the first and second dispersals, alongside ancient genomes from the North American Pacific Coast (that is, excluding present-day Indigenous Americans and Ceramic-period Caribbeans). We identified feasible models in which source contributions summed to 100% and plotted their probabilities and admixture proportions (Extended Data Fig. 11). Full model statistics are reported in Supplementary Table 10.

Effective population size history and IBD sharing

We used IBDNe[81] v.07May18.6a4 to infer the effective population size (Ne) histories of present-day Indigenous Americans. IBD segments were identified using the Refined IBD[82] v.12Jul18.a0b by merging those with short gaps (maximum gap = 0.6, maximum discordant homozygotes = 1). Ne was inferred by pooling individuals by language families or genetic clusters (minimum ten individuals; populations from Mesoamerica and Aridoamerica were pooled together), as previous studies indicate that historical Ne trajectories remain robust with smaller sample sizes (Supplementary Note 5). Segments greater than 2 cM were analysed using the default IBDNe parameters.

IBD sharing patterns were assessed by categorizing segments by length, which reflected the time since a shared ancestor, to examine shared IBD within and between populations over time. The IBD networks were estimated using the ‘as_tbl_graph’ function (directed = FALSE) and visualized with the ‘ggraph’ function (layout = ‘fr’) from the ‘tidygraph’ v.1.3.1 and ‘ggraph’ v.2.2.1 R packages, respectively.

Effective population size, coalescence rates and divergence times

We applied the coalescent-based method Relate[83] v.1.2.2 to the phased WGS data to infer historical changes in Ne and estimate divergence times between contemporary populations. Input files were converted from variant call format to the haps/sample format using the RelateFileFormats script, and haplotypes were flipped according to the ancestral genome using the PrepareInputFiles script, which also filtered SNPs and adjusted the distances on the basis of the genomic mappability mask. Input preparation was performed using the GRCh38 ancestral genome (human_ancestor_GRCh38), the GRCh38 genome mask (20160622_genome_mask_GRCh38) and GRCh38 recombination maps provided by the developer.

Ancestral recombination graphs were inferred using the parameters −m = 1.25 × 10−8 (mutation rate) and −N = 30,000 (effective population size), and the coalescence rate trajectories for each population were estimated using the EstimatePopulationSize script. Ne estimates were obtained by calculating the inverted coalescence rate in the form of 0.5/(coalescence rate), and putative divergence times between groups were identified as the time points at which the inverted coalescence rate values in each population started to diverge from one another, whereas the inverted cross-coalescence rate values between populations increased. This indicates a decrease in cross-coalescence rates between populations, indicating genetic separation and increasing divergence over time. We also inferred the Ne history by inferring the ancestral recombination graph, while considering all individuals as a single population.

ROHs

ROHs were inferred for unrelated and unadmixed Indigenous Americans using PLINK v.1.9 with a sliding window of 50 SNPs, allowing for up to one heterozygous site and five missing calls, a minimum density of one SNP per 50 kb, a maximum gap of 100 kb and a minimum ROH length of 500 kb. We compared the total and average ROH lengths per individual across global populations and Indigenous American clusters of genetic similarity. For global populations, we visualized individual total ROH counts and lengths, whereas for Indigenous Americans, we additionally plotted population-wise averages. We estimated the inbreeding coefficient (FROH − ROH-base inbreeding coefficient) and average inbreeding per population and tested the correlations between ROH counts and ancestry proportions. Additionally, ROH hotspots, defined as regions with an above-average ROH occurrence (more than three standard deviations), were identified (Supplementary Note 5).

Excess affinity with Australasian populations

We analysed the excess genetic affinity between Indigenous American and Australasian populations by computing D(Mbuti, Australasian; X, Y), where X and Y are Indigenous American groups, and Australasians are represented by Australian, Bougainville, Jarawa, Onge and Papuan (‘PapuanHighlands’ and ‘PapuanSepik’) populations. This included comparisons between present-day and ancient Indigenous American populations to trace the Ypykuéra ancestry across space and time. To minimize bias from missing data, we used a subset of unrelated and unadmixed Indigenous American individuals to ensure more reliable results when integrating present-day and ancient genomes, the latter generally having high missingness levels. We also investigated the signatures of natural selection in genomic regions with excess genetic affinity for Australasian populations (Ypykuéra ancestry) and their potential functional effects (Supplementary Note 7).

Selection scans

Natural selection analysis was performed on a subset of unrelated individuals, masking segments of non-Indigenous American ancestry. To detect positive selection signals, we used two approaches based on population differentiation (di statistic[84] and PBS[85]) and extended haplotype homozygosity (iHS[86] and xpEHH[87]). For all four statistics, we conducted sliding-window analysis using 200 SNPs per window with a step size of 50 SNPs. We then combined the genome-wide ranks of the four statistics for each window using Fisher’s combined score[88]. This score is calculated as the sum, over the four statistics, of −ln(rank of the statistic/number of windows tested). Outlier regions were defined as windows with Fisher’s combined score scores in the 99.9th percentile (Supplementary Note 8).

Archaic introgression inference

To identify genomic regions exhibiting signals of archaic introgression in ancestry-masked Indigenous Americans, we used segments detected by the SPrime method[89], using Indigenous Americans as targets and African Mbuti from the HGDP as unadmixed outgroups. To enhance robustness, we applied filtering steps following ref. [64], retaining only (1) high-confidence archaic sites and (2) those found at low frequency in Africa (less than 0.01) but present at greater than or equal to 0.01 in at least one non-African population (Supplementary Note 9).

For sites passing through these filters, we classified a match when the Archaic genotype contained the putative Archaic-specific allele (present in both Neanderthals and Denisovans). Additionally, we identified Neanderthal-specific (matching Neanderthals but differing from Denisovans) and Denisovan-specific (matching Denisovans but differing from Neanderthals) sites.

ORA

ORA was performed using the WEB-based GEne SeT AnaLysis Toolkit (WEB-GESTALT)[90], focusing on phenotypes and Gene Ontology categories, including Biological Processes, Cellular Components and Molecular Functions. To address redundancy, we applied a weighted set cover approach, which identified the minimum subset of gene sets that covered all genes from the enriched sets. The weight or cost of adding a gene set was based on P.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The corresponding author (T.H.) can be contacted at tabita.hunemeier@csic.es or hunemeier@usp.br. All data needed to evaluate the conclusions of this study are presented in the paper and/or Supplementary Information. The datasets generated in this study have been deposited in the European Genome-Phenome Archive (EGA) and are available under accession number EGAD50000002396. Access to individual-level genomic data is controlled and granted through the EGA Data Access Committee for research in human evolutionary genetics and related fields, in accordance with the ethical approvals, informed consent and community agreements governing the use of Indigenous genomic data. Requests for access will be evaluated by the Data Access Committee, and applicants can expect an initial response in about 2–4 weeks. Allele frequencies are freely accessible through the Indigenous American Genomic Diversity Project Variant Browser (IAGDP Browser).

Code availability

Newly generated code essential for reproducing the results has been deposited on GitHub (https://github.com/macscastro/lamask). All remaining data processing and analyses were performed using previously published software, as described and referenced in the Methods.

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Acknowledgements

We thank all individuals and Indigenous communities who participated in this study, as well as V. Acuña Alonzo and G. Macín for their valuable assistance in working with Indigenous populations from Mexico. We also thank Google Cloud Brazil for providing support with cloud computing. Funding was provided by grant nos. FAPESP 21/06860-8 and 15/26875-9 (T.H.), grant no. CSIC 20223AT005 (T.H.), European Union’s Horizon 2020 Research and Innovation Programme/Marie Skłodowska-Curie COFUND/EUTOPIA grant no. 945380 (M.A.C.S.), grant no. JDC2022-049175-I funded by MICIU/AEI/10.13039/501100011033 and ‘European Union NextGenerationEU/PRTR’ (M.A.C.S.), CAPES grant no. 88887.505029/2020 (K.N.), grant no. CNPq INCT 406864/2022-5 (F.R.S.), URPP ‘Evolution in Action’ of the University of Zurich (C.B.), the NCCR Evolving Language Swiss National Science Foundation Agreement grant no. 51NF40_180888 (C.B.), the National Institute of General Medical Sciences of the National Institutes of Health under grant no. R35GM142939 (C.E.G.A.) and the Max Planck Society (D.I.H.-Z., A.W., T.N.C. and R.B.).

Author information

Author notes

1. These authors contributed equally: Marcos Araújo Castro e Silva, Kelly Nunes, Maíra R. Ribeiro

Authors and Affiliations

1. Institut de Biologia Evolutiva, CSIC/Universitat Pompeu Fabra, Barcelona, Spain

Marcos Araújo Castro e Silva, David Comas & Tábita Hünemeier

2. Departament de Medicina i Ciències de la Vida, Universitat Pompeu Fabra, Barcelona, Spain

Marcos Araújo Castro e Silva & David Comas

3. Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil

Marcos Araújo Castro e Silva, Kelly Nunes, Maíra R. Ribeiro, Renan Barbosa Lemes, Lilian Kimura & Tábita Hünemeier

4. Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Hemanoel Passarelli-Araujo

5. Laboratório de Genética Humana e Médica, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil

Putira Sacuena & João Farias Guerreiro

6. Institute of Human Origins and School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

Carlos Eduardo G. Amorim

7. Departamento de Genética, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Maria Cátira Bortolini

8. Departamento de Fisiologia, Universidade Federal do Espírito Santo, Espírito Santo, Brazil

José Geraldo Mill

9. Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy

Chiara Barbieri

10. Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

Chiara Barbieri

11. Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology (MPI-EVA), Leipzig, Germany

Diana Iraíz Hernández-Zaragoza, Antonia Walter, Trija Nag Chowdhury & Rodrigo Barquera

12. Escuela Nacional de Antropología e Historia (ENAH), Mexico City, Mexico

Diana Iraíz Hernández-Zaragoza & Daniela Macías-Herrera

13. Chemistry Faculty, Universidad Autónoma de Yucatán (UADY), Mérida, Mexico

Julio César Lara-Riegos & Julio César Torres-Romero

14. Centro INAH Yucatán, Instituto Nacional de Antropología e Historia (INAH), Mérida, Mexico

Oana Del Castillo-Chávez

15. Biomedicine Research Unit, Zurita & Zurita Laboratorios, Quito, Ecuador

Camilo Zurita

16. Nursing School, Faculty of Health Sciences, Universidad de las Américas (UDLA), Quito, Ecuador

Ana María Tito-Álvarez

17. Department of Biotechnology, Faculty of Engineering and Applied Sciences, Universidad de las Américas (UDLA), Quito, Ecuador

Emilia Vásquez-Domínguez

18. Nursing Faculty, Universidad Autónoma de Yucatán (UADY), Mérida, Mexico

María Ermila Moo-Mezeta

19. Blood Bank Laboratory, Hospital General de Zona #30, Instituto Mexicano del Seguro Social (IMSS), Mexicali, Mexico

Abraham Aguilar-Campos

20. Clinical Laboratory, Unidad Médica de Alta Especialidad (UMAE) #2, Instituto Mexicano del Seguro Social (IMSS), Ciudad Obregón, Mexico

Ricardo Serrano-Osuna

21. Instituto de Diversidad y Evolución Austral (IDEAus, CCT CONICET-CENPAT), Puerto Madryn, Argentina

Maria Laura Parolín

22. Laboratorio de Genética Molecular Poblacional, Instituto Multidisciplinario de Biología Celular (IMBICE), Buenos Aires, Argentina

Claudio M. Bravi & Graciela Bailliet

23. Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn, Argentina

Virgínia Ramallo

24. Universidad Mayor de San Andrés (UMSA), La Paz, Bolivia

Susana Revollo

25. Centro de Genética y Biología Molecular (CGBM), Instituto de Investigación, Facultad de Medicina Humana, Universidad de San Martín de Porres, Lima, Perú

José R. Sandoval & Ricardo Fujita

26. Laboratório de Biodiversidade e Evolução Molecular (LBEM), Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Fabrício R. Santos

Contributions

Conceptualization: T.H. Methodology: M.A.C.S., K.N., M.R.R., H.P.-A., R.B.L and L.K. Investigation: M.A.C.S., K.N., M.R.R., D.C. and T.H. Data curation: P.S., C.E.G.A., M.C.B., J.G.M., J.F.G., C.B., D.I.H.-Z., A.W., T.N.C., D.H.-M., J.C.L.R., O.D.C.-C., C.Z., A.M.T.-A., E.V.-D., M.E.M.-M., J.C.T.-R., A.A.-C., R.S.-O., M.L.P., C.M.B., V.R., G.B., S.R., J.R.S., R.F. and F.R.S. Funding acquisition: T.H. Project administration: T.H. Supervision: T.H. Writing—original draft: M.A.C.S., K.N. and T.H. Writing—review and editing: T.H. All authors reviewed the final version of the manuscript.

Corresponding author

Correspondence to Tábita Hünemeier.

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The authors declare no competing interests.

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Nature thanks María Ávila-Arcos, Ryan Hernandez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Geographic distribution, linguistic affiliation, and sample sizes of newly-generated whole genome sequences.

The approximate locations of newly sampled Indigenous American communities are shown on a partial map of the Americas, with language families represented by different symbol shapes and colors. A bar plot below lists ethnic groups and language families (x-axis) along with the number of sequenced individuals per community (y-axis), with numbers linking communities between the map and bar plot. Countries with at least one sampled Indigenous community are highlighted in white.

Extended Data Fig. 2 Present-day patterns of genetic similarity and demographic history.

(a) Neighbour-Joining (NJ) tree constructed from genetic distances calculated as 1 − f_3(Mbuti; X, Y), where X and Y represent pairs of contemporary Indigenous American populations, masked for non-Indigenous American ancestry. Genetic clusters are color-coded and branch lengths are omitted to emphasize topology. Population-averaged ancestry proportions (from Fig. 2d) are shown to the right of each tree tip. (b) Multidimensional scaling (MDS) plot based on the same genetic distance matrix, illustrating genetic similarities in reduced dimensional space. (c) Geographic distribution of populations, color-coded by genetic cluster. Effective population size (Ne) trajectories for contemporary Indigenous Americans were inferred (d) using IBDNe, based on identity-by-descent (IBD) segments exceeding 2 cM (y-axis log_10-transformed), and (e) using Relate, based on coalescence rates (axes log_10-transformed), with individuals pooled and color-coded according to the genetic clusters defined in (a), assuming a generation time of 28 years.

Extended Data Fig. 3 Indigenous American individual and population-wise FROH distribution.

(a) Boxplot showing individual-wise ROH-based inbreeding coefficients (FROH) for each contemporary Indigenous American population, color-coded by genetic cluster (see Extended Data Fig. 2). (b) A partial map of the Americas displaying the approximate geographic location of each population, color-scaled by their average FROH.

Extended Data Fig. 4 Effective migration rates in Indigenous American populations.

Effective migration surface modelling was applied to the ancestry-masked Indigenous Americans. Blue shades indicate increased migration rates, while orange shades indicate migration barriers. The model used 1,200 demes and ran for 4×10^6 iterations with a 2×10^5 burn-in period. Migration rates and model fit were visualized using scripts provided by the developers. The estimated effective migration rates are shown (top left), and posterior probabilities >0.9 (top right) highlight regions with rates significantly higher (blue) or lower (orange) than expected under an isolation-by-distance model. The bottom panels show the correlation between observed genetic dissimilarity and geographic distance (bottom left), and between observed and fitted dissimilarities under the EEMS model (bottom right).

Extended Data Fig. 5 Worldwide individual and population-wise ROH distribution.

(a) Boxplot of individual-wise total ROH length (y-axis) across different segment length categories (x-axis), with individuals from different continental regions separated and color-coded as shown in the legend. (b) Scatterplot of individual-wise total ROH length (x-axis) versus count (y-axis), with points color-coded by their continental region of origin. (c) Population average total ROH length (x-axis) and count (y-axis), overlaid on individual-wise values shown in panel B, with points color-coded according to genetic cluster (Extended Data Fig. 2).

Extended Data Fig. 6 Patterns of IBD sharing between individuals.

Identity by descent (IBD) segments were identified between all pairs of contemporary Indigenous Americans. These segments were categorized by length and independently analysed in network analysis. IBD segments shared among individuals from the same population are color-coded in blue, while those shared between individuals from different populations are color-coded in red. The number of segments shared between pairs of individuals is represented by the thickness of the connecting lines. Individuals are color-coded according to their genetic cluster (Extended Data Fig. 2).

Extended Data Fig. 7 Coalescence and cross-coalescence rates in contemporary Indigenous Americans and worldwide populations.

Inverse coalescence rates (ICR estimated within groups) and inverse cross-coalescence rates (ICCR; estimated between groups) were inferred using Relate (0.5/coalescence rate in both cases) to estimate divergence times, grouping individuals by genetic clusters (Extended Data Fig. 2). Each panel displays the ICR and ICCR for a given group, with ICCR represented by color-coded lines corresponding to comparisons with other groups. The ICR for each group is depicted by a thicker line color-coded with the same group as the panel’s name. Both axes are log_10-transformed, and key periods marking the onset of group divergence are highlighted. Other continental regions are represented by the following populations: YRI (African); IBS (European); CHB (East Asian); GIH (South Asian); Yakut (Central Asian/Siberian); Jarawa and Onge (Andamanese).

Extended Data Fig. 8 Continental-wide long-term patterns of genetic similarity.

Neighbour-joining (NJ) trees based on genetic distances calculated as 1−f_3(Mbuti; X, Y), where X and Y are (a) pairs of present-day Indigenous American groups or (c) combinations of present-day and ancient groups. color-coded clades define genetic clusters, with branch lengths representing genetic distances. (b,d) Geographic distribution of the genetic clusters shown in the corresponding NJ trees (a,c). In (b), all populations are labelled; in (d), selected present-day (blue text) and ancient (black text) groups are labelled. In (c) and (d), present-day individuals are outlined in black and ancient individuals in white. In (c), numbers to the right of tree branches indicate genetic clusters as listed in the figure, and shades below group names denote the associated dispersal events.

Extended Data Fig. 9 Global ancestry inference of contemporary and ancient Indigenous Americans.

Ancestry proportions inferred from unsupervised ADMIXTURE analysis for K = 2 to 12. Individual estimates are shown for each K, with individuals ordered according to the NJ tree clustering (Fig. 3; Extended Data Fig. 8). Genetic clusters are indicated in the bottom bar, with selected present-day and ancient individuals distinguished in blue and black, respectively.

Extended Data Fig. 10 Admixture graph modelling of Indigenous American populations.

Population history models were inferred using ‘find_graphs’ from ‘admixtools’ in R, with 10 replicates and 200 iterations per admixture event. The best-fitting graph for each scenario (0–5 admixture events) was selected based on the highest score using f_2 statistics with transversions. One representative per genetic cluster (Fig. 3) was included, alongside Late Pleistocene individuals and outgroups necessary to model the ancestry of Indigenous Americans. (a–c) Models were refined by progressively adding admixture events (examples of models with 0 to 2 admixture events are shown), (d–g) then expanded to include present-day populations with genetic continuity to ancient Southern Cone and Andean individuals (Tehuelche and Quechua, respectively; examples of models with 0, 1, 2 and 5 admixture events are shown).

Extended Data Fig. 11 Ancestry modelling of contemporary Indigenous American populations.

Ancestry sources and proportions were estimated using qpADM with a rotating strategy, based on transversions and a maximum per-site missing rate of 10%. (a) Models restricted to source populations representing the first and second dispersals. (b) Models incorporating source populations from all defined genetic clusters. For each target population, the top 50% of feasible models are shown, ordered from lowest to highest model probability (left to right). Bar plots indicate inferred ancestry proportions, and model probabilities are shown on the y-axis. Full model statistics are available in Supplementary Table 10.

Extended Data Fig. 12 Spatiotemporal distribution of relative excess genetic affinity of Indigenous Americans to Australasians.

Excess Australasian affinity in Indigenous Americans was assessed using D(Mbuti, Australasian; X, Y), where X and Y represent Indigenous American groups, and Australasian affinity (Ypykuéra ancestry) was modelled using Australian, Bougainville, Jarawa, Onge, and Papuan (PapuanHighlands and PapuanSepik) populations. To minimize biases from missing data, only groups with less than 50% missing data were analysed, and unadmixed individuals were used instead of the masked dataset. The number of significant tests (Z > 3) for each Y population is represented by point size and color on a partial map of the Americas and a timeline of sample dates (x-axis) by genetic cluster (y-axis). Ancient and present-day individuals are indicated by diamonds and circles, respectively. In the bottom panel, shaded areas denote the earliest and latest presence of individuals associated with the first (gray), second (red), and third (green) dispersals in South America and the Caribbean, while the top bar indicates the geological timescale: Late Pleistocene (red), Early (orange), Middle (gold), and Late Holocene (yellow).

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Castro e Silva, M.A., Nunes, K., Ribeiro, M.R. et al. The evolutionary history and unique genetic diversity of Indigenous Americans. Nature 653, 134–145 (2026). https://doi.org/10.1038/s41586-026-10406-w

- Received: 15 May 2025

- Accepted: 11 March 2026

- Published: 22 April 2026

- Version of record: 22 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10406-w

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Rapid concerted switching of the neural code in the inferotemporal cortex

Yuelin Shi,

Dasheng Bi,

Janis K. Hesse,

Frank F. Lanfranchi,

Shi Chen &

…

Doris Y. Tsao

Nature volume 653, pages 146–155 (2026)

Abstract

A fundamental paradigm in neuroscience is that neurons represent the world through fixed tuning functions, with stable mappings from stimulus features to firing rates[1]. Here, we report that tuning can instead shift rapidly and coherently across a neural population, enabling a dynamic transition from detecting a broad category to discriminating individual exemplars. We set out to address a longstanding debate in visual neuroscience about whether the inferotemporal cortex uses a specialized code for specific object categories or a general-purpose code that applies to all objects. We found that face-selective cells in macaque inferotemporal cortex initially adopted a general code optimized for face detection. However, after a rapid concerted population event lasting less than 20 ms, the neural code transformed into a face-specific one, with two striking features: response gradients to principal detection-related dimensions reversed direction, and new tuning emerged for multiple higher-dimensional features that support fine face discrimination. These dynamics in face patches were specific to face stimuli and did not occur in response to non-face objects. Thus, for faces, face cells transition from detection to discrimination by switching from an object-general code to a face-specific one. More broadly, our findings indicate that there is a previously unknown mechanism for neural representation: concerted stimulus-dependent switching of the neural code used by a cortical area.

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Main

An important challenge in visual neuroscience is to understand how populations of neurons encode visual stimuli. In the past decade, considerable progress on this question has been made in macaque inferotemporal cortex2,3,4,5,6, a large brain region dedicated to high-level object recognition7,8,9. Anatomically, the inferotemporal cortex is organized into parallel networks, each specialized for encoding distinct aspects of object shape and colour6,10,11,12,13. Among these networks, the macaque face patch system has become a powerful model for testing competing theories of high-level object representation14,15,16. Here, we leverage this system to address a central debate in the field: whether the inferotemporal cortex relies on specialized mechanisms for processing specific object categories17,18,19, or whether it relies on general-purpose mechanisms that process all object types in the same way20,21,22.

According to one theory, face processing is ‘domain specific’, being reliant on specialized mechanisms dedicated to faces17,23. This idea was motivated by the observation that humans are best at discriminating face parts presented in the context of a whole, upright face24,25 (Fig. 1a, top-right inset). This observation led to the suggestion that a face-detection gate—a switch determining whether an image is a face or not—is implemented before detailed feature processing begins[23] (Fig. 1a, top). The selectivity of face patches for faces15,22,26,27, the exquisite sensitivity of cells in face patches to facial geometry and texture3,14,28, and the striking perceptual distortions of faces but not non-face objects induced by face-patch stimulation[29] all point to a special role for face patches in representing faces.

Fig. 1: Two contrasting models for face processing.

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a, Top, a specialized coding mechanism. Extraction of detailed face features is preceded by a face-detection gate, ensuring that only upright faces undergo detailed processing. Top right, face features are perceived much better when embedded in an upright face[24]. Bottom, a general-purpose coding mechanism. Face cells are modelled by units in a late layer of a DNN trained on general object classification. Conv, convolutional layers; FC, fully-connected layers. b, The response of a unit to a stimulus is modelled by the projection of the features of the stimulus onto the unit’s preferred axis. N denotes the number of stimuli, and M denotes the number of features. c, A 2D general object space can be computed by PCA of a DNN representation of faces and objects[6]. d, Schematic illustrating the predictions of domain-general (top) and domain-specific (bottom) models of face processing. Left, each dot corresponds to the projection of one object image onto the first two dimensions of object space, and the colour of the dot indicates the firing rate of a cell elicited by the image. The green arrow is the cell’s object axis. Right, each dot represents the projection of one face image onto the first two dimensions of the same object space. The purple arrow indicates the cell’s face axis. If cells use a domain-general mechanism (top), the face and object axes should align. If cells use a domain-specific mechanism (bottom), the face and object axes should differ. e, Schematic of the experiment. f, In the main experiment, a large set of human faces (left) and non-face objects (right) were presented. g, Right, mean responses of all visually responsive cells in ML (n = 310). The small grey horizontal bar at the bottom indicates images of animals with greyed-out heads. Left, face selectivity d′ for each cell. The dotted vertical line marks d′ = 0.2. h, As in g but for AM cells (n = 153). Credits: object images in a, c and f are from ref. [6], Springer Nature Limited; face images in a, c, e and f are reproduced from the FEI database (https://fei.edu.br/~cet/facedatabase.html)[47]; face images in f are reproduced from the CVL Face Database (http://lrv.fri.uni-lj.si/facedb.html)[48].

However, a competing theory proposes that the computations performed by face patches are domain general rather than face specific20,22. Supporting this view, several studies have shown that general-purpose deep neural networks (DNNs) trained on object categorization effectively model neural responses across the inferotemporal cortex, including in face patches2,6,22,30,31,32 (Fig. 1a, bottom). Specifically, inferotemporal cell responses can be modelled as linear combinations of features represented in late layers of these networks: r = c·f, where r is the cell’s response, f is a feature vector representing the stimulus in the DNN space, and c defines the cell’s ‘preferred axis’2,6,22,30 (Fig. 1b). According to this picture, the DNN-derived feature space forms a general object space in which all stimuli, including faces, reside (Fig. 1c). A neuron’s preferred axis can be thought of as the direction in this space pointing from stimuli that elicit weak responses to those that elicit strong responses. In this framework, face cells encode stimuli, just like other inferotemporal cells, by projecting them onto their preferred axes, regardless of object category6,22. Consistent with this domain-general view, the inferotemporal cortex has been shown to contain a topographic map of object space defined by the top two principal components of DNN space6,31 (Fig. 1c). Face patches occupy one quadrant of this map, alongside other category-selective regions, indicating that face cells participate in a shared object-coding framework.

According to the domain-general theory of face-patch coding, each cell should use a single preferred axis, and mapping with either faces or non-face objects should yield the same axis6,22 (Fig. 1d, top). By contrast, the domain-specific view posits that fine feature discrimination is selectively engaged for faces, so mapping with faces should produce a unique axis for each cell supporting fine-grained face identification (Fig. 1d, bottom).

To investigate these competing theories, we recorded responses of face cells in macaque face patches to a large set of faces and non-face objects. We parameterized the stimuli using layer fc6 of AlexNet[32], whose performance in modelling inferotemporal cells is representative of a large class of feedforward DNNs6,30,33. Comparison of the encoding axes for faces versus objects revealed an unexpected phenomenon: stimulus-dependent switching of the neural code. Multiple analyses converged on this conclusion. First, the preferred axes derived from responses to non-face objects were uncorrelated with those derived from responses to faces: object axes consistently pointed towards the face quadrant of feature space, supporting detection, whereas face axes were more diverse. Second, time-resolved analysis revealed that the face-encoding axis initially aligned with the object-encoding axis, consistent with domain-general processing. However, about 100 ms after the stimulus onset, a clear switch occurred: the face-encoding axis reversed direction in low dimensions of the general object space, neural responses became sparse and tuning to multiple face-specific features rapidly emerged. Strikingly, these dynamics were specific to faces and absent for non-face objects. This late-emerging tuning improved the reconstruction of face identity from neural responses, demonstrating the computational importance of the switch. Together, these results show that face cells achieve domain specificity through a rapid, concerted change in their neural code, resolving a longstanding debate on domain specificity in the inferotemporal cortex17,18,19,20,21,22. More broadly, the results reveal a new mechanism for neural representation: stimulus-dependent switching of the neural code.

We identified face patches ML (middle lateral) and AM (anterior medial) in three macaque monkeys by using functional MRI (fMRI)[15] (Methods). We then targeted NHP Neuropixels probes[34] to the ML and AM face patches of the three monkeys (Fig. 1e and Extended Data Fig. 1a–d). Neural responses were recorded while the animals passively fixated 1,525 images of real human faces and 1,392 images of diverse non-face objects (Fig. 1f). Each stimulus was shown for 150 ms followed by an interstimulus interval of 150 ms. In the figures, we show results from the ML face patch of monkey A. Corresponding data from the ML face patch in monkey J are shown in Extended Data Fig. 2, and results from the AM face patch in monkeys A and M are shown in Extended Data Fig. 3.

Distinct axes for faces and objects

Cells in both the ML and AM face patches responded more to faces than to objects (Fig. 1g,h and Extended Data Fig. 1e,f), with 54.5% of ML cells (n = 310) and 51.6% of AM cells (n = 153) having a face selectivity d′ ≥ 0.2 (Extended Data Fig. 1g,h and Methods).

We first passed a combined set of face and object stimuli through AlexNet and performed principal component analysis (PCA) on the fc6 layer embedding to extract a general 60-dimension feature space capturing features of both faces and objects[6]. For each face cell, we used responses to the non-face object stimuli (averaged over 50–220 ms) to compute a preferred axis, the object axis, by linearly regressing responses of the cell to the 60-dimension feature vectors corresponding to different objects (Fig. 1b). Similarly, we used responses to face stimuli to compute a preferred face axis. We confirmed that the object and face axes could explain significantly more variance in responses to objects and faces, respectively, compared with a shuffle control (Extended Data Fig. 4a,b).

We wanted to know whether single face cells encode the entire object space using a single axis, or whether they use multiple axes (Fig. 1d). We first compared the face and object axes by visualizing them in a 2D space corresponding to the first two PCs of the 60-dimension feature space. In agreement with a previous study[6], we found that the object axes of face cells consistently pointed towards the upper right quadrant of the PC1–PC2 space (Fig. 2a, left; standard deviation (s.d.) of angles = 37.4°), where faces reside (Extended Data Fig. 4c). By contrast, the face axes of the same cells pointed in diverse directions (Fig. 2a, right; s.d. of angles = 81.9°; comparing face and object axis variance, F(1,150) = 214.7, P < 4.9 × 10−37). To ensure that the consistency of the object axes was not due to any inherent non-uniform distribution of the object stimuli, we selected a subset of object stimuli that were Gaussian distributed (Extended Data Fig. 4d,e). This did not change the qualitative difference in angular distributions for object compared with face axes (Extended Data Fig. 4f,g), which was confirmed to be destroyed by stimulus shuffling (Extended Data Fig. 4h,i). A scatter plot of face and object axis weights for PC1 confirmed the lack of a clear correlation (Fig. 2b, left; r = 0.16, P = 0.04), and similarly for PC2 (Fig. 2b, right; r = −0.07, P = 0.37). Further confirming the lack of correlation between face and object axes, the face axis could not explain any variance in the object responses, and vice versa (Fig. 2c).

Fig. 2: Face cells use distinct axes to represent faces and objects.

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a, Distribution of object (left, green) and face (right, purple) axes, projected onto the top two dimensions of the 60-dimension object space (note that faces occupy the upper right quadrant of this space; Extended Data Fig. 4c) (n = 151 cells; only cells with R^2 > 0 on the test set for both object and face axes are included; Methods). b, Scatter plots of object versus face axis weights for PC1 (left) and PC2 (right). c, Cross-prediction accuracies. Left, distribution of explained variances when using face axes to explain object responses (grey), or object axes computed using responses to a random object subset to explain responses to the left-out object subset (green). Right, as in the left, except to explain face responses. Units with R^2 < −2 are not shown. d, Responses of an example cell with opposite tuning to objects and faces. Top left, scatter plot showing actual responses to objects versus predicted responses computed by projecting images onto the object axis (R^2 = 0.62). Top right, scatter plot showing actual responses to faces versus predicted responses computed by projecting images onto the face axis (R^2 = 0.54). Bottom: responses of this cell to objects (large cluster in the lower left) and faces (small cluster in the upper right), colour coded by response magnitude (Extended Data Fig. 4c). The object (face) axis of the cell is indicated by the green (purple) arrow.

The lack of correlation between the face and object axes was evident in the raw responses of single cells. For example, Fig. 2d shows a cell with diametrically opposite face and object axes. Responses of this cell to objects were well captured by the object axis (Fig. 2d, top left), and responses to faces were well captured by the face axis (Fig. 2d, top right), but the two axes clearly pointed in opposite directions in the PC1–PC2 space (Fig. 2d, bottom). Thus, it is clear from the raw responses that a single axis cannot explain the selectivity of this cell for faces and objects (Fig. 1d).

Cells in the AM face patch showed a similar pattern (Extended Data Fig. 3a–d), with object axes consistently pointing towards the face quadrant and face axes pointing in diverse directions.

The difference between the face and object axes was not dependent on prior expectations or adaptation, because we observed the same results when the face and object stimuli were presented in separate blocks (Fig. 2 and Extended Data Fig. 3a–d,q) or randomly interleaved (Extended Data Figs. 2a–d and 3t) (Methods). The axis divergence also could not be attributed to out-of-distribution generalization failure[22], because artificial AlexNet fc6 units, which have single axes in the object space by construction, showed correlated face and object axes (Extended Data Fig. 4j–m).

Face axis dynamics

If a face cell’s face and object axes are distinct, we wanted to know when the cell decides which axis to use. We wondered whether it was evident in the first spikes, or whether it would emerge dynamically over time. Single face cells showed rich response dynamics that differed between faces and objects (Fig. 3a). The axis-encoding framework allowed us to directly address whether such firing-rate changes over time reflect dynamic changes in the neural code for faces and objects. To examine axis-change dynamics, we computed face and object axes using a 20-ms sliding window. Figure 3b shows matrices of mean similarities across the population for (object, object), (face, face) and (face, object) axis pairs at different latencies. The object axis stabilized early at 60 ms, whereas the face axis stabilized later, at 118 ms (Fig. 3b, left and middle). Surprisingly, the (face, object) matrix showed a positive correlation very early, in the 50–100 ms interval (Fig. 3b (right),c). This interval overlapped with the peak of face-evoked population activity (93 ms; Fig. 3d). After 100 ms, however, the face–object axis correlation became negative (Fig. 3b (right),c). Thus, the face axis is transiently aligned to the object axis at short latency before later diverging (see ref. [22], which compared face and object coding but did not analyse tuning dynamics).

Fig. 3: Face and object axes transiently align before diverging.

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a, Raster plot of responses of an example ML cell to two face and two non-face stimuli. The grey bar at the bottom indicates stimulus on time. b, Left to right, matrices of mean cosine similarities across the population between (object, object), (face, face) and (face, object) axes (computed using all 60 dimensions) for different pairs of latencies (n = 151 cells; only cells with R^2 > 0 on the test set for both object and face axes are included; Methods). c, Correlation between face and object axes as a function of time (diagonal values in the right-most similarity matrix in b). The shaded region indicates s.e.m. d, Mean response time course to each face and object stimulus, averaged across cells and trials. e, Cosine similarity between each cell’s overall object axis (computed using a time window of 50–220 ms) and its time-varying face (top) and object (bottom) axes, sorted from top to bottom according to face selectivity, d′ (left). f, Axis change dynamics of a single cell. Top, mean response time course of this cell to each face and object stimulus, averaged across trials. Expanded row 1: time-resolved scatter plots of face and object stimuli projected onto PC1 and PC2 of object space, colour coded by the cell’s response magnitude. Expanded row 2: time-resolved face and object axis directions computed using a time window of 20 ms. Expanded row 3: explained variance (R^2) on the test set for object axis (green) and face axis (purple) as a function of time. g, Eight example cells showing axis reversal in PC1–PC2 space for faces but not for objects. Purple arrows indicate the approximate face axis direction in each time window. h, Response sparseness as a function of time for faces and objects (Methods). Credits: object images in a are from ref. [6], Springer Nature Limited; face images in a are reproduced from the CVL Face Database (http://lrv.fri.uni-lj.si/facedb.html)[48].

To examine axis dynamics in single cells, we computed the cosine similarity between the overall object axis for each cell (computed using a time window of 50–220 ms) and its time-varying face and object axes (Fig. 3e). The face axis showed strong alignment with the object axis at early latencies (50–100 ms) but then diverged (Fig. 3e, top). The axis divergence times were concerted across the population (Extended Data Fig. 5a). The object axis, by contrast, showed high correlation throughout the entire presentation duration (Fig. 3e, bottom). The cells in Fig. 3e are sorted according to their face selectivity d′; divergence was more prominent for highly face-selective cells.

Inspection of raw responses of an example cell projected onto PC1–PC2 space (Fig. 3f) revealed that, over time, the face axis did not merely diverge from the object axis; it actually reversed direction. In this cell, face and object axes were aligned at 80–100 ms, both pointing towards the face quadrant of PC1–PC2 space. Thereafter, the face axis suddenly reversed direction at 120–140 ms. By contrast, the object axis did not change over time.

This pattern of face-axis reversal in the PC1–PC2 subspace of the general object space at around 100 ms was extremely common across the population (Extended Data Fig. 5b,c). Figure 3g illustrates this phenomenon in eight further example cells. Across the population, 62% of cells clearly flipped tuning in PC1–PC2 space (Extended Data Fig. 5d). Notably, this reversal was accompanied by increased response sparseness across the face-cell population (Fig. 3h). This reversal is consistent with the earlier observation of divergent face axes using a time-averaged response (Fig. 2a), which probably reflected temporal blurring, underscoring the value of time-resolved analysis. The early alignment of face and object axes in a direction pointing towards faces followed by subsequent specific reversal of face axes is consistent with a transition from face detection to face discrimination (Fig. 1a, top).

Cells in the AM face patch showed a largely similar pattern of dynamics, but with longer latency overall (Extended Data Fig. 3e–j,r,s,u,v). As with ML, face axes in the AM face patch initially aligned with the object axis (at around 100 ms) before reversing in low dimensions (interestingly, there was also a weak, brief second period of re-alignment at about 160 ms that was not observed in ML). Overall, 57% of AM cells exhibited a clear tuning reversal (Extended Data Fig. 5e). The fact that face axis reversal in AM occurred later than in ML indicates that the ML reversal was due to local recurrence, rather than to long-range feedback from AM.

Direct assessment of tuning from the raw neural responses yielded results consistent with the axis-based analyses (Extended Data Fig. 5f,h). Given that face axis reversal is evident in the raw responses and occurs consistently across face-selective cells, we reasoned that the response rank across the population should reverse when the monkey sees a face. If true, this would offer a powerful way to assess axis change in response to single images. We computed a single-stimulus axis-change score by tracking shifts in population rank over time (Extended Data Fig. 5j and Methods). Applied to human faces, monkey faces, dog faces and pareidolia, the metric revealed robust axis reversal for individual human and monkey faces, variable effects for dog faces and weaker effects for pareidolia (Extended Data Fig. 5k,l). This approach bypassed the need for human-defined face and object categories[4], providing an unbiased, data-driven measure of whether a face patch treats an image as a face based solely on neural population dynamics.

Finally, we investigated whether the observed axis-change behaviour is unique to neurons within face patches in the inferotemporal cortex. We recorded from neurons outside face patches while we presented a large, diverse set of objects (Extended Data Fig. 6 and Methods). Many neurons outside face patches, including isolated neurons selective for faces, also exhibited axis change for their preferred stimuli, although this was typically weaker and less consistent than that observed in face-selective populations. This indicates that stimulus-gated axis change may be a general computational motif in the inferotemporal cortex, with the face system representing a particularly robust instance.

We next wanted to investigate the mechanism responsible for stimulus-gated axis change. Control analyses ruled out ‘cell-intrinsic’ mechanisms that depend only on a neuron’s intrinsic input–output properties, including explanations based on mean response differences to faces versus objects (Extended Data Fig. 7a–i), distinct response time courses to high- versus low-responding faces (Extended Data Fig. 7j–l), adaptation (Extended Data Fig. 7m,n) and differences in spatial frequency content of faces versus objects (Extended Data Fig. 7o–v). In particular, we found that faceless animal bodies drove even stronger mean responses than some faces did[35], yet they failed to trigger axis change, whereas those faces did (Extended Data Fig. 7b–i). Conversely, some degraded face stimuli elicited relatively weak responses but still triggered axis change (Extended Data Fig. 8a–e). These analyses, together with our earlier finding ruling out top-down feedback based on later occurrence of axis change in the AM face patch compared to ML (Extended Data Fig. 3e–j), indicate a mechanism for axis change driven by local recurrent dynamics.

Emergent tuning in higher dimensions

To investigate whether new tuning to face features emerges over time, we first generated a 60-dimension face space by passing face stimuli through AlexNet and performing PCA on the fc6 representation. This new AlexNet face space emphasized differences between face identities, rather than between faces and objects. A plot of face and object axis weights computed in this new space using short (80–100 ms) and long (120–140 ms) latency windows revealed a markedly different pattern for faces (Fig. 4a, top and middle) compared with objects (Fig. 4b, top and middle). For faces, short- versus long-latency axis weights were anti-correlated in low dimensions and decorrelated in higher ones, resulting in a distribution of correlations skewed towards negative values when computed over all 60 dimensions (Fig. 4c, purple; one-sample Student’s t-test, t(150) = −7.78, P < 1.1 × 10−12). By contrast, for objects, the weights were clearly correlated between the two time windows across all dimensions (Fig. 4c, green; one-sample Student’s t-test, t(150) = 16.01, P < 2.8 × 10−34). Importantly, we confirmed that axis weights for faces computed using short and long time windows in higher dimensions (6–60) were not positively correlated (Fig. 4d, purple; one-sample Student’s t-test, t(150) = −3.13, P = 0.002), in contrast to axis weights for objects (Fig. 4d, green; one-sample Student’s t-test, t(150) = 14.56, P < 1.7 × 10−30); thus, the changes in tuning were not confined to low dimensions of the face space. Following the drastic change in face axis weights between 80–100 ms and 120–140 ms, the face axis then stabilized (Fig. 4a, middle and bottom, and Fig. 4e; face: one-sample Student’s t-test, t(150) = 29.3, P < 5.8 × 10−64; object: one-sample Student’s t-test, t(150) = 19.1, P < 5.5 × 10−42).

Fig. 4: Face axis reversal in low dimensions is accompanied by the emergence of diverse new tuning to higher dimensions of face space.

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a, Matrix of face axis weights in the AlexNet face space for each cell, computed using short (80–100 ms, top), long-early (120–140 ms, middle) and long-late (160–180 ms, bottom) latency windows (n = 151 cells; only cells with R^2 > 0 on the test set for both object and face axes are included; Methods). b, Matrix of object axis weights computed at three different latencies; conventions are the same as in a. c, Purple (green) distribution shows cosine similarities across units between face (object) axis weights at short and long-early latency. All the dimensions (1–60) were used to compute the cosine similarities. d, As in c, but using higher face space dimensions 6–60 to compute cosine similarities for each cell. e, As in c, but showing cosine similarities across units between face (object) axis weights at long-early and long-late latencies (120–140 ms and 160–180 ms), using all 60 dimensions. f, Explained variance in z-scored population responses to faces at short (80–100 ms, orange) and long (120–140 ms, blue) latencies, as a function of the number of PCs. Data are shown as mean ± s.d. g, Schematic showing the projection of a cell’s encoding axis at 120–140 ms (ν_2) onto the component orthogonal (ν⊥) to the cell’s encoding axis at 80–100 ms (ν_1). h–k, Top, scatter plots of responses to faces projected onto ν⊥ (top) at three different time windows (80–100 ms, 120–140 ms and 140–160 ms) for four example cells. Bottom, faces spanning ν⊥ for each cell, sampled at [−4, −2, −1, 0, 1, 2, 4] σ, where σ is the s.d. of the projections of stimuli along ν⊥ (Methods).

Another signature of new tuning to higher dimensions would be an increase in the dimensionality of the neural state space over time. To test this, we performed PCA on raw neural population responses to faces at short (80–100 ms) and long (120–140 ms) latencies. Figure 4f shows that more dimensions were required to explain 90% of the response variance at long compared to short latency (mean ± s.d.: 91 ± 5.28 dimensions for long versus 79 ± 2.85 dimensions for short). This difference was significant (paired Student’s t-test across 100 bootstrap samples, t(99) = –21.52, P < 0.05), supporting the emergence of new tuning dimensions at later stages of processing.

To directly visualize the new tuning dimensions emerging at long latency, for each cell, we first computed its preferred face axis at 80–100 ms (ν_1) and at 120–140 ms (ν_2). We then derived the component of ν_2 orthogonal to ν_1 (ν⊥) (Fig. 4g). This component represents a new tuning direction orthogonal to the cell’s previous tuning direction. We projected the features of each face onto ν⊥ (x axis) and the principal orthogonal direction to ν⊥ (y axis) and plotted neural responses at 80–100 ms, 120–140 ms, 160–180 ms for four example cells. In each of the four example cells shown, new tuning is apparent along ν⊥ at 120–140 ms (Fig. 4h–k, top). To further illustrate what these new dimensions encode, we generated faces varied along ν⊥ (Fig. 4h–k, bottom). Each cell showed new tuning to different features, for example ν⊥ for cell 1 varied from small inter-eye distance, sharp chin, masculine appearance and light complexion to large inter-eye distance, round chin, feminine appearance and dark complexion.

Cells in the face patch AM showed a very similar pattern, with reversal in low dimensions, new feature tuning in higher dimensions for faces and no change in tuning for objects (Extended Data Fig. 3k–p).

Similar results were obtained using a face space derived from an alternative deep network (ResNet-50 trained on VGGFace2; ref. [36]), demonstrating that the axis dynamics are robust to the choice of feature representation (Extended Data Fig. 8f–i). Analyses of tuning directly from raw neural responses further corroborated the axis-based findings (Extended Data Fig. 5g,i).

Overall, these results show that for faces, a marked change in tuning occurs across all dimensions of face space at about 100 ms, involving reversal in low dimensions and loss or gain of tuning in higher dimensions.

Late axes improve face discrimination

We wanted to know what the newly emergent encoding axes for faces accomplish. We therefore investigated whether changes in tuning improve face discrimination. To assess the population-level impact, we reconstructed faces from neural activity using linear decoders trained on responses from three different windows: a short latency window; a long latency window; and a combined latency window. Each decoder estimated face features (f = Mfacerface), which were then passed to a variational autoencoder trained to generate faces from latent features (Fig. 5a and Methods).

Fig. 5: Newly emergent face-encoding axes improve face discrimination.

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a, The reconstruction pipeline. We trained an image generator (encoder: images → 512D latent features; decoder: latent features → images; Methods). Latent features were linearly decoded from neural responses in three windows (short, long and combined) to reconstruct faces. Each window comprised two sub-windows, ensuring equal degrees of freedom. Data from the ML (n = 332 cells) and AM (n = 154 cells) face patches were pooled, with AM windows delayed by 20 ms. b, Row 1: eight example faces. Row 2: best possible reconstructions using ground-truth latent features. Row 3: reconstructions from combined-window activity using a decoder trained and tested on combined responses. Row 4: decoder trained and tested on long-latency responses. Row 5: decoder trained and tested on short-latency responses. Row 6: decoder trained on short- and tested on long-latency responses. Row 7: decoder trained on long- and tested on short-latency responses. c, A face-discrimination DNN was used to identify reconstructions; a reconstruction was correct if it was closest (in DNN space) to the optimal reconstruction of the same face. d, Face identification performance on faces reconstructed from neural activity in different time windows as a function of the number of cells, following the scheme in c. The shaded area denotes the s.e.m. e, As in d, but computed using simulated units in which short-latency responses had similar tuning across units and were concentrated in a small number of low (high-variance-capturing) dimensions (5 of 60), whereas long-latency responses had more varied tuning across units and spanned many dimensions (60 of 60) (Methods). f, Summary schematic showing how a face-detection gate can be implemented through axis dynamics. Early in the response, face and object axes align, both pointing towards the face quadrant, supporting detection. Later in the response, face axes reverse in low dimensions while new tuning directions emerge in face space, enabling fine discrimination. Credits: face images in a and b are reproduced from the FEI database (https://fei.edu.br/~cet/facedatabase.html)[47]; face images in b are reproduced from the CVL Face Database (http://lrv.fri.uni-lj.si/facedb.html)[48] and the Chicago Face Database (chicagofaces.org)[52].

For the long and combined windows, this approach gave reasonable reconstructions compared with the best possible reconstruction (the reconstruction from veridical face features; Methods) (Fig. 5b, rows 3 and 4). Reconstructions were less accurate using the short window (Fig. 5b, row 5). When we attempted to perform cross-window decoding by training a decoder using short (long) window responses and then applying the decoder to long (short) window responses, face reconstruction completely failed (Fig. 5b, rows 6 and 7). This underscores the complete change in neural code occurring at about 100 ms.

We quantified reconstruction quality by identifying the reconstructed faces using a DNN trained to discriminate faces36,37,38. We compared faces reconstructed using short, long and combined windows, to see which reconstruction was closest to the best possible reconstruction in the DNN space (Fig. 5c). For small numbers of cells, the short-latency response performed best (Fig. 5d, orange curve). However, as cell number increased, the long and combined responses outperformed the short-latency response (Fig. 5d, blue and cyan curves).

This crossover pattern aligns with the idea that short-latency responses are optimized for face detection, whereas long-latency responses support face discrimination through more diverse tuning. Cells showed stronger tuning to low dimensions of face space at short latency than at long latency (Extended Data Fig. 9a,b). These low face-space dimensions, which account for the most variance in faces, are well-explained by low object-space dimensions (Extended Data Fig. 9c), which support face detection. When only a few cells, and thus a limited number of dimensions, are available, it is advantageous to use these low, face-detection dimensions from short latency, because they are also the ones that explain the most variance in faces. By contrast, as more cells become available, more dimensions can be leveraged to encode subtle differences between individual faces, improving discrimination. A simulation of this trade-off, between using a few high-variance, face-detection dimensions and many diverse dimensions, produced the same pattern observed in the actual cell population (Fig. 5e).

We also evaluated face categorization and identity discrimination performance directly from the population responses using a 20-ms sliding window (Methods), without relying on reconstruction. This analysis confirmed that face discrimination accuracy increased at later time points, rising after the peak in categorization accuracy (Extended Data Fig. 9d), consistent with previous studies15,39,40.

To what extent does the improvement in face discrimination accuracy at long latency depend on the emergence of new tuning to higher dimensions of face space? To test this, we designed an experiment to knock out the contribution of low-dimensional features and assess whether the performance gain at later latencies persisted. We created synthetic faces whose variation along low-dimensional features was fixed (Methods). Despite this, discrimination performance still improved at later time points. This supports the idea that the observed improvement is, at least in part, driven by new tuning to higher-dimensional features essential for fine face discrimination (Extended Data Fig. 9e,f).

Overall, these results show that the marked changes in feature tuning occurring at about 100 ms have a functional consequence, greatly improving the ability of the neural population to perform fine face discrimination.

Discussion

In this study, we resolve a longstanding debate over whether coding mechanisms in the inferotemporal cortex are domain specific17,18,19 or domain general20,21,22. We show that face-selective cells initially use a domain-general code, but following a rapid and concerted population-level event lasting less than 20 ms, the neural code transitions into a domain-specific one. Our findings further indicate that the early, domain-general phase supports face detection, whereas the subsequent domain-specific phase supports face discrimination, offering a concrete neural implementation of the long-hypothesized face-detection gate proposed to explain domain-specific face processing[23] (Fig. 5f). At short latencies, face cells encode stimuli using a shared axis for both faces and objects, oriented towards faces in a DNN-derived feature space, optimally subserving face detection. At longer latencies, however, the same population switches to new axes selectively for faces to optimally distinguish face identities. This transition is marked by a reversal of the face axis in low dimensions of the object space (effectively subtracting the ‘DC component’ of faces to shift processing away from detection[41]), a change to sparser responses, and emergence of tuning to new facial feature dimensions that improve discrimination between individual faces.

These findings demonstrate that the neural code for faces is not fixed but can change completely in just 20 ms (Fig. 3b–h), complementing a previous report of a long-latency axis change triggered by stimulus familiarity[42]. Crucially, we are not merely observing different latencies for different feature representations, which would be expected given the multiple synaptic pathways through which visual input can reach the inferotemporal cortex. Rather, we have identified a wholesale switch in neural code, from axes mediating face detection to axes mediating face discrimination. This switch is stimulus gated: it occurs only for faces, while non-face objects continue to evoke responses along fixed object axes throughout the stimulus presentation.

Our results challenge a prevailing view that ‘core object recognition’—the ability to rapidly recognize objects in 200 ms, despite substantial variation in appearance—can be explained primarily by feedforward processes[43]. They also call into question the dominant view that deep networks, which are widely considered to be the best models of the inferotemporal cortex, are sufficient to capture core object recognition processes in the inferotemporal cortex. The recognition of clear, isolated faces in the absence of any background clutter is a textbook example of core object recognition. Yet we find that even this process is consistently accompanied by a rapid switch in neural code in the key brain structures mediating face recognition[29]. We speculate that the dynamic switch in encoding axis is mediated by the rich local and long-range recurrent connections in the inferotemporal cortex5,42. Supporting this idea, a recurrent neural network model showed that a simple form of lateral inhibition can lead to axis reversal (Extended Data Fig. 10). Lateral inhibition was one of the earliest circuit motifs to be identified44,45, but we are only beginning to understand its full range of computational functions[46].

Methods

Three male rhesus macaques (Macaca mulatta) were used in this study. All procedures conformed to local and US National Institutes of Health guidelines, including the US National Institutes of Health Guide for the Care and Use of Laboratory Animals. All experiments were performed with the approval of the UC Berkeley Animal Care and Use Committee.

No statistical methods were used to predetermine sample size. The experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment.

Visual stimuli

Face-patch localizer

The fMRI localizer stimulus contained five types of blocks, consisting of images of faces, hands, technological objects, vegetables and fruits, and bodies. Face blocks were presented in alternation with non-face blocks. Each block lasted 24 s (each image lasted 500 ms). In each run, the face block was repeated four times and each of the non-face blocks was shown once. A block of grid-scrambled noise patterns was presented between each stimulus block and at the beginning and end of each run. Each run lasted 408 s.

Stimuli for electrophysiology experiments

Human faces

We acquired 2,000 frontal views of faces, as in ref. [42], from various face databases: FERET49,50; CVL (Peter Peer, CVL Face Database, Computer Vision Laboratory, University of Ljubljana, Slovenia; http://www.lrv.fri.uni-lj.si/facedb.html)[48]; MR2, ref. [51]; Chicago Face Database[52]; CelebA[53]; FEI (fei.edu.br/~cet/facedatabase.html)[47]; PICS (pics.stir.ac.uk); Caltech Face Dataset 1999 (Caltech DATA, 2022; https://doi.org/10.22002/D1.20237); Essex (Face Recognition Data, University of Essex, UK); and MUCT (www.milbo.org/muct)[54]. The faces were aligned using facial landmarks as in ref. [42] with an open-source face aligner (github.com/jrosebr1/imutils).

For Extended Data Fig. 4c (right), we used synthetic face images from Syn-Vis-v0[55].

Non-face objects

We used 1,392 different images of isolated objects, previously used in ref. [6].

For monkey A, we presented a subset of 1,525 faces and 1,392 objects. For monkey J, we presented all 2,000 faces and 1,392 objects. For monkey M, we presented a subset of 1,050 faces and 1,392 objects. We presented a smaller number of faces to monkeys A and M to increase the number of repetitions per stimulus; all the analyses for monkey J used only the same subset of 1,525 faces that was presented to monkey A. For all experiments using monkey A and Extended Data Fig. 3q–s for monkey M, we showed the human faces and objects in separate blocks. In each block, we showed a training set (a random subset of 1,425 faces or 1,292 objects) for one repetition, followed by the test set (the remaining 100 faces or 100 objects) for three repetitions. The face and object blocks were interleaved and repeated until enough repetitions were acquired (around 10 repetitions per training image and 30 repetitions per test image). For all experiments using monkey J and Extended Data Fig. 3t–v for monkey M, stimuli were not separated into blocks; instead, images were drawn randomly from the combined pool of faces and objects. All images were rendered in greyscale and subtended 3.9° × 3.9° of visual angle.

Mixed image pool

To characterize neurons outside face patches, we presented a combination of six image sets: stubby objects (n = 600), spiky objects (n = 600), monkey bodies (n = 600), animals (n = 1,087), faces (n = 600) and other general objects (n = 1,593). The stubby, spiky, animal and general-object images were previously used in ref. [6]. The monkey body images were sourced from Flickr (https://www.flickr.com/) and edited manually to mask visible faces. The face images were randomly selected from the human face set described above.

Non-human and ambiguous face-like stimuli

To probe responses to non-human and ambiguous face stimuli, we used monkey faces (n = 1,100, previously used in ref. [42]), dog faces (n = 600, from the AFHQ (Animal Faces-HQ) dataset, https://www.kaggle.com/datasets/andrewmvd/animal-faces) and pareidolia images with matched controls (n = 400, from ref. [56]).

The illustrative dog photograph in Extended Data Fig. 5l and the illustrative monkey-body photographs in Extended Data Fig. 6a,b were obtained from StockSnap (stocksnap.io; CC0).

Degraded faces

To test responses to degraded faces, we rendered occluded, noisy (blended with 50% spectrally equivalent noise) and Mooney versions of 200 images from the human face set described above.

Synthetic faces

We generated 2,000 synthetic faces using the face model introduced in ref. [3], which defines a 50D face feature space consisting of 25 shape and 25 appearance dimensions. To constrain variation along low-level features, we restricted the first five shape and first five appearance dimensions (a 10D low-level subspace) to five discrete vectors and generated 400 faces per low-level position. The remaining higher-dimensional features were allowed to vary freely, sampled from Gaussian distributions, ensuring that identity differences in this set were driven primarily by high-dimensional variation.

Face cell screening set

We presented a screening stimulus set consisting of six object categories (faces, bodies, hands, gadgets, fruits and scrambled patterns) with 16 identities each (Extended Data Fig. 1e–h).

Behavioural task

For electrophysiology experiments, monkeys were head-fixed and passively viewed a screen in a dark room. Stimuli were presented on an LCD monitor (Asus ROG Swift PG43UQ). Screen size measured 26.0° × 43.9° visual angle. Gaze position was monitored using an infrared-camera eye-tracking system (Eyelink, SR Research) sampled at 1,000 Hz.

All monkeys performed a passive fixation task for both fMRI scanning and electrophysiological recording. Juice reward was delivered every 2–4 s in return for monkeys maintaining fixation on a small spot (0.2° in diameter).

MRI scanning and analysis

Subjects were scanned in a 3T PRISMA (Siemens) magnet. First, anatomical scans were done using a single-loop coil at isotropic 0.5 mm resolution. Then functional scans were done using a custom eight-channel coil (MGH) at isotropic 1 mm resolution, while the monkeys performed a passive fixation task. Contrast agent (Molday ION) was injected to improve the signal-to-noise ratio. Further details about the scanning protocol can be found in ref. [28].

Analysis of functional volumes was done using the FreeSurfer Functional Analysis Stream[57] and FSL[58]. Volumes were corrected for motion and undistorted based on the acquired field map. Runs in which the norm of the residuals of a quadratic fit of displacement during the run exceeded 5 mm and the maximum displacement exceeded 0.55 mm were discarded. The resulting data were analysed using a standard general linear model. The face contrast was computed as the average of all face blocks compared with the average of all non-face blocks.

Electrophysiological recording

We used Neuropixels 1.0 NHP (non-human primate) probes[34] (probes 45 mm long with 4,416 contacts along the shaft, of which 384 are selectable at any time) to perform electrophysiology targeted to the face patches ML and AM. To insert the probes, we developed a custom insertion system composed of a linear rail bearing and 3D-printed fixture enabling a precise insertion trajectory. Face patches were initially targeted with single tungsten electrodes before the Neuropixels recordings, following methods for MRI-guided electrophysiology described previously[59]. All Neuropixels data were acquired using SpikeGLX or OpenEphys[60] acquisition software and spike sorted using Kilosort 3 or 461,62 with the threshold parameter set to (10, 4).

We included data from monkey A for 13 sessions, from monkey J for one session and from monkey M for one session. Results across all sessions were consistent. Data shown in Figs. 1–5 and Extended Data Fig. 3a–p were from one session using monkey A; data shown in Extended Data Fig. 2 were from one session using monkey J; and data shown in Extended Data Fig. 3q–v were from one session using monkey M.

The percentages of face-selective cells recorded with Neuropixels probes are lower than those reported previously using single tungsten recordings15,26. This is probably because Neuropixels probes capture activity from many nearby neurons, including smaller or less well-isolated units, as well as neurons that are not strongly visually driven, and because portions of the probe often extended beyond the face-patch boundaries (Fig. 1g,h and Extended Data Fig. 1g,h).

Data analysis

Python v.3.10 and MATLAB R2023a were used for analysis. Only visually responsive cells were included for analysis. To determine visual responsiveness, a two-sided Student’s t-test was done comparing activity between −50 ms and 0 ms with that of 50–300 ms after the stimulus onset. Cells with P < 0.05 were included. Wherever further cell selection was done (for example, to cull cells whose activity could be well explained by an axis model, as determined by R^2), it is indicated in the relevant section of the paper.

Here, we summarize these inclusion criteria. In Figs. 2–4, we analysed the same subset of cells that satisfied all the following criteria: significant visual responsiveness to our main stimulus set consisting of 1,525 faces and 1,392 objects; non-zero response variance across stimuli; peak d′ ≥ 0.2 within 80–140 ms (see ‘Face selectivity index’ section); and the presence of positive R^2 on a held-out test set for both face and object axes, in both the general object space and the face space (see ‘AlexNet general-object space and face space’ section), at 80–140 ms. This resulted in 151 of 563 units in ML of monkey A, 76 of 248 units in AM of monkey A, 131 of 467 units in ML of monkey J, and 84 of 353 units in AM of monkey M. For Fig. 5, to include a larger population of cells for face reconstruction, we used a different inclusion criterion. Specifically, we included cells that were visually responsive to the face cell screening set (see ‘Visual stimuli’ section) and showed significantly different responses to faces versus non-face objects in that set (two-sided Student’s t-test, P < 0.05).

Face selectivity index

The face selectivity, d′, was defined for each cell and each 1 ms time bin as:

$${d}_{t}^{{\prime} }=\frac{E({r}_{{\rm{face}},t})-E({r}_{{\rm{object}},t})}{\sqrt{\frac{1}{2}({{\sigma }}_{{\rm{face}},t}^{2}+{{\sigma }}_{{\rm{object}},t}^{2})}}$$

Where E(rt) and σt^2 are the mean and variance of responses of the cell over stimuli at time t, respectively. We further calculated the peak d′ over a 20-ms sliding window between 80 ms and 140 ms (corresponding to the time interval in which we observed the main face-selective responses in raw time courses; Fig. 3d):

$$\mathrm{peak\ }{d}^{{\prime} }=\mathop{\text{max}}\limits_{T\in [80,120]}\,{E}_{t\in [T,T+20]}({d}_{t}^{{\prime} }).$$

We then selected cells with peak d′ ≥ 0.2.

In Extended Data Fig. 1f–h, the face selectivity index (FSI) was defined for each cell as:

$${\rm{FSI}}=\frac{{r}_{{\rm{face}}}-{r}_{{\rm{non}}-{\rm{face}}}}{{r}_{{\rm{face}}}+{r}_{{\rm{non}}-{\rm{face}}}}$$

where r is the average neuronal response in a 50–220 ms window after stimulus onset.

Average response profile

Mean responses of each cell to each stimulus were computed in a 50–220 ms window after stimulus onset. Responses were then normalized for each cell to the range [0, 1], where the minimum response was assigned 0 and maximum was assigned 1.

AlexNet general-object space and face space

We used AlexNet[32] to embed stimulus images into a high-dimensional latent space. Specifically, images were passed through a pretrained version of the model in MATLAB (Mathworks) and 4,096-dimension features were extracted from layer fc6. To further reduce dimensionality, we performed PCA. We built two feature spaces using this approach: a 60-dimension general-object space and a 60-dimension face space.

To build the 60-dimension general-object space, we performed PCA on fc6 responses to a set of 100 face images (randomly selected from the 2,000 faces in our face database) and 1,292 object images. This general-object space could explain 80.9% (61.6%) of the variance in the fc6 features of the object (face) stimuli. We normalized each dimension such that the projection of all stimuli along the dimension had a mean of 0 and an s.d. of 1. This general-object space was used for the analyses shown in Figs. 2 and 3 and Extended Data Figs. 2a–j, 3a–j, q–v, 4, 5a–e. For visualization purposes, we also used the 2D subspace consisting of the first two PCs of this space, as in ref. [6].

To build a 60-dimension face space capturing variation in face-specific features, we performed PCA on fc6 responses to a set of 1,425 face images. The face space explained 86.4% of the variance in the fc6 features of the face stimuli. This face space was used for the analyses shown in Fig. 4 and Extended Data Figs. 2k–p, 3k–p and 9a–c.

To build a 60-dimension face feature space using a ResNet-50 model trained on the VGGFace2 dataset[36], we used a PyTorch implementation of the VGGFace2 model available at https://github.com/cydonia999/VGGFace2-pytorch. We extracted responses from the final fully connected (fc) layer to a set of 1,425 face images and performed PCA. The resulting 60 principal components defined the face feature space used in the analyses shown in Extended Data Fig. 8f–i.

Preferred axis of cells

The preferred axis of each cell was computed using linear regression as follows:

$${P}_{{\rm{l}}{\rm{i}}{\rm{n}}}=({\bf{r}}-\bar{r})F{({F}^{T}F)}^{-1}$$

where r is a 1 × n vector of the firing-rate response to a set of n face stimuli, \(\bar{r}\) is the mean firing rate and F is an n × d matrix, where each row consists of the d parameters representing each image in the feature space. As mentioned above in ‘Stimuli for electrophysiology experiments’, we split the full image set into training and test sets, and linear regression models were trained on the training set and tested on the held-out test set. In all figures, R^2 for the held-out test set is shown.

Stimulus distribution control

To evaluate whether the shape of our stimulus distribution had any effect on the axis directions (Extended Data Fig. 4d–i), we fitted multivariate Gaussian distributions to the face and object training sets, and then identified subsets of 500 faces and 500 objects with the highest probability density under the face and object Gaussian distributions, respectively.

Cross prediction between face and object axes

To evaluate how well the face axis generalizes to object responses and vice versa, we trained a linear regression model with the training set for faces (objects) and then tested on the test set for objects (faces) (Fig. 2c and Extended Data Figs. 2c, 3c and 4l).

Normalized face–object axis correlation

To quantify the correlation between the face and object axes of each cell while accounting for the out-of-distribution effect, we first measured the within-category axis correlation by calculating the correlation between axes estimated from two random subsets of images from the same category. We then used the mean of the face subset–face subset correlation and the object subset–object subset correlation for each cell as the upper bound imposed by the out-of-distribution effect. Finally, we calculated the correlation between the face and object axes of each cell and divided this raw correlation by the upper bound to obtain the normalized face–object axis correlation (Extended Data Figs. 2d, 3d and 4m).

Artificial-unit comparison

To observe how a unit with a single axis across both face and object space would behave, we identified artificial face-selective units from the AlexNet fc6 layer and redid our analyses for real neural units on them (Extended Data Fig. 4j–m). Because the general-object space was built by PCA from the activities of AlexNet fc6-layer unit activities, a unit in the same layer should respond linearly to the features in the general-object space, thus providing a model neuron with a single encoding axis. Given that there are features not perfectly explained by the feature space, the artificial units should preserve effects from the out-of-distribution generalization. We first identified face-selective AlexNet fc6 units by calculating the FSI for each unit using its response to the face cell screening set. We then repeated the analyses of Fig. 2a–c using the responses of the 126 most face-selective units (FSI ranging from 0.23 to 0.46).

Time-varying axis analysis

We fitted axes to the average neural responses in 20-ms windows over the trial duration of 0–300 ms after stimulus onset (Figs. 3 and 4 and the related Extended Data figures). To quantify the alignment between axes at different latencies, we computed cosine similarity between the time-varying face or object axis at each latency and the trial-wide object axis computed across 50–220 ms (Fig. 3e and Extended Data Figs. 2h, 3h and 8a–e, rightmost column).

Population response sparseness

To characterize the sparseness of face-cell population responses to faces and objects, we computed a modified Treves-Rolls population sparseness[63]. Specifically, we calculated

$${S}_{i,t}=1-\frac{{\left({\sum }_{j=1}^{N}{r}_{j,i,t}\right)}^{2}}{N{\sum }_{j=1}^{N}{r}_{j,i,t}^{2}}$$

where N is the number of neurons and rj,i,t is the response of neuron j to stimulus i at time t, for all stimuli and times. To compare population response sparseness to faces and objects, we took the mean over all face and all object stimuli, respectively, at each time point.

Identifying top- and bottom-projected images for response PC1 and PC2

We performed PCA on the time-averaged population response matrix across all stimuli and treated the first two PCs (PC1 and PC2) as pseudo-units. For each response PC, we computed its face and object axes using a 20-ms sliding time window. We then projected all the stimuli onto each axis and identified the top five and bottom five images based on projection values, representing the most and least preferred stimuli for each axis over time.

Single-stimulus axis-change score

To assess whether an individual face image elicits a change in the population code, we avoided any per-cell normalization and worked with raw firing rates. Because large between-cell firing rate differences can mask population rank-order changes, we excluded extreme-firing-rate cells before analysis.

For each image, we computed the Pearson correlation between the population response vectors at an early (60–80 ms) and a late (100–120 ms) window across all face-selective cells. We then mapped the correlation values to face probabilities using a simple single-feature logistic regression classifier trained to discriminate between face and object labels from the correlations. The classifier output is reported as the single-stimulus axis-change score: higher values indicate a greater ‘faceness’ of an image (more negative early-versus-late population correlation).

Recording outside face patches and quantifying image selectivity

We used fMRI localizers[6] to target the middle stubby, spiky and body patches in the monkeys as in ref. [6]. Neuropixels probes were inserted into these regions and neural activity was recorded while the monkeys viewed a mixed image pool (see ‘Visual stimuli’ section). For each neuron, we quantified discriminability (peak d′) for its most responsive stimulus category using the same procedure described above for quantifying face selectivity. Neurons were pooled across sessions for subsequent analyses (Extended Data Fig. 6).

Computing axis correlation between short and long latencies

To quantify changes in face and object axes (Fig. 4 and related Extended Data figures), we computed the cosine similarity between the face axes at three different latencies (80−100 ms, 120–140 ms and 160–180 ms), and similarly for object axes. We chose to focus on these three time windows because they straddled the population response peak of face cells (Fig. 3d). We note that individual cells sometimes showed salient changes in response to objects over time; our analyses and conclusions are intended to capture population-wide changes in tuning.

In Fig. 4c,d, to better account for different latencies in responses of different units to faces and objects (see the example cell in Fig. 3f, top), for each unit, we took the short latency to be the first 20-ms window in which the mean response of that unit was at least 2 s.d. above the mean baseline (−25–25 ms) response of that unit to faces and objects, respectively.

Control analysis to rule out cell-intrinsic axis-change hypotheses

In hypothetical scenario one, axis dynamics is always accompanied by a high mean response magnitude (Extended Data Fig. 7a). To address this, we identified the 100 most-effective non-face stimuli, as well as the 100 least-effective face stimuli (Extended Data Fig. 7b). The former evoked a greater mean response, averaged over 50–220 ms, than the latter (Extended Data Fig. 7c; mean response ratio = 1.29). However, the two types of stimuli evoked very different response dynamics (Extended Data Fig. 7d), and comparison of axis weights across different time windows revealed axis change only in response to the face stimuli (Extended Data Fig. 7e–i). This shows that the presence of discriminable face features is necessary to trigger axis change; high mean response magnitude is insufficient.

In hypothetical scenario two, axis-change dynamics could be due to delayed responses to weaker stimuli leading to a change in tuning (Extended Data Fig. 7j). Specifically, if weaker responses are more delayed, then at longer latencies, stronger responses may already have subsided while weaker responses persist. This temporal offset could result in weaker responses appearing stronger than the diminished stronger responses, thereby giving the phenomenon of a reversed tuning profile. To address this, we first identified the 50% most-effective and 50% least-effective face stimuli for each cell, determined by the mean response in the early time window 80–100 ms. We then computed face axes separately using these two groups of stimuli, at both short (80–100 ms) and long (120–140 ms) latency. If axis-change dynamics were driven solely by delayed onset of weak stimuli, then one would predict: a lack of correlation between axes computed using the most-effective and least-effective faces at long latency; and positive correlation between axes at short and long latency computed using the most-effective faces (determined by response at short latency). Neither of these predictions was supported by the data (Extended Data Fig. 7k,l). In particular, the negative correlation that we observed experimentally between axes computed using the most-effective faces at short and long latency rules out the possibility that axis reversal is driven solely by responses to non-optimal, low-contrast stimuli (Extended Data Fig. 7l).

In hypothetical scenario three, axis-change dynamics could be due to a cell-intrinsic increase in firing threshold following a strong transient response (adaptation; Extended Data Fig. 7m). To investigate this possibility, we measured the axis tuning of model cells encoding a single axis with and without application of a raised threshold. We found that the resulting axes were still highly correlated (Extended Data Fig. 7n). This contrasts with our actual results (Fig. 4a–d), ruling out cell-intrinsic adaptation as the source of the change in neural code.

Generating low and high spatial frequency-filtered images

Each image from our original set of 1,525 face images was first convolved with a Gaussian filter (σ = 0.044°) to generate a low spatial frequency-filtered image. This was then subtracted from the original to compute a high spatial frequency image (Extended Data Fig. 7o–v).

Identifying new tuning directions for each cell

In Fig. 4c,d, to better identify new tuning directions that emerge at long latency (Fig. 4g–k and related Extended Data figures), we compared the face axes of each cell at 80–100 ms, denoted as ν_1, to the axes at 120–140 ms, denoted as ν_2 (AM time windows were delayed by 20 ms). We decomposed ν_2 =νǁ + ν⊥, where νǁ = ⟨ν_1, ν_2⟩(ν_1/|ν_1|2) is the component of ν_2 parallel or antiparallel to ν_1, and ν⊥, the component of ν_2 orthogonal to ν_1, is given by ν⊥ = ν_2 − νǁ. For visualizations in Fig. 4h–k, we computed the principal orthogonal direction for each ν⊥. Specifically, for each stimulus with embedding u in the face space, we orthogonalized u with respect to ν⊥ by computing u⊥ = u − ⟨u, ν⊥⟩(ν⊥/|ν⊥|2). Then we performed PCA over all u⊥ and took the first PC as the principal orthogonal direction to ν⊥.

Face reconstruction

To reconstruct faces from neural activity (Fig. 5 and Extended Data Fig. 2q,r), we leveraged a probabilistic generative model[64]. The model followed the design of variational autoencoders[65], with an encoder mapping the input images (resized to 128 × 128) into latent features defined with a variational distribution and a decoder projecting the features to the original inputs. The encoder and decoder each consisted of five convolutional layers, and the latent features were set to 512 dimensions. A regularizer was used to minimize discrepancies between the latent distribution and a Gaussian prior, which better supports data on low-dimensional manifolds. We also included an additional objective to align the latent features (after linear projection) with fc6 features from AlexNet, to ensure that the latent space described similar features to the general-object space. We trained the generative model on 1,900 faces and 1,292 objects, and validated the model on the 200 held-out images (100 faces and 100 objects).

To compare the reconstructed faces from neural responses at short and long latency, we used averaged population responses from several time windows. For ML cells, short-latency responses were averaged from 50–75 ms and 75–100 ms, and long-latency responses from 120–145 ms and 145–170 ms. We also used a combined window composed of windows from 62–87 ms and 132–157 ms. Thus, each of the three windows comprised two sub-windows. Note that the three windows each spanned exactly the same duration of time. The AM time windows were delayed by 20 ms. For each window, we treated responses from the two sub-windows as if they were from distinct cells, enabling the decoder to assign distinct axes to each sub-window (Fig. 5a).

For reconstruction, after training and freezing the generative model, we linearly decoded 512-dimension feature vectors from neural responses to each image using responses from the short, long and combined time windows (thus three separate linear decoders were trained). We then fed the latent features into the decoder of the probabilistic generative model to reconstruct faces. For learning linear decoders mapping neural activity to latent features, we trained on 1,425 face images (a subset of the 1,900 images used for generative model training) and tested on the remaining 100.

We also passed each original face image through the encoder and decoder of the generative model directly to obtain the best possible reconstruction of each face, allowing us to separate fine face-feature loss in the generative model itself from decoding inaccuracy due to noise in the neural signal.

Visualizing new tuning directions

To visualize new tuning directions of cells (ν⊥; Fig. 4h–k), for each cell we reconstructed a series of faces with features that gradually varied along ν⊥, leveraging the probabilistic generative model described above (‘Face reconstruction’ section). Because ν⊥ lies in our 60-dimension face space, we also trained a linear transformation matrix to transform features from this 60-dimension face space to the generative model’s latent space (512 dimensions). We first normalized each cell’s axis to unit length and then sampled seven locations along it, at projection lengths of [−4, −2, −1, 0, 1, 2, 4] σ, where σ is the s.d. of the projections of our 1,425 original faces onto ν⊥. This yielded seven 60-dimension feature vectors, which we then transformed to 512 dimensions. Finally, we fed these 512-dimension features into the generative model to generate the reconstructed faces.

Using a DNN to quantify face identification performance

To quantify the goodness of reconstruction (beyond human visual evaluation; Fig. 5c,d), we leveraged a DNN pretrained to discriminate faces[38] (using the VGGFace2 dataset[36]), whose feature space provides an objective metric for evaluating similarity between faces. We embedded all our neural-reconstructed faces and best possible reconstructed faces into the feature space of this DNN and then calculated the Euclidean distance between each face’s neural reconstruction and its best possible counterpart.

To compare the goodness of reconstruction among the three time windows, in Fig. 5c,d, we compared the distance of reconstructions from the three time windows to the best possible reconstruction for each face. The closest face of the three was considered to be ‘correct’. We repeated this recognition task for all 1,525 faces. We further repeated this whole process using only subsets of the neurons to investigate how the number of cells affects the reconstruction quality for different time windows. For each cell count, we randomly resampled the cells 30 times to estimate the s.e.m.

Simulating results of face reconstruction analyses

We simulated a neural population whose short-latency responses tuned to a smaller set of face dimensions and whose long-latency responses tuned to a larger set of face dimensions, to test our hypothesis that the results of Fig. 5d might be explained by a trade-off between redundancy and diversity.

Simulated neuron responses were created by linearly combining different dimensions of the latent space features of the DNN[38] mentioned above. We first performed PCA on this DNN’s latent space to get a 60-dimension feature for each image. Then, for simulating short-latency responses of each artificial neuron, we linearly combined a random subset of the first five dimensions of the features and applied Gaussian noise. For long-latency responses, we linearly combined a random subset of dimensions from the full 60 dimensions and applied Gaussian noise. These simulated neurons were then treated as real neurons and underwent the face reconstruction and recognition analyses described above.

Face categorization and discrimination time course

For both face categorization (face versus object) and face identity discrimination, we summarized how well the two categories or single identities are separated in population space. In each time window, let \({{\bf{r}}}_{i}\in {{\bf{r}}}^{N}\) be the population response vector (across N units) to face image \(i\) (distinct identity). We computed all pairwise Euclidean distances \({d}_{{ij}}={{||}{{\bf{r}}}_{i}-{{\bf{r}}}_{j}{||}}_{2}\) across categories or across identities and reported a population separation index (PSI):

$${\rm{PSI}}=\frac{{{\rm{mean}}}_{i < j}{d}_{{ij}}}{{\sigma }_{{\bf{r}}}}$$

where σr is the pooled s.d. of responses across units and identities in the window. PSI is unitless and increases when category- or identity-specific responses are more dispersed relative to the overall response variability. We repeated this computation for each 20-ms window to obtain the categorization and discrimination time courses (Extended Data Fig. 9d,f).

Recurrent neural network training

We trained a recurrent neural network with 100 units whose dynamics at each timestep were described by

$${{\bf{h}}}_{t}=\tanh ({{\bf{x}}}_{t}+W{{\bf{h}}}_{t-1})$$

where ht is the vector of unit activations at time t, W is the weight matrix and xt is the external input at time t. During training, the network was run for two timesteps with the same random linear gradient provided as input at both timesteps, and the mean squared error between the output at time 2 and the reversed input gradient was used as the loss. The network was trained for 10,000 iterations, each with a random linear gradient, using the Adam optimizer (learning rate 0.001).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are available from the corresponding authors (Y.S. and D.Y.T.) upon reasonable request.

Code availability

All the code is available from the corresponding authors (Y.S. and D.Y.T.) upon reasonable request.

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Acknowledgements

We thank C. Sohn for animal care support; members of the Tsao laboratory, P. Bao, X. Dai, J. Gallant, J. Gao, S. Kornblith, Y. Ma, F. Tong and A. Tsao for discussions and comments; and P. Bao and L. She for technical advice. Funding: this work was supported by the NSF (to D.B.), NIH (EY030650-01), the Office of Naval Research, and the Howard Hughes Medical Institute.

Author information

Authors and Affiliations

1. Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA

Yuelin Shi & Frank F. Lanfranchi

2. Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

Yuelin Shi, Dasheng Bi, Janis K. Hesse, Frank F. Lanfranchi, Shi Chen & Doris Y. Tsao

3. Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA, USA

Doris Y. Tsao

Contributions

Y.S. and D.Y.T. conceived the project and designed the experiments; Y.S., J.K.H. and F.F.L. collected the data; Y.S. and D.B. analysed the data, with help from S.C. Y.S., D.B. and D.Y.T. interpreted the data. Y.S., D.B., J.K.H. and D.Y.T. wrote the paper, with feedback from F.F.L. and S.C.

Corresponding authors

Correspondence to Yuelin Shi or Doris Y. Tsao.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Rodrigo Quian Quiroga and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 ML and AM targeting and face selectivity index (FSI) distribution.

a. Tungsten probe targeting ML in monkey A. Activations for the face vs. object contrast are shown, at uncorrected p values shown in -log10. b. Neuropixels probe targeting AM in monkey A. p values as in (a). c. Tungsten probe targeting ML in monkey J. p values as in (a). d. Tungsten probe targeting AM in monkey M. p values as in (a). e. Mean responses of cells in ML (left) and AM (right) to 96 screening stimuli (16 images from each of the categories shown) in the main monkey A session, computed using a 50–220 ms window and normalized to the range [0, 1] for each cell. The same cells as in Fig. 1g,h are shown. f. Distributions of FSI (Methods) for cells in ML (left) and AM (right) from the same session as in (e). g. Fraction of face-selective cells among visually responsive cells across all Neuropixels-recorded sessions (n = 67 sessions, includes sessions not used in the analyses). For each session, we first excluded non-visually responsive units, then identified the putative face patch span along the probe as the longest contiguous stretch with FSI ≥ 0.33, allowing gaps of up to three consecutive sub-threshold units. The reported fraction is the proportion of face-selective cells within this span. Left: sessions targeting ML; Right: sessions targeting AM. Red triangles mark the main monkey A session. Black asterisks in the left panel denote the three example sessions shown in (h). h. Responses of all recorded units to the screening stimulus set for the three representative sessions indicated in (g) (low, median, and high face-cell fractions). Units are sorted by depth along the probe. The vertical dashed line indicates FSI of 0.33. Horizontal red lines show the putative face-patch boundaries. Credits: face images in e are reproduced from the CVL Face Database[48] (http://lrv.fri.uni-lj.si/facedb.html).

Extended Data Fig. 2 Main results computed for monkey J, ML.

a. Distribution of object (left, green) and face (right, purple) axes, projected onto the top two dimensions of the 60-dimensional (60-d) object space (N = 131 cells, only cells with R^2 > 0 on the test set for both object and face axes are included). Face and object stimuli in this experiment were presented with random interleaving. b. Scatter plots of object vs. face axis weights for PC1 (left) and PC2 (right). c. Cross prediction accuracies, same conventions as Fig. 2c. Units with R^2 < −2 are not shown. d. Histogram of correlations between the face and object axes for each cell in the 60-d object space for real (dark grey) and AlexNet (light grey) units, normalized by the correlation between axes fitted from two disjoint subsets of the same category. e. From left to right: matrices of mean cosine similarities across the population between (object, object), (face, face), and (face, object) axes for different pairs of latencies (N = 131 cells). f. Correlation between face and object axes as a function of time (diagonal values in the rightmost similarity matrix in (e)). g. Mean response time course to each face and object stimulus, averaged across cells and trials. h. Cosine similarity between the overall object axis for each cell (computed using a 50–220 ms window) and its time-varying face (top) and object (bottom) axes, sorted according to face selectivity d′ (left). i. Response sparseness as a function of time for faces and objects. j. Time-resolved scatter plots of face and object stimuli projected onto PC1 and PC2 of object space, colour coded by the mean response magnitude across the population (N = 131 cells) to each stimulus. Purple arrows indicate the approximate face-axis direction in each time window. k. Matrix of face axis weights in AlexNet face space for each cell, computed using a short (80–100 ms, top), long-early (120–140 ms, middle), and long-late (160–180 ms, bottom) latency window; same conventions as Fig. 4a. l. Matrix of object axis weights; conventions same as (k). m. Purple (green) distribution shows correlation coefficients across units between face (object) axis weights at short and long-early latency. Conventions as in Fig. 4c. Face axis weights were negatively correlated (two-sided one-sample Student’s t-test, t(130) = −6.03, p = 1.62 × 10−8); object axis weights were positively correlated (two-sided one-sample Student’s t-test, t(130) = 12.75, p = 1.20 × 10−24). n. Same as (m), using higher face space dimensions 6–60 to compute axis correlation for each cell. Face axis weights were uncorrelated (two-sided one-sample Student’s t-test, t(130) = 0.52, p = 0.60); object axis weights were positively correlated (two-sided one-sample Student’s t-test, t(130) = 6.96, p = 1.47 × 10−10). o. Same as (m), using axis weights at long-early and long-late latencies (120–140 ms and 160–180 ms) and all 60 dimensions. Face and object axis weights were both positively correlated (face: two-sided one-sample Student’s t-test, t(130) = 21.83, p = 2.56 × 10−45, object: two-sided one-sample Student’s t-test, t(130) = 22.39, p = 1.91 × 10−46). p. Explained variance in z-scored population responses to faces at short (80–100 ms, orange) and long (120–140 ms, blue) latencies, as a function of the number of PCs. More dimensions were required to explain 90% of response variance at long compared to short latency (mean ± s.d.: 90.5 ± 4.53 for long vs. 78.8 ± 4.47 for short, two-sided paired Student’s t-test, t(99) = −20.54, p = 1.81 × 10−37). q. Reconstructions of faces using responses from different time windows. Same conventions as Fig. 5b. r. Face identification performance on faces reconstructed from neural activity in different time windows. Same conventions as Fig. 5d. Credits: face images in q are reproduced from the CVL Face Database[48] (http://lrv.fri.uni-lj.si/facedb.html), the Chicago Face Database[52] (chicagofaces.org) and MUCT[54] (www.milbo.org/muct).

Extended Data Fig. 3 Data from the AM face patch in monkeys A and M.

a-p show data from monkey A (AM). a. Distribution of object (left, green) and face (right, purple) axes, projected onto the top two dimensions of the 60-d object space (N = 76 cells, only cells with R^2 > 0 on the test set for both object and face axes are included). b. Scatter plots of object vs. face axis weights for PC1 (left) and PC2 (right). c. Cross prediction accuracies, same conventions as Fig. 2c. Units with R^2 < −2 are not shown. d. Histogram of correlations between the face and object axes for each cell in the 60-d object space for real (dark grey) and AlexNet (light grey) units, same conventions as Extended Data Fig. 2d. e. From left to right: matrices of mean cosine similarities across the population between (object, object), (face, face), and (face, object) axes for different pairs of latencies (N = 76 cells). f. Correlation between face and object axes as a function of time (diagonal values in the rightmost similarity matrix in (e)). g. Mean response time course to each face and object stimulus, averaged across cells and trials. h. Cosine similarity between the overall object axis for each cell (computed using a time window of 50–220 ms) and its time-varying face (top) and object (bottom) axes, sorted according to face selectivity d′ (left). i. Time-resolved scatter plots of face and object stimuli projected onto PC1 and PC2 of object space, colour coded by the mean response magnitude across the population (N = 76 cells) to each stimulus. Purple arrows indicate the approximate face-axis direction in each time window. j. Response sparseness as a function of time for faces and objects. k. Matrix of face axis weights in AlexNet face space for each cell, computed using a short (100–120 ms, top), long-early (140–160 ms, middle), and long-late (180–200 ms, bottom) latency window; same conventions as Fig. 4a. l. Matrix of object axis weights; conventions same as (k). m. Purple (green) distribution shows correlation coefficients across units between face (object) axis weights at short and long-early latency. Conventions as in Fig. 4c. Face axis weights were negatively correlated (two-sided one-sample Student’s t-test, t(75) = −12.09, p = 2.70 × 10−19); object axis weights were positively correlated (two-sided one-sample Student’s t-test, t(75) = 10.22, p = 7.35 × 10−16). n. Same as (m), using higher face space dimensions 6–60 to compute correlation for each cell. Face axis weights were not significantly correlated (two-sided one-sample Student’s t-test, t(75) = −1.79, p = 0.08); object axis weights were positively correlated (two-sided one-sample Student’s t-test, t(75) = 8.82, p = 3.27 × 10−13). o. Same as (m), using axis weights at long-early and long-late latencies and all 60 dimensions. Face and object axis weights were both positively correlated (face: two-sided one-sample Student’s t-test, t(75) = 32.21, p = 1.13 × 10−45, object: two-sided one-sample Student’s t-test, t(75) = 19.13, p = 1.41 × 10−30). p. Explained variance in population responses at short (100–120 ms, orange) and long (140–160 ms, blue) latencies, as a function of the number of PCs. More dimensions were required to explain 90% of response variance at long compared to short latency (mean ± s.d.: 52.3 ± 2.98 for long vs. 48.6 ± 2.21 for short, two-sided paired Student’s t-test, t(99) = −9.69, p = 5.31 × 10−16). q–v show data from monkey M (AM). In q–s: stimuli were shown in blocks. In t–v: stimuli were interleaved. q. Distribution of object (left, green) and face (right, purple) axes, projected onto the top two dimensions of the 60-d object space (N = 84 cells, only cells with R^2 > 0 on the test set for both object and face axes are included). r. Mean response time course to each face and object stimulus, averaged across cells and trials. s. From left to right: matrices of mean cosine similarities across the population between (object, object), (face, face), and (face, object) axes for different pairs of latencies (N = 84 cells). t–v. Same analyses as (q–s), using interleaved stimulus presentation during the same recording session (Methods).

Extended Data Fig. 4 Quantification and controls of axis tuning.

a. Left: Explained variance of object responses for each ML cell using the object axis (green) together with explained variance for stimulus-shuffled data (grey; data are presented as mean ± s.d.). Right: Explained variance of face responses for each cell using the face axis (purple) and stimulus-shuffled control (grey). Across the population, 147/151 (151/151) units had significantly higher face (object) axis R^2 than a random shuffle. b. Same as (a) for AM; 58/76 (73/76) units had significantly higher face (object) axis R^2 than a random shuffle. c. Face and object images embedded in object space by projecting onto PC1 and PC2 subspace. Left: Face images used in the experiments are represented by a single template face placed at the corresponding embedding locations; we do not show the original face photographs because the dataset license does not permit reproduction of all images. Right: The same analysis repeated with a set of synthetic face images (Syn-Vis-v0; not shown to the monkey) to illustrate how face features distribute within this space. d. Distribution of the full training set of 1425 face (purple) and 1,292 object (green) stimuli projected onto the top two dimensions of the 60-d feature space. e. Distribution of a subset of face (purple) and object (green) stimuli chosen to make the face and object stimulus distributions maximally Gaussian. f. Distribution of object axes computed using responses to the subset of object images selected in (e), projected onto the top two dimensions of the 60-d feature space. g. Same as (f), for face axes. h. Same as (f), computed for stimulus-shuffled data. i. Same as (g), computed for stimulus-shuffled data. j–l. Same as Fig. 2a–c, computed using the 230 most face-selective units from AlexNet layer fc6 (FSI ranging from 0.2 to 0.46). m. Histogram of correlations between the face and object axes for each cell in the 60-d space for real (dark grey; N = 151 cells from the main ML session in monkey A) and AlexNet (light grey) units, same conventions as Extended Data Fig. 2d. Credits: object images in c are used with permission from ref. [6], Springer Nature Limited; face images in c are reproduced from the FEI database[47] (https://fei.edu.br/~cet/facedatabase.html) and Syn-Vis-v0[55] (https://huggingface.co/datasets/retowyss/Syn-Vis-v0).

Extended Data Fig. 5 Figures related to axis dynamics.

a. Histogram showing the distribution of face axis switch times across cells in the ML face patch, defined as the first time point at which the face–object axis correlation drops below half of its peak value for each unit. The dashed line indicates the average response latency across the population (76.9 ms), computed by averaging the half-peak time of each unit’s response. b. Images that project most strongly onto the extremes of the object (top) and face (bottom) axis of the first response component of ML at different time windows (Methods). For the face images, original identities are replaced with visually similar substitute images. c. Same as (b), but for the second response component of ML. d. Distribution of cosine similarities between face axis (140–160 ms) and the overall object axis (50–220 ms), computed using only their PC1 and PC2 components, for ML. Cells with cosine similarity less than −0.5 (corresponding to an angle of at least 120°) between the two axes were counted as reversing. e. Same as (d) for AM computed over 160–180 ms. f. From left to right: matrices of mean correlations across the population between (object, object) and (face, face) population responses for different pairs of latencies (N = 151 cells; only cells with R^2 > 0 on the test set for both object and face axes were used). g. Left: Purple (green) distribution shows correlations across units between face (object) responses at short and long-early latency. Right: Correlations across units between face (object) responses at long-early and long-late latencies. h, i. Same as (f), (g) for AM. j. Population response cross-time correlation of each individual stimulus calculated from the ML face cell responses. k. Histogram of single-stimulus axis-change score (Methods) to various categories of stimuli, including human faces, monkey faces, pareidolia, dog faces, and objects. l. Single-stimulus axis-change score for example human face, monkey face, dog face, object, and 2 pareidolia images. The scores are annotated below each image. The dog image is a substitute illustrative image. Credits: object images in b are used with permission from ref. [6], Springer Nature Limited; face images in b are reproduced from the CVL Face Database[48] (http://lrv.fri.uni-lj.si/facedb.html); face images in l are reproduced from MUCT[54] (www.milbo.org/muct).

Extended Data Fig. 6 Axis dynamics of units outside face patches.

a. Top: Example images from the monkey-body image set (monkey-body images shown here are illustrative replacements; images from all other categories are the original stimuli shown to the monkey). Bottom: Distribution of features for monkey-body stimuli and other objects in PC1–PC2 of the object space. b. Time-averaged responses of all monkey-body-selective units (peak d′ > 0.1, Methods) to monkey-body and non-monkey-body stimuli. c. Distribution of monkey-body (left, purple) and object (right, green) axes projected onto the top two dimensions of the 60-d object space, for early (top) and late (bottom) latency responses of monkey-body-selective units. (Only units with peak d′ > 0.2 are used). d. Histogram of correlations of monkey-body (purple)/object (green) axes between early and late latencies for each unit. (The early and late time windows were defined individually for each unit: early window was centered on a unit’s peak response time; late window began 20 ms after the early window.) e–h. Same as (a-d) for stubby-object-selective units. i–l. Same as (a-d) for spiky-object-selective units. m–p. Same as (a-d) for animal-selective units. For these units, we did not observe axis switch over time—their tuning axes remained stable. q–t. Same as (a-d) for face cells outside face patches. Credits: object images in e, f, i, j, m, n are used with permission from ref. [6], Springer Nature Limited; face images in q and r are reproduced from the CVL Face Database[48] (http://lrv.fri.uni-lj.si/facedb.html).

Extended Data Fig. 7 Testing three cell-intrinsic mechanisms and the contributions of low and high spatial frequency components to axis change.

a. Hypothetical scenario 1: a high mean response magnitude triggers axis change. b. The 10 most-effective object stimuli and 10 faces selected from the 75 least-effective faces; shown faces are limited to stimuli with reuse permission. c. Top: Mean responses averaged over 50–220 ms to the 100 most-effective non-face object stimuli, other objects, the 100 least-effective face stimuli, and other faces. Bottom: bar graph of the mean response to each stimulus across cells (N = 151 cells). d. Response time courses to the 100 least-effective faces (top) and 100 most-effective non-face objects (bottom), averaged across cells. e. Matrix of face axis weights in AlexNet face space for each cell, computed using responses to the least-effective faces in (c), same conventions as in Fig. 4a. Here, we only fit axes to the top 10 face space dimensions due to the smaller number of images used to fit the axes. f. Matrix of object axis weights in AlexNet face space for each cell, computed using responses to the most-effective objects in (c), same conventions as in (e). g. Purple (green) distribution shows cosine similarities across units between face (object) axis weights at short and long-early latency; conventions as in Fig. 4c. All dimensions (1–10) were used to compute cosine similarities shown here. Face axis weights were negatively correlated (two-sided one-sample Student’s t-test, t(150) = −6.60, p = 6.72 × 10−10); object axis weights were positively correlated (two-sided one-sample Student’s t-test, t(150) = 4.85, p = 3.10 × 10−6). h. Same as (g), using higher face space dimensions 3–10 to compute cosine similarities for each cell. Face axis weights were negatively correlated (two-sided one-sample Student’s t-test, t(150) = −7.31, p = 1.50 × 10−11); object axis weights were positively correlated (two-sided one-sample Student’s t-test, t(150) = 2.55, p = 0.012). i. Same as (g), showing cosine similarities across units between face (object) axis weights at long-early and long-late latencies (120–140 ms and 160–180 ms), using all 10 dimensions. Face and object axis weights were both positively correlated (face: two-sided one-sample Student’s t-test, t(150) = 4.13, p = 5.98 × 10−5, object: two-sided one- sample Student’s t-test, t(150) = 6.60, p = 6.58 × 10−10). j. Hypothetical scenario 2: delayed responses to weaker stimuli lead to a change in tuning. To address this, we first identified the 50% most-effective and 50% least-effective face stimuli for each cell, determined by the mean response in the time window 80–100 ms. We then computed face axes separately using these two groups of stimuli, at both short (80–100 ms) and long (120–140 ms) latency. k. Purple distribution shows cosine similarities across units between face axis weights at long (120–140 ms) latency computed using the 50% most-effective and 50% least-effective faces for each cell; the two sets of face axes were positively correlated (two-sided one-sample Student’s t-test, t(150) = 12.13, p = 4.89 × 10−24). Grey distributions: cosine similarities between the two sets of face axes and the object axes at the same latency; the two sets of face axes were both negatively correlated with object axes (low face-object: two-sided one-sample Student’s t-test, t(150) = −5.69, p = 6.57 × 10−8; high face-object: two-sided one-sample Student’s t-test, t(150) = −4.52, p = 1.27 × 10−5). l. Distribution of cosine similarities across units between face axis weights at short (80–100 ms) and long (120–140 ms) latency computed using the 50% most-effective faces for each cell; axes were negatively correlated (two-sided one-sample Student’s t-test, t(150) = −8.70, p = 5.59 × 10−15). The result rules out the possibility that axis reversal is driven solely by responses to non-optimal, low-contrast stimuli. m. Hypothetical scenario 3: cell-intrinsic adaptation leads to axis change—face cells increase firing threshold following a strong transient response. To investigate this possibility, we measured axis tuning of model cells encoding a single axis with and without application of a raised threshold. n. Distribution of cosine similarities across units between face axes mapped for the same units with and without application of a raised threshold (set to one s.d. above mean response); axes were significantly correlated (two-sided one-sample Student’s t-test, t(150) = 146.67, p = 6.96 × 10−164). o–v. Hypothetical scenario 4: a ‘coarse to fine’ progression in arrival of visual information to a face patch triggers the change in face axis. o. Two example faces (left) filtered to show low (middle) and high (right) spatial frequency components (Methods). p. Explained variance for short (left, 80–100 ms) and long (right, 120–140 ms) latency responses, using low spatial frequency features, high spatial frequency features, or a combined set of features. The number of features used to compute explained variance was the same in all three conditions (low: 120, high: 120, combined: 60 + 60). q–s. Axis weights and axis weight correlations computed using low spatial frequency features, same conventions as Fig. 4a–e. Face axis weights showed negative correlation between the two time windows (all dimensions: two-sided one-sample Student’s t-test, t(150) = −7.47, p = 6.25 × 10−12; higher dimensions: two-sided one-sample Student’s t-test, t(150) = −4.56, p = 1.03 × 10−5). Object axis weights were positively correlated between the two time windows (all dimensions: two-sided one-sample Student’s t-test, t(150) = 11.27, p = 9.88 × 10−22; higher dimensions: two-sided one-sample Student’s t-test, t(150) = 12.69, p = 1.60 × 10−25). Both face and object axis weights were positively correlated between the two long latency windows (face: two-sided one-sample Student’s t-test, t(150) = 29.28, p = 6.66 × 10−64; object: two-sided one-sample Student’s t-test, t(150) = 13.70, p = 3.15 × 10−28). t–v. Axis weights and axis weight correlations computed using high spatial frequency features, same conventions as Fig. 4a–e. Face axis weights showed negative correlation between the two time windows (all dimensions: two-sided one-sample Student’s t-test, t(150) = −8.05, p = 2.33 × 10−13; higher dimensions: two-sided one-sample Student’s t-test, t(150) = −7.59, p = 3.11 × 10−12). Object axis weights were positively correlated between the two time windows (all dimensions: two-sided one-sample Student’s t-test, t(150) = 16.07, p = 2.02 × 10−34; higher dimensions: two-sided one-sample Student’s t-test, t(150) = 14.84, p = 3.09 × 10−31). Both face and object axis weights were positively correlated between the two long latency windows (face: two-sided one-sample Student’s t-test, t(150) = 26.80, p = 4.55 × 10−59; object: two-sided one-sample Student’s t-test, t(150) = 17.50, p = 4.51 × 10−38). Credits: object images in b are used with permission from ref. [6], Springer Nature Limited; face images in b and o are reproduced from the CVL Face Database[48] (http://lrv.fri.uni-lj.si/facedb.html); face images in b are reproduced from the Chicago Face Database[52] (chicagofaces.org), the FEI database[47] (https://fei.edu.br/~cet/facedatabase.html) and MUCT[54] (www.milbo.org/muct).

Extended Data Fig. 8 Axis dynamics for degraded faces and axis change using VGGFace2 face space.

a. Time course of responses and axis correlations for clear faces. First column, an example clear face image. Second column, PSTH of the cell-averaged response to each clear face. Third column, average response time course across all units and all clear face identities. Fourth column, time course of the correlation between the time-varying face axis and the cell’s overall object axis, averaged across cells (same conventions as Fig. 3e). Fifth column, time courses of correlations between each unit’s time-varying face axis and its overall object axis. b–e. Same as (a), but applied to degraded faces (occluded, noise-masked, and Mooney) and to non-face objects. In the third and fourth columns, the degraded-face or object time course is plotted in blue; the clear-face time course is plotted in grey as a control baseline (Methods). f. Matrix of face axis weights in VGGFace2 face space (Methods) for each cell, computed using short (80–100 ms, top), long-early (120–140 ms, middle), and long-late (160–180 ms, bottom) latency windows; same conventions as Fig. 4a. g. Matrix of object axis weights; conventions same as (f). h. Top row: Purple (green) distribution shows correlation coefficients across units between face (object) axis weights at short and long-early latency. Conventions as in Fig. 4c. Face axis weights were negatively correlated; object axis weights were positively correlated. Middle row: Using higher face space dimensions 6–60 to compute correlation for each cell. Face axis weights were not significantly correlated; object axis weights were positively correlated. Bottom row: Using axis weights at long-early and long-late latencies and all 60 dimensions. Face and object axis weights were both positively correlated. i. Explained variance in population responses at short (80–100 ms, orange) and long (120−140 ms, blue) latencies, as a function of the number of PCs. More dimensions were required to explain 90% of response variance at long compared to short latency. Data are presented as mean ± s.d. Credits: object images in e are used with permission from ref. [6], Springer Nature Limited; face images are reproduced from the CVL Face Database[48] (http://lrv.fri.uni-lj.si/facedb.html).

Extended Data Fig. 9 Decoding face category vs. identity over time.

a. Projection lengths of short-latency (65–85 ms, blue) and long-latency (135–155 ms, orange) face axes onto face-space dimensions 1–5 (left) and 6–60 (right) in ML. b. Same as (a) in AM. c. Left: regression weights from linear models predicting each face-space dimension using all object-space dimensions. Lower face-space dimensions are predicted primarily by lower object-space dimensions, indicating that the low-dimensional structure of the two spaces is aligned. Right: explained variance (R²) for predicting each face-space dimension. d. Time course of face categorization (face vs. object, blue) and face identity discrimination (orange), computed from population responses of all ML cells using a 20-ms sliding window (Methods). e. Schematic of the synthetic face generation. We constrained the first 5 shape PCs and the first 5 appearance PCs to take one of five discrete positions within the resulting 10-dimension subspace, while leaving all higher-dimensional features randomized (Methods). f. Time courses of face categorization (blue) and identity discrimination (orange) using the low-dimensionality-restricted synthetic face set. The five grey dashed lines show identity-discrimination time courses for each of the five face subsets in which the low-dimensional PCs were fixed (with higher-dimensional features randomized).

Extended Data Fig. 10 An RNN model of face axis reversal.

a. Architecture of a simple RNN trained to reverse a gradient in its input representation (Methods). b. Activity of each RNN unit over time for several input gradients (colours denote different input gradients), demonstrating stable gradient reversal (see Fig. 3e,f). c. Learned RNN weight matrix. The matrix is dominated by local inhibition and long-range excitation. d. Weight matrix of a second RNN explicitly incorporating only local inhibition and long-range excitation. e. Activity of each RNN unit over time for several different input gradients, for an RNN with weights as in (d).

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Shi, Y., Bi, D., Hesse, J.K. et al. Rapid concerted switching of the neural code in the inferotemporal cortex. Nature 653, 146–155 (2026). https://doi.org/10.1038/s41586-026-10267-3

- Received: 13 December 2023

- Accepted: 11 February 2026

- Published: 25 March 2026

- Version of record: 25 March 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10267-3

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Adaptive evolution of gene regulatory networks in mammalian neocortex

Zhuo Li,

Navjot Kaur,

Gabriel Santpere,

Sydney K. Muchnik,

Suvimal Kumar Sindhu,

Cai Qi,

Mikihito Shibata,

Olivier Clément,

Thomas S. Klarić,

Xabier de Martin,

Victor Luria,

Hyesun Cho,

Mingfeng Li,

Akemi Shibata,

Sang-Hun Choi,

Hyojin Kim,

Andrew T. N. Tebbenkamp,

Shaojie Ma,

Wenqi Han,

Suel-Kee Kim,

Sirisha Pochareddy,

Phan Q. Duy,

Xiaojun Xing,

Yunhua Bao,

Xuming Xu,

Ivan Enghian Gladwyn-Ng,

Hayley Daniella Cullen,

Annalisa Paolino,

Laura R. Fenlon,

Peter Kozulin,

Rodrigo Suárez,

Ryan D. Risgaard,

Forrest O. Gulden,

Amir Karger,

Ikuo K. Suzuki,

Tatsumi Hirata,

Kevin T. Gobeske,

Linda J. Richards,

André M. M. Sousa,

Julian I.-T. Heng &

…

Nenad Sestan

Nature volume 653, pages 156–166 (2026)

Abstract

Mammals have evolved a more complex brain, exemplified by the transformation of the single-layer dorsal cortex of excitatory projection neurons (ExNs) in ancestors into a multilayered cerebral neocortex1,2,3,4 enriched with diverse intratelencephalic and extratelencephalic ExN subtypes5,6,7, thereby establishing specialized projection systems that enhance brain connectivity and functionality5,6,7,8. This is in contrast to modern reptiles and birds with single-layered or pseudolayered columnar organization of ExNs4,9,10,11,12. However, the mechanisms underlying these mammalian-specific adaptations remain elusive. By comparing the landscape of gene expression and putative cis-regulatory elements (CREs) in mouse ExN subtypes and through cross-species examination, we identified mammalian-specific CREs, including a subset bound by the transcription factor ZBTB18 (also RP58, ZFP238 or ZNF238) and associated with genes defining intratelencephalic and extratelencephalic subtypes and connectivity, which have been implicated in intellectual disability and autism. Deletion of Zbtb18 in mouse ExNs dysregulated target gene expression, reduced molecular diversity, diminished cortico-spinal and callosal projections and increased intrahemispheric cortico-cortical association projections to the prefrontal cortex, thereby resembling non-mammalian brain. ZBTB18 binding motifs are highly enriched in callosally projecting intratelencephalic-biased putative CREs and show higher conservation specifically in mammals. This study uncovers critical components and mammalian-specific evolutionary adaptations within a regulatory node essential for neocortical ExN identity and connectivity.

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Main

Previous studies identified transcription factors (TFs) that guide subtype specification, laminar positioning and connectivity of mammalian neocortical ExNs5,6,7; however, the evolutionary adaptations and precise molecular mechanisms remain elusive. Here we uncovered gene regulatory subcircuits, particularly involving ZBTB18–CRE interactions that govern key features of neocortical ExNs that have undergone modifications in the mammalian lineage.

Subtype-specific CREs and TFs in neocortical ExNs

To characterize CREs and TFs for neocortical ExNs, we used Arpp21–Gfp or Fezf2–Gfp transgenic mice and enriched GFP-expressing neocortical upper layer (L2–4) intratelencephalic (IT) neurons or deep layer (L5–6) predominantly extratelencephalic (ET) neurons, respectively (Fig. 1a and Supplementary Fig. 1), from neonatal mice (postnatal day (PD) 0), an age at which neocortical ExN identity and connectivity are established. Cells were processed for RNA sequencing (RNA-seq) and chromatin immunoprecipitation (ChIP) assays with DNA sequencing (ChIP–seq) for H3K27ac13,14,15,16,17 (Fig. 1b).

Fig. 1: Mammalian-specific changes in the ZBTB18-associated CREs and TF expression in neocortical ExNs.

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a, Immunolabelling for Arpp21–Gfp+ IT (IT neurons onwards) and Fezf2–Gfp+ ET (ET neurons onwards) neurons and BCL11B in mouse PD 0 neocortex. b, Schematic for isolation and processing of Arpp21–Gfp-positive and Fezf2–Gfp-positive cells from neocortex for RNA-seq and H3K27ac ChIP–seq. c, TFBS for 69 TFs enriched among both IT-biased H3K27ac peaks (red circle) and H3K27ac peaks near IT-biased genes (blue circle) compared with ET-biased peaks and genes, respectively. TFBS enrichments were tested using Fisher’s exact test. Significant TF motifs had a Benjamini–Hochberg-corrected P value < 0.05 and odds ratio > 1. For RNA-seq, n = 3 biological replicates per condition. For ChIP–seq, n = 2 biological replicates per condition. d, Schematic showing two dorsal pallial regions (H and M) microdissected from E17 chicken embryo. e, Venn diagram showing the co-occurrence of peaks derived from ZBTB18-HA ChIP–seq and H3K27ac peaks preferably found in IT and ET neurons and in chicken dorsal pallium. X are peaks in both IT and ET neurons excluded from analysis. f, Heat map showing pairwise alignment distances among vertebrates for six putative CREs overlapping ZBTB18 ChIP–seq peaks linked to IT neurons and axon guidance. The left grid indicates ChIP–seq peak identification method. Columns on the right show if the regions (H3K27ac or ZBTB18 peaks) are orthologous and active in the chicken embryonic dorsal pallium. Grey columns mark non-orthologous regions; red and black columns show the presence or absence of H3K27ac peaks in orthologous regions. Distances are relative to mice. g, Dot plot of Zbtb18-expressing neurons in the dorsal pallium showing cell percentages and co-expression of genes with the ZBTB18 binding motif in IT neurons across mammalian (mouse) and non-mammalian species (chicken, lizard and turtle). h,i, Coronal sections of chicken (h) and mouse (i) brains showing co-localization of BCL11B and SATB2 with ZBTB18. j, Bar plots showing the co-localization percentage for BCL11B and ZBTB18 over total BCL11B+ cells and SATB2 and ZBTB18 over total SATB2+ cells in chicken and mouse. A standard unpaired two-tailed t-test was applied. The graph shows the mean ± s.e.m. **P = 0.0011; ***P = 0.0004 (n = 3 per species). E, entopallium; fL, fetal (immature) layer; H, hyperpallium; HA, apical hyperpallium; Hp, hippocampus; M, mesopallium; N, nidopallium; RPKM, reads per kilobase of transcript per million mapped reads; Str, striatum. Scale bars, 100 µm (a), 1 mm (h,i (mouse brain)), 150 µm (h,i (mouse inset)), 1 mm (h,i (chicken brain)), 300 µm (h,i (chicken inset)), 250 µm (h,i (Cux2)).

Commensurate with the shared developmental lineage of IT and ET neurons, most of the expressed genes (15,120 of 15,902) and the genomic regions enriched for H3K27ac (peaks) (35,173 of 62,448) were common to both cell types (Supplementary Tables 1 and 2). Gene Ontology analysis of the genes and peaks enriched specifically in either IT (304 genes and 54 peaks) or ET (478 genes and 73 peaks) neurons (Extended Data Fig. 1a–c and Supplementary Tables 1–3) revealed terms broadly relevant for IT and ET neurons, respectively (Extended Data Fig. 1d and Supplementary Tables 4–7).

To prioritize TFs for IT and ET neuron development, we identified those highly and differentially expressed and with TF binding sites (TFBSs) enriched among H3K27ac peaks specific to Arpp21–Gfp+ cells (222 TFs; Fig. 1c and Supplementary Table 8). We also highlighted TFBS proximal to genes upregulated in the same cells (154 TFs) compared with Fezf2–Gfp+ cells, and vice versa (Fig. 1c and Supplementary Table 9). Some prioritized TFs are implicated in the development of IT and ET neurons5,6,7, including Meis2, Rara, Rarb and Rxrg, which mediate retinoic acid signalling during the prefrontal cortex (PFC) development[18]. In Arpp21–Gfp+ cells, 69 TFs had binding motifs enriched in ChIP–seq and showed enhanced expression in RNA-seq datasets. Among these, ZBTB18 was the most highly expressed TF (Fig. 1c and Supplementary Table 8). In Fezf2–Gfp+ cells, 54 TFs had motifs enriched and showed increased expression, with NR2F1 being the most highly expressed (Fig. 1c and Supplementary Table 9). Through integrated analysis, we discovered both shared and cell-type-specific TFs and putative CREs. Notably, our findings highlighted ZBTB18, prompting us to investigate its regulatory network and functions in developing neocortical ExNs.

ZBTB18 targets mammalian-specific ExN enhancers

We sought to validate ZBTB18 binding sites in the developing mouse neocortex using ChIP–seq. Because our trials with commercially available antibodies did not yield high-quality ChIP–seq findings, despite working well for immunostaining (Extended Data Fig. 4a), we conducted ChIP–seq by expressing HA-tagged mouse Zbtb18 plasmid electroporated into PD 0 neocortical ExNs. This revealed that ZBTB18 binds to putative developmental CREs marked by H3K27ac peaks near crucial genes previously implicated in the development and diversification of ExNs, such as Bcl11b, Cux2 and Satb2 (Supplementary Table 11).

We compared mouse ZBTB18 ChIP–seq and H3K27ac peaks with H3K27ac peaks from chicken, a non-mammal (Fig. 1d and Supplementary Table 10). On the basis of multispecies transcriptome datasets4,10,11, we performed this analysis on microdissected chicken hyperpallium and mesopallium regions within the dorsal pallium, considered homologous to the mammalian neocortex, at embryonic day 17 (corresponding to mouse age of PD 0). We identified ten distinct enhancer or promoter peaks bound by ZBTB18 that were also biased to IT neurons in mice but not in chicken (Fig. 1e and Supplementary Table 11). Among these ten peaks, five were in proximity to crucial genes differentially expressed between IT and ET neurons and implicated in their development5,6,7: Cux2 (Cux2 enhancer (E) E1 and Cux2-E4), Satb2 (Satb2-E1), Robo1 (Robo1-E1), and Bcl11b (Bcl11b promoter (P) P1) (Fig. 1e–f and Extended Data Figs. 2a–c and 9a). Within the ZBTB18-bound peak associated with mouse Cux2-E1, no overlapping H3K27ac peaks were detected in the orthologous region in chicken, suggesting that this region serves as an active enhancer in mouse cortex but not in chicken (Fig. 1f and Extended Data Fig. 2a). No orthologous regions or overlapping H3K27ac peaks of mouse Cux2-E4 and Satb2-E1 were seen in chicken (Fig. 1f and Extended Data Fig. 2a,b).

The ZBTB18-bound peak within the mouse Bcl11b-P1 did not have an orthologous region in chicken (Fig. 1f and Extended Data Fig. 2c). We did not identify an orthologous region for mouse Robo1-E1 in chicken or any other non-mammalian species analysed (Fig. 1f and Extended Data Fig. 9a), indicating they lack this putative enhancer. We also identified ZBTB18-bound H3K27ac peaks within corresponding regions in mouse ExNs and chicken dorsal pallium, including promoter region of Zbtb18 (Extended Data Fig. 2d), suggesting a potential conserved autoregulatory role. These findings also indicate that specific ZBTB18-bound neocortical ExN subtype-biased enhancers exhibit distinct mammalian features.

For further review of ZBTB18-interacting regions, we selected Cux2-E1 and Satb2-E1 that contained ZBTB18 binding sites in all analysed mammals. Either lacked H3K27ac peaks, or regions associated with ZBTB18-bound peaks were absent in chicken (Extended Data Fig. 2a,b). In humans, the putative enhancers identified for CUX2 and SATB2 exhibited H3K27ac enrichment in fetal dorsolateral PFC (dlPFC)[15], suggesting that these enhancers are conserved in the mammalian developing neocortex (Extended Data Fig. 2a,b). Additionally, we analysed co-expression patterns of Zbtb18 with genes that have ZBTB18-bound peaks in IT neurons (Fig. 1e) using publicly available, single-cell RNA-seq datasets for mouse cortex and dorsal pallium of birds, lizards and turtles12,19,20,21. Among these genes, Zbtb18 exhibited the highest co-expression with Cux2, Satb2 and Robo1 in mice but not in non-mammalian species (Fig. 1g). Conversely, in chickens, lizards and turtles, Zbtb18 showed the highest co-expression with Bcl11b, an ET-specific TF in mammals, suggesting a stronger association with IT neurons in mammals but potentially less ExN class selectivity earlier in evolution (Fig. 1g).

Further, using independently validated anti-ZBTB18 antibodies (Extended Data Fig. 4a), we conducted immunolabelling on ExNs in mouse. ZBTB18 exhibited strong co-localization with IT-enriched TFs SATB2 and Cux2 but not with BCL11B (Fig. 1i,j and Extended Data Fig. 4b). Analysis of embryonic chicken pallium revealed regionally extensive ZBTB18 expression, with a higher density of labelled nuclei in the apical hyperpallium, particularly the interstitial apical hyperpallium (IHA), the main sensory input subregion of the apical hyperpallium, containing neurons molecularly resembling mammalian neocortical thalamorecipient IT ExNs[11] (Fig. 1h and Extended Data Fig. 1e). Consistent with previous analyses in embryonic chick pallium4,11, we observed stronger expression of BCL11B and Fezf2 in the apical hyperpallium than in the mesopallium region, whereas SATB2 was expressed predominantly in the mesopallium region (Extended Data Fig. 1e). In contrast to the developing mouse neocortex, ZBTB18 exhibited increased co-localization with BCL11B rather than SATB2 in the apical hyperpallium, IHA and adjacent mesopallium region in chicken pallium (Fig. 1h–j and Extended Data Fig. 1e). We did not detect appreciable Cux2 expression in most regions of the dorsal pallium, except the posterior ventral pallium (Fig. 1h and Extended Data Fig. 1e). Analysis of single-cell RNA-seq datasets12,19,20,21 also revealed little or no appreciable Cux2 expression in Zbtb18-expressing neurons within the dorsal pallium of birds, lizards and turtles (Fig. 1g). Therefore, in mammals, ZBTB18-bound CREs may regulate expression of genes, such as Cux2, Satb2 and Robo1, which are not active or are absent in non-mammalian species, suggesting that the ZBTB18-dependent gene regulatory network contributes to the enhanced diversity and connectivity of mammalian neocortical ExNs.

ZBTB18 regulates mammalian Cux2 neocortex enhancer

Because Cux2 is IT neuron-specific in mice, with minimal expression in embryonic chicken dorsal pallium (Fig. 1h and Extended Data Fig. 1e), and Cux2 H3K27ac peaks in IT neurons were prominent (Extended Data Fig. 2a), we selected Cux2 as a potential target. To validate the enhancer activity, we generated multiple transgenic founders, in which Cux2-E1 was placed 5′ to the human BGN promoter and linked with a Gfp reporter gene. We observed GFP expression in the forebrain by post-conception day (PCD) 14.5, recapitulating the native expression of Cux2 (refs. 22,23) (Extended Data Fig. 3a). By PCD 16.5, forebrain GFP expression was restricted to a subset of neurons in the neocortical plate (Fig. 2a,b) and co-localized with ZBTB18 in the neocortex but not in striatum (Fig. 2b). Consistent with the importance of Cux2-E1 for gene expression broadly in the neocortex and specifically in IT neurons, we found that the Cux2-E1–Gfp expression was predominantly co-localized with IT neuron marker SATB2 but not with ET neuron marker BCL11B at PCD 16.5, 17.5 and PD 0 (Fig. 2c,d and Extended Data Fig. 3b,c). By PD 15, GFP-labelled cells were in L2–L4 and mostly immunopositive for CUX1 but not BCL11B (Extended Data Fig. 3d–f). Further, we observed GFP in the corpus callosum but not in the cortico-spinal tract when surveyed at the pontine region on the ventral surface of the brain at PD 0 (Extended Data Fig. 3c). In contrast to the specificity of Cux2-E1, two putative Cux2 enhancers (Cux2-E2 and Cux2-E3) were active in both IT and ET neurons and lacked ZBTB18 ChIP–seq peak (Extended Data Fig. 3g,h). As expected, neither was able to drive expression selectively in IT neurons but instead expressed mainly in RELN+ L1 neurons and LHX6+ interneurons, respectively (Extended Data Fig. 3g,h).

Fig. 2: ZBTB18 directly regulates the mammalian-specific neocortical enhancer of Cux2.

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a, Cux2-E1–Gfp expression in the PCD 16.5 neocortex. b, Cux2-E1–Gfp expression co-localizes with ZBTB18 (closed arrows) in the neocortex. c,d, At PCD 16.5, Cux2- E1–Gfp co-expression with SATB2 (closed arrows) but not with BCL11B (c). Double open arrowheads and triple open arrowheads indicate SATB2+ and BCL11B+ cells, respectively. An unpaired two-tailed t-test was used to detect differences between groups (d). The graph shows the mean ± s.e.m.; n = 349 cells from four biological replicates, *P = 0.0001. e, Line graphs showing the H3K27ac peaks from the IT neurons, ET neurons and ZBTB18-HA ChIP–seq peaks associated with mouse Cux2. f, Luciferase activity of the Cux2-E1 enhancer with ZBTB18. An unpaired two-tailed t-test was applied. The graph represents mean ± s.e.m. *P = 0.000040, 0.00029 and 0.000057 (ZBTB18, POU3F2 and HDAC2+ SIN3A, respectively). g, Luciferase activity of the Cux2-E1 enhancer with ZBTB18 from different species. Ordinary two-way analysis of variance (ANOVA) with Bonferroni’s multiple comparisons test was applied. The graph represents mean ± s.e.m. (n = 6). P values are listed in Supplementary Table 16. h, Luciferase reporter assay comparing the activity of wild-type and ZBTB18-binding site-deleted Cux2-E1 (Δ) enhancer constructs from human and mouse with ZBTB18. The graph represents the mean ± s.e.m. (n = 4 per condition). Two-way ANOVA with Bonferroni’s multiple comparisons correction was applied. P values are listed in Supplementary Table 16. i, Cux2-E1–Gfp expression in Zbtb18fl/+ and Zbtb18fl/fl brains electroporated with pCag–Cre and p-Calsl–Rfp plasmids. Co-localization shown with open (in Zbtb18fl/+) and closed (Zbtb18fl/fl) arrows. j, Activity of the mouse Cux2-E1–Gfp or Cux2-ΔE1–Gfp enhancer in E7 chicken hyperpallial neurons. The graph represents GFP intensity normalized to DAPI; n = 3 (control), 42 (Cux2-E1–Gfp) and 51 (Cux2-DE1–Gfp) cells from three independent experiments. Ordinary two-way ANOVA with Bonferroni’s multiple comparisons test, with single pooled variance, was applied. The graph represents mean ± s.e.m. ***P = 0.0001; **P = 0.0048; *P = 0.0430. k, Reverse transcription–polymerase chain reaction (RT–PCR) for Cux2 and Bcl11b at PCD 14.5 and PD 0. Ordinary two-way ANOVA with Bonferroni’s multiple comparisons test, with single pooled variance, was applied. The graph represents mean ± s.e.m.; n = 3 per condition per time point. *P = 0.0327 and 0.0178 (wild type versus Zbtb18 cKO at E15.5 and PD 0); P = 0.0001 (wild type versus Zbtb18 cKO at E15.5 and PD 0). l,m, Distribution of CUX1 (l) or SATB2 (m) and BCL11B is shown in Neurod6–Cre, Zbtb18 cKO and control brains at PD 1. Quantification of cell-type laminar distribution from l and m, respectively. Standard t-tests were used for cell counts per bin. Mean ± s.e.m. for each bin. For P values, see Supplementary Table 16. For RNA-seq and RT–PCR, n = 3 per time point. For immunofluorescent analyses, we counted neurons from independent sections (n = 3 per condition). CP, cortical plate; h, human; IUE, in utero electroporation; IZ, intermediate zone; lCP, lower cortical plate; m, mouse; SP, subplate; SVZ, subventricular zone; uCP, upper cortical plate; VZ, ventricular zone. Scale bars, 1 mm (a), 100 µm (b,l,m), 50 µm (c,i), 10 µm (j).

We conducted luciferase assays to identify the TFs responsible for neocortical Cux2-E1 activity. Several TFs expressed in the developing mouse and human forebrain, including ZBTB18, exhibit predicted binding motifs. Of the tested TFs, ZBTB18 and POU3F2 increased the Cux2-E1 activity, and a combination of HDAC2 and SIN3A repressed it (Fig. 2f). This is consistent with previous findings that POU3F2 has a conserved function in neocortical progenitors[24] and potentially interacts with ZBTB18, which was shown to have dual roles in gene regulation[25]. Combined with ChIP–seq (Fig. 2e) demonstrating ZBTB18 binding to Cux2-E1, these results indicate that ZBTB18 acts as a direct activator of Cux2. Because Zbtb18 and Cux2 are highly co-expressed in mice and humans (Extended Data Fig. 4), but not in non-mammalian species (Fig. 1g), to assess whether this function is specific to mammals, we generated and tested luciferase constructs containing orthologous Cux2-E1 sequences from human, chimpanzee, macaque, mouse, opossum and chicken. Notably, only the sequences from placental mammals, but not those from opossum or chicken, exhibited a significant increase in luciferase activity upon ZBTB18 co-expression (Fig. 2g), indicating that this activation is an evolutionary adaptation specific to placental mammals. When the ZBTB18 binding site from human and mouse Cux2-E1 was removed (hCux2-ΔE1 and mCux2-ΔE1), their ZBTB18-induced luciferase activity decreased (Fig. 2h), indicating that ZBTB18 motif contributes to this placental mammal-specific activation.

To determine the potential of ZBTB18 for transactivation of Cux2-E1 in vivo, we co-electroporated a Cux2-E1–Gfp plasmid, a CALSL–Rfp reporter plasmid and a CAG–Cre plasmid into mice carrying homozygous (fl/fl) or heterozygous (fl/+) floxed Zbtb18 alleles. Electroporation was performed at PCD 15.5 to predominantly target IT neurons. At PD 0, we observed co-localization of GFP and RFP in upper-layer neurons in the Zbtb18fl/+ brain. By contrast, we observed RFP without commensurate GFP expression in electroporated Zbtb18fl/fl cells (Fig. 2i), indicating ZBTB18 is required for Cux2-E1 transactivation in vivo.

To further investigate whether ZBTB18 activates mammalian Cux2-E1 through its motif, we examined its activity in non-mammalian embryonic chicken hyperpallium neurons expressing ZBTB18 by introducing the Cux2-E1–Gfp plasmid into them. Mouse Cux2-E1 drove robust GFP expression in ZBTB18-immuno-positive chicken hyperpallial neurons, whereas deleting the ZBTB18 binding site (Cux2-ΔE1) resulted in a significant reduction in GFP expression (Fig. 2j). These findings, together with the luciferase data (Fig. 2g,h), demonstrate that Cux2-E1 regulation by ZBTB18 in mammalian cortical ExNs is probably motif-dependent. Consistent with Cux2 being enriched in mammalian IT neurons[22] and controls their neurite growth[26], we observed that overexpression of mouse CUX2 in chicken ZBTB18 and SATB2 co-immunopositive ExNs increased neurite outgrowth (Extended Data Fig. 3i,j), similar to the reported role of CUX2 in mammalian IT ExNs[26].

We identified a consensus ZBTB18 binding site within Satb2-E1 (Extended Data Fig. 2b) and performed luciferase assays by co-expressing Zbtb18 with DNA constructs for either Satb2-E1 or Satb2-ΔE1 (lacking the ZBTB18 site). Zbtb18 markedly increased Satb2-E1 activity, whereas luciferase activity from Satb2-ΔE1 constructs was reduced but not to basal levels (Extended Data Fig. 3k). We further revealed overlaps of putative CREs Cux2-E1, Satb2-E1, Bcl11b-P1, Zbtb18-P1 and Robo1-E1 in both human and developing mouse mammalian neocortex by analysing an independent H3K27ac ChIP–seq database from human midfetal dlPFC[15] (Extended Data Figs. 2a–d and 9a), indicating functional conservation of these CREs in mammals and their potential involvement in fundamental neocortical developmental processes. These results highlight the pivotal role of ZBTB18 in regulating these CREs, underscoring the need for a comprehensive investigation.

Enrichment of ZBTB18 in postmigratory IT neurons

We examined ZBTB18 expression at the postmigratory stage, a stage previously shown to be critical for ExN specification5,6,7. It is co-expressed with CUX2 in upper-layer IT neurons in the mouse at PD 0 (Fig. 1i,j). This pattern was replicated in post-conception week (PCW) 20 human neocortex, where strong immunolabelling for ZBTB18 was found in nuclei positive for SATB2 and CUX2 within the upper-layer prospective IT neurons (Extended Data Figs. 4c and 5b,c). However, ZBTB18 immunolabelling was diminished in the BCL11B-immunopositive nuclei of prospective L5B ET neurons (Fig. 1i,j and Extended Data Fig. 5b,c). Analysis of a publicly available mouse single-cell RNA-seq dataset[27] revealed Zbtb18 expression was higher in developing IT neurons than in L5 and L6 ET neurons (Extended Data Fig. 5a). The enrichment and sustained ZBTB18 expression in IT neurons suggest its crucial role in their specification.

ZBTB18 regulates postmitotic ExN subspecification

Previously, the role of ZBTB18 in neurogenesis and neuronal migration has been reported28,29,30,31,32, and mutations in the human ZBTB18 gene are linked to intellectual disability and autism33,34,35,36,37. To investigate the effects of Zbtb18 on ExN specification, we tested the expression of genes enriched in IT neurons versus ET neurons following Zbtb18 KO. We performed RNA-seq on Zbtb18−/− (KO) and Zbtb18+/− (control) neocortex at PCD 14.5, a time coinciding with the generation of upper-layer IT neurons (Extended Data Fig. 6a and Supplementary Table 12). Of genes enriched in IT or ET neurons and differentially regulated in Zbtb18 KO at PCD 14.5, disproportionally more genes enriched in IT neurons were downregulated following KO (82.3%), as opposed to the upregulated genes. The opposite trend was observed with ET neuron-enriched genes in Zbtb18 KO (92.4% were upregulated) (Extended Data Fig. 6a and Supplementary Table 12). To test whether ZBTB18 is required for ExN specification postmitotically, we conditionally deleted Zbtb18 in these neurons by crossing Zbtb18fl/fl mice to Neurod6–Cre (also known as Nex1–Cre) transgenic mice and conducted RNA-seq on neocortical tissue derived from Neurod6–Cre, Zbtb18fl/fl (Zbtb18 conditional knockout (cKO)) and Neurod6–Cre; Zbtb18fl/+ (control) mice at PD 0 (Extended Data Fig. 6b and Supplementary Table 13). Similarly, at PD 0, 58.7% of genes enriched in IT neurons and differentially expressed in Zbtb18 cKO were downregulated, whereas 96.7% of genes enriched in ET neurons were upregulated (Extended Data Fig. 6b and Supplementary Table 13). Reduced expression of key IT neuron marker genes within the neocortical plate of Zbtb18 KO and cKO mice, including Cux1, Cux2, Rorb and Satb2, was confirmed by multiple assays (Fig. 2k and Extended Data Fig. 6c,h). Conversely, the ET neuron marker BCL11B expression increased in these same mice (Fig. 2k).

Neurod6–Cre; Zbtb18 cKO mice at PD 0 showed a minor reduction in neocortical size, contrasting with constitutive or Emx1–Cre KO mice28,31 (Extended Data Fig. 6d), but at PD 8, it was substantially smaller than the control (Extended Data Fig. 6d). In Neurod6–Cre; Zbtb18 cKO mice, ZBTB18 protein was detected in neocortical progenitor cells but not in postmitotic cells of the intermediate zone, subplate or cortical plate from PCD 15.5 onwards (Extended Data Fig. 6e), suggesting that the effects observed are not attributable to progenitor-related processes. In Neurod6–Cre; Zbtb18 cKO, CUX2 protein and messenger RNA (mRNA) expression in cortical plate were reduced (Extended Data Fig. 6f–h and Supplementary Fig. 2), similar to Emx1–Cre; Zbtb18 cKO mice and whole-body KO. We also found expansion of the laminar distribution and the number of BCL11B-immunopositive and TBR1-immunopositive neurons. This may occur at the expense of both SATB2-immunopositive and CUX1-immunopositive upper-layer IT neurons at PD 0 (Fig. 2j,k and Extended Data Fig. 7d,e).

We next conducted 5-iodo-2-deoxyuridine (IdU) or 5-chloro-2-deoxyuridine (CIdU) labelling at developmental stages in Neurod6–Cre; Zbtb18 cKO mice and control littermates. At PCD 12.5, we labelled early-born neurons destined for deep layers (L5–6), whereas at PCD 14.5 and 15.5, we labelled later-born neurons primarily destined for L4 or L2–3 (Extended Data Fig. 7a–c). We then analysed the co-localization of IdU or CldU with BCL11B for ET neurons and SATB2 for IT neurons. Following labelling at PCD 12.5, most of IdU-positive nuclei were in the central region of cortical plate in both cKO and control mice, typically where immature L5 neurons resided (Extended Data Fig. 7a). Although there was a significant increase in BCL11B-positive cell numbers and laminar distribution in cKO mice, there was no corresponding increase of cells double positive for IdU and BCL11B (Extended Data Fig. 7a), suggesting that the laminar identity and position of L5 ET neurons are not substantially altered at this age. Following IdU injection at PCD 14.5, in cKO mice, we observed increased proportions of IdU and BCL11B double-labelled nuclei (Extended Data Fig. 7b), although the numbers of IdU-positive and BCL11B-positive cells were greater overall. This suggests that Zbtb18 postmitotic deficiency influences cell fate of late-born nascent neurons. Similarly, we observed a decrease in the percentage of nuclei double positive for CldU and SATB2 following injection at PCD 15.5 in cKO mouse, explained in part by a broader laminar distribution of double-positive neurons (Extended Data Fig. 7c). These results indicate that postmitotic and postmigratory ZBTB18 is required cell autonomously for proper specification of IT neurons, and its deletion leads to misspecification of some IT neurons, including acquisition of certain molecular properties associated with ET neurons.

ZBTB18 regulates mammalian neocortical projections

The above findings suggest that ZBTB18 may also regulate axonal projections, a distinguishing feature of ExN subclasses and crucial for the unique mammalian connectivity. We analysed early postnatal Neurod6–Cre Zbtb18 cKO mice harbouring CAG–CAT–Gfp, a CRE-responsive GFP transgene. The cKO mice exhibited reduced white matter and absent corpus callosum (Fig. 3a). Very few callosal axons reached the midline, and those misrouted ventrally into the septum. Other forebrain commissural projections, including the anterior commissure, were also disrupted, reflecting an overall defect in commissural axon growth (Fig. 3a). Additionally, the internal capsule and different subcerebral projections were significantly reduced (Fig. 3a).

Fig. 3: ZBTB18 depletion reduces callosal and subcerebral projections while increasing cortico-cortical association projections.

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a, Visualization of axonal projections by GFP expression in the Neurod6–Cre; Zbtb18 cKO mouse brain. Full arrows and arrowheads depict wild type, and open arrowheads depict defective tracts in the Zbtb18 cKO brain. b, Whole brain image (left top) and coronal sections of the brain at P7 showing the injection site and extent of the GFP expression after AAvrg–CAG–Gfp injections into the medial PFC at PD 3. c, Left, bar plot showing GFP-positive neurons projecting to PFC in the neocortex of Zbtb18 cKO mice compared with control brains at PD 7, as shown in b. Unpaired two-tailed t-test. The graph represents mean ± s.e.m. *P = 0.03 (n = 3). Right, line graph data showing the percentage distribution of GFP+ neurons projecting to PFC in each bin of the neocortex of Zbtb18 cKO mice compared with wild-type brains at PD 7. The number of labelled neurons in each bin was compared using unpaired t-test. The graph represents mean ± s.e.m. *P = 0.03 (bin 3), 0.004031 (bin 6), 0.03 (bin 7) and 0.012 (bin 14) (n = 3). AC, anterior commissure; AUD, auditory cortex; BLA, basolateral amygdala (excitatory nucleus within amygdala); CC, corpus callosum; CP, cerebral peduncle; cPERI, contralateral perirhinal; CST, cortico-spinal tract; Hip, hippocampus; IC, internal capsule; nLOT, nucleus of lateral olfactory tract; PERI, perirhinal cortex; Str, striatum; Th, thalamus. Scale bars, 1 mm (a), 20 µm (a (CP panel)), 500 μm (b), 125 μm (b (inset)).

To determine the intrahemispheric intracortical connectivity, we injected AAVrg–CAG–Gfp into the PFC of PD 3 mice for retrograde tracing. In control mice, we observed typical intrahemispheric cortico-cortical connectivity originating from transmodal and unimodal association areas, including perirhinal cortex, secondary visual cortex, retrosplenial cortex and parts of the amygdala projecting to PFC (Fig. 3b and Extended Data Fig. 8a,b). However, in Zbtb18 cKOs, this pattern was significantly disrupted: we noted a substantial increase in unilateral afferent connections to the PFC originating from the central region of the neocortex where primary areas are located. Notably, there was a noticeable input reduction from perirhinal cortex, secondary visual cortex and retrosplenial cortex (Fig. 3b and Extended Data Fig. 8a,b). Within neocortex, we also observed an overall increase in GFP-positive cells projecting to PFC (Fig. 3c). Additionally, we observed a reduction in the projections from most of the contralateral inputs and subcortical connections as seen in striatum and thalamus (Fig. 3b and Extended Data Fig. 8b). These results suggest compensatory effects after ZBTB18-mediated loss of callosal and subcortical connections, and collectively demonstrate that ZBTB18 is postmitotically required for the mammalian characteristic neocortical long-range connectivity.

ZBTB18 regulates Robo1 enhancer and callosal wiring

We co-electroporated Neurod1–Cre and CALNL–Gfp reporter vectors into the neocortex of PCD 15 Zbtb18fl/fl mice. This Cre-mediated deletion of Zbtb18 from ExNs resulted in diminished GFP-labelled axons projecting towards the midline at PD 0 (Fig. 4a). Co-electroporation with a Neurod1–Zbtb18 expression vector that restores ZBTB18 in the same cell populations rescued the projection of GFP-positive callosal axons (Fig. 4a), suggesting that ZBTB18 is cell-autonomously required for midline crossing of callosal axons.

Fig. 4: ZBTB18 regulates axon guidance genes and the mammalian-specific Robo1 enhancer.

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a, Coronal sections from PD 0 brain showing ZBTB18 expression and GFP-labelled corpus callosum axons after IUE at PCD 15.5 with Neurod1–Cre and CALNL–Gfp (left) or Neurod1–Zbtb18 (right) plasmids into Zbtb18fl/fl mice. Enlarged panels show GFP images in greyscale. b, Top five Gene Ontology terms for genes that are downregulated (red) or upregulated (blue) in the Zbtb18−/− (KO) and mouse compared with Zbtb18+/− (control) at PCD 14.5 (upper bar plot) and in Neurod6–Cre; Zbtb18 cKO compared with control at PD 0 (lower bar plot). c, Genes encoding axon guidance molecules and upregulated and downregulated in the Zbtb18 KO mouse compared with Zbtb18+/− (control). d, Line graphs showing the H3K27ac peaks from IT neurons, ET neurons and ZBTB18-HA ChIP–seq peaks associated with mouse Robo1. e, Luciferase reporter activity with Robo1-E1 enhancer and ZBTB18. An unpaired two-tailed t-test was performed to detect differences between the control and experimental conditions. The graph represents mean ± s.e.m. **P = 0.00672 (n = 3). f, ISH shows Robo1, Robo2 and Robo3 expressions in the Zbtb18 −/− mouse in the neocortical plate. g–i, Representative images of the IUE with pCag–Robo1 allow Gfp-expressing co-electroporated upper-layer neurons to project GFP-positive axons to and across the corpus callosum (arrows in the inset) analysed at PD 0 (g) and PD 21 (h). Top, number of axons crossing at the midline on the contralateral side of the IUE at PD 21 (i). Bottom, number of cells electroporated on the ipsilateral side (i). An unpaired two-tailed t-test was performed to detect differences between the control and experimental conditions. The graph represents mean ± s.e.m. *P = 0.030. For each IUE experiment, n = 6 (control) and n = 3 (Robo1) animals were analysed at PD 0; n = 3 (control) and n = 3 (Robo1) animals were analysed at PD 21. For RNA-seq, n = 3 per condition. The ISH data shown are representative of the data generated from multiple sections (n = 3 animals). Scale bars, 500 μm (a,g,h), 150 μm (f), 250 μm (g,h (inset)).

We analysed our RNA-seq data from Zbtb18 KO mice to understand the potential downstream effectors. Gene Ontology terms associated with upregulated and downregulated genes at both PCD 14.5 and PD 0 referenced general categories, including nervous system development. Several terms implicating DNA replication and cell cycle were enriched among upregulated genes at PCD 14.5. Axonogenesis, axon guidance and neuron projection development were also enriched among genes downregulated in the Zbtb18 KO at both PCD 14.5 and PD 0 (Fig. 4b). These observations indicate that ZBTB18 not only promotes cell cycle exit and neuronal migration, as previously described28,31,32, but also promotes postmitotic neocortical ExN diversification and axonogenesis. We analysed axon guidance and cell adhesion molecules and found that receptors implicated in callosal development, including members of the Netrin (Dcc and Unc5d38,39,40) and Slit–Robo families, were either downregulated (Robo1 and Robo2; refs. 41,42) or upregulated (Robo3; ref. [43]) in Zbtb18 KO neocortex (Fig. 4c). Notably, the Slit receptor Robo1 was the most downregulated. Additionally, ZBTB18 ChIP–seq data revealed an IT neuron-specific putative enhancer Robo1-E1 within the Robo1 locus harbouring a consensus ZBTB18 motif (Fig. 4d and Extended Data Figs. 9a and 11d). Luciferase assays demonstrated significant activation of Robo1-E1 by ZBTB18 (Fig. 4e). In situ hybridization (ISH) at PCD 15.5 confirmed the downregulation of Robo1 and Robo2, as well as the upregulation of Robo3 in the Zbtb18 cKO neocortex (Fig. 4f).

Given these observations and reported callosal defects resulting from disruptions in both mouse and human ROBO1 homologues41,42,44, we electroporated a control (CAG–empty) or Robo1-expression vector (CAG–Robo1) together with Neurod1–Cre and CALNL–Gfp (or CALNL–Rfp) into the PCD 15.5 Zbtb18fl/fl mouse neocortex. We found negligible axons projecting towards or across the midline at PD 0 in any of the six control electroporated Zbtb18fl/fl brains (Fig. 4g). By contrast, we observed recovery of a proportion of GFP+ axons in the corpus callosum following Robo1 overexpression in all electroporated animals (Fig. 4g). Further, we observed similar restoration of a subset of GFP+ axons in the corpus callosum upon overexpression of Robo1 at PD 21, a stage when its development is complete (Fig. 4g,i). The fact that the recovery was not extensive suggests that midline crossing is a complex process, and other factors, such as cell-intrinsic effectors (including DCC and ROBO2), the actions of midline glia[8] or a combination of these, may be required, especially in humans and in relation to pathology.

ZBTB18 binding sites conserved in mammalian IT CREs

ExN subtype-specific CREs controlled by ZBTB18 showing either mammal-specific characteristics or unique mammalian ZBTB18 binding sites led us to explore whether this regulatory system might mark an evolutionary milestone. We analysed the protein sequences and dorsal pallial expression of Zbtb18 and found that they are highly conserved across vertebrates (Fig. 1h,i and Extended Data Figs. 1e and 10a). Next, we considered the conservation of broad populations of ZBTB18 binding motifs across placental mammals, marsupials, monotremes and non-mammalian species. We then assessed evolutionary footprints of specific CREs and/or ZBTB18 binding motifs plausibly regulating key genes associated with major subtypes of ExNs, including Cux2, Satb2, Bcl11b and Robo1. However, because variations in TFBSs may lead to interspecies differences13,14,16,17, we sought to determine whether variations are conserved within specific clades.

We assessed the relative conservation of putative ZBTB18 binding sites among putative CREs near genes enriched in IT neurons (Extended Data Fig. 10a) and compared this to putative CREs unrelated to IT or ET neuron subspecifications or corpus callosum formation (Methods). We also considered putative CREs near genes enriched in ET neurons or was upregulated or downregulated in Zbtb18 KO mice. We devised a bootstrap-based strategy to evaluate robustness (Extended Data Fig. 10d and Methods). When precisely matching the core motif as in the JASPAR database (Methods), ZBTB18 binding sites were significantly conserved in IT neuron enhancers in placental mammals and marsupials but not in monotremes or non-mammals (Fig. 5b), an enrichment not observed in ET neuron enhancers. Moreover, we found significant conservation of ZBTB18 binding sites in CREs of genes upregulated in Zbtb18 KO mice specifically throughout Eutheria but no other mammals (Fig. 5a). We also assessed the conservation of ZBTB18 binding sites with one, two or three mutations in the core sequence, reasoning that heavily mutated sites should not exhibit similar enrichment. Expectedly, we found no enrichment among putative IT neuron enhancers or those upregulated in Zbtb18 KO mice when assessed with either two or three mutations, suggesting that the ZBTB18 binding site conservation is specific among IT neurons (Fig. 5a).

Fig. 5: Increased conservation of ZBTB18 binding motifs in mammalian IT neuron-biased CREs.

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a, Conserved ZBTB18 binding motifs are identified in putative CREs associated with genes exhibiting enriched expression in Arpp21–Gfp+ IT neurons. b, Between marsupials and placental mammals, enhancers associated with Arpp21–Gfp+ IT neurons have a significantly higher percentage of conserved ZBTB18 motifs than those associated with Fezf2–Gfp+ ET neurons or background sequences. No differences in ZBTB18 motif conservation in non-mammals or monotremes were detected. Asterisks indicate where Fisher’s exact test revealed a significant enrichment of conservation, with a false discovery rate (FDR)-corrected P value < 0.05. c, Among the TFBS catalogued in the JASPAR database and expressed in either IT neurons or ET neurons, the ZBTB18 motif stands out as one of three motifs with consensus sequences uniquely conserved within enhancers linked to genes enriched in IT neurons across placental mammals and marsupials. Fisher’s exact test was used to assess enrichment of conservation. Red dots represent motifs with FDR-corrected P value < 0.05 (see Supplementary Table 17 for detailed statistics). Mut., mutation.

Considering the possibility that this conservation may be nonspecific, we performed a similar analysis using 267 motifs from the JASPAR database, corresponding to TFs expressed in either IT or ET neurons, or both, at PD 0 according to our RNA-seq dataset. We again assessed enrichment of binding sites with perfect core motif conservation in putative CREs associated with IT or ET neuron-enriched genes or those differentially regulated in the Zbtb18 KO (Fig. 5c). We grouped the significant motifs according to independently tested enrichment in consecutive phylogenetic groups (Placentalia + Marsupalia, Placentalia + Marsupalia + Monotrema, and so on). Of the 267 motifs, only two other motifs (GMEB2 and SMAD2, SMAD3 and SMAD4, involved in transforming growth factor-β signalling) were specifically conserved in IT putative CREs in Theria, demonstrating that ZBTB18 is a member of a rare group of TFs with binding sites that are singularly conserved among placental mammals and marsupials in IT neuron-related CREs (Fig. 5c). This further supports a critical role for ZBTB18 in the evolution of the mammalian diversification of ExNs. Among genes significantly upregulated in the neocortex of Zbtb18 KO mice, the enriched conservation of ZBTB18 motifs was only significant across placental mammals (Extended Data Fig. 10b).

We next examined the motif evolutionary footprints within ZBTB18-bound CREs of critical IT and ET genes, including those we functionally validated to regulate Cux2, Satb2 and Robo1, and the promoter region of Bcl11b and Zbtb18, by performing alignment across 60 vertebrates (Extended Data Figs. 2a–d and 9c). Although motifs within Zbtb18-P1 demonstrate high conservation across vertebrates (Extended Data Fig. 11a), motifs in key IT and ET neuron CREs are eutherian-specific or mammalian-specific (Extended Data Fig. 11b–f). We next inferred the emergence time of these motifs (Extended Data Fig. 10c). Notably, all consensus ZBTB18 motifs in Zbtb18-P1 evolved before the divergence of monotremes from non-mammalian vertebrates (Fig. 1h,i and Extended Data Fig. 1e), suggesting a possible conserved self-regulation mechanism of ZBTB18. By contrast, most motifs in key IT and ET CREs appeared after the divergence of placental mammals from marsupials, suggesting ZBTB18 may have used eutherian-evolved IT and ET CREs to finely tune gene expression.

Finally, we estimated the evolutionary age of ZBTB18 genes by protein sequence similarity and found that, although they are ancient, their protein sequences do not change among chordates (Extended Data Fig. 10a and Methods). These results further suggest that the evolutionary-derived CREs are essential to drive the expression pattern of key genes involved in the subspecification and connectivity of mammalian neocortical ExNs.

Discussion

We addressed a fundamental question at the intersection of neuroscience and evolutionary biology: the emergence of the mammalian neocortex, particularly its diverse ExN subtypes and intricate long-range projections. We revealed mammalian-specific adaptations at the level of CREs linked to TFs associated with ET and IT neuron specification, contributing to the hallmarks of mammalian neocortex evolution. Consistent with these findings, the loss of ZBTB18 function in mice leads to simplified neocortical laminar organization and long-range axonal projection patterns that resemble ancestral forms. This research also expands upon the study of ExNs in mouse piriform cortex (palaeocortex) that revealed diminished ExN subspecification compared with neocortex, with piriform ExNs retaining molecular signatures reminiscent of those shared with reptiles and amphibians[45].

Human genetic studies associated ZBTB18 variants with corpus callosum agenesis, microcephaly, autism and intellectual disability35,36,37,46,47. Mutations in the identified target genes, including CUX2, SATB2, BCL11B and ROBO1, are associated with similar disorders36,48,49,50. We revealed that CREs and the expression patterns of these genes in neocortex are conserved across mammalian species and differ from their non-mammalian counterparts. ZBTB18 binding sites are highly conserved in eutherian IT-biased CREs. This heightened evolutionary constraint in mammalian neocortex, although advantageous, may render them more susceptible to various neurodevelopmental and neuropsychiatric disorders. Our research has demonstrated potential predictive value for autism pathogenesis, as individuals with ZBTB18 loss-of-function mutations may show increased cortico-cortical connectivity between PFC and temporal areas, a pattern also seen in functional magnetic resonance imaging studies of autism51,52,53. Although we did not directly examine connectivity within sensory-motor and visual networks, owing to an apparent expansion of temporal heteromodal projections in the Zbtb18 cKO mice, we predict that these areas are hypoconnected in our model. Further, similar to ZBTB18, we identified that its direct target Satb2 also maintains spatially organized connectivity patterns across primary sensorimotor and association neocortical areas in mammals[54]. These findings highlight how evolutionary and developmental insights relate to neurodevelopmental and psychiatric disorders.

Methods

Mice

All experiments involving animals were approved by the Yale University Institutional Animal Care and Use Committee and conducted in compliance with all relevant university, state and federal guidelines. The day of vaginal plug detection was designated as PCD 0.5. The day of birth was designated as PD 0. Fezf2–Gfp (000293-UNC) and Arpp21–Gfp (011848-UCD) transgenic mice were obtained from the Gene Expression Nervous System Atlas[55]. Cux2-E1–Gfp, Cux2-E2–Gfp and Cux2-E3–Gfp transgenic mice were generated by delivering linearized DNA constructs by pronuclear injection. Three to seven founders from each line were examined for reproducible GFP expression. Zbtb18 KO mice were generated by the Masai laboratory[28], and Zbtb18fl/fl mice were generated by the Heng laboratory. Emx1–Cre (The Jackson Laboratory; 005628) mice, Cux2–Cre mice, Neurod6–Cre (also known as Nex1–Cre) mice and CAG–Cat–Gfp (The Jackson Laboratory; 024636) mice were previously generated and described56,57,58,59 (Cre Driver Network at the National Institutes of Health (NIH) Blueprint for Neuroscience Research). Genotyping primers are shown in Supplementary Table 15.

The mice were provided with food and water ad libitum, maintained on a 12-h light/12-h dark cycle, provided veterinary care by the Yale Animal Resources Center and housed in clean groups. All mice used or bred for these experiments were in good health, as approved by the Yale Animal Resources Center and confirmed through regular veterinary monitoring. To maintain genetic diversity, multiple concurrent breeding pairs were maintained and siblings were never mated. Both males and females were used randomly throughout this study.

Although blinding was not relevant for the primary mutant versus control comparison, other aspects ofthe study required careful design to minimize bias. Randomization was implemented during data acquisition. Littermates (WT, HET and KO) were housed together to avoid confounding housing effects on statistical analyses. The experimental cohort comprised age-matched male and female littermates. Including samples from multiple litters further enhanced reproducibility.

Tissue preparation and fluorescence-activated cell sorting

Neocortices from PD 0.5 Fezf2–Gfp, with GFP-expressing neurons enriched in the deep layer (L5–6) predominantly ET neurons (7), and Arpp21–Gfp, with GFP-expressing neurons enriched in predominantly IT neurons[55], were dissected under a dissection microscope and minced with a sterile blade. Tails were collected for determining sex and genotyping. Single-cell suspensions from neocortical tissue were prepared by dissociation with a papain-based solution[60] and incubated at 37 °C for 15 min with intermittent trituration using autoclaved, fire-polished glass Pasteur pipettes. Cells were then pelleted by centrifugation at 4 °C for 5 min, washed with sterile 1× phosphate-buffered saline (PBS) and filtered through a 40-μm strainer. Cells were then sorted to collect GFP+ cells by fluorescence-activated cell sorting (FACS) using FACSAria II (BD Biosciences) sorter or Beckman Coulter MoFlo sorter. Hibernate solution (Gibco) supplemented with 2% fetal bovine serum (Gibco) was used to collect FACS-sorted cells. Cell preparations were maintained at 4 °C during the entire process. For RNA-seq, immediately after FACS, cells were pelleted by centrifugation at 350g at 4 °C for 10 min, washed with PBS, pelleted and flash-frozen in liquid nitrogen and stored at −80 °C. For ChIP–seq, FACS-sorted GFP+ cells were pelleted by centrifugation at 350g for 10 min at 4 °C, washed with PBS and immediately crosslinked with formaldehyde solution at a final concentration of 1% for 10 min at room temperature. Glycine (AmericanBio) was added at a final concentration of 125 mM, and samples were incubated for 5 min at room temperature to quench crosslinking. Cells were washed again with PBS, pelleted, flash-frozen in liquid nitrogen and stored at −80 °C.

RNA-seq and initial analysis

Total RNA was extracted from FACS-purified cells or neocortical tissue using TRIzol reagent, according to the manufacturer’s instructions. DNase I (Invitrogen) was added to the extracted total RNA, incubated for 15 min at 37 °C to eliminate DNA contaminants and then inactivated according to the manufacturer’s instructions. RNA concentration and integrity were measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific) and TapeStation 2200 (Agilent). Samples with RNA integrity number of 8 or higher were used for subsequent experiments. Libraries were prepared using the TruSeq Stranded Total RNA with Ribo-Zero preparation kit (Illumina), according to the manufacturer’s instructions. Libraries were quality controlled using the TapeStation 2200 (Agilent) and sequenced on the HiSeq 2000 platform (Illumina) at the Yale Center for Genome Analysis (YCGA) to generate 75-bp single-end reads. Sequencing data were quality controlled using FastQC and aligned to the mouse genome (NCBI38/mm10) using TopHat (v.1.0.13) with up to two mismatches[61]. An average of 40 million uniquely mapped reads were obtained for each sample. Differential expression analysis was performed using the R package DESeq, and principal component analysis was performed using the R package prompt. Differential expression of transcripts was detected using FDR < 0.01.

ChIP–seq and initial analysis

Pooled GFP+ cells from FACS were used for ChIP–seq. A total of 2.5 × 10^7 cells per condition were crosslinked with a formaldehyde solution (Sigma-Aldrich) at a final concentration of 1% for 10 min at room temperature. L-Glycine (AmericanBio) was added at a final concentration of 125 mM and incubated for 5 min at room temperature to quench the crosslinking. Cells were washed with PBS and then disrupted using lysis buffer I (50 mM HEPES–KOH (pH 7.5), 140 mM NaCl, 1 M EDTA (pH 8.0), 10% glycerol, 0.5% Nonidet P-40 (NP-40), 0.25% Triton X-100 and 1× protease inhibitor) for 20 min at 4 °C and lysis buffer II (200 mM NaCl, 1 M EDTA (pH 8.0), 0.5 mM EGTA (pH 8.0), 10 mM Tris–HCl (pH 8.0) and 1× protease inhibitor) for 10 min at room temperature. Cells were centrifuged at 300g for 15 min at 4 °C, and pellets were dissolved in 400–600 μl of lysis buffer III (1 mM EDTA (pH 8.0), 0.5 mM EGTA (pH 8.0), 10 mM Tris–HCl (pH 8.0), 0.5% sarkosyl and 1× protease inhibitor) before being sheared into 200–500 bp fragments with a sonicator (Bioruptor; Diagenode). Dynabeads Protein G (Invitrogen) was pre-blocked with 5 mg ml−1 of ice-cold bovine serum albumin (BSA) and incubated with 5 μg anti-H3K27ac antibody (Abcam) at 4 °C with constant rotation for 12 h. Chromatin (25 μg) was added to the bead–antibody complex mixture per reaction and incubated with constant rotation for 16 h at 4 °C. Beads were washed with ice-cold radioimmunoprecipitation assay (RIPA) buffer (Thermo Fisher Scientific) eight times, rinsed with 1× Tris–EDTA (1× Tris–EDTA) solution, eluted by adding 200 μl of ChIP elution buffer (1% SDS; 1× Tris–EDTA) and incubated in a shaker for 20 min at 65 °C. ChIP DNA was incubated for 12 h at 65 °C for reverse crosslinking, treated with RNAse A (Thermo Fisher Scientific; 1 h; 37 °C) and Proteinase K (Sigma-Aldrich; 2 h; 55 °C) and then purified on PCR purification columns. For input control, 5 μg of chromatin from whole-cell extract of each sample was subjected to reverse crosslinking, RNase A treatment (Thermo Fisher Scientific; EN0531) and Proteinase K treatment (Sigma-Aldrich; 3115887001), together with immunoprecipitated samples, and purified using PCR purification columns. DNA amounts were quantified using the PicoGreen assay (Thermo Fisher Scientific; P7589). Immunoprecipitated DNA (5 ng) and input from each sample were used to prepare ChIP libraries with TruSeq ChIP Library Preparation Kit (Illumina; IP-202-1012), according to the manufacturer’s instructions. Libraries were size selected to enrich 300–400 bp size fragments, quality controlled and sequenced on Hiseq 2000 platform (Illumina) (YCGA). Approximately 20–25 million reads were obtained from each sample. FASTA files were mapped to the mouse genome (NCBI37/mm10) using TopHat v.1.0.13 (http://tophat.cbcb.umd.edu/) and Bowtie 2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)[62]. Peaks were identified using MACS2 (SCR_013291)[63].

H3K27ac ChIP–seq in the chicken

Embryonic day 15 or Hamilton–Hamburger stage 41 (ref. [64]) chicken eggs were obtained from Charles River Laboratories and incubated in the laboratory for 2 days at 37 °C in a humidified chamber. The dorsal pallial regions (hyperpallium apicale and mesopallium) were microdissected from the embryonic day 17 (Hamilton–Hamburger stage 43) chicken embryos. Ten pallia were pooled and crosslinked with a formaldehyde solution (Sigma-Aldrich) at a final concentration of 1% for 10 min at room temperature per sample. L-Glycine (AmericanBio; 56-40-6) at a final concentration of 125 mM was added and incubated for 5 min at room temperature to quench the crosslinking. Cells were washed with PBS thrice and lysed in a hypotonic solution (50 mM Tris–HCl (pH 7.5), 0.5% NP-40, 0.25% sodium deoxycholate, 0.1% SDS and 150 mM NaCl) on ice for 10 min to obtain the nuclei. The nuclei were centrifuged at 600g for 5 min at 4 °C, and pellets were resuspended in the SDS lysis buffer (1% SDS, 10 mM EDTA and 50 mM Tris–HCl (pH 8.1)) before being sheared into 200–500 bp size fragments using a sonicator (M220 Focused-ultrasonicator; Covaris). The sheared DNA was diluted with the ChIP dilution buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris–HCl (pH 8.1) and 167 mM NaCl) and pre-cleared with Magnetic Protein A/G Beads (MilliporeSigma) for 1 h at 4 °C. The beads were discarded, and anti-H3K27ac antibody (Abcam; ab4729) was added. The samples were incubated on constant rotation for overnight at 4 °C. Magnetic Protein A/G Beads (MilliporeSigma) blocked with 1 mg ml−1 BSA (Sigma-Aldrich) and transfer RNA were added to the chromatin–antibody complexes for 4 h at 4 °C. The beads were washed with low salt (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris–HCl (pH 8.1) and 150 mM NaCl), high salt (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris–HCl (pH 8.1) and 500 mM NaCl), LiCl (0.25 M LiCl, 1% IGEPAL CA-630, 1% deoxycholic acid (sodium salt), 1 mM EDTA and 10 mM Tris–HCl (pH 8.1)) and 1× Tris–EDTA (AmericanBio; AB14033-01000), sequentially for 3 min each. ChIP DNA was incubated overnight at 65 °C for reverse crosslinking and subjected to RNase A (Thermo Fisher Scientific; EN0531) treatment (37 °C; 1 h) and Proteinase K (Sigma-Aldrich; 3115887001) treatment (55 °C; 2 h) and then purified on PCR purification columns. For input control, 5 μg of crosslinked chromatin from each sample was also treated for reverse crosslinking, RNase A (Thermo Fisher Scientific; EN0531) and Proteinase K (Sigma-Aldrich; 3115887001), together with immunoprecipitated samples and purified by PCR purification columns. DNA amounts were quantified using PicoGreen assay (Thermo Fisher Scientific; P7589). Immunoprecipitated DNA (10 ng) and input from each sample were used to prepare libraries using the KAPA HyperPrep Kit (Roche; KK8500), according to the manufacturer’s instructions. Sample multiplexing was done using combinatorial dual indexes (Illumina) (YCGA). Libraries were size selected to enrich 300–400 bp size fragments and quality controlled and sequenced on a HiSeq 2000 platform (Illumina). Approximately 60 million reads were obtained per sample. Reads were mapped to the chicken genome (Galgal6) using Bowtie 2 v.2.4.2 (Research Resource Identifier (RRID): SCR_016368; http://bowtie-bio.sourceforge.net/bowtie2/index.shtml). Duplicates were removed, and unique reads with mapping quality > 20 were selected using SAMtools v.1.16 (RRID: SCR_003030; https://github.com/samtools/samtools). Peaks were called using MACS2 v.2.2.7.1 (RRID: SCR_008036).

ZBTB18 ChIP–seq in the mouse

Epitope-tagged ChIP was performed to identify ZBTB18 binding sites. Cortices from PD 0 were isolated and subjected to enzymatic dissociation. Cells from 15 cortices were pooled and treated as one sample. Cells were washed and transfected with HA-tagged Zbtb18 using Amaxa Basic Nucleofector Kit (Lonza Bioscience; VPI-1003) for primary mammalian neurons following the manufacturer’s instructions. After 48 h of culture, the medium was removed, and the cells were crosslinked with a formaldehyde solution at a final concentration of 1% for 10 min at room temperature. L-Glycine (AmericanBio; 56-40-6) was added at a final concentration of 125 mM and incubated for 5 min at room temperature to quench the crosslinking. The cells were scraped and collected in a 50-ml conical tube and lysed in the hypotonic solution (50 mM Tris–HCl (pH 7.5), 0.5% NP-40, 0.25% sodium deoxycholate, 0.1% SDS and 150 mM NaCl) on ice for 10 min to obtain the nuclei. The nuclei were centrifuged at 600g for 5 min at 4 °C, and pellets were resuspended in the SDS lysis buffer (1% SDS, 10 mM EDTA and 50 mM Tris–HCl (pH 8.1)) before being sheared into 200–500 bp size fragments using a sonicator (M220 Focused-ultrasonicator; Covaris). The sheared DNA was diluted with the ChIP dilution buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris–HCl (pH 8.1) and 167 mM NaCl) and pre-cleared with Protein A/G Magnetic BEads (Thermo Fisher Scientific) for 1 h at 4 °C. For epitope-tagged ChIP, 5 μg of HA antibody (MilliporeSigma; 11867423001) was used. Samples were incubated on constant rotation for overnight at 4 °C. Magnetic Protein A/G Beads (MilliporeSigma; 88803) were blocked with 1 mg ml−1 BSA (Sigma-Aldrich; A2153), and transfer RNAs were added to the chromatin–antibody complexes for 4 h at 4 °C. The beads were washed with low salt (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris–HCl (pH 8.1) and 150 mM NaCl), high salt (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris–HCl (pH 8.1) and 500 mM NaCl), LiCl (0.25 M LiCl, 1% IGEPAL CA-630, 1% deoxycholic acid (sodium salt), 1 mM EDTA and 10 mM Tris–HCl (pH 8.1)) and 1× Tris–EDTA (AmericanBio) sequentially for 3 min each. ChIP DNA was incubated overnight at 65 °C for reverse crosslinking, treated with RNase A (37 °C for 1 h) and Proteinase K (55 °C for 2 h) and then purified using PCR purification columns. For input control, 5 μg of crosslinked chromatin from each sample was also treated by reverse crosslinking, RNase A (Thermo Fisher Scientific) and Proteinase K (MilliporeSigma), together with immunoprecipitated samples, and purified using PCR purification columns. DNA amounts were quantified by PicoGreen assay (Thermo Fisher Scientific; P7589). Immunoprecipitated DNA (10 ng) and input from each sample were used to prepare libraries using KAPA HyperPrep Kit (Roche; KK8500) following the manufacturer’s instructions and multiplexing using combinatorial dual indexes (Illumina). The libraries were size-selected to enrich 300–400 bp size fragments, quality controlled and sequenced on the HiSeq 2000 platform (Illumina) (YCGA). Approximately 60 million reads were obtained per sample. The reads were mapped to the mouse genome (mm10) using Bowtie 2 v.2.4.2. Duplicates were removed, and unique reads with mapping quality > 20 were selected using SAMtools (v.1.16). We explored different options to determine putative ZBTB18 peaks in terms of replicates and parameters, including using TRANSFAC[65], PROMO[66], MatInspector[67] and JASPAR databases[68]. Hence, peaks were called using four different approaches: (1) MACS2 (ref. [63]) with default parameters, requiring the peak to be present in at least two biological replicates; (2) MACS2 with the ‘--nomodel’ and ‘--nolambda’ parameters, requiring consistency across replicates; (3) MACS2 with default parameters but only requiring the peak to be present in replicate 2; and (4) MACS3 with the peaks of replicate 2.

TF binding site prediction of candidate CREs

We obtained the DNA sequence for all 62,448 H3K27ac peaks using the twoBitToFa[69] tool (https://genome.ucsc.edu/goldenPath/help/twoBit.html). We then ran the tool Find Individual Motif Occurrences (https://meme-suite.org/meme/doc/fimo.html) with default parameters to predict TFBSs in those sequences[70]. We used the JASPAR 2016 CORE dataset[71] in MEME format containing 635 motifs. For the ET neuron-specific TFBS enrichment analysis, we calculated the number of bases present in the binding sites for a particular TF but not in the binding sites within IT neuron-associated peaks and compared them with the same numbers in ET neuron-associated peaks through Fisher’s exact test. Motifs with an FDR-corrected P value < 0.05 were considered significantly enriched.

For the six key CREs (Cux2-E1, Cux2-E4, Satb2-E1, Robo1-E1, Bcl11b-P1 and Zbtb18-P1), we further used the regular expression (Regex) method with the ZBTB18 consensus motif sequence 5′-CAGATGT-3′, allowing 1-nt mismatch to scan for any non-canonical or low-affinity motifs. Next, we used PROMO[66] to test whether there are more sites in all six CREs. Finally, we reanalysed public ZBTB18 ChIP–seq data from HEK293 cells[72] to derive an empirical motif using MEME–ChIP, obtaining the top motif significantly enriched in centrality through CentriMo, and searched for the top motif (MEME–TOP) in all six CREs.

Generation of enhancer reporter transgenic mice

Putative enhancers (Cux2-E1, Cux2-E2 and Cux2-E3) were amplified from mouse genomic DNA, cloned into pBgn–Gfp vector (Sestan laboratory) and sequence verified by Sanger sequencing (YCGA). The enhancers were placed upstream of the human BGN minimal promoter[73] to drive GFP expression. The primers and oligonucleotides used for cloning DNA constructs are shown in Supplementary Table 15. Enhancer-inserted plasmids were linearized using appropriate restriction enzymes, size-selected by gel electrophoresis and then purified by phenol/chloroform extraction. A final concentration of 2.5 ng μl−1 of DNA was used for pronuclear injection. At PD 0, pups were examined for GFP expression under a fluorescence microscope, and tail samples were collected for genotyping to confirm the presence of Gfp loci. For GFP+ founders, brains were collected and proceeded with immunohistological analysis to examine the GFP expression pattern. Three to seven founders from each transgenic line with stable GFP expression patterns were acquired and analysed.

Quantitative RT–PCR

Total RNA was extracted using TRIzol reagent (Invitrogen; 15596018) from freshly isolated neocortical tissue from PCD 15.5 and PD 0 wild-type, Neurod6–Cre; Zbtb18 fl/fl cKO and Neurod6–Cre; Zbtb18fl/+ (control) mice and subjected to DNase I (Invitrogen; AM1907) treatment as previously described. Complementary DNA (cDNA) was synthesized using reverse transcriptase (Invitrogen; 18080093) following the manufacturer’s instructions, and quantitative PCR was performed in triplicate for each sample using an RT–PCR machine (iQ5 system; Bio-Rad) with primer sets spanning exon junctions of the targeted transcripts. Identical or near-identical-sized transcript fragments from the mouse Tbp gene were used as an internal control, and the expression level of each gene was normalized to wild type for relative fold changes. Sequences of primers used are provided in the Supplementary Table 15. Unpaired two-tailed t-test was used to detect differences between samples.

Single-cell RNA-seq analysis across species

To assess the expression patterns of Zbtb18 across different species, we reanalysed public single-nucleus transcriptome datasets for amygdala in turtle (Trachemys scripta elegans), lizard (Pogona vitticeps), chicken (Gallus gallus) and mouse (Mus musculus). We checked the expression level of Cux2, Satb2, Robo1, Bcl11b, Ccdc1167, Rpl38-ps1, Arhgef16, n-R5s136, 3830408C21Rik, Gm24089 and Gm42489 across major cell types, as identified by the original studies12,19,20,21. For cross-species comparison, we included only the excitatory neurons.

Postmortem human tissue

De-identified postmortem human brain tissue was acquired from the Sestan laboratory collection or the NIH NeuroBioBank (https://neurobiobank.nih.gov), as previously published[30]. All tissue was collected with informed consent from parents or next of kin and with approval of all relevant review boards or committees of the Yale University School of Medicine and the NIH. Similarly, tissue was handled in accordance with the appropriate constraints, regulations and ethical guidelines for the research use of human brain tissue set forth by the NIH (http://bioethics.od.nih.gov/humantissue.html) and the World Medical Association Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/). The samples used in this study were analysed without regard to gender, and the influence of sex-specific characteristics of male versus female samples was not considered in this study.

The samples were fixed in 4% paraformaldehyde (PFA) (Electron Microscopy Sciences) for 2 days at 4 °C. Tissue sections were mounted and dried overnight. Antigen retrieval was performed using R-Buffer A pH 6.0 (Electron Microscopy Sciences) and the bench-top Antigen Retriever device (Electron Microscopy Sciences) according to the manufacturer’s instructions. Sections were washed three times in PBS, each for 15 min, and incubated in blocking solution containing 5% (v/v) normal donkey serum (Jackson ImmunoResearch Laboratories), 1% (w/v) BSA and 0.4% (v/v) Triton X-100 (Sigma-Aldrich) in PBS for 1 h at room temperature. Primary antibodies were diluted in blocking solution as follows: ZBTB18 (Proteintech; 12714-1-AP), 1:1,000; BCL11B (Abcam; ab18465), 1:500; and SATB2 (Genway Biotech; GWB-9F2D9F), 1:200. Tissue sections were incubated with these antibodies for two nights at 4 °C. Sections were washed three times with PBS, each for 15 min, before being incubated with the appropriate fluorescent secondary antibodies (Jackson ImmunoResearch Laboratories) for 1.5 h at room temperature. All secondary antibodies were raised in donkey and diluted at 1:250 in blocking solution. Finally, sections were washed with PBS with 0.3% Triton X-100, treated with the Autofluorescence Eliminator Reagent (MilliporeSigma; 2160) according to the manufacturer’s instructions and coverslipped with aqueous mounting medium (VECTASHIELD; Vector Laboratories). These sections were digitized using confocal microscope (ZEISS; LSM 510 Meta), and images were assembled in ZEISS ZEN, ImageJ, Adobe Photoshop and Adobe Illustrator.

Cell line and transfection

Neuro-2a cells (American Type Culture Collection; CCL-131) were maintained in Dulbecco’s modified Eagle’s medium (Gibco) supplied with 10% fetal bovine serum (Gibco), L-Glutamine (Thermo Fisher Scientific), penicillin (Gibco) and streptomycin (Gibco). The cell line was authenticated by morphology or genotyping, and no commonly misidentified lines were used. All lines tested negative for mycoplasma contamination, checked monthly using the MycoAlert Mycoplasma Detection Kit (Lonza). The cells were passaged every 2–3 days upon reaching 80% confluency. For transfection experiments, the Neuro-2a cells were seeded at an appropriate density the day before transfection; when the cells reached 60–70% confluency, transfection was performed 20–24 h later, with Lipofectamine 2000 (Thermo Fisher Scientific; 11668027) mixed with appropriate vectors at a DNA:lipid ratio of 1:3. Either 24 h or 48 h after transfection, the cells were dissociated with 0.25% trypsin (Gibco), washed with PBS and processed for corresponding assays.

In utero electroporation

IUE was performed on PCD 14.5 and PCD 15.5 timed-pregnancy embryos (n = 3–6 for each condition), and littermates were used as controls. A 0.5-μl DNA preparation (4 μg μl−1 of DNA mixed with 0.05% Fast Green FCF Dye (Sigma-Aldrich) was injected into the lateral ventricle of the embryos and electroporated using a square-wave pulse electroporator (Harvard Apparatus; BTX) at 35–38 V, five pulses, 50 ms ON and 950 ms OFF to deliver DNA constructs to the ventricular zone. At PD 0, pups were screened for GFP or RFP expression under a fluorescence-attached dissection microscope. Pups with the fluorescence signal were euthanized, and brains were analysed as previously described.

Neuronal tracing and imaging

Neonatal mice injections were performed with a motorized microinjector (RWD Life Science; MM-500 and R480). In brief, mice at PD 3 were anaesthetized through hypothermia, and approximately 50 nl of retrograde AAV viral particles carrying pCAG–Gfp cassette (Addgene; 37825-AAVrg; titre ≥ 7 × 10^12 vg ml−1) was injected into the medial prefrontal cortex visually with the guide of a stereo binocular microscope. At PD 7, the injected mice that were properly targeted were perfused with ice-cold PBS, followed by 4% PFA (Electron Microscopy Sciences). The collected brains underwent postfixation at 4 °C overnight and washed with PBS before cutting coronally at 60-μm thickness. The sections were stained with anti-GFP antibody (Abcam; ab13970) before imaging. Images were taken using an automated slide scanner (Olympus; VS200) with ×20 objective. For analysis, the images from wild-type mice were aligned with the Allen Brain Atlas coordinates at PD 6, and the Neurod6–Cre Zbtb18 cKO was approximated with the wild-type counterpart. The cell numbers from each bin were counted using the ImageJ tool (RRID: SCR_003070). The total cell number from the neocortex, as shown in Fig. 3c from three mice of each genotype, was compared. For the percentage of distribution, we divided the cell number from each bin by the total number of cells in the neocortex, and comparisons between each bin were made with an unpaired t-test.

BrdU/CIdU/IdU birth-date labelling

Either 5′-bromo-2′-deoxyuridine (BrdU) (Sigma-Aldrich; B5002) or CIdU (Sigma-Aldrich; C6891) was dissolved in distilled water at 100 mg ml−1, and IdU (Sigma-Aldrich; I7125) was dissolved in distilled water at 50 mg ml−1 and stored at −20 °C in the dark. At the proscribed ages, a single dose of IdU, or CIdU was redissolved by intermittent vortexing and given to the timed-pregnant mice by intraperitoneal injection at a dosage of 1 mg (20 g)−1 body weight (10 μl of CIdU or 20 μl of IdU). Pups were euthanized within the PD 1, and brains were fixed in 4% PFA (Electron Microscopy Sciences) for 12 h at 4 °C. Heterozygous littermates were used as controls. The brains were embedded with 4% agarose and sectioned at 50-μm thickness on a vibratome (Leica; VT1000S). Sections were treated with 2 M HCl at room temperature for 30 min and co-immunostained with BrdU (Sigma-Aldrich) and other markers for immunofluorescence.

Recombinant DNA

DNA constructs used for making transgenic mice were subcloned from the pBgn–GFP (Sestan laboratory) backbone with Cux2-E1, Cux2-E2 and Cux2-E3 inserted. Vectors for overexpression experiments were subcloned from the pCAGIG backbone (Addgene; 11159), with inserts of the full coding sequences of the following genes: mouse Zbtb18 (also known as Rp58, Znf238 and Zfp238), BC054529; mouse Pou3f2, NM_008899; mouse Trim28 (also known as Rnf96), BC058391; mouse Lhx2, BC055741; mouse Satb2, BC138626; mouse Hdac2, BC138517; mouse E2F1, BC052160; mouse Sin3A, BC052716; mouse Foxn2, NM_001355743; mouse Coup-tf1 (also known as Nr2f1), BC108408; and mouse Robo1, NM_019413.2. Coding sequences in DNA constructs used for IUE experiments were cloned into an expression construct with a Neurod1 promoter[74] and verified by Sanger sequencing (YCGA). Other DNA constructs used in this study included pCAG–Cre, pCALNL–GFP, pCAGIG, pCALSL–RFP, pNeurod1–GFP and subcloned plasmids. The plasmids pCAG–Cre, pCALNL–GFP and pCAGIG were gifts from C. Cepko (Addgene; 11159, 13770 and 13775, respectively), whereas pCALSL–RFP and pNeurod1–GFP have been previously described74,75. Vectors carrying enhancers for luciferase assay were subcloned from pGL4.24 backbone (Promega). For enhancer mutagenesis, desired sites within specific enhancers were mutated using a site-directed mutagenesis kit (New England Biolabs; Q5 Site-Directed Mutagenesis Kit) following the manufacturer’s instructions. The mutated products were validated by Sanger sequencing. Oligos and primers used for cloning are listed in Supplementary Table 15. For neurite outgrowth analysis in chicken primary neurons, pCagen (empty vector; a gift from C. Cepko; Addgene; 11160), pCag–mCherry (a gift from Phil Sharp laboratory, through Addgene; 41583) and pCag–Cux2 (mouse Cux2 expression construct, cloned in pCAG backbone using NsiI and NotI restriction enzymes, with Cux2 open reading frame subcloned from MGC clone ID 30532644) were used.

Cux2-E1 enhancer activity assay and Cux2 overexpression in chicken hyperpallial neurons

Primary hyperpallial cells were isolated from chick embryos at embryonic days 7 and 17. After carefully opening the eggshells, the embryos were collected and immediately transferred into ice-cold artificial cerebrospinal fluid supplemented with the following components: 92 mM N-methyl-D-glucamine (Sigma-Aldrich; M2004), 20 mM HEPES (Sigma-Aldrich; H3375), 5.5 mM glucose (Sigma-Aldrich; G7021), 30 mM sodium bicarbonate (Sigma-Aldrich; S5761), 5 mM sodium L-ascorbate (Sigma-Aldrich; A4034), 2.5 mM potassium chloride (Sigma-Aldrich; P9541), 1.25 mM sodium phosphate (Sigma-Aldrich; S0751), 2 mM thiourea (Sigma-Aldrich; T7875), 3 mM 196 sodium pyruvate (Sigma-Aldrich; P2256), 5.5 mM urea (Sigma-Aldrich; U5128), 10 mM magnesium sulfate (Sigma-Aldrich; M7506) and 0.5 mM calcium chloride (Sigma-Aldrich; 21115). Following dissection, tissues were enzymatically dissociated by incubation in 2 mg ml−1 of papain (Transnetyx; PAP) for 20–30 min at 37 °C. After enzymatic digestion, 0.1 mg ml−1 of DNase I (STEMCELL Technologies; 07900) was added, and the tissue was gently triturated using a 1-ml glass pipette to obtain a single-cell suspension. Dissociated cells were transfected with various plasmids using the Chicken Neuron Nucleofector Kit (Lonza; VPG-1002) according to the manufacturer’s instructions. A total of 4 µg of each plasmid was used in all experiments. Post-transfection cells were seeded at a density of 4 × 10^5 cells per well in 24-well plates (ibidi; 82406) precoated with poly-D-lysine (Gibco; A3890401) and laminin (Gibco; 23017015). Cultures were maintained in neurobasal medium (Gibco; 21103049) supplemented with 1× B27 (Gibco; 17504044), 1% GlutaMAX (Gibco; 35050061), 30 nM sodium selenite (Sigma-Aldrich; S5261), 5 µg ml−1 of human insulin (Sigma-Aldrich; I9278), 1% penicillin–streptomycin (Gibco; 15140122), 10 ng ml−1 of brain-derived neurotrophic factor (PeproTech; 450-02) and 10 ng ml−1 of NT-3 (PeproTech; 450-03). Media were changed every 2 days. After 48 h, primary chicken neuronal cultures were fixed with 4% PFA for 10 min, followed by three washes with PBS. Cells were permeabilized with 0.3% Triton X-100 (Sigma-Aldrich; T8787) in PBS for 10 min at room temperature and blocked with 10% (v/v) normal donkey serum (Jackson ImmunoResearch Laboratoires; 017-000-121) in PBS for 1 h at room temperature. The primary antibodies were diluted in the same blocking solution and incubated with the cells overnight at 4 °C. The primary antibodies used were anti-SATB2 (1:100; Abcam), anti-ZBTB18 (1:250; Proteintech), anti-GFP (1:2,000; Abcam) and anti-mCherry (1:1,000; Aves Labs). After three washes with PBS, cells were incubated with the respective secondary antibodies prepared in blocking solution for 2 h at room temperature. Nuclei were stained with DAPI (1 μg ml−1; Sigma-Aldrich; D9542) for 5 min at room temperature. Fluorescent images were acquired using a confocal microscope (LSM880; Zeiss). Post hoc, the GFP intensity was quantified using the ImageJ tool (RRID: SCR_003070), and the neurite length was traced following mCherry signal using the NeuronJ plugin of the ImageJ tool. Because deletion constructs used as controls may alter the local DNA context and possibly disrupt cryptic overlapping motifs, we further performed in silico analysis of the 30-bp DNA sequencing flanking the deleted region and found no high-affinity binding motifs for other relevant TFs.

Luciferase assay

Neuro-2a (American Type Culture Collection; CCL-131) cells were plated into 96-well or 24-well plates at a density of 10,000 or 50,000 cells per well, respectively. Sixteen hours after plating, cells were transfected with a DNA mixture consisting of 100 ng and 500 ng overexpression plasmids, 30 ng and 500 ng of firefly luciferase (pGL4.24; Promega) plasmids containing candidate enhancers, together with 20 ng and 25 ng pRL-SV40 plasmid (Promega) as a control for 96-well and 24-well plates, respectively, using Lipofectamine 2000 (Thermo Fisher Scientific). Transfected cells were lysed and assayed 24–48 h after transfection using the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer’s instructions. Relative luciferase activity was calculated by normalizing the firefly luciferase luminescence to the Renilla luciferase luminescence value. Luciferase activity was measured and quantified by GloMax-Multi Detection System (Promega). Primers used for amplifying the candidate enhancer and promoter regions are shown in Supplementary Table 15. Experiments were performed in triplicate or quadruplicate, and the data were normalized to the empty vector control (pCAGEN versus the pZBTB18). P values were calculated using unpaired two-tailed t-tests to compare the overexpression and control. To perform the luciferase assay on multiple mutated enhancers, a one-way ANOVA test was used to detect differences among more than two enhancers; alpha value = 0.05.

In situ hybridization for the human, mouse and chicken brain sections

RNA probes for mouse and human were generated using the respective neocortical tissue cDNA as template (mouse Zbtb18, ENSMUSG00000063659; mouse Cux2, ENSMUSG00000042589; mouse Satb2, ENSMUSG00000038331; mouse Robo1, ENSMUSG00000022883; mouse Robo2, ENSMUSG00000052516; mouse Robo3, ENSMUSG00000032128; mouse Rorb, ENSMUSG00000036192; mouse Dcc, ENSMUSG00000060534; human ZBTB18, ENSG00000179456; and human CUX2, ENSG00000111249) by TA Cloning Kit (Invitrogen) and followed by in vitro transcription (Roche) according to the manufacturer’s instructions. For chicken probes, the cDNA fragments coding for SATB2 (NM_001199110) and CUX2 (XM_415167), were amplified by RT–PCR and subcloned into the plasmid vector pTA2 (Toyobo). Templates purified by phenol/chloroform extraction and digoxigenin (DIG)-labelled probes were synthetized using T3 (Roche) and T7 RNA polymerases (Roche), respectively. RNA labelling was performed mix (Roche) according to the manufacturer’s instructions. Probes were purified by phenol/chloroform extraction, quantified, quality controlled and stored at −80 °C until hybridization. Primers used for generating probes are provided in Supplementary Table 15.

For single-colour ISH, free-floating or slide-mounted cryosections at 20–30 μm thickness were processed according to a previously described protocol[76]. In brief, brains were fixed overnight at 4 °C in 4% PFA (Electron Microscopy Sciences) diluted in Dulbecco’s phosphate-buffered saline (DPBS) (Thermo Fisher Scientific), equilibrated for 12 h at 4 °C in 10% sucrose and another 12 h at 4 °C in 30% sucrose (AmericanBio) in DPBS (Thermo Fisher Scientific). Fixed brains were then embedded in O.C.T. (Scigen) and sliced on a cryostat (Leica Biosystems; CM1800). Slides were stored at −80 °C until processed for ISH. Sections were first postfixed in 4% PFA (Electron Microscopy Sciences) in PBS for 15 min at room temperature, washed with PBS and submerged in hybridization buffer (5× saline–sodium citrate, 50% formamide, 5× Denhardt’s solution, 500 µg ml−1 of salmon sperm DNA and 250 µg ml−1 of torula yeast RNA) supplemented with 1,000 ng ml−1 of the appropriate DIG-labelled probe at 70 °C overnight. Sections were washed twice for 60 min at 70 °C in 2× saline–sodium citrate, 50% formamide and 0.1% Tween, followed by washing with 100 mM Tris–HCl (pH 7.5), 150 mM NaCl and 0.1% Tween. These sections were then blocked with 10% sheep inactivated serum (Sigma-Aldrich) and incubated overnight at 4 °C with an anti-DIG antibody conjugated to alkaline phosphatase (1:2,000; Roche). The sections were then rinsed in the substrate buffer (100 mM Tris–HCl (pH 9.5), 100 mM NaCl, 50 mM MgCl_2 and 0.1% Tween-20) before being overlaid with NBT–BCIP substrate (Roche). Revelation was done at room temperature in the dark until the desired signal was reached. Finally, the sections were rinsed in DPBS (Thermo Fisher Scientific), postfixed with 4% PFA (Electron Microscopy Sciences) in DPBS (Thermo Fisher Scientific), washed with water and mounted with Permount mounting medium (Electron Microscopy Sciences).

For two-colour ISH, probes were synthesized by either DIG-labelled (Roche) or fluorescein-labelled (Roche) RNA labelling mixes in vitro transcription (Roche). Free-floating or slide-mounted cryosections (20–30 μm thickness) were postfixed in 4% PFA (Electron Microscopy Sciences) for 15 min, washed and hybridized overnight at 70 °C in 14-ml hybridization solution with 500 ng ml−1 of DIG-labelled (Roche) human ZBTB18 probe and 500 ng ml−1 of fluorescein-labelled (Roche) human CUX2 probe for human tissue. For mouse tissue, 500 ng ml−1 of DIG-labelled mouse Zbtb18 and 500 ng μl−1 of fluorescein-labelled mouse Cux2 probes were used. The signals were sequentially detected with an alkaline phosphatase-conjugated anti-DIG antibody (MilliporeSigma), NBT–BCIP substrate (Roche) and horseradish peroxidase-conjugated anti-fluorescein antibody. Horseradish peroxidase activity was initially amplified using TSA (Akoya) and subsequently strengthened with 3,3′-diaminobenzidine tetrahydrochloride hydrate (DAB) (Sigma-Aldrich).

Immunostaining and immunoblotting

Brains dissected from embryonic and neonatal mice were fixed by immersion in 4% PFA (Electron Microscopy Sciences) overnight at 4 °C. Adult brains were perfused with 10 ml PBS followed by 10 ml of 4% PFA (Electron Microscopy Sciences), isolated and postfixed by immersion in 4% PFA (Electron Microscopy Sciences) overnight at 4 °C. The brains were then sectioned at a thickness of 50 μm (postnatal) and 80 μm (embryonic) on a vibratome (Leica; VT1000S). The brain sections were blocked using blocking solution (5% BSA, 10% donkey serum and 0.3% Triton X-100 in 1× PBS solution) for 1 h at room temperature and incubated with appropriate primary antibodies for 12–24 h at 4 °C, washed three times with 1× PBS and incubated with appropriate secondary antibodies for 1 h at room temperature. DAPI was used to stain nuclei. The antibody dilutions used for immunostaining were as follows: anti-ZBTB18 (rabbit; 1:1,000; Proteintech), anti-GFP (chicken; 1:3,000; Abcam), anti-RFP (rabbit; 1:1,000; Abcam), anti-CUX1 (rabbit; 1:250; Santa Cruz Biotechnology), anti-CUX2 (rabbit; 1:250; Abcam), anti-SATB2 (mouse; 1:200; Genway), anti-BCL11B (rat; 1:500; Abcam), anti-TBR1 (rabbit; 1:250; Santa Cruz Biotechnology), anti-LHX6 (mouse; 1:300; Santa Cruz Biotechnology), anti-LHX6 (goat; 1:250; Santa Cruz Biotechnology), anti-RELN (mouse; 1:300; Millipore), anti-BrDU (mouse; 1:250; BD Biosciences) and anti-BrDU (rat; 1:250; Accurate Chemical). For the quantitative analysis of total number of BCL11B+ and SATB2+ cells, z-stack images from three per species were used to segment DAPI signals from individual nuclei, and fluorescence signals from the nuclei were obtained using Volocity (v.6.3.1). The positive signals for each marker protein were defined on the basis of the median nuclear intensity from all cells analysed using the Spotfire software (v.11.2.0).

Immunoblot analyses were performed using a previously described protocol[77]. In brief, neocortical tissue from PD 0 Zbtb18fl/fl; Emx1–Cre mice (n = 3), Zbtb18fl/+; Emx1–Cre mice (n = 3) and wild-type mice (n = 3) were isolated and snap-frozen in liquid nitrogen. The samples were then minced and mixed with lysis buffer (150 mM NaCl, 1.0% NP-40 and 50 mM Tris–HCl (pH 8.0)), including protease inhibitors (Roche). Equal amounts of protein from each sample were loaded for SDS–PAGE, followed by western blot analysis with signals detected using the enhanced chemiluminescence detection reagent. Antibody dilutions used for immunoblot were as follows: anti-GAPDH (rabbit; 1:5,000; Abcam; ab9485), anti-CUX2 (rabbit; 1:1,000; Abcam), anti-ZBTB18 (rabbit; 1:2,000; Proteintech) and anti-ZBTB18 (goat; 1:2,000; Santa Cruz Biotechnology). For detailed antibody information, refer to Supplementary Table 14.

Obtaining multispecies alignments of TF binding sites

Beginning with the lists of putative TFBSs identified by Find Individual Motif Occurrences, as described earlier[70], we selected only those motifs that are recognized by TFs expressed (reads per kilobase of transcript per million mapped reads ≥ 1) in our RNA-seq samples derived from either IT or ET neurons for further analysis. This produced a list of 267 motifs. The total number of binding sites for those motifs ranged from 0 to 244,332.

For each of the 267 motifs, we obtained the multiple-species sequence alignment of 60 vertebrate species from the MULTIZ60 (ref. [78]) track in the University of California, Santa Cruz (UCSC) browser using mafsInRegion[69] from the UCSC tools. We stitched maf alignments and converted them to fasta using the script maf_to_concat_fasta.py from bx-python (https://github.com/bxlab/bx-python/blob/main/scripts/maf_to_concat_fasta.py).

Conservation of TF binding sites across species

We used an in-house script to analyse the multiple sequence alignment of each binding site. First, we calculated the information content for each position in each MEME motif using the R package TFBSTools[79]. For each motif, we identified positions with high information content (0.75 or higher) and considered them ‘core’ positions. Second, we divided the species in the 60-way multiple sequence alignment into four groups: ‘placental mammals’, ‘marsupials’, ‘monotremes’ and ‘non-mammals’. Finally, for each binding site, we counted the number of species in each group in which the motif was present without any single nucleotide substitutions in any of the core positions compared with the mouse reference sequence mm10. We repeated the analysis with one, two and three mutations. A motif was not considered conserved if it contained an indel spanning the core positions.

For each group of species, we produced a binary vector in which, for each binding site, a value of 1 was assigned if the binding site was conserved and 0 if it was non-conserved. For placental mammals, we required at least six species fulfilling the conservation criteria stated above. For marsupials, monotremes and non-mammalian species, we required at least one species.

Enrichment in TF binding sites in sets of H3K27ac peaks

We selected seven sets of ChIP–seq peaks: peaks annotated to differentially expressed genes between IT and ET neurons (DESeq2 PAdj < 0.05; one set for upregulated genes and another for downregulated genes), peaks differentially enriched between IT and ET neurons (as determined by DESeq2 with FC > 2 and PAdj < 0.1; one set for IT neuron-biased peaks and another for ET neuron-biased peaks), peaks annotated to genes differentially expressed between the Zbtb18 knockout (KO) or Zbtb18 heterozygous mice (DESeq2 PAdj < 0.05; one set for KO and another for heterozygous-biased genes) and a final set composed by a manually curated list of genes important for corpus callosum formation extracted from the literature. In addition, we produced a list of background peaks composed of all H3K27ac peaks, which did not fall in any of the previous categories. We then grouped these CREs into four categories to evaluate the enrichment of TFBSs across species: (1) H3K27ac peaks associated with ET neuron-biased genes (n = 1,433), which include peaks annotated to ET neuron-enriched genes and peaks enriched in ET neurons; (2) peaks associated with IT neuron-biased genes (n = 1,409), which include peaks annotated to IT neuron-enriched genes and peaks enriched in IT neurons; (3) peaks associated with CREs near genes with expression that was downregulated in Zbtb18 KO (n = 8,187); and (4) peaks associated with CREs near genes with expression that was upregulated in Zbtb18 KO (n = 3,705). For associating peaks with genes in the identification of putative CREs, we associated peaks with the nearest genes.

For each group of species, we counted the number of conserved and non-conserved binding sites observed in each set of peaks. We compared the proportion of conserved versus non-conserved binding sites to the same ratio in the background peaks using Fisher’s exact test. Motifs with FDR-adjusted P values < 0.05 were considered significant.

The number of motifs tested in each category of enhancers, including those selected as background supplementary, is shown in Supplementary Table 17. To evaluate the robustness of these enrichments, we devised a bootstrap-based strategy (Extended Data Fig. 10d). For each comparison, we counted ZBTB18 motifs within each category and performed 1,000 random samplings, with replacement, of an equal number of ZBTB18 motifs from background H3K27ac regions (considered invariant putative regulatory elements). We then compared the observed ratios of conserved and non-conserved motifs at each phylogenetic depth to the distribution of 1,000 such ratios obtained from the background peaks. The percentile occupied by the observed ratio represents an empirical P value, indicating how likely it is to obtain this ratio by chance among the invariant peaks.

Conservation of H3K27ac and ZBTB18 peaks

We used mafsInRegion[69] from the UCSC tools to obtain MULTIZ60 alignments for each selected enhancer. We stitched maf alignments and converted them to fasta using the script maf_to_concat_fasta.py from bx-python. The pairwise alignment distance between species of each of the selected H3K27ac peaks was obtained using the function dist.alignment from the seqinr package in R.

The intersection of chicken and mouse H3K27ac and ZBTB18 ChIP–seq peaks was conducted using the ‘IntersectBed’ function within bedtools. To facilitate this intersection, chicken peaks were initially converted into mouse coordinates (from gg6 to mm10) using LiftOver with -minMatch = 0.1. Orthologous regions in chicken of all ZBTB18 ChIP–seq peaks identified in mice were determined using LiftOver from mm10 to gg6 using the same minMatch value.

Evolutionary age of the ZBTB18 gene

To evaluate the evolutionary age of the ZBTB18 gene, we used protein sequence similarity (BLASTP[80] and HMMER[81]) to search our reference database, which is adapted from UniProt[82] and trimmed with a taxonomically informed procedure that optimizes database size while ensuring that species with well-resolved genomes are kept in all major branches of the phylogenetic tree of life[83]. We partitioned the similarity hits according to their evolutionary distance from humans and the species in which they appear. Here the evolutionary age of a gene is defined as the taxonomic restriction of the most ancient fragment of the protein encoded by the gene83,84,85,86. If an ancestral gene duplicated and expanded, all descendant genes are considered of the same evolutionary gene as the ancestral gene[86]. For any protein of at least 40 amino acids, the taxonomic restriction is indicated as the index of the NCBI taxonomic node[87] at which the query species and subject species diverged. The evolutionary age of each taxonomic (phylogenetic) node is provided by the TimeTree database[88]. Thus, a human protein also present in bacteria is ancient; a human protein also present in sea squirts is restricted to the Chordata phylum; a human protein also present in platypus is restricted to the Mammalia class; a human protein present also in the Coquerel’s sifaka lemur is restricted to the Primates order; a human protein present only in humans is restricted to the human species. First, we found that few bacterial species in our database harbour proteins similar to the human ZBTB18 protein (four species with hits in 1,600 species of our database; 0.025%). Of the four bacterial proteins similar to ZBTB18, three of them have two or four zinc finger domains, whereas the remaining protein has a BTB domain. Most eukaryotic species harbour proteins similar to the human ZBTB18 (67 of 100 eukaryotic species; 67%). The three eukaryotic proteins most similar to ZBTB18 have two or four zinc finger domains. Second, we found that in all subsequent evolutionary nodes of the human lineage, all outgroup species for each node harbour proteins similar to ZBTB18. Thus, ZBTB18 is an ancient gene that probably arose at the origin of eukaryotes. The few bacterial hits may indicate an even more ancient origin in all cellular organisms. The presence of genes similar to ZBTB18 in only 0.025% of bacterial species is consistent either with an extremely ancient origin in all cellular organisms followed by many events of gene loss in most bacterial species, or with two to four events of horizontal gene transfer from eukaryotes to a few bacterial species. Although both explanations are possible, the second requires fewer events. In any case, ZBTB18 is an ancient gene that arose in eukaryotes approximately two billion years ago or even earlier at the origin of cellular organisms approximately 3.6 billion years ago.

Cell counting and data analysis

For each cell counting analysis, neocortical somatosensory regions from three different brains of each condition were used. For PD 0 mouse and late to midfetal human brain tissues, 200-μm-wide neocortical columns covering from layer 1 to subplate were used as standard fields to conduct counting. For PCD 14.5 and PCD 16.5 mouse tissues, 200-μm-wide neocortical columns covering from layer 1 to ventricular zone were used as standard fields. Unpaired two-tailed t-tests were used to detect differences between samples. P < 0.05 (α = 0.05) was set as the cutoff for significance.

Replicates

For ChIP–seq, two replicates were used for each condition. For RNA-seq, a minimum of three replicates were used for each condition. For ChIP–quantitative polymerase chain reaction analysis, a minimum of four replicates were used for each condition. For RT–PCR analysis, three replicates from each time point were used for each condition. For Cux2-E1, Cux2-E2 and Cux2-E3 transgenic mice, three to seven founders from each line were analysed. For IUE experiments, three to six successfully electroporated animals were examined for each analysis. For IDU/CIDU in vivo labelling, a minimum of two litters were used for each experiment, and three to five brains from each litter were examined. For ISH and immunofluorescence experiments using mouse tissue, a minimum of three animals were used for each experiment, and between 3 and 20 sections from each animal were examined. For ISH and immunofluorescence experiments using non-mouse mammals, two to five sections from two individuals were used. For cell culture experiments, cells were isolated from three different mouse brains per condition, and two replicates from each brain were used for subsequent observations. For luciferase assays, a minimum of three replicates were used for each transfection and analysis. Statistical methods were not used to determine sample size. For all experiments, n = 3 or >3 was used.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data were deposited into the NCBI Sequence Read Archive under BioProject accession number PRJNA1391524.

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Acknowledgements

We thank members of the Heng, Richards and Sestan laboratories for their helpful comments. We also thank C. Cepko, E. Grove, F. Polleux and P. Rakic for providing reagents. This study was supported by the Australian Research Council (DP 160103958 to R.S. and L.J.R. and Discovery Early Career Researcher Award Fellowship 160101394 to R.S.), the National Health and Medical Research Council (Principal Research Fellowship to L.J.R. and C.J. Martin Fellowship to P.K.), the Australia Postgraduate Award (to L.R.F.), the UQ-QBI Doctoral Scholarship (to A.P.), the Victorian Government through the Operational Infrastructure Scheme and the National Health and Medical Research Council of Australia (ID 1011505 to J.I.-T.H.), Instituto de Salud Carlos III Spain and European Social Fund grant MS20/00064 (to G.S.), grants PID2019-104700GA-I00 and PID2022-140137NB-100 funded by MICIU/AEI/10.13039/501100011033 and co-funded by FEDER, European Union (to G.S.), Fundació LaMarató de TV3 (to G.S.), NIH grants (MH129981 to K.T.G., NS095654, MH106934, MH116488, MH110926, MH122678, MH124619 and HG010898) and the Simons Foundation 736706 (to N.S.).

Author information

Author notes

1. These authors contributed equally: Zhuo Li, Navjot Kaur, Gabriel Santpere, Sydney K. Muchnik, Suvimal Kumar Sindhu, Cai Qi, Mikihito Shibata

Authors and Affiliations

1. Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA

Zhuo Li, Navjot Kaur, Gabriel Santpere, Sydney K. Muchnik, Suvimal Kumar Sindhu, Cai Qi, Mikihito Shibata, Thomas S. Klarić, Victor Luria, Hyesun Cho, Mingfeng Li, Akemi Shibata, Sang-Hun Choi, Hyojin Kim, Andrew T. N. Tebbenkamp, Shaojie Ma, Wenqi Han, Suel-Kee Kim, Sirisha Pochareddy, Phan Q. Duy, Xiaojun Xing, Yunhua Bao, Xuming Xu, Forrest O. Gulden, Kevin T. Gobeske & Nenad Sestan

2. Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Zhuo Li

3. Neurogenomics Group, Hospital del Mar Research Institute, PRBB, Barcelona, Spain

Gabriel Santpere & Xabier de Martin

4. Department of Genetics, Yale School of Medicine, New Haven, CT, USA

Sydney K. Muchnik & Nenad Sestan

5. Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia

Olivier Clément, Ivan Enghian Gladwyn-Ng, Hayley Daniella Cullen & Julian I.-T. Heng

6. Centre for Medical Research, University of Western Australia, Perth, Western Australia, Australia

Olivier Clément, Ivan Enghian Gladwyn-Ng, Hayley Daniella Cullen & Julian I.-T. Heng

7. Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA

Phan Q. Duy & Nenad Sestan

8. Yale Genome Editing Center, Yale School of Medicine, New Haven, CT, USA

Xiaojun Xing, Yunhua Bao & Nenad Sestan

9. EMBL Australia, Australian Regenerative Medicine Institute, Monash University, Melbourne, Victoria, Australia

Hayley Daniella Cullen

10. School of Biomedical Science, University of Queensland, Brisbane, Queensland, Australia

Annalisa Paolino, Laura R. Fenlon, Peter Kozulin, Rodrigo Suárez & Linda J. Richards

11. Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia

Annalisa Paolino, Laura R. Fenlon, Peter Kozulin & Rodrigo Suárez

12. Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, USA

Ryan D. Risgaard & André M. M. Sousa

13. Waisman Center, University of Wisconsin-Madison, Madison, WI, USA

Ryan D. Risgaard & André M. M. Sousa

14. IT-Research Computing, Harvard Medical School, Boston, MA, USA

Amir Karger

15. Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan

Ikuo K. Suzuki

16. Brain Function Laboratory, National Institute of Genetics, Mishima-shi, Japan

Tatsumi Hirata

17. Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA

Linda J. Richards

18. Curtin Health Innovation Research Institute, Curtin University, Perth, Western Australia, Australia

Julian I.-T. Heng

19. Department of Comparative Medicine, Yale University, New Haven, CT, USA

Nenad Sestan

20. Department of Neurosurgery, Yale University, New Haven, CT, USA

Nenad Sestan

21. Department of Psychiatry, Yale University, New Haven, CT, USA

Nenad Sestan

22. Wu Tsai Institute, Yale University, New Haven, CT, USA

Nenad Sestan

23. Program in Cellular Neuroscience, Neurodegeneration and Repair, Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA

Nenad Sestan

24. Yale Child Study Center, Yale University, New Haven, CT, USA

Nenad Sestan

Contributions

N.S. conceived and designed the study. Z.L., N.K., S.K.S., C.Q. and M.S. performed most of the genetic, molecular and other experiments described in this study. S.K.M., G.S. and X.d.M. performed most of the computational, bioinformatic and related analyses for this study. M.L., X. Xu, P.K., R.S., V.L., A.K., H.C., S.M., S.-K.K. performed further computational analysis. Z.L., O.C., A.S., I.K.S., T.H. and F.O.G. conducted ISH experiments. Z.L., N.K., S.K.S., A.M.M.S., R.D.R., P.Q.D., A.T.N.T., T.S.K., W.H., A.P. and L.R.F. performed analysis of immunohistochemistry or protein content. Z.L., W.H., S.K.S. and Y.B. performed IUE experiments. S.-H.C., H.K. and S.K.S. performed tissue culture experiments for chicken. Z.L., A.T.N.T. and S.P. developed/acquired human tissue resources. Z.L., X. Xing, I.E.G.-N. and H.D.C. developed/acquired mouse model resources. Z.L., S.K.M., G.S., F.O.G. and N.S. prepared and wrote the initial draft of the paper. All authors provided edits and/or comments on subsequent drafts. F.O.G., N.K., A.M.M.S., K.T.G., L.J.R., J.I.-T.H. and N.S. provided supervision and guidance for this study. L.J.R., J.I.-T.H., G.S. and N.S. obtained and provided funding for this study.

Corresponding author

Correspondence to Nenad Sestan.

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Competing interests

The authors declare no competing interests.

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Nature thanks Tomomi Shimogori and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Integration of RNA-Seq and ChIP-Seq to Identify Gene Enrichment and CREs in IT and ET Neurons.

a-b) Venn diagrams illustrating the overlap between genes differentially expressed and genes regulated by differentially enriched H3K27ac peaks between Arpp21- and Fezf2- labeled cells. b) As in A but representing overlap of H3K27ac peaks rather than genes. c) Most genes and H3K27ac peaks were not differentially expressed or enriched, respectively, between IT neurons and ET neurons (left panel). However, certain genes were enriched in either IT neurons or ET neurons and associated with putative CREs enriched or present in that same cell subtype (middle and right panels). Data shown represent ChIP-Seq data collected from 2 independent biological replicates per condition. d) Bar graph showing the top 15 gene ontology terms in the H3K27ac peaks either present in both (grey) or in Arpp21-Gfp + (green) or in Fezf2-Gfp + (red) cells but are absent in the H3K27ac peaks from E 17 chicken. The X-axis depicts the log2 P-value. e) Schematics showing different areas of the chicken brain across the anterior-posterior axis in the coronal plane. Coronal sections of chicken brain showing ZBTB18, Cux2, Satb2, BCL11B, and Fezf2 expression domain ZBTB18 and BCL11B images showing ZBTB18 and BCL11b immunostaining are inverted to enhance visualization. In inset Cux2 in situ expression signal is shown at posterior plane. This demonstrates the Cux2 probe’s sensitivity to chicken Cux2. Scale bar: ZBTB18, Cux2, (500 µm); Satb2, BCL11B, Fezf2 (250 µm). Hp, hippocampus; HA, hyperpallium apicale; M, mesopallium; N, nidopallium; E, entopallium; Str, striatum; D, dorsal; L, lateral.

Extended Data Fig. 2 CREs bound by ZBTB18 at the Genomic Loci of Cux2, Satb2, Bcl11b and Zbtb18 and Luciferase Reporter Assay.

a-d) Line graphs showing the H3K27ac peaks from, Arpp21-Gfp+ cells (line 1 and 2), Fezf2-Gfp+ cells (line 3 and 4), from PCD 14.5, and PD 0 forebrain DNaseI Hypersensitivity tracks (DHS) by Digital DNaseI from ENCODE, (line 5 and 6, respectively) and mammal conservation as reference (line 7), ZBTB18-HA ChIP-seq peaks from mouse (line 8), H3K27ac peaks from fetal (dlPFC (line 9), gene locus in mice (line 10) H3K27ac peaks collapsed together in the Arpp21-Gfp + IT neurons and Fezf2-Gfp + ET neurons (line 11) and H3K27ac peaks from E17 chicken dorsal pallium (12) at the genomic locus of Cux2, Satb2, Bcl11b and Zbtb18. The H3K27ac and ZBTB18- ChIP peaks in mice are shown in light beige. In the chicken genome, the regions orthologous to the ZBTB18-ChIP peaks are shown in a dark beige color, with the dotted blue boxes indicating the exact orthologous regions obtained after liftover.

Extended Data Fig. 3 Assessment of Cux2 Enhancer Activities Through Mouse Transgenic Reporter Assays.

a) Transgenic mouse Cux2-E1-Gfp shows no GFP expression at PCD 12.5, but strong GFP expression at PCD 14.5 GFP in the embryonic mouse forebrain. Scale bar: 1 mm (left), 2 mm (right). b) At PCD 17.5, GFP is expressed in the BCL11B negative cells in upper CP and CC in Cux2-E1-Gfp mice. Scale bar: 1 mm (whole brain), 50 µm (right). c) At PD 0, GFP is mostly expressed in BCL11B negative upper layer neurons and absent from CST as seen at pons in Cux2-E1-Gfp mice. Scale bar: 1 mm (whole brain), 50 µm (right). (d-f). At PD 15, the majority of Cux2-E1-Gfp positive cells reside in upper layers and co-label with CUX1 (closed arrows). Double open arrowheads (CUX1) and triple open arrowheads (BCL11B) again indicate cells immunolabeled for specific molecular markers but not by Cux2-E1-Gfp. CUX1 and BCL11B positive cell populations were compared using an unpaired, two-tailed t-test. The graph represents mean ± s.e.m. n = 1,116 cells from 3 biological replicates * P = 0.000352. Scale bars: 100 µm (e); inset, 50 µm (f) ;100 µm. (g) In the PCD 17.5, Cux2-E3-Gfp transgenic mice brain, GFP is mostly expressed in L1 cell and co-localize with RELN, but not with BCL11B, GFP labeled axons can be seen passing through CC. Scale bar: 100 µm (left, right), 20 µm (middle). (h) In the PCD 17.5 Cux2-E2-Gfp transgenic mice brain, GFP-positive cells are scattered throughout CP and largely co-localize with LHX6; GFP is not expressed in CC. Scale bar: 100 µm (left, right), 20 µm (middle). i) Cux2 overexpression increases neurite length of the SATB2 and ZBTB18 immunopositive developing chicken neurons. The image shows chicken neurons from E7 chicken embryonic forebrain overexpressing Cux2 using pCag-Cux2 and pCaggs-mCherry to label electroporated neurons. Scale bar: 100 µm. n = 43 neurons for control & n = 24 for Cux2 OE. j) The graph represents the length of neurites of the pCag-Cux2 + pCaggs-mCherry neurons that co-express SATB2 and ZBTB18 over controls pCagen + pCaggs-mCherry. Ordinary two-way ANOVA with Bonferroni’s multiple comparisons test, with single pooled variance, was applied. The graph represents mean ± s.e.m. n = 210 from control and 139 from Cux2 overexpressing cells from 3 experimental replicates. **** P = 0.0001. k) Luciferase reporter activity driven by the Satb2 E1 enhancer is significantly increased by ZBTB18 and reduces when the ZBTB18 binding site is mutated (Satb2 ΔE1). The graph represents mean ± s.e.m. Ordinary two-way ANOVA with Bonferroni’s multiple comparisons test, with single pooled variance, was applied. **** P = 0.0001 (Satb2 E1 (Control vs Zbtb18)), (Satb2 ΔE1 (Control vs Zbtb18)), and P = 0.003 for Satb2 E1 vs Satb2 ΔE1 (Zbtb18), n = 3.

Extended Data Fig. 4 ZBTB18 Expression in the Developing Cerebral Cortex of Humans and Mice.

a) Coronal sections showing the immunolabeling for three anti- ZBTB18 antibodies on Neurod6-Cre; Zbtb18 cKO as compared to the control. Arrow points to the expression of ZBTB18 in the ventral region of the cKO where Neurod6-Cre appears not be active. Scale bar: 150 µm. b) Zbtb18 mRNA are co-expressed with Cux2 mRNA in PD 0 mouse neocortex in L2-3 (arrows) as well as in L5 (arrowheads). Scale bar: 1 mm, 20 µm (right). c) ZBTB18 mRNA is co-expressed with CUX2 mRNA (arrow) in 20 PCW human frontal cortex. Scale bar: 2 mm, 40 µm (right) CP, cortical-plate; IZ, intermediate zone; SVZ, subventricular zone; VZ, ventricular zone. All images of mouse neocortex are representative of at least 3 independent biological replicates. In situ hybridization of a postmortem postconception weeks (PCW 20) human neocortex reflects multiple sections from a single brain but is consistent with an additional PCW 18 postmortem human brain sample (not shown).

Extended Data Fig. 5 Enrichment of ZBTB18 in Postmigratory IT Neurons.

a) Dot-plot showing expression of Zbtb18 and other canonical layer markers across ExN types along developmental ages of mouse cortex[27]. Zbtb18 co-expresses Satb2 and Cux2 in IT neurons but not with ET neurons’ TFs Bcl11b, Fezf2, Sox5, Foxp2, and Zfpm2. b-c) ZBTB18 co-localizes with SATB2, but not BCL11B, in the 20 PCW human neocortex. Scale bar: 100 µm. Immunofluorescent analysis of the mouse brain utilized independent sections from at least 3 animals. Images and quantification shown in A are representative of analysis conducted on at least 2 sections from the postmortem human brain. The graph represents mean ± s.e.m. An unpaired two tailed t-test was applied. * P = 0.0001, n = 3.

Extended Data Fig. 6 Downregulation of Essential IT Neuron Marker Genes in Mice Lacking ZBTB18.

a-b) Bulk RNA-Seq of PCD 14.5 and PD 0 neocortex was performed in whole body Zbtb18 -/- knockout (KO) and Zbtb18 +/- (Control) mice and in Neurod6-Cre; Zbtb18fl/fl (Neurod6-Cre; Zbtb18 cKO) and Neurod6-Cre; Zbtb18fl/+ (Neurod6-Cre; Zbtb18 cHET) (Control) respectively. 82.3% of genes upregulated in Arpp21-Gfp+ cells were downregulated in the Neurod6-Cre; Zbtb18 cKO [93 of 113], while 92.4% of genes enriched in Fezf2-Gfp+ cells were upregulated in the cKO [219 of 237]. c) In situ hybridization shows mRNA expression of upper layer marker genes Cux1, Satb2, and Rorb. Scale bar: 150 µm. d) The size of the dorsal brain of Neurod6-Cre; Zbtb18fl/fl (cKO) mice don’t change at PD 0 but appears to be smaller at PD 8 as compared with Neurod6-Cre; Zbtb18fl/+; mice. (e) Immunofluorescence staining of ZBTB18 is shown in the PCD 15.5 Neurod6-Cre; Zbtb18 cKO brain, and ZBTB18 expression is lost in postmitotic neurons but not SVZ progenitors. f) Western blot shows ZBTB18 protein expression is abolished, and CUX2 expression is significantly diminished in PD 0 Emx-Cre; Zbtb18 cKO mouse brain. g) Quantified signal intensity shows CUX2 expression is reduced by 70% in Emx-Cre; Zbtb18 cKO mouse brain compared with control. The graph represents mean ± s.e.m. n = 3 biological replicates. ZBTB18, CUX2 and GAPDH were run on individual gels with equal amount of lysate from same samples to avoid overlapping of smaller isoforms. Raw image of uncropped gel is in Supplementary Fig. 1. An unpaired t-test was used to compare groups. * P = 0.0465. h) Cux2 mRNA expression is significantly reduced in the neocortical plate but not the striatum of Zbtb18 knockout mice at multiple developmental periods. 150 µm. i) CUX2 expression in control and ZBTB18 cKO brains detected by CUX2 antibody. Representative image of coronal sections of the P0 brains stained with anti-Cux2 antibody are shown. Upper panel: Neurod6-Cre + ; Zbtb18fl/+; Lower panel: Neurod6-Cre + ; Zbtb18fl/fl. Scale bar: 500 µm. CP, cortical plate; IZ, intermediate zone; SP, subplate; SVZ, subventricular zone; VZ, ventricular zone. All immunofluorescent analyses and in situ hybridization represent images selected from multiple sections from at least 3 independent biological replicates. Western blotting is representative of 3 independent biological replicates.

Extended Data Fig. 7 Impact of Postmitotic Depletion of ZBTB18 on IT Neuron Development and Upper Layer Formation.

a) Top: IdU labeling (red) expected deep layer, BCL11B-immunopositive neurons at PCD 12.5 and immunofluorescence analysis with BCL11B (green) at PD 0 shows a greater number and broader distribution of BCL11B- positive neurons without a concomitant change in BCL11B and, IdU double-positive neurons. Bottom: Quantification of cells per laminar position (bins). b) Similar to A, with IdU labeling putative upper layer neurons at PCD 14.5, we observed a significant increase in the number of BCL11B and IdU double-positive neurons at PD 0. c) Similar to a and b, with CIdU labeling (green) expected upper layer neurons at E15.5 and immunofluorescence analysis with SATB2 (red) indicating upper layer neurons at P1. Zbtb18 deficient cells exhibited an alteration in the distribution of SATB2-immunopositive neurons that are also CIdU- positive relative to control mice. For each experiment, 145 to 356 cells from independent sections from at least three different mice were analyzed. Two-tailed T-tests were used to compare control and knockout cell counts per bin. * Bonferroni corrected P-value < 0.05. Please refer to the Supplementary Table 18 for detailed p values. Scale bar: 100 µm. d-e) In early postnatal Emx1-Cre; Zbtb18fl/fl mice brain, layers 2-4 are absent, BCL11B- positive cells populate the top layers of the neocortex, CUX1 expression is absent from upper layers, but present in SP. Scale bars: 50 µm. All analyses depicted in this figure were conducted on at least 3 independent biological replicates.

Extended Data Fig. 8 ZBTB18 Depletion Leads to Increased Intrahemispheric Cortico-Cortical Projections into the mPFC.

a) Coordinates (33) of bins used to analyze tracing experiments depicted in b. b) Coronal sections of traced PD 7 brains showing more intensive GFP labeling in GI, S2, S1Ul in Neurod6-Cre/+; Zbtb18flfl cKO mice than control. GI, granular insular cortex; S2, secondary somatosensory cortex; S1UL, primary somatosensory cortex, upper lip. Scale bar: 500 µm.

Extended Data Fig. 9 ZBTB18 Enhances Robo1 E1 Activity through Direct Binding.

a) Line graphs showing the H3K27ac peaks from, Arpp21-Gfp+ cells (line 1 and 2), Fezf2-Gfp+ cells (line 3 and 4), from PCD 14.5, and PD 0 forebrain DNaseI Hypersensitivity tracks (DHS) by Digital DNaseI from ENCODE, (line 5 and 6, respectively) and mammal conservation as reference (line 7), ZBTB18-HA ChIP-seq peaks from mouse (line 8), H3K27ac peaks from fetal dlPFC (line 9), gene locus in mice (line 10) H3K27ac peaks collapsed together in the Arpp21-Gfp + IT neurons and Fezf2-Gfp + ET neurons (line 11) and H3K27ac peaks from E17 chicken dorsal pallium (12) at the genomic locus of Robo1. The regions detected by H3K27ac and ZBTB18- ChIP seq are in light beige color.

Extended Data Fig. 10 ZBTB18 Binding Motifs Exhibit High Conservation in Putative CREs of Eutherian IT Neurons.

a) Taxonomic tree of the 23 Chordate species ZBTB18 SST protein sequence features of ZBTB18 were examined. For every species, the Pfam architecture of protein domains is indicated to the right. b) The ZBTB18 binding motif, as well as 16 other motifs from the JASPAR database, have consensus sequences enriched in regulatory regions related to genes downregulated in the Zbtb18 KO that are specifically conserved in placental mammals. Fisher’s exact test was used to test for an enrichment of conservation. Red dots represent motifs with a False Discovery Rate corrected P-value < 0.05. For detailed statistics, please refer to Supplementary Table 17. c) Evolutionary divergence of the key enhancers and/or ZBTB18 binding motifs. The evolutionary divergence of ZBTB18 motifs in key enhancers (Zbtb18 P1, Cux2 E1, Cux2 E4, Robo1 E1, Satb2 E1, and Bcl11b P1) are plotted onto a phylogenetic tree that shows the divergence of placental mammals (black line), marsupials (red line), monotremes (green line) and non-mammal vertebrates (purple line). All three motifs in Zbtb18 P1(Zbtb18 P1_motif1, Zbtb18 P1_motif2, Zbtb18 P1_motif3) and Cux2 E1_motif2 (purple) had evolved before the divergence of monotremes from non-mammal vertebrates, and Satb2 E1_motif2 appeared between the divergence of monotremes to the rest of mammals. In contrast, Cux2 E1_motif1, Satb2 E1_motif1, and all motifs in Cux2 E4, Robo1 E1, and Bcl11b P1 appeared after the divergence of placental mammals from marsupials. MYA: million years ago. d) Bootstrap-based strategy to evaluate the robustness of motif enrichments. The percentile occupied by the observed ratio represents an empirical P-value (Red dash line), indicating how likely it is to obtain this ratio by chance among the invariant peaks. The categories IT Placentalia, IT Marsupialia, and KO up Placentalia present significantly extreme observed ratios (P = 0.005, P = 0.003, and P < 0.001, respectively).

Extended Data Fig. 11 Evolutionary footprints analysis of ZBTB18 motifs in key CREs.

DNA sequence from mouse Zbtb18 P1, Cux2 E1, Cux2 E4, Robo1 E1, Satb2 E1, and Bcl11b P1 are analyzed for ZBTB18 motif discovery, including non-canonical motifs. For Regex motif, identifying the canonical “CAGATGT” pattern allowing one mismatch, only the core sequence is shown. Multi-species alignment was performed by comparing ZBTB18 motifs across 60 vertebrate species, which were sub-grouped in placental mammal (black), marsupial (red), monotreme (green), and non-mammal vertebrate (purple). Gaps are represented by black vertical bars. a) Three Jaspar motifs are detected in Zbtb18 P1, all of which show a relatively high level of conservation in a majority of the 60 species examined, but they are less conserved in species belonging to the fish clades. b) Two Regex motifs are detected in Cux2 E1, and Cux2 E1_ motif1 shows universal conservation in placental mammals, and 1 bp mutation is observed in marsupials, but the motif is lost in non-mammal vertebrates because of gaps (black vertical bar, panel b). Cux2 E1_ motif2 is less conserved with 1 bp mutation across mammalian species. c) Two motifs are detected in Cux2 E4, one is Jaspar motif (red) and the other is Regex motif (blue). d) Three motifs are detected in Robo1 E1, one is Jaspar motif (red), one is Regex motif (blue), and the other is detected by both Regex(blue) and MEME-TOP (#). All three motifs are only present in placental mammals. e) Two Jaspar motifs(red) and one PROMO (*) /Regex(blue) motif are detected in Satb2 E1, showing high level of conservation in mammals. All motifs are not present in monotremes or non-mammal vertebrates. f) Four motifs are detected in Bcl11b P1, one being Jaspar motif(red), two being Regex motifs(blue), and the last one being MEME-TOP (#). While motif1 and motif2 show a higher level of conservation, motif3 and motif4 have more sequence variation across mammals. Jaspar consensus motif (Red). Regular expression motif (regex motif, Blue). * PROMO motif. # MEME-TOP motif. The E-box core sequences are highlighted in rectangles.

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Li, Z., Kaur, N., Santpere, G. et al. Adaptive evolution of gene regulatory networks in mammalian neocortex. Nature 653, 156–166 (2026). https://doi.org/10.1038/s41586-026-10226-y

- Received: 16 May 2024

- Accepted: 03 February 2026

- Published: 18 March 2026

- Version of record: 18 March 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10226-y

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Nature volume 653, pages 167–179 (2026)

Abstract

The human maternal–fetal interface is characterized by mosaic intermingling of maternal and fetal cells[1]. Yet the underlying cellular, molecular and spatial programmes remain incompletely defined. Here we generate a comprehensive atlas of the human maternal–fetal interface across normal pregnancies from early gestation to term by integrating large-scale paired single-nucleus transcriptomic and chromatin accessibility profiling with submicrometre-resolution spatial transcriptomics and CODEX multiplex protein imaging[2], substantially boosting the spatiotemporal resolution of prior research[3]. This framework delineates common and transient cell types, states and spatial niches across the fetal and maternal compartments, reconstructs transcriptional programmes that guide cytotrophoblast and decidual stromal cell differentiation, and resolves recurrent architecture structural units that build this interface. We identify previously unrecognized arterial endothelial state transitions during cytotrophoblast-mediated spiral artery remodelling and develop a machine learning model that predicts cytotrophoblast invasiveness from transcriptomic signatures. We further discover a decidual stromal cell subtype that suppresses cytotrophoblast invasion via endocannabinoid signalling at the human maternal–fetal interface. By integrating the atlas with genome-wide association data, we pinpoint maternal and fetal cells that are most vulnerable to pre-eclampsia, preterm birth or miscarriage. This resource provides a comprehensive spatially resolved single-cell multiomic reference of the human placenta and decidua and offers a framework for decoding their normal and disordered development.

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Main

The human maternal–fetal interface (MFI) is a transient hemi-allogeneic amalgam in which maternal decidual stromal cells (DSCs) support placental attachment, recruit immune cells and create a tolerogenic milieu for patterning fetal cytotrophoblast invasion[1]. During placental development, fetal villous cytotrophoblasts (VCTs) in floating villi fuse into syncytiotrophoblasts (SCTs)[1], which mediate nutrient and waste exchange and hormone and growth factor secretion, and limit fetal cortisol exposure1,4. Alternatively, they form anchoring villi with cell columns that generate invasive extravillous trophoblasts (EVTs)[1]. EVTs invade the decidua and uterine spiral arteries. By the end of the first trimester, EVT remodelling of these vessels establishes low-resistance arteries that enable high-velocity blood flow to the placenta[1]. Previous single-cell studies have been confined to limited gestational windows3,5,6,7,8,9 (Supplementary Table 1); here we generate a reference of the human MFI from early gestation to term across normal pregnancies by integrating single-nucleus multiomics with submicrometre spatial mapping. This framework catalogues diverse cell types, resolves transient states and trajectories, maps intercellular communication in situ, and pinpoints vulnerable cell states in pregnancy complications.

A single-cell map of the interface

Our single-nucleus multiomics profiling targeted the human MFI from known normal (term gestation) or presumed normal (early/mid gestation) pregnancies (Methods; Supplementary Table 1). Samples encompassed the decidua basalis with embedded EVTs (early gestation) and the basal plate (mid/late gestation) from gestational week (GW) 5 to 39 (Fig. 1a). Sample collection, inspection and validation are detailed in Methods (Extended Data Fig. 1a–d). We performed paired single-nucleus RNA-sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC–seq) using the 10x Genomics platform, yielding high-quality datasets of 221,380 nuclei (snRNA-seq) and 210,191 nuclei (snATAC–seq) after stringent quality control (Methods). Among these, 191,735 nuclei had paired snRNA-seq and snATAC–seq profiles (Supplementary Table 1). On average, 8,336 nuclei were multiomically profiled per sample. The distribution of sequenced nuclei across gestational week intervals is shown in Fig. 1a and Extended Data Fig. 1e.

Fig. 1: Overview of single-cell multiomics data from the human MFI across gestation.

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a, Experimental design. Nuclei (191,735) from the human MFI were profiled spanning five intervals from early gestation to term. n represents the number of nuclei per group. Pie charts show major cell-type proportions in each group. Right, stacked area plot illustrating cell-type composition changes over gestation (cell type abbreviations defined in key below b). Image credit: YG Studio/Shutterstock.com. b, UMAP projections of snATAC–seq (left) and snRNA-seq (right) for 19 broadly identified cell types (key). c, Expression (snRNA-seq) dot plot of selected marker genes across the 19 cell types. Dot size represents the percentage of expressing cells. d, Assignment as maternal or fetal for each nucleus by genotype phasing. Bottom, pie chart illustrating the proportion of maternal or fetal fibroblasts. e, ATAC–seq footprinting identifies binding sites of cell-type-enriched transcription factors: TP63 (VCT), GATA3 (EVT), SPI1 (M) and RUNX2 (dNK). Binding sites are marked by depletion of Tn5 insertion flanking the motifs. f, Chromatin accessibility heat map showing the activity of the ten most active cell-type-specific enhancers. Regions analysed were cap analysis gene expression (CAGE)-validated enhancers from the FAMTOM5 collection[13]. g, Rewiring of GRNs during VCT differentiation into EVT and SCT lineages. The reconstructed EVT GRN is shown. Red and blue denote activation and repression, respectively, relative to VCTs. Inset, a FOS-mediated subnetwork. The top transcription factors with the highest ranked regulatory interactions are shown as examples. h, Venn diagram showing uniquely upregulated transcription factors (TFs) in EVTs or SCTs compared to VCTs. i, Expression patterns of transcription factors and their target genes reflect the coordinated regulation across two trophoblast lineages. EVT transcription factors activated EVT genes and repressed SCT genes in EVTs, with the reciprocal pattern in SCTs. In box plots, the centre line is the median, box represents the interquartile range (IQR; 25th to 75th percentile), and whiskers show minima and maxima (0.5× IQR) end-points; outliers are not displayed. P values, two-tailed Wilcoxon rank-sum tests after Benjamini–Hochberg correction. j, Schematic toggle switch model illustrating trophoblast fate reinforcement and suppression of the alternative programme.

Source data

We annotated cell clusters in the open-chromatin epigenome and transcriptome spaces (Fig. 1b) on the basis of known markers (Supplementary Table 2), which showed strong cell-type specificity (Fig. 1c) and overall concordance between the two modalities (Extended Data Fig. 2a). To confirm annotation accuracy, we verified batch correction (Extended Data Fig. 2b) and performed label transfer by projecting our cell-type annotations onto a prior placental atlas[3], yielding concordant assignments across major cell classes (Extended Data Fig. 2c). Notably, our study identified many more novel cell subtypes and states. Figure 1a summarizes cell-type composition across developmental windows. Because microanatomy (especially spiral artery distribution) varies across biopsy sites, cross-sample cell-type proportion comparisons can be misleading without spatial context. We therefore emphasized within-cell-type molecular profiles and performed compositional analyses only when spatial information was available or required.

We used Souporcell[10] to assign maternal versus fetal origin for more than 95% of cells (182,773 out of 191,735; Fig. 1d), validated by Y chromosome markers (Extended Data Fig. 2d,e). Some cell types contained both origins (for example, fibroblasts, FB in Fig. 1d), whereas vascular endothelial cells formed distinct maternal and fetal clusters (Fig. 1b), indicating molecular divergence.

Cell-type-specific gene regulation

Cell-type-specific genes aligned with cell-type-specific promoter assay for ATAC–seq peaks (Extended Data Fig. 2f). Using chromVar[11], we inferred transcription factor-binding motifs enriched in ATAC–seq pseudobulk peaks in each cell type (Extended Data Fig. 3a). snATAC–seq footprinting[12] further resolved transcription factor-binding sites of approximately 20 bp in size within pseudobulk peaks (Fig. 1e). We then mapped open-chromatin peaks to experimentally defined enhancers[13]. The top ten most enriched cis-enhancers for each cell type are shown in Fig. 1f (Supplementary Table 2). At the HLA-G locus (an EVT marker), we detected an EVT-specific promoter peak and three upstream open-chromatin peaks, including a known enhancer about 10 kb upstream[14] and two putative distal enhancers (Extended Data Fig. 3b). Together, these analyses revealed extensive cell-type-specific regulatory rewiring.

A toggle switch model for EVT fate

We used CellOracle[15] to reconstruct gene regulatory networks (GRNs) in each cell type by integrating snATAC–seq and snRNA-seq data. We focused on the trophoblast lineage to identify regulatory rewiring that directs progenitor VCTs towards terminal fates: EVTs or SCTs. Compared to VCTs, we identified 71 and 30 transcription factors that were specifically upregulated in EVTs and SCTs, respectively (false discovery rate (FDR) ≤ 0.01; top transcription factors in Fig. 1g and Extended Data Fig. 3c; representative motifs in Extended Data Fig. 3d; EVT–SCT overlap in Fig. 1h; full lists in Supplementary Table 3; Methods). CellOracle inferred target genes for these transcription factors. In EVTs, the 20 most highly upregulated transcription factors included ASCL2, FOS, KLF6 and STAT1, known regulators of VCT-to-EVT differentiation16,17,18,19, which mediated 167 high-confidence regulatory interactions (strength coefficients >0.1) with 116 target genes (Fig. 1g, Extended Data Fig. 3e and Supplementary Table 3). Among these, FOS positively regulated canonical EVT genes such as HLA-G, KRT8 and FN1 (Fig. 1g, inset). The same transcription factors activated in EVTs also showed negative associations with genes, which, as expected, were down-regulated in EVTs, but many, in fact, were known SCT markers (for example, CGA, TFPI2 and PLAC4; Fig. 1g and Extended Data Fig. 3f). Genome-wide, genes that were positively associated with EVT transcription factors were upregulated in EVTs, whereas negatively associated targets were suppressed in EVTs but enriched in SCTs (Fig. 1i). In EVTs, these negative associations between EVT transcription factors and SCT genes, predicted as putative repressive interactions, are consistent with recent reports that EVT-specific transcription factors functionally suppress the SCT programme in EVTs16,20,21. The same analysis of SCT-specific transcription factors showed the reciprocal pattern: activating the SCT programme while negatively associating with EVT-enriched genes in SCTs (Fig. 1i). Together, these observations suggest a bistable toggle switch model that enforces commitment to either the EVT or SCT lineage, locking cells into one fate while actively suppressing the alternative (Fig. 1j). Notably, only a few activating transcription factors were shared between the two lineages (Fig. 1h), including GCM1, whose loss impairs formation of both EVTs and SCTs[22]. Even these shared factors engaged distinct target gene sets in the two cell types (Extended Data Fig. 3g), highlighting extensive regulatory rewiring that secures mutually exclusive trophoblast fates.

From single cells to tissue architecture

To enable spatial mapping of deeply sequenced cell states in a native tissue context, we generated submicrometre (0.5-µm) spatial whole-transcriptomic maps using STOmics Stereo-seq23,24 (1 cm × 1 cm chips) for 16 wide-swath basal plate biopsy sections from normal pregnancies (after RNA quality control; Methods), each from an independent donor (Fig. 2a). Stereo-seq enables single-cell segmentation from full-tissue sections and reconstructs single-cell whole transcriptomes by aggregating transcripts captured at 0.5-µm spatial resolution (Extended Data Fig. 4a–d). Focusing on the peak phase of EVT invasion and spiral artery remodelling, we profiled only second-trimester basal plate specimens that contained a well-defined MFI with anchoring villi and abundant spiral arteries (Supplementary Table 4 and Methods). The technology permits immunostaining prior to transcriptomic profiling. Most of the samples (12 out of 16) were immunostained with anti-pan-cytokeratin (CK) to label trophoblasts, and a subset that contained numerous blood vessels was co-stained with anti-CD31 to mark the vasculature. The remaining four samples were deliberately processed without immunostaining to assess its impact on transcriptomic quality and to validate annotations using transcriptomes alone. Extensive quality control confirmed data quality and the manufacturer’s recommendation that pre-run immunostaining did not compromise Stereo-seq performance (Extended Data Fig. 4e and Methods). Cell-type assignments were concordant across technical variations, such as staining protocols and chemistry versions (Extended Data Fig. 4f–h), supporting consistency across preparation methods and justifying integrating all 16 whole-slide samples for downstream analysis (Fig. 2a).

Fig. 2: Overview of submicrometre spatial whole-transcriptome profiling at the MFI.

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a, STOmics Stereo-seq profiling of basal-plate sections from 16 second-trimester samples of normal pregnancy. Each dot represents a single cell, colour-coded by cell type. b, UMAP projection of around 1.1 million single-cell spatial transcriptomic profiles across 16 samples. c, Spatial cell community analysis identified six recurrent communities. D1, decidua-1; D2, decidua-2; FV, floating villus; Junction, maternal–fetal junction; VCV, villous core vessel. d, Each spatial community exhibited a characteristic cell-type composition. e, Distances (in 0.5-μm pixels) to the MFI for each cell. Positive distances are maternal; negative distances are fetal. px, pixels. f, Cell-type proportions binned across the MFI. g, Spatial distances (in 0.5-μm pixels) to the nearest blood vessel (BV) wall in the decidua. Positive, intraluminal; negative, extraluminal. h, Cell-type proportion in decidua by proximity to blood vessel walls (BVWs). i, Quantification of EVT density adjacent to blood vessel walls (n = 62) compared to randomized decidual regions of same size. In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P value, two-tailed Wilcoxon rank-sum test. j, Two representative spiral arteries at distinct phases of EVT-mediated remodelling, visualized by joint immunostaining and spatial transcriptomics. Box 1, early stage; box 2, advanced. k, Spiral artery cross-sections (n = 17) with PECAM1+ endothelial cells illustrating active EVT invasion in a representative GW20 sample. l, Single-cell transcriptomic profiling revealed endothelial state transitions: caEC → R0 → R1 → R2. m, PDE3A and VIM distinguished R0–R2 states with high accuracy (n = 10 bootstrap replications). Bars show mean area under the receiver operating characteristic curve (AUROC) and error bars represent s.d. n, R2 endothelial cells are enriched for apoptosis-related genes. o,p, R0–R2 endothelial states in samples 010-GW20 (o) and 016-GW21 (p) were independently validated via paired spatial transcriptomics and pan-CK and CD31 immunostaining. Fields from one representative sample. q, Absolute pixel distances of endothelial states to the vessel walls from 17 remodelling spiral arteries (010-GW20). In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P values, two-tailed Wilcoxon rank-sum test after Benjamini–Hochberg correction.

Source data

Our spatial profiling reconstructed around 1.1 million cells across all samples; their transcriptomes were harmonized and embedded in a unified uniform manifold approximation and projection (UMAP) that recapitulated the major cell types (Fig. 2b, Extended Data Fig. 4i–l and Methods). We performed spatial co-occurrence analysis to identify significantly co-localizing cell populations in the decidua, which revealed the strongest associations between decidual stromal and immune cells (Extended Data Fig. 4m), suggesting stromal–immune crosstalk[25]. We next performed spatial cell community analysis to identify fundamental ‘neighbourhoods’, spatial niches of adjoining cells that recur across tissues and share characteristic cell-type compositions26,27. This analysis revealed six major communities, each corresponding to a distinct anatomical niche: two decidual niches (D1 and D2), maternal arterial and fetal villous core vascular niches, the maternal–fetal junction, and the floating villi niche (see Fig. 2c for a representative section and Extended Data Fig. 5a for all tissues). Each niche had a characteristic cell-type composition (Fig. 2d).

The decidual niches D1 and D2 had distinct microenvironments. D1 was enriched for EVTs, whereas D2 had fewer EVTs and was dominated by decidual stromal and immune cells (Fig. 2d). The vascular communities also diverged. The maternal arterial niche was enriched for EVTs that were probably engaged in vessel remodelling, whereas the villous core vascular niche was enriched for ACTA2+ perivascular cells (Extended Data Fig. 5b, bottom). At the maternal–fetal junction, anchoring villus VCTs and SCTs intermingled with decidual stromal and endothelial cells, whereas the floating villi niche comprised SCTs, VCTs and fetal endothelial cells lacking a perivascular sheath (Fig. 2d), consistent with capillary networks beneath the trophoblast basement membrane. Thus, spatial community analysis delineated recurrent cellular niches that constitute fundamental structural units of the human MFI.

Mosaic EVTs in maternal vessel remodelling

For each cell, we computed the pixel distance (pixel size, 0.5 µm) to two anatomic landmarks: the nearest portion of the MFI and the nearest maternal spiral artery. In each sample, we first defined the interface as the boundary formed by the termini of dense anchoring villi between the placenta and decidua (Extended Data Fig. 5b, right). Cells were then binned by distance to the interface (Fig. 2e for a representative section; Extended Data Fig. 5c for all sections), and distance-stratified cell-type composition profiles were computed across samples (Fig. 2f). Overall, EVTs were more abundant in superficial decidua than in deep decidua, consistent with only the most invasive EVTs reaching those depths. However, immune and DSCs were enriched in deep decidua. The fetal cell-type composition within chorionic villi was less variable, irrespective of their distance to the interface (Fig. 2f).

Across all sections, CD31 immunostaining together with PECAM1 expression (CD31 is encoded by PECAM1) delineated 62 uterine blood vessels. Detailed review confirmed spiral artery morphology and the expected enrichment of adjacent CK+ EVTs. Setting the vessel wall as distance zero, we quantified cell-type composition in concentric 100-pixel distance bins (Fig. 2g for a representative section; Extended Data Fig. 5d for all vessel-containing sections), assigning positive distances to extraluminal cells and negative distances to intraluminal cells. Distal cells extending into chorionic villi were excluded from the analysis. Luminal and perivascular regions were enriched for maternal endothelial cells and EVTs, with only a small fraction of immune cells (Fig. 2h). Moving outward, endothelial cells were reduced and EVTs remained; decidual and immune cells were the major adjacent populations.

To quantitatively assess EVT aggregation around blood vessels, we estimated their density relative to randomly sampled size-equivalent decidua areas that lacked uterine blood vessels, revealing strong enrichment of EVTs surrounding uterine vessels (Fig. 2i, Methods and Supplementary Table 4). Magnified views of two representative vessels from the same sample (Fig. 2j) highlight the power of integrated immunostaining and spatial transcriptomics to capture distinct stages of EVT-mediated vascular remodelling. The first spiral artery (Fig. 2j, box 1) displayed a mostly intact CD31+ endothelial lining, with sparse CK+ EVTs clustered on one side (Fig. 2j), indicating an early stage of remodelling. By contrast, the second vessel (Fig. 2j, box 2) had a mosaic EVT phenotype: NCAM1+ (endovascular EVT marker[28]; Fig. 2j) EVTs co-expressed varying levels of HLA-G (Fig. 2j) and AOC1 (interstitial EVT marker29,30) (Fig. 2j). Residual CD31+ endothelial cells were sparse and fragmented along the vessel wall, consistent with EVT-mediated displacement and advanced remodelling. Decidual stromal and scattered immune cells were also present (Extended Data Fig. 5e), suggesting multicellular coordination during vessel remodulation.

Mapping arterial endothelial states

In sample 010-GW20, from a coiled spiral artery, we identified 17 cross-sections that traversed the decidua, and each had varying densities of endothelial cells and EVTs lining the vessel walls (Fig. 2k and Extended Data Fig. 5f,g for endothelial and EVT localization, respectively). This series in a single sample enabled detailed mapping of endothelial state transitions without confounding inter-sample variability. We later replicated these transitions in additional samples and validated them at the protein level. We used PDE3A, a recently identified arterial endothelial marker[31] that we independently validated by reanalysis (Extended Data Fig. 6a,b) and immunostaining of uterine spiral arteries (Extended Data Fig. 6c–e), to confirm that these vessels were lined by arterial endothelium (Extended Data Fig. 5g). Whole-transcriptome profiling of PECAM1+ arterial endothelial cells within these arteries followed by pseudotime reconstruction resolved four sequential states (Fig. 2l). Differential expression analysis between adjacent states showed that co-expression of PDE3A and VIM distinguished each cell state (Fig. 2m, Methods and Supplementary Table 5): canonical arterial endothelial cells (caECs; VIMhiPDE3Ahi), which progressed through R0 (VIMlowPDE3Ahi), R1 (VIMlowPDE3Alow) and R2 (VIMhiPDE3Alow) (Fig. 2l,m), charting the endothelial response to EVT-mediated remodelling. Functional enrichment analysis confirmed the trajectory direction: genes upregulated in R2 relative to caECs were significantly associated with apoptosis (Fig. 2n and Supplementary Table 5), confirming R2 as the terminal state. The same cell states (R0, R1 and R2) were observed in independent tissue samples (Fig. 2o,p). Of note, the strong expression of the pro-apoptotic gene GADD45G was specific to the R2 terminal state (Fig. 2o). Spatial measurements further validated this trajectory (Fig. 2l). We calculated the absolute distance of each endothelial cell from the vessel wall. caECs and R0 cells were proximate, R1 cells were modestly displaced, and R2 cells were most distant (Fig. 2q). Therefore, the loss of caECs during spiral artery remodelling begins with vessel wall displacement in the R1 state, leading to the full detachment, apoptosis and eventual clearance of R2 cells.

We next performed CODEX imaging[2] for protein-level validation by multiplexing nine cell-type-specific antibodies (Supplementary Table 6 and Supplementary Note), which accurately detected the major cell types at the human MFI (Fig. 3a and Extended Data Fig. 6f–i; independent validation in Extended Data Fig. 7a; Methods), including CD31 (PECAM1), PDE3A and vimentin (VIM) to label the evolving endothelial cell states. At the protein level, the cell states (R0, R1 and R2) were observed in arteries at different stages of vascular remodelling (SA-A and SA-B at GW15.2 in Fig. 3b–e and a vessel at GW19 in Fig. 3f–i). These CODEX observations were validated by immunolocalization on independent samples (Extended Data Fig. 7b).

Fig. 3: CODEX validation of stepwise arterial endothelial state transitions during EVT remodelling of spiral arteries.

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a, An overview of multiplexed CODEX imaging of a representative tissue section from a human basal plate sample (GW15). The selected markers (and their specificity): DAPI (nuclei), CD31 (endothelial cells), CK18 (cytotrophoblasts, particularly EVTs), CDH1 (endometrial epithelium and cytotrophoblasts), Ki67 (proliferation), CD206 (macrophages and Hofbauer cells), PDE3A (arterial endothelial and perivascular smooth muscle cells), vimentin (decidua stromal cells and a subset of vascular and lymphatic endothelial cells), CD3 (T cells) and PDPN (lymphatic endothelium and fetal fibroblasts). Marker specificities are shown in Extended Data Fig. 6g. SA, spiral artery; AV, anchoring villi. Scale bar, 400 μm. b–e, Higher-magnification CODEX imaging of spiral arteries SA-A highlighting CD31 (b) and PDE3A (c), and SA-B highlighting CD31 (d) and PDE3A (e) recapitulated the transient arterial endothelial states at the protein level: R0 (PDE3A+VIM−), R1 (PDE3A−VIM−) and R2 (PDE3A−VIM+). Biological replicates of the CODEX analysis are included in the Extended Data Fig. 7a. Scale bars, 50 μm. f, Overview of CODEX imaging near an extensively remodelled spiral artery from a GW19 basal plate sample. Scale bar, 100 μm. g–i, Magnification of the boxed region from f, showing the details of the distinct endothelial states across selected channels: CD31, PDE3A, CK18 and vimentin (g), and CD31 and CK18 (h). i, R0 (green arrows) and R2 cells (yellow arrows) are spatially segregated. Scale bars, 50 μm. j,k, Differential gene expression analysis comparing spatial transcriptomic profiles: R0 versus caECs (j) and R1 versus caECs (k). Genes involved in cell–cell junctions (for example, PLAUR, CDH5 and JUP) were down-regulated in R0 versus caECs (j), whereas genes that define arterial identity (for example, DLL4, EFNB2, HEY1 and ENG) were down-regulated in R1 versus caECs (k). l, Proposed stepwise model of endothelial (CD31+) state transitions during spiral artery remodelling. CaECs (PDE3AhiVIMhi) → R0 (PDE3AhiVIMlow) → R1 (PDE3AlowVIMlow) → R2 (PDE3AlowVIMhi).

For functional characterization, we compared each endothelial state to caECs in the spatial transcriptomes (Fig. 2l). R0 cells exhibited down-regulation of junctional genes (FDR = 1.8 × 10−2) and antigen-presentation pathways (FDR = 1.9 × 10−2; Fig. 3j, Extended Data Fig. 7c and Supplementary Table 5), defining a vessel wall-adherent ‘primed’ state (Fig. 2q) with reduced structural stability and immune signalling. R1 cells down-regulated PDE3A and other arterial markers (for example, EFNB2, HEY1 and DLL4; Fig. 3k and Supplementary Table 5), indicating loss of arterial identity, coincident with early detachment from the vessel wall (Fig. 2q). R2, the terminal EVT remodelling state, showed apoptotic gene activation (Fig. 2n and Extended Data Fig. 7d) and complete detachment (Figs. 2q and 3f–i), suggesting anoikis. Notably, caECs were furthest from EVTs, which came progressively closer to R1 and R2 (Extended Data Fig. 7e), supporting a spatial gradient in which increasing EVT proximity drives sequential endothelial transitions from loss of arterial identity (R1) to apoptosis (R2) (Fig. 3l).

In this model (Fig. 3l), SA-A cells (Fig. 3b) were primed for remodelling (R0). SA-B showed more advanced remodelling, with R1 cells along the upper wall and EVT-displaced R2 cells along the lower wall. The artery in Fig. 3d represented a near-terminal stage, with EVTs largely replacing the endothelium.

Trophoblast developmental trajectories

Deep-coverage snRNA-seq enabled sensitive identification of intermediate trophoblast states, which were then mapped in situ using spatial transcriptomics. We clustered 95,872 trophoblasts on the basis of snRNA-seq, identifying VCT, SCT and EVT subpopulations distinguished by canonical markers (Fig. 4a–c). Within each lineage, we uncovered multiple intermediate cell states that varied across gestational stages (Fig. 4d and Extended Data Fig. 8a). Pseudotime analysis using Palantir[32] reconstructed the canonical bifurcation of VCTs into SCTs and EVTs (Fig. 4e) and resolved the timing of emerging trophoblast states along developmental continua (Fig. 4f and Extended Data Fig. 8b). First, we focused on the EVT lineage (Fig. 4e), which originated from a VCT subtype, anchoring villi VCTs (Fig. 4d,f), and was distinct from another subtype, floating villi VCTs, that primarily gave rise to SCTs (Fig. 4d,f). We identified a progenitor EVT population marked by ITGA2, ITGB6 and VIT expression[33] (Fig. 4e and Extended Data Fig. 8c–e), and further resolved three terminal EVT subtypes: endovascular (eEVT), interstitial (iEVT) and perivascular (pEVT) EVTs, each defined by established markers (Fig. 4e). Additionally, we identified a population of potential trophoblast giant cells (Fig. 4d), marked by CD81 (ref. [3]) (Extended Data Fig. 8f). These cells also expressed canonical iEVT markers (Extended Data Fig. 8g), suggesting their formation from iEVTs; however, their transcriptomes clustered with SCTs (Fig. 4d), probably reflecting shared cell fusion mechanisms.

Fig. 4: Molecular and spatial characterization of human trophoblast development and function.

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a–c, UMAPs of trophoblast subtypes by marker expression: TP63+ VCTs (a), HOPX+ SCTs (b) and HLA-G+ EVTs (c). d, Clustering of trophoblast subtypes: iEVT, eEVT, pEVT, giant cell (GC), floating villi VCT (fvVCT), anchoring villi VCT (avVCT), EVT progenitor (proEVT) and SCT progenitor (proSCT). e, Reconstructed pseudotime trajectory reveals divergent developmental paths in floating versus anchoring villi by UMAP (top). Bottom, expression of EVT subtype-specific markers on UMAP: proEVT (ITGA2 and ITGB6), eEVT (FLT4 and NCAM1), iEVT (AOC1 and PRG2) and pEVT (SLIT2). f, Stream plot mapping VCT differentiation. Major branches are shown. g,h, Proportion of EVT subtypes identified in the decidua binned as the distance from the MFI (g) or maternal blood vessel walls (h). i, Spatial transcriptomics with pan-CK and CD31 immunostaining localized EVT subtypes, which were marked by HLA-G versus ITGB6 (proEVT), PRG2 and AOC1 (iEVT), SLIT2 (pEVT) and NCAM1 (eEVT). Images highlight EVT positioning near anchoring villi or blood vessels. j, Differential expression analysis (pEVT versus iEVT and eEVT versus iEVT) identified subtype-specific signatures. DEG, differentially expressed gene. k, CellChat identified distinct outgoing (top) and incoming (bottom) interactions, reciprocal signalling (eEVTs and mVECs), and crosstalk (EVTs and decidual cells). iEVTs showed strong outgoing interactions towards NK cells. l, Validation of the EVT pseudovascularization model. Left, EVTs adjacent to vessel walls exhibited high expression of endothelial signatures. Right, positive correlation of endothelial gene scores in EVTs with proximity to vessel walls (n = 9,374 EVTs from 16 samples). In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P value, Spearman (two-tailed) correlation test. m, A sparse learning model predicts EVT invasiveness from single-cell transcriptomes. Invasiveness scores (iScores) were positively correlated with EVT decidual depth in the test set (n = 63,916 EVTs from 16 samples). In box plots, the centre line is the median, box represent the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P value, Spearman (two-tailed) correlation test. n, Benchmark iScores in independent studies. Left, reduced invasiveness of smooth chorion EVTs[40]. Right, EVT shows increased invasiveness in placental accreta spectrum[41]. In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points; outliers are not shown. P values, two-tailed Wilcoxon rank-sum test. o, Comparison of EVT invasiveness in superficial and deep decidual compartments (left and middle) and near blood vessels (right). p, Invasiveness (iScore; bottom) and expression of ITGA2 (progenitor), HLA-G (cell column exit) and AOC1 (advanced maturation) at EVT maturation stages along the reconstructed trajectories. q,r, Identification of a novel SCT terminal state with distinct expression of KLRD1 (encoding CD94) (q) and GPC5 (r) in SCT-A versus SCT-B. s, Immunolocalization (CD94) and RNAscope (GPC5) identifies CD94+ SCT-A in syncytium and GPC5+ SCT-B in syncytial knots of floating villi cells in a representative GW23 sample (n = 3). IF, immunofluorescence. Scale bar, 25 μm. t, Spatial transcriptomics independently confirmed the presence of the SCT-B subtype in a representative sample (016-GW24, n = 16). Immunostaining: pan-CK (green). Scale bar, 25 μm.

Source data

Spatial localization of EVT subtypes

We next leveraged the aggregated spatial dataset to localize EVT subtypes as a function of distance from the MFI and vessel wall (Fig. 4g,h). EVT progenitors were confined to regions adjacent to the interface, including an ITGB6+ subpopulation[33] that was restricted to cell columns (Fig. 4i). The proportion of eEVTs increased with decidual depth and iEVTs were the predominant subtype across decidual layers (Fig. 4g), initiating HLA-G expression as they exited the columns (Fig. 4i). Within iEVTs, PRG2+ and AOC1+ subpopulations[29] (Fig. 4e) were spatially intermingled (Fig. 4i). AOC1 was enriched in developmentally advanced iEVTs (Fig. 4e), consistent with its role as a late-stage EVT marker[34]. Stratifying by vessel proximity, NCAM1+ eEVTs were restricted to intraluminal and perivascular zones with concomitant iEVT depletion, whereas SLIT2+NCAM1− pEVTs[35] (Fig. 4e) were specifically enriched in the perivascular region (Fig. 4h). These observations delineate a mosaic EVT organization in which HLA-G+ iEVTs encase the vessel, SLIT2+ pEVTs occupy the perivascular zone, and NCAM1+ eEVTs populate the lumen and vessel wall (Fig. 4i).

Functional specializations of EVT subtypes

We compared the transcriptomes of terminally differentiated eEVTs, iEVTs, and pEVTs (Fig. 4j and Supplementary Table 7). Gene ontology analysis highlighted their distinct molecular functions (Extended Data Fig. 8h). CellChat[36] analyses, which integrates ligand–receptor co-expression with spatial proximity, revealed reciprocal signalling between eEVTs and maternal vascular endothelial cells (mVECs; Fig. 4k). Signals from endothelial cells to eEVTs are likely to recruit eEVTs to spiral arteries, whereas signals from eEVTs to endothelial cells are likely to contribute to the caEC-to-R2 transition (Fig. 3f). This analysis also revealed crosstalk between iEVTs and DSCs (Fig. 4k). Additionally, iEVTs showed strong outgoing signalling towards natural killer (NK) cells, suggesting that iEVTs have a role in modulating NK cells[37].

The pseudovascularization model

Previously, we proposed a pseudovascularization model in which eEVT remodelling of spiral arteries entails their mimicry of endothelial phenotypes, supported by their CDH5 expression[38]. Across all spatial transcriptomic samples (Fig. 2a), we validated this model transcriptome-wide. As EVTs approached the vessel wall, they shifted from an interstitial to an endovascular phenotype, down-regulating PRG2 and up-regulating NCAM1. Meanwhile, their expression of endothelial-associated genes peaked in wall-adjacent eEVTs (Fig. 4l, left). Furthermore, an ‘endothelium-like’ score[39] assigned to each EVT on the basis of the 100 genes that were most enriched in mVECs (from snRNA-seq; Fig. 1b), was significantly inversely correlated with EVT distance from the vessel wall (Fig. 4l, right), suggesting progressive acquisition of an endothelial programme as EVTs occupy the spiral artery niche. Thus, spatially resolved data bolster the pseudovascularization model, showing that eEVTs systematically adopt an endothelial-like identity. In addition, eEVTs co-clustering with other EVT subtypes (from snRNA-seq; Fig. 4d) indicate preserved trophoblast identity alongside endothelial mimicry.

Machine learning to infer EVT invasiveness

EVTs exhibit heterogeneous invasiveness, prompting us to test whether single-cell transcriptomes predict invasive potential. This analysis was guided by three considerations. First, by focusing on the MFI, deep decidual EVTs were spatially separated from the superficial myometrium, reducing confounding from peri-myometrial EVTs that may have ceased invading. Second, most superficial decidual EVTs (around 77.3%) expressed AOC1 (Fig. 4i), a marker of mature EVTs[34], limiting confounding from developmental heterogeneity. Third, because EVT transcriptomes integrate both cell-autonomous and microenvironmental cues, transcriptome-based inference captures both sources of variation relevant to invasion.

We first considered EVTs that were not associated with blood vessels (that is, eEVTs and pEVTs) and trained a sparse learning model to compare EVT transcriptomes at varying decidual depths. The model agnostically selected 54 of 3,192 EVT-enriched genes (from snRNA-seq; Fig. 4e and Supplementary Table 8) and aggregated their expression into an invasiveness score (iScore; Supplementary Note). In the randomly held-out test set (EVTs not used in training), iScores tightly tracked iEVT invasion depth (Fig. 4m), and the up-regulated genes were enriched for migration and extracellular matrix (ECM)-remodelling programmes in high- versus low-scoring cells (Extended Data Fig. 8i,j and Supplementary Note), confirming biological relevance. In independent single-cell datasets, EVTs from the smooth chorion[40], known to have minimal invasion, indeed showed reduced iScores (Fig. 4n, left). Conversely, EVTs from placenta accreta spectrum pregnancies[41], characterized by excessive invasion, had higher iScores regardless of whether they were sampled from decidua-adherent or non-adherent regions (Fig. 4n, right and Extended Data Fig. 8k). These independent validations confirmed biological specificity. Across iEVTs from the spatial data, high iScores generally localized to deep decidua, whereas low iScores were superficial (Fig. 4o, left). Outliers included superficial high-iScore iEVTs (ongoing invasion, pink arrows; Fig. 4o, middle) and deep low-iScore iEVTs (attenuated invasion, white arrows; Fig. 4o, middle), suggesting that iScores captured heterogeneous invasive states beyond spatial position alone. Extending to eEVTs, they showed high iScores near vessels (Fig. 4o, right and Extended Data Fig. 8l). Scoring EVTs from our snRNA-seq data, progenitors exhibited the lowest iScores, whereas eEVTs and pEVTs had the highest (Extended Data Fig. 8m). iEVTs had a stage-dependent pattern: iScores rose sharply as HLA-G+ cells entered the decidua and then declined with AOC1+ expression and maturation (Fig. 4p). Thus, iEVT invasive potential is inversely correlated with developmental maturation; this antagonism suggests a mechanistic basis for tightly controlled uterine invasion.

A novel SCT subtype in syncytial knots

Our 10x snRNA-seq data confirmed the previously described SCT progenitor population (proSCT; Fig. 4d), marked by syncytin-2 (ERVFRD-1) and SLC26A2 (refs. 3,9) (Extended Data Fig. 9a–c). Immediately downstream, we identified an SCT-A subtype with increased KLRD1 expression (encoding CD94) (Fig. 4q), and a terminal SCT-B state marked by GPC5 (Fig. 4r). CD94 immunostaining localized SCT-A to the syncytium of floating villi, whereas RNAscope and spatial transcriptomics showed GPC5 enrichment in syncytial knots, a finding that was supported by immunolocalization in term villi (Fig. 4s,t, Extended Data Fig. 9d–g and Supplementary Table 9). Consistent with the accumulation of syncytial knots in late gestation[42], SCT-B nuclei were enriched at later gestational stages (Extended Data Fig. 9h), supporting a trajectory from proSCT to SCT-A and finally to SCT-B in syncytial knots.

Novel DSC states

snRNA-seq of 20,579 DSCs across gestation identified five distinct clusters (DSC0–DSC4; Fig. 5a and Extended Data Fig. 10a). Pseudotime analysis revealed two trajectories rooted in DSC0: path A (DSC0 → DSC1 → DSC3) and path B (DSC0 → DSC2 → DSC4; Fig. 5b). DSC0 predominated in early gestation, whereas DSC3 and DSC4 emerged later (Fig. 5c). A prior early-pregnancy atlas described only path A[5] (markers below); thus, path B and its terminal DSC4 population represent a previously unrecognized decidual lineage. Notably, DSC4 was also absent from non-pregnant human endometrium (proliferative and secretory phases)[43], indicating pregnancy-specific emergence.

Fig. 5: Molecular and spatial characterization of DSC heterogeneity and function.

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a, UMAP projection of five DSC subtypes (DSC0–DSC4) from snRNA-seq. b, Reconstructed trajectory of the major decidualization paths: path A (DSC0 → DSC1 → DSC3) and path B (DSC0 → DSC2 → DSC4). c, Distribution of DSC subtypes across gestation (GW5–39). d, Expression of DSC subtype-specific markers on UMAP: ACTA2 (undecidualized DSC0), IGFBP1 and PRL (decidualized DSCs, DSC3/DSC4), SERPINF1 (path A: DSC1/DSC3), SEMA3A and CNR1 (path B: DSC2/DSC4). e, Differential expression analysis highlighted SERPINF1 (encoding PEDF) as a key marker that distinguishes path A from path B DSCs. f, Immunolocalization of DSC subtypes: PEDF+IGFBP1+ DSC3 cells were localized in interstitial decidua (left); PEDF−IGFBP1+ DSC4 cells were enriched in superficial decidua and clustered near anchoring villi (right). Images from n = 3 representative samples. g, Spatial transcriptomics: DSC3 cells (SERPINF1+PRL+) were adjacent to blood vessels in deep decidua (top); DSC4 cells (CNR1+IGFBP1+) were localized near anchoring villi in superficial decidua (bottom). Images from n = 16 representative samples. Scale bars, 100 μm. h, Spatial distance analysis (n = 78,993 DSCs) showed that DSC4 were proximal to the MFI compared with other subtypes (16 samples). In box plots, the centre line is the median, box represents the IQR, and whiskers show observed minimum and maximum values. P value, Wilcoxon rank-sum test (two-tailed) after Benjamini–Hochberg correction. i, Comparing DSC3 and DSC4 proportions across cell communities. P value, chi-square (two-tailed) test after Benjamini–Hochberg correction. j, EVTs adjacent to DSC4 exhibited lower iScores than those near DSC3, other DSCs or EVTs at comparable decidual depths (16 samples). In box plots, the centre line is the median, box represents the IQR, and whiskers show observed minimum and maximum values. P values, Wilcoxon rank-sum test (two-tailed) after Benjamini–Hochberg correction. k, In vitro decidualized HuFs recapitulated the DSC4 transcriptomic signatures (left), manifested by CNR1 (right). l, Gene set enrichment analysis (GSEA) of DSC3 versus DSC4 responses to mAEA after in vitro decidualization. mAEA induces pro-apoptotic genes in DSC3, which were repressed in DSC4. Ctrl, control; NES, normalized enrichment score. m, Schematic of Transwell invasion assay. n, Quantification of primary cytotrophoblast invasion (n = 6). Data are mean ± s.e.m.; P values, Student’s t-test (two-tailed) after Benjamini–Hochberg correction. o, Schematic of model. mAEA exposure protects DSC4 from apoptosis and reshapes DSC4 paracrine cues to enhance local suppression on adjacent EVT invasiveness. CTB, cytotrophoblast.

Source data

Marker analysis supported these trajectories. DSC0 cells expressed high levels of ACTA2 and low IGFBP1 and PRL, indicating an undecidualized state, whereas DSC3 and DSC4 showed low expression of ACTA2 and strong expression of IGFBP1, demonstrating decidualization (Fig. 5d and Extended Data Fig. 10b). SERPINF1 (encoding PEDF) was specifically up-regulated along path A (Fig. 5d,e), distinguishing DSC3 (PEDF+IGFBP1+) from DSC4 (PEDF−IGFBP1+). Immunolocalization identified ACTA2+ DSC0 cells deep in the decidua (Extended Data Fig. 10c), whereas DSC3 (PEDF+IGFBP1+) cells were broadly distributed (Fig. 5f, left), often in perivascular regions (Extended Data Fig. 10d). We also observed a small population of CK+ cytotrophoblasts that immunostained for PEDF (Fig. 5f and Extended Data Fig. 10e). By contrast, the novel DSC4 subtype (PEDF−IGFBP1+) localized to the superficial decidual surface, and many were adjacent to the termini of anchoring villi (Fig. 5f, right and Extended Data Fig. 10f,g for biological replicates), a pattern later confirmed by spatial transcriptomics. Finally, independent validation using single-cell RNA-seq from 13 additional decidual samples (28,626 cells) reproduced the DSC3 and DSC4 subtypes (Extended Data Fig. 10h).

We next functionally characterized the two DSC developmental pathways (Fig. 5e). Genes that were up-regulated along path A were enriched for endothelial cell migration (FDR = 9.31 × 10−3; Supplementary Table 10), consistent with the frequent perivascular localization of DSC3 (Extended Data Fig. 10d), and many other functions (Extended Data Fig. 10i and Supplementary Table 10). At the terminus of path A (DSC3), we observed bifurcation into two subclusters, DSC3.1 and DSC3.2 (Fig. 5b and Extended Data Fig. 10j), both of which exhibited IGFBP1 expression. DSC3.2 cells, which emerged mainly after GW22, broadly down-regulated angiogenic inhibitors, MHC class I molecules, complement components and anti-apoptotic genes (Extended Data Fig. 10k). These features support a senescent-like decidual state, consistent with prior reports that senescent DSCs accumulate at later gestation and contribute to parturition[44].

By contrast, genes that were up-regulated along path B (Fig. 5e) were enriched for ECM organization (FDR = 3.7 × 10−2; Supplementary Table 10). DSC4 cells specifically expressed SEMA3A, WNT5A and CNR1 (Fig. 5d and Extended Data Fig. 11a). SEMA3A and WNT5A promote ECM rigidity and stabilization, restraining cell motility45,46. The marker CNR1 encodes the cannabinoid receptor CB1. Because DSC4 cells are in the superficial decidua, often near the termini of anchoring villi (Fig. 5f, right) where EVTs initiate invasion, DSC4 marker expression suggests that DSC4 mediates endocannabinoid signalling at the human MFI and likely serves as a localized brake limiting EVT invasion.

Regulation of DSC4 on EVT invasion

We first spatially mapped the DSC states and then leveraged the iScore metrics (Fig. 4m) to quantify EVT invasiveness adjacent to DSC4. We used gene signature scores[39] from snRNA-seq to identify DSC3 and DSC4 subtypes from IGFBP1+ DSCs on the spatial map (Extended Data Fig. 11b and Methods), whose spatial localizations were consistent with immunostaining (Fig. 5f). ACTA2+VIM+ undecidualized stromal cells (Extended Data Fig. 11c,d) and SERPINF+IGFBP1+ DSC3 cells (Fig. 5g) were enriched in the deep decidua. CNR1+IGFBP1+ DSC4 cells often localized near anchoring villi (Fig. 5g). DSC3 lay farthest from and DSC4 was closest to the MFI (Fig. 5h). When mapped onto spatial niches (Fig. 2c,d), DSC4 were enriched in the EVT-rich D1 niche (Fig. 5i), suggesting interactions between EVT and DSC4. For confirmation, we compared iScores of EVTs adjacent to DSC4 with: (1) EVTs adjacent to other DSC subtypes; and (2) depth-matched EVTs (non-significant depth difference; P = 0.85, Wilcoxon rank-sum test). EVTs adjacent to DSC4 had significantly lower iScores than both comparison groups (P = 8.4 × 10−91 and P = 3.8 × 10−142, respectively; Wilcoxon rank-sum test; Fig. 5j), revealing a local DSC4-mediated suppression of EVT invasiveness.

DSC4 cells mediate cannabinoid signalling

Because the cannabinoid receptor CB1 (encoded by CNR1) was specifically expressed in DSC4 cells (Fig. 5d,g), and CB1 is activated by the endocannabinoid anandamide (AEA), whose balance at the MFI is critical for pregnancy[47], we investigated how CB1 agonism affects DSC4 and their regulation of trophoblast invasion. Using our in vitro decidualization model[48], human uterine fibroblasts (HuFs) were differentiated into decidualized stromal cells and exposed to 0.5 μM methanandamide (mAEA, an AEA analogue) or vehicle for 72 h. Single-cell RNA-seq of 33,088 cells (13,493 control, 19,595 mAEA-exposed; Methods) identified undecidualized ACTA2+ and decidualized IGFBP1+ clusters, as well as DSC3- and DSC4-like subtypes as defined by markers of these cell types in primary tissues (Fig. 5k, left and Extended Data Fig. 11e–h). DSC4-like cells retained robust CNR1 expression (Fig. 5k, right), indicating that the model recapitulated DSC3 and DSC4 differentiation.

Given the absence of CNR1 in DSC3 and its specific expression in DSC4, we performed subtype-specific differential expression after mAEA exposure (Extended Data Fig. 11i and Supplementary Table 11). Gene set enrichment analysis (Fig. 5l) revealed divergent responses. In DSC3, mAEA tended to up-regulate pro-apoptotic genes (GSEA FDR = 9.7 × 10−2), consistent with prior reports of mAEA-induced apoptosis in DSCs[49]. In CB1+ DSC4 cells, the apoptosis process was significantly down-regulated (GSEA FDR = 7.0 × 10−3), including expression of pro-apoptotic genes (DAPK3, FADD and GADD45G; Extended Data Fig. 11i), suggesting CB1-mediated protection from apoptosis after mAEA exposure.

We tested whether endocannabinoid signalling altered DSC regulation of primary human cytotrophoblasts invasion. Decidualized DSCs were treated for 72 h with mAEA (CB1 agonist), rimonabant (SR141716A, CB1 antagonist), both, or vehicle. After washing, conditioned medium was collected for 24 h and applied to second-trimester human cytotrophoblasts in a Transwell invasion assay (Fig. 5m, Methods and Supplementary Table 12). Conditioned medium from mAEA-treated DSCs reduced cytotrophoblast invasion (P = 3.7 × 10−5), whereas conditioned medium from rimonabant-treated DSCs increased invasion (P = 4.8 × 10−3); co-treatment restored invasion towards control (P = 5.4 × 10−3) (Fig. 5n). Together, these data suggest that endocannabinoid exposure protects DSC4 from apoptosis (Fig. 5l) and modulates DSC4 paracrine cues (Fig. 5n), which enhanced its capacity in constraining adjacent EVT invasiveness (Fig. 5o).

Cell types that are most vulnerable in disease

We next investigated whether our single-cell atlas could explain the genetic architecture of major pregnancy complications. We used SCAVENGE[50] to integrate single-cell open-chromatin architecture and large-scale (n > 10,000) maternal and fetal genome-wide association studies (GWASs) for pre-eclampsia[51], spontaneous preterm birth52,53 and sporadic miscarriage[54], generating cell-type-resolved maps of genetic risk for each condition (Fig. 6a and Supplementary Table 13). Conventional pre-eclampsia GWAS, despite large cohorts, yielded only a few loci with modest effect sizes, leaving most heritability unexplained[51]. By contrast, SCAVENGE[50] aggregated GWAS signals across open-chromatin regions in each cell into a per-cell trait relevance score. Pairing maternal cells with maternal pre-eclampsia GWAS[51] and fetal cells with fetal pre-eclampsia GWAS[51], we identified 6,221 maternal and 8,232 fetal cells with significant pre-eclampsia risk enrichment. In the fetal compartment, only EVTs, particularly iEVTs, were enriched for pre-eclampsia risk (FDR ≤ 2.2 × 10−96) (Fig. 6b). On the maternal side, enriched populations included the DSC3 subtype, arterial endothelium (not venous), perivascular cell, fibroblasts and T cells, whereas decidual macrophages and NK cells were not enriched (Fig. 6c). A distinct POU5F1+LGR5+ endometrial epithelial population, restricted to early gestation and expressing stem cell markers (Extended Data Fig. 12a–d), also showed strong enrichment, implicating early endometrial defects in pre-eclampsia. Validation using 151 DisGeNET[55]-curated pre-eclampsia genes confirmed expression enrichment in these vulnerable cell types (Extended Data Fig. 12e and Supplementary Table 13). Negative control experiments paired the same GWASs with fetal brain[56] or adult brain[57] single-cell datasets and detected no significant enrichment (Extended Data Fig. 12f,g), demonstrating specificity. Note that this analysis focused on population genetic risk (common variants), and other cell types may contribute through rare or somatic variants or non-genetic factors.

Fig. 6: Single-cell risk map of major pregnancy complications.

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a, Single-cell multiomics was integrated with maternal and fetal pre-eclampsia GWASs by mapping high-risk variants onto open chromatin in single cells, revealing cell-type-specific genetic vulnerabilities and pinpointing risk-enriched populations. TRS, trait relevance scores. b,c, Maternal or fetal cell types and subtypes showing significant associations with pre-eclampsia. The proportion of risk-associated cells within each source was compared to the maternal or fetal background. Enrichment is shown as fold change; P values, Fisher’s exact test (two-tailed) with Benjamini–Hochberg correction. b, Fetal GWAS risk associations in fetal cell types. c, Maternal GWAS risk associations in maternal cell types. EM, endometrium. LEC, lymphatic endothelial cell. d,e, Parallel enrichment analyses for maternal GWAS risk in spontaneous preterm birth (d) and sporadic miscarriage (e) across maternal cell types indicate distinct maternal cellular contributors. Only subtypes with more than 200 cells were included. P values, Fisher’s exact test (two-tailed) with Benjamini–Hochberg correction.

Source data

We next applied the same strategy to maternal GWASs for spontaneous preterm birth and sporadic miscarriage, pairing analysis to maternal cell types52,54. For both conditions, only POU5F1+LGR5+ endometrial epithelial cells showed significant enrichment (Fig. 6d,e), an association that was replicated in an independent spontaneous preterm birth cohort of 233,290 women[53] (Extended Data Fig. 12h). The shared vulnerability of this epithelial population across pre-eclampsia, spontaneous preterm birth and miscarriage supports the concept of ‘endometrium spectrum disorders’[58].

Discussion

Across gestation, our multimodal atlas systematically delineated key cell states, spatial niches, and developmental trajectories at the human MFI and pinpointed genetically vulnerable cell types in major pregnancy complications. In trophoblasts, our study suggested a bistable regulatory circuit that directs commitment to terminal fates from progenitor VCTs (Fig. 1j). Thus, perturbing key regulators could shift this circuit across the EVT–SCT boundary, triggering an aberrant fate switch that may predispose to pathology. A key pathological manifestation of trophoblast dysfunction is dysregulated EVT invasion. This study developed a quantitative framework to quantify EVT invasiveness directly from transcriptomes, with proof-of-concept validation in placenta accreta spectrum cases. While appropriate EVT invasiveness is essential for normal spiral artery remodelling, endothelial cell–intrinsic changes within the vessel wall are also likely to be critical. Our data uncovered discrete endothelial state transitions during vascular remodelling[59], suggesting that disruption of these short-lived states could impair this process and contribute to pathological changes. The same is likely to extend to other perivascular populations, particularly the decidual stromal subtype DSC3, which often surrounds vessels (Extended Data Fig. 10d) and is genetically associated with pre-eclampsia (Fig. 6c). Its potential role in regulating vascular function warrants future investigation. Together, from a single-cell perspective, coordinated perturbations of trophoblast, endothelial and stromal states are likely to collectively drive many pregnancy complications, shifting the focus from fetal versus maternal contributions to failed integration at the MFI. Although immune cells were not the primary focus of this study, their contribution was evident and will be important to dissect in future work.

We also identified novel cell subtypes and cellular interactions. DSC4, first described here, modulates EVT invasion of the superficial decidua via endocannabinoid signalling. This finding raises public health concerns about cannabis use during pregnancy, as its principal ingredient, Δ9-tetrahydrocannabinol, is a CB1 agonist that is likel to perturb DSC4-mediated endocannabinoid signalling and thereby dysregulate EVT invasion. Consistent with this possibility, prenatal cannabis exposure has been epidemiologically associated with spontaneous preterm birth, stillbirth, reduced birthweight and other adverse outcomes[60].

Integrating GWASs with our single-cell datasets showed that fetal EVTs strongly mediate population genetic risk of pre-eclampsia. Meanwhile, the POU5F1+LGR5+ endometrial epithelial cells exhibited shared genetic risk enrichment across pre-eclampsia, spontaneous preterm birth and miscarriage, highlighting a convergent and previously underappreciated cell population for future mechanistic investigation.

Despite extensive profiling in this study, many more cell states and subtypes almost certainly remain to be discovered, including populations in the deep decidua, at very early gestation and in pregnancy complications. Further work expanding spatial and temporal coverage will be essential to define these elusive populations and to clarify their roles in normal pregnancy and pathological deviations.

Methods

Tissue acquisition and processing for joint single-nucleus multiome profiling and spatial transcriptomics

Snap-frozen decidual and basal plate samples were obtained from the existing placenta tissue banks at Stanford University and University of California, San Francisco (Supplementary Tables 1 and 4). All samples were collected with written informed consent. Tissues were derived from women undergoing elective termination of presumed normal pregnancies (first and second-trimester samples; no known or predicted fetal chromosomal abnormalities, Extended Data Fig. 1c) or after term delivery (≥37 gestational weeks). For term samples, clinical records were reviewed to exclude placenta-associated complications (for example, chorioamnionitis); cases with NICU admission and preterm premature rupture of membranes were also excluded. Fresh placental tissues were grossly inspected and dissected under a microscope (Leica Microsystems) by pathologists. Decidua basalis was micro-dissected on ice from the MFI and distinguished from decidua parietalis and capsularis on the basis of characteristic histological and morphological features (Extended Data Fig. 1a,b). Dissected tissues were sequentially washed to remove residual blood cells in DMEM/H-21 medium, supplemented with 12.5% FBS (Hyclone), 1% L-glutamine (Atlanta Biologicals), 1% penicillin/streptomycin and 0.1% gentamicin and cold 1× PBS (Gibco, Thermofisher). Samples used for single-cell or spatial transcriptomic profiling were flash-frozen in liquid nitrogen and stored at −80 °C until processing. RNA quality was assessed from adjacent cryosections using a Bioanalyzer or Tapestation. For fresh frozen samples, only samples with RIN ≥ 7.0 were included.

Isolation of single nucleus from snap-frozen tissues

Single nuclei were isolated from snap-frozen tissues as previously described[61] with modification. In brief, tissues were ground on dry ice, and 30–50 mg was homogenized into a pre-chilled 7 ml PYREX dounce homogenizer (Corning Life Science). Tissue was homogenized in 2 ml ice-cold buffer (250 mM sucrose, 0.3% NP-40, 5 mM MgCl_2, 25 mM KCl, 10 mM Tris-HCl pH 7.8) supplemented with protease inhibitors (Roche, cOmplete) and 0.6 U µl−1 Ribolock RNase inhibitor (thermofisher). Debris was removed by 40 μm filtration, and nuclei were purified by OptiPrep iodixanol gradient centrifugation (25%, 30%, 40%). After centrifugation in a swinging bucket centrifuge (Eppendorf 5810R) for 30 min at 3,000g, nuclei were collected from the 30–40% interface, washed, and assessed by trypan blue staining and microscopy to ensure nuclei integrity. Approximately 15,000 nuclei per sample were processed using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression platform (10x Genomics).

Tissue preparation and CODEX imaging

Placenta samples for CODEX were OCT-embedded, cryosectioned at 10 µm, mounted on poly-L-lysine-coated slides and stored at −80 °C. On the day of staining, sections were equilibrated, acetone-treated, rehydrated, fixed with 1.6% paraformaldehyde, blocked, and incubated with a barcoded antibody cocktail (200 µl/section) for 3 h at room temperature. Sections were then washed, post-fixed with 4% paraformaldehyde and cold methanol, stabilized using CODEX fixative reagent, and stored in storage buffer at 4 °C (≤2 weeks) before imaging. Multiplex imaging was performed on an Akoya CODEX microfluidic system coupled to an inverted fluorescence microscope using a 7-cycle protocol (including blank cycles for alignment) across 4 channels (DAPI, FITC, Cy3 and Cy5) with a 20×/0.75 NA objective. Images were processed using CODEX Analysis Manager. The antibody panel included Akoya-validated barcoded antibodies and custom-conjugated antibodies generated using Akoya oligo barcodes following the manufacturer’s protocol (Supplementary Table 6). Additional details are provided in the Supplementary Note.

Single-nucleus multiome library construction and sequencing

Single-nucleus Multiome libraries were prepared using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit (10x Genomics) following the manufacturer’s protocol, using one reagent kit per sample. Around 15,000 isolated nuclei per sample were encapsulated into Gel Bead-In Emulsions (GEMs) containing unique cell barcodes, where reverse transcription and transposition occurred, followed by library amplification, and separation of gene expression and chromatin accessibility libraries. Libraries were sequenced on an Illumina NovaSeq 6000 using paired-end reads, with sequencing depth selected on the basis of recommendations from 10x Genomics. On average, each sample yielded approximately 250 million paired-end reads for ATAC and RNA libraries. Raw BCL files were demultiplexed, aligned to the GRCh38 (v.3.0.0) reference genome, and processed for barcode assignment, UMI counting, and quality control using Cell Ranger ARC v.2.0.0 (10x Genomics) (https://support.10xgenomics.com/single-cell-geneexpression/software/pipelines/latest/advanced/references).

Single-nucleus multiome data processing

High-quality nuclei with paired snRNA-seq and snATAC–seq profiles were retained using the following criteria: RNA UMI counts 1,000–50,000; detected genes >400; mitochondrial reads <20%; ATAC fragment counts 1,000–100,000; transcription start site enrichment >1.0; and nucleosome signal <2.0. Doublets were identified and removed using Scrublet[62] with prior set to 0.1. After filtering, 191,735 nuclei were retained with paired snATAC- and snRNA-seq data. For snATAC–seq, open-chromatin peaks were called per sample using MACS2 (v.2.2.7)[63], and merged into a unified peak set after excluding ENCODE blacklist regions[64]. Peak-by-cell count matrices were integrated across samples using reciprocal latent semantic indexing (LSI) projection in Signac[12]. For snRNA-seq, gene expression count matrices were integrated using reciprocal principal components analysis projection in Seurat (v.4)[39]. Prior to integration, data were normalized, scaled and feature-selected following best practices recommended in Seurat/Signac workflows[12].

Dimensionality reduction was performed using principal components analysis on the top 3,000 variable genes (RNA) and LSI on the top variable ATAC peaks observed in at least 10 cells. UMAP embeddings for RNA/ATAC spaces were projected individually for visualization. Clustering was performed using the Louvain algorithm in Seurat/Signac12,39, and clusters were annotated on the basis of canonical marker genes (Supplementary Table 2) using gene expression and gene activity scores. Concordance between RNA- and ATAC-based clustering was assessed using the Jaccard index and percentage of cells assigned to same cell type.

Cell-of-origin assignment was performed using Souporcell[10] with default parameters. Variants were called using FreeBayes based on 1000 Genomes Project reference, and only informative heterozygous loci (≥5 cells with both reference and alternative alleles) were retained. Trophoblasts were used as fetal reference populations, whereas lymphatic endothelial cells and DSCs were used as maternal reference populations to inform Souporcell assignment. Assignment accuracy was validated using UTY expression in samples from male fetuses, in which 94.3% of UTY+ nuclei were correctly classified as fetal.

Transcriptional regulation and ATAC–seq footprinting analysis

Putative enhancer peaks were identified by intersecting ATAC–seq fragments with experimentally validated enhancers from FANTOM5 datasets[13]. An enhancer-by-cell accessibility matrix was constructed by counting overlapping fragments per cell. Cell-type–specific enhancers were identified using a log-ratio test, comparing chromatin accessibility in each cell type to all others; enhancers with enriched accessibility (FDR < 0.05) were considered cell-type-specific.

Transcription factor activity was inferred using chromVAR[11], which quantifies motif-associated accessibility deviations while correcting for GC content. Motif annotations were obtained from the JASPAR2020 database[65]. Cell-type-enriched transcription factors were those with significantly elevated chromVAR deviation scores in each cell type, identified by Wilcoxon rank-sum test. To validate transcription factor activities at the DNA level, transcription factor footprinting was analysed using Signac[12]. For each candidate transcription factor, footprinting profiles were generated over corresponding binding motif(s) within accessible chromatin regions after correcting for Tn5 insertion bias.

Gene regulatory network construction

Cell type-specific GRNs were constructed using the CellOracle workflow[15], integrating chromatin accessibility and gene expression data to model transcription factor–target gene interactions in a lineage-resolved manner. A base GRN scaffold was first constructed from snATAC–seq data via scanning transcription factor-binding motifs within proximal promoters and distal regulatory elements using GimmeMotifs (v.5.0)[66]. Proximal elements were annotated with HOMER[67], and distal regulatory interactions were inferred on the basis of Cicero[68] co-accessibility scores to link distal enhancers to putative gene targets. Using this scaffold, cell type-specific GRNs were inferred from snRNA-seq data via regularized Ridge regression implemented in CellOracle. Only statistically significant transcription factor–target gene interactions were retained. Among all transcription factors in the GRNs, DEG analysis identified 71 and 30 transcription factors significantly up-regulated (FDR < 0.01) in EVTs and SCTs, respectively, relative to VCTs. Regulatory interaction strength was evaluated by the absolute model-derived coefficients. Interactions with absolute coefficients exceeding 0.1 were visualized using Cytoscape[69], and all identified regulatory interactions were included in our downstream analyses.

To identify lineage-specific regulatory rewiring, transcription factor–target gene pairs were classified into four categories: (1) EVT-specific activation (n = 224); (2) EVT-specific repression (n = 18); (3) SCT-specific activation (n = 234); and (4) SCT-specific repression (n = 48). Expression distributions of target genes in each category were compared with genome-wide background expression in EVT or SCT populations. For transcription factors up-regulated in both lineages, overlap between their target gene sets was quantified using the Jaccard index to assess shared regulatory programmes. Statistical comparisons were performed using the two-tailed Wilcoxon rank-sum test with Benjamini–Hochberg correction.

DEG and gene ontology analysis

DEGs between cell types or subtypes were compared using Wilcoxon rank-sums test by SCANPY[70]. A composite DEG score was derived for each gene that summarizes fold changes and the differences in the percentage of expressing cells between two cell types. DEGs were determined by FDR (FDR < 0.05). All non-ribosomal DEGs receiving the highest DEG scores were selected as signature genes to define: (1) aEC states (|DEG score| > 10); (2) eEVT, iEVT and pEVT (|DEG score| > 20); and (3) path A DSC and path B DSC (|DEG score|>20). These top ranked DEGs were included for the downstream gene ontology (GO) analysis using Enrichr[71], and gene ontology terms with an FDR < 0.05 were considered statistically significant.

Pseudotime trajectory inference

For pseudotime analysis within arterial endothelial cells (spatial transcriptomics), trophoblasts (snRNA-seq) and DSCs (snRNA-seq), the initial and terminal cell states during cell differentiation were inferred by CellRank (v.1.5.1)[72]. Inferred initial states were selected as the developmental origin for pseudotime analysis by Palantir (v.1.1.0)[32] with default settings. The fate probability to each identified terminal state was estimated for subsequent visualization. The major developmental branches identified by Palantir were visualized on UMAP projection. For trophoblast analysis, major branches with more than 500 cells were aggregated for stream plot.

Spatial transcriptomic library preparation and sequencing

The Stereo-seq (STOmics) spatial whole-transcriptome sequencing platform was applied to construct a high-resolution spatial transcriptomic atlas of the human MFI. Stereo-seq captures polyadenylated mRNA directly from tissue sections using spatially barcoded DNA nanoball (DNB) arrays, achieving subcellular resolution (~0.5 µm) with an effective spot diameter of ~220 nm. 16 basal plate samples (GW20–24) were embedded in Tissue-Tek OCT compound (4583, Sakura Finetek) and stored at −80 °C. The anatomical structure of cryosections was visually confirmed under a microscope by a pathologist on the day of the experiments. 10 µm cryosections were dissected using a Leica cryostat and mounted onto Stereo-seq transcriptomics T chips (Complete Genomics). Stereo-seq library preparation was performed per the manufacturer’s guidance (Supplementary Note). In brief, tissue sections were methanol-fixed and subjected to antibody or single stranded DNA (ssDNA) staining prior to tissue permeabilization. Released RNA was reverse-transcribed in situ, followed by tissue removal, cDNA amplification and SPRISelect bead purification. Libraries were sequenced on the DNBSEQ T7 platform (50 bp read 1, 100 bp read 2).

Spatial transcriptomic data analysis

Raw FASTQ files were processed using the STOmics SAW v.8.1 pipeline. Spatial barcodes (CIDs) were aligned to the chip coordinate grid, and high-quality reads were mapped to the human genome (GRCh38) using STAR[73]. Reads with identical CID–UMI pairs were collapsed to generate the final spatial gene expression matrix at bin1 resolution (0.5 nm × 0.5 nm). DAPI and ssDNA images were used to delineate cell boundaries using deep learning–based segmentation pipeline in the SAW workflow. To interrogate gene expression at single-cell resolution, read counts from all bins within a single cell’s boundary were aggregated to achieve CellBin resolution. Cells with low complexity (n_genes <100), excessive transcript counts (>10,000 MIDs or genes), or high mitochondrial gene content (>20%) were excluded. The count matrices in cells passing quality control were processed and normalized using Stereopy (v.1.6.1)[27], in which the top 3,000 variable genes were selected for principal components analysis-based dimensionality reduction. Batch effects across donors were corrected by Harmony before UMAP visualization and clustering analysis[74]. Clusters were annotated on the basis of known cell-type- and subtype-specific gene signatures from snRNA-seq or known markers, and were then mapped to spatial coordinates for localization validation. Cell community and neighbourhood analysis were performed using Stereopy (v.1.6.1)[27]. Integrated visualization of cell segmentation, gene expression, and immunofluorescence signals was performed on StereoMap (v.4.1). Detailed experimental protocol and downstream spatial data analysis are described in Supplementary Note.

Spatially resolved cell–cell communication analysis

Intercellular signalling among spatially resolved cell types was analysed using CellChat (v.2)[36]. Analyses were performed with the CellChatDB (the human ligand–receptor database), following the standard workflow. Spatial coordinates were incorporated to constrain inferred intercellular interactions within a maximum Euclidean distance of 200 µm, consistent with the expected physical range of paracrine signalling. Ligand–receptor pairs were grouped into signalling pathways, and communication probabilities were aggregated to estimate pathway-level and overall interaction strengths between cell-type pairs. Communication scores were standardized for comparison. Statistical significance was assessed by label-shuffled permutation (n = 1,000) and interactions with Benjamini–Hochberg corrected P < 0.05 were considered significant.

Model-based prediction of EVT invasiveness

Gene signatures defining EVT invasiveness potential were identified via a supervised machine learning framework using L1-regularized regression (LASSO) based on spatial transcriptomic profiles. All identified EVTs excluding those associated with spiral arteries from sixteen sections were split into training and testing datasets at a 1:1 ratio. EVTs were ordered by their distance to the MFI. We then grouped ten cells with similar MFI distances within a narrow depth window to generate pseudobulk profiles, thereby matching spatial context across samples and reducing sparsity and batch effects during model training. A LASSO regression model was trained on the averaged gene expression as predictors and distance to the MFI as the response variable. The feature space comprised 3,192 genes enriched in EVTs relative to other cell types from the independent snRNA-seq data and analysis. Gene expression from training and test datasets were independently standardized while preventing information leakage from the training data into the test data. The model agnostically identified 54 predicting EVT invasiveness potential, with nonzero coefficients, where positive and negative coefficients indicated pro-invasive and anti-invasive effects, respectively. The trained model was applied to single-cell data to generate predicted invasion scores (iScores). Model performance was evaluated by Spearman correlation between predicted iScores and observed distance of each EVT (the held-out test dataset) to the MFI. iScores were normalized within each tissue section for cross-sample comparisons.

To benchmark iScores, the trained model was applied to two independent EVT scRNA-seq datasets, including second-trimester smooth chorion and placental villi (GSE198373)[40], term placental tissues from placenta accreta spectrum (PAS) patients and healthy controls (GSE212505)[41]. Gene expression matrices were log-normalized, z-score standardized, and used as input to the trained model. Predicted iScores were compared across tissue contexts and disease states to evaluate differences in the predicted EVT invasiveness. To assess the effects of DSC subtypes on neighbouring EVTs, iScores of EVTs in direct spatial proximity to annotated DSC subtypes (within five neighbouring tiles on grids, by Squidpy[75]) were analysed and compared with depth-matched EVTs that were not in spatial proximity to any DSCs. Statistical comparisons were performed using the two-tailed Wilcoxon rank-sum test with Benjamini–Hochberg correction for multiple testing.

Immunofluorescence

Immunofluorescence was performed as previously described40,76. Placenta tissues were fixed in 3% paraformaldehyde in PBS for 30 min, followed by cryoprotection in a sucrose gradient (5%, 10%, 15% in PBS). Tissues were embedded in OCT (Tissue-Tek, Sakura Finetec), sectioned at 10 µm using a Leica cryostat, and mounted on poly-l-lysine-coated slides (Electron Microscopy Sciences). The tissue sections on coverslips were permeabilized using 0.3% Triton X-100 for 10 min and blocked in 1% BSA in PBS for 30 min. Sections were incubated with primary antibodies (Supplementary Table 6) in blocking buffer for 2 h at room temperature, washed four times with PBS, and incubated with Alexa Fluor 488- or 647-conjugated secondary antibodies (1:200; Jackson ImmunoResearch) for 1 h. After PBS washes, nuclei were counterstained with DAPI. Slides were mounted using ProLong Gold Antifade Mountant and stored at 4 °C in the dark prior to imaging. Images were acquired using a Leica DM5000 B microscope under optimized settings.

Single-molecule fluorescent in situ hybridization with RNAscope

Freshly dissected basal plate samples were fixed in 4% paraformaldehyde for 30 min at room temperature, embedded in OCT, and cryosectioned at 15 μm. Sections were permeabilized and subjected to target retrieval by protease prior to probe hybridization. In situ hybridization was performed using the RNAscope Multiplex Fluorescent Reagent Kit v.2 (Advanced Cell Diagnostics) following the manufacturer’s instructions. Sections were hybridized with a GPC5-pecific probe for 2 h at 40 °C in a humidified chamber, followed by signal amplification and detection using TSA-conjugated fluorophores. After hybridization, sections were washed in PBS and processed for immunofluorescence staining with a FITC-conjugated anti-CD94 antibody (BD Biosciences, 555888). Nuclei were counterstained with DAPI. Sections were mounted with ProLong Gold Antifade Mountant (Invitrogen) and stored at 4 °C prior to imaging. Images were acquired using a STELLARIS 5 Confocal Microscope (Leica Microsystems) with consistent acquisition settings across samples. Multiple representative fields were captured per section from three biological replicates. Quantification of GPC5+ cells was performed in QuPath using automated cell segmentation within defined regions of interest (syncytial aggregates or membrane), with manual verification. Results were reported as the percentage of GPC5+ cells among total DAPI+ nuclei. Statistical significance was assessed using two-tailed Student’s t-test.

In vitro decidualization and mAEA treatment

The HuF cells were collected from human term placenta the decidualized in vitro as described[48]. In brief, the decidual tissues were micro-dissected from the chorionic layer and enzymatically dispersed to reach over 95% purity. The cells are cultured in RPMI medium supplemented with 2% fetal bovine serum and antibiotics. The cells were decidualized for a continuous 7 days by adding medroxyprogesterone acetate (1 μM), oestradiol (10 nM) and prostaglandin E_2 (1 μM) after reaching 90% confluence. On day 5, 500 nM mAEA or ethanol was added to the culture for 72 h. The conditioned media were collected 24 h after the medium replacement. In parallel experiments, the cells were collected by trypsin digestion on day 7 for 10× Chromium 3′ single-cell RNA sequencing. To identify enriched pathways in decidualized stromal cells following mAEA treatment, gene set enrichment analysis was performed. Genes were ranked by their log-fold changes from differential expression analysis, and pathways were considered significantly enriched at a threshold of FDR < 0.25, as recommended by gene set enrichment analysis guidelines.

Transwell invasion assay

Transwell invasion assays were performed using 24-well inserts with 8 µm pore-size polycarbonate membranes (Corning Costar, 3422). Membranes were coated with 10 µl of Matrigel (Corning) diluted 1:1 in serum-free DMEM/H-21 supplemented with 2% Nutridoma, 1% Penicillin-Streptomycin, 1% HEPES and 0.1% gentamicin. Trophoblasts isolated from second-trimester basal plate were resuspended in serum-free medium and seeded into the upper chamber at 2.5 × 10^5 cells per insert. The lower chamber contained conditioned medium from in vitro decidualized HuFs treated with vehicle, mAEA (methanandamide), rimonabant (SRI141716) or mAEA plus rimonabant. Cells were incubated at 37 °C with 5% CO_2 for 48 h to allow invasion through the Matrigel-coated membrane. Following incubation, inserts were fixed and permeabilized in ice-cold methanol for 20 min, washed with PBS, and stained with DAPI for 5 min at room temperature. Membranes were mounted on slides and imaged at 40× magnification under a fluorescence microscope (Leica Microsystems), with at least 5 non-overlapping fields acquired per insert. Each condition was tested on primarily isolated cytotrophoblasts from three independent placentas. Invaded cells stained with DAPI were counted manually and normalized to the total number of DAPI-positive cells per field. Comparisons between conditions were performed using a two-tailed Student’s t-test, with P values < 0.05 considered statistically significant.

Single-cell risk map for pregnancy complications

Single-cell trait relevance scores (TRS) were computed using SCAVENGE by integrating snATAC–seq with GWAS summary statistics of pregnancy complications[50]. Cell-type-specific ATAC peaks were identified using MACS2 (v.2.2.7.1; default)[63], and cell-by-peak matrices were generated as input for recommended SCAVENGE workflow. Analyses were performed using GWAS datasets for pre-eclampsia (maternal and fetal genomes; EGAD00010001984 and EGAD00010001986)[51], spontaneous preterm birth (maternal genomes)52,53, and sporadic miscarriage (maternal genomes)[54]. Risk-associated cells were defined as cells receiving top 5% TRS scores by permutation testing within SCAVENGE[50]. Genetically vulnerable cell types were determined by the two-tailed Fisher’s exact after Benjamini–Hochberg correction, in which cell types with fewer than 200 cells were excluded. As negative controls, identical analyses using pre-eclampsia GWAS data were performed on adult and prenatal brain single-cell datasets56,57 (adult: GSE204684; fetal: https://assets.nemoarchive.org/dat-oiif74w), in which the percentage of genetically vulnerable cells in each cell type was normalized for visualization.

Ethics statement

All human pregnancy tissue samples were obtained with informed consent and processed in accordance with the Declaration of Helsinki. Study protocols were approved by the Stanford institutional review board (31552, 34745, 48255 and 46584) and University of California, San Francisco institutional review board (11-05530 and 10-00350). All samples were de-identified before processing.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The COSMOS (Cellular Omics and Spatial Mapping Of States) explorer for data visualization is available at https://cell.ucsf.edu/. Data used in this study can be accessed interactively and downloaded on https://cell.ucsf.edu/snPlacenta/. The raw FASTQ data can be accessed from NIH database of Genotypes and Phenotypes (dbGaP) under controlled access under the accession number phs004305.v1. The data can be accessed by submitting a data access request to the dbGaP Data Access Committee. GWAS data for maternal or fetal compartments in pre-eclampsia can be downloaded from https://ega-archive.org/studies/EGAS00001001266. The full GWAS summary statistics from the meta-analysis of spontaneous preterm birth are available at the Early Growth Genetics Consortium website (https://egg-consortium.org/). Summary statistics of the spontaneous preterm birth cohort from 23andMe can be obtained under an agreement that protects the privacy of the 23andMe participants. Please visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data. The GWAS summary for sporadic miscarriage can be downloaded from http://www.geenivaramu.ee/tools/misc_sumstats.zip. The single-cell datasets used in this study can be accessed from the Gene Expression Omnibus under accessions GSE198373, GSE212505 and GSE204684, and the NeMO archive (https://assets.nemoarchive.org/dat-oiif74w). The source data for visualization can be found in Supplementary Information. Source data are provided with this paper.

Code availability

The code used for data analysis in this study is available at GitHub (https://github.com/complexdisease/mf.interface).

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Acknowledgements

This work was supported by the Chan-Zuckerberg Initiative (the Silicon Valley Community Foundation, DAF2021-239932 and DAF2020-217686) and the National Institute of General Medical Sciences (R35GM142983) to J.L. X.S. discloses support for the research of this work from the National Institute of Child Health and Human Development (NICHD) (R01HD068524). D.K.S. was supported by Christopher Hess Research Fund (Stanford University). S.K.E. was supported by the March of Dimes Fund (Prematurity Research Center, Washington University). L.C.G was supported by NICHD (P01HD106414); R.B. was supported by NICHD (P50HD112034); T.C.M., A.J.C., and A.E. were supported by Bakar ImmunoX Initiative Pilot Grant (University of California, San Francisco). We thank P. Narasimhan Iyer for assistance with tissue procurement.

Author information

Author notes

1. These authors contributed equally: Cheng Wang, Yan Zhou, Yuejun Wang

Authors and Affiliations

1. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA

Cheng Wang, Yan Zhou, Yuejun Wang, Zhida Luo, Tara I. McIntyre, Marisa E. Schwab, Robert Blelloch, Linda C. Giudice, Tippi C. MacKenzie, Susan J. Fisher & Jingjing Li

2. Department of Neurology, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA

Cheng Wang, Yuejun Wang, Zhida Luo & Jingjing Li

3. Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA

Cheng Wang, Yuejun Wang, Zhida Luo & Jingjing Li

4. Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA

Cheng Wang, Yan Zhou, Yuejun Wang, Zhida Luo, Tara I. McIntyre, Robert Blelloch, Linda C. Giudice, Adrian Erlebacher, Susan J. Fisher & Jingjing Li

5. The Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA

Cheng Wang, Yuejun Wang, Zhida Luo & Jingjing Li

6. Department of Obstetrics, Gynecology and Reproductive Sciences, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA

Yan Zhou, Juan M. Gonzalez, Linda C. Giudice & Susan J. Fisher

7. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

Tuhin Kumar Guha & Michael P. Snyder

8. Reproductive Sciences Center, Division of Developmental Biology, Cincinnati Children’s Hospital, Cincinnati, OH, USA

Anxhela Mustafaraj & Xiaofei Sun

9. Department of Surgery, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA

Marisa E. Schwab & Tippi C. MacKenzie

10. UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA

Brittany R. Davidson, Gabriella C. Reeder & Alexis J. Combes

11. Department of Pathology, University of California San Francisco, San Francisco, CA, USA

Gabriella C. Reeder & Alexis J. Combes

12. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA

Ronald J. Wong

13. Center for Reproductive Health Sciences, Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA

Sarah K. England, David K. Stevenson & Gary M. Shaw

14. Department of Urology, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA

Robert Blelloch

15. Bakar ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA

Adrian Erlebacher

16. Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA

Adrian Erlebacher

17. The Center for Maternal–Fetal Precision Medicine, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA

Tippi C. MacKenzie

18. College of Medicine, University of Cincinnati, Cincinnati, OH, USA

Xiaofei Sun

19. Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA

Virginia D. Winn

Contributions

J.L. conceived and acquired funding support for this study. S.J.F. designed the functional experiments. S.J.F. and V.D.W. coordinated sample acquisition, clinical annotation and ethics approvals. C.W. performed sample and library preparation for single-cell data production. C.W. and Y.Z. processed the tissues for spatial transcriptome profiling, with assistance from the vendor. J.L. and C.W. designed the analytical framework and defined the key questions to be examined prior to data generation. C.W. performed computational analysis, with contributions from Y.W. and Z.L. Y.Z. performed immunostaining and invasion assays. C.W. performed RNAscope in situ hybridization. T.K.G. performed CODEX imaging, with supervision from M.P.S. X.S. and A.M. designed and performed in vitro decidualization and endocannabinoid experiments. C.W. analysed and curated the data and developed the atlas portal. T.I.M., M.E.S., B.R.D., G.C.R., J.M.G., A.J.C., T.C.M. and A.E. contributed to the replicated single-cell data from decidua swap. R.J.W., S.K.E., R.B., L.C.G., D.K.S. and G.M.S., contribute to data interpretation and provide critical suggestions. J.L., S.J.F. and C.W. wrote and revised the manuscript. All authors reviewed, edited and approved the final manuscript.

Corresponding authors

Correspondence to Susan J. Fisher or Jingjing Li.

Ethics declarations

Competing interests

J.L. is a cofounder of SensOmics and serves on its scientific advisory board. M.P.S. is a cofounder of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos and NiMo, and serves on the scientific advisory boards of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos, NiMo and Genapsys. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks John Aplin, D. Stephen Charnock-Jones, Xi Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Sample validation and quality-controlled single-nucleus (sn) multi-omic integration.

(a) Representative brightfield images of early gestation decidua basalis, decidua capsularis and decidua parietalis (left, GW 8.2; right, GW 8.6). Samples from decidua basalis were collected for snMultiome library preparation. (b) Immunostaining of Vimentin (green) and pan-cytokeratin (CK, red), highlighted the distinct anatomical structure of decidua basalis vs. other decidual regions (left, GW 8.2; right, GW 8.6). CK+ trophoblasts confirmed decidua basalis identity. (c) Proportion of fetal cells with putative copy number variations (CNVs) inferred from snATAC-seq using Copy-scAT. No significant germline CNVs (>25% of cells) were detected. (d) Scrublet score distributions for predicted singlets and doublets/multiplets; the predicted doublets/multiplets (n = 6,008) were excluded from downstream analyses. Box plots, center line=median, box=IQR (25th-75th percentile), minima/maxima=5%/95% of data range; P value, two-tailed Wilcoxon rank-sum test. (e) UMAP of nuclei grouped by five gestational stages: early (GW 5–8) and late (GW 9–12) first trimester, early (GW 13–18) and late (GW 19–24) second trimester, and term.

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Extended Data Fig. 2 Cell origin assignment and cross-modality annotation.

(a) Heatmap of concordance between snATAC-seq and snRNA-seq cell type annotations based on gene activity and expression, respectively; colors, fraction of cells with consistent labels. (b) UMAPs before and after integration showing effective batch effect correction across three sequencing batches for all gestational age groups. (c) Heatmap showing concordance between published annotations in Arutyunyan et al.[3] and label-transfer predictions by this study. Colors, percentage of cells with matched labels. (d) UMAPs showing Y chromosome gene UTY expression across 191,735 nuclei. (e) Benchmarking Souporcell-based origin assignment by UTY expression. Maternal and fetal cell distributions were stratified by UTY positivity in pregnancies with male fetuses; 94.3% of UTY+ cells were classified as fetal (two-tailed χ^2 test, ***, P < 2.2e-96). (f) Cell type-specific ATAC-seq peaks at promoters of canonical marker genes in each cell type, shown as normalized ATAC-seq signal across ±10 kb of transcription start sites.

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Extended Data Fig. 3 Decoding gene regulation underlying trophoblast differentiation.

(a) Heatmap of chromVAR activity of cell type-specific TFs. The representative highly expressed and specific TFs/cell-type are shown. (b) Chromatin accessibility at the HLA-G locus (chr6:29,790,000–29,840,000) across major cell types. Three potential EVT-specific distal enhancer clusters (red) are located within 30 kb upstream of the HLA-G promoter (cyan). (c) Representative transcription factors (TFs) upregulated in SCTs/EVTs relative to VCTs. (d) DNA-binding motifs of the 20 most highly upregulated TFs during VCT-to-EVT transition, shown as PWM logos. (e) Gene regulatory networks (GRNs) underlying VCT-to-EVT (upper panel) and VCT-to-SCT (lower panel) differentiation. (f) UMAP and dot plots showing that EVT TFs negatively targeted genes in EVTs, many of which had preferential expression in SCTs. (g) Bar charts showing the proportion of shared target genes regulated by 16 TFs in EVT and SCT GRNs. The 16 TFs regulate distinct set of target genes despite upregulation during both VCT-to-EVT and VCT-to-SCT transition.

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Extended Data Fig. 4 Spatial transcriptomics data quality, integration and clustering.

(a) Stereo-seq spatial map of a full placental section (010-GW20), with spatial spots colored by total MID counts. (b) Zoomed view near a blood vessel (BV) illustrating cell segmentation by Cellpose; each segmented cell (geometric bin) is colored by a cell cluster, with MIDs aggregated within its cell boundary. (c-d) Comparison of binning resolutions: (c) raw barcodes at 1-pixel (0.5 µm, bin1) resolution and (d) CellBin-aggregated counts per segmented cell, colored by total MID counts; cell boundaries (white) and nuclei (blue) are shown. (e) Comparison of quality metrics between samples processed with mIF or ssDNA staining, showing no significant differences in reconstructed cell numbers, per-cell MID counts, or gene counts. Center line=median, box=IQR (25th-75th percentile), minima/maxima=observed minimum and maximum values; n.s., not significant, two-tailed Wilcoxon rank-sum test. (f) UMAPs showing consistent cell type identifications across STOmics chemistry versions, staining strategies (mIF vs. ssDNA), and sequencing batches. (g) Silhouette scores for technical variables before and after Harmony correction, showing effective batch correction and improved mixing post-correction (Bar plots, mean ± s.d.; ***, P < 0.001; two-tailed Wilcoxon rank-sum test with Benjamini-Hochberg correction). (h) Cell type proportions stratified by tissue slide (16 samples on 10 slides), chemistry version, and sequencing batch, indicating minimal technical bias and consistent cell type representation after integration. (i) Dot plot of key marker gene expression across major cell types in spatial transcriptomic data. (j) UMAPs showing representative marker genes for major cell types (e.g., TENM3, VCT; HLA-G, EVT; HOPX, SCT; MMP1, EC; ACTA2, PV; IGFBP1/PRL, DSC; CD14, HB/macrophages; NKG7, NK; PAEP, epithelium). (k) UMAPs of immune cell subtypes identified from spatial transcriptomics (DC, dendritic cells; M, macrophages; NK, natural killer cells; T, T cells). (l) High-resolution spatial map of a section (016-GW21), with cells colored by cell types. (m) Heatmap of normalized inter-cell-type co-occurrence frequencies within a 300-pixel radius.

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Extended Data Fig. 5 Spatial mapping of cell types.

(a) Six cell communities identified across 16 samples, with regions annotated as villous core vessels (VCV), maternal artery, floating villi (FV), maternal-fetal junction (Junction), decidua-1 (D1), and decidua-2 (D2). (b) Zoomed views of a VCV niche (bottom; ACTA2+ perivascular cells) and a junction niche (right) at GW21, immunostained for pan-CK (green) and CD31 (red); interface boundary=orange dashed line. AV, anchoring villi; BV, blood vessel. (c) Spatial map of distance to the maternal-fetal interface across 16 samples (positive, maternal; negative, fetal). (d) Spatial map of distance to maternal spiral artery vessel walls (BVWs) across 8 samples with detectable uterine vessels. Positive values, extraluminal; negative values, intraluminal. (e) Spatial views (GW20 basal plate) showing immune cells adjacent to a BV. Arrows indicate NK cells (NKG7) and macrophages (CD14). (f-g) Spatial maps showing aggregation of HLA-G+ EVTs (f) around PDE3A+ spiral arteries (g) undergoing active remodeling.

Extended Data Fig. 6 Molecular and spatial validation of arterial endothelial cell markers.

(a-b) UMAPs showing specific expression of PDE3A in arterial endothelial cells (aECs) (a) among all endothelial subtypes in Schupp et al.[31] (b). (c-e) Immunolocalization validating PDE3A as an aEC marker in spiral arteries (SA). aECs in SA with (c) or without trophoblast plugs (d) are VIM+, CD31+, and PDE3A+. Trophoblast plugs and partial loss of aECs along vessel wall confirm arterial identity. PDE3A is absent from venous ECs (e), which lack dense surrounding CK+ trophoblasts. Representative images from one of three biological replicates were shown. (f) UMAPs of snRNA-seq data showing specific expression of nine marker genes selected for the CODEX panel. (g) Single-channel CODEX images (GW15 basal plate) illustrating spatial localization of each marker: CD3 (T cells), CDH1 (endometrial epithelium and cytotrophoblasts), PDPN (lymphatic endothelium and fetal fibroblasts), Vimentin (decidual stromal cells and subsets of vascular/lymphatic endothelium), CD31 (ECs), CK18 (KRT18, cytotrophoblasts), CD206 (macrophages and Hofbauer cells), and PDE3A (aECs and perivascular smooth muscle). SA, spiral artery; FV, floating villi; AV, anchoring villi. (h–i) Representative zoomed views showing VCT-specific CDH1 expression (cyan; arrows) in placental villi (GW15 basal plate).

Extended Data Fig. 7 Spatial and functional characterization of the arterial endothelial state transitions.

(a) Validation of cell type localization at the maternal-fetal interface by CODEX on an independent sample (GW22 basal plate), integrating 9 antibody channels. (b) Independent validations using immunolocalization identified one spiral artery (GW 20) undergoing remodeling with discontinuous endothelial lining. Invading EVTs (pan-CK+) displace aECs along one side of the vessel (left), while the remaining segment is lined by aECs with distinct molecular states, including R0 (arrows), characterized by VIM− (middle) and PDE3A+ (right). (c) GO analysis for genes downregulated in R0 aECs vs. canonical aECs. (d) Differentially expressed gene analysis in R2 vs. canonical aECs (caECs), showing upregulation of apoptosis-related genes. (e) Comparison of spatial proximity to EVTs across aEC states from 17 SAs (010-GW20), quantified as Euclidean distance to the nearest EVT. Box plots, center line=median, box=IQR (25th-75th percentile), minima/maxima=lower/upper whisker (0.5×IQR) endpoints, and outliers are not displayed; P values, two-tailed Wilcoxon rank-sum test with Benjamini-Hochberg correction; n.s., not significant.

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Extended Data Fig. 8 Spatial and functional characterization of EVTs.

(a) UMAP of all trophoblasts grouped by gestational stages. (b) Reconstructed trophoblast differentiation trajectories showing bifurcation from VCT into SCT and EVT lineages. The EVT branch: proEVT, interstitial EVT (iEVT), and endovascular EVT (eEVT), colored by pseudotime. (c) Co-immunolocalization validating ITGA2 expression in EVT progenitors in cell column. Representative fields from one of three biological replicates were shown. (d-g) UMAPs showing subtype-specific gene expression in snRNA-seq: ITGB6 (d) and VIT (e), proEVT; AOC1 (f), iEVT; and CD81 (g), giant cells (GC). (h) Functional enrichment by GO analysis for differentially expressed genes in each EVT subtype. (i) Differential expression analysis between highly invasive EVTs (highest 5% iScore) and minimally invasive EVTs (lowest 5%) from the held-out test. (j) GO enrichment analysis comparing EVTs with high vs. low iScores. (k) Comparison of EVT iScores across normal pregnancy, placenta accreta spectrum (PAS)-adherent, and PAS-nonadherent placentas. Box plots, center line=median, box=IQR (25th-75th percentile), minima/maxima=lower/upper whisker (0.5×IQR) endpoints, and outliers were not displayed; P values, two-tailed Wilcoxon rank-sum test with Benjamini-Hochberg correction. (l) Spatial distribution of EVT iScores near the maternal-fetal interface and blood vessels (GW20). (m) iScore distributions across EVT subtypes (proEVT, iEVT, eEVT, pEVT). Center line=median, box=IQR (25th-75th percentile), minima/maxima=observed minimum and maximum values; P values, two-tailed Wilcoxon rank-sum test with Benjamini-Hochberg correction.

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Extended Data Fig. 9 Molecular and spatial characterization of syncytiotrophoblast subtypes.

(a-b) snRNA-seq UMAPs showing ERVFRD-1 (syncytin-2) (a) and SLC26A2 (b) expression specific to progenitor SCTs (proSCT). (c) Immunolocalization of SLC26A2 (green) and pan-CK (red) in placental villi (left) and basal plate (right) marking proSCTs in floating villi. FV, floating villi; AV, anchoring villi; col, cell column (GW19). Representative images from FVs and AVs from one of three replicates were shown. (d) Joint RNAscope FISH for GPC5 (magenta) and immunostaining for CD94 (green) in FV from two GW23 placentas as biological replicates, identifying GPC5+ SCT-b cells enriched in syncytial aggregates/knots and CD94+ SCT-a cells along the trophoblast basement membrane. (e) Representative zoomed views from spatial transcriptomes showing GPC5+ SCT-b localization within syncytial aggregates/knots (011-GW21.5 and 014-GW24), with pan-CK (green) and CD31 (red) co-immunostaining. (f) Quantification of GPC5+ SCTs in syncytial aggregates vs. the entire syncytium by RNAscope (left) and Stereo-seq (right). Box plots, center line=median, box=IQR (25th-75th percentile), minima/maxima=lower/upper whisker (0.5×IQR) endpoints; P values, two-tailed Student’s t test. (g) Immunolocalization of GPC5 in two term placentas (GW39, GW41) showing preferential cytoplasmic localization of SCT-b cells within syncytial aggregates/knots. (h) Increased proportion of the SCT-b subtype with advancing gestation. P values, two-tailed Fisher’s exact test with Benjamini-Hochberg correction.

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Extended Data Fig. 10 Spatial and functional characterization of decidual stromal cells (DSCs).

(a) UMAP of DSCs across gestational ages colored by subtypes. (b) Dot plot showing scaled expression of representative path A and B markers across five DSC subtypes. (c-g) Immunolocalization confirming DSC subtype identity and spatial localization (second trimester basal plate); representative fields from at least three biological replicates were shown. (c) Undecidualized cells (ACTA2+, red) were localized in the deep decidua (GW20); (d) PEDF+IGFBP1+ DSC3 near blood vessels (PEDF, red; IGFBP1, green) in GW22 decidua; (e) A subset of CK+ (red) EVTs also displayed PEDF protein abundance (green) in the decidua (GW22); (f-g) PEDF− (red) /IGFBP1+ (green) DSC4 adjacent to anchoring villi (GW17 and GW16). (h) Replication scRNA-seq of 28,626 cells from decidual swabs at term recapitulated DSC subtype signatures. (i) Enriched GO terms for genes upregulated in path A and path B DSCs. (j) UMAP showing two DSC3 subclusters (3.1 and 3.2) identified in snRNA-seq. (k) DSC3.2 displayed significant downregulation of genes in anti-angiogenesis, antigen presentation, complement activation, and anti-apoptosis, relative to DSC3.1 cells.

Extended Data Fig. 11 Characterization of DSC4 in in vitro model.

(a) UMAP showing specific WNT5A expression in DSC4. (b) Identification of DSC3/DSC4 subtypes from subclusters within the spatial DSC compartment, validated by snRNA-seq signature scoring. Center line=median, box=IQR (25th-75th percentile), minima/maxima=observed minimum and maximum values; P values, two-tailed Wilcoxon rank-sum test with Benjamini-Hochberg correction. (c-d) Spatial localization of ACTA2+/VIM+ undecidualized DSC0 (yellow arrows) overlaid with immunostaining for pan-CK (green) and CD31 (red) in one representative 2nd trimester sample (002-GW22.3, n = 16). (e-h) UMAPs of scRNA-seq from in vitro decidualized cells confirming the expected DSC subtypes, marked by ACTA2 (e, clusters c0, c1 & c5), IGFBP1 (f, clusters c2–c4), and SERPINF1 (g, clusters c2 & c3). Clusters c2–c3 resembled DSC3, while cluster c4 resembled DSC4 from primary decidua. (i) Differential gene expression showing down-regulation of apoptotic genes in DSC4 upon mAEA exposure.

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Extended Data Fig. 12 Identify vulnerable cell types in pregnancy complications.

(a-d) UMAPs showing specific expression of POU5F1 (OCT4, a), LGR5 (b), PAX8 (c), and B4GALNT3 (d) in endometrial epithelial cells. (e) Aggregated preeclampsia gene set scores from DisGeNET across maternal and fetal cell types. Box plots, center line=median, box=IQR (25th-75th percentile), minima/maxima=lower/upper whisker (0.5×IQR) endpoints, and outliers are not shown; P values, two-tailed Wilcoxon rank-sum test with Benjamini-Hochberg correction. (f-g) Negative controls when performing SCAVENGE analysis in disease irrelevant tissues; no significant preeclampsia risk enrichment in prenatal (f) or adult (g) neuronal cells; (h) Enrichment of maternal GWAS risk for preterm delivery from an independent replication cohort[53]; P values, two-tailed Fisher’s exact test after Benjamini-Hochberg correction.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Methods describe the additional details of sample processing and computational analysis not listed in Methods; Supplementary Notes contain the additional information of CODEX imaging and single-cell GWAS analysis not described in the main text.

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Supplementary Tables 1–13 and Supplementary Tables guide

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Wang, C., Zhou, Y., Wang, Y. et al. Single-cell spatiotemporal dissection of the human maternal–fetal interface. Nature 653, 167–179 (2026). https://doi.org/10.1038/s41586-026-10316-x

- Received: 25 September 2024

- Accepted: 23 February 2026

- Published: 08 April 2026

- Version of record: 08 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10316-x

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Heart-nosed bat alphacoronaviruses use human CEACAM6 to enter cells

Giulia Gallo,

Antonello Di Nardo,

Doreen Lugano,

Adam J. Roberts,

Bernadette Ataku Kutima,

Moses Okombo,

Aghnianditya Kresno Dewantari,

Florence M. M. Buckley,

Gavin J. Wright,

James Nyagwange,

Bernard Agwanda,

Stephen C. Graham &

…

Dalan Bailey

Nature volume 653, pages 180–189 (2026)

Abstract

Identifying viruses with zoonotic potential on the basis of their ability to enter human cells is a critical component of pandemic prediction, prevention and preparedness. Here using a computational approach that retains maximum phylogenetic diversity, we selected an optimal subset of alphacoronavirus spike proteins to screen against broad coronavirus receptor libraries. Most of the selected spike proteins did not use any of the established coronavirus receptors. However, the pseudotyped spike protein of Cardioderma cor (heart-nosed bat) coronavirus KY43 (CcCoV-KY43) could enter human cells. Using a recombinant CcCoV receptor-binding domain (RBD) and a human receptor screening platform, we identified direct interactions with the human CEACAM proteins CEACAM3, CEACAM5 and CEACAM6. Overexpression of human CEACAM6—a protein widely expressed in the human lung—conferred permissivity to otherwise refractory human cells. A crystal structure showed that the RBD binds the amino-terminal IgV-like domain of human CEACAM6. Immune surveillance studies using sera of individuals from the Taveta region of Kenya, where CcCoV-KY43 was identified, did not show significant evidence of recent spillover. Wider characterization of alphacoronaviruses related to CcCoV-KY43 showed that human CEACAM6 is used by two other CcCoVs collected in Kenya. Moreover, there was more restricted nonhuman CEACAM6 tropism for viruses isolated from Rhinolophus bats from Russia and China. Thus, alphacoronaviruses that use CEACAM6 are probably geographically widespread, and viruses from East Africa show potential for transmission to humans.

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Main

Following the COVID-19 pandemic, there has been a renewed focus on the characterization and prediction of viruses with zoonotic potential, particularly among the coronaviruses. Between 60% and 75% of human pathogens are thought to have a zoonotic origin[1]. However, acquiring comprehensive information on the zoonotic potential of all viruses is technically challenging because of their extensive diversity. Detailed data are usually available for only a few species of human viruses, or their close relatives, which belies the fact that the next pandemic is likely to be a previously undescribed virus, as SARS-CoV-2 was to virologists in 2019.

The first barrier for any cross-species viral jump is cell entry, a process that relies on the binding of viral attachment proteins to cellular receptors. To attempt wider characterization and identification of viruses with zoonotic potential, we focused on alphacoronavirus entry as a model, owing to the lack of detailed genus-wide information on the host range of these predominantly bat-borne viruses. So far, only two cellular receptors have been characterized for alphacoronaviruses. Human, porcine, canine and feline aminopeptidase N (APN) receptors provide entry to group I viruses, which include human coronavirus 229E (HCoV-229E)2,3, transmissible gastroenteritis virus and the related porcine respiratory coronavirus (PRCV)[4], as well as some canine and feline coronaviruses[5]. Human coronavirus NL63 (HCoV-NL63) is the only known alphacoronavirus to use ACE2 as a receptor[6]. Apart from ACE2 (used by SARS-CoV-1 and SARS-CoV-2), human betacoronaviruses use the receptors DPP4 (used by MERS-CoV)[7], O-acetylated sialic acids (used by HKU1 (ref. [8]) and OC43 (ref. [9])) and TMPRSS2 (used by HKU1)[10]. Recently, porcine and human DPEP1 were both shown to be efficient receptors for porcine haemagglutinating encephalomyelitis virus, although there is no evidence for zoonotic infection[11]. Entry of the betacoronavirus mouse hepatitis virus (MHV) is facilitated by mouse CEACAM1 (ref. [12]); although in this case, the human orthologue is not efficiently used[13]. How receptor usage partitions across the entire alphacoronavirus genus and whether there are other human tropic viruses and/or other permissive receptors are unclear.

APN- and ACE2-dependent entry is rare for alphacoronaviruses

For all coronaviruses, the spike protein is the major determinant of viral entry, and cleavage by cellular proteases produces two subunits: S1 and S2. S1 is involved in binding to the cellular receptor, whereas S2 is involved in fusion of the viral and cellular membranes. To select alphacoronavirus spike proteins that accurately represent the known diversity, we used a greedy algorithm[14] on all full-length spike protein sequences (n = 2,714) retrieved from the Virus Pathogen Database and Analysis Resource database[15] and deposited into GenBank (as of May 2021). Our goal was to scale down the number of spike proteins tested without significantly losing the richness and heterogeneity of diversity in this genus. The final number chosen (n = 40) was based on our capacity for pseudotyping and receptor screening (Fig. 1a and Supplementary Table 1). Among these sequences, which included representatives from taxonomically classified subgenera such as Colacovirus, Pedacovirus, Minacovirus, Tegacovirus, Nyctacovirus and Luchacovirus, the overall level of amino acid conservation was low, especially in the predicted RBD (Supplementary Fig. 1). The selected 40-sequence spike protein panel (around 1.5% of the full dataset) captured 53.4% of the total phylogenetic diversity, which substantially exceeded size-matched random panels (13.7 ± 3.2%, 10,000 permutations, around 3.9-fold enrichment, empirical P < 0.0001). Most selected spike proteins were from poorly characterized alphacoronaviruses isolated from bats (27 out of 40); however, the two endemic human viruses HCoV-229E and HCoV-NL63 were selected. To confirm that the commercially synthesized, cloned, codon-optimized spike protein open-reading frames (ORFs) could efficiently produce spike proteins that are pseudotyped onto HIV-1-based lentiviruses, pseudoviruses were purified and spike protein incorporation was confirmed by immunoblotting (Extended Data Fig. 1a). Concurrent to the establishment and validation of this alphacoronavirus spike protein pseudotype library, we developed equivalent plasmid-based APN and ACE2 expression libraries that represented 25 and 34 mammalian species, respectively (Supplementary Tables 2 and 3). Expression of these tagged receptors was verified by flow cytometry (Supplementary Fig. 2). First, we assessed use of known human receptors (APN, ACE2, DPP4, TMPRSS2 and DPEP1), which identified only HCoV-229E and HCoV-NL63 as human-tropic (Fig. 1b). The algorithm-selected HCoV-229E spike protein was not functional; therefore, we replaced it with the reference sequence for the virus (Supplementary Fig. 3a). Next, we assayed whether the pseudotyped spike protein from our alphacoronavirus library could use any APN or ACE2 from our receptor libraries (Fig. 1c (selected data) and Supplementary Figs. 4a and 5a (all data)). Canine coronaviruses (CCoVs) could enter cells that express APN, whereas despite a feline coronavirus being included in the library, it did not pseudotype. Moreover, two different porcine epidemic diarrhoea virus (PEDV) spike proteins did not use porcine APN in our experimental settings, which could be explained by the dynamics of PEDV spike protein internalization in non-susceptible cells[16]. However, another porcine alphacoronavirus, PRCV, was able to use the same receptor (Supplementary Fig. 3b). Where possible, we included the species for which individual viruses were identified or the closest relative available (Supplementary Figs. 4b and 5b). This selection included genetically divergent Chiroptera species to account for the reservoir biology of coronaviruses and domesticated and peridomestic animals that might bridge the gap between unknown reservoirs and humans. In general, APN-using viruses seemed to be restricted to lower numbers of host species than the ACE2-using HCoV-NL63. We suggest that this difference is determined by the spike protein–receptor interaction surface area, affinity and physicochemical nature (for example, charge and hydrophobicity), combined with receptor sequence variability across hosts. Notably, two bat-origin alphacoronaviruses, BtCoV-WA1087 and BtCoV-AT1A-F41, used APN receptors to enter cells, albeit not human APN (Fig. 1c). To our knowledge, this is the first study to confirm APN usage by alphacoronaviruses isolated from bats. Most of the alphacoronaviruses tested did not use any of the expressed APN or ACE2 receptors to enter cells. Some coronaviruses are only infectious after protease treatment, which facilitates transition of the RBD from a closed to open conformation and/or exposure of the fusion peptide at the S2′ site. To address this possibility, we repeated experiments in the presence or absence of human serine protease TMPRSS2 or trypsin (Extended Data Fig. 2a (TMPRSS2) and 2b (trypsin)). Co-expression or pretreatment did not affect host range, although there was some evidence of digestion of spike proteins by trypsin (Extended Data Fig. 1b). To examine whether closed RBD conformations were leading to false negatives in our screens, we purified a selection of RBDs and performed flow cytometry to assess receptor binding at the cell surface (Extended Data Fig. 3a,b). The observed correlation between entry and receptor binding indicated that an absence of pseudotype entry reflects a genuine inability to bind APN and ACE2. Together, these results suggest that use of the known receptors ACE2 and APN may be a relatively rare phenomenon in the alphacoronavirus genus.

Fig. 1: Construction of the alphacoronavirus spike protein library and screening of the receptor libraries.

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a, Schematic of the selection process used to create the spike protein library. Full-length amino acid sequences of alphacoronaviruses were recovered from public repositories (1) and aligned (2) to infer the alphacoronavirus genus phylogeny (3), from which cophenetic distances between all taxa were estimated. In total, 40 representatives spike protein sequences were selected using an optimal greedy-based algorithm (4). b, Screening of known human coronavirus receptors using the alphacoronavirus spike protein library pseudotypes. As expected, entry was observed for HCoV-229E inf1 and HCoV-NL63 with APN and ACE2, respectively. Entry of other viruses in the library was not supported by previously described human coronavirus receptors. c, Wider receptor usage by the alphacoronavirus library. To assess whether APN or ACE2 proteins from nonhuman species could be used as receptors for spike proteins in the alphacoronavirus library, we examined receptors from different mammalian species. Positive results were reproduced at least three times using independent biological replicates. All experiments were performed in technical triplicate with pseudotype (PV) entry indicated as log_10 relative light units (RLU). The average of the repeats, minus background, is shown.

Fig. 2: Human CEACAM6 supports entry of CcCoV-KY43.

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a, Different human cell lines were assessed for their permissivity to alphacoronavirus pseudotyped spike proteins. CcCoV-KY43 could enter Calu3 (lung) and Caco2 (intestinal) cells. The mean entry (fold change (FC) compared with non-enveloped (NE) controls) from technical triplicates is shown. b, Recombinantly produced CcCoV-KY43 RBD shows potentially high levels of glycosylation. c, Screening an array of human receptor ectodomains identified CEACAM6, CEACAM3 and CEACAM5 as interactors of the CcCoV-KY43 RBD. d, CEACAM proteins were overexpressed in refractory HEK293T cells, and the assays showed that pseudotyped CcCoV-KY43 spike protein could only enter in the presence of CEACAM6. Two biological replicates (technical triplicates) along with their s.d. values, are shown. ***P = 0.0003 using two-way analysis of variance (ANOVA). NS, not significant. e, Interaction of the CcCoV-KY43 RBD and CEACAM6 was confirmed by ELISA, whereas no binding was observed with CEACAM3 or CEACAM5. The mean of two experiments (technical triplicate) is shown with s.d. values. f, ITC (showing differential power (DP) values) of CEACAM6 and CEACAM5 binding the CcCoV-KY3 RBD. Data are representative of two (CEACAM5) or three (CEACAM6) independent experiments. For CEACAM6, mean Kd, number of binding sites (n) and enthalpy change (ΔH) are shown. g, Pseudotyped CcCoV-KY43 spike protein was incubated for 1 h with recombinant CEACAM6, followed by titration on HEK293T cells expressing human CEACAM6 (hCEACAM6). Raw data of the mean of two independent experiments (technical triplicate) are shown with s.d. values. h, Monoclonal antibodies against CEACAM6 (B6.2 and clone 439424) were used to neutralize CcCoV-KY43 entry into HEK293T cells expressing hCEACAM6. The mean of two independent experiments (technical triplicate) was normalized to the untreated condition and plotted with s.d. values. i, Left, CEACAM6-specific siRNAs, or negative control, were electroporated into permissive cells and then infected with CcCoV-KY43. To confirm CEACAM6 reduction, cell lysates were analysed by immunoblotting (bottom). Mean entry reduction relative to the scrambled siRNA control from three independent experiments (technical triplicates) along with s.d. values, is shown. Significance of log_10 fold change was determined using a one-sample t-test and P values were adjusted for multiple comparisons (Caco2: P = 0.004 for siRNA-1, P = 0.001 for siRNA-2 and P = 0.009 for siRNA-3; Calu3: P = 0.022 for siRNA-1, P = 0.04 for siRNA-2 and siRNA-3). Right, using lentivirus expressing shRNA, stable knockdown of CEACAM6 expression in Caco2 and Calu3 cell lines was induced and validated by immunoblotting. One biological replicate (technical triplicates) is shown, along with s.d. values. WT, wild type. j, Left, lung-specific single-cell transcriptomic data show the expression of CEACAM6. Right, comparison of the expression of coronavirus receptors in lung cells: CEACAM6, ACE2, APN, DDP4 and TMPRSS2 are shown with the dot plot indicating both average and per cent expression.

Fig. 3: CcCoV-KY43 RBD interacts with the IgV domain of CEACAM6.

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a, Structure of the CcCoV-KY43 RBD interacting with the human receptor CEACAM6. The three loops protruding from the RBD that are responsible for the interaction with CEACAM6 are highlighted. b, Open-book representation of the interfaces of interaction between the two proteins. The surface formed by residues constituting the three loops of the RBD are coloured as in a. c, Zoom-in to key residues involved in binding between the CcCoV-KY43 RBD and human CEACAM6. Residues of loop 1 (R517 and S519) and loop 2 (T552 and S555) establish hydrogen bonds with the surface polar regions of CEACAM6, whereas W600 and I605 on loop 3 of the RBD interact with the hydrophobic pocket of the receptor. d, Alignment of human CEACAM5 and CEACAM6, showing the residues interacting with CcCOV-KY43 RBD (in bold). An I63F substitution was introduced in CEACAM6, and the complementary substitution F63I in CEACAM5, followed by entry assays. The average of three independent experiments, performed in technical triplicates, is shown with s.d. values. ***P = 0.0001, ****P < 0.0001 statistical analysis using two-way ANOVA. e, Structure of alphacoronavirus RBDs in complex with human receptors used for entry: CcCoV-KY43 with CEACAM6, HCoV-229E with APN (Protein Data Bank (PDB) ID: 6ATK)[28] and HCoV-NL63 with ACE2 (PDB ID: 3KBH)[48]. Despite binding architecturally different proteins, the RBDs share similar overall folds and all bind their receptors using the same three surface loops.

Human CEACAM6 supports entry of CcCoV-KY43

Recent high-throughput analyses of a diverse range of pseudotyped viruses (screened against the NCI-60 panel from the National Institutes of Health) concluded that coronavirus entry is a key determinant of host range[17]. As most alphacoronavirus spike proteins did not use human APN, ACE2, DPP4, TMPRSS2 or DPEP1 for entry, the next step in our zoonotic risk assessment was to examine pseudotype infection of a wider range of human cell lines. Across our screen (Fig. 2a), the only examples of infection we detected were with pseudotyped HCoV-NL63 and HCoV-229E, a finding consistent with our receptor screening results and their known human tropism (Fig. 1b), and with BtCoV-KY43 (Fig. 2a). The spike protein sequence of BtCoV-KY43 (hereafter referred to as CcCoV-KY43) was originally isolated from C. cor bats in Kenya in 2006 (ref. [18]), with a partial genome sequence published in 2012 (GenBank accession: HQ728480)[19]. We speculated that successful infection of Calu3 and Caco2 cells by CcCoV-KY43 spike protein pseudotypes is facilitated by an unknown human receptor. To identify this receptor, we used an avidity-based method designed to systematically identify direct extracellular interactions with a library of 759 human receptor ectodomains[20], using the CcCoV-KY43 spike protein RBD (CcCoV-KY43 RBD) as prey (Fig. 2b). We identified three interactions, all of which were paralogues of the human carcinoembryonic antigen cell adhesion molecule (CEACAM) protein: CEACAM3, CEACAM5 and CEACAM6 (Fig. 2c and Supplementary Fig. 6). These cell surface glycoproteins contain immunoglobulin-like domains, with both CEACAM5 and CEACAM6 having a glycosylphosphatidylinositol anchor. Subsequent overexpression of a panel of full-length human CEACAM proteins in HEK293 cells, which are normally refractory to CcCoV-KY43 spike protein pseudotype infection, demonstrated that only human CEACAM6 expression resulted in a significant increase in permissivity (Fig. 2d). A split luciferase-based cell–cell fusion reporter assay subsequently confirmed this receptor usage profile in a separate cellular context (Extended Data Fig. 4). ELISA experiments with recombinant CcCoV-KY43 RBD and the extracellular domains of human CEACAM3, CEACAM5 and CEACAM6 enabled us to attribute this entry specificity to a higher affinity for CEACAM6 (Fig. 2e). Thermodynamic analyses using isothermal titration calorimetry (ITC) confirmed a 1:1 interaction between the CcCoV-KY43 RBD and CEACAM6, with a dissociation constant (Kd) of 271 ± 68 nM (mean ± s.d., n = 3), whereas the RBD did not measurably bind CEACAM5 (Fig. 2f and Supplementary Table 4). Dose-dependent neutralization of CcCoV-KY43 spike protein pseudotypes was demonstrated using soluble, recombinant CEACAM6 (Fig. 2g), and cell entry was blocked when using monoclonal antibodies against human CEACAM6 (Fig. 2h and Extended Data Fig. 5a). To further confirm the role of human CEACAM6 in CcCoV-KY43 entry, we used CEACAM6-specific short interfering RNAs (siRNAs) to knockdown endogenous protein expression in the susceptible cell lines Caco2 and Calu3. These experiments revealed reduced virus entry for CcCoV-KY43 (Fig. 2i, left) but not HCoV-229E inf1 (Supplementary Fig. 7). The same results were obtained in Calu3 and Caco2 cells treated with short hairpin RNA (shRNA) that were selected to stably downregulate the expression of human CEACAM6 (Fig. 2i, right). To assess whether human CEACAM6 could support entry of other greedy-selected alphacoronaviruses, we re-screened our library but did not find other examples of entry (Extended Data Fig. 6).

In humans, pathogenic coronaviruses often have tropism for the respiratory and/or gastrointestinal tract. Analyses of datasets from The Human Cell Atlas[21], ranked by the number of cells expressing CEACAM6 in individual tissues, identified the lung, colon and bronchus as the three tissues with the most numerous CEACAM6-expressing cells (Supplementary Fig. 8). Stratification of single-cell transcriptomic data for the lung[22] by cell type identified pulmonary alveolar type 1, goblet (bronchial, tracheobronchial and nasal) and epithelial cells of the lower respiratory tract as having the most prevalent CEACAM6 expression (Fig. 2j and Supplementary Fig. 9a). These cell types are frequent targets for respiratory virus infections. The only precedents for CEACAM6 being used by pathogens for infection is as an adhesion factor for Gram-negative bacteria23,24, for example by Escherichia coli in patients with Crohn’s disease who have aberrant CEACAM6 expression in their ileal epithelium[25], and by the pathogenic yeast Candida albicans[26]. Of note, expression of CEACAM6 in the human lung is both higher and more widespread than any of the established human coronavirus proteinaceous receptors APN, ACE2 or DPP4 (Fig. 2j and Supplementary Fig. 9b). Together, these data demonstrate that CcCoV-KY43 spike protein uses human CEACAM6 as a receptor to infect cells, and this receptor is expressed in the human lung.

KY43 RBD binds CEACAM6 N-terminal immunoglobulin domain

We used crystallography to solve the structure of a complex comprising the CcCoV-KY43 RBD and the three immunoglobulin-like domains that form the ectodomain of CEACAM6 to define the molecular architecture of their interaction (Fig. 3a). The structure was refined (R/Rfree = 0.256/0.317) from crystals with anisotropic diffraction (3.0 and 5.1 Å in the best and worst direction, respectively) that contained one heterodimer of the CcCoV-KY43 RBD plus CEACAM6 per asymmetric unit (Supplementary Table 5 and Supplementary Fig. 10a). The RBD binds the tip of the N-terminal V-set immunoglobulin-like domain of CEACAM6, with receptor binding loops 1–3 of the RBD engaging the surface formed by CEACAM6 strands G-F-C-C′-C″ plus the F-G and C-C′ loops (Fig. 3b and Supplementary Table 6). This region is the same surface that mediates both homodimerization of CEACAM6 and its heterodimerization with CEACAM8 (ref. [27]). Moreover, the N-terminal domain of CEACAM6 is highly similar between the complexes with CcCoV-KY43 RBD and CEACAM6 and CEACAM8, with 0.51–0.54 Å root-mean-squared deviation across 99–103 Cα atoms (Supplementary Fig. 11). The interaction buries 1,300 Å^2 of the surface area, less extensive than CEACAM6 homodimerization or heterodimerization (1,600 Å^2) but more than the interaction between the HCoV-229E RBD and human APN (1,000 Å^2)[28] and comparable with that of other coronavirus–receptor interactions (Supplementary Fig. 12). The interaction is dominated by the hydrophobic interaction between CcCoV-KY43 RBD loop 3, especially residue W600, and the hydrophobic surface formed by strands C, C′ and F of CEACAM6, centred on residue L129 (Fig. 3c and Supplementary Table 6).

Elucidation of the structure of the binding interface enabled us to dissect the genetic determinants of CEACAM6 receptor specificity. Loops 1–3 in the RBDs from alphacoronavirus species included in our library differed markedly from CcCoV-KY43 in both their length (Supplementary Fig. 13a) and amino acid composition (Supplementary Fig. 13b). This result is consistent with the observation that only CcCoV-KY43 uses CEACAM6 for entry (Extended Data Fig. 6). Although human CEACAM5 was identified as an entry receptor in our sensitive avidity-based receptor discovery experiment, the ELISA and ITC experiments showed that it has reduced affinity for the CcCoV-KY43 RBD (Fig. 2e,f). Moreover, CEACAM5 overexpression was not sufficient for pseudotype entry (Fig. 2d). Alignment of the surface contact interface identified two important amino acid substitutions, I63F and Q123H, in CEACAM5 relative to CEACAM6 (Fig. 3d). A F63I, but not H123Q, substitution in CEACAM5 partially overcame the entry restriction of CcCoV-KY43 pseudotypes, with the corresponding substitutions in CEACAM6—I63F, Q123H or both—reducing pseudotype entry (Fig. 3d and Extended Data Fig. 7a). Note that the mutated receptors were expressed at comparable levels to the wild type for these experiments (Extended Data Fig. 7b). Detailed structural mechanics of coronavirus receptor binding have been extensively characterized, which have highlighted a fascinating pattern of either convergent or continued evolution. The architecture of the CcCoV-KY43 RBD is similar to those of the other two human alphacoronaviruses HCoV-229E and HCoV-NL63, even though their cellular receptors are structurally different (Fig. 3e). Although our data provide evidence that CEACAM6 can be used as a direct proteinaceous receptor by viruses, we note that mouse CEACAM1 is used as a receptor by the betacoronavirus MHV[29]. MHV receptor binding is mediated through the N-terminal domain of S1, whereas most protein-binding coronavirus RBDs are found in the S1 C-terminal domain, as is the case for CcCoV-KY43. Nevertheless, the target region on the CEACAM receptor remains orthologous: the N-terminal immunoglobulin domain (Supplementary Fig. 14). As the human CEACAM N-terminal immunoglobulin domains are relatively conserved (Supplementary Fig. 15, binding region in teal), we also examined CEACAM1, CEACAM4, CEACAM8 and CEACAM20 in CcCoV-KY43 entry assays (Extended Data Fig. 8 and Supplementary Table 7). None of these CEACAM proteins was able to facilitate entry of CcCoV-KY43 pseudotypes. Moreover, CEACAM1, CEACAM4 and CEACAM8 did not show appreciable binding in biolayer interferometry assays, nor did the I63F mutant of human CEACAM6 (Extended Data Fig. 9). These data highlight another example of the diversity of receptor recognition modalities among coronaviruses, with two coronaviruses from distinct genera evolving to bind the same family of receptors (in the same orthologous domain on those receptors), but through different S1 domains. These results further confirm that CEACAM usage across the rich coronavirus diversity may be underexplored.

CEACAM6-using coronaviruses are globally distributed

CcCoV-KY43 was isolated from a bat collected in a rural setting: an artisanal mine in southeastern Kenya[18] (Fig. 4a). C. cor is naturally distributed across Eastern Africa[30]. However, more granular population distribution data for Kenya are currently lacking. To provide an interim assessment of sites where bat–human spillover might occur, we summarized our bat sampling data from across Kenya over the past 20 years (Fig. 4a, derived geographical range of C. cor coloured in teal). This analysis revealed that coastal regions in the southeast are human population centres at increased risk. Focusing on this region, we examined human sera from our biobank and assessed for the presence of CcCoV-KY43 RBD-specific antibodies (Fig. 4b). We analysed a total of 368 blood donor samples collected in 2020 and 2021 from mainly male individuals (95%) under 35 years of age (72%) living in the Tana River and Taita-Taveta counties where CcCoV-KY43 was identified (Supplementary Table 8). The samples were examined for CcCoV-KY43, HCoV-229E, HCoV-NL63 and SARS-CoV-2 S-protein-specific responses by ELISA (Fig. 4b, inset, Tana River and Taita-Taveta counties highlighted in pale red). We also included two RBDs from more distantly related alphacoronaviruses (BtCoV-HlYN18 and BtCoV-A701, both isolated from bats in China) as comparators. Across the full dataset, Spearman’s rank correlation analysis revealed weak monotonic associations between CcCoV-KY43 and HCoV-229E, SARS-CoV-2, BtCov-HlYN18 and BtCoV-A701 (ρ = 0.14–0.30), whereas no association was observed with HCoV-NL63 (ρ = 0.06) (Fig. 4b). Although ELISA reactivity against the human alphacoronaviruses HCoV-229E and HCoV-NL63 was commonly observed, increased CcCoV-KY43 ELISA signals were detected only sporadically, with optical density (OD) values exceeding one in nine samples. Using this OD threshold as a descriptive marker of high reactivity rather than evidence of seropositivity, similarly infrequent high OD signals were observed for BtCoV-HlYN18 (n = 5) and BtCoV-A701 (n = 2). Restricting the analysis to samples comprising the top 10% of CcCoV-KY43 ELISA signals, Pearson’s correlation analysis of matched sera revealed moderate correlations with HCoV-NL63 and SARS-CoV-2, but only weak correlation with HCoV-229E, BtCoV-HlYN18 and BtCoV-A701 (Supplementary Fig. 16). Together, these data suggest that widespread CcCoV-KY43 spillover is unlikely in these populations. Alternatively, sporadic CcCoV-KY43 reactivity may reflect cross-reactive antibody responses induced by exposure to antigenically related coronaviruses.

Fig. 4: Human CEACAM6 is the receptor for other CcCoVs identified in Kenya.

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a, Sites of bat sampling in Kenya and the distribution of C. cor (highlighted in teal). C. cor is found in mines (squares), natural (circles) and tree (triangles) caves and in two instances in human habitations (houses). Other sampling locations where C. cor was not observed are labelled with a cross. Inset, human population-dense areas of Kenya do not generally overlap with C. cor roosting sites. The individual sites where CcCoV were identified are shown in pale brown (CcCoV-KY43) and dark brown (CcCoV-2A and CcCoV-2B). b, Human sera from individuals (n = 368) from Tana River and Taita-Taveta counties (highlighted in pale red in the inset map; teal indicates distribution of C. cor) were analysed for their reactivity to different human coronavirus glycoproteins. Individual Spearman’s rank correlations (ρ) for each dataset (compared with CcCoV-KY43) are provided. c, Alignment of C. cor-derived alphacoronavirus RBD amino acid sequences, showing high identity in the loops interacting with CEACAM6 (loops are highlighted as in Fig. 3a). The secondary structure of the RBD, based on the CcCoV-KY43 RBD, is depicted above the sequences. Residues that interact with CEACAM6 are in bold. d, Entry assays with a pseudotyped spike protein of CcCoV-2A showed that human CEACAM6 confers permissivity to HEK293T cells at levels similar to CcCoV-KY43. Average of the raw data of three independent experiments, performed in technical triplicate, are shown, with s.d. values. **P = 0.0036 for CcCoV-KY43; **P = 0.0014 for CcCoV-2A. Statistical analysis used two-way ANOVA. e, Recombinant CcCoV-2B spike protein RBD was purified and used to assess binding to human CEACAM3, CEACAM5 and CEACAM6 by ELISA. Raw data of three independent experiments, performed in technical triplicate, are shown with s.d. values. f, ITC showed that the CcCoV-2B RBD binds CEACAM6 with high affinity. g, Crystal structure of CcCoV-2B in complex with human CEACAM6. Inset shows the CcCoV-2B and CcCoV-KY43 RBDs from the two complexes superposed, which highlights the similar folds of the RBDs and conserved interaction with the receptor. h, Zoom-in of key residues at the CEACAM6-binding interfaces of CcCoV-2B and CcCoV-KY43, highlighting the similarity of the interactions.

Recently, two more CcCoV sequences from Meru National Park in central Kenya, about 600 km from the site of the isolation of CcCoV-KY43, were published[31]: CcCoV 2A/Kenya/BAT2621/2015 (CcCoV-2A; GenBank identifier: PP273172.1) and CcCoV 2B/Kenya/BAT2618/2015 (CcCoV-2B; GenBank identifier: PP273173.1) (Fig. 4a). Notably, both viruses are relatively divergent. CcCoV-2B is more related to CcCoV-KY43; however, it shares only 79% and 83% nucleotide and amino acid identity, respectively, in the spike protein (79% and 85%, respectively, in the RBD). The more distantly related isolate, CcCoV-2A, shares only 70% and 77% nucleotide and amino acid identity, respectively, across the spike protein (72% and 76%, respectively, for the RBD) with CcCoV-KY43 (Fig. 4c). Despite this variability, entry assays using CcCoV-2A spike protein pseudotypes showed human CEACAM6-dependent entry (Fig. 4d). In accordance with the CcCoV-KY43 data, human CEACAM5 did not support pseudotype entry. Similarly, the F63I mutant in CEACAM5 led to increased entry to levels seen with human CEACAM6 (Extended Data Fig. 10a), whereas the I63F substitution in CEACAM6 reduced entry (Extended Data Fig. 10b). In parallel, an ELISA with a CcCoV-2B recombinant RBD confirmed binding to human CEACAM6 (Fig. 4e), and ITC showed that it had higher affinity for CEACAM6 than CcCoV-KY43 (70 ± 16 nM; Fig. 4f and Supplementary Table 4). The structure of the CcCoV-2B RBD in complex with human CEACAM6 was solved from crystals with an anisotropic diffraction limit of 3 Å (Supplementary Table 5). The structures showed an identical quaternary organization to the structure of human CEACAM6 in complex with the CcCoV-KY43 RBD (Fig. 4g) and highly similar residues at the interaction interface (Fig. 4h and Supplementary Fig. 10b).

The use of human CEACAM6 by genetically divergent CcCoVs strongly suggest that CEACAM6 usage might be a conserved ancestral trait of a larger group of uncharacterized alphacoronaviruses. To explore the evolutionary history of CEACAM6 receptor usage, we assembled a library of spike proteins from related viruses and reconstructed the ‘local phylogeny’ of CcCoVs on the alphacoronavirus tree (Supplementary Table 9 and Supplementary Fig. 17a). Overall, CcCoVs were phylogenetically placed in a sister clade to alphacoronaviruses isolated from Rhinolophus ferrumequinum bats in South China (Fig. 5a). The CcCoV clade was returned as monophyletic in all sampled posterior trees (n = 9,000, 95% binomial confidence interval (CI) of 0.999–1.000), with its most recent common ancestor (MRCA) estimated to have evolved around 1833 (95% highest posterior density (HPD) of 1794–1884). Time-dependent rate effects in viral molecular evolution suggest that our date estimates for the CEACAM6-adapted lineage are likely to be conservative (that is, biased towards more recent times). Across diverse viruses, substitution rate estimates are systematically higher over short time scales and decay as the measurement interval increases, consistent with a general time-dependent rate phenomenon[32]. Mechanistic work further implicated purifying selection and multiple-hit saturation as key drivers of this bias, which causes long-term evolutionary change to accumulate more slowly than expected under a simple, constant-rate clock[32]. For coronaviruses specifically, models that explicitly accommodated variable selection pressure and substitution saturation pushed the inferred origin of coronavirus radiation from around 10^4 years to an ancient time scale of millions of years, a result in closer agreement with the diversification of their hosts[33]. A full investigation of the time-dependent rate phenomenon (for example, using epoch or heterogenous clock models[34]) is beyond the scope of this study. However, we acknowledge that estimates for the time to MRCA reported here should be interpreted as lower bounds and that the acquisition of CEACAM6 usage may predate our point estimates. To understand the potential host range of these viruses in more detail, entry through additional CEACAM6-like proteins from primates, bats and other mammals was examined (Fig. 5b, Supplementary Table 10 and Supplementary Fig. 17b). C. cor are taxonomically classified in the family Megadermatidae (African false vampire bat) and the superfamily Rhinolophoidea, which also contains the common coronavirus reservoir bat species Hipposideros and Rhinolophus. Typically, nonhuman primates and bats in the Yinpterochiroptera suborder (Megadermatidae, Rhinolophidae, Hipossideridae, Rhinopomatidae, Craseonycteridae and all fruit bats) have four to six CEACAM genes that produce CEACAM6-like proteins[35]. Notably, this is not the case for Yangochiroptera bats, which have undergone large expansions in their CEACAM gene repertoire[36]. As there are currently no Megadermatidae CEACAM6 sequences publicly available, we screened a wide diversity of other bat species. Our screens identified that human CEACAM6 tropism is exclusive to the Kenyan CcCoV isolates (Fig. 5b). The CcCoV-KY43 and CcCoV-2A pseudotypes showed broad use of primate CEACAM6 receptors as well as various Yinpterochiroptera bats, including Indian flying fox, Egyptian fruit bat, large flying fox and greater horseshoe bat. Moreover, pseudotyped spike proteins from alphacoronaviruses isolated from R. ferrumequinum bats in South China[37] and Southern European Russia[38] were able to use CEACAM6 as receptors to enter cells, but only from Egyptian fruit bat and/or greater horseshoe bat (Fig. 5b). ELISA experiments showed that the recombinant RBD from a related virus, BtCoV-977, which did not pseudotype, can bind Egyptian fruit bat, but not human, CEACAM6 (Extended Data Fig. 11). No consistent amino acid substitution pattern in these viral RBDs distinguished CEACAM6-like proteins that permit CcCoV entry from those that do not (Extended Data Fig. 12a). However, viruses that use human CEACAM6 conserve multiple RBD sequence features that are absent from other RBDs, for example, the length and amino acid compositions of loops 1 and 2 (Extended Data Fig. 12a). We introduced point mutations into the BtCoV-LN20 RBD, which uses various CEACAM6-like proteins but not the human orthologue, to better define the genetic determinants of human receptor usage and zoonotic spillover (Extended Data Fig. 12b). Although we could expand the host range phenotype of BtCoV-LN20, individual changes did not confer human CEACAM6 tropism, which indicated that multiple changes are needed to induce this characteristic and that the risk of rapid adaptation of CcCoV-related viruses to zoonotic spillover is low. To investigate the potential acquisition (or loss) of CEACAM6 usage along the full evolutionary history of alphacoronavirus, we further explicitly modelled CEACAM6 usage as a discrete trait in a Markov jump framework to assess whether the data support single versus multiple gains (or losses). The posterior distribution of CEACAM6 transitions supports ≥2 independent gains (median number of gains = 2; 95% HPD of 1−3), whereas losses are rare and are not mapped on the resulting tree generated using the method highest independent posterior subtree reconstruction (median number of gains = 0, 95% HPD of 0−1). Consistent with this result, the posterior probability that the ‘focal MRCA’ used CEACAM6 is 0.97. In the reconstructed history, CEACAM6 usage in Rhinolophus ferrumequinum bat (BtRf) CoV-Kudep arises on a branch leading to the MRCA of a sister clade to the local phylogeny, whereas the MRCA of these two sister clades itself is reconstructed as not using CEACAM6 (Supplementary Fig. 18). This pattern is most parsimoniously explained by two independent acquisitions of CEACAM6 usage: one along the lineage giving rise to the local phylogeny and one along the lineage leading to the Rhinolophus sinicus (BtRs) CoV-YN20 and BtRfCoV-Kudep strains. Collectively, these data provide evidence that CEACAM6 is a receptor for a broad range of geographically divergent alphacoronaviruses found across East Africa, European Russia and China.

Fig. 5: CEACAM6 is the receptor for CcCoV-related viruses identified in Asia.

The alternative text for this image may have been generated using AI.

Full size image

a, Evolutionary reconstruction of the spike protein-encoding gene for selected alphacoronaviruses demonstrates the relatively recent acquisition of CEACAM6 usage. Viruses reported to utilize human receptors are underlined. Inset map shows locations of isolation of indicated CcCoV-related viruses. b, Pseudotype entry assays for CcCoV and related viruses with CEACAM6-like proteins from different mammalian species (percentage amino acid identity compared with human CEACAM6 or CcCoV-KY43 for the full-length spike protein (FL-S) or the IgV RBD domain (RBD), respectively, is shown). Viruses closely related to CcCoV-2A, CcCoV-2B and CcCoV-KY43 users were included in the analysis, which informed the evolutionary acquisition of receptor usage. Positive results were reproduced three times, using independent biological replicates. All experiments were performed in technical triplicate with PV entry indicated as the fold change above background.

Discussion

As a consequence of the COVID-19 pandemic, there has been a significant increase in research on coronavirus discovery, reservoir characterization and spillover, as well as the development of broad-acting antivirals and therapeutics39,40,41,42. However, most studies have focused on betacoronaviruses, and the zoonotic and pandemic potential of alphacoronaviruses has remained relatively uncharacterized. Indeed, sequencing efforts to understand the origin of alphacoronaviruses has identified a rich diversity of virus genotypes in reservoir species such as rodents and bats31,43. Here we used an approach aimed at analysing and capitalizing on viral heterogeneity at the genus-wide level to gain a broad understanding of receptor usage and host tropism. We confirmed the importance of APN and ACE2 for human, livestock and companion animal alphacoronavirus infections. However, the broader trend was that use of these two receptors is poorly conserved across the genus. This was especially true for spike proteins for which ORFs were sequenced after sampling of bats. Indeed, only 2 out of 25 functionally pseudotyped bat coronavirus spike proteins use a recognized alphacoronavirus receptor. One limitation of our study is that only 28 out of the 36 pseudotyped viruses have a single species assigned as a host, and for these 28 species, the corresponding receptor sequences are known in only 50% (APN) and 79% (ACE2) of cases. We directly matched 10 out of 28 and 9 out of 28 of viruses to their cognate host’s APN and ACE2 receptors, respectively. These numbers increased to 13 out of 28 and 18 out of 28, respectively, when the identity threshold for the receptors was reduced to >85%; however, only 5 pseudotypes used APN and only 1 used ACE2 (Supplementary Figs. 4 and 5). Although we cannot formally discount the hypothesis that some bat alphacoronaviruses are hyperspecialized to only one cognate bat APN or ACE2 that was not included in our libraries, our results strongly indicated the presence of other receptors. The subsequent discovery that one of these viruses—CcCoV-KY43 from Kenya—has at least partial tropism for human cells provides a critical risk assessment for regional and global health communities to prepare for any potential spillover of these viruses. This assessment is strengthened by the following findings: identification of the CcCoV receptor CEACAM6; examination of host reservoir distribution in Kenya; sero-surveillance in human populations; and finally, wider elucidation of receptor use by related bat viruses from outside Africa. These results are crucial when considering that bat coronaviruses closely related to the endemic human alphacoronaviruses HCoV-229E and HCoV-NL63 have been found across Sub-Saharan Africa (Supplementary Fig. 19), consistent with previous alphacoronavirus zoonotic spillover events in this region44,45.

Many viruses use the N-terminal V-set immunoglobulin domain of cellular adhesion molecules for cell attachment[46], including MHV, which binds CEACAM1 (ref. [29]). The high abundance of CEACAM family proteins on the apical surfaces of mucosal epithelia make them highly suitable for pathogen attachment. Indeed, C. albicans[26] and several Gram-negative bacteria[24] use CEACAM proteins for adhesion. Given their immunoglobulin-domain architecture and abundance on barrier membranes, we propose that additional coronaviruses use CEACAM family proteins for cell entry. The ultimate goal of pandemic preparedness is to be able to predict and risk assess the zoonotic potential of viruses from their genome sequence alone. Various approaches are being leveraged to do this, one such being the application of machine-learning algorithms to predict zoonotic potential[47]. By integrating computational, unbiased selection of sequences with high-throughput screening, receptor identification, structural characterization, field epidemiology and sero-surveillance, we identified a previously uncharacterized receptor used by alphacoronaviruses from Africa, Europe and East Asia. This finding both identifies a potential threat to human health and provides the underpinning characterization to enhance pandemic preparedness and prevention efforts.

Methods

Ethics statement

The use of human sera for this study was approved by the Scientific and Ethics Review Unit of the Kenya Medical Research Institute (protocol SSC 3426). Before the blood draw, donors gave individual consent for the use of their samples for research.

Construction of gene libraries

A schematic of the downstream analysis pipeline used for retrieving alphacoronavirus data and for generating the final spike protein library used in this study (n = 40) is provided in Fig. 1a. All publicly available alphacoronavirus genome sequences were retrieved from the Virus Pathogen Database and Analysis Resource platform hosted by the Bioinformatics Resource Center at the National Institute of Allergy and Infectious Diseases[15]. As of May 2021, the full database consists of 19,082 alphacoronavirus genomes, from which we extracted sequences of the whole spike protein-coding region to obtain a final database of 2,714 sequences. We constructed the spike protein-coding DNA sequence alignment using MAFFT (v.7.526)49,50 by integrating structural alignments of homologous spike protein structures queried from the UniProt Reference Clusters[51]. Maximum-likelihood phylogenetic reconstruction was performed using IQTREE (v.2.3.4)[52] with 1,000 ultrafast bootstrap replicates (UFBoot) and 1,000 SH-like approximate likelihood ratio tests[53] (Supplementary Fig. 20). We performed codon model reconstruction by determining the best-fit model using the model selection procedure implemented in IQTREE. Patristic distances (the sum of branch lengths along the shortest path) were then computed between all pairs of tips in R (v.4.4.1) using the ape package[54], which informed the unbiased selection of the n = 40 spike protein-coding sequences by applying a greedy algorithm. In brief, let δ(i,j) denote the patristic distance between tips i and j on the reconstructed maximum-likelihood tree, with the branch length estimated in substitutions or sites, and let k be a previous number of tips to be selected. The greedy algorithm identifies the farthest pair arg maxi,j δ(i,j) and initializes the final selection set S with these two tips. Next, for each subsequent selection step, it adds the tip x∉S that maximize its nearest-neighbour distance to the current set x = arg maxx∉S miny∈S δ(i,j) and repeats the process until |S| = k. Although heuristic, this approach ensures that an optimal subset of tips (evolutionary units) is returned under the assumption of maximizing both minimum phylogenetic distance and phylogenetic diversity, as previously theoretically proposed and discussed[14]. We report Faith’s phylogenetic diversity[55] of the induced minimal subtree and benchmarked against a 10,000 random panels of matching size. For the most divergent alphacoronaviruses, a RBD could not be readily identified for two unclassified viruses and viruses classified in the Soracivirus and Luchacovirus subgenera.

Plasmids used for pseudotyping

Selected alphacoronavirus spike protein-coding sequences were ordered from Biobasic as codon-optimized synthetic genes and subcloned in pcDNA3.1 with a HA tag in the C-terminal cytoplasmic tail. For the APN library, genes were ordered from GenScript and subcloned with a N-terminal V5 tag in the vector pCAGGS. For the ACE2 library and human DPP4 (GenBank: NP_001926), the ectodomains of the ORFs were subcloned with a N-terminal HA tag in pDisplay. CEACAM libraries were obtained from GenScript and subcloned in pcDNA3.1(+) with a C-terminal 8×His tag. Flag-tagged human TMPRSS2 (GenBank: NP_001128571.1) and human DPEP1 (GenBank: NP_001121613.1) were cloned into pcDNA3.1. GenBank accession numbers of the sequences used for the study are provided in Supplementary Table 1 for the alphacoronavirus spike protein library, Supplementary Table 2 for APN, Supplementary Table 3 for ACE2, Supplementary Table 7 for human CEACAM receptors, Supplementary Table 9 for the CcCoV local phylogeny spike protein library and Supplementary Table 10 for CEACAM6-like proteins of different mammalian species.

Cells

Mycoplasma-free HEK293T (human kidney), Calu3 (human lung), Caco2 (human colorectal adenocarcinoma) and HuH7 (human hepatoma) were cultured in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% FBS, 100 U ml–1 penicillin plus 100 µg ml–1 streptomycin (PenStrep) and 1 mM sodium pyruvate. Mycoplasma-free THP1 (human monocytes) and LCL (human B lymphocytes) cell lines were maintained in suspension in RPMI1640 medium supplemented with 10% FBS and PenStrep. All reagents for cell culture were purchased from Gibco. All cells were cultured in a humidified atmosphere at 37 °C, 5% CO_2.

Pseudotype virus production

To pseudotype alphacoronavirus spike proteins, plasmids encoding their ORF were transfected in confluent HEK293T cells seeded in a 6-well plate using polyethylenimine (PEI, 5 µg ml–1). HIV-1-based lentiviral vectors coding for viral structural proteins (p8.91), particle packaging signals and a luciferase reporter gene (pCSFLW) were also included in the transfection mix[56]. The following day, the medium was replaced and pseudoviruses in the supernatant were collected 48 and 72 h after transfection and pooled together. Following the final collection, the supernatant was centrifuged for 10 min at 4,000 rpm to remove cellular debris. Finally, pseudoviruses were aliquoted and stored at −80 °C until further use. To verify spike protein incorporation, pseudoparticles were purified by ultracentrifugation at 23,000 rpm, 4 °C for 2 h using a 20% sucrose gradient. Supernatants were discarded and pellets were resuspended in PBS. Concentrated pseudoparticles were lysed by boiling with Laemmli (Bio-Rad) and spike protein expression was analysed by SDS–PAGE. Separated proteins were transferred onto a 0.45 µm nitrocellulose membrane (Cytiva), blocked in PBS supplemented with 0.05% Tween-20 and 5% (w/v) unskimmed milk powder and incubated with mouse monoclonal anti-HA (clone 6E2, Cell Signaling Technology, 1:5,000) and anti-p24 (clone 5, Abcam, 1:2,000) overnight at 4 °C. The following day, goat anti-mouse DyLight 680 (Invitrogen, 1:10,000) was used to probe primary antibodies, and signals were detected with an Odyssey DLx imaging system (Li-Cor Biosciences). Of note, for those spike proteins for which we saw entry (Fig. 1; HCoV-NL63, CcCoV-SD F3, CcCoV-A76, HCoV-229E, BtCoV-AT1A F41 and BtCoV-WA1087), protein expression in the purified pseudoparticle immunoblots (Extended Data Fig. 1) did not quantitatively correlate with entry signals, which indicated that it is difficult to establish a minimum threshold of spike protein incorporation needed for membrane fusion. For the six spike proteins that did not pseudotype (Supplementary Fig. 1), we attempted to substitute them for spike proteins that have been reported to pseudotype and that share at least 95% amino acid similarity (Supplementary Fig. 21). However, the only match was for PEDV-KDJ, which was replaced by PEDV-Colorado[57]. The selected HCoV-229E spike protein sequence pseudotyped to low levels and was not functional in our downstream applications (Supplementary Fig. 3a); therefore, we replaced it with a different strain of the same species. Original uncropped immunoblots are provided in the Zenodo repository (https://doi.org/10.5281/zenodo.17951484)[58].

Pseudovirus entry assay

For receptor usage screening, HEK293T cells were transfected with plasmids coding for the receptors of interest. The following day, the medium was removed and replaced with fresh DMEM supplemented with 10% FBS to a final concentration of 2 × 10^4 cells per ml. Next, 100 µl of cell preparation was aliquoted in each well of a 96-well plate and treated with 100 µl pseudovirus preparation, diluted 1:1 with fresh medium. Two days later, the supernatant was removed and cells were treated with Bright-Glo (Promega), diluted 1:1 with PBS. Luciferase signals were acquired using a Glomax Discover luminometer (Promega). To assess cell line permissivity to pseudovirus infection, confluent human cell lines were transduced with undiluted pseudoparticle supernatant in a 96-well plate. Two days later, plates were spun down, the medium replaced with 50 µl Bright-Glo and luciferase signals were acquired on a luminometer. To inhibit human CEACAM6-dependent entry, pseudotyped CcCoV-KY43 spike protein was incubated with commercial monoclonal antibodies against human CEACAM6, clone B6.2 (ThermoFisher) and clone 439424 (ThermoFisher), or recombinant C-terminally Fc-tagged human CEACAM6, for 1 h at room temperature in a 96-well plate. HEK293T cells transiently transfected with pcDNA-human CEACAM6 was then added to the plate at a concentration of 2 × 10^4 cells per well. Two days later, luciferase signals were measured as described above.

Flow cytometry for expression of the receptor libraries

Plasmids were transfected in HEK293T cells using Trans-IT X2 (Mirus Bio). The following day, cells were resuspended in PBS, fixed using 2% paraformaldehyde for 20 min at 4 °C and permeabilized using PBS supplemented with 0.5% Triton X-100 for 5 min at 4 °C. After washing, cells were incubated for 1 h with anti-tag antibodies conjugated with PE for APN (V5 tag, Invitrogen, clone TCM5, 1:500) and APC for ACE2 (Flag-tag, Miltenyi Biotec, clone REA216, 1:500) and CEACAM (His-tag, Miltenyi Biotec, clone GG11-8F3.5.1, 1:500) libraries. Samples were run on a MACSQuant Analyzer 10 cytometer (Miltenyi Biotec), and data analysis was performed using FlowJo (BD Biosciences). To assess the specificity of anti-human CEACAM6 monoclonal antibodies, HEK293T cells were transfected with a library of different human CEACAM proteins. The following day, cells were washed in PBS and fixed using 2% paraformaldehyde for 20 min at 4 °C. After washing, cells were incubated with clone B6.2 (1:500) or clone 439424 (1:500) for 1 h on ice. Cells were washed three times before incubation with the secondary antibody conjugated with FITC and recognizing mouse IgG(H+L) (Invitrogen, 1:5,000). Samples were run on a MACSQuant Analyzer 10 cytometer and data were analysed using FlowJo.

Human virus receptor discovery

Human receptor ectodomains were expressed as enzymatically monobiotinylated soluble proteins as previously described[20] by co-transfecting HEK293 cells with a secreted BirA protein biotin[59], as previously described[60]. Protein-containing supernatants were collected after 5 days of expression at 37 °C with 5% CO_2 and 70% humidity. Cells were removed by centrifugation 2,000g for 20 mins, before filtering away particulates (Acrodisc PF PES 0.8/0.2 micron filters, 4658). The imidazole concentration was adjusted to 20 mM and applied to a 96-well HisTrapHP plate as previously described[61]. Non-captured proteins were removed using 3 washes with buffer (20 mM sodium phosphate buffer, 400 mM NaCl and 40 mM imidazole, pH 7.4). Captured proteins were eluted after 15 min of incubation with elution buffer (20 mM sodium phosphate, 400 mM NaCl and 400 mM imidazole, pH adjusted to 7.4). Protein concentrations were determined using a Pierce Bradford protein assay kit, comparing the absorbance values for the purified proteins against values obtained for a dilution series of bovine serum albumin (BSA). Protein purity was assessed by separating protein preparations under reducing conditions (NuPAGE sample reducing agent and LDS sample loading buffer) by SDS–PAGE in MOPS–SDS buffer (NuPAGE) and visualized using Coomassie dye (InstantBlue). Human receptor screening assays were carried out as previously described[20]. In brief, horseradish-peroxidase-labelled preys were produced by complexing monobiotinylated proteins (1.785 ml at 17.5 nM) with streptavidin HRP (714 µl of a 1 in 1,000 dilution, Pierce: 21130) for 1 h at 23 °C, diluted in HEPES buffered saline (HBS) containing 2% (w/v) BSA. Preys were further diluted 20-fold in the same buffer and applied to 2 × 384-well plates containing 759 immobilized human receptor ectodomains and incubated for 1 h at 23 °C. Plates were washed twice with HBS containing Tween-20 (0.1% w/v), followed by a final wash in HBS. Protein interactions were visualized using TMB/E solution (Merck ES001), stopping the reaction by the addition of NaF to a final concentration 0.15% (w/v). Absorbance readings at 652 nm were processed using a median polished Z score with the significance threshold set to Z > 2.

Site-directed mutagenesis

Substitutions in human CEACAM5 and CEACAM6 were introduced using a Quikchange Lightning Site-Directed Mutagenesis kit (Agilent) following the manufacturer’s instructions. Primers were designed using the Agilent online tool (https://www.agilent.com/store/primerDesignProgram.jsp).

Cell–cell fusion assay

HEK293T cells were transfected with either the human CEACAM receptor constructs and rLuc-GFP 1-7 plasmid[62] or with plasmids encoding CcCoV-KY43 spike protein and rLuc-GFP 8-11 using Transit-X2 transfection reagent (Mirus) according to the manufacturer’s instructions. The following day, cells were resuspended in fresh medium and co-cultured at a ratio of 1:1 to a final density of 4 × 10^4 cells per well in a 96-well plate. Two days later, medium was removed and cells were lysed in Passive Lysis buffer (Promega). Renilla luciferase substrate coelenterazine-H (Promega) was added and signals were read using a Glomax Discover Reader (Promega).

Knockdown of CEACAM6 in human cells

siRNA targeting human CEACAM6 (Dharmacon) was introduced in Calu3 and Caco2 cells by electroporation (Neon NxT, ThermoFisher). In brief, cells were washed, resuspended in electroporation buffer at a final concentration of 1 × 10^8 cells per ml and mixed with siRNA at 100 mM. Electroporation was carried out for 2 pulses of 20 ms at 1,400 V. Afterwards, cells were grown for 24 h in DMEM supplemented with 20% FBS before transduction with pseudotyped CcCoV-KY43 spike protein. Entry signals using firefly luciferase were detected as described above. To evaluate reduction in protein expression, cellular lysates were obtained at the same time. Western blot membranes were incubated with mouse anti-human CEACAM6 (Fisher Scientific, clone B6.2, 1:1,000) and rabbit anti-human GAPDH (Proteintech, 1:10,000) as a loading control. The following day, goat anti-mouse DyLight 680 (Invitrogen, 1:10,000) was used to probe primary antibodies, and signals were detected using an Odyssey DLx imaging system (Li-Cor Biosciences). In parallel, stable Caco2 and Calu3 cell lines for which expression of human CEACAM6 was knocked down using shRNA were obtained. Hairpin sequences of 21-mers targeting the gene were obtained from the web portal of the Genetic Perturbation Consortium (Broad Institute, https://portals.broadinstitute.org/gpp/public/seq/search). The sequences TRCN0000424513 and TRCN000062298 were purchased as oligomers from IDT and cloned into the pKLO.1C vector (Addgene, 139470; PMID 16564017). The final constructs were transfected into HEK293T cells, along with the lentiviral packaging vector p8.91 and a plasmid encoding for VSV glycoprotein G. Pseudoviruses were collected as described above. Caco2 and Calu3 cells were plated in a 6-well plate and transduced when 80% confluency was reached. Pseudoviruses were added for 3 days, then removed and replaced with DMEM 20% FBS containing 8 µg ml–1 puromycin. Functional entry assays were performed as described above, and reduced expression of CEACAM6 was again confirmed by immunoblotting. Original uncropped immunoblots are provided in the Zenodo repository (https://doi.org/10.5281/zenodo.17951484)[58].

Transcriptomic analysis of available datasets

Publicly available single-cell RNA-sequencing data from the Human Protein Atlas were downloaded (https://www.proteinatlas.org/humanproteome/single+cell/single+cell+type/data#datasets). The dataset included gene read counts per cell for 31 human organs. Count matrices per organ were imported into R (v.4.3.3) and processed using Seurat (v.5.3.0) in RStudio (v.2025.05.1). To further zoom in on lung-specific single-cell transcriptomic data, we downloaded the Human Lung Cell Atlas (HLCA v.1.0) dataset for normal human lungs samples (https://data.humancellatlas.org/hca-bio-networks/lung/atlases/lung-v1-0). Processed cell-type annotations and expression matrices were loaded into Seurat (v.4.3.3.) for downstream analyses.

Recombinant protein production

Receptors (human CEACAM6, residues 1–326; human CEACAM5, residues 1–684; human CEACAM3, residues 1–215; human CEACAM1, residues 1–428; human CEACAM8, residues 1–327; and Egyptian fruit bat CEACAM6, residues 1–388) were cloned into pOPINTT vectors, upstream of the human rhinovirus 3C protease site and human IgG-Fc tag. Full-length spike proteins (SARS-CoV-2, HCoV-NL63 and HCoV-229E) were cloned into pCDNA3.1 vectors. RBDs (CcCoV-KY43, residues 500–630; CcCoV-2B, residues 496–625; BtCoV-HlYN18, residues 490–627; BtCoV-A701, residues 488–622; BtMfCoV-HuB2013, residues 497–641; BtCoV-WA3607, residues 497–652; BtCoV-CpYN11, residues 491–641; BtCoV-180, residues 477–633; BtCoV-HKU8, residues 497–644; BtCoV-RmYN17, residues 506–632; BtCoV-77, residues 495–634, BtCoV-WA2028, residues 497–648; BtCoV-977, residues 508-638; mink coronavirus, residues 518–660; and CCoV-SD-F3, residues 525–679) were cloned into pOPINTT vectors, which encode a 6×His tag at the C terminus of the protein. Mycoplasma-free Expi293 cells were cultured in Expi293 Expression medium (Gibco) at 37 °C, 8% CO_2. Expression plasmids were transfected using Polyethylenimine Max (PEI 40K, Polyscience) at a mass ratio of 1:1.5 according to the manufacturer’s instructions. After 18 h, valproic acid (Merck), sodium propionate (Merck) and glucose (Merck) were added to the cellular suspension at a final concentration of 5 mM, 6.7 mM and 46 mM, respectively. After 3 days, supernatant was collected by centrifugation (3,800g for 10 min) and sterile filtered before storage at 4 °C. Fc-tagged proteins were purified using HiTrap protein G HP (Cytiva) prepacked affinity columns, washing with 10 mM sodium phosphate pH 7 buffer, eluting with 0.1 M glycine pH 2.7 that was immediately neutralized with 1 M Tris pH 8.0. His-tagged proteins were purified using HisTrap FF (Cytiva) prepacked affinity columns, washing with 10 mM sodium phosphate pH 7.5, 150 mM NaCl, 20 mM imidazole and eluting with 10 mM sodium phosphate pH 7.5, 150 mM NaCl and 1 M imidazole. For all samples, eluted fractions were analysed by SDS–PAGE, and fractions containing the relevant protein were pooled. Pooled protein was exchanged into 10 mM Tris pH 7.5, 150 mM NaCl via repeated concentration and dilution using Amicon Ultra Centrifugal filters (Merck). For crystallization, the CEACAM6 Fc tag was removed by overnight incubation at 4 °C with human rhinovirus 3C protease, and both the Fc tag plus uncleaved CEACAM6-Fc were depleted using a HiTrap protein G HP affinity column. For crystallography and biophysics (ITC and biolayer interferometry assays), proteins were further purified by size-exclusion chromatography using a Superdex 200 10/300 GL column (Cytiva) equilibrated in 10 mM Tris pH 7.5, 150 mM NaCl. Proteins were stored at 4 °C (<2 weeks) or snap-frozen and stored at –80 °C (long term).

Protein binding determined by ELISA

Recombinant RBDs were coated onto Maxisorp NUNC-immuno flat-bottomed 96-well plates (Thermo Scientific) at 1 μg ml–1 overnight at 4 °C in carbonate–bicarbonate solution (0.6 M, pH 9.6). Plates were blocked for 1 h at room temperature with PBS Tween-20 containing 2% BSA (Merck), after which a dilution series of CEACAM proteins in PBS BSA 2% was added for 1 h at room temperature. Plates were washed 3 times with PBS Tween-20 0.1% (Sigma), followed by the addition of anti-human-Fc HRP conjugate diluted 1:10,000 for 1 h at room temperature. 1-step Ultra TMB (Merck) was added to each well, incubated for 5–10 min at room temperature and the reactions stopped with an equivalent volume of 2 M sulfuric acid solution. The OD at 450 nm was measured using a Glomax Discover luminometer (Promega).

The binding IgG ELISAs with human sera were performed according to previously published protocols[63]. In brief, Maxisorp NUNC-immuno flat-bottomed 96-well plates were coated with 2 µg ml–1 coronavirus spike antigens and CcCoV-KY43, BtCoV-HlYN18 and BtCoV-A701 RBDs at 37 °C for 1 h, then washed 3 times in 0.1% Tween-20 and blocked with blocker casein (Thermo Fisher) for 1 h. As full-length spike protein ectodomains could not be produced in sufficient quantities for CcCoV-KY43, ELISAs used stabilized full-length spike proteins for HCoV-229E, HCoV-NL63 and SARS-CoV-2 and RBDs for CcCoV-KY43, BtCoV-HlYN18 and BtCoV-A701; this modification represents a limitation of the study and precludes direct quantitative comparison of ELISA signal magnitudes across antigens. Samples were diluted 1:800 in blocker casein and added to both receptor binding domain and spike protein-coated plates, and incubated for 2 h at room temperature. After washing with 0.1% Tween-20, a 1:10,000 dilution of horseradish peroxidase-conjugated goat antihuman IgA antibody (Sigma) in wash buffer was added to plates, incubated for 1 h at room temperature, washed and O-phenylenediamine dihydrochloride substrate (Sigma) was added for colour development for 10 min. Absorbance was measured at 492 nm. Spearman’s rank correlations were applied to assess associations across the full dataset, whereas Pearson’s correlations were applied to the top 10% of CcCoV-KY43 ELISA signals to explore co-variation among high responders. As true negative control sera are unavailable in this emergent-virus setting, OD thresholds were used solely as descriptive markers of high reactivity (in each antigen-specific ELISA) and not to infer seropositivity or to compare absolute signal magnitudes across assays.

Flow cytometry for receptor–viral protein binding

HEK293T cells were transiently transfected with plasmids encoding APN and ACE2 from different species, as well as human TMPRSS2, human DPP4 and human DPEP1. The following day, cells were washed in PBS and fixed using 2% formaldehyde in PBS for 20 min on ice. After incubation, cells were permeabilized with Triton 1% in PBS, for 5 min on ice. Cells were washed twice in PBS and incubated with 10 mg ml–1 viral-predicted RBD diluted in PBS BSA 1%, for 1 h on ice. After washing, the secondary antibodies were incubated with the cells for 1 h on ice. To label the receptors, PE-conjugated anti-V5 (Invitrogen, clone TCM5, 1:500) was used for the APN library, PE-conjugated anti-HA (Miltenyi Biotec, clone GG8-1F3.3.1, 1:500) was used for the ACE2 library and human DPP4 and PE conjugated anti-Flag (BioLegend, clone L5, 1:500) was used for human TMPRSS2 and human DPEP1. To recognize the His-tagged viral predicted RBDs, APC-conjugated anti-6×His was used (Miltenyi Biotec, 1:500). Cells were run on a MACSQuant flow cytometer (Miltenyi Biotec), and at least 25,000 events were acquired per sample. Data were analysed using FlowJo (BD Biosciences). The gating strategy is shown in Supplementary Fig. 22.

Crystallization

Complexes of CEACAM6 with RBDs from CcCoV-KY43 and CcCoV-2B were formed by mixing the proteins at a 1:1.2 molar ratio and incubating for 60 min at 22 °C. Proteins were crystallized in 96-well nanolitre-scale sitting drops (200 nl protein plus 200 nl of reservoir) equilibrated at 293 K against 80 μl reservoir solution as follows: 2 mg ml−1 CcCoV-KY43 complex, 0.1 M sodium acetate pH 4.3, 21.5% PEG5K MME and 5% glycerol; 2.7 mg ml−1 CcCoV-2B complex, 0.1 M sodium acetate, 20% PEG 6K and 0.2 M NaCl. Crystals were cryo-preserved by brief immersion in reservoir supplemented with 20–25% glycerol before collecting in SPINE standard nylon loops and flash-cryocooling in liquid nitrogen.

X-ray data collection and structure determination

Diffraction data were recorded from single crystals on the Diamond beamline I24 at 20 keV using an Eiger2 9M detector (CcCoV-KY43 complex) or on the beamline I04 at 13 keV using an Eiger2 X 16M detector (CcCoV-2B complex). Both crystals produced severely anisotropic diffraction; therefore, data were integrated using DIALS[64] before anisotropic scaling and merging using the STARANISO ‘aniso merge’ data processing pipeline[65]. The structure of human CEACAM6 in complex with CcCoV-KY43 was solved by molecular replacement using PHASER[66] with AlphaFold 3 (ref. [67]) models of the ectodomain (residues 35–326) of CEACAM6 and the RBD (residues 500–630) of CcCoV-KY43, for which the atomic pLDDT values had been converted into pseudo-atomic displacement factors[68]. The model was manually improved using ISOLDE[69] with adaptive restraints based on the AlphaFold quality metrics[68] before refinement using phenix.refine[70], with the coordinates from ISOLDE used as a reference model to prevent deterioration of model geometry. Subsequent rounds of iterative model building were performed using COOT[71] and ISOLDE in consultation with the validation statistics provided by MolProbity[72], in each case using the coordinates from ISOLDE as a reference model in phenix.refine. The final model had one molecule of CEACAM6 and CcCoV-KY43 RBD per asymmetric unit, with 95.6% of residues in the favoured area of the Ramachandran plot and no outliers, and it included 10 N-acetylglucosamine residues (9 attached to CEACAM6 and 1 to CcCoV-KY43 RBD). The structure of human CEACAM6 in complex with CcCoV-2B was solved by molecular replacement using PHASER[66] with models of the CEACAM6 ectodomain and CcCoV-2B RBD (residues 496–625) that were generated with ColabFold[73] using the crystal structure of CEACAM6 in complex with the CcCoV-KY43 RBD as a template. The structure was refined as described above. The final model had one molecule of CEACAM6 and CcCoV-2B RBD per asymmetric unit, with 96.2% of residues in the favoured area of the Ramachandran plot and no outliers, and it included 12 N-acetylglucosamine residues (9 attached to CEACAM6 and 3 to CcCoV-2B RBD).

Biolayer interferometry

To compare affinities of human CEACAM proteins for CcCoV-KY43 and CcCoV-2B, the recombinant viral RBDs were immobilized at 20 μg ml–1 to Ni-NTA biosensors (Sartorius) pre-hydrated in 10 mM HEPES pH 7.5, 150 mM NaCl and 0.02% Tween-20. Biosensors were incubated with a a 2-fold serial dilution of CEACAM proteins from 600 nM to 37.5 nM (association) then buffer alone (dissociation). Between incubations, biosensors were regenerated by thrice incubating for 5 s with 10 mM glycine pH 1.7 then 5 s in buffer to neutralize, and biosensors were recharged using 10 mM NiCl_2 before fresh immobilization of the RBD. Two independent experiments were performed at 30 °C using a Octect Red (ForteBio). Results were analysed using data analysis software of the instrument (Sartorius).

ITC

ITC experiments were performed using a MicroCal PEAQ-ITC automated calorimeter (Malvern Panalytical) at 25 °C. CcCoV RBDs were titrated into human CEACAM proteins using 13 × 3 µl or 19 × 2 µl injections. Results were analysed using the analysis software of the instrument and fitted using a one-site binding model. The experiments were performed twice (CEACAM5) or thrice (CEACAM6) independently, and data from each experiment are shown in Supplementary Table 4.

C. cor and human population distribution data in Kenya

GPS points from all the study areas in Kenya where bats were surveyed were uploaded onto QGIS (2025, https://www.qgis.org). The points were later grouped based on bats species as either ‘other species of bats’ or ‘heart-nosed bat’. A shape file of the known extent of C. cor was added to ascertain the accuracy of the sampling. Sampling points containing C. cor were later classified based on the cave type as either natural cave, tree cave or mine cave. In two instances, bats were found in human habitations.

Phylogenetic reconstruction

Alphacoronavirus spike gene sequences of the 40 selected strains, in addition to those representing the local phylogeny of CcCoV-KY43, were aligned using MAFFT (v.7.526)[49] and molecular clock calibration was performed in BEAST (v.1.10.5)[74]. Bayesian analysis was parameterized using the SRD06 codon position model (which implements a HKY_112 partition)75,76, a relaxed clock defined by an underlying log-normal distribution to model variability in rates across branches[77], and a nonparametric skygrid prior with 50 grid points distributed over 65 years[78]. An uninformative continuous-time Markov chain rate was set as reference prior on the clock rate, whereas other priors were left at their default values. Moreover, to investigate the potential acquisition (or loss) of CEACAM6 usage along the full evolutionary history of alphacoronavirus, discrete CEACAM6 usage was modelled as a binary trait under an asymmetric continuous-time Markov chain process with Markov jump counting in BEAST[79]. CEACAM6 trait evolution was jointly inferred in BEAST (v.1.10.5) with time-calibrated phylogenies estimated under a SRD06 codon-partitioned substitution model, an uncorrelated log-normal relaxed molecular clock and a skygrid coalescent prior. Markov jumps were used to obtain posterior distributions for the number and direction of CEACAM6 transitions (gains and losses) and to reconstruct the most probable trait state at internal nodes. All Markov chain Monte Carlo analyses were run for 200 million iterations, with samples collected every 20,000 steps. Convergence and mixing of the chain were evaluated by ensuring that all posterior parameters returned effective sample size values greater than 200. Highest independent posterior subtree reconstructed[80] trees were constructed in TreeAnnotator by discarding the initial 10% of the chain.

Additional computational analysis

Molecular graphics were generated using PyMOL (Schrödinger) and ChimeraX[81]. Structure interfaces were analysed using PDBePISA[82]. For analysis of relative changes in pseudotype entry, significance was quantified using the Python3 module statsmodels (https://www.statsmodels.org/) with technical replicates averaged and treatment effects quantified as log_10-transformed fold changes relative to the control (untreated) sample. Significance was determined using a one-sample t-test against a theoretical mean of 0, with standard errors calculated using a pooled variance estimate across all groups to ensure robust variance estimation given the small sample sizes. P values were adjusted for multiple comparisons using the Holm–Bonferroni correction. GraphPad Prism 9 was used to generate all the other figures and to perform all other statistical analyses. Non-significant difference is shown when P > 0.032.

Biosecurity statement

Following the identification of CEACAM6 as a receptor in early 2024, we recognized the potential health implications of our findings and the need to notify and improve preparedness and biosecurity in Kenya. Contacting B. A. at the National Museum of Kenya, who first identified CcCoV-KY43 and is a co-author on this study, we let his team know of the potential risk, especially concerning sampling of this bat species. In consultation with biosecurity colleagues in the United Kingdom, we also made the decision to not attempt rescuing or propagating the virus in our laboratories. As such, all work detailed in this paper has been done without live virus work in UK laboratories.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All sequence data analysed were sourced from public databases, as described in the Methods. These, along with the raw, when necessary, analysed data presented in this article, have been deposited into Zenodo (https://doi.org/10.5281/zenodo.17951484)[58] and made freely accessible. Atomic coordinates, structure factors and protein sequences have been deposited into the Protein Data Bank (PDB) with the following accession numbers: 9RCS for human CEACAM6 in complex with the CcCoV-KY43 RBD and 9RCU for human CEACAM6 in complex with the CcCoV-2B RBD. In addition to the structures determined in this study, the following previously experimentally determined 3D structures were included: CEACAM6 homodimer (PDB:4Y8A); CEACAM6–CEACAM8 heterodimer (PDB: 4YIQ); BtCoV-MOW15-22 (PDB: 9C6O); HCoV-SARS2 (PDB: 6M0J); HCoV-SARS1 (PDB: 2AJF4); NeoCoV (PDB: 7WPO); BtCoV-HKU5 (PDB: 9D32); BtCoV-KY72 (PDB: 8K4U); BtCoV-PRD-0038 (PDB: 8U0T); HCoV-NL63 (PDB: 3KBH); HCoV-229E (PDB: 6ATK); CCoV strain HuPn2018 (PDB: 7U0L); PRCV (PDB: 4F5C); PDCoV (PDB: 7VPQ); and MHV spike trimer in complex with mouse CEACAM1 (PDB: 6VSJ). Human population data were obtained from the Kenya National Bureau of Statistics (2019, https://www.knbs.or.ke/download/2019-kenya-population-and-housing-census-volume-iii-distribution-of-population-byage-sex-and-administrative-units/) based on the 2019 national census. Viral sequences used for this study were downloaded (https://www.bv-brc.org/view/Virus/10239). Publicly available single-cell RNA-sequencing data from the Human Protein Atlas were downloaded (https://www.proteinatlas.org/humanproteome/single+cell/single+cell+type/data#datasets). Lung Cell Atlas (HLCA v1.0) dataset for normal human lungs samples was downloaded https://data.humancellatlas.org/hca-bio-networks/lung/atlases/lung-v1-0).

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Acknowledgements

We thank T. Peacock, N. Thakur and J. Newman at The Pirbright Institute for provision of the TMPRSS2 plasmids, other protease constructs and the ACE2 expression library; S. Uyoga at the KEMRI–Wellcome Trust Research Program for the blood donor samples used in the serological assays; all the coronavirus research teams that deposited sequence information to GenBank that were selected by our greedy-algorithm-based approach; staff at the Diamond Light Source for beamtime (proposal mx36838) and staff of beamlines I04 and I24 for assistance with crystal testing and data collection. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. D.B., A.D.N., S.C.G. and G.G. were supported by a BBSRC grant (BB/W006162/1). D.B. is supported by a BBSRC Institute Strategic Program Grant (BBS/E/PI/230002B) to The Pirbright Institute. D.B. and G.G. acknowledge the Pirbright Institute Flow Cytometry Facility (BBS/E/PI/23NB0003). A.D.N. is supported by a grant funded by the UK Department for Environment, Food and Rural Affairs (Defra, SE2947). The human receptor screening platform was supported by a MRC Partnership Grant (MR/X019705/1) awarded to D.B. and G.J.W. J.N., D.L. and B.A. are funded by Wellcome Trust grants 226141/Z/22/Z and 226130/Z/22/Z. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.

Author information

Authors and Affiliations

1. The Pirbright Institute, Woking, UK

Giulia Gallo, Antonello Di Nardo, Aghnianditya Kresno Dewantari & Dalan Bailey

2. Department of Pathology, University of Cambridge, Cambridge, UK

Giulia Gallo, Florence M. M. Buckley & Stephen C. Graham

3. KEMRI–Wellcome Trust Research Programme, Kilifi, Kenya

Doreen Lugano, Bernadette Ataku Kutima & James Nyagwange

4. Hull York Medical School, Department of Biology, York Biomedical Research Institute, University of York, York, UK

Adam J. Roberts & Gavin J. Wright

5. Loisaba Conservancy, Nanyuki, Kenya

Moses Okombo

6. Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK

Aghnianditya Kresno Dewantari

7. Department of Zoology, National Museum of Kenya, Nairobi, Kenya

Bernard Agwanda

Contributions

G.G., A.D.N., S.C.G. and D.B. conceptualized the study and designed the methodology. G.G., A.J.R., B.A.K., A.K.D., F.M.M.B. and B.A. performed the investigation. G.G., D.L. and A.J.R. conducted formal analyses. G.G., A.K.D. and F.M.M.B. performed validation. G.G., A.D.N., D.L. and M.O. performed visualization. G.G., S.C.G. and D.B. wrote the original draft of the manuscript. All authors reviewed and edited the manuscript. S.C.G. and D.B. supervised the study. G.J.W., J.N., A.D.N., S.C.G. and D.B. acquired funding.

Corresponding authors

Correspondence to Stephen C. Graham or Dalan Bailey.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature thanks Huan Yan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Lentiviral pseudotyping of the alphaCoV library.

(a) Pseudoparticles were purified from the cellular supernatant using a 20% sucrose gradient, and centrifugated at 33,000 g for 2 h, at 4 °C. Pellets were resuspended in DMEM and lysed for immunoblot analysis. Spikes were detected using an antibody recognizing the attached HA tag, while efficiency of pseudoparticle purification was assessed using an antibody against the p24 capsid protein of HIV. (b) Pseudoparticles were also pre-treated with TDPK-trypsin for one hour at room temperature, before purification and immunoblot analysis, as described in (a). (c) Pseudoviruses of CcCoV-related viruses were purified from cellular supernatant using 20% sucrose, as previously described, and the incorporation of S on the particles assessed by immunoblot against an HA-tag fused to the S. To validate correct purification, expression of the lentiviral structural protein p24 was also assayed.

Extended Data Fig. 2 APN and ACE2 receptor screening in presence of human TMPRSS2, or preincubation with trypsin.

(a) HEK293T overexpressing libraries of APN or ACE2 from various mammals, as indicated, plus human TMPRSS2 were infected with the alphaCoV library of S pseudotypes. Expression of hTMPRSS2 did not impact the overall pattern of receptor usage. Initial screening was performed in technical triplicate, and positive results were validated in two additional independent experiments. (b) Treatment of pseudotyped alphaCoV S with trypsin does not affect usage of APN and ACE2 receptors. Pseudoviruses were incubated with 250μg/mL of TDPK-trypsin for one hour at room temperature, before infection of HEK293T overexpressing selected APN or ACE2. Cleavage of S did not impact receptor usage, or host range (through comparison to results shown in Fig. 1b and Fig. 1c). Experiments were performed once in technical triplicate.

Extended Data Fig. 3 Binding of a subset of alphaCoV predicted RBDs to known coronavirus receptors, as assessed by flow cytometry.

(a) Top: Phylogenetic tree of the alphaCoV library. Viruses with RBDs that were successfully produced recombinantly are highlighted in green, while those we could not obtain are coloured in red. Those viruses highlighted in grey were not tested (no outline) or alternatively the RBD could not be confidently identified by alignment (black outline). Below: Coomassie staining of the recombinant RBDs used in the binding assay. (b) Binding of viral RBDs to relevant APN, ACE2, TMPRSS2, DPP4 and DPEP1 receptors, as determined by antibody staining and flow cytometry. The numbers by each sample indicate the percentage of PE+ positive cells among the singlets (receptor + population). The numbers in coloured boxes show the percentage of APC+ cells (viral RBD + population) in this PE+ population. At least 25,000 events were acquired per sample.

Extended Data Fig. 4 Human CEACAM6 supports cell-cell fusion in the presence of CcCoV-KY43 S.

A fusion assay, based on a split firefly luciferase and GFP, was used to validate usage of human CEACAM6 as a cognate receptor for CcCoV-KY43 S. The average fold change from at least three independent experiments, each performed in triplicate, is shown along with the standard deviation. Statistical significance was examined using a one-sample t test of log_10 fold change against a theoretical mean of 0, with standard errors calculated using a pooled variance estimate across all groups to ensure robust variance estimation given the small sample sizes. P-values were adjusted for multiple comparisons using the Holm-Bonferroni correction (*p-value = 0.01, ns: non-significant difference).

Extended Data Fig. 5 Monoclonal antibodies B6.2 and 493424 are specific to human CEACAM6.

HEK293T cells transfected with plasmids encoding the indicated human CEACAMs were stained using the anti-CEACAM6 commercial monoclonal antibodies, B6.2 and 493424. FITC-conjugated anti-mouse was used as secondary antibody to detect binding of the monoclonals. Surface expression was then quantified using a flow cytometer.

Extended Data Fig. 6 Human CEACAM6 usage within the alphaCoV pseudotype S library.

Pseudovirus (PV) entry assays showed that only CcCoV-KY43 requires CEACAM6 for entry. Experiment was performed in technical triplicate, with the mean and SD relative light units (RLU) plotted.

Extended Data Fig. 7 Additional amino-acid substitutions in human CEACAM5 and CEACAM6 confirm a major contribution of position 63 to receptor specificity.

(a) Entry assays using CcCoV-KY43 S pseudotypes with HEK293T cells transiently transfected with human CEACAM5 and CEACAM6, or mutated forms of these proteins. The substitutions I63F and Q123H confirms that position 63 is a major determinant of CEACAM usage for KY43. (b) Flow cytometry data shows comparable expression of human CEACAM5 and CEACAM6, and their derived mutants, in HEK293T cells.

Extended Data Fig. 8 Entry of pseudotyped CcCoV|KY43 S using a wider library of human CEACAMs.

HEK293T cells were transiently transfected to individually overexpress human CEACAMs, as indicated. The experiment was performed with two independent replicates, both containing technical triplicates. The technical triplicates of two biological replicates, with SD, are shown as relative light units (RLU), compared to non-enveloped (NE) controls. ns: non-significant, ****p = <0.001 statistical analysis using two-way ANOVA.

Extended Data Fig. 9 Comparison of RBD binding to different human CEACAMs using bio-layer interferometry (BLI).

Ni-NTA sensors were loaded with His-tagged CcCoV|KY43 RBD (a) or CcCoV-2B (b) and then incubated with different human CEACAMs in a two-fold concentration gradient from 600 nM (dark colours) to 37.5 nM (light colours). BLI responses following subtraction of a reference sensor incubated with buffer are shown. The end of the association phase is marked (dashed vertical line). Human CEACAM6 binds strongly to CcCoV RBDs, and this binding is lost upon introduction of an I63F substitution. CcCoV RBDs bind weakly to human CEACAM5 and displays negligible binding to CEACAM1, CEACAM3 and CEACAM8. Data are representative of two independent experiments.

Extended Data Fig. 10 Usage of human CEACAM5, CEACAM6, and individual point mutants, by pseudotyped CcCoV-2A S.

(a) Pseudovirus entry of CcCoV-2A S in HEK293T cells expressing the indicated CEACAM protein. The F63I substitution in CEACAM5 increases CcCoV-2A S-mediated entry to the level seen with CEACAM6. Values are presented as relative light units (RLU). Assays were performed twice in technical triplicates with mean RLU and SD shown. (b) CcCoV-2A S entry into cells expressing CEACAM6 substitution I63F is reduced relative to wild type human CEACAM6 (hCEACAM6). Assays were performed twice in technical triplicates in triplicate with percentage PV entry and SD shown. Statistical analysis was performed using paired one-way ANOVA (****p-value < 0.0001) and paired t-test (***p-value = 0.0002), respectively.

Extended Data Fig. 11 Binding of recombinant btCoV-977 to Egyptian fruit bat, but not human, CEACAM6.

ELISA using the recombinant RBD of btCoV-977 to assess binding to soluble Egyptian fruit bat CEACAM6-like, or human CEACAM6, proteins. The mean OD from a representative experiment, performed in technical quadruplicates, is shown together with the SD.

Extended Data Fig. 12 Sequence determinants of human CEACAM6 usage.

(a) Alignment of RBD sequences from the CEACAM6-using alphaCoV RBDs included in this study. Loops that interact with human CEACAM6 are highlighted in red. Residues that interact with human CEACAM6 in the CcCoV-KY43 and CcCoV-2B complex structure are shown in bold red. Residues of btCoV-LN20 that were mutated to probe the sequence determinants of human CEACAM6 usage (see below) are underlined in grey. (b) Host range of btCoV-LN20, a CEACAM6 user, cannot be defined by point substitutions in its RBD. Entry assays using pseudotyped btCoV-LN20 S show that single mutations in the RBD, making it more like CcCoV-KY43 S, cannot recapitulate the broad host range of CcCoV-KY43, including usage of human CEACAM6. The background-corrected average of three independent experiments, each performed in triplicate, is shown.

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Gallo, G., Di Nardo, A., Lugano, D. et al. Heart-nosed bat alphacoronaviruses use human CEACAM6 to enter cells. Nature 653, 180–189 (2026). https://doi.org/10.1038/s41586-026-10394-x

- Received: 23 July 2025

- Accepted: 10 March 2026

- Published: 22 April 2026

- Version of record: 22 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10394-x

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A mechanism to initiate emergency type 2 myelopoiesis

Alexandre Fagnan,

Cristina Di Genua,

Yiran Meng  (孟一然),

Roy Drissen,

Zishan Zhang  (张紫姗),

Bowen Zhang  (张伯闻),

Padraic G. Fallon,

Vassilis Pachnis,

Erika J. Mancini,

Fränze Progatzky &

…

Claus Nerlov

Nature volume 653, pages 212–220 (2026)

Abstract

Immune responses to parasite infection involve the increased production of basophils and eosinophils. These two myeloid cell types have key roles in type 2 anti-parasite immunity[1] and rely on GATA family transcription factors for their specification2,3. The first committed step in basophil and eosinophil production is generation of basophil–eosinophil–mast cell progenitors (BEMPs) from oligopotent erythroid-primed multipotent progenitors (EMPPs). However, it is not well established how immune responses act on progenitors to initiate type 2 myelopoiesis. Here we show that infection with the helminth Heligmosomoides polygyrus increases EMPP commitment to myeloid fate at the expense of erythropoiesis. Upon infection with H. polygyrus, the IL-33 alarmin accumulated in the bone marrow, causing EMPPs to upregulate the GATA co-factor LMO4 and preferentially differentiate into myeloid cells. LMO4 was sufficient to instruct myeloid fate in EMPPs by interacting with GATA2, displacing the FOG1 co-factor and redistributing GATA binding from megakaryocyte–erythroid-specific to basophil, eosinophil and mast cell (BEM)-specific chromatin. Accordingly, mice carrying a GATA2 mutation that selectively impairs the LMO4–GATA2 interaction were deficient in GATA factor allocation to BEM chromatin, myeloid lineage commitment, basophil and eosinophil production, and parasite control. This identifies LMO4 as an IL-33-regulated master regulator of type 2 myelopoiesis, and transcription factor reallocation as a mechanism of lineage commitment.

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BEMs are key effector cells of the type 2 immune response, originating in the bone marrow and acting in barrier tissues to detect and fight parasites[1]. Dysregulated type 2 immunity is commonly associated with allergic inflammation[1], and excessive production of mast cells[4] or eosinophils[5] can lead to severe morbidity due to toxicity and inflammatory properties of the antimicrobial proteins produced by these cell types. Therefore, understanding the cellular pathways and molecular mechanism through which type 2 myeloid cells arise is critical if we are to understand how type 2 inflammation is regulated, and develop targeted strategies to correct imbalanced myeloid cell production.

Upon helminth infection and allergic inflammation, alarmins such as thymic stromal lymphopoietin, interleukin-25 (IL-25) and IL-33, are released from injured cells, inducing and enhancing the recruitment and expansion of type 2 immune cells[1]. Although direct action of type 2 inflammatory signals on bone marrow-resident-restricted basophil[6] or eosinophil[7] progenitors is well established, whether such signalling initiates type 2 myelopoiesis by promoting commitment to a BEM fate remains to be understood.

Recent studies have shown that BEMs are specified via a cellular pathway distinct from that generating neutrophils and monocytes in both mice and humans. In this pathway, the first myeloid-committed step is the generation of BEMPs from EMPPs, which in addition to BEMs generate erythrocytes and platelets via pre-megakaryocyte–erythroid progenitors (preMegEs)8,9,10,11. GATA2 has been shown to have a key role in the specification of BEMs[12]. However, Gata2 is also critical for megakaryocyte and erythroid lineage development[13] and expressed at a similar level in BEMP and preMegEs[8]. Furthermore, FOG1, a GATA co-factor identified as interacting with the zinc-finger 1 (ZnF1) of GATA1 (ref. [14]), is essential for the development of embryonic[15] and definitive[16] megakaryocyte and erythroid lineages, and ectopic expression of FOG1 has been shown to block both eosinophil[17] and mast cell[18] differentiation, consistent with GATA2 interactors, rather than the GATA2 expression level, having a key role in the BEMP versus preMegE lineage choice. However, a GATA2 co-factor that promotes BEM lineage commitment is yet to be identified.

Here we show that initiation of a type 2 immune response in mice by helminth infection induces myeloid bias in EMPPs, concomitant with an increase in bone marrow IL-33. IL-33 directly induced Lmo4 expression in EMPPs, leading to GATA factor reallocation to BEM-specific chromatin through FOG1 displacement, EMPP myeloid bias and increased basophil and eosinophil formation. Conversely, a GATA2 mutation that selectively blocked the LMO4–GATA2 interaction had the opposite effect. Together, these results identify LMO4 as an IL-33-regulated inducer of type 2 myelopoiesis, and GATA factor reallocation as its mechanism of action.

Helminth infection induces myeloid bias

To determine the effect of helminth infection on type 2 myelopoiesis, we infected mice with H. polygyrus and analysed the bone marrow by flow cytometry at 4 and 11 days post-infection (Fig. 1a and Extended Data Fig. 1a,b). As previously reported[6], we observed a progressive increase in basophils and eosinophils, concomitant with a decreased frequency of erythroid cells in the bone marrow of infected mice (Fig. 1b). In parallel, we observed an increased BEMP and decreased preMegE frequency within Lin−Sca-1−Kit+ (LK) progenitors (Fig. 1c and Extended Data Fig. 1c,d), indicating that increased myeloid output was initiated by altered EMPP lineage bias. To test this hypothesis, we analysed EMPPs isolated 4 days post-infection in colony-forming assays, and analysed the resulting colonies by flow cytometry (Extended Data Fig. 1e). This showed increased myeloid and decreased erythroid–megakaryocyte (Ery–Meg) output from EMPP isolated from infected mice compared with EMPPs from naive mice (Fig. 1d and Extended Data Fig. 1f,g). To address the underlying molecular mechanism, we compared EMPP purified from infected mice to naive EMPPs using RNA sequencing (RNA-seq; Supplementary Tables 1 and 2). Gene set enrichment analysis (GSEA) showed upregulation of inflammatory signatures both day 4 and day 11 post-infection (Fig. 1e). Furthermore, using RNA-seq of EMPPs as well as the downstream BEMP and preMegE populations (Extended Data Fig. 2a) to identify BEMP-specific and preMegE-specific gene expression signatures (Extended Data Fig. 2b and Supplementary Table 3), we found that the BEMP signature was upregulated in EMPPs isolated from infected compared with naive mice, with a corresponding downregulation of the preMegE signature (Fig. 1f). Therefore, H. polygyrus infection generates an inflammatory environment within the bone marrow that induces molecular and functional myeloid bias in oligopotent myelo-erythroid progenitors.

Fig. 1: H. polygyrus infection promotes EMPP myeloid bias.

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a, Experimental workflow of H. polygyrus infection. Mice were infected for 4 or 11 days and compared with non-infected (naive) mice from the same cohort. CFU, colony-forming unit. b, Frequency of erythrocyte (Ery), eosinophil (Eos) and basophil (Bas) within the Gata1-expressing cell fraction in the bone marrow of mice days 4 and 11 post-infection as outlined in a. n = 8 biological replicates, 4 independent experiments. c, Frequency of EMPPs, BEMPs and preMegEs within the bone marrow LK cell fraction of mice from a. d, Quantification of clonal EMPP fates from c. Ery/Meg, Ery and/or Meg colonies; Myelo, mast cell (Mas) and/or Eos; mixed, Myelo and Ery/Meg; other, monocyte (Mon)/neutrophil (Neu). e, GSEA analysis showing enrichment of MSigDB hallmark gene sets (P < 0.05) comparing naive and day 4 or day 11 infected EMPPs as indicated using RNA-seq (n = 4 per condition). The normalized enrichment score (NES) is shown for each comparison. f, GSEA comparing expression of the BEMP (top) and preMegE (bottom) gene expression signatures between EMPPs isolated from naive mice to day 4 or day 11 infected mice. For RNA-seq, n = 4 per condition. NES and P values are shown for each comparison. g, The concentration of IL-33 was measured by ELISA in bone marrow supernatant of naive mice (n = 6) or mice infected with H. polygyrus for 4 or 11 days (n = 7 for each). h, Experimental workflow for analysis of mice treated with IL-33 or vehicle (PBS). i, Frequency of EMPPs, BEMPs and preMegEs in bone marrow LK cells of mice treated as outlined in h. PBS day 2, n = 6; PBS day 6, n = 6; IL-33 day 2, n = 9; and IL-33 day 6, n = 9 biological replicates; 3 independent experiments. j, Frequency of clonal EMPP fates from Extended Data Fig. 1f,g; six independent experiments.

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IL-33 induces Lmo4 expression

Alarmins are released rapidly in response to parasite-induced tissue damage and have been shown to induce basophil[19] and eosinophil[20] expansion in the bone marrow. We therefore investigated whether alarmins could contribute to the observed myeloid bias in EMPPs after H. polygyrus infection. The genes encoding the IL-25 (Il17ra) and IL-33 (Il1rl1) receptors, but not the thymic stromal lymphopoietin receptor (Crlf2), were robustly expressed in EMPPs (Extended Data Fig. 2c). Furthermore, Il1rl1, but not Il17ra, expression was upregulated in BEMPs, and the concentration of IL-33 was higher in the bone marrow fluid from infected mice than in naive mice (Fig. 1g), consistent with a role for IL-33 in the induction of type 2 myelopoiesis. To test this possibility, mice received daily injections of IL-33 (or vehicle; Fig. 1h). This resulted in an increased BEMP and decreased preMegE frequency after 2 days of treatment, with a further decrease in frequency of preMegEs after 6 days (Fig. 1i and Extended Data Fig. 2d). In line with previous reports7,20, treatment with IL-33 induced a large increase of eosinophils in the bone marrow after 6 days at the expense of erythroid cells (Extended Data Fig. 2e,f). Furthermore, a colony-forming assay of EMPPs from mice IL-33 treated for 2 days showed increased myeloid colony frequency and decreased frequency of Ery–Meg colonies compared with control mice (Fig. 1j and Extended Data Fig. 2g). Accordingly, RNA-seq and GSEA of EMPPs isolated from mice after 2 days of IL-33 treatment showed increased BEMP signature and decreased preMegE signature expression (Extended Data Fig. 2h). IL-33 therefore instructs molecularly and functional myeloid bias in EMPPs, similarly to what is observed after H. polygyrus infection.

Lmo4 instructs myeloid fate in EMPPs

To identify putative molecular mechanisms underlying myeloid bias induced by the type 2 immune response, we performed differential transcription factor gene expression analysis of EMPPs after H. polygyrus infection or IL-33 treatment (Extended Data Fig. 3a–c), and identified transcription factors showing the same regulation in both conditions (Fig. 2a). This identified Klf1, Zfpm1 and Tcerg1 as preMegE-specific transcription factors downregulated in both scenarios. Conversely, four transcription factors (Jup, Zbtb33, Runx1 and Lmo4) were upregulated in both conditions, of which Runx1 and Lmo4 were more highly expressed in BEMPs compared with preMegEs (Fig. 2a and Supplementary Table 4). Of these, Lmo4 was of particular interest, as it is a GATA2 co-factor[21], and is highly expressed in basophil and eosinophil progenitors11,22, consistent with a role in specification of these lineages. However, although lmo4 knockdown in zebrafish embryos impaired definitive haematopoiesis[23], the role of Lmo4 in lineage specification remains to be understood.

Fig. 2: Lmo4 expression instructs myeloid fate in EMPPs.

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a, Differential EMPP transcription factor gene expression between mice treated with IL-33 or PBS (2 days, n = 4 per group) and H. polygyrus (HP)-infected versus naive mice (4 days, n = 3 per group). The shapes indicate significant upregulation (green) or downregulation (blue) in both comparisons (P < 0.05). The shapes identify genes from BEMP (square) or preMegE signatures (triangle), or neither (circle). b, Experimental workflow for colony assays of EMPPs from Gata1–eGFP mice transduced with Lmo4-expressing (Lmo4) or control lentivirus. LTR, long-terminal repeat; SFFV, spleen focus forming virus; SIN, self-inactivating. c, Frequency of clonal EMPP fates from b; three independent experiments. Ctrl, control. d, Clonal fates of preMegEs transduced with Lmo4-expressing or control lentivirus quantified as in c; three independent experiments. e, Quantification of Lmo4 gene expression in single EMPPs, preMegEs and BEMPs using quantitative PCR with reverse transcription (RT–qPCR); n = 32 cells per condition. f, Histogram showing LMO4 protein levels measured by intracellular flow cytometry in EMPPs, BEMPs and preMegE progenitors. Corresponding isotype controls are shown. The graph is representative of three independent experiments. g, Experimental workflow for transplantation of Kit+ bone marrow cells transduced with Lmo4-expressing or empty backbone (control) lentivirus. h, Frequency of mCherry+ mononuclear cells (MNCs), red blood cells (RBCs) and platelets (PLTs) in peripheral blood of mice from g; n = 19 per condition, 3 independent experiments. i, Quantification of Gata1+ blood cell types in mCherry+ bone marrow cells from mice in h; n = 9 from 3 independent experiments. j, Frequency of progenitor cell populations within the LK cell fraction in the bone marrow of mice transplanted as in g; n = 10 per condition from 3 independent experiments. k, Frequency of clonal EMPP fates from Extended Data Fig. 3j quantified as in c. For Lmo4, n = 54; for control, n = 61 from 6 independent experiments. l, Clonal fates of preMegEs quantified as in k; three independent experiments. Values are mean ± s.d. P values were determined by two-tailed Welch’s t-test unless otherwise specified.

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We therefore expressed Lmo4 by lentiviral delivery in EMPPs (Fig. 2b), using EMPPs isolated from Gata1–eGFP mice[8] to facilitate both EMPP isolation and subsequent lineage readout, and flow cytometry to quantify their lineage output at the single-cell level after 10 days. Lmo4 expression led to an increase in the frequency of myeloid colonies, with a commensurate decrease in Ery–Meg colonies (Fig. 2c and Extended Data Fig. 3d). This lineage-instructive property of LMO4 was further supported by induction of ectopic myeloid potential in preMegEs after Lmo4 overexpression (Fig. 2d). Consistent with the myeloid lineage-instructive capacity of LMO4, both Lmo4 mRNA (Fig. 2e) and LMO4 protein (Fig. 2f) levels were increased in BEMPs compared with EMPPs.

To confirm this observation in vivo, Kit+ cells isolated from CD45.1 Gata1–eGFP mice were transduced with Lmo4 or control virus, followed by transplantation into lethally irradiated CD45.2 recipients (Fig. 2g). We observed the same level of mononuclear cell engraftment by Lmo4 and control grafts, whereas Lmo4 expression significantly decreased the output of platelets and erythrocytes from transduced haematopoietic stem cells (Fig. 2h and Extended Data Fig. 3e,f). Furthermore, analysis of bone marrow cells showed an increased frequency of eosinophils and basophils and decreased erythroid cells (Fig. 2i and Extended Data Fig. 3g,h). Finally, Lmo4 expression induced a significant increase in the number of BEMPs and decrease in preMegEs (Fig. 2j and Extended Data Fig. 3i), and Lmo4-transduced EMPPs and preMegEs were myeloid biased in vitro (Fig. 2k,l and Extended Data Fig. 3j).

To determine whether IL-33 acted directly on EMPPs, we generated bone marrow chimeras between wild-type and IL-33 receptor-knockout cells (IL-33 receptor is also known as ST2 and is encoded by Il1rl1) (Fig. 3a). In chimeras, CD45.2 Il1rl1-KO cells generated fewer BEMPs than wild-type CD45.2 cells, an effect that was exacerbated by IL-33 administration (Fig. 3b and Extended Data Fig. 4a,b). Microfluidics-based single-cell gene expression profiling of CD45.2 wild-type and Il1rl1-KO EMPPs from both control and IL-33-exposed mice using a panel of BEMP-affiliated and Ery–Meg lineage-affiliated genes showed that the EMPP population was coherent and distinct from both BEMPs and preMegEs (Fig. 3c). IL-33 treatment induced a molecular shift of EMPPs towards BEMPs (Fig. 3d), and significantly upregulated Lmo4 in an ST2-dependent manner (Fig. 3e), with loss of ST2 (and hence IL-33 signalling) leading to the converse shift towards preMegEs (Fig. 3d). ST2 also maintained baseline EMPP Lmo4 expression (Fig. 3e) and was required for induction of functional myeloid bias in EMPPs (Extended Data Fig. 4c,d). Finally, in vitro IL-33 stimulation of EMPPs showed Lmo4 upregulation by IL-33 to be NF-κB dependent (Extended Data Fig. 4e). To test whether IL-33 acted similarly in humans, we generated human haematopoietic xenografts in NSGW41 mice and treated these with IL-33 or vehicle, followed by microfluidics-based single-cell expression profiling of lineage-affiliated genes in the CD34+CD38–CD131+ population (Extended Data Fig. 5a), which contains the GATA1-expressing progenitors[24]. Clustering (Fig. 3f) and lineage signature analysis (Extended Data Fig. 5b,c) identified the human EMPP (cluster 1, expressing intermediate levels of both BMCP (basophil–mast cell progenitor) and MEP (megakaryocyte–erythroid progenitor) signatures), BEMP and preMegE equivalents, and showed that IL-33 treatment upregulated LMO4 expression in hEMPPs (Fig. 3g) and expanded hBEMPs (Fig. 3h). Accordingly, IL-33-treated human CD131+ common myeloid progenitors (CMPs) generated more myeloid-restricted colonies (Fig. 3i and Extended Data Fig. 5d–f). Therefore, the IL-33–LMO4 axis is an evolutionarily conserved inducer of type 2 myelopoiesis.

Fig. 3: Lmo4 reallocates GATA2.

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a, Generation of bone marrow chimeras with Il1rl1−/− or wild-type (WT) CD45.2 and wild-type CD45.1 bone marrow. Reconstituted recipients were IL-33 treated as indicated. b, Frequency of BEMPs within CD45.1+ and CD45.2+ LK populations in chimeras from a treated with IL-33 or PBS for 2 days. n = 7 per condition except WT PBS (n = 6) from 2 experiments. P values compare CD45.1+ and CD45.2+ BEMP frequencies. c, UMAP projection of single cells of the indicated cell population using RT–qPCR gene expression data. For BEMP and preMegE, n = 30; for WT PBS, n = 56; for WT IL-33, n = 64; for Il1rl1−/− PBS, n = 62; and for Il1rl1−/− IL-33, n = 62. d, Plot showing ssGSEA scores of BEMP and preMegE signatures in single cells from c. e, Lmo4 gene expression in a single EMPP from c for the indicated conditions. P values were determined using two-tailed Wilcoxon rank-sum test. f, UMAP projection of clustered human CD34+CD38−CD131+ cells from xenografted mice (n = 3 per condition) injected with PBS or human recombinant IL-33 for 3 or 7 days using RT–qPCR gene expression data. g, Quantification of LMO4 gene expression in human EMPPs (hEMPPs; cluster 1) from f for the indicated conditions. For PBS, n = 29; for IL-33 day 3, n = 26; and for IL-33 day 7, n = 21. P values are as in e. h, Fraction of clusters in f for each indicated condition. Clusters were annotated as follows: hEMPP (1), hBEMP (2), hMEP-1 (3) and hMEP-2 (4). Error bars denote s.d. (n = 3). i, Quantification of clonal fates of human CD131+ CMPs cultured with human IL-33 or PBS; three independent experiments. For PBS, n = 71; for IL-33, n = 72. j, Comparison of differential transcription factor motif occupancy within global chromatin and BEMP-specific chromatin regions using TOBIAS. The large shapes indicate motifs showing significant binding differences. k, Heatmap representing the GATA2 ChIP–seq signal in the indicated chromatin regions of control and Lmo4-overexpressing HPC-7 cells; three biological replicates. l, GSEA comparing GATA2 binding to BEMP or preMegE chromatin accessible in EMPPs, and predicted to be bound by GATA2 using TOBIAS, in data from k. NES and P values are shown. Values are mean ± s.d. P values were determined by two-tailed Welch’s t-test unless otherwise specified.

Source data

Lmo4 induces GATA factor reallocation

To address how Lmo4 promotes myeloid fate, we performed RNA-seq and assay for transposase-accessible chromatin using sequencing (ATAC-seq) of Lmo4-expressing and control EMPPs isolated from mice transplanted with lentivirally transduced Kit+ progenitors. GSEA analysis showed that EMPPs expressing Lmo4 were enriched for the BEMP signatures and depleted for the preMegE signatures at both the transcriptional and the chromatin level (Extended Data Fig. 6a,b and Supplementary Table 5), indicating coordinated transcriptional and epigenetic reprogramming. Lmo4-induced changes to transcription factor motif occupancy were inferred from ATAC-seq-based digital footprinting using TOBIAS[25]. This showed that Lmo4 expression led to a global decrease in GATA motif binding in EMPPs, whereas occupancy of RUNX, SPI1/SPIB (that is, PU.1 binding) and JUN (AP-1) motifs was significantly increased (Extended Data Fig. 6c and Supplementary Table 6). Similar analysis of BEMP-specific and preMegE-specific chromatin domains showed increased GATA motif occupancy of BEMP chromatin (Extended Data Fig. 6d and Supplementary Table 7) and decreased occupancy of preMegE chromatin (Extended Data Fig. 6e and Supplementary Table 8). Therefore, global GATA motif occupancy and GATA binding to BEMP chromatin were discordantly regulated (Fig. 3j), indicating that LMO4 reallocates GATA factors to BEMP chromatin in EMPPs, with no change to the mRNA levels of Gata1 and Gata2 in any of the populations investigated (Extended Data Fig. 6f). To directly test this, we expressed Lmo4 in the mouse HPC-7 progenitor cell line[26] (Extended Data Fig. 6g), a well-established cell line model for studying transcription factor colocalization in haematopoietic progenitors[27], and measured GATA2 chromatin distribution by chromatin immunoprecipitation followed by sequencing (ChIP–seq). Consistent with in silico footprinting, this showed large-scale GATA2 redistribution upon Lmo4 expression (Fig. 3k), with GATA2 being lost from preMegE chromatin and enriched within BEMP chromatin (Fig. 3l). In particular, Lmo4 expression increased GATA2 binding to the Lmo4 gene itself, and depleted GATA2 from the Zfpm1 gene (which encodes FOG1; Extended Data Fig. 6h).

LMO4 displaces FOG1 from GATA2

As LMO4 is known to interact with GATA factors, these results were consistent with LMO4 altering the chromatin distribution of GATA factors through protein–protein interaction. Although both Gata1 and Gata2 are expressed in the Ery–Meg and BEM lineages, we previously found that Gata1 is dispensable for BEMP commitment[8], whereas GATA2 has been shown to instruct BEM lineage differentiation[12].

GATA factors are found in complexes that include SCL, E47, FOG1 and LMO2 (ref. [13]), with LMO2 and FOG1 capable of simultaneously interacting with GATA1 ZnF1 (ref. [28]) (Fig. 4a). To address how LMO4 affects the GATA2 ZnF1 interactome, we co-expressed GATA2, FOG1, LMO2 and LMO4 in HEK293T cells using different epitope tags for each protein, and performed co-immunoprecipitation (co-IP) using the LMO2 and LMO4 tags. Anti-LMO2 IP pulled down both FOG1 and GATA2, confirming the presence of a GATA2–LMO2–FOG1 complex. However, no LMO4 was detected in anti-LMO2 co-IPs. Conversely, neither FOG1 nor LMO2 were detected in anti-LMO4 co-IPs (Fig. 4b and Extended Data Fig. 7a), indicating that LMO4 binding to GATA2 is mutually exclusive with LMO2 and FOG1. To further explore this, we performed anti-GATA2 IP of GATA2–LMO2–FOG1 in the presence or absence of LMO4, finding that LMO4 was able to displace both LMO2 and FOG1 from the GATA2 ZnF1 (Fig. 4c and Extended Data Fig. 7b). Modelling the GATA2–LMO2–LDB1 complex using AlphaFold3 (ref. [29]) showed that consistent with NMR-based structure prediction[28], GATA2 V296, critical for FOG1 interaction, is exposed and accessible for binding (Fig. 4d). By contrast, in the predicted GATA2–LMO4–LDB1 complex, V296 is internal and inaccessible to FOG1 (Fig. 4e). The LMO2–GATA2 contact occurred through the LMO2 LIM-2 domain. Replacing the LMO2 LIM-2 domain with the corresponding LMO4 sequence (Extended Data Fig. 7c) was sufficient to make V296 inaccessible (Extended Data Fig. 7d), confer myeloid lineage-instructive capacity on LMO2 (Extended Data Fig. 7e) and block FOG1 binding to the LMO2–GATA2 complex (Extended Data Fig. 7f,g), further linking these activities.

Fig. 4: GATA2(G320D) mutation blocks LMO4 interaction.

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a, The GATA2 complex containing FOG1, LMO2, LDB1 and SCL–E47, capable of binding to a combined GATA–E-box motif. The effect of LMO4 on this complex is not known. b, HA–FOG1, V5–LMO2, MYC–LMO4 and Flag–GATA2 were co-expressed in HEK293T cells, and co-immunoprecipitated with anti-V5, anti-MYC or control IgG, followed by western blotting with the indicated antibodies (Abs). Input denotes 0.75% of IP material. c, HA–FOG1, V5–LMO2 and Flag–GATA2 were co-expressed in HEK293T cells in the presence or absence of MYC–LMO4 as indicated, and co-immunoprecipitated with anti-Flag (GATA2), antibody or control IgG, followed by western blotting with the indicated anti-tag antibodies. Input represents 0.75% of the material used for IP. d, The structure of LMO2 (amino acids 1–158; orange), LDB1 LID (337–375; yellow) and GATA2 ZnF1 (291–339; magenta) as modelled by AlphaFold3. GATA2 V296 is highlighted with a mesh surface. FOG1 ZnF1 (red) was subsequently docked onto the structure. e, The structure and binding interface of LMO4 (1–165; blue), LDB1 LID (337–375; yellow) and GATA2 N-terminal zinc finger (magenta) as modelled by AlphaFold3. GATA2 V296 is highlighted with a mesh surface. f, HA–FOG1, V5–LMO2 and MYC–LMO4 were co-expressed in HEK293T cells with either wild-type or G320D-mutant Flag–GATA2 as indicated, and co-immunoprecipitated with anti-Flag (GATA2) antibody or control IgG, followed by western blotting with the indicated anti-tag antibodies. Input denotes 0.75% of IP material. g, Plot of the LMO2 (left) or LMO4 (right) ChIP–seq signal performed in HEK293T cells co-transfected as in f; two biological replicates per condition. h, Workflow for the colony-forming assay of lentivirally transduced EMPPs. i, Mas, Ery and Meg frequency in mCherry+ cells from pooled colonies of EMPPs transduced as shown in h; five biological replicates per condition. Values are mean ± s.d. P values (determined by two-tailed Welch’s t-test) are shown for Mas prevalence. Western blots are representative of three experiments.

Source data

The observation that LMO2 and LMO4 form structurally distinct complexes with GATA2 raised the possibility that the LMO4–GATA2 interaction could be selectively disrupted. We previously generated and characterized a mouse line carrying a GATA2 ZnF1 point mutation (G320 to D; Gata2D allele)[30] that is commonly observed in patients with acute erythroleukaemia[31], and found that in myeloid progenitors, this mutation increased accessibility of GATA motifs, while decreasing the accessibility of SPI1/SPIB motifs[30], the converse effect of that observed after ectopic Lmo4 expression. Co-IP of GATA2 with LMO2, LMO4 and FOG1 showed that the G320D mutation selectively disrupted the LMO4–GATA2 interaction, with both LMO2–GATA2 and FOG1–GATA2 interactions unaffected (Fig. 4f and Extended Data Fig. 8a), and ChIP–seq of GATA2, LMO2 and LMO4 in HEK293T cells transfected with either wild-type or mutant GATA2 showed a selective loss of LMO4 association with GATA2(G320D)-bound sites (Fig. 4g). Furthermore, in vitro differentiation of EMPPs isolated from Gata2+/+ or Gata2G320D/G320D (Gata2D/D) mice, with or without Lmo4 transduction (Fig. 4h), showed that Gata2D/D EMPPs were highly deficient in myeloid colony formation and completely refractory to Lmo4 fate instruction (Fig. 4i). Finally, knockdown of Lmo4 by viral transduction in Kit+ bone marrow cells followed by transplantation and IL-33 treatment (Extended Data Fig. 8b,c) impaired BEMP formation (Extended Data Fig. 8d) and EMPP myeloid colony formation (Extended Data Fig. 8e). Together, these results demonstrate a critical role of the LMO4–GATA2 interaction in both baseline and emergency type 2 myelopoiesis.

LMO4 interaction reallocates GATA2

Characterization of the bone marrow (Fig. 5a and Extended Data Fig. 9a,b) of Gata2D/D mice showed a significant decrease in the number of eosinophils and basophils compared with Gata2+/+ and Gata2D/+ controls. Similarly, we observed a decreased frequency of BEMPs and increased frequency of preMegEs in Gata2D/D compared with Gata2+/+ mice (Fig. 5b). To determine whether the observed phenotype was intrinsic to the haematopoietic system, we transplanted total bone marrow cells from Gata2+/+ and Gata2D/D mice into lethally irradiated recipients and analysed their bone marrow (Fig. 5c). This analysis showed a decrease in the number of eosinophils and basophils in the donor cell fraction derived from Gata2D/D compared with Gata2+/+ mice (Fig. 5d), associated with a decreased frequency of BEMPs and increase of preMegEs (Fig. 5e). A similar phenotype was observed in the spleen (Extended Data Fig. 9c,d).

Fig. 5: GATA2–LMO4 promotes parasite control.

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a, Frequency of Ery, Eos and Bas cells in the bone marrow of Gata1–eGFP mice with a Gata2+/+ (n = 6), Gata2D/+ (n = 7) or Gata2D/D (n = 6) genotype; 2 independent experiments. b, Frequency of EMPP, BEMP and preMegE cell populations within the LK bone marrow cell fraction in genotypes from a. n = 5 per genotype; 2 independent experiments. c, Workflow for transplantation of bone marrow (BM) from Gata1–eGFP/CD45.1 mice with a Gata2+/+ or Gata2D/D genotype into lethally irradiated CD45.2 recipient mice. d, Frequency of Eos and Bas in the eGFP+ bone marrow cell fraction of mice transplanted as in c. n = 9 per genotype; 2 independent experiments. e, Frequency of EMPPs, BEMPs and preMegEs within the LK bone marrow cell fraction of mice transplanted as in c. n = 8 per genotype; 2 independent experiments. f, UMAP projection of single cells of the indicated cell population, generated using microfluidics RT–qPCR gene expression data. For BEMPs, n = 55; for preMegEs, n = 63; for Gata2+/+ EMPPs, n = 71; and for Gata2D/D EMPPs, n = 75; 2 independent experiments. g, Plot showing the ssGSEA enrichment scores of BEMP and preMegE signatures in single cells from f. h, Comparison of differential transcription factor motif occupancy within global chromatin and BEMP-specific chromatin regions using TOBIAS. The large shapes indicate motifs showing significant binding differences. i, Quantification of worm settlement sites in the intestine of Gata2+/+ and Gata2D/D mice infected with H. polygyrus for 14 days. n = 7; 3 experiments. j, Quantification of worm number in the intestine of mice from i. k, Model for induction of type 2 myelopoiesis by helminth infection. IL-33 released from cells damaged by infection induces Lmo4 in oligopotent EMPPs via NF-κB, leading to displacement of FOG1 and LMO2 from GATA2, GATA2 reallocation from Ery–Meg to BEM chromatin, and myeloid fate. Values are mean ± s.d. P values were determined by two-tailed Welch’s t-test unless otherwise specified.

Source data

Comparison of Gata2+/+ and Gata2D/D EMPPs by GSEA using RNA-seq and ATAC-seq data showed that loss of the GATA2–LMO4 interaction led to decreased expression of both transcriptional and epigenetic BEMP signatures, with a corresponding increase in preMegE signature expression (Extended Data Fig. 9e,f and Supplementary Table 9). Consistent with this, Gata2D/D EMPPs acquired transcriptional Ery–Meg bias, while remaining distinct from preMegEs (Fig. 5f,g), and digital transcription factor footprinting showed a global increase in GATA motif occupancy in Gata2D/D, compared with Gata2+/+, EMPPs (Extended Data Fig. 9g and Supplementary Table 10). Furthermore, comparison of global and BEMP-specific chromatin showed a selective loss of GATA motif occupancy within the BEMP chromatin domain in the absence of GATA2–LMO4 interaction (Fig. 5h, Extended Data Fig. 9h and Supplementary Table 11), whereas preMegE-specific chromatin showed a significant increase in GATA motif occupancy (Extended Data Fig. 9i and Supplementary Table 12). Finally, after infection of Gata2D/D mice and wild-type littermates with H. polygyrus, we observed a significant increase in the number of worms and worm settlement sites in the small intestine of mutant mice (Fig. 5i,j), showing that GATA2–LMO4 interaction is required for parasite control.

Collectively, these data confirmed that LMO4 binding to GATA2 is critical for the lineage-instructive properties of LMO4: in the absence of the GATA2–LMO4 interaction, GATA factor recruitment to BEMP-specific chromatin is selectively decreased in EMPPs and commitment to a BEMP fate, subsequent myeloid differentiation and anti-parasite immunity severely impaired.

Discussion

The presence of a dedicated cellular pathway of type 2 myelopoiesis raised the possibility of coordinated intrinsic and extrinsic regulation of BEM production. Here we identified LMO4 as a GATA2 co-factor that is upregulated during BEMP commitment, which has the capacity to promote the generation of all BEM cell types. Furthermore, Lmo4 expression is upregulated in the upstream EMPPs after H. polygyrus infection or direct IL-33 administration, and Lmo4 expression is sufficient to bias EMPPs towards BEMP commitment. IL-33 also upregulated LMO4 expression in human EMPPs, inducing myeloid fate bias. LMO4 is therefore a conserved master regulator of type 2 myelopoiesis that initiates BEM production at the oligopotent progenitor level in response to type 2 inflammatory signalling, which may, in addition to the observed direct IL-33 signalling to EMPPs, involve indirect effects of IL-33-induced cytokines[32]. Emergency production of erythrocytes, platelets, monocytes and neutrophils involves induction of bias in multipotent and oligopotent haematopoietic stem and progenitor cells by cytokines in response to inflammatory or infectious challenge33,34. However, in experimental models of basophilia[6] and eosinophilia[7], expansion of committed progenitor populations has been observed, but regulation of BEM fate commitment has not been reported, probably because the relevant progenitor populations were not well defined. EMPPs and BEMPs constitute a relatively small fraction of the commonly used CMP and GMP progenitor definitions8,35, which could obscure changes to their abundance if these populations were used as reference. It will therefore be important to re-evaluate the action of other relevant cytokines such as KitL, IL-3, IL-5 and GM-CSF, all of which promote the differentiation of one or more BEM cell types36,37.

The haematopoietic system has long served as a paradigm for cell-fate regulation, and GATA2 is a master regulator of haematopoiesis[38]. Consequently, Gata2 ablation[39], dysregulation[39] or pathogenic GATA2 mutations8,40 all have profound and pleiotropic effects on gene regulation and haematopoietic lineage development. Understanding how dynamic changes to GATA2 protein–protein interactions execute cellular decisions and establish cell-type-specific gene expression patterns is therefore central to understanding both normal and defective blood cell production. Here we found that regulation of the GATA2 ZnF1 interactome controls EMPP fate. Both biochemical analysis and structural modelling showed that the binding of LMO4 (but not LMO2) to GATA2 ZnF1 was incompatible with the GATA2–FOG1 interaction. Furthermore, cells containing GATA2(G320D), which is defective in LMO4 binding, were refractory to LMO4-mediated myeloid reprogramming and defective in BEM generation. Therefore, LMO4 modulates the GATA2 proteome to induce EMPP lineage bias and myeloid differentiation. Given the essential role of FOG1 in Ery–Meg lineage commitment[16], FOG1 displacement from GATA2 provides a simple mechanism by which LMO4 inhibits EMPP commitment to Ery–Meg fate, and conversely, the ability of FOG1 to inhibit both eosinophil[17] and mast cell[18] differentiation can now be explained by competition between LMO4 and FOG1 for GATA2 ZnF1 interaction. Overall, this identifies the LMO4–GATA2 interaction as a specific and essential requirement for type 2 myelopoiesis, and interference with LMO4 or its interaction with GATA2 as a molecular strategy for selective modulation of type 2 inflammation.

A key consequence of LMO4–GATA2 interaction was reallocation of GATA factors, and GATA2 in particular, from preMegE-specific to BEMP-specific chromatin. GATA motif occupancy within BEMP chromatin increased after LMO4 expression, even as global GATA motif occupancy decreased. Conversely, in GATA2(G320D)-mutant EMPPs, GATA motif occupancy increased both globally and within preMegE chromatin, whereas decreased within BEMP chromatin, demonstrating that GATA factor allocation is an active mechanism, a conclusion further supported by the significant GATA2 reallocation observed in HPC-7 cells upon LMO4 expression. Reallocation of Runx1 by PU.1 has previously been shown to coordinate gene expression during thymocyte differentiation[41], and to control the balance between differentiation and stemness in myeloid progenitors[42]. This type of mechanism can therefore control multiple processes during blood formation. Furthermore, BEMP-specific transcription in EMPPs was correlated to GATA factor allocation, indicating that GATA factor chromatin distribution directly determines lineage commitment and is controlled by competition between lineage-specific GATA co-factors, a notion supported by the observation that FOG1 is capable of displacing GATA1 from mast cell-specific regulatory elements when ectopically expressed in mast cells[43]. Given the ability of LDB1-containing transcription factor complexes to mediate long-range interactions[44], this is likely to involve both changes to local transcription factor occupancy and reorganization of the 3D chromatin structure.

In addition to blood, GATA factors are central to the development of endothelium, skin, neurons, heart and multiple epithelia, functions executed through their participation in diverse, tissue-specific protein complexes[13]. The principles herein identified are therefore likely to apply to other GATA co-factor-regulated developmental processes, including neuronal specification[21]. Furthermore, our finding that GATA distribution is regulated via the H. polygyrus–IL-33–Lmo4 axis (Fig. 5k) indicates that GATA-regulated fate decisions can be directly linked to the physiological needs of the organism, identifying GATA factor chromatin reallocation as a novel stress response mechanism.

Methods

Animal studies

All animal studies were performed in accordance with UK Home Office regulations with approval by the University of Oxford Animal Welfare and Ethical Review Body (project license number 30/3359, PP2240412 and PP3246892) and the Francis Crick Institute Animal Welfare and Ethical Review Body (project license number PP8468807), as well as the Irish Health Products Regulatory Authority regulations License AE19136/P108 and P196, approved by Trinity College Dublin’s Animal Research Ethics Committee. NOD.Cg-KitW-41JTyr+PrkdcscidIl2rgtm1Wjl/ThomJ (NSGW41) mice[45], Il1rl1-KO mice[46], Gata2G320D knock-in mice[30] and Gata1–eGFP BAC transgenic mice[8] have been previously described. For IL-33 administration 9-week-old male C57BL/6J or Gata1–eGFP mice or short hairpin RNA (shRNA)-targeting Scramble or Lmo4, or control or St2−/− transplanted mice were intraperitoneally injected with 1 μg of recombinant mouse IL-33 (Peprotech) or PBS. For parasite infection, 9–12-week-old male C57BL/6J were used. Eight-nine-week-old Gata2D/D mice and wild-type littermates were infected with 200 H. polygyrus third-stage larvae, obtained from faecal cultures of H. polygyrus-infected mice, by oral gavage. Mice were maintained under specific pathogen-free conditions at 19–23 °C and 45–65% humidity on a 12-h light–12-h dark cycle. Sex and age-matched animals were randomly selected and allocated to experimental group, expect when assignment based on mouse genotype. Experiment and data collection were not performed blind to the condition of the experiment.

Plasmid construction

Flag–LMO2, MYC–LMO4, shRNA-targeting Scramble and shRNA-targeting Lmo4 constructs were obtained from Addgene (#64893, #22965, #59299 and #59292, respectively). The HA–FOG1 construct has been previously described[17]. Gata2 as well as LMO2/4 chimeras LMO-C1 and LMO-C2 cDNA were synthetized and subcloned into pLeGO-G (Addgene #27347) or pLeGO-C2 (Addgene #27339) under a mCherry-P2A reporter. For lentiviral transduction, Lmo4 cDNA was amplified and subcloned into pLeGO-C2 (Addgene #27339) or LT3-GEPIR (Addgene #111177) under a mCherry-P2A reporter.

Lentiviral production

HEK-293T (American Type Culture Collection CRL-11268) cells were plated in DMEM (Gibco) supplemented with 10% FBS (Gibco), 2 mM l-glutamine (Gibco) and 100 U ml−1 penicillin–streptomycin (Gibco), a day before co-transfection. Co-transfection was performed using PEIpro (Polyplus) according to the manufacturer’s recommendation, with psPAX2 (Addgene #12260), pMD2-G (Addgene #12259) and LeGO-C2-P2A-Lmo4/empty vectors. The viral supernatant was collected 48 h post-transfection, filtered using 0.45-µm cellulose acetate filter (Milipore) and concentrated using an Optima XPN-80 (Beckman Coulter) at 23.000 rpm for 1.5 h at 4 °C. HEK-293T cells were purchased from the supplier (Sigma) as a cell line authenticated by STR profiling, and was tested negative for mycoplasma.

Bone marrow transplantation

A total of 5 × 10^5 bone marrow cells were isolated from 12-week-old female Gata1–eGFP and Gata2+/+ or Gata2D/D mice and engrafted in lethally irradiated (10 Gy) 8–10-week-old female CD45.2 recipient mice by intravenous injection. To generate Il1rl1−/− chimeras, 5 × 10^5 total bone marrow cells from CD45.2 Il1rl1−/− or wild-type control mice were co-transplanted with an equal number of wild-type CD45.1 bone marrow cells into lethally irradiated (10 Gy) 8–10-week-old female CD45.1 recipient mice by intravenous injection.

Transplantation of genetically modified HSPCs

Bone marrow was isolated from 10–14-week-old female C57BL/6J or Gata1–eGFP mice[8]. Haematopoietic stem and progenitor cells (HSPCs) were enriched using CD117 MicroBeads and LS columns, according to the manufacturer’s recommendation (Miltenyi Biotec). Transduction of empty/Lmo4-expressing or shRNA-targeting Scramble/Lmo4 lentivirus was at a multiplicity of infection of 50 in StemSpan SFEM (STEMCELL Technologies) supplemented with 2 mM l-glutamine (Gibco), 100 U ml−1 penicillin–streptomycin (Gibco), 5 μg ml−1 polybrene (Merck), 100 ng ml−1 mouse SCF (Peprotech) and 100 ng ml−1 human TPO (Peprotech). Cells were spinfected at 33 °C and 700g for 1 h and incubated at 37 °C for 7 h or overnight. A total of 5 × 10^5 cells were injected intravenously into lethally irradiated (12 Gy; split dose) 8–10-week-old female CD45.2-recipient or CD45.1-recipient mice.

Colony-forming unit assay

Sorted cells (100–5,000) were plated in MethoCult GF M3434 (STEMCELL Technologies) supplemented with 25 ng ml−1 human TPO (Peprotech), 50 ng ml−1 mouse IL-5 (Peprotech), 50 ng ml−1 mouse IL-9 (Peprotech) and 100 U ml−1 penicillin–streptomycin (Gibco). Bulk or single-colony analysis was performed 10 days after cell plating. For single-colony readout, individual colonies were picked up in PBS (Gibco) supplemented with 5% FBS (Gibco) in 96-well plates and analysed by flow cytometry.

Human single-cell culture and xenotransplantation

Bone marrow from healthy volunteer was purchased from Lonza (Lonza Bioscience). CMP CD131+ was isolated by flow cytometry and cultured at the single-cell level in round-bottom 96-well plates in 50 μl of StemSpan (STEMCELL Technologies) with 20 ng ml−1 human SCF, 20 ng ml−1 human Flt3L, 20 ng ml−1 human IL-3, 50 ng ml−1 human IL-5, 20 ng ml−1 human IL-6, 50 ng ml−1 human GM-CSF, 20 ng ml−1 human G-CSF, 40 μg ml−1 human LDL (Sigma-Aldrich), 0.5 U ml−1 erythropoietin, 100 ng ml−1 thrombopoietin with 100 U ml−1 penicillin–streptomycin (Gibco), 0.1 mg ml−1 streptomycin and with or without 100 ng ml−1 human IL-33. Cytokines supplied by Peprotech. Lineage analysis was performed by flow cytometry, 20 days after cell plating. For xenotransplantation, 10^4 CD34+ cells isolated by flow cytometry were injected in NSGW41 mice by intrafemoral injection.

ELISA assay

Bone marrow supernatant was collected from tibias and femur by flushing. IL-33 was quantified with mouse/rat IL-33 Quantikine ELISA kit (R&D) according to the manufacturer’s recommendation, using CLARIOstar Plus (BMG labtech).

Flow cytometry analysis and cell sorting

Mouse bone marrow cells were isolated by crushing leg bones in FACS media that consisted of PBS (Gibco) supplemented with 2% FBS (Gibco). For HSPC hierarchy analysis, bone marrow cells were subjected to CD117 enrichment using CD117 MicroBeads and LS column, following the manufacturer’s recommendation (Miltenyi Biotec). Spleen cells were obtained by crushing the mouse spleen on a 0.70-μM filter using FACS media. Isolation of peritoneal cavity cells was performed by intraperitoneal injection of PBS (Gibco) in culled mice. After peritoneum massage, peritoneal suspension containing cells was collected, spun down at 500g for 5 min at 4 °C and cell pellet resuspended in FACS media. Peripheral blood was collected from the mouse tail vein into lithium heparin-coated microvettes (Sarstedt). An aliquot was collected for analysis of red blood cells and platelets. For mononuclear cell fraction analysis, samples were subjected to red blood cell lysis using ammonium chloride solution (STEMCELL Technologies) for 10 min in ice. After centrifugation at 500g for 5 min at 4 °C, the cell pellet was resuspended in FACS media. For xenografted human cell sorting, bone marrow cells isolated from NSGW41-transplanted mice were subjected to mCD45/Ter119 depletion using mCD45 and Ter119 MicroBeads and LS column, following the manufacturer’s recommendation (Miltenyi Biotec). Cells were incubated in FACS media containing Fc-block antibody (eBioscience) and subsequently stained with antibody cocktail for 20 min in ice. Samples were washed with FACS media, spun down at 500g for 5 min at 4 °C and resuspended in FACS media supplemented with living/dead cell dye: 1 μg ml−1 7AAD (Insight Biotechnology) or 0.1 μg ml−1 DAPI (Miltenyi Biotec). Intracellular LMO4 staining was conducted using the True-Nuclear Transcription Factor Buffer Set (BioLegend) according to the manufacturer’s protocol. Stained cells were analysed on LSR Fortessa or LSR X-20 flow cytometers (BD Biosciences). Cell sorting was carried out on FACSAria III, FACSAria Fusion or FACSymphony S6 (BD Biosciences) using FACS DIVA software (v9.0). Analysis was performed using FlowJo software (v10.8.1). The antibodies used are listed in Supplementary Table 13.

Co-IP and western blot

Transfection of HEK293T cells was performed using PEIpro (Polyplus) according to the manufacturer’s recommendation. Forty-eight hours post-transfection, cells were washed in PBS (Gibco) and isolated using trypsin–EDTA 0.05% (Gibco). Total cell lysis was performed using RIPA buffer (25 mM Tris-HCl, 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate and 0.1% SDS) supplemented with cOmplete protease inhibitor cocktail (Roche) for 20 min on ice. Samples were sonicated for one cycle (30 s ON/OFF) using Bioruptor Pico (Diagenode) and spun down at 10,000g for 25 min at 4 °C. Proteins in supernatant were quantified using Qubit Protein broad range assay (Invitrogen). The following antibodies were used for immunoprecipitation: anti-GATA2 (R&D), anti-Flag (Sigma and Cell Signaling) or anti-MYC (Invitrogen and Cell Signaling). Immune complexes were purified using protein G magnetic beads (NEB) followed by serial washes in RIPA buffer supplemented with cOmplete protease inhibitor cocktail (Roche). Magnetic beads were resuspended in 2X Laemmli buffer (Bio-Rad) supplemented with 100 mM dithiothreitol (Merck) and incubated 15 min at 96 °C. Beads were separated on a magnetic rack and supernatant collected for western blotting, according to Pierce protocols.

In vitro signalling inhibitor treatment

Sorted EMPP isolated from Gata1–eGFP mice were plated in round-bottom 96-well plates in 50 µl of StemSpan SFEM (STEMCELL Technologies) supplemented with 100 U ml−1 penicillin and 0.1 mg ml−1 streptomycin, 2 mM l-glutamine, 20 ng ml−1 mouse SCF (PeproTech), 20 ng ml−1 human Flt3-ligand (PeproTech), 20 ng ml−1 human thrombopoietin (PeproTech) and PBS or recombinant IL-33 (PeproTech). Treatment was performed with SP600125 (1 μM; Medchem Express), U0125 (5 μM, Medchem Express), IKK-16 (0.5 μM, Medchem Express) or PBS for 16 h. Equal living cell numbers between treatment groups were sorted by flow cytometry directly in lysis buffer using CellsDirect One-Step RT–qPCR kit, immediately followed by cDNA synthesis and RT–qPCR using TaqMan Gene Expression Master Mix with QuantStudio 3 (Applied Biosystems), according to the manufacturer’s protocol.

Microfluidics single-cell gene expression analysis

Single cells were sorted in a 96-well PCR plate in 5 μl of CellsDirect One-Step RT–qPCR lysis buffer (Life Technologies) supplemented with 0.2X TaqMan probes for each targeted gene (Supplementary Table 14). cDNA synthesis and pre-amplification were conducted with the following program: 15 min at 50 °C, 2 min at 95 °C and 22 cycles of (15 s at 95 °C; 4 min at 60 °C). Samples were diluted in 5 volumes of TE buffer (Invitrogen) and gene expression quantified using the BioMark 192.24 Dynamic Array platform (Fluidigm) according to the manufacturer’s protocol.

Gene expression was measured according to ΔCt method using Hprt1 and B2m (for mouse) or HPRT and B2M (for human) as housekeeping genes with the geometric mean normalization method (Supplementary Tables 15–17). Low-quality cells marked by non-detectable housekeeping gene expression were excluded, as well as non-targeted cells characterized by non-detectable expression of Gata1 and expression of lymphoid genes (Dntt, Irf8, Pax5, Ebf1 or Tcf7). Principal component analysis, UMAP projection and clustering were performed using Seurat (v5)[47]. Single-cell GSEA was performed using ssGSEA (https://rpubs.com/pranali018/SSGSEA).

RNA-seq

Bulk RNA-seq was performed as previously described[48]. In brief, 100 cells were sorted in lysis buffer containing 0.18% Triton X-100 (Sigma-Aldrich), 4 U RNase inhibitor (Takara), 2.5 mM dNTP (Invitrogen) and 2.5 μM oligo-dT (Biomers.net). Reverse transcription was performed using 50 U SMARTScribe reserve transcriptase (Takara), and cDNA amplification was performed using 50 U SeqAmp Polymerase (Takara). cDNA was purified using SPRIselect beads (Beckman Coulter) and the library prepared using Nextera XT DNA Library Preparation Kit and Nextera XT Index kit (Illumina) according to the manufacturer’s recommendation. Libraries were sequenced on Illumina NovaSeq6000 or NovaSeq X Plus (150 bp; paired-end read).

ATAC-seq

Bulk ATAC-seq was performed based on the Omni-ATAC protocol[49]. In brief, 500–1,000 cells were sorted into tagmentation mix containing 1X TD buffer (Illumina), 0.01% digitonin (Promega) and 0.1% Tween-20 (Bio-Rad), and tagmentation was performed using TDE1 enzyme (Illumina) at 37 °C for 30 min. DNA was purified using the MinElute PCR Purification Kit (Qiagen) and amplified using NEBNext High-Fidelity 2×PCR master mix (NEB) and customized Nextera primers (IDT). PCR product was purified using SPRIselect beads (Beckman Coulter), quantified using the NEBNext Library Quant Kit (NEB) and sequenced on Illumina NovaSeq X Plus (150 bp; paired-end read).

ChIP–seq

ChIP was performed as previously described[50]. In brief, the HPC-7 cell line was transduced using doxycycline-inducible Lmo4/empty-expressing lentivirus and the transduced cell selected using puromycin. Doxycycline (1 μg ml−1) was added in culture media for 48 h and mCherry+ cells sorted by flow cytometry. Cells (1 × 10^6) were incubated with 2 mM disuccinimidyl glutarate and 1% paraformaldehyde, followed by addition of glycine to 125 mM and incubation for 5 min. Cells were pelleted and washed in PBS (Gibco) supplemented with cOmplete protease inhibitor cocktail (Roche). Pelleted cells were resuspended in lysis buffer A (50 mM HEPES pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40 and 0.25% Triton X-100) supplemented with cOmplete protease inhibitor cocktail (Roche) and incubated in ice for 15 min. Nuclei were spun down and washed in lysis buffer B (200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA and 10 mM Tris-HCl pH 7.5), then pelleted and resuspended in wash buffer (50 mM Tris-HCl pH 7.5 and 50 mM CaCl_2) supplemented with cOmplete protease inhibitor cocktail (Roche). Nuclei were pelleted and resuspended in chromatin digestion mix (1X micrococcal nuclease buffer (NEB), 2,000 U micrococcal nuclease (NEB) and 0.1 mg ml−1 BSA) and incubated at 37 °C for 10 min. The reaction was stopped by adding Stop buffer (0.5% SDS, 5 mM EDTA and 5 mM EGTA final concentration) and tubes transferred in ice. Digested nuclei were sonicated for one cycle 30 s ON/OFF using Bioruptor Pico (Diagenode) and diluted in 4 volumes of ChIP dilution buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl pH 8.1 and 167 mM NaCl). Relevant antibodies (Supplementary Table 13) were added and incubated at 4 °C overnight. Protein G magnetic beads (NEB) were added and incubated for 2 h at 4 °C. Bead-bound chromatin was washed twice in low-salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.1 and 150 mM NaCl), twice in high-salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.1 and 500 mM NaCl), twice in LiCl buffer (0.25 M LiCl, 1% NP-40, 1% deoxycholic acid, 1 mM EDTA and 10 mM Tris-HCl pH 8.1) and once in TE buffer (1 mM EDTA and 10 mM Tris-HCl pH 8.1). DNA was reverse crosslinked by adding 1.6 U proteinase K (NEB) and incubated at 55 °C for 3 h, then at 65 °C overnight. Finally, DNA was purified with a 1.8X ratio SPRI clean-up and quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). Sequencing libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB) according to the manufacturer’s recommendation, and sequenced aiming 40 million reads per sample in a NextSeq550 or NovaSeq-X Plus (Illumina). The HPC-7 cell line was authenticated by differentiation into erythroid, megakaryocyte and myeloid lineages, and tested negative for mycoplasma.

RNA-seq analysis

Quality control and adaptor sequences were removed from fastq file using Trim Galore! (v0.6.5) and subsequent reads were quantified with Salmon (v1.2.0)[51] using the mouse mm10 reference genome (https://www.gencodegenes.org/mouse/release_M10.html). Read count normalization and differential gene expression were performed using DESeq2 with default parameters (v1.42.0)[52]. GSEA was performed using the javaGSEA application (v4.2.3)[53] and FGSEA R package (v1.34.2; http://bioconductor.org/packages/fgsea/).

ATAC-seq analysis

Quality control and adaptor sequences were removed from Fastq files using Trim Galore! (v0.6.5). Reads were aligned to the mm10 reference genome using Bowtie2 (v2.4.1)[54]. Subsequent SAM files were converted to BAM files, and PCR duplicate reads were filtered out using SAMtools (v1.17)[55]. Peak calling was performed using MACS2 (v2.1.2)[56] with the following parameters: -q 0.05-nomodel–shift -100-extsize 200. A normalized Bigwig file was generated using bamCoverage (deepTools v3.5.3)[57] and visualized on Integrative Genomics Viewer (v2.19.1)[58]. Unique reads mapped to these regions were quantified using featuresCounts (v2.0.1)[59]. Differential open chromatin regions were measured using DESeq2 (v1.42.0)[52]. For GSEA, peaks were ranked by statistic from DESeq2 to generate a rank-ordered list that was imported into the javaGSEA application (v4.2.3)[53] and FGSEA R package (v1.34.2; http://bioconductor.org/packages/fgsea/). Transcription factor footprint analysis was performed using TOBIAS (v0.12.1)[25] with the JASPAR2020 vertebrate core database[60]. BAM files from replicates were aggregated into a single BAM file and used for transposition insertion bias correction using the ATACorrection function. The transcription factor footprint score within selected open chromatin regions was computed using corrected files with ScoreBigwig function. Finally, differential transcription factor occupancy was quantified using the BINDetect function.

ChIP–seq analysis

FASTQ files were processed to BAM files similarly as described in ATAC-seq analysis section, using mm10 (https://www.gencodegenes.org/mouse/release_M10.html) or hg19 (https://www.gencodegenes.org/human/release_19.html) reference genomes for experiment using HPC-7 or HEK293T cell lines, respectively. Peak calling was performed using MACS2 (v2.1.2)[56] with the following parameters: -q 0.05-nomodel-extsize 200. Read counts mapped on these regions were quantified using featureCounts (v2.0.1)[59] and a normalized Bigwig file was generated using bamCoverage (deepTools v3.5.3)[57]. Differential binding was measured using DESeq2 (v1.42.0)[52] and visualized with deepTools (v3.5.3)[57]. GSEA was performed using FGSEA R package (v1.34.2).

Protein structural modelling

The structures and binding interfaces of the protein complexes were generated using AlphaFold3 (ref. [29]). The interaction between LMO2 (residues 1–158), LDB1 LID (residues 337–375) and the GATA2 N-terminal zinc finger (residues 291–339) was modelled, and the binding interface between the GATA2 N-terminal zinc finger and FOG1 ZnF1 was based on the previously determined NMR structure (Protein Data Bank ID 1Y0J) of the GATA1 N-terminal zinc finger complexed with Drosophila melanogaster homologue, ush ZnF1 (dFOG; residues 202–235)[61]. Similarly, the complex containing full-length LMO4 (residues 1–165), LDB1 LID and GATA2 N-terminal zinc finger was also modelled using the same method. A third model was generated for the LMO-C2 construct, which included all residues from LMO2 aside from 112–151 of LMO4, in complex with LDB1 LID and GATA-2 N-terminal zinc finger. All models were visualized using the PyMOL Molecular Graphics System (v2.1.0; http://www.pymol.org/pymol), where proteins were rendered as cartoon diagrams.

Statistics and reproducibility

Statistical analysis and representation were performed using R software. For normally distributed data, two-tailed t-tests were applied with Welch’s correction, which did not assume equal variance. Normality was tested using the Shapiro–Wilk test. The statistical tests used have been described for each analysis in the corresponding figure legend. Sample sizes were chosen based on the variance previously observed in similar experiments, to allow detection of a 50% difference at P = 0.05 with 80% power. Block designs were used to allow variance estimates to be adjusted if necessary. Data collection and analysis were not performed blind to the conditions of the experiments. The boxplots display the median as the centre line of the box, with the box representing the distribution’s 25th (minima) and 75th (maxima) percentiles; the whiskers extend up to 1.5 times the interquartile range (Q3–Q1) from the minima and maxima.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Raw data generated by Illumina sequencing and processed data files are available at Gene Expression Omnibus as super-series GSE276830. Source data are provided with this paper.

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Acknowledgements

We thank M. de Bruijn for the HPC-7 cell line; T. Milne for advice on ChIP–seq; the Weatherall Institute of Molecular Medicine (WIMM) flow cytometry facility for assistance with cell sorting; the Oxford University Biomedical Service for assistance with animal experiments; the WIMM single-cell facility for expert assistance; the WIMM CBRG for computational support; and staff at the Crick Science Technology Platforms for expert support. This work was supported by MRC Unit grant (MC_UU_12009/7 and MC_UU_00029/9) to C.N.; European Hematology Association Junior Research Grant (RG-202112-01553) to A.F.; and by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001128 and FC001159), the UK Medical Research Council (FC001128 and FC001159) and the Wellcome Trust (FC001128 and FC001159). V.P. (212300/Z/18/Z) and F.P. (226579/Z/22/Z) acknowledge additional funding from the Wellcome Trust and P.G.F. is supported by Research Ireland (24/FFP-P/12948). The WIMM FACS Core facility is supported by MRC HIU, MRC MHU (MC_UU_12009), NIHR Oxford BRC and the John Fell Fund (131/030 and 101/517), the EPA fund (CF182 and CF170) and by WIMM Strategic Alliance awards (G0902418 and MC_UU_12025).

Author information

Authors and Affiliations

1. MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK

Alexandre Fagnan, Cristina Di Genua, Yiran Meng  (孟一然), Roy Drissen, Zishan Zhang  (张紫姗), Bowen Zhang  (张伯闻) & Claus Nerlov

2. School of Medicine, Trinity College Dublin, Dublin, Ireland

Padraic G. Fallon

3. Nervous System Development and Homeostasis Laboratory, Francis Crick Institute, London, UK

Vassilis Pachnis & Fränze Progatzky

4. School of Life Sciences and Sussex Drug Discovery Centre, University of Sussex, Brighton, UK

Erika J. Mancini

5. Kennedy Institute of Rheumatology, Oxford, UK

Fränze Progatzky

Contributions

C.N. and A.F. conceived the study. C.N., F.P. and V.P. supervised the experiments. A.F., C.D.G., Y.M., R.D., Z.Z., B.Z. and F.P. performed the experiments. P.G.F. generated the Il1rl1-knockout mice. E.J.M. performed the structural analysis. C.N. and A.F. performed the bioinformatics analysis, analysed the data and wrote the manuscript.

Corresponding author

Correspondence to Claus Nerlov.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Harinder Singh, Mitchell Weiss and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Effect of H. polygyrus on EMPP lineage potential.

a, Gating strategy used to quantify the frequency of erythroid (Ery), eosinophil (Eos) and basophil (Bas) cells in mouse BM. b, Quantification of erythroid (Ery), eosinophils (Eos) and basophils (Bas) cell number isolated from the bone marrow of mice, related to Fig. 1a,b. c, Gating strategy used for sorting EMPPs, preMegEs and BEMPs from mouse BM. d, Quantification of EMPP, BEMP and preMegE cell number isolated from the bone marrow of mice, related to Fig. 1a,c. e, Gating strategy used to analyse single cell colony forming assay. f, Clonal fate heatmap showing the cell type frequency in each colony in EMPP colonies from day 4 H.Polygyrus-infected (n = 68) or naïve mice (n = 62). 6 independent experiments. Ery, Meg, Mas, Eos and Mon/Ne) frequencies were quantified by flow cytometry. g, Quantification of clonal fates from (f) as colonies per 150 plated cells. Myelo: Mas and/or Eos; Ery/Meg: Ery and/or Meg colonies; Mixed: Myelo and Ery/Meg. Other: Mon/Neu. Values are mean ± SD. Statistical significance was determined by two-tailed Welch’s t-test.

Source data

Extended Data Fig. 2 IL-33 regulates EMPP lineage output.

a, Schematic of the differentiation hierarchy of Gata1 expressing progenitors. b, Heatmap of gene expression of BEMP and preMegE signatures, defined as genes differentially expressed between BEMPs and preMegEs (Padj<0.05), in EMPPs, BEMPs and preMegEs. c, Expression level of Il17ra, Il1rl1 and Crlf2 genes in EMPP, preMegE and BEMP cell population measured by RNA-seq; 3 biological replicates. d, Quantification of EMPP, BEMP and preMegE cell number isolated from the bone marrow of mice, related to Fig. 1h,i. e, Quantification of erythroid (Ery), eosinophils (Eos) and basophils (Bas) cell number isolated from the bone marrow of mice related to Fig. 1h,i. f, Frequency of Ery, Eos and Bas within the Gata1 expressing cell fraction in BM from (f). PBS day 2, n = 6; PBS day 6, n = 6; IL-33 day 2, n = 9; IL-33 day 6, n = 9. 3 independent experiments. g, Clonal fate heatmap showing the cell type frequency in colonies generated by EMPP isolated from mice treated with IL-33 (n = 105 colonies) or PBS (n = 91 colonies) for 2 days. Ery, Meg, Mas, Eos and Mon/Neu frequencies were quantified by flow cytometry. 6 independent experiments. h, GSEA analysis comparing the expression of the BEMP (left panel) and preMegE gene expression signatures (right panel) between EMPPs isolated from mice treated with IL-33 or PBS for 2 days using RNA-seq (n = 4/condition). NES and p-values are shown for each comparison. Values are mean ± SD. P-values were determined by two-tailed Welch’s t-test unless otherwise specified.

Source data

Extended Data Fig. 3 Lmo4 is induced by both H. Polygyrus and IL-33.

a, Differential transcription factor (TF) gene expression between EMPP purified from naïve mice and H.polygyrus infected mice (day 4). n = 4/condition. b, Differential transcription factor (TF) gene expression between EMPP purified from naïve mice and H.polygyrus infected mice (day 11). n = 4/condition. c, Differential transcription factor (TF) gene expression between EMPP purified from mice injected with IL-33 and control PBS-injected mice (day 2). n = 3/condition. d, Clonal fate heatmap showing the cell type frequency colonies generated by EMPP transduced ex vivo with Lmo4-expressing (n = 101) or control (n = 99) lentivirus. Ery, Meg, Mas, Eos and Mon/Neu frequencies were quantified by flow cytometry. e, Gating strategy used to quantify the frequency of mCherry+ cells in donor derived (EGFP+) red blood cells (RBC) and platelets (PLT) in the peripheral blood of recipient mice in Fig. 2h. f, Gating strategy used to quantify the frequency of mCherry+ cells in donor derived mononuclear cells (CD45.1+), in the peripheral blood of recipient mice in Fig. 2h. g, Flow cytometry workflow for quantification of Gata1+ blood cell types within the mCherry+ BM cell fraction in mice from Fig. 2i. The number of cells is shown as percentage of the parental gate. h, Representative example of quantification of Lmo4 transduced and control (Ctrl) erythroid (Ery), eosinophil (Eos) and basophil (Bas) cells from recipient mice represented in Fig. 2i. i, Gating strategy used to quantify the frequency of EMPP, preMegE and BEMP cells in the Lin–Sca-1–c-Kit+mCherry+ cell fraction in BM of recipient mice in Fig. 2j. j, Clonal fate heatmap showing the cell type frequency in colonies generated by mCherry+ EMPP isolated from mice transplanted as shown in Fig. 2g, Lmo4 (n = 54) or control transduced (n = 61). Cell types were quantified by flow cytometry. 3 independent experiments. Differential gene expression P-values were computed using a two-sided Wilcoxon Rank-Sum test with Bonferroni correction (a-c).

Source data

Extended Data Fig. 4 IL-33 acts on EMPPs to control their lineage bias.

a, Gating strategy used to quantify the frequency of EMPP, BEMP and preMegE with the LK CD45.1+ and LK CD45.2+ population, related to Fig. 3a,b. b, Frequency of EMPP and BEMP progenitors within LK CD45.1+ or LK CD45.2+ populations in Il1rl1-/- bone marrow chimeras treated with mouse recombinant IL-33 or PBS for 2 or 6 consecutive days as shown in Fig. 3a. For the day 2 timepoint, n = 7 for each condition except WT PBS (n = 6); for the day 6 timepoint, n = 5 per condition. Indicated P-values have been calculated comparing the number of CD45.1+ and CD45.2+ BEMPs within each mouse using a paired Student’s t-test. c, Quantification of clonal EMPP fates as number of colonies per 500 plated cells from the indicated condition as illustrated in Fig. 3a classified as follows: Mixed: Ery and/or Meg with Mas and/or Eos; Myelo: Mas and/or Eos; Ery/Meg: Ery and/or Meg colonies. Other: Mon/Neu d, Clonal EMPP fates from (c) as frequencies of total colonies. 4 independent experiments. e, Lmo4 gene expression in EMPP populations isolated from Gata1-EGFP mice, treated for 16 h with IL-33 and inhibitors as indicated. DMSO (n = 11), IL-33 (n = 9), IL-33/IKK-16 (n = 10), IL-33/SP600125 (n = 11) and IL-33/U0126 (n = 11). 2 independent experiments. Values are mean ± SD. Statistical significance was determined by two-tailed Welch’s t-test.

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Extended Data Fig. 5 Effect of hIL-33 on human EMPPs.

a, Gating strategy used to sort human CD34+CD38–CD131+ cells from bone marrow of xenografted mice, related to Fig. 3f–h. b, Histogram showing the human MEP (hMEP) ssGSEA signature score of the cell clusters from Fig. 3f. c, Histogram showing the human BMCP (hBMCP) ssGSEA signature score of the cell clusters from Fig. 3f. d, Gating strategy used to sort human CMP CD131+ cells, related to Fig. 3i. e, Gating strategy used to quantify lineage clonal fate from human CMP CD131+ cell, related to Fig. 3i. f, Clonal fate heatmap showing the cell type frequency colonies generated by human CMP CD131+ cell treated with recombinant human IL-33 or PBS, related to Fig. 3i. Ery, Mk, Mas, Eos, Bas and Mon/Neu frequencies were quantified by flow cytometry as in (e).

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Extended Data Fig. 6 Lmo4 reprograms EMPPs.

a, Heatmap of chromatin accessibility in EMPP, BEMP and preMegE of BEMP (orange) and preMegE (grey) signatures identified as peaks differentially accessible (Padj<0.05) between BEMPs and preMegEs. b, GSEA analysis of BEMP and preMegE gene expression (RNA-seq) and chromatin accessibility signatures (ATAC-seq) comparing Lmo4 and control virus transduced mCherry+ EMPPs from mice transplanted as in Fig. 2g. n = 3/condition. NES and p-values are shown for each comparison. c, TF motifs differentially occupied between Lmo4 and control virus transduced mCherry+ EMPPs isolated from mice transplanted as shown in Fig. 2g identified using TOBIAS. n = 3/condition. Large dots represent differentially bound TFs and colours indicate increased binding in Lmo4 (green) or in Ctrl (purple) conditions, respectively. d, TF motifs differentially occupied within BEMP-specific chromatin between LMO4 transduced and control mCherry+ EMPPs identified as in (c). e, TF motifs differentially occupied within preMegE-specific chromatin between LMO4 transduced and control mCherry+ EMPPs identified as in (c). f, Expression level (TPM) of Lmo4, Cpa3, Ly6c2, Ces2g, Gata1 and Gata2 genes in mCherry+ BEMP, EMPP and preMegE cells purified from recipient mice transplanted as shown in Fig. 2g (n = 3/condition). P-values were computed using a two-sided Wilcoxon Rank-Sum test with Bonferroni correction (a-c). g, Gating strategy used to sort HPC-7 cells ectopically expressing Lmo4 or empty backbone (Ctrl) using a doxycycline (Dox) inducible lentiviral vector. No Dox control is also shown. h, Genome browser tracks showing ATAC-seq and GATA-2 ChIP-seq signals in the indicated cell population for the Zfpm1 and Lmo4 gene loci. P-values for differential TF motif occupancy (c-e) were computed using TOBIAS.

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Extended Data Fig. 7 Analysis of GATA-2–Lmo4 interaction.

a, Replicates of co-immunoprecipitation assay from Fig. 4b. b, Replicates of co-immunoprecipitation assay from Fig. 4c. c, Schematic of LMO2, LMO4, and the LMO-C1 and LMO-C2 chimeras. Domain start and end positions are indicated. d, The structure and binding interface of the LMO-C2 construct (residues 112-151 from LMO4 highlighted in blue), LDB1 LID (337-375) (yellow) and GATA-2 NF (light magenta) as modelled by Alphafold3. GATA-2 V296 is highlighted with a mesh surface. e, Clonal fates of isolated Gata1-EGFP EMPPs transduced with lentivirus expressing the indicated LMO constructs. Mixed: Ery and/or Meg with Mas and/or Eos; Myelo: Mas and/or Eos; Ery/Meg: Ery and/or Meg colonies. Other: Mon/Neu. 500 cells/assay, 3 biological replicates. f, HA-FOG-1, Flag-GATA-2 and Myc-LMO were co-expressed in HEK293T cells and co-immunoprecipitated with anti-Myc antibodies or control IgG followed by Western blotting with the indicated anti-tag antibodies. The input represents 0.75% of the material used for immunoprecipitation. g, Replicates of co-immunoprecipitation assay from (f).

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Extended Data Fig. 8 Lmo4 is required for myeloid commitment.

a, Replicates of co-immunoprecipitation assay from Fig. 4f. b, Experimental workflow schematic showing the transduction of c-Kit+ isolated from 9-week old C57BL/6 mice with lentivirus expressing shRNA targeting Lmo4 or Scramble, and transplanted into lethally irradiated 10-week old C57BL/6 female mice. Treatment with recombinant mouse IL-33 or PBS for 2 consecutive days is also represented. c, Quantification of Lmo4 gene expression in EMPP isolated from transplanted and treated mice as shown in h). n = 5 biological replicate per condition. d, Frequency of EMPP, BEMP and preMegE cell populations within the LK BM cell fraction in transplanted and treated mice as shown in (a). 5 biological replicates. e, Quantification of clonal EMPP fates isolated from transplanted and treated mice as shown in h). n = 5 biological replicate per condition. Mixed: Ery and/or Meg with Mas and/or Eos; Myelo: Mas and/or Eos; Ery/Meg: Ery and/or Meg colonies. Other: Mon/Neu. Values are mean ± SD. P-values were determined by two-tailed Welch’s t-test.

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Extended Data Fig. 9 GATA-2–Lmo4 interaction controls EMPP lineage bias.

a, Quantification of erythroid (Ery), eosinophils (Eos) and basophils (Bas) in the bone marrow of Gata2+/+ (n = 6), Gata2D/+ (n = 7) and Gata2D/D (n = 6) mice. Related to Fig. 5a. b, Quantification of EMPP, BEMP and preMegE cell number isolated from the bone marrow of mice, related to Fig. 5b. n = 5 per genotype. c, Frequency of erythroid (Ery), eosinophil (Eos) and basophil (Bas) cells in the spleen of Gata1-EGFP mice with a Gata2+/+ (n = 6), Gata2D/+ (n = 7) and Gata2D/D (n = 6) genotype measured by flow cytometry. Values are mean frequencies ±SD from 3 independent experiments. P-values are shown. d, Frequency of eosinophils (Eos) and basophils (Bas) in the EGFP+ spleen cell fraction of mice from Fig. 5c. n = 9/genotype. e, GSEA analysis of BEMP and preMegE gene expression (RNA-seq) and chromatin accessibility signatures (ATAC-seq) comparing Gata2+/+ and Gata2D/D EMPPs. n = 3/condition. NES and p-values are shown for each comparison. f, Expression level (TPM) of Nfe2, Cpa3, Ly6c2 and Ces2g in BEMP, EMPP or preMegE cells isolated from Gata1-EGFP mice with a Gata2+/+ (n = 3), or Gata2D/D (n = 3) genotype measured by RNA-seq. Values are mean ± SD from 3 independent experiments. P-values were computed using a two-sided Wilcoxon Rank-Sum test with Bonferroni correction. g, TF motifs differentially occupied between EMPPs with a Gata2+/+ or Gata2D/D genotype isolated from mice transplanted as in Fig. 5c identified using TOBIAS. Large dots indicate motifs showing significant binding differences. h, TF motifs differentially occupied within BEMP-specific chromatin in EMPPs with a Gata2+/+ or Gata2D/D genotype identified as in (g). i, TF motifs differentially occupied within preMegE-specific chromatin in EMPPs with a Gata2+/+ or Gata2D/D genotype identified as in (g). P-values for differential TF motif occupancy (c-e) were computed using TOBIAS. Statistical significance was determined by two-tailed Welch’s t-test except when otherwise specified.

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Fagnan, A., Di Genua, C., Meng, Y. et al. A mechanism to initiate emergency type 2 myelopoiesis. Nature 653, 212–220 (2026). https://doi.org/10.1038/s41586-026-10256-6

- Received: 03 October 2024

- Accepted: 10 February 2026

- Published: 11 March 2026

- Version of record: 11 March 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10256-6

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Cell-type-targeted mitochondrial transplantation rescues cell degeneration

Temurkhan Ayupov,

Verónica Moreno-Juan,

Serena Curtoni,

Alex Fratzl,

Upnishad Sharma,

Susana Posada-Céspedes,

Ramona Ratiu,

Rei Morikawa,

Alexandra Graff Meyer,

Margherita Pezzoli,

Glenn Bantug,

Morgan Chevalier,

Yanyan Hou,

Sarah A. Nadeau,

Álvaro Herrero-Navarro,

Vikram Ayinampudi,

Elizabeth Kastanaki,

Natasha Whitehead,

Rebecca A. Siwicki,

Mariana M. Ribeiro,

Ji Hoon Han,

Annalisa Bucci,

Christoph Hess,

Simone Picelli,

Magdalena Renner,

Daniel J. Müller,

Cameron S. Cowan,

Simon Hansen &

…

Botond Roska

Nature volume 653, pages 221–231 (2026)

Abstract

A number of currently untreatable diseases, including neurodegenerative disorders, optic nerve atrophy and heart failure, are associated with mitochondrial dysfunction. Transplantation of healthy mitochondria has been proposed as a potential therapeutic strategy1,2,3. However, the lack of methods to target donor mitochondria to disease-affected cell types limits treatment specificity and efficacy. Here we developed MitoCatch as a system to deliver mitochondria to specific cell types using different types of protein binders. Donor mitochondria are captured by target cells by cell-surface-displayed monospecific binders, mitochondrion-displayed monospecific binders or bispecific binders linking mitochondria to target cells. Using MitoCatch, we show that donor mitochondria are efficiently internalized, exposed to the cytosol, move, and undergo fusion and fission inside target cells. By engineering binders with different affinities, we tune the efficiency of mitochondrial delivery. We demonstrate targeted mitochondrial transplantation to retinal cell types, neurons and cardiac, endothelial and immune cells in humans and mice. Transplanted mitochondria promoted the survival of damaged neurons from an individual with optic nerve atrophy in vitro and after neuronal injury in mice in vivo. MitoCatch is a potential strategy to target disease-affected cell types with mitochondria in organs affected by diseases associated with mitochondrial dysfunction.

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Delivery of isolated healthy mitochondria to cells defective in mitochondrial function has been proposed as a form of organelle therapy and has been demonstrated in vitro and in vivo in animals and in humans2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18. However, mitochondrial delivery in its current form has low efficiency and cannot be targeted to disease-affected cell types.

Cell-type-targeted entry by some viruses uses viral coat proteins that bind to cell surface proteins. By artificially modifying viral or cellular surfaces with engineered or naturally occurring binders, viruses can be targeted to specific cell types19,20. We therefore reasoned that, similar to viruses, isolated mitochondria could potentially be targeted to and enter cells by enabling mitochondria to bind to cell surface proteins.

Here we developed MitoCatch, a cell-type-specific mitochondrion-targeting system that links mitochondria and the cell surface by protein binders and brings mitochondria into the target cell. We designed three complementary approaches to enable the most versatile use of MitoCatch. The first (MitoCatch-C) involves display of binders on the cell surface of a specific cell type that in turn can interact with proteins on the surface of mitochondria. The second approach involves display of binders on the surface of mitochondria (MitoCatch-M) that selectively bind to specific membrane proteins of a given cell type. The third strategy uses bispecific binders (MitoCatch-Bi) that link cell-type-specific membrane proteins and mitochondria outer membrane proteins (Fig. 1a).

Fig. 1: Cell-type-specific delivery of mitochondria and their internalization.

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a, Schematic of MitoCatch. b, iHNeurons with anti-mCherry (top) or anti-GFP (bottom) nanobodies (magenta, grey dashed lines), 1 day after mito-GFP (green) transplantation. c, Quantification of b. n = 8. Two-sided Welch’s t-test, P < 0.0001. d, Schematic of visualization of transplanted mitochondria (left). TEM image of miniSOG-positive mitochondria (dark stained) in iHNeurons, 1 day after transplantation (right). The white dashed lines show miniSOG-negative mitochondrion. e, Mito-GFP movement (white arrows) in neurites (grey dashed lines) of iHNeurons, 3 days after transplantation. f, Mitochondrial fission and fusion events (white arrows). g, Transplanted mito-GFP (green, white arrows) and dsRed2 matrix-labelled native mitochondria (red, blue arrows) movement 6 h after transplantation in neurites (grey dashed lines) of iHNeurons. h, Mito-GFP movement (green, white arrows) in neurites (grey dashed lines) of iHNeurons with TagBFP–KDEL-labelled endoplasmic reticulum (ER, magenta, blue arrows), 2 days after transplantation. i, Mito-GFP movement (green, white arrows) in the soma of an endothelial cell with TagBFP–KDEL-labelled endoplasmic reticulum (magenta, blue arrows), 6 h after transplantation. j, Mito-GFP movement (green, white arrows) in the soma of an endothelial cell with pHLys-Red-labelled lysosomes (magenta, blue arrows), 1 day after transplantation. k, Schematic of measuring cytosolic accessibility of donor mitochondria. l, iHNeurons expressing cell-surface anti-mCherry (top) or anti-GFP (bottom) nanobodies (magenta, grey dashed lines) and destabilized nanobodies fused to TagBFP (cyan) 1 day after mito-GFP (green) transplantation. m, Quantification of l. n = 6. Two-sided Mann–Whitney U-test; P = 0.0087. n, Cells in the ganglion cell layer of human retina expressing anti-mCherry (top) or anti-GFP (bottom) nanobodies (magenta, grey dashed lines) 1 day after mito-GFP (green) transplantation. o, Quantification of n. n = 5 (control nanobody) and n = 4 (anti-GFP nanobody). Two-sided Mann–Whitney U-tests; P = 0.0159; *P < 0.05, **P < 0.01, ***P < 0.001. Data are mean ± s.e.m. Scale bars, 10 µm (b, l and n), 0.5 µm (d) and 5 µm (e–j).

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Mitochondrial delivery using MitoCatch-C

We fused an anti-GFP nanobody[21] to an OLLAS tag and PDGFRβ transmembrane domain to anchor it on the surface of HEK293T cells. We then directed a GFP to the outer membrane of mitochondria (mito-GFP) by fusing GFP to OMP25[22]. Co-labelling mitochondrial matrix with dsRed2 fused to COX8 signal peptide or with MitoTracker dye affirmed the localization of GFP at the outer membrane. We isolated mitochondria using differential centrifugation and confirmed their purity and intactness using western blotting, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) and oxygen-consumption assays (Extended Data Fig. 1a–f).

We added isolated mito-GFP to HEK293T cells displaying either an anti-GFP nanobody or a control nanobody that binds to mCherry[23]. GFP fluorescence in anti-GFP-nanobody-positive cells was 1,300% higher than in anti-GFP-nanobody-negative cells, while fluorescence in control-nanobody-positive cells was only 62% higher than in the control-nanobody-negative cells (Extended Data Fig. 1g,h).

We quantified mitochondrial enrichment in target cells by comparing experiments performed with anti-GFP versus control nanobodies. First, we determined the percentage of GFP+ cells among the nanobody-positive cells. In total, 94.5% of anti-GFP-nanobody-positive cells and 10.5% of control-nanobody-positive cells were GFP+, an 804% increase. Alternatively, we quantified mitochondrial enrichment in target cells by determining the ratios of GFP fluorescence (GFP-ratio) in nanobody-positive cells and in nanobody-negative cells in the same experiment. Anti-GFP-nanobody-positive cells had an 808% higher GFP-ratio compared with the control-nanobody-positive cells. GFP and TOMM20 were colocalized, confirming that GFP was displayed on the surface of mitochondria. As another control, we transplanted mito-GFP into HEK293T cells expressing cytosolic tdTomato and quantified mitochondrial enrichment between tdTomato+ and tdTomato− cells. Similar to the control nanobody condition, there was no significant enrichment of mito-GFP in tdTomato+ cells (Extended Data Fig. 1i–o). Finally, we defined the specificity of mitochondrial targeting as a normalized ratio between the percentage of GFP+ cells in anti-GFP-nanobody-positive cells and the sum of the percentages of GFP+ cells in both anti-GFP-nanobody-positive and control-nanobody-positive cells. A value of 1 indicates maximal specificity and 0 indicates no specificity. The specificity of mitochondrial targeting with the anti-GFP nanobody was 0.8. Taken together, we describe mitochondrial targeting efficacy and specificity with three values: (1) the percentage increase in donor-mitochondrion-positive cells (PI) when using a matched binder–ligand pair relative to an unmatched binder–ligand pair (here control nanobody); (2) the percentage increase in fluorescence ratio (FR, here the GFP-ratio); and (3) the specificity (S) (Supplementary Table 1).

Next, we tested mitochondrion targeting to induced human neurons (iHNeurons)[24]. In total, 91% of iHNeurons displaying the anti-GFP nanobody were GFP+, as opposed to 11% of cells with the control nanobody (PI = 708%, FR = 488%, S = 0.78). Using confocal microscopy, correlated bright-field microscopy and SEM, we detected GFP+ mitochondria bound to iHNeuron membranes and GFP and TOMM20 were co-localized (Fig. 1b,c and Extended Data Fig. 2a–g). We also performed proteomics analysis of iHNeurons transplanted or not with mito-GFP. We detected enrichment of GFP and mitochondrial proteins in iHNeurons displaying anti-GFP nanobody, relative to both iHNeurons displaying the control nanobody and untreated iHNeurons. Changes in the abundance of lysosomal, endoplasmic reticulum and nuclear proteins were not detected after mitochondrial delivery (Extended Data Fig. 2h,i). Thus, mitochondria can be targeted selectively and effectively to human cells in vitro by cell-surface-displayed nanobodies binding to mitochondrial outer membrane proteins.

Transplanted mitochondria within cells

To determine whether mitochondria enter the targeted cells, we performed TEM analysis of cells after transplantation using mini Single Oxygen Generator (miniSOG)-labelled mito-GFP[25]. We detected miniSOG+ mitochondria within iHNeurons displaying anti-GFP nanobody. MiniSOG+ mitochondria had a distinct cristae structure and a thin elongated morphology and were smaller than mitochondria in donor HEK293T cells or miniSOG− mitochondria in target iHNeurons (Fig. 1d and Extended Data Fig. 3a–g).

Live imaging of iHNeurons transplanted with mito-GFP revealed movement of mito-GFP along neurites and within cell bodies, as well as both fission and fusion events. Mito-GFP, native mitochondria labelled with dsRed2 (mito-dsRed2), as well as GFP and dsRed2 double-positive mitochondria, were present within the same neurites. Transplanted and native mitochondria moved at similar median and maximum speeds along the neurites. Furthermore, mito-GFP was associated with endoplasmic reticulum labelled with TagBFP (Fig. 1e–h, Extended Data Fig. 3h–k and Supplementary Videos 1–4).

Next, we targeted mito-GFP to primary human umbilical vein endothelial cells (HUVECs) displaying anti-GFP nanobodies. To test whether entire mitochondria, rather than fragments, were transferred, we labelled the mitochondria matrix with dsRed2 and the outer membrane with GFP. Both signals were detected within HUVECs, indicating internalization of intact mitochondria. Moreover, donor mitochondria remained motile and were detectable for at least 6 days after transplantation. To examine interactions with other organelles, we transplanted mito-GFP into HUVECs with TagBFP-labelled endoplasmic reticulum. Donor mitochondria were motile and some were closely associated with the endoplasmic reticulum. Next, we transplanted mito-GFP into HUVECs with lysosomes labelled using the lysosome-specific pH sensitive dye, pHLysRed. We detected mito-GFP free from lysosomes and some associated with lysosomes. Most donor mitochondria were more motile than lysosomes, indicating that the two organelles move independently. Finally, transplanting mito-GFP into HUVECs with native mitochondria labelled with matrix-targeted dsRed2 demonstrated an interaction between donor and native mitochondria. The mito-GFP signal persisted for at least 2 days, the time at which we finished the experiment, with some mito-GFP also positive for dsRed2 (Fig. 1i,j, Extended Data Fig. 4a–g and Supplementary Videos 5–7).

Mitochondria are exposed to the cytosol

To test whether the outer membrane of the transplanted mitochondria is exposed to the cytosol, we used a destabilized anti-GFP nanobody (dGBP1) fused to TagBFP[26]. In the absence of GFP, dGBP1–TagBFP undergoes degradation. If GFP is present, dGBP1–TagBFP binds to GFP, resulting in stabilization of dGBP1–TagBFP and blue fluorescence in the target cell (Fig. 1k).

We first co-expressed dGBP1–TagBFP together with GFP or tdTomato in HEK293T cells. Live imaging showed that TagBFP was expressed 420% higher in GFP+ cells than in tdTomato+ cells, confirming GFP-dependent stabilization of dGBP1–TagBFP. Next, we co-expressed dGBP1–TagBFP with GFP in iHNeurons. We confirmed GFP-dependent dGBP1–TagBFP stabilization in fixed GFP+ iHNeurons by staining with anti-TagBFP and anti-GFP antibodies. In total, 70.3% of GFP+ iHNeurons were positive for TagBFP, and the fluorescence of TagBFP was 1,200% higher in GFP+ iHNeurons than in GFP− iHNeurons (Extended Data Fig. 4h–l).

Subsequently, we expressed both dGBP1–TagBFP and cell-surface-displayed anti-GFP or control nanobodies in iHNeurons. We transplanted mito-GFP into these cells and assessed TagBFP expression using anti-TagBFP antibodies: 51.7% of anti-GFP-nanobody-positive cells had a TagBFP signal, as opposed to 19.8% of control-nanobody-positive cells. Moreover, anti-GFP-nanobody-positive cells had a 100% higher TagBFP-ratio than control-nanobody-positive cells (Fig. 1l,m). Furthermore, we measured the proportion of mito-GFP that were positive for TagBFP in anti-GFP-nanobody-positive cells and were therefore accessible to the cytosol: 41.3% of GFP+ mitochondria were colocalized with TagBFP. While the quantity of internalized mitochondria was proportional to the cell body size, the cytosolic accessibility of internalized mitochondria was unaffected by cell size (Extended Data Fig. 4m–p). Using live imaging, we confirmed that TagBFP-labelled particles colocalized with GFP-labelled mitochondria in iHNeurons displaying anti-GFP nanobodies. By contrast, transplanted iHNeurons displaying control nanobodies did not internalize mito-GFP and therefore showed no intracellular TagBFP signal, confirming that BFP fluorescence is detected only after mitochondrial internalization (Extended Data Fig. 4q–v and Supplementary Video 8). Thus, the outer membranes of a proportion of transplanted mitochondria are exposed to the cytosol, suggesting the escape of mitochondria from endocytic vesicles after internalization.

Targeting in human retina ex vivo

To determine whether mitochondrial targeting also works in human tissues, we displayed anti-GFP or control nanobodies on cells of ex vivo-cultured post-mortem human peripheral retinas[27] and injected mito-GFP. In total, 30.2% of anti-GFP-nanobody-positive human retina cells had detectable GFP fluorescence, as opposed to 6.1% of control-nanobody-positive cells (PI = 394%, FR = 98%, S = 0.66). The lower percentage of targeted cells with mito-GFP can be attributed to the thick inner limiting membrane of the peripheral human retina[28]. Moreover, live imaging showed movement of mito-GFP along neuronal processes and in cell bodies, confirming their internalization (Fig. 1n,o, Extended Data Fig. 4w,x and Supplementary Video 9).

Mitochondrial delivery using MitoCatch-M

We next tested mitochondrial transplantation by displaying protein binders on surface of mitochondria (MitoCatch-M).

First, we directed camelid-derived anti-GFP nanobody to the outer surface of mitochondria by fusing it with OMP25. We then isolated these mitochondria and transplanted them into cells expressing either mCherry or GFP on their surface (Extended Data Fig. 5a,b). In total, 61.3% of GFP-positive cells were anti-GFP-nanobody-positive, as opposed to 2.2% of mCherry-positive cells (PI = 2,600%, FR = 938%, S = 0.93). Similarly, we tested delivery of mitochondria displaying anti-mCherry nanobodies into cells displaying either cell-surface GFP or mCherry. In total, 39.1% of mCherry-positive cells were anti-mCherry-nanobody positive, as opposed to 8% of GFP-positive cells (PI = 386%, FR = 326%, S = 0.66) (Extended Data Fig. 5c–f).

Second, we transplanted mitochondria labelled with mTagBFP2 in the matrix (mito-mTagBFP2) and displaying anti-GFP nanobodies into HUVECs expressing cell-surface-GFP and endosome-targeted RAB5A–TagRFP. Donor mitochondria were internalized by endosomes but, already after 6 h, we detected mito-mTagBFP2 free from the endosomes. Additional labelling of lysosomes in the cells with LysoTracker Deep Red dye showed that endosome-free mito-mTagBFP2 can also be free from lysosomes (Extended Data Fig. 5g–j and Supplementary Video 10). Moreover, we transplanted mito-mTagBFP2 lacking any binders into HUVECs co-expressing cell-surface GFP and endosome-targeted RAB5A–TagRFP. Although the targeting efficiency was low, we also observed endosome-free mito-mTagBFP2 in cells that received mitochondria. The proportion of endosome-free donor mitochondria in mito-mTagBFP2-positive cells was comparable between the two conditions—whether mitochondria displayed the anti-GFP nanobody or lacked any binder. However, the overall total abundance of endosome-free mitochondria was fourfold higher in cells transplanted with mito-mTagBFP2 displaying the anti-GFP nanobody. Furthermore, we examined whether donor mitochondria influence lysosomal acidification after their internalization. To this end, we transplanted mito-mTagBFP2, either displaying the anti-GFP nanobody or lacking any binder, into HUVECs expressing cell surface GFP, and stained lysosomes with the pH-sensitive dye pHLys Red. No changes in lysosomal pH were detected in donor-mitochondrion-positive cells under either condition. As a positive control, we treated cells with bafilomycin A1, a lysosomal acidification inhibitor, which resulted in a marked decrease in lysosomal acidification. Moreover, both the proportion and the abundance of lysosome-free mitochondria were higher when cells were transplanted with mito-mTagBFP2 displaying the anti-GFP nanobody compared with mitochondria without any binder (Extended Data Fig. 5k–n and Supplementary Fig. 3a–d).

Third, we transplanted mito-dsRed2 displaying the anti-GFP nanobody into HUVECs with TagBFP-labelled endoplasmic reticulum and expressing cell surface GFP. Transferred mito-dsRed2 were detected for at least 4 days after transplantation and were arranged in a tubular-like network structure. Next, we stained the cells with MitoTracker Deep Red dye, which labelled both native and donor mitochondria. The differential staining demonstrated active interaction of a newly formed donor mitochondrial network with a native mitochondrial network (Extended Data Fig. 5o–q and Supplementary Videos 11 and 12).

Fourth, we generated nanobodies against human CD71 protein (KD = 113 pM). No cross-reactivity with mouse CD71 (mCD71) was detected. We expressed human CD71 and control CD73 or CD142 membrane proteins together with cytosolic GFP in HEK293T cells. We then treated these cells with anti-CD71 nanobody-displaying mitochondria. In total, 56.4% of CD71+ cells were nanobody positive as visualized by anti-alpaca VHH antibodies, compared with 5.8% and 10.4% in CD73+ and CD142+ cells (mean PI = 590%, mean FR = 170%, mean S = 0.76), respectively (Extended Data Fig. 6a–f and Supplementary Table 3).

Fifth, we assessed the targeting efficiency of mitochondria with the anti-CD71 nanobody to primary human cardiac cells that natively express CD71. We treated these cells with isolated mitochondria displaying either anti-CD71 or control nanobodies (anti-mCherry). In total, 86.3% of cells that were transplanted with anti-CD71-nanobody-displaying mitochondria had a nanobody signal visualized by anti-alpaca VHH antibodies, as opposed to 30.2% of cells transplanted with anti-mCherry-nanobody-displaying mitochondria (PI = 180%, FR = 150%, S = 0.48). We confirmed that cell-bound nanobody particles were mitochondria by co-staining with the mitochondrion-specific antibody anti-MT-CO1 (Fig. 2a,b and Extended Data Fig. 6g,h).

Fig. 2: Mitochondrial delivery using mitochondrion-surface-displayed binders.

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a, Primary human cardiac cells (cyan, grey dashed lines) transplanted with mitochondria displaying control anti-GFP (top) or anti-CD71 (bottom) nanobodies (magenta), 2 h after transplantation. b, Quantification of a. n = 6. Two-sided Mann–Whitney U-tests; P = 0.0043 (top), P = 0.0152 (bottom). c, Human rods (green) in retinal organoids transplanted with mito-DsRed2 (cyan) displaying control anti-mCherry (top) or anti-CD73 (bottom) nanobodies, 1 day after transplantation. The grey dashed lines indicate the photoreceptor layer. d, Quantification of c. n = 3 (control nanobody) and n = 5 (anti-CD73 nanobody). Two-sided Welch’s t-test; P = 0.0025. e, HEK293T cells with surface-displayed GFP (green, grey dashed lines) transplanted with mitochondria displaying control anti-mCherry (top) or anti-GFP (bottom) DARPins (magenta), 2 h after transplantation. f, Quantification of e. n = 6 (control DARPin) and n = 5 (anti-GFP DARPin). Two-sided Welch’s t-tests; P = 0.0058 (top) and P = 0.0007 (bottom). g, Endothelial cells (cyan) in vessel organoids targeted by mitochondria (magenta) displaying IgG1 (top) or anti-CD31 (bottom) antibodies, 1 day after transplantation. h, Quantification of g. n = 5 (BG–IgG1) and n = 6 (BG–anti-CD31). Two-sided Mann–Whitney U-test; P = 0.0043. i, Quantification of antibody-fused mitochondrial delivery into non-activated CD8+ T cells, 4 h after transplantation. n = 6. Two-sided Welch’s t-test; P = 0.0032. n values represent independent cultures of T cells from two human donors with mitochondria from multiple preparations. j, Fluorescence intensities of donor mitochondria stained with anti-SNAP-tag antibodies displaying isotype control IgG1 (left) or anti-CD8 (right) antibodies in unstimulated CD8+ T cells, 4 h after transplantation. k, The factors required for efficient and specific mitochondrial delivery. l–n, Schematic of AFM experiments (l), force–distance curves (m) and mitochondrion–cell adhesion profiling (n). o, Example force–distance retraction curves. The control is untreated cells. p, Quantification of mitochondrion–cell adhesion force per approach. Control: n = 115 (30 cells); anti-GFP nanobody: n = 93 (29 cells). Two-sided Mann–Whitney U-test; P < 0.0001. q, The distribution of rupture events per approach. Data are mean ± s.e.m. Scale bars, 50 µm (a and c), 25 µm (e) and 100 µm (g).

Source data

Sixth, we tested whether T cells can be targeted with mitochondria by using their native cell-surface receptors. Given that CD71 was shown to be upregulated after activation of T cells29,30, we used the anti-CD71 nanobody displayed on the surface of mitochondria to deliver mitochondria to activated human CD8+ T cells. We isolated CD8+ T cells from healthy donors and co-stimulated them with anti-CD2, anti-CD3 and anti-CD28 antibodies. We confirmed an increase in the CD71 protein level in activated cells. We then transplanted mito-DsRed2 displaying either anti-CD71 or control nanobodies (anti-GFP) into activated CD8+ T cells. In total, 15.1% of the CD71+ T cells that were transplanted with anti-CD71-nanobody-displaying mitochondria were positive for DsRed2, as opposed to 9.4% of cells transplanted with control-nanobody-displaying mitochondria (PI = 60%, S = 0.23) (Extended Data Fig. 6i–m). We hypothesized that the relatively low targeting efficacy and specificity results from the low abundance of CD71 on the cell surface. To verify this, we performed super-resolution imaging on activated CD8+ T cells and compared the distributions of CD71 and the abundant marker CD8 proteins on the cell surface. CD71 was fourfold less abundant than CD8, suggesting a low overall binding strength arising from low avidity (Extended Data Fig. 6n–p).

Seventh, we generated nanobodies against human CD73 (KD = 4.3 nM), a photoreceptor-specific cell-surface-protein in the retina31,32. We transplanted mito-DsRed2 displaying either anti-CD73 or control nanobodies (anti-mCherry) into human retinal organoids[33]. The abundance of anti-CD73-nanobody-displaying mito-DsRed2 was 50% higher at the photoreceptor layer than the control nanobody displaying mitochondria (Fig. 2c,d and Supplementary Table 3).

Eighth, we tested whether displaying a binder on the surface of mitochondria increases their immunogenicity. We injected native or anti-GFP-nanobody-displaying mitochondria derived from mouse mesenchymal stem cells (mMSCs) into mouse eye and brain, or systemically in the blood, and collected serum samples at different timepoints. In all of the tested conditions, we found no increase in antibody titres against mitochondrial outer membrane proteins (TOMM20, TOMM70), against isolated mitochondria or against the anti-GFP nanobody over time (Extended Data Fig. 6q,r).

Tuning mitochondrial targeting efficiency

We assessed whether higher binder affinity improves mitochondrial delivery efficiency. First, we generated nanobodies against mCD71 (KD = 4.2 nM) and increased their affinity 76-fold (KD = 55 pM, L47F and L104W mutations) using affinity maturation[34] (Extended Data Fig. 7a–h). We transplanted mitochondria displaying original or affinity-matured anti-mCD71 nanobodies at low (0.5 µg) and high (2.5 µg) doses into HEK293T cells expressing mCD71 or control CD73, together with cytosolic GFP. With the affinity-matured nanobody at low dose, 21.6% of mCD71-expressing cells were mitochondria positive versus 6.9% of CD73-expressing cells (PI = 210%, FR = 100%, S = 0.52); and, at high dose, 23.4% versus 6.2% cells were mitochondria positive, respectively (PI = 280%, FR = 70%, S = 0.58). The original anti-mCD71 nanobody showed no targeting enrichment in mCD71-expressing over CD73-expressing cells at either dose (Extended Data Fig. 7i–k and Supplementary Table 3).

Second, we increased the affinity of anti-CD73 nanobody by 6.5-fold (A24T, F29I, T31N mutations), from KD = 4.3 nM to KD = 0.6 nM. We transplanted mitochondria displaying original or affinity-matured anti-CD73 nanobody at 0.5 or 2.5 µg into HEK293T cells expressing CD73 or control CD142, together with cytosolic GFP. At 0.5 µg, the matured anti-CD73 nanobody targeted 29.2% of CD73-expressing cells versus 2.1% of CD142-expressing cells (PI = 1,300%, FR = 160%, S = 0.86), while the original nanobody showed no enrichment. At 2.5 µg, both original and matured nanobodies achieved comparable targeted delivery into CD73-expressing cells (Extended Data Fig. 7l–o and Supplementary Table 3). These results indicate that higher binder affinity can improve targeting efficiency, enabling effective delivery at lower mitochondrial doses.

Targeting with different binder types

So far, we used nanobodies as binders. To broaden the applicability of MitoCatch, we explored targeting using other types of binders (Extended Data Fig. 8a).

We tested DARPins[35] first. We displayed GFP on HEK293T cells and transplanted mitochondria surface-decorated with anti-GFP- or control-DARPins (anti-mCherry[36]) through OMP25–OLLAS-tag fusion. In total, 31.9% of GFP-positive cells were positive for anti-GFP-DARPin-displaying mitochondria, compared with 7.9% with control-DARPin mitochondria (PI = 290%, FR = 130%, S = 0.6) (Fig. 2e,f).

We next tested full-length antibodies. To link antibodies to mitochondria, we expressed a SNAP-tag at the outer membrane of mitochondria by fusing it to OMP25 (Extended Data Fig. 8b,c) and covalently bound the SNAP-tag to benzylguanine-(BG)-conjugated antibodies.

First, we incubated SNAP-tagged mitochondria with either BG–anti-CD31 or control BG–IgG1 at different concentrations from 0 to 500 nM and confirmed antibody fusion to mitochondria by immunostaining and western blotting (Extended Data Fig. 8d–g). This yielded mitochondria with different avidities. Next, we transplanted antibody-displaying mitochondria into HUVECs. At 500 nM BG–anti-CD31, 93% of HUVECs were SNAP-tag-mitochondrion-positive, versus 52.8% with control IgG1 (PI = 80%, FR = 170%, S = 0.27). Increased avidity enhanced anti-CD31-fused mitochondrial enrichment but not control IgG, where higher surface density instead reduced targeting efficiency, possibly by blocking non-specific interactions (Extended Data Fig. 8h,i). To address the low specificity in HUVECs, we performed co-culture experiments with cardiac fibroblasts; specificity increased to 0.51, suggesting that binder-mediated targeting is advantageous in complex cellular environments (Extended Data Fig. 8j–l).

Second, we evaluated mitochondrial targeting through SNAP-tagged anti-CD31 antibodies to endothelial cells in human blood vessel organoids containing endothelial cells (anti-CD31 positive) and pericytes (anti-PDGFRβ positive)[37] made from induced pluripotent stem (iPS) cells expressing tdTomato (Extended Data Fig. 8m). We incubated SNAP-tagged mitochondria with either BG–anti-CD31 or control BG–IgG1 antibodies at 500 nM and transplanted them into vessel organoids. In total, 26.4% of endothelial cells were positive for SNAP-tag-fused mitochondria as detected by anti-SNAP-tag antibodies, in contrast to 8.5% with control antibody-fused mitochondria (PI = 210%, FR = 175%, S = 0.51) (Fig. 2g,h).

Finally, we assessed the delivery of SNAP-tagged mitochondria displaying anti-CD8 monoclonal antibody to unstimulated CD8+ T cells isolated from human donors. In total, 40.2% of T cells were positive for SNAP-tagged mitochondria detected by anti-SNAP-tag antibodies when BG–anti-CD8 was fused to mitochondria, as opposed to 9.3% when control BG–IgG1 was used (PI = 330%, S = 0.62). Targeting CD8+ T cells with anti-CD8 antibodies was more efficient in comparison to anti-CD71 nanobodies, suggesting that the reduced specificity observed when targeting CD71+ T cells is probably due to the lower abundance of the CD71 protein (Fig. 2i–k and Extended Data Fig. 6n–p).

Adhesion of mitochondria to target cells

To quantify the strength of adhesion of a single mitochondrion to a cell we used atomic force microscopy (AFM)38,39. First, we characterized the total adhesion force and number of unbinding events between a single mito-GFP and a HEK293T cell with or without displayed anti-GFP nanobody (Extended Data Fig. 9a–f and Supplementary Video 13). The total adhesion force was 101 pN with binders and 46.7 pN without binders. Single-molecule unbinding events occurred in 92% of approaches with and 55% without binders. Multiple unbinding events were detected in 77% of approaches with binders and 25% without binders (Extended Data Fig. 9g–j).

Second, we tested the adhesion of a single mito-dsRed2 displaying affinity-matured anti-mCD71 nanobodies to a HEK293T cell expressing or not mCD71 protein. The total adhesion force was 86 pN with and 39.2 pN without mCD71. Moreover, a mitochondrion displaying the anti-mCD71 nanobody exhibited a higher number of unbinding events with mCD71+ cells in comparison to control cells (Fig. 2l–q and Extended Data Fig. 9k,l).

Third, it has been proposed that heparan sulfate (HS) mediates interactions between cells and exogenous mitochondria40,41. We measured the adhesion force of mito-GFP, either coated with HS or untreated, to HEK293T cells displaying anti-GFP nanobodies. Coating mito-GFP with HS strongly reduced adhesion to cells lacking anti-GFP nanobodies, while adhesion to anti-GFP nanobody-displaying cells remained high (Extended Data Fig. 9m–p). Thus, HS contributes to cell–mitochondrion interactions but does not block binder–receptor interactions.

We hypothesized that HS coating could increase MitoCatch specificity by blocking non-specific interactions. We tested this in CD8+ and CD71+ activated T cells, in which low CD71 abundance initially yielded low specificity (Extended Data Fig. 6n–p). We transplanted HS-treated or untreated mitochondria displaying anti-CD71 or control nanobodies into CD71+ cells. HS treatment did not affect anti-CD71 nanobody targeting efficiency but significantly reduced non-specific targeting by control nanobody mitochondria, increasing specificity from 0.22 to 0.45 (Extended Data Fig. 9q). Thus, HS pretreatment reduces non-specific mitochondrial interactions.

Mitochondrial delivery using MitoCatch-Bi

As the final MitoCatch approach, we developed bispecific protein binders that bind to mitochondrial outer membrane proteins at one end and to cell surface proteins at the other.

First, we made recombinant anti-GFP–anti-mCherry and control anti-GFP–anti-lysozyme bispecific nanobodies and assessed the delivery of mito-GFP incubated with these bispecific nanobodies to HEK293T cells displaying mCherry at their surface. Delivery of mito-GFP with anti-GFP–anti-mCherry bispecific nanobodies yielded 97.1% GFP-positive cells compared with 12.6% without bispecific nanobodies (PI = 670%, FR = 860%, S = 0.77) and 2.2% with control bispecific nanobodies (PI = 4,300%, FR = 2,100%, S = 0.95) (Fig. 3a,b and Extended Data Fig. 10a–c).

Fig. 3: Mitochondrial delivery using bispecific binders.

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a, Mito-GFP (green) with control anti-GFP–anti-lysozyme (left) or anti-GFP–anti-mCherry (right) nanobodies transplanted into mCherry-displaying HEK293T cells (cyan, grey dashed lines), 2 h after transplantation. b, Quantification of a. n = 4. Welch’s analysis of variance (ANOVA) followed by two-sided Dunnett’s test for multiple comparisons; anti-GFP–anti-mCherry versus no binder: P = 0.0024 (left) and P = 0.0394 (right); anti-GFP–anti-mCherry versus anti-GFP–anti-lysozyme: P < 0.0001 (left) and P = 0.0314 (right). c, Mito-GFP (green) with control anti-GFP–anti-lysozyme (left) or anti-GFP–anti-mCherry (right) nanobodies transplanted into mouse mCherry-displaying retinal ganglion cells in vivo (cyan, grey dashed lines), 1 day after transplantation. d, Quantification of c. n = 7 (no binder), n = 5 (anti-GFP–anti-lysozyme) and n = 6 (anti-GFP–anti-mCherry). Welch’s ANOVA followed by two-sided Dunnett’s test for multiple comparisons; anti-GFP–anti-mCherry versus no binder: P < 0.0001 (left) and P = 0.0168 (right); anti-GFP–anti-mCherry versus anti-GFP–anti-lysozyme: P < 0.0001 (left) and P = 0.0066 (right). e, Mito-dsRed2 (cyan) with control anti-TOMM20–anti-lysozyme (left) or anti-TOMM20–anti-CD71 (right) nanobodies transplanted into HEK293T cells expressing human CD71 and GFP (green, grey dashed lines), 2 h after transplantation. f, Quantification of e. n = 6. Welch’s ANOVA followed by two-sided Dunnett’s test for multiple comparisons; anti-TOMM20–anti-CD71 versus no binder: P = 0.0078 (left) and P = 0.0254 (right); anti-TOMM20–anti-CD71 versus anti-TOMM20–anti-lysozyme: P = 0.0126 (left) and P = 0.0484 (right). g, Schematic of mito-dsRed2 delivery into CD4+ T cells using anti-TOMM20–anti-CD4 nanobodies. The diagram was created using BioRender; Ayupov, T. https://BioRender.com/fv9sxoi (2026). h, Quantification of mito-dsRed2 delivery as shown in g, 4 h after transplantation. n = 4. Welch’s ANOVA followed by two-sided Dunnett’s test for multiple comparisons; anti-TOMM20–anti-CD4 clone 1 versus anti-TOMM20–anti-lysozyme: P = 0.0112 (0.5 µg), P < 0.0001 (2.5 µg), P < 0.0001 (5 µg); anti-TOMM20–anti-CD4 clone 2 versus anti-TOMM20–anti-lysozyme: P = 0.0319 (0.5 µg), P < 0.0001 (2.5 µg), P < 0.0001 (5 µg); anti-TOMM20–anti-CD4 clone 1 versus no binder: P < 0.0001; anti-TOMM20–anti-CD4 clone 2 versus no binder: P < 0.0001; anti-TOMM20–anti-lysozyme versus no binder: P = 0.9986. i, Detection of mito-dsRed2 delivered by bispecific nanobodies (5 µg) in CD3+ T cells using flow cytometry. Double-positive cells (CD4+dsRed2+) are outlined in red. Data are mean ± s.e.m. Scale bars, 25 µm. NS, not significant.

Source data

Second, we expressed mCherry on the surface of mouse retinal ganglion cells using the ganglion-cell-specific ProA5 promoter[27] and injected mito-GFP with bispecific nanobodies into the eye in vivo. In total, 68.4% of mCherry-expressing ganglion cells were GFP-positive after injection of mito-GFP with anti-GFP–anti-mCherry bispecific nanobodies, compared with 11.8% without bispecific nanobodies (PI = 480%, FR = 150%, S = 0.70) and 7.3% with control bispecific nanobodies (PI = 837%, FR = 316%, S = 0.81) (Fig. 3c,d and Extended Data Fig. 10d,e).

Third, we aimed to deliver mitochondria by linking natively expressed mitochondrion-specific and cell-type-specific surface proteins. We isolated high-affinity nanobodies from humanized libraries against the mitochondrial outer-membrane proteins TOMM20 and TOMM70 (Extended Data Fig. 10f–j). We first evaluated mito-dsRed2 delivery using bispecific nanobodies targeting either TOMM20 or TOMM70 and human CD71 in CD71- and GFP-expressing HEK293T cells. In total, 39.3% of CD71-expressing cells were mito-dsRed2 positive with the anti-TOMM20–anti-CD71 bispecific nanobody, compared with 5.8% (PI = 580%, FR = 400%, S = 0.74) and 10.6% (PI = 270%, FR = 220%, S = 0.57) without the binder or with the control anti-TOMM20–anti-lysozyme bispecific nanobody, respectively (Fig. 3e,f). The anti-TOMM70–anti-CD71 bispecific nanobody was less effective than anti-TOMM20–anti-CD71 (PI = 259%, FR = 135%, S = 0.56) (Extended Data Fig. 10k–m and Supplementary Table 3).

We next assessed mitochondrial delivery into human CD4+ T cells using anti-TOMM20–anti-CD4 bispecific nanobodies42,43. As controls we used no binder or anti-TOMM20–anti-lysozyme bispecific nanobodies. We added mito-dsRed2 with bispecific nanobodies to bulk CD3+ T cells (containing CD4+ and CD8+ populations) at three doses: 0.5, 2.5 and 5 µg. At the lowest dose, 56.6% and 45.4% of CD4+ cells were targeted with two different anti-TOMM20–anti-CD4 bispecific nanobodies (different anti-CD4 nanobody clones), as opposed to 1.7% when the control bispecific nanobody was used (PI = 3,230%, S = 0.94 for clone 1 and PI = 2,570%, S = 0.93 for clone 2). At the highest dose, the anti-TOMM20–anti-CD4 bispecific nanobody targeted up to 95% of CD4+ T cells (PI = 614%, S = 0.75), compared with 3% with control bispecific nanobody and 12.6% without any binder (PI = 654%, S = 0.77) (Fig. 3g–i and Extended Data Fig. 10n–p). Thus, when using bispecific-binders targeting natively expressed mitochondrion- and cell-type-specific proteins, the increase in efficacy relative to the control binders reached over 30-fold.

Protection of diseased cells in vitro

We next examined whether targeting healthy human mitochondria to cells with primary mitochondrial disease could reduce degeneration of these cells. We generated a homoplasmic Leber’s hereditary optic neuropathy (LHON)44,45,46 patient-derived iPS cell line carrying the mt11778G>A mutation in the mitochondrial ND4 gene. We displayed anti-GFP nanobodies on the surface of LHON iPS-cell-derived iHNeurons, transplanted mito-GFP and performed full-length single-cell RNA sequencing (scRNA-seq; Fig. 4a,b and Supplementary Fig. 5a–f). We detected different proportions of ND4 RNA transcripts originating from wild-type donor and intrinsic (mt11778G>A) mitochondria. The proportion of wild-type ND4 transcripts correlated with the proportion of total mitochondrially encoded transcripts. Furthermore, the expression of nuclear-encoded oxidative phosphorylation, ATP synthesis and respiratory chain complex I genes was significantly enriched after healthy mitochondria uptake (Fig. 4c,d). This suggests that iHNeurons react to the increase in mitochondrial content by upregulation of nuclear-encoded mitochondrial proteins. We also detected ND4 transcripts from donor mitochondria in trypsin-treated LHON iHNeurons, but not from mitochondrial DNA (mtDNA)-depleted donor mitochondria (Extended Data Fig. 11a–h). We next measured the oxygen consumption rates after transplanting mito-GFP into LHON iHNeurons expressing anti-GFP or control nanobody. Cells displaying anti-GFP nanobody and transplanted with mitochondria exhibited dose-dependent, higher levels of basal, ATP-linked and maximal respiration than untreated cells, while control-nanobody-expressing cells transplanted with mitochondria were not affected. Similarly, treatment of cells with mtDNA-depleted mito-GFP did not increase respiration (Fig. 4e,f and Extended Data Fig. 11i).

Fig. 4: Mitochondrial transplantation to LHON neurons increases survival.

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a, Schematic of mitochondrial delivery into LHON iHNeurons. A single-cell picking example is shown in Supplementary Fig. 5g. AAV, adeno-associated virus. b, The relative frequency of ND4 variants in single LHON iHNeurons. Left, non-transplanted cells (n = 12); right, mito-GFP-transplanted cells (n = 30). See also Supplementary Fig. 5j,k. c, Correlation of total mitochondrial transcripts and the relative frequency of wild-type ND4 in non-transplanted and mito-GFP-transplanted cells. Two outlier points were removed. Two-sided Pearson correlation coefficient, r = 0.67, P < 0.0001. The black line is the linear fit. d, Top 14 Gene Ontology classification of differentially expressed genes in LHON iHNeurons 2 days after transplantation of mito-GFP. Permutation tests were used to estimate P values. e, Representative oxygen consumption rate (OCR) of LHON iHNeurons without transplantation and 2 days after transplantation with mito-GFP. n = 3 (non-transplanted), n = 3 (control nanobody + mito-GFP) and n = 5 (anti-GFP nanobody + mito-GFP). f, Quantification of respiration in LHON iHNeurons 2 d after transplantation. Data are normalized to non-transplanted samples within each experiment. n = 13 (non-transplanted), n = 11 (control nanobody + mito-GFP), n = 11 (anti-GFP nanobody + mito-GFP). One-way ANOVA with Tukey’s test for multiple comparisons; non-transplanted versus control nanobody + mito-GFP: P = 0.5799 (basal), P = 0.9418 (ATP-linked), P = 0.5330 (maximal); anti-GFP nanobody + mito-GFP versus non-transplanted: P = 0.001 (basal), P = 0.0056 (ATP-linked), P = 0.0116 (maximal); anti-GFP nanobody + mito-GFP versus control nanobody + mito-GFP: P = 0.0182 (basal), P = 0.0174 (ATP-linked), P = 0.0116 (maximal). g, Live imaging of LHON iHNeurons expressing cytosolic tdTomato (cyan) before and 2 days after glucose substitution with galactose. h, Quantification of the cell survival rate in galactose medium in g. n = 4 (non-transplanted), n = 5 (control nanobody + mito-GFP) and n = 5 (anti-GFP nanobody + mito-GFP). Welch’s ANOVA followed by two-sided Dunnett’s test for multiple comparisons; non-transplanted versus control nanobody + mito-GFP: P = 0.7815; anti-GFP nanobody + mito-GFP versus non-transplanted: P = 0.0019; anti-GFP nanobody + mito-GFP versus control nanobody + mito-GFP: P = 0.0023. Data are mean ± s.e.m. Scale bars, 50 µm.

Source data

To induce degeneration of LHON iHNeurons47,48,49, we slowed down glycolysis by substituting glucose with galactose[50]. To quantify LHON iHNeuron viability, we expressed cytosolic tdTomato together with either cell-surface-displayed anti-GFP or control nanobodies. We confirmed that over 95% of surviving tdTomato+ cells are viable using calcein, a cell viability dye (Extended Data Fig. 11j–m and Supplementary Video 14). Mito-GFP-transplanted cells displaying anti-GFP nanobody showed 23.6% higher survival compared with non-transplanted cells, while those displaying a control nanobody showed no survival difference. Neither uridine nor mtDNA-depleted mito-GFP delivery promoted cell survival, although mtDNA-depleted mito-GFP were internalized by cells displaying anti-GFP nanobody but not by cells displaying control nanobody. Moreover, as the control nanobody (anti-mCherry) interacts with tdTomato, we confirmed that co-expression of the cell-surface-targeted nanobody together with cytosolic tdTomato had no effect on its subcellular localization (Fig. 4g,h and Extended Data Fig. 11n–s). Together, these results suggest that targeted transplantation of healthy mitochondria can promote at least short-term survival of cells with primary mitochondrial disease.

Increasing cellular function in vitro

We performed a cell migration assay in HUVECs using an in vitro wound-healing model. Cells were transplanted with mitochondria displaying either the BG-conjugated anti-CD31 antibody or the control BG-conjugated isotype IgG1 antibody, or they were treated only with anti-CD31 antibodies as another control. Cells transplanted with mitochondria displaying BG–CD31 antibodies exhibited higher gap closure efficiency in comparison to the two controls (Extended Data Fig. 11t,u). Thus, targeted transplantation of mitochondria resulted in an increased cellular function.

Protection of damaged cells in vivo

We examined whether targeted mitochondrial transplantation could decrease neurodegeneration in vivo. We displayed anti-GFP or control nanobodies (anti-mCherry) on the surface of tdTomato expressing parvalbumin (PV)-positive retinal ganglion cells and transplanted mito-GFP into the eyes of adult mice by injection. In total, 81.9% of PV-retinal ganglion cells expressing the anti-GFP nanobody became GFP positive, as opposed to 7.7% of PV-retinal ganglion cells expressing the control nanobody (PI = 960%, FR = 380%, S = 0.83) (Fig. 5a–c).

Fig. 5: Cell-type-targeted mitochondrial delivery promotes ganglion cell survival after optic nerve injury.

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a, Schematic of in vivo mitochondrial targeting of retinal ganglion cells. b, PV-retinal ganglion cells (cyan) with control (anti-mCherry) or anti-GFP nanobodies (magenta) targeted with mito-GFP (green), 1 day after transplantation. c, Quantification of b. n = 4 (control nanobody) and n = 6 (anti-GFP nanobody). Two-sided Welch’s t-tests; P < 0.0001 (top) and P = 0.0003 (bottom). d, TdTomato-expressing PV-retinal ganglion cells (anti-RFP antibodies, cyan), 10 days after ONC. e, The density of surviving tdTomato-expressing PV cells with and without ONC and with injected mito-GFP at different doses. PV cells were detected by co-staining with anti-RFP and anti-RBPMS antibodies. Untreated: n = 8; ONC + anti-GFP nanobody + mito-GFP: n = 6 (0 µg), n = 6 (2.5 µg), n = 8 (5 µg); ONC + control nanobody + mito-GFP: n = 6 (0 µg), n = 5 (2.5 µg), n = 7 (5 µg). Two-way ANOVA with Tukey’s test for multiple comparisons; ONC + anti-GFP nanobody + mito-GFP: P < 0.0001 (0 µg versus 2.5 µg), P < 0.0001 (0 µg versus 5 µg), P = 0.9171 (2.5 µg versus 5 µg); ONC + control nanobody + mito-GFP: P < 0.6848 (0 µg versus 2.5 µg), P < 0.0077 (0 µg versus 5 µg), P = 0.0845 (2.5 µg versus 5 µg); anti-GFP nanobody versus control nanobody: P = 0.9811 (0 µg), P = 0.0002 (2.5 µg), P = 0.0091 (5 µg). f, Schematic of imaging activity of PV cells after ONC and with or without mitochondrial transplantation. g, Two-photon image of PV cells expressing GCaMP8m. h, Example light responses of On, Off, and On–off PV cells after ONC and mito-GFP transplantation. Grey shades show the timing of light stimulation. i, The density of PV cells responding to light stimulation. ONC + buffer: n = 4 (total 509 cells); ONC + mito-GFP: n = 4 (total 550 cells). Two-sided Welch’s t-test; P = 0.0273. Data are mean ± s.e.m. Scale bars, 25 µm (b), 50 µm (g) and 100 µm (d).

Source data

We induced retinal ganglion cell degeneration by crushing the optic nerve and confirmed its efficacy using anterograde tracers (Extended Data Fig. 12a,b). We then evaluated the effect of targeted mitochondrial transplantation on PV-retinal ganglion cell survival after optic nerve crush (ONC) in vivo. Ten days after ONC, we quantified the number of PV-retinal ganglion cells per unit retinal area using tdTomato and the retinal ganglion cell marker RBPMS (Extended Data Fig. 12c). Transplantation of 2.5 µg mito-GFP into retinas expressing the anti-GFP nanobody resulted in a 46.8% increase in PV-retinal ganglion cell survival compared with eyes injected with buffer. Transplantation of 2.5 µg mito-GFP into retinas expressing the control nanobody was not sufficient to promote ganglion cell survival. Moreover, after transplanting mito-GFP into anti-GFP-nanobody-expressing retinas at different doses, we observed saturation in the number of surviving PV-retinal ganglion cells at 2.5 µg of mito-GFP. Doubling the mito-GFP dose to 5 µg promoted the survival of ganglion cells in retinas expressing the control nanobody, but the therapeutic effect after mitochondrial transplantation to retinas expressing the anti-GFP nanobody was still significantly higher. Furthermore, transplantation of 5 µg mtDNA-depleted mitochondria was not efficient in promoting cellular rescue compared with healthy mitochondria (Fig. 5d,e and Extended Data Fig. 12d,e).

Finally, we recorded light-evoked responses from anti-GFP nanobody-displaying PV-retinal ganglion cells expressing GCaMP8m activity sensor in retinas of mice after the ONC, followed by either control buffer or mito-GFP injections. The density of light-responding PV-retinal ganglion cells after the ONC was significantly higher in mitochondrion-treated retinas than in buffer-injected retinas (Fig. 5f–i). Moreover, we observed that buffer-treated retinas had more degenerating axons with a fourfold higher amount of axonal beading compared with mitochondrion-treated retinas (Extended Data Fig. 12f,g). These results suggest that mitochondrial transplantation promotes functional rescue of surviving cells in vivo.

Discussion

Two key parameters were essential for efficient and specific mitochondrial delivery into target cell types: the affinity of individual binders and the overall avidity resulting from multivalent interactions. We have shown short- or mid-term neuroprotection by targeted mitochondrial transplantation in two cell types: LHON iHNeurons in vitro and mouse retinal ganglion cells in vivo. Future studies should investigate the long-term efficacy of mitochondrial delivery to protect damaged retinal ganglion cells. Regarding the potential immunogenicity of targeted mitochondrial transplantation, at least two factors can be considered. The human blood contains over 2 × 10^5 cell-free mitochondria per ml (refs. 51,52,53) that do not evoke immune reactions after blood transfusion. Furthermore, we observed no immune response when binder-displaying mitochondria (MitoCatch-M) were injected into the blood, brain or eye of mice (Supplementary Discussion).

Methods

Animals

Animal experiments were performed following standard ethical guidelines (European Communities Guidelines on the Care and Use of Laboratory Animals, 86/609/EEC), and were approved by the Veterinary Department of the Canton of Basel-Stadt. C57BL6, PV-cre (The Jackson Laboratory, 008069) mice and PV-cre mice crossed with Ai9(RCL-tdT) mice (The Jackson Laboratory, 007909) in the C57BL6 background were used at 55–120 days old. Both females and males were used for this study. They were maintained on a normal 12 h–12 h light–dark cycle in a pathogen-free environment with ad libitum access to food and drinking water. Mice were euthanized by exposure to CO_2. No formal statistical methods were used to predetermine sample sizes. Mice were allocated randomly to treatment conditions. No blinding to group allocation was performed during the experiments.

Post-mortem human retina donations

Human retina tissue was obtained from multi-organ donors through the sampling of non-transplantable eye tissue extracted during cornea collection for transplantation. Retinas from individuals with a documented history of eye disease were excluded. The collection of tissue samples adhered to the principles outlined in the Declaration of Helsinki, and all experimental protocols received the approval of the local ethics committee.

HEK293T cells

All cell cultures were maintained at 37 °C in a humidified incubator (5% CO_2). HEK293T cells (ATCC, CRL-3216) were cultured in DMEM (Gibco, 10566016) supplemented with 10% FBS and 1% penicillin–streptomycin (Gibco, 15140-122). Cells were passaged at 90% confluency. Cell dissociation was performed using TrypLE Express (Thermo Fisher Scientific, 12604021).

Bone marrow mMSCs

Bone marrow mMSCs were obtained from Cyagen (MUBMX-01001). Cells were cultured in MesenCult Proliferation medium (StemCell Technologies, 05411) up to passage 11. Cells were passaged with TrypLE Express at 70–80% confluency.

Human cardiac cells

Human cardiac cells isolated from the ventricles of the adult heart (Promocell, C-12810) were maintained in myocyte growth medium (Promocell, C-22070). For passaging, cells were detached with TrypLE Express at 70% confluency and seeded at a density of 10,000–15,000 cells per cm^2.

Human cardiac fibroblasts

Human cardiac fibroblasts were obtained from LifeLine Cell Technology (FC-0060) and were maintained in FibroLife S2 Fibroblast Medium (Complete Kit, LifeLine Cell Technology, LL-0011) supplemented with 1% penicillin–streptomycin (Gibco, 15140-122). Cells were grown in a dish precoated with attachment factor (Thermo Fisher Scientific, S006100) and passaged with TrypLE Express at 70–80% confluency.

HUVECs

Primary HUVECs isolated from the umbilical cord vein (Sigma-Aldrich, C-12203) were maintained in endothelial cell growth medium (Bio-techne, CCM027). Cells were passaged at 80–90% confluency and seeded at 5,000–10,000 cells per cm^2 density. Cells were dissociated using TrypLE Express. For co-culturing experiments, HUVECs were cultured together with cardiac fibroblasts in 1:1 ratio (20,000 cells in total) in an eight-well slide (Ibidi, 80826-IBI) using endothelial cell growth medium.

Generation and maintenance of induced human neurons

Neuronal differentiation was performed as described previously[24] with some modifications. For inductions, IMR90 (iPS(IMR90)-4; WiCell, WISCi004-B)) and patient-derived LHON (mt11778G>A, iPS(HS-0565-B-LHON-C12), this study) iPS cell lines. Freshly thawed iPS cells were cultured in mTeSR Plus medium for several passages. At 70–90% confluency, iPS cells were washed with PBS and dissociated with 0.5 mM EDTA (Invitrogen, 15575020) to form small clumps. The clumps were seeded onto six-well plates coated with Matrigel growth-factor reduced (Corning, 356230) in mTeSR Plus medium and incubated for 1 h at 37 °C (5% CO_2). After incubation, mTeSR Plus was replaced with N2B27 medium (DMEM/F12 with GlutaMAX) (Gibco, 31331-028) and Neurobasal (Gibco, 21103-049) medium in 1:1 ratio, 0.5% additional GlutaMAX (Thermo Fisher Scientific, 35050038), 1% N2 (Gibco, 17502-048), 2% B27 without vitamin A (Gibco, 12587-010) and 1% penicillin–streptomycin (Gibco, 15140-122) containing 2% Matrigel growth factor reduced and incubated overnight at 37 °C (5% CO_2). The next day, the medium was exchanged with fresh N2B27 supplemented with 25 μM forskolin (StemCell, 100-0249). Cells were fed every second day. After 35 days post-induction, cells were dissociated using TrypLE Express (Thermo Fisher Scientific, 12604021), diluted in N2B27 medium, filtered through a 70-μm cell strainer (pluriSelect, 43-50070-51) and centrifuged for 10 min at 300g at 4 °C. The cell pellet was resuspended in MACS buffer (80 µl for 10^7 cells) containing autoMACS Rinsing solution (Miltenyi Biotec, 130-091-222) and 0.5% BSA solution (Miltenyi Biotec, 130-091-376). CD90 microbeads (Miltenyi Biotec, 130-096-253) were added to the cells (20 µl for 10^7 cells) and incubated for 15 min on ice. CD90+ cells were sorted on a MidiMACS magnetic separator in an LS column (Miltenyi Biotec, 130-042-302, 130-042-401). The magnetically enriched CD90+ cell population was counted on the Countess system (Invitrogen). Cells were centrifuged at 4 °C for 10 min at 300g, resuspended in N2B27 and the sorted cell suspension was subsequently seeded onto a Matrigel-coated plate. Seeded cells were fed every second day with N2B27 supplemented with 10 μM forskolin and 10 ng ml−1 CNTF (Peprotech, 450-13). Experiments used sorted cells at 80 to 130 days after induction.

Induced pluripotent stem cell culture

The human iPS cell lines 01F49i-N-B7[33] and IMR90 (iPS(IMR90)-4, WiCell; WISCi004-B) and the IMR90-tdTomato reporter line (IMR90-4 AAVS1-tdTomato, developed according to a published protocol[54]) were used. Cells were cultured at 37 °C and 5% CO_2 in a humidified incubator. mTesR1 and mTeSR Plus media were used (StemCell Technologies, 85850, 100-0276) on Matrigel-coated six-well plates (Corning, 356230). The culture medium was replaced daily, and cells were passaged weekly using 0.5 mM EDTA (Invitrogen, 15575020) in PBS without applied CaCl_2/MgCl_2, for 3–5 min. Cells were tested for mycoplasma on a regular basis using the MycoAlert PLUS Mycoplasma Detection Kit (Lonza, CABRLT07–710).

Reprogramming and characterization of LHON iPS cells

The iPS cell line IOBHS-i-0565B-12 (short name, LHON) was derived from coded male donor peripheral blood mononuclear cells (PBMCs) with the m11778G>A mutation in the mitochondrial ND4 gene, 100% homoplasmic. Blood samples were received in ACD solution B (BD, 367756) then diluted 1:1 in PBS (Thermo Fisher Scientific, 70011051) and applied over lymphocyte separation medium 1077 (Promocell, C44010). The samples were centrifuged, PBMCs were isolated and resuspended in RPMI medium (Gibco, 21875). Activation of PBMCs for 1 day was completed using Stemspan II medium (StemCell Technologies, 09605) and erythrocyte supplement (StemCell Technologies, 02692). PBMCs were reprogrammed using the CytoTune-iPS Reprogramming Kit 2.0 (Thermo Fisher Scientific, A16517) according to the manufacturer’s instructions. In brief, 3.5 × 10^6 PBMCs were plated in one well of a 24-well ultra-low-attachment culture plate (Sigma-Aldrich, CLS347) with StemSpan medium and infected with Sendai virus at a multiplicity of infection (MOI) of 5:5:3 (KOS, MOI = 5; hc-MYC, MOI = 5; hKLF4, MOI = 3) for approximately 16 h. On day 3 after transfection cells were plated in different densities from 10,000 to 25,000 cells on six-well Matrigel-coated plates. On day 7, 50% of the medium was replaced by Essential 8 Flex medium (Thermo Fisher Scientific, A2858501). After iPS cell colonies formed, they were picked, plated on 24-well Corning Synthemax-R plates (Corning, 3979), cultured in Essential 8 FLEX medium (Thermo Fisher Scientific, A2858501) and transitioned to six-well Corning plates (Corning, 3506). iPS cells were subsequently cultured in mTeSR Plus medium (StemCell Technologies, 100-0276) up to passage 34 when cells were characterized and used for experiments.

iPS cell pluripotency was assessed 4–6 days after splitting by staining iPS cell colonies cultured in six-well plates for alkaline phosphatase using the alkaline phosphatase live stain according to the manufacturer’s instructions (Thermo Fisher Scientific, 12807552). To assess pluripotency iPS cells were seeded into Matrigel-coated (Corning, 354277) four-well chamber slides (Vitaris, 80426-IBI) and further stained for the pluripotency markers SOX2 (1:200; Millipore, AB5603), OCT4 (1:100; Abcam, Ab181557), NANOG (1:200; R&D Systems, AF1997) and TRA-1-60 (1:200; BD Pharmingen, 560193). The potential to differentiate into the three embryonic germ layers was assessed by directed differentiation into ectoderm; nestin (1:200, Sigma-Aldrich, N5413) and PAX6 (1:200, ATLAS, AMAb91372), mesoderm; Brachyury (1:200, R&D Systems, AF2085) and endoderm; FOXA2 (1:400, Abcam, ab108422) and SOX17 (1:200, R&D Systems, AF1924) using the STEMdiff Trilineage Differentiation Kit (StemCell Technologies, 05230) according to the manufacturer’s instructions on cells maintained in Matrigel-coated (Corning, 356230) four-well chamber slides (Vitaris, 80426-IBI). Secondary antibodies used were Invitrogen, A10037, A11055 and A21206 at 1:1,000. To test for chromosomal aberrations, genomic integrity testing was performed (StemGenomics) on genomic DNA extracted from iPS cells by the DNeasy Blood & Tissue Kit (Qiagen, 69504).

Generation and maintenance of retinal organoids

Retinal organoids were generated as described previously33,55. iPS cells were detached using EDTA at 0.5 mM for 3 min. EDTA was then removed and Accutase was added (Thermo Fisher Scientific, 00-4555-56) for 4 min at 37 °C to generate single-cell suspensions of which 300 to 600 cells were placed per microwell in an 81-microwell hydrogel. The hydrogels were generated using the MicroTissues 3D Petri Dish micro-mould (Sigma-Aldrich, Z764000) and 2% agarose (Thermo Fisher Scientific, R0491). Each hydrogel carrying the iPS cells was cultured in a 12-well plate (Corning, 3513) in neural induction medium DMEM/F12 (Gibco, 31331-028), 1% N2 supplement (Gibco, 17502-048), 1% NEAA solution (Sigma-Aldrich, M7145) and 2 mg ml−1 heparin (Sigma-Aldrich, H3149-50KU) for 1 week with daily medium exchanges. Embryoid bodies formed in each microwell were detached from the hydrogel and distributed across three wells of a Matrigel-coated six-well plate. Organoids in six-well plates were cultured with daily medium exchanges and began to form 2D confluent structures. For the first 16 days, they were cultured in neural induction medium. Subsequently, the medium was changed to 3:1 medium (3 parts DMEM (Gibco, 10569–010):1 part F12 medium (Gibco, 31765–027), supplemented with 2% B27 without vitamin A (Gibco, 12587010), 1% NEAA solution (Merck, M7145) and 1% penicillin–streptomycin (Gibco, 15140–122). Between days 28 and 30 of culture, checkerboard scraping was performed as previously described[33]. From week 6, organoids were cultured in 3:1 medium supplemented with 10% heat-inactivated FBS (Millipore, es009–b) and 100 mM taurine (Sigma-Aldrich, T0625–25 G) with medium changes three times per week. At week 10, the culture medium was supplemented with 1 mM retinoic acid (Sigma-Aldrich, R2625). From week 14, the B27 supplement in 3:1 medium was replaced by N2 supplement (Gibco, 17502–048) and retinoic acid was reduced to 0.5 mM.

Generation and maintenance of vascular organoids

Vessel organoids were induced from the IMR90 AAVS1-tdTomato human iPS cell line. The iPS cell reporter line was generated by insertion of a reporter construct into the safe harbour AAVS1 locus with TALEN technology as described previously[54]. The growth and cultivation of blood vessel organoids was carried out as previously described with minor modifications[37]. Human iPS cells were dissociated into single cells using Accutase (Invitrogen, E141421) and 150 cells per microwell were distributed in a 256-microwell hydrogel (2% agarose) in mTeSR1 containing the CEPT cocktail (Tocris, 7991). On day 1 after induction, the embryoid bodies were treated with N2B27 with 12 µm CHIR99021 (StemCell, 72054) and 30 ng ml−1 BMP4 (StemCell Technologies, 78211). On day 4, the medium was changed to N2B27 100 ng ml−1 VEGF-A (Peprotech, 100-20) and 30 ng ml−1 forskolin (Sigma-Aldrich, F3917). On day 7, the embryoid bodies were embedded in collagen I (Sigma-Aldrich, CC300) Matrigel mix and treated with StemPro34 (Thermo Fisher Scientific, 10639011) containing 15% inactivated FBS (Merck, 21C662-A), 100 ng ml−1 VEGF-A and 100 ng ml−1 FGF-2 (Miltenyi Biotec, 130-093-564). On day 11, individual blood vessel organoids were dissected out of the gel and maintained in 96-well ultra-low-attachment plates until day 16.

Generation of mtDNA-depleted cells

mtDNA-depleted HEK293T cells and mMSCs were generated by treatment with a combination of 2′,3′-dideoxycytidine (ddC) and ethidium bromide (EtBr) according to established protocols56,57. For HEK293T cells, the base medium (DMEM + GlutaMAX containing sodium pyruvate; Thermo Fisher Scientific, 10569010) was supplemented with 10% FBS (Sigma-Aldrich, F7524), 1% penicillin–streptomycin (Thermo Fisher Scientific, 15140122), 62 μg ml−1 uridine (Thermo Fisher Scientific, A15227.14), 40 µM ddC (MedChemExpress, HY-17392) and 0.5 μg ml−1 EtBr (Thermo Fisher Scientific, 15585011). For mMSCs, they were cultured in MesenCult Proliferation Kit medium (StemCell Technologies, 05411) with 1 mM sodium pyruvate (Thermo Fisher Scientific, 11360070) and the same additives were used with a final concentration of 200 μM ddC and 1 μg ml−1 EtBr. The medium was replaced every 2 days. After mtDNA depletion, ddC and EtBr were removed from the medium and cells were maintained in culture medium containing uridine and sodium pyruvate. The depletion was monitored using PCR as previously described. Total DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, 69506). The copy number of the mtDNA was assessed using droplet digital PCR (ddPCR). The assay was performed using the ddPCR Supermix for Probes (Bio-Rad, 1863010), as described in the product’s technical bulletin. In brief, the reaction mixes (22 μl per reaction) were prepared as follows: 500 nM primers, 250 nM probes, 1× ddPCR Supermix for probes and 1 ng of DNA. The reactions were loaded into DG8 cartridges (Bio-Rad, 1864008) with droplet generation oil for probes (Bio-Rad, 1863005). The cartridge was then fitted with a DG8 gasket (Bio-Rad, 1863009) and run on the QX200 droplet generator (Bio-Rad, 10031907). Subsequently, the droplet–oil emulsions were transferred to a ddPCR-compatible 96-well plate that was sealed with a PX1 PCR Plate Sealer (Bio-Rad, 1814000) and the PCR reactions were run on a Mastercycler X50s (Eppendorf, 6311000010) according to the PCR protocol detailed in the technical bulletin. Afterwards, data were acquired on the QX200 Droplet Reader (Bio-Rad, 1864003) using the QuantaSoft software (v.1.7.4.0917, Bio-Rad). Negative controls were processed in parallel, where only ddH_2O and non-mtDNA depleted cells were used as input. Primers for ND1 (for HEK293T)[58] and Nd5 (for mMSCs)[57] genes were used for mtDNA amplification. For nuclear genome amplification, primers for B2M (human) and Rpp30 (mouse) genes were used.

Post-mortem human retina cultures

Post-mortem human retinas were maintained as described previously[27]. The tissue used in our experiments was obtained from one donor. The periphery of the donor retina was cut into pieces with fine surgical scissors and cultured individually in a Transwell with a polycarbonate membrane of 0.4 µm pore size (Corning, 7910). The medium was changed every second day. The retina pieces were cultured for up to 6 weeks. For live imaging, cultured retina pieces were transferred to a transparent Millicell cell culture insert (Merck, PICM0RG50).

Blood sample collections from healthy donors for PBMC isolation

Blood samples were obtained as buffy coats from healthy male and female donors after written informed consent (Blood Donor Center, University Hospital Basel).

Isolation and activation of human CD8+ T cells

PBMCs were isolated by density-gradient centrifugation using Lymphoprep (Serumwerk Bernburg, 1858). CD8+ T cells were enriched using CD8+ magnetic beads (Miltenyi Biotec, 130-045-201) by positive selection. CD8+ T cells were activated using soluble antibody complexes containing anti-CD3, anti-CD28 and anti-CD2 antibodies (Immunocult, StemCell Technologies, 10970) for 2 days.

Generation of constructs for cell-surface binder or antigen display

The sequence for anti-GFP nanobody was retrieved from pcDNA3_NSlmb-vhhGFP4 plasmid (Addgene, 35579), and the sequence for LaM-2 anti-mCherry nanobody was obtained from pET21-pelB-LaM-2 plasmid (Addgene, 172768). To generate the pAAV-EF1α-IgK-VHH_GFP4-OLLAS-TMD-WPRE and pAAV-EF1α-IgK-VHH_LaM2-OLLAS-TMD-WPRE constructs for cell-surface anti-GFP and anti-mCherry nanobody display, respectively, nanobody sequences codon optimized for mammalian expression were fused to the IgK signal peptide at the N terminus and an OLLAS-tag followed by PDGFRβ transmembrane domain at the C terminus. The sequences were chemically synthesized (Twist Bioscience) with overhangs and subcloned into the pAAV backbone containing the EF1a promoter via HiFi DNA assembly (NEB, E2621X), where the GFP transgene was removed using BamHI/EcoRI restriction sites (NEB). Similarly, cell-surface mCherry and enhanced GFP (eGFP) constructs (pAAV-EF1α-IgK-mCherry-OLLAS-TMD-WPRE and pAAV-EF1α-IgK-eGFP-HA-TMD-WPRE, respectively) were generated as described above. Sequences encoding either mCherry or eGFP with an N-terminal IgK and C-terminal OLLAS-tag (for mCherry) or an HA tag (for eGFP) and a PDGFRβ transmembrane domain were chemically synthesized (Twist Bioscience) and subcloned into the pAAV-EF1α-GFP backbone. To generate the pAAV-ProA5-DIO-IgK-VHH_GFP4-OLLAS-TMD-WPRE and pAAV-ProA5-DIO-IgK-LaM2_VHH-OLLAS-TMD-WPRE constructs, synthesized inserts (described above) were inverted by PCR (forward primer: 5′-TACATTATACGAAGTTATGGCGCGCCCTAACGTGGCTTCTTCTG-3′, reverse primer: 5′-ATACTTTATACGAAGTTATGCTAGCCACCATGGAGACAGACAC-3′) using Q5 DNA polymerase (NEB, M0492S) and subcloned into the pAAV-ProA5-DIO-DsRed2 backbone (Roska laboratory plasmid collection) through HiFi DNA assembly, whereby the DsRed2 transgene was removed using AscI/NheI restriction sites (NEB).

Generation of constructs for mitochondrial surface binders or antigen display

For mitochondrial outer membrane GFP targeting, the pCMV-GFP-OMP25 plasmid was used (Addgene, 141150). To display proteins of interest (for example, nanobodies, DARPins or SNAP-tag) on the surface of mitochondria, sequences were chemically synthesized (Twist Biosciences), fused to the C terminus of rat OMP25 and subcloned into the pcDNA3.1(+) backbone under control of the CMV by HiFi DNA assembly. Anti-GFP and anti-mCherry nanobody DNA sequences were obtained as described above. Nanobody sequences for cell-type-specific surface markers including human and mouse CD71, human CD73 and human CD142 were developed in this study and are described below. Anti-GFP (3G86.32) and anti-mCherry (2m22) DARPin sequences were retrieved from a published study[36], the SNAP-tag sequence was obtained from pSNAP-tag (m) vector (Addgene, 101135).

Generation of constructs for ubiquitous intracellular expression of destabilized nanobody

To generate the destabilized nanobody construct in an AAV backbone (pAAV-EF1α-dGBP1-TagBFP-WPRE), dGBP1-TagBFP from pCAG-dGBP1-TagBFP (Addgene, 80086) was PCR amplified via Q5 DNA polymerase (forward primer: 5′-TCAGGTGTCGTGAGGTACCGGATCCGCCACCATGGCCGACGTG-3′, reverse primer: 5′-TATCGATAAGCTTGATATCGAATTCTTAATTAAGCTTGTGCCCCAGTTTGCTAG-3′; NEB, M0492) and subcloned into an AAV backbone carrying the EF1A promoter through HiFi DNA assembly.

Generation of constructs for recombinant binder production in E. coli

Genes encoding nanobodies developed in this study, and bispecific binders were synthesized by Twist Biosciences. Nanobodies were inserted into a pET28-derived vector with a modified N-terminal pelB signal peptide[59] (pET28-NPelB(DN5v3)-6×His) between BamHI and XhoI sites through HiFi DNA assembly. The anti-mCherry–(G_3S)2G–anti-GFP bispecific nanobody construct was inserted into a pET24 vector (pET24-NPelB-6×His). Bispecific nanobodies produced from Escherichia coli were used for mitochondrial targeting experiments in in vitro assays. For in vivo experiments, the mammalian expression system was used as described below.

Generation of CD71, CD73, CD142 and bispecific nanobody constructs for the mammalian expression system

Open reading frames of human CD71 (NCBI: NM_001128148.3), CD73 (NCBI: NM_002526.4) and CD142 (NCBI: NM_001993.5) genes were synthesized (GenScript Biotech) and subcloned into a pcDNA3.1(+)-P2A-eGFP backbone containing a CMV promoter by using HindIII/XbaI restriction sites. Bispecific nanobodies were fused through a (G_3S)2G linker. The anti-mCherry–(G_3S)2G–anti-GFP bispecific nanobody sequence was codon optimized and synthesized (Twist Biosciences) with an N-terminal heavy chain signal peptide (IgHC) and a C-terminal 6×His tag. The DNA fragment was subcloned into a pcDNA3.1(+) backbone containing a CMV promoter using HindIII/EcoRI restriction sites. The anti-lysozyme–(G_3S)2G–anti-GFP bispecific nanobody construct was synthesized with an N-terminal mouse Igκ signal peptide and cloned into the pCSG_IBaA103 vector (IBA Life Sciences) containing a CMV promoter using BamHI/EcoRI restriction sites. The sequence for the anti-lysozyme nanobody was obtained from a published study[60].

Constructs for labelling cellular organelles

Constructs to target MiniSOG (pCMV-MiniSOG-Mito-7) and DsRed2 (pCMV-DsRed2-Mito-7) into the mitochondrial matrix were obtained from Addgene (Addgene, 57773 and 55838, respectively). For TagBFP2 targeting into the matrix, the mTagBFP2 coding DNA sequence was fused to COX8 matrix-targeting signal peptide, synthesized by Twist Biosciences, and inserted into a pCMV backbone. For endoplasmic reticulum labelling, TagBFP was fused to a KDELR1 peptide sequence and inserted it into an AAV backbone containing Ef1a promoter and WPRE sequence. For mito-dsRed2 expression in iHNeurons and HUVECs, the dsRed2 sequence was inserted into the same AAV backbone. The generation of AAVs is described below.

Plasmid DNA transfection

For mitochondria labelling with outer-membrane-targeted GFP, matrix-targeted dsRed2 and TagBFP, nanobodies, DARPins or SNAP-tag, HEK293T cells were grown in a 150 mm culture dish and transfected with 20 µg of plasmid DNA (a list of the constructs is provided in Supplementary Table 4) at 70–80% confluency using the jetPrime reagent (Polyplus transfection, 101000046) according to the manufacturer’s instructions. Mitochondria were isolated 24 h after transfection. mMSCs were used for the isolation of mouse-derived mito-GFP. For this, the cells were grown in a 225 cm^2 culture flask (Thermo Fisher Scientific, 159934) and transfected with 40 µg of plasmid DNA. The culture medium was exchanged after 24 h and mitochondria were isolated 48 h after transfection.

For cell-surface labelling with GFP, mCherry and nanobodies or for overexpression of CD markers (CD71, CD142 and CD73), HEK293T cells were seeded on a 96-well plate (Ibidi, 89626-90) at 30,000 cells per cm^2 density and transfected at 40–50% confluency as described above.

AAV production

AAVs were produced as described previously[61]. In brief, adherent HEK293T cells were co-transfected with the AAV transgene plasmid, an AAV helper plasmid encoding the AAV Rep2 and Cap proteins specific to the chosen serotypes, and the pHGT1-Adeno1 helper plasmid (provided by C. Cepko) using branched polyethyleneimine (PEImax, Polysciences). AAVs were collected from cells and cell culture supernatant 72 h after transfection and isolated using a discontinuous iodixanol density gradient prepared with OptiPrep (Sigma-Aldrich, D1556) and ultracentrifugation. The AAVs underwent concentration and buffer exchange into PBS containing 0.001% Pluronic F 68 using Amicon Ultra-15 100K spin filters (Millipore, UFC9100). Genomic copies were titrated using quantitative PCR for ITR and ddPCR for WPRE, with the following primer sequences: ITR forward: 5′-GGAACCCCTAGTGATGGAGTT-3′, ITR reverse: 5′-CGGCCTCAGTGAGCGA-3′, ITR probe: 5′-(6FAM)CACTCCCTCTCTGCGCGCTCG(MGB)-3′; WPRE forward: 5′-GGCTGTTGGGCACTGACAA-3′, WPRE reverse: 5′-CCAAGGAAAGGACGATGATTTC-3′, WPRE probe: 5′(6FAM)TCCGTGGTGTTGTCG(MGB)-3′. The WPRE ddPCR value was used for AAV titre matching of vector preparations for in vivo experiments. The validation of identity and purity of input plasmids was confirmed by complete plasmid next-generation sequencing (NGS) at MGH CCIB DNA Core.

AAV transduction of iHNeurons, primary endothelial cells, post-mortem human retinas and mouse retinas

For iHNeurons and primary endothelial cells cultured in an eight-well slide (Ibidi, 80826-IBI), AAVs were diluted to a titre of 1 × 10^11 genome copies per ml. The existing medium in the well was replaced with 50 µl of culture medium containing diluted AAVs and incubated for 1 h. An additional 150 µl of culture medium was then added on top of the well. The medium was exchanged after 48 h and cells were fed every second day. For iHNeurons in a 35-mm MatTek gridded coverslips dish, AAVs were diluted to a titre of 2.5 × 10^10 genome copies per ml. The existing medium in the well was replaced with 200 µl of culture medium containing diluted AAVs to cover the gridded coverslip area. After 1 h, an additional 1,800 µl of medium was applied on top. The medium was exchanged after 48 h and cells were fed every second day. The cells were cultured for a further 3 weeks before the experiments were conducted.

For post-mortem human retinas, AAVs were diluted to a titre of 3.8 × 10^12 genome copies per ml. For each explant, 30 µl of culture medium containing diluted AAVs was applied topically covering the whole explant. The explant tissues were then kept at room temperature for 10 min before transfer to an incubator. The medium was exchanged after 48 h and the explants fed every second day. The infected explants were cultured for at least 5 weeks before the experiments were conducted.

For in vivo transduction in mouse retinas, 1 × 10^11–10^12 genome copies were delivered bilaterally by intravitreal injection using a nanoinjector (Nanoliter 2020, WPI) and 1 × 10^11 genome copies per gram were injected retro-orbitally. Treated mice were given carprofen (50 mg ml−1, Rimadyl) 24 h before the surgery for up to 48 h. The injected eye was post-treated with Novesin 0.4% (OmniVision). Mice were used for the experiments after 3 weeks.

Western blotting

Mitochondria were freshly isolated and 5 µg of mitochondria was mixed with loading buffer (Thermo Fisher Scientific, NP0007) and reducing agent (Thermo Fisher Scientific, NP0004) and heated for 10 min at 90 °C. A total of 2.5 µg of each sample was loaded onto SDS–PAGE gel (4–12% acrylamide gel, Thermo Fisher Scientific, NP0322) and run in MOPS running buffer (Thermo Fisher Scientific, NP0001). The gel was moved onto a PVDF membrane (Millipore, IPVH00010) in Tris-glycine transfer buffer with 20% methanol (Thermo Fisher Scientific, LC3675). The transfer was done at 20 V for 2 h at 4 °C. The membrane was then blocked in 5% milk (Roth, T145.3) in TBS-T buffer (TBS + 0.1% Tween-20) at room temperature for 1 h and incubated overnight at 4 °C in primary antibodies in TBS-T containing 2.5% milk. The membrane was washed three times with TBS-T and incubated with secondary antibodies for 1 h at room temperature in 2.5% milk in TBS-T. After washing with TBS-T, the membrane was developed using Pierce ECL western blotting substrate (Thermo Fisher Scientific, 32106). Primary antibodies were as follows: anti-SNAP-tag (1:1,000, NEB, P9310S), anti-TOMM20 (1:2,500, Sigma-Aldrich, HPA011562), MCU (1:1,000, AMAB91189), GRP78 (1:2,000, G8918), PDIA3 (1:500, AMAB90988), KDELR1 (1:1,000, NBP2-12873) and membrane-integrity antibody cocktail (1:232, Abcam, ab110414). Secondary antibodies were as follows: anti-rabbit IgG-HRP antibodies (1:1,000, Jackson, 711-036-152), anti-mouse IgG-HRP antibodies (Thermo Fisher Scientific, A16017). All original membrane images are shown in Supplementary Fig. 1.

Expression and purification of TOMM20 cytoplasmic domain

The gene encoding the cytoplasmic domain of TOMM20 (UniProt: Q15388, residues 59–145, IDT) was cloned into a pET28-derived vector behind an N-terminal 6×His tag, SUMO domain and 3C-protease cleavage site and transformed to Shuffle T7 express cells (NEB, C3029J). A single, sequence verified clone was grown in LB with 50 μg ml−1 kanamycin overnight. The next day, 100 ml TB auto induction medium (Formedium, AIMTB0210) with 50 μg ml−1 kanamycin was inoculated with 1 ml of the overnight culture, grown at 37 °C for 2.5 h, and overnight at 26 °C. Cells were pelleted (15 min, 8,000g), lysed with 1× BugBuster extraction buffer (Millipore, 70584-3), protease inhibitor tablets (Thermo Fisher Scientific, A32955) in 50 mM Tris pH 8 and 100 mM NaCl for 30 min at room temperature. Cell debris was removed by centrifugation for 15 min at 15,000g. The supernatant was loaded onto a Streptactin XT column (IBA Lifesciences, 2-5028-001), washed with 5 column volumes (CV) of wash buffer (IBA Lifesciences, 2-1003-100) and eluted with 10 CV elution buffer (IBA Lifesciences, 2-1042-025). The elution fractions were pooled and concentrated to 2 ml and incubated overnight at 4 °C with homemade produced 3C protease (Addgene, 162795) added at 2% of total TOMM20 weight. The mixture was diluted to 25 ml with 20 mM Tris HCl pH 8.0 and incubated with 1 ml PureCube Ni-INDIGO MagBeads (CUBE Biotech, 75225) for 2 h at 4 °C to remove cleaved SUMO domain. The supernatant was loaded onto a Capto HiRes Q 5/50 column and eluted with a gradient to 35% 20 mM Tris HCl pH 8.0, 500 mM NaCl. Fractions containing pure TOMM20 were identified by SDS–PAGE, pooled and frozen at –80 °C until further use.

Expression and purification of TOMM70 cytoplasmic domain

A gene fragment of the cytoplasmic domain of human TOMM70 (amino acids 111–608) was synthesized (Twist Bioscience) and subcloned into pET28 vector containing an N-terminal 7×His-tag followed by a TEV protease cleavage site. The construct was transformed into the Rosetta 2 T7 express E. coli strain for protein expression and purification. The protein was produced as previously described[62], with some modifications. The genes were expressed at 17 °C in E. coli T7 Express cells (NEB) co-transformed with the pRARE2 plasmid. The purification protocol comprised mechanical cell lysis by sonication (SONOPULS HD 2200, Bandelin), affinity chromatography on the 5 ml HisTrap FF crude column (Cytiva), 7×His-tag cleavage with 1:30 (w/w) tobacco etch virus protease while dialysing into 20 mM HEPES-NaOH pH 7.5, 0.25 M NaCl, 5% glycerol and 1 mM DTT, a 5 ml HiTrap Heparin HP column (Cytiva) equilibrated with 20 mM HEPES-NaOH pH 7.5 and 0.15 M NaCl, and size-exclusion chromatography on the 26/600 Superdex 200 prep grade gel-filtration column (Cytiva) equilibrated with PBS buffer pH 7.4. The purified protein was concentrated to 4 mg ml−1, sterile filtered, flash-frozen in small aliquots with liquid nitrogen and stored at −70 °C until further use. The intact molecular mass was confirmed by liquid chromatography coupled with time-of-flight mass spectrometry (LC–TOF-MS).

Expression and purification of fluorescent proteins

Gene fragments were obtained from IDT or Twist Biosciences and subcloned by Gibson assembly (NEB HiFi DNA assembly master mix). Fluorescent proteins were subcloned into modified pET28 vectors with either N-terminal 6×His tag, HRV 3C protease cleavage site and avi-tag or C-terminal MYC tag and 6×His tag. The plasmids were transformed into BL21 (DE3) cells, and the avi-tagged constructs were co-transfected with the pBirAcm plasmid (Avidity).

Starter cultures were grown in LB medium and 25 μg ml−1 kanamycin. Expression was performed in autoinduction TB medium with 25 μg ml−1 kanamycin (Formedium, AIMTB0260) by inoculation with 1/100 volume of starter culture incubation at 37 °C for 2–3 h followed by 25 °C overnight. Cells were collected by centrifugation at 5,000g for 15 min, resuspended in PBS and lysed by sonication. Cell debris was removed by centrifugation. Avi-tagged fluorescent proteins was removed with NT*-HRV3CP (Addgene, 162795) by incubation with 2 wt% of protease for 2 h at room temperature. Uncleaved proteins and protease were removed using PureCube Ni-INDIGO MagBeads.

TOMM70 biotinylation

TOMM70 (4 mg ml−1) in PBS was incubated with a fivefold molar excess of EZ-Link NHS-PEG4-Biotin (Thermo Fisher Scientific, A39259) for about 1 h at room temperature. Unreacted reagent was blocked by adding 100 mM Tris/HCl pH 7.4 and removed with PBS equilibrated Zeba Spin Desalting columns (Thermo Fisher Scientific, 89882). The degree of biotinylation was determined using the QuantTag Biotin Quantitation Kit (Vector Labs, BDK-2000) according to the manufacturer’s instructions, and was determined to be 4.4 biotins per TOMM70 molecule.

Camelid-derived nanobody generation and selection

The general workflow for nanobody generation, production and selection was based on published protocol[63]. Nanobodies for human CD73 and mouse CD71 were generated at VIB Nanobody Core (VUB). Commercially available recombinant human CD73 (Sino Biological, 10904-H08H) and mouse CD71 (Sino Biological, 50741-M07H) proteins were used for llama immunizations. Llamas were immunized up to six times once a week with 120 µg of recombinant protein per each immunization. VHH DNA libraries were constructed from the animal’s peripheral lymphocytes cDNA, subcloned into the pMECS-GG phagemid vector[64] and transformed into E. coli TG1 cells for subsequent nanobody selection through phage-display. Phages from the constructed VHH DNA libraries were panned on solid phase coated with target protein (100 µg ml−1 in 0.1 M NaHCO_3 pH 8.2) for three rounds. Periplasmic extracts from the enriched clones after the second and third selection rounds were analysed by enzyme-linked immunosorbent assay (ELISA). Validated clones were sequenced by Sanger sequencing using the MP057 primer (5′-TTATGCTTCCGGCTCGTATG-3′).

Nanobodies for human CD71 were produced at the Nanobody Service Facility (University of Zurich). Commercially available recombinant human CD71 (Sino Biological, 11020-H07H) was used for alpaca immunization. The llama was immunized four times every second week with 200 μg of recombinant protein per injection. Similarly, cDNA was synthesized from RNA isolated from peripheral lymphocytes. VHH DNA libraries were constructed by PCR and subcloned into the pDX phagemid vector[34]. The library was transformed into E. coli TG1 cells for subsequent nanobody selection through phage-display. Phages from the constructed VHH DNA libraries were panned on solid phase coated with target protein (10 µg ml−1 in 0.1 M NaHCO_3 pH 8.4) for three rounds. Periplasmic extracts from the enriched clones after the second and third selection rounds were analysed using ELISA. Validated clones were sequenced by Sanger sequencing using SQ-NbREV primer (5′-GTAGCATTCCACAGA CAGCCCTCATAGT-3′).

Synthetic nanobody library design

Nanobody libraries were designed with two different CDR3 lengths (long and short CDR3 libraries). The framework regions were partially humanized using human germline V3-23*J04 as a template, hallmark camelid residues in framework 2 were kept (long CDR3 library) or partially kept (short CDR3 library) similar to the framework designs reported previously65,66. The design of the CDR3 regions and the randomization strategy of all complementarity-determining regions (CDRs) were inspired by the strategy used previously[67]. Further mutations beneficial for nanobody stability were incorporated into the library[68]. The final sequences of the libraries are shown in Extended Data Fig. 10. Libraries flanked by regulatory regions necessary for ribosome display were ordered from Twist Biosciences.

Nanobody selections against TOMM70 and TOMM20 using mRNA ribosome display

The nanobody selections followed the published protocol[69], with a few modifications: chemically biotinylated TOMM70 and TOMM20 with a TwinStrep tag were used as target proteins. The panning procedures were automated on a KingFisher DUO instrument (Thermo Fisher Scientific, 5400110) using Streptavidin-coated magnetic beads for TOMM70 (Thermo Fisher Scientific, 65001 and 65601) or Streptactin-XT-coated beads for TOMM20 (IBA Lifesciences, 2-5090-002). All nanobody sequences generated in this study are shown in Supplementary Data 1 and their antigen binding kinetics are described in Supplementary Table 3.

Prokaryotic expression and purification of camelid-derived nanobodies

Newly generated and affinity matured nanobodies were produced in E. coli Shuffle T7 express strain (NEB, C3029J). Starter cultures were grown in LB medium with 1% glucose and 25 μg ml−1 kanamycin. Expression was performed in autoinduction TB medium with 25 μg ml−1 kanamycin (Formedium, AIMTB0260) by inoculation with 1/100 volume of starter culture incubation at 37 °C for 2–3 h followed by 25 °C overnight incubation.

Prokaryotic expression of nanobodies from synthetic libraries and bispecific nanobodies

Gene fragments were obtained from IDT, Twist Biosciences and Genscript, or were generated by PCR (Q5 High-Fidelity 2× Master Mix, NEB, M0492L). Nanobodies from synthetic libraries were cloned by Gibson assembly (NEB HiFi DNA assembly master mix) into a pET28-derived vector without a PelB signal sequence and with a C-terminal 6×His tag. Expression was carried out from a single, sequence-verified colony of Shuffle T7 express cells (NEB, C3029J). TB Autoinduction medium (Formedium, AIMTB0210) was inoculated with overnight culture grown in LB (100:1 ratio), grown for 4 h at 30 °C, and overnight at 25 °C. Pelleted cells were lysed in 5 ml detergent extraction buffer per 50 ml expression culture consisting of 50 mM Tris HCl pH 8.0, 500 mM NaCl, 1% CHAPS, 1% NP40, 10 mM imidazole and protease inhibitors (Thermo Fisher Scientific, A32955) and incubated for 30 min. Cell debris was removed by centrifugation. Proteins were purified from the supernatant with PureCube Ni-INDIGO MagBeads (CUBE Biotech, 75225) according to the manufacturer’s instructions. Elution fractions were pooled and polished by size-exclusion chromatography with PBS as the running buffer (Sepax, SRT-10C SEC300 column). Monomeric fractions were collected, pooled and concentrated if necessary.

Bispecific nanobodies were subcloned into pET28-derived vectors with or without a modified PelB signal peptide (DN5v3.1) and transformed into E. coli Shuffle T7 express strain (NEB, C3029J) as described above. 2YT medium with 0.5% glucose and 25 μg ml−1 kanamycin was inoculated with 1/100 of starter culture grown in LB with 1% glucose and 25 μg ml−1 kanamycin. Cultures were grown at 37 °C until they reached an optical density at 600 nm of 0.7, induced with 1 mM IPTG and grown overnight at 25 °C. Constructs without PelB signal peptide were purified as described above. Cells carrying constructs with PelB signal peptides were resuspended in periplasmic extraction buffer (3–5 ml per 50 ml expression culture, 20% sucrose 50 mM Tris/HCl pH 8.0, 0.5 mM EDTA and 0.5 μg ml−1 lysozyme) and incubated for 30 min on ice. Four times the volume of 20 mM Tris/HCl pH 7.5, 150 mM NaCl and 2 mM MgCl_2 was added. Cells were separated by centrifugation (5,000g, 15 min) and the supernatant was decanted. Purification was continued as described above.

Mammalian expression and purification of bispecific nanobodies

For in vivo experiments to reduce endotoxin contamination, the bispecific nanobodies were produced in ExpiCHO-S cells (Thermo Fisher Scientific, A29127). For the anti-mCherry–(G_3S)2G–anti-GFP bispecific nanobody production (produced at the EPFL Protein Production Platform), the cells were cultured in ProCHO5 medium (Lonza, BELN12-766Q) supplemented with 2% DMSO. The plasmid DNA encoding the anti-mCherry–(G_3S)2G–anti-GFP bispecific nanobody was transfected with PEI-MAX (Polysciences, POL24765-1). After 6 days, the supernatant was filtered and incubated with Fastback Ni Advance Resin (Protein Ark, Fastback-Ni-Adv) overnight at 4 °C. The resin was loaded into a disposable column and washed with 500 mM NaCl, 50 mM HEPES pH 7.5 and the nanobody was eluted with 500 mM NaCl, 50 mM HEPES pH 7.5, 500 mM imidazole. Eluted protein was dialysed overnight in PBS at 4 °C and concentrated using an Amicon 30 kDa cut-off column (Merck, UFC9030). For the anti-lysozyme–(G_3S)2G–anti-GFP bispecific nanobody production (produced in-house), the cells were cultured in ExpiCHO Expression Medium (Thermo Fisher Scientific, A29100). The plasmid was transfected with Expifectamine CHO reagent (Thermo Fisher Scientific, A29129). The protein from the supernatant was purified using Ni-IMAC (column: HisTrap excel; Cytiva, 17371206). The column was washed with 500 mM NaCl, 50 mM Tris pH 8.0 and 20 mM imidazole, and the nanobody was eluted with 500 mM NaCl, 50 mM Tris pH 8.0 and 500 mM imidazole. Pure protein fractions were subsequently purified by anion-exchange chromatography and then size-exclusion chromatography for monomeric protein fractions. The pure protein fractions were pooled and concentrated in PBS.

Nanobody affinity maturations

Site saturation libraries targeting all three CDRs and adjacent sequences of initial VHH hits were synthesized by Twist Biosciences. Libraries were subcloned into the pDX plasmid and electroporated into TG1 DUOs electrocompetent cells (Biosearch Technologies, 60502-2). Transformed cells were superinfected with M13K07 helper phage (NEB, N0315S) and phagemids were purified from culture supernatant. Phagemids were cycled through 3−4 rounds of phage display with increasing stringency. Mutations that were enriched over sequential rounds were identified by NGS (Illumina MiSeq) of PCR amplified VHH inserts from phages. Data analysis was performed using the PipeBio platform. Genes of clones with single or combined enriched mutations were synthesized (eblocks, IDT) and expressed as described above.

Nanobody affinity measurement using surface plasmon resonance

All measurements were conducted on a Biacore T200 instrument using PBS with 0.05% Tween-20 as running buffer. CM5 chip (Cytiva, BR100399) surfaces were functionalized using an amine coupling kit (Cytiva, BR100050) according to the manufacturer’s instructions with a goat anti-human IgG Fc antibody (Southern Biotech, 2048-01) or a goat anti-mouse IgG2a Fc antibody (Southern Biotech, 1081-01) to immobilize Fc-tagged target proteins. Recombinant human CD71 (110202-H01H) and human CD142 (13133-H02H) proteins were obtained from Sino biologics. Recombinant mouse CD71 (TFR-M5263) and human CD73 (CD3-H5252) proteins were obtained from Acro Biosystems. To immobilize biotinylated proteins, we used the RGD200M sensor chip (Xantec) in combination with oligonucleotide-modified Streptavidin reagent (Xantec, C RG-SA-50I) according to the manufacturer’s instructions. To immobilize TwinStrep-tagged proteins, we modified a CM5 chip surface with Streptactin XT using the Twin-Strep-tag Capture Kit (IBA Lifesciences, 2-4370-000) according to the manufacturer’s instructions. Increasing concentrations of VHHs were injected with a single cycle protocol. Regeneration of measurement surfaces was achieved with 200 mM H_3PO_4 for anti-Fc surfaces or regeneration solutions according to the manufacturer’s instructions for other capture reagents. Data were double referenced and fitted to a 1:1 binding model in the instrument software.

Thermostability measurement of selected nanobodies

Nanobodies were diluted to 0.3–0.5 mg ml−1 in PBS. Data were collected from 20–95 °C on the Prometheus nanoDSF instrument (Nanotemper) in a standard capillary. Fitting was performed in the instrument software.

BG conjugation to monoclonal antibodies

Anti-CD31 monoclonal antibodies (130-108-038) and mouse IgG1 isotype control (130-106-545) were obtained from Miltenyi Biotec. BG conjugation to antibodies was as described previously[20]. In brief, BG-GLA-NHS (NEB, S9151S) were incubated with the antibodies (1 mg ml−1) at a 60-fold molar excess for 30 min at room temperature. The antibodies were column purified using Zeba Spin Columns (7 K MWCO; Thermo Fisher Scientific, 89882) and eluted in 1× PBS, pH 7.4.

BG-conjugated antibody linking to mitochondria through the SNAP-tag

BG-conjugated antibodies at different concentrations (0–500 nM) were incubated with 2.5 µg of SNAP-tag-displaying freshly isolated mitochondria at room temperature for 20 min. The mitochondria were then transferred to ice and incubated for an additional 10 min before transplantation.

AlphaFold2 and AlphaFold3 predictions

Structure predictions were done with AlphaFold2 implemented in a google colab notebook[70] available online (https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb.Predictions); predictions using AlphaFold3[71] were done at https://alphafoldserver.com/.

Mitochondria isolation

Mitochondria were freshly isolated from donor cells using the Qproteome Mitochondria Isolation Kit (Qiagen, 37612) according to the manufacturer’s instructions. The mitochondria sources were HEK293T cells for targeting human cells (for example, HEK293T, iHNeurons, human retina) and mMSCs for targeting mouse cells (mouse retina). The amounts of freshly isolated mitochondria used for the transplantation experiments corresponded to the total protein amount in the isolated mitochondrial fractions, measured using the Qubit Protein Assay Kit (Thermo Fisher Scientific, Q33212).

Mitochondrial transplantation

Freshly isolated mitochondria were transplanted within 1 h of isolation in the storage buffer provided in the kit. For proliferating cells in 2D culture, we used a 2 h incubation for quantification of mitochondrial delivery efficiency, avoiding any possible dilution of mitochondria due to cell division. For cytosolic accessibility, we incubated cells for 1 day to allow enrichment of BFP at the GFP-labelled mitochondria. For post-mitotic cells such as neurons, organoids and retinal ganglion cells, we waited for 1 day to allow the distribution of donor mitochondria along the processes of the cells. The amounts of mitochondria used were as follows: for transplantation targeting experiments in cultured cells, 0.5–10 µg; for mitochondrion-surface display experiments in HEK293T cells with affinity matured nanobodies, 0.5–2.5 µg; for transplantation in LHON iHNeurons respiration assays, 2.5–10 µg; for transplantation in glycolysis inhibition experiments involving LHON iHNeurons, 5 µg; and, for retinal and blood vessel organoids, 10 µg. For isolated suspensions of CD4 and CD8+ T cells, the amount of mitochondria was normalized to 2.5 µg per 100,000 cells. For ex vivo cultured post-mortem human retinal explants, 3 µg of mitochondria was injected per piece. For in vivo targeted mitochondrion-delivery experiments, 2.5 µg of mitochondria was injected, while for cell rescue experiments the amount injected intravitreally was 0.5–5 µg. For MitoCatch experiments involving anti-mCherry–(G_3S)2G–anti-GFP bispecific binders, 2.5 µg of isolated mitochondria was co-incubated with bispecific binders (4 µM) for 25 min on ice before they were added to cells or injected in vivo. MitoCatch experiments involving anti-TOMM20–(G_3S)2G–anti-CD4 bispecific binders, 0.5–5 µg of isolated mitochondria was co-incubated with bispecific binders (0.5 µM) for 1 h on ice before the mitochondria were added to cells. For MitoCatch experiments involving a SNAP-tag, 2.5 µg of isolated mitochondria was incubated with either 0.5 µM anti-CD31 or 0.5 µM IgG1 and transplanted into endothelial cells; 10 µg of mitochondria was incubated with 0.5 µM anti-CD31 or 0.5 µM IgG1 and transplanted into individual blood vessel organoids. For unstimulated CD8+ T cells, 5 µg of mitochondria was incubated with either 1 µM CD8 or 1 µM IgG1 and transplanted into CD8+ T cells (2.5 µg per 100,000 cells).

For in vitro transplantations, HEK293T cells, cardiac cells or endothelial cells were cultured in 96-well plates (Ibidi, 89626). iHNeurons, were cultured in MatTek gridded coverslips dishes (MatTek, P35G-1.5-14-C-GRD) or in 8-well slides (Ibidi, 80826-IBI). For retinal and vascular organoids or T cells, 96-well cell culture plates with a conical bottom (Thermo Fisher Scientific, 277143) were used.

For ex vivo transplantation into post-mortem human retinas, mitochondria were injected with a nanoinjector (Nanoliter 2020, WPI); five injections per explant were performed in a volume of 150 nl (0.6 μg). For in vivo transplantation into eyes, mitochondria were injected intravitreally with a Hamilton syringe (5 µl, 32 gauge, Hamilton; 87931) in a volume of 1.5 µl. Treated mice were given carprofen (50 mg ml−1, Rimadyl) 24 h before the surgery for up to 48 h, and the injected eye post-treated with Novesin 0.4% (OmniVision).

In targeted mitochondrial delivery experiments in HEK293T, cardiac cells and endothelial cells, the cells were washed 2 h after transplantation three times with PBS and fixed with 4% PFA for further processing. T cells were washed and stained for flow cytometry after 4 h, vascular organoids were fixed after 6 h, and iHNeurons, retinal organoids, mouse retina and post-mortem human retinal explants were washed with 1× PBS and fixed the day after mitochondrial transplantation.

Flow cytometry

Mitochondrial targeting efficiency in T cells was assessed by flow cytometry. After incubation with donor mitochondria, T cells were transferred to a U-bottom 96-well plate (Thermo Fisher Scientific, 163320) for antibody staining; at each step, cells were maintained on ice and stained with antibodies for 30 min. Cells were washed before and after staining with staining buffer (1% FBS in PBS). For mitochondrion-targeting experiments involving the anti-CD71 nanobody, both unstimulated and activated CD8+ T cells were stained with monoclonal mouse anti-CD71-FITC antibodies (1:100, BioLegend, 334104) to identify the CD71+ population. For CD4+ T cell targeting experiments, anti-CD4-FITC antibodies (1:100, BioLegend, 344604) were used to identify the CD4+ population. As the transplanted mitochondria exhibited DsRed2 fluorescence, no additional staining was necessary.

In mitochondrial targeting experiments involving SNAP-tag strategy to target CD8 receptor in unstimulated CD8+ T cells, cells were stained initially with polyclonal rabbit anti-SNAP antibodies (1:100, NEB, P9310S), followed by washing and incubation with donkey anti-rabbit IgG (H+L) secondary antibody conjugated to Alexa Fluor 568 (Thermo Fisher Scientific, A10042). Data were acquired using the Cytoflex flow cytometer (Beckman Coulter) and analysed in FlowJo.

Respiration measurements of isolated mitochondria

Respiration analysis of freshly isolated mitochondria was performed using the Seahorse XF96 extracellular flux analyser (Seahorse Bioscience, Agilent). In total, 5 µg of mitochondria was resuspended in 25 µl of storage buffer containing 2 µM rotenone, 10 mM succinate and 10 mM ADP. Mitochondria were added into a prechilled 96-well plate and centrifuged for 20 min at 2,000g at 4 °C. The wells were then supplemented with 155 µl of pre-warmed storage buffer containing the substrates. For the control condition, succinate and ADP were not added. To stop mitochondrial respiration 4 µM antimycin A was injected.

Respiration measurements of LHON iHNeurons

Patient-derived LHON iHNeurons were induced from iPS cells as described previously. CD90+ sorted cells were seeded on a 96-well Seahorse well plate (Agilent, 103793-100) coated with Matrigel at 40,000–120,000 cells per well (ranged from induction batch). Seeded cells were allowed to recover for a week and then infected with a PHP.eB[72] serotyped AAV encoding cell-surface anti-GFP nanobody (1 × 10^11 genomic copies). The cells were incubated for 2 weeks before adding mitochondria and the oxygen consumption rate of the cells was measured 2 days after transplantation.

Respiration measurements were performed using the Seahorse XF96 extracellular flux analyzer (Seahorse Bioscience, Agilent). The day before the assay, the sensor cartridge was hydrated overnight in a calibrant in a CO_2-free incubator at 37 °C. On the day of the assay, the cells were washed three times in unbuffered DMEM supplemented with glucose, pyruvate and glutamine (Agilent, 103680-100) and allowed to equilibrate for at least 30 min in a CO_2-free incubator at 37 °C. During the assay, metabolic modulators—1 µM oligomycin (Sigma-Aldrich, 75351), 2 µM carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP, Sigma-Aldrich, C2920), 1 µM rotenone (Sigma-Aldrich, R8875) and 1 µM antimycin A (Sigma-Aldrich, A8674)—were added sequentially and the oxygen consumption rates were measured in real time. Oligomycin, an ATP synthase inhibitor, was used to assess ATP-linked respiration. FCCP, an uncoupling compound, was used to assess maximal respiration. Rotenone and antimycin A (mitochondrial electron-transport chain complex I and III inhibitors, respectively) were used to block mitochondrial respiration.

Endosomal escape and lysosomal pH acidification experiments in primary endothelial cells

HUVECs were seeded in eight-well slides (10,000 cells per well) and were infected on the next day with an AAV expressing cell surface GFP or co-infected with the AAV (PHP.eB serotype, Ef1a promoter) and baculovirus expressing RAB5A–TagRFP for labelling early endosomes. After 2 days, cells were transplanted with 10 µg of mito-mTagBFP2 donor mitochondria displaying anti-GFP nanobody or without any binder. Cells were washed 2 h after transplantation with culture medium and live imaged after 6 h and 1 day. For assessment of lysosomal pH changes, a lysosomal acid pH detection kit was used according to the manufacturer’s instructions (Dojindo, L266-10).

Glycolysis inhibition experiments by glucose substitution with galactose in LHON iHNeurons

Patient-derived LHON iHNeurons were induced from iPS cells and seeded on a Matrigel-coated MatTek gridded coverslips dish or an eight-well Ibidi slide (50,000 cells per dish/well). The cells were allowed to recover for a week and then infected with AAVs to express cell-surface anti-GFP or control nanobody and cytosolic tdTomato (PHP.eB serotype, Ef1a promoter). After 3 weeks, cells were transplanted with 5 µg of mito-GFP or remained untreated. After 2 days, the cells were carefully washed with prewarmed PBS and the culture medium was substituted with glucose-free Neurobasal-A medium supplemented with D-galactose (50 mM) and 1% penicillin–streptomycin. Cells were live imaged and tracked over 2 days during starvation using grids in the plate. For cells treated with mitochondria, survival of tdTomato-positive cells positive for mito-GFP was assessed, and survival of tdTomato-positive cells was evaluated for control samples. Moreover, survival of tdTomato+ cells were assessed by using a cell viability calcein dye (Thermo Fisher Scientific, C1430). We restricted the starvation to 2 days due to cell detachment that occurs over a longer period.

Immune response profiling after mitochondria injection

Mitochondria either displaying anti-GFP nanobody or no binder were injected into the retina, systemically through the bloodstream or locally into the brain. For retinal injections, intravitreal injections were performed as previously described. A total of 2.5 µg of mitochondria in 1 µl of buffer was injected into the vitreous chamber. For systemic injections, 20 µg of mitochondria in 30 µl of buffer was injected retro-orbitally. Mice were anaesthetized with isoflurane and received a topical drop of anaesthetic on the eye (oxybuprocaine hydrochloride, 0.4%). The eye was gently protruded from the socket, and the injection was administered at the medial canthus into the retro-orbital sinus with a 28 G syringe. Carprofen (4 mg per kg) was administered before the procedure and provided in the drinking water (10 mg per kg) for 24 h after surgery. For brain injections, mice were anaesthetized with a combination of fentanyl (0.05 mg per kg), midazolam (5 mg per kg) and medetomidine (0.5 mg per kg) and placed onto a heating pad. The surgical area was sterilized with betadine and a midline scalp incision was made from lambda to bregma. A small craniotomy was performed to allow for intracortical injections. Mitochondria were delivered into the cortex of the brain with a nanoinjector (Nanoliter 2020, WPI). A total volume of 150 nl (1.5 µg μl−1) of mitochondria was injected into three different locations of the cortex in each mouse. The skin incision was sutured and the mice received a reversal cocktail of flumazenil (0.5 mg per kg) and atipamezol (2.5 mg per kg). Post-operative analgesia was maintained for 2 days. Blood samples were collected at three timepoints: the day of the injection, and at 7- and 28 days after injection. Tail-vein collection was performed under isoflurane anaesthesia. Tails were warmed to promote vasodilation and approximately 50 µl of blood was collected from the lateral tail vein using a needle. Haemostasis was achieved by applying a sterile swab to the puncture site. ELISAs on blood serum were performed to assess the presence of antibodies against mitochondrial proteins and described below in the ‘ELISA’ section.

Wound-healing assay

One day before the assay, HUVECs were seeded at a density of 25,000 cells per well (50,000 cells per insert) in Ibidi two-well silicone inserts with a cell-free gap (Ibidi, 80209), placed into 24-well plates and topped with endothelial cell growth medium (Bio-techne, CCM027). Cells were kept at 37 °C in a humidified incubator (5% CO_2) and grown overnight to reach confluence. On the day of the assay, mitochondria were isolated from HEK293T cells transfected with pcDNA3.1-SNAP-OMP25, enabling SNAP-tag display on the surface of mitochondria. Isolated mitochondria were incubated for 1 h on ice in one of two conditions: (1) anti-CD31 monoclonal antibodies (130-108-038) or (2) mouse IgG1 isotype control (130-106-545), both conjugated to BG. Moreover, control BG-conjugated anti-CD31 antibodies without mitochondria were added to cells. Mitochondria were added to the HUVECs in inserts, 10 μg per insert side (20 μg per insert) suspended in the storage buffer provided with the Qproteome Mitochondria Isolation Kit (Qiagen, 37612). HUVECs were incubated with mitochondria for 3 h at 37 °C. Next, the inserts were gently and swiftly removed to create a defined wound gap. The HUVECs were washed with warm PBS to remove excess mitochondria and cell debris and fresh medium was added to the wells. Time-lapse imaging was initiated immediately using an Olympus confocal spinning-disc microscope capturing one image per hour for 10 h to monitor wound closure. The gap area was quantified over time using ImageJ.

ONC for disease modelling

ONC was performed in PV-cre/tdTomato mice previously transduced with the AAV-ProA5-DIO-αGFP nanobody (PhP.eB). We used the ProA5 promoter to exclude amacrine cells, which also express PV. Mice were deeply anaesthetized with isoflurane and buprenorphine (Temgesic, 0.05 mg per kg) injected subcutaneously. Oxybuprocaine hydrochloride (Novesin) drops were applied locally for analgesia. The eyeball was extracted from the orbit by wide opening of the eyelids, the optic nerve gripped and pressed for 10 s with the tip of surgical forceps and the eye was placed back into the orbit. The ONC was performed unilaterally with the second eye as a control. A suspension of isolated mitochondria was injected intravitreally 1 day after ONC. A similar volume of storage buffer was injected into control eyes without donor mitochondria. Then, 10 days after ONC, the retinas were fixed in 4% PFA in PBS for 1 h for further analysis.

To validate the model, C57BL6 mice were injected intravitreally with retrograde tracer cholera toxin subunit B-Alexa Fluor-555, 647 or 488 (Thermo Fisher Scientific, C34776, C34778 and C34775) in crushed and control eyes at 8 days after crush to label axons of retinal ganglion cells in the brain. Then, at 10 days after crush, mice were transcardially perfused with 4% PFA in PBS and brains were postfixed overnight in the same solution, embedded in 3% agarose in PBS and sliced at a thickness of 100 μm using a vibratome (Leica, VT1000S) for further analysis.

TEM analysis of freshly isolated mitochondria

A pellet of isolated mitochondria was fixed with 2.5% glutaraldehyde (Electron Microscopy Science; 16200) in 0.1 M sodium cacodylate buffer pH 7.4 (Sigma-Aldrich, C0250). The pellet was post-fixed in 2% aqueous osmium tetroxide (Electron Microscopy Science, 19160), dehydrated in gradual ethanol (30–100%), embedded in EMbed 812 epoxy resin (Electron Microscopy Science, 14120) and cured for 24 h at 60 °C. A region of interest was selected under light for TEM analysis. After trimming, silver/grey thin sections (50 nm) were collected on formvar-coated single-slot copper grids (Electron Microscopy Science, FF2010-CU-50). After post-staining with 1% uranyl acetate and lead citrate (5 min each), images were recorded using a FEI Tecnai Spirit (FEI Company) operated at 120 keV using a side-mounted 2 K × 2 K CCD camera (Veleta, Olympus).

SEM analysis of cultured iHNeurons

iHNeurons (15,000–20,000 cells) were cultured on a Matrigel-coated MatTek gridded coverslips dish (MatTek, P35G-1.5-14-C-GRD) for live-cell imaging and subsequent localization. After 3 h, coordinates of cells positive and negative for GFP-tagged donor mitochondria were recorded by both fluorescent and transmitted light microscopy. After imaging, the cells were fixed with 2.5% glutaraldehyde (Electron Microscopy Science, 16200) and 2% paraformaldehyde (Electron Microscopy Science; 15710) in 0.1 M cacodylate buffer pH 7.4 for 1 h. After rinsing with 0.1 M cacodylate buffer pH 7.4, the cells were dehydrated in a graded ethanol series and dried with hexamethyldisilazane (Sigma-Aldrich, 379212). The cells were further dried in an oven at 60 °C for 5 min, and mounted onto aluminium stubs by fixing the coverslip with double-sided carbon tape and sputter coated (Quorum, SC7620) with gold/palladium (5–8 nm). Cells were localized using the coordinates on the gridded coverslips and imaged at 3 kV and 200 pA with a scanning electron microscope (SEM Merlin, Zeiss) using the HE-SE2 detector.

Photooxidation of cultured cells and localization of miniSOG-labelled mitochondria using TEM

Photo-oxidations of cells and sample preparations were performed as previously described[25]. For HEK293T cells, 10,000–15,000 cells were seeded on a MatTek gridded dish and transfected with miniSOG-encoding plasmid DNA. The next day, cells expressing miniSOG in the matrix of mitochondria were localized by live imaging using an inverted Olympus confocal spinning-disc microscope and their coordinates were determined from the dish grid. The cells were fixed with 2% glutaraldehyde in 0.1 M cacodylate buffer (pH 7.4) for 1 h, rinsed several times with ice cold cacodylate buffer and blocked for 30 min in 50 mM glycine, 10 mM KCN and 5 mM aminotriazole. Diaminobenzidine (DAB) in 0.1 M cacodylate buffer (pH 7.4) solution (1 mg ml−1) was saturated with pure oxygen and poured onto the cells. Live recorded cells were identified and photooxidized for 10 min using the OBIS 488 nm LS 100 mW Laser (Coherent, 88-025) and the Olympus UPlanSApo ×20 objective (Olympus, N1480500). For iHNeurons, 50,000 cells per dish were seeded on the MatTek gridded dish coated with Matrigel and infected with AAVs to express the cell-surface anti-GFP nanobody. After 2 weeks, the cells were transplanted with mito-GFP (2.5 µg). The next day iHNeurons were live imaged to locate cells positive for mito-GFP, fixed with 2% glutaraldehyde in 0.1 M cacodylate buffer (pH 7.4) for 1 h and processed further as described above. Subsequently, dishes with cells were placed on ice and washed with ice-cold cacodylate buffer (5 × 2 min) to remove unpolymerized DAB. After washing, cells were post-fixed with 1% osmium tetroxide (Electron Microscopy Science, 19160) in 0.1 M cacodylate buffer (pH 7.4) for 30 min on ice. The cells were then washed three times with double-distilled H_2O and stained with 1% uranyl acetate in double-distilled H_2O for 20 min. After five washes in double-distilled H_2O and dehydration steps in graded alcohol series, the cells were infiltrated with EMbed 812 epoxy resin, using the hard recipe (Electron Microscopy Science, 14120), for 12 h. The next day, the resin was replaced with freshly prepared resin and polymerized at 60 °C for 24 h. Subsequently, photooxidized regions of interest (ROIs) were identified by transmitted light, sawed out using a jeweller’s saw and mounted onto dummy acrylic blocks with cyanoacrylic adhesive. The glass coverslip was carefully removed, and ultrathin sections (50 nm) containing the photooxidized region of interest were collected on formvar-coated single-slot copper grids (EMS). Images were recorded using a FEI Tecnai Spirit (FEI Company) operated at 120 keV using a side-mounted 2 K × 2 K CCD camera (Veleta, Olympus).

AFM analysis

Cantilever coating for collection of an isolated mitochondrion was done as follows. Silica beads with 5 μm diameter (Kisker Biotech, PSI-5.0) were glued to the free end of a tipless triangular microcantilever (NP-O-D, Bruker Instruments, nominal spring constant, ~0.06 N m−1) using ultraviolet curable glue (Dymax, OP-29) and were cured with ultraviolet light for 30 min. Beaded cantilevers were then cleaned for 2 min using a plasma cleaner (Harrick Plasma)[73]. Thereafter, the beaded cantilevers were coated with 2 mg ml−1 concanavalin A (Sigma-Aldrich, C5275) for 30 min at 25 °C. Any extra concanavalin A was removed by washing the cantilevers with 1× PBS. Next, the cantilevers were coated with  anti-TOMM22 antibody-conjugated microbeads (Miltenyi Biotech, 130-127-693) for 1 h at 25 °C. Any excess beads were removed by washing the cantilevers with 1× PBS.

Freshly isolated mitochondria were added to an empty well of the PDMS insert containing glass-bottomed fluodish (WPI, FD35) at a concentration of 2 µg ml−1. The mitochondria pick-up experiments were performed using an atomic force microscope (Nanowizard II with CellHesion Module, JPK Instruments) mounted onto an inverted epifluorescence microscope (Observer.Z1, Zeiss). The temperature was maintained at 37 °C throughout the experiment by a Petri dish heater (JPK Instruments). The AFM cantilever coated with 5-µm anti-TOMM22-antibody-conjugated microbeads was moved towards the surface at a speed of 5 µm s−1 and was softly pushed (set point force = 2 nN) onto the mitochondria for 60 s. After retraction of the cantilever, the successful pick-up of mitochondria was monitored using the epifluorescence signal.

Mitochondrion–cell unbinding forces were measured using an atomic force microscope (Nanowizard II with CellHesion Module, JPK Instruments) mounted onto an inverted epifluorescence microscope (Observer.Z1, Zeiss). The temperature was maintained at 37 °C throughout the experiment by a Petri dish heater (JPK Instruments). The cantilever-attached mitochondrion was moved towards a cell at a speed of 2 µm s−1 and pushed gently (set point force = 0.5 nN) onto the cell for ~50 ms (0 s contact time). Subsequently, the mitochondrion was retracted at 2 µm s−1 and allowed to recover for a time period equal to that of the contact time with the cell[74]. A minimum of three unbinding measurements was performed on each cell. Adhesion forces were determined after the retraction force-distance (F–D) curves were drift- and baseline-corrected using the JPK data-processing software (v.8.0.92, Bruker Nano). The maximum adhesive force of the F–D curve was determined by measuring the difference between the minimum force value (maximum cantilever deflection) and the corrected baseline. This is a measure of the force required to separate the mitochondria from a cell. Single events were counted as each force step between the maximum detachment force and the final force step before the F–D curve merges with the baseline and recorded using JPK data processing software (v.8.0.92, Bruker Nano). A cut-off of ~20 pN, which corresponds to twice the noise in our AFM experiments, was used before analysing single force steps[75]. This is a measure of the frequency with which receptor–ligand interactions occur between the mitochondria and a cell.

Confocal imaging of immunostained and live samples

Images of cultured cells and tissue were acquired using a spinning-disc confocal microscope (Olympus IXplore SpinSR). For fixed samples, objectives with ×10 (UPLXAPO10X, Olympus, N5701900), ×20 (UPlanSApo, Olympus, N1480500) and ×40 (UPLSAPO40XS, Olympus, N4274100) magnifications were used for the acquisitions. For live imaging, the microscope included a cellVivo incubation system (Olympus) to maintain samples at 37 °C and 5% CO_2. The samples were imaged with an UPlanSApo ×20 objective and an extra ×3.2 optical zoom for 2 min at 1–2 s intervals.

Super-resolution imaging of immunostained samples

Three-dimensional structured illumination microscopy (3D-SIM) was performed on the DeltaVision OMX-Blaze V4 system (Leica) equipped with 405, 445, 488, 514, 568 and 642 nm solid-state lasers. Images were acquired using a Plan Apo N ×60/1.42 NA oil-immersion objective lens (Olympus) and four liquid-cooled sCMOS cameras (pco.edge 5.5, full frame 2,560 × 2,160; PCO). Exciting light was directed through a movable optical grating to generate a fine-striped interference pattern on the sample plane. The pattern was shifted laterally through five phases and three angular rotations of 60° for each z section. The 405, 488 568 and 642 nm laser lines were used during acquisition and the optical z sections were separated by 0.125 μm. The laser power was attenuated to 10%–50% and exposure times were typically between 5 and 100 ms. The power of each laser was adjusted to achieve optimal intensities of between 5,000 and 8,000 counts in a raw image of 15-bit dynamic range at the lowest laser power possible to minimize photobleaching. Multichannel imaging was achieved through sequential acquisition of wavelengths by separate cameras. Raw 3D-SIM images were processed and reconstructed using the DeltaVision OMX SoftWoRx software package (v.7.0.0 release RC 6, GE Healthcare). The resulting size of the reconstructed images was 1,024 × 1,024 pixels from an initial set of 512 × 512 raw images. The channels were aligned in the image plane and around the optical axis using predetermined shifts as measured using a target lens and the SoftWoRx alignment tool. The channels were then carefully aligned using an alignment parameter from control measurements with 0.5 µm diameter multi-spectral fluorescent beads (Invitrogen, Thermo Fisher Scientific).

Ex vivo two-photon calcium imaging of mouse retinal ganglion cells

For ex vivo calcium imaging, we expressed a Cre-dependent GCaMP8m calcium indicator (Addgene, 162378) specifically in retinal ganglion cells using ProA5 promoter using AAVs[76]. Two-photon live imaging of ganglion cells was performed as previously described[77]. In brief, retinas from light-adapted mice were isolated under red dim light in oxygenated Ringer’s medium. Isolated retinas were mounted with the ganglion-cell-side up on filter paper with an aperture of 1.5 × 3 mm positioned under the ventral side of the retina. The retina was perfused in a recording chamber with Ringer’s medium with a flow rate of 1.5 ml min−1 at 35 °C for the entire duration of the experiment. The ganglion cell bodies in the ventral retina were imaged using a custom-built two-photon imaging system equipped with a Mai Tai two-photon laser (Spectra Physics) tuned to 920 nm, a ×60 water-immersion objective (Fluor, 1.0 NA, Nikon) and a 510/20 nm band-pass emission filter. The laser power at the sample was kept lower than 20 mW. The system acquired images of 200 × 200 µm at 1 µm px−1 at a frame rate of 5.71 Hz. For measuring the light responses, light stimulation (LED 405 nm) of a spot (250 µm diameter) was delivered onto the retinal region inside of the paper filter aperture for 2 s. The light intensities for stimulation (100%) and background (0%) were 134 and 0.153 µW cm−2, corresponding to 2.31 × 10^5 and 2.64 × 10^2 R* s−1, respectively. The stimulation light was turned on during the fly-back periods of the horizontal scanning mirror to minimize PMT noise originating from the stimulation light. Each light stimulation was repeated three times.

The data were analysed with Python and MATLAB. Ganglion cell bodies were segmented manually. To compute ∆F/F of each region of interest, the background was first calculated as the mean of the 10% dimmest pixels in each frame, and this was subtracted from all pixels. Then, all of the pixels in each region of interest were averaged in each frame and ∆F/F was calculated. F was the baseline calculated as the mean fluorescence within the 2 s time window before the stimulation onset. Three repetitions were averaged to give the mean ∆F/F curve. The amplitude of the response was calculated as the maximum ∆F/F in the 2 s time window after the onset/offset of the light stimulation. Those peaks with signal-to-noise ratio (SNR) higher than 2 were considered as light responses, where SNR is the response amplitude/s.d. of the baseline.

Immunostaining of cultured cells, organoids and mouse and human retinas

The blocking solution used for the immunostaining contained 10% normal donkey serum (NDS, Sigma-Aldrich, S30-M), 1% BSA (Sigma-Aldrich, A4503), 0.5% Triton X-100 (Sigma-Aldrich, 93443) and 0.01% sodium azide (Sigma-Aldrich, S2002) in PBS. The staining solution consisted of PBS containing 3% NDS, 1% BSA, 0.5% Triton X-100 and 0.01% sodium azide in PBS, and was supplemented with primary antibodies.

Cultured cells were fixed for 30 min in 4% PFA, washed three times with PBS and blocked for 1 h in blocking solution. The blocking solution was exchanged with staining solution and the cells were stained with the primary antibodies at 4 °C overnight. The cells were then washed with PBS and stained for 1 h at room temperature with secondary antibodies diluted in the staining solution. The cells were stored and imaged in PBS; the images were acquired within several days. For long-term storage, 0.01% sodium azide was added to the PBS.

Organoids and mouse and human retinas were fixed for 1 h in 4% paraformaldehyde diluted in PBS. The tissues were washed in PBS and placed in 30% sucrose in PBS for cryopreservation. Before sectioning or staining, the tissues underwent three freeze–thaw cycles using dry ice. The sucrose was then removed by washing several times with PBS. For mouse and human whole-mount retinas, the tissue was blocked for 1 h in blocking solution. For organoids, they were embedded in 3% agarose (Lonza, 50110), cut into 80–100μm vibratome sections (Leica, VT1000S vibratome) and blocked for 1 h in blocking solution. The tissue was then stained for 3–5 days at room temperature with primary antibodies in the staining solution. After washing with PBS, the tissue was stained with secondary antibodies in the staining solution for 2 h at room temperature and mounted on Super-Frost Ultra Plus GOLD slides (Thermo Fisher Scientific, 11976299), embedded with ProLong Gold antifade mounting medium (Thermo Fisher Scientific, P36934).

The primary antibodies used in this study for immunostaining were as follows: polyclonal chicken anti-GFP (1:500, Thermo Fisher Scientific, A10262), polyclonal chicken anti-RFP (1:500, Rockland, 600-901-379), polyclonal rabbit anti-OLLAS (1:500, Genscript, A01658), polyclonal rabbit anti-TagRFP (1:250, Evrogen, AB233), polyclonal rabbit anti-TOMM20 (1:500, Sigma-Aldrich, HPA011562), polyclonal rabbit anti-CD71 (1:250, Thermo Fisher Scientific, PA5-85936), monoclonal mouse anti-Rhodopsin (1:500, Sigma-Aldrich, R5403), polyclonal rabbit anti-SNAP (1:200, NEB, P9310S), monoclonal mouse anti-MT-CO1 (1:250, Thermo Fisher Scientific, 459600), monoclonal mouse anti-CD31 (1:500, BD Biosciences, 550389), monoclonal mouse anti-CD90 (1:250, Thermo Fisher Scientific, 14-0909-82), monoclonal mouse anti-TUBB3 (1:250, BioLegend, 801202), polyclonal rabbit anti-RBPMS (1:500, phosphosolutions, 1830-RBPMS) and monoclonal rabbit anti-CD73 (1:250, Abcam, AB133582).

The secondary antibodies used in this study for immunostaining were as follows: Alexa Fluor 647 goat anti-alpaca IgG (against VHH domain, Jackson, 128-605-232), Alexa Fluor 405 donkey anti-rabbit IgG (H+L, Thermo Fisher Scientific, A48258), Alexa Fluor 488 donkey anti-rabbit IgG (H+L, Thermo Fisher Scientific, A21206), Alexa Fluor 568 donkey anti-rabbit IgG (H+L, Thermo Fisher Scientific, A10042), Alexa Fluor 647 donkey anti-rabbit IgG (H+L, Thermo Fisher Scientific, A31573), Alexa Fluor 488 donkey anti-mouse IgG (H+L, Thermo Fisher Scientific, A21202), Alexa Fluor 568 donkey anti-mouse IgG (H+L, Thermo Fisher Scientific, A10037), Alexa Fluor 647 donkey anti-mouse IgG (H+L, Thermo Fisher Scientific, A31571), Alexa Fluor 488 goat anti-chicken IgY (H+L, Thermo Fisher Scientific, A11039) and Alexa Fluor 568 goat anti-chicken IgY (H+L, Thermo Fisher Scientific, A11041). All secondary antibodies were used at a dilution of 1:500.

Antibodies used for pluripotency validation are described previously in the ‘Reprogramming and characterization of LHON iPS cells’ section. For mitochondria staining, MitoTracker Deep Red was used (50 nM, Thermo Fisher Scientific, M22426) For actin filament staining, Phalloidin conjugated to Alexa Fluor 488 was used (1:1,000, Abcam, ab176753). For lysosome labelling, pHLys Red (1:1,000, Dojindo, L266-10) and LysoTracker Deep Red (5 nM, Thermo Fisher Scientific, L12492) dyes were used. To label early endosomes, CellLight Early Endosomes-RFP baculovirus (Thermo Fisher Scientific, C10587) was used according to the manufacturer’s instructions.

ELISA

All assays were run in 384-well Maxisorp plates (Nunc). All reagents had a volume of 25 µl. Immobilization of capturing reagents or target proteins was performed at the following concentrations in PBS over night at 4 °C: neutravidin (1.25 µg ml−1, Invitrogen, A2666), anti-hFc antibody (1 µg ml−1, Southern Biotech, 2048-01), fluorescent proteins (eGFP, mCherry, dsRed and tdTomato all 5 μg ml−1, produced in house), anti-GFP VHH (2 µg ml−1, produced in house), TOMM70 (1 μg ml−1, produced in house), TOMM20 (1 µg ml−1, produced in house), purified mitochondria (5 µg ml−1, produced in house). Blocking was performed by adding 100 µl of PBS with 1% BSA for 1.5 h at room temperature. All of the following protein steps were performed in PBS with 0.05% Tween-20 and 1% BSA for >30 min at room temperature. After each step, the plates were washed three times with 70 µl PBS with 0.05% Tween-20 (six times after detection antibody). The following proteins were used in the proteins steps in different combinations: biotinylated TOMM70 (1.8 µg ml−1, produced in house, see above), hCD71-Fc (1.5 µg ml−1, Sino Biological, 11020-H01H), avi-tagged mCherry (2.5 µg ml−1, produced in house), MYC-tagged mCherry (2 µg ml−1, produced in house), VHHs and VHH bispecifics (50 nM or concentration series, produced in house, see above), mouse serums (dilution series starting at 1:200). Positive control antibodies were as follows: goat anti-alpaca IgG, VHH domain (Jackson IR, 128-005-230, 1:1,000), anti-TOMM70 antibody (SantaCruz, sc-390545, 1:1,000), anti-TOMM20 (SantaCruz, sc-17764, 1:2,000). Detection antibodies were as follows: anti-human IgG Fc antibody (HRP) (Genscript, A01854-40, 1:5,000), anti-His-tag antibody (HRP) (Genscript, A00612, 1:2,500), anti-MYC antibody (HRP) (Genscript, A00863, 1:5,000) and rabbit anti-goat IgG(H+L)-HRP (Southern Biotech, 6160-05, 1:5,000). 1-Step Ultra TMB-ELISA substrate (Thermo Fisher Scientific, 34024) or OBP substrate (Thermo Fisher Scientific, 34006) was added for 5–10 min and stopped with 2 M sulphuric acid. Absorbance was measured at 450 nm (TMB substrate) or 490 nm (OBP substrate) and plotting and fitting were performed in GraphPad Prism 10.

mtDNA sequencing of patient-derived iPS cell clones

Total DNA from iPS cells was isolated using the DNeasy Blood & Tissue Kit (Qiagen, 69504). Furthermore, mtDNA was amplified using the REPLI-g Mitochondrial DNA Kit (Qiagen, 151023). Primers for human mtDNA amplification were included in the kit. The amplified product was column purified using the DNA Clean & Concentrator-5 Kit (Zymo Research, D4014) and eluted in ultrapure water. All of the above-mentioned procedures were performed according to the manufacturer’s instructions. Whole-genome sequencing was performed at CeGaT using the NovaSeq 6000 (Illumina) system as 100-bp paired-end reads.

Genomic mapping, identification of genetic variations and variant annotation

For each sample, we aligned the raw genome-sequencing data to the human reference genome sequence (build hg19/GRCh37) using BWA-MEM (v.0.7.17)[78]. The alignment results in SAM format were converted to the BAM format using the SortSam tool from the Picard suite (v.2.14.0-SNAPSHOT), and samtools (v.1.10) for indexing. We identified duplicate reads in the BAM files using Picard’s MarkDuplicates module. On the basis of the GATK Best Practices workflow[79], base quality score recalibration (BQSR) was performed (v.4.1.4.1), followed by variant calling using HaplotypeCaller in gVCF mode[80]. The gVCF files from each sequencing batch were first merged with CombineGVCFs. Subsequently, these merged gVCF files were converted into a single VCF file per batch using GenotypeGVCFs[81]. We used VariantRecalibrator and ApplyVQSR[81] to recalibrate the quality of single-nucleotide polymorphisms and insertions–deletions (indels). The view and norm functions in bcftools (v.1.10.2) were applied to process each VCF file. Annotation of variants obtained from genome sequencing was performed by ANNOVAR[82].

Proteomics for iHNeurons transplanted with mitochondria

Cells were lysed with buffer containing 100 mM TEAB pH 8.5, 5% SDS, 10 mM TCEP by heating for 10 min at 95 °C, followed by 10 min sonication (30 s on, 30 s off per cycle) on a Pixul system (Active Motif, 53130) using the following settings: Pulse[N]: 50; PRF[kHz]: 1; burst rate[Hz]: 20. Protein extracts were alkylated using 15 mM iodoacetamide at 25 °C in the dark for 30 min. For each sample, 100 µg of protein lysate was captured, digested and desalted using S-Trap cartridges (Protifi, CO2-micro). In brief, samples were acidified by addition of 10% TFA (1:10) and then S-trap buffer (90% methanol, 100 mM TEAB pH 7.1) was added to the samples (6:1). Samples were briefly vortexed and loaded onto S-trap micro spin-columns (Protifi, CO2-micro) and centrifuged for 1 min at 4,000g. The flow-through was discarded and spin-columns were then washed three times with 150 µl of S-trap buffer (each time samples were centrifuged for 1 min at 4,000g and flow-through was removed). S-trap columns were then moved to the clean tubes and 20 µl of digestion buffer (50 mM TEAB pH 8.0) and trypsin (at 1:25 enzyme to protein ratio) were added to the samples. Digestion was allowed to proceed for 1 h at 47 °C. Next, 40 µl of digestion buffer was added to the samples and the peptides were collected by centrifugation at 4,000g for 1 min. To increase recovery, S-trap columns were washed with 40 µl of 0.2% formic acid in water (400g, 1 min) and 35 µl of 0.2% formic acid in 50% acetonitrile. Eluted peptides were dried under vacuum and stored at −20 °C until further processing. Moreover, phosphopeptides were enriched using AssayMAP Fe(III)-NTA cartridges, but these enriched fractions were not included in the final analysis. The peptides were subjected to LC–MS/MS analysis using the Orbitrap Exploris 480 Mass Spectrometer fitted with an Vanquish Neo (both Thermo Fisher Scientific) and a custom-made column heater set to 60 °C. Peptides were resolved using a RP-HPLC column (75 μm × 30 cm) packed in-house with C18 resin (ReproSil-Pur C18–AQ, 1.9 μm resin; Dr. Maisch) at a flow rate of 0.2 μl min−1. Separation of peptides was achieved using the following gradient: from 2% buffer B to 10% buffer B over 5 min; to 35% buffer B over 45 min; to 50% buffer B over 10 min. The mass spectrometer was operated in data-independent acquisition (DIA) mode. Each MS1 scan was acquired at a resolution of 120,000 FWHM (at 200 m/z) and a scan mass range from 390 to 910 m/z. The maximum injection time was set to auto mode with a normalized AGC target set to 300%. Each survey scan was followed by a set of DIA scans acquired at a resolution of 15,000 FWHM. Precursor mass range was set to 400–900 m/z, the isolation windows size to 10 m/z and the HCD normalized collision energy to 28%. For MS2 scans, the maximum injection time was set to 22 ms with a normalized AGC target set to 3000%. The acquired raw files were searched using SpectroNaut (v.19.0, directDIA workflow, default settings) against a human database (downloaded from UniProt on 22 February 2022) using the following search criteria: full tryptic specificity was required (cleavage after lysine or arginine residues, unless followed by proline); three missed cleavages were allowed; carbamidomethylation (C) was set as a fixed modification; oxidation (M), N-acetylation (N-term) The identified transitions were exported as .tsv files and processed using the MSstats R package (v.4.0.1)[83].

Proteomics analysis

Data were normalized by sample-wise equalizing medians and missing values were retained without imputation. Limma (v.3.62.2)[84] was used to identify differentially abundant proteins for each pairwise comparison of treatments based on t-tests with empirical Bayes variance moderation. Pre-rank gene set enrichment analysis was performed for each comparison using clusterProfiler (v.4.14.0)[85], incorporating gene ontologies for biological processes, molecular functionality and cellular components. Proteins were ranked based on the log-transformed fold change estimated by limma, with no log fold change threshold applied. Benjamini–Hochberg adjustment was performed to correct for multiple testing. The most significant terms in the anti-GFP nanobody treatment protein data versus untreated cells comparison were selected for visualization. Terms that were child terms of other selected terms were removed. To assess the degree of mitochondrial enrichment compared with other cellular compartments, gene set enrichment analysis was performed on custom protein lists of known marker proteins (Supplementary Table 2).

Single-cell FLASH-seq in transplanted LHON iHNeurons

GFP-positive LHON cells were collected 2 days after mitochondrial transplantation by single-cell picking, and full-length scRNA-seq was performed. Strip tubes containing 4.5 µl of guanidine lysis buffer were prepared for single-cell collection. The buffer consisted of 248 mM GuHCl, 6 mM dNTPs, 1.8 µM oligo-dT, 1.2 mM DTT, 1 M betaine and nuclease-free water. Then, 2 days after mitochondrial transplantation, cells were washed and perfused with Ringer solution (110 mM NaCl, 2.5 mM KCl, 1 mM CaCl_2, 1.6 mM MgCl_2, 22 mM NaHCO_3 and 10 mM d-glucose) bubbled with 95% O_2 and 5% CO_2, at 37 °C. The flow rate was maintained at 1.5 ml min−1. The presence of GFP was assessed using a collimated LED (470 nm, 487 mW). Glass pipettes of diameter 10–20 µm were used to collect GFP+ cells by placing the pipette near the cell and applying negative pressure. For trypsin pretreatment experiments, 0.05% trypsin-EDTA (Thermo Fisher Scientific, 25300-054) was applied locally to a single cell through a glass micropipette for 1 min without perfusion. The application pipette was then withdrawn and the cell was collected with a second glass pipette. Cells were transferred to tubes containing lysis buffer and immediately stored at −70 °C. The samples were then processed according to the FLASH-seq protocol86,87, starting with sample denaturation. RNA from LHON cells transplanted with mito-GFP (LHON_mito_transplanted_2d) and control non-transplanted LHON cells (Control_LHON_no_mito_transplanted) was converted to cDNA using Superscript IV (Thermo Fisher Scientific, 18090200). RNA from LHON cells transplanted with mito-GFP and treated with trypsin-EDTA (LHON_mito_transplanted_2d_protease) and untreated controls (Control_LHON_no_mito_transplanted_protease), and LHON cells transplanted with mtDNA-free mito-GFP (LHON_mito_transplanted_2_empty_mito) was converted to cDNA using Maxima H Minus Reverse Transcriptase (Thermo Fisher Scientific). All cDNA was amplified using the KAPA HiFi HotStart (Roche, KK2602) and then cleaned using a 0.8× ratio of homebrew SeraMag beads in 18% PEG (Cytivia, GE24152105050250). cDNA concentrations and quality were measured using the Qubit 1× dsDNA High Sensitivity Assay Kit (Thermo Fisher Scientific, Q33231) and the Agilent Bioanalyzer (Agilent, 5067-4626). The cDNA was normalized to 200 pg μl−1 before tagmentation using 0.2 μM of homemade Tn5[86]. The reaction was halted with 0.2% SDS. An indexing PCR was performed to add index adaptors carrying an 8-bp barcode (IDT Technologies) using KAPA HiFi reagents (Roche, KK2102). Libraries were pooled in equal volumes and a final 0.8× cleanup was performed with homebrew SeraMag beads before measuring sample concentration and quality. LHON_mito_transplanted_2d and Control_LHON_no_mito_transplanted libraries were sequenced on the NextSeq 550 (Illumina) system to either 75- or 100-bp single-end reads with 8-bp dual indexes. LHON_mito_transplanted_2d_protease were sequenced on either the NextSeq 550 (Illumina) or AVITI (Element Biosciences) system to 75-bp paired-end reads with 8 bp dual indexes. LHON_mito_transplanted_2d_empty_mito libraries were sequenced on the NextSeq550 (Illumina) system to 75-bp paired-end reads with 8-bp dual indexes.

Single-cell transcriptome analysis

Sequencing reads were processed to obtain gene counts using Snakemake (v.7.21.0)[88], a robust workflow management system. The workflow comprised three key steps. The first step is quality control. The tool BBDuk from BBMap (v.39.01) was used for removing remaining sequencing adapters, while Trimmomatic (v.2.6.0)[89] was used for cropping potentially low-quality bases from the 3′-end and removing short reads such that reads had at most 75 bp and at least 45 bp. In a second step, reads were aligned to the GRCh38 reference genome (Ensembl release 110) using STAR (v.2.7.10b)[90]. This yielded two essential outputs: alignments translated into transcript coordinates (--quantMode TranscriptomeSAM) and the quantification of read counts per gene (--quantMode GeneCounts). Gene counts per cell were subsequently aggregated using custom Python scripts. The third step includes downstream quality control and counting of nucleotide polymorphisms within the MT-ND4 gene. Before variant counting, duplicate reads were marked with Samtools markdup (v.1.17) to avoid inflation of nucleotide counts. Quality assurance measures were implemented by calculating, for each sequencing run, the mean and s.d. of per-sample read depth at the MT-ND4 gene and flagging samples with mean depth below one s.d. from the group mean, or—when this threshold was non-positive—below the fifth percentile of the group distribution. Moreover, we retained only samples with a read depth in MT-ND4 of greater than 30. For the MT-ND4 gene, nucleotide counts were calculated while excluding marked duplicates and spliced alignments spanning the MT-ND4 region to retain only variant-supporting reads. We then estimated the fraction of donor mitochondria using the frequency of MT11778G relative to the total of MT11778G and MT11778A.

Single-cell annotation followed the strategy outlined in a previously published study[33], with initial quality-control filtering to remove cells with fewer than 200 expressed genes and genes expressed in fewer than three cells. Using the Leiden algorithm (leidenalg, v.0.10.1), we clustered single cells based on a neighbourhood graph constructed from highly variable genes. Highly variable genes were called using a batch-aware procedure, with batches defined based on the experimental batch. We ran Scanpy’s (v.1.9.5) highly variable genes routine with the default settings, except for 40 mean-variance bins, and selected the top 200 genes per batch; a gene was retained as a highly variable gene if it was called variable in ≥2 batches. During the embedding and clustering stages, transcripts from mitochondrial and ribosomal protein-coding genes—commonly characterized by high expression and dispersion, independent of cell type identity—were excluded. Cells were then manually annotated into major types using established markers[33].

For downstream analyses, we focused on neurons and retained cells with ≥1,000 expressed genes, which led to the removal of 24 low-quality cells (5 control cells, 0 transplanted with donor mitochondria, 15 transplanted with donor mitochondria and then trypsin-treated, and 4 transplanted with mtDNA-depleted, ρ^0, donor mitochondria). We used DESeq2 (v.1.38)[91] to identify genes that were differentially expressed dependent on the frequency of donor mitochondria. This analysis used a log_2-transformed fold change threshold of 1 and a 5% significance level after the Benjamini–Hochberg correction for multiple-hypothesis testing. Next, the GSEApy (v.1.0.6)[92] pre-rank functionality was used for gene set enrichment analysis, incorporating gene ontologies for biological processes, molecular functionality and cellular components. In particular, the genes were ranked based on the Wald test statistic from DESeq2.

Measurement of an individual mitochondria area in TEM images

iHNeurons with transplanted mitochondria (miniSOG-mito-GFP) were localized, photooxidized and imaged under EM as described above. After photo-oxidation by blue light and oxygen, miniSOG generated a strong dark stain with high contrast visible under light and electron microscopes; thus, mitochondria were classified as miniSOG-positive and miniSOG-negative based on contrast levels. Mitochondria area in TEM images was measured in ImageJ by manually selecting ROIs in the photooxidized and neighbouring cells.

Quantification of LHON iHNeuron survival after glycolysis inhibition

The survival rate of LHON iHNeurons after glycolysis inhibition was evaluated by quantifying tdTomato+ cells in a gridded dish. The imaging set-up included one channel to detect tdTomato+ cells, a second bright-field channel to locate cells within the gridded dish for easier tracking over time, and a third channel to monitor the fluorescence of donor mitochondria (mito-GFP) in transplanted iHNeurons. For each biological replicate, images were captured from three different locations on the gridded dish. At the outset (day 0), control samples with tdTomato+ cells were defined as ROIs, with 30–50 cells per dish, and monitored for changes over a 2-day period. Similarly, in the day 0 mito-GFP transplanted samples, ROIs were established for tdTomato-and mito-GFP-positive cells and tracked for the same duration. The leak of tdTomato fluorescence from the cells served as an indicator of cell death. The cell survival rate was defined as the ratio between number of ROIs at day 2 and number of ROIs at day 0.

Quantification of retinal ganglion cell survival after optic nerve injury

The survival rate of retinal ganglion cells following optic nerve injury (ONC) was assessed by quantifying the density of tdTomato-labelled cells in the retina positive for RBPMS, a retinal ganglion cell marker. Quantification was conducted manually using the Cell Counter plugin in ImageJ. For each whole-mounted retina, images were captured at eight locations using a ×20 objective, encompassing both central and peripheral regions. In each image, five ROIs (100 µm × 100 µm) were selected to ensure comprehensive coverage of the entire retinal area. The numbers of counted cells were averaged per retina and shown as cell density (cells per mm^2).

Quantification of RGC axonal projections to the dLGN after optic nerve injury

To assess the absence of axonal projections from retinal ganglion cells to the dorsal lateral geniculate nucleus (dLGN) after ONC, the fluorescence intensity of retinal ganglion cell axon terminals in the dLGN was quantified using ImageJ. For ONC samples, mice with retinas correctly injected with cholera toxin were selected prior to brain analysis. Images of the dLGN were captured in three different brain slices from both control and ONC mice using a ×10 objective. The dLGN was outlined as a ROI based on cell density, while another ROI was selected outside the dLGN for background measurements. Mean grey values were measured within both the dLGN and the background ROIs. A ratio was calculated by dividing the mean grey value obtained from the dLGN ROI by that of the background ROI. This ratio reflects the normalized fluorescence intensity within the dLGN region, accounting for background fluorescence or noise. The three values obtained from each subject were averaged to represent a sample.

Mitochondrion enrichment in HEK293T cells, iHNeurons, blood vessel organoids, and mouse and human retina

Mitochondrion enrichment in cells was determined using custom-written routines in MATLAB (MathWorks). Per experiment, several (2–5) images were acquired at different spatial locations on the biological sample. Each image contained a channel representing cell-type-specific surface marker fluorescence (further referred to as marker-positive and marker-negative), a second channel representing donor mitochondria fluorescence and the last channel labelling all cells in the image.

First, for each acquired image, marker-positive and marker-negative cells were manually chosen as ROIs in ImageJ (https://imagej.net). Then, all channels as well as ROIs were loaded into MATLAB. The mean mitochondria fluorescence was computed for each ROI. To correct for non-specific background fluorescence in the acquired mitochondria images, as is commonly done for neuropil correction in calcium imaging[93], the median fluorescence within a 10-pixel-wide ring at 10-pixel distance from the ROI edge was subtracted from the computed mean mitochondria fluorescence for each ROI. To ensure that the ring does not contain other marker-positive cells, only the quarter of pixels with the lowest marker fluorescence was included. After obtaining a single corrected mitochondria fluorescence value for each marker-positive and marker-negative cell, the mitochondrial enrichment of the whole image was defined as the ratio between the mean of the corrected mitochondria fluorescence in marker-positive cells and the mean of the corrected mitochondria fluorescence in marker-negative cells. Moreover, the percentage of marker-positive cells significantly enriched in mitochondria was defined as the fraction of marker-positive cells with a corrected mitochondria fluorescence of more than 2 s.d. above the mean of the corrected mitochondria fluorescence of marker-negative cells.

For mitochondrial targeting experiment involving cytosolic tdTomato cells in HEK293T cells (Extended Data Fig. 1) additional analysis was performed. Owing to strong expression of tdTomato, it led to substantial bleedthrough in the green mitochondria channel; therefore, a linear regression correction was applied. The contribution of tdTomato fluorescence to the green signal was estimated by considering pixels which simultaneously had more than median intensity in the red channel (to only consider tdTomato-positive locations) and less than the 99th-percentile intensity in the green channel (to ignore a true mitochondria signal). The green intensity contributed by tdTomato fluorescence was then determined by a linear fit with offset and removed from the green channel. Negative values were set to 0. Finally, the normal enrichment algorithm comparing mitochondria fluorescence in tdTomato+ and tdTomato− cells was implemented as usual.

Mitochondrial enrichment in human cardiac cells, fibroblasts and HUVECs

Mitochondrial enrichment in the cells was computed slightly differently than previously described, given that there were no marker-negative cells. As before, several (2–5) images were acquired per experiment at different spatial locations on the biological sample. Each image contained a channel representing cell-type-specific surface marker fluorescence and a second channel representing donor mitochondria fluorescence.

For each acquired image, marker-positive cells were manually chosen as ROIs in ImageJ (https://imagej.net). Given that no marker-negative cells could be chosen that way, they were instead randomly simulated. For each marker-positive ROI in a given image, a marker-negative ROI was defined by randomly rotating and repositioning the marker-positive ROI mask in the original image. It was ensured that simulated ROIs did not overlap each other or the original marker-positive ROIs. Moreover, for each simulated ROI, it was verified that they were marker-negative by ensuring that their mean fluorescence was below an intensity-based threshold dependent on the mean marker fluorescence of the marker-positive ROIs in that image. For human cardiomyocytes, the threshold was defined as one fifth of the median fluorescence intensity across all marker-positive ROIs. For samples of endothelial cells in which the background fluorescence was higher, the threshold was defined as half of the median fluorescence intensity across marker-positive ROIs. The random simulation algorithm was repeated until it converged and produced the right number of marker-negative ROIs satisfying all defined criteria. The mitochondrial enrichment in marker-positive and marker-negative ROIs was then computed as described above.

Given that marker-negative ROIs were, by design, randomly computed, the randomization procedure was repeated ten times independently for each image. The obtained values of mitochondrial enrichment and percentage of significantly enriched cells were then averaged across repeats from a single image before being combined across images from the same experiment as before.

Mitochondrial enrichment in retinal organoids

Mitochondrial enrichment in retinal organoids was computed differently. For each organoid, several (2–3) organoid sections were acquired for analysis. Each image contained a channel representing photoreceptor layer, a second channel representing all cells and a third channel representing mitochondria fluorescence. For this dataset, the total enrichment of mitochondria fluorescence in the photoreceptor layer was computed. For each image, photoreceptor-positive and photoreceptor-negative regions were selected manually in ImageJ. Mean fluorescence of the channel representing mitochondria for each ROI was computed. The mitochondrial enrichment of the whole image was defined as the ratio between the mean of the nanobody fluorescence in photoreceptor-positive ROI and the mean of the nanobody fluorescence in photoreceptor− ROI. Obtained enrichment values were averaged across several images of a given sample.

Mitochondrial enrichment in T cells

Data for targeting T cells were acquired by flow cytometry, not confocal imaging. Thus, the mitochondrial enrichment in these cells were quantified differently. The targeting was performed on non-activated CD8+ T cells, activated CD8+ T cells overexpressing CD71, and non-activated CD4+ cells. Depending on the experiment, the CD8+, CD71+ or CD4+ populations were first identified and then the fraction of cells positive for mito-DsRed2 within this population was defined. Gating strategies to define the cell populations are described in the ‘Flow cytometry’ section.

TagBFP (destabilized nanobody fused to TagBFP) enrichment in iHNeurons expressing either GFP or TdTomato in fixed cells

Per experiment, at least three images were acquired at different spatial locations of the biological sample. Each image contained a channel representing GFP fluorescence, a second channel representing destabilized nanobody fused to TagBFP and the last channel labelling all neurons in the image. Enrichment of destabilized nanobody was computed as the previously described mitochondrial enrichment quantifying TagBFP fluorescence enrichment in GFP-positive versus GFP-negative cells.

TagBFP enrichment in iHNeurons expressing either GFP or tdTomato in live cells

Enrichment of destabilized nanobody during live imaging of GFP-positive and tdTomato-positive cells was computed in a different way to the fixed samples, as it was not possible to trivially identify GFP- or tdTomato-negative cells. Thus, an approach was used based solely on an automated image segmentation procedure, subdividing the GFP or tdTomato fluorescence image into three distinct regions (GFP positive or tdTomato positive, intermediate and GFP negative or tdTomato negative) depending on the relative local fluorescence in the image, taking into account possible heterogeneity in background fluorescence.

To determine GFP- or tdTomato-positive regions, the GFP or tdTomato fluorescence image was first smoothed by a relatively large 2D-Gaussian filter of 30-pixel s.d. The filtered image was then subtracted from the original image to estimate local fluorescence above the background. The resulting image underwent a combination of small morphological filtering operations (closing followed by opening with a circular 3-pixel mask) to remove small noise in the processed image. The outcome was then thresholded based on intensity (40 in the case of neurons and 20 in the case of HEK293T cells) and finally dilated by a morphological circular filter of pixel size 2.

To estimate conservative GFP- or tdTomato-negative regions, a similar method was used by first determining an intermediate region computed by finding all pixels above a lower intensity threshold than before (10 in the case of neurons and 5 in the case of HEK cells). Smaller morphological filters (circular 1 pixel mask) were used to remove less noise. Moreover, the intermediate region was dilated by a large circular filter of pixel size 20. The GFP- or tdTomato-negative region was defined as any part of the image that is not part of the intermediate region or the GFP-positive or tdTomato-positive region.

Enrichment of destabilized nanobodies was finally computed as the ratio between the mean nanobody fluorescence in the GFP- or tdTomato-positive regions and the GFP- or tdTomato-negative regions. As before, to obtain a single value of this quantity per experimental repetition, the nanobody enrichment was averaged across 2–5 acquired images of the same sample.

TagBFP enrichment in iHNeurons transplanted with mito-GFP

Per experiment, at least three images were acquired at different spatial locations of the biological sample. Each image contained a channel representing cell-surface nanobody fluorescence, a second channel representing destabilized nanobody fused to TagBFP and the last channel labelling all neurons in the image. Enrichment of destabilized nanobodies was computed in the same way as the previously described mitochondrial enrichment quantifying TagBFP fluorescence enrichment in nanobody-positive versus nanobody-negative cells.

Mitochondrial targeting specificity

We defined the specificity of mitochondrial targeting as the ratio between the percentage of donor-mitochondrion-positive cells targeted with a specific binder and the sum of the percentages of donor-mitochondrion-positive cells targeted by both specific and control binders. To normalize this index, the ratio was first subtracted by a value of 0.5 and then multiplied by 2 to have values ranging from −1 to 1. A value of 1 indicated maximal specificity, 0 indicated no specificity and −1 indicated maximal negative specificity.

Co-localization of donor mitochondria with a mitochondrial marker

The co-localization of donor mitochondria with a mitochondrial marker (TOMM20 or MT-CO1) was determined using custom-written routines in MATLAB (MathWorks). First, TOMM20- or MTCO1-positive cells were manually chosen as ROIs in ImageJ (https://imagej.net) before loading the donor mitochondria and mitochondrial marker images, as well as the ROIs, into MATLAB. Then, both the donor mitochondria and mitochondrial marker images were thresholded based on their intensity: 1 s.d. above the mean of the donor mitochondria fluorescence for HEK293T cells, 0.5 s.d. above the mean of the donor mitochondria fluorescence for iHNeurons and heart cells; 3 s.d. above the mean of the mitochondrial marker fluorescence for HEK293T cells and 1 s.d. above the mean of the mitochondrial marker fluorescence for iHNeurons and heart cells, to obtain binary images. For each ROI, the fraction of donor-mitochondrion-positive pixels among TOMM20- or MT-CO1-positive pixels was computed to assess the amount of colocalization. TOMM20- or MT-CO1-negative pixels, or pixels outside of ROIs were ignored in the analysis.

To check for unspecific co-localization values that would increase with increasing amounts of donor mitochondria within ROIs, a spatial randomization method was applied. For any given ROI, the donor-mitochondrion-positive pixels were spatially randomized within the ROI, while the location of TOMM20- or MT-CO1-positive pixels was maintained constant. The colocalization value was then computed as before. The randomization process was repeated 1,000 times for each ROI and the final unspecific co-localization value defined as the mean across the 1,000 iterations.

Co-localization of donor mitochondria displaying SNAP-tags with BG–anti-CD31 or BG–IgG1

The co-localization of SNAP-tags with BG-conjugated anti-CD31 antibodies or IgG1 isotype control was performed as discussed above. Images contained a channel representing SNAP-tag fluorescence and a channel representing either anti-CD31 or IgG1 fluorescence. The values were averaged across two to five acquired images of the same sample.

Co-localization of donor mitochondria with endosomes and lysosomes

To determine endosome/lysosome-free mitochondria, a procedure similar to the colocalization analysis of donor mitochondria with mitochondrial markers described previously was employed. Per field of view, one cell was selected for analysis and a background region was designated for threshold determination. Within the selected cell, pixels were classified as mitochondrion-positive if their fluorescence intensity exceeded the mean background fluorescence by more than 5 s.d. in the mitochondrion-containing channel. Similarly, pixels were classified as endosome or lysosome positive if their fluorescence intensity exceeded the mean background fluorescence by more than 5 s.d. in the endosome/lysosome-containing channel. To assess the colocalization rate, the fraction of endosome- or lysosome-positive pixels among mitochondrion-positive pixels was computed. The proportion of endosome/lysosome-free mitochondria was calculated as 1 minus the colocalization rate. The abundance of endosome/lysosome-free mitochondria was then calculated by multiplying this proportion by the total surface area covered by mitochondria in the selected cell.

Quantification of transplanted mito-GFP accessibility in iHNeurons

Mito-GFP accessibility was evaluated in fixed iHNeurons expressing the anti-GFP nanobody and destabilized nanobodies fused to TagBFP (dGBP1–TagBFP). Mitochondrial accessibility in cells was determined using custom-written routines in Python. The proportion of transplanted mitochondria labelled with GFP (mito-GFP) positive for TagBFP was computed. Images of individual neurons expressing the anti-GFP nanobody and positive for mito-GFP were used for the analysis. Each image contained a channel representing anti-GFP nanobody fluorescence, a second channel representing donor mitochondria fluorescence (mito-GFP) and the last channel representing TagBFP fluorescence (destabilized nanobody). For each neuron, the cell area was annotated as an ROI in ImageJ and used for mito-GFP segmentation. Mito-GFP within a cell were segmented using a custom-trained model in Cellpose[94]. Segmented objects from the mito-GFP channel were mapped onto the TagBFP channel. Segmented objects of less than 3 pixels were excluded from the analysis. For each segmented object, a 5-pixel-wide ring was used to compute the median background fluorescence (TagBFP). Overlapping segmented objects were removed from the background fluorescence measurement. The ratio between the median TagBFP fluorescence in segmented objects and TagBFP fluorescence in the defined background area was used as a contrast measurement. The segmented objects with a contrast value higher than 1 were considered positive for TagBFP.

Quantification of TagBFP co-localization with mito-GFP in live-imaged iHNeurons

TagBFP colocalization with mito-GFP was evaluated in live iHNeurons expressing the anti-GFP nanobody and destabilized nanobodies fused to TagBFP (dGBP1–TagBFP). Individual neurons transplanted with mito-GFP were imaged at 1 day after transplantation for 2 min at 2 s intervals. Each recording contained a channel representing donor mitochondria fluorescence (mito-GFP) and a channel representing TagBFP fluorescence. TagBFP colocalization with mito-GFP in cells was determined using custom-written routines in Python. Due to a weak signal to noise ratio in the TagBFP channel, a Noise2Void tool was used for image denoising[95]. A subset of images from a recorded dataset was used for the model training. Training parameters were as follows: 192 × 192 patch size, batch size 128, neighbourhood radius 2 and learning rate 0.001, 120 epochs. A Gaussian filter was applied to all images with TagBFP fluorescence before applying the Noise2Void model. To generate kymographs for each recorded neuron, denoised images with TagBFP fluorescence and corresponding images with GFP fluorescence were projected onto the xT and yT planes. In the generated kymographs, the TagBFP and GFP tracks were annotated using the Labelme tool (http://labelme.csail.mit.edu/). For each annotated TagBFP track, the fraction of TagBFP-positive pixels positive for GFP-positive pixels was computed by assessing the distance of each individual TagBFP-positive pixel to neighbouring pixels from annotated GFP tracks. Pixels with a distance of less than 15 pixels were considered overlapping. To calculate the overlapping fraction in a given TagBFP track, the number of overlapping pixels was divided by the total number of TagBFP-positive pixels. For any given recording, GFP-positive tracks were spatially randomized within the cell area, while the location of BFP tracks was maintained constant. The colocalization value was then computed as before. The randomization process was repeated 100 times for each recording and the final unspecific colocalization value defined as the mean across the 100 iterations.

Tracking of mitochondria and lysosomes

Tracking of donor and native mitochondria in iHNeurons and donor mitochondria and lysosomes in HUVECs was performed manually in live-imaged cells using Trackmate96,97. Median and maximum speeds of generated tracks in Trackmate were used for quantification and illustration.

Quantification of axonal beading in retinal ganglion cells axons after ONC

Axonal beading in retinal ganglion cell axons after optical nerve crush experiments was quantified using a semi-automatic full image segmentation method. This method, based on the Segment Anything Model (SAM)[98] in MATLAB (R2025a, MathWorks), involved first labelling the total surface of RGC axons in two-photon mean projection images of ex-vivo retinas with the SAM algorithm. Subsequently, axonal beads were manually labelled based on their distinctive shape and size. The axonal beading ratio for each image was calculated by dividing the total area covered by axonal beads by the sum of the areas covered by axonal beads and labelled axons. For each retina, three to four projection images were independently analysed, and their mean value was reported.

Statistical analyses

All quantification and statistical analyses were performed using Python, MATLAB, ImageJ or GraphPad Prism. The cumulative distributions of two independent groups were compared using the Kolmogorov–Smirnov test. Normality was assessed using the Shapiro–Wilk test for datasets with more than four samples. For unpaired datasets with multiple groups, each group was tested individually for normality; for paired datasets, normality was tested on the differences between pairs. If the null hypothesis of normality was rejected (P < 0.05), nonparametric tests were applied. For unpaired datasets with two independent groups, parametric comparisons were performed using an unpaired two-tailed t-test with Welch’s correction; for nonparametric comparisons, we used the Mann–Whitney U-test. When the sample size was fewer than five, an unpaired two-tailed t-test with Welch’s correction was applied. For unpaired datasets with more than two groups (n > 4) and a single independent variable, ordinary one-way ANOVA (unequal variance) or Welch’s ANOVA (equal variance) was used for parametric analyses, with Bartlett’s test applied to assess homogeneity of variance. The Kruskal–Wallis test was used for nonparametric analyses. For groups with sample size fewer than five, Welch’s ANOVA was used. For datasets with two independent variables, two-way ANOVA was used. For paired datasets, parametric and nonparametric comparisons were performed using the paired t-test and Wilcoxon signed-rank test, respectively. Data are mean ± s.e.m., unless otherwise stated.

For cultured cells in mitochondrial transplantation experiments, each n value refers to the number of wells or dishes of cells used per condition from independent cultures. For induced organoids, it refers to the number of organoids used per condition. For ex vivo and in vivo experiments, each n value refers to the number of explants or retinas used per condition. In some cases, n values refer to the number of human donors used in this study.

Biological materials availability

All unique biological materials used are readily available from the authors.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Sequences of newly generated nanobodies are provided in Supplementary Data 1. The proteomics dataset and scRNA-seq count matrix have been uploaded to Zenodo[99] and are publicly available as of the date of publication (https://doi.org/10.5281/zenodo.17909630). Raw scRNA-sequencing data are publicly available in the NCBI SRA repository under BioProject PRJNA1380367. Source data are provided with this paper.

Code availability

Custom codes have been uploaded to Zenodo[99] (https://doi.org/10.5281/zenodo.17909630). Any additional code required to reanalyse the data reported in this Article is available on request.

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Acknowledgements

We thank Z. Raics for assistance with imaging data analysis; L. Utz and S. Spirig for providing retinal organoids; D. Rey, N. Ledergerber and T. Marzolla for their efforts in animal genotyping; K. Drumm, A. Hammel, M. Kohlweg and N. Walter for their support in animal care; the staff at the Complex Viruses Platform, including J. Jüttner, T. Kleindienst, J. Imbach and A. Volak, for AAV production; the members of the Human Retina Donation Team, including D. Disco, Z. Gkatzioufas, M. Znidaric, H. Schreyer, J. Matsell and A. Kusnyerik, for their help with post-mortem retina cultures; the patient with LHON who provided their blood donation; N. Schaerer, D. Hauenstein, A. Palmowski-Wolfe and the patient’s family for the assistance in blood collection; the members of the EPFL Protein Production Platform team, particularly K. Lau and P. Florence, for recombinant nanobody production; A. Schütz and the team of the Protein Production & Characterization Technology Platform of the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (https://www.mdc-berlin.de/protein-production-characterization) for assisting with expression construct design and producing human TOMM70 protein; S. Stefanic and the staff at the Nanobody Service Facility of the University of Zurich, along with the VIB Nanobody Core, for their assistance in generating and selecting novel nanobodies; the members of the Imaging Core facility (PCF, Biozentrum, University of Basel) and in particular A. Ferrand for the technical assistance provided on the OMX 3D-SIM microscope; the members of the Proteomics Core facility (IMCF, Biozentrum, University of Basel) and in particular K. Buczak for technical assistance; K. Kolar for discussions and assistance in bispecific nanobody design; V. Juvin from SciArtWork for illustrations; and A. Bharioke, A. Muller, B. György, M. Altermatt, M. Munz, S. Spirig and T. Rodrigues for commenting on the manuscript. Some icons in Fig. 3g and Extended Data Figs. 6i, 7c and 8b,j were created using BioRender (https://BioRender.com/fv9sxoi). This study was funded by EMBO postdoctoral fellowships ALTF 688-2022 to A.F. and ALTF 151-2022 to Á.H.-N., Swiss National Science Foundation Synergia grant (CRSII3_141801), European Research Council advanced grant (HURET no. 883781), Carigest grant, FONDAZIONE TEOFILO ROSSI DI MONTELERA E DI PREMUDA, Louis-Jeantet Foundation award, Körber Foundation award, Sedinum Foundation Grant and Program, Swiss National Science Foundation grant (31003A_182523) and the NCCR ‘Molecular Systems Engineering’ to B.R.

Author information

Author notes

1. These authors contributed equally: Temurkhan Ayupov, Verónica Moreno-Juan

Authors and Affiliations

1. Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland

Temurkhan Ayupov, Verónica Moreno-Juan, Serena Curtoni, Alex Fratzl, Susana Posada-Céspedes, Ramona Ratiu, Rei Morikawa, Morgan Chevalier, Yanyan Hou, Sarah A. Nadeau, Álvaro Herrero-Navarro, Vikram Ayinampudi, Elizabeth Kastanaki, Natasha Whitehead, Rebecca A. Siwicki, Mariana M. Ribeiro, Ji Hoon Han, Annalisa Bucci, Simone Picelli, Magdalena Renner, Cameron S. Cowan, Simon Hansen & Botond Roska

2. Department of Ophthalmology, University of Basel, Basel, Switzerland

Temurkhan Ayupov, Ji Hoon Han & Botond Roska

3. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

Upnishad Sharma & Daniel J. Müller

4. Facility for Advanced Imaging and Microscopy, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

Alexandra Graff Meyer

5. Immunobiology Laboratory, Department of Biomedicine, University of Basel and University Hospital of Basel, Basel, Switzerland

Margherita Pezzoli, Glenn Bantug & Christoph Hess

6. Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Department of Medicine, University of Cambridge, Cambridge, UK

Christoph Hess

Contributions

T.A. conceptualized the study, designed and conducted experiments, analysed and interpreted data, and wrote and revised the paper. V.M.-J. designed and conducted experiments, analysed and interpreted data, and wrote and revised the paper. S.C. conducted experiments. A.F. developed image analysis algorithms and analysed data. U.S. performed AFM experiments and analysed and interpreted data. R.R. conducted experiments. R.M. performed ex vivo two-photon calcium imaging experiments and analysed and interpreted data. S.P.-C. analysed single-cell transcriptomic data. A.G.M performed experiments related to EM and analysed data. M.P. and G.B. assisted with oxygen consumption and primary T cell-targeting experiments. M.C. performed single-cell collection experiments for scRNA-seq. Y.H. optimized the protocol for culturing induced neurons and reprogrammed patient-derived samples S.A.N. analysed proteomics data. Á.H-N. and V.A. performed nanobody affinity maturation experiments. E.K. characterized patient-derived iPS cell clones. N.W. generated blood vessel organoids. R.A.S. and M.M.R. performed single-cell sequencing experiments. J.H.H. analysed whole-genome sequencing data from patient-derived samples. A.B. conducted post-mortem human retina collection and culturing. C.H. supervised experiments with immune cells. S.P. supervised RNA-seq experiments. M.R. supervised the generation of induced human retinal neurons, retinal organoids and blood vessel organoids. C.S.C. supervised the analysis of RNA-seq experiments. D.J.M. supervised AFM experiments. S.H. conduced and supervised nanobody production and affinity maturation experiments. B.R. conceptualized the study, designed and supervised experiments, interpreted data, and wrote and revised the paper.

Corresponding author

Correspondence to Botond Roska.

Ethics declarations

Competing interests

The Institute of Molecular and Clinical Ophthalmology Basel is the applicant of a pending international patent application that relates to aspects of this Article and names T.A., V.M.-J., S.H. and B.R. as inventors.

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Nature thanks Jonathan Brestoff, José Sahel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Components of cell-surface binder display-based MitoCatch-C.

a. Schematic diagram of a construct designed to display nanobodies on the surface of a cell (top) and super-resolution image of a HEK293T cell expressing the cell-surface anti-GFP nanobody (bottom). Nanobodies are identified by anti-OLLAS antibodies (magenta), and cell nuclei are labelled with Hoechst (blue). Igκ SP, immunoglobulin kappa light chain signal peptide; TM, transmembrane domain; 3D SIM, three-dimensional structural illumination microscopy. The construct was validated in at least three independent experiments. b. Schematic diagram of a construct designed to target GFP to the outer membrane of mitochondria (top) and super-resolution image of a HEK293T cell expressing GFP at the outer membrane of mitochondria (bottom, green). Matrix of mitochondria is labelled with dsRed2 protein fused to COX8 signal peptide (red) and MitoTracker Deep Red (magenta). The construct was validated in at least three independent experiments. c. Western blotting on different subcellular fractions. Depletion of endoplasmic reticulum proteins in the isolated mitochondrial fraction was assessed with anti-GRP78, anti-PDIA3, and anti-KDELR1 antibodies. Enrichment of mitochondrial proteins in the mitochondrial fraction was assessed with anti-MCU and anti-TOMM20 antibodies in three independent mitochondrial preparations. For gel source data, see Supplementary Fig. 1. d. Western blotting on isolated mitochondria to assess the presence of mitochondrial proteins from matrix, intermembrane space, inner and outer membranes validated with an antibody cocktail consisting of anti-ATP5a, anti-UQCRC1 (Complex III Core Protein I), anti-VDAC1, anti-Cyclophilin D, and anti-Cytochrome c antibodies in three independent mitochondrial preparations. For gel source data, see Supplementary Fig. 1. e. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images of isolated mitochondria from HEK293T cells. At least three independent mitochondrial preparations were used for integrity validations using the EM techniques. f. Oxygen consumption rate (OCR) of isolated mitochondria with and without substrates (10 mM succinate and 10 mM ADP). For all conditions, 2 µM rotenone is added to inhibit Complex I. Dashed line illustrates the time point when 4 µM Antimycin A is added. n = 3, Succinate + ADP vs. Succinate + ADP + Antimycin A: P = 0.0032, Succinate + ADP vs. No substrates: P = 0.014, Succinate + ADP + Antimycin A vs. No substrates: P = 0.85, Welch’s ANOVA test with two-sided Dunnett’s multiple comparisons test. g. Binding of anti-GFP and anti-mCherry nanobodies to GFP, mCherry, dsRed2, and tdTomato proteins assayed by enzyme-linked immunosorbent assay. OD490, optical density (OD) values measured at 490 nm. n = 3, each ‘n’ refers to a technical replicate. The assay was repeated two times independently. h. Histograms displaying the distribution of mean GFP fluorescence in nanobody-positive and nanobody-negative cells. Threshold for significant enrichment is shown with a black dashed line. Numbers indicate the percentage of cells that pass the threshold. P < 0.0001, Kolmogorov-Smirnov test. i. HEK293 cells expressing either a control nanobody (top) or the anti-GFP nanobody (bottom) on the cell surface after two hours of mito-GFP transplantation. Nanobody-positive cells are identified by anti-OLLAS antibodies (magenta). Transplanted mito-GFP are stained with anti-GFP antibodies (green). Nanobody-positive cells are outlined with grey dashed lines. j. Quantification of the efficacy of mito-GFP delivery in HEK293T cells two hours after transplantation. Percentage of GFP-positive cells among all nanobody-positive and the mean GFP fluorescence ratio between nanobody-positive cells and nanobody-negative cells. n = 7, P < 0.0001 (top) and P = 0.0014 (bottom), two-sided Welch’s t test. k. Co-localization of GFP from transplanted mito-GFP and TOMM20 among total mitochondria in HEK293T cells. Mito-GFP are stained with anti-GFP antibodies (green); anti-GFP nanobody is identified by anti-OLLAS antibodies (magenta); TOMM20 is identified by anti-TOMM20 antibodies (red); nuclei labelled with Hoechst (blue). Anti-GFP nanobody-positive cells are outlined with white dashed lines. l. Quantification of the co-localization of GFP and TOMM20 in (k). Donor mitochondria-positive pixels (GFP channel) were spatially randomized within a cell region to compute unspecific co-localization values. n = 10, P < 0.0001, two-sided paired t test. m. Histograms displaying the distribution of mean GFP fluorescence in tdTomato-positive and tdTomato-negative cells. Threshold for significant enrichment is shown with a black dashed line. Numbers indicate the percentage of cells that pass the threshold. P < 0.3766, two-sided Kolmogorov-Smirnov test. n. HEK293 cells expressing cytosolic tdTomato after two hours of mito-GFP transplantation. TdTomato-positive cells are identified by anti-RFP antibody immunostaining (red). Transplanted mito-GFP are stained with anti-GFP antibodies (green). Brightfield image is shown in grey. o. Quantification of the efficacy of mito-GFP delivery in (n). Percentage of GFP-positive cells among all tdTomato-positive cells (top, n = 6) and the mean GFP fluorescence ratio between tdTomato-positive cells and tdTomato-negative cells (bottom, n = 6). *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m. Scale bars, 5 µm (a, i), 1 µm (b, e), 10 µm (k), 50 µm (n).

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Extended Data Fig. 2 Delivery of mito-GFP into iHNeurons.

a. Differentiation of induced human neurons (iHNeurons). Developing iHNeurons at three different stages are shown in brightfield images. The neuronal differentiation was confirmed in at least three induction batches. b. Sorted iHNeurons from induction in (a) expressing a CD90 marker at day 80 (green). Cell nuclei are labelled with Hoechst (blue). c. An example of a transplanted (mito-GFP-positive) iHNeuron used for a correlated SEM. Left, brightfield; right, GFP fluorescence image. d. Brightfield image of cultured iHNeurons (left) and a correlated SEM image (right) three hours after mito-GFP transplantation. e. Left, schematic of an experiment showing expression of the anti-GFP nanobody on the surface of the iHNeuron identified by anti-OLLAS antibodies (magenta) and the transplanted mito-GFP stained with anti-GFP antibodies (green). Right, SEM images of an iHNeuron with surface-bound donor mitochondria (mito-GFP-positive). The image is captured on iHNeurons three hours post-transplantation. Total five different regions (n > 10 cells) were used for SEM correlations across multiple experiments. f. Co-localization of GFP from transplanted mito-GFP stained with anti-GFP antibodies (green) and TOMM20 identified by anti-TOMM20 antibodies (cyan) among total mitochondria in iHNeurons expressing the anti-GFP nanobody stained by anti-OLLAS antibodies (magenta). The mito-GFP-targeted cell is outlined with a white dashed line. g. Quantification of the co-localization of GFP and TOMM20 in (f). Donor mitochondria-positive pixels (GFP channel) were spatially randomized within a cell region to compute unspecific co-localization values. n = 8, P < 0.0001, two-sided paired t test. h. Gene ontology classification of differentially abundant proteins in iHNeurons four hours after mitochondrial transplantation. Anti-GFP and control nanobody, iHNeurons expressed the cell surface nanobodies and were transplanted with mito-GFP. Untreated, iHNeurons expressed anti-GFP nanobody without mito-GFP treatment. Benjamini-Hochberg adjustment was performed to correct for multiple testing for quality control data, see Supplementary Fig. 2. i. Assessment of mitochondrial, lysosomal, endoplasmic reticulum and nuclear protein enrichment using gene set enrichment analysis. The analysis was performed on custom protein lists that contained organelle-specific markers (Supplementary Table 2). A green dashed line indicates GFP. Permutation test corrected with Benjamini-Hochberg test for multiple comparisons was used to calculate p values. *P < 0.05, **P < 0.01,***P < 0.001. Data, mean. Scale bars, 500 µm (a), 100 µm (b), 25 µm (c, d, f), 1 µm (e).

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Extended Data Fig. 3 Internalization of donor mitochondria in iHNeurons.

a. Schematic of an experiment to test the internalization of transplanted mitochondria in HEK293T cells using miniSOG. b. Detection of miniSOG-labelled mitochondria in HEK293T cells. Left, schematic of an experiment involving photooxidation of HEK293T cells with miniSOG-labelled mitochondria. Middle, HEK293T cells expressing miniSOG (green) in mitochondria imaged by live microscopy. Right, correlated TEM images of miniSOG-positive and miniSOG-negative mitochondria after photooxidation. Cells containing miniSOG-labelled mitochondria in the live image are shown in green in the TEM image. Validations of miniSOG-labelling under EM were performed in at least three different photooxidized locations. c. Schematic of an experiment to test the internalization of transplanted mitochondria in iHNeurons using miniSOG. d. Brightfield images of iHNeurons before and after photooxidation. The photooxidized region is shown with a white dashed line. In small insets, iHNeurons with transplanted mitochondria are shown (green) before and after photooxidation. In total 15 photoxoxidations were performed in five independent experiments. e. TEM images of miniSOG-negative mitochondria in donor HEK293T cells as described in (b). f. TEM images of miniSOG-labelled mitochondria-transplanted iHNeurons after photooxidation, one day after transplantation. White arrows indicate miniSOG-negative mitochondria, magenta arrows indicate miniSOG-positive mitochondria and magenta asterisks indicate lysosomes. Representative images were collected from five different photooxidized locations from at least three independent experiments. g. Surface area for donor mitochondria in HEK293T cells, and miniSOG-positive or miniSOG-negative mitochondria in iHNeurons. Five outlier points were removed for plotting. Donor mitochondria in donor cells: n = 162; miniSOG-negative mitochondria: n = 77; miniSOG-positive mitochondria: n = 111, P < 0.0001, Kruskal-Wallis test corrected with two-sided Dunn’s test for multiple comparisons. h. Live imaging of a single mito-GFP (green) movement in the neurite of an iHNeuron at different time points, three days after mitochondrial transplantation. Moving mito-GFP is indicated with a white arrow. Neurites are indicated with grey arrows in the saturated image. Mito-GFP movement in neurites was confirmed in at least three independent experiments. i. Live imaging of a transplanted mito-GFP movement in iHNeurons. Sequential frames showing the movement of a single transplanted mito-GFP (green) within the neurite of an iHNeuron expressing cytosolic tdTomato (grey), recorded at different time points, one day after mitochondrial transplantation. The moving mito-GFP is indicated with a white arrow. Right image, maximal intensity projection of the live imaging sequence. Mito-GFP movement in neurites expressing cytosolic tdTomato was confirmed in at least three independent experiments. j. Live imaging of mito-GFP (green) in the neurites of iHNeurons expressing mitochondria matrix-targeted dsRed2 (red) at different time points, one day after mitochondrial transplantation. Examples of Mito-GFP positive for dsRed2 are indicated with white arrows, confirmed in at least three independent experiments. k. Quantification of the median (left) and maximal (right) speed of the native and the transplanted mitochondria as shown in (j), one day after mitochondrial transplantation. Eight data points are not shown for the median speed graph (left), and 54 data points for the maximum speed graph (right) were removed for plotting. For mitochondria tracking, native mitochondria were labelled with mitochondria matrix-targeted dsRed2 and donor mitochondria were labelled with outer membrane-targeted GFP. Median speed for only dsRed2-positive mitochondria: 0.32 µm/s, n = 97 tracks; median speed for GFP-positive mitochondria: 0.49 µm/s, n = 105 tracks, P = 0.08, two-sided Mann-Whitney U test. Maximum speed for only dsRed2-positive mitochondria: 0.61 µm/s, n = 97 tracks; maximum speed for GFP-positive mitochondria: 0.72 ± 0.07 µm/s, n = 105 tracks, P = 0.52, two-sided Mann-Whitney U test. NS not significant, ***P < 0.001. Data, mean ± s.e.m (g) and median (k). Scale bars, 5 µm (left and middle b, and h, i), 0.5 µm (right b), 50 µm (d), 0.5 µm (e, f).

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Extended Data Fig. 4 Cytosolic accessibility of transplanted mitochondria.

a. Live-recorded endothelial cell transplanted with donor mitochondria labelled with GFP in the outer membrane (green) and dsRed2 in the matrix (red). The presence of mito-GFP-dsRed2 in endothelial cells were confirmed in at least three independent experiments. b. Live imaging of mito-GFP-dsRed2 mitochondria in an endothelial cell at different time points, one day after mitochondrial transplantation. White arrows indicate mitochondria undergoing fusion. c. Live-recorded endothelial cell transplanted with mito-GFP-dsRed2 six days after mitochondrial transplantation. Top, a single time frame. Bottom, time projections of the selected regions illustrating active transport of donor mitochondria. White arrows, starting point (first frame). Grey arrows, end point (last frame). d. Top, a single frame of a live-recorded endothelial cell transplanted with mito-GFP (green) and labelled endogenous lysosomes (magenta). Bottom, example tracks of mito-GFP and lysosomes movement. Lysosomes are labelled with a pH-sensitive pHLys Red dye. e. Quantification of mito-GFP and native lysosomes travelled distance in live-imaged endothelial cells in (d). Mitochondria and lysosome movements were tracked manually. Individual tracks are plotted and combined from multiple live-recorded cells (n = 4). Seven outlier points were removed for plotting. Lysosomes: n = 110; mito-GFP: n = 116, P < 0.0001, two-sided Mann-Whitney U test. f,g. Live imaging of an endothelial cell expressing cell surface anti-GFP nanobody and mitochondria matrix-targeted dsRed2 transplanted with mito-GFP, five hours (f) and 48 h (g) after mitochondrial transplantation. White arrows, motile mito-GFP (f), and examples of mito-GFP positive for dsRed2 (g). The uptake and interaction of mito-GFP with native mito-dsRed2 were confirmed in at least three independent experiments. h. Schematic diagram of a destabilized anti-GFP nanobody (dGBP1) fused to TagBFP (dGBP1-TagBFP). i. Left, HEK293T cells expressing cytosolic tdTomato (red) and dGBP1-TagBFP (cyan). Right, HEK293T cells expressing cytosolic GFP (green) and dGBP1-TagBFP (cyan). j. Quantification of TagBFP enrichment in tdTomato- and GFP-positive cells in (i). n = 3, P = 0.0016, two-sided Welch’s t test. k. Anti-TUBB3 stained iHNeurons (grey) co-expressing dGBP1-TagBFP (cyan) and GFP (green). White arrows indicate GFP- and TagBFP-positive cells. Grey arrows indicate GFP-positive TagBFP-negative cells. l. Quantification of TagBFP- and GFP-positive iHNeurons in (k). Total n = 7. m. Schematics of an experiment to assess the accessibility of the internalized mito-GFP via destabilized nanobody (dGBP1-TagBFP) in iHNeurons expressing cell-surface anti-GFP nanobody. n. Quantification of internalized mito-GFP accessibility to the cytosol. Each n represents a single cell (n = 29). o. Mito-GFP number as a function of cell soma size in fixed iHNeurons (n = 29), two-sided Pearson correlation test. p. Mito-GFP accessibility as a function of cell soma size in fixed iHNeurons (n = 29), two-sided Pearson correlation test. q. Live-recorded iHNeurons expressing cytosolic destabilized nanobody fused to TagBFP (cyan) transplanted with mito-GFP (green) (from n-p). Left, a saturated image of a single frame in the GFP channel. Right, Z projections of an image stack, right. White arrows indicate GFP-positive and TagBFP negative donor mitochondria in the extracellular space. r. X (top) and Y (bottom) projected kymographs of live-recorded iHNeurons transplanted with mito-GFP. One frame is 2 s. s. Example of TagBFP (top, cyan) and mito-GFP tracks (bottom, green). White arrows, overlapping tracks; red arrow, only mito-GFP track. t. Colocalization of TagBFP and GFP tracks from X- and Y-projected kymographs in (r) and (s). Track colocalization values were averaged per cell (X projection, n = 13; Y projection, n = 15). u. An iHNeuron expressing a control nanobody stained by anti-OLLAS antibodies (magenta) on the cell surface and cytosolic destabilized nanobody stained with anti-TagBFP antibodies (cyan) transplanted with mito-GFP stained with anti-GFP (green), one day after mitochondrial transplantation from the experiment in Fig. 1l. Cells are outlined with grey dashed lines. Extracellular mito-GFP are outlined with red dashed lines. v. Live-imaged iHNeuron expressing control nanobody together with cytosolic dGBP1-TagBFP and transplanted with mito-GFP, one day after transplantation. White dashed lines, outlined cell. Red arrows, extracellular mito-GFP. The experiment was repeated independently at least three times. w. X- (top) and Y- (bottom) projected kymographs from live-recorded human retinal cells transplanted with mito-GFP in post-mortem retinas. x. Quantification of the median (left) and maximum (right) speed of transplanted mitochondria in (w); 18 data points were removed for plotting. Median speed: 0.45 µm/s; maximum speed: 1.01 µm/s, n = 83 tracks. *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m and median for x. Scale bars, 50 µm (a, i, k), 5 µm (b, c, d, f, g, q, r, s, u, v, w).

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Extended Data Fig. 5 Targeted mitochondria delivery based on nanobody display on the surface of mitochondria.

a. Top, schematic diagram of the construct used for directing a nanobody to the outer membrane of mitochondria. Bottom, a super-resolution image of an HEK293T cell with mitochondria-targeted nanobody detected by anti-alpacaVHH antibodies (magenta). Cell nuclei are labelled with Hoechst (blue). Mitochondria matrix is labelled with dsRed2 (cyan). 3D SIM, three-dimensional structural illumination microscopy. The construct was validated in at least three independent experiments. b. Top, schematic diagram of the construct used for directing GFP to the cell surface. Bottom, a super-resolution image of an HEK293T cell with cell surface-targeted GFP detected by anti-GFP antibodies (green). Cell nuclei are labelled with Hoechst (blue). The construct was validated in at least three independent experiments. c. HEK239T cells transplanted with donor mitochondria displaying the anti-GFP nanobody, two hours after transplantation. HEK293T cells are transfected with cell-surface mCherry (cyan) or GFP (green). Nanobodies are detected by anti-alpacaVHH antibodies (magenta). d. Quantification of the efficacy of the delivery of nanobody-displaying mitochondria two hours after transplantation. n = 6, P < 0.0001 (top) and P = 0.0012 (bottom), two-sided Welch’s t test. e. HEK239T cells transplanted with donor mitochondria displaying the anti-mCherry nanobody, two hours after transplantation. HEK293T cells were transfected with cell-surface GFP (green) or mCherry (cyan). Nanobodies detected by anti-alpacaVHH antibodies (magenta). f. Quantification of the efficacy of the delivery of nanobody-displaying mitochondria, two hours after transplantation. n = 8, P = 0.0003 (top) and P = 0.0012 (bottom), two-sided Welch’s test. g. Live-imaged endothelial cells expressing cell surface GFP and endosome-targeted RAB5A-TagRFP with (top) and without (bottom) transplanted with mito-mTagBFP2 (cyan) displaying anti-GFP nanobody, six hours after mitochondrial transplantation. White arrows, endosome-free donor mitochondria (confirmed in at least three independent experiments). For an example of mito-mTagBP2 in endothelial cells transplanted without the anti-GFP nanobody, see Supplementary Fig. 3a. h. Donor mito-mTagBFP2 (cyan) inside endocytic vesicles (red arrows) labelled with RAB5A-TagRFP (magenta) (from g), six hours after mitochondrial transplantation. i. Donor mito-mTagBFP2 (cyan) free from endocytic vesicles (white arrows) labelled with RAB5A-TagRFP (magenta) (from g), six hours after mitochondrial transplantation. j. Donor mito-mTagBFP2 (cyan) free from endocytic vesicles (white arrows) labelled with RAB5A-TagRFP (magenta), 24 h after mitochondrial transplantation. Outlined region, mito-mTagBFP2 free from endocytic vesicles and lysosomes (yellow). Lysosomes are stained with LysoTracker Deep Red dye. The experiment was repeated at least three times with similar results. k. Quantification of proportion of endosome-free mitochondria by pixel-based co-localization analysis. Mito-mTagBFP2: n = 30; mito-mTagBFP2 + anti-GFP nanobody: n = 52, P = 0.0756, two-sided Mann-Whitney U test. l. Quantification of abundance of endosome-free mitochondria by pixel-based co-localization analysis. The values were normalized to cell size (µm^2 of donor mitochondria area per 1 µm^2 cell area). Mito-mTagBFP2: n = 30; mito-mTagBFP2 + anti-GFP nanobody: n = 52, P < 0.0001, two-sided Mann-Whitney U test. m. Endothelial cells stained with pH-dependent lysosome staining dye pHLys Red (yellow). Cells expressed cell surface GFP and were transplanted with mito-mTagBFP2 displaying anti-GFP nanobody or no binder. In addition, Bafilomycin A1 was used as a positive control for pH acidification change in lysosomes. For mitochondria transplanted conditions, images of cells positive for mito-mTagBFP2 are shown (Supplementary Fig. 3b–d). n. Quantification of pH changes in lysosomes relative to untreated condition. Untreated: n = 6; Bafilomycin A1: n = 4; mito-mTagBFP2: n = 4; mito-mTagBFP2 + anti-GFP nanobody: n = 6, Untreated vs. Bafilomycin A1: P = 0.0036, Untreated vs. mito-mTagBFP2: P = 0.8235, Untreated vs. mito-mTagBFP2 + anti-GFP nanobody: P = 0.9648, Welch’s ANOVA test corrected with two-sided Dunnett’s test for multiple comparisons. o. Live-imaged endothelial cell expressing cell surface GFP (green), and transplanted with mito-dsRed2 (cyan) displaying anti-GFP nanobody, four days after mitochondrial transplantation. The cell is outlined with a grey dashed line. The zoomed-in region is outlined with a white dashed square. Two timeframes are shown on the right. The tracked mitochondrion is indicated with a red arrow. The experiment was repeated at least three times with similar results. p. Live-imaged endothelial cell expressing cell surface GFP and transplanted with mito-dsRed2 (cyan) displaying outer membrane anti-GFP nanobody, four days after mitochondrial transplantation. Mitochondria are labelled with 50 nM MitoTracker Deep Red (magenta). The zoomed-in region is outlined with a white dashed square and the tracked mitochondrion is indicated with a white arrow. The experiment was repeated at least three times with similar results. q. Labelling of donor and native mitochondria with MitoTracker Deep Red dye in live-recorded endothelial cells. At the used concentration, the dye stained both native (black) and donor mitochondria (cyan) with stronger enrichment in the native mitochondria. Donor mitochondria positive for matrix-labelled dsRed2 and MitoTracker Deep Red are indicated with red arrows. NS not significant, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m and median for k, l. Scale bars, 2.5 µm (a, b), 25 µm (c, e), 5 µm (g, h, i, o, p, q), 10 µm (j), 20 µm (m).

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Extended Data Fig. 6 Targeted mitochondria delivery using binders displayed on mitochondria against natively existing cell-surface proteins.

a. Schematic of an experiment using a cell-surface protein binder with the anti-CD71 nanobody to bind to cells expressing CD71. b,c. Binding kinetics the of the anti-CD71 nanobody (clone H08) to either human CD71 (b) or mouse CD71 (c) measured by surface plasmon resonance. Red line, responses after anti-CD71 nanobody injections at 0.195, 0.78, 3.13, 12.5, 50 nM; black line, fit with 1:1 binding model. KD (in molar (M)), equilibrium dissociation constant, ka, association rate (1/M × s), kd, dissociation rate (1/s). d. Top, schematic diagram of the construct used to express human CD71 together with cytosolic GFP. Middle, schematic of mitochondria displaying the anti-CD71 nanobody targeted to HEK293T cells expressing the CD71 protein and cytosolic GFP. Bottom, HEK293T cells expressing human CD71 on the cell surface (red) and cytosolic GFP (green). The construct was validated in at least three independent experiments. e. HEK293T cells expressing human CD71, CD73, or CD142 together with cytosolic GFP (green) transplanted with donor mitochondria displaying the anti-CD71 nanobody two hours after transplantation. Nanobodies are stained with anti-alpacaVHH antibodies (magenta). f. Quantification of the efficacy of the delivery of nanobody-displaying mitochondria into CD71, CD73, or CD142-expressing cells two hours after transplantation. n = 4, P < 0.0001, Welch’s ANOVA followed by two-sided Dunnett’s test for multiple comparisons. g. Primary human cardiac cells transplanted with donor mitochondria displaying the anti-CD71 nanobody from Fig. 2a,b. Transplanted and native mitochondria are detected by anti-MT-CO1 antibodies (green). Mitochondria-displayed nanobodies are detected with anti-alpacaVHH antibodies (magenta). h. Co-localization of nanobody with MT-CO1 in (g). For the colocalization analysis, donor mitochondria-positive pixels (magenta) were spatially randomized within a cell region to compute unspecific co-localization values. n = 12, P < 0.0001, two-sided paired t test. i. Schematic of an experiment for CD8-positive T cell activation via co-stimulation with anti-CD3, anti-CD28 and anti-CD2 antibodies, and the subsequent increase in CD71 expression. j. Proportion of CD71-positive cells in non-activated and activated T cells two days after stimulation. n = 4, P = 0.0003, two-sided Welch’s t test. k. Fluorescence intensities of CD71 protein in non-activated and activated CD8- conjugated to FITC. l. Schematic (left) and quantification (right) of the efficacy of the delivery of nanobody-displaying mitochondria into CD71-positive T cells from four healthy donors. n = 20, P = 0.0025, two-sided Welch’s t test. Each ‘n’ refers to independent cultures of donor cells with mitochondria from multiple mitochondrial preparations. See Supplementary Fig. 4a,b for gating strategy and flow cytometry data. m. Quantification of the efficacy of the delivery of nanobody-displaying mitochondria into activated CD8-positive cells paired per donor. n = 4, P = 0.0142, two-sided paired ratio t test. n. Activated CD8-positive T cell stained with anti-CD8 (left) and anti-CD71 (right) antibodies imaged by 3D SIM. CD proteins are shown in cyan and black. Nuclei are stained with Hoechst (blue). o,p. Quantification of CD8 and CD71 distribution in activated CD8-positive T cells. Each ‘n’ refers to a single cell derived from a healthy human donor. n = 13, P = 0.0003, two-sided paired t test. q. Immune response profiling in mice after a single injection of mitochondria either displaying anti-GFP nanobody or lacking a binder, assayed by enzyme-linked immunosorbent assay. Mitochondria were injected either into the eye, brain, or systemically. 0, 7, and 28 days refer to a time point when blood serum was collected after the mitochondrial injection. Recombinantly produced anti-GFP nanobody, TOMM20, TOMM70 proteins, and isolated mitochondria were used as targets to assess the presence of antibodies in serum specific to mitochondrial proteins and the anti-GFP nanobody. OD490, optical density (OD) values measured at 490 nm. Three mice were used for each condition. All serum samples were applied at 200-fold dilution. r. Antibody titre assessment from collected serum samples (n = 18) by enzyme-linked immunosorbent assay. Antibodies from the serum were captured with donkey anti-mouse IgG antibodies. *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m and median for p. Scale bars, 25 µm (d, e), 50 µm (g), 2.5 µm (n). The diagram in i was created using BioRender; Ayupov, T. https://BioRender.com/fv9sxoi (2026).

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Extended Data Fig. 7 Affinity maturation of nanobodies for enhanced cell type-targeted mitochondria delivery.

a. AlphaFold model of an anti-mCD71 nanobody (clone 2MCS25). Complementarity-determining regions (CDRs) in white, regions adjacent to CDRs in grey, nanobody scaffold in magenta. b. Next-generation sequencing of a single-site mutagenesis phage library of the anti-mCD71 nanobody. Single amino acid positions in CDRs and adjacent regions were mutated to all possible amino acids (except cysteine), shown in red dashed red lines. Counts from the original clone are excluded. c. Schematic of phage library screen to select nanobody variants with improved affinities. d. Enrichment of anti-mCD71 nanobody variants with single mutations over the course of selection rounds. e. Coomassie-stained polyacrylamide gel showing purified nanobodies after size-exclusion chromatography. Lane 1, protein ladder; lanes 2–7, purified individual anti-mCD71 nanobody variants. Bottom, schematic diagram of the construct used for expression in E.coli. PelB, signal peptide for periplasmic secretion. Purified nanobodies were generated for use in subsequent experiments. f. Size-exclusion chromatography of the purified 2MCS25 anti-mCD71 nanobody. mAU, milliarbitrary units. g. Binding kinetics of anti-mCD71 nanobody variants to mouse CD71 measured by surface plasmon resonance. Red line, responses after anti-mCD71 nanobody injections at 0.39, 1.56, 6.25, 25, 100 nM; black line, fit to 1:1 binding model. KD (in molar (M)), equilibrium dissociation constant, ka, ligand association rate (1/M × s), kd, ligand dissociation rate (1/s). h. Enrichment of anti-mCD71 nanobody mutant variants after two rounds of selection versus measured KD. Original clone, 2MCS25 nanobody. i. Top, schematic of mitochondria displaying the anti-CD71 nanobody targeted to HEK293T cells expressing mouse CD71 protein and cytosolic GFP. Middle, HEK293T cells expressing mouse CD71 on the cell surface (red) and cytosolic GFP (green). Bottom, HEK293T cells expressing human CD73 on the cell surface (red) and cytosolic GFP (green). j,k. Quantification of the efficacy of the delivery of nanobody-displaying mitochondria into mCD71 or CD73-expressing cells at low (0.5 µg, j) and high (2.5 µg, k) amounts of donor mitochondria two hours after transplantation. For j, mCD71: n = 3; CD73: n = 4, original nanobody: P = 0.7234 (left) and P = 0.7149 (right), matured nanobody: P = 0.02 (left) and P = 0.0009 (right), two-sided Welch’s t test. For k, mCD71: n = 5; CD73: n = 4, original nanobody: P = 0.6990 (left) and P = 0.1318 (right), matured nanobody: P = 0.0051 (left) and P < 0.0001 (right), two-sided Welch’s t test. l. Binding kinetics of anti-CD73 nanobody variants (original clone 2NKT30) to human CD73 measured by surface plasmon resonance. Red line, responses after anti-CD73 nanobody injections at 0.39, 1.56, 6.25, 25, 100 nM; black line, fit to 1:1 binding model. KD (in molar (M)), equilibrium dissociation constant, ka, ligand association rate (1/M × s), kd, ligand dissociation rate (1/s). m. Enrichment of anti-CD73 nanobody mutant variants after three rounds of selection versus measured KD. Original clone, 2NKT30 nanobody. n,o. Quantification of the efficacy of the delivery of nanobody-displaying mitochondria into CD73- or CD142-expressing cells; left (0.5 µg, l) and right (2.5 µg, m) amounts of donor mitochondria two hours after transplantation. For n, n = 5, original nanobody: P = 0.7946 (left) and P = 0.3220 (right), matured nanobody: P = 0.0074 (left) and P = 0.0059 (right), two-sided Welch’s t test. For o, n = 5, original nanobody: P = 0.0301 (left) and P = 0.0048 (right), matured nanobody: P = 0.0079 (left) and P = 0.0004 (right), two-sided Welch’s t test and two-sided Mann-Whitney U test (matured nanobody, left). *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m. Scale bar, 25 µm. The diagram in c was created using BioRender; Ayupov, T. https://BioRender.com/fv9sxoi (2026).

Source data

Extended Data Fig. 8 Full-length antibody conjugation to donor mitochondria.

a. Binder scaffolds used for mitochondria delivery. b. Schematic of full-length antibody conjugation to SNAP-tag-displaying mitochondria. c. HEK293T cells expressing a SNAP-tag at the outer membrane of mitochondria. The SNAP-tag is stained by anti-SNAP antibodies (magenta). Mitochondria are stained by anti-MT-CO1 antibodies (green). The SNAP-tag display on the mitochondrial surface was validated in at least three independent experiments. d. Immunostaining of isolated mitochondria displaying benzylguanine (BG)-conjugated anti-CD31 antibodies bound to the mitochondria outer membrane displayed SNAP-tag (bottom). Isolated mitochondria without BG-anti-CD31 antibodies are shown at the top. Mitochondria were detected by anti-SNAP-tag antibodies (magenta). Mitochondria-displayed anti-CD31 antibodies were detected with anti-mouse IgG conjugated with Alexa fluor-647 conjugated antibodies (cyan). e. Immunostaining of isolated mitochondria displaying BG-conjugated isotype control IgG1 bound to the mitochondria outer membrane-displayed SNAP-tag (bottom). Isolated mitochondria without BG-IgG1 (top). Mitochondria were detected by anti-SNAP-tag antibodies (magenta). Mitochondria-displayed IgG1 were detected with anti-mouseIgG conjugated with Alexa fluor-647 conjugated antibodies (cyan). f. Colocalization of SNAP-tag with either BG-anti-CD31 (left) or BG-IgG1 (right) in (d) and (e), respectively. n = 5, P < 0.0001, two-sided paired t test. g. Western blotting on isolated mitochondria displaying a SNAP-tag fused to a full-length antibody. Isolated mitochondria were incubated at different concentrations with either BG-conjugated anti-CD31 antibodies (BG-anti-CD31) or IgG1 (BG-IgG1). For controls, BG-free antibodies were incubated with isolated mitochondria displaying a SNAP-tag (SNAP-OMP25). Loading control, anti-TOMM20 antibody. A SNAP-tag interacting with the BG-conjugated antibody was detected with anti-SNAP-tag antibodies. Shifting in weight indicates SNAP-tag interaction with the light or heavy chain (LC/HC) of the BG-conjugated antibody. n × SNAP-OMP25, multiple SNAP-tag bound to LC or HC. For gel source data, see Supplementary Fig. 1. The experiment was repeated two times. h. Endothelial cells targeted by donor mitochondria displaying either IgG1 or anti-CD31 antibodies two hours after transplantation. Endothelial cells are stained by Phalloidin dye (green). Donor mitochondria displaying antibodies are stained by anti-SNAP antibodies (magenta). Cells are outlined with grey dashed lines. i. Quantification of the efficacy of the delivery of antibody-displaying mitochondria two hours after transplantation. n = 5, top: P = 0.1051 (10 nM), 0.0016 (100 nM), 0.0008 (500 nM) and bottom P = 0.7501 (10 nM), 0.0079 (100 nM), 0.0013 (500 nM), two-sided Welch’s t test and Mann-Whitney test (bottom 100 nM). Effect of avidity increase on mitochondria targeting efficiency (percentage) for anti-CD31: P = 0.0133 and for control IgG: P = 0.1432, Welch’s ANOVA test. Effect of avidity increase on mitochondria targeting efficiency (ratio) for control IgG: P = 0.0057, Welch’s ANOVA test. j. Schematic of mitochondria delivery displaying either BG-anti-CD31 or BG-IgG1 into primary endothelial cells (shown in magenta, CD31-positive) and cardiac fibroblasts (shown in brown, CD31-negative). k. Endothelial cells and cardiac fibroblasts targeted by donor mitochondria displaying either IgG1 or anti-CD31 antibodies two hours after transplantation. Endothelial cells are stained by anti-CD31 antibodies (red). All cells are stained with Phalloidin dye (green). Donor mitochondria displaying antibodies are stained by anti-SNAP antibodies (magenta). CD31-positive cells are outlined with grey dashed lines. l. Quantification of the efficacy of the delivery of antibody-displaying mitochondria two hours after transplantation. n = 4, P < 0.0001 (left) and P = 0.0158 (right), two-sided Welch’s t test. m. Immunostaining of tdTomato-expressing blood vessel organoids for CD31 (cyan), PDGFRβ (magenta), and RFP (red). The presence of vascular organoid cell types was validated in at least three induction batches. *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m. Scale bars, 10 µm (c, d, e), 50 µm (h, k, m). The diagrams in b and j were created using BioRender; Ayupov, T. https://BioRender.com/fv9sxoi (2026).

Source data

Extended Data Fig. 9 Mitochondria adhesion profiling with single cell force spectroscopy.

a. Schematic of AFM microcantilever functionalization. A silicon microbead (5 µm diameter), which had been glued to the free end of the microcantilever, is pre-coated with concanavalin A. Afterwards anti-TOMM22 antibody-conjugated nanobeads (50 nm, diameter) are attached to the microbead. b. Schematic of an isolated mitochondrion attached to the functionalized microcantilever bead. Anti-TOMM22 antibodies specifically bind TOMM22 proteins at the outer membrane of mitochondria. c. Attaching an isolated mito-GFP to the microbead glued to the microcantilever. White arrows, mito-GFP. d. A microcantilever functionalized with a mito-GFP (green). e. Schematic of an experiment for mitochondria-cell adhesion profiling with AFM-based single-cell force spectroscopy. The mitochondria-attached AFM microcantilever is approached to the cell surface with 2 µm s−1 (1), resulting in mitochondria-cell contact (~100 ms) (2), followed by a retraction with 2 µm s−1 (3), finalizing in mitochondria-cell separation (4). f. Schematic diagram of a construct designed to display the anti-GFP nanobody on the cell surface together with cytosolic mCherry (red) expression (top). Brightfield and fluorescence images of a HEK293T cell expressing the cell-surface anti-GFP nanobody and cytosolic mCherry (bottom). Black and white arrows indicate cells not expressing the anti-GFP nanobody and mCherry. Red arrows indicate a cell positive for the anti-GFP nanobody and mCherry. White dashed lines highlight the AFM microcantilever. The construct was validated in at least three independent experiments. g. Schematic of mitochondria-cell adhesion profiling in HEK293T cells expressing the cell surface anti-GFP nanobody and cytosolic mCherry with mitochondria displaying GFP. h. Example force-distance retraction curves. Total adhesion forces are indicated with dark red arrows. Untreated cells were used as a control. i. Quantification of mitochondrion-cell adhesion force per individual approach. Control: n = 112 (total 22 cells); anti-GFP nanobody: n = 95 (total 29 cells), P < 0.0001, two-sided Mann-Whitney U test. j. Distribution of rupture events per individual approach. k. Quantification of single mitochondrion-cell adhesion force averaged per cell. Control: n = 22, anti-GFP nanobody: n = 29, P < 0.0001, two-sided Mann-Whitney test. l. Quantification of single mitochondrion-cell adhesion force averaged per cell. Control: n = 30, anti-mCD71 nanobody: n = 29, P < 0.0001, two-sided Mann-Whitney test. m. Schematic of the mitochondrion-cell adhesion profiling in the presence of heparan sulfate. Isolated mito-GFP were pre-incubated with heparan sulfate before measuring their adhesion force to either control cells or anti-GFP nanobody expressing cells. n. Force-distance retraction curves (average from n > 25 selected curves per condition). Control cells (top) and cells expressing cell surface anti-GFP nanobody (bottom) were used to profile the adhesion to mito-GFP pre-coated with heparan sulfate. The anti-GFP nanobody-positive cells were identified via cytoplasmic mCherry fluorescence. o. Quantification of mitochondrion-cell adhesion force per approach. Nine data points for the anti-GFP nanobody + heparan sulfate (HS) condition are not shown for plotting. Control: n = 112; Control + HS: n = 110; anti-GFP nanobody + HS: n = 83; Control vs. Control + HS: P < 0.0002, anti-GFP nanobody + HS vs. Control + HS: P < 0.0001, anti-GFP nanobody + HS vs. Control: P < 0.0001, two-sided Kruskal-Wallis test followed by Dunn’s test for multiple comparisons. p. Quantification of mitochondrion-cell adhesion force averaged per cell. One data point for the anti-GFP nanobody + HS condition is not shown for plotting. Control: n = 22; Control + HS: n = 28; anti-GFP nanobody + HS: n = 27; Control vs. Control + HS: P < 0.5172, anti-GFP nanobody + HS vs. Control + HS: P < 0.0001, anti-GFP nanobody + HS vs. Control: P = 0.0006, Kruskal-Wallis test corrected with Dunn’s test for multiple comparisons. q. Schematic (top) and quantification (bottom) of the efficacy of the delivery of nanobody-displaying mitochondria pre-coated with heparan sulfate into CD71-positive T cells four hours after transplantation. n = 6, Control nanobody vs. Control nanobody + HS: P = 0.0152, anti-CD71 nanobody vs. anti-CD71 nanobody + HS: P = 0.0931, two-sided Mann-Whitney test. See Supplementary Fig. 4c for flow cytometry graphs. *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m for q and median for i, k, l, o, p. Scale bars, 5 µm (c, d) and 10 µm (f).

Source data

Extended Data Fig. 10 Generation of bispecific binders for targeted mitochondria delivery.

a. AlphaFold model of anti-GFP and anti-mCherry bispecific nanobody linked via a (GGGS)2G flexible linker. The nanobody scaffold is coloured with dark (anti-GFP) and light magenta (anti-mCherry) and complementarity-determining regions are shown in grey. b. Coomassie-stained polyacrylamide gel showing purified anti-mCherry-anti-GFP and control anti-Lysozyme-anti-GFP bispecific nanobodies used in Fig. 3. Lane 1, protein ladder; lane 2, purified anti-mCherry-anti-GFP nanobody produced in E.coli; lane 3, purified anti-mCherry-anti-GFP nanobody produced in CHO cells; lane 4, purified anti-Lysozyme-anti-GFP nanobody produced in E.coli; lane 5, purified anti-Lysozyme-anti-GFP nanobody produced in CHO cells. In all bispecific nanobody constructs, nanobodies were linked with a (GGGS)2G flexible linker. c. HEK293T cells expressing cell-surface mCherry (cyan) transplanted with mito-GFP (green) without an anti-GFP-anti-mCherry bispecific nanobody, two hours after transplantation (related to Fig. 3a,b). d. Mouse mesenchymal stem cells expressing GFP at the outer membrane of mitochondria (green), cell nuclei (blue) (related to Fig. 3c,d). e. Mouse retinal ganglion cells expressing cell surface-displayed mCherry (cyan) targeted by mito-GFP (green) without an anti-GFP-anti-mCherry bispecific nanobody, one day after transplantation (related to Fig. 3c,d). f. Design of two partially humanized synthetic nanobody libraries with short and long CDR3 regions. Top, 3D protein structure of a synthetic binder depicting framework and complementary-determining regions. Bottom, alignment of the humanized synthetic libraries and the closest human immunoglobulin germline (hV3-23*J4). Non-humanized framework positions are highlighted in red. g. Schematic of mitochondria outer membrane-specific binder selection steps in vitro from a synthetic binder library using ribosome and phage display technologies (top). AlphaFold model of anti-TOMM20 selected binder interacting with TOMM20 cytosolic domain shown as an illustration (bottom). h. Coomassie-stained polyacrylamide gel showing purified human TOMM20 and TOMM70 cytosolic domains used for in vitro binder selection. Lane 1, protein ladder; lane 2, purified TOMM20 produced in E.coli; lane 3, purified TOMM70 produced in E.coli. i. Binding kinetics of anti-TOMM20 (clone T20S.E1, top) and anti-TOMM70 (clone T70S.D7, bottom) nanobody variants to human TOMM20 and TOMM70, respectively, measured by surface plasmon resonance. Red line, responses after the nanobody injections at 0.62, 1.85, 5.55, 16.66, 50 nM for TOMM20 (top) and 0.195, 0.78, 3.13, 12.5, 50 for TOMM70 (bottom); black line, fit to 1:1 binding model. KD (in molar (M)), equilibrium dissociation constant, ka, ligand association rate (1/M × s), kd, ligand dissociation rate (1/s). j. Western blotting on isolated mitochondria lysate with anti-TOMM20 (top) and anti-TOMM70 (bottom) nanobodies (outlined in red). Commercial antibodies for TOMM20 and TOMM70 were used as positive controls (outlined in black). The experiment was repeated two times. For gel source data, see Supplementary Fig. 1. k. Coomassie-stained polyacrylamide gel showing purified anti-TOMM20, anti-TOMM70 nanobodies and anti-TOMM20-anti-CD71, anti-TOMM70-anti-CD71, anti-TOMM20-anti-Lysozyme bispecific nanobodies used in Fig. 3e,f. Lane 1, protein ladder; lane 2, anti-TOMM20 nanobody; lane 3, anti-TOMM70 nanobody; lane 4, anti-TOMM20-αCD71 bispecific nanobody; lane 5, anti-TOMM70-anti-CD71 bispecific nanobody; lane 6, anti-TOMM20-anti-Lysozyme control bispecific nanobody. All proteins were produced in E.coli. In all bispecific nanobody constructs, nanobodies were linked with a (GGGS)2G flexible linker. l. HEK293T cells expressing cytosolic GFP and human CD71 (green) transplanted with mito-dsRed2 (cyan) without any binder (top) and with anti-TOMM70-anti-CD71 bispecific nanobody (bottom) two hours after transplantation. Cells positive for GFP are outlined with grey dashed lines. m. Quantification of the efficacy of the delivery of mitochondria conjugated to bispecific nanobodies two hours after transplantation. Data for mito-DsRed2 is replotted from Fig. 3. n = 6, P = 0.0015 (left) and P = 0.001 (right), two-sided Welch’s t test. n. Coomassie-stained polyacrylamide gel showing purified anti-TOMM20-anti-CD4 bispecific nanobodies used in Fig. 3g–i. Lane 1, protein ladder; lane 2, anti-TOMM20-anti-CD4.Nb457 (clone 2); lane 3, anti-TOMM20-anti-CD4.Nb1 (clone 1). In all bispecific nanobody constructs, nanobodies were linked with a (GGGS)2G flexible linker. o. Gating strategy of bulk CD3-positive T cells for the identification of single cells. Left, cells were selected from debris by forward scatter area (FSC-A) and side scatter area (SSC-A). Right, single cells were selected by FSC-A and forward scatter height (FSC-H). p. Gating strategy of bulk CD3-positive T cells for the identification of CD4-positive T cells. Left, unstained cells. Right, cells stained with anti-CD4 antibodies conjugated to FITC. *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m. Scale bars, 25 µm.

Source data

Extended Data Fig. 11 Targeted mitochondria delivery into starving LHON iHNeurons and primary endothelial cells.

a. Schematic of a single-cell picking experiment on LHON iHNeurons transplanted with mito-GFP and pre-treated with trypsin. For a single cell trypsin treatment example, see Supplementary Fig. 5h,i. b. Relative frequency of ND4 variants (LHON:mt11778A and wild type:mt11778G) in mito-GFP transplanted LHON iHNeurons (n = 39) pre-treated with trypsin before single-cell picking. For additional cell type annotation data, see Supplementary Fig. 5j,k. c. Schematic of mitochondrial DNA (mtDNA) depletion experiment in HEK293T cells using 2′-3′-dideoxycytidine (ddC) and ethidium bromide (EtBr). d. Genotyping of cells for the presence of mtDNA. Untreated HEK293T cells without any addition of ddC and EtBr. ddC + EtBr, HEK293T cells treated with 40 µM ddC + 0.5 ug/ml EtBr treatment. Two sets of pre-mixed primers were used for PCR: the first set is specific for a hypervariable region in the D-loop of mtDNA, the second set is for the nuclear-encoded hPOLRMT gene. Due to the high abundance of mtDNA, the genomic DNA (gDNA) is not amplified for untreated cells. e. Quantifications of mtDNA copy number by ddPCR. The abundance of mtDNA is shown as the ratio between mtDNA/gDNA. n = 3, P = 0.009, two-sided Welch’s t test. f. Schematic of a single-cell picking experiment on LHON iHNeurons transplanted with mtDNA-depleted mito-GFP. g. Relative frequency of ND4 variants (LHON:mt11778A and wild type:mt11778G) in mtDNA-depleted mito-GFP transplanted LHON iHNeurons (n = 19). h. Quantification of mtDNA-encoded mitochondrial transcripts in LHON iHNeurons. Mito-GFP: n = 31; mito-GFP + trypsin pre-treatment: n = 39; mtDNA-depleted mito-GFP: n = 19; Non-transplanted: n = 41 (combined controls from all experiments), mito-GFP vs. mito-GFP + trypsin pre-treatment: P > 0.9999, mito-GFP vs. mtDNA-depleted mito-GFP: P = 0.0156, mito-GFP + trypsin pre-treatment vs. mtDNA-depleted mito-GFP: P = 0.0009, mito-GFP + trypsin pre-treatment vs. Non-transplanted: P < 0.0001, mtDNA-depleted mito-GFP vs. Non-transplanted: P > 0.9999, Kruskal-Wallis test with Dunn’s test for multiple comparisons. i. Quantification of basal, ATP-linked, and maximum respiration in LHON iHNeurons two days after mito-GFP transplantation. The data were normalized to ‘non-transplanted’ samples within each experiment. Non-transplanted: n = 13, Control nanobody + mito-GFP (10 µg): n = 11; anti-GFP nanobody + mito-GFP (10 µg): n = 11; anti-GFP nanobody + mtDNA-depleted mito-GFP (10 µg): n = 8; anti-GFP nanobody + mito-GFP (2.5 and 5 µg): n = 5, Non-transplanted vs. Control nanobody + mito-GFP (10 µg): P = 0.7689 (basal), P = 0.9983 (ATP-linked), P = 0.4911 (maximal), Non-transplanted vs. anti-GFP nanobody + mtDNA-depleted mito-GFP (10 µg): P = 0.6991 (basal), P = 0.7164 (ATP-linked), P > 0.9999 (maximal), Non-transplanted vs. anti-GFP nanobody + mtDNA-depleted mito-GFP (2.5 µg): P = 0.3535 (basal), P = 0.4903 (ATP-linked), P > 0.9999 (maximal), Non-transplanted vs. anti-GFP nanobody + mtDNA-depleted mito-GFP (5 µg): P = 0.0166 (basal), P < 0.0001 (ATP-linked), P = 0.1313 (maximal), Non-transplanted vs. anti-GFP nanobody + mtDNA-depleted mito-GFP (10 µg): P = 0.0005 (basal), P = 0.0289 (ATP-linked), P = 0.0176 (maximal). Basal respiration, one-way ANOVA with two-sided Dunnet’s test for multiple comparisons. ATP-linked respiration, Welch’s ANOVA with two-sided Dunnet’s test for multiple comparisons. Maximal respiration, Kruskal-Wallis test with Dunn’s test for multiple comparisons. j. Live imaging of LHON iHNeurons expressing cytosolic tdTomato (cyan) during starvation imaged for 48 h related to Fig. 4g,h. The degeneration of iHNeurons was over the course of starvation was confirmed in at least three independent experiments. k. Examples of live-imaged LHON iHNeurons detachment during starvation for 96 h. Cells expressed cytosolic tdTomato (cyan) related to Fig. 4g,h. l. Live-imaged LHON iHNeurons expressing cytosolic tdTomato (cyan) co-stained with calcein viability dye (black) after two days starvation. m. Quantification of tdTomato-expressing cells positive for calcein after two days of starvation (n = 5) in (l). n. Live imaging of LHON iHNeurons expressing cytosolic tdTomato (cyan) before and after glucose substitution with galactose. Left, LHON iHNeurons expressing anti-GFP nanobody treated with uridine (50 ng/ml). Right, LHON iHNeurons expressing anti-GFP nanobody transplanted with mtDNA-depleted mito-GFP. o. Quantification of the cell survival rate in galactose media in (n). Non-transplanted: n = 4; Uridine treated: n = 5; mtDNA-depleted mito-GFP treated: n = 5, Non-transplanted vs. Uridine treated: P = 0.8373, Non-transplanted vs. mtDNA-depleted mito-GFP: P = 0.9336, Welch’s ANOVA followed by two-sided Dunnett’s test for multiple comparisons. p. Live imaging of LHON iHNeurons expressing cytosolic tdTomato (cyan) together with either control nanobody (top) or the anti-GFP nanobody (bottom) and transplanted with mtDNA-depleted mito-GFP (green) after two days post-transplantation related to Fig. 2g,h. Mito-GFP movement was manually tracked over time. The tracks are shown with green lines. White arrows, internalized mito-GFP. Red arrows, extracellular mito-GFP. q. Quantification of mito-GFP travelled distance in LHON iHNeurons expressing either control nanobody or the anti-GFP nanobody in (p). Control nanobody: n = 29 (individual tracks from 3 cells); anti-GFP nanobody: n = 47 (individual tracks from 3 cells), P < 0.0001, two-sided Mann-Whitney U test. r. LHON iHNeurons expressing either cytosolic tdTomato (cyan) or cytosolic GFP (green) together with cell surface-targeted anti-mCherry nanobody (magenta). s. Histograms displaying the distribution of mean nanobody fluorescence in tdTomato-positive (n = 195 cells) and GFP-positive cells (n = 152 cells) in (r). P = 0.0946, two-sided Kolmogorov-Smirnov test. t. Wound-healing assay with endothelial cells transplanted with mitochondria displaying either BG-anti-CD31 antibodies or control BG-IgG1. For controls, cells were incubated with BG-anti-CD31 antibodies. u. Quantifications of gap closure 10 h after wound generation. Anti-CD31 only: n = 9; mito-BG-IgG1: n = 6; mito-BG-anti-CD31: n = 6, P = 0.0287 (mito-BG-anti-CD31 vs. αCD31 only) and P = 0.0314 (mito-BG-anti-CD31 vs. mito-BG-IgG1), Kruskal-Wallis test with Dunn’s test for multiple comparisons. NS not significant, *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m and median for h, q. Scale bars, 100 µm (j), 500 µm (k), 25 µm (l, n, r), 5 µm (p), 200 µm (t).

Source data

Extended Data Fig. 12 Targeted delivery of mitochondria to mouse retinal ganglion cells after ONC.

a. Ganglion cell axon terminals in the dorsal lateral geniculate nucleus (dLGN) of the thalamus with and without optic nerve crush (ONC). Ganglion cell axons are labelled with cholera toxin subunit B (ctb) fused to Alexa Fluor dye injected into the eye (cyan). b. Quantification of axonal projections from retinal ganglion cells to dLGN after optic nerve injury (ONC). Control: n = 5; ONC: n = 5, P = 0.0387, two-sided Welch’s t test. c. PV-retinal ganglion cells 10 days after ONC stained for tdTomato (cyan) and RBPMS (red). The retinas are from the experiment in Fig. 5d. d. Left, schematic of mitochondrial DNA (mtDNA) depletion experiment in primary mouse mesenchymal stem cells (mMSCs) using 2′-3′-dideoxycytidine (ddC) and ethidium bromide (EtBr). Top right, quantifications of mtDNA copy number by ddPCR. Bottom right, mtDNA-depleted mMSCs expressing mito-GFP (green). The abundance of mtDNA is shown as a ratio between mtDNA/gDNA. n = 3, P = 0.0013, two-sided Welch’s t test. e. Quantification of the density of surviving tdTomato-expressing retinal ganglion cells after ONC followed by mito-GFP injection at different doses. PV-retinal ganglion cells were detected and quantified by co-staining with anti-RFP and anti-RBPMS antibodies. The 2.5 µg and 5 µg datasets are replotted from Fig. 5e. Anti-GFP nanobody + ONC + mito-GFP: n = 6 (1 µg and 2.5 µg) and n = 8 (5 µg); anti-GFP nanobody + ONC + mtDNA-depleted mito-GFP: n = 6 (5 µg), P = 0.002 (1 µg vs. 2.5 µg), P = 0.0045 (1 µg vs. 5 µg), P = 0.2089 (1 µg vs. mtDNA-depleted 5 µg), P = 0.9997 (2.5 µg vs. 5 µg), P = 0.0233 (2.5 µg vs. mtDNA-depleted 5 µg), P = 0.0359 (5 µg vs. mtDNA-depleted 5 µg), Welch’s ANOVA followed by two-sided Dunnett’s test for multiple comparisons. f. Live-imaged PV-retinal ganglion cells expressing the anti-GFP nanobody together with cytosolic GCaMP8m (grey). ONC + buffer, injected with respiration buffer after ONC and imaged after 10 days. ONC + mito-GFP, injected with 5 µg of mito-GFP after ONC and imaged after 10 days. Red arrows, axonal beading areas. g. Quantifications of axonal beading in mouse retinas after ONC. n = 4, ONC + buffer vs. ONC + mito-GFP: P = 0.0003, two-sided Welch’s t test. *P < 0.05, **P < 0.01, ***P < 0.001. Data, mean ± s.e.m. Scale bars, 100 µm (a), 50 µm (c,f), 10 µm (d).

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Supplementary information

Supplementary Information (download PDF )

Supplementary Discussion, Supplementary Figures and Supplementary Data.

Reporting Summary (download PDF )

Supplementary Tables 1–4 (download ZIP )

Supplementary Table 1: mitochondria targeting specificity in all MitoCatch experiments. Supplementary Table 2: protein list used for enrichment analysis in Extended Data Fig. 2. Supplementary Table 3: characteristics of newly generated nanobodies and their affinity matured variants. Supplementary Table 4: list of constructs used in this study.

Peer Review file (download PDF )

Supplementary Video 1 (download MP4 )

Transport of mito-GFP in iHNeurons. iHNeurons expressing cell-surface anti-GFP nanobody transplanted with mito-GFP (green). A neurite is outlined with white dashed lines. The video was recorded 3 days after mitochondrial transplantation. Scale bar, 5 mm.

Supplementary Video 2 (download MP4 )

Fission and fusion events of transplanted mito-GFP. iHNeurons expressing cell-surface anti-GFP nanobody transplanted with mito-GFP (green). Fission and fusion events of transplanted mitochondria are illustrated with white arrows. The video was recorded 3 days after mitochondrial transplantation. Scale bar, 5 mm.

Supplementary Video 3 (download MP4 )

Transport of mito-GFP in iHNeurons with labelled native mitochondria. iHNeurons expressing cell-surface anti-GFP nanobody together with mitochondria matrix-targeted dsRed2 (red) transplanted with mito-GFP (green). The video was recorded 6 h after mitochondrial transplantation. Scale bar, 5 µm.

Supplementary Video 4 (download MP4 )

Transport of mito-GFP in iHNeurons with labelled endoplasmic reticulum. iHNeurons expressing cell-surface anti-GFP nanobody together with endoplasmic reticulum-targeted TagBFP (magenta) transplanted with mito-GFP (green). The video was recorded 2 days after mitochondrial transplantation. Scale bar, 5 µm.

Supplementary Video 5 (download MP4 )

Transport of mito-GFP in HUVECs. HUVECs expressing cell-surface anti-GFP nanobody transplanted with mito-GFP (green). The video was recorded 1 day after mitochondrial transplantation. Scale bar, 10 mm.

Supplementary Video 6 (download MP4 )

Transport of mito-GFP in HUVECs with labelled endoplasmic reticulum. HUVECs expressing cell-surface anti-GFP nanobody together with endoplasmic-reticulum-targeted TagBFP (magenta) transplanted with mito-GFP (green). The video was recorded 6 h after mitochondrial transplantation. Scale bar, 5 µm.

Supplementary Video 7 (download MP4 )

Donor mito-GFP can be free from lysosomes. HUVECs expressing cell-surface anti-GFP nanobody transplanted with mito-GFP (green). Lysosomes are labelled with pHLys Red dye (magenta). The video was recorded 1 day after mitochondrial transplantation. Scale bar, 5 µm.

Supplementary Video 8 (download MP4 )

Accessibility of transplanted mito-GFP to the cytosol of an iHNeuron. Right, iHNeurons expressing cell-surface anti-GFP nanobody transplanted with mito-GFP (green). Left, mito-GFP that are accessible to the cytosol (cyan). Video was recorded 1 day after mitochondrial transplantation. TagBFP channel was denoised using N2V. Scale bar, 5 mm.

Supplementary Video 9 (download MP4 )

Transport of mito-GFP in human retinal cells. Human retinal cells expressing cell-surface anti-GFP nanobody transplanted with mito-GFP (green). A neurite is outlined with white dashed lines. The video was recorded 1 day after mitochondrial transplantation. Scale bar, 5 mm.

Supplementary Video 10 (download MP4 )

Donor mito-mTagBFP2 can be free from endocytic vesicles. HUVECs expressing cell surface GFP and endosome-targeted Rab5a-TagRFP (magenta) transplanted with mito-mTagBFP2 displaying anti-GFP nanobody (cyan). The videos were recorded 6 h and 1 day after mitochondrial transplantation, respectively. Two different cells are shown. Scale bar, 5 mm.

Supplementary Video 11 (download MP4 )

Donor mito-dsRed2 form a tubular network. HUVECs expressing cell surface GFP transplanted with mito-dsRed2 displaying anti-GFP nanobody (cyan). The video was recorded 4 days after mitochondrial transplantation. Scale bar, 10 mm.

Supplementary Video 12 (download MP4 )

Donor mito-dsRed2 interact with native mitochondria. HUVECs expressing cell surface GFP transplanted with mito-dsRed2 displaying anti-GFP nanobody (cyan). Cells were additionally stained with MitoTracker to label native mitochondria (magenta). The video was recorded 4 days after mitochondrial transplantation. Scale bar, 10 mm.

Supplementary Video 13 (download MP4 )

Functionalization of an AFM microcantilever with a single mitochondrion. Isolated mito-GFP is collected by a anti-TOMM22 antibody precoated microcantilever. Scale bar, 10 mm.

Supplementary Video 14 (download MP4 )

Starvation of LHON iHNeurons expressing cytosolic tdTomato. LHON iHNeurons expressing cytosolic tdTomato (cyan) cultured in glucose-free medium supplemented with galactose for 2 days. Scale bar, 100 mm.

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Ayupov, T., Moreno-Juan, V., Curtoni, S. et al. Cell-type-targeted mitochondrial transplantation rescues cell degeneration. Nature 653, 221–231 (2026). https://doi.org/10.1038/s41586-026-10391-0

- Received: 09 July 2024

- Accepted: 10 March 2026

- Published: 15 April 2026

- Version of record: 15 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10391-0

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Synthetic super-enhancers enable precision viral immunotherapy

Ute Koeber,

Mantas Matjusaitis,

Neza Alfazema,

Katharine Furlong,

Zeyu Wang,

Rachel White,

Alhafidz Hamdan,

Pooran Dewari,

Gregoire Morisse,

Mariela Navarette,

Rosie Willis,

Jin Wang,

Michelle P. Clark,

Carla Jacinto de Sousa,

Hei Ip Hong,

Shahida Sheraz,

Ben Southgate,

Justyna Cholewa-Waclaw,

Sabine Gogolok,

Gillian M. Morrison,

Felipe Galvez Cancino,

Faye Robertson,

Anna Williams,

Susan J. Rosser,

Paul M. Brennan,

Dirk Sieger,

Abdenour Soufi,

Sergio A. Quezada &

…

Steven M. Pollard

Nature volume 653, pages 232–241 (2026)

Abstract

Cell-type-specific promoters are used in gene therapy to restrict expression of the therapeutic payload. However, these promoters often have suboptimal strength, selectivity and size. Here, leveraging recent insights into the function of enhancers, we developed synthetic super-enhancers (SSEs) by assembling functionally validated enhancer fragments into multipart arrays. Focusing on the core SOX2-driven and SOX9-driven transcriptional regulatory network in glioblastoma stem cells (GSCs)[1], we engineered SSEs with robust activity and high selectivity. Single-cell profiling, biochemical analyses and genome-binding data indicated that SSEs integrate neurodevelopmental and signalling-state transcription factors to trigger the formation of large multimeric complexes of transcription factors. Moreover, GSC-selective expression of a combination of cytotoxic (HSV-TK and ganciclovir) and immunomodulatory (IL-12) payloads, delivered using adeno-associated virus vectors, as a single treatment led to curative outcomes in a mouse model of aggressive glioblastoma. Notably, IL-12 induced an immunological memory that prevented tumour recurrence. The activity and selectivity of the adeno-associated virus and SSE were validated using primary human glioblastoma tissue and normal cortex samples. In summary, SSEs harness the unique core transcriptional programs that define the GSC phenotype and enable precision immune activation. This approach may have broader applications in other contexts when precise control of transgene expression in specific cell states is necessary.

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Main

Gene therapies require the targeted expression of payloads in specific cell populations to achieve appropriate therapeutic dosing and to minimize off-target effects. Various strategies to attain this selectivity can be explored, including delivery routes, capsid engineering and regulatory elements (for example, promoters, enhancers, untranslated regions and termination signals)[2]. Enhancers determine cell-type-specific expression by recruiting complementary transcription factors (TFs) at high density via TF-binding motifs (TFBMs)3,4. However, natural enhancers often have suboptimal features for application in gene therapy vectors, such as size, strength, selectivity and quality of the sequence (for example, GC-rich or repetitive sequences). Strategies for creating artificial promoters and enhancers have typically focused on screening small (around 10 bp) synthetic TFBMs and assembling them into concatamers5,6,7. However, this approach is inadequate owing to the lack of a natural TFBM grammar, that is, the spacing, order, orientation and affinity of TFBMs, which are necessary for cell-type selectivity[8].

Cell-type-specific enhancers are often clustered in the genome to create super-enhancers9,10. Super-enhancers typically regulate transcriptional programs associated with cell identity through the local clustering of low-affinity motifs[8]. This leads to increased high-density binding of lineage and signalling TFs to promote RNA polymerase II recruitment and high transcriptional output11,12. We propose that synthetic super-enhancers (SSEs) can be engineered by assembling natural enhancer fragments associated with different genes into multipart arrays and then functionally screening them to identify desired activity and selectivity. The resulting transgene regulatory elements would capture the fundamental grammar of the combinations of TFs that define a specific cell type and signalling state. Such SSEs could have substantial value in gene therapy applications.

We focused on glioblastoma (GBM) to test this platform. GBM is an incurable brain cancer that is driven by cells with fetal neural stem-like phenotypes termed GBM stem cells (GSCs). These cells express high levels of master regulatory TFs associated with neural stem and progenitor cells, including SOX2 (ref. [13]). SOX2 is a pioneer TF and reprogramming factor essential for inducing and maintaining the identity of neural stem cells (NSCs)14,15 and GSCs1,16,17,18. SOX2 is also a crucial master regulatory TF that is broadly expressed across diverse GSC subtypes[18].

Here we develop SSEs for the targeted expression of anticancer payloads in GSCs. A single, locally delivered dose of adeno-associated virus (AAV)-SSE-7[19] was evaluated in an immunocompetent model of aggressive GBM. We demonstrate that tumours are safely cleared using this precise and controlled method to express a dual payload of a cytotoxic (herpes simplex virus thymidine kinase and ganciclovir (HSV-TK/GCV)) and a cytokine (IL-12).

GSC-specific enhancer fragment design

TFs bind to thousands of sites in the genome, but only a small subset are functional enhancers[20]. To identify candidate GSC-selective enhancers, we first re-analysed previously published datasets[1] that mapped SOX2-binding sites and used chromatin immunoprecipitation with sequencing (ChIP–seq) to define those specific to GSCs and lost in their differentiated progeny. Of the 1,710 candidate GSC-specific peaks identified (range of 115–3,366 bp, mean of 400 bp), a library of 160-bp double-stranded DNA oligonucleotides, with overlapping 100 bp segments, was synthesized (Extended Data Fig. 1). The 160 bp length was chosen as this was deemed to be the optimum size to capture the core set of TFBMs with the necessary natural grammar and motif diversity that might confer selectivity. Moreover, this length is compatible with nucleosome formation for pioneer factors such as SOX2 (ref. [21]). Candidate fragments were amplified by PCR, cloned into a luciferase–mNeonGreen reporter plasmid and assembled into an arrayed plasmid library (48 × 96-well plates with 4,579 individual plasmids) (Fig. 1a).

Fig. 1: Functional enhancer screening of an arrayed plasmid library.

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a, Strategy for enhancer fragment (160 bp) functional screening using an arrayed library of 4,579 plasmids with luciferase (Luc; yellow) and mNeonGreen (mNGreen; green) reporters (single mRNA linked by P2A; grey), with an mCMV promoter (light blue). b, Results for the enhancer fragment screen in GSCs (G7 cells) (n = 1). c, Validation of 135 hits from the primary screen using independent luciferase reporter assays (NanoLuc DLR assay) (n = 3 biological replicates, error bars represent the s.d. of the arithmetic mean). The inset shows the enriched SOX dimer motif identified using the MEME tool. d, Validation of the top 16 significant hits from c, using flow cytometry to determine the percentage of mNeonGreen reporter-positive cells in an independent GSC line (E28), and the MFI per cell for each fragment (right). mCMV and the full-length CMV promoter were used as negative and positive reference controls, respectively (each dot represents a biological replicate (n = 3), error bars represent the s.d. of the arithmetic mean). Ordinary one-way analysis of variance (ANOVA) with Dunnet’s multiple comparison test against mCMV was performed. For the mNeonGreen (%) data, adjusted P < 0.0001 for ID3146, P = 0.0018 for ID1101 and P = 0.0003 for ID2339. In the MFI mNeonGreen panel, P = 0.0171 for ID2706, P = 0.0092 for ID3146, P = 0.0038 for ID1101, P = 0.0034 for ID2339 and P = 0.0037 for ID2904. e, Negative control normal human dermal fibroblasts (huFib70) using the same assays as d (each dot represents a biological replicate (n = 3), error bars represent the s.d. of the arithmetic mean). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

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Discovery of functional GSC enhancers

Patient-derived GSCs can be routinely propagated in a monolayer and can be efficiently transfected[13]. We screened the enhancer fragment plasmid library in GSCs (specifically GSC7 cells) using a luciferase assay to identify functional hits[22]. From this initial screen, 135 plasmids exhibited more than 10-fold activity over a minimal CMV (mCMV) promoter (Fig. 1b). Several hits were identified recurrently, which provided confidence in the screening assay.

Sanger sequencing confirmed sequences of the 135 initial hits, and 32 unique sequences were validated in triplicate with >20-fold activity compared with mCMV (Fig. 1c, Extended Data Table 1 and Supplementary Table 1). The top 16 candidates were also validated using an independent flow cytometry assay to detect the mNeonGreen transcriptional reporter, which provides single-cell quantification of activity and mean fluorescence intensity (MFI). All 16 validated hits were active in an independent patient-derived GSC line (E28), but were inactive in fibroblasts and HEK293 cells, which were used as SOX2-negative controls (Fig. 1d,e and Extended Data Fig. 1c,d). As anticipated, these hit sequences contained TFBMs for many known neurodevelopmental or GBM-associated TF families, including motifs for SOX, bHLH, FOX, TCF and SMAD (Extended Data Fig. 1e).

Notably, de novo motif searches using the MEME tool revealed that the 32 active fragments contained a specific SOX dimer motif that resembles a previously reported SOX9 motif[23] (Fig. 1c). Although SOX2 typically operates as a monomer, SOX9—a known NSC self-renewal factor[24]—can function as a homodimer[23]. This result suggests that the functional enhancer fragments potentially bind SOX9.

To functionally validate the importance of this motif in the enhancer fragments, we created a series of mutants in the dimer motif of ID2904 (which was chosen because it only has a single motif) with nucleotide substitutions, inversions or changes in spacing. Each of these alterations abolished the transcriptional activity of the fragment (Extended Data Fig. 2a–f). Higher activity was observed when the distance between the half-sites was reduced by 2 bp. This result suggests that dimer binding is a crucial feature of the activity of the motif and is probably important for SOX9 binding. Indeed, using biochemical pull-down, we confirmed that SOX9 binds to ID2904 and that binding depends on the SOX dimer motif (Extended Data Fig. 2g).

The above findings led us to speculate that SOX2 and SOX9 might work together at shared target enhancers in GSCs. To find their putative shared target sites, we mapped genome-wide binding of SOX2 and SOX9 in seven independent GSC patient-derived cell lines using ChIP–seq (Fig. 2a). Most SOX9 peaks co-bound with SOX2, and these overlapped with previously reported super-enhancers found across GSCs (Fig. 2b–d). These co-bound peaks were located near genes involved in stem cell function (Fig. 2e), including essential regulators of neural stem and progenitor cells (for example, CDK6 and PTPRZ1; Fig. 2h). The expression of SOX2 and SOX9 was positively correlated, particularly in GBM (relative to most other cancers), which implied that these two factors have a disease-specific function and/or regulatory interactions (Fig. 2f). Notably, the SOX2 and SOX9 peaks in co-bound sites at enhancers were enriched with multiple SOX monomer and dimer motifs, respectively, with an optimal dimer motif spacing of 2–3 bp (Fig. 2g,i). Altogether, these data indicate a specific requirement for SOX9 dimerization at loci in which SOX2 and SOX9 are co-bound.

Fig. 2: ChIP–seq analysis of SOX2 and SOX9 binding in seven different patient-derived GSC cultures.

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a, Schematic of SOX2 and SOX9 ChIP–seq experimental design and analysis. GO, gene ontology. b, Venn diagram showing the overlap between SOX2 and SOX9 peaks for the sample E28 (left), and heatmaps of SOX2 and SOX9 binding intensity over SOX9 and SOX2 peaks, respectively (right), demonstrating co-binding events. c, Bar plots showing the proportion of SOX9 peaks located in co-bound sites. d, Venn diagrams showing the overlaps between consensus SOX2 and SOX9 peaks and co-bound SOX2–SOX9 peaks with a consensus set of GSC super-enhancers (n = 44 publicly available GSC H3K27ac datasets; Methods). e, Gene ontology analysis of co-bound SOX2–SOX9 peaks in super-enhancers. f, Bar plot showing the distribution of SOX2–SOX9 expression correlation values (y axis) across diverse cancer types from The Cancer Genome Atlas (x axis). g, Bar plot showing the motif co-occurrences of inverted palindromic SOX motifs, grouped by the distances between the primary and secondary SOX motif pairings. h, UCSC genome browser coverage tracks of SOX2 and SOX9 ChIP–seq in the seven individual GSCs at PTPRZ1 and CDK6 loci, annotated by the presence of GSC super-enhancers, co-bound SOX2–SOX9 peaks and the functional SOX dimer motif. cCREs, candidate cis-regulatory elements; Cons 100 Verts, conservation across 100 vertebrates. i, The distributions of centrally enriched de novo SOX monomer and dimer motifs at SOX2 and SOX9 peaks, respectively, restricted to those in co-bound SOX2–SOX9 enhancers.

In summary, using functional screening in patient-derived GSCs, we identified candidate 160 bp SOX2 and SOX9 co-bound enhancer fragments with high selectivity but low activity (relative to CMV). Together with previously published findings, such as genome-wide CRISPR screens[18], these results highlight the importance of SOX2 and SOX9 as core components of a TF circuit that determines and sustains GSC identity. Moreover, they share crucial transcriptional targets.

SSEs for GBM

Our goal was to create a GSC-selective transcriptional switch for cancer gene therapy that combines strong expression and high selectivity. Such a SSE would provide the necessary therapeutic window to target the tumour but spare surrounding normal CNS tissue. We proposed that combining functional and selective enhancer fragments into a larger artificial multipart assembly might increase activity by facilitating increased high-density recruitment of SOX2 and SOX9 alongside other signalling-associated transcriptional cofactors to mimic the mechanism of action of natural super-enhancers11,12. We chose four-part assemblies, as this represents an optimal size compatible with AAV vector applications. These assemblies were constructed using sequences from the top 16 validated hits grouped into four sets: 1–4, 5–8, 9–12 and 13–16 (Fig. 3a,c).

Fig. 3: Combining SOX2 functional enhancer fragments results in synergistic increases in activity without compromising selectivity.

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a, Activity of the 16 top hits relative to mCMV and CMV assessed by flow cytometry in GSCs (G7 cells) (each dot represents a biological replicate, error bars represent the s.d. of the mean). b, SSE activity detected using an mNeonGreen reporter across GSC lines (GSC7 and E28) and control fibroblasts. Error bars represent the s.d. of the mean. One-way ANOVA with Dunnett’s multiple comparisons test against mCMV, adjusted P values for G7: P = 0.0001 for SSE-1, P = 0.0009 for SSE-3, P = 0.0003 for SSE-5 and P < 0.0001 for SSE-7. Adjusted P values for E28: P = 0.0099 for SSE-1, P = 0.0048 for SSE-3, P = 0.0075 for SSE-5 and P = 0.0008 for SSE-7. UT, untreated. c, Schematic of each SSE-1, SSE-3, SSE-5 and SSE-7 transgene design. d, EMSA of SSE-7 DNA following incubation with GSC lysates compared with differentiated GSCs (representative image; n = 3). e, EMSA showing SSE-7 DNA with fibroblast (hFib) lysate (left) and hFib with SOX2 and SOX9 expressed individually (middle) or in combination (right). Representative images shown (n = 3). f, EMSA of SSE-7 DNA (left, representative images of n = 3) and its constituent fragments (right, n = 1), incubated with SOX2 and SOX9. Schematic of SOX motifs (thin black bar, partial SOX motif; thicker, SOX motif; thickest, dimer motif) is shown underneath the images. g, SABER–FISH analysis of transgenes. Representative images are shown (left), with quantification (right). For SSE-7, mean copy number = 167 (median, 119; Q1–Q3, 76–209; min–max, 8–806). For CMV, mean copy number = 115 (median, 87; Q1–Q3, 57–127; min–max, 5–761). Scale bars, 20 μm. h, scRNA-seq analysis shows no correlation between SSE-7 activity transcriptional subtypes (AC, astrocyte; MES, mesenchymal; NPC, neural progenitor cell). Neftel subtype, subtype as identified by Neftel et al.[27]. Distribution of SSE-7–mCherryhigh cells using UMAP plots of scRNA-seq analysis in E21 cells (n = 23,384 cells) and E28 cells (n = 35,879). i, SCENIC analysis to determine TF enrichment across replicate libraries. Combined Z scores from each library. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001; NS, not significant. P values were calculated using repeated-measures ANOVA, followed by Bonferroni-adjusted pairwise comparisons of estimated marginal means. Boxes show the interquartile range, centre lines the median, and whiskers the minimum and maximum values.

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Each variant displayed strong activity that matched or exceeded that of a full-length CMV promoter in GSCs. By contrast, the variants had low activity in both fibroblasts and HEK293 cells, such that levels were comparable to mCMV-like levels (Fig. 3b and Extended Data Fig. 4a). Given the principles that underpin their design, their high activity (switch-like behaviour) and their high cell-type selectivity, we herein refer to these artificial elements as SSEs.

To initially explore selectivity in glial cell lineage differentiation, we evaluated SSE activity during GSC differentiation into astrocyte-like cells1,13. We observed a twofold reduction in MFI per cell within 24 h, followed by an approximately tenfold decrease by day 10 of differentiation (Extended Data Fig. 4b,c). By contrast, the constitutive CMV promoter displayed high activity across all conditions. SSE activity is therefore highest in the immature stem cell-like state of GSCs and is rapidly reduced as cells differentiate.

Focusing on SSE-7, we next tested a larger set of patient-derived GSCs that span diverse genetic and transcriptional subtypes25,26. SSE-7 was active across all seven patient-derived GSCs but had reduced levels in E37 (an atypical cell line with MYC amplifications and reduced SOX2 and SOX9 levels) (Extended Data Fig. 3). However, there was variability across the cell lines in the percentage of positive cells in the population compared with CMV, which indicated heterogeneity in the cultures. This result was not explained by SOX2 or SOX9 protein levels, as these were similar across lines, which suggests that additional cooperating TFs might be necessary for SSE-7 activity.

We next explored the biochemical mechanism of the switch-like behaviour and selectivity of SSE-7 and the roles of SOX2 and SOX9 in this process. To that end, we first used cell-free electrophoretic mobility shift assays (EMSAs) to analyse their interactions with SSE-7 alone and in combination using both cell lysates and cell-free recombinant SOX proteins.

SSE-7 incubation with GSC cell extracts resulted in a major super-shift and the formation of large higher-order multimeric complexes, but this was not observed in GSCs that had differentiated into astrocytes (Fig. 3d). Using human fibroblasts with SOX2 and/or SOX9 forced expression, we also observed a clear super-shifted band only when both factors were incubated together with SSE-7, but this effect was weaker compared with GSCs. This result highlights the importance of GSC-associated cofactors for triggering SSE activity (Fig. 3e).

Next, to validate whether synergy exists with SOX2 and SOX9 co-binding, we used purified recombinant SOX2 and SOX9 proteins and tested their binding. When SOX2 alone was bound with SSE-7, it displayed a typical ladder-like banding pattern indicative of SOX2 binding individually at each motif in the 640-bp-long SSE-7 (Fig. 3f and Extended Data Fig. 4d–f). Notably, the binding of SOX9 led to the formation of the higher-order complexes, and this effect was enhanced in the presence of SOX2 (Fig. 3f). The analysis of each constituent 160 bp enhancer fragment in isolation demonstrated that the formation of higher-order complexes depends on the architecture of the SSE (Fig. 3f). No higher-order complexes were formed when using a control 40 bp FGF4 enhancer that contains a single SOX motif (Extended Data Fig. 4g). We therefore conclude that a key feature of the mechanism of action of SSE-7 is its ability to induce higher-order TF complexes through high-density local recruitment of SOX2 and SOX9.

Mechanistic basis of SSE activity in GSCs

We next aimed to identify candidate SOX-cooperating TFs and the cofactors required for the SSE switch-like transcriptional activation seen in GSCs. To that end, we performed single-cell RNA sequencing (scRNA-seq) analysis of 2,711 SSE-7–mCherry-positive cells across 8 different GSC lines, comparing these to matched mCherry-negative populations. We first used the single-molecule imaging technique signal amplification by exchange reaction and fluorescence in situ hybridization (SABER–FISH) and confirmed that transduction rates and nuclear transgene copy number were similar among the different cell lines and that there was no correlation with mCherry levels (Fig. 3g). Therefore, differential transduction or nuclear processing of the AAV does not explain the restricted SSE-7 activity observed in heterogeneous GSC cultures compared with CMV.

Uniform manifold approximation and projection (UMAP) analysis confirmed that these GSC cultures have, as expected, a diverse range of transcriptional subtypes or states[27]. This transcriptional heterogeneity mirrored that seen in patient tumour samples and reflected distinct differentiation states (Fig. 3h). We did not find a simple correlation of SSE-7high activity with previously reported transcriptional subtypes (Fig. 3h), SOX2 and SOX9 expression or cell cycle phase (Extended Data Figs. 5 and 6a).

To perform an unbiased search for TF activities enriched in SSE-7high cells, we used the analysis tool SCENIC. The results revealed a clear correlation of multiple TF activities linked to cancer signalling pathways, including STAT, IRF, SMAD and FOS–JUN (Fig. 3i). Such signalling end-point TFs are often highly activated in cancers, including GBM, and motifs for such TF families are present in SSE-7. This finding is consistent with the idea that these TFs have a potential functional role (Fig. 3i and Extended Data Fig. 6b,c). These data suggest that SSE-7 is activated in cells that display a combination of activated signalling end-point TFs and an immature stem cell-like identity driven by SOX2 and SOX9.

We next explored the requirements needed for signalling pathway activation for SSE-7 function. We performed a screen of 160 different small-molecule kinase inhibitors, spanning 70 distinct families, to identify those that would reduce SSE-7 activity (Extended Data Fig. 7a). Given the diverse signalling states across GSCs and the results from the SCENIC analysis, we speculated that activity would be highly dependent on the cell line. We therefore focused the screen on E55 and E31, two GSC lines with typical phenotypic heterogeneity and distinct transcriptional states.

Initial hits were validated, and a clear dose-dependent reduction in mCherry MFI was confirmed for four of these hits (Extended Data Fig. 7b,c). These four inhibitors all operate in the MAPK–ERK signalling pathway: three MEK inhibitors (PD0325901, AS-703026 and PD184161) and one ERK inhibitor (SC-1) (Extended Data Fig. 7d). For these specific GSC lines (E55 and E31), SOX2 and SOX9 activity together with hyperactive MAPK signalling seemed to be necessary for SSE-7 activity. Finally, a subpopulation of sorted SSE-7-negative GSCs could reactivate SSE-7 expression at later time points. This result is consistent with the finding that SSE activity depends on a specific signalling state (Extended Data Fig. 7e).

Altogether, these functional genetic, biochemical and genomic data indicate that SSE-7 is a dynamic transcriptional regulatory element that is strongly activated in specific GSC states that have a combination of both SOX2 and SOX9 expression and hyperactive signalling-associated TFs. The specific signalling TFs involved are likely to vary among the heterogeneous GSC genetic and transcriptional subtypes. However, our SSE design strategy (that is, mixing distinct enhancer fragments from diverse genes) enabled us to achieve robust and strong expression across GSCs regardless of this variability.

Cell-state-specific activation of SSE-7

Our functional enhancer fragments typically had high vertebrate evolutionary conservation, which suggested that SSEs may work in different species. To further explore selectivity across diverse tissues and organs, we first tested SSE activity in developing zebrafish (around 48 h after fertilization) (Extended Data Fig. 8). Overlapping expression domains for each SSE-driven eGFP reporter was observed for all four SSEs tested, with consistently high levels of expression in the optic placodes and a subset of forebrain and spinal cord neural progenitors (Extended Data Fig. 8a,b). This restricted developmental expression pattern was consistent with previously reported zebrafish Sox2 and Sox9 expression[27],28, and confirms that not only are the SSEs highly active but they are also highly tissue restricted. Notably, SSE-1, SSE-3 and SSE-5 had an additional weaker expression domain in more posterior CNS tissues, whereas for SSE-3, we detected expression in the posterior endoderm (Extended Data Fig. 8b). We therefore prioritized SSE-7 for subsequent experiments.

We created a stable transgenic zebrafish line and used this to look at later larval stages in the nervous system (Extended Data Fig. 8c). SSE-7 activity was nonoverlapping with mature neuronal markers, which confirmed that SSE-7 is not expressed in neurons and is only seen in a subset of fetal neural progenitor cells (Extended Data Fig. 8d–g). Notably, when we forced the expression of a potent oncogenic form of Akt in zebrafish, we observed that SSE-7 activity increased in those cells. This result is consistent with the requirement of hyperactive signalling together with Sox expression to support SSE activation, as seen in human GSCs (Extended Data Fig. 8h). SSE-7 had low activity in oligodendrocyte progenitor cells (OPCs) derived from human induced pluripotent stem (iPS) cells relative to CMV (Extended Data Fig. 8i), but was expressed in human fetal NSC cultures (Extended Data Fig. 8j). Altogether, these data suggest that SSE-7 activity is highly restricted to specific cell states and may be most active in a subset of fetal neural progenitors.

SSE-7 activity in GBM core and margin

AAV is an excellent gene therapy vector for use in GBM owing to the following characteristics: (1) its small physical size (for enhanced distribution); (2) persistence in quiescent cancer cells; (3) low immunogenicity; (4) diverse natural capsids that might enable improved transduction; (5) validated efficient brain delivery via convection-enhanced delivery; and (6) well-understood manufacturing and clinical safety profile.

To further explore SSE-7 selectivity using relevant adult human neural cell types and the effectiveness of AAV as the delivery vector, we generated human glutamatergic and GABAergic differentiated neurons (ioGlutamatergic and ioGABAergic, respectively), and microglia (ioMicroglia) from iPS cells, and tested AAV1-SSE-7–mCherry activity (Fig. 4a). SSE-7 was strongly expressed in GSCs within a few days of transduction, with similar levels to the CMV positive control. However, in postmitotic neurons, we did not observe SSE-7 activity (Fig. 4a) at either day 3 or day 10. For microglia, there was poor transduction with AAV1.

Fig. 4: GSC selectivity of SSE-7 confirmed in vitro and in tissue slice cultures.

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a, AAV1-SSE-7–mCherry reporter activity in GSCs and human iPS-cell-derived differentiated neurons and microglia assessed by flow cytometry at day 3 (left) and day 10 (right) following transduction. For day 3 for ioGABAergic neurons and ioMicroglia, n = 3 biological independent cell experiments; ioGlutamatergic neurons and GSCs, n = 4 biological independent cell experiments. For day 10, n = 3 biological independent cell experiments excluding ioGABAergic neurons, for which n = 2. Data are the mean ± s.e.m., and statistical analysis was performed using an unpaired t-test. **P ≤ 0.01, ***P ≤ 0.001. b, Experimental schematic of AAV1-SSE-7 to test selectivity in fresh human GBM tumour mass and tumour margin tissue samples (infiltrating cortex) processed into slice cultures (Methods). c, Immunostaining and confocal images of tissue for mCherry (red) and GSC markers (Nestin and SOX2) demonstrates GSC selectivity of SSE-7 relative to CMV. Tumour mass and adjacent matched normal brain tissue (tumour margin) are shown. d, Percentage of mCherry-expressing cells co-expressing SOX2. Each point represents a biological replicate, matched tumour and tumour margin samples are indicated by matching symbols. n = 3 for margin and n = 4 for the tumour mass. Statistical analysis was performed using two-way ANOVA with multiple-comparisons correction (P = 0.0436). Scale bars, 10 µm. Schematic in b created in BioRender. Pollard, S. M. (2026) https://BioRender.com/l774d5t.

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To directly test whether SSE-7 would have strong differential activity in GBM tumour tissue compared with the surrounding normal brain, we determined the transduction and expression of AAV1-SSE-7–mCherry in ex vivo fresh tumour tissue slices obtained from patients with GBM (Fig. 4b) and compared these with the tumour margin (which is macroscopically normal tissue but contains infiltrating GBM cells). We first tested whether AAV1-SSE-7 would be active in the tissue, focusing on the mCherry reporter only. After 7 days of incubation with a single dose of the virus, we fixed and stained the tumour tissue and compared CMV and SSE-7 activity using the mCherry reporter.

There were significantly fewer mCherry-positive cells when using SSE-7 compared with CMV (Fig. 4c and Extended Data Fig. 9b) in the tumour, and this was even more significant in the tumour margin tissue. This result is consistent with SSE-7 labelling a subset of GBM cells but not normal healthy tissue. We performed this analysis for four independent patient samples—grade 4 IDH mutant astrocytoma, IDH wild-type GBM, a grade 3 astrocytoma and recurrent GBM (Extended Data Fig. 9a)—and obtained similar results. By contrast, as expected, CMV induced widespread high expression across diverse normal CNS cell types, including cells with neuronal and endothelial morphology (Fig. 4c and Extended Data Fig. 9).

To next confirm whether mCherry is enriched in the tumour cell population, we performed and quantified SOX2 co-staining. This experiment revealed significant differences between CMV and SSE-7, with SSE-7 activated in around 90% of mCherry-positive tumour cells that co-expressed SOX2 (Fig. 4d). We conclude that SSE-7 transgene expression is differentially active in tumour versus normal tissue (tumour margin) compared with CMV. Moreover, SSE-7 provides a strong and selective gene switch that can restrict transgene activity to GSCs in the tumour mass and the infiltrating tumour margin.

Complete responses in a mouse model of GBM

We next determined whether AAV1-SSE-7 could be used to selectively kill GSCs using expression of the HSV-TK/GCV enzyme prodrug system, which has previously been applied in the clinic as a gene-directed enzyme prodrug therapy[29]. SSE-7 retained activity and provided selective expression of the HSV-TK payload in GSCs (Extended Data Fig. 10a–d). This enabled cytotoxic killing of GSCs, after GCV treatment, whereas fibroblasts were unaffected (Extended Data Fig. 10e–g). Screening of common natural AAV serotypes confirmed that AAV1 is the optimal serotype of choice, as it efficiently transduced all seven patient-derived GSCs with high efficiency (Extended Data Fig. 11a,b).

Despite the promise of HSV-TK as a suicide gene therapy approach for oncology, there has been limited success in the clinic. This is partially explained by the challenges of efficient delivery to all cancer cells[30]. As immune payloads can act non-autonomously to trigger an immune response against residual disease, we reasoned that combining an immune payload with a cytotoxic payload might be required to achieve this goal. Indeed, such a strategy could induce multiple pathways of the immune response to polarize local macrophages in the tumour microenvironment while enabling systemic adaptive responses by T cells. We proposed that this combination—tumour cell killing, tumour-associated macrophage polarization and T cell education and activation—would be potent and reduce chances of tumour regrowth from residual disease[30].

We chose the cytokine IL-12 for evaluation as it triggers a potent inflammatory response (stimulating adaptive and innate immunity) and is effective in promoting anticancer immune responses31,32. We reasoned that by using the AAV1-SSE-7 platform, we could avoid the toxicities previously seen when using systemic delivery[31]. This would enable controlled release of IL-12 alongside tumour killing and induce effective antitumour immune responses, with IL-12 levels being reduced as tumour cells are lost. We designed an AAV1 vector with SSE-7 driving the expression of a single mRNA encoding HSV-TK linked via the P2A sequence to the mouse IL-12 heterodimer fusion (Il12a and Il12b, which encode P40 and P35, respectively) (Fig. 5a). This dual payload design ensures that the cytokine and cytotoxic effects are synchronized and serves as a safety switch to prevent uncontrolled IL-12 levels.

Fig. 5: The combination of a cytotoxic and immunomodulatory payload is highly effective at clearing established GBM tumours.

The alternative text for this image may have been generated using AI.

Full size image

a, Architecture of the AAV1 transgene used to treat NPE-IE GBM in mice. ITR, inverted terminal repeat; mCMV, minimal CMV promoter; mIL-12, mouse IL-12. b, Survival curves for the mice treated with AAV1-SSE-7–HSV-TK–IL-12 (n = 24) versus control (UT (PBS); n = 22). P < 0.0001 for pooled (n = 3) independent cohorts. Survival was analysed using the Kaplan–Meier method. Significance was determined using a log-rank (Mantel–Cox) test. Mice were selected at week 1 using bioluminescence imaging to ensure a similar starting size for tumours across the cohorts. c, Longitudinal tracking of tumour growth in mice once a week using bioluminescence imaging (three examples shown for experimental and control; see Extended Data Fig. 12 for the larger cohort). d,e, Bar plots of the abundance of total T (CD3) cells, effector T (Teff) cells, CD8+ T cells, regulatory T (Treg) cells and natural killer (NK) cells (d), and myeloid-derived suppressor cells (MDSCs), monocytes, bone marrow-derived macrophages (BMDMs), dendritic cells (DCs) and microglia (e) out of total live CD45+ cells in the tumour microenvironment of untreated mice and mice treated with a single dose of AAV-SSE-7-HSV-TK–IL-12 or with the AAV-SSE-7-HSV-TK–IL-12 + GCV combination, determined by flow cytometry. f,g, Bar plots of the abundance of PD1−Ki67−, PD1−Ki67+, PD1+Ki67− and PD1+Ki67+ cells from Teff cells (f) and from CD8+ T cells (g) in the tumour microenvironment of untreated mice and mice treated with a single dose of AAV-SSE-7-HSV-TK–IL-12 or with the AAV-SSE-7-HSV-TK-IL-12 + GCV combination, determined by flow cytometry. n = 7 biologically independent animals per group, examined in one experiment. Data are the mean ± s.d. Significance was assessed using one-way ANOVA with Tukey’s multiple comparison test. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

Source data

For in vivo preclinical efficacy studies, we used our previously reported disease-relevant syngeneic mouse model of GBM, termed NPE-IE[25], which exhibits a myelosuppressive tumour microenvironment and transcriptional programs seen in human GBMs[25]. AAV1-SSE-7 transduction and SSE activity were first validated using mouse NPE-IE cells in vitro (Extended Data Fig. 11c,d).

AAV1-SSE-7–HSV-TK–P2A-IL-12 was delivered to a cohort of mice with established orthotopic tumours (14 days after tumour cell orthotopic engraftment) using direct intratumoral injection with a microinjection pump to deliver a single dose of the virus. We observed substantial tumour regression within 1–2 weeks and complete clearance of tumours in 20–24 treated mice over the subsequent 2–3 weeks, whereas control mice died within 1–2 weeks (Fig. 5b,c).

Using bioluminescence imaging to track tumour burden in live mice, no further regrowth of tumours was observed after the initial tumours had cleared (Extended Data Fig. 12a). Moreover, no prominent toxicity, scored by weight loss and neurological monitoring, was seen over the subsequent 11 months (the experimental end point). We re-challenged treated mice (n = 10 that cleared the tumour) with fresh NPE-IE cell transplants into the striatum (5 months after initiation of the first therapy). Notably, there was no detectable tumour formation in any of the mice after the re-challenge, whereas parallel control mice formed tumours within 2–3 weeks (Extended Data Fig. 12a). Finally, antitumour activity was dose-dependent (Extended Data Fig. 12b).

IL-12 with HSV-TK/GCV drives antitumour immunity

To determine whether IL-12 alone would be sufficient for eradicating tumours without the concomitant HSV-TK/GCV-induced killing (that is, single versus dual payloads, respectively), we treated mice with AAV1-SSE-7-HSV-TK–P2A-IL-12 alone or with this virus and with GCV. The dual payload was the most effective and enhanced the effects of IL-12 alone (Extended Data Fig. 13a). To explore safety in normal CNS, we also assessed responses in mice without tumours to either CMV-driven or SSE-7-driven HSV-TK–IL12. As expected for CMV, there were rapid neurological issues within weeks, consistent with the expected toxicities with continuous expression of IL-12 in normal CNS. By contrast, we observed no toxicity with SSE-7, a result consistent with our earlier in vitro data confirming the selectivity of this SSE (Extended Data Fig. 13b).

Characterization of the immune cell composition after treatment using either IL-12 alone or IL-12 with HSV-TK/GCV revealed that the latter performed better, with significantly increased levels of IFNγ and a trend towards increased TNF (Extended Data Fig. 13c–i). This finding is consistent with the observed increase in cytotoxic T cells and reduction in myelosuppressive cells that occurs through T helper 1-like responses induced by IL-12 (Fig. 5d–g).

There was also an increase in proliferative Ki67+ T cells, with a shift towards increased CD44+, a marker of T cell activation and memory (Extended Data Fig. 13d,e). To assess the percentage of transduction in the tumour mass, we used the SSE-7–mCherry reporter. Tumours were collected, dissociated and assessed using flow cytometry, which revealed around 20% positive tumour cells (GFP+mCherry+) (Extended Data Fig. 13j). This transduction efficiency was achieved using the same viral doses as the survival analysis experiments. This result confirms that only a fraction of the tumour mass needs to be transduced to achieve immune clearance of residual disease.

In conclusion, AAV1-SSE-7–HSV-TK–IL-12 is highly effective at eliminating tumours in an aggressive syngeneic mouse GBM model. Treated mice were resistant to subsequent re-challenge with fresh NPE-IE cells. A single dose of AAV1-SSE-7–HSV-TK–IL-12 induced complete responses with durable immune memory without off-target toxicity.

Discussion

The development of effective gene therapies for oncology will require more effective payload combinations and more selective delivery mechanisms than has been possible so far30,33,34. We showed that SSEs, combined with delivery via AAV1, can address one of the significant challenges in oncology: namely, how to specifically deliver combinations of anticancer payloads at high doses without off-target toxicity. Across genetically diverse GSC lines and high-grade glioma tissues, SSE-7 consistently read out a core transcriptional program characteristic of GSC identity: the immature NSC-like state.

Our findings indicated that SOX2 and SOX9 share crucial target genes and may represent a core transcriptional module, an ‘oncofetal’ program, that defines aggressive tumour-initiating cells in GBM18,35. Using AAV-mediated delivery and SSE control of a transgene, we achieved specific elimination of active GSCs by targeting them from within, akin to a Trojan horse approach. The persistence of AAV vectors, and our focus on the shared SOX circuits, meant that we were able to guard against plasticity, as transgene expression will be reactivated if cells transit through this specific cell state.

Immunotherapy approaches have generally had limited success in GBM36,37,38. Our AAV–SSE approach will now require careful safety studies to determine the potential for translation to patients. The goal is to deliver cytotoxic payloads alongside innate and adaptive immune activation to achieve meaningful and durable suppression of high-grade gliomas without significant off-target toxicities.

Future studies will explore the molecular mechanisms that underlie SSE activity, which may involve transcriptional hubs or condensates. Our findings from exploring the biochemical binding of SOX2 and SOX9 suggests that there is clear synergy between these two factors. However, additional cofactors are required for the creation of a multimeric complex. Detailed structural studies are needed to precisely determine how SOX2 and SOX9 operate and the signalling TF requirements and whether nucleosome context is critical[39]. In the future, improved computational methods and artificial-intelligence-based tools for the design of enhancers and synthetic associated motifs will complement the functional screening approaches used here40,41,42. In conclusion, our study demonstrated the potential of SSEs and AAV vectors to deliver precision immune activation for the treatment of cancer.

Methods

Destination vector cloning

We built a specific custom destination vector for efficient Golden Gate cloning of enhancer fragments. This included the reporter gene cassette NanoLuc-Ires-mNGreen-pA, downstream of a mCMV promoter. A bacterial suicide ccdB cassette spanning the enhancer position enabled assembly of a single enhancer for selection and efficient cloning. This was based on the EMMA Golden Gate cloning system[43]. For the GSC long-term differentiation experiments, the same vector was built but included PiggyBac transposase recognition sites flanking the entire cassette.

Bioinformatics to design a SOX2 enhancer oligonucleotide pool

We re-analysed previously published[1] SOX2 ChIP–seq and H3K27ac GSC cell line data to identify GSC-specific SOX2 peaks that were overlapping with H3K27ac and absent in differentiated cells (cells in serum culture). The resulting shared peaks were then combined into one set and manually curated to remove centromeres. This resulted in 1,721 peaks with an average length of 402 bp. These peaks were then used to design a set of 160 bp sequences. Next, 20 bp adapters were included to flank the sequences and these sequences were synthesized as an oligonucleotide pool (Twist Bio).

Construction of an arrayed plasmid library of enhancer fragments

The oligonucleotide pool (9,523 unique sequences) was first amplified for 10 cycles with 0.25 µl (2.5 ng input) volume, and 0.5 µl of this reaction was used for the subsequent 15 cycles of amplification to reduce PCR ‘jackpot’ amplification. The final products were cloned into the expression vector using an efficient Golden Gate reaction. We used KAPA HiFi Hotstart polymerase with GC buffer (Roche, KK2501), 68 °C annealing temperature and 5 s of extension time at 72 °C. A total of 4,579 individual plasmids were then randomly picked, miniprepped and plated as an arrayed plasmid DNA library on 96-well plates. This was deemed a practical number for arrayed library screening. A magnetic-bead-based SPRI purification method was used to minimize loss and to clean up the DNA before Golden Gate cloning. Sanger sequencing for quality checks of a sample of the plasmids confirmed that they were diverse, and all sequences could be mapped back to the original library design.

Screening platform for 384-well plates

Cells were seeded in 384-well plates using a multidrop and transfected the following day using CyBio Felix (CyBio). Two days later, a NanoGlo DLR assay was used to identify hits with a fold change to mCMV of >10. These hits were Sanger sequenced to determine the specific sequence and mapped back to the genome to validate overlap with the original SOX2 ChIP–seq data. The GREAT tool predicted the target gene for hit sequences[44].

General cell culture procedures

The GSC lines GCGR-E17 (E17), GCGR-E21 (E21), GCGR-E27 (E27), GCGR-E28 (E28), GCGR-E31 (E31), GCGR-E34 (E34), GCGR-E37 (E37) and GCGR-E55 (E55) and the human NSC lines NS9FB_B (NS9), NS12ST_A (NS12) and NS17ST_A (NS17) were generated in the Pollard Laboratory and are available upon request from the Glioma Cellular Genetics Resource. Informed consent was obtained for use of patient tissue. All procedures on patient brain tissue received ethics approval from the NHS Health Research Authority, East of Scotland Research Ethics Service (REC reference 15/ES/0094), and all procedures on embryonic and fetal brain tissue received ethics approval from the Lothian NHS Board, South East Scotland Research Ethics Committee (REC reference 08/S1101/1).

Cells were grown as adherent monolayers under serum-free conditions as previously described[13]. All cells lines tested negative for mycoplasma at passage 3 using a MycoAlert Mycoplasma Detection kit (Lonza, LT07-318). Cell line authentication by STR profiling was performed as a service by the European Collection of Authenticated Cell Cultures (ECACC). Reports are available upon request from the Glioma Cellular Genetics Resource. Analysis was conducted using the Promega Fusion system (DC2402), analysing differences at 24 distinct hypervariable genetic loci, 16 of which are used for the final STR profile and report. The ioGlutamatergic (io1001S) and ioGABAergic (ioEA1003S) neurons and ioMicroglia (ioA021) were obtained from bit.bio and grown per the manufacturer’s specifications. GSC7 was previously characterized[22].

For lipofection, Plus reagent and Lipofectamine LTX (Life Technologies, 15338030) were each diluted in half the volume of Opti-MEM I reduced-serum medium (hereafter referred to as Optimem) (Life Technologies, 31985062). Following this step, the Plus reagent and Optimem premix was added to all DNA samples followed by the Lipofectamine LTX and Optimem premix. The transfection mix was incubated for 5 min at room temperature and then carefully dropped onto the cells. Generally, no change in medium was carried out. Cells were analysed 2 days after transfection.

HEK293 cells were seeded at the specified density in the respective plate format. The next day GMEM medium, DNA and polyethylenimine were mixed and incubated for 15 min at room temperature to facilitate formation of the complex. The transfection mix was subsequently dropped onto the cells, and analysis was carried out 2 days later.

Screening assay using the Nano-Glo Dual-Luciferase reporter assay system

This assay consists of two steps. Transfection was performed using a normalization plasmid PGK-Firefly-Luciferase (Promega, E5011) transfected in a 1:10 ratio with the plasmid of interest. Therefore, in this assay, the firefly luciferase activity is first measured, which enables normalizing of the transfection efficiency. Cells were washed 3 times with PBS and 20 µl was left in a 96-well plate (25 µl in a 384-well plate). Oneglo buffer was added, and the plate was shaken for 5 min at 480 rpm to allow cell lysis. Next, 20 µl cell lysate in a 96-well format (25 µl cell lysate in a 384-well format) was transferred into an opaque white 96-well format (or 384-well format) and light was measured for 0.1 s per well (Ensight Multimode Plate Reader, Perkin Elmer). For the subsequent NanoLuc reaction in a 96-well format, 2 µl cell lysate was transferred into an opaque white plate containing 40 µl PBS, and 20 µl Stopglo buffer, supplemented with substrate, was added to each well. In a 384-well format, 20 µl Stopglo buffer containing substrate was added on top of the undiluted cell lysate. The plate was shaken again for 5 min at 480 rpm to quench the firefly luciferase reaction and to ensure good mixing. NanoLuc activity was measured for 0.1 s per well. Data obtained by the NanoLuc reaction were normalized to the Firefly reaction to account for well-to-well variability of transfection efficiency. Normalized data were used to calculate the fold change compared with the empty vector control, which contained mCMV only and no enhancer fragments.

Immunocytochemistry

Cells were washed twice with PBS and fixed in 4% paraformaldehyde (PFA) for 10 min. Cells were permeabilized with 0.1% Triton-100 in PBS (PBST). Cells were blocked with blocking solution (1% BSA in PBST with 3% goat serum) and incubated with the primary antibody at 4 °C overnight. The next day, cells were washed 3  times in PBST, and the respective secondary antibody was applied in blocking solution and incubated for 45–60 min at room temperature. Cells were washed with PBS and incubated for 5 min with a DAPI nuclear counterstain at 1 µg ml–1 final concentration. Images were acquired using a Nikon TiE microscope and NIS elements software (Nikon).

Flow cytometry

For flow cytometry, cells were detached, pelleted and resuspended in an appropriate volume of flow cytometry buffer (1% BSA in PBS, v/v) or PBS. Cells were stained with Draq7 (Abcam, ab109202, final concentration of 0.1 µM) or DAPI (ThermoFisher Scientific, D3571) as a live/dead stain and analysed using a BD LSRFortessa cell analyser (4 lasers, BD Bioscience). Analysis of flow cytometry data was carried out using FlowJo Analysis software (v.10.6.2) or FCS Express 7 flow cytometry software (v.7.22.x).

RNA isolation, cDNA synthesis and RT–qPCR

RNA was extracted using a MasterPure RNA Purification kit (Epicentre). RNA was stored at −80 °C, and concentration was determined using a NanoDrop spectrophotometer. cDNA synthesis was carried out using a SuperScript III Reverse Transcriptase kit (Life Technologies) according to the manufacturer’s instructions. Around 200–500 ng RNA was used for the cDNA reaction. The same amount of RNA was used for each experiment. After the reaction, cDNA was diluted to the required volume using nuclease-free water.

For RT–qPCR, TaqMan Universal PCR master mix (Applied Biosystems) and TaqMan gene expression assays (Life Technologies) were used on a Quant Studio7 Flex Real-Time qPCR machine. RNA samples that did not undergo reverse transcription (to assess DNA contamination) and water controls were used on every plate. RT–qPCR was carried out in technical duplicates. Data analysis was performed using the ddCt method, which assumes 100% PCR efficiency and is guaranteed with TaqMan assays. In brief, the mean was calculated for technical replicates and normalized to the housekeeping gene GAPDH, which produces the dCt value.

Western immunoblotting

Cell lysates were prepared by resuspending cells in RIPA buffer (50 mM HEPES pH 7.7, 150 mM NaCl, 1% NP-40, 0.5% DOC and 0.1% SDS), incubating on ice for 5 min, centrifuging at maximum speed for 10 min in a tabletop centrifuge (5415D, Eppendorf) and collecting the supernatant (lysate). Protein extracts were quantified using a Pierce BCA Protein Assay kit (Thermo Scientific, 23225). SDS–PAGE was performed using homemade 4–12% Bis-Tris gels. Gels were transferred onto PVDF membranes (Millipore, IPVH00010), previously activated in methanol, by wet electroblotting or semi-dry blotting using a Bio-Rad Trans-blot turbo system. Western blots were revealed using HRP-conjugated antibodies, homemade ECL solutions or Clarity Western ECL (Bio-Rad), and imaged using X-ray films or a Bio-Rad ChemiDoc Imaging system.

Precipitation of interacting proteins

PCR amplification with biotinylated primers and purification were used to generate templates that could pull down bound proteins. Enhancer fragments were amplified with 5′ biotinylated primers using PrimeStar Max (Takara) according to the manufacturer’s instructions.The following primers were used: forward primer 595: TGATCCGTCTCGCCCTACTAGGTTACTGGTGCATGC; reverse primer 596: ACTAACGTCTCGGAGCACCCAAACTATTGGAGCGAG (bold sequences, adaptors).

PCR purification using Agencourt AMPure XP magnetic beads (Beckman Coulter) was carried out according to the manufacturer’s instructions. PCR product quantification using Tapestation and reagents was carried out according to the manufacturer’s instructions.

All the buffers were prepared the day before, passed through a 22 µm filter (except the dialysis buffer) and left at 4 °C overnight. DTT and protease inhibitors were added immediately before use. Cell pellets were resuspended in 5 ml per 40 million cells of ice-cold buffer A (10 mM HEPES pH 7.9, 1.5 mM MgCl_2, 10 mM KCl, 0.5 mM DTT and protease inhibitors (complete, Roche, 11697498001)) and incubated on ice for 10 min. The cell suspension was transferred to a glass Dounce homogenizer and dounced 40 times on ice, transferred to a Falcon tube and centrifuged at 1,350 rcf for 10 min at 4 °C. The supernatant was discarded (cytosolic extract) and the pellet was resuspended in 100 µl per 10 million cells in ice-cold buffer B (20 mM HEPES pH 7.9, 5% glycerol, 1 M NaCl, 1.5 mM MgCl_2, 0.2 mM EDTA pH 8.0, 0.5 mM DTT and protease inhibitors (complete, Roche, 11697498001)). The suspension was rotated for 30 min at 4 °C and transferred into a dialysis membrane (SnakeSkin, Thermo Scientific, 68100). Dialysis was carried out in 500 ml dialysis buffer (20 mM HEPES pH 7.9, 5% glycerol, 100 mM KCl, 0.83 mM EDTA pH 8.0, 1.66 mM DTT and protease inhibitors (complete, Roche, 11697498001)) for 2 h at 4 °C with rotation. The dialysis buffer was changed (500 ml), and dialysis was continued overnight at 4 °C. The extract was collected in a 1.5 ml tube and spun at maximum speed for 15 min at 4 °C (centrifuge 5415D Eppendorf). The supernatant was collected into new 1.5 ml tubes and quantified (Pierce BCA Protein Assay kit, Thermo Scientific, 23225). Next, 100 µg aliquots were flash-frozen in liquid nitrogen and stored at −80 °C. The following primary antibodies were used for western blotting: anti-SOX2 (rabbit) 1:1,000 Abcam (ab92494); anti-SOX9 (rabbit) 1:500 Millipore (ab5535); and anti-GAPDH (mouse) 1:10,000 Ambion (AM4300). The secondary antibodies used for western blotting were anti-rabbit 1:5,000 Novex (A16110) and anti-mouse 1:5,000 Novex (A16027).

Streptavidin magnetic beads (NEB, S1420S) were used according to the manufacturer’s instructions. In brief, 10 µl beads were aliquoted into low-bind tubes and washed 3 times with binding buffer (20 mM Tris-HCl pH 7.5, 0.5 M NaCl and 1 mM EDTA) on a magnetic stand. Next, 20 µl biotinylated DNA (around 20 pmol) was mixed with 200 µl binding buffer and was added to the beads. The suspension was rotated at room temperature for 2 h. The beads were washed 3 times with binding buffer, and 50 µg nuclear extract was added (total volume of 50 µl). Beads, DNA and nuclear extract, to which 150 µl dialysis buffer (20 mM HEPES pH 7.9, 5% glycerol, 100 mM KCl, 0.83 mM EDTA pH 8.0, 1.66 mM DTT and protease inhibitors (complete, Roche, 11697498001)) was added, were rotated overnight at 4 °C. The following morning, beads were washed 3 times with wash buffer (20 mM HEPES pH 7.9, 5% glycerol, 250 mM NaCl, 0.83 mM EDTA pH 8.0 and 1.66 mM DTT). For western blotting, proteins were eluted in 20 µl loading buffer (lithium dodecyl sulfate buffer containing 50 mM DTT) and boiled for 5 min to denature the proteins. For mass spectrometry, the beads were sent dry to the Institute for Genetics and Cancer core facility (University of Edinburgh) for analyses.

SOX2 and SOX9 ChIP–seq library preparation and analysis

In brief, GSCs cultured in 150 mm dishes to 70% confluence were crosslinked in 1% formaldehyde for 10 min at room temperature. Excess formaldehyde was quenched by incubating cells with 0.25 M glycine for 5 min at room temperature. The cell pellet was washed twice with ice-cold PBS before storage at −80 °C. For each GSC line, cell pellets from eight 150 mm dishes were combined before proceeding for cell lysis and sonication. The cell pellet was lysed in buffer LB3 (10 mM Tris-HCl pH 8.0, 100 mM NaCl, 1 mM EDTA pH 8.0, 0.5 mM EGTA pH 8.0, 0.1% sodium deoxycholate, 0.5% N-lauroylsarcosine and 0.1% SDS) and chromatin was fragmented to 200–600 bp size on a Covaris M220 sonicator (total of 4 cycles, with the following treatment settings: 600 s per cycle, peak power 75, duty factor 10, cycles/burst 200; temperature: minimum 5 °C, set point 7 °C, maximum 9 °C). For each ChIP assay, 15–20 µg fragmented chromatin was incubated overnight in a cold room with 10 μg antibodies (R&D SOX2 AF2018, Millipore, SOX9 AB5535) and 30 μl Protein G Dynabeads (10003D). The next day, magnetic beads were washed 5 times with RIPA (50 mM HEPES-KOH pH 7.5, 0.5 M LiCl, 1 mM EDTA pH 8.0, 1% NP-40 and 0.7% sodium deoxycholate), once with TE NaCl (10 mM Tris-HCl pH 8.0, 1 mM EDTA pH 8.0 and 50 mM NaCl) and finally eluted in 200 μl ChIP elution buffer (50 mM Tris-HCl pH 8.0, 10 mM EDTA pH 8.0 and 1% SDS). Immunoprecipitated chromatin was reverse crosslinked by incubating samples at 65 °C for 10 h and extracted using the phenol–chloroform–IAA (11896714) method. To account for intertumoral heterogeneity, ChIP–seq was performed on seven independent primary GSC lines. Libraries were prepared for two technical ChIP replicates and one input control using a NEBNext Ultra II DNA Library Prep kit for Illumina (E7645S). The libraries were barcoded using NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1 kit, E7600S) and sequenced on an Illumina Novaseq (50 bp read length).

Paired-end reads were aligned to the hg38 genome using BWA, filtering out poor-quality (MAPQ < 10), duplicates, mitochondrial genome and blacklisted regions (https://www.encodeproject.org/annotations/ENCSR636HFF/). Technical replicates were merged, and peaks were called using MACS2 with default settings. Consensus SOX2 and SOX9 peak sets were derived by taking the overlap of peaks occurring in 5 out of 7 GSCs for each TF set. The overlap significance between the consensus SOX2 and SOX9 peak sets was determined using a circular permutation test in regioneR (ntimes = 10,000). Genomic regions near SOX2 and SOX9 peaks were annotated using HOMER[45].

To perform overlapping analysis with GSC super-enhancers, we downloaded raw fastq files from publicly available H3K27ac datasets on GSCs (GSE119834, GSE74529, GSE121601 and GSE92458) and called super-enhancers using ROSE with default settings. A consensus set of GSC super-enhancers was derived by considering super-enhancers that occurred in at least two GSCs. The overlap significance between the consensus co-bound SOX2–SOX9 sites and consensus GSC super-enhancers was performed using a circular permutation test as described above. Gene ontology term association analysis was performed using GREAT. To find centrally enriched de novo motifs at SOX2 and SOX9 peaks in co-bound SOX2–SOX9 enhancers, and to identify the spacing between the most significant de novo motifs from each peak set, we used CentriMo and SpaMo from the MEME-ChIP suite of tools[46].

scRNA-seq sample preparation

On the day of cell seeding, cells were detached as described above. Cells were counted using a haemocytometer and seeded into a 6-well Corning plate at 400,000 cells per well in 2 ml. Plates were incubated at 37 °C with 5% CO_2. The next day, AAV vector stocks were thawed at room temperature. The appropriate amount of virus stock was added to the culture medium to achieve a final multiplicity of infection (MOI) of 1,000,000, which ensured high transduction efficiency. Viral-containing medium was added to cells without replacing the existing medium. The cells were then returned to the incubator at 37 °C with 5% CO_2.

After 3 days of incubation with the viral particles, cells were detached as described above. Next, 10% of the cells were prepared for flow cytometry using DAPI as a live/dead stain (see Supplementary Information for the gating strategy). The remaining 90% of the cells were processed for single-cell transcriptome sequencing.

Cells were fixed using a Parse Evercode v.2 Cell Fixation kit following the manufacturer’s instructions, with an average of around 400,000 cells per sample. Fresh reagents were prepared for each fixation, and 7.5% Gibco BSA fraction V was added per the manufacturer’s recommendations. In brief, cells were washed with PBS and resuspended in cold Cell Prefixation buffer. Cells were filtered through a 40 µm strainer after 5 min of centrifugation at 300g at 4 °C. Following this step, Cell Fixation solution and Cell Permeabilization solution were subsequently added, with incubation on ice for 10 min and 3 min, respectively. Cell Neutralization buffer was then added, and cells were centrifuged again at 300g for 5 min at 4 °C. Finally, cells were resuspended in a volume of 50–90 µl Cell Buffer containing 1% DMSO based on the live cell number before fixation. Cells were filtered through a 40 µm strainer and stored at −80 °C for up to 3 months.

scRNA-seq library preparation

Libraries were generated using a Parse Evercode WT Mega v.2 kit according to the manufacturer’s instructions. In brief, 72 samples were loaded into a 96-well RT barcoding plate for reverse transcription, with each well containing a unique barcoded RT primer. Following cDNA synthesis, cells were pooled and subjected to two additional rounds of barcoding, achieving three rounds of barcoding. Barcoded cDNA was then amplified via template switching and pooled into 15 sublibraries, each containing around 62,500 cells. A fourth barcode was incorporated during PCR amplification of the sequencing libraries. Sublibraries were then fragmented, ligated with Illumina adapters and purified using Ampure XP beads. Library quality was assessed on an Agilent BioAnalyzer 2100 before sequencing. Sublibraries, with 5% PhiX spike-in, were sequenced on an Illumina NovaSeq X using 300 cycles kits as paired-end, dual-index reads.

scRNA-seq analysis

Sequencing reads were aligned to the human (hg38) genome, and UMI counting data were generated following the standard Parse Bioscience pipeline, ‘splitpipe (v.1.1.2)’, with default parameters. Three custom genes were added to the genome (mCherry, HSV-TK and bGHployA) to capture ‘activated’ SSE-7 cells. In total, 345,495 cells were included in the initial dataset. Low-quality cells were filtered out based on the following thresholds: (1) the percentage of mitochondrial gene was >20%; (2) the number of genes was <300; (3) the number of uniquely aligned reads was <500; or (4) the number of uniquely aligned reads was >20,000. Genes detected in fewer than five cells were filtered out as low-quality genes. Potential doublets were identified using the R package ‘DoubletFinder’ (v.2.0.4), with an expected doublets rate of 3%, as guided by Parse. Doublets and clusters containing more than 20% doublets were removed. Ambient RNA-contaminated cells (cell score > 0.2) were removed using ‘decontX’ from the R package ‘celda’ (v.1.18.2). After filtration, 270,842 cells remained for further analysis.

The data were normalized using ‘LogNormalize’, and principal component analysis was performed based on the top 2,000 variant genes using the R package ‘Seurat’ (v.5.0.3). The first 15 principal components were used as input for Louvain-based graphing. SSE-7-activated cells (2,711 cells) were labelled according to the following criteria: mCherry > 1 or HSV-TK > 1 or bGHpolyA > 0. Cells with a single count of mCherry or HSV-TK from the virus-treated group were labelled as false negative (9,583 cells), whereas the remaining cells were labelled as SSE-7 non-activated cells (125,525 cells). Meanwhile, cells expressing mCherry or HSV-TK in the control group were labelled as false positive (1,998 cells). Transcriptional subtypes were predicted on the basis of a previously described gene signature[27] using the function ‘sigScores’ and ‘as_four_state_gbm’ from the R package ‘scalop’ (v.1.1.0). Cell cycle was predicted using ‘CellCycleScoring’ from the R package ‘Seurat’.

SCENIC analysis of scRNA-seq data

False-negative and false-positive cells were removed, and the dataset was divided into 15 libraries as per the sequencing library preparation. The dataset was then analysed following the standard ‘pyscenic (v.0.12.1)’ pipeline. Auxiliary human datasets (hg38 mc_v10_clust) were downloaded from the cisTarget resources website (https://resources.aertslab.org/cistarget/). Motif–TF annotation was based on 10 kbp upstream and downstream around the transcription start sit (20 kbp in total). SSE-7-enriched TF modules were selected on the basis of the following criteria: (1) Z score > 0.7 and (2) Z score in the SSE-7-activated group was higher than in other groups, as determined by repeated-measures ANOVA test. Enrichment for candidate TF modules across 15 libraries was as follows: SOX8 (found in 9 libraries), STAT1 (found in 15 libraries), IRF9 (found in 12 libraries), ETS1 (found in 13 libraries), MAF (found in 3 libraries), SMAD1 (found in 12 libraries), FOS (found in 11 libraries), HIF1A (found in 7 libraries), SOX9 (found in 10 libraries), STAT3 (found in 3 libraries), NEF2L2 (found in 13 libraries), JUN (found in 2 libraries) and SOX2 (found in 2 libraries).

Lentivirus production and titration

To produce SOX2, SOX9 and rtTA2M2 individual lentiviral supernatant samples, nearly 2.4 million HEK293T cells per 15 cm plate were cultured for 24 h in GMEM medium supplemented with 10% fetal calf serum (FCS), 1 mM sodium pyruvate, 1 mM glutamine and non-essential amino acids (HEK medium) at 37 °C and 5% CO_2. At 24 h after seeding, HEK293T cells were transfected with a plasmid cocktail containing 7.5 μg expression plasmid, 5.1 μg psPAX2 packaging vector, 2.4 μg envelope vector mixed in 45 μl Fugene6 (Roche) and 855 μl Opti-MEM medium (Invitrogen, Thermo Fisher Scientific). The cells were further cultured for 16 h at 37 °C and 5% CO_2 before the medium was changed for HEK medium. At 65 h after transfection, virus-containing supernatant was collected and cleared by centrifugation and filtered through a 0.45 μm syringe filter (Merck Millipore). Virus was concentrated (roughly 100-fold) by transferring to thin-walled ultracentrifuge tubes and pelleted by ultracentrifugation at 25,000 rpm for 2.5 h using a SW32-TI rotor in a Beckman Optima XPN ultracentrifuge (Beckman Coulter). The pelleted virus was resuspended in 300 μl plain GMEM medium and incubated at 4 °C for 16 h before aliquoting and flash-freezing in liquid nitrogen for storage at −80 °C. The viral titre, calculated by flow cytometry, was 6 × 10^7 infectious units per ml.

Overexpression of SOX2 and SOX9

Early passage (passage 4) human fibroblasts (hFibs) were seeded, 1.5 million per 15 cm plate, and cultured in hFib medium (GMEM medium supplemented with 10% FCS, 1 mM sodium pyruvate, 1 mM glutamine, MEM 1× non-essential amino acids solution, 50 μM 2-mercaptoethanol (Gibco, Thermo Fisher Scientific) and 2 ml penicillin–streptomycin (Invitrogen, 15140122)) 24 h before transduction at 37 °C and 5% CO_2. The cells were transduced by exchanging the growth culture medium with lentiviral medium, without antibiotics, containing 8 μg ml–1 polybrene, and 65 μl concentrated rtTA2M2 and either SOX2 or SOX9 virus, for single factor overexpression, or 75 μl rtTA2M2, SOX2 and SOX9 viruses for double factor overexpression. At 48 h after transduction, the cells were split 1:2 and cultured for 24 h, after which the culture medium was replaced with induction medium containing 1 μg ml–1 doxycycline (induction day 0). After 48 h of induction, the cells were collected for lysis and nuclear protein extraction.

Nuclear extraction of GSC7 and hFib cells

GSC7 cells were cultured as adherent monolayers[13]. For differentiation, cells were cultured without the supplements EGF and FGF and in 5% serum for 2 weeks. GSC7 and hFib nuclear lysates were prepared in the same way; cells were resuspended to 1 million cells per 100 μl 10 mM HEPES pH 7–8, 1.5 mM MgCl_2, 10 mM KCl, 0.5 mM DTT, supplemented with complete Ultra EDTA-free protease inhibitors (Roche, 05892970001) and homogenized using a dounce and tight-fit pestle with 1 stroke per 1 million cells. Samples were centrifuged at 1,350 rcf for 5 min at 4 °C. The nucleus pellet was resuspended to 10 μl per 1 million starting cells in 20 mM HEPES pH 7–8, 30% glycerol, 420 mM NaCl, 1.5 mM MgCl_2, 0.2 mM EDTA, 0.5 mM DTT and complete Ultra EDTA-free protease inhibitors (Roche, 05892970001). Samples were dialysed for 4 h into a minimum of 1–1,000 volume excess of 20 mM HEPES pH 7–8, 30% glycerol, 100 mM NaCl, 0.83 mM EDTA, 1.66 mM DTT, 0.2 mM PMSF and complete Ultra EDTA-free protease inhibitors (Roche 05892953001) using Slide-A-lyzer mini dialysis units with 3,500 MWCO cassettes (Thermo). Samples were divided into single-use aliquots (11 μl) and flash-frozen in liquid nitrogen for storage at −80 °C.

Western blot analysis of overexpression

The protein concentrations of the lysates were quantified using a Pierce BCA Protein Assay kit according to the manufacturer’s 96-well format instructions (Thermo Fisher Scientific). The following overexpression (OE) values were used: SOX2 OE = 10.54 mg ml–1, SOX9 OE = 5.56 mg ml–1, SOX2 and SOX9 OE = 7.56 mg ml–1, GSC7 GSCs = 1.02 mg ml–1 and GSC7 differentiated cells = 0.43 mg ml–1. Verification of SOX protein overexpression, relative to untransfected hFib lysate, was resolved by SDS–PAGE and electroblotting onto a PVDF membrane for western blotting. The primary antibody incubations with goat anti-human SOX2 antibody (1:363, AF2018, Abcam) and rabbit anti-mouse SOX9 (1:2,000 AB5535, Chemicon) were performed at 4 °C for 16 h. The secondary antibody incubations with donkey anti-goat IgG-HRP (1:2,000 dilution of ab97110, Abcam) and goat anti-rabbit (1:2,000 of 32460, Invitrogen) were performed for 1 h at room temperature. Blots were visualized using SuperSignal West Pico Chemiluminescent substrate (Thermo Fisher Scientific) and HRP was visualized using a Bio-Rad ChemiDoc MP imaging system.

Recombinant SOX protein production

His-tagged human SOX2 and SOX9 were expressed from a pET28a backbone (cloned by G. Roberts in the Soufi Laboratory) using Rosetta 2(DE3) pLys competent cells (Novagen).

SOX2 was expressed as previously described[47]. Cells in LB medium (1% Bacto-tryptone, 0.5% yeast extract and 1% NaCl), supplemented with 30 µg ml–1 kanamycin and chloramphenicol, were grown at 37 °C overnight. SOX9 was expressed in 2× TY medium (1.6% Bacto-tryptone, 1% yeast extract and 0.5% NaCl), supplemented with 30 µg ml–1 kanamycin and chloramphenicol, at 16 °C overnight. After expression, bacteria cell pellets were lysed, 100 ml buffer per 1 l of culture, in GuHCl Denaturing buffer (6 M guanidine-HCl, 50 mM Tris-HCl (pH 8) and 500 mM NaCl, 5% (v/v) glycerol) overnight. Lysate was sonicated for 200–300 s at 25–30 microns using a Soniprep 150 with intermittent chilling on ice. The supernatant was clarified by centrifugation at 18,000 rpm in a FLA21-8x50y rotor for 30 min. The lysate was further sonicated for 120 s, as above, then filtered through a 0.45 µm filter before loading into a HisTrapHP column (Cytivia) for affinity purification of His-tagged proteins using an AKTA pure system. The column was equilibrated with 5 CV of denaturing 10 mM imidazole buffer (6 M urea, 500 mM NaCl, 50 mM Tris-HCl (pH 8), 10 mM imidazole and 5% (v/v) glycerol), after sample loading was washed with 20 CV of denaturing 50 mM imidazole buffer (6 M urea, 500 mM NaCl, 50 mM Tris-HCl (pH 8), 30 mM imidazole and 10% (v/v) glycerol), and bound proteins were eluted with denaturing 300 mM imidazole (6 M urea, 500 mM NaCl, 50 mM Tris-HCl (pH 8), 300 mM imidazole and 10% glycerol). Peak fractions of the elution were analysed by SDS–PAGE, and samples with appropriately sized bands (40 kDa for His–SOX2, and 60 kDa for His–SOX9) were combined and desalted into 2 M urea buffer using a HiPrep 26/10 desalting column (GE Healthcare). The SOX2 desalting buffer comprised 50 mM Tris-HCl (pH 8), 240 mM NaCl, 10 mM KCl, 2 mM MgCl_2, 2 mM CaCl_2, 0.8 M urea, 30% (v/v) glycerol, 0.1% NP40 substitute, 0.05% Triton-X-100, 2 mM EDTA and 5 mM DTT resuspended in 1× PBS. The SOX9 desalting buffer comprised 50 mM HEPES NaOH (pH 7.5), 240 mM NaCl, 10 mM KCl, 2 mM MgCl_2, 2 mM CaCl_2, 2 M urea, 30% (v/v) glycerol, 0.1% NP40 substitute, 0.05% Triton-X-100, 2 mM EDTA and 5 mM DTT. The fractions corresponding to a dip in the absorbance at A_280 were collected and visualized by SDS–PAGE before combining. Protein concentration was determined by SDS–PAGE, comparing to rAlbumin standards. The gels were imaged by detecting Coomassie blue staining using a Bio-Rad ChemiDoc MP imaging system. The resulting images were visualized, and the bands quantified using ImageJ 2.8/1.54i.

Small-molecule inhibitor screening

E55 cells were screened using inhibitors included in the Kinase Screening Library (Cayman, 10505) as well as two YAP–TEAD pathway inhibitors, MGH-CP1 (HY-139330) and TED-347 (HY-125269). Around 18,000 cells were seeded per well in a 96-well plate and transduced with the corresponding AAVs at a MOI of 2,000,000 on the next day. Cells were incubated at 37 °C, 5% CO_2. In the primary screen, the cells were treated with a single concentration of 3 μM per inhibitor at 24 h after transduction. After 48 h of inhibitor treatment at 37 °C and 5% CO_2, the cells were detached and analysed using an Agilent NovoCyte Penteon five laser flow cytometer and FCS Express 7 Research (v.7.24.0030). The results were expressed as the fold change in mCherry median fluorescence intensity in the inhibitor-treated samples relative to the DMSO control. After performing the first round of screening, inhibitors that gave more than 20% changes were selected for the second round of screening.

In the second-round screen, the cells were seeded and transduced with AAVs using the same procedures as the primary screen. At 24 h after transduction, the cells were treated with 3 different doses (0.1, 1 and 3 μM), and incubated at 37 °C and 5% CO_2 for 48 h. Cells were then detached and analysed using a Agilent NovoCyte Penteon five laser flow cytometer. Draq7 was used as the live/dead stain in both rounds of screening. The results of the second round screen were expressed as the fold change in mCherry MFI and as the percentages of mCherry-positive cells, each normalized to the DMSO control.

EMSAs

Cy5-labelled SSE-7 bait for EMSAs was generated by PCR amplification of SSE-7 from the plasmid using Cy5-labelled primers (Sigma): 5′-ACTAGGTTACTGGTGCATGCTTGTCCTGCCTTTGAGAACA-3′ and 5′-ACCCAAACTATTGGAGCGAGAGAAAGGAAAGAAAGAGGTC-3′. The following primers were used for Cy5-labelled fragments for EMSA: ID4310: 5′-ACTAGGTTACTGGTGCATGCTTGTCCTGCCTTTGAGAACA-3′ and 5′-ACCCAAACTATTGGAGCGAGCTCATTTGAAGCAGAAGAAT-3′; ID4428: 5′-ACTAGGTTACTGGTGCATGCTCTAAATGGAGATCCTCCCA-3′ and 5′-ACCCAAACTATTGGAGCGAGACAGGAGGAAGTAGTAAATC-3′; ID0785: 5′-ACTAGGTTACTGGTGCATGCAAGAATGAACTGGGCCCAGC-3′ and 5′-ACCCAAACTATTGGAGCGAGTGAAAACCAGAGGCATCTCA-3′; and ID3836: 5′-ACTAGGTTACTGGTGCATGCTACCCTTCCAGGGGAGCAGT-3′ and 5′-ACCCAAACTATTGGAGCGAGAGAAAGGAAAGAAAGAGGTC-3′.

The binding to Cy5-end-labelled SSE-7 was analysed in native 1% agarose gels (12 × 13 × 1 cm), which were prepared in 0.5× TBE (45 mM Tris-borate and 1 mM EDTA). Gels were stored overnight at 4 °C before pre-running at 120 V (approximately 10 V cm–1) for 1 h. For affinity analysis, a 10 μl mixture typically containing 50 nM Cy5-labelled full-length or fragmented SSE-7, 100 ng µl–1 poly(dA:dT) (InvivoGen, tlrl-patn) and 0, 0.5, 1, 2 or 4 μl of hFib lysates, with and without SOX overexpression (5.56 mg ml for OE lysates and 3.47 mg ml–1 for hFib control lysates), GSC7 GBM stem, or differentiated, cell lysates (at 0.43 mg ml–1), or purified recombinant SOX protein (at 3.17 µM). For SOX2 and SOX9 in combination, equimolar amounts of SOX2 and SOX9 were mixed for 0.5, 1, 2, 4 and 8 µl of protein in total. Next, 8 µl SOX protein was added to 25 nM SSE-7 DNA in a 20 µl volume, and 20 µl of sample was loaded for gels, which were prepared in 1× binding buffer (10 mM Tris HCl pH 7.5, 1 mM MgCl_2, 10 μM ZnCl_2, 10 mM KCl, 1 mM DTT, 5% (v/v) glycerol and 0.5 mg ml–1 BSA). The mixtures were incubated at 20 ± 1 °C in the dark for 1 h using DNA LoBind tubes (Eppendorf). The entirety of each sample was then loaded onto agarose gels and electrophoresis was performed at 120 V for 3 h at 4 °C. The gels were imaged by detecting Cy5 fluorescence using a Bio-Rad ChemiDoc MP imaging system. The resulting images were visualized, and the bands were quantified using ImageJ 2.8/1.54i.

Adult human cortex and brain tumour slice cultures

Tumour and non-tumour brain tissue were transferred to a 10-cm^2 tissue culture dish with sterile PBS and placed on ice. Visibly damaged tissue was removed and the remaining tissue was transferred into a 35-mm^2 dish with pre-warmed 3% SeaPlaque agarose (50100, Lonza). After cooling in ice, the block was removed and cut using a scalpel into an approximately 2 cm cube around the brain. Before starting to cut, a 6-well plate was prepared. In each well, we introduced one cell culture insert (PICMORG50, Millicell) and added below it 1 ml culture medium in basal NSC medium, Dulbecco’s modified Eagle medium: F12 supplemented with EGF and FGF (Life Technologies). The embedded brain was placed in a circular vibratome plate with glue. The vibratome (VT1000 S, Leica) plate was fixed in the platform and filled with PBS and penicillin–streptomycin (15140-122, Gibco, 1:100). Then, 300-µm-thick slices were cut, with vibrating frequency set at 10 and speed at 1. Each slice was transferred using a small brush onto the top of a Millipore culture insert. The platform was maintained cool at all times. The 6-well plate was placed in an incubator at 37 °C with 5% CO_2.

Addition of virus

A 5 µl volume of virus was pipetted onto the centre of each tissue slice, using a 10 µl tip without touching the tissue. Three repeat doses of 5 µl virus was added to each slice at 5 min intervals (20 µl in total per slice). Slices were incubated for 7 days. The medium was replaced on day 3 or 4.

Immunostaining

The medium was removed and exchanged for 1 ml freshly prepared 4% PFA; 1–2 ml PFA was also placed gently on top to cover the slice. After 2 h, PFA was removed, brain slices were washed 3 times with PBS and transferred using a brush to a 24-well plate. Slices were incubated at room temperature for 1.5 h in blocking solution (0.5% Triton X-100 and 3% goat serum; Sigma-Aldrich) followed by incubation with primary antibody for 2 days at 4 °C. Sections were washed five times with PBS, the respective secondary antibody was applied in blocking solution overnight. The next day, slices were washed 5 times with PBS mounted and cleared in RapiClear 1.49 (RC149001, Sunjin Lab).

Images were acquired using an Opera Phenix Plus high-content imaging system (Revvity) equipped with a ×40/1.1 NA water-immersion objective. Image analysis was performed in Harmony software (v.5.2; Revvity). For the full pipeline see Supplementary Information. In brief, nuclei were segmented using DAPI with the common thresholding method, and the nuclear mean intensity of channels SOX2 (488) and mCherry (555) were then calculated. Thresholds to define positive cells were set for each tissue slice individually based on 3–5 regions of interest. Nestin was segmented using ‘find image region’ with channel 647, and positive cells were defined as those with nuclei in these regions. Analysis was performed for a single z plane. Data were exported and analysed using GraphPad Prism. Representative images of the segmentation can be found in the Supplementary Information.

Zebrafish experiments

All embryos were obtained by natural spawning and collected in conditioned aquarium water in 0.00001% methylene blue. Embryos were raised at 28.5 °C in embryo medium (E3) on a 14 h light–10 h dark photoperiod and were treated with 200 μM N-phenylthiourea (Sigma) from 6 h post fertilization to inhibit pigmentation. Zygotes were injected at the one-cell stage of development. DNA constructs were created using the Tol2Kit system48,49. Approximately 2 nl of plasmid DNA (30 ng μl–1) containing Tol2-capped mRNA (20 ng μl–1), supplemented with 0.2% w/v phenol red (Sigma) to facilitate visualization of injected volume, was injected. To induce Akt1 overexpression in neural progenitor cells, a combination of Tol2-pDEST-NBT:DlexPR-lexOP-pA (20 ng μl–1) and Tol2-pDEST-lexOP:AKT1-lexOP:tagRFP (30 ng μl–1) plasmids was injected as previously described[50]. The newly generated tgSSE-7:eGFP was outcrossed to tg(Xla.Tubb:DsRed) and tg(olig1:mScarlet). Live imaging was performed using either a Leica MS205 stereomicroscope or a Leica SP8 confocal microscope with a ×40/NA 1.1 objective. Animal experimentation was approved by the ethics review committee of the University of Edinburgh and the Home Office in accordance with the Scientific Procedure Act 1986.

AAV medium-exchange transduction assay

HEK cells were seeded into a 6-well plate a day before transfection, aiming for 60–70% confluence the next day. On the day of transfection, the culture medium was exchanged and HEK cells were transfected with RepCap, pHelper and AAV-ITR transgene plasmids using polyethylenimine. Two days later, conditioned HEK culture medium (now containing rAAV particles) was collected and centrifuged at 1,300 rpm for 4 min to remove any cells. rAAV-conditioned medium was transferred to HEK or GSC7 cells seeded at low density (10–20%) in 6-well or 12-well plates. Two to three days later, cells were analysed for transduction. For AAV1 in vivo experiments, we purchased research-grade viral vectors from Vector Biolabs (iodixanol gradient ultracentrifugation purified).

AAV transduction assay in iPS cell-derived neurons

GSC7 and E55 cells were seeded in a 24-well plate format the day before transduction, whereas ioGlutamatergic neurons, ioGABAergic neurons and microglia (bit.bio) were seeded in a 48-well format before differentiation. After differentiation, cells were transduced with AAVs at a MOI of 5 × 10^5. Cells were incubated at 37 °C and 5% CO_2 for 3 or 10 days.

Cell seeding and transduction for GCV killing assay

On the day of cell seeding, cells were detached using the method described above. Cells were counted using a haemocytometer and seeded into 96-well (1,000 cells per well in 50 µl), 24-well (30,000 cells per well in 500 µl) or 6-well (60,000 cells per well in 2 ml) Corning plate and placed in an incubator at 37 °C and 5% CO_2. The next day, AAV stocks were thawed at room temperature, and an appropriate amount of virus stock was added to the required amount of culture medium to achieve the final MOI of 5 × 10^5. Culture medium containing viral particles was added to cells without the replacement of existing medium. Cells were returned to a 37 °C and 5% CO_2 incubator.

Prodrug treatment

Lyophilized GCV was diluted in DMSO to achieve 100 mM stock concentration. GCV stocks were aliquoted and were stored at −20 °C for no longer than 1 month. To make a working stock concentration, GCV was diluted 1:100 in appropriate culture medium, and 20 µl was added to wells already containing 80 µl culture medium for a final concentration of 200 µM. As a negative control, DMSO was diluted 1:100 in appropriate culture medium to achieve a working stock. Next, 20 µl of working stock was added to wells already containing 80 µl culture medium. As a positive control, 20 µl DMSO was added to wells with 80 µl culture medium to achieve a final concentration of 20% DMSO.

Incucyte live-cell imaging

To track cell proliferation and morphological changes during treatments, cells were monitored using an Incucyte live-cell imaging system. Whole-well imaging of Corning 96-well plates was performed every 4 h. Basic confluence scoring analysis software (Incucyte) was used to estimate confluence. Images at specific time points were extracted to verify cell confluence and morphology.

MTT assay

On the day of assay, the culture medium was replaced with 0.3 mg ml–1 MTT solution (diluted in cell line-appropriate culture medium). Cells were placed in incubator at 37 °C and 5% CO_2 for 3 h. After incubation, the medium was removed and 70 µl DMSO was added to each well. Each plate was kept in the dark at 37 °C for 20 min, shaking it occasionally. Before reading the plate, each well was visually inspected to make sure that all (formazan) crystals were dissolved. Plates were read with plate reader at 560 nm absorbance.

Statistical analysis

Data analysis was performed using Microsoft Excel (v.16.23 for Mac), GraphPad Prism (v7) and RStudio (v1.1.456). Error bars are shown as the s.d. of the mean. For illustrations, BioRender and Adobe Illustrator 22.0.1 were used.

Furthermore, we used open-source programmes such as bedtools, GREAT, fastasplitter, HOMER and MEME for various analyses45,46.

Tumour initiation by transplantation and intratumoral AAV delivery

All animal procedures were approved by the University of Edinburgh Animal Welfare and Ethical Review Body (AWERB) and conducted under UK Home Office licence (PPL number: PP8631583) in accordance with the Animals (Scientific Procedures) Act 1986 and ARRIVE guidelines. Male and female C57BL/6 mice (6–8 weeks old) were obtained from Charles River. Mice were housed in Individually ventilated cages with sterile bedding plus enrichment inside a pathogen-free facility on a 12-h light–dark cycle, at a temperature range of 20–24 °C and with relative humidity level of 45–65%. Transplantation experiments were performed as previously described[25]. We used 7-week-old male mice. Mice were transplanted with 200,000 NPE-IE cells in 2 µl PBS per mouse. NPE-IE cells have been previously described[25] and have Nf1 and Pten inactivating mutations, alongside EGFvIII overexpression and a GFP-Luc reporter construct. At 2 weeks, when tumours were visible by IVIS imaging (bioluminescence) and of similar size, AAV1-SSE-7–HSV-TK–IL-12 virus (1.2 × 10^13 viral genomes per ml, Vector Biolabs) was delivered directly into the mouse brain tumour (2.5 µl per mouse) using a microsyringe linked to an injection pump, at a rate of 0.17 µl min–1 and 2.3 mm depth, higher than the previous depth of 2.4 mm for tumour cells. Each mouse received 3.00 × 10^10 viral genomes. The following day after virus injections, GCV was intraperitoneally injected (500 µl per mouse at 2 mg ml–1) once daily for 20 days. Mice were imaged by IVIS imaging (PerkinElmer) once a week for luciferase signals. Animals were observed regularly for any neurological symptoms. Mice with tumours were culled based on the following criteria; (1) body weight loss of ≥20% relative to baseline (weights of mice were recorded once weekly); (2) abnormal neurological signs, including ataxia, head tilt, circling behaviour, seizures, paresis or inability to right itself within 3 s; (3) hunched posture with reduced locomotor activity and lack of reach response; and (4) any extracranial tumours ≥ 15 mm in diameter or ulcerated as per the Workman (2010) guidelines[51]. All mice reaching the humane end point were culled using the schedule 1 method. The end-point limits were not exceeded in any of the experiments.

Immune characterization of mouse GBMs

Brains were collected and processed 5 days after treatment. First, they were microdissected under a Leica stereo microscope to avoid contamination with healthy tissue. Tissues were mechanically disaggregated using scissors, followed by 30 min of enzymatic digestion using a mix of 0.35 µg ml–1 Liberase TL (05401119001, Roche) and 0.23 µg ml–1 DNAse (101041590001, Sigma) in plain RPMI. After digestion, tissues were dispersed through a 70 nm cell strainer to obtain a single-cell suspension. After centrifugation, cells were resuspended in PBS for further flow cytometry staining. Cell suspensions were incubated for 10 min at 4 °C with a blocking mixture of mouse, rat and calf serum containing anti-CD16/32 blocking antibodies (clone 2.4G2, BioXcell, BE0307). Cells were washed with PBS and incubated for 20 min in antibody-containing staining buffer plus Fixable Viability Dye eFluor-780 (eBioscience, 65-0865-14) to distinguish live and dead cells. Cells were then washed and resuspended in fixation/permeabilization buffer (BD CytoFix/Cytoperm; 554714 or FOXP3 Transcription Factor Staining Buffer set, eBioscience) followed by intracellular target staining.

SABER–FISH detection of transgene copy number

SABER–FISH was performed as previously described52,53. Non-overlapping 36 bp probes were designed to target the HSV-TK transgene, and probes were extended using primer exchange reaction to approximately 500 bp. For imaging, cells were first stained with mCherry,then fixed with PFA and subsequently processed for immuno-SABER–FISH[54].

Assessment of the fraction of the tumour cell population that activates AAV1-SSE-7–mCherry

AAV1-SSE-7-mCMV–HSV-TK-V5–mCherry was injected at around 3.58 × 10^14 viral genomes per ml and 7.2 × 10^13 viral genomes per ml (1:5 dilution) in PBS. AAV was injected into tumours as described above. After 7 days, tumours were imaged using a stereomicroscope and surgically excised.

Each tumour sample was added to 1 ml digest mix (RPMI + 1 mg ml–1 collagenase IV, 12.6 µg ml–1 DNase I and 1% penicillin–streptomycin) and incubated for 1 h in a shaking incubator at 40 rpm at 37 °C. Samples were filtered through a 100 μm cell strainer, washed with 9 ml FACS buffer and centrifuged at 250g for 15 min at room temperature. The pellet was then resuspended in 1 ml 37.5% Percoll in PBS and gradient centrifuged at 900g, room temperature, for 12 min with no brake. The supernatant was discarded, and the bottom layer of cells resuspend in 500 µl PBS and filtered into a FACS tube. Cells were stained with DAPI as a live/dead stain and analysed using a BD LSR Fortessa cell analyser (four lasers, BD Bioscience).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The scRNA-seq data have been deposited at the European Nucleotide Archive (ENA) (accession number: PRJEB81816). The project number for ChIP–seq data is PRJEB107008. For ChIP–seq, raw (fastq) and processed (bigwig and narrowPeak) files have been deposited and made public (https://www.ebi.ac.uk/ena/browser/view/PRJEB107008). The ENA does not take processed files; therefore, these have been uploaded to BioStudies, which links to the same ENA accession (PRJEB107008), otherwise they are at https://www.ebi.ac.uk/biostudies/studies/S-BSST2733. All the codes and processing steps for scRNA-seq, SCENIC analysis and ChIP–seq data are available at GitHub (https://github.com/alhafidzhamdan/sse_gene_therapy). The GitHub repository includes some processed files, including all the narrowPeak files and consensus sets; using these in conjunction with the .Rmd document for reproduction of Fig. 2. This also includes a link to the SOX2 and SOX9 tracks on the UCSC browser. The linked and minted a repo is available at Zenodo (https://zenodo.org/records/18676096)[55]. Source data is provided with this paper.

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Acknowledgements

U.K., M.M., S.J.R. and S.M.P. are supported by the BBSRC UK Mammalian Synthetic Biology Centre (BB/M018040/1). The GSC and NSC lines were obtained from the GCGR. The GCGR was funded by a Cancer Research UK (CRUK) Accelerator Award (C157/A21992). A.W. was supported by a BBSRC funded project grant. A.H. was supported by CRUK and the Edinburgh Clinical Academic Training (ECAT) programme (C157/A29279). D.S. is supported by a CRUK programme foundation award. S.M.P. is supported by CRUK programme grant (DRCNPG-Nov21\100002). S.A.Q. is funded by a CRUK Senior Cancer Research Fellowship (C36463/A22246) and a CRUK Biotherapeutic Program grant (C36463/A20764). This work was undertaken at University College London with support from the CRUK City of London Centre (C7893/A26233). S.M.P. and S.A.Q. also received support from the CRUK Brain Tumour Award (A28592). We thank J. Mendrychowski and V. Kho for technical support; I. Chambers and J. M. Iglesias for their comments on the manuscript; and A. Corsinotti for technical advice on scRNA-seq. For open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

Author information

Author notes

1. Ute Koeber

Present address: IQVIA RDS, Frankfurt am Main, Germany

2. Mantas Matjusaitis

Present address: Plurify, Cambridge, UK

3. Rachel White, Michelle P. Clark, Carla Jacinto de Sousa & Ben Southgate

Present address: Trogenix, Edinburgh, UK

4. Felipe Galvez Cancino

Present address: Laboratory of Immune Regulation, NDM Centre for Immuno-Oncology, University of Oxford, Oxford, UK

5. These authors contributed equally: Ute Koeber, Mantas Matjusaitis

Authors and Affiliations

1. Centre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK

Ute Koeber, Mantas Matjusaitis, Neza Alfazema, Katharine Furlong, Zeyu Wang, Rachel White, Alhafidz Hamdan, Pooran Dewari, Rosie Willis, Michelle P. Clark, Carla Jacinto de Sousa, Hei Ip Hong, Shahida Sheraz, Ben Southgate, Justyna Cholewa-Waclaw, Sabine Gogolok, Gillian M. Morrison, Faye Robertson, Anna Williams, Paul M. Brennan, Abdenour Soufi & Steven M. Pollard

2. Cancer Research UK Scotland Centre, Edinburgh, UK

Ute Koeber, Mantas Matjusaitis, Neza Alfazema, Zeyu Wang, Rachel White, Alhafidz Hamdan, Pooran Dewari, Gregoire Morisse, Rosie Willis, Jin Wang, Michelle P. Clark, Carla Jacinto de Sousa, Hei Ip Hong, Shahida Sheraz, Ben Southgate, Justyna Cholewa-Waclaw, Sabine Gogolok, Gillian M. Morrison, Faye Robertson, Paul M. Brennan, Dirk Sieger & Steven M. Pollard

3. Edinburgh Pathology, Royal Infirmary Edinburgh, NHS Lothian, Edinburgh, UK

Alhafidz Hamdan

4. Institute for Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh, UK

Gregoire Morisse, Jin Wang & Dirk Sieger

5. Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London, UK

Mariela Navarette, Felipe Galvez Cancino & Sergio A. Quezada

6. School of Biological Sciences, University of Edinburgh, Edinburgh, UK

Susan J. Rosser

7. Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

Paul M. Brennan

Contributions

S.M.P. conceived the study, secured funding and supervised the project. U.K. designed and performed in vitro experiments to construct the SSEs, with bioinformatic support from B.S. M.M. designed and performed in vitro experiments related to the therapeutic transgene experiments and selectivity, and with N.A. in the animal efficacy studies. A.W. provided human OPCs. M.P.C. led the human in vitro selectivity experiments. The ChIP–seq analysis was performed by P.D., and A.H. led the bioinformatic analyses. Z.W. designed and performed the scRNA-seq experiments. R. Willis performed SABER–FISH. K.F. performed the biochemical analysis of SOX interactions, with supervision from A.S. Key technical support for all the molecular biology was provided by R. White and S.G. All zebrafish studies were performed by G.M. and J.W., with design and supervision from D.S. Manufacture and quality control of all viral vectors was performed by M.M., R. White and C.J.d.S. The inhibitor screen was designed and performed by H.I.H., with supervision from S.M.P. The human tissue slices were provided by P.M.B., with AAV testing performed by R. Willis, S.S., M.P.C. and J.C.-W. The initial support for the transgene optimization and patient-derived GSC models was from S.G. and G.M.M. F.R. supported mouse model experimental designs and translational relevance. The plasmid library manufacture was supported by S.J.R. and staff at the Edinburgh Genome Foundry. All immune characterization was designed and performed by M.N. and F.G.C., with supervision from S.A.Q. The manuscript and figures were drafted by S.M.P., with contributions from all authors. All authors reviewed, edited and approved the final version.

Corresponding author

Correspondence to Steven M. Pollard.

Ethics declarations

Competing interests

S.M.P., U.K. and M.M. are inventors of patents filed by the University of Edinburgh related to the discoveries in this study and to SSEs and associated sequences (WO2022162361A1). S.M.P. is founder of Trogenix, which is developing novel gene therapies for oncology, and is a paid consultant (part-time chief scientific officer, since February 2024) and shareholder. B.S., R. White, C.J.d.S. and M.P.C. have recently joined Trogenix as employees (since October 2025). S.A.Q. is a paid member of the scientific advisory board of Trogenix. P.M.B. and F.R. are paid external consultants for Trogenix related to ongoing clinical translation of these technologies. All other authors (N.A., K.F., Z.W., A.H., P.D., G.M., M.N., R. Willis., J.W., H.I.H., S.S., J.C.-W., S.G., G.M.M., F.G.C., A.W., S.J.R., D.S. and A.S.) declare no competing interests.

Peer review

Peer review information

Nature thanks Ralf Jauch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 Design of candidate SOX2 GSC-specific peaks and their conversion into a library of candidate enhancer fragments.

(a) Summary of the workflow to design and build the enhancer fragment library (DGC: differentiated glioma cell). (b) The genomic annotation of the final library of candidate peaks. (c) The top 16 fragment hits identified from the initial screen (Fig. 1) were validated in GSC7 GSCs for mNeongreen expression using flow cytometry (median fluorescence intensity shown (left). (d) The fragments have the same level of expression in HEK negative control cells as the minimal CMV (MFI and % of total cell population shown, left and right, respectively). (e) De novo motif analysis using HOMER for the Top32 hits of the validation (using shuffled sequences as background) from Fig. 1, confirms the repertoire of candidate TF motifs that are enriched in the functional enhancer fragments.

Source data

Extended Data Fig. 2 A dimeric SOX motif that binds SOX9 is a major contributor to activity.

(a) A specific dimeric SOX dimer motif was identified using MEME for de novo motif discovery and is enriched in the set of functional enhancer hits (ACAAAGRGBVNHTKK) and was mutated as depicted (below) for 10 different variants of the ID2904 sequence to determine the functional impact. (b-e) Plasmid-based flow cytometry assay to determine the activity of each variant, in GSCs (GSC7 and E28, respectively). Interestingly, increased activity was seen with 2 bp reduction of the spacing consistent with the importance of this motif for activity. Each dot represents a biological replicate (2e) One-way Anova test with Dunnett’s multiple comparisons test against ID2904 WT; ** represent p-value of 0.0040 (f) Examples of flow cytometry data used for panels c-f. M(f) Examples of flow cytometry data used for panels c-f. (g) SOX9 protein selectively interacts with ID1101 and ID2904 through the dimer motif.

Source data

Extended Data Fig. 3 Assessment of SSE-7 activity across a set of seven different patient-derived GSC lines.

(a) Reporter reference controls were used to determine relative SSE activity by flow cytometry across each cell line, with minimal CMV (mCMV) and CMV as negative and positive controls, respectively. (c, e, g, i, k, m and o) shows the % mCherry positive cells, whereas (d, f, h, j, l, n and p) shows the median fluorescence intensity (MFI) for each cell. E37 is an outlier line and has an atypical MYC amplification and lower levels of SOX2. Error bars represent the SD of the mean, each dot represents a biological replicate.

Source data

Extended Data Fig. 4 Characterisation of SSEs.

(a) Comparison of activity from all seven SSE variants in GSC7 using flow cytometry to monitor expression of the mNeonGreen reporter. Each dot represents a biological replicate. (b) Time-course of GSC astrocyte differentiation to monitor SSE activity using flow cytometry. (n = 2 biological replicates, each dot symbol represents the mean, the error bars represent the SD of the mean) (c) Live cell wide-field immunofluorescence imaging for the cells assessed in (b). (d-e) Quantitation of the band intensities from the EMSA experiments in Fig. 2d,e. Data points are mean from n = 3 with error bars of the SD. Solid line represents the Hill equation with the Kd values displayed above the graph. (f) EMSA of SSE-7 DNA using purified recombinant SOX2 and SOX9, alone or in combination (representative image of n = 3). (g) EMSA showing the individual and combinatorial binding of purified recombinant SOX2 and SOX9 to FGF4 enhancer element (40 bp, n = 1) containing a typical SOX2 motif.

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Extended Data Fig. 5 Single cell mRNA profiling of multiple patient derived GSC lines following transduction with AAV1-SSE-7-mCherry virus.

(a) Distribution of transcriptional subtypes in scRNA data. UMAP of transcriptional subtype in E17 (n = 29,829 cells), E20 (n = 23,301 cells), E31 (n = 39,823 cells), E34 (n = 27,766 cells), E43 (n = 31,538 cells), E55 (n = 47,291 cells). *P < 0.05, **P < 0.01, ***P < 0.001; NS, not significant. (b) SOX2 and SOX9 are broadly expressed across the diverse transcriptional subtype cell states in GSCs, consistent with their roles as core regulators. See also Extended Data Fig. 6 for cell cycle analysis.

Extended Data Fig. 6 Single cell analysis of cell cycle and SCENIC analysis.

(a) Distribution of cell cycle phases across GSC cell states in scRNA data (as per Extended Data Fig. 5). (b) Full data for SCENIC analysis of transcription factor modules identifies signalling endpoint transcription factors alongside SOX as correlating with SSE-7 activity. (c) Heatmap across technical replicates of libraries from Fig. 3i.

Extended Data Fig. 7 A screen of small molecule pharmacological inhibitors to identify signalling pathways that may impact the activity of SSE-7 in E55 GSCs.

(a) The primary screen was performed in the E55 cells. Heatmap summarising the fold change of the normalised mCherry MFI in inhibitor-treated samples compared with DMSO. Initial hits identified with red font. The relative comparator is the CMV-SSE7-mCherry positive control. Any reduction in mCherry selectively in the E55 is due to the different properties of the SSE7. (n = 2 biological replicate; independent screens) (b) Validation of the hit compounds using flow cytometry for mCherry detection across three different concentrations of compound (0.1, 0.3 or 3μM). The heatmap shows the fold change of the normalised mCherry MFI and percentages of mCherry positive cells of inhibitor-treated samples relative to the DMSO-treated control. (n = 2 biological replicates). Total % mCherry cells (top panel) and mean fluorescence intensity (MFI) bottom panel. Red font are MAPK signalling-related compounds validated with dose response and show selective effects on the SSE-7 compared to CMV promoter. (c) Flow cytometry histograms for each validated hit compound. Typical example of flow cytometry overlay histograms for E55 GSCs treated with 3 different doses of MAPK signalling-related inhibitors (left panels) and E31 GSCs (right panels). E55 and E31 have distinct transcriptional subtype identities (mesenchymal and developmental/proneural, respectively). (d) Summary of validated inhibitor hits (red font) and their mechanism of action within the MAPK signalling pathway. (e) Flow cytometry analysis of positive and negative SSE-7mCherry cells sorted and reanalysed over a 72-hr time-course shows that SSE-7 activity can be activated in cells that are initially negative, indicative of a readout that is sensitive to signalling state. n = 3 biological replicates. 2-way ANOVAs followed by two stage setup method were carried out to compare with mCMV neg. *P < 0.05, **P < 0.01, ***P < 0.001; NS, not significant. Note: SC-1 was toxic at the higher doses for E31 and so could not be assessed.

Source data

Extended Data Fig. 8 Selectivity in tissue expression in transgenic zebrafish embryos.

(a) Reporter transgene for selective expression of SSEs in a zebrafish transgenic reporter model with a GFP reporter, with a transposase (Tol2) reporter designed track transgene insertion using mCherry expression in the eye from the Crystal promoter. (b) SSE activity is tracked by eGFP levels in live cell imaging of the embryos at 48hpf and is tissue-restricted compared parallel CMV controls for each SSE tested. Note: the minCMV control exhibited GFP-expression in the eyes due to spurious transcription from the Crystal eye promoter. (c) A stable transgenic SSE-7:eGFP reporter zebrafish line confirms expression is restricted to the developing nervous system. Images were acquired using a Leica MS205 stereomicroscope. Scale bars represent 500 µm for (b) and 728 µm for (c). (d) Higher magnification image of the spinal cord and nototchord with neuronal reporter (Cyan, as per (e)). (e) Outcross of tgSSE-7:eGFP to tg(Xla.Tubb:DsRed) reveals no expression in neurons; in (f) outcross of tgSSE-7:eGFP to tg(olig1:mScarlet) shows expression in a subset of oligodendrocyte precursor cells. Images represent maximum intensity projections of confocal stacks (optic tectum and cerebellum shown). Images were captured using a Leica SP8 confocal microscope with a 40X/NA 1.1 objective. Scale bars represent 30 µm. (g) Quantification of the OPC data from (f). (h) Overexpression of human Akt1 in neuronal cells results in strong activity of SSE-7. Images represent maximum intensity projections of confocal stacks. Images were captured using a Leica SP8 confocal microscope with a 40X/NA 1.1 objective. Scale bars represent 30 µm. (i) Immunostaining and flow cytometry (top and bottom, respectively) of human ESC differentiated oligodendrocyte progenitor cells shows that SSE-7 is not active compared to CMV (n = 1). (j) SSE7 versus CMV across a set of three human foetal neural stem cell (NSC) cultures (derived from week of gestation 9, 12 and 17 tissue; NS9FB_B, NS12ST_A and NS17ST_A, respectively). Each dot represents a biological replicate. Error bars represent the SD of the mean.

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Extended Data Fig. 9 Selective activity of AAV1-SSE7-mCherry in SOX2 expressing cells within the tumour tissue mass and infiltrating margin.

(a) Tissue slice-culture experiments were performed across diverse subtypes of high-grade glioma similarly to Fig. 2. Immunostaining and confocal for mCherry across distinct tumour subtypes shows restricted activity of SSE-7-mCherry in the tumour margin relative to CMV. (b) Quantitation of the % of mCherry expression in the tumour versus the margin shows SSE-7 is activated in a subset of GBM cells. (n = 4 for both tumour and margin with both CMV and SSE7). Matched tumour and margin samples were used across multiple tumour types; GBM IDH-WT (isocitrate dehydrogenase wild-type), Grade 4 Astrocytoma IDH-MUT (isocitrate dehydrogenase mutant), Grade 3 high-grade astrocytoma and denoted by distinct symbols, each point is a biological replicate. Error bars represent SD. Statistical significance was assessed by two-way ANOVA with multiple-comparisons correction (p = 0.0182). n = 4. Image analysis and segmentation pipelines provided in the Supplementary Data. Scale bar = 100 micron. (c) Staining for mCherry alongside SOX2 and Nestin in a recurrent GBM sample treated with the AAV1-SSE7-mCherry vector, confirming that SSE-7 is active in a subset of the tumour cells. Left panels, low magnification (no matched normal tissue was available for this recurrent sample).

Source data

Extended Data Fig. 10 In vitro killing of GSCs, but not fibroblasts, is achieved using SSE7-driven HSV-TK.

(a) Schematic of the AAV-2 transgene encoding both HSV-tk and mCherry used in vitro to assess selective cytotoxicity. (b and c) Flow cytometry was performed 4 days post-transduction. Each dot represents biological replicate (n = 3), error bars represent SD. (b) % mCherry positive cells and (c) median fluorescent intensity (MFI) of mCherry positive cells. (d) Western blot image developed against V5 and GAPDH in transduced and non-transduced GSC7 and huFB170 cells; the higher weaker band is the small fraction that was uncleaved. (e) Incucyte live cell imaging generate proliferation curves to determine the change in confluence over 10-day time-course for human GSC7 cells transduced with CMV or SSE-7-driven TKv5-P2A-mCherry in presence of 200 µM GCV, 0.2% DMSO (vehicle control) or 20% DMSO (positive control). (f; top panels) Live images of GSC7 cells at day 8 demonstrating cell number and morphological changes in cells transduced with constructs in the presence of 200 µM GCV. (f; bottom panels) Live images of huFB170 cells at day 17 demonstrating cell number and morphological changes in cells transduced with constructs in the presence of 200 µM GCV. (g) MTT assay for cell viability in GSC-7 and fibroblasts. Each dot represents an independent biological replicate. MTT values were normalized to vehicle-only control. Error bars represent SD (n = 3).

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Extended Data Fig. 11 Evaluating SSE7-TKv5-P2A-mCherry activity from an AAV vector.

(a) Example of flow cytometry data for GSCs (E17) cells following transduction at MOI of 5E5. (b) Heatmap summarizing mean AAV transduction efficiency in different patient-derived GSC cultures (n = 3, except E34 and E21, which are n = 2). (c) SSE-7 activity in mouse fibroblasts and GSCs. (c) Flow cytometry data comparing transcriptional activity between SSE-7- and CMV-driven TKv5-mCherry cassette in mouse NPE-IE cells (GSCs) and NIH3T3 cells when delivered using rAAV2. (d) MTT viability assay demonstrating GCV dose response kill curves in NPE-IE cells transduced with rAAV carrying specified constructs.

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Extended Data Fig. 12 Treatment of mice with AAV-SSE7-TK-IL12 results in tumour clearance and long-term protection against rechallenge.

(a) Bioluminescence imaging to track tumour growth with either control (PBS) injection (right panels) or virus plus 3-week ganciclovir treatment (shown are n = 10 mice from the first cohort of mice in Fig. 5b). Right panel are a cohort of parallel fresh controls using the same cells in new cohort of mice, confirming tumour initiation potential. Blue font indicates the new timeline after re-challenge with fresh tumour cells injected (male mice used). (b) Dose de-escalation using IVIS to track tumour growth following treatment with different doses of AAV1-SSE7-HSV-tk-IL12. (male mice used).

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Extended Data Fig. 13 Characterisation of immune cells with single or double payloads.

(a) Survival curves of tumour-bearing animals for either single (IL-12) or combination payloads (IL-12 plus TK/gcv), n = 7 male mice per group. Survival was analysed using Kaplan-Meier method. Statistical significance was determined using log-rank (Mantel-Cox) test with p = 0.0009. (b) Non-tumour bearing mice treated with AAV-SSE7-TK-IL12 versus the CMV variant. n = 5 male mice per group. Survival was analysed using Kaplan-Meier method. Statistical significance was determined using log-rank (Mantel-Cox) test with p = 0.0034. The CMV driven payloads are toxic. (c) Bar plots of the percentage of PD-L1+ cells in MDSCs, monocytes, BMDMs, DCs and microglia. (d, e) Bar plots of the percentage of CD62L-CD44-, CD62L-CD44+, CD62L+CD44- and CD62L+CD44+ cells in T effector cells or CD8+ T cells, respectively. (f,g) Bar plots of the percentage of IFNg producing cells in T effector cells. (h,i) Bar plots of the percentage of IFNg producing cells in CD8+ T cells. (j) Flow cytometry analysis of tumor mass 7 days following AAV1-SSE7-mCherry transduction, reveals that ~20% of the NPE-IE-GFP tumor cell population has strong mCherry expression (left panel, gating of tumor cells; middle panel, mCherry histogram; right panel, quantitation n = 4 independent tumours); 2ul of 3.6E14/ml (full dose) and 7.2E13/ml (1:5 dose). (c-i) n = 7 biologically independent animals per group, examined in one experiment. Data are represented as mean values +/− SD. Statistical significance was assessed using one-way ANOVA with Tukey’s multiple comparison test. *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001.

Source data

Extended Data Table 1 Predicted target genes and distance from enhancer fragment to transcription start site (TSS)

Supplementary information

Supplementary Information (download PDF )

Supplementary Sections 1–8, including Supplementary Tables 1 and 2.

Reporting Summary (download PDF )

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Source Data Figs. 1 and 3–5 and Source Data Extended Data Figs. 1–4 and 7–13 (download XLSX )

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Koeber, U., Matjusaitis, M., Alfazema, N. et al. Synthetic super-enhancers enable precision viral immunotherapy. Nature 653, 232–241 (2026). https://doi.org/10.1038/s41586-026-10329-6

- Received: 07 December 2023

- Accepted: 26 February 2026

- Published: 08 April 2026

- Version of record: 08 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10329-6

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Precancerous niche remodelling dictates nascent tumour persistence

G. Skrupskelyte,

J. E. Rojo Arias,

H. Ajith,

Y. Dang,

D. Rossetti,

S. Han,

M. K. S. Tang,

M. T. Bejar,

B. Colom,

J. C. Fowler,

K. Murai,

W. Knight,

D. Aust,

M. H. H. Schmidt,

J. Jászai,

S. Zeki,

A. Noorani,

P. H. Jones,

S. Rulands,

B. D. Simons &

…

M. P. Alcolea

Nature volume 653, pages 242–253 (2026)

Abstract

Interactions between mutant cells and their environment have a key role in determining cancer susceptibility1,2,3. However, understanding of how the precancerous microenvironment contributes to early tumorigenesis remains limited. Here we show that newly emerging tumours at their most incipient stages shape their microenvironment in a critical process that determines their survival. Analysis of nascent squamous tumours in the upper gastrointestinal tract of the mouse reveals that the stress response of early tumour cells instructs the underlying mesenchyme to form a supportive ‘precancerous niche’, which dictates the long-term outcome of epithelial lesions. Stimulated fibroblasts beneath emerging tumours activate a wound-healing response that triggers a marked remodelling of the underlying extracellular matrix, resulting in the formation of a fibronectin-rich stromal scaffold that promotes tumour growth. Functional heterotypic 3D culture assays and in vivo grafting experiments, combining carcinogen-free healthy epithelium and tumour-derived stroma, demonstrate that the precancerous niche alone is sufficient to confer tumour properties to normal epithelial cells. We propose a model in which both mutations and the stromal response to genetic stress together define the likelihood of early tumours to persist and progress towards more advanced disease stages.

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Main

Groundbreaking studies in human genomics over the past decade have revealed that our healthy tissues accumulate cancer-associated mutations with age4,5,6,7,8. These observations highlight new levels of complexity in the early pathophysiology of cancer, raising the question of what other factors, beyond cancer mutations, may have a role during early carcinogenesis.

Models of early tumours spanning a range of epithelial tissues, including oesophagus, skin and intestine, have started to offer a clearer understanding of what drives tumour initiation1,2,3,9,10,11. Work in this area has shown that tumour formation represents more than the mere accumulation of genetic alterations, highlighting the important role of environmental cues and non-genetic mechanisms in this process3,12,13,14,15. Indeed, mounting evidence indicates that the predisposition of a mutated epithelium to develop tumoral lesions depends on complex interactions between mutant cells and their dynamic surroundings. Coexisting mutant clones could either synergize or compete, contributing to early tumour initiation16,17. Indeed, even after tumours have formed, the presence of neighbouring mutant clones can continue to influence tumorigenesis[1]. Alternative environmental cues, such as the stiffness of the extracellular matrix (ECM)3,13, as well as direct cell–cell communication between mutant cells and non-mutant cells2,9,10,11,14, have also been shown to affect the expansion of mutant clones, susceptibility to tumour initiation and invasion18,19,20. Despite this, understanding of the mechanisms by which environmental factors determine the formation and long-term persistence of emerging tumours remains limited.

Previous studies using an oesophageal early-tumour model demonstrated that not all nascent tumours have the same chance of survival. Most tumours are cleared from the tissue soon after formation by competition with neighbouring mutant clones. Surviving tumours instead persist long term, becoming susceptible to cancer progression[1]. But a key question remains: how are precancerous tumours able to withstand the competitive mutant environment that surrounds them? Understanding the processes underlying early tumour persistence and the relevance of the microenvironment at pre-neoplastic stages provides a critical opportunity to dissect the mechanisms driving precancer progression, opening new avenues to halt cancer in its tracks. Here we combine single-cell RNA sequencing with lineage tracing and 3D heterotypic cultures to study the unique features of the few nascent tumours that escape the existing protective barriers preventing tumorigenesis. We demonstrate that, during the earliest stages of tumour development, fibroblasts react to the pre-neoplastic epithelium by promoting the formation of a fibrotic precancer niche that, in turn, feeds back on the epithelium favouring early tumour growth and survival.

Precancerous tumour persistence

To study the processes that underlie the persistence of pre-neoplastic nascent tumours, we used a well-established, clinically relevant mouse model of upper gastrointestinal tract (including oesophagus and forestomach) tumorigenesis driven by a mutagen found in tobacco smoke (diethyl-nitrosamine (DEN))1,21 (Extended Data Fig. 1a–j). After DEN treatment, the tissue becomes an evolving patchwork of mutant clones competing for space and survival, recapitulating the complex mutational landscape of the normal human ageing oesophagus[22]. This results in the emergence of pre-neoplastic squamous tumours with the potential to persist long-term (Extended Data Fig. 1a–c).

Nascent epithelial tumours, marked by KRT17 (keratin 6A (KRT6A) and keratin 17)[1], can be detected in tissue whole-mounts from their most incipient stages, from as early as 10 days after DEN treatment (Extended Data Fig. 1d–e). The emerging tumours are microscopic, containing as few as 10 cells, and are characterized by their distinctive rosette-like structure1,21 (Extended Data Fig. 1d,f). This brief window of formation is followed by a tumour-clearing process, in which more than one-third of the initial tumours are progressively eliminated[1]. The surviving tumours can persist in the tissue for more than a year, largely as low-grade dysplasia (pre-neoplastic or precancer stages), with sporadic progression to invasive squamous cell carcinomas[1] (Extended Data Fig. 1c,g,h), mimicking human carcinogenesis[23]. As a result, only a subset of the original tumours survive long term, enabling us to study the mechanisms that modulate precancerous tumour persistence.

To understand what drives early tumour survival, we first set out to compare the phenotypic traits of nascent tumours and those persisting long term (10 days and more than 8 months, respectively, after DEN treatment; Fig. 1a). Histological analysis showed that persistent dysplastic tumours (Extended Data Fig. 1h) were characterized by a prominent stromal remodelling (Fig. 1b). These nest-like structures were formed by stromal fibroblasts (PDGFRα+) that protruded towards the epithelial compartment, seemingly enclosing early tumours to create a supportive scaffold or a ‘precancerous niche’ (Fig. 1b,c). Unlike in persisting tumours, at nascent stages, most epithelial lesions (around 70%; 199 of 296) showed no apparent stromal reorganization (Fig. 1c, d), denoting the existence of two phenotypically different nascent tumour subtypes, referred to here as Niche+ and Niche− (Fig. 1c).

Fig. 1: Precancerous niche remodelling is linked to long-term tumour persistence.

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a, The experimental DEN carcinogen protocol. Wild-type mice were exposed to DEN in the drinking water for 2 months. Tissues were collected at 10 days, 2 months, 8 months and 1 year. b,c, Representative confocal images of long-term-persisting tumours 8 months after DEN treatment (b) and nascent tumours 10 days after DEN treatment (c), stained for DAPI (blue), KRT6A (tumour marker; red) and PDGFRα (fibroblast marker; grey). Scale bars: 50 µm (b) and 10 µm (c). Image settings were adjusted to the upper stromal layer. d, Percentage of Niche+ and Niche− tumours at the indicated time points after DEN administration from three mice per time point; statistical significance was determined by a one-sided chi-squared test. e, Confocal images showing the incorporation of 5-ethynyl-2′-deoxyuridine (Edu; green) in KRT6A+ nascent tumours (red, dashed line) 10 days after DEN treatment. Scale bars: 10 µm. Images were generated omitting the uppermost suprabasal layer. f, Diameter (×100 µm) of Niche+ (red) and Niche– (blue) tumours at the indicated time points after DEN treatment. Tumours were quantified in three mice per time point: at 10 days, n = 128 tumours; at 2 months, n = 84 tumours; at 8 months, n = 53 tumours; and at 1 year, n = 49 tumours. Data are expressed as mean ± s.e.m. Two-tailed Welch’s t-test comparing Niche– versus Niche+ tumours. g, Cartoon illustrating the association between tumour niche remodelling and long-term tumour survival. Red, surviving tumours; blue, disappearing tumours. Illustrations in a and g were created in BioRender; Alcolea, M. https://BioRender.com/0g5wodl (2026).

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Next, we assessed the dynamic nature of these two nascent tumour subtypes. We found that the number of Niche+ tumours, despite constituting the minority of all initial tumours, remained constant over time, whereas the number of Niche– tumours decreased markedly (Extended Data Fig. 2a). As a result, the tissue became progressively enriched in Niche+ tumours, with most (around 82%; 65 of 79) showing a supportive stromal scaffold by 8 months following DEN treatment (Fig. 1d). This enrichment in Niche+ tumours prompted us to explore whether stromal remodelling was associated with nascent tumour persistence. We found that Niche+ lesions were hyperproliferative and were more likely to persist and enlarge than Niche– tumours were (Fig. 1e–g and Extended Data Fig. 2b–f). Close analysis revealed that keratinocytes in contact with the niche showed a particularly high proliferative activity (Extended Data Fig. 2e), indicating active epithelial–stromal communication at precancerous stages. These results were further reinforced by observations in the squamous forestomach, where long-term pre-neoplastic tumours also exhibited a remodelled stromal niche (Extended Data Fig. 2g,h).

Collectively, our data support a model in which the remodelled stromal scaffold acts as a ‘precancerous niche’ promoting tumour growth and survival. These observations link stromal remodelling in nascent tumours with pre-neoplastic tumour progression.

Niche signals drive tumour traits

The importance of the precancerous niche during early tumorigenesis became evident in 3D heterotypic cultures. These cocultures revealed that signals from the early tumour stroma are sufficient to confer tumour features to epithelial cells that had never been exposed to carcinogens[24] (Extended Data Fig. 2i–l). Using reporter mouse lines to track the tissue origin, we found that untreated phenotypically normal epithelium directly exposed to the denuded tumour niche (lacking the epithelial compartment) acquired a tumour-like morphology and became highly proliferative, reaching levels similar to those of early tumours in vivo (Extended Data Fig. 2k,l).

Moreover, heterotypic tissue constructs grafted into immune-deficient NOD-SCID-γ mice (NSG; NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) showed that the pro-survival phenotype conferred by the tumour niche was also observed in vivo. Normal epithelial cells were more likely to engraft long term when exposed to early tumour stromal signals (Extended Data Fig. 2m–o).

Overall, these results demonstrate that the early tumour microenvironment promotes epithelial cell growth, favouring precancerous tumour survival and, ultimately, disease progression.

Local fibroblasts form nascent tumour niche

Given the key role of the niche in nascent tumour survival, we next explored its cellular composition.

Under normal conditions, the squamous upper gastrointestinal tract is characterized by three distinct layers of stromal tissue: the lamina propria, a thin loose connective tissue directly beneath the epithelium; the muscularis mucosae, a layer of smooth muscle cells; and the submucosae, a dense irregular lower stromal compartment[25] (Extended Data Fig. 3a–d). In line with observations in other epithelial tissues26,27, immunofluorescence analysis revealed two fibroblast populations that showed distinctive tissue compartmentalization and morphology and different expression levels of the pan-fibroblast marker PDGFRα (Extended Data Fig. 3b–d). PDGFRαlow fibroblasts resided in the upper stroma (the lamina propria), whereas PDGFRαhigh fibroblasts populated the deepest stromal layer (the submucosae; Extended Data Fig. 3c,d). Histological analysis of emerging tumours revealed that the main cellular component of the precancerous niche was PDGFRαlow fibroblasts, phenotypically indistinguishable from neighbouring lamina propria fibroblasts (Fig. 2a). Further characterization showed that endothelial and immune cells were largely absent from the niche in nascent tumours (10 days after DEN treatment; Fig. 2b,c).

Fig. 2: The early tumour niche is formed by a PDGFRαlow fibroblast population.

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a, Representative confocal image of a nascent Niche+ tumour 10 days after DEN withdrawal. The side view shows that the niche is composed of lamina propria (Lp), not submucosae (Sb), fibroblasts. Mm, muscularis mucosae; Ep, epithelium. Dashed lines show the layers. The white arrowhead points to the nascent tumour niche arising from lamina propria. Blue, DAPI; red, KRT6A; grey, PDGFRα. Scale bar, 25 µm. b, Number of stromal cells in tumour-free DEN tissue, Niche− and Niche+ tumours per unit of surface area, 10 days after DEN withdrawal; n = 27 Niche−, n = 22 Niche+, n = 15 (DEN) areas, from 3 mice; dots represent each area; PDGFRα+ fibroblasts, CD45+ immune cells and CD31+ endothelial cells are shown. Data are expressed as mean ± s.e.m. Statistical significance was assessed by one way Welch’s analysis of variance (ANOVA) with multiple comparisons. c, 3D-rendered confocal side views of Niche− and Niche+ tumours 10 days after DEN withdrawal; green, CD45; orange, CD31 (absent); red, KRT6A; grey, PDGFRα. Dashed lines show the basal membrane. Scale bar, 10 μm. Image settings were adjusted to the upper stromal layer. d, Experimental protocol for fibroblast lineage tracing: Col1a2CreER and R26FlConfetti/wt mice received a dose of tamoxifen (TAM) followed by DEN treatment. Samples were collected 6 months after DEN treatment. e, Representative top-down (top) and side views (bottom) of control and tumour tissue from d, Grey, PDGFRα; yellow and red, lineage-traced Confetti+ cells. Scale bars, 50 µm. Dashed lines separate stromal compartments. Control, n = 3 mice; DEN, n = 4 mice. f, A single channel from e shows a marked difference in PDGFRα expression across the two stromal compartments, both in control and tumour samples; lamina propria, PDGFRαlow (underlying epithelium); submucosae, PDGFRαhigh (deeper stromal layer). The illustration in d was created in BioRender; Alcolea, M. https://BioRender.com/hwbs32m (2026).

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Since the role of cancer-associated fibroblasts (CAFs) in tumorigenesis, drug resistance and disease progression is well recognized28,29, we next assessed whether niche-forming fibroblasts exhibited CAF features. Except for the nuclear localization of YAP (active YAP, aYAP; Extended Data Fig. 3e), the expression of CAF markers, such as fibroblast activation protein (FAP) and α-smooth muscle actin (α-SMA), was not detectable in 10-day tumour fibroblasts (Extended Data Fig. 3f). We conclude that, although Niche+ fibroblasts in incipient tumours lack a full CAF phenotype, their aYAP status is consistent with a pre-CAF transitional state in the nascent tumour niche[30].

Another important stromal contributor to tumorigenesis and cancer progression is the immune compartment[31]. Immune-cell characterization of Niche+ and Niche– tumours, however, did not show significant differences (Fig. 2b,c and Extended Data Fig. 3g). Accordingly, the emergence and persistence of Niche+ and Niche− tumours remained unaltered in immune-deficient mice (NSG; Extended Data Fig. 3h–l). These results indicate that immune cells do not discriminate between nascent Niche+ and Niche− tumours, acting as bystanders in early tumour persistence.

Precancerous stromal reorganization

To better understand the contribution of stromal fibroblast to tumour niche formation, we used an unbiased genetic lineage-tracing approach to target fibroblasts. Sporadic confetti labelling of fibroblasts, across the lamina propria and submucosae compartments, was induced in Col1a2-CreER;R26FlConfetti/WT mice followed by DEN treatment (Fig. 2d and Extended Data Fig. 4a–d). To trace the origin of the tumour ‘niche’, recombination of the confetti cassette was induced before DEN treatment (Fig. 2d). Analysis of confetti clones 6 months after DEN treatment showed that fibroblasts in the niche underwent clonal expansion (Fig. 2e, Extended Data Fig. 4e–i and Supplementary Table 1). Immunostaining further revealed that clones in the niche were formed by lamina propria PDGFRαlow fibroblasts (Fig. 3e,f and Extended Data Fig. 4e).

Fig. 3: Nascent tumour heterogeneity in the epithelial compartment is linked to stromal remodelling.

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a, Microdissection of squamous upper gastrointestinal tract 8 months after DEN treatment for single-cell RNA sequencing. b, Uniform manifold approximation and projection (UMAP) showing cell-type annotation. Krt, keratinocytes. c, Heterogeneous expression of Pdgfra in fibroblasts in the UMAP space. The inset shows fibroblast clusters. d, Violin plots showing levels of Pdgfra and Fn1 expression in fibroblast clusters. Black line, mean. e, Representative images from 6 mice of the DEN area and nascent tumour stroma 10 days after DEN treatment, showing the accumulation (white arrowhead) of fibronectin (FN1, green) in the niche. Blue, DAPI. Scale bars, 10 µm. f, Heatmap (left) of the top 1,500 differentially expressed genes along the basal keratinocyte pseudotime trajectory. Pseudotime trajectories, top right (blue, committed; yellow, basal; magenta, tumour). Populations representing two tumour transcriptional modules (tumour 1, red; and tumour 12, cyan; bottom right UMAP). g, Representative images from 6 mice, showing tumour 12 markers in nascent tumours 10 days after DEN treatment, showing homogeneous KRT6A (red) and KRT17 (yellow); heterogenous SOX9 and EGR1 (cyan); and AREG and RUNX1 (magenta); DAPI (blue). Scale bars, 10 µm. h, Representative images showing SOX9 distribution in Niche− and Niche+ tumours 10 days after DEN treatment. White arrowheads highlight SOX9+ keratinocytes (cyan), KRT6A (red) and PDGFRα (grey). Scale bars, 10 µm. i, Images of SOX9+ clusters in DEN-treated tumour-free areas 10 days after DEN treatment. The control was the adjacent area negative for SOX9. Blue, DAPI; cyan, SOX9; grey, PDGFRα. Scale bars, 10 µm. White arrowheads highlight keratinocyte to fibroblast proximity; the white dashed line shows the epithelia to stroma border. j, Left, experimental protocol: Krt14CreER;Sox9flox/flox mice received tamoxifen (TAM) followed by DEN. Tissues were collected 1 month after DEN treatment and compared with DEN-treated uninduced controls. Right, quantification of tumour burden; n = 3 mice per condition; data shown as mean ± s.e.m.; one-tailed Mann–Whitney test. Images captured by confocal microscopy. Illustrations in a and j were created in BioRender; Alcolea, M. https://BioRender.com/xjxwb1m (2026).

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To confirm this, we traced the PDGFRαlow and PDGFRαhigh fibroblast populations separately in Pdgfra-CreER;R26FlConfetti/WT mice (Extended Data Fig. 4j–o). We reasoned that differential PDGFRα expression levels would enable us to control the level of recombination in the lamina propria and submucosae. Paradoxically, the PDGFRαlow fibroblast population showed a markedly higher recombination efficiency, with negligible recombination detected in the lower PDGFRαhigh compartment (Extended Data Fig. 4j–m), potentially owing to different tamoxifen accessibility between stromal layers[32].

The distinctive recombination efficiency enabled us to trace PDGFRαlow fibroblasts at early tumour stages (6 weeks after DEN treatment) to explore their contribution to the niche-formation process (Extended Data Fig. 4n,o). We observed that the early tumour niche was composed of PDGFRαlow-derived fibroblast clones that expanded locally in the upper stromal compartment. No clonal expansion events were found in the lower stroma (PDGFRαhigh compartment) or spanning across stromal compartments (Extended Data Fig. 4o).

Overall, these observations demonstrate that local PDGFRαlow fibroblasts in the lamina propria not only maintain, but also contribute to, the formation of the pre-neoplastic tumour niche.

Pro-fibrotic PDGFRαlow niche fibroblasts

To determine the mechanisms underlying the formation of the precancerous niche, we set out to study epithelial–mesenchymal communication. Single-cell RNA sequencing (scRNA-seq) was done in individually micro-dissected dysplastic tumours of the squamous upper gastrointestinal tract 8 months after DEN treatment (Fig. 3a,b, Extended Data Fig. 5a–f and Supplementary Tables 2 and 3).

The transcriptional profile of pre-neoplastic tumour cells was compared with that of cells from adjacent tumour-free areas in DEN-treated mice (internal control), and with that of cells from healthy untreated control animals (Extended Data Fig. 5g).

In line with our histological and lineage-tracing characterization (Fig. 2 and Extended Data Figs. 3c,d and 4), the scRNA-seq analysis revealed a marked degree of heterogeneity in the expression of the fibroblast marker Pdgfra (Fig. 3c). Distinctive Pdgfralow and Pdgfrahigh fibroblast populations were present across conditions (Supplementary Table 5). Pdgfralow fibroblasts expressed higher levels of genes encoding structural (scaffolding matrix) ECM components (such as Fn1, Fbn1, Has1, Has2, Loxl2, Mfap5, Cd248 and Col1a1)[33] common in loose connective tissue. Pdgfrahigh fibroblasts instead showed an enrichment of genes encoding ECM fibrillar (underlying matrix) collagens (Col6a3 and Col5a3), vascular support collagens (such as Col4a1/2, Col8a1, Col15a1 and Col13a1) and other basement-membrane components[33] (such as Lama1 and Thbs1/2; Fig. 3d, Extended Data Fig. 6a–c and Supplementary Table 6). Accordingly, immunolabelling of Pdgfra-derived clones (Extended Data Fig. 4j) in the lamina propria and submucosae, respectively, revealed increased fibronectin production in the Pdgfralow fibroblast compartment (Extended Data Fig. 6d).

We then investigated the tumour-specific transcriptional signature of the Pdgfralow niche-forming fibroblasts. Fibroblasts in cluster 19 (C19), enriched in pre-neoplastic tumour stroma (Extended Data Fig. 6e), showed a significant upregulation of matrisome genes associated with wound healing/fibrosis9,34 (matrix deposition: Fn1, Fbln5, Tnxb, Cd248, Vim, Plaur, Mfap5; thrombospondins: Thbs1, Thbs3; collagens: Col3a1, Col1a1/2, Col5a3; remodelling: Timp1, Adam9, Loxl1; other factors: Fgfr2, Bmp1, Bmp6, Cx3cr1; Extended Data Fig. 6f and Supplementary Tables 7 and 8). The pro-fibrotic nature of these niche fibroblasts was supported by immunolabelling of both nascent and surviving tumours (Fig. 3e and Extended Data Fig. 6g), as well as by second-harmonic generation (SHG) imaging, which showed a marked ECM remodelling in the tumour niche (Extended Data Fig. 6h–j). Accordingly, alterations in integrin α6 (CD49f) expression in nascent epithelial tumour cells hinted at ‘wound healing-like’ changes in epithelial–ECM interactions[35] (Extended Data Fig. 6k). Together, these findings pointed at the activation of a tissue repair response in the stroma of early tumours, in line with the long-standing notion that tumours are “wounds that do not heal”[36].

We next explored whether niche-forming fibroblasts (C19) in long-term surviving tumours presented a CAF signature. Despite a subtle upregulation of a reduced subset of CAF-associated genes12,28,37,38,39 (Vim, S100a4, Mfap5 and Col1a2; Extended Data Fig. 6l–n), their expression at the protein level (VIM, FSP and FAP) remained largely unaltered or undetectable (Extended Data Fig. 6o). Accordingly, fibroblast proliferation did not show significant changes[28] (Extended Data Fig. 6p). These observations indicate that Niche+ fibroblasts in surviving dysplastic tumours lack a fully established CAF phenotype. However, the presence of nuclear YAP and the profibrotic nature of Niche+ fibroblasts point to a pre-CAF state[30] (Extended Data Fig. 3e), transitioning to myCAFs at more advanced stages (invasive squamous cell carcinomas 14 months after treatment; Extended Data Fig. 6q). Label-transfer analysis from the fibroblast atlas in ref. [40] indicated that tumour niche fibroblasts probably derive from universal, rather than tissue-specific, fibroblast populations. Pdgfralow fibroblasts, including tumour-enriched cluster 19, partly recapitulated the transcriptional signature of the Pi16+ universal population, whereas Pdgfrahigh fibroblasts aligned with the Col15a1+ universal fibroblast subset (Extended Data Fig. 6r,s).

To assess whether stromal genetic alterations drive the tumour niche phenotype, we performed deep-targeted sequencing of 192 cancer-related genes[22] (Supplementary Table 9). The results argued against somatic mutations in fibroblasts being responsible for precancerous niche formation. The data revealed that DEN treatment induces gene perturbations, mainly in the epithelial compartment, showing a minimal mutational burden in the tumour stroma that matched the level of untreated or internal DEN control samples (Extended Data Fig. 7a,b).

In contrast to nascent stages (Fig. 2b,c), we found that, as tumours progressed, they showed a marked remodelling of the vascular network and an increased immune infiltrate (Extended Data Fig. 7c,d). In line with previous findings, transcriptional analysis revealed notable changes in the immune-cell composition in long-term surviving tumours, indicative of active immune-cell recruitment with progression towards an immunosuppressive microenvironment at later stages[41] (Extended Data Fig. 7e–h and Supplementary Table 10).

Taken together, our data reveal a significant stromal reorganization in surviving tumours. Analysis of lamina propria-derived Pdgfralow fibroblasts was consistent with a notable fibrotic ECM remodelling in the precancerous niche, before the emergence of a fully established CAF phenotype.

Nascent tumour heterogeneity

Next, we explored the epithelial transition from healthy and normal to pre-neoplastic states. Pseudotime analysis revealed two distinctive basal cell trajectories denoting tumour and non-tumour states. These trajectories largely converged in committed and differentiating cells (Fig. 3f and Extended Data Fig. 8a–f). Gene score enrichment analysis of tumour-specific gene modules identified by pseudotime analysis revealed further heterogeneity in epithelial tumour states, referred to as Tumour 1 and Tumour 12 (Fig. 3f, Extended Data Fig. 8d–h and Supplementary Tables 11–13), that expressed increased levels of the early tumour markers Krt6a and Krt17 (refs. 1,21) (Extended Data Fig. 8i).

Gene set enrichment analysis revealed a unique signature in Tumour 12 cells (Extended Data Fig. 8j,k), enriched for genes related to cancer-associated processes such as tissue development and morphogenesis (Cdh1, Cdh4, Sox4, Klf4, Sox9 and Foxa1), hypoxia (Hmox1, Vegfa, Edn1, Cited2 and Sirt1) and stress-induced pathways, including EGFR (Areg, Hbegf, Nrg1, Nrg2, Egfr, Epha2 (ref. [42]) and Lamc2 (ref. [43])), Hippo, TNF, p53 and TGF-β (Tead1, Tnfaip3, Nfkbia, Ccng2, Rela, Nfkb1, Ptgs2, Gadd45a, Cdkn1a and Id1-4) (Extended Data Fig. 8k and Supplementary Table 14). This was reinforced by increased levels of genes encoding transcription factors associated with a tumour stress response (Jun, Fos, Fosb, Runx1, Atf3, Egr1, Egr3 and Myc)41,44,45,46,47,48 (Extended Data Fig. 8l). Crucially, validation at the protein level further supported the heterogenous nature of the epithelial cells populating nascent early tumours, with Tumour 12 markers staining only a subset of tumour cells (Fig. 3g and Extended Data Fig. 8m,n).

Further expression changes in Tumour 12 comprised the upregulation of genes associated with stromal communication, including cell adhesion (Col12a1, Itgav, Itga2, Itgb6, Lama3, Vcl, Cadm1, Icam1 and Runx1 (ref. [49])), as well as ECM breakdown (Adamst1 (ref. [50])). Increased expression of the cell-adhesion genes Ccn1, Ccn2 (ref. [51]) and Thbs1 (ref. [52] was of particular interest, owing to their recognized role in communication with fibroblasts (Extended Data Fig. 8l).

Since the data so far had indicated that there was close communication between Tumour 12 epithelial and stromal cells, we reasoned that Tumour 12 cells could be linked to the emergence of the tumour niche, and thereby to tumour persistence and survival. Indeed, SOX9 expression, used as a proxy to mark the Tumour 12 state (alongside KRT6A and/or KRT17 (refs. 1,21)), was expressed mainly in early tumour cells in direct contact with the niche (Extended Data Fig. 8o), indicative of their close interaction. Accordingly, nascent Niche+ tumours showed significantly higher SOX9 expression than Niche− tumours did (Fig. 3h and Extended Data Fig. 9a–c).

To explore whether SOX9+ cells were associated with the formation of the pre-neoplastic tumour niche, we took advantage of sporadic clusters of KRT6A+ and SOX9+ cells in phenotypically normal (non-tumour) regions of DEN-treated tissue, potentially marking prospective tumour cells before lesion formation. Isolated SOX9+ cell clusters showed signs of fibroblast attraction, presenting fibroblasts in closer proximity and at a higher density than in the surrounding tissue (Fig. 3i and Extended Data Fig. 9d–f). Overall, these data establish the Tumour 12 state as a relevant player in early tumour stromal remodelling and niche formation. Accordingly, SOX9 depletion in Krt14-CreER; Sox9flox/flox DEN-treated mice (Fig. 3j and Extended Data Fig. 9g) led not only to a significant reduction in early tumour survival (1 month after DEN treatment; Fig. 3j) but also to a reduction in the size of nascent Niche+ tumours compared with that of Niche− tumours (Extended Data Fig. 9h).

Cell–cell communication in early tumours

To study epithelial–mesenchymal communication in surviving tumours, we assessed ligand–receptor interactions[53] enriched across coexisting cell populations in DEN-treated tissue (Fig. 4a,b and Extended Data Fig. 10a; fibroblasts: Fig. 3c and Extended Data Fig. 6e; epithelia: Fig. 3f and Extended Data Fig. 8g–k). We analysed interactions between non-tumour cells, including control keratinocytes (non-tumour Krt) and control fibroblasts (Pdgfralow; C19 DEN Fbr used as an internal control), as well as among tumour associated cells; that is, tumour keratinocytes (Tumour 1), tumour niche keratinocytes (Tumour 12) and tumour niche fibroblasts (Pdgfralow; C19 Tumour Fbr). Pro-fibrotic ECM-related pathways were among the top outgoing interactions predicted to preferentially signal from tumour niche fibroblasts to tumour niche keratinocytes. These pathways included laminin, collagen, fibronectin, thrombospondin and tenascin (Fig. 4c). Given its well-established role in ECM assembly and association with fibrosis and advanced cancer progression[54], we reasoned that fibronectin (FN1) might be a central player modulating ECM interactions across tumour cell compartments. The relevance of FN1 interactions was supported both by the specific upregulation of FN1 receptor genes in tumour niche keratinocytes (receiver cells) and by the increased expression of FN1, at both mRNA and protein level, in tumour niche fibroblasts (sender cells; Fig. 3d,e and Extended Data Figs. 6f,g and 10b). Overall, our analyses predicted robust pro-fibrotic and wound healing epithelial–mesenchymal interactions in surviving tumours.

Fig. 4: EGF–SOX9–FN1 axis promotes tumour survival.

The alternative text for this image may have been generated using AI.

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a, Cartoon showing the composition of a tumour cell. b, The cell–cell communication network. Circles represent cells; circle sizes show cell numbers; and the thickness of the connections represents the number of significant interactions. c, Heatmap showing the top 10 signalling predictions. Interactions in Tumour (Tmr) 12 keratinocytes and Tmr fibroblasts are highlighted in yellow. Signals with the strongest interactions in Tmr fibroblasts are highlighted in pink; in Tmr 12, keratinocytes are highlighted in cyan. d, Top: schematics of the chemoattractant assay. Bottom: representative images. Scale bars, 25 µm; inset, 10 µm. FBS, fetal bovine serum. e, Percentage of fibroblasts crossing the membrane. Data are expressed as mean ± s.e.m. Dots represent replicate cultures from n = 4 mice. Significance assessed by one-way Welch’s ANOVA with multiple comparisons (other groups in EDF 10c). f, Schematic representation of epithelioid and fibroblast co-culture treated with gefitinib (GFT) or vehicle (DMSO). g, Representative images from f showing fibroblast interaction with keratinocytes in DMSO and EGFR inhibition (GFT) conditions. Top insets show PDGFRα+ fibroblast (red) heterogeneity: PDGFRαlow fibroblasts assemble adjacent to keratinocytes, whereas PDGFRαhigh fibroblasts position further away in controls. Fibroblast activation at the border is labelled by VIM (white) and FN1 (green). This was inhibited in GFT (bottom). Green, CDH1; nT, nuclear Tomato; KRT, keratinocytes, red; blue, DAPI. Scale bars: main, 500 µm; insets, 50 µm. h, Schematic representation of keratinocyte–fibroblast interactions under DMSO and GFT conditions. i, Experimental protocol of drug intervention with a fibronectin assembly-inhibiting peptide (FUD) or GFT 20-day regimen with the DEN treatment. j, Tumour burden decreased in the GFT and FUD groups compared with vehicle (VHC) or scrambled (SCR) control, respectively. Tissues were collected 10 days after DEN treatment. Data are from n = 3 (VHC), n = 4 (GFT), n = 5 (SCR) or n = 5 (FUD) mice. Data represent mean ± s.e.m. Significance was assessed by one-tailed Mann–Whitney test. Images were captured by confocal microscopy. Illustrations in a, d, f, h and i were created in BioRender; Alcolea, M. https://BioRender.com/eghet5p (2026).

Source data

Next, we explored outgoing signals from tumour niche keratinocytes. Here, EGF was identified as one of the strongest incoming signals for tumour niche fibroblasts (Fig. 4c). EGF ligands (including AREG and HBEGF) were enriched in tumour niche keratinocytes (sender cells) at both the mRNA and protein levels (Fig. 3g and Extended Data Figs. 8l, m and 10b), with its receptor (EGFR) being markedly expressed in the underlying tumour niche fibroblast population (receiver cells; Extended Data Fig. 10b).

The upregulation of both AREG (amphiregulin) and FN1 in nascent tumours (10 days after DEN treatment; Fig. 3e,g and Extended Data Figs. 6g and 8m) highlighted the importance of epithelial–mesenchymal communication from nascent tumour stages.

EGF–SOX9–FN1 supports tumour survival

Given the well-known role of EGF and FN1 signalling in epithelial–stromal communication during wound healing and tissue damage55,56, we reasoned that they might have a similar role in response to DEN-induced genetic stress.

To determine whether tumour niche keratinocytes (Tumour 12) exert their mesenchymal remodelling effect (Fig. 3h,i and Extended Data Figs. 8k,o and 9a–f) through the EGFR pathway (Fig. 4c), we used a chemoattractant assay (Fig. 4d). We found that the EGFR ligand AREG positively stimulated fibroblast migration, confirming that keratinocytes in nascent tumours can promote fibroblast chemotaxis and mesenchymal remodelling through paracrine EGF stimulation (Fig. 4d,e and Extended Data Fig. 10c).

We gained further insights into the dynamic nature of epithelial–mesenchymal communication by coculturing regenerative 3D oesophageal cultures (epithelioids[57]) with primary fibroblasts (Fig. 4f). We found that expanding keratinocytes, which exhibit increased levels of stress markers (including SOX9; Extended Data Fig. 10d), prompted fibroblasts to segregate spatially into two distinct populations, mirroring the in vivo scenario. PDGFRαlow fibroblasts localized immediately adjacent to the growing epithelium, whereas PDGFRαhigh fibroblasts were found in distant areas (Fig. 4g and Extended Data Fig. 10e). The interaction between expanding epithelial cells and fibroblasts also led to FN1 deposition and vimentin upregulation in the PDGFRαlow population (Fig. 4g and Extended Data Fig. 10e). Gefitinib-mediated inhibition of EGFR signalling in epithelioids further confirmed the role of EGFR signalling in epithelial–mesenchymal communication in a regenerative or stress context, showing reduced epithelial SOX9 expression, diminished fibroblast segregation or compartmentalization, and hindered fibroblast ECM remodelling (Fig. 4f–h and Extended Data Fig. 10d,e). These data directly link EGFR signalling and SOX9 expression in the epithelium with mesenchymal remodelling.

FN1, which is an important component of the fibrotic niche in early tumours (Fig. 3d, e and Extended Data Fig. 6b–g), also represents a critical and well-established regulator of the stromal wound-healing response[58]. To determine whether the newly formed fibronectin-rich tumour niche has a critical role in promoting early tumour growth and survival, we treated established 3D epithelioids (exhibiting steady-state levels of proliferation)[57] with soluble FN1 for 24 hours. Indeed, FN1 promoted epithelial proliferation (Extended Data Fig. 10f). By contrast, in vivo bleomycin treatment, which promotes tissue fibrosis, resulted in a negligible increase in SOX9 expression in DEN-untreated mice (Extended Data Fig. 10g). These data indicate that, although fibrosis promotes tumour growth, a fibrotic environment alone is not sufficient to drive a pre-neoplastic response.

These ex vivo experiments revealed that the EGF–SOX9–FN1 axis governs epithelial–mesenchymal communication and subsequent tissue reorganization in response to epithelial perturbations. Accordingly, in vivo experiments showed that inhibition of either fibronectin fibrillogenesis (using the functional upstream domain (FUD) peptide)[59] or EGFR signalling with Gefitinib (GFT) led to a significant reduction in the number of Niche+ tumours (Fig. 4i,j).

Taken together, our results demonstrate the central role of the EGF–SOX9–FN1 axis in early tumour niche formation. In response to genetic stress, SOX9+ epithelial cells stimulate fibroblast migration and ECM remodelling through EGF signalling. This, in turn, promotes the formation of a pro-fibrotic, fibronectin-rich tumour scaffold, which favours tumour persistence and progression by perpetuating the pro-tumorigenic phenotype.

Human tumour niche remodelling

To validate the relevance of our observations in a human context, we analysed chemo-naive early-stage human oesophageal squamous cell carcinomas (T1a, T1b and Tis; Fig. 5a–d) and residual dysplastic tissues after chemotherapy (ypT0; Fig. 5e,f).

Fig. 5: Early tumour niche remodelling in human oesophageal squamous cell carcinoma.

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a, Schematic representation of the tumour sample origin shown in b–d; tumour resections from human patients performed before chemotherapy (chemo-naive). b, Representative confocal image (from n = 4 patients) of a T1B tumour section showing widespread KRT6A (red), and heterogenous SOX9 (cyan) and AREG (magenta) expression; blue, DAPI. The control sample is normal area identified by a pathologist in the tumour section. Scale bar, 200 µm; in insets, 50 µm. c, Representative confocal image (from n = 5 patients) of a T1A tumour section showing widespread KRT17 (yellow), heterogenous SOX9 (cyan) expression and fibroblast attraction to areas marked by PDGFRα (grey) expression. Blue, DAPI. Scale bars, 200 µm; in insets, 10 µm. d, Representative confocal image (from n = 1 patient) of a carcinoma in situ (Tis) whole mount showing heterogenous SOX9 (cyan) expression and fibronectin (FN1, green) accumulation underneath (white arrowhead). Blue, DAPI. Scale bars, 50 µm. e, Schematic representation of the tumour sample origin shown in f; resections were performed after chemotherapy. f, Representative confocal image (from n = 1 patient) of a post-treatment, pathological staging T0 (dysplasia) whole mount, showing heterogenous expression of SOX9 (cyan) and AREG (magenta) and fibronectin (FN1, green) accumulation underneath. Blue, DAPI. The control sample is a biopsy distal to the tumour. Scale bar, 500 µm; in insets, 50 µm. g, Schematic of proposed model whereby epithelial cells respond to genetic perturbation by activating a stress gene signature (tumour 12), denoted by SOX9 and EFG ligand overexpression. This drives the migration of underlying fibroblasts towards the nascent tumour, followed by the formation of a fibrotic scaffold. The establishment of this EGF–SOX9–FN1 signalling axis between epithelial and mesenchymal nascent tumour cells results in the formation of an early tumour niche that favours tumour growth and promotes long-term persistence. Illustrations in a, e and g were created in BioRender; Alcolea, M. https://BioRender.com/e94fdb9 (2026).

Immunolabelling showed that patient tumours, unlike adjacent tissue, recapitulate observations in the DEN mouse model. T1a and T1b tumours displayed homogeneous expression of KRT6A and KRT17 (pan-tumour markers in mice) and heterogeneous expression of SOX9 (marking tumour cells associated with stromal remodelling in mice; Tumour 12; Fig. 5b,c). Accordingly, SOX9-expressing cells exhibited high AREG expression levels (Fig. 5b) and were found next to areas with increased fibroblast density (PDGFRα; Fig. 5c). Analysis of tissue whole-mounts reinforced the link between SOX9 expression and stromal remodelling, with marked FN1 deposition in the vicinity of SOX9+ tumour cells (Fig. 5d).

These features were also observed in residual dysplastic tissue after chemotherapy (Fig. 5e,f), which showed marked ECM remodelling (fibronectin deposition) in the proximity of AREG+ or SOX9+ tumour cells.

Our observations support the presence of a heterogeneous AREG+ and/or SOX9+ population in early-stage squamous tumours of the human oesophagus. The data further reinforce the association between this population, mesenchymal changes and ECM remodelling in early human oesophageal tumorigenesis, revealing the potential clinical relevance of our study.

Discussion

Studies in the past decade have shown that mutations, conventionally thought to be the sole cause of cancer, can also be found in healthy ageing tissues4,5,6,7,8, where they form part of normal tissue physiology. This has redirected the interest of the cancer community to fill the knowledge gap around the earliest disease stages, and particularly to understand how mutant cells interact with adjacent tissue compartments1,2,3,13,21,22. This study provides mechanistic insights into the processes that determine whether tumours emerging in complex mutant landscapes persist long term or are outcompeted and eliminated from the tissue[1]. We show that early tumour survival and subsequent progression rely on intricate interactions between nascent tumour cells and their dynamic niche.

This work reveals that exposure to mutagens activates a heterogeneous ‘tissue stress’ response, whereby incipient epithelial and mesenchymal tumour cells signal and feedback onto each other through the EGF–SOX9–FN1 axis. Tumours failing to activate this communication axis are less likely to persist and grow. In particular, a tumour-specific stress state, defined by high SOX9 expression, promotes the recruitment of fibroblasts to the nascent tumour through EGF signalling. This in turn facilitates the formation of a precancerous niche, rich in fibronectin, that perpetuates a pro-tumorigenic phenotype, favouring tumour growth and persistence. Interfering with fibronectin fibrillogenesis in vivo impaired niche formation, prevented tumour survival and reduced the overall tumour burden. These findings support a self-sustaining process in which the reciprocal communication between niche mesenchymal cells and SOX9high epithelial cells supports tumour survival, favouring disease progression over time (Fig. 5g).

These data are relevant to early human carcinogenesis. The heterogeneous expression of SOX9, EGFR ligands and associated deposition of FN1 are recapitulated in early stage human oesophageal squamous cell carcinomas, consistent with active epithelial–stromal communication in nascent human tumours. Whether interfering with ECM assembly represents a valid approach to prevent cancer progression in patients, and whether analogous mechanisms operate in other tumour types, requires further investigation.

Overall, our data demonstrate the unprecedented capacity of the early tumour niche to perpetuate tumour survival signals beyond intrinsic changes driven by genetic alterations, enabling nascent tumours to persist in highly competitive mutant landscapes. This offers the new perspective that not only mutations, but also the environmental response to genetic stress, defines the likelihood of tumours to progress towards more advanced disease stages. Our findings indicate that strategies targeting tumour cells, as well as supporting neighbouring cells, could open new avenues for cancer prevention and improve long-term outcomes.

Methods

Clinical samples

High-grade dysplasia, squamous cell carcinoma and macroscopically normal, healthy clinical samples, as well as the corresponding clinical information, were collected following research ethics approval and individual informed consent from patients who underwent oesophagectomy for oesophageal cancer.

The T1A and T1B stage chemo-naive surgical tumour samples were donated by patients who had undergone surgery at the Clinic for Visceral, Thoracic and Vascular Surgery at TU Dresden or at the Medical Department I of the Carl Gustav Carus University Hospital. Macroscopically normal samples adjacent to the proximal resection margin were sampled from cancer resection specimens. The corresponding formalin-fixed, paraffin-embedded material (tumour and healthy tissue) from a total of ten characterized oesophageal squamous cell carcinomas was selected from the archive of the Institute of Pathology of the University Hospital Carl Gustav Carus (EK 59032007) by the Tumour and Normal Tissue Bank (TNTB) Dresden. Studies presented in the manuscript involving early chemo-naive human oesophageal tumour samples from Dresden were approved by the Ethics Committee of TU Dresden, Germany (ref. SR+BO-ff (Mono)-EK-161042025). Studies presenting chemo-naive or post-chemo human oesophageal tumour samples from Guy’s and St Thomas’ (London) and Addenbrooke’s Hospital (Cambridge, UK), respectively, were approved by the East of Scotland Research Ethics approval committee (REC 18/ES/133). Histological sectioning of the tissue samples and haematoxylin and eosin staining of reference slide series for determining the tumour cell content of the individual patient samples were done at the Institute of Pathology, University Hospital CGC Dresden, TU Dresden.

Mice strains

All animal experiments were approved by the local ethical review committees of the University of Cambridge and conducted according to the Home Office project licences PPL70/8866 and PP7037913 of the Cambridge Stem Cell Institute, University of Cambridge.

Unless otherwise specified, C57BL/6J mice (Charles River, strain code 632) were used. Other mouse strains used include: cell-cycle reporter line R26Fucci2aR (Fucci2a)[60], provided by I. J. Jackson; PdgfraEGFP (007669, Jackson Laboratory); Sox9flox/flox (ref. [61]; MRC-Harwell, on behalf of the European Mouse Mutant Archive; https://www.infrafrontier.eu); K14CreER (005107, Jackson laboratory); R26mT−mG (mTmG, Jackson Laboratory); Col1a2CreER (029567, Jackson Laboratory); R26FlConfetti (ref. [62]; 017492, Jackson laboratory, provided by H. Clevers)[62]; R26nT−nG (nTnG; 023537, Jackson Laboratory); H2B-EGFP (CAG::H2B-EGFP; 006069, Jackson Laboratory); NOD-SCID-γ (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ; 005557, Jackson Laboratory); and PdgfraCreER (018280, Jackson Laboratory)[63]. Further information about the experimental mouse lines can be found in the Supplementary Methods section Experimental mouse lines.

Recombination of Col1a2CreERR26FlConfetti/WT mice was induced by a single intraperitoneal tamoxifen injection (3 mg per 20 g body weight). The Col1a2CreERR26FlConfetti/WT mice were induced by a single intraperitoneal tamoxifen injection (0.5 mg or 5.0 mg per 20 g body weight). The K14CreERSox9flox/flox received two subcutaneous tamoxifen injections (5 mg per 20 g body weight) 48 h apart. Tamoxifen was prepared by dissolving in ethanol (less than 10% total volume) and diluting in sunflower-seed oil.

All strains were maintained in a C57BL/6 background. All experiments used a mixture of male and female mice with no gender-specific differences observed (unless specified otherwise). For RNA-sequencing experiments, only male animals were used to avoid confounding effects from the oestrous cycle. All animals exposed to the carcinogen and their respective controls were adults between 8 and 14 weeks of age (see the section on chemically induced mutagenesis below). Mice were bred and maintained under specific-pathogen-free conditions at the Gurdon Institute and the Anne McLaren Building, University of Cambridge. All animals were housed at 20–24 °C, 45–65% humidity and a 12 h:12 h light:dark cycle.

Chemical tumorigenesis model

Mice were treated with DEN (Sigma-Aldrich; N0756) at 40 mg l−1 in Ribena-flavoured water for 24 h, three times a week (Monday, Wednesday and Friday) for 8 weeks1,21. Mice received sweetened water between DEN dosages and normal water after the completion of DEN treatment. Control mice received sweetened water as a vehicle control for the length of the treatment. Animals exposed to DEN were monitored for adverse effects as stated in our Home Office project licences (PPL70/8866 and PP7037913) for regulated procedures on protected animals. In summary, animals were weighed daily on weekdays for the first week, weekly for the next month and then monthly thereafter. Animals were also checked every day for any clinical signs or abnormal behaviour. Any concerning animals were weighed every other day or daily, if necessary, until the weight was stable again. If the weight loss approached 10%, animals were weighed daily until stable and received wet mash or palatable diet. Animals showing 15% weight loss measured for 2 consecutive days were killed immediately.

EdU tracing

For EdU labelling experiments, mice received 100 µg EdU in PBS (Life Technologies, A10044) intraperitoneally 2 h before tissue collection. In vitro 3D cultures (see above) received media supplemented with 10 µM EdU and were incubated for 2 h at 37 °C and 5% CO_2 before fixation. EdU incorporation in tissue whole-mounts (see above) was detected using a Click-iT EdU kit according to the manufacturer’s instructions (Invitrogen, C10337). EdU+ cells were quantified using confocal microscopy.

Inhibitor treatment in vivo

Mice were treated with Gefitinib for 20 days at 80 mg per kg body weight (or vehicle control) three times a week to inhibit the EGFR pathway. Treatment started 10 days before the end of DEN treatment and ended 10 days after it. Gefitinib was prepared in concentrated form by dissolving it in DMSO and was diluted in corn oil.

Pharmacological inhibition of FN1 fibrillogenesis was achieved by treating mice with functional upstream domain (FUD) peptide[64] intraperitoneally for 20 days at a concentration of 12.5 mg per kg body weight. Control mice were treated with scrambled (SCR) control peptide. Treatment started 10 days before the end of DEN treatment and ended 10 days after it. Peptides were synthesized at more than 95% purity (WatsonBio; peptide sequence below). Lyophilized peptides were reconstituted in PBS.

The peptide sequences were:

FUD, - Cys-GSKDQSPLAGESGETEYITEVYGNQQNPVDIDKKLPNETGFSGNMVETEDTKLN;

SCR, - Cys-QGQTGPVNSKVKIDNYELESNPEKIEANDLQVEGTTTYESKFMGDLTGSGNPED.

Whole-mount preparation

The upper gastrointestinal tract (oesophagus and forestomach) from control and DEN-treated mice was dissected at the time points indicated in the main text and/or figure legends. Oesophagi were excised and cut open longitudinally. The muscle layer was then removed and the tissue was flattened under a dissecting microscope using fine forceps. Stomachs were cut open longitudinally and rinsed twice with PBS to remove any food remains. The glandular stomach was excised away and the forestomach kept and flattened for downstream analysis. For epithelial-only and stromal-only whole-mounts, tissues were incubated in 5 mM ethylene-diamine-tetraacetic acid (EDTA) (Life Technologies, 15575020) in PBS for 3 h at 37 °C. After incubation, the epithelium was gently peeled from the stroma using fine forceps. Subsequently, each of these layers was flattened individually.

Oesophageal and forestomach whole-mounts (either peeled or unpeeled) were fixed in 4% paraformaldehyde (Alfa Aesar, 043368) in PBS for 30 min at room temperature.

Histology

For histology, tissues were fixed in 10% formalin in PBS overnight at room temperature before storage at 4 °C in absolute ethanol. Haematoxylin and eosin staining was done in 7-μm paraffin-embedded sections by the Histology Core Service at the Cambridge Stem Cell Institute and imaged using a Zeiss AxioScan Z1 microscope. Histological analysis of murine tumour samples was done by B. Mahler-Araujo at the MRC Metabolic Diseases Unit (MC_UU_00014/5).

Ex vivo tissue recombination assay

Under a dissecting microscope in a laminar flow hood, oesophagi were dissected and epithelial–stromal layers isolated as described above in the section ‘Whole-mount preparation’. Thereafter, tissues were rinsed in 1% P/S in PBS three times to remove residual EDTA and flattened. Combinations of epithelium and stroma from different experimental conditions (DEN treated and/or control) were prepared by carefully placing the epithelial layer over the relevant stroma (referred to as ‘tissue recombination’ composites). The remaining epithelium was trimmed to match the size of the stroma, and the resulting construct was cut in half. Flattened epithelium–stromal constructs were cultured in six-well plate inserts (ThinCert Greiner Bio-One, 657641). Size-matched polydimethylsiloxane (PDMS) stencil frames were placed around the tissue construct to prevent cell expansion (see the ‘Stencil production’ section below). The tissue was allowed to settle for 10 min before adding 2 ml of minimal medium (mFAD) containing one-part DMEM (Fisher Scientific, 41966029) and one-part DMEM/F12 (Fisher Scientific, 11320033) supplemented with 5 μg ml−1 insulin (Sigma-Aldrich, 15500), 5% fetal calf serum (Fisher Scientific, 26140079), 1% P/S and 5 µg ml−1 Apo-Transferrin (Sigma-Aldrich, T2036), as previously described24,65. The 3D heterotypic cultures were maintained in standard humidified cell-culture incubators at 37 °C with 5% CO_2 for up to 7 days. At the end point, samples were fixed in 4% PFA in PBS for 30 min at room temperature and stored for downstream confocal analysis.

Stencil production

Silicone elastomer (PDMS) was mixed with a curing agent (Avantor VWR; Sylgard 184 Elastomer Kit, 634165S) at a 10:1 ratio and centrifuged at 300g for 10 min to remove the bubbles. The resulting mix was poured on a dish at around 70 mg cm−2 and left on an even surface to polymerize overnight at 37 °C. The next day, the resulting polymer was cut into 2 × 5 mm rectangle-shaped frames, sterilized in 70% ethanol overnight, and treated with 1% pluronic acid (Sigma-Aldrich, P2443-250g) in PBS for 1 h at 37 °C. The frames were then left to air dry before use.

In vivo tissue recombination grafting

Tissue recombination composites (as described above in the section ‘Ex vivo tissue recombination assay’) of DEN-treated oesophageal stroma and untreated (control) oesophageal epithelium were prepared for in vivo grafting adapting the strategy described above. Before separating the epithelium from the stroma, all visible tumours were marked with a partial incision using a punch biopsy tool (1 mm diameter; Merck, WHAWB100040). After separating the tissue layers, all the stromal compartments were assessed for peeling efficiency under a fluorescence dissecting microscope (Leica M165 FC), and any remaining epithelium, identified by the dense epithelial nuclei clusters, were excised from the tissue using a 1 mm biopsy punch. For heterotypic tissue constructs, 2 mm biopsies (Selles Medical, instrument BP20F) of tumour or control stroma were excised and a 2 mm healthy untreated epithelium biopsy placed above. Composites were cultured overnight as described above and grafted in the back skin of anaesthetized shaved NSG female mice (two constructs per incision, and two incisions per animal). Longitudinal incisions for grafting were approximately 5 mm in length. The wounds were closed with GLUture glue (Fisher Scientific, NC0632797) and the mice were left to recover. Then, 3–6 months later, the mice were killed and the back skin fixed with 4% PFA in PBS for 30 min at room temperature and stored for downstream confocal analysis.

Primary mouse fibroblast isolation and migration assay

Oesophagi were dissected as described above and cut in half. Tissue was incubated in 0.5 mg ml−1 Dispase (Sigma-Aldrich, D4818) for 10 min at 37 °C while rotating. After incubation, the epithelium was peeled away and the stroma was minced finely and incubated in Trypsin-EDTA (0.25%) (ThermoFisher, 25200056) for 15 min at 37 °C while rotating. The resulting suspension was mixed by pipetting and DMEM supplemented with 10% FBS and 1% P/S was added (1:1 v/v). The suspension was passed through a 70 µm filter (PluriSelect, 43-10070-40) and cells were pelleted by centrifugation at 300g for 5 min at 4 °C. Pellets were resuspended in 0.5% FBS, 1% P/S in DMEM, and seeded on 8.0 µm pore transwell insert (24-well plates; ThinCert Greiner Bio-One, 662638). The lower compartment of the transwell contained 1% P/S DMEM supplemented either with 0.5% FBS, 2% FBS, 10% FBS or 2% FBS with 1 µg ml−1 Amphiregulin (AREG) (R&D, 989-AR-100/CF). Primary fibroblasts were cultured for 48 h before fixation in 4% PFA in PBS for 10 min. Membranes were incubated with 1 µg ml−1 DAPI (Sigma-Aldrich, D9542) in PBS for 30 min at room temperature, cut and mounted in 1.52 Rapiclear mounting medium (SUNJin Lab, RC152001) keeping their original orientation, followed by confocal analysis. Further information on quantification can be found in the Supplementary Methods section ‘Analysis of fibroblast migration assay’.

Keratinocyte cultures and fibronectin treatment in vitro

Oesophagi were cultured using the 3D epithelioid organ culture approach[65]. In brief, tissues were dissected, cut into 3 × 5 mm rectangles and placed on a transwell insert with the epithelium side up. The tissue was left to settle for about 5 min. Explants were expanded in complete medium (cFAD) containing mFAD supplemented with 1 × 10−10 M cholera toxin (Sigma-Aldrich, C8052), 10 ng ml−1 EGF (Fisher Scientific PeproTech, AF-100-15), 0.5 μg ml−1 hydrocortisone (Calbiochem, 386698). Tissue explants were removed by aspiration 5 days after culture set-up and maintained in mFAD for 2 weeks to confluence. Soluble fibronectin (Fisher Scientific Corning, 356008) was added to the medium for 24 h at 100 μg ml−1 after diluting it in mFAD with 25 mM HEPES (Fisher Scientific, 15630056). Samples were fixed with 4% PFA in PBS for 30 min at room temperature, after a 2 h EdU chase, and kept for downstream confocal analysis.

Keratinocyte and fibroblast interaction assay

Epithelioids were set up as described above and maintained in mFAD until keratinocyte migration started. The original tissue was then removed and the explant left overnight before adding freshly isolated oesophageal fibroblasts (as described above). DMSO as vehicle control or 2 µM of Gefitinib prepared in DMSO were added together with the fibroblast suspension. Then, 3 days after the fibroblasts were introduced to culture, samples were fixed with 4% PFA in PBS for 30 min at room temperature and kept for downstream confocal analysis.

Immunostaining

After fixation, epithelial–stromal composites or human tissue whole-mounts were incubated for 30 min in permeabilization buffer (PB1; 0.5% bovine serum albumin (VWR International, 126575-10), 0.25% fish-skin gelatin (Sigma-Aldrich, G7765), 1% Triton X-100 (Fisher Scientific, 10102913) in PBS), then blocked for 2 h in PB1 containing 10% donkey serum (DS) (Scientific Laboratory Supplies, D9663). Next, tissues were incubated with primary antibodies diluted in 10% DS in PB1 for 3 days at 4 °C followed by four washes of 30 min each with 0.2% Tween-20 (Promega UK, H5151) in PBS. Thereafter, tissues were incubated overnight with secondary antibodies diluted 1:500 in 10% DS in PB1 at room temperature. Unbound antibody was removed by four washes with 0.2% Tween-20 in PBS throughout the next day. Antibody details are provided in Supplementary Table 15. To stain cell nuclei, tissues were incubated with 1 µg ml−1 DAPI in PBS at 4 °C overnight. Afterwards, samples were rinsed three times in PBS and mounted in 1.52 RapiClear mounting media for imaging.

Immunolabelling of individual tissue layers (epithelium or stroma) or sections consisted of an incubation for 30 min in permeabilization buffer (PB2; 0.5% bovine serum albumin, 0.25% fish-skin gelatin, 0.5% Triton X-100 in PBS). Tissues were then blocked for 2 h in PB2 containing 10% DS. Next, samples were incubated overnight at room temperature with primary antibodies diluted in 10% DS in PB2 followed by three washes with 0.2% Tween-20 in PBS for 30 min each. Secondary antibodies were diluted 1:500 in 10% DS in PB2 and incubated with tissues overnight at 4 °C, after which unbound antibody was removed by three washes with 0.2% Tween-20 in PBS, and staining continued as above. Thick cryosections of fixed tissues embedded in optimal cutting temperature compound (OCT; Thermo Scientific, 12678646), cut with a thickness of 50 µm onto glass slides, were immunolabelled using the same protocol. Likewise, 7-μm paraffin-embedded sections were immunolabelled according to the protocol described above, after antigen retrieval performed by heating of tissue sections in either 1 mM EDTA buffer (pH 8.0) or 10 mM sodium citrate buffer (pH 6.0) for 10 min at 95 °C.

When staining with primary antibodies raised in the same host, one of the antibodies was acquired as preconjugated with a fluorophore or conjugated in house following the manufacturer’s instructions (Invitrogen, A20186/A20187). The staining proceeded as described above with the unconjugated primary antibodies. After incubation with the corresponding secondary antibodies, the samples were blocked for 3 h at room temperature with 10% DS in PB with the IgG from the relevant host species (1:500). Afterwards, samples were incubated with conjugated antibodies diluted in PB containing 10% DS and the relevant host IgG (1:500) overnight at room temperature. At this point, staining proceeded as described above. Immunostained samples were analysed by confocal imaging.

Confocal imaging

Confocal images were acquired using either an inverted Leica SP5 microscope with standard laser configuration or a Stellaris 8 FALCON FLIM microscope with a white-light laser using LAS X 4.7.0.28176 or 3.5.5.19976 software. Typical confocal settings used included: bidirectional scanning, a 40× immersion objective lens, an optimal pinhole size (as defined by the software), a scan speed of 400–600 Hz with 2–3× line averaging, optimal Z-step size (as defined by the software) and a resolution of 512 × 512 or 1,024 × 1,024 pixels, unless stated otherwise. Then, sD reconstructions from optical sections and their corresponding image renders were generated using Volocity 5.5.5 (PerkinElmer) and Volocity 7 (Quorum), Zen 3.2 and Arivis 3.5.1. Further information about specific types of image analysis, such as second-harmonic generation imaging, can be found in the Supplementary Methods.

Transcriptomics

Library preparation and scRNA-seq

Sample preparation methods for libraries can be found in the Supplementary Methods in the section ‘Single-cell and RNA isolation for single-cell RNA-sequencing (scRNA-seq)’.

The scRNA-seq libraries were generated using the 10× Genomics Chromium Next GEM Single Cell 3′ Reagent Kit (v.3) and sequenced at the Genomics Core Facility of Cancer Research UK (CRUK), Cambridge Institute. Libraries were generated in two different batches. Information about library batches can be found in Supplementary Table 2. Control samples were included in both batches to provide a reference to assess potential batch effects. The cells for each biological replicate were loaded into a 10× Chromium microfluidics chip channel to generate one library from each. In total, 17 libraries were sequenced on either an Illumina HiSeqx4000 or a NovaSeq6000 system using one SP, two S1 and two S2 flow cells. Note that, given the punch biopsy approach used, DEN samples could contain sporadic tumour cells from tumours not visible under the dissection microscope.

scRNA-seq preprocessing, dimensionality reduction and visualization

The raw scRNA-seq data were processed with CellRanger (v.7.0.1). Reads were aligned to the mouse reference genome (mm10 2020-A), empty droplets were filtered and unique molecular identifiers were counted to generate gene-expression matrices. Doublets were identified using Scrublet[66] (v.0.2.3) and removed, along with low-quality cells, on the basis of per-sample quality-control metrics (Supplementary Table 2); cells with more than 15% mitochondrial reads or genes expressed in fewer than three cells were excluded, resulting in 91,347 high-quality cells. Count matrices were processed using a standard Seurat workflow[67] (v.5.0.3) up to dimensionality reduction. Data were integrated by tissue of origin (oesophagus or forestomach) using Harmony[68] (v.1.2.3), and the integrated embeddings were projected in two dimensions using Seurat’s RunUMAP function. Further details on data clustering, annotation, and trajectory and communication analysis can be found in the Supplementary Methods and on the Alcolea lab’s GitHub page.

Low-input targeted DNA sequencing

Oesophagi from control and DEN-treated animals were dissected as described above. Tissues were flattened with the epithelial side up and visible tumour lesions were marked with a partial incision using a 1 mm diameter punch tool under a dissecting microscope. Tissues were then incubated in 5 mM EDTA for 3 h at 37 °C while rotating. After incubation, the epithelium was removed, the stroma was flattened and tissues were fixed as described above.

Immunofluorescent labelling against KRT14 was done as described above. Only tumour stroma footprints negative for KRT14 (lacking epithelial cells) were considered for DNA sequencing to avoid the identification of genetic mutations present in epithelial cells. Tumour stroma matching the criteria was dissected under a fluorescence dissecting microscope (Leica M165 FC) using a 1 mm punch biopsy tool. Punch biopsies of equivalent size were collected from untreated healthy tissues as a control. The DNA from individual biopsies was extracted using the Arcturus PicoPure DNA Extraction Kit (Fisher Scientific, KIT0103) following the manufacturer’s instructions. In brief, proteinase K was reconstituted in 155 µl and the sample was lysed in 20 µl at 65 °C overnight. Thereafter, proteinase K was inactivated by incubation at 95 °C for 10 min.

Samples were then sheared, libraries prepped using the NEBNext Ultra II Fragmentase System, and index tags applied (Sanger 168 tag set). Material was subjected to 12 PCR cycles (initial denaturation; 95 °C for 5 min; 12 cycles of 98 °C for 30 s; 65 °C for 30 s; 72 °C for 2 min; final elongation, 72 °C for 10 min) and quantified (Accuclear dsDNA Quantitation Solution, Biotium). Then, 500 ng of pooled material was taken forward for hybridization capture and enrichment (SureSelect Target enrichment system, Agilent Technologies) using a previously designed bait panel of 192 genes (Supplementary Table 9), including those commonly mutated in squamous cancers[22]. After clean-up, libraries were normalized to around 6 nM and submitted to cluster formation for sequencing on a Novaseq6000 (Illumina) to generate 100-base pair paired-end reads.

Aligned reads were mapped to the mouse GRCm38 reference genome using BWA-mem (v.0.7.17)[69]. Duplicate reads were marked using SAMtools[70] (v.1.11). Depth of coverage was also calculated using SAMtools to exclude reads that were unmapped, not in the primary alignment, failed platform or vendor quality checks, or PCR or optical duplicates. Bedtools (v.2.23.0)[71] was then used to calculate the depth of coverage per base across samples.

Variant calling was done using the deepSNV R package (also commonly referred to as ShearwaterML; v.1.21.3; https://github.com/gerstung-lab/deepSNV). Variants were annotated using VAGrENT. R v.3.3.0 was used7,72.

Mutations called by deepSNV ShearwaterML were filtered using the following criteria: first, positions of called single nucleotide variants (SNVs) have a coverage of at least 100 reads; second, germline variants called from the same individual are omitted from the list of called variants; third, adjustment for false discovery rate and mutations use support from at least one read from both strands for the mutations identified; and finally, pairs of SNVs on adjacent nucleotides in the same sample are merged into a dinucleotide variant if at least 90% of the mapped DNA reads containing at least one of the SNV pairs also contained the other one. DeepSNV ShearwaterML was run with a normal panel of approximately 12,000 reads.

Statistics and reproducibility

The numbers of biological replicates and animals are indicated in the figure legends (n refers to the number of independent replicates per time point and/or condition). A minimum of three independent mice or ex vivo cultures were used in all cases. All experiments were done independently at least three times with similar results, unless otherwise stated. The reproducibility of all key findings was confirmed in independent experiments conducted on different days and using independent biological samples. For image analysis, a minimum of three independent samples were inspected in all cases. All figures show representative images. The data are expressed as mean values ± s.e.m. unless otherwise indicated.

All statistical tests were done comparing biological replicates. Differences in tumour burden were assessed by one-tailed unpaired non-parametric Mann–Whitney U-tests. Differences between Niche− and Niche+ tumour distribution in ageing animals was assessed by one-sided chi-squared test. For large datasets, normality was assessed using a Kolmogorov–Smirnov test; for normally distributed data, differences between two groups were assessed by two-tailed Welch’s t-tests; for non-normally distributed data, a two-tailed Mann–Whitney U-test was used. Differences between more than two groups were calculated using either one-way Welch’s ANOVA, followed by a Dunnett’s T3 multiple-comparison test, or Kruskal–Wallis one-way ANOVA, followed by Dunn’s multiple-comparison test, for normally distributed or non-normally distributed data, respectively, unless specified otherwise. Exact P-values are indicated in the relevant figures with a precision of up to four decimal places. Statistical tests were conducted in GraphPad Prism (v.10.5.0) with 95% confidence intervals. No statistical method was used to predetermine sample size. The experiments were done without randomization. Blinding was done for tumour count per condition and in vitro sample analyses by confocal microscopy. In cases for which quantification was done in tumours and morphologically normal areas, blinding was not possible owing to differences in physical sample appearance.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Mouse reference genomes GRCm38 and mm10 2020-A were used. The single-cell RNA sequencing data generated in this study have been deposited in the Gene Expression Omnibus (GEO) repository under accession code GSE271962. The DNA sequencing dataset was deposited at the European Nucleotide Archive (ENA) under dataset accession number ERP134942. Source data are provided with this paper.

Code availability

No new algorithms were developed for this paper. The analysis code is available on the Alcolea lab’s GitHub page.

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Acknowledgements

We thank members of the Alcolea lab for comments and suggestions; I. Michalk for coordinating human sample collection and documentation at the Tumour and Normal Tissue Bank Dresden; J. Cordle for coordinating human sample collection and delivery from Guy’s and St Thomas’ NHS Foundation Trust; the Cancer Aging and Somatic Mutation (CASM) teams for accommodating work related to human samples, especially E. Anderson, L.-A. Gilbey and the rest of the CASM research management team; P. Humphreys and D. Clements for guiding imaging analysis at the Jeffrey Cheah Biomedical Centre (JCBC); N. Lawrence for guiding imaging analysis at the Gurdon Institute Imaging Facility; I. Pshenichaya for technical histology support at the JCBC; B. Mahler-Araujo and J. Warner for histopathology analysis; D. Streichert for technical support with human-sample staining; M. Paramor and V. Murray; K. Kania; I. Mohorianu’s team for their contribution to scRNA-seq sample processing, library preparation and data pre-processing; the staff of the University Biomedical Services Gurdon Institute and the Anne McLaren Building technical biomedical assistance; and I. J. Jackson (Fucci2a) and H. Clevers (Rosa26Confetti) for donating mouse lines. This work was supported by grants to M.P.A. from the Wellcome and The Royal Society (105942/Z/14/Z and 105942/Z/14/A), the Medical Research Council (MR/P019013/1), the Worldwide Cancer Research and Guts UK (19-0192 and 23-0063). This research was funded in whole, or in part, by Wellcome (203151/Z/16/Z, 203151/A/16/Z) and the UKRI Medical Research Council (MC_PC_17230), and core support grant for Cambridge Stem Cell Institute Discovery Research Wellcome Platform Discovery Research Platform for Tissue Scale Biology (226795/Z/22/Z). This work also received funding from the European Research Council (ERC) Executive Agency under HORIZON ERC Synergy Grant Programme (grant agreement 101167202 — ClonEScape — ERC-2024-SyG, to M.P.A.). G.S. was funded by the Isaac Newton Trust (21.07(a)), the Medical Research Council (MR/P019013/1) and Worldwide Cancer Research (19-0192 and 23-0063). J.E.R.A. was supported by a University of Cambridge/Wellcome Junior Interdisciplinary Fellowship (ISSF 11/2/2020) and the Medical Research Council (MR/P019013/1). Y.D. was funded by an ELBE Postdoctoral Fellowship from the Center for Systems Biology Dresden (CSBD). M.T.B. received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement 794664 (OESOPHAGEAL FATE). H.A. and M.T.B were also supported by the Isaac Newton Trust (research grants 16.24(e)) and the Leverhulme Trust (RPG-2023-136). D.R. was funded by the Cancer Research UK Cambridge Centre (CANCTA-2023/100003). S.H. acknowledges funding from the Human Frontier Science Program (LT000092/2016-L). P.H.J., K.M. and J.C.F. were supported by Wellcome (108413/A/15/D). P.H.J. was also supported by Cancer Research UK programme grants (C609/A27326 andDRCRPG-Nov24/100003). A.N. and W.K. were funded by CRUK (RCCCSF-Nov22/100005). D.A., M.H.H.S. and J.J were funded by intramural sources of TU Dresden allocated to the Institute of Pathology and Institute of Anatomy. S.R. received funding from the ERC under the European Union’s Horizon 2020 research and innovation program (grant agreement 950349) and acknowledges the Center for Nanoscience (CeNS), Munich. B.D.S. acknowledges funding from the Royal Society (E.P. Abraham Research Professorship, RSRP\R\231004) and Wellcome (219478/Z/19/Z).

Author information

Author notes

1. J. E. Rojo Arias

Present address: RhyGaze, Basel, Switzerland

2. B. Colom

Present address: Cambridge Institute of Science, Altos Labs, Cambridge, UK

3. These authors contributed equally: G. Skrupskelyte, J. E. Rojo Arias

Authors and Affiliations

1. Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

G. Skrupskelyte, J. E. Rojo Arias, H. Ajith, D. Rossetti, M. K. S. Tang, M. T. Bejar, B. D. Simons & M. P. Alcolea

2. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

G. Skrupskelyte, J. E. Rojo Arias, H. Ajith, D. Rossetti, M. K. S. Tang, M. T. Bejar & M. P. Alcolea

3. Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany

Y. Dang

4. Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

Y. Dang & S. Rulands

5. Center for Systems Biology, Dresden, Germany

Y. Dang

6. Gurdon Institute, University of Cambridge, Cambridge, UK

S. Han & B. D. Simons

7. Wellcome Sanger Institute, Hinxton, UK

B. Colom, J. C. Fowler, K. Murai, W. Knight, A. Noorani & P. H. Jones

8. University Hospital Carl Gustav Carus Dresden, Faculty of Medicine of TUD Dresden University of Technology, Dresden, Germany

D. Aust

9. Institute of Pathology, University Hospital CGC Dresden, TU Dresden, Dresden, Germany

D. Aust

10. Institute of Anatomy, Faculty of Medicine of TUD, University of Technology, Dresden, Germany

M. H. H. Schmidt & J. Jászai

11. Department of Gastroenterology, Guy’s and St. Thomas’ Hospital, London, UK

S. Zeki

12. Addenbrooke’s Hospital, Cambridge University Hospital NHS Trust, Cambridge, UK

A. Noorani

13. Department of Oncology, University of Cambridge, Hutchison Research Centre, Cambridge Biomedical Campus, Cambridge, UK

P. H. Jones

14. Arnold Sommerfeld Center for Theoretical Physics, Ludwigs-Maximilians-Universität Munchen, Munich, Germany

S. Rulands

15. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Science, University of Cambridge, Cambridge, UK

B. D. Simons

Contributions

G.S., J.E.R.A. and M.P.A. designed, validated and conducted experiments; S.H. guided the experimental design for single-cell RNA sequencing and, together with H.A. and J.E.R.A., did the data processing. H.A. and J.E.R.A. did the trajectory inference analysis. H.A. and Y.D. did cell-to-cell communication analysis and advised and supported the single-cell RNA sequencing analysis. S.R. analysed lineage-tracing data and provided advice and guidance on single-cell RNA sequencing data analysis. J.C.F. processed DNA sequencing samples and did data analysis. B.C. provided insights and technical expertise in targeted DNA sequencing. M.T.B. did in vivo transplantation assays. A.N. and S. Z. helped with human tissue recruitment and D.R., K.M. and W.K. assisted with human tissue experiments. D.A. did histopathological staging of human tumour samples. M.H.H.S. and J.J. advised on comparative anatomical aspects of human and rodent oesophagus and sample processing. M.K.S.T. assisted with in vitro validation experiments. M.T.B., B.C., P.H.J. and B.D.S. advised on parts of the study, provided expertise regarding epithelial stem cells and tumour biology, and assisted with writing the manuscript. G.S. and M.P.A. conceived the project, supervised and performed experiments, and wrote the manuscript with input from all authors. Review and editing of the manuscript was done by all authors. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Corresponding authors

Correspondence to G. Skrupskelyte or M. P. Alcolea.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Early tumorigenesis model in squamous upper-gastrointestinal tract of a mouse; (Corresponds to Fig. 1).

a, H&E image of transversal cross-sections from the murine upper gastro-intestinal tract (including oesophagus and forestomach) displaying multiple tumours (black arrowheads) 6 m after DEN treatment. Scale bars, 100 µm. b, Number of tumours per oesophagus (OE) at the indicated time points after DEN treatment, n = 3 mice per time point. A line is drawn across the means of each time point. One way Welch’s ANOVA with multiple comparison was used to assess significance, indicated p values show two consecutive timepoint comparison indicated by the black line. c, Cartoon (edges) illustrating tumour clearance over time, with examples of confocal images (middle) of growing persistent tumours stained for DAPI (blue) and KRT6A (red). Tissues were collected at the indicated time points after DEN treatment: 10 d, 2 m and 1 y from 5 mice per timepoint. Scale bars, 25 µm. d-e, Confocal images showing early tumour marker expression in nascent tumours 10 d after DEN treatment from 18 mice. DAPI (blue) (d & e); KRT6A (red) (d); KRT17 (yellow) (e). Scale bars: 25 µm. f, H&E image of a transversal cross-section depicting a nascent tumour 10 d after DEN treatment (black triangle), representative image from 4 mice. g, H&E images of tumours transversally cross-sectioned at different time points after DEN treatment at different stages of progression in the squamous upper gastro-intestinal tract. Scale bars, 50 µm. n = 4 (1 m), 4 (9 m), 10 (12 m), 2 (14 m) mice per timepoint. h, Bar plot (left) showing distribution of DEN induced pathology before (<) and after (>) 9 m after DEN treatment. Note, 10 d tumours are not identifiable to pathologists due to their size. i, Representative whole mount confocal images from control (ctrl) or 10 d post-DEN oesophagi showing localized KRT17 expression (yellow) in tumours, otherwise absent in surrounding or age-matched control tissues. Scale bar, 500 µm. j, Percentage of proliferating (KI67+) fibroblasts in DEN-treated oesophagi (0 d post-DEN) and in normal age-matched controls from PdgfraEGFP mice. n = 17 and 9 areas per condition respectively, each from 3 animals. Data is expressed as mean±s.e.m. Statistical significance was assessed by a two-tailed Mann-Whitney U test. Days (d), months (m), year (y). Illustrations in c,h were created in BioRender; Alcolea, M. https://BioRender.com/z50odrm (2026). Source data (b,h,j).

Source data

Extended Data Fig. 2 Pre-cancerous niche promotes epithelia proliferation and tumour growth; (Corresponds to Fig. 1).

a, Density of Niche+ and Niche– tumours at the indicated time points after DEN treatment. n = 3 mice per time point; solid lines represent means. One-tailed Mann-Whitney test Niche– versus Niche+ tumours per time-point. b, Confocal images showing EdU incorporation (green) in KRT6A+ nascent tumours (red, dashed line) 10 d after DEN treatment. DAPI (blue). Scale bars, 10 µm. c, Number of EdU+ basal keratinocytes normalised per basal cell density at the indicated time points after DEN treatment. N, number of normal areas or Niche– or Niche+ tumours; n = 84 (10 d), 20 (2 m) Niche– tumours, n = 35 (10 d), 38 (2 m) Niche+ tumours, and n = 20 (10 d), 14 (2 m) DEN areas from 3 mice per condition. Data expressed as violin plots with mean (solid lines) and quartiles (dashed lines). One-way Welch’s ANOVA with multiple comparison was used to assess significance. d, Quantification of basal keratinocyte cells per area in DEN, Niche– or Niche+ early tumours. N is number of DEN or tumour areas from 3 mice. Data expressed as mean+s.e.m. One-way Welch’s ANOVA with multiple comparisons was used to assess significance. e, 3D-rendered confocal side-views of a tumour 3 m after DEN treatment and its respective age-matched control showing R26Fucci2aR tissue (mCherry, G1 cells; red mVenus, S/G2/M cells; green). PDGFRα (fibroblasts, grey). Scale bar, 50 μm. White arrows mark proliferating cells in the basal layer. f, 3D-rendered confocal side-views showing growing Niche+ tumours at the indicated time points after DEN treatment; DAPI (blue); KRT6A (red); PDGFRα (greyscale). g, Representative confocal images of forestomach epithelia 10 d and 3 m after DEN treatment. DAPI (blue); KRT6A (red). White arrowheads highlight tumours at indicated time points. Scale bars, 1.5 mm. Data form n = 3 animals per timepoint. h, Representative confocal images of a transversal cross-section of a forestomach tumour showing the presence of the stromal niche 8 m afterDEN treatment. DAPI (blue); KRT6A (red); KRT17 (yellow); PDGFRα (grey). Scale bar, 25 µm. Data from n = 4 animals. i, Schematic illustration of ex vivo 3D heterotypic organ cultures. After separating epithelial and stromal layers, control epithelia were combined with stroma of surviving tumours 3 m after DEN withdrawal (dashed lines). This was compared to tissue constructs combining control epithelia and control stroma. j, Schematic representation of the heterotypic construct approach used (left). Ep, epithelium; WT, wild-type. Images (middle and right) show the emerging tumour like structure in control epithelium, side and top-down views, respectively. Grafted epithelium expressed nuclear tdTomato (nT, red, from nTnG mice). DAPI (blue); β-catenin (CTNNB1, green), PDGFRα (greys). Scale bars, 25 µm. Data from 10 biological replicates. k, Experimental protocol (left) and representative side-views of 3D-rendered confocal images (right) of 3D heterotypic tissue constructs 7 d post-culture and in vivo control or tumour tissue for comparison. PDGFRα labels fibroblasts (greyscale); in R26Fucci2aR tissue mCherry, G1 cells (red); mVenus, S/G2/M cells (green). Scale bars, 25 µm. Ep, epithelium; WT, wild-type; Ctrl, control. Tumour bearing tissue was collected 3 m after DEN treatment. l, Quantification of cycling (mVenus+) basal keratinocytes (Krt) from k, expressed as percentage of total number of basal cells. Dots are data from an individual tumour (Tmr, in vivo), tumour-like structure (Tmr-like, heterotypic culture), and the respective in vivo and in vitro control areas. Dots represent areas assessed across different biological replicates n = 9 control areas, from 3 mice in vivo, 23 control areas from 3 mice in vitro and n = 10 tumours from 6 mice in vivo and 7 tumour like structures from 5 mice in vitro. Data expressed as mean±s.e.m. One-way Welch’s ANOVA with multiple comparisons were used to assess significance. m, Schematic representation of the subcutaneous heterotypic grafting approach to NOD SCID gamma (NSG) immunodeficient mice. Grafted epithelium expressed nuclear tdTomato (nT, red, nTnG mice), while accompanying stroma was EGFP (green, from H2B-EGFP mice) to distinguish the origin of cells in the graft. DEN treated tissue was collected 3 m after DEN treatment. n, Representative confocal images (from m) showing the long-term survival of a graft combining healthy untreated nT epithelium with EGFP tumour or control stroma. Scale bars 500 µm. o, Quantification of surviving and lost graft constructs from n, 3-6 m post-transplantation expressed as percentage. 19 control and 23 DEN constructs were transplanted across 10 animals, respectively. Statistical significance was determined by a one-sided Chi-squared test. Days (d), months (m), control (ctrl). Illustrations in i,j,k,m were created in BioRender; Alcolea, M. https://BioRender.com/kvs2ti9 (2026). Source data (a,c,d,l,o).

Source data

Extended Data Fig. 3 Nascent tumour niche is mainly comprised of fibroblast; (Corresponds to Fig. 2).

a, Schematic representation of tissue layers of the murine upper-gastrointestinal tract. b, Schematic representation of the imaging angle used according to the sample preparation method. c, Image of tissue whole-mounts showing differential PDGFRα (grey) expression between the fibroblast layers. α-SMA smooth muscle layer (green), DAPI (blue). Scale bar, 25 µm. Lamina propria-Lp, Muscularis mucosae- Mm, Submucosae- Sb. d, Image of a tissue section showing reduced PDGFRα expression in upper stromal layers (i.e. lamina propria). Dashed lines (c,d) separate the tissue layers as listed in schematic. e, Confocal images of nascent tumour stroma 10 d after DEN treatment. Images show active YAP (aYAP) expression in PDGFRα labelled fibroblasts (white arrowheads). aYAP (magenta); CD31 and CD45 (green); PDGFRα (grey); DAPI (blue). Scale bars, 25 µm. f, Representative confocal images of Niche+ tumours 10 d after DEN treatment, showing the absence of typical cancer associate fibroblast (CAF) marker in the niche forming fibroblasts. g, Number of CD45+ (immune) cells in epithelia of Niche−/Niche+ tumours and adjacent DEN area normalised to surface area, 10 d post-DEN. n = 27 (Niche−), 22 (Niche+) tumours, from 3 mice each, dots represent a tumour. Data expressed as mean±s.e.m. Statistical significance assessed by one-way Welch’s ANOVA with multiple comparisons. h, Experimental DEN carcinogen protocol in wild-type (wt) and immunocompromised NOD SCID gamma (NSG) mice. i, j, Niche+ (i) and Niche− (j) tumours per area in wt and NSG mice at 10 d and 2 m post-DEN withdrawal. N = 3 wt and 4 NSG mice at 10 d and n = 3 wt and 3 NSG at 2 m after DEN treatment time point; expressed as mean±s.e.m. One-tailed Mann-Whitney test was used to assess statistical significance. k, Representative confocal images of Niche+ nascent tumours from 10 d post-DEN showing fibroblast (PDGRα, grey) recruitment (white arrow) to a forming epithelial tumour (KRT6A, red) in wt and NSG mice. CD45 (green), DAPI (blue). Scale bar, 10 µm, l, Diameter (µm) of Niche+ and Niche− tumours, n = 126 (tumours in wt from 3 mice) and n = 189 (tumours in NSG from 4 mice) at 10 d post DEN and n = 57 (tumours in wt from 3 mice) and n = 36 tumours in NSG from 3 mice) at 2 m after DEN treatment. Data expressed as mean±s.e.m. Two-tailed Welch’s t-test was used to assess statistical significance. Days (d), months (m). Illustrations in a, b, h were created in BioRender; Alcolea, M. https://BioRender.com/8klr068 (2026). Source data (g, i, j, l).

Source data

Extended Data Fig. 4 Expansion of PDGRαlow fibroblast population in the early tumour niche; (Corresponds to Fig. 2).

a, Experimental protocol of fibroblast lineage tracing in Col1a2CreERRs26FConfetti mice. b, Confocal images of confetti-labelled cells 24 h after induction in different stromal layers. Scale bars, 25 µm. c, d, Efficiency of confetti construct recombination after a 72 h chase in the PDGFRα+ fibroblast population. Total percentage of recombined cells in the PDGFRα+ fibroblast population (c), and split colour percentage within the recombined population (d). N represents number of fields from 3 animals, n = 36; each replicate shown with a different shade of colour. e, Confocal 3D top-down or side-view images of a DEN area. Scale bars, 50 µm. Corresponds to Fig. 2d-f. f, Distance to the nearest neighbour fibroblast (fbr) labelled in the same colour in external control (Ctrl), internal control (DEN) and tumours. The horizontal line denotes the average, and the box signifies 95% confidence intervals obtained via bootstrapping. Pairwise Wilcoxon signed-rank tests were performed, and P-values were adjusted using the Holm-Bonferroni correction. g, Cartoon illustrating fibroblast expansion in the early tumour niche: lineage tracing results revealed that stroma between tumour lobes show increased fibroblast density and decreased distance between the same colour fibroblasts (black arrowheads) if compared to the tissue outside of lobes or control. h, Histogram showing the percentage of cells in a given bin of the distance to the nearest lobe contour. i, The average distance to the nearest neighbour of the same colour as a function of the distance to the lobe boundary. Results show that labelled cells are closer together on the lobe contour than elsewhere. Data expressed as mean and error bars denote 95% confidence intervals obtained from boot strapping. The vertical dashed line in h and i denote 10 µm, the estimated width of the tumour lobe contour. f,h and i data from n = 18 areas in control from 3 animals, n = 18 areas in DEN from 3 animals and n = 16 tumours from 4 animals. j, Schematic representation of the confetti cassette and experimental protocol of fibroblast lineage tracing in PdgfraCreERTRs26FConfetti mice. k, Images of labelled fibroblasts (from j) in lamina propria and submucosae; data from 5 animals. l, Percentage of fibroblasts labelled in lamina propria (Lp) and submucosae (Sb) (from j). m, Representative confocal images of tissue from j showing top-down projections and confetti labelled fibroblast; 5 and 3 animals for low and high TAM dose, respectively. Quantified induction efficiency per field of view shown as an average above the images. n, Experimental DEN protocol in TAM induced PdgfraCreERTRs26FConfetti mice. o, Representative confocal images of confetti labelled fibroblasts in early tumours 6w after DEN treatment. Dashed lines mark tumour and stromal layer margins. 0.5 mg/20 g body weight is low; 5 mg/20 g body weight is high tamoxifen dose. DEN treated animals represent n = 4 (high TAM) and 3 (low TAM). PDGFRα (grey), α-SMA (in b grey); CONFETTI (cyan, yellow and red) in (b). Dashed lines in (e,k,o) label lamina propria and submucosae layers in side views. Hours (h), weeks (w), days (d), months (m), tamoxifen (TAM). Scale bars (k, m, o) 50 µm. Illustrations in a,g,j,n were created in BioRender; Alcolea, M. https://BioRender.com/7zgcsff (2026). Source data (c, d, l).

Source data

Extended Data Fig. 5 Single cell RNA sequencing annotation; (Corresponds to Fig. 3).

a, Schematic visualisation of representative DEN-induced tumours under the dissection microscope, before and after microdissection using punch biopsy tool. b, Image composite depicting sample collection strategy for scRNA-seq. Squamous oesophagus and forestomach (outlined in red) first underwent tumour marking and were then peeled to separate epithelial (Ep) and Stromal (Str) compartments. This was followed by sample biopsying, where tumours and size-matched control biopsies (adjacent tissue as internal control, DEN; untreated tissue as external control, Control) were micro-dissected for scRNA-seq. c, UMAP representing cell cluster distribution as defined by Seurat. d, UMAP cell distribution of cell-types identified using label transfer from ref. [73]. e, Heatmap showing expression of representative marker genes used for curated cell type annotation across the 22 clusters. Log-transformed normalised expression levels were averaged by cluster for each gene and scaled across all cells belonging to each group. Scale bar denotes expression range (scale: -4 to 6). f, UMAPs showing expression of representative genes for each cell type identified. Colour bars of UMAPs indicate log-transformed normalised expression levels. g, UMAP cell distribution representing identified cell types in each condition. Illustration in a) was created in BioRender; Alcolea, M. https://BioRender.com/l73dcie (2026).

Extended Data Fig. 6 Tumour niche fibroblasts transition towards Cancer Associated Fibroblast (CAF) identity; (Corresponds to Fig. 3).

a, Heatmap showing the log_2 fold-change (FC) of differentially expressed genes in Pdgfralow relative to Pdgfrahigh fibroblasts. Genes characterising Pdgfralow shown in red, Pdgfrahigh in blue. DAPI (blue). Scale bars, 10 µm. Dashed line marks fibroblast contour. b, UMAP projection denoting the heterogenous expression of Fn1 (like Pdgfra) in fibroblasts; inset shows fibroblast cluster distribution. c, Violin plots showing the levels of Pdgfra and Fn1 expression in fibroblast clusters i.e., 3, 2, 4, and 19 in control conditions. Fn1 expression levels are inversely proportional to those seen of Pdgfra. Black line across violin plots shows is mean. d, Representative images of stromal whole mount showing fibronectin (FN1, green) expression in lamina propria PDGFRαlow (white arrowhead) and submucosae PDGFRαhigh fibroblasts, labelled in yellow (from induced PdgfraCreERRs26FConfetti mice; EDF 4j; n = 3 mice). e, Stacked bar plot (left) showing fibroblast cluster enrichment across conditions. Significance was assessed by one-sided Chi-squared test, standardised residual (Std Res) values are shown in the heatmap on the right, indicating Cluster 19 (C19) to be the most enriched cluster in tumour conditions f, Volcano plot showing differential gene expression between Tumour and DEN conditions in Cluster 19, Pdgfralow fibroblast cluster enriched in tumours. Non-significant genes (p ≥ 0.05), green and grey. Significant genes with FC > 1, red; FC ≤ 1, blue. Genes defining the profibrotic nature of cluster 19 in tumours are labelled on the right. g Representative confocal images of a DEN adjacent area and nascent tumours 10 d after DEN treatment or persisting tumour 6 m after DEN treatment showing the accumulation of Fibronectin (FN1, green). DAPI (blue). Scale bars, 25 µm (10 d), 100 µm (6 m). h, Second harmonic generation (SHG) image of lamina propria (directly underneath the epithelium) in a control (Ctrl) and tumour areas 3 m post-DEN. Dashed lines mark insets. Extracellular matrix (ECM) fibres detected by SHG, (cyan); nuclei (red). Scale bars, 25 µm. The yellow diamonds represent the longitudinal orientation of the oesophagus. i, ECM fibre density was scored as SHG signal intensity 3 m after DEN treatment. N is the number of regions or tumours assessed from 6 animals; n = 36 (Ctrl), 20 (DEN), 31 (Tmr). Bars indicate mean±s.e.m. One-way Welch’s ANOVA with multiple comparison was used to assess significance. j, Plot depicting the orientation of ECM fibres in control regions and tumours 9 m after DEN treatment. N is the number of fields of view assessed in 3 animals per condition, n = 12 (Ctrl) and 8 (Tmr). Data expressed as average ±s.d. k, Representative confocal image of a nascent tumour from 6 animals, showing integrin α6 (CD49f, magenta) sequestering in the early tumour niche. Samples were also labelled for KRT6A (red), PDGFRα (grey) and DAPI (blue). Image settings adjusted to upper stromal layer. l, Venn diagram displaying overlapping genes between known CAF markers (top left, red) and differentially expressed genes (DEGs) in DEN tumour (Tmr) fibroblasts (Cluster 19) relative to external control (Ctrl; top right, yellow) or to internal control cells (DEN; bottom, green). Overlapping genes are listed. Known subtypes of CAFs: Myofibrotic (my), immune (i), and antigen-presenting (ap) were considered. m,n, Violin plots showing expression of signature genes found to be enriched in Cluster 19 tumour fibroblasts (m) or other canonical CAF markers (n); external control (ctrl, green), internal control (DEN, blue), and tumours (Tmr) (red). Black line is mean. o, Representative 3D-rendered side-view images of control tissue and tumours 6-7 m post-DEN. Expression of CAF markers (FAP, VIM, FSP) was not detected in niche fibroblasts. KRT6A (red); CD45 (immune), and CD31 (endothelia) (cyan); Vimentin (VIM) (green); PDGFRα (top left and right) and FSP (bottom), (grey); FAP (green), DAPI, blue. Scale bars, 50 µm. Dashed line outlines tumour area. p, Quantification of Ki67+ EGFP+ fibroblasts from PdgfraEGFP mice in control and Niche+ tumours 6-12 m after DEN treatment. N = 25 control areas (from 7 animals) and 50 tumours (from 8 animals). Data is expressed as mean±s.e.m. Two-tailed Mann Whitney test. q, Representative bright field (H&E) and confocal (IF) images of invasive carcinoma from 14 m, post-DEN withdrawal tissue. H&E was used to diagnose the pathology. In different panels αSMA, FAP, VIM (green) co-expression with PDGFRα (grey) show CAF emergence in DEN model. DAPI (blue). Scale bars, 100 µm (black) 50 µm (white); representative data from 5 animals >12 m after DEN treatment. r, Violin plots showing the prediction score from label transfer for Pi16+ and Col15a1+ cross-tissue universal fibroblast population[40] in upper GI fibroblast clusters. s, upper GI fibroblast clusters (top left). Remaining UMAPS show Pdgfra expression in UMAP space of upper GI and cross-tissue fibroblasts[40]. Days (d), months (m). Source data (i,p).

Source data

Extended Data Fig. 7 Niche of growing tumours recruits other stromal cells; (Corresponds to Fig. 3).

a, Experimental protocol for targeted DNA sequencing of stromal niche 7 m after DEN treatment. b, Substitutions per megabase (Sb/Mb) were calculated and used as indicative of the mutation burden in stroma across conditions. n = 6 control (from 4 animals), 13 DEN (from 6 animals) and 18 tumour (from 6 animals). Epithelial tissue (12 m after DEN treatment) was used as benchmark and includes both tumour and tumour-free tissues (DEN+Tmr; from 3 mice). Data is expressed as mean±s.e.m. Two-tailed Welch’s t-test comparing tumour stroma with internal or external control stroma or with epithelium. c, Stacked bar plot showing scRNA-seq captured cell type distribution across conditions. d, Representative 3D-rendered confocal image of an early tumour (KRT6A, red) 4 m (left) and 7 m (right) post-DEN withdrawal showing the recruitment of immune cells (CD45, green) and blood vessels (CD31, orange) to the persisting tumour niche. DAPI (blue). Scale bar, 50 µm. e, Heatmap showing expression of representative marker genes across the 14 immune cell types identified in the scRNA-seq data. Log-transformed normalised expression levels were averaged by cluster for each gene and scaled across all cells belonging to each group. Scale bar denotes expression range (scale: -2 to 2). f, UMAP of identified immune cell types split by condition. g, Top, stacked bar plot showing immune cell type enrichment across conditions. Significance was assessed by a one-sided Chi-square test. (Bottom) heatmap showing standardised residuals values from Chi-square test. Asterisk (*) marks cell types used in CellChat analysis. h, Heatmaps representing outgoing (left) and incoming (right) interaction strengths as identified by CellChat and rescaled to their row maxima. Cumulative interaction strength is depicted by bars on the right. Months (m). Illustration in a was created in BioRender; Alcolea, M. https://BioRender.com/udeggcf (2026). Source data (b).

Source data

Extended Data Fig. 8 Pseudotime analysis of basal keratinocytes denotes distinctive early tumour state; (Corresponds to Fig. 3).

a, Left (inset), basal keratinocyte cell distribution in the UMAP space annotated by condition (right, UMAP consisting of all cell types). b, UMAP of basal keratinocyte cell distribution representing cells in each condition. c, UMAPs showing expression of representative basal keratinocytes genes (Col17a1, basal; Top2a, cycling basal; Krt4, committed). Colour bars of UMAPs indicate log-transformed normalised expression levels. d, UMAP showing cell clusters as defined by Seurat. e, Mapped RNA velocity of basal keratinocytes colour coded by condition. Black arrows are velocity vectors that show predicted cellular state change overtime. f, Basal keratinocyte two-dimensional pseudotime (PST) UMAP (top) and trajectories (black line) (bottom) inferred by Monocle 3. Legend shows the range of pseudotime values g, Heatmap (left) of the top 1500 genes identified as differentially expressed between cells in the tumour and basal trajectories. Log-transformed expression levels were scaled in a gene-wise fashion from −4 to 4 (scale) and are shown after hierarchical clustering along trajectories from committed to basal (blue to orange) and from committed to tumour (blue to magenta). The average expression patterns of the genes contained in each cluster are depicted on the right, with two modules (1 and 12) identified as tumour unique based on their preferential upregulation at the end of the tumour trajectory (Δ_tumour > 2, Δ_basal <1). (Blue, committed; Magenta, tumour; Orange, basal). Dashed vertical lines label the highest PST score of committed. h, Tumour cell populations representing Tumour 1 and Tumour 12 clustering in UMAP space. i, Violin plots highlighting Krt6a and Krt17 expression between Tumour 12 and Tumour 1 states. j, Volcano plot showing differential gene expression between Tumour 12 and Tumour 1 states. Non-significant genes (p value higher than p = 0.05), green. Significant genes with FC ≤ 1, red. Genes driving stress and extracellular matrix related processes are labelled. k, Gene Set Enrichment Analysis of Tumour 12 versus Tumour 1 differentially expressed genes. Terms ranked by FDR (false discovery rate); representative genes listed below the bar plot. GO:BP (Gene Ontology Biological Processes); KEGG (Kyoto Encyclopedia of Genes and Genomes). Cartoon (top right) representing Tumour 12 keratinocyte communicates with stroma. l, Violin plots highlighting differences between Tumour 12 and Tumour 1 states. m, Representative confocal images from 6 mice showing expression of tumour markers: SOX9 and EGR1 (cyan); AREG and RUNX1, (magenta); KRT6A, red; KRT17 (yellow). DEN images were acquired 10 d after DEN treatment in regions adjacent to tumours. DAPI (blue). Scale bars, 10 µm. n, Relative frequency (%) (top) of tumours expressing cells positive for SOX9 (Tumour 12 state marker) per 1,000 µm^2, tumours 10 d post-DEN, n- number of tumours=119 from 3 biological replicates. o, Representative confocal image from 10 mice demonstrating SOX9+ (cyan) keratinocytes are situated in direct contact to stroma (PDGFRα, grey). KRT6A (red), DAPI (blue). Scale bar 50 µm. Days (d), months (m). Illustration in k was created in BioRender; Alcolea, M. https://BioRender.com/mhoqqbw (2026). Source data (n).

Source data

Extended Data Fig. 9 SOX9+ early tumour keratinocytes are associated with stromal reorganisation; (Corresponds to Fig. 3).

a, 3D-rendered confocal images of Niche+ and Niche- tumours showing KRT6A (red) and SOX9 (cyan) 10 d after DEN treatment. PDGFRα (grey), DAPI (blue). Scale bars, 10 µm. Corresponds to Fig. 3h. b, c Quantification of SOX9+ keratinocytes (krt) (b) and SOX9 intensity (c) in a, normalised to surface area. DEN, equivalent morphologically normal areas from DEN-treated tissues. 3 mice per condition with n = 20 DEN areas, 84 Niche- and 35 Niche+ tumours. Statistical significance assessed by one-way Welch’s ANOVA with multiple comparisons. d, 3D-rendered confocal images (basal view, left; side-view, right) of SOX9+ keratinocyte clusters exclusively found in DEN condition. SOX9 (cyan), β catenin (green), PDGFRα (grey), DAPI (blue). Scale bar, 10 µm. Corresponds to Fig. 3i. e, Quantification of fibroblast proximity to basal layer from d, expressed as the inverse of the distance between basal keratinocytes and underlying fibroblasts. 1/distance (µm−1). f, Number of fibroblasts directly underneath the basal layer corrected per area. The number of control (n = 27) or SOX9 expressing regions (n = 42) assessed in tissues from 4 mice. Two-tailed Welch’s t-test. g, Strategy to validate SOX9 knock-out in basal keratinocytes (left). SOX9 expression was induced by culturing oesophageal tissue using the 3D epithelioid approach. Established cultures from tamoxifen-induced Krt14CreER R26mTmGSox9flox/flox mice were confocal imaged (right), confirming the presence of areas lacking SOX9 (cyan) expression. n = 3, biological replicates. White arrowheads mark the SOX9 loss. Membrane Tomato (purple), DAPI (blue). Scale bar, 25 µm. h, Top, experimental protocol: Krt14CreERSox9flox/flox mice received a dose of Tamoxifen (TAM) followed by the DEN treatment. Tissues were collected 1 m after DEN treatment (from n = 3 mice) and tumour diameter was compared to DEN-treated un-induced controls (n = 3 mice). Bottom, tumour diameter (µm) of Niche+ and Niche- tumours, from the above. Data expressed as mean±s.e.m. Significance was assessed using two-tailed Welch’s t-test. Days (d), months (m). Corresponds to Fig. 4j. Illustrations in g,h were created in BioRender; Alcolea, M. https://BioRender.com/ip2ihnq (2026). Source data (b,c,e,f,h).

Source data

Extended Data Fig. 10 Tumour epithelial cells are associated with early tumour stromal remodelling via the EGF-SOX9-Fibronectin axis (Corresponds to Fig. 5).

a, Scatterplot displaying the dominant senders and receivers as identified by CellChat. Circle size represents ‘communication probabilities’. b, Heatmaps (top) showing the relative importance of each cell group based on the computed network centrality scores for the Fibronectin (FN1) and EGF signalling network, respectively. Violin plots of relevant ligands and receptors across different cell types (bottom). These include expression of fibronectin (Fn1) and fibronectin-binding receptors (Itga3, Itgav, Itgb6), as well as EGF ligands (Areg, Hbegf) and binding receptors (Egfr, Erbb2). c, Migration of fibroblast through cell culture insert membrane (cartoon, top) in low (0.5%) or high (10%) serum (FBS) conditions was quantified (bottom) and expressed as a percentage of the total number of cells. Data expressed as mean±s.e.m. Each dot represents a technical replicate; data 4 biological replicates. One way Welch’s ANOVA test with multiple comparison was used to assess significance between groups presented in the main Fig. 4e. d, schematic representation of regions imaged in epithelioid cultures from (top) and representative confocal images showing SOX9 (cyan) downregulation in keratinocytes in central epithelioid areas as well as on the border of GFT-treated epithelioids. Representative images of the DMSO (ctrl) condition shown for comparison. Nuclear tdTomato (nT) keratinocytes (krt) (red); CDH1 (green); DAPI (blue). Scale bar, 50 µm. e, Schematic representation of an experiment (left) and representative confocal images (right) of expanding epithelioid cultures exposed to fibroblasts under DMSO (control) or Gefitinib (GFT) conditions. Vimentin, VIM (grey); PDGFRα (red); nuclear Tomato (nT) keratinocytes (krt) (red); DAPI (blue), E-Cadherin, CDH1 (green); Fibronectin, FN1 (green). Scale bar 500 µm (inset 50 µm). Corresponds to Fig. 4g. f, Top, experimental schematics, bottom, quantification of EdU incorporation (2 h chase) in confluent 3D epithelial cultures (Epithelioids) exposed to Fibronectin for 24 h. Data expressed as a percentage of the total number of cells (mean±s.e.m). Each dot represents a technical replicate; data from 3 biological replicates. Two-tailed Welch’s t-test comparing EdU incorporation in control (Ctrl) and fibronectin-treated cells. g, Schematic representation of Bleomycin (BLEO) administration to mice (top) and representative confocal images (bottom) of SOX9 (cyan) expression in epithelia of bleomycin treated animals. DAPI (blue). Scale bars, 50 µm. N = 5 (control) 4 (BLEO) mice. Illustrations in c,d,e,f,g were created in BioRender; Alcolea, M. https://BioRender.com/nm3lx8y (2026). Source data (c, f).

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Skrupskelyte, G., Rojo Arias, J.E., Ajith, H. et al. Precancerous niche remodelling dictates nascent tumour persistence. Nature 653, 242–253 (2026). https://doi.org/10.1038/s41586-026-10157-8

- Received: 10 July 2024

- Accepted: 19 January 2026

- Published: 04 March 2026

- Version of record: 04 March 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10157-8

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Early fibrotic niches establish tumour-permissive microenvironments

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Early fibrotic niches establish tumour-permissive microenvironments

- Erik C. Cardoso

- Hyeyoung Lee

- Joo-Hyeon Lee

Nature Article Open Access 22 Apr 2026

Article Open access Published: 22 April 2026

Early fibrotic niches establish tumour-permissive microenvironments

Erik C. Cardoso,

Hyeyoung Lee,

Frances J. England,

Hyunjin Cho,

Robin Lu,

Sagar S. Varankar,

Moo Suk Park,

Natasha Rekhtman,

Bon-Kyoung Koo,

Benjamin D. Simons,

Jinwook Choi &

…

Joo-Hyeon Lee

Nature volume 653, pages 254–264 (2026)

Abstract

Pathologic transformation represents a critical yet poorly defined window during which mutant epithelial stem cells actively construct the microenvironment that enables tumour initiation1,2. Here using integrated single-cell, spatial and functional analyses, we define the earliest multicellular events that licence this transition following oncogenic activation in the lung. KrasG12D-mutant alveolar type II cells rapidly adopt regenerative-like states that act as signalling hubs, orchestrating coordinated stromal and immune reprogramming while enhancing epithelial plasticity. Through secretion of amphiregulin, mutant epithelial cells activate EGFR signalling in adjacent fibroblasts, inducing a fibrotic, injury-like programme. Reprogrammed fibroblasts, in turn, expand and reprogramme alveolar macrophages, amplifying inflammatory signalling and reinforcing epithelial plasticity. These reciprocal interactions establish a self-sustaining epithelial–stromal–immune circuit that generates a tumour-permissive niche before malignant outgrowth. Disruption of the amphiregulin–EGFR axis prevents early niche formation and abrogates tumour initiation. Conservation of this programme in KRASG12D-inducible human alveolar organoids and early-stage lung adenocarcinoma tissues identifies epithelial–microenvironment communication as a therapeutically actionable vulnerability and suggests that intercepting niche formation may prevent progression to treatment-resistant disease.

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Main

Oncogenic mutations can disrupt stem cell homeostasis, driving uncontrolled expansion of mutant cells and initiating malignant transformation[2]. However, tumour development extends beyond genetic alterations, involving dynamic interactions between mutant cells and their non-cancerous neighbours that shape evolving tumour ecosystems through spatiotemporal remodelling of the microenvironment1,3,4. Recent single-cell profiling studies have revealed early pathologic changes in both mutant cells and the surrounding stroma. However, it remains unclear how mutant cells orchestrate niche remodelling during tumour initiation—a process establishing tumour-permissive microenvironments and defining a crucial window for intervention4,5. This knowledge gap is particularly critical for lung adenocarcinoma (LUAD), in which early targeting of initial ecosystem changes could improve patient survival, but most cases are diagnosed at advanced, treatment-resistant stages with limited therapeutic options and poor outcomes.

In the lung, alveolar type II (AT2) cells serve as resident stem cells responsible for maintaining homeostasis in gas-exchange regions and enabling epithelial repair after injury6,7,8,9. Dysregulated expansion and differentiation of AT2 cells following oncogenic activation contribute to LUAD pathogenesis, with AT2 cells identified as a major cell of origin5,7,8,10,11,12. Pdgfrα-expressing alveolar fibroblasts support AT2 cell function and regeneration, providing structural scaffolding and essential paracrine signals6,13,14,15. Injury repair induces distinct fibroblast states that mediate extracellular matrix (ECM) remodelling and reciprocal epithelial regulation, whereas immune cells, especially interstitial macrophages, modulate AT2 cell fate through inflammatory signals16,17,18,19,20,21. Despite these advances, the mechanisms by which early tumour–niche interactions establish preneoplastic microenvironments remain unknown. Resolving how initial signals from mutant cell reprogramme the niche could identify effective intervention strategies before treatment resistance emerges.

We previously identified a regenerative epithelial state that emerges during lung regeneration and is co-opted in early tumorigenesis[7]. Here we define niche remodelling that co-evolves with KrasG12D-mutant AT2 cells as they transition through this state. Using lineage tracing, single-cell profiling and human organoid models, we show that mutant AT2 cells activate an amphiregulin (Areg)–EGFR axis that reprogrammes fibroblasts and alveolar macrophages (AMs). Disrupting this circuit blocks mutant cell reprogramming and expansion. Inducible KRASG12D human LUAD organoids identify AREGhigh epithelial states sufficient to initiate fibrotic niche formation. EGFRL858R-mutant AT2 cells engage a conserved Areg–EGFR circuit, indicating a shared mechanism of niche construction across subtypes. Together, these findings define a spatiotemporal signalling axis driving tumour–niche co-evolution and uncover a targetable window to prevent treatment-resistant disease.

Fibrotic niches arise through regenerative programmes

To define early microenvironmental changes during lung tumorigenesis, we used the KrasG12D multicolour reporter (Red2Kras) crossed with Sftpc–CreERT2 mice, enabling stochastic labelling and tracking of mutant AT2 cells7,22 (Fig. 1a). As RFP+KrasG12D AT2 cells undergo clonal expansion through a regenerative-like state within 2 weeks of oncogenic activation, we performed single-cell transcriptomic profiling of mesenchymal and immune compartments at this nascent stage to delineate niche establishment preceding tumour formation[7].

Fig. 1: Niche reprogramming supports early tumorigenesis.

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a, Experimental design for labelling wild-type and mutant cells in Confetti and Red2Kras lungs. b, Uniform manifold approximation and projection (UMAP) showing the distribution of fibroblasts in Confetti and Red2Kras lungs. Cells are coloured by population. Dashed lines indicate reprogrammed fibroblasts enriched in Red2Kras lungs. c, Percentage of fibroblast subsets in b. d, Dot plot of key fibroblast marker genes annotated in b. e, Representative confocal images of reprogrammed fibroblast and lineage-labelled AT2 cells at 2 weeks post-oncogenic activation. Runx1, blue; Acta2, yellow; Pdgfrβ, grey and RFP,red. Images representative of n = 3 animals. f, UMAP showing all macrophage subsets. g, UMAP showing the distribution of Confetti and Red2Kras macrophages. h, Dot plot of key macrophage markers annotated in f. i, Flow cytometry of alveolar and monocyte-derived interstitial macrophages at 4 weeks post-oncogenic activation. j, Quantification of macrophage subsets from i. Data are presented as mean ± s.e.m. Each dot represents one mouse. Confetti (n = 3) and Red2Kras (n = 3) mice. P values were calculated using two-tailed unpaired t-test. k, Relative mean fluorescence intensity (MFI) of MHC-II expression in AMs (SiglecF+CD64+) gated in i. l,m, Representative confocal images of AMs and lineage-labelled AT2 cells at 4 weeks post-oncogenic activation. 4′,6-Diamidino-2-phenylindole (DAPI), blue; F4/80 (pan macrophage), green; PD-L1, grey and RFP, red (l). DAPI, blue; F4/80, grey; Msr1, green and RFP, red (m). Images representative of n = 3 animals. n, Experimental scheme for macrophage depletion by clodronate liposomes. o, Representative haematoxylin and eosin staining of control and clodronate-liposome-treated lungs. p, Quantification of average tumour size. Data are presented as mean ± s.e.m. Each dot represents an individual tumour mass. Control (n = 4) and clodronate-treated (n = 3) mice. P values were calculated using two-tailed unpaired t-test. Scale bars, 50 µm (e,m), 100 μm (l,o).

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We first analysed mesenchymal cells (CD31−CD45−EpCAM−) from Sftpc–CreERT2;Confetti (homeostasis) and Sftpc–CreERT2;Red2Kras (oncogenesis) lungs 2 weeks after induction (Fig. 1a and Extended Data Fig. 1a). Single-cell profiling of 9,210 cells identified major mesenchymal populations, including alveolar fibroblasts (Col13a1, Tcf21 and Scube2), adventitial fibroblasts (Col14a1, Pi16 and Dcn), smooth muscle cells (Acta2, Myh11 and Thsd4), peri-bronchial fibroblasts (Csmd1, Hhip and Fgf18), pericytes (Pdgfrβ, Cspg4 and Postn), mesothelium (Wt1, Msln and Aqp1) and proliferating cells (Mki67 and Birc5) (refs. 23,24) (Extended Data Fig. 1b–d). Notably, a distinct fibroblast cluster emerged exclusively in Red2Kras lungs, marked by Fst, Tnc, Runx1 and Runx2, here termed ‘reprogrammed fibroblasts’ (Extended Data Fig. 1b–d). Red2Kras lungs also showed enrichment of cycling and mesothelial-like cells (Celf4, Lgals7, Npl and Wt1os) (Extended Data Fig. 1b–d).

Subclustering alveolar, adventitial and reprogrammed fibroblasts confirmed the selective presence of reprogrammed fibroblasts in tumours (Fig. 1b,c). Trajectory analyses suggested transition from alveolar fibroblasts (Extended Data Fig. 1e). Differential gene expression analysis revealed upregulation of fibrotic and injury-associated genes, including Acta2, Pdgfrβ, Runx1 and Runx2, with Gene Ontology enrichment for ECM remodelling, wound repair and tissue development16,17,18,19,20,24 (Fig. 1d and Extended Data Fig. 1f,g). Immunofluorescence confirmed Pdgfrβ+Acta2+Runx1+ fibroblasts with reduced Pdgfrα adjacent to RFP+ mutant cells within tumours, indicating fibrotic transition[16], whereas such markers were rare in homeostatic lungs (Fig. 1e and Extended Data Fig. 1h). Integration with transcriptomic datasets from bleomycin-induced alveolar injury revealed a shared fibroblast population enriched in both injury and Red2Kras tumours, with parallel alveolar-to-reprogrammed transitions[24] (Extended Data Fig. 1i–o).

Collectively, these data demonstrate that at the preneoplastic stages, alveolar fibroblasts undergo regenerative-like fibrotic reprogramming, establishing a tumour-associated mesenchymal niche.

Immune landscape shifts at tumour onset

To profile immune cell dynamics, stromal preparations (1:1 mixture of EpCAM−CD45− and EpCAM−CD45+ fractions) were analysed, focusing on CD45+ (Ptprc) immune cells, which comprised most captured cells (Extended Data Fig. 2a). Sixteen immune cell clusters were identified on the basis of canonical markers, including monocyte-derived macrophages, AMs, neutrophils and diverse lymphocyte subtypes (Extended Data Fig. 2b–d)21,23,25.

Subclustering macrophages identified a distinct AM population in Red2Kras lungs transcriptionally divergent from homeostatic AMs (Fig. 1f,g). These ‘reprogrammed AMs’ retained canonical AM markers, such as SiglecF and MertK, but were distinguished by induction of genes, including Msr1, Cdh1 and Ch25h (Fig. 1h). They showed a hybrid inflammatory profile, co-expressing pro-inflammatory (IL-1a and IL-1b) and anti-inflammatory (Mrc1, Chil3 and Arg1) genes, with reduced MHC-II expression (H2-Ab1 and H2-Eb) and elevated chemokines (Cxcl2 and Cxcl16) implicated in neutrophil and γδ T cell recruitment26,27 (Fig. 1h and Extended Data Fig. 2e). Flow cytometry confirmed expansion of CD64+SiglecF+ AMs with diminished MHC-II expression, alongside increased CD64+SiglecF− interstitial and/or monocyte-derived macrophages (Fig. 1i–k). Immunofluorescence revealed marked accumulation of macrophages with high PD-L1 and Msr1 but low MHC-II expression specifically in intertumour regions (Fig. 1l,m and Extended Data Fig. 2f,g). Orthotopic engraftment of RFP+ mutant organoids into CCR2–CreERT2;ZsGreen mice showed that most tumour-associated macrophages were ZsGreen–, indicating a resident AM origin rather than monocyte recruitment (Extended Data Fig. 3a,b).

Concomitantly, immunosuppressive T cell subsets, including regulatory T and PD-1+ T cells, were expanded in Red2Kras lungs28,29,30 (Extended Data Fig. 2d,h,i). Despite no noticeable expansion, CD8+ T cells showed exhaustion features, upregulating Cd160, Btla and Havcr2 and shifting metabolism from oxidative phosphorylation towards glycolysis (Extended Data Fig. 2d,j–l)[31]. Neutrophils, including SiglecFhigh mature neutrophils, and γδ T cells were also enriched (Extended Data Fig. 2d,m–q)32,33,34. Spatial mapping revealed the accumulation of neutrophils and γδ T cells within tumours, whereas AMs were preferentially enriched in intertumour regions (Extended Data Fig. 2r,s).

Functional depletion of AMs by intratracheal clodronate liposomes markedly reduced tumour growth and impaired neutrophil and γδ T cell recruitment (Fig. 1n–p and Extended Data Fig. 3c–l). This was consistent with the upregulation of Cxcl2 and Cxcl16 in reprogrammed AMs, with their cognate receptors Cxcr2 and Cxcr6 predominantly expressed by neutrophils and γδ T cells, respectively, in Red2Kras lungs32,35 (Fig. 1i and Extended Data Figs. 2e and 3f).

Altogether, these findings demonstrate early alveolar immune remodelling, in which expansion and reprogramming of resident AMs coordinate inflammatory and immunosuppressive circuits that establish a tumour-supportive microenvironment at the preneoplastic stage.

Spatiotemporal tumour–niche circuits

To delineate how stromal populations shape the oncogenic niche, we mapped the temporal and spatial dynamics of fibroblasts and macrophages relative to RFP+ mutant AT2 clones over 1–8 weeks following oncogenic activation. By 1 week, Pdgfrβ+Runx1+ reprogrammed fibroblasts were detected in direct contact with nascent RFP+ tumours, and by 2 weeks nearly all expanding tumours were surrounded by fibrotic fibroblasts that persisted thereafter, indicating that fibrotic reprogramming initiates at tumour onset (Fig. 2a,c). By contrast, macrophage remodelling emerged later. Msr1+ reprogrammed macrophages remained comparable with homeostasis at 1 week but became prominent from 2 weeks to 4 weeks, suggesting fibroblast reprogramming precedes major macrophage expansion and phenotypic changes (Fig. 2b,d,e). At this later stage, Pdgfrβ+ fibroblasts were found both within RFP+ tumours and along their borders, where they closely associated with expanded macrophages in intertumour regions (Fig. 2f). To investigate whether reprogrammed fibroblasts directly modulate AM phenotype, wild-type AMs were co-cultured with mesenchymal cells isolated from wild-type or 4-week Red2Kras lungs. Red2Kras mesenchyme promoted AM expansion and decreased MHC-II expression compared with controls, indicating direct fibroblast-mediated regulation of AM phenotype (Extended Data Fig. 4a–c).

Fig. 2: Stromal crosstalk orchestrates spatiotemporal niche evolution during tumour initiation.

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a,b, Representative confocal images of lineage-labelled AT2 cells with reprogrammed fibroblasts (a) and macrophages (b) in Confetti and Red2Kras lungs. DAPI, blue; Pdgfrβ, green; Runx1, grey and RFP, red (a). DAPI, blue; Msr1, green; F4/80, grey and RFP, red (b). c, Percentage of RFP+ tumours containing fibrotic fibroblasts. Data are presented as mean ± s.e.m. Each dot represents one mouse. 1 week, n = 4; 2 weeks, n = 3; 4 weeks, n = 3; 8 weeks, n = 3. P values were calculated using two-tailed unpaired t-test. d,e, Quantification of F4/80+ macrophages (d) and Msr1+ macrophages (e) within RFP– stromal regions. Data are presented as mean ± s.e.m. Each dot represents one mouse. Confetti, n = 3; 1 week, n = 6; 2 weeks, n = 4; 4 weeks, n = 4; 8 weeks, n = 3. P values were calculated using two-tailed unpaired t-test. f, Representative confocal images showing close interactions between macrophages and reprogrammed fibrotic fibroblasts adjacent to lineage-labelled mutant AT2 cells at 4 weeks post-induction. Pdgfrβ, grey; Tnc, green; CD68, yellow and RFP, red. Images representative of n = 3 mice. g, Representative confocal images showing close interactions between inflammatory fibroblasts and macrophages adjacent to lineage-labelled mutant AT2 cells 4 weeks post-induction. Lcn2, yellow; F4/80, green; Pdgfrα, grey and RFP, red. h, Quantification of inflammatory fibroblasts. Peripheral and interior tumour regions were separately analysed at 4 weeks and 8 weeks. Data are presented as mean ± s.e.m. Each dot represents one mouse; n = 3 mice. P values were calculated using two-tailed unpaired t-test. i, Schematic illustrating mesenchymal–immune niche remodelling. At tumour onset, Pdgfrα+ fibroblasts adjacent to tumour cells reprogramme into fibrotic fibroblasts (Pdgfrβ+Runx1+Tnc+), which expand at tumour borders and remodel Msr1+ AMs, amplifying inflammatory cues and inducing Lcn2+ fibroblasts at the tumour periphery. I, interior; P, peripheral. Scale bars, 50 µm (a,b,f), 25 µm (g), 20 µm (a, magnified panels).

Source data

To identify mediators of this interaction, we examined the top differentially expressed gene between reprogrammed and homeostatic alveolar fibroblasts and found tenascin-C (Tnc), an immunomodulatory ECM protein, markedly enriched in reprogrammed fibroblasts36,37,38 (Extended Data Fig. 4d). Notably, its receptor, Toll-like receptor 4 (TLR4), was selectively and highly expressed on AMs, suggesting a Tnc–TLR4 axis in fibroblast-driven AM remodelling (Extended Data Fig. 4d). Immunofluorescence confirmed high Tnc expression in fibrotic Pdgfrβ+ fibroblasts adjacent to RFP+ mutant cells, especially at tumour borders, where Tnc+Pdgfrβ+ fibroblasts frequently engaged CD68+ macrophages at 4 weeks, coincident with macrophage remodelling (Fig. 2f). In vitro Tnc treatment of wild-type AMs induced proliferation and phenotypic remodelling, with increased Msr1 and Ki67 expression, effects abrogated by the TLR4 inhibitor TAK-242 (Extended Data Fig. 4e–h). Likewise, co-culture of wild-type AMs with Red2Kras mesenchymal cells, but not wild-type mesenchyme, induced AM reprogramming that was abrogated by TLR4 inhibition (Extended Data Fig. 4i–k), identifying Tnc as fibrotic-fibroblast-derived cues driving AM expansion and remodelling through TLR4.

Given that inflammatory fibroblasts arise from alveolar fibroblasts before fibrotic changes during injury repair16,17,19, we asked whether a similar population emerges in early oncogenesis. Single-cell profiling revealed a subset of Red2Kras reprogrammed fibroblasts enriched for inflammatory markers, including Lcn2, Saa3, Sfrp1 and Cxcl12 (Extended Data Fig. 4l,m). Immunofluorescence identified Lcn2+Pdgfrα+ inflammatory fibroblasts emerging from 4 weeks post-induction, localizing predominantly with expanded macrophages at tumour peripheries and absent from inner RFP+ tumour areas (Fig. 2g,h). Unlike injury repair, these cells were rare at 1 week and lacked fibrotic markers, such as Tnc, indicating that inflammatory and fibrotic fibroblasts represent distinct mesenchymal populations arising during early tumorigenesis (Fig. 2h and Extended Data Fig. 4n,o). Finally, co-culture with AMs from Red2Kras lungs robustly induced Lcn2 expression in wild-type Pdgfrα+ fibroblasts, an effect recapitulated by interleukin-1β (IL-1β) treatment (Extended Data Fig. 4p,q).

Altogether, these findings demonstrate sequential tumour–niche remodelling initiated by early fibrotic fibroblast reprogramming at tumour onset. Fibrotic fibroblasts drive AM expansion and phenotypic rewiring through a Tnc–TLR4 axis, whereas expanded AMs amplify local inflammatory signalling to induce inflammatory fibroblasts and immune cell recruitment, reinforcing early multicellular niche circuits (Fig. 2i).

Mutant epithelial hubs drive niche remodelling

We next asked how oncogenic AT2 cells initiate dynamic tumour–niche formation. Building on our recent findings that KrasG12D activation drives AT2 cell reprogramming into a damage-associated transient progenitor (DATP)-like regenerative state, we integrated single-cell transcriptomic datasets from Red2Kras fibroblasts and lineage-labelled RFP+ mutant cells to identify signals orchestrating fibroblast reprogramming[7] (Extended Data Fig. 5a,b). CellChat revealed enhanced interactions between DATP-like cells and alveolar fibroblasts compared with AT2 cells, with the EGF–EGFR axis emerging as a top candidate (Extended Data Fig. 5c–g). Among enriched ligands, Areg displayed the highest interaction probability and was specifically upregulated in DATP-like states (Extended Data Fig. 5e–h). Immunofluorescence confirmed robust Areg induction in DATP-like cells from day 4 post-induction, persisting during tumour expansion and absent in homeostasis, establishing Areg upregulation as a defining feature of Kras-driven AT2 reprogramming (Fig. 3a,b and Extended Data Fig. 6a,b).

Fig. 3: Nascent tumour cells drive niche remodelling to sustain mutant cell states.

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a,b, Representative confocal images showing Areg expression in DATP-like cells in Red2Kras lungs. DAPI, blue; Areg, grey; Itgα2, green and RFP, red (a). DAPI, blue; Areg, grey; pro-SPC, green and RFP, red (b). n = 2 mice. c, Schematic of 3D organoid co-cultures of lineage-labelled AT2 cells with wild-type AMs and mesenchyme, treated with dimethyl sulfoxide (DMSO) or gefitinib. d,e, Flow cytometry (d) and quantification (e) of MHC-II expression in AMs from c. Data are presented as mean ± s.e.m. Each dot represents an independent experiment. Wild-type DMSO, n = 2; wild-type gefitinib, n = 2; Red2Kras DMSO, n = 3; Red2Kras gefitinib, n = 2. f,g, Flow cytometry (f) and quantification (g) of Msr1 expression in AMs co-cultured with RFP+ mutant cells and wild-type mesenchyme, treated with DMSO or gefitinib. Data are presented as mean ± s.e.m. Each dot represents an independent experiment. DMSO, n = 4; gefitinib, n = 4. P values were calculated using two-tailed unpaired t-test. h, Experimental design for KrasG12D inhibitor (MRTX1133) administration. i–k, Representative confocal images showing lineage-labelled mutant cells. DAPI, blue; Sox9, yellow and RFP, red (i). DAPI, blue; Cd177, yellow and RFP, red (j). DAPI, blue; Ager, yellow; LpCAT1, grey and RFP, red (k). l, Quantification of cell states within RFP+ mutant cells from i–k. Data are presented as mean ± s.e.m. Each dot represents one mouse. Untreated, n = 3; MRTX1133, n = 3. P values were calculated using two-tailed unpaired t-test. m,n, Representative confocal images showing lineage-labelled cells with reprogrammed fibroblasts (m) and macrophages (n). DAPI, blue; Pdgfrβ, grey; Tnc, green and RFP, red (m). DAPI, blue; F4/80, grey; Msr1, green and RFP, red (n). Scale bar, 50 µm. o,p, Quantification of macrophages (o) and Msr1+ macrophages (p) from n. Data are presented as mean ± s.e.m. Each dot represents one mouse. Untreated, n = 3; MRTX1133, n = 3. P values were calculated using two-tailed unpaired t-test. Scale bars, 50 µm.

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To directly test whether oncogenic AT2 cells remodel fibroblasts through EGFR activation, we first treated lung mesenchymal cells with Areg. Areg induced morphological changes and fibrotic programmes, marked by elevated Pdgfrβ and Acta2 expression (Extended Data Fig. 6c–e). By contrast, Areg stimulation did not alter the phenotype in AMs or RFP+ mutant cells, indicating selective fibroblast responsiveness (Extended Data Fig. 6f–n). To further interrogate mutant AT2–fibroblast crosstalk, we established organoid co-cultures of lineage-labelled Pdgfrα+ fibroblasts from Pdgfrα–CreERT2;ZsGreen lungs with either tdTomato+ wild-type or RFP+ mutant AT2 cells, from tdTomato or Red2Kras lungs, and treated with the EGFR inhibitor gefitinib (Extended Data Fig. 6o). Fibroblasts co-cultured with mutant AT2 cells acquired a fibrotic phenotype, marked by Pdgfrβ upregulation and organoid wrapping that recapitulated the in vivo architecture. Both effects were decreased by EGFR inhibition (Extended Data Fig. 6p). CellChat highlighted ECM-related pathways as potential mediators of fibroblast–tumour interactions (Extended Data Fig. 5i–j). These data demonstrate that DATP-like RFP+ mutant states reprogramme the surrounding fibroblasts towards a fibrotic phenotype through EGFR activation to initiate niche formation.

To dissect downstream immune effects, we established organoid tri-cultures combining freshly isolated wild-type mesenchyme, AMs and either wild-type or mutant AT2 cells from tdTomato or Red2Kras lungs (Fig. 3c). AMs co-cultured with fibroblasts and mutant AT2 cells showed reduced MHC-II expression and increased Msr1 expression relative to AMs with wild-type AT2 cells, and both changes were prevented by gefitinib treatment (Fig. 3d–g). To determine whether mutant cells act directly on AMs or primarily through fibroblasts, we compared AMs co-cultured with RFP+ mutant cells alone versus mutant cells plus wild-type mesenchyme (Extended Data Fig. 7a,b). Msr1 induction occurred only when fibroblasts were present, demonstrating fibroblast dependence (Extended Data Fig. 7c,d). AM expansion, however, was triggered by tumour cells even without fibroblasts (Extended Data Fig. 7e). These data indicate that fibrotic fibroblasts reprogramme AM phenotype downstream of EGFR-mediated signals from mutant cells, whereas mutant cells can independently support AM expansion. Exogeneous Areg or Areg/Ereg stimulation in tri-cultures of wild-type mesenchyme, AMs and AT2 cells was sufficient to induce AM expansion and reprogramming, reinforcing the role of DATP-derived EGF signals in launching niche remodelling cascades (Extended Data Fig. 7f–n).

We next asked whether blocking mutant AT2–fibroblast interactions would alter mutant epithelial states. Gefitinib treatment of RFP+ mutant organoids co-cultured with fibroblasts reduced Sox9+ DATP-like populations and increased Lpcat1+ AT2 cells, demonstrating that EGFR-driven fibroblast inputs sustain epithelial reprogramming (Extended Data Fig. 7o–s). Notably, organoid size seemed largely unchanged, suggesting that EGFR inhibition uncouples epithelial identity from proliferation and highlights the primarily instructive role of reprogrammed fibroblasts. By contrast, gefitinib treatment had no direct effect on RFP+ mutant organoids cultured without fibroblasts, confirming that fibroblast-derived signals are required to maintain DATP-like states (Extended Data Fig. 7t–v). Consistently, reprogrammed fibroblasts revealed upregulated AT2 regulatory factors, including Wnt5a, Igf1 and Spp1 (Extended Data Fig. 5i,j).

To investigate whether fibrotic niche maintenance depends on mutant DATP-like cells, we treated Red2Kras mice with the KrasG12D-specific inhibitor MRTX1133 for 10 days from 4 weeks post-induction39,40 (Fig. 3h). MRTX1133 caused a pronounced reduction in Sox9+ DATP-like and CD177+ reprogrammed populations while increasing AT1 cells and restoring an AT2:AT1 ratio of approximately 2:1, similar to homeostasis[41] (Fig. 3i–l). Targeting DATP-like states also reversed niche remodelling, with a marked loss of Pdgfrβ+Tnc+ fibrotic fibroblasts and decreased AM expansion and reprogramming (Fig. 3m–p). These findings demonstrate that tumour fibroblasts maintain a reversible, injury-like fibrotic phenotype dependent on continuous signals from reprogrammed mutant cells[16].

Collectively, our results demonstrate that DATP-like states in nascent KrasG12D-mutant AT2 cells act as central signalling hubs that coordinate fibrotic and immune niche remodelling through Areg-driven EGFR activation of fibroblasts. Disrupting this signalling circuit reverses early niche reprogramming, underscoring a therapeutic window for intercepting tumour-permissive niche formation as its onset.

Areg–EGFR drives tumour–niche assembly

To determine the requirement for fibrotic fibroblasts during early niche formation in vivo, we orthotopically engrafted RFP+ tumour organoids into Pdgfrα–CreERT2;ZsGreen;DTR mice, enabling lineage tracing and selective depletion of resident Pdgfrα+ fibroblasts following intratracheal diphtheria toxin administration (Extended Data Fig. 8a). One week after engraftment, diphtheria toxin was administered every other day for 14 days, and tissues were analysed 19 days post-injection. In controls, lineage-labelled fibroblasts localized to RFP+ tumours and upregulated Pdgfrβ, confirming tumour-induced fibrotic reprogramming of resident Pdgfrα+ fibroblasts (Extended Data Fig. 8b). Diphtheria-toxin-mediated depletion markedly reduced expansion of lineage-labelled Pdgfrβ+ fibroblasts, resulting in impeded tumour growth, diminished Sox9+ reprogrammed mutant cells and reduced macrophage expansion (Extended Data Fig. 8b–d). These data demonstrate that fibrotic fibroblasts that originated from resident alveolar fibroblasts are critical for early tumour development and immune niche establishment.

We next evaluated whether interrupting the Areg–EGFR axis disrupts tumour-induced niche remodelling in vivo. Pharmacologic EGFR inhibition with gefitinib in Red2Kras mice reduced tumour burden, with reduced Tnc+ fibrotic fibroblasts, decreased macrophage activation and impaired neutrophil recruitment (Extended Data Fig. 8e–n). Gefitinib also decreased Sox9+ DATP-like and CD177+ mutant states while increasing Lpcat1+ AT2 cells, indicating that EGFR-dependent fibroblast signalling is required to sustain both mutant epithelial reprogramming and immune remodelling (Extended Data Fig. 8o–r). To pinpoint the contribution of mutant-cell-derived Areg in vivo, we genetically deleted Areg in AT2 cells (Aregflox/flox;Sftpc–CreERT2;Red2Kras) (Fig. 4a). At 2 weeks post-induction, Areg-depleted lungs revealed reduced tumour formation compared with haplodeficient controls (Fig. 4b,c). Single-cell profiling on niche compartments (1:1 mixture of CD31–CD45–EpCAM– mesenchymal and CD31–CD45+EpCAM– immune cells) and lineage-labelled RFP+ mutant cells from Areg-deficient and haplodeficient lungs identified 8,206 mesenchymal cells comprising established stromal populations (Extended Data Fig. 9a–e). Subclustering fibroblasts revealed a reprogrammed subset expressing Runx1, Acta2, Tnc and Pdgfrβ (Fig. 4d and Extended Data Fig. 9f). Consistent with gefitinib treatment, Areg-deficient lungs showed reduced reprogrammed fibroblasts, confirmed by decreased Tnc+Pdgfrβ+ fibrotic fibroblasts, establishing mutant-derived Areg as a critical initiating signal for fibrotic niche remodelling (Fig. 4e–g).

Fig. 4: Areg–EGFR axis establishes precancer niches essential for tumour development.

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Full size image

a, Experimental design for AT2-cell-specific genetic deletion of Areg in Red2Kras lungs. b, Representative whole-lobe tile scans of Aregflox/+ and Aregflox/flox lungs. DAPI, blue and RFP, red. c, Percentage of RFP+ cell area relative to lobe area. Data are presented as mean ± s.e.m. Each dot represents one mouse. Aregflox/+, n = 15; Aregflox/flox, n = 15. Mice with no detectable RFP+ expansion were assigned a value of 0. P values were calculated using two-tailed Mann–Whitney test. d, UMAP of fibroblast subclusters. e, UMAP showing the distribution of Aregflox/+ or Aregflox/flox fibroblasts. f, Percentage of cells distributed across each cluster annotated in d. g,h, Representative confocal images of reprogrammed fibroblasts (g) and macrophages (h) with lineage-labelled cells in Aregflox/+ and Aregflox/flox lungs. DAPI, blue; Pdgfrβ, grey; Tnc, green and RFP, red (g). DAPI, blue; F4/80, grey; Msr1, green and RFP, red (h). Images representative of n = 3 mice. i, Quantification of F4/80+ macrophages within the RFP– stromal cells assessed in h. Data are presented as mean ± s.e.m. Each dot represents one mouse. Aregflox/+, n = 5; Aregflox/flox, n = 6. P values were calculated using two-tailed unpaired t-test. j, Percentage of Msr1+ macrophages assessed in h. Data are presented as mean ± s.e.m. Each dot represents one mouse. Aregflox/+, n = 5; Aregflox/flox, n = 6. P values were calculated using two-tailed unpaired t-test. k, UMAP of lineage-labelled RFP+ cells from Aregflox/+ and Aregflox/flox lungs. l, Dot plot of epithelial state marker genes in Aregflox/+ and Aregflox/flox lungs. m, Percentage of RFP+ cells distributed across each cluster defined in k. n, Schematic illustrating the sequential events establishing precancer niches. Scale bars, 1,000 µm (b), 50 µm (g,h).

Source data

Analysis of 7,731 immune cells identified 14 immune clusters (Extended Data Fig. 10a,c). In Aregflox/+ lungs, AMs and neutrophils predominated, whereas Areg deletion reduced immune remodelling (Extended Data Fig. 10b). AMs from Aregflox/+ lungs showed elevated pro-inflammatory signatures (Cxcl2, Ccl6 and Ccl9) and reduced MHC-II complex genes compared with Aregflox/flox lungs (Extended Data Fig. 10d,e). Immunofluorescence confirmed reduced Msr1+ macrophages in Areg-deleted lungs (Fig. 4h–j). Reclustering neutrophils revealed Areg-dependent transcriptional reshaping (Extended Data Fig. 10f,g). Neutrophils from Aregflox/+ lungs upregulated SiglecF, Tlr4, Clec5a and Cd177, implicated in neutrophil activation and LUAD progression42,43,44. These data confirm that blocking EGFR-mediated fibrotic reprogramming prevents immune niche remodelling required for tumour formation in vivo.

Finally, profiling of 12,219 RFP+ mutant cells recovered previously defined clusters[7] (Fig. 4k,l). Areg deletion in KrasG12D-mutant AT2 cells significantly reduced DATP-like and CD177+ reprogrammed populations while increasing Sftpc+ AT2 cells compared with controls (Fig. 4m).

Collectively, our findings elucidate a hierarchical signalling cascade, in which oncogenic AT2 reprogramming creates Areg-secreting DATP-like states that activate EGFR on adjacent fibroblasts, initiating fibrotic niche assembly. These remodelled fibroblasts feedback to sustain mutant epithelial plasticity and drive immune remodelling. Disrupting this circuit halts stromal and immune reprogramming, establishing Areg–EGFR signalling as a central regulator of early tumour-permissive niche formation and plasticity (Fig. 4n).

Conserved niche circuits in human LUAD

Single-cell studies have uncovered early epithelial reprogramming in tissues of patients with LUAD[5]; however, direct evidence linking tumour–niche interactions to early human LUAD progression remains limited. To address this, we analysed published single-cell transcriptomic data from early-stage LUAD and matched normal lung tissues[45] (six pairs total; five matched pairs for analysis; Extended Data Fig. 11a). Given the mutual exclusivity of KRAS and EGFR mutations in LUAD46,47, we focused on EGFR wild-type cases to enrich for KRAS-driven events (Extended Data Fig. 11a). Subclustering EpCAM+ epithelial cells identified AT2, AT1 and five tumour-specific populations (Extended Data Fig. 11b–e). Two clusters (1 and 2) showed high expression of DATP markers (CLDN4, KRT8 and KRT19), consistent with regenerative transitional states in early tumorigenesis5,21 (Extended Data Fig. 11f). Although cluster 2 was enriched in a single patient, cluster 1 was consistently detected across samples, including in stage I LUAD tissues, suggesting a conserved DATP-like state across early LUAD (Extended Data Fig. 11g,h). This conserved cluster exhibited high AREG and EREG expression, indicating upregulation of EGFR ligands during early tumour evolution (Extended Data Fig. 11i).

To investigate stromal remodelling, we reclustered COL1A1+ mesenchymal cells and subclustered fibroblasts expressing PDGFRα, COL14A1 and COL13A1 (Extended Data Fig. 11k). A LUAD-enriched fibroblast subset (fibroblasts_6) expresses fibrotic markers (RUNX1, PDGFRβ, ACTA2 and CTHRC1), along with elevated ECM components, mirroring fibrotic reprogramming observed in Red2Kras lungs (Extended Data Fig. 11l–p). Immunofluorescence in early-stage KRASG12D LUAD specimens confirmed KRT8+SOX9+ DATP-like cells with high AREG expression (Fig. 5a and Extended Data Fig. 11j). Fibrotic fibroblasts expressing ACTA2, RUNX1 and CTHRC1 were located within tumours and frequently abutted KRT8+ tumour cells, indicating spatially coordinated epithelial–fibroblast interactions in human LUAD (Fig. 5b and Extended Data Fig. 11q).

Fig. 5: Oncogenic activation of human AT2 cells drives fibrotic niches.

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Full size image

a,b, Representative confocal images showing fibrotic fibroblasts and DATP-like AREG-expressing cells in matched background and early-stage KRASG12D tissues from patients with LUAD. DAPI, blue; KRT8, green; AREG, red (a). DAPI, blue; ACTA2, red and KRT8, green (b). c, Schematics of inducible viral constructs and experimental design for establishing inducible KRASG12D human alveolar organoid. d, Representative fluorescent images of alveolar organoids transduced with lentivirus vectors expressing empty or KRASG12D. e, Quantitative polymerase chain reaction analysis showing KRAS expression upon doxycycline treatment in KRASG12D and empty-vector-transduced cells. Data are presented as mean ± s.e.m. Each dot represents an independent experiment. Empty, n = 2; KRASG12D, n = 2. f, UMAP of cells derived from control or KRASG12D organoids. Cells are coloured by dataset of origin. g, UMAP showing epithelial cell state diversification following KRASG12D induction. h, Dot pot of key marker genes defining epithelial populations in g. i, Representative confocal images showing PDGFRβ+ fibroblasts around KRASG12D-expressing organoids and suppression of fibrotic remodelling upon EGFR inhibition. RFP, tumour organoid; PDGFRβ, green; PDGFRα, grey and DAPI, blue. Images representative of n = 3 experiments. Scale bars, 50 µm (a), 2,000 µm (d), 100 μm (b,i).

Source data

To functionally model sequential transitions in tumour and niche interactions, we developed an ex vivo three-dimensional (3D) inducible human LUAD system by introducing doxycycline-inducible KRASG12D and an RFP reporter in primary human AT2 (hAT2) cells (Fig. 5c). EpCAM+HTII-280+ hAT2 cells from non-tumour lung parenchyma were expanded as organoids, transduced at day 14 and replated as purified RFP+ KRASG12D–hAT2 organoids (Fig. 5d,e)[48]. Single-cell profiling showed that control organoids consisted largely of SFTPC+ AT2 cells, whereas KRAS activation shifted cells into transitional states with reduced SFTPC expression, paralleling Red2Kras findings (Fig. 5f–h). KRASG12D–hAT2 organoids also retained SFTPC+SCGB3A2+ and SFTPC–SCGB3A2+SCGB1a1– subsets, corresponding to AT0 and terminal and respiratory bronchiole secretory cells, previously shown to originate from AT2 cells (Fig. 5f–h)49,50. Notably, we identified an AREGhigh populations co-expressing SOX9, KRT8 and ITGA2, indicative of DATP-like states (Fig. 5f–h). Co-culture of KRASG12D–hAT2 organoids with primary human lung mesenchymal cells (CD31–CD45–EpCAM–) isolated from non-tumour lung parenchyma induced fibrotic phenotypes marked by PDGFRβ, which were fully abrogated by gefitinib treatment (Fig. 5i). These findings demonstrate that KRASG12D-driven AT2 reprogramming into DATP-like states is conserved across mouse and human lungs and is sufficient to activate fibroblasts through the AREG–EGFR axis in early tumorigenesis.

To test whether this mechanism extends beyond KRAS, we developed a 3D inducible EGFR-mutant LUAD model by expressing EGFRL858R with an EGFP reporter in primary mouse AT2 cells (Extended Data Fig. 12a). Lineage-labelled AT2 cells were cultured as organoids, transduced at day 14 and orthotopically engrafted into NSG mouse lungs (Extended Data Fig. 12a,b). After 3 weeks, tdTomato+EGFP+ mutant cells formed expanding lesions enriched for Krt8 and Areg, hallmarks of DATP-like states observed in KrasG12D models (Extended Data Fig. 12c,d). EGFRL858R-mutant cells induced Pdgfrβ+ fibrotic fibroblasts and macrophage expansion in adjacent niches (Extended Data Fig. 12e).

These results demonstrate that oncogenic KRAS and EGFR mutations reprogramme AT2 cells into Areg+ DATP-like states that activate EGFR in surrounding fibroblasts, driving fibrotic and immune niche assembly. AREG–EGFR signalling thus represents a conserved central mechanism of early tumour–microenvironment co-evolution across LUAD subtypes.

Discussion

Tissue homeostasis relies on tightly regulated stem cell–stromal interactions[51]. Tumour initiation disrupts these networks, triggering spatial and temporal microenvironmental remodelling. Although cancer hijacks regenerative programmes, the earliest steps converting normal tissue into a tumour-permissive niche remain unresolved. Here we define these transitions at lung tumour onset. KrasG12D-mutant AT2 cells rapidly adopt a regenerative, Areghigh DATP-like state that functions as a central signalling hub. Through Areg–EGFR activation, these cells induce fibrotic fibroblasts that remodel the ECM and reprogramme AMs through the Tnc–TLR4 axis, establishing a self-reinforcing fibrotic–immune niche that sustains epithelial plasticity and accelerates tumour expansion. EGFRL858R-mutant AT2 cells engage a convergent epithelial–fibrosis circuit, identifying regenerative epithelial states as a conserved early driver of niche construction across LUAD subtypes.

We demonstrate that oncogenic signals co-opt regenerative pathways to generate mutant epithelial states and spatially distinct fibroblast populations. At tumour onset, DATP-like cells induce fibrotic fibroblasts that activate injury-like ECM remodelling, establishing the matrix required for tumour initiation[52]. The Areg–EGFR axis emerges as an early determinant of this epithelial–mesenchymal crosstalk. Notably, this circuit is reversible. Kras inhibition reduces DATP-like and CD177+ mutant states, restores the AT2–AT1 balance and reverses niche remodelling, whereas genetic or pharmacologic blockage of Areg–EGFR signalling prevents fibroblast reprogramming, extinguishes reprogrammed mutant states and limits immune remodelling. These findings reveal reciprocal dependency between DATP-like cells and their niches, identifying Areg–EGFR signalling as a therapeutically actionable vulnerability at the preneoplastic stage, consistent with previous clonal analyses showing that mutant AT2 expansion requires sustained niche support[7].

Unlike injury repair in which inflammatory fibroblasts precede fibrotic differentiation, Lcn2+ inflammatory fibroblasts emerge later in tumour development, coinciding with macrophage remodelling16,17,19. Probably induced by IL-1β from reprogrammed AMs, they localize to tumour peripheries and lack fibrotic markers such as Tnc, establishing spatial hierarchy in which fibrotic cues dominate tumour cores, whereas inflammatory programmes persist at the periphery. This compartmentalization mirrors fibroblast hierarchies in repair but diverges in timing, as inflammatory fibroblasts are rare at tumour onset[17]. Early fibrotic fibroblasts directly modulate mutant epithelial states, whereas inflammatory fibroblasts, together with AMs, shape the immune milieu later by upregulating chemoattractants such as Cxcl12, a feature absent in reprogrammed AMs, indicating distinct recruitment mechanisms[17]. Notably, advanced LUAD harbours heterogeneous cancer-associated fibroblasts, including p16+ ApoE-secreting fibroblasts53,54, which are not detected in early lesions, indicating temporally distinct fibroblast programmes, with early Areg-dependent fibrotic states remaining plastic and reversible (Extended Data Fig. 1f). Our findings suggest that later cancer-associated fibroblast complexity arises from initial epithelial–stromal interactions.

Immune remodelling occurs downstream of fibroblast activation. Resident AMs undergo phenotypic rewiring, acquiring hybrid inflammatory profiles and reduced MHC-II expression resembling tumour-associated macrophages in solid tumours[55]. These AMs derive primarily from resident AMs and are remodelled by fibrotic fibroblasts through the Tnc–TLR4 axis, establishing a sequential stromal–immune signalling cascade. AM depletion impairs tumour growth and prevents neutrophil and γδ T cell recruitment, demonstrating that AM remodelling is essential for assembling immunosuppressive niches. Spatial segregation further defines immune roles. AMs accumulate peri-tumourally, whereas neutrophils and γδ T cells infiltrate tumour cores33,34. Although reprogrammed AMs facilitate tumour development through niche remodelling, neutrophils expressing high IL-1β probably provide dominant inflammatory cues sustaining tumour reprogramming alongside fibrotic fibroblasts, consistent with restricted tumour development in IL1R1-deficient KrasG12D AT2 cells[7]. Collectively, immune remodelling represents a critical downstream consequence of fibroblast activation in preneoplastic niche assembly.

Our findings revealed both parallels and distinctions between injury repair and oncogenesis. In both contexts, fibrotic fibroblasts support AT2 cell expansion and reprogramming. However, during regeneration, they resolve as DATP states differentiate, whereas in oncogenesis sustained NF-κB activation maintains DATP-like mutant cells, creating a self-reinforcing loop7,16. This pathological circuit echoes persistent fibrosis in LUAD and idiopathic pulmonary fibrosis. Early-stage human LUAD confirms conservation of this epithelial–fibrotic interactions, and similar EGFR-dependent epithelial–fibroblast circuits have been implicated in idiopathic pulmonary fibrosis[56]. The Areg–EGFR signalling thus represents the apex of a hierarchical cascade orchestrating tumour–niche formation across fibrotic lung pathologies.

To overcome limitations in modelling early tumour–niche interactions in humans, we established an inducible LUAD organoid platform enabling temporal KRAS activation and sequential mutant reprogramming in primary hAT2 cells. This system captures the transition from normal to pre-malignant states, recapitulating emergence of AREGhigh DATP-like cells and de-differentiation into AT0 or terminal and respiratory bronchiole secretory cell populations, indicating redeployment of regeneration-associated transitional states during early LUAD49,50. KRASG12D-expressing hAT2 cells induce fibrotic reprogramming of human lung mesenchyme in an EGFR-dependent manner, recapitulating early-stage KRASG12D LUAD architecture. This platform enables dissection of epithelial–stromal signalling and testing of preventive interventions. Notably, clinical responses to EGFR tyrosine kinase inhibitors in EGFR wild-type non-small cell lung cancer, particularly in AREGhigh tumours, suggest that such EGFR-dependent states may already be exploited therapeutically[57].

In summary, we map the dynamic interplay between mutant epithelial cells and their microenvironment during early lung tumorigenesis. Oncogenic AT2 reprogramming activates an Areg-driven EGFR axis that initiates sequential fibrotic and immune niche assembly. The reversibility of these preneoplastic circuits defines a therapeutic window before progression to treatment-resistant disease. Our inducible human LUAD platform provides a framework to interrogate patient-specific tumour–niche interactions and target conserved EGFR-dependent mechanisms of tumour ecosystem formation.

Methods

Mice

Sftpc–CreERT2 (028054), R26R–Confetti (013731), Pdgfrα–CreERT2 (032770), R26R–iDTR (007900), NOD/Scid Il2rg null Tg (NSG: 005557) and Ai6/RCL–ZsGreen (007906) animals were obtained from The Jackson Laboratory. AregloxP/loxP animals were kindly provided by M. Clatworthy from the University of Cambridge. Red2Kras mice were generated in-house as previously described[22]. CCR2–CreERT2 mice were kindly provided by B. Becher (University of Zurich)[58]. All transgenic mouse strains were maintained on a C57BL or C57BL/6Brd-Tyr 597 c-Brd mixed background. Mouse studies in the UK were approved under UK Home Office Project Licences PC7F8AE82 and PP3176550, and experiments in the US and Korea were approved by the Memorial Sloan Kettering Cancer Center (MSKCC) Institutional Animal Care and Use Committee (protocol no. 24-04-003) and Gwangju Institute of Science and Technology (GIST) Institutional Animal Care and Use Committee (protocol no. GIST-2022-043). All procedures complied with institutional and national guidelines. The mice were housed under specific pathogen-free conditions at the Gurdon Institute (University of Cambridge), MSKCC and GIST on a 12-h light/dark cycle with food and water provided ad libitum.

Both male and female mice aged 6–15 weeks were used. Experiments were randomized where feasible. Blinding was not performed, as treatment effects on tumour volume were readily distinguishable between groups. Humane end points were defined as a single tumour exceeding 2 cm in diameter, a tumour burden exceeding 10% of body mass or multiple tumours with a cumulative volume greater than 3,000 mm^3. As this study focused on early tumour development, these limits were not approached or exceeded in any experiment.

Mouse procedures

Tamoxifen administration

Tamoxifen (Sigma; T5648) was dissolved in corn oil (Sigma; C8267) at 20 mg ml−1. Aliquots were heated to 50 °C and vortexed before administration. Animals were weighed and received tamoxifen by oral gavage. They received either a single dose (0.1 mg g−1 body weight) for clonal analysis or two to four doses (0.2 mg g−1 body weight) administered every other day. Tissue collection time points are specified in the relevant figures and detailed in Methods.

MRTX1133 administration

Red2Kras animals received two doses of tamoxifen through oral gavage (0.2 mg g−1 body weight) every other day to induce KrasG12D expression. At 4 weeks post-induction, the mice received freshly prepared MRTX1133 (MedChemExpress; HY-134813) through intraperitoneal injection at 15 mg kg−1 twice daily for 10 days. The stock solution was prepared in DMSO and diluted in 40% polyethylene glycol 300 (PEG300; MedChemExpress; HY-Y0873), 5% Tween-80 (MedChemExpress; HY-Y1891) and 45% phosphate-buffered saline (PBS) for injection, as recommended by the manufacturer.

Gefitinib administration

Red2Kras animals received two doses of tamoxifen through oral gavage (0.2 mg g−1 body weight) every other day to induce KrasG12D expression. Four days after the final tamoxifen dose, the mice received freshly prepared gefitinib (80 mg kg−1 in 50 μl of DMSO) or DMSO (vehicle control) through intraperitoneal injection every 4 days for 20 days.

Clodronate administration

Red2Kras animals received four doses of tamoxifen through oral gavage (0.2 mg g−1 body weight) every other day to induce KrasG12D expression. For selective depletion of macrophages in the lungs, five doses of PBS-loaded or clodronate-loaded liposomes (5 mg ml−1; LIPOSOMA) were administered through intratracheal injection (25 µl) at the time points depicted in the experiment scheme, beginning 2 days after the final tamoxifen dose. Lungs were collected and analysed 7 days after the final clodronate liposome administration.

Diphtheria toxin administration

To deplete fibroblasts, 8–10-week-old Pdgfrα–CreERT2;ZsGreen;iDTR mice were used. The animals received four doses of tamoxifen through oral gavage (0.2 mg g−1 body weight) every other day to mark Pdgfrα+ cells. Following organoid engraftment (see section “Orthotopic engraftment of KrasG12D organoids”), diphtheria toxin (Sigma) was dissolved in PBS and administered intratracheally at 50 ng per mouse every other day for seven doses, starting 21 days after the final tamoxifen dose. Lungs were collected 7 days after the final diphtheria toxin injection for analysis.

Orthotopic engraftment of Kras G12D organoids

RFP+ KrasG12D-mutant organoids co-cultured with mesenchymal cells were orthotopically engrafted into CCR2–CreERT2;ZsGreen or Pdgfrα–CreERT2;ZsGreen;iDTR mice. A total of 50,000 RFP+ mutant epithelial cells from one to two passages of organoids were isolated by fluorescence-activated cell sorting (FACS) and mixed with 20,000 freshly isolated lung mesenchymal cells (CD31–CD45–EpCAM–) from wild-type lungs to enhance epithelial cell recovery during engraftment. The epithelial–stromal cell mixture was resuspended in 20 µl of PBS and transplanted intratracheally into recipient mice 1 day after a single dose of bleomycin treatment (1.0 U kg−1). Lungs were collected and analysed 21 days post-engraftment to assess differentiation of the engrafted cells.

Generation of EGFR L858R -transduced organoids for orthotopic engraftment

AT2 organoids expressing tdTomato (passages 1 and 2) derived from Sftpc–CreERT2;tdTomato lungs were dissociated into single cells and transduced with lentivirus encoding pHAGE–EGFRL858R–EGFP (Addgene plasmid no. 116276) by spin infection (2,000 rpm; 32 °C; 60 min) in the presence of polybrene (8 μg ml−1; Sigma). Transduced cells were subsequently co-cultured with mesenchymal cells. FACS was used to isolate tdTomato+GFP+ cells on day 10 post-infection, which were then expanded in co-culture with mesenchymal cells for further passages. Organoids at passages 1 and 2 were used for orthotopic engraftments. A total of 50,000 FACS-isolated epithelial cells were combined with 20,000 freshly isolated lung mesenchymal cells (CD31–CD45–EpCAM–) from wild-type lungs to support epithelial cell survival. The cell mixture was resuspended in 20 µl of PBS and transplanted intratracheally into NSG mice 1 day after a single dose of bleomycin treatment (1.0 U kg−1). Lungs were collected and analysed 21 days post-engraftment to assess differentiation of the transplanted cells.

Human adult lung tissue

The Royal Papworth Hospital NHS Foundation Trust (Research Tissue Bank Generic Research Ethics Committee approval, Tissue Bank Project no. T02233) provided de-identified LUAD and matched normal background lung tissues obtained from lobectomies. Fresh tissues were either dissociated to establish human alveolar organoids, followed by KRASG12D introduction for single-cell profiling, or fixed overnight in 4% paraformaldehyde (PFA; Thermo Fisher Scientific; 10131580) at 4 °C and processed into paraffin-embedded sections (7 μm) for immunofluorescence analysis. Paraffin-embedded sections of human LUAD tissues harbouring confirmed KRASG12D mutations were obtained from MSKCC following surgical lobectomy (Institutional Review Board no. 12-245). For human alveolar organoid co-cultures, de-identified non-tumour lung parenchymal tissues were obtained from lobectomies at Severance Hospital (IRB nos. 4-2019-0447, 4-2012-0685 and 4-2013-0770). Written informed consent was obtained from all donors before tissue collection under approved IRB protocols. No human participants were recruited specifically for this study.

Tissue collection, fixation and sectioning

All animals were euthanized by cervical dislocation, and lungs were perfused with 10 ml of PBS (Sigma; D8537) to remove blood. Lungs were slowly inflated through intratracheal injection of 2–3 ml of 4% PFA (Thermo Fisher Scientific; 10131580) in PBS, dissected and fixed in 4% PFA for 2–4 h at 4 °C. Tissues were washed three times in PBS at room temperature for 15–20 min each and then stored in PBS overnight at 4 °C. The lungs were dehydrated sequentially in 15% then 20% sucrose (Sigma; S5016) in PBS at room temperature for 1 h each, followed by immersion in 30% sucrose in PBS overnight at 4 °C. Individual lobes were separated, trimmed into smaller pieces and embedded in cryomolds filled with optimal cutting temperature compound (VWR; 361603E). Moulds were frozen on dry ice and stored at −80 °C. Frozen tissues were sectioned at 15–20 µm thickness using a cryostat, mounted onto glass slides and stored at −80 °C until staining.

Lung tissue dissociation

For lung cell isolation, mice were euthanized by cervical dislocation, and lungs were perfused with 10 ml of PBS to remove blood. The lungs were inflated through intratracheal instillation with 2–3 ml of dispase solution (Thermo Fisher Scientific; 11553550) through intratracheal injection. For mesenchymal cell isolation, collagenase I (Gibco; 17100017) was added to the dispase solution at 350 U ml−1 before inflation. The lungs were carefully dissected from the thoracic cavity and then placed on ice. Individual lobes were separated, transferred to 50-ml tubes and minced into small fragments. Tissue fragments were washed down with 3 ml of PBS. For epithelial cell isolation, 60 µl of 100 mg ml−1 of collagenase–dispase solution was added per tube. Samples were incubated in a shaking incubator at 37 °C, 190 rpm, for 45 min. DNase I (7.5 µl of 1% solution; Sigma; D4527) was added during the final 10 min of incubation. Cell suspensions were sequentially filtered through 100-µm and 40-µm strainers and washed with 2 ml of PF10 (10% fetal bovine serum (FBS) in PBS). Samples were centrifuged at 800 rpm for 5 min at 4 °C. Supernatants were removed, and pellets were resuspended in 1 ml of red blood cell lysis buffer (prepared in-house: 150 mM NH_4Cl and 10 mM KHCO_3 in distilled H_2O) for 60 s at room temperature. Lysis was neutralized with 6 ml of Dulbecco’s modified Eagle’s medium (DMEM)/F12 (Invitrogen; 11330057). To enrich for viable cells, 500 µl of filtered FBS was carefully layered at the bottom of each tube, followed by centrifugation at 800 rpm for 5 min at 4 °C. Final cell pellets were resuspended in PF10 and transferred to 1.5-ml tubes for antibody staining.

Flow cytometry analysis

Fluorophore-conjugated antibodies were added to each sample according to the cell population being sorted. Antibodies (Supplementary Table 1) were used at a 1:200 dilution in PF10 and incubated for 20–40 min at 4 °C. DAPI was added during the final 10 min of incubation to label dead cells. A small aliquot of each sample was reserved for unstained and single-stained controls. Following incubation, cells were centrifuged, and pellets were resuspended in PF10. Cell suspensions were filtered through a 35-µm cell strainer (VWR; 352235) into polypropylene FACS tubes (Corning; 352063). Samples were sorted using a BD Influx cell sorter equipped with a 100-µm nozzle, and individual cell populations were collected into chilled 1.5-ml tubes containing 500 µl of FBS.

Primary 3D mouse lung organoid cultures

All established organoids were validated by genotyping and routinely tested for Mycoplasma contamination.

Feeder-free organoid cultures

At 7–10 days after three doses of tamoxifen (0.2 mg g−1 body weight) injection every other day, RFP+ cells were obtained from Red2Kras lungs. Organoids from RFP+ labelled cells were established as previously described[7]. Briefly, sorted CD31–CD45–EpCAM+RFP+ lineage-labelled cells were centrifuged at 300g for 10 min at 4 °C and resuspended in Wnt basal medium (Cambridge Stem Cell Institute’s Tissue Culture Core Facility) containing advanced DMEM (Thermo Fisher Scientific; 12491023) supplemented with 10 mM HEPES buffer (Invitrogen; 15630080), 1% penicillin–streptomycin and 1% l-glutamine (Cambridge Stem Cell Institute). Cells were counted, centrifuged and resuspended in growth-factor-reduced Matrigel (Matrigel Growth Factor Reduced (GFR); Corning; 356231). A total of 5,000–10,000 cells in 20 µl of Matrigel GFR were plated per well in eight-well LabTek Chamber Slides (Thermo Fisher Scientific; 154534 K). Matrigel was allowed to solidify at 37 °C for 30 min before adding 300 µl complete Wnt medium per well. Complete Wnt medium comprised Wnt basal medium supplemented with 1× B-27 (Thermo Fisher Scientific; 17504044), 100 ng ml−1 of recombinant FGF7 (PeproTech; 100-19-100), FGF10 (PeproTech; 100-26-100), Noggin (PeproTech; 250-38), 50 ng ml−1 of recombinant EGF (Life Technologies; PMG8043), 1 mM N-acetylcysteine, 10 mM nicotinamide and 2 µM CHIR99021 (Tocris Bioscience; 4423). Organoids were cultured at 37 °C with 5% CO_2, and the medium was changed every other day. The rho kinase (ROCK) inhibitor Y-27632 (10 µM; Cambridge Bioscience; SM02-1) was added during the first 48 h of culture. For EGFR inhibition experiments, gefitinib (Selleckchem; S1025) was added from day 2 post-plating at a final concentration of 5 µM and maintained throughout the experiment. DMSO-treated cultures served as controls.

Organoid co-cultures with lung fibroblasts

At 7–10 days after three doses of tamoxifen (0.2 mg g−1 body weight) injection every other day, RFP+ mutant (CD31–CD45–EpCAM+RFP+) and tdTomato+ AT2 (CD31–CD45–EpCAM+tdTomato+) cells were obtained from Red2Kras and Sftpc–CreERT2;tdTomato lungs, respectively. To isolate lineage-labelled fibroblasts (CD31–CD45–EpCAM–ZsGreen+), Pdgfrα–CreERT2;ZsGreen mice received five daily doses of tamoxifen (0.2 mg g−1 body weight), and lungs were collected 7 days post-induction. Lung organoid co-cultures were established as previously described[21]. Briefly, freshly sorted epithelial cells and fibroblasts were centrifuged at 300g for 10 min at 4 °C and resuspended in 3D basic medium comprising DMEM/F12 (Gibco; 11330-032) supplemented with 10% FBS and insulin–transferrin–selenium (Corning; 25-800-CR). Cells were counted and combined at a ratio of 7,000–9,000 epithelial cells to 45,000–50,000 fibroblasts per well. Following centrifugation, cell pellets were resuspended in Matrigel GFR. A 30-µl Matrigel–cell mixture was plated per well in eight-well LabTek Chamber Slides for whole-mount staining. Domes were allowed to solidify at 37 °C for 30 min before adding 300 µl of 3D basal medium per well. Cultures were maintained at 37 °C with 5% CO_2. The medium was changed every other day. The ROCK inhibitor Y-27632 (10 µM) was included for the first 48 h of culture. For EGFR inhibition experiments, gefitinib (5 µM) was added from day 2 post-plating and maintained until the end of the culture period. DMSO-treated cultures served as controls.

Organoid tri-cultures with lung mesenchymal cells and alveolar macrophages

To investigate the effect of gefitinib in organoid co-cultures, RFP+ mutant and tdTomato+ AT2 cells were isolated as described above. AMs (CD45+CD64+SiglecF+) and lung mesenchymal cells (CD31–CD45–EpCAM–) were isolated from wild-type lungs. Freshly sorted cells were centrifuged at 300g for 10 min at 4 °C and resuspended in 3D basic medium. Cells were counted and combined at a ratio of 1:5:10 (5,000 AT2 cells, 30,000 AMs and 50,000 mesenchymal cells per well). The cell mixture was centrifuged, resuspended in 100-μl Matrigel GFR containing 50% 3D basic medium and plated into 24-well Transwell inserts with 0.4-μm pore size. A total of 500 μl of 3D basic medium was added to the lower chamber, and cultures were maintained at 37 °C with 5% CO_2. For inhibition of EGFR signalling, gefitinib was added at a final concentration of 5 μM. DMSO-treated cultures served as controls.

To investigate the effect of Areg and Ereg in initiating niche remodelling, tdTomato+ AT2 cells were co-cultured with wild-type mesenchymal cells and AMs as described above. A total of 500 μl 3D basic medium supplemented with granulocyte–macrophage colony-stimulating factor (20 ng ml−1; PeproTech) was added to the lower chamber, and cultures were maintained at 37 °C with 5% CO_2. Recombinant Areg (20 ng ml−1; PeproTech; 315-36) and/or Ereg (20 ng ml−1; PeproTech; 100-04-5) was added to the medium and maintained for 5 days.

Co-cultures of lung mesenchymal cells and alveolar macrophages

To evaluate whether tumour-derived mesenchymal cells modulate AMs, co-cultures were established. AMs (CD45+CD64+SiglecF+) were isolated from wild-type lungs, and mesenchymal cells (CD31–CD45–EpCAM–) were isolated from either wild-type or Red2Kras lungs at 4 weeks after three doses of tamoxifen (0.2 mg g−1 body weight) injection on alternate days. Freshly sorted AMs and mesenchymal cells were centrifuged at 300g for 10 min at 4 °C and resuspended in 3D basic medium. Cells were counted and combined to create mixtures of 30,000 AMs with either 50,000 wild-type or 25,000 Red2Kras mesenchymal cells per well. Cells were centrifuged, resuspended in 100-μl Matrigel GFR containing 50% 3D basic medium and plated into 24-well Transwell inserts with 0.4-μm pore size. A total of 500-μl 3D basic medium was added to the lower chamber and replaced every other day. Cultures were maintained for 14 days, after which AMs were analysed.

To assess the effect of TLR4 inhibition on fibroblast–AM interactions, wild-type AMs and mesenchymal cells from either wild-type or Red2Kras lungs were isolated as described above. Cells were counted, combined at a ratio of 30,000 AMs to 40,000 mesenchymal cells and then plated in eight-well LabTek Chamber Slides. Co-cultures were maintained for 4 days. For TLR4 inhibition, selected wells were treated with the TLR4 inhibitor TAK-242 (3 µM; Sigma; 614316) beginning at plating (day 0) and continuing throughout the experiment. To assess the effect of inflammatory cues in fibroblasts, wild-type mesenchymal cells were treated with IL-1β (20 ng ml–1; PeproTech 211-11B-10UG) or co-cultured with AMs isolated from Red2Kras lungs for 48 h.

Mesenchymal cultures

To test whether EGFR activation induces fibrotic phenotypes, mesenchymal cells (CD31–CD45–EpCAM–) were isolated from wild-type lungs, centrifuged at 300g for 10 min at 4 °C and resuspended in 30-μl Matrigel GFR. A total of 50,000 mesenchymal cells per well were seeded in eight-well LabTek Chamber Slides for whole-mount staining. The Matrigel GFR domes were left to set for 30 min at 37 °C, before 300-µl 3D basic medium was added to each well. Recombinant Areg (100 ng ml−1; PeproTech; 315-36) was added to the medium for 5 days.

Alveolar macrophage cultures

AMs sorted from wild-type lungs were cultured under three conditions: (1) AMs alone; (2) AMs co-cultured with tumour cells; and (3) AMs co-cultured with tumour cells and fibroblasts. For each condition, cell numbers were as follows: 50,000 AMs for condition 1, 50,000 AMs with 5,000 RFP+ AT2 tumour cells for condition 2 and 50,000 AMs with 5,000 RFP+ tumour AT2 cells and 25,000 fibroblasts for condition 3. Cells were embedded in 20-µl Matrigel GFR domes and cultured in 3D basic medium for 7 days. For Areg treatment, recombinant Areg (20 ng ml−1) was added to the culture medium.

To assess responses to Tnc, AMs were isolated from wild-type lungs, plated in eight-well LabTek Chamber Slides and left to adhere overnight. Cells were then treated with Tnc (2 µg ml−1; MedChemExpress; HY-P700834) alone or in combination with the TLR4 inhibitor TAK-242 (3 µM; Sigma; 614316) for 48 h.

Inducible human LUAD organoid development

Primary human lung alveolar organoid cultures

Human alveolar organoids were established following a previous study[48]. AT2 cells (CD45–EpCAM+HTII-280+) were isolated from non-tumour lung parenchyma tissues using FACS, resuspended in 20-μl Matrigel GFR and plated in 48-well plates. Domes were incubated at 37 °C for 15 min to allow solidification before adding 250-μl alveolar medium. Alveolar medium consisted of DMEM/F12 supplemented with 1× B27 (Thermo Fisher Scientific; 17504044), 50 ng ml−1 of murine EGF (PeproTech; 100-15), 100 ng ml−1 of human FGF7/KGF (PeproTech; 100-19), 100 ng ml−1 of human FGF10 (PeproTech; 100-26), 100 ng ml−1 of human NOGGIN (PeproTech; 120-10 C), 3 μM CHIR99021 (Tocris; 4423), 500 nM A83-01 (Tocris; 2939), 10 μM SB431542 (Tocris; 616461), 1× penicillin–streptomycin, 500 μg ml−1 of Primocin (InvivoGen; ant-pm-1) and 1.25 mM N-acetylcysteine (Merck; A9165). The ROCK inhibitor Y-27632 (10 μM) was added to the medium for the first 2 days of culture, and the medium was replaced every 2–3 days.

Inducible vector construction, viral production and organoid infection

The plasmid pHAGE–KRASG12D was a gift from G. Mills and K. Scott (Addgene plasmid no. 116423; PIRD: Addgene_116423). For the inducible system, the KRASG12D sequence was cloned into the EF1a–TagRFP–2A–tet3G vector using In-Fusion cloning (vector kindly provided by the Emma Rawlins laboratory, University of Cambridge). Lentivirus was produced by transfecting HEK293T cells (American Type Culture Collection; CRL-11268) using a calcium phosphate protocol, and viral supernatants were collected 48 h post-transfection. Human AT2-cell-derived alveolar organoids (passages 0–2) were recovered from Matrigel GFR using dispase (1 mg ml−1; 40 min; 37 °C) and dissociated to single cells with TrypLE (5 min; 37 °C). Cells were subjected to spin infection (2,000 rpm; 32 °C; 60 min) with viral supernatant in the presence of polybrene (8 μg ml−1; Sigma), followed by feeder-free culture in Matrigel GFR supplemented with alveolar medium as described above. RFP+ cells were then isolated by FACS on day 14 or 21 post-infection, and approximately 50,000 cells were embedded in Matrigel GFR for co-culture experiments or for single-cell profiling under feeder-free conditions, respectively. For induction of the KRASG12D gene, doxycycline (2 μg ml−1; Merck) was added every 2 days, starting on day 7.

Primary human lung alveolar organoid co-cultures with primary human mesenchymal cells

Fourteen days post-infection, RFP+ infected cells were sorted by FACS and co-cultured with freshly isolated primary human lung mesenchymal cells (EpCAM−CD31−CD45−) at a 1:5 ratio (approximately 1,000 RFP+ cells with 5,000 mesenchymal cells per well) in 20-μl Matrigel GFR. Cultures were established in eight-well chamber slides (μ-Slide 8 wells; ibidi) and maintained in co-culture medium consisting of a 1:1 mixture of alveolar medium and PneumaCult (STEMCELL Technologies). KRASG12D expression was induced with doxycycline (2 μg ml−1; Merck), added every 2 days starting on day 7. Immunofluorescence analysis was performed 7 days post-induction. For EGFR inhibition experiments, gefitinib (Selleckchem; S1025; 5 µM) was added from day 7 following doxycycline induction and maintained for extra 7 days. DMSO-treated cultures served as controls.

Immunofluorescence staining

Mouse lung tissue sections

Individual cryosections were circled using a Hydrophobic PAP Pen (Sigma; Z377821) and placed in a humidified chamber. Sections were permeabilized with 0.3% Triton X-100 (Sigma; X100) in PBS for 15 min, followed by blocking with 0.3% Triton X-100 in PBS containing 5% normal donkey serum (Jackson ImmunoResearch Labs; 017-000-121) for 1 h at room temperature. Primary antibodies (Supplementary Table 1) were diluted in blocking buffer and incubated overnight at 4 °C. Sections were washed three times in 0.2% Tween-20 (Sigma; P9416) in PBS for 5 min each at room temperature, followed by incubation with secondary antibodies (Supplementary Table 1) diluted in PBS for 1 h at room temperature. The nuclear staining DAPI (Sigma; D9542) was added to the secondary antibody mix at 0.5 µg ml−1. Following staining, sections were washed three times in PBS, mounted in RapiClear 1.52 (SUNJin Lab; RC152002), enclosed with glass coverslips (VWR; 631-1574) and sealed with nail polish.

Human lung tissue sections

Human paraffin-embedded tissue sections were deparaffinized, and antigen retrieval was performed by incubation at 95 °C for 15 min in sodium citrate buffer (pH 6) (Sigma; S4641) containing 0.05% Tween-20. For immunofluorescence staining, the protocol described above for cryosections was followed.

Organoid whole-mount staining

Organoid cultures grown in LabTek Chamber Slides were fixed with 200 µl of 4% PFA for 20 min at room temperature and washed three times with PBS. Cells were permeabilized with 0.5% Triton X-100 in PBS for 15 min at room temperature, followed by blocking in 0.3% Triton X-100 in PBS containing 5% normal donkey serum for 1 h. Primary antibodies (Supplementary Table 1) diluted in blocking buffer were added to each well and incubated overnight at 4 °C. Samples were washed three times with 0.2% Tween-20 in PBS and incubated with secondary antibodies (Supplementary Table 1) diluted in 0.2% Tween-20 in PBS for 2 h at room temperature. DAPI (0.5 µg ml−1) was included in the secondary antibody solution for nuclear staining. Wells were washed three times with PBS. Chambers were then removed according to the manufacturer’s instructions. Samples were mounted in RapiClear 1.52, enclosed with glass coverslips and sealed with nail polish. The slides were allowed to dry at room temperature before imaging.

Confocal imaging, processing and quantification

Immunofluorescence images of stained sections and organoids were acquired using a Leica STELLARIS 8 white light laser inverted confocal microscope or an Olympus FV3000RS. Standard configurations were used for all experiments. All representative images were acquired using ×20 or ×40 oil objectives, except for whole-lobe tile scans, which were acquired using a ×10 objective. Confocal images were processed and analysed using Fiji (ImageJ). Signal thresholds were manually adjusted during image processing, and identical settings were applied to all representative images within the same experiment. An exception was made for endogenous fluorescent reporters (RFP and tdTomato), for which thresholds were adjusted as necessary to enable clear visualization of labelled cells in representative images.

Cell quantification in tissue sections was performed manually using the CellCounter plugin or by automated detection of DAPI+ nuclei using the ‘Analyze particles’ function. Signal thresholds were manually adjusted to distinguish marker-positive and marker-negative cells, with identical settings applied to all images within the same experiment. For most analyses, representative images and quantifications were obtained from a minimum of seven fields of view per mouse sample or experimental condition. For analyses of tumours containing fibrotic fibroblasts, 10–20 individual mutant clones were analysed per mouse. Tumour burden was quantified by defining RFP+ signal thresholds and measuring total RFP+ area using the ‘Analyze particles’ function. The RFP+ area was normalized to the total area of the whole-lobe cross section, with two to four independent tissue sections analysed per mouse. For quantification of cell numbers in organoids, detection thresholds were manually adjusted, and DAPI+ and SOX9+ nuclei were quantified using the ‘Analyze particles’ function with a minimum particle size of 10 µm^2.

Quantitative reverse transcription–polymerase chain reaction

Total RNA was isolated using a QIAGEN RNeasy Micro or Mini-plus Kit according to the manufacturer’s instructions. Equivalent quantities of total RNA were reverse transcribed with SuperScript IV complementary DNA (cDNA) Synthesis Kit (Life Technologies). Diluted cDNA was analysed by real-time polymerase chain reaction (StepOnePlus; Applied Biosystems). SYBR Green assays were used for human or mouse gene expression with SYBR Green Master Mix (2×; Thermo Fisher Scientific). The primer sequences are as follows:

Mouse Gapdh: F-AGGTCGGTGTGAACGGATTTG, R-TGTAGACCATGTAGTTGAGGTCA

Mouse Arg1: F-CTCCAAGCCAAAGTCCTTAGAG, R- AGGAGCTGTCATTAGGGACATC

Mouse Ym-1: F-TGGAATTGGTGCCCCTACAA, R- CCACGGCACCTCCTAAATTG

Mouse Tnf: F-CCCTCACACTCAGATCATCTTCT, R- GCTACGACGTGGGCTACAG

Human GAPDH: F-GGAGCGAGATCCCTCCAAAAT, R- GGCTGTTGTCATACTTCTCATGG

Human KRAS: F-AGTGCCTTGACGATACAGCT, R-CCTCCCCAGTCCTCATGTAC.

Single-cell transcriptomics

Library preparation and sequencing

Lung mesenchymal cells from Confetti and Red2Kras lungs

Two weeks after three doses of tamoxifen induction (0.2 mg g−1 body weight; administered every other day), lung tissues were collected, and mesenchymal cells (CD45–CD31–EpCAM–) were isolated from Sftpc–CreERT2;Confetti and Sftpc–CreERT2;Red2Kras mice. Cells from three mice of the same genotype were pooled into a single-cell suspension to generate two separate libraries (1× Confetti and 1× Red2Kras). Cell suspensions were spun down, counted and resuspended in 0.04% bovine serum albumin (BSA; Sigma; A3294) in PBS to achieve a cell concentration of approximately 345 cells µl−1. Single-cell 3′ RNA sequencing libraries were generated according to the manufacturer’s instructions (Chromium Single Cell 3′ Reagent v.3 Chemistry Kit; 10X Genomics), and cDNA quality was assessed. Libraries were sequenced to a minimum depth of approximately 20,000 reads per cell using Illumina NovaSeq X 1.5B.

Lung stromal and immune cells from Confetti and Red2Kras lungs

Two weeks after three doses of tamoxifen induction (0.2 mg g−1 body weight; administered every other day), lung tissues were collected, and stromal and immune cells (1:1 mixture of immune cells (CD45+EpCAM–) and stromal cells (CD45–EpCAM–)) were isolated from Sftpc–CreERT2;Confetti and Sftpc–CreERT2;Red2Kras mice. Cells from three mice of the same genotype were pooled into a single-cell suspension to generate two separate libraries (1× Confetti and 1× Red2Kras). Libraries were generated as described above and sequenced to a minimum depth of approximately 20,000 reads per cell using the Illumina NovaSeq 6000.

RFP+ mutant, mesenchymal and immune cells from Areg flox/+ and Areg flox/flox lungs

Two weeks after two doses of tamoxifen induction (0.2 mg g−1 body weight; administered every other day), lung tissues were collected from Sftpc–CreERT2;Red2Kras;Aregflox/+ and Sftpc–CreERT2;Red2Kras;Aregflox/flox animals. Lungs from three mice of the same genotype were dissociated and pooled into a single-cell suspension. Lineage-labelled epithelial cells (EpCAM+RFP+) and a mixed population of mesenchymal and immune cells (1:1 ratio of mesenchymal cells (CD45–CD31–EpCAM–) and immune cells (CD45+CD31–EpCAM–)) were then sorted. For each genotype, two separate libraries were generated, resulting in a total of four libraries (2× Aregflox/+ and 2× Aregflox/flox). Libraries were sequenced to a minimum depth of approximately 20,000 reads per cell using Illumina NovaSeq 6000.

Human lung alveolar organoids

Feeder-free organoids from control or KRASG12D-induced RFP+ cells were used for single-cell RNA sequencing (scRNA-seq) analysis by isolating cells on day 7 following doxycycline-mediated induction. For cell isolation, organoids were incubated with dispase (1 mg ml−1; 30–60 min), dissociated with TripLE (Gibco) for 5 min and washed with PBS. Libraries were prepared as described above and sequenced to a minimum depth of approximately 20,000 reads per cell using Illumina NovaSeq 6000.

Read alignment

Raw FASTQ files containing droplet-based sequencing data were preprocessed in CellRanger (v.6.0.2). Reads were aligned to the Ensembl Mus musculus GRCm38 reference genome or Homo sapiens GRCh38 (GENCODE v.38), empty droplets were filtered out and the number of unique molecular identifiers (UMIs) mapped to each protein-coding gene was quantified to generate the final count matrices.

Quality control

Analysis of count matrices was performed in R using the Seurat package[59] or Scanpy[60] pipeline (v.1.9.1). Quality control metrics for each library were first assessed and used to define thresholds for filtering out low-quality cells and possible doublets. Standard cutoffs of less than 10% mitochondrial genes, more than 1,000 detected genes, more than 2,000 UMIs and fewer than 50,000 UMIs were used for most cases. For the immune cell dataset from the Red2Kras versus Confetti experiment and human alveolar organoid, cells were filtered by custom cutoff (more than 500 and less than 7,000 detected genes and more than 2,000 UMI count).

Dimensionality reduction, clustering and analysis

Seurat pipeline was used for further data processing. Briefly, filtered data were log-normalized and scaled, and the top 2,000 highly variable genes were used for principal component analysis (PCA). The first 30 PCAs were used for downstream analyses. Nearest neighbours were calculated. Cells were clustered using Louvain algorithm and visualized using UMAP. Seurat objects were integrated using Harmony. After integration, count matrices were renormalized, and PCA-based dimensionality reduction, clustering and UMAP visualization were performed. Markers for each clusters were identified using the FindAllMarkers() function, and individual populations were annotated on the basis of previously described gene markers for immune, mesenchymal and epithelial lung cell types7,23,24. Unwanted cell types were manually removed where appropriate. Cell populations of interest were subset, reclustered and reprocessed. Gene expression between clusters was visualized using the DotPlot(), VlnPlot(), FeaturePlot() and Heatmap() functions. Gene Ontology terms for ‘biological processes’ were obtained using g:Profiler on the top differentially expressed genes, applying a significance threshold of P < 0.05. Selected biologically relevant terms were presented in the figures. Cell trajectory analysis for selected populations was performed using the Monocle 3 package[61]. Identification of communication networks and ligand–receptor pairs between epithelial cells and fibroblasts was performed using CellChat by following standard analysis protocols[62].

Integration of oncogenesis and regeneration mesenchyme datasets

To compare mesenchymal transcriptional profiles during injury response and early oncogenesis, our scRNA-seq dataset was integrated with a previously published dataset of mesenchymal cells during lung regeneration[24]. Both datasets were processed and filtered as described above. A total of 10,000 anchor features were identified using the FindIntegrationAnchors() function, and the datasets were integrated using IntegrateData() function. Dimensional reduction, log normalization, scaling, clustering and downstream analyses were performed as described above.

Analysis of early-stage human LUAD scRNA-seq datasets

To investigate the presence of mutant epithelial states and fibrotic fibroblasts in early-stage human LUAD, we reanalysed a previously generated scRNA-seq dataset for primary LUAD tumours (stages I–III) and distant normal tissue[45]. Only samples with confirmed EGFR wild-type status were used for the analysis. For quality control, cutoffs of less than 20% mitochondrial genes, more than 200 detected genes and fewer than 3,000 detected genes were used. Dimensional reduction, log normalization, scaling, clustering and downstream analyses were performed as described above using the Seurat (v.5) package on R studio (v.4.4.2). From the resulting dataset, epithelial cells were reclustered on the basis of EPCAM expression, with ciliated cells (FOXJ1) excluded. Mesenchymal cells were reclustered on the basis of COL1A1 expression, and fibroblasts were further reclustered on the basis of PDGFRα, COL13A1 and COL14A1 expression. Gene expression between cell clusters was visualized as described above.

Statistical analysis and reproducibility

All in vivo experiments were performed in at least two independent experiments, with individual animals considered as biological replicates. All in vitro assays were performed in at least three independent experiments, and summary statistics were calculated from experiment-level mean values, unless otherwise stated. For most quantifications, 10–20 tumours or a minimum of seven fields of view were analysed per mouse or per experimental condition, and measurements were treated as nested within each mouse or condition and averaged to obtain mouse-level or experiment-level values for statistical analysis. For tumour burden, tumour area was normalized to the total lobe area across sections, with two to four independent tissue sections analysed per mouse, and section-level measurements were averaged to yield a single mouse-level value for statistical analysis. For individual tumour size, two slides per animal, spaced 100 µm apart, were evaluated. Total lobes and lesion areas were defined manually and measured using QuPath open-source software (v.0.6.0).

Data are presented as mean ± s.e.m. Statistical analyses were performed using Prism software (GraphPad; v.7.0) or R. Statistical significance was assessed using two-tailed unpaired Student’s t-test or two-tailed Mann–Whitney test, as indicated in the figure legends. The number of animals or in vitro assays is stated in the figure legends (n = x mice per group; n = x independent experiments per condition). Representative images are shown, and the corresponding quantifications are derived from the indicated numbers of animals and experiments. Nested statistical analyses that accounted for within-mouse or within-experiment variability yielded results consistent with analyses of mouse-level or experiment-level means. Therefore, these summary values were used for statistical comparisons, and the specific statistical tests and exact P values are reported in the figures and figure legends.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Single-cell RNA sequencing datasets have been deposited in the Gene Expression Omnibus under the following accession numbers: human alveolar organoids (GSE310335); mesenchymal (GSE316241) and immune (GSE316243) cells from Confetti and Red2Kras lungs, respectively; and tumours and stromal cells from Aregflox/+ and Aregflox/flox lungs (GSE316244). Source data are provided with this paper.

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Acknowledgements

We thank members of the Lee laboratory for discussions; C. Rudin for coordinating the acquisition of human lung tissue; and the Royal Papworth Hospital NHS Foundation Trust, NIHR Cambridge BRC Cell Phenotyping Hub, Cambridge Research UK Cancer Institute Genomics Core Facility, UBS Biofacility, Cambridge Stem Cell Institute Core Facilities, Sloan Kettering Institute′s Molecular Cytology Core Facility, Sloan Kettering Institute′s Flow Cytometry Core Facility and GIST Advanced Institute of Instrumental Analysis for technical assistance. This study was supported by a Wellcome Trust Senior Research Fellowship (221857/Z/20/Z) and Suh Kyung-bae Foundation Award (SUHF-20010033). J.C. was supported by the National Research Foundation of Korea grant funded by the Korean government (MSIT) (NRF-2022R1A2C1091644, RS-2024-00438368 and RS-2024-00411768) and by the Ministry of Health and Welfare (RS-2025-25459531 and RS-2025-24536036). H.L. was supported by the National Research Foundation (RS-2025-25428221). F.J.E. was supported by a Wellcome PhD Studentship (220088/Z/20/Z). B.D.S. is supported by the Wellcome Trust (219478/Z/19/Z) and the Royal Society EP Abraham Research Professorship (RP\R\231004).

Author information

Author notes

1. These authors contributed equally: Erik C. Cardoso, Hyeyoung Lee

Authors and Affiliations

1. Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

Erik C. Cardoso, Frances J. England, Sagar S. Varankar, Benjamin D. Simons, Jinwook Choi & Joo-Hyeon Lee

2. Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Erik C. Cardoso, Robin Lu & Joo-Hyeon Lee

3. Department of Life Sciences, Integrated Institute of Biomedical Research, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea

Hyeyoung Lee, Hyunjin Cho & Jinwook Choi

4. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

Moo Suk Park

5. Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Natasha Rekhtman

6. Center for Genome Engineering, Institute for Basic Science, Daejeon, Republic of Korea

Bon-Kyoung Koo

7. Gurdon Institute, University of Cambridge, Cambridge, UK

Benjamin D. Simons

8. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Science, University of Cambridge, Cambridge, UK

Benjamin D. Simons

Contributions

E.C.C., J.C., F.J.E. and J.-H.L. conceived and designed the study and interpreted the data. E.C.C., J.C. and J.-H.L. wrote the paper. E.C.C. led the project and performed most of the experiments and analyses. J.C. performed immune cell analyses and generated single-cell data from human lung organoids. H.L. performed clodronate, engraftment and organoid assays under the supervision of J.C. F.J.E. identified fibroblast reprogramming and generated single-cell data of mutant epithelial and immune cells. H.C. supported organoid assays. R.L. supported the immunofluorescence of human tissues. S.S.V. optimized mesenchymal cell isolation. M.S.P. and N.R. provided human samples. B.-K.K. shared the Red2KrasG12D mouse. B.D.S. co-supervised E.C.C. and F.J.E. and provided the discussion. J.-H.L. supervised the study, directed the project and secured funding.

Corresponding authors

Correspondence to Jinwook Choi or Joo-Hyeon Lee.

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Competing interests

The authors declare no competing interests.

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Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Alveolar fibroblasts acquire a regenerative-like signature during early oncogenesis.

a, Fluorescence-Activated Cell Sorting (FACS) gating strategy for isolating mesenchymal cells (CD45–CD31–EpCAM–) from Confetti (top) and Red2Kras (bottom) lungs for single-cell RNA sequencing (scRNA-seq). Red boxes highlight the sorted population used for library generation in each sample. b, UMAP plot showing all integrated single-cell transcriptomics of mesenchymal cells obtained from Confetti and Red2Kras datasets, annotated into the nine identified populations. Cells are coloured and numbered by population. c, Dotplot showing the expression of key marker genes distinguishing each cluster annotated in (b). Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. d, UMAP plots showing the distribution of mesenchymal cells in Confetti and Red2Kras lungs separately. Dashed lines highlight a cluster of reprogrammed fibroblasts enriched in Red2Kras lungs. Cells are coloured by population. e, All predicted trajectories generated from the dataset of fibroblasts from Red2Kras lungs. Cells are coloured by populations. Analysis was performed on the dataset prior to annotation. Lines illustrate all different cell trajectories predicted by the analysis. f, Dotplot showing the expression of ECM-related and senescence marker genes in each fibroblast population annotated in Fig. 1b. Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. g, Gene Ontology (GO) terms significantly enriched in reprogrammed fibroblast gene signatures compared to alveolar fibroblasts, plotted against their corresponding -log_10 adjusted p-values. Only the top 300 differentially expressed genes (DEGs) were used for the analysis. h, Representative confocal images showing reprogrammed fibroblast markers and lineage-labelled AT2 cells in Confetti and Red2Kras lungs. DAPI (blue), Pdgfrα (grey), Pdgfrβ (yellow) and RFP (red, tumour). Right panels are magnifications of left panels. Blue arrows, Pdgfrα+Pdgfrβ+ fibroblasts; Orange arrows, PdgfrαlowPdgfrβ+ fibroblasts. Scale bar, 50 µm. Images representative of n = 3 mice. i, UMAP plot showing the distribution of mesenchymal cells in bleomycin injury and our Red2Kras datasets after integration of mesenchymal cell transcriptomics[24]. Cells are coloured according to their dataset of origin. j, UMAP feature plot showing fibroblasts expressing Pdgfra in (i), as indicated by dashed lines in the plot. k, UMAP plot showing all fibroblasts obtained from the integration of the Red2Kras and bleomycin-injury datasets[24], clustered into three distinct populations. Cells are coloured and numbered by population. l, Percentage of cells distributed across each cluster annotated in (k). Percentages smaller than 5% are omitted from the bar plots. m, Dotplot showing the expression of key marker genes for each fibroblast state annotated in (k). Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene, as shown. n, UMAP plots showing the distribution of Red2Kras and bleomycin-injury datasets separately. Dashed lines highlight a cluster of reprogrammed fibroblasts enriched in Red2Kras lungs. Cells are coloured by population. o, All predicted trajectories generated for fibroblasts from Red2Kras (left) and bleomycin-injury (right) datasets. Cells are coloured by populations. Analysis was performed on the dataset prior to annotation. Lines illustrate all different cell trajectories predicted by the analysis.

Source data

Extended Data Fig. 2 Dynamic immune cell landscape during early tumour development.

a, Fluorescence-Activated Cell Sorting (FACS) gating strategy for isolating immune cells (CD45+) and both mesenchymal and endothelial cells (CD45–EpCAM–) from Confetti (top) and Red2Kras (bottom) lungs for single-cell RNA sequencing (scRNA-seq). Red boxes highlight the sorted populations used for library generation in each sample. b, UMAP plot showing all integrated single-cell transcriptomics of immune cells obtained from Confetti and Red2Kras datasets, annotated into the 16 identified populations. Cells are coloured and numbered by population. c, Heatmap plot showing key gene markers for each immune population annotated in (b). d, Distribution of each immune cell cluster in Confetti and Red2Kras lungs. Number of cells in the individual cluster is depicted in the figure. e, Heatmap plot showing the expression of genes altered in reprogrammed macrophages from Red2Kras lungs relative to alveolar macrophages from Confetti lungs, indicating enhanced potential for recruiting other immune cells. f,g, Representative confocal images showing alveolar macrophages and lineage-labelled AT2 cells in Red2Kras lungs at 4 weeks following oncogenic activation. DAPI (blue), F4/80 (pan macrophage, green), CD11c (alveolar macrophage, grey) and RFP (red, tumour) (f). DAPI (blue), CD11c (alveolar macrophage, grey), MHC-II (green) and RFP (red, tumour) (g). Scale bar, 100 μm. Images representative of n = 3 mice. h, Flow cytometry analysis of CD153+PD-1+CD4+ T cells in Confetti and Red2Kras lungs at 4 weeks following oncogenic activation. i, Representative confocal images for regulatory T cells and lineage-labelled AT2 cells in Confetti and Red2Kras lungs at 4 weeks following oncogenic activation. DAPI (blue), CD4 (green), FOXP3 (grey) and RFP (red, tumour). Scale bar, 100 μm. Images representative of n = 3 mice. j, Representative confocal images for CD8+ T cells and lineage-labelled AT2 cells in Confetti and Red2Kras lungs at 4 weeks following oncogenic activation. DAPI (blue), CD8 (green) and RFP (red, tumour). Scale bar, 100 μm. Images representative of n = 3 mice. k, UMAP plot showing all sub-clustered CD8+ T cells annotated based on the dataset of origin. l, Heatmap plot showing exhaustion markers and metabolic signatures of CD8+ T cells from (k). m, Flow cytometric analysis for neutrophils in Confetti and Red2Kras lungs, at 4 weeks following oncogenic activation. n,o, Quantification of the percentage of Ly6G+ neutrophils (n) and Ly6G+SiglecF+ neutrophils (o) in Confetti (n = 3) and Red2Kras (n = 3) lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. Data are mean ± s.e.m. P values were calculated using two-tailed unpaired t-test. p, Flow cytometric analysis for γδ T cells in Confetti and Red2Kras lungs, at 4 weeks following oncogenic activation. GL3 (TCR gamma/delta), CD3ε (T cells), and CD45 (pan immune cells). q, Quantification of the percentage of γδ T cells in Confetti (n = 3) and Red2Kras (n = 3) lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. P values were calculated using two-tailed unpaired t-test. r, s, Representative confocal images for neutrophils (q), γδ T cells (r) and lineage-labelled AT2 cells in Confetti and Red2Kras lungs at 4 weeks following oncogenic activation. DAPI (blue), Ly6G (neutrophils, green), Slc7a11 (neutrophils, grey) and RFP (red, tumour) (q). DAPI (blue), Gl3 (TCR gamma/delta, green) and RFP (red) (r). Scale bar, 100 μm. Images representative of n = 3 mice.

Source data

Extended Data Fig. 3 Reprogrammed resident alveolar macrophages establish the early tumour niche.

a, Experiment scheme of orthotopic engraftment of tumour organoids into the lungs of CCR2-CreERT2;ZsGreen mouse. b, Representative confocal images showing lineage+ and lineage- macrophages in the lungs of CCR2-CreERT2;ZsGreen mice after tumour organoid engraftment. DAPI (blue), ZsGreen (lineage-traced monocytes, green), CD64 (grey), and RFP (red, tumour). Scale bar, 100 μm. Insets (bottom) are enlargements of the dashed boxes (top). Scale bars, 50 μm. Images representative of n = 3 mice. c, Flow cytometry analysis of alveolar macrophages in control and clodronate liposome-treated lungs. d, Quantification of alveolar macrophages in control (n = 3) and clodronate liposome-treated (n = 3) lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. P values were calculated using two-tailed unpaired t-test. e, Representative confocal images of macrophages and lineage-labelled AT2 cells in control and clodronate liposome-treated lungs. DAPI (blue), F4/80 (grey) and RFP (red, tumour). Scale bar, 100 μm. Images representative of n = 3 mice. f, Dotplot of key chemokine receptors in neutrophils and γδ T cells. Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene, as shown. g,h, Representative confocal images of neutrophils (g), γδ T cells (h) and lineage-labelled AT2 cells in control and clodronate liposome-treated lungs. DAPI (blue), Ly6G (green) and RFP (red) (g). DAPI (blue), GL3 (green) and RFP (red) (h). Scale bar, 100 μm. Images representative of n = 3 mice. i, Flow cytometry analysis of neutrophils from control and clodronate liposome-treated lungs, at 4 weeks following oncogenic activation. Ly6G (neutrophils), CD11B (leukocytes/granulocytes). j, Quantification of neutrophils from control (n = 3) and clodronate liposome-treated (n = 3) lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. P values were calculated using two-tailed unpaired t-test. k, Flow cytometry analysis of γδ T cells from control and clodronate liposome-treated lungs, at 4 weeks following oncogenic activation. GL3 (TCR gamma/delta), CD3ε (T cells). l, Quantification of γδ T cells from control (n = 3) and clodronate liposome-treated (n = 3) lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. P values were calculated using two-tailed unpaired t-test.

Source data

Extended Data Fig. 4 A Tnc-TLR4 axis induces alveolar macrophage reprogramming.

a, Schematic illustrating 3D Matrigel co-cultures of wildtype (WT) AMs with mesenchymal cells isolated from WT (homeostatic) or Red2Kras (oncogenic) lungs. b, Quantification of relative AMs numbers in the co-cultures from (a). Data are mean ± s.e.m. Each dot represents an independent experiment. Homeostatic (n = 4) and Oncogenic (n = 4). P values were calculated using two-tailed unpaired t-test. c, Quantification of the MFI for MHC-II of AMs in the co-cultures from (a). Data are mean ± s.e.m. Each dot represents an independent experiment. Homeostatic (n = 3) and Oncogenic (n = 3). P values were calculated using two-tailed unpaired t-test. d, DotPlots showing the expression of Tnc within mesenchymal cells and TLR4 within all immune cell populations. Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. e, Schematic of WT AM culture conditions: 1. Untreated; 2. Tnc-treated; 3. Treatment with Tnc and TLR4 inhibitor (TAK-242). f, Representative confocal images for AMs from cultures in (e). DAPI (blue), Msr1 (yellow) and Ki67 (red). Scale bar, 50 μm. g, h, Quantification of the percentage of Msr1+ (g) and Ki67+ (h) AMs. Data are mean ± s.e.m. Each dot represents an independent experiment. Untreated (n = 3). Tnc (n = 3) and Tnc plus TAK-242 (n = 3). P values were calculated using two-tailed unpaired t-test. i, Schematic illustrating 2D co-cultures of WT AMs with mesenchymal cells isolated from WT or Red2Kras lungs. The TLR4 inhibitor TAK-242 was added to some of the co-cultures. j, Representative confocal images showing reprogrammed macrophages in the co-cultures from (i). DAPI (blue), Msr1 (red), F4/80 (green). Scale bar, 50 µm. k, Quantification of Msr1+ macrophages in the co-cultures from (i). Data are as mean ± s.e.m. Each dot represents an independent experiment. n = 3 for all experimental conditions. P values were calculated using two-tailed unpaired t-test. l, UMAP plot showing fibroblast subclusters annotated into three distinct populations (from Fig. 1b). Cells are coloured and numbered by population. m, Feature plots showing the expression of inflammatory markers within a sub-set of reprogrammed fibroblasts. n, Representative confocal images for inflammatory fibroblasts and lineage-labelled AT2 in Red2Kras lungs at 1 week following oncogenic activation. Mice received a single tamoxifen dose (0.1 mg/gbw). Lcn2 (yellow), Pdgfra (grey) and RFP (red, tumour). Scale bar, 40 μm. o, Representative confocal images for fibrotic and inflammatory fibroblasts and lineage-labelled AT2 in Red2Kras lungs at 4 week following oncogenic activation. Lcn2 (yellow), Pdgfra (blue), Tnc (grey) and RFP (red, tumour). Right panels are magnifications of the left panels. Orange arrows highlight Lcn2+ inflammatory fibroblasts with no Tnc expression. Scale bar, 50 μm. p, Schematic illustrating WT fibroblast cultures treated with IL-1β, or co-cultured with AMs isolated from Red2Kras lungs. q, Representative confocal images of the cultures from (p). DAPI (blue), Lcn2 (red), Pdgfra (green), F4/80 (grey). Scale bar, 50 μm.

Source data

Extended Data Fig. 5 Regenerative mutant cell state serves as the signalling source for niche remodelling.

a, UMAP showing the integration of our single-cell transcriptomics datasets from lineage-labelled RFP+ cells[7] and lung fibroblasts in Red2Kras and Confetti lungs. Cells and numbers are coloured by population. b, Dotplot showing the expression of key epithelial and fibroblast cell marker genes in the identified populations, annotated in (a). Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. c, Aggregated cell-cell communication networks between epithelial and fibroblast states. The thickness of the connections represents the number of significant interactions identified by CellChat. d, Aggregated cell-cell communication networks with AT2 or DATP-like cells as the signalling source. The thickness of the connections represents the number of significant interactions identified by CellChat. e, All significant outgoing signals from DATP-like cells to alveolar fibroblasts identified by CellChat. Dashed lines highlight the top ligand-receptor pairs identified. Each dot is coloured according to the cellular communication probability. f, Outgoing EGF signalling from DATP-like mutant cells to alveolar fibroblasts highlighting Areg-EGFR as the pair with the highest integration probability. Each dot is coloured according to the cellular communication probability. g, Dotplot showing the expression of EGFR and EGFR-binding ligands in the cell populations annotated in (a). Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. h, Violin plot showing the expression of ligands identified as the top signals in the communication analysis from DATP-like cells to alveolar fibroblasts. i, All significant outgoing signals from fibroblasts to epithelial cells identified by CellChat. Dashed lines highlight the top ligand-receptor pairs identified. Each dot is coloured according to the cellular communication probability. j, Violin plot showing the expression of ligands identified as the top signals in the communication analysis from fibroblasts to epithelial cells.

Extended Data Fig. 6 AREG–EGFR signalling reprograms fibroblasts but does not directly drive the reprogramming of tumour cells and macrophages.

a, Representative confocal images confirming an absence of detectable Areg expression in homeostatic lungs (Confetti). Right panels are the magnifications of left panel. DAPI (blue), pro-SPC (green), Areg (grey), YFP (yellow) and RFP (red). Scale bar, 50 µm. Image representative of n = 2 mice. b, Violin plot showing the expression of Areg and EGFR across epithelia, mesenchymal and immune cell populations. c, Experimental scheme to investigate the effect of Areg on wildtype mesenchymal cells. d, Representative brightfield images showing morphological changes of lung mesenchymal cells treated with Areg after 7 days in 3D Matrigel culture. Images are representative of n = 3 independent experiments. Scale bar, 50 µm. e, Representative confocal images showing the expression of reprogrammed fibroblast markers in lung mesenchyme cultures in (c). Top panel: DAPI (blue) and Acta2 (red). Bottom panel: DAPI (blue) and Pdgfrβ (red). Scale bar, 50 µm. f, Experimental scheme to investigate the effect of Areg in AMs. g, Flow cytometry analysis of AMs numbers after Areg treatment. h, Flow cytometry analysis showing Msr1 expression in AMs from (f). i, Quantification of the MFI for Msr1 in AMs from (f). Data are mean ± s.e.m. Each dot represents an independent experiment. Control (n = 6) and Areg (n = 6). P values were calculated using two-tailed unpaired t-test. n.s., not significant. j, Flow cytometry analysis showing MHCII expression on AMs from (f). k, Quantification of the MFI for MHCII in AMs from (f). Data are mean ± s.e.m. Each dot represents an independent experiment. Control (n = 6) and Areg (n = 6). P values were calculated using two-tailed unpaired t-test. n.s., not significant. l, Experimental scheme to investigate the effect of Areg on RFP+ tumour cells isolated from Red2Kras lungs. m, Bright-field image from (l). n, Representative confocal image of RFP+ tumour organoids from (l). DAPI (blue), Krt8 (grey), pro-SPC (green) and RFP (red). Images representative of n = 3 independent experiments. Scale bar, 100 µm. o, Schematic illustrating 3D organoid co-cultures of fibroblasts isolated from Pdgfra-CreERT2; ZsGreen lungs with lineage-labelled cells isolated from Sftpc-CreERT2; tdTomato or Red2Kras lungs. Gefitinib or DMSO was treated for the indicated duration. p, Representative confocal images showing reprogrammed fibroblasts and lineage-labelled cells in (o). Right panels are magnifications of left panel for each condition. DAPI (blue), ZsGreen (green), Pdgfrβ (grey), and RFP (red). Scale bar, 50 µm. Images of representative n = 3 independent experiments.

Source data

Extended Data Fig. 7 Areg–EGFR signalling mediates reciprocal reprogramming between fibroblasts and mutant AT2 cells.

a, Schematic illustrating 3D organoid co-cultures of wildtype AMs and mesenchymal cells with lineage-labelled RFP+ cells isolated Red2Kras lungs. b, Representative flow cytometry plots showing the populations of macrophages (CD45+EpCAM−), tumour cells (CD45−EpCAM+), and mesenchymal cells (CD45−EpCAM−) gated from CD31− populations and stablished in (a). c, Flow cytometry analysis of Msr1 expression in AMs from (a). d, e, Quantification of the MFI for Msr1 (d) and relative cell numbers (e) of AMs obtained from (a). Data are mean ± s.e.m. Each dot represents one independent experiment. 1 (n = 4), 2 (n = 4) and 3 (n = 4). P values were calculated using two-tailed unpaired t-test. f, Experimental scheme to investigate the effect of Areg on AMs co-cultured with wildtype mesenchymal and AT2 cells. g, Flow cytometry analysis showing the proportion of AMs from (f). h, Quantification of frequency of AMs from (f). Data are mean ± s.e.m. Each dot represents one independent experiment. Control (n = 3) and Areg (n = 3). P values were calculated using two-tailed unpaired t-test. i, Flow cytometry analysis showing MHCII expression in AMs from (f). j, Quantification of the MFI for MHCII in AMs from (f). Data are mean ± s.e.m. Each dot represents one independent experiment. Control (n = 3) and Areg (n = 3). P values were calculated using two-tailed unpaired t-test. k, Experimental scheme to investigate the effect of Areg/Ereg on AMs co-cultured with wildtype mesenchymal and AT2 cells. l, Quantification of the relative cell numbers of AMs from (k). Data are mean ± s.e.m. Each dot represents one independent experiment. Control (n = 3) and Areg/Ereg (n = 3). P values were calculated using two-tailed unpaired t-test. m, Quantification of MFI for MHCII in AMs from (k). Data are mean ± s.e.m. Each dot represents one independent experiment. Control (n = 4) and Areg/Ereg (n = 4). P values were calculated using two-tailed unpaired t-test. n, qPCR analysis of Arg1, Ym-1, and Tnfa expression in AMs co-cultured with wildtype mesenchyme and AT2 cells treated with PBS, Areg alone, or Areg/Ereg. Data are mean ± s.e.m. Each dot represents one independent experiment. Control (n = 4), Areg (n = 4) and Areg/Ereg (n = 4). P values were calculated using two-tailed unpaired t-test. o, Schematic illustrating 3D organoid co-cultures of fibroblasts isolated from Pdgfra-CreERT2; ZsGreen lungs with lineage-labelled cells isolated from Sftpc-CreERT2;tdTomato or Red2Kras lungs. Gefitinib or DMSO was treated for the indicated duration. p, Representative confocal images showing lineage-labelled DATP-like mutant cells in 3D organoid co-cultures from (o). DAPI (blue), ZsGreen (green), Sox9 (grey), and RFP (red, tumour). Scale bar, 50 µm. q, Quantification of the percentage of Sox9 expressing cells within RFP+ mutant organoids assessed in (p). Data are presented as mean ± s.e.m. Each dot represents one organoid from four independent experiments. DMSO (n = 4), Gefitinib (n = 4). P values were calculated using Mann–Whitney test. r, Representative confocal images showing lineage-labelled cells in 3D organoid co-cultures with wild-type mesenchymal cells and treated with Gefitinib or DMSO. DAPI (blue), LpCat1 (green) and RFP (red, tumour). Scale bar, 100 µm. s, Quantification of the percentage of LpCat1 expressing cells within RFP+ mutant organoids. Data are presented as mean ± s.e.m. Each dot represents one organoid from three independent experiments. DMSO (n = 3), Gefitinib (n = 3). P values were calculated using Mann–Whitney test. t, Schematic illustrating 3D organoid cultures of mutant AT2 cells isolated from Red2Kras lungs. Gefitinib or DMSO was treated for the indicated duration. u, Representative confocal images showing the expression of DATP-like cell state marker in Red2Kras RFP+ mutant organoids from (t). DAPI (blue), Sox9 (green) and RFP (red, tumour). Scale bar, 50 µm. v, Percentage of DATP-like cell states expressing Sox9 in Red2Kras RFP+ mutant organoids from (u). Data are presented as mean ± s.e.m.; Each dot represents an individual organoid from four independent experiments. DMSO (n = 4), Gefitinib (n = 4). P values were calculated using two-tailed unpaired t-test.

Source data

Extended Data Fig. 8 EGFR inhibition disrupts sustained mutant epithelial states.

a. Experiment scheme of orthotopic engraftment of tumour organoids into the lungs of Pdgfra-CreERT2;ZsGreen;iDTR mice. b-d. Representative confocal images for fibrotic fibroblasts (b), macrophage (c) and mutant epithelial cells (d) in PBS-treated and DTR-treated lungs. DAPI (blue), Pdgfrβ (grey), ZsGreen (green, lineage-traced fibroblasts) and RFP (red, tumour) (b). DAPI (blue), Cd68 (grey), ZsGreen (green, lineage-traced fibroblasts) and RFP (red, tumour) (c). DAPI (blue), Sox9 (grey), and RFP (red, tumour) (d). Images are representative of n = 2 mice. Scale bar, 100 μm. e, Experimental design for EGFR tyrosine kinase inhibitor administration. Red2Kras mice received two doses of tamoxifen and were treated with DMSO or Gefitinib (80 mg/kg) at the indicated timepoints. Lung tissues were collected for histological analyses. f, Representative tile scans of entire lung lobe cross-sections from Red2Kras animals treated with DMSO or Gefitinib. DAPI (blue) and RFP (red, tumour). Scale bar, 1,000 µm. g, Percentage of RFP+ mutant cell area relative to lobe area assessed in (f). Data are as mean ± s.e.m. Each dot represents a biologically independent mouse replicate. DMSO (n = 2), Gefitinib (n = 3). h,i Representative confocal images for reprogrammed fibroblasts, macrophages and lineage-labelled cells in DMSO or Gefitinib-treated lungs. DAPI (blue), Tnc (grey) and RFP (red, tumour) (h). Images representative of n = 2 (DMSO) and n = 2 (Gefitinib) mice. DAPI (blue), F4/80 (grey) and RFP (red, tumour) (i). Images representative of n = 2 (DMSO) and n = 3 (Gefitinib) mice. Scale bar, 50 µm. j, Flow cytometry analysis for AMs from Red2Kras lungs treated with DMSO or Gefitinib. k, Quantification of the percentage of AMs from DMSO- or Gefitinib-treated Red2Kras lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. DMSO (n = 3) and Gefitinib (n = 3). P values were calculated using two-tailed unpaired t-test. l, Flow cytometry analysis for MHC-II expression on AMs from DMSO- or Gefitinib-treated Red2Kras lungs. m, Quantification of the MFI for MHC-II in AMs from DMSO- or Gefitinib-treated Red2Kras lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. DMSO (n = 2) and Gefitinib (n = 3). n, Representative confocal images for neutrophils and lineage-labelled cells in DMSO- or Gefitinib-treated Red2Kras lungs. DAPI (blue), Ly6G (green) and RFP (red, tumour). Images representative of n = 3 biologically independent mice. Scale bar, 50 µm. o-q, Representative confocal images of the different epithelial states in DMSO- or Gefitinib-treated Red2Kras lungs. DAPI (blue), Sox9 (grey) and RFP (red, tumour) (o). DAPI (blue), Cd177 (grey) and RFP (red, tumour) (p). DAPI (blue), Ager (grey), LpCat1 (yellow) and RFP (red, tumour) (q). Scale bar, 50 µm. r, Quantification of the percentage of each cell state within the RFP+ mutant cell population in DMSO- or Gefitinib-treated Red2Kras lungs. Data are mean ± s.e.m. Each dot represents a biologically independent mouse replicate. DMSO (n = 2), Gefitinib (n = 3).

Source data

Extended Data Fig. 9 Transcriptional landscape of lung mesenchymal cells upon mutant cell-specific Areg deletion.

a, FACS gating strategy for isolating mesenchymal (CD45–CD31–EpCAM–), immune (CD45+EpCAM–), and lineage-labelled RFP+ mutant cells from Aregflox/+ and Aregflox/flox lungs for scRNA-seq. Each sorted population, indicated by red boxes, was used for library generation. b, UMAP plot showing all immune and mesenchymal cell populations obtained after integration of Aregflox/+ and Aregflox/flox datasets. Cells are coloured by cell cluster. c, Feature plot showing the expression of Col1a1 (mesenchymal cells) and Ptprc (immune cells) in the UMAP from (b). d, UMAP plot showing all sub-clustered mesenchymal cells, annotated into the nine identified population in the dataset. Cells are coloured and numbered by population. e, Dotplot showing the expression of key marker genes for mesenchymal cells annotated in (d). Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. f, Dotplot showing the expression of key marker genes for the sub-clustered fibroblast annotated in Fig. 4d. Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene.

Extended Data Fig. 10 Transcriptional landscape of lung immune cells upon mutant cell-specific Areg deletion.

a, UMAP plot showing all sub-clustered immune cells, annotated into the fourteen identified populations in the dataset. Cells are coloured and numbered by population. b, UMAP plot showing the distribution of immune cells in Aregflox/+ and Aregflox/flox datasets. Cells are coloured according to their dataset of origin. c, Dotplot showing the expression of key marker genes for immune cells annotated in (a). Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. d, UMAP plot showing the sub-clustering and distribution of AMs in Aregflox/+ and Aregflox/flox datasets. Cells are coloured according to their dataset of origin. e, Violin plots comparing the expression of pro-inflammatory cytokines and MHC-II complex components in AMs between Aregflox/+ and Aregflox/flox lungs. f, UMAP plot showing the distribution of neutrophils in Aregflox/+ and Aregflox/flox lungs. Cells are coloured according to dataset of origin. g, Violin plots showing increased expression of genes associated with neutrophil activation in neutrophils from Aregflox/+ lungs compared to Aregflox/flox lungs.

Extended Data Fig. 11 Early-stage human lung adenocarcinoma reveals distinct transcriptional reprogramming and fibrotic niche.

a, Schematic of the workflow for analysing the single-cell transcriptomics dataset of early-stage (I-III) human lung adenocarcinoma (LUAD) patient samples[45]. Patients with confirmed EGFR wildtype status were selected for analysis, with a total of six LUAD and six distant normal lung tissue samples (5 matched pairs). b, UMAP plot showing the integrated single-cell transcriptomics datasets of early-stage (I-III) human lung adenocarcinoma (LUAD) patient and matched distant normal lung tissue samples (5 matched pairs, 1 LUAD-only, and 1 normal-only sample). Cells are coloured by population. c, UMAP feature plots showing the expression of epithelial (EPCAM), immune (PTPRC) and mesenchymal (COL1A1) marker genes in the integrated UMAP from (b). d, UMAP plots showing all sub-clustered epithelial cells annotated into the seven distinct populations, colour-coded by cellular states. e, UMAP plot showing the distribution of epithelial cells, colour-coded by the group of datasets of origin (LUAD vs normal). f, Heatmap displaying expression of key marker genes distinguishing each cluster annotated in (d). g, UMAP plot showing the distribution of epithelial cells in normal and LUAD lung tissues. Cells are coloured according to dataset of origin (a total of twelve samples: six LUAD and six normal patient tissue samples). h, UMAP plot showing the distribution of epithelial cells in normal and LUAD lung tissues. Cells are coloured according to LUAD stage of dataset origin. i, Violin plots depicting the expression of DATP-like cell marker genes and EGFR ligands in AT2 cells and cluster 1. j, Representative confocal images showing SOX9+ intermediate epithelial cell states in early-stage KRASG12D LUAD patient tissues. DAPI (blue), KRT8 (green) and SOX9 (red). Scale bar, 100 µm k, UMAP feature plot showing the expression of fibroblast marker gene PDGFRA in the UMAP of human mesenchymal cells. l, UMAP plot showing all sub-clustered fibroblasts annotated into six distinct populations. Cells are coloured and numbered by population. m, UMAP plot showing the distribution of fibroblasts in normal and LUAD lung tissues. Cells are coloured according to dataset of origin (LUAD vs normal). n, UMAP plots showing all sub-clustered fibroblasts colour-coded by dataset of origin. o, UMAP plot showing the distribution of fibroblasts, colour-coded by LUAD stage of dataset origin. p, Dotplot showing the expression of key fibroblast marker genes in each cluster annotated in (l). Each dot is coloured based on the average expression and sized based on the percentage of cells expressing that gene. q, Representative confocal images showing reprogrammed fibroblasts and DATP-like intermediate cells in matched background and LUAD patient tissues. DAPI (blue), RUNX1 (grey), ACTA2 (red), and KRT8 (green) (top panel). DAPI (blue), CTHRC1 (red), and KRT8 (green) (bottom panel). Yellow arrows highlight reprogrammed fibroblasts in close proximity to KRT8+ intermediate cells. Scale bar, 50 µm.

Extended Data Fig. 12 Establishment of pre-neoplastic niches driven by EGFR-mutant cells.

a. Experiment scheme of the 3D inducible LUAD model established by introducing EGFRL858R mutation into primary AT2 cells, followed by orthotopic engraftment into NSG mouse lungs. b, Flow cytometry analysis showing the AT2 cells transduced with EGFRL858R mutation. c, Representative confocal images showing the engraftment of tdTom+EGFP+ mutant organoids. DAPI (blue), tdTomato (red), GFP (green) and Krt8 (grey). Images representative of n = 2 mice. Scale bar, 100 μm. d,e, Representative confocal images showing Areg expression in Krt8+ epithelial cell states (d) and the associated remodelling of fibroblasts and macrophages surrounding tumour cells (e). DAPI (blue), tdTomato (red), Areg (green) and Krt8 (grey) (d). DAPI (blue), tdTomato (red), Pdgfrβ (green) and Cd68 (grey) (e). Images representative of n = 2 mice. Scale bar, 100 μm.

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Cardoso, E.C., Lee, H., England, F.J. et al. Early fibrotic niches establish tumour-permissive microenvironments. Nature 653, 254–264 (2026). https://doi.org/10.1038/s41586-026-10399-6

- Received: 16 March 2025

- Accepted: 11 March 2026

- Published: 22 April 2026

- Version of record: 22 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10399-6

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A disease model resource reveals core principles of tissue-specific cancer evolution

Sebastian Mueller,

Niklas de Andrade Krätzig,

Markus Tschurtschenthaler,

Miguel G. Silva,

Chiara Thordsen,

Riccardo Trozzo,

Perrine Simon,

Frederic Saab,

Thorsten Kaltenbacher,

Magdalena Zukowska,

Daniele Lucarelli,

Rupert Öllinger,

Joscha Griger,

Nina Groß,

Tanja Groll,

Jessica Löprich,

Antonio E. Zaurito,

Linus R. Schömig,

Jeroen M. Bugter,

Stefanie Bärthel,

Chiara Falcomatà,

Alexander Strong,

Cordelia Brandt,

Mulham Najajreh,

Aristeidis Papargyriou,

Roman Maresch,

Katharina A. N. Collins,

David Sailer,

Christian Schneeweis,

Sebastian Burger,

Lisa M. Fröhlich,

Christine Klement,

Alexander Belka,

Juan J. Montero,

Ute Jungwirth,

Maximilian Reichert,

Markus Moser,

Jens Neumann,

George Vassiliou,

Juan Cadiñanos,

Ignacio Varela,

Carsten Marr,

Daniel F. Alonso,

Pier-Luigi Lollini,

Jean Zhao,

Louis Chesler,

Clare M. Isacke,

Angela Riedel,

Christian J. Braun,

Martin L. Sos,

Filippo Beleggia,

Hans C. Reinhardt,

Monica Musteanu,

Mariano Barbacid,

Michael Quante,

Marc Schmidt-Supprian,

Günter Schneider,

Simon Clare,

Trevor D. Lawley,

Gordon Dougan,

Katja Steiger,

Nathalie Conte,

Allan Bradley,

Lena Rad,

Dieter Saur &

…

Roland Rad

Nature volume 653, pages 265–276 (2026)

Abstract

Oncogenes such as KRAS display marked tissue specificity in their oncogenic potential, genetic interactions and phenotypic effects, but the underlying determinants remain largely unresolved1,2,3,4,5. Here, to address these questions, we developed the Mouse Cancer Cell line Atlas, a broad-utility resource of 590 comprehensively characterized models across a wide range of entities (www.mcca.tum.de). Comparative and functional studies using this platform, human cohorts and mice identified core principles underlying tissue-specific evolution of KRAS-initiated cancers. First, we show that mutant KRAS dosage gain through allelic imbalance exerts cell-type-specific effects, defining its timing across entities, as exemplified by dosage-sensitive developmental reprogramming during pancreatic cancer initiation. Second, we highlight how tissue- and stage-specific evolutionary requirements, such as block of differentiation in the intestine, select for KRAS-collaborating alterations. Third, we identified context-dependent epistatic KRAS–tumour suppressor interactions and show that reciprocal dosage sensitivities dictate the entity-specific patterns of cancer gene alterations, explaining their frequency, zygosity and acquisition chronology. These findings highlight how intrinsic and acquired determinants instruct cancer evolution in different tissues, with predictable molecular patterns, temporal dynamics and phenotypic outcomes. Our study provides major advances towards a mechanistic understanding of cancer genomes.

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Main

Cancer genome sequencing efforts have catalogued genetic alterations for all major human cancer types and revealed considerable differences between tissues1,2,3. However, the causes and evolutionary principles shaping cancer genomic landscapes are only partly understood4,5. Cell types differ in their susceptibility to transformation by individual oncogenes, and cancer types initiated by the same oncogene vary in their aggressiveness. Moreover, the same oncogene collaborates with distinct secondary alterations in different tissues, and heterogenous patterns of allelic imbalance at cancer genes further complicate the picture. Mechanistically, these and many other observations in cancer evolution remain largely unexplored.

Comprehensively characterized human cell line collections, such as the Cancer Cell Line Encyclopedia (CCLE)6,7, have become indispensable resources and sources of major discoveries in cancer research[8]. Although the mouse is the most important mammalian model organism9,10,11, there is no comparable pan-cancer cell line resource available for this species. The mouse offers some unique opportunities, such as the possibility to engineer defined molecular contexts or to model rare cancer types and assemble required sample sizes. Complementarity to human resources also emerges from the possibility to capture desired timepoints or conditions, such as treatment-naive contexts or defined progression stages. Likewise, the potential transplantability of mouse cell lines into immunocompetent hosts can be decisive in a broad spectrum of research contexts, such as the study of cancer ecosystems or the testing of (immuno)therapies9,12.

To address the need for such a mouse resource, we assembled cancer cell lines from a broad spectrum of cancer types. The collection encompasses 590 models, for which we provide multilayered molecular, phenotypic and clinical metadata through an interactive web portal (www.mcca.tum.de). We developed analytical tools to infer immunophenotypes from genomic sequencing data to guide the in vivo use of Mouse Cancer Cell line Atlas (MCCA) lines in immunocompetent settings. By combining MCCA data analyses with functional studies in mice and human investigations, we set out to examine cellular, molecular and temporal parameters in the evolution of cancers initiated by KRAS. Through analysis of prototype entities originating from terminally differentiated or stem cells (pancreas, lung and intestine), we describe hallmark events and mechanisms underlying tissue-specific oncogenesis. Overall, our study supports a deterministic model of cancer evolution that explains genomic alteration patterns in different cancer types.

Development and characterization of the MCCA

To address the limited availability of non-human cell line resources, we developed the MCCA (Fig. 1a,b). We derived primary cell cultures (hereafter, cell lines) from 81 mouse models of cancer, encompassing tumours induced by engineered oncogene/tumour suppressor alleles or exogenous triggers (Supplementary Table 1). Alongside established genetically engineered mouse models, we also developed models to study genetic, inflammation-associated or irradiation-induced cancers. Examples include genetically engineered cholangiocarcinomas, Helicobacter-induced stomach adenocarcinomas or numerous cancer types triggered by γ-irradiation (Supplementary Table 1). Moreover, we characterized 36 publicly available cancer cell lines commonly used in basic and translational research. In total, the MCCA encompasses 590 cell lines, covering 22 lineages and 46 disease types (Supplementary Table 1). To ensure the long-term preservation of cell lines and the high-quality nature of related data, we established rigorous protocols for MCCA handling and characterization (Methods and Extended Data Fig. 1a–j).

Fig. 1: Development and characterization of the MCCA.

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a, Workflow describing the development and characterization of MCCA cell lines. The website providing access to related datasets is indicated. b, Overview of selected MCCA datasets derived from the characterization of 590 cell lines, including 36 previously available lines. Cancer types are ordered by unsupervised hierarchical clustering of their transcriptome mean values. Within individual cancer types, each cell line is sorted by transcriptome clustering. Full MCCA annotation is provided in Supplementary Table 1. CNS, central nervous system; PNS, peripheral nervous system.

For each cell line, we provide multiple data layers in the MCCA, including some not systematically captured in human collections (Fig. 1a,b). First, we sequenced MCCA lines and generated genomic and transcriptomic profiles using our computational pipelines specifically tailored to the mouse genome (MoCaSeq)13,14. Second, we assembled clinical metadata, such as survival and metastasis. Third, after microscopy-based grading of cellular morphology, we assigned each line to one of four distinct epithelial-to-mesenchymal transition (EMT) states (Extended Data Fig. 2a). Fourth, we performed histopathological classification of tumour tissue related to individual MCCA lines (Supplementary Table 1). MCCA therefore represents a comprehensive cell line (and related data) resource for the mouse—the most widely used experimental model in biomedical research.

Integrative analyses of MCCA data

When evaluating MCCA’s use in examining the relationships between molecular and phenotypic data, we observed that separation of transcriptomes is driven by various parameters, including cell lineage, cell state, disease type within a lineage/organ, genotype, disease stage and culture conditions (Extended Data Fig. 2a–q). To facilitate such integrative analyses of molecular, cellular, organismal and temporal data layers, we made all MCCA data accessible through a user-friendly mouse-adapted cBioPortal15,16,17 web interface (www.mcca.tum.de). Exemplary data mining is showcased by correlating pancreatic cancer phenotypes, such as survival or metastasis, with molecular data (Supplementary Video 1).

To examine cross-species relationships, we compared transcriptomes of MCCA and the human CCLE using a correlation-based approach (Methods and Extended Data Fig. 3a–i). For example, among lymphoid cancers, MCCA T cell neoplasms cluster with human T cell leukaemia/lymphoma, while mouse B cell neoplasms align with their human counterparts, including B lymphoblastic leukaemia/lymphoma, multiple myeloma or mature B cell neoplasms. Equivalent analyses within an entity are shown for pancreatic cancer, where mouse and human cell lines with a mesenchymal phenotype and increased dosage of mutant KRAS co-cluster—consistent with oncogenic dosage increase promoting EMT and a basal-like differentiation with poor prognosis18,19. These and similar data for other cancer types (Extended Data Fig. 3a–k) show the broad spectrum of human disease phenotypes and molecular contexts covered by the MCCA. To support the identification of MCCA counterparts of human disease subtypes or CCLE models, we provide Pearson correlation coefficients for all mouse–human cell line pairs (Supplementary Table 4) along with broad molecular and phenotypic annotations (Supplementary Table 1) and further cross-species comparisons at the genomic level (Extended Data Fig. 4a–k).

MCCA immunophenotyping

Immunocompetent transplantation models are of growing importance for biomedical research and preclinical drug testing, especially as immunotherapy landscapes expand at an rapid pace. As cancer cell lines often originate from models with mixed genetic backgrounds, matching donor–recipient immunocompatibility requires immunophenotyping. In principle, relevant information (MHC haplotypes, genetic background, sex) can be obtained from genomic sequencing data, but analytical tools are lacking. We therefore developed methodological approaches and computational tools addressing this need (Methods).

First, for strain background detection, we extracted single-nucleotide polymorphisms (SNPs) for 29 inbred mouse strains using Mouse Genomes Project[20] data. By correlating SNP patterns between strains, we identified 15 clusters, which we defined as genealogically related strain groups (Extended Data Fig. 5a). This assignment was critical for delineating strain-specific signature SNPs (SNPs unique for each of the 15 strain groups but allowed to be shared within the same group). These signature SNPs (n = 1,097,314) enabled highly accurate detection of strain composition (Fig. 2a–c and Extended Data Fig. 5b,c). Supplementary Table 5 lists corresponding data for all of the MCCA lines. Notably, non-dominant genetic backgrounds can remain detectable despite extensive backcrossing, as exemplified for cell line MCCA0417, which was derived from a C57BL/6-backcrossed mouse carrying the Ptf1acre, KrasLSL-G12D and Trp53LSL-R172H alleles originally engineered in 129-related stem cells. Owing to genetic linkage, 129 signature SNPs in close genomic proximity to engineered alleles ‘withstand’ backcrossing to C57BL/6 mice (Fig. 2a), thereby contributing around 4% 129 background. Such effects are critical for estimating backcrossing status from genomic data (Methods, Extended Data Fig. 5d and Supplementary Table 5).

Fig. 2: Immunophenotyping of MCCA and application for immunocompetent transplantation studies.

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a, Inference of strain background composition, MHC haplotypes and sex for MCCA lines from genome sequencing data. Top, overview of MCCA immunophenotypes. Cell lines are ordered by unsupervised hierarchical clustering of genetic background composition. Bottom, genome-wide SNP plot of a C57BL/6-backcrossed cell line (MCCA0417). Note the persistence of the 129-mouse signature SNPs in proximity to genetically engineered alleles. b–e, Immunophenotype characteristics across the MCCA. b, The distribution of genetic backgrounds and corresponding MHC haplotypes. Prioritized (#) and feasible (§) lines are defined in the caption for e. c, Genetic background (strain composition) per cell line, considering the two highest-ranking strains only. d, MHC haplotype distribution for both alleles (A,B) per cell line. e, Choice of MCCA transplant recipients: recommendations are indicated for each MCCA line based on their genetic background(s), MHC haplotype(s) and sex information. Prioritized (#), MCCA lines with one or two genetic backgrounds (in a few cases, with a third genetic background contributing <1%; exemplary transplant scenarios are shown in h). Feasible (§), MCCA lines with two dominant genetic backgrounds and a third background contributing 1–9% of SNPs (exemplary transplant scenarios are shown in Extended Data Fig. 6f). Not recommended, MCCA lines with three dominant backgrounds. f, Survival of immunocompetent mice transplanted with MHC-matched mPACA lines with various degrees of strain SNP mismatch (n = 63 transplantations). Only mPACA lines with incomplete Cdkn2a inactivation were compared. g,h, TMB (considering protein-changing alterations, pTMB) of MCCA lines in different experimental contexts, shown for PACA lines with C57BL/6-derived MHC and C57BL/6;129-SNP contributions (n = 27). g, In autochthonous tumours, somatic protein-altering mutations define pTMB. Human PACA lines are displayed for comparison (CCLE, n = 41). h, In MHC-matched transplantations using the corresponding cell lines, the ‘effective’ pTMB is recipient dependent. Scenario 1 (C57BL/6;129-F1 recipients): only somatic mutations contribute to ‘effective’ pTMB. Scenario 2 (C57BL/6 recipients): somatic mutations and 129-strain-specific germline variants can be immunogenic (increased ‘effective’ pTMB).

Second, for MHC haplotype detection, we divided the MHC locus into six gene clusters (H2-K, -A, -E, -D, -Q and -T) on the basis of MHC subclasses. Precise classification of MHC clusters is crucial for preventing T cell-mediated transplant rejection. We correlated SNP data for each gene cluster to assign 29 inbred strains into genetically conserved MHC subclass haplotypes, defined by 44,219 signature SNPs (Extended Data Fig. 5e). These MHC signature SNPs were used to determine MHC-subclass-specific haplotypes, which enabled us to define the combined/full MHC haplotype (Extended Data Fig. 5f,g). Overall, 83% of cell lines possess MHC alleles from C57BL/6-related (H2b) and/or 129-related (H2bc) strains (Fig. 2d). In rare cases, additional complexity can arise from meiotic crossover events in mouse cohorts with mixed genetic backgrounds. For example, we detected mosaic MHC haplotypes generated through recombination of 129- and FVB-derived MHC H2-T gene clusters (Fig. 2d and Extended Data Fig. 5h).

Third, we determined immunophenotypes for all cell lines by combining genetic background (SNP composition), MHC haplotype and sex information (Supplementary Table 5). We found that 60% of cell lines possess immunophenotypes from one or two inbred strains, allowing transplantation into one strain or matched F_1 hybrid mice (most commonly 129;C57BL/6; Fig. 2c,e and Extended Data Fig. 6a). Importantly, most entities are represented in this group. The remaining lines had immunophenotype contributions from more than two strains. Although donor–recipient matching is still possible at the MHC level, SNP mismatching could affect transplantability. However, in 56% of cases (23% of the MCCA), the third-background SNP contribution is less than 10%, which is often tolerated in transplantation experiments. To exemplify this, we performed MHC-matched transplantations (Fig. 2f). As expected, cell lines with the highest SNP mismatch to recipients (42% and 45%) did not engraft, whereas lines with ≤12% mismatch engrafted robustly. Notably, one line with 41% mismatch and intact MHC expression/competence (Extended Data Fig. 6b–d) formed cancers, albeit with long survival.

Overall, these results highlight the importance of annotating MCCA immunophenotypes, which will guide precise recipient selection in future studies (Supplementary Table 5).

Somatic and germline variants in MCCA

In transplantation experiments, not only somatic mutations in cell lines, but also strain-specific germline variants, can contribute to tumour mutational burden (TMB) and immunogenicity, depending on the recipient. This is illustrated by transplanting pancreatic cancer cell line MCCA0349 into distinct MHC-matched recipients. MCCA0349 was derived from a C57BL/6-backcrossed mouse with Ptf1acre and KrasLSL-G12D alleles originally engineered in the 129 background. Its MHC haplotype is therefore C57BL/6, but 129-associated SNPs in proximity to Ptf1acre and KrasLSL-G12D ‘withstood’ backcrossing.

In the first scenario, MCCA0349 is transplanted into C57BL/6;129-F1 hybrid mice. Here, only somatic mutations (n = 26 protein-altering mutations, yielding a protein-altering TMB (pTMB) of 0.4 per Mb exome), but not strain-specific germline variants, contribute to immunogenicity and pTMB. Equivalent data for other pancreatic cancer cell lines with similar MHC and strain characteristics (Fig. 2g,h) show that this transplantation scenario exhibits a lower pTMB compared with human cancers.

In the second scenario, MCCA0349 is transplanted into C57BL/6 mice. Here, the ‘effective’ pTMB amounts to 103 protein-altering mutations (1.4 per Mb exome; 26 somatic mutations plus 77 129-related SNPs). Modelling this scenario for all MCCA pancreatic cancer lines with similar MHC and strain contributions (Fig. 2h) revealed that the majority possesses an ‘effective’ pTMB comparable to human cancers (Fig. 2g). These results corroborate previous observations that syngeneic transplant models often respond better to immunotherapies compared with their autochthonous counterparts[12].

Notably, in scenario 2, the MCCA lines with the highest 129-strain contributions displayed ‘effective’ pTMB levels similar to the MSH2-mutant human line SNU324. Moreover, considering MCCA lines with over two genetic backgrounds for MHC-matched transplantations gives further flexibility to create experimental settings with high mutational burden (Extended Data Fig. 6e,f). Thus, strain-specific germline variants can be exploited to design immunocompetent transplantation experiments with desired levels of ‘effective’ pTMB (Supplementary Table 6).

KRAS gene dosage variation across entities

KRAS is the most frequently mutated human oncogene[21]. While allelic imbalance at mutated KRAS has been shown to exacerbate oncogenic signalling18,19,22,23,24, its timing, biological consequences and genetic interaction partners in different tissues are mostly unclear. To address these questions, we analyse Kras allelic imbalance in MCCA lines from pancreatic (mPACA), lung (mLUCA) and intestinal (mINCA) carcinomas initiated in KrasLSL-G12D mice[25]. By integrating single-nucleotide variant (SNV) and copy-number variation (CNV) data, we defined three KrasG12D-allelic states: decreased gene dosage (dGD), heterozygous (HET) and increased gene dosage (iGD) (Methods and Extended Data Fig. 7a). Whereas KrasG12D-iGD was common across entities (Fig. 3a and Supplementary Table 7), KrasG12D-dGD was not detected. To perform equivalent analyses in humans, we used cell lines[6] and tissues from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) with KRAS G12* or G13* mutations (hereafter, KRASMUT). For cancer tissues, we inferred pure KRASMUT gene dosage states using purity-corrected SNV and CNV values (Methods). As in mice, KRASMUT-iGD was frequent across cancer types, whereas KRASMUT-dGD was very rare (Fig. 3b,c and Supplementary Tables 8 and 9), consistent with KRASMUT increase in gene dosage being under positive selection rather than a neutral event in cancer evolution. Moreover, we found that KRASMUT-iGD was associated with reduced patient survival (Fig. 3d and Extended Data Fig. 7b). Thus, KRASMUT-iGD is of clinical relevance and is positively selected in pancreatic, lung and intestinal cancer.

Fig. 3: Frequency, clinical relevance and timing of KRASMUT allelic imbalance across tissues.

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a–c, Inference of KRASMUT allelic status in mouse and human pancreatic, lung and intestinal cancer cohorts using genomics data. Three main KRASMUT allelic states were defined as dGD (decreased gene dosage, allelic imbalance favouring the WT allele), HET (heterozygous KRASMUT) and iGD (increased KRASMUT dosage) (Methods; examples are provided in Extended Data Fig. 7a). Enrichment of KRASMUT-iGD, but not KRASMUT-dGD, across entities and species demonstrates positive selection of increased gene dosage (P ≤ 0.0001, two-sided Mann–Whitney U-test). Amp, high-level amplification. a, MCCA lines and tissues derived from genetically engineered mouse models: Ptf1acre/+;KrasLSL-G12D/+ (pancreas), Ela-creERTM;KrasLSL-G12D/+ (lung) and Vil-cre;KrasLSL-G12D/+ (intestine). b, Human cancer cell lines from pancreas (CCLE-PAAD), lung (CCLE-NSCLC) and intestine (CCLE-COAD). c, Human cancer tissues from pancreas (ICGC-PanCuRx), lung (TCGA-LUAD) and intestine (TCGA-COAD). d, Disease-specific survival of patients with KRASMUT-HET versus KRASMUT-iGD cancers (top, pancreatic cancers; middle, lung cancers; bottom, intestinal cancers). TCGA-LUAD survival including event censoring is shown in Extended Data Fig. 7b. P values were calculated using two-sided log-rank tests. e, KrasG12D allelic imbalance at defined stages of lung cancer evolution in Ela-creERTM;KrasLSL-G12D/+ mice. Purity-corrected KrasG12D allele frequencies of adenomas (ade; n = 40) and carcinomas (car; n = 44) microdissected from MCCA tissues. Statistical analysis was performed using two-sided Mann–Whitney U-tests; ****P < 0.0001. f, KrasG12D allelic imbalance in MCCA organoids isolated at defined stages of serrated, intestinal cancer evolution from Vil-cre;KrasLSL-G12D/+ mice: hyperplasia (hyp; n = 9), adenoma (n = 24), carcinoma (n = 20) and metastasis (met; n = 10). For e and f, statistical analysis was performed using two-sided Mann–Whitney U-tests; ****P < 0.0001. VAF, variant allele frequency.

Tissue-specific timing of KRAS MUT-iGD

We next investigated the evolutionary timing of KRASMUT-iGD in different cancer types. For the pancreas, we found that KRASMUT gene dosage increase in mouse (Extended Data Fig. 7c,d) and human[18] pancreatic intraepithelial neoplasia (PanIN), highlighting its acquisition during the earliest stages of PACA evolution. By contrast, acquisition of KrasG12D allelic imbalance in the lung was linked to carcinomas in Kras/Trp53 compound mutant mice[22]. As Trp53 barrier loss can facilitate KrasG12D allelic imbalance[18], we studied macrodissected lung adenomas and carcinomas from Trp53-wild-type (WT) Ela-creERTM;KrasLSL-G12D/+ mice (Methods and Extended Data Fig. 7e,f). We found that KrasG12D-iGD is frequently acquired in carcinomas, but not adenomas (Fig. 3e), confirming the later acquisition of KrasG12D-iGD in the lung.

To study the timing of KrasMUT-iGD in RAS-initiated (serrated) intestinal tumorigenesis, we analysed KrasG12D allelic status in 63 MCCA organoid lines isolated at distinct disease stages from Vil-cre;KrasLSL-G12D/+ mice. We found that KrasG12D-iGD is rare in hyperplasias and adenomas but frequent in carcinomas and metastases (Fig. 3f and Extended Data Fig. 7g). These data suggest positive selection of KrasG12D-iGD during intestinal adenoma to carcinoma progression, particularly given the lack of KrasWT gain (Fig. 3f) or of increased genomic instability in KrasG12D-iGD carcinomas (Extended Data Fig. 7h).

To functionally interrogate the contribution of KrasMUT-iGD to intestinal cancer progression, we transplanted KrasG12D-HET adenoma organoids of MCCA with or without subclonal KrasG12D-iGD into mice. All engrafted adenomas progressed to carcinomas with frequent acquisition or clonal expansion of KrasG12D-iGD (Extended Data Fig. 7i). Notably, individual MCCA adenoma lines displayed markedly different engraftment rates that strongly correlated with the extent of subclonal KrasG12D-iGD at transplantation, but not with SNV or CNV load (Extended Data Fig. 7j–n). Thus, KrasMUT allelic imbalance is functionally relevant and selected for during adenoma-to-carcinoma progression. Overall, these results highlight tissue-specific differences in the evolutionary timing of KrasMUT-iGD acquisition.

KRAS effects are dosage and tissue dependent

To identify the biological basis of these observations, we next examined the molecular and cellular effects exerted by different KRASMUT dosages across tissues. We modelled early tumour evolution by developing non-transformed human cell lines with doxycycline-titratable KRASG12D expression. Growing them under three-dimensional (3D) (instead of two-dimensional (2D)) conditions enabled accurate examination of related processes like de-differentiation or invasion (Extended Data Fig. 8a). We optimized doxycycline concentration ranges to achieve KRASG12D mRNA induction over a wide dynamic range for each cell model (Extended Data Fig. 8b) and performed RNA-sequencing (RNA-seq) to capture transcriptome changes at each condition.

Principal component analyses (PCA) revealed that KRASG12D is driving transcriptomic changes in a dosage-dependent manner (mainly along PC1; Fig. 4a). These effects started emerging at doxycycline concentrations at which KRASG12D exceeds KRASWT expression (Extended Data Fig. 8c,d). To examine the nature of KRASG12D-driven transcriptional responses in PC1, we conducted gene set enrichment analyses (Methods). While all of the models displayed dosage-dependent KRASG12D effects, the induced molecular processes differed between entities (Fig. 4b, Extended Data Fig. 8e and Supplementary Table 10). Characteristic for pancreas and lung were signatures related to invasion (EMT, focal adhesion) and reactivation of fetal programs (endoderm differentiation/formation). The intestine instead displayed prominent dosage-dependent proliferation signatures, as also confirmed in a mouse model (Extended Data Fig. 8f–h and Supplementary Table 11).

Fig. 4: KRASMUT effects are dosage dependent and shaped by cellular context.

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a–c, Doxycycline (dox)-titratable induction of KRASG12D or GFP in the indicated non-transformed human cellular models (Methods and Extended Data Fig. 8a). a, PCA of KRASG12D-induced transcriptome changes and GFP controls. b, Gene set enrichment analyses of the top 250 genes driving transcriptome separation on PC1 (negative, PC1−; positive, PC1+). The circular bar plots show selected gene sets. FDR, false-discovery rate. Epi. cell diff., epithelial cell differentiation; interm. filament, intermediate filament. c, Microscopy-based analysis of KRASG12D-induced cellular phenotypes and GFP controls. Left, the frequency of adhesive/discohesive phenotypes at each doxycycline concentration (n ≥ 20 spheroids per condition; Methods). Right, representative images exemplify each phenotype (ntotal ≥ 160 spheroids imaged per model). Scale bars, 60 µm. d, Inference of developmental programs induced by KRASG12D. Upregulation/downregulation of eight developmental signatures in HPDE cells after induction of different KRASG12D levels (data are from a and b). The heat map shows gene set variation scores. The eight signatures signify developmental stages during in vitro differentiation of human embryonic stem cells into ductal cells[26]. DE, definitive endoderm; hPSC, human pluripotent stem cell; PDLO, pancreatic duct-like organoid; PP, pancreatic progenitor; PTrLO, pancreatic-trunk-like organoid. e, Doxycycline-titratable induction of KRASG12D or GFP in a pancreatic acinar cell model (266-6). Left, PCA of KRASG12D-induced transcriptome changes, and GFP controls. Right, clustered heat map showing expression changes for acinar (top cluster) or ductal transcription factors/marker genes (bottom cluster) as well as of ductal[55] (duct.) and progenitor[56] (prog.) duct signatures (sign.). Genes, z-transformed expression values; signatures, gene set variation scores. f, Somatic WNT pathway mutations in organoids isolated from defined disease stages of Vil-cre;KrasLSL-G12D/+ mice or WT controls. n = 4 (WT), n = 9 (hyperplasia), n = 27 (adenoma), n = 5 (carcinoma). *P = 0.0164, two-sided χ^2 test. g, The transcriptomic gene set variation scores of the indicated signatures in MCCA tissues isolated from defined diseases stages of Vil-cre;KrasLSL-G12D/+ mice or WT controls.

Source data

To examine whether KRASG12D dosage-dependent signatures relate to specific cellular phenotypes, we imaged spheroids at each doxycycline dilution and classified them as either adhesive or discohesive, based on protrusion formation, cellular invasion and loss of epithelial organization (Methods). Discohesive growth appeared in the pancreas and lung, but not in the intestine (Fig. 4c and Extended Data Fig. 8g)—mirroring the tissue specificity of invasion-related transcriptional signatures (Fig. 4b). Thus, the biological effects of KRAS are not only dosage dependent but also profoundly shaped by tissue context: invasion and developmental programs in pancreas/lung versus proliferation in the intestine.

Pancreatic de-differentiation is KRAS dosage-sensitive

KRASMUT allelic imbalance is detectable in early pancreatic cancer precursors. These lesions arise through cellular de-differentiation, which might therefore be KRAS-dosage sensitive. To test this possibility, we overlayed the transcriptomic effects exerted by different KRASG12D levels in human pancreatic ductal epithelial (HPDE) cells with cell-type-specific signatures representing the consecutive stages of pancreatic duct cell differentiation (Fig. 4d). For the latter, we used stage-specific transcriptomic signatures from an in vitro human stem-to-ductal cell differentiation series[26] and performed gene set variation analysis (GSVA) to determine the upregulation/downregulation of each developmental signature in the transcriptomes of HPDE cells. We found that high—but not low—levels of KRASG12D induce a switch from differentiated duct-like (days 30, 45, 59) to early pancreatic progenitor-like (days 3, 13, 20) signatures in HPDE cells (Fig. 4d).

As PACA can originate not only from ductal cells but also from acinar cells, we studied a pancreatic acinar cell model after equipping it with doxycycline-inducible KRASG12D (Extended Data Fig. 8i). We found that KRASG12D induces de-differentiation of acinar cells in a dosage-dependent manner (Fig. 4e): while markers of acinar cell differentiation (such as Cpa2, Prss1 and Ptf1a) were progressively lost, duct-like precursor cell markers (such as Sox9) increased with doxycycline concentrations.

The dependence of transcriptional reprogramming (the first step in PACA initiation) on increased KRASMUT levels rationalizes the selective pressure to amplify oncogenic signalling early in oncogenesis. We therefore hypothesized that other triggers causing de-differentiation would release the selective pressure to amplify KrasMUT during early evolution. Indeed, we previously found that cancers in Ptf1acre/+;KrasLSL-G12D/+;Tgfbr2fl/fl mice are largely KrasG12D-HET (ref. [18]), which might reflect the role of TGFβ in maintaining the acinar cell identity[27]. To test this, we somatically inactivated Tgfbr2 in fully differentiated acinar cells of adult Ptf1acre/+;KrasLSL-G12D/+,Rosa26CAG-LSL-Cas9/CAG-LSL-Cas9 mice using scAAV8-based sgRNA delivery, as described previously[28]. We found that Tgfbr2 targeting substantially increased acinar de-differentiation 8 weeks after mutagenesis compared with control mice (Extended Data Fig. 9a,b). Tgfbr2 inactivation therefore promotes loss of acinar cell identity, thereby reducing the selective pressure for early acquisition of KrasMUT-iGD.

Together, these results highlight the importance of KrasMUT-iGD for early pancreatic de-differentiation. As genetic duplication of KRASMUT seems sufficient to trigger reprogramming (high-copy amplification is uncommon), we examined whether non-genetic mechanisms further enhance transcriptional output at the locus during de-differentiation. To this end, we first used single-cell RNA-seq (scRNA-seq) data from pancreata of Ptf1acre-ERTM/+;KrasLSL-G12D/+,Rosa26LSL-CAG-tdTomato/+ mice[29]. In this model, tdTomato marks acinar cells (Cpa1+tdTomato+Krt19−) and their de-differentiated duct-like progeny (Cpa1−tdTomato+Krt19+, acinar-to-ductal metaplasia (ADM)/PanIN cells), while adult ductal cells are Cpa1−tdTomato−Krt19+. Comparative analyses revealed that endogenous Kras expression increases 5.5-fold during acinar cell de-differentiation and is further enhanced in the cancer cell state (Extended Data Fig. 9c). To verify this effect, we performed an ex vivo ADM assay, in which healthy acini from Ptf1acre/+;KrasLSL-G12D/+ mice spontaneously transdifferentiate into duct-like precursor cells. As in mice, we found 5.7-fold upregulation of endogenous Kras expression in de-differentiated metaplastic cells (Extended Data Fig. 9d–f). Thus, the effect of (low-level) KrasMUT-iGD is further amplified through non-genetic mechanisms during successive cell state transitions in PACA evolution.

De-differentiation also contributes to lung oncogenesis. It has been associated with progressive amplification of MAPK signalling30,31, triggered by cell-extrinsic or cell-intrinsic (genetic) events. Given the low prevalence of KrasG12D-iGD in early-stage lung adenomas (Fig. 3e), cell-extrinsic triggers might have a more important role for early de-differentiation in the lung.

KRAS–WNT collaboration in the intestine

In intestinal tumour evolution, monoallelic KrasG12D activation in tissue-resident stem cells induces hyperplasia, while KrasG12D-iGD emerges at the carcinoma stage (Fig. 3f). This raises the question of what triggers adenoma formation. Given the rapid turnover of intestinal epithelia, which leaves little time to acquire mutations, we reasoned that a block of differentiation might be needed at this evolutionary step.

As this process seems to be independent of KrasG12D in the intestine (Fig. 4a–c and Extended Data Fig. 8f–h), we screened MCCA organoids from KrasG12D-induced hyperplasias, adenomas and carcinomas for mutations in the WNT pathway—the key regulator of intestinal differentiation. We indeed found that Apc and Ctnnb1 mutations specifically emerged at the adenoma stage, affecting 48% of cases (Fig. 4f and Supplementary Table 12). Moreover, transcriptome analyses of MCCA tissues highlighted WNT pathway upregulation in all adenomas, independent of the Apc or Ctnnb1 mutation status (Fig. 4g).

In the classical model of intestinal tumorigenesis (initiated by APC mutation), WNT pathway activation is the gatekeeper event for adenoma formation[32]. Our results suggest that the same applies to KRAS-initiated serrated tumorigenesis. As KRASMUT cannot block intestinal differentiation, stochastic KRASMUT-iGD events are rapidly lost in shedding cells of hyperplastic tissue, explaining why KRASMUT-iGD is not observed in early oncogenesis. It is only after WNT-induced block of differentiation at the adenoma stage that the competitive growth advantage exerted by KRASMUT-iGD translates into clonal outgrowth.

KRAS–TSG interactions are entity specific

Given the tissue-specific timing and output of KRASMUT dosage variation, we suspected that interactions of the oncogene with tumour suppressor genes (TSGs) might also be context-dependent. RAS signalling is known to engage endogenous tumour suppression, most notably through CDKN2A activation[33]. We therefore analysed Cdkn2a alteration patterns across pancreatic, lung and intestinal carcinomas. As Cdkn2a might be lost during in vitro cell culture[34], we studied microdissected MCCA tissues. These analyses revealed substantial differences between cancer types, with homozygous Cdkn2a loss being frequent in KrasG12D-mutant pancreatic carcinomas (82%), but rare in lung (5%) or intestinal (11%) carcinomas (Fig. 5a and Supplementary Table 13). Likewise, examination of human KRASMUT cancers exposed frequent biallelic CDKN2A inactivation in human PACA (64%), but not in human LUCA (15%) and human COCA (2%) (Fig. 5b and Supplementary Table 14). Thus, although all cancers are KRAS mutant, selective pressure for CDKN2A inactivation differs substantially between tissues.

Fig. 5: KRAS–tumour suppressor interactions are cell-type specific.

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a,b, Somatic CDKN2A inactivation patterns in the indicated cancer cohorts based on genomics data (Methods). Microdissected MCCA cancers (a): from Ptf1acre/+;KrasLSL-G12D/+ (n = 17), Ela-creERTM;KrasLSL-G12D/+ (n = 44) and Vil-cre;KrasLSL-G12D/+ (n = 19) mice. Human cancer tissues (b): ICGC-PanCuRx (n = 114), TCGA-LUAD (n = 135) and TCGA-COAD (n = 136). c, CDKN2A chromatin status and mRNA expression in healthy human pancreas, lung and intestine inferred from ROADMAP data. Top, transcript coverage plots (reads per kilobase million (RPKM)). Middle, histone modification signal plots. H3K4me3, promoter activation; H3K27me3, Polycomb repression. Signal score, −log_10[P]. Bottom, selected chromatin states of a core 15-state model based on five histone marks (Extended Data Fig. 10c). Coordinates based on the GRCh37 human reference genome. d, Cell-type-specific CDKN2A expression based on pseudobulk analyses of public scRNA-seq data (Methods). RPM, reads per million. P = 0.0009, Kruskal–Wallis test. Data are median. e,f, The responsiveness of Cdkn2a to KrasG12D during early tumour evolution. e, Hyperplasia and adenoma (Vil-cre;KrasLSL-G12D/+) versus healthy (KrasWT) intestinal organoids. f, Acinar and metaplastic/PanIN (Ptf1acre-ERTM/+;KrasLSL-G12D/+,Rosa26LSL-CAG-tdTomato/+) versus healthy (KrasWT) acinar cells (data are from ref. [29]). FC, fold change. g, Representative senescence-associated β-galactosidase (SA-βGal) in precursor lesions of pancreatic (Ptf1acre/+;KrasLSL-G12D/+, n = 4 mice), lung (Ela-creERTM;KrasLSL-G12D/+, n = 4 mice) and serrated intestinal (Vil-cre;KrasLSL-G12D/+, n = 8 mice) cancer. Scale bars, 200 µm (top) and 50 µm (bottom). h, Representative duodenal Ki-67 staining of WT mice (n = 3) and hyperplastic crypts of Vil-cre;KrasLSL-G12D/+ mice (n = 3). Scale bars, 50 µm. i, The selective advantage conferred by Cdkn2a and WNT pathway alterations in the indicated tissues, as determined by transposon-based forward genetic screens in Pdx1-cre;KrasLSL-G12D/+,Rosa26LSL-piggyBac/+;ATP1-S2 (n = 49)[43] and Vil-cre;KrasLSL-G12D/+,Rosa26LSL-piggyBac/+;ATP1-S2 (n = 219) mice (Methods). **P = 0.0018; two-sided χ^2 test. j, The chronological order of KrasG12D and Cdkn2a alterations during oncogenesis, as determined by phylogenetic analyses in matched samples from Ptf1acre/+;KrasLSL-G12D/+, Ela-creERTM;KrasLSL-G12D/+ and Vil-cre;KrasLSL-G12D/+ mice (Extended Data Fig. 12a). A, adenoma; P, primary cancer; Li/LN, liver/lymph-node metastasis. Non-bold and bold labels indicate cell lines and tissues, respectively. k, KRASMUT allelic status of CDKN2A/TP53-proficient human pancreatic (ICGC-PanCuRx and TCGA-PAAD, ntotal = 201), lung (TCGA-LUAD, ntotal = 135) and intestinal (TCGA-COAD, ntotal = 136) cancer tissues (Methods). l,m, KRASMUT allelic status (l) and CDKN2A inactivation patterns (m) in human bladder (TCGA-BLCA, n = 12), rectal (TCGA-READ, n = 35), stomach (TCGA-STAD, n = 25) and uterine (TCGA-UCEC, n = 67) carcinomas (Methods). n, CDKN2A mRNA expression for cell-of-origin candidates of the indicated cancer types based on pseudobulk analyses of public scRNA-seq data47,48,49 (Methods). P = 0.0087, Kruskal–Wallis test. The bars show the median value. o, Oncogene–tumour suppressor interactions and cellular processes in KRASMUT-initiated pancreatic, lung and serrated intestinal cancer evolution. The simplified model displays interactions between KRASMUT and CDKN2A, but not other tumour suppressors such as TP53. See the Discussion for details.

Cell-type-specific repression of CDKN2A

To study the mechanistic basis of this observation, we used ROADMAP and ENCODE epigenomics data35,36 to assess the chromatin states at CDKN2A, the regulation of which occurs chiefly at the transcriptional level[37] (Supplementary Table 15). In the pancreas, CDKN2A expression was readily detectable, in accordance with trimethylation of histone H3 at lysine 4 (H3K4me3) occupancy at transcription start sites marking the actively transcribed promoter. Conversely, in the intestine, we found very low CDKN2A expression and high occupancy of H3K27me3, a repressive mark catalysed by Polycomb repressive complexes (PRCs) (Fig. 5c and Extended Data Fig. 10a,b).

We next integrated H3K4me1, H3K36me3 and H3K9me3 histone marks to investigate 15 distinct chromatin states, as provided through ROADMAP[35]. Indeed, active chromatin states characterized the CDKN2A locus in the pancreas, whereas in the intestine Polycomb-repressed states dominated (Fig. 5c and Extended Data Fig. 10c). In the lung, CDKN2A showed a bivalent pattern of active and repressive states associated with low expression. Notably, we found no tissue-specific differences for H3K4me3, H3K27me3 or chromatin states at KRAS (Extended Data Fig. 10d–f).

To examine CDKN2A transcriptional activity specifically in the cell of origin of each cancer type, we analysed human scRNA-seq data38,39,40. Consistent with our epigenetic analyses, CDKN2A expression is detectable in pancreatic acinar and ductal cells, but minimal or absent in lung alveolar or intestinal stem cells (Fig. 5d and Extended Data Fig. 10g). By contrast, KRAS is expressed across cell types (Extended Data Fig. 10h).

We also examined scRNA-seq and chromatin immunoprecipitation followed by sequencing (ChIP–seq) data from healthy mouse tissues to assess whether Cdkn2a regulation is conserved across species (Supplementary Table 15). We found that Cdkn2a expression was generally low, consistent with low H3K4me3 promoter occupancy and previous reports of minimal expression in adult mouse tissues[41]. Notably, repressive H3K27me3 occupancy is lower in the pancreas than in the intestine, while the lung exhibited intermediate levels, probably reflecting the bivalent Cdkn2a chromatin state (Extended Data Fig. 10i–k). Thus, in humans and mice, Polycomb repression at CDKN2A is strong in the intestine, moderate in the lung and weak in the pancreas.

We next tested whether PRC2-catalysed H3K27me3 is mechanistically involved in cell-type-specific repression of Cdkn2a. To this end, we pharmacologically inhibited PRC2 (PRC2i) in non-transformed mouse intestinal and pancreatic ductal organoids, followed by quantification of Cdkn2a expression and cellular growth (Extended Data Fig. 11a,b). We found that PRC2i induced Cdkn2a expression and cellular growth arrest in intestinal organoids, but not in pancreatic cells. Moreover, knocking out Cdkn2a rescued the PRC2i-induced inhibition of intestinal cell proliferation (Extended Data Fig. 11c–e). Thus, tissue-specific chromatin states of Cdkn2a directly affect its function.

Together, these analyses demonstrate Polycomb-mediated chromatin repression at CDKN2A in a tissue-specific manner, directly affecting tumour suppressor expression and function.

CDKN2A chromatin states and response to KRAS

Given these tissue-specific differences in PRC2-mediated repression at Cdkn2a, we next investigated possible functional consequences for in vivo KrasMUT-driven tumour evolution.

First, we examined whether Cdkn2a responsiveness to KrasG12D differs between intestinal and pancreatic cells during early tumour evolution. Analysing Vil-cre;KrasLSL-G12D/+ MCCA-mouse cohorts, we found that Cdkn2a expression only slightly increases as healthy intestinal cells progress to the hyperplasia and adenoma stage (Fig. 5e). By contrast, Cdkn2a is strongly induced during KrasG12D-initiated early pancreatic oncogenesis, when healthy acinar cells develop into metaplastic PanINs[29] (Fig. 5f; data from Ptf1acre-ERTM/+;KrasLSL-G12D/+,Rosa26LSL-CAG-tdTomato/+ mice).

Second, we investigated KrasG12D-induced cellular senescence across tissues—a key tumour-suppressive mechanism that is mediated by Cdkn2a. To this end, we performed senescence-associated β-galactosidase staining of non-transformed MCCA tissues from KrasG12D-mutant pancreatic, intestinal and lung cancer models. We found that senescence was prominent in early pancreatic lesions (ADM, PanIN), but minimal or absent in lung and intestinal precursors (Fig. 5g). Instead, KrasG12D causes pronounced hyperproliferation in the intestine (Fig. 5h). Thus, tissue-specific CDKN2A responsiveness results in distinct functional outputs, with robust tumour-suppressive senescence being mounted only in the pancreas. This suggests that the selective pressure to lose CDKN2A is highest in the pancreas, as are its pro-tumorigenic effects.

Third, to quantify selection and clonal outgrowth conferred by Cdkn2a inactivation in different organs, we pursued a forward genetic screening approach in mice. We performed insertional mutagenesis using the insect-derived piggyBac transposon system, which we adapted earlier for applications in mice42,43. In cancers derived from corresponding mouse colonies (Pdx1-cre;KrasLSL-G12D/+,Rosa26LSL-piggyBac/+;ATP1-S2 and Vil-cre;KrasLSL-G12D/+,Rosa26LSL-piggyBac/+;ATP1-S2), we sequenced and mapped transposon insertions as described previously[44]. To infer the selective advantage conferred by Cdkn2a inactivation, we ranked Cdkn2a insertions by read coverage relative to all other insertions in each sample (Methods). These analyses revealed that 44% of Cdkn2a insertions were top-10 ranked in pancreatic cancers, but only 9% in the intestine (Fig. 5i). Thus, Cdkn2a inactivation confers a significantly stronger selective advantage in the pancreas compared with in the intestine. Conversely, transposon insertions in WNT pathway genes were typically top-10 ranked in the intestine, but not in the pancreas (Fig. 5i)—confirming the dependence of KrasG12D-initiated intestinal cancer evolution on WNT pathway activation (Fig. 4f,g).

Fourth, further evidence linking tissue-specific Cdkn2a activity and natural selection for its loss comes from our prospective studies in mouse models of KrasG12D-initiated pancreatic, lung and intestinal cancer, where genomic Cdkn2a loss is near-ubiquitous in the pancreas but rare in the other organs (Fig. 5a).

Finally, by comparing KrasG12D-mutant mouse cohorts with or without Cdkn2a inactivation across tissues, we determined context-dependent effects of Cdkn2a loss on oncogenesis. We found that homozygous loss of Cdkn2a substantially accelerates KrasG12D-initiated pancreatic cancer evolution (Ptf1acre/+;KrasLSL-G12D/+;Cdkn2afl/fl (PKC) versus Ptf1acre/+;KrasLSL-G12D/+ (PK) mice; Extended Data Fig. 11f). Notably, deletions affecting chromosome 4 (containing Cdkn2a) are frequent in PK mice but absent in PKC mice (Extended Data Fig. 11g), excluding a major contribution of genes nearby Cdkn2a to this phenotype, which can be relevant in other contexts[45]. In contrast to the pancreas, Cdkn2a loss had a far less pronounced effect on tumour evolution in our intestinal cancer models (Vil-cre;KrasLSL-G12D/+;Cdkn2afl/fl versus Vil-cre;KrasLSL-G12D/+ mice; Extended Data Fig. 11f). Likewise, the Cdkn2a effects are relatively modest in KrasG12D-driven lung adenocarcinoma models (Extended Data Fig. 11f, data are from ref. [46]).

Taken together, these functional studies in KRASMUT-initiated cancers establish a chain of causality linking cell-type-specific CDKN2A chromatin states to differential CDKN2A expression and tumour suppression, ultimately defining the distinct selective pressures to lose this locus in different tissues.

Order of gene alteration varies by tissue

We previously observed that KrasMUT allelic imbalance in the pancreas is contingent on homozygous Cdkn2a loss[18]. Given the reduced ability of Kras to engage Cdkn2a in the lungs and intestine, we hypothesized that the sequence of genetic events during cancer evolution might differ between the three tissues. To study the temporal order of gene alterations, we performed phylogenetic studies analysing the allelic imbalance at Cdkn2a and Kras in matched primary cancers and metastases from our mouse models. Out of 81 lung and intestinal MCCA samples, 5 matched cases were Cdkn2aHOMKrasG12D-iGD and were therefore amenable to such analyses. One pair displayed identical CNV and loss of heterozygosity (LOH) patterns at Cdkn2a and Kras, preventing the reconstruction of their sequentiality. In three out of the remaining four cases (one lung, two intestine), we found that Cdkn2aHOM was acquired after KrasG12D-iGD (Fig. 5j and Extended Data Fig. 12a), a scenario that we did not observe for the pancreas in a previous study[18]. To further confirm the latter, we investigated 45 novel cell lines from matched primary–metastasis mPACA pairs. In all cases with inferable sequence of genetic events, Cdkn2aHOM preceded acquisition of KrasG12D-iGD (Fig. 5j and Extended Data Fig. 12a). These results are consistent with Cdkn2a loss licensing oncogenic signalling amplification in the pancreas. Indeed, we observed that PanINs of Ptf1acre/+;KrasLSL-G12D/+;Cdkn2afl/fl mice have strongly increased rates of KrasG12D-iGD as compared to Ptf1acre/+;KrasLSL-G12D/+ mice (Extended Data Fig. 7c,d). Overall, these findings highlight that KrasMUT gene dosage increase is contingent on homozygous Cdkn2a inactivation in the pancreas, but not in the lung and intestine.

To investigate whether mutation patterns in human samples can be reconciled with the evolutionary principles identified in mice, we used TCGA and ICGC data for various correlative analyses. The low number of CDKN2AHOM intestinal cancers (2 out of 136) prevented meaningful analyses in this entity. In the mouse lung, acquisition of Cdkn2aHOM after KrasG12D-iGD suggests that Cdkn2a inactivation provides a late-stage selective advantage. Indeed, human LUCA with CDKN2AHOM loss displayed enhanced KRAS signalling and reduced survival compared with CDKN2AHET/WT cancers (Extended Data Fig. 12b,c and Supplementary Table 16). Such late evolutionary advantage could explain why CDKN2AHOM is more frequent in LUCA cell lines versus tissue collections (Fig. 5a,b, Extended Data Fig. 12d,e and Supplementary Table 17). Thus, while CDKN2AHOM is not required for KRASMUT-iGD acquisition in the lung, it might subsequently provide a selective advantage by allowing unrestrained KRAS signalling.

In the mouse pancreas, we showed earlier that KrasG12D-iGD can be licensed by Cdkn2aHOM but also by Trp53 loss[18]. To study these genetic dependencies in humans, we examined the frequency of KRASMUT-iGD in PACA proficient for CDKN2A and TP53 (CDKN2APROFTP53PROF). As CDKN2APROFTP53PROF pancreatic cancers are infrequent (<10%), we combined the COMPASS and TCGA-PAAD cohorts. Notably, KRASMUT-iGD was frequent in CDKN2AHOM and/or TP53HOM cancers (68 out of 185) but did not occur in CDKN2APROFTP53PROF pancreatic cancers (0 out of 16) (Fig. 5k and Supplementary Table 18). These data demonstrate that the contingency of KRASMUT-iGD on preceding CDKN2A loss not only applies to mice but also to human PACA evolution. By contrast, in human LUCA and COCA, the frequency of KRASMUT-iGD is independent of TSG status (Fig. 5k and Supplementary Table 18)—consistent with the mouse data.

Thus, oncogene–tumour suppressor interactions display marked tissue specificity. In the pancreas, CDKN2AHOM precedes and licences KRASMUT-iGD during early cancer evolution, whereas KRASMUT-iGD is compatible with CDKN2A proficiency in the lung and intestine. However, in these organs, late-stage CDKN2AHOM acquisition can provide a selective advantage through increased oncogenic signalling and tumour aggressiveness.

KRAS–TSG interactions across cancer types

To examine whether these principles of oncogene–tumour suppressor interaction are generalizable beyond pancreatic, lung and intestinal cancer, we extended our analyses to TCGA data encompassing over 10,000 human samples from 33 cancer types. We selected entities comprising large numbers of cases with KRAS mutations, including bladder, rectal, stomach and uterine carcinomas. To determine KRASMUT and CDKN2A gene dosage, we purity-corrected tissue-derived SNV and CNV data (Methods). Moreover, we analysed public human scRNA-seq data to quantify CDKN2A expression in healthy epithelial cells, serving as potential cell of origin for the selected cancer types47,48,49. Integrated data analysis revealed that CDKN2AHOM is most frequent in bladder cancer (50% of cases), consistent with frequent occurrence of KRASMUT-iGD in this cancer type and high CDKN2A activity in bladder epithelial cells (Fig. 5l–n). By contrast, CDKN2A loss is less frequent in tissues with low CDKN2A expression and reduced occurrence of KRASMUT-iGD (CDKN2AHOM in 0% of rectal, 12% of stomach and 0% of uterine carcinomas). Overall, findings from this expanded set of tissues support a model in which KRAS signalling strength and tissue-specific CDKN2A chromatin states jointly shape the CDKN2A response and the selective pressure to inactivate this tumour-suppressive barrier.

Discussion

Our study describes the MCCA, a comprehensive cancer cell resource for mice, encompassing 590 lines from 46 cancer entities and subentities. MCCA comprises large datasets, including molecular profiles and phenotypic annotations of cell lines and mice. The availability of clinical metadata, biobanked tissues and matched normal material further supports broad applicability of the MCCA in biomedical research. All data are accessible through a user-friendly cBioPortal web interface (www.mcca.tum.de) for versatile data mining and integrative analyses, such as the exploration of genotype–phenotype relationships and their context dependencies.

Cellular models constitute a major pillar of functional experimentation, not least given their ease of manipulation and (high throughput) perturbation6,7,8,50. The opportunities for mechanistic research offered by MCCA are potentiated by the immunocompetent transplantability of the resource. We developed computational methods to precisely determine strain composition and MHC haplotypes from genomic data, enabling us to predict immunocompatibility and guide selection of suitable hosts. We found that even extensive SNP mismatch can be compatible with engraftment in MHC-matched transplantations, but the extent of SNP divergence affects the experimental outcomes. This highlights the importance of strain and MHC annotation for MCCA, which will facilitate the in vivo investigation of a broad spectrum of research questions.

MCCA-based analyses of KRAS-initiated cancers together with human investigations and functional studies in mice highlighted general principles of tissue-specific cancer evolution, with marked differences between quiescent and proliferative organs (Fig. 5o).

The pancreas is a prototypical tissue in which oncogenesis starts in a terminally differentiated non-proliferative compartment (acinar or ductal cells). Here, the critical first step in oncogenesis is de-differentiation (Fig. 5o). We found that this process is KRASMUT dosage sensitive, explaining our previous observation that KRASMUT-iGD is acquired early during cancer evolution[18]. The associated engagement of CDKN2A, enabled by the active CDKN2A chromatin state in differentiated pancreatic cells, constitutes a strong barrier to early tumour progression. Consequently, the selective pressure to inactivate CDKN2A is high, and its loss needs to precede KRASMUT-iGD.

In the lung, we also observed KRASMUT-dosage-dependent induction of developmental programs. However, in contrast to pancreatic acinar or ductal cells, lung alveolar cells displayed low CDKN2A activity. Increased tolerance to KRASMUT signalling amplification in these cells explains why homozygous CDKN2A loss is less frequent in human LUCA (15%) than in human PACA (64%). Moreover, as KRASMUT gene dosage increase is not severely constrained by CDKN2A in the lung, the order of KRASMUT and CDKN2AHOM acquisition can be reverse to that observed in the pancreas (Fig. 5o). Finally, cell-type-specific chromatin states (and the resulting gatekeeper activity) of CDKN2A explain tissue-specific oncogenicity of KRASMUT: whereas carcinogenesis is rapid and multifocal in the mouse lung, only one cancer evolves after long time frames in the mouse pancreas.

The intestine is the prototype of a highly proliferative epithelial tissue. Owing to high cellular turnover rates, oncogenesis requires acquisition of KRASMUT in long-lived stem or precursor cells. However, clonal expansion of KRASMUT beyond the crypt relies on block of differentiation, as does expansion of subsequent KRASMUT-iGD. In the intestine, this cannot be induced by KRAS, but depends on WNT signalling activation, the hallmark event for adenoma formation. Thus, only from this stage onwards can KRASMUT-iGD clonally expand and drive aggressive growth and invasion (Fig. 5o). Although CDKN2A activity is very low in the intestine (as in lung), Vil-cre;KrasLSL-G12D/+ mice rarely develop carcinomas, even when aged up to 2 years. This apparent paradox can be explained by the contingency of KRASMUT on sporadic acquisition of WNT pathway alterations to drive cancer evolution in the intestine.

Our results shed light on central mechanisms underlying tissue-specific oncogene–tumour suppressor collaboration. We highlight genetic interactions, pinpoint the processes that they drive, the stage at which they occur and the dosage at which they interact in different tissues. The varying ability of CDKN2A to restrain KRAS-induced oncogenesis in different tissues translates into distinct frequencies of complete, partial or lacking CDKN2A inactivation (Fig. 5a,b). Moreover, CDKN2A haploinsufficiency displays genetic context dependencies, even within the same tissue, as exemplified in the pancreas, in which partial CDKN2A inactivation is associated with KRASMUT-HET (Fig. 5k). Such findings are consistent with a continuum model of tumour suppressor and oncogene function[51] and the existence of extensive dosage sensitivities in oncogenesis[52]. We show that gene-dosage sweet spots in genetic interactions are not random but are demarcated by context-specific evolutionary constraints and contingencies. These considerations might also be relevant to cancer prevention and treatment, as both oncogenic dosage increase and co-deletions with CDKN2A have been implicated in therapy response and resistance53,54.

Overall, our study identifies rules, molecular hallmarks and mechanistic principles defining KRAS-initiated cancer evolution in different tissues. The results support a deterministic model of cancer evolution with predictable trajectories that explain the tissue-specific patterns of genomic alterations in human cancers. This work was triggered and developed by key investigations of the MCCA resource, which will be continuously expanded to advance mechanistic and translational cancer research.

Methods

Cell line collection, characterization, maintenance and dissemination

MCCA lines were generated either in-house, provided by collaborators or obtained from public repositories. Details of the original source (laboratory or commercial vendor) of each MCCA line are provided in Supplementary Table 1. In case a cell line has been published previously, the original research article for each individual MCCA line is referenced in Supplementary Table 1. MCCA lines were maintained under cell-line-specific conditions. For each MCCA line, the medium composition (basal medium, growth factors and so on) and cell culture requirements (adherent/suspension, extracellular matrix/coating) are provided in Supplementary Table 1.

To ensure the quality, utility and long-term preservation of MCCA cell lines, a strategy for MCCA handling and maintenance was established that covers several aspects. First, rigorous quality controls are performed, ranging from regular mycoplasma testing and human DNA detection PCRs to regenotyping of mouse alleles to prevent contamination or misidentification of cell lines. Moreover, the recombination of genetically engineered alleles was tested in cell lines directly after their isolation from mice to detect potential fibroblast contaminations (which were removed through differential trypsinization). Second, standardized protocols for the culture and maintenance of cell lines were implemented, encompassing (1) the assessment of cell density before molecular characterization; (2) the propagation of cell lines using splitting ratios adapted to proliferation rate; and (3) the amplification of cell lines from the same pool of original cells to minimize variation between batches of cells. Third, comprehensive phenotypic and molecular characterization of cell lines were performed using standardized analytical approaches. To ensure a high quality of analyses, computational approaches optimized for the mouse were applied[13]. Fourth, to ensure secure long-term preservation of the MCCA resource, backup vials for each line have been archived at at least two independent locations. Finally, all information relevant for the request of MCCA lines can be found on the ‘Resource availability’ page at www.mcca.tum.de.

All characterization data generated as part of MCCA are publicly available through a mouse-specific cBioPortal instance (branched from main v.3.7.1) at www.mcca.tum.de.

Animal cohorts and experiments

Mice were housed under specific-pathogen-free conditions in groups of up to five animals per cage under a 12 h–12 h light–dark cycle at 21–22 °C temperature and 45–65% relative humidity, and supplied ad libitum with standard chow and water. Female and male mice were randomly submitted to respective tumour cohorts. The maximal tumour size/burden permitted by the IACUC and the local authorities (Regierung von Oberbayern) is 1.5 cm in diameter, which was not exceeded in our study. All animal studies were conducted in compliance with European guidelines for the care and use of laboratory animals and were approved by the Institutional Animal Care and Use Committees (IACUC) of the Technische Universität München, Regierung von Oberbayern and the UK Home Office. All genetically engineered mouse alleles included in this study are referenced in Supplementary Table 1.

Histopathological analyses

For histological characterization, 2-μm-thick specimens from formalin-fixed paraffin-embedded material were routinely stained with haematoxylin and eosin (H&E), scanned (Leica LAS X, v.3.7.5.24914; Leica Aperio ImageScope, v.12.4.3.5008) and submitted to at least two veterinary pathologists experienced in comparative cancer pathology in mouse models. Histomorphological evaluation and tumour grading were performed according to the guidelines of the MMHCC (Mouse Models of Human Cancers Consortium (NIH/National Cancer Institute)) and organ-specific INHAND classifications[57]. If required, immunohistochemistry was performed to validate H&E-based histopathological classification.

gDNA and RNA isolation

Cells were cultured according to the conditions described in Supplementary Table 1. Isolation of genomic DNA (gDNA) and RNA was conducted according to the manufacturer’s instructions using the DNeasy Blood & Tissue Kit (Qiagen) and the RNeasy Kit (Qiagen), respectively. For gDNA isolation, frozen cell pellets were used. For the collection of RNA, cells were cultured in the corresponding culture medium and immediately lysed with RLT buffer (Qiagen) containing β-mercaptoethanol and homogenized with QIAshredder columns (Qiagen) before proceeding with RNA isolation. gDNA and RNA concentrations were determined using a Qubit fluorometer (Thermo Fisher Scientific).

Genomic sequencing of MCCA lines

Whole-exome sequencing (WES) was performed using 450 ng of gDNA from mouse cell lines and matched normal samples from mouse tail biopsies. Coding exons were enriched by whole-exome pull-down using the Agilent SureSelect XT Mouse All Exon Kit according to the manufacturer’s instructions and sequenced on the NovaSeq 6000 (Illumina) system.

Low-coverage whole-genome sequencing (lcWGS) was performed using 200 ng of gDNA from mouse cell lines and matched normal samples from mouse tail biopsies when available. Libraries were prepared using the TruSeq DNA Nanokit (Illumina) according to the manufacturer’s instructions. The resulting libraries were analysed on the 2100 Bioanalyzer instrument (Agilent Technologies) and sequenced on the NextSeq 550 (Illumina) or NovaSeq 6000 (Illumina) system.

Analysis of genomic sequencing data

The analysis of WES data from mouse tumour–normal sample pairs was performed according to the GATK best practice suggestions. The established MoCaSeq analysis pipeline (v.0.4.54)[13] was used for processing all samples. Raw BCL files were converted into demultiplexed FASTQ files using bcl2fastq (v.2.20.0.422). Raw sequencing reads were trimmed using Trimmomatic (v.0.39)[58], removing leading and trailing bases with Phred scores below 25 and reads with less than 50 nucleotides. Moreover, an average base quality of 25 was enforced with a sliding window of 10 nucleotides for the reads. Passing reads were then aligned to the GRCm38.p6 reference genome using BWA-MEM (v.0.7.17)[59] with the default settings. The mapped reads were processed using samblaster (v.0.1.26)[60], sambamba (v.0.7.0)[61] and Picard tools (v.2.20.0). Mutect2 from the GATK toolkit (v.4.2.0.0)[62] was used to call indels and somatic mutations with the default settings. Variants were filtered for read orientation artefacts using GATK. For each tumour sample, the corresponding normal sample was used to filter germline variants. Moreover, candidate somatic mutations were filtered for SNPs by excluding variants listed in the Wellcome Trust Sanger Mouse Genome Project SNP database (v5) (ENA study PRJEB11471). Furthermore, somatic mutations were filtered if (1) the read coverage was below 5 in both the control and tumour; (2) the variant allele frequency (VAF) was below 5%; and (3) the number of reads carrying the variant was below 2 in the tumour sample and equal to 1 or 0 in the normal sample. Annotation of somatic variants was performed using SNPeff (v.4.3)[63]. SNVs with a low predicted impact as well as variants at non-exonic sites were excluded from further analysis. DNA tumour/normal copy ratios were determined using CNVKit (v.0.9.9)[64]. The copy-number calling was performed using the batch command of the CNVKit pipeline for read coverage estimation, normalization and segmentation. The probe regions of the Agilent SureSelect XT Mouse All Exon Kit were used as on-target regions.

The analysis of WES and WGS data from human tumour/normal sample pairs was performed based on a modified version of MoCaSeq adapted to the human genome. Raw sequencing data were obtained from TCGA (dbGAP study phs000178.v11.p8) and the ICGC PanCuRx study (EGA studies EGAD00001003585, EGAD00001004551, EGAD00001006081 and EGAD00001006152). Reads were aligned to the GRCh38.p12 reference genome. Variants were called using Mutect2 from the GATK toolkit (v.4.2.0.0)[62]. Genes were annotated using SNPeff (Ensembl 92) and Gencode (v.31)[65]. Potential somatic variants were filtered for SNPs by excluding SNVs listed in the GnomAD (>1%)[66] and dbSNP (>5%)[67] database. The copy ratios were determined by using the batch command of CNVKit (v.0.9.9) in WGS mode and Agilent SureSelect Human All Exon V7 exon probes (S31285117) as on-target regions. For the detection of microsatellite instability (MSI), MSIsensor (v.0.5)[68] was run with the default parameters and using the GRCm38.p6 microsatellites data provided by the authors (but no evidence for MSI was found in the MCCA; Supplementary Table 6).

The lcWGS data were analysed analogously to the WES data regarding trimming, mapping and postprocessing. Copy-number calling was performed with CNVKit[64] using the whole-genome sequencing mode combined with the Agilent SureSelect XT Mouse All Exon Kit exon probe regions as on-target regions, according to the CNVKit best practice suggestions. Postprocessing and data visualization were performed in R (v.4.4.1) using data.table (v.1.14.8), ggplot2 (v.3.4.2), pheatmap (v.1.0.12) and ComplexHeatmap (v.2.16.0).

Purity correction and gene allele state analyses

To determine purity and ploidy values for cancer tissues, we reanalysed TCGA cancer tissue samples using ABSOLUTE[69] (v.1.0.6) with the default parameters and total copy number as well as mutation data as input. Purity and ploidy estimates from ABSOLUTE were reviewed and curated based on manual inspection of copy-number profiles and SNP/SNV frequencies for each sample and compared to the purity and ploidy values as provided by the PanCanAtlas (https://gdc.cancer.gov/about-data/publications/pancanatlas). The consensus genomic tumour purity value of each sample was then used to bioinformatically adjust the VAF of SNVs as well as the copy ratio of CNV segments as they would be detected in pure cancer cells. The purity and ploidy values for cancer tissues of the ICGC PanCuRx cohort were estimated similarly to as described above (including manual review of purity/ploidy solutions and bioinformatical purity adjustment of SNV VAFs and CNV copy ratios). Only human cancer tissues with KRAS exon 2 hotspot mutations (G12* or G13*; referred to as KRASMUT) were considered for further downstream analyses. Moreover, human cancers with a purity below 20% were excluded from the analyses of KRAS, CDKN2A and TP53 allele states to ensure robust detection of allelic imbalances and/or deletions. Estimating tumour purity and gene allele state based on genomics data from bulk tissue samples can be complicated by purity–ploidy ambiguity (multiple possible combinations matching a single genomic profile), intratumour heterogeneity (subclonal copy-number alterations appear with attenuated log_2 ratios) and low tumour content (increasing stroma and immune cell admixture masking tumour-derived signals). These potential confounders were accounted for in the data analysis by manual review of all candidate purity–ploidy solutions through two independent experts, by evaluation of allelic imbalance in the dominant tumour clone and by exclusion of samples with tumour purity below 20%, respectively. Given the typical low purity of pancreatic cancer tissue samples, analyses of gene allele states in this entity were primarily performed using the ICGC PanCuRx cohort, which encompasses genomics data generated from laser microdissected pancreatic cancer tissues (as opposed to the TCGA-PAAD dataset).

For microdissected MCCA tissues, purity correction of KrasG12D VAFs was performed based on the quantification of non-recombined KrasLSL-G12D alleles. Custom-designed TaqMan quantitative PCR (qPCR) assays were used to determine KrasLSL-G12D allele and total Kras locus copy (KrasCopy) quantities (Supplementary Table 19). KrasLSL-G12D quantities were normalized to KrasCopy to account for potential copy-number changes at the Kras locus in cancer cells. Normalized KrasLSL-G12D values directly reflect stroma contamination and were used for bioinformatical purity adjustment of tissue-based KrasG12D VAFs. For this, contaminating stroma reads were subtracted from tissue-based amplicon-based next-generation-sequencing data of the Kras locus to finally obtain pure KrasG12D VAFs.

For the analysis of gene allele states, processed VAFs and copy number ratios were integrated. Purity-adjusted VAF and copy ratio (CR) values were used for cancer tissues, but not cell lines. For analyses of PAAD, NSCLC and COAD cohorts of the CCLE dataset[6], processed VAF and CR data were downloaded from the cBioPortal study, Cancer Cell Line Encyclopedia[6] (https://www.cbioportal.org/study/summary?id=cellline_ccle_broad). KrasG12D (mouse) and KRASMUT (human) allelic states were classified using the following thresholds for VAF and CR: dGD (0.05 ≤ VAF < 0.4), HET (0.4 ≤ VAF < 0.61), gain (VAF ≥ 0.61 and 1.3 ≤ CR < 2.8), amp (VAF ≥ 0.61 and CR ≥ 2.8) and LOH (VAF ≥ 0.61 and CR < 1.3). Cdkn2a (mouse) and CDKN2A (human) allelic states were classified as follows: WT (VAF = 0 and CR ≥ 0.87), HET (0 < VAF < 0.85 or 0.19 < CR < 0.87) and HOM (VAF ≥ 0.85 or CR ≤ 0.19).

Analysis of TMB and CNV load

For the analysis of TMB in MCCA, cell lines with available WES data of the cancer and matched normal control were used, resulting in a final set of 190 samples from the intestine, liver, lung, pancreas and stomach. Somatic mutations were retained if they met the following criteria: (1) read coverage ≥10 at the variant site in both tumour and matched normal sample; (2) VAF ≥ 10%; (3) ≥3 variant-supporting reads in the tumour; and (4) no variant-supporting reads in the matched normal. Protein-coding exon coordinates were obtained from the GENCODE M25 annotation (Mus musculus), including all exons from protein-coding transcripts. Overlapping regions were collapsed to generate a non-redundant, non-overlapping set of exonic intervals representing the protein-coding exome. Mutations outside these regions were excluded. TMB was calculated as the number of all protein-coding exonic mutations divided by the total exonic length (in megabases). A detailed description for the analysis of the ‘effective’ pTMB in different transplantation scenarios is provided in the ‘Immunophenotyping’ section. For the TMB analyses in human cancer cell lines, pre-processed somatic mutation calls were retrieved from DepMap 24Q4 (https://doi.org/10.25452/figshare.plus.27993248.v1; file, OmicsSomaticMutations.csv)[50] for the same set of CCLE samples of ref. [7] that was also used for the cross-species comparison of MCCA and CCLE transcriptomes. Samples corresponding to the same tissue types as those selected in MCCA were retained, resulting in 336 CCLE samples. The exome was defined using GENCODE v38 (Homo sapiens) according to the same collapsing strategy as for MCCA. As matched normal samples are not available for CCLE, mutations were retained if they met the following criteria: (1) read coverage ≥ 10; (2) VAF ≥ 10%; and (3) ≥3 variant-supporting reads in the tumour. TMB was computed as above (and pTMB as described in the ‘Immunophenotyping’ section). For TCGA samples, WES data were obtained for tumours and matched normal controls as described above. Samples with annotated low quality or low tumour purity estimates (<40%) were excluded. To avoid patient-level redundancy, when multiple samples were available from the same individual, one sample was randomly selected. Samples matching the selected MCCA tissue types were retained, yielding 1,551 samples. For the selected samples, somatic mutation calls were obtained from the GDC data portal and VAFs were purity-corrected as described below. Mutations were filtered using the same criteria as for MCCA, and the exome was defined as for CCLE. TMB was calculated using the same approach.

For the analysis of CNV load in MCCA, copy-number profiles generated by CNVkit (see the ‘Analysis of genomic sequencing data’ section) were obtained for 590 MCCA cell lines as determined by WES (n = 200) or lcWGS (n = 390). Tissues represented by at least five MCCA lines were selected for downstream analyses, including soft tissue, bile duct, intestine, liver, lung, pancreas, stomach, lymphoid T, lymphoid B, myeloid, nervous system and oesophagus, resulting in a final set of 562 samples. CNV load was calculated as the percentage of the autosomal genome affected by copy-number alterations. In detail, genomic segments with an absolute log_2-transformed copy ratio of >0.2 were defined as altered, and their total length was divided by the total autosomal genome length and multiplied by 100. For the CCLE cell lines reported in ref. [7], preprocessed copy-number segment profiles were obtained from cBioPortal[15]. Samples corresponding to the same tissue types as those selected in MCCA were retained, resulting in 606 samples. CNV load was computed using the same approach as for MCCA using R (v.4.4.1) and with data.table (v.1.14.8). For TCGA, pre-processed copy-number segment data were obtained as described above and samples with annotated low quality or low tumour purity estimates (<40%) were excluded. When multiple samples were available from the same individual, one was randomly selected. Samples matching the selected MCCA tissue types were retained, yielding 1,962 samples. Copy-number ratios for the selected samples were retrieved from the GDC portal and purity-corrected as described below. CNV load was calculated as described for MCCA and CCLE.

The weighted genome instability index (wGII) was determined as described previously[70]. wGII estimates copy-number instability based on the proportion of the genome with aberrant copy number compared with the median ploidy, weighted on a per chromosome basis; and correlates with copy-number instability in cancer cell lines.

Immunophenotyping

For the analysis of mouse genetic background (strain) from genomic sequencing data, a library of strain-specific signature SNPs was first established. For this, a total of 21,923,209 SNPs was extracted from the whole-genome sequencing catalogue of the Mouse Genomes Project, which comprises 29 widely used inbred mouse strains[20]. Pearson correlation of SNP patterns was calculated for each pairwise inbred strain combination. Correlation values were used for hierarchical clustering to assign the 29 inbred mouse strains into 15 genealogically related strain groups. Strain-specific marker SNPs were required to be unique to a particular strain group but were allowed to be shared within the same group. In total, 1,097,314 genome-wide signature SNPs were determined. This library of signature SNPs was then used to infer the percentage strain composition of MCCA lines from genomic sequencing data. For this purpose, the genome was binned into consecutive 10 Mb bins. Strain-specific signature SNPs were assigned to each bin according to their genomic position. Genomic sequencing data were used for performing variant calling and SNP detection in MCCA lines. The list of identified SNP variants was matched against the library of strain-specific signature SNPs. An enrichment score was calculated for each bin per strain, based on the number of matching SNPs and normalized to the number of expected SNPs. The length of all bins with a sufficient enrichment score was summed to derive a genome-wide enrichment score for each of the 29 inbred strains. Genome-wide enrichment scores for the top three highest scoring strains were reported for each MCCA line (Supplementary Table 5).

The backcross status of MCCA lines was estimated from sequencing data (genomic strain composition; Supplementary Table 5) based on the following equation:

$${\mathrm{cBS}}_{{N}}={\log }_{2}\,\left(\frac{2\times 100 \% }{\sum {\mathrm{FS}}_{ \% }-\sum {\mathrm{AS}}_{ \% }}\right)$$

where cBSN is the computational backcross status, \(\sum {{\rm{FS}}}_{ \% }\) is the sum of foreign strain contribution and \(\sum {{\rm{AS}}}_{ \% }\) is the sum of allele-clustered SNP contribution). Three specific considerations are accounted for in the equation. Related to \({\log }_{2}()\): each successive backcross generation reduces the genetic contribution of the original strain background by 50% in the genome of the backcrossed mouse. Backcross generation can therefore be inferred by calculating the log_2 from the fraction of the original strain background remaining in the backcrossed mouse genome. Related to \((2\times 100 \% )\): Both copies of a diploid genome (the maternal and paternal) need to be backcrossed and considered in the analyses. Related to \((\sum {{\rm{FS}}}_{ \% }-\sum {{\rm{AS}}}_{ \% })\): SNPs clustered around non-congenic alleles withstand backcrossing and thereby confound the calculation of backcross status. We therefore filter SNPs found in close genomic proximity to non-congenic mouse alleles. To this end, the genetic background (strain) of the ES cell, in which a mouse allele was originally engineered, was annotated for the mouse alleles present in each individual MCCA line (Supplementary Table 1). This information was then used to remove SNPs corresponding to the strain of a given allele within a 25 Mb window of its genomic integration site (for mouse alleles with unknown genomic integration sites, a value of 1% per allele was subtracted from the total enrichment score of the corresponding genetic background). This procedure was performed for all genetically engineered alleles of a given MCCA line to finally compute allele-adjusted enrichment scores for each detected strain contribution (Supplementary Table 5).

For MHC haplotype analysis from genomic sequencing data, a library of MHC-specific signature SNPs was generated first. For this, the MHC locus was divided into 6 gene clusters (H2-K, -A, -E, -D, -Q and -T) based on their MHC subclass assignment (class I or II, classical or non-classical). The identification of MHC-specific signature SNPs was performed in a way that is comparable to the identification of strain signature SNPs but for each H2 gene cluster individually. First, a total of 375,097 MHC SNPs was derived from the whole-genome sequencing catalogue of the Mouse Genomes Project, comprising 29 inbred mouse strains[20]. In the second step, Pearson correlation of SNP patterns with hierarchical clustering was used to define MHC haplotypes for each MHC gene cluster individually. Identified H2 gene cluster haplotype groups were verified using existing MHC haplotype information, if available (https://www.imgt.org/IMGTrepertoireMH/Polymorphism/haplotypes/mouse/MHC/Mu_haplotypes.html). For each individual H2 gene cluster, signature SNPs were required to be exclusive to a particular haplotype group but were allowed to be shared within the same group. In total, 44,219 MHC gene cluster signature SNPs were identified. This panel of signature SNPs was finally used to determine H2 gene cluster-specific MHC haplotypes for MCCA lines based on genomic sequencing data. Such as for the genome-wide strain analysis, MHC gene clusters were binned (here into segments of 1 Mb size) and the enrichment score for each bin was calculated. On the basis of the enrichment scores, the MHC haplotype was assigned to each of the 6 H2 gene clusters, which, in combination, defines the full MHC haplotype of an individual MCCA line (Supplementary Table 5).

For sex analysis, the number of uniquely mapped reads was calculated for each chromosome using samtools coverage (v.1.17). Next, the ratio of Y-chromosome-specific read counts over the sum of all autosome-specific read counts was calculated. On the basis of samples with available sex annotation, a cut-off of 0.05 was selected for the Y chromosome mapping ratio to assign male (≥0.05) or female (<0.05) sex (Supplementary Table 5).

For the quantification of the ‘effective’ pTMB of MCCA lines in defined immunocompetent transplantation scenarios, information on genetic background (strain), MHC haplotyping and TMB were integrated. The TMB was analysed as described in the ‘Analysis of TMB and CNV load’ section, but with two modifications. First, variants detected in the tumour were not filtered for their presence or absence in the matched normal sample, as somatic and germline variants need to be considered for this type of analysis. Second, as only non-synonymous (protein-altering) variants can be potentially immunogenic in immunocompetent settings, the TMB output was filtered for variants with a predicted impact classified as MODERATE or HIGH based on the Ensembl Variant Effect Predictor (referred to as the TMB of protein-coding alterations, pTMB). These variants were then annotated as either somatic (present in the tumour, absent in the normal) or germline (present in both tumour and normal). Germline variants were further required (1) to be reported as a strain-specific germline variant in the SNP catalogue of the Mouse Genomes Project[20]; (2) to match the genetic background annotation of the corresponding MCCA line as provided in Supplementary Table 5; and (3) to localize outside of SNP-dense genomic regions (which are indicative of errors in the genome assembly, as reported previously[71]). The ‘effective’ pTMB of a given MCCA line was then calculated from its protein-altering germline variants that do not match the genetic background of the corresponding recipient mouse (plus the protein-altering somatic mutations found in the same MCCA line). As the ‘effective’ pTMB of an MCCA line is recipient dependent, two distinct scenarios of immunocompetent transplantation were analysed. Scenario 1: MHC and the first (if possible, also the second) most dominant genetic background detected in an MCCA line match the recipient (reduced contribution of germline variants to the ‘effective’ pTMB). Scenario 2: MHC but only the most dominant genetic background detected in an MCCA line do match the recipient (elevated contribution of germline variants to the ‘effective’ pTMB).

3′ RNA-seq

Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously[72]. In brief, barcoded cDNA of each sample was generated with Maxima RT polymerase (Thermo Fisher Scientific) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. The ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO site and the adaptor. The NEBNext Ultra II FS kit was used to fragment cDNA. After end-repair and A-tailing, a TruSeq adapter was ligated, and 3′-end fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to a previous study[72], the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read1 and barcodes and UMIs in read2 to achieve a better cluster recognition. The library was sequenced on the NextSeq 550 (Illumina) system with 63 cycles for the cDNA in read1 and 16 cycles for the barcodes and UMIs in read2.

The 3′ RNA-seq data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables[73]. The reference genomes GRCm38 and GRCh38 were used for alignment of mouse and human samples, respectively. Transcript and gene definitions were used according to the Gencode (v.38)[65]. The data were processed in R using the DESeq2 package (v.1.46.0) for read normalization and variance stabilizing transformation[74].

Analysis of transcriptome data

Batch correction was performed to obtain a single, full MCCA transcriptome dataset. Transcriptomes of MCCA lines were generated by 3′ RNA-seq in three independent batches (batch B1–B3), and each individual cell line was sequenced in technical replicates. To facilitate batch correction, the largest batch (B1) included 53 reference samples that were matched to B2 (27 reference samples) and B3 (26 reference samples). B1, B2 and B3 count matrices were transformed using the variance stabilizing transformation (vst()) function of the DESeq2 package (v.1.46.0)[74]. Next, the batch effect was examined based on the clustering of MCCA batches and reference samples in dimensionality-reduction plots (PCA and UMAP). One technical replicate from B1 was removed owing to a clear separation from the other technical replicates of the same sample. Next, the duplicate correlation (dupcor()) function of limma (v.3.5.4)[75] was used to compute the correlation of technical replicates for matching reference samples across batches. The dupcor() function estimates the correlation between duplicates (here matching reference samples) by fitting a mixed linear model individually for each gene. Finally, to remove the batch effect, the removeBatchEffect function of limma was applied by using lineage information (as covariate), the batch information and the consensus duplicate correlation (returned by dupcor). The resulting batch corrected expression matrix was evaluated for the absence of batch effects using dimensionality-reduction plots (PCA and UMAP). Last, the gene coefficients obtained from removeBatchEffect were retained and the duplicated reference samples (from B2 and B3) were removed from the dataset.

GSVA was performed on rlog-normalized gene expression data using the GSVA R package (v.1.52.3)[76]. Gene set libraries MSigDb-Hallmark-2020, KEGG-2021 and WikiPathway-2021 of enrichR (v.1.62.0)[77] as well as gene sets described previously26,55,56 were used for analyses. Gaussian kernel was used for nonparametric estimation of the cumulative distribution function of (sorted) expression levels, and normalized GSVA scores were extracted.

To assess pathways enriched in hepatic cell lines of MCCA cultured in 2D versus 3D Matrigel conditions, RNA-seq raw counts were normalized using the median-of-ratios method and variance-stabilized with the rlog transformation in DESeq2 (v.1.46.0) under R (v.4.4.1). Gene set enrichment analysis was conducted in enrichR (v.1.62.0), using the MSigDb-Hallmark-2020 gene set library.

For KRASG12D overexpression experiments, count matrices were transformed using variance-stabilizing transformation as described in the ‘3′ RNA-seq’ section. Genes with a low sum of counts across all samples were removed (≥30 for CACO2, HBEC3KT and HPDE; ≥60 for HCEC1CT, MODEK and 266-6). The PCA was conducted on rlog-normalized data by selecting the top 10% of the most variable protein-coding genes based on their s.d. across samples. For a given principal component, the 250 genes with the highest positive loadings and the 250 genes with the most negative loadings were extracted. Loadings represent the coefficients that quantify how strongly each gene contributes to the variance captured by a principal component. Positive and negative loadings correspond to genes driving the separation of samples toward opposite directions along the principal component axis, reflecting, for example, the KRASG12D dosage-dependent induction of transcriptional changes on PC1. Gene set enrichment analysis of these gene sets was performed using enrichR (v.1.62.0)[77]. The gene set libraries MSigDb-Hallmark-2020, KEGG-2021 and GO-Biological-Process2023 of enrichR (v.1.62.0)[77] were analysed.

For the comparison of transcriptomes of acinar cells 0 and 24 h after explantation from Ptf1acre/+;KrasLSL-G12D/+ mice, RNA-seq raw counts were normalized using the median-of-ratios method and variance-stabilized with rlog transformation in DESeq2 (v.1.46.0) under R (v.4.4.1). Differential expression analysis was performed with DESeq2, and genes were considered to be differentially expressed at a FDR < 0.05. Significance values for Kras were extracted accordingly.

Raw count RNA-seq data from the TCGA-LUAD were downloaded through the GDC data portal (https://portal.gdc.cancer.gov/). Only cancer tissues with KRAS exon2 hotspot mutations (G12* or G13*) were considered for analysis. Differential expression analysis was performed in R (v.4.4.1) with Limma (v.3.5.4)[75]. A gene was considered to be differentially expressed if its FDR-adjusted P value was below 0.05. Gene set enrichment analysis was performed using enrichR (v.1.62.0)[77] with the gene set libraries MSigDb-Hallmark-2020 and KEGG-2021. Postprocessing and data visualization were performed in R (v4.4.1) using data.table (v.1.14.8), ggplot2 (v.3.4.2), pheatmap (v.1.0.12) and ComplexHeatmap (v.2.16.0).

Genome and transcriptome stability of cell lines

To determine the stability of genomes and transcriptomes of mouse cancer cell lines, pancreatic cancer cell lines that have been cultured and characterized multiple times over a period of 10 years (up to 13 passages difference) was used (Supplementary Table 2). Cell lines that contained mixed populations of epithelial and mesenchymal cells were not included (to avoid confounding effects on the analysis of transcriptome stability), resulting in a set of 30 cell lines shared between both datasets.

The stability of mouse pancreatic cancer cell line transcriptomes was assessed by comparing cell lines cultured and profiled by RNA-seq in 2022 (data from MCCA) and 2016 (data from ref. [18]). RNA-seq raw counts from both cohorts were normalized using the median-of-ratios method and variance-stabilized with rlog transformation using DESeq2 (v.1.46.0) in R (v.4.4.1). The top 10% most variable genes, ranked by s.d., were selected and used to cluster the samples by Euclidean distance and complete linkage. Cell lines were assigned into transcriptomic clusters in each dataset to assess the similarity of transcriptome clustering for each individual sample over time and passages.

Genome stability was investigated by comparing the log_2-transformed copy ratio values of copy-number profiles for the same cell line as detected by WES in 2012 (data from ref. [18]) or lcWGS in 2022 (data from MCCA). Both datasets were analysed using MoCaSeq (described in the ‘Analysis of genomic sequencing data’ section). Copy-number segments generated by HMMCopy were binned to 1 Mb intervals for each chromosome. If a bin contained two different log_2-transformed values, a weighted mean (based on the size of each overlap) was calculated to assign a single log_2-transformed value to the bin. The bins were then smoothed using the median and a window size of 5 bins, excluding chromosomes associated with chromothripsis and other complex rearrangements. For a cell line with sufficient copy-number changes (median log_2 > 0), the median log_2-transformed value was subtracted from every bin to recentre the data. To quantify the stability of a cell line, the Euclidean distance between the log_2-transformed copy ratio value of WES and lcWGS data was calculated per bin and averaged across each chromosome.

To compare the genome stability of MCCA lines to human models, two separately generated copy-number datasets of large-scale human cancer cell line projects were used: CCLE (Broad Institute) and Genomics of Drug Sensitivity in Cancer (GDSC, Sanger Institute). Genome stability was investigated by comparing the log_2-transformed copy ratio values of copy-number profiles for the same cell line as detected by WES (CCLE, data from ref. [7]) or SNP array (GDSC, data obtained from https://cellmodelpassports.sanger.ac.uk/downloads). The copy-number profiles of 625 cell lines matched between CCLE and GDSC were analysed using the same analytical workflow as described above for the comparison of mouse cancer cell line genomes. Notably, a direct comparison of genome stability in mouse and human cancer cell lines has certain limitations. For example, while the majority of cell lines in CCLE and GDSC (473 out of 625) were obtained from the same supplier (Supplementary Table 2), the exact number of passages separating individual cell line pairs in both projects is unclear. Furthermore, the increased genome instability in human cell line pairs might be linked to the increased copy-number load observed in human cancers.

Mouse–human cross-species comparison of cancer genomes and transcriptomes

For the mouse–human cross-species genome analyses, CNV profiles of MCCA lines were generated using CNVkit (v.0.9.9) with a bin size of 20 kb. Germline CNVs were manually filtered out before the analysis. For each cancer entity, consensus CNV profiles were obtained by binning the genome and calculating the average of normalized copy-number values at each genomic bin across all cancers of a given disease type. The resulting consensus plots provide entity-specific CNV landscapes and were used for the annotation of orthologues of recurrently amplified or deleted human cancer genes. These genes were identified for each disease type individually, by first assembling all genes affected by recurrent copy-number alterations in the corresponding human cancer type (CCLE and TCGA cohorts). In a second step, this list of genes was filtered for those genes reported in the Cancer Gene Census. Finally, a representative set of cancer genes was selected for each disease type, based on gene amplification/deletion frequency and literature search. These overlays enable direct comparison of mouse and human CNV patterns, highlighting conserved lineage- and entity-specific alterations.

For the mouse–human cross-species transcriptome analyses, gene-level RNA-seq count data for human cancer cell lines were obtained from the CCLE dataset provided by ref. [7] (file, CCLE_RNAseq_genes_counts_20180929.gct.gz). Counts were normalized using the median-of-ratios method and variance-stabilized by vst transformation using DESeq2 (v.1.46.0) in R (v.4.4.1). For mouse cancer cell lines from the MCCA dataset, previously generated batch-corrected variance-stabilized counts were used. Only tissues and tumour types represented in both MCCA and CCLE datasets were retained. Tissue nomenclature was manually curated to harmonize labels between datasets. Human–mouse orthologous gene names were obtained from Ensembl (v.103, https://doi.org/10.1093/nar/gkae1071) using biomaRt (v.2.60.1), retaining only 1:1 orthologues (n = 13,134). Genes with one-to-many or many-to-one relationships were excluded. For each tissue, the top 10% most variable orthologous genes were selected based on standard deviation, separately for MCCA and CCLE. The intersection of these sets was defined as the set of common top variable genes. Pearson correlation coefficients were computed between MCCA and CCLE samples using these genes. The resulting correlation matrices were clustered using the Ward.D2 method. The morphology of human pancreatic cancer cell lines was classified based on the expression of epithelial (CDH1 associated) and mesenchymal (VIM associated) gene signatures described previously[78]. The upregulation/downregulation of each signature was determined by GSVA, performed on rlog-normalized gene expression data using the GSVA R package (v.1.52.3)[76].

Epigenetic analyses

ROADMAP RNA-seq and ChIP–seq data shown in Fig. 5c were obtained from the NIH ROADMAP epigenomics web portal (https://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html)[35]. For the pancreas (E098), lung (E096), small intestine (E109) and large intestine (E106) tissues, the consolidated and normalized bigWig files (GRCh37, hg19) were converted to the bedGraph format using the UCSC utility tool bigWigToBedGraph (v.1.04.00)[79]. For quantification of CDKN2A and KRAS mRNA expression levels, the normalized reads per kilobase million counts were used. For analysing histone modifications at the CDKN2A or KRAS locus, the signal scores were calculated as negative log_10 of the Poisson P values.

For the chromHMM analysis provided by ROADMAP, the core 15-state model was selected, and the joint mnemonics bed files were obtained from the NIH ROADMAP epigenomics web portal[35] (https://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html). This model was previously trained by integrating 5 chromatin marks (H3K4me3, H3K4me1, H3K36me3, H3K9me3, H3K27me3) for 127 reference epigenomes, whereby the chromatin state with the highest posterior probability given by the model was assigned to each genomic bin. The states were additionally stratified into eight active (TssA, TssAFlnk, TxFlnk, Tx, TxWk, EnhG, Enh, ZNF/Rpts) and seven inactive or repressed (Het, TssBiv, BivFlnk, EnhBiv, ReprPC, ReprPCWk, Quies) states. The chromHMM data were filtered to the pancreas (E098), lung (E096), small intestine (E109) and large intestine (E106) tissues and processed/harmonized using RNA-seq and ChIP–seq analyses.

For the quantitative comparison of ChIP–seq signals at CDKN2A and KRAS across healthy human tissues (Extended Data Fig. 10a,b,d,e), H3K4me3 and H3K27me3 ChIP–seq raw data of ENCODE and ROADMAP reference epigenomes were downloaded as FASTQ format from the ENCODE web portal80,81 (Supplementary Table 15). All datasets were processed with nf-core’s ChIP–seq pipeline (v.2.0.0)[82], run separately for each histone mark, with narrow settings for H3K4me3 and broad settings for H3K27me3. Normalized coverage tracks (BigWig files) generated by the pipeline were then used to compute the H3K4me3 or H3K27me3 signals, defined as the sum of continuous signal values across the genomic region of interest normalized to its kb length. For H3K27me3, the ChIP–seq signal was quantified by determining histone occupancy across all exonic regions of CDKN2A (ENST00000304494.10 and ENST00000579755.2) or KRAS (ENST00000256078.10 and ENST00000311936.8). For H3K4me3, the ChIP–seq signal only at the promoter containing exons of CDKN2A or KRAS was analysed. The CDKN2A Ex1β promoter region was excluded from the analyses, as the H3K4me3 peak at this promoter could not be discriminated from the promoter signal of CDKN2B-AS1 (300 bp upstream of CDKN2A Ex1β).

H3K4me3 and H3K27me3 ChIP–seq data of healthy mouse tissues were obtained from refs. 83,84,85,86,87,88,89,90 (Supplementary Table 15). The analytical workflow described for human tissues above was applied with identical parameter settings to ChIP–seq data from healthy mouse tissues. ENSMUST00000060501.4 and ENSMUST00000107131.1 transcript annotations were used for Cdkn2a. This approach provided a quantitative measure of H3K4me3 and H3K27me3 signals that could be compared across tissues and between species.

Single-cell sequencing and pseudobulk analyses

Single-cell RNA-seq datasets from multiple human tissues were processed for tissue-specific pseudobulk analysis using Python (v.3.9.12) with Scanpy (v.1.9.3), Pandas (v.1.5.3) and Numpy (v.1.24.4). Datasets included epithelial cells from the pancreas, lung, rectum, stomach, bladder and uterus as well as small and large intestine. For the pancreas, lung and intestinal datasets, raw count matrices were downloaded38,39,40 and processed by filtering cells based on gene counts (min., 300; max., 2,500) and mitochondrial content (<50%), removing genes expressed in fewer than three cells, normalizing and log-transforming. Next the PCA, neighbourhood graphs (n_neighbours = 50), Leiden clustering and UMAP embeddings were computed. Only epithelial cells were retained, and clusters with fewer than 100 cells were excluded. For the bladder[47], uterus[49], stomach and rectum[48], epithelial cells were selected from publicly available datasets, filtering for healthy samples and relevant tissue annotations. For cell-of-origin profiling, specific epithelial subtypes were extracted from each tissue: acinar and ductal (pancreas), type I and type II pneumocytes (lung), stem cells (small intestine, large intestine, rectum), surface foveolar and cycling cells (stomach), bladder urothelial cells (bladder) and non-ciliated epithelial cells (uterus). CDKN2A and KRAS mRNA expression levels were determined through pseudobulk analysis. For each group of cells in a specific tissue, the counts of gene-specific reads detected per cell were summed and divided by the sum of total reads detected in all cells of an individual donor. These normalized gene mRNA expression levels were independent of cluster size and sequencing depth, and therefore comparable across cell types and datasets. Donor samples contributing <50 cells of a particular group of cells were aggregated into a single sample. Donor-specific pseudobulk CDKN2A and KRAS mRNA expression values were used for statistical comparison of gene expression between cell types.

A comparable analysis was performed for mouse single-cell RNA-seq data[91], focusing on the lung, pancreas and intestine datasets. Similarly, samples were subset based on the cell-of-origin classifications defined for the human data, and pseudobulk aggregation was applied to these samples.

For the analysis of scRNA-seq data from ref. [29], the dataset was subset to defined cell states of KrasG12D-driven pancreatic cancer evolution: KrasG12D-mutant acinar cells (Cpa1+tdTomato+Krt19−) and KrasG12D-mutant metaplastic ADM/PanIN cells (Cpa1−tdTomato+Krt19+), as well as KrasG12D-mutant cancer cells of an additional 15-month-old mouse. KrasWT acinar cells (Cpa1+tdTomato−Krt19−) of the dataset were used as control. These four groups of cells were assigned based on the localization of marker gene expression in UMAPs, which were computed from a PCA (n_combs = 30), neighbourhood graphs (n_pcs = 30) and Leiden clustering. The normalized mRNA expression of Kras or Cdkn2a was determined by calculating the mean of normalized read counts in each cell state and were statistically compared across independent samples.

In vivo transposon mutagenesis screens, QiSeq and CIS analysis

In vivo transposon mutagenesis screens in the pancreas and intestine have been performed as described previously42,43,52. Quantitative transposon insertion site sequencing (QiSeq) was performed as described previously43,44. In brief, genomic DNA was sheared to around 250 bp fragments using a Covaris M220 ultrasonicator. Fragmented DNA was end-repaired, A-tailed and ligated to a Y-shaped splinkerette adapter consisting of a double-stranded region with a hairpin loop and a single-stranded overhang. Library preparation was carried out separately for the 5′ and 3′ transposon ends. In a first PCR, junction-containing fragments were selectively amplified using transposon-specific and splinkerette-specific primers, as the adapter design prevents priming from the splinkerette during the initial cycle. A nested PCR introduced Illumina P5 and P7 adapters together with sample-specific barcodes. Libraries were quantified by qPCR with P5/P7-specific primers, pooled in equimolar amounts and sequenced on the Illumina MiSeq platform (paired-end, 65 bp). For the analysis of sequencing raw data, reads containing transposon-genome junctions were extracted, aligned to the mm10 reference genome using SSAHA2 and collapsed to unique insertion sites per tumour, with read counts quantified per site. For the pancreas samples, sequencing data were obtained from a previous study[43] and analysed using the same computational workflow.

The subsequent common insertion site (CIS) analysis was conducted as follows. For each sample, insertion counts were normalized to library size and expressed as counts per hundred reads by dividing the counts per insertion by the total number of reads and multiplying by 100. Insertions with normalized counts of <0.02 were excluded. Insertion coordinates from all samples of the same cohort were merged into a single BED file. CISs were identified with MACS2 (v.2.2.7.1) (https://github.com/macs3-project/MACS) using four window sizes (5, 30, 60 and 100 kb) with genome size, shift and extension parameters adjusted accordingly, and with the nomodel and nolambda options enabled. Peaks were filtered at q < 0.05, and neighbouring peaks within 10 kb were collapsed. CISs were annotated with overlapping and flanking genomic features. Insertion sites with fewer than ten supporting reads and/or normalized counts of <0.02 were excluded from further analysis. Insertion sites within the Cdkn2a locus (including Cdkn2a and Ncruc/Gm12610) or in WNT pathway-related genes (including Apc, Ctnnd1, Rnf4 and Rspo2) were then ranked according to sequencing read coverage and normalized insertion counts relative to all other insertions in the respective cancer. Finally, Cdkn2a locus or WNT pathway gene transposon insertions were classified as either high-ranked (top 10) or low-ranked based on their rank in each cancer sample. Assuming that the stronger the selective advantage conferred by a gene perturbation, the more pronounced the expansion of the affected cell, such ranking can serve as a direct measure for the selective advantage conferred by a transposon insertion during cancer evolution.

Orthotopic transplantation

For mPACA transplantation experiments, 2,500–10,000 cancer cells were orthotopically grafted into the pancreas of syngeneic immunocompetent C57BL/6J or 129 WT mice. For orthotopic transplantation, mice were anaesthetized with a combination of medetomidine, midazolam and fentanyl. The left flank was carefully shaved, the eyes were protected with ointment and the abdomen was disinfected. When anaesthesia was complete, a 1.5 cm left-lateral incision caudal to the spleen was made, and the pancreas was located, and was then carefully pulled out of the abdomen to make it accessible for intraparenchymal injection. Cell suspension was administered slowly using a 27 G needle at a depth of 3–4 mm. The needle was left in this position for at least 30 s to avoid leakage of the bubble. The pancreas and spleen were carefully placed back in their anatomical position and covered with PBS to avoid organ adhesion. The peritoneum was closed with interrupted sutures (5-0 Ethilon) and the skin with wound clips. The mice were kept in a 37 °C heating chamber until they woke up.

For mCACO, transplantation experiments were performed as previously described[92]. In brief, organoids were dissociated into clusters of 5–10 cells and resuspended in a transplantation medium consisting of PBS, B27, N2, l-Glutamine (Thermo Fisher Scientific), 10% Matrigel (Corning) and 10 µM Y-27632 (Stem Cell Technologies). For each injection (2–3 per mouse), 50 dissociated organoids were prepared in a volume of 100 µl. The procedure involved anaesthetizing the mice, followed by gently rinsing the colon with PBS using a syringe and a straight oral gavage needle. Colonoscopy was performed using a rigid endoscope from Karl STORZ (1.9 mm in diameter) with linear Hopkins lens optics (ColoView System). Organoids were injected into the submucosa of the colon using a flexible fine needle (Hamilton; 33 gauge, custom length of 16 inches, custom point style of 4 at 45°). Correct submucosal injections were identified by the formation of a bubble that occluded the intestinal lumen. A scoring system was used to correlate the quality of injections with the experimental outcomes.

Note that MCCA lines might occasionally not engraft in fully immunophenotype-matched hosts due to non-immunological reasons, such as a lack of specific niche factors. These effects are especially relevant when transplanting low cell numbers (<10,000), which can, however, be rescued by increasing the number of injected cells.

scAAV8-based somatic mutagenesis in mice

scAAV8-based somatic mutagenesis in mice was performed as described in detail previously[28]. In brief, scAAV8 particles were produced by transfecting HEK293T cells with scAAV and helper plasmid pDP8.ape. scAAV8-producing HEK293T cells were collected, resuspended and lysed through repeated freeze–thaw cycles. Free nucleic acids were digested with Benzonase nuclease (Sigma-Aldrich) and scAAV8 particle purified from the supernatant using iodixanol-based gradient ultracentrifugation (Backman Coulter). Vivaspin 20 centrifugal concentrator columns (Sigma-Aldrich) and Ringer lactate solution were used for buffer exchange of the extracted scAAV8-containing iodixanol solution. scAAV8 titres were subsequently determined by qPCR. For this, scAAV8 viral capsids were first disrupted using alkaline lysis. The sample was neutralized before qPCR-based quantification of scAAV8 viral genomes. Next, 1 × 10^12 scAAV8 viral genomes were diluted in PBS and intraperitoneally injected into 8-week-old Ptf1acre/+;KrasLSL-G12D/+,Rosa26CAG-LSL-Cas9/CAG-LSL-Cas9 mice. Pancreata were dissected from mice 8 weeks after injection of scAAV8-Tgfbr2-sgRNA or scAAV8-Rosa26-sgRNA, or from age-matched non-injected mice. Pancreas tissue was formalin-fixed and paraffin-embedded to prepare H&E stains for the quantification of acini. The acini number was determined in H&E sections by counting acini per field of view in at least five images with ×40 magnification per animal. The averaged acini count per animal was finally used to compare pancreatic remodelling across conditions.

Amplicon-based deep sequencing

Amplicon-based deep sequencing of the mouse KrasG12D mutation, human KRASG12D mutation or the mouse Ctnnb1 exon3 hotspot mutations was performed using either 50 ng of genomic DNA (gDNA) or 1.5 µl of reverse-transcribed mRNA (cDNA). In brief, the exon2 of Kras and KRAS or exon3 of Ctnnb1 was amplified using Kapa HiFi HotStart ReadyMix (Roche, 30 cycles) and primers with TruSeq adaptor overhangs (Supplementary Table 19). In a second PCR step (ten cycles), TruSeq index primer sequences (Illumina) were added. After each PCR step, solid-phase reversible immobilization clean-up (0.8×) was performed using an Agencourt AMPure XP kit (Beckman Coulter). The pooled library was quantified by SYBR Green qPCR (Sigma-Aldrich) and a Kapa Biosystems library quantification kit (Roche). The resulting library was sequenced on a NextSeq 550 (Illumina) system. Raw reads were mapped to the matching mouse (GRCm38) or human (GRCh38) reference genome assembly. G12D mutation-specific VAFs were calculated at the corresponding genomic position. Ctnnb1 exon3 hotspot mutations were determined using Mutect2 from the GATK toolkit (v.4.2.0.0)[62].

For amplicon-based deep sequencing of all mouse Apc-coding exons, 50 ng of gDNA was used as input for PCR-based amplification with pools of primers listed in Supplementary Table 19. PCR products were enzymatically fragmented, and libraries were prepared using the TruSeq DNA Nanokit (Illumina) according to the manufacturer’s instructions. After read mapping to GRCm38, mutation calling was conducted with Mutect2 from the GATK toolkit (v.4.2.0.0)[62].

To determine KrasG12D VAFs from laser microdissected tissue, lysates were directly prepared in the sample collection tube by adding proteinase K to a final concentration of 0.4 mg ml−1 and incubating overnight at 56 °C, followed by heat inactivation at 95 °C for 15 min. Kras exon 2 was amplified using a nested PCR strategy: an initial PCR with outer primers (KAPA HiFi HotStart, 25 cycles, annealing 59 °C) was performed, followed by purification with 0.8× AMPure XP beads (Beckman Coulter). A second PCR with inner primers and Illumina TruSeq overhangs (10 cycles, annealing 55 °C) was conducted. Finally, a third PCR was used to add sample-specific barcodes and P5/P7 adapters (10 cycles, annealing 65 °C). Cycling conditions for all PCRs were as follows: 98 °C for 20 s, annealing at the indicated temperature for 20 s, and extension at 72 °C for 45 s, with a final extension at 72 °C for 2 min. A list of all of the primers used for the three PCR steps is provided in Supplementary Table 19. Final libraries were purified (0.8× AMPure XP) and sequenced on the Illumina NextSeq 1000 system. Sequencing reads were aligned to the Kras reference (GRCm38), and G12D mutation-specific VAFs were calculated at the corresponding genomic position.

cDNA synthesis and TaqMan qPCR

cDNA synthesis was synthesized from 1 mg of RNA by using SuperScript II Reverse Transcriptase (Thermo Fisher Scientific) according to standard protocols. Notably, reverse transcription was performed using random hexamers to avoid biased reverse transcription of endogenous versus lentiviral transcripts (in case oligo(dT) primers are used). TaqMan qPCR was performed using TaqMan chemistry (Thermo Fisher Scientific) and a list of the primers and probes is provided in Supplementary Table 19. Quantification of KrasLSL-G12D and KRASMUT mRNA was normalized to Kras or GAPDH, respectively. TaqMan qPCR was conducted on the StepOnePlus system (Applied Biosystems).

Flow cytometry

Mouse pancreatic ductal adenocarcinoma (PDAC) cell lines were cultured under standard conditions and treated with recombinant mouse IFNγ (BioLegend) at a final concentration of 100 ng ml−1 for 3 days. Untreated cells served as controls. After treatment, surface expression of MHC class I was assessed by flow cytometry. Cells were collected and transferred into 96-well V-bottom plates for staining. After centrifugation (5 min at 1,500 rpm, 4 °C), cells were washed with FACS buffer (PBS supplemented with 1% BSA and 5 mM EDTA) and incubated with an extracellular staining mix containing Fc block and either anti-MHC class I antibody (H-2Kb, AF6-88.5.5.3, eFluor 450, eBioscience, 48-5958-82) or the corresponding isotype control (mouse IgG2a κ, eFluor 450, eBioscience, 48-4724-82). Staining was carried out for 30 min at 4 °C, protected from light. After surface staining, cells were washed and incubated for 15 min at 4 °C with a viability dye (iFluor 840 maleimide, AAT Bioquest). After a final wash, cells were resuspended in FACS buffer and acquired on the CytoFlex Flow Cytometer (Beckman Coulter). Flow cytometry data were analysed using FlowJo software (v.10.10.0, FlowJo, BD). Appropriate gating strategies were applied to exclude debris, dead cells and doublets, and to quantify MHC-I surface expression.

Doxycycline-titratable gene overexpression

The pINDUCER20 (ref. [93]) vector system was used for doxycycline-inducible KRASG12D and GFP overexpression. HEK293FT cells were used for lentivirus production and maintained in DMEM supplemented with 10% FCS and 1% penicillin–streptomycin. In brief, the puromycin resistance was first exchanged with a hygromycin cassette and the cDNAs of oncogenic KRASG12D (CCDS 8702.1, 35G>A) or GFP were cloned in a second step into the pINDUCER20 lentiviral vector. Stbl3 bacteria (Thermo Fisher Scientific) were chemically transformed, and the plasmid DNA sequence was verified. For lentivirus production, HEK293FT cells were transfected using TransIT-LT1 (Mirus Bioscience) with standard virus packaging plasmids and the respective pINDUCER20 vectors according to the manufacturer’s recommendations. Virus-containing supernatant was pooled 48 h and 72 h after transfection, briefly centrifuged to pellet detached HEK293FT cells and filtered through 0.45-mm filters (Filtropur, Sarstedt). Lentiviral particles were stored at −80 °C until use.

For lentiviral transduction, 100,000–200,000 HPDE[94], HBEC3KT[95], HCEC1CT[96], MODEK[97] or 266-6 (ref. [98]) cells were seeded per well of a six-well plate. Acinar WT cells are not viable in vitro; thus, the acinar carcinoma cell line 266-6 was selected as model system. The cells were transduced in the presence of 1 μg μl−1 polybrene (Sigma-Aldrich). Then, 2 days after transduction, cells were selected with hygromycin (Sigma-Aldrich) for at least 7 days. HPDE and HBEC3KT cells were cultured in keratinocyte-SFM medium (Thermo Fisher Scientific), supplemented with bovine pituitary extract, EGF (Thermo Fisher Scientific) and 1% penicillin–streptomycin; HCEC1CT cells in a mixture of DMEM (80%) and MEM199 (20%) supplemented with 2% FCS, EGF, insulin-transferrin-selenium, hydrocortisone (Thermo Fisher Scientific) and 1% penicillin–streptomycin; and MODEK and 266-6 cells in DMEM supplemented with 10% FCS and 1% penicillin–streptomycin. After successful transduction, the inducibility of KRASG12D expression was tested using 1:10 doxycycline dilution series ranging from 0.1 to 1,000 ng ml−1. HCEC1CT and MODEK showed a reduced sensitivity/response of KRASG12D induction to doxycycline treatment. To cover the dynamic induction of KRASG12D expression levels across cell lines, the doxycycline concentration range was therefore adapted for HCEC1CT and MODEK. For doxycycline-titratable induction of KRASG12D or GFP expression, cells were either seeded in 3D (HPDE, HBEC3KT, HCEC1CT, MODEK) or 2D conditions (266-6; gelatin type A coating). For 3D conditions, 150 cells were seeded per dome consisting of 50% Matrigel (Corning). After 7 days of initial growth, target gene expression was induced for 3 days by adding the indicated amounts of doxycycline (Sigma-Aldrich) to the corresponding penicillin–streptomycin-free culturing medium. At the end point, for each cell line and doxycycline concentration, at least 20 individual spheroids were imaged to assess phenotypes and RNA was isolated by pooling four domes from the identical condition to analyse transcriptomic changes. Bright-field images were used to classify spheroids into cohesive and discohesive phenotypes. Criteria included the extent of cell-to-cell adhesion, epithelial architecture of clusters, occurrence of detached single cells, and the emergence of cell membrane protrusions as compared to the growth pattern observed for spheroids of the respective untreated model. The expert biologist was blinded for phenotype grading of bright-field spheroid images. For 2D conditions, 250,000 cells were seeded per well of a six-well plate. Target gene expression was induced the next day for 3 days using the indicated doxycycline concentrations. At the end point, RNA was isolated from one well of a six-well plate per condition. TaqMan qPCR and 3′ RNA-seq library preparation were performed as described above. The 3′ RNA-seq data analysis was conducted as described below.

Microdissection

From formalin-fixed paraffin-embedded material, one 2-µm-thick and five 10-μm-thick consecutive tissue sections were prepared and air-dried overnight. The 2 µm section was stained with H&E according to standard procedures and submitted for histopathological grading and annotation of tumour areas for microdissection. The five consecutive 10 µm sections were used for tumour microdissection. Paraffin was removed through short incubation with xylene. The specimens were briefly stained with haematoxylin and kept wet for the microdissection procedure. Individually assessed and scored samples were then microdissected under a Primovert microscope (Zeiss). gDNA was extracted using the QIAamp DNA Mini Kit (Qiagen) according to the manufacturer’s instructions, which included the use of carrier RNA to increase DNA binding during purification and a 90 °C ATL buffer incubation step to reverse formaldehyde modifications. gDNA concentrations were measured using the Qubit fluorometer (Thermo Fisher Scientific). Depending on the total available gDNA, 20–80 ng of gDNA was used as input for lcWGS, 5–20 ng for each TaqMan qPCR reaction (KrasLSL-G12D, KrasCopy) and 4–20 ng for amplicon-based deep sequencing of the KrasG12D mutation. TaqMan qPCR was performed in technical quadruplicates for each target. Ratios of KrasLSL-G12D to KrasCopy quantifications were calculated for purity estimation of microdissected tumour tissue samples. Finally, these purity values facilitated the computational subtraction of stroma contamination from lcWGS and KrasG12D amplicon sequencing data.

Laser microdissection

Laser microdissection (LMD) was performed using the Leica LMD6 system (Leica Microsystems) to isolate defined lesions from either cryosections or paraffin-embedded sections. For cryosections, tissue was sectioned at a thickness of 7 µm and mounted onto FrameSlides with a 4.0 µm PEN membrane (Leica Microsystems). The slides were thawed at room temperature for 1 h, briefly fixed in freshly prepared 80% ethanol for 20 s and then air-dried for at least 20 min. The sections were stained with methylene blue (1:10 dilution in distilled H_2O) for 20 s and rinsed twice in 80% ethanol, followed by an additional drying step (≥20 min) to optimize the laser-cutting efficiency. Paraffin sections (7 µm thick) were mounted onto glass slides with a 2.0 µm PEN membrane (Leica Microsystems), deparaffinized externally (Institute of Pathology, CEP) and transported in distilled H_2O. Methylene blue staining and drying steps were performed as described for cryosections. LMD was carried out using Leica software (v.8.4). Annotated regions of interest were identified under ×10 magnification using the Leica LMD software and excised. Dissected tissue was collected directly into the lids of eight-well PCR strip tubes containing 10 µl of lysis buffer (1:1 dilution in distilled H_2O; DirectPCR, Viagen Biotech), and the samples were immediately sealed and stored at −80 °C until further downstream analysis (see the ‘Amplicon-based deep sequencing’ section (the last paragraph is related to laser microdissected tissues)).

ADM ex vivo assay

Pancreata of 8-week-old Ptf1acre/+;KrasLSL-G12D/+ mice were collected, cut into pieces and digested twice in McCoy’s 5A Medium (Sigma-Aldrich), containing 0.5 mg ml−1 collagenase P (Sigma-Aldrich), 0.002% trypsin inhibitor from soybean (Sigma-Aldrich) and 0.1% BSA, for 10 min at 37 °C. Cells were passed through a 100 µm mesh, washed with McCoy’s 5A Medium (Sigma-Aldrich) containing 0.02% trypsin inhibitor from soybean (Sigma-Aldrich) and 0.1% BSA, and spun down for 5 min at 100g. The cells were then recovered in culture medium (Waymouth’s MB752/1 medium (Gibco), supplemented with 0.1% FCS (Merck), 1× insulin–transferrin–selenium (Gibco), 50 µg ml−1 bovine pituitary extract (Gibco), 10 mM HEPES (Gibco), 0.1% BSA, 0.01% trypsin inhibitor from soybean (Sigma-Aldrich), 2.6 mg ml−1 NaHCO_3 and 30% FCS) and were incubated for 30 to 60 min at 37 °C. After recovery (which defines the 0 h timepoint), acinar cells were cultured under suspension conditions in ultra-low-attachment plates using culture medium for 24 h at 37 °C (which defines the 24 h timepoint). For the isolation of RNA, acinar cells were pelleted at the defined timepoints. The 3′ RNA-seq and analysis of transcriptome data were conducted as described in the corresponding methods sections.

SA-βGal and Ki-67 staining

For SA-βGal staining, mouse tissues of mice were fixed in 4% paraformaldehyde (methanol free, for 1 h at 4 °C) and subsequently cryoprotected in 15% and 30% sucrose (each at least for 2 h at 4 °C) before being embedded in Tissue-Tek O.C.T. compound. The tissue blocks were frozen in a dry-ice ethanol bath and stored at −80 °C. Cryosections at a thickness of 5 μm were performed using the Leica Biosystems Cryostat (CM3050, Leica). β-Galactosidase staining on cryosections was performed using the Senescence β-Galactosidase Staining Kit (Cell Signaling) according to the manufacturer’s protocol. Nuclear counterstaining was performed using Nuclear Fast Red (Certistain, Merck).

For Ki-67 staining, formalin-fixed paraffin-embedded duodenal sections from WT and Vil-cre;KrasLSL-G12D/+ mice were deparaffinized and rehydrated through graded ethanol series. Antigen retrieval was performed according to the manufacturer’s protocol. The sections were incubated with anti-Ki-67 antibody (Abcam, ab16667) at the recommended dilution, followed by detection using an appropriate HRP-conjugated secondary antibody and chromogenic substrate (according to the manufacturer’s instructions). Nuclei were counterstained with haematoxylin.

PRC2 inhibition in organoids

Intestinal and pancreatic ductal organoids were treated with a combination of A-395 hydrochloride (Sigma-Aldrich) and UNC1999 (Sigma-Aldrich), as described previously[99]. Treatment was initiated immediately after seeding into Matrigel and continued over the course of two passages (pancreas, 12 days; intestine, 10 days) to not only facilitate the block of de novo H3K27me3, but also to allow for the dilution of existing H3K27me3 through cell division. To ensure sufficient cell proliferation during the treatment period, organoids were split once between passages. The final concentrations were 4 µM A-395 and 2 µM UNC1999, added directly to the organoid culture medium. Control organoids were treated with the corresponding vehicle controls (distilled H_2O for A-395, DMSO for UNC1999). Organoids were cultured under standard conditions and monitored throughout the treatment period. At the end of treatment, organoids were counted, and cell pellets were collected for RNA isolation and western blot analysis.

Western blotting of histone marks

Cell pellets were lysed in RIPA buffer (Thermo Fisher Scientific) containing protease (Pierce Mini Tablets) and phosphatase inhibitor mixes I and II (SERVA), and sheared using a Covaris M220 (20 °C, 2 min, peak power 50, duty factor 20, 200 cycles per burst). The protein concentration was measured using Pierce BCA Protein Assay Kit (Thermo Fisher Scientific), and 40 µg per sample was denatured in 5× Lämmli buffer at 95 °C for 5 min. Proteins were separated on Mini-PROTEAN TGX gels (BioRad) at 65 V (stacking) and 90 V (resolving) and transferred to 0.45 µm nitrocellulose membranes (Thermo Fisher Scientific), soaked in Power Blotter 1-Step transfer buffer (Thermo Fisher Scientific) using the Power Blotter Station PB0010 (Thermo Fisher Scientific). Membranes were blocked in 5% BSA/TBS for 1 h, incubated overnight at 4 °C with anti-H3K27me3 (tri-methyl-histone H3 (Lys27) rabbit monoclonal antibody, Cell Signaling, 9733, 1:1,000) and anti-H4 (histone H4 (L64C1) mouse monoclonal antibody, Cell Signaling, 2935, 1:1,000) in 2.5% BSA/TBST, washed and incubated with anti-mouse Dylight 680 (Cell Signaling, 5470, 1:8,000) or anti-rabbit Dylight 800 (Cell Signaling, 5151P, 1:8,000) for 1 h. After the final washes, the blots were imaged on the LI-COR Odyssey Fc system and analysed using Image Studio.

Statistics and reproducibility

For each experiment, all statistics were performed as indicated in the respective Figure and Extended Data Figure legends. Statistical testing across all classes was performed to account for multiple testing. Continuous variables were tested for normal distribution. Non-parametric tests were used for non-normally distributed data. GraphPad Prism (v.8.0.1) was used for significance calculations. Complex statistical techniques are explained in detail in the relevant Methods section.

Materials availability

MCCA lines are available from the lead contact or the original contributor on request. All detailed information can be found at the ‘Resource availability’ and ‘Contacts’ pages on www.mcca.tum.de, or in Supplementary Table 1.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The following reference genomes were used: GRCm38.p6 (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001635.26/) and GRCh38.p12 (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.38/). The following gene annotations were used: mouse gene annotations (GENCODE mouse M25; https://www.gencodegenes.org/mouse/release_M25.html), human gene annotations (GENCODE human v38; https://www.gencodegenes.org/human/release_38.html), Agilent WES mouse target regions (Agilent SureSelect XT Mouse All Exon, V1; https://earray.chem.agilent.com/suredesign/), Agilent WES human target regions (Agilent SureSelect Human All Exon V7 exon, S31285117; https://earray.chem.agilent.com/suredesign/) and Ensembl human-mouse orthologous gene names (v103; https://doi.org/10.1093/nar/gkae1071). The following SNP annotations were used: MGP SNP database (v5 from https://www.sanger.ac.uk/data/mouse-genomes-project/), GnomAD database (v.2.0.1 from https://gnomad.broadinstitute.org/) and dbSNP database (9606-b150 from https://www.ncbi.nlm.nih.gov/snp/). TCGA data were downloaded from dbGAP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v11.p8) through GDC (https://portal.gdc.cancer.gov/). TCGA purity/ploidy reference values were obtained from the PanCanAtlas (https://gdc.cancer.gov/about-data/publications/pancanatlas). PanCuRx data were downloaded from EGA (EGAD00001003585, EGAD00001004551, EGAD00001006081 and EGAD00001006152 as part of https://ega-archive.org/studies/EGAS00001002543). CCLE data were downloaded from cBioPortal (https://www.cbioportal.org/; studies: Cancer Cell Line Encyclopedia6,7 and DepMap 24Q4 (https://doi.org/10.25452/figshare.plus.27993248.v1))[50]. GDSC data were downloaded from the CellModelPassports database (https://cellmodelpassports.sanger.ac.uk/downloads). ROADMAP data were downloaded from the ROADMAP database (https://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html). ROADMAP and ENCODE ChIP–seq data were downloaded from the ENCODE database (https://www.encodeproject.org/). Mouse scRNA-seq data were downloaded from the GEO (GSE141017) and the Tabula Muris Senis consortium (https://doi.org/10.6084/m9.figshare.8273102.v2)[100]. Mouse pancreatic cancer WES data were downloaded from the ENA (PRJEB23116). Human scRNA-seq data were downloaded from GEO (GSE84133 and GSE185224), from refs. 39,48,49 and the Tabula Sapiens consortium (https://figshare.com/articles/dataset/Tabula_Sapiens_v2/27921984)[101]. Mouse ChIP–seq data were downloaded from ENA (PRJNA63471, PRJNA737464, PRJNA529029, PRJNA246383, PRJNA291874, PRJNA1094907, PRJNA664361 and PRJNA892467). lcWGS, WES and 3′ RNA-seq data generated in this study are deposited under ENA accession number PRJEB78428. Processed genomic and transcriptomic data of MCCA lines are publicly available through www.mcca.tum.de. Source data are provided with this paper.

Code availability

Bioinformatics analysis was performed using publicly available programs and parameters described in the Methods. MoCaSeq (v.0.4.54) source code is available at GitHub (https://github.com/roland-rad-lab/MoCaSeq). StrainMapper (v.1.0.0) source code generated in this study is available at GitHub (https://github.com/roland-rad-lab/StrainMapper).

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Acknowledgements

We thank J. Eichinger, A. Grotloh, V. Aigner, L. Lunglmeir, L. Suresh, O. Seelbach and the members of the comparative experimental pathology team for technical assistance; M. Wassef and R. Margueron for their expertise and input on Polycomb-mediated repression; M. Wagner for bioinformatic support; and G. Multhoff for providing resources. This Article used data generated by the TCGA Research Network. This study was supported by the European Research Council (consolidator grant CoG PACA-MET-819642 and MSCA-ITN-ETN-861196 to R.R.; CoG no. 648521 to D. Saur; H2020-MSCA-IF-2016 no. 753058 to M.T.); the Deutsche Forschungsgemeinschaft (DFG RA1629/4-1, RA1629/9-1, RA1629/11-1 and TRR378 project ID 514894665 to R.R.; and SFB1371 project ID 395357507 to M.T., K.S. and D. Saur); DFG SA 1374/11-1 and RA 4046/2-1 Project-ID 538132723 to L.R. and D. Saur; DFG SA 1374/8-1 Project-ID 515991405 to D. Saur; DFG SA 1374/7-1 Project-ID 515571394 to D. Saur and M.S.S.; DFG SA 1374/6-1 Project-ID 458890590 to D. Saur); Deutsche Krebshilfe (70114314 and 70115167 (CUPIDO) to R.R.; DEFEAT-PDAC of the German Pancreatic Cancer Alliance no. 70117118 to D. Saur, R.R., M.S.S., G.S., C.F.; DKH Excellence Program no. 70115743 to D. Saur; no. 70116843 to D. Saur and no. 70115995 to M.T.); the German Federal Ministry of Education and Research (Cluster4Future: CNATM to R.R.); the NUCLEATE Cluster of Excellence to R.R.; the TUM Innovation Network NextGenDrugs (to R.R.); the Cura Placida Foundation (2301BGA to R.R.); the Wilhelm Sander-Stiftung (2020.174.1 and 2017.091.2 to D. Saur), the German Cancer Consortium to R.R., M.S.S and D. Saur, the DKTK BACTORG Joint Funding Program to D. Saur and the DKFZ-MOST cooperation program (Ca-217) to D. Saur. M.T. was supported by a European Molecular Biology Organization (EMBO) Long-Term Fellowship (ALTF 1290–2016); and G.D. by the Health@InnoHK, Innovation Technology Commission Funding.

Funding

Open access funding provided by Technische Universität München.

Author information

Author notes

1. These authors contributed equally: Sebastian Mueller, Niklas de Andrade Krätzig, Markus Tschurtschenthaler

2. These authors jointly supervised this work: Dieter Saur, Roland Rad

Authors and Affiliations

1. Institute of Molecular Oncology and Functional Genomics, School of Medicine and Health, TU Munich, Munich, Germany

Sebastian Mueller, Niklas de Andrade Krätzig, Miguel G. Silva, Chiara Thordsen, Riccardo Trozzo, Perrine Simon, Thorsten Kaltenbacher, Rupert Öllinger, Joscha Griger, Nina Groß, Jessica Löprich, Jeroen M. Bugter, Roman Maresch, Katharina A. N. Collins, David Sailer, Sebastian Burger, Christine Klement, Alexander Belka, Juan J. Montero, Christian J. Braun & Roland Rad

2. Institute for Translational Cancer Research (TranslaTUM), School of Medicine and Health, TU Munich, Munich, Germany

Sebastian Mueller, Niklas de Andrade Krätzig, Markus Tschurtschenthaler, Miguel G. Silva, Chiara Thordsen, Riccardo Trozzo, Perrine Simon, Frederic Saab, Thorsten Kaltenbacher, Magdalena Zukowska, Daniele Lucarelli, Rupert Öllinger, Joscha Griger, Nina Groß, Jessica Löprich, Antonio E. Zaurito, Jeroen M. Bugter, Stefanie Bärthel, Chiara Falcomatà, Roman Maresch, Katharina A. N. Collins, David Sailer, Christian Schneeweis, Sebastian Burger, Christine Klement, Alexander Belka, Juan J. Montero, Marc Schmidt-Supprian, Günter Schneider, Lena Rad, Dieter Saur & Roland Rad

3. Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany

Markus Tschurtschenthaler, Frederic Saab, Magdalena Zukowska, Daniele Lucarelli, Antonio E. Zaurito, Stefanie Bärthel, Chiara Falcomatà, Christian Schneeweis & Dieter Saur

4. Institute of Experimental Cancer Therapy, School of Medicine and Health, TU Munich, Munich, Germany

Markus Tschurtschenthaler, Frederic Saab, Magdalena Zukowska, Daniele Lucarelli, Antonio E. Zaurito, Stefanie Bärthel, Chiara Falcomatà, Christian Schneeweis, Lena Rad & Dieter Saur

5. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

Markus Tschurtschenthaler, Lisa M. Fröhlich, Maximilian Reichert, Carsten Marr, Martin L. Sos, Hans C. Reinhardt, Michael Quante, Marc Schmidt-Supprian, Katja Steiger, Lena Rad, Dieter Saur & Roland Rad

6. Institute of Computational Biology, Helmholtz Munich, Munich, Germany

Daniele Lucarelli

7. Comparative Experimental Pathology, School of Medicine and Health, TU Munich, Munich, Germany

Tanja Groll & Katja Steiger

8. Institute of Pathology, School of Medicine and Health, TU Munich, Munich, Germany

Tanja Groll & Katja Steiger

9. Klinik für Innere Medizin II, Universitätsklinikum Freiburg, Freiburg, Germany

Linus R. Schömig & Michael Quante

10. Genome Campus, Wellcome Trust Sanger Institute, Hinxton, UK

Alexander Strong, Cordelia Brandt, Simon Clare, Trevor D. Lawley, Gordon Dougan, Nathalie Conte & Allan Bradley

11. Center for Functional Protein Assemblies, TU Munich, Garching, Germany

Mulham Najajreh, Aristeidis Papargyriou & Maximilian Reichert

12. Center for Organoid Systems (COS), TU Munich, Garching, Germany

Mulham Najajreh, Aristeidis Papargyriou & Maximilian Reichert

13. Department of Medicine II, TUM University Hospital, TU Munich, Munich, Germany

Mulham Najajreh, Aristeidis Papargyriou, Maximilian Reichert & Dieter Saur

14. Translational Pancreatic Cancer Research Center, Department of Medicine II, TUM University Hospital, TU Munich, Munich, Germany

Mulham Najajreh, Aristeidis Papargyriou & Maximilian Reichert

15. Institute of Stem Cell Research, Helmholtz Munich, Munich, Germany

Aristeidis Papargyriou

16. Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany

Lisa M. Fröhlich & Martin L. Sos

17. Department of Translational Oncology, German Cancer Research Center (DKFZ), LMU Munich, Munich, Germany

Lisa M. Fröhlich & Martin L. Sos

18. Newcastle Drug Discovery Group, Translational and Clinical Research Institute, Newcastle University, Newcastle, UK

Ute Jungwirth

19. Institute of Experimental Hematology, School of Medicine and Health, TU Munich, Munich, Germany

Markus Moser & Marc Schmidt-Supprian

20. Institute of Pathology, Faculty of Medicine, LMU Munich, Munich, Germany

Jens Neumann

21. Department of Haematology, Cambridge University Hospitals NHS Trust, Cambridge, UK

George Vassiliou

22. Fundación Centro Médico de Asturias, Oviedo, Spain

Juan Cadiñanos

23. Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), Universidad de Cantabria-CSIC, Santander, Spain

Ignacio Varela

24. Institute of AI for Health, Helmholtz Munich, Neuherberg, Germany

Carsten Marr

25. Center for Molecular and Translational Oncology (COMTra), National University of Quilmes (UNQ), Buenos Aires, Argentina

Daniel F. Alonso

26. National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

Daniel F. Alonso

27. Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy

Pier-Luigi Lollini

28. IRCCS Azienda Ospedaliera Universitaria di Bologna, Bologna, Italy

Pier-Luigi Lollini

29. Broad Institute of Harvard and MIT, Cambridge, MA, USA

Jean Zhao

30. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

Jean Zhao

31. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

Jean Zhao

32. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA

Jean Zhao

33. Division of Clinical Studies, The Institute of Cancer Research (ICR) and Royal Marsden NHS Trust, Sutton, UK

Louis Chesler

34. Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK

Clare M. Isacke

35. Mildred Scheel Early Career Centre, University Hospital of Würzburg, Würzburg, Germany

Angela Riedel

36. Department of Pediatrics, Dr. von Hauner Children’s Hospital, LMU University Hospital, LMU Munich, Munich, Germany

Christian J. Braun

37. Hopp Children’s Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ), Heidelberg, Germany

Christian J. Braun

38. Department of Translational Genomics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Martin L. Sos & Filippo Beleggia

39. Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Filippo Beleggia

40. Mildred Scheel School of Oncology Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Filippo Beleggia

41. Department of Hematology and Stem Cell Transplantation, West German Cancer Center Essen, University Hospital Essen, Essen, Germany

Hans C. Reinhardt

42. Centro de Investigación Biomédica en Red de Cáncer, Instituto de Salud Carlos III, Madrid, Spain

Monica Musteanu & Mariano Barbacid

43. Department of Biochemistry and Molecular Biology, Faculty of Pharmacy, Complutense University of Madrid, Madrid, Spain

Monica Musteanu

44. Experimental Oncology Group, Centro Nacional de Investigaciones Oncológicas, Madrid, Spain

Mariano Barbacid

45. Clinical Research Unit 5002, KFO5002, University Medical Center Göttingen, Göttingen, Germany

Günter Schneider

46. Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Göttingen, Germany

Günter Schneider

47. Centre for Translational Stem Cell Biology, Hong Kong, China

Gordon Dougan

Contributions

S.M. and R.R. designed and supervised the study. S.M. and R.R. wrote the manuscript. N.d.A.K. and M.T. edited the manuscript. S.M., N.d.A.K., M.T., M.G.S., R.T., P.S., F.S., T.K. and R.R. interpreted and visualized data. N.d.A.K., R.T., P.S., F.S. and D.L. conducted bioinformatic analyses. S.M., M.T., M.G.S., T.K., M.Z., J.G., N.G., J.L., A.E.Z., S. Bärthel, C.F., A.S., C.B., C.K., N.C. and L.R. performed mouse work. S.M., M.T., M.G.S., C.T., M.Z., R.Ö., J.G., N.G., A.E.Z., L.R.S., R.M., K.A.N.C., D. Sailer, S. Burger, L.M.F. and C.K. characterized MCCA lines. T.G. and K.S. performed pathological assessment. S.M., M.T., M.G.S., C.T., T.K., M.Z., A.E.Z., S. Bärthel, C.F. and M.N. performed experiments. S.M., M.T., M.G.S., T.K., M.Z., J.G., N.G., A.E.Z., L.R.S., A.S., A.P., L.M.F., C.K., U.J., D.F.A., P.-L.L., J.Z., L.C., C.M.I., A.R., C.J.B., M.L.S., F.B., H.C.R., M. Musteanu, M.B., M.Q., M.S.-S., G.S., N.C., A. Bradley, L.R., D. Saur and R.R. contributed cell lines. J.M.B., C.S., A. Belka, J.J.M., M.R., M. Moser, J.N., G.V., J.C., I.V., C.M., S.C., T.D.L., G.D., A. Bradley and D. Saur provided critical resources and input.

Corresponding author

Correspondence to Roland Rad.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Lukas Dow and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Stability of MCCA cell line transcriptomes and genomes.

a-h, Stability of transcriptomes and genomes of a set of 30 MCCA pancreatic cancer cell lines characterized at distinct timepoints and passages (Supplementary Table 2). a,b, Unbiased hierarchical clustering of transcriptomes from 30 MCCA pancreatic cancer cell lines cultured and profiled by RNA-seq in 2022 (a, data from MCCA) or 2016 (b, data from[18], and with up to 13 passages difference. The top 10% most variable expressed genes, as detected by bulk RNA-seq, were used for clustering. Three major transcriptomic clusters (C1, C2, C3) and two subclusters of C3 (C3a and C3b) were identified in both datasets. c, Assignment of pancreatic cancer cell line transcriptomes into major transcriptomic clusters (C1, C2, C3) is largely preserved between 2022 and 2016 data sets. Two cell lines were found to switch from C3b to C3a. This observed discordance is however minor, given the small Euclidean distance between both subclusters (a,b). d, Histogram showing the genomic stability of the same set of 30 pancreatic cancer cell lines cultured and profiled by lcWGS in 2022 (data from MCCA) or WES in 2012 (data from[18], and with up to 12 passages difference. The conservation of two corresponding copy number profiles was quantified by calculating their Euclidean distance based on the overlap of copy number segment positions as well as on the similarity of corresponding log_2 copy ratio changes (see Methods). Twenty-eight of 30 cell lines showed relatively stable copy number profiles (Euclidian distance <0.06), while genomes of two cell lines were less conserved (Euclidian distance >0.09). e,f, Representative plots of overlayed copy number profiles are shown for cell lines with relatively stable genomes (Euclidean distance <0.06) in (d). g,h, Plots showing overlayed copy number profiles of both pancreatic cancer cell lines with relatively unstable genomes (Euclidean distance >0.09) in (d). Discordance of genomes largely arises from chromosome arm-level gains or losses. i,j, Histograms showing the genomic stability of human cancer cell lines cultured and profiled in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC) projects. Both projects conducted independent copy number profiling of largely overlapping cell line panels that were propagated over years at different institutions. The conservation of two corresponding copy number profiles was quantified by calculating their Euclidean distance similar to the analysis of mouse pancreatic cancer cell lines shown in (d) (see Methods). The conservation of copy number profiles between two human cancer cell lines is shown for all 625 (i) or the subset of human pancreatic cancer cell lines ((j); for its comparison to mouse pancreatic cancer in (d)).

Extended Data Fig. 2 Characterization of the MCCA and integrative analyses of various data layers.

a, Exemplary microscopic images showing the spectrum of morphologies observed in solid cancer cell lines grown in 2D conditions (n = 439). The panel shows representative images of MCCA lines with epithelial (Epi), quasimesenchymal (QM, hybrid state), weak mesenchymal (MesLOW, low spindle-shaped cell morphology) or strong mesenchymal morphology (MesHIGH, high spindle-shaped cell morphology). Scale bars, 25 µm. b, Transcriptome-based UMAP providing an overview on the clustering of cell lines across the MCCA cohort. The culture type of each MCCA line is annotated: 2D (solid cancers, grown as adherent monolayer), 3D (solid cancers, grown as organoid), or suspension (hematopoietic cancers, grown as non-adherent suspension). c,d, Assessing the effect of 2D versus 3D culture conditions on global transcriptomes and EMT states of MCCA lines. Four hepatic cancer lines of MCCA covering the full spectrum of EMT states were cultured in either 2D or 3D Matrigel conditions and profiled by RNA-seq (Epi, epithelial; QM, quasi-mesenchymal; MesLOW, MesHIGH, mesenchymal with low/high spindle-shaped cell morphology). Principal component analysis (PCA) shows culture condition-induced transcriptome changes (c). Pathways upregulated in 2D versus 3D culture conditions were determined by gene set enrichment analysis and revealed predominant effects on proliferative and metabolic programs, but not EMT (d) (Supplementary Table 3). e-q, Transcriptome-based UMAPs showing the context-dependence of transcriptome clustering for indicated subsets of MCCA lines, considering distinct phenotypic and molecular contexts. e,f, Cellular lineage identity, including hematopoietic lines (e) or solid cancer lines grown in 2D conditions (excluding 3D organoids) (f). g-i, Morphology, shown for 2D solid cancer lines of as classified by microscopy into progressive EMT states Epi>QM>MesLOW>MesHIGH (Epi, epithelial; QM, quasimesenchymal, hybrid state; MesLOW/HIGH, mesenchymal with low/high spindle-shaped cell morphology) (g); or classified by transcriptional EMT state based on EMT scores determined by gene set variation analysis using the MSigDB EMT hallmark gene set (h). The precise recording of EMT phenotypes in MCCA will allow users to account for this potential ‘confounder’, for example when comparing cell line transcriptomes – a consideration that is likely also relevant to human cell line resources. i, Transcriptome-clustering (top) and morphology (bottom) of MCCA soft tissue sarcoma cell lines (n = 18). UMAP is corresponding to (f-h). Representative microscopic images of MESHIGH sarcoma cell lines are shown on the right. Scale bars, 25 µm. j,k, Disease type, shown for lung cancers (NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer) (j), and hepatic cancer cell lines (Disease type: iCCC, intrahepatic cholangiocellular carcinoma; HCC, hepatocellular carcinoma; Genotype/trigger: Blm, Bloom∆; A, Alb-Cre; I, Rosa26PIK3CA-H1047R; K, KrasG12D; T, Trp53∆; N, Pten-sgRNA; C, Cdkn2a∆ or Cdkn2a-sgRNA) (k). Note: Disease type can be one driver of transcriptome clustering in the liver as indicated by the differential enrichment of iCCC and HCC liver cancer cell lines in two transcriptomic clusters (*P = 0.0437, two-sided Chi-squared test). l, Genotype (encompassing engineered and somatic alterations), shown for pancreatic cancers. Co-clustering of PK with PKC or PKT cell lines can be explained by their somatic acquisition of Cdkn2a or Trp53 alterations, respectively. PKB cell lines cluster with a subset of PK cells that are characterized by genetic Cdkn2a proficiency (cluster in the lower right) – an alteration profile shared by the PKB model[18]. Vertical spread of some genotypes is linked to the differential EMT/morphology status of individual cell lines (see also m,n). Genotype: P, PancCre or PancFlp; K, KrasG12D; I, Rosa26PIK3CA-H1047R; C, Cdkn2a∆; T, Trp53∆; B, Tgfbr2∆. m,n, Transcriptome-based UMAPs showing the clustering of MCCA pancreatic cancer lines, considering their microscopy-based morphology (Epi, epithelial; QM, quasimesenchymal, hybrid state; MesLOW/HIGH, mesenchymal with low/high spindle-shaped cell morphology) (m), or transcriptional EMT state (EMT scores based on gene set variation analysis using the MSigDB EMT hallmark gene set) (n). o,p, Bar plots showing the effect of mouse genotype on cell line morphology as exemplified in KrasG12D-driven pancreatic (o) and liver cancer cell lines (p) of MCCA with Cdkn2a or Trp53 inactivation (PKC, n = 22; PKT, n = 32; AKC, n = 19; AKT, n = 25). Genotype/trigger: P, PancCre; A, Alb-Cre; K, KrasG12D; C, Cdkn2a∆; T, Trp53∆. *P = 0.0372, ****P < 0.0001, two-sided Chi-squared test. q, Evolutionary disease stage, shown for serrated, intestinal cancer evolution.

Extended Data Fig. 3 Cross-species comparison of cancer transcriptomes in mice (MCCA) and humans.

a-i, Transcriptome similarity of MCCA (mouse) and CCLE (human) cancer cell lines. Heatmaps show unbiased hierarchical clustering of Pearson correlation coefficients derived from the pairwise comparison of mouse and human cell line transcriptomes for indicated cancer types. For intestinal cancers, only cell lines cultured as adherent monolayer (2D) were compared. Pearson correlation coefficients were calculated based on the top 10% most variable expressed orthologous genes within each lineage or entity (see Methods and Supplementary Table 4). An extended list of Pearson correlation coefficients for the comparison of MCCA to CCLE lines, and of CCLE to MCCA lines within the respective lineages or disease types are also provided in Supplementary Table 4. Combining the mouse/human transcriptome correlation analyses with the broad molecular and phenotypic annotation of MCCA lines will guide users to select mouse cell lines representing defined human cancer subtypes/models – and vice versa. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; HCC, hepatocellular carcinoma; PDAC, pancreatic ductal adenocarcinoma. Corr., Pearson correlation coefficient. a-c, Entity-specific clustering of indicated mouse and human neoplasms. Mouse cancer subtypes co-cluster with their respective human counterparts. Leuk., leukaemia; lym., lymphoma. d-i, Within disease type comparison of indicated mouse and human cancers. For pancreatic cancer (PDAC) (i), mesenchymal mouse and human cancer cell lines co-cluster and are both enriched for increased KRASMUT gene dosage (see also j,k). Human cell line morphology was classified based on epithelial/mesenchymal marker gene expression (see Methods). PDAC, pancreatic ductal adenocarcinoma; KRASMUT, KRAS G12* or Q61*; HET, heterozygous; iGD, increased gene dosage; NA, unknown; Epi, epithelial; QM, quasimesenchymal; MesLOW/HIGH, mesenchymal with low/high spindle-shaped cell morphology. j,k, Comparison of KrasG12D or KRASMUT gene dosage status in epithelial versus mesenchymal pancreatic ductal adenocarcinoma (PDAC) cell lines of MCCA (j) or CCLE (k), respectively. Acquisition of increased KrasG12D gene dosage is more frequent in mouse PDAC cell lines with mesenchymal morphology as compared to epithelial lines – as previously reported by us[18]. Similarly, KRASMUT (G12* or Q61*) gene dosage increase is enriched in human PDAC cell lines with mesenchymal-like morphology. (Supplementary Table 4; MCCA: 84% versus 67%, *P = 0.0467, Chi-squared test; CCLE: 100% versus 48%, **P = 0.0057, two-sided Chi-squared test). For the analysis of MCCA cell lines in (j), KrasG12D-mutant pancreatic cancer with or without engineered genetic alterations in PDAC hallmark genes Cdkn2a, Trp53 or Tgfbr2 derived from independent cancers were included in the analysis. The morphology of human PDAC cell lines (k) was classified based on epithelial/mesenchymal marker gene expression (see Methods).

Extended Data Fig. 4 Cross-species comparison of cancer genomes in mice (MCCA) and humans.

a,b, Tumour mutational burden (TMB, somatic) (a) and copy number variation (CNV) load (b) across MCCA, TCGA and CCLE cohorts. Stroma cell contributions in TCGA tissue data were corrected using cancer cell purity estimates. Only human cancer cohorts matching to MCCA cancer types were analysed (see Methods). ****P < 0.0001, two-sided Mann-Whitney test; bars, median. c-k, Consensus copy number profiles of nine MCCA cancer entities, annotated with the orthologues of genes that are recurrently amplified or deleted in the corresponding human cancer type. MCCA cancer types with sufficiently large numbers of samples and alterations for meaningful detection of recurrent copy number alterations are shown. Consensus profiles were generated by binning the genome and calculating the average copy number change at each genomic bin across all cancers of a given disease type. Y axis shows the averaged copy number value of a genomic region relative to a diploid genome (Amplification, >2; Deletion, <2). For the annotation of human cancer gene orthologues, all genes affected by recurrent copy number alterations in individual human cancer types (CCLE and TCGA datasets) were first assembled and filtered for those reported in the Cancer Gene Census. From these genes, a representative set of cancer genes was then selected for each disease type, based on gene amplification/deletion frequency and literature search. Overall, these profiles highlight that mouse and human cancers share critical disease-specific genomic alterations. Selected explanatory notes: (i) In pancreatic cancer, genes frequently affected by copy number alterations in the human disease (e.g. KRAS, MYC, CDKN2A, TP63, SMAD4) are also altered in mice: while SMAD4 deletions are less prevalent in mice than in humans, KRAS amplifications and CDKN2A deletions are frequent in both species. (c) Note that in intestinal cancer copy number changes affecting APC or CTNNB1 are infrequent, as both mouse genes are typically altered by mutations (see Fig. 4f), as in humans. (f,g) Mouse small cell lung cancers (SCLC) and non-small cell lung cancers (NSCLC) show distinct copy number alterations, as in humans. For example, amplifications of MYCL are frequent in mouse and human SCLC, but not in NSCLC. Moreover, both SCLC and NSCLC show amplifications of chromosome 6, but the oncogenic drivers of this event are likely different: amplification of the oncogenic KrasG12D knock-in allele in NSCLC versus Ccnd2 gain in SCLC (which are Kras-wildtype). HCC, hepatocellular carcinoma; iCCC, intrahepatic cholangiocarcinoma; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; PDAC, pancreatic ductal adenocarcinoma.

Extended Data Fig. 5 Analytical tools for immunophenotyping of MCCA lines using genomics data.

a, Schematic workflow illustrating the identification of mouse strain-specific signature SNPs for genome-wide detection of genetic background composition from genomic sequencing data. First, a total of 21,923,209 SNPs were extracted from the whole genome sequencing catalogue of the Mouse Genomes Project comprising 29 inbred mouse strains[20]. Next, these SNPs were used to compute the Pearson correlation of SNP patterns across mouse strains (heatmap). Based on the hierarchical clustering of these 29 inbred strains, 15 genealogically-related strains groups were defined. Finally, a total of 1,097,314 strain signature SNPs were determined based on variants that are mutually exclusive between strain groups (while signature SNPs are allowed to be shared within the same strain group). The enrichment or absence of strain-specific signature SNPs was subsequently used for precise analysis of strain composition across the entire genome. b,c, Bubble plots showing the genome-wide strain composition for a given sample. Percent genomic enrichments of signature-SNPs for each of the 29 inbred mouse strain is annotated on the right. Boxes indicate the 15 groups of genealogically-related strains. b, Strain composition in a pure FVB/NJ mouse as determined by whole exome sequencing (MMR-4805, sequencing data obtained from the Jackson Laboratory). The FVB/NJ strain is highlighted in blue. c, Analysis of strain composition for cell line MCCA0508 of the MCCA resource which was isolated from a mouse with unknown genetic background. An enrichment of FVB/NJ (90%) and 129-related strains (10–14%) was found, based on whole exome sequencing. The FVB/NJ strain and 129-related strains are highlighted in blue. d, Computational backcross generation for MCCA pancreatic cancer cell lines derived PK mice with distinct backcross status. Backcross generation was inferred from the strain composition of a given cell line as detected in genomic sequencing data (see Methods). All cell lines derived from backcrossed pancreatic cancer mouse colonies were identified as ‘highly backcrossed’ (≥ 8 generations, n = 11), which corresponds to less than 1% non-dominant genetic background contribution. In contrast, cell lines with unknown backcross status (n = 105) showed a mixed level of backcrossing. Corresponding information across the MCCA cohort is provided in Supplementary Table 5. e, Schematic workflow visualizing the identification of major histocompatibility complex (MHC) signature SNPs for MHC haplotype classification from genomic sequencing data. For this, the murine MHC locus was first divided into 6 gene clusters (H2-K, -A, -E, -D, -Q and -T) based on their MHC subclass assignment (class I or II, classical or non-classical). Data from the whole genome sequencing catalogue of the Mouse Genomes Project comprising 29 inbred mouse strains[20] was used to examine the SNP composition at MHC gene clusters and to derive a total of 375,097 SNPs. Next, these MHC-related SNPs were used to compute Pearson correlations and to perform hierarchical clustering of SNP patterns for each MHC gene cluster across the 29 inbred strains. MHC haplotype assignment and signature SNP identification were conducted for each MHC gene cluster individually, as the combination of MHC gene cluster haplotypes do not need to be conserved across strains within the same genealogical strain group. These analyses resulted in a set of 44,219 mutually exclusive MHC signature SNPs. Ultimately, MHC-specific signature SNPs were utilized to infer the haplotype for each of the 6 MHC gene clusters – which in combination defines the full MHC haplotype. f-h, Bubble plots showing the enrichment of MHC-specific signature SNPs across MHC gene clusters (H2-K,-A,-E,-D,-Q,-T) and for distinct MHC haplotypes (a,b,d,f,I,k,N,q,z). The MHC haplotypes follow the lowercase letter nomenclature, except ‘I’ and ‘N’ which were specifically defined in this study for strains I/LnJ and NOD/ShiLtJ, respectively. Horizontal boxes represent groups of inbred mouse strains with identical haplotypes in at least one MHC gene cluster. Of note, individual strains can be annotated multiple times in the MHC haplotype plot. This is because the assignment of inbred strains into MHC haplotype groups does vary across the 6 defined MHC gene clusters (as explained in (e)). f, MHC haplotype composition of each MHC gene cluster is shown for a pure FVB/NJ mouse as determined by whole exome sequencing (MMR-4805, sequencing data obtained from the Jackson Laboratory; same sample as shown in (b)). The MHC haplotype combination for this sample was predicted to be ‘q-q-q-q-q-a’, which is line with to the available MHC annotations for the FVB/NJ strain. Bubbles corresponding to FVB/NJ-specific MHC gene cluster haplotypes are highlighted in blue. g, Analysis of MHC haplotype composition for cell line MCCA0508 of the MCCA resource which was isolated from a mouse with unknown genetic background. The MHC haplotype composition was determined to be ‘q-q-q-q-q-a’ as inferred from whole exome sequencing data. This MHC locus haplotype is consistent with the high enrichment of FVB/NJ-specific strain SNPs in this sample (c). Bubbles corresponding to FVB/NJ-specific MHC gene cluster haplotypes are highlighted in blue. h, MHC haplotype composition at the MHC gene cluster H2-T in MCCA line MCCA0022 based on low coverage whole genome sequencing (lcWGS). The bubble plot shows a mosaic MHC haplotype at H2-T with enrichment for 129- (‘f’ haplotype at the 5′-end) and FVB/NJ-specific (‘a’ haplotype at the 3′-end) signature SNPs. Such mosaic MHC haplotypes are rarely observed in MCCA (0.5%) and are generated de novo through meiotic crossover events at the MHC locus in mouse cohorts maintained on a mixed genetic background. 129- and FVB/NJ-related SNP enrichment is highlighted in blue.

Extended Data Fig. 6 Relevance of genetic background, MHC-I haplotype and ‘effective’ pTMB of MCCA lines in immunocompetent transplantations.

a, Engraftment of mouse pancreatic cancer cell lines with indicated MHC haplotypes and strain contributions from C57BL/6 and 129 backgrounds when transplanted into immunocompetent C57BL/6 or C57BL/6;129-F1 hybrid mice. After their transplantation into C57BL/6 recipients, engraftment was observed in only one out of nine cases and occurred with long latency (66 days, MCCA0323). In contrast, all three cell lines engrafted when transplanted into immunophenotype-matched C57BL/6;129-F1 hybrid mice (13/13 transplantations) and formed pancreatic cancers within 16–42 days. These exemplary data show that MCCA lines carrying MHC haplotypes and/or high strain contributions from two genetic backgrounds can engraft in corresponding F1 hybrid recipients. Note that MCCA lines might occasionally not engraft in fully immunophenotype-matched hosts due to non-immunological reasons, such as the lack of specific niche factors. These effects are especially relevant when transplanting low cell numbers (< 10,000), which can however be rescued by increasing the number of injected cells. b, mRNA expression of MHC class I genes (H2-D1, H2-K1, H2-T22, H2-T23) in transplanted pancreatic cancer cell lines from Fig. 2f. MCCA0336 (highlighted in red), which engrafted despite high strain mismatch (Fig. 2f), does not show down-regulation of MHC class I gene expression in comparison to all other pancreatic cancer cells lines. Only MHC class I genes robustly expressed in this set of cell lines are shown (median vst expression >5). Batch-corrected, variance-stabilizing transformed (vst) gene expression values are shown because transcriptomes originate from distinct RNA-seq batches of MCCA. c, Flow cytometry gating strategy for the quantification of cell surface MHC class I protein expression. Sequential gating illustrates the selection of singlets, cells of interest, and viable cells. Top: Staining with anti-MHC class I antibody (H-2Kb). Bottom: Isotype control (IgG2a κ). d, Bar plot depicting cell surface MHC class I protein levels for transplanted pancreatic cancer cell lines from Fig. 2f, either untreated (baseline) or stimulated with IFN-γ (100 ng/mL, 3 days). Neither baseline nor IFN-γ-stimulated MHC levels of MCCA0336 were reduced in comparison to matched/engrafted or mismatched/non-engrafted pancreatic cancer cell lines of Fig. 2f. Order of cell lines is identical to Fig. 2f. e,f, Tumour mutational burden of non-synonymous (protein-altering) mutations (pTMB) in human (CCLE, n = 41) and mouse pancreatic cancer cell lines (MCCA, n = 27). MCCA lines with homozygous C57BL/6 MHC haplotype, C57BL/6;129 dominant genetic background and a 3rd genetic background contributing 1–9% of SNPs (which typically display higher germline SNP burdens) were selected to illustrate how these cell lines can be used to model high mutational burden. Compare to Fig. 2g,h where MCCA pancreatic cancer cell lines with only C57BL/6;129 strain contribution are shown. In the human or mouse autochthonous setting, somatic protein-altering mutations define the pTMB (e). Arrowhead indicates human pancreatic cancer cell line SNU324 which shows a high pTMB of 11.1 due to micro-satellite instability caused by a homozygous MSH2 missense mutation. In MHC-matched transplantations, the ‘effective’ pTMB of the selected MCCA lines is recipient-dependent (f). Scenario-1: transplantation into C57BL/6;129-F1 hybrid mice, somatic mutations and strain-specific germline variants of the 3rd, non-dominant strain contribute to the ‘effective’ pTMB. Scenario-2: transplantation into C57BL/6 mice, somatic mutations and strain-specific germline variants of the 3rd as well as the 129 genetic background can be immunogenic, thereby further elevating the ‘effective’ pTMB.

Extended Data Fig. 7 Allelic states of KRASMUT dosage variation, their detection in mouse tissues and their role for driving cancer evolution.

a, Overview of KrasG12D allelic ‘states’ as defined in this study. For each individual KrasG12D allelic status, exemplary CNV plots and corresponding KrasG12D variant allele frequencies (VAF) are shown for representative MCCA lines. The hypothetical dGD example (in grey) was not found in MCCA lines and is rarely detected in human cancer cohorts. dGD, decreased KrasG12D gene dosage, allelic imbalance favouring the wildtype allele; HET, heterozygous KrasG12D; iGD, increased KrasG12D dosage; gain, low-level amplification with tumour/normal copy ratio <2.8; Amp, high-level amplification; LOH, loss of heterozygosity. b, Disease-specific patient survival related to lung adenocarcinomas (TCGA-LUAD) with heterozygous or increased KRASMUT gene dosage (related to Fig. 3d). Survival curves (including censored events) show early separation but intercross at later timepoints due to event censoring. c, Experimental workflow illustrating the analysis of KrasG12D allelic imbalance in single mouse PanIN lesions. Individual PanINs were excised by laser microdissection (LMD), followed by DNA isolation and amplicon-based next-generation sequencing of Kras exon 2 using a customized nested PCR protocol (see Methods). Developing a customized protocol for amplicon-based Kras exon 2 sequencing was necessary to enable the quantification of KrasG12D allelic imbalance in single mouse PanINs, which are up to ten times smaller and contain fewer cells than their human counterparts. d, Quantification of KrasG12D allelic states in laser-microdissected PanINs from Ptf1aCre/+;KrasLSL-G12D/+ (PK) or Ptf1aCre/+;KrasLSL-G12D/+;Cdkn2aFL/FL (PKC) mice. KrasG12D allelic status was assigned based on KrasG12D variant allele frequencies (VAF) using identical thresholds as for all other mouse samples. In the PK model, mouse PanINs acquired KrasG12D-iGD, as observed earlier in humans[18]. However, the proportion of PanINs with KrasG12D-iGD was low (2 of 52 mPanINs, 3.8%), consistent with the fact that most PanINs of this mouse model do not progress to invasive carcinoma. By contrast, in the PKC model 16 of 52 PanINs (30.8%) acquired KrasG12D-iGD, in line with earlier findings (i) that tumour progression in PK mice following acquisition of KrasG12D-iGD is contingent on loss of Cdkn2a and (ii) that KRASMUT-iGD and early loss of CDKN2A occur frequent in human PanINs18,102. ***P = 0.0002, two-sided Chi-squared test. e, Scheme illustrating the inference of ‘pure’ KrasG12D variant allele frequencies (VAF) in microdissected tumour tissues of MCCA. After microdissection of tumour tissues and subsequent DNA isolation, stroma cell contamination is measured by quantifying non-recombined KrasLSL-G12D alleles using a custom-designed TaqMan qPCR assay. Non-recombined LSL-cassettes at the Kras locus are specific to stroma cells but cannot be found in tumour cells, where the LSL-cassette is recombined. To account for artifacts arising from (potential) Kras copy number changes in tumour cells, a second TaqMan qPCR assay for quantifying total Kras copy number was used to normalize KrasLSL-G12D quantities. Normalized KrasLSL-G12D values directly reflect stroma cell contamination of a given tumour tissue sample and were finally utilized for mathematical ‘purity’-correction of its KrasG12D VAF (as determined by amplicon-based next generation sequencing of the tumour tissue sample). HE stains show an example for the microdissection of lung carcinoma tissue. NGS, next generation sequencing. Scale bars, left: 1 mm, right: 200 µm. f, Representative HE stains depict lung adenoma and carcinoma disease stages in Ela-CreERTM;KrasLSL-G12D/+ mice and correspond to the analysis of KrasG12D allelic imbalance shown in Fig. 3e. Scale bars, 100 µm. g, Representative HE stains show hyperplasia, adenoma, carcinoma and metastasis disease stages of serrated, intestinal cancer evolution in Vil-Cre;KrasLSL-G12D/+ mice and correspond to the analysis of KrasG12D allelic imbalance shown in Fig. 3f. Scale bars, 200 µm. h, Genomic copy number instability of intestinal carcinoma organoid lines from Vil-Cre;KrasLSL-G12D/+ mice. The weighted Genome Instability Index (wGII) indicates the proportion of the genome with aberrant copy number, weighted on a per chromosome basis[70]. wGII correlates with copy number instability in cancer cell lines[70]. No significant difference of wGII was observed between intestinal carcinomas with one copy of KrasG12D or more than one copy of KrasG12D. P, two-sided Mann-Whitney test. i,j, Functional interrogation of the importance of KrasG12D allelic imbalance for intestinal adenoma-to-carcinoma progression. MCCA adenoma organoids from Vil-Cre;KrasLSL-G12D/+ mice with varying levels of subclonal KrasG12D allelic imbalance were selected for colorectal transplantations using mouse endoscopy. Adenoma organoid with highest KrasG12D variant allele frequency (VAF) was used as control. i, KrasG12D VAF in MCCA adenoma organoids (pre-transplantation) and corresponding carcinomas/metastases (post-transplantation, n = 21 organoids). Cartoon depicts experimental procedure. j, Correlation analysis of pre-transplantation KrasG12D allelic imbalance and engraftment rates for MCCA adenoma organoid lines (n = 81 transplantations). Dots correspond to adenoma lines shown in (i). rs, Spearman correlation coefficient; P, two-sided t-test. k, KrasG12D allelic states in carcinomas derived from transplanted intestinal adenomas with subclonal KrasG12D allelic imbalance. Kras variant allele frequencies for individual adenomas are shown in (i). High-level amplifications of KrasG12D were not detected (KrasG12D-Amp, tumour/normal copy ratio ≥2.8). Instead, all carcinomas exhibited low-level KrasG12D amplification, with trisomy of KrasG12D being the most frequent state (KrasG12D-Gain, 7 of 8 cases). Thus, subclonal KrasG12D dosage increase observed in intestinal adenomas in (i) is based on low level amplification of KrasG12D. l, Accuracy of KrasG12D variant allele frequency (VAF) quantification by amplicon-based next generation sequencing. Six samples with KrasG12D VAFs between 0.50 and 0.63 were each examined by eight independent library preparations and sequencing experiments. Of 48 total measurements, 45 deviated by less than 0.01 (1%) from the expected mean. The remaining 3 quantifications deviated by <2%. Expected mean is indicated on top of the bar for each sample. This demonstrates that KrasG12D -VAF quantification by amplicon-based next generation sequencing is highly accurate and suitable for determining subclonal KrasG12D dosage increase during early cancer evolution. m,n, Correlation analysis of pre-transplantation SNV (m) or CNV load (n) with engraftment rates for MCCA adenoma organoid lines (n = 81 transplantations). Neither SNV nor CNV load of transplanted adenomas correlate with their engraftment rate in vivo. Instead, subclonal KrasG12D -VAF displays marked correlation with engraftment (j). Dots correspond to adenoma lines shown in (i). SNV, single-nucleotide variant; CNV, copy number variation. rS, Spearman correlation coefficient; P, two-sided t-test.

Extended Data Fig. 8 Titratable induction of KRASMUT expression for modelling the dosage-dependency of its molecular and cellular effects.

a, Experimental outline for doxycycline-titratable expression of KRASG12D in pancreatic, lung and intestinal cell lines. To model early cancer evolution, non-transformed/immortal cells were used instead of full-blown cancer cell lines. Further, cells were cultured in optimized 3D conditions to model processes related to initiation of carcinogenesis, such as epithelial de-differentiation or invasion. To this end, cells were first seeded at very low density in Matrigel domes and allowed to form clonal spheroids. After 7 days of initial growth, distinct levels of KRASG12D expression were induced by using defined doxycycline concentrations. KRASG12D expression was continued for the following 3 days before spheroid phenotypes (at least 20 spheroids imaged per condition), and mRNA transcriptomes were assessed. GFP was used as control for doxycycline-mediated effects. b, Total KRAS mRNA levels induced by increasing concentrations of doxycycline in non-transformed human cell lines. KRAS mRNA levels were normalized to GAPDH. Both targets were quantified by TaqMan qPCR. c, Relative ratio of KRASG12D to total KRAS mRNA transcripts induced by increasing doxycycline concentrations in non-transformed human cell lines, and as detected by amplicon-based cDNA sequencing. The dotted horizontal line at 0.5 ratio indicates the threshold at which KRASG12D levels start to increase over KRASWT – a point which might resemble the transition from heterozygous to increased KRASG12D dosage. d,e, Modelling low-level KRASG12D dosage increase in non-transformed, human pancreatic ductal epithelial (HPDE) cells using a fine-graded titration series of doxycycline concentrations between 0.6 and 15.8 ng/mL. d, KRASG12D to total KRAS transcript ratios steadily increase with doxycycline concentration as detected by amplicon-based deep cDNA sequencing (top). PCA of KRASG12D-induced transcriptome changes at different doxycycline concentrations (bottom). Progressive transcriptomic changes along PC1 are driven by KRASG12D in a dosage-dependent manner. Of note, the tipping point at which strong transcriptomic shifts in the PCA start to emerge is at a KRASG12D to total KRAS transcript ratio of ~80%. e, Gene set enrichment analysis of the top250 genes driving transcriptome separation on PC1 (negative, PC1Neg.; positive, PC1Pos.). Circular bar plot shows selected gene sets enriched on each principal component, which mirror the transcriptomic changes observed in the wider-range titration experiment (Fig. 4b). A full list of enriched gene sets is provided in Supplementary Table 10. FDR, false discovery rate. f-h, Doxycycline-titratable induction of KRASG12D expression in a non-transformed, murine intestinal cell line from MCCA. GFP served as control for doxycycline-mediated effects. f, PCA of KRASG12D-induced transcriptome changes at defined doxycycline concentrations (left). Circular bar plot (right) depicts gene set enrichment analyses of top250 genes driving transcriptome separation on PC1 (negative, PC1Neg.; positive, PC1Pos.). Selected gene sets are shown (a full list of enriched gene sets is provided in Supplementary Table 11). FDR, false discovery rate. g, KRASG12D dosage-dependent induction of cellular phenotypes as detected by microscopy (n = 21 spheroids per doxycycline concentration). Graphs depict the frequency of adhesive/discohesive phenotypes at each doxycycline concentration. Representative images exemplify each phenotype for this cell line. h, KRASG12D-specific mRNA expression levels normalized to Gapdh, both determined by TaqMan qPCR. i, Doxycycline-titratable induction of KRASG12D mRNA expression in murine pancreatic acinar carcinoma cells from MCCA. KRASG12D-specific mRNA levels were normalized to Gapdh. Both targets were quantified by TaqMan qPCR.

Extended Data Fig. 9 Drivers of early de-differentiation in the pancreas beyond KRASMUT gene dosage increase.

a,b, Loss of Tgfbr2 enhances KrasG12D-driven de-differentiation of acinar cells during early stages of pancreatic cancer evolution in vivo. a, Top: Overview of the experimental procedure. Single-stranded AAV8 (scAAV8) carrying Tgfbr2 or Rosa26 control sgRNA was injected into Ptf1aCre/+;KrasLSL-G12D/+,Rosa26CAG-LSL-Cas9/CAG-LSL-Cas9 (PKR) mice for CRISPR/Cas9-mediated somatic gene inactivation in adult pancreatic acinar cells. Non-injected PKR mice served as control for the delivery of scAAV8. Bottom: Representative hematoxylin and eosin-stained pancreas sections of PKR mice eight weeks post-sgRNA delivery or of age-matched non-injected controls. Scale bar: 50 µm. b, Quantification of acini per field of view in the pancreas of Tgfbr2-sgRNA-, sgRosa26-sgRNA- and non-injected PKR mice. Dots represent independent mice. For each mouse, the mean acinus count from at least five hematoxylin and eosin-stained images is shown. Loss of Tgfbr2 dramatically enhances KrasG12D-driven acinar cell de-differentiation, consistent with essential role of TGFβ in maintaining acinar cell identity. The results are also in line with the reduced selective pressure for the acquisition of KrasG12D allelic imbalance during early pancreatic tumorigenesis in Ptf1aCre/+;KrasLSL-G12D/+;Tgfbr2∆ mice[18]. *P = 0.0286, two-sided Mann–Whitney test; bars, median. c, Kras mRNA expression from its endogenous locus during KrasG12D-driven pancreatic cancer evolution in vivo. Induction of Kras mRNA levels was measured in acinar, metaplastic/PanIN and cancer cells from Ptf1aCre-ERTM/+;KrasLSL-G12D/+,Rosa26LSL-CAG-tdTomato/+ mice as compared to healthy, Kras wildtype acinar cells (data from[29]. Endogenous Kras expression increases 5.5-fold during acinar cell de-differentiation (and is further enhanced in the cancer cell state). Normalized reads, Mean of normalized reads per cell. P = 0.0079, two-sided Mann–Whitney test; bars, median. d-f, Kras mRNA expression from its endogenous locus during de-differentiation of KrasG12D-mutant acinar cells in vitro. d, Overview on the experimental procedure of the ex vivo acinar-to-ductal metaplasia (ADM) assay. Healthy acini were explanted from 8 weeks-old Ptf1aCre/+;KrasLSL-G12D/+ mice and cultured under ultra-low attachment conditions in vitro, where they spontaneously transdifferentiate into duct-like precursor cells within 24 h. e, Clustered heatmap illustrates the downregulation of acinar and upregulation of ductal marker gene expression in transdifferentiated (24 h) versus freshly isolated acini (0 h), as determined by RNA-seq. f, Bar plot showing Kras mRNA expression from its endogenous locus in de-differentiated metaplastic (24 h) versus freshly isolated acini (0 h), as quantified by RNA-seq. A 5.7-fold upregulation of Kras expression from its endogenous locus is observed during acinar cell de-differentiation, as observed during de novo tumorigenesis in mice (see (c) above). P = 2.04 × 10−7, two-sided Wald test in DESeq2; bars, median.

Extended Data Fig. 10 Transcriptional activity and epigenetic status of CDKN2A or KRAS across tissues and species.

a,b, Quantitative comparisons of H3K4me3 (a) and H3K27me3 (b) ChIP-seq signals at the CDKN2A locus across human pancreatic, lung, and intestinal tissues using ROADMAP and ENCODE epigenomics datasets (Supplementary Table 15, and as described in Methods). H3K27me3 signal or H3K4me3 signal, normalized reads per kb. ***P = 0.0007, two-sided Mann-Whitney test; bars, median. c, mRNA expression, histone modification patterns and chromatin states at the CDKN2A locus in healthy human pancreas, lung and small/large intestine as inferred from ROADMAP reference transcriptomes and epigenomes[35] (related to Fig. 5c). Top: mRNA expression of the CDKN2A locus (as detected by full-length mRNA sequencing). Coverage plots depicting the mapping of mRNA transcripts either to the forward or the reverse strand of the genome are shown (CKDN2A is transcribed from the reverse DNA strand). Cartoons in-between forward and reverse strand coverage plots illustrate CKDN2A exon structure. RPKM, reads per kilobase million. Middle: Histone modifications at the CDKN2A locus. Signal plots for histone marks are shown: H3K4me3 (associated with promoter regions), H3K4me1 (associated with enhancer regions), H3K36me3 (associated with transcribed regions), H3K9me3 (associated with heterochromatin regions) and H3K27me3 (associated with Polycomb repression). Genome-wide signal plots were calculated using the Model-based Analysis of ChIP-seq 2 (MACS2) tool. After normalization, the enrichment/signal scores were provided as negative log_10 of the Poisson p-values. Signal score, -log_10(p-value). Bottom: Chromatin states found at the CDKN2A locus as inferred from the ChromHMM ‘core’ 15-state model. This model was previously trained by integrating 5 chromatin marks (H3K4me3, H3K4me1, H3K36me3, H3K9me3 and H3K27me3) across 127 reference epigenomes covering a broad spectrum of tissues and cell types[35]. Active chromatin states of the model are highlighted in shades of red (states 1–8) and repressed chromatin states in shades of blue (states 9–15). To visualize chromatin states at the CDKN2A, the locus was binned, and each genomic bin was then labelled with the chromatin state having the highest posterior probability at the given genomic position. Coordinates at the bottom indicate the genomic position for mRNA expression, histone modifications and chromatin states according to human genome assembly GRCh37. d,e, Quantitative comparisons of H3K4me3 (d) and H3K27me3 (e) ChIP-seq signals at the KRAS locus across human pancreatic, lung, and intestinal tissues using ROADMAP and ENCODE epigenomics datasets (Supplementary Table 15, and as described in Methods). Note the different scales of the y axes compared to (a,b). No significant differences of histone mark occupancy were detected between tissues (two-sided Mann-Whitney test). H3K27me3 or H3K4me3 signal, normalized reads per kb. Bars, median. f, mRNA expression, histone modification patterns and chromatin states at the KRAS locus in healthy human pancreas, lung and small/large intestine as inferred from ROADMAP reference transcriptomes and epigenomes[35]. Analyses as in (c). g,h, Cell type-specific KRAS mRNA expression for the cell-of-origin of each cancer type determined by single-cell RNA-seq (human data from38,39,40. Analysis as for CDKN2A mRNA expression in Fig. 5d. g, UMAPs of pancreatic, lung and intestinal cell types. Only cell types with n ≥ 100 cells are shown for lung and intestine. AT1/AT2, alveolar cells type I/II; SI, small intestine; LI, large intestine. h, Bar plot showing cell type-specific KRAS mRNA expression based on pseudobulk analyses per donor (see Methods). Expr., expression; RPM, reads per million. Bars, median. i, scRNA-seq pseudobulk analysis of Cdkn2a mRNA expression across indicated mouse cell types (data from[91]). The number of reads mapping to Cdkn2a is extremely low in all cell types – and substantially reduced when compared to their human counterparts (Fig. 5d). This finding is supported by previous observations that expression of Cdkn2a is very low or absent in healthy adult tissues of the mouse[41]. AT2, alveolar cells type II; SI, small intestine; RPM, reads per million. j, Quantification of H3K4me3 signals at the Cdkn2a locus based on public ChIP-seq data from healthy mouse pancreas, lung, and intestinal tissues (Supplementary Table 15, and as described in Methods). Abundance of H3K4me3 at Cdkn2a is very low and does not differ between tissues, consistent with the extremely low expression of Cdkn2a in the mouse scRNA-seq pseudobulk analyses shown in (i). H3K4me3 signal, normalized reads per kb. P, two-sided Mann-Whitney test; bars, median. k, Quantification of H3K27me3 signals at the Cdkn2a locus based on public ChIP-seq data from healthy mouse pancreas, lung, and intestinal tissues (Supplementary Table 15). H3K27me3 signal, normalized reads per kb. Compare to human in (b). *P = 0.0167, two-sided Mann-Whitney test; bars, median.

Extended Data Fig. 11 Polycomb-mediated repression of the Cdkn2a locus is cell type-specific and shapes tissue-specific Cdkn2a tumour suppressor function.

a, Experimental outline for the inhibition of Polycomb Repressive Complex 2 (PRC2i) in non-transformed pancreatic and intestinal organoids through combinatorial treatment with UNC1999 (2 µM) and A-395 (4 µM). Following standard protocols, cells were treated with PRC2i for 10 to 12 days to not only block de novo H3K27 trimethylation but also to allow dilution of pre-existing H3K27me3 marks through cell division[99]. b, Western blot analysis of H3K27me3 in non-transformed pancreatic organoids and Cdkn2a-deficient intestinal organoids following Mock-treatment (Ctrl) or PRC2 inhibition (PRC2i). Histone H4 was used as loading control. c, Induction of Cdkn2a mRNA expression following PRC2 inhibition (PRC2i) in non-transformed pancreatic and intestinal organoids as determined by qPCR. Cdkn2a expression levels are normalized to Gapdh and presented as relative expression compared to the corresponding Mock-treated control. Cdkn2a∆, homozygous knockout of Cdkn2a. FC, fold change. *P = 0.0124; two-sided t-test on log_2-transformed data; bars, mean. d, Organoid counts following PRC2 inhibition (PRC2i) in non-transformed pancreatic and intestinal organoids. Organoid counts of PRC2i-treated cells are shown as percentage relative to organoid counts of the corresponding Mock-treated control. Of note, the marked growth inhibitory effect of PRC2i in intestinal cells is completely rescued by knock-out of Cdkn2a, demonstrating that PRC2i-induced block of proliferation is Cdkn2a-dependent in this cell type. Cdkn2aWT, wildtype Cdkn2a; Cdkn2a∆, homozygous knockout of Cdkn2a. **P = 0.0047; two-sided t-test on log_2-transformed data; bars, mean. e, Representative images of pancreatic and intestinal organoids following Mock-treatment (Ctrl) or PRC2 inhibition (PRC2i), corresponding to the quantification of organoid counts shown in (d). f, Survival analysis of KrasLSL-G12D/+ mice with or without Cdkn2a inactivation in the pancreas, intestine and lung, for determining the effect of Cdkn2a loss on oncogenesis in different tissues. Homozygous loss of Cdkn2a dramatically accelerates KrasG12D-initiated cancer evolution in the pancreas, while having only moderate effects in mouse models of KrasG12D-initiated intestinal or lung cancer. The survival comparison of KrasLSL-G12D/+ versus KrasLSL-G12D/+;Cdkn2aFL/FL lung cancer mouse models is based on data from[46]. The number of animals included in each group is indicated in the table. *Only two of the five Vil-Cre;KrasLSL-G12D/+;Cdkn2aFL/FL mice developed intestinal tumours. The remaining three animals did not show signs of intestinal neoplasia even after extended observation periods and died from tumours unrelated to intestinal cancer, most likely due to leakiness of Cre expression. g, Frequency of homozygous somatic deletions affecting chromosome 4, which harbours the Cdkn2a locus, in pancreatic cancer cell lines derived from Ptf1aCre/+;KrasLSL-G12D/+ (PK, n = 49) or Ptf1aCre/+;KrasLSL-G12D/+;Cdkn2aFL/FL (PKC, n = 18) mice. While such deletions are frequent in PK cancers, their absence in the PKC model indicate that Cdkn2a, rather than neighbouring genes, drive this phenotype. However, the co-deletion of genes with Cdkn2a can be relevant for other phenotypes, such as for promoting immune evasion and cancer metastasis[45]. **P = 0.0055, two-sided Chi-squared test.

Source data

Extended Data Fig. 12 Chronological order of KrasMUT and Cdkn2a alterations in different tissues, and late evolutionary advantage of CDKN2AHOM in lung cancer.

a, Chronological order of KrasG12D gene dosage increase and bi-allelic Cdkn2a inactivation during evolution of mouse pancreatic (Ptf1aCre/+;KrasLSL-G12D/+), lung (Ela-CreERTM;KrasLSL-G12D/+) and intestinal cancer (Vil-Cre;KrasLSL-G12D/+). Selected cases from Fig. 5j are shown to illustrate how the sequential order of genetic events was inferred using CNV profiling and KrasG12D VAF data. Top: Phylogenetic trees indicating sequential order of genetic events. Length of lines does not represent evolutionary distances. Stromal contamination of MCCA cancer tissues was accounted for by the data analysis in order to study ‘pure’ cancer genomes (see Methods). Bottom: Plots showing Cdkn2a or chromosome 6 (home of Kras) CNV patterns as determined by WES or lcWGS, and KrasG12D VAF as detected by amplicon-based deep sequencing. In the pancreas (case R2604), all corresponding cancer lesions share the identical pattern of homozygous Cdkn2a deletion, while distinct types of KrasG12D gene dosage increase are detected in each individual sample. Accordingly, Cdkn2aHOM preceded the acquisition of KrasG12D-iGD during the evolution of pancreatic cancer in this mouse. By contrast, in the intestine (case TM5805), the type of KrasG12D gene dosage increase is shared between corresponding cancer lesions, but patterns of homozygous Cdkn2a deletion are heterogenous. Thus, Cdkn2aHOM occurred after KrasG12D-iGD was already acquired during serrated intestinal cancer evolution in this mouse. A similar order of genetic events was detected in lung cancer case 10701. A, adenoma; P, primary cancer; Li/LN, liver/lymph-node metastasis. b, Overall patient survival related to lung adenocarcinomas (TCGA-LUAD) proficient (CDKN2AHET/WT) or deficient for CDKN2A (CDKN2AHOM). Only KRASMUT cancers were included in the analysis. P, two-sided log-rank test. c, Gene set enrichment analysis of genes upregulated in CDKN2AHOM versus CDKN2AHET/WT human lung adenocarcinoma tissues (TCGA-LUAD) as detected by RNA-seq. Bar plot shows selected gene sets (a full list of enriched gene sets is provided in Supplementary Table 16). Only KRASMUT cancers were included in the analysis. FDR, false discovery rate. d, Frequency of distinct somatic CDKN2A inactivation states in human, non-small cell lung cancer cell lines (CCLE-NSCLC) (Supplementary Table 17). Re-analysis of data provided by the Cancer Cell Line Encyclopedia[6]. HOM, homozygous; HET, heterozygous; WT, wildtype. e, Frequency of defined somatic Cdkn2a inactivation states in murine lung carcinoma (mLUCA) cell lines of MCCA (Supplementary Table 17). HOM, homozygous; HET, heterozygous; WT, wildtype.

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Mueller, S., de Andrade Krätzig, N., Tschurtschenthaler, M. et al. A disease model resource reveals core principles of tissue-specific cancer evolution. Nature 653, 265–276 (2026). https://doi.org/10.1038/s41586-026-10187-2

- Received: 11 October 2024

- Accepted: 23 January 2026

- Published: 25 February 2026

- Version of record: 25 February 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10187-2

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The molecular basis of force selectivity by PIEZO2

Eric M. Mulhall,

Oleg Yarishkin,

Rose Z. Hill,

Anna K. Koster &

…

Ardem Patapoutian

Nature volume 653, pages 297–305 (2026)

This article has been updated

Abstract

PIEZOs are mechanically gated ion channels that transduce force into electrochemical signals[1]. PIEZO1 responds to diverse stimuli including membrane stretch[2] and shear stress[3], whereas PIEZO2 is generally tuned to detect cellular indentation4,5. The functional specialization of PIEZO2 is proposed to underlie its distinct physiological roles, including mediating the sense of touch6,7. How PIEZO2 achieves this selectivity despite its close structural similarity to PIEZO1 is unclear. Here we combine single-molecule MINFLUX fluorescence nanoscopy with electrophysiology to link the conformational states of PIEZO2 to channel gating in intact cells. We find that PIEZO2 is intrinsically more rigid than PIEZO1, and that disparate mechanical stimuli paradoxically evoke opposite conformational and gating responses in each channel. These unique gating properties arise in part from a connection to the actin cytoskeleton, and we identify filamin-B (FLNB) as a molecular tether that is required for this interaction. This complex alters how force is transmitted to PIEZO2 and confers heightened sensitivity to and selectivity for cellular indentation. PIEZO2 and FLNB are co-expressed in somatosensory neurons and colocalize within tens of nanometres at the end organs of cutaneous mechanosensory afferents. These findings help to explain why PIEZO2 is a specialized mechanosensor and provide a molecular blueprint for understanding how cells decode diverse mechanical stimuli across tissues and organ systems.

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PIEZO2 is a mechanotransduction channel that mediates an array of physiological processes in vertebrates, including touch[6], proprioception[8] and respiration[9]. Pathogenic loss- or gain-of-function mutations in this channel can cause extensive mechanosensory defects or debilitating neurological diseases such as Gordon syndrome, Marden–Walker syndrome or distal arthrogryposis type 5 (ref. [10]). Despite recent advances in understanding the physiological roles of PIEZO2, the underlying structural correlates of function remain poorly understood.

PIEZO1 and PIEZO2 are homotrimeric membrane proteins with three identical subunits that assemble to form a triskelion[11] (Fig. 1a). Each subunit has a blade of 36 transmembrane domains extending outward and upward from a central C-terminal pore domain, forming a concave shape around 24 nm in diameter in membrane-free structural models12,13,14,15. The blades are proposed to function as the primary sensors of mechanical force because they directly connect to the ion-conducting pore and, in the case of PIEZO1, blade expansion by membrane tension correlates with channel activation16,17,18. Mechanosensitive ion channels are generally gated through either a ‘force from lipid’ mechanism driven by membrane tension, a ‘force from filament’ mechanism involving force transmission through cytoskeletal tethers, or a hybrid of the two[19]. While membrane tension has been established as the primary gating stimulus for PIEZO12,12,20,21, comparatively little is known about how PIEZO2 is gated by physiologically relevant forces.

Fig. 1: The divergent structural mechanics of PIEZO1 and PIEZO2 in a cell membrane.

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a, Structural models of PIEZO1 and PIEZO2, showing the tag positions (magenta stars). b, Interblade distances for mouse PIEZO1 tagged at residue 103 (grey; n = 36 molecules, n = 4 cells) and PIEZO2 tagged at residue 105 (blue; n = 52 molecules, n = 4 cells) in unstimulated PtK2 cells. Kolmogorov–Smirnov test, ****P = 6.3 × 10−5, D = 0.44. c, The variance and 95% confidence intervals (CI) of the mean interblade distances in b. F test of equality of variances, P = 8.5 × 10−8, F = 5.6. d, PIEZO1 interblade distances from expansive membrane stretch (red; median = 34.7 nm; n = 41 molecules, n = 3 cells) versus unstimulated (grey; median = 26.5 nm). Kolmogorov–Smirnov test, *P = 0.01, D = 0.38. e, PIEZO2 during expansive membrane stretch (magenta; median: 18.9 nm; n = 53 molecules, n = 3 cells) versus unstimulated (blue; median = 19.9 nm). Kolmogorov–Smirnov test, *P = 0.01, D = 0.26. f, Current–voltage curves of hypo-osmotic-swelling-evoked current density for PIEZO1 (n = 9 cells) and PIEZO2 (n = 8 cells) in SWELL1-KO HEK293 cells. The arrow denotes the current at +80 mV, used for quantification. g, Peak stretch-induced current density at +80 mV for f, including untransfected controls (n = 7 cells). Kruskal–Wallis test with Dunn’s post hoc test, ****P = 6.2 × 10−5. h, PIEZO1 interblade distances from hyperosmotic stimulus (yellow; median = 19.8 nm; n = 21 molecules, n = 3 cells) versus unstimulated (grey). Kolmogorov–Smirnov test, **P = 0.008, D = 0.46. i, PIEZO2 during hyperosmotic stimulus (green; median = 30.6 nm; n = 57 molecules, n = 3 cells). Kolmogorov–Smirnov test; ****P = 1.3 × 10−9, D = 0.54. j, Peak stretch-induced current from hyperosmotic stimulus in SWELL1-KO HEK293 cells expressing PIEZO2 (n = 8 cells) and untransfected controls (n = 6 cells). Mann–Whitney U-test, P = 0.181. k, The peak induced current density from hyperosmotic stimulus in PtK2 control cells (n = 6 cells) versus cells expressing PIEZO1 or PIEZO2 (n = 7 cells each). Kruskal–Wallis test, **P = 0.001. Data are mean ± s.d. (b), median ± 95% CI (d, e, h and i) and mean ± s.e.m. (f, g, j and k). All statistical tests were two-sided.

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PIEZO1 and PIEZO2 have distinct roles in physiology. While PIEZO1 is predominantly expressed in non-neuronal cells, such as erythrocytes[22] and chondrocytes[23], PIEZO2 is primarily expressed in somatosensory neurons[6], such as those that mediate proprioception and touch. These divergent physiological roles are consistent with their distinct functional properties. PIEZO1 is primarily activated by membrane tension, but PIEZO2 is preferentially activated by cellular indentation[4], the mode of stimulus that many cutaneous mechanosensory neurons are tuned to detect[24]. Indentation stimuli, for example by poking a cell with a blunt probe, apply a localized force that both deforms the membrane and strains the underlying cytoskeleton[25]. By contrast, techniques like cell-attached pressure clamp recordings expand the membrane like a balloon and produce relatively uniform lateral membrane tension[25]. Comparing the responses of mechanotransduction channels to each of these stimulus modalities can provide clues about their gating mechanism. Notably, atomic-force-microscopy experiments show that PIEZO2 is approximately threefold more sensitive to indentation compared with PIEZO1 in heterologous cells[5] yet, paradoxically, PIEZO2 is equally less sensitive to membrane tension in cell-attached or excised patch recordings[26]. Moreover, only a small minority of PIEZO2-transfected cells exhibits mechanosensitive currents in cell-attached recordings, and these currents are consistently small4,27. These observations suggest that PIEZO2 might not be gated purely through a force-from-lipid mechanism. Despite these clear differences in function, the cryo-electron microscopy (cryo-EM) structures of each channel are highly similar (Fig. 1a) and provide few clues about their functional specialization, highlighting the need to investigate their structural mechanics in a cellular context.

Distinct nanomechanics of PIEZO channels

To compare the structural mechanics of PIEZO1 and PIEZO2, we combined MINFLUX fluorescence nanoscopy with fluorogenic DNA PAINT to measure the conformation of individual molecules in a cell membrane. We previously used MINFLUX to measure the conformational states of PIEZO1 using conjugated spontaneously photoblinking dyes[16]; however, these dyes irreversibly bleach on excitation and produce variable photon counts. To increase the photon yield per molecular position, we used DNA PAINT, which exploits transient binding of fluorophore-labelled oligonucleotides[28]. Programmable binding kinetics through sequence design and the use of more stable dyes can yield far more localization events per molecule, which in turn can resolve a molecular position with higher confidence. We chose Potorous tridactylus kidney (PtK2) cells for imaging because they have an exceptionally flat morphology[29] that allows imaging probes to easily reach molecules on the apical membrane adjacent to the coverslip. This minimizes optical aberrations, eliminates the complication of restricted diffusion of probes between the basal membrane and the coverslip and removes the need for permeabilization, which could otherwise expose intracellular channels that are not functionally active.

Using genetic code expansion, the click chemistry substrate trans-cyclooctene-lysine (TCO*K) was incorporated into equivalent extracellular loops of the distal blade in PIEZO1 and PIEZO2 (Methods). These sites were labelled with a tetrazine-conjugated DNA oligonucleotide that served as a transient docking site for a complementary oligonucleotide conjugated to the fluorescent dye ATTO 643 freely diffusing in the imaging buffer (Extended Data Fig. 1a). The cells were fixed in an isosmotic cross-linking solution to preserve morphology and imaged without permeabilization to keep the plasma membrane intact. Conventional DNA PAINT is hindered by background from unbound fluorophores, so we used self-quenching fluorogenic imager probes with ATTO 643 and an IowaBlack fluorescence quencher on opposite termini that become unquenched when bound to a docking strand[30]. Under identical imaging conditions, fluorogenic DNA PAINT yielded lower background, higher localization precision and more detections per binding event (Extended Data Fig. 1b–d). We verified that fluorogenic DNA-PAINT did not introduce measurable bias in MINFLUX localizations due to quencher–fluorophore interactions, and that we maintained nanometre-scale stability over extended acquisitions (Extended Data Fig. 2). Raw localizations were clustered and fit with three-dimensional (3D) Gaussian mixture models to obtain precise fluorophore positions, enabling us to directly measure 3D distances between the distal blade domains of individual PIEZO channels (Methods). The resulting enhanced localization density, especially compared with traditional photoblinking dyes[16], enabled us to apply more stringent parameters in our 3D clustering algorithm (Methods and Extended Data Fig. 3), increasing confidence that each cluster corresponds to a single PIEZO protomer.

Compared with PIEZO1, the blades of PIEZO2 are significantly less expanded in the absence of stimulation (Fig. 1b). Notably, while thermal fluctuations alone are thought to cause large deformations in the shape of PIEZO116,17, the markedly narrower range of conformational states in PIEZO2 also reveals that the blade domains are much more conformationally rigid than PIEZO1 (Fig. 1c). If blade expansion generally correlates with activation, the larger force required to gate PIEZO2 in excised membrane patches[26] might reflect the extra energy required to bend the more rigid blades by the same distance. These differences in flexibility might also explain why the distal blades of PIEZO2 were resolved by cryo-EM but were not for PIEZO112,14.

The primary gating stimulus for PIEZO1 is membrane tension2,12,20,21. Expansive membrane stretch from osmotic swelling increases tension[31], similarly to cell-attached pressure clamp recordings[25]. To isolate PIEZO-mediated currents, we used SWELL1-knockout (KO) HEK293 cells, which lack the ubiquitous SWELL1-dependent chloride current evoked by hypotonic stimuli that otherwise obscures the measurements of mechanosensitive cation channels[16]. We replicated our previous findings[16], and show that membrane tension from osmotic swelling expands the blades of PIEZO1 (Fig. 1d) and gates the channel (Fig. 1f). This behaviour is well explained by a force-from-lipids model, whereby mechanical energy from the bilayer is transmitted directly to the channel without requiring intermediary components[19]. Notably, osmotic swelling significantly compacted the blade domains of PIEZO2 by 1.9 nm on average (Fig. 1e) and did not significantly activate the channel (Fig. 1f,g). This lack of activation appears consistent with cell-attached pressure-clamp recordings in heterologous cells, in which only a small minority of cells expressing PIEZO2 displays mechanosensitive currents, and those with responses are invariably small4,27. These distinct gating properties indicate that PIEZO2 does not follow a strict force-from-lipids mechanism and may instead recruit additional cellular components that alter how force is transmitted to the channel.

The mismatch in curvature between the concave PIEZO1 structure and planar lipid bilayer results in a bending stress that can substantially expand the blades without externally applied tension16,17. A hypertonic extracellular environment reduces cell volume and causes plasma membrane slackening and folding[32]. Consequently, the blades should compact as residual tension is relieved and as the channel partitions into membrane folds matching its intrinsic curvature. While we observed this effect for PIEZO1 (Fig. 1h), we found that the blade domains of PIEZO2 are significantly expanded by the same stimulus (Fig. 1i). This raises the question of how a stimulus that reduces membrane tension can expand the blades of PIEZO2. In addition to slackening, hyperosmotic shrinkage is thought to fold slack membrane into the gaps of the comparably rigid cortical actin meshwork[32]. If PIEZO2 is tightly coupled to actin, we hypothesize that this might recruit a membrane bending force that expands the blade domains. Consistent with this idea, a hypertonic stimulus does not significantly activate PIEZO2 in SWELL1-KO HEK293 cells (Fig. 1j), but it does activate the channel in PtK2 cells (Fig. 1k), of which the actin cortex is roughly one order of magnitude stiffer33,34 and more tightly anchored to the plasma membrane[35]. Together, these data favour a tether-coupled membrane gating model for PIEZO2, whereby deformation of the membrane relative to a connection to actin can apply a gating force to the channel (Fig. 2a).

Fig. 2: A connection to the actin cytoskeleton regulates the structural mechanics of PIEZO2.

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a, Tether-coupled gating model: changes in membrane properties (for example, curvature) relative to a cytoskeletal tether exert force on the blade domains. b, Cytochalasin D (CytoD) decreases the resting PIEZO2 interblade distance (dark blue; median = 20.1 nm; n = 51 molecules, n = 3 cells; Kolmogorov–Smirnov test, ****P = 3.4 × 10−5, D = 0.40) and permits blade expansion from expansive membrane stretch (green; median = 28.3 nm; n = 24 molecules, n = 5 cells; Kolmogorov–Smirnov test, ***P = 0.0002, D = 0.53). c, CytoD enables stretch-evoked PIEZO2 activation in SWELL1-KO HEK293 cells. n = 5 (control), n = 7 (PIEZO2) and n = 5 (PIEZO2 + CytoD) cells. Kruskal–Wallis test, *P = 0.048. d, Schematic of PIEZO2 blade expansion after actin disruption. e, PIEZO2(ΔIDR5) shows reduced interblade distance at rest (light blue; median = 16.6 nm; n = 30 molecules, n = 4 cells; Kolmogorov–Smirnov test, ****P = 9.7 × 10−6, D = 0.57) and robust expansion from hypo-osmotic swelling (orange; median = 37.2 nm; n = 21 molecules, n = 6 cells; Kolmogorov–Smirnov test, ****P = 3.9 × 10−7, D = 0.79). f, Hypo-osmotic swelling gates PIEZO2(ΔIDR5) in SWELL1-KO cells. n = 5 (control), n = 10 (PIEZO2) and n = 4 (PIEZO2(ΔIDR5)). Kruskal–Wallis test, *P = 0.011. g, Schematic of PIEZO2(ΔIDR5) blade expansion from expansive membrane stretch. h, Overlaid MINFLUX single-molecule trajectories aligned by centre of mass (≥1 s) (top). Bottom, MSD analysis for trajectories with ≥200 localizations. Trajectories (grey) were averaged (50 bins in 350 ms; black circles) and fit at 5–50 ms (cyan, microscopic D: PIEZO1 = 0.0218, PIEZO2 = 0.0096, PIEZO2(ΔIDR5) = 0.0264 µm^2 s−1) and 50–350 ms (magenta, macroscopic D: PIEZO1 = 0.0197, PIEZO2 = 0.0041, PIEZO2(ΔIDR5) = 0.0149 µm^2 s−1). n = 55 (PIEZO1), n = 61 (PIEZO2) and n = 36 (PIEZO2(ΔIDR5)) trajectories. i, Diffusion coefficients from individual trajectories in h. Top, microscopic D for PIEZO2 (blue; median = 0.011 µm^2 s−1), PIEZO1 (grey; median = 0.024 µm^2 s−1) and PIEZO2 ΔIDR5 (dark blue; median = 0.032 µm^2 s−1). Kruskal–Wallis test, ****P = 3.8 × 10−9 (PIEZO1 versus PIEZO2), ****P = 1.3 × 10−14 (PIEZO2 versus PIEZO2(ΔIDR5)). n = 104 (PIEZO1), n = 87 (PIEZO2) and n = 65 (PIEZO2(ΔIDR5)) trajectories. Bottom, macroscopic D for PIEZO2 (blue circles; median = 0.011 µm^2 s−1), PIEZO1 (grey; median = 0.024 µm^2 s−1) and PIEZO2(ΔIDR5) (dark blue; median = 0.036 µm^2 s−1). Kruskal–Wallis test, **P = 0.0023, ****P = 1.2 × 10−9. n = 55 (PIEZO1), n = 61 (PIEZO2) and n = 36 (PIEZO2(ΔIDR5)) trajectories. Data are mean ± s.e.m. (c, f and h) and median ± 95% CI (b, e and i). All statistical tests are two-sided. The diagrams in a, d and g were created using BioRender; Mulhall, E. M. https://BioRender.com/5k5114d (2026).

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Cytoskeletal modulation of PIEZO2 gating

If actin tethering controls how force is transmitted to PIEZO2, then destabilizing the actin cytoskeleton should revert the channel to a force-from-lipids mechanism of gating, consistent with its close structural similarity to PIEZO1 (Fig. 2d). Indeed, when cells were treated with the actin-destabilizing drug cytochalasin D, expansive membrane stretch from osmotic swelling significantly expanded the blades of PIEZO2 and activated the channel (Fig. 2b,c). This could explain why pressure-evoked PIEZO2 currents recorded in cell-attached recordings become larger in magnitude and more frequent after treatment with actin-disrupting compounds[4]. Notably, the unstimulated conformation of PIEZO2 significantly decreased under these conditions but had no measurable effect on the conformation PIEZO1, suggesting that specific tethering of PIEZO2 to actin is responsible for a resting state of blade expansion (Fig. 2b and Extended Data Fig. 4). The significantly smaller distribution of conformational states compared with PIEZO1 in the same conditions also implies that the relative rigidity of PIEZO2 is an intrinsic property of the channel rather than a result of mechanical coupling to actin.

Previous work has implicated the intrinsically disordered intracellular domain between transmembrane helices 12 and 13 (IDR5) in PIEZO2 in activation by indentation forces, and this domain was hypothesized to be involved with a connection to the actin cortex[4] (Fig. 2g). We found that, like actin disruption, deletion of the IDR5 domain in PIEZO2 (PIEZO2(ΔIDR5)) decreased the resting state of expansion of the channel, and expansive membrane stretch from osmotic swelling significantly expanded the blades and activated the channel (Fig. 2e,f). If PIEZO2 does scaffold to actin, we also reasoned that the channel should be immobilized in the cell membrane and that disruption of this connection should increase its mobility. To test this, we performed single-molecule 3D MINFLUX tracking in live cells expressing PIEZO proteins tagged at the C terminus with a HaloTag and labelled with a Janelia Fluor 635 HaloTag ligand positioned near the channel’s centre of mass. We ensured that only individual molecules were tracked by exploiting the roughly quantal photon emission intensity pattern to isolate single emitters resident in the plasma membrane (Methods and Extended Data Fig. 5a,b). Single-molecule trajectories were reconstructed from 3D MINFLUX localizations and diffusion coefficients were obtained by weighted fits of mean-squared displacement (MSD) versus time (Methods). We found that PIEZO1 freely diffuses with a median diffusion coefficient of D = 0.02 µm^2 s−1 (Fig. 2h), consistent with independent measurements of PIEZO1 diffusion in red blood cells[36], neural stem cells[37] and fibroblasts[38]. By contrast, PIEZO2 is significantly more immobilized than PIEZO1 (median D = 0.004 µm^2 s−1) (Fig. 2h). Analysis of microscopic diffusion during the first 50 ms of the trajectories, before the channel will tend to interact with diffusional barriers created by membrane–cytoskeletal interactions[36], shows that diffusion is restricted essentially from the outset of measuring its motion (Fig. 2i (top)), indicative of a strong tethering interaction.

Compared with the wild-type channel, PIEZO2(ΔIDR5) showed markedly increased mobility (Fig. 2h,i), suggesting that the interaction responsible for immobilization is abolished when IDR5 is removed. Notably, these data differ from an earlier study reporting no change in diffusion after deletion of PIEZO2 IDR5[4]. This discrepancy probably arises from the use of 2D TIRF single-particle tracking on densely labelled channels, which cannot reliably resolve individual molecules nor discriminate those in the plasma membrane from intracellular pools in the secretory pathway. Substitution of PIEZO2 IDR5 into the corresponding intracellular loop of PIEZO1 did not alter the diffusion properties relative to wild-type PIEZO1 (Extended Data Fig. 5c). This implies that the IDR5 domain alone is not sufficient to confer tethering, and that additional PIEZO2-specific elements are required for immobilization. Overall, these findings demonstrate that actin-dependent immobilization of PIEZO2 modifies how force is conveyed to the channel, and that IDR5 is necessary but not sufficient by itself for this interaction.

FLNB tethering shapes PIEZO2 function

As IDR5 does not possess a canonical actin-binding motif, and most anchored membrane proteins such as integrins, cadherins or ion channels rely on intermediate adaptors to couple to actin[39], we hypothesized that PIEZO2 tethers to actin through an intermediate protein that binds at least partially to IDR5. To identify candidate tethers, we used protein cross-linking and mass spectrometry (MS)-based proteomics (Fig. 3a). We compared the abundance ratio of proteins that cross-linked to either PIEZO2 or PIEZO2(ΔIDR5) and identified candidates that are known to mediate interactions between actin and membrane proteins (Fig. 3b). As disruption of the connection to actin is expected to render PIEZO2 sensitive to expansive membrane stretch, we next performed a small interfering RNA (siRNA) knockdown screen targeting mRNAs of candidate tethering proteins and measured PIEZO2 responses to osmotic swelling. We found that only knockdown of FLNB, an actin-binding scaffold protein that binds to cortical actin filaments and anchors membrane proteins to the cytoskeleton[40], elicited significant stretch responses from PIEZO2 (Fig. 3c). Notably, knockdown of the closely related family member FLNA had no effect on stretch responses. We next made CRISPR-mediated clonal FLNB-knockout cells on the SWELL1-KO HEK293 background to confirm that the effects seen with siRNA knockdown are indeed due to loss of FLNB (Extended Data Fig. 6). PIEZO2 in these cells showed significant responses to expansive membrane stretch in all cells tested (Fig. 3d), suggesting that FLNB is a component of the molecular tether that confers force selectivity to PIEZO2.

Fig. 3: FLNB is a molecular tether that confers force selectivity and sensitivity to PIEZO2.

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a, The cross-linking MS workflow. LC, liquid chromatography. b, The exponentially modified protein abundance index (emPAI) ratio of proteins cross-linked to PIEZO2 versus PIEZO2(ΔIDR5). Candidate cytoskeletal–membrane tethers highlighted (magenta). c, Stretch-induced current in SWELL1-KO HEK293 cells expressing PIEZO2 with non-targeting siRNA (n = 46), FLNB siRNA (n = 24) or other candidate siRNAs (VINC (n = 7), MOES (n = 11), CTNB1 (n = 10), FLNA (n = 9) and TAGL2 (n = 11)). d, Stretch-induced current in SWELL1-KO HEK293 cells electroporated with scrambled CRISPR sgRNA (untransfected or expressing PIEZO2; n = 4 cells each) compared with clonal SWELL1-KO/FLNB-KO cells (untransfected (n = 4) or expressing PIEZO2 (n = 8)). Mann–Whitney U-test, **P = 0.004. e, The PIEZO2 interblade distance in WT PtK2 cells (blue; median = 18.0 nm; n = 30 molecules, n = 4 cells) versus PtK2 cells with Potoroo Flnb DsiRNA (cyan; median = 16.8 nm; n = 41 molecules, n = 3 cells; Kolmogorov–Smirnov test, ****P = 4.5 × 10−7, D = 0.79) versus PtK2 cells with Flnb DsiRNA + hypo-osmotic swelling (purple; median = 37.0 nm; n = 39 molecules, n = 3 cells; Kolmogorov–Smirnov test, ****P = 3.2 × 10−10, D = 0.51). f, Schematic of expansive stretch without FLNB. g, MINFLUX tracking MSD fits: 5–50 ms (cyan, PIEZO2 + non-targeting DsiRNA: D = 0.0106 µm^2 s−1; PIEZO2 + Flnb DsiRNA: D = 0.0181 µm^2 s−1) and 50–350 ms (magenta, PIEZO2 + non-targeting DsiRNA: D = 0.00495 µm^2 s−1; PIEZO2 + Flnb DsiRNA: D = 0.0090 µm^2 s−1). n = 55 (PIEZO1), n = 61 (PIEZO2), n = 81 (PIEZO2 Flnb DsiRNA) and n = 50 (PIEZO2 NT DsiRNA) trajectories. h, Microscopic D from individual trajectories. n = 104 (PIEZO1), n = 87(PIEZO2), n = 126 (PIEZO2 Flnb DsiRNA) and n = 138 (PIEZO2 + non-targeting DsiRNA) trajectories. Kruskal–Wallis test, ****P = 2.1 × 10−10. i, Macroscopic D from individual trajectories. n = 55(PIEZO1), n = 61 (PIEZO2), n = 81 (PIEZO2 Flnb DsiRNA) and n = 50 (PIEZO2 non-targeting DsiRNA) trajectories. Kruskal–Wallis test, *P  = 0.0183. j, Indentation-evoked current in SWELL1-KO cells. n = 26 (PIEZO2), n = 7 (PIEZO2(ΔIDR5)) and n = 20 (PIEZO2 FLNB KO) cells. k, The indentation threshold for macroscopic current from j. Kruskal–Wallis test, *P = 0.0214, ***P = 0.0002. The box plots show the median (centre line), interquartile range (box limits, 25th–75th percentiles) and the minimum and maximum values (whiskers). l, Measured PIEZO2 blade conformation relative to cryo-EM (18.04 nm). For PIEZO2 + latrunculin A, median = 16.9 nm; n = 18 molecules, n = 4 cells; other values and n are described in Figs. 1b, 2b,e and 3e. Data are mean ± s.e.m. (c, d, g, j and k) and median ± 95% CI (e, h, i and l). All statistical tests were two-sided. The diagrams in a and f were created using BioRender; Mulhall, E. M. https://BioRender.com/5k5114d (2026).

Source data

We next assessed conformational changes of PIEZO2 in the absence of FLNB using MINFLUX. As the P. tridactylus genome remains unsequenced and 21-nucleotide siRNAs produced only partial knockdown in SWELL1-KO HEK293 cells after 48 h, we instead used 27-nucleotide double-stranded Dicer-substrate siRNAs (DsiRNAs), which can be up to 100-fold more potent than the corresponding 21-nucleotide single-stranded siRNAs[41]. We transfected four arrayed DsiRNAs against the mRNA encoding Potoroo Flnb—designed using the published PtK2 cell transcriptome as a ref. [42]—twice over a five-day period to obtain robust knockdown before imaging. Like actin disruption and deletion of PIEZO2 IDR5, Flnb knockdown significantly decreased the resting blade conformation in the absence of a stimulus and allowed the blades to expand with expansive membrane stretch (Fig. 3e,f). We also performed live-cell single-molecule MINFLUX tracking and observed that Flnb knockdown significantly increased the diffusion rate of PIEZO2 (Fig. 3g–i and Extended Data Fig. 7). These data suggest that FLNB is a component of the molecular tether that links PIEZO2 to actin and is required for conferring selectivity to indentation forces.

PIEZO2 is more sensitive to cellular indentation than PIEZO1[5]. To determine whether actin tethering through FLNB contributes to this heightened sensitivity, we measured mechanically evoked responses from cellular indentation with a blunt glass probe. In SWELL1-KO/FLNB-KO HEK293 cells, PIEZO2 showed reduced responses at all of the tested indentation depths (Fig. 3j), significantly lower peak current densities (Extended Data Fig. 8a) and a significantly higher gating threshold (Fig. 3k) compared with the control cells. These results demonstrate that FLNB confers heightened sensitivity to cellular indentation to PIEZO2. By contrast, PIEZO1 responses to both indentation and membrane stretch did not differ significantly between FLNB-KO and control cells (Extended Data Fig. 8b–d), indicating that PIEZO1 gating is independent of FLNB under these conditions. PIEZO2(ΔIDR5) also exhibited significantly reduced indentation responses and increased indentation threshold[4], although to a larger degree than those observed for FLNB deletion (Fig. 3j,k). We suspect that deletion of IDR5 might disrupt the intrinsic structural mechanics of the channel or that FLNB functions as part of a larger protein complex that interacts with this domain.

Compared with the conformation of the membrane-free cryo-EM structure, PIEZO2 is significantly expanded in the cell membrane of PtK2 cells by 2.76 nm, on average, without stimulation (Fig. 3l). Notably, while actin disruption with cytochalasin D compacts the blades to almost exactly that of the cryo-EM structure on average, the more potent drug latrunculin A, along with IDR5 deletion and Flnb knockdown, decreased the blade conformation even further (Fig. 3l). This is surprising because the blades presumably experience the same membrane bending forces that expand the blades of PIEZO1 by ~7 nm on average[16], yet we measure conformations even more compact on average than the membrane-free cryo-EM structure. This discrepancy could arise from the focused-refinement steps of the single-particle cryo-EM reconstruction of the blades of PIEZO2[14], which tends to overly weight conformations that align most readily rather than those that might reflect the true average conformation[43]. It could also arise, for example, from the presence of a separate unidentified binding protein that modulates the conformation of the blades. Overall, we propose that, if blade expansion correlates with channel open probability, a more expanded resting state resulting from FLNB tethering might be responsible for lowering the force threshold for gating by decreasing the distance the blades need to be deformed, thereby keeping it primed in a more sensitive state.

As FLNB is responsible for conferring selectivity and sensitivity to indentation forces in heterologous cells, we wondered whether it is present alongside PIEZO2 in peripheral mechanosensory neurons tuned to detect innocuous touch in the skin. We first used single-molecule fluorescence in situ hybridization (smFISH) to quantify Piezo2 and Flnb co-expression in mouse dorsal root ganglion (DRG) neuronal cell bodies positive for Ntrk2 or Ntrk3—markers that collectively cover most of the cutaneous low-threshold mechanoreceptors (LTMRs)[44]—and found 95% and 96% co-expression, respectively (Extended Data Fig. 9a–d). We next co-stained PIEZO2 and FLNB protein in fixed skin sections from Piezo2smFP-Flag knock-in mice using antibodies against endogenous FLNB and the smFlag epitope, focusing on two specialized mechanoreceptor terminals defined by these LTMR markers: Meissner corpuscles, which detect touch and low-frequency vibration in glabrous skin; and lanceolate endings, which encircle hair follicles in hairy skin to sense their deflection[24]. We found that FLNB and PIEZO2 co-localize prominently within both types of mechanoreceptor endings (Fig. 4a). To determine whether FLNB and PIEZO2 lie within a reasonable distance needed for direct interaction, we performed STED super-resolution imaging of lanceolate endings stained for PIEZO2 and FLNB with a full width at half maximum (FWHM) resolution of around 60–80 nm and quantified puncta overlap using intensity-profile correlations (Fig. 4b–e, Methods and Extended Data Fig. 9e–g). Analysis of FLNB and PIEZO2 intensity profiles in lanceolate endings yielded a mean Spearman’s rank correlation of ρ = 0.72 (Fig. 4e), indicating that the two proteins lie within just tens of nanometres of each other. This nanoscale proximity implies that FLNB is ideally positioned to tether PIEZO2, supporting a model in which they form a physically contiguous mechanotransduction complex that facilitates gating by low-threshold indentation forces.

Fig. 4: FLNB and PIEZO2 in somatosensory neurons.

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a, Representative images of sectioned skin from Piezo2smFP-Flag mice that contain end-organs formed by Ntrk2+ and/or Ntrk3+ LTMRs: lanceolate endings around hair follicles from back skin (top, five sections from two mice) and Meissner corpuscles from glabrous skin of paw digits (bottom, six sections from one mouse). Each section was co-stained with antibodies against NFH, PIEZO2–Flag and FLNB. Scale bars, 10 μm. b, Representative super-resolution STED microscopy image of PIEZO2–Flag and FLNB immunostaining in lanceolate endings showing co-localization of single puncta (six sections from two mice). Scale bars, 10 μm. c, Isolated single lanceolates from the yellow boxes in b (representative images of six sections from two mice). Scale bars, 500 nm. d, FWHM resolution of all identified peaks. Mean ± s.d. FWHM: anti-PIEZO2–Flag = 83 ± 31 nm, anti-FLNB = 62 ± 19 nm. n = 201(PIEZO2) and n = 469 (FLNB) puncta. e, Quantification of colocalization using Spearman’s rank correlation value ρ = 0.72 ± 0.12 (mean ± s.d.). n = 50 lanceolates from 5 follicles and 2 mice. f, The amplitude of stretch-induced currents in dissociated large diameter (≥70 µm) DRG neurons without any treatment (control, n = 8 cells), nucleofected with non-targeting DsiRNA (n = 8 cells) or nucleofected with DsiRNAs targeting Flnb (n = 9 cells). n = 3 mice for each condition. Two-sided Kruskal–Wallis test with Dunn’s multiple-comparison test; P = 0.9999 (control versus non-targeting DsiRNA), *P = 0.0105 (control versus Flnb DsiRNA), *P = 0.0201 (non-targeting DsiRNA versus Flnb DsiRNA).

Source data

Finally, to test whether FLNB modulates the force selectivity endogenous PIEZO2 in somatosensory neurons, we recorded mechanically evoked currents from dissociated mouse DRG neurons. We restricted our analysis to large-diameter (≥70 µm) neurons, of which the mechanical responses are predominantly mediated by PIEZO2[45]. We elicited expansive membrane stretch with osmotic swelling in the presence of the SWELL1 inhibitor DCPIB, but did not observe significant currents above the baseline (Fig. 4f). This indicates both that SWELL1 is sufficiently blocked by DCPIB, and that endogenous PIEZO2 does not respond to expansive membrane stretch. By contrast, DsiRNA-mediated Flnb knockdown elicited significant responses to expansive membrane stretch compared with control cells (Fig. 4f), indicating that FLNB is required for conferring force selectivity to PIEZO2 in somatosensory neurons.

Discussion

Here we provide evidence that PIEZO2 acquires its exquisite sensitivity to and selectivity for cellular indentation from a physical tether to the cortical actin network through FLNB. This scaffold fundamentally alters how force is transmitted to the channel. Whereas the primary gating force for PIEZO1 is lateral membrane tension, PIEZO2 appears to instead sense deformation of the bilayer against its FLNB–actin linkage. This difference yields distinct mechanisms of activation, highlighted by the fact that identical mechanical stimuli drive opposite conformational and gating responses (Fig. 1d–k).

Even without FLNB, the structural mechanics of PIEZO1 and PIEZO2 are intrinsically different (Figs. 1b and 3e). The blades of PIEZO2 have far higher apparent rigidity compared with those of PIEZO1 because they explore a smaller distribution of conformational states and are bent significantly less by the curvature mismatch of the planar plasma membrane and bowl-shaped structure of the channel (Fig. 1b,c). Notably, PIEZO2 blades in cells are even more compacted than in the membrane-free cryo-EM structure (Fig. 3l), despite ostensibly experiencing the same bending forces that expand PIEZO1 blades by around 7 nm on average[16]. Future studies could use targeted mutagenesis of predicted high-affinity interfaces to pinpoint the regions that confer this rigidity and reveal their impact on gating mechanics.

FLNB tethering biases the blades of PIEZO2 into a pre-expanded state, which correlates with a lower activation threshold by cellular indentation (Fig. 3). This indicates that tethering makes the channel more sensitive to indentation forces by reducing the distance the blades need to be deflected to open the ion-conducting pore. In cells or tissues that naturally do not express FLNB, we therefore predict PIEZO2 to act as a higher-threshold sensor of membrane tension, consistent with its higher pressure activation threshold in cell-attached recordings[26]. Differentially expressing PIEZO1 and PIEZO2 along with FLNB or other modulators might be a basic mechanism by which cells can calibrate mechanical sensitivity across a wide dynamic range.

Filamins are approximately 280 kDa actin-scaffolding proteins that consist of three family members—FLNA, FLNB and FLNC[40]. Each protein contains an N-terminal actin-binding domain, two rod regions of 24 immunoglobulin-like repeats separated by a flexible hinge and a C-terminal self-association domain (Extended Data Fig. 6c). Filamins are broadly implicated in cytoskeletal organization and are proposed to function as actin filament cross-linkers that influence cell shape and mechanical properties[40]. Both strong cortical coupling to the membrane and a stiff, highly cross-linked cortex tends to inhibit mechanosensitive ion channels by dampening the propagation of membrane tension46,47,48. For example, FLNA deletion increases PIEZO1 activity in smooth muscle cells, probably through changes in cytoskeletal stiffness rather than direct tethering[49]. However, FLNB KO did not significantly affect PIEZO1 responses to indentation or membrane stretch (Extended Data Fig. 8b–d), suggesting that FLNB does not substantially impact cell stiffness in our conditions. Filamins also prominently act as a scaffold for membrane proteins such as integrins, β-spectrins, G-protein-coupled receptors and ion channels[40]. Consistent with this role, our results identify FLNB as a component of the scaffold required for a direct channel–actin connection.

FLNB is expressed in many cell types throughout the body and in a variety of immortalized cells lines[50], which might explain why PIEZO2 appears to broadly retain its mechanosensitive specialization. Filamins are also regulated by various processes including calcium-dependent proteolysis, phosphorylation and mechanical unfolding[51], which may in turn impact PIEZO2 function. For example, protein kinase A (PKA) modulates the activity of both PIEZO2 and filamins52,53. Notably, PKA modulation of PIEZO2 requires the cytoskeleton-binding IDR5 segment—despite the absence of PKA phosphorylation sites within this region—suggesting that FLNB could be the phosphorylation target[52]. This type of modulation might tune the PIEZO2–actin linkage dynamically and could be involved in switching PIEZO2 from a lower-threshold sensor of indentation to a higher-threshold sensor of membrane tension depending on the needs of the cell. Clinically, loss of FLNB function in humans and mice can cause perinatal respiratory distress, musculoskeletal defects and distal arthrogryposis[44]. These phenotypes significantly overlap with those caused by deleterious mutations in PIEZO2[10], suggesting shared physiological roles. Although it is beyond the scope of this current study, future studies should carefully examine how disruption of the PIEZO2–FLNB connection alters touch discrimination, proprioception and related behaviours.

In summary, we directly measured the force-dependent conformational changes of PIEZO2 during channel gating and established a direct link between the structural state of the channel and its function. Compared with PIEZO1, differences in intrinsic structural mechanics and actin tethering through FLNB fundamentally alter the way PIEZO2 is gated by mechanical force. These data lay the foundation for understanding how PIEZOs are functionally specialized to detect diverse mechanical stimuli. Peripheral mechanosensory neurons distinguish indentation, membrane tension, phasic displacement and osmotic pressure to encode touch, proprioception, vibration and bladder fullness. This functional diversity, despite a common reliance on PIEZO2, suggests that sensory end organs contain additional specialized ultrastructural features beyond FLNB, such as extracellular tethers or caveolae54,55, that modulate how PIEZO2 responds to mechanical stimuli, enabling them to generate specific sensory outputs. Identifying and characterizing these specialized ultrastructural features will be essential for defining the molecular basis of PIEZO2-mediated mechanotransduction.

Methods

Study design

No statistical methods were used to predetermine sample size and the sample size was based on a previous study[16]. All attempts at replication were successful, and all experiments were repeated more than once, as indicated in the figure legends. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment. For MINFLUX, all experiments were repeated at least three times with separate biological and technical replicates, imaged over at least two separate days. Label-free controls were included to ensure signal specificity. For electrophysiology, experiments were conducted as previously described without blinding1,6,16. Untransfected control groups were measured on each day of experimentation, and each separate manipulation was paired with wild-type PIEZO1 and PIEZO2 control measurements.

Expression constructs

The coding sequence of mouse PIEZO2 (UniProtKB: Q8CD54) was codon optimized, synthesized and cloned into the pcDNA3.1 plasmid. For imaging and MS experiments, an amber stop codon was inserted after amino acid 104 (TCO*K 105) through site-directed mutagenesis using the Q5 Site-Directed Mutagenesis Kit (New England Biolabs), and a C-terminal HaloTag and Strep-Tag II. Labelled PIEZO1 plasmids with a tag after position 102 (TCO*K-103) were prepared the same way from mouse PIEZO1 (UniProtKB: E2JF22), as described previously[16]. For genetic code expansion, the tRNA and tRNA synthetase expression plasmid of pNEU-hMbPylRS-4xU6M15 (Addgene, 105830) was modified to have a mNeonGreen sequence upstream of the tRNA synthetase sequence separated by a T2A self-cleaving peptide to mark transfected cells. For electrophysiology experiments, codon-optimized mouse Piezo2 was cloned into the pcDNA3.1 vector upstream of an IRES mNeonGreen sequence, and mouse Piezo1 was cloned into the pcDNA3.1 vector upstream of an IRES eGFP sequence. PIEZO2(ΔIDR5) was created by deleting the coding sequence of amino acids 621–673 as described previously[4]. The PIEZO1 + IDR5 chimera was created by replacing the fifth IDR of PIEZO1 (amino acids 551–575) with the corresponding IDR5 region from PIEZO2 (amino acids 620–672). The sequences of each plasmid were verified using whole-plasmid sequencing (Plasmidsaurus). All DNA sequences were viewed and designed in SnapGene software (Dotmatics).

Coverslips with embedded gold fiducials

#1.5 D263 borosilicate coverglass (Warner Instruments) were cleaned by boiling in 1% Hellmanex III detergent (Hellma) in MilliQ water and sonicating for 15 min in a water bath sonicator. The coverslips were washed five times with MilliQ water, exchanged into 100% ethanol and then dried and placed onto a sheet of 4 in × 4 in × 1/8 in 304 stainless steel. Then, 150 nm gold nanoparticles with Nanopartz Surface Polymer (Nanopartz) were prepared by diluting to 5 µg ml−1 in 100% ethanol and sonicated in a bath sonicator to break up aggregates. This solution was applied to the coverslips at 0.079 µl mm−2 of surface area and allowed to dry. The coverslips were then covered with a borosilicate glass Petri dish and placed into a muffle furnace. The furnace was heated to 600 °C at a rate of 30 °C min−1, held at this temperature for 5 h and then allowed to cool overnight. The coverslips were stored at room temperature until use. Immediately before plating cells, the coverslips were coated with a 1:100 dilution of LDEV-free Matrigel (Corning) in DMEM (Thermo Fisher Scientific).

Cell preparation for structural MINFLUX imaging

PtK2 cells (ATCC, CCL-56) were maintained at 37 °C with 5% CO_2 in minimum essential medium (MEM) supplemented with 2 mM GlutaMAX, 25 mM HEPES (Thermo Fisher Scientific), 10% FBS, 1 mM sodium pyruvate (Gibco), 1× MEM non-essential amino acids solution (Gibco) and 100 µg ml−1 penicillin and streptomycin. Cells were authenticated by the supplier by morphological analysis, species verification by isoenzymology, short-tandem-repeat profiling and mycoplasma testing. Cells were further verified to be free of mycoplasma using the using the MycoAlert Mycoplasma Detection Kit (Lonza). The cells were plated onto Matrigel-coated coverslips with embedded gold fiducials, exchanged into a medium containing 250 µM of the click amino acid trans-cyclooct-2-en-l-lysine (axial isomer) (SiChem), and transfected with 1 µg of an equimolar ratio of PIEZO expression plasmid and of the tRNA/tRNA synthetase expression plasmids using TransfeX transfection reagent (ATCC). After 48 h, the cells were washed four times with prewarmed medium in 15 min intervals to remove excess click amino acid. The cells were washed in prewarmed 1× PBS and then fixed in prewarmed 1× PBS containing 0.8% PFA and 0.1% glutaraldehyde for 15 min.

For osmotic stimulation experiments, the cells were washed in 1× PBS and then exposed to a modified Ringer’s solution at 120 mOsm (48.8 mM NaCl, 5 mM KCl, 10 mM HEPES (pH 7.40) and 10 mM D-glucose) or at 480 mOsm (140 mM NaCl, 5 mM KCl, 10 mM HEPES (pH 7.40), 10 mM D-glucose and 190.3 mM mannitol) for 2.5 min at room temperature. The cells were then fixed in the same osmotic solution containing 0.8% PFA and 0.1% glutaraldehyde for 15 min and quenched in 1× PBS containing 25 mM Tris, pH 8.0. The osmolality of all solutions was determined to be ±5 mOsm using a vapour pressure osmometer.

For experiments with cytochalasin D or latrunculin A, a stock solution of 10 mM in DMSO was diluted to 10 µM in prewarmed medium and added to the cells. After incubation at 37 °C for 30 min, the cells were washed and fixed as described above, except each solution, including fixatives, contained 10 µM cytochalasin D or latrunculin A.

After fixation, all of the coverslips were washed in 1× PBS and quenched with 1× PBS + 25 mM Tris pH 8.0 for 10 min. The coverslips were then blocked in 1× PBS + 1% BSA for 10 min, PIEZOs were labelled with a custom DNA PAINT docking strand modified with a 3′ tetrazine (Supplementary Table 1) at 1 µM in blocking solution for 15 min and washed with blocking solution and 1× PBS. After labelling, cells were washed with and mounted in DNA PAINT imaging buffer (1× PBS + 500 mM NaCl + 0.5 mM EDTA) containing an enzymatic oxygen-scavenging system of 3,4-dihydroxybenzoic acid and protocatechuate 3,4-dioxygenase (from Pseudomonas) in addition to the triplet-state quencher Trolox ((+/−)−6-hydroxy-2,5,7,8-tetra-methylchromane-2-carboxylic acid) as described previously[56]. This imaging solution containing 1–10 nM of a custom, complementary fluorogenic DNA PAINT imaging strand modified with a 5′ ATTO 643 dye and a 3′ IowaBlack fluorescence quencher (Supplementary Table 1). The coverslip was placed onto a glass slide containing a cavity well (Globe Scientific) filled with imaging buffer and was then sealed onto the slide using Elite Double 22 dental epoxy (Zhermack).

Cell preparation for MINFLUX tracking

For tracking single PIEZO ion channels, the cells were transfected and prepared as for structural MINFLUX imaging. After washing away excess click amino acid, the cells were labelled with 0.5–2 nM Janelia Fluor 635-HaloTag ligand (Janelia Materials) in culture medium for 15 min at 37 °C, and then washed several times with fresh medium before incubating the cells for 30 min at 37 °C. This labelling concentration was chosen to obtain sparsely labelled channels conjugated to single dyes with non-overlapping fluorescent puncta. After another wash step, the cells were maintained at 37 °C for up to 4 h before imaging. Immediately before imaging, the cells were exchanged into supplemented culture medium without Phenol Red and mounted and sealed onto a glass slide with a cavity well as described above. The coverslip was imaged for a maximum of 1 h before discarding.

3D MINFLUX imaging

MINFLUX data were acquired on the commercial MINFLUX 3D microscope an Olympus IX83 microscope body (Abberior Instruments) using Imspector software (v.16.3.15645-m2205) with MINFLUX drivers. A ×100 oil-immersion objective lens (UPL SAPO100XO/1.4, Olympus) and a 642-nm excitation laser was used for imaging. Transfected cells were identified by expression of the mNeonGreen fluorescent marker driven by the tRNA/tRNA synthetase plasmid. A field of view around the cell was chosen containing at least three separate embedded gold nanoparticles for active sample stabilization through back-scattering from a 980 nm laser source through a closed control loop, typically resulting in less than 1 nm mean s.d. in the x,y,z axes. A 5–25 µm^2 region of interest (ROI) was chosen at the top face of the flat extension of a PtK2 cell. For structural MINFLUX imaging, at least three isolated gold fiducial nanoparticles were chosen for use by the active beamline stabilization system, and the localization error was verified to be less than 5 nm for each fiducial. The sample was imaged using a 5–12% 642-nm laser power, measured to be around 4.30 µW per percent set power at the sample plane. The pinhole diameter was set to be 0.47–0.6 a.u. The total measurement time varied between 5 and 24 h. For MINFLUX tracking, the sample was imaged using 2% 642-nm excitation laser power with a pinhole diameter of 0.8 a.u.

MINFLUX data analysis for 3D structural imaging in fixed cells

For structural MINFLUX imaging, raw final valid localizations from the last targeting iteration of the 3D imaging sequence (Supplementary Table 2) were exported from Imspector as a .mat file. Custom MATLAB analysis software was used to identify and segregate clusters of three localizations essentially as previously described[16], with some modifications. All clustering and distance calculations were performed in 3D on xyz coordinates and the reported interblade distances are 3D Euclidean distances. In brief, xyz coordinates were imported, and a 0.7 correction factor was applied to z coordinates to correct for refractive index mismatch. To remove poorly localized molecules, data were filtered so that each trace contained over 10–20 localizations, and we required a raw s.d. per dimension of less than 10–20 nm, much larger than the median localization precision. A z threshold was applied manually based on the apparent plane of the plasma membrane isolate those molecules at or near the membrane. An effective photon frequency at offset (EFO) threshold was applied between 120,000 and 150,000 depending on the first peak of photon emission frequency to remove localizations that might come from multiple fluorescent emitters. The data were then processed using a density-based clustering algorithm that uses two-step DBSCAN clustering followed by an expectation maximization Gaussian mixture model (GMM) fit to assign the 3D position of fluorophores, as previously described16,57. All data were analysed using the same parameters so that fair comparisons can be drawn between conditions. The first DBSCAN step was used to preassign localizations, and to identify and remove noise. The DBSCAN parameters in this first step were set to an epsilon of 10 nm and required five neighbours for a core point. The second DBSCAN step was set to epsilon = 7 nm and a minimum of 5 points. The initial GMM fit sigma was set to 5 nm, approximately equal to the localization error. The fluorophore centre positions were estimated as the mean values of the GMM fit, and the error was determined as the s.d. of the localizations within a cluster. After this clustering, an error threshold of 10 nm removed any poorly localized positions. We identified PIEZO trimers as clusters of three fluorophore positions that were isolated from all other detected fluorophore positions by more than 60 nm by subjecting identified molecular positions to DBSCAN clustering with epsilon = 60 nm and 3 minimum points. Next, a nearest-neighbour analysis required each point to have 2 neighbours between 6 and 60 nm, a window that spans the maximum expected range of interblade separations from available PIEZO cryo-EM structures. Clusters of three localizations passing each step was segmented and the average 3D interblade distance was calculated directly from the assigned molecular positions. As interblade distances are computed in 3D, the random orientation of individual channels relative to the imaging axes only rotates the trimer in space and does not bias the distribution of interblade distances. To verify the accuracy of our clustering algorithm in assigning molecular positions, we calculated the mean position of each trace after EFO thresholding and reanalysed the data using the same density-based clustering algorithm, with minpts set to 1 in both steps. Example data are shown in Extended Data Fig. 2c. Variability between replicates was assessed using a Kruskal–Wallis test with Dunn’s post hoc test (Supplementary Table 1).

MINFLUX data analysis for 3D tracking in live cells

For MINFLUX tracking experiments, raw valid localizations from the final targeting iteration of the 3D tracking sequence (Supplementary Table 3) were exported from Imspector as .mat files, imported into MATLAB and analysed using custom MATLAB analysis code to analyse tracks and obtain diffusion coefficients. For each localization, we used the x, y and z coordinates, the trace identifier, the time stamps and the EFO. A 0.7 refractive index correction factor was applied to z values. Localizations were grouped by trace ID to isolate individual trajectories. To ensure robust MSD estimation from well-localized single emitters, tracks were prefiltered using empirical thresholds that were kept fixed across all conditions. A maximum EFO cut-off of 130,000 was applied. The EFO cut-off ensured that only single dyes were imaged, as the photon emission frequency peak of a single dye was determined to be around 75 kHz, and we observed a second emission peak at approximately twice the single-dye emission frequency (~150 kHz) corresponding to two dyes, as described in a previous report that similarly used 3D MINFLUX tracking and an under-labelling strategy[58] (Extended Data Fig. 5a,b). A maximum allowed time gap of 18 ms between successive localizations was imposed, and any trajectory was truncated at the first gap exceeding this threshold, preventing artificial linking of positions across long dark periods. Trajectories were required to have greater than 200 localizations per trajectory so that the MSD could be computed over a sufficiently long timescale.

For each filtered trajectory, we calculated the MSD of the PIEZO molecules undergoing diffusion in three dimensions as a function of lag time τ with a weighted linear model MSD(τ) = 6Dτ. All MSDs and diffusion coefficients were calculated from 3D displacements. Although the confinement of PIEZO channels to the plasma membrane means that z excursions are small, all motion is explicitly included. Microscopic diffusion coefficients were obtained for each trajectory by fitting MSD(τ) over 5–50 ms. This window was chosen because, at this timescale, the channel will tend not to interact with diffusional barriers created by membrane–cytoskeletal interactions[36]. Macroscopic diffusion coefficients were obtained using the same model from 50–350 ms. To obtain ensemble diffusion coefficients, all accepted trajectories were pooled, binned into 50 linearly spaced lag-time bins between 0 and 350 ms, and a weighted mean and s.e.m. were computed for each bin using the number of displacement pairs as weights. The ensemble MSD curve was then fit with a weighted linear MSD model. For visualization only (Fig. 2h and Extended Data Fig. 7), accepted 3D trajectories were centred on their centre of mass and the first 1 s of motion was overlaid.

Electrophysiology analysis of heterologous cells

Cells were transfected with 1 µg DNA 48 h before measurements and plated onto 12-mm poly-D-lysine-coated glass coverslips (Corning, 354086) before recordings. Transfected cells were identified by fluorescence. Whole-cell currents were recorded using a Multiclamp 700A amplifier, Digidata 1550B digitizer and pClamp10.7 software (all from Molecular Devices). To record indentation-evoked currents, data were sampled at 20 kHz and low-pass filtered at 10 kHz. Patch pipette electrodes were pulled using borosilicate glass (34BF150-86-10, Sutter Instruments) and had a resistance of 3–5 MΩ when filled with the pipette solution (see below). The standard extracellular recording solution contained 135 mM NaCl, 3 mM KCl, 1 mM MgCl_2, 2.5 mM CaCl_2, 10 mM D-glucose, 10 mM HEPES (pH 7.3 with NaOH; 300 ± 5 mOsm was adjusted with D-mannitol). The pipette solution contained 133 mM CsCl, 5 mM EGTA, 1 mM MgCl_2, 1 mM CaCl_2, 10 mM HEPES, 4 mM Mg-ATP, 0.4 mM Na_2-GTP (pH 7.3 with CsOH; 294 ± 2 mOsm). Cells were mechanically stimulated for 145 ms at a holding potential of −80 mV, using a glass probe heat-polished to a 3–4-μm diameter (34B150-86-10, Sutter Instruments) and driven by a piezoelectric controller and actuator (E625 LVPZT Controller/Amplifier; Physik Instrumente) attached to the micromanipulator with a custom dye-anodized aluminium adapter. The probe was positioned at an 80° angle. The probe was initially positioned at about 2–4 mm from the cell and advanced at 0.5 µm ms−1 in 0.5 µm increments. The interstimulus intervals were 20 s. The maximum current (Imax) was identified from the family of peak current responses to increased membrane indentation. Cells with high access resistance (>20 MΩ) or low seal resistance (<1 GΩ) were excluded from data analysis. Cells that changed their morphology during repetitive poke stimulation were also excluded from data analysis. No series resistance compensation was applied.

To record cell swelling- and shrinking-induced currents, cells were perfused at a rate of 3–4 ml min−1 with an iso-osmotic solution containing 45 mM NaCl, 2.4 mM KCl, 1 mM MgCl_2, 2 mM CaCl_2, 10 mM D-glucose, 10 mM HEPES (pH 7.3 with NaOH; 300 ± 5 mOsm was adjusted with D-mannitol) using the VC-6 valve control system (Warner Instruments). The hypo-osmotic and hyper-osmotic solutions had the same composition, but mannitol was omitted in the hypo-osmotic solution (125 ± 5 mOsm), and the osmolarity of the hyper-osmotic solution was adjusted to 400 mOsm with mannitol. The pipette solution was identical to that described in the previous paragraph. The whole-cell currents were elicited by voltage ramps from −80 mV to 80 mV from the holding potential of −40 mV; the voltage ramps were applied at 0.1 Hz and with a 1 s duration. The interstimulus interval was 20 s. The cell membrane capacitance was estimated using the membrane test of pClamp 10.7, and the amplitude values of recorded current were normalized to the membrane capacitance to obtain the current density. The current–voltage relationship was reconstructed by plotting the current density versus the test voltage. Whole-cell currents were sampled at 10 kHz and low-pass filtered at 2 kHz. All patch-clamp experiments were conducted at the room temperature (20–23 °C). Swelling- or shrinking-induced currents were obtained by subtracting the currents recorded before application of either hypo- or hyper-osmotic solution, respectively, from the maximum current recorded during perfusion with the corresponding solution. Cells that developed membrane blebs during hypo-osmotic challenge were excluded from data analysis.

Cross-linking MS

Expi293 cells (Thermo Fisher Scientific) were maintained at 37 °C with 8% CO_2 in Expi293 medium, shaking at 125 rpm on a rotator with a 19-mm orbit diameter, and were verified to be free of mycoplasma using the using the MycoAlert Mycoplasma Detection Kit (Lonza). Cells were authenticated by the supplier for post-thaw viability, mycoplasma testing and sterility. The cells were cultured to a density of 4 × 10^6 cells per ml, exchanged into fresh medium containing 250–500 µM of the click amino acid trans-cyclooct-2-en-l-lysine (axial isomer) (SiChem), and three separate flasks were transfected with a 1:1 ratio of (1) PIEZO2 plasmid and tRNA/synthetase expression vector; (2) PIEZO2(ΔIDR5) and tRNA/synthetase expression vector, each using EndoFectin Expi293 transfection reagent (GeneCopeia); or (3) were not transfected. After 48 h of expression, the cells were washed three times at 15-min intervals with medium to wash out excess click amino acid. On the final wash, the cells were resuspended in 10 ml of medium supplemented with 1% BSA and blocked for 5 min. Tetrazine-PEG4-Biotin (Thermo Fisher Scientific) was added to a final concentration of 4 µM and allowed to react for 15 min with occasional mixing. After washing twice in BSA-supplemented medium, the cells were washed with HBSS with 20 mM HEPES pH 8.0. On the final wash, the cells were resuspended in HBSS + 20 mM HEPES pH 8.0 + 1 mM DSP dithiobis(succinimidylpropionate) cross-linker and incubated for 30 min at room temperature with occasional mixing. The reaction was quenched by adding 200 µl of 1 M Tris pH 8.0 and incubating for 15 min with occasional mixing. The cells were next pelleted by centrifugation, solubilized in ice-cold 25 mM HEPES (pH 7.4), 0.15 M NaCl, 1% C12E9, 0.1% GDN, 1× HALT protease inhibitor and rotated at 4 °C for 1 h. Insoluble cell debris was pelleted at 40,000g for 10 min, and the supernatant was kept on ice. The solubilized proteins were next processed for tandem affinity purification. First, biotinylated proteins were isolated using Pierce Monomeric Avidin Agarose on a polyprep column according to the manufacturer’s instructions using a wash buffer (25 mM HEPES (pH 7.4), 0.15 M NaCl, 0.1% C12E9) and eluted with wash buffer containing 2 mM D-biotin. Next, Halo-Tagged proteins were isolated using Magne HaloTag Beads (Promega). After immobilizing for 90 min at room temperature, the beads were washed with RIPA buffer to remove non-specifically bound protein. Cross-linked proteins were released from covalently immobilized Halo-tagged PIEZO molecules using RIPA buffer + 30 mM dithiothreitol at 50 °C for 15 min. The eluate was collected and stored on ice. Eluted proteins were run on a 4–20% polyacrylamide gels with Tris-Glycine buffer for 1 h and silver stained for visualization. For proteomics MS, the proteins were run on the same gel for 20 min and stained with SimplyBlue Safe Stain (Coomassie G-250) (Thermo Fisher Scientific). The lane was cut out from just below the well to ~10 kDa and submitted for nano-flow liquid chromatography coupled with tandem MS (nano-LC–MS/MS) analysis at the Scripps Research Center for Metabolomics and Mass Spectrometry. In brief, the gel was destained, and the proteins were denatured, reduced and alkylated before digestion with trypsin overnight. The peptides were analysed by nano-LC–MS/MS, and the data were searched against the predicted fragment ions from the trypsin digestion of human proteins using the proteomics search engine Mascot (Matrix Science Limited). The analysed results contained proteins identified at the 95% confidence interval. Protein identifiers were cross-referenced against the UniProt database, and each protein ID was appended with an exponentially modified protein abundance index (emPAI). Proteins that were detected in untransfected cells were determined to be background and removed from the PIEZO2 and PIEZO2(ΔIDR5) datasets.

siRNA-mediated knockdown of candidate scaffolding proteins

SWELL1-KO HEK293 cells16,59 were maintained in Freestyle 293 medium (Thermo Fisher Scientific) at 37 °C with 8% CO_2, shaking at 125 RPM on a rotator with a 19-mm orbit diameter, and were verified to be free of mycoplasma using the using the MycoAlert Mycoplasma Detection Kit (Lonza). Before transfection, 3 ml of cells were grown in a 30 mm diameter uncoated Petri dish to a density of 1 × 10^6 cells per ml. Then, 1.5 µg of mPiezo2-IRES-mNG was co-transfected with 2 µl of a 40 µM stock (80 pmol total) of ON-TARGETplus SMARTpool siRNAs (Dharmacon) targeted against the mRNAs of candidate scaffolding proteins using EndoFectin Expi293 transfection reagent (GeneCopeia). After 48 h, cells were plated onto 10 mm poly-D-lysine-coated coverslips, allowed to settle for 1 h, and assessed using patch-clamp electrophysiology.

Clonal FLNB-KO cells

Clonal FLNB-KO cells were created using SWELL1-KO HEK293 cells16,59 using the EditCo Bio Gene Knockout Kit. SWELL1-KO cells were authenticated as previously described: successful KO of SWELL1 genes was determined by PCR genotyping and Sanger sequencing targeted regions for frameshift mutations, and verified by MS analysis16,59. Three sgRNAs were designed against exon 8 of the human FLNB gene (NCBI: NM_001164317.2). The sgRNAs were precomplexed with spCas9-2NLS (Synthego) and 1.5 × 10^6 cells were nucleofected using a Lonza 4D Nucleofector System, the P3 Primary Cell Kit S and a nucleocuvette strip (Lonza) using the default HEK293 electroporation program. Cells were recovered for 10 min in Freestyle 293 medium (Thermo Fisher Scientific) and then grown for 3 days with shaking at 125 rpm. Single cells were isolated using a Propel Bigfoot flow cytometer (Thermo Fisher Scientific) at the Scripps Research Flow Cytometry Core in the wells of three 96-well plates containing DMEM + 10% FBS + 1× penicillin–streptomycin and grown for 3 weeks at 37 °C, 5% CO_2 to form clonal colonies. After visible colonies were formed, cells were dissociated using Tryple Express (Thermo Fisher Scientific), quenched with DMEM + 10% FBS, and 50% of the dissociated mixture was plated into a single well of a 12-well plate to expand further. Meanwhile, the remaining mixture (containing around 300,000 cells) was centrifuged and genomic DNA was isolated using the QuickExtract DNA Extraction Solution (BioSearch Technologies). A 1.245 kb fragment of genomic DNA from each colony was amplified using two PCR primers using Q5 DNA Polymerase (NEB), isolated with a PCR purification column (Zymo Research) and Sanger sequenced using a nested sequencing primer (Genewiz). Sequencing traces were analysed using the EditCo ICE analysis tool. A single SWELL1-KO/FLNB-KO clone was chosen that contained a homozygous deletion of 52 bp in exon 8 of FLNB (chromosome 3: 58098750–58098801 of the GRCh38/hg38 reference assembly), resulting in a frameshift. The clone was expanded and maintained in adherent culture in DMEM + 10% FBS + 1× penicillin–streptomycin in an incubator at 37 °C, 5% CO_2.

Flnb knockdown in PtK2 cells

Four arrayed Dicer-substrate interfering RNAs (DsiRNAs) (IDT) were designed against the P. tridactylus Flnb sequence (Supplementary Table 1) using the published PtK2 cell transcriptome as a guide[42]. A commercial non-targeting dsiRNA was included as a control (IDT 51-01-19-08). PtK2 cells were plated into a six-well plate containing 3 ml of medium 1–3 h before transfection. Each well was transfected with 20 pmol of each of the 4 dsiRNAs in 300 µl Optimem with 7.5 µl TransfeX transfection reagent (ATCC). After 48 h, the cells were split and plated onto Matrigel-coated coverslips with embedded gold fiducials, exchanged into a medium containing 250 µM of the click amino acid trans-cyclooct-2-en-l-lysine (axial isomer) (SiChem) and transfected a second time with identical amounts of dsiRNA, with the addition of 1.5 µg of an equimolar ratio of the mPiezo2 and the tRNA/tRNA synthetase expression plasmids. The cells were allowed to express for an additional 48 h and then prepared for MINFLUX imaging as described above. Knockdown efficiency was verified using qPCR.

Mice

All experiments were performed under the policies and recommendations of the International Association for the Study of Pain and approved by the Scripps Research Animal Care and Use Committee. Mice were kept in standard housing under a 12 h–12 h light–dark cycle at 22 °C with humidity between 30% and 80% (not controlled). Mice were kept on pelleted paper bedding and provided with paper square nestlets and polyvinyl chloride pipe enrichment with ad libitum access to food and water. PCR genotyping was performed from tail snip DNA samples using Transnetyx. All mice received metal identification tags on their ears at 18–30 days old. After weaning (21–30 days old), mice were co-housed in groups of 2–5 littermates of the same sex. Animal sample sizes were based on similar studies in the literature6,60.

Immunohistochemical co-localization of PIEZO2–smFlag and FLNB in mouse skin

Two male Piezo2smFP-Flag/smFP-Flag mice[60] (Piezo2em1.1Ddg/J, CD-1 genetic background, Jackson Laboratories, 039935, a gift from D. Ginty, one at 2.5 weeks of age and one at 4 weeks of age) and two male CD-1 mice (one at 6 weeks of age and one at 4 weeks of age) were used for hairy skin immunohistochemistry[60]. One 4-week-old male Piezo2smFP-Flag/smFP-Flag mouse and one 4-week-old CD-1 control mouse were used for glabrous skin immunohistochemistry. In brief, mice were euthanized with isoflurane and killed by cervical dislocation. The dorsal surface of the mouse was dehaired with depilatory cream (Nair Cocoa Butter) for 3 min, the skin was thoroughly rinsed with industrial water and gentle manual massaging for 30 s and patted dry with paper towels. The dorsal back skin was rapidly collected, the epidermis was pinned down onto Styrofoam and scraped with a surgical scalpel to remove subcutaneous fat, taking care to note the rostrocaudal axis. The plantar (glabrous) surface of the hind paw, not including the digit tips, was collected using spring scissors, with the underlying tissues removed. White cardstock was pressed onto the dermis to flatten the samples, and the dorsal back skin was trimmed to approximately 1 × 1 cm. Skin samples were drop-fixed in freshly prepared and prechilled 1% paraformaldehyde in PBS pH 7.4 for 2 h on ice. Skin samples were rinsed twice in cold PBS, then transferred into ice-cold 30% sucrose in PBS and incubated for 18 h at 4 °C until the tissues sank. The samples were briefly washed in ice-cold OCT medium (Sakura Finetech, 4583) and embedded in OCT medium in cryomolds (with the cardstock) on crushed dry ice. All tissues were sectioned dermis-first at 25 µm at −20 °C onto gelatin-coated slides (FD NeuroTechnololgies, PO101). Hairy skin was sectioned normal to the rostrocaudal axis, and glabrous skin was sectioned normal to the proximodistal axis, beginning with the distal portion. Slides were air-dried at room temperature for 1 h and a hydrophobic barrier was drawn (ImmEdge Vector Laboratories H-4000) around the tissue sections. The slides were rehydrated in 200 ml PBS to remove OCT and cardstock and, from then on, all washes were performed with a volume of 40 ml. All washes were performed for 10 min. The samples were washed in PBS and then blocked for 2 h at room temperature in 5% normal goat serum (Life Technologies, PCN5000) in 0.1% PBST. Block was aspirated and primary antibody was applied as follows in blocking buffer for 48 h at 4 °C: 1:500 polyclonal guinea pig anti-Flag[60] (a gift from D. Ginty), 1:500 polyclonal rabbit anti-FLNB (Thermo Fisher Scientific, PA5-52098) and 1:1,000 polyclonal chicken anti-NFH (Abcam, ab4680). The slides were washed three times in PBST followed by PBS and incubated in the following highly cross-adsorbed secondary antibodies for 24 h at 4 °C: for confocal imaging: 1:2,000 goat anti-guinea pig Alexa Fluor 594 (Life Technologies, A11076), 1:2,000 goat anti-rabbit Alexa Fluor 647 (Life Technologies, A21245), 1:2,000 goat anti-chicken Alexa Fluor 488 (Life Technologies, A32931); for STED imaging: 1:2,000 goat anti-guinea pig STAR RED (Abberior, STRED-1006), 1:750 goat anti-rabbit STAR ORANGE (Abberior, STORANGE-1002). A no-primary control was always performed. The slides were washed three times in 0.1% PBST, then PBS, and then mounted in SlowFade Diamond (Life Technologies, S36967). The slides were sealed with nail polish, dried for 2 h, stored at 4 °C, and imaged on the Nikon AX confocal microscope or Abberior Instruments Facility Line 3D STED microscope.

smFISH

Mouse dorsal root ganglia were dissected fresh from two adult C57BL6/J male mice, embedded in optimal cutting temperature compound (OCT, Sakura), and flash-frozen in liquid nitrogen. The protocols for the RNAscope Multiplex Fluorescent Reagent Kit V2 (ACDBio, 323100) and tyramide signal amplification dyes (Perkin Elmer) were followed exactly according to the manufacturers’ instructions. Protease IV was applied for 22 min. Probes (all from ACDBio) for mouse Flnb (572481), Ntrk2 (423611-C2), Ntrk3 (423621-C2) and Piezo2 (400191-C3) were applied to detect transcript. Slides were imaged on a Nikon AX confocal microscope using a ×16 water-immersion objective with Nyquist zoom. Cell borders were drawn around highly expressed marker transcript (Ntrk2 and Ntrk3) signals to define individual cells.

Confocal imaging and data analysis

Images were acquired on the Nikon AX confocal microscope with NIS Elements software and the image settings (laser power, gain, resolution, pixel dwell time, objective and pixel dimension settings) were kept the same for all conditions. Meissner corpuscles were imaged with a Nikon ×60/1.4 NA oil-immersion objective and hair follicle lanceolates were imaged with a Nikon ×100/1.42 NA oil-immersion objective. Images were analysed in Fiji. For all images, the brightness and contrast adjustments were applied uniformly to the entire image.

STED imaging and data analysis

STED imaging was performed on an Abberior Instruments Facility Line 3D STED microscope on an Olympus IX83 microscope body. The excitation lasers and STED depletion lasers were autoaligned with a fluorescent bead fiducial sample. The samples were imaged in 2D mode with a ×60/1.42 NA oil-immersion Olympus objective with 561 and 640 nm excitation lasers with a 775 nm depletion laser with fluorescent lifetime imaging (TIMEBOW) enabled for all of the experiments. All of the images were acquired with identical excitation power, STED depletion power, pinhole diameter and line accumulations so that the fluorescence intensities were comparable across samples. STED images were acquired at 20 nm per pixel. Co-registered confocal and STED images were acquired in two channels (STAR RED for PIEZO2-smFlag, STAR ORANGE for FLNB) for each field of view. Single-label controls were used to confirm detection bandwidths and verify negligible bleed-through between channels. STED data were analysed and deconvolved in Lightbox 2025 software (v.2024.48.21878-gc86bbd647c) using lifetime-based PHASOR deconvolution. Deconvolution was used to improve lateral resolution and signal-to-noise. The deconvolution model incorporated the refractive index of the mounting medium (1.420), the measured axial distance from the coverslip to correct for depth-dependent point spread function changes and the lifetime information from the dyes, which helps to distinguish fluorophore signal from the background compared with intensity-only deconvolution. All images were processed with the same deconvolution and background subtraction parameters. In the Lightbox program, deconvolution parameters were set to 50 iterations and a sharpness value of 10, and PHASOR background weights of 1.50 were applied to the STAR RED and STAR ORANGE channels. The background was removed using a rolling-ball subtraction (kernel size = 20, weight = 1.0). These values were empirically chosen to yield stable FWHM values of approximately 60–80 nm without oversharpening or ringing artifacts.

The deconvolved images were exported to Fiji (v.2.16.0/1.54p) for segmentation and co-localization analysis. To restrict measurements to individual lanceolate endings, we generated a binary mask from the confocal STAR RED channel, which provides a continuous representation of PIEZO2-positive lanceolate endings (Extended Data Fig. 9e). The confocal STAR RED (PIEZO2-smFlag) image was Gaussian blurred with a radius of 10 pixels to smooth local intensity fluctuations and then thresholded using a fixed intensity threshold applied identically to all images. This value was chosen from the intensity histograms to include lanceolate signal and exclude background. The FLNB channel was hidden during ROI generation to avoid bias. These ROIs were applied to the deconvolved STED images of both channels. Within each lanceolate ROI, co-localization between PIEZO2-smFlag and FLNB was quantified on the deconvolved STED images by pixel intensity correlation using the Coloc2 plugin in Fiji with a point-spread-function diameter of 4.0 pixels, chosen based on the measured STED FWHM. No additional intensity thresholds were applied within the ROIs. We report Pearson’s correlation coefficient r and Spearman’s rank correlation coefficient ρ per lanceolate ending. FWHM was measured from the deconvolved STED images in Fiji using the ‘fwhm_on_spots’ jython-fiji macro (https://github.com/sommerc/spots_fwhm).

DRG neurons

Isolation and culture of mouse DRG neurons from wild type C57BL/6J mice aged 3–5 months were performed as previously described1,6. In brief, DRGs were dissected and incubated for 1 h at 37 °C in serum-free medium containing 1.25% collagenase IV (Life Technologies), followed by incubation with 1 U ml−1 papain (Thermo Fisher Scientific) for 30 min at 37 °C. Cells were then triturated and transferred into complete growth medium (Ham’s F12/DMEM + 10% FBS supplemented with the following growth factors (from Gibco): 50 ng ml−1 GDNF, 100 ng ml−1 NGF, 50 ng ml−1 NT-4, 50 ng ml−1 NT-3, 50 ng ml−1 BDNF and 10 µM cytosine arabinoside (AraC)) and plated onto laminin-coated poly-D-lysine coverslips (Corning). Cells were allowed to adhere for 1–2 h before the addition of extra complete medium. For DsiRNA knockdown experiments, cells were nucleofected using the Amaxa P3 Primary Cell 4D-Nucleofector X Kit S (Lonza) as previously described1,8. Four arrayed DsiRNAs (IDT) designed against mouse Flnb mRNA were used to knockdown Flnb and a non-targeting dsiRNA was included as a control (IDT 51-01-19-08). In total, 120 pmol of DsiRNA and 400 ng of pmaxGFP vector (Lonza) were nucleofected per reaction. After nucleofection, cells were allowed to recover in serum-free medium for 10 min at 37 °C and then plated with complete medium containing growth factors, except without the addition of AraC. Cells were allowed to adhere for 1–2 h before the addition of extra complete medium. Measurement of swelling-induced current was performed as described for heterologous cells, except the extracellular solution contained 30 µM DCPIB to block SWELL1-mediated chloride currents. Patch-clamp recordings were performed on the fourth and fifth day after nucleofection. Only large-diameter cells (>70 µm) with neuronal morphology were patched. The extracellular iso-osmotic and hypo-osmotic solutions, intracellular recording solution and other conditions were identical to those used for heterologous expression experiments (see above), with the exception that cells were patched in the standard extracellular recording solution followed by perfusion with DCPIB (30 µM)-containing iso-osmotic solution for at least 5 min before 2 min perfusion with the hypo-osmotic solution (also containing 30 µM DCPIB). The holding potential between voltage ramps was −40 mV. Cells that exhibited a sudden increase in current of several hundred pA or several nA after the application of hypotonic solution, without recovering after reintroduction to the iso-osmotic solution, were excluded from data analysis due to the potential compromise of the gigaseal stability or plasma membrane integrity during cell swelling.

Structural models

Structural models from single-particle cryo-EM for PIEZO1 (6B3R) and PIEZO2 (6KG7) were obtained from the Protein Data Bank (PDB). The PIEZO2 cryo-EM structure lacks the extracellular loop containing the tagging location at amino acid 105, so an AlphaFold III model was generated for a monomer of mouse PIEZO2, and the last PIEZO repeat domain was superposed onto the equivalent domain of the 6KG7 cryo-EM structure in UCSF Chimera software. Interblade distances were measured using amino acid Gln105 of this model.

Data visualization and statistical tests

Data were visualized and statistical tests performed in MATLAB (MathWorks) and Prism (GraphPad) software. Molecular structures were visualized in MolStar Viewer (https://molstar.org/viewer/) and Chimera (UCSF) software. DNA and mRNA sequences were designed and analysed in SnapGene analysis software (Dotmatics).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Published protein structures were obtained from the RSCB Protein Data Bank (6B3R (PIEZO1) and 6KG7 (PIEZO2)). Protein sequences were obtained from UniprotKB. AlphaFold III models were generated with the Google DeepMind AlphaFold Server. Raw data are available at Zenodo[61] (https://doi.org/10.5281/zenodo.17644763). All reagents that are not commercially available are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

Custom MATLAB code for analysis of MINFLUX structural and tracking data are available at GitHub (https://github.com/PatapoutianLab/MINFLUX_Localization_and_Tracking_Analysis) and Zenodo[62] (https://doi.org/10.5281/zenodo.17625937).

Change history

- 17 March 2026

This paper was originally published under Creative Commons Attribution 4.0 International license, CC-BY-NC-ND, © The Author(s). It is now available under a Creative Commons Attribution 4.0 International license, CC-BY, © The Author(s). The error has been corrected in the online version of the article.

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Acknowledgements

We thank J. Matthias for help with MINFLUX instrument and software troubleshooting; D. Ginty for sharing Piezo2smFP-Flag/smFP-Flag mice and anti-Flag antibodies; K. Spencer and S. Henderson for assistance with imaging; the staff at the Scripps Research Department of Animal Resources for resources and support services; and the members of the Patapoutian laboratory for feedback and discussions. MINFLUX and STED imaging was performed at and supported by the Scripps Research Core Microscopy Facility. MS was performed at the Scripps Center for Metabolomics and Mass Spectrometry. This work is supported by NIH grants K99 GM155547 (to E.M.M.) and R35 NS105067 (to A.P.). E.M.M. was supported by a George E. Hewitt Foundation for Medical Research postdoctoral fellowship and The Warren Alpert Distinguished Scholar Award in Neuroscience. A.P. is a Howard Hughes Medical Institute Investigator.

Author information

Author notes

1. Rose Z. Hill

Present address: Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, OR, USA

Authors and Affiliations

1. Howard Hughes Medical Institute, Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, La Jolla, CA, USA

Eric M. Mulhall, Oleg Yarishkin, Rose Z. Hill, Anna K. Koster & Ardem Patapoutian

2. Vollum Institute, Oregon Health & Science University, Portland, OR, USA

Rose Z. Hill

Contributions

E.M.M. conceived and designed the project, performed MINFLUX, STED and confocal imaging, molecular biology experiments, cross-linking MS, wrote the MINFLUX analysis and tracking code, analysed and interpreted all data, prepared figures and wrote the manuscript. O.Y. performed electrophysiology experiments. R.Z.H. performed skin immunostaining, smFISH and confocal imaging. A.K.K. assisted with cross-linking MS. A.P. contributed to project design and supervised the project. All of authors discussed results and contributed to manuscript editing.

Corresponding authors

Correspondence to Eric M. Mulhall or Ardem Patapoutian.

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Extended data figures and tables

Extended Data Fig. 1 Fluorogenic DNA PAINT with MINFLUX nanoscopy.

a, Schematic of DNA PAINT with traditional and fluorogenic DNA PAINT. A DNA docking strand is conjugated to the distal extracellular blade of PIEZO1 or PIEZO2. Traditional DNA PAINT imaging strands remain fluorescent in solution when not bound to the docking strand, but fluorogenic imaging strands are quenched, reducing background. b-c, PtK2 cells expressing mPIEZO1 TCO*K 103 imaged with 5 nM imaging strand, with and without a fluorescence quencher on the imaging strand. Imaging parameters were exactly the same (5% excitation power, 0.60 AU pinhole). Left, determined fluorescence background photon emission frequency in the sample. Middle, effective photon emission frequency at offset TCP position for the final iteration of a localization. Right, Number of localizations collected per trace. n = 2 cells per condition. d, Standard deviation per trace calculated in each dimension for unfiltered traces containing >10 localizations.

Extended Data Fig. 2 MINFLUX imaging stability with fluorogenic DNA PAINT.

a, Representative examples of triple-labelled PIEZO1 molecules imaged with 3D MINFLUX over a total acquisition period of 19 h. The left panels show the mean positions of individual traces (coloured circles) within identified trimeric clusters. The right panels show histograms of the time elapsed since the start of the experiment for each trace in the corresponding cluster. b, Localization precision of independent detection events within any cluster for clusters with >1 trace. c, Comparison of measured inter-blade distances for triple-labelled PIEZO1 molecules when clustering localizations versus the mean position of each trace. In both cases, data were subjected to the same density-based clustering algorithm. Statistical test: two-sided Kolmogorov–Smirnov (KS) test. PIEZO1 Isotonic DBSCAN: mean = 26.4 nm, n = 36 molecules, n = 4 cells; PIEZO1 Hypotonic DBSCAN: mean = 35.6 nm, n = 39 molecules, n = 3 cells; PIEZO1 Isotonic mean values: mean = 25.0 nm, n = 110 molecules, n = 4 cells; PIEZO1 Hypotonic mean values: mean = 33.4 nm, n = 129 molecules, n = 3 cells. d, Representative standard deviation of photon counts per trace for PIEZO1 molecules imaged with direct Alexa 647 dye conjugation (itr 8: mean = 15.3, itr 9: mean = 12.1; n = 2014 traces), fluorogenic DNA PAINT (itr 8: mean = 15.8, itr 9: mean = 14.9; n = 7254 traces), and traditional DNA PAINT (itr 8: mean = 43.2, itr 9: mean = 28.4; n = 1013 traces). Error bars: mean ± SD (c,d).

Source data

Extended Data Fig. 3 Representative MINFLUX data and particle segmentation.

a, A representative rendered MINFLUX image of valid final localizations (5-nm spheres coloured by trace ID) from a PtK2 cell membrane expressing mPIEZO1 TCO*K 103 (from 4 cells) and imaged with 5 nM fluorogenic imager strand. Scale bar = 1 µm. b, Zoomed-in renderings of localizations (5-nm spheres) of the magenta boxes in a showing single-labelled (red), double-labelled (yellow) and triple-labelled (green) PIEZO1 molecules (scale bar, 50 nm). c, PIEZO1 trimers identified by the clustering algorithm in a 100×100 nm bounding box for the PIEZO1 TCO*K 103 Isotonic condition in Fig. 1d. Raw localizations (6-nm spheres) coloured by DBSCAN cluster ID. The centre positions were determined by a 3D GMM fit (black circles). Segmented particles are viewed top-down from a flat plane fit between each of the three centre positions.

Source data

Extended Data Fig. 4 Actin disruption with Cytochalasin D does not significantly alter PIEZO1 structural mechanics.

a, Disruption of the actin cytoskeleton with 10 µM cytochalasin D (magenta circles; median = 26.6 nm; n = 29 molecules, n = 3 cells) does not alter the resting conformation of PIEZO1 relative to untreated cells (grey circles; data from Fig. 1b). b, Expansive membrane stretch from hypo-osmotic expansion in cytochalasin D treated cells results in blade expansion in PIEZO1 (dark magenta circles; median = 35.1 nm; n = 40 molecules, n = 3 cells). c, Comparison of blade expansion from hypo-osmotic expansion (dark red circles; data from Fig. 1d). All statistical tests performed with a two-sided KS test, error bars shown as median and 95% CI.

Extended Data Fig. 5 MINFLUX single-molecule tracking: data filtering and PIEZO1-IDR5 chimera.

a, Histograms of EFO (effective photon frequency measured at TCP offset) for each MINFLUX tracking experiment. The photon emission peaks corresponding to one dye (Peak 1) or two dyes (Peak 2) are noted. An upper EFO threshold of 150 kHz was applied to each dataset to eliminate signal arising from multiple dyes. b, Histograms of EFO for PIEZO1 measured with 0.5 nM dye (from Extended Data Fig. 5a) and 4 nM dye. Increased labelling concentration led to a disproportionate increase in the higher-frequency EFO peak (~ 150 kHz) relative to the single-molecule peak (~ 75 kHz), consistent with a higher fraction of multi-molecule events at higher labelling densities. c, Results from single-molecule tracking of the PIEZO1 + IDR5 chimera in which the fifth IDR of PIEZO1 (amino acids 551–575) was replaced with the corresponding IDR5 region from PIEZO2 (amino acids 620–672). Left, Ensemble Mean Squared Displacement (MSD) against time for trajectories containing at least 200 localizations fit between 5–50 ms (PIEZO1: D = 0.0218 µm^2 s−1, PIEZO1 + IDR5: D = 0.0204 µm^2 s−1) and between 50–350 ms (PIEZO1: D = 0.0197 µm^2 s−1, PIEZO1 + IDR5: D = 0.0153 µm^2 s−1). (n, trajectories: PIEZO1: n = 55, PIEZO1 + IDR5: n = 29). Error bars: mean ± SEM. Middle, microscale diffusion coefficients for PIEZO1 (grey circles; median = 0.024 µm^2 s−1, n = 104), PIEZO1 + IDR5 (magenta circles; median = 0.022 µm^2 s−1, n = 62), and PIEZO2 (blue circles; median = 0.011 µm^2 s−1, n = 87). KW test: ****P = 2.0×10−9. Error bars: median ± 95% CI. Right, macroscale diffusion coefficients for PIEZO1 (grey circles; median = 0.024 µm^2 s−1, n = 55), PIEZO1 + IDR5 (magenta circles; median = 0.018 µm^2 s−1, n = 29), and PIEZO2 (blue circles; median = 0.011 µm^2 s−1, n = 61). KW test: **P = 0.0027. Error bars: median ± 95% CI. All statistical tests are two-sided.

Source data

Extended Data Fig. 6 FLNB and SWELL1 knockout HEK293 cells.

a, Genomic DNA sequence surrounding and including exon 8 of human FLNB. The position of the indel in the SWELL1-KO + FLNB KO HEK293 cells (magenta) is shown within the exon (blue). Arrayed CRISPR sgRNA hybridization locations are shown in grey. b, Human FLNB protein sequences encoded by Refseq cDNA NM_001457.4 surrounding the indel, starting after amino acid 382. Indel location is shown with a red caret and introduced stop codons are shown as red asterisks. c, A schematic of the domains in FLNB with the red arrowhead denoting the location of truncation in the FLNB KO cells.

Extended Data Fig. 7 Single-molecule tracks and individual trajectories for FLNB DsiRNA knockdown.

a, Top, overlaid trajectories of PIEZO molecules from single molecule tracking with MINFLUX aligned by centre of mass for trajectories lasting at least 1 s for PtK2 cells expressing PIEZO2 with non-targeting DsiRNA. Bottom, Mean Squared Displacement against time for trajectories containing at least 200 localizations for the trajectories shown in Fig. 3g. n = 50 trajectories. b, Top, overlaid single-molecule trajectories for cells expressing PIEZO2 with Flnb DsiRNA. Bottom, Mean Squared Displacement against time for trajectories shown in Fig. 3g. n = 81 trajectories. Error bars: mean ± SEM (a,b).

Extended Data Fig. 8 Mechanically Evoked Currents from PIEZO2 and PIEZO1 in FLNB Knockout Cells.

a, Quantification of the peak indentation-evoked current for the data in Fig. 3j. Piezo2: 183.5 ± 42.1 pA/pF, n = 19 cells; Piezo2 Flnb KO: 90.2 ± 20.7 pA/pF, n = 19 cells; Piezo2 ΔIDR5: 59.0 ± 22.3 pA/pF, n = 7 cells. KW test: **P = 0.0015, ***P = 0.0010. b, Indentation threshold to elicit a macroscopic current from Piezo1 in Swell1 KO and Swell1 KO + Flnb KO cells (n = 7 cells each). Mann-Whitney test: P = 0.5897. Box plot shows median (centre line) and interquartile range (box, 25th–75th percentiles); whiskers indicate minimum and maximum. c, Peak indentation-evoked current from Piezo1 in Swell1 KO and Swell1 KO + Flnb KO cells. Piezo1: 327.4 ± 123.8 pA/pF, n = 7 cells; Piezo2 Flnb KO: 397.4 ± 150.2 pA/pF, n = 7 cells. Mann-Whitney test: P = 0.2593. d, Amplitude of stretch-induced currents from osmotic swelling for untransfected Swell1 KO cells (−1.9 ± 0.6 pA/pF, n = 11 cells), Swell1 KO cells transfected with Piezo1 (14.6 ± 4.9 pA/pF, n = 9 cells), and Swell1 + Flnb KO cells transfected with Piezo1 (15.5 ± 4.5 pA/pF, n = 12 cells). KW test: ****P  = 0.0002. Error bars in a, c, and d are mean ± SEM. All statistical tests are two-sided.

Source data

Extended Data Fig. 9 Analysis of FLNB and PIEZO2 co-localization.

a, Representative images (11 images from 2 mice) of smFISH on sectioned mouse DRG for Ntrk2, Flnb, Piezo2 and merged with DAPI. Yellow outlines indicate Ntrk2+ cells. Scale bar = 100 μm. b, Quantification of DRG smFISH images with the percentage of Ntrk2+ cells expressing Piezo2, Flnb, or both. The number of co-expressing neurons is above the bar. c, Representative images (11 images from 2 mice) of sectioned mouse DRG smFISH for Ntrk3, Flnb, Piezo2 and merged with DAPI. Scale bar = 100 μm. d, Quantification of DRG smFISH images with the percentage of Ntrk3+ cells expressing Piezo2, Flnb, or both. e, Overview of lanceolate ROI assignment for the hair follicle shown in Fig. 4b. f, Pearson’s correlation coefficient (r = 0.54 ± 0.16) for the α-PIEZO2-FLAG and α-FLNB channels of the representative segmented lanceolate ROIs for the hair follicle in e. n = 16 lanceolates from 1 representative image. g, Spearman’s rank correlation coefficient for the same ROIs (ρ = 0.75 ± 0.08). Error bars are mean ± SD. n = 16 lanceolates from 1 representative image.

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Mulhall, E.M., Yarishkin, O., Hill, R.Z. et al. The molecular basis of force selectivity by PIEZO2. Nature 653, 297–305 (2026). https://doi.org/10.1038/s41586-026-10182-7

- Received: 22 May 2025

- Accepted: 23 January 2026

- Published: 04 March 2026

- Version of record: 04 March 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10182-7

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Biosynthesis of cinchona alkaloids

Blaise Kimbadi Lombe,

Tingan Zhou  (周庭安),

Gyumin Kang,

Joshua C. Wood,

John P. Hamilton,

Klaus Gase,

Yoko Nakamura,

Ryan M. Alam,

Ron P. Dirks,

Lorenzo Caputi,

C. Robin Buell &

…

Sarah E. O’Connor

Nature volume 653, pages 306–314 (2026)

Abstract

Cinchona alkaloids, which have been studied for more than 250 years, are plant-derived natural products that have collectively had a substantial impact in medicine and basic science1,2,3,4,5. Examples of cinchona alkaloids include quinine, a historically important antimalarial drug, and cinchonidine, a chiral catalyst widely used in process chemistry. However, it is still largely unknown how plants synthesize these well-known compounds. Here we report the discovery of genes responsible for the biosynthesis of the distinctive quinoline–quinuclidine scaffold of cinchona alkaloids. A combination of isotopic labelling, gene silencing, single-nucleus RNA sequencing and comparative transcriptomics revealed the involvement of several unexpected biosynthetic transformations. We also describe a previously unreported quaternary amine intermediate that is generated through an unusual enzymatic cyclization. We show that dihydroquini(di)none, dihydrocinchoni(di)none and cinchoni(di)none can be produced when these genes are heterologously expressed in Nicotiana benthamiana. Furthermore, we demonstrate that this N. benthamiana expression platform can convert non-native fluorinated and chlorinated tryptamine substrates into dihydrocinchoni(di)none analogues, which suggests that these biosynthetic enzymes can be leveraged to produce halogenated cinchona alkaloid derivatives. These discoveries uncover the long-standing mystery of how the cinchona alkaloid scaffold is biosynthesized and highlight prospects for access to these compounds through metabolic engineering approaches.

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Main

Cinchona alkaloids are a group of structurally diverse, nitrogen-containing compounds that are primarily found in Cinchona plants. The best known of these alkaloids, quinine (1a), is the earliest known naturally occurring treatment for malaria[6] and is used as a bittering agent in foods and beverages such as tonic water and bitter lemon. Cinchona alkaloids also include quinidine (2a), an antiarrhythmic drug that acts through the inhibition of sodium and potassium channels7,8,9, and the non-methoxylated analogues cinchonidine (1) and cinchonine (2), which are widely used as chiral catalysts for synthetic chemistry10,11. Dihydro analogues of these alkaloids (dihydrocinchonidine (1′), dihydrocinchonine (2′), dihydroquinine (1a′) and dihydroquinidine (2a′), congeners with a single bond at C-18–C-19) are produced in parallel by plants and display comparable properties to 1(a)–2(a)1,12. Cinchona alkaloids have also been the subject of longstanding investigation in the field of organic chemistry2,13. Quinine was isolated in 1820 (ref. [4]), subjected to structural characterization in 1908 (ref. [14]) and the chemical structure was fully established in the 1940s[15]. Unsuccessful attempts to synthetize quinine in the 1850s resulted in a serendipitous discovery of mauve, the world’s first synthetic dye16,17,18. A racemic formal synthesis process was eventually accomplished in 1945 (ref. [19]) and a stereoselective total synthetic route was achieved in 2001 (ref. [20]). Overall, cinchona alkaloids have had seminal roles in the fields of medicine, organic synthesis and natural product chemistry1,2.

From a biosynthetic perspective, quinine and other cinchona alkaloids are classified as monoterpene indole alkaloids12,21. Monoterpene indole alkaloids are natural products that originate from the enzymatic condensation of tryptamine (3, derived from tryptophan[22]) and the geraniol-derived monoterpene[23] secologanin (4) to form the tetrahydro-β-carboline intermediate strictosidine (5)[21]. This reaction is catalysed by the enzyme strictosidine synthase (STR) (note that throughout, protein and gene symbols relate to Cinchona pubescens unless otherwise indicated) (Fig. 1 and Supplementary Fig. 1). Strictosidine, after enzymatic deglucosylation, is acted on by a reductase (dihydrocorynantheine aldehyde synthase (DCS)) and an esterase (dihydrocorynantheine aldehyde esterase (DCE)) to form the dihydro congener of corynantheal (dihydrocorynantheal (6′))[24]. Plant feeding studies suggest that both 6′ and corynantheal (6) congeners undergo, in parallel, a series of unknown steps to form the hypothetical indole–quinuclidine intermediates cinchonaminal (7) and dihydrocinchonaminal (7′)[25], which are thought to undergo conversion, again through a series of unknown chemical transformations, to form the quinoline core of 1, 1′, 2 and 2′. Recent work has shown that the methoxy group observed in 1a, 1a′, 2a and 2a′ is introduced onto the starting precursor tryptamine by tryptamine-5-hydroxylase (T5H) and the O-methyltransferase OMT1 (ref. [26]). Feeding studies have confirmed that the biosynthesis of methoxylated congeners proceeds in the same way as for 1 and 2 via a parallel biosynthetic pathway, with 5-methoxytryptamine (3a′) instead of 3 as the indole starting precursor[26] (Fig. 1 and Supplementary Figs. 1 and 2). However, despite these insights into the biosynthetic process of cinchona alkaloids, the mechanism by which the corynantheal scaffold is converted to these alkaloids has remained elusive.

Fig. 1: Proposed biosynthetic pathways to cinchona alkaloids.

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Methoxylated alkaloids and non-methoxylated analogues are produced in a similar manner via parallel routes by the same enzymes. An unknown series of transformations generating the cinchona alkaloid scaffold core is shaded in grey. The dashed double bond at C_18−C_19 shown in 6,6a–10 and 10a indicates that these compounds also occur in the dihydro form (that is, with a single bond) and are biogenetic precursors to the corresponding alkaloids, for example, 1′–2′. This hypothetical pathway is based on early investigations in plants fed with isotopically labelled precursors. A detailed and updated biosynthetic schematic and a summary of key feeding experiments are given in Supplementary Figs. 1 and 2. The asterisk on SGD indicates that functional characterization of this enzyme is reported here (Supplementary Fig. 3). Red designates previously reported catalytic activity that has been observed in crude samples of Cinchona tissues, but the corresponding protein and gene were not reported[40]. For a complete list of compound names, structures and numbers mentioned in this work, see Supplementary Information.

Discovery of additional biosynthetic intermediates

As there is no substantiated hypothesis for how 6 is converted to the hypothetical indole–quinuclidine intermediate 7, we set out to identify additional on-pathway biosynthetic intermediates. Early experiments showed that 6 was rapidly reduced to the corresponding alcohol corynantheol (11) when fed to cell cultures of Cinchona ledgeneria[27]. These observations led us to speculate that corynantheol—as well as the dihydro and methoxy corynantheol congeners—could be intermediates of downstream cinchona alkaloids. To test this hypothesis, we chemically synthesized the isotopically labelled analogue d_5-corynantheol (11b) and fed it to C. pubescens tissues (leaf, stem and root). Liquid chromatography and mass spectrometry (LC–MS) analyses of fed tissues confirmed the formation of both d_5-cinchonidine (1b) and d_5-cinchonine (2b), and the indole–quinuclidine d_5-cinchonamine (10b). This result establishes 11 as an on-pathway intermediate (Fig. 2a–c).

Fig. 2: Identification of three key on-pathway intermediates (11–13) through feeding studies.

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a, Schematic illustrating the natural conversion of 6 into the corresponding alcohol congener 11, along with the structure of the synthetic isotopically labelled analogue 11b that was used to feed tissues from C. pubescens plantlets. MS/MS (20 to 50 eV) spectrum of the synthetic subtract 11b is shown. b,c, Extracted ion chromatograms (EICs) and MS/MS (20 to 50 eV) spectra evidencing the incorporation of 11b into 1, 2 and 10. Labelled compounds elute slightly faster than the non-labelled counterparts (a retention time difference of about 0.05 min). In b, the asterisk indicates an unknown compound that is not part of the cinchona alkaloid pathway as evidenced by re-feeding experiments with partially purified material of that labelled compound. Data showing incorporation of 11b into keto-quinoline intermediates 8 and 9 are provided in Supplementary Fig. 4. d–g, Identification of a previously unknown compound, 12, and cyclized cinchonaminal (13) as on-pathway intermediates. EIC and MS/MS spectra evidencing the incorporation of 11b into 12 and 13 are shown. Details on the structural characterization and synthesis of 12 and 13 are provided in the Supplementary Information. EIC and MS/MS spectra demonstrating the incorporation of 12b and 13b into downstream alkaloids 1, 2, 10 and 13 are similar to the data shown in b, c and f (Supplementary Fig. 5). Mock denotes samples fed with water instead of the labelled compound (control group).

Notably, we also detected efficient isotopic incorporation of 11b into an unknown metabolite with a pseudomolecular ion at m/z 279.1856 (denoted compound 12) (Fig. 2d and Supplementary Fig. 4). To test whether 12 is also an on-pathway intermediate, we fractionated the isotopically labelled form of this compound from C. pubescens tissues that had been fed with 11b. The fractionated part was then re-fed to fresh C. pubescens tissues. Subsequent LC–MS analyses confirmed that this metabolite is a precursor for the alkaloids 1, 2 and cinchonamine (10) (Fig. 2e, and Supplementary Fig. 5). Large-scale isolation, semi-synthesis and NMR analyses established that this major metabolite is a quaternary ammonium, a scaffold previously unreported in the metabolism of Cinchona. Therefore we named 12 as cinchonium (Fig. 2d).

Feeding of 11b and d_5-cinchonium (12b) to C. pubescens tissues also led to the detection of a native compound with a molecular mass (m/z of 295.1805) and a tandem MS (MS/MS) fragmentation pattern similar to commercially available 10 (an alcohol with an m/z of 297.1961), which suggested that the detected metabolite was the long-predicted[25] aldehyde intermediate cinchonaminal (Figs. 1 and 2f and Supplementary Fig. 6). As with 12, an isotope-labelled derivative of this target (putative cinchonaminal) was partially purified from fed plant tissues, tested as an intermediate by re-feeding to C. pubescens tissues and confirmed by LC–MS analysis to be incorporated into 1 and 2 (Fig. 2g). Isolation, semi-synthesis and NMR and MS/MS analyses confirmed that the target compound is the predicted cinchonaminal, but predominantly existing in the hemiaminal cyclized form 13 (herein referred to as cyclocinchonaminal; Fig. 2f). To our knowledge, this result marks the first time that this intermediate has been isolated and structurally characterized (Supplementary Fig. 7).

In summary, the identification and incorporation of these three intermediates—11, 12 and (cyclo)cinchonaminal (13)—led us to propose a revised biosynthetic hypothesis. Corynantheal is reduced to 11, which is then cyclized to form the newly identified quaternary amine 12. Cinchonium is converted into 7 (or the cyclized form 13), the first biosynthetic intermediate with the distinctive indole–quinuclidine moiety of cinchona alkaloids. (Cyclo)cinchonaminal then undergoes oxidative rearrangement[25] to form the quinoline scaffold found in all cinchona alkaloids, followed by reduction of the ketone function to produce the end products 1 and 2.

Conversion of corynantheal to malonyl-corynantheol

With this revised biosynthetic hypothesis in hand, we set out to identify the reductase responsible for converting 6 to 11. We mined a RNA sequencing (RNA-seq) dataset from C. pubescens and selected eight putative reductases that were co-expressed with DCS (Pearson’s coefficient (r) > 0.5; Extended Data Fig. 1a). During reconstruction of the early-stage pathway in N. benthamiana, the last known intermediate, 6′, was observed to undergo reduction to its corresponding alcohol, dihydrocorynantheol (11′)[26], probably through the action of native reductases in N. benthamiana. Therefore, the selected candidates were expressed in Escherichia coli, purified and tested in vitro using the enzymatically accessible dihydro analogue of 6′ as the substrate. All tested enzymes (named KR1–KR8) displayed various levels of reductive activity. This result suggests that 6′ can be readily reduced in C. pubescens (Extended Data Fig. 1a).

We next investigated how the scaffold 11 is converted to 12. Model chemistry has shown that 11′ can be converted to dihydrocinchonium (12′) via tosylation of the alcohol followed by refluxing28,29. We proposed that biosynthesis in Cinchona could proceed in a similar manner if a suitable leaving group is enzymatically installed on the corynantheol scaffold. This leaving group could then be eliminated through intramolecular nucleophilic substitution to generate the quinuclidine ring of 12. We mined transcriptomic data obtained from C. pubescens bulk tissue for genes encoding putative transferases (sulfotransferases, acyltransferases, kinases and O-methyltransferases). A total of 30 candidates that were co-expressed with genes encoding the upstream DCE, DCS and STR enzymes were selected and transiently expressed in N. benthamiana leaves, followed by infiltration of 11. LC–MS analyses of crude extracts of the transformed leaves indicated that only one enzyme, encoded by a putative BAHD-acyltransferase gene30,31, could metabolize 11 to produce a new compound with a mass that corresponded to a malonylated derivative of corynantheol (m/z of 383.197; Extended Data Fig. 1b,c). The same product was observed when the enzyme was assayed in vitro with corynantheol in the presence of malonyl-CoA (Supplementary Fig. 8a,b). MS/MS analysis (Extended Data Fig. 1c) and NMR data (Supplementary Fig. 8c) of the partially purified product established that the produced compound is the O-malonylated derivative of corynantheol (14, herein referred to as malonyl-corynantheol), thereby confirming that this enzyme is an O-malonyltransferase (MAT). Although MAT showed acetyltransferase activity in vitro, this substrate specificity was not observed in N. benthamiana, even when acetyl-CoA was exogenously infiltrated along with 11. This result suggests that this observed in vitro acetylation is probably due to promiscuous enzyme activity (Supplementary Fig. 8d).

Notably, 14 could not be detected in any Cinchona tissues. To validate that 14 is a true pathway intermediate, we attempted to feed enzymatically synthesized isotopically labelled d_5-malonyl-corynantheol (14b) to C. pubescens root tissue. However, NMR analysis in protic solvent (methanol-d_3) indicated that 14b hydrolyses to corynantheol over time (Supplementary Fig. 9). Moreover, LC–MS analysis of C. pubescens tissues fed with d_5-malonyl-corynantheol revealed the presence of the demalonylated compound (11b). Therefore, although we observed isotopically labelled late-stage alkaloids (for example, 2) in these feeding experiments (Supplementary Fig. 10), we could not unequivocally establish whether it was 11b or 14b that was being incorporated. We therefore developed a virus-induced gene silencing (VIGS) assay using in vitro germinated C. pubescens plantlets to assess the function of MAT in vivo. After silencing MAT, both congeners 11 and 11′, and methoxylated analogues, accumulated in high levels in the VIGS-treated Cinchona leaves (Extended Data Fig. 1e,f and Supplementary Fig. 11a). Although levels of the downstream alkaloids were not affected after silencing (Supplementary Fig. 11b), presumably because leaves have high levels of the downstream 12 intermediate that would compensate for low levels of the upstream intermediate 11, the substantial accumulation of corynantheol congeners strongly suggests that MAT acts on these substrates in vivo.

Conversion of malonyl-corynantheol to cinchonium

With the biosynthetic function of MAT established in planta, we set out to identify the enzyme responsible for the proposed unusual cyclization-mediated displacement of malonate that would generate 12 from 14. A wide range of candidate enzymes, including decarboxylases, esterases, hydrolases and oxidases, were tested in N. benthamiana. However, none exhibited detectable catalytic activity on 14. At this point, annotation-based candidate selection became uninformative. Because a large number of putative biosynthetic genes (>5,000) were co-expressed with MAT in the RNA-seq dataset, we used a combination of approaches to narrow down the number of candidates to be screened. First, we generated a cell-type-resolved transcriptome through single-nucleus RNA sequencing (snRNA-seq) of C. pubescens young leaves (Fig. 3a and Supplementary Fig. 12). This dataset revealed a high enrichment of previously identified pathway genes that encode TDC, STR, STTr, DCS and DCE, as well as the newly identified MAT, in three specific clusters (clusters 4, 5 and 6; Fig. 3b). These clusters were putatively assigned as epidermal cells based on their homology to cell markers from the phylogenetically related plant Catharanthus roseus[32]. Second, crude soluble protein extracts obtained from C. pubescens root, leaf and leaf apoplastic fluid could cyclize 14 to form 12 (Fig. 3c and Supplementary Fig. 13). These protein extracts were then partially purified using ion exchange and size-exclusion chromatography, and catalytically active fractions were subjected to proteomic analyses. The proteomes of these three samples were then compared with each other and with the available transcriptomic data (Fig. 3c). Third, we noted a previously published report describing the presence of a cinchonium analogue (hydroxylated dihydrocinchonium), ophiorrhizine[33], in Ophiorrhiza major, which, like Cinchona, is a member of the Rubiaceae family. Feeding the leaves of an available species from the Ophiorrhiza genus (Ophiorrhiza mungos) with 14b led to high incorporation into d_5-cinchonium (12b). This result suggests that the cyclase is conserved in this phylogenetically related plant (Fig. 3d and Supplementary Fig. 14). Notably, feeding d_5-malonyl-corynantheol to another Rubiaceae species, Mitragyna speciosa, also led to the formation of 12b, although cinchonium is not normally present in the metabolome of this plant. On the basis of these findings, we generated an O. mungos leaf transcriptomic dataset and compared it to available C. pubescens and M. speciosa transcriptome datasets to identify conserved biosynthetic genes34,35. Arabidopsis thaliana, which did not show cyclization activity and is phylogenetically distant to the three other plant species (Supplementary Figs. 14 and 15), was included in this analysis to exclude broadly conserved plant genes (Fig. 3e).

Fig. 3: Discovery and functional characterization of MCC.

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a, Uniform manifold approximation and projection (UMAP) plot of C. pubescens leaf nuclei with high-quality snRNA-seq data, coloured by cell clusters. b, Gene expression heatmap of C. pubescens known upstream alkaloid biosynthetic genes across cell clusters shown in a. Genes marked with an asterisk are reported herein. c, Top, schematic showing cyclization activity of protein extracts from C. pubescens. Bottom, Venn diagram showing shared peptides among the proteomic fractions from C. pubescens that displayed catalytic activity. Assay results with protein extracts are shown in Supplementary Fig. 13. d, Detection of cyclization activity of C. pubescens, O. mungosand M. speciosa by feeding isotopically labelled 14b to leaves. For LC–MS data showing results of incorporation, see Supplementary Fig. 14. e, Comparison of cross-species transcriptomes using the OrthoFinder algorithm (with A. thaliana included in the analysis to exclude broadly conserved plant genes). The numbers shown in the diagram are numbers of orthologue clusters. f, Integration of proteomics, cross-species transcriptomic comparison and leaf snRNA-seq data to refine candidate gene selection. The numbers shown in the diagram are numbers of genes. g, Transient expression of MAT alone and with MCC in N. benthamiana along with co-infiltration of 11. Left, EICs for 14 (m/z 383.197 ± 0.05 Da [M+H]+, black) and for 12 (m/z  279.185 ± 0.05 Da, red). Chromatogram of the standard 12 is also shown (blue). This experiment was repeated three times with similar results. Right, MS/MS (20–50 eV) spectra of the standard 12 and the MCC product (m/z 279.1852 Da). h, Schematic of the reaction catalysed by MCC.

Source data

By integrating data from snRNA-seq (genes coexpressed with MAT, r  > 0.4), proteomics (shared peptides among all three C. pubescens protein fractions) and transcriptomic cross-species comparison (orthologous genes present in C. pubescens, O. mungos and M. speciosa but absent in A. thaliana), we compiled a list of 20 candidate genes (Fig. 3f). We expressed MAT and all these candidates together in N. benthamiana. Infiltration of 11 and subsequent LC–MS analyses revealed the formation of 12. Unexpectedly, deconvolution of the enzyme candidates traced the cyclization activity to a gene annotated as a BAHD malonyltransferase, which we thereafter named malonyl-corynantheol cyclase (MCC) (Fig. 3g,h). Transiently knocking down the MCC-encoding gene in C. pubescens plantlets using VIGS led to an accumulation of 14 and related dihydro and methoxylated congeners. This result confirmed the function of this cyclase in vivo (Supplementary Fig. 16a). In vitro assays showed that MCC did not require cofactors for catalysis and exhibited strict substrate specificity, accepting only malonylated, but not acetylated, corynantheol (Supplementary Fig. 16b). Notably, although both MCC and MAT belong to the BAHD acyltransferase family, these two enzymes do not share high sequence similarity (19.5%) and are phylogenetically distant. MAT clusters with other known BAHD acyltransferases involved in monoterpene indole alkaloid biosynthesis, whereas MCC appears in an evolutionary distant clade (Supplementary Fig. 17). Moreover, MCC did not show malonylation activity and could not install the malonyl group on 11 required for subsequent cyclization to 12 (Supplementary Fig. 18). A potential explanation for the loss of this loss of acyl transferase activity is that its binding pocket for the malonyl-CoA substrate has been extensively mutagenized (Supplementary Fig. 19). A homology model indicates the presence of a binding pocket deeper in the active site. This could conformationally orient 14 such that the nucleophilic amine is proximal to the electrophilic carbon, and the alkyl-chain-bearing malonyl group is in the axial conformation, which would lead to the observed product stereochemistry (Supplementary Fig. 20). The discovery of MAT and MCC as key enzymes that mediate malonylation and subsequent demalonylative cyclization reveals a previously unknown biosynthetic strategy to form this quaternary amine and broadens the functional range of BAHD acyltransferases beyond canonical acyl transfer reactions[36].

Conversion of cinchonium to quinoline alkaloids

We then set out to find the gene responsible for the conversion of 12 to the next known biosynthetic intermediate cinchonamimal 7. Hydroxylation of cinchonium would directly produce 13, the cyclic form of 7. Therefore, we searched for biosynthetic genes predicted to encode oxidases. All annotated cytochrome P450 enzymes and oxoglutarate-dependent dioxygenases were ranked on the basis of Pearson’s correlation coefficients with MCC using the snRNA-seq data (Extended Data Fig. 2a). A candidate annotated as a 2-oxoglutarate-dependent dioxygenase was confirmed to catalyse this transformation in both N. benthamiana and in vitro enzymatic assays, and was subsequently designated as cinchonaminal synthase (CiS) (peak A, Extended Data Fig. 2b,c and Supplementary Fig. 21a). Notably, CiS further oxidized cinchonaminal to form a compound with spectral data consistent with the carboxylic acid (herein named as cinchonaminic acid (15); peak B, Extended Data Fig. 2b–d). This compound was not observed in Cinchona, which indicated that the next pathway intermediate rapidly intercepts the initial enzymatic product before overoxidation can occur. Conversely, however, Cinchona is postulated to reduce 7 to form cinchonamine 10 (Fig. 1). Screening of candidate reductases, obtained through coexpression correlation with CiS (Supplementary Fig. 21b), led to the identification of an alcohol dehydrogenase that converts 7 to 10, which we named cinchonaminal reductase (CiR) (peak C, Extended Data Fig. 2e–g). CiS and CiR were also enriched in the same clusters (clusters 4–6; Fig. 3b) as the upstream biosynthetic genes (for example, MAT and MCC) according to the snRNA-seq data. This result suggests that cell-type localization is maintained in these downstream pathway steps.

Conversion of cinchonaminal 7 into a quinoline moiety probably involves oxidative opening of the indole, followed by cyclization and dehydration to form the quinoline scaffold. As the phylogenetically related plant M. speciosa uses a cytochrome P450 enzyme (family CYP71) to oxidize an indole to a spirooxindole35,37, we speculated that a Cinchona CYP71 P450 enzyme might also be involved in the oxidative transformation of this indole substrate. Because the feeding studies and metabolite accumulation levels indicated that these late-stage steps were more highly expressed in root and stem tissues (Supplementary Figs. 4 and 22–27), we performed a clustering analysis to prioritize gene candidates based on this expression pattern (Supplementary Fig. 28). Among the top CYP71 P450 candidates selected on the basis of the coexpression analysis with CiS, a functional enzyme was identified, which we named cinchonaminal oxidase (CiO) (Extended Data Fig. 3a,b). CiO catalysed the oxidation of 7, which produced the two ketone quinoline isomers cinchonidinone (8) and cinchoninone (9). These stereoisomers, which are known to exist in equilibrium38,39, were observed in a 1:0.23 ratio, which is approximately the same ratio observed in C. pubescens. The enzymatic activity of CiO was further confirmed through in vitro assays using yeast microsomes (Supplementary Fig. 29), which demonstrated that this single P450 enzyme can catalyse the indole-to-quinoline ring expansion. VIGS of CiO was also conducted in C. pubescens. Leaves in which CiO was silenced showed a significant accumulation of 13 and 10 along with decreased biosynthesis of both ketone quinoline alkaloid stereoisomers (8 and 9). This result provides evidence for the physiological function of CiO in planta (Extended Data Fig. 3c–e).

The final step in cinchona alkaloid biosynthesis involves the reduction of the ketone moiety that is generated after the formation of the quinoline scaffold. Previous studies have suggested that this transformation is probably catalysed by a NADPH-dependent oxidoreductase[40]. A total of 60 NADPH-dependent reductase genes that were coexpressed with CiO in the bulk transcriptomic dataset and/or snRNA-seq were cloned and tested (Supplementary Dataset 1). Screening in N. benthamiana did not lead to an active candidate. Therefore, we prioritized the selected candidate genes on the basis of the analysis of expression trends (Supplementary Fig. 28a–c) and re-expressed 15 putative aldo-keto reductases and alcohol dehydrogenases (including the above mentioned KR1–KR8) in E. coli for assays in vitro. Only one enzyme, KR4, displayed clear reductase activity, converting both non-methoxylated and methoxylated quinoline ketone stereoisomers into the corresponding alcohols (Extended Data Fig. 4a,b). This functional enzyme also reduced the aldehyde function of corynantheal scaffolds (Extended Data Fig. 1a), a result that further supported its involvement in the biosynthesis of cinchona alkaloids. Notably, despite robust activity in vitro, KR4 did not show detectable activity when assayed in N. benthamiana leaves after transient expression and infiltration of quinoline ketones. We designed a fusion protein comprising CiO and KR4 for transient expression in N. benthamiana. Although oxidation activity of CiO was observed, no reduced product was obtained. To test whether N. benthamiana contained endogenous components that inhibit this aldo-keto reductase, diluted crude extracts from N. benthamiana leaves were added to in vitro reactions, substituting 10% of the reaction volume. The addition of N. benthamiana extract abolished KR4 activity in vitro, a result that implicates the presence of an unknown inhibitory factor that suppresses KR4 function (Extended Data Fig. 4a,b and Supplementary Fig. 30a,b). These results provide a reasonable explanation for the lack of detectable activity in N. benthamiana and highlights a potential limitation in using this heterologous system for functional validation of reductases.

Although the two late-stage genes CiO and KR4 were selectively and highly expressed in stem and root tissues, these genes also showed clear enrichment in clusters 4–6 of the leaf snRNA-seq dataset, clusters previously associated with nearly all upstream alkaloid biosynthetic genes (Fig. 3b). Notably, the expression of monoterpene indole alkaloid pathway genes in the Apocynaceae species C. roseus seems to switch from epidermal cells to specialized idioblast cells in the late stages of alkaloid biosynthesis[32]. By contrast, in Cinchona, a member of the Rubiaceae family, all alkaloid biosynthetic genes identified in this study were selectively enriched in epidermal cells (clusters 4–6). This consistent cell-type-specific expression pattern of the biosynthetic genes in Cinchona, along with high coexpression in bulk tissue (Supplementary Fig. 31), provides further support that these newly identified genes function in the same metabolic pathway and contribute to the biosynthesis of cinchona alkaloids. However, we note that KR4 did not exhibit absolute stereoselectivity with the ketone quinoline alkaloids (8, 9, 8a and 9a). In particular, with the methoxylated analogues 8a–9a, a stereoisomer that is not observed in the plant was formed in higher levels than the naturally observed stereoisomers (Supplementary Fig. 30c,d). Although this reductase showed clear activity with the quinolines, it is possible that a reductase with higher stereoselectivity remains to be identified.

Reconstitution of natural and unnatural alkaloids

With these enzymes identified and functionally characterized, the cinchona alkaloid biosynthetic pathway could be reconstituted in the heterologous host N. benthamiana. The well-known central monoterpene indole alkaloid precursor 5 was externally supplied as a substrate to discs taken from N. benthamiana leaves that had been transformed with DCS, DCE, MAT, MCC, CiS and CiO, along with a construct encoding strictosidine β-glucosidase (SGD) from the phylogenetically related plant C. roseus (CrSGD), an orthologue of SGD with high catalytic activity (Supplementary Fig. 3). No corynantheal reductase gene was included because the endogenous N. benthamiana enzymes efficiently reduced (dihydro)corynantheal. As anticipated, strictosidine was efficiently converted to 8′ and 9′ (Extended Data Fig. 5 and Supplementary Figs. 32 and 33). Reconstruction with the methoxylated analogue 10-OMe strictosidine (5a) instead led to the formation of 8a′ and 9a′. This result confirms the substrate flexibility of the downstream enzymes and provides clear experimental support for the parallel biosynthetic route previously proposed for methoxylated alkaloids[26] (Extended Data Fig. 5). Notably, the addition of STR, T5H, OMT1 and the vacuolar strictosidine transporter[26] STTr to the gene stack, along with replacement of 5 with 3 and 4, led to the formation of a mixture of methoxylated and non-methoxylated keto quinolines (8′, 9′, 8a′ and 9a′), which mimicked the occurrence of these dihydro alkaloids in Cinchona (Fig. 4a and Supplementary Fig. 34). Because under these assay conditions, DCS leads to 6′, we tested synthetic 11 as an exogenous substrate for N. benthamiana leaf discs (transformed with MAT, MCC, CiS and CiO) to observe the formation of 8 and 9 (Fig. 4b). Moreover, intermediates that we had identified at the outset of this study—11, 12 and 13—were detected when these transformed N. benthamiana leaves were subjected to analysis. By contrast, 14 was undetectable when enzymes downstream of MAT (MCC, CiS and CiO) were present, a result consistent with the absence of this transient intermediate in native C. pubescens metabolite profiles (Fig. 4b). Collectively, these findings highlight the potential for the biosynthetic production of medicinally relevant quinoline alkaloids using these biosynthetic genes.

Fig. 4: Biosynthesis of quinoline cinchona alkaloids.

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a, Production of both methoxylated and non-methoxylated cinchona alkaloids in N. benthamiana following transient expression of the indicated biosynthetic enzymes and co-infiltration of 3 and 4. TIC, total ion chromatogram. b, LC–MS peak areas of products detected in N. benthamiana following transient expression (+) or not (–) of the indicated biosynthetic enzymes from C. pubescens and incubation with 11 using a leaf-disc assay. Data are the mean ± s.d. (n = 4 biological replicates).

Source data

Directed biosynthesis, in which the host generating the product is supplied with an exogenous unnatural starting substrate, is a method that has been used for decades to produce industrially important natural product analogues34,41,42,43,44,45. However, for successful production of end-product analogues, this approach requires that all of the downstream enzymes catalyse conversion of all corresponding unnatural biosynthetic intermediates. As Cinchona biosynthetic enzymes naturally act on both methoxy and dihydro derivatives, we rationalized that these biosynthetic enzymes would be well suited for the production of unnatural alkaloid analogues. Transformed N. benthamiana leaves (STR, CrSGD, DCS, DCE, MAT, MCC, CiS and CiO) were infiltrated with a series of analogues of 3 (5-fluorotryptamine, 5-chlorotryptamine, 6-fluorotryptamine, 6-chlorotryptamine, 7-fluorotryptamine and 7-chlorotryptamine), along with the natural co-substrate 4. In all cases, we saw consumption of the halogenated tryptamine substrate, along with the presence of compounds with a mass and MS/MS pattern consistent with the formation of the corresponding dihydrocinchoninone and dihydrocinchonidinone analogues (Fig. 5 and Supplementary Figs. 35 and 36). These results suggest that the identified biosynthetic genes can be used to produce halogenated cinchona alkaloid analogues, which will be potentially useful for medicinal chemistry applications given the clinical use and attractiveness of halogenated quinolines[46].

Fig. 5: Biosynthesis of halogenated analogues of quinoline cinchona alkaloids.

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Directed biosynthesis of fluorinated and chlorinated analogues of cinchona alkaloids in N. benthamiana following transient expression of the indicated biosynthetic enzymes and co-infiltration of 4 and halogenated tryptamines (3b–3g). LC–MS peak areas are shown and data are the mean ± s.e.m. (n = 4 biological replicates). Data from assays using the native substrates, 3 and 3a, are included for relative comparison of the efficiency of the enzymes. For EICs and MS/MS data, see Supplementary Figs. 35 and 36.

Source data

Conclusion

Here we described the genetic basis that underlies the biosynthesis of the quinoline–quinuclidine scaffold that characterizes cinchona alkaloids. Although aspects of the biosynthesis, such as the mechanism underlying the formation of the C-18–C-19 double bond, remain to be resolved, the discovery of these genes enables biosynthetic conversion from known starting materials to (dihydro)cinchoni(di)ne and (dihydro)quini(di)ne. These discoveries were achieved by integrating modern omics approaches—namely single nucleus sequencing and comparative transcriptomics—along with classical methods of isotopic feeding and enzyme activity fractionation from plant tissues, and the development of a functional in planta assay using VIGS. We demonstrated how simultaneous application of these approaches can substantially streamline the list of gene candidates to be screened. We further demonstrated the application of these biosynthetic genes for the production of natural cinchona alkaloids and non-natural halogenated alkaloid analogues in a N. benthamiana host. These discoveries reveal how the distinctive cinchona alkaloid scaffold is biosynthesized and highlights prospects for the metabolic engineering of these complex bioactive alkaloids.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data supporting the findings of this work are available within this paper and its Supplementary Information. Sequences of reported genes are included in the Supplementary Information and have been deposited into the National Center for Biotechnology (NCBI) GenBank under the following accession numbers: MAT (PX842829), MCC (PX842830), CiS (PX842831), CiR (PX842832), CiO (PX842833), SGD1 (PX842834), SGD2 (PX842835), SGD3 (PX842836), KR1 (PX842837), KR2 (PX842838), KR3 (PX842839), KR4 (PX842840), KR5 (PX842841), KR6 (PX842842), KR7 (PX842843) and KR8 (PX842844). C. pubescens bulk RNA-seq and snRNA-seq, ONT full-length cDNA and ONT gDNA reads have been deposited into the NCBI under BioProject PRJNA1347772. Raw reads from RNA-seq of O. mungos leaves have been deposited into the NCBI (accession PRJNA1413194). Bulk RNA-seq of M. speciosa from a previous study was downloaded from the NCBI (accession PRJNA1244102) and the transcriptome of A. thaliana used is implemented and available in OrthoVenn3 (https://orthovenn3.bioinfotoolkits.net). Proteomic data have been deposited into the ProteomeXchange Consortium (dataset identifier PXD068683). Source data are provided with this paper.

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Acknowledgements

We acknowledge K. Ploß for plant culture cultivation; E. Rothe for plant cultivation; J. Wurlitzer for technical assistance; M. Kunert and S. Heinecke for assistance with MS; F. Trenti for isolation of C. pubescens DNA; and B. Vaillancourt for assistance with management of the sequencing data. We acknowledge NIH (AT012783-02) for funding. The plant and tissue art in Extended Data Figs. 1d and 2g were created in BioRender; Lombe, B. https://biorender.com/8hz4n59 (2025) (agreement number OA2956EHDC).

Funding

Open access funding provided by Max Planck Society.

Author information

Author notes

1. These authors contributed equally: Blaise Kimbadi Lombe, Tingan Zhou

Authors and Affiliations

1. Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, Jena, Germany

Blaise Kimbadi Lombe, Tingan Zhou  (周庭安), Gyumin Kang, Klaus Gase, Yoko Nakamura, Ryan M. Alam, Lorenzo Caputi & Sarah E. O’Connor

2. Center for Applied Genetic Technologies, University of Georgia, Athens, GA, USA

Joshua C. Wood, John P. Hamilton & C. Robin Buell

3. Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA

John P. Hamilton

4. Future Genomics Technologies, Leiden, The Netherlands

Ron P. Dirks

5. Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, USA

C. Robin Buell

6. The Plant Center, University of Georgia, Athens, GA, USA

C. Robin Buell

Contributions

B.K.L., T.Z., S.E.O. and C.R.B. conceived the study and wrote the manuscript. B.K.L and T.Z. performed feeding experiments, bioinformatic analyses of datasets, identification of gene candidates, isolation of compounds, and in vitro and in planta assays of enzymes. G.K. synthesized compounds and provided key mechanistic insights. J.C.W. performed the snRNA-seq experiments. J.C.W. and J.P.H. processed the snRNA-seq data. B.K.L. and Y.N. performed NMR analyses. B.K.L. and K.G. conducted the VIGS experiments. R.M.A. provided early access to cinchonaminal. R.P.D. performed C. pubescens genome sequencing. J.C.W., J.P.H. and R.P.D. conducted genome assembly and annotation. L.C. assisted with protein purification and analyses. S.E.O. and C.R.B. raised funds and coordinated the study.

Corresponding authors

Correspondence to C. Robin Buell or Sarah E. O’Connor.

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Competing interests

The authors declare no competing interests.

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Nature thanks Yanran Li, Shanhui Xu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Functional characterization of corynantheal reductases (CpKR1-8) and corynantheol malonyltransferase CpMAT.

a. Reaction catalyzed by CpKR1-8 with varied level of catalytic efficiency and co-expression with other pathway upstream genes CpDCS, CpDE and CpSTR. Data on CpKR1-8 activity are mean ± s.e.m. (n = 3). This experiment was repeated two times with similar results. b. Transient expression of CpMAT in N. benthamiana with co-infiltration of corynantheol (11). Extracted ion chromatograms for corynantheol (11) (m/z 297.196 ± 0.05 Da [M + H]+, green-highlighted) and malonyl-corynantheol (14) (m/z 383.197 ± 0.05 Da [M + H]+, red-highlighted). This experiment was repeated three times. Similar results were obtained in vitro using purified recombinant enzyme (see Supplementary Fig. 8). c. MS/MS (20 to 50 eV) spectrum of the CpMAT product and putative ion fragments of generated m/z 383.1966 [M + H]+. d. Reaction catalyzed by CpMAT. e. VIGS-treated C. pubescens plantlets (transformed with a vector designed to silence magnesium chelatase subunit H (CpMgChl) encoding gene) and untreated plantlets. Leaf bleaching (the yellow areas indicated by arrows) is the phenotypic characteristic of effective CpMgChl silencing. f. Relative expression of the gene encoding CpMAT in C. pubescens plantlets transformed with a vector designed to silence both CpMAT encoding gene and the marker gene CpMgChl (VIGS-CpMAT) and in control plantlets (transformed with a vector designed to silence only the marker gene CpMgChl) indicate the knockdown of CpMAT. g. Representative total ion chromatogram (TIC) of leaf tissue extracts from VIGS-CpMAT-transformed C. pubescens plantlets and control plantlets. Chromatograms of the standards (dihydro)corynantheol (11 and 11′) and bar graphs for metabolites levels of the CpMAT knockdown tissue in comparison to controls are shown, corroborating the effective silencing of CpMAT encoding gene. Data in f and g are mean ± s.e.m. (n = 6 biological replicates) and statistical analyses were performed using a two-sided, unpaired Welch’s t-test. VIGS assays were repeated two times and one additional time with C. pubescens plantlets grown under various conditions with similar results. The icon in d was created in BioRender; Lombe, B. https://biorender.com/8hz4n59 (2025).

Source data

Extended Data Fig. 2 Functional characterization of cinchonaminal synthase (CpCiS) and cinchonaminal reductase CpCiR.

a. Ranking of hydroxylase (OGD and P450) gene candidates based on coexpression with CpMCC. b. Extracted ion chromatograms of the extract from the transient expression of CpCiS in N. benthamiana with infiltration of cinchonium (12, left) and cyclocinchonaminal (13, right), which show that CpCiS converts 12 into 13 (peak A) and then transforms 13 into an overoxidized compound (peak B). This experiment was repeated five times with similar results. c. MS/MS (20 to 50 eV) spectra of the CpCiS product peak A and a standard cinchonaminal (13). d. MS/MS (20 to 50 eV) spectra in positive (top) and negative (bottom) mode of the CpCiS product peak B and putative fragments revealing the compound to be the carboxylic acid analog of cinchonaminal, herein named cinchonaminic acid (15). e. Extracted ion chromatograms of the extract from the transient expression of CpCiS and/or CpCiR in N. benthamiana with infiltration of cinchonium (12) or cyclocinchonaminal (13), which show that CpCiR reduces 13 into cinchonamine 10 (peak C) and prevents an over oxidation of 13 by CpCiS. This experiment was repeated five times with similar results. f. MS/MS (20 to 50 eV) spectra of the CpCiR product peak C and a standard cinchonamine (10). g. Reaction catalyzed by CpCiS and CpCiR. The icon in g was created in BioRender; Lombe, B. https://biorender.com/8hz4n59 (2025).

Extended Data Fig. 3 Functional characterization of cinchonamine oxidase (CpCiO).

a. Extracted ion chromatograms (EIC) of the extract from the transient expression of CpCiS in N. benthamiana with infiltration of cinchonaminal (13), showing that CpCiO converts 13 into quinoline compounds cinchonidinone (8) and cinchoninone (9), which exist in a tautomeric equilibrium. This experiment was repeated five times with similar results. b. MS/MS (20 to 50 eV) spectra of the CpCiO product and standard 8. c. VIGS-treated C. pubescens plantlets transformed with a vector designed to silence CpCiO and the marker gene CpMgChl. The phenotypic characteristic resulting from CpMgChl silencing (leaf bleaching) is indicated by an arrow and the relative expression of CpCiO is shown. d. Representative total ion chromatogram (TIC) of leaf tissue extracts from VIGS-CpCiO-transformed Cinchona plantlets and control plantlets, along with standards cinchonamine and cyclocinchonaminal (10 and 13); EIC of cinchonidinone (8) and cinchoninone (9), and bar graphs for metabolite levels of the CpCiO knockdown tissue in comparison to controls are shown, corroborating the effective silencing of CpCiO. In c and d, the values represent mean ± s.e.m. (n = 5 biological replicates) and statistical analyses were performed using a two-sided, unpaired Welch’s t-test. e. Reaction catalyzed by CpCiO.

Source data

Extended Data Fig. 4 Functional characterization of the keto-reductase CpKR4.

a. in vitro reaction of CpKR4 in the presence or absence of N. benthamania aqueous extract. b. EIC along with the LC-MS peak areas of reactions products, showing that CpKR4 reduces the ketone-quinolines to the Cinchona alkaloids, but this activity is inhibited by N. benthamania metabolites present in the aqueous extract. Another stereoisomer is also produced (Supplementary Fig. 31). Data are mean ± s.d. (n = 3). This experiment was repeated three times with similar results.

Source data

Extended Data Fig. 5 Step-by-step biosynthesis of Cinchona alkaloids in N. benthamiana.

LC-MS peak areas of products detected in N. benthamiana following transient expression of the indicated biosynthetic enzymes and incubation with either strictosidine (5) or 10-methoxystrictosidine (5a). Data are presented as mean ± s.d. (n = 4 biological replicates).

Source data

Supplementary information

Supplementary Information (download PDF )

This file contains Supplementary Materials and Methods, supplementary list of compound names, structures and numbers mentioned in this work, Supplementary Figs. 1–70, Supplementary Tables 1–11 and Supplementary References.

Reporting Summary (download PDF )

Supplementary Data (download XLSX )

List of 20 genes screened for cyclase activity.

Supplementary Data (download ZIP )

FASTA file of all genes discovered in this study.

Supplementary Data (download ZIP )

Source data for Supplementary Figs. 4, 11, 16, 18, 22 and 31.

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Source Data Fig. 3 (download XLSX )

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Source Data Extended Data Fig. 5 (download XLSX )

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Lombe, B.K., Zhou, T., Kang, G. et al. Biosynthesis of cinchona alkaloids. Nature 653, 306–314 (2026). https://doi.org/10.1038/s41586-026-10227-x

- Received: 27 October 2025

- Accepted: 03 February 2026

- Published: 18 March 2026

- Version of record: 18 March 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10227-x

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Amendments & Corrections

- Author Correction: Robust cytoplasmic partitioning by solving a cytoskeletal instability

- Author Correction: Rewiring an E3 ligase enhances cold resilience and phosphate use in maize

Author Correction Open access Published: 22 April 2026

Author Correction: Robust cytoplasmic partitioning by solving a cytoskeletal instability

Melissa Rinaldin,

Alison Kickuth,

Adam Lamson,

Benjamin Dalton,

Yitong Xu,

Pavel Mejstřík,

Stefano Di Talia &

…

Jan Brugués

Nature volume 653, page E1 (2026)

The Original Article was published on 28 January 2026

Correction to: Nature https://doi.org/10.1038/s41586-025-10023-z Published online 28 January 2026

In the originally published version of the article, there were two inadvertent plotting errors. In two graphs, while the original data are plotted correctly on the left curves (Fig. 1l and Extended Data Fig. 8b), the profiles were visually mirrored to help in the interface visualization, inadvertently introducing plotting errors.

In Fig. 1l, the confidence interval of the mirrored simulated data, represented by a grey shaded area, was erroneously shifted along the x-axis. In Extended Data Fig. 8b, a point was missing from the plot in the mirrored simulated profile. These errors were purely aesthetic and did not affect the data, results or conclusions of the study. The figures and their associated source data have been updated in the HTML and PDF versions of this article. For comparison, the original and corrected figures are available as Supplementary Information accompanying this amendment.

Supplementary information is available in the online version of this amendment.

Author information

Author notes

1. Yitong Xu

Present address: Program in Developmental Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

2. These authors contributed equally: Alison Kickuth, Adam Lamson

Authors and Affiliations

1. Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany

Melissa Rinaldin, Alison Kickuth, Adam Lamson & Jan Brugués

2. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

Melissa Rinaldin, Alison Kickuth, Pavel Mejstřík & Jan Brugués

3. Fachbereich Physik, Freie Universität Berlin, Berlin, Germany

Benjamin Dalton

4. Department of Cell Biology, Duke University Medical Center, Durham, NC, USA

Yitong Xu & Stefano Di Talia

5. Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

Jan Brugués

Corresponding authors

Correspondence to Melissa Rinaldin or Jan Brugués.

Supplementary information

Original and revised Fig. 1l and Extended Data Fig. 8b (download PDF )

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Rinaldin, M., Kickuth, A., Lamson, A. et al. Author Correction: Robust cytoplasmic partitioning by solving a cytoskeletal instability. Nature 653, E1 (2026). https://doi.org/10.1038/s41586-026-10390-1

- Published: 22 April 2026

- Version of record: 22 April 2026

- Issue date: 07 May 2026

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Author Correction Published: 23 April 2026

Author Correction: Rewiring an E3 ligase enhances cold resilience and phosphate use in maize

Huan Liao,

Xiaoyun Zhao,

Keyu Ren,

Li Guo,

Zhuoyang Li,

Zhicheng Liu,

Xiaoyan Zhang,

Tianhang Su,

Diyi Fu,

Zhaoyang Zhang,

Junhong Zhuang,

Xiaohong Yang,

Feng Tian,

Zhizhong Gong,

Wen Song,

Zhen Li,

Yiting Shi &

…

Shuhua Yang

Nature volume 653, page E2 (2026)

The Original Article was published on 25 February 2026

Correction to: Nature https://doi.org/10.1038/s41586-026-10142-1 Published online 25 February 2026

Since the version of the article initially published, several errors have been corrected. In Extended Data Fig. 4j, the coordinates were incorrectly given as “521-558” and have been corrected to “1386-1423” based on the full genomic DNA sequence. The mutation site depicted in the figure remains accurate. In Extended Data Figs. 1c and 7c, the wild-type (WT) sequence was inadvertently missing nine nucleotides due to a typographical error during figure assembly. The correct sequence has now been provided. In addition, in Extended Data Fig. 7c, a coordinate label was incorrectly given as “+8” and has been corrected to “+9”. The mutation sites in these figures have been re-verified against original sequencing data and remain correct. In the main text, in the “NLA targets PT4 for Pi uptake in cold conditions” section, the text “In vitro ubiquitination assays” has been corrected to “In vivo ubiquitination assays”. The changes have been made to the HTML and PDF versions of the article. The conclusions of the study are unaffected.

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Authors and Affiliations

1. State Key Laboratory of Plant Environmental Resilience, Frontiers Science Center for Molecular Design Breeding, College of Biological Sciences, China Agricultural University, Beijing, China

Huan Liao, Xiaoyun Zhao, Keyu Ren, Zhuoyang Li, Zhicheng Liu, Xiaoyan Zhang, Tianhang Su, Diyi Fu, Zhaoyang Zhang, Zhizhong Gong, Wen Song, Zhen Li, Yiting Shi & Shuhua Yang

2. State Key Laboratory of Plant Environmental Resilience, Frontiers Science Center for Molecular Design Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, China

Li Guo, Xiaohong Yang & Feng Tian

3. State Key Laboratory of Plant Environmental Resilience, Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, China

Junhong Zhuang

4. School of Life Sciences, Institute of Life Science and Green Development, Hebei University, Baoding, China

Zhizhong Gong

Corresponding authors

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Liao, H., Zhao, X., Ren, K. et al. Author Correction: Rewiring an E3 ligase enhances cold resilience and phosphate use in maize. Nature 653, E2 (2026). https://doi.org/10.1038/s41586-026-10560-1

- Published: 23 April 2026

- Version of record: 23 April 2026

- Issue date: 07 May 2026

- DOI: https://doi.org/10.1038/s41586-026-10560-1

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