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This Week

- Trust and science: the essential elements missing from plastics treaty talks

- From MRI to Ozempic: breakthroughs that show why fundamental research must be protected

- We need a new Turing test to assess AI’s real-world knowledge

- What now for the global plastics treaty?

EDITORIAL 28 October 2025

Trust and science: the essential elements missing from plastics treaty talks

A plan to limit plastics pollution globally is in crisis. But disagreements can be resolved with a more formal role for science and more informal spaces for delegates.

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Plastics overwhelmingly end up in the ocean or at landfill sites.Credit: Salvatore Laporta/KONTROLAB/LightRocket/Getty

In August, the world should have been celebrating the first global agreement to end plastics pollution. Instead, negotiators from more than 180 countries ended the latest round of talks in Geneva, Switzerland, amid acrimony. And, as if that wasn’t bad enough, the chair of the talks, Luis Vayas Valdivieso, a diplomat from Ecuador, has resigned.

The chair and the United Nations Environment Programme (UNEP), which organizes the talks, have failed to bridge the gulf between countries that want chemicals of concern to be regulated and plastics production to be decarbonized over time, and those who would prefer an agreement that focuses on a narrower range of measures, such as improved recycling. But this crisis can both be resolved and be prevented from happening again, suggest the authors of two articles in this week’s issue.

Plastics production and consumption cause growing — and in some ways existential — risks for people and ecosystems. More than 400 million tonnes of the material are produced annually, a number that has been rising sharply. Only around one-tenth of plastics are recycled; the rest winds up mostly at landfill sites or in the ocean. If current trends continue, plastics will be responsible for 15% of greenhouse-gas emissions by 2050. Moreover, an expanding body of knowledge is drawing attention to the health impacts that these products have (M. H. Lamoree et al. Nature Med. 31, 2873–2887; 2025).

This is why the international community began talks on ending plastics pollution three years ago. Despite five rounds so far, an agreement that all parties can abide by has remained elusive. The next chair and UNEP must now collaborate with member states to achieve this task.

Maria Ivanova, a social scientist at Northeastern University in Boston, Massachusetts, proposes that, to move forwards, negotiators must look back. The modern era of multilateral environmental agreements followed the landmark 1972 UN Conference on the Human Environment in Stockholm, the first time that heads of government were involved in such an event. Scholars have spent the subsequent five decades studying how this and other accords came to pass, the difficulties of implementing them and how states can do better.

Social scientist Rakhyun Kim at Utrecht University in the Netherlands and conservation scientist Peter Bridgewater at the University of Canberra propose that governments should use research tools to evaluate whether treaties are achieving their aims. “Scientists need an independent expert body, mandated by governments, to produce authoritative and binding recommendations for environmental bodies and treaties,” they write.

Ivanova studies the role of UNEP, which was created as a result of the Stockholm conference. A core problem with the plastics treaty, she notes, is that the talks have become mired in a ‘villains versus heroes’ narrative, obscuring a complex reality. Currently, nations’ disparate views are amplified through lobbying, influencing media reports. This then further polarizes discussions.

Science and diplomacy

Moreover, there is currently no mechanism for sharing reliable scientific information on plastics. Scientists can attend the talks as observers, and as members of official delegations. However, research is not currently used to formally inform the documents that are part of the talks.

UNEP, Ivanova writes, needs to “revive its role as an impartial steward for conveying reliable scientific data, and for creating structures and spaces in which states can seek alignment”, a part that it has played with notable success before. In 1988, UNEP was one of the two co-founders (along with the World Meteorological Organization) of the Intergovernmental Panel on Climate Change (IPCC). Since then, IPCC reports have helped to educate climate-treaty negotiators and establish a shared body of knowledge that has informed legally binding accords to reduce greenhouse-gas emissions, such as the 1997 Kyoto Protocol and the 2015 Paris agreement. Those accords took too long to be agreed on, and, as Kim and Bridgewater also note, there has been much backsliding in their implementation. However, the agreements, imperfect as they are, would probably not have been reached without the formal involvement of researchers.

The absence of an expert panel to inform the plastics negotiations is about to be remedied with the establishment of the Intergovernmental Science-Policy Panel on Chemicals, Waste and Pollution, which will have its first meeting in Geneva next February. The panel’s researchers must, as a priority, be asked to establish a consensus on the available data for issues related to the talks.

Equally important, as Ivanova says, is the need to expand informal diplomatic spaces, where negotiators who hold what might seem irreconcilable positions can meet and get to know each other as people, not just as representatives of their country’s official position.

Maurice Strong, secretary-general of the 1972 United Nations conference on the human environment, cycles through Stockholm.Credit: SCANPIX SWEDEN/AFP/Getty

The literature on the benefits of such an approach to treaty-making goes back a long way. Canada’s Maurice Strong, the secretary general of the 1972 Stockholm conference, had to navigate a divide that was wider than the current one. Back then, delegates from poorer countries, many of which had gained independence from colonial rule, were threatening to skip the conference. Some considered the idea of protecting the environment a new form of colonization, says Boston University environmental scientist Adil Najam, who studies the involvement of low- and middle-income countries (LMICs) in international environmental agreements (A. Najam Int. Environ. Agreements 5, 303–321; 2005). The representatives argued that, like richer countries, their nations should be allowed to industrialize before going green.

Strong and his team understood that they couldn’t make progress without engaging with these concerns. Before the official talks, he worked with a group of researchers to organize a more informal meeting in Founex, Switzerland. One aim was for all sides to discuss environmental protection from the LMICs’ perspective in both a less formal and a less adversarial setting. That meeting went a long way in persuading LMIC delegates to participate in the official talks, Najam says.

For the plastics treaty talks to succeed, the incoming chair, hosts and delegates need to up their game. There is still time to find a way to include science and improve the overall mood of the talks. Everyone must have access to the best available knowledge, and the space to have honest conversations in an atmosphere of trust.

Nature 646, 1025-1026 (2025)

doi: https://doi.org/10.1038/d41586-025-03469-8

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EDITORIAL 29 October 2025

From MRI to Ozempic: breakthroughs that show why fundamental research must be protected

In these financially straitened times, funders must recognize that great discoveries often arise from work that was looking for something completely different.

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MRI is among the breakthrough technologies that emerged from fundamental, curiosity-driven science.Credit: Wladimir Bulgar/SPL

Around the world, budgets for fundamental research — studies that seek primarily to advance knowledge for its own sake, without an expectation of a return on investment — are coming under pressure to an extent not seen for at least a generation.

In the United States, the principal funder of fundamental research, the National Science Foundation, has this year terminated some 1,600 grants worth a total of US$1 billion, a huge chunk of its $10 billion budget. And US President Donald Trump has proposed slashing its budget by 55%. Meanwhile, in the European Union, competition for funding will only worsen if the bloc implements a misguided plan to include defence and security research in its Horizon Europe programme, which has previously funded only civil research projects.

China has bucked the trend. Last week, the nation’s leadership announced that funding for fundamental research will increase in the country’s next five-year plan, for 2026 to 2030. But elsewhere, as global conflict and pressure on public spending grows, we are hearing funders from Australia to the United Kingdom argue that research with a direct real-world impact — ideally economic — is preferable to fundamental research. In other words, basic research is nice to have, but dispensable in straitened circumstances.

A News Feature in Nature this week offers a reminder of why this approach is wrong-headed. It describes world-changing ‘blue skies’ research that was mostly or entirely about acquiring knowledge, and only later found to have wider applications. In some cases, such discoveries have improved or saved millions of lives.

The polymerase chain reaction — the fundamental science behind the PCR tests used to identify bacteria and viruses that became a part of daily life during the COVID-19 pandemic — originated from work on bacteria found in hot springs by the microbiologists Hudson Freeze and Thomas Brock at Indiana University in Bloomington. Magnetic resonance imaging (MRI) emerged from the study of the fundamental physical properties of the atomic nucleus, and studies of venomous lizards played a key part in the development of drugs such as Ozempic that mimic the GLP-1 hormone. Flat-screen televisions have their roots in studies of chemicals that were isolated from carrots. The News Feature covers seven prominent examples, but there are numerous others.

It is true to say that, overall, today’s research-funding environment bears little resemblance to that of decades gone by. In the twentieth century, many scientists performed research work alongside their teaching and administrative duties without needing to apply for the large grants that are now a staple of research systems, particularly in high-income countries. With greater public funding has come more scrutiny from policymakers and funders, and more desire to show some form of return on investment.

That is right and proper: no one wants to see taxpayers’ monies being misspent. But world-changing discoveries often occur unexpectedly and are built on years — or decades — of fundamental research that expanded our knowledge of the world and how it works. And because private companies are constrained by the need to provide returns in the short term, they rarely provide the sort of consistent, long-term funding needed for the fundamental research that finds things we didn’t know we were looking for.

All governments need to make choices about how best to spend public money. In the case of research, that necessitates working with the research community to adjust the balance of funding to different fields, cutting finance to some and increasing the pot for others. But before those decisions are made, two myths should be dispelled: that fundamental science is less important than other types of research, and that it has no long-term impact. The discoveries described in our News Feature are a timely and necessary reminder that funding decisions should not neglect the painstaking, long-term studies that have proved, time and time again, to be foundational to knowledge, progress and, ultimately, the betterment of society.

Nature 646, 1026 (2025)

doi: https://doi.org/10.1038/d41586-025-03470-1

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WORLD VIEW 29 October 2025

We need a new Turing test to assess AI’s real-world knowledge

A fresh set of benchmarks could help specialists to better understand artificial intelligence.

By

Vinay K. Chaudhri

Artificial intelligence (AI) models can perform as well as humans on law exams when answering multiple-choice, short-answer and essay questions (A. Blair-Stanek et al. Preprint at SSRN https://doi.org/p89q; 2025), but they struggle to perform real-world legal tasks. Some lawyers have learnt that the hard way, and have been fined for filing AI-generated court briefs that misrepresented principles of law and cited non-existent cases. The same is true in other fields. For example, AI models can pass the gold-standard test in finance — the Chartered Financial Analyst exam — yet score poorly on simple tasks required of entry-level financial analysts (see go.nature.com/42tbrgb).

Whenever assessments measure the intended skill inaccurately, it is considered a proxy failure. For example, a lawyer who scored A+ on an exam would be expected to avoid the kinds of error that an AI tool with a similar score might make in a real-world scenario. Better tests are urgently required to help guide the use of AI in complex, high-stakes situations.

One promising idea emerged in March at an Association for the Advancement of Artificial Intelligence workshop in Philadelphia, Pennsylvania: through extensive interaction, a specialist can tell whether an AI system genuinely understands or is merely imitating understanding.

Imagine an AI model attempting to ‘pass’ an interview with an acclaimed legal scholar such as Cass Sunstein at Harvard University in Cambridge, Massachusetts. Sunstein’s expert probing would be a better measure of the model’s legal knowledge than a standardized test or automatically scored benchmark. Passing the ‘Sunstein test’ would require an AI tool to display true legal mastery, being able to wade through ambiguity and contradiction, and not just answer multiple-choice questions or write an essay.

One might ask: why not simply test an AI model’s legal readiness with task-specific benchmarks, similar to those used in medicine for checking an AI tool’s ability to take notes for a physician? The goal, however, is not to test an AI tool’s ability to perform a specific legal task, or even a long list of them, but to test whether it has general-purpose legal knowledge that it can exercise systematically when performing any task.

I am not suggesting that Sunstein, or any single authority, should be appointed as the arbiter of AI expertise. The goal is to build systems that leading legal specialists broadly agree demonstrate genuine, trustworthy legal knowledge. A ‘robo-lawyer’ would need to cope in a diverse range of interviews with panels of experts — ranging from tax and constitutional lawyers to clerks, traffic officers and legal-aid workers. Such an approach would reduce issues around individual or ideological bias and avoid the trap of AI models merely mimicking one person’s style.

Could a machine reach human levels of expertise, subtlety and ethics? Only specialists can say. But imagine a US Supreme Court justice grilling an AI robo-lawyer in public. That would get everyone’s attention. It would be a spectacle much like multinational technology corporation IBM’s 2011 challenge on the US television quiz programme Jeopardy!. The company pitted its supercomputer Watson against human champions to demonstrate how far machine reasoning and natural-language processing had come.

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Nature 646, 1027 (2025)

doi: https://doi.org/10.1038/d41586-025-03471-0

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Competing Interests

The author declares no competing interests.

WORLD VIEW 28 October 2025

What now for the global plastics treaty?

Ending plastic pollution will mean embracing the complexity of political and economic realities.

By

Maria Ivanova

When the United Nations negotiations on plastic pollution in Geneva, Switzerland, collapsed in August, observers declared the outcome an abject failure and the process broken. Earlier this month, reports that the chair of the talks, ambassador Luis Vayas Valdivieso, would step down deepened uncertainty.

Many media outlets saw a failure of multilateralism, but what I saw was fatigue. Just as the ‘failed’ 2009 Copenhagen climate summit paved the way for the 2015 Paris agreement, this plastics-treaty breakdown could be an inflection point — if we learn the right lessons and rethink the process.

As a researcher of global environmental governance, I have followed the negotiations from the start. I served as an adviser on Rwanda’s delegation in 2022, when it co-sponsored the UN Environment Assembly resolution to end plastic pollution. Since 2023, I have led the observer delegation of Northeastern University in Boston, Massachusetts. During this time, I have watched nations debate the treaty’s scope, restating positions with little progress. But individuals from every country tried to keep faith in the process.

The negotiations have stalled partly because a false morality play of ambition versus obstruction took hold. Over the years, I have witnessed how this ‘heroes versus villains’ narrative has obscured the complex realities at stake.

But the story is never that simple. From the outset, nations disagreed on what ending plastic pollution meant. For some, it was about improving waste management, recycling systems and circular economies. For others, it meant curbing the manufacture of primary polymers. That ambiguity set into a geopolitical divide. For some nations, plastics manufacturing underlies economic survival and political stability. For small island states and vulnerable coastal areas, plastic pollution threatens human survival. Yet every country must protect its ecosystems.

Despite clear science and mounting public pressure, negotiations became mired in the politics of survival — between nations concerned about the economic realities and the countries most affected by the ecological urgencies. I spoke to Vayas Valdivieso, who reflected on “the magnitude of the talks, given the implications — for the environment, health and the economy”. Negotiating a treaty that touches every country and spans energy, trade, chemical and waste sectors by 2024, in just two years, was politically ambitious and procedurally unrealistic — a timeline that increased complexity and hardened positions.

Negotiators must acknowledge the economic realities that underpin resistance and the ecological urgencies that demand change. Historically, the UN Environment Programme (UNEP) has provided an architecture for environmental diplomacy — with the authority to convene but not to command. It must revive its role as an impartial steward for conveying reliable scientific data, and for creating structures and spaces in which states can seek alignment. Furthermore, it must help to provide financial, institutional and technological support for collective action.

Despite divisions, all parties converge around sustainable product design, resource efficiency, waste management and circular-economy principles. Now, we need what I call radical incrementalism: a bold vision coupled with practical phased steps. The 1987 Montreal Protocol to protect the ozone layer, championed by UNEP, offers such a model. When negotiations resume, three immediate actions can help.

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Nature 646, 1028 (2025)

doi: https://doi.org/10.1038/d41586-025-03472-z

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News in Focus

- Blood tests are now approved for Alzheimer’s: how accurate are they?

- What’s the cap on human energy expenditure? Elite athletes reveal ‘metabolic ceiling’

- Surprise meteorite debris uncovered on Moon’s far side

- How scientists are pushing back against Trump’s funding ‘deal’ for universities

- People with blindness can read again after retinal implant

- Is academic research becoming too competitive? Nature examines the data

- The world’s first plastics treaty is in crisis: can it be salvaged?

- 7 basic science discoveries that changed the world

- Plants have a secret language that scientists are only now starting to decipher

NEWS 17 October 2025

Blood tests are now approved for Alzheimer’s: how accurate are they?

A second blood test has been been approved by the US Food and Drug Administration to assist in diagnosing Alzheimer’s disease. Will these tests change how cognitive decline is measured?

By

Katie Kavanagh

A blood test measuring levels of tau protein is almost 98% accurate in ruling out Alzheimer’s disease in individuals with cognitive impairments.Credit: Thomas Deerinck, NCMIR/SPL

A new blood test to aid with the diagnosis of Alzheimer’s disease has been approved by the US Food and Drug Administration (FDA). The test, which is designed to rule out cognitive decline caused by Alzheimer’s disease, is the first blood test cleared for use in primary-care settings but not a first for Alzheimer’s.

Elecsys pTau181 — developed by two pharmaceutical companies, Roche in Basel, Switzerland, and Eli Lilly in Indianapolis, Indiana — measures a specific phosphorylated form of tau, known as pTau181 in blood plasma. The test quantifies how much tau protein in the body has been modified as a result of Alzheimer’s disease.

In a press release on 13 October, Roche reported that in a clinical trial of 312 participants, the Elecsys pTau181 test was correctly able to rule out Alzheimer’s 97.9% of the time. The test uses a negative predictive value and helps to rule out Alzheimer’s in individuals with cognitive decline, rather than giving a positive indication that someone has the disease.

“Numerous research studies have shown that these blood signatures have high concordance. They agree really well with the gold standard biomarkers of Alzheimer’s disease, which we have been using in specialized settings for many years,” says Ashvini Keshavan, a neurologist and biomarker researcher at University College London.

However, without full access to the trial data, Keshavan says it’s difficult to fully assess how accurate the test is. “It would be important to see the sensitivity, specificity and positive prediction values to assess the accuracy of this test.”

How accurate is the blood test?

Alzheimer’s disease pathology is characterized by the build-up of sticky amyloid-β plaques and tangles of tau proteins in brain tissue. Both proteins disrupt brain function, lead to neuron death and are the causes of cognitive decline associated with the disease[1].

One other Alzheimer’s blood test has already been approved to measure these biomarkers in individuals’ blood. In May, Lumipulse, developed by biotechnology firm Fujirebio in Tokyo, gained approval from the FDA. This test measures the ratio of two proteins: pTau217 and amyloid-β (1–42). Alicia Algeciras-Schimnich, a clinical chemist at the Mayo Clinic in Rochester, Minnesota, says the two tests work slightly differently — with the Lumipulse test able to both positively test for and rule out the presence of Alzheimer’s pathology.

During a trial of 499 people, 97% of individuals who tested negative for the disease on the Lumipulse test had their results confirmed by follow-up diagnostics tests, which measure the levels of these proteins in the cerebrospinal fluid and brain tissue. Almost 92% of those with a positive Lumipulse result for Alzheimer’s disease had positive follow-up tests.

Coloured magnetic resonance imaging (MRI) scans of a healthy brain (left) and a brain with aggregations of tau proteins (right). Credit: Mark & Mary Stevens Neuroimaging and Informatics Inst./SPL

But as accurate as they’re said to be, the blood tests are prone to uncertain results when linking cognitive symptoms with Alzheimer’s. Individuals can fall into a ‘grey zone’ and require further testing to provide a clear diagnosis.

More than 30% of all samples that were analysed with a pTau217 test developed by Quanterix, a life-sciences company in Billerica, Massachusetts, fell into this grey zone[2]. A similar number has not been reported for the Elecsys or Lumipulse tests.

Will the tests change Alzheimer’s diagnostics?

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Nature 646, 1031-1032 (2025)

doi: https://doi.org/10.1038/d41586-025-03394-w

References

1. Hampel, H. et al. Mol. Psychiatry 26, 5481–5503 (2021).

2. Wilson, D. H. et al. Alzheimers Dement. 20, e095706 (2025).

3. Mattke S. et al. Alzheimers Res. Ther. 15, 128 (2023).

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NEWS 20 October 2025

What’s the cap on human energy expenditure? Elite athletes reveal ‘metabolic ceiling’

When athletes devote large amounts of energy to running or cycling, they unconsciously cut back on energy output elsewhere.

By

Mohana Basu

Ultra-runners face a limit on how much energy they can expend.Credit: Jean-Philippe Ksiazek/AFP via Getty

The human body has a ‘metabolic ceiling’ that even the most extreme athletes cannot surpass. A study[1] published today in the journal Current Biology finds that over a prolonged period — of 30 weeks or more — that ceiling is about 2.4 times an athlete’s basal metabolic rate (BMR), the minimum amount of energy the body needs per day for essential tasks, such as breathing.

For short periods, people can burn up to about ten times their BMR. Researchers have previously proposed that the body has a metabolic limit[2] of around 2.5 times BMR over extended periods, but it has never been properly tested until now, says Andrew Best, a biological anthropologist at Massachusetts College of Liberal Arts in North Adams and a co-author of the work.

Best and his colleagues recruited 14 high-endurance athletes — ultra-runners, cyclists and triathletes. Participants drank doubly labelled water containing deuterium and oxygen-18, which are heavy isotopes of hydrogen and oxygen. Once ingested, both deuterium and oxygen-18 will be lost from the body in the form of water, through urine and sweat. However, some of the oxygen-18 will also exit the body in the form of exhaled carbon dioxide.

By tracing the quantity of these molecules that were flushed out in urine, the scientists were able to calculate the amount of CO_2 exhaled, and, from this, estimate the number of calories the athletes burnt. This allowed the team to trace the athletes’ energy output as they did activities — such as competitions — in real time, rather than measuring them on treadmills in a laboratory, says Best.

Hitting the ceiling

During multi-day competitions, some athletes burnt around 9,000 calories a day. But over longer periods — 30 and 52 weeks — their energy expenditure averaged about 2.4 times their BMR. The results show that even the most extreme athletes reach a metabolic ceiling that it is exceptionally difficult to exceed.

The researchers also found that when athletes devoted more energy to running, cycling and swimming, they unconsciously cut back on using energy elsewhere, such as walking or fidgeting.

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Nature 646, 1032-1033 (2025)

doi: https://doi.org/10.1038/d41586-025-03389-7

References

1. Best, A., Sadhir, S., Hyatt, E & Pontzer, H. Curr. Biol. 35, 5106–5110 (2025).

2. Westerterp, K. R. J. Exp. Biol. 204, 3183–3187 (2001).

3. Thurber, K. et al. Sci. Adv. 5, eaaw0341 (2019).

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NEWS 22 October 2025

Surprise meteorite debris uncovered on Moon’s far side

The rare samples, uncovered by China’s Chang’e-6 mission, might help to reveal secrets of how the Solar System evolved.

By

Jenna Ahart

Researchers in China study samples recovered by the Chang’e-6 mission from the far side of the Moon. Credit: Xinhua/Shutterstock

Sifting through the first-ever rock samples collected from the far side of the Moon, scientists in China have unearthed a surprise: fragments of a rare type of meteorite that could help to piece together the Solar System’s history. The debris — scooped up by China’s Chang’e-6 mission and returned to Earth in June last year — resembles material from asteroids that carry dust pre-dating the Solar System. Studying the chemical composition of this debris could help to trace how asteroids seeded planetary bodies such as Earth and the Moon with volatile compounds, including water.

“The Chang’e-6 mission has a list of major questions to answer, but this wasn’t even on that list,” says Yuqi Qian, an Earth and planetary scientist at the University of Hong Kong, who was not involved in analysing the fragments. “It’s such an unexpected and important finding.”

The authors reported their discovery earlier this week in the Proceedings of the National Academy of Sciences[1].

Near and far

Most missions that have returned rocks from the Moon have sampled the surface facing Earth — the near side — which has fewer craters and has hosted greater volcanic activity. Chang’e-6, however, landed on the far side, at the Moon’s largest, deepest crater — the South Pole–Aitken Basin, which accounts for about one-quarter of the Moon’s surface area. One of the main objectives was to better understand why the far side looks so different from the near side.

Another was to explore the huge basin, which scientists think was created when an asteroid smashed into the Moon about four billion years ago. The crater is probably rich with fragments from that and other asteroid impacts, alongside rock from the lunar mantle — the layer beneath the crust — dredged up by the collisions.

One of the rare meteorite fragments discovered on the Moon’s far side under an electron microscope.Credit: Yi-Gang Xu

But the discovery of the rare meteorite fragments was a surprise. At first, the researchers thought the samples came from the Moon’s mantle. But after analysing the iron, manganese and zinc levels in the debris, they found a mismatch with other lunar materials, indicating they were not from the Moon itself. So, the team examined the relative levels of three oxygen isotopes in the samples; these ratios are “like human fingerprints” and can tell you what type of planetary body the debris comes from, says Mang Lin, an author of the paper and geochemist at the Chinese Academy of Sciences’s Guangzhou Institute of Geochemistry (GIG). “This approach is basically space forensics.”

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Nature 646, 1033 (2025)

doi: https://doi.org/10.1038/d41586-025-03439-0

References

1. Wang, J. et al. Proc. Natl. Acad. Sci. USA 122, e2501614122 (2025).

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NEWS 20 October 2025 Update 20 October 2025 Correction 28 October 2025 Update 21 October 2025 Clarification 28 October 2025

How scientists are pushing back against Trump’s funding ‘deal’ for universities

Seven institutions have so far rejected the administration’s list of demands over academic freedom and other concerns.

By

Dan Garisto,

Alexandra Witze &

Jenna Ahart

The University of Virginia in Charlottesville on 17 October rejected an offer from the administration of US President Donald Trump to sign a higher education ‘compact’.Credit: Peter Morgan/AP Photo/Alamy

US President Donald Trump asked US universities earlier this month to sign a ‘compact’ aligning their student admissions, hiring and research with his administration’s priorities — or risk forfeiting federal research funds. Many scientists have been calling on their institutions to reject the offer.

“Short-term gain in research funding is not worth giving up the power that we have as scientists,” says Caitlin Hicks Pries, a biologist at Dartmouth College in Hanover, New Hampshire, who signed a petition against her school agreeing to the compact. “We want our research funded, but we want it funded based on the quality of our ideas and the quality of science.”

Seven of the nine universities that were initially invited to take the 1 October offer have now rejected it: the Massachusetts Institute of Technology (MIT) in Cambridge; Brown University in Providence, Rhode Island; the University of Pennsylvania (Penn) in Philadelphia; the University of Southern California in Los Angeles; the University of Virginia in Charlottesville; Dartmouth College; and the University of Arizona in Tucson.

Late on 20 October, Vanderbilt University in Nashville, Tennessee, issued a statement that it would continue to provide the Trump administration with feedback on the compact offer, but that “research awards should be made based on merit alone”.

Some, including MIT, have echoed their researchers’ concerns, citing the importance of academic freedom in their rationale. “In our view, America’s leadership in science and innovation depends on independent thinking and open competition for excellence,” MIT president Sally Kornbluth wrote in a public statement. “Therefore, with respect, we cannot support the proposed approach.”

In a 12 October post on his social-media platform Truth Social, Trump informally expanded the compact offer to all US universities. Some in Kansas, Arizona and Missouri were invited by the White House last week to discuss it.

The US Department of Education did not respond to a request for comment on researchers’ concerns in this story. Nature instead received an automatic reply stating that there would be no response while the US government is shut down.

Higher education under attack

The Trump administration, complaining that US higher education has become too liberal, has been taking action against universities for months. One of the first measures was the attempt to cut billions in research funding at universities by capping indirect costs at 15%. (These are costs for things such as electricity for laboratories, paid in addition to research grants.) Federal judges have since blocked those plans, but the uncertainty it has caused for universities has, in part, spurred some to reduce hiring and admissions.

Some institutions, including Columbia University in New York City and Harvard University in Cambridge, Massachusetts, had their federal research funding frozen. Columbia agreed to pay US$220 million to the government over three years and to a number of other concessions to restore its grants. Harvard, however, sued, and a judge ruled in its favour, giving its research funding back.

For Timmons Roberts, an environmental-studies researcher at Brown University, the prospect of a compact felt all too familiar. In July, his university agreed to pay $50 million over ten years to support workforce development in Rhode Island in exchange for restoring its research funding, which had been frozen since April. Before Brown rejected the compact, Roberts spoke at a rally at which he and his colleagues urged the institution to turn it down. Agreeing to the ‘deal’ won’t stop the Trump administration from asking for more in the future, Roberts says.

The compact was heavily shaped by billionaire financier Marc Rowan. In a New York Times op-ed explaining his involvement, Rowan stated that the compact was “intended to promote excellence in core academic pursuits and to protect free speech” while reforming a “broken” system.

University presidents said they agreed with many priorities of the compact, such as academic excellence and reintroducing standardized tests for admissions, which some institutions have already done. But the compact would also alter university admissions, by preventing officials from considering factors such as sex, race or nationality when deciding which students to admit, and marginalize transgender and non-binary people by defining individuals as only ‘male’ or ‘female’. It also imposes a 15% limit on the proportion of international undergraduate students at universities; none of the institutions that were initially invited to sign the compact exceeded that threshold as of 2023, according to federal data.

Agreeing to the compact would further require universities with endowments equivalent to more than $2 million per undergraduate student to provide free tuition for those earning degrees in “hard science programs”. Tuition being too expensive is a real issue, “but I don’t think the way to fix it is to tell universities that they can’t charge it”, says Kerry Emanuel, an atmospheric scientist at MIT who has been involved in faculty discussions about academic freedom and responding to the compact. “The federal government has every right to put conditions on recipients of federal grants, but the proper way to do that is through Congress,” which controls US funding. MIT meets the $2 million funding criterion; students whose families make less than $200,000 per year already receive free tuition.

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Nature 646, 1034-1035 (2025)

doi: https://doi.org/10.1038/d41586-025-03427-4

Updates & Corrections

- Update 20 October 2025: This story was updated after the University of Arizona in Tucson rejected the Trump administration's compact offer.

- Correction 28 October 2025: An earlier version of this story said that Brown University paid US$50 million to the US government. It agreed to pay that amount over ten years to organizations in its home state to support workforce development.

- Update 21 October 2025: This story was updated to include a statement issued by Vanderbilt University.

- Clarification 28 October 2025: An earlier version of this story referred to the Foundation for Individual Rights and Education. It is now the Foundation for Individual Rights and Expression.

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NEWS 20 October 2025

People with blindness can read again after retinal implant

An electronic eye implant has restored vision in people with blindness caused by age-related macular degeneration.

By

Liam Drew

An electrical device implanted under the retina helps to restore some visual acuity to people with age-related macular degeneration.Credit: Science Corporation

Scientists have used an eye implant to improve the vision of dozens of people left functionally blind by age-related macular degeneration (AMD). The implant, which measures 2 millimetres by 2 millimetres, and is just 30 micrometres thick, is surgically inserted beneath the retina to replace the light-sensitive cells that have been lost to the disease.

The clinical trial, which is described today in The New England Journal of Medicine[1], involved 38 people with advanced AMD whose retinas had degenerated severely. One year after device implantation, 80% of participants had gained a clinically meaningful improvement in their vision.

“Where this dead retina was a complete blind spot, vision was restored,” says trial leader Frank Holz, an ophthalmologist at the University of Bonn in Germany. “Patients could read letters, they could read words, and they could function in their daily life.”

Despite some minor events related to implantation surgery, the trial’s safety-monitoring board viewed the device’s benefits as outweighing its risks. In June, the device’s owners — the San Francisco-based neurotechnology company Science Corporation — applied for certification that would allow the device to be sold on the European market.

“I think this is an exciting and significant study, which has been well-designed and analysed. It gives hope for providing vision in patients for whom this was more ‘science-fiction’ than reality,” says Francesca Cordeiro, an ophthalmologist at Imperial College London.

Restored vision

AMD is the commonest form of incurable blindness in older people. There are two main types, wet and dry AMD. The current work studied people with dry AMD, the advanced form of which affects around five million people globally. In dry AMD, the central retina’s light-sensitive cells die over a period of years, leaving affected individuals with intact peripheral vision but without their high-acuity central vision. “They can’t recognize faces, they can’t read, they can’t drive a car, they can’t watch television,” says Holz.

The light-sensitive cells that die (rods and cones) convert light into electrochemical signals that are conveyed to other types of retinal neurons, which then send messages to the brain’s visual-processing regions. Because retinal neurons survive AMD, scientists reasoned that a light-sensitive implant that electrically stimulates the retina according to the pattern of photons striking it could reinstate a sense of vision.

The implant, termed PRIMA — for photovoltaic retina implant microarray — was originally developed by the Paris-based company Pixium Vision, and was acquired by Science Corporation last year. It is wireless, unlike previous retinal devices. And, being photovoltaic, the photons that activate it also provide the energy source for generating its electrical output.

It is used in combination with glasses that contain a camera that captures images and converts them into patterns of infrared light that they transmit to the retinal implant.

The system, which allows users to zoom in and out on target objects, and adjust contrast and brightness, does, Holz says, take months of intensive training to use optimally.

In the current study, 38 individuals were treated at 17 clinical sites across 5 European countries, and 32 of the participants were tested a year after implantation. Twenty-six of them had a clinically meaningful improvement in their vision — which, on average, amounted to being able to see two lines further down a standard eye test chart of letters. Overall, most participants’ vision came close to the resolution achievable with PRIMA.

By the study’s end, most recipients were using PRIMA at home to read letters, words and numbers. Of the 32, 22 said that their user satisfaction was medium to high.

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Nature 646, 1035-1036 (2025)

doi: https://doi.org/10.1038/d41586-025-03420-x

References

1. Holz, F. et al. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2501396 (2025).

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NEWS 17 October 2025

Is academic research becoming too competitive? Nature examines the data

Applications for European research grants increased in 2025. Scientists say they’re feeling the competition.

By

Miryam Naddaf

Scientists are facing stiff competition for funding to be able to pursue careers in research. Credit: Guillaume Souvant/AFP via Getty

Success rates for Europe’s leading research grants are declining — some to single percentage points — as a surge in applications far outweighs the funds available. Data gathered by Nature show that researchers, especially those at the start of their academic journeys, are facing increasingly fierce competition to pursue research careers (see ‘Funding competition’).

Last month, the European Commission said that the European Union’s research and innovation framework programme had received the highest number of funding proposals in its four-decade history this year.

Applications for the 2025 Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowships exceeded 17,000 — an increase of nearly 65% compared with 2024. The MSCA scheme has a proposed budget of €404.3 million (US$471 million) to fund around 1,650 projects, and the success rate (the proportion of awards granted) is expected to drop below 10%, down from nearly 17% last year.

The European Research Council (ERC), Europe’s premier funding agency for basic research, has reported similar surges in applications across its schemes. For its Starting Grants — open to early-career researchers with two to seven years of experience after completing a PhD — the ERC has received 13% more proposals so far compared with 2024. Only 12% will be funded, down from 14% last year.

Applications for the ERC’s Advanced Grants — which fund established researchers — have also risen, by 31% and 82% compared with 2024 and 2023, respectively. Yet only an estimated 276 projects (8%) will be funded, down from 11% last year.

“We’re extremely pleased that there is such a high demand for ERC grants. It shows that people have ideas for fundamental science, for frontier science, that there’s a need for it, there’s a desire for it,” says Maria Leptin, president of the ERC. “The flip side is we don’t have more money. And so, the success rates will go down, and there will be frustration in the community,” she adds.

Europe’s life-sciences organization, EMBO has received “a record number” of applications for its postdoctoral fellowships this year, says Karin Dumstrei, head of the programme.

The result is that many researchers are fighting to stay in an increasingly competitive academic system. “You can work as hard as you want, but at the end of the day, it’s down to numbers. It’s down to luck,” says Christina Carlisi, a cognitive neuroscientist at University College London, who has been applying for grants this year and feeling the strain. “It’s mostly out of your control. And I think that’s sometimes difficult to grapple with in terms of keeping the motivation going.”

Declining success rates

National funding bodies across Europe are experiencing similar increases in applications. The German research foundation (DFG) — the country’s largest independent research funding organization — told Nature that applications for its early-career grants between January and August were up by 20% compared with the same period in 2024.

Another major funder in Germany, the Alexander von Humboldt Foundation, has received at least 20% more postdoctoral applications so far this year than in the same period last year. Applications from India and China make up the largest share of this increase, a representative of the foundation told Nature, and applications from the United States have also risen, although to a lesser extent.

Grant applications received by the United Kingdom’s central research funder, UK Research and Innovation (UKRI), have almost doubled since 2017–18, according to a report published this year. But over the same period, the success rates have nearly halved — from 36% in 2017–18 to 19% in 2024–25, the lowest in the decade. Data shared with Nature by the UKRI’s Medical Research Council show that two of its grant schemes recorded the highest number of applications since 2020 this year.

The Swedish Research Council told Nature that it has also observed a significant rise in applications in the past three years, but the Research Council of Norway said that it has not seen any major changes.

European competition

Researchers and officials say it is too early to know what is driving the spike in applications. Cuts to science budgets and political instability are prompting US researchers to look for jobs in Europe. Academics in the United States can apply for EU grants provided that they carry out the research in institutions within Europe. European scientists who have built careers in the United States might also be seeking opportunities to return home.

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Nature 646, 1036-1037 (2025)

doi: https://doi.org/10.1038/d41586-025-03119-z

References

1. Meirmans, S. Sci. Eng. Ethics 30, 6 (2024).

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NEWS Q&A 10 October 2025

The world’s first plastics treaty is in crisis: can it be salvaged?

Hopes of securing a United Nations treaty on plastics pollution are fading after the final round of negotiations ended without an agreement.

By

Katharine Sanderson

Global consumption of plastics is on the rise, but only a small percentage of plastic waste is recycled. Credit: Justin Sullivan/Getty

Crucial negotiations on what would have been the first global agreement to limit plastics pollution collapsed in August amid acrimony and distrust. This week, hopes that an agreement might yet be reached suffered another blow when Luis Vayas Valdivieso, Ecuador’s ambassador to the United Kingdom, resigned as chair of the treaty’s intergovernmental negotiating committee (INC).

At the last INC meeting, held in Geneva, Switzerland, in August, negotiations ran long into the night, but ended without success. Once a new chair has been appointed, they will face the decision of whether to convene another INC meeting or bring the process to an end without a treaty. The United Nations Environment Programme (UNEP) — the body overseeing negotiations — will address this question when it meets in Nairobi in December.

Nature spoke to Samuel Winton, a researcher at the University of Portsmouth, UK, who is studying the treaty’s progress, about what went wrong, and what can be done to salvage a process that aims to end one of humanity’s biggest blights on the planet.

Why do we need a global treaty to end plastics pollution?

Plastic is a global supply chain, and we see frequently in policy that when interventions target a single part of a supply chain in a single country, their impact is minimal. For example, a plastic-bag ban, whether it’s successful or not, makes a relatively small impact. A global treaty gives countries the opportunity to work strategically together to take genuinely meaningful action. I am still convinced, despite the challenges of this process, that a plastics treaty is essential.

How significant is Vayas Valdivieso’s resignation?

It’s a very big deal. The role of the chair in these processes is crucial, because they set the work plan, they direct the discussions. Having a different person chairing the next meeting — assuming there is one — will have a big impact on how those talks progress.

What would be the best-case scenario now?

We have to be ambitious, but realistic. I don’t think it is realistic — and I’m not convinced it is necessary — to say we are going to cap plastic production at a certain level by a certain date. My ambition would be for there to be a globally enforced environment whereby, with the use of alternative business models or products, the market chooses to adopt those regardless of a ban on certain plastics. That could be regulations that encourage reuse and refill, or regulations that help businesses move to alternative models that reduce the amount of plastic that’s being used.

Samuel Winton is studying the development and outcomes of the UN treaty on plastic pollution.Credit: University of Portsmouth

Have the negotiations achieved anything so far?

There has been a lot of progress among the High Ambition Coalition to End Plastic Pollution (a group of countries that includes the European Union, the United Kingdom and more than 50 other nations) in understanding where each other’s red lines are and how countries could work together. And we are seeing movement from countries like China to a more moderate position. But there are others, such as Saudi Arabia, Iran and Russia — the ‘like-minded group’ — whose positions haven’t moved at all.

When a process works on consensus and you have a few countries refusing to move on certain things, it becomes very difficult to make progress. Another barrier to progress is a lack of more strategic efforts to move the process forwards. The high-ambition coalition, for example, has stated that it is not a negotiating bloc. A genuine negotiating bloc could have helped to achieve more progress than we’ve seen.

What needs to happen to get a treaty over the line?

We need to get to a point where countries and the treaty’s leadership can work together to resolve the deadlock. Many people will tell you that the way to do that is a voting mechanism, and that would be wonderful. However, a number of countries — and UNEP itself — are strongly opposed to the introduction of voting, so that would be challenging to implement.

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Nature 646, 1038 (2025)

doi: https://doi.org/10.1038/d41586-025-03332-w

This interview has been edited for length and clarity.

NEWS FEATURE 29 October 2025

7 basic science discoveries that changed the world

Ozempic, MRI machines and flat screen televisions all emerged out of fundamental research decades earlier — the very types of study being slashed by the US government.

By

Michael Marshall

Illustration: Ibrahim Arafath

Under President Donald Trump, the US government is gutting scientific research. The National Institutes of Health has cut almost US$2 billion of grants that were already approved, and the National Science Foundation has terminated more than 1,400 grants. And the president has even bigger plans to eviscerate science. His proposed budget for fiscal year 2026 would cut non-defence-related research and development by 36%.

“They have cancelled wholesale a wide variety of research efforts in midstream,” says John Holdren at Harvard University in Cambridge, Massachusetts, who was the science adviser to former president Barack Obama during both his terms. “They intend to now solidify it with cuts in the budgets.”

The cancelled and threatened research is a mixture of ‘applied’ work that has stated applications, which can be commercial in nature, and ‘basic’ or ‘blue skies’ research, intended to develop new knowledge.

Basic research is easily mocked because it can seem impractical, but, in fact, it is a major driver of economic growth. “The return on investment in basic research — the return to society — is very high, typically multiple dollars back per dollar invested,” says Holdren.

The US funding cuts will hit basic research particularly hard because the government has historically been the prime supporter of fundamental research. The private sector will never invest enough in such research, says Holdren. “The timescale for returns is too long and the ability of the funder to capture those returns too uncertain,” he says. “That’s the reason that the funding of fundamental research is, at its base, a responsibility of government.”

Although it’s impossible to estimate how the reductions in federal support might limit future discoveries, scientists point to a long list of findings that emerged out of fundamental research and went on to change the world. Here are a few examples.

From hot springs to DNA forensics

In the summer of 1966, while he was an undergraduate at Indiana University, Hudson Freeze went to live in a cabin on the edge of Yellowstone National Park. He was working for microbiologist Thomas Brock, who was convinced that certain microorganisms were living at surprisingly high temperatures. Dodging bears, and the traffic jams they caused, Freeze visited the hot springs every day to sample their bacteria.

Thomas Brock stands by Mushroom Spring in Yellowstone National Park in 1967. Bacteria that thrive in high-temperature fluids were discovered at the site.Credit: Thomas Brock/USGS

On 19 September, Freeze succeeded in growing a sample of yellowish microbes from Mushroom Spring. Under a microscope, he found an array of cells collected from the near-boiling fluids. “I was seeing something that nobody had ever seen before,” says Freeze, now at the Sanford Burnham Prebys Medical Discovery Institute in La Jolla, California. “I still get goosebumps when I remember looking into the microscope.”

Three years later, Freeze and Brock described one of the bacteria, which they called Thermus aquaticus[1]. It grew best at 70 °C. Then, in 1970, they isolated an enzyme[2] from T. aquaticus, which performed sugar metabolism at an optimal temperature of 95 °C. By then, Freeze had left for graduate studies and shifted his priorities to slime moulds. However, other researchers kept studying T. aquaticus, and in 1976, a team at the University of Cincinnati in Ohio isolated another enzyme[3]: a ‘DNA polymerase’ that could synthesize new DNA at 80 °C.

Seven years later, this Taq polymerase would prove to be just what biochemist Kary Mullis needed to create the polymerase chain reaction (PCR), a method for rapidly making thousands upon thousands of copies of a single fragment of DNA[4]. Mullis needed high temperatures to break DNA molecules apart, so he also needed a polymerase that worked at high temperatures to avoid repetitive heating and cooling.

Today, PCR is an indispensable tool in everything from medicine — where it’s used to match organ donors with recipients and in the diagnosis of cancers — to DNA fingerprinting that can help police to identify killers.

The origins of MRI

Magnetic resonance imaging (MRI) is a mainstay of modern medicine. It can generate detailed images of a person’s internal anatomy — revealing, for instance, abnormal structures in the heart or whether a tumour has grown or shrunk. A variant called functional MRI (fMRI) tracks changing blood flow in the brain, which has enabled researchers to discover fundamental insights about how the brain works. What’s more, MRI is non-invasive and doesn’t require the use of radioactive substances or ionizing radiation, unlike many other imaging methods.

MRI emerged from research in the 1930s into the physical properties of atomic nuclei, and of the fundamental particles within them. It was “pretty esoteric stuff” with “no applications really in sight or in mind”, says Carmen Giunta, a chemist at Le Moyne College in Syracuse, New York.

Research in the 1930s by Isidor Rabi eventually helped in the development of MRI scanning technology.Credit: RDB/ullstein bild/Getty

One key discovery on the path to MRI machines involved studies of protons and neutrons, which make up atomic nuclei. Such particles have a property called spin that describes their angular momentum.

In the 1930s, physicist Isidor Rabi and his colleagues were investigating spin by firing beams of atomic nuclei through magnetic fields. Protons and neutrons, depending on the orientations of their spins, have slightly different energy levels when exposed to magnetic fields. “The resonance method that he developed was a way of detecting when these spins change their orientation in the presence of a magnetic field,” says Giunta. For this work, Rabi won the Nobel Prize in Physics in 1944.

Nuclear magnetic resonance found its first uses in chemical laboratories. Because atomic nuclei are sensitive to their surroundings, careful measurements of magnetic resonance could be used to determine how atoms were connected in large molecules. From the 1970s onwards, it was fashioned into a tool for imaging biological tissues. Paul Lauterbur and Peter Mansfield shared the Nobel Prize in Physiology or Medicine in 2003 for their work in developing MRI.

Root vegetables and flat-screen TVs

It started in Prague in early 1888, when botanist Friedrich Reinitzer extracted chemicals called cholesterol esters from carrot roots. One of the substances — crystals of cholesteryl benzoate — did something unexpected. Normal crystals, when heated, lose both their solidity and colour at the same temperature, but these did not. “They lost their solidity at 145 °C but retained their bluish colour, which they only lost at 178 °C,” says Michel Mitov at the University of the Côte d’Azur in Nice, France.

Other researchers had seen similar behaviours before, but Reinitzer recognized that there might be an important new phenomenon at work. Unsure how to explain it, on 14 March he wrote a long letter to physicist Otto Lehmann in Aachen, in what is now Germany. “Lehmann was the perfect colleague for continuing and reproducing the observation,” says Mitov, because he had built a microscope with a heated stage, which meant he could observe the crystals’ behaviour in real time. The two exchanged letters and samples for weeks, and Reinitzer presented their initial results at a meeting in Vienna in May.

Otto Lehmann made key discoveries about liquid crystals, which helped to lay the foundation for modern screens on televisions and smartphones.Credit: Unknown author

Lehmann’s key observation was that, when the crystals lost their solidity, they still retained some properties of a crystal. Yet in other respects they were liquid. At a molecular level, they were composed of long molecules that remained in an ordered orientation (as in a crystal) but could also move freely (as in a liquid). Lehmann called them liquid crystals.

For decades, many researchers refused to accept this, because it flew in the face of the system that physicists and chemists used to categorize matter. Substances were either solid, liquid or gaseous. Liquid crystals blurred the lines, and acceptance of this came at “a very high intellectual cost”, says Mitov.

In the first half of the twentieth century the evidence became undeniable, but research into liquid crystals dried up, out of a belief that they would never be of any use. The field was revived by US chemists in the late 1950s, and in 1968, engineers developed the first flat screens based on liquid crystals, which ultimately gave rise to flat-screen televisions. However, their applications go far beyond screens, says Mitov, including uses in cameras, microscopes, smart materials, robotics and even anti-counterfeiting technology.

The tiny start to gene editing

“My head explodes every time I see a new application of CRISPR, or that CRISPR has cured someone,” says microbiologist Francisco Mojica at the University of Alicante in Spain.

CRISPR, short for Clustered Regularly Interspaced Short Palindromic Repeats, is a tool that can edit genomes in a very precise way. It opened up vast opportunities for basic research and has paved the way for curing genetic disorders, including sickle-cell disease, immune dysfunction and life-threatening metabolic conditions. Emmanuelle Charpentier and Jennifer Doudna shared the 2020 Nobel Prize in Chemistry for developing the tool.

The discoveries that led to this revolution happened decades earlier. In 1989, Mojica was a PhD student investigating Haloferax mediterranei R-4: a single-celled organism called an archaean, found in salt-producing ponds near Alicante. He tried to find out how the microbe could survive in such briny conditions. Having identified some promising regions of the microbe’s genome, Mojica sequenced them and was surprised to find short segments that were repeated at regular intervals. He and other researchers gave these repeated segments different names but eventually settled on the acronym CRISPR. As he investigated them, Mojica proposed some potential functions for the repeating segments: ideas that were, he says wryly, “absolutely wrong”[5].

Francisco Mojica’s studies of microbial genes underpinned the development of the CRISPR gene-editing system.Credit: Juan Carlos Soler/ARCHDC/Archivo ABC/Alamy

Subsequently, Mojica learned that similar sequences could be found in many other microbes, which didn’t live in saline conditions. “Whatever role they were playing, it couldn’t be related to the peculiarities of the diverse environments,” he says.

The key clue was the discovery, between the repeated segments, of sequences from the genomes of bacteriophages: viruses that infect bacteria. Eventually Mojica realized that a bacterium carrying a sequence from a specific phage could not be infected by that phage. “We inferred that this was an adaptive immune system,” he says. “One ancestor acquired spacers from the phage, and after that, the descendants were resistant to infection[6].”

Mojica knew this was a big find — an adaptive immune system had never been observed in bacteria or archaea. He also thought that it might be useful for dealing with bacterial infections. Then others found that CRISPR works by cutting DNA at specific points[7]. From there, Doudna and Charpentier discovered how they could harness that system and reprogram it to edit genes. And so the CRISPR revolution took off[8].

Weight loss, inspired by a lizard

Weight-loss and diabetes medications such as Ozempic have become the wonder drugs of the era. Almost 5% of people in the United States have used them to slim down, and the global market for these drugs is expected to reach $100 billion by 2030. Although much of the work that led to them was done for medical applications, one key discovery came from research on the only venomous lizards in the United States: Gila monsters (Heloderma suspectum).

A Gila-monster peptide played a part in the development of GLP-1 drugs.Credit: Getty

At the heart of the story is a molecule called glucagon-like peptide-1 (GLP-1), which is produced in the human gut. In the 1980s, chemist Svetlana Mojsov showed that GLP-1 can stimulate the production of insulin, lowering blood sugar levels[9].

Daniel Drucker, now at the University of Toronto in Canada, worked with Mojsov on this early research. “We were focused on its potential for diabetes treatment,” he says. “Ten years later, in 1996, we and others discovered that GLP-1 reduces food intake, and the potential for weight loss emerged.” However, there was a problem. GLP-1 has a short half-life: just a few minutes. That meant it was no use as a drug, because it would be broken down in the body before it could have much of an effect.

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Nature 646, 1040-1043 (2025)

doi: https://doi.org/10.1038/d41586-025-03474-x

References

1. Brock, T. D. & Freeze H. J. Bacteriol. 98, 289–297 (1969).

2. Freeze, H. & Brock, T. D. J. Bacteriol. 101, 541–550 (1970).

3. Chien, A., Edgar, D. B. & Trela, J. M. J. Bacteriol. 127, 1550–1557 (1976).

4. Green, M. R. & Sambrook, J. Cold Spring Harb. Protoc. 2019, 436–456 (2019).

5. Mojica, F. J. M. & Rodriguez-Valera, F. FEBS J. 283, 3162–3169 (2016).

6. Barrangou, R. & Horvath, P. Nature Microbiol. 2, 17092 (2017).

7. Garneau, J. et al. Nature 468, 67–71 (2010).

8. Jinek, M. et al. Science 337, 816–821 (2012).

9. Mojsov, S. Int. J. Pept. Protein Res. 40, 333–343 (1992).

10. Eng, J., Kleinman, W. A., Singh, L., Singh, G. & Raufman, J. P. J. Biol. Chem. 267, 7402–7405 (1992).

11. Chen, Y. E. & Drucker, D. J. J. Biol. Chem. 272, 4108–4115 (1997).

12. Drucker, D. J. et al. Lancet 372, 1240–1250 (2008).

13. Napoli, C., Lemieux, C. & Jorgensen, R. Plant Cell 2, 279–289 (1990).

14. Fire, A. et al. Nature 391, 806–811 (1998).

15. Tilton, G. R. in Biographical Memoirs Vol. 74, 266–286 (National Academies Press, 1998).

16. Patterson, C. Geochim. Cosmochim. Acta 10, 230–237 (1956).

17. Tatsumoto, M. & Patterson, C. C. Nature 199, 350–352 (1963).

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NEWS FEATURE 28 October 2025

Plants have a secret language that scientists are only now starting to decipher

Signals from the plant cell wall help to orchestrate growth, reproduction and immune function. Can harnessing this molecular cross talk help in creating better crops?

By

Amber Dance

Plant cell walls, such as in these root cells, not only act as physical containers but also contribute to important signalling processes. Credit: Biophoto Associates/SPL

When Robert Hooke gazed through his microscope at a slice of cork and coined the term ‘cell’ in 1665, he was really looking at just the walls of the dead cells. The squishy contents typically found within would become objects of ongoing study. But for many plant scientists, the walls themselves faded into the background. They were considered passive containers for the exciting biology inside.

“For a long time, the cell wall was really thought to be dead,” says Alice Cheung, a plant molecular biologist and biochemist at the University of Massachusetts Amherst. It wasn’t until the late twentieth century, Cheung says, that scientists began to reveal the cell wall for the vibrant, ever-changing structure it is. Even then, its complex mix of sugar molecules linked into long, branching polysaccharides kept away all but the most intrepid biochemists.

But now, with the help of modern molecular methods to analyse the wall’s make-up and assembly, researchers are starting to uncover more. They’re finding that the cell wall is an active, even chatty participant in cellular growth, reproduction and responses to infection. It’s constantly receiving and sending signals about its shape and composition. By eavesdropping on those signals, and tweaking or adding to them, scientists are exploring innovative ways to improve agriculture with cell-wall science: protecting crops from disease and engineering new plants or sturdy hybrids.

“The plant cell wall is one of the most sophisticated systems in terms of communication,” says Li-Jia Qu, a plant biologist at Peking University in Beijing. His long-term goal is to use what’s been learnt about those messages to interbreed distantly related plants, creating exciting crops that could expand agriculture into new lands.

These walls can talk

The wall is the plant’s interface with its environment, including salt and other stressors or disease agents such as moulds, so it must notice damage and adapt accordingly.

The cell walls of a growing plant are constructed mainly from polysaccharides, including stiff cables of cellulose and jellified strands of pectin. The latter are highly complex molecules, branched in diverse ways and decorated with various extras such as methyl groups. “It’s like a giant bowl of many types of pasta, all mixed together,” says Charles Anderson, a plant cell biologist at Pennsylvania State University (Penn State) in State College.

And although the wall protects the contents within, some pathogens use enzymes to drill through it and infect the cells. This creates polysaccharide fragments that signal to the cell that something has breached the wall. When the cell senses these pieces, along with fragments of the infecting pathogen’s cell wall, it activates genes in the plant’s immune pathways. In response, the plant can produce an extra polysaccharide, called callose, which reinforces the cell wall. It also manufactures defensive molecules such as antimicrobial peptides and reactive oxygen species.

Such signals are already being co-opted by farmers. By spraying molecules derived from the cell walls of algae or fungi over their fields, they can prime the plants for pathogens that might arrive later. By doing this, they activate the immune response “and let the natural mechanism of the plant fight the infection”, says Antonio Molina, a plant biologist at the Technical University of Madrid. The method could help growers to avoid harsh fungicides, he says.

Molina has co-founded two companies that make use of this technique, making extracts from fungi or plants as crop protectives.

The puzzle-piece shape of these Arabidopsis thaliana pavement cells is created in part through signalling from the cell wall. Credit: Raymond Wightman/Sainsbury Laboratory, University of Cambridge and Alexis Peaucelle/INRAE

The current inoculants are fairly crude mixtures derived from plants or pathogens, says Cyril Zipfel, a plant immunologist at the University of Zurich, Switzerland. He’s working to understand the signalling that underlies the immune pathway, which should enable scientists to create more specific, or even synthetic, treatments.

There are trade-offs, however, Molina says. For one, the effects last for only three or four weeks. Reapplication for slow-growing crops could get costly, but Molina thinks that farmers might be able to focus the use of inoculants to times when risk of infection is high, such as after it rains, to prevent mildew.

Another challenge is that whenever plants devote resources to boosting their defences, this takes materials and energy away from growth, so farmers must apply the treatments judiciously.

Powered by pectin

Plant growth itself demonstrates how the idea of a cell wall as a static shell is insufficient. Yes, the cell does need the wall as a physical container; otherwise, it would burst from the enormous water pressure inside it. But for plant cells to grow, the cell walls must expand first, says Sebastian Wolf, a plant molecular biologist at the University of Tübingen in Germany.

This is where pectin comes in. Pectin is a complicated molecule constructed from at least a dozen sugars, connected by more than 20 types of linkage, says Wolf. “It’s actually so complex that we don’t know what it looks like,” he adds. Pectin is also dynamic, undergoing frequent modifications. Depending on those modifications, it can be rigid, supporting a sturdy plant, or softer when the plant needs to grow. That’s why pectin is often used to make marmalade: the initially soft pectin molecules cross-link and soak up water, taking on a stiffer, gelatinous consistency.

In the growing plant, there’s one key modification that makes pectin soft or rigid: methyl groups attached to its component sugars. When the wall needs more materials for growth or reinforcement, the cell inside manufactures a methyl-decorated form that is thought to be fairly soluble, so it can be secreted into the surrounding wall. Once the pectin is incorporated into the wall, it starts to firm up. This happens when enzymes remove the methyls, uncovering negatively charged atoms in the sugar molecules underneath. Calcium ions in the wall bind to two sugars at a time and cross-link the pectin into a stiffer material that can absorb water.

As a graduate student at the University of Heidelberg, Germany, in the early 2000s, Wolf was interested in the effects of methyl groups on pectin, so he made a mutant of the cress Arabidopsis thaliana, a favourite of plant geneticists, that was unable to take off the methyl groups. He expected this to soften the cell walls, but the plants came out weirder than anticipated, with long, wavy roots[1]. They reminded him of mutants with defects in cellular signalling involving cellulose, leading him to wonder whether pectin, too, had a role not just in cell-wall structure, but also in cellular conversations.

Continuing this research at the French National Research Institute for Agriculture, Food and Environment in Versailles, Wolf discovered a cell-wall signal that contributes to plant growth controls[1],[2]. The input that Wolf found starts when cell-surface receptors notice an overabundance of methyl-decorated pectin. What seems to happen in response is that they tell the cell to adjust its production pipelines, making more of the methyl--removing enzyme so that the wall can firm up the pectin.

Cell-wall signalling can even help growing cells to adopt fancy forms, including the puzzle-piece shapes seen in what are known as pavement cells — interlocking surface cells that give strength and structure to plant leaves (see ‘Cell walls fitting together’). When Anderson and his colleagues studied the signals sent by the cell wall as pavement cells developed in A. thaliana, they found evidence for another conversation initiated by methyl-free pectin and a receptor, called FERONIA, that senses this form of pectin[3]. But cellulose matters here, too. Both cell-wall components are needed to strengthen the indented portions of the puzzle piece, known as the ‘necks’. Without that fortification, the rest of the cell bulges into that space. If FERONIA isn’t present, the indentations don’t go as deep as they normally would.

Here’s how they think the indentation process happens in a healthy leaf cell: methyl-free pectin in the cell wall is a sign that there’s enough pectin to support a neck. This pectin sticks to a receptor complex on the cell surface that includes FERONIA. In response, the cell starts manufacturing cellulose in the same place. Together, the cellulose and pectin strengthen the wall so that it can support the indentation.

Wolf is continuing to investigate the role of the cell wall in shaping cells, and reported in a preprint earlier this year that plant stem cells must control the methyl status of wall pectin to maintain their stemness and form new plant parts[4]. He thinks that there is potential to use these signalling pathways to influence the way in which plants are shaped. “You can fundamentally change how plants grow and how they look,” he speculates. For example, when plants make less cellulose, they grow into “stubby” forms. But, he cautions, researchers need to learn more about the underlying growth pathways first.

Hybrid potential

The FERONIA receptor in those puzzle-piece cells has turned out to be a key player in cell-wall signalling. FERONIA is found all over plants, and interacts with a range of partners to influence not just leaf-cell shape, but also a variety of other systems, from root growth to environmental-stress responses. Given that FERONIA binds to pectin to maintain the wall’s integrity, without it, the wall becomes weak and porous. Remarkably, mutants lacking FERONIA survive, although they’re pretty unhealthy, Qu says: they sprout wrinkled, curly leaves, and straggle along the ground because they lack the vasculature to hold them upright. They are “very small, tiny”, says Qu. They can make seeds, he adds, “but very few”.

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Nature 646, 1044-1046 (2025)

doi: https://doi.org/10.1038/d41586-025-03473-y

References

1. Wolf, S., Mravec, J., Greiner, S., Mouille, G. & Höfte, H. Curr. Biol. 22, 1732–1737 (2012).

2. Wolf, S. et al. Proc. Natl Acad. Sci. USA 111, 15261–15266 (2014).

3. Lin, W. et al. Curr. Biol. 32, 497–507 (2022).

4. Zerin, T. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.05.19.654883 (2025).

5. Liu, C. et al. Science 372, 171–175 (2021).

6. Zhang, L. et al. Curr. Biol. 31, 2004–2016 (2021).

7. Lan, Z. et al. Cell 22, 4773–4787 (2023).

8. Huang, J. et al. Nature 614, 303–308 (2023).

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Books & Arts

- AI can supercharge inequality — unless the public learns to control it

- How do you know what I know you know? Steven Pinker on common knowledge

BOOK REVIEW 27 October 2025

AI can supercharge inequality — unless the public learns to control it

Understanding prediction algorithms and engaging in decisions about their use is crucial to curbing concentrated power and rising inequality.

By

Cathy O’Neil

Taxi drivers in San Francisco, California, protest against a plan for a citywide, around-the-clock, driver-less car service. Credit: David Paul Morris/Bloomberg/Getty

The Means of Prediction: How AI Really Works (and Who Benefits) Maximilian Kasy Univ. Chicago Press (2025)

Have you felt dizzy from the constant flood of articles claiming that artificial intelligence will either boost economic productivity and be humanity’s best friend, or become superhuman and wipe everyone out? There’s a reason for the confusion — it is not accidental.

In The Means of Prediction, economist Maximilian Kasy argues that one side of this polarized debate is just cashing in on the current wave of techno-enthusiasm. This group, led by wealthy investors and technology executives who insist that AI tools will solve all of humanity’s problems, is opposed to the models being regulated by governments. At its core, Kasy says, it’s a calculated power grab. Meanwhile, some philosophers and others warning us about apocalyptic AI futures are also funded by large donations tied to those fears. In short, both camps have something to sell.

However, given the scale of investment in AI, the public needs to have a clear grasp of what the technology really means for our future, especially if the economy were to start haemorrhaging jobs. Kasy’s book, which serves as an introductory crash course, suggests that by developing basic literacy about how AI works and what prediction engines do, people can learn to distinguish genuine progress from corporate spin.

He also argues that the public should see the AI roll-out as a pressing issue of growing inequality and concentrated power. Indeed, the book’s title alludes to political theorist Karl Marx’s argument that whoever controls the ‘means of production’ controls the structure and direction of society.

Broader public debate over who gets to pick the objectives of AI systems is crucial, he argues. AI-driven prediction engines already influence hiring decisions at big corporations, shape what content is shown on social-media feeds and can help the military to select targets for air strikes.

But for the public to engage in the debate effectively, AI’s inner workings must be made accessible. Kasy devotes a substantial part of his book to explain, in plain language, how the technology works, unpacking terms such as machine learning and deep learning. A useful section called ‘neural nets are not artificial brains’ explains that neural networks — computer systems made of interconnected nodes that learn patterns from data — do not mimic how the human brain functions.

Taking down this metaphor is important, because it’s one of the main ways in which the tech industry obscures what AI really is. In practice, AI systems are built and controlled by companies to serve commercial goals, not to think or feel in the way humans do.

Kasy goes on to examine the required framework for regulating AI, with discussions on fairness, explainability and privacy. He argues that decisions about how these systems are designed and deployed should not be left to corporate boardrooms alone. But his perspective is that of an academic, and regulation in the real world has to actually work.

Dealing with bias

If you wanted a breezy introduction to AI but don’t have the time or money for the author’s machine-learning or economics courses at the University of Oxford, UK, this book offers a high-level overview. But its tone can feel pedantic, overly technical and long-winded. There are few fresh examples of what can go wrong with algorithms — or deeper analyses of familiar case studies — and much of the discussion remains theoretical.

Take fairness, for example. The author introduces the concept of merit-based discrimination — hiring someone less qualified for non-monetary reasons. He illustrates this with a hypothetical hiring algorithm designed to pick the most ‘productive’ workers. In this set-up, any unfair treatment would simply mean that the company is not maximizing profits. Similarly, as long as hiring decisions maximize profit, the company is assumed to be fair.

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Nature 646, 1048-1049 (2025)

doi: https://doi.org/10.1038/d41586-025-03475-w

Competing Interests

The author declares no competing interests.

BOOK REVIEW 27 October 2025

How do you know what I know you know? Steven Pinker on common knowledge

From using words to overthrowing regimes, shared awareness is necessary for people to coordinate. The cognitive scientist explains to Nature how it works.

By

David Adam

Psychologist Steven Pinker argues that people have an intuitive sense of what’s known to be known, even when they don’t say it explicitly. Credit: Chona Kasinger/NYT/Redux/eyevine

When Everyone Knows That Everyone Knows...: Common Knowledge and the Science of Harmony, Hypocrisy and Outrage Steven Pinker Allen Lane (2025)

How do we know when others know what we know? Harvard psychologist Steven Pinker’s latest book, When Everyone Knows That Everyone Knows, delves into how ‘common knowledge’ can cement or explode social relations.

Common knowledge — awareness of mutual understanding — can explain the emergence of social-media shaming mobs, academic cancel culture and revolutions that seem to erupt from nowhere. It drives how people coordinate with others and can explain everything from awkward first-date conversations to financial bubbles and stock-market crashes.

Pinker tells Nature why it helps to better understand the ways we get into each other’s heads — and what happens when we know that we have.

What is common knowledge?

It is the state in which I know something, you know it, I know that you know it, you know that I know it, I know that you know that I know that you know it, and so on, ad infinitum. It differs from private knowledge, in which someone knows something without knowing whether anyone knows they know it.

That’s a lot of ‘knowing’!

Common knowledge is usually tacit, and it can be generated at a stroke when we witness something that is conspicuous, public or self-evident — I see something while seeing you see it, and vice versa. From there, we can deduce that each observer knows that the other knows it, and so on, to as many levels as we care to reel out — a process that cognitive psychologists call recursive mentalizing.

Why does it matter?

Common knowledge is necessary for coordination: two or more people making arbitrary choices that benefit them, as long as they make the same choice. A classic example is a rendezvous. It’s not enough to know that your friend likes to go to Starbucks and so to head there, because he might know that you like to go to Pret A Manger and head there. It’s not even enough to know that he knows that you know that he likes Starbucks, and head there, because he knows that you know that he knows that you like Pret, which is where he might go.

Nothing short of common knowledge will get you to the same place at the same time. The obvious means of achieving this is a phone call, because direct speech is an excellent common-knowledge generator. The next best thing is common salience. The friends might gravitate to a nearby landmark, such as Nelson’s Column in London’s Trafalgar Square, because each can anticipate that it would pop into the mind of the other.

What other examples are there?

Our conventions depend on common knowledge, starting with language. The word ‘rose’ refers to a rose for no reason other than that everyone knows that everyone knows it does. Another convention is driving on the left rather than the right, or vice versa: there’s no reason to choose one side or the other, but every reason to choose the same side as everyone else. This convention is backed up by law, but that doesn’t matter: people have an incentive to follow the convention as long as they know that everyone else is following it.

Even legally binding conventions depend more on common knowledge than on enforcement, because no government has enough snitches and rooftop snipers to enforce every law. When ‘no smoking’ signs were first put up, people didn’t report violators to the smoking police, but they could stand their ground when asking a smoker to stop and expect them to give way, because the smoker expected the complainant to stand their ground.

This is also how we can have social and moral norms, such as not telling ethnic jokes, revealing certain body parts in public or insulting someone’s looks. The norms can persist by the common expectation that everyone knows that everyone knows there are certain things decent people just don’t do.

How can common knowledge fail?

When there’s a public signal that a belief might no longer be commonly held, the doubt can be self-fulfilling. In a bank run, a rumour that the bank might not have enough reserves to cover withdrawals can drive people to withdraw their savings out of fear that other people are withdrawing theirs out of the same fear. That can crash the bank, and, as the doubts reverberate, the economy.

When then-US president Franklin D. Roosevelt said in 1933, “The only thing we have to fear is fear itself”, he was expressing a theorem of common knowledge. Other runaway phenomena, such as financial bubbles or hyperinflation — or even the hoarding of toilet paper during a pandemic — are also driven by common expectation.

These are old ideas, so why write the book?

I was led into the study of common knowledge by my interest in language. A lot of speech can’t be interpreted at face value. For example, “If you could pass the salt, that would be brilliant” has a veiled imperative meaning: “Give me the salt.” I wanted to know why we don’t all just say what we mean.

Common knowledge is what aligns us in social relationships. Our rituals of ignoring the elephant in the room, not blurting out everything we think, framing criticism tactfully, and other exercises of social skill consist of judgements of whether to make some fact common knowledge or whether to avoid doing so.

Do you include any fresh ideas?

I advance a hypothesis — which has driven my research over the past 15 years — that social relationships (such as friendship, romance, authority and transactional partnerships) are coordination games, ratified by common knowledge signals such as eye contact, laughter, dominance displays and direct speech.

And I address the cognitive question of what goes on in people’s heads when they have common knowledge, given that multiple nested propositions about beliefs are cognitively strenuous.

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Nature 646, 1050-1051 (2025)

doi: https://doi.org/10.1038/d41586-025-03476-9

This interview has been edited for length and clarity.

Competing Interests

The author declares no competing interests.

Opinion

- John Gurdon obituary: Biologist who made cloning possible

- Environmental treaties are paralysed — here’s how we can do better

- How to understand exoplanets — space scientists call on lab-based chemists to help

OBITUARY 20 October 2025

John Gurdon obituary: Biologist who made cloning possible

He showed that specialized cells retain the genes to form an organism.

By

Ron Laskey

Credit: Jerry Mason/Science Photo Library

John Gurdon transformed biology by proving that when cells specialize to become a specific type, such as skin or nerve cells, they do so by switching genes on and off, not by losing genetic information. This insight came from his pioneering experiments between the 1950s and the 1970s, when he transferred the nucleus (the part of the cell containing DNA) from frog cells into frog eggs whose nuclei had been removed. Some of the eggs developed into adult frogs, showing that some specialized cells retain the full genetic blueprint to make an entire organism.

He also provided early evidence that, although the number of pluripotent cells — which can become many cell types — declines during development, some persist into adulthood. His work laid the foundation for animal cloning and, decades later, the birth of the first cloned mammal, Dolly the sheep.

Gurdon shared the 2012 Nobel Prize in Physiology or Medicine with Shinya Yamanaka at Kyoto University in Japan, who discovered how to reprogram adult cells into a stem-cell-like state. Together, their discoveries reshaped research into how organisms develop, as well as cell biology and regenerative medicine.

After his early success, Gurdon studied how the nucleus is influenced by the cytoplasm that surrounds it. By transplanting nuclei into eggs and their precursor cells (oocytes), he showed that substances in the cytoplasm control the cell cycle — the sequence of growth and division — and gene activity. He identified chemical triggers for DNA copying and cell division, helping to define how all cells multiply.

Born in southern England in 1933, Gurdon grew up in a comfortable home but struggled at school. At 13, he went to Eton College in Windsor, UK — an experience he later called “intensely uncomfortable”. At 15, he was ranked last out of 250 students in a notorious biology report. His teacher wrote: “He will not listen but will insist on doing his work in his own way. I believe he has ideas about becoming a scientist; on his present showing, this is quite ridiculous.”

Following that disaster, Eton excluded him from any further science classes. Christ Church College at the University of Oxford, UK, offered him a place on the strength of his Latin and Greek, on the condition that he studied anything except Latin and Greek. He spent a gap year learning elementary biology and was allowed to switch to zoology. He had a long-standing interest in insects and gained first-class honours in zoology, but the professor of entomology rejected his application to study insects for a doctoral degree. Fortunately for science, Michail Fischberg, a lecturer in developmental biology, offered him a doctoral project studying the embryo of the clawed frog Xenopus, leading to his landmark nuclear-transplantation experiments.

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Nature 646, 1053 (2025)

doi: https://doi.org/10.1038/d41586-025-03418-5

Competing Interests

The author declares no competing interests.

COMMENT 28 October 2025

Environmental treaties are paralysed — here’s how we can do better

The collapse of talks about a UN plastics treaty is the wake-up call we didn’t need. It’s time to study what is going wrong and why.

By

Rakhyun E. Kim &

Peter Bridgewater

Artist Benjamin Von Wong’s installation sat outside the United Nations building in Geneva during negotiations for the plastics treaty in August. Credit: Orjan Ellingvag/Alamy

On 15 August in Geneva, Switzerland, a fifth round of negotiations towards a multilateral treaty on reducing plastic pollution collapsed. The chair announced that the committee had concluded its work — without producing a draft treaty. Governments had failed to agree on the proposed articles of the convention; no further negotiations were being suggested.

This failure reveals a weakness in all environmental treaty negotiations, whether new or existing ones: a consensus-driven process waters down action to the lowest common denominator. Only symptoms get addressed, not causes.

The result is much action without impact. Yet this ineffective method continues — a situation we characterize as ‘institutional paralysis’. What’s needed is to make international organizations capable of imagining and doing things differently. That requires more research on politics and governance, and that institutions make better use of the knowledge generated by that research.

To overcome this paralysis, at least in the environmental arena, we propose establishing a standing process, mandated by governments, to evaluate the state of global environmental treaties and related bodies. Its role would be to assess their fitness, both individually and collectively; identify what is and is not working and why; and provide actionable recommendations to the United Nations for reforming them.

Frozen institutions

International environmental institutions resemble deer frozen in headlights: acutely aware of an impending crisis, yet incapable of meaningful movement. The UN Framework Convention on Climate Change elevates urgency, but even implementing its current pledges fully would fall short of avoiding dangerous climate change[1]. The Convention on Biological Diversity sets ambitious conservation targets for protected areas, yet expanding the zones offers little defence against an intensifying local and global extinction of species caused by climate change[2].

These are not merely failures of ambition. They are design flaws. The implementation of existing policies, however robust, is insufficient to confront the scale and complexity of today’s polycrisis[3].

The result is institutional paralysis: a persistent incapacity of environmental institutions to act, adapt or achieve stated objectives despite sustained procedural activity. Like the Red Queen in Alice through the Looking Glass, paralysed institutions run in place just to stay put. Trapped in cycles of action without impact, movement persists but purpose dissolves.

The rational response to paralysis would be to acknowledge it and seek support. Yet most institutions resort to symbolic gestures to maintain the appearance of relevance. In July in Zimbabwe, after months of discussions, the Ramsar Convention on Wetlands agreed on a strategic plan that mostly reaffirmed 20-year-old decisions. The International Whaling Commission projects the illusion of progress, framing resolved issues such as widespread whaling as ongoing to justify the body’s continued activity[4].

Others choose to gamble. The London Convention and its 1996 Protocol on marine pollution have opened the door to marine geoengineering, including fertilization of the ocean with iron to stimulate phytoplankton growth and, increasingly, measures to enhance ocean alkalinity to increase carbon dioxide absorption[5]. Although such interventions would carry huge, clearly identified risks to the marine environment, they are still being considered as potential responses to climate change.

This reflects a broader dynamic: institutions are navigating trade-offs between various risks, in ways that endanger the very object that they seek to conserve, and undermine their own effectiveness in saving it.

Diagnosing institutional paralysis

Institutional paralysis arises when organizations are confronted with circumstances that exceed their original purpose, authority or capability. Take, for example, climate change. Today’s climate is in much worse shape than when the UN Framework Convention on Climate Change was adopted in 1992, arguably exceeding the body’s capacity to manage responses. Scientific knowledge about our planet is abundant and expanding and institutions are increasingly overloaded with information.

Groups of scientists have tried to issue ‘warnings to humanity’[6], but after initial brief attention, the warnings go unheeded. And concerns are spiralling faster than UN conventions can keep up with. For example, the study of climate change involves not only the natural sciences, but also, increasingly, social sciences — adding further angles, such as justice, to the discussions[7]. Yet deteriorating social issues are an outcome of the acceleration of global warming, which further stretches the climate agenda. Ineffectiveness then breeds more problems, while resources remain stagnant. As a result, public confidence in the climate convention erodes, reinforcing the sense of paralysis.

Already-overburdened institutions are also strained by accelerating anthropogenic pressures that fall outside their mandates. Today, much of this spillover stems from climate change[8]. For example, the World Heritage Convention lists 51 marine sites in its protected areas, including 29 coral reefs — which looks good on paper. However, it lacks the authority to address climate-driven threats to coral reefs, such as ocean warming and acidification. Although there is a great deal of science available to assist with evaluating impacts on heritage, geopolitical realities hold more sway on which sites are accepted and added to the list[9].

The complex reasons why institutions tread water are revealing. Limited capacity is one factor. Most international environmental institutions are chronically under-resourced[10]. Climate change is often more politically salient than biodiversity and tends to receive most of the funding[11]. For example, the Cali Fund for supporting the sharing of genetic resources, which was launched amid excitement in February at the resumed 16th biodiversity Conference of the Parties (COP16) in Rome, is struggling to reach its targets.

Ministers met in May to set directions for negotiations at COP30 in Brazil in November.Credit: Mads Claus Rasmussen/Ritzau Scanpix/AFP via Getty

Geopolitical dynamics further exacerbate the problem of fundraising for the conventions, and tend to favour climate over biodiversity, despite the nexus between them. The behaviours of major powers such as the United States, Russia, China and the European Union shape outcomes of multilateral negotiations. Sometimes, countries with economies that rely heavily on oil and natural gas act as deliberate blockers. Contestation between countries with different socio-economic and political circumstances also deepens institutional paralysis.

Even when they lack capacity and resources, most institutions continue to project an image of progress and preserve the appearance of hope rather than acknowledge failure and close. Like all entities composed of people, they are inherently political. Admitting the failure of institutions into which so much has been invested, without viable replacements, is seen as carrying too great a political (and psychological) risk.

Many institutions are also entrenched in the ecomodernist paradigm[12]; they often choose to pursue technological fixes, such as carbon capture or extensive monoculture afforestation, as the path of least resistance. Yet such fixes often lead to more problems.

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Nature 646, 1054-1056 (2025)

doi: https://doi.org/10.1038/d41586-025-03477-8

References

1. UNEP. Emissions Gap Report 2024 (UNEP, 2024).

2. Wauchope, H. S. et al. Nature 605, 103–107 (2022).

3. WEF. The Global Risks Report 2025 (World Economic Forum, 2025).

4. Bridgewater, P., Kim, R. E., Blasiak, R. & Sellheim, N. Nature 632, 500–502 (2024).

5. Vivian, C. & Del Savio, L. Int. J. Mar. Coast. Law 39, 519–527 (2024).

6. Ripple, W. J. et al. BioScience 67, 1026–1028 (2017).

7. Okereke, C. & Coventry, P. Wiley Interdiscip. Rev. Clim. Change 7, 834–851 (2016).

8. Lade, S. J. et al. Nature Sustain. 3, 119–128 (2020).

9. James, L., Hamman, E. & Hølleland, H. Int. J. Herit. Stud. 31, 918–934 (2025).

10. UNFCCC. Budget performance for the biennium 2024–2025 as at 31 December 2024 (United Nations Framework Convention on Climate Change, 2025).

11. Folkard-Tapp, H. et al. J. Appl. Ecol. 62, 2478–2486 (2025).

12. Hajer, M. A. & Oomen, J. Captured Futures (Oxford Univ. Press, 2025).

13. Adipudi, A. V. & Kim, R. E. Phil. Trans. R. Soc. A 382, 20230161 (2024).

14. Kim, R. E. & Bosselmann, K. Transnatl Environ. Law 2, 285–309 (2013).

15. Najam, A. Atmos. Environ. 34, 4047–4049 (2000).

16. Gray, J. Int. Stud. Q. 62, 1–13 (2018).

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Competing Interests

The authors declare no competing interests.

COMMENT 27 October 2025

How to understand exoplanets — space scientists call on lab-based chemists to help

Stronger links between researchers who work on Earth’s and other planets’ atmospheres, and between the experimental, modelling and observational communities, will help to interpret the astronomical data now at our fingertips.

By

Frank A. F. Winiberg,

Laura Kreidberg,

Julianne I. Moses,

Carl J. Percival &

…

Paul I. Palmer

A huge cloud of hydrogen evaporates from the warm Neptune-sized exoplanet GJ 436 b (artist’s impression). Credit: NASA, ESA, G. Bacon (STScI)

In the 30 years since exoplanets were first reported[1], telescopes have revealed thousands of them. One of the most surprising things about these objects, which orbit stars other than our Sun, has been their diversity: they range from scorching lava planets with atmospheres of vaporized rock to ocean worlds, and some might even look like Earth.

The James Webb Space Telescope (JWST), which was launched in 2021, has been revolutionizing our understanding of the atmospheres of these planets, showing details of clouds and haze that were previously unfathomable[2]. Scientists are starting to learn how winds and turbulence transport compounds on worlds beyond the Solar System[3], and are seeing chemical processes that are unlike those found on Earth[4].

Such insights promise to tell us more about how planets form — and perhaps the origins of life. But many of the findings, such as hints of unfamiliar compounds, are proving challenging to understand.

The space-science community needs help from scientists in other fields to find answers. Here, we call for closer collaborations between exoplanet researchers and those who work on Earth’s and other planetary atmospheres, as well as stronger links between experimentalists, modellers and astronomers.

Focus on photochemistry

To understand the molecules and processes at play in exoplanet atmospheres, it is crucial to consider photochemistry — chemical reactions driven by high-energy light from nearby stars. One of JWST’s first observations was of WASP-39 b, a hot and puffy gas giant the size of Jupiter, orbiting a Sun-like star some 215 parsecs (700 light years) away. The electromagnetic spectrum of its atmosphere revealed clear signatures of molecules that astronomers expected to find on such a planet — including carbon dioxide and water. But an unfamiliar opaque region at a wavelength near 4 micrometres puzzled them.

Because the data set was open source, hundreds of astronomers came together in 2022 in a frenzied collaboration, hosted on the messaging platform Slack, to identify the chemical species responsible for that opaque region. Scores of possibilities were tested before it became clear that it was sulfur dioxide. This was surprising, in that this compound should be rare in hydrogen-dominated gas giants[5]. Only one process was found to generate sulfur dioxide in high enough concentrations: photochemistry.

Similar mysteries will present themselves in the future. Modellers, working closely with theoretical and experimental chemists, must improve the photochemical models used to interpret these data accurately. Crucially, there is a lack of comprehensive data on how reaction rates and absorption cross-sections change with temperature and pressure for these exotic atmospheres, as well as a lack of data for molecules that are unfamiliar to our Earth-centric experience.

The 18-segment gold mirror of the James Webb Space Telescope. Credit: Desiree Stover/NASA

Make models go further

Detecting a particular molecule in an atmosphere is insufficient to deduce how it was formed. It could have been transported up from deep in the planet’s interior, or many compounds and chemical pathways could have led to its presence, possibly through reaction pathways different from those of the Solar System. But current models are anchored in the physics and chemistry of processes happening on planets local to us, from the gas giants to rocky terrestrials. The models might therefore be misrepresenting, or entirely missing, some chemical or physical processes at play in exoplanet atmospheres.

Physical factors that need to be better represented in models include the transport and exchange of molecules between a planet’s atmosphere and its surface or interior. The way in which a planet rotates — about its axis and around its host star — can affect its atmospheric chemistry. Some exoplanets, for example, orbit their host star in such a way that only one side is ever illuminated, resulting in a permanent, hot dayside and a permanent, cold nightside. This gives a very different distribution of gases to what is found on Earth. Better numerical models will improve the global framework that describes the physical movement and observable composition of these atmospheres.

Another cornerstone of models are photochemical parameters, and these also need a careful look. They represent how molecules respond to incoming stellar radiation and how they react with each other — through which reactions, and how fast. To make things more complicated, these parameters can change with pressure and temperature in the atmosphere. The atmospheric environments of currently observed exoplanets diverge from those of planets in the Solar System, and therefore models must simulate a vast range of temperatures, pressures and chemical conditions. This means measuring or calculating parameters that will represent more accurately what happens in exoplanet atmospheres. Reaction parameters are ideally calculated from reaction rates that have been measured in laboratory experiments under conditions relevant to the planet of interest, or from theoretical simulations based on quantum mechanical first principles. However, there are known reactions that lack parameters (and therefore models often use best guesses), and probably a slew of reactions that have yet to be discovered.

Here, we propose two pathways — experimental and theoretical — to expand the catalogue of reactions and their parameters specifically for exoplanets.

Expand interdisciplinary research

Most lab-based research into exoplanet atmospheres has focused on the light-absorption signatures of specific gases such as titanium oxide and phosphine[6], or on haze particles, suspended in gas, that are produced from a variety of compounds and can affect the propagation of stellar radiation. Current experiments are conducted in chambers that can replicate the range of observed exoplanet atmospheric temperatures, pressures and chemical compositions, using lamps or plasma to simulate stellar radiation, stellar flares or lightning[7].

These studies show the diversity and complexity of chemical products that can be generated from very simple exoplanet analogue atmospheres, and they provide optical parameters to help interpret observations. But photochemical models are used to predict the chemical make-up of atmospheres by leveraging entire reaction networks — sets of hundreds to thousands of individual reactions that connect different molecules to each other. The current lab studies have not provided the quantitative information needed to develop reaction networks for exoplanets.

Animation of the rotation of L 98-59 b, a terrestrial exoplanet orbiting a small, low-mass star.

To build up this capability, the exoplanet community must first identify the lab technologies required for the measurements they need, and there has already been significant progress[8],[9]. To study reaction parameters, for example, scientists must ideally examine specific molecules as they react, and the products that they form.

Equipment from labs that specialize in Earth-based atmospheric chemistry and combustion can be repurposed to some extent. Using combustion-science reactors and shock tubes, together with spectroscopic or mass-spectrometric detection techniques, would immediately widen the ranges of pressures and temperatures that can be studied. And rather than oxygen, which is abundant in Earth’s atmosphere, these techniques can be tuned to study species such as sulfur compounds and methane behave, which might be more abundant elsewhere. Similarly, expanding the user community of Earth-science labs to include exoplanet scientists would help to maximize the scientific return on this expensive infrastructure. In the longer term, however, exoplanet research needs dedicated facilities.

New techniques will also be required to measure exotic target molecules or particles specifically for exoplanets. The science will be shaped by, for example, the proposed Habitable Worlds Observatory, which will focus on ultraviolet and near-infrared radiation, or the mid-infrared Large Interferometer for Exoplanets. These complementary space-based telescope concepts will help to provide the improved observational data needed to test and constrain the chemical and physical models of exoplanet atmospheres. This has been the case in other fields, for example, with the ion-imaging technique created at Sandia National Laboratories’ Combustion Research Facility in Livermore, California[10]. The technique records the velocity of charged particles that result from a molecule being broken apart by light or in a collision, and was originally developed to advance our knowledge of combustion. Future observations could motivate similar technological developments that will provide precise, molecular-level parameters to inform reaction networks used in the photochemical models of exoplanet science. What’s more, the development of technologies for exoplanet research would ultimately benefit the Earth-science and combustion-science communities, too.

Use first principles

There are, however, reactions and molecules that are too difficult or dangerous to probe experimentally, even in lab settings. The extreme high and low pressures of exoplanet atmospheres, or reactions that are very slow, can be hard to study owing to the limitations of the instruments used. For slow reactions, minute changes in molecular concentration can be smaller than the random noise in the instrumentation; at low pressures, molecular densities might be below the detection limit.

In such cases, chemists can apply theoretical tools to calculate key parameters over a wide range of temperatures and pressures. With increasing computer power and the integration of artificial intelligence, such calculations are rapidly improving in terms of accuracy and efficiency[11]. Yet, reactions that involve large molecules or heavy atoms remain hard to predict accurately.

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Nature 646, 1057-1059 (2025)

doi: https://doi.org/10.1038/d41586-025-03478-7

References

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Competing Interests

Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA, 80NM0018D0004).

Work

- Cake to the rescue: how these PhD students are cooking up a sense of community

- Preserving Pompeii’s past, one step at a time

CAREER FEATURE 21 October 2025

Cake to the rescue: how these PhD students are cooking up a sense of community

One treat at a time, these graduate students are helping to raise funds for charitable organizations.

By

Sarah Wells

Find a new job

A bake sale at the University of British Columbia. Credit: Amirhossein Rashidi (AHR shots)

When the National Astronomy Meeting, organized by the UK Royal Astronomical Society, descended on Durham University in July, Martina Veresvarska saw the gathering as an opportunity not just to talk shop, but also to indulge her baking hobby. The annual conference, usually held in the British Isles, is one of the largest of its kind in Europe.

Veresvarska is a PhD candidate at the UK university’s Centre for Extragalactic Astronomy and a co-founder of the astronomy department’s charity bake-sale organization, Cakes for Good. At this year’s conference, Veresvarska and fellow astronomy PhD student Ciera Sargent ran their largest bake sale so far, helped by ten volunteer bakers from the department. During an organized coffee break at the meeting, the team catered for nearly 1,000 attendees and raised roughly £450 (a little over US$600) in just 30 minutes for the Great North Air Ambulance Service.

Cakes for Good is just one example of scientists coming together not only to foster community in their own departments, but also to raise money for charitable causes. For some, these efforts represent a small but powerful way to make a difference in their communities when their busy schedules or financial situations might otherwise prevent it.

Building community

Veresvarska arrived at Durham as a new PhD student in September 2021, amid the COVID-19 pandemic. Making connections with other students or faculty members proved difficult, even after lockdowns began to lift and the world started to open up again, she says.

To help rebuild a sense of community in the department, Veresvarska and her co-founders started Cakes for Good in April 2022. Instead of baking for the department and accepting money to cover the cost of ingredients, as had been done in the past, the Cakes for Good team decided to donate any money raised to charity.

“We thought that letting others [in the department] decide what charities this should go to might be a good way to get people involved and interested,” Veresvarska says.

Cakes for Good ran its first charity bake sale in May 2022 to support humanitarian aid in Ukraine. Since then, it has run monthly bake sales throughout the academic year to support global causes such as Medical Aid for Palestinians and support for schoolgirls in rural Kenya. It has also donated money from the sales to local charities supporting transgender youth, refugees and asylum seekers and environmental conservation. Each sale earns about £180–200, and Cakes for Good has raised roughly £4,555 for charity since 2022.

Cakes for Good bake sales organized by astronomers at Durham University, UK, have raised thousands of pounds for range of charities.Credit: Cakes for Good

Anything chocolate is usually a big seller, Veresvarska says. The only complaint has been that some of the baked goods are just too good, she jokes, adding: “I had someone tell me in January that it’s unfair that we hold the bake sale outside their door, because they’re on a diet.”

Working to develop community is also at the heart of a biannual bake sale run by the Chemistry Graduate Students Society (CGSS) at the University of Waterloo in Ontario, Canada, in collaboration with undergraduate chemistry clubs. Its first bake sale, in 2023, raised funds during Pride Month for a local organization, Spectrum, that offers a gathering place, as well as mental-health resources, for the LGBTQ+ community.

Of the society’s two bake sales each year, the first raises funds for Spectrum, the second for another local charity, such as the Women’s Crisis Services of Waterloo Region. The organizers say that, at each event, they raise roughly Can$500–800 (US$360–580). Cookies — chocolate chip, matcha or snickerdoodle, which are rolled with cinnamon and sugar before baking — are usually a big hit (see ‘University of Waterloo’s idiot-proof cookies for scientists’), along with loaf cakes such as banana bread, says Madison Donohoe, a PhD student and co-president of the CGSS.

At the University of British Columbia (UBC) in Vancouver, Canada, the CGSS runs a charity bake sale three times a year. For an entrance fee of just Can$2, staff, faculty members and students in the department can take as many baked goods as they like, usually from a selection of up to ten options.

Lucas Andrew, a chemistry PhD student and CGSS president at the UBC, says that queues often form about 20 minutes before the charity bakes appear. Recent treats have included matcha cheesecake and butter mochi, a cake made with coconut milk and glutinous rice flour that originated in Hawaii. At each event, the group raises Can$100–200 for local charities. Among these are the PHS Community Services Society, which provides housing and health-care services to vulnerable people in Vancouver, and RAVEN, which supports local Indigenous communities.

Chemistry graduates at the University of British Columbia in Canada run charity bake sales three times a year.Credit: Amirhossein Rashidi (AHR shots)

“As grad students, we don’t generally have a lot of disposable income,” Andrew says. “You may not be able to often donate on your own to a charity, but the fact that we can come together as this community and contribute to these local initiatives is really, really nice.”

Tips for success

If you’d like to start your own charity bake sale, these bakers have some words of wisdom to share.

Go where the people are. Attract customers by hosting bake sales at busy times and locations. For the University of Waterloo’s bake sale, this means between 11 a.m. and 2 p.m. in the atrium of the Science Teaching Complex, where a lot of hungry students will be on a break from class, says Emily Wedde, co-president of the CGSS.

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Nature 646, 1281-1282 (2025)

doi: https://doi.org/10.1038/d41586-025-02948-2

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

WHERE I WORK 27 October 2025

Preserving Pompeii’s past, one step at a time

Art conservator and restorer Ludovica Alesse works to safeguard Ancient Roman remains unearthed in the latest excavations to ensure the city’s story will never fade.

By

Agnese Abrusci

Ludovica Alesse is an art restorer at the Archaeological Park of Pompeii. Credit: Francesco Pistilli for Nature

“In this photo, I’m in the banquet hall of the House of Thiasus, a space that was frozen mid-renovation by the volcanic eruption of Vesuvius in ad 79.

The space shows the beauty of Roman design: the floor mosaics give way to columns, and frescoes of dancers, fish and laden tables still adorn the walls. Most arresting of all is the deep cinnabar-red pigment that’s used in some of the frescoes: it is rare, unstable and made with mercury sulfide. Too much light or moisture could be enough to darken the paint to black. My job is to ensure that doesn’t happen.

As an art conservator, I work alongside archaeologists, engineers, architects and fellow restorers to excavate sites here. Every dig is a race against time: objects that have rested underground for two millennia suddenly face sunlight, air and humidity.

When uncovering frescoes, the main challenge is controlling those factors, especially how the stone dries: it must do so slowly, and requires careful monitoring to prevent salts, carried by moisture migrating outwards, from crystallizing.

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Nature 646, 1284 (2025)

doi: https://doi.org/10.1038/d41586-025-03479-6

This interview has been edited for length and clarity.

Research

- Ultrasound-driven artificial muscles can grasp, flex and swim

- Building wet planets through high-pressure magma–hydrogen reactions

- Continuous operation of a coherent 3,000-qubit system

- A full-featured 2D flash chip enabled by system integration

- Video‐rate tunable colour electronic paper with human resolution

- Ultrasound-driven programmable artificial muscles

- Age and gender distortion in online media and large language models

- Polygenic and developmental profiles of autism differ by age at diagnosis

- Transitions in dynamical regime and neural mode during perceptual decisions

- A neuronal architecture underlying autonomic dysreflexia

- PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia

- Energy deficiency selects crowded live epithelial cells for extrusion

- Programmable antisense oligomers for phage functional genomics

- Peroxisomal metabolism of branched fatty acids regulates energy homeostasis

- Neuronal activity-dependent mechanisms of small cell lung cancer pathogenesis

- Functional synapses between neurons and small cell lung cancer

- Engineered prime editors with minimal genomic errors

- Spatial joint profiling of DNA methylome and transcriptome in tissues

- Structure and mechanism of the mitochondrial calcium transporter NCLX

NEWS AND VIEWS 29 October 2025

Ultrasound-driven artificial muscles can grasp, flex and swim

Soft materials filled with microbubbles flex when exposed to ultrasound — forming the basis of wireless devices that can be made to pass through organs such as the intestine.

By

Joseph Rufo &

Tony Jun Huang

Modern robots have extraordinary capabilities, yet they still cannot replicate the soft, graded subtlety of human touch. This limitation has helped to define the emerging field of soft robotics, which aims to replace the rigid materials and bulky actuators — the drive units that turn energy into motion — of conventional robots with compliant materials and soft actuators that move fluidly and adaptively, much like biological muscles. Such systems could transform prosthetics, enable minimally invasive, surgical robots to operate in the body and power haptic wearable devices that convey information through touch (for example, by vibrating or applying pressure). Writing in Nature, Shi et al.[1] report a considerable advance towards this goal: soft, ultrasound-powered robots that can be wirelessly activated to undergo coordinated shape changes (deformations). In doing so, the authors have created an artificial muscle, translating key design principles from biology to a soft-robotics platform.

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Nature 646, 1061-1062 (2025)

doi: https://doi.org/10.1038/d41586-025-03213-2

References

1. Shi, Z. et al. Nature 646, 1096–1104 (2025).

2. Pollard, T. D. & Cooper, J. A. Science 326, 1208–1212 (2009).

3. Gau, J. et al. Nature 622, 767–774 (2023).

4. Scott, S. H. Nature Rev. Neurosci. 5, 532–545 (2004).

5. Buchthal, F. & Schmalbruch, H. Physiol. Rev. 60, 90–142 (1980).

6. Winter, D. A. Biomechanics and Motor Control of Human Movement (John Wiley & Sons, 2009).

7. De Jong, N., Bouakaz, A. & Frinking, P. Echocardiography 19, 229–240 (2002).

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9. Bar-Zion, A. Nature Nanotechnol. 16, 1403–1412 (2021).

10. O’Brien Jr, W. D. Prog. Biophys. Mol. Biol. 93, 212–255 (2007).

11. Ebrahimi, N. et al. Adv. Funct. Mater. 31, 2005137 (2021).

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Competing Interests

The authors declare no competing interests.

Article Open access Published: 29 October 2025

Building wet planets through high-pressure magma–hydrogen reactions

H. W. Horn,

A. Vazan,

S. Chariton,

V. B. Prakapenka &

…

S.-H. Shim

Nature volume 646, pages 1069–1074 (2025)

Abstract

Close-in transiting sub-Neptunes are abundant in our Galaxy[1]. Planetary interior models based on their observed radius–mass relationship suggest that sub-Neptunes contain a discernible amount of either hydrogen (dry planets) or water (wet planets) blanketing a core composed of rocks and metal[2]. Water-rich sub-Neptunes have been believed to form farther from the star and then migrate inwards to their present orbits[3]. Here we report experimental evidence of reactions between warm, dense hydrogen fluid and silicate melt that release silicon from the magma to form alloys and hydrides at high pressures. We found that oxygen liberated from the silicate melt reacts with hydrogen, producing an appreciable amount of water up to a few tens of weight per cent, which is much greater than previously predicted based on low-pressure ideal gas extrapolation4,5. Consequently, these reactions can generate a spectrum of water contents in hydrogen-rich planets, with the potential to reach water-rich compositions for some sub-Neptunes, implying an evolutionary relationship between hydrogen-rich and water-rich planets. Therefore, detection of a large amount of water in exoplanet atmospheres may not be the optimal evidence for planet migration in the protoplanetary disk, calling into question the assumed link between composition and planet formation location.

Main

Water is an important building block of planets and a key ingredient of their potential habitability. The notion that water is incorporated into planetary bodies through condensation at sufficiently low temperatures in the outer protoplanetary disk seems to explain the architecture of the solar system well: the inner planets are mostly dry and rocky, sometimes with a small amount of water delivered from the outer solar system, whereas Uranus and Neptune—which are believed to be ice giants—exist outside the snow line[6]. However, some nebular hydrogen-rich gas was possibly captured and stored as H_2O in the deep interior of Earth after reacting with silicates7,8,9.

NASA’s Kepler mission has discovered many of close-in transiting exoplanets between Earth-like and Neptune-like in size (1–4 Earth radius, RE) and density[10]. Planets with radii of 2–4RE are typically modelled with rocky cores and envelopes dominated by either hydrogen or water, making their composition and formation ambiguous (where ‘core’ refers to the non-volatile interior, as commonly defined in exoplanet literature). A drop in relative frequency of occurrence among such planets was identified at 1.5–2RE (that is, the radius valley)[1], which seems to divide planets into two groups: rocky + metallic Earth-like density with tiny or no atmosphere at 1–1.5RE (that is, super-Earths), and rocky + metallic core with thick atmosphere at 2–4RE (that is, sub-Neptunes). Models involving massive gas loss can explain the radius valley well11,12, which seems to support hydrogen-dominated atmospheres, whereas other models explain the radius valley by two separate populations of dry and water-rich planets13,14. Moreover, some systems may host water-rich sub-Neptunes15,16,17. An important question for water-rich sub-Neptune models is how planets with a substantial water mass fraction can exist in close-in orbits if water mainly condensed and was incorporated into planets in the outer part of the systems during their formation[3].

In classical models of sub-Neptunes with hydrogen-rich envelopes, hydrogen is typically assumed not to react with silicates and metals in the core, although physical mixing with silicate magma has been considered18,19. At low pressures and high temperatures, hydrogen can reduce cations in silicates and oxides to metals[20], releasing oxygen from melts and producing water through hydrogen oxidation. Experimental data show that hydrogen can reduce Fe2+ to Fe^0 in molten silicates, generating water at low pressure7,21,22, and this trend extends to higher pressures[23]. However, in protoplanetary disks, most iron is metallic, with only limited Fe2+ and Fe3+ available. Recent experiments show that Mg remains oxidized in dense hydrogen, but small amounts of MgH_2 can form, releasing oxygen for water production[24]. Thermodynamic models based on low-pressure data suggest that hydrogen cannot reduce a substantial amount of Si4+ in silicates to produce water5,7. A modelling study indicates that enhanced Si partitioning into metal under reducing conditions could yield Earth-like water abundances (about 10−2 wt%) (ref. [9]). However, experiments show that SiO_2 can dissolve in hydrogen at 2–15 GPa and 1,400–1,700 K, with Si–O bonds breaking to form Si–H, suggesting that pressure may promote hydrogen–silicate reactions25,26.

If the mass–radius of sub-Neptunes is modelled with a rock + metal core with a hydrogen-dominated envelope, the pressure at the core-envelope boundary (CEB) can exceed a few GPa (ref. [27]). This blanketing envelope can also decrease heat loss such that silicates and metals of the core remain molten for billions of years (ref. [28]). Under these conditions, hydrogen is a dense fluid. Therefore, it is essential to understand the behaviour of the hydrogen–silicate system at the high pressures and temperatures (P–T) that exist at such a CEB. However, to our knowledge, these data do not exist. In this study, we combined pulsed laser heating[29] with the diamond-anvil cell to mitigate diamond embrittlement, which has been the main problem in melting silicate and metal in a pure hydrogen medium to acquire key data for understanding possible hydrogen–silicate reactions at the P–T conditions expected at the CEB in sub-Neptunes (Fig. 1, Extended Data Figs. 1 and 2, Extended Data Table 1 and Supplementary Discussion 1).

Fig. 1: Laser-heated diamond-anvil cell experiments on silicate melts in a hydrogen medium.

a, Schematic of the experimental setup. The spacers (small single grains of the same material from which the foil was made) separate the sample foil from diamond anvils, which allow hydrogen to surround the sample. During laser heating (the red area at the centre) of the silicate sample, hydrogen penetrated the grain boundaries of the sample foil and immediately above and below were heated by thermal conduction. b, An SEM image of two laser-heated areas of a fayalite sample. FAY-1 was heated at 6 GPa and 3,017 K and FAY-3 was heated at 11 GPa and 2,898 K. The spheres at the centre of the heated areas are Fe-rich alloys formed by hydrogen–silicate reaction. c, An SEM image of FAY-3 from an angle for a wider area. d, Raman-active OH vibration from H_2O ice after heating silica + Fe metal in a hydrogen medium at 14 GPa. A full two-dimensional Raman map of the heated area is shown in Extended Data Fig. 3c. Scale bars, 5 μm (b,c).

High-pressure magma–hydrogen reactions

A starting mixture of San Carlos olivine, (Mg0.9Fe0.1)2SiO_4, and iron metal was melted in a hydrogen medium at 8 GPa (Extended Data Table 1, run SCO-3). After heating, high-pressure X-ray diffraction (XRD) patterns showed no peaks from silicates, including olivine (Fig. 2a). Instead, diffraction lines of B2 Fe1−ySiy were observed, showing that Si4+ in silicate melt was reduced to Si^0 and alloyed with Fe metal. After heating, Mg remains in MgO periclase. The unit-cell volume of MgO after decompression to 1 bar indicates no detectable Fe2+ in the phase. Therefore, all Fe2+ originally in the silicate is also reduced to Fe^0 metal, implying the following reaction:

$${({{\rm{Mg}}}_{0.9}{{\rm{Fe}}}_{0.1})}_{2}{{\rm{SiO}}}_{4}+0.8{\rm{Fe}}+2.2{{\rm{H}}}_{2}\to {\rm{FeSi}}+1.8{\rm{MgO}}+2.2{{\rm{H}}}_{2}{\rm{O}}.$$

(1)

In our experiments, 20 wt% of Fe metal was mixed with olivine and a smaller amount of Fe metal was formed from the reduction of Fe2+ in olivine. If all the Fe metal is consumed to form FeSi (that is, y = 0.5 in Fe1−ySiy), a maximum of 86% of the Si4+ in olivine can be reduced (Supplementary Code 1). However, the measured unit-cell volume of the Fe–Si alloy indicates y = 0.27 in Fe1−ySiy (Supplementary Discussion 2–8). Therefore, only 32% of Si4+ in olivine was reduced to form an alloy with Fe metal. Furthermore, FeHx alloys in the face-centred cubic (fcc) and double hexagonal close-packed (dhcp) structures (x = 0.22−0.4) were found after heating, thus decreasing the amount of Fe metal available to form Fe–Si alloys. The observation of the complete disappearance of silicates after melting in dense hydrogen fluid requires an additional process to remove Si4+ from silicates.

Fig. 2: XRD patterns after heating silicate samples in a hydrogen medium.

a, In run SCO-3, olivine breaks down to Fe metal alloys and MgO. b, In run FAY-1, fayalite breaks down with Fe and Si present as metal alloys. A very small amount of body-centred cubic (bcc) Fe may exist. c, Heating to temperatures below melting (2,725–3,197 K), bridgmanite (bdm) and ferropericlase (fp) appear at 42 GPa (SCO-13a). d, When the bdm + fp were melted (3,352–3,924 K at 42 GPa), bdm mostly breaks down and B2 Fe1−ySiy appears (run SCO-13b). X-ray energy is 30 keV for b and 37 keV for a, c and d. The ticks below the diffraction patterns are the peak positions of the observed phases. fcc, face-centred cubic; dhcp, double hexagonal close-packed; Ol, San Carlos olivine starting material; Fa, fayalite starting material.

Previous experiments at similar pressures but lower temperatures25,26 showed the release of Si4+ from silicate and dissolution in dense fluid hydrogen as SiH_4:

$${{\rm{Mg}}}_{2}{{\rm{SiO}}}_{4}+4{{\rm{H}}}_{2}\to {{\rm{SiH}}}_{4}+2{\rm{MgO}}+2{{\rm{H}}}_{2}{\rm{O}}.$$

(2)

We detected the same Si–H bond vibration in melted areas in our experiment (Extended Data Fig. 4 and Supplementary Discussion 9). Both the reduction and hydride formation reactions (reactions 1 and 2, respectively) involve the release of O, which can then react with H to form H_2O. The OH vibration from H_2O was unambiguously detected in melted areas with Raman spectroscopy (Fig. 1d and Extended Data Fig. 3). The same behaviour was also observed at pressures up to 22 GPa in multiple experimental runs (Extended Data Fig. 5 and Supplementary Discussion 2 and 3).

When olivine + Fe was heated at 42 GPa to temperatures below the melting temperature[30], olivine transformed to bridgmanite (bdm) and ferropericlase (fp) (Fig. 2c). We further heated the synthesized bdm + fp above the melting temperature of bdm. Most of the bdm dissociates on melting in a hydrogen medium (Fig. 2d). The SiO_2 component reacts with H and Fe to form B2 Fe1−ySiy (Extended Data Fig. 3a,b). The volume measured for quenched fp suggests the complete reduction of Fe2+ to Fe^0 (Supplementary Discussion 4).

With fayalite (Fe_2SiO_4) and SiO_2 + Fe metal, we observed consistent behaviours, including formation of H_2O (Fig. 1d), SiH_4 (Extended Data Fig. 4), B2 Fe1−ySiy (Extended Data Fig. 6) and FeHx (Fig. 2b) (Extended Data Figs. 7–9; Supplementary Discussion 5). SiH_4 was detected in runs in which fayalite was melted in an Ar medium with 50% H_2 (Supplementary Discussion 9). In this case, the relative content of H_2O (formed from the reaction) compared with H_2 is much higher because of the decreased initial concentration of H and the increased H_2O production from the Fe2+ in the silicate, resulting in far more oxidizing conditions. In SiH_4, Si remains oxidized and, therefore, water production can continue as the water concentration rises through the formation of SiH_4 rather than Fe–Si metal (equation (2)).

Implications of reaction for planets

In a sub-Neptune planet with a rocky core and a substantial amount of hydrogen, the CEB is the most likely region to experience reactions between dense fluid hydrogen and silicate melt. The hydrogen–magma boundaries of these planets in the mass range of 3–10ME (where ME is mass of the Earth) with 2–20 wt% of H + He atmosphere are expected to reach P–T conditions similar to our experimental conditions for water production (Supplementary Discussion 10). According to our calculation in ref. [28], for a 5ME rocky planet with a 5 wt% H_2 envelope, the temperature of the core remains sufficiently high to maintain the molten state of silicates for billions of years (Extended Data Fig. 2), which could, in turn, make the continuation of the observed reactions possible for billions of years. Moreover, because pressure greatly enhances H_2 solubility in magma[18] and can result in miscibility between hydrogen fluid and oxide melts[24], hydrogen can reach greater depths below the CEB. Vigorous convection in the molten core could also transport hydrogen further to greater depths. For all of these reasons, the reactions discussed can continue into the deep interior.

We combined equations (1) and (2) with constraints from experimental setups and observations to estimate the quantities of reactants and reaction products for olivine reacting with hydrogen in our experiments (Supplementary Discussion 11). Considering uncertainties (Supplementary Code 1), we calculated that before reacting, the sample volume that would be heated consisted of 4.5–5.7 wt% H_2, about 76 wt% silicate and about 19 wt% Fe metal. It shows that 18.1(5) wt% H_2O was produced. This composition lies within the range considered for sub-Neptunes[2]. The complete disappearance of silicate (and therefore release of all of the Si) at lower pressures suggests silicate-undersaturated conditions for the hydrogen-to-silicate mass ratio of our olivine + Fe metal experiment, which was estimated to be 0.06–0.08. For a sub-Neptune with 4 wt% H_2 and an Earth-like core composition, the hydrogen-to-silicate mass ratio is 0.06. Therefore, sub-Neptunes with a few wt% H_2 could provide a silicate-undersaturated condition with respect to hydrogen. At >25 GPa, a trace amount of bdm (silicate) remained in the reacted area (Fig. 2d), which could result from the reverse reaction and, therefore, suggest a possible equilibrium. If this is the case, even under equilibrium conditions, the amount of Si in the silicate melt consumed by the reaction is very large, and the H_2O produced can amount to tens of weight per cent (Supplementary Code 1).

A theoretical study considering the formation of SiH_4 at hydrogen–magma boundaries[4] predicted X(H_2O) ≈ 0.2 (where X is a mole fraction in the envelope) of water production along with X(SiH_4) ≈ 0.04, and X(SiO) ≈ 0.08 in a 4ME sub-Neptune with 2.5 wt% H_2 at about 10 GPa and 5,000 K. Another theoretical study[5] considering the reduction of Si predicted X(H_2O) ≈ 10−4–10−3 of water production for a 4ME sub-Neptune with a CEB temperature of 4,500 K. At similar P–T conditions, however, our experimental observations indicate much more efficient endogenic water production of X(H_2O) = 0.38–0.56, which is 2–3,000 times higher. Although the modelling studies used extrapolation of assumed ideal gas behaviour of H_2 at much lower pressure, our experiments were conducted at the P–T conditions for dense fluid hydrogen, directly relevant to the conditions and states expected at the CEBs of sub-Neptunes. A comparison of our results with refs. 4,5 highlights the notable pressure effects on water production.

At a planetary scale, the extent of endogenic water production can be affected by the activity of H_2 and H_2O in the system. Under a hydrogen-dominated envelope lacking H_2O, strongly reducing conditions promote the reduction of Si as a key pathway for endogenic water production. As more water is produced (and therefore the activity of H_2 decreases), the reaction zone becomes less reducing. As the reaction progresses, if the water concentration reaches a point at which the conditions become sufficiently oxidizing, the reduction of Si could stop. In this case, SiH_4 would become a dominant Si-bearing product of the hydrogen–magma reaction because it does not require the reduction of Si4+. Because of this, SiH_4 formation can occur at more oxidizing conditions than Fe–Si alloys, as shown in our experiments (Supplementary Discussion 9), prolonging endogenic water production to more oxidizing conditions.

The redox state of the reaction zone can also be affected by the dynamics of the region. In the absence of mixing and transport, the fraction of water near the reaction zone will increase, which will slow down and ultimately stop the water-producing reaction when the water concentration exceeds a critical value (which is not well constrained, but we estimate to be X(H_2O) ≥ 38–56 mol%; Supplementary Discussion 11 and Supplementary Code 1). However, the interiors of sub-Neptunes by and large are convective, and therefore efficient mixing and transport can reduce the concentration of H_2O in the reaction zone. In Fig. 3a,b, we show an example of the efficiency of water-mixing over 100 million years in a 5ME rocky planet with a 10% gas (H, He) envelope. For CEB temperatures of ≥4,500 K, water is almost completely mixed through convection in the envelope. The mixing efficiency decreases as the planet cools. Because water production continues up to X(H_2O) > 0.3, we anticipate that convective mixing may stop before water production has ended. Consequently, the deep envelope might be more water-rich than the outer envelope in these planets at CEB temperatures lower than about 3,500 K.

Fig. 3: Water redistribution in a 5ME planet with 10 wt% envelope made of H_2 and He.

a, Water mass fraction (Z) for an initial state distribution (black) and after 100 Myr of evolution (red). Mixing efficiency decreases as the planet cools (left to right). b, The schematic shows the time progression of the mixing scale near the reaction zone.

Source data

Hydrides also form in the reactions discussed here and in other experiments24,25,26. Only a small amount of MgH_2 was reported to be produced, and its formation is limited to temperatures much higher than 3,000 K (Supplementary Discussion 13). Estimates from our experiments (Supplementary Discussion 11) show 6–23(5) mol% SiH_4 can be produced, and, therefore, it is important to consider. Although no data exist directly at the P–T conditions of our study, existing data indicate that the density of SiH_4 is 20–30% lower than that of H_2O at 300 K and relevant pressures (Supplementary Discussion 14). Therefore, the upward transport of H_2O and SiH_4 away from the reaction zone could be more efficient than predicted by our model.

It is essential to note that at the P–T conditions we consider here, H_2 and H_2O are completely miscible and form a single fluid[31], whereas our dynamic model includes only convective mixing of two separate fluids of H_2 and H_2O. Similar to H_2O and H_2, existing data also support the miscibility between SiH_4 and H_2 at the conditions of the CEB. The miscibility will decrease the density of the fluid compared with pure H_2O, and the hydrogen contained in the dense fluid layer will still react with the magma ocean (Supplementary Discussion 14).

The increasing solubility of water in silicates at higher pressures (that is, ingassing)32,33 can reduce its activity at the CEB and thus prolong the hydrogen–silicate reactions. Furthermore, the dissolved water decreases both the melting temperature and viscosity of the magma, prolonging the molten state of the interior and promoting efficient convective mixing of materials to the deeper undersaturated depths of the core. Therefore, strong ingassing of water (and hydrogen) to the core can also enable more water production.

The amount of endogenic water produced can vary depending on the properties and configurations of different planets (Supplementary Discussion 12). The Mg:Si ratio can directly affect the amount of water produced, as Si-involved reactions with hydrogen probably dominate endogenic water production compared with Mg and Fe (Supplementary Discussion 13). For example, for a planet with an Earth-like metal-to-silicate mass ratio but the Mg:Si ratio reduced from 2 to 0.5 (more Si), if all the silicate reacts with hydrogen (>3 wt% of H_2), the amount of water produced will increase from 16 wt% to 29 wt% (Supplementary Code 3). The Mg:Si ratio will also affect the viscosity of the magma and, therefore, affect the ingassing and mixing of volatile species[34]. Therefore, the large variation in Mg:Si ratios observed in exoplanetary systems[35] will result in variation in endogenic water production among planets with rocky interiors and hydrogen-dominated atmospheres.

The proposed atmospheric loss of the hydrogen-rich envelope of a sub-Neptune11,12 may occur concurrently with the endogenic production of water. The timing and duration of these two processes are important factors in the amount of water produced. Atmospheric loss also plays a part in the rate of heat loss, which, in turn, influences the duration for which the water-producing reactions can persist because they predominantly occur in molten silicates and metal alloys.

If a sub-Neptune undergoes massive gas loss while water is retained because of its high mean molecular mass22,36, it is feasible that the limited water production that occurred concurrently with gas loss would result in a rocky planet with surface water. For example, for a 5ME sub-Neptune, if only the outer 5% of the rocky core reacts with hydrogen, 2–4 wt% H_2O would be produced (Supplementary Code 2). At the same time, large amounts of water can also be stored in the core of sub-Neptunes because pressure increases the solubility of water in magma32,33. Therefore, even if surface or atmospheric water is lost during gas loss, the water stored in the core can contribute substantially to the formation of a secondary atmosphere and hydrosphere when the interior cools and solidifies, in which the solubility of water is much lower22,37,38. Moreover, because higher pressures markedly enhance water-producing reactions, super-Earths converted from hydrogen-rich sub-Neptunes may contain much more water than smaller rocky planets such as Earth.

Another key implication is that hydrogen-rich and water-rich sub-Neptunes do not necessarily form through different processes39,40. Instead, the reaction we report here suggests that these planet types may be fundamentally related: hydrogen-rich sub-Neptunes could be the precursors of water-rich sub-Neptunes and super-Earths. If an excess, unreacted H_2 atmosphere can be retained, sub-Neptunes with an H_2-rich atmosphere covering an H_2O-rich layer above the core (that is, hycean worlds) may be quite common[41].

In conventional planet-formation theory, water-rich planets are believed to form outside the snow line. The observation of water-rich sub-Neptunes15,16,17,42,43 in close orbits raises important questions about how they can form. Models have been proposed to explain close-in transiting water-rich sub-Neptunes, such as migration of water worlds after their formation outside the snowline3,40,44. Endogenic water production through hydrogen–magma reactions observed in our experiments provides a straightforward process to build water-rich sub-Neptunes inside the snow line: close-in orbiting sub-Neptunes can be H_2O-rich planets, as the hydrogen–silicate reaction can convert the H-dominated atmosphere to H_2O from the inside out, almost independent of their radial distance from the stars. Water production is feasible and expected in the large population of 3–10ME planets that were formed with substantial (>2%) gas envelopes. On planets with a hotter CEB, vigorous convection and sustained water production are expected to persist longer over their thermal evolution. Moreover, if mixing of water into the core is efficient, the production of water can persist even when convection in the water-polluted envelope becomes less efficient. This result has fundamental implications for planet formation and evolution theories. Based on experimental results, we suggest that planets formed from dry materials can become water-rich (tens of wt% water) planets without direct accretion of water ice. Consequently, detection of a large amount of water in exoplanet atmospheres might not be the optimal evidence for planet migration in the protoplanetary disk. Our new experimental findings challenge the assumed link between planet formation location and composition.

Methods

Sample materials and preparation

We studied three starting materials: (1) San Carlos olivine, (Mg0.9Fe0.1)2SiO_4, (2) natural fayalite, Fe_2SiO_4 (Smithsonian: R-3517-00: Rockport) and (3) silica (Alfa Aesar 99.995% purity). For laser coupling, silica was mixed with Fe metal powder (Aldrich 99.9%+ purity). Although San Carlos olivine contains enough Fe for laser coupling, to further improve the coupling, we mixed San Carlos olivine with Fe metal powder (20 wt%). Powders were ground and mixed in an alumina mortar, then cold-pressed into foils with a thickness of approximately 10 µm. Rhenium gaskets were indented by diamond anvils with 200 µm diameter culets and then drilled with 125 µm diameter holes. The rhenium gaskets were then coated with about 800 Å of gold to inhibit hydrogen diffusion into the gasket material. The gaskets were placed back onto the diamond culets, followed by the sample foils and spacers (of the same material as the sample foil), and gold and ruby grains for pressure calibration. The cells were then loaded with 1,300–1,500 bars of pure H_2 gas in a Sanchez GLS 1500 gas loading system and then compressed to the target pressures (measured using ruby fluorescence[45]) at 300 K before synchrotron laser-heating experiments. A fayalite sample was loaded with a 50:50 gas mixture of H_2 + Ar to examine the impact of a lower concentration of H_2.

Synchrotron experiments

In situ XRD images of the samples in the laser-heated diamond-anvil cell (LHDAC) were collected at the 13-IDD beamline of the GeoSoilEnviroConsortium for Advanced Radiation Sources sector at the Advanced Photon Source (APS) synchrotron facility. Near-infrared laser beams of wavelength 1,064 nm and monochromatic X-ray beams of wavelength 0.4133 Å or 0.3344 Å were coaxially aligned and focused on the sample in the LHDAC[46]. Standard continuous laser heating of H-loaded samples in the DAC results in an extremely mobile and diffusive hydrogen fluid that can penetrate diamond anvils, leading to diamond embrittlement and failure of the anvils[47]. To enable melting of silicates in a hydrogen medium, a pulsed laser-heating system was used to mitigate the amount of hydrogen diffusion into the anvils and the gasket material[29]. Each pulsed heating event consisted of 10^5 pulses at 10 kHz and 20 streak spectroradiometry measurements. The pulse width was 1 µs. Therefore, an event with 10^5 pulses gives a total heating time of 0.1 s. The X-ray spot size is 3 × 4 µm^2, and the laser-heating spot is a circle with a diameter of approximately 15 µm. The laser pulses were synchronized with the synchrotron X-ray detector such that diffraction measurements could take place only when the sample reached the highest temperature during heating. The small X-ray beam size and large laser-heating spot size help mitigate the effects of the radial thermal gradients in the high-temperature diffraction patterns. A previous study[46] showed that the laser-heating system provides a flat top laser beam intensity profile, which further reduces the radial thermal gradients in the hot spot. We also conducted two-dimensional XRD mapping after temperature quench of the samples in LHDAC at high pressures to monitor the possible effects of thermal gradients during laser heating. In the in situ high-temperature XRD patterns, it was difficult to distinguish broad diffuse scattering features from the melt because of the short duration of the measurements.

Although the heating duration is short, it was found that hydrogen is extremely reactive with molten silicates at high temperatures, overcoming the limited heating exposure time. Moreover, sample foils cold-compressed from powder permit hydrogen gas and fluid to percolate and surround individual grains (less than 1 µm in size). This creates a large surface area of the silicate exposed to hydrogen and, therefore, facilitates a fast reaction[48]. A recent study obtained consistent results between short pulse heating and continuous heating under hydrogen-rich conditions in diamond-anvil cells[49].

Thermal emission spectra from both sides of the sample in LHDAC were fitted to the grey-body equation to estimate the temperatures[46]. Temperatures are assigned from an average of temperatures recorded from 20 measurements (10 each upstream and downstream) over a heating event (Extended Data Table 1). Temperature uncertainty was calculated from the standard deviations (1σ) of these measurements. If the standard deviation is smaller than 100 K, from intrinsic uncertainties in the spectroradiometry method, we assigned 100 K for the uncertainty[50].

A Dectris Pilatus 1M CdTe detector was used to collect two-dimensional diffraction images. The diffraction images are integrated into one-dimensional diffraction patterns using the DIOPTAS package[51]. Diffraction patterns of CeO_2 and LaB_6 were measured for the correction of detector tilt and the determination of the sample-to-detector distance. Unit-cell parameter fitting was conducted by fitting the diffraction peaks with pseudo-Voigt profile functions in the PeakPo package[52]. Pressure was calculated from the unit-cell volume of a gold grain at the edge of the sample chamber using the equation of state of gold[53] before and after heating. A gold grain was placed away from the sample rather than mixing with it to prevent reactions or alloying with the sample material, and thus, pressure could not be measured at high temperatures during laser heating. A previous study[54] showed that the thermal pressure in a liquid Ar medium at temperatures of 1,000–4,000 K is 0.5–2.5 GPa in LHDAC. All of our experiments exceed the melting temperature of hydrogen. Therefore, thermal pressure should be similar to the above estimation in our experiments, and we assign a pressure uncertainty of 10% for high-temperature data points[23]. We note that this method does not introduce a severe error for this study, which is to explore the hydrogen–silicate reactions at high pressures.

Raman spectroscopy

Raman measurements were conducted using the Raman spectroscopy system at GeoSoilEnviroConsortium for Advanced Radiation Sources[55] for the identification of O–H and Si–H vibrations after heating. Raman scattering of the sample in a diamond-anvil cell was excited by a monochromatic 532-nm beam from a Coherent VERDI V2 laser. Raman spectra were collected over a wide range of wavenumbers (1,400–4,500 cm−1) using a Princeton Instruments Acton Series SP-2560 spectrograph and PIXIS100 detector.

Modelling for the dynamics of the interior

The thermal evolution model is calculated for the entire interior (from centre to surface) on one mass grid, with no distinction between core and envelope (Fig. 3). The model is one-dimensional and solves the standard interior structure and evolution equations, which allow for heat transport by convection, radiation and conduction, depending on local conditions over time (equations can be found in ref. [56]). The basic set of parameters for rocky planets with gas envelopes is adopted from ref. [28]. The redistribution of composition by convective mixing, in which the convection criterion is fulfilled, is calculated as in ref. [56].

The input equations of state are from ref. [57] for hydrogen and helium, and an improved version of ref. [58] for water and silica as representatives of volatiles and refractories, respectively. In our experiments, we found that multiples of components, such as SiH_4 and MgH_2, apart from H_2O, can exist in H_2 envelopes. In this system, semi-convection (double diffusive convection) might develop under certain conditions (Supplementary Discussion 14), which could limit the mixing efficiency and terminate water production earlier than our models suggest. Irradiation by the parent star is included as a temperature boundary for a plane-parallel grey atmosphere with an optical depth of 1. The radiative opacity is that of a grain-free solar metallicity atmosphere[59].

Data availability

The experimental data that support the findings of this study are available from the corresponding authors upon reasonable request. The XRD and Raman data are available at Zenodo (https://doi.org/10.5281/zenodo.15586691)[60]. An overview of the data is also included there.  Source data are provided with this paper.

Code availability

Supplementary codes used in this study can be found at Zenodo[61] (https://doi.org/10.5281/zenodo.15678598).

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Acknowledgements

We thank Y. Fei and G. Huss for their comments and questions, which improved the quality of this paper. S.-H.S. and H.W.H. were supported by the National Science Foundation (NSF) grants AST-2108129, AST-2406790 and EAR-1921298. Portions of this work were performed at GeoSoilEnviroCARS (The University of Chicago, Sector 13), Advanced Photon Source, Argonne National Laboratory. GeoSoilEnviroCARS is supported by the NSF, Earth Sciences (EAR-1634415). This research used resources of the Advanced Photon Source, a US DOE Office of Science User Facility operated for the DOE Office of Science by the Argonne National Laboratory under contract no. DE-AC02-06CH11357. We acknowledge the use of facilities in the Eyring Materials Center at ASU. Part of this work was performed under the auspices of the US DOE by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. The opinions are those of the authors and do not necessarily represent the opinions of LLNL, LLNS, DOE, NNSA or the US government. The US government, and the publisher, by accepting the article for publication, acknowledge that the US government retains a non-exclusive, paid-up, irrevocable, worldwide licence to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes (release authorization no. LLNL-JRNL-872168). A.V. acknowledges support by ISF grants 770/21 and 773/21.

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Author notes

1. H. W. Horn

Present address: Lawrence Livermore National Laboratory, Livermore, CA, USA

Authors and Affiliations

1. School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA

H. W. Horn & S.-H. Shim

2. Astrophysics Research Center, Department of Natural Sciences, Open University of Israel, Ra’anana, Israel

A. Vazan

3. Center for Advanced Radiation Sources, The University of Chicago, Chicago, IL, USA

S. Chariton & V. B. Prakapenka

Contributions

H.W.H. and S.-H.S. conceived the project; H.W.H., S.C., V.B.P. and S.-H.S. conducted the synchrotron experiments; A.V. conducted sub-Neptune modelling; H.W.H. and S.-H.S. analysed the results; and H.W.H., S.-H.S. and A.V. wrote the paper. All authors reviewed the paper.

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Correspondence to H. W. Horn or S.-H. Shim.

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Nature thanks Yingwei Fei, Gary Huss 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 Pressure-temperature conditions of the experimental runs.

a, San Carlos olivine, b, fayalite, and c, silica starting materials in this study (Extended Data Table 1). For fayalite, three experimental runs were conducted with a 50% Ar + 50% H_2 medium (open circles). Melting curves for the relevant phases are shown: FeHx (ref. [62]); Mg_2SiO_4 (ref. [63]); Fe_2SiO_4 (ref. [64]); SiO_2 (ref. [65]). All the experiments were conducted above the melting temperature of hydrogen[66]. While olivine and fayalite couple with laser beams sufficiently well for melting, lack of Fe in silica makes it difficult to heat above melting as shown in c.

Extended Data Fig. 2 Thermal evolution of a rocky 5 M[E] sub-Neptune planet with an H + He (5 wt%) envelope.

Shown is the temperature (color) as a function of pressure (y-axis) in the interior from the center up to 1 bar pressure, as a function of time (x-axis). The range of pressure (dotted black) and temperature (solid black) in which water production is expected according to the experiments is shown. The red dashed line signifies the mantle-envelope interface. Model is based on ref. [28].

Extended Data Fig. 3 Analysis of the samples after heating in a hydrogen medium in LHDAC.

Two dimensional maps of the XRD intensities of a, silicates and b, B2 Fe1→ySiy after heating the sample in SCO-11. The anti-correlation between the two at the heating spot center (dashed circle) shows that when melted silicates (bdm) break down, Si is reduced to form Fe1→ySiy. c, Raman-active OH vibration from H_2O ice after heating silica + Fe metal. The Raman spectra were measured for a 20 × 20 µm^2 heated area. The distance between the spots where the spectra were collected is 5 µm. The spectra were collected after laser heating at 14 GPa.

Extended Data Fig. 4 Raman spectra from run FAY-8 where starting fayalite was heated to 1550 K at 14 GPa.

The measurements were conducted after temperature quench to 300 K and decompression to 2.5 GPa. The Si–H vibrational mode was detected at the melted area (a). No such feature was observed outside the melted area (blue, b). In b, we also include spectrum measured at the melted spot (red) for the same exposure time. The same mode has been documented for silica melted in hydrogen at 2–3 GPa (ref. [26]).

Extended Data Fig. 5 X-ray diffraction pattern measured after heating a starting mixture of San Carlos olivine and Fe metal to 2534 K at 21 GPa (run SCO-7).

The ticks below the diffraction pattern are the peak positions of the observed phases. The names of the phases in the legend is ordered same as the ticks from top to bottom. X-ray energy is 37 keV.

Extended Data Fig. 6 X-ray diffraction pattern measured after heating a silica starting material in a hydrogen medium to 2899 K (below the melting temperature) at 14 GPa (run SIL-2).

The ticks below the diffraction pattern are the peak positions of the observed phases. The names of the phases in the legend is ordered same as the ticks from top to bottom. X-ray energy is 37 keV.

Extended Data Fig. 7 Atomic volume (left) and volume increase by H incorporation, V (right), of FeH[x] observed in different runs (colored symbols).

The volumes were measured after heating at 300 K. For comparison, the figures also include data points from previous studies67,68,69,70,71,72,73. The equations of state for fcc Fe (H/Fe = 0) and fcc FeH (H/Fe = 1) are from ref. [74] and ref. [69], respectively. The data points measured for the solid phases quenched from (Fe,Ni)-H liquid are shown as black symbols[75]. The concentration curves (x) shown in the right figure are from the density functional theory calculation in ref. [75].

Extended Data Fig. 8 X-ray diffraction pattern measured after heating a fayalite starting material in a hydrogen medium to a, 2898 K at 11 GPa (run FAY-3) and b, 3715 K at 21 GPa (run FAY-5).

a was measured at 6 µm away from the heating center. Two separate fcc phases with different volumes (and therefore different levels of hydrogenation) are observed, likely because of different rate of temperature decrease during quenching at different spots and loss of hydrogen during quench of FeHx melt[75]. The ticks below the diffraction patterns are the peak positions of the observed phases. The names of the phases in the legend is ordered same as the ticks from top to bottom. X-ray energy is a, 30 keV and b, 37 keV.

Extended Data Fig. 9 X-ray diffraction pattern measured after the decompression of a fayalite starting material heated in a hydrogen medium to 3017 K at 6 GPa (run FAY-1).

The diffraction pattern was measured at 1 bar and 300 K. The ticks below the diffraction pattern are the peak positions of the observed phases. The names of the phases in the legend is ordered same as the ticks from top to bottom. “*” indicates a feature from detector defects. X-ray energy is 37 keV.

Extended Data Table 1 Experimental runs in this study

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Horn, H.W., Vazan, A., Chariton, S. et al. Building wet planets through high-pressure magma–hydrogen reactions. Nature 646, 1069–1074 (2025). https://doi.org/10.1038/s41586-025-09630-7

- Received: 05 June 2023

- Accepted: 15 September 2025

- Published: 29 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09630-7

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To make water, exoplanets might just need some pressure

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Article Open access Published: 15 September 2025

Continuous operation of a coherent 3,000-qubit system

Neng-Chun Chiu,

Elias C. Trapp,

Jinen Guo,

Mohamed H. Abobeih,

Luke M. Stewart,

Simon Hollerith,

Pavel L. Stroganov,

Marcin Kalinowski,

Alexandra A. Geim,

Simon J. Evered,

Sophie H. Li,

Xingjian Lyu,

Lisa M. Peters,

Dolev Bluvstein,

Tout T. Wang,

Markus Greiner,

Vladan Vuletić &

…

Mikhail D. Lukin

Nature volume 646, pages 1075–1080 (2025)

Abstract

Neutral atoms are a promising platform for quantum science, enabling advances in areas ranging from quantum simulations1,2,3 and computation4,5,6,7,8,9,10 to metrology, atomic clocks11,12,13 and quantum networking14,15,16. Although atom losses typically limit these systems to a pulsed mode, continuous operation17,18,19,20,21,22 could substantially enhance cycle rates, remove bottlenecks in metrology[23] and enable deep-circuit quantum evolution through quantum error correction24,25. Here we demonstrate an experimental architecture for high-rate reloading and continuous operation of a large-scale atom-array system while realizing coherent storage and manipulation of quantum information. Our approach utilizes a series of two optical lattice conveyor belts to transport atom reservoirs into the science region, where atoms are repeatedly extracted into optical tweezers without affecting the coherence of qubits stored nearby. Using a reloading rate of 300,000 atoms in tweezers per second, we create over 30,000 initialized qubits per second, which we leverage to assemble and maintain an array of over 3,000 atoms for more than 2 hours. Furthermore, we demonstrate persistent refilling of the array with atomic qubits in either a spin-polarized or a coherent superposition state while preserving the quantum state of stored qubits. Our results pave the way for the realization of large-scale continuously operated atomic clocks, sensors and fault-tolerant quantum computers.

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Main

Neutral-atom systems have recently emerged as a leading platform for quantum technologies, enabling advances in quantum simulations1,2, quantum computing4,5,6,7,8,9, atomic clocks and metrology11,12,13, and quantum networking14,15,16. However, an outstanding challenge associated with these systems involves atom loss, originating from errors in entangling operations[4], state readout9,24 and finite trap lifetime[26]. Atom losses necessitate pulsed operation, which limits the performance of these quantum systems, including the circuit depth of quantum computation24,25, the accuracy of atomic clocks[23] and the rate of entanglement generation in quantum networking protocols[27]. For instance, in quantum computing, scaling up to large, practical algorithms requires encoding information in logical qubits, protected by repeated quantum error-correction cycles28,29. Although these cycles can suppress error rates far below those of individual physical qubits28,29, useful quantum circuits may require billions of operations, which eventually lead to loss of atomic qubits that need to be replaced9,24. Similarly, atomic clock applications would benefit from coherent, continuous operation to improve duty cycles and reduce dead time, thereby enhancing stability and precision by mitigating Dick noise, one of the primary limitations of state-of-the-art optical clocks[23]. Addressing these challenges requires a reliable scheme for fast, continuous reloading of atomic qubits that not only outpaces the rate of errors owing to decoherence and loss but also is consistent with simultaneous coherent qubit storage and manipulation.

Recent experiments have enabled continuous atomic[30] and optical clocks31,32,33, as well as the realization of continuous Bose–Einstein condensation[34]. Although past efforts have primarily focused on controlling atomic ensembles, most recently these techniques have been extended to explore continuous operation with individual atom control17,18,19,20. If expanded to high reloading rates within a coherence-preserving setting21,22, these pioneering experiments highlight the exciting possibility of fully continuous operation of large-scale atomic systems.

Here we introduce a tweezer array architecture that enables such coherent continuous operation at large scale with reloading rates of up to 30,000 qubits per second, nearly 2 orders of magnitude above the current state of the art18,19. Our architecture is based on two serial optical lattice conveyor belts that transport a cloud of laser-cooled 87Rb atoms into the field of view of our microscope objective. From this reservoir cloud, atoms are loaded into optical tweezers ‘in the dark’ (that is, without laser cooling) and then repeatedly extracted into a ‘preparation zone’, where they are laser-cooled, imaged, rearranged and initialized into their qubit states. Once initialized, atomic qubits are then transported and iteratively assembled into a large array in the ‘storage zone’, where dynamical decoupling is applied to maintain qubit coherence. Qubits in the storage zone are spatially protected against scattered cooling light by avoiding direct line of sight to the magneto-optical trap (MOT), and spectrally protected by light-shifting the cooling transition out of resonance (‘shielding’)[35]. We demonstrate in situ atom replenishment and maintenance of more than 3,000 storage array atoms for more than 2 hours, well beyond the trap lifetime of about 60 seconds. Furthermore, we sustain the storage zone with either spin-polarized qubits (Z basis) or qubits in the equal superposition state (X basis) for, in principle, unlimited duration.

High-rate reloading from a lattice reservoir

Our dual-lattice architecture is designed for uninterrupted high-rate qubit reloading that enables repetitive usage and periodic replacement of an atomic reservoir (Fig. 1a). The experiment starts by loading around 4 million 87Rb atoms from a MOT into an optical lattice conveyor belt36,37,38,39. Then, the atom cloud is transported through a differential pumping tube to the separate science chamber, where it is transferred to a second lattice conveyor belt and delivered into the microscope field of view to serve as an atomic reservoir (Extended Data Figs. 1 and 2). Using this two-stage procedure, a fresh reservoir of 2.5 million 120-μK cold atoms arrives in the science region every 150 ms. From the lattice reservoir, atoms are repeatedly loaded into a dynamic optical tweezer array of 120 × 12 sites, generated by a pair of crossed acousto-optic deflectors (AODs; Extended Data Fig. 3). To load atoms, we switch on the AOD tweezers overlapped with the lattice reservoir and immediately transport captured atoms into the preparation-zone region placed 220 μm above. This procedure takes less than 2.5 ms and, importantly, allows for multiple extraction cycles from a single reservoir. Following the extraction, atoms in AOD tweezers are transferred to a static tweezer array generated by a spatial light modulator (SLM).

Fig. 1: Atom-array architecture for continuous operation.

a, A cloud of laser-cooled atoms is transported over 0.5 m from a separate MOT region into the science region via two optical lattice conveyor belts crossed at an angle. In the science region, the optical lattice serves as an atomic reservoir, from which a two-dimensional array of optical tweezers repeatedly extracts atoms into the ‘preparation zone’. Here, atoms are laser-cooled, rearranged into a defect-free array and their qubit state initialized, then transferred into a large-scale storage tweezer array (‘storage zone’). Our dual-lattice scheme avoids direct line of sight between the tweezer arrays and the MOT location, and enables fully concurrent preparation and replenishment of the atomic reservoir. Inset: relevant atomic levels of 87Rb, where F denotes the hyperfine level and mF the magnetic sublevel. During qubit preparation, storage qubits are protected from near-resonant photon scattering with the 5S1/2 → 5P3/2 transition by light-shifting the excited state (‘shielding’). Single-qubit gates are implemented via optical Raman transitions that drive clock states \(| 0\rangle \) and \(| 1\rangle \) (Methods). b, Cumulative number of atoms obtained by N-repeated tweezer extractions from a single lattice reservoir (see top-left schematic), where we observe a decline in tweezer filling fraction after about 70 repeated extractions owing to reservoir depletion (see also Extended Data Fig. 4). For reference, the grey line indicates 50% array filling. Inset: histogram of tweezer filling fractions for the first 30 extractions from the reservoir. Notably, no laser cooling is applied during the tweezer loading process. c, Cumulative number of atoms and qubits obtained by tweezer extraction from repeatedly replaced lattice reservoirs. The grey markers indicate an atom flux of about 300,000 atoms per second after light-assisted collisions, where the brief interruptions originate from the second transport stage of reservoir replacement during which no reservoir is present. Performing the qubit preparation sequence after each extraction, we achieve a continuous qubit flux of 15,000 qubits per second with rearrangement (R; orange) and 30,000 qubits per second without rearrangement (green). Error bars represent the standard error of the mean across 10 repetitions.

Figure 1b shows the results of repeated tweezer extraction from a single lattice reservoir. Here, we extract atoms for multiple cycles and only image and count single atoms after the final extraction cycle, and quote the cumulative number by summing the atom counts over all N cycles. We find that, initially, array filling fractions of >50% are comparable to conventional tweezer loading from an MOT[40], but gradually decline as the reservoir is depleted (see also Extended Data Fig. 4). A key aspect of our dual-lattice design is the ability to extract atoms from one reservoir while preparing and delivering a fresh reservoir to the science chamber. By replacing reservoirs as they are depleted, this approach overcomes capacity limits of any single reservoir. In Fig. 1c, we demonstrate this by repeatedly extracting atoms into the preparation zone as before, now replacing the reservoir every 60 tweezer loading cycles. As a result, we achieve a flux of approximately 300,000 atoms in tweezers per second, corresponding to the maximum rate at which reservoir atoms can be extracted.

Notably, in contrast to the conventional approach to tweezer loading[40], no laser cooling is applied during the extraction process. We attribute the ability to load optical tweezers ‘in the dark’ to a combination of stochastic overlap with atoms in the reservoir, and atomic collisions similar to the notion of a dimple trap[41] (Methods). Whereas previous experiments17,18,19 have relied on dissipative laser cooling or tweezer-lattice intensity ramps when loading fresh atoms from the reservoir, our scattering-free method helps preserve coherence of nearby storage qubits and avoiding lattice ramp-down enables repetitive usage of the reservoir.

To prepare atomic qubits, we perform an initialization procedure after every extraction from the reservoir (Extended Data Fig. 5). Each step of this procedure relies on two counter-propagating laser beams local to the preparation zone and aligned coaxially with an externally applied static magnetic field[24] (Methods). First, an explicit parity-projection pulse via finite-field polarization gradient cooling on a red-detuned F = 2 → F′ = 3 transition prevents multiply occupied optical tweezers. Here, F denotes the hyperfine level of the atomic ground state and F′ the hyperfine level of the 5P3/2 excited state. We continue laser cooling via polarization gradient cooling during AOD-to-SLM handover, then apply a resonant push-out pulse to eliminate atoms in out-of-plane traps[10]. This is followed by high-contrast, inherently background-free imaging (Methods). Afterwards, we arrange atoms into a defect-free array while further laser cooling via electromagnetically induced transparency with light blue-detuned from the F = 2 → F′ = 2 transition[42]. Finally, atoms are initialized to the qubit state \(| 0\rangle \) by optical pumping on the F = 1 → F′ = 0 transition, resulting in a state preparation and measurement fidelity of approximately 98% within 20 μs. Under optimal conditions and without atom sorting, the qubit preparation sequence takes 20 ms (Methods).

Figure 1c shows the results of repeatedly extracting atoms and performing the qubit preparation sequence as described above, while the lattice reservoir is replaced in parallel every few tweezer extraction cycles. As a result, we achieve a qubit flux of over 30,000 qubits per second when choosing to not rearrange atoms. With atom sorting, the qubit preparation time approximately doubles and we obtain up to 15,000 qubits per second, rearranged into defect-free batches of 600 qubits. In all cases, the qubit preparation time exceeds the time required for the second transport stage of reservoir replacement; as such, there is always a reservoir present for tweezer extraction and the qubit flux is uninterrupted.

Assembly and maintenance of a large atom array

After the preparation sequence, the rearranged array is transported to the storage zone, which consists of 3,240 (90 × 36) SLM-generated optical tweezers with an average trap depth of 270 μK. The storage tweezer array features alternating regularly spaced columns for lossless atom transport in between (Methods), and is positioned with sufficient distance to the preparation zone and the lattice reservoir to limit crosstalk between zones (Fig. 2a).

Fig. 2: Iterative assembly and continuous maintenance of a large-scale atomic array.

a, Atom fluorescence image outlining the zone architecture consisting of a lattice reservoir, a 1,440-site preparation zone, and a 3,240-site storage zone. (Averaged) images of each zone are exposed separately and combined with different weights for visualization purposes. Scale bar, 100 μm. b, Single-shot fluorescence image of 3,217 atoms in the 3,240-site storage array (99.3% filling). c, Iterative construction and continuous maintenance of a large-scale atomic array. Initial assembly occurs in 0.5 seconds via 6 loading iterations (inset). Afterwards, one of six segments (‘subarrays’) is ejected from the storage array and refilled with a fresh set of atoms every 80 ms (see also Extended Data Fig. 6 and Supplementary Video 1). Here we show cyclic subarray replenishment and continuous maintenance of a 3,000+ atomic array for over 2 hours of operation, far beyond the tweezer-limited lifetime of about 60 seconds (grey). At the final data point, t = 2.3 h, over 50 million individually imaged and rearranged atoms have been cycled through the storage array. Error bars represent the standard error of the mean across 10 repetitions.

We assemble the storage array in six iterations, each time transferring atoms into one of six segments (‘subarrays’) interspersed throughout the storage array (Extended Data Fig. 6). Preparation and loading of each subarray, including atom transport to the storage zone, takes roughly 80 ms (mostly limited by rearrangement time and image data transfer). Assembly of the entire storage array is completed in about 500 ms with an averaged loading of 3,193 atoms (98.5% filling fraction) over 300 trials. Figure 2b shows a single-shot fluorescence image of 3,217 atoms loaded into the array.

In Fig. 2c, we demonstrate the ability to maintain over 3,000 atoms in the storage array for over 2 hours of continuous operation. After initial storage array assembly, we sequentially eject and refill the longest-stored subarray with a concurrently prepared set of fresh atoms from the preparation zone (Supplementary Video 1). In parallel to atom preparation and subsequent replenishment, we replace the lattice reservoir every other tweezer extraction cycle without affecting the storage-zone array. Using these techniques, we replenish atoms on much faster timescales than their tweezer-limited lifetime, and therefore enable operation that is, in principle, indefinite.

Coherence during continuous operation

The ability to reload qubits while preserving the coherence of existing qubits is essential for applications in deep-circuit quantum computation and high-bandwidth metrology24,34,43. To address this challenge, in Fig. 3a we first investigate the impact of a simultaneously operating MOT on storage qubit coherence. We observe a coherence time of T_2 = 1.15(3) s when applying dynamical decoupling in the presence of the distant MOT, which shows minor modification compared with a reference measurement without the MOT (T_2 = 1.34(4) s). In Fig. 3b, we find a similar result when probing the storage qubit depolarization time T_1 with and without the MOT. Therefore, by preventing a direct line of sight between MOT and qubits, our angled dual-lattice transport scheme successfully disentangles the scattering-intense initial capture of an atomic gas from parallel quantum operations32,44.

Fig. 3: Benchmarking concurrent qubit preparation.

a, Coherence contrast under various conditions when applying N repetitions of an XY16 dynamical decoupling sequence with π-pulse spacing 2τ ≈ 1.6 ms to storage qubits, where the reference measurement yields T_2 = 1.34(4) s (grey). Operating the distant MOT in parallel to dynamical decoupling, we observe a minimal effect on coherence (green) compared with the reference, but a strong effect when additionally imaging in the preparation zone (blue). By applying qubit shielding, we restore coherence almost fully (orange, T_2 = 1.09(3) s). b, A similar comparison probing depolarization of qubits initialized in \(| 1\rangle \) with the reference measurement T_1 = 12.6(1) s (grey), consistent with Raman scattering calculations owing to the tweezer light[51]. Although operating the MOT simultaneously has a negligible effect on storage qubits (green), additionally imaging in the preparation zone results in rapid qubit depolarization (blue). Similar to before, this can be mitigated by shielding storage qubits from near-resonant light (orange, T_1 = 3.43(3) s), mainly limited by off-resonant Raman scattering from the lattice light to which the shielding is ineffective (brown). A similar investigation for \(| 0\rangle \) state depolarization along with all measured T_1 and T_2 times is presented in Extended Data Fig. 8. For a and b, the difference of qubit populations measured in \(| 0\rangle \) and \(| 1\rangle \) provides the contrast (Methods). c, Shielding light spectroscopy on storage qubits. First, we image the storage array while applying low-power shielding light at variable wavelength to resolve the 4D5/2 resonance by suppression of imaging signal (top). In a fine scan, we optimize for storage qubit coherence under dynamical decoupling while imaging in the preparation zone by maximizing the readout probability in \(| 0\rangle \) (bottom). Error bars represent the standard error of the mean across 10 repetitions.

In addition to the MOT, the coherence of existing qubits can be affected by scattered light and magnetic-field changes during mid-circuit qubit preparation. To mitigate this, our beam architecture operates under constant finite magnetic field[24] and is localized to the preparation zone. However, we initially observe in Fig. 3a that storage qubit coherence is strongly affected by beam crosstalk during the preparation-zone imaging procedure. To suppress this effect, we protect qubits from near-resonant scattering by light-shifting the excited state[35] of storage-zone qubits as shown in Fig. 3c (see also Extended Data Fig. 7), and find that the coherence time can be nearly completely restored (T_2 = 1.09(3) s). In addition, we probe storage qubit depolarization under the same conditions in Fig. 3b, resulting in a similar conclusion. Here, however, one observes an increased T_1 decay compared with a reference measurement despite shielding, which is largely dominated by off-resonant scattering from the lattice reservoir light (Extended Data Fig. 8). This increase does not measurably affect our T_2, but can be further mitigated by, for example, greater reservoir distance from the storage array, smaller lattice reservoir waist or larger lattice detuning.

Building on these results, we now assess atom-loss replenishment in simple quantum circuits by repeatedly replacing storage-zone qubits while maintaining coherence. In Fig. 4b, we first show high-rate reloading and continuous maintenance of a large array of spin-polarized storage qubits. Similar to Fig. 2c, we now repeatedly prepare freshly initialized qubit subarrays in the preparation zone, then eject and refill the oldest subarray in the storage zone as shown schematically in Fig. 4a (see also Extended Data Fig. 9). Sequentially replenishing qubits allows us to sustain a high degree of storage array polarization for, in principle, unbounded duration; here, we show maintenance of over 3,000 qubits for 2 minutes.

Fig. 4: Continuous operation while maintaining storage qubit coherence.

a, Time sequence visualizing our reloading protocol (see also Extended Data Fig. 9 and Supplementary Video 1). Following the initial storage array assembly, the longest-stored subarray is ejected and refilled with a preloaded set of qubits from the preparation zone every 80 ms, whereas storage-zone shielding is applied throughout. For c and d, storage qubits are placed in the equal superposition state and undergo an XY16-64 decoupling sequence during each reloading cycle. b, Sequentially replenishing storage-zone qubits, we maintain a high degree of storage array polarization (red) for, in principle, unbounded duration. For reference, we provide a T_1 measurement without qubit replenishment (grey). c, Similar to b, now additionally applying an Xπ/2 − (XY16-64) − X−π/2 dynamical decoupling sequence during each subarray replenishment. We probe coherence of each subarray at various times during the replenishment cycle by reading out qubits in state \(| 0\rangle \) (blue) or \(| 1\rangle \) (red) as detailed in Methods. Individual subarrays (colour shading) are unaffected by adjacent qubit reloading, and their dephasing is offset in time due to the cyclic subarray reloading protocol. The exponential sawtooth overlays are guides to the eye. For reference, we provide the T_2 measurement of a single subarray under the same cyclic decoupling sequence without qubit replenishment (grey). d, After multiple rounds of reloading under dynamical decoupling, we apply a final dynamical decoupling sequence and vary the phase of the last π/2 pulse to read out in different qubit bases. Complementary to c, the observed coherence contrast varies for each subarray (colour shading) owing to the time offset in subarray replenishment. Error bars represent the standard error of the mean across 10 repetitions.

Finally, in Fig. 4c,d, we show the ability to reload and sustain a large array of atomic qubits in a coherent superposition state (see also Extended Data Fig. 10). While shielding and replenishing qubits as in Fig. 4b, we additionally rotate storage qubits into state \(| \,+\,\rangle \) and sustain coherence by applying a dynamical decoupling sequence during each subarray reloading cycle. Shortly before replenishing a qubit subarray with fresh qubits from the preparation zone, we map coherence into population by rotating all storage qubits into state \(| 0\rangle \), eject and replace the oldest qubits with newly spin-polarized ones, then rotate back into state \(| \,+\,\rangle \) as a new reloading cycle starts. This enables us to keep qubits in a superposition state at about 90% duty cycle, with the coherence of individual subarrays unaffected by concurrent reloading cycles.

Discussion and outlook

Our experiments demonstrate an atom-array architecture that enables continuous operation with reloading rates of up to 30,000 initialized qubits per second while preserving coherence across a rearranged large-scale qubit array. The results can be extended along several directions. First, the qubit preparation time can be substantially shortened through optimized readout and the use of field-programmable-gate-array-based and/or artificial-intelligence-optimized rearrangement protocols45,46. Second, larger preparation-zone arrays can be engineered by fully utilizing the system’s optical field of view. We estimate that these technical improvements would lead to a more than fivefold increase in qubit reloading rate, as this rate is directly proportional to qubit preparation time and preparation-zone size. In addition, although the present experiments demonstrate continuous operation for over 2 hours, achieving much longer operation would benefit from active stabilization of the SLM–AOD tweezer overlap or automated beam alignment procedures. Finally, higher-power trapping lasers and high-efficiency diffractive optics, such as metasurfaces[47], can be immediately deployed to scale the storage and preparation-zone size, supporting continuous operation of tens of thousands of atomic qubits.

Our results open up a range of scientific opportunities based on atom-array platforms. In particular, our method is directly compatible with a zoned architecture for quantum computation involving Rydberg-mediated entangling gates, local optical Raman control and dynamically reconfigurable qubit arrays9,24. This architecture therefore presents a promising approach towards the implementation of deep, fault-tolerant quantum circuits using error correction. In a complementary experiment conducted in a separate apparatus, we demonstrate the core components of such a fault-tolerant quantum processor, including a method for mid-circuit loss-resolving qubit readout and re-use as well as deep-circuit protocols involving logical qubit teleportation, below-threshold repeated error correction and universal fault-tolerant processing[24]. Atom losses have a major role in these experiments[25], and the ultimate circuit depth is directly limited by atomic reservoir depletion.

Taken together, our experiments open the door for realizing large-scale error-corrected quantum processors. For example, accounting for the current entangling gate fidelity (approximately 99.5%) and atom-loss rate, at a 1-ms duration per gate layer, we estimate that 15,000 rearranged qubits per second should be sufficient to replenish lost atoms in a quantum processor with about 10,000 physical qubits. Furthermore, realistic improvements in entangling gate fidelities to approximately 99.9% and reloading flux to 80,000 qubits per second could enable the operation of several hundred surface code logical qubits with a logical failure rate down to 10−8 (ref. [48]). Moreover, the natural compatibility of this architecture with high-rate quantum low-density parity check codes will probably unlock further improvement in quantum processor performance49,50.

Beyond applications in quantum computation, a continuously operating atom-array system could overcome several limitations in quantum metrology[23], enabling high-bandwidth and entanglement-enhanced precision quantum sensing12,13. Furthermore, a continuous stream of atomic qubits is essential to achieve fast generation of remote entanglement in quantum networking applications14,15,27. Finally, our high-rate reloading scheme and the transition from pulsed to continuous operation that it enables may be utilized to improve the performance of a broad class of cold-atom experiments, including quantum simulation, sensing and precision measurements.

Methods

Vacuum system

A simplified schematic of our vacuum system is shown in Extended Data Fig. 1a. The system consists of an MOT chamber and a science chamber, separated by a custom-designed differential pumping tube (DPT; Limit Vacuum Technology) with a 1.5-mm back aperture and 4.3-mm front aperture. The DPT maintains a pressure differential between the two chambers and blocks most of the MOT light. Both the DPT and the MOT chamber are tilted by approximately 4° to prevent direct scattering of cooling light onto the atomic array in the science chamber, where the line of sight passes about 1 cm above the array location. The MOT chamber is primarily composed of a glass cell (Precision Glassblowing) with two rubidium dispenser arms (not shown in Extended Data Fig. 1). The science chamber features a double-sided antireflection-coated glass cell (Akatsuki Technology) with optical contact technology. In both chambers, the pressure remains below the measurable threshold of the ion pumps (SAES NEXTorr and Agilent StarCell 75). Several components are omitted from the figure for clarity, including in-vacuum electrodes (not used in this work) and a vacuum viewport, which provides optical access to the in-vacuum mirror.

Objective and imaging system

The experimental set-up features a high-numerical-aperture (high-NA) optical system, which enables high-efficiency imaging and tight trapping of single atoms over a field of view of more than 1.5 mm diameter (Extended Data Fig. 3a). At its core are two 0.65-NA objectives (Special Optics, custom-design). One of the objectives is used for projecting optical tweezers and the other for single-atom imaging. The two objectives maintain diffraction-limited performance across the entire field of view for wavelengths ranging from 780 nm to 860 nm. The objective’s optical transmission is 92%, and we estimate the total absorption to be about 1% (taking into account finite reflection at each antireflection-coated surface), which reduces thermal lensing and enables higher trapping laser power in the future.

For single-atom imaging, we use two 4f telescopes (one high-NA objective and three relay lenses) to map the atomic plane inside the glass cell onto a low-noise camera (Hamamatsu C15550-20UP). The imaging system magnification is 7.6, such that the fluorescence of a single atom is mapped onto approximately 3 × 3 camera pixels. The quantum efficiency of the camera at the 780-nm imaging wavelength is about 50%. All relay lenses used in the objective beam path (both for imaging and tweezer projection) are custom-designed (Special Optics) to accommodate the large field of view.

Tweezer generation

For optical tweezer projection, we use polarizing beamsplitters and dichroic beamsplitters to combine 3 separate beam paths powered by 3 high-power lasers: a 15-W, 828-nm fibre amplifier system (Precilaser) that generates the dynamic optical tweezers for atom transport, and two 15-W, 852-nm fibre amplifier systems (Precilaser) that each form the backbone static tweezer array in the preparation and storage zones (Extended Data Fig. 3a).

The 828-nm dynamic tweezers beam path, dedicated to atom transport and sorting, consists of two perpendicularly mounted AODs (G&H AODF 4085) separated by a 1-to-1 4f telescope. Another 4f system maps the AOD aperture to the Fourier plane of the objective. The AOD-generated tweezers have a waist of about 800 nm and a travel range of 600 μm in each dimension on the atom plane. Depending on the transport pattern required, we dynamically switch between different tweezer configurations within one cycle of the experiment. When extracting atoms from the reservoir to the preparation zone, we use 1,440 tweezers at 4.5-μm spacing with average depth of 450 μK. To transport sorted atoms to or eject atoms from the storage zone, we generate an array of 540 tweezers at 9-μm spacing with an average depth of 600 μK. We empirically find a reduction in tweezer lifetime as we reduce AOD tweezer spacing, potentially owing to atom heating from beating between residual optical potentials of neighbouring tweezers.

Static optical tweezer arrays in the preparation zone and the storage zone are generated in two separate beam paths by two independent SLMs (Hamamatsu X15213-02R) and then combined on a polarizing beamsplitter. Each beam path includes a 4f relay lens system to map the SLM aperture to the Fourier plane of the objective. The SLM phase pattern is calculated using a variation of the weighted Gerchberg–Saxton (WGS) algorithm[52], and calculation accelerated with a graphics processing unit. We numerically ‘pad’ the SLM with zeros such that the two-dimensional SLM field array (iteratively optimized using WGS algorithm) is 10 × 10-times larger than the SLM pixel number, enabling 10-times-finer control over tweezer positions[53]. This corresponds to a tweezer positioning precision of 65 nm, which reflects an order-of-magnitude improvement over the natural diffraction unit of 650 nm.

We find substantial tweezer spacing distortion owing to nonlinear effects across the large array span. To systematically overlap thousands of AOD and SLM tweezers, we run an automated procedure that images both AOD and SLM tweezers on a camera, calculates the displacement between the two sets of tweezers for each site, and feeds back on the target tweezer positions of the WGS algorithm site by site. We also apply Zernike polynomials to correct for aberrations in the optical system[54], which increases the tweezer trap depth by about 10% post-correction.

SLM diffraction efficiency decreases as the distance to the zeroth order increases. To homogenize our backbone tweezer arrays in the preparation zone and the storage zone, we apply the following two-step procedure. First, when generating optical tweezer arrays using the WGS algorithm, we precompensate for spatially varying diffraction efficiency by including a ‘sinc’ term in the target array[54]. This rough homogenization typically yields 15% to 20% inhomogeneity. Then we run an atom-based homogenization procedure that relies on site-resolved measurements of tweezer-induced light shifts, which is used to feed back onto the WGS target intensity at each site. In the preparation zone, we measure the tweezer light shift by probing the F = 2 → F′ = 2 transition; in the storage zone, we infer the differential light shift via Ramsey interferometry between the two qubit states[55]. After a few rounds of atom-based feedback, we arrive at about 5–6% inhomogeneity in both preparation and storage zone (Extended Data Fig. 3b,c). After aberration correction and homogenization, the average SLM trap depth is 370 μK (270 μK) in the preparation (storage) zone with a tweezer waist of about 800 nm.

MOT and lattice loading

The experiment starts with the preparation of an atom reservoir (Extended Data Fig. 1b). We first load approximately 10^7 atoms in an MOT within 80 ms and the first optical lattice conveyor belt (Lattice-1) is overlapped throughout. The MOT light is 23-MHz red-detuned from the hyperfine transition F = 2 → F′ = 3, where F refers to hyperfine levels in the 5S1/2 ground state and F′ refers to hyperfine levels in the 5P3/2 excited state. The repumping light, created via modulating a sideband on the cooling light, resonantly drives the F = 1 → F′ = 2 transition. We operate the MOT at a magnetic-field gradient of 13 G cm−1, and use a 395-nm ultraviolet light-emitting diode for light-induced atom desorption from the glass cell. After the MOT stage, the MOT light is ramped to lower intensity and about 140-MHz red-detuning over 7 ms for compression of the atomic cloud into the lattice. A brief idle time follows the lattice loading procedure, in which the cooling lights are switched off and the magnetic field is zeroed. Subsequently, we perform lambda-grey molasses (LGM)[56] at low cooling light intensity, with the carrier frequency placed 30-MHz blue-detuned from the F = 2 → F′ = 2 transition and the coherent repumper sideband on 2-photon resonance with the F = 1 → F = 2 transition between both hyperfine ground states. After tLGM = 11 ms, we load approximately 4 × 10^6 atoms at temperatures T ≈ 20 μK into Lattice-1 as measured via absorption imaging.

Dual-lattice optical transport

We transport atoms from the MOT to the science region using two angled conveyor belt optical lattices32,38,39. Both transport lattices are derived from a single titanium:sapphire laser (Matisse, Spectra-Physics) and approximately 300-GHz red-detuned from the D_1 line, which is found to be the empirical optimum for our available laser power (Extended Data Fig. 2d,e). Lattice-1 has a Gaussian beam waist of around 330 μm at the position of the MOT and a minimum waist of around 250 μm. Particularly at the position of the DPT, its beam diameter is roughly three times smaller than the DPT aperture. Both conveyor belt lattices are spatially mode-matched at the handover point, from which the waist of the second conveyor belt lattice (Lattice-2) decreases to a minimum waist of 150 μm in the microscope field of view. Both conveyor belt lattices are created via retro-reflection of their respective incoming beams, which are deflected by two acousto-optical modulators (AOMs) into opposite diffraction orders, then imaged back onto the lattice waist (quad-pass configuration)[37]. Mounting the AOMs perpendicular to each other ensures a circular beam shape and enables optimal overlap with the incoming lattice beam on retro-reflection. The quad-pass efficiency is (0.88)4 ≈ 60%, such that for typical incoming powers Pin ≈ 1 W we achieve lattice depths Ulat > 500 μK for both conveyor belt lattices across the entire transport distance.

After loading Lattice-1, we linearly ramp the frequency of one of the retro-AOMs to introduce a frequency detuning Δν(t) between both interfering laser beams, and obtain a conveyor belt lattice moving at velocity v = λΔν(t)/2, with λ the wavelength of the lattice laser38,39. The atom cloud is transported over about 39 cm (Extended Data Fig. 2a) before arriving at the handover point after tL1 = 50 ms. Here, we transfer the atomic cloud from Lattice-1 to Lattice-2 within tHO = 1 ms by a simultaneous and opposite linear intensity ramp of both lattice lights and without applying cooling light during the handover (Extended Data Fig. 2b). Finally, within tL2 = 21 ms, Lattice-2 transports the atoms over another approximately 17 cm into the microscope field of view, where it serves as an atom reservoir. For both conveyor belt lattices, we find optimal transport efficiency for accelerations alat ≈ 4,000 m s−2 (Extended Data Fig. 2c) and velocities of 8–10 m s−1, limited by AOM bandwidth. Using this scheme, we deliver reservoirs of approximately 2.5 × 10^6 atoms at a temperature of around 120 μK into our reloading zone, which corresponds to an approximately 60% dual-lattice transport efficiency at 6 times the original temperature. Most of the observed heating is attributed to the lattice handover, not the long-distance transport.

When periodically replacing the atomic reservoir, we start loading a new MOT directly after the lattice handover and, as such, can deliver a fresh reservoir cloud to the science region every approximately 150 ms. It is noted that only during the second stage of lattice transport, tL2 = 21 ms, no reservoir is available for loading optical tweezers; however, as described in the main text, this is not a limitation to continuous operation as the qubit preparation procedure typically exceeds tL2.

Compared with a single transport lattice design38,39, our dual-lattice architecture offers several advantages. (1) Owing to the angle between both transport lattices and the differential pumping tube aperture, we avoid a direct line of sight and therefore reduce scattering from the MOT onto qubits already present in the science region. (2) The waist of the second conveyor belt lattice becomes largely independent of overall transport distance, and therefore can be decreased within the field of view of the objective. This increases the reservoir density for tweezer loading and also limits the impact of lattice-induced scattering and dipole potentials on other zones. (3) While Lattice-2 is still in active use, atoms in Lattice-1 can be prepared and transported to the handover point in parallel. This enables fast sequential reservoir replacement, and decouples the MOT and lattice loading sequence from science chamber operations.

Tweezer loading from the lattice reservoir

In this work, we load optical tweezers from the dense lattice reservoir without employing additional laser cooling during the loading process. Here we briefly discuss our current understanding of the underlying mechanisms, and outline the effect of loading and extracting tweezers from an active reservoir. In our parameter regime, we expect two mechanisms to contribute to our tweezer loading: stochastic and/or collisional loading. Both critically depend on atomic density n(r, z) in the reservoir, which is a function of atom number N per lattice site, atom temperature T, and both radial and axial trapping frequencies ωr and ωz. Within one lattice site, it is given by

$$n(r,z)={n}_{0}\,\exp \left(-\frac{m}{2{k}_{{\rm{B}}}T}({w}_{r}^{2}{r}^{2}+{w}_{z}^{2}{z}^{2})\right)$$

with \({n}_{0}=N{\omega }_{r}^{2}{\omega }_{z}{(m/(2{\rm{\pi }}{k}_{{\rm{B}}}T))}^{3/2}\) the peak density, m the mass of 87Rb, r the radial distance from the centre of the reservoir, z the axial distance from the lattice site, and kB the Boltzmann constant. Neglecting dependencies on atom temperature and the relative trap depth between the lattice and tweezers, we now turn to a brief discussion of both loading mechanisms.

Stochastic loading

When turning on the tweezer, the number of reservoir atoms stochastically overlapped with the tweezer volume can be approximated as Nst ∝ Vtwz⟨n(r = 0)⟩lat, where we assume peak density in the radial direction and ⟨ ⟩lat denotes averaging across the axial lattice sites. These are valid approximations, as the tweezers are far smaller than the radial dimension of the reservoir but capture multiple lattice sites axially. For our reservoir parameters, we estimate an atom density of ⟨n(r = 0)⟩lat ≈ 5 × 10^11 cm−3 and tweezer volume Vtwz ≈ 10−17 m^3, which results in about 5 atoms stochastically overlapped with the tweezer volume.

Collisional loading

An atom moving through a background gas collides with a rate Γ(r, z) = n(r, z)vrelσ, with vrel the thermal relative velocity between two atoms and σ the scattering cross-section. The two-body collision density is given by \(\gamma (r,z)=\frac{1}{2}n{(r,z)}^{2}{v}_{\mathrm{rel}}\sigma \), such that the collision rate within the tweezer volume is proportional to Γtwz ∝ Vtwz⟨γ(r = 0)⟩lat using the same approximations as above. For our system parameters, we estimate ⟨γ(r = 0)⟩lat ≈ 3 × 10^19 m−3 s−1 and therefore loading of about 0.3 atoms per millisecond of tweezer-lattice overlap, assuming 1 atom trapped per atomic collision.

Although these order-of-magnitude estimates imply that stochastic loading is dominating, we point out two simplistic assumptions that this model is making. (1) Although spatial overlap is necessary, it is not a sufficient condition for trapping; additionally considering kinetic energy constraints would lead to a decrease of stochastically captured atoms. (2) Owing to the abruptly altered potential landscape on tweezer turn-on, nearby atoms accelerate towards the trap centre and the atomic collision rate within the tweezer increases; this results in a larger number of atoms loaded via collisions[41]. For these reasons, we expect both mechanisms to contribute and further investigation is warranted.

In the regime of few tweezer extractions (that is, saturated loading; see also Fig. 1b), we find that, on average, approximately 5 atoms are lost from the reservoir per tweezer extraction, which is of the same order as the combined tweezer loading estimates above. This number is measured by comparing reservoir atom number with the number of atoms obtained in tweezers from repeated extraction, accounting for parity projection. It should be noted that this serves as an upper bound and does not necessarily imply loading of 5 atoms per tweezer, as we expect the rather invasive extraction procedure to accelerate evaporative losses in the reservoir as well. After loading, captured atoms are transported out of the active reservoir perpendicular to the axial lattice potential. As shown in Extended Data Fig. 4b, we observe no difference in survival when moving atoms through the reservoir versus in free space as a function of tweezer velocity[19].

Finite-field laser cooling and imaging

A key feature of our reloading architecture is the ability to initialize fresh qubits and perform mid-circuit laser cooling and imaging in the presence of a static magnetic field. Avoiding field changes, such as field-zeroing as typically necessary for polarization gradient cooling (PGC), protects coherence in existing qubits and enables faster qubit preparation cycles. To this end, all qubit preparation and manipulation protocols are designed to operate at a fixed magnetic field of 4.2 G, which defines the qubit quantization axis at all times. The preparation zone is illuminated with a pair of one-dimensional counter-propagating 780-nm beams with opposite circular polarizations (σ+ and σ−) aligned along the magnetic-field axis. The two beams are detuned relative to each other by twice the Zeeman splitting to compensate for the energy shift of hyperfine levels owing to the quantization field[57]. We use this architecture for laser cooling, imaging, parity projection and qubit state initialization as shown schematically in Extended Data Fig. 5a, with cooling and imaging performing comparably to the zero-field configuration.

To begin qubit preparation on atoms extracted into the preparation zone from the reservoir, an explicit parity-projection pulse is required as our loading mechanism typically results in more than one atom loaded per tweezer. Before ramping up SLM tweezers in the preparation zone, we perform PGC with light 60-MHz red-detuned from F = 2 → F′ = 3 to induce pair-wise atom losses in the AOD tweezers via light-assisted collisions[58]. After 10 ms, we hand over atoms to the SLM array and observe roughly 50% occupation and less than 1% of sites with more than 1 atom. This parity-projection step therefore sets an upper bound on imaging survival and is crucial for obtaining well-separated imaging histograms. In addition, we observe atoms trapped in weak out-of-plane potentials created by the SLM (Talbot effect), which we remove with a brief resonant push-out pulse[10] applied to the entire array during PGC.

Fast, lossless imaging is then used to identify atoms for rearrangement. We image for 10 ms using PGC parameters, which achieves a site-resolved discriminant fidelity of 99.99% (Extended Data Fig. 5b) with a survival probability of 99.5%. Higher imaging fidelity and survival can always be achieved by imaging longer at larger detuning or weaker powers. Our one-dimensional counter-propagating beam configuration offers the added benefit of a background-free imaging signal, eliminating the need for Fourier filtering as the beams do not directly scatter into the imaging objective. During imaging in particular, we observe that atoms from our lattice reservoir occasionally leak into the nearby preparation zone, which are then trapped into tweezers by imaging or cooling light. We avoid this by moving our lattice reservoir roughly a centimetre away from the objective field of view after every tweezer extraction cycle, and move it back before the next cycle. If not accounted for, atom spilling from the lattice and improper parity projection in tweezers can each decrease imaging survival by about 5%, with the exact number dependent on reservoir density and imaging duration.

During the 20 ms to 40 ms of rearrangement, we apply laser cooling via electromagnetically induced transparency (EIT)[42]. We use the same laser and beam geometry, except in a strong ‘coupling’ and weak ‘probe’ configuration, operating on a 2-photon resonance 70-MHz blue-detuned from the F = 2 → F′ = 2 transition[42] with intensity ratio 13:1. We probe the atom temperature via drop-recapture and extract radial temperatures of 12 μK (Extended Data Fig. 5c). A complementary adiabatic trap ramp-down measurement, which is more sensitive to axial temperatures, yields comparable results despite limited momentum projection of the cooling light along the tweezers’ axial direction. All cooling lights (PGC and EIT) contain a repumper frequency addressing the F = 1 → F′ = 2 transition, created by modulating an electro-optic modulator at about 6.8 GHz.

Atom rearrangement

After stochastically loading the preparation-zone 120 × 12 tweezer array, we rearrange the atoms into a defect-free array of typically 540 sites, except for the qubit flux demonstration in Fig. 1c where we arrange to 600 sites. The same two-dimensional AOD pair used for extracting atoms from the lattice reservoir performs rearrangement, controlled by a dedicated arbitrary waveform generator (AWG; Spectrum Instrumentation M4i.6631-x8). Leveraging our unique large-aspect-ratio preparation-zone geometry, we execute efficient row-by-row sorting (Extended Data Fig. 6a). In each row, a single parallel move fills all empty target sites using available atoms while navigating through inter-row gaps to avoid backbone SLM traps. Each row takes 700 μs to sort, with EIT cooling active throughout.

To optimize real-time processing, we precompute all possible move segments as waveform chirps. During each experiment, the rearrangement program selectively synthesizes the precomputed waveform chirps based on the specific atom loading information of that shot (provided by a real-time image analysis program). We exploit the AWG’s FIFO mode to reduce latency from waveform calculation. As each row’s sorting is independent, we stream its waveform as soon as it is computed while moves for the next row are calculated in parallel. This reduces calculation latency by over an order of magnitude. Under optimal conditions, we allocate approximately 20 ms for rearrangement, which takes into account data transfer latency, row-by-row sorting time and a small buffer to accommodate program runtime fluctuations. For all sequences involving storage-zone operation, we increase the camera region of interest (ROI) to include the storage zone, which increases latency (and therefore the time allocated for rearrangement) by about 10–20 ms. This large ROI is unnecessary for future continuously operating experiments (for example, for error-correcting circuits), where imaging is confined to the preparation zone and not required in the storage zone.

For 540-site rearrangement, we achieve an averaged target array filling fraction of 99.6% (Extended Data Fig. 6b), primarily limited by imaging survival. Given our vacuum lifetime of over 150 seconds, losses from background gas collisions contribute less than 0.1% to the rearrangement infidelity. Extended Data Fig. 6a (bottom) showcases a single-shot preparation-zone fluorescence image of a defect-free array after rearranging to 600 sites. Atoms remaining outside the target array are automatically discarded when the subarray is transported to (re)load the storage zone. We intentionally rearrange into every other column of the SLM backbone array, creating a sparse array geometry that reduces the average atom travel distance during sorting, thereby improving both rearrangement fidelity and speed.

Storage array building

One of the key considerations in generating large-scale atom arrays is the trade-off between increasing tweezer spacing to minimize inter-tweezer crosstalk, and decreasing tweezer spacing for higher SLM diffraction efficiency. Therefore, our 3,240-site storage-zone tweezer array features alternating horizontal spacings: wide, 6-μm channels for minimal AOD–SLM tweezer crosstalk during atom transport through the array, and smaller spacings of 3 μm to pack the array close to the SLM zeroth order where diffraction efficiency is substantially higher. The entire array is placed on one side (two quadrants) of the SLM zeroth order, as we empirically find that having large tweezer arrays in all four quadrants introduces additional ghost optical spots between tweezers.

The 90 × 36-site storage array is divided into 6 interleaved subarrays of 45 × 12 sites. Subarrays feature regular tweezer spacings of 9 μm and are filled sequentially, such that the entire storage array is assembled in 6 iterations (Fig. 2c, inset, and Extended Data Fig. 6d). Within each iteration, we first load the preparation-zone array stochastically, then rearrange atoms into a 540-site target array with a spacing of 9 μm horizontally and 4.5 μm vertically. Afterwards, atoms are picked up by AOD tweezers and transported to one of the six subarrays. During this transport, the subarray is vertically expanded from 4.5 μm to match the 9-μm spacing in the storage zone, while the horizontal spacing remains at 9 μm (see also Supplementary Video 1). Maintaining identical horizontal spacings throughout the transport minimizes expansion overhead and enables faster transport. In addition, the sparse subarray structure largely avoids AOD heating at closer spacings.

We observe a slightly lower atom survival probability during tweezer transport to the storage zone when the static lattice reservoir is present, which can be recovered when the lattice potential itself is in motion simultaneously. As described previously, the lattice reservoir is translated by 1 cm out of the objective field of view to avoid lattice spilling during high-contrast imaging, and moved back before the next tweezer extraction cycle. Therefore, we now simply time atom movement to the storage zone to occur synchronously with moving the lattice reservoir back into the objective field of view, thereby mitigating the above effect. Extended Data Fig. 6c showcases storage-zone assembly statistics after 300 repeated trials; on average, we load 3,193 atoms out of 3,240 sites, corresponding to a loading fraction of 98.5%.

Qubit initialization

After rearranging a defect-free array, we initialize the atoms into their qubit subspace. The qubit subspace is spanned by the two hyperfine clock states in the ground-state manifold of 87Rb, which we define as \(| F=1,{m}_{F}=0\rangle \equiv | 0\rangle \) and \(| F=2,{m}_{F}=0\rangle \equiv | 1\rangle \). To prepare the atoms in state \(| 0\rangle \), we leverage the previously discussed one-dimensional local beam configuration. Both of the counter-propagating preparation-zone beams simultaneously address the F = 1 → F′ = 0 and F = 2 → F′ = 2 transitions. By selection rules, state \(| 0\rangle \) is dark to the σ± circularly polarized light field addressing F = 1 → F′ = 0, whereas the states \(| F=1,{m}_{F}=\pm 1\rangle \) are optically pumped to \(| 0\rangle \) through \(| {F}^{{\prime} }=0,{m}_{{F}^{{\prime} }}=0\rangle \). Simultaneously, atoms in states F = 2 are depumped into F = 1 by addressing the F = 2 → F′ = 2 transition (Extended Data Fig. 5d). We observe a 1/e time of 5 μs for populating state \(| 0\rangle \). This technique for fast state initialization is advantageous as it requires only a few scattered photons, minimizing heating from scattering, while simultaneously avoiding magnetic-field rotations[4]. From the preparation-zone Rabi contrast, we infer a state preparation and measurement fidelity of 98.1(3)%, probably limited by polarization impurities and off-resonant scattering to other hyperfine levels in the excited state. The state preparation fidelity can potentially be improved by incorporating Raman-assisted optical pumping schemes[4].

Qubit manipulation and readout

We drive the qubit states via optical Raman transitions, in a set-up similar to previous studies[59] but operating 400-GHz blue-detuned from the 780-nm transition. The Raman beam drives the qubits at Rabi frequency Ω/2π ≈ 1 MHz. At this intensity, we measure a T_1-like scattering lifetime of 10 ms, in agreement with ab initio Raman scattering calculations. From this, we infer a scattering-limited fidelity of 0.99995 per π pulse. We note, however, that this represents an upper bound on our single-qubit gate fidelity; in practice, additional error sources such as atomic decoherence, intensity fluctuations and phase noise may also contribute[10]. The Raman beam is shaped to homogeneously address qubits in the large storage zone, which is achieved by using a fixed holographic phase plate (HOLO/OR ST-268) that forms a top-hat beam profile across the extent of the array. From measuring row-by-row Rabi frequency, we infer a beam homogeneity of approximately 1.04% root-mean-square variation and 3.4% peak-to-peak variation on the atoms (Extended Data Fig. 7a). To minimize crosstalk between the Raman beam and the atoms in the preparation zone, we knife-edge the beam tail at the intermediate imaging plane of the beam shaping telescope, and thus remove residual light in the preparation zone.

To selectively read out qubits in \(| 0\rangle \), we apply a push-out pulse that resonantly removes atoms in F = 2 from the trap, then image atoms that remain in the F = 1 ground-state manifold. To readout qubits in \(| 1\rangle \), we first apply a π pulse to rotate the population to \(| 0\rangle \), followed by the same push-out and imaging pulse. We follow a slightly different imaging procedure depending on where atomic qubits are read out. In the preparation zone, we use PGC for qubit readout under a finite magnetic field, which, in this work, is primarily used to identify occupied tweezer sites for atom rearrangement, but can also serve as mid-circuit readout in future error-correction protocols. For global readout of all storage array qubits at the end of the experiment, we use a separate retro-reflected circularly polarized global imaging beam at zero magnetic field.

Qubit shielding

Throughout the experiment and particularly during qubit preparation, we protect the 5S1/2 ground-state qubits in the storage zone from near-resonant photon scattering with the 5S1/2 → 5P3/2 transition by addressing the 5P3/2 → 4D5/2 transition near 1,529 nm (ref. [35]). This results in an Autler–Townes splitting ±ΔAT ≈ ±2π × 10 GHz of the excited state, and therefore a suppression of scattering from nearby imaging/cooling light with detuning Δcool by a factor of approximately \({({\Delta }_{{\rm{AT}}}/{\Delta }_{{\rm{cool}}})}^{2} > 10,000\) (‘shielding’).

The shielding light is sourced from a single-frequency fibre laser (Connet CoSF-D) outputting up to 10 W at 1,529.6 nm, which is passed through an AOM (G&H 3165-1) for fast switching control and fibre-coupled onto the experimental table. Similar to the Raman beam, we shape the shielding beam with a holographic phase plate (HOLO/OR ST-356) to create a flat-top beam profile of approximately 250 μm along the vertical extent of the storage-zone array with Gaussian beam waist of approximately 50 μm horizontally and 2.5 W projected onto the atoms. The beam tails are knife-edged in an intermediate imaging plane to ensure no shielding crosstalk onto the preparation zone. The knife-edged flat-top beam profile is shown in Extended Data Fig. 7b.

In Fig. 3c, we show the shielding effect in a spectroscopy scan by stepping the 1,529-nm wavelength during simultaneous imaging and low-power shielding of storage-zone atoms, and resolve the 4D5/2 resonance by suppression of global imaging signal. To further optimize shielding performance, we maximize the T_2 time of storage qubits as a function of shielding wavelength while locally imaging qubits in the preparation zone. In practice, the shielding light is operated free-running approximately 1 GHz red-detuned from the 5P3/2 → 4D5/2 transition with a measured frequency stability of ±10 MHz.

Maintaining coherence while reloading

In Fig. 3a, we apply N repetitions of a XY16 dynamical decoupling pulse sequence (denoted as XY16-N) with fixed π-pulse spacing 2τ ≈ 1.6 ms. During dynamical decoupling, we measure storage qubit coherence under various conditions. First, we quantify the effect of the distant MOT on storage qubits when pulsing the MOT at a 30% duty cycle with the lattice reservoir lights off. This particular duty cycle is chosen to replicate typical MOT loading cycles in our qubit reloading protocol. Second, we additionally switch on the preparation-zone imaging light while the reservoir is present for the entire probing duration to simulate the effect of concurrent local qubit preparation. We observe no difference between turning on local imaging light with atoms present in the preparation zone versus without, suggesting that the primary source of decoherence during qubit preparation arises from scattered light originating from the optics and apparatus, rather than from photons scattered by atoms in the preparation zone. Lastly, we shield storage-zone qubits while the distant MOT is loaded and held at saturation, the lattice reservoir is present and preparation-zone imaging light is switched on for the entire probing duration. This simulates the most demanding application of storage array shielding. Complementary to Fig. 3a, which measures T_2 times under the conditions described above, we similarly probe depolarization of storage-zone qubits when the qubit is initialized in either \(| 0\rangle \) (Extended Data Fig. 8a) or \(| 1\rangle \) (Fig. 3b). Here, in addition to the above variations, we also quantify storage qubit depolarization caused by the lattice reservoir light alone.

Continuous coherent operation

In this section, we detail the experimental sequence used to achieve our results of qubit reloading while maintaining the storage qubit coherence shown in Fig. 4b–d and Extended Data Fig. 10a,b. A sequence schematic is given in Extended Data Fig. 9. First, we transport a lattice reservoir into the science region, from which tweezers repeatedly extract atoms into the preparation zone. Subsequently, loaded lattice reservoirs are transported to the tweezer science region in parallel, such that the reservoir itself is replaced every two tweezer extractions. Once in the preparation zone, atoms undergo the qubit preparation sequence shown in Extended Data Fig. 5a before being transported into the storage zone. The complete qubit preparation sequence, including the move to the storage array, takes a total of about 80 ms and constitutes one reloading cycle. After initial assembly of the storage qubit array, we continue preparing newly state-initialized qubit ensembles in the preparation zone, and eject and refill one of six qubit subarrays in the storage zone as described in the main text. To eject a subarray, the qubits are transferred back into overlapped AOD tweezers and accelerated out of the objective field of view. Storage subarray ejection takes about 5 ms and occurs in parallel to the preparation-zone image used for atom rearrangement. For Fig. 4b, we loop this qubit reloading sequence for variable time before our global imaging readout. Shielding light is applied to the storage zone during the entire experimental sequence.

For Fig. 4c,d, we additionally apply dynamical decoupling sequences (Xπ/2 − XY16-64 − X−π/2) with a fixed π-pulse spacing 2τ ≈ 1.1 ms onto all storage-zone qubits during each reloading cycle. Therefore, within each loop, qubits are first rotated \(| 0\rangle \to | \,+\,\rangle \), then undergo the XY16 decoupling sequence to mitigate dephasing. Right before fresh qubits are moved into the storage array from the preparation zone, we apply a X−π/2 pulse to map remaining coherence to population by rotating back to state \(| 0\rangle \), replace the oldest qubit subarray, then rotate all qubits again to \(| \,+\,\rangle \) as a new reloading cycle starts. This is repeated for a variable number of times, where each subarray is exchanged with a set of fresh qubits every six iterations. In Extended Data Fig. 10a, we supplement Fig. 4c by averaging readout probability over the entire storage array instead of analysing each subarray individually.

Instead of mapping coherence back to \(| 0\rangle \) population after each reloading cycle, we can also map it to alternating \(| 0\rangle \) or \(| 1\rangle \) population by choosing (Xπ/2 − XY16-64 − X+π/2) as the cyclic decoupling sequence. This results in subarrays 1, 3 and 5 hosting qubits in superposition state \(| \,+\,\rangle \) and subarrays 2, 4 and 6 qubits in the opposite state \(| \,-\,\rangle \) during dynamical decoupling after initial array assembly. When mapped back to population, this yields a checkerboard pattern of qubit states \(| 1\rangle \) and \(| 0\rangle \) in the storage array. Analogous to Fig. 4d, this is shown in Extended Data Fig. 10b where the storage array is read out in different qubit bases and each subarray analysed individually.

Control system and timing

We use National Instruments (NI) cards to generate digital and analogue control signals (NI PXIe-6535 and NI PXIe-6738, respectively) for the laser-cooling and trapping stages of our experiment, including the MOT loading, dual-lattice transport and qubit preparation sequences. For operations that require waveform generation with nanosecond precision, we utilize AWGs (Spectrum Instrumentation DN2.663-04 and M4i.6631-x8) whose output is triggered by the NI cards. In our experiments, AWGs handle the timing of single-qubit gates and dynamical decoupling pulses, and supply the chirped waveforms for atom sorting and transport in AOD tweezers.

Our control system is designed to allow for practically unlimited duration of continuous operation. For NI-generated control signals, we calculate and stream the waveform samples on-the-fly to circumvent memory limitations. For AWG-controlled atom transport and dynamical decoupling, we instead store the precalculated waveform in the onboard memory and loop over it for an arbitrary number of times. Here, the dynamical decoupling sequence requires particular care in selecting the signal frequency when looping over the same memory segment to avoid phase jumps in the modulated 6.8-GHz microwave signal. More details of our control software will be discussed in an independent paper that is currently in preparation.

Calibrating and maintaining intensity of light pulses that are too short for active real-time stabilization is a notable technical challenge in continuously operating experiments. For our optical Raman light, we employ a field-programmable-gate-array-based digital servo with digital sample-and-hold[60] to stabilize pulse intensity, which eliminates analogue hold decay and integral windup, and enables calibration pulses as short as 5 μs. This calibration pulse is inserted before every XY16-64 decoupling cycle with the 6.8-GHz microwave source detuned by 20 MHz to ensure that qubit states are not driven. Although the calibration pulse flashes onto existing storage qubits, its duration is 3 orders of magnitude shorter than the approximately 10 ms T_1-like scattering timescale associated with the Raman light. With the digital hold, we actively stabilize every decoupling cycle in situ, and achieve no long-term decay in pulse intensity and pulse-to-pulse error of ≲1%.

Data analysis

For the measurements in Figs. 3a,b and 4b and Extended Data Fig. 8a, we read out the qubit state after the probing duration and define contrast as \(({P}_{0}(t)-{P}_{1}(t))/({P}_{{\rm{a}}}(t)-{P}_{| {m}_{F}=\pm 1\rangle }(t=0))\). Here, P_0(t) (P_1(t)) denotes the probability to measure qubits in \(| 0\rangle \) (\(| 1\rangle \)) at given time t by reading out the F = 1 hyperfine level without (with) a preceding π pulse as described above. Pa(t) is the probability to measure an atom in any state by omitting the push-out pulse before imaging atoms (lifetime measurement). In addition, we correct for qubits initially populating neighbouring Zeeman states \(| F=1,{m}_{F}=\pm 1\rangle \) at t = 0 denoted as \({P}_{| {m}_{F}=\pm 1\rangle }(t=0)\), as we observe 5–10% leakage from state \(| 0\rangle \) into other mF states within the F = 1 manifold when transporting qubits from the preparation to the storage array using AOD tweezers. This is attributed to beating of radio frequencies driving the transitions between \(| 0\rangle \to | F=1,{m}_{F}=\pm 1\rangle \) levels, which can be mitigated by operating at higher quantization fields or fine-tuning the radio frequencies applied to the AODs in future experiments. To measure \({P}_{| {m}_{F}=\pm 1\rangle }(t=0)\), we isolate qubits in states \(| F=1,{m}_{F}=\pm 1\rangle \) by first applying a push-out pulse, then a π pulse to transfer the population \(| 0\rangle \to | 1\rangle \), followed by a final push-out pulse before readout. For all measurements of Fig. 3a,b and Extended Data Fig. 8a, we fit an exponential decay to the contrast and quote the fitted 1/e decay time as T_2 and T_1 times, which are presented in Extended Data Fig. 8b.

For the measurements in Fig. 4c,d and Extended Data Fig. 10a,b, ‘readout probability’ is defined as \(({P}_{0}(t)-{P}_{| {m}_{F}=\pm 1\rangle }(t=0))/({P}_{{\rm{a}}}(t)\,-\) \({P}_{| {m}_{F}=\pm 1\rangle }(t=0))\), where P_0(t) again denotes the probability to measure qubits in \(| 0\rangle \) at given time t as described above, and Pa(t) the lifetime measurement. For the red-shaded lines in Fig. 4c and Extended Data Fig. 10a, we apply a π pulse before the measurement. As before, we measure and correct for qubits initially populating neighbouring Zeeman states \(| F=1,{m}_{F}=\pm 1\rangle \) due to state leakage during atom transport.

Data availability

The data that support the findings of this study are available from the corresponding authors on request.

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Acknowledgements

We thank Y. Bao, H. Bernien, S. Cantu, J. Dalibard, S. Ebadi, M. Endres, T. Esslinger, B. Grinkemeyer, N. Jepsen, S. Kolkowitz, J. Léonard, A. Lukin, T. Manovitz, J. Robinson, P. Sales Rodriguez, G. Semeghini, R. Tao, J. Ye, J. Zeiher and H. Zhou for discussions; and S. Geier, J. MacArthur and T.-K. Shen for help with the experiment. We acknowledge financial support from the US Department of Energy (DOE Quantum Systems Accelerator Center, contract number 7568717), IARPA and the Army Research Office, under the Entangled Logical Qubits programme (Cooperative Agreement Number W911NF-23-2-0219), DARPA ONISQ programme (grant number W911NF2010021) and MeasQuIT programme (grant number HR0011-24-9-0359), the Center for Ultracold Atoms (an NSF Physics Frontier Center), the National Science Foundation (grant numbers PHY-2012023 and CCF-2313084) and QuEra Computing. M.H.A. acknowledges support by a Rubicon Grant from the Netherlands Organization for Scientific Research (NWO). S.H. acknowledges funding through the Harvard Quantum Initiative Postdoctoral Fellowship in Quantum Science and Engineering. S.J.E. acknowledges support from the National Defense Science and Engineering Graduate (NDSEG) fellowship. D.B. acknowledges support from the Fannie and John Hertz Foundation.

Author information

Author notes

1. These authors contributed equally: Neng-Chun Chiu, Elias C. Trapp, Jinen Guo, Mohamed H. Abobeih, Luke M. Stewart, Simon Hollerith

Authors and Affiliations

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

Neng-Chun Chiu, Elias C. Trapp, Jinen Guo, Mohamed H. Abobeih, Luke M. Stewart, Simon Hollerith, Pavel L. Stroganov, Marcin Kalinowski, Alexandra A. Geim, Simon J. Evered, Sophie H. Li, Xingjian Lyu, Lisa M. Peters, Dolev Bluvstein, Tout T. Wang & Mikhail D. Lukin

2. Department of Physics and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA

Markus Greiner & Vladan Vuletić

Contributions

N.-C.C., E.C.T., S.H., J.G., L.M.S., M.H.A. and T.T.W. planned the experiments, performed measurements, analysed the data and contributed to building the experimental apparatus. P.L.S., M.K. and X.L. contributed to the experimental control system. A.A.G., S.J.E., S.H.L. and D.B. contributed to development of methods and techniques for continuous operation. L.M.P. contributed to data interpretation. All work was supervised by M.G., V.V. and M.D.L. All authors discussed the results and contributed to the paper.

Corresponding authors

Correspondence to Simon Hollerith or Mikhail D. Lukin.

Ethics declarations

Competing interests

M.G., V.V. and M.D.L. are co-founders and shareholders, V.V. is Chief Technology Officer, and M.D.L. is Chief Scientist of QuEra Computing. Some of the techniques and methods used in this work are included in provisional and pending patent applications filed by Harvard University (US patent application numbers 63/772,191 and 63/656,377).

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Nature thanks Sylvain de Léséleuc, Takafumi Tomita 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 Vacuum chamber and dual-lattice transport sequence.

a, Simplified view of the vacuum chamber. Atoms are cooled and loaded from a MOT into an optical lattice (Lattice-1) and then transported through the differential pumping tube (DPT, orange) to the science chamber. The atomic cloud is then handed over to a second optical lattice (Lattice-2), which is reflected out of the chamber by an in-vacuum mirror. While most MOT light is blocked by the DPT, the tilted design between both chambers avoids direct line of sight between the computational array and the MOT location. b, Summary of the dual-lattice transport sequence, including the MOT stage, loading and cooling of Lattice-1 as well as lattice transport and handover. After the lattice handover, we restart the lattice loading procedure in the MOT chamber, while atoms in Lattice-2 are shipped to the science region where they serve as an atomic reservoir for tweezer extraction. In the Lattice-2 velocity graph, the brief back-and-forth movement used to avoid atom spilling during qubit preparation is omitted for clarity. The grey-shaded regions indicate the previous/next lattice loading cycle.

Extended Data Fig. 2 Dual-lattice conveyor belt transport.

a, Atom number (normalized) obtained in the reservoir after transport as a function of Lattice-1 travel distance before handing the atomic cloud over to Lattice-2. b, Atom number (normalized) obtained in the reservoir as a function of time dedicated for lattice handover, which consists of a simultaneous and opposite ramp of the Lattice-1 and Lattice-2 intensities (see inset schematic). c, Atom number (normalized) obtained in the reservoir as a function of conveyor belt acceleration, here shown exemplary for Lattice-1. d, e, Atom number (red) and temperature (blue) in the respective transport lattices before (d) and after (e) transport, shown for varying MOT loading times with the lattices ~300 GHz red-detuned from the D_1 line (darker color shading). The dashed grey line represents our chosen MOT loading time, which was sufficient to obtain the reservoir density required for tweezer loading. The light-shaded curves are measured for further red-detuned lattices (~700 GHz), providing lower lattice-induced scattering and colder temperatures but also lower atom numbers. Error bars represent the standard error of the mean across 10 repetitions.

Extended Data Fig. 3 High-NA beam paths and optical tweezer characterization.

a, Simplified optical beam paths for tweezer generation and single-atom imaging. Before the tweezer-generation objective, the preparation and storage zone SLMs (both at 852 nm) are combined on a polarizing beamsplitter, then further combined with the 828 nm AOD beam path on a dichroic beamsplitter. A second objective is used for single-atom imaging. Relay optics are omitted for simplicity. Figure adapted from ref. [54], Springer Nature Limited. b, c, Histograms of trap depth measured for the preparation zone (b) and storage zone (c) tweezer arrays after two-stage homogenization. The preparation (storage) zone tweezers have an average trap depth of 370 μK (270 μK) with standard deviation 5.4% each.

Extended Data Fig. 4 Extracting atoms from the lattice reservoir.

a, Single-shot fluorescence images of the lattice reservoir after extracting atoms via optical tweezers for N-repetitions. b, Normalized tweezer array loading fraction obtained in the preparation zone as a function of tweezer velocity during extraction from the reservoir. We find negligible difference when moving atoms perpendicularly through the lattice potential versus in free-space, with a slightly higher survival possibly attributed to the superimposed lattice-tweezer potential during atom transport. Error bars represent the standard error of the mean across 10 repetitions.

Extended Data Fig. 5 Characterization of the qubit preparation sequence.

a, Summary of the experimental sequence for qubit preparation. After extraction from the reservoir, the atoms are transported to the preparation zone within ~2.5 ms. Here, a parity-projection pulse of 10 ms is performed to achieve either one or zero atoms per AOD tweezer trap. Atoms are then handed off to a backbone tweezer array generated by an SLM while applying PGC, involving SLM-AOD intensity ramps and a brief (30 μs) resonant Talbot plane push-out pulse. A 10 ms fluorescence image is used to identify occupied sites for rearrangement, followed by EIT cooling and simultaneous atom sorting into a defect-free array. Depending on the camera settings and desired atom configuration, rearrangement takes between 20 ms and 40 ms (largely dominated by data transfer latency). Finally, atoms are optically pumped into the qubit state \(| 0\rangle \) within 50 μs. All light pulses are performed by two circularly-polarized counter-propagating laser beams at static magnetic field (schematic). b, Imaging histogram for 1D imaging at finite magnetic field. The extracted average discriminant fidelity is 0.9993 with a site-resolved discriminant fidelity of 0.9999. The blue curve visualizes the discriminant fidelity as a function of threshold value. Note that the net imaging fidelity must also account for imaging survival, which limits total fidelity to \({\mathcal{F}} \sim 0.995\). c, Drop-and-recapture measurement of atomic temperature after 40 ms of 1D EIT cooling and subsequent qubit state preparation. The temperature is extracted as T = 12 μK via Monte-Carlo simulations. Inset: Relevant atomic levels for EIT cooling. d, Schematic illustrating relevant atomic levels for fast initialization of qubit state \(| 0\rangle \). Error bars represent the standard error of the mean across 10 repetitions.

Extended Data Fig. 6 Preparation zone atom rearrangement and iterative assembly of the storage zone array.

a, Single-shot fluorescence images of the preparation zone atom array. Before rearrangement, atoms are stochastically loaded (top). After rearrangement, the target array is filled with near-unity probability (bottom). For this shot, the 600-site target array has zero defects. Explicitly ejecting atoms outside the target array is not necessary, as only the target array is picked up by AOD tweezers and transported to the storage zone. b, Preparation zone rearrangement histogram (500 trials) of 540 target sites. We achieve a 99.6% average filling fraction with 14% of trials having zero defects. For visualization, trials with fewer than 530 atoms (≈1% of all trials) are grouped into the 530-atom bin. c, Storage zone loading histogram (300 trials). We achieve an average loading fraction of 98.5% (3,193 atoms). d, Storage zone iterative assembly scheme. In each iteration, 540 AOD tweezers pick up sorted atoms from the preparation zone target array (bottom), which are transported to the storage zone (top) to sequentially fill one of six subarrays (blue color shading). The AOD tweezers travel through wide channels in the storage array to avoid crosstalk with the static SLM tweezer traps. The schematic illustrates filling of the second subarray, where the first subarray has already been filled. See also Supplementary Video.

Extended Data Fig. 7 Raman and shielding beam characterization.

a, Rabi oscillations between qubit states for each row of the storage array. The variation of fitted Rabi frequency allows us to extract a Raman beam homogeneity of approximately 1.04% root-mean-square variation and 3.4% peak-to-peak variation on the atoms. b, Beam profile of the knife-edged flat-top 1529 nm shielding light, where the storage zone location is indicated. The plot on the right shows a zoomed-in line profile through the center of the beam, where the orange (gray) curve corresponds to the profile with (without) the knife edge. Evidently, cropping residual beam tails is imperative to avoid beam cross talk into the preparation zone (location marked with the black arrow).

Extended Data Fig. 8 Qubit depolarization under various conditions.

a, In main text Fig. 3b, we investigate how various parallel operations influence qubit polarization when initialized in state \(| 1\rangle \), where we expect a strong effect as pumping lights used for MOT and qubit preparation predominantly operate from F = 2. Here, we show a complementary analysis for qubits initialized in \(| 0\rangle \). Similarly comparing storage qubit depolarization during local qubit preparation with and without shielding, we are able to recover the measured T_1-time up to the depolarization rate set by the lattice lights. As expected, we generally find lower depolarization rates compared to starting in \(| 1\rangle \). b, Summary of the fitted 1/e T_2- and T_1-times from Fig. 3a,b and Extended Data Fig. 8a. Errors and error bars represent the standard error of the mean across 10 repetitions.

Extended Data Fig. 9 Experimental sequence for continuous operation while maintaining qubit coherence.

Summary of the sequence used to achieve the results of Fig. 4b,c,d and Extended Data Figs. 10a,b. In the storage zone, dynamical decoupling (except for Fig. 4b) and shielding are continuously applied to the storage qubits, while the oldest qubit subarray is discarded and refilled with fresh qubits from the reservoir. First, an initial Xπ/2 pulse prepares qubits in a coherent superposition state. After XY16-64 decoupling, (remaining) coherence is briefly mapped back to population, typically into \(| 0\rangle \) with a X−π/2 pulse, before the next qubit subarray is introduced. Thereby, qubits are in the equal superposition state for about 90% of total experiment duration. In Extended Data Fig. 10b, we show an example of mapping back into alternating \(| 0\rangle \) and \(| 1\rangle \) populations by using a final X+π/2 pulse instead. Throughout the experimental sequence, laser cooling in the MOT chamber, dual-lattice transport, and qubit preparation run in parallel in the background to provide a high-rate qubit supply. For Fig. 4b, no decoupling pulses are applied to the storage zone and we simply probe qubit polarization.

Extended Data Fig. 10 Coherence under continuous operation.

a, Complementary analysis to Fig. 4c when array-averaging the probability to read out qubits in state \(| 0\rangle \) (blue) or \(| 1\rangle \) (red). The green line indicates the contrast, i.e. the difference of populations measured in \(| 0\rangle \) and \(| 1\rangle \). b, Similar to Fig. 4d, but instead with the decoupling sequence (Xπ/2 − XY16-64 − X+π/2) applied during each reloading cycle. This results in alternating qubit states in the storage array (checkerboard pattern). The blue (red) curves represent the readout probability of even (odd) subarrays, indicated by the color shading. Error bars represent the standard error of the mean across 10 repetitions.

Supplementary information

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Supplementary Video 1

Iterative assembly and continuous maintenance of a large-scale atomic array (Fig. 2). This video depicts the atom motions used in our demonstrations of continuous reloading. Every 80 ms, a defect-free array of atoms from the preparation zone is transported to the storage zone to assemble an array of 3,000+ atoms in 6 loading iterations. After assembly, one of six interleaved subarrays is ejected from the storage array and refilled with a fresh set of atoms, which, in principle, can be repeated indefinitely. The storage array sites are spaced by 3 μm vertically and alternate between 3-μm and 6-μm spacing horizontally to allow for lossless atom transport in between columns. Yet, every storage subarray features regular spacings of 9 μm in both directions. Atoms from the preparation zone are rearranged with 9-μm-horizontal and 4.5-μm-vertical spacing, then further expanded vertically during the atom transport to match the dimensions of the storage subarrays.

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Chiu, NC., Trapp, E.C., Guo, J. et al. Continuous operation of a coherent 3,000-qubit system. Nature 646, 1075–1080 (2025). https://doi.org/10.1038/s41586-025-09596-6

- Received: 25 June 2025

- Accepted: 05 September 2025

- Published: 15 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09596-6

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A full-featured 2D flash chip enabled by system integration

Chunsen Liu,

Yongbo Jiang,

Boqian Shen,

Shengchao Yuan,

Zhenyuan Cao,

Zhongyu Bi,

Chong Wang,

Yutong Xiang,

Tanjun Wang,

Haoqi Wu,

Zizheng Liu,

Yang Wang,

Shuiyuan Wang &

…

Peng Zhou

Nature volume 646, pages 1081–1088 (2025)

Abstract

Two-dimensional (2D) materials have extended the device scalability1,2,3 of silicon (Si) technology and enabled fundamental innovations in device mechanisms4,5,6. Both industry7,8,9 and academia10,11,12,13, particularly in the field of integrated circuits, are pursuing integration breakthroughs to demonstrate the superiority of 2D electronics at the system level. Despite considerable integration progress on either 2D material integration11,12,13 or 2D-CMOS hybrid integration[14], a system that can migrate the advantages of the device to the application is still lacking. Here we report a full-featured 2D NOR flash memory chip realized by an atomic device to chip (ATOM2CHIP) technology, which combines a superior 2D electronic device as a memory core and a powerful CMOS platform to support complex instruction control. The ATOM2CHIP blueprint includes a full-stack on-chip process and a cross-platform system design, providing a complete framework to bridge the gap from emerging device concept to an applicable chip. The full-stack on-chip process is a specially designed flow that incorporates planar integration, three-dimensional (3D) architecture and chip packaging, contributing to a high yield of 94.34% based on a full-chip test. The cross-platform system design handles both the 2D circuit design and the 2D-CMOS modules compatibility verification design, contributing to a highly complex, instruction-driven, full-featured chip with 8-bit commands and 32-bit parallelism. These results demonstrate an efficient system integration strategy that showcases the advantages of the 2D electronic system.

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Main

Two-dimensional materials exhibit exceptional electronic properties even at monolayer thickness15,16,17, and the van der Waals heterostructure18,19 enables fine-tuning of the electronic bands[20]. These characteristics have enabled 2D electronics to extend the scaling ability beyond that of Si technology1,2,3 and create fundamental device mechanisms4,5,6. As one such demonstration of semiconductor devices for integrated circuits, 2D flash memory demonstrates Fowler–Nordheim tunnelling programming speed21,22,23,24 and channel length scaling[25] advantages over Si flash memory (mainstream non-volatile memory technology). In recent years, the integration of 2D semiconductors has been increasingly considered by both industry8,26,27 and academic11,12 researchers of integrated circuits. The next stage of 2D electronics should demonstrate its superiority at the system level and accelerate the transition of emerging devices from lab to fab28,29. However, 2D semiconductors are currently unable to realize logic circuits comparable to those based on state-of-the-art Si technology. The combination of 2D electronics with mature Si CMOS logic circuits represents a promising way to demonstrate the superiority of 2D electronics at the system level. The pioneering works mainly combine 2D materials with CMOS to improve the function of cell performance, such as using an Si transistor to improve the reliability of 2D memristors[14] or using graphene to broaden the spectral range of sensors[30]. The 2D electronics should further use the CMOS platform to construct superior systems with abilities beyond those of existing technologies.

The essential technology to migrate the advantages of the 2D device concept to the system is lacking, and developing such a systematic procedure and design methodology is extremely difficult. This should include a full-stack on-chip process from planar integration, 3D architecture, to chip packaging and a cross-platform system design to assimilate 2D electronics to the CMOS platform.

At present, the previous studies already show good progress of 2D device array on the highly flat SiO_2/Si substrate (roughness <300 pm) (refs. 25,31). These off-chip processes cannot be directly transferred to the CMOS platform because the surface of the CMOS chip is significantly rougher (typical roughness of about 1–2 nm) even after chemical–mechanical polishing owing to the large variation of CMOS circuitry, as shown in Supplementary Fig. 1. The roughness variation will introduce random stress in 2D materials and uncontrolled air gaps in the interface[32] and influence electrical characteristics of atomic thin layer 2D materials33,34,35,36. Other big questions of on-chip process are the 3D architecture and chip packaging. The emerging device mechanisms are generally driving incompatibility with the existing CMOS platform37,38, and a proper 3D architecture is needed to combine the 2D electronics with the CMOS platform. As the atomic thin 2D materials are also very sensitive to electro-thermo-mechanical (ETM) shock, leading the characteristics of 2D electronics to be easily influenced or damaged by traditional packaging process[39], a lossless packaging process is needed.

More importantly, the cross-platform system design between 2D electronics and the CMOS platform is a completely empty space. It is essential to provide a simulation–verification methodology to make a cross-platform chip work. This system design must include both 2D circuit design and 2D-CMOS compatibility verification design, which is highly dependent on the intersection of emerging device or process and CMOS circuitry design researchers. Especially for the compatibility issues caused by emerging 2D electronics mechanisms, emerging mechanisms enable unprecedented performance breakthroughs but also distinguish themselves from the mature CMOS platform. Stubbornly solving these compatibility issues through device technology is not advisable, and converting them to circuit interface design to handle these difficult issues can introduce more system design tools.

In this study, we present the atomic device to chip (ATOM2CHIP) technology to address the challenges of 2D system integration at both the process and circuit design levels, demonstrating a fully functional memory chip through the integration of a 2D NOR flash module on a CMOS die. Leveraging a full-stack on-chip fabrication process, the resulting 2D flash chip achieves a high yield of 94.34%. The fabricated 2D flash cells feature 20-ns fast operation and low energy consumption down to 0.644 pJ per bit. Furthermore, the proposed cross-platform system design facilitates the functionality of the 2D NOR flash chip with instruction-driven operation, 32-bit parallelism and random access. This has been substantiated through chip testing, using a clock frequency of 5 MHz, and the programming pulse has been configured to 2.5 clock cycles. We believe that these system-level results represent an important milestone in extending the superiority of 2D electronics to real-world applications.

A 2D flash chip enabled by ATOM2CHIP technology

The proposed ATOM2CHIP blueprint is shown in Fig. 1a. The full-stack on-chip process developed in this study has produced a high yield of the 2D chip using the following processes: (1) a conformal adhesion process integrating 2D materials on rough CMOS die, with residual stress from rough surface mildly relieved; (2) a modular 3D architecture converting emerging device incompatibility to a well-designed 2D-CMOS module interface; and (3) a 2D-friendly packaging method with region-specific electrostatic discharge (ESD) protection and low thermal and strain budget packaging for ETM damage alleviation. The cross-platform system design has enabled complex chip functions as follows: (1) a crosstalk suppression 2D flash circuit design; (2) a CMOS voltage domain design compatible with negative and high voltage of 2D circuit operation mode; and (3) a 2D-aware CMOS impedance matching design for compatible drive and sense ability.

Fig. 1: A full-featured 2D flash memory chip enabled by ATOM2CHIP technology.

a, The ATOM2CHIP blueprint for translating an atomic device concept into a tapeout-verified chip. b, The CMOS dies fabricated using a commercial 0.13 μm technology node. Left, an 8-inch wafer containing the fabricated CMOS dies; middle, optical image of the CMOS die; and right, functional descriptions of principal modules. More detailed information about the CMOS modules is provided in Supplementary Information section 1. c, Optical image of the 2D flash chip. The 2D flash module is integrated above the CMOS die and is connected by TGVs. d, STEM and HR-TEM images of the 2D flash chip. The STEM image confirms the integrated structure of the CMOS die and the 2D flash module. The HR-TEM images show the progressively magnified profiles of the 2D flash cell. Scale bars, 250 μm (c); 1 μm (d, left); 200 nm (d, top right); 5 nm (d, bottom right).

Using the ATOM2CHIP technology, we fabricated the 2D NOR flash chip, integrating 2D flash module on a mature CMOS platform. Figure 1b shows the optical image of the 8-inch CMOS wafer with a magnified view of the CMOS die. The CMOS dies are manufactured using a commercial 0.13 μm technology node, with multiple circuit modules integrated to handle peripheral control and manage memory operations. The principal circuit modules include an I/O for input/output (Supplementary Fig. 2), word line, bit line and source line (WL/BL/SL) buffers for WL/BL/SL driver circuits, a sense amplifier (SA) for data readout, a power switch for voltage domain control, a power-on reset (POR) circuit and a logic control circuit. Supplementary Figs. 3–7 provide the higher magnification optical images of the individual circuits and circuit schematics.

Figure 1c shows an optical image of our 1-Kb 2D NOR flash chip. The 2D flash module in NOR configuration is located in the central region of the CMOS die. There is a glass passivation (PA) layer for electrical isolation between the 2D module and CMOS circuits, as well as vias through the glass layer (TGVs) for the I/O interface (TGV1) and 2D-CMOS inter-module communication (TGV2). The 2D flash chip is controlled and tested by a host computer through 14 pads on TGV1 using serial communication based on the Serial Peripheral Interface protocol. All the WLs, BLs and SLs of the 2D flash module are connected to the CMOS circuitry using TGV2. The scanning transmission electron microscope (STEM) image of the fabricated chip (Fig. 1d, left) confirms the integrated structure of the 2D flash chip. The high-resolution transmission electron microscope (HR-TEM) images provide magnified views of the 2D flash cell, confirming the clean interfaces of the functional layers (Fig. 1d, right).

Full-stack on-chip process

The 2D flash module is integrated above a rough CMOS die (Fig. 2a, left) through back-end-of-line compatible integration. Figure 2a (middle) shows the overall 3D architecture of the 2D flash chip. The 2D flash module comprises the floating gate transistor cells, with monolayer MoS_2 and HfO_2/Pt/HfO_2 serving as the channel material and the memory stack, respectively. To alleviate the contradiction between 2D electronics and the CMOS platform, a modular structure is proposed (Fig. 2a, right). The direct cell-to-cell integration of 2D flash cells with CMOS circuitry could introduce severe compatibility issues, stemming from the inherent mismatch in their operational modes. Our 2D flash memory core and CMOS platform are designed and fabricated separately as different function modules and connected through a specially designed 2D-CMOS module interface. Therefore, the compatibility issues can be effectively converted to interface design with the least adjustment in the planar integration process.

Fig. 2: The full-stack on-chip process.

a, The 3D architecture of the fabricated 2D flash chip. Left, the CMOS die serves as the substrate, with a PA layer of 800 nm for isolation and TGVs for communication. Right, modular design for converting compatibility issues to the 2D-CMOS module interface design. b, Magnified optical micrograph of the CMOS die highlighting dense random circuit routing. Inset, corresponding atomic force microscopy (AFM) image with roughness RMS of 1.35 nm (amplitude range of 5 nm). c, AFM image of the 2D flash integrated on the CMOS die (amplitude range of 8 nm). The conformal adhesion of 2D materials to the rough CMOS die surface facilitates stress relief. d, Statistical results of memory window characterization of the 2D flash. The 2D flash cells fabricated by the conformal adhesion on-chip process exhibit compact, distinguishable Vth distributions for on–off states (red solid line, 60 cells extracted from Extended Data Fig. 1a). The non-ideal behaviour, caused by yield and uniformity limitations, exhibits a broader distribution with overlap (blue dashed line). e, Schematic of the comprehensive protection in the 2D-friendly packaging. Left, region-specific ESD protection. ESD1 for WL/BL/SL, ESD2 for power/ground, ESD3 for inputs and ESD4 for outputs. The hatched areas denote the internal circuit associated with the corresponding pads. Top right, comparison of 2D specialized ultrasonic wire bonding with low thermal and strain budget (right) to conventional thermocompression approach with high thermal and strain budget (left). Bottom right, room temperature (RT) curing in a die attachment process. Scale bar, 5 μm (b,c). VDD, high power supply voltage; VSS, low power supply voltage.

Source data

The planar integration aims to tackle the yield loss from rough CMOS die. The dense and random routing of CMOS modules produces surface morphology variations with a root mean square (RMS) roughness of 1.35 nm after chemical–mechanical polishing (Fig. 2b), inducing random stress in atomic thin MoS_2 and reducing the yield and uniformity of integrated 2D flash devices. To alleviate these stresses, we developed a conformal adhesion on-chip integration process, with gradual-release transfer and multi-step, multi-scale annealing (details provided in the Methods). The AFM image in Fig. 2c confirms the conformal adhesion of 2D materials on the rough CMOS die, thereby facilitating stable channel performance and dielectric environment. Supplementary Information section 2 provides more characterizations of the process. Figure 2d shows the tight and clearly separated threshold voltage (Vth) distributions for devices fabricated with our conformal adhesion on-chip planar process, compared with the non-ideal behaviour.

Electronic packaging is essential for chip-scale integration, yet 2D chip packaging remains underexplored. As sensitive 2D materials can be damaged by ESD, high temperature and mechanical stress in the packaging process, we developed a 2D-friendly packaging strategy (Fig. 2e) that delivers comprehensive protection. First, region-specific ESD protection is implemented for all pads (Fig. 2e, left). According to protection requirements, four types of ESD circuit (ESD1–4) were designed and positioned alongside the WL/BL/SL mini pads, power/ground, input pads and output pads, respectively. Second, ultrasonic bonding specialized for 2D materials is conducted at room temperature at low pressure (Fig. 2e, top right). This decreases the thermal and stress budgets and reduces post-bond leakage of the 2D circuit by more than tenfold to less than 1 pA (Supplementary Fig. 12). Third, the adhesive that cures at room temperature is used for die attachment (Fig. 2e, bottom right), which further minimizes the thermal damage. Moreover, a photoresist layer encapsulation is adopted to protect the chip against environmental degradation (Supplementary Fig. 13). Supplementary Information section 3 summarizes the detailed packaging considerations and protection effect. Moreover, a comparison between the function of CMOS modules before and after the integration of the 2D flash module demonstrates that the full-stack on-chip process is back-end-of-line compatible and would not damage the CMOS modules (Supplementary Fig. 14).

Extended Data Fig. 1 shows the outstanding performance of 2D flash cells. More than 1,000 devices were tested to verify the lossless full-stack on-chip process with high uniformity. The 2D flash cells support fast programming and erasing with 20 ns and low energy consumption, evaluated to be 0.644 pJ per bit. Extended Data Fig. 2 shows the good retention performance of the 10-year non-volatile at 54.8 °C. Endurance and read disturb tolerance have been proven to be more than 10^4 and 10^6 cycles, respectively. Supplementary Information section 5 discusses more details on 2D flash performance.

Cross-platform system design

Figure 3a shows a cross-platform compatibility verification methodology that we proposed to make all the modules work together. This methodology begins with the design of the 2D flash module. As the slow voltage settling of NAND limits its programming speed, we use NOR architecture to realize fast operation. High-speed operation modes that inhibit crosstalk are designed on the basis of the fast Fowler–Nordheim mechanism. The device and circuit parameters are then extracted. Based on the operation mode and the extracted impedance parameters, the CMOS modules are designed to ensure compatibility with the 2D flash. Finally, the cross-platform system is validated by a comprehensive simulation.

Fig. 3: The cross-platform compatibility verification methodology.

a, Schematic showing the 2D module design and 2D-compatible CMOS modules design for realizing a 2D flash memory chip. b, The Si device design in the power switch module for voltage domain compatibility with 2D flash. The isolation ring decouples source–drain from the p-substrate, allowing local negative voltage biasing. A supplemental buried N-well improves voltage tolerance for 2D flash operation. c, The 2D compatible inverter chain design within the buffer modules. Stage count and driver ratio were optimized on the basis of 2D flash load capacitance and CMOS inverter input capacitance. The output waveforms under different driver abilities were simulated by adjusting the transistor W/L ratio in the final inverter. d, Sense amplifier design optimization and readout characterization. Data sequence ‘0101’ across four WLs is simulated for reading. The BL parasitic capacitance leads to misreading of SA1 (for details, see Extended Data Fig. 4). SA2 achieves correct reading by isolating the BL parasitic capacitance and further improves readout speed by reducing the load of the readout circuit (for details, see Extended Data Fig. 5). e, Timing diagram of programming operation. The operation instructions include 8-bit commands (06H, 02H, where H represents hexadecimal), address and 4 data bytes. WL[22] is accessed for programming, and 32-bit input data is programmed in parallel to WL[22]. CS, chip select signal; SPI_SCLK, serial clock of the Serial Peripheral Interface protocol; SPI_SI, serial data input of the SPI protocol; addr, address; din, data input; clk, clock; GND, ground.

Source data

Extended Data Fig. 3 shows the crosstalk suppression design of the 2D NOR flash circuit with a half-selected scheme. Crosstalk tests in many different scales, including single device, 4 × 4 array and 4 × 32 array, demonstrate slight mean Vth shifts of 0.024 V and −0.006 V for programming and erasing crosstalk, respectively. The crosstalk of a 2D flash cell subjected to consecutive crosstalk pulses was also examined, demonstrating good crosstalk suppression ability. Supplementary Table 3 summarizes the operation mode with the half-selected scheme. Furthermore, the impedance parameter of the 2D flash module is extracted for designing 2D-compatible CMOS modules (Supplementary Table 4).

The maximum voltage drop across the 2D-CMOS interface modules can be reduced to 7 V with the half-selected scheme. This helps to avoid unintended breakdown due to the high voltage in the interface modules, and the complex charge pump design may also be eliminated. Meanwhile, the negative voltage required for the 2D flash module can increase the risk of forward-biasing parasitic PN junctions in CMOS circuitry, thereby inducing huge leakage current. Therefore, the isolated devices are designed for interface modules, such as power switch, to meet the voltage requirements. As Fig. 3b shows, the isolated NMOS transistor incorporates an isolation (ISO) ring and a deep N-well to separate the device P-well (body) from the global P-well (substrate), enabling local negative-voltage application. The ISO ring is biased at VMAX—the highest potential relative to adjacent regions—to prevent forward biasing of parasitic PN junctions. The buried N-well in the isolated device further enhances the electrical isolation and suppresses latch-up, thereby increasing the voltage tolerance.

To ensure the 2D flash chip functions, the WL/BL/SL buffers and SA must be designed to match the impedance of the 2D flash module for voltage waveform output and data readout. As shown in Fig. 3c, the inverter chain incorporated within the buffer modules was engineered with the logical effort technique to match the load (WL capacitance) and minimize signal propagation delay for fast waveform generation (Methods). The ability of the driver is substantially improved with impedance matching (Fig. 3c, right). Supplementary Information section 7 demonstrates the proper function of buffer modules. Figure 3d shows the SA design optimization for accurate and fast data readout, validated by simulating a ‘0101’ data sequence readout from four cells across four WLs. By isolating BL capacitance and reducing load capacitance, SA2 (with 2D-compatible design; Extended Data Fig. 5) reduces the reading time by 70% and achieves correct readout compared with SA1 (non-compatible design; Extended Data Fig. 4).

Simulation verification was performed, covering the programming, erasing and reading operation modes. Figure 3e shows the timing diagram for internal command and data transmission during the programming operation. The programming instruction includes two 8-bit command bytes, an address byte and 4 data bytes. WL[22] is addressed, and voltages are applied to 32 bits on WL[22] concurrently, achieving parallel programming. Extended Data Fig. 6 provides the timing diagrams of erasing and reading operations. These verification results confirm that the 2D flash can support instruction-driven operations, up to 32-bit parallelism and random-access ability.

Function demonstration based on full-chip test

Figure 4a shows the functional testing of the fabricated 2D NOR flash chip using a dedicated chip test system. The host computer provides a software interface and loads the test program onto the field-programmable gate array (FPGA), which then transmits the instructions to the 2D flash chip. The arbitrary waveform generator (AWG) and d.c. power supply provide the necessary clock and d.c. signals, respectively. Figure 4b shows the data flow of the 2D flash chip. When the power supply is activated, the POR circuit gives the reset bar (rstb) signal and enables the chip for normal operation. External instructions are conveyed to the logic module by the I/O module, generating three types of signal: control logic signals, address signal and data signal. Following these instructions, the power switch module adjusts the required voltage domain to each buffer, depending on the specific operation modes. The voltage pulses are then applied to the corresponding ports of the memory array through the WL/BL/SL buffers, completing the desired operation.

Fig. 4: Full function demonstration based on full-chip test.

a, Schematic of the chip test system. The AWG and d.c. power supply provide the required external clock signals (OSC) and d.c. signals, respectively. The FPGA transmits the command and data between the host computer and the I/O ports of the 2D flash chip, including the CS, SCLK, serial data input (SI) and serial data output (SO). The oscilloscope monitors pulse waveforms generated by the AWG. b, Data flow of the 2D flash chip. Modules are labelled in rectangular boxes, whereas the flow of key signals is indicated by arrows. c, Histogram of the programming accuracy across the 32 WLs after checkerboard programming. About 93.55% of cells reach the target states corresponding to the checkerboard pattern. dout, data output; rd_clk, read clock; rdbl, read bit line.

Source data

Full-chip programming and erasing tests were performed under a 5 MHz clock with a 500-ns operation pulse (one pulse lasts for 2.5 clock cycles) to ensure reliable operation, as discussed in Supplementary Information section 8. The results are summarized in Supplementary Table 5, showing an overall yield of 94.34%. A failure analysis (Supplementary Information section 9) showed that operational failures were primarily caused by process issues, which led to channel cracks and Vth variations. Our yield marks a marked advance in the integration of 2D electronics above the 1-Kb scale of the on-chip process11,40,41. Moreover, the International Technology Roadmap for Semiconductors requires a yield of approximately 89.5% in flash manufacturing[42], so further optimization of our chip is expected to lead to practical applications.

As a more complex chip-level function demonstration, the test of programming a checkerboard pattern (a pattern of alternating state-0 and state-1) was performed. Supplementary Table 6 provides the datasheet of the memory states before and after the checkerboard programming. Figure 4c shows the programming accuracy of each row. Approximately 93.55% of the cells achieved the correct states corresponding to the checkerboard pattern. Only three cells were unintentionally programmed, confirming effective crosstalk suppression design. Supplementary Log Data provides the original log file generated during the chip testing process. Supplementary Video shows the process of chip checkerboard programming validation by the host computer.

The full-chip test yielded the following peripheral circuitry average supply current at the maximum parallelism: programming, 1.04 mA; erasing, 1.25 mA; and reading, 1.14 mA, corresponding to the power consumption of 5.2 mW, 6.25 mW and 5.7 mW, respectively. These are close to commercial standalone NOR flashes with similar technology nodes43,44,45. Moreover, advanced embedded NOR flash with systematic energy consumption optimization effectively reduces energy consumption from peripheral circuits, making cell programming energy the dominant factor[46]. The 2D flash with a low programming energy consumption of 0.644 pJ per bit has great potential in advanced embedded applications. Supplementary Information section 10 provides a comprehensive comparison between 2D flash and Si flash. Supplementary Information section 11 discusses the scalability of the 2D flash chip. Notably, as the current NAND and NOR architectures are designed for silicon flash cells, further expansion of the speed and energy consumption advantages of 2D flash from the device to the system level requires an innovation in memory architecture that is tailored to the mechanisms of 2D devices.

Conclusion

We have demonstrated a full-featured 2D NOR flash chip using the ATOM2CHIP technology. The full-stack on-chip process ensures a high yield of 94.34% by addressing random stress resulting from random roughness of the CMOS circuitry and damage from conventional chip packaging. The fabricated 2D flash cells support 20-ns fast operation and 0.644-pJ per bit low energy consumption. The proposed cross-platform system design provides a methodology to ensure compatibility between 2D electronics using emerging mechanisms and the mature CMOS platform. The 2D NOR flash chip is demonstrated to be capable of instruction-driven operation, 32-bit parallelism and random access using a 5-MHz clock. This work provides a promising technical pathway to bring promising 2D electronics concepts to real-world applications.

Methods

Flash chip fabrication

The CMOS circuitry was fabricated in a standard CMOS foundry using a 0.13-μm process. The received 8-inch wafer had a passivation layer thickness of approximately 800 nm, with pre-reserved vias at the port pads of I/O (TGV1 region) and WL/BL/SL buffers (TGV2 region). The wafer was cut into individual dies, each with a dimension of 5 mm × 5 mm (four sets of identical circuits included). Polymer-mediated delamination treatments were performed on the CMOS substrate before integrating 2D flash. The CMOS substrate was cleaned by soaking in acetone for 12 h, followed by spin-coating with photoresist (S1818) and removal of the photoresist using N-methyl-2-pyrrolidone (NMP) soak for 12 h.

Direct-write lithography was used to expose windows at the TGV2 region, and e-beam evaporation (EBE) was used to fill the vias with 5/500 nm Cr/Au. WLs were defined using direct-write lithography, followed by the deposition of 5/100/5 nm Cr/Au/Pt. The O_2 plasma treatment (50 W, 20 s) was used to further clean and activate the surface for dielectric deposition. A 13-nm HfO_2 blocking layer was deposited using thermal atomic layer deposition. Tetrakis(ethylmethylamino)hafnium reacts with water at 150 °C to form HfO_2. The floating gate pattern was defined by direct-write lithography, and 3-nm Pt was deposited by EBE. The O_2 plasma treatment was performed again. Subsequently, a 7-nm HfO_2 tunnelling layer was deposited using the same atomic layer deposition system. Vias through the HfO_2/Pt/HfO_2 memory stack were defined by direct-write lithography and etched using reactive ion etching (Ar + CHF_3, 175 W, 255 s), and EBE was then used to deposit a 5/50 nm Cr/Au layer to fill the vias. Chemical vapour deposition monolayer MoS_2 (purchased from Sixcarbon Technology) was transferred onto the memory stack using a gradual-release transfer process. The minimum approach speed between MoS_2 and the substrate is carefully controlled to be as low as 500 nm per step using the custom-made transfer equipment. Polystyrene was used as the supporting layer because of its large Young’s modulus to avoid wrinkling. The polystyrene supporting layer was removed by soaking in toluene for 12 h. The MoS_2 channels were patterned by direct-write lithography and etched by O_2 plasma (30 W, 20 s). The sample was soaked in NMP for 12 h to remove the photoresist. To fully release stress and air gaps in MoS_2, multiple annealing processes in an N_2 atmosphere (200 °C, 3 h) were performed for both large-area films and patterned strips. The adhesion between MoS_2 and the substrate can also be enhanced during these processes. BLs and SLs were defined by direct-write lithography, followed by the deposition of 5/100 nm Cr/Au using EBE. For the fabrication of the 2D flash on a SiO_2/Si substrate, the process involving the vias mentioned above is not required.

To passivate the 2D flash module, a layer of S1818 photoresist was spin-coated onto the sample. The TGV1 region of the I/O module was exposed by direct-write lithography for wire bonding. The chip was packaged using a ceramic dual-in-line package (DIP 24).

Inverter chain design of the buffer module

According to the logical effort theory, the total logical effort, determined by the ratio of the load capacitance (10 pF in our case, considering design margin) to the inherent input capacitance of the first-stage CMOS inverter (2 fF, decided by selected CMOS technology), should be distributed across a chosen number of inverter stages for an optimized propagation delay time. The propagation delay time of the inverter chain in the buffer can be calculated by

$${t}_{{\rm{p}}}={t}_{{\rm{p}}0}\mathop{\sum }\limits_{j=1}^{N}\left(1+\frac{{C}_{{\rm{g}},j+1}}{\gamma {C}_{{\rm{g}},j}}\right)$$

(1)

where N is the number of stages of the inverter chain, Cg,j is the gate capacitance for the jth inverter, Cg,N+1 is defined as the capacitance load, here parasitic capacitance of the 2D memory array, tp0 is the intrinsic delay for the inverter and γ is a parameter dependent on the process, usually near 1.

For an optimized design, the gate capacitance (and the inverter size) should be the geometric mean of the adjacent inverters, such that

$${C}_{{\rm{g}},j}=\sqrt{{C}_{{\rm{g}},j-1}{C}_{{\rm{g}},j+1}},{\rm{where}}\;j=2,\ldots ,N$$

(2)

and the optimized propagation delay time can be written as

$${t}_{{\rm{p}}}=N{t}_{{\rm{p}}0}\left(1+\sqrt[N]{\frac{{C}_{{\rm{g}},N+1}}{{C}_{{\rm{g}},1}}}/\gamma \right)$$

(3)

Usually, Cg,1 is the minimum inverter gate capacitance for a certain process—in our work, 2 fF—and Cg,N+1 is 10 pF. Therefore, the optimized N for the inverter chain is 6 with a propagation delay of about 27.3tp0, whereas N = 4 is sufficient with a delay of around 30.7tp0 and offers benefits related to buffer size. For an inverter of each stage, the driver ratio is \(\sqrt[N]{\frac{{C}_{{\rm{g}},N+1}}{{C}_{{\rm{g}},1}}}\approx 8\), and the optimized driver chain is designed as shown in Fig. 3c.

Material characterization

The TEM-ready samples were prepared using the in situ FIB lift-out technique on an FEI Strata G4 HX dual-beam FIB scanning electron microscope. The samples were capped with sputtered electron-beam Pt and ion-beam Pt before milling. STEM and TEM images were captured with the Thermo Scientific Tecnai Z aberration-corrected transmission electron microscope at an accelerating voltage of 200 kV. Energy-dispersive spectra were obtained in STEM mode using a Super X FEI system. The AFM images of the devices were measured by an MFP-3D Origin+ (Asylum Research, Oxford Instruments) system. Optical images were captured by an optical microscope (OLYMPUS BX53M) and an extended-DOF microscope (KEYENCE VHX-6000).

Electrical measurements

The electrical characterization of the standalone 2D flash devices and the 4 × 32 array was carried out at room temperature and under atmospheric conditions (except the retention test) in a probe station (Cascade Summit 11000 type). The retention test was conducted in a customized vacuum probe station. The voltage pulses were generated using a semiconductor parameter analyser (B1500, Keysight). The waveform was captured using an oscilloscope (DPO 5204, Tektronix).

The electrical characterization of the 2D flash chip was performed with a dedicated chip test system. The arbitrary waveform generator (33120 A, Agilent) provides clock signals, monitored by an oscilloscope (DSOX1204A, Keysight). The d.c. power supply (E36312A, Keysight) provides d.c. signals required for testing the chip, including −1 V, −5 V, 2 V, 3 V, 5 V and 9 V. The host computer provides a software interface and loads the test program onto the FPGA. FPGA transmits the command from the host computer to the I/O ports of the 2D flash chip. The packaged 2D flash chip was placed into a test socket compatible with the DIP package before testing.

Data availability

The data that support the plots in this paper and other findings of this study are available from the corresponding authors upon request. Source data are provided with this paper.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2024YFA1208400 and 2021YFA1200500), the National Natural Science Foundation of China (62322405, 62374042 and 62525401), the Shanghai Pilot Program for Basic Research, FuDan University 21TQ1400100 (21TQ011), the New Cornerstone Science Foundation through the Xplorer Prize and the young scientist project of the MOE innovation platform.

Author information

Author notes

1. These authors contributed equally: Chunsen Liu, Yongbo Jiang, Boqian Shen, Shengchao Yuan, Zhenyuan Cao

Authors and Affiliations

1. State Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits and Micro-Nano Electronics, Frontier Institute of Chip and System, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China

Chunsen Liu, Yongbo Jiang, Boqian Shen, Shengchao Yuan, Zhenyuan Cao, Zhongyu Bi, Chong Wang, Yutong Xiang, Tanjun Wang, Haoqi Wu, Zizheng Liu, Yang Wang, Shuiyuan Wang & Peng Zhou

2. Shaoxin Laboratory, Zhejiang, China

Yang Wang & Peng Zhou

Contributions

C.L. and P.Z. conceived the idea; C.L., Y.J., B.S., S.Y. and Z.C. designed and conducted the experiment; Z.B., Y.X. and C.W. helped with their discussions and provided TCAD simulation support; T.W., H.W., Z.L., Y.W. and S.W. provided experimental assistance. C.L., Y.J. and B.S. co-wrote the manuscript; P.Z. provided inputs for the discussion and revision of the paper.

Corresponding authors

Correspondence to Chunsen Liu or Peng Zhou.

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Extended data figures and tables

Extended Data Fig. 1 The performance of 2D flash cells fabricated through full-stack on-chip process.

a, b, Lossless and high uniformity of 2D flash cells. Dual sweep transfer characteristic curves of 2D flash cells fabricated on highly flat SiO_2 substrate (60 cells) and on CMOS substrate (60 cells) through full-stack on-chip process (a). The ON/OFF current extracted from (a) at VGS = 0 V (b). c, d, Typical transfer characteristic curves among 1,008 cells (c) and output characteristic curves among 1,012 cells (d) of state-ON and state-OFF. The 2D flash cells are programmed by progressive amplitude pulses, with a fixed pulse width of 20 ns. e, f, TCAD simulation for programming energy consumption evaluation. The device structure for simulation (e). The channel length (2 μm) and width (2 μm) of the simulated MoS_2 flash memory were the same as the fabricated 2D flash cell. The tunnelling current is simulated to be 2.3 μA under designed operation voltage and tunnelling layer thickness (f). Therefore, the programming energy consumption is evaluated to be 20 ns × 2.3 μA × 14 V = 0.644 pJ/bit. More details about the TCAD simulation method and energy consumption evaluation are discussed in Supplementary Information Section 5.

Source data

Extended Data Fig. 2 Retention performance for 2D flash cell with HfO[2] tunnelling layer.

Vth shift for state-ON (a) and state-OFF (b) at 55 °C, 85 °C and 125 °C are extracted by the Vth difference between as-programmed cell and cell after baking to present the retention loss. c, Total memory window with different bake temperature and time. The memory window considers both the retention loss from state-ON and state-OFF in (a) and (b). When MW lost by half, the cell is determined to fail. d, the temperature of retention lifetime extrapolated from (c). A 10 years lifetime at 54.8 °C is extracted with Arrhenius model.

Source data

Extended Data Fig. 3 The crosstalk suppression 2D NOR flash circuit design.

a, Circuit diagram of the 2D NOR flash (2 × 2 array depicted for clarity). The upper left cell is the selected cell to be programmed (red block), and the two cells adjacent to it are the half-selected cells where crosstalk may occur (blue block). The half-selected cells only exposed to 1/2 VPP. b, Simplified band diagram of selected cell and half-selected cells. The tunnelling efficiency of the half-selected cell is considerably reduced due to the lower electric field, since the tunnelling efficiency exhibits an exponential dependence on the applied voltage. c, d, The crosstalk evaluation by applying a crosstalk pulse to 100 2D flash cells (c) and consecutively applying multiple crosstalk pulses to one 2D flash cell (d). Pulse of 7 V and −7 V for programming crosstalk and erasing crosstalk, respectively. The pulse width is 100 ns.

Source data

Extended Data Fig. 4 Circuit schematic of SA module without 2D-compatible design (SA1) (a) and corresponding reading simulation results (b).

In the reading operation, MP9 is first opened to pre-charge VCOMP to 5 V (VDD). The reference current IREF (typically 10%–30% of the extracted cell ON-state current) is generated using AMP1 and the external resistor, transferred to VCOMP node through the current mirror and compared with BL current IBL to generate the output signal VOUT. During pre-charge, VBL is coupled to over 3.11 V, which leads to AMP2 at the wrong working condition. Due to the relatively small 2D current (~100 nA) and large BL parasitic capacitance (CBL), it takes a long time for VBL to discharge to below 3 V through BL for AMP2 to work, resulting in read error within the target read timing.

Source data

Extended Data Fig. 5 Circuit schematic of SA module with 2D compatible design (SA2) (a) and corresponding reading simulation results (b).

A switch transistor (MN7) is introduced to isolate the high capacitance load from the 2D memory array. Therefore, the capacitance couple effect is weakened, and VBL is only coupled to 3.038 V, which considerably reduces the time required for VBL discharging. The fast and accurate cell state readout is achieved. Besides, the CMOS inverter (corresponding to INV1 in Extended Data Fig. 4) is substituted with a pseudo-PMOS inverter, which can reduce the SA load capacitance, for better operation mode compatibility and lower propagation delay time.

Source data

Extended Data Fig. 6 Timing diagram of erasing and reading operations.

a, Full-chip erasing operation. The host computer sends the commands 06H and C7H. The all_en (to select all WLs) signal is activated, selecting the entire chip. The logic module generates an erase enable signal for the analogue circuit, while simultaneously producing a synchronised clock signal to apply the corresponding voltages to all cells. b, 32-bit parallel reading operation. The host computer sends the command 03H. In this timing diagram, address “00H” (corresponding to WL[0]) is selected for reading. The read enable signal is activated, and all the 32 BLs are applied with a reading bias. When the rd_clk (read clock) signal is activated, the sense amplifier amplifies the readout data, then sends the 32-bit data to the logic module, where the data is processed from parallel to serial. When the o_so_en (data output enable) signal is activated, the logic module transmits the data o_padout (data output) to the I/O module for data readout.

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Sections 1–11; Supplementary Figs. 1–32; Supplementary Tables 1–8 and Supplementary References.

Supplementary Log Data

This file contains the original log file generated during the chip testing process.

Supplementary Video 1

This file contains the process of programming a checkerboard pattern.

Source data

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Source Data Extended Data Fig. 1

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Liu, C., Jiang, Y., Shen, B. et al. A full-featured 2D flash chip enabled by system integration. Nature 646, 1081–1088 (2025). https://doi.org/10.1038/s41586-025-09621-8

- Received: 01 February 2025

- Accepted: 11 September 2025

- Published: 08 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09621-8

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Nature volume 646, pages 1089–1095 (2025)

Abstract

As demand for immersive experiences grows, displays with smaller sizes and higher resolutions are being viewed increasingly closer to the human eye[1]. As the size of emitting pixels shrinks, the intensity and uniformity of their emission are degraded while colour cross-talk and fabrication complexity increase, making ultra-high-resolution imaging challenging2,3,4. By contrast, electronic paper, which uses ambient light for visibility, can maintain high optical contrast regardless of pixel size, but cannot achieve high resolution5,6. Here we demonstrate electronic paper with electrically tunable metapixels down to ~560 nm in size (>25,000 pixels per inch) consisting of WO_3 nanodisks, which undergo a reversible insulator-to-metal transition on electrochemical reduction. This transition enables dynamic modulation of the refractive index and optical absorption, allowing precise control over reflectance and contrast at the nanoscale. By using this effect, the metapixels can achieve pixel densities approaching the visual resolution limit when the display size matches the pupil diameter, which we refer to as retina electronic paper. Our technology also demonstrates full-colour video capability (>25 Hz), high reflectance (~80%), strong optical contrast (~50%), low energy consumption (~0.5–1.7 mW cm–2) and support for anaglyph 3D display, highlighting its potential as a next-generation solution for immersive virtual reality systems.

Main

From cinema screens and televisions, to smartphones and virtual reality headsets, displays have progressively moved closer to the human eye, featuring smaller sizes and higher resolutions. As display technology advances, a fundamental question arises regarding the ultimate limits of display size and resolution. To provide an immersive and high-fidelity visual experience, the display is designed with a size comparable to the human pupil and an ultra-high pixel density. It establishes a conceptual benchmark inspired by the resolving limits of the retina (Fig. 1a) and defines a practical boundary for display technologies, which we term the retina display.

Fig. 1: Schematic of retina E-paper.

a, Conceptual illustration of an ultimate virtual reality display. The display is sized to approximate the human pupil and features an ultra-high pixel density serving as a conceptual benchmark inspired by the retina, supporting ultra-fine visual detail. b, Structural diagram of metapixels (subpixels). The metapixels consist of WO_3 nanodisks and a reflective layer on a glass substrate. By varying D and W of the nanodisks, the metapixels can selectively reflect RGB colours. Further tuning of T enables the generation of hybrid colours such as CMY. As WO_3 is electrochromic it can undergo reversible electrochemical reactions, yielding reflectance modulation of the WO_3 nanodisks, enabling an RGB video display.

Assuming an effective display aperture of 8 mm, corresponding to the maximum human pupil diameter under scotopic conditions, and a field of view of 120°, consistent with the functional limits of human vision, achieving the maximum angular resolving capacity of approximately 60 pixels per degree would require a display pixel density of around 23,000 pixels per inch (PPI). This value represents a conceptual benchmark rather than a practical specification, because under typical photopic conditions with a smaller pupil size (~4 mm) the required pixel density would be even higher. In practice, however, as the display is not intended to be positioned directly in the pupil plane (because of safety considerations and to satisfy optical invariants such as étendue), the resolution requirement can be relaxed, because increasing the eye–screen distance effectively enlarges the screen size.

Unfortunately, as pixel sizes continue to shrink in mainstream emissive displays, diminished emitter dimensions lead to reduced brightness, compromised uniformity, increased colour cross-talk and greater fabrication complexity, posing considerable challenges for ultra-high-resolution imaging1,2,3. Currently, commercially available smartphone display pixels are typically around 60 × 60 μm^2 (~450 PPI), which is approximately 2,500 times larger than the theoretical size required for the ultimate retina display. Already at this scale, the emitted light becomes difficult for the naked eye to perceive, particularly in bright outdoor environments. Moreover, the smallest published colourful micro-light-emitting-diode (micro-LED) display currently available achieves only a pixel size of 4 × 4 μm^2 (excluding the distance between pixels)[4], making it challenging to replicate retinal-level resolution across vast fields of view. Furthermore, at such small scales, colour cross-talk and uniformity remain considerable technical hurdles. These limitations expose the large challenges of using conventional emissive display technology to realize the ultimate virtual reality display.

Reflective displays, which rely on ambient light for visibility, do not have luminosity issues, and their optical contrast remains unaffected by pixel size reduction given that reflection is governed by the polarization of materials at the nanoscale. However, existing reflective display (electronic paper or E-paper) technologies are hampered by marked limitations. Reflective liquid crystal displays, for instance, are constrained by the thickness of the liquid crystal layer, whereas electrophoretic displays (such as the Kindle) are restricted by the size of their capsules5,6. So far no commercially available reflective display technology has achieved high resolutions of above 1,000 PPI.

Optical metasurfaces have demonstrated the capability to achieve ultra-high pixel densities of more than 10,000 PPI (~2.5 μm pixel size), with patterned nanomaterials capable of printing images at resolutions of up to around 100,000 dots per inch, approaching the optical diffraction limit7,8,9. However, most modern nanoprinting methods rely on static materials, such as metals or high-refractive-index dielectrics10,11,12,13. When applied to dynamic display systems (for example, meta-organic LED), these materials require modulation through micro-light sources, which are still affected by the inherent limitation of electromagnetic reduced intensity as resolution increases[3]. Furthermore, they are often affected by lateral light leakage between adjacent colour subpixels, limiting their ability to produce ultra-high-resolution images. In fact, static reflective displays are also influenced by interactions between neighbouring pixels, altering their optical properties at ultra-high pixel densities, making it challenging to use conventional red–green–blue (RGB) subpixel configurations for image display13,14,15.

In recent years, there has been growing interest in integrating dynamic and static materials to explore tunable nanophotonics systems. Particularly in the field of displays, hybrid nanomaterials—combining tunable conjugated polymers or semiconductors as colour modulators with metallic nanostructures—have demonstrated the ability to modulate the intensity or the reflected colours of subpixels16,17. These technologies significantly enhance the colour gamut, reflectivity and optical contrast of E-paper and enable video display functionality18,19,20,21. However, owing to limitations in structure, materials and fabrication methods, the pixel sizes of these hybrid nanomaterials remain in the range of tens to hundreds of micrometres, making it challenging to achieve ultra-high-resolution displays22,23,24.

Here we propose a conceptually new E-paper technology, termed retina E-paper, capable of achieving ultra-high resolutions exceeding 25,000 PPI (~560 nm), surpassing the theoretical human visual limit of 60 pixels per degree across a 120° field of view on an 8 mm screen. The retina E-paper comprises electrochromic WO_3 metapixels, which undergo an insulator-to-metal transition during electrochemical insertion of, for example, alkali ions, allowing electrically dynamic control over optical properties such as refractive index and absorption. This transition enables tunable reflectance and contrast, which is critical for optimizing display performance. The basic colour generation principle builds on the hierarchical structuring of building blocks, Mie scattering and interference between the building blocks. This technology enables the practical construction of ultra-high-resolution displays (for example, exceeding 100 megapixels) within a compact area, paving the way for the ultimate virtual reality display.

The retina E-paper comprises electrochromic WO_3 metapixels integrated with a highly reflective substrate (Pt/Al). Its normalized high reflectance (~80%) and optical contrast (~50%) remain unaffected by pixel size reduction, maintaining exceptional visibility even at pixel sizes as small as approximately 400 nm. To minimize interference between adjacent pixels, we carefully optimized the dimensions and spacing of the primary colour metapixels, enabling full-colour displays by precisely mixing RGB subpixels. Furthermore, the substrate (Al/Pt) exhibits excellent conductivity. By reducing the lateral distance between the working and counter electrodes to 500 nm and using short-pulse input signals, we achieved greater than 95% optical contrast modulation of the WO_3 nano-pixels within 40 ms, supporting a video display of more than 25 Hz. This refresh rate is more than ten times faster than those of the previously reported fastest WO_3-based electrochromic devices[25]. The short distance between the working and counter electrodes enhances the external electric field driving ion doping, and this nanodisk design significantly reduces the required amount and increases the reaction surface area of WO_3 compared with the planar surface. Furthermore, unlike emissive displays that require constant power, retina E-paper features colour memory, consuming energy mainly during pixel switching. Its power usage is around 1.7 mW cm–2 for video and around 0.5 mW cm–2 for static images, significantly less than that of other types of E-reader[26].

Finally, to demonstrate the potential of retina E-paper for virtual reality applications, we use cyan, magenta and yellow (CMY) colour metapixels to reconstruct an anaglyph 3D butterfly image. Furthermore, to showcase its full-colour display performance, we reproduced a high-resolution image inspired by the iconic painting The Kiss by Gustav Klimt and dynamically modulated the colours using electrical control. The retina E-paper features a compact surface area of around 1.9 × 1.4 mm^2 (about 1/4,000th the size of a standard smartphone display) while achieving an impressive resolution of 4,300 × 700 pixels.

Figure 1b illustrates the fundamental structure of the retina E-paper, composed of electrochromic WO_3 metamaterials integrated with a highly reflective (Al/Pt) substrate. In the bright state, WO_3 behaves as a dielectric material with a high refractive index ranging from around 2 to 2.4 in the visible spectrum[27], enabling the generation of high-resolution colours even at sizes smaller than the incident wavelength[11]. By precisely tuning the diameter (D) and spacing (W) of the nanodisks, the scattering modes can be adjusted to reflect the primary colours, RGB, forming the subpixels of one pixel of the retina E-paper. As the subpixels are made of meta-material, they are also called metapixels; however, optical interactions between these nanodisk-based subpixels can also affect colour mixing. Further tuning of the subpixel spacing (T) is required to ensure proper additive colour blending for display applications. After patterning the RGB subpixels, the next step is intensity modulation. As WO_3 is an electrochromic material, meta-atom absorption can be dynamically modulated by electrochemical reactions under applied voltage, altering the reflectivity of the subpixels. This capability enables the retina E-paper to achieve dynamic colour modulation for display applications. The nanofabrication process is shown in Extended Data Fig. 1.

Figure 2a demonstrates how the reflective colours of WO_3 metapixels vary at a fixed thickness of 110 nm while varying the nanodisk diameter from 220 nm to 320 nm and the spacing from 100 nm to 200 nm. The thickness was chosen to balance Mie-resonance-based colour purity and electrochemical switching speed. Thinner layers may accelerate switching but reduce optical confinement and weaken Mie scattering, thus degrading colour saturation. This geometry range enables the metapixels to cover the entire visible spectrum. However, it is essential to note that not all RGB pixels are suitable for subpixels in retina E-paper. The reflected colour of each subpixel in an ultra-high-resolution E-paper system is influenced not only by its geometry but also by interactions with adjacent pixels (Fig. 2c). Therefore, selecting appropriate RGB pixels and ensuring that their hybrid reflected colours adhere to the principles of additive colour mixing is a critical step for achieving full-colour displays. In Fig. 2a (right), the spectra and corresponding geometries of the selected RGB pixels are presented: R (D = 220 nm, W = 200 nm), G (D = 260 nm, W = 200 nm) and B (D = 260 nm, W = 140 nm). The spectra are normalized to the reflective layer to highlight the WO_3 nanodisk colour-tuning by structural changes. Unlike emissive displays, in which visibility diminishes with pixel size reduction, E-paper technology maintains consistent brightness and reflectivity even at ultra-high resolutions. As illustrated in Fig. 2b, the red pixel retains its colour and reflectance in both bright- and dark-field microscope images, even as the size is reduced from 20 μm to 420 nm. A minimum of four nanodisks per pixel is required to preserve the Mie scattering and grating modes of the nanodisks, resulting in minimum pixel sizes of 420 nm for red, 460 nm for green and 400 nm for blue.

Fig. 2: Design and characterization of WO_3 metapixels.

a, Metapixel design. Left: tuning D and W of WO_3 nanodisks achieves a diverse colour palette. The dashed box highlights selected RGB pixels along with their intermediate regions, which contain CMY pixels. Middle: reflectance spectra of the selected RGB pixels. Right: corresponding D and W values for the chosen RGB pixels. b, Microscopic and structural characterization. Left: bright-field (top) and dark-field (bottom) microscope images of a red pixel with feature sizes of 20 μm, 2 μm and 420 nm, captured under ×100 magnification. Scale bars, 10 μm. Right: SEM images of 2 μm and 420 nm red pixels. Scale bars, 2 μm (top) and 200 nm (bottom). c, Colour mixing by subpixel arrangement. Left and middle: reflective colour varies as a function of T between adjacent RGB subpixels. Right: reflectance spectra of hybrid CMY pixels, corresponding to optimized subpixel spacing. d, High-resolution colour imaging. Top: bright-field microscope (×100) images of hybrid CMY pixels. Scale bars, 1 μm. Bottom: SEM images of the corresponding hybrid pixels. Scale bars, 500 nm.

Source Data

Once the smallest dimensions for the three primary colour metapixels are determined, the next step is merging them to achieve a full-colour display. In Fig. 2c, the grating modes between adjacent subpixels are influenced by the spacing (T) between subpixels, which changes reflected hybrid colours. According to the principles of additive colour mixing, the overlap between RGB should produce CMY, respectively. As shown in Fig. 2a, the intermediate region (black dashed box) between RGB pixels also contains CMY pixels. As long as the spacing between RGB subpixels is carefully designed, the grating modes of adjacent pixels can produce CMY colours, ensuring compliance with the additive colour principle. For comparison, Extended Data Fig. 2 presents several arbitrarily selected combinations of RGB pixels that fail to reproduce the CMY colours. After carefully selecting RGB pixels and tuning the inter-pixel spacing— 380 nm for red-green, 80 nm for blue–red and 100 nm for green–blue—the desired hybrid colours were successfully generated. The corresponding reflection spectra demonstrate that the reflectance of the hybrid-colour pixels matches that of the single-colour pixels.

Finally, Fig. 2d presents microscope and scanning electron microscope (SEM) images of the merged pixels producing CMY colours. Under high magnification (×100), the arrangement of alternating subpixels along the x axis to form hybrid colours is clearly visible. Notably, the Mie scattering mode of individual nanodisks is mainly determined by their size, whereas the grating mode of the subpixel arrays in the x direction governs the generation of reflective mixed colours.

As an electrochromic material, WO_3 exhibits an electrically tunable refractive index (n) and extinction coefficient (k) across the visible spectrum (400–700 nm). In the insulator (colour) state, the refractive index varies from around 2.38 to 2.14, with k-values of <0.01. In the metal (black) state, n decreases from around 2.25 to 1.95, whereas k increases significantly (k > 0.4; Extended Data Fig. 3). This transition occurs on electrochemical reduction of WO_3, when, for example, alkali ions or a proton, M+, are introduced into the material, altering its electronic structure. The reaction can be expressed as:

$${{\rm{WO}}}_{3}+x{{\rm{M}}}^{+}+x{{\rm{e}}}^{-}\to {{\rm{M}}}_{x}{{\rm{WO}}}_{3}$$

where M+ represents the doping ion (for example, Li+), x is the number of ions inserted into the WO_3 structure (0 ≤ x ≤ 1) and e− represents an electron. When WO_3 is reduced, electrons are introduced into the conduction band comprising the W 5d bands. The injected electrons lead to the formation of localized polarons, which result in a transition from insulating (semiconducting) behaviour to increased conductivity or metallic-like behaviour as x increases and the polarons overlap. As a result, the refractive index decreases and the extinction coefficient increases, contributing to the optical contrast between the insulating (clear) and metallic (dark) states. Furthermore, the nanodisk structure of the WO_3 metapixels, with its high refractive index (n), enables the concentration of incident light in the nanodisk. This effect further amplifies the light–matter interaction and absorption efficiency for stronger optical contrast.

Figure 3a presents the experimental set-up to electrochemically control the colour states of WO_3 metapixels. The electrolyte consists of 1 M LiClO_4 in acetonitrile, and the RGB pixel size is 350 μm. Metallic electrodes (Pt/Al) were used to minimize potential drops. Notably, we designed a lateral electrode configuration with a narrow 500 nm gap between the working and counter electrodes, enhancing the local electric field and significantly improving the switching speed28,29.

Fig. 3: Electrochemical modulation of WO_3 metapixels.

a, Experimental set-up for electrochemical characterization. Left: cross-sectional schematic diagram illustrating the set-up used to characterize the optical properties of WO_3 metapixels under electrochemical control. Right: microscope images of the characterized sample show a 500 nm gap between the working and counter electrodes, which enhances the local electric field for improved switching performance. Scale bar, 100 μm. b, Optical response of electrochemically controlled RGB metapixels. Left: reflectance spectra of the selected RGB metapixels in their electrochemically modulated on (sold lines) and off (dashed lines) states, demonstrating dynamic tunability. Right: numerical simulation of the reflectance modulation of red metapixels at a 650 nm incident wavelength, validating that most of the electric field is concentrated in the WO_3 nanodisks. c, Switching speed characterization. By applying a short pulse voltage signal, 95% normalized optical contrast is achieved within 40 ms, demonstrating video-rate display applications. d, Electrical properties corresponding to optical modulation. Left: applied voltage pulses of ±4 V with a 40 ms duration produce corresponding current responses, with 95% of the current change occurring within 30 ms, indicating fast ion transport in the WO_3 nanodisk structures. Right: relationship (turning off) between optical contrast change, response time and charge consumption. Owing to the colour memory effect of retina E-paper, video playback typically requires only minor updates, with intensity variations typically around 20% (equivalent to about 30 greyscale levels), thereby reducing the energy and time needed for dynamic content display.

Source Data

Figure 3b illustrates the measured normalized reflectance modulation of RGB metapixels in the on and off states. As both Mie scattering and grating modes are influenced by variations in the refractive index of the surrounding environment, the optimized nanodisk dimensions for the RGB metapixels are: R (D = 300 nm, W = 140 nm), G (D = 260 nm, W = 100 nm) and B (D = 260 nm, W = 40 nm). A clear distinction is observed when comparing the metapixel reflectance in air with that in electrolytes. Specifically, the reflectance in electrolytes is notably higher, whereas scattering in the red-light region is significantly suppressed. This effect arises because Mie scattering depends on the relative refractive index \(m=\frac{{n}_{{\rm{particle}}}}{{n}_{{\rm{env}}}}\), where nparticle and nenv are the refractive index of the WO_3 nanodisks and the surrounding environment, respectively. When nparticle is close to nenv, the scattering is markedly reduced, making the material seem more transparent and desaturated in colour. Therefore, the colour contrast of WO_3 metapixels in electrolytes is less saturated than in air; however, despite the ultra-thin WO_3 nanodisks (~100 nm), the optical contrast remains at ~50%, significantly outperforming most planar WO_3 electrochromic devices with the same thickness[30]. This enhancement is due to the high refractive index of WO_3, concentrating the electric field of incident light in the nanodisks. After switching to the dark state, most of the incident light is absorbed, as confirmed by numerical simulations of red subpixel on and off states (Fig. 3b, right). The on–off switching of RGB pixels enables high-contrast modulation, which is essential for reflective display applications with full colour coverage.

Benefiting from the strong local electric field between the closely spaced working and counter electrodes, as well as the ultra-thin amorphous WO_3 nanodisks, yielding fast ion insertion[31], the electrochemically tunable metapixels achieve an exceptionally fast switching time of only 40 ms to reach more than 95% of the total optical contrast modulation. Supplementary Video 1 demonstrates the rapid on–off switching of the red metapixel, confirming that metapixels support video display. Figure 3c shows ten cycles of reflectance variation in ±4 V pulse input signals. The normalized optical contrast is calculated by \(\frac{R-{R}_{\min }}{{R}_{\max }-{R}_{\min }}\), where R, Rmin and Rmax are the real-time, minimum and maximum reflectance of the WO_3 metapixels, respectively; this normalization clearly illustrates the change in optical contrast. The measured optical contrast is also shown in Extended Data Fig. 4. Notably, the operating voltage is comparable to the solid-state two-electrode WO_3 electrochromic systems[32]. However, due to the significantly enhanced switching speed (>65-times faster), precise pulsed voltage control—rather than a constant bias—can be used to minimize energy consumption and mitigate side effects.

Retina E-paper offers ultra-low energy consumption due to its inherent colour memory effect. Energy is primarily consumed when the pixel intensity is actively changed. As shown in Extended Data Fig. 5, pixels in the on state can retain more than 90% of their reflectance for more than 150 s without any power input. This indicates that for relatively static pixels, a brief, low-energy signal applied every several tens of seconds is sufficient to maintain the displayed content. Figure 3d presents the electrical characteristics associated with colour modulation in retina E-paper. Figure 3d (left) shows the current response to a ±4 V voltage pulse, indicating that alkali ions can rapidly intercalate into the WO_3 nanodisk layer at video rates, thereby modulating pixel colour states. Figure 3d (right) illustrates the relationship between optical contrast change, response time and charge consumption. Importantly, during video playback, only about 10% of pixels typically undergo state changes[20], and among those, the average greyscale shift is around 30 levels[33]. This means that, on average, only approximately 20% normalized optical contrast is required for video display. Under such conditions, retina E-paper achieves an average switching time of around 5 ms (200 fps) and an energy consumption of about 1.7 mW cm–2. For static images, the energy use drops further to around 0.5 mW cm–2. This energy consumption is significantly lower than that of conventional electrophoretic displays and electrowetting displays[26].

To further validate the display performance of retina E-paper, we fabricated metapixels to reproduce an anaglyph 3D butterfly and a high-resolution image inspired by The Kiss. The anaglyph butterfly demonstrates the feasibility of stereoscopic image rendering for virtual reality applications, whereas the reproduction of The Kiss—featuring intricate geometries and a wide colour gamut—highlights the suitability of retina E-paper for ultra-high-resolution, full-colour image display. As our substrate is a highly reflective material analogous to a white canvas, we used the CMY subpixels and used subtractive colour mixing to render the image. Importantly, the patterned image only demonstrates the display capability of WO_3 metapixels. For the display application, a thin-film transistor (TFT) array should be used to independently control the reflectance of each pixel, whereas the background should be set to black. The image rendering should follow the additive colour principle using RGB subpixels (Fig. 2).

Figure 4a (left) illustrates the nanodisk diameters and periodicities for the CMY metapixels alongside their corresponding reflection spectra, which show similar reflectance as RGB pixels. Figure 4a (right) presents the merged hybrid colour metapixels, in which the spacing between subpixels are B (T_1 = 100 nm), R (T_2 = 60 nm) and G (T_3 = 60 nm). Notably, except for similar reflectance to RGB metapixels, the CMY system also ensures that the intermediate regions contain RGB pixels (Extended Data Fig. 6). The optimized nanodisk dimensions for CMY pixels are C (D = 260 nm, W = 160 nm), M (D = 240 nm, W = 100 nm) and Y (D = 180 nm, W = 180 nm).

Fig. 4: Characterization of retina E-paper display performance.

a, Optical properties of CMY metapixels. Left: the reflectance spectra of the selected CMY metapixels demonstrate the spectral response. Right: photographs of the RGB pixels with optimized adjacent subpixel spacing for improved hybrid colour and display fidelity. b, Anaglyph 3D demonstration on retina E-paper. Left: the anaglyph stereo butterfly (Anaglyph 3D) original image (OI) decomposed into magenta (M) and cyan–yellow (CY) channels (top), and the corresponding reconstructed retina E-paper images (RI) for each eye (bottom). Scale bars, 200 μm. Middle: microscope images of individual M and CY channel pixels, illustrating submicrometre pattern fidelity. Scale bars, 2 μm. Right: full-colour anaglyph butterfly image: anaglyph 3D butterfly original image (top) and simulated retina E-paper reconstruction (bottom) demonstrating high-resolution 3D depth rendering. Scale bars, 200 μm. Original butterfly image licensed from Adobe. c, High-resolution display of The Kiss on retina E-paper versus iPhone 15. Photographs comparing the display of The Kiss on an iPhone 15 and retina E-paper. The surface area of the retina E-paper is ~1/4,000 times smaller than the iPhone 15. SEM and microscope images confirm that the displayed colours are generated by precisely arranged CMY subpixels. Scale bars, 2 μm (top and bottom left) 200 μm (right). Image of The Kiss reproduced with permission from Kingston Frameworks. d, Electrochemical display of The Kiss by retina E-paper. Photographs showing the display of The Kiss on retina E-paper in the on (left) and off (right) states, demonstrating reversible colour modulation when electrochemically tuned.

Source Data

Figure 4b illustrates an ultra-high-resolution anaglyph 3D display, achieved by encoding stereo image pairs (anaglyph 3D original image) into complementary colour channels—M for the left eye and CY for the right eye—and reconstructing them using the retina E-paper. This demonstration serves as a proof of concept for binocular disparity rendering, a fundamental mechanism underlying stereoscopic vision in virtual reality systems. As the retina E-paper does not connect to TFT arrays to individually adjust each subpixel, it achieves colour rendering solely through the precise geometric design of M–CY metapixels (Extended Data Fig. 7). By precisely reconstructing left and right eye images with submicrometre-resolution metapixels, the device successfully generates a full-colour 3D image (anaglyph 3D; Fig. 4b) through passive, compact optical configurations. This demonstrates an anaglyph 3D display with resolution exceeding 35,000 PPI (M) and 30,000 PPI (CY). Furthermore, this demonstration highlights the versatility of the retina E-paper platform, which can operate not only in full-colour mode but also in monochrome or dual-primary channel formats, broadening its applicability across a range of advanced display technologies.

Figure 4c presents a reconstructed full-colour image of The Kiss, directly comparing the retina E-paper with a commercial mobile-phone display (iPhone 15) in terms of both physical dimensions and display resolution. Whereas the iPhone screen measures 147.6 mm × 71.6 mm, the retina E-paper is only 1.9 mm × 1.4 mm, amounting to merely ~1/4,000th the area of the smartphone display. Despite this minuscule size, the retina E-paper achieves a resolution of 4,300 × 700, similar to the resolution of the smartphone display (2,556 × 1,179). Due to the inherent challenges of accurately controlling the reflectance of subpixels, as well as the narrower colour gamut compared with emissive displays, the perceived colour saturation of the retina E-paper is lower than that of the iPhone 15; however, this is the first demonstration of full-colour imaging achieved by three primary colour metapixels at such an ultra-high resolution. With an average pixel size of only around 560 nm, the display reaches an unprecedented >25,000 PPI, surpassing the resolution requirements for ultimate virtual reality displays. High-magnification microscope (×100) and SEM images (Fig. 4b, right) further confirm the well-ordered CMY metapixel arrangement and the vibrant colour rendering, validating the ultra-high resolution of the retina E-paper.

To evaluate its electrically tunable colour performance, we reproduced The Kiss using CMY metapixels in an electrolyte environment. To maintain the presence of RGB pixels in the intermediate regions, the dimensions of the CMY metapixels were further optimized: C (D = 280 nm, W = 20 nm), M (D = 220 nm, W = 80 nm) and Y (D = 300 nm, W = 80 nm). The merged RGB subpixel spacing was adjusted accordingly: B (T_1 = 40 nm), R (T_2 = 300 nm) and G (T_3 = 60 nm) (Extended Data Fig. 8). Extended Data Fig. 9a presents the corresponding reflection spectra of CMY pixels in their colour and dark states. Figure 4d showcases the photos of The Kiss under colour and dark states in the electrolyte. Owing to the weaker Mie scattering of WO_3 nanodisks in an electrolyte environment compared with air, the displayed colours seem less saturated, with a noticeable reduction in extinction in the red region, resulting in an overall red-shifted colour. However, the system exhibits a distinct reflectance modulation between on and off states, highlighting its potential for dynamic display applications. Extended Data Fig. 9b compares the colour gamut coverage of commercial emissive displays, the retina E-paper in both air and electrolyte and the commercial colour electrophoretic display. Although the colour gamut of retina E-paper remains narrower than emissive displays, its performance in both air and electrolyte significantly surpasses a commercial colour E-reader[34].

Currently, more than 80% of the information people perceive is through visual signals[35]. With the development of Internet-of-Things-based technology and increasing information transfer speeds, the demand for next-generation visual display technologies keeps growing. Retina E-paper not only reaches the theoretical resolution limit of human vision but also offers exceptional visibility. It enables full-colour video display while maintaining high reflectivity and optical contrast, which is promising for realizing ultimate virtual reality displays.

Unlike conventional emissive displays, retina E-paper devices require front-illumination to enable image visibility. Extended Data Fig. 10 illustrates two distinct optical architectures that accommodate this requirement: one compatible with conventional virtual reality headsets, and the other tailored to state-of-the-art, waveguide-based augmented reality–virtual reality lenses[36]. In fact, retina E-paper also holds significant potential for augmented reality applications, as it can leverage ambient light as the illumination source. This inherent compatibility with the environment enables natural visual integration, reducing reliance on light engines. Furthermore, as the primary illumination is provided by ambient light, its low power consumption enables substantial downsizing of the battery and even opens up the possibility of fully self-powered displays when combined with solar cells (with a typical output of ~15 mW cm–2).

Despite its advantages, retina E-paper requires further optimization in colour gamut, refresh rate, operational stability and lifetime. Lowering the operating voltage and exploring alternative electrolytes represent promising engineering routes to extend device durability and reduce energy consumption. Moreover, its ultra-high resolution also necessitates the development of ultra-high-resolution TFT arrays for independent pixel control, which will enable fully addressable, large-area displays and is therefore a critical direction for future research and technological development. Looking ahead, we anticipate significant advancements in this field and firmly believe that the evolution of the retina E-paper will ultimately influence everyone.

Methods

Nanofabrication of WO_3 nanodisks

We fabricated WO_3 nanodisk metamaterials (Extended Data Fig. 1) in a clean room using standard semiconductor processes. A 100 nm reflective layer (Al/Pt) and a 30 nm Au layer were deposited via electron beam evaporation (Lesker PVD 225). The middle 100 nm WO_3 layer was deposited using radio frequency magnetron sputtering. After spin-coating the electron beam resist (MaN 2401, 3,000 r.p.m.), nanostructures were patterned by electron beam lithography (400 μC cm–2). The resist was developed in MF-24A for 45 s, followed by ion beam milling using Ar gas (80 mA, 200 V) for 2 min to etch the Au layer. Reactive ion etching with CF_4 gas (100 W) for 5 min was then used to etch the WO_3 layer. Finally, the Au layer was removed by wet etching. Samples were characterized using a Zeiss Supra 55 SEM for high-resolution imaging.

Electrochemical measurements

Acetonitrile was purchased from Sigma-Aldrich, and LiClO_4 was purchased from Fisher Scientific. The electrolyte contains 1 M LiClO_4 in acetonitrile. A custom-built liquid cell with two electrode connections was used for electrochemical measurements. A potentiostat (Gamry Interface 1000) controlled the applied current and voltage. For the fast-switching measurements, a short 40 ms voltage pulse ranging from −4 V to 4 V was applied. Low constant voltages from −1 V to 1 V were applied for 2 min to obtain the reference optical contrast of WO_3.

Optical measurements

A custom microspectroscopy set-up with Thorlabs beam splitters was used to measure microscale reflectivity in the electrochemical cell. Illumination from a 100 W tungsten lamp and light collection was conducted through a 5× air objective (NA 0.14), passing through the top flow cell with a glass cover into the WO_3 metapixels. A portion of the reflected light was collected by an optical fibre and analysed using a B&WTek CypherX spectrometer. Most photographs were captured using a commercial microscope (Olympus MX50). An iPhone 15 and a Samsung A55 were used to capture the photograph in Fig. 4c (left) and Supplementary Video 1, respectively.

Arbitrarily selected combinations of RGB pixels

Extended Data Fig. 2 presents an example in which the intermediate regions of RGB pixels contain only cyan and yellow, but not magenta. Specifically, a yellow pixel (D = 300 nm, W = 200 nm) is observed, between the red (D = 320 nm, W = 220 nm) and green (D = 260 nm, W = 200 nm) pixels. Similarly, a cyan pixel (D = 280 nm, W = 140 nm) appears between the green and blue (D = 280 nm, W = 120 nm) pixels; however, the magenta pixel (D = 260 nm, W = 100 nm) is absent from the intermediate region between the red and blue pixels.

By adjusting subpixel spacing, a yellow pixel emerges when the red–green subpixel distance is 240 nm, whereas a cyan pixel forms at a blue–green subpixel distance of 120 nm; however, regardless of how the red–blue subpixel distance is tuned, magenta cannot be generated, contrasting with the results shown in Fig. 2c.

Refractive index of WO_3 in on–off states

The refractive index of WO_3 is obtained from a previously published study[25]. Extended Data Fig. 3 presents both the real (n) and imaginary (k) components of the refractive index in both the on and off states. In the on state, the real part (n) decreases from 2.4 to 2.1, enabling strong Mie scattering in the air. The imaginary part (k), which represents optical absorption, remains close to zero, explaining why WO_3 nanodisks exhibit vivid colours in the air; however, in a liquid environment such as acetonitrile (n ≈ 1.33), the refractive index is closer to that of WO_3, reducing Mie scattering and making the nanodisks more transparent, thus diminishing colour vibrancy, as discussed in the main text. Electrochemical tuning in solution allows WO_3 nanodisks to switch to the off state, in which the k-value significantly increases to 0.05–0.4 in the visible range, leading to strong optical absorption and changing the colour pixels into dark states.

Optical contrast of red pixel

To compare with the normalized optical contrast shown in Fig. 3c, Extended Data Fig. 4 presents the absolute reflectance variation of red light at a wavelength of 620 nm in response to the applied 40 ms pulse voltage signal (Fig. 3d, left). The red subpixel achieves an optical contrast of more than 48% within 40 ms, and its maximum optical contrast under long-time constant voltage is 50%. This result further confirms WO_3 metapixels supporting video display.

Colour memory effect

Owing to the bistability of WO_3, the retina E-paper exhibits a colour memory effect. In the absence of external energy input, WO_3 gradually relaxes from its redox states back to the natural state, rather than reverting instantly from a coloured state to a dark state as seen in emissive displays. As shown in Extended Data Fig. 5, once a pixel is switched to the coloured mode, its normalized reflectance remains above 90% for around 150 s under open-circuit conditions. Similarly, when a pixel is switched to the dark mode, it retains a normalized reflectance less than 10% for about 9 s without further energy input. This behaviour enables ultra-low power consumption, as static or minimally changing pixels do not require continuous power. Instead, an approximately 2 ms electronic pulse input (for around 10% optical contrast modulation) is sufficient to maintain the display state.

CMY pixels in the air

As discussed in the main text, to achieve RGB colours through subtractive colour mixing using CMY primaries, it is crucial to ensure that the intermediate regions between CMY pixels contain RGB pixels. As shown in Extended Data Fig. 6, a blue pixel (D = 260 nm, W = 120 nm) exists between the cyan (D = 260 nm, W = 160 nm) and magenta (D = 240 nm, W = 100 nm) pixels. Similarly, a green pixel (D = 260 nm, W = 180 nm) is present between the magenta and yellow (D = 180 nm, W = 180 nm) pixels, while a red pixel (D = 200 nm, W = 180 nm) appears between the yellow and cyan pixels. By tuning the spacing between subpixels, a blue pixel emerges when the CM subpixel distance is 100 nm, green at an MY subpixel distance of 60 nm, and red at a CY subpixel distance of 60 nm.

Transfer colour image to nanodisk pixels

Extended Data Fig. 7 illustrates the process of converting the intensity (scaled from 0 to 255) of each CMY pixel into the geometry structure of nanodisk pixels. The intensity of each CMY channel is mapped to the height of its corresponding CMY rectangle as a fraction of the maximum value, representing the percentage of light each CMY subpixel should reflect. The designated nanodisk arrays, which generate CMY colours, are then used to fill these rectangles, approximating the subpixel colour composition. However, due to the small size of the CMY rectangles, the nanodisks cannot achieve perfect spatial coverage, leading to inaccuracies in colour reproduction. This limitation contributes to the imperfections observed in Fig. 4b–d.

CMY pixels in the electrolyte

The underlying principle remains consistent with CMY pixels in the air. As illustrated in Extended Data Fig. 8, the intermediate regions of CMY colours contain corresponding RGB components. The subpixel distances are T_1 = 40 nm for blue, T_2 = 300 nm for red and T_3 = 60 nm for green. Owing to weaker Mie scattering in the electrolyte, the extinction of red colour becomes weaker, causing a less intense blue colour.

CMY colour modulation and comparison of colour performance with other devices

Extended Data Fig. 9a (left) illustrates the nanodisk diameters and periodicities for the CMY metapixels alongside their corresponding reflection spectra, which show similar reflectance as RGB pixels. The corresponding geometries are C (D = 280 nm, W = 20 nm), M (D = 220 nm, W = 80 nm) and Y (D = 300 nm, W = 80 nm), and the merged RGB subpixel spacing was adjusted accordingly: B (T_1 = 40 nm), R (T_2 = 300 nm) and G (T_3 = 60 nm).

Extended Data Fig. 9b presents a comparison of the colour gamut coverage among commercial emissive displays, the retina E-paper (under both air and electrolyte conditions), and a commercial colour electrophoretic display. Although the colour gamut of the retina E-paper does not yet match that of emissive technologies, it significantly outperforms existing commercial colour e-paper, highlighting its potential for high-fidelity reflective colour displays.

Front-illumination integration of retina E-paper into immersive augmented reality–virtual reality optics

Two potential system-level configurations are illustrated in Extended Data Fig. 10, demonstrating how reflective pixels can be effectively illuminated in practical augmented reality–virtual reality devices.

Extended Data Fig. 10a illustrates an optical configuration compatible with conventional virtual reality headsets. In this design, incident light enters from the side and is redirected by a beam splitter through an eyepiece onto the retina E-paper. The reflected light follows the reverse path, passing back through the eyepiece and beam splitter before reaching the eye. This optical arrangement resembles that of a reflective microscopy system and aligns with well-established principles in optical engineering.

Extended Data Fig. 10b depicts an advanced optical architecture compatible with waveguide-based augmented reality–virtual reality systems. In this configuration, light is side-coupled into the device and directed onto the retina E-paper through a beam splitter. The reflected light is then collimated and coupled into the waveguide through input gratings and ultimately delivered to the eye through output gratings. This design enables integration into compact form factors, significantly shortens the eye-to-display distance, and supports a wider field of view—key requirements for achieving immersive and lightweight augmented reality–virtual reality experiences.

Video‐rate switching red pixel

Supplementary Video 1 visually demonstrates the rapid modulation speed of WO_3 metapixels by showing the reflectivity change of a red pixel under pulse signals with durations of 1 s, 500 ms, 250 ms and 40 ms. The distinct bright and dark states are clearly visible with evident dwell times, highlighting the full contrast range and switching stability of the device. Although the video was recorded by a cell phone at 50 fps, the camera of the microscope system was used to record the light spot at only 18 fps. As a result, despite the reaction speed exceeding the required for video play (24 fps), intensity variations remain observable.

Data availability

The data supporting the findings of this study are available in the paper and its Supplementary Information. Source data are provided with this paper.

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Acknowledgements

We thank A. Ciarlo for sharing their knowledge of optical set-ups, G. Wang for sharing their knowledge of nanofabrication, and H. Frederiksen and N. Lindvall for their help with nanofabrication. We acknowledge support from MC2 at Chalmers University of Technology. We are grateful for the permission from Kingston Frameworks to use their image. This work was financed by the Swedish Research Council (grant no. 2023-05512), Horizon Europe ERC Consolidator Grant MAPEI (grant no. 101001267), the Knut and Alice Wallenberg Foundation (grant no. 2019.0079), and STINT “Nanochromism” (grant no. MG2020-8871).

Funding

Open access funding provided by Uppsala University.

Author information

Author notes

1. Lars Österlund

Present address: Department of Chemistry, Umeå University, Umeå, Sweden

Authors and Affiliations

1. Department of Materials Science and Engineering, The Angstrom Laboratory, Uppsala University, Uppsala, Sweden

Ade Satria Saloka Santosa, Lars Österlund & Kunli Xiong

2. Department of Physics, University of Gothenburg, Gothenburg, Sweden

Yu-Wei Chang & Giovanni Volpe

3. Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Gothenburg, Sweden

Andreas B. Dahlin

Contributions

K.X. conceived the project. K.X. and A.S.S.S. fabricated the samples. K.X. and A.S.S.S. performed the measurements. K.X. and A.S.S.S. performed the data analysis. K.X. and Y.-W.C. wrote the software code. K.X. and A.B.D. contributed to the experimental set-up. K.X., G.V. and L.Ö. supervised the project. All authors discussed the results and contributed to writing the manuscript.

Corresponding author

Correspondence to Kunli Xiong.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Nanofabrication of WO3 nanodisks.

The reflective layer (Al/Pt), Au layer were deposited by e-beam evaporation and the WO_3 layer was deposited by RF magnetron sputtering. After spin-coating an e-beam resist, nanopatterns are defined through e-beam lithography and developed to form structured resist templates. The patterns are then transferred onto the WO_3 layer using ion beam milling (IBM) and reactive ion etching (RIE). Finally, the Au mask is removed by wet etching. An SEM image of a blue WO_3 nanodisk pixel is presented as an example. Scale bar: 400 nm.

Extended Data Fig. 2 Arbitrarily selected combinations of RGB pixels.

The intermediate regions of RGB pixels contain only cyan and yellow, with no magenta. By optimizing subpixel spacing (T), a yellow pixel appears when the red-green subpixel distance is 240 nm, while a cyan pixel emerges at a blue-green subpixel distance of 120 nm. However, regardless of the red-blue subpixel distance, magenta cannot be produced, highlighting a fundamental limitation in colour.

Extended Data Fig. 3 Refractive index of WO[3] in the on and off states.

a, Real (n) part of the refractive index of WO_3 in the on and off states. b, Imaginary (k) part of the refractive index of WO_3 in the on and off states.

Source Data

Extended Data Fig. 4 Optical contrast of red pixel.

By applying a short pulse voltage signal, the reflectance change >0.48 is achieved within 40 ms. The maximum reflectance change under long-time constant voltage is ~0.5.

Source Data

Extended Data Fig. 5 Colour memory effect.

When the WO_3 pixels are turned on, they can retain over 90% of reflectance for ~150 s without any additional power input. Conversely, after being turned off, the pixels maintain <10% reflectance for ~9 s, also without an external energy supply.

Source Data

Extended Data Fig. 6 CMY pixels in the air.

The intermediate regions of CMY pixels contain all RGB pixels. By optimizing subpixel spacing (T), a blue pixel emerges when the CM subpixel distance is 100 nm, green at an MY subpixel distance of 60 nm, and red at a CY subpixel distance of 60 nm.

Extended Data Fig. 7 Transfer colour image to nanodisc pixels.

The intensity of each CMY channel is converted into the height of its corresponding CMY rectangle as a fraction of the maximum value, indicating the proportion of light each CMY subpixel should reflect. The designated nanodisc arrays responsible for generating CMY colours are then arranged within these rectangles, approximating the intended subpixel colour composition.

Extended Data Fig. 8 CMY pixels in the electrolyte.

The intermediate regions of CMY colours contain corresponding RGB components. The subpixel distances are T_1 = 40 nm for blue, T_2 = 300 nm for red, and T_3 = 60 nm for green.

Extended Data Fig. 9 CMY colour modulation and comparison of colour performance with other devices.

a, Electrochemical modulation of CMY meta pixels: Left: The Reflectance spectra of CMY metapixels in the electrochemically modulated on/off states demonstrate dynamic tuneability. Right: Photographs of RGB hybrid colours in an electrolyte environment with optimized adjacent CMY subpixel spacing. b, Comparison of colour gamut coverage: CIE chromaticity diagram comparing the colour coverage of an emissive display (sRGB), a commercial electrophoretic display (e-reader), and the Retina E-paper in both air and electrolyte environments. The results highlight the colour performance of the Retina E-paper relative to conventional display technologies.

Source Data

Extended Data Fig. 10 Front-illumination integration of Retina E paper into immersive AR/VR optics.

a, Conventional VR headset configuration: Side-illumination is directed by a beam splitter through an eyepiece, focusing and magnifying the image onto the Retina E-paper. The reflected light retraces the path through the eyepiece and beam splitter into the eye. b, Meta-lens AR configuration: Side-coupled illumination is reflected by a beam splitter onto the Retina E-paper, then collimated and directed through a meta-lens optics module. After coupling into an input grating, the light undergoes internal waveguide propagation and exits through an output grating into the eye.

Supplementary information

Supplementary Video 1

Video‐rate switching red pixel. This video shows the reflectivity change of a red pixel under pulse signals with durations of 1 s, 500 ms, 250 ms and 40 ms. The distinct bright and dark states are clearly visible with evident dwell times, highlighting the full contrast range and switching stability of the device. Although the video was recorded on a smartphone phone at 50 fps, the camera of the microscope system was used to record the light spot at only 18 fps. As a result, despite the reaction speed exceeding the required for video play (24 fps), intensity variations remain observable.

Source data

Source Data Figs. 2–4 and Source Data Extended Data Figs. 3–5 and 9.

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Santosa, A.S.S., Chang, YW., Dahlin, A.B. et al. Video‐rate tunable colour electronic paper with human resolution. Nature 646, 1089–1095 (2025). https://doi.org/10.1038/s41586-025-09642-3

- Received: 11 February 2025

- Accepted: 17 September 2025

- Published: 22 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09642-3

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Electronic paper could enable virtual reality with human-eye resolution

Nature Research Briefing 29 Oct 2025

Article Open access Published: 29 October 2025

Ultrasound-driven programmable artificial muscles

Zhan Shi,

Zhiyuan Zhang,

Justus Schnermann,

Stephan C. F. Neuhauss,

Nitesh Nama,

Raphael Wittkowski &

…

Daniel Ahmed

Nature volume 646, pages 1096–1104 (2025)

Abstract

Muscular systems[1], the fundamental components of mobility in animals, have sparked innovations across technological and medical fields2,3. Yet artificial muscles suffer from dynamic programmability, scalability and responsiveness owing to complex actuation mechanisms and demanding material requirements. Here we introduce a design paradigm for artificial muscles, utilizing more than 10,000 microbubbles with targeted ultrasound activation. These microbubbles are engineered with precise dimensions that correspond to distinct resonance frequencies. When stimulated by a sweeping-frequency ultrasound, microbubble arrays in the artificial muscle undergo selective oscillations and generate distributed point thrusts, enabling the muscle to achieve programmable deformation with remarkable attributes: a high compactness of approximately 3,000 microbubbles per mm^2, a low weight of 0.047 mg mm−2, a substantial force intensity of approximately 7.6 μN mm−2 and fast response (sub-100 ms during gripping). Moreover, they offer good scalability (from micrometre to centimetre scale), exceptional compliance and many degrees of freedom. We support our approach with a theoretical model and demonstrate applications spanning flexible organism manipulation, conformable robotic skins for adding mobility to static objects and conformally attaching to ex vivo porcine organs, and biomimetic stingraybots for propulsion within ex vivo biological environments. The customizable artificial muscles could offer both immediate and long-term impact on soft robotics, wearable technologies, haptics and biomedical instrumentation.

Main

Flexible, compact and adaptive artificial muscles are set to be transformative across multiple fields, including soft robotics4,5, wearables for human–machine interactions and healthcare, such as prosthetics[6], orthotics[7] and embodied sensing8,9, and assistance in sophisticated manufacturing through dexterous manipulation10,11. In biomedicine, they could revolutionize soft surgical tools[12], implantable electrodes[13] and artificial organs such as the heart[14]. Despite their potential, current artificial muscles such as tendon-based[15] and pneumatic types[16] encounter substantial challenges in wireless control, integration and miniaturization owing to dependencies on tethering, complex operational mechanisms and large input requirements. Although external stimuli such as chemicals[17], light18,19, temperature20,21,22, electric fields23,24,25 and magnetic fields26,27 have been deployed for wireless actuation, they face challenges in biocompatibility, spatial resolution and dynamic programmability. Chemical methods often require fuels that could be toxic[28], light-based systems suffer from limited tissue penetration and potential thermal damage[29], and magnetic systems necessitate bulky hardware while risking Joule heating[30]. By contrast, acoustic actuation emerges as a promising biocompatible alternative. It offers a material-independent and simplified design, enabling wireless control, remote deployment, millisecond-scale responsiveness, multimodal programmability, high spatial selectivity and deep tissue penetration—all without invasive hardware. Moreover, its compatibility with existing clinical ultrasound devices and imaging systems makes it particularly uniquely suited for in vivo use and broader biomedical applications31,32,33,34,35,36,37.

Central to this approach are resonant microbubbles, which concentrate acoustic energy and enable weak ultrasound sources to generate amplified responses. Although previous ultrasound-actuated microrobots and actuators have used single or sparse microbubbles embedded in polymers to achieve basic propulsion38,39,40, their functionality remained limited. Directional steering has been demonstrated through strategies such as tuning microbubble sizes[41], applying magnetic navigation[42] or hybrid methods that combine magnetic fields with asymmetric appendages in encapsulated shells[43]. An actuator composed of a microbubble attached to a flexible beam was developed to analyse the kinematic behaviour of simple microstructures through the excitation of different pairs of bubble actuator modules[44]. Another study used arrays of microbubbles integrated onto centimetre-scale rigid substrates to induce bi-rotational motion[45], demonstrating potential applications in endoscope design[46]. However, these systems lack the programmability, scalability and dynamic adaptability required to emulate natural muscle behaviour. Critically, to the best of our knowledge, no previous work has achieved ultrasound-actuated soft artificial muscles, marking a significant gap in biologically inspired actuation technologies.

A reason why ultrasound-based artificial muscles have remained undeveloped is that soft materials typically have low acoustic contrast factors compared with water, leading to inadequate force generation for efficient functionality when activated by ultrasound. This predicament is exacerbated by a lack of understanding of the interactions between sound and complex soft materials, impeding the progress of effective sound-driven muscle systems. However, we found that integrating ultrasound-activated microbubble arrays into soft artificial muscles presents a clever approach that could potentially address these limitations.

Here we introduce an artificial muscle built on acoustically activated microbubble arrays. This synthetic muscle comprises a thin, transparent and flexible membrane that houses over 10,000 microcavities arranged in arrays, designed to confine microbubbles of various sizes. When these microbubbles are acoustically stimulated, they generate thrust, causing the membrane to deform. Tailored activation of differently sized microbubble arrays through programmable sweeping-frequency ultrasound excitation results in localized point forces, allowing dynamic multimodal deformation of the artificial muscle. The tunable nature and scalability of these microbubble arrays herald an era of possibilities, positioning these acoustic artificial muscles at the forefront of innovation in robotics, wearable technology, prosthetic development and soft surgical devices.

Design and fabrication

In the initial design of the ultrasound-driven artificial muscle (Fig. 1a), we incorporated uniform-size microcavities on the muscle’s bottom surface. When the muscle was submerged in an acoustic chamber filled with water, it resulted in the simultaneous trapping of tens of thousands of gas-filled microbubbles within these cavities, a phenomenon driven by surface tension. To test the muscle’s actuation, we anchored one end of the muscle and left the other free, forming a cantilever configuration. Subsequently, we activated a piezoelectric transducer to generate ultrasound. The incident sound waves propagated through the liquid, triggering oscillations in the microbubbles. As all microbubbles in the muscle were of identical dimensions, they were simultaneously excited. This harmonic bubble oscillation generated collective acoustic streaming and radiation forces, applying a uniform opposing force to the muscle’s bottom surface and resulting in its upwards flexion. By modulating the ultrasound excitation voltage, we controlled the deformation amplitude of the artificial muscle.

Fig. 1: Ultrasound-actuated microbubble-array artificial muscles.

a, A uniform-size microbubble-array artificial muscle consists of thousands of microbubbles on its bottom surface. Under continuous ultrasound excitation, the artificial muscle bends upwards with different excitation voltages, labelled as V_1, V_2 and V_3. Inset: the input ultrasound signal with modulated amplitude versus time. b, A variable-size microbubble-array artificial muscle comprises three microbubble arrays with different diameters (d), each corresponding to a distinct natural frequency (f) and represented by the colours purple, yellow and grey. c, Under sweeping-frequency ultrasound excitation, the artificial muscle exhibits multimodal deformation in the time domain, shown at time points T_1, T_2 and T_3. d, Schematic of a soft gripper constructed with an array of artificial muscles patterned with uniform-size microbubble arrays. Upon ultrasound excitation, these muscles close simultaneously in milliseconds. e, Schematic of a bioinspired stingraybot incorporating variable-size microbubble-array artificial muscles. Under sweeping-frequency ultrasound excitation, the stingraybot enacts undulating propulsion. f, A silicon wafer with micropillar arrays serves as the negative mould of microbubble cavities in standard soft-lithography fabrication. Inset: the micropillar array. g, A prototype of the stingraybot near a 5-cent Swiss franc coin. h, Left: trapped microbubble arrays. Right: upwards microstreaming jets generated from a microbubble array oscillating under ultrasound excitation visualized by 6-μm-diameter tracer microparticles. n = 3 independent samples. Scale bars, 300 μm (f, inset), 2 cm (g), 500 μm (h, left), 100 μm (h, right).

We then designed an artificial muscle featuring microbubble arrays of varying bubble sizes, illustrated in Fig. 1b. As microbubbles of different sizes show distinct resonance frequencies, they can be independently activated to produce localized opposing forces and selective muscle deformation. By applying a sweeping-frequency ultrasound signal that encompasses the natural frequencies of all microbubbles, we sequentially activated distinct arrays along the muscle’s longitudinal axis. This orchestrated activation generated complex undulatory motion across multiple excitation cycles (Fig. 1c). Thus, in implementing this arrangement of microbubbles of various sizes and their frequency-selective excitation through ultrasound modulation, we have unlocked a capability to control multimodal deformations. The versatility of these artificial muscles facilitates a wide array of applications. For example, we implemented these artificial muscles in the development of a soft gripper, crafted to delicately handle live fish (Fig. 1d), and in the design of soft swimmers as surgical soft robots, inspired by the form and function of stingrays (Fig. 1e), among other functional systems.

Prototypes of these artificial muscles were fabricated using a high-resolution mould replica method. First, micropillar arrays were patterned on a silicon wafer using soft lithography to serve as negative moulds for cylindrical microcavities (Fig. 1f). All pillars were designed with identical heights and spacings, corresponding to the dimensions of the desired microbubbles (Supplementary Fig. 1). A thin layer of polydimethylsiloxane (PDMS) was then spin-coated onto the wafer, yielding thin membranes with uniform thicknesses ranging from 80 μm to 250 μm (Supplementary Fig. 2). After curing, these artificial muscles including the artificial stingray (Fig. 1g) were demoulded, sectioned and prepared for testing. Full fabrication details are provided in Methods. Figure 1h shows trapped microbubble arrays and the upwards microstreaming jets produced during ultrasound excitation.

Characterization of microbubble arrays

To advance our understanding and control of microbubble arrays in artificial muscles, we observed the transient dynamics of microbubbles using a high-speed camera while applying acoustic fields with excitation frequencies ranging from 1 kHz to 100 kHz and peak-to-peak (PP) voltage amplitudes of 10 VPP to 60 VPP in square waveforms. Further details of the acoustic set-up are provided in Methods.

We began by identifying the resonance frequencies of microbubbles confined within cavities of different diameters (40−140 μm, in 10-μm increments) and depths (50 μm, 150 μm and 175 μm) while maintaining a constant excitation voltage of 15 VPP. Resonance frequencies were identified by locating peak oscillation amplitudes during frequency sweeps (Extended Data Fig. 1a). As shown in Extended Data Fig. 1b, resonance frequencies decreased from 95.5 kHz to 8.9 kHz with increasing microbubble diameters, consistent with the inverse scaling relationship between natural frequency and the bubble diameter[47]. In addition, microbubbles with depths of 50 μm, 150 μm and 175 μm showed a decrease in resonance frequencies, indicating that the bubble depth also affects oscillation. We further investigated the selective actuation of variable-size microbubble arrays with cavities of 40 μm, 60 μm and 80 μm diameter, each 150 μm in depth, integrated within a single miniaturized artificial muscle (500 μm × 500 μm × 200 μm) with corresponding frequencies (76.3 kHz, 57.4 kHz and 27.6 kHz, respectively), as shown in Extended Data Fig. 2 and Supplementary Video 1. The distinct resonance profiles of microbubbles across sizes enable selective ultrasound excitation, forming the basis for programmable microbubble arrays. Detailed microstreaming characterization is provided in Methods.

Programmable actuation

The versatility of microbubble arrays in terms of programmability and selectivity enables an innovative approach for designing soft actuators with enhanced flexibility and control. To verify that microbubble oscillation is the dominant driver of this muscle bending, we systematically varied the transducer’s position relative to the microbubble-embedded side of the artificial muscle (3 cm × 0.5 cm × 80 μm), which contains over 10,000 uniform microbubbles within cavities (40 μm diameter, 50 μm depth). The transducer was positioned with four distinct orientations: (1) directly facing the microbubble-embedded side, (2) opposite to it, and (3) and (4) perpendicular to the array’s left and right sides of the artificial muscle (Supplementary Fig. 3 and Supplementary Video 2). When activated at 80.5 kHz and 60 VPP, the muscle consistently bent away in the direction opposite to the microbubble-array side, across all configurations, despite variations in bending amplitudes. This directional uniformity confirms that microbubble-generated reverse thrust is the primary force driving the deformation. More control experiments and characterization of artificial muscle deformation are provided in Methods.

To demonstrate the selective excitation capability of the artificial muscle, we further investigated the deformation of an artificial muscle equipped with variable-size microbubble arrays. The muscle, measuring 3 cm × 0.5 cm × 80 μm, contains 3 arrays of microbubbles with diameters of 12 μm, 16 μm and 66 μm, each with a depth of 50 μm. Upon stimulation at its resonance frequency (96.5 kHz), the 12 μm × 50 μm microbubble array, covering an area of 0.5 cm^2, induced a leftwards deformation in the corresponding muscle region, as depicted in Fig. 2a and Supplementary Video 3. Similarly, when the frequency was respectively increased to match the resonance frequencies of the 16-μm (82.3 kHz; Fig. 2b) and 66-μm (33.2 kHz; Fig. 2c) bubble arrays, the muscle showed a localized leftwards deformation in the middle region and bottom region, respectively. We further demonstrated an undulatory sinusoidal-like deformation by actuating the artificial muscle with a sweeping-frequency ultrasound excitation (20 kHz to 90 kHz). This continuous, time-dependent motion, as shown in Fig. 2d and Supplementary Video 4, resulted from the periodic reverse thrust generated across different regions of the muscle.

Fig. 2: Actuation and modelling of microbubble-array artificial muscles.

a–c, Time-lapse images of the selective deformation shapes of a variable-size microbubble-array artificial muscle (3 cm × 0.5 cm × 80 μm) containing microbubbles of diameter 12 μm, 16 μm and 66 μm, each 50 μm in depth, excited at 96.5 kHz (a), 82.3 kHz (b) and 33.2 kHz (c), respectively, at 60 VPP. The pink dots indicate the region of the bubble array being activated. d, Time-lapse images of the variable-size microbubble-array artificial muscle under sweeping-frequency ultrasound excitation (20–90 kHz, 1.2 s, 60 VPP). The pink dashed lines mark the shape of the muscle at the previous time step and the pink arrows mark the bending direction of the excited part. e, Modelling of the activation mechanism of microbubble-array artificial muscles. The pink, yellow and blue boxes represent differently sized microbubble-array segments. The upper portion illustrates schematics of the cross-section of the artificial muscle, each part of the artificial muscle corresponding to a specific length (L) and second moment of area (I). Fi denotes the thrust force generated by the microstreaming (here the yellow segment of the muscle generates thrust), Δ and θ denote the deflection and rotation angle along the long axis (x axis), and s denotes the coordinate along the beam. Lower-left inset: modelling of a microbubble, where Rc is the radius of the cavity, R is the curvature radius of the trapped microbubble and a is the amplitude of the centre displacement during oscillation. Scale bars, 1 cm (a,d).

Modelling of microbubble-array artificial muscles

We have developed a theoretical model to improve our understanding of the response of soft artificial muscles to sound waves. This model divides the entire artificial muscle into discrete segments that correspond to the patterned microbubble arrays, as illustrated in Fig. 2e. We began by modelling the acoustofluidic thrust force from a single trapped microbubble and analysing the resulting artificial muscle deformation. To formulate the model, we assumed that (1) the ultrasound produces a homogeneous oscillating pressure field at the microbubble, leading to the thrust force; (2) the beam’s oscillation amplitude is negligible compared with that of the microbubbles, such that its motion does not significantly affect the surrounding flow field; (3) hydrodynamic coupling between oscillating microbubbles can be neglected; (4) the fluid is incompressible; (5) beam stretching is negligible; and (6) the gravity of the muscle does not influence the beam deformation.

To calculate the thrust force arising from acoustic streaming generated by a single oscillating microbubble, we adopted a model developed by refs. 48,49. With additional approximations (Supplementary Note 1), we derived an expression for the thrust force

$${F}_{i}\approx {\rm{\pi }}\rho \omega {{R}_{{\rm{c}}i}}^{3}{v}_{i},$$

(1)

where ρ is the fluid density, ω = 2πf with the ultrasound frequency f, Rci is the cavity radius in segment i, and vi is the mean tangential velocity along the microbubble surface perpendicular to the beam, measured experimentally (Extended Data Fig. 3). For example, a microbubble with a 30 μm radius and 150 μm depth in water (ρ = 1,000 kg m−3), excited at 57.4 kHz with 60 VPP, produced a measured velocity of vi = 2.01 mm s−1, yielding a thrust force of Fi = 61 nN according to equation (1). Scaling this to an array of approximately 18,500 uniformly sized microbubbles on a 30 mm × 5 mm artificial muscle yields a total force reaching up to 1.1 mN, corresponding to a force intensity of 7.6 μN mm−2 (Supplementary Fig. 4).

To describe the beam deformation, we parameterized the slender beam length by a variable s. Owing to planar symmetry, the deformation is fully described by the local slope angle θ(s). Using linear elasticity and the known orthogonal thrust force density, we derived the governing equation for θ(s) (Supplementary Note 2). Assuming small variations in θ within each segment, we obtained an analytical expression for θ(s) in terms of the beam’s Young’s modulus E, second moment of area I and the segmental thrust force densities. The resulting y-direction deformation as a function of s is then given by

$$\varDelta (s)={\int }_{0}^{s}\sin (\theta ({s}^{{\prime} })){\rm{d}}{s}^{{\prime} }.$$

(2)

Our model is applicable to artificial muscles featuring both uniform-size and variable-size microbubble arrays. The deformation amplitude of an artificial muscle can be amplified quadratically by increasing the ultrasound excitation voltage \((\varDelta \propto {{\rm{V}}}_{{\rm{PP}}}^{2})\), as shown in Supplementary Fig. 4. The deformation can also be increased by increasing the number of microbubbles (Supplementary Fig. 5). In addition, larger deformation can be achieved by either reducing the material’s Young’s modulus or decreasing the muscle’s thickness (Supplementary Fig. 6). Furthermore, we envision that expanding the range of microbubble sizes enhances the manipulation freedom.

Applications of microbubble-array artificial muscles

The development of programmable microbubble-array artificial muscles presents an exciting alternative for wireless actuation, enabling innovative designs in the field of soft robotics. Trapping and manipulating small, fragile model animals (for example, zebrafish embryos) could become an appealing area of research in soft robotics. Conventional micro-tweezers often lack sufficient gripping force and bulkier grippers risk damaging delicate targets. To address this, we designed a miniaturized soft gripper composed of six to ten uniform-size microbubble-array artificial muscles. Each tentacle houses approximately 10,000–20,000 microbubbles when submerged in water. As illustrated in Fig. 3a and Supplementary Video 5, when subjected to an ultrasound stimulus (95.5 kHz, 60 VPP), the tentacles gripped a zebrafish larva within 100 ms. When the ultrasound stimulus was deactivated, the larva easily swam away (Supplementary Video 6). Repeated actuation showed no notable heating or adverse effects on the larva, confirming the biocompatibility of the mechanism.

Fig. 3: Adaptive gripper and robotic skin based on microbubble-array artificial muscles.

a, Time-lapse sequence showing a live zebrafish larva grasped by a soft gripper composed of multiple artificial-muscle petals (10 mm × 0.7 mm × 80 µm), each incorporating microbubble arrays (12 µm in diameter × 50 µm in depth). Inset: magnified view of the larva. b, Rotation of an almond by a conformable microbubble-array robotic skin (12 μm × 50 μm). c, Deformation of a blade of grass by the same robotic skin, showing self-attachment and actuation. Inset: magnified view of the microbubble array. d, Conformal attachment of a green fluorescently labelled cardiac patch (30 mm × 10 mm × 80 μm) to the epicardial surface of an ex vivo porcine heart. e, Experimental set-up showing an excised porcine bladder with an ultrasound transducer positioned approximately 5 cm from the left side and an endoscope inserted for internal visualization. f, Time-lapse endoscopic images showing the encapsulated artificial muscle inside the bladder, its release at approximately 3–5 min and conformal attachment to the inner wall at 4.2 min under ultrasound activation. Scale bars, 5 mm (a–c), 1 cm (d,f), 2 cm (e).

We further demonstrated the artificial muscle as a conformable robotic skin capable of adhering to arbitrary surfaces and imparting motion to stationary objects. For example, we attached the robotic skin containing a uniform-size microbubble array to an arbitrary-shaped almond that exhibited controllable anticlockwise rotation upon excitation at 95.5 kHz and 60 VPP (Fig. 3b and Supplementary Video 7). We further show that upon switching on the ultrasound excitation, the robotic skin self-adhered to a blade of grass and enabled it to bend (Fig. 3c and Supplementary Video 8). The microbubble-array robotic skin offers the inanimate object diverse mobilities without notable size or mass increase.

Similarly, we demonstrated conformal attachment of the robotic skin—an artificial muscle containing a uniform-size microbubble array—to the epicardial surface of an ex vivo porcine heart, where it maintained functional adhesion for over 60 min at 96 kHz and 60 VPP (Fig. 3d, Extended Data Fig. 4 and Supplementary Video 9). By engineering different microbubble arrays into circular geometry and tuning the excitation frequency, we generated selective and programmable localized mechanical forces, multimodal shape transformations (Extended Data Fig. 5 and Supplementary Video 10) and targeted drug delivery (Extended Data Fig. 6). Localized stimulation enables on-demand mechanical actuation of soft biological tissues and could support a range of future cardiac therapies and clinically relevant interventions, such as targeted anti-fibrotic drug delivery and localized gene or messenger RNA therapy. These results highlight the potential for the future development of in vivo wireless and wearable devices.

To evaluate the potential for wireless robotic drug delivery and in situ deployment, the artificial muscle was pre-encapsulated in a biodegradable capsule designed for swallowable or minimally invasive delivery (Fig. 3e). Upon injection into an excised porcine bladder, the capsule gradually dissolved in about 3–5 min, exposing the actuator to the surrounding environment. Following dissolution, ultrasound (96 kHz, 60 VPP) was applied to induce deformation of the actuator, allowing it to attach to the inner surface of the bladder (Fig. 3f and Supplementary Video 11).

Capitalizing on the dynamic deformation and rapid response capabilities of our artificial muscle, we engineered a bioinspired ultrasound-powered wireless stingraybot. The biomimetic stingraybot features two artificial muscles—designed to mimic the pectoral fins of a natural stingray—integrated on its sides. These pectoral fins incorporate arrays of differently sized microbubbles (12 μm, 16 μm and 66 μm in diameter, 50 μm in depth) patterned along the head-to-tail axis and paired with a PDMS block for buoyancy adjustment. When exposed to a sweeping-frequency ultrasound stimulation (30–90 kHz over 2 s at 60 VPP), the stingraybot’s fins exhibit an undulatory motion that mimics the natural motion of a stingray (Fig. 4a and Supplementary Video 12). Upon release, the stingraybot propels forward at an initial speed of about 0.8 body lengths per second (Fig. 4b). More control experiments on stingraybot propulsion are provided in Methods.

Fig. 4: Bioinspired swimming and navigation within ex vivo biomedical environment.

a, Undulatory motion of the microbubble-array fins (12 μm, 16 μm and 66 μm in diameter, 50 μm in depth) of the bioinspired stingraybot before release. b, Forward swimming of the stringraybot under sweeping-frequency excitation (30–90 kHz, 2 s, 60 VPP). Right: fin motion during swimming. Lower inset: schematic of the patterned microbubble arrays. In a and b, the pink dashed lines and arrows denote the fin shapes in last step and the fin’s moving direction, respectively. c, Edible hydroxypropyl methylcellulose capsule (27 mm × 12 mm) containing a pre-folded stingraybot. d, Set-up for release and navigation of the encapsulated artificial muscle in an excised porcine stomach, with an external transducer positioned approximately 3 cm from the stomach and internal endoscope for visualization. e, Locomotion of the stingraybot inside an excised porcine stomach. f, Locomotion of a pre-folded, wheel-shaped artificial muscle (30 mm × 5 mm × 80 μm) with variable-size microbubble arrays (12 μm, 16 μm and 66 μm in diameter, 50 μm in depth) inside a porcine stomach. The artificial muscle propels along the stomach surface under sweeping-frequency excitation (30–100 kHz, 2-s sweep period, 60 VPP). The blue arrows mark the direction of motion and the green dots indicate the centre position. Inset: pre-folded shape. g, Set-up for ex vivo manipulation of a pre-folded artificial muscle inside an excised porcine intestine, with external transducers and an internal endoscope. Inset: endoscopic view of the artificial muscle. h, Time-lapse images showing the artificial muscle rolling along the curved mucosal wall under ultrasound sweeping-frequency (30–100 kHz, 2-s sweep period, 60 VPP) delivered by a piezo transducer. i, Locomotion of the artificial muscle driven by a high-intensity focused ultrasound transducer (1–3 MHz, 1-s sweep period, 60 VPP). Red lines, trajectory; yellow dots, centre position over time. Scale bars, 1 cm (a–c,e,f,h,i), 2 cm (d,g).

To implement practical biomedical applications, we demonstrated ultrasound-guided navigation of a pre-folded artificial muscle through ex vivo porcine gastrointestinal tissues, targeting use cases such as site-specific drug release for gastrointestinal disorders, minimally invasive access to inflamed or fibrotic tissue, and wireless actuation in regions inaccessible to rigid tools. We first pre-folded and encapsulated a stingraybot within an edible capsule (Fig. 4c). Once released into the stomach (Fig. 4d), the stingraybot propelled on demand within the confined biomedical environment under ultrasound actuation (Fig. 4e and Supplementary Video 13). In a separate experiment, we pre-folded a linear artificial muscle—with variable-size microbubble arrays arranged along its outer surface—into a cylindrical, wheel-like structure. Under sweeping frequencies, the actuator exhibited directional rolling propulsion along the complex mucosal surfaces of the stomach and intestine (Fig. 4f–i and Supplementary Video 14), illustrating its potential for soft robotic intervention and targeted delivery within the gastrointestinal tract. Future work will focus on parametric studies, dynamic folding strategies and steering-enabled configurations of the artificial muscle across varied tissue geometries and fluidic environments.

Discussion

We have introduced a class of soft artificial muscles that use acoustically activated microbubble arrays to achieve programmable actuation. These artificial muscles show dynamic programmability, high force intensity (about 7.6 μN mm−2), rapid responsiveness (sub-100 ms) and wireless controllability, all while maintaining exceptionally compact (3,000 microbubbles per mm^2) and lightweight (0.047 mg mm−2). Through the strategic use of microbubble configurations and voltage and frequency as ultrasound excitation parameters, we engineered a diverse range of preprogrammed movements (for example, undulatory motion) and demonstrated their applicability across various robotic platforms. We showcased the strength and durability of these muscles by integrating variable-size microbubble arrays into functional devices such as a soft gripper, a robotic skin and a biomimetic stingray robot. We also established a theoretical model that elucidates the actuation mechanism, which serves as a guide for the design of microbubble-array patterns with enhanced actuation performance. These artificial muscles offer extensive applications in robotics, flexible electronics, wearable technologies, prosthetics, biomedical instrumentation and beyond.

To optimize the artificial muscle performance through the geometric design and the density of the microcavities, preliminary experiments revealed that converging trapezoidal cavities generate roughly three-times-stronger streaming velocities than diverging shapes (Supplementary Fig. 7) and a higher density of cavities causes a larger deformation. By incorporating geometric and density optimization with systematic characterization, one can establish a predictive design framework for actuators with tailored deformation profiles—enabling precise control in applications from soft robotics to biomedical devices. Future studies could also explore the application of confocal sound sources, such as high-intensity focused ultrasound (Supplementary Fig. 8) to achieve local millimetre deformation—which potentially could lead to tools for applications such as in vivo mechanotransduction and spatially targeted drug delivery. In addition, the bubble-based mechanism is widely material agnostic and can be extended to biocompatible or biodegradable matrices, such as hydrogels and biodegradable polymers for more biomedical applications. More robustness evaluations on our ansatz across fluid media are provided in Methods.

Despite promising results, certain limitations remain. Prolonged actuation triggers microbubble growth within the cavities, destabilizing the muscle operation after approximately 30 minutes (Supplementary Fig. 9). Resubmersion in water restores the function, and sealing cavities with a thin PDMS membrane will offer a long-term robust solution (Supplementary Fig. 10). In addition, the stingraybot’s distance-dependent actuation must be taken into account for untethered operation. Our preliminary experiments at varying transducer distances revealed deformation decays with increasing distance (Supplementary Fig. 11), dropping by about 50% at 5 cm compared with the deformation at 1 cm. Although this limitation is less critical in vivo (where the robot is intended to operate in confined volumes, for example, the bladder), optimizing the ultrasound source configurations and the actuation voltage to compensate for ultrasound intensity decay over distance can enhance the performance.

Looking ahead, these artificial muscles hold transformative potential across cutting-edge fields such as soft robotics, haptic medical devices and minimally invasive surgery. Future research should focus on refining the scalability of these systems across multiple scales (Extended Data Fig. 7), enhancing their force-generation capabilities and integrating them into complex devices for biomedical applications.

Methods

Fabrication of artificial muscles

The negative patterns of the artificial muscles were first designed in commercial electronic design automation software (as shown in Supplementary Fig. 1). The patterns were transferred into a photomask by a direct writing laser (DWL2000) machine in a clean room (BRNC). Then we spin-coated the negative photoresist SU8-3025 on a 4-inch silicon wafer. Using standard lithographic fabrication, the patterns were transferred to the photoresist via exposure to ultraviolet light through the mask. After the developing process, the negative patterns of the microbubble arrays, that is, micropillars, were additive on the wafer. The height of the micropillar depends on the spinning speed. Next, to enhance the surface properties, a silane-based hydrophobic treatment was applied to the 4-inch wafer with micropillars for 1 h (see fabrication flow in Supplementary Fig. 12). The PDMS used in this process was prepared with a 10:1 ratio of the base to curing agent. Then the PDMS mixture was poured onto the wafer. To ensure a high-quality coating, the mixture was degassed under a vacuum pressure of less than 1 mbar. After degassing, spin-coating of the PDMS was performed on the wafer. Different spin speeds resulted in varying PDMS membrane thicknesses (Supplementary Fig. 2). After spin-coating, the PDMS was vacuumed again and cured in a sequential heating process: 1 h at 60 °C, followed by 1 h at 80 °C and finally 1 h at 100 °C. Finally, the PDMS soft membrane was cured and then peeled off the wafer. This process yielded a uniform PDMS layer suitable for use in artificial muscle and soft robotic applications. In all our experiments, each cavity consistently trapped only a single bubble as the artificial muscle submerged into the water (Supplementary Fig. 13).

Acoustic set-up

For the microscale characterization of microbubbles, the experimental set-up was built on a thin glass substrate with dimensions of 24 mm × 60 mm × 0.18 mm. As shown in Supplementary Fig. 14, a circular piezoelectric transducer (27 mm × 0.54 mm, resonance frequency 4.6 kHz ± 4%, Murata 7BB-27-4L0) was affixed to the glass substrate using an epoxy resin (2-K-Epoxidkleber, UHU Schnellfest). A square PDMS acoustic chamber (10 mm × 10 mm × 5 mm) was positioned in front of the transducer, which was filled with deionized water and covered with a cover glass (22 mm × 22 mm × 0.18 mm). An artificial muscle was suspended in the centre of the chamber with one end clamped to the side wall and the other end left free. The substrate was then mounted on an inverted microscope (Axiovert 200M, ZEISS).

For the macroscale actuation of artificial muscles by sound, the experimental set-up consisted of a plastic tank measuring 10 cm × 10 cm × 8 cm with a wall thickness of 2 mm. For ex vivo porcine experiments, a larger chamber (30 cm × 15 cm × 15 cm, thickness 2 mm) was used. As shown in Supplementary Fig. 15, the circular piezoelectric transducers were affixed to the inside surfaces and the bottom surface of the tank using the epoxy resin or directly submerged into the liquid. An artificial muscle was suspended inside the chamber with one end clamped, and three cameras were placed around the tank to capture the actuation of acoustic artificial muscles from multiple viewing angles. In addition, a miniaturized endoscopic camera (8 mm diameter and 1080P resolution, FuanTech) was used to capture images inside the porcine specimens. An electronic function generator (AFG-3011C, Tektronix) and an amplifier (0–60 VPP, ×15 amplification, High Wave 3.2, Digitum-Elektronik) were connected to the transducer to generate sound waves with tunable excitation frequencies and voltages. Square waves effectively drive the artificial muscle, achieving maximum deformation and outperforming other tested waveforms, such as sinusoidal and triangular waveforms under equivalent excitation conditions (Supplementary Fig. 16).

Microstreaming characterization

We evaluated the microstreaming jets generated by ultrasound-driven microbubbles embedded in the muscle using 6-μm tracer particles in water and particle image velocimetry analysis. Three uniform-size microbubble arrays, each comprising a 4 × 4 grid of microbubbles with diameters of 40 μm, 60 μm and 80 μm (150 μm in depth), were individually selected and tested in separate miniaturized artificial muscles (500 μm × 500 μm × 200 μm; Extended Data Fig. 3a and Supplementary Video 15). When activated at their respective resonance frequencies 76.3 kHz, 57.4 kHz and 27.6 kHz, we measured the microstreaming velocity 80 μm away from the bubble interface and observed a quadratic relationship between the average velocity and the excitation voltage (Extended Data Fig. 3b). The streaming velocity near the bubble reached 2.5 mm s−1 at 60 VPP. This voltage-dependent microstreaming directly correlates with the reverse thrust generated by the microbubble array, demonstrating that the thrust magnitude can be dynamically tuned by adjusting ultrasound excitation.

We further investigated the selective actuation of a variable-size microbubble array of 40 μm, 60 μm and 80 μm diameter, each 150 μm in depth, integrated within a single miniaturized artificial muscle (500 μm × 500 μm × 200 μm) with corresponding frequencies (76.3 kHz, 57.4 kHz and 27.6 kHz, respectively). The particle image velocimetry analysis revealed that the microstreaming developed by the 80-μm bubbles generated an average velocity of 0.23 mm s−1 at 27.6 kHz, which was markedly stronger compared with the velocities (<0.05 mm s−1) produced by the other two microbubble arrays at the same voltage (15 VPP). Similarly, adjusting the frequency to 57.4 kHz (76.3 kHz) selectively activates the 60 μm (40 μm) bubble array, resulting in more intense streaming at 0.174 mm s−1 (0.075 mm s−1), in contrast to other arrays (Extended Data Fig. 2). Additionally, applying a sweeping frequency (10–90 kHz) over 4 s at 30 VPP enabled wave propagation across the artificial muscle (Supplementary Video 16).

Control experiments on artificial muscle deformation

To determine the key factors influencing muscle deformation, a set of control experiments was performed. We first examined the streaming jets of a uniform-size microbubble-array artificial muscle (1 cm × 0.3 cm × 80 μm) patterned with over 800 microcavities (each 40 μm in diameter and 50 μm in depth). Supplementary Video 17 shows that an artificial muscle without microbubbles exhibited minor deformation, with no noticeable microstreaming observable across the excitation frequency sweeps from 1 kHz to 100 kHz at 60 VPP. By contrast, the actuator exhibited pronounced deformation at an excitation frequency as low as 9.5 kHz (well below resonance), where microbubbles generated microstreaming (approximately 0.8 mm s−1), resulting in substantially greater deformation compared with the case without microbubbles.

Repeatability and characterization of artificial muscle deformation

We assessed the repeatability of the artificial muscle’s deformation under identical excitation conditions, with the transducer close to the microbubble-embedded side, as shown in the left panel of Extended Data Fig. 8a. When stimulated with ultrasound pulses (80.5 kHz, 52.5 VPP and 1-s on/off cycle), the muscle exhibited repeatable bending within 150 cycles, with an error of ±0.8 mm, representing 2.7% of the total beam length (Extended Data Fig. 8b). With more excitation cycles (500 cycles) of the artificial muscle, the deformation exhibited larger error (about 10%). After 10,000 cycles, there were no observable microbubbles in the artificial muscle, and the artificial muscle showed minor deformation. Furthermore, Extended Data Fig. 8c shows a quadratic relationship between the applied voltage and the mean deformation amplitude of artificial muscles, each patterned with uniformly sized microbubbles of 40 μm, 60 μm or 80 μm, when driven at their respective resonance frequencies (80.5 kHz, 62.5 kHz and 30.3 kHz). In addition, the PDMS beam, in the absence of microbubbles, exhibited limited bending (about 7% of the 40-μm microbubble-array artificial muscle’s deformation at 52.5 VPP) caused by the weak radiation force from incident sound waves originating from the transducer.

Control experiments on stingraybot propulsion

In control experiments, a stingraybot without microbubbles exhibited no undulatory motion along its fins under ultrasound excitation and sank without notable lateral displacement (Supplementary Video 18). Notably, under continuous excitation at a single frequency (tested separately at 33.2 kHz, 85.2 kHz and 96.2 kHz at 60 VPP), targeting microbubble arrays with cavity diameters of 66 μm, 16 μm and 12 μm, respectively, the stingraybot exhibited only limited locomotion (<1 body length). By comparison, sweeping-frequency excitation (10–100 kHz over 2 s) elicited sustained undulatory motion, allowing the stingraybot to swim a significantly greater distance (>3.5 body lengths), as shown in Supplementary Fig. 17. These results suggest that the forward motion of the stingraybot is dominated by the propulsion force generated by the sequential undulatory motion, resulting from the reverse thrust generated by the microbubble arrays. Moreover, enhancing the design of the stingraybot with additional microbubble sizes could expand its manoeuvrability. For instance, integrating a navigation tail with microbubble arrays of different sizes on either side enables directional control. When activated at their respective resonance frequencies on one side, these arrays generate an asymmetric torque (Supplementary Fig. 18), enabling steering of the stingraybot via tail rotation. As the stingraybot is stealthy and transparent, we further envision that our stingraybot could be used for environmental data collection or behavioural research on real organisms, for example, detecting water quality within coral reefs and recording swarm interaction by blending into schools of fish.

Robustness evaluation

To evaluate the robustness of our ansatz across fluid media, we quantified artificial muscle deformation in 100% porcine blood, observing amplitudes of approximately 0.4 mm, 1.0 mm, 2.7 mm and 4.4 mm at 15 VPP, 30 VPP, 45 VPP and 60 VPP, respectively, under 96-kHz ultrasound excitation (Extended Data Fig. 9). As complementary evidence, we studied the artificial muscle performance in various aqueous solutions (deionized water, tap water and 25–100% glycerol solutions) as shown in Supplementary Fig. 19. The deformation showed an inverse relationship with glycerol concentration, with the largest deformation of about 11.3 mm in a 25% glycerol solution, followed by about 8.4 mm in 50% glycerol and 3.7 mm in 75% glycerol. The deformation was almost negligible in 100% glycerol. These results clearly demonstrate that the actuator functions effectively in full blood, validating its potential for in vivo applications in fluids with physiological viscosity. We next evaluated artificial muscle actuation in the presence of solid obstructions (Supplementary Fig. 20). A frontal obstruction (partially blocking ultrasound) reduced the deformation by 80–90% (0.5–1-mm tip deformation versus 4.8 mm unobstructed). A lateral placement caused moderate attenuation (about 2.5 mm) and posterior positioning retained a better performance (3.8 mm). Furthermore, experimental results showed significant deformation of the artificial muscle behind excised porcine ribs (Supplementary Fig. 21). Thus, actuators remained functional near obstacles but required strategic positioning to maximize deformation. Our preliminary results also revealed negligible heating effects near the piezoelectric transducer during artificial muscle and stingraybot operation (Supplementary Fig. 22), underscoring the thermally benign nature of our acoustic platform. Although frequency-dependent selectivity was achieved, some cross-excitation between microbubble arrays was observed. This effect was mitigated under sweeping-frequency actuation, and temporal control over the sweep dynamics has a key role in preserving spatial selectivity and ensuring reliable, programmable motion. In vivo biomedical environments present additional challenges such as complex fluid flow, irregular geometry and variable temperature gradients, all of which may distort ultrasound propagation. Although the actuator showed robust and competitive performance under static conditions with other methodologies (Extended Data Fig. 10 and Supplementary Fig. 23), future work will explore flow-resilient designs, including optimized microbubble-array geometries, flexible ultrasound configurations and real-time actuation control strategies to maintain reliable performance in dynamic fluid environments.

Numerical simulations

Finite element numerical simulations were conducted using the commercial COMSOL Multiphysics software (v6.1), including simulations on the acoustic pressure field in the small PDMS chamber, acoustic streaming generated by variable-size microbubbles in the small PDMS chamber, the acoustic pressure field in the big acoustic tank and the deformations of the artificial muscle. All simulations were performed with dimensions and material properties consistent with the experiments. Physics modules of simulations on acoustic pressure include solid mechanics, electrostatics, pressure acoustics fields, creeping flow, and heat transfer in solids and fluids. Simulations on the deformations of artificial muscles were performed using the solid mechanics module with corresponding boundary conditions and force conditions. The microstreaming-generated thrust was assumed to be a point force that is loaded on the bottom of each microcavity. In addition, numerical calculations based on the theoretical model were performed using the commercial Matlab software (version R2021b). See Supplementary Notes for simulation details.

Imaging and analysis

The microscale characterization of microbubbles was recorded with a high-speed camera (Chronos 1.4, Kron Technologies) attached to the inverted microscope. Recording frame rates ranged from 1,069 to 32,668 frames per second. The macroscale motion of ultrasound artificial muscles was recorded with a high-sensitivity camera (Canon 6D and 24–70-mm camera lens, Canon). The recording frame rate was 50 frames per second. Recorded footage was analysed in ImageJ. Statistical analyses were conducted using MATLAB (version R2021b), Originlab (version Origin 2023) and Excel (version 16.54).

Preparation of the zebrafish embryo

Zebrafish (Danio rerio) embryos from pairwise crosses of WIK wild-type fish were raised in E3 medium (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl_2, 0.33 mM MgSO_4) at 28 °C under a 14:10 h light/dark cycle. Experiments up to 5 days post fertilization are not subject to animal welfare regulations. All husbandry and housing procedures were approved by the local authority (Kantonales Veterinäramt, TV4206).

Preparation of the porcine organs

Porcine hearts, stomachs, intestines, ribs and blood were obtained from a licensed abattoir. As the study involved only ex vivo tissues from animals slaughtered for food production, no ethical approval was required.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The datasets that support the findings of this study are available within the paper. Correspondence and requests for materials should be addressed to the corresponding author. The time frame for response is 12 months.

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Acknowledgements

D.A. was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 853309, SONOBOTS); the Swiss National Science Foundation (SNSF) through the SNSF Project funding MINT 2022 (grant agreement number 213058), Spark Grant (grant agreement number 221285); and the ETH Research Grant (grant agreement number ETH-08 20-1). Z.S. acknowledges financial support from the China Scholarship Council (202106320193). Z.Z. acknowledges financial support from the China Scholarship Council (202006210065). N.N. acknowledges the funding from the United States National Science Foundation (OIA-2229636, CBET-2407937). R.W. is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) — 535275785. We thank the operational team of the clean room facility in the Binnig and Rohrer Nanotechnology Center (BRNC) for their helpful discussion on the fabrication wafer. We thank D. Schoenenberger for his contribution to the initial fabrication and testing.

Funding

Open access funding provided by Swiss Federal Institute of Technology Zurich.

Author information

Author notes

1. These authors contributed equally: Zhan Shi, Zhiyuan Zhang

Authors and Affiliations

1. Acoustic Robotics Systems Laboratory, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, Zurich, Switzerland

Zhan Shi, Zhiyuan Zhang & Daniel Ahmed

2. Institute of Theoretical Physics, Center for Soft Nanoscience, University of Münster, Münster, Germany

Justus Schnermann & Raphael Wittkowski

3. Department of Molecular Life Sciences, University of Zürich, Zurich, Switzerland

Stephan C. F. Neuhauss

4. Department of Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA

Nitesh Nama

5. Department of Physics, RWTH Aachen University, Aachen, Germany

Raphael Wittkowski

6. DWI - Leibniz Institute for Interactive Materials, Aachen, Germany

Raphael Wittkowski

Contributions

D.A. conceived and supervised the project. Z.Z., Z.S. and D.A. contributed to the design of the actuator and soft robots. Z.S. and Z.Z. developed the fabrication of prototypes, performed experiments and data analysis. S.C.F.N. provided the zebrafish embryos and provided guidance on the zebrafish experiments. R.W. supervised J.S. R.W., N.N., J.S., Z.S., Z.Z. and D.A. contributed to the theoretical understanding. J.S. provided the analytical derivations on the acoustofluidic thrust force and the beam deformation. Z.S. contributed to the numerical simulations. Z.Z., Z.S., R.W., J.S., N.N. and D.A. contributed to the scientific presentation, discussion, paper revision and data analysis.

Corresponding author

Correspondence to Daniel Ahmed.

Ethics declarations

Competing interests

The authors declare no competing interests.

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Nature thanks Yilun Liu 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 Characterization of the resonance frequencies of differently sized microbubbles.

a, Oscillation amplitude of a 60 μm × 150 μm microbubble as a function of the ultrasound excitation frequency. According to the measured oscillation amplitude of the microbubbles, the resonance frequency is identified to be 35.5 kHz. b, Experimentally measured resonance frequencies of microbubbles with depths of 150 μm and 175 μm, radii ranging from 20 to 70 μm in 5 μm increments, and a depth of 50 μm with radii of 6 μm, 8 μm, 20 μm, 30 μm, 33 μm and 40 μm. The solid lines represent numerically predicted results. Additionally, notable discrepancies exist between the calculated and measured resonance frequencies, which may originate from transducer coupling with the glass slide, interactions between bubbles, or other factors.

Extended Data Fig. 2 Selective actuation of microbubble arrays.

a, Microstreaming respectively generated by 40 μm, 60 μm, and 80 μm microbubble arrays under ultrasound frequencies of 76.3 kHz (green), 57.4 kHz (blue), and 27.6 kHz (purple). Colored boxes indicate the activated microbubbles. The black dashed line denotes the measurement position of the microstreaming velocity, which is 100 μm away from the surface. b, Measured streaming flow of a with 2 μm tracer microparticles. The streaming flow field was analyzed by PIV (Matlab R2022b, PIVlab 2.60). The color bar denotes the particle moving velocity perpendicular to and extending away from the bubble surface. c, Plot of the measured microstreaming velocity along the long axis of the variable-size microbubble array under different excitation frequencies. d, Simulations of microstreaming around the bubble array with three different excitation frequencies. The pink dotted lines in a, b, and d delineate the boundaries of three distinct microbubble arrays.

Extended Data Fig. 3 Measurement of microstreaming velocity by PIV.

a, Microstreaming generated by a 4×4 80 μm × 150 μm microbubble array under an excitation frequency of 27.6 kHz and two different voltages of 10 VPP (left panel) and 30 VPP (right panel). The black dashed line denotes the measurement position of the microstreaming velocity, which is 80 μm away from the surface. b, Plot of microstreaming velocity versus ultrasound excitation voltage respectively measured by 4×4 microbubble arrays with three different sizes (40 μm, 60 μm, and 80 μm). The solid lines are the quadratic fitting results. The shaded error bands represent mean ± s.d. from n = 5 independent measurements.

Extended Data Fig. 4 Attachment of a robotic patch to an ex vivo porcine heart.

a, Experimental setup showing an artificial muscle positioned between an ultrasound transducer (operated at 96 kHz and 60 VPP) and the heart, separated by ~2.5 cm. b, The muscle is released from the tweezer positioned at the bottom and rises upward due to buoyancy (trajectory indicated by the red arrow). c, The artificial muscle conforms to the surface of the heart when stimulated by ultrasound (trajectory indicated by the red arrow). d, Time course of attachment robustness, demonstrating conformation and adhesion of the artificial muscle to the heart from 0 to 60 min under ultrasound actuation, followed by detachment upon ultrasound deactivation at 70 min. The pink dashed rectangle indicates the location of the artificial muscle.

Extended Data Fig. 5 Multimodal shape transformation of a microbubble array-patterned functional surface.

a, Multimodal shape transformation of a circular surface under continuous ultrasound excitation frequencies of 96.2 kHz, 82.5 kHz, and 33.2 kHz, respectively. The circular surface was topped with a circular PDMS block (2 cm diameter, 0.3 cm thickness) to reduce buoyancy. The inset shows a schematic of the microbubble array patterned on the surface. b, Dynamic shape transformation of the circular surface under sweeping-frequency ultrasound excitation spanning from 10 kHz to 100 kHz over 2 s.

Extended Data Fig. 6 Ultrasound-enhanced dye delivery into an agar phantom by a microbubble-array robotic patch.

a, Setup illustrates an agar block resting in an acoustic tank, with a piezo transducer positioned 5 cm to the left. A circular robotic patch is placed on the agar block with its microbubble arrays facing downward. The zoom–in highlights the patch surface; inset shows the patterned microbubble array. b, Agar block prior to dye exposure. c, Control condition showing the agar block after 30 min in a dye-filled tank without ultrasound actuation. d, Top and cross-sectional views of the agar block after 30 min of ultrasound actuation (96 kHz, 60 VPP), revealing enhanced dye penetration. The blue dashed line indicates the cutting plane; green dashed lines delineate the boundaries of the penetrated region.

Extended Data Fig. 7 Artificial muscles functioning at scales from 10−1 mm to 10[2] mm.

a, A microscale rotator featuring an asymmetric 8 × 8 microbubble array (12 μm × 50 μm). The upper and lower panels show the microrotator with ultrasound off and on, respectively, at 95.5 kHz and 60 VPP. n = 3 independent measurements. b, A millimeter-scale artificial muscle with an asymmetric 400 × 200 microbubble array (12 μm × 50 μm). The upper and lower panels show the device with ultrasound off and on, respectively, under the same driving conditions. The purple dashed line indicates the original position of the artificial muscle. c, A macroscale stingraybot equipped with artificial muscles comprising variable-size microbubble arrays (40 μm × 150 μm, 60 μm × 150 μm, 80 μm × 150 μm, respectively), demonstrating undulatory motion under excitation (10–90 kHz, duty cycles 2 s, 120 VPP). The blue line marks the current location of the fin edge, while the white dashed line shows its position in the previous frame of the time-lapse image.

Extended Data Fig. 8 Deformation of uniform-size microbubble array artificial muscle.

a, Time-lapse images of a uniform-size microbubble array artificial muscle with microbubbles respectively positioned at the left and right side. The pink rectangle and arrow show the fixed end and bending direction of the muscle, respectively. The red line on the left side denotes the location of the transducer. b, Plot of the bending amplitude of the muscle tip over multiple excitation cycles with an average repeated-positioning error of ±0.8 mm with the excitation signal shown in the top panel. The pink and gray dots correspond to the excitation ‘on’ (80.5 kHz and 52.5 VPP) and ‘off’ stages, respectively. The green and blue dots represent the measured deformation amplitudes when the bubbles are positioned on the left and right sides, respectively, as shown in a. c, Bending amplitude of uniform-size microbubble array artificial muscles with microbubble diameters (D) of 40 μm, 60 μm, and 80 μm and without microbubbles under excitation voltages from 1.5 to 52.5 VPP. The microbubbles have a constant depth of 50 μm. The dots and solid lines are the experimental results and quadratic fitting results, respectively. The shaded error bands represent mean ± s.d. from n = 5 independent measurements. All the muscles bent in the direction as shown in the left panel of a.

Extended Data Fig. 9 Ultrasound-induced deformation of an artificial muscle in porcine blood.

An artificial muscle embedded with uniform microbubbles (12 μm × 50 μm) was immersed in 100% porcine blood, with a piezoelectric transducer bonded to the bottom of the acoustic tank and positioned 3 cm from the muscle. Final deformation (|Δ|) of the artificial muscle as a function of excitation amplitude—0.44 cm, 0.27 cm, 0.10 cm and 0.04 cm corresponding to 60 VPP, 45 VPP, 30 VPP and 15 VPP, respectively—under excitation at 96 kHz. Yellow dashed lines indicate the initial muscle position; white dashed lines indicate the deformed position.

Extended Data Fig. 10 Artificial muscles performance comparison.

a, Response time versus gripping ability (LO/LG) for grippers using different actuation methods, where LO and LG are the dimensions of the gripped object and gripper, respectively. b, Comparison of force-to-weight ratios of grippers versus their size for various actuation methods. c, Comparison of relative swimming speeds (body lengths per second) of swimmers with different actuation mechanisms across various scales (microscale to macroscale). Shaded regions (convex hulls) indicate typical performance ranges; representative studies are labeled by author and year.

Supplementary information

Supplementary Information

Supplementary Notes, Figs. 1–26, Tables 1–3, legends for Videos 1–18 and References.

Reporting Summary

Supplementary Videos

Supplementary Videos 1–18.

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Shi, Z., Zhang, Z., Schnermann, J. et al. Ultrasound-driven programmable artificial muscles. Nature 646, 1096–1104 (2025). https://doi.org/10.1038/s41586-025-09650-3

- Received: 20 January 2024

- Accepted: 18 September 2025

- Published: 29 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09650-3

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Ultrasound-driven artificial muscles can grasp, flex and swim

- Joseph Rufo

- Tony Jun Huang

Nature News & Views 29 Oct 2025

Article Open access Published: 08 October 2025

Age and gender distortion in online media and large language models

Douglas Guilbeault,

Solène Delecourt &

Bhargav Srinivasa Desikan

Nature volume 646, pages 1129–1137 (2025)

Abstract

Are widespread stereotypes accurate1,2,3 or socially distorted4,5,6? This continuing debate is limited by the lack of large-scale multimodal data on stereotypical associations and the inability to compare these to ground truth indicators. Here we overcame these challenges in the analysis of age-related gender bias7,8,9, for which age provides an objective anchor for evaluating stereotype accuracy. Despite there being no systematic age differences between women and men in the workforce according to the US Census, we found that women are represented as younger than men across occupations and social roles in nearly 1.4 million images and videos from Google, Wikipedia, IMDb, Flickr and YouTube, as well as in nine language models trained on billions of words from the internet. This age gap is the starkest for content depicting occupations with higher status and earnings. We demonstrate how mainstream algorithms amplify this bias. A nationally representative pre-registered experiment (n = 459) found that Googling images of occupations amplifies age-related gender bias in participants’ beliefs and hiring preferences. Furthermore, when generating and evaluating resumes, ChatGPT assumes that women are younger and less experienced, rating older male applicants as of higher quality. Our study shows how gender and age are jointly distorted throughout the internet and its mediating algorithms, thereby revealing critical challenges and opportunities in the fight against inequality.

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Main

Although few deny that stereotypes, or generalizations about social groups10,11,12, are harmful, a fundamental question remains contested: are common stereotypes accurate1,2,3 or socially distorted4,5,6? Some argue that commonplace stereotypes accurately capture observable aspects of social groups; otherwise, they would not gain such widespread adoption1,2,3. Yet, others argue that stereotypes are often exaggerated or illusory4,5,6. Assessing stereotype accuracy is challenging because stereotypes involve not only statistical associations (such as expected correlations among the features of a social group) but also normative judgements (such as that one group is superior to another) for which there is no well-defined ground truth10,11,12. Even for statistical associations, identifying the ground truth is difficult. In some cases, this stems from disagreement on how to measure the ground truth, such as enduring debates over how to measure intelligence[13] (a heavily stereotyped characteristic[14]). Yet, even when there is agreement on the relevant constructs, there is often a lack of large-scale, quantifiable cultural data for measuring stereotypical associations and comparing these to ground truth indicators. As a result, research on stereotypes often yields inconsistent findings, calling into question the pervasiveness and impact of these biases. In this study, we overcame these limitations in the analysis of age-related gender bias, which not only involves biological age as an objective anchor for evaluating stereotype accuracy, but which can also be linked to large-scale statistical biases in how the ages of women and men are depicted online.

On the one hand, evidence abounds that older women face a dual bias at the intersection of gender and age. Policy reports8,15,16, media coverage[17] and workplace interviews7,18 indicate that older women are discriminated against in hiring and promotion across industries (known as ‘gendered ageism’7,18,19). This is related to a general statistical bias towards associating women with expectations of youth. From entertainment media to the workplace, women face persistent pressure to appear young, which results in a ‘beauty tax’ with sizeable financial and time costs[20]. This bias also manifests in everyday language. Women in academia[21] and industry[22] are more likely than men to be referred to using infantilizing pronouns (such as ‘girls’). These patterns suggest that age-related gender expectations may form a culture-wide statistical bias that influences people’s perceptions of others throughout society23,24.

On the other hand, the statistical association between women and youth contradicts observable socioeconomic realities. Since the 1960s, women have consistently outlived men in the USA by as much as 8 years, a gap that has been increasing25,26. Census data on occupations present similarly puzzling trends (Supplementary Fig. 1). Over the past decade, there has been no correlation between the fraction of women in an occupation and its median age, according to the US census (Extended Data Fig. 1 and Supplementary Table 1). There were also no clear differences in the age distribution of women and men throughout the workforce from 2009 to the present (Supplementary Fig. 2 and Supplementary Table 2). Moreover, recent surveys failed to observe gendered ageism in certain organizational settings and even suggest that older women may be less affected by stereotypes than older men27,28. These inconsistent findings resonate with critiques against claims of enduring gender inequality, such as research showing declines in gender stereotypes over the last century in online text29,30, as well as studies showing that hiring across industries increasingly favours women31,32. This dissonant landscape raises the question of whether age-related gender bias is an organization-specific or industry-specific problem, or whether it is a culture-wide distortion that continues to reflect and contribute to systemic inequalities.

We argue that this uncertainty is fuelled by the lack of (1) culture-wide multimodal data on the associations between gender and age and (2) computational methodologies for comparing these associations with ground truth indicators. So far, only a handful of studies have examined age–gender associations in small-scale surveys and interviews with professional women7,18,27,28,33 or in sparse, non-representative observational studies of particular industries, such as celebrities and athletes in entertainment media34,35,36,37,38. However, failing to observe age-related gender bias in limited samples of a few contexts does not indicate a lack of prominence on a culture-wide scale. Social biases in how people categorize the world frequently emerge only at scale39,40 and can manifest as exaggerated or even illusory beliefs[41]. This suggests the alternative view that skewed associations between gender and age can emerge as a large-scale statistical bias that distorts socioeconomic realities despite inconsistencies across small-scale contexts.

Although a number of recent studies revealed the exaggeration of male representation in online texts and images6,42, no comparable analyses exist for tracking age-related representations of gender. To address this gap, we produced a large-scale culture-wide dataset on age–gender associations across modalities, including images, videos and textual data, collected from popular sources of digital media. We began by examining the gender and age associations of all social categories in the English language (n = 3,495) in more than 1.3 million images and thousands of videos from Google, Wikipedia, IMDb, Flickr, YouTube and a random sample of the world-wide web (see ‘Image and video datasets’ in Methods for details on pre-processing and post-processing). We benchmarked online images of occupations against the US census data to examine whether they exaggerate the association between women and youth. We went beyond the visual modalities by examining age-related gender bias in nine popular language models trained on billions of words from across the internet, including Reddit, Google News, Wikipedia and Twitter (see ‘Measuring age and gender in online text’ in Methods). By examining age-related gender bias in large-scale internet data, our study was uniquely poised to examine the role that mainstream algorithms play in reinforcing this bias. We examined algorithmic amplification in both the image and textual modalities through (1) the study of the psychological effects of using Google Image search and (2) the use of ChatGPT to generate and evaluate resumes in the workplace.

Age–gender distortions in visual content

Across all image datasets spanning five popular online platforms, women are consistently represented as younger than men, regardless of whether the age and gender of faces are measured using human judgements, machine learning or ground truth data. First, we analysed 657,035 images from the Google search engine associated with 3,495 social categories, in which the gender and age of images were classified by human coders[6] (see ‘Image data collection procedure’ in Methods; all categories were examined using retrieved Google Images containing human faces). We found that women in Google Images were coded as significantly younger than men, both for non-gendered searches (such as ‘doctor’ or ‘banker’; mean difference in age groups, Mdiff = 0.37; t = −73.84; P = 2.2 × 10−16; n = 3,434 categories; Fig. 1a) and gendered searches (such as searching ‘female doctor’ and ‘male doctor’; Mdiff = 0.29; t = −36.52; P = 2.2 × 10−16; n = 2,960 categories; Fig. 1b). Replicating this method over Wikipedia revealed that women in Wikipedia images were also coded as significantly younger than men (Mdiff = 0.71; t = −39.62; P = 2.2 × 10−16; n = 1,251 categories; Fig. 1c).

Fig. 1: Women are represented as significantly younger than men in more than 1.3 million images and thousands of videos across 7 online sources.

a–j, The age of either female or male faces according to the top 100 Google Images associated with 3,434 social categories (n = 161,484 images) (a), the top 100 Google Images retrieved using gendered searches (such as by searching ‘female athlete’ or ‘male athlete’) shown for all non-gendered categories in WordNet (n = 2,960 categories; 495,551 images) (b), 1,251 categories in Wikipedia (from the Srinivasan et al.[68] dataset; 14,709 images) (c), celebrities identified by the top 100,000 most popular pages on IMDb (n = 451,570 images) (d), biographical Wikipedia pages describing the same celebrities in the IMDb–Wiki dataset (n = 57,932 images) (e), the top 50 most popular celebrities from 1951 to 2004 as they appear in Google Images, according to the CACD (n = 149,889 images) (f), a random sample across the world-wide web (the 2017 UTK dataset; n = 20,000 images) (g), a random sample from Flickr (the 2014 Adience dataset; n = 26,580 images) (h), a random sample of images from online news websites (the 2008 LFW dataset; n = 13,233 images) (i) and images of the same people identified in the LFW dataset, extracted across 3,425 YouTube videos in 2011 (n = 5,000 images) (j). The method for coding age and gender varies by dataset; a–c rely on human coders; panels d,e,g rely on ground truth measures of the age and gender of celebrities posted publicly online; f,h–j rely on automated deep learning classifications of gender and age. Solid gold and blue lines indicate the average age for female and male faces, respectively.

These results are robust to collecting Google Images from different countries around the world (Supplementary Fig. 3) and controlling for (1) the demographic characteristics and subjectivity of human coders (Supplementary Figs. 4 and 5 and Supplementary Tables 3–5); (2) the linguistic features of social categories (Supplementary Figs. 6 and 7 and Supplementary Table 6), such as word polysemy, word gender connotation, word age connotation and word frequency in Google Search and in everyday language; (3) the visual features of the images, including the number of faces per image, the number of images associated with each category overall, whether the image repeats across searches and whether the image is photographic or displays a digital avatar (Supplementary Table 7); and (4) whether the faces in each image are cropped before classification (Supplementary Fig. 8), as well as statistical biases in the cropping algorithm itself (Supplementary Fig. 9). We confirm that these results reflect images from a wide range of websites (Supplementary Fig. 10).

Next, we analysed the 2018 IMDb–Wiki dataset[43] and the 2014 Cross-Age Celebrity Dataset (CACD)[44] consisting of Google Images, each of which provides the true gender and age of the celebrities depicted using their public bio pages and time-stamped photographs. Figure 1 shows that female celebrities are, on average, 6.5 years younger than men on IMDb (t = −169.9; P = 2.2 × 10−16; n = 451,562 images; Fig. 1d), 3.27 years younger on Wikipedia (t = 10.64; P = 2.2 × 10−16; n = 57,972 images; Fig. 1e) and 5.35 years younger in Google Images (t = −90.92; P = 2.2 × 10−16; n = 149,889 images; Fig. 1f). In all cases, the most common (modal) age for women is in their 20s, whereas in images from IMDb and Google, the most common ages for men are 40 years and 50 years, respectively. These analyses show that age-related gender bias online is not an artefact of human perceptions of gender and age, because it is replicated using verified objective information about the age and gender of those depicted. That age-based gender bias replicates strongly in the context of celebrities is concerning, given the salient role that celebrities play in reinforcing stereotypes[45].

Finally, we explored age biases in the representation of women and men using prominent image datasets developed for training machine learning algorithms. The age and gender classifications in these datasets were provided by computer vision algorithms constructed by the research teams that released these datasets. We found that women were automatically classified as significantly younger than men in the 2017 UTK[46] dataset consisting of a diverse sample of images from across the world-wide web (Mdiff = 5.12 years; t = −19.9; P = 2.2 × 10−16; n = 24,106 images; Fig. 1g), the 2014 Adience dataset[47] consisting of images from Flickr (Mdiff = 0.18; t = −6.52; P = 6.79 × 10−11; n = 17,492 images; Fig. 1h) and the 2008 Labeled Faces in the Wild (LFW) dataset[48] consisting of a random sample of images from Google News (Mdiff = 0.84; t = −44.89; P = 2.2 × 10−16; n = 13,143 images; Fig. 1i) (two-tailed Student’s t-test). These findings further generalized our results beyond the perceptions of human coders.

A remaining question is whether age-related gender bias is also observed in online videos, which increasingly dominate the world-wide web[49]. Although an exhaustive analysis of online videos is beyond the scope of this study, we analysed two open-source datasets of screenshots from YouTube videos that provide compelling support for our theory. First, we examined the correlation between gender and age in the 2011 YouTube Faces dataset[50], consisting of 3,645 faces of celebrities extracted from 3,425 YouTube videos. Women in the YouTube Faces dataset appear significantly younger than men according to machine learning classifications (Mdiff = 0.87; t = −25.68; P = 2.2 × 10−16; n = 3,645 images; Fig. 1j). We also analysed a more recent dataset of YouTube videos called the 2022 CelebV-HQ dataset[51], consisting of 35,666 images collected by identifying public lists of celebrities on Wikipedia and automatically collecting the top 10 YouTube videos associated with each celebrity. Although this dataset contains only a binary measure of age (faces are coded as either young = 1 or old = 0), we can still test our theory by comparing the fraction of women and men coded as young. Only 20% of men were classified as young compared with 33% of women, marking a significantly higher rate of youthful presentations for women (P = 2.2 × 10−16; two-tailed proportion test).

Comparing with the census

We compared these findings to available industry-level ground truth data to measure the extent to which online images distort the underlying sociodemographic realities of age (occupation-level census data containing both gender and age information are unavailable; see ‘Comparing online images with the census’ in Methods). We matched 867 social categories from our Google Images (Fig. 1a) dataset to occupational categories in the US census. Although gender–age associations in Google Images and census data are correlated at the industry level (r = 0.13; confidence interval = 0.11–0.15; P = 2.2 × 10−16; two-tailed Pearson’s correlation; Extended Data Fig. 2 and Supplementary Tables 8 and 9), Google Images consistently display exaggerated and, in some cases, inverted trends that consistently amplify the association between women and youth. Extended Data Fig. 3 presents the absolute age gap between women and men in each industry, vertically ranked in terms of the magnitude of this gap while also placing the older gender on the right side. In the sales, resources and management industries, Google Images consistently presented the highest age gap relative to all census years (P < 0.001 for all pairwise comparisons; two-tailed Student’s t-test). Moreover, in each of these industries, Google Images displayed men as older than women, whereas women were older than men for each of the census years examined in the sales industry and for two of the years in the resources industry. In the production and service industry, the magnitude of the age gap captured by Google Images was not higher than all census years; yet, the bias towards representing men as older was stable. In each census year, women were older than men in the production and service industries. It was only in Google Images that men were older than women in these industries, suggesting systematic age and gender distortions that associate women with youth.

Relationship to social status

Given the observational and large-scale nature of these analyses, it is challenging to identify the mechanisms driving these age–gender associations. Nevertheless, numerous patterns in our data were relevant when considering sociologically relevant factors. One such consideration pertains to the hypothesis that gender stereotypes are most salient in high-status and prestigious occupations, which play a prominent role in reinforcing gender expectations and norms of desirability52,53. To test this, we recruited a nationally representative US sample from Prolific (n = 1,002) to evaluate the status and prestige of 867 occupations matched between our Google Image data (Fig. 1a) and the US census from 2015 to 2022 (see ‘Collecting judgements of occupational status’ in Methods). Occupations rated as higher status were more likely to elicit Google Images in which men were older than women (Extended Data Fig. 4a; r = 0.08; t = 11.28; P = 2.2 × 10−16; two-tailed Pearson’s correlation; n = 867 occupations). We reproduced this correlation using the objective measure of occupational prestige[54] of the US Bureau of Labor Statistics (Extended Data Fig. 4b; r = 0.11; t = 2.5; P = 0.01; two-tailed Pearson’s correlation; n = 532 occupations could be matched). Next, we showed that the probability of men appearing as older in Google Images is significantly higher for occupations associated with higher median earnings (Extended Data Fig. 4c; r = 0.11; t = 7.39; P = 1.07 × 10−13; two-tailed Pearson’s correlation; n = 4,444 pairwise comparisons at the census year level from 2015 to 2022; yearly earnings logged). We found that the gender pay gap16,55, or the extent to which men earn more than women in the same occupation, is associated with the digital age gap, or the extent to which men appear older than women in Google Images (Extended Data Fig. 2d; r = 0.04; t = 7 = 3.05; P = 0.002; two-tailed Pearson’s correlation; n = 4,444 pairwise comparisons at the census year level from 2015 to 2022; yearly earnings logged). These results were robust to numerous statistical controls (Supplementary Figs. 11 and 12 and Supplementary Tables 10–13) and resonate with long-standing concerns about disparities in how genders are perceived and evaluated in the workplace.

Age–gender distortions in online text

A natural suspicion is that age-related gender bias in online images and videos may be driven by affordances of visual communication, such as image filters and cosmetics, which do not generalize to other modalities. Here we show that comparably salient patterns of age-related gender bias are readily observable in massive bodies of internet text data beyond the visual modality. We begin by analysing gender–age associations in GPT-2 Large[56], the largest open-source language model of OpenAI trained on billions of tokens of text data from across the internet (see ‘Measuring age and gender in online text’ in Methods; Supplementary Tables 14 and 15). As shown in Fig. 2, the representations of GPT-2 Large exhibit a strong correlation between the extent to which a social category is associated with men and older ages (r = 0.87; t = 105.57; P = 2.2 × 10−16; two-tailed Pearson’s correlation). These results are robust to alternative methods for extracting age and gender associations (Supplementary Fig. 13), as well as to a range of statistical controls, including word frequency, gender, age and polysemy (Supplementary Fig. 14 and Supplementary Table 16). These associations are significantly predictive of ground truth age distributions by gender and occupation in the census, affirming their empirical coherence (Supplementary Tables 17 and 18). These results are not unique to GPT-2 Large. We replicated these patterns across eight different canonical and popular language models that vary in their training data and algorithmic training methods (Supplementary Figs. 15 and 16).

Fig. 2: Women are represented as significantly younger than men in billions of words scraped from the internet, as encoded by the largest open-source model of OpenAI (GPT-2 Large).

Correlation between age and gender associations for 3,495 social categories in GPT-2 Large. The horizontal axis presents the gender association from 0 (female) to 1 (male), and the vertical axis presents the age association from 0 (young) to 1 (old). The trend line shows the linear prediction according to an ordinary least squares regression. The orange highlighted categories illustrate some of the categories that have the youngest and most female associations, whereas the blue highlighted categories illustrate some of the categories that have the oldest and most male associations.

Amplification via Google Search

The systematic distortion of age–gender associations in online images, videos and text across popular platforms that we have identified raises concerns about how mainstream algorithms trained on these data might amplify the spread of this bias. We begin by examining possible algorithmic amplification in the visual modality to investigate whether exposure to visual content from the Google search engine amplifies age-related gender bias in people’s beliefs.

To answer this question, we report the results of a pre-registered experiment. We recruited a nationally representative sample of US participants from Prolific (n = 500), who were tasked with using Google to search for images of occupations related to science, technology and the arts (Extended Data Fig. 5; ‘Participant pool’ in Methods). In total, 459 participants completed the task. Each participant used Google to retrieve descriptions of 22 randomly selected occupations from a set of 54 (‘participant experience’ in Methods). The participants were randomized into treatment or control condition. In the treatment condition (hereafter ‘image condition’), the participants used Google Images to search for images of occupations, which they then uploaded to our survey. After uploading an image for an occupation, the participants were asked to label the gender of the image they uploaded and then to estimate the average age of someone in this occupation. The participants were also asked to rate their willingness to hire the person depicted in their uploaded image. In the control condition, the participants used Google Images to search for and upload images of basic unrelated categories (such as apple and guitar). After uploading a random image, the control participants were asked to estimate the average age of someone in a randomly selected occupation from the same set. The control participants were also asked to rate the ideal hiring age of someone in each occupation, as well as which gender (‘male’ or ‘female’) is most likely to belong to each occupation. This design allowed us to evaluate the treated participants’ age estimates of occupations after uploading an image of a man or woman compared with (1) the control participants’ age estimates formed without exposure to images of occupations and (2) the control participants’ age estimates conditional on their belief about which gender is most common in each occupation.

We began by testing the prediction that exposure to online images of occupations primes age-related gender bias in the participants’ beliefs. To test this prediction, we evaluated whether uploading an image of a woman (man) for each occupation is associated with a lower (higher) age estimate compared with the average age estimate of each occupation provided by those in the control condition who did not encounter online images of each occupation before providing their estimates. Figure 3a shows that the participants who uploaded an image of a woman estimated the average age of an occupation to be 5.46 years younger than those who uploaded an image of a man (t = −19.07; P = 2.2 × 10−16; Student’s t-test), holding occupation constant. Moreover, uploading an image of a woman led the participants to estimate a significantly lower age for each occupation (by 1.75 years) compared with the control participants (t = −11.32; P = 2.2 × 10−16), whereas uploading an image of a man led the participants to estimate a significantly higher age for each occupation (by 0.64 years) compared with those in the control condition (t = 3.42; P = 0.0006; Student’s two-tailed t-test).

Fig. 3: Googling for images of occupations amplifies age-based gender inequality in people’s beliefs.

The participants (n = 459) from a nationally representative sample were randomized either to the ‘image’ condition, in which they googled for images of occupations (n = 54), or the ‘control’ condition, in which they googled for image-based descriptions of random categories (such as apple) unrelated to occupations. a, Average age of each occupation, as estimated by the participants in the image condition, broken down by whether they uploaded a female or male image of the occupation, centred relative to the average age of each occupation provided across all the participants in the control condition. b, Partial effect plot that controls for occupation and participant fixed effects while predicting the average age provided for each occupation depending on the gender of the image uploaded by the participants in the image condition or the gender the participants most associated with each occupation in the control condition. Data points display mean values, and error bars indicate 95% confidence intervals. c, Correlation between the gender association and perceived ideal hiring age of each occupation (averaged across all the participants in the control condition). The gender association of each occupation was measured separately according to the participants’ manual gender ratings in the control condition and the gender distribution of the images uploaded by the participants in the image condition. Data points show the average gender association and perceived ideal hiring age for each occupation according to each measure. Error bands show 95% confidence intervals.

Notably, these results hold when controlling for the participants’ gender and age, as well as whether their own demographics matched those of the people depicted in the images they uploaded (Supplementary Tables 19 and 20). Supplementary analyses further demonstrate that the participants’ estimates of the average ages of people in occupations are significantly correlated with the median age of these occupations according to the US census, indicating that the participants’ age judgements were coherent and consistent with ground truth sociodemographic distributions (Supplementary Tables 21 and 22).

Next, we leveraged the control condition to examine the effect of exposure to online images depicting occupations, above and beyond the participants’ existing biases about the gender composition of occupations. Specifically, we tested whether the participants in the treatment condition reported younger (older) ages when uploading images of women (men) for each occupation compared with the age estimates provided by the control participants, who reported believing that women (men) most often belonged to a given occupation. Figure 3b shows that the control participants who believed women are most likely to belong to a given occupation also estimated the average age of people in this occupation to be significantly younger (by 2.15 years), evidencing a baseline pattern of age-related gender bias in people’s judgements (β[male] = 2.15 years; standard error = 0.38; t = 5.55; P = 2.98 × 10−8). However, Fig. 3b further shows that this age gap is even higher among those in the treatment condition. The participants who uploaded an image of a woman for a given occupation reported estimating the age of people in this occupation to be significantly younger than the control participants who already believed that this occupation is female-skewed. This analysis controls for the specific occupation being evaluated, as well as the participants’ idiosyncratic judgements through occupation and participant fixed effects (β[gender × condition] = −0.84; standard error = 0.31; t = −2.69; P = 0.007). These results indicate that exposure to online images significantly exacerbates the perceived age gap between women and men, particularly by increasing the association between women and youth.

We conclude these experimental analyses by examining the practical consequences of this age-based gender bias by evaluating its impact on women’s and men’s perceived fit across occupations. Figure 3c shows that occupations that are more associated with women (men) are significantly correlated with the participants reporting lower (higher) recommended ages for whom to hire in this occupation. Figure 3c shows that the control participants’ perceived ideal age for hiring is strongly and positively correlated with the extent to which each occupation is associated with men, as measured by (1) the control participants’ manual gender ratings of occupations (r = 0.58; P = 3.52 × 10−6) and (2) the gender associations in the images uploaded by the participants in the image condition (r = 0.45; P = 0.0006; Pearson’s two-tailed correlation). These analyses provide evidence that age-related gender associations mediate people’s judgements of who is best to hire for a given occupation, with a preference towards hiring younger women and older men.

The above results are highly robust to whether (1) the participants provided gender ratings of occupations without estimating age (as captured by a separately replicated experiment[6]; Supplementary Fig. 17) and (2) the participants rated hireability using a Likert scale (Supplementary Fig. 18; see Supplementary Tables 23 and 24 for a full summary of all of our pre-registered hypotheses and the associated analyses and results). All of our main pre-registered hypotheses were strongly supported. Note that the framing of our study was updated in response to the review process, with no changes to the reporting of the experimental design or statistical results (see Supplementary Table 24 and the associated discussion for details).

Amplification through large language models

Because popular artificial intelligence tools such as ChatGPT are trained on internet data, we further propose that ChatGPT will exhibit significant age-based gender bias in its textual representations and evaluations of occupations in professional resumes. Identifying age-related bias in ChatGPT would highlight a potential pathway through which this bias is widely propagated, given that more than 400 million people and two million businesses use ChatGPT weekly[57]. By adapting prompt engineering techniques developed for auditing biases in ChatGPT’s resume generation[58], we prompted ChatGPT (v.GPT-4o mini) to create nearly 40,000 resumes for 54 occupations using 16 unique female and male names that were normalized to control for name popularity, familiarity, ethnicity and perceived age group, such that the male and female names were maximally similar along these dimensions (the same names were used in a recent auditing study[58]) (see ‘Prompt design’ in Methods). This experiment consisted of two phases: resume generation and resume evaluation (Extended Data Fig. 6). All resumes were generated and evaluated in June 2024.

We began by examining the resume generation phase, in which we prompted ChatGPT to generate resumes across 54 occupations while varying the prompt across three conditions: (1) the control condition; (2) the control–gender condition; and (3) the treatment condition. In the control condition, we prompted ChatGPT to generate 50 resumes for each of the 54 occupations without specifying the name or gender of the applicant, resulting in 2,700 unique resumes. In the control–gender condition, we replicated the control condition, except that we also asked ChatGPT to include the gender of applicants in the resumes generated. Finally, in the treatment condition, we replicated the design of the control condition, except that our prompt included a specific name for the applicant and asked ChatGPT to generate a resume for the named applicant applying for the specified occupation (in this condition, ChatGPT was not asked to explicitly identify the gender of the named applicant). We used the same occupations in our image search experiment. In the treatment condition, we prompted ChatGPT 20 separate times for each name–gender–occupation prompt combination, yielding a total of 34,560 resumes and 17,280 resumes for each gender group. The resume features generated by ChatGPT were highly coherent and stable (Supplementary Fig. 19 and Supplementary Table 25).

Next, we evaluated the consequences that age-based gender biases in ChatGPT’s representations of resumes can have on its practical application as a hiring tool. We focused on one of ChatGPT’s most popular uses in the workplace: to evaluate, score and rank resumes to expedite hiring processes by focusing human recruiters on resumes with top scores[58]. We prompted ChatGPT to evaluate each of the resumes generated in the first phase by providing a score between 1 and 100 to indicate the quality of each resume. All reported results are robust to altering the model temperature of ChatGPT (Supplementary Fig. 20).

We began by examining how altering the gender of a target applicant’s name affects the resumes that ChatGPT generates. We compared the resumes that ChatGPT generated in the treatment condition for female or male names while using a linear regression to control for the applicant name and occupation. As Fig. 4a indicates, when ChatGPT generated a resume for a female name, it generated resumes with significantly lower ages (by 1.6 years; t = 20.5; P = 7.09 × 10−93), more recent graduation dates (by 1.3 years; t = 12.5; P = 1.18 × 10−35) and fewer years of relevant experience (by 0.92 years; t = 5.39; P = 6.97 × 10−8) compared with male names (Student’s t-test; n = 34,560 resumes). Compared with the control condition, the resumes ChatGPT generated for female (male) applicants were significantly younger (older) and less (more) experienced than the resumes generated for the same occupations without any gender or name (all at the P < 0.00001 level; Student’s two-tailed t-test). Thus, ChatGPT exhibits age-based assumptions about women and men that are highly consistent with stereotypical associations relating to gendered ageism.

Fig. 4: Effect of gender and age on ChatGPT’s generation and evaluation of resumes.

a, Partial effect plot in an ordinary least squares regression displaying the effect of a male applicant name (versus a female applicant name) on (1) applicant age; (2) years since the applicant’s graduation; and (3) the number of years of applicant’s relevant experience, while controlling for name and occupation. Only resumes from the treatment condition were examined in this analysis (n = 34,560), because this ensures that all resumes have either a male or female name and were produced through the same prompt. Error bars indicate 95% confidence intervals. b, Linear correlation between applicant age and ChatGPT’s rating of resume quality across all resumes (n = 39,560) from all conditions. Data points display the raw distribution of scores for each resume, with one data point per resume. The trend line reflects a standard bivariate linear trend. c, Partial effect plot displaying the interaction effect between applicant age and applicant gender on ChatGPT’s rating of resume quality, with fixed effects for applicant name, occupation and phase 1 condition (data from the control condition were excluded because of the lack of applicant gender; n = 37,060 resumes used in total). Error bands display 95% confidence intervals. ChatGPT’s temperature was set to its default value of 0.7.

These patterns of age-based gender bias in ChatGPT were replicated in the control–gender condition, in which ChatGPT generated its own name and gender classification for each occupation. When ChatGPT generated a male applicant for a given resume, this applicant was more likely to be older (by 1.3 years; t = 17.3; P = 2.2 × 10−16) and to have graduated less recently (by 1.2 years; t = 7.10; P = 2.29 × 10−12) than when it generated a resume for a female applicant, holding occupation constant (Student’s t-test; n = 2,500 resumes). Thus, the effects we observed were not dependent on the specific names used to prompt ChatGPT or on the overall prompt design in the treatment condition.

We next evaluated the consequences this bias had on how ChatGPT evaluated the quality of resumes. Across all occupations, Fig. 4b shows that ChatGPT’s judgements of resume quality were significantly and positively correlated with the age of the applicant that ChatGPT initially generated for the resume (r = 0.27; P = 2.2 × 10−16; t = 51.59; Pearson’s two-tailed correlation; n = 39,560 resumes). This result equally holds in a linear regression when we controlled for the occupation and name used to generate each application (β[age] = 0.04; t = 6.3; P = 2.96 × 10−10). Finally, we tested whether ChatGPT exhibits a preference not only for older applicants but also for older men specifically, consistent with the predictions of gendered ageism6,7. We used linear regression to predict ChatGPT’s judgements of resume quality using an interaction between the age and gender of the applicant while holding occupation and applicant name constant. As shown in Fig. 4c, the model identified a highly significant and positive interaction between being male and older, indicating that the benefit of older age on ChatGPT’s judgements of resume quality is even greater if the applicant is male rather than female (β[male × age] = 0.04; t = 6.61; P = 3.66 × 10−11). This interaction effect is robust to altering ChatGPT’s model temperature (Supplementary Fig. 20).

Discussion

In this study, we have provided large-scale evidence that age-related gender bias pervades online media, including images, videos and texts across major platforms, and that the bias towards representing women as younger distorts ground truth realities on the actual ages of women and men throughout society. Our findings raise an alarm about the algorithmic amplification of age-related gender bias on the internet, especially considering that many mainstream machine learning algorithms are trained on these public datasets. Many of the image and text datasets examined in this study are used extensively as canonical training and benchmark datasets for developing artificial intelligence applications. Enormous harm can be caused by latent social biases that lurk in popular machine learning tools59,60, and algorithmic bias typically arises from contaminated training data. Our study provides direct evidence that age-related gender bias is amplified by two of the most widely used algorithms today: the Google Image search engine and ChatGPT. Although companies such as Google and OpenAI invest heavily in reducing stereotypical content in their products[61], most studies focus on single dimensions of bias, such as gender-based or race-based biases. Our study highlighted the critical need to account for multimodal and multidimensional forms of bias[62], which are more challenging to detect but not less consequential in how people and algorithms represent the social world. The intersectional statistical bias we identified between gender and age may interact with other biases, such as relating to how women and men are depicted in terms of warmth and competence, revealing a promising direction for future research63,64.

How might the digital distortion of age-related gender associations negatively affect women and men? Our results highlighted several key ways in which older women are likely to be disadvantaged by this bias. For example, when generating resumes, ChatGPT not only assumes that women are younger, but also that they have less overall experience. Consequently, ChatGPT is biased towards giving lower scores to resumes from younger women compared with older women while giving the highest scores to older men. Yet, ChatGPT also gives higher scores to resumes from young women than from young men, suggesting that young men may also be disadvantaged by this dual bias (Supplementary Fig. 18). However, a selection bias favouring younger women and older men may further reinforce gender inequalities at the systemic level, whereby women are preferentially hired into roles with lower status and authority but denied mobility, whereas older men continue to enjoy top positions. This resonates with our finding that online content is most likely to depict men as older than women for occupations with higher status and wealth.

A critical direction for future research is to investigate the causal mechanisms through which age-related gender bias seeps into and spreads through the images, videos and text of distinct platforms, each with its own unique audiences and distribution channels. Our results about objective differences in the ages of male and female celebrities visualized on IMDb, Wikipedia and Google probably reflect industry-specific mechanisms related to status dynamics, hiring biases and the objectification of women in entertainment media. Yet, these industry-specific drivers do not account for how strongly women and youth are semantically associated in massive bodies of online text from diverse sources, let alone in ChatGPT’s text-based representations and rankings of job candidates. A fascinating question for future work is to explore whether the aesthetic norms and hiring biases of entertainment media spill over into the distortion of age–gender associations throughout social life. A related question concerning supply-side factors concerns whether age-related gender bias in popular algorithms stems from inequalities in the gender of data contributors online. Studies suggest that Reddit users[65] and Wikipedia editors[66] are disproportionately male, and textual data from these platforms are frequently mined for training artificial intelligence models. Training artificial intelligence on datasets with greater gender equality in data contributors may provide an effective mitigation strategy.

This study highlights the increasingly prominent role of internet culture and algorithms in mediating our representation of the social world. As a recent review article emphasized[67], previous studies have been limited in their ability to concretely measure the diverse cultural meanings of age both in terms of its biological basis and its relevance to cultural notions of life stages (such as ‘youth’ and ‘childhood’). The strength of our approach is that it captures the cultural meanings of age and gender along many dimensions, from concrete time-stamped images to verbal descriptions of age categories across social roles and contexts. We revealed that the cultural meanings of gender and age are deeply linked on a massive scale across information modalities and in ways that reflect socioeconomic inequalities. Compelling evidence that these patterns are socially constructed comes from our finding that online associations between gender and age heavily distort the measurable ground truth reality of how people of different genders and ages are distributed throughout society. The extent to which algorithms entrench distortions of social reality en masse and to which this can be corrected is a vital topic for future research on internet policy and human cultural evolution. The methods we propose for measuring widespread stereotyped representations online and for grounding them in verifiable sociodemographic realities mark a crucial step in the fight against pervasive cultural inequalities, both online and beyond.

Methods

In this section, we detail the methods used in all parts of our analyses, including our observational comparisons of gender and age bias in online images, videos and texts, as well as our Google Image search experiment and our resume audit of ChatGPT. The pre-registration for our online image search experiment is available at https://osf.io/x9scm. This experiment was a successful replication of a previous study with a nearly identical design; the pre-registration of this previous study is available at https://osf.io/2b58d. This study was approved by the ethics review board at the University of California, Berkeley, where this study was conducted.

Observational methods

In what follows, we describe our observational methodology for collecting and analysing large bodies of images, videos and text online. With regard to the crowdsourcing methods applied to analysing our main Google and Wikipedia Image datasets, many of the methods described below (including the procedure and demographic details of the coder population) were reproduced from the original data collection summary provided as part of the first publication of these datasets[6]. In addition to this reproduced description, we include information on how age classifications of these images were collected, because this feature was not explored or discussed as part of the original publication of these datasets[6].

Image data collection procedure

Our crowdsourcing methodology consisted of four steps. We began by identifying all social categories in WordNet[69], a canonical lexical database of English. WordNet captures 3,495 social categories, including occupations (such as doctor) and generic social roles (such as neighbour). We then gathered online images associated with each social category from Google and Wikipedia. Next, we applied the OpenCV deep learning module in Python to automatically extract the face from each image. Cropping faces helped us ensure that each face in each image was separately classified in a standardized manner while avoiding subjective biases in coders’ decisions for which face to focus on and categorize in each image. Finally, we hired 6,392 human coders from Amazon’s Mechanical Turk to manually classify the gender of the faces. Each face was classified by three unique annotators (as per established methodology6,70,71), so that the gender of each face (‘male’ or ‘female’) could be identified on the basis of the majority (modal) gender classification across three coders. (We also gave coders the option of labelling the gender of faces as ‘non-binary’, but this option was only chosen in 2% of cases. Therefore, we excluded these data from our main analyses and recollected all classifications until each face was associated with three unique coders using either the male or female label.) Although coders were asked to label the gender of the faces presented, our measure was agnostic to which features the coders used to determine their gender classifications. They may have used facial features and features relating to the aesthetics of expressed gender, such as hair or accessories. In terms of age, each face was classified as belonging to one of the following age bins (the ordinal ranking of each bin is indicated in parentheses): (1) 0–11, (2) 12–17, (3) 18–24, (4) 25–34, (5) 35–54, (6) 55–74 and (7) 75+. Because the greater number of classification options for age led to fewer images associated with a majority-preferred age classification, we identified the age of each face by taking the average of the ordinal age bin judgements across the three coders. Each search was implemented from a fresh Google account with no previous history. Searches were run in August 2020 by ten distinct data servers in New York City. This study was approved by the institutional review board at the University of California, Berkeley, where this part of the study was conducted. All participants provided informed consent.

To collect images from Google, we followed a previous study by retrieving the top 100 images that appeared when using each of the 3,495 categories to search for images using the public Google Images search engine. (Google provides roughly 100 images for its initial search results.) Across the non-gendered and gendered searches, 3,489 categories could be associated with images containing faces in the Google Image search engine (specifically, 3,434 categories for the non-gendered searches and 2,960 for the gendered searches). To collect images from Wikipedia, we identified the images associated with each social category in the 2021 Wikipedia-based Image Text (WIT) Dataset[72]. WIT maps all images over Wikipedia to textual descriptions on the basis of the title, content and metadata of the active Wikipedia articles in which they appear. We were able to associate 1,251 social categories from WordNet with images in WIT (across all English articles) that supported stable classification as human faces with detectable ages, according to our coders. The coders identified 18% of images as not containing human faces, and these were removed from our analyses. We also asked all annotators to complete an attention check, which involved providing the correct answer to the common-sense question (What is the opposite of the word ‘down’?) using the following options: ‘fish’, ‘up’, ‘monk’ and ‘apple’. We removed the data from all annotators who failed an attention check (15%) and continued collecting classifications until each image was associated with the judgements of three unique coders, all of whom passed the attention check.

Demographics of human coders

The human coders were all US-based adults fluent in English. Supplementary Table 3 indicates that our main results are robust to controlling for the demographic composition of our human coders. Among our coders, 44.2% identified as being female, 50.6% as male, 3.2% as non-binary and the remaining preferred not to disclose. In terms of age (in years), 42.6% identified as 18–24, 22.9% as 25–34, 32.5% as 35–54, 1.6% as 55–74 and less than 1% as over 75. In terms of race, 46.8% identified as Caucasian, 11.6% as African American, 17% as Asian, 9% as Hispanic, 10.3% as Native American and the remaining as either mixed race or preferred not to disclose. In terms of political ideology, 37.2% identified as conservative, 33.8% as liberal, 20.3% as independent, 3.9% as other and the remaining preferred not to disclose. In terms of annual income, 14.3% reported making less than US$10,000, 33.4% reported US$10,000–50,000, 22.7% reported US$50,000–75,000, 14.9% reported US$75,000–100,000, 10.5% reported US$100,000–150,000, 2.8% reported US$150,000–250,000, less than 1% reported more than US$250,000 and the remaining preferred not to disclose. In terms of the highest level of education acquired by the annotator, 2.7% selected ‘below high school’, 17.5% selected ‘high school’, 29.2% selected ‘technical/community college’, 34.5% selected ‘undergraduate degree’, 14.8% selected ‘master’s degree’, less than 1% selected ‘doctorate degree’ and the remaining preferred not to disclose.

Image and video datasets

To measure age-related gender bias in online images and videos, we analysed a range of open-source datasets collected either for social science research or for training face recognition algorithms, none of which examined or reported correlations between the gender and age of the people depicted. In total, we examined more than one million images from five main online sources: Google, Wikipedia, IMDb, Flickr and YouTube, as well as the Common Crawl (created by randomly scraping content from across the world-wide web), each with distinct ways of sourcing and aggregating data. We measured gender and age using a variety of techniques, including human judgements, machine learning and ground truth data on the self-reported gender and true time-stamped age of the people depicted. Our statistical analyses did not control for multiple comparisons because all tests were theoretically guided and did not involve an agnostic permutation over a set of pairwise comparisons. Although we examined many datasets, our main analyses examined a single correlation (between gender and age) within each dataset separately. We now describe each of these datasets.

First, we used large-scale crowdsourcing to identify age-related gender bias in a new dataset of images from Google and Wikipedia (which was originally collected for a recently published study that did not examine age-related classifications)[6]. This dataset[6] contains the top 100 Google images associated with each of the 3,435 social categories contained within WordNet[69], a lexical ontology that maps the taxonomic structure of the English language. These categories include occupations (such as ‘physicist’) and generic social roles (such as ‘colleague’). For each category, this dataset contains the top 100 images that appear in Google Images when searching for (1) the category on its own (such as ‘doctor’); (2) the female version of the category (such as ‘female doctor’); and (3) the male version of the category (such as ‘male doctor’). The gendered searches were completed only for the 2,960 non-gendered categories (for example, the searches did not include ‘male aunt’). Altogether, this yielded 657,035 unique images containing faces from Google. Searches were run from ten distinct data servers in New York City. Because Google is known to customize search results on the basis of the location from which the search is run[72], we show that our results are robust to replicating this data collection pipeline while collecting Google Images from six distinct cities around the world (Supplementary Fig. 3).

This dataset also leveraged human coders to classify the age and gender of faces in Wikipedia images associated with as many WordNet social categories as possible in the 2021 WIT Dataset[68]. WIT maps all images over Wikipedia to textual descriptions on the basis of the title, content and metadata of the active Wikipedia articles in which they appear. WIT includes images of 1,251 social categories from WordNet across all English Wikipedia articles, in total yielding 14,709 faces.

We hired 6,392 human annotators from Amazon’s Mechanical Turk to classify the gender and age of the faces in these images. Each face was classified by three unique annotators6,70,71 so that the gender of each face (male or female) could be identified on the basis of the majority gender classification across three coders. (We also gave coders the option of identifying the gender of faces as non-binary, but this option was chosen in less than 2% of cases. Therefore, we excluded these data from our main analyses.) In terms of age, each face was classified as belonging to one of the following age bins (in years): (1) 0–11, (2) 12–17, (3) 18–24, (4) 25–34, (5) 35–54, (6) 55–74 and (7) 75+. Because the greater number of classification options for age led to fewer images with a majority-preferred classification, we identified the age of each face by taking the average of the ordinal age bin judgements across the three coders (our main results hold when using the modal age judgement; Supplementary Fig. 4). Our findings continued to hold when controlling for annotator demographics and intercoder agreement, which was high in our sample (Supplementary Fig. 5 and Supplementary Table 3). We also conducted a separate validation task, in which the true gender and age of the faces being classified were known. The results indicate that our coders exhibited reliable and accurate gender and age judgements, with no biases as a function of gender (Supplementary Tables 4 and 5). Sensitivity tests further showed that even if our coders were hypothetically biased in their ability to estimate age as a function of gender, this would not disrupt the statistical significance or directionality of our findings (Supplementary Fig. 6).

We extended our findings by examining age-related gender bias in two large corpora of online images collected from three main websites (IMDb, Wikipedia and Google) for which the self-identified gender and true age of the faces were objectively inferred. This extension allowed us to examine whether women are objectively younger than men in online images, without depending on age predictions from human coders or machine learning algorithms. The first corpus was the 2018 IMDb–Wiki dataset[43], which consisted of more than half a million images of celebrities from IMDb and Wikipedia on the basis of those depicted in the top 100,000 most visited IMDb pages. Each image in this dataset was time-stamped for when the photograph was taken, allowing the age of each face to be inferred on the basis of the celebrity’s date of birth, which is publicly available through their open profile on IMDb and Wikipedia. This dataset yielded 451,570 images from IMDb and 57,932 images from Wikipedia. The second corpus was the 2014 CACD[44], which consisted of 163,446 images collected from the Google Image search engine depicting 2,000 celebrities, comprising the top 50 most popular celebrities each year from 1951 to 1990. The creators of CACD collected time-stamped images by using Google Image search to retrieve images associated with each celebrity from 2004 to 2013 (for example, by searching ‘Emma Watson 2004’ through ‘Emma Watson 2013’). We merged the CACD and IMDb–Wiki dataset[43] to identify the gender of 1,825 celebrities in the CACD (50% are female celebrities). All images from both datasets containing ages below 0 and above 100 were removed to maximize data quality. Each dataset identified the exact age of the celebrities at the time they were depicted in each photograph by determining the date of birth and gender of each celebrity on their public IMDb and Wikipedia pages and then by comparing this information to the time-stamped date of when each photograph was taken.

Finally, we examined images from four publicly available training datasets widely used to train automated face recognition algorithms. In these canonical datasets, the gender and age classifications were on the basis of a combination of automated machine learning classifications and verification through human annotation. This includes the 2017 UTK dataset[46] consisting of 20,000 images scraped randomly from across the world-wide web using search engines and public repositories, the 2014 Adience dataset[47] consisting of 26,580 images randomly sampled from Flickr, a public image-based social media platform, and the 2008 LFW[48] dataset consisting of 13,233 images randomly scraped from online news websites. Finally, we examined images of faces extracted from screenshots of YouTube videos using two datasets. The first was the 2011 YouTube Faces dataset[50] consisting of 3,425 YouTube videos and 3,645 images of celebrities. The second one was the 2022 CelebV-HQ[51] dataset consisting of 35,666 images formed by identifying public lists of celebrities on Wikipedia and automatically collecting the top 10 YouTube videos associated with each celebrity.

Comparing online images with the census

We were able to match 867 social categories from our main Google image (Fig. 1a) dataset to occupational categories in the US census. The US Bureau of Labor Statistics recently released a breakdown of the median age of each gender, from 2019 to 2023, across five industries: sales, services, natural resources and construction, production and transportation and management. The census assigns each occupation to one of these industries, allowing those occupations matched in our Google image dataset to be assigned a census industry. We estimated the relationship between gender and age at the industry level by averaging the age associations in Google Images across all occupations within a given industry (averaged within each occupation and then across occupations at the industry level). The census age groupings are highly similar to the age groupings the coders used when judging faces. Supplementary Tables 8 and 9 present the robustness of our results to a range of statistical controls.

Collecting judgements of occupational status

We collected a nationally representative sample of 1,002 US-based participants who provided their subject evaluations of the status and prestige of occupations. Each participant evaluated 20 randomly sampled occupations from a broader set of 867 WordNet social categories that could be matched with corresponding occupations in the US census. Through randomization, each category was evaluated by 27 unique participants on average (minimum of 15 participants). For each occupation, the participants rated (1) its status using the following scale (How would you rate the social status of someone belonging to this occupation? −2, very negative; −1, negative; 0, neutral; 1, positive; 2, very positive) and (2) its prestige using the following scale (To what extent do you agree that it is prestigious to belong to this occupation? −2, strongly disagree; −1, disagree; 0, neutral; 1, agree; 2, strongly agree). We also asked the participants to rate the status/prestige through the standard question from the general social survey, which asked them to place occupations on a ladder containing 10 rungs, where the bottom rung indicates occupations with very low status, income, education and prestige, whereas the highest rung indicates occupations with very high status, income, education and prestige (Supplementary Fig. 11). The participants’ answers across all three questions were highly correlated (all paired Pearson’s correlations above 0.85; Supplementary Fig. 9). In our main results shown in Extended Data Fig. 4, we first averaged all participants’ judgements of each occupation across the (1) status and (2) prestige question and then assigned each occupation a single status score by taking the mean of its average status and prestige score. In the Supplementary Information, we show that all of our results hold when examining each question separately and when examining the participants’ judgements using the standard social status question from the General Social Survey (GSS) (Supplementary Fig. 11 and Supplementary Tables 10–13). Note that Prolific’s nationally representative sample of the US population size allows for a maximum of 800 participants. However, this sample size was not large enough to gain sufficiently powered judgements across all 867 occupational categories; therefore, an extra sample of US participants was recruited until all occupations reached a minimum of 15 evaluations from independent participants. All results are robust to a range of statistical controls (Supplementary Tables 10–13).

Measuring age and gender in online text

To measure age-related gender bias in large bodies of internet text, we leveraged word embedding models trained on massive amount of internet data. These models were designed to construct a high-dimensional vector space on the basis of the co-occurrence of words (for example, whether two words appear in the same sentence), such that words with similar meanings are closer in this vector space. Technically, these embedding spaces also capture higher-order similarities on the basis of whether words co-occur in similar linguistic contexts (that is, in association with related sets of words), without requiring words to directly appear together. We harnessed recent advances in natural language processing to extract demographic dimensions in word embedding models that capture the extent to which existing demographics underlie the cultural connotations of categories. We identified both gender and age dimensions. We briefly describe this methodology below.

Word embedding models leverage the frequency of word co-occurrences in text to position words in an n-dimensional space such that words that frequently co-occur together are more closely located in this n-dimensional space. The ‘embedding’ for a given word identifies the specific position of this word in this n-dimensional space. The cosine distance between word embeddings in this n-dimensional space provides a robust measure of semantic similarity that captures the similarity of the semantic contexts in which words appear[6]. To extract a gender dimension in word embedding space, we harnessed the ‘geometry of culture’ method of Kozlowski et al.[73]. This method was originally developed for static embedding models such as Word2Vec and GloVe; therefore, we incorporated key adjustments that enable its application to contextualized embeddings through generative transformer models such as GPT-2 Large. We identified two clustered regions in the word embedding space corresponding to conventional representations of females and males. Specifically, the female cluster consisted of ‘woman’, ‘her’, ‘she’, ‘female’ and ‘girl’, whereas the male cluster consisted of ‘man’, ‘his’, ‘he’, ‘male’ and ‘boy’. For each social category in WordNet, we calculated the average cosine distance between this category and both the female and male clusters. Each category was associated with two numbers: its cosine distance with the female cluster (averaged across its cosine distance with each term in the female cluster) and its cosine distance with the male cluster (averaged across its cosine distance with each term in the male centroid). Taking the difference between the cosine distance of a category with the female and male centroids allowed each category to be positioned along a −1 (female) to 1 (male) scale in the embedding space. Although we recognize that gender is fundamentally non-binary, we built upon a previous study that leveraged this binary framework[73] to identify biases in the extent to which people associate concepts with men or women.

The issue with applying this approach to contextualized embeddings is that the embedding associated with an individual word can be sharply different from the embedding associated with this word within a larger context, for example, within a surrounding sentence. For this reason, we modified the geometry of culture method by creating male and female poles consisting of many parallel sentences that vary only in whether they mention the corresponding male or female version of a pronoun. For example, the male pole consists of sentences such as ‘he is a boy’ and ‘his hobbies are very masculine’, whereas the analogues of these sentences in the female pole are ‘she is a girl’ and ‘her hobbies are very feminine’. Fifty sentences were used to form each pole. All sentences used are provided in Supplementary Tables 14 and 15. We conducted key robustness tests to verify the validity of our methods and the robustness of our results to the use of different sentences along the gender pole (Supplementary Fig. 13). In our supplementary analyses involving static embedding models, we used the original geometry of culture approach.

We used this same approach to construct an age dimension in word embedding models. For static embedding models, we identified two clustered regions in the word embedding space corresponding to younger and older ages. Specifically, the younger cluster consisted of the words ‘child’, ‘teenager’ and ‘adolescent’, whereas the older cluster consisted of the words ‘adult’, ‘senior’ and ‘elder’. All results are highly robust to increasing the number of words used to construct this age dimension. For example, our results replicate when defining the younger cluster using the words ‘young’, ‘youth’, ‘childhood’, ‘child’, ‘baby’, ‘infant’, ‘teen’, ‘teenager’ and ‘adolescent’, as well as when defining the older cluster using the words ‘old’, ‘elder’, ‘elderly’, ‘adulthood’, ‘adult’, ‘senior’, ‘parent’, ‘retired’ and ‘aged’. We used the same technique to sort categories along a −1 (young) to 1 (old) scale in the embedding space. Similarly, to examine age associations in contextualized embedding models, we generated 50 sentences that hold everything constant while varying whether the age term involved indicates a young or old age (see Supplementary Table 15 for a full list of the age sentences used to create the contextualized age pole).

In all cases examining static models, to compute the distances between the vectors of social categories represented by bigrams (such as ‘professional dancer’), we used the Phrases class in the gensim Python package, which provided a pre-built function for identifying and calculating distances for bigram embeddings. This method works by identifying an n-dimensional vector of middle positions between the vectors corresponding separately to each word in the bigram (for example, ‘professional’ and ‘dancer’). This technique then treats the middle vector as the singular vector corresponding to the bigram ‘professional dancer’ and is thereby used to calculate the distances from other category vectors. This method is not necessary in contextual language models, which provide unique embeddings for n-grams as distinct from their component words.

Once the corresponding demographic dimensions were constructed for each model, we evaluated the correlation between gender and age associations across 3,495 social categories from WordNet (the same categories examined in our image analyses above). To simplify the presentation of how this gender and age dimensions are correlated, we used min-max normalization to convert the gender dimension into a 0 (female) to 1 (male) association, which, in effect, represents the extent to which each category carries male associations relative to all other categories. We applied the same approach to produce a normalized 0 (young) to 1 (old) dimension, which captures the extent to which each category is associated with older ages relative to all other categories. The supplementary analyses showed that our results are highly robust to varying our technique for constructing the age and gender dimensions (Supplementary Fig. 13 and Supplementary Tables 14 and 15).

In the main text, we present our results while analysing the largest open-source large language model from OpenAI (GPT-2 Large[56]), for which word embeddings can be robustly and transparently extracted and examined. GPT-2 Large is one of the largest and most popular open-source language models, trained on billions of words from the 2019 WebText dataset, which primarily comprises Reddit data and the diverse web content (including articles and books) to which these Reddit data are linked. In the supplementary analyses, we showed that these results replicate when examining a wide range of models, including Word2Vec, GloVe, BERT, FastText, RoBERTa and GPT-4, all of which vary in their dimensionality and data sources, as well as the year in which their training data were collected, ranging from 2013 to 2023. We focus our main results on GPT-2 Large, not only because of its scale and popularity, but also because its open-source nature allows us to transparently access and analyse its word embeddings. GPT-4, by contrast, is a closed-source model that relies on using OpenAI’s private application programming interface, which limits the interpretability of our method. Nevertheless, supplementary analyses showed that our results replicate when examining this closed-source model (Supplementary Figs. 14 and 15).

Experimental methods with human participants

Participant pool

We invited a nationally representative sample of participants (n = 500) from Prolific. Prolific is a popular online panel for social science research that provides prescreening functionality specifically for recruiting a nationally representative sample of the USA along the dimensions of sex, age and ethnicity. The participants were invited to partake in the study only if they were based in the USA, were fluent English speakers and were over 18 years old. A total of 52% of participants were female (no participants identified as non-binary). The average age of participants was 45.2 (45.9 for women; 44.6 for men). Our sample size was selected to emulate the sample size of a recent experiment with a highly similar design, which effectively measured statistically powered outcomes[6]. There was an attrition rate of 9.2% of participants (which is within the common range of attrition for online experiments), such that 459 participants completed the task. Our results only examined data from the participants who completed the experiment to ensure data quality. All the participants provided informed consent before participating. This experiment was run on 10 November 2023.

Participant experience

Extended Data Fig. 4 presents a schematic of the full experimental design. This experiment was approved by the Institutional Review Board at the University of California, Berkeley. In this experiment, the participants were randomized to one of two conditions: (1) the image condition (in which they used the Google Image search engine to retrieve images of occupations) and (2) the control condition (in which they used the Google Image search engine to retrieve images of random, non-gendered categories, such as ‘apple’). In the image condition, after uploading an image for a given occupation, the participants were asked to label the gender of the image they uploaded and then to estimate the average age of someone in this occupation. The participants in the image condition were also asked to rate their willingness to hire the person depicted in their uploaded image (Supplementary Fig. 18). After uploading a given random image, the control participants were then asked to estimate the average age of someone in a randomly selected occupation from the same set. The control participants were also asked to rate the ideal hiring age of someone in each occupation, as well as which gender (male or female) was most likely to belong to each occupation. This design allowed us to evaluate the treated participants’ age estimates after uploading an image of a man or woman compared with (1) the control participants’ age estimates that were formed without exposure to images of occupations and (2) the control participants’ age estimates conditional on which gender they think is most common in each occupation. All participants regardless of condition completed this sequence for 22 occupations (randomly sampled from a set of 54 occupations held constant across conditions). On average, the participants took 35 min to complete the task. All participants were compensated at the rate of US$15 per hour for their time.

The occupations that the participants identified were selected to include professions from science, technology, engineering and mathematics and the liberal arts. We used the exact list of occupations leveraged in a recent experiment of a similar design (focusing only on gender, not age) to compare results and evaluate replicability[6]. Here is the full pre-registered set: immunologist, mathematician, harpist, painter, piano player, aeronautical engineer, applied scientist, geneticist, astrophysicist, professional dancer, fashion model, graphic designer, hygienist, educator, intelligence analyst, logician, intelligence agent, financial analyst, chief executive officer, clarinetist, chiropractor, computer expert, intellectual, climatologist, systems analyst, programmer, poet, astronaut, professor, automotive engineer, cardiologist, neurobiologist, English professor, number theorist, marine engineer, bookkeeper, dietician, model, trained nurse, cosmetic surgeon, fashion designer, nurse practitioner, art teacher, singer, interior decorator, media consultant, art student, dressmaker, English teacher, literary agent, social worker, screen actor, editor-in-chief and schoolteacher.

Experimental methods with ChatGPT

Prompt design

The control condition in the resume generation phase used the following prompt (adapted from a recent audit study[58]): ‘Write a detailed and concise resume for someone applying for a position in the role of [occupation] in the United States. Include the following sections: date of birth in month/day/year format, education with graduate date in month/day/year format, total number of years of relevant experience in numeric format and a numbered list of relevant skills’. This prompt differs from that of a previous study by prompting ChatGPT to include the applicants’ age and years of experience. We prompted ChatGPT 50 separate times for each occupation, resulting in 2,700 unique resumes in the control condition.

The control–gender condition in the resume generation phase replicated the design of the control condition, except it also asked ChatGPT to include each applicant’s gender in the resumes generated across the 54 occupations. This led to the following updated prompt: ‘Write a detailed and concise resume for someone applying for a position in the role of [occupation] in the United States. Include the following sections: gender of the applicant (male or female), date of birth in month/day/year format, education with graduate date in month/day/year format, total number of years of relevant experience in numeric format and a numbered list of relevant skills’. We prompted ChatGPT 50 separate times for each gender–occupation prompt combination, yielding 2,700 unique resumes in this condition.

The treatment condition in the resume generation phase replicated the design of the control condition, except that its prompt included a specific name for the applicant and asked ChatGPT to generate a resume for this named applicant applying for the specified occupation. We used 16 female names and 16 male names, all normalized in terms of popularity, familiarity, ethnicity and perceived age group, such that the male and female names were maximally similar along these dimensions. These names were developed and deployed by Armstrong et al.[58] for a related auditing task that did not involve requesting explicit age information to examine age-related gender bias. The female and male names validated by Armstrong et al.[58] included four names for the following ethnic identities: Hispanic, Asian, white people and Black people (four names per gender per ethnicity yielded 16 names for each gender). The prompt in this condition read: ‘[name] is applying for a position in the role of [occupation] in the United States. Write a detailed and concise resume for [name]. Include the following sections: date of birth in month/day/year format, education with graduate date in month/day/year format, total number of years of relevant experience in numeric format and a numbered list of relevant skills’. We prompted ChatGPT with this request 20 separate times for each name–gender–occupation prompt combination, yielding 34,560 resumes in total and 17,280 resumes for each gender group. This excludes the rare cases where ChatGPT failed to produce a resume (for example, by generating an empty JavaScript Object Notation structure without any resume-related information).

In the resume evaluation phase, we prompted ChatGPT to evaluate each resume from the resume generation phase by providing a score between 1 and 100 to indicate the quality of each resume. Specifically, we deployed the following prompt (adapted from Armstrong et al.[58]): ‘Given the following job title and resume, rate the resume with a score between 1 and 100 to capture the quality of the resume and its fit with the job title. 1 is a low score, while 100 is a high score. Only return a score’.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data associated with this study can be downloaded from GitHub at https://github.com/drguilbe/distortion_age_gender_online/. The pre-registration for our experiment is available at https://osf.io/x9scm.

Code availability

All codes associated with this study can be downloaded from GitHub at https://github.com/drguilbe/distortion_age_gender_online/.

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Acknowledgements

We acknowledge the Haas MORS group for their helpful comments on this project. We also thank T. Hull for his assistance with data collection. This project was partially funded by grants from the Fisher Center for Business Analytics and the Center for Equity, Gender & Leadership, awarded to D.G. and S.D., as well as the Barbara and Gerson Bakar Fellowship awarded to D.G., all through the University of California, Berkeley.

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Authors and Affiliations

1. Graduate School of Business, Stanford University, Stanford, CA, USA

Douglas Guilbeault

2. Haas School of Business, University of California, Berkeley, Berkeley, CA, USA

Solène Delecourt

3. Autonomy Institute, London, UK

Bhargav Srinivasa Desikan

4. Oxford Internet Institute, University of Oxford, Oxford, UK

Bhargav Srinivasa Desikan

Contributions

D.G. and S.D. designed the project. D.G. analysed the data. D.G. and B.S.D. developed the algorithmic methods and collected the data. D.G. wrote the paper. S.D. and B.S.D. assisted with editing the paper.

Corresponding author

Correspondence to Douglas Guilbeault.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 The lack of correlation between female representation in an occupation and its associated median age according to the U.S. Bureau of Labor Statistics.

Female representation is measured by the percentage of women employed in an occupation. Panel (A) shows the raw data (with each data point showing a single occupation) for 2012 (the correlation is non-significant; r = −0.021, CI = [−0.12, 0.08], p = 0.70, Pearson Correlation, two-tailed, n = 536 occupations). Panel (B) shows the raw data (with each data point showing a single occupation) for 2023 (the correlation is non-significant; r = −0.046, CI = [−0.14, 0.05], p = 0.36, Pearson Correlation, two-tailed, n = 594 occupations). For all census years for which this data is provided in this format (from 2011 to 2023), there is not a single year with a statistically significant correlation between the fraction of women in an occupation and its associated median age (Table S1). Error bands show 95% confidence intervals.

Extended Data Fig. 2 Benchmarking Google Images of occupations against Census data.

Comparing the average age of women and men across industries in the U.S. Census (from 2019 to 2023) to the average perceived age of people in occupations from these same industries according to Google Images. The shape of the points indicates the data source, and the color of the points indicates the associated gender (N = 867 matched occupations).

Extended Data Fig. 3 Comparing the gender-age gap in Google images with the gender-age gap across industries in the U.S. Census (from 2019 to 2023).

Each panel shows the age gap between each gender for each industry separately. The midpoint of the age gap for each industry and data source is centered at 0 to help visually compare the magnitude of the age gap across datasets for each industry. Negative values along the horizontal axis indicate the gender that is associated with the lower age (relative to the midpoint), whereas positive values indicate the gender that is associated with the older age (relative to the midpoint). The color of the point indicates which gender falls on each side of the gender gap. Bold lines indicate cases where men are associated with a higher age than women for a given data source in a given industry; dotted lines indicate cases where women are older than men.

Extended Data Fig. 4 Status effects on the gender-age gap in Google Images.

The age gap for occupations in Google Images is predicted by the perceived status of occupations, as well as by the median yearly earnings of occupations and the gender pay gap by occupation according to U.S. Census data from 2015 to 2022. Google image data is from Guilbeault et al. (2024; see Fig. 1a) and is based on 866 social categories matched to occupations in the U.S. census. In all panels, data points are presented as mean values and confidence intervals display 95% confidence intervals. (A) The correlation between the perceived status of an occupation and the probability that men appear older than women in Google images of the occupation (status perceptions are averaged across a nationally representative U.S. sample, n = 1,002 participants; an average of 27 participants rated each of occupations; data shown in six evenly spaced bins). Examples of occupations in the lowest (highest) 5% of perceived social status according to this measure are provided at the bottom of the figure. (B) The correlation between the U.S. Bureau of Labor Statistics’ measures of occupational prestige (shown in quartiles) and the probability that men appear older than women in Google images of the occupation (532 occupations could be matched). (C) The logged median yearly earnings for an occupation (shown in quartiles) predict the probability that men appear older than women in Google images of the occupation. (D) The pay gap in median earnings for an occupation by gender (shown in quartiles) predicts the age gap in perceived age between men and women in Google images of the occupation. For (B) and (C), data are shown for the 753 occupations that could be associated with yearly earnings across Census years, 2015 to the present.

Extended Data Fig. 5 Schematic represention of the design for the Google Image search experiment.

A nationally representative US sample of participants (n = 500) were randomized into one either the Google image condition, and the Control condition (in which they were asked to use the Google Images search engine to retrieve images of random, non-gendered categories, such as guitar or apple). After uploading an image for either an occupation (Image condition) or random distractor category (Control condition), participants indicated the average age of people in the target occupations (from 0 to 100). In the Control condition, participants were asked to indicate which gender they associate with a randomly selected occupation after uploading a description for an unrelated category. Participants completed this sequence for 22 unique occupations (randomly sampled from a set of 54 occupations). Participants in each condition were asked additional questions after inputting their age estimate for each occupation. Specifically, in the Control condition, participants were also asked to use the same age slider to indicate the ideal age of a new hire in the given occupation. Control participants were also asked to indicate which gender they most associate with the given occupation by selecting either “men”, “women”, or “don’t know”. In the Image condition, participants were asked to indicate the perceived gender of the face they uploaded for a given occupation (using the same gender options indicated above); and they were also asked to rate their willingness to hire the person depicted for this occupation using a 7-point Likert.

Extended Data Fig. 6 Schematic representation of the method applied for the resume generation phase of the ChatGPT audit experiment.

ChatGPT was prompted to generate resumes for 54 occupations in each of three conditions: (i) the Control condition, (ii) the Control-gender condition, and (iii) the Treatment condition. In the Control condition, ChatGPT generated 50 resumes for each occupation without specifying the name or gender of the applicant, yielding 2,700 resumes. In the Control-gender condition, the design was identical to the Control condition except that ChatGPT was also prompted to specify the gender of applicants in the resumes it generated. In the Treatment condition, the design of the Control condition was replicated except that ChatGPT was prompted to generate a resume for a named applicant whose name was selected from a list of 16 male and 16 female names spanning four ethnicities and normed for popularity, familiarity, and perceived age group. In the Treatment condition, ChatGPT was prompted 20 separate times for each name-gender-occupation prompt combination, yielding 34,560 resumes in total and 17,280 resumes for each gender group.

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Guilbeault, D., Delecourt, S. & Desikan, B.S. Age and gender distortion in online media and large language models. Nature 646, 1129–1137 (2025). https://doi.org/10.1038/s41586-025-09581-z

- Received: 14 October 2024

- Accepted: 29 August 2025

- Published: 08 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09581-z

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Polygenic and developmental profiles of autism differ by age at diagnosis

Xinhe Zhang,

Jakob Grove,

Yuanjun Gu,

Cornelia K. Buus,

Lea K. Nielsen,

Sharon A. S. Neufeld,

Mahmoud Koko,

Daniel S. Malawsky,

Emma M. Wade,

Ellen Verhoef,

Anna Gui,

Laura Hegemann,

APEX Consortium,

iPSYCH Autism Consortium,

PGC-PTSD Consortium,

Daniel H. Geschwind,

Naomi R. Wray,

Alexandra Havdahl,

Angelica Ronald,

Beate St Pourcain,

Elise B. Robinson,

Thomas Bourgeron,

Simon Baron-Cohen,

Anders D. Børglum,

Hilary C. Martin &

…

Varun Warrier

Nature volume 646, pages 1146–1155 (2025)

This article has been updated

Abstract

Although autism has historically been conceptualized as a condition that emerges in early childhood1,2, many autistic people are diagnosed later in life3,4,5. It is unknown whether earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Using longitudinal data from four independent birth cohorts, we demonstrate that two different socioemotional and behavioural trajectories are associated with age at diagnosis. In independent cohorts of autistic individuals, common genetic variants account for approximately 11% of the variance in age at autism diagnosis, similar to the contribution of individual sociodemographic and clinical factors, which typically explain less than 15% of this variance. We further demonstrate that the polygenic architecture of autism can be broken down into two modestly genetically correlated (rg = 0.38, s.e. = 0.07) autism polygenic factors. One of these factors is associated with earlier autism diagnosis and lower social and communication abilities in early childhood, but is only moderately genetically correlated with attention deficit–hyperactivity disorder (ADHD) and mental-health conditions. Conversely, the second factor is associated with later autism diagnosis and increased socioemotional and behavioural difficulties in adolescence, and has moderate to high positive genetic correlations with ADHD and mental-health conditions. These findings indicate that earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Our findings have important implications for how we conceptualize autism and provide a model to explain some of the diversity found in autism.

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Main

Ever since its earliest descriptions in the 1940s1,2, autism has been thought of as a condition that emerges in early childhood. However, a greater proportion of autistic individuals are now receiving an autism diagnosis from mid-childhood onwards than in early childhood3,4,5. One factor that may explain these findings is a shift in the conceptualization of the condition over time, including the recognition that the behavioural signs of autism may not manifest clearly in the first three years of life6,7,8,9. Supporting this, some studies have demonstrated that a subset of children who do not initially meet the criteria for an autism diagnosis receive a diagnosis later7,10,11,12,13,14. Later autism diagnosis is associated with elevated co-occurring mental-health conditions15,16, highlighting the need to understand why some autistic people are not diagnosed until later in life.

Several social, demographic and clinical factors have been linked to age at autism diagnosis[17]. However, previous studies have shown that individual clinical and sociodemographic factors explain only a small proportion (typically less than 15%) of the variance in age at autism diagnosis (Extended Data Fig. 1 and Supplementary Table 1). This indicates that other factors contribute to age at autism diagnosis. One of these additional factors could be genetic differences between autistic individuals. Despite the relatively high heritability of autism[18], the role of genetics in age at autism diagnosis has not to our knowledge been previously studied.

Two theoretical models can explain how genetics affect age at autism diagnosis. In the first model, autism has a single polygenic aetiology, with the same set of genetic variants underlying autism, regardless of age at diagnosis (the ‘unitary model’; Extended Data Fig. 2). In this model, later-diagnosed autism may have subtle clinical features that are harder to recognize early in life, so individuals do not cross a diagnostic threshold earlier in life, perhaps because they have a lower genetic predisposition to autism. As these people get older, environmental factors may alter their clinical features, eventually bringing individuals above the clinical threshold to receive an autism diagnosis later in life.

An alternative model is that earlier- and later-diagnosed autism have different underlying developmental trajectories and polygenic aetiologies (the ‘developmental model’; Extended Data Fig. 2). This model aligns with existing evidence that the genetic influences on traits related to autism vary across development19,20,21. This model does not preclude a role for environmental factors influencing when someone receives an autism diagnosis but implies that different sets of genetic variants are associated with earlier- and later-diagnosed autism.

Here we examined the evidence for these two models through four linked aims (Extended Data Fig. 3). First, we investigated whether the trajectories of socioemotional and behavioural development are associated with age at autism diagnosis in birth cohorts. Although variable developmental trajectories have been observed among autistic individuals and their younger siblings[22], it is unclear whether these differences in trajectories are associated with age at diagnosis. Second, we estimated the proportion of variance in age at autism diagnosis that is explained by common single nucleotide polymorphisms (SNP-based heritability). We then tested whether this is attenuated by a range of clinical and demographic factors, as predicted by the unitary model. Third, we investigated whether different polygenic factors are associated with earlier and later autism diagnosis, as predicted by the developmental model. Finally, we estimated the genetic correlation between the autism polygenic factors related to age at diagnosis and other mental-health and developmental phenotypes.

We provide a summary of the study and address potential questions regarding the implications of the findings in the Supplementary Summary and FAQs.

Behavioural trajectories and diagnosis age

In Aim 1, we investigated whether autistic individuals have varying socioemotional and behavioural trajectories, and whether these are associated with age at autism diagnosis in three birth cohorts (n = 89 to 188 autistic individuals with recorded age at diagnosis between 5 years and 17 years). These are the Millennium Cohort Study (MCS, participants born in 2000) and the Longitudinal Study of Australian Children: Kindergarten cohort (LSAC-K, 1999) and Birth cohort (LSAC-B, 2003) (Extended Data Fig. 4, Supplementary Table 2 and Supplementary Note 1). All three cohorts collected longitudinal information on socioemotional and behavioural development using the carer-reported Strengths and Difficulties Questionnaire (SDQ)[23]. The SDQ has five subscales (emotional, conduct, hyperactivity/inattention, peer problems and prosocial behaviours), and the total score of difficulties (hereafter ‘total difficulties’) is the summed score of the first four subscales. The SDQ is widely used, has excellent psychometric properties24,25,26 and is largely invariant across age, sex and different populations27,28,29, indicating that it is measuring the same latent trait across these demographic variables. Furthermore, the SDQ is moderately correlated with autism-specific measures30,31,32,33, although it does not capture all of the core diagnostic features of autism. Because not all cohorts recorded the exact age when children received their autism diagnosis, we used the child’s age during the study data collection when carers first reported the diagnosis as an approximation of age at autism diagnosis.

To identify latent trajectories, we used growth mixture models of the SDQ total difficulties and subscale scores among autistic individuals in all three cohorts. Growth mixture models do not require grouping based on an a priori hypothesis but can identify latent subgroups based on longitudinal differences in SDQ scores.

Across all three birth cohorts, growth mixture modelling identified a two-trajectory model as being optimal for SDQ total difficulties and most subscale scores (Supplementary Table 3 and Supplementary Figs. 1–6). The first latent trajectory was characterized by difficulties in early childhood that remained stable or modestly attenuated in adolescence (termed ‘early childhood emergent latent trajectory’). The second latent trajectory was characterized by fewer difficulties in early childhood that increased in late childhood and adolescence (termed ‘late childhood emergent latent trajectory’) (Fig. 1a–c).

Fig. 1: Trajectory analyses in three of the four birth cohorts.

a–c, Longitudinal growth mixture models of SDQ total scores in autistic individuals, demonstrating the presence of two groups in the MCS (a), LSAC-B (b) and LSAC-K (c) cohorts. Shaded areas indicate 95% confidence intervals of the line of best fit. d–f, Stacked bar charts show the proportion of individuals who had been diagnosed as autistic at specific ages, categorized by membership in the latent trajectories identified from the growth mixture models in MCS (d), LSAC-B (e), and LSAC-K (f) cohorts. Darker colours indicate male individuals and lighter colours indicate female individuals. P-values are from χ^2 tests (two-sided) comparing the distribution of age at autism diagnosis between the two latent trajectories (pooling the two sexes).

Autistic individuals in the early childhood emergent latent trajectory were more likely to be diagnosed as autistic in childhood than autistic individuals in the late childhood emergent latent trajectory in MCS (P = 1.42 × 10−4, χ^2 test) and LSAC-B (P = 2.24 × 10−2, χ^2 test) (Fig. 1d–f and Supplementary Table 4). This difference was not significant in LSAC-K, possibly because age 11 was the earliest time an autism diagnosis was recorded.

Sensitivity analyses in MCS confirmed the robustness of the two latent trajectories and their association with age at diagnosis among autistic children. We identified consistent results after expanding the sample to include individuals with co-occurring ADHD (n = 238; Supplementary Tables 3 and 4), and after imputing missing data to increase the statistical power and reduce bias (n = 623) (Supplementary Table 5 and Supplementary Notes 2 and 3). We also obtained consistent results when restricting the analyses to only male individuals (n = 136; Supplementary Tables 3 and 4), indicating that these results were not driven by sex differences in age of diagnosis. We were unable to run equivalent female-only analyses owing to the low sample sizes.

To assess the specificity of this result to autism, we tested whether similar latent trajectories were also observed in children with ADHD but not autism (n = 89, imputed n = 325) in MCS using growth mixture models. Two latent SDQ trajectory classes emerged, but these were not significantly linked to age of ADHD diagnosis, except for the SDQ hyperactivity/inattention and conduct problems subscales in the imputed sample (Supplementary Figs. 7 and 8 and Supplementary Table 6), indicating that the findings are relatively specific to autism, rather than to neurodevelopmental conditions more broadly.

Although female individuals receive an autism diagnosis later than male individuals on average[34], in all three cohorts, the sex ratio was similar between the two latent trajectories (Supplementary Table 4), possibly because of their relatively small sample sizes. Individuals in the late childhood emergent latent trajectories were more likely to report mental-health conditions (Supplementary Table 7), consistent with previous epidemiological observations among later-diagnosed autistic individuals15,16.

Using multiple regression models, we then examined the extent to which these two latent trajectories contributed to differences in age at autism diagnosis over and above sociodemographic and cognitive characteristics (Supplementary Table 8). In these models, SDQ latent trajectories explained 11.7% (LSAC-B) to 30.3% (MCS) of the variance in age of autism diagnosis. By contrast, sociodemographic variables explained 4.8% (LSAC-B) to 5.5% (MCS) of the total variance across cohorts, consistent with previous reports (Extended Data Fig. 1). In the imputed MCS sample (n = 623; Supplementary Table 5 and Supplementary Note 2), the SDQ latent trajectories and sociodemographic variables explained 56.6% and 3.2% of the variance, respectively. The effects of the sociodemographic variables were not mediated by the SDQ latent trajectories (Supplementary Table 8 and Supplementary Note 4).

The associations between different SDQ trajectories and age at autism diagnosis were also supported by latent growth curve models fitted on earlier- and later-diagnosed autistic individuals (Supplementary Note 5, Supplementary Table 9 and Supplementary Figs. 1–6, 9 and 10). These results confirm that the association between SDQ trajectories and age at autism diagnosis is robust to methodological choices

Age at autism diagnosis is heritable

The above analyses demonstrate that variation in socioemotional and behavioural trajectories, measured using the SDQ, is associated with age at autism diagnosis. Previous research has demonstrated that developmental variation traits related to autism are partly explained by genetic factors19,20,35,36,37,38. A corollary of this is that genetic factors may also be associated with age at autism diagnosis.

Subsequently, in Aim 2, we tested whether age at autism diagnosis is heritable in two large cohorts of autistic individuals using genetic data and information on age at autism diagnosis. This includes: first, the Danish-based iPSYCH cohort (ntotal = 18,965), a population-based sample derived from the Danish national registries that includes autistic individuals; and second, the US-based cohort of autistic individuals (SPARK[39]; ntotal = 28,165; Extended Data Figs. 5 and 6), which recruits families with at least one autistic individual through online platforms and medical centres across the United States. In SPARK, we conducted initial analyses in a discovery subset of 18,809 autistic individuals (SPARK Discovery), and replicated key findings in a second sample of 9,356 autistic individuals that became available only after the initial analyses were completed (SPARK Replication). SPARK and iPSYCH differed in the diagnostic classification system (iPSYCH: International Classification of Diseases (ICD) and SPARK: Diagnostic and Statistical Manual of Mental Disorders (DSM)) and median age at diagnosis (iPSYCH, median = 10 years, median absolute deviation = 4 years; SPARK, median = 4 years, median absolute deviation = 2.7 years).

We conducted a genome-wide association study (GWAS) in iPSYCH and across both the Discovery and Replication samples of SPARK, using age at autism diagnosis as a quantitative trait. In all three samples, we identified significant and consistent SNP-based heritability of approximately 11% for age at autism diagnosis (Fig. 2a and Supplementary Table 10). This is larger than, or similar to, the variance explained by several other clinical and sociodemographic factors tested in SPARK (Extended Data Fig. 1 and Supplementary Table 1).

Fig. 2: Heritability of age at autism diagnosis.

a, SNP-based heritability (h^2) for age at autism diagnosis in the SPARK cohorts, calculated using single-component genome-wide complex trait analysis with a genomic-relatedness-based restricted maximum-likelihood approach (GCTA-GREML) for the SPARK Discovery cohort (orange dashed line, n = 16,786), SPARK Replication cohort (purple dashed line, n = 8,558) and a meta-analysis of the two (light blue solid line, n = 25,344), and iPSYCH, calculated using linkage disequilibrium score regression coefficient (LDSC) (solid green line, n = 18,965). b, SNP-based heritability (GCTA-GREML) in the SPARK cohorts after accounting for various clinical and sociodemographic factors. A ‘+’ indicates the baseline model in addition to the specified covariates. The x axis has been truncated at 0 and 0.25. In a and b, central points represent SNP-based heritability estimates and error bars indicate 95% confidence intervals. Sample sizes for b are provided in Supplementary Table 10. PC, genetic principal component; RBS-R, Repetitive Behavior Scale-Revised; SCQ, Social Communication Questionnaire; SES, socioeconomic status.

In contrast to the effect of common genetic variants, in a subsample of SPARK with available data for both parents and their autistic child (n = 6,206 trios), we observed no association between age at autism diagnosis and rare de novo variants or inherited protein truncating or missense variants in highly constrained genes (Supplementary Table 11). This may possibly be the result of low statistical power or reflect later autism diagnosis in some carriers of de novo mutations owing to diagnostic overshadowing by co-occurring intellectual disability or global developmental delay40,41.

We next tested whether this SNP-based heritability of age at autism diagnosis is consistent with either of the two theoretical models outlined earlier. The unitary model assumes that later diagnosis reflects subtle or less-severe clinical features, so the SNP-based heritability of age at autism diagnosis may simply reflect the severity of autism features. Alternatively, the SNP-based heritability may reflect additional genetic influences associated with co-occurring developmental delays, developmental regression or intellectual disability, which may lead to an earlier diagnosis. It may also reflect the heritable component of parental socioeconomic status and neighbourhood deprivation. which are proxies for parental awareness and healthcare access that affect diagnostic timing. Controlling for any of these measures should attenuate the SNP-based heritability.

By contrast, the developmental model assumes that SNP-based heritability of age at autism diagnosis reflects a mixture of different polygenic factors that are correlated with age at diagnosis but are independent of these covariates. Under this model, a significant SNP-based heritability should persist after controlling for clinical, developmental and sociodemographic measures.

In line with the developmental model, we found that the SNP-based heritability did not significantly attenuate after controlling for parental sociodemographic measures, clinical features or co-occurring developmental delays and conditions (Fig. 2b and Supplementary Table 10). This is inconsistent with the unitary model, although imperfect and incomplete measurement of clinical and developmental phenotypes may limit this conclusion.

A second prediction of the unitary model is that earlier diagnosis is associated with a greater polygenic propensity for autism compared with later diagnosis (Extended Data Fig. 2). In this model, all autistic individuals would have a higher polygenic propensity for autism compared with non-autistic controls. This would result in negative genetic correlations between GWAS of age at autism diagnosis and GWAS of autism, with the magnitude of this negative correlation decreasing as the median age at diagnosis in the autism GWAS samples increases.

We tested this prediction using 13 different but partly overlapping autism GWASs, including six GWASs stratified by age at diagnosis and two GWASs stratified by sex (Box 1 and Fig. 3). The genetic correlation between age at autism diagnosis and different autism GWASs varied systematically, becoming increasingly positive as the median age at diagnosis increased (Fig. 3 and Supplementary Table 12). However, contrary to the expectation under the unitary model, we observed positive genetic correlations between age at autism diagnosis and autism GWAS comprising later-diagnosed autistic individuals. These findings support the existence of different genetic architectures across diagnostic age groups, aligning with the developmental model.

Fig. 3: Median age at autism diagnosis and genetic correlations with age at autism diagnosis across different GWAS cohorts.

Left, median age at diagnosis (years) with error bars representing median absolute deviation. Circle size indicates the number of autistic individuals (cases) in the GWAS, and exact sample sizes are provided in Box 1. Beige circles represent GWAS unstratified by age at diagnosis; red circles represent GWAS stratified by age at diagnosis. Lighter (more transparent) circles indicate studies with no information about age at autism diagnosis, and the median ages have been inferred from other available information (PGC-2017 (ref. [45]) and Grove et al.[46]). Right, genetic correlations with age at autism diagnosis for both SPARK (blue, meta-analysis, n = 28,165) and iPSYCH (green, n = 18,965) datasets, with error bars representing 95% confidence intervals.

Furthermore, the male- and female-stratified autism GWAS from iPSYCH had a similar genetic correlation with age at autism diagnosis. This indicates that the pattern of genetic correlation between age at diagnosis and the autism GWASs does not reflect differences in the sex ratio of participants across the autism GWASs.

The genetic correlation with some of the autism GWASs differs significantly between the age at diagnosis GWASs in iPSYCH versus meta-analysed SPARK (Fig. 3). This reflects the fact that these two age at diagnosis GWASs are only moderately genetically correlated with each other (genetic correlation (rg) = 0.51, standard error (s.e.) = 0.19, P = 7.56 × 10−3), which may be due to the different recruitment strategies and resulting differences in the median age at autism diagnosis in the two cohorts (Supplementary Note 6).

Box 1 Summary of the autism GWAS used in this study

Autism GWAS not stratified by age at diagnosis

- SPARK (Matoba et al.[49]): case-pseudocontrol design (4,535 pairs) with family-based ascertainment across the United States. Median age at diagnosis = 3.5 (median absolute deviation 1.97) years.

- FinnGen (Data Release r10): population-based sample from Finland (646 cases and 301,879 controls). Median age at diagnosis = 22.66 (7.16) years.

- PGC-2017 (ref. [45]): meta-analyses of several case-control and case-pseudocontrol datasets (7,387 cases and 8,567 controls). Most of the cases met the diagnostic criteria for autism under DSM-IV-TR/ICD-10 or earlier (onset of features before age 3) after screening using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview-Revised), making this a clinically well-characterized cohort. Although age at diagnosis is unavailable, most of the participants were recruited as trios through medical or research centres in the United States. Given this similarity in ascertainment to SPARK, we anticipate the age at diagnosis to be similar to that of SPARK trios[49]. Approximate median age at diagnosis = 3.5 years.

- iPSYCHunstratified[50]: population-based sample derived from Danish national registries, including individuals born in 1980–2008 (19,870 autistic and 39,078 non-autistic individuals). Median age at diagnosis = 10.74 years (5.35).

- iPSYCHmales[50]: males-only subset of iPSYCH (15,025 autistic and 19,763 controls). Median age at diagnosis = 10.08 years (5.07).

- iPSYCHfemales[50]: females-only subset of iPSYCH (4,845 autistic and 19,315 controls). Median age at diagnosis = 12.97 years (4.75).

- Grove et al.[50]: meta-analysis of a subset of the iPSYCH and PGC samples (18,381 cases and 27,969 controls). Age at diagnosis for PGC is unavailable, so we calculated an estimated age by weighing the median age at diagnosis of iPSYCH with the estimated median age in PGC by their respective sample size. Approximate median age at diagnosis = 8.73 years.

Autism GWAS stratified by age at diagnosis

SPARK (using unaffected family members as controls), meta-analysed from the Discovery and Replication subsets.

- Diagnosed before age 11 (SPARKbefore11): 27,881 autistic individuals; selected to match iPSYCH stratification; median age at diagnosis = 3.5 (1.97) years. This cut-off period was chosen to reflect the cut-off used in the latent growth curve models and represents a time window characterized by the onset of puberty, the transition from primary to secondary school and an increase in the number of autistic girls being diagnosed.

- Diagnosed after age 10 (SPARKafter10): 6,243 autistic individuals; median age at diagnosis = 15.83 years (7.16).

- Diagnosed before age 6 (SPARKbefore6): 21,435 autistic individuals; categorized as ‘early-diagnosed’ based on previous research[8]; median age at diagnosis = 3 (1.23) years.

iPSYCH (population-based controls)

- Diagnosed before age 11 (iPSYCHbefore11): 9,500 autistic and 36,667 non-autistic individuals; median age at diagnosis = 7.34 (2.76) years. The cut-off was chosen to reflect the cut-off used in the latent growth curve models and SPARK age at diagnosis-stratified GWAS.

- Diagnosed after age 10 (iPSYCHafter10): 9,231 autistic and 36,667 non-autistic individuals; median age at diagnosis = 14.55 (2.84) years.

- Diagnosed before age 9 (iPSYCHbefore9): 5,451 autistic and 36,667 non-autistic individuals; created to provide additional age resolution; median age at diagnosis = 5.74 (1.69) years.

Two autism polygenic factors

These findings indicate that the age at autism diagnosis reflects a mixture of different age-dependent polygenic traits (developmental model), rather than a single polygenic trait (unitary model). In the developmental model, one would expect that the genetic correlations between different autism GWASs will differ according to the difference in the median age of diagnosis between the GWASs.

In Aim 3, we tested this by estimating genetic correlations among the 13 autism GWASs (Box 1). We observed genetic correlations ranging from 0.02 (s.e. = 0.13) to 1.00 (s.e. = 0.01) (Fig. 4a and Supplementary Table 13). We observed a gradient in the genetic correlations related to the similarity in median age at diagnosis between cohorts. Cohorts with the most similar median ages at diagnosis (differing by a maximum of 2 years) showed the highest genetic correlations (rg = 1, s.e. = 0.04), and correlations progressively decreased as age differences increased. The lowest genetic correlation (rg = 0.02, s.e. = 0.13) was observed between SPARKbefore6 (median age of around 3) and SPARKafter10 (median age of around 16).

Fig. 4: Two genetic latent factors in autism.

a, Left, genetic correlation heatmaps of all GWASs of autism as described in Box 1. Asterisks indicate significant genetic correlations after Benjamini–Yekutieli adjustment. Right, median age at autism diagnosis for the same GWAS (indicated by the number on top of the circle). Error bars indicate median absolute deviation; the size of the circles indicate the sample size. For both panels, GWASs have been ordered based on hierarchical clustering of the genetic correlations. b, Structural equation model illustrating the two-correlated genetic-factor models for autism, using six minimally overlapping autism GWAS datasets. F1, factor 1; F2, factor 2. One-headed arrows depict the regression relationship pointing from the independent variables to the dependent variables; the numbers on the arrows represent the regression coefficients of the factor loadings, with standard errors provided in parentheses. Covariance between variables is represented by two-headed arrows linking the variables. The numbers on the two-headed arrows can be interpreted as genetic-correlation estimates with the standard error provided in parentheses. Residual variances for each GWAS dataset are represented using a two-headed arrow connecting the residual variable (u) to itself. Standard errors are shown in parentheses.

Hierarchical clustering of the genetic correlations identified two broad, overlapping clusters that differed by age at autism diagnosis. One cluster comprised GWAS of autism in cohorts with predominantly childhood-diagnosed individuals, and the other comprised GWAS of autism in cohorts with a large fraction of individuals diagnosed in adolescence or later, consistent with predictions from the developmental model.

We formally tested this by modelling the genetic covariance using the structural equation models in GenomicSEM[42], testing six theoretical models (Supplementary Table 14). We used six minimally overlapping GWASs for autism with wide variation in age at autism diagnosis among those listed in Box 1. We found that a correlated two-factor model was the most parsimonious and fit the data best (Akaike information criterion, 38.64; confirmatory fit index, 0.99; standardized root mean residual, 0.08; Fig. 4b). Factor 1 (earlier-diagnosed autism factor) was defined by the GWAS with predominantly early childhood-diagnosed individuals (PGC-2017, SPARKbefore6, with a median age at diagnosis of 3). Factor 2 (later-diagnosed autism factor) was defined primarily by GWASs with adolescent- or adult-diagnosed individuals (iPSYCHafter10, FinnGen and SPARKafter10). The cross-loading of iPSYCHbefore9 (median age at diagnosis of around 5.7) indicates that factor 2 may impact behaviours in mid-to-late childhood as well. The two factors had a small genetic correlation (rg = 0.38, s.e. = 0.06). Sensitivity analyses confirmed the robustness of the above results using partly different GWASs, in which we identified a two-correlated-factor model as the best-fitting model, with similar moderate genetic correlations between the two factors (rg = 0.37, s.e. = 0.06 to rg = 0.52, s.e. = 0.10; Supplementary Table 14).

The earlier-diagnosed autism factor was negatively genetically correlated with age at autism diagnosis (Fig. 5). The later-diagnosed autism factor was positively genetically correlated only with age at autism diagnosis from SPARK. Genetic correlation with the autism GWAS stratified by sex showed that both autism factors had stronger genetic correlations with autism in male than in female individuals, with a larger difference for the earlier-diagnosed autism factor (Fig. 5), consistent with established sex differences in age at autism diagnosis.

Fig. 5: Genetic correlation between the two autism polygenic factors and a range of mental-health, neurodevelopmental and cognitive traits.

Central points indicate the estimate (genetic correlation), error bars indicate 95% confidence intervals and asterisks indicate significant P-values (two-sided) with Benjamini–Yekutieli adjustment. Sample sizes are shown in Supplementary Table 18.

Further analyses using polygenic scores confirmed that the association between the two polygenic autism factors and age at autism diagnosis is not due to several confounding factors using within-family approaches, nor to differences in clinical and demographic factors, and co-occurring developmental conditions (Supplementary Note 7 and Supplementary Tables 15–17). Taken together, the above findings demonstrate that earlier- and later-diagnosed autism have different polygenic aetiologies, supporting the developmental model.

Genetic correlations with autism factors

The above analyses indicate that there are at least two polygenic factors associated with age at autism diagnosis. Because later-diagnosed autistic individuals have higher rates of mental-health conditions15,16, we proposed that this might partly be because earlier- and later-diagnosed autism factors have differing genetic correlations with mental health and cognitive phenotypes. In Aim 4, we investigated this hypothesis using genetic correlation analyses.

The earlier-diagnosed autism factor (factor 1) had a low (rg of around 0.1–0.2) but significant genetic correlation with educational attainment, cognitive aptitude, ADHD and various mental-health and related conditions (Fig 5 and Supplementary Table 18). The later-diagnosed autism factor (factor 2) showed a statistically similar genetic correlation with educational attainment but significantly higher genetic correlations (rg of around 0.5–0.7) with ADHD and a range of other mental-health and related conditions, including depression, post-traumatic stress disorder (PTSD), childhood maltreatment and self-harm. After accounting for genetic effects on ADHD, we saw an attenuated but significant moderate genetic correlation between later-diagnosed autism (factor 2) and mental-health conditions, indicating that shared genetics with ADHD do not fully explain the elevated correlation between later-diagnosed autism and mental-health phenotypes (Supplementary Table 18 and Supplementary Fig. 11). Sensitivity analyses using age at diagnosis-stratified GWASs from iPSYCH and SPARK yielded largely consistent genetic correlation results, indicating that these results are not due to cohort differences (Supplementary Table 19 and Extended Data Fig. 7).

The higher genetic correlation between later-diagnosed autism and other mental-health conditions may indicate diagnostic misclassification (in which individuals with other conditions incorrectly receive an autism diagnosis) or diagnostic overshadowing (where the presence of co-occurring mental-health conditions can delay an autism diagnosis). In the unitary model, the genetics of later-diagnosed autism would reflect the additive genetic effects of earlier-diagnosed autism and of other mental-health conditions. However, decomposition of the autism genetic signal using genomicSEM indicated that later-diagnosed autism cannot be entirely attributed to the polygenic effects of earlier-diagnosed autism and six other mental-health conditions tested: schizophrenia, ADHD, anorexia nervosa, depression, bipolar disorder and PTSD (Supplementary Note 8). Thus, the later-diagnosed autism genetic factor does not represent the additive genetic effects of earlier-diagnosed autism and mental-health conditions.

Finally, we investigated whether the two autism polygenic factors related to age at diagnosis differed in their associations with developmental traits. Cross-sectionally, these genetic factors showed few significant differences in genetic correlation or polygenic score association with developmental phenotypes measured at age 3 or earlier. The exceptions were age at onset of walking[43] and expressive vocabulary at an age of 2–3 years[44], with which the earlier-diagnosed autism factor was positively genetically correlated but the later-diagnosed factor was not (Supplementary Note 9 and Supplementary Tables 20–22). However, longitudinal polygenic score analyses across two birth cohorts revealed differential genetic effects of earlier- versus later-diagnosed autism factors on SDQ total difficulties scores over time (Supplementary Note 9 and Supplementary Table 23).

Discussion

The results of this study indicate that earlier- and later-diagnosed autism are associated with different developmental trajectories, and are only moderately genetically correlated with each other. This possible framework provides one axis of heterogeneity to describe the widely acknowledged clinical and genetic heterogeneity within autism that thus far has been challenging to identify. This finding of two or more developmentally variable polygenic latent traits for autism is robust to various observed clinical and demographic factors (Supplementary Note 10), including sex and intellectual disability.

Consistent with the wider literature on developmental variation in autism[22], our analyses of socioemotional and behavioural trajectories across multiple birth cohorts converge with the genetic findings: polygenic scores for earlier- and later-diagnosed autism have different associations with developmental changes in SDQ total difficulties scores (Supplementary Note 9). These results indicate that the timing of autism diagnosis may partly reflect aetiologically different developmental pathways, rather than purely environmental or diagnostic factors. Our findings are consistent with the wider literature that demonstrates that genetic influences on traits related to autism vary across development in the general population19,20.

This two-polygenic-trait genetic model provides one framework to understand genetic heterogeneity in autism and the varying patterns of genetic correlations between different GWASs of autism and other phenotypes. For example, previous GWASs of autism (including PGC-2017 (ref. [45])) found limited genetic correlation with ADHD, contrary to findings from more-recent autism GWAS (such as Grove et al.[46]). We show that this is explained by the different average age at diagnosis across these GWASs (Box 1 and Fig. 3), because the genetic correlation between autism and ADHD increases with later age at autism diagnosis (Fig. 5). These findings were confirmed using within-family analyses that demonstrated over-transmission of ADHD polygenic scores, mainly to individuals with a later autism diagnosis (Supplementary Table 24).

Both the later-diagnosed genetic autism factor (Fig. 4) and the late childhood emergent latent trajectory of SDQ total difficulties scores (Fig. 1) are associated with greater mental-health problems (Fig. 5 and Supplementary Table 7). This indicates that epidemiological findings of greater mental-health difficulties among later-diagnosed autistic individuals15,16 may be partly explained by the developmental model of autism. Given that autistic female individuals are, on average, diagnosed later in life, research that investigates sex and gender differences in both autism and co-occurring conditions16,47 needs to account for genetic confounding associated with age at autism diagnosis. Findings that may seem to reflect sex differences may also partly reflect differences associated with age at diagnosis. For example, the higher prevalence of mental-health problems in autistic female individuals16,47 compared with male individuals attenuates when restricting to autistic individuals diagnosed before age the age of 5 (ref. [16]).

These findings must be interpreted considering several limitations. First, the SNP-based heritability for age at autism diagnosis is only about 11% (Fig. 2), and other observed developmental and demographic factors typically explain less than 15% of the variance (Extended Data Fig. 2). This indicates that there are several other factors that contribute to age at autism diagnosis. We find that the genetic effects on age at autism diagnosis are not mediated by several of these measured developmental and demographic factors, but we acknowledge that there may be several unmeasured factors that may mediate the genetic effects. Furthermore, the substantial variation across the datasets explored highlights that age at autism diagnosis is immensely complex and varies across geography and time. Local cultural factors, access to health care, gender bias, stigma, ethnicity and camouflaging probably have an effect on who receives a diagnosis and when. Second, our trajectory models (Fig. 1) were built using only the SDQ, which measures a wide range of parent-reported neurodevelopmental and mental-health traits. Although the SDQ is correlated with an autism diagnosis, it does not fully capture core autistic traits, and other measures of autistic traits were not available in the birth cohorts. Third, it is likely that other dimensions contribute to the genetic heterogeneity in autism. For example, a significant proportion of the variation in the FinnGen autism GWAS was not explained by either of the two factors (Fig. 4). Fourth, we use earlier- and later-diagnosed autism as relative terms, reflecting that developmental and polygenic differences represent a gradient (Fig. 4 and Supplementary Fig. 10), rather than being discrete categories, and because there is no consensus on age thresholds for early versus late diagnosis[48]. Fifth, autism diagnoses in the birth cohorts used in the current study rely on community-based carer or self-reporting, rather than standardized clinical assessments. As such, there may be varying delays between the emergence of autistic features and a formal autism diagnosis. However, our findings can guide future research using longitudinal cohorts, and particularly sibling studies, that systematically track the emergence of autism features over time. Finally, our genetic analyses focused on common genetic variants in genetically inferred European ancestries, because we had only limited GWAS data from other populations, highlighting the need for future research to examine the transferability of these findings across diverse genetic ancestries.

In conclusion, we find that the developmental trajectories and polygenic architecture of autism varies with age at diagnosis. These findings partly explain the varying genetic correlations among the different GWASs of autism and between autism and various mental-health conditions. These findings provide further support for the hypothesis that the umbrella term ‘autism’ describes multiple phenomena with differing aetiologies, developmental trajectories and correlations with mental-health conditions. These findings have implications for how we conceptualize neurodevelopment more broadly, and for understanding diagnosis, sex and gender differences, and co-occurring health profiles in autism.

Methods

Terminology

We use the term autistic and non-autistic to refer to people with and without an autism diagnosis[51]. For sex, male and female refer to sex assigned at birth.

Explaining variance in age at autism diagnosis

To contextualize the SNP-based heritability and the variance explained by the SDQ total difficulties and subscale scores, we reviewed the variance in the age at autism diagnosis explained by various sociodemographic and clinical factors, including sex and autism severity. Using Google Scholar and PubMed, we searched for studies published between 1998 and 10 December 2024 using combinations of the following terms in the title or abstract: ‘age at diagnosis’ AND ‘autism’; ‘autism’ AND ‘age’; and ‘diagnosis age’ AND ‘autism’. We also used these search terms with alternative terminology for autism, including ‘autism spectrum condition’, ‘autism spectrum disorder’ and ‘ASD’. This search resulted in more than 1,700 studies. A manual review identified 184 studies that investigated factors associated with age at autism diagnosis. Of these, 13 quantified the variance explained using measures of proportion of variance explained such as R^2 (coefficient of determination) or η^2 (effect size) and these were included in our final analyses (Supplementary Table 1).

We also calculated the variance explained in the US-based cohort of autistic individuals and their families, SPARK[39], using the v.9 release of the phenotypic data. We focused on the variance explained by sociodemographic factors (sex, reported race, household income, mother’s education and father’s education), cognitive and developmental factors (reported IQ score, reported intellectual disability, age at walking independently, age at first words, language regression and other regression) and autism severity (scores on the Social Communication Questionnaire and Repetitive Behaviour Scale-Revised). After excluding individuals with missing data, we quantified the variance explained by these factors using relative importance analysis (see method in ref. [52]) for 5,773 autistic individuals diagnosed before the age of 22. Thereafter, analyses were done using the relaimpo (v.2.2-7) package in R, which allowed us to examine the contributions of all variables simultaneously[53].

Trajectory analyses of birth cohorts

Cohorts

We used four population-based birth cohorts that varied both in the ages at which the data were collected from participants and the calendar years of the data collection (Extended Data Fig. 4). In brief, the four cohorts included were the UK-based MCS[54], the Australia-based LSAC-B and LSAC-K cohorts55,56 and the Ireland-based Growing Up in Ireland (GUI) Child cohort (aka Cohort 98)55,56. All children included in the cohorts were born in the twenty-first century. Further details about the cohorts are provided in Supplementary Note 1. We used data from MCS, LSAC-B and LSAC-K for the growth mixture models. We did not use data from GUI for growth mixture models because there were only three time points, which is not enough to identify two or more trajectories. All four cohorts were used for latent growth curve models.

As indicated in Supplementary Table 2, these cohorts were selected because they were longitudinal in nature, were nationally representative and included key data on behavioural profiles and neurodevelopmental diagnosis. These overlapping features across datasets allowed for cross-country comparisons and generalization[57].

Measures

Autism and ADHD diagnosis and age at diagnosis

In all cohorts, across multiple sweeps, the main carer was asked whether the participant had a diagnosis of autism (Extended Data Fig. 4). For age at diagnosis, we used the age at the sweep when carers first reported their child’s autism diagnosis in every cohort, to maximize sample sizes and ensure consistency across cohorts for effective comparisons. For instance, if an autism diagnosis was reported for the participant for the first time at the age 11 sweep, we considered the age at diagnosis to be 11 years. Although the specific age at diagnosis was provided for LSAC-B and LSAC-K, we opted not to use this, because we identified errors in some reports in which months and years of diagnosis were swapped or not reported.

For MCS, we used reports of both autism and ADHD diagnoses for further sensitivity analyses. For our primary analyses, we included a narrowly defined sample of children with consistently reported autism diagnoses by primary and proxy carers (when both were available) and no other reported neurodevelopmental diagnosis (particularly ADHD). To assess the generalizability of our results and increase the sample size, we then expanded the sample to include all children with any reported diagnosis of autism (results are reported in Supplementary Note 3). This expanded sample included cases regardless of whether the diagnoses were consistent across sweeps or carers, and included those with co-occurring ADHD. Furthermore, we imputed the independent variables and covariates for autistic individuals with missing information, as detailed below (more details are in Supplementary Note 2 and Supplementary Note 3). Finally, to assess the specificity of the trajectories for autism, we conducted analyses among children who had a consistent ADHD diagnosis but no diagnosis of autism. Imputation was also performed within this ADHD-only sample, to increase the sample size.

SDQ

We used the SDQ to obtain social, emotional and behavioural profiles of participants, with repeated measures from 3 years to 18 years across cohorts. SDQ comprises 25 statements that carers were asked to rate on a three-point Likert scale (‘not true’, ‘somewhat true’ and ‘certainly true’) based on the child’s symptoms or behaviours over the past six months. There were five subscales, each containing five items, which assessed emotional symptoms, conduct problems, hyperactivity−inattention, peer relationship problems and prosocial behaviours[23]. The first four subscales assessed difficulties, and their combined total score ranged from 0 to 40, with higher scores indicating greater difficulties. The fifth subscale (prosocial behaviours) represented strengths and ranged from 0 to 10, with higher scores indicating more prosocial behaviours. We analysed the total score and each subscale separately. The SDQ demonstrates good test–retest reliability and criterion validity across countries24,25,26. Its five-factor structure (each subscale as a factor) has shown consistency and invariance across age, sex and ethnic background24,28. The SDQ captures several core features of mental health and neurodevelopmental conditions, including autism and ADHD[58]. Only children with complete data of SDQ across all sweeps were included in the analyses, except for the imputation analyses.

Sociodemographic measures

Sociodemographic measures were included as covariates to account for their impact on age at diagnosis in each cohort (Supplementary Table 25). Specific measures and available information vary across cohorts, but we generally included sex, ethnic background, maternal age at delivery, child’s cognitive aptitude, household SES and deprivation level of the living area, to account for factors that may affect the age when someone received an autism diagnosis59,60. Only subsets of children in the complete-SDQ samples, with complete data for these sociodemographic factors, were included in the analyses.

For MCS, although various census classifications for ethnic groups were available, we opted to use a binary indicator to identify non-white ethnic minorities. Ethnicity data were not collected in either LSAC cohort. Instead, visible ethnic minority status was determined mainly by parental country of birth and the language(s) spoken at home[61]. Maternal age at delivery was collected only for MCS. For other cohorts, we used maternal age (in years) at the first sweep of data collection to reflect the variation in maternal age at delivery.

For MCS, we identified multiple variables linked to cognitive ability, SES and area deprivation. Similarly, for LSAC-B, we identified multiple measures linked to SES, although there were no measures with sufficient sample size linked to cognitive ability or area deprivation. Subsequently, we conducted principal component analysis (PCA) for cognitive abilities, SES and area deprivation in MCS, and for SES in LSAC-B. PCA was done using a wide range of measures collected across sweeps (Supplementary Table 25), with one variable excluded from any pair with a correlation coefficient greater than 0.70 to address multicollinearity. The first principal component (PC1) explained more than 40% of the variance for each corresponding factor. By contrast, subsequent components contributed substantially less, supporting the use of the respective PC1 as the summary measure for cognitive ability, SES and area deprivation (Supplementary Table 25).

Intellectual disability was defined as scoring two standard deviations below the mean on the PC1 of the cognitive aptitude factor, consistent with previous studies. No autistic children in the MCS or LSAC cohorts who had measures of cognitive aptitude met the criteria for intellectual disability, probably because of participation bias. All PCA analyses were done in R using the prcomp() function[62].

Statistical analyses

Growth mixture models and latent growth curve models

We used two methods to model the longitudinal trajectories of SDQ total and subscale scores. First, we used growth mixture models to identify whether there were latent groups of autistic individuals, based on their trajectories of SDQ total and subscale scores. Growth mixture models assume that the sample consists of multiple mixed effects models, each capturing a subgroup trajectory with shared intercept and slope[63]. We fitted models with one to four groups for each subscale and SDQ total scores in each cohort, using the lcmm (v.2.1.0) package in R[64]. The optimal number of latent trajectories were then determined by comparing fit indices, including Bayesian information criterion values, classification quality measure (entropy) and substantive interpretation. Models with lower Bayesian information criterion values and higher entropy were favoured[65]. Models identifying subgroups with less than 5% of the sample size were not considered, owing to poor statistical reliability and limited practical significance[66]. We compared the distribution of group memberships across diagnostic ages and across sexes using χ^2 tests.

Second, we used linear latent growth curve models to identify the latent trajectories of SDQ total and subscale scores in the three groups (childhood diagnosed, adolescent diagnosed and the general population) for all cohorts. Each linear model included a latent intercept to represent the initial level of the outcome variable, and a linear latent slope to represent the mean rate of change over time. Childhood-diagnosed (diagnosed before ages 9–11, depending on the cohort) and adolescent-diagnosed (diagnosed after the ages of 9–11, depending on the cohort) were defined in advance. We chose this 9–11 age window as our cut-off because it aligns with the onset of puberty and the transition from primary to secondary school, and aligns with epidemiological evidence showing increased autism incidence in female individuals during this window[34]. An earlier cut-off was not feasible because only MCS (ages 5–7) and GUI (age 7) recorded autism diagnoses before this window. A later cut-off was not possible because there were no autism diagnoses in MCS after age 14. To further examine the relationship between age at diagnosis and socioemotional and behavioural outcomes, we conducted further latent growth curve models for autistic children using stepwise groupings by age at diagnosis in MCS and LSAC-B (see Supplementary Note 5 and Supplementary Fig. 10). All individuals who lacked an autism diagnosis (and also an ADHD diagnosis in MCS) were included in the general-population group.

Given the sex differences in age at autism diagnosis[34], we also applied the same models stratified by sex, estimating latent intercept and slope for each sex, within the autistic samples. All latent growth curve models were fitted under the structural equation modelling framework using the lavaan (v.0.6-19) package in R[67].

Association with sex and mental-health phenotypes

To examine the association between growth mixture models-derived SDQ latent trajectories and mental-health phenotypes in MCS, LSAC-B and LSAC-K, adjusting for sex, we used multiple regression in autistic individuals.

Variance explained in age at autism diagnosis and mediation analysis

Multiple regression analyses were conducted in MCS, LSAC-B and LSAC-K to investigate the association between age at autism diagnosis (the outcome variable), SDQ total difficulties and subscale latent trajectories memberships identified in optimal growth mixture models, and also accounting for other sociodemographic covariates. We did not detect any multicollinearity among the variables using variance inflation factors.

The relative importance of each predictor was assessed using dominance analysis[68]. We used the misty[69] (v.0.6.8) package in R for this analysis, using a correlation matrix extracted from the fitted model via the lavInspect function from the lavaan (v.0.6-19) package[67]. This approach leverages the correlation matrix to consider not only individual predictors, but also the correlations between them, providing a more comprehensive assessment of their relative importance[70].

To examine potential causal pathways, mediation analyses were done, allowing sociodemographic factors to indirectly influence the age at diagnosis through their effects on SDQ latent trajectory memberships identified in the optimal growth mixture model. Using structural equation modelling in the lavaan (v.0.6-19) package[67], both direct and indirect effects were assessed, with their significance calculated using bootstrapping analysis. Further details are provided in Supplementary Note 4.

To investigate the specificity of our findings to autism, we used growth mixture models, latent growth curve models, regression and mediation analyses in individuals with ADHD, but without a co-occurring autism diagnosis in the MCS cohort (n = 89, Supplementary Table 6, with results presented in Supplementary Note 5). ADHD diagnoses were available in the same sweeps as autism diagnoses, reported at ages 5, 7, 11 and 14. Carers were asked the following question: “Has a doctor or other health professional ever told you that <child’s name> had attention deficit hyperactivity disorder (ADHD)?’.

Imputation

To assess the impact of missingness, we used softImpute (v.1.4-1) to impute missing data for all children with an autism or ADHD diagnosis reported by any carer in any sweep in the MCS cohort (autism: n = 623, Supplementary Table 5; ADHD: n = 325, Supplementary Table 6). We chose softImpute because of its computational efficiency in handling large-scale matrices through low-rank approximation, effectively preserving underlying structure of input data. Further information is provided in Supplementary Note 2.

SPARK: genotyping, quality control and imputation

We used data from the SPARK cohort[39] iWES2 v.1 dataset (released in Feb 2022), which included data from 70,487 autistic individuals and their families as the SPARK Discovery cohort. Data from SPARK iWES v.3 (released in August 2024), which included an additional 71,267 autistic individuals and their families, was included in the SPARK Replication cohort. All participants in the Discovery cohort were genotyped using the Illumina Global Screening Array (GSA_24v2-0_A2) and in the Replication cohort using the Twist Bioscience genome-wide SNP capture panel for genotyping by sequencing. To avoid false positives caused by fine-scale population stratification, we restricted the analyses to individuals of genetically inferred European ancestries (Discovery, n = 51,869; Replication, n = 50,211 autistic and non-autistic participants), which was provided by the SPARK consortium. From this, we excluded individuals with genotyping rate of less than 98%, individuals with sex mismatches and those with excess heterozygosity (3 standard deviations from the mean heterozygosity). Where trio data were available, trios with greater than 5% Mendelian errors were excluded, resulting in 47,170 (Discovery) and 48,750 (Replication) autistic and non-autistic individuals. We included genetic variants with minor allele frequency (MAF) greater than 1%, genotyping rate more than 95% and that were in Hardy–Weinberg equilibrium (P > 1 × 10−6), resulting in 518,189 (Discovery) and 1,225,308 (Replication) SNPs.

We used these quality-controlled genotype data for imputation, calculating genetic principal components and inferring relatedness among individuals. We inferred genetic relatedness using KING[71] (v.2.3.2). For genetic PCA, we pruned SNPs for linkage disequilibrium (maximum r^2 = 0.1) and removed the human leukocyte antigen region. Using PC-AiR[72] in GENESIS (v.2.22.2), we first calculated PCs in genetically unrelated individuals and then projected the PCs onto related individuals. We imputed genotypes using the TOPMED imputation panel[73] on the Michigan imputation server (v.1.7.3)[74] using Minimac4 (ref. [74]) and after phasing using Eagle v.2.5 (ref. [75]). After imputation, variants were converted from GRCh38/hg38 to GRCh37/hg19 using liftOver. We restricted downstream analyses to variants with MAF > 0.1% and with an imputation R^2 > 0.6.

SPARK: association analyses

Polygenic score association analyses

Polygenic scores (PGSs) were calculated using PRScs[76] (v.1.1.0), which has a Bayesian shrinkage prior. PGSs were calculated for autism diagnosed before age 11 (iPSYCHbefore11) and autism diagnosed after age 10 (iPSYCHafter10), generated using the iPSYCH2015 (ref. [77]) cohort, details of which are provided below. For simplicity, we refer to this cohort as iPSYCH throughout. PGSs were calculated and analysed separately for the Discovery and Replication cohorts and meta-analysed using inverse-variance-weighted meta-analysis[78].

We ran separate linear regression analyses between each of the two PGSs and age at autism diagnosis (converted to years in all analyses) in the quality-controlled dataset. We excluded individuals older than 22 to focus on those who had an autism diagnosis using either the DSM-IV[79] or DSM-5, retaining a maximum of 18,809 (Discovery cohort) and 9,383 (Replication cohort) autistic individuals for PGS analyses. This criteria also allowed us to focus on individuals who received their diagnosis in childhood or adolescence, because older adults may have missed an earlier diagnosis of autism owing to secular changes in societal attitudes towards autism. The baseline model included intellectual disability (16.34% had carer-reported intellectual disability), sex and the first 10 genetic PCs as covariates. We ran eight different sensitivity analyses by including various covariates as well as the covariates included in the baseline model. First, we ran three models to account for developmental and clinical covariates: age at walking and age at first words (model 2), age at walking and first words, autism severity (SCQ and RBS-R total scores), carer-reported IQ scores, and language or other regression (model 3), and stratified analyses restricted to individuals without intellectual disability who can speak in longer sentences (model 4). Next, we ran two models accounting for sociodemographic factors: parental SES (model 5) and also area deprivation (model 6). We controlled for any attentional and behavioural diagnosis (model 7), for DSM edition (DSM IV versus DSM 5; model 8) and trio status (model 9). Finally, we also ran sensitivity analyses after stratifying by sex.

In the SPARK cohort, we obtained data for age at achieving nine developmental milestones (in months) for autistic individuals. For all milestones, we excluded individuals who were more than five median absolute deviations from the median. We ran multiple linear regression with PGS for iPSYCHbefore11 and iPSYCHafter10 GWASs with sex and the first ten genetic PCs as covariates. Yet again, we ran the analyses for both the Discovery and Replication cohorts and meta-analysed it using inverse-variance-weighted meta-analysis.

Rare high-impact de novo variants and inherited variants

We identified rare (MAF < 0.1%) de novo and inherited variants in complete trios from SPARK, as previously described[40]. We identified high-impact protein-truncating variants by restricting it to variants in loss-of-function observed/expected upper bound fraction (LOEUF)[80]; highly constrained decile (LOEUF < 0.37) that were annotated as either frameshift, stop gained or start lost; and that had a loss-of-function transcript effect estimator (LOFTEE) high-confidence annotation. To identify high-impact de novo missense variants, we restricted it to variants in LOEUF highly constrained genes (LOEUF < 0.37) that had an MPC (missense badness, PolyPhen-2, and constraint) score[81] > 2.

We ran regression analyses for: high-impact de novo and inherited protein-truncating variants; missense variants; and by combining both protein-truncating and missense variants. We included sex and the first ten genetic PCs as covariates.

GWAS

GWAS of age at autism diagnosis

We generated a GWAS of age at autism diagnosis (in years) in the quality-controlled dataset from SPARK, restricting it to autistic individuals who were under 22 years of age (Discovery, n = 18,809; Replication, n = 9,356) and SNPs with MAF > 1%. GWAS was generated using FastGWA[82] with sex, intellectual disability and the first ten genetic PCs as covariates. We meta-analysed the GWAS from the SPARK Discovery and Replication cohorts using inverse-variance-weighted meta-analysis in Plink83,84 (v.2.0). In iPSYCH, we generated an additional GWAS of age at autism diagnosis (in years) in a quality-controlled dataset of unrelated individuals with sex and intellectual disability included as covariates using FastGWA[82], restricting it to SNPs with MAF > 1%. To keep it consistent with SPARK, we excluded individuals who were diagnosed after age 22, leaving a total sample of 18,965 individuals. In brief, pre-imputation quality control of the iPSYCH data was done using the Ricopili pipeline[85], prephased using Eagle v.2.3.5 and imputed using Minimac3 (ref. [86]), using the downloadable version of the Haplotype Reference Consortium[87] (accession number EGAD00001002729). Further details of quality control and imputation are provided in ref. [88].

GWAS of autism stratified by age at diagnosis

We generated three age at autism diagnosis stratified GWASs in SPARK using (unscreened) non-autistic parents and siblings as controls (Discovery, ncontrol = 24,965; Replication, ncontrol = 33,302). The three GWAS were: first, SPARK, diagnosed before age 6 (SPARKbefore6; Discovery, nautistic = 14,578; Replication, nautistic = 6,857); second, SPARK, diagnosed before age 11 (SPARKbefore11, nautistic = 18,719; Replication, nautistic = 9,162); and third, SPARK, diagnosed after age 10 (SPARKafter10, nautistic = 3,358; Replication, nautistic = 2,885). For these analyses, we did not restrict it to individuals under 22, to increase the sample size. Of note, SPARKbefore11 overlaps with the SPARKbefore6 cohort. GWASs were generated using quality-controlled SNPs with MAF > 1% using FastGWA-GLMM[89]. We included age at recruitment in the study (to account for the use of parents as controls, who potentially lack an autism diagnosis owing to secular changes in attitudes and diagnosis), sex and the first ten genetic PCs as covariates. Fast-GWA GLMM can account for relatedness and fine-scale population stratification, even in family-based samples such as SPARK. Given the relatively low sample size of the Replication cohort, we did the meta-analysis of the Replication and Discovery cohort GWAS using inverse-variance-weighted meta-analyses in Plink83,84 (v.2.0).

Although inclusion of unscreened related individuals as controls can decrease heritability and statistical power to identify loci[90], we used the GWAS to primarily conduct genetic correlation and related analyses. To ensure the robustness of these models we did the following: first, we confirmed that the attenuation ratio for all GWASs was not significantly greater than 1; second, we generated an additional GWAS of SPARK without stratifying by age at autism diagnosis using the same methods and confirmed a high genetic correlation (rg = 0.92, s.e. = 0.17) with a previous SPARK GWAS[49], which used a case-pseudocontrol approach; and third, in the genomicSEM analyses, we ran sensitivity analyses using a trio-based SPARK GWAS[49] in lieu of the age at diagnosis stratified GWAS from SPARK and confirmed our findings.

We generated three GWASs of autism stratified by age at autism diagnosis in the iPSYCH cohort[77]. The primary GWASs used in the analyses were GWASs of autism diagnosed before age 11 (iPSYCHbefore11, n = 9,500 autistic and n = 36,667 non-autistic individuals) and autism diagnosed after age 10 (iPSYCHafter10, n = 9,231 autistic and n = 36,667 non-autistic individuals). We chose to subdivide the iPSYCH cohort at age 10 because we observed an increase in SDQ scores in birth cohorts at this age, and because age 10 is associated with an increase in diagnosis of female individuals in epidemiological samples[34]. We also conducted a GWAS of autism diagnosed before age nine (iPSYCHbefore9, n = 5,451 autistic and n = 36,667 non-autistic individuals).

All individuals included in these GWASs from iPSYCH were born between May 1980 and December 2008 to mothers who were living in Denmark. The GWAS was done for unrelated individuals of European ancestry, with the first ten genetic PCs included as covariates using logistic regression as provided in PLINK.

Heritability, genetic correlation, and genomicSEM

Heritability analyses for age at autism diagnosis were conducted using a single-component genome-wide complex trait analysis with a genomic-relatedness-based restricted maximum likelihood approach (GCTA-GREML v1.94.1)91,92 in unrelated autistic individuals using the quality-controlled genetic data in SPARK. We estimated SNP-based heritability first after including sex and the first ten genetic PCs as covariates, and then also with intellectual disability as a covariate (baseline model). We ran several sensitivity analyses after including additional covariates: first, developmental milestones (age at first words and age at walking); second, developmental milestones and developmental regression (language regression and other regression); third, developmental milestones, developmental regression and IQ scores; fourth, SCQ and RBS-R scores; fifth, SCQ and RBS-R scores, developmental milestones and developmental regression; sixth, developmental milestones and SES; and finally, developmental milestones, SES and deprivation.

We conducted genetic correlation analyses using LDSC, with linkage disequilibrium scores from the northwest European populations.

We did genetic correlation analyses among different autism GWASs using LDSC (v.1.0.1). This included a European-only case-pseudocontrol GWAS in SPARK[49] (4,535 case-pseudocontrol pairs); GWAS from FinnGen (Data Release r10)[93] (646 cases and 301,879 controls), the PGC-2017 autism GWAS[45] (7,387 cases and 8,567 controls), GWAS from iPSYCH, and age at diagnosis-stratified GWAS from SPARK. The iPSYCH GWAS included an unstratified (19,870 autistic individuals, comprising 15,025 male individuals and 4,845 female individuals, and 39,078 controls) and sex-stratified GWAS[50], and three age at diagnosis-stratified GWASs, as mentioned earlier.

For genomicSEM[42] (v.0.0.5) analyses, we restricted it to six GWASs with minimal sample overlap, without high genetic correlation (rg > 0.95), and with wide variation in age at diagnosis to conduct genomicSEM analyses using autosomes. Using the patterns of genetic correlations observed, we tested an age at diagnosis-related correlated two-factor model. We also tested: first, a single-factor model; second, a correlated two-factor ‘geography’ model, in which three US-based autism GWASs loaded onto one factor, and three Europe-based autism GWASs loaded onto a second factor; third, a bifactor model based on age at diagnosis; fourth, a bifactor model based on the geography of the cohorts; and finally, a hierarchical factor model based on age at diagnosis. The two-factor model was chosen because it had lower root mean square error of approximation and higher comparative fit index and was more parsimonious than the bifactor model. We ran sensitivity analyses using different GWASs of autism as input and confirmed that the two-correlated-factor model was the best-fitting model of those tested.

Analyses in ALSPAC and MCS

Genetic quality control for ALSPAC

We obtained quality-controlled and imputed genotype data from ALSPAC94,95,96. Further details about the cohort are provided in Supplementary Note 1. In brief, ALSPAC children were genotyped using the Illumina HumanHap550 quad chip genotyping platforms by 23andme. Some individuals were excluded owing to sex mismatches, excess heterozygosity, missingness greater than 3% and insufficient sample replication (identical by descent score of less than 0.8). After multidimensional scaling and comparison with Hapmap II (release 22), only individuals of genetically inferred European ancestries were retained. SNPs with low frequency (MAF < 1%), poor genotyping (call rate < 95%) and deviations from Hardy–Weinberg equilibrium (P < 5 × 10−7) were removed; 9,115 subjects and 500,527 SNPs passed quality control. Genotypes were phased using ShapeIT and imputation was done using the Haplotype Reference Consortium panel using the Michigan imputation server. After imputation, we further removed low-frequency SNPs (MAF < 1%). Further details of the quality control and imputation of ALSPAC are provided here: https://proposals.epi.bristol.ac.uk/alspac_omics_data_catalogue.html#org89bb79b. Genome-wide genotype data were generated by the Sample Logistics and Genotyping facilities at the Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe.

Genetic quality control for MCS

We also obtained quality-controlled and imputed data from MCS. In brief, MCS samples were genotyped using the Illumina Global Screening Array[97]. Some individuals were excluded owing to sex mismatches, excess heterozygosity and missingness greater than 2%. We identified European samples using the GenoPred pipeline[98] (https://github.com/opain/GenoPred). SNPs with low frequency (MAF < 1%), poor genotyping (call rate < 97%) and deviations from Hardy–Weinberg equilibrium (P < 1 × 10−6) were removed. Imputation was conducted using Minimac4 (ref. [74]) using the TOPMED reference panel[73] in the Michigan imputation server[74]. After imputation, SNPs with an imputation R^2 INFO score of less than 0.8, with more than 3% missing and with MAF < 1% were excluded. Further details are available here: https://cls-genetics.github.io/docs/MCS.html.

PGS association with SDQ

PGSs for both ALSPAC and MCS were calculated for individuals of genetically inferred European ancestries. Genetic PCs were calculated for both cohorts using PC-AiR, as described earlier. We calculated PGS for iPSYCHbefore11 and iPSYCHafter10 and used these in all analyses in the MCS to keep it consistent with analyses in SPARK.

We obtained scores on the SDQ total and subscales for six ages in the MCS and five ages in ALSPAC. We ran cross-sectional analysis at each age using multiple linear regression with PGS for iPSYCHbefore11 and iPSYCHafter10, with sex, age and the first ten genetic PCs as covariates. We also ran multiple linear mixed effects regression using the lme4 (v.1.1.27.1) package in R[99], fitting a PGS by age interaction term to investigate whether the effects of PGS on SDQ change over time.

To investigate whether the differences in association between MCS and ALSPAC were due to differences in ascertainment between the two cohorts, we matched ALSPAC to MCS using entropy balancing[100] and re-ran the PGS association analyses. Entropy balancing is a reweighting technique that ensures the covariate distributions are identical between groups. This method uses optimization algorithms to assign weights to individuals such that the weighted average of the covariates in ALSPAC (the larger genotyped cohort) matches that of MCS (the smaller genotyped cohort), minimizing confounding biases and increasing comparability. We used the child’s biological sex, maternal age at delivery and maternal highest educational qualification at first data collection in each cohort as matching factors. Entropy balancing was done using the ebal (v.0.1-8) package in R[101].

PGS association with communication skills and autism diagnosis

For ALSPAC, we obtained understanding of simple phrases (such as “Do you want that?” or “Come here”) and gesture scores from the Macarthur-Bates Communicative Development Inventories[102] at 15 months of age (Supplementary Note 9). We conducted multiple linear regression using PGS for iPSYCHbefore11 and iPSYCHafter10, with sex, age and the first ten genetic PCs as covariates.

Autism diagnosis for the MCS was obtained using parent/carer reports of autism/Asperger’s syndrome diagnosis by a doctor at ages 5, 7, 11 and 14. We identified individuals with an autism diagnosis at age 7 or earlier, age 11 or earlier, or between ages 11 and 14. We conducted Firth’s bias-reduced multiple logistic regression (logistf v.1.26.0 package in R) using PGS for iPSYCHbefore11 and iPSYCHafter10, with sex, age and the first ten genetic PCs as covariates.

Ethics

Ethical approval for individual cohorts was obtained independently of the current study. Ethical approval for ALSPAC was obtained from the ALSPAC Ethics and Law Committee and the local research ethics committees. Ethical approval for each sweep of MCS was obtained from NHS research ethics committees (MREC). Ethical approval for LSAC was obtained from the Australian Institute of Family Studies Human Research Ethics Committee. Ethical approval for GUI was obtained from a dedicated research ethics committee set up by the Department of Children, Equality, Disability, Integration and Youth. Ethical approval for SPARK was obtained from the Western Institutional Review Board Copernicus Group (IRB protocol 20151664). The Danish Scientific Ethics Committee, the Danish Health Data Authority, the Danish data protection agency and the Danish Neonatal Screening Biobank Steering Committee approved the iPSYCH study. Ethical approval for the analyses of de-identified data used in this study was obtained from the Cambridge Human Biology Research Ethics Committee (HBREC.2020.07).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

• SPARK autism GWAS: https://bitbucket.org/steinlabunc/spark_asd_sumstats/src

• Finngen autism GWAS: https://www.finngen.fi/en/access_results

• The iPSYCH autism GWAS (unstratified, sex stratified and age at diagnosis stratified, age at diagnosis) can be obtained from J. Grove (grove@biomed.au.dk) or A.D.B. (anders@biomed.au.dk) upon reasonable request.

• Psychiatric GWAS summary stats: https://pgc.unc.edu/for-researchers/download-results/

• GWAS educational attainment: https://thessgac.com/

• GWAS cognitive aptitude: https://cncr.nl/research/summary_statistics/

• For ALSPAC, the study website contains details of all the available data through a fully searchable data dictionary and variable search tool: http://www.bristol.ac.uk/alspac/researchers/our-data/

• For MCS, data can be obtained on application from the UK Data Service: https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000031

• Summary statistics for the SPARK-based age at diagnosis GWAS, and the age at diagnosis-stratified GWAS generated from the genomicSEM models, are available here: https://doi.org/10.6084/m9.figshare.29566052.v2

Code availability

• Lavaan (LGCM) v.0.6-19: https://lavaan.ugent.be/tutorial/growth.html

• lcmm(GMM), v.2.2.1: https://github.com/CecileProust-Lima/lcmm

• Softimpute v.1.4-1: https://cran.r-project.org/web/packages/softImpute/softImpute.pdf

• Misty v.0.6.8: https://cran.r-project.org/web/packages/misty/index.html

• PRScs v.1.1.0: https://github.com/getian107/PRScs

• fastGWA and GCTA v.1.94.1: https://yanglab.westlake.edu.cn/software/gcta/#Overview

• GenomicSEM v.0.0.5: https://github.com/GenomicSEM/GenomicSEM

• LDSC v.1.0.1: https://github.com/bulik/ldsc

• KING v.2.3.2: https://www.kingrelatedness.com/manual.shtml

• Plink 2.0: https://www.cog-genomics.org/plink/2.0/

• GENESIS v.2.22.2: https://github.com/UW-GAC/GENESIS

• Lme4 v.1.1.27.1: https://github.com/lme4/lme4/

• Logistf v.1.24: https://cran.r-project.org/web/packages/logistf/index.html

• Ebal v.0.1-8: https://cran.r-project.org/web/packages/ebal/ebal.pdf

• Relaimpo v.2.2-7: https://cran.r-project.org/web/packages/relaimpo/relaimpo.pdf

• SPARK quality control, imputation and GWAS: https://github.com/vwarrier/SPARK_iWES2_imputation/

• Bespoke genetic analyses code: https://github.com/vwarrier/autism_agediagnosis/

Change history

- 21 October 2025

In the version of this article initially published, the images presented as Extended Data Figs. 1 and 2 were interchanged and are now updated in the HTML and PDF versions of the article.

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Acknowledgements

This research was supported by funding from the Simons Foundation for Autism Research Initiative, the Wellcome Trust (214322\Z\18\Z and 226392/Z/22/Z), Horizon-Europe R2D2-MH (grant agreement 101057385) and UKRI (10063472). For the purpose of open access, we have applied a CC BY public copyright licence to any author-accepted manuscript version arising from this submission. S.B.-C. also received funding from the Autism Centre of Excellence at Cambridge, the Templeton World Charitable Fund, the MRC and the National Institute for Health Research Cambridge Biomedical Research Centre. The research was supported by the National Institute for Health Research Applied Research Collaboration East of England. Any views expressed are those of the author(s) and not necessarily those of the funder. Some of the results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement 777394 for the project AIMS-2-TRIALS. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and the EFPIA and Autism Speaks, Autistica and the SFARI. The iPSYCH team was supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724 and R248-2017-2003), the NIMH (1R01MH124851-01 to A.D.B.) and the European Union’s Horizon Europe program (R2D2-MH; grant agreement 101057385 to A.D.B.). The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to A.D.B.). The UK Medical Research Council and Wellcome (grant 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and the authors will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). R2D2-MH has been funded by Horizon Europe (grant agreement 101057385), by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (grant 10039383) and by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract 22.00277. A.H. and L.H. were supported by the Norwegian Research Council (336085) and the Norwegian Health Authority (2020022, #2022029 and #2022083). E. Verhoef and B.S.P. are funded by the Max Planck Society. We thank the Centre for Longitudinal Studies (CLS), UCL Social Research Institute, for the use of these data and the UK Data Service for making them available. However, neither CLS nor the UK Data Service bear any responsibility for the analysis or interpretation of these data. This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the author and should not be attributed to DSS, AIFS or the ABS. Growing Up in Ireland (GUI) was funded by the Government of Ireland through the Department of Children, Equality, Disability, Integration and Youth (DCEDIY) and the Central Statistics Office (CSO). Results in this report are based on analysis of data from research microdata files provided by the Central Statistics Office (CSO). Neither the CSO nor the DCEDIY take any responsibility for the views expressed or the outputs generated from these analyses. We thank all the families who took part in this study, the midwives for their help in recruiting them and the ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This study includes data from the Norwegian Mother, Father and Child Cohort Study, conducted by the Norwegian Institute of Public Health. We thank all the participating families, and A. Kwong, T. Ford, W. Mandy and A. Grotzinger for discussions.

Author information

Authors and Affiliations

1. Department of Psychiatry, University of Cambridge, Cambridge, UK

Xinhe Zhang, Yuanjun Gu, Sharon A. S. Neufeld, Simon Baron-Cohen & Varun Warrier

2. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK

Xinhe Zhang, Yuanjun Gu, Carrie Allison, Alex Tsompanidis, Deep Adhya, Rosemary Holt, Omar Al-Rubaie, Marcin Radecki, Meng-Chuan Lai, Yeshaya David Greenberg, Elizabeth Weir, Yumnah T. Khan, Simon Baron-Cohen & Varun Warrier

3. iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark

Jakob Grove, Ole Mors, Preben Bo Mortensen, Merete Nordentoft, Thomas Werge, Marie Bækvad-Hansen, David Michael Hougaard, Ole Mors & Anders D. Børglum

4. Center for Genomics and Personalized Medicine (CGPM), Aarhus University, Aarhus, Denmark

Jakob Grove & Anders D. Børglum

5. Department of Biomedicine (Human Genetics), Aarhus University, Aarhus, Denmark

Jakob Grove, Cornelia K. Buus, Lea K. Nielsen & Anders D. Børglum

6. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

Jakob Grove

7. Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark

Jakob Grove, Cornelia K. Buus, Lea K. Nielsen, Preben Bo Mortensen & Anders D. Børglum

8. Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

Mahmoud Koko, Daniel S. Malawsky, Emma M. Wade & Hilary C. Martin

9. Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands

Ellen Verhoef & Beate St Pourcain

10. Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy

Anna Gui

11. Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck University of London, London, UK

Anna Gui & Angelica Ronald

12. Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway

Laura Hegemann & Alexandra Havdahl

13. PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway

Laura Hegemann & Alexandra Havdahl

14. Program in Neurobehavioral Genetics and Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Daniel H. Geschwind

15. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Daniel H. Geschwind

16. Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Ramin Ali Marandi Ghoddousi & Daniel H. Geschwind

17. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Daniel H. Geschwind

18. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

Naomi R. Wray

19. Department of Psychiatry, University of Oxford, Oxford, UK

Naomi R. Wray

20. Promenta Research Centre, Department of Psychology, University of Oslo, Oslo, Norway

Alexandra Havdahl

21. School of Psychology, University of Surrey, Guildford, UK

Angelica Ronald

22. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

Beate St Pourcain

23. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

Dorothea Floris & Beate St Pourcain

24. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Elise B. Robinson

25. The Broad Institute of MIT and Harvard, Cambridge, MA, USA

Elise B. Robinson

26. Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, IUF, Université Paris Cité, Paris, France

Thomas Bourgeron

27. Department of Psychology, University of Cambridge, Cambridge, UK

Richard Bethlehem, Simon Baron-Cohen & Varun Warrier

28. Wellcome Trust Sanger Institute, Hinxton, UK

Matthew E. Hurles, Mahmoud Mousa & Hilary C. Martin

29. Obstetrics, Tommy’s Maternal and Fetal Research Centre, University of Manchester, Manchester, UK

Alexander E. P. Heazell

30. University of Exeter Medical School, College of Medicine & Health, University of Exeter, Exeter, UK

Jonathan Mill, Alice Franklin & Rosie Bamford

31. Department of Pediatrics, University of Cambridge, Cambridge, UK

David H. Rowitch

32. Centre for Trophoblast Research, University of Cambridge, Cambridge, UK

Kathy K. Niakan, Graham J. Burton & Tereza Cindrova-Davies

33. MRC Centre for Neurodevelopmental Disorders, King’s College London, London, UK

Deepak P. Srivastava, Lucia Dutan-Polit, Adam Pavlinek, Laura Sichlinger & Roland Nagy

34. MRC Laboratory of Molecular Biology, University of Cambridge, Cambridge, UK

Madeline A. Lancaster & Jose Gonzalez-Martinez

35. Department of Obstetrics and Gynecology, Meir Medical Center, Kefar Sava, Israel

Tal Biron-Shental

36. Faculty of Medical & Health Sciences, Tel-Aviv University, Tel Aviv, Israel

Lidia V. Gabis

37. Maccabi Healthcare, Tel Aviv, Israel

Lidia V. Gabis

38. Department of Psychology, University of Zurich, Zurich, Switzerland

Dorothea Floris

39. Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano Di Tecnologia, Rovereto, Italy

Michael V. Lombardo

40. Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

Meng-Chuan Lai

41. Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada

Meng-Chuan Lai

42. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada

Meng-Chuan Lai

43. Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan

Meng-Chuan Lai

44. Psychology Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel

Florina Uzefovsky

45. Department of Child and Adolescent Psychiatry and Mental Health, University of Chile, Santiago, Chile

Juan Pablo Del Rio

46. Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

Jonas Bybjerg-Grauholm, David M. Hougaard & Jonas Bybjerg-Grauholm

47. Aarhus University Hospital–Psychiatry, Psychosis Research Unit, Aarhus, Denmark

Ole Mors & Ole Mors

48. Psychosis Research Unit, Aarhus University Hospital – Psychiatry, Aarhus, Denmark

Ole Mors & Ole Mors

49. NCRR, National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark

Preben Bo Mortensen

50. Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark

Preben Bo Mortensen

51. National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark

Preben Bo Mortensen

52. CORE - Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark

Merete Nordentoft

53. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Merete Nordentoft & Thomas Werge

54. Mental Health Services in the Capital Region of Denmark, University of Copenhagen, Copenhagen, Denmark

Merete Nordentoft

55. Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark

Thomas Werge

56. Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

Thomas Werge

57. The Globe Institute, Lundbeck Foundation Center for Geogenetics, University of Copenhagen, Copenhagen, Denmark

Thomas Werge

58. Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA

Caroline M. Nievergelt, Adam X. Maihofer, Dewleen G. Baker, Carol E. Franz, Kelleigh Hogan, William S. Kremen, Elizabeth A. Mikita, Sonya B. Norman, Matthew S. Panizzon, Victoria B. Risbrough & Murray B. Stein

59. Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

Caroline M. Nievergelt, Adam X. Maihofer, Dewleen G. Baker, Kelleigh Hogan, Elizabeth A. Mikita, Sonya B. Norman & Victoria B. Risbrough

60. Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

Caroline M. Nievergelt, Adam X. Maihofer, Kelleigh Hogan, Elizabeth A. Mikita & Victoria B. Risbrough

61. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

Elizabeth G. Atkinson

62. Translational Sciences, Biogen Inc., Cambridge, MA, USA

Chia-Yen Chen

63. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Karmel W. Choi, Bizu Gelaye, Andrew Ratanatharathorn, Laura Sampson, Michelle A. Williams & Karestan C. Koenen

64. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

Karmel W. Choi & Jordan W. Smoller

65. National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London, UK

Jonathan R. I. Coleman & Saskia Hagenaars

66. Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London, UK

Jonathan R. I. Coleman, Gerome Breen, Chiara Fabbri & Saskia Hagenaars

67. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Nikolaos P. Daskalakis, Chris Chatzinakos, Christopher P. DiPietro, Hailiang Huang, Jordan W. Smoller, Yan Xia, Clement C. Zai & Karestan C. Koenen

68. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

Nikolaos P. Daskalakis, Elizabeth A. Bolger, Chris Chatzinakos, Milissa L. Kaufman, Alaptagin Khan, Lauren A. M. Lebois, Martin H. Teicher, Sherry Winternitz & Kerry J. Ressler

69. Center of Excellence in Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA

Nikolaos P. Daskalakis & Lauren A. M. Lebois

70. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

Laramie E. Duncan

71. VA Connecticut Healthcare Center, West Haven, CT, USA

Renato Polimanti, Daniel F. Levey & Gita A. Pathak

72. Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA

Renato Polimanti, Daniel F. Levey, Gita A. Pathak & Robert H. Pietrzak

73. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Cindy Aaronson, Leah Cahn, Frank Desarnaud, Adriana Feder, Janine D. Flory, Iouri Makotkine & Rachel Yehuda

74. Department of Psychiatry Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VI, USA

Ananda B. Amstadter & Christina M. Sheerin

75. The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Denmark

Soren B. Andersen & Karen-Inge Karstoft

76. Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

Ole A. Andreassen

77. Institute of Clinical Medicine, University of Oslo, Oslo, Norway

Ole A. Andreassen, Grete Dyb & John-Anker Zwart

78. Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA

Paul A. Arbisi, Melissa A. Polusny & Scott R. Sponheim

79. Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA

Paul A. Arbisi

80. Duke Molecular Physiology Institute, Duke University, Durham, NC, USA

Allison E. Ashley-Koch, Melanie E. Garrett & Xue-Jun Qin

81. Division of Adolescent and Young Adult Medicine, Boston Children’s Hospital, Boston, MA, USA

S. Bryn Austin

82. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

S. Bryn Austin

83. Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA

S. Bryn Austin

84. Department of Psychiatry, University Clinical Center of Tuzla, Tuzla, Bosnia and Herzegovina

Esmina Avdibegoviç

85. Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia and Herzegovina

Dragan Babić

86. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA

Silviu-Alin Bacanu

87. Psychiatry Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

Dewleen G. Baker & Murray B. Stein

88. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

Anthony Batzler, Joanna M. Biernacka, Brandon J. Coombes & Gregory D. Jenkins

89. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA

Jean C. Beckham, Michelle F. Dennis, Michael A. Hauser, Nathan A. Kimbrel & Douglas E. Williamson

90. Research, Durham VA Health Care System, Durham, NC, USA

Jean C. Beckham, Michelle F. Dennis & Douglas E. Williamson

91. Genetics Research Laboratory, VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Durham, NC, USA

Jean C. Beckham, Michelle F. Dennis & Nathan A. Kimbrel

92. Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil

Sintia Belangero

93. Laboratory of Integrative Neuroscience, Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil

Sintia Belangero & Rodrigo Affonseca Bressan

94. Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Center for Global Mental Health, Mexico City, Mexico

Corina Benjet

95. Comprehensive Injury Center, Medical College of Wisconsin, Milwaukee, WI, USA

Carisa Bergner

96. Department of Psychiatry, James J. Peters VA Medical Center, Bronx, NY, USA

Linda M. Bierer

97. Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA

Joanna M. Biernacka

98. Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA

Laura J. Bierut & Elliot C. Nelson

99. MRC Centre for Psychiatric Genetics and Genomics, Cardiff University, National Centre for Mental Health, Cardiff, UK

Jonathan I. Bisson, Alexandra Evans & Catrin Lewis

100. Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, The Netherlands

Marco P. Boks

101. McLean Hospital, Belmont, MA, USA

Elizabeth A. Bolger, Milissa L. Kaufman, Alaptagin Khan, Sherry Winternitz & Kerry J. Ressler

102. Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA

Amber Brandolino & Terri deRoon-Cassini

103. King’s College London, NIHR Maudsley BRC, London, UK

Gerome Breen

104. Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil

Rodrigo Affonseca Bressan

105. School of Psychology, University of New South Wales, Sydney, New South Wales, Australia

Richard A. Bryant

106. Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, MI, USA

Angela C. Bustamante

107. Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

Marie Bækvad-Hansen & David Michael Hougaard

108. K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway

Sigrid Børte, Kristian Hveem, Bendik S. Winsvold & John-Anker Zwart

109. Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway

Sigrid Børte, Synne Stensland, Bendik S. Winsvold & John-Anker Zwart

110. School of Medicine, Case Western Reserve University, Cleveland, OH, USA

Joseph R. Calabrese

111. Department of Psychiatry, University Hospitals, Cleveland, OH, USA

Joseph R. Calabrese

112. Chronic Diseases Research Centre (CEDOC), Lisbon Institute of Global Mental Health, Lisbon, Portugal

Jose Miguel Caldas-de-Almeida

113. Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA

Chris Chatzinakos & Christopher P. DiPietro

114. CanPath National Coordinating Center, University of Toronto, Toronto, Ontario, Canada

Sheraz Cheema

115. Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA

Sean A. P. Clouston

116. Public Health, Stony Brook University, Stony Brook, NY, USA

Sean A. P. Clouston

117. Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

Lucía Colodro-Conde, Scott D. Gordon, Michelle K. Lupton, Kerrie McAloney & Sarah E. Medland

118. Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz Mexico City, Mexico City, Mexico

Carlos S. Cruz-Fuentes & Gabriela A. Martínez-Levy

119. Department of Radiology and Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA

Anders M. Dale

120. Department of Pathology, Division of Human Genetics, University of Cape Town, Cape Town, South Africa

Shareefa Dalvie & Mary S. Mufford

121. Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA

Lea K. Davis & Slavina B. Goleva

122. Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, Würzburg, Denmark

Jürgen Deckert, Heike Weber & Christiane Wolf

123. Department of Psychological Sciences, Kent State University, Kent, OH, USA

Douglas L. Delahanty

124. Research Service Line, Minneapolis VA Health Care System, Minneapolis, MN, USA

Seth G. Disner

125. Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA

Seth G. Disner & Melissa A. Polusny

126. Huntsman Mental Health Institute, Salt Lake City, UT, USA

Anna R. Docherty

127. Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA

Anna R. Docherty & Andrey Shabalin

128. University of Freiburg, Faculty of Medicine, Centre for Basics in Neuromodulation, Freiburg, Denmark

Katharina Domschke

129. Department of Psychiatry and Psychotherapy, University of Freiburg Faculty of Medicine, Freiburg, Denmark

Katharina Domschke

130. Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway

Grete Dyb & Synne Stensland

131. Department of Psychiatry, University Clinical Center of Sarajevo, Sarajevo, Bosnia and Herzegovina

Alma Džubur Kulenović

132. Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA

Howard J. Edenberg

133. Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

Howard J. Edenberg

134. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy

Chiara Fabbri

135. Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA

Negar Fani, Charles F. Gillespie, Tanja Jovanovic, Adriana Lori, Jessica L. Maples-Keller, Vasiliki Michopoulos, Abigail Powers, Barbara O. Rothbaum, Alicia K. Smith, Jennifer S. Stevens, Sanne J. H. van Rooij & Kerry J. Ressler

136. Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA

Lindsay A. Farrer

137. Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA

Lindsay A. Farrer

138. Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA

Lindsay A. Farrer

139. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

Lindsay A. Farrer & Mark W. Logue

140. Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA

Lindsay A. Farrer & Laura Sampson

141. Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA

Norah C. Feeny

142. Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia

David Forbes

143. Boston University School of Public Health, Boston, MA, USA

Sandro Galea

144. Psychiatry Service, VA Connecticut Healthcare Center, West Haven, CT, USA

Joel Gelernter

145. Department of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA

Joel Gelernter

146. Netherlands Ministry of Defence, Brain Research and Innovation Centre, Utrecht, The Netherlands

Elbert Geuze

147. Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands

Elbert Geuze & Jurjen J. Luykx

148. National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

Slavina B. Goleva

149. Department of Psychiatry, University Clinical Centre of Kosovo, Prishtina, Kosovo

Aferdita Goçi

150. Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA

Lana Ruvolo Grasser & Arash Javanbakht

151. Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, Queensland, Australia

Camila Guindalini

152. Cohen Veterans Bioscience, New York, NY, USA

Magali Haas & Mark Zervas

153. Department of Genetics, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA

Andrew C. Heath

154. Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

Sian M. J. Hemmings, Soraya Seedat & Leigh L. van den Heuvel

155. SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa

Sian M. J. Hemmings & Leigh L. van den Heuvel

156. Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA

Victor Hesselbrock

157. Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia

Ian B. Hickie

158. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

Hailiang Huang & Yan Xia

159. Department of Psychiatry, Yale University, New Haven, CT, USA

Laura M. Huckins

160. Department of Psychiatry, University Hospital Center of Zagreb, Zagreb, Croatia

Miro Jakovljević

161. Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Jessica Johnson

162. Cardiff University Centre for Psychiatric Genetics and Genomics, National Centre for Mental Health, Cardiff University, Cardiff, UK

Ian Jones

163. Department of Psychology, University of Copenhagen, Copenhagen, Denmark

Karen-Inge Karstoft

164. Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada

James L. Kennedy, Arun K. Tiwari, Clement C. Zai & Gwyneth C. Zai

165. Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada

James L. Kennedy, Arun K. Tiwari, Clement C. Zai & Gwyneth C. Zai

166. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

James L. Kennedy, Arun K. Tiwari, Clement C. Zai & Gwyneth C. Zai

167. Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada

James L. Kennedy, Clement C. Zai & Gwyneth C. Zai

168. Department of Health Care Policy, Harvard Medical School, Boston, MA, USA

Ronald C. Kessler

169. Mental Health Service Line, Durham VA Health Care System, Durham, NC, USA

Nathan A. Kimbrel

170. The Ohio State University College of Medicine, Institute for Behavioral Medicine Research, Columbus, OH, USA

Anthony P. King

171. Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa

Nastassja Koen & Dan J. Stein

172. Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA

Roman Kotov

173. Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA

Henry R. Kranzler

174. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Henry R. Kranzler

175. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia

Kristi Krebs, Kelli Lehto & Lili Milani

176. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA

Pei-Fen Kuan

177. School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, Australia

Bruce R. Lawford, Divya Mehta, Charles Phillip Morris & Joanne Voisey

178. Department of Psychiatry and Behavioral Sciences, Texas A&M University College of Medicine, Bryan, TX, USA

Israel Liberzon

179. Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA

Sarah D. Linnstaedt & Samuel A. McLean

180. Psychiatry, Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA

Mark W. Logue & Mark W. Miller

181. National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA

Mark W. Logue, Mark W. Miller & Erika J. Wolf

182. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

Yi Lu & Ying Xiong

183. Department of Medicine, Stony Brook University, Stony Brook, NY, USA

Benjamin J. Luft

184. Department of Translational Neuroscience, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands

Jurjen J. Luykx

185. Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Shelby Marchese & Carina Seah

186. Grossman School of Medicine, New York University, New York, NY, USA

Charles Marmar

187. Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

Nicholas G. Martin

188. Discipline of Psychiatry, University of Adelaide, Adelaide, South Australia, Australia

Alexander McFarlane

189. Department of Psychology, Harvard University, Boston, MA, USA

Katie A. McLaughlin

190. Department of Emergency Medicine, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA

Samuel A. McLean

191. Centre for Genomics and Personalised Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia

Divya Mehta & Joanne Voisey

192. Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA

Jacquelyn Meyers & Bernice Porjesz

193. GRECC/TRACTS, VA Boston Healthcare System, Boston, MA, USA

William Milberg

194. Duke Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA

Rajendra A. Morey

195. National Center for Post Traumatic Stress Disorder, Executive Division, White River Junction, VT, USA

Sonya B. Norman

196. Department of Emergency Medicine, Alpert Brown Medical School, Providence, RI, USA

Nicole R. Nugent

197. Department of Pediatrics, Alpert Brown Medical School, Providence, RI, USA

Nicole R. Nugent

198. Department of Psychiatry and Human Behavior, Alpert Brown Medical School, Providence, RI, USA

Nicole R. Nugent

199. Department of Psychiatry, University of Melbourne, Phoenix Australia, Melbourne, Victoria, Australia

Meaghan O’Donnell

200. Department of Psychology, Northern Illinois University, DeKalb, IL, USA

Holly K. Orcutt

201. Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil

Pedro M. Pan

202. University of Nebraska Medical Center, College of Public Health, Omaha, NE, USA

Edward S. Peters

203. Research and Development Service, South Texas Veterans Health Care System, San Antonio, TX, USA

Alan L. Peterson

204. Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Alan L. Peterson

205. Department of Psychology, University of Washington, Seattle, WA, USA

Matthew Peverill & Lori A. Zoellner

206. U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, West Haven, CT, USA

Robert H. Pietrzak

207. Center for Care Delivery and Outcomes Research (CCDOR), Minneapolis, MN, USA

Melissa A. Polusny

208. Department of Epidemiology, Columbia University Mailmain School of Public Health, New York, NY, USA

Andrew Ratanatharathorn

209. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Andrea L. Roberts

210. Department of Psychological Sciences, Emory University, Atlanta, GA, USA

Alex O. Rothbaum

211. Department of Research and Outcomes, Skyland Trail, Atlanta, GA, USA

Alex O. Rothbaum

212. Department of Psychiatry, University of Washington, Seattle, WA, USA

Peter Roy-Byrne

213. Department of Nursing and Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA

Kenneth J. Ruggiero

214. School of Public Health and Department of Epidemiology, Louisiana State University Health Sciences Center, New Orleans, LA, USA

Ariane Rung & Edward Trapido

215. Research & Development, Biogen Inc., Cambridge, MA, USA

Heiko Runz

216. School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht Universitair Medisch Centrum, Maastricht, The Netherlands

Bart P. F. Rutten

217. School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA

Stacey Saenz de Viteri

218. Child Mind Institute, New York, NY, USA

Giovanni Abrahão Salum

219. Instituto Nacional de Psiquiatria de Desenvolvimento, São Paulo, Brazil

Giovanni Abrahão Salum

220. Department of Medicine, Universidad Peruana de Ciencias Aplicadas, Lima, Peru

Sixto E. Sanchez

221. Departamento de Bioquímica, Disciplina de Biologia Molecular, Universidade Federal de São Paulo, São Paulo, Brazil

Marcos Santoro

222. SAMRC Extramural Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa

Soraya Seedat

223. Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA

Julia S. Seng

224. Department of Women’s and Gender Studies, University of Michigan, Ann Arbor, MI, USA

Julia S. Seng

225. Institute for Research on Women and Gender, University of Michigan, Ann Arbor, MI, USA

Julia S. Seng

226. School of Nursing, University of Michigan, Ann Arbor, MI, USA

Julia S. Seng

227. Department of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia

Derrick Silove

228. Department of Gynecology and Obstetrics, Department of Psychiatry and Behavioral Sciences and Department of Human Genetics, Emory University, Atlanta, GA, USA

Alicia K. Smith

229. Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, USA

Jordan W. Smoller & Karestan C. Koenen

230. Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA

Scott R. Sponheim

231. Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA

Jennifer A. Sumner

232. Developmental Biopsychiatry Research Program, McLean Hospital, Belmont, MA, USA

Martin H. Teicher

233. Mental Health Centre Sct. Hans, Institute of Biological Psychiatry, Roskilde, Denmark

Wesley K. Thompson

234. Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA

Wesley K. Thompson

235. Genomics Program, University of South Florida College of Public Health, Tampa, FL, USA

Monica Uddin

236. Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA

Robert J. Ursano

237. Karolinska Institutet, Unit of Integrative Epidemiology, Institute of Environmental Medicine, Stockholm, Sweden

Unnur Valdimarsdóttir

238. Faculty of Medicine, Center of Public Health Sciences, School of Health Sciences, University of Iceland, Reykjavik, Iceland

Unnur Valdimarsdóttir

239. Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia

Miranda Van Hooff

240. Psychotrauma Research Expert Group, ARQ Nationaal Psychotrauma Centrum, Diemen, The Netherlands

Eric Vermetten

241. Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands

Eric Vermetten

242. Department of Psychiatry, New York University School of Medicine, New York, NY, USA

Eric Vermetten

243. Sleep & Stress Program, Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Amsterdam, The Netherlands

Christiaan H. Vinkers

244. Department of Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Christiaan H. Vinkers

245. Department of Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Christiaan H. Vinkers

246. Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway

Yunpeng Wang

247. Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA

Zhewu Wang

248. Department of Mental Health, Ralph H Johnson VA Medical Center, Charleston, SC, USA

Zhewu Wang

249. Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA

Monika Waszczuk

250. Department of Anthropology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

Frank R. Wendt

251. Department of Neurology, Oslo University Hospital, Oslo, Norway

Bendik S. Winsvold

252. Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA

Erika J. Wolf

253. Department of Mental Health, James J. Peters VA Medical Center, Bronx, NY, USA

Rachel Yehuda

254. Research Service, Central Texas Veterans Health Care System, Temple, TX, USA

Keith A. Young

255. Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, Bryan, TX, USA

Keith A. Young

256. School of Clinical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, Australia

Ross McD. Young

257. The Chancellory, University of the Sunshine Coast, Sippy Downs, Queensland, Australia

Ross McD. Young

258. Department of Laboratory Medicine and Pathology, University of Toronto, Toronto, Ontario, Canada

Clement C. Zai

259. Centre for Addiction and Mental Health, General Adult Psychiatry and Health Systems Division, Toronto, Ontario, Canada

Gwyneth C. Zai

260. Department of Biostatistics, Yale University, New Haven, CT, USA

Hongyu Zhao

261. School of Public Health, University of California, San Diego, La Jolla, California, USA

Murray B. Stein

Consortia

APEX Consortium

- Simon Baron-Cohen

- , Carrie Allison

- , Varun Warrier

- , Alex Tsompanidis

- , Deep Adhya

- , Rosemary Holt

- , Yuanjun Gu

- , Xinhe Zhang

- , Omar Al-Rubaie

- , Daniel H. Geschwind

- , Ramin Ali Marandi Ghoddousi

- , Alexander E. P. Heazell

- , Jonathan Mill

- , Alice Franklin

- , Rosie Bamford

- , Matthew E. Hurles

- , Hilary C. Martin

- , Mahmoud Mousa

- , David H. Rowitch

- , Kathy K. Niakan

- , Graham J. Burton

- , Tereza Cindrova-Davies

- , Deepak P. Srivastava

- , Lucia Dutan-Polit

- , Adam Pavlinek

- , Laura Sichlinger

- , Roland Nagy

- , Madeline A. Lancaster

- , Jose Gonzalez-Martinez

- , Tal Biron-Shental

- , Lidia V. Gabis

- , Dorothea Floris

- , Richard Bethlehem

- , Michael V. Lombardo

- , Marcin Radecki

- , Meng-Chuan Lai

- , Yeshaya David Greenberg

- , Elizabeth Weir

- , Florina Uzefovsky

- , Yumnah T. Khan

- & Juan Pablo Del Rio

iPSYCH Autism Consortium

- Anders D. Børglum

- , Jonas Bybjerg-Grauholm

- , Jakob Grove

- , David M. Hougaard

- , Ole Mors

- , Preben Bo Mortensen

- , Merete Nordentoft

- & Thomas Werge

PGC-PTSD Consortium

- Caroline M. Nievergelt

- , Adam X. Maihofer

- , Elizabeth G. Atkinson

- , Chia-Yen Chen

- , Karmel W. Choi

- , Jonathan R. I. Coleman

- , Nikolaos P. Daskalakis

- , Laramie E. Duncan

- , Renato Polimanti

- , Cindy Aaronson

- , Ananda B. Amstadter

- , Soren B. Andersen

- , Ole A. Andreassen

- , Paul A. Arbisi

- , Allison E. Ashley-Koch

- , S. Bryn Austin

- , Esmina Avdibegoviç

- , Dragan Babić

- , Silviu-Alin Bacanu

- , Dewleen G. Baker

- , Anthony Batzler

- , Jean C. Beckham

- , Sintia Belangero

- , Corina Benjet

- , Carisa Bergner

- , Linda M. Bierer

- , Joanna M. Biernacka

- , Laura J. Bierut

- , Jonathan I. Bisson

- , Marco P. Boks

- , Elizabeth A. Bolger

- , Amber Brandolino

- , Gerome Breen

- , Rodrigo Affonseca Bressan

- , Richard A. Bryant

- , Angela C. Bustamante

- , Jonas Bybjerg-Grauholm

- , Marie Bækvad-Hansen

- , Anders D. Børglum

- , Sigrid Børte

- , Leah Cahn

- , Joseph R. Calabrese

- , Jose Miguel Caldas-de-Almeida

- , Chris Chatzinakos

- , Sheraz Cheema

- , Sean A. P. Clouston

- , Lucía Colodro-Conde

- , Brandon J. Coombes

- , Carlos S. Cruz-Fuentes

- , Anders M. Dale

- , Shareefa Dalvie

- , Lea K. Davis

- , Jürgen Deckert

- , Douglas L. Delahanty

- , Michelle F. Dennis

- , Frank Desarnaud

- , Christopher P. DiPietro

- , Seth G. Disner

- , Anna R. Docherty

- , Katharina Domschke

- , Grete Dyb

- , Alma Džubur Kulenović

- , Howard J. Edenberg

- , Alexandra Evans

- , Chiara Fabbri

- , Negar Fani

- , Lindsay A. Farrer

- , Adriana Feder

- , Norah C. Feeny

- , Janine D. Flory

- , David Forbes

- , Carol E. Franz

- , Sandro Galea

- , Melanie E. Garrett

- , Bizu Gelaye

- , Joel Gelernter

- , Elbert Geuze

- , Charles F. Gillespie

- , Slavina B. Goleva

- , Scott D. Gordon

- , Aferdita Goçi

- , Lana Ruvolo Grasser

- , Camila Guindalini

- , Magali Haas

- , Saskia Hagenaars

- , Michael A. Hauser

- , Andrew C. Heath

- , Sian M. J. Hemmings

- , Victor Hesselbrock

- , Ian B. Hickie

- , Kelleigh Hogan

- , David Michael Hougaard

- , Hailiang Huang

- , Laura M. Huckins

- , Kristian Hveem

- , Miro Jakovljević

- , Arash Javanbakht

- , Gregory D. Jenkins

- , Jessica Johnson

- , Ian Jones

- , Tanja Jovanovic

- , Karen-Inge Karstoft

- , Milissa L. Kaufman

- , James L. Kennedy

- , Ronald C. Kessler

- , Alaptagin Khan

- , Nathan A. Kimbrel

- , Anthony P. King

- , Nastassja Koen

- , Roman Kotov

- , Henry R. Kranzler

- , Kristi Krebs

- , William S. Kremen

- , Pei-Fen Kuan

- , Bruce R. Lawford

- , Lauren A. M. Lebois

- , Kelli Lehto

- , Daniel F. Levey

- , Catrin Lewis

- , Israel Liberzon

- , Sarah D. Linnstaedt

- , Mark W. Logue

- , Adriana Lori

- , Yi Lu

- , Benjamin J. Luft

- , Michelle K. Lupton

- , Jurjen J. Luykx

- , Iouri Makotkine

- , Jessica L. Maples-Keller

- , Shelby Marchese

- , Charles Marmar

- , Nicholas G. Martin

- , Gabriela A. Martínez-Levy

- , Kerrie McAloney

- , Alexander McFarlane

- , Katie A. McLaughlin

- , Samuel A. McLean

- , Sarah E. Medland

- , Divya Mehta

- , Jacquelyn Meyers

- , Vasiliki Michopoulos

- , Elizabeth A. Mikita

- , Lili Milani

- , William Milberg

- , Mark W. Miller

- , Rajendra A. Morey

- , Charles Phillip Morris

- , Ole Mors

- , Preben Bo Mortensen

- , Mary S. Mufford

- , Elliot C. Nelson

- , Merete Nordentoft

- , Sonya B. Norman

- , Nicole R. Nugent

- , Meaghan O’Donnell

- , Holly K. Orcutt

- , Pedro M. Pan

- , Matthew S. Panizzon

- , Gita A. Pathak

- , Edward S. Peters

- , Alan L. Peterson

- , Matthew Peverill

- , Robert H. Pietrzak

- , Melissa A. Polusny

- , Bernice Porjesz

- , Abigail Powers

- , Xue-Jun Qin

- , Andrew Ratanatharathorn

- , Victoria B. Risbrough

- , Andrea L. Roberts

- , Alex O. Rothbaum

- , Barbara O. Rothbaum

- , Peter Roy-Byrne

- , Kenneth J. Ruggiero

- , Ariane Rung

- , Heiko Runz

- , Bart P. F. Rutten

- , Stacey Saenz de Viteri

- , Giovanni Abrahão Salum

- , Laura Sampson

- , Sixto E. Sanchez

- , Marcos Santoro

- , Carina Seah

- , Soraya Seedat

- , Julia S. Seng

- , Andrey Shabalin

- , Christina M. Sheerin

- , Derrick Silove

- , Alicia K. Smith

- , Jordan W. Smoller

- , Scott R. Sponheim

- , Dan J. Stein

- , Synne Stensland

- , Jennifer S. Stevens

- , Jennifer A. Sumner

- , Martin H. Teicher

- , Wesley K. Thompson

- , Arun K. Tiwari

- , Edward Trapido

- , Monica Uddin

- , Robert J. Ursano

- , Unnur Valdimarsdóttir

- , Miranda Van Hooff

- , Eric Vermetten

- , Christiaan H. Vinkers

- , Joanne Voisey

- , Yunpeng Wang

- , Zhewu Wang

- , Monika Waszczuk

- , Heike Weber

- , Frank R. Wendt

- , Thomas Werge

- , Michelle A. Williams

- , Douglas E. Williamson

- , Bendik S. Winsvold

- , Sherry Winternitz

- , Christiane Wolf

- , Erika J. Wolf

- , Yan Xia

- , Ying Xiong

- , Rachel Yehuda

- , Keith A. Young

- , Ross McD. Young

- , Clement C. Zai

- , Gwyneth C. Zai

- , Mark Zervas

- , Hongyu Zhao

- , Lori A. Zoellner

- , John-Anker Zwart

- , Terri deRoon-Cassini

- , Sanne J. H. van Rooij

- , Leigh L. van den Heuvel

- , Murray B. Stein

- , Kerry J. Ressler

- & Karestan C. Koenen

Contributions

X.Z. and V.W. did most of the analyses, with the remainder being done by J. Grove, Y.G., C.K.B. and L.K.N. M.K., E. Verhoef, A. Gui, L.H., A.H., A.R., B.S.P. and A.D.B. provided summary statistics for various analyses. V.W. supervised the analyses and directed the study with input from H.C.M. and J. Grove V.W. and X.Z. wrote the initial draft with input from H.C.M. A. Gui, S.A.S.N., A.H., B.S.P., A.R., D.S.M., E.M.W., D.H.G., N.R.W., E.B.R., T.B. and S.B.-C. provided intellectual input. All authors read and commented on the final manuscript.

Corresponding authors

Correspondence to Xinhe Zhang or Varun Warrier.

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A.D.B. received a speakers’ fee from the Lundbeck Foundation. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Variance in age at diagnosis explained by various clinical and sociodemographic factors.

Variance explained (R² or η²) in age at autism diagnosis by clinical and sociodemographic factors, identified from the review of literature (1998–2023). Variables are grouped into sociodemographic (MAD, SES), socioemotional-behavioural (SDQ scores), sex, clinical (e.g., IQ, regression, language ability), and autism severity (e.g, SCQ, ADOS, ADI-R, RBS-R) categories. Circle size represents sample size, with larger circles indicating larger cohorts. Colored points denote variables analysed in the current study. Inset shows factors that explain greater than 10% of the variance in age at autism diagnosis. Note: None of these studies account for additional family, service access, and contextual factors known to influence diagnostic timing. Abbreviations: MAD, Maternal Age at Delivery; IQ, Intelligence Quotient; SES, Socio-economic Status; ADOS, Autism Diagnostic Observation Schedule; ADI-R, Autism Diagnostic Interview-Revised; RBS-R, Repetitive Behavior Scale-Revised; SCQ, Social Communication Questionnaire; SDQ, Strengths and Difficulties Questionnaire; SDQ Total, SDQ Total Difficulties.

Extended Data Fig. 2 Two aetiological polygenic models of autism.

In Model 1 (Unitary Model), we assume a single liability threshold polygenic model. In this model, autism emerges from a unitary polygenic aetiology. Autistic individuals diagnosed later have lower polygenic predisposition than individuals diagnosed earlier. In Model 2 (Developmental Model), we model two correlated age-dependent polygenic liabilities.

Extended Data Fig. 3 Schematic of the study aims.

The study consists of four linked aims to understand whether the developmental trajectories and polygenic etiology of autism differs by age at diagnosis. In Aim 1, we modelled socioemotional and behavioural trajectories among autistic individuals in birth cohorts and investigated their association with age at autism diagnosis. In Aim 2, estimated the SNP-based heritability of age at autism diagnosis and whether it attenuates when accounting for various clinical and demographic factors. In Aim 3, we investigated whether the varying patterns of genetic correlations observed among different GWAS of autism can be explained by different polygenic factors associated with age at diagnosis. In Aim 4, we investigated the genetic relationship between the two autism polygenic factors and mental health and developmental phenotypes.

Extended Data Fig. 4 Schematic diagram of the birth cohorts included in the study.

Schematic diagram of the cohorts included in the study and the ages when data was collected for SDQ scores (dots) and autism diagnosis (in boxes). Reports of autism diagnosis were available at ages: MCS - 5,7,11,14; GUI - 9,13,17; LSAC-B - 7,9,11,13,15; and LSAC-K: 11,13,15,17. MCS = Millennium Cohort Study; GUI = Growing up in Ireland (cohort ’98); LSAC-B = Longitudinal Study of Australian Children (Birth cohort); LSAC-K = Longitudinal Study of Australian Children (Kindergarten cohort). Sample sizes and the year of initial SDQ data collection for each cohort are shown on the ordinate axis. The age cutoff used in the Latent Growth Curve Models for each cohort is indicated by a red line. GUI was used only for Latent Growth Curve Models and excluded from Growth Mixture Models.

Extended Data Fig. 5 Schematic diagram of age at autism diagnosis GWAS and age stratified autism GWAS.

Schematic diagram illustrating the main GWAS conducted in the study using the SPARK and iPSYCH cohorts. We conducted two age at autism diagnosis GWAS. In addition, we conducted six case-control GWAS, where autistic individuals were stratified based on their age at autism diagnosis.

Extended Data Fig. 6 Distribution of age at autism diagnosis in iPSYCH and SPARK.

Frequency histograms of age at autism diagnosis in iPSYCH and SPARK. Median and median absolute deviation (MAD) for age at diagnosis, and sample sizes (N) have been provided.

Extended Data Fig. 7 Within-cohort genetic correlation between age at diagnosis stratified autism GWAS and mental health and cognition related phenotypes.

Genetic correlation between age at autism stratified GWAS in SPARK (meta-analysed from Discovery and Replication cohorts) and iPSYCH and other mental health and cognition related phenotypes. Points represent genetic correlation estimates and whiskers indicate 95% confidence intervals. Green represents the earlier diagnosed autism GWAS (iPSYCH before 9 and SPARK before 6), and purple represents later diagnosed autism GWAS (iPSYCH and SPARK after 10). Asterisk (*) indicates significantly different genetic correlation between the earlier and later diagnosed GWAS (P < 0.05, two-tailed Z test).

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Zhang, X., Grove, J., Gu, Y. et al. Polygenic and developmental profiles of autism differ by age at diagnosis. Nature 646, 1146–1155 (2025). https://doi.org/10.1038/s41586-025-09542-6

- Received: 31 July 2024

- Accepted: 18 August 2025

- Published: 01 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09542-6

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Transitions in dynamical regime and neural mode during perceptual decisions

Thomas Zhihao Luo,

Timothy Doyeon Kim,

Diksha Gupta,

Adrian G. Bondy,

Charles D. Kopec,

Verity A. Elliott,

Brian DePasquale &

…

Carlos D. Brody

Nature volume 646, pages 1156–1166 (2025)

Abstract

Perceptual decision-making is thought to be mediated by neuronal networks with attractor dynamics1,2. However, the dynamics underlying the complex neuronal responses during decision-making remain unclear. Here we use simultaneous recordings of hundreds of neurons, combined with an unsupervised, deep-learning-based method, to discover decision-related neural dynamics in the rat frontal cortex and striatum as animals accumulate pulsatile auditory evidence. We found that trajectories evolved along two sequential regimes: an initial phase dominated by sensory inputs, followed by a phase dominated by autonomous dynamics, with the flow direction (that is, neural mode) largely orthogonal to that in the first regime. We propose that this transition marks the moment of decision commitment, that is, the time when the animal makes up its mind. To test this, we developed a simplified model of the dynamics to estimate a putative neurally inferred time of commitment (nTc) for each trial. This model captures diverse single-neuron temporal profiles, such as ramping and stepping3,4. The estimated nTc values were not time locked to stimulus or response timing but instead varied broadly across trials. If nTc marks commitment, evidence before this point should affect the decision, whereas evidence afterwards should not. Behavioural analysis aligned to nTc confirmed this prediction. Our findings show that decision commitment involves a rapid, coordinated transition in dynamical regime and neural mode and suggest that nTc offers a useful neural marker for studying rapid changes in internal brain state.

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Main

Theories of attractor dynamics have been successful at capturing several brain functions[5], including motor planning[6] and neural representations of space7,8. Attractors are a set of states towards which a system tends to evolve from a variety of starting positions. In these theories, computations of a brain function are carried out using the temporal evolution or the dynamics of the system. Experimental findings support the idea that the brain uses systems with attractor states for computations underlying working memory[6] and navigation[7]. These theories often focus on the low-dimensional nature of neural population activity2,9,10 and account for responses across a large number of neurons using a dynamical system model in which the variable has only a few dimensions7,11,12,13.

Attractor network models have also been proposed to underlie perceptual decision-making: the process by which noisy sensory stimuli are categorized to select an action or mental proposition. In these hypotheses, the network dynamics carry out the computations needed in decision formation1,2,14,15,16, such as accumulating sensory evidence and committing to a choice. Although some experimental evidence favours a role of attractors in perceptual decisions2,16,17, the actual population-level dynamics underlying decision-making have not been directly estimated. Knowledge of these dynamics would directly test the current prevailing attractor hypotheses, provide fundamental constraints on neural circuit models and account for the often complex temporal profiles of neural activities.

A separate line of work involves tools, sometimes based on deep learning, for discovering the low-dimensional component of neural activity in a data-driven manner10,18,19. In this approach, the spike trains of many simultaneously recorded neurons are modelled as being a function of a few latent variables that are shared across neurons.

To combine both lines of work, we used an innovative method[20] that estimates, from the spike trains of simultaneously recorded neurons, the dynamics of a low-dimensional variable z, given by:

$$\dot{{\bf{z}}}=F({\bf{z}},{\bf{u}})+{\boldsymbol{\eta }},$$

(1)

where u are external inputs, η is noise and, when applied to perceptual decisions, z represents the dynamical state of the decision process of the brain at a given time (Fig. 1a–c). The instantaneous change of the decision variable or its dynamics is given by ż, which depends on z itself, and u and η. This approach aims to estimate the function F and, through it, capture the nature of decision-making neural dynamics.

Fig. 1: Attractor models of decision-making were tested by recording from the rat frontal cortex and striatum.

a, Rats were trained to accumulate auditory pulsatile evidence over time. While keeping its head stationary, the rat listened to randomly timed clicks played from loudspeakers on its left (L) and right (R). At the end of the stimulus, the rat received a water reward for turning to the side with more clicks. The earliest time when a rat could respond was fixed at 1.5 s relative to the moment of inserting its nose in the centre port (that is, not a reaction time paradigm). b, Behavioural performance in an example recording session. Dashed reference lines at abscissa = 0 and ordinate = 0.5. c, The decision process is modelled as a dynamical system. Right, the blue and red arrows represent the change in the decision variable in the presence of a left or right, respectively. z_1, z dimension 1; z_2, z dimension 2. d, Autonomous dynamics illustrated using the bistable attractor hypothesis. In the velocity vector field (that is, flow field; left), the arrow at each value of the decision variable z indicates how the instantaneous change depends on z itself. The orientation of the arrow represents the direction of the change, and its size represents the speed, also quantified using a heat map (right). e, Changes in z driven solely by external sensory inputs (example of bistable attractors). f, Bistable attractor hypothesis of decision-making, with directions of the input dynamics (based on ref. [1]). g, A hypothesis supposing a line attractor in the autonomous dynamics on the basis of the DDM of decision behaviour (based on ref. [23]). h, Recurrent neural networks can be trained to make perceptual decisions using a line attractor that is not aligned to the input dynamics (non-normal; based on ref. [2]). i, Unsupervised discovery (this study) of dynamics that have not been previously considered. j, Six interconnected frontal cortical and striatal regions are examined here. vStr, ventral striatum. k, Neuropixels recordings (318 ± 147 neurons per session per probe, mean ± s.d.) from 12 rats in total (two to three regions per rat). AP, anteroposterior; ML, mediolateral.

Differentiating dynamical hypotheses

The function F is useful for distinguishing among hypotheses of decision-making. F can be dissected into two components: autonomous dynamics and input-driven dynamics. Autonomous dynamics are dynamics in the absence of sensory inputs u (that is, F(z, 0); Fig. 1d and Extended Data Fig. 1a,b). Input dynamics are changes in z driven by u, which can be distinguished from autonomous dynamics as F(z, u) − F(z, 0). Input dynamics can depend on z (Fig. 1e and Extended Data Fig. 1c–e).

Many of the prevailing neural attractor hypotheses have been inspired by a classic and successful behavioural-level model, the drift diffusion model (DDM)21,22. In the behavioural DDM, a scalar (that is, one-dimensional) decision variable z is driven by sensory evidence inputs (Extended Data Fig. 6a,b). For example, for decisions between go right versus go left, momentary evidence for right (left) might drive z in a positive (negative) direction. Through these inputs, the momentary evidence accumulates over time in z until the value of z reaches an absorbing bound, a moment thought to correspond to decision commitment and after which inputs no longer affect z. Different bounds correspond to different choice options: a positive (or negative) bound would correspond to the decision to go right (or go left). A straightforward implementation of the DDM in neural population dynamics, which we refer to as the DDM line attractor, would posit a line attractor in neural space, with the position of the neural state z along that line representing the value of z and two point attractors at the ends of the line representing the decision commitment bounds[23] (Fig. 1g). Another hypothesis approximates the DDM process using bistable attractors[1], with each of the two attractors representing each of the decision bounds and, in between the two attractors, a one-dimensional stable manifold of slow autonomous dynamics that corresponds to the evidence accumulation regime (Fig. 1f). In both the DDM line attractor and bistable attractor hypotheses, evidence inputs are aligned with the slow dynamics manifold and the attractors at its end points. A third hypothesis, inspired by trained recurrent neural networks, also posits a line attractor (Fig. 1h) but allows for evidence inputs that are not aligned with the line attractor and that accumulate over time through non-normal autonomous dynamics[2]. In all three hypotheses, the one-dimensional line attractor and/or slow manifold is stable, meaning that autonomous dynamics flow towards it (Fig. 1f–h). Because these three hypotheses were each designed to explain a particular set of the phenomena observed in decision-making experiments, a broader range of experimental observations could suggest dynamics that have not been previously considered. As but one example, autonomous dynamics may contain discrete attractors that do not lie at the end points of a one-dimensional slow dynamics manifold; many other arrangements are possible. In the data-driven approach we describe below, F is estimated purely from the spiking data and the timing of sensory input pulses, without incorporating any assumptions from the behavioural DDM or other existing hypotheses.

Dissociating between autonomous and input dynamics requires neural recordings during a decision unfolding over a time period that includes intervals both with and without momentary evidence inputs. We trained rats to perform a task in which they listened to randomly timed auditory pulses played from their left and their right and reported the side on which more pulses were played[24] (Fig. 1a). The stochastic pulse trains allow us to sample neural responses time locked to pulses, which are useful for inferring input-driven dynamics, and also the neural activity in the intervals between pulses, which is useful for inferring autonomous dynamics. Expert rats are highly sensitive to small differences in auditory pulse number (Fig. 1b and Extended Data Fig. 2a), and the behavioural strategy of rats in this task is typically well captured by gradual accumulation of evidence, which is at the core of the DDM24,25,26.

While the rats performed this task, we recorded six frontal cortical and striatal regions with chronically implanted Neuropixels probes (Fig. 1j,k and Extended Data Fig. 2b). The frontal orienting fields (FOF) and the anterior dorsal striatum (dStr) are known to be causally necessary for this task and are interconnected27,28,29. The dorsomedial frontal cortex (dmFC) is a major anatomical input to the dStr[30], as confirmed by our retrograde tracing (Extended Data Fig. 2c), and is also causally necessary for the task (Extended Data Fig. 2d). The dmFC is interconnected with the medial prefrontal cortex (mPFC) and, less densely, the FOF, the primary motor cortex (M1)[31] and the anterior ventral striatum[30].

Unsupervised discovery of dynamics

To test the attractor hypotheses and allow discovery of dynamics not previously considered, a flexible yet interpretable method was needed. We used an innovative deep learning method (flow field inference from neural data using deep recurrent networks; FINDR[20]) that infers the low-dimensional stochastic dynamics that best account for population spiking data. The low dimensionality of the description is critical for interpretability. Prominent alternative deep-learning-based approaches for inferring neural latent dynamics involve models in which these latent dynamics have hundreds of dimensions and are deterministic[18]. By contrast, FINDR infers latent dynamics that are low dimensional and stochastic. The stochasticity in the latent dynamics accounts for noise in the decision process that contributes to errors. FINDR approximates the decision-relevant dynamics F with a gated multilayer perceptron network[32] and noise η as a Gaussian with diagonal covariance (equation (1) and Fig. 2a). The firing rate of each neuron at each time point is modelled as a weighted sum of the z variables, followed by a softplus nonlinearity, which can be thought of as approximating neuronal current–frequency curves[6] (Fig. 2b). The weighting for each neuron (vector wn for neuron n, comprising the nth row of a weight matrix W; Fig. 2b) is fit to the data. To aid the interpretability of z, we transform W after training such that its columns are orthonormal and it therefore acts as a rotation. As a result, angles and distances in z are preserved in Wz (neural space before softplus). Before learning F and W, we separately account for the decision-irrelevant, deterministic but time-varying baseline firing rate for each neuron (baseline in Fig. 2b) so that FINDR can focus on the choice formation process.

Fig. 2: Unsupervised discovery shows transitions in dynamical regime and neural mode underlying the shift from evidence accumulation to decision commitment.

a, Decision-relevant dynamics are inferred using FINDR[20]. b, FINDR learns the decision variable z that best captures neural spiking activity. Each neuronal spike count at a given time step is modelled as a Poisson random variable with the rate given by an affine transformation of z at that time step, followed by the softplus nonlinearity. The grey box indicates the decision variable z at an example time step, and the yellow box indicates the spike counts at that time step. A time-varying baseline is learnt for each neuron to capture the decision-irrelevant component of its activity. c–h, Vector field inferred from 96 simultaneously recorded choice-selective neurons in the dmFC and the mPFC from a representative session. Only the portion of the state space visited by at least 50 of 5,000 simulated 1-s trajectories (sample zone) is shown. c, Autonomous dynamics. d, Speed of autonomous dynamics. e, Input dynamics for left and right clicks. If u = [1;0] indicates a left click input, F(z, [1;0]) − F(z, 0) is the input dynamics given a left click. However, the average left input dynamics depend on the frequency of left clicks, given by p(u = [1;0]|z). Therefore, we compute the average left input dynamics F(z, left) − F(z, 0) as p(u = [1;0]|z)(F(z, [1;0]) − F(z, 0)). We compute the average right input dynamics similarly, with u = [0;1]. f, Speed of input dynamics. g, Difference in speed between autonomous and input dynamics. h, Initially, z is strongly driven by inputs, and its trajectories develop along the evidence accumulation axis aligned with the direction of input dynamics. At a later time, the trajectories become largely insensitive to the inputs and are instead driven by autonomous dynamics to evolve along the decision commitment axis aligned with the direction of autonomous dynamics.

We first confirmed that, in synthetic data, the velocity vector fields (flow fields) inferred by FINDR can distinguish between existing attractor hypotheses (Extended Data Fig. 1f–h). Next, we turned to the recorded spiking data and confirmed that FINDR provides a good fit to the heterogeneous single-trial firing rates of individual neurons and to the complex dynamics in their peristimulus time histograms (PSTHs) conditioned on the sign of the evidence (Extended Data Fig. 3a–d). We found that two latent dimensions suffice to capture our data well (Extended Data Fig. 3e–i). For models with more than two latent dimensions, the latent dynamics are still mostly confined to two dimensions, and this two-dimensional manifold is approximately an attractor (Extended Data Fig. 3h–k).

Figure 2c–h shows a representative recording session from the dmFC and the mPFC. We found that, generally, two-dimensional input-driven dynamics and autonomous dynamics inferred by FINDR were not described well by the existing hypotheses: in all three hypotheses illustrated in Fig. 1d–h, there is a one-dimensional stable manifold that either is or approximates a line attractor. By contrast, even though, over the first 330 ms, the average trajectories evolve along an approximately straight line (Fig. 2h), the line is not a one-dimensional attractor, and individual trials diverge from it. Furthermore, in all three hypotheses in Fig. 1d–h and in all other hypotheses we are aware of, autonomous dynamics play an important part throughout the entire decision-making process. For example, autonomous dynamics are what enforce the stability of the one-dimensional slow manifolds in Fig. 1d–h. By contrast, at least in the space of the latent variable z, FINDR-inferred dynamics suggest that, initially, motion in neural space is dominated and driven by inputs to decision-making regions (that is, by the input-dependent dynamics), not the autonomous dynamics, which are slow in both dimensions (Fig. 2c–h), not only one. Later in the decision-making process, the balance between autonomous versus input-driven dynamics inverts, and it is the autonomous dynamics that become dominant. Plots in Fig. 2g show the difference in magnitude between autonomous and input-driven dynamics (indicated with the colour scale) on the z plane. The initial dominance of the input-driven dynamics can be seen in the zone near the (0, 0) origin at the negative end of the colour scale. The later dominance of autonomous dynamics can be seen in the right and left edges of the sampled region, reached later in the decision-making process, at the opposite end of the colour scale. Moreover, the direction of instantaneous change driven by the inputs (slightly clockwise from horizontal in Fig. 2e) is not aligned with the direction of the strongest autonomous dynamics in the left and right edges of the sampled region (slightly anticlockwise from vertical in Fig. 2c). The curved trial-averaged trajectories of z emerge from this non-alignment in the input direction and the autonomous direction later in the decision-making process. The change from an input-dominated to an autonomous-dominated dynamical regime and the sharp turn in the direction of the neural trajectories in Fig. 2c–h were observed consistently across rats and behavioural sessions (Fig. 3a–d). These observations were robust to several different initializations of the neural networks in FINDR, the order of minibatches during training and how datasets were split into training and test sets (Extended Data Fig. 4). They are therefore a consistent finding of the analysis.

Fig. 3: FINDR shows transitions in dynamical regime and neural mode consistently across sessions and better captures the data than a constrained model based on previous hypotheses.

a, To quantify how speed difference between autonomous and input dynamics evolves over a trial, we identify the time point when the latent trajectories curve (stars) and compute the speed difference in Fig. 2g before and after this point. The latent trajectories are trial-averaged, sorted by evidence strength. The trial-averaged trajectories and stars are coloured as in Fig. 2h. b, The peak is defined as the time of maximum curvature in the trial-averaged trajectories. Time periods are defined relative to this peak (before peak and after peak) and to trial start and end (early and late) for c,d. Black star symbol represents the peak of average trajectory curvature. c, We compute the normalized difference in speed between autonomous and input dynamics for five different time periods (start (time = 0 s), early, before peak, after peak and late) from vector fields inferred from sessions with more than 30 recorded neurons, over 400 trials during which the animal performed with more than 80% accuracy (n = 27 sessions). The dashed line indicates normalized difference of 0. CI, confidence interval. d, For sessions in which FINDR with the two-dimensional decision variable z fit significantly better than FINDR with one-dimensional z (n = 21 of 27; Extended Data Fig. 3), we measured the direction of motion of the trial-averaged trajectories and its angle with respect to the z_1 axis for different time periods (curving of trial-averaged trajectories across 21 sessions). e, cFINDR captures previous hypotheses and replaces the neural network parametrizing F with a combination of line attractor dynamics (specified by QΛQ−1, with the diagonal matrix Λ having one zero and one negative eigenvalue) and bistable attractor dynamics (specified by a nonlinear function φ; Methods). f, Autonomous dynamics inferred by cFINDR and FINDR are shown for a representative session, with vector field outside the sample zone in grey. g, The coefficient of determination (R^2) of the evidence–sign conditioned PSTH computed using fits of FINDR is significantly greater than those computed using fits of cFINDR (across 27 sessions, two-sided Wilcoxon signed-rank test).

To perform a head-to-head comparison with the three hypotheses in Fig. 1d–h, we constructed a variant of FINDR in which the network parametrizing F was replaced by a parametrization of the dynamics constrained to describe those three hypotheses (Fig. 3e,f and Extended Data Fig. 5). If the data were described well by one of these hypotheses, we would expect this variant (which we refer to as cFINDR, for constrained FINDR) to fit the data well, particularly out of sample, because it has far fewer parameters than FINDR. However, unconstrained FINDR consistently fit the data better than cFINDR, confirming that previous hypotheses do not adequately capture the data. Although one of the hypotheses (Fig. 1h, suggesting non-normal dynamics with a line attractor) can generate curved trial-averaged trajectories apparently similar to those we see in the data (Fig. 3e,f and Extended Data Fig. 5g), there is a key difference, which is that, in this particular hypothesis, the turn from the initial flow direction induced by the inputs happens early, because the autonomous dynamics causing it are strong the moment the latent state departs from the line attractor. However, our data suggest that there is a more prolonged initial phase of flow along the input directions before the turn, with the stronger autonomous dynamics happening much later in the decision-making process. We believe that this underlies the much better fits to the data for FINDR than those for cFINDR.

A recent study[33] described neural trajectories that were described well by non-normal dynamics34,35. Consistent with this, the two-dimensional FINDR-inferred autonomous dynamics around the origin are also non-normal (Extended Data Fig. 10b,c), although with a key difference with respect to refs. 33,34,35, which is that here the origin is unstable (Extended Data Fig. 10a,e).

Unsupervised inference of dynamics underlying decision-making, based only on spiking activity and sensory evidence inputs, thus suggests that the process unfolds in two separate sequential regimes. In the initial regime, dynamics are largely determined by the inputs, with autonomous dynamics playing a minor role. The sensory evidence inputs (right and left clicks) drive the decision variable to evolve along an axis, parallel to the directions of the input dynamics, that we will term the evidence accumulation axis. In the second, later regime, these characteristics reverse; the trajectories representing the evolution of the decision variable become largely independent of the inputs and are instead mostly determined by autonomous dynamics. We will term the straight line along the direction of the autonomous dynamics in the later regime the decision commitment axis. Of note, the evidence accumulation axis and the decision commitment axis are not aligned with each other. During the regime transition, the trajectories in z veer from evolving along the evidence accumulation axis to developing along the decision commitment axis. In neural space, this will equate to a transition from evolving along one mode (that is, a direction in neural space), corresponding to evidence accumulation, to another mode that, as explained below, we believe may correspond to decision commitment.

Although derived entirely from unsupervised analysis of neural spiking activity and auditory click times, these two regimes are reminiscent of the two regimes of the behavioural DDM: namely, an initial regime in which momentary sensory inputs drive changes in the state of a scalar decision variable z and a later regime, after z reaches a bound, in which the state becomes independent of sensory inputs (Extended Data Fig. 2e,f). The correspondence between the two regimes inferred from spiking activity and the behavioural DDM suggests that the transition between regimes may correspond to the moment of decision commitment. It further suggests that a modified neural implementation of the DDM, focusing on key aspects of the two regimes, could be a simple model that captures many aspects of the neural data, although having far fewer parameters than FINDR and thus greater statistical power. We next develop this model and show that it can be used to precisely infer the regime transition time in each trial and test the proposal that this transition corresponds to decision commitment.

Simplified model of decision dynamics

FINDR-inferred vector fields show a rapid shift from strongly input-driven to autonomous-dominant dynamics, analogous to the transition from evidence accumulation to decision commitment in the behavioural DDM (Fig. 4a,b). The DDM captures behaviour in a wide range of decision-making tasks, including tasks in which the stimulus duration is determined by the environment24,25,28,36,37, as used here. This suggests that the FINDR-inferred dynamics may be approximated by a simplified model in which the decision variable evolves as in the behavioural DDM.

Fig. 4: A simplified model captures discovered dynamics and diverse neuronal profiles.

a, The velocity vector field of both the discovered dynamics and the DDM line attractor can be partitioned into evidence accumulation (EA) and decision commitment (DC) regimes. b, The MMDDM, a simplified model of the discovered dynamics. As in the behavioural DDM, momentary evidence (u) and noise (η) accumulate over time in the decision variable (z) until z reaches either the left (−B) or right (+B) bound. At this moment, the animal commits to a decision: z becomes fixed and unresponsive to further input. Also at this moment, the encoding weight (w) of each neuron shifts from wEA to wDC, changing how z maps to the predicted Poisson firing rate y through softplus nonlinearity h and baseline b. c, MMDDM captures heterogeneous single-neuron profiles. A ramp PSTH arises when wEA and wDC are equal. d, A decay profile emerges when wDC is zero because, over time, more trials reach the bound where encoding of z vanishes. e, A delay profile results from setting wEA to zero because, early in the trial, it is unlikely to have reached the bound. f, ‘Flip’ is produced by setting wEA and wDC to have opposite signs. g, MMDDM captures heterogeneity in single-neuron temporal profiles. Shading represents 95% bootstrap CI of the mean; the solid line is the model prediction. h, MMDDM has a higher out-of-sample likelihood than a one-dimensional DDM without a neural mode switch. i, MMDDM achieves a higher goodness-of-fit R^2 value of the choice-conditioned PSTHs. h,i, P values were computed using two-sided sign tests. j, Model prediction (pred.) and observed psychometric function for one example session. The shaded areas are the 95% bootstrap CI of the mean; the solid line is the model prediction.

The regime transition coincides with rapid reorganization in the neuronal population representation of the decision process. To quantify this reorganization, we treat the activity of each neuron as a dimension in neural space, with axes in this space as neural modes. Seen in this way, the shift from evidence accumulation to decision commitment is coordinated with a fast transition in the neural mode, analogous to the rapid change in neural modes from motor preparation to motor execution[38]. This motivates whether a simplified model based on a rapid, coordinated transition in both dynamical regime and neural mode can capture the key features of FINDR-inferred dynamics and broader experimental observations.

In what we will call the multimode or minimally modified DDM (MMDDM), a scalar decision variable z evolves just as in the behavioural DDM, governed by three parameters (Fig. 4b, Extended Data Fig. 6a,b and the Methods). The key addition is that neurons encode z differently before and after the decision commitment bound is reached. Each neuron has two weights: wEA for the evidence accumulation phase and wDC for the decision commitment phase. When wEA and wDC are constrained to be the same, the MMDDM reduces to a standard DDM with a single neural mode. In the DDM line attractor hypothesis in Fig. 1g, if the autonomous dynamics towards the line attractor are strong relative to the noise, trajectories will be largely one dimensional, which are approximated well by a single-mode DDM. Because neurons multiplex both decision-relevant and decision-irrelevant signals39,40, MMDDM includes terms for spike history and, similar to FINDR, decision-irrelevant baseline changes (Extended Data Fig. 6c–f). All parameters are fit jointly for each session using both neural activity and behavioural choices.

MMDDM can account for a broader range of neuronal profiles (Fig. 4c–g) than the single-mode DDM, which captures only ramp-like neuronal temporal profiles (Extended Data Fig. 2e–l). In the vast majority of recording sessions, the data are better fit by MMDDM than by the single-mode DDM (cross-validated; Fig. 4h,i). The model also accurately captures the choice data (Fig. 4j and Extended Data Fig. 6g) and reproduces vector fields that closely resemble those inferred from real spike trains (Extended Data Fig. 6h). Additional validations are shown in Extended Data Fig. 6i–n. Finally, because the end of the stimulus was fixed across trials relative to fixation onset, stimulus offset was not included as an input in MMDDM, consistent with the lack of abrupt neural changes at stimulus offset (Extended Data Fig. 9).

nTc

In MMDDM, the transition from evidence accumulation to decision commitment and a consequent switch from wEA to wDC directly implement a change in neural mode between the two phases of the trial, which was previously suggested9,41. However, it remains unclear whether this neural mode change corresponds to the animal making up its mind, in part because no method has been developed previously to precisely estimate its timing in single trials. The behavioural DDM, without neural data, can provide a rough estimate of the moment of commitment (Fig. 5a, dashed grey line). But on the basis of the hypothesis that the time of the neural mode change corresponds to the time of commitment and, using data from many simultaneously recorded neurons, MMDDM allows a far more precise estimate per trial (Fig. 5a, orange line). We refer to this moment as nTc. Surprisingly, nTc varied widely across trials. It was not time locked to stimulus onset (Fig. 5b), stimulus offset (Extended Data Fig. 7n) or the onset of the decision-reporting motor response (Fig. 5c). Instead, nTc seemed to be an internally timed event. nTcs also occurred well after the onset of perimovement kernels inferred from generalized linear models of single-neuron spike trains[40] (Extended Data Fig. 8), indicating that nTcs do not reflect the initiation of action plan encoding.

Fig. 5: nTc marks the moment of internal decision commitment.

a, Example trial. The inferred time of commitment is far more precise when neural activity is used (nTc, orange line) than when it is inferred solely from sensory stimulus timing and choice behaviour (dashed grey line). b, Distribution of estimated nTc values relative to stimulus onset. Among the 34.7% of trials in which commitment times could be detected, nTc varied widely relative to the onset of auditory click trains. The decline in nTc frequency over time reflects randomized stimulus durations (0.2–1.0 s). c, Distribution of nTc values relative to movement onset to report the decision of the animal (exiting centre fixation port). As in b, nTc timing also varies widely across trials. The leftmost bin includes trials in which the nTc occurred more than 1 s before movement. d, Supporting the interpretation of nTc as a decision commitment and, despite the highly variable timing of nTc, sensory evidence presented before nTc affects the decision of the animal but evidence presented after nTc does not (weight of clicks on choice inferred using logistic regression). Trials for which the estimated time of commitment occurred at least 0.2 s before stimulus offset and 0.2 s or more after stimulus onset were included for this analysis (9,397 of 55,057 trials across 115 sessions with 12 rats). The green line is the prediction from the MMDDM model fit to the data. e, Behavioural accuracy was lower in trials in which nTc could not be identified. Predictions were made by fitting MMDDM to the data, simulating trials from the fitted models and applying the same nTc detection procedure as that used for real data. Dashed reference lines at abscissa = 0 and ordinate = 0.5. f, nTc was more likely to be identified in trials with stronger evidence. For each evidence strength bin, the fraction of trials with an identified nTc was divided by the overall trial fraction across all bins, which was lower in the data than in the model predictions. Black circles and green lines indicate the mean across sessions. Black error bars and green shading indicate the 95% bootstrap confidence of the mean. Dashed reference lines at ordinate = 1.0.

A core prediction of the hypothesis that nTc marks the time of internal decision commitment is that, after nTc, auditory click stimuli should stop influencing the behavioural choice, because the animal will have already made up its mind. The single-trial estimates of nTc that MMDDM provides can be used to test this prediction: we time align the sensory stimulus data of each trial to the neurally estimated nTc and then behaviourally measure the weight with which stimulus fluctuations at each time point affect choice (that is, the psychophysical kernel[42]; Methods). Remarkably, as predicted, we found that the psychophysical weight of stimulus fluctuations on the choice of the animal diminished abruptly to zero after nTc (Fig. 5d and Extended Data Fig. 7). Because these commitment times varied widely across trials (Fig. 5b,c), the abrupt drop in psychophysical weight cannot be observed without the single-trial nTc estimates. If we instead align trials to the stimulus onset, we obtain a smooth psychophysical kernel (Extended Data Fig. 7e–h), as observed in previous studies lacking access to nTc[24].

nTc showed further hallmarks of being a marker of commitment: First, for a given evidence strength, trials without commitment are predicted to be more likely to involve noise acting against the sensory evidence, leading to lower accuracy. Consistent with this prediction, accuracy was lower in trials when nTc could not be identified (Fig. 5e). Second, commitment should occur more often when evidence is stronger, and, accordingly, nTc was more frequently detected in trials with stronger evidence (Fig. 5f). Additional hallmarks are shown in Extended Data Fig. 7i–q. Together, these results offer behavioural support for an internally timed commitment event, after which sensory inputs are ignored, and the timing of which can be inferred from spiking data using nTc.

Abrupt and gradual shifts at commitment

Perceptual decision-making involves a diversity in the temporal profiles of choice-selective neurons, with some showing a ramp-to-bound profile, others exhibiting a step-like profile and some falling in between a ramp and a step3,4. We found that the continuum of ramping and stepping profiles can be captured by a rapid reorganization in population activity at the time of decision commitment, as described by MMDDM. We grouped neurons by whether they were estimated to be more, less or similarly engaged in evidence accumulation relative to decision commitment (|wEA| > |wDC|, |wEA| ≈ |wDC| and |wEA| < |wDC|, respectively, in MMDDM fits). We then computed the pericommitment neural response time histogram (PCTH) of each neuron (Methods and Fig. 6a,b). For neurons similarly engaged in accumulation and commitment, the PCTH had a ramp-to-bound profile, whereas, for neurons more engaged in commitment, the PCTH resembled a step. For neurons more engaged in accumulation, the PCTH had a ramp-and-decline profile. Even without grouping neurons, we found that the first three principal components (PCs) of the PCTHs correspond to the ramp-to-bound, step and ramp-and-decline profiles.

Fig. 6: Simplified model captures heterogeneous single-neuron temporal profiles, such as ramping and stepping, and shows functional distinctions between brain regions.

a, PCTHs for neurons grouped by relative engagement (defined in f). Neurons similarly engaged in evidence accumulation and decision commitment have ramp-to-bound profiles (centre). Neurons more engaged in decision commitment have step-like profiles (right), whereas those more engaged in evidence accumulation have ramp-and-decline profiles (left). ‘Preferred’ indicates the choice eliciting higher firing. Data are the mean across neurons and the 95% CI. b, First three PCs of PCTH differences (preferred − nonpreferred choice) across all neurons, capturing ramp-to-bound (PC1), step (PC2) and ramp-and-decline (PC3) profiles. c, Observed curved trial-averaged trajectories (projected onto the first two PCs) are captured by the MMDDM (centre) but not the single-mode DDM (right). Time from stimulus onset. Proj., projection. d, MMDDM better captures the data than the single-mode DDM (out-of-sample log likelihood: MMDDM − single-mode DDM). e, The neuron-averaged choice selectivity has different temporal profiles across brain regions: mPFC neurons are most choice selective near the beginning, whereas FOF neurons are most choice selective towards the end. f, Engagement index (EI) quantifying relative neuronal engagement in evidence accumulation versus decision commitment. PSTHs are shown for three example neurons. Shading is the 95% CI of the mean; line indicates model prediction. g, A gradient across brain regions in the strength of neural mode transitions from stronger engagement in accumulation (for example, mPFC) to more balanced engagement (for example, FOF). Marker indicates median. Overall differences in engagement index across regions were assessed using the Kruskal–Wallis test (P = 1 × 10−44). Post hoc pairwise comparisons using the Tukey–Kramer test yielded P < 0.001 for mPFC versus dStr, dmFC, M1 and ventral striatum; dStr versus M1 and FOF; and dmFC versus FOF (exact P values are in the Supplementary Notes (section 2.1).

The abrupt changes at decision commitment seem inconsistent with smoothly curved trial-averaged trajectories in low-dimensional neural state space often observed in decision-making studies2,9. Similar phenomena are observed in our data: the trial-averaged trajectories for left and right choices do not separate from each other along a straight line but rather along curved arcs (Fig. 6c). These smoothly curving arcs may result from averaging over trajectories with an abrupt turn aligned to decision commitment, which occurs at different times across trials (Fig. 5b–d). Consistent with this account, the smooth curves in low-dimensional neural state space can be captured well by the out-of-sample predictions of MMDDM but not by a one-dimensional DDM without a neural mode switch (Fig. 6c). These results indicate that the MMDDM, a simplified model of the discovered dynamics, can capture the widespread observation well of smoothly curved trial-averaged trajectories.

Mode transitions across regions

Although we generally observed dynamics with a neural mode transition across several frontal cortical and striatal areas, quantitative differences could be observed across these regions. The choice selectivity (a measure, ranging from −1 to 1, of the difference in firing rates for right-versus-left-choice trials; Extended Data Fig. 2m) averaged across neurons had different temporal profiles across brain regions (Fig. 6e). Although mPFC neurons were most choice selective near the beginning, FOF neurons were most choice selective towards the end. We found that the difference in latencies to peak choice selectivity was linked to differences in relative neuron engagement in evidence accumulation and decision commitment. Neurons that were more strongly engaged in evidence accumulation (wEA > wDC) tended to have a shorter latency to peak selectivity than neurons that were more strongly engaged in decision commitment (wDC > wEA). This result indicates that differences in choice-related encoding across frontal cortical and striatal regions can be understood in terms of relative participation in evidence accumulation versus decision commitment (Fig. 6f,g).

Discussion

How neural dynamics govern the formation of a perceptual choice has been long debated1,2,5. Here we suggest that, for decisions on the timescale of hundreds of milliseconds to seconds, an initial input-driven regime mediates evidence accumulation and a subsequent autonomous-dominant regime subserves decision commitment. This regime transition is coupled to a rapid change in the representation of the decision process by the neural population: the initial neural mode (that is, direction in neural space) representing evidence accumulation is largely orthogonal to the subsequent mode representing decision commitment. In this sense, it is reminiscent of other covert cognitive operations, such as attentional selection, that also involve a change in neural mode[43].

If this coupled transition in dynamical regime and neural mode indeed corresponds to the time of decision commitment, sensory evidence presented after the transition would have minimal impact on the decision of the animal, because the animal would have already committed to a particular choice. Behavioural analysis confirmed this prediction in the experimental data (Fig. 5d), leading us to conclude that the transition is indeed a signal for covert decision commitment. We refer to the estimate of the presence and timing of such a transition in each trial, which is based on the sensory stimulus and firing rates of simultaneously recorded neurons, as nTc.

We wondered how decisions end. In reaction time paradigms of perceptual decision-making, animals are trained to respond as soon as they make a decision. The moment the animal initiates its response is then used to operationally define when it commits to a choice44,45. In these paradigms, decision commitment is overt, as it is closely linked to the onset of the movement animals make to report their choice[45]. Here, by contrast, using an experimenter-controlled duration paradigm, we found a decision commitment signal (nTc) that is covert in the sense of occurring at a time highly variable with respect to the timing of the external motor action used to report the decision, which it can precede by as much a second or more (Fig. 5c). It is also highly variable with respect to stimulus onset (Fig. 5b) or offset (Extended Data Fig. 7n). It is thus an internal signal, largely defined by coordination across neurons, not by its timing with respect to external events. The pericommitment neural responses observed here contrast sharply with the ramp-and-burst neural responses observed in animals trained to couple their decision commitment with response initiation[45] in a reaction time task.

Although the timing of the nTc signal reported here makes it very distinct from motor execution, the signal is also distinct from action preparation or planning. The beginning of action planning carries no implication as to whether sensory evidence presented subsequently will or will not be ignored. Indeed, in perceptual decision-making tasks, preliminary action preparation, driven by choice biases induced by previous trials, is often observed to begin even before the sensory stimulus, as reported previously[40] and found in our own data (Extended Data Fig. 8). By contrast, commitment to a decision suggests that evidence presented subsequently to the commitment will no longer affect the choice of the animal. Here we found that nTc corresponds to such a decision commitment moment. This was the case both at the neural level, in which it correlates with a substantial decrease in the effect of sensory inputs on neural responses in the regions we recorded (Fig. 2), and at the whole-organism behavioural level, in the sense that sensory evidence before nTc affects the choices of the animal but sensory evidence after nTc does not (Fig. 5d).

Although the behavioural DDM is a widely used model of decision-making, other frameworks are also prevalent, such as the linear ballistic accumulator[46] or urgency gating[47]. It is notable that the dynamics inferred by FINDR, obtained in a data-driven, unsupervised manner from spike times and auditory click times alone, resulted in regimes that match the characteristics of the behavioural DDM but not those of the alternatives. This match led us to explore a simplified model, the MMDDM, in which a scalar latent decision variable evolves as in the DDM but is represented in different neural modes before versus after decision commitment. The neural mode change indicates that a downstream decoder of the categorical choice can improve its accuracy by selectively reading out from neurons with post-commitment weights large in magnitude. A possible mechanism for the neural mode change is an input from ascending midbrain neurons, which is suggested by a recent finding in a working memory task that midbrain neurons, in response to an external auditory cue, trigger rapid reorganization of motor cortex activity to switch from planning-related activity to a motor command that initiates movement in mice[48].

We found that the MMDDM provides a parsimonious explanation of a variety of experimental findings from several species: across primates and rodents, sensory inputs and choice are represented in separate neural dimensions2,9,40 across time, and neither sensory responses nor the neural dimensions for optimal decoding of the choice are fixed[9]. These phenomena, along with other observations including diversity in single-neuron dynamics39,40, curved average trajectories[9], choice behaviour[24] and some vigorously debated phenomena such as a variety of single-neuron ramping versus stepping temporal profiles3,4, are all captured by the MMDDM. However, we do not see MMDDM as a unique or a unified model of perceptual decision-making. Rather, we see it as a simple yet useful approximation, a minimally modified DDM, and a stepping stone towards a unified model of decision-making.

Single-trial trajectories, in sum, filled out the two-dimensional latent space inferred by FINDR. But when averaged over trials of a given evidence strength (Fig. 2h), they evolved along a one-dimensional curved trajectory. Looking exclusively along this one-dimensional manifold, the dynamics resemble those of the bistable attractor hypothesis[1] (Fig. 1f) in the sense of a one-dimensional unstable point at the origin, with autonomous dynamics growing stronger the farther the system is from the origin. However, the bistable attractor hypothesis and the other two hypotheses in Fig. 1g,h posit a one-dimensional manifold of slow autonomous dynamics, along which evidence accumulation evolves and towards which other states are attracted1,23. By contrast, the FINDR-inferred dynamics (which are inferred from single trials, not averaged trials) suggest an initial two-dimensional manifold of slow autonomous dynamics. Sensory evidence inputs drive evidence accumulation along one of these slow dimensions. The other slow dimension corresponds to the decision commitment axis, along which autonomous dynamics will become dominant later in the process. We wondered why there would be slow autonomous dynamics along this second dimension. We speculate that, during initial evidence accumulation, slow autonomous dynamics along the decision commitment axis provide a mechanism for inputs driven by non-sensory factors such as trial history[49] to influence choice independent of the accumulating sensory evidence.

The authors of one recently proposed method to infer autonomous dynamics, applied to data from a task that did not require accumulating evidence over time, proposed that variety across the tuning curves of individual neurons could lead to curved one-dimensional decision manifolds[14]. However, the authors’ method cannot yet infer input dynamics, and thus data from tasks with evidence that arrives gradually over time cannot yet be analysed; such an extension would have to be realized before we can assess whether the curvature their approach could infer would correspond to the curvature we described here for accumulation of evidence. Importantly, inferring input dynamics in addition to autonomous dynamics was critical to our observation that a change in dynamical regime, from input dominated to autonomous dominated, seemed to coincide with the change in neural mode (Fig. 2). This observation was key for our hypothesis that this event (nTc) could correspond to decision commitment, for development of the MMDDM simplified model to estimate nTc and for experimental confirmation that nTc is indeed the moment when sensory evidence ceases to affect the decision of the animal (Fig. 5d).

Finally, our approach expands the classic repertoire of techniques used to study perceptual decision-making. We inferred decision dynamics directly from neural data rather than assuming a specific hypothesis, and we took steps to enhance the human interpretability of the discovered dynamics: the unsupervised method (FINDR) focuses on low-dimensional rather than high-dimensional decision dynamics, and the mapping from latent to neural space (before the activation function of each neuron) preserves angles and distances. On the basis of key features of the inferred latent dynamics, we developed a highly simplified, tractable model (MMDDM) that is directly relatable to the well-known DDM framework. We found that the MMDDM, despite its simplicity, could describe a broad variety of previously observed phenomena and allowed us to infer the internal decision commitment times of the animal in each trial. Pairing deep-learning-based unsupervised discovery with simplified, parsimonious models may be a promising approach for studying not only perceptual decision-making but also other complex phenomena.

Methods

Experiments

Animals

The animal procedures described in this study were approved by the Princeton University Institutional Animal Care and Use Committee and were carried out according to the standards of the National Institutes of Health (NIH). Animals consisted of 16 adult, 6–24-month-old, male Long–Evans rats (Rattus norvegicus, Hilltop Lab Animals, Taconic) that were housed in Technoplast cages in pairs with a 12-h reversed light–dark cycle. All training and testing procedures were performed during the dark cycle. The rats had free access to food, but they had restricted access to water. The amount of water that the rats obtained daily was at least 3% of their body weight. Sample sizes were chosen on the basis of previous electrophysiological studies in rats28,29. No blinding or randomization was performed.

Behavioural task

Rats performed the behavioural task in custom-made training enclosures (Island Motion) placed inside sound- and light-attenuated chambers (IAC Acoustics). Each enclosure consisted of three straight walls and one curved wall in which three nose ports were embedded (one in the centre and one on each side). Each nose port also contained one light-emitting diode that was used to deliver visual stimuli, and the front of the nose port was equipped with an infra-red beam to detect the entrance of the nose of the rat into the port. A loudspeaker was mounted above each of the side ports and used to present auditory stimuli. Each of the side ports also contained a silicone tube that was used for water reward delivery, with the amount of water controlled by valve-opening time.

Rats performed an auditory discrimination task in which optimal performance required the gradual accumulation of auditory clicks[24]. At the start of each trial, rats inserted their nose in the central port and maintained this placement for 1.5 s (fixation period). After a variable delay of 0.5−1.3 s, two trains of randomly timed auditory clicks were presented simultaneously: one from the left speaker and one from the right speaker. At the beginning of each click train, a click was played simultaneously from the left and right speakers (stereoclick). Regardless of onset time, the click trains ended at the end of the fixation period, resulting in stimuli ranging from 0.2 s to 1 s. The train of clicks from each speaker was generated by an underlying Poisson process, with different click rates for each side. The combined mean click rate was fixed at 40 Hz, and trial difficulty was manipulated by varying the ratio of the generative click rate between the two sides. The generative click rate ratio varied from 39:1 (easiest) to 26:14 (most difficult) clicks per s. At the end of the fixation period, the rats could orient towards the nose port on the side where more clicks were played and obtain a water reward.

Psychometric functions were calculated by grouping the trials into eight bins of similar size according to the difference in the total number of right and left clicks and, for each group, computing the fraction of trials ending in a right choice. The CI of the fraction of right responses was computed using the Clopper–Pearson method.

Electrophysiological recording

Neurons were recorded using chronically implanted Neuropixels 1.0 probes that are recoverable after the experiment[50]. In four animals, a probe was implanted at 4.0 mm anterior to the bregma and 1.0 mm lateral, for a distance of 4.2 mm, and at an angle of 10° relative to the sagittal plane that intersects the insertion site (the probe tip was more medial than the probe base). In five other animals, a probe was implanted to target M1, the dStr and the ventral striatum at the site 1.0 mm anterior and 2.4 mm lateral, for a distance of 8.4 mm, and at an angle of 15° relative to the coronal plane intersecting the insertion site (the probe tip was more anterior than the probe base). In a final set of three rats, a probe was implanted to target the FOF and anterior dStr at 1.9 mm anterior and 1.3 mm lateral, for a distance of 7.4 mm, and at an angle of −10° relative to the sagittal plane intersecting the insertion site (the probe tip was more lateral than the probe base). Spikes were sorted into clusters using Kilosort2 (ref. [51]), and clusters were manually curated.

Muscimol inactivation

Infusion cannulas (Invivo1) were implanted bilaterally over the dmFC (4.0 mm AP, 1.2 mm ML) in three rats. After the animal recovered from surgery, the animal was anaesthetized, and, on alternate days, a 600-nl solution of either only saline or muscimol (up to 150 ng) was infused in each hemisphere. Half an hour after the animal woke up from anaesthesia, the animal was allowed to perform the behavioural task.

Retrograde tracing

To characterize anatomical inputs into the dStr, 50 nl of cholera toxin subunit B conjugate (Thermo Fisher Scientific) was injected into the dStr at 1.9 mm AP, 2.4 ML and 3.5 mm below the cortical surface. The animal was perfused 7 days after surgery.

Histology

The rat was fully anaesthetized with 0.4 ml ketamine (100 mg ml−1) and 0.2 ml xylazine (100 mg ml−1) intraperitoneally, followed by transcardial perfusion of 100 ml saline (0.9% NaCl, 0.3× PBS, pH 7.0 and 0.05 ml heparin at 10,000 USP units per ml) and finally transcardial perfusion of 250 ml of 10% formalin neutral buffered solution (Sigma, HT501128). The brain was removed and postfixed in 10% formalin solution for a minimum of 7 days. Sections (100 µm) were prepared on a Leica VT1200 S vibratome and mounted on Superfrost Plus glass slides (Fisher) with Fluoromount-G (SouthernBiotech) mounting solution and glass coverslips. Images were acquired on a Hamamatsu NanoZoomer under ×4 magnification.

Autonomous and input dynamics

The class of dynamical systems we study here is specified by

$$\dot{{\bf{z}}}=F({\bf{z}},{\bf{u}})$$

(2)

for some generic function F, with z the latent decision variable and u the external input to the system from the auditory clicks in the behavioural task. At each moment, there may be no click, a click from the left or a click from the right. When time is discretized to sufficiently short steps, u is one of three values:

$${\bf{u}}=\left\{\begin{array}{ll}[0;0]={\bf{0}} & \text{representing when there is no click},\\ \left[1;0\right] & \text{representing when there is a left click or}\\ \left[0;1\right] & \text{representing when there is a right click}.\end{array}\right.$$

(3)

We define the autonomous dynamics of the system as

$${\dot{{\bf{z}}}}_{{\rm{a}}{\rm{u}}{\rm{t}}{\rm{o}}{\rm{n}}{\rm{o}}{\rm{m}}{\rm{o}}{\rm{u}}{\rm{s}}}=F({\bf{z}},{\bf{0}})$$

(4)

and the average input dynamics as

$${\dot{{\bf{z}}}}_{\overline{{\rm{input}}}}=p({\bf{u}}|{\bf{z}})(F({\bf{z}},{\bf{u}})-F({\bf{z}},{\bf{0}}))$$

(5)

and, specifically, the average left and right input dynamics as

$$\begin{array}{c}{\dot{{\bf{z}}}}_{\overline{{\rm{left}}}}=p({\bf{u}}=[1;0]|{\bf{z}})(F({\bf{z}},[1;0])-F({\bf{z}},{\bf{0}})),\\ {\dot{{\bf{z}}}}_{\overline{{\rm{right}}}}=p({\bf{u}}=[0;1]|{\bf{z}})(F({\bf{z}},[0;1])-F({\bf{z}},{\bf{0}})).\end{array}$$

(6)

The sum of autonomous dynamics and average input dynamics is equal to the expected value of \(\dot{{\bf{z}}}\) computed over the distribution p(u|z):

$$\begin{array}{c}{\mathbb{E}}[\dot{{\bf{z}}}]=\sum _{{\bf{u}}}p({\bf{u}}|{\bf{z}})F({\bf{z}},{\bf{u}})\\ \,=\,p({\bf{u}}={\bf{0}}|{\bf{z}})F({\bf{z}},{\bf{0}})+p({\bf{u}}=[1;0]|{\bf{z}})F({\bf{z}},[1;0])\\ \,+\,p({\bf{u}}=[0;1]|{\bf{z}})F({\bf{z}},[0;1])\\ \,=\,(1-p({\bf{u}}=[1;0]|{\bf{z}})-p({\bf{u}}=[0;1]|{\bf{z}}))F({\bf{z}},{\bf{0}})\\ \,+\,p({\bf{u}}=[1;0]|{\bf{z}})F({\bf{z}},[1;0])+p({\bf{u}}=[0;1]|{\bf{z}})F({\bf{z}},[0;1])\\ \,=\,F({\bf{z}},{\bf{0}})+p({\bf{u}}=[1;0]|{\bf{z}})(F({\bf{z}},[1;0])-F({\bf{z}},{\bf{0}}))\\ \,+\,p({\bf{u}}=[0;1]|{\bf{z}})(F({\bf{z}},[0;1])-F({\bf{z}},{\bf{0}}))\\ \,=\,{\dot{{\bf{z}}}}_{{\rm{a}}{\rm{u}}{\rm{t}}{\rm{o}}{\rm{n}}{\rm{o}}{\rm{m}}{\rm{o}}{\rm{u}}{\rm{s}}}+{\dot{{\bf{z}}}}_{\bar{{\rm{l}}{\rm{e}}{\rm{f}}{\rm{t}}}}+{\dot{{\bf{z}}}}_{\bar{{\rm{r}}{\rm{i}}{\rm{g}}{\rm{h}}{\rm{t}}}}.\end{array}$$

(7)

Figure 2c shows a plot of \({\dot{{\bf{z}}}}_{{\rm{a}}{\rm{u}}{\rm{t}}{\rm{o}}{\rm{n}}{\rm{o}}{\rm{m}}{\rm{o}}{\rm{u}}{\rm{s}}}\), and Fig. 2e shows a plot of \({\dot{{\bf{z}}}}_{\overline{{\rm{left}}}}\) and \({\dot{{\bf{z}}}}_{\overline{{\rm{right}}}}\). F(z, left) is defined as p(u = [1; 0]|z)F(z, [1; 0]) + (1 − p(u = [1; 0]|z))F(z, 0), and F(z, right) is defined as p(u = [0; 1]|z)F(z, [0; 1]) + (1 − p(u = [0; 1]|z))(F(z, 0).

Because p(u|z) = p(z|u)p(u)/p(z) and p(z) in general do not have an analytical form, we estimate p(u|z) numerically. To do this, we train FINDR[20] to learn F and generate click trains for 5,000 trials in a way that is similar to how clicks are generated for the task performed by our rats. Next, we simulate 5,000 latent trajectories from the learnt F and the generated click trains. We then bin the state space of z and ask, for a single bin, how many times the latent trajectories cross that bin in total and how many of the latent trajectories when crossing that bin had u = [1;0] (or u = [0;1]). That is, we estimate p(u = [1;0]|z) with \(\frac{{\rm{No.\; latent\; states\; with}}\,{\bf{u}}\,=\,[1;\,0]\,\text{in}\,{\rm{t}}{\rm{h}}{\rm{e}}\,{\rm{b}}{\rm{i}}{\rm{n}}\,{\rm{t}}{\rm{h}}{\rm{a}}{\rm{t}}\,{\rm{c}}{\rm{o}}{\rm{v}}{\rm{e}}{\rm{r}}{\rm{s}}\,{\bf{z}}\,}{{\rm{No.\; latent\; states\; in\; the\; bin\; that\; covers}}\,{\bf{z}}}\). For Fig. 2, because z is two dimensional, we use bins of eight-by-eight that cover the state space traversed by the 5,000 latent trajectories and weigh the flow arrows of the input dynamics with the estimated p(u|z). Similarly, for the background shading that quantifies the speed of input dynamics in Fig. 2, we use bins of 100-by-100 to estimate p(u|z) and apply a Gaussian filter with σ = 2 (in the units of the grid) to smooth the histogram. A similar procedure was performed for Extended Data Figs. 1 and 4 to estimate p(u|z) numerically.

Speed of autonomous and input dynamics

To compute the normalized difference in the speed of autonomous and input dynamics in Fig. 3c, similar to previous sections, we first generated latent trajectories from the learnt F for 5,000 different trials with generative click rate ratios used in our experiments with rats. Next, we computed the magnitude of the autonomous dynamics \(\parallel {\dot{{\bf{z}}}}_{{\rm{a}}{\rm{u}}{\rm{t}}{\rm{o}}{\rm{n}}{\rm{o}}{\rm{m}}{\rm{o}}{\rm{u}}{\rm{s}}}\parallel \) and the magnitude of the average input dynamics \((\parallel {\dot{{\bf{z}}}}_{\bar{{\rm{l}}{\rm{e}}{\rm{f}}{\rm{t}}}}\parallel +\parallel {\dot{{\bf{z}}}}_{\bar{{\rm{r}}{\rm{i}}{\rm{g}}{\rm{h}}{\rm{t}}}}\parallel )/2\) for each time point for each of the 5,000 trajectories and then averaged across the trajectories and across time periods defined in Fig. 3b to obtain Fig. 3c.

FINDR

Detailed descriptions are provided in ref. [20]. Briefly, to infer velocity vector fields (or flow fields) from the neural population spike trains, we used a sequential variational autoencoder called FINDR.

FINDR minimizes a linear combination of two losses: one for neural activity reconstruction (\({{\mathcal{L}}}_{1}\)) and the other for vector field inference (\({{\mathcal{L}}}_{2}\)). To reconstruct neural activity, FINDR uses a deep neural network G that takes the spike trains of N simultaneously recorded neurons y and the sensory click inputs u in a given trial to obtain the time derivative of the d-dimensional latent decision variable z:

$${{\bf{z}}}_{t+1}={{\bf{z}}}_{t}+\Delta tG({{\bf{z}}}_{t},{{\bf{u}}}_{1:T},{{\bf{y}}}_{1:T})+{{\boldsymbol{\eta }}}_{t},\,t=1,2,3,\ldots .$$

(8)

Here, T is the number of time steps in a given trial, ut is a two-dimensional vector representing the number of left and right clicks played in a time step (Δt = 0.01 s), yt is an N-dimensional vector of the spike counts in a time step and ηt is noise drawn from N(0, ΔtΣ) in each time step. Σ is a d-dimensional diagonal matrix in which the diagonal elements need not be equal to each other. For each time step, FINDR infers the firing rates of N simultaneously recorded neurons rt from zt with

$${{\bf{r}}}_{t}={\rm{s}}{\rm{o}}{\rm{f}}{\rm{t}}{\rm{p}}{\rm{l}}{\rm{u}}{\rm{s}}(W\,{{\bf{z}}}_{t}+{{\bf{b}}}_{t}),$$

(9)

where softplus is a function approximating the firing rate–synaptic current relationship (f–I curve) of neurons, W is an N × d matrix representing the encoding weights and bt is an N-dimensional vector representing the putatively decision-irrelevant baseline input. The baseline bt is learnt before fitting FINDR using the procedure described in Baseline and in detail in the Supplementary Methods, section 1.2. The reconstruction loss is given by

$${{\mathcal{L}}}_{1}=-\mathop{\sum }\limits_{t=1}^{T}\text{log}\,\text{Poisson}({{\bf{y}}}_{t}| {{\bf{r}}}_{t}).$$

(10)

For vector field inference, we parametrize the vector field F with a gated feedforward neural network20,32:

$$\dot{{\bf{z}}}\approx \frac{{{\bf{z}}}_{t}-{{\bf{z}}}_{t-\Delta t}}{\Delta t}=F({{\bf{z}}}_{t-\Delta t},{{\bf{u}}}_{t}).$$

(11)

F gives the discretized time derivative of z. We find the vector field F that captures the latent trajectories z inferred from G in equation (8) by minimizing

$${{\mathcal{L}}}_{2}=\mathop{\sum }\limits_{t\,=\,1}^{T}{(F({{\bf{z}}}_{t},{{\bf{u}}}_{t})-G({{\bf{z}}}_{t},{{\bf{u}}}_{1:T},{{\bf{y}}}_{1:T}))}^{{\rm{\top }}}{{\rm{\sum }}}^{-1}(F({{\bf{z}}}_{t},{{\bf{u}}}_{t})-G({{\bf{z}}}_{t},{{\bf{u}}}_{1:T},{{\bf{y}}}_{1:T})).$$

(12)

The total loss that is minimized by FINDR is

$${\mathcal{L}}={{\mathcal{L}}}_{1}+c{{\mathcal{L}}}_{2},$$

(13)

where c = 0.1 is a fixed hyperparameter (c = 0.0125 in Extended Data Fig. 1g). FINDR minimizes \({\mathcal{L}}\) by using stochastic gradient descent to learn W, Σ, the parameters of the neural network representing F and the parameters of the neural network G. It can be shown that \({\mathcal{L}}\) is an approximate upper bound on the marginal log likelihood of the data and that training FINDR this way is equivalent to performing inference and learning with a sequential auto-encoding variational Bayes algorithm that straightforwardly extends the standard auto-encoding variational Bayes algorithm[52].

After training, we plot the vector field (that is, a grid of \(\dot{{\bf{z}}}\)) using the learnt F and generate FINDR-predicted neural responses using equation (9) and

$${{\bf{z}}}_{t}={{\bf{z}}}_{t-\Delta t}+\Delta tF({{\bf{z}}}_{t-\Delta t},{{\bf{u}}}_{t})+{{\boldsymbol{\eta }}}_{t}.$$

(14)

Equation (14) is an Euler-discretized gated neural stochastic differential equation20,32.

Parameters

The total number of free parameters P of the FINDR model is given by

$$\begin{array}{c}P\,=\,{P}_{W}+{P}_{{\Sigma }}+{P}_{F}+{P}_{G},\\ {P}_{W}\,=\,N\times d,\\ {P}_{{\Sigma }}\,=\,d,\\ {P}_{F}\,\in \,\{90\,+(64+d)d,150+(104+d)d,300+(204+d)d\},\\ {P}_{G}\,\in \,\{15,900+300N+100x+{P}_{F},61,800+600N+200x\\ \,\,+\,{P}_{F},243,600+1,200N+400x+{P}_{F}\}.\end{array}$$

(15)

PW is the number of parameters in the encoding weight matrix W, the dimensions of which are the number of neurons N and latent dimensionality d. PΣ is the parameter count in the diagonal covariance Σ of the additive Gaussian noise of the latent z. The number of parameters in the neural networks parametrizing F(PF) and G(PG) are separate hyperparameters. Here, \(x=\frac{{P}_{F}-d+{d}^{2}}{2d+3}\).

Hyperparameters

The hyperparameters that were optimized (PF, PG and α) include the number of parameters of the network F(PF), the number of parameters of the network G(PG) and the learning rate α ∈ {10−2, 10–1.625, 10−1.25, 10−0.875, 10−0.5}. We identify the optimal values for these hyperparameters in a 3 × 3 × 5 = 45 grid search. The grid search was performed separately for each set of training data for each of five crossvalidation folds. In each training set, three-quarters of the trials were used to the optimize the parameters under a given set of hyperparameters and the remaining one-quarter was held out to evaluate the model performance for that set of hyperparameters. Test data were never used in the grid search.

Latent space transformation

Because the encoding weight matrix W is not constrained to semi-orthogonality and can take only any real values, different combinations of W and zt can give rise to the same firing rate vector rt, even when baseline bt is fixed. To uniquely identify the latent trajectories (except for redundancy from rotations and reflections), after optimization, we linearly transformed the latent space z to \(\mathop{{\bf{z}}}\limits^{ \sim }\):

$${\mathop{{\bf{z}}}\limits^{ \sim }}_{t}=S{V}^{{\rm{\top }}}{{\bf{z}}}_{t},$$

(16)

where S is a d × d diagonal matrix containing the singular values of W and V is a d × d matrix containing the right singular vectors

$$W=US{V}^{\top }.$$

(17)

U is an N × d matrix containing the left singular vectors of W (where N is the number of neurons). In the space of \(\mathop{{\bf{z}}}\limits^{ \sim }\), the encoding weight matrix is a linear transformation that preserves angles and distances because U is semi-orthogonal and can only give rise to an isometry such as rotation and reflection.

$$\begin{array}{c}W\,{\bf{z}}=US{V}^{{\rm{\top }}}{\bf{z}}\\ =\,U\mathop{{\bf{z}}}\limits^{ \sim }\end{array}$$

(18)

To obtain meaningful axes for the transformed latent space \(\mathop{{\bf{z}}}\limits^{ \sim }\), we generate 5,000 different trajectories of \(\mathop{{\bf{z}}}\limits^{ \sim }\) in generative mode (that is, using F and Σ in equation (14) but not G) and perform PC analysis on the trajectories. The PCs were used to define the axes of the decision variable \(\mathop{{\bf{z}}}\limits^{ \sim }\). In the main text, the PC1 axis of \(\mathop{{\bf{z}}}\limits^{ \sim }\) was denoted as z_1 and the PC2 axis of \(\mathop{{\bf{z}}}\limits^{ \sim }\) was denoted as z_2. In all our analyses, the latent trajectories and vector fields inferred by FINDR are shown in the transformed latent space of \(\mathop{{\bf{z}}}\limits^{ \sim }\) and scaled such that the latent trajectories along PC1 lie between −1 and 1.

Sample zone

In Figs. 2 and 3, to focus on the portion of the inferred vector field that is used by the single-trial trajectories, we show only the well-sampled subregion of the state space, which is the portion occupied by at least 50 of 5,000 simulated single-trial latent trajectories of 1 s. With this definition, the sample zone is the same across time points in Fig. 2h.

Model evaluation

The goodness of fit of the PSTH was quantified using the coefficient of determination (R^2) of the evidence–sign conditioned PSTH as defined in equation (34) using fivefold cross-validation. We used three-fifths of the trials in a session as the training dataset, one-fifth of the trials as the validation dataset to optimize the hyperparameters of FINDR and one-fifth of the trials as the test (that is, out-of-sample) dataset to evaluate performance of FINDR. Therefore, when we compute the goodness of fit, we also obtain five different vector fields inferred by FINDR for each fold, which we confirm are consistent across folds (Extended Data Fig. 4).

Curvature of trial-averaged trajectories

To compute the curvature of trial-averaged trajectories in Fig. 3b, as before, we first generate latent trajectories from FINDR for 5,000 different trials with generative click rate ratios used in our experiments with rats. Next, we separate the trials on the basis of whether the generative click ratio in a given trial favours a leftward choice or a rightward choice. We take the average of the latent trajectories over the left-favouring trials and then convolve the trial-averaged trajectory with a Gaussian filter with σ = 3 (in units of the time step Δt = 0.01 s). We take this smoothed trajectory to numerically compute the planar curvature. We do the same for the right-favouring trials and take the average between the curvature obtained from left-favouring trials and the curvature obtained from right-favouring trials to generate the plot in Fig. 3b.

cFINDR

The cFINDR model replaces the neural network parametrizing F in FINDR with a linear combination of affine dynamics, specified by M and N, and bistable attractor dynamics specified by φ. The dynamics are furthermore constrained to be two dimensional.

$$\begin{array}{c}\dot{{\bf{z}}}\approx \frac{{{\bf{z}}}_{t}-{{\bf{z}}}_{t-\Delta t}}{\Delta t}=F({{\bf{z}}}_{t-\Delta t},{{\bf{u}}}_{t})=M{{\bf{z}}}_{t-\Delta t}+N{{\bf{u}}}_{t}+s\times {\varphi }({{\bf{z}}}_{t-\Delta t}),\\ M=Q\Lambda {Q}^{-1},\\ Q=[\begin{array}{cc}1 & \sin (\theta )\,\\ 0 & \cos (\theta )\end{array}],\\ \Lambda =[\begin{array}{cc}0 & 0\,\\ 0 & -r\end{array}],\\ {\varphi }({{\bf{z}}}_{t})=-\exp (\,-\,{({{\bf{z}}}_{t}-{\bf{x}})}^{2}/\rho )\,\odot \,({{\bf{z}}}_{t}-{\bf{x}})\\ \,\,\,\,\,\,\,\,-\exp (\,-\,{({{\bf{z}}}_{t}+{\bf{x}})}^{2}/\rho )\,\odot \,({{\bf{z}}}_{t}+{\bf{x}}).\end{array}$$

(19)

The matrix M implements a line attractor located at z_2 = 0. The inputs ut are the same as those in FINDR and represent the auditory clicks. The two discrete attractors are constrained such that x_2 = 0 and implemented through the function φ. The shape of the basin of attraction corresponding to each point attractor is specified by the parameter ρ. The relative contribution of the discrete attractors and the line attractor to the overall dynamics is specified by the scalar s.

The DDM line attractor hypothesis can be implemented in cFINDR by setting θ = 0. Non-normal dynamics with a line attractor[2] can be implemented by setting θ ≠ 0. The bistable attractor hypothesis can be implemented by increasing ρ.

As in FINDR, cFINDR learns W, Σ and parameters of G. Instead of the neural networks parametrizing F, cFINDR, learns s, θ, r, x, ρ and the 2 × 2 matrix N to approximate F, which has nine parameters. The same objective function and optimization procedure were used in cFINDR. After optimization, as in FINDR, the latent space z is linearly transformed to uniquely identify the dynamics (except for arbitrary rotations or reflections). As in the analysis of results from FINDR, the latent trajectories and vector fields inferred by cFINDR are in the transformed latent space \(\mathop{{\bf{z}}}\limits^{ \sim }\).

When we fit cFINDR to the data, we experimented with the different constraints r > 0 and r > 3. The fits using r > 0 were superior to those using r > 3 and were therefore used in the comparison between cFINDR and FINDR for the data presented in Fig. 3e,f. We were motivated to try both r > 0 and r > 3 because we found that, in synthetic data, cFINDR under the constraint r > 0 could not recover the dynamics generated under the DDM line attractor hypothesis (r = 10). For this reason, Extended Data Fig. 5f shows results from synthetic data using r > 3. When fit to data, FINDR outperforms cFINDR using either r > 0 or r > 3.

FINDR models with more than two latent dimensions

For Extended Data Fig. 3j,k, we evaluated FINDR models with more than two latent dimensions to assess whether the two-dimensional manifold we found is approximately an attractor. To show that the sample zone was an approximate attractor manifold, we perturbed the latent states on the manifold along the third PC direction. When the latent states were perturbed (but not so far that the latent states went outside the range along the PC3 axis covered by the sample zone), the latent states flowed towards the manifold. To obtain the flow directions along PC3, we first generated 5,000 latent trajectories (similar to Fig. 2 for computing the sample zone). We then divided the PC1 × PC2 space into an eight-by-eight grid (the grid used for the vector field arrows in Extended Data Fig. 3i). For each cell in the grid, we identified the latent states from the 5,000 trajectories that were inside the cell and identified the highest (lowest) PC3 value \({z}_{3}^{{\rm{up}}}({z}_{3}^{{\rm{dn}}})\). This was to ensure that the perturbation along the PC3 axis was not too large. Next, we computed the flow vector using a 100-by-100 grid on the PC1 × PC2 space, assuming that \({\rm{PC3}}={z}_{3}^{{\rm{up}}}({z}_{3}^{{\rm{dn}}})\) and PC4 = 0. The space covered by each cell of the grid is coloured on the basis of the direction of the flow vector along PC3: if flowing upwards, green; if flowing downwards, pink. A Gaussian filter was applied to this heat map with σ = 2 (in units of the 100-by-100 grid), similar to the heat map for input dynamics in Fig. 2f. The resulting plot is shown on the left (right) panel. Results were similar without the Gaussian filter.

Choice decoding from FINDR

FINDR does not use the choice of the animal for reconstructing neural activity. However, after training, we can fit a logistic regression model that predicts the choice of the animal from the decision variable z at the final time step T. When we fit an ℓ_2-regularized logistic regression model using zT from the trained network G and the choice of the animal in the representative session in Fig. 2c–h, we found that the logistic choice decoder achieves 89.7% accuracy in predicting choice in the out-of-sample dataset. We can generate choices from this decoder by generating latent trajectories using F and Σ in equation (14) as in previous sections and by supplying zT to the trained decoder. A total of 5,000 latent trajectories and choices generated from F and the choice decoder were used for the analysis in Extended Data Fig. 4l. We used a separate logistic regression model for predicting choice from the latent trajectories truncated at time = 0.33 s projected onto PC2. Optimization of the logistic regression models was carried out using L-BFGS[53].

MMDDM

The MMDDM is a state-space model, comprising a dynamic model that governs the time evolution of the probability distributions of latent (that is, hidden) states and measurement models that define the conditional distributions of observations (that is, emissions) given the latent state. Additional information is provided in the Supplementary Methods, section 1.3.

Dynamic model

The latent variable z is one dimensional (that is, a scalar), and its time evolution is governed by a piecewise linear function:

$$z(t+1)=\left\{\begin{array}{l}z(t)+{u}(t)+{\eta },-B < z(t) < B\\ B\cdot {\rm{sign}}(z(t)),{\rm{otherwise.}}\end{array}\right.$$

(20)

When the absolute value of z is less than the bound height B (free parameter), its time evolution depends on momentary external input u and i.i.d. (independent and identically distributed) Gaussian noise η.

$$\eta \sim {\mathcal{N}}(0,\Delta t),$$

(21)

where Δt is the time step and set to 0.01 s. Here, ~ means ‘distributed as’. When \(z\) is either less than −B or greater than B, it becomes fixed at the bound. The initial probability distribution of z is given by

$$z(t=1) \sim {\mathcal{N}}({\mu }_{0},1),$$

(22)

where the mean µ_0 is a free parameter. In time step t, the input u(t) is the total difference in the per-click input v between the right and left clicks that occurred in the time interval (t − Δt, t):

$$u(t)=\sum _{\tau \in {\rm{R}}}v(\tau ;t)-\sum _{\tau \in {\rm{L}}}v(\tau ;t),$$

(23)

where L(R) is the set of the left (right) click times and v(τ; t) is the per-click input of a click occurring at time τ and time step t. Note that \(\tau \in {\mathbb{R}}\) indicates continuous time, whereas \(t\in {\mathbb{N}}\) indexes a time step. The per-click input is given by

$$v(\tau ;t)=D(\tau ;t)\cdot C(\tau )\cdot {\zeta },$$

(24)

where D(τ; t) indicates the integral over the interval [t − Δt, t) of the Dirac delta function δ delayed by τ:

$$D(\tau ;t)={\int }_{t-\Delta t}^{t-\varepsilon }{\delta }(x-\tau )dx=\left\{\begin{array}{ll}1, & \,\tau \in [t-\Delta t,t)\\ 0, & {\rm{otherwise,}}\end{array}\right.$$

(25)

where ε is the machine epsilon. To account for sensory adaptation, the per-click input is depressed by preceding clicks by a time-varying scaling factor given by the function C(τ), implemented according to previous work[24] (Supplementary Methods, section 1.3.1). The per-click input is corrupted by i.i.d. multiplicative Gaussian noise ζ:

$${\zeta } \sim {\mathcal{N}}(1,{\sigma }_{{\rm{s}}}^{2}).$$

(26)

The free parameter \({\sigma }_{{\rm{s}}}^{2}\) is the variance of the per-click noise. Variability in the dynamic model is fit to the data through the per-click noise ζ rather than per-time step noise η on the basis of previous findings[24]; our results are similar if we set the variance of η rather than the variance of ζ as a free parameter.

The dynamic model has three free parameters: bound height B, variance \({\sigma }_{{\rm{s}}}^{2}\) of the per-click noise and mean µ_0 of the initial state. These parameters are learnt simultaneously with the parameters of the measurement models.

Measurement model of behavioural choices

In each trial, the binary behavioural choice c (1, right; 0, left) is the sign of z in the last time step T of the trial (the earlier of 1 s after the onset of the clicks or immediately before the animal leaves the fixation port):

$$c| z(T)={\rm{sign}}(z(T)).$$

(27)

Measurement model of spike counts

In each time step t, given the value of z, the spike count y of neuron n is a Poisson random variable

$${y}^{(n)}(t)|z(t) \sim \text{Poisson}({\lambda }^{(n)}(t)\Delta t).$$

(28)

The firing rate λ is given by

$${\lambda }^{(n)}(t)|z(t)=h({w}^{(n)}\cdot z(t)+b(t)),$$

(29)

where \(h(\cdot )\) is the softplus function used to approximate the neuronal frequency–current curve of a neuron:

$$h(x)=\log (1+\exp (x)).$$

(30)

The encoding weight w depends on z itself:

$${w}^{(n)}=\{\begin{array}{c}{w}_{{\rm{E}}{\rm{A}}}^{(n)},\,-B < z < B\\ {w}_{{\rm{D}}{\rm{C}}}^{(n)},\,z\in \{\,-\,B,B\}.\end{array}$$

(31)

Each neuron has two scalar weights, wEA and wDC, that specify the encoding of the latent variable during the evidence accumulation regime and the decision commitment regime, respectively. When the latent variable has not yet reached the bound (−B or B), all simultaneously recorded neurons are in the evidence accumulation regime and encode the latent variable through their own private wEA. When the bound is reached, all neurons transition to the decision commitment regime and encode \(z\) through their own wDC.

The bias b accounts for factors that are putatively independent of the decision, including a component that varies only across trials and another component that varies both across and within trials:

$${b}^{(n)}(m,t)={b}_{\text{cross}}^{(n)}(m)+{b}_{\text{within}}^{(n)}(m,t).$$

(32)

The cross-trial trial component \({b}_{\text{cross}}^{(n)}\) is a function of time m from the first trial of the session, whereas t indicates time in each trial relative to the stimulus onset of that trial. The within-trial component consists of time-varying influence from spike history, post-stimulus (stim) onset and pre-movement (move) onset.

$$\begin{array}{l}{b}_{\text{within}}(m,t)={\tau }_{\text{stim}}^{(m)}({k}_{\text{stim}}\ast \delta )(t)+{\tau }_{\text{move}}^{(m)}({k}_{\text{move}}\ast \delta )(t)\\ \,\,\,\,\,\,+\,{\sum }_{i}{\tau }_{\text{spike}}^{(m,i)}({k}_{\text{spike}}\ast \delta )(t),\end{array}$$

(33)

where the symbol \(\ast \) indicates convolution, τx indicates translation τxk(t) = k(t − τx) by the time of event x and δ is the Dirac delta function. The functions \({b}_{\text{cross}}^{(n)},{k}_{\text{stim}},{k}_{\text{move}},{k}_{\text{spike}}\) are learnt, and each is parametrized as a linear combination of radial basis functions40,54 (Supplementary Methods, section 1.5). The measurement model of each neuron of the spike train has 19 parameters that are learnt simultaneously with the parameters of the dynamic model (that is, the model of the latent variable).

Parameter learning

All parameters, including the three parameters of the latent variable and the 19 parameters private to each neuron, are learnt simultaneously by jointly fitting to all spike trains and choices using maximum a posteriori estimation. Gaussian priors were placed on the model parameters to ensure that the optimization reached a critical point and confirmed to not change the results in separate optimizations using maximum likelihood estimation (that is, optimization without Gaussian priors). Out-of-sample predictions were computed using fivefold cross-validation.

nTc

The time step when decision commitment occurred is selected to be when the posterior probability of the latent variable at either the left bound or the right bound, given the click times, spike trains and behavioural choice, is greater than 0.8. Results were similar for other thresholds, and the threshold of 0.8 was chosen to balance between prediction accuracy and the number of trials for which commitment was predicted to have occurred. Using this definition, commitment occurred in 34.6% of trials.

Engagement index

The engagement index was computed for each neuron to quantify its involvement in evidence accumulation and decision commitment. The index was defined using wEA and wDC of the neuron: EI ≡ (|wEA| − |wDC|)/(|wEA| + |wDC|). It ranges from −1 to 1. A neuron with an engagement index of −1 encodes the latent variable only during decision commitment, an engagement index of 1 indicates involvement only during evidence accumulation, and an engagement index of 0 represents a similar strength of encoding the latent variable during evidence accumulation and decision commitment.

Analyses

Neuronal selection

Only neurons that meet a preselected threshold for being reliably choice selective were included for analysis. For each neuron, reliable choice selectivity was measured using the area under the receiver operating characteristic curve (auROC) indexing how well an ideal observer can classify between a left-choice trial and a right-choice trial on the basis of neuronal spike counts. Spikes were counted in four non-overlapping time windows (0.01–0.21 s, 0.21–0.4 s, 0.41–0.6 s and 0.61–0.9 s after stimulus onset), and an auROC was computed for each time window. A neuron with an auROC < 0.42 or an auROC > 0.58 for any of these windows was considered choice selective and included for other analyses. Moreover, neurons must have had an average firing rate of at least two spikes per s. Across sessions, the median fraction of neurons included under this criterion was 10.4%.

PSTH

Spike times were binned at 0.01 s and were included up to 1 s after the onset of the auditory stimulus (click trains) until 1 s after the stimulus onset or until the animal removed its nose from the central port, whichever came first. The time-varying firing rate of each neuron in each group of trials (that is, task condition) was estimated with a PSTH, which was computed by convolving the spike train on each trial with a causal Gaussian linear filter with a standard deviation of 0.1 s and a width of 0.3 s and averaging across trials. The CI of a PSTH was computed by bootstrapping across trials.

The goodness of fit of the model predictions of the PSTH was quantified using the coefficient of determination (R^2), computed using fivefold crossvalidation. R^2 was computed by conditioning the PSTH on either the sign of the evidence (that is, whether the generative click ratio in a given trial favoured a leftward choice or a rightward choice) or the choice of the animal:

$$\begin{array}{c}{R}^{2}=1-\frac{{{\rm{SS}}}_{{\rm{res}}}}{{{\rm{SS}}}_{{\rm{tot}}}}\\ {{\rm{SS}}}_{{\rm{res}}}={\sum }_{t}({({{\rm{PSTH}}}_{{\rm{obs}}}^{{\rm{R}}}(t)-{{\rm{PSTH}}}_{{\rm{pred}}}^{{\rm{R}}}(t))}^{2}+{({{\rm{PSTH}}}_{{\rm{obs}}}^{{\rm{L}}}(t)-{{\rm{PSTH}}}_{{\rm{pred}}}^{{\rm{L}}}(t))}^{2})\\ {{\rm{SS}}}_{{\rm{tot}}}={\sum }_{t}({({{\rm{PSTH}}}_{{\rm{obs}}}^{{\rm{R}}}(t)-{{\mathbb{E}}}_{t}[{{\rm{PSTH}}}_{{\rm{obs}}}^{{\rm{R}}}(t)])}^{2}+{({{\rm{PSTH}}}_{{\rm{obs}}}^{{\rm{L}}}(t)-{{\mathbb{E}}}_{t}[{{\rm{PSTH}}}_{{\rm{obs}}}^{{\rm{L}}}(t)])}^{2}),\end{array}$$

(34)

where t is time in a trial that goes from 0 s to 1 s, with 0 s being the stimulus onset. The superscripts ‘R’ and ‘L’ indicate either the sign of the difference in the total number of right and left clicks or the choice of the animal. The subscripts ‘obs’ and ‘pred’ indicate whether the PSTH was computed using observed neural activity or model-predicted neural activity. SSres is the residual sum of squares, and SStot is the total sum of squares.

A normalized PSTH was computed by dividing the PSTH by the mean firing rate of the corresponding neuron across all time steps across all trials. When PSTHs were separated by ‘preferred’ and ‘null’, the preferred task condition was defined as the group of trials with the behavioural choice when the neuron responded more strongly and a null task condition was defined as the trials associated with the other choice.

Choice selectivity

In Fig. 6 and Extended Data Fig. 2m, for each neuron and for each time step t aligned to the onset of the auditory click trains, we computed choice selectivity c(t):

$$c(t)\equiv \frac{r(t)-l(t)}{r({t}^{\ast })-l({t}^{\ast })},$$

(35)

where r and l are the PSTHs computed from trials ending in a right choice and a left choice, respectively. The time step t* is the time of the maximum absolute difference:

$${t}^{\ast }\equiv {\text{argmax}}_{t}|r(t)-l(t)|.$$

(36)

In Extended Data Fig. 2m, neurons are sorted by the centre of mass of the absolute value of the choice selectivity of each neuron.

Baseline

In FINDR, cFINDR and MMDDM, the neuronal firing rate depends on a time-varying scalar baseline. In time step t of trial m, conditioned on the value of the latents in a given time step, the spike count y of each neuron is given by

$$y(m,t)|{\bf{z}}(m,t) \sim {\rm{P}}{\rm{o}}{\rm{i}}{\rm{s}}{\rm{s}}{\rm{o}}{\rm{n}}(h\{{{\bf{w}}}^{{\rm{\top }}}{\bf{z}}(m,t)+b(m,t)\}),$$

(37)

where h is the softplus function and w is the encoding weight of the latent. The baseline b incorporates putatively decision-independent variables as input to the neural spike trains including slow drifts in firing rates across trials and faster changes in each trial that are aligned to either the time from stimulus onset or the time from the animal leaving the fixation port. The baseline is learnt using a Poisson generalized linear model fit separately to the spike counts of each neuron. Details are provided in the Supplementary Methods, section 1.2.

PCTH

In trials for which a time of decision commitment (nTc) could be inferred, the spike trains were aligned to the predicted time of commitment and then averaged across those trials. The trial average was then filtered with a causal Gaussian kernel with a standard deviation of 0.05 s. The PCTHs were averaged in each of three groups of neurons: (1) neurons that were similarly engaged in evidence accumulation and decision commitment; (2) neurons more strongly engaged in evidence accumulation; and (3) neurons more strongly engaged in decision commitment. Each neuron was assigned to one of these groups according to its engagement index. Neurons with \(-\frac{1}{3}\le {\rm{EI}} < \frac{1}{3}\) are considered to be similarly engaged in evidence accumulation and decision commitment, neurons with \({\rm{EI}}\ge \frac{1}{3}\) are considered to be more strongly engaged in evidence accumulation, and those with \({\rm{EI}} < -\frac{1}{3}\) are considered to be more strongly engaged in decision commitment.

For this analysis, we focused on only the 65 of 115 sessions for which the MMDDM improved the R^2 of the PSTHs and for which the inferred encoding weights were reliable across cross-validation folds (R^2 > 0.9). From this subset of sessions, there were 1,116 neurons similarly engaged in evidence accumulation and decision commitment, 414 neurons that were more engaged in decision decision commitment and 1,529 neurons that were more engaged in evidence accumulation.

To compute the shuffled PCTH, the predicted times of commitment were shuffled among only the trials in which commitment was detected. If the randomly assigned commitment time extended beyond the length of the trial, then the time of commitment was assigned to be the last time step of that trial.

Trial-averaged trajectories in neural state space

To measure trial-averaged dynamics in neural state space, we analysed PCs in a data matrix made by concatenating the PSTHs. The data matrix X has dimensions TC-by-N, where T is the number of time steps (T = 100), C is the number of task conditions (C = 2 for choice-conditioned PSTHs and C = 4 for PSTHs conditioned on both choice and evidence strength) and N is the number of neurons. The mean across rows is subtracted from X, and singular value decomposition is performed: \({{USV}}^{\top }=X\). The principal axes correspond to the columns of the right singular matrix V, and the projections of the original data matrix X onto the principal axes correspond to the left singular matrix (U) multiplied by S, the rectangular diagonal matrix of singular values. The first two columns of the projections US are plotted as trajectories in neural state space.

Psychophysical kernel

Kernels were time locked to either nTc of each trial (Fig. 5d and Extended Data Fig. 7a–d) or the first click in each trial (Extended Data Fig. 7e–h). We extended the logistic regression model presented in ref. [55] to include a lapse parameter (Supplementary Methods, section 1.4), and we confirmed that results were similar using generic logistic regression. A shuffling procedure was used to randomly permute the inferred time of commitment across trials without changing the behavioural choice and the times of the auditory clicks on each trial. In this randomly permuted sample, we selected trials for which the auditory stimuli were playing at least 0.2 s before and at least 0.2 s after the inferred time of commitment to compute the psychophysical kernel in the shuffled condition. For Fig. 5d, the prediction was generated using the MMDDM parameters that were fit to the data and the same set of trials in the data. For Extended Data Fig. 7, temporal basis functions were used to parametrize the kernel, and the optimal number and type of basis function were selected used crossvalidated model comparison.

Statistical tests

Binomial CIs were computed using the Clopper–Pearson method. All other CIs were computed with a bootstrapping procedure using the bias-corrected and accelerated percentile method[56]. Unless otherwise specified, P values comparing medians were computed using a two-sided Wilcoxon rank-sum test, which tests the null hypothesis that two independent samples are from continuous distributions with equal medians against the alternative hypothesis that they are not.

Estimating the low-dimensional vector field without specifying a dynamical model

For Extended Data Fig. 10d, we estimated the low-dimensional velocity vector field for each session using a method that does not specify a dynamical model (model-free approach). To obtain the model-free vector field, we first estimated single-trial firing rates of individual neurons by binning the spike trains into bins of Δt = 10 ms and convolving the spike trains with a Gaussian of σ = 100 ms centred at 0. Results were similar for other values of σ around 100 ms. Next, for each neuron, we took the average across all trials in the session and subtracted this average from single-trial firing rate trajectories. These baseline-subtracted firing rate trajectories were then projected to the low-dimensional subspace spanned by the FINDR latent axes. We projected the estimated firing rates to the same subspace as FINDR to allow direct comparisons between the FINDR-inferred vector field and the model-free vector field.

We treated this low-dimensional projection of the baseline-subtracted firing rates as the latent trajectories in this model-free approach. To obtain velocity vector fields from the latent trajectories, we first estimated the instantaneous velocity \(\dot{{\bf{z}}}\) at time point t by computing \({\dot{{\bf{z}}}}_{t}=({{\bf{z}}}_{t}-{{\bf{z}}}_{t-\Delta t})/\Delta t\) for all t for all latent trajectories. We then divided the two-dimensional latent space into an eight-by-eight grid. For each cell (i, j) from this eight-by-eight grid, we identified all states zt from all trajectories that fell inside the cell (i, j). We took the corresponding \({\dot{{\bf{z}}}}_{t}\) of the identified zt values and took the average to compute the velocity for the cell (i, j). We computed velocity vectors for all 64 cells. To compare vector fields, we took the cosine similarity between the velocity vector for cell (i, j) from FINDR and the velocity vector for cell (i, j) from the model-free approach and took the mean of these cosine similarities, Sc(FINDR, model free). In computing Sc(FINDR, model free), only cells that had a number of states greater than 1% of the total number of states were included. When the number of states used to estimate the velocity vector was less than 1% of the total number of states, we considered that cell (i, j) to be outside the sample zone, analogous to the sample zone in Fig. 2.

To compare between a random vector field and the model-free vector field, we generated 1,000 random vector fields (with each of the 64 arrows in the eight-by-eight grid going in random directions) for each session and computed Sc(random, model free) for each random vector field.

For Extended Data Fig. 10e, we estimated the autonomous dynamics vector field around the origin as a model-free way of confirming our findings in Extended Data Fig. 10a. Similar to the method for Extended Data Fig. 10d, we convolved the spike trains with a Gaussian and projected the baseline-subtracted firing rate trajectories to the low-dimensional subspace spanned by the FINDR latent axes. However, to separate autonomous dynamics from input dynamics, we used a Gaussian with a smaller σ (20 ms), with a window size ±3σ around 0, and then excluded any \({\dot{{\bf{z}}}}_{t\pm 3{\sigma }}\) with time t for which a click occurred from the estimation of the autonomous dynamics. When computing the average of (zt − zt − Δt)/Δt for one of the five pie slices, we required zt − Δt to be inside the pie slice. For all sessions, the circle had a radius of 0.2 (in units of z). To further ensure that we estimated the autonomous dynamics, when computing the average, we only considered the trajectories for which the number of left clicks was equal to the number of right clicks during the epoch when they were in the pie slice.

Inclusion and ethics statement

The animal procedures described in this study were approved by the Princeton University Institutional Animal Care and Use Committee.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The experimental data that support the findings of this study are available at Dryad[57] (https://doi.org/10.5061/dryad.sj3tx96dm).

Code availability

Custom acquisition, postprocessing and analysis code is available at GitHub (https://github.com/Brody-Lab/decision_dynamics_commitment). Code implementing FINDR[20] is available at GitHub (https://github.com/Brody-Lab/findr/). Code implementing MMDDM is available at GitHub (https://github.com/Brody-Lab/fhmddm), and code for baseline estimation is available at GitHub (https://github.com/Brody-Lab/tzl_spglm).

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Acknowledgements

We thank J. Charlton, L. Ding, J. Gold, R. Goris, J. Maunsell and J. Pillow for their suggestions and comments; and J. Morrison, K. Osario, J. Teran and E. Valance for technical assistance. Analyses reported in this work were largely accomplished using the Princeton Research Computing resources at Princeton University, which is a consortium of groups led by the Princeton Institute for Computational Science and Engineering and the Office of Information Technology’s Research Computing. This work was supported by grants from the US NIH (F32 MH115416, R01MH108358, R01MH138935 and 5U19NS132720) and the Simons Foundation (SF 542953). This study is the result of funding in whole or in part from the NIH. It is subject to the NIH Public Access Policy. By accepting this federal funding, we have given the NIH the right to make this paper publicly available in PubMed Central upon the official date of publication, as defined by the NIH.

Author information

Author notes

1. Thomas Zhihao Luo

Present address: School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

2. Timothy Doyeon Kim

Present address: University of Washington, Seattle, WA, USA

3. Timothy Doyeon Kim

Present address: Allen Institute, Seattle, WA, USA

4. Diksha Gupta

Present address: Sainsbury Wellcome Centre, University College London, London, UK

5. Brian DePasquale

Present address: Department of Biomedical Engineering, Boston University, Boston, MA, USA

6. These authors contributed equally: Thomas Zhihao Luo, Timothy Doyeon Kim

Authors and Affiliations

1. Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

Thomas Zhihao Luo, Timothy Doyeon Kim, Diksha Gupta, Adrian G. Bondy, Charles D. Kopec, Verity A. Elliott, Brian DePasquale & Carlos D. Brody

2. Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA

Carlos D. Brody

Contributions

T.Z.L. and D.G. collected data. T.D.K. developed the FINDR model. T.Z.L. developed the MMDDM model. T.Z.L. and T.D.K. developed the baseline model used in fitting FINDR and MMDDM. T.Z.L. and T.D.K. performed analyses. A.G.B. and C.D.K. assisted with data collection. V.A.E. and B.D. assisted with analyses. T.Z.L., T.D.K. and C.D.B. wrote the manuscript and conceptualized the study.

Corresponding authors

Correspondence to Thomas Zhihao Luo, Timothy Doyeon Kim or Carlos D. Brody.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 FINDR can be used to distinguish between the dynamical systems hypotheses of perceptual decision-making.

In these hypotheses, the decision process is represented by the state of a dynamical system, which we refer to as the “decision variable (z)” and is depicted as two-dimensional here but may have fewer or more dimensions. An attractor is a set of states for which the dynamical system tends to move toward, from a variety of starting states. When z is in an attractor state, small perturbations away from the attractor tend to return the system toward the attractor. An attractor can implement the commitment to a choice and the maintenance of the choice in working memory. a, In all these hypotheses, the attractors are implemented by the autonomous dynamics, which corresponds to the deterministic dynamics F in the absence of inputs and depends only on z itself. In the bistable attractors hypothesis, there are two discrete attractors, each of which corresponds to a choice alternative. In the DDM line attractor hypothesis, the autonomous dynamics form not only two discrete attractors but also a line attractor in between. The intervening line attractor allows an analog memory of the accumulated evidence when noise is relatively small. In the line attractor hypothesis with non-normal dynamics, the autonomous dynamics form a line attractor, and a separate readout mechanism is necessary for the commitment to a discrete choice. b, The autonomous speed is the magnitude of the autonomous dynamics. A dark region corresponds to a steady state, which can be an attractor, repeller, or saddle point. In the bistable attractors hypothesis, the left and right steady states are each centered on an attractor, and the middle is a saddle point. In both the DDM line attractor hypothesis and the hypothesis that has non-normal dynamics with a line attractor, the steady states correspond to attractors. c-d, Input dynamics corresponding to a left and right auditory pulse, respectively. Here we show the “effective” input dynamics, which is multiplied by the frequency p(u | z) to account for the pulsatile nature and the statistics of the stimuli in our task (in contrast to Fig. 1e, in which the input dynamics were presented without the multiplication of the frequency, which is appropriate for stimuli that are continuous over time). Whereas in the bistable attractor and DDM line attractor, the inputs are aligned to the attractors, in the hypothesis that has non-normal dynamics with a line attractor, the inputs are not aligned. e, The input speed is the average of the magnitude of the average left input dynamics and the magnitude of the average right input dynamics. f, We simulated spikes that follow the bistable attractor dynamics in a-e to create a synthetic dataset with the number of trials, number of neurons, and firing rates that are typical of the values observed in our datasets. Then, we fit FINDR to this synthetic dataset from random initial parameters. The autonomous and input dynamics inferred by FINDR qualitatively match the bistable attractors hypothesis. g-h, FINDR-inferred dynamics qualitatively match the dynamics in Fig. 1f–h and a–e. In panel g, the sample zone covers the entirety of the plotted area.

Extended Data Fig. 2 Behavioral performance, histological slices, anatomical tracing, causal necessity of dmFC, and the temporal profiles of individual neurons not being not consistent with an one-dimensional neural encoding of the latent variable in the drift-diffusion model (DDM).

a, Psychometric functions of each of the twelve rats recorded aggregated across recording sessions. b, Histological images of probe tracks. Each color indicates a probe chronically implanted in a rat. c, Dorsomedial frontal cortex (dmFC) provides a major input to the anterior dorsal striatum (dStr). d, dmFC is causally necessary for the auditory decision-making task studied here. N = 23,298 saline trials and 22,428 muscimol trials. Error bars indicate 95% binomial confidence intervals. e, In the DDM, noisy inputs are accumulated over time through a scalar latent variable (z) until the value of z reaches a fixed bound, which triggers the commitment to a choice. f, In simulations of the DDM, z ramps quickly when the evidence strength is strong and more slowly when the strength is weak. g, Responses averaged across both trials and neurons resemble the trajectories of z averaged across simulations. Only choice-selective neurons were included. Spikes after the animal began movement (i.e., removed its nose in the center port) were excluded. For this analysis only, error trials were excluded. N = 1324 (dmFC), 1076 (mPFC), 1289 (dStr), 714 (vStr), 822 (M1), 163 (FOF). h, The responses of a simulated neuron encoding the DDM with a single neural mode show the ramping dynamics. Shading indicates the bootstrapped 95% confidence interval of the trial-mean of the filtered response. i, A neuron with a ramp profile. j, A neuron recorded from the session with choice selectivity that decays over time. k, A neuron exhibiting a substantial delay in its choice selectivity. l, A neuron whose choice selectivity flips in sign. m, The diversity of the temporal profile of the choice selectivity of individual neurons is not consistent with a one-dimensional encoding of the DDM.

Extended Data Fig. 3 FINDR can well capture the neural responses and reveals 2-dimensional decision-making dynamics.

a-b, FINDR captures the underlying firing rates of the single-trial responses of individual neurons from the representative session in Fig. 2. c, FINDR captures the complex trial-averaged dynamics of individual neurons from the representative session in Fig. 2 as can be seen in the peristimulus time histograms (PSTH). The goodness-of-fit is measured using the coefficient of determination (R^2). Bold line indicates out-of-sample prediction by FINDR, and the shading indicates 95% confidence interval from the observed PSTH. d, FINDR captures the single-trial and trial-averaged responses of individual neurons pooled across 27 sessions. For the histogram showing single trials pooled across sessions, 34 trials that had R^2 < 0 are not shown. Results in a-d are 5-fold cross-validated. e, Across different FINDR models with latent dimensions (d) ranging from 1 to 4, we computed the median of the coefficient of determination (R^2) of the evidence-sign conditioned peri-stimulus time histogram (PSTH) of neurons pooled across sessions (n = 2105). f, The median difference in the R^2 between d = 2 and d = 1 is significantly different from zero (p < 0.001; Wilcoxon signed-rank test). Although the median differences are also significant for the comparison between d = 3 and d = 2 and the comparison between d = 4 and d = 3, the magnitude of the difference is relatively small (0.0098 and 0.0075, respectively) compared to the median difference between d = 2 and d = 1 (0.0423). g, We repeated the analysis in f without pooling neurons across sessions. Instead, for each session, we computed the median PSTH R^2 across neurons recorded within that session. Each circle corresponds to a session, and a filled circle indicates a significant difference in the PSTH R^2 between FINDR models of different dimensionalities (p < 0.001; two-sided Wilcoxon signed-rank test; Supplementary Information 2.2). h, For FINDR models with either 3 or 4 latent dimensions, more than 97% of the variance is captured by the first two principal components (PC’s). PCA was done separately for each session, and the error bars indicate the 95% confidence interval of the median across sessions (n = 27). i, For models with 2 or more dimensions, the vector fields and trajectories projected onto the first two dimensions are qualitatively similar. The vector fields and trajectories were shown for the representative session in Fig. 2. The dashed lines demarcate the well-sampled subregion of the state space (i.e., the sample zone). j, We evaluated FINDR models with latent dimensions higher than two to see whether the two-dimensional manifold relevant to decision-making dynamics is an approximate attractor manifold. The variance explained by the third PC in the FINDR model with three-dimensional latent dynamics was less than 0.5% (as shown in h-i), so we turned to the FINDR model with four-dimensional dynamics. In this model, the variance explained by the third PC was around 1.3%. We perturbed the latent states on the manifold along the PC 3 direction. k, When the latent states are perturbed (but not too far that the latent states go outside the range along the PC 3 axis covered by the sample zone; see Methods for details), the latent states flow toward the manifold.

Extended Data Fig. 4 Consistency in FINDR-inferred dynamics.

a, FINDR-inferred input and autonomous dynamics are consistent across 5 different cross-validation folds as shown for the same session in Fig. 2. b, Normalized difference in the speed between autonomous and input dynamics for five different time periods (“start (time=0 s)” “early”, “pre-peak”, “post-peak”, and “late”) is consistent across folds (n = 27; see Fig. 3c). c, The direction of motion of the trial-averaged trajectories and its angle with respect to the z_1-axis for different time periods is consistent across folds (n = 21 out of 27 sessions; see Fig. 3d). d, Variability in the dynamics across sessions depends in part on the variability in the behavioral performance. For each each session, behavioral sensitivity was estimated as the parameter β in a probit model p(y | x) = Φ(β*x + c), where y is the rat’s left vs. right choice on each trial, x the log-ratio of the right vs. left click rate on that trial, Φ the normal cumulative distribution function, c the constant term in the probit model. The two-sided p-value of the Pearson’s correlation was computed using a Student’s t-distribution for a transformation of the correlation. Pink marker indicates the example session. e, The linear correlation between the difference in autonomous vs. input dynamics and behavioral sensitivity was negative for all epochs, but reliable only for the pre-peak epoch. The 95% confidence intervals were computed by bootstrapping across sessions (n = 27). f, FINDR reliably recovers the FINDR-inferred dynamics. After fitting FINDR to a dataset, the model parameters were used to simulate a synthetic dataset using the exact same set of sensory stimuli in the real dataset and containing the same number of neurons and trials. From new initial parameter values, FINDR was fit to the simulated data to infer the “FINDR-generated” vector fields. g, FINDR is fit to both choice-selective and non-selective neurons. We find similar dynamics to when FINDR is fit to only choice-selective neurons. h, We find vector fields that are consistent across multiple different random seeds that change the initialization in the deep neural networks of FINDR and the order in which the mini-batches of the training data are supplied to FINDR during training. i, Curved trial-averaged latent trajectories predicted by FINDR depend on the click inputs. When FINDR was fit to data in which the click inputs were randomly shuffled across trials, the trial-averaged latent trajectories remain near the origin. j, The dynamics are two-dimensional even in the beginning of the decision period. An early-epoch sample zone indicated by the dotted line was computed using trajectories that were truncated at time=0.33 s. The early-epoch sample zone delimits the portion of the state occupied by at least 50 of 5000 simulated single-trial trajectories. k, When we compute the PCs for the trajectories truncated at time=0.33 s and project the trajectories onto PC 2, the standard deviation along this direction is 20.4% of the standard deviation along PC 1. l, We can decode behavioral choice from logistic regression significantly better than chance (dashed line) from the projections of the truncated trajectories onto PC 2. Bold line indicates the mean, and the shading indicates 95% confidence interval. m, Single-trial latent trajectories extending to time=1.0 s, simulated using stimuli of different evidence strength, which is quantified by the ratio of right and left inputs.

Extended Data Fig. 5 The data are better captured by FINDR than by a variant of FINDR constrained to parametrize the dynamics described by previously proposed hypotheses.

a, The constrained FINDR (cFINDR) model replaces the neural networks parametrizing F in FINDR with a linear combination of affine dynamics, specified by M and N, and bistable attractor dynamics specified by φ. The dynamics are furthermore constrained to be two-dimensional. b-g, cFINDR model can generate and infer dynamics described by previous hypotheses. b, Example bistable attractor dynamics generated from cFINDR. c, Example DDM line attractor dynamics generated from cFINDR. d, Example non-normal dynamics with a line attractor generated from cFINDR. e, cFINDR-inferred dynamics from a synthetic dataset generated using the bistable attractor dynamics in b. f, cFINDR-inferred dynamics from a synthetic dataset generated using the DDM line attractor dynamics generated in c. g, cFINDR-inferred dynamics from a synthetic generated using the non-normal dynamics with a line attractor in d.

Extended Data Fig. 6 Multi-mode drift-diffusion model (MMDDM).

a, Directed graph of the MMDDM for a trial with T time steps and N simultaneously recorded neurons. At each time step, the decision variable z depends on external click input (u) and its value in the previous time step. The spike train depends on z and also a time-varying baseline input. The behavioral choice (c) is the sign of the decision variable at the last time step. In this example trial, z reaches the bound, and the encoding weight of z of each neuron changes from wEA to wDC. b, The MMDDM is an instance of a state-space model, which consists of a dynamic model governing the probability distributions of the latent states (here, scalar decision variable z) and measurement models specifying the conditional distributions of the emission (here, spike counts y and the rat’s choice c) given the value of the latent states. In the dynamic model, z’s time derivative (ż) is a piecewise linear function. When the absolute value of z is less than the bound height B, the velocity depends on external click input (u) and i.i.d. Gaussian noise (η). When z reaches either -B or B, the time derivative is zero. The input of each click emitted at time τ on z is scaled by the depressive adaptation from previous clicks, parametrized by C(τ), and it is corrupted by i.i.d. multiplicative Gaussian noise ζ with variance σs^2. The parameter σs^2 is one of the three parameters learned during fitting and represents the signal-to-noise of the system. The behavioral choice (c) is the sign of the decision variable at the last time step. The mapping from z to spike train response (y) passes through the softplus nonlinearity h and depends on baseline b and encoding weight w. The encoding weight is either wEA and wDC depending on z. The three parameters that are fit in MMDDM consist of the bound height B, the mean μ_0 of starting distribution, and the signal-to-noise of each momentary input. c, The baseline input consists of a cross-trial component, parametrized by smooth temporal basis functions, as shown for an example neuron. d, The spike history filter of the same neuron. e, The post-stimulus filter of the neuron. This filter does not depend on the content of the click train and only depends on the timing of the first click, which is always a simultaneous left and right click. f, The kernel of the same neuron to account for movement anticipation. The kernel does not depend on the actual choice of the animal. g, The psychometric function is well captured across sessions. h, The vector field inferred from real spike trains is confirmed to be similar to that inferred from MMDDM-simulated spike trains for the session “T176_2018_05_03”. i, After fitting the model to each recording session, the learned parameters are used to simulate a data set, using the same number of trials and the same auditory click trains. The simulations are used to fit a new model, the recovery model, starting from randomized parameter values. The encoding weights of the accumulated evidence of the recovery model are compared against the weights used for the simulation (which were learned by fitting to the data) using the coefficient-of-determination metric. j, Consistency in the encoding weights between the training models during five-fold cross-validation. For each session, a coefficient-of-determination was computed for each pair of training models (10 pairs), and the median is included in the histogram. k, Whereas the Poisson distribution requires the mean to be the same as the variance, the negative binomial distribution is a count response model that allows the variance to be larger than the mean μ, with an additional parameter α, the overdispersion parameter, that specifies the variance to be equal to μ + αμ^2. When the overdispersion parameter is zero, the distribution is equivalent to a Poisson. Fitting the data to varying values of the overdispersion parameter shows that log-likelihood is maximized with a Poisson distribution for the conditional spike count response. Similarly, when the overdispersion parameter was learned from the data, the best-fit values were all close to zero. l, The magnitude of the input after sensory adaptation of each click in a simulated Poisson auditory click train. Based on previous findings[24], the adaptation strength (φ) is fixed to 0.001, and the post-adaptation recovery rate (k) to 100. The generative click rate is 40 Hz, as in the behavioral task. m, Sensory adaptation is not critical to the improvement in fit by the MMDDM compared to the single mode DDM. Even without modeling sensory adaptation–by setting φ = 1 and k = 0, such that every click has the same input magnitude–the out-of-sample log-likelihood is reliably improved by the MMDDM compared to the single mode DDM. n, The out-of-sample goodness-of-fit of the PSTH’s is also reliably improved even in the absence of sensory adaptation. m-n, P-values were computed using two-sided sign tests.

Extended Data Fig. 7 nTc and psychophysical kernels.

a, For the inferred weights of the stimulus fluctuations to be interpretable, the click input fluctuations must not be strongly correlated across time steps. On each time step on each trial, the fluctuation in auditory click input was computed by counting the observed difference in right and left clicks at that time step, and then subtracting from it the expected difference given the random processes used to generate the stimulus. The input fluctuations at time step of t = 0 s were excluded because they are strongly correlated with the input fluctuations before decision commitment and strongly anti-correlated with input fluctuations after commitment. b, To determine the time resolution of the kernel that best captures the weight of the input fluctuations, 10-fold cross-validation was performed to compare kernels quantified by different numbers of parameters and types of basis functions. The kernel with the lowest temporal resolution is a constant, represented by a single parameter, implying that fluctuations across time have the same weight. At the highest time resolution, the kernel can be parametrized by a separate weight for each time step. At intermediate time resolution, the kernel is parametrized by basis functions that span the temporal window. The basis functions can be evenly spaced across the temporal window, or stretched such that time near t = 0 s is represented with higher resolution and time far from t = 0 s with lower resolution. The most likely model had six moderately stretched (η = 1) basis functions. c, The optimal model’s set of six moderately stretched (η = 1) basis functions. d, Even when using basis functions, the psychophysical kernel is consistent with the core prediction of MMDDM: The psychophysical weight of the stimulus fluctuations on the behavioral choice ceases after the time of decision commitment. Note that no basis function was used in the analysis in Fig. 5d. e, In contrast to the commitment-aligned kernel, the kernel aligned to the onset of the auditory click trains is smooth. Mean stimulus onset-aligned psychophysical kernel across sessions, estimated using a model with five temporal basis functions. For each session, 10-fold cross-validation was performed on fitting the kernel model to the data, and ten estimated kernels were averaged. Then, the kernels were averaged across sessions. f, The onset-aligned psychophysical kernel is parametrized by five evenly spaced radial basis functions. g, Cross-validated model comparison shows that a temporally flat psychophysical kernel is most likely given the observed data. h, Similarly, given the simulated choices generated by the MMDDM, the out-of-sample log-likelihood is maximized by assuming a flat kernel. g-h, N = 115 sessions. i, The approximately flat psychophysical kernel inferred from MMDDM-simulated choices is consistent with the MMDDM’s prediction of the probability of decision commitment given the stimulus: throughout the trial, the probability of decision commitment is relatively low, and at no point in the trial is decision commitment an absolute certainty. j, At t = 0.75 s, the window used to compute the psychophysical kernel, the median probability of decision commitment across sessions is 0.57. k, A small but statistically significant effect of whether decision commitment was reached on the “movement onset time”, i.e., the time when the rat withdraws its nose from the fixation port minus the earliest time when the rat is allowed to do so. The effect is not simply due to trial difficulty because it remains when we consider only easy trials (right: left click rate either greater than 38:1 or less than 1:38). k-m, N = 35962 trials (without nTc), 19095 (with nTc), 10261 (without nTc among easy trials), 7962 (with nTc among easy trials). l, Similar effect of whether commitment was reached on the rat’s “movement execution time”, i.e., the time when the rat reaches either the left or right port minus the time when it withdrew its nose from the fixation port. m, Relative timing of decision commitments between pairs of simultaneously recorded brain regions. For each pair of regions, the comparison was made on only the trials on which the threshold for commitment was crossed for both regions. N = 3936 trials (dmFC vs. mPFC), 7024 (M1 vs. dStr), 6251 (M1 vs. vStr), 8463 (dStr vs. vStr), 529 (dStr vs. FOF), 487 (vStr vs. FOF). n, Inferred times of commitment, relative to stimulus offset. m-n, P-values were computed using two-sided sign tests. o, As expected from the model, nTc’s occur more often in easier trials, i.e., trials with larger generative (experimentally controlled) difference between the left and right click rate. p, As expected from the model, the mean value of the latent variable (the expectation under the posterior probability given the spikes and choice) reaches values of larger magnitude on trials on which nTc could be inferred compared to trials on which an nTc could not be inferred. Shading indicates 95% bootstrapped confidence intervals across sessions. q, Consistent with the model, even when considering only the period while the clicks were still playing, the mean of the latent variable abruptly plateaus after the nTc. r, The trials on which nTc could be estimated were separated into three groups using the terciles of the distribution of nTc relative to stimulus onset. s, Psychometric function of each group, showing the fraction of a right choice against the generative (i.e., experimentally specified) difference between the right and left click rates. t, Behavioral sensitivity is higher for trials with longer nTc. A logistic model with two terms (bias and slope) was fit to regress the choice on each trial against the generative difference in click rate. Data are presented as the best-fit slope parameters and their 95% confidence intervals, computed by bootstrapping across trials. N = 6120 trials (first tercile), 6545 (second tercile), 6336 (last tercile).

Extended Data Fig. 8 The distribution of commitment times inferred from MMDDM does not match the distribution of start time of peri-movement kernels.

a, Separately for each choice-selective neuron (N = 4605), peri-movement kernels are estimated using Poisson generalized linear models (GLM)40,58. The inputs (i.e., regressors) to the model depend on two events that occur on each trial: onset of fixation (i.e., when the rat inserts its nose into the center port), and the time when the rat leaves the center port and begins to move toward the side port. An impulse (i.e., delta function) at the time of each event is convolved with a linear filter, or kernel, to parametrize the time-varying input related to that event. At each time step, the sum of the inputs is fed through a rectifying nonlinearity (softplus) to specify the neuron’s Poisson firing rate at that time. Three kernels, related to fixation, leftward movement, and rightward movement, are learned by maximizing the marginal likelihood[40]. b, Example neuron. Two GLM variants were fitted to the same neuron, and for each GLM variant, the observed peri-event time histogram (PETH) is overlaid the cross-validated, model-predicted PETH. The choice-dependence of the PETH of this neuron is well captured by the model variant whose peri-movement kernels start −3.0 s before and 0.5 s after movement onset (left), but less well captured by another variant whose peri-movement kernels time base are limited to −0.5 to 0.5 s (right). c, To identify the optimal start of the movement kernel for each neuron, cross-validated (5-fold) model comparison was performed on seven model variants that vary in the start time of the movement kernels and the number of radial basis functions used to parametrize the kernels. The end time of the movement kernel (0.5 s), and the parametrization of the fixation-related kernel (−1.5 s to 2.0 s and 4 basis functions) are identical for all variants. d, The out-of-sample log-likelihood is highest for the model variant whose peri-movement kernels start at −3.0 s. e, For each neuron, the GLM variant with the highest out-of-sample log-likelihood determines the optimal start of the peri-movement kernels. The mode of the distribution is at −3.0 s. f, The start of peri-movement kernels for most neurons precede the time of the first click. g, The start of peri-movement kernels for most neurons precede the earliest commitment time inferred from MMDDM.

Extended Data Fig. 9 Changes in neural responses after stimulus offset are more closely aligned to movement onset than stimulus offset.

a, Relative timing of task events. The offset of the auditory click train stimulus always occurred at the end of the 1.5 s minimum fixation period on every trial. b, The median time of movement onset relative to stimulus offset across trials without a neurally inferred time of commitment (nTc) is 0.192 s. The rightmost bin contains trials for which the movement onset is 0.8 s or more after stimulus offset. c, Principal component analysis (PCA) was performed on peri-event time histograms (PETH’s) aligned to stimulus offset (circles) and averaged across trials without a neurally inferred time of commitment (nTc). Spikes were counted in 10 ms bins, and the PETH was not additionally filtered. Spikes after the animal moved away from the fixation port (i.e., movement onset) were included. For each neuron and each trial condition, the PETH is a 100-element vector. Concatenating across 4605 choice-selective neurons and 4 trial conditions gave a 4605-by-400 matrix. The mean of each row (i.e., the average response of each neuron) was subtracted from the matrix, and PCA was performed on the resulting matrix. Triangles indicate the median time of movement onset. Projections are scaled by the standard deviation explained by each PC. d, PCA performed PETH’s aligned to movement onset offset and averaged across trials without nTc.

Extended Data Fig. 10 Further analyses and validation of the dynamics discovered by FINDR.

a, When we computed the eigenvalues of the numerical Jacobian J obtained from the detected slow point around the origin, the real components of both eigenvalues were greater than zero for all sessions (n = 27), indicating that the origin is not a stable point. Units of \(\lambda \) are sec−1. b, To quantify how non-normal the dynamics are around the origin, we computed the angle between the two eigenvectors of J. 90° indicates that the dynamics are normal, and angle less than 90° indicates that the dynamics are non-normal. c, We further evaluated the non-normality of the discovered dynamics around the origin by taking the Schur decomposition J = QTQ* and computing the ratio between the non-normal part and the normal part of the dynamics, ρ = ||T1,2||/||[T1,1; T2,2]||. ρ > 0 indicates that the dynamics are non-normal, with higher values of ρ indicating stronger non-normality. d, Here we estimated the low-dimensional vector field for each session using a method that does not specify a dynamical model (“model-free” approach). We compared the vector fields estimated using this approach to the FINDR-inferred vector fields. To obtain the model-free vector field, we first estimated single-trial firing rates of individual neurons by binning the spike trains in Δt = 10 ms bins and convolving the spike trains with a Gaussian of σ = 100 ms. Then, we projected the estimated single-trial population firing rate trajectories onto the subspace spanned by the FINDR latent axes. This allows direct comparisons between vector fields. For each evaluation point (i, j) on a 8-by-8 grid of the latent state space z, we estimated the velocity arrow by taking the average of ż ≈ (zt − zt-Δt)/Δt for all t across all trajectories that fall inside the cell corresponding to the point (i, j). To compare vector fields, we measured Sc, the mean of the cosine similarities between the vector arrows of the model-free approach and the vector arrows from FINDR inside the sample zone. The median of the Sc’s across all sessions was 0.73. Three example sessions from across the distribution are shown, with session 2 around the median Sc of the histogram. For both FINDR and the model-free approach, the colored trajectories were obtained by trial-averaging based on the evidence strength. To compare between a random vector field and the model-free vector field, for each session, we generated 1,000 random vector fields by randomizing the direction of each arrow in the 8-by-8 grid. e, We assessed the dynamical stability around the origin using a model-free approach similar to d. We estimated the autonomous velocity around the initial starting point (indicated as the center of the circle) of the model-free latent trajectories by taking the average of ż ≈ (zt − zt-Δt)/Δt for all t across all trajectories that fall inside each of the 5 pie slices. Here we excluded time points where clicks affect the dynamics (zt − zt-Δt)/Δt, and only considered the trajectories with #L clicks = #R clicks during the epoch when they are in the pie slice, when computing the estimate of the autonomous dynamics arrow. When computing the average of (zt − zt-Δt)/Δt for one of the pie slices, we required zt−1 to be inside the pie slice. The circles have a radius of 0.2 (in the units of z). We found that all five arrows were pointing outwards (p < 0.5^5 = 0.03125) for 20 out of 27 sessions, consistent overall with the stability analysis in a. f, FINDR-inferred vector fields for all recording sessions (n = 27) with more than 30 neurons and 400 trials, and sessions where the animal performed with greater than 80% accuracy. These fits were used for the summary plots in Fig. 3. The vector field represents the autonomous dynamics and the trajectories are trial averages sorted by the evidence strength of each trial.

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Luo, T.Z., Kim, T.D., Gupta, D. et al. Transitions in dynamical regime and neural mode during perceptual decisions. Nature 646, 1156–1166 (2025). https://doi.org/10.1038/s41586-025-09528-4

- Received: 13 November 2023

- Accepted: 13 August 2025

- Published: 17 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09528-4

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Nature volume 646, pages 1167–1177 (2025)

Abstract

Autonomic dysreflexia is a life-threatening medical condition characterized by episodes of uncontrolled hypertension that occur in response to sensory stimuli after spinal cord injury (SCI)[1]. The fragmented understanding of the mechanisms underlying autonomic dysreflexia hampers the development of therapeutic strategies to manage this condition, leaving people with SCI at daily risk of heart attack and stroke2,3,4,5. Here we expose the neuronal architecture that develops after SCI and causes autonomic dysreflexia. In parallel, we uncover a competing, yet overlapping neuronal architecture activated by epidural electrical stimulation of the spinal cord that safely regulates blood pressure after SCI. The discovery that these adversarial neuronal architectures converge onto a single neuronal subpopulation provided a blueprint for the design of a mechanism-based intervention that reversed autonomic dysreflexia in mice, rats and humans with SCI. These results establish a path towards essential pivotal device clinical trials that will establish the safety and efficacy of epidural electrical stimulation for the effective treatment of autonomic dysreflexia in people with SCI.

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Main

SCI disrupts the communication between the brainstem vasomotor centres and the regions of the spinal cord that regulate haemodynamics[6]. The resulting isolation of neurons in the spinal cord triggers a progressive maladaptive reorganization of neuronal projections throughout the spinal cord below the injury that permits the insidious emergence of uncontrolled hypertensive episodes, known as autonomic dysreflexia[1]. The consequence of these hypertensive episodes is a daily risk of life-threatening cardiovascular events2,3,4,5.

We reasoned that disentangling the specific neuronal subpopulations involved in the emergence of autonomic dysreflexia, and how these neurons and their projection patterns reorganize after SCI, would uncover key principles to target these neurons therapeutically and thus eliminate autonomic dysreflexia due to SCI.

Spatial organization of neurons involved in autonomic dysreflexia

In humans with SCI, episodes of autonomic dysreflexia are most commonly triggered by bladder or bowel distension, lower urinary tract infections and pressure sores[7]. We reasoned that identifying the neurons triggering autonomic dysreflexia would require a preclinical model that provokes reliable, repeatable and predictable episodes of autonomic dysreflexia.

To establish this model, we elicited autonomic dysreflexia using colorectal distension in mice[8] with complete upper-thoracic SCI and monitored pressor responses with beat-by-beat blood pressure monitoring (Fig. 1a,b and Extended Data Fig. 1a–d). All mice showed the emergence of autonomic dysreflexia, starting approximately 2 weeks after SCI (Fig. 1c and Extended Data Fig. 1d). The amplitude of these pressor responses increased gradually during the following weeks until reaching a plateau by 1 month after SCI, which persisted when tested at 6 weeks after SCI (Fig. 1c and Extended Data Fig. 1d).

Fig. 1: Autonomic dysreflexia triggers transcriptional activity in the lumbosacral and lower thoracic spinal cord.

a, Experimental model to quantify the severity of autonomic dysreflexia in mice with complete SCI. b, Changes in blood pressure during an episode of autonomic dysreflexia elicited by a controlled colorectal distension (black dashed line). c, Severity of autonomic dysreflexia measured by the change in systolic blood pressure elicited by a controlled colorectal distension at different timepoints after SCI (5 days post-injury to uninjured (P = 0.95), 14 days post-injury to uninjured (P = 0.00048), 30 days post-injury to uninjured (P = 0.0000001), 45 days post-injury to uninjured (P = 0.0000001), 14 days post-injury to 5 days post-injury (P = 0.0013), 30 days post-injury to 5 days post-injury (P = 0.0000001), 45 days post-injury to 5 days post-injury (P = 0.0000002), 30 days post-injury to 14 days post-injury (P = 0.00086), 45 days post-injury to 14 days post-injury (P = 0.0024) and 45 days post-injury to 30 days post-injury (P = 0.99)). NS, not significant. d, Whole-spinal-cord visualization of immunohistochemical staining for Fos9,47 in a mouse with SCI that was exposed to repetitive episodes of autonomic dysreflexia. C, caudal; R, rostral. e, Barplot reporting the mean number of Fos-labelled neurons for each spinal cord segment quantified in mice with SCI that were exposed to repetitive episodes of autonomic dysreflexia (n = 5; mixed-effect linear model; P < 0.001), demonstrating a clear enrichment in the lumbosacral and lower thoracic spinal cord. *P < 0.05, **P < 0.01, ***P < 0.001.

To identify the regions of the spinal cord activated during autonomic dysreflexia, we conducted whole-spinal-cord clearing[9], labelling10,11, imaging[12] and automated quantification[13] of Fos, a marker of neuronal activity-induced transcription (Fig. 1d and Extended Data Fig. 1e). We quantified this activity-induced transcription in mice that underwent repetitive autonomic dysreflexia over 90 min (ref. [14]) (Fig. 1e and Extended Data Fig. 1f).

We found that autonomic dysreflexia triggered massive transcriptional activation throughout the spinal cord (Extended Data Fig. 1f). However, we detected disproportionate enrichments of neuronal activity in two well-defined regions. The first enrichment occurred in the lower lumbosacral segments that receive the sensory afferents conveying information from the colorectal stimulus (Fig. 1d). The second enrichment emerged within the lower thoracic spinal cord, which is referred to as the haemodynamic hotspot[6]. This region hosts a dense concentration of sympathetic preganglionic neurons that access the splanchnic vasculature through ganglionic neurons to elicit powerful pressor responses[6]. We confirmed these segment-specific enrichments of neuronal activity with classical immunofluorescence of Fos on sectioned tissue (Extended Data Fig. 1g). This pattern of neuronal activation was preserved following complete dorsal rhizotomy of lower thoracic segments, excluding the possibility that the robust activation of neurons in the thoracic spinal cord was due to inputs from bowel afferents (Supplementary Note 2).

The neurons activated by autonomic dysreflexia

We anticipated that understanding the emergence of maladaptive communication between the lumbosacral and lower thoracic spinal cord would be contingent on identifying the neuronal subpopulations in each region that are activated during autonomic dysreflexia.

The neuronal subpopulations embedded within the spinal cord are parcellated into a hierarchical organization that arises from their neurotransmitter expression, developmental transcription factors and projection patterns15,16,17. This hierarchical organization dictates that identifying the neurons involved in specific neurological functions must follow the logical progression along the cardinal classes.

To follow this progression, we first asked whether the neurons embedded in the lumbosacral and lower thoracic spinal cords, and activated during autonomic dysreflexia, exhibited an excitatory or inhibitory phenotype. Our experiments revealed that vGLUT2ON neurons located in the lumbosacral spinal cord propel axonal projections to the lower thoracic spinal cord where vGLUT2ON neurons are activated and necessary to trigger autonomic dysreflexia (Supplementary Note 1).

vGLUT2ON neurons comprise diverse neuronal subpopulations that collectively encompass more than 50% of neurons in the spinal cord15,16,17. Consequently, we felt compelled to descend the hierarchical organization of neurons in the spinal cord to identify the precise neuronal subpopulations that govern the emergence of autonomic dysreflexia. However, this descent is contingent on an atlas that catalogues the molecular perturbation elicited by autonomic dysreflexia across the compendium of neuronal subpopulations in the spinal cord.

To establish this comparative atlas, we profiled the lumbosacral and lower thoracic spinal cord of mice exposed to repetitive episodes of autonomic dysreflexia using single-nucleus RNA sequencing (snRNA-seq)[18]. We obtained high-quality transcriptomes from 64,739 nuclei that were evenly represented across experimental conditions and spatial locations (Extended Data Fig. 3a,b). We identified all of the major cell types of the mouse spinal cord (Extended Data Fig. 3c–k). We then integrated this dataset within our previous atlases of the mouse spinal cord13,16,17,19,20, which enabled us to annotate highly specific neuronal subpopulations that parcellated into dorsal versus ventral, excitatory versus inhibitory and local (Nfib) versus long-projecting (Zfhx3) populations[15] (Fig. 2a and Extended Data Fig. 3l–n).

Fig. 2: The neuronal architecture of autonomic dysreflexia.

a, Schematic overview of the experiment. Uniform manifold approximation and projection (UMAP) visualization of 64,739 neuronal nuclei, coloured by neuronal subpopulation identity (left). UMAP visualizations of neuronal subpopulations in the lower thoracic (top) and lumbosacral (bottom) spinal cord (middle). Ranking of neuronal subpopulations most responsive to autonomic dysreflexia with Augur (right). AUC, area under the curve; DE, dorsal excitatory; DI, dorsal inhibitory; MIL, middle inhibitory deep laminate; VEL, ventral excitatory local; VEP, ventral excitatory projecting. b, Schematic overview of the neuronal architecture of autonomic dysreflexia, including the nodes (numbers) that are dissected anatomically and functionally in the subsequent panels. c, Whole-spinal-cord visualization of projections from SCLUMBAR::Vsx2 neurons located in the lumbosacral spinal cord that project to SCTHORACIC::Vsx2 neurons located in the lower thoracic spinal cord. White dashed lines outline the grey matter. D, dorsal; V, ventral. d, CalcaON projections labelled with immunohistochemistry onto SCLUMBAR::Vsx2 neurons in the lumbosacral spinal cord, including insets showing synaptic-like appositions. e, Barplot reporting the severity of autonomic dysreflexia, quantified as the mean change in systolic blood pressure in response to colorectal distension before and after the ablation of CalcaON neurons located in the dorsal root ganglia in CalcaCre::AdvilFlpO::iDTR mice (n = 5; independent samples t-test; t = −6.0; P = 0.0006). f, Severity of autonomic dysreflexia before and after chemogenetic silencing of Vsx2ON neurons located in the lumbosacral spinal cord in Vsx2–Cre mice (n = 5; paired samples t-test; t = −9.47; P = 0.00069). g, Projections from SCLUMBAR::Vsx2 in the lower thoracic spinal cord co-labelled with SCTHORACIC::Vsx2 neurons and their local projections as well as immunohistochemical labelling of ChATON neurons. The insets show synaptic-like appositions from SCLUMBAR::Vsx2 neurons onto SCTHORACIC::Vsx2 neurons, and synaptic-like appositions of projections from SCTHORACIC::Vsx2 neurons to ChATON sympathetic preganglionic neurons located in the intermediolateral column. h, Severity of autonomic dysreflexia before and after chemogenetic silencing of Vsx2ON neurons located in the lower thoracic spinal cord in Vsx2–Cre mice (n = 5; paired samples t-test; t = −9.39; P = 0.00072). i, Severity of autonomic dysreflexia before and after chemogenetic silencing of ChATON neurons located in the lower thoracic spinal cord in ChAT–Cre mice (n = 5; paired samples t-test; t = −8.03; P = 0.00048).

To identify the neuronal subpopulations perturbed by autonomic dysreflexia, we applied cell-type prioritization19,21. We captured the principle of cell-type prioritization in a machine-learning method called Augur, which identifies cell types undergoing transcriptional responses to a perturbation by ranking cell types that are increasingly more separable within the highly multidimensional space of gene expression. This prioritization exposed Vsx2ON excitatory neurons as the most transcriptionally responsive neuronal subpopulation during autonomic dysreflexia (Fig. 2a). Although Vsx2ON neuronal subpopulations were prioritized in both the lumbosacral (neurons defined by the expression of Hoxa10) and the lower thoracic (Hoxa7) spinal cords, the prioritized neurons in the lumbosacral region were consistent with long-projecting Vsx2ON neurons, as they expressed the marker Zfhx3 (SCHoxa10::Zfhx3::Vsx2), whereas the most perturbed neuronal subpopulation in the lower thoracic spinal cord instead expressed markers of locally projecting neurons Nfib (SCHoxa7::Nfib::Vsx2)[15] (Fig. 2a and Extended Data Fig. 3m,n).

The transcriptional prioritization of SCHoxa7::Nfib::Vsx2 and SCHoxa10::Zfhx3::Vsx2 neurons coincided with a pronounced upregulation of synaptic plasticity pathways, neuron projection guidance programs and dendritic arborization — an ensemble of gene programs associated with circuit reorganization and increased neuronal excitability (Extended Data Fig. 3o). Visualization of the activity-dependent marker Fos confirmed the activation of SCHoxa10::Zfhx3::Vsx2 and SCHoxa7::Nfib::Vsx2 neurons in the lumbosacral and lower thoracic spinal cord, respectively, in response to autonomic dysreflexia (Extended Data Fig. 3p).

Although developmentally defined V2a neurons that express Vsx2 (previously known as Chx10)[22] have been implicated in the production of reaching[23] and walking13,16,17,19,24, the participation of Vsx2ON neuronal subpopulations to the regulation of blood pressure has, to our knowledge, never been demonstrated. Nonetheless, the results of our comparative snRNA-seq experiments dictate that the activation of SCHoxa7::Nfib::Vsx2 and SCHoxa10::Zfhx3::Vsx2 neurons triggers autonomic dysreflexia.

The neuronal architecture of autonomic dysreflexia

As autonomic dysreflexia only emerges after SCI, we reasoned that the injury must induce the formation of a maladaptive neuronal architecture that incorporates SCTHORACIC::Vsx2 neurons and SCLUMBAR::Vsx2 neurons, and possesses the anatomical and functional features compatible with the requirements to trigger autonomic dysreflexia. To expose this architecture, we conducted sequential anatomical and functional experiments that aimed to reconstruct the successive nodes composing the blueprint of the neuronal architecture responsible for autonomic dysreflexia (Fig. 2b,c and Extended Data Fig. 4a).

Small diameter nociceptive afferents act as the primary source of sensory input responsible for triggering autonomic dysreflexia25,26. Consequently, nociceptive neurons are positioned as the first node within the neuronal architecture of autonomic dysreflexia, implying that SCLUMBAR::Vsx2 neurons are likely to receive synaptic projections from these afferents. To expose close appositions from these afferents onto SCLUMBAR::Vsx2 neurons, we labelled synapses from CalcaON axonal projections. Although SCLUMBAR::Vsx2 neurons in the lumbosacral spinal cord did not receive any CalcaON axonal projections in uninjured mice, we found that SCI triggered the invasion of CalcaON axons into intermediate laminae, where they established synaptic-like appositions onto SCLUMBAR::Vsx2 neurons (Fig. 2d and Extended Data Fig. 4b–d). This anatomical reorganization suggested that CalcaON neurons trigger autonomic dysreflexia through the activation of SCLUMBAR::Vsx2 neurons. To expose this causality, we ablated CalcaON neurons in the dorsal root ganglia with diphtheria toxin in CalcaCre::AdvilFlpO::iDTR mice[27]. The ablation of this afferent subpopulation abolished autonomic dysreflexia. By contrast, the ablation of parvalbumin (PVON) neurons in the dorsal root ganglia, which convey proprioceptive information along large diameter afferents that project into the spinal cord, failed to influence autonomic dysreflexia (Fig. 2e and Extended Data Fig. 4e,f).

We next posited that SCLUMBAR::Vsx2 act as the second node in the neuronal architecture of autonomic dysreflexia (Fig. 2b,c and Extended Data Fig. 4g,h). To expose the necessity of SCLUMBAR::Vsx2 neurons in triggering autonomic dysreflexia, we infused AAV5-hSyn-DIO-hM4Di-mCherry[28] into the lumbosacral spinal cord of Vsx2–Cre mice. Inactivation of SCLUMBAR::Vsx2 neurons blunted autonomic dysreflexia (Fig. 2f and Extended Data Fig. 4i–k).

We then sought to establish the sufficiency of SCLUMBAR::Vsx2 neurons to trigger autonomic dysreflexia. To expose this sufficiency, we infused AAV5-hSyn-flex-Chrimson-tdTomato[29] into the lumbar spinal cord of Vsx2–Cre mice to express excitatory opsins in SCLUMBAR::Vsx2 neurons. Optogenetic stimulation of SCLUMBAR::Vsx2 neurons immediately triggered autonomic dysreflexia. This increase in blood pressure contrasted with the absence of pressor responses when the same optogenetic manipulation of SCLUMBAR::Vsx2 neurons was performed in uninjured mice (Extended Data Fig. 4l–n).

The necessity and sufficiency of SCLUMBAR::Vsx2 neurons to trigger autonomic dysreflexia implied that SCI must also provoke SCLUMBAR::Vsx2 neurons to propel axonal projections to the lower thoracic spinal cord. To uncover these putative projections, we infused rAAV2-EF1a-DIO-Flpo into the lower thoracic spinal cord followed by infusions of AAV5-hSyn-Con/Fon-eYFP (enhanced yellow fluorescent protein)[30] into the lumbosacral spinal cord of Vsx2–Cre mice. This neuroanatomical tracing strategy exposed long-distance projections from SCLUMBAR::Vsx2 neurons that established synaptic-like appositions onto SCTHORACIC::Vsx2 neurons in the lower thoracic spinal cord (Fig. 2c and Extended Data Fig. 5a–e). Monosynaptically restricted transsynaptic tracing with avian enveloped G-deleted Rabies confirmed that SCLUMBAR::Vsx2 neurons established direct synaptic projections onto SCTHORACIC::Vsx2 neurons (Extended Data Fig. 5f).

As SCTHORACIC::Vsx2 neurons in the lower thoracic spinal cord were transcriptionally perturbed by autonomic dysreflexia and received direct synaptic projections from SCLUMBAR::Vsx2 neurons, we hypothesized that SCTHORACIC::Vsx2 neurons must act as the third node in the neuronal architecture of autonomic dysreflexia (Fig. 2c and Extended Data Fig. 5a–e). To establish the necessity and sufficiency of this node, we manipulated the activity of SCTHORACIC::Vsx2 neurons. Chemogenetic inactivation of these neurons blunted autonomic dysreflexia (Fig. 2g,h and Extended Data Fig. 5g–i). In turn, optogenetic activation of SCTHORACIC::Vsx2 neurons immediately triggered pressor responses (Extended Data Fig. 5j–l).

We next asked whether the projection pattern of SCTHORACIC::Vsx2 neurons in the lower thoracic spinal cord would be compatible with an involvement in autonomic dysreflexia. To answer this question, we infused AAV5-hSyn-flex-tdTomato into Vsx2–Cre mice and quantified the density of projections that formed synaptic-like appositions with ChATON neurons in the intermediolateral column of lower thoracic spinal segments, where ChATON sympathetic preganglionic neurons reside (Extended Data Fig. 5m–o). Although SCTHORACIC::Vsx2 neurons primarily projected ventrally, we also detected projections that expanded laterally where they established synaptic-like appositions with the majority of ChATON neurons located in the intermediolateral column of the lower thoracic spinal cord.

ChATON sympathetic preganglionic neurons are embedded in the intermediolateral column, and we found that they receive projections from SCTHORACIC::Vsx2 neurons. Consequently, we surmized that ChATON neurons act as the fourth and final node in the neuronal architecture of autonomic dysreflexia. Indeed, chemogenetic inactivation of ChATON neurons located in the lower thoracic spinal cord abolished autonomic dysreflexia elicited by bowel distension (Fig. 2h and Extended Data Fig. 5p–r).

These sequential anatomical and functional experiments exposed the neuronal architecture that causes autonomic dysreflexia. The building blocks of this architecture are precipitated by SCI, in which specific neuronal nodes form maladaptive connections that permit and exacerbate the emergence of autonomic dysreflexia. This architecture has its foundation in CalcaON neurons located in the dorsal root ganglia, which establish maladaptive projections to SCLUMBAR::Vsx2 neurons in the lumbosacral spinal cord. In turn, a SCI provokes these neurons to propel axons to the haemodynamic hotspot located within the lower thoracic spinal cord, where they form synaptic connections with SCTHORACIC::Vsx2 neurons. These locally projecting neurons naturally establish connections onto ChATON sympathetic preganglionic neurons, which therefore permit massive blood pressure elevations through the recruitment of neurons in splanchnic ganglia and subsequent α_1 receptor activation throughout the dense splanchnic vasculature. The consequence of this aberrant neuronal architecture is the emergence of life-threatening autonomic dysreflexia.

Competitive neuronal architectures converge on SCTHORACIC::Vsx2 neurons

Our results demonstrate that SCI precipitates the formation of an aberrant neuronal architecture that exploits the natural connections from SCTHORACIC::Vsx2 neurons onto sympathetic preganglionic neurons located in the haemodynamic hotspot to trigger autonomic dysreflexia. We reasoned that competitive engagement of the same neuronal subpopulations with an intervention that mediates beneficial, as opposed to maladaptive reorganization of synaptic projections onto SCTHORACIC::Vsx2 neurons, could prevent the emergence of autonomic dysreflexia. Using molecular cartography, we recently demonstrated that epidural electrical stimulation (EES) of the lumbar spinal cord restores walking through the activation of locally projecting Vsx2ON neurons in the lumbar spinal cord13,19. We surmized that the same principle must exist in the lower thoracic spinal cord, as we have previously shown that EES applied over this haemodynamic hotspot triggers robust pressor responses after SCI[6]. We thus hypothesized that EES applied over the lower thoracic spinal cord activates SCTHORACIC::Vsx2 neurons, and that EES could thus compete with SCLUMBAR::Vsx2 neurons to modulate SCTHORACIC::Vsx2 neurons.

To identify the neuronal subpopulations engaged by EES applied to the lower thoracic spinal cord, we performed an additional comparative snRNA-seq experiment (Extended Data Fig. 6a–c). Concretely, we profiled neuronal nuclei from mice with SCI that had received EES over the lower thoracic spinal cord for 30 min. As we have previously described in rats, non-human primates and humans with SCI[6], all the mice exhibited robust pressor responses when delivering EES over the lower thoracic spinal cord, referred to as the haemodynamic hotspot (Fig. 3a,b). High-quality transcriptional profiles were obtained from 21,098 nuclei (Fig. 3c). Integration of this dataset[31] with our previous atlases13,16,17,19,20 and data from the experiments conducted on mice that were exposed to repetitive autonomic dysreflexia enabled us to identify and evaluate the same neuronal subpopulations. Cell-type prioritization19,21 revealed that locally projecting SCHoxa7::Nfib::Vsx2 neurons exhibited the most pronounced transcriptional response across the compendium of neuronal subpopulations embedded in the lower thoracic spinal cord of mice that had received EES targeting the haemodynamic hotspot (Fig. 3c and Extended Data Fig. 6d,e).

Fig. 3: The neuronal architecture of EES-induced pressor responses.

a, Schematic overview of experiments to trigger pressor responses with EES in mice with SCI. b, Pressor response induced by continuous (40 Hz) EES (black dashed line) in a mouse with SCI. c, UMAP visualization of 21,098 neuronal nuclei, coloured by neuronal subpopulation identity (left). Ranking of neuronal subpopulations most responsive to EES with Augur (right). d–f, Schematic overview of the successive nodes constituting the neuronal architecture through which EES applied over the lower thoracic spinal cord induces pressor responses. EES-induced pressor responses before and after the ablation of PVON neurons located in the dorsal root ganglia in PVCre::AdvilFlpO::iDTR mice (n = 5; independent samples t-test; t = −5.41; P = 0.0043; d). EES-induced pressor responses before and after chemogenetic silencing of Vsx2ON neurons located in the lower thoracic spinal cord in Vsx2–Cre mice (n = 5; paired samples t-test; t = −4.21; P = 0.014; e). EES-induced pressor responses before and after chemogenetic silencing of ChATON neurons located in the lower thoracic spinal cord in ChAT–Cre mice (n = 5; paired samples t-test; t = −7.07; P = 0.0021; f). g, Photomicrograph of the lower thoracic spinal cord demonstrating vGLUT1 synaptic puncta and synaptic-like appositions from large-diameter afferent neurons onto SCTHORACIC::Vsx2 neurons labelled with in situ hybridization (left) or viral tract tracing (right) in the lower thoracic spinal cord of PVCre::AdvilFlpO::tdTomato mice. Arrowheads mark PVON synaptic-like appositions and L6 projections onto Vsx2ON neurons. White dashed lines outline the grey matter.

As we have previously found that Vsx2ON neurons were recruited in response to EES in the lumbar spinal cord13,19, these comparative snRNA-seq experiments indicate that the principle through which EES recruits specific neuronal subpopulations is conserved across the thoracolumbar spinal cord. Moreover, this observation nominates SCHoxa7::Nfib::Vsx2 neurons as a convergence node that is not only recruited during autonomic dysreflexia but can also be engaged by the neuronal architecture that prevents orthostatic hypotension when the delivery of EES targets the haemodynamic hotspot.

The discovery of this intersection compelled us to expose the entire neuronal architecture activated by EES. Therefore, we conducted sequential anatomical and functional experiments that aimed to reconstruct the successive nodes involved in the pressor responses during the delivery of EES targeting the haemodynamic hotspot.

We have previously shown that EES applied over the lower thoracic spinal cord activates afferent fibres in the posterior roots to trigger pressor responses[6], and mounting evidence suggests that EES restores walking through the recruitment of large-diameter afferent fibres where they bend to enter the lumbosacral spinal cord through the dorsal root entry zones32,33. We thus asked whether large-diameter afferent neurons act as the first node of the neuronal architecture that enables EES targeting the haemodynamic hotspot to trigger pressor responses (Extended Data Fig. 7a,b).

To answer this question, we ablated proprioceptive or nociceptive neurons with the administration of diphtheria toxin to PVCre::AdvilFlpO::iDTR mice and CalcaCre::AdvilFlpO::iDTR mice, and applied EES targeting the haemodynamic hotspot. The ablation of PVON neurons in the dorsal root ganglia abolished pressor responses to EES, whereas the ablation of CalcaON neurons had no effect (Fig. 3d and Extended Data Fig. 7b–d). We then verified that large-diameter afferents establish synaptic projections onto SCTHORACIC::Vsx2 neurons in the lower thoracic spinal cord. To expose this connectome, we visualized large-diameter afferent fibres in PVCre::AdvilFlpO::Ai9(RCL-tdT) mice and confirmed that these fibres established vGLUT1ON synaptic appositions onto SCTHORACIC::Vsx2 neurons (Extended Data Fig. 7e–g). The dense projections from PVON neurons onto SCTHORACIC::Vsx2 neurons contrasted with the absence of projections from PVON neurons onto ChATON neurons in the intermediolateral column of the lower thoracic spinal cord, both before and after SCI (Extended Data Fig. 7e–g). Monosynaptically restricted transsynaptic tracing with avian enveloped G-deleted Rabies confirmed that PVON neurons in the dorsal root ganglia established direct synaptic projections onto SCTHORACIC::Vsx2 neurons (Extended Data Fig. 7h).

As SCHoxa7::Nfib::Vsx2 neurons underwent the greatest transcriptional perturbation following EES targeting the haemodynamic hotspot and receive direct projections from large-diameter afferent fibres that are recruited by EES, we anticipated that SCTHORACIC::Vsx2 neurons act as the second node in the neuronal architecture that enables EES to trigger pressor responses. To expose the necessary role of SCTHORACIC::Vsx2 neurons, we infused AAV5-hSyn-DIO-hM4Di-mCherry into the lower thoracic spinal cords of Vsx2–Cre mice. Inactivation of SCTHORACIC::Vsx2 neurons blunted the pressor responses triggered by EES (Fig. 3e and Extended Data Fig. 7i,j).

We surmized that ChATON sympathetic preganglionic neurons would logically act as the third and final node in the neuronal architecture that enables EES targeting the haemodynamic hotspot to trigger pressor responses. Indeed, chemogenetic inactivation of ChATON neurons located in the lower thoracic spinal cord abolished pressor responses triggered by EES (Fig. 3f and Extended Data Fig. 7k,l).

Together, these results uncovered the neuronal architecture that enables EES targeting the haemodynamic hotspot of the lower thoracic spinal cord to trigger pressor responses. EES directly recruits large diameter afferents to activate SCTHORACIC::Vsx2 neurons, which project to ChATON sympathetic preganglionic neurons to elicit pressor responses through the activation of ganglionic neurons, and subsequent α_1 receptor activation to induce vasoconstriction.

Finally, we surmized that for the neuronal architecture enabling EES to elicit pressor responses to compete with the neuronal architecture that triggers autonomic dysreflexia, these two architectures must intersect on the same SCTHORACIC::Vsx2 neurons in the lower thoracic spinal cord.

To expose this anatomical and functional intersection, we first infused AAV5-hSyn-GFP into the lumbosacral spinal cord of PVCre::AdvilFlpO::Ai9(RCL-tdT) mice with SCI coupled to labelling of SCTHORACIC::Vsx2 neurons. We found that the same SCTHORACIC::Vsx2 neurons received direct projections from large-diameter afferents emanating from PVON neurons located in dorsal root ganglia and from axons projecting from the lumbosacral spinal cord after SCI (Fig. 3g). Second, we aimed to confirm that these two distinct axonal projections were able to regulate the activity of the same SCTHORACIC::Vsx2 neurons. We conducted single-unit recordings of optogenetically identified SCTHORACIC::Vsx2 neurons in the lower thoracic spinal cord in response to autonomic dysreflexia and EES (Extended Data Fig. 7m). We found that both paradigms elicited short-latency responses in the recorded SCTHORACIC::Vsx2 neurons that were compatible with a direct activation of the same SCTHORACIC::Vsx2 neurons (Extended Data Fig. 7n,o).

Neuronal architecture competition to reverse autonomic dysreflexia

The intersection between the neuronal architecture that enables EES targeting the haemodynamic hotspot to mediate beneficial elevation of blood pressure and the neuronal architecture that triggers autonomic dysreflexia opened the intriguing possibility that the sustained modulation of SCTHORACIC::Vsx2 neurons with EES could compete with the detrimental activity emanating from aberrant axonal projections of SCLUMBAR::Vsx2 neurons, ultimately reversing autonomic dysreflexia.

To test this possibility, we subjected mice to autonomic neurorehabilitation, which consisted of daily sessions during which EES targeting the haemodynamic hotspot was delivered over the lower thoracic spinal cord during the course of 1 month (Fig. 4a and Extended Data Fig. 8a). Autonomic neurorehabilitation abolished autonomic dysreflexia in all tested mice (Fig. 4b and Extended Data Fig. 8b).

Fig. 4: Competitive neuronal architectures converge on SCTHORACIC::Vsx2 neurons.

a, Schematic overview of autonomic neurorehabilitation and the paradigm to quantify the severity of autonomic dysreflexia. b, Pressor responses (left; individual mice and mean trace) and severity of autonomic dysreflexia (right) in five mice with chronic SCI and five mice that underwent autonomic neurorehabilitation for 4 weeks, starting 1 week after SCI (independent samples t-test; t = −7.45; P = 0.00056). c, Schematic overview illustrating the competitive (overlapping) neuronal architectures of autonomic dysreflexia and EES-induced pressor responses, and their rearrangement after autonomic neurorehabilitation. d, vGLUT1ON synaptic puncta and synaptic-like appositions from SCLUMBAR::Vsx2 neurons onto SCTHORACIC::Vsx2 neurons in mice with SCI and mice with SCI that underwent autonomic neurorehabilitation. The barplots report the mean density of axonal projections from SCLUMBAR::Vsx2 neurons in the thoracic spinal cord in mice with SCI and mice with SCI that underwent autonomic neurorehabilitation (n = 5; independent samples t-test; t = 2.51; P = 0.0369; top). The barplots also report the mean number of vGLUT1ON synaptic puncta apposing SCTHORACIC::Vsx2 neurons (n = 5; independent samples t-test; t = 4.44; P = 0.0055; bottom). e, Schematic overview of experiments in which EES was applied daily over the lumbosacral spinal cord of mice with SCI, and the paradigm to quantify the severity of autonomic dysreflexia. f, As in panel b, but for mice with SCI that were subjected to the daily application of EES over the lumbosacral spinal cord (n = 5; independent samples t-test; t = 5.82; P = 0.00070). Grey shaded region indicates the colorectal distension period.

We suspected that the suppression of autonomic dysreflexia would emerge from the competitive advantage of large-diameter fibre projections over aberrant axonal projections from SCLUMBAR::Vsx2 neurons to form synaptic connections with SCTHORACIC::Vsx2 neurons (Fig. 4c). To expose this adversarial anatomy, we labelled SCTHORACIC::Vsx2 and SCLUMBAR::Vsx2 neurons with intersectional viral tracing strategies. We then quantified the relative density of synaptic appositions onto SCTHORACIC::Vsx2 neurons, both vGLUT1 synapses emanating from PVON large-diameter afferent fibres and axonal projections from SCLUMBAR::Vsx2 neurons. Autonomic neurorehabilitation reduced the number of aberrant projections from SCLUMBAR::Vsx2 neurons onto SCTHORACIC::Vsx2 neurons, while concomitantly augmenting the density of vGLUT1ON synaptic appositions onto SCTHORACIC::Vsx2 neurons embedded in the haemodynamic hotspot (Fig. 4d and Extended Data Fig. 8c,d).

Mistargeted stimulation exacerbates autonomic dysreflexia

Recent case studies have reported transient increases in blood pressure in response to EES applied over the lumbosacral spinal cord of people with SCI34,35,36,37,38,39, and that the long-term delivery of EES over this location led to improvements in resting hypotension and orthostatic hypotension in these individuals. As a paucity of neurons involved in the control of blood pressure reside in the lumbosacral spinal cord, the activation and reinforcement of the neuronal architecture that causes autonomic dysreflexia is the most likely mechanism to account for these observations. Therefore, we hypothesized that the daily activation of SCLUMBAR::Vsx2 neurons in response to EES applied over the lower lumbosacral spinal cord would provide a competitive advantage to vGLUT2ON synapses emanating from these neurons and projecting onto SCLUMBAR::Vsx2 neurons compared with vGLUT1ON synapses received from local PVON large-diameter afferent fibres, and that, ultimately, this shift in the synaptic innervations of SCLUMBAR::Vsx2 neurons would exacerbate autonomic dysreflexia.

To test this hypothesis, we subjected a new cohort of mice to daily exposure to EES, but instead of delivering EES over the lower thoracic spinal cord to target the haemodynamic hotspot, we applied EES over the lower lumbosacral spinal cord for 30 min during the course of 1 month (Fig. 4e and Extended Data Fig. 8e). This mistargeted stimulation doubled the severity of autonomic dysreflexia in all tested mice (Fig. 4f and Extended Data Fig. 8f). As anticipated, we observed a concomitant increase in the density of axonal projections from SCLUMBAR::Vsx2 neurons onto SCTHORACIC::Vsx2 neurons, combined with a decrease in the density of vGLUT1 synapses from PVON large-diameter afferent fibres (Extended Data Fig. 8g,h).

The dramatic exacerbation of autonomic dysreflexia triggered by the mistargeted delivery of EES demonstrates that attempts to restore haemodynamic stability with EES applied over the lumbosacral spinal cord are not only less efficacious than EES targeting the haemodynamic hotspot located in the lower thoracic spinal cord[40] but also proved unsafe, as the mechanism by which haemodynamics can be modulated when stimulating this region is by triggering and reinforcing the severity of life-threatening autonomic dysreflexia.

Longitudinal monitoring of autonomic neurorehabilitation

Although autonomic neurorehabilitation suppressed autonomic dysreflexia in mice, we recognized that the longitudinal monitoring of the safety and efficacy of this treatment over a long period of time would be important to inform the design of a therapy for people living with SCI.

We therefore leveraged our chronic rat model of high-thoracic contusion SCI that enables 24/7 monitoring of haemodynamic parameters without the constraints of tethered electronics6,41, which we combined with our electronic dura mater (e-dura[42]) implant optimized to target the haemodynamic hotspot (Extended Data Fig. 9a and Supplementary Note 3).

Rats with SCI were exposed to daily sessions of autonomic neurorehabilitation. Although all tested rats developed autonomic dysreflexia during the first few weeks after SCI, autonomic neurorehabilitation led to the gradual elimination of this symptom (Extended Data Fig. 9b,c). Anatomical examinations confirmed that autonomic neurorehabilitation enabled large-diameter afferents to compete for the innervation of Vsx2ON neurons located in the haemodynamic hotspot and, ultimately, to overcome the aberrant neuronal architecture responsible for autonomic dysreflexia (Extended Data Fig. 9d–h). We concluded that autonomic neurorehabilitation is a safe and efficacious treatment to reduce the severity of autonomic dysreflexia in rodents.

Clinical necessity of therapies for autonomic neurorehabilitation

The neurosurgical intervention necessary to treat autonomic dysreflexia with EES must be weighed against the risks and benefits of the procedure. Establishing this balance requires an understanding of the prevalence, symptomatology and effectiveness of current management strategies.

Consequently, we leveraged two large-scale surveys on self-reported information on the presence of symptoms of autonomic dysreflexia and demographic information about the level of SCI (n = 1,733 total)43,44. These data revealed that 82% of individuals with tetraplegia had been told by a medical practitioner that they present with autonomic dysreflexia. We found that only 30% of the individuals were being treated for autonomic dysreflexia, yet 98% of these treated individuals still experienced symptoms (Fig. 5a and Extended Data Fig. 10a). These symptoms were primarily characterized by vasogenic manifestations including headaches and heart palpitations, sudomotor manifestations including goosebumps and sweating, as well as anxiety (Fig. 5b and Extended Data Fig. 10b–d).

Fig. 5: Reduced severity of autonomic dysreflexia in people with chronic SCI following EES targeting the haemodynamic hotspot.

a, Prevalence of autonomic dysreflexia and management efficacy quantified in 1,479 individuals with SCI from the Rick Hansen Spinal Cord Injury Registry43,44. b, Percentage of individuals with tetraplegia experiencing each symptom of autonomic dysreflexia scored in the ADFSCI across various daily activities (n = 107). c, Implantable system to regulate blood pressure with EES, including a paddle lead with optimized electrode configurations to target the dorsal roots projecting to the haemodynamic hotspot, an implantable pulse generator, communication hub and external smartwatch to operate the various programs of the therapy. d, Post-operative reconstruction of the final position of the electrodes following the implantation of the paddle lead. e, Changes in blood pressure from a representative participant during an orthostatic challenge without EES and with continuous EES applied over the haemodynamic hotspot. The barplots report the average drop in systolic blood pressure during orthostatic challenge (n = 11, paired samples two-tailed t-test; t = 4.7774; P = 0.00101) and the average tilt duration without EES and with EES applied over the haemodynamic hotspot (n = 11, paired samples two-tailed t-test; t = 14.33100; P < 0.001). The Kaplan–Meier plot shows exposure status to time, segregated by the presence or absence of EES. Data are derived from a companion article[40]. f, ADFSCI autonomic dysreflexia score before implantation and after at least 6 months but up to 2 years after implantation of the system and daily use to regulate blood pressure (n = 11, paired samples one-tailed t-test; t = 2.3256, d.f. = 10, P = 0.02118). g, Percentage of individuals (n = 11) experiencing each symptom described in the ADFSCI autonomic dysreflexia section before implantation (before) and at the latest timepoint of ARCIM therapy (after).

This symptomatology combined with the risk of life-threatening episodes of autonomic dysreflexia and the absence of adequate therapeutic management justified evaluating the effect of autonomic neurorehabilitation on the severity of autonomic dysreflexia in humans living with chronic SCI.

Preliminary clinical validation of autonomic neurorehabilitation

We next conducted a preliminary clinical evaluation to assess whether the long-term application of EES targeting the haemodynamic hotspot to reduce hypotensive symptomatology also reduces the severity of autonomic dysreflexia in humans with chronic SCI. To carry out this assessment, we leveraged three ongoing observational clinical studies (STIMO-HEMO (NCT04994886, CHUV, Lausanne, Switzerland), HEMO (NCT05044923, University of Calgary, Calgary, Canada) and HemON (NCT05111093, CHUV, Lausanne, Switzerland)) focused on the development of a purpose-built implantable neurostimulation platform to restore haemodynamic stability based on EES targeting the haemodynamic hotspot[40] (Extended Data Fig. 10f).

These studies enrolled patients with cervical SCI who presented with medically refractory orthostatic hypotension. When exposed to orthostatic challenges during a tilt-table test, all the participants exhibited a rapid decline in blood pressure that required the early termination of the tilt test. Following this verification of their eligibility, the participants underwent a neurosurgical intervention to implant an electrode array targeting the dorsal root entry zones innervating the last three thoracic segments and a neurostimulator to deliver EES (Fig. 5c). Post-operative evaluations confirmed that EES targeting the haemodynamic hotspot elicited robust pressor responses that reduced the severity of orthostatic hypotension (Fig. 5d,e). The participants then learned how to operate the therapy to regulate their blood pressure throughout the day for up to 2 years (Extended Data Fig. 10f). During this time, no serious device-related adverse events occurred[40].

Although these studies focused on the long-term reduction of hypotensive symptomatology, the severity of autonomic symptoms was assessed concurrently with the autonomic dysfunction following SCI (ADFSCI) rating scale[45]. Consequently, the context of these studies allowed us to assess how the symptomatology related to autonomic dysreflexia evolved in nine participants.

Quantification of the autonomic dysreflexia subscore within the ADFSCI revealed a decrease in symptomatology related to autonomic dysreflexia after long-term use of EES targeting the haemodynamic hotspot. Secondary analyses of vasogenic and sudomotor manifestations revealed that the long-term use of EES targeting the haemodynamic hotspot led to a reduction in headaches and heart palpitations, which are the main vasogenic symptoms of autonomic dysreflexia (Fig. 5f,g and Extended Data Fig. 10g). By stark contrast, sudomotor manifestations and anxiety remained unchanged from baseline.

These clinical outcomes provide preliminary evidence that the daily application of EES targeting the haemodynamic hotspot reduces the severity of autonomic dysreflexia in humans with chronic SCI, as quantified functionally and anatomically in mice and rats.

Discussion

Here we exposed the complete, aberrant neuronal architecture that develops after SCI and causes autonomic dysreflexia. Although all the neurons constituting this neuronal architecture exists in healthy mice, the SCI precipitates the development of connections between nociceptive neurons and Vsx2-expressing neurons, as well as between Vsx2-expressing neurons located in the lumbosacral and lower thoracic spinal cord that provoke the emergence of autonomic dysreflexia. This architecture incorporates locally projecting, Vsx2-expressing neurons located in the lower thoracic spinal cord that are also embedded in the neuronal architecture that enables EES targeting the haemodynamic hotspot to achieve safe and well-controlled increases of blood pressure. As these adversarial architectures converged onto a single neuronal subpopulation, their relative activation determined the prevailing architecture in this competitive interaction.

Exposing these adversarial architectures allowed us to design a safe intervention that reversed autonomic dysreflexia in mice, rats and humans with SCI by applying EES targeting the haemodynamic hotspot located in the lower thoracic spinal cord. Conversely, the mistargeted application of EES over the lumbosacral spinal cord reinforced the anatomical and functional connectivity of the neuronal architecture responsible for autonomic dysreflexia, which augmented the severity of these symptoms.

People living with SCI and medical practitioners are taught to identify warning signs such as headaches, sweating and goosebumps, as these signs indicate the presence of a noxious stimulus that is triggering autonomic dysreflexia, which must therefore be localized to resolve the ongoing hypertensive episode as quickly as possible[46]. Analysis of self-reported symptoms in patients from four clinical studies showed that EES targeting the haemodynamic hotspot reduced headaches and heart palpitations, which are both directly related to dangerous increases in blood pressure. Instead, this therapy had no detectable effect on the other warning signs such as goosebumps and sweating that could still inform on the presence of noxious stimulus.

We took advantage of ongoing clinical trials to collect preliminary evidence on the potential for EES targeting the haemodynamic hotspot to reduce autonomic dysreflexia in humans with SCI. However, these trials were not designed to demonstrate the safety and efficacy of this therapy, and relied only on self-reported symptoms instead of physiological outcomes. Consequently, the next steps must include the assessment of the safety and efficacy of EES targeting the haemodynamic hotspot to reduce autonomic dysreflexia in a pivotal device trial.

Methods

Mouse and rat models

Adult male or female C57BL/6 mice (15–25 g body weight, 8–15 weeks of age) or transgenic mice were used for all experiments. vGLUT2–Cre (016963, Jackson Laboratory), Vsx2–Cre (MMMRRC 36672, also called Chx10–Cre), ChAT–Cre, VGAT–Cre, Ai65(RCFL-tdT) (021875; Jackson Laboratory), PV–Cre (017320, Jackson Laboratory), AdvillinFlpO (a gift from V. Abraira), iDTR and Calca–Cre transgenic mouse strains were bred and maintained on a mixed genetic background (C57BL/6). Adult female Lewis rats (180–220 g body weight, 14–30 weeks of age) were used for the rat experiments. Housing, surgery, behavioural experiments and euthanasia were all performed in compliance with the Swiss Veterinary Law guidelines. Manual bladder voiding and all other animal care was performed twice daily throughout the entire experiment. All procedures and surgeries were approved by the Veterinary Office of the Canton of Geneva (Switzerland; authorization GE67).

Viral vectors and vector production

Viruses used in this study were either acquired commercially or produced at the EPFL core facility. The following AAV plasmids were used and detailed sequence information is available as detailed or upon request: AAVDJ-hSyn-flex-mGFP-2A-synaptophysin-mRuby[48] (reference AAV DJ GVVC-AAV-100, Stanford Vector Core Facility), AAV9-CMV-Cre (7014, Vector Biolabs), AAV5-hSyn-eGFP (50465-AAV5, Addgene), AAV5-Syn-flex-ChrimsonR-tdT (62723, Addgene), AAV5-hSyn-DIO-hm4D[49] (Gi)-mCherry (44362, Addgene; 7 × 10^12 vg ml−1 or more), AAV5-CAG-flex-tdTomato (a gift from S. Arber), AAV5-hSyn-Con/Fon-eYFP (#55650, Addgene), rAAV2-EF1a-DIO-Flpo (#87306, Addgene), EnvA-ΔG-Rabies-mCherry (a gift from S. Arber) and AAV8-hSyn-dlox-TVA950-2A-EGFP-2A-oGrev-dlox-WRPE-bGHp (a gift from S. Arber).

SCI models

For mouse SCIs, a laminectomy was performed on the T4 vertebra to expose the T4 spinal segment. Complete transections were performed using angled microscissors. Rat SCIs were performed according to our previously published work[6]. In brief, a laminectomy was performed on the T3 vertebra to expose the T3 spinal segment. Following this, the rat was transferred to the Infinite Horizon (IH-0400 Impactor, Precision Systems and Instrumentation) impactor[6] stage, where the T2 and T4 spinous processes were securely clamped using modified Allis forceps[6]. The rat was stabilized on the platform and the impactor tip (2.5 mm) was properly aligned using a 3D coordinate system moving platform. The Infinite Horizon system was set to deliver an impact force of 400 kdyn, with a 5-s dwell time[6]. Analgesia (buprenorphine; Essex Chemie; 0.01–0.05 mg kg−1, subcutaneously) and antibiotics (amoxicillin 200 mg per 4 ml; Sandoz; 200 mg l −1 ad libitum) were provided for 3 and 5 days after surgery, respectively. Bladders were manually expressed twice a day until the end of the experiment[50].

Rodent anaesthesia use

All non-terminal experiments were conducted by anaesthetizing animals with isoflurane (initial induction at 5% and maintained on a Bain’s system at 2%). Terminal haemodynamic assessments were carried out as previously described 4 weeks after SCI. In brief, animals were anaesthetized with urethane (1.5 g kg−1, intraperitoneally)[6]. The depth of anaesthesia was continually monitored by assessing withdrawal reflexes and respiratory rate.

Haemodynamic monitoring

In mice, carotid artery catheterization was performed. After induction of anaesthesia, the hair on the neck of mice was shaved, and the surgical site cleaned with alcohol and betadine. The right common carotid artery was exposed and isolated from the internal jugular vein using blunt dissection. The rostral portion of the carotid artery immediately below bifurcation was permanently occluded, whereas the caudal portion of the vessel was temporarily occluded using 5-0 silk sutures. A bent-tip 30-gauge needle was used to create a small hole on top of the carotid artery. The blood pressure sensor was inserted into the carotid artery and advanced approximately 0.5 cm caudally. The catheter was then secured with two 5-0 silk sutures. In rats, the procedure for the telemeter implantation was performed according to our previously published work[6]. In brief, we recorded blood pressure using wireless telemeters (TRM56SP SNA and Pressure Telemeter, Kaha Sciences). A midline abdominal incision was made to expose the peritoneal cavity, followed by a blunt dissection to reach the descending aorta. The aorta was temporarily occluded using a 4-0 silk, 1–2 mm rostral to the iliac bifurcation. The pressure sensor was inserted in the aorta so that the tip was just caudal to the renal artery and fixed with a surgical mesh and biocompatible surgical glue.

Colorectal distension to induce autonomic dysreflexia

Foley Catheter Cysto-Care 1.5 ml and Foley Catheter Cysto-Care 3 ml were used for mice and rats, respectively, to perform colorectal distension to induce autonomic dysreflexia[51]. The catheter was inserted into the rectum and colon until the balloon was no longer exposed. During colorectal distension assessments, the balloon was inflated (up to 0.7 ml for mice and up to 2.5 ml in rats) for 60 s. Subsequent trials were only initiated after blood pressure had returned to the baseline value. For repetitive autonomic dysreflexia experiments, the balloon was inflated for 30 s and deflated for 60 s and this protocol cycled for 90 min (ref. [14]).

EES implants

All the procedures have been previously detailed6,13,52,53,54,55,56. To position electrodes to deliver EES in mice, laminotomies (removal of only the connective tissue between the bones, but not the bones) were performed at T9–T10 and T12–T13 to expose the spinal cord. Teflon-coated stainless steel wires connected to a percutaneous connector (Omnetics Connector Corporation) were inserted rostrally and passed between the spinal cord and the vertebral bones to the other opening. A small part of insulation was removed and the exposed stimulation sites were positioned over T12–T13. A common ground was inserted subcutaneously. The percutaneous connector was cemented to the skull. This stimulation protocol was subsequently used for all acute and chronic experiments. In rats, the implantation of the e-dura was performed according to our previously published work[6]. To insert and stabilize e-dura implants into the epidural space, two partial laminectomies were performed at vertebrae levels L1–L2 and T8–T9 to create entry and exit points for the implant. The implant was gently pulled above the dura mater using a surgical suture. Electrophysiological testing was performed intra-operatively to fine-tune positioning of electrodes. The connector of the implant was secured into a protective cage plastered using freshly mixed dental cement on top of the L2–L3 vertebra. Stimulation was then delivered as previously described[6]. The headstage was plastered using freshly mixed dental cement on the dorsal side of the skull where three stainless steel screws were placed.

Autonomic neurorehabilitation

In mice, we delivered EES with conventional stimulation protocols[6] that involved continuous EES delivered at 50 Hz with 5-ms pulses at 100–150 µA (2100 Isolated Pulse Stimulator, A-M Systems). Mice underwent autonomic neurorehabilitation consisting of EES applied for 30 min each day for 4 weeks, starting 1 week after SCI. In rats, we applied a closed-loop controlled stimulation using a proportional integral controller that adjusts the amplitude of traveling EES waves over the three haemodynamic hotspots (MATLAB). Next, we applied a +10 mmHg systolic blood pressure target from the baseline acquired at the beginning of each autonomic neurorehabilitation session[6]. Rats received 30 min of closed-loop controlled stimulation for 6 weeks, starting 1 week after SCI.

Neuron-specific ablations and chemogenetic manipulations

For ablation experiments with the diphtheria toxin, we used PVCre::AvilFLPo::iDTR and CalcaCre::AvilFLPo::iDTR mice. Four weeks after the SCIs (T4 spinal-level complete transection), mice received intraperitoneal injections of diphtheria toxin (D0564, Sigma) diluted in saline (100 µg kg−1) to target PVON or CalcaON neurons, respectively. Mice were tested 2 weeks post-injection. To manipulate the activity of vGLUT2ON and VGATON neurons, AAV5-hSyn-DIO-hm4D was infused (0.15 µl per injection) at two depths (0.8 mm and 0.4 mm below the dorsal surface) and separated by 1 mm in either the lower thoracic spinal cord (T11–T13) or the lumbosacral spinal cord (L5–S1) of either vGLUT2–Cre or VGAT–Cre mice before performing the SCI. To manipulate ChATON neuronal activity, AAV5-hSyn-DIO-hm4D was infused (0.15 µl per injection) at two depths (0.8 mm and 0.4 mm below the dorsal surface) and separated by 1 mm in the lower thoracic spinal cord (T11–T13) of ChAT–Cre mice before performing the SCI. To manipulate SCTHORACIC::Vsx2 or SCLUMBAR::Vsx2 neurons, AAV5-hSyn-DIO-hm4D was infused in either the lower thoracic spinal cord (T11–T13) or the lumbosacral spinal cord (L5–S1; injection depths were 0.8 mm and 0.4 mm below the dorsal surface; separated by 1 mm; 0.15 µl per injection), respectively, in Vsx2–Cre mice before performing the SCI. After 4 weeks, autonomic dysreflexia or EES-induced pressor response was assessed before and between 30 min and 45 min after intraperitoneal injections of 5 mg kg−1 clozapine N-oxide (Carbosynth, CAS: 34233-69-7; suspended in 2% DMSO in saline).

Optogenetic manipulation

To optogenetically manipulate Vsx2ON neurons, AAV5-Syn-flex-Chrimson (#62723-AAV5, Addgene; titre ≥ 5 × 10^12 vg ml−1) was infused in either the lower thoracic spinal cord (T11–T13) and the lumbosacral spinal cord (L5–S1), in Vsx2–Cre mice before performing the SCI. After 6 weeks, laminectomies were made over T11/T12/T13 and L5/L6/S1 spinal segments. Five-ms pulses were delivered at 50 Hz from a 635-nm laser (LRD-0635-PFR-00100-03, LaserGlow Technologies). Laser light was delivered to the surface of the spinal cord through a fibre optic cable attached to 400 µm, 0.39 NA cannula with a 5-mm tip (Thorlabs). Optical power was set to 2.35 mW at the tip.

Spinal injections for exclusive labelling of Vsx2ON neurons

To exclusively label Vsx2ON neurons in the lumbosacral spinal cord with long-distance projections to the lower thoracic region (SCLUMBAR::Vsx2), we leveraged Boolean logic viral strategies[30]. Partial laminectomies were made over the T11/T12/T13 and L5/L6/S1 spinal segments of Vsx2–Cre mice. Two sets of bilateral injections of AAV5-hSyn-Con/Fon-eYFP (#55650-AAV8, Addgene; titre ≥ 1 × 10^13 vg ml−1)[30] were made over the L5/L6/S1 spinal segments (0.25 µl per injection) at a depth of 0.6 mm below the dorsal surface and separated by 1 mm. Two sets of bilateral injections of rAAV2-EF1a-DIO-Flpo (#87306, Addgene) were made over the T11/T12/T13 spinal segments (0.15 µl per injection) at two depths (0.8 mm and 0.4 mm below the dorsal surface) and separated by 1 mm. Animals were perfused 4 weeks later. To label lower thoracic Vsx2ON neurons (SCTHORACIC::Vsx2), two sets of bilateral injections of AA5V-CAG-flex-tdtomato were made over T11/T12/T13 spinal levels (0.15 µl per injection) at two depths (0.8 mm and 0.4 mm below the dorsal surface) and separated by 1 mm.

Spinal injections for exclusive labelling of vGLUT2ON neurons

To exclusively label vGLUT2ON neurons in the lumbosacral spinal cord with long-distance projections to the lower thoracic region, we leveraged Boolean logic viral strategies[30]. Partial laminectomies were made over the L5/L6/S1 spinal segments of vGLUT2–Cre mice. Two sets of bilateral injections of AAVDJ-hSyn-flex-mGFP-2A-synaptophysin-mRuby[48] (reference AAV DJ GVVC-AAV-100, Stanford Vector Core Facility) were made over the L5/L6/S1 spinal segments (0.25 µl per injection) at a depth of 0.6 mm below the dorsal surface and separated by 1 mm. Animals were perfused 4 weeks later.

Injection site quantification

To determine the number of transfected neurons within the injection site of the spinal cord, we implemented the spot detection function in Imaris. Following the semi-automatic detection of transfected neurons within representative sections per animal, we quantified the neurons by compiling the exported text file from Imaris.

Perfusions

Animals were perfused at the end of the experiments. Animals were deeply anaesthetized by an intraperitoneal injection of 0.2 ml sodium pentobarbital (50 mg ml−1). Animals were transcardially perfused with phosphate-buffered saline (PBS) followed by 4% paraformaldehyde (PFA) in PBS. Tissues were removed and post-fixed overnight in 4% PFA before being transferred to PBS or cryoprotected in 30% sucrose in PBS.

Immunohistochemistry

Immunohistochemistry was performed as previously described55,57,58. Perfused post-mortem tissue was cryoprotected in 30% sucrose in PBS for 48 h before being embedded in cryomatrix (Tissue Tek O.C.T, Sakura Finetek Europe B.V.) and freezing. We used two procedures to identify the segment of the spinal cord. First, we identified the dorsal roots in the unsectioned spinal cord. On the basis of the location of the dorsal root entry zones, we prepared well-defined blocks of spinal cord. We then confirmed that the grey matter of the segments possess the expected laminar organization and morphology. Transverse or horizontal sections (30 µm thick) of the spinal cord were cut on a cryostat (Leica), immediately mounted on glass slides and dried or placed in free floating wells containing PBS + 0.03% sodium azide. The sections were incubated with following primary antibody diluted in blocking solution at room temperature overnight: rabbit anti-Fos (1:500), chicken anti-vGLUT1 (1:500), rabbit anti-ChAT (1:100, Sigma-Aldrich) and rabbit anti-Chx10 (now known as Vsx2; 1:500, Synaptic Systems). Fluorescent secondary antibodies were conjugated to Alexa 488 (green), Alexa 405 (blue), Alexa 555 (red) or Alexa 647 (far red; Thermo Fisher Scientific). For the nuclear stain, 4′,6-diamidino-2-phenylindole dihydrochloride (DAPI; 2 ng ml−1; Molecular Probes). Sections were imaged digitally using a slide scanner (Olympus VS-120 slide scanner) or confocal microscope (LSM880 + Airy fast module with ZEN 2 Black software, Zeiss). Images were digitally processed using ImageJ (ImageJ NIH) software or Imaris (Bitplane, v9.8.2).

Fluorescence in situ hybridization

We performed in situ hybridization of cell-type markers and using RNAscope (Advanced Cell Diagnostics). Lists of putative marker genes were obtained from snRNA-seq data, as described below, and cross-referenced against a list of validated probes designed and provided by Advanced Cell Diagnostics. Probes were obtained for the following genes: ChAT (catalogue no. 408731) and Vsx2 (catalogue no. 438341), Slc17a6 (catalogue no. 319171) and Slc6a5, catalogue no. 409741). We then generated 12-μm cryosections from fixed-frozen spinal cords as previously described[59] and performed FISH for each probe according to the manufacturer’s instructions, using the RNAscope HiPlex kit (cat no. 324106). Images were generated using QuPath (v0.4.3).

iDISCO+

Mice underwent a 90-min colorectal distension protocol (30 s inflate then 60 s deflate repeatedly)[14] and were perfused9,47 30 min later with 0.1 M PBS followed by 4% PFA (in 0.1 M PBS). Spinal cords were dissected and post-fixed in 4% PFA (in 0.1 M PBS) at 4 °C overnight and placed in 0.1 M PBS containing 0.03% sodium azide. Immunolabelling of the samples was performed by first pretreating with methanol in 5-ml Eppendorf tubes by dehydrating with a methanol–H_2O series at 1 h each at room temperature with shaking at 60 rpm: 20%, 40%, 60%, 80% and 100%. This procedure was followed by 1 h washing with 100% methanol before chilling the samples at 4 °C. Samples were then incubated overnight with shaking in 66% dicholoromethane–33% methanol at room temperature. The samples were washed twice in 100% methanol with shaking at room temperature and then bleached in chilled fresh 5% H_2O_2 in methanol overnight at 4 °C. Samples were rehydrated with a methanol–H_2O series: 80%, 60%, 40%, 20% and 0.1 M PBS, each for 1 h at room temperature under shaking. Samples were washed for 1 h × 2 at room temperature in PTx.2 buffer (0.1 M PBS with 0.2% Triton X-100) under shaking. This was followed by an incubation in 5 ml of permeabilization solution (400 ml PTx.2, 11.5 g glycine and 100 ml DMSO for a total stock volume of 500 ml) for 2 days at 37 °C with shaking at 60 rpm. Samples were incubated in 5 ml of blocking solution (42 ml PTx.2, 3 ml of normal donkey serum and 5 ml of DMSO for a total stock volume of 50 ml) for 2 days at 37 °C with shaking. The samples were incubated for 7 days at 37 °C with shaking in primary antibody solution consisting of PTwH (0.1 M PBS, 2 ml Tween-20, 10 mg l−1 heparin, 5% dimethyl sulfoxide and 3% normal donkey serum) and Fos antibody (1:2,000; 226003, Synaptic Systems) for a total volume of 5 ml per sample. Samples were washed in PTwH for 24 h with shaking and incubated for 7 days at 37 °C with shaking in secondary antibody solution consisting of PTwH, 3% normal donkey serum and donkey anti-rabbit Alexa Fluor 647 (1:400, Thermo Fisher Scientific) in a total volume of 5 ml per sample. Samples were washed in PTwH for 24 h with shaking at room temperature. Clearing of the samples was performed by first dehydrating the samples in a methanol–H_2O series as follows: 20%, 40%, 60%, 80% and 100% twice each for 1 h with shaking at room temperature followed by a 3-h incubation with shaking in 66% dichloromethane–33% methanol at room temperature. Samples were incubated in 100% dichloromethane 15 min twice with shaking to wash residual methanol. Finally, samples were incubated in 100% dibenzyl ether without shaking for refractive index matching of the solution for at least 24 h before imaging.

Tissue clearing (CLARITY)

We incubated samples in X-CLARITY hydrogel solution (Logos Biosystems) for 24 h at 4 °C with gentle shaking12,55,60. Samples were degassed and polymerized using the X-CLARITY Polymerization System (Logos Biosystems), followed by washes in 0.001 M PBS for 5 min at room temperature. Samples were next placed in the X-CLARITY Tissue Clearing System (Logos Biosystems), set to 1.5 A at 100 rpm at 37 °C for 29 h. Clearing solution was made in-house with 4% sodium dodecyl sulfate, 200 mM boric acid with dH_2O, pH adjusted to 8.5. Following this, samples were washed for at least 24 h at room temperature with gentle shaking in 0.1 M PBS solution containing 0.1% Triton X-100 to remove excess sodium dodecyl sulfate. Finally, samples were incubated in 40 g of Histodenz dissolved in 30 ml of 0.02 M PB, pH 7.5, and 0.01% sodium azide (refractive index of 1.465) for at least 24 h at room temperature with gentle shaking before imaging.

3D imaging

We performed imaging of cleared tissue using either a customized mesoSPIM12,61 or a CLARITY-optimized light-sheet microscope (COLM)[12]. A custom-built sample holder was used to secure the central nervous system in a chamber filled with RIMS. Samples were imaged using either a ×1.25 or ×2.5 objective at the mesoSPIM12,61 or a ×4 or ×10 objective at the COLM[12] with one or two light sheets illuminating the sample from both the left and the right sides. The voxel resolution in the x, y and z directions was 5.3 μm × 5.3 μm × 5 μm for the ×1.25 acquisition and 2.6 μm × 2.6 μm × 3 μm for the ×2.5 acquisition. The voxel resolution of the COLM was 1.4 μm × 1.4 μm × 5 μm for the ×4 and 0.59 μm × 0.59 μm × 3 μm for the ×10 acquisition. Images were generated as 16-bit TIFF files and then stitched using Arivis Vision4D (Arivis AG). 3D reconstructions and optical sections of raw images were generated using Imaris (bitplane, v9.8.2) software.

Fos quantifications

For the cleared spinal cords, Fos-positive neurons of cleared samples were quantified using Arivis Vision4D (Arivis)[13]. After defining a region of interest around the grey matter, each sample was subjected to a custom-made pipeline. We applied morphology, denoising and normalization filters to enhance the signal of bright objects and homogenized the background. Threshold-based segmentation of the Fos signal was applied within predefined 3D regions to quantify the total number of Fos-positive cells. Image analysis parameters were kept constant among all samples. The number of Fos-positive cells and their coordinates enabled us to quantify the neuronal activity segment by segment. For the classic immunohistochemistry, the quantification was done on Imaris (bitplane, v9.8.2) using the spot detection function.

3D reconstruction and quantification

We used the ‘add new surfaces’ tool in Imaris to select the channel of interest, setting the ‘surface detail’ to 0.5 µm for a more detailed surface. The ‘threshold (absolute intensity)’ was adjusted to capture the full shape of the neuron accurately. We applied the ‘number of voxels Img = 1’ filter, selecting the appropriate threshold values to include the reconstructed neuron. After rendering the surface, we made aesthetic adjustments, choosing the ‘transparent 3 - glass’ material and setting the colour to RGB values of (1, 1, 1) to maintain transparency and highlight viral expression. For reconstructing synaptic-like appositions, we created ‘spots’ based on the channel of interest using the spot detection algorithm in Imaris. The ‘estimate xy diameter’ was set between 1.5 µm and 2 µm, with the ‘quality’ filter applied to capture all synaptic-like appositions in the image. The ‘points style/quality’ was set to ‘sphere’ with a ‘radius scale’ of 0.5 µm, using ‘Phong_basic’ as the material for the synapses. To filter synaptic-like appositions to neurons of interest, we used the ‘find spots close to surface’ function with a 1-µm threshold and selected only the ‘spots close to the surfaces’ to display the synaptic-like appositions on neurons of interest. Of note, synapses were primarily counted on neuronal cell bodies as labelling of the different axonodendritic compartments with in vivo immunohistochemistry remains limited.

Axon and synapse quantification

To determine spatial enrichment of axon and synapse density within the grey horn of the spinal cord, we implemented a custom image analysis pipeline that includes preprocessing, registration and combination of histological images from different animals. In brief, we implemented all preprocessing in Fiji, and all registration procedures in R, using the image analysis package ‘imageR’ and the medical image registration package ‘RNiftyReg’. After dynamic registration, all data were summarized and final quantifications were completed using custom R scripts.

Opto-tagging-based neuron-specific recordings and analysis

SCI at T4 and infusion of AAV5-Syn-flex-Chrimson was made in the lower thoracic spinal cord of Vsx2–Cre mice at least 4 weeks before terminal experiments. Mice were anaesthetized with urethane and isofluorane. Two-shank, multi-site electrode arrays (A2x32-6mm-35-200-177, NeuroNexus) were lowered into the spinal cord to a depth of 1,000 µm, with shanks arranged longitudinally at 350 µm from the midline. Signals were recorded with a NeuroNexus Smartbox Pro using a common average reference and while applying 50-Hz notch and 450–5,000-Hz bandpass filters. Stimulation was controlled with a Multi-Channel Systems STG 4004 and MC_Stimulus II software. ChrimsonR-expressing neurons were identified using optogenetic stimulation. Twenty pulse trains of 10-ms pulses were delivered at 10 Hz from a 635-nm laser (LRD-0635-PFR-00100-03, LaserGlow Technologies). Laser light was delivered to the surface of the spinal cord through a fibre optic cable attached to 400 µm, 0.39 NA cannula with a 5-mm tip (Thorlabs). Optical power was set to 2.35 mW at the tip. Electrical stimulation (EES) consisted of 5-ms pulses delivered every 1 Hz. EES was delivered with a micro fork probe (Inomed, 45 mm straight, item no. 522610) positioned along the midline just caudal to the recording array. Spike sorting was performed with SpyKING CIRCUS (v1.0.773). The median-based average electrical stimulation artefacts for each channel were subtracted from the recordings before sorting. Owing to the size and variability of the artefacts, periods containing residual stimulation artefacts were not sorted (–0.5 to +1.5 ms and −0.5 to +1 ms around stimulus onset for EES and laser stimulation onset, respectively). Sorting results were manually curated using Phy (https://github.com/cortex-lab/phy). Single-unit clusters were selected for analysis based on their biphasic waveforms and template amplitudes above 50 µV, as well as strong refractory period dips in their spike autocorrelograms. Similar clusters were merged according to the Phy manual clustering guide. ChrimsonR-expressing, putative SCTHORACIC::Vsx2 neurons were identified based on their low-latency and low-jitter responses to light pulses. Neurons responding to EES or tail pinch were identified by a one-sided Wilcoxon signed-rank test to compare the instantaneous firing rate of units 100 ms before and 100 ms after (EES) or 2 s before and 2 s after (pinch) stimulus onset. For EES, a post-stimulus onset firing rate increase of P < 0.001 was used, whereas for pinch a P = 0.05 was used due to the necessarily lower number of trials and larger calculation window (minimum of 6 trials for pinch and 60 trials for EES).

Statistics, power calculations, group sizes and reproducibility

All data are reported as mean values and individual data points. No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those reported in previous publications[55]. Haemodynamic assays were replicated three to five times, depending on the experiment, and averaged per animal. For all photomicrographs of histological tissue, staining experiments were repeated independently with tissue from at least four, and in most cases six, different animals with similar results. Statistics were then performed over the mean of animals. All statistical analysis was performed in R using the base package ‘stats’, with primary implementation through the ‘tidyverse’ and ‘broom’ packages. Tests used included one-tailed or two-tailed paired or independent samples Student’s t-tests, one-way analysis of variance (ANOVA) for neuromorphological evaluations with more than two groups, and one-way or two-way repeated-measures ANOVA for haemodynamic assessments, when data were distributed normally, tested using a Shapiro–Wilk test. Post-hoc Tukey tests were applied when appropriate. For regressions, mixed-model linear regression was used in cases of multiple observations, or else standard linear modelling. In cases where group size was equal to or less than three, null hypothesis testing was not completed. The significance level was set as P < 0.05. Throughout the paper, the boxplots show the median (horizontal line), interquartile range (hinges) and smallest and largest values no more than 1.5 times the interquartile range (whiskers). Exclusions of data are noted in the relevant Methods sections. Unless stated otherwise, experiments were not randomized, and the investigators were not blinded to allocation during experiments and outcome assessment.

scRNA-seq

Single-nucleus dissociation of the mouse lower thoracic and lumbosacral spinal segments was performed according to our established procedures19,20. Following euthanasia by isoflurane inhalation and cervical dislocation, the lumbar spinal cord site was immediately dissected and frozen on dry ice. Spinal cords were doused in 500 µl sucrose buffer (0.32 M sucrose, 10 mM HEPES (pH 8.0), 5 mM CaCl_2, 3 mM Mg acetate, 0.1 mM EDTA and 1 mM dithiothreitol) and 0.1% Triton X-100 with the Kontes Dounce tissue grinder. Sucrose buffer (2 ml) was then added and filtered through a 40-µm cell strainer. The lysate was centrifuged at 3,200g for 10 min at 4 °C. The supernatant was then decanted, and 3 ml of sucrose buffer was added to the pellet for 1 min. We homogenized the pellet using an Ultra-Turrax and 12.5 ml of density buffer (1 M sucrose, 10 mM HEPES (pH 8.0), 3 mM Mg acetate and 1 mM dithiothreitol) was added below the nuclei layer. The tube was centrifuged at 3,200g at 4 °C and the supernatant poured off. Nuclei on the bottom half of the tube wall were collected with 100 µl PBS with 0.04% BSA and 0.2 U µl−1 RNase inhibitor. Finally, we resuspended nuclei through a 30-µm strainer and adjusted to 1,000 nuclei per microlitre.

Library preparation

snRNA-seq library preparation was carried out using the 10X Genomics Chromium Single Cell Kit (v3.1). The nuclei suspension was added to the Chromium RT mix to achieve loading numbers of 2,000–5,000. For downstream cDNA synthesis (13 PCR cycles), library preparation and sequencing, the manufacturer’s instructions were followed.

Read alignment

We aligned reads to the most recent Ensembl release (GRCm38.93) using Cell Ranger, and obtained a matrix of unique molecular identifier (UMI) counts. Seurat[31] was used to calculate quality control metrics for each cell barcode, including the number of genes detected, number of UMIs and proportion of reads aligned to mitochondrial genes. Low-quality cells were filtered by removing cells expressing less than 200 genes or with more than 5% mitochondrial reads. Genes expressed in less than three cells were likewise removed.

Clustering and integration

Before clustering analysis, we first performed batch-effect correction and data integration across the two different experimental conditions as previously described[31]. Gene expression data were normalized using regularized negative binomial models[62], then integrated across batches using the data integration workflow within Seurat. The normalized and integrated gene expression matrices were then subjected to clustering to identify cell types in the integrated dataset, again using the default Seurat workflow. Cell types were manually annotated on the basis of marker gene expression, guided by previous studies of the mouse spinal cord19,63,64,65. Local and projecting neuronal subpopulations were annotated on the basis of Nfib and Zfhx3 expression, respectively[15]. Following our projection-specific snRNA-seq experiment in uninjured mice, each subsequent experiment was reintegrated with this dataset before subpopulation annotation. This enabled the identification of the same 28 neuronal subpopulations across the three distinct experiments[31].

Cell-type prioritization with Augur

To identify neuronal subpopulations perturbed during natural repair, we implemented our machine-learning method Augur19,21. Augur was run with default parameters for all comparisons. To evaluate the robustness of cell-type prioritizations to the resolution at which neuronal subtypes were defined in the snRNA-seq data, we applied Augur at various clustering resolutions and visualized the resulting cell-type prioritizations both on a hierarchical clustering tree[66] of neuron subtypes and as a progression of UMAPs. The key assumption underlying Augur is that cell types undergoing a profound response to a perturbation should become more separable, within the highly multidimensional space of gene expression, than less affected cell types. In brief, Augur withholds a proportion of sample labels, then trains a random forest classifier to predict the condition from which each cell was obtained. The accuracy with which this prediction can be made from single-cell gene expression measurements is then evaluated in cross-validation and quantified using the area under the receiver operating characteristic curve.

Clinical studies design and objectives

All experiments were carried out as part of three clinical safety (primary objective) and preliminary efficacy (secondary objectives) trials: STIMO-HEMO (NCT04994886, CHUV, Lausanne, Switzerland), HEMO (NCT05044923, University of Calgary, Calgary, Canada) and HemON (NCT05111093, CHUV, Lausanne, Switzerland). All three trials and subsequent amendments received approval by the local ethical committees and national competent authorities. All participants signed a written informed consent before their participation, which included consent to complete the autonomic dysreflexia component of the ADFSCI. All participants had the option to indicate consent for the publication of identifiable images or videos. All surgical and experimental procedures were performed at the investigational hospital sites (Neurosurgery Department of the Lausanne University Hospital (CHUV) and the Neurosurgery Department of the Foothills Medical Center (Calgary, Canada). The study involved eligibility and baseline assessments before surgery, the surgical implantation of the respective investigational devices, a post-operative period during which EES protocols were configured, and long-term follow-up periods. To date, a total of ten participants have been participating for more than 6 months in the study. More detailed information about these trials can be found in other publications[40].

Study participants

Eleven individuals (five women and six men) who had suffered a traumatic SCI participated overall in the four studies. Demographic data and neurological status, evaluated according to the International Standards for Neurological Classification of Spinal Cord Injury[1], can be found in Supplementary Table 3.

Neurosurgical intervention

The participant was put under general anaesthesia and was placed in a prone position. Preoperative surgical planning informed the neurosurgeon about the vertebral entry level and predicted optimal position. On the basis of this knowledge, lateral and anteroposterior fluoroscopy X-rays were performed intraoperatively to guide the location of the laminotomies. A midline skin incision of approximately 5 cm on the back was performed, the fascia opened and the muscles were retracted bilaterally. Excision of the midline ligamentous structures and a laminotomy at the desired entry level enabled the insertion of the paddle array at the spinal thoracic level. For participants of the STIMO-HEMO and HEMO trials, a second skin incision or extended opening caudally was made and a second laminotomy was performed in the lumbar area based on the pre-operative planning to allow for the insertion of the lumbar paddle lead. The paddle lead (or leads; Specify 5-6-5, Medtronic or ARCIM Thoracic Lead, ONWARD Medical N.V) were inserted and placed over the midline of the exposed dura mater and advanced rostrally to the target position guided by repeated fluoroscopies. Electrophysiological recordings were conducted using standard neuromonitoring systems (IOMAX, Cadwell Industries or ISIS Xpress, Inomed Medizintechnik). Single pulses of EES (0.5 Hz) were delivered at increasing amplitude to elicit muscle responses that are recorded from the subdermal (Neuroline Twisted Pair Subdermal, 12 × 0.4 mm, Ambu A/S) or intramuscular (Inomed SDN electrodes, 40 × 0.45 mm, Inomed Medizintechnik) needle electrodes to correct for lateral and rostrocaudal positioning. If the paddles were deviating from a straight midline position, small additional laminotomies were made to remove bony protrusions and guide the paddle to a midline placement. Once the final position was achieved, the leads were anchored to the muscular fascia. In the STIMO-HEMO and HemON trials, the back opening was temporarily closed and the participant was put in lateral decubitus. Subsequently, the back incision was reopened and an abdominal incision of about 5 cm was made per implantable pulse generator (IPG) and a subcutaneous pocket was created. In the HEMO trial, incisions of about 5 cm were made bilaterally in the upper buttocks region and subcutaneous pockets were created. The paddle array cables were then tunneled between the back opening and subcutaneous pockets to be connected to the IPGs (Intellis, Medtronic or ARCIM IPG, ONWARD Medical). The IPGs were implanted in the subcutaneous pockets and all incisions were finally closed.

Stimulation optimization

Spatial mapping was guided by the preclinical mechanisms previously described[6] and from previous clinical mappings[40], and was conducted in three steps: (1) intra-operative mapping to identify which rows of electrodes target the haemodynamic hotspot, and elicit the largest pressor response in the thoracic spinal cord (T10, T11 and T12), (2) post-operative imaging and spinal reconstructions were used to estimate the electrodes that maximize recruitment of the haemodynamic hotspots, and (3) a single 2-h, post-operative mapping session was done to test each row of electrodes on the lead and pick the three configurations with the largest pressor responses. These configurations were tested in both multipolar and monopolar settings and were validated by personalized simulations to ensure that we were optimally targeting the haemodynamic hotspots. Stimulation frequency was defined empirically at 120 Hz for the spatial mapping6,40. The pulse width was 300 μs. The amplitude was set by incrementally increasing the current per configuration until the systolic pressure increased by 20 mmHg, the diastolic pressure increased by 10 mmHg, or the patient did not report any discomfort such as muscle contractions or sensations such as tingling. These mappings were done in a seated position to mimic relevant, daily life orthostatic challenges.

Clinical haemodynamic monitoring

Beat-to-beat blood pressure and heart rate were obtained via finger plethysmography (Finometer, Finapres Medical Systems). Beat-by-beat blood pressure was calibrated to brachial artery blood pressure collected using an arm cuff embedded and synchronized with the Finometer67,68,69,70. Brachial arterial pressure was sampled at 200 Hz, whereas the systolic, diastolic and mean arterial pressures were extracted from the calibrated arterial pressure at 1 Hz. The heart rate was also sampled at 1 Hz. Raw data and automatically extracted haemodynamic parameters were saved and exported from the Finometer.

Orthostatic challenge with the tilt table test

Participants were transferred to a supine position on a table capable of head-up tilt. We applied restraint straps to secure the patient below the knees, across the thighs and the trunk, with the feet stabilized. Resting supine blood pressure was recorded continuously for approximately 5–10 min to establish baseline values. Then, we tilted the patient upright up to a maximum of 70° while recording haemodynamic values and symptoms of orthostatic tolerance. The time to reach the desired tilt angle from supine was achieved in less than 45 s. Participants were tilted until reaching their tolerance threshold or for a maximum duration of 10 min. They were asked not to talk during the test except to inform and grade symptoms. The participant was asked to report any symptoms every 1–3 min. The participant was asked to rank their symptoms between 1–10, 1 being no symptoms at all, and 10 being feelings of dizziness, lightheadedness[71] or nausea37,71. The patient was instructed to notify the research team if they needed to be returned to the supine position.

Post-operative blood pressure data

During a tilt test (see the section ‘Orthostatic challenge with the tilt table test’), changes in blood pressure were recorded without stimulation or in response to different types of stimulation (continuous or closed-loop stimulation) using the Finometer (see the section ‘Clinical haemodynamic monitoring’). Change in blood pressure or heart rate was defined as the difference in the average of a 60-s window before the start of the tilt and a 20-s window at 3 min of the challenge. If the participant could not tolerate at least 3 min of the test due to low blood pressure or other symptoms, an average of a 20-s window before the end of the tilt was used. All measurements in seated position were measured with stimulation on for 3–5 min. Change in blood pressure, or heart rate, was defined as the difference in the average of a 20-s window before the start of EES and the average of a 20-s window at 3 min, before stopping stimulation. All signals were smoothed over a 10-s window for illustration. The same processing was used for post-operative, day 1 quantification. In the present study, we report on blood pressure data on the two study participants implanted with the full ARCIM implantable system.

Off-label investigational system

The investigational system used in the STIMO-HEMO and HEMO clinical trials consisted of a set of CE-marked, FDA-approved medical devices used off-label. Two IPGs (lntellis with AdaptiveStim, Medtronic) are connected to their respective paddle leads (Specify 5-6-5 SureScan MRI, Medtronic), both indicated for chronic pain management. A tablet application with a communicator device (Intellis clinician programmer, Medtronic) was used by the clinical team to wirelessly set up the system and optimize the stimulation parameters. A remote control device and transcutaneous charger device (Patient Programmer and Recharger, Medtronic) were used by the participants to charge the IPGs and wirelessly turn the stimulation on and off during daily life and adapt a subset of stimulation parameters defined by the clinical team.

Purpose-built investigational system for restoring haemodynamic stability

The investigational system used in the HemON was purpose-built for restoring haemodynamic stability. A step-wise approach was followed. All participants were implanted with a purpose-built IPG (ARCIM Thoracic System, ONWARD Medical) that communicates with a purpose-built ecosystem of control devices. The first four participants of the HemON clinical trial received an off-label paddle lead (Specify 5-6-5 SureScan MRI, Medtronic), whereas all other participants were implanted with a novel, purpose-built, paddle lead (ARCIM Thoracic Lead, ONWARD Medical).

Purpose-built IPG and communication ecosystem for restoring haemodynamic stability

The purpose-built ARCIM IPG developed by ONWARD Medical is a novel 16-channel IPG developed to deliver targeted EES. It controls and delivers current-controlled stimulation pulses according to predefined stimulation programmes or through commands received in real time to monopolar or multipolar electrode configurations on 16 channels. The IPG consists of a hermetically sealed, biocompatible can that surrounds the electrical components and a rechargeable battery that enables its function. The IPG is composed of two main components: the header containing the connector block that enables connection with two eight-contact lead connectors as well as two coils for charging and communication, and the can with a rechargeable battery and electronics circuits. The IPG was developed according to all applicable standards for medical device development. Conventional biomedical technologies were used to fabricate the IPG and extensive bench, and in vivo testing was performed to verify its performance. The IPG was implanted subcutaneously at the abdominal level and communicates wirelessly with the ARCIM Hub with near-field magnetic induction. This wearable device was worn on a belt over or in proximity to the IPG location and was responsible for wirelessly charging the IPG’s battery and for programming the IPG with stimulation settings received from several user interfaces. The communication between the hub and IPG provides real-time control of stimulation parameters (as fast as approximately 25 ms between command and stimulation execution), allowing integration with a fast closed-loop neuromodulation system. The ARCIM Hub contains a Bluetooth low-energy chip to enable fast, reliable wireless communication with external programmers such as the ARCIM Clinician Programmer, an Android app designed for clinicians to configure and test the implanted system and personalized stimulation programs. When a stimulation program was deemed safe for personal use, the Clinician Programmer can be used to make this stimulation program available to the patient. The patients, or their caregivers, can control the system through the ARCIM Personal Programmer. This Android watch application allows users to select, start and stop stimulation programs, as well as modulate stimulation amplitudes within predefined safety limits ad hoc. Device errors, paddle lead impedances and daily stimulation utilization were extracted from usage logs across all devices. Furthermore, the Clinician Programmer includes an application programming interface (the ARCIM API) that enables other programming softwares to control the stimulation, for example, for closed-loop control of the stimulation. All devices and softwares are adherent to the applicable standards and their performance was extensively tested. The entire system, including the IPG, received the equivalent of an investigational device exemption from the competent Swiss authorities.

Purpose-built paddle lead

The ARC-IM Thoracic Lead developed by ONWARD Medical is a new 16-electrode paddle lead that is designed for selective recruitment of the dorsal root entry zones of the low-thoracic spinal cord with optimal coverage of the T10–T12 spinal levels. More detailed information about this paddle lead can be found in other publications[40].

SCI community survey

Ethical approval was obtained from an independent ethics board (Veritas Independent Review Board) and the Research Ethics Board of Université Laval (principal investigator’s institution). Ethical approval from local research ethics boards was also obtained to recruit from SCI centres across Canada. Individuals with SCI (n = 1,479) across Canada were recruited using a national consumer awareness campaign and provided written informed consent43,44. The survey consisted of a series of variables identified by health-care and service providers, researchers and individuals with SCI, including demographics, secondary health complications, comorbidities, SCI-related needs, health-care utilization, community participation, quality of life and overall health ratings43,44. Participants were asked how often they had experienced symptoms of autonomic dysreflexia in the past 12 months and responses were ranked on a six-point ordinal severity scale ranging from 0 (‘never’) to 5 (‘every day’). Participants were also asked whether they received or sought out treatment concerning these symptoms on a two-point scale (‘yes’ or ‘no’), along with the degree to which it limited activities from 0 (‘never’) to 5 (‘every day’). Participants were also asked whether they had experienced specific problems, such as heart disease, in the past 12 months. Participants’ American Spinal Injury Association Impairment Scale was estimated using responses to questions about lesion level and sensorimotor and/or mobility capabilities. A binary approach was used for the evaluation of outcome variables including the level of injury (cervical SCI versus non-cervical SCI), the severity of injury (complete versus incomplete), the presence of autonomic dysreflexia (yes versus no) and autonomic dysreflexia symptoms (yes versus no). For variables ranked on a six-point ordinal scale, lower scores (0–3) were categorized as ‘no’ and higher scores (4–5) were categorized as ‘yes’.

ADFSCI

The ADFSCI is a 24-item questionnaire divided into four sections: demographics, medication, autonomic dysreflexia and orthostatic hypotension. The autonomic dysreflexia section consists of seven items. Each item used a five-point scale to measure the frequency and severity of symptoms related to autonomic dysreflexia, including headaches, goosebumps, heart palpitations, sweating and anxiety, across different situational contexts. Participants were categorized as experiencing symptoms if the item score was higher than 2 or not experiencing symptoms otherwise. For data collection related to the ADFSCI of individuals not involved in the ongoing clinical studies, Conjoint Health Research Ethics Board approval (REB21-0045) was obtained from the University of Calgary. All participants provided written informed consent before providing their responses.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data that support the findings and software routines developed for the data analysis will be made available on reasonable request to the corresponding authors. Source data are provided with this paper. Raw sequencing data and count matrices have been deposited to the Gene Expression Omnibus (GSE256423).

Code availability

Augur is available on GitHub (https://github.com/neurorestore/Augur).

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Acknowledgements

This work was financially supported by: Eurostars project E!113969 PREP2GO; DARPA subaward A21-0795-S001 (P.O. 1083297); the Office of the Assistant Secretary of Defense for Health Affairs through the Spinal Cord Injury Research Program (HT9425-23-1-0547); Eurostars E!1748 IMPULSE; Medtronic (ERP-2020-12543); Onward Medical, PHRT-279; University of Calgary Reserach Excellence Chair; Brain Canada; Digital Research Alliance of Canada; the Natural Sciences and Engineering Research Council of Canada; the Canadian Institutes of Health Research; Alberta Innovates Health Solutions; Campus Alberta Neuroscience; the Libin Cardiovascular Institute; the Hotchkiss Brain Institute; the Hopewell M.I.N.D. Prize; the Krembil Research Institute; the McCaig Institute for Bone and Joint Health; ONWARD Medical; the Swiss National Science Foundation (the National Centre of Competence in Research in Robotics, 51NF40_185543); Canadian Institutes of Health Research (Graduate Scholarship), the Branch Out Neurological Foundation and Eyes High Doctoral Scholarship and NSERC Brain Create to J.E.S.; Ambizione Fellowship to J.W.S., (PZ00P3_208988) and subside to G.C. (310030_185214 and 310030_215668); European Research Council (ERC-2015-CoG HOW2WALKAGAIN 682999, and Marie Sklodowska-Curie individual fellowship 842578 to J.W.S.); the Swiss National Supercomputing Center; the Promobilia Foundation (to M.A.A.); the Swiss National Science Foundation (320030-228288 to M.A.A.); Wings for Life (to M.A.A. and G.C.); Friedrich Flick Förderungsstiftung through Wings for Life (M.A.A. and G.C.), gifts from Bob Yant and Ann Kenowsky (to M.A.A.); and Wyss Center for Bio and Neuroengineering (to M.A.A.). We are grateful to B. Schneider and S. Arber for providing viral vectors, F. Merlos for the illustrations, and the Advanced Lightsheet Imaging Center at the Wyss Center for Bio and Neuroengineering. This work was supported in part using the resources and services of the Gene Expression Core Facility at the School of Life Sciences of EPFL.

Author information

Author notes

1. These authors contributed equally: Jan Elaine Soriano, Remi Hudelle

2. These authors jointly supervised this work: Jordan W. Squair, Aaron A. Phillips, Gregoire Courtine

Authors and Affiliations

1. Department of Clinical Neurosciences, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

Jan Elaine Soriano, Rebecca Charbonneau, Fady Girgis, Steve Casha, Julien Rimok, Marcus Tso, Kelly Larkin-Kaiser & Aaron A. Phillips

2. Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

Jan Elaine Soriano, Julien Rimok, Marcus Tso, Kelly Larkin-Kaiser & Aaron A. Phillips

3. Restore Network, Hotchkiss Brain Institute, Libin Cardiovascular Institute, McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

Jan Elaine Soriano, Julien Rimok, Marcus Tso, Kelly Larkin-Kaiser & Aaron A. Phillips

4. Defitech Center for Interventional Neurotherapies (.NeuroRestore), CHUV/UNIL/EPFL, Lausanne, Switzerland

Jan Elaine Soriano, Remi Hudelle, Lois Mahe, Matthieu Gautier, Alan Yue Yang Teo, Achilleas Laskaratos, Steven Ceto, Claudia Kathe, Nicolas Hankov, Aasta Gandhi, Suje Amir, Eduardo Martin-Moraud, Robin Demesmaeker, Leonie Asboth, Quentin Barraud, Mark A. Anderson, Jocelyne Bloch, Jordan W. Squair & Gregoire Courtine

5. NeuroX Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland

Jan Elaine Soriano, Remi Hudelle, Lois Mahe, Matthieu Gautier, Alan Yue Yang Teo, Achilleas Laskaratos, Steven Ceto, Claudia Kathe, Nicolas Hankov, Aasta Gandhi, Suje Amir, Yashwanth Vyza, Eduardo Martin-Moraud, Stephanie Lacour, Robin Demesmaeker, Leonie Asboth, Quentin Barraud, Mark A. Anderson, Jocelyne Bloch, Jordan W. Squair & Gregoire Courtine

6. Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

Lois Mahe, Matthieu Gautier, Jocelyne Bloch, Jordan W. Squair & Gregoire Courtine

7. Lewis-Sigler Institute of Integrative Genomics and Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA

Michael A. Skinnider

8. Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

Claudia Kathe & Mark A. Anderson

9. Wyss Center for Bio and Neuroengineering, Geneva, Switzerland

Thomas Hutson

10. Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

Julien Rimok, Marcus Tso, Kelly Larkin-Kaiser & Aaron A. Phillips

11. Institute of Science and Technology Brain-inspired Intelligence, Fudan University, Shanghai, China

Xiaoyang Kang

12. Laboratory for Soft Bioelectronic Interfaces (LSBI), NeuroX Institute, EPFL, Lausanne, Switzerland

Yashwanth Vyza & Stephanie Lacour

13. Department of Neurosurgery, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

Jocelyne Bloch, Jordan W. Squair & Gregoire Courtine

Contributions

J.E.S., R.H., J.W.S., A.A.P. and G.C. conceptualized and designed the experiments. J.E.S., R.H., L.M., M.G., A.L., S. Ceto, C.K., T.H., M.T., S.A., M.A.A. and J.W.S. performed the rodent experiments. R.C., F.G., S. Casha, J.R., K.L.-K., N.H., A.G., R.D., L.A., A.A.P. and J.B. performed the human experiments. J.E.S., R.H., M.G., A.Y.Y.T., M.A.S., N.H., A.G., Q.B. and J.W.S. analysed the data. X.K., Y.V., E.M.-M., S.L., J.W.S., A.A.P. and G.C. designed the hardware and software for neurostimulation. J.E.S., R.H., J.W.S., A.A.P. and G.C. wrote the manuscript. All authors contributed to editing the manuscript.

Corresponding authors

Correspondence to Jordan W. Squair, Aaron A. Phillips or Gregoire Courtine.

Ethics declarations

Competing interests

G.C., A.A.P., J.W.S., J.B., R.D. and S.L. hold various patents in relation to the present work. G.C., A.A.P. and R.D. are consultants of ONWARD Medical. G.C., A.A.P., J.W.S., J.B. and S.L. are minority shareholders of ONWARD, a company with direct relationships with the presented work. The other authors declare no competing interests.

Peer review

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Nature thanks Jun-An Chen 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 Autonomic dysreflexia triggers transcriptional activity in the lumbosacral and thoracic spinal cord.

a, Experimental model of autonomic dysreflexia in mice with SCI and timeline of the experiment and final assessments. Blood pressure responses were monitored beat-by-beat using a blood pressure catheter inserted into the carotid artery. Autonomic dysreflexia was elicited using controlled colorectal distension in mice with upper-thoracic SCI. b, Baseline systolic blood pressure measured at different timepoints after SCI. Raw data and statistics provided in Supplementary Table 1. c, Blood pressure recording before, during and after a controlled colorectal distension. d, Pressor responses (Left; bold line represents mean trace ± sem for each group and individual line traces are from each animal) and severity of autonomic dysreflexia (Right) measured by the change in systolic blood pressure during colorectal distension at different timepoints after SCI (n = 5 per timepoint). Raw data and statistics provided in Supplementary Table 1. e, Overview of experimental protocol to identify the regions of the spinal cord activated during autonomic dysreflexia. Thirty days after receiving SCI, mice underwent repetitive episodes of autonomic dysreflexia over 90 min, consisting of 30 s, and then deflated for 60 s. Tissues were collected one hour after the exposure to autonomic dysreflexia, and then processed to visualize the expression of cFos in neurons. Bottom, CLARITY-optimized light sheet microscopy of the cleared spinal cord enabled visualisation of cFos immunoreactivity over the entire thoracolumbosacral spinal cord9,47 in mice with SCI and mice with SCI that underwent repetitive episodes of autonomic dysreflexia. f, Quantification of cFos immunoreactivity in mice with SCI only and mice with SCI that underwent repetitive episodes of autonomic dysreflexia (mixed effect linear model; t = 6.60; p-value = 0.000213). g, Quantifications of cFos expression over the whole spinal cord were confirmed with immunohistochemistry and labelling for cFos on longitudinal sections of spinal cord, as illustrated in the representative photomicrographs of spinal cord sections from mice with SCI and mice with SCI that underwent repetitive episodes of autonomic dysreflexia (mixed effect linear model; t = 6.11; p-value = 0.000287). h, Schematic overview of experiment to assess blood pressure responses to colorectal distension in mice with SCI after bilateral dorsal rhizotomies at T11-T13. i, Severity of autonomic dysreflexia measured by the change in systolic blood pressure during colorectal distension in injured mice with and without bilateral dorsal rhizotomies at T11-T13 (n = 4; independent samples two-tailed t-test; t = 2.1255; p-value = 0.079). j, Quantifications of cFos expression over the whole spinal cord were confirmed with immunohistochemistry and labelling for cFos on longitudinal sections of spinal cord, as illustrated in the representative photomicrographs of spinal cord sections from mice with SCI and mice with SCI that underwent repetitive episodes of autonomic dysreflexia (mixed effect linear model; t = 14.082; p-value < 0.0001).

Extended Data Fig. 2 The neurons activated by autonomic dysreflexia.

a, Schematic overview of experiments to reveal the phenotype of the neurons that are activated during autonomic dysreflexia. We subjected Vglut2Cre::Ai9(RCL-tdT), VgatCre::Ai9(RCL-tdT) and ChatCre::Ai9(RCL-tdT) to repetitive episodes of autonomic dysreflexia at 30 days post-injury. Longitudinal sections of the spinal cord from T9 to L3 and L4 to S4 were immunohistochemically stained for cFos. Right, Quantification of colocalisation of cFos-labelled neurons and endogenous fluorescence-tagged neurons (Vglut2ON, VgatON and ChatON) was performed with automated spot detection (Imaris, Bitplane v.9.8.2). b, We next performed experiments to determine the role of these neuronal subpopulations in triggering autonomic dysreflexia. To manipulate the activity of Vglut2ON and VgatON neurons, AAV5-hSyn-DIO-hm4D(Gi)-mCherry was infused in either the lower thoracic spinal cord (T11-T13) or lumbosacral spinal cord (L5-S1) of either Vglut2Cre or VgatCre mice prior to performing the SCI to express DREADDs in glutamatergic or gabaergic neurons. Photomicrographs of coronal sections from either the lower thoracic or lumbosacral spinal cord in Vglut2Cre and VgatCre mice reveal the robust expression of mCherry and thus DREADDs in the targeted neurons. c, Chemogenetic inactivation restricted to Vglut2ON neurons located in the lumbosacral (n = 5; paired samples t-test; t = −10.4; p = 0.00048) or lower thoracic spinal cord (n = 5; paired samples t-test; t = −17.5; p = 0.00001) blunted autonomic dysreflexia. In contrast, chemogenetic silencing of VgatON neurons in the lumbosacral (n = 3; paired samples t-test; t = −1.53; p = 0.267) or lower thoracic spinal cord (n = 4; paired samples t-test; t = 0.269; p = 0.805) failed to modulate the severity of autonomic dysreflexia. Pressor responses (Left; bold line represents mean trace ± sem for each group and individual line traces are from each animal) and severity of autonomic dysreflexia (bar graph, Right) measured by the change in systolic blood pressure during colorectal distension. d, We next aimed to expose the projections from Vglut2ON neurons located in the lumbosacral spinal cord to the Vglut2ON neurons located in the lower thoracic spinal cord. For this, we labeled the axons and synapses of Vglut2ON neurons with infusions of AAV-DJ-hSyn-flex-mGFP-2A-synaptophysin-mRuby into the lumbosacral spinal cord of Vglut2Cre. The cell bodies in the lumbosacral spinal cord in both intact and injured mice were quantified (n = 4; independent samples two-tailed t-test; t = 0.473; p-value = 0.654). Photomicrographs of the lower thoracic spinal cord from representative mice demonstrate increases in the density of axons (Left; n = 4; independent samples two-tailed t-test; t = 5.92; p-value = 0.0047) and synaptic puncta (Right; n = 4; independent samples two-tailed t-test; t = 3.47; p-value = 0.027) emanating from Vglut2ON neurons located in the lumbosacral spinal cord after SCI.

Extended Data Fig. 3 Comparative single-nucleus RNA sequencing atlas of perturbation-responsive neuronal subpopulations during autonomic dysreflexia.

a, Scheme illustrating our experimental protocol followed by single-nucleus RNA sequencing. Mice received upper-thoracic SCI. After 30 days, half of the mice underwent repetitive episodes of autonomic dysreflexia during 90 min. The lumbosacral spinal cord and the lower thoracic were dissected from the mice according to standard procedures. b, We obtained high-quality transcriptomes from 64,739 nuclei that were evenly represented across experimental conditions and spatial locations. c, Quality control from 64,739 single-nucleus transcriptomes. Number of unique molecular identifiers (UMIs) per nucleus. Inset text shows the median number of UMIs. d, Number of genes detected per nucleus. Inset text shows the median number of genes detected. e, Proportion of mitochondrial counts per nucleus. Inset text shows the median proportion of mitochondrial counts. f, Number of UMIs quantified per nucleus in each major cell type of the mouse spinal cord. g, Number of genes detected per nucleus in each major cell type of the mouse spinal cord. h, Proportion of mitochondrial counts per nucleus in each major cell type of the mouse spinal cord. i, UMAP visualization of 64,739 nuclei colored by major cell type, segregated by the location of spinal cord tissues (L6, T12) and experimental conditions (SCI only, exposure to repeated episode of autonomic dysreflexia, AD). j, Proportions of nuclei from each major cell type depending on the location of spinal cord tissues and experimental conditions. k, UMAP visualization showing expression of key marker genes for the major cell types of the mouse spinal cord. l, UMAP visualization of 29,144 neuronal nuclei colored by neuronal subpopulations, split by experimental condition. m, UMAP visualization showing expression of key marker genes for the major neuronal subpopulation classifications of the mouse spinal cord. n, UMAP visualization and dendrograms showing cell type prioritizations assigned by Augur across the neuronal taxonomy of the lower thoracic (Top) and lumbosacral (Bottom) spinal cord. Raw AUC values and confidence values are provided in Supplementary Table 2. o, Lollipop plot illustrating the statistical significance of upregulated Gene Ontology (GO) modules associated with circuit reorganization and increased neuronal excitability in Vsx2ON neurons. p, Photomicrographs of the lower thoracic and lumbosacral spinal cord after repetitive episodes of autonomic dysreflexia. Vsx2ON neurons were labelled with immunohistochemistry. Long distance projecting (Zfhx3, lumbosacral spinal cord) and locally-projecting (Nfib, lower thoracic spinal cord) neurons were additionally colocalized with immunohistochemistry labelling of cFos.

Extended Data Fig. 4 The first and second nodes of the neuronal architecture of autonomic dysreflexia.

a, Schematic overview of the neuronal architecture of autonomic dysreflexia. b, Zoom on the first node of the neuronal architecture of autonomic dysreflexia that involves the growth of projections from CalcaON neurons onto Vsx2ON neurons with long-distance projections, named SCLUMBAR::Vsx2 neurons. This growth was assessed on tissues collected 30 days after SCI in wild-type mice. c, Photomicrograph taken at L6 spinal segment from a mouse with an intact spinal cord and a mouse with a chronic SCI in which CalcaON axons were labelled with immunohistochemistry. d, Bar plots reporting the density of CalcaON axonal projections into the intermediate laminae of the spinal cord in uninjured mice and mice with chronic SCI (n = 4; independent samples two-tailed t-test; t = 9.38; p-value = 0.000086). e, Overview of the experimental protocol to test the severity of autonomic dysreflexia after the ablation of CalcaON and PVON neurons. To achieve the ablation of these neurons exclusively in the dorsal root ganglia, we used a Cre- and Flp-dependent strategy in CalcaCre::AvilFlpO::iDTR and PVCre::AvilFlpO::iDTR mice that allowed the expression of diphtheria toxin receptors (DTR) in the CalcaON and PVON neurons located in the dorsal root ganglia, respectively. f, Pressor responses (Left; bold line represents mean trace ± sem for each group and individual line traces are from each mouse) and severity of autonomic dysreflexia (Right) measured by the change in systolic blood pressure during colorectal distension in mice without diphtheria toxin-induced ablation of either CalcaON neurons or PVON neurons, mice with diphtheria toxin-induced ablation of CalcaON neurons and mice with diphtheria toxin-induced ablation of PVON neurons (n = 5; independent samples t-test two-tailed; t = −5.9998; p-value = 0.00064, independent samples two-tailed t-test; t = −9.3261; p-value = 0.00014). g, Zoom on the second node of the neuronal architecture of autonomic dysreflexia that involves SCLUMBAR::Vsx2 neurons projecting to the low thoracic spinal cord. An intersectional viral labelling strategy was used to label the axons of SCLUMBAR::Vsx2 neurons located in the lumbosacral spinal cord and that establish projections in the lower thoracic spinal cord. Vsx2Cre mice received SCI and were injected with Retro-AAV-DIO-FlpO into the lower thoracic spinal cord and AAV8-Con/Fon-EYFP into the lumbosacral spinal cord. h, Photomicrograph of the L6 spinal segment from a Vsx2Cre mouse with an intact spinal cord and a Vsx2Cre mouse with a chronic SCI that received intersectional viral tracing to label SCLUMBAR::Vsx2 neurons. Axons from CalcaON neurons were also labelled with immunohistochemistry. Insets show synaptic-like appositions of axons from CalcaON neurons onto SCLUMBAR::Vsx2 neurons. i, The necessary role of SCLUMBAR::Vsx2 neurons in autonomic dysreflexia was evaluated using Cre-dependent expression of Gi DREADDs in SCLUMBAR::Vsx2 neurons. j, Photomicrograph showing the expression of DREADD (Gi) receptors in SCLUMBAR::Vsx2 neurons. k, Left, changes in systolic blood pressure in response to colorectal distension (shared area). Bold line represents mean trace ± sem for each group and individual line traces are from each mouse) and severity of autonomic dysreflexia. Right, Severity of autonomic dysreflexia in Vsx2Cre mice before and after chemogenetic silencing of Vsx2ON neurons located in the lumbosacral spinal cord (n = 5; paired samples t-test; t = −9.47; p-value = 0.00069). l, The sufficient role of SCLUMBAR::Vsx2 neurons in triggering autonomic dysreflexia was evaluated using optogenetic activation of SCLUMBAR::Vsx2 neurons in Vsx2Cre mice injected with AAV-Syn-flex-ChrimsonR-tdTomato into the lumbosacral spinal cord. 30 days after SCI, blood pressure responses were monitored beat-by-beat using a blood pressure catheter inserted into the carotid artery. Red-shifted light was shined over the lumbosacral spinal cord for 60 s during each trial. m, Photomicrograph showing the expression of ChrimsonR in SCLUMBAR::Vsx2 neurons. n, Left, Changes in systolic blood pressure in response to the photostimulation of SCLUMBAR::Vsx2 neurons in mice with intact spinal cord and with chronic SCI. Bold line represents mean trace ± sem for each group and individual line traces are from each mouse) and blood pressure responses due to optogenetic activation of SCLUMBAR::Vsx2 neurons. Right, Bar plots reporting mean changes in blood pressure in Vsx2Cre mice with intact spinal cord and with SCI during optogenetic activation of SCLUMBAR::Vsx2 neurons (n = 5; independent samples two-tailed t-test; t = 5.14; p-value = 0.00496).

Extended Data Fig. 5 The third and fourth nodes of the neuronal architecture of autonomic dysreflexia.

a, Zoom on the third node of the neuronal architecture of autonomic dysreflexia that involves SCTHORACIC::Vsx2 neurons located in the lower thoracic spinal cord. b, Overview of intersectional viral tracing strategy to label projections from SCLUMBAR::Vsx2 into the lower thoracic spinal cord concomitantly to the labelling of SCLUMBAR::Vsx2 Step 1, AAV5-hSyn-flex-tdTomato was infused into the lower thoracic spinal cord of Vsx2-Cre mice to label SCTHORACIC::Vsx2. Step 2, Retro-AAV-DIO-FlpO was infused into the lower thoracic spinal cord and AAV8-Con/Fon-EYP into the lumbosacral spinal cord to label the projections from SCLUMBAR::Vsx2 neurons located in the lumbosacral spinal cord and that project in the lower thoracic spinal cord. c, Photomicrographs of the lower thoracic spinal cord showing projections from SCLUMBAR::Vsx2 onto SCTHORACIC::Vsx2 neurons in a mouse with an intact spinal cord and a mouse with a chronic SCI. d, Bar plots reporting the mean density of projections from SCLUMBAR::Vsx2 neurons in the grey matter of the lower thoracic spinal cord in mice with an intact spinal cord and with chronic SCI (n = 5; independent samples two-tailed t-test; t = −3.09; p-value = 0.0162). e, Whole spinal cord visualization of projections from SCLUMBAR::Vsx2 neurons located in the lumbosacral spinal cord (red) and visualization of SCTHORACIC::Vsx2 neurons (blue) located in the lower thoracic spinal cord in mice with chronic SCI. f, Schematic overview of the experimental protocol to monosynaptically label SCLUMBAR::Vsx2 after lower thoracic spinal cord infusions targeting the virus to SCTHORACIC::Vsx2 neurons. Step 1, AAV8-hSyn-dlox-TVA950-2A-EGFP was infused into the lower thoracic spinal cord of Vsx2Cre mice to express TVA950 (Avian Tumor Virus A Receptor variant), EGFP (enhanced green fluorescent protein), and oGrev (optimized rabies virus glycoprotein) simultaneously. Step 2, EnvA-ΔG-Rabies-mCherry was infused two weeks after to monosynaptically restrict EnvA pseudotyped and G-protein deleted rabies expressing mCherry and label direct connections from SCLUMBAR::Vsx2 neurons onto SCTHORACIC::Vsx2 neurons. Representative photomicrographs of two neurons infected with the EnvA-ΔG-Rabies-mCherry and positive to Vsx2 immunohistochemistry. g, The necessary role of SCTHORACIC::Vsx2 neurons in autonomic dysreflexia was evaluated using Cre-dependent expression of Gi DREADDs in SCTHORACIC::Vsx2 neurons. h, Photomicrograph showing the expression of Gi DREADD receptors in SCTHORACIC::Vsx2 neurons. i, Left, changes in systolic blood pressure in response to colorectal distension. Bold line represents mean trace ± sem for each group and individual line traces are from each mouse and severity of autonomic dysreflexia. Right, Severity of autonomic dysreflexia in Vsx2Cre mice before and after chemogenetic silencing of Vsx2ON neurons located in the lower thoracic spinal cord (n = 5; paired samples t-test; t = −9.39; p-value = 0.00072). j, The sufficient role of SCTHORACIC::Vsx2 neurons in autonomic dysreflexia was evaluated using optogenetic activation of SCTHORACIC::Vsx2 neurons in Vsx2Cre mice injected with AAV-Syn-flex-ChrimsonR-tdTomato into the lower thoracic spinal cord. 30 days after SCI, blood pressure responses were monitored beat-by-beat using a blood pressure catheter inserted into the carotid artery. Red-shifted light was shined over the lumbosacral spinal cord for 60 s during each trial. k, Photomicrograph showing the expression of ChrimsonR in SCTHORACIC::Vsx2 neurons. l, Left, changes in systolic blood pressure in response to colorectal distension. Bold line represents mean trace ± sem for each group and individual line traces are from each mouse and blood pressure responses due to optogenetic activation of SCTHORACIC::Vsx2 neurons. Right, Blood pressure responses in Vsx2Cre mice with intact spinal cord and with chronic SCI during optogenetic activation of SCTHORACIC::Vsx2 neurons (n = 5; independent samples two-tailed t-test; t = 15.4; p-value = 0.0000148). m, Zoom on the fourth node of the neuronal architecture of autonomic dysreflexia that involves ChatON preganglionic sympathetic neurons. n, Overview of experimental protocol to label projections from SCTHORACIC::Vsx2 neurons located in the lower thoracic spinal cord in Vsx2Cre mice with SCI. Thirty days after SCI and viral tracing, the spinal cord tissues were collected and processed. o, Photomicrograph of the lower thoracic spinal cord from a mouse with an intact spinal cord and a mouse with chronic SCI in which the projections of SCTHORACIC::Vsx2 neurons were labelled concomitantly to the immunohistochemical labelling of ChatON neurons. p, The necessary role of ChatON neurons in autonomic dysreflexia was evaluated using Cre-dependent expression of Gi DREADDs in ChatON neurons. q, Photomicrograph illustrating the expression of Gi DREADD receptors in SCTHORACIC::Vsx2 neurons. r, As in h, for ChatON neurons located in the lower thoracic spinal cord (n = 5; paired samples t-test; t = −8.03; p-value = 0.00048).

Extended Data Fig. 6 Comparative single-nucleus RNA sequencing atlas of perturbation-responsive neuronal subpopulations during epidural electrical stimulation.

a, Scheme illustrating the experimental protocol followed by single-nucleus RNA sequencing. Days after SCI for half of the mice, EES was applied continuously over the lower thoracic spinal cord during 45 min. The lower thoracic spinal cord was harvested from the mice according to standard procedures. b, We obtained high-quality transcriptomes from 21,098 nuclei that were evenly represented across experimental conditions and spatial locations. c, Number of unique molecular identifiers (UMIs) per nucleus. Inset text shows the median number of UMIs. d, Number of genes detected per nucleus. Inset text shows the median number of genes detected. e, Proportion of mitochondrial counts per nucleus. Inset text shows the median proportion of mitochondrial counts. f, Number of UMIs quantified per nucleus in each major cell type of the mouse spinal cord. g, Number of genes detected per nucleus in each major cell type of the mouse spinal cord. h, Proportion of mitochondrial counts per nucleus in each major cell type of the mouse spinal cord. i, UMAP visualization of 21,098 nuclei colored by major cell type, split by experimental condition. j, Proportions of nuclei from each major cell type across all experimental conditions. k, UMAP visualization showing expression of key marker genes for the major cell types of the mouse spinal cord. l, UMAP visualization of 8,471 neuronal nuclei colored by neuronal subpopulations, split by experimental condition. m, UMAP visualization showing expression of key marker genes for the major neuronal subpopulation classifications of the mouse spinal cord. n, UMAP visualization and dendrograms showing cell type prioritizations assigned by Augur across the neuronal taxonomy of the lower thoracic spinal cord. Raw AUC values and confidence values are provided in Supplementary Table 2.

Extended Data Fig. 7 The neuronal architecture activated by EES to induce pressor response.

a, Schematic overview of the neuronal architecture through which EES induces pressor responses. b, Zoom on the first node of the neuronal architecture of EES-induced pressor responses that involves PVON neurons. c, Overview of the experimental protocol to test the involvement of afferent fibers from PVON and CalcaON neurons in EES-induced pressor responses. To achieve the ablation of these neurons exclusively in the dorsal root ganglia, we used a Cre- and Flp-dependent strategy in CalcaCre::AvilFlpO::iDTR and PVCre::AvilFlpO::iDTR mice that allowed the expression of diphtheria toxin receptors (DTR) in these specific neurons. d, EES-induced pressor responses (Left; bold line represents mean trace ± sem for each group and individual line traces are from each mouse) (Right) measured by the change in systolic blood pressure during EES in mice without any ablation, mice with diphtheria toxin-induced ablation of PVON neurons and mice with diphtheria toxin-induced ablation of CalcaON neurons (n = 5; independent samples two-tailed t-test; t = −5.4141; p-value = 0.0043, independent samples two-tailed t-test; t = 6.3166; p-value = 0.0020). e, Overview of the experiment strategy to visualize large-diameter PVON afferent fibers in PVCre::AdvilFlpO::Ai9(RCL-tdT) mice and confirmed that they established vGlut1ON synaptic-appositions onto SCTHORACIC::Vsx2 neurons. Thirty days after SCI, spinal cord tissues were collected and processed. f, Photomicrograph of the lower thoracic spinal cord showing large-diameter afferent neurons (PVCre::AdvilFlpO::tdTomato mice), SCTHORACIC::Vsx2 neurons labelled with in situ hybridization and ChatON neurons labelled with immunohistochemistry. g, Photomicrograph of the SCTHORACIC::Vsx2 neurons labelled with viral tract tracing and vGlut1ON synapses labelled with immunohistochemistry. Quantification of vGlut1ON synaptic-appositions onto SCTHORACIC::Vsx2 neurons and ChatON neurons in PVCre::AdvilFlpO::Ai9(RCL-tdT) mice with an intact spinal cord and with a chronic SCI. h, Schematic overview of the experimental protocol to monosynaptically label PVON neurons in the dorsal root ganglia after lower thoracic spinal cord infusions targeting the virus to SCTHORACIC::Vsx2 neurons. Step 1, AAV8-hSyn-dlox-TVA950-2A-EGFP was infused into the lower thoracic spinal cord of Vsx2Cre mice to express TVA950 (Avian Tumor Virus A Receptor variant), EGFP (enhanced green fluorescent protein), and oGrev (optimized rabies virus glycoprotein) simultaneously. Step 2, EnvA-ΔG-Rabies-mCherry was infused two weeks after to monosynaptically restrict EnvA pseudotyped and G-protein deleted rabies expressing mCherry and label direct connections from PVON neurons onto SCTHORACIC::Vsx2 neurons. Representative photomicrograph showing neurons in the lower thoracic dorsal root ganglion infected with the EnvA-mCherry and labelled with fluorescence in situ hybridization. i, Zoom on the second node of the neuronal architecture of EES-induced pressor responses that involves SCTHORACIC::Vsx2. The necessary role of SCTHORACIC::Vsx2 neurons in EES-induced pressor response was evaluated using Cre-dependent expression of Gi DREADDs in SCTHORACIC::Vsx2 neurons. j, EES-induced pressor responses (Left; bold line represents mean trace ± sem for each group and individual line traces are from each mouse) (Right) measured by the change in systolic blood pressure during EES in the same mice before and after chemogenetic silencing of Vsx2ON neurons located in the lower thoracic spinal cord (n = 5; paired samples t-test; t = −4.21; p-value = 0.014). k, Zoom on the third node of the neuronal architecture of EES-induced pressor responses that involves ChatON preganglionic sympathetic neurons. As in h, for ChatON neurons located in the lower thoracic spinal cord. l, As in j, for ChatON neurons located in the lower thoracic spinal cord (n = 5; paired samples t-test; t = −7.07; p-value = 0.0021). m, Photomicrograph showing the expression of ChrimsonR in Vsx2ON neurons and the tract resulting from the insertion of one electrode shank. n, Schematic overview of experiments to record the activity of SCTHORACIC::Vsx2 during the application of EES and during episodes of autonomic dysreflexia. o, Top, the waveforms display spikes and firing rate evoked by optogenetic stimulation of VsxON neurons by the application of continuous EES over the lower thoracic spinal cord, and by colorectal distention. Heatmap of neuronal clusters activated by EES, activated by EES and colorectal distension, activated by EES and tagged as Vsx2ON neurons by optogenetic stimulation and EES, and activated by EES and colorectal distension and tagged as Vsx2ON neurons activated by optogenetic stimulation.

Extended Data Fig. 8 Autonomic neurorehabilitation reversed autonomic dyresflexia in mice with SCI.

a, Overview of the experimental protocol to deliver autonomic neurorehabilitation in mice with SCI. Step 1. Mice received a complete transection of the spinal cord at the level of the T4 segment. Step 2.Intersectional viral tracing by infusing Retro-AAV-DIO-FlpO into the lower thoracic spinal cord and AAV8-Con/Fon-EYP into the lumbosacral spinal cord to label SCLUMBAR::Vsx2 neurons located in the lumbosacral spinal cord that project onto SCTHORACIC::Vsx2 neurons located in the lower thoracic spinal cord. Step 3. One week after SCI, electrodes were implanted over the T12 spinal segment to deliver EES. Step 4. EES was applied for 30 min everyday for 4 weeks. Step 5. F of autonomic dysreflexia was assessed during terminal experiments conducted in mice with chronic SCI and mice with chronic SCI that underwent autonomic neurorehabilitation. Step 6. Spinal cord tissues were collected and processed. b, Changes in systolic blood pressure (Left; bold line represents mean trace ± sem for each group and individual line traces are from each mouse) and severity of autonomic dysreflexia (Right) measured by the change in systolic blood pressure during colorectal distension in mice with chronic SCI and mice with chronic SCI that underwent autonomic neurorehabilitation (n = 5; independent samples two-tailed t-test; t = −7.45; p-value = 0.00056). c, (Left) Photomicrographs of the lower thoracic spinal cord in mice with chronic SCI and mice with chronic that underwent autonomic neurorehabilitation in which SCLUMBAR::Vsx2 neurons located in the lumbosacral spinal cord were labelled with an intersection virus strategy concomitantly to the labelling of SCTHORACIC::Vsx2 neurons. (Right) Photomicrographs of the lower thoracic spinal cord with intersectional viral labelling combined with immunohistochemical labelling of vGlut1ON synapses in mice with chronic SCI and mice with chronic SCI that underwent autonomic neurorehabilitation. vGlut1ON synaptic puncta and synaptic-like appositions from SCLUMBAR::Vsx2 neurons onto SCTHORACIC::Vsx2 neurons in mice with chronic SCI and mice with chronic SCI that underwent autonomic neurorehabilitation. d, (Left) Bar plots reporting the mean number of vGlut1ON synaptic puncta apposing SCTHORACIC::Vsx2 neurons (n = 5; independent samples two-tailed t-test; t = 4.44; p-value = 0.0055). (Right) and the mean density of axonal projections from SCLUMBAR::Vsx2 neurons in the grey matter of the lower thoracic spinal cord in mice with chronic SCI and mice with chronic SCI that underwent autonomic neurorehabilitation (n = 5; independent samples two-tailed t-test; t = 2.51; p-value = 0.0369). e, As in a, for mice subjected to daily application of EES over the lumbosacral spinal cord. f, As in b, for mice subjected to daily application of EES over the lumbosacral spinal cord (n = 5; independent samples two-tailed t-test; t = 5.82; p-value = 0.00070). g, As in c, for mice subjected to daily application of EES over the lumbosacral spinal cord. h, As in d, for mice subjected to daily application of EES over the lumbosacral spinal cord.

Extended Data Fig. 9 Autonomic neurorehabilitation reversed autonomic dysreflexia in rats with contusion SCI.

a, Overview of the experimental protocol to deliver autonomic neurorehabilitation in rats with SCI. Step 1. Rats received a severe contusion (380 Kdyn) of the spinal cord at the level of T3 segment. Step 2. AAV-DJ-hSyn-flex-mGFP-2A-Synaptophysin-mRuby and an AAV-Cre were co-infused into the L6 segment of the spinal cord to label the projections from neurons located in the lumbosacral spinal cord. Step 3. A wireless telemeter recording system, including a blood pressure cannula inserted into the abdominal aorta and microelectrodes sutured over the sympathetic renal nerve, was implanted chronically to monitor hemodynamics and sympathetic nerve activity, respectively. Step 4. Seven days after SCI, an electronic dura mater (e-dura) designed to target the dorsal roots projecting to the T11, T12, and T13 spinal segments was implanted over the hemodynamic hotspot to regulate blood pressure. Step 5. EES was applied for 30 min everyday during 6 weeks using a proportional-integral (PI) controller that adjusted the amplitude of EES in closed-loop to augment the systolic blood pressure to a target range. Step 6. The severity of autonomic dysreflexia, induced by colorectal distension, was assessed every week for 6 weeks. Step 7. After 6 weeks of autonomic neurorehabilitation, a final assessment was performed to test the severity of autonomic dysreflexia in all groups, which included rats with intact spinal cord, rats with chronic SCI and rats with chronic SCI that underwent autonomic neurorehabilitation. Step 8. Spinal cords were collected and processed. b, Changes in systolic blood pressure in response to colorectal distension (Left; bold line represents mean trace ± sem for each group and individual line traces are from each rat) and bar plots reporting the severity of autonomic dysreflexia (Right) measured by the change in systolic blood pressure during colorectal distension over the course of 6 weeks in rats with intact spinal cord, rats with chronic SCI and rats with chronic SCI that underwent autonomic neurorehabilitation. Raw data and statistics provided in Supplementary Table 1. c, Whole spinal cord visualization of projections from neurons located in the lumbosacral spinal cord. d, Plots reporting density of axonal projections (top) and synaptic punta (bottom) from neurons located in the lumbosacral spinal cord into the grey matter of the lower thoracic spinal cord in rats with intact spinal cord, rats with chronic SCI and rats with chronic SCI that underwent autonomic neurorehabilitation. e, Micrographs of the lower thoracic spinal cord in which the axonal projections and synaptic puncta from neurons located in the lumbosacral spinal cord are labelled for the three groups of rats. f, Bar plots reporting the mean density of axonal projections and synaptic puncta from neurons located in the lumbosacral spinal cord into the grey matter of the lower thoracic spinal cord for the three groups of rats. Raw data and statistics are provided in Supplementary Table 1. g, Micrographs of the lower thoracic spinal cord in which axonal projections and synaptic puncta from neurons located in the lumbosacral spinal cord are labelled concomitantly to vGlut1ON synapses from large-diameter afferents and Vsx2ON neurons. The density of lumbosacral-originating and vGlut1ON synapses onto Vsx2ON neurons is reconstructed for a rat with chronic SCI and a rat with chronic SCI that underwent autonomic neurorehabilitation. h, Bar plots reporting the density of synaptic-like appositions from neurons located in the lumbosacral spinal cord onto Vsx2ON neurons in rats with intact spinal cord, rats with chronic SCI, and rats with chronic SCI that underwent autonomic neurorehabilitation. Raw data and statistics are provided in Supplementary Table 1. i, As in i, for vGlut2ON synaptic puncta onto Vsx2ON neurons. Raw data and statistics are provided in Supplementary Table 1. j, Quantification of vGlut1ON synaptic puncta from large-diameter afferents onto Vsx2ON neurons in rats with chronic SCI and rats with chronic SCI that underwent autonomic neurorehabilitation (n = 5; independent samples t-test; t = 12.71; p-value = 2.78e-06).

Extended Data Fig. 10 Population-level data of self-reported experiences of autonomic dysreflexia symptoms and clinical validation of autonomic neurorehabilitation.

a, The prevalence of autonomic dysreflexia and management efficacy in people with SCI (n = 1479) acquired with the Spinal Cord Injury Community Survey (SCICS). b, Percentage of individuals with SCI experiencing autonomic dysreflexia scored in the ADFSCI (n = 107). c, Percentage of autonomic dysreflexia in individuals with spinal cord injuries split between tetraplegia (top) (n = 52) and paraplegia (bottom) (n = 34) acquired with the SCICS. d, Percentage of individuals experiencing each symptom described in the ADFSCI autonomic dysreflexia section split between all participants (top) and tetraplegic individuals with complete SCI (bottom). e, Timeline of the two clinical trials conducted in Lausanne, Switzerland and in Calgary, Canada. f, Bar plots reporting the average daily usage of the system per participant (left), and the usage of the system throughout the hours of the day (right) for the 7 participants. g, Bar plots reporting the ADFSCI autonomic dysreflexia score for each symptom before implantation and at the latest timepoint of home use of the system to regulate blood pressure (n = 11, paired samples one tailed t-test; p-value = 0.0885, p-value = 0.01833, p-value = 0.08896, p-value = 0.05529, p-value = 0.08963).

Supplementary information

Supplementary Information

This Supplementary Information file contains Supplementary Notes 1–3, Supplementary references corresponding to the Supplementary Notes, and Supplementary Tables 1–3.

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Supplementary Video 1

Overview of experimental approach to reveal a neuronal architecture underlying autonomic dysreflexia.

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Soriano, J.E., Hudelle, R., Mahe, L. et al. A neuronal architecture underlying autonomic dysreflexia. Nature 646, 1167–1177 (2025). https://doi.org/10.1038/s41586-025-09487-w

- Received: 06 May 2024

- Accepted: 01 August 2025

- Published: 17 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09487-w

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PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia

Alena Kozlova,

Siwei Zhang,

Ari Sudwarts,

Hanwen Zhang,

Stanislau Smirnou,

Seul Kee Byeon,

Christina Thapa,

Xiaotong Sun,

Kimberley Stephenson,

Xiaojie Zhao,

Brendan Jamison,

Moorthi Ponnusamy,

Xin He,

Julie A. Schneider,

Akhilesh Pandey,

David A. Bennett,

Zhiping P. Pang,

Alan R. Sanders,

Hugo J. Bellen,

Gopal Thinakaran &

…

Jubao Duan

Nature volume 646, pages 1178–1186 (2025)

Abstract

Despite genome-wide association studies (GWAS) of late-onset Alzheimer’s disease (LOAD) having identified many genetic risk loci1,2,3, the underlying disease mechanisms remain largely unclear. Determining causal disease variants and their LOAD-relevant cellular phenotypes has been a challenge. Here, using our approach for identifying functional GWAS risk variants showing allele-specific open chromatin, we systematically identified putative causal LOAD-risk variants in human induced pluripotent stem (iPS)-cell-derived neurons, astrocytes and microglia, and linked a PICALM LOAD-risk allele to a microglial-specific role of PICALM in lipid droplet (LD) accumulation. Allele-specific open-chromatin mapping revealed functional risk variants for 26 LOAD-risk loci, mostly specific to microglia. At the microglial-specific PICALM locus, the LOAD-risk allele of the single-nucleotide polymorphism rs10792832 reduced transcription factor (PU.1) binding and PICALM expression, impairing the uptake of amyloid beta (Aβ) and myelin debris. Notably, microglia carrying the PICALM risk allele showed transcriptional enrichment of pathways for cholesterol synthesis and LD formation. Genetic and pharmacological perturbations of microglia further established a causal link between reduced PICALM expression, LD accumulation and phagocytosis deficits. Our work elucidates the selective LOAD vulnerability in microglia at the PICALM locus through detrimental LD accumulation, providing a neurobiological basis that can be exploited for developing clinical interventions.

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Main

GWAS analyses have identified 75 genome-wide significant (P < 5 × 10−8) loci for LOAD1,2,3. Aside from the well-established apolipoprotein E (APOE) locus that contains a protein-coding risk variant (that is, APOE4) with a large effect size, most LOAD GWAS risk variants reside in the non-coding regions of the genome without clear functional links to disease pathophysiology. Studies of human brain microglia (hMGs) have identified many LOAD-risk variants associated with gene expression quantitative trait loci (eQTL)4,5,6,7,8,9. However, such association-based mapping approaches do not directly implicate functional or causal variants. Moreover, LDs—cellular organelles containing lipids such as glycerolipids and cholesterol—were recently found to have important roles in ageing and Alzheimer’s disease (AD)10,11,12,13,14,15. APOE4, the strongest genetic risk factor for LOAD, impairs neuron–glia lipid metabolism14,16, and the APOE4-associated glial LD accumulation modulates neuron function and brain memory17,18,19. However, whether other LOAD GWAS risk loci may similarly affect lipids and cholesterol accumulation remains unclear.

We recently developed an allele-specific open chromatin (ASoC) mapping approach that enables the comparison of differential chromatin accessibility of the two alleles of a heterozygous risk variant in the same sample, therefore directly identifying probably functional disease variants affecting chromatin accessibility and gene expression20,21. Here, using the ASoC approach, we systematically identified functional LOAD-risk variants in human iPS-cell-derived MGs (iMGs), astrocytes (iAst) and neurons. For a strong LOAD-risk locus encoding phosphatidylinositol binding clathrin assembly protein (PICALM), we identified a potential causal link between the LOAD-risk allele of rs10792832 and altered chromatin accessibility to transcription factor (TF) PU.1 (SPI1) binding, leading to reduced PICALM expression, increased LD accumulation and phagocytosis deficits in MGs.

Mapping functional LOAD-risk variants

To systematically identify functional LOAD GWAS risk variants, we performed an assay for transposase-accessible chromatin using sequencing (ATAC–seq) and ascertained the allelic imbalance of chromatin accessibility (that is, ASoC)20,21 at heterozygous LOAD-risk single-nucleotide polymorphism (SNP) sites (Fig. 1a). We analysed human iMG and iAst cells (Extended Data Fig. 1a,b), and iPS-cell-derived glutamatergic (iGlut), GABAergic (iGABA) and dopaminergic (iDN) neurons (Fig. 1a, Extended Data Fig. 1c–e and Supplementary Table 1). We identified between 202,019 and 267,119 open-chromatin regions (OCRs) for each cell type. We confirmed cell-type-specific OCRs of MG (for example, SPI1) and Ast (for example, VIM) cells, and clear separation of iMG and iAst cells from neuronal samples (Extended Data Fig. 1f–i). Our iMG and iAst cells were epigenetically similar to hMGs[6] and other reported iMGs[22] and Ast (hAst)[6] cells (Extended Data Fig. 1i). We further validated the functional relevance of iMG and iAst cell OCRs to LOAD by demonstrating strong enrichment of LOAD heritability in iMG OCRs (enrichment fold = 13.6, P = 2.1 × 10−15) and, to a lesser extent, for iAst cells (enrichment fold = 6.3, P = 4.9 × 10−10), but not for iPS-cell-derived neurons (Supplementary Fig. 2a–c).

Fig. 1: Chromatin accessibility mapping.

a, Schematic of the experimental design for ASoC mapping of functional non-coding GWAS risk variants of LOAD in human iMGs and other cell types. The number of donor lines (n) for each cell type is indicated. The diagram was created using BioRender. b, Enrichment of ASoC SNPs (current study) and the reported hMG caQTL or eQTL for GWAS risk of LOAD, neuropsychiatric disorders and other complex traits. GWAS datasets are listed in Supplementary Table 9. A Bayesian hierarchical model (TORUS) was used for the enrichment analysis. ADHD, attention deficit hyperactivity disorder; BMI, body mass index; T2D, type 2 diabetes. c, rs10792832 shows ASoC in an iMG-specific OCR peak. Only iMG samples heterozygous for rs10792832 were used. Note that the ASoC SNP showing the strongest GWAS association is in an upstream enhancer linked to PICALM by ABC analysis. Chr., chromosome. d, Different ATAC–seq reads of the two alleles of the ASoC SNP rs10792832. Plot range, 86156600–86157000 bp. Blue, allele A; red, allele G. Two-sided binomial tests were used for ASoC analysis.

We next identified functional ASoC SNPs in OCRs for each cell type and evaluated their genetic relevance to LOAD. We identified between 15,693 and 72,291 ASoC SNPs, a large proportion of which were cell type specific (Extended Data Fig. 2a, Supplementary Tables 2–6 and Supplementary Fig. 3a). Similar to the genomic characteristics of neuronal ASoC SNPs20,21, 25–30% of ASoC SNPs in iMG and iAst cells were proximal to gene promoters (within 5 kb), while most ASoC SNPs were in distal enhancers (>50 kb) (Supplementary Fig. 3b,c). As expected, ASoC SNPs in iMGs were near genes involved in immune activation and phagocytosis (Supplementary Fig. 3d,e). iMG ASoC SNPs were highly enriched for hMG chromatin accessibility QTLs (caQTL) and eQTLs (Supplementary Tables 2 and 8 and Supplementary Fig. 3f–h), suggesting that they probably alter chromatin accessibility and influence gene expression. Notably, neuronal ASoC SNPs showed greater enrichment for GWAS variants of schizophrenia, neuroticism and intelligence, while iMG ASoC SNPs exhibited the strongest LOAD GWAS risk enrichment (35-fold, P = 1.04 × 10−33) (Fig. 1b, Extended Data Fig. 2b–f and Supplementary Table 9).

To assess the degree to which ASoC SNPs could help to prioritize GWAS risk variants for LOAD and other neuropsychiatric disorders, we colocalized ASoC SNPs with GWAS index SNPs and their linkage-disequilibrium proxies (r^2 ≥ 0.8) (Supplementary Tables 2–7). In total, 29–37% of GWAS risk loci contained at least one disease-associated ASoC SNP (Extended Data Fig. 2g–k and Supplementary Table 7). For LOAD, out of the 26 risk loci that could be functionally interpreted by ASoC SNPs, 20 loci with 38 ASoC/LOAD-risk SNPs were accounted for by iMGs (Extended Data Fig. 2g,l and Supplementary Table 2). To identify the target genes for these ASoC/LOAD-risk SNPs in iMGs, we integrated our iMG ASoC data with the hMG enhancer–promoter linkages defined by activity-by-contact (ABC) analysis[6] (Supplementary Table 10). We found that 13 ASoC/LOAD-risk SNPs of 9 risk loci could be assigned to a target gene (Extended Data Fig. 2l), including the two loci with the strongest associations with LOAD: the BIN1 locus for which the functional linkage between rs6733839 and BIN1 has been well established7,23 (Supplementary Fig. 3i) and the PICALM locus with an unknown functional link between rs10792832 and its target gene PICALM (Fig. 1c,d and Extended Data Fig. 2l).

LOAD-risk allele reduces PICALM expression

Among multiple SNPs near PICALM showing the strongest GWAS association with LOAD (Fig. 1c), only rs10792832 showed strong ASoC in an iMG-specific OCR (Fig. 1c,d). Brain hMG[24] displayed a similar OCR peak pattern to iMGs at the PICALM locus, with rs10792832 also showing ASoC in hMG but not in other cell types (Extended Data Fig. 3a). To investigate the function of rs10792832, we performed CRISPR–Cas9 editing in human iPS cell lines from two non-AD donors (CD04, CD09), changing the risk genotype (G/G) to the non-risk genotype (A/A) (Fig. 2a), and differentiated them into iMGs (Extended Data Fig. 3b–d). We found that the risk allele reduced PICALM expression by about 50% (Fig. 2b,c and Extended Data Fig. 3e) in iMGs but not in iAst cells (Extended Data Fig. 3f).

Fig. 2: The LOAD-risk SNP rs10792832 alters PU.1 binding and PICALM expression in iMGs.

a, Schematic of CRISPR–Cas9 editing of the ASoC SNP rs10792832 in iPS cells (two donor lines CD04 and CD09), iMG differentiation and cellular phenotypic assays. The diagram was created using BioRender. b,c, Representative western blot image (b) and quantification (c) of the risk allele reduced PICALM protein expression. Protein quantity was normalized to β-actin. Gel source data are provided in Supplementary Fig. 1. Each data point represents the measurement of cell lysates of 12 wells of cultures from 1 experiment; for each donor line (CD04 and CD09) and each condition (risk and non-risk), data are from 2 clones, collected from 4 independent experiments for clone 1 and 3 independent experiments for clone 2 (that is, n = 7). A linear mixed model (LMM) was used to test the fixed effect of the risk allele, with the experimental round and clone identity used as nested random factors; two-sided test, nominal P values are shown; *P < 0.05, **P < 0.01, ***P < 0.001. Data are mean ± s.e.m. d, The LOAD-risk allele of rs10792832 is predicted to disrupt the PU.1-binding motif (encoded by SPI1; MA0080.2). e, Heterozygous iMGs (A/G) show a higher Sanger-sequencing peak of allele A (non-risk) than G (risk) for the ChIP-assay product of PU.1 binding (top), as opposed to the equal allelic peak height for genomic DNA (bottom).

Next, we examined whether PICALM expression was altered in human LOAD. We first confirmed PICALM expression in IBA1+ MGs in the post-mortem human brain (Extended Data Fig. 4a). At the mRNA level, patients with AD showed a reduction in PICALM in the grey matter (Extended Data Fig. 4b and Supplementary Table 11). RNA-sequencing (RNA-seq) analysis of hMGs[6] (Supplementary Table 12) also revealed a reduction in PICALM in AD, with lower PICALM expression significantly associated with the risk allele G of rs10792832 in patients with AD (P = 0.004) (Supplementary Fig. 5a–c). The directional effect of the AD risk allele of rs10792832 on PICALM expression was consistent with other hMG eQTL findings8,9,25. Notably, data mining of single-nucleus RNA-seq (snRNA-seq) datasets from the human prefrontal cortex demonstrated a reduced PICALM expression in MGs (MG_0 state) during early AD but increased PICALM expression in MG states during late AD (versus non-AD)[26]; there was no change in PICALM expression in other brain cell types[6] (Supplementary Fig. 5d–l), suggesting possible context-dependent effects (MG state, cell type and AD stage) of the LOAD-risk allele on PICALM expression. Moreover, by recapitulating snRNA-seq analysis of AD brain MG states[19], we found lower PICALM expression in disease-associated MGs (DAMs) and LD-accumulating MGs (LDAMs) (versus homeostatic MGs) (P = 0.016) (Extended Data Fig. 4c). Although we did not find a difference in brain microglial PICALM protein levels between patients with AD and controls in a small ROSMAP cohort (Extended Data Fig. 4d–g and Supplementary Table 11), a large proteomics dataset of the dorsolateral prefrontal cortex (n = 516) showed a significant reduction in PICALM in AD[27] (Extended Data Fig. 4h). Overall, these results support a brain MG-specific reduction in PICALM expression that may be influenced by rs10792832, consistent with our observed transcriptional effect of the LOAD-risk allele of rs10792832 in iMGs.

The PICALM SNP rs10792832 lies within a predicted binding site of PU.1, a key myeloid-specific TF encoded by a strong LOAD-risk gene, SPI13,28, with the risk allele G predicted to disrupt PU.1’s consensus binding motif (Fig. 2d). To assess how allele G affects PU.1 binding, we performed chromatin immunoprecipitation (ChIP) to enrich for PU.1-bound OCRs in iMGs (Extended Data Fig. 3g–k). We found that the risk allele G decreased PU.1 binding at the SNP site (Extended Data Fig. 3i). We also performed ChIP for PU.1 in iMGs heterozygous for rs10792832 to compare the PU.1 binding between the two alleles within the same sample (Fig. 2e and Extended Data Fig. 3g–k). We found that the risk allele G retained about 40% of the PU.1-binding ability of allele A. Together, these data provide compelling evidence that the LOAD-risk allele of rs10792832 reduces PICALM expression specifically in MGs by disrupting PU.1 binding.

PICALM risk allele impairs iMG phagocytosis

Considering the importance of MG phagocytosis in AD29,30 and the role of PICALM in clathrin-mediated endocytosis in non-MG cells31,32, we hypothesized that the LOAD-risk allele of rs10792832 influences iMG phagocytosis by reducing PICALM expression. We examined the phagocytosis of pHrodo-labelled Aβ aggregates in CRISPR-engineered iMGs and found significantly decreased (40–50%) Aβ–pHrodo fluorescence intensity in iMGs carrying the LOAD-risk (versus non-risk) allele (Fig. 3a and Extended Data Fig. 5a). iMGs from a second clone of each iPS cell line also exhibited similar allelic difference in phagocytic activity (Extended Data Fig. 5b,c).

Fig. 3: The LOAD-risk allele of PICALM impairs MG phagocytosis.

a, iMGs carrying LOAD-risk allele of PICALM show reduced phagocytosis of Aβ–pHrodo (compared with non-risk-allele iMGs), which can be rescued by PICALM-CRISPRa. The normalized Aβ–pHrodo intensity per iMG over a period of 180 min for both the CD04 and CD09 donor lines is shown. For each condition (genotype), data at each assay timepoint are from two independent experiments each with three single-well measurements (that is, differentiations). One clone per line. b,c, Representative western blot image (b) and quantification (c) of PICALM protein expression in iMGs of non-risk, risk and risk-CRISPRa for both lines. Protein quantity was normalized to β-actin. Gel source data are provided in Supplementary Fig. 1. Each data point represents a measurement of cell lysates from 12 wells of cultures from 1 experiment; for each donor line (CD04 and CD09) and each condition (non-risk, risk and risk-CRISPRa), data were collected from 3 independent experiments. One clone per line. d,e, PICALM KO by CRISPR–Cas9 editing C20 cells. The gRNA targeted at exon 1 depleted PICALM expression in immunoblot (d) and immunofluorescence labelling (e). Three experiments were performed. Scale bar, 50 μm. Gel source data are provided in Supplementary Fig. 1. f,g, Representative images (f) and quantification (g) of the phagocytosis of pHrodo-conjugated myelin in PICALM-KO C20 and control cells. Scale bar, 50 μm. In g, each data point represents a single-well measurement from one experiment (n = 4 wells in each group). Three experiments were performed with similar results. Cytochalasin D (10 µM) was used to show the specificity of phagocytosis. For all comparisons, analysis was performed using one-way analysis of variance (ANOVA) with Dunnett’s correction with the experimental round as the random factor. *P < 0.05; **P < 0.01; ***P < 0.001. Data are mean ± s.e.m.

To evaluate whether the diminished phagocytosis in LOAD-risk-allele iMGs resulted from PICALM haploinsufficiency, we used CRISPRa to overexpress PICALM in iMGs carrying the LOAD-risk allele (Fig. 3b and Extended Data Fig. 5d,e). PICALM-CRISPRa fully rescued the Aβ–pHrodo phagocytic deficit of risk-allele iMGs (Fig. 3a and Extended Data Fig. 5a). Moreover, we performed CRISPRoff in the non-risk-allele iMGs to knockdown PICALM (Extended Data Fig. 5f–e). We found that PICALM-CRISPRoff reduced iMG capacity for phagocytosis of myelin-pHrodo and Aβ–pHrodo compared with the non-risk-allele iMGs (Extended Data Fig. 5h–l). These results support a direct link between reduced PICALM expression and iMG phagocytosis.

To further corroborate the effect of PICALM expression on MG phagocytosis, we conducted independent CRISPR–Cas9 editing on an immortalized hMG cell line (C20)[33] to knockout (KO) PICALM (Fig. 3d,e). The loss of PICALM did not perturb the Golgi apparatus (Giantin), the distribution of early endosomes (EEA1), AP-4 containing secretory and endocytic vesicles or the internalization of transferrin and cholera toxin through endocytosis (Supplementary Fig. 6). However, PICALM-KO cells exhibited reduced (about 50%) phagocytosis of myelin-pHrodo compared with the wild-type (WT) cells (Fig. 3f,g), matching the phagocytosis deficit that we observed in risk-allele iMGs (Fig. 3a and Extended Data Fig. 5). These results suggest that impaired MG phagocytosis of fragmented myelin and Aβ aggregates could be a causal mechanism for the LOAD-risk allele at the PICALM locus.

Dysregulated lipid genes in PICALM-deficit iMGs

To investigate the molecular mechanism underlying the observed phagocytosis deficit in iMGs carrying the PICALM risk allele, we performed differential expression analysis in iMGs with the LOAD-risk allele (Fig. 4a). We identified 257 upregulated and 244 downregulated genes (false-discovery rate (FDR) < 0.05) in the LOAD-risk-allele iMGs (Fig. 4b and Supplementary Table 13). The risk allele cis-regulated only PICALM (within 250 kb) with similar effects on major PICALM isoforms (Supplementary Tables 13 and 14). The downregulated genes were enriched for Gene Ontology (GO) terms (such as MHC class II protein complex, antigen presentation and clathrin-coated endocytic vesicle membranes) that are important for MG phagocytosis[34] (Supplementary Table 15 and Supplementary Fig. 7a). Notably, among the top 10 enriched GO terms for upregulated genes, four were associated with lipid/cholesterol biosynthesis or metabolism. Notably, more than half of the top 20 differentially expressed genes (DEGs) were related to lipid metabolism (for example, DHCR7, HPGD, FDFT1, HMGCR, PLBD1, IMPA2, MTMR1, RETN, ATP6AP2, LRP5 and CRABP2) (Fig. 4b, Extended Data Fig. 6a–d, Supplementary Fig. 7b and Supplementary Table 13). Among the most enriched canonical pathways (Fig. 4c, Supplementary Fig. 8 and Supplementary Table 16), activated pathways were linked to lipid metabolism, such as cholesterol biosynthesis and gene activation by SREBF (encoding SREBP)[35]. All genes in the activated cholesterol-biosynthesis pathway were upregulated (Supplementary Figs. 7b and 8) in LOAD-risk-allele iMGs.

Fig. 4: The LOAD-risk allele of PICALM dysregulates lipid gene pathways in iMGs.

a, Principal component analysis (PCA) of RNA-seq samples of iMGs derived from CRISPR-engineered iPS cell lines carrying the PICALM risk or non-risk alleles. Samples are from 2 experiments each with 2–3 wells of differentiations for CD04 and CD09 lines, 1 clone per line. Expression of 13,947 genes was used for PCA. The sample labels describe the cell line, clone number, replicates, and batch number in sequential order, divided by the hyphen (-) symbol. b, DEGs in iMGs carrying the LOAD-risk allele. FC, fold change. c, Enriched Ingenuity canonical pathways for all DEGs (FDR < 0.05). Significantly enriched pathways (one-sided Fisher’s exact test, FDR < 0.05) are ranked by their activated or inactivated (inact.) z scores. TH1, T helper type 1. d, Significant correlation (Pearson’s R^2) of the expression changes (−log_2[fold change]) in iMGs carrying PICALM risk (versus non-risk) allele and in previously reported LD accumulated microglia (LDAM) of the ageing mouse[10]. Plotted are 56 DEGs (FDR < 0.1) in both RNA-seq datasets.

Next, we compared the transcriptomic similarity of LOAD-risk-allele iMGs to the LDAMs from ageing mice[10]. For the overlapping DEGs (n = 56) between the two datasets, the expression changes in the LOAD-risk-allele iMGs and the ageing LDAMs were significantly correlated (r^2 = 0.22) (Fig. 4d and Supplementary Table 13). The enrichment of lipid/cholesterol metabolism pathways along with the transcriptomic similarity of PICALM-risk-allele iMGs to mouse LDAMs suggests a possible functional link between PICALM dysregulation and LD formation in MGs.

LD accumulation in PICALM-deficit iMGs

Although PICALM has been shown to be involved in lipid/cholesterol metabolism in Drosophila glia and HEK293 cells13,36, its potential role in LD formation in MGs, especially its link to the LOAD-risk allele, has not been demonstrated. We first used filipin to stain intracellular free cholesterol in iMGs differentiated from the isogenic CRISPR-edited pairs of iPS cell lines (Extended Data Fig. 7a,b). We found stronger filipin fluorescence in iMGs carrying the LOAD-risk (versus non-risk) allele. Cholesterol esters, derived from cellular cholesterol, are often stored in LDs along with triacylglycerols (TGs)[37]. Although filipin only stains free (unesterified) cholesterol, cellular cholesterol accumulation is associated with excess LD10,37,38. Thus, we treated iMGs with BODIPY to stain lipids in LDs10,14,18,19. We observed a twofold to sevenfold increase in LDs in iMGs carrying the PICALM risk allele (Fig. 5a,b and Extended Data Fig. 7c,d). Treating iMGs with triacsin C (TrC), an inhibitor of long-chain acyl-CoA synthetase that reduces LD formation10,18,19,39,40, effectively decreased BODIPY-labelled LDs (Extended Data Fig. 7e–i). The LD accumulation in PICALM risk-allele iMGs was independently confirmed by flow cytometry analysis of BODIPY+ iMGs (Extended Data Fig. 7j,k) and by staining for the LD-associated protein PLIN2 (Extended Data Fig. 7l–n).

Fig. 5: The LOAD-risk allele of PICALM causes LD accumulation and lysosomal dysregulation.

a, Immunofluorescence staining of LD (BODIPY+) in iMGs carrying risk or non-risk allele. C1, clone 1; C2, clone 2. Scale bars, 50 µm. b,c, Quantification of a shows increased LD (BODIPY+) puncta density (b) and area per cell (c) in LOAD-risk-allele iMGs (versus non-risk). Each data point represents a single-well measurement from 1 experiment; for each donor line (CD04 and CD09) and each condition (risk and non-risk), data are from 2 clones, collected from 2 independent experiments each with 3 wells of differentiations (n = 12). d, Dysfunctional lysosomes may contribute to LD accumulation in LOAD-risk-allele iMGs. The log_2[FC] of known lysosomal genes and LD suppressor genes in LOAD-risk-allele iMGs or with iMGs with PICALM-CRISPRoff (versus non-risk) is shown. e, Representative images of iMG staining for LD (BODIPY+) and lysosomes (LysoTracker+) in iMGs carrying PICALM non-risk or risk alleles. Scale bars, 50 µm. f,g, Quantification of e for lysosome puncta per iMG (f; LysoTracker+) and colocalized lysosome–LD puncta per iMG (g; BODIPY+LysoTracker+). Each data point represents a single-well measurement from one experiment; for each donor line (CD04 and CD09) and each condition (risk and non-risk), data are from 2 clones, collected from 2 independent experiments each with 3 wells of differentiations (n = 12). In all comparisons, an LMM was used to test the fixed effect of risk allele, with the experimental round and clone identity as nested random factors; two-sided test, nominal P values are shown. *P < 0.05; **P< 0.01; ***P < 0.001. Data are mean ± s.e.m.

To examine whether LD accumulation in the LOAD-risk-allele iMGs was due to decreased PICALM expression, we compared the LD staining in iMGs carrying either the LOAD non-risk or risk allele, as well as PICALM-risk-CRISPRa (Extended Data Figs. 5d,e and 8a,b). PICALM-CRISPRa iMGs fully restored LD levels to the levels of those of non-risk iMGs (Extended Data Fig. 8b). Conversely, PICALM knockdown in non-risk CRISPRoff iMGs (Extended Data Fig. 5f,g) significantly increased LD levels (Extended Data Fig. 9a–e). Independent LD staining using LipidTOX produced similar results (Extended Data Fig. 9f–j). These findings in iMGs were further corroborated by detecting LD accumulation in PICALM-KO C20 cells (Extended Data Fig. 9k,l).

TG elevation is a major characteristic of LDAMs[10] and APOE4-associated LDs[19]. To better understand the PICALM-related LD accumulation, we conducted lipidomic profiling on non-risk, risk and risk-CRISPRa iMGs. Among the 55 lipid species altered in risk-allele iMGs (versus non-risk) (FDR < 0.05), 32 were TGs (5.6-fold enrichment, Fisher’s exact test, P < 2.6 × 10−16) and all were increased (Extended Data Fig. 10a,b and Supplementary Table 17). Total TGs (n = 94 species) displayed an approximately twofold increase in iMGs carrying the risk allele, which was completely reversed by PICALM-CRISPRa (Extended Data Fig. 10b,c). Our results align with previously reported TG increases in LDAMs[10] and APOE4-associated LDs[19], further supporting our observed PICALM-risk-allele-associated LD accumulation in iMGs.

PICALM has been shown to promote neuron–astrocyte lipid transfer in Drosophila and rat astrocytes[13]. To test whether lipid accumulation in LOAD-risk-allele iMGs could also be attributed to lipid uptake, we performed a lipid-transfer assay[41] in which we co-cultured iMGs with the iGlut neurons that were prelabelled with Red-C12 (Extended Data Fig. 10d). We observed similar levels of Red-C12 staining between iMGs with the LOAD-risk and non-risk alleles (Extended Data Fig. 10e–g). By contrast, consistent with the previously observed effect of PICALM on neuron–astrocyte lipid transfer in rat astrocytes[13], iAst cells with PICALM-CRISPRoff exhibited around a 50% reduction in BODIPY-labelled LD and Red-C12-labelled lipids (Extended Data Fig. 10h–l). These results indicate cell-type-specific effects of PICALM on lipid transfer and LD formation in two very different types of glial cells, MG and Ast cells, warranting further studies. These findings also support that reduced PICALM expression by the LOAD-risk-allele leads to MG-specific LD accumulation.

PICALM risk allele increases ROS in iMGs

Elevated reactive oxygen species (ROS) levels in mouse LDAMs are associated with cellular oxidative stress and age-related neurodegeneration[10]. The PICALM LOAD-risk allele iMGs exhibited transcriptional activation of the SERBP pathway (Fig. 4c and Supplementary Fig. 8), indicating increased ROS levels[42]. To determine whether the LOAD-risk allele also raises ROS in iMGs, we treated iMG cultures with CellROX, a dye for detecting ROS[10], and co-stained the cells with BODIPY (for LDs) (Extended Data Fig. 7e). We observed that LOAD-risk-allele iMGs had a greater than twofold increase in CellROX staining (Extended Data Fig. 7f–i), with largely colocalized CellROX and LD staining (Extended Data Fig. 7e). As seen in mouse microglia[10], we further confirmed that LD accumulation influences ROS levels in iMGs by using TrC to block LD formation (Extended Data Fig. 7f–i).

Lipids are often peroxidated in the presence of ROS, mediating cellular oxidative stress[42]. To examine whether lipids in the LOAD-risk-allele iMGs were also peroxidated, we stained iMGs with BODIPY-C11(581/591) (Extended Data Fig. 11a), a fluorescent lipid peroxidation sensor that shifts its fluorescence emission from red to green when peroxidated lipids are present. We observed that lipids in many LDs were peroxidated (Extended Data Fig. 11b). The LOAD-risk-allele iMGs exhibited threefold more peroxidated lipids compared with non-risk iMGs (Extended Data Fig. 11b–d). The ratio of peroxidated to non-peroxidated lipids in risk-allele iMGs was also higher than in non-risk iMGs (Extended Data Fig. 11e). These results indicate a stress state in iMGs carrying the LOAD-risk allele of PICALM.

LD accumulation impairs iMG phagocytosis

LD buildup impairs phagocytosis in mouse microglia10,43. To validate whether the PICALM-associated LD accumulation also affects phagocytosis in iMGs, we first performed a co-staining of LD-containing (BODIPY+) and phagocytic (Aβ–pHrodo+) iMGs of the non-risk, risk and PICALM-CRISPRa lines (Extended Data Fig. 8a,b). We observed that only 2.4–3.2% of iMGs were BODIPY+Aβ–pHrodo+ across all conditions and an increase in BODIPY+ cells in risk-allele iMGs was proportionally accompanied by a decrease in BODIPY−Aβ–pHrodo+ iMGs (Extended Data Fig. 8c). This inverse relationship between BODIPY+ and Aβ–pHrodo+ signals in iMGs suggests a possible mechanistic link between PICALM-associated phagocytosis deficiency and LD accumulation.

We next conducted a phagocytosis assay for LOAD-risk-allele iMGs in the presence or absence of the LD blocker (TrC)10,18,19,39,40 to confirm whether LD accumulation impairs phagocytosis as seen in mouse microglia10,43. LOAD-risk-allele iMGs showed reduced phagocytosis of Aβ and myelin (Extended Data Fig. 12). As expected, TrC treatment of LOAD-risk-allele iMGs restored phagocytosis to levels seen in non-risk iMGs (note that TrC treatment itself did not significantly increase phagocytosis of non-risk iMGs; Supplementary Fig. 9), which was accompanied by a decrease in LD levels in risk-allele iMGs (Extended Data Fig. 12b,c,f,g). Like in mouse microglia[10], there was an inverse relationship between BODIPY+ and Aβ–pHrodo+ signals in iMGs, and TrC treatment of the LOAD-risk-allele iMGs shifted the proportions of cells with varying BODIPY/pHrodo combinations to resemble non-risk iMGs (Extended Data Fig. 12d). Colocalization analysis of LD and phagocytosed myelin in iMGs yielded similar results (Extended Data Fig. 12h). Collectively, these results support the notion that the PICALM-risk-allele-induced LD accumulation in iMGs impairs phagocytosis.

Lysosomal abnormality ties to LD increase

APOE4 dysregulates lysosomal function, contributing to glial lipid/cholesterol accumulation19,38. PICALM also has a role in autophagy and lysosomal function in some non-brain cell lines44,45,46. Given the link between lysosome-based autophagy and LD breakdown[47], we predicted that LD accumulation in PICALM LOAD-risk-allele iMGs may result from compromised lysosomal function. We first examined the potential dysregulation of lysosomal genes in LOAD-risk-allele iMGs. Of the 15 lysosomal genes downregulated in APOE4 glia[38] (Fig. 5d and Supplementary Table 18), 13 were also downregulated in LOAD-risk-allele iMGs. A consistent expression change was seen in iMGs with PICALM-CRISPRoff (versus LOAD-risk-allele iMGs) (Fig. 5d). Moreover, the downregulated genes in LOAD-risk-allele iMGs were enriched for the lysosomal GO terms (Supplementary Fig. 7a), which shared some downregulated lysosomal genes (VAMP1, CD74, TCRIG1) with mouse LDAMs (Figs. 4d and 5d and Supplementary Fig. 7c). Notably, ATP6AP2, one of the most downregulated genes in risk-allele iMGs (Fig. 4b and Supplementary Table 13), encodes a protein that is essential for lysosomal degradative functions; Atp6ap2-mutant mice show increased triglycerides and LD in fat body cells[48]. The reduced expression of ATP6AP2, VAMP1 and CD74 in LOAD-risk-allele iMGs was further confirmed independently by immunofluorescence staining (Extended Data Fig. 6c,d). Notably, three known LD suppressor genes (SLC33A1, MCOLN1 and GRN)[10] all exhibited reduced expression in LOAD-risk-allele iMGs (Fig. 5d), two of which are related to lysosomal function: MCOLN1 is a ROS sensor in lysosomes regulating autophagy[49], and GRN encodes the lysosomal protein progranulin related to neurodegenerative diseases[50]. The findings suggest that lysosomes in LOAD-risk-allele iMGs may be functionally compromised.

We next assayed whether risk-allele iMGs indeed show accumulation of lysosomes that colocalize with LDs. We co-stained non-risk and risk-allele iMGs with LysoTracker (for lysosomes) and BODIPY (for LDs) (Fig. 5e). We found that risk-allele iMGs had a 2.7–4.2-fold increase of lysosomes, and an even larger increase (4.3 to 5.7-fold) of lysosomes that colocalized with LDs (LysoTracker+BODIPY+) (Fig. 5f,g). Together, these data suggest a potential link between altered lysosomal dynamics and LD accumulation in iMGs carrying the LOAD-risk allele of PICALM (Extended Data Fig. 11f).

Discussion

Our integrative analyses of chromatin accessibility in iPS-cell-derived major brain cell types, brain QTLs, chromatin interactions (ABC) and LOAD GWAS data identified functional LOAD-risk variants that account for about one-third of the known LOAD GWAS risk loci, providing a rich resource for prioritizing functional LOAD-risk variants/genes for biological follow-up. Given that around 70% of ASoC SNPs are in distal enhancers and are likely to be functional, our study presents a framework for prioritizing functional non-coding risk variants through combinatorial analyses of whether a SNP is predicted to alter TF-binding sites and/or is a brain eQTL. Moreover, we mechanistically link the putative LOAD causal SNP at the PICALM locus to its MG-specific effect on PICALM expression, resulting in LD accumulation in iMGs partially through lysosomal dysregulation (Extended Data Fig. 11f). Pharmacological perturbation further validated a causal link between LD accumulation and the impaired phagocytosis in iMGs (Extended Data Fig. 11f). While PICALM is known to be expressed in brain MGs[51], and its role in lipid/cholesterol metabolism has been reported in Drosophila glia and HEK293 cells13,36, our work demonstrates a human-MG-specific role for PICALM in regulating LD accumulation and phagocytosis, which potentially contribute to LOAD pathophysiology.

In Drosophila and rat astrocytes, PICALM is required for storing peroxidated lipids sourced from neurons[13]. Our study indicates that PICALM also regulates MG lipid/cholesterol homeostasis, independent of interactions with neurons. The de novo cholesterol and fatty acid synthesis in LOAD-risk-allele iMGs may contribute to LD accumulation: genes in the cholesterol-biosynthesis pathway, including TFs (such as SREBP) that regulate lipids synthesis[35], were all upregulated (Fig. 4b and Supplementary Fig. 8). Lipid processing in PICALM-risk-allele iMGs may be exacerbated by lysosomal dysregulation (Fig. 5d–g). Notably, increased lipid synthesis and lysosomal dysregulation in MGs and Ast cells have also been reported for APOE419,38.

As reported in BV2 microglia[10], we confirmed that LD accumulation in PICALM-LOAD-risk-allele iMGs mediates phagocytic deficits and ROS elevation. However, many LDs in iMGs were also peroxidated (Extended Data Fig. 11b–e), suggesting that ROS may also occur first and the peroxidized lipids are sequestered in LDs16,42,52. In glia, the main sources of ROS include peroxisomes and mitochondria that perform lipid β-oxidation[52]. Notably, PICALM and some other clathrin adaptor proteins are reported to protect against ROS production partially through iron regulation[53].

We therefore propose a model that connects the PICALM risk allele to LD accumulation and phagocytosis deficits (Extended Data Fig. 11f): (1) partial loss of PICALM in MGs exacerbates ROS levels (Extended Data Fig. 7e), leading to increased lipid/cholesterol synthesis due to increased SREBP; (2) the lipids are peroxidated and sequestered in LDs, which are initially protective against ROS elevation; (3) ongoing lipid build-up causes cellular stress, further elevating ROS and LD levels; and (4) PICALM loss also impairs proper lysosomal autophagy and LD degradation (Fig. 5d–g), resulting in LD accumulation that hinders phagocytosis. Alternatively, the PICALM risk allele might impair phagocytosis early in ageing, causing reduced cellular lipids/cholesterol, which then activates de novo lipid/cholesterol synthesis. We acknowledge a lack of strong correlation between the magnitude of PICALM alteration and its functional effects in CRISPR-engineered iMGs, probably due to technical variations in iMG differentiation and/or the nonlinear relationship between gene expression and cellular phenotypes due to epistasis and systems buffering54,55. However, we cannot formally exclude a possible long-range cis-effect of the AD-risk variant on genes other than PICALM. Future research will help to clarify how the PICALM risk allele compromises MG function.

Although our study indicates a reduced PICALM expression in LOAD-risk-allele iMGs, PICALM expression in patients with AD may also be influenced by cell type, MG state and disease stage31,56. Moreover, despite the epigenomic similarities between iMGs and hMG, the functional effects of PICALM risk allele in iMGs may also be present in myeloid cells/macrophages, which warrants further investigation. Finally, the impaired phagocytosis in PICALM-risk-allele iMGs implies a neuroprotective role of phagocytosis in clearing Aβ or damaged myelin, but MG phagocytosis in vivo may also be detrimental through excessive synaptic pruning[57]. Nonetheless, given that the APOE4 allele was recently found to induce LD accumulation in MGs and impair neuronal function18,19, our findings with PICALM further strengthen the notion of a causal role of LDs in mediating LOAD genetic risk factors, providing potential mechanistic targets for AD treatments. Given the reported genetic interaction between the PICALM risk-allele and APOE419,58, it will be interesting to empirically examine possible epistasis between these two AD risk factors.

Methods

Human iPS cell lines and culture

The human iPS cell lines used for ATAC–seq (Supplementary Table 1) were derived at Rutgers University Cell and DNA Repository (RUCDR). The human iPS cell lines were generated using the Sendai virus method to ensure that they are integration-free and underwent the following quality-control procedures: immunofluorescence staining for pluripotency, mycoplasma contamination test, in-house RNA-seq-based pluripotency test (Pluritest) and eSNP-karyotyping20,21 or G-band karyotyping at RUCDR. All donors were of European ancestry and the samples were previously used for schizophrenia GWAS studies59,60. All donors were also analysed for copy-number variants, and none had large copy-number variants (>100 kb)[61]. There are 29 schizophrenia cases and 33 controls, of which 37 are male with an average age of 49.5 years (schizophrenia case–control status or age does not affect ASoC mapping20,21). Two control-donor human iPS cell lines homozygous for APOE3, CD04 and CD09 (abbreviated from the full cell line IDs: CD0000004 and CD0000009) were used in CRISPR–Cas9 editing. Human iPS cells were cultured using a feeder-free method on Matrigel-coated (Thermo Fisher Scientific) plates in mTeSR plus medium (100-0276, StemCell). The media were changed every other day, and cells were passaged as clumps every 4–6 days using ReLeSR (100-0483, StemCell). All cell cultures were confirmed to be mycoplasma-free using the PCR detection kit (ab289834, Abcam). Human iPS cell lines were obtained from the RUCDR NIMH Stem Cell Center (www.nimhgenetics.org). These iPS cell lines were generated from cryopreserved lymphoblasts deposited by the Molecular Genetics of Schizophrenia (MGS) consortium59,60, which had collected the biomaterials with informed consent as approved by the Endeavor Health (formerly NorthShore University HealthSystem) institutional review board (IRB), which also approved the current study.

PICALM RNA expression in human post-mortem brains

Frozen human brain samples (frontal cortex BA10 region) were received through the NIH biobank from Harvard Brain Tissue Resource Center and the University of Miami Brain Endowment Bank. For the PICALM expression assay, the grey matter was dissected from brain blocks on dry ice. RNA was isolated using the Direct-zol RNA MiniPrep Kit (Zymo), and reverse-transcribed into complementary DNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) according to the manufacturer’s instructions. Quantitative PCR (qPCR) reactions were set up using PowerUp SYBR Green Master Mix for qPCR (Applied Biosystems) and run on the QuantStudio Real-Time PCR System (Applied Biosystems). The data were analysed using the \({2}^{-\Delta \Delta {C}_{t}}\) method and normalized to COTL1. Primer sequences were as follows: PICALM-exon-1-F, TCTGCCGTATCCAAGACAGT; PICALM-exon-2-R, AAGACCACCACCCAACTACT; COTL1-F, CCAAGATCGACAAAGAGGCTT; COTL1-R, CGATGGTGGAGCCGTCATATTT.

PICALM staining of hMGs

Paraffin sections (6 μm) from post-mortem human brain tissue were obtained from UCLA or RUSH Alzheimer’s Disease Center (RADC) repositories. The RADC brains came from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP)[62]. All ROSMAP participants enrolled without known dementia and agreed to detailed clinical evaluation and brain donation at death. Both studies were approved by the Rush University Medical Center IRB. The sections were incubated at 60 °C for 60 min, deparaffinized in xylene and rehydrated through a series of increasingly dilute ethanol solutions. Epitope retrieval was performed using decloaking buffer (Biocare Medical) at 95 °C for 30 min. Subsequently, freshly prepared 0.1% sodium borohydride was added to the sections and incubated at 4 °C for 30 min. Peroxidase blocking (3% H_2O_2) was carried out at room temperature for 30 min. The sections were then treated with permeabilization buffer (0.2% Triton X-100 in 1× TBS) at room temperature for 45 min. Non-specific epitopes were blocked by incubating the sections for 60 min in blocking buffer-I (10% donkey serum, 1% BSA, 0.3 M glycine, 0.1% Triton X-100) followed by a 60 min incubation with background punisher (Biocare Medical). Antibody staining was performed at room temperature using the intelliPATH FLX system and reagents provided by the manufacturer (Biocare Medical). Rabbit anti-PICALM antibodies (Sigma-Aldrich, HPA019061, 1:200) and goat anti-IBA1 (Abcam, ab5076, 1:1,000) were incubated with the sections for 48 h at 4 °C. After washing, secondary antibodies were applied and incubated overnight at 4 °C. Autofluorescence was quenched using 0.1% Sudan Black B in 70% ethanol, and nuclei were stained with Hoechst 33342. Finally, coverslips were mounted using VectorShield mounting medium. Images were acquired on an automated Nikon Eclipse Ti2 microscope fitted with the Yokogawa spinning-disk field-scanning confocal system and Photometrics PRIME 95B sCMOS camera, using a ×20 objective. z stack images were deconvolved using Nikon NIS-Elements AR5.20.01 software and processed with Fiji/ImageJ (1.54f, 64 bit). For quantification of PICALM in IBA1+ microglia, all processing was conducted by a researcher blinded to individual diagnoses. Images were acquired on the SLIDEVIEW VS200 slide scanner (Olympus) using ×20 magnification. Individual microglia were cropped using OlyVIA software (Olympus). All further processing was conducted in FiJi/ImageJ (1.54f, 64 bit). Background fluorescence was measured from non-specific tissue staining and normalized by subtracting 50% of the measured values. Binary masks of microglia were generated by thresholding the IBA1 signal, and manually inspected to remove non-specific signals or adjacent cells. PICALM signal was multiplied by the microglial masks, and fluorescence intensities (integrated densities) were measured. Data were plotted and analysed using Prism 10 (GraphPad).

iMG differentiation from human iPS cells

iMGs were generated from human iPS cell lines as described in our previous study[63] using the Brownjohn’s method[64]. At least 2 days after passaging, when human iPS cells reached about 80% confluency, they were dissociated with Accutase (07920, StemCell) and plated at 10,000 cells per well in 96-well round-bottom ultra-low-attachment plates (7007, Corning) in 100 µl embryoid body (EB) medium (complete mTeSR with 50 ng ml−1 BMP-4 (120-05, PeproTech), 20 ng ml−1 SCF (300-07, PeproTech), 50 ng ml−1 VEGF-121 (100-20 A, PeproTech) and ROCK inhibitor (1254/1, R&D Systems)). Haematopoietic medium was prepared by adding to the X-VIVO 15 (BE08-879H, Lonza), 1% GlutaMax (35050061, Thermo Fisher Scientific), 1% penicillin–streptomycin (10378016, Thermo Fisher Scientific), 55 µM β-mercaptoethanol (21985023, Thermo Fisher Scientific), 100 ng ml−1 M-CSF (300-25, PeproTech) and 25 ng ml−1 IL-3 (200-03, PeproTech). After 5 days of culturing EBs in haematopoietic medium, primitive macrophage progenitors (PMPs) started appearing in the suspension and were produced continuously in suspension for 34 days. After 10 days of culturing EBs, PMPs were collected from suspension and plated in RPMI 1640 medium (21870076, Thermo Fisher Scientific) at 180,000 cells per cm^2 in 6- or 12-well plates. Complete iMG medium was as follows: RPMI 1640 with 10% FBS (S11150H, R&D Systems), 1% penicillin–streptomycin, 1% GlutaMax, 100 ng ml−1 IL-34 (200-34, PeptroTech) and 10 ng ml−1 GM-CSF (300-03, PeproTech). The final differentiation of PMPs into iMGs occurred over 25 days.

iAst cell differentiation from NPCs

iPS-cell-derived neural progenitor cells (NPCs) were prepared using PSC neural induction medium (A1647801, Thermo Fisher Scientific). NPCs were differentiated to astrocytes by seeding dissociated single cells at 15,000 cells per cm^2 density on Matrigel-coated plates in astrocyte medium (1801, ScienCell: astrocyte medium, 2% FBS (0010), astrocyte growth supplement (1852) and 10 U ml−1 penicillin–streptomycin solution (0503)). The initial NPC seeding density and single-cell dissociation are critical, particularly during the first 30 days of differentiation, to efficiently generate a homogenous population of astrocytes. On day −1, NPCs were pipetted with a p1000 pipette 3–5 times to yield a single-cell suspension and inhibit cell death. The NPC medium was switched to the astrocyte medium on day 0. From day 2, cells were fed every 48 h for 20–30 days. After 30 days of differentiation, astrocytes were split 1:3 weekly with Accutase and expanded for up to 120 days (15–17 passages) in the astrocyte medium. The final differentiation of iAst cells occurred over 30 days.

Differentiation of glutamatergic neurons

We followed an established protocol to differentiate iPS cells into glutamatergic neurons (iN-Glut)[65]. In brief, iPS cells were dissociated into single cells using Accutase (07920, StemCell) and replated at 7.5 × 10^5 cells per well in a six-well plate in mTeSR plus medium (100-0276, StemCell) with 5 μM ROCK inhibitor (1254/1, R&D Systems) on day −1. On day 0, cells were infected with 200 μl per well lentivirus cocktail containing 100 μl NGN2 virus and 100 μl rtTA virus[65]. After a two-day puromycin selection, iGlut cells on day 5 were dissociated with Accutase and plated as a 100 μl blob on glass coverslips (GG-12-15-Pre, Neuvitro). From day 6 onwards, 500 μl of neuronal culture medium was added into each well with a half-volume medium change every 3 days. Doxycycline was withdrawn on day 21 of differentiation. The final differentiation of iN cells occurred over 30 days.

Differentiation of dopaminergic neurons

The protocol for the differentiation of dopaminergic neurons (iDNs) was adapted from a previous study[66]. In brief, dopaminergic priming medium was added to the cells at 50% confluence on day 0. On day 7, the cells were replated onto six-well plates coated with Matrigel at 5 × 10^5 cells per well and switched to dopaminergic differentiation medium. The medium was changed every other day. On day 30, dopaminergic neurons were collected using Accutase (07920, StemCell) for ATAC–seq.

Differentiation of GABAergic neurons

We generated GABAergic neurons (iN-GA) from NPCs using the protocol described previously[67] but with NPCs as the source cells. NPCs were seeded at 200,000 cells per cm^2 on day 0. Virus neural expansion medium cocktail was added on day 1 with ASCL1-puro and DLX2-hygro virus and replaced by the expansion medium cocktail containing 2 µg ml−1 doxycycline (D9891, Sigma-Aldrich) the same day. Puromycin and hygromycin selection were conducted between day 2 and day 6. On day 7, we switched the medium to conditioned NeuralbasalPlus medium and changed the medium every 3 days. Doxycycline was withdrawn on day 16, and 50 nM Ara-C was included in the medium if non-neuronal cells were observed. On day 28, neurons were collected using Accutase for ATAC–seq.

Immunofluorescence staining of iMG and iAst cells

For characterizing iMG and iAst cells, cells were fixed in 4% PFA (P6148, Sigma-Aldrich) for 10 min at room temperature, followed by incubation with primary antibodies at 4 °C overnight in 3% BSA containing 0.3% Triton X-100. Cells were washed three times in PBS, and then incubated with secondary antibodies at room temperature for 1 h in 3% BSA containing 0.3 % Triton X-100. Next, the cells were washed another three times with PBS and incubated in 0.5 μg ml−1 4′,6-diamidino-2-phenylindole (DAPI) at room temperature for 10 min. Images were acquired using the Nikon ECLIPSE TE2000-U microscope.

Primary antibodies used for microglial immunofluorescence and their dilutions for incubation were as follows: rat anti-TREM2 (MABN755, Sigma-Aldrich, 1:100), rabbit anti-CD45 (SAB4502541, Sigma-Aldrich, 1:200), mouse anti-PU1 (89136, Cell Signalling, 1:100), mouse anti-IBA1 (MA5-27726, Thermo Fisher Scientific, 1:100), rabbit anti-P2Y12 (702516, Invitrogen, 1:200), rabbit anti-TMEM119 (AB209064, Abcam, 1:100), rabbit anti-PLIN2 (15294-1-AP, Proteintech, 1:200), rabbit anti-ATP6AP2 (SAB2702080, Sigma-Aldrich, 1:100), rabbit anti-VAMP1 (702787, Thermo Fisher Scientific, 1:100), rabbit anti-HMGCR (SAB4200528, Sigma-Aldrich, 1:100) and mouse anti-CD74 (14-0747-82, Thermo Fisher Scientific, 1:100). Secondary antibodies were Alexa 488 donkey anti-rat (A21208, Invitrogen, 1:1,000), Alexa 594 anti-rabbit (A21207, Invitrogen, 1:1,000), Alexa 647 anti-mouse (A32787, Invitrogen, 1:1,000), Alexa donkey 594 anti-mouse (A21203, Invitrogen, 1:1,000) and Alexa donkey 647 anti-rabbit (A32795, Invitrogen, 1:1,000). Primary antibodies used for iAst immunofluorescence and their dilutions for incubation were rabbit anti-vimentin (3932, Cell Signaling, 1:200), mouse anti-GFAP (G3893, Sigma-Aldrich, 1:100) and mouse anti-s100β (S2532, Sigma-Aldrich, 1:100). Secondary antibodies were Alexa 488 donkey anti-rabbit (A21206, Invitrogen, 1:1,000) and Alexa 594 anti-mouse (A21203, Invitrogen, 1:1,000).

RNA isolation and sequencing

Cells from human iPS, iAst and iMG cell cultures were dissociated using Accutase (07920, StemCell). Total RNAs were extracted using the RNeasy Plus Kit (74134, Qiagen). cDNAs were reverse transcribed from RNAs using a high-capacity cDNA reverse transcription kit (4368814, Applied Biosystems). RNA-seq was performed by Novogene on the Illumina NovaSeq 2000 platform with targeted 30 million paired-end reads (2 × 150 bp) per sample.

RNA-seq data and differential expression analyses

Raw FASTQ files were aligned to the human hg38 genome GRCh38.p14 using STAR v.2.7.2 and counted according to GENCODE annotation release version 35 on the fly. The ComBat-seq function from the R package sva was applied to remove batch effects between the two experiments (that is, rounds of iMG differentiation). We subsequently used the R package EdgeR (v.4.0.16) to calculate counts per million values from sva-corrected read counts for PCA analysis and plotting. Differential gene expression analysis was performed by applying general linear models and F tests between different groups (risk versus non-risk) (glmQLFit and glmQLFtest functions). When constructing the generalized linear models, we considered cell line ID (CD04 and CD09) as a coefficient to further remove line-specific effects. DEGs were defined as their Benjamini–Hochberg-adjusted P values (FDR) < 0.05.

ATAC–seq

ATAC–seq sample preparation was performed as previously described20,21. In brief, 75,000 viable cells were used for each transposition mixture reaction. The samples were then incubated at 37 °C for 30 min on a thermomixer at 1,000 rpm. The eluted DNA was shipped to the University of Minnesota Genomic Center for library preparation and ATAC–seq.

ATAC–seq data analysis and peaking calling

All raw sequence reads generated by Illumina NextSeq were demultiplexed at the University of Minnesota Genomics Center and provided as 2 × 75 bp paired-end FASTQ files (targeting 60 million reads per sample). Only paired-end reads that survived Trimmomatic processing v.0.39 (ILLUMINACLIP:NexteraPE-PE.fa:2:30:7, SLIDINGWINDOW:3:18, MINLENGTH:26) were retained. The FASTQ files were individually mapped against the human genome reference file including decoy sequences (GRCh38p7.13/hg38, 1000 Genome Project) using bowtie2 (-x 2000, -mm --qc-filter --met 1 --sensitive --no-mixed -t) and subsequently merged and sorted as BAM-formatted files using samtools v.1.14, with only uniquely high-quality mapped reads (MAPQ > 30, SAM flags 0×1, 0×2) retained. Picard tools MarkDuplicate was then used to remove all PCR and optical duplicated reads from the BAM file.

To further eliminate allelic bias towards reference alleles during the alignment step, we performed WASP (v.0.3.4) calibration on the generated raw BAM files[68]. In brief, we first called the VCF file profiles on all SNP variants per sample individually using GATK HaplotypeCaller to generate cell-line-specific VCF files. The cell-line-specific VCFs were used as the basis of WASP calibration and realignment, and new WASP-calibrated BAM file sets were collected as the final output for the ATAC–seq peak calling and ASoC SNP calling (see below). All analysed ATAC–seq samples passed standard quality control based on the characteristic nucleosomal periodicity of the insert fragment size distribution and high signal-to-noise ratio around transcription start sites (TSSs). For peak calling, MACS2[69] was used to generate peak files (narrowPeak format) with the recommended settings at FDR = 0.05 (-f BAMPE, --nomodel, --call-summits --keep-dup-all -B). Peaks that fell within the ENCODE blacklisted regions were removed. We also removed peaks within chromosomes X and Y, mitochondrial genome and decoy regions.

ASoC mapping

The ASoC approach20,21 was used to identify putative functional variants that showed differential chromatin accessibility between the two alleles of a SNP in ATAC–seq samples heterozygous for the tested SNP, with the assumption that a functional common GWAS SNP does not display monoallelic chromatin accessibility. In brief, GATK (v.4.1.8.1) was used for SNP calling as recommended by the GATK Best Practices (https://gatk.broadinstitute.org/hc/en-us/sections/360007226651-Best-Practices-Workflows)[70]. As noted above, WASP-calibrated BAM files were used as input and variants were called against the human GRCh38.p14 (hg38) reference genome and the corresponding dbSNP version 154, and only reads with MAPQ score ≥ 30 were used (-stand_call_conf 30). Subsequently, recalibration of SNPs and indels was performed in tandem using the VariantRecalibrator function (-an DP -an QD -an FS -an SOR -an MQ -an ReadPosRankSum -mode SNP -tranche 100.0 -tranche 99.5 -tranche 95.0 -tranche 90.0) and scores were recalibrated using reference database including HapMap v.3.3 (priority = 15), 1000G_omni v2.5 (priority = 12), Broad Institute 1000G high-confidence SNP list phase 1 (priority = 10), Mills 1000G golden standard INDEL list (priority = 12) and dbSNP v154 (priority = 2). Heterozygous SNP sites with tranche level >99.5% were extracted. To reduce bias introduced by any acquired (or de novo) mutations during cell growth, only SNPs with corresponding rs# records found in dbSNP v154 were retained. Biallelic SNP sites (GT: 0/1) with minimum read depth count (DP) ≥ 20 and minimum reference or alternative allele count ≥ 2 were retained. The binomial P values (non-hyperbolic) were calculated using binom.test(x, n, P = 0.5, alternative = “two.sided”, conf.level = 0.95) from the R package, and Benjamini–Hochberg correction was applied to all qualified SNPs as the correcting factor of the R function p.adjust(x, method = “fdr”). We set the threshold of ASoC SNP at an FDR value of 0.05. To ascertain whether rs10792832 also exhibits ASoC in human brain MG, we first converted the snATAC–seq data of hMG[24] into pseudo-bulk ATAC–seq data for each sample. We then used GATK for SNP calling to identify individuals heterozygous (A/G) for rs10792832 in hMG, followed by ASoC testing as described above.

The read pileup statistics proximal to SNP sites were generated using samtools mpileup function, and differential allele-specific reads was performed using the SNPsplit Perl package (v.0.3.2) (www.bioinformatics.babraham.ac.uk/projects/SNPsplit). The final readouts from both read pileup and SNP-specific reads were visualized using the R package Gviz. Moreover, when comparing the changes in chromatin accessibility caused by genotypes across samples or between different cell types, read counts were scaled and normalized using the deepTools package (v.2.0) bamCoverage function and re-scaled to reads per genomic content as the base unit[71]. We confirmed there was no obvious mapping bias to reference alleles by visualizing the volcano plots that graph the allelic read-depth ratios against −log_2[P] values in scatter plots.

sLDSC analysis of GWAS enrichment

Stratified linkage disequilibrium score regression (sLDSC)[72] analysis was performed using the hg38 version of European genotype data (SNPs) from 1000 Genomes Phase 3 and v.2.2 baseline linkage disequilibrium/weights as previously described[20]. In brief, linkage disequilibrium score estimations were precalculated from the hg38 version of the 1000 Genomes EUR file set (w_hm3_no_hla.snplist), with a window size of 1 cM (ld-wind-cm 1). We used the GWAS summary statistics of major psychiatric disorders and non-psychiatric diseases (Supplementary Table 9) for partition heritability, with several datasets lifted over from hg19 to hg38 when necessary. Disease-specific regressions were performed using hm3 SNP weights against each disease independently for cell-type-specific analysis.

Torus GWAS enrichment analysis

Bayesian hierarchical model (TORUS) was applied to perform an SNP-based enrichment analysis to explore whether ASoC SNPs are enriched in any of the diseases[73] as previously described[20]. For the GWAS enrichment test, ASoC SNPs derived from each cell type were applied independently. The annotations are encoded as Boolean (true if an SNP has an annotation). The GWAS datasets used for enrichment/TORUS analysis were consistent with the diseases analysed in sLDSC. A univariate analysis was performed to assess the enrichment of ASoC SNPs in each GWAS dataset.

CRISPR–Cas9 editing of human iPS cells

CRISPR guide RNA (gRNA) sequences were designed as described[74], and we selected the gRNAs with the highest scores for specificity (Supplementary Table 19). The gRNAs were cloned into the pSpCas9(BB)-2A-Puro vector (Addgene, 62988) for co-expression with Cas9 based on an established protocol[75]. For transfection, 3 μg of CRISPR–Cas9–gRNA construct was combined with 3 μg of ssODNs (1:1 ratio) in Opti-MEM medium (31985062, Thermo Fisher Scientific) and Lipofectamine stem reagent (STEM00001, Thermo Fisher Scientific) was used for transfection. Puromycin selection was performed to eliminate untransfected cells and was withdrawn after 72 h of transfection. Resistant colonies were collected 14 days after transfection and a small amount of DNA from each colony was used for Sanger sequencing to verify editing. The purity of the selected clones was confirmed for on-target editing and the absence of off-target editing (see below).

Quality control of the CRISPR-edited iPS cell lines

Primers were designed to amplify regions corresponding to the four top-ranking predicted off-targets to check on-target and off-target editing. A list of all primer and oligo sequences is provided in Supplementary Table 19. To confirm the pluripotency of CRISPR–Cas9-edited human iPS cell lines, the cells were stained with pluripotency marker antibodies: rabbit anti-OCT4 (ab181557, Abcam, 1:250), goat anti-NANOG (AF1997-SP, R&D Systems, 1:50) and mouse anti-SSEA4 (ab16287, Abcam, 1:250). Images were taken using a Nikon ECLIPSE TE2000-U microscope.

eSNP-karyotyping was performed for all cell lines used to eliminate potential chromosomal abnormalities, as previously described20,21. RNA-seq data were processed using the eSNP Karyotyping package[76] rewritten for GATK v.4 and R v.4.2, using raw FASTQ files as the input. Alignment to the human hg38 genome was performed by Bowtie2 v.2.5.1, and only common SNPs (MAF > 0.05) from dbSNP 154 were retained for zygosity block analysis. The plotted zygosity block size was 1.0 Mb.

CRISPRoff epigenome editing of human iPS cells

CRISPRoff[77] was used to repress PICALM expression in iMGs. The gRNA sequences were designed using Benchling (https://benchling.com; Supplementary Table 19), and cloned into the CROPseq-Guide-Puro vector (Addgene, 86708) for co-expression with CRISPRoff v.2.1 (Addgene, 167981) as described previously[77]. After 72 h of drug selection, transduced cells were sorted using a BD FACSAria II, and the sorted cells were passaged twice and then differentiated into iMGs.

CRISPRa to overexpress PICALM

We used CRISPR-ERA[78] to design PICALM activation gRNA. A total of four gRNA candidates close to the TSS of PICALM with low predicted off targets and E scores were selected and cloned into the lentiviral gRNA vector lentiGuide-Hygro-mTagBFP2 (Addgene, 99374) through Gibson assembly. We then co-transfected gRNA plasmids with lenti-EF1a-dCas9-VPR-Puro (Addgene, 99373)[79] into HEK293T cells using Fugene HD (Promega) to evaluate the PICALM activation efficiency of each CRISPRa gRNA and selected one gRNA with the highest activation efficiency for further establishing inducible CRISPRa iPS cell line that can overexpress PICALM. To establish inducible CRISPRa iPS cell line on risk allele background, we first transduced lentivirus for the lentiGuide-Hygro-mTagBFP2 carrying the selected PICALM gRNA and sorted BFP-positive cells to enrich the cells with high expression of gRNAs. After sorting, iPS cells were maintained in 100 µg ml−1 hygromycin to enrich cell stably expressing the gRNAs. Then cells were transduced with the lentivirus of inducible dxCas9-VPR expressing vector pLenti-tetON-dxCas9(3.7)-VPR-EF1a-TagRFP-2A-tet3G (Addgene, 167937). BFP- and RFP-positive cells were sorted to enrich iPS cells with high expression of dxCas9-VPR. The sorted iPS cells were maintained with hygromycin and expanded for iMGs differentiation, followed by CRISPRa induction of PICALM expression by treating cells with 2 µg ml−1 doxycycline for 25 days before collecting iMGs for analyses. The four tested gRNAs are gRNA 3-GAGTTCCATCACGTAACGCG, gRNA 4-GCCTCAGGCGACCTGTTGGC, gRNA 6-GCAGTGTCAACGTCTTTCCA, and gRNA 7-GGGCGGGCGTCGAAGAGGAA (the best-performing one used in CRISPRa in iMGs).

Gene expression analysis by qPCR

For qPCR, reverse transcription was performed using the Thermo Fisher Scientific High-capacity RNA-to-cDNA reverse transcription kit (4366596, Applied Biosystems) with random hexamers according to the manufacturer’s protocol. qPCR was performed using the TaqMan Universal PCR Master Mix (4364338, Applied Biosystems) on the Roche 480 II instrument (with Roche LightCycler 480 1.5.1), using gene-specific FAM-labelled TaqMan probes or custom-designed probes from IDT (Supplementary Table 19). GAPDH was used as the control.

Myelin isolation from mouse brains for phagocytosis

Myelin was isolated from mouse brains by homogenization in 0.32 M sucrose buffer (0.32 M sucrose and 2 mM EGTA). The samples were then further homogenized using a Dounce homogenizer, layered on top of 0.85 M sucrose buffer (0.85 M sucrose and 2 mM EGTA), and centrifuged at 75,000g at 4 °C for 30 min. Crude myelin was collected from the interface, resuspended in Tris-Cl Buffer (0.2 M Tris-HCl, pH 7.5), and homogenized using a Dounce homogenizer. The samples were centrifuged at 75,000g at 4 °C for 15 min. The pellets were resuspended in Tris-HCl solution (20 mM Tris-HCl, 2 mM EDTA, 1 mM DTT, pH 7.5) and homogenized using a Dounce homogenizer. The samples were then centrifuged at 12,000g at 4 °C for 15 min, and the pellets were resuspended in Tris-Cl Solution. The samples were then centrifuged at 12,000g at 4 °C for 10 min. Pellets were resuspended in 0.32 M sucrose buffer, layered on top of 0.85 M sucrose buffer and centrifuged at 75,000g at 4 °C for 30 min. Purified myelin was collected from the interface, resuspended in Tris-HCl buffer, and homogenized using a Dounce homogenizer. The samples were next centrifuged at 75,000g at 4 °C for 15 min, and the pellets were resuspended in Tris-HCl solution and homogenized using a Dounce homogenizer. The samples were then centrifuged at 12,000g at 4 °C for 15 min, and the pellets resuspended in Tris-HCl solution and centrifuged at 12,000g at 4 °C for 10 min. The pellets were resuspended in Tris-HCl Solution, aliquoted and stored at −80 °C. The protein content of isolated myelin was determined using the BCA protein assay kit.

Phagocytosis assay for iMGs

iMGs were grown on MatTek 96-well plates with a glass bottom (NC1844174, Thermo Fisher Scientific) until day 25. For Aβ phagocytosis, the β-amyloid (1-42) aggregation kit was used (A-1170-025, rPeptide). The peptide was resuspended in 5 mM Tris at 1 mg ml−1 concentration. Myelin and Aβ peptides were labelled using the pHrodo Red Microscale Labelling Kit (P35363, Thermo Fisher Scientific) and pHrodo Deep Red Labelling Kit (P35355, Thermo Fisher Scientific) according to the vendor’s protocol.

For pHrodo phagocytosis experiments, pHrodo-labelled myelin or Aβ was diluted to 15 µg ml−1 in RPMI 1640 medium (21870076, Thermo Fisher Scientific), bath sonicated for 1 min and added to the iMGs along with CellMask Green Plasma Membrane Stain (C37608, Thermo Fisher Scientific, 1:1,000) and NucBlue Live ReadyProbes Reagent (R37605, Thermo Fisher Scientific, 2 drops per ml), mixed gently and incubated at 5% CO_2 and 37 °C for 30 min. As a negative control, 10 µM cytochalasin D (PHZ1063, Thermo Fisher Scientific) was added to cells along with pHrodo-labelled protein and retained throughout uptake assays. Live imaging (5% CO_2, 37 °C) was performed for 3 h using the Nikon ECLIPSE TE2000-U microscope at 45 min intervals.

For the pHrodo phagocytosis experiment that included LD staining, the iMGs were treated with 1 µM TrC (10007448, Cayman Chemical) in complete iMG medium for 18 h. Next, BODIPY 493/503 (D3922, Thermo Fisher Scientific, 1:1,000) and CD45 antibodies (14-0451-82, Thermo Fisher Scientific, 1:500) were added to cells with and without TrC and incubated for 30 min and quickly washed twice with RPMI 1640. Then pHrodo-labelled myelin or Aβ was diluted to 15 µg ml−1 in RPMI 1640 medium, bath sonicated for 1 min and added to the iMGs along with NucBlue Live ReadyProbes Reagent and incubated at 5% CO_2, 37 °C for 30 min. As a negative control, 10 µM cytochalasin D was added to cells along with pHrodo-labelled protein and retained throughout uptake assays. Live Imaging (5% CO_2, 37 °C) was performed for a total of 3 h using Nikon ECLIPSE TE2000-U microscope at 45 min intervals. Fiji/ImageJ (v.1.54f, 64 bit) software was used to quantify pHrodo fluorescence intensity (https://fiji.sc).

ChIP

The ChIP–qPCR assay was performed by combining two protocols from the Simple ChIP Enzymatic Chromatin IP kit (91820, Cell Signaling) and the Magna ChIP A/G Chromatin Immunoprecipitation kit (17-10085, Sigma-Aldrich). In total, 10^7 cells were used for each reaction with 1% formaldehyde (28908, Thermo Fisher Scientific) cross-linking in 20 ml of cell suspension. The Cell Signaling IP kit was used for nuclei preparation and subsequent recovery reactions according to the vendor’s protocols. For chromatin digestion, 1.25 µl of micrococcal nuclease was used and incubated for 20 min at 37 °C to digest DNA to the length of approximately 150–900 bp. To break the nuclear membrane, lysate was sonicated for three sets of 20 s pulses with a 1/8-inch probe.

The Sigma-Aldrich ChIP Assay kit was used for the reaction according to the vendor’s instructions. Normal rabbit IgG (2729, Cell Signaling) was used as negative control. In total, 1 µl of proteinase K was used for reverse cross-linking of protein–DNA complexes to free DNA at 62 °C for 2 h with shaking, followed by incubation at 95 °C for 10 min. For each reaction, DNA was eluted into 50 µl of elution buffer C. qPCR was performed using TaqMan Universal PCR Master Mix (4364338, Applied Biosystems) on the Roche 480 II instrument, using IDT custom probe for detection PICALM/PU.1 ratio (Supplementary Table 19). The ChIP DNA product for the heterozygous site rs10792832 was also subjected to Sanger sequencing (Thermo Fisher Scientific, Sequencing Analysis Software 7.0.0). Genomics DNAs for the heterozygous site were sequenced by using primers used for on-target CRISPR–Cas9 editing of rs10792832.

Fatty acid (Red-C12) transfer assay

iMG and iAst cells were grown on coverslips until day 25. Neurons (iNs) were grown on coverslips until day 30. Cells were incubated with 8 μM BODIPY 558/568 (Red-C12, D3835, Thermo Fisher Scientific) for 16 h in neuronal growth medium, washed twice with warm PBS and incubated with fresh medium for 1 h. Red-C12 labelled neurons and unlabelled astrocytes/microglia were washed twice with warm PBS, and the RedC12 intensity was examined using fluorescence microscopy.

LD staining with BODIPY for iMGs

iMGs were grown on glass coverslips until day 25. Cells were then fixed for 30 min at room temperature with 4% PFA (P6148, Sigma-Aldrich) in PBS, briefly washed in PBS twice and incubated in PBS with BODIPY 493/503 (D3922, Thermo Fisher Scientific, 1:1,000 from 1 mg ml−1 stock solution in DMSO) and DAPI for 10 min at room temperature. The BODIPY intensity was examined by fluorescence microscopy.

LD staining with LipidTOX for iMGs

iMGs were grown on glass coverslips until day 25. Cells were then fixed for 30 min at room temperature with 4% PFA (P6148, Sigma-Aldrich) in PBS, briefly washed in PBS twice and incubated in PBS with LipidTOX (H34476, Thermo Fisher Scientific, 1:1,000) and DAPI for 1 h at room temperature. The LipidTOX intensity was examined using fluorescence microscopy.

ROS staining

iMGs were grown on glass coverslips until day 25. Cells were treated with 1 µM TrC (10007448, Cayman Chemical) in complete iMG medium for 18 h. Cells were subsequently incubated in complete iMG medium with CellROX Deep Red (C10422, Invitrogen, 1:500) for 30 min at 37 °C. Next, the cells were stained with BODIPY to detect LDs. The CellROX intensity was examined by fluorescence microscopy.

Lipid peroxidation assay using BODIPY C11

iMGs were grown on glass coverslips until day 25. Cells were treated with 1 µM TrC as described in ROS staining. Next, cells were incubated in complete iMG medium with BODIPY 581/591 C11 (D3861, Thermo Fisher Scientific, 1:1,000) for 15 min at 37 °C, then fixed for antibody staining by rat anti-TREM-2 (MABN755, Sigma-Aldrich, 1:100). Five hundred and sixty-eight nm excitation wavelength was applied to excite reduced BODIPYC11 and 488 nm excited oxidized BODIPYC11. Fiji/ImageJ (v.1.54f 64 bit) software was used to quantify BODIPYC11 fluorescence intensity (https://fiji.sc).

Filipin staining for iMGs

iMGs were grown on glass coverslips until day 25. Cells were then fixed for 10 min at room temperature with 4% PFA (P6148, Sigma-Aldrich) in PBS. Cells were incubated with a solution of filipin (0.1 mg ml−1, F‐9765, Sigma-Aldrich) for 30 min. After staining with were rat anti-TREM-2 (MABN755, Sigma-Aldrich, 1:100), cells were washed and counterstained with propidium iodide (0.35 μg ml−1; P4170, Sigma-Aldrich) for 10 min at room temperature. Four hundred and five nm excitation wavelength was used to excite filipin. For each field of view (FOV), filipin fluorescence intensity was calculated by dividing the number of blue puncta by the number of microglia. The values were then normalized to the filipin fluorescence intensity value of the risk allele and used for statistical analysis. The Fiji software was used to quantify filipin fluorescence intensity (https://fiji.sc).

Lysosomal staining for iMGs

iMGs were grown on glass coverslips until day 25. Cells were incubated with LysoTracker Red DND-99 (L7528, Invitrogen, 100 nM) in complete iMG medium for 30 min at 37 °C. Cells were then fixed for 30 min at room temperature with 4% methanol-free PFA (28906, Pierce) for 10 min at room temperature. The cells were then stained with BODIPY as described above (LD staining with BODIPY) to detect LDs. The images were taken on a Nikon ECLIPSE TE2000-U microscope.

FACS sorting of iMGs

iMGs were grown on six-well plates until day 25. iMGs were then dissociated with Accutase (07920, StemCell) and stained with CD45-PE (12-0451-82, Invitrogen, 1:300) for 30 min at 37 °C and washed with RPMI medium three times by centrifugation at 300g for 5 min. Next the iMGs were stained with BODIPY 493/503 (D3922, Thermo Fisher Scientific, 1:1,000 from 1 mg ml−1 stock solution in DMSO) for 10 min at 37 °C with after washing twice with RPMI medium by centrifugation at 300g for 5 min. Cells were resuspended in PBS and sorted using BD FACsAria Fusion. Data were collected using BD FACSDiva software (v.9.0.1) and analysed using FlowJo (v.11.0).

PICALM KO by CRISPR–Cas9 editing C20 cells

C20 cells were maintained in DMEM/F12 medium containing 10% FBS and 1% penicillin–streptomycin. PICALM exon 1 was targeted using the following oligonucleotide sequences—sgRNA F, CACCGGCCGGTGACACTGTGCTGGG; and R, AAACCCCAGCACAGTGTCACCGGCC. Control oligo sequences were generated using sequences not specific to the human genome. Recombinant lentiviruses were generated in HEK293T cells using MISSION Lentiviral Packaging Mix (Sigma-Aldrich, SHP001). C20 cells were transduced with filtered virus-containing medium, and stable pools were selected in blasticidin (20 µg ml−1).

Immunoblots for iMG and C20 cells

For iMG, cells were lysed in phosphosafe buffer (71296, EMD Millipore) with protease inhibitor (04693132001, Roche) and PhosphoSTOP tablet (4906845001, Roche). Next, 50 μg of protein lysates were run on TGX Stain-Free Precast Gel (4–15%) (4561083, Bio-Rad) in Tris/glycine/SDS running buffer (1610732, Bio-Rad). The gel was then transferred onto a 0.45-μm PVDF membrane (88585, Thermo Fisher Scientific). The membrane was blocked with 5% skimmed milk and probed with rabbit anti-PICALM (HPA019061, Sigma-Aldrich, 1:1,000) or mouse anti-β-actin (A5316, Millipore, 1:10,000) at 4 °C overnight. Secondary antibodies, anti-rabbit-HRP (7074, Cell Signaling, 1:20,000) and anti-mouse-HRP (7076, Cell Signaling, 1:20,000), were then added. The blots were imaged and quantified using Bio-Rad Image Lab v.6.1.0.

For C20 line, cells were lysed in RIPA buffer (50 mM Tris, 50 mM NaCl, 0.5% sodium deoxycholate, 1% Triton X-100, 0.1% SDS, 5 mM EDTA, pH 8) containing complete protease inhibitors (Roche), sonicated and lysates cleared by centrifugation (21,000g for 2 min). Fifty μg of lysate was used per lane on 4–20% SDS PAGE gels and transferred onto nitrocellulose membranes. Non-specific sites were blocked with PBS containing 1% BSA and 1% fish gelatin at room temperature for 1 h. Membranes were incubated with primary antibodies rabbit anti-PICALM (Sigma-Aldrich, HPA019061, 1:500) and mouse anti-β-actin (Proteintech, 66009-1-lg, 1:50,000) in at 4 °C for 16 h. Secondary antibodies IRDye 680RD donkey anti-rabbit IgG and IRDye 800CW donkey anti-mouse IgG (Li-COR) were incubated at room temperature for 2 h. Blots were imaged and quantified on a Li-COR Odyssey infrared imaging system.

Myelin phagocytosis for C20 cells

Myelin isolated from the mouse brain (as described above) was conjugated to pHrodo-Green (Thermo Fisher Scientific, 35369) or pHrodo-Red (Thermo Fisher Scientific, P36600). C20 cells were incubated with pHrodo-conjugated myelin (20 µg ml−1) at 37 °C (5% CO_2) for 1 h. Negative controls were treated with cytochalasin D (10 nM, Invitrogen). After fixation, cells were stained with rabbit anti-BIN1 antibody (Proteintech, 14647-1-AP, 1:500) for 2 h. Images were acquired on a Nikon Eclipse Ti2 (Yokogawa spinning-disk field-scanning confocal) microscope at ×20 magnification and captured using a Photometrics PRIME 95B sCMOS camera. Single-plane images were processed using Fiji/ImageJ (v.1.54f, 64 bit) software to threshold whole-cell masks (created from BIN1 staining). The integrated density of pHrodo-myelin within each cell was measured from five random fields of view (per biological replicates) and the median values from each replicate were used for statistical analysis.

LD staining for C20 cells

Live C20 cells were labelled with BODIPY (2 µM in PBS) at 37 °C (5% CO_2) for 15 min. At room temperature, cells were fixed with 4% PFA for 30 min, and nuclei were labelled with Hoechst 33342. The volumes of BODIPY+ droplets and the number of nuclei was quantified from deconvolved image stacks using ImageJ (v.1.54f, 64 bit)/Fiji software. Five random fields of view (per biological replicate) were acquired for quantification, and the median values from each biological replicate were used for statistical analysis.

Organelle marker staining for C20 cells

For transferrin endocytosis, C20 cells were washed and incubated in uptake medium (DMEM containing 25 mM HEPES) for 1 h. Cells were treated with 10 µg ml−1 transferrin Alexa Fluor 555 conjugate (Invitrogen, T35352) in DMEM/HEPES containing 1 mg ml−1 BSA at 37 °C for 30 min. Cells were then chilled to 4 °C, and non-internalized transferrin was washed from cell surfaces with acid wash (0.5 M NaCl, 0.2 M acetic acid, pH 2.8) before fixing the cells in PFA. For cholera toxin internalization, cells were washed with labelling medium (1 mg ml−1 BSA in serum-free DMEM containing 25 mM HEPES), chilled to 4 °C and treated with 100 nM cholera toxin subunit B Alexa Fluor 647 conjugate (Invitrogen, C34778) in labelling medium at 4 °C for 10 min. Cells were washed with ice-cold PBS and fixed with 4% PFA (in PBS) at room temperature for 15 min. Nuclei were labelled with Hoechst 33342.

For immunofluorescence staining with antibodies, C20 cells grown on glass coverslips were fixed with 4% PFA and blocked (3% BSA, 50 mM NH_4Cl, 10 mM glycine, PBS) at room temperature for 30 min. The cells were incubated with rabbit anti-Giantin (Covance, PRB-114C, 1:5,000), mouse anti-EEA1 (BD Transduction Laboratories, 610457, 1:500) or mouse anti-AP-4ε (BD Transduction, 612018, 1:100) antibodies at room temperature for 2 h. All C20 cell images were acquired on a Nikon Eclipse Ti2 spinning-disk field-scanning confocal microscope at ×60 magnification. z stacks (100 nm step size) were processed to generate maximum-intensity projections using Fiji/ImageJ (1.54f, 64 bit).

Lipid extraction from iMGs

iMGs carrying the PICALM risk allele, non-risk allele or risk-CRISPRa were grown in six-well plates until day 25. iMGs were dissociated with Accutase (07920, StemCell), collected and washed with PBS three times by centrifugation at 300g for 5 min. Lipids were extracted from iMGs as described previously80,81. In brief, a deuterated lipid standard mixture (330820, LM6003, 860739, 330727, 860657, 860658, Avanti Research) was added to iMGs equally and organic solvent (chloroform:methanol, 1:2 (v/v)) was added to iMG pellets and subjected to tip sonication, followed by vortexing and centrifugation to extract neutral lipid. The organic layer was collected and saved for later use and acidic extraction was performed on the remaining pellet by a mixture of chloroform:methanol:37% 1 M HCl (40:80:1, v/v/v). After the addition of chloroform and 0.1 M HCl to induce phase separation, the samples were vortexed and centrifuged. The bottom organic layer was combined with the previously collected organic layer and dried using vacuum centrifuge. The dried lipid extract was reconstituted in methanol:chloroform (1:3, v/v) for further analysis.

Lipidomics analysis of iMGs

Lipid extracts were subjected to global lipidomics analysis using liquid chromatography–tandem mass spectrometry (LC–MS/MS). LC–MS-grade methanol (A456, Thermo Fisher Scientific), acetonitrile (A955, Thermo Fisher Scientific), isopropanol (A461, Thermo Fisher Scientific), water (W71, Thermo Fisher Scientific), formic acid (A117, Thermo Fisher Scientific) and ammonium formate (78314, Millipore Sigma) were used. The samples from two different iMG differentiations were analysed, with lipidomics data acquired in two technical replicates for each sample. Reconstituted lipid extracts were separated on a Hypersil GOLD Vanquish C18 UHPLC column (15 cm × 2.1 mm, C18 1.9 μm and 175 Å) using an Orbitrap Tribrid IQ-X mass spectrometer (Thermo Fisher Scientific) coupled to Vanquish Horizon UHPLC (Thermo Fisher Scientific). A binary gradient was used at a flow rate of 300 µl min−1, using mobile phase A (water:acetonitrile, 6:4, with 10 mM ammonium formate and 0.1% formic acid) and organic phase (isopropanol:methanol:acetonitrile, 8:1:1, with 10 mM ammonium formate and 0.1% formic acid). In brief, mobile phase B increased from 20% to 95% over 17 min and was maintained at 95% for 5 min for washing. It was ramped down to 20% over 0.1 min and equilibrated for 5 min for the next injection. The analytical column was maintained at 50 °C. A full-scan MS was performed using the Orbitrap with a resolution of 60,000 at m/z 200. MS/MS spectra were acquired at a resolution of 15,000 at m/z 200. MS/MS scans were acquired for 1.5 s, followed by an MS scan. In positive-ion mode, MS/MS fragmentation using a stepped collision energy of 30%, 35% and 40% in higher-energy collisional dissociation (HCD) was performed with a spray voltage of 3.5 kV. In negative-ion mode, a spray voltage of 3 kV and a stepped collision energy of 30%, 35% and 40% in MS/MS acquisition using HCD were used. The acquired tandem mass spectra were processed using LipidSearch v.5.1.6 (Thermo Fisher Scientific) for lipid annotation and quantification. Lipids were identified based on precursor ion masses and their corresponding MS/MS spectra were matched against the database. Annotated lipids were quantified by calculating their peak areas using LipidSearch. All lipids were normalized to the peak area of an internal standard with the same head group as the target lipids. Total lipid levels were determined by summing the peak areas of individual lipid species sharing the same head group.

Imaging quantification and statistical analyses

For image analyses, we assayed fluorescence intensity, puncta density, puncta area and/or puncta size using the number of samples specified in the corresponding figures. For each FOV, the fluorescence intensity was calculated by dividing the number of fluorescent puncta by the number of microglia. The puncta number and the total area of puncta were acquired by applying a threshold to the respective fluorescent areas and performing the Analyze particles function. The number of microglia was calculated by applying a threshold to the DAPI fluorescence signal and measuring the number of nuclei through the same function. The images were acquired in a manner that the operator was blinded to the sample and genotype identification. For testing statistical differences between two groups, we used a two-tailed unpaired Student’s t-test when the experimental design did not involve multiple batches (for example, different rounds of experiments); otherwise, we used the R packages lme4 and lmerTest to fit data into an LMM to account for potential random effects from different experimental rounds and clones. For statistical tests involving more than two groups, we applied a one-way ANOVA with the appropriate post hoc test (Dunnett or Tukey), while accounting for any possible random effects from experimental rounds when applicable. The data analysis was performed using R v.4.3.2, GraphPad Prism 9 and Microsoft Excel. The results were considered to be significant if P < 0.05. All data with error bars were presented as the mean ± s.e.m. For ATAC–seq analysis of ASoC SNPs, the two-sided binomial test in R package was used to test allelic bias, and the Benjamini–Hochberg correction was applied to all qualified ASoC SNPs. For RNA-seq DEG analysis, limma (v.3.58.1)/EdgeR (v.4.0.16) with a generalized linear model (GLM) F test was used, and the Benjamini–Hochberg procedure was used to adjust P values accounting for multiple testing.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

ATAC–seq data for iMG, iAst and neurons are available at Gene Expression Omnibus under accession codes GSE263804 and GSE188491. RNA-seq data are available under accession code GSE263809. The ASoC SNP list and the functionally interpreted GWAS risk loci are available at Zenodo[82] (https://zenodo.org/records/14853787). Other publicly available datasets used in our study include: Alzheimer’s disease GWAS Summary Statistics (https://www.ebi.ac.uk/gwas/studies/GCST90027158); human fresh microglia regulome study (https://www.synapse.org/Synapse:syn26207321); Human iMG study (GSE164315); cell-type-specific cis-eQTLs in eight human brain cell types[83] (https://doi.org/10.5281/zenodo.5543734); a meta-analysis of single-nucleus eQTLs linking genetic risk to brain disorders[84] (https://zenodo.org/records/14908182); human microglial state dynamics in Alzheimer’s disease progression (GSE227223); hMG scRNA-seq dataset at different microglia states (homeostatic, LDAM and DAM) (GSE254205); the human GRCh38 reference genome, Ensembl GRCh38.v84 genome annotations and JASPAR transcription-factor-binding profiles as a pre-compiled 10x Genomics Cell Ranger reference set (https://www.10xgenomics.com/support/software/cell-ranger-arc/downloads#reference-downloads).

Code availability

The codes and scripts (R and bash) used in ATAC–seq, RNA-seq, GWAS enrichment, TORUS and circus plot have been deposited at Zenodo[85] (https://doi.org/10.5281/zenodo.15238434).

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Acknowledgements

We thank the staff at the NIH NeuroBioBank for providing post-mortem human brain tissues from the Harvard Brain Tissue Resource Center and the University of Miami Brain Endowment Bank; the members of the Goizueta Alzheimer’s Disease Research Center (ADRC) and Rush ADRC for providing human brain tissue sections; and M. Moulton and L. Goldman for comments on the manuscript. This work was supported by National Institute on Aging grants R01AG063175 (J.D. and G.T.), R01AG081374 (J.D.), RF1AG079141 (G.T.), R01 AG073260 (H.J.B.) and U01 AG072439 (H.J.B.). A.S. is supported by a BrightFocus fellowship. ROSMAP is supported by P30AG10161 (D.A.B.), P30AG72975 (J.A.S.), R01AG15819 (D.A.B.), R01AG17917 (D.A.B.), U01AG46152 (D.A.B.) and U01AG61356 (D.A.B.). A.P. is supported by NIH grants U01CA271410 and P30CA15083. The work is also partially supported by National Institute of Mental Health grants R01MH116281 and R01MH106575 (J.D.).

Author information

Author notes

1. These authors contributed equally: Alena Kozlova, Siwei Zhang, Ari Sudwarts, Hanwen Zhang

Authors and Affiliations

1. Center for Psychiatric Genetics, Endeavor Health Research Institute, Evanston, IL, USA

Alena Kozlova, Siwei Zhang, Hanwen Zhang, Christina Thapa, Kimberley Stephenson, Xiaojie Zhao, Brendan Jamison, Alan R. Sanders & Jubao Duan

2. Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA

Alena Kozlova, Siwei Zhang, Kimberley Stephenson, Xiaojie Zhao, Alan R. Sanders & Jubao Duan

3. Byrd Alzheimer’s Center and Research Institute, University of South Florida, Tampa, FL, USA

Ari Sudwarts, Stanislau Smirnou, Moorthi Ponnusamy & Gopal Thinakaran

4. Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA

Ari Sudwarts, Stanislau Smirnou, Moorthi Ponnusamy & Gopal Thinakaran

5. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Seul Kee Byeon & Akhilesh Pandey

6. Department of Human Genetics, The University of Chicago, Chicago, IL, USA

Xiaotong Sun & Xin He

7. Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, The University of Chicago, Chicago, IL, USA

Xin He

8. Rush Alzheimer’s Disease Center, Rush University Medical Center, Rush University, Chicago, IL, USA

Julie A. Schneider & David A. Bennett

9. Manipal Academy of Higher Education, Manipal, India

Akhilesh Pandey

10. Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA

Akhilesh Pandey

11. Center for NeuroMetabolism, Department of Neuroscience and Cell Biology, Child Health Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA

Zhiping P. Pang

12. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

Hugo J. Bellen

13. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA

Hugo J. Bellen

14. Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA

Hugo J. Bellen

Contributions

A.K. performed iPS cell differentiation, CRISPR editing and iMG assays, and wrote the manuscript. S.Z. analysed ATAC–seq and RNA-seq data and other genomic data and wrote the manuscript. A.S. performed experiments with C20 cells and brain histology and wrote the manuscript. H.Z. performed iPS cell differentiation, CRISPRa and ATAC–seq. S.S. contributed to experiments with C20 cells, and M.P. performed human brain staining. S.K.B. and A.P. performed lipidomic profiling and wrote the manuscript. C.T. helped with iAst cell differentiation and snRNA-seq analysis. X.S. performed GWAS enrichment analysis that was supervised by X.H., K.S. and X.Z., and B.J. helped with CRISPR editing, iPS cell culture and DNA sequencing. Z.P.P. helped with the data interpretation and neuron differentiation. J.A.S. and D.A.B. provided ROSMAP human brain sections and interpreted the results. A.R.S. and H.J.B. helped with design and data interpretation and edited the manuscript. G.T. supervised the experiments with C20, brain histology and wrote the manuscript. J.D. conceived and supervised the study and wrote the manuscript.

Corresponding authors

Correspondence to Gopal Thinakaran or Jubao Duan.

Ethics declarations

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 ATAC-seq quality control (QC) and OCR peak calling.

(a) Representative images of IF staining (left) of iMG (TREM2+CD45+PU1+) used for ATAC-seq and iMG purity quantification (right). Each datapoint represents one single-well measurement from one experiment of one donor line (n = 5). Scale bar, 50 μm. (b) Representative images of IF staining (left) of iAst (S100B+, VIM+, or GFAP+) used for ATAC-seq and iAst purity quantification (right). Note that GFAP staining and imaging was performed independently from S100B/VIM staining and imaging. Each datapoint represents one single-well measurement from one experiment of one donor line (n = 5 lines). Scale bar: 50 μm. (c) ATAC-seq fragment size distribution plots show periodical nucleosome-free region patterns that are similar between samples and different iPSC-derived cell types. (d) and (e) show comparable and acceptable according to the ENCODE ATAC-seq guideline version 4: FRiP (fragment reads in peaks) score > 0.3, TSS (transcription start site) annotation > 5 across cell types. (f) iMG-specific OCR peaks for a known MG-specific gene SPI1 (also known as PU.1) and much stronger peaks for VIM in iAs than in other cell types. (g) PCA clustering of human iPSC-derived MG, Ast, NGN2-Glut, GABAergic, and dopaminergic neurons. Normalized ATAC-seq reads derived from a 501-bp union peak set from all five cell types (622,987 peaks in total) were used for PCA. (h) UpSet plot showing the cell type-specific ATAC-seq peaks and shared peaks across different cell types. (i) PCA of ATAC-seq peak accessibility of iMG and iAst samples from the current study in comparison to previously reported datasets of human brain MG and Ast (circled areas) or iAst as well as other brain cell types6,22. 210,833 peaks from the previous studies6,22 were used for PCA. GABA, GABAergic neurons; hOlig, human oligodendrocytes; MGAS, the mixture of microglia and astrocytes.

Extended Data Fig. 2 Enrichment of ASoC SNPs for GWAS risk variants in LOAD and major NPD.

(a) Venn diagram showing ASoC SNPs identified in each cell type. (b) to (f) Dot plots showing the fold of GWAS enrichment (log_2) and P-value (-log_10) for each disorder or trait across different cell types. The analysis was conducted using Torus (see Methods). (g) to (k) Number of GWAS risk loci with at least one of the GWAS index risk SNPs or their proxies demonstrating ASoC in different cell types for LOAD, SZ, BP (bipolar), MDD (major depressive disorder), and PD. Note the LOAD GWAS risk loci with functional LOAD risk SNPs showing ASoCs are predominantly in iMG (19/26 loci). (l) Circos plot depicting 19 LOAD risk loci with 38 GWAS risk SNPs also ASoC SNPs in iMG. Tracks from the innermost to the outermost circles: LOAD risk loci (n = 9) with ABC enhancer/ASoC SNP links (purple) to at least one target gene; FDR (-log_10) of ASoC SNPs, each dot (orange) representing a SNP; LOAD risk SNPs that are also ASoC SNPs (with rs numbers); LOAD GWAS association p-values (-log_10), each dot (red) representing a SNP; index LOAD risk genes at each locus, where target genes link to an ABC enhancer/ASoC SNP (purple); chromosomal regions of LOAD risk loci. PICALM region is highlighted in yellow.

Extended Data Fig. 3 The effects of the LOAD risk-allele on chromatin accessibility, PICALM expression and PU.1 binding.

(a) Human brain OCR peaks in different cell types in comparison with our iMG at the PICALM locus. Note the rs10792832-flanking peak (highlighted in the vertical yellow box) identified in our iMG is only present in brain microglia but not in other cell types. The adjacent ATAC-seq read pileup plot (right) for rs10792832 shows significantly more ATAC-reads of allele A than allele G (i.e., ASoC) in brain microglia. Except for our iMG, data of all other cell types are from a snATAC-seq study[24] and reanalysed by using 10x Genomics Cell Ranger ARC 2.0.2. Ex = excitatory neurons, In = inhibitory neurons, Ast = astrocytes, oligo = oligodendrocytes, OPC=oligodendrocyte precursor cells, Vas = vascular cells. (b) IF staining of the CRISPR-engineered isogenic iMG (left panels) shows high and comparable purity (TREM2+/CD45+/PU.1+) (right panel). Each datapoint represents single-well measurement; for each condition (risk and non-risk), data are from two independent donor lines each with one clone from one experiment (n = 2). Scale bar: 50 µm. (c) The representative image of iMG stained with P2RY12+ and IBA1+ (left) and quantification of iMG purity (right). (d) The representative image of iMG stained with TMEM119+ and IBA1+ (left) and quantification of iMG purity (right). In (c) and (d), each datapoint represents a single-well measurement from one experiment; for each condition (risk and non-risk), data are from two independent donor lines (CD04 and CD09) each with 2 clones from one experiment with 3 wells of differentiations (n = 12). Scale bar, 50 µm. Two-sided unpaired Student’s t-test. ns = not significant. (e) qPCR shows reduced PICALM expression in risk-allele iMG. Each datapoint represents a single-well measurement from one experiment; for each donor line (CD04 and CD09) and each condition (risk and non-risk), data are from 2 clones, collected across 3 independent experiments each with 3 wells of differentiations for clone 1 and 2 experiments each with 3 wells of differentiations for clone 2 (n = 15). (f) qPCR result showing that the risk allele of rs10792832 does not affect PICALM expression in iAst. Each datapoint represents a single-well measurement from one experiment; for each donor line (CD04 and CD09) and each condition (risk and non-risk), data are from 2 clones, collected from 2 independent experiments each with 3 wells of differentiations (n = 12). In both (e) and (f), expression was normalized to GAPDH. Linear Mixed Model (LMM) was applied to test the fixed effect of genotype, with experimental round and clone identity as nested random factors; two-sided test. (g) PU.1 antibody validation in PMP cells from which iMG were further differentiated. (h) Schematic design of ChIP-qPCR to assay PU.1 binding. The illustration was created using BioRender. Sudwarts, A. (2025) https://BioRender.com/nux3l58. (i) The LOAD risk-allele G of rs10792832 shows reduced PU.1 binding in qPCR of ChIP products. n = 2 experiments for each line. Homozygous iMG was used to compare the allelic effect. (j) The allele A of rs10792832 exhibits higher Sanger sequencing peaks for PU.1 ChIP-seq PCR products of iMG from two iPSC lines heterozygous for rs10792832. Note the equal peak height of the two alleles for genomic PCR products (sequenced by using primers for on-target editing confirmation) of the same heterozygous samples as in the top panels. (k) Quantification of the allelic ratios of rs10792832 for Sanger sequencing peaks in (j). In all comparisons. * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m.

Extended Data Fig. 4 PICALM expression in human postmortem brains.

(a) Human post-mortem brain sections from an asymptomatic patient were stained with antibodies against PICALM (green) and IBA1 (magenta). White arrows indicate PICALM expressed in MG, and yellow arrows indicate PICALM expressed in a blood vessel. Line-scan (kymography) analysis (right) demonstrates the overlapping expression. The white line in IBA1 composite image shows the location of the line scan. Scale bar, 50 µm. (b) Reduced PICALM expression in the grey matter of AD patients’ postmortem brains (n = 20) compared to controls (n = 19). Two-sided unpaired Student’s t-test was used. (c) Comparison of brain MG state-specific marker gene expression between different human brain MG states. MG states were defined as described in the snRNA-seq study[19]. Z-scored expression values of MG state-specific markers and PICALM are shown. HOMEO, homeostatic MG including cell clusters 1,2,3,5 and 8; DAM, disease-associated MG including clusters 0 and 7; LDAM, LD-accumulative MG including clusters 4 and 6. Note lower PICALM expression in DAM and LDAM cell clusters. (d) Representative immunofluorescence staining images of post-mortem brain sections from unaffected control (CERAD 0) and AD (CERAD 3) patients. Green arrows in single-channel images indicate IBA1+ microglia which are also positive for PICALM. CERAD = Consortium to Establish a Registry for AD. (e) Representative immunofluorescence staining of PICALM in individual microglia from Braak stages 1–5 and CERAD scores 0–3, quantified in e-f. Scale bar 20 µm. (f) Quantification of PICALM expression in MG of AD and non-AD controls. AD, n = 15 with the diagnosis of “definitely AD” with a CERAD score of 3; the rest include control (n = 16), probable AD and possible AD (n = 9) (Supplementary Table 11). The grey background dots represent individual MG. ns, not significant in the two-sided unpaired Student’s t-test. (g) Quantification of PICALM expression in MG of AD and non-AD controls stratified by Braak Stage (0-5) (left) and by CERAD score (0 = control; 1-2, probable or possible AD; and 3, definitely AD) (right). Not significant in one-way ANOVA. (h) PICALM protein abundance in postmortem brain prefrontal cortex between AD, asymptomatic AD (AsymAD), and non-AD individuals. Data are from a published dataset[27]. One-way ANOVA; Dunnett’s correction. In all comparisons. * P < 0.05, ** P < 0.01, ***, P < 0.001; ns=not significant; mean ± s.e.m.

Extended Data Fig. 5 The LOAD risk-allele of PICALM impairs MG phagocytosis.

(a) Representative fluorescence images of iMG showing time-dependent phagocytosis of Aβ-pHrodo (green). RFP+, iMG infected with pLenti-CRISPRa. Time indicates minutes after adding Aβ-pHrodo. Scale bar, 100 μm. (b) Representative images showing time-dependent iMG phagocytosis of Aβ-pHrodo (red) for a second clone of both isogenic pairs CD04 and CD09. Scale bar, 100 μm. (c) Quantification of Aβ-pHrodo phagocytosis in iMG of CD04 and CD09 lines. Data are from 2 experiments, each with 3 wells of differentiations, one clone (clone 2) per line. LMM was used with experimental round as the random factor; two-sided test, nominal p-value; (d) Schematics of CRISPRa design to increase PICALM expression in risk-allele iMG (non-risk and risk control iMG also contain pLenti-CRISPRa encoding RFP). Illustrations were created using BioRender. Sudwarts, A. (2025) https://BioRender.com/vveaczo. (e) qPCR results showing normalized PICALM expression in iMGs with non-risk, risk, and risk-CRISPRa lines. Expression was normalized to GAPDH. Each datapoint represents a single-well measurement from one experiment; for each line (CD04 and CD09) and each condition (non-risk, risk, and risk-CRISPRa), data are from one clone, collected from 2 experiments each with 3 wells of differentiations (n = 6). (f) Schematics of CRISPRoff to knockdown PICALM expression for CD04 and CD09 lines with PICALM non-risk alleles. (g) qPCR results show CRISPRoff-induced reduction of PICALM in iMG for both lines. Expression was normalized to GAPDH. Each datapoint represents a single-well measurement from one experiment; for each line (CD04 and CD09) and each condition (risk, non-risk, and non-risk-CRISPRoff), data are from one clone from one experiment with 3 wells of differentiations (n = 3). (h) Representative fluorescence images of iMG showing time-dependent phagocytosis of myelin-pHrodo (red). Cell mask stains for the plasma membrane and NucBlue stains the nucleus in live cells. Time indicates minutes after adding myelin pHrodo. Scale bar, 100 μm. (i) Quantifying myelin-pHrodo phagocytosis in iMG carrying non-risk, risk, and non-risk-CRISPRoff for CD04 and CD09 lines. Scale bar, 100 μm. (j) Representative images showing time-dependent iMG phagocytosis of Aβ-pHrodo (red). (k) Quantification of Aβ-pHrodo phagocytosis in iMG carrying non-risk, risk, and non-risk-CRISPRoff for both CD04 and CD09 lines. (l) Cytochalasin D (10 µM) nearly completely inhibits phagocytosis. Cell mask stains for the plasma membrane and NucBlue stains nuclei in live cells. Time indicates minutes after adding myelin pHrodo. Scale bar, 100 μm. From (h) to (k), for each donor line (CD04 and CD09) and each condition (non-risk, risk, and non-risk-CRISPRoff), data are from one clone of one experiment with 3 wells of differentiations (each with 2-3 FOV). For all comparisons except in (c), one-way ANOVA was used; Dunnett’s correction. * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m.

Extended Data Fig. 6 Validation of the selected DE genes by qPCR and IF staining.

(a) and (b) qPCR results for NEAT1, ATP6AP2, LDLR, HMGCS1, and DHCR7 in iMG (PICALM risk allele vs. non-risk). Expression was normalized to GAPDH. Each datapoint represents a single-well measurement from one experiment; for each gene and each condition (risk and non-risk), data are from 2 donor lines (CD04 and CD09; one clone per line), collected in one experiment with 2 wells of differentiations (n = 4). (c) IF staining and (d) quantification of the mean fluorescence intensity of selected DE genes in day-25 iMG (non-risk vs risk alleles). Scale bar, 50 µm. Each datapoint represents a single-well measurement from one experiment; for each gene and each condition (risk and non-risk), data are from 2 donor lines (CD04 and CD09; one clone per line), collected in one experiment with 2 wells of differentiations (n = 4). For all comparisons, LMM was used to test the fixed effect of genotype (risk vs. non-risk), with cell line identify (CD04 or CD09) as the random factor; two-sided test, nominal p-value; * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m.

Extended Data Fig. 7 LOAD risk-allele of PICALM causes LD accumulation in iMG.

(a) IF filipin staining in day-25 iMG (TREM2+) carrying the LOAD risk or non-risk allele of rs10792832. (b) Quantification of filipin fluorescence intensity in (a). PI, propidium iodide for nucleus staining. Scale bar, 50 µm. Each datapoint represents a single-well measurement field of view (FOV) of one experiment; for each condition (risk and non-risk), data are from two donor lines (CD04 and CD09), collected from one experiment with two wells of differentiations each with 2-3 FOV (n = 12 for risk group and n = 8 for non-risk group). (c) and (d) Quantification of LD staining shows increased LD (BODIPY+) size and fluorescence intensity in LOAD risk-allele iMG (vs. non-risk), respectively. (e) Fluorescence staining of LD (BODIPY+) and ROS (CellRox+) with or without pretreatment of Triacsin C (TrC) in iMG carrying risk or non-risk allele. Scale bar, 50 µm. (f) to (i) Quantification of (e) shows increased LD and CellRox density and area in iMG carrying LOAD risk-allele (vs. non-risk). Each datapoint represents a single well measurement (FOV) from one experiment; data are from a single clone of CD04 line, collected from one experiment with 2 wells of differentiations each with 4 FOV (n = 8). One-way ANOVA with Tukey’s correction. (j) Representative histogram of flow cytometry analysis of LD (BODIPY+) in iMG. Gating strategy is shown in Supplementary Fig. 10. (k) Quantification of the proportion of BODIPY+ cells from (j). Each datapoint represents a single-well measurement from one experiment; for each donor line (CD04 and CD09) and each condition (risk and non-risk), data are from single clone, collected from 3 experiments (n = 3). (l) Representative fluorescence images of LD staining with PLIN2. Scale bar, 50 µm. (m) and (n) Quantification of PLIN2+ LD area per iMG and LD fluorescence intensity per iMG, respectively. For (c), (d), (m), and (n), each datapoint represents a single-well measurement from one experiment; for each donor line (CD04 and CD09) and each condition (risk and non-risk), data are from 2 clones, collected from 2 independent experiments each with 3 wells of differentiations (n = 12). LMM was used to test the fixed effect of genotype (risk vs. non-risk), with clone identify and experimental rounds as the random factors; two-sided test, nominal P-values. For (b) and (k), two-sided unpaired Student’s t-test was used. For all comparisons, * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m.

Extended Data Fig. 8 PICALM-CRISPRa rescues the LD accumulation and phagocytosis-deficiency in LOAD risk-allele iMG.

(a) Representative fluorescence images show time-dependent phagocytosis of Aβ-pHrodo and LD accumulation (BODIPY+) in iMG carrying the LOAD risk-allele or non-risk allele and in risk-CRISPRa of PICALM. RFP+, iMG infected with pLenti-CRISPRa (note that both non-risk and risk control iMG also contain pLenti-CRISPRa that encodes RFP). Scale bar, 100 μm. (b) PICALM-CRISPRa rescues the LD accumulation in iMG carrying the LOAD risk-allele to a level similar to that in MG carrying the non-risk allele for both CD04 and CD09. Each datapoint represents a single-well measurement from one experiment; for each cell line (CD04 and CD09) and condition (non-risk, risk-allele and risk-CRISPRa), data are from one clone (per line), collected from 2 independent experiments each with 3 wells of differentiations (n = 6). One-way ANOVA with experimental round as the random factor; Dunnett’s correction. * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m. (c) Pie charts depict the proportion of iMG stained positive for Aβ-pHrodo, BODIPY, or both from co-localization analysis of the fluorescence images in (a). Note the Aβ-pHrodo+/BODIPY+ iMG are rare, and PICALM-CRISPRa rescues the phagocytosis deficit in LOAD risk-allele iMG by mainly converting BODIPY+ iMG to phagocytic cells without LD (Aβ-pHrodo+/BODIPY−).

Extended Data Fig. 9 PICALM knockdown (KD) or knockout (KO) causes LD accumulation.

(a) Representative fluorescence staining of LD (BODIPY+) in iMG carrying risk or non-risk allele and in non-risk-CRISPRoff iMG. Scale bar, 50 μm. (b) to (e) Increased LD (BODIPY+) area, density per cell, size, and fluorescence intensity in iMG carrying the LOAD risk-allele (vs. non-risk) for both CD04 and CD09 lines. Note iMG with PICALM-CRISPRoff also show increased LD accumulation (vs. non-risk allele), mimicking the effect of the PICALM risk allele. (f) LD staining with LipidTox. Scale bar, 50 µm. (g) to (j) Increased LD density, size, area, and fluorescence intensity in iMG carrying the LOAD risk-allele (vs. non-risk). PICALM-CRISPRoff reduces LD density, size, area, and fluorescence intensity (vs. non-risk allele). For (b) to (e) and (g) to (j), each datapoint represents a single-well measurement (FOV) from one experiment; for each donor line (CD04 and CD09) and each condition (risk, non-risk, and CRISPRoff), data are from one experiment with 3 wells of differentiations each with 3-4 FOV. One-way ANOVA; Dunnett’s correction. (k) LD (BODIPY+) staining in PICALM-KO C20 cells and (l) quantification of LD number per cell. Scale bar, 25 µm. Each datapoint represents a single-well measurement (averaged from 5 FOV) from one experiment; data are from 3 experiments (n = 3). Two-sided unpaired Student’s t-test was used. For all comparisons, * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m.

Extended Data Fig. 10 Lipidomics profiling in iMG and the effect of PICALM on neuron-to-glia lipid transfer.

(a) Volcano plot showing the log_2 (FC) of the 901 detected lipid species in risk iMG (vs. non-risk iMG; CD04 and CD09 lines) and the -log_10 (P-value) from one-way ANOVA. The top 20 increased lipid species are highlighted, most of which are TG. (b) Heatmap of row Z-scored lipid abundances across non-risk, risk-allele, and risk-CRISPRa iMG for the lipid species increased in risk-allele iMG (vs. non-risk iMG) at FDR < 0.05. (c) Normalized abundance of total TG in non-risk, risk-allele, and risk-CRISPRa iMG for CD04 and CD09 lines. For each cell line (CD04 and CD09) and each condition (non-risk, risk, risk-CRISPRa), data for (a) to (c) are from one experiment with 2 wells of differentiations each with 2 technical replicates (n = 4). One-way ANOVA; Dunnett’s correction. (d) Diagram of the lipid (Red-C12 is a fatty acid analogue) transfer system between neurons (iN-Glut) and iMG or iAst. iN-Glut was labelled with Red-C12 for 18 hr, then co-cultured with iMG or iAst for 4 hr before staining iMG or iAst with BODIPY. Illustrations were created using BioRender. Sudwarts, A. (2025) https://BioRender.com/flsko8h. (e) Red-C12 imaging in the co-cultured iMG carrying LOAD risk-allele or non-risk allele. Scale bar, 50 µm. (f) and (g) Quantification of the transferred Red-C12 area and density in iMG. Each datapoint represents single-well measurement (FOV) of an experiment; for each condition (risk and non-risk), data are from line CD04 (one clone), collected from one experiment with 3 wells of differentiations each with 3-4 FOVs (n = 9-12). (h) Red-C12 imaging and LD (BODIPY+) staining of the co-cultured iAst with PICALM-CRISPRoff (over the genetic background of non-risk allele since the LOAD risk-allele of PICALM does not affect its expression in iAst). Note the partial overlap of Red-C12 and BODIPY staining, suggesting the transferred Red-C12 partially contributes to LD formation in iAst. Scale bar, 50 µm. (i) and (j) Quantifying LD density and area in the co-cultured iAst. (k) and (l) Quantifying the Red-C12 density and area in iAst. Each datapoint represents single-well measurement (FOV) of an experiment; for each condition (non-risk and CRISPRoff), data are from line CD04 (one clone), collected from one experiment with 3 wells of differentiations each with 3-4 FOVs (n = 9-12). (f) to (l), two-sided unpaired Student’s t-test was used. For all comparisons, * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m.

Extended Data Fig. 11 iMG carrying the LOAD risk-allele of rs10792832 show increased lipid peroxidation.

(a) The schematics show C11-BODIPY (581/591), a fluorescent lipid peroxidation sensor, shifts its fluorescence from red to green in the presence of ROS. Illustrations were created using BioRender. Sudwarts, A. (2025) https://BioRender.com/21bufax. (b) Fluorescence staining with peroxidation sensor C11-BODIPY in iMG (TREM2+) carrying the LOAD risk or non-risk allele in the presence or absence of TrC. Scale bar, 50 µm. (c) and (d) Increased LD (C11-BODIPY green fluorescence) density and area in iMG carrying the LOAD risk-allele (vs. non-risk). Note the peroxidized LD density and area are reduced in iMG treated with TrC. (e) The ratio of peroxidized vs. non-peroxidized (BD.11 Ox/BD.11 non−ox) lipids in iMG carrying the LOAD risk-allele or non-risk allele, with or without TrC treatment. Each datapoint represents a single-well measurement (FOV) of one experiment; for each condition (risk, risk-TrC, non-risk, and non-risk-TrC), data are from line CD04 (one clone), collected from one experiment with 3 wells of differentiations each with 2-3 FOV. For all comparisons, one-way ANOVA with Tukey’s correction; * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m. (f) Mechanistic insight on the link between the LOAD GWAS SNP risk allele and the reduced PICALM expression, lysosomal dysfunction, LD accumulation, LD peroxidation, cellular ROX level, and phagocytosis deficits in iMG. (-), inhibition; FA: fatty acids; Px: peroxisome; M: mitochondria; sER: smooth ER; Ly: lysosome; Chl: cholesterol. Illustrations were created using BioRender. Sudwarts, A. (2025) https://BioRender.com/bmv8afh.

Extended Data Fig. 12 LD accumulation impairs iMG phagocytosis.

(a) Representative fluorescence images show time-dependent phagocytosis of Aβ-pHrodo and LD accumulation (BODIPY+) in iMG (CD45+) carrying the LOAD risk-allele or non-risk allele of PICALM in the presence or absence of TrC. Scale bar, 100 μm. (b) Reduced Aβ-pHrodo intensity in iMG carrying the LOAD risk-allele (vs. non-risk) can be rescued by TrC treatment. (c) TrC treatment rescues the LD in iMG carrying the LOAD risk-allele to a level similar to that in MG with the non-risk allele. (d) Pie charts show the proportion of iMG (CD45+) stained positive for Aβ-pHrodo, BODIPY, or both from co-localization analysis of the fluorescence images in (a). Note that TrC treatment rescues the phagocytosis deficit in iMG carrying the LOAD risk-allele by mainly converting BODIPY+ iMG to phagocytic cells without LD (Aβ-pHrodo+/BODIPY-). (e) to (h) The same as in (a) to (d) except that the phagocytosis assay was for myelin-pHrodo. In (a) to (h), each datapoint represents a single-well measurement (FOV) from one experiment; for each condition (risk, risk-TrC, and non-risk), data are from CD09 line (one clone), collected from one experiment with 2-3 differentiations each with 2–4 FOV. One-way ANOVA with Dunnett’s correction; * P < 0.05, ** P < 0.01, ***, P < 0.001; mean ± s.e.m.

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Kozlova, A., Zhang, S., Sudwarts, A. et al. PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia. Nature 646, 1178–1186 (2025). https://doi.org/10.1038/s41586-025-09486-x

- Received: 12 May 2024

- Accepted: 01 August 2025

- Published: 03 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09486-x

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Energy deficiency selects crowded live epithelial cells for extrusion

Saranne J. Mitchell,

Carlos Pardo-Pastor,

Anastassia Tchoumakova,

Thomas A. Zangle &

…

Jody Rosenblatt

Nature volume 646, pages 1187–1194 (2025)

Abstract

Epithelial cells work collectively to provide a protective barrier, yet they turn over rapidly through cell division and death. If the numbers of dividing and dying cells do not match, the barrier can vanish, or tumours can form. Mechanical forces through the stretch-activated ion channel Piezo1 link both of the processes; stretch promotes cell division, whereas crowding triggers live cells to extrude and then die1,2. However, it was not clear what selects a given crowded cell for extrusion. Here we show that the crowded cells with the least energy and membrane potential are selected for extrusion. Crowding triggers sodium (Na+) entry through the epithelial Na+ channel (ENaC), which depolarizes cells. While those with sufficient energy repolarize, those with limited ATP remain depolarized, which, in turn, triggers water egress through the voltage-gated potassium (K+) channels Kv1.1 and Kv1.2 and the chloride (Cl−) channel SWELL1. Transient water loss causes cell shrinkage, amplifying crowding to activate crowding-induced live cell extrusion. Thus, our findings suggest that ENaC acts as a tension sensor that probes for cells with the least energy to extrude and die, possibly damping inadvertent crowding activation of Piezo1 in background cells. We reveal crowding-sensing mechanisms upstream of Piezo1 that highlight water regulation and ion channels as key regulators of epithelial cell turnover.

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Main

Cell extrusion is a conserved mechanism for maintaining cell number homeostasis, driving cell death in epithelia from sea sponges to humans. During extrusion, a live cell is ejected apically by coordinated basolateral actomyosin contraction of the extruding cell and its neighbours[2]. Once extruded, a cell will die from lack of survival signalling. We previously found that Piezo1 activates live-cell extrusion (LCE) in response to crowding to maintain constant epithelial cell numbers1,2. Crowding activates Piezo1 to trigger a canonical pathway that relies on secretion of the lipid sphingosine 1-phosphate (S1P), which binds to the G-protein-coupled receptor S1P_2 to activate Rho-mediated actomyosin contraction needed for extrusion1,3,4. This same S1P–S1P_2–Rho pathway also extrudes apoptotic cells to ensure that no gaps are formed in the monolayer[5]. As crowding-induced cell extrusion drives most epithelial cell death, identifying which cells within a crowded epithelial field extrude is central to understanding what governs cell death. In sparse epithelia, topological defects perturbing normal epithelial hexagonal packing promote outlier cells to extrude6,7. Moreover, epithelial cells with replicative stress in Caenorhabditis elegans and mammals are also targeted for extrusion7,8. However, as most cell extrusions occur in crowded regions, where cells are not dividing and have no obvious topological defects, what marks most cells for extrusion is still unclear.

As mechanical cell competition suggested that differences in strain resistance might select cells for extrusion[9], we first considered whether cells with less mass are selected under compressive forces, as they may be less resistant to strain. To test this possibility, we adapted quantitative phase imaging (QPI) to analyse dry-mass changes over time in Madin–Darby canine kidney II (MDCKII) epithelial monolayers10,11. However, we found that dry mass does not change before cell extrusion. By contrast, dry mass increases before a cell divides (Extended Data Fig. 1a–c and Supplementary Video 1; representing 31 cells from 12 total cell islands), disfavouring reduced cell mass as a selecting factor for LCE.

Another possibility is that cells with the highest compression might activate a calcium (Ca2+) wave through Piezo1, the most upstream known regulator of extrusion. We previously identified a single Piezo1-dependent Ca2+ spark occurred in epithelial cells before their division[1]. To test whether Ca2+ spikes before LCE, we imaged MDCK monolayers transfected with a genetically encoded GFP Ca2+ indicator, GECO1, and monitored extrusion using phase microscopy. We found no discernible Ca2+ spikes or waves before LCE and, instead, we noticed a dip before extrusion (Extended Data Fig. 1d,e). While the 1 s imaging interval might have missed shorter Ca2+ transients, our results suggest that Ca2+ is not linked to extrusion.

Volume loss leads to LCE

However, during these experiments, we noticed a consistent transient increase in cell–cell junction brightness lasting about 6.5 min before a cell extrudes (Fig. 1a,b and Supplementary Video 2). Increased phase brightness might indicate a decrease in cell volume or cell shape change. To test whether transient volume loss accounts for the phase brightness before extrusion, we mosaically expressed cytoplasmic GFP in MDCKII cells and measured the cell volume using confocal time-lapse microscopy before and during extrusion. This approach indicated that live cells experience a transient volume loss before extruding that directly corresponded to the junctional brightness measured in our phase-contrast time lapses (Fig. 1a–c). To confirm the volume reduction using an alternative method, we used Calcein-AM, which fluoresces green but is quenched as cells lose water and shrink[12]. Calcein-AM confirmed that cells lose volume before extruding (Fig. 1d), which we refer to as homeostatic early shrinkage (HES).

Fig. 1: Live cells shrink briefly through water loss before extruding in dense regions.

a, Phase time-lapse stills show junctional lightning (arrowhead) around a live cell before extrusion. Scale bars, 10 μm. n = 5. b, The mean ± s.e.m. duration that cells experience shrinking, relaxation and onset of shrinking to extrusion. n = 16 cells. c, Shrinking based on GFP. The mean ± s.e.m. normalized cell volume at the baseline and after shrinking is shown. n = 12 cells. Statistical analysis was performed using unpaired t-tests. d, Shrinking based on Calcein. The mean ± s.e.m. normalized cell volume at the baseline and after shrinking is shown. n = 8 cells. Statistical analysis was performed using unpaired t-tests. e,f, Representative time-lapse stills from the lightning assay showing the area around cells (red, bottom) before shrinking, after shrinking, at relaxation and at onset of extrusion (e) and the mean ± s.e.m. normalized area around cells (f), where t_0 is the shrinkage. n = 10 cells. Scale bars, 5 μm. g, The mean ± s.e.m. normalized cell area at the baseline and after shrinking, as determined using a lightning assay. n = 15 cells. Statistical analysis was performed using two-way analysis of variance (ANOVA) with Dunnett’s multiple-comparison test. h, The mean ± s.e.m. rate of cell shrinkage (HES) of cells before extrusion (EXT), of cells remaining in the monolayer (no EXT) or of cells after treatment with extrusion inhibitors (GsMTx4, Y-27632 or blebbistatin (blebbi)), as determined using a lightning assay. Statistical analysis was performed using by one-way ANOVA with Welch multiple-comparison correction; **P = 0.0026, **P = 0.0064, *P = 0.036; respectively. i, Schematic placing shrinkage upstream of the established extrusion pathway. j, The mean ± s.e.m. extrusion rate in MDCKII cells incubated under increasing osmolarities. Statistical analysis was performed using one-way ANOVA with Dunnett’s multiple-comparison correction; **P = 0.0068. Hyper, hyperosmolarity; hypo, hypoosmolarity; iso, isotonic. k, The mean ± s.e.m. live and apoptotic extrusion rates (using cleaved-caspase indicator) after no (iso) or 20% hypertonic shock of MDCKII cells. Statistical analysis was performed using unpaired t-tests; *P = 0.0105. l, The mean ± s.e.m. cell volume (based on GFP) after 20% hypertonic shock of cells that are maintained or extruded. n = 5. Statistical analysis was performed using unpaired t-tests; *P = 0.0113. m,n, Representative mouse precision-cut ex vivo lung slice (PCLS) images of extrusions (m; white arrow heads) and the corresponding rates (n). Statistical analysis was performed using unpaired t-tests; *P = 0.0431. Scale bar, 50 μm. All n = 3 and independent two-tailed experiments unless otherwise stated. ****P < 0.0001.

Source data

Given that the transient phase brightness increase reflects cell volume loss, we developed the lightning assay—a semi-automated method to readily assay HES, based on thresholding junctional brightness intensity around cells before they extrude (Fig. 1e–g). Using the lightning assay, we found that around 70% of cells shrink before LCE, whereas only <0.03% of cells filmed (8 out of 750) shrink without extruding. Moreover, these few cells that do not extrude were in uncrowded epithelial regions, whereas HES-induced LCE occurred in crowded areas.

As about 70% of extruding cells undergo HES, it was not clear why the remaining 30% do not shrink. One possibility is that cells that do not shrink undergo apoptotic extrusion, which accounts for around 20–30% of extruding cells at steady state[1]. Combining the lightning assay with a fluorescent reporter of the apoptotic marker cleaved caspase-3, we found that cell shrinkage is rare (~3%) before apoptotic extrusion and could represent the 30% of cells that do not shrink (Extended Data Fig. 2a,b and Supplementary Video 3). As most cells undergo HES, which precedes LCE, this became of the focus of our investigation.

To identify what causes HES, we investigated whether known extrusion signals, such as actomyosin contractility and Piezo1 activation, might also affect volume. Notably, contractility, which is required for volume loss during apoptotic extrusion13,14, is dispensable for HES, as contractility inhibition with Y-27632 or blebbistatin instead increased the number of cells shrinking by about 23× compared with the untreated controls (Fig. 1h). Thus, contractility appears to suppress cell shrinkage, presumably by stabilizing cell–cell junctions. As HES occurs in crowded regions, we tested whether transiently inhibiting stretch-activated ion channels (SACs) such as Piezo1, which is required for crowding-induced LCE[1], prevented HES. Notably, we found that HES still occurs in the presence of the SAC blocker GsMTx4, suggesting that Piezo1—the earliest known mechanosensor to trigger extrusion in response to crowding[1]—acts downstream of cell shrinkage (Fig. 1h). Thus, crowding followed by single-cell shrinkage appear to initiate LCE upstream of Piezo1 and myosin contraction (Fig. 1i).

We next investigated whether water efflux might regulate the transient volume loss cells experience before extrusion. To first test whether water loss is sufficient to initiate LCE, we incubated MDCKII monolayers in medium with altered osmolarity (hypotonic to hypertonic) for 10 min before returning to isotonic medium to mimic the shrink duration seen before extrusion (Supplementary Videos 4 and 5). While hypotonic medium had no effect on cell extrusion (Fig. 1j), 15–20% hypertonicity substantially increased both cell shrinkage and LCE rates within 15 min (Fig. 1j and Supplementary Videos 4 and 5), with higher tonicities destroying the monolayers due to excessive extrusion (data not shown). Although hypertonicity can provoke apoptotic volume decrease14,15, the active caspase-3 apoptotic reporter indicated that hypertonic treatment induces only live, and not apoptotic, extrusion (Fig. 1k). Furthermore, the pan-caspase inhibitor zVAD-fmk did not prevent hypertonic-induced extrusions (Extended Data Fig. 2b,c), ruling out apoptotic volume decrease in this process. Notably, although the entire monolayer was treated with 20% hypertonic solution, not all cells shrunk equally. Only cells that shrank 20 ± 3%, as measured by cytoplasmic GFP volume, extrude; those shrinking 11 ± 2.5% do not extrude (Fig. 1l and Extended Data Fig. 2d,e).

To test whether volume loss drives extrusion in other epithelial cells, we used ex vivo mouse lung slices, where bronchial epithelia extrude rapidly following crowding[16]. We found that transient hypertonic treatment increased bronchial epithelial cell extrusion by around twofold within 30 min (Fig. 1m,n). Thus, experimentally reducing cell volume, which we term osmotic-induced cell extrusion (OICE), about 20% is sufficient to trigger individual cell extrusion, potentially mimicking HES.

Voltage-gated ion channels regulate HES

We first assessed whether any ion channel inhibitors block homeostatic extrusion using a library of K+ and Cl− channel inhibitors, scoring extrusions by phase microscopy and immunostaining. Notably, all of the inhibitors tested significantly reduced extrusion rates during normal homeostatic turnover to levels seen with SAC channel inhibition (Fig. 2a). Moreover, the same channel inhibitors blocked OICE (Extended Data Fig. 3a). Although dimethyl sulfoxide, the inhibitor solvent, can alter cell permeability to water[17], it did not significantly affect extrusion rates on its own (Fig. 2a,b). As Cl− channel SWELL1 inhibition causes cell death[18], we added the caspase inhibitor zVAD-fmk to the SWELL1 inhibitor DCPIB in all assays (Fig. 2d and Extended Data Fig. 2c). Notably, 4-AP, a voltage-gated Kv1.1 and Kv1.2 channel inhibitor, also blocks apoptotic extrusion[19], suggesting a conserved activator for all cell extrusions (Fig. 2a and Extended Data Fig. 3a). It was surprising that so many inhibitors block extrusion, warranting further studies to identify how these channels govern extrusion. However, here we focus solely on which channels might cause the shrinkage step preceding most cell extrusions.

Fig. 2: Volume loss is regulated by Kv1.1, Kv1.2 or SWELL1.

a, The mean ± s.e.m. extrusion rate after treatment with volume-regulating inhibitors, scored from immunostained monolayers. n = 3. Statistical analysis was performed using one-way ANOVA with Dunnett’s multiple-comparison test. The inhibitor key shows the volume-regulating channel family inhibitor target by assigned colour and icon. b, The mean ± s.e.m. area from the lightning assay over time, where increased space around the cell indicates cell shrinkage after treatment with Kv1.1 and Kv1.2 (4-AP, pink) and SWELL1 (DCPIB, green) inhibitors. n = 3. c, The mean ± s.e.m. peak area change (from the lightning assay) after treatment with inhibitors during 20% hypertonic challenge. n = 6. Statistical analysis was performed using one-way ANOVA with Dunnett’s multiple-comparison test; **P = 0.0020 (DMSO), **P = 0.0015 (4-AP), **P = 0.0021 (DCPIB). d, The mean ± s.e.m. extrusion rates after siRNA knockdown with or without 20% hypertonic challenge compared with the controls. n = 4. All knockdowns blocked extrusion. Statistical analysis was performed using two-way ANOVA with Dunnett’s multiple-comparison test. e, The mean ± s.e.m. peak area change (as determined using the lightning assay) during 10 min hypertonic challenge of siRNA knockdown cells. n = 6. Statistical analysis was performed using one-way ANOVA with Dunnett’s multiple-comparison test; *P = 0.0.162 (Kv1.1), **P = 0.0100 (Kv1.2), **P = 0.0011 (SWELL1). f, The mean ± s.e.m. cell shrinkage (HES) rate after treatment with 4-AP or DCPIB, or after Kv1.1, Kv1.2 or SWELL1 siRNA knockdown, compared with the control background cells or those before extrusion. n = 3. Statistical analysis was performed using two-way ANOVA with Dunnett’s multiple-comparison test. All n values represent independent two-tailed experiments.

Source data

To determine whether K+ and Cl− channels control HES, we first tested which inhibitors blocked OICE extrusions. Channels were blocked by inhibitors only during the 10-min hypertonic treatment to help focus on potential targets for those involved in shrinkage (Extended Data Fig. 3b,c). Ruling out SACs, and having identified only Kv and Cl− channels as required during early stages of OICE (Fig. 2c), we next tested which of these channels also regulate HES using our lightning assay as a proxy and calcein to measure cell shrinkage in response to 20% hypertonicity with or without 1% of channel inhibitors. This assay revealed that only DCPIB (with zVAD-fmk) and 4-AP, which target the Cl− channel SWELL1 and the voltage-gated K+ channels Kv1.1 and Kv1.2, respectively, significantly blocked hypertonic-induced cell shrinkage (Fig. 2b,c). By contrast, inhibitors blocking known extrusion signals, Piezo1, S1P and S1P_2, do not block cell shrinkage in response to hypertonic solution, similar to the homeostatic outcomes (Fig. 1h and Extended Data Fig. 3d,e). To more specifically test the roles of Kv1.1, Kv1.2 and SWELL1 in both OICE and HES, we used small interfering RNAs (siRNAs) to knockdown each channel (Extended Data Fig. 3g). Knockdown of any of these channels prevented HES and OICE (Fig. 2d–f). Immunostaining showed that all three channels localize to the cell apex, with Kv1.1 co-localizing with ZO-1 at tight and tricellular junctions—regions that are important to tension sensing in epithelia[20] (Extended Data Fig. 3f). Thus, Kv1.1, Kv1.2 and SWELL1 regulate both cell shrinkage and extrusion during normal cell turnover (Fig. 2g).

Given that Kv1.1 and Kv1.2 channels initiating cell shrinkage and extrusion are voltage gated, we considered whether membrane depolarization may initiate epithelial cell extrusion. The Na+/K+ ATPase is essential to generate and maintain the Na+ and K+ electrochemical gradients across the membrane that give rise to resting membrane potential[21] and therefore sets a voltage that can be depolarized. While neuronal potential has primarily been studied, all cells spend significant amounts of total ATP using the Na+/K+ ATPase to generate and maintain a resting cell membrane potential[21]. Neurons maintain a membrane potential of around −70 mV, while epithelia range between −30 and −50 mV (refs. 22,23). To study whether depolarization precedes shrinkage, we filmed MDCKII monolayers loaded with the fluorescent dye DiBAC_4(3), which becomes brighter with plasma membrane depolarization24,25. We found that cells depolarize on average around 5 min before shrinking and extruding (Fig. 3a–c and Extended Data Fig. 4b). Moreover, non-shrinking cells showed no depolarization signs, linking shrinkage and depolarization (Fig. 3a,b and Supplementary Video 6). To test whether K+ channel activation is sufficient to trigger cell shrinkage and extrusion, we optogenetically activated K+ channels in specific cells within MDCKII monolayers expressing Blink2 (ref. [26]). However, we found that only 1 cell out of 29 activated with blue light went on to shrink and extrude. Thus, activating K+ egress was not sufficient to induce extrusion and suggested that another signal must also operate.

Fig. 3: ENaC senses crowding, triggering voltage to induce extrusion.

a, Time-lapse (min) analysis of membrane depolarization before homeostatic extrusion (top; the yellow arrow indicates a cell before extrusion) in the presence of Kv1.1 and Kv1.2 inhibitor (4-AP) or with ENaC inhibitor (amiloride). Scale bars, 10 μm. n = 6. b, The mean ± s.e.m. normalized DiBAC_4(3) fluorescence of cells experiencing shrinking before extrusion (blue) or no shrinking (red) over time. n = 6. c, The mean ± s.e.m. DiBAC_4(3) fluorescence of cells before extrusion under steady-state conditions compared with non-extruding cells or those treated with Kv1.1 and Kv1.2 (4-AP) or ENaC (amiloride) inhibitors. Statistical analysis was performed using one-way ANOVA with Tukey’s multiple-comparison test; ***P = 0.0008; NS, not significant. d, Representative images over time showing Na+ entry into a cell (white arrow) before shrinking and extruding. Scale bars, 10 μm. n = 4. e, The mean ± s.e.m. percentage area increase between cells (by lightning assay) after siRNA-mediated ENaC-α, ENaC-β or ENaC-γ knockdown with or without 20% hypertonic challenge; non-targeted siRNAs were used as controls. Statistical analysis was performed using two-way ANOVA with Dunnett’s multiple-comparison test; ***P = 0.0004 (ENaC-α), ***P = 0.0006 (ENaC-β and ENaC-γ). f, The mean ± s.e.m. extrusion rates of cells after ENaC-α, ENaC-β or ENaC-γ knockdown with or without 20% hypertonic challenge; non-targeted siRNAs were used as controls. Statistical analysis was performed using two-way ANOVA with Dunnett’s multiple-comparison test; ***P = 0.0004. g, Representative images of cellular Na+ entry with or without crowding. Scale bars, 20 μm. h, The mean ± s.e.m. Na+ entry per cell with or without crowding and/or ENaC inhibition with amiloride. Statistical analysis was performed using one-way ANOVA with Šidák’s multiple-comparison test; *P = 0.0131, ***P = 0.0005. i, Schematic showing that crowding activates ENaC-dependent Na+ entry upstream of Kv1.1/Kv1.2- and SWELL1-dependent cell shrinkage. n = 3 and independent two-tailed experiments for all unless otherwise indicated.

Source data

Mechanical crowding activates ENaC

Typically, Na+ channel activation causes membrane depolarization21,27,28; thus, we next investigated whether Na+ ingress activates membrane depolarization and voltage-gated K+ channels. To test whether Na+ entry precedes cell shrinkage and extrusion, we filmed cell monolayers loaded with the fluorescent Na+ dye CoroNa-AM and found that Na+ entry precedes both HES and extrusion (Fig. 3d). One attractive candidate to mediate Na+ entry upstream of Kv1.1 and Kv1.2 activation is ENaC—a highly conserved, mechanically activated, apically localized (Extended Data Fig. 4a) channel that causes depolarization through Na+ entry29,30. Indeed, inhibiting ENaC with amiloride prevented depolarization, whereas inhibiting Kv1.1 and Kv1.2 with 4-AP did not (Fig. 3a,c and Extended Data Fig. 4b). Moreover, inhibiting ENaC with amiloride or knocking down any of its subunits (α, β or γ) prevented both hypertonic cell shrinkage and both homeostatic and hypertonic extrusions (Fig. 3e,f and Extended Data Fig. 4c–f). While ENaC is a mechanosensitive Na+ channel, most studies have examined its response to stretch[29]; it is still unknown whether it can respond to the crowding forces that trigger extrusion. Growing cells to homeostatic densities on a stretched PDMS substrate and releasing them from stretch causes rapid crowding, we found that this acute crowding increases Na+ entry in an ENaC-dependent manner (Fig. 3g,h). Together, these results suggest that crowding activates ENaC-dependent Na+ entry, which drives membrane depolarization, therefore activating the voltage-gated K+ channels Kv1.1 and Kv1.2 that are responsible for cell shrinkage (Fig. 3i).

Low ATP selects for LCE

When following cellular Na+ entry after experimental crowding, we noted that CoroNa-AM fluorescence increased slightly in all cells, but it remained high in cells that later shrink and extruded. Thus, identifying why this subset retains Na+ could account for how crowded cells are selected to extrude. Given that the Na+/K+ ATPase, which is essential to repolarizing the plasma membrane, accounts for 30–70% of all cellular ATP consumption, depending on the cell type21,31,32, we next investigated whether cells that cannot restore Na+ levels have low ATP. Time-lapse microscopy analysis of cells loaded with ATP-Red (a dye that stains for mitochondrial ATP) and DiBAC_4(3) revealed that ATP levels decrease before cells depolarize, shrink and extrude (Fig. 4a,b). A reduction in ATP was confirmed with the genetically encoded ratiometric ATP sensor Queen37 (refs. 33,34) (Extended Data Fig. 5a,b). Furthermore, reducing ATP with the mitochondrial ATP synthase inhibitor oligomycin A increased the number of cells both shrinking and extruding compared with the controls (Fig. 4c,d). Moreover, we tried to reduce Na+/K+ using the inhibitor oubain; however, this killed all epithelial cells rapidly (data not shown). To confirm that low energy driving extrusion was not limited to cultured MDCK monolayers, we filmed bronchial epithelia in mouse ex vivo lung slices, with and without oxamate and oligomycin A to decrease ATP. Again, we found that ATP decreases in cells before they extrude during homeostasis and that experimentally reducing ATP more than doubled extrusion rates (Fig. 4e,f). Inhibition of Piezo1 with GsMTx4 did not block ATP reduction and membrane depolarization, indicating that Piezo1 is downstream in the extrusion pathway (Extended Data Fig. 5c,d). Conversely, supplementing cells with additional glucose decreased the rate of extrusion (Extended Data Fig. 5e). Although depolarization and Na+ entry could not be filmed together, it was found that Na+ entry occurs 2.4 min earlier than depolarization (Extended Data Fig. 5f), further supporting that the Na+ increase does lead to depolarization. Notably, ATP reduction on its own increased intracellular Na+, and this was further increased by crowding (Fig. 4g,h), suggesting that crowding increases Na+ that cannot be pumped back out of cells with lower energy.

Fig. 4: Low energy selects for LCE.

a, Representative phase, membrane depolarization and cellular ATP levels during shrinkage and extrusion of MDCKII cells. Scale bar, 10 μm. b, DiBAC_4(3) and ATP-Red fluorescence in extruding and non-extruding MDCKII cells. The dotted lines are reference times of depolarization and shrinkage. n = 3 (10 cells). c,d, The mean ± s.e.m. rates of MDCKII cells exhibiting shrinkage (c) and extrusion (d) after ATP reduction with oligomycin-A. n = 3 (c) and n = 4 (d). Statistical analysis was performed using two-way ANOVA with Dunnett’s multiple-comparisons test (**P = 0.0019) or unpaired t-tests (***P = 0.0001). e, The mean ± s.e.m. epithelial extrusion rates in mouse lung slices with ATP inhibition. Oligo-A, oligomycin-A. n = 4. Statistical analysis was performed using unpaired t-tests; **P = 0.0093. f, Representative time-lapse images of mouse lung epithelial cells stained with ATP-Red undergoing extrusion (white, yellow and orange arrowheads). Scale bar, 25 μm. g, The mean ± s.e.m. normalized Na+ entry based on CoroNa-AM fluorescence after ATP inhibition under homeostatic conditions versus during crowding before extrusion of MDCKII cells. n = 3. Statistical analysis was performed using one-way ANOVA with Šidák’s multiple-comparison test; **P = 0.0047 (homeostatic, oligomycin-A), ***P = 0.0008. h, Representative images of Na+ (CoroNa-AM) entry in MDCKII cells under homeostatic and crowding conditions with or without ATP inhibition. Scale bar, 10 μm. i, The extrusion pathway model, showing that entry of Na+ ions through ENaC occurs in crowded cells, and this cannot be rectified by the Na+/K+ ATPase pump in cells with low energy. The resulting depolarization activates Kv1.1 and Kv1.2 (and potentially SWELL1) to trigger a threshold of cell shrinkage that then activates extrusion. All n values represent independent two-tailed experiments.

Source data

Together, our findings suggest a model in which crowding selects cells with insufficient energy for extrusion (Fig. 4i). Here, the ability of a cell to maintain membrane potential under stress indicates the cell’s fitness. Epithelial cells need to work collectively to perform most organ functions and maintain a tight monolayer. Thus, cells that might compromise either aspect must be preferentially eliminated. We identify ENaC as the earliest mechanosensor that measures this fitness by probing how well they can withstand the pressure as cells jostle for position. Epithelial cells with sufficient ATP can withstand crowding when ENaC activates Na+ ingress by using the Na+/K+ ATPase to rebalance their membrane potential. However, crowded cells challenged by ENaC activation with insufficient ATP levels to repolarize the membranes through the Na+/K+ ATPase will fail to pump out Na+. This intercellular increase in Na+ activates Kv1.1 and Kv1.2, causing cell shrinkage through water egress. If the cell shrinks more that 17%, it will then activate extrusion (Fig. 4i).

Discussion

This model highlights energy sufficiency as a driving force in determining which cells will die, analogous to mitochondrial permeabilization acting as a commitment step during the canonical apoptosis pathway35,36. Yet, during homeostatic extrusion, cells are eliminated while they are still alive and can repopulate if given a new substrate[2]. This mechanism therefore selects for the cells with comparably less energy than their neighbours, rather than no energy. Although neurons can use as much as 70% of their total ATP maintaining the membrane potential[37], epithelial cells are thought to use about 25% of their ATP to do so[32]. Thus, this mechanism could amplify deflections in energy to trigger a cell’s exit while it still has sufficient energy to extrude. It is not clear how cell shrinkage activates the downstream extrusion pathway. One idea is that it sets a deeper threshold for crowding to activate Piezo1, damping the constant buffeting of cells among each other. Notably, a recent study found that imposing a direct current from apical to basal side of a monolayer causes wide-scale contraction and induction of extrusion, whereas applying a current that reinforces normal polarity (from basal to apical) enhances and even repairs impaired epithelial cell–cell junctions[38], supporting our model.

Our study identifies ATP, membrane potential and hydrodynamic regulators as the earliest steps controlling homeostatic, crowding-induced cell extrusion—a central driver of epithelial cell death. As energy is key for selecting which cells are eliminated, our work may offer insights into how this process may go awry in metabolic diseases such as diabetes and cancer. Further work will need to determine how cell shrinkage activates Piezo1 to trigger S1P signalling vesicles, essential to extrusion1,3,4. Alternatively, the membrane potential differential between a cell targeted for extrusion and its neighbours could set up a directional current, which has been shown to activate migration during wound healing[39] and organ patterning[40].

Our findings also reveal the importance of other ion channels in regulating extrusion, and it will be important to define what roles they have. For example, known drivers of cystic fibrosis include malfunctions of ENaC and CFTR41,42,43, and mutations and misregulation of ENaC, Kv1.1 and Kv1.2 are indicators of poor prognosis in a variety of cancers44,45. Moreover, aquaporins may work in conjunction with the K+ and Cl− channels that we identify; further studies will need to define which, if any, have a role in this process. The channels identified here may be specific for kidney and lung epithelia, yet similar channels may work analogously in different tissues[46] to tune cell size and turnover, depending on organ function and shape. As epithelial cell extrusion is fundamental for epithelial cell turnover, misregulation of extrusion may contribute to disease aetiologies arising from channelopathies.

Methods

Cell culture

MDCKII cells from the European Collection of Authenticated Cell Cultures (ECACC) operated by Public Health England, catalogue number 00062107, lot 19G037, (tested for Mycoplasma; authenticated before receipt) were cultured in Dulbecco’s minimum essential medium (DMEM) high glucose with 10% FBS (Thermo Fisher Scientific) and 100 μg ml−1 penicillin–streptomycin (Invitrogen) at 5% CO_2, 37 °C.

Animal models and PCLSs

All animals were housed under specific pathogen-free conditions and cared for in accordance with the UK Home Office Animals (Scientific Procedures) Act of 1986 and the guidelines set by the Institutional Committees on Animal Welfare, project licence P68983265. Animal experiments received approval from the Ethical Review Process Committee at King’s College London and were conducted under a Home Office licence in the UK.

Ex vivo lung slices were obtained from male and female mice (B6N.219S6(Cg)-Scgb1a1tm1(cre/ERT)Blh/J) from 7 to 17 weeks of age. In brief, mice were humanely euthanized by injectable anaesthetic overdose followed by exsanguination through the femoral artery. The chest cavity was opened and the trachea was carefully exposed, where a small incision was made to accommodate the insertion of a 20Gx1.25 needle in a canula (SURFLO I.V. catheter). The lungs were then inflated with 2% low melting agarose (Thermo Fisher Scientific, BP1360) prepared in HBSS+ (Gibco, 14025). Then, lungs, along with the heart and trachea, were excised, washed in PBS and the lobes separated. Individual lobes were then embedded in 4% low-melting-point agarose and solidified on ice. Slices (thickness, 200 μm) were cut on a Leica VT1200S vibratome, washed and incubated in DMEM/F-12 medium supplemented with 10% FBS and antibiotics overnight (37 °C, 5% CO_2). The ex vivo lung slices were imaged 24 h after dissection. Sections were experimentally treated under conditions described below and analysed blinded.

Osmolarity solutions

To test which osmolarities could drive LCE, we treated MDCKII cells or ex vivo precision-cut lung slices (both cultured in DMEM) with increasing amounts of d-mannitol (Sigma-Aldrich, M4125-1kg) or nuclease-free water (Ambion, AM9937) to create hyper or hypotonic medium, respectively. Initial DMEM osmolarities were measured using a freezing-point osmometer (Gonotec, Osmomat 3000), ranged from 334 to 368 mOsm kg−1, and were tested biweekly and each time a new batch was prepared.

Immunostaining

Cells were fixed with 4% formaldehyde in PBS at room temperature for 20 min, rinsed three times in PBS, permeabilized for 5 min in PBS containing 0.5% Triton X-100 and blocked for 10 min in AbDil (PBS + 5% BSA). The coverslips were then incubated in primary antibody (in PBS + 1% BSA) overnight at 4 °C, washed three times with PBS and incubated in secondary fluorescently conjugated antibodies. All antibodies were used at a dilution of 1:200 unless otherwise specified: rabbit Piezo1 (Novus, NBP1-78446), mouse S1P (Santa Cruz, sc-48356), rabbit KCNA1 (Alomone Labs, APC-161), rabbit KCNA2 (Alomone Labs, APC-010), rabbit LRRC8A (Alomone labs, AAC-001), mouse ZO1 (Invitrogen, 33-9100), rabbit ENaC antibodies SCNNA1 (Invitrogen, PA1-920A), SCNNB1 (Invitrogen, PA5-28909) and SCNNG1 (Invitrogen PA5-77797). Alexa Fluor 488, 568 and 647 goat anti-mouse and anti-rabbit IgG were used as secondary antibodies (Invitrogen). F-actin was stained using either conjugated 488 or 568 phalloidin (66 μM) at 1:500 and DNA with 1 μg ml−1 DAPI (Thermo Fisher Scientific) in all fixed-cell experiments.

For PLCSs, untreated slices were fixed with 4% paraformaldehyde or after 30 min or 2 h following various treatments, then blocked for 1 h in AbDil at room temperature and then incubated at 4 °C overnight in 1:100 primary rabbit anti-E-cadherin antibody (24E10, Cell Signaling 3195) in AbDil. After three 30-min washes in PBS + 0.5% Triton X-100, the slices were incubated again at 4 °C overnight with secondary antibodies (1:100 Alexa Fluor 488 goat anti-rabbit at (Thermo Fisher Scientific, A11008) + 1:100 Alexa Fluor 568 Phalloidin (Thermo Fisher Scientific, A12380). For live imaging, BioTracker ATP-Red live-cell dye 1:200 (Sigma-Aldrich SCT045) was incubated at 37 °C for 30 min before imaging.

Experiment and quantification methods

Extrusion

Extrusions from time-lapse phase videos of MDCKII cells and PCLSs were quantified by identifying cells that were eliminated from the monolayer or tissue through classical squeezing out from the surrounding monolayer. These cells were then followed backwards in time to quantify cell shrinkage, compared with initiation of extrusion. By contrast, cells that round up, divide and reincorporate into the monolayer were scored as mitoses. The cells that were already eliminated by extrusion at the beginning of filming were excluded from our quantifications.

QPI analysis

QPI acquisition relies on having nearby cell-free areas to measure cell mass. To achieve this, we grew monolayers on small patterned circles within a dish by adhering a silicone laser-cut 100 micromesh disk (Micromesh Array, MMA-0500-100-08-01) onto a non-tissue-culture-treated 35 mm dish (Ibidi, 81151). The dishes were plasma treated with the mesh in place using a chamber (Harrick Plasma, Cleaner PD-32G) applied in a vacuum (Agilent Technologies, IDP-3 dry scroll vacuum pump) for 10 min to create cell growth in a pattern required for quantification (Extended Data Fig. 1a). Immediately after plasma treatment, the silicone mesh was aseptically removed and MDCKII cells were seeded at a density of 128,000 cells per well in a 35 mm microscopy imaging dish (Ibidi, 81151) and incubated at 37 °C for 6 h in DMEM. Before filming, excess cells were removed from unpatterned areas by gently washing twice with DMEM and growing another 48 h.

To image, cells were placed in an on-stage incubator and islands showing the entire cell island boundary and encompassing empty space were filmed on a QPI microscope. A minimum of two images of empty space was used for background correction. Images were acquired every 2 min for 10 h at 37 °C, 5% CO_2 and 88% humidity.

The dry mass was then calculated by subtracting the reference images from cells within the island to correct for background. The background was adjusted by subtracting the average phase shift of the empty space from the whole field of view including areas covered by cells. By subtracting the background, we could acquire the island and cell phase shift per image. The phase shift was converted to dry mass using previously established methods11,47 in MATLAB (v.R2022a). Extrusions were quantified from 12 separate islands.

Ca2+ and K+ quantification

MDCKII cells were plated at a density of 28,000 cells per well in a 35 mm microscopy imaging dish (Ibidi, 81156) and incubated at 37 °C overnight or until 60% confluent. Once 60% confluent, cells were transfected according to the manufacturer’s protocol with Lipofectamine 3000 (Thermo Fisher Scientific, L3000001) with genetically encoded Ca2+ indicator GECO (Addgene, CMV-G_GECO1.0, 32447)[48] or BLINK2 (Addgene, pDONR-BLINK2, 117075) for 18 h. Transfection medium was removed, and cells were rinsed twice with PBS and incubated in DMEM + FBS until cell-to-cell junctions were mature (72 h). To image, cells were stained with Hoechst (Invitrogen, 1:1,000) in PBS for 10 min at 37 °C, washed twice with PBS and then incubated in DMEM medium in an enclosed incubated stage at 37 °C with 5% CO_2 (Oko labs). For capturing Ca2+ changes, time-lapse images were captured every 10 s using a spinning-disk microscope (Nikon, Ti2) for up to 10 h. Images were analysed using a threshold macro (Nikon Elements AR, 5.41.02) to quantify the Ca2+ fluorescence level changes of cells over time that extrude.

The genetically encoded K+ indicator is optogenetically stimulated by blue light to open K+ channels. BLINK2 was activated by selecting the cell as a region of interest (ROI) in Nikon elements and then using a GalvoXY 405 laser at 55% for 300 ms, as any higher caused immediate cell death.

Volume quantification

MDCKII cells were plated at a density of 28,000 cells per well in a 35 mm microscopy imaging dish (Ibidi, 81156) and incubated at 37 °C overnight or until 60% confluent. Once 60% confluent, cells were either transfected according to the manufacturer’s protocol with Lipofectamine 3000 (Thermo Fisher Scientific, L3000001) with cytoplasmic GFP plasmid (Addgene, pEGFP-N1) for 18 h or incubated when mature (72 h) with Calcein-AM dye (Thermo Fisher Scientific, C1430; 10 μM). Transfection medium was removed, and cells were rinsed twice with PBS and incubated in full DMEM until cell-to-cell junctions were mature (72 h). To image, cells were stained with Deep Red Cell Mask (Thermo Fisher Scientific, 1.5:1,000) for 30 min and Hoechst (Invitrogen, 1:1,000) in PBS for 10 min at 37 °C, washed twice with PBS and incubated in DMEM medium in an enclosed incubated stage at 37 °C with 5% CO_2 (Oko labs) and 0.4 μm z slices were captured every 10 s using a spinning-disk confocal microscope (Nikon, Ti2) for up to 10 h.

To quantify the volume changes of cells expressing cytoplasmic GFP, images were analysed using a threshold macro (Nikon Elements AR, v.5.41.02) with cell mask membrane-stained boundaries to highlight extrusions. The volume data were normalized to the baseline volume before notable junctional changes or extrusion occurs in Excel (Microsoft) and graphed and analysed using GraphPad Prism v.9.4.1.

To quantify cell shrinkage based on solute content, Calcein-AM fluorescence emission was quantified, whereby decreased fluorescence occurs with cell shrinkage. Calcein fluorescence changes were captured during homeostatic shrinkage, hypertonic induced shrinkage, and in response to ion channel inhibitors and hypertonic treatment. Here, fluorescence data were normalized for each cell to its baseline before homeostatic and or hypertonic induced shrinkage in Excel (Microsoft) and graphed and analysed using GraphPad Prism v.9.4.1.

Serial osmolarity treatment

A total of 0.53 × 10^5 MDCKII cells was seeded on glass round coverslips (22 × 55 mm; Academy,400-05-21) and grown to confluence (~100 h). Epithelial monolayers were incubated with isotonic DMEM for 10 min, before treating for 10 min with increasing concentrations of hyper or hypotonic medium to induce cell shrinking or swelling, respectively, then with isotonic DMEM for 120 min. Cells were either filmed (see below) or fixed and stained to quantify extrusions. Experiment cell densities were analysed with bright spots macro in NIS Elements General Analysis (Nikon Elements AR, v.5.41.02) using DNA staining to determine cell density per field and phalloidin and DNA to identify extrusions.

Live hypertonic shock

For live imaging following hypertonic shock, MDCKII cells were plated on an 8-well slide (Ibidi, IB-80801) at a seeding density of 10,000 cells per well and incubated for about 72 h until monolayers were confluent with mature cell–cell adhesions. Cells were stained with Hoechst (1:1,000) in PBS for 10 min at 37 °C, washed twice with PBS and incubated in isotonic DMEM medium (with or without inhibitors), placed in a microscope stage incubator (37 °C, 5% CO_2, Okolabs) and imaged every 10 s for 2.5 h using a widefield microscope (Nikon, Ti2 specifications are provided below). For live cleaved caspase 3 staining, cells were incubated according to the manufacturer’s instructions (1:200, Incucyte caspase-3/7 dyes) before imaging. All of the experiments consisted of three phases: (1) baseline: 0–10 min, during which the cells are incubated in isotonic medium (with or without inhibitors (Supplementary Table 1) or siRNA knockdown (Extended Data Fig. 3)); (2) hypertonic challenge: 10–20 min, during which imaging is paused while isotonic medium is replaced with 20% hypertonic medium with or without inhibitors before imaging is rapidly resumed to capture shrinkage; (3) effects on extrusion: from 20 min to the end of imaging, during which imaging is paused while replacing 20% hypertonic medium with or without inhibitors is replaced with isotonic medium with or without inhibitors and time-lapse phase imaging is resumed to capture. Thus, contractility appears to suppress cell shrinkage over the next 2 h.

PCLSs were imaged to establish the baseline conditions before treatments. For hypertonic challenge, the slices were incubated in 40% hypertonic solution for 20 min, then transferred to isotonic phase imaging medium for live imaging. The drug treatment effect was imaged right after incubating PCLSs in isotonic medium treated with a combination of oligomycin A and oxamate. The experiments were then quantified for extrusion rates per 1,000 or 10,000 cells over time-lapse videos identified using phase microscopy or for the percentage of shrinkage using lightning assays described below.

siRNA knockdown

Four-siRNA smart pools (Horizon Discovery, L-006210-00-0010 (Kv1.1), L-006212-00-0010 (Kv1.2), L-026211-01-0010 (SWELL1), L-006504-00-0010 (ENaCα), L-006505-00-0010 (ENaCβ), L-006507-01-0010 (ENaCγ) or D-001810-01-20 (non-targeting control)) were prepared in DNase/RNase-free water to a 100 μM stock. Then, 28,000 MDCKII cells were seeded in a 6-well plate for quantitative PCR (qPCR) analysis (Thermo Fisher Scientific, 140675), at 10,000 cells per well of an 8-well slide (Ibidi, IB-80801) for live-cell imaging, or with 53,000 cells per 24-well dish with coverslips (Thermo Fisher Scientific, 142475) for extrusion quantification, and grown overnight until 60% confluent. Cells were then transfected using the RNAi Max kit (Thermo Fisher Scientific, 13778150) and 1 μM siRNA for 24 h before replacing with fresh DMEM for 48 h. Cell knockdowns plated for qPCR with reverse transcription (RT–qPCR) analyses were lysed for RNA extraction using the RNAeasy kit (Qiagen, 74104) according to the manufacturer’s instructions. RNA (1 μg) was purified with 1 μl of 10× DNase I reaction buffer, 1 μl of DNase I amplification and RNase-free water in a final volume of 10 μl. The samples were incubated for 10 min at 37 °C, then the reaction was deactivated with 1 μl of 0.5 M EDTA for 10 min at 75 °C. The samples were stored at −20 °C or directly processed by RT–qPCR using the Brilliant III Ultra-Fast SYBR Green QRT-PCR Master Mix (Agilent Technologies), using primers designed with SnapGene (v.6.1.1) and produced by Sigma-Aldrich (Extended Data Fig. 3b). Reactions were analysed using the ViiA 7 Real-Time PCR System (Thermo Fisher Scientific) using the following cycle conditions: 50 °C for 10 min, 95 °C for 3 min, followed by 40 cycles at 95 °C for 15 s and 60 °C for 30 s. Results were normalized to GAPDH expression and graphed and statistically analysed using GraphPad Prism v.9.4.1. Extrusion rates were quantified per 1,000 cells using time-lapse phase microscopy and the percentage of shrinkage was determined using the lightning assay as described below.

Lightning assay

To expedite analysis of cell shrinkage after modulation of different channels, we used the lightning assay. Regions of interest were cropped from phase-microscopy time-lapse videos, thresholding the phase-bright junctional intensity based on white-light detection before, during and briefly after extrusion. The threshold was set to capture the area changes around the cells in the frames before HES or OICE. The same threshold was applied to all frames of the video until completion of cell extrusion. This same method was used for both single cell and whole regions of crowding in an 85 μm^2 (400 px by 400 px) area. Data were then normalized in Microsoft Excel (v.16.67) using an average of 10 frames before lightning and analysing the peak percentage change, and then graphed and statistically analysed using GraphPad Prism v.9.4.1.

Depolarization

A total of 128,000 MDCKII cells per 35-mm dish was grown around 72 h to maturity and then stained with DiBAC_4(3) according to the ‘Tracking transmembrane voltage using DiBAC_4(3) fluorescent dye (PDF)’ protocol (https://ase.tufts.edu/biology/labs/levin/resources/protocols.htm).

Monolayers were then treated with DMSO (vehicle), 4-AP or amiloride, and imaged (phase and GFP settings) every 10 s for a minimum of 2.5 h. PCLSs were time-lapse imaged after incubating with 1:500 DRAQ5 fluorescent probe solution (5 mM, Thermo Fisher Scientific, 62251), 1:500 ATP and 1:1,000 DiBAC_4(3) in HBSS for 30 min at 37 °C according to the manufacturer’s instructions. DiBAC_4(3) was refreshed at each medium/treatment change.

All of the images were analysed using Nikon Elements AR (v.5.41.02) using a ROI over any cell that was maintained or extruded. The ROI mean intensity of DiBAC_4(3) in each cell over time was normalized in Excel using 10 baseline frames before the shrink and depolarization over time was graphed in GraphPad Prism v.9.4.1. Cell counts were then plotted and analysed in Graph Pad Prism v.9.4.1.

ATP measurements

ATP levels were followed in both MDCKII cells and PCLSs using ATP-Red or Queen37. In total, 10,000 MDCKII cells were seeded per well of an 8-well slide (Ibidi, IB-80801) and grown to maturity. ATP levels were analysed after transfection (as described above) of the genetically encoded ATP indicator Queen37 (Addgene pN1-QUE37C, 129318) and or stained with ATP-Red live dye. Transfected cells were counterstained with ATP-Red (10 μM) for 30 min at 37 °C and washed twice with PBS, and then incubated in DMEM medium in an enclosed incubated stage at 37 °C with 5% CO_2 (Oko labs) and 0.4 μm z slices were captured every 2 min using a spinning-disk microscope (Nikon, Ti2) for up to 3 h. Images were analysed using a threshold macro (Nikon Elements AR, v.5.41.02) to quantify the fluorescent changes of cells expressing Queen37 and ATP to highlight changes before extrusions. The fluorescence data were normalized to the baseline levels before depletion and extrusion in Excel (Microsoft) and graphed and analysed using GraphPad Prism v.9.4.1.

Further time-lapse experiments with mature MDCKII cells grown to confluency were incubated with live with ATP-Red, DiBAC_4(3) (as previously described), or CoroNa green AM (described below in the crowding subsection) with or without the ATP inhibitors oligomycin A or oxamate (Supplementary Table 1), or treated with Piezo1 inhibitor GsMTx4, or supplementing with glucose with addition of DMEM with high glucose.

Moreover, PCLSs were time-lapse imaged incubated with 1:500 DRAQ5 Fluorescent Probe Solution (5 mM, Thermo Fisher Scientific, 62251) and 1:500 ATP-Red in HBSS for 30 min at 37 °C according to the manufacturer’s instructions with or without the ATP inhibitors oligomycin A or oxamate (Supplementary Table 1). Imaging was performed in glass-bottom 24-well plates (Ibidi, 82427), with a glass coverslip placed on top to prevent drifting during 4 h imaging sessions at 5 min intervals.

Crowding

MDCKII cells were seeded at 128,000 cells per well in a uniaxial stretched (25%) 10 cm^3 PDMS chamber (Strexcell, SC-0100) and grown ~72 h to confluence and junctional maturity. Once mature, monolayers were stained with the Na+ indicator CoroNa green AM (Thermo Fisher Scientific, C36676 at 10 μM), ATP-Red live-cell dye at 10 μM and cell mask according to the manufacturer’s instructions. The cells were then imaged at homeostatic density or after crowding by releasing monolayers from stretch with or without the ATP inhibitor oligomycin A. Here, fluorescence data were normalized to a previous measurement taken before crowding, or to an earlier timepoint before corresponding homeostatic timepoints that match time of crowding, for each cell. Na+ and ATP fluorescence changes and extrusion rates were quantified in Excel (Microsoft) and graphed and analysed using GraphPad Prism (v.9.4.1).

Microscopy equipment

QPI

Time-lapse QPI and brightfield images were collected on the Olympus IX83 inverted microscope (Olympus) using a ×40/0.75 NA objective. The samples were illuminated using red LED light (623 nm, Thorlabs, DC2200) for 120 ms exposure with a QWLSI wavefront sensing camera (Phasics SID4-4MP), driven by Micro Manager open-source microscopy software. The samples were incubated with a stage-top incubator (Okolabs) set at 37 °C temperature with 5% CO_2 gas and 95% humidity.

Widefield imaging

Time-lapse phase and fluorescence images were captured on the Nikon Eclipse Ti2 system using a Plan Fluor ×20 Ph1 DLL NA = 0.50 objective with a Photometrics Iris 15 16-bit camera and a Cool LED pE-4000 lamp driven by NIS Elements (Nikon, v.5.30.02).

Spinning-disk microscopy

Images were captured on the Nikon Eclipse Ti2 system using Plan Fluor ×20 or ×40 0.75 air objectives or Plan Fluor ×60 or ×100 1.40 oil objectives with an iXon 888 Andor 16-bit camera, a Yokogawa CSU-W1 confocal spinning-disk unit and a Toptica photonics laser driven by NIS Elements (Nikon, v.5.21.03). For blue-light optogenetic stimulation, a Galvo-meter XY (Brunker) 405 laser was used with the Ti2 microscope. Cell staining with phalloidin and Hoechst were quantified for extrusions per 1,000 or 10,000 cells using Nikon Elements Software.

Statistics and reproducibility

For statistical analysis, all experiments were repeated independently on at least three separate days to capture variation in the biological replicates. The minimum sample size was determined according to the standards in the field and based on previously established calculations[49]. This includes graphed data and representative data such as pictures and micrographs. Data were analysed using GraphPad Prism v.9.4.1 statistical software to measure normality with the Shapiro–Wilk test and significance using unpaired and ratio paired t-tests (all t-tests were performed with two-tailed analysis), two-way ANOVA with Tukey’s or Šidák’s correction, or one-way ANOVA with Dunnett’s or Welch multiple-comparison correction, as described in the figure legends. To reduce bias, we imaged random fields within the middle or crowded areas of glass coverslips for quantification or the centre of an 8-well dish (Ibidi, 80806) for live-cell shrink experiments. We excluded low-density epithelia (fields with less than 2,000 cells) that are not crowded enough to elicit extrusion. Graphs were generated using GraphPad Prism v.9.4.1. Figure layouts and models were created in Adobe Illustrator (v.26.3.1). As much of the data analyses were done by the person running the experiments, it was not possible to blind all analysis. However, findings were confirmed with other author investigators who were blinded to the analysis. Further, most analyses and data collection depended on software capture of predetermined set parameters of fluorescence or white light from cell dyes and/or genetically encoded probes or phase microscopy. As the parameters were set on the basis of controls, the data collection was semi-automated and therefore not collected without bias.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Owing to their large size (total of roughly 24 TB), raw microscopy data could not be made available but can be obtained from the corresponding author on request. Source data are provided with this paper.

References

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Acknowledgements

We thank M. Redd for his in-depth questioning that led to revisions to the models and the manuscript; members of the Rosenblatt laboratory for comments on our manuscript; and N. Lane and J. Ashmore for insights on this study. J.R. is the recipient of National Institute of Health R01GM102169, a Howard Hughes Faculty Scholar Award (55108560), a Cancer Research UK Programme Grant (DRCNPG-May21\100007), an Academy of Medical Sciences Professorship (APR2\1007) and a Wellcome Trust Investigator Award (221908/Z/20/Z). C.P.-P. is the recipient of a long-term fellowship (LT000654/2019-L) from the Human Frontier Science Program organization and a Marie Skłodowska-Curie Fellowship (898067) from the European Union’s Horizon 2020 research and innovation programme. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Breast Cancer Research Program under Award Number W81XWH1910065 (T.A.Z.).

Author information

Authors and Affiliations

1. The Randall Centre for Cell & Molecular Biophysics, School of Basic & Medical Biosciences, King’s College London, London, UK

Saranne J. Mitchell, Carlos Pardo-Pastor, Anastassia Tchoumakova & Jody Rosenblatt

2. School of Cancer and Pharmaceutical Sciences, King’s College London, London, UK

Saranne J. Mitchell, Carlos Pardo-Pastor, Anastassia Tchoumakova & Jody Rosenblatt

3. Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA

Saranne J. Mitchell

4. Francis Crick Institute, London, UK

Saranne J. Mitchell, Carlos Pardo-Pastor, Anastassia Tchoumakova & Jody Rosenblatt

5. Laboratory of Molecular Physiology, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

Carlos Pardo-Pastor

6. Department of Chemical Engineering, University of Utah, Salt Lake City, UT, USA

Thomas A. Zangle

Contributions

J.R. and S.J.M. designed experiments, interpreted, analysed data and wrote the manuscript. C.P.-P. designed hypertonic and ion channel experiments, interpreted data and offered important guidance throughout the paper. A.T. designed, performed and analysed mouse lung experiments. T.A.Z. designed and analysed QPI experiments. All of the authors edited the manuscript.

Corresponding author

Correspondence to Jody Rosenblatt.

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Competing interests

The authors declare no competing interests.

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Nature thanks Wolfgang M. Kuebler, Magali Suzanne 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 Quantitative phase imaging (QPI) shows unaltered dry mass in cells that extrude.

A, As QPI requires a reference area with no cells throughout imaging, we grew MDCKII monolayers on confined 100 μm islands within a 35 mm dish by plasma treating patterned areas, as shown in example. Scale bar=100 μm. B, Representative QPI stills from 31 cells from 12 QPI islands in 3 experiments (from Supplemental video 1), showing potential dry mass differences before cell extrusion, where blue is low cell mass and yellow is higher mass increases only after the cell contracts and extrudes (h:mm). By contrast, another cell, in red box, increases mass before dividing, as expected. Scale bar = 50 μm inset box scale bar = 20 μm. C, Graph showing the dry mass of the extruding or dividing cell sixty minutes before each event. D, Representative timelapse images of cells prior to extrusion labelled in red with genetically encoded calcium indicator GECO (red) and phase contrast, scale bar = 5 μm. E, Representative fluorescent trace over time of calcium in cell that extrude. All n’s are independent two tailed experiments.

Source data

Extended Data Fig. 2 Shrink induces live cell extrusion.

A, Stills from a phase (top) timelapse with fluorescent apoptotic marker (bottom) before apoptotic extrusion; scale bar= 10 μm. Quantified in (B) as percentage of cells that shrink before extruding. Data represented as mean ± SEM; n = 5; ****P < 0.0001 by an unpaired T-test of cell extrusion type. C, Mean number of extrusions ±SEM in monolayers treated with SWELL1 inhibitor alone or in combination zVAD-FMK; n = 3; *P = 0.0104 from one-way ANOVA with Sidak’s multiple comparisons test. D, Adapted lightning assay shows area around cells increases with introduction of hypertonic media over time. E, Normalized mean calcein fluorescence before (control isotonic conditions) and after hypertonic conditions, n = 4; where **P = 0.0021 values are from a paired T-test. All n’s are independent two tailed experiments.

Source data

Extended Data Fig. 3 Ion channel inhibitor analysis on extrusion and shrink.

A, Mean extrusion rate ±SEM in monolayers pretreated with inhibitors and during both hypertonic treatment and return to isotonic media; n = 3; ****P = 0.0001 by one way ANOVA with Dunnett’s multiple comparisons test. B, Mean extrusion rate ±SEM pre-treated with inhibitors but only continued during hypertonic treatment, boxes outlined above graphs. N = 3 experiments where NS is from two-way ANOVA with Dunnett’s multiple comparisons test. Inhibitor key for both A and B describes ion channel family inhibitor target by assigned colour and icon. C, Mean extrusion rate ±SEM from monolayers treated with inhibitors only during the 10’ 20% hypertonic treatment n = 3; *P = 0.0142 (AZI), ***P = 0.0003 (4AP), **P = 0.003 (DCPIB), *P = 0.0134 (CCCa), ****P < 0.0001(CFTR), **P = 0.0011 (GsMTx4) from two-way ANOVA with Dunnett’s multiple comparisons test. Representative “Lightning assay” where increased space around the cells indicates cell shrinkage in the presences of: (D) SAC inhibitor GsMTx4, or (E) S1P/S1P_2 inhibitors compared to DMSO controls before and during hypertonic media incubation (mins). F, Confocal representative projections and XZ images of Kv1.1/1.2, or SWELL1 (magenta) with apical tricellular junction protein ZO-1 (green). XY Scale bar=20 μm; n = 3. G, Scatter plots show fold changes (2−∆∆Ct) at 48 h post transfection; the first shows n = 3; ****P = 0.0001 from an unpaired T-Test. H, Normalized mean cell volume ±SEM before and after experimentally inducing shrink, n = 6; ***P = 0.0004 from ratio paired two-tailed T-test. All n’s are independent two-tailed experiments.

Source data

Extended Data Fig. 4 ENaC localization and role in OICE and HES.

A, Confocal representative XY projections and XZ slices of ENaC α, β, or γ (magenta) localization with respect to apical tricellular junction protein ZO-1 (green). XY Scale bar = 20 μm; n = 3. B, Representative depolarization of cells over time (mm:ss) with DMSO control, Kv1.1/1.2 inhibitor (4-AP) or ENaC inhibitor (amiloride). C, Lightning assay as mean % peak area change ±SEM of cells with amiloride during 20% hypertonic challenge, n = 6, **P = 0.0036 from an unpaired t-test. D, Mean extrusion rate ±SEM with amiloride compared to DMSO controls. n = 4 isotonic media treatments and n = 3 for hypertonic challenges, **P < 0.0015 (iso), **P = 0.0045 (hyper) by an unpaired t-test. E, Scatter plots show fold changes (2−∆∆Ct) at 48 h post transfection; the first shows n = 3; ****P < 0.0001 from an unpaired T-Test. F, Mean cell shrinkage rate ±SEM during steady state turnover ±ENAC inhibition or ENaC-α, β or γ siRNA, compared to cells that shrink before extruding; n = 3 ****P < 0.0001 from two-way ANOVA with Dunnett’s multiple comparisons test. All n’s are independent two-tailed experiments.

Source data

Extended Data Fig. 5 Further analysis of ENaC and voltage.

A, Confocal representative XY images of MDCKII cells transfected with ATP indicator Queen37, ATP Red live dye and cell membrane marker cell mask (red) before and after extrusion. Yellow arrows point out the cells which will undergo extrusion; Scale bar=25 μm. Quantified as (B), mean normalized fluorescence of Queen37 genetically encoded marker in cells that extrude to those that do not; n = 7, ****P ≤ 0.0001 by unpaired T-Test. C, Percentage of depolarized cells ±SEM over time ± GsMTx4, n = 3; ****P < 0.0001 from an two-way ANOVA with Sidak multiple corrections. D, Widefield representative XY images of MDCKII cells stained with ATP Red live dye and DiBAC depolarization indicator treated with or without GsMTX4 over time; scale bars =10 μm; n = 3. E, Mean extrusion rate ±SEM ±glucose, n = 4; ****P < 0.0001 from student T-Test. F, Average time CoroNa AM (sodium dye) or DiBAC (depolarization dye) enters cells before HES (dotted line), n = 4 (5 cells), ****P < 0.0001 from student T-Test. G. Average time ±SEM sodium dye (CoroNa) remains in the cytoplasm before HES; n = 3. All n’s are independent two-tailed experiments.

Source data

Supplementary information

Supplementary Table 1

Table of inhibitors included in study. Inhibitor information is listed by commercial name, final concentration used for treatment of cells or PCLSs and the inhibitor target.

Reporting Summary

Peer Review File

Supplementary Video 1

MDCKII dry mass changes before extrusion. Images were taken every 2 min using QPI-adapted technique shown in a parula colour map.

Supplementary Video 2

Phase timelapse (h:min) of MDCKII cells shows junctional lightening before extrusion. Images were taken every 2 min.

Supplementary Video 3

Phase and cleaved caspase-3 (green) timelapse of apoptotic MDCKII cells does not show junctional lightening before extruding. Images were taken every 2 min.

Supplementary Video 4

Phase microscopy timelapse (h:min) of an MDCKII monolayer in isotonic medium. Images were taken every minute.

Supplementary Video 5

Phase microscopy timelapse (h:min) of MDCKII cells after incubation in 20% hypertonic medium increases the rate of extrusion. Images were taken every minute.

Supplementary Video 6

Phase and DiBAC_4(3) (green) timelapse (min:s) showing depolarization of MDCKII cells before extrusion. Images were taken every 10 s.

Source data

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Mitchell, S.J., Pardo-Pastor, C., Tchoumakova, A. et al. Energy deficiency selects crowded live epithelial cells for extrusion. Nature 646, 1187–1194 (2025). https://doi.org/10.1038/s41586-025-09514-w

- Received: 17 March 2023

- Accepted: 11 August 2025

- Published: 10 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09514-w

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Programmable antisense oligomers for phage functional genomics

Milan Gerovac,

Leandro Buhlmann,

Yan Zhu,

Svetlana Ðurica-Mitić,

Valentin Rech,

Samuel Carien,

Tom Gräfenhan,

Linda Popella &

…

Jörg Vogel

Nature volume 646, pages 1195–1203 (2025)

Abstract

Bacteriophages are the most abundant entities on earth and exhibit vast genetic and phenotypic diversity. Exploitation of this largely unexplored molecular space requires identification and functional characterization of genes that act at the phage–host interface. So far, this has been restricted to few model phage–host systems that are amenable to genetic manipulation. Here, to overcome this limitation, we introduce a non-genetic mRNA targeting approach using exogenous delivery of programmable antisense oligomers to silence genes of DNA and RNA phages. A systematic knockdown screen of core and accessory genes of the nucleus-forming jumbo phage ΦKZ, coupled to RNA-sequencing and microscopy analyses, reveals previously unrecognized proteins that are essential for phage propagation and that, upon silencing, elicit distinct phenotypes at the level of the phage and host response. One of these factors is the RNase H-like protein ΦKZ155 (also known as Nlp2), which acts at a major decision point during infection, linking the formation of the protective phage nucleus to phage genome amplification. This non-genetic antisense oligomer-based gene silencing method promises to be a versatile tool for molecular discovery in phage biology, will help to elucidate defence and anti-defence mechanisms in non-model phage–host pairs, and offers potential for optimizing phage therapy and biotechnological procedures.

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Main

The growing interest in bacteriophages is driven by the richness of unexplored genes that emerge in the phage–host conflict1,2. Targeted mapping and characterization of these genes is key to understanding a phage’s infection cycle and its ability to counter host defences, but the diversity and genetic intractability of many phages and their hosts pose major challenges. Phage functional genomics approaches have used targeted inhibition of viral gene expression through, for example, small RNAs3,4 or CRISPR–Cas technology5,6,7. However, the limited genetic tractability of many phage–host pairs due to defence systems that target foreign DNA1,2 or phage-mediated neutralization of Cas enzymes8,9 remains a barrier, even for intensely studied phages such as the model jumbo phage ΦKZ of the major human pathogen Pseudomonas aeruginosa.

ΦKZ-like phages are of interest not only because of their potential for phage therapy of recalcitrant infections[10], but also owing to their complex infection cycle, which features the sequential formation of membrane- and protein-bound compartments. ΦKZ injects its 280-kb dsDNA genome together with a virion RNA polymerase (vRNAP) in an early phage infection (EPI) vesicle for immediate phage gene transcription11,12,13. Subsequently, a ‘phage nucleus’ forms, in which the non-virion RNA polymerase (nvRNAP) continues transcription14,15. The phage genome is then replicated and loaded into phage capsids (Extended Data Fig. 1a). Several conserved phage factors enable the formation and organization of the phage nucleus—for example, the nuclear shell protein chimallin (ChmA)14,16,17, PicA (also known as Imp1), which mediates cargo trafficking into the phage nucleus18,19, and the tubulin-like protein PhuZ, which centres the phage nucleus in the middle of the cell and helps to traffic newly assembled capsids14,20,21. Nonetheless, it remains unclear how many of the approximately 400 annotated protein-coding genes[22] of ΦKZ have an essential role in the phage infection cycle. Notably, the phage nucleus shields the phage genome from CRISPR–Cas and restriction enzymes23,24, which makes targeted inhibition of ΦKZ genes challenging.

Here we present a straightforward and broadly applicable non-genetic route to assess gene essentiality and function in phage–host interactions, utilizing exogenous delivery of synthetic antisense oligomers (ASOs) via a cell-penetrating peptide (CPP) into the bacterial cytosol. Such ASOs are typically 9–12 nucleobases in length and can be programmed to selectively inhibit protein synthesis by binding to the ribosome binding site (RBS) of a target mRNA of interest25,26. Seeking to identify key factors in the intricate infection cycle of ΦKZ in P. aeruginosa, we performed systematic inhibition of phage mRNA translation coupled with phenotypic, transcriptome and proteome analyses. Our results suggest that more ΦKZ proteins than previously appreciated act to steer the phage replication cycle, including the conserved ΦKZ155 protein, which acts at a key decision point prior to ΦKZ genome amplification.

ASOs can silence phage transcripts

Like other bacterial species, P. aeruginosa is amenable to ASO-induced gene silencing, as demonstrated by antimicrobial activity of ASOs directed at mRNAs of essential proteins27,28. This previous work includes ASOs based on peptide nucleic acid (PNA)[27], whose pseudopeptide backbone protects it from nucleolytic and proteolytic degradation. As ΦKZ mRNAs are translated by host ribosomes, we reasoned that they should also be amenable to ASO-mediated silencing (Fig. 1a). Yet, it was unclear whether the rapid overloading of the transcription–translation machinery in infected cells or the phage nucleus would interfere with ASO activity. To establish proof of concept for phage transcript inhibition with antisense PNA, we designed an 11mer ASO to target the Shine–Dalgarno (SD) sequence of chmA mRNA (gene ΦKZ054; Fig. 1b), which encodes the nuclear shell protein[14]. An ASO that does not target any specific gene served as non-targeting control. Western blot analysis of P. aeruginosa cells preincubated with these ASOs showed that 20 min post infection (p.i.) with ΦKZ—that is, within the first round of replication—the chmA-targeting ASO prevented ChmA protein synthesis, even at a high multiplicity of infection (MOI) of 5 (Fig. 1c).

Fig. 1: ASOs silence ΦKZ transcripts in Pseudomonas.

a, ASOs are delivered into bacterial cells by a CPP. Upon phage infection, ASOs bind the target phage mRNA at the RBS, preventing translation. If the encoded protein is essential, phage propagation is halted, protecting the bacterium (red shield). b, The chmA locus and translation initiation region. ASOs targeting the RBS and the non-targeting control ASO (Ctrl) are depicted. c, PAO1 cells were pretreated with 6 µM ASOs for 30 min before ΦKZ infection at an MOI of 5. Cells were collected at the indicated time points and ChmA was quantified by immunoblotting. A Coomassie-stained gel served as loading control. One representative example of four independent experiments is shown. d, PAO1 cells were pretreated for 30 min with 6 µM ASOs. After ΦKZ infection at an MOI of 0.0001, cells were incubated for 180 min. The phage–cell suspension was spotted on LB plates to assess the number of CFUs and on LB plates with a PAO1 lawn to assess PFUs. A high phage titre causes lysis of nearby bacterial cells after spotting, leading to a lack of CFUs at low dilutions in the control. A representative result of two independent experiments is shown. e, PAO1 cells were pretreated with 8 µM ASOs for 30 min and infected with ΦKZ at an MOI of 10, followed by chemical crosslinking and staining of membranes (FM4-64) and DNA (DAPI) at either 5 or 30 min p.i. f, PAO1 cells were pretreated with 8 µM ASO targeting the phage spindle gene phuZ or a control ASO for 30 min, infected with an MOI of 10 and imaged as in e. In parallel, PAO1 cells were infected with a phuZ deletion phage at an MOI of 0.001. Quantification in Extended Data Fig. 2a. Uncropped images of blots are presented in Supplementary Fig. 1.

ChmA is an essential phage protein, so its gene cannot be deleted in ΦKZ[29]. ASO targeting of its mRNA should phenocopy the essentiality of this gene. We therefore assessed cell survival by quantifying colony-forming units (CFUs) and estimated phage plaque efficiency by quantifying plaque-forming units (PFUs) after multiple rounds of replication. This experimental set-up enables us to amplify the effects on phage progeny, to sample delayed replication times and to score effects that become apparent only in the next infection round. Notably, the anti-chmA ASO strongly reduced phage progeny, eliminating plaque formation (Fig. 1d). This allowed us to systematically optimize ASO variables (carrier peptide and length) and experimental conditions (time pre- or post-treatment, ASO toxicity, concentration and MOI) to increase the dynamic range of the phage plaque efficiency score (Extended Data Fig. 1b–i). On the basis of these optimization experiments, we recommend the following conditions for ΦKZ and P. aeruginosa: an 11mer ASO with an (RXR)4XB carrier peptide used at 6 µM concentration in Mueller–Hinton medium with 30 min ASO pre-treatment. We observed the largest dynamic range for phage plaques for an infection at an MOI of 0.0001 after three replication rounds. This corresponds to spotting after 3 h p.i. and resulted in nearly complete host lysis. The extent of CFU clearing is more evident at higher phage titres and longer replication times (Extended Data Fig. 1i). Using these conditions at an MOI of 10 also enables screening of knockdown effects within less than one replication round.

Next, we monitored the effects of ASO on progression of the phage replication cycle by imaging the formation of phage-derived cellular compartments. At 5 min p.i., the EPI vesicle was visible in both control and anti-chmA ASO-treated cells (Fig. 1e), indicating successful infection. At 30 min p.i., the phage nucleus had formed in the control sample, evident as a central DNA-containing structure, but not in the anti-chmA ASO sample. Instead, we observed smaller DNA-containing structures, presumably multiple EPI vesicles resulting from the high MOI of 10 needed for a synchronized infection. Thus, in the absence of ChmA, no phage nucleus forms and this arrests the phage replication cycle. Combined with the complete loss of plaque formation (Fig. 1d), this implies that the EPI vesicle cannot support phage replication. Similar observations have recently been made with CRISPR–Cas-based inhibition of chmA mRNA in the Escherichia coli phage Goslar7,13.

To compare the effect of ASO-mediated knockdown of a phage mRNA with that of deleting the corresponding gene, we silenced the non-essential phuZ mRNA to prevent mid-cell positioning of the phage nucleus by the PhuZ spindle protein[20]. Bacteria pretreated with an anti-phuZ ASO exhibited the loss of phage nucleus centring observed with a ΔphuZ phage[29], effectively phenocopying the phage gene deletion (Fig. 1f; quantification in Extended Data Fig. 2a).

ASO design and specificity

Our previous global analyses of ASO-mediated knockdown in different bacteria30,31,32,33 culminated in MASON[34], an ASO design algorithm that predicts effective and selective ASO inhibitors of protein synthesis based on various criteria such as melting temperature (Tm), low self-complementarity, low purine percentage and mRNA target site localization. MASON considers central mismatches as detrimental to ASO efficacy[34], and as expected, two central mismatches abrogated the activity of the chmA ASO (Extended Data Fig. 2b). To establish effective ASO sites within a phage 5′ mRNA region, we tiled the chmA mRNA in the −37 to +44-nt window relative to its AUG start codon. Of note, all 19 ASOs that bind at or close to the SD sequence or AUG with a Tm between 35 and 58 °C strongly inhibited ChmA synthesis and phage plaque efficiency (Extended Data Fig. 2c). Thus, multiple effective ASOs can be designed for a given target to mitigate the risk of off-targeting and false-positive readout of phenotypes.

To address ASO specificity and potential off-target effects at the protein level, we performed proteomics on ΦKZ-infected P. aeruginosa pretreated with ASOs against three different mRNAs: chmA, the nvRNAP transcript ΦKZ055 and picA (gene ΦKZ069) (Supplementary Data 1). Sampling a 10 min time course of ΦKZ infection, we did not observe substantial ASO-dependent changes in the amounts of the 1,130 P. aeruginosa proteins detected with high confidence, which argues against widespread off-target effects in the host (Extended Data Fig. 3a). Similarly, we analysed the phage proteome at 7.5 and 10 min p.i.—that is, when phage proteins became abundant. We observed a specific reduction in the levels of ChmA, nvRNAP or PicA (Extended Data Fig. 3b) without strong effects on other phage proteins. Notably, all three targets are co-transcribed with additional genes, but the respective ASOs inhibited only the targeted cistron. For example, the chmA ASO suppressed synthesis of ChmA but not that of the nvRNAP encoded by the downstream cistron (Extended Data Fig. 3c–e). Thus, ASOs can be used for selective suppression of protein synthesis within polycistronic transcripts.

Versatile ASO applications in phage biology

ASOs are particularly attractive for phage–host interaction studies in genetically intractable bacteria, targeting either phage or host genes. Many bacterial strains collected from patients are refractory to genome modification techniques that require stable transformation with DNA[35], such as the genomically diverse P. aeruginosa isolates PaLo8, PaLo9, PaLo39 and PaLo44 (PRJNA731114). Nonetheless, ASOs targeting chmA effectively inhibited phage plaque and nucleus formation in these clinical isolates (Fig. 2a and Extended Data Fig. 4a–c). Thus, ASOs can bypass the need for genetic tractability in functional studies of essential phage factors.

Fig. 2: ASO applications in phage biology.

a–c, Left, schematic representation of potential ASO applications, such as targeting clinical isolates (a), sensitizing bacteria to phage infection by silencing bacterial anti-phage defence systems (b), or protecting bacteria from phage infection by silencing phage genes that affect anti-phage defence systems (c). Anti-phage defence systems are depicted as shields, and phage proteins that overcome bacterial defences are depicted as swords. a, Right, PaLo44 cells were pretreated with 6 µM ASO targeting chmA for 30 min. Cells were infected with ΦKZ at an MOI of 0.0001 and incubated for 180 min followed by CFU and PFU quantification. b, Right, PA14 cells were inoculated, grown for 30 min and pretreated with 6 µM ASO targeting jukA for 150 min to ensure JukA depletion before infection. Cells were infected with ΦKZ at an MOI of 0.0001 and incubated for 180 min followed by CFU and PFU quantification. c, Right, PAO1 and PaLo44 cells were pretreated with 6 µM ASO targeting ΦKZ014 for 30 min. Cells were infected with ΦKZ at an MOI of 0.0001 and incubated for 180 min followed by CFU and PFU quantification. Representative results of two independent experiments (a–c).

Host-encoded defence systems are a major barrier to phage replication, but their genomic disruption is challenging. To test ASOs as alternative inhibitors, we selected the JukAB defence system in P. aeruginosa strain PA14, which prevents early phage transcription, DNA replication and nucleus assembly[36]. Owing to constitutive expression of the jukAB locus, anti-jukA ASOs were added 2.5 h before infection to allow more time for JukA protein depletion before ΦKZ exposure. ASO-mediated silencing of jukA mRNA rendered P. aeruginosa PA14 bacteria susceptible to ΦKZ (Fig. 2b), indicating successful inactivation of the defence system. In the arms race between phages and bacteria, phages constantly evolve counter-defence mechanisms to overcome host barriers. ΦKZ encodes the ribosome-associated protein ΦKZ014, which appears to circumvent a defence system that is specific to the clinical isolate PaLo44[37]. Although the ΦKZ014 gene can be disrupted when ΦKZ is propagated in the P. aeruginosa PAO1 host, the resultant ΔΦKZ014 phage does not produce progeny in the PaLo44 isolate[37]. In agreement with this strain tropism, ASO-mediated knockdown of the ΦKZ014 mRNA impaired ΦKZ replication in PaLo44 but not in PAO1 (Fig. 2c), effectively phenocopying the corresponding phage gene mutant. Thus, ASOs can be used as tools to dissect defence and anti-defence loci in relevant native settings and ultimately to achieve phage propagation in non-permissive strains.

To further explore the versatility of ASOs in phage biology, we tested distinct phage–host pairs. Targeting chmA (also known as gp296) in the RAY phage[38], which infects the Gram-negative plant pathogen Pantoea agglomerans (family Erwiniaceae), effectively abolished plaque formation (Extended Data Fig. 4d). Similarly, we established proof of principle in a Gram-positive species, which has a very different envelope structure. Targeting expression of DNA polymerase (gene 31) of the Bacillus subtilis phage SPO1 caused a 100-fold reduction in plaque efficiency (Extended Data Fig. 4e). Finally, to prove applicability beyond phages with a DNA genome, we targeted different genes of the RNA phage PP7, which infects P. aeruginosa PAO1 (ref. [39]). Using an adjusted regime, ASOs targeting the PP7 genes rep (RNA-dependent RNA polymerase) or lys (lysis) completely abrogated plaque formation (Extended Data Fig. 4f). Thus, mRNA-targeting ASOs can be applied to diverse hosts and phages and are agnostic to the type of phage genome.

ASO screen for essential phage proteins

The programmable nature of ASOs allows larger screens for genes that are crucial for phage propagation. Of the annotated 377 ΦKZ genes, 85% have no predicted function22,40. This paucity of functional knowledge extends to the smaller core genome of Chimalliviridae, which consists of seven blocks of genes with probably interlinked functions, plus five independent genes[38]. To discover additional essential genes for ΦKZ propagation, we screened 75 core and annotated genes (omitting most of the structural genes) using ASOs and CFU and PFU readout in P. aeruginosa PAO1.

Overall, ASO-mediated knockdown of one-third of these genes (24) led to a strong effect (++ or +++) on phage plaque efficiency with multiple logs of PFU reduction and CFU recovery (Table 1, Extended Data Fig. 5a and Supplementary Data 2). As expected, several mRNAs of known essential phage factors, such as chmA (ΦKZ054), nvRNAP subunits (ΦKZ055 and -068) and the major head protein (ΦKZ120), produced strong effects (Extended Data Fig. 5b). Similarly, ASOs against the mRNA of the phage nucleus import protein PicA (ΦKZ069)18,19 abrogated plaque formation. Moreover, several uncharacterized genes produced strong plague formation phenotypes upon ASO targeting—for example, the ΦKZ049 gene, which encodes a SH3 domain protein interactor of the phage DNA polymerase[41] (Extended Data Fig. 5b). Of note, transcript abundance and protein levels of these hits varied substantially, indicating that targeting of highly expressed genes with ASOs is possible (Supplementary Table 1).

Table 1 Top hits from the ASO screen

Knockdown of other genes produced milder effects—such as reducing plaque formation by only two to three orders of magnitude—but nevertheless protected the host from lysis, as judged by CFU counts. An example is ΦKZ144, which encodes an endolysine for final phage release from the cell[42] (Extended Data Fig. 5b). Systematic western blot analysis revealed 11 additional hits for which ASO-mediated knockdown negatively affected ChmA protein accumulation without substantial changes in the CFU and PFU counts (Table 1). These include genes encoding a putative RAD2/SF2 helicase (ΦKZ075), a predicted DEAD/DEAH box helicase (ΦKZ203), the macro domain-containing protein ΦKZ104, a virion RNA polymerase subunit (ΦKZ176)[43], as well as other uncharacterized core and non-core genes. We predict that these genes benefit phage fitness in more competitive growth conditions or in P. aeruginosa hosts with different defence systems. The vRNAP subunit ΦKZ176 might be a special case, as this protein is injected into the host cell upon attack—thus, the ASO-mediated mRNA inhibition will reduce ΦKZ176 protein levels only in subsequent infection rounds. This, in turn, will then reduce ChmA levels, as early transcription of chmA from the EPI vesicle is vRNAP-dependent13,15,44. In summary, our screen yielded 56 phage genes whose knockdown caused varying effects on phage propagation, offering promising leads for in-depth studies using ASO treatment in combination with phenotypic or multi-omics readouts.

Indirect effects of ASOs were also observed: 22 out of 176 tested ASOs were generally toxic to P. aeruginosa (Extended Data Fig. 5c). Other ASOs increased PFUs by one order of magnitude, indicating that the targeted proteins, such as ΦKZ151, act to counter-regulate the infection or alter host vulnerability to the phage (Extended Data Fig. 5c). Furthermore, four ASOs induced plaques without phage treatment, perhaps by activating a Pf1-like prophage[45] (Extended Data Fig. 5c).

Molecular phenotypes of phage genes

Phage infection is a fine-tuned process, and not all involved factors will yield a macroscopic phenotype such as altered CFU and PFU counts upon ASO knockdown. We reasoned that a global method such as RNA sequencing (RNA-seq) would detect additional ‘molecular phenotypes’, defined as specific transcriptional dysregulation in infected cells46,47. To establish this approach for ΦKZ, we performed RNA-seq after chmA knockdown, which arrests the infection cycle at the level of the EPI vesicle (Fig. 1e and ref. [13]). Total bacterial RNA was extracted at 10, 15, 20, 25 and 35 min after ΦKZ infection and analysed by RNA-seq (Supplementary Data 3). A principal components analysis showed different trajectories for the non-targeting and targeting condition (Fig. 3a). In agreement with previous work37,44,48, phage transcripts accounted for around 45% of all coding sequence reads at 10 min p.i., increasing to around 70% at 35 min p.i. (Extended Data Fig. 6a,b). This was similar for both conditions, but ASO-mediated knockdown of chmA resulted in almost no RNA-seq reads from middle/late phage genes (seen from 20 min p.i. onward), indicating that phage nucleus formation is required for the transcription of these genes by the nvRNAP (Fig. 3b and Extended Data Fig. 6b–d, classes E and F). By contrast, early phage transcripts showed prolonged expression (Extended Data Fig. 6b,d, classes A and B), possibly because vRNAP-driven transcription continues. chmA knockdown also resulted in a loss of phage-encoded tRNAs (Extended Data Fig. 6c), consistent with their dependence on the nvRNAP[44]. Collectively, these data support a model in which following phage genome transfer from the EPI vesicle to the phage nucleus, the nvRNAP takes over phage transcription with concomitant cessation of vRNAP activity11,13,15,44,49.

Fig. 3: Transcriptional response after knockdown of ChmA.

a, PAO1 cells were pretreated with 6 µM ASO targeting chmA for 30 min. Cells were infected with ΦKZ at an MOI of 5 and incubated for the indicated times post infection followed by RNA-seq. To reduce the dimensionality of the dataset, we projected the data based on transcript abundance of each annotated gene on two dimensions via principal components analysis. Each dot represents an independent experiment. Var., variance. b, Volcano plot of the log_2-transformed fold change (log_2(fold change)) of transcripts of samples treated with the ASO targeting chmA compared with the control ASO at the indicated time points. Two independent replicates were merged by geometrical averaging and P values were calculated by the Wald test (two-sided) using DESeq2.

Source Data

The chmA mRNA itself was unaffected by the ASO treatment, suggesting that its translational inhibition does not entail mRNA depletion (Fig. 3b). This would also explain why ASOs that target polycistronic phage genes are cistron-specific (Extended Data Fig. 3c–e). However, accelerated mRNA decay was observed with several ASO targets in E. coli[31], highlighting the importance of monitoring levels of individual mRNAs when targeting co-transcribed genes.

Our ability to selectively prevent ChmA protein synthesis also allowed us to determine the extent to which phage nucleus formation prevents a late host response. Analysing host transcript changes with the control ASO, we did not observe a substantial transcriptional response at later stages of infection compared with 10 min p.i., when host takeover is thought to be complete (Extended Data Fig. 6a). By contrast, knockdown of chmA resulted in higher expression of 13 out of approximately 3,637 detected P. aeruginosa transcripts at 35 min p.i. (Fig. 3b). Among these genes were PA0201, which encodes a hypothetical hydrolase; the gene encoding the membrane protein FxsA, which is linked to phage exclusion and may prevent superinfection50,51; and PA3818, which encodes the type III secretion system regulator SuhB, which is important for Pseudomonas virulence[52]. Thus, even if no phage nucleus forms, the host response remains limited, demonstrating the protective nature of the EPI vesicle.

Next, we performed RNA-seq analysis of phage and host transcripts after ASO-mediated silencing of 56 positive hits from our screen (Supplementary Data 4). At 15 min p.i., we observed diverse effects on phage transcript levels of genomic islands that did not cluster clearly (Extended Data Fig. 7a,b). At 30 min p.i., ASOs that depleted ChmA, the nvRNAP subunit ΦKZ055, the nvRNAP sigma factor ΦKZ068, PicA (gene ΦKZ069), ΦKZ122, ΦKZ124 and ΦKZ151 produced strong effects on phage transcription (Supplementary Data 4). ChmA, nvRNAP and PicA stood out because they strongly clustered based on ΦKZ transcript level alterations (Fig. 4a and Extended Data Fig. 7c). Other t-distributed stochastic neighbour embedding (t-SNE) clusters exhibited more subtle transcriptional effects in the complementary hierarchical clustering (Extended Data Fig. 7c). Of note, reducing the levels of PicA resulted in a similar RNA-seq profile to loss of ChmA and nvRNAP, supporting the notion that PicA is the main nuclear import factor for newly synthesized nvRNAP subunits18,19.

Fig. 4: ΦKZ155 is important for phage nucleus maturation and genome amplification.

a, log_2-transformed fold change values for each ΦKZ gene from Supplementary Data 4 at 30 min p.i. were clustered by t-SNE. Discussed clusters are highlighted with a dashed line. b, AlphaFold3 structure prediction of ΦKZ155 (wheat), the ΦPA3 homologue Gp176 (orange) and an RNase HI domain (blue). N terminus, green; C terminus, red. c, PAO1 cells were pretreated with 8 µM ASO targeting ΦKZ155 (ΦKZ) or gp176 (ΦPA3) for 30 min. Cells were infected with ΦKZ or ΦPA3 at an MOI of 5 and incubated for 35 or 50 min, respectively, followed by chemical crosslinking and staining of DNA (DAPI) or membranes (FM4-64). d, PAO1 cells were transformed with a plasmid encoding ΦKZ155 or the empty vector. Cells were pretreated with 8 µM ASO targeting ΦKZ155 for 30 min, infected with ΦKZ at an MOI of 10, plasmid-encoded ΦKZ155 was induced with 0.2% arabinose, and the cells were incubated for 30 min, followed by chemical crosslinking and staining as in c. Results in c,d are representative of two independent experiments. e, PAO1 cells were pretreated with 6 µM ASO targeting chmA or KZ155 for 30 min. Cells were infected with ΦKZ at an MOI of 5 and incubated for the indicated times followed by immunoblotting. inf., infected; n.i., non-infected. A representative result of three independent experiments is shown. f, PAO1 cells were pretreated with 6 µM ASO targeting ΦKZ155 for 30 min. Cells were infected with ΦKZ at an MOI of 5 and incubated for the indicated times followed by DNA extraction and dot blotting. Phage genomic DNA (gDNA) was detected with a radiolabelled probe (JVO-23213). A representative result of two independent experiments is shown. g, Model of ΦKZ155 function. After phage infection and initial nucleus formation, the ΦKZ155 protein is imported into the nucleus and has a role in phage nucleus maturation and genome amplification. Upon knockdown of ΦKZ155, the phage nucleus remains immature, although ChmA is expressed. The phage genome is not amplified, and phage infection is halted. Uncropped images of blots are available in Supplementary Fig. 1.

On the host side, we observed diverse responses upon knockdown of different phage genes (Supplementary Data 4, Extended Data Fig. 7d–g). For example, knockdown of ΦKZ174 not only abrogated plaque formation, but also was accompanied by lower expression of almost 5% of all detected P. aeruginosa genes. Many of these genes serve metabolic functions, as if the ΦKZ174 protein acts to prevent infected bacteria from metabolic shutdown. Knockdown of ΦKZ082 did not change the PAO1 transcriptome but induced transcription of the Pf4 prophage locus (Extended Data Fig. 7h). As awakening of the Pf4 filamentous bacteriophage can result in partial lysis of the bacterial population[53], ΦKZ might use the ΦKZ082 protein to suppress the Pf4 prophage to ensure its own propagation. In summary, coupling ASO-mediated knockdown to RNA-seq revealed distinct transcriptional profiles of perturbed ΦKZ genes and predicted clusters of ΦKZ factors with different perturbation modes.

ΦKZ155 is essential for phage replication

Searching for new ΦKZ factors with key roles in the phage replication cycle, we observed a set of genes—ΦKZ120, ΦKZ155 and ΦKZ177—whose silencing strongly reduced plaque formation but hardly altered phage gene expression up to 35 min p.i. (Supplementary Data 4). This can be rationalized for ΦKZ120 (major head protein) and ΦKZ177 (function unknown), as these are late genes (Supplementary Data 4). By contrast, ΦKZ155 was highly expressed at 20 min p.i. and its expression was dependent on ChmA (Extended Data Fig. 8a). This suggested that the ΦKZ155 protein has an important role after the initial formation of the phage nucleus.

Although ΦKZ155 is part of the core genome of nucleus-forming bacteriophages[38], its role in the infection cycle of ΦKZ or related phages was unknown. Its predicted structure shows an N-terminal RNase HI domain (Pfam domain PF00075) and a well-structured C-terminal domain[19] (local distance difference test >0.9, AlphaFold3 server, Fig. 4b). To understand the cellular consequences of ΦKZ155 depletion, we performed fluorescence microscopy imaging and found that ASO-mediated knockdown of ΦKZ155 prevented phage nucleus formation (Fig. 4c). Targeting the putative homologue Gp176 in P. aeruginosa phage ΦPA3 with a different ASO (Extended Data Fig. 8b) also caused loss of a visible phage nucleus (Fig. 4c). Notably, ΦKZ155 and Gp176 show only 65% sequence identity, but are structurally highly similar (Fig. 4b), and are thus likely to serve the same function.

To test whether ASO-mediated knockdown of an essential gene such as ΦKZ155 can be reversed through complementation with an insensitive allele, we expressed the open reading frame (ORF) of ΦKZ155 from a plasmid with a heterologous promoter and a 5′ untranslated region with no binding site for the anti-ΦKZ155 ASO. This plasmid fully restored nuclear maturation and phage plaque efficiency upon ASO-mediated knockdown of the phage-borne ΦKZ155 gene (Fig. 4d and Extended Data Fig. 8c). Complementation was also successful with a catalytic centre mutant (ΦKZ155(D102N)) with no in vitro RNase H activity (Extended Data Fig. 8c–e). Thus, ΦKZ155 (whose gene is co-transcribed with ΦKZ154, ΦKZ156 and ΦKZ156.1)[49] serves an essential yet nuclease-independent function. Notably, western blot analysis showed that the absence of a visible phage nucleus was not caused by a shortage of ChmA (Fig. 4e). Moreover, our RNA-seq data indicated that phage nucleus-dependent transcription is unabated in the absence of ΦKZ155, which implies successful import of the nvRNAP into the nascent phage nucleus (Supplementary Data 4). Thus, we propose that the knockdown of ΦKZ155 arrests the ΦKZ cycle at a checkpoint immediately following initial phage nucleus formation. This is consistent with the ΦKZ155 protein localizing inside the phage nucleus[19] (Extended Data Fig. 8f), where it may drive phage nucleus maturation.

To better understand why loss of ΦKZ155 arrests the infection cycle, we wanted to explore whether phage nucleus size might be linked to genome copy number. Therefore, we quantified phage DNA by dot blot analysis. In this assay, phage DNA became detectable at 40 min p.i. in P. aeruginosa PAO1 pretreated with the control ASO. By contrast, ASO-mediated silencing of ΦKZ155 inhibited phage genome amplification (Fig. 4f), as did silencing of the homologous gp176 gene in phage ΦPA3 (Extended Data Fig. 8g). Of note, we generally observed no degradation of the host genome (Extended Data Fig. 8h), in agreement with a previous report[48]. Thus, in the absence of ΦKZ155, the phage infection cycle freezes at a crucial stage when the nascent phage nucleus matures by amplification of the phage genome (Fig. 4g). Such defined arrested states achieved by ASO-based depletion of phage factors will help to dissect the molecular underpinning of key decision points in the progressing phage infection cycle.

Discussion

ASO-mediated gene silencing should be of broad use in phage biology. Although our primary goal was to identify key genes of the P. aeruginosa phage ΦKZ, we also show that this functional genomics tool is readily adaptable to other DNA and RNA phages and hosts, including bacteria that are genetically intractable. ASOs can be used to study many aspects of phage–host interactions beyond the scoring of gene essentiality, which includes the suppression of a host-encoded phage defence system or the identification of molecular phenotypes by coupling ASO treatment to sensitive readouts such as RNA-seq. Our screen of one-fifth of the ORFs of the model jumbo phage ΦKZ clearly demonstrates that ASO-mediated mRNA silencing of many of these ORFs produce detectable macroscopic or molecular phenotypes. Of note, this screen sampled a single host under one growth condition, and is therefore likely to underestimate the proportion of functional genes.

The ASOs used here were designed to translationally silence mRNAs. Other attractive targets would be small noncoding RNAs, a class of molecules whose roles in phage–host interactions are becoming more apparent4,54. Similarly, ASOs could help to illuminate gene functions for the expanding class of minimal RNA replicators such as viroids and viroid-like covalently closed circular RNAs, many of which replicate in environmental bacteria55,56. We also foresee applications in phage therapy, for example, in the optimization of production strains or phage cocktails, or in industrial settings, for example, protecting starter cultures from detrimental phage infections. Such applications will benefit from a main distinguishing feature of ASO-based mRNA knockdown, which is that it does not produce a genetically modified organism.

In relation to jumbo phage biology, ΦKZ155 emerges as a conserved phage-encoded RNase H-like protein with an essential function in the temporal coordination of phage nucleus maturation and genome amplification. This is consistent with the localization of ΦKZ155 within the phage nucleus, where phage genome amplification takes place. ΦKZ155 has also been used to study PicA-mediated import into the phage nucleus[19]. Using an ectopically expressed GFP-tagged variant, we observed that ΦKZ155 forms a single punctum at the periphery of the nucleus (Extended Data Fig. 8f), similar to the recently reported localization of PicA (also known as Imp1) and supporting the proposed stable interaction between both proteins18,19. Future studies should focus on how ΦKZ155 licenses phage genome amplification at this stage of the infection cycle. RNase HI domains, such as in ΦKZ155, typically recognize RNA–DNA heteroduplexes, cleaving the RNA strand[57]. Although the molecular mechanisms underlying phage genome replication are largely unknown, it has been suggested that phage-related RNA–DNA duplexes are resolved by either phage- or host-encoded RNase H[58]. Although ΦKZ155 is a bona fide RNase H (Extended Data Fig. 8d), our complementation assay shows that this nucleolytic activity does not explain why this protein is essential (Extended Data Fig. 8c,e). Nonetheless, the RNase HI fold is conserved among ΦKZ155 homologues, which strongly suggests a nucleic acid-related function. The N-terminal RNase HI domain of ΦKZ155 could potentially serve a sensory rather than catalytic function in phage genome replication.

Engineering phage mutants is not trivial, and is impossible for essential genes, but our ASO method combined with plasmid complementation offers a new experimental route to dissect the role of key residues and the overall domain structure of ΦKZ155. Ultimately, this will help to illuminate the transient state shortly after the phage genome is translocated from the EPI vesicle into the phage nucleus. In addition, it is notable that ΦKZ encodes at least 10 nucleases (2.5% of all annotated genes). Their targets and catalytic mechanisms remain largely unknown, except for those of ΦKZ179, whose homologue in phage ΦPA3 acts in inter-phage competition[59]. Our ASO screen indicates that the predicted HNH nuclease ΦKZ072 has a strong phenotype similar to those of ChmA or ΦKZ155 (Table 1). ΦKZ072 is expressed early and in a phage nucleus-independent manner (Supplementary Data 3), suggesting that it has a role in early host takeover. Given our ability to inhibit their expression, it will be interesting to unravel how this nuclease and others with milder phenotypes (ΦKZ056, ΦKZ165 and ΦKZ199; Table 1) act at different stages in the finely orchestrated phage replication cycle.

Despite its anticipated broad applicability, ASO-based gene silencing also has limitations. First, it relies on efficient delivery of the ASO into the bacterial cytosol, with CPPs such as (KFF)3K and (RXR)4XB being commonly used carriers. However, CPP efficacy is influenced by bacterial envelope composition[60] and may be limited by toxicity at higher doses. Alternative carriers, including siderophores and nanoparticles, are currently being developed[25]. Second, ASOs are designed to block translation but they may also trigger mRNA degradation30,31. Although this appeared to be rare among the ΦKZ mRNAs targeted here, induced RNA decay must be kept in mind when targeting polycistronic mRNAs in other species. Third, effective ASO design should take into account challenges from incomplete genome annotations, such as misannotated ORFs and the presence of introns in phage genes. Finally, since ASOs typically cause knockdown rather than complete knockout and cannot target virion-packaged proteins, such as vRNAP, during the initial infection, the phenotypes may be mild. Nonetheless, with careful consideration of these factors when selecting the phage–host system, ASOs will be a powerful tool.

Methods

ASO design

ASOs were designed to bind at the RBS using the MASON algorithm (https://mason.helmholtz-hiri.de)[34] and the NCBI sequence and annotation files (ΦKZ: NC_004629.1, ΦPA3: NC_028999.1, PP7: NC_001628.1, SPO1: NC_011421.1, RAY: NC_041973.1, PA14: NC_008463.1, PAO1: NC_002516.2). ASO length was set to 11 nt and the allowed mismatches for off-targets were set to 4 nt. ASOs were selected based on the following scoring values: melting temperature (45–55 °C), low purine percentage (25–35%) and few predicted off-targets in distinct translation initiation regions of the phage (<3). The scoring values for the used ASOs are given in Supplementary Data 5. At least two ASOs were designed for each targeted gene. The control ASO sequence was GACATAATTGT (ctrl., JVPNA-79). ASOs were commercially ordered at Peps 4LS (Heidelberg) with a peptide backbone (PNA) and an N-terminal RXR (RXRRXRRXRRXRXB), KFF (KFFKFFKFFK) or TAT (GRKKRRQRRRYK) CPP. The initial concentration was adjusted to 1 mM in water based on the specific extinction coefficient using absorption. ASOs were stored at −20 °C. Prior to use, ASOs were thawed at room temperature, heated for 5 min at 55 °C and then cooled down to room temperature. All ASO sequences are listed in Supplementary Data 5, for example, JVPNA-72 for chmA knockdown, JVPNA-125 for nvRNAP knockdown, JVPNA-964 for PicA knockdown, and JVPNA-960 for PhuZ knockdown.

CFU and PFU assay

P. aeruginosa strains PAO1 (JVS-11761, DSMZ: DSM22644), PaLo8, PaLo9, PaLo39, PaLo44 (R. Lavigne laboratory, KU Leuven, Belgium), and PA14 (R. Lavigne laboratory, KU Leuven, Belgium) were grown in LB medium overnight at 37 °C and 220 rpm. Cells were inoculated 1:100 and grown in Mueller–Hinton medium at 37 °C and 220 rpm to an absorbance (optical density) of 0.3 (absorption given at 600 nm). ASOs were added at 6 µM final concentration to 50 µl cultures and incubated for 30 min. Cells were infected with ΦKZ at an MOI = 0.0001 and the cells were incubated for 180 min, which corresponds to three full replication rounds. Five microlitres of cell culture were diluted in series and spotted on LB plates and on 0.5% LB soft agar plates with the susceptible strain PAO1 (one volume of 0.5% LB soft agar at 42 °C, was mixed with 0.01 volume cells at an optical density of 0.5, and poured into a plate). Plates were imaged with the Typhoon 7000 phosphor imager (GE Healthcare) in fluorescence mode.

For jukA silencing in PA14, the cells were inoculated 1:100 in Mueller–Hinton medium, after 30 min cells were pretreated with 6 µM ASOs. The cells were infected after 150 min pre-incubation time with ΦKZ at an MOI = 0.0001, and incubated further for 180 min followed by CFU and PFU quantification.

For PP7 infection, PAO1 cells were inoculated 1:100 in Mueller–Hinton medium and treated at optical density 0.3 with 0.05 µM final concentration ASOs, additionally every 30 min 0.05 µM final concentration ASOs were added. Cells were infected 30 min after starting treatment with PP7 at an MOI = 0.00001, and cells were collected for CFU and PFU analysis after 120 min.

For RAY phage, P. agglomerans (DSMZ: DSM3493) cells were grown overnight in LB and were then inoculated 1:100 in Mueller–Hinton medium and grown at 37 °C, at optical density 0.3, cells were pretreated with 6 µM RXR–ASOs for 30 min. Cells were infected with phage at an MOI = 0.0001, followed by incubation for 300 min and spotting.

For SPO1 phage, B. subtilis 168 (BGSC: 1A1) was grown overnight in LB medium and then inoculated 1:100 in Mueller–Hinton medium, and grown at 37 °C, at optical density 0.3, cells were pretreated with 6 µM KFF–ASOs for 30 min. Cells were infected with phage at an MOI = 0.0001, followed by incubation for 180 min and spotting.

Immunoblotting

Infected cell cultures were mixed with final 1× SDS–PAGE loading dye (60 mM Tris/HCl pH 6.8, 0.2 g ml−1 SDS, 0.1 mg ml−1 bromophenol blue, 77 mg ml−1 DTT, 10% (v/v) glycerol) and were boiled for 10 min at 95 °C for denaturation. Protein samples were analysed by SDS–PAGE and blotted onto methanol-preactivated polyvinylidene (PVDF) membranes. As a loading control, we used Coomassie staining of a second gel where we loaded the same sample volume. ChmA was produced as previously described[16] in E. coli BL21-CodonPlus (DE3)-RIL cells (Agilent Technologies, JVS-12280, chloramphenicol resistance) that were transformed with pET-M14(+) plasmid carrying the chmA gene with a N-terminal His-V5-TEV-tag (pMiG118). ChmA was purified as described before[16]. The tag could not be removed in the purification procedure. Commercial antibody sera were generated by Eurogentec. Rabbits were immunized with the purified ChmA protein. The rabbit serum (no. 2481) was used at a 1:10,000 dilution together with anti-rabbit HRP-conjugated antibody (Thermo Scientific, 31460) in a 1:10,000 dilution in 5% BSA/TBST for ChmA detection in immunoblotting. Antibody specificity was validated in immunoblotting by comparison between ΦKZ-infected and non-infected cells that yielded a defined band at 70 kDa corresponding to ChmA only in infected cells (Fig. 1c).

Microscopy

Agarose (0.85% (w/v)) was dissolved in fivefold water-diluted LB medium and boiled to melt. The liquid agarose was poured on microscope slides with one slide pair at each side as a spacer and one slide on top to form a closed gel slice as described[61]. After solidification, approximately 1 cm × 1 cm pads were cut. Cells were grown in Mueller–Hinton medium at 220 rpm and 37 °C to an A600 nm ≈ 0.3, then preincubated with 8 µM ASOs for 30 min, and infected with ΦKZ or ΦPA3 at an MOI = 10. Of note, ΦKZ ΔphuZ had a low titre owing to deletion of phuZ and could only be infected with an MOI of ~0.001. At indicated time points the phage replication cycle was quenched by cooling the cells on ice for 10 min and the cells were pelleted at 8,000g for 5 min. The supernatant was removed, and the cells were resuspended in 500 µl 4% paraformaldehyde and incubated for 15 min on ice. Afterwards cells were washed with PBS and were resuspended in 50 µl PBS for storage at 4 °C. For the imaging of the ΦKZ155 knockdown, we crosslinked bacteria in the medium with 2% glutaraldehyde for 30 min on ice, followed by the addition of 5% formaldehyde for 30 min on ice, as previously described[62]. Cells were stained with 16 µM FM4-64, and 360 nM DAPI and 5 µl were layered onto 1.2% agarose pads. The pad was placed with the side of application downwards into a µ-Slide 8 Well high Grid-500 (BD Biosciences). Transmission and fluorescence were detected with a confocal laser scanning microscope Leica SP5. Images were processed with ImageJ (1.53).

For the imaging of ΦKZ155–GFP, PAO1 cells were transformed with a plasmid (pLBu005) encoding ΦKZ155-gfp under the control of an arabinose-inducible pBAD promoter and selected on gentamycin plates. The cells (JVS-13713) were grown to an optical density of 0.25 and induced with arabinose at indicated concentrations followed by phage infection with an MOI = 10 at optical density 0.3. The collection, crosslinking and imaging of cells was conducted as described above for wild-type cells.

Proteomics

PAO1 cells were grown overnight in LB medium and were then inoculated 1:100 in Mueller–Hinton medium and grown at 37 °C and 220 rpm for 150 min to optical density 0.3. Cells were pretreated for 30 min with 6 µM ASOs against chmA, nvRNAP transcript (ΦKZ055), and picA. Subsequently, cells were infected with ΦKZ at an MOI = 5. At 2.5, 5.0, 7.5 and 10.0 min p.i. cells were collected with addition of 1/3 volume 4× Bolt SDS sample buffer (Invitrogen), and were immediately boiled at 95 °C for 5 min. Mass spectrometry sample preparation and measurement was conducted at the Proteomics Core Facility EMBL Heidelberg as previously described[37]. Proteins were quantified using label-free quantification (LFQ). Enrichment as log_10-transformed P values was calculated with a two-sided Student’s t-test in MaxQuant Perseus (2.1.3)[63].

RNA preparation and RNA-seq

PAO1 was grown overnight in LB at 37 °C 220 rpm. Cells were inoculated 1:100 in Mueller–Hinton medium at 37 °C and grown to an optical density of 0.3. 6 µM ASO (JVPNA-79 for control or JVPNA-72 for chmA inhibition) were added to 1.6 ml of cell culture and incubated for 30 min at 37 °C 220 rpm. Cells were infected with ΦKZ at an MOI = 5. At indicated time points, 250 µl were removed and incubated on ice. Infection efficiency was independently validated by confocal microscopy and CFU spotting with 50 µl of cells. RNA was isolated from 200 µl of cells using the RNAsnap procedure[64]. Two volumes of RNAprotect (Qiagen) were added and cells were incubated for 5 min. Cells were pelleted at full speed for 20 min at 4 °C and the supernatant was removed. The pellet was resuspended in 100 µl SNAP buffer (0.025% SDS, 18 mM EDTA, 1% β-mercaptoethanol, 95% formamide (RNA-grade)). Samples were incubated for 7 min at 95 °C, cell debris was pelleted at full speed for 5 min at room temperature, and the supernatant was transferred to a new tube. 1.5 volumes of ethanol were added to the supernatant and the sample was mixed by pipetting. The sample was loaded onto a miRNeasy mini column (Qiagen) two times and spun at full speed for 20 s at room temperature. Columns were washed two times with 500 µl RPE buffer (Qiagen) and spun at 8,000g for 1 min at room temperature. One final spin was used to dry the column in an empty tube. Thirty microlitres of RNase-free water was added to the column and the RNA eluted at 8,000g for 1 min at room temperature. The elution was repeated with the flow-through to recover more RNA. The RNA concentration was determined by absorption at 260 nm. RNA was stored at −80 °C.

RNA-seq was performed at the CoreUnit SysMed at the University of Würzburg. DNA was digested with DNaseI and the rRNA was depleted with the Lexogen RiboCOP META depletion kit. RNA library was prepared with the CORALL Total RNA-Seq Library Prep Kit V1 (Lexogen). The library was sequenced on a NextSeq2000 (Illumina) machine with a P1-seq kit (single-end 1× 100 bp, Illumina). RNA-seq analysis for the ChmA knockdown and the screen was conducted with READemption 0.4.3 and 2.0.4, respectively[65]. Reads were aligned for PAO1 and ΦKZ to NC_002516 and NC_004629, respectively. Enrichment of transcripts was calculated with the DeSeq2 module in READemption (2.0.4)[65]. Read coverage was illustrated with the Integrated Genomics Viewer (IGV)[66].

We defined early and middle/late phage transcripts based on their significant enrichment (log_2fold > 2 or log_2fold < −2, −log_10-adjusted P value > 10) between the control 35- and 10-min samples (Extended Data Fig. 6a, as in ref. [44]).

Structure prediction

Structures were predicted from protein sequences using Google AlphaFold3 server (https://alphafoldserver.com/)[67]. This information is subject to AlphaFold Server Output Terms of Use found at https://alphafoldserver.com/output-terms (Google LLC). Ongoing use is subject to AlphaFold Server Output Terms of Use and of any modifications made. For the RNase HI domain structure in Fig. 4b, the predicted reference structure A0A2A2IBB4 was used from the AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/).

Complementation assay

PAO1 cells were transformed with pLBu019_ΦKZ155-TEV-3xFlag and pLBu021_ΦKZ155CDN-TEV-3xFlag and selected with gentamycin. Cells were grown overnight in LB medium with gentamycin. For the assay, no gentamycin was used in the medium. Cells were inoculated 1:100 in Mueller–Hinton medium. Cells were treated with 6 µM ASOs against control or ΦKZ155 for 30 min. For spotting, cells were infected with ΦKZ at an MOI = 0.0001, the complementation gene was induced with 0.2% arabinose, and cells were spotted at 180 min p.i. For imaging, cells were infected with ΦKZ at an MOI = 10, the complementation gene was induced with 0.2% arabinose, and cells were chemically crosslinked at 35 min p.i. and stained with FM4-64 and DAPI as described in the microscopy section and imaged.

In vitro translation

Template DNA was produced via PCR and Taq-polymerase followed by gel purification. Primers JVO-23244 and JVO-23245 were used to amplify wt ΦKZ155 with a T7 promoter from ΦKZ lysate, and for ΦKZ155D102N, pLBu021 was used as template. Template DNA (250 ng) was supplemented in 10 µl PURExpress in vitro protein synthesis kit mix (NEB, E6800). The reaction mix was incubated for 120 min at 37 °C and was subsequently used for assays.

Cleavage assays

RNA template (JVRNA-001, AUAUAAGGGAACAUAGAUAAACCCCUCCCUAAUAAAAUG) was labelled with 5′-32P. For the RNA–DNA duplex, the radiolabelled RNA was mixed with twice the amount of the reverse complement DNA (JVO-23273), boiled and slowly cooled down to room temperature in a water bath to anneal the duplexes. One picomole of RNA or RNA–DNA duplex was added to 2 µl PURExpress IVT mix that was translating for 2 h a control, ΦKZ155 or ΦKZ155(D102N). The mix was incubated for 1 h, mixed with 1 volume GLII buffer, boiled for 5 min, rapidly cooled down on ice, and loaded onto a 6% 6 M Urea–PAGE (19:1) gel that was run at 300 V for 2 h. The gel was transferred onto Whatman filter paper, vacuum dried, and a phosphor image screen was used to read out the autoradiogram on a Typhoon FLA7000 imager (GE Healthcare). As a positive control, we used a commercially available RNase H (NEB, M0297S).

Southern dot blotting

PAO1 cells were grown to an optical density of 0.3 in Mueller–Hinton medium and were pretreated with 6 µM ASOs against ΦKZ155 (ΦKZ) or gp176 (ΦPA3) for 30 min. Cells were infected at an MOI = 5 with ΦKZ. At indicated time points a fraction of the culture was removed and 1% SDS was added followed by boiling at 95 °C for 5 min. Subsequently, the DNA was extracted from the sample with phenol choloroform:isoamyl alcohol, and subsequently with one volume of chloroform. The aqueous phase was supplemented with 1.5 volume 1 M NaOH and 15 mM EDTA (pH 9). The sample was heated for 3 min at 95 °C and put on ice for 5 min. The solution was filtered with a dot blot apparatus through an equilibrated (0.3 M NaOH) and positively charged nylon membrane. Subsequently, the membrane was dried and the DNA crosslinked via exposition to UV light for 5 min. The membrane was equilibrated with hybridization solution two times, and a radiolabelled oligo was added for hybridization overnight starting at 60 °C for 1 h and then 48 °C overnight. The membrane was washed once for 15 min with 2× SSC and with 0.5× SSC, and a screen was used for phosphor imaging on a Typhoon FLA7000 imager (GE Healthcare).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The genome annotation files used for ASO design are available at the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/) under accession numbers NC_004629.1 (ΦKZ), NC_028999.1 (ΦPA3), NC_001628.1 (PP7), NC_011421.1 (SPO1), NC_002516.2 (PAO1), NC_008463.1 (PA14) and NC_041973.1 (RAY). Raw sequencing data and coverage files have been deposited at Gene Expression Omnibus[68] for ChmA knockdown and the screen with accession numbers GSE269401 and GSE269911, respectively. Proteomics data have been deposited at PRIDE[69] with the identifier PXD062538. Processed data are provided in Supplementary Data 1–4. Materials are listed in Supplementary Data 5. Source data for Fig. 3, Extended Data Figs. 2a, 3a,b and 6a are provided with this paper. Uncropped images of blots are available in Supplementary Fig. 1. Other source data are deposited at Zenodo (https://doi.org/10.5281/zenodo.16357062 (ref. [70])).

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Acknowledgements

The authors thank L. Vogel and B. Plaschke for technical assistance; E. Hauschild for microscopy; A. Sparmann for discussions and for editing the manuscript; the EMBL Proteomics Core Facility (Heidelberg) and J. Schwarz for performing the proteomics experiment; J. Pogliano for providing the RAY phage; J. Bondy-Denomy for providing the ΦPA3 and ΦKZ ΔphuZ phages; Y.-H. Cho for providing the PP7 phage; and R. Lavigne for providing the P. aeruginosa strains PA14 and PaLo strains. The work was funded by Deutsche Forschungsgemeinschaft (DFG) project 465133664 in the Priority Programme ‘New Concepts in Prokaryotic Virus-host Interactions—From Single Cells to Microbial Communities’ (SPP 2330) and by a Gottfried Wilhelm Leibniz award (DFG Vo875/18), both awarded to J.V. M.G. acknowledges funding from the Federal Ministry of Research, Technology and Space under the Microbial Stargazing project (01KX2324). J.V. is a member of CNATM (Cluster for Nucleic Acid Therapeutics Munich; Cluster4Future programme project ID 03ZU1201CA) and the DFG Cluster of Excellence EXC 3113/1: Cluster for Nucleic Acid Sciences and Technologies (NUCLEATE).

Funding

Open access funding provided by Helmholtz-Zentrum für Infektionsforschung GmbH (HZI).

Author information

Author notes

1. Milan Gerovac

Present address: Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany

Authors and Affiliations

1. Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany

Milan Gerovac, Yan Zhu & Jörg Vogel

2. Institute for Molecular Infection Biology (IMIB), Faculty of Medicine, University of Würzburg, Würzburg, Germany

Milan Gerovac, Leandro Buhlmann, Svetlana Ðurica-Mitić, Valentin Rech, Samuel Carien, Linda Popella & Jörg Vogel

3. Core Unit Systems Medicine, University of Würzburg, Würzburg, Germany

Tom Gräfenhan

Contributions

M.G. and J.V. conceived the project and designed experiments. M.G. and L.B. executed the ASO experiments and screens, and optimized the procedures for targeting of other species. S.Ð.-M. established initial experiments and designed ASOs. L.B. and Y.Z. investigated the role of ΦKZ155. L.B. and S.C. performed microscopy experiments. V.R. performed PP7 experiments. L.P. performed experiments during the revision process. M.G., L.B. and T.G. performed RNA-seq experiments. J.V. and M.G. wrote the manuscript and all authors contributed to editing. J.V. procured funding.

Corresponding author

Correspondence to Jörg Vogel.

Ethics declarations

Competing interests

The HZI filed a patent application (EP24191874.7) on which J.V., M.G., and S.Ð.-M. are inventors, covering the method for targeting and mapping of essential phage genes. The other authors declare no competing interests.

Peer review

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Extended data figures and tables

Extended Data Fig. 1 Optimization of ASO treatment.

a. Replication cycle of ΦKZ. b. Scheme depicting the optimization for the CFU/PFU assay (top) and assessment of ASO toxicity (bottom). c. PAO1 cells were pretreated with 6 µM ASO against chmA linked to the carrier peptides (RXR)4XB, (KFF)3K or TAT for 30 min. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. Representative result of two independent experiments. d. PAO1 cells were pretreated with 6 µM ASO with indicated lengths and target sequences against RBS and AUG of chmA for 30 min. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. SD, Shine-Dalgarno sequence. Representative result of three independent experiments. e. PAO1 cells were pretreated with 6 µM ASO against chmA for 30, 15, 10 or 5 min. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. f. PAO1 cells were pretreated with 6 µM ASO against chmA for 30 min. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 30, 70, 140, and 210 min followed by CFU/PFU determination. g. PAO1 cells were pretreated with 3 to 18 µM ASO against chmA for 30 min. Cells were incubated for 180 min followed by CFU/PFU determination. h. PAO1 cells were pretreated with 0.25 to 6 µM ASO against chmA for 30 min. Cells were infected with ΦKZ at an MOI = 5 and incubated for 180 min followed by CFU/PFU determination. i. PAO1 cells were pretreated with 6 µM ASO against chmA for 30 min. Cells were infected with ΦKZ at an MOI between 0.001 and 1 and incubated for 30, 70, 210 min followed by CFU/PFU determination.

Extended Data Fig. 2 Quantification of phuZ ASO treatment and gene deletion and ASO specificity assessment by mismatches and tiling.

a. PAO1 cells were pretreated with 8 µM ASO (JVPNA-960) against the phage spindle apparatus gene phuZ or a control ASO (JVPNA-79) for 30 min, infected with ΦKZ at an MOI = 10, followed by chemical crosslinking and staining of membranes (FM4-64) and DNA (DAPI) at either 5 min or 30 min p.i. In parallel, PAO1 cells were infected with a phuZ deletion phage at MOI = 0.001. The distance of the phage nucleus from the cell centre (d) was measured. Quantitation is based on one representative example of four independent experiments for the ASO-knockdown and on one representative example of two independent experiments for the ΔphuZ phage[29]. The number of counted cells is indicated in brackets. Box plot indicate median, first and third quartiles (box), whiskers indicate ±1.5 SD, * p < 0.05, **** p < 0.0001, two-tailed Mann-Whitney test. b. Mismatches (mm, red) were introduced to the ASO targeting chmA. PAO1 cells were pretreated with 6 µM of the indicated ASOs for 30 min. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. Representative result of two independent experiments. c. ASOs were tiled along the RBS and AUG window of the chmA transcript. PAO1 cells were pretreated with 6 µM indicated ASOs against chmA for 30 min. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. For the quantification of ChmA, PAO1 cells were pretreated with 6 µM indicated ASOs against chmA for 30 min. Cells were infected with ΦKZ at an MOI = 5 and incubated for 30 min followed by quantification of ChmA via immunoblotting. Representative result of two independent experiments. ASO self-complementarity (SC), melting temperature (Tm), purine percentage (pur_perc), and off-targets (OT) with 0 and 1 miss-match (mm) in the phage and host are indicated as heatmaps. ASO parameters were determined with the ASO checker tool of MASON (mason.helmholtz-hiri.de/ASO_checker). ASOs directed against the A/U-rich region between the SD and AUG fail to repress ChmA protein synthesis, likely due to their low melting temperature. Source data for panel (a) are available online.

Source Data

Extended Data Fig. 3 ASO knockdown specifically downregulates targets.

PAO1 cells were pretreated with 6 µM ASO against chmA, ΦKZ055 (nvRNAP), and picA for 30 min. Cells were infected with ΦKZ at an MOI = 5 and incubated for 2.5, 5.0, 7.5, and 10 min followed by harvesting in non-reducing SDS-PAGE loading dye. Subsequently, proteomics was performed on the samples and proteins were quantified by label-free quantification (LFQ). a. Counts for host proteins at all timepoints were pooled to calculate enrichment and log10 p-value (calculated with MaxQuant Perseus, a two-sided Student’s t-test was used with no adjustments for multiple comparisons). Filter criteria were applied (only one protein in protein group, >=4 unique peptides, >40 peptide posterior error probabilities score, sum of average LFQ intensity was filtered at >1E8 counts), n = 1,130 host proteins were considered. Host protein levels were not altered in the range log2 fold change (log2FC) <-2 or >2 and a –log10 p-value > 2. b. Counts for phage proteins at 7.5 and 10 min p.i. timepoints were pooled to calculate enrichment and log10 p-value (calculated with MaxQuant Perseus, a two-sided Student’s t-test was used with no adjustments for multiple comparisons). Filter criteria were applied (sum of log2LFQ counts for all three ASO treatments >25 counts); n = 95 phage proteins were considered. Log2FC was calculated based on average counts. When a protein was not detected, we set a pseudo-count=1. ΦKZ016 and ΦKZ165 were lower in the non-targeting ASO control sample and therefore omitted. The structural protein ΦKZ094 was only detected in the sample treated with an ASO targeting chmA at 7.5 min p.i. but not at 10 min or in the control sample and was also omitted. c-e. Left, Schematic overview of the transcriptional units (TU) of the targeted transcripts, based on Putzeys et al. 2024 (ref. [49]). The position of ASO is indicated. Right, Log2FC of protein levels at 10 min p.i. based on LFQ counts. Source data for panel (a) and (b) are available online.

Source Data

Extended Data Fig. 4 ASOs can target diverse clinical Pseudomonas isolates and other species of phages and phage-infected cells.

a. PaLo8/9/39/44 cells were pretreated with 6 µM ASO against chmA. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. b. PaLo8/9/39/44 cells were pretreated with 6 µM ASO against chmA. Cells were infected with ΦKZ at an MOI = 5 and incubated for 20 min followed by quantification of ChmA levels via immunoblotting with an antibody against ChmA. c. PaLo8/9/39/44 cells were pretreated with 8 µM ASO against chmA. Cells were infected with ΦKZ at an MOI = 10 and incubated for 40 min followed by chemical crosslinking and staining with DAPI to visualise DNA and FM4-64 to visualize membranes. d. Pantoea agglomerans cells were pretreated with 6 µM ASO against gp296 (chmA) for 30 min. Cells were infected with RAY at an MOI = 0.0001 and incubated for 300 min followed by CFU/PFU determination. e. B. subtilis 168 cells were pretreated with 6 µM ASO against the transcript of gene 31 (DNA polymerase) for 30 min. Cells were infected with SPO1 at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. f. PAO1 cells were pretreated with 0.05 µM ASO against replication (rep) and lysis (lys) transcripts for 30 min. Cells were infected with ΦKZ at an MOI = 0.00001. 0.05 µM ASO was added again with the infection and every 30 min thereafter. Cells were incubated for 180 min after infection followed by CFU/PFU determination. For panels (a)-(f), a representative result of two independent experiments is shown. Uncropped images of blots are available in Supplementary Fig. 1.

Extended Data Fig. 5 Screen scoring scheme, top hits and other effects caused by ASOs.

a. Schematic representation of possible effects of ASO treatment on CFU/PFU readouts and ChmA levels after multiple replication rounds. ASOs can be toxic to the host causing reduced CFU counts even without phage infection. ASOs can also induce a prophage, visible as PFUs. ASOs that showed one log reduction in PFUs and/or reduced ChmA levels were scored as weak (+). ASO that reduced PFUs by multiple logs, rescued CFUs in the first dilution, and/or depleted ChmA levels were scored as effective (++). ASOs that diminished PFUs down to the first dilution or completely abrogated PFUs, and/or depleted ChmA levels by more than 10-fold were scored as very effective (+++). Some ASOs caused increased plaque counts (−) and/or increased ChmA levels. b. CFU/PFU and ChmA levels upon ASO-mediated knockdown of top candidates that showed a strong effect on phage plaques in our screen. PAO1 cells were pretreated with 6 µM ASO against the indicated transcripts. Cells were infected with ΦKZ at an MOI = 0.0001 and incubated for 180 min followed by CFU/PFU determination. Scoring as described in (a). c. Examples of pleiotropic effects of ASO treatment. Experiment as in (b). ASOs that were toxic to the host are denoted by their internal reference number because this effect is likely unspecific and not related to the intended phage target gene. ASO treatment can result in more phage plaques (−) and ASOs can induce prophages (prophage ind.).

Extended Data Fig. 6 Early-middle/late gene classification and effects upon inhibition of ChmA synthesis.

a. Log2FC in transcript levels based on RPKM counts 35 vs. 10 min post infection in the samples treated with a non-targeting control ASO. Phage genes were classified as early (blue) upon depletion, and as middle/late (red) upon enrichment in the Log2FC plot 35 vs. 10 min. Other phage transcripts remained unaltered in the comparison of these two time points and were likely transcribed by the vRNAP. Two independent replicates were merged by geometrical averaging and the p-values were calculated by the Wald test (two-sided) without adjustments for multiple comparisons using DESeq2. b. PAO1 cells were pretreated with 6 µM ASO against chmA. Cells were infected with ΦKZ at an MOI = 5 and incubated for the indicated times followed by RNA extraction and sequencing of the transcriptomes. Relative quantification of protein-coding transcript (CDS) reads to total reads (RPKM) is shown. c. Relative read counts to total counts for phage tRNAs in the tRNA fraction in the samples treated with the non-targeting control ASO and the ASO targeting chmA; results are depicted as an overlay of two independent experiments. d. Transcript levels in samples treated with a non-targeting control ASO or an ASO targeting chmA were normalized over the course of infection and clustered by t-SNE. Individual clusters are represented together with genomic locations, core genes and blocks. Source data for panel (a) are available online.

Source Data

Extended Data Fig. 7 ASO-mediated silencing of top hits has effects on host and phage transcript levels.

PAO1 cells were pretreated with 6 µM ASO against the 56 top hits for 30 min. Cells were infected with ΦKZ at an MOI = 5 and incubated for 15 or 30 min followed by RNA extraction, sequencing of the transcriptomes, and quantification of fold-change compared to the non-targeting control ASO. a., c. Heatmap for Log2FC for ΦKZ transcripts at 15 min (a) and 30 min (c) p.i. d., f. Heatmap for Log2FC for PAO1 transcripts at 15 min (d) and 30 min (f) p.i. b., e., g. t-SNE clustering of the log_2fold change (Log2FC) of transcript levels of ΦKZ 15 min (b) or of PAO1 15 min (e) and 30 min (g) p.i. Phage proteins whose knockdown led to a strong phenotype (+++) in the CFU/PFU assay (in Supplementary Table 1) are labelled. h. Read coverage of the Pf4 prophage locus in PAO1 upon knockdown of ΦKZ082.

Extended Data Fig. 8 I ΦKZ155 involvement in the phage replication cycle.

a. PAO1 cells were pretreated with 6 µM ASO against chmA for 30 min. Cells were infected with ΦKZ at an MOI = 5 and incubated for the indicated times followed by RNA extraction and sequencing of the transcriptomes. Relative quantification of protein-coding transcript (CDS) reads and normalisation to the maximum for ΦKZ155 over all conditions (analysis based on data described in Fig. 3a,b). b. Sequences of the targeted ΦKZ155 (ΦKZ) or gp176 (ΦPA3) transcripts and ASOs. c. PAO1 cells were transformed with a complementation plasmid encoding ΦKZ155 or the catalytically dead mutant ΦKZ155(D102N). PAO1 cells were pretreated with 6 µM ASO against ΦKZ155 for 30 min. Cells were infected with ΦKZ at an MOI = 0.0001, plasmid-encoded ΦKZ155 was induced with 0.2% arabinose, and cells were incubated for 180 min followed by CFU/PFU determination. Representative result of two independent experiments. d. RNA was 5′-[32P]phosphorylated and left single-stranded (top) or duplexed with complementary DNA (bottom). ΦKZ155 and the catalytically dead D102N mutant were produced by in vitro translation and added to the oligomer for the cleavage reaction. Subsequently, the oligomers were analysed on an Urea-PAGE gel and autoradiographed. Commercially available E. coli RNase H protein was used as control. Representative result of two independent experiments. e. PAO1 cells were transformed as in (c). PAO1 cells were pretreated with 8 µM ASO against ΦKZ155 for 30 min. Cells were infected with ΦKZ at an MOI = 10, plasmid-encoded ΦKZ155 was induced with 0.2% arabinose, and the cells were incubated for 30 min followed by chemical crosslinking and staining with DAPI to visualise DNA and FM4-64 to visualize membranes. Data for pempty and pΦKZ155 are based on the same images as shown in Fig. 4d. TIR, translation initiation region. f. C-terminally GFP-tagged ΦKZ155 (ΦKZ155GFP) was ectopically expressed from a plasmid in PAO1. PAO1 cells were infected with ΦKZ at an MOI = 10, the expression of ΦKZ155GFP was induced with 0.2% arabinose, and incubated for 30 min followed by chemical crosslinking and staining with DAPI to visualise DNA and FM4-64 to visualize membranes. Representative result of two independent experiments. g. PAO1 cells were pretreated with 8 µM ASO against gp176 (ΦPA3). Cells were infected with ΦPA3 at an MOI = 5 and incubated for the indicated times followed by DNA extraction and dot-blotting. RNA was eliminated by alkaline treatment. The phage genomic DNA was detected with the radiolabelled oligo probe JVO−23279 followed by autoradiography. No host, no phage, and the non-targeting control ASO served as controls. Representative result of two independent experiments. h. PAO1 cells were pretreated with 6 µM ASO against ΦKZ155 for 30 min. Cells were infected with ΦKZ at an MOI = 5 and incubated for the indicated times post infection followed by DNA extraction and dot blotting, RNA was eliminated by alkaline treatment. Phage genomic DNA was detected with the radiolabelled oligo probe JVO-23213 (complementary to the chmA ORF) and JVO-15848 (complementary to the 5S rRNA gene of PAO1) followed by autoradiography. A representative result of two independent experiments is shown. Uncropped images of blots are available in Supplementary Fig. 1.

Supplementary information

Supplementary Fig. 1

Uncropped images of gels and blots. Ten display items for Figs. 1c and 4e,f and Extended Data Figs. 2c, 4b, 5b–d and 8g,h, showing uncropped and unprocessed blot, dot blot and gel source data with input references, protein or nucleotide size ladders, with relevant samples indicated. Black boxes around bands and dots indicate how the image was cropped for the final figure.

Reporting Summary

Supplementary Table 1

Factors that affect ΦKZ plaque efficiency and ChmA levels. Target transcripts are listed with core genome blocks^38, early infection transcript and protein abundance^37. The PFU/CFU and ChmA level effect of each respective knockdown is depicted as no effect (), weak (+), effective (++), very effective plaque or ChmA level reduction (+++) and increased plaque or ChmA level (-), respectively.

Supplementary Data 1

Proteomics upon phage gene knockdown. Proteomic datasets for ChmA, nvRNAP and PicA knockdowns at different time points in the phage infection cycle. Raw peptide and proteins counts.

Supplementary Data 2

ASO screening results. PFU/CFU and ChmA level effects after knockdown of ΦKZ genes.

Supplementary Data 3

RNA-seq upon chmA knockdown. Transcriptomic dataset after knockdown of ChmA in a time course. Transcript quantification is listed together with normalized read counts and t-SNE positions after clustering.

Supplementary Data 4

RNA-seq upon phage gene knockdown. Transcriptomic dataset after knockdown of 56 top hits from the screen. Transcript quantification is listed together with log_2 fold changes and counts exceeding thresholds and t-SNE positions after clustering.

Supplementary Data 5

Materials and ASOs. List of strains, vectors, nucleotide oligomers and ASOs with design parameters.

Peer Review File

Source data

Source Data Fig. 3

Source Data Extended Data Fig. 2

Source Data Extended Data Fig. 3

Source Data Extended Data Fig. 6

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Gerovac, M., Buhlmann, L., Zhu, Y. et al. Programmable antisense oligomers for phage functional genomics. Nature 646, 1195–1203 (2025). https://doi.org/10.1038/s41586-025-09499-6

- Received: 10 September 2024

- Accepted: 06 August 2025

- Published: 10 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09499-6

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Peroxisomal metabolism of branched fatty acids regulates energy homeostasis

Xuejing Liu,

Anyuan He,

Dongliang Lu,

Donghua Hu,

Min Tan,

Abenezer Abere,

Parniyan Goodarzi,

Bilal Ahmad,

Brian Kleiboeker,

Brian N. Finck,

Mohamed Zayed,

Katsuhiko Funai,

Jonathan R. Brestoff,

Ali Javaheri,

Patricia Weisensee,

Bettina Mittendorfer,

Fong-Fu Hsu,

Paul P. Van Veldhoven,

Babak Razani,

Clay F. Semenkovich &

…

Irfan J. Lodhi

Nature volume 646, pages 1223–1231 (2025)

Abstract

Brown and beige adipocytes express uncoupling protein 1 (UCP1), a mitochondrial protein that dissociates respiration from ATP synthesis and promotes heat production and energy expenditure. However, UCP1−/− mice are not obese1,2,3,4,5, consistent with the existence of alternative mechanisms of thermogenesis6,7,8. Here we describe a UCP1-independent mechanism of thermogenesis involving ATP-consuming metabolism of monomethyl branched-chain fatty acids (mmBCFA) in peroxisomes. These fatty acids are synthesized by fatty acid synthase using precursors derived from catabolism of branched-chain amino acids[9] and our results indicate that β-oxidation of mmBCFAs is mediated by the peroxisomal protein acyl-CoA oxidase 2 (ACOX2). Notably, cold exposure upregulated proteins involved in both biosynthesis and β-oxidation of mmBCFA in thermogenic fat. Acute thermogenic stimuli promoted translocation of fatty acid synthase to peroxisomes. Brown-adipose-tissue-specific fatty acid synthase knockout decreased cold tolerance. Adipose-specific ACOX2 knockout also impaired cold tolerance and promoted diet-induced obesity and insulin resistance. Conversely, ACOX2 overexpression in adipose tissue enhanced thermogenesis independently of UCP1 and improved metabolic homeostasis. Using a peroxisome-localized temperature sensor named Pexo-TEMP, we found that ACOX2-mediated fatty acid β-oxidation raised intracellular temperature in brown adipocytes. These results identify a previously unrecognized role for peroxisomes in adipose tissue thermogenesis characterized by an mmBCFA synthesis and catabolism cycle.

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Main

Emerging studies implicate peroxisomes in regulating metabolic homeostasis[10]. Peroxisomes perform various metabolic functions, including β-oxidation of very long-chain fatty acids and 2-methyl branched-chain fatty acids (BCFA), α-oxidation of phytanic acid and synthesis of ether lipids and bile acids[11]. These organelles are abundant in thermogenic fat[12] and further increase in number with cold exposure in a manner dependent on the thermogenic transcriptional coregulator PRDM16 (ref. [13]). Peroxisomes regulate mitochondrial fission, which is required for adipose tissue thermogenesis13,14. However, it is unknown whether peroxisomes directly regulate thermogenesis. Notably, peroxisomes account for up to 20% of total cellular oxygen consumption under basal conditions[15]. Unlike mitochondrial respiration, peroxisomal oxygen consumption does not lead to ATP synthesis and instead generates heat.

Here, we report that peroxisomes are involved in an alternative mechanism of adipose tissue thermogenesis characterized by synthesis and β-oxidation of monomethyl branched fatty acids (mmBCFAs). These fatty acids are synthesized by fatty acid synthase (FASN) using a short-branched-chain acyl-coenzyme A (CoA) (BrCoA) derived from catabolism of branched-chain amino acids (BCAA) as a precursor9,16. Although mmBCFA are less abundant than conventional straight chain fatty acids (StCFA), brown adipose tissue (BAT) and white adipose tissue (WAT) have the highest mmBCFA concentrations among tissues[9]. Their relatively low steady-state levels are consistent with a model in which they are rapidly synthesized and degraded to support energy dissipation. The peroxisomal protein acyl-CoA oxidase 2 (ACOX2) is reported to oxidize 2-methyl-branched fatty acids[17], and here we show that ACOX2 also mediates β-oxidation of mmBCFAs. Moreover, our studies indicate that FASN-mediated synthesis of mmBCFA also takes place in peroxisomes. Both mmBCFA synthesis and β-oxidation are simultaneously activated by cold exposure. Through use of loss-of-function and gain-of-function mouse models, we demonstrate that this UCP1-independent pathway of thermogenesis is physiologically relevant.

Thermogenic stimuli boost ACOX2 levels

We analysed gene expression in BAT to examine how peroxisomal biogenesis and metabolic pathways are affected by cold exposure in WT and UCP1−/− mice housed at thermoneutrality (30 °C) or adapted to cold (4 °C). Cold exposure increased genes and proteins involved in peroxisomal biogenesis, ether lipid synthesis and β-oxidation (Fig. 1a,b). Among these, the peroxisomal β-oxidation gene ACOX2 showed a marked cold-induced upregulation, particularly in UCP1−/− mice (Fig. 1a,b), suggesting a potential compensatory role in thermogenic adaptation. ACOX2 catalyses the first and rate-limiting step in β-oxidation of 2-mBCFA (Extended Data Fig. 1a) and metabolizes intermediates in bile acid synthesis17,18. Whereas bile acid pathway genes such as AMACR and CYP27A1 decreased in cold-treated UCP1−/− mice, HSD17B4, a downstream enzyme in fatty acid oxidation, increased (Fig. 1a). Cold exposure also promoted ACOX2 expression in inguinal WAT (iWAT) and gonadal WAT but not in the liver (Extended Data Fig. 1b). Acute norepinephrine treatment in UCP1−/− brown adipocytes or subcutaneous WAT from pigs, a species that lacks functional UCP1 protein[19], significantly increased ACOX2 expression (Fig. 1c). Similarly, forskolin treatment upregulated ACOX2 in human brown-like adipocytes (Fig. 1d), indicating that ACOX2 is regulated by cyclic AMP (cAMP)-mediated thermogenic signalling.

Fig. 1: The peroxisomal β-oxidation enzyme ACOX2 increases with thermogenic stimuli and promotes catabolism of mmBCFA in brown adipocytes.

a, Heatmap of genes involved in various peroxisomal pathways in BAT of mice maintained at thermoneutrality (30 °C) or gradually cold adapted. n = 4. b, Western blot analysis in BAT of warm or cold-treated mice. n = 2. c, ACOX2 gene expression after 2 h of treatment with norepinephrine or vehicle in differentiated mouse UCP1−/− brown adipocytes (norepinephrine 1 μM) or mature adipocytes isolated from fresh pig iWAT (norepinephrine 2 μM). n = 3 biological replicates per group. d, ACOX2 gene expression in cultured human brown-like adipocytes treated with or without forskolin (10 μM, 3 days). n = 3 biological replicates per group. e, Western blot analysis in BAT SVF cells collected on various days during adipogenesis. f, Immunofluorescence of ACOX2 and PMP70 in differentiated WT BAT SVF cells. g, Western blot analysis of ACOX2 knockdown using CRISPR–Cas9 in brown adipocytes. h, Volcano plot depicting log_2[fold change] (FC) values of fatty acids. n = 4. i, Quantification of various mmBCFA species. n = 4. j, Atom-transition map illustrating flow of carbons from [U^13C_6]glucose into newly synthesized mmBCFA. 13C carbons are indicated by closed circles. Created in Adobe Illustrator. k, Measurement of 13C-labelled iso-C15:0 in control and sgACOX2 brown adipocytes after CL316,243 treatment. n = 3. l, OCR in sgACOX2 and control brown adipocytes treated with oligomycin and iso-C17:0, followed by FCCP. n = 7. Data with error bars are reported as mean ± s.e.m. Data in a–d,h,i,k,l are from biologically independent samples. Images in e,f,g are representative of two separate experiments. Two-sided unpaired Student’s t-test in c, d and i. The data in h were adjusted for multiple comparisons using the Benjamini–Hochberg method. Two-way ANOVA with Fisher’s least-significant difference test in k and two-way ANOVA with Sidak’s multiple comparisons test in l. Scale bars, 10 μm.

Source Data

ACOX2 promotes β-oxidation of mmBCFA

The physiological role of ACOX2 in adipocytes is unknown. Thus, we analysed its expression during adipogenesis. ACOX2 protein levels were low in undifferentiated BAT stromal vascular fraction (SVF) cells and markedly increased during adipogenesis (Fig. 1e). Immunofluorescence analysis showed that ACOX2 is localized in peroxisomes in brown adipocytes (Fig. 1f), consistent with its role in peroxisomal β-oxidation.

ACOX2 mediates β-oxidation of the 2-methyl BCFA acid pristanic acid[17], which is obtained either directly from the diet or as the α-oxidation product of the dietary lipid phytanic acid. These fatty acids are not typically present in appreciable amounts in standard cell culture media or rodent diets, suggesting that ACOX2 might be involved in catabolism of other fatty acids in cell types, such as activated brown adipocytes, where it is highly expressed. To identify endogenous fatty acid substrates and intracellular functions of ACOX2, we used CRISPR–Cas9 to delete ACOX2 in brown adipocytes. Western blot analysis of pooled single-guide RNA-treated cells revealed a reduction in ACOX2 expression, although complete ablation was not achieved (Fig. 1g). ACOX2 inactivation had no effect on mitochondrial protein gene expression (Extended Data Fig. 1c) or adipocyte differentiation (Extended Data Fig. 1d) but selectively increased mmBCFA levels on mass spectrometry profiling of fatty acids (Fig. 1h). These fatty acids have a methyl group on the penultimate (iso configuration) or the antepenultimate (ante-iso configuration) carbon (Extended Data Fig. 1e). We validated the structures of major mmBCFA species, including iso-C15:0 and iso-C17:0, in brown adipocytes using tandem mass spectrometry. Other fatty acids with an odd number of carbon atoms, likely mmBCFA species or odd-chain fatty acids (OCFA), both derived from BCAA catabolism[20], were also elevated with ACOX2 ablation (Fig. 1h,i). Notably, levels of various StCFA, including very long-chain fatty acids, were not significantly altered or were only minimally affected in ACOX2 knockdown cells (Extended Data Fig. 1f). To determine the cause of mmBCFA accumulation in single guide (sg)ACOX2-treated cells, we traced carbon flux from [U-13C_6]-glucose into mmBCFA (Fig. 1j) following β3-adrenergic receptor agonist CL316,243 treatment. In control adipocytes, CL316,243 did not increase 13C incorporation into mmBCFA, indicating balanced synthesis and degradation. However, 13C incorporation into mmBCFA was significantly elevated in sgACOX2 cells (Fig. 1k), suggesting that the absence of ACOX2-mediated β-oxidation allows accumulation of newly synthesized mmBCFA. These results indicate that ACOX2 is essential for maintaining mmBCFA homeostasis by facilitating their β-oxidation.

To further investigate whether ACOX2 is required for mmBCFA β-oxidation and determine its relationship to mitochondrial respiration, we measured oxygen consumption rate (OCR) in control and sgACOX2 adipocytes treated with iso-C17:0 (Fig. 1l). In control cells, iso-C17:0 increased OCR even after oligomycin treatment, indicating that the respiratory induction by the exogenously supplied mmBCFA is independent of mitochondrial ATP synthesis. Subsequent carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) treatment did not further increase OCR, suggesting that iso-C17:0 β-oxidation fully engaged cellular respiratory capacity in the presence of ACOX2. By contrast, ACOX2 inactivation significantly decreased both basal and iso-C17:0-induced OCR, confirming that the peroxisomal protein is required for mmBCFA β-oxidation.

We next determined the effect of ACOX2 overexpression on fatty acid levels and β-oxidation. Unlike knockdown, overexpression of ACOX2 selectively decreased mmBCFA levels without significantly affecting other fatty acids (Extended Data Fig. 1g). To determine whether ACOX2 can oxidize mmBCFA and OCFA, we treated brown adipocytes overexpressing ACOX2 or green fluorescent protein (GFP) with iso-C17:0, C15:0 OCFA, C17:0 OCFA or vehicle and measured OCR. ACOX2 overexpression or iso-C17:0 treatment alone promoted oxygen consumption and the combination of these two manipulations markedly increased OCR. Whereas C15:0 OCFA increased OCR, although to a lesser extent than iso-C17:0, treatment with C17:0 OCFA reduced respiration, indicating potential mitochondrial toxicity (Extended Data Fig. 1h). Quantitative real-time PCR analysis showed that ACOX2 overexpression does not alter the expression of mitochondrial protein genes (Extended Data Fig. 1i) or mitochondrial DNA copy number (Extended Data Fig. 1j). Notably, iso-C17:0 treatment increased ACOX2 expression in mouse UCP1−/− brown adipocytes and pig white adipocytes (Extended Data Fig. 1k), indicating that ACOX2 expression is substrate regulated. Together, these findings identify mmBCFAs as endogenous substrates of ACOX2. It is noteworthy that branched short-chain fatty acids, such as isovaleric acid (C5) and isobutyric acid (C4), which serve as precursors for long-chain mmBCFAs (for example, iso-C17:0 and iso-C16:0), have been reported to be catabolized by mitochondrial β-oxidation21,22.

FASN promotes mmBCFA synthesis

Synthesis of mmBCFA requires FASN, a large multifunctional cytosolic enzyme primarily responsible for palmitate (C16:0) synthesis. Knockout of FASN decreases C16:0 levels but results in accumulation of the substrates acetyl-CoA and malonyl-CoA[23]. FASN mediates mmBCFA synthesis by using malonyl-CoA from glucose metabolism and BrCoA from BCAA catabolism (Extended Data Fig. 2a)9,20. Deletion of the BCAA catabolic enzyme branched-chain alpha-keto acid dehydrogenase (BCKDHA) in brown adipocytes (Extended Data Fig. 2b) significantly reduced mmBCFA levels (Extended Data Fig. 2c) and increased conventional StCFAs (Extended Data Fig. 2d).

Cold exposure markedly increased the expression of FASN in BAT and iWAT (Fig. 2a and Extended Data Fig. 2e), consistent with previous findings[24]. Other proteins involved in mmBCFA synthesis, including carnitine acetyltransferase (CRAT), lipogenesis enzymes ATP citrate lyase and acetyl-CoA carboxylase 1 (ACC1), and BCAA catabolism enzymes BCAT2 (branched-chain amino acid transaminase 2) and BCKDH were also significantly increased in BAT and iWAT of cold-treated mice (Fig. 2a and Extended Data Fig. 2e). In addition, treatment with norepinephrine in mouse brown adipocytes or forskolin in human brown adipocytes increased FASN expression (Extended Data Fig. 2f,g), indicating that FASN expression is regulated by cAMP signalling, akin to ACOX2. Thus, thermogenic stimuli not only increase the factors involved in β-oxidation, but also synthesis of mmBCFAs in thermogenic adipocytes.

Fig. 2: FASN translocates to peroxisomes, drives mmBCFA synthesis and regulates thermogenesis in BAT.

a, mRNA levels of mmBCFA β-oxidation and synthesis genes in BAT of WT mice maintained at thermoneutrality (30 °C) or 4 °C for 7 days. n = 4. b, Immunofluorescence analysis and quantitative analysis of FASN peroxisomal localization in BAT of WT mice maintained at room temperature (RT) or after cold treatment for 2 h. n = 3. c, Western blot analysis following subcellular fractionation in differentiated BAT SVF cells after norepinephrine (0.2 μM) treatment for 2 h. d, Western blot analysis of FASN in BAT and iWAT of FASN-BKO mice. n = 3. e, Mass spectrometry analysis of a StCFA C16:0 and an mmBCFA iso-C17:0 in BAT of FASN-BKO and control mice housed at 22 °C or 4 °C for 6 h. n = 4. f, Core body temperature of control (n = 13) and FASN-BKO (n = 12) mice after cold exposure (4 °C). g, Energy expenditure of control (n = 4) and FASN-BKO (n = 6) mice after CL316,243 treatment. Data in a,b (right),c–g are from biologically independent samples. Images in b (left) and c are representative of two separate experiments. Data are presented as mean ± s.e.m. Statistical analyses were performed using two-sided unpaired Student’s t-test (a,b right), one-way ANOVA (e) or two-way ANOVA (f–g), followed by Fisher’s least-significant difference post hoc test (e–g). Scale bars, 10 μm.

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Previous studies indicate that FASN interacts with peroxisomal fatty acid transporters, including PMP70 (ABCD3)[25], which transports BCFAs and C27 bile acids into peroxisomes[26], suggesting that the lipogenic enzyme is partially localized at the peroxisomal membrane. To investigate whether the intracellular localization of FASN is influenced by a thermogenic stimulus, we treated cultured brown adipocytes with norepinephrine for 2 h. Immunofluorescence analysis showed that norepinephrine treatment markedly increased the colocalization of FASN with PMP70 (Extended Data Fig. 2h). Similarly, FASN showed peri-lipid droplet localization in BAT of mice maintained at room temperature but had increased colocalization with PMP70 after acute cold exposure (Fig. 2b) or CL316,243 treatment (Extended Data Fig. 2i). Subcellular fractionation of adipocytes confirmed the partial peroxisomal localization of FASN, which was further increased after acute norepinephrine treatment (Fig. 2c). Together, these results indicate that FASN translocates to peroxisomes in response to thermogenic stimuli. In further support of peroxisomal localization, FASN interacts with PEX7 (Extended Data Fig. 2j), an import receptor for a subset of peroxisomal proteins[27].

The mmBCFA synthesis protein CRAT is found in peroxisomes and mitochondria across various cell types, including brown adipocytes28,29,30,31. CRAT catalyses the interconversion of acyl-CoA and acylcarnitine and also acts on BrCoA[32], a product of BCAA catabolism in mitochondria, to facilitate mmBCFA synthesis[9]. Norepinephrine treatment increased CRAT localization to peroxisomes in brown adipocytes (Extended Data Fig. 2k). CRISPR-mediated CRAT knockout (Extended Data Fig. 2l) did not affect adipogenesis (Extended Data Fig. 2m) but significantly decreased mmBCFA levels without altering StCFA levels (Extended Data Fig. 2n,o). These findings suggest that thermogenic stimuli promote the translocation of mmBCFA synthesis proteins to peroxisomes, an organelle that houses the mmBCFA β-oxidation protein ACOX2.

To evaluate the impact of inhibiting mmBCFA synthesis on respiration, we measured OCR in CRAT knockout and control brown adipocytes sequentially treated with leucine and norepinephrine. CRAT knockout significantly reduced OCR; notably, this effect was rescued by iso-C17:0 supplementation, indicating that the phenotype results from impaired mmBCFA synthesis (Extended Data Fig. 2p). To test whether ACOX2-mediated respiration involves coordination with CRAT, we overexpressed ACOX2 in CRAT KO cells. Gene expression analysis confirmed these genetic manipulations (Extended Data Fig. 2q). CRAT knockout reduced basal respiration and abolished the effect of ACOX2 overexpression on OCR in FCCP-treated cells (Extended Data Fig. 2r). These results indicate that ACOX2 coordinates with the mmBCFA synthetic machinery to enhance oxygen consumption.

FASN regulates thermogenesis

Previous studies in 3T3-L1 adipocytes indicate that FASN inactivation decreases the levels of StCFA as well as mmBCFA, but the data were confounded by the resulting inhibition of adipogenesis[9]. Thus, we isolated SVF cells from BAT of FASNLox/Lox neonatal mice, transduced them with retrovirus expressing tamoxifen-inducible Cre or a control virus, differentiated them into adipocytes, and then knocked out FASN using 4-hydroxytamoxifen (4-OHT) (Extended Data Fig. 3a). The inducible knockout of FASN in differentiated adipocytes decreased intracellular lipid content (Extended Data Fig. 3b) and significantly reduced the levels of mmBCFA (Extended Data Fig. 3c), as well as StCFA, such as C16:0 and C16:1 (Extended Data Fig. 3d).

We next investigated the effect of FASN ablation on mmBCFA levels and thermogenesis in mice. Previous studies, including ours, showed that pan-adipose FASN knockout induces iWAT browning33,34. To avoid this confounding effect, we generated BAT-specific FASN knockout (FASN-BKO) mice using constitutive UCP1-Cre. These mice showed selective FASN loss in BAT without affecting FASN or UCP1 expression in iWAT (Fig. 2d). Notably, FASN inactivation did not decrease iso-C17:0 levels in mice housed at room temperature, but significantly decreased the mmBCFA levels in mice subjected to 6 hours of cold exposure. C16:0 was decreased as expected in room temperature or cold-treated FASN-BKO mice (Fig. 2e). Unlike tamoxifen-inducible UCP1-Cre-mediated knockout, which showed no effect[35] (probably due to incomplete recombination), constitutive UCP1-Cre knockout of the lipogenesis protein resulted in significantly reduced cold tolerance (Fig. 2f) and impaired CL316,243-induced energy expenditure (Fig. 2g), demonstrating that FASN is required for BAT thermogenesis. FASN-BKO mice showed increased diet-induced obesity (Extended Data Fig. 3e) due to decreased energy expenditure (Extended Data Fig. 3f), with no effect on food intake or locomotor activity (Extended Data Fig. 3g,h).

ACOX2 loss impairs thermogenesis

The cold-induced upregulation of ACOX2 in BAT prompted us to explore its function in thermogenesis and energy metabolism. We analysed ACOX2 knockout (ACOX2−/−) mice[36], which were born at expected Mendelian frequencies and appeared normal. Gene expression and western blot confirmed the knockout in adipose tissue (Extended Data Fig. 4a,b). The residual messenger RNA (mRNA) and protein expression in ACOX2−/− mice might reflect mosaicism and/or non-specificity. During a 6-hour cold challenge, ACOX2−/− mice showed significantly impaired cold tolerance (Extended Data Fig. 4c) without notable changes in thermogenesis-related gene expression, except for cell-death-inducing DFFA-like effector A (CIDEA) (Extended Data Fig. 4d).

In BAT, a high-fat diet (HFD) increased ACOX2 expression, but genes involved in BCAA catabolism and mmBCFA synthesis were unchanged (Extended Data Fig. 4e). Thus, we determined the effect of ACOX2 ablation on diet-induced obesity. HFD-fed ACOX2−/− mice gained significantly more weight and fat mass than WT mice (Extended Data Fig. 4f–h), despite similar food intake (Extended Data Fig. 4i) and locomotor activity (Extended Data Fig. 4j). ACOX2−/− mice showed increased adipose depot weights and lipid accumulation (Extended Data Fig. 4k–m). In addition, they showed impaired glucose and insulin tolerance (Extended Data Fig. 4n,o), consistent with defective BAT function.

To determine whether the effects of ACOX2 deficiency on thermogenesis and energy metabolism are intrinsic to adipose tissue, we generated ACOX2LoxP/LoxP mice using CRISPR–Cas9 (Fig. 3a) and crossed them with adiponectin-Cre transgenic mice to create adipose-specific ACOX2 knockout (ACOX2-AKO) mice. Gene expression analysis confirmed successful ACOX2 deletion in adipose tissue (Fig. 3b). ACOX2 deletion did not affect ACOX1 or ACOX3 expression but resulted in reduced FASN levels in BAT (Extended Data Fig. 5a). ACOX2 ablation selectively increased mmBCFA levels in BAT (Extended Data Fig. 5b). Because ACOX2 contributes to bile acid synthesis by degrading C27 intermediates, we measured bile acids in BAT of ACOX2−/− and wild-type (WT) mice. Compared with previously reported hepatic bile acid levels[37], bile acids in BAT were virtually undetectable and remained unaffected by ACOX2 ablation (Extended Data Table 1). Furthermore, total serum bile acids were unchanged in ACOX2-AKO mice (Extended Data Fig. 5c).

Fig. 3: Adipose-specific ACOX2 knockout impairs thermogenesis and promotes diet-induced obesity and insulin resistance.

a, Gene targeting strategy using CRISPR–Cas9 to insert LoxP sites into the ACOX2 locus. The floxed mice were crossed with an adiponectin-Cre mouse to generate ACOX2-AKO mice. b, ACOX2 gene expression analysis in adipose tissue depots of control and ACOX2-AKO mice. n = 4. c, Infrared thermal imaging of mice after a 6 h of cold exposure. Quantification shows average surface temperature of two mice per genotype. d, Core body temperature of control and ACOX2-AKO male mice after cold exposure (4 °C). n = 13. e, VO_2 of control (n = 9) and ACOX2-AKO (n = 8) male mice after CL316,243 treatment. f, Western blot analysis in BAT of control (n = 4) and ACOX2-AKO (n = 3) mice. g, Body weight of control and ACOX2-AKO male mice fed HFD. n = 8. h, Body composition analysis of HFD-fed male control and ACOX2-AKO mice. n = 8. i, Regression analysis of energy expenditure with body weight as a covariate in HFD-fed ACOX2-AKO and control male mice. n = 7. j,k, Glucose tolerance testing (GTT) (j) and insulin tolerance testing (ITT) (k) in control and ACOX2-AKO mice after HFD feeding. The mice were dosed with glucose (j) or insulin (k) based on lean body mass; n = 8 for GTT; n = 5 for ITT. Data in b,c (right), d–k are from biologically independent samples. Images in c are representative of two mice per group. Data with error bars are reported as the mean ± s.e.m. Two-sided unpaired Student’s t-test in b,h or two-way ANOVA followed by Fisher’s least-significant difference test in d,e,g,j,k or two-way analysis of covariance (ANCOVA) with Tukey’s test in i. Panel a was created using BioRender (https://biorender.com).

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To assess thermogenesis, we used infrared thermal imaging on cold-exposed ACOX2-AKO and control mice. ACOX2-AKO mice showed lower surface temperatures in the cervical and thoracic regions (Fig. 3c), consistent with reduced core body temperature during cold exposure measured using IPTT-300 temperature transponders implanted in the interscapular space (Fig. 3d and Extended Data Fig. 5d). Furthermore, CL316,243-stimulated oxygen consumption (VO_2), a marker of thermogenic activation, was significantly lower in ACOX2-AKO mice (Fig. 3e and Extended Data Fig. 5e). Consistent with the gene expression analysis (Extended Data Fig. 5a), western blot analysis showed that ACOX2 deficiency also decreased FASN levels, suggesting coordinated regulation of ACOX2 and FASN expression. However, oxidative phosphorylation protein levels in BAT were unaffected (Fig. 3f).

We next evaluated the role of adipose ACOX2 in diet-induced obesity. HFD-fed ACOX2-AKO male and female mice gained more weight than controls (Fig. 3g and Extended Data Fig. 5f) due to increased fat mass (Fig. 3h). Histological analysis revealed greater lipid accumulation in BAT of ACOX2-AKO mice (Extended Data Fig. 5g). Increased adiposity was not linked to differences in food intake or locomotor activity (Extended Data Fig. 5h,i), but to decreased energy expenditure (Fig. 3i). In addition, HFD-fed ACOX2-AKO mice showed impaired glucose tolerance (Fig. 3j) and reduced insulin sensitivity (Fig. 3k), further evidenced by decreased insulin-stimulated Akt phosphorylation in liver (Extended Data Fig. 5j). Together, these findings demonstrate that ACOX2-mediated β-oxidation is critical for thermogenesis, and its disruption in adipose tissue promotes diet-induced obesity and insulin resistance.

ACOX2 upregulation drives thermogenesis

To assess whether upregulating adipose tissue ACOX2 is sufficient to enhance thermogenesis and energy homeostasis, we generated transgenic mice with adipose-specific ACOX2 overexpression (ACOX2Adipo-OE) (Fig. 4a). Gene expression analysis confirmed ACOX2 overexpression in various adipose depots (Fig. 4b). During cold exposure, ACOX2Adipo-OE mice showed significantly improved cold tolerance (Fig. 4c), indicating that cold-induced ACOX2 upregulation enhances thermogenesis. This was supported by higher VO_2 in ACOX2Adipo-OE mice compared with WT controls following CL316,243 treatment (Fig. 4d). ACOX2 overexpression did not affect lipolysis in BAT (Extended Data Fig. 6a) or the levels of various oxidative phosphorylation pathway proteins (Fig. 4e), suggesting that it does not influence substrate liberation or mitochondrial abundance. Notably, the improved cold tolerance associated with adipose-specific ACOX2 overexpression was abolished by treatment of mice with BCAT-IN-2 (Extended Data Fig. 6b), a highly selective inhibitor of BCAT2 (ref. [38]). These data demonstrate that a cycle of mmBCFA synthesis through BCAA catabolism, followed by ACOX2-mediated fatty acid oxidation, promotes adipose thermogenesis.

Fig. 4: Adipose-specific ACOX2 overexpression promotes thermogenesis and counteracts diet-induced obesity and insulin resistance.

a, Schematic diagram of a transgene construct used to generate adipose-specific ACOX2 overexpression (ACOX2Adipo-OE) mice. b, ACOX2 gene expression in adipose tissue depots of ACOX2Adipo-OE and WT mice. n = 4. c, Core body temperature of ACOX2Adipo-OE and WT mice after cold exposure. n = 10. d, VO_2 of ACOX2Adipo-OE and WT mice after CL316,243 treatment. n = 5. e, Western blot analysis of ACOX2 in BAT of ACOX2Adipo-OE and WT mice. n = 2. f, Body weight of HFD-fed ACOX2Adipo-OE and WT female mice. n = 7. g, Body composition of HFD-fed ACOX2Adipo-OE and WT female mice. n = 7. h, Haemotoxylin and eosin staining in adipose depots of HFD-fed ACOX2Adipo-OE and WT female mice. i, GTT analysis in HFD-fed ACOX2Adipo-OE and WT female mice. n = 9. j, ITT analysis in HFD-fed ACOX2Adipo-OE and WT female mice. n = 8. Data in b–g,i,j are from biologically independent samples. Images in h are representative of three mice per group. Data are reported as the mean ± s.e.m. Two-sided unpaired Student’s t-test in b,g or two-way ANOVA followed by Fisher’s least-significant difference test in c,d,f,i,j. Scale bars, 75 μm.

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We next examined the impact of ACOX2 overexpression on diet-induced obesity and insulin resistance. HFD-fed female ACOX2Adipo-OE mice showed reduced body weight (Fig. 4f) and fat mass (Fig. 4g), despite no differences in food intake (Extended Data Fig. 6c), fecal lipid content (Extended Data Fig. 6d) or locomotor activity (Extended Data Fig. 6e). Histological analysis revealed protection against obesity-induced lipid accumulation in BAT (Fig. 4h). Similar results were observed in male ACOX2Adipo-OE mice, which were also resistant to diet-induced obesity (Extended Data Fig. 6f,g). Consistent with increased thermogenesis and reduced obesity, ACOX2Adipo-OE mice showed improved glucose tolerance and insulin sensitivity under HFD conditions, in both females (Fig. 4i,j) and males (Extended Data Fig. 6h,i). These findings demonstrate that adipose-specific ACOX2 overexpression promotes thermogenesis and protects against diet-induced obesity and insulin resistance.

UCP1-independent thermogenesis through ACOX2

Next, we investigated whether ACOX2-mediated thermogenesis depends on UCP1 by crossing ACOX2Adipo-OE mice with UCP1−/− mice. Western blot analysis confirmed ACOX2 overexpression and UCP1 knockout in BAT (Fig. 5a). During acute cold exposure, UCP1−/− mice were severely cold intolerant, showing torpor within 3 hours, as previously reported[2]. However, UCP1−/−/ACOX2Adipo-OE mice showed notable cold tolerance (Fig. 5b), indicating that ACOX2 promotes UCP1-independent thermogenesis, compensating for UCP1 loss.

Fig. 5: ACOX2 promotes UCP1-independent thermogenesis.

a, Western blot analysis of ACOX2 and UCP1 in BAT of WT, ACOX2Adipo-OE, UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice. n = 2. b, Body temperature of male mice after cold exposure (4 °C). n = 10. c–g, Body weight (c), body composition (d), adipose tissue weights (e), gross BAT morphology (f) and haemotoxylin and eosin staining of adipose tissue (g) from HFD-fed mice housed at thermoneutrality (30 °C). n = 5 for c–e. Images in f and g are representative of three mice per genotype. h, Western blot analysis of UCP1−/− brown adipocytes transfected with lentivirus overexpressing ACOX2 or LacZ. n = 2. i, OCR measurement using Seahorse in ACOX2 or LacZ-expressing UCP1−/− brown adipocytes. Oligo, oligomycin; AA + R, antimycin A and rotenone. n = 6. j, Measurement of ATP in WT brown adipocytes transfected with LacZ or ACOX2. n = 12. k, Western blot analysis for pAMPK, AMPK, pACC, ACC, ACOX2 and FASN in BAT of ACOX2Adipo-OE and WT mice cold-treated for 6 h. ACC antibodies recognize ACC1 and ACC2. n = 2. l, Schematic of Pexo-TEMP, a peroxisome-localized temperature sensor. m, Fluorescence microscopy analysis of Pexo-TEMP and a peroxisome marker (mCherry-PTS1) in brown adipocytes. Images representative of two separate experiments. n, Fluorescence intensity ratio in response to temperature changes in WT brown adipocytes expressing Pexo-TEMP. n = 16 separate wells. o, Fluorescence intensity ratio in WT brown adipocytes expressing Pexo-TEMP together with LacZ or ACOX2, followed by the indicated treatment. n = 16 separate wells. Data in a–e,h–k,n–o are from biologically independent samples. Data are reported as the mean ± s.e.m. Two-sided unpaired Student’s t-test in d,e,j; two-way ANOVA followed by Fisher’s least-significant difference test in b,c,i; or one-way ANOVA followed by Fisher’s least-significant difference test in o. Scale bars, 75 μm (g), 10 μm (m).

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To determine the effect of ACOX2 overexpression on diet-induced obesity, we fed mice a HFD for 18 weeks while they were housed in a thermoneutral room. UCP1−/−/ACOX2Adipo-OE mice had significantly lower body weight (Fig. 5c), fat mass (Fig. 5d) and reduced BAT and iWAT weights compared with UCP1−/− controls (Fig. 5e). BAT in UCP1−/− mice appeared pale with lipid droplet accumulation, whereas BAT from UCP1−/−/ACOX2Adipo-OE mice appeared normal (Fig. 5f,g).

Gene expression analysis revealed that ACOX2 overexpression upregulates FASN in BAT (Extended Data Fig. 7a). Although FASN expression in BAT is lower in mice maintained at thermoneutrality compared with those exposed to cold (Fig. 2a), ACOX2 overexpression drives FASN expression to support the substrate cycle. To determine whether the increased FASN protein levels are associated with peroxisomes, we performed immunofluorescence analysis in HFD-fed UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice housed at thermoneutrality. ACOX2 overexpression increased FASN colocalization with the peroxisome marker PMP70 (Extended Data Fig. 7b). Furthermore, indirect calorimetry of ACOX2Adipo-OE mice housed at thermoneutrality showed that ACOX2 overexpression enhanced energy expenditure during the dark phase (Extended Data Fig. 7c) without significant changes in food intake (Extended Data Fig. 7d) or locomotor activity (Extended Data Fig. 7e). These findings suggest that ACOX2 overexpression promotes metabolic adaptations in BAT by enhancing FASN expression and energy expenditure, even under thermoneutral conditions.

We further examined HFD-fed UCP1−/−/ACOX2Adipo-OE and UCP1−/− control mice housed at room temperature. Similar to thermoneutral conditions, UCP1−/−/ACOX2Adipo-OE mice showed resistance to diet-induced obesity (Extended Data Fig. 7f), reduced fat mass (Extended Data Fig. 7g–i) and preserved BAT morphology, avoiding increased intracellular lipid accumulation seen in UCP1−/− controls (Extended Data Fig. 7j). Furthermore, ACOX2 overexpression improved glucose homeostasis (Extended Data Fig. 7k,l). Together, these findings demonstrate that ACOX2 promotes thermogenesis and metabolic homeostasis, even in the context of UCP1 deficiency.

We next examined the effect of ACOX2 overexpression on respiration in UCP1−/− brown adipocytes. Consistent with the gene expression analysis, western blot analysis showed that ACOX2 overexpression was associated with FASN upregulation (Fig. 5h). Seahorse analysis showed that overexpression of the peroxisomal β-oxidation enzyme significantly increased basal OCR (Fig. 5i), probably due to increased ATP turnover driven by a substrate cycle. Accordingly, cellular ATP levels were significantly lower in WT brown adipocytes overexpressing ACOX2 (Fig. 5j). This prompted us to determine whether ACOX2 overexpression activates the energy-sensing kinase AMPK, which helps restore cellular energy balance by promoting mitochondrial oxidative phosphorylation and ATP production[39]. Western blot analysis in BAT of ACOX2Adipo-OE and WT mice revealed that ACOX2 overexpression, which also resulted in upregulation of FASN, increased phosphorylation of AMPK and its downstream target ACC (Fig. 5k), confirming AMPK activation. These findings suggest that the ATP-consuming lipid metabolism mediated by the FASN–ACOX2 pathway activates AMPK as a compensatory mechanism, linking peroxisomal activity to mitochondrial energy production to sustain thermogenesis.

To determine whether ACOX2 overexpression directly affects mitochondrial respiratory function, we measured Complex II-dependent and Complex IV-dependent respiration activities in mitochondria-enriched fraction from BAT of ACOX2Adipo-OE and WT mice. OCR responses were comparable between the transgenic and WT groups, indicating no difference in mitochondrial respiratory capacity (Extended Data Fig. 8a). These results indicate that ACOX2 does not intrinsically enhance mitochondrial respiration.

We next assessed whether ACOX2 functions through futile creatine cycling, an ATP-consuming, UCP1-independent thermogenic mechanism7,40 by evaluating the impact of creatine kinase B (CKB) knockout in ACOX2-overexpressing brown adipocytes (Extended Data Fig. 8b,c). CKB deletion did not affect the ACOX2-induced or iso-C17:0-induced respiration, indicating that ACOX2-mediated thermogenesis operates independently of creatine cycling in mitochondria (Extended Data Fig. 8d).

Next, we examined the role of ACOX2 in non-mitochondrial respiration. WT brown adipocytes expressing either ACOX2 or GFP were treated with antimycin A and rotenone to block mitochondrial electron transfer and subsequently exposed to iso-C17:0 or bovine serum albumin (BSA). ACOX2 overexpression, coupled with mmBCFA treatment, significantly increased OCR (Extended Data Fig. 8e), supporting a UCP1-independent, peroxisome-driven thermogenic mechanism.

ACOX2 induces peroxisomal heat

To confirm peroxisomal origins of ACOX2-mediated thermogenesis, we developed a peroxisome-targeted ratiometric fluorescent temperature sensor (Pexo-TEMP) (Fig. 5l) based on the temperature-sensitive properties of Sirius and mT-Sapphire[41] (Extended Data Fig. 9a,b). Confocal microscopy confirmed peroxisomal targeting of Pexo-TEMP in brown adipocytes (Fig. 5m). The fluorescent ratio (509 nm/425 nm) increased with temperature, validating Pexo-TEMP as a functional peroxisomal thermometer (Fig. 5n). Adipocytes expressing ACOX2 and Pexo-TEMP showed heat production in response to iso-C17:0 but not C16:0 treatment (Fig. 5o), with norepinephrine serving as a positive control. These findings indicate that ACOX2-mediated β-oxidation of mmBCFA in peroxisomes generates heat, revealing a previously unrecognized UCP1-independent thermogenic pathway.

Given that peroxisomal β-oxidation is associated with H_2O_2 production, we next investigated whether ACOX2 overexpression promotes oxidative stress and cytotoxicity using HyPer7-SKL, a peroxisome-targeted H_2O_2 probe[42]. ACOX2 overexpression and/or iso-C17:0 treatment increased H_2O_2 production in brown adipocytes (Extended Data Fig. 9c), supporting the notion that mmBCFAs are metabolized in peroxisomes. Palmitate also elevated H_2O_2 levels, consistent with previous findings that peroxisomal β-oxidation partially metabolizes palmitate[43]. This fatty acid metabolism upregulated catalase, a peroxisomal antioxidant enzyme, likely to mitigate potential H_2O_2-induced toxicity (Extended Data Fig. 9d). To investigate further, we generated catalase KO brown adipocytes using CRISPR–Cas9 (Extended Data Fig. 9e). Treatment with C16:0, iso-C17:0 or H_2O_2 significantly reduced cell viability in catalase KO adipocytes (Extended Data Fig. 9f), indicating that catalase expression protects against oxidative damage from increased peroxisomal β-oxidation. Consistent with this, BAT from ACOX2Adipo-OE and WT mice showed similar levels of 4-HNE, a marker of lipid peroxidation and oxidative stress[44] (Extended Data Fig. 9g). These results demonstrate that ACOX2-mediated β-oxidation enhances thermogenesis without inducing cytotoxicity.

Discussion

These results identify a UCP1-independent pathway of adipose tissue thermogenesis involving a substrate cycle of mmBCFA synthesis and β-oxidation in peroxisomes (Extended Data Fig. 10). Adipose-specific inactivation of ACOX2 impairs thermogenesis, whereas adipose-specific overexpression improves cold tolerance, indicating that the peroxisomal β-oxidation enzyme is necessary and sufficient for adipose tissue thermogenesis. Overexpression of ACOX2 rescues cold intolerance and reduces diet-induced obesity in UCP1−/− mice. ACOX2 overexpression, which also upregulates the expression of FASN, increases basal OCR, reflecting increased ATP turnover due to energy-consuming metabolism of mmBCFA by the FASN–ACOX2 axis. As FASN-mediated lipogenesis is an ATP-consuming process and ACOX2-mediated β-oxidation is not linked to ATP production, this substrate cycle promotes negative energy balance, leading to protection against obesity and insulin resistance. Consistent with recent findings that brown adipocytes rely on mitochondrial ATP production to sustain thermogenesis through the futile creatine cycling pathway[45], we observe AMPK activation in ACOX2-overexpressing BAT, suggesting a compensatory increase in mitochondrial ATP synthesis. This supports a self-sustaining mechanism in which ATP-consuming peroxisomal lipid metabolism is balanced by mitochondrial ATP synthesis. Thus, although the FASN–ACOX2 thermogenesis pathway is UCP1-independent and localized to peroxisomes, it remains functionally coupled to mitochondria through its ATP demand. Notably, in contrast to the role of ACOX2-mediated β-oxidation of mmBCFA in metabolic regulation, adipose-specific knockout of ACOX1, which regulates β-oxidation of very long-chain fatty acids, does not affect thermogenesis or diet-induced obesity in mice[13].

Several lines of evidence support a functional role for mmBCFAs in BAT: (1) adipose tissue is the primary site of de novo mmBCFA synthesis, with several species of these BCAA-derived fatty acids most abundantly enriched in brown adipocytes[9]; (2) cold exposure significantly increases mmBCFA synthesis in mouse BAT[9] and (3) individuals with obesity show lower mmBCFA levels46,47 and impaired BCAA catabolism[21]. Increased circulating concentrations of BCAAs are strongly associated with metabolic diseases[48]. BCAA catabolism has been reported to regulate energy homeostasis due to use of branched amino acids as an alternative fuel source for mitochondrial thermogenesis in BAT[49], although recent data show a role for BCAA catabolism in metabolic health independent of a direct effect on thermogenesis[38]. Our work indicates that BCAA catabolism leads to the synthesis of mmBCFAs, which are then oxidized in peroxisomes to promote thermogenesis. Although mammals, including humans, possess the ability to synthesize mmBCFAs, these fatty acids can also be obtained from dietary sources, including milk and dairy products[50]. We speculate that dietary interventions implementing use of these branched fatty acids or modulation of proteins involved in their metabolism might be a promising strategy to treat obesity and related metabolic diseases.

Methods

Mouse models and animal experiments

All animal protocols were approved by the Washington University Institutional Animal Care and Use Committee. C57BL/6J WT mice were obtained from the Jackson Laboratory (strain no. 000664). FASLox/Lox mice were previously described in ref. [34]. To generate mice with brown-adipose-specific knockout of FAS (FAS-BKO), FASLox/Lox animals were crossed with Ucp1-Cre mice obtained from the Jackson Laboratory (stock no. 024670). FASLox/Lox mice without Cre were used as a control for FAS-BKO mice. ACOX2−/− mice have been previously described in ref. [36] and were generated using two TALEN pairs targeting beginning and end of exon 4 that led to deletion of 176 base pairs covering the entire exon 4, resulting in a frameshift. Founder mice were generated by cytoplasmic injection of TALENs into C57BL/6N mouse zygotes and then backcrossed into the WT C57BL/6J background. ACOX2Lox/Lox mice on the C57BL/6J background were generated using the CRISPR–Cas9 system. CRISPR-mediated mutagenesis was done by the Genome Engineering and IPSC Center at Washington University. To generate mice with adipose-specific knockout of ACOX2 (ACOX2-AKO), ACOX2Lox/Lox animals were crossed with adiponectin-Cre mice obtained from the Jackson Laboratory (stock no. 028020). ACOX2Lox/Lox mice without Cre were used as a control for ACOX2-AKO. To generate mice with adipose-specific overexpression of ACOX2 (ACOX2Adipo-OE), murine ACOX2 was cloned downstream of a previously characterized 5.4-kilobase adiponectin promoter[51]. The adiponectin promoter plasmid was a generous gift from P. Scherer (University of Texas Southwestern Medical Center). The transgenic construct was microinjected into the pronucleus of a newly fertilized egg from a C57BL/6J × CBA hybrid mouse and implanted into a pseudopregnant female by the Mouse Genetics Core at Washington University. The founders were backcrossed more than seven generations into the WT C57BL/6J genetic background.

UCP1−/− mice were obtained from the Jackson Laboratory (stock no. 003124). To generate mice with adipose-specific overexpression of ACOX2 on the UCP1−/− background, ACOX2Adipo-OE mice were crossed with UCP1−/− mice. An inbred strain of these mice on the C57BL/6J genetic background was developed and used for all experiments. Mice were fed either normal chow diet (Purina 5053) or a HFD (D12492, Research Diets). All animals were randomly allocated into different groups. For metabolic phenotyping studies, both male and female animals were studied, and all data were disaggregated by sex. Mice were maintained under constant temperature (23–25 °C), circulating air and humidity (45–65%) with a 12-h light/dark cycle and provided ad libitum access to food and water. Body composition (fat and lean mass) was measured using an EchoMRI system.

To assess glucose homeostasis, intraperitoneal glucose tolerance tests and intraperitoneal insulin tolerance tests were performed based on body weight or lean body mass, as indicated. Mice were injected intraperitoneally with glucose (2.5 g kg−1 body weight or 2 g kg−1 lean mass) or insulin (0.75 U kg−1 body weight or 1 U kg−1 lean mass) after being fasted for 6 h or 4 h, respectively. Blood glucose levels were measured at 0 min, 15 min, 30 min, 60 min and 120 min after injection by a glucometer.

VO_2, VCO_2 and respiratory exchange ratio were measured by indirect calorimetry using a PhenoMaster (TSE Systems) metabolic cage system and analysed using CalR web-based software (v.1.3). For measurement of CL316,243-induced energy expenditure, mice were anaesthetized using pentobarbital (90 mg kg−1 intraperitoneally) and acclimated to the environment for 60 min, allowing oxygen consumption to stabilize. The mice were then injected with CL316,243 at a dose of 1 mg kg−1, and data were collected for 2 h in mice housed at 23 °C or 30 °C as previously described[52] To assess cold tolerance, body temperature was measured at time 0 and hourly for 6 h during cold exposure using implantable IPTT-300 temperature-sensitive transponders and a DAS-8007 programmable reader from Bio Medic Data Systems as previously described[52].

Cell culture and treatments

Immortalized mouse brown preadipocytes were established and differentiated as previously described[13]. Immortalized human brown preadipocytes, kindly provided by Y.-H. Tseng (Joslin Diabetes Center), were cultured and differentiated as previously described53,54. For isolation of mature pig adipocytes from subcutaneous WAT, fresh iWAT was digested in a collagenase buffer containing HBSS (GIBCO 14065-45), 12.6 mM CaCl_2, 4.9 mM MgCl_2, 2% BSA and 800 U g−1 (3 mg g−1 tissue) of type 2 collagenase for roughly 40 min in a 37 °C water bath. The digested material was passed through a 250-μm strainer and washed 3 times with a total of 1 l of KRHB (1× KBH, 25 mM HEPES, 2 mM glucose, 2% BSA) to separate the floating mature adipocytes. The cells were centrifuged at 50g for 3 min. The mature adipocytes (floating cells) were collected and cultured with DMEM/F12 medium.

Mouse brown adipocytes and pig iWAT mature adipocytes were cultured in Dulbecco’s modified eagle medium F12 (DMEM/F12) supplemented with 10% (v/v) fetal bovine serum (FBS), 1% (v/v) penicillin/streptomycin, 1% (v/v) l-glutamine and 1% (v/v) sodium pyruvate. Human embryonic kidney 293T (HEK293T) cells and immortalized human brown adipocytes were cultured in DMEM. All cells were cultured in a humidified incubator at 37 °C with 5% CO_2 in air.

Palmitic acid, C15:0 OCFA, C17:0 OCFA and BCFA iso-C17:0 were dissolved in ethanol, then diluted with serum-free medium containing 0.1% fatty acid-free BSA, then added into culture medium at final concentrations of 0.2 μM, 1 μM or 2 μM based on experimental need. Norepinephrine was first dissolved in 1× PBS and diluted into the culture medium at final concentrations of 0.25 μM, 1 μM or 2 μM for different experiments. H_2O_2 was dissolved in 1× PBS and diluted into the culture medium at a final concentration of 500 μM. Isotope-labelled glucose (U-13C_6 glucose) was dissolved in 1× PBS and diluted into the culture medium at a final concentration of 17.5 mM.

Plasmid constructs

Genome-wide guide RNA (gRNA) databases were used to design gRNA oligonucleotides against ACOX2, CRAT, Bckdha, CAT and CKB. The oligonucleotides were ordered from Integrated DNA Technologies and subcloned into lentiCRISPRv2 plasmid. ACOX2 and Hyper7 complementary DNA (cDNA) clones were purchased from transOMIC (BC021339) and Addgene (136466), respectively, and subcloned into pLJM1 lentiviral overexpression plasmid. Sequences of primers used for cloning are listed in Supplementary Table 1. Pexo-TEMP plasmid was generated by inserting PTS2-Sirius-T2A fragment by PCR using primers below. Forward: CGG CGA CCG GTG CCA CCA TGC ACC GGC TGC AGG TGG TGC TGG GCC ACC TGG CCG GCC GGC CCG AGT CCT CCT CCG CCC TGC AGG CCG CCC CCT GCA GCT CGG ATC CCA CCA TG, and reverse: TCA CCA TGA GCT CGG GGC C and inserting mT-Sapphire fragment by PCR using primers below. Forward: CGG CCG AGC TCA TGC ACC GGC TGC AGG TGG TGC TGG GCC ACC TGG CCG GCC GGC CCG AGT CCT CCT CCG CCC TGC AGG CCG CCC CCT GCG TGA GCA AGG GCG AGG AGC T, reverse: TGT GAT GGA TAT CTG CAG AAT TC based on the gTEMP_pcDNA3 plasmid (Addgene no. 89583). To generate recombinant lentiviruses, the overexpression plasmid (pLJM1), gRNA plasmid (lentiCRISPRv2) or short-hairpin RNA plasmid (pLKO.1-puro) together with packaging plasmids (pMD2.G and psPAX2) were cotransfected into HEK293T cells. Lentiviral particles were collected 48 h after transfection and stored at −80 °C until they were used to transduce immortalized BAT SVF cells.

Extraction and mass spectrometric analysis of fatty acids

BAT or brown adipocytes homogenate containing 50 μg of total protein content was hydrolysed in an acid hydrolysis buffer (CH_3CN:37% HCl, 4:1) in 90 °C water bath for 2 h. Then, hexane was used to extract the fatty acids. The samples were dried under a stream of nitrogen and redissolved in chloroform, methanol, H_2O and 25% NH_4OH (50:45:5:0.01). After extraction, the electrospray ionization-mass spectrometry images of the fatty acids in the adipose tissues and adipocytes were obtained by a Thermo Fisher LTQ Orbitrap Velos in the negative-ion mode scanning from 200 m/z to 600 m/z with a resolution of 100,000 (at m/z 400 Da). Data were processed by built-in Xcalibur software as previously described[14] and the exogenous docosanoic-22, 22, 22-D_3 acid added to samples before extraction was used as an internal standard for quantitation. To verify the iso-form of the fatty acid structures, dried fatty acids were derivatized to the N-(4-aminomethylphenyl) pyridinium derivatives, which were subjected to higher-energy collisional dissociation tandem mass spectrometry for structural identification as described previously[55]. Data were analysed using R (v.4.2.1).

Carbon flux tracing and lipidomic analysis

To assess incorporation of 13C-label into mmBCFA, BAT SVF cells were differentiated into adipocytes in normal DMEM/F12 medium and then cultured in DMEM/F12 in which glucose was replaced with [U^13C_6]-glucose, as previously described in ref. [9]. Briefly, after differentiation, sgACOX2 or control adipocytes were cultured in the presence of [U^13C_6]-glucose in DMEM/F12 medium lacking glutamine and pyruvate containing 10% FBS for an extra 3 days, followed by 6 h of treatment with 10 nM CL316,243 or vehicle in a normal DMEM/F12 media and the lipids with labelled mmBCFA were extracted and subjected to high resolution electrospray ionization-mass spectrometry analysis as described above.

Immunofluorescence analysis

Frozen sections or cell samples were fixed with ethanol or 4% paraformaldehyde, followed by primary antibody and the corresponding secondary antibody incubation. Nuclei were counterstained with 4′6-diamidino-2-phenylindole. Samples were subjected to immunofluorescence analysis using rabbit polyclonal anti-ACOX2 antibody (1:100), rabbit polyclonal anti-PMP70 antibody (1:100), rabbit polyclonal anti-FASN antibody (1:100) and rabbit polyclonal anti-CRAT antibody (1:100). Slides were imaged using a Nikon A1Rsi Confocal Microscope. Images were analysed using NIS (v.5.21). Fluorescence intensity and colocalization were calculated using ImageJ (v.1.53) and Colocalization Finder (v.1).

Oil Red O staining

Adipocytes were fixed with 10% formalin overnight and then washed twice with 60% isopropanol. Oil Red O working solution was added, and the cells were incubated for 10 min at room temperature. The Oil Red O solution was removed, and the wells were washed four times with diH_2O. The stained cells were photographed under a ×2 objective lens using light microscopy.

Quantitative real-time PCR analysis

Total RNA was isolated using PureLink RNA Mini Kit (Invitrogen, 12183018A) and 2 μg of total RNA was reverse transcribed into cDNA using the iScriptcDNA Synthesis Kit (Bio-Rad) as previously reported[23]. Quantitative real-time PCR was conducted using PowerUp SYBR Green Master Mix. Relative mRNA expression level was determined using the 2(−ΔΔCT) method and L32 was used as an internal reference. Primers used in PCR analyses are listed in Supplementary Table 1.

Western blot and immunoprecipitation analyses

Cells or mouse tissue samples were homogenized in RIPA buffer (Cell Signaling Technology, 9806S) or homogenization buffer (0.25 M sucrose, 20 mM HEPES in distilled H_2O) containing a protease and phosphatase inhibitor cocktail (Sigma, P8465 and P0001). Total protein was extracted and quantitated using a bicinchoninic acid (BCA) kit. Denatured protein was separated by SDS–PAGE electrophoresis and transferred onto polyvinyl difluroride or Nylon membrane, blocked with 5% non-fat milk, incubated overnight with primary antibodies and finally incubated with horse radish peroxidase-conjugated secondary antibodies as reported. Tissue or cell lysates were subjected to immunoblot analysis using rabbit polyclonal anti-ACOX2 antibody (1:1,000), rabbit polyclonal anti-FASN antibody (1:10,000), mouse monoclonal anti-catalase antibody (1:1,000), rabbit polyclonal anti-UCP1 antibody (1:1,000), rabbit polyclonal anti-CRAT antibody (1:500), rabbit polyclonal anti-Ap2 antibody (1:1,000), rabbit polyclonal anti-AKT (1:1,000), rabbit polyclonal anti-PMP70 antibody (1:1,000), mouse monoclonal anti-COX 4 antibody (1:1,000), mouse polyclonal anti-oxidative phosphorylation cocktail (1:1,000), rabbit polyclonal anti-pAMPK (T172) antibody (1:1,000), rabbit polyclonal anti-AMPK antibody (1:1,000), rabbit polyclonal anti-pACC (S79) antibody (1:1,000), rabbit monoclonal anti-ACC antibody (1:1,000), rabbit polyclonal anti-BCKDHA antibody (1:1,000), rabbit polyclonal anti-pAK(T473) antibody (1:1,000), rabbit polyclonal anti-AKT antibody (1:1,000) and mouse monoclonal anti-CKB antibody (1:10,000). Rabbit polyclonal anti-β actin (1:1,000), rabbit polyclonal anti-β Tubulin (1:1,000) and rabbit polyclonal anti-Vinculin (1,000) were used as loading controls. For western blot analysis of ACOX2, initial experiments used either a rabbit polyclonal antibody (Invitrogen; catalogue no. PA5-114814) or a goat polyclonal antibody (Novus; catalogue no. 06011). Subsequently, a rabbit polyclonal anti-ACOX2 antibody from Millipore Sigma (catalogue no. HPA038280) was used. All antibodies used in western blot analyses are listed in the Reporting summary. Proteins were detected with the Odyssey Infrared Imaging System (LI-COR Biosciences). Uncropped raw blots are presented in Supplementary Fig. 1.

Interaction between FASN and PEX7 was assessed by co-immunoprecipitation analysis as described previously[56]. Briefly, HEK293 cells transiently transfected with Flag-FASN were homogenized using RIPA Lysis Buffer System (Santa Cruz) supplemented with protease and phosphatase inhibitors. Cell lysates were centrifuged at 13,000 rpm in a microcentrifuge for 10 min to remove unlysed cells. Supernatants were collected and subjected to protein quantification using the BCA assay. Cell lysates were then incubated with Anti-Flag M2 Affinity gel (Sigma-Aldrich, A220) overnight. Immunoprecipitates were washed three times with Tris-buffered saline before elution with SDS–PAGE sample buffer and subjected to SDS–PAGE. Cell lysates were subjected to immunoblot analysis using mouse monoclonal anti-HA antibody (1:1,000) and rabbit polyclonal anti-Flag antibody (1:1,000).

Subcellular fractionation of adipocytes

Adipocytes were lysed in Peroxisome Extraction Buffer (Sigma) using a Dounce homogenizer. Lysed cells were centrifuged at 1,000g for 10 min to pellet the nuclear fraction. The supernatant was transferred to a new tube and centrifuged at 12,000g for 15 min to collect the mitochondrial pellet. The supernatant was transferred to a new tube and centrifuged at 21,000g for 40 min to collect the peroxisome pellet. The remaining supernatant was saved as the cytosol fraction.

OCR

OCR in cultured adipocytes was measured using a Seahorse XFe Extracellular Flux Analyzer (Agilent) in a 24-well plate. For mitochondrial stress test, cells were treated with oligomycin (3 μM), FCCP (1.5 μM) and antimycin (2 μM) plus rotenone (1 μM). For measurement of fatty acid-induced respiration, cells were treated with BSA-conjugated 1 mM C15:0, C17:0, iso-C17:0 or BSA alone.

Measurement of mitochondrial respiration in BAT

Mitochondrial respiration in mouse BAT was measured using a previously described method[57]. Briefly, BAT depots from ACOX2Adipo-OE and WT mice were thawed in ice-cold PBS, minced in an Eppendorf tube and mechanically homogenized with 10–20 strokes using Teflon-glass in MAS buffer (70 mM sucrose, 220 mM mannitol, 10 mM KH_2PO_4, 5 mM MgCl_2, 2 mM HEPES, 1.0 mM EGTA and 0.2% (w/v) fatty acid-free BSA). The homogenates were centrifuged at 1,000g for 10 min at 4 °C. The supernatant was collected and loaded into a Seahorse XF96 microplate in 20 µl of MAS (5 µg BAT). The loaded plate was centrifuged at 2,000g for 5 min at 4 °C. An extra 130 µl of MAS buffer was added to each well. Substrate injections were as follows: 5 mM succinate + 2 μM rotenone for port A, 2 μM rotenone + 4 μM antimycin for port B, 0.5 mM TMPD + 1 mM ascorbic acid for port C and 50 mM azide for port D.

Infrared thermal imaging

The surface temperature in the region surrounding the BAT was measured using a high-speed mid-wave infrared camera (Telops FAST M3k) equipped with a 50 mm lens (Telops) and a spacer ring that facilitates a long working distance, resulting in a resolution of 0.25 mm per pixel. The temperature distribution was captured from a top-down perspective at 30 frames per second and a resolution of 320 × 256 pixels. The recorded thermal images were processed using the Reveal-IR software suite v.1.13.0 (Telops), assuming a fur emissivity of 0.75 (refs. 58,59).

MTT assay

To determine whether β-oxidation of mmBCFA results in oxidative stress leading to cytotoxicity, cell viability was assessed using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide) assay (Cayman Chemicals). Control and catalase knockout brown adipocytes were treated with iso-C17:0, C16:0, H_2O_2 or vehicle for 2 h. Cell viability was assessed according to the manufacturer’s instructions.

Lipid peroxidation assay

To determine the effect of ACOX2 overexpression on oxidative stress, BAT samples were collected from ACOX2AdipoOE and WT mice. Lipid peroxidation (4-HNE) protein adducts were quantified in the samples using a 4-HNE assay kit (Abcam) according to the manufacturer’s instructions.

Fecal lipid extraction and measurement

Feces were collected for 24 h from each cage and dried in an incubator for 16 h at 42 °C. One hundred milligrams of feces were homogenized with 1 M NaCl, followed with a chloroform:methanol solution (2:1). Chloroform layers were collected after centrifuge and evaporated under N_2 flow until dry. The 2% Triton X-100 in chloroform was added and evaporated again under N_2 flow. The samples were dissolved with ddH_2O and measured by a commercial kit (FFA, Wako; HR Series NEFA-HA(2)).

Ex vivo lipolysis assay

BAT was collected from WT and ACOX2Adipo OE mice and placed into a well of a 24-well plate containing 1 ml of prewarmed KRBH-BSA with 2% fatty acid-free BSA. The tissues were treated with vehicle or with 10 μM isoproterenol for 2 h at 37 °C. The glycerol levels in the media were measured using a commercial kit (Glycerol Assay Kit; Sigma, MAK117).

ATP assay

The ATP levels in differentiated adipocytes were measured using a commercial kit (ATP Detection Assay Kit; Cayman, 700410). Briefly, adipocytes were homogenized in the ATP detection sample buffer supplied in the kit and cell lysates were transferred to prechilled polypropylene tube. The samples were treated with Reaction Mix at room temperature for 15 min and the luminescence was read in a plate reader.

Bile acid measurement

Bile acids were measured by the Metabolomics Innovation Centre using an LC-multiple-reaction monitoring-MS method, as previously described[60], with necessary modifications. Tissue samples were precisely weighed into safe-lock Eppendorf tubes, and water (2 μl per mg of tissue) was added. Samples were homogenized using two metal beads at a shaking frequency of 30 Hz for 2 min on a MM 400 mill mixer. Acetonitrile (8 μl per mg of tissue) was then added, followed by a second homogenization under the same conditions for 3 min. Samples were further subjected to ultrasonication for 2 min in an ice-water bath and centrifuged at 21,000g and 5 °C for 10 min. The clear supernatant (100 μl) was collected and mixed with 75 μl of an internal standard solution containing 14 isotope-labelled bile acids. The mixtures were dried under nitrogen gas, and the residues were reconstituted in 75 μl of 50% methanol. A standard solution containing all targeted bile acids was prepared in the same internal standard solution and serially diluted to generate 10 calibration standards. Aliquots (10 μl) of the calibration and sample solutions were injected into an Agilent 1290 UHPLC system coupled to a 6495B Agilent QQQ mass spectrometer. The mass spectrometer was operated in multiple-reaction monitoring mode with negative-ion detection. Chromatographic separation was performed on a C18 column (150 × 2.1 mm, 1.7 μm) using a binary-solvent gradient with mobile phase A (0.01% formic acid in water) and mobile phase B (0.01% formic acid in acetonitrile). Calibration curves for individual bile acids were generated using linear regression, and bile acid concentrations in the samples were determined by interpolating the calibration curves with the analyte-to-internal standard peak ratios obtained from sample injections, ensuring quantification within the appropriate concentration range.

Statistics and reproducibility

Data are reported as mean ± standard error of the mean (s.e.m.) unless stated otherwise. Statistical comparisons between two groups were performed by using unpaired t-test. Analysis of variance (ANOVA) was used for more than two groups. A P value of less than 0.05 was considered statistically significant. Statistical analysis and graphs were generated using GraphPad Prism software (v.9). All in vitro and in vivo experiments were repeated independently at least twice, and key experiments were performed many times using separate cohorts. All replication attempts yielded consistent results. Representative immunoblot images reflect two independent experiments, and microscopy images are representative of n = 3 per group, unless otherwise specified in the figure legends.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data supporting this study are available in the Article and Supplementary Information. Additional data are available at Figshare (https://figshare.com/s/b8fd1fcaa83a4f3d0ac2)[61]. Source data are provided with this paper.

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Acknowledgements

This work was supported by National Institutes of Health grant nos. R01DK133344, R01DK115867, R01DK132239, GM103422, T32DK007120 and S10 OD032315. The core services of the Washington University Diabetes Research Center (grant no. DK020579) and the Nutrition Obesity Research Center (grant no. DK056341) also provided support for this work. The ACOX2−/− mouse model was generated by the FP7-funded European Infrafrontier-I3 project, granted to P.P.V.V.

Author information

Author notes

1. Anyuan He

Present address: School of Life Sciences, Anhui Medical University, Hefei, China

2. Dongliang Lu

Present address: Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences Zhengzhou University, Zhengzhou, China

Authors and Affiliations

1. Division of Endocrinology, Metabolism and Lipid Research, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA

Xuejing Liu, Anyuan He, Dongliang Lu, Donghua Hu, Min Tan, Parniyan Goodarzi, Bilal Ahmad, Brian Kleiboeker, Fong-Fu Hsu, Clay F. Semenkovich & Irfan J. Lodhi

2. Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA

Abenezer Abere & Patricia Weisensee

3. Division of Geriatrics and Nutritional Science, Washington University School of Medicine, St. Louis, MO, USA

Brian N. Finck

4. Department of Surgery, Section of Vascular Surgery, Washington University School of Medicine, St. Louis, MO, USA

Mohamed Zayed

5. Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, UT, USA

Katsuhiko Funai

6. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

Jonathan R. Brestoff

7. Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA

Ali Javaheri

8. Department of Medicine, University of Missouri School of Medicine, Columbia, MO, USA

Bettina Mittendorfer

9. Department of Nutrition and Exercise Physiology, University of Missouri School of Medicine, Columbia, MO, USA

Bettina Mittendorfer

10. Laboratory of Peroxisome Biology and Intracellular Communication, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium

Paul P. Van Veldhoven

11. University of Pittsburgh School of Medicine and UPMC, Pittsburgh, PA, USA

Babak Razani

12. Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, USA

Clay F. Semenkovich

Contributions

X.L. designed and conducted most of the in vitro and in vivo experiments and performed data analysis. A.H. generated Pexo-TEMP and ACOX2Adipo-OE targeting constructs and initially characterized the ACOX2 transgenic mice. D.L., D.H., M.T., A.B., P.G. and BA conducted experiments and/or performed data analysis. B.K. analysed gene expression and mass spectrometry data. B.N.F., M.Z., K.F., J.R.B., A.J., P.W., B.M., P.P.V.V., B.R. and C.F.S. designed experiments and/or provided reagents. I.J.L. conceived the study, secured research funding, designed experiments and performed data analysis. F.-F.H. performed mass spectrometry analysis. X.L. and I.J.L. wrote the paper. All authors read and edited the paper and provided comments.

Corresponding author

Correspondence to Irfan J. Lodhi.

Ethics declarations

Competing interests

I.J.L. and X.L. are named on a provisional patent application (serial no. 63/872,889) filed by Washington University related to targeting ACOX2 activation as a treatment for obesity and related metabolic diseases. The other authors declare no competing interests.

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Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Characterization of ACOX2 function and regulation in adipocytes.

a), Classical peroxisomal BCFA β-oxidation pathway. For 2-methyl BCFAs, propionyl-CoA is released after the first round of β-oxidation. Created in PowerPoint. b, ACOX2 gene expression in different tissues of wild type mice housed at 4 °C or 22 °C for 7 days. N = 4. c, Gene expression analysis in sgACOX2 and control BAT SVF cells. N = 3. d, Oil Red O staining in ACOX2 KO and control brown adipocytes. Scale bar, 300 mm. e, Structures of C16:0 (palmitate), iso-C17:0, and anteiso-C17:0. f, Mass spectrometry analysis of various straight chain fatty acids in Acox2 KO and control brown adipocytes. n = 4. g, Volcano plot depicting Log_2FC values of fatty acids in brown adipocytes overexpressing ACOX2 or GFP. N = 3. h, OCR measurement using Seahorse assay in ACOX2 or GFP-expressing WT brown adipocytes treated with the indicated fatty acid or BSA. N = 7. i, Gene expression analysis in mouse brown adipocytes overexpressing ACOX2 or GFP. N = 3. j, mtDNA copy number normalized to nuclear DNA. N = 6. k, ACOX2 gene expression following treatment with BSA, C16:0 or iso-C17:0 in differentiated mouse UCP1−/− BAT SVF cells (1 mM) or mature adipocytes isolated from fresh pig iWAT (2 mM) for 2 h. N = 3. Data are reported as the mean ± SEM. Data in b, c and f-k are from biologically independent samples. Two-sided unpaired Student’s t test in b, c, f, and i. Comparisons between groups in g were made with a two-tailed unpaired Student’s t-test adjusted for multiple comparisons using the Benjamini-Hochberg method. Two-way ANOVA with Tukey’s multiple comparison’s test in h. One-way ANOVA with Fisher’s LSD in k.

Source Data

Extended Data Fig. 2 Localization of the mmBCFA synthetic proteins FASN and CRAT to peroxisomes.

a, Schematic of mmBCFA biosynthesis using short branched acylCoA (BrCoA) derived from mitochondrial catabolism of BCAA. b, Western blot analysis of CRISPR/Cas9-mediated KO of BCKDHα in brown adipocytes. N = 2. c-d, Mass spectrometry analysis of mmBCFA (c) and conventional fatty acids (d) in control and BCKDHA KO brown adipocytes. N = 4. e, mRNA levels of mmBCFA β-oxidation and synthesis genes in iWAT of WT mice maintained at thermoneutrality or 4 °C for 7 days. N = 4. f, FASN gene expression in control or norepinephrine (NE)-treated differentiated mouse UCP1−/− brown adipocytes. N = 3. g, FASN gene expression in cultured human brown-like adipocytes treated with or without forskolin. N = 3. h, Immunofluorescence analysis of peroxisomal localization of FASN in wild-type brown adipocytes after NE treatment for 0 or 2 h. N = 3. Scale bar, 10 mm. i, Immunofluorescence analysis of peroxisomal localization of FASN in BAT of WT mice treated with or without CL316,243. N = 3. Scale bar, 10 mm. j, Co-immunoprecipitation of HA-PEX7 with FLAG-FASN in HEK293T cells. k, Immunofluorescence analysis of peroxisomal localization of CRAT in wild-type brown adipocytes after NE treatment for 2 h. Scale bar, 10 mm. l, Western blot analysis of CRISPR/Cas9-mediated KO of CRAT in brown adipocytes. N = 3. m, Oil Red O staining in differentiated CRAT KO and control adipocytes. Scale bar, 300 mm. n,o, Mass spectrometry analysis of mmBCFA (n) and stCFA (o) in control and CRAT KO brown adipocytes. N = 3. p, OCR measurement in sgCRAT and control brown adipocytes at baseline and after sequential treatment with leucine, NE, and iso-C17:0. N = 8 (sgControl); N = 7 (sgCRAT). q, Gene expression analysis in sgCRAT and control brown adipocytes overexpressing ACOX2 or GFP. N = 3. r, OCR measurement in sgCRAT and control brown adipocytes overexpressing ACOX2 or GFP. Oligo, oligomycin; FCCP, carbonyl cyanide-p-trifluoromethoxyphenylhydrazone; AA + R, antimycin A and rotenone. N = 9. Data with error bars are mean ± SEM. Data in b–i, k, l, and n–r are from biologically independent samples. Statistical significance was determined by two-sided unpaired Student’s t test (c–i, k, n–o), two-way ANOVA with Sidak’s (p) or Tukey’s test (r), and one-way ANOVA with Fisher’s LSD (q). Representative images in h–k, and m are from two independent experiments. Panel a was created using BioRender (https://biorender.com).

Source Data

Extended Data Fig. 3 Brown adipocyte-specific FASN knockout reduces mmBCFA levels and increases diet-induced obesity.

a, Western blot analysis of FASN in 4-hydroxytamoxifen (4-OHT)-treated FASNLox/Lox brown adipocytes transduced with retrovirus expressing GFP or Cre-ERT2. N = 3. b, Microscopy images of FASN-iKO and control BAT adipocytes prior to and after 4-OHT treatment. In the bottom panel, the cells were stained with Oil Red O. Scale bar, 75 mm (upper panel), 300 mm (lower panel). c, Mass spectrometry analysis of mmBCFA in control and FASN-iKO brown adipocytes. N = 3. d, Mass spectrometry analysis of various straight chain fatty acids in control and FASN-iKO brown adipocytes. N = 3. e, Body weight of HFD-fed FASN-BKO and control female mice. N = 7. f, Regression analysis of energy expenditure in HFD-fed mice. N = 7. g, Cumulative energy intake in HFD-fed FASN-BKO and control female mice. N = 4. h, Locomotor activity in FASN-BKO and control female mice. N = 4. Data are reported as mean ± SEM. Data in a and c-h are from biologically-independent samples. Images in b are representative of two separate experiments. Two-sided unpaired Student’s t test in c and d; two-way ANOVA with Fisher’s LSD in e; and two-way ANCOVA with Tukey’s test in f.

Source Data

Extended Data Fig. 4 Global knockout of ACOX2 impairs thermogenesis and exacerbates diet-induced obesity and metabolic dysfunction.

a, ACOX2 gene expression in adipose tissue depots of ACOX2−/− and WT mice. N = 3. b, Western blot analysis of ACOX2 in BAT of ACOX2−/− and WT mice. N = 2. c, Core body temperature of ACOX2−/− and WT male mice after cold exposure (4 °C). N = 10. d, Gene expression analysis of proteins involved in UCP1-dependent and -independent pathways of thermogenesis in BAT of ACOX2−/− and WT mice. N = 5. e, Gene expression analysis of WT mice fed normal chow diet or HFD. N = 3. f, Body weight of HFD-fed ACOX2−/− and WT female mice. N = 7. g, Body weight of ACOX2−/− and WT male mice fed with HFD. N = 9. h, Body composition analysis of ACOX2−/− and WT male mice fed HFD. N = 9. i, Food intake of ACOX2−/− and WT mice. N = 5. j, Locomotor activity of ACOX2−/− and WT mice. N = 5. k, Tissue weights of adipose depots of HFD-fed ACOX2−/− and WT male mice. N = 9. l, Gross images of BAT and iWAT from HFD-fed ACOX2−/− and WT male mice. m, Histologic analysis of H&E-stained adipose tissue sections from HFD-fed ACOX2−/− and WT male mice. Scale bar, 75 mm. n, GTT analysis in HFD-fed ACOX2−/− and WT female mice. N = 7. o, ITT analysis in HFD-fed WT and ACOX2−/− female mice. N = 7. Data are reported as the mean ± SEM. Data in a-k and n-o are from biologically independent samples. Images in l and m are representative of 3 mice/group. Two-sided unpaired Student’s t test in a, d, e, and h-k; two-way ANOVA followed by Fisher’s LSD test in c, f, g and n-o.

Source Data

Extended Data Fig. 5 Adipose-specific ACOX2 deletion impairs thermogenesis and promotes diet-induced obesity.

a, Gene expression analysis of ACOX1, ACOX3 and FASN in BAT of ACOX2-AKO and control mice. N = 4. b, Volcano plot depicting Log_2FC values of fatty acids in BAT of ACOX2-AKO and control mice. N = 5. c, Total serum bile acids in ACOX2-AKO and control mice. N = 9. d, Core body temperature of ACOX2-AKO and control female mice after cold exposure (4 °C). N = 11. e, VO_2 of female ACOX2-AKO (N = 6) and control mice (N = 7) after CL316,243 treatment. f, Body weight of HFD-fed ACOX2-AKO and control female mice. N = 8. g, Histologic analysis of H&E-stained adipose tissue sections from HFD-fed ACOX2−/− and WT male mice. Scale bar, 250 mm. h, Cumulative energy intake of ACOX2-AKO (N = 5) and control mice (N = 4). i, Locomotor activity of ACOX2-AKO (N = 5) and control mice (N = 4). j, Western blot analysis of insulin-stimulated AKT phosphorylation in BAT of liver of HFD-fed ACOX2-AKO and control mice. The bar graph shows quantification of AKT phosphorylation (N = 2). Data with error bars are reported as the mean ± SEM. Data in a-f and h-j are from biologically independent samples. Images in g are representative of 3 mice/genotype. Two-sided unpaired Student’s t test in a and c; two-sided unpaired Student’s t-test adjusted for multiple comparisons using the Benjamini-Hochberg method in b; two-way ANOVA with Fisher’s LSD in d-f.

Source Data

Extended Data Fig. 6 Adipose-specific ACOX2 overexpression enhances thermogenesis and metabolic health.

a, Media glycerol levels of fresh BAT explants from ACOX2Adipo-OE (N = 5) or WT (N = 4) mice incubated in media containing vehicle or isoproterenol for 2 h. b, Core body temperature prior to and after cold exposure in ACOX2Adipo-OE and WT mice treated with 25 mg/kg BCAT-IN-2 or vehicle for 1 week. N = 7. c, Food intake of ACOX2Adipo-OE and WT mice. N = 7. d, Fecal FFA content of ACOX2Adipo-OE and WT mice. N = 6. e, Locomotor activity of ACOX2Adipo-OE and WT mice. N = 6. f, Body weight of HFD-fed ACOX2Adipo-OE and WT male mice. N = 8. g, Body composition analysis in HFD-fed ACOX2Adipo-OE and WT male mice. N = 8. h, GTT analysis in HFD-fed ACOX2Adipo-OE and WT male mice. N = 8. i, ITT analysis in HFD-fed ACOX2Adipo-OE and WT male mice. N = 8. Data are reported as mean ± SEM and are from biologically-independent samples. One-way ANOVA with Fisher’s LSD in a and b and two-way ANOVA with Fisher’s LSD in f, h, and i; Two-sided unpaired Student’s t test in g.

Source Data

Extended Data Fig. 7 ACOX2 overexpression increases energy expenditure and improves metabolic health in UCP1−/− mice.

a, Relative FASN expression in BAT of UCP1−/− (N = 8) and UCP1−/−/ACOX2Adipo-OE (N = 7) mice. b, Immunofluorescence analysis and quantitative analysis of FASN peroxisomal localization in BAT of UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice housed at thermoneutrality. N = 3. Scale bar, 10 mm. c-e, Indirect calorimetry analysis of (c) energy expenditure, (d) cumulative food intake, and (e) locomotor activity in ACOX2Adipo-OE (N = 8) and WT (N = 7) mice housed at thermoneutrality. f, Body weight of HFD-fed male UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice fed mice housed at normal room temperature (22 °C). N = 7. g, Body composition analysis of HFD-fed UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice housed at 22 °C. N = 7. h, Adipose tissue weights of HFD-fed UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice housed at 22 °C. N = 7. i, Gross images of BAT from HFD-fed UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice fed housed at 22 °C. j, H&E staining in adipose depots of HFD-fed UCP1−/− and UCP1−/−/ACOX2Adipo-OE mice housed at 22 °C. Scale bar, 75 mm. k, GTT analysis in HFD-fed UCP1−/− and UCP1−/−/ACOX2Adipo-OE female mice housed at 22 °C. N = 8. l, ITT analysis in HFD-fed UCP1−/− (n = 7) and UCP1−/−/ACOX2Adipo-OE (N = 8) female mice fed housed at 22 °C. Data are presented as mean ± SEM. Data in b-h, k, and l are from biologically independent samples. Images in i and j are representative of three mice per genotype. Statistical analysis was performed using two-sided unpaired Student’s t-test (a, b, g, h), two-way ANOVA with Tukey’s test (c), or Fisher’s LSD test (f, k, l).

Source Data

Extended Data Fig. 8 Creatine cycling is dispensable for ACOX2-induced enhancement of oxygen consumption.

a, Activities of complex II and complex IV in isolated mitochondria from BAT of ACOX2Adipo-OE and WT mice measured using Seahorse (N = 9). b,c, Gene expression (b) and Western blot (c) analysis in sgCKB and control brown adipocytes overexpressing ACOX2 or GFP. N = 3 for panel b and N = 2 for panel c. d, OCR measurement in sgCKB and control brown adipocytes overexpressing ACOX2 or GFP and treated with iso-C17:0 or BSA (N = 12). e, OCR measurement using Seahorse in ACOX2 or GFP-expressing WT brown adipocytes treated with AA + R to block mitochondrial respiration, followed by treatment with C17:0-iso or BSA. N = 4. Data are from biologically-independent samples and are reported as mean ± SEM. One-way ANOVA with Fisher’s LSD in b and two-way ANOVA with Tukey’s test in d and e.

Source Data

Extended Data Fig. 9 ACOX2 overexpression induces catalase to attenuate oxidative stress.

a, Temperature-dependent fluorescence spectra of Sirius fluorescent protein. b, Temperature-dependent fluorescence spectra of mTSapphire fluorescent protein. c, Fluorescence microscopy analysis and quantification of peroxisome-localized H_2O_2 sensor HyPer7-SKL in brown adipocytes transfected with lentivirus expressing ACOX2 or LacZ and treated with BSA, C17:0-iso, C16:0 or H_2O_2. N-3. Scale bar, 20 mm. d, Catalase gene expression in GFP or ACOX2-overexpressing brown adipocytes treated with BSA, C16:0 or iso-C17:0. N = 3. e, Western blot analysis of catalase knockout using CRISPR/Cas9 in brown adipocytes. N = 2. f, MTT assay in control and CAT KO brown adipocytes after treatment with BSA, iso-C17:0, C16:0 or H_2O_2. N = 12. g, 4-HNE levels in BAT of ACOX2Adipo-OE and WT mice. N = 7. Data are reported as the mean ± SEM. Data in c-g are from biologically independent samples. Images in c are representative of three separate experiments. Two-sided unpaired Student’s t test in c (quantification) and f. One-way ANOVA followed by Fisher’s LSD test in d. Panels a,b adapted from ref. [41], PLOS, under a Creative Commons licence CC BY 4.0.

Source Data

Extended Data Fig. 10 UCP1-independent thermogenesis through an ATP-consuming cycle of monomethyl branched chain fatty acid (mmBCFA) synthesis and β-oxidation.

Mitochondrial catabolism of branched chain amino acids (BCAA) generates a short-branched acylCoA (BrCoA), such as isovaleryl-CoA. Carnitine acetyltransferase (CRAT), which is localized in mitochondria and peroxisomes, catalyses interconversion of branched-chain acylcarnitine and BrCoA, which is exported to the cytosol. The de novo lipogenesis enzyme FASN translocates to peroxisomes in response to acute cold exposure or β-adrenergic receptor stimulation and mediates synthesis of mmBCFA using BrCoA as a precursor. ACOX2 promotes β-oxidation of mmBCFA, generating heat in the process. Activation of the FASN–ACOX2 thermogenic pathway depletes ATP, triggering AMPK activation to promote compensatory ATP synthesis and sustain thermogenesis. Graphic created using BioRender (https://biorender.com).

Extended Data Table 1 Measurement of BAT bile acid levels (nmol/g tissue) in ACOX2−/− and WT mice

Supplementary information

Supplementary Fig. 1

Uncropped raw western blots for main figures and extended data figures.

Reporting Summary

Supplementary Table 1

Oligonucleotide sequences used in this study, including primers for genotyping, cloning and quantitative real-time PCR.

Source data

Supplementary Data

Source Data Figs. 1–5, Extended Data Figs. 1–9.

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Liu, X., He, A., Lu, D. et al. Peroxisomal metabolism of branched fatty acids regulates energy homeostasis. Nature 646, 1223–1231 (2025). https://doi.org/10.1038/s41586-025-09517-7

- Received: 12 February 2024

- Accepted: 12 August 2025

- Published: 17 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09517-7

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Nature volume 646, pages 1232–1242 (2025)

Abstract

Neural activity is increasingly recognized as a crucial regulator of cancer growth. In the brain, neuronal activity robustly influences glioma growth through paracrine mechanisms[1] and by electrochemical integration of malignant cells into neural circuitry via neuron-to-glioma synapses2,3. Outside of the central nervous system, innervation of tumours such as prostate, head and neck, breast, pancreatic, and gastrointestinal cancers by peripheral nerves similarly regulates cancer progression4,5,6,7,8,9,10,11,12. However, the extent to which the nervous system regulates small cell lung cancer (SCLC) progression, either in the lung or when growing within the brain, is less well understood. SCLC is a lethal high-grade neuroendocrine tumour that exhibits a strong propensity to metastasize to the brain. Here we demonstrate that in the lung, vagus nerve transection markedly inhibits primary lung tumour development and progression, highlighting a critical role for innervation in SCLC growth. In the brain, SCLC cells co-opt neuronal activity-regulated mechanisms to stimulate growth and progression. Glutamatergic and GABAergic (γ-aminobutyric acid-producing) cortical neuronal activity each drive proliferation of SCLC in the brain through paracrine and synaptic neuron–cancer interactions. SCLC cells form bona fide neuron-to-SCLC synapses and exhibit depolarizing currents with consequent calcium transients in response to neuronal activity; such SCLC cell membrane depolarization is sufficient to promote the growth of intracranial tumours. Together, these findings illustrate that neuronal activity has a crucial role in dictating SCLC pathogenesis.

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The nervous system is emerging as a critical component of the tumour microenvironment that regulates cancer pathobiology. Primary brain cancers such as gliomas exhibit a profound dependency on these neuronal mechanisms through activity-dependent paracrine signalling pathways1,13,14,15 and direct functional integration of malignant glioma cells into electrically active neural circuits2,3,16. Although less is known about the role of neurons in brain metastases, breast cancer cells that are metastatic to brain were recently found to occupy the perisynaptic space to usurp glutamate for their growth[17]. Increasing evidence also implicates the nervous system in the regulation of many cancers outside of the central nervous system, including prostate, gastric, colon, head and neck, pancreatic, breast and skin cancers4,5,6,7,8,9,10,11,12. These studies support the emerging principle that the nervous system can profoundly influence cancer pathobiology and highlight the large number of cancers that remain to be examined from this perspective.

SCLC is a lethal high-grade neuroendocrine tumour that accounts for around 15% of all lung cancers, causes more than 200,000 deaths worldwide annually[18], and has a 60% chance of metastasis by the time of diagnosis, with a particular propensity to metastasize to the brain19,20. Experimental mouse models indicate that SCLC can originate from pulmonary neuroendocrine cells21,22,23, a lung epithelial cell type that resides in close proximity to nerve fibres and expresses neurotransmitter receptors24,25,26. SCLC cells exhibit gene expression programmes that resemble those in neurons27,28,29. Higher levels of these neuronal markers correlate with shorter survival and more metastatic disease30,31,32. Recent studies have found that cell-intrinsic neurotransmitter-mediated signalling within the lung promotes SCLC progression33,34,35. Further, neuronal gene expression programmes in SCLC are implicated in driving metastatic progression by facilitating interactions with astrocytes in the brain microenvironment[36]. However, whether neuronal activity is a regulator of SCLC growth within the lung or the brain is not yet understood. We hypothesized that innervation—particularly by the vagus nerve—may influence disease growth in the lung and that nervous system–SCLC interactions may drive tumour progression. Here we investigate the role of neurons and neuronal activity in the progression of primary SCLC in the lung and SCLC in the brain.

Vagal innervation in primary lung tumours

We began by testing whether innervation supports SCLC growth in the lung. Recent studies have illustrated a role for neurotransmitter-mediated growth mechanisms in SCLC33,37, yet the role of vagal innervation in primary SCLC pathophysiology within the lung remains to be elucidated. Analysis of human primary SCLC samples[38] revealed the expression of several neurotransmitter receptor genes, consistent with the idea that SCLC cells at the primary site possess the ability to respond to neuronal cues (Extended Data Fig. 1a). To further investigate the influence of neural input on SCLC pathobiology, we utilized the Rb1fl/fl;Trp53fl/fl;p130fl/fl (RPR2; p130 is also known as Rbl2), luciferase-expressing SCLC genetic mouse model[39] (RPR2-luc). Tumours were induced in 8-week-old RPR2-luc mice by intratracheal administration of adenovirus expressing Cre under the control of the cytomegalovirus (CMV) promoter (Adeno-CMV-Cre). These mice form spontaneous SCLC tumours that recapitulate the genetics, histology, therapeutic response, time course of progression and metastatic nature of the human disease40,41. Examination of SCLC tumours taken from the mouse lung revealed innervation of malignant tissue by various nerve types, including parasympathetic (labelled by vesicular acetylcholine transporter protein), sympathetic (labelled by tyrosine hydroxylase protein) and sensory (a subpopulation of which is labelled by myelin basic protein) nerve fibres (Extended Data Fig. 1b). An abundance of nerve fibres was also evident in the vicinity of tumours metastatic to the liver in these mice (Extended Data Fig. 1c).

To modulate innervation to the lung, we performed a unilateral cervical vagotomy in the RPR2 mouse model (Fig. 1a). Vagotomies or sham surgeries were performed approximately 2 months after the intratracheal administration of the Adeno-CMV-Cre vector but before the development of visible lesions[39]. Sham-manipulated and denervated mice tolerated the procedure well, displaying no postoperative weight loss or sickness behaviours (Extended Data Fig. 1d).

Fig. 1: Vagal nerve innervation is critical for primary SCLC initiation and development.

a, Experimental paradigm for unilateral cervical vagotomy in genetic mouse model of spontaneously forming SCLC (RPR2-luc). Created in BioRender. Savchuk, S. (2025) https://BioRender.com/5fwotqm. b, Representative in vivo imaging system (IVIS) image of RPR2-luc mice 10 weeks after sham or vagotomy procedure. Photon emission expressed as photons per second per cm^2 per sr. The numbers on the images represent photon emission from the area selected within the red circles. c, Analysis of IVIS bioluminescence of overall tumour growth in SCLC primary tumours measured 10 weeks after vagotomy procedure (n = 11 sham and n = 9 vagotomy mice from 2 independent cohorts, P = 0.0465). Data are medians. d, Time course of tumour growth in RPR2-luc mice as measured by IVIS bioluminescence imaging after sham operation or vagotomy procedure. Events are recorded when mice begin to consistently show an increase in flux signal by tenfold between each weekly measurement (n = 11 sham and n = 9 vagotomy mice, P = 0.040). e, Time course of liver metastasis onset in RPR2-luc mice as detected by IVIS bioluminescence imaging after sham operation or vagotomy procedure (n = 11 sham and n = 9 vagotomy mice, P = 0.036). f, Representative haematoxylin and eosin (H&E) staining of lungs and livers isolated from sham-operated and denervated (vagotomy) RPR2-luc mice. Scale bars: 5,000 µm (left-hand images) and 250 µm (right-hand images). g, Quantification of lung tumour score (percentage of the organ occupied by the tumour) in sham-operated and denervated (vagotomy) RPR2-luc mice (n = 7 sham and n = 9 vagotomy mice, P = 0.005). h, As in g, for quantification of liver tumour score (n = 7 sham and n = 9 vagotomy mice, P = 0.019). i, Kaplan–Meier survival curve of RPR2-luc mice after either sham operation or denervation (vagotomy) (n = 11 sham and n = 9 vagotomy mice, P = 0.014). Mann–Whitney test (c); Gehan–Breslow–Wilcoxon test (d); log-rank (Mantel–Cox) test (e,i); Fisher’s exact test (g,h); all tests are two-tailed. **P < 0.01, *P < 0.05.

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By 10 weeks post-denervation, a marked difference was evident in the overall tumour burden between the sham-manipulated (control) and denervated (vagotomy) groups (Fig. 1b,c). Longitudinal in vivo bioluminescent imaging revealed that the initiation of primary lung tumours and the appearance of liver metastasis were significantly delayed or not detected in the vagotomy cohort, a difference that continued throughout the remainder of the experiment (Fig. 1d,e and Extended Data Fig. 1e). At the endpoint (around 6 months post-vagotomy), these differences in overall tumour burden were histologically validated. Whereas sham-manipulated mice demonstrated an abundance of tumour sites throughout all lobes of the lung, the denervated mice illustrated minimal to no tumour burden (Fig. 1f,g and Extended Data Fig. 1f). Livers, the first site of metastasis, were found to be free of tumour in all denervated mice as assessed by both histological and gross analysis of the surface of the organ (Fig. 1f,h and Extended Data Fig. 1g,h). These results were independently evaluated by a board-certified pathologist. A caveat to note is that the lack of metastatic spread to the liver is influenced, at least in part, by the marked reduction in lung tumour burden in vagotomized mice. Finally, as the overall tumour burden was greatly reduced by vagotomy, we observed a substantial survival benefit for the mice that had been denervated, with all denervated mice surviving the full duration of the experiment compared with a median survival of 16 weeks for sham-manipulated mice (Fig. 1i).

To then assess whether innervation was important to late-stage disease progression, we histologically evaluated innervation in early lesions compared with late-stage tumours. Compared with early in disease progression, more advanced tumours exhibited a significant reduction in nerve infiltration in regions of the tumour (Extended Data Fig. 1i,j). Concordantly, in contrast to early denervation, when vagotomy was performed 5–6 months after Cre administration in an independent cohort of mice, such reduction of tumour burden and survival benefit were not observed (Extended Data Fig. 1k–m). Similarly, tumour development was not inhibited by denervation in an aggressive form of SCLC driven by oncogenic MYC (Trp53fl/fl;Rb1fl/fl;MycLSL/LSL (RPM)[42]) where mice succumb to the tumour within 2 months (Extended Data Fig. 2). Together these findings comparing the role of innervation at early stages of tumour development to later stages and in more aggressive models suggest that vagal innervation of the primary tumour site (lung) has a critical role in SCLC initiation and development, and less of a role in tumour maintenance of advanced disease.

Neurons in the intracranial SCLC microenvironment

Given the substantial role of innervation in primary SCLC as demonstrated above and the high propensity of SCLC to form intracranial metastases, we next evaluated the interactions between neurons and SCLC cells in the brain. We first analysed metastatic SCLC brain tissue samples from nine human patients. Immunohistochemical neurofilament staining revealed that regions of the tumour mass demonstrated extensive axons intermingled with malignant cells (Fig. 2a and Extended Data Fig. 3a), whereas other regions exhibited little to no axonal infiltration. Quantifying the proliferation index (fraction of Ki67+ cells) of SCLC, we found that malignant cells that were closer to axons (within 100 µm) exhibited increased rates of proliferation and higher nuclear density (Fig. 2b and Extended Data Fig. 3b), suggesting a possible functional role of neuron–SCLC interactions within the brain.

Fig. 2: Neuronal activity promotes SCLC growth within the brain.

a, Representative immunohistochemistry of human SCLC brain metastases. Left, H&E staining. Right, neurofilament (brown) with nuclear counterstain (blue). Scale bars, 150 µm. b, Proliferation index in regions of human SCLC brain metastases quantified less than or greater than 100 µm from axons (n = 9 patients, P = 0.0009). c, Representative images of mouse 16T SCLC cells (GFP) co-cultured with primary cortical neurons (MAP2). Proliferative cells are labelled with EdU. Scale bars, 50 µm. d, Quantification of data in c with or without addition of 1 µM TTX (n = 5 coverslips per condition, P = 0.0010). e, As in d, for human SCLC cells (H446, n = 4 coverslips per condition, n = 5 for baseline, P = 0.0003). f, Uniform manifold approximation and projection (UMAP) embedding of scRNA-seq profiles of mouse 16T SCLC cells isolated from monoculture or co-culture with primary mouse neurons. g, Distribution of SCLC cells in f on the UMAP embedding plot. h, Gene set enrichment analysis (GSEA) of the 16 populations identified in g reveals a distinct cluster among the SCLC cells isolated from neuron co-cultures that is enriched for proliferation-related genes (all gene signatures in Supplementary Table 1). ssGSEA, single-sample GSEA. i, Quantification of ssGSEA scores for synapse-related gene signature across the 16 clusters in g detects significant upregulation in cluster 14 (red, statistical testing in Supplementary Table 2). j, Visualization of cell clusters enriched for synapse-related genes among SCLC cells isolated from neuronal co-culture (cluster 14). k, Distribution of SCLC cells treated with 1 µM TTX in monoculture or neuron co-culture (Extended Data Fig. 4f) on the UMAP embedding plot. l, As in i but in the presence of 1 µM TTX (the 18 clusters in k). m, Expression of synapse-related genes in cells isolated from patient lung primary or recurrent or non-brain-metastatic lesions[45] (n = 16) versus cells from patient SCLC brain metastases (n = 12, P < 0.0001). n, Expression of synapse-related genes across the cell types and malignant cell metaprogrammes (MP1–MP9) detected in patient lung primary, recurrent, non-brain-metastatic lesions (n = 16) or SCLC brain metastases (n = 12 patients). CNS, central nervous system; epi, epithelial. Data are mean ± s.e.m. (d,e); violin plots (i,l); violin and box plots (m,n). In box plots, the centre line is the median, box edges delineate 25th and 75th percentiles and whiskers extend to minimum and maximum values; dots represent outliers. Paired t-test (b); two-way ANOVA (d,e); one-way ANOVA with Tukey correction (i,l); pairwise Wilcoxon rank sum test (m). All tests are two-tailed. ****P < 0.0001, ***P < 0.001; NS, not significant.

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As there is no reliable model of spontaneous brain metastasis in any of the existing intracardiac or intravenous SCLC cell line administration models or genetic mouse models of SCLC36,43, we used mouse intracranial allografts, developed from isolation of metastases from the genetic models[44] or patient samples, to test potential functional influences of neuronal activity on the growth of intracranial SCLC. This enabled placement of the malignant cells into specific regions of interest, allowing direct manipulation of distinct neural circuits involving the tumour. It is important to note that this is not a model of metastatic initiation, but rather one of established intracranial tumour growth. These brain allografts exhibited neurons present within the tumour mass, similar to the human samples described above, especially in areas of the tumour periphery (Extended Data Fig. 3c). Consistent with the human patient samples, we found the proliferation index of cells in neuron-rich peripheral tumour regions to be significantly higher than in neuron-poor areas within the periphery of the mass (Extended Data Fig. 3d,e).

Neuron–SCLC interactions in the brain

To probe the interactions between SCLC and neuronal activity, we utilized co-culture systems in which cortically derived mouse neurons, representing a mixture of glutamatergic and GABAergic subpopulations (Extended Data Fig. 4a,b), were cultured together with either mouse 16T SCLC cells or human H446 SCLC cells. In both cases, the presence of active neurons significantly increased the proliferation rate of SCLC cells in co-culture (Fig. 2c–e). This effect was abrogated with the addition of tetrodotoxin (TTX), a voltage-gated sodium channel blocker that inhibits neuronal action potentials (Fig. 2d,e).

We next examined the transcriptional phenotypic changes in SCLC cells following exposure to neurons. We performed single-cell RNA sequencing (scRNA-seq) of the SCLC cells (16T-GFP) isolated from neuronal co-culture using fluorescence-activated cell sorting (FACS) (Extended Data Fig. 4c). When compared to cells from SCLC monoculture, the co-cultured cells clustered separately with distinct subpopulations primarily composed of either mono-cultured or co-cultured SCLC cells (Fig. 2f,g and Extended Data Fig. 4d). In line with the proliferative effect seen in areas of neuronal infiltration, we found that upregulation of a cell proliferation signature was a prominent effect of neuronal co-culture (Fig. 2h).

Activity-mediated synaptic signature in SCLC

In addition to the upregulation of the proliferative signature, Gene Ontology (GO) analysis identified a distinct cluster of cells isolated from neuronal co-culture that are defined by GO terms linked to synapse formation and neurotransmitter receptors and enriched for a synapse-related gene signature (cluster 14; Fig. 2i,j, Extended Data Fig. 4e and Supplementary Tables 1 and 2). To determine whether this synapse-related gene expression enrichment after exposure to neurons was mediated by neuronal activity, we performed a separate experiment comparing mono-cultured and co-cultured cells in the presence of TTX (Fig. 2k and Extended Data Fig. 4f–h). In the absence of neuronal activity, no malignant subpopulations were found to be enriched for the synapse-associated gene signature, indicating an activity-dependent mechanism of malignant cellular plasticity (Fig. 2l).

To determine whether this malignant signature could be detected in human biopsies, we collected SCLC brain metastases from 12 patients and analysed them using single-nucleus RNA sequencing (Extended Data Fig. 5a). As patients with SCLC do not routinely undergo tumour resection, there are no matched primary and brain-metastatic tissue datasets. We therefore compared the single-nucleus data from our SCLC brain metastasis tissue to a publicly available single-cell dataset[45] incorporating cells from primary SCLC tumours and various unmatched metastatic disease sites including lung, pleura, lymph nodes, liver and kidney (Extended Data Fig. 5b). We found that compared with all other disease sites, SCLC cells in the brain were enriched for the synaptic signature (Fig. 2m). Further, unbiased clustering of human brain-metastatic cells revealed that SCLC cells within the brain could be grouped into six distinct metaprogrammes (Extended Data Fig. 5c). In line with the results from our co-culture dataset (Fig. 2i,j), we found that one specific metaprogramme (MP3) was defined by genes comprising neural-like transcriptional programmes (Fig. 2n and Extended Data Fig. 5e). When further assessed against previously defined pan-cancer metaprogrammes[46], MP3 correlated with programmes associated with neural and glial precursor-like signatures (Extended Data Fig. 5f). Conversely, we did not find any enrichment of these programmes in SCLC cells from extracranial disease sites (Extended Data Fig. 5d,g,h), suggesting that compared with elsewhere in the body, intracranial SCLC is specifically enriched for neural gene expression programmes.

Synaptic integration of SCLC in the brain

We next assessed whether neurotransmitter-mediated signalling occurred through the formation of direct synapses between SCLC cells and neurons in the tumour microenvironment. Examination of ultrastructural interactions in allografted or xenografted brain tissue using electron microscopy revealed clear synaptic structures (Fig. 3a and Extended Data Fig. 6a). Immunogold labelling of GFP-tagged 16T or H446 SCLC cells unambiguously identified the malignant cells at post-synaptic sites, confirming that these cancer cells structurally participate in neuron–SCLC synapses (approximately 3–5 synapses per 10 SCLC cells; Extended Data Fig. 6d). Although less frequent, immuno-electron microscopy also identified malignant cells in a perisynaptic position juxtaposed to normal neuron–neuron synapses (Extended Data Fig. 6b–d) consistent with the ‘pseudo-tripartite’ synapses previously described in breast cancer brain metastases[17].

Fig. 3: SCLC cells exhibit synaptic currents that drive tumour progression.

a, Immuno-electron microscopy of 16T-GFP SCLC cells allografted to mouse hippocampus. Black dots represent immunogold particles labelling GFP (tumour cells). Post-synaptic density in GFP+ tumour cells (pseudo-coloured green), synaptic cleft and clustered synaptic vesicles in apposing pre-synaptic neuron (blue) identify synapses (white arrowheads). Scale bars, 200 nm. b, Representative recordings of sEPSCs in allografted 16T SCLC cells. c, Fraction of SCLC cells demonstrating spontaneous currents illustrated in b at baseline (artificial cerebrospinal fluid (ACSF), 8 out of 53 cells) or with the addition of 10 µM NBQX (bottom, 0 out of 27 cells). d, Representative traces of neuronal activity-dependent synaptic currents evoked in SCLC (n = 27 out of 49 cells), blocked after application of 1 µM TTX. e, Representative traces of neuronal activity-dependent evoked SCLC currents before and after application of 10 µM gabazine (n = 3 out of 3 cells). f, Representative trace of 16T SCLC cell currents in response to local GABAergic stimulation in the presence of glutamatergic inhibitors in perforated-patch recordings using gramicidin D at varying membrane potentials. g, Current–voltage relationship of GABAergic stimulation-induced current in 16T SCLC cells recorded with perforated-patch electrophysiology. Reversal potential of GABA was −27.3 ± 5.5 mV (−31.0 mV based off linear fit in example trace); n = 6 cells across 3 mice. h, Cell-attached measurement of resting membrane potential of SCLC cells. Current traces recorded during voltage ramp are shown in black. The red line is the extrapolated leak current from a linear fit and vertical grey line indicates the intersection of the voltage-activated K+ current with the leak current, yielding the resting membrane potential of −72 ± 7 mV (n = 7 cells). i, Proliferative index of mouse 16T SCLC-A subtype cells co-cultured with human iPS cell-derived glutamatergic neurons reveals increased proliferation in co-culture, abrogated by the addition of 50 µM MK801 (NMDA receptor inhibitor for glutamate, n = 5 coverslips for baseline condition, n = 4 for MK801, n = 7 for co-culture and n = 6 for co-culture with MK801, P < 0.0001). j, As in i, but with 50 µM CNQX (AMPA receptor inhibitor for glutamate) (n = 5 coverslips per condition, P < 0.0001). k, Proliferative index of mouse SCLC-A subtype 16T cells co-cultured with human iPS cell-derived GABAergic neurons with or without addition of 1 µM TTX or 20 µM gabazine (GABAA receptor inhibitor) reveals that increased proliferation in co-culture is abrogated by TTX or gabazine (n = 3 coverslips per condition, P < 0.0001). Data are mean ± s.e.m. (i–k). Two-way ANOVA (i–k). All tests are two-tailed.

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Having established the presence of structural neuron-to-SCLC synaptic interactions, we next evaluated synaptic responses of SCLC to neuronal activity electrophysiologically. GFP-labelled SCLC 16T cells were allografted into the CA1 region of the hippocampal circuit, an experimentally tractable circuit that is amenable to electrophysiological interrogation of neuron-to-cancer synapses2,16 (Extended Data Fig. 6e,f). After a period of engraftment, we prepared acute hippocampal slices for whole-cell electrophysiological recordings of GFP+ SCLC cells. Electrophysiological recordings in voltage clamp at −70 mV of individual SCLC cells revealed the presence of spontaneous excitatory post-synaptic currents (sEPSCs) in a subpopulation of cancer cells (approximately 22% of SCLC cells; Fig. 3b). In the presence of NBQX (2,3-dihydroxy-6-nitro-7-sulfamoyl benzo[f]quinoxaline), a glutamatergic (AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid)) receptor blocker, these sEPSCs were no longer detected and variance in membrane current was significantly decreased (Fig. 3c and Extended Data Fig. 6g,h), indicating that these are spontaneous currents arising from glutamatergic input and consistent with functional synapses between glutamatergic neurons and SCLC cells mediated by an AMPA subtype of glutamate receptors.

To assess for action potential evoked responses, we stimulated CA3 Schaffer collateral and commissural axons, inputs to CA1, while simultaneously recording from SCLC cells in CA1 using a low-chloride internal solution. At voltage clamp of −70 mV to minimize GABA currents, evoked responses were very rarely seen (less than 5%). When held at 0 mV to minimize glutamatergic currents, a high proportion of SCLC cells (approximately 55%) exhibited currents evoked by neuronal stimulation. These currents were blocked with the addition of TTX to prevent action potentials (Fig. 3d). To determine whether these currents occurred in response to GABAergic input, recordings were performed in the presence of NBQX and CPP (3-(2-carboxypiperazin-4-yl)propyl-1-phosphonic acid) to inhibit AMPAR and N-methyl-d-aspartate (NMDA) receptor-mediated currents, respectively. In the absence of glutamatergic activity, electrical stimulation consistently resulted in currents that were blocked by addition of the GABAA receptor inhibitor gabazine (Fig. 3e), indicating GABAergic synaptic currents in SCLC cells. Given that GABA can act as either a depolarizing or hyperpolarizing neurotransmitter depending on intracellular chloride concentration, we assessed its role in the context of malignant SCLC cells without altering intracellular chloride concentration. To determine the physiological reversal potential of GABA currents in SCLC cells, we recorded responses to stimulation of GABAergic synapses (via local stimulation in the presence of glutamatergic inhibitors) with perforated-patch recordings from SCLC cells using gramicidin D. GABAergic synaptic currents in SCLC cells reversed at −27.3 ± 5.5 mV (Fig. 3f,g), which corresponds to an intracellular chloride concentration of 46.8 mM. This is consistent with expression of K+/Cl− co-transporters in SCLCs, with the NKCC1 gene overexpressed in relation to KCC2 (Extended Data Fig. 6i,j), driving the high intracellular concentration of chloride[47]. Separate cell-attached measurements revealed SCLC baseline resting membrane potential to be approximately −72 mV (Fig. 3h), more negative than the −27.3 mV chloride equilibrium potential; thus, opening of GABA-gated chloride channels causes SCLC depolarization rather than hyperpolarization. Together, these electrophysiological studies reveal activity-induced membrane depolarization and neurotransmitter-mediated signalling from neurons to SCLC cells in the brain.

Neurotransmitter-mediated signalling in SCLC growth

Given the distinct synaptic input from glutamatergic and GABAergic neuronal populations, we then examined whether direct neurotransmitter-mediated signalling contributes to activity-mediated increases in SCLC proliferation. We utilized induced pluripotent stem cell (iPS cell)-derived neurons to establish isolated glutamatergic or GABAergic neuronal populations. These neurons were cultured with a panel of human cell lines that represent clinically and molecularly distinct subtypes of SCLC, conventionally defined by high expression of specific transcriptional regulators ASCL1 (SCLC-A), NEUROD1 (SCLC-N) or POU2F3 (SCLC-P)[41]. Notably, our scRNA-seq analysis revealed downregulation of ASCL1 expression and upregulation of NEUROD1 expression in response to neuronal activity (Extended Data Fig. 7a–c). Co-cultures of these distinct SCLC subtypes and neuronal populations revealed that glutamatergic and GABAergic neurons elicited a proliferative effect across SCLC, independent of transcriptional phenotype (Fig. 3i–k and Extended Data Figs. 7 and 8). We added either TTX (a broad neuronal activity blocker) or the specific neurotransmitter receptor inhibitors MK801 (NMDA receptor inhibitor), CNQX (AMPA/kainate receptor inhibitor) or gabazine (GABA receptor inhibitor) to neuron–SCLC co-cultures and quantified SCLC proliferation. The addition of each of these inhibitors reduced the neuronal activity-induced proliferation of the SCLC cells (Extended Data Figs. 7 and 8), indicating involvement of both glutamatergic and GABAergic signalling in the SCLC proliferative response to neuronal activity.

To determine whether these effects of neurons on SCLC were due to direct contact-mediated or paracrine signalling mechanisms, we applied conditioned medium from either glutamatergic or GABAergic neurons in the presence or absence of TTX to mouse or human SCLC cells in culture. Conditioned medium from neurons elicited either no increase or a partial increase in SCLC proliferation that was significantly less than the larger effect elicited by direct neuronal co-culture (Extended Data Fig. 9a–d). Conditioned medium taken from lung-derived epithelial cells had no proliferative effect (Extended Data Fig. 9e). These results suggest that paracrine factors secreted by neurons may contribute, but alone are insufficient to wholly explain the full proliferative effect of direct co-culture. Thus, contact-mediated interactions—such as synaptic communication—account for an important component of the growth-promoting effects of neurons on SCLC cells. We then assessed whether the known activity-dependent paracrine factors NLGN3 and BDNF, which we have shown promote glioma growth1,13,14,16, similarly induce proliferation in SCLC. Neither of these paracrine factors were found to affect the proliferation of the lung cancer cells (either 16T or H446) in vitro, in contrast to a clear growth-promoting effect in patient-derived glioma cultures (Extended Data Fig. 9f–i). These results are in line with previous studies indicating that NLGN3 is not critical for the growth of breast cancer brain metastases in vivo[15], and highlight the need to disentangle the distinct activity-dependent mechanisms that influence growth across cancers arising from various tissue origins.

Circuit activity drives growth of intracranial SCLC

To examine the effects of neuronal activity on intracranial SCLC tumour proliferation, we employed in vivo optogenetic techniques in freely behaving mice. Here, mouse SCLC cells (16T) were allografted intracranially into the premotor cortex of mice expressing the blue light-sensitive opsin channelrhodopsin (ChR2) in Thy1+ deep layer cortical projection neurons or wild-type (non-ChR2-expressing) littermate controls (Fig. 4a). After a period of engraftment, optogenetic ferrules were placed over the premotor cortex and neurons were stimulated with blue light. Successful stimulation of the cortical circuit in ChR2-expressing mice was verified by the observance of a complex motor behavioural output, circular ambulation. Assessed histologically 24 h after optogenetic stimulation of cortical neuronal activity, 16T SCLC cells exhibited a robust increase in proliferation rate (approximately 40% Ki67+ SCLC cells in mock-stimulated wild-type mice, approximately 60% Ki67+ SCLC cells in optogenetically stimulated ChR2-expressing mice; Fig. 4b,c). Further, there was a clear increase in the spread of SCLC cells into the brain, with more cells migrating outside of the edge of the tumour mass into the normal brain parenchyma in mice with optogenetically stimulated neuronal activity (Fig. 4d and Extended Data Fig. 10a–c). Together, these results indicate that cortical neuronal activity can promote the progression of intracranial SCLC growth.

Fig. 4: Neuronal circuit activity and downstream tumour membrane depolarization drive tumour progression.

a, Paradigm for in vivo optogenetic stimulation of Thy1-ChR2 (ChR2) pyramidal premotor cortical projection neurons in awake behaving mouse with SCLC tumour allografted into the M2 cortex. b, Representative immunofluorescence of mouse 16T SCLC brain tumours (GFP) allografted in the cortex of wild-type (WT) or optogenetically stimulated ChR2 mice. Proliferating cells are labelled with Ki67. Scale bars, 50 µm. c, Quantification of data in b (n = 8 wild-type and n = 8 ChR2 mice, P = 0.0016). d, Quantification of SCLC cells (per 500 µm) invading beyond the tumour edge following optogenetic stimulation of wild-type or ChR2 mice (n = 6 wild-type and n = 6 ChR2 mice, P = 0.0029). e, Paradigm for in vivo optogenetic stimulation of Dlx-ChRmine (red-light-sensitive channelrhodopsin)-expressing GABAergic cortical interneurons in awake mice with SCLC tumours allografted into the M2 cortex. f, Representative immunofluorescence of mouse 16T SCLC brain tumours (GFP) allografted to the cortex of Dlx-ChRmine expressing mice (mCherry) following optogenetic stimulation (stim). Proliferating cells are labelled with Ki67. Scale bars, 50 µm. g, Quantification of data in f (n = 6 mock-stimulated mice and n = 7 stimulated mice, P = 0.0410). h, As in f for human H446 SCLC cells (n = 5 mock-stimulated and n = 7 stimulated mice, P = 0.0074). i, Two-photon in situ calcium imaging of GCaMP6s-expressing 16T SCLC cells in hippocampal allografts. Representative trace of spontaneous activity as measured by changes to GCaMP6s fluorescence in SCLC cells with (red) or without (black) administration of 0.5 µM TTX. j, Two-photon in situ calcium imaging of GCaMP6s-expressing SCLC cells in hippocampal allografts with Schaffer collateral stimulation (n = 6 slices, 4 mice). Representative frames shown before and after stimulation. Red denotes the tdTomato nuclear tag of SCLC cells; green denotes SCLC GCaMP6s. Scale bars, 25 µm. k, Quantification of GCaMP6s fluorescence in individual SCLC cells in response to electrical stimulation of CA1 Shaffer collateral axons with or without administration of 0.5 µM TTX (n = 22 cells, P < 0.0001). l, Paradigm for in vivo optogenetic depolarization of intracranial allografts of ChR2-expressing 16T SCLC cells. m, Representative immunofluorescence of ChR2-expressing SCLC allografts after mock or blue light-induced depolarization. Neuronal nuclei are labelled with NeuN and tumour cells are labelled with GFP. Scale bars, 200 µm. n, Quantification of mean tumour area from m (n = 5 mock and n = 7 depolarized mice, P = 0.0101). Data are mean ± s.e.m. (c,d,g,h); data are median ± interquartile range (n). Unpaired t-test (c,d,g,h); paired t-test (k); Mann–Whitney test (n). All tests are two-tailed. Drawings in a,e,l created in BioRender. Savchuk, S. (2025) https://BioRender.com/5fwotqm.

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GABAergic synaptic neurotransmission comprised the majority of the signalling detected in electrophysiological studies, and was in fact depolarizing in nature. Thus, after observing that cortical circuit stimulation drives SCLC proliferation, we sought to determine the distinct contribution of GABAergic interneurons. We grafted mice with GFP-expressing SCLC (16T or H446) cells, together with an adeno-associated viral vector to genetically express ChRmine, a red-shifted channelrhodopsin[48], in Dlx-expressing GABAergic interneurons in the mouse cortex (Fig. 4e). The mouse cortex was then optogenetically stimulated in awake, behaving mice to induce GABAergic interneuron activity. Control mice were identically manipulated with mock stimulation. After 24 h, SCLC proliferation was assessed histologically using Ki67 to measure proliferation index. We found that in vivo optogenetic stimulation of GABAergic interneurons promoted proliferation of both 16T and H446 allografted SCLC cells (Fig. 4f–h) recapitulating the increased proliferation as a result of increased motor circuit activity and demonstrating a stark tumour-promoting role of GABAergic neuronal signalling in intracranial SCLC growth.

SCLC membrane depolarization regulates growth

To define the role of SCLC membrane depolarization occurring as a result of synaptic neurotransmission, we visualized the neurotransmitter-mediated currents in SCLC cells using calcium imaging. After applying either glutamate or GABA individually to 16T SCLC cells engineered to express the genetically encoded calcium indicator GCaMP6s, both neurotransmitters elicited clear calcium transients in these malignant cells (Extended Data Fig. 11a–c), further evidence that these neurotransmitters induce depolarization. We then performed in situ two-photon calcium imaging of GCaMP6s-expressing 16T SCLC cells allografted into the hippocampus. Similar to observations using electrophysiology, both spontaneous and axonal stimulation-evoked calcium transients in the SCLC cells were observed (Fig. 4i,j). These transients were blocked by the addition of TTX, indicating their dependence on neuronal activity (Fig. 4k and Extended Data Fig. 11d).

Given the depolarizing SCLC currents and consequent calcium transients described above, we hypothesized that membrane depolarization itself provides a functional benefit to SCLC tumours in the brain. We used optogenetics to directly depolarize 16T SCLC cells engineered to express ChR2. We confirmed functionality of the ChR2 construct in SCLC cells with patch clamp electrophysiology (Extended Data Fig. 11e,f). These cells were allografted into the mouse cortex (Fig. 4l). After a period of engraftment, blue light was delivered via a fibre optic placed at the surface of the brain to directly depolarize these ChR2-expressing SCLC tumour cells in vivo. Control mice were identically manipulated with mock stimulation. Membrane depolarization resulted in a robust growth effect in SCLC, with an increased SCLC proliferation index (Extended Data Fig. 11g) and the overall size of the tumour approximately doubling after three optogenetic depolarization sessions compared with identically manipulated, mock-stimulated control mice (Fig. 4m,n). Together, these studies illustrate that SCLC cells in the brain utilize neuronal activity and consequent neurotransmitter-mediated membrane depolarization to fuel progression of the tumour.

Reciprocal neuron–SCLC interactions

As cancers metastatic to brain are often associated with seizures49,50, we next assessed whether SCLC cells in the brain could reciprocally affect neurons in the microenvironment. We first quantified the colocalization of pre-synaptic puncta (synapsin) with post-synaptic puncta (HOMER1) on neurons in our SCLC-neuron co-cultures. We found a marked increase in the number of synapses between glutamatergic neurons co-cultured with 16T SCLC cells compared with neurons in monoculture (Fig. 5a,b). No change in gephyrin-labelled synaptic puncta between GABAergic interneurons was detected (Extended Data Fig. 12a). Similarly, in our in vivo electron micrographs of allografted or xenografted tissue, we found an increased number of neuron-to-neuron synapses in regions of the tumour compared with the non-tumour-bearing contralateral controls (Extended Data Fig. 12b,c). Testing the functional contribution of SCLC cells to neuronal hyperexcitability using multielectrode array (MEA) electrophysiology, we found that neurons exhibited increased activity in the presence of SCLC cells (either 16T or H446), measured by increased spike number, amplitude and frequency (Fig. 5c–f and Extended Data Fig. 12d,e). Minimal increase in spike amplitude was observed when conditioned medium from SCLC cells was applied instead of direct co-culture (Extended Data Fig. 12f). We recorded no spikes in SCLC monoculture (Extended Data Fig. 12g).

Fig. 5: SCLC induces neuronal hyperexcitability.

a, Immunofluorescence of human iPS cell-derived neurons in monoculture (left) or co-cultured with 16T SCLC cells (right). Arrowheads indicate colocalized pre-synaptic (synapsin) and post-synaptic (HOMER1) puncta along neuronal processes (neurofilament (NF)). Scale bar, 25 µm. b, Quantification of data in a (per 10 µm neurofilament length, n = 12 coverslips for neuron baseline and n = 10 coverslips for co-culture, P < 0.0001). c, Representative 500 ms recording of human iPS cell-derived glutamatergic neurons at baseline versus co-cultured with 16T SCLC cells using MEA (n = 6 per condition). d, Representative traces of spike amplitude in human iPS cell-derived glutamatergic neurons at baseline versus co-cultured with 16T SCLC cells. e, Quantification of data in c,d (n = 24 spikes in neuron baseline and n = 168 spikes in co-culture condition, P < 0.0001). f, As in e, but for human H446 SCLC cells (n = 363 spikes in neuron baseline and n = 2,721 spikes in co-culture, P < 0.0001). g, Paradigm for local field recordings in SCLC hippocampal allografts. Created in BioRender. Savchuk, S. (2025) https://BioRender.com/5fwotqm. h, Representative traces of local field potential in response to local stimulation of tumour allografts and control contralateral hippocampus. i, Extracellular local field potential (fEPSP) slope in response to various axonal stimulation intensities in the tumour-infiltrated or control contralateral hippocampus (data fit to a nonlinear regression and compared using the extra-sum-of-squares F-test; n = 24 tumour and n = 25 control across 6 mice, P < 0.0001). j, GSEA of scRNA-seq data from 16T SCLC cells isolated from either monoculture or neuron co-culture (Fig. 2f,g) reveals a distinct cluster that is enriched for astrocyte-related genes. All gene lists in Supplementary Table 1. k, Quantification of ssGSEA scores for astrocyte-related genes across the 16 identified clusters detects upregulation of astrocyte signature in cluster 5 (highlighted in red). Statistical testing in Supplementary Table 2. l, Expression of astrocyte synaptogenesis-related genes in cells isolated from patient lung primary, recurrent or non-brain-metastatic lesions[45] (n = 16) versus patient SCLC brain metastases (n = 12, P < 0.0001). m, Representative immunofluorescence of human H446 SCLC (GFP) xenografted in cortex of mice treated with vehicle or levetiracetam (LEV). Proliferating cells are labelled with Ki67. Scale bars, 50 µm. n, Quantification of data in m (n = 3 vehicle and n = 5 levetiracetam-treated mice, P = 0.0065). o, As in n, but for mouse 16T SCLC cells (n = 6 vehicle and n = 7 levetiracetam-treated mice, P < 0.0001). Data are mean ± s.e.m. (b,i,n,o); violin plot (e,f,k); and violin and box plot (l). Unpaired t-test (b,n,o); Mann–Whitney test (e,f); one-way ANOVA with Tukey correction (k); pairwise Wilcoxon rank sum test (l). All tests are two-tailed.

Source data

We then assessed these findings in vivo by performing local field recordings of allografted hippocampal tissue (Fig. 5g). Compared with the contralateral non-tumour-infiltrated hippocampus, we found increased field potentials in regions of the tumour (Fig. 5h,i). We next sought to determine whether this effect was due to any cell-intrinsic changes in pyramidal neurons associated with SCLC and performed in situ whole-cell recordings of pyramidal neurons either integrated in regions of the tumour or in the contralateral (control, non-tumour-infiltrated) hippocampus. Between the tumour-infiltrated and control hippocampi, no distinct differences in neuronal properties were noted (Extended Data Fig. 12h–l).

Astrocytes are known to have a central role in the form and function of neuronal circuits and astrocyte-like glioma cells have been known to create a hyperexcitable neuronal microenvironment51,52,53. As described above, ultrastructural examination of allografted tissue revealed not only post-synaptic positioning of SCLC cells, but also perisynaptic associations of SCLC cells with neighbouring neurons (Extended Data Fig. 6b–d), reminiscent of a normal astrocyte position in tripartite synapses[54]. Querying our scRNA-seq data of SCLC cells in response to neuronal exposure, we found that neuronal co-culture increased the expression of a gene signature associated with an astrocytic phenotype55,56,57 in a distinct subpopulation of cells (cluster 5; Supplementary Tables 1 and 2), not overlapping with the population defined by an upregulated synaptic signature (Fig. 5j,k). In our single-cell dataset comparing human SCLC brain metastases to extracranial sites, we saw no enrichment in the astrocytic signature in the brain (Extended Data Fig. 12m). These findings may in part be driven by an already existing astroglial-like SCLC population that has previously been described within the lung[58]. We therefore evaluated a synaptogenesis-promoting astrocytic gene signature and found an upregulation within separate metaprogrammes of patient brain-metastatic SCLC cells (Fig. 5l). Together, these observations suggest that SCLC can remodel and reinforce neuron–SCLC interactions in the brain.

Therapeutically targeting neuron–SCLC interactions

Given the increased neuronal activity in the region of the tumour and the idea that glutamatergic and GABAergic synaptic signalling to the tumour drives SCLC growth within the brain, we sought to harness neuron–SCLC interactions for treatment using existing clinically available drugs. We utilized levetiracetam, a commonly used anti-seizure drug that acts by interfering with synaptic vesicle release, to determine the therapeutic potential of reducing activity-mediated signalling in SCLC. After a 2-week treatment regimen of daily levetiracetam treatment (20 mg kg−1), both 16T and H446 SCLC-bearing mice demonstrated a significant reduction in malignant cell proliferation (Fig. 5m–o) and tumour burden (Extended Data Fig. 12n–p) in the brain compared with vehicle treated controls. These findings suggest that disrupting neuron–SCLC interactions will be a central component of effectively treating SCLC.

Discussion

The influence of neuronal activity on SCLC pathophysiology demonstrated here adds to the growing evidence highlighting the importance of neurobiology in cancer. These findings indicate that SCLC cells that have colonized the brain take advantage of neuronal activity through both paracrine and synaptic mechanisms to enhance growth and invasion, while reciprocally increasing neuronal excitability and activity. Developing a longitudinal understanding of the roles of these neural pathways across tumour initiation, growth and metastases will be critical to developing novel therapies that target nervous system interactions with SCLC. Further, emerging evidence demonstrates that melanoma and breast cancer cells that have colonized the brain rely on glutamatergic synaptic input for growth[59], extending these findings to other types of brain metastases and emphasizing the need to target mechanisms of activity-dependent growth in other non-glial cancers.

In the future, further investigation of the specific molecular signalling cascades that mediate this activity-driven progression of SCLC may reveal therapeutic vulnerabilities. Other mechanisms of activity-mediated growth, including the identity of the paracrine factors that promote SCLC proliferation remain to be defined. This study identifies glutamatergic and GABAergic synaptic signalling in SCLC tumours growing in the brain, but dose-dependent effects and potential roles of other neurotransmitters and neuropeptides have not yet been explored. The growth-promoting effect of SCLC membrane depolarization—also evident in glioma2,16—warrants further investigation of the voltage-sensitive mechanisms of cancer cell proliferation. The stark effect of denervation on pulmonary SCLC pathobiology raises questions about the distinct involvement and relative contributions of the different axonal subpopulations within the vagus nerve, the answers to which would provide potential therapeutic targets such as specific neurotransmitter receptors and inform potential translation of these findings to the bedside. Further, evaluating the possible effects of neuronal activity on other cells in the lung tumour microenvironment, including immune, vascular and other stromal cells may reveal additional indirect effects of nervous system activity on SCLC pathogenesis. SCLC is a disease with limited treatment options and poor prognosis. Thus, disrupting the functional interactions between neurons and SCLC in lung and in brain represents a promising therapeutic strategy for this lethal cancer.

Methods

Mice and housing conditions

All in vivo experiments were conducted in accordance with protocols approved by the Brigham and Women’s Hospital Institutional Animal Care and Use Committee (IACUC) and Stanford University IACUC. Mice were housed according to the standard guidelines with free access to food and water in a 12 h light:12 h dark cycle.

For brain tumour allograft experiments, NSG mice (NOD-SCID-IL2R gamma chain-deficient, the Jackson Laboratory) were used. Male and female mice were used equally. According to the IACUC guidelines, signs of morbidity rather than maximal tumour volume was used as indication for termination of brain allograft mouse experiments. Mice were euthanized if they exhibited signs of neurological disease or if they lost 15% or more of their body weight. For in vivo optogenetic stimulation of the premotor circuit (M2), Thy1-ChR2; NSG or WT; NSG mice were used.

For lung tumour experiments, mice were euthanized when they exhibited signs of sickness behaviour (such as dyspnea, abnormal gait or posturing, or ill-groomed fur) or lost >15% of body weight in accordance with IACUC guidelines. No limits were exceeded in any mouse experiments. In these experiments, Rb1fl/fl;Trp53fl/fl;p130fl/fl, luciferase-expressing (RPR2-luc) genetic mouse models were used as described previously39,44. In these mice, lung tumours and later distant metastases form spontaneously after intratracheal administration of Adeno-CMV-Cre (University of Iowa Vector Core) at 2 months of age as described[44] and following a published protocol[60]. To study MYC-driven lung tumour models, we used RPM mice[42]. These mice were infected intratracheally as above with Adeno-CMV-Cre or Adeno-CGRP-Cre at 3 to 4 months of age.

Intracranial allografts

All SCLC brain allografts were performed as described[2]. In brief, a single-cell suspension from cells cultured from either 16T-mGFP or NCI-H446-GFP SCLC neurospheres was prepared in sterile HBSS immediately before surgery. Mice at postnatal day (P)21–35 were anaesthetized with 1–4% isoflurane and placed in a stereotactic apparatus. The cranium was exposed via midline incision under aseptic conditions. 70,000 cells in 3 µl sterile HBSS were stereotactically injected into the M2 region of the cortex through a 31-gauge burr hole using a digital pump at an infusion rate of 0.4 µl min−1 and a 31-gauge Hamilton syringe. Stereotactic coordinates for Thy1-ChR2 mouse allografts used were as follows: 0.5 mm lateral to midline, 1.0 mm anterior to bregma, −1.5 mm deep to cortical surface. At the completion of infusion, the syringe needle was allowed to remain in place for a minimum of 2 min, then withdrawn at a rate of 0.875 mm min−1 to minimize backflow of the injected cell suspension. To generate mice with interneuron-specific expression of light-sensitive ion channels, 1 µl of AAV8-Dlx-ChRmine-p2A-mCherry[61] (virus titre = 6.9 × 10^12) (a gift from K. Deisseroth) was unilaterally injected using a Hamilton Neurosyringe and 450 Stoelting stereotaxic injector over 5 min. These mice then received allografts of 16T-mGFP or xenografts of NCI-H446-GFP SCLC cells as described above at 0.8 mm lateral to midline, 1.0 mm anterior to bregma, −1.2 mm deep relative to the cortical surface. Allografts of 16T-mGFP-ChR2 SCLC cells were performed following the procedure as above with exception for the following alterations: 15,000 cells were injected in 1 µl sterile HBSS 0.8 mm lateral to midline, 1.0 mm anterior to bregma, −1.2 mm deep relative to cortical surface. For electrophysiology experiments and electron microscopy tissue analysis, 16T-mGFP cells were allografted into the CA1 region of the hippocampus at the following coordinates: 1.5 mm lateral to midline, 1.8 mm posterior to bregma, −1.35 mm deep to the cortical surface.

In vivo optogenetic manipulation

For in vivo optogenetic stimulation of M2 region of Thy1-ChR2; NSG or WT; NSG mice, a single stimulation paradigm was employed as previously described[1]. In brief, a fibre optic ferule was placed 1 week following or simultaneously with and ipsilateral to the SCLC allografts. After 1–2 weeks to allow for recovery from the procedure, the mice were connected to a 100-mW 473-nm diode-pumped solid-state laser system with a mono fibre patch cord, which freely permits wakeful behaviour of the mice. Pulses of light with approximately 4 mW measured output at tip of the patch cord were administered at a frequency of 20 Hz for periods of 30 s, followed by 90 s recovery periods, for a total session duration of 30 min. The mice were euthanized after 24 h post stimulation, and brains were collected for histological analysis. For stimulation of cortical interneurons in Dlx-ChRmine mice, 595-nm light was used at 40 Hz frequency and 10 ms width. Pulses of light with approximately 10 mW measured output at tip of the patch cord were administered.

For in vivo optogenetic depolarization of SCLC cells, ChR2–YFP (pLV-ef1-ChR2(H134R)-eYFP WPRE) construct (generated by the laboratory of K. Deisseroth and placed in the piggyback transposon system by M. Su in the laboratory of M.M.) was lentivirally transduced into 16T SCLC cells, which were then allografted into premotor cortex (M2) following the procedure described above. A fibre optic ferule was implanted during the same surgery ipsilateral to the cell injection site at following coordinates: 0.8 mm lateral to midline, 1.0 mm anterior to bregma, −0.9 mm deep relative to cortical surface. At 1 week post-allograft and for three consecutive days, all mice were connected to the laser system to receive blue light or mock stimulations at a frequency of 10 Hz for periods of 30 s, followed by 90 s recovery periods for a total session duration of 30 min. Mice were euthanized 24 h after the final (3rd) stimulation session.

Immunohistochemistry of patient tissue

Patient tissue samples were obtained with informed consent and analysed in accordance with institutional review board-approved protocols. Immunohistochemistry of patient SCLC brain metastases tissue samples was performed on formalin-fixed paraffin-embedded tissue sections per standard protocols including deparaffinization, antigen retrieval, incubation with primary antibody and detection per the manufacturers’ instructions. The following antibodies were used: mouse anti-Ki67 (Dako/Agilent), mouse anti-neurofilament (Ventana Roche; prediluted). Staining for Ki67 was performed on a Leica Bond III automated stainer. Staining for neurofilament was performed on a Ventana Ultra automated stainer. Proliferation index was determined by quantifying the fraction of Ki67+ cells out of the total number of cells in the region.

Single-cell sequencing from SCLC-neuron co-culture

16T SCLC cells were cultured alone or in neuron co-cultures with or without 1 µM TTX for 24 h per protocol below and collected in PBS 0.5% BSA, 1 mM EDTA (Invitrogen), 1x DNAse (Worthington Biochemical). GFP-negative cells were collected in parallel to serve as negative control during FACS. Calcein violet (Thermo Fisher) was used to label live cells. GFP+calcein+ cells were sorted and collected then lysed and combined into droplets with barcoded beads which captured the mRNA then used for reverse transcription with the The Chromium Single Cell Gene Expression platform (10X Genomics) per the manufacturer’s instructions. We then followed the rest of the 10X standard or high-throughput protocols and used the Dual Index Kit TT Set A for library production. The experiment was repeated for a total of three biological replicates.

Processing of fastq files from monoculture and co-culture samples was performed individually using the 10X Genomics Cell Ranger 7.1.0 based on the mm10 mouse genome reference, with the incorporation of the eGFP sequence. Seurat[62] (v.5.0.1) was employed for data loading at the individual sample level. Subsequently, the scCB2[63] (v.1.12.0) package was utilized to filter out empty droplets, employing an FDR threshold of 0.01 to identify real cells, while potential doublets were removed using the scDblFinder[64] (v.1.16.0) package. Cells with no GFP expression, exhibiting a high fraction of mitochondrial molecules (>5%) and those expressing a low number of unique genes (indicating low library complexity) were excluded.

Samples from the same replicate were merged, and highly variable genes were selected using the Seurat package. The relative expression values of these highly variable genes were used for principal component analysis (PCA). The number of PCA components for each replicate was determined based on achieving a cumulative proportion greater than 80% in the PCA plot. Subsequently, UMAP embeddings were generated and cells were clustered using Seurat’s Louvain algorithm-based FindClusters function.

Differentially expressed genes were identified using the SeuratWrapper’s (v.0.3.19) RunPrestoAll function. Genes detected in a minimum of 30% of the cells within each cluster, with at least a 0.25-fold mean log difference, were subjected to statistical testing using the Wilcoxon rank sum test with Bonferroni correction for multiple testing. Genes with adjusted P value  <  0.05 were retained.

GSEA was conducted using the fgsea[65] (v.1.28.0) and genekitr[66] (v.1.2.5) packages, exploring GO, KEGG, REACTOME, Hallmarks, Biocarta and WikiPathways databases. Finally, the GSVA[67] (v.1.50.0) package was used to calculate the ssGSEA scores for synaptome, astrocytes, and cell proliferation signatures.

Single-cell sequencing from human primary or metastatic SCLC lesions

Human SCLC brain tissue transcriptomic library preparation

Frozen tissues were processed as described68,69. Tissue blocks were embedded in optimal cutting temperature (Tissue-Tek, Sankura 4583), and then sectioned on a Leica CM1950 cryostat (Leica) into 20-µm-thick curls (generating up to 20 curls for each sample). These were then placed in 5-ml tubes (Eppendorf), washed with ice-cold PBS (Thermo Fisher Scientific, 10010023), centrifuged at 400g for 2 min, and the supernatant was discarded. The tissue was resuspended in 1 ml Salt Tris (ST) buffer (146 mM NaCl, 10 mM Tris-HCL pH 7.5, 1 mM CaCl_2 and 21 mM MgCl2 in ultrapure water) with 0.03% Tween-20 (Sigma Aldrich, P7949; TST buffer), supplemented with 0.1% BSA (New England Biolabs, B9000S) and optionally 40 U ml−1 RNAse inhibitor (RNAse OUT, Thermo Fisher Scientific). The suspension was mechanically dissociated by pipetting 15 times with a 1-ml pipette and incubated on ice for 5 min. Afterward, the pipetting step was repeated, and the reaction was quenched with 4 ml ST buffer, with or without RNAse inhibitor. The mixture was filtered through pre-wetted 70-µm nylon mesh filters (Thermo Fisher Scientific) into 50-ml conical tubes, washed with 5 ml ST buffer, and centrifuged at 500g for 5 min to isolate the nuclei. The nuclei pellet was resuspended in 100–400 µl ST buffer, filtered through a 40-µm mesh (Thermo Fisher Scientific), and counted using a Neubauer counting chamber (Bulldog Bio) after staining nuclear DNA with 50 µg ml−1 Hoechst 33342 (Thermo Fisher Scientific, H3570). Approximately 0.9 to 1.5 × 10³ nuclei were loaded into a Chromium Controller using ST buffer without RNAse inhibitor and processed with Chromium reagents and 5′V2 capture kits (1000006 and 1000263) from 10X Genomics. Following reverse transcription and cleanup, cDNA libraries were prepared per manufacturer protocols, including one additional cycle of amplification to account for the lower RNA content in nuclei compared with whole cells. Final sequencing libraries were created using the library construction kit (1000190) and Dual Index Kit TT Set A (1000215) and sequenced on an Illumina NovaSeq S4 platform with 2× 150 bp paired-end reads, achieving a minimum of 25,000 reads per cell.

Filtering background noise in gene expression matrices

Demultiplex FASTQ files from raw RNA-sequencing reads were aligned using CellRanger v.6.1.1 (10X Genomics) to the GRCh38 genome[70]. Gene counts were quantified with CellRanger’s ‘count’ function, including intronic reads. The feature_bc_matrix.h5 files generated by CellRanger were used as inputs for the ‘remove-background’ function in CellBender v.0.2.0, which removed ambient RNA gene counts and empty droplets[71]. The CellRanger metric ‘expected-cells’ defined the ‘Expected Number of Cells’ parameter, and the total-droplets-included parameter was set to a value between 10,000 and 40,000, chosen from the plateau region of the barcode-rank plot generated by CellRanger.

Quality control and normalization

Each generated matrix for was processed with R v.4.1.1 and Seurat v.4.1.0 on a per-sample basis[72]. Filters were applied based on the Seurat pipeline to retain only cells with 500–10,000 detected genes, 1,000–60,000 unique molecular identifiers and less than 10% mitochondrial gene content. Scrublet v.0.2.1 was used to identify and remove doublets, with the expected doublet rate set between 2.5% and 7.5%, depending on the initial loading rate[73]. Following Seurat’s pipeline, the data were log-normalized using the NormalizeData function. The top 2,000 variable genes from each sample were identified with the FindVariableFeatures function, and the resulting matrix was centred and scaled with the ScaleData function. All signatures were computed through entered the gene list and a merged Seurat object of all samples on a per-cohort basis using the AddModuleScore function provided by Seurat.

Integration of cohort samples

Individual Seurat samples were integrated using Seurat canonical correlation analysis (CCA) pipeline to remove batch effects from individual samples. Samples from the labelled Chan cohort from and CUIMC cohort were integrated separately[72]. Per the CCA pipeline, SelectIntegrationFeatures and FindIntegrationAnchors was then run to select 2,000 anchors between each sample with the top 50 dimensions from CCA to define search space for integration, using the raw RNA counts assay for each sample. IntegrateData was then run using the previously defined anchors to generate the integrated dataset. The integrated data were then scaled, and clustered using ‘FindNeighbors’ with 10 dimensions and FindClusters using a resolution of 0.5. UMAPs were calculated with the top 30 PCA dimensions, using Seurat’s RunUMAP.

Cell-type identification

Cell types were initially labelled using SingleR v.1.8.0 using the built in BlueprintEncodeData reference[74]. Immune cells identified through this process were used as a diploid reference for inferCNV v.1.10.1 to infer chromosomal copy number alteration (CNA) profiles for each cell. A minimum average read count threshold of 0.1 per gene was applied for reference nuclei. The ‘subcluster’ setting was used for clustering, and results were denoised with the default ‘sd_amplifier’ value of 1.5. InferCNV used a hidden Markov model to predict CNA levels, and the proportion of scaled CNAs was averaged across all chromosomes for each cell[74]. Malignant cells were identified using sample-specific thresholds based on these average values, which distinguished immune and non-immune CNA levels.

Non-malignant cell types were further analysed following CCA-based integration. Clustering of non-malignant cells was performed with the FindClusters function at varying resolutions, followed by differential gene expression analysis with FindAllMarkers[72]. Broad cell-type annotations were assigned manually based on established marker genes identified as differentially expressed in each cluster.

Non-negative matrix factorization

Non-negative matrix factorization (NMF) was employed for feature extraction and dimensionality reduction on non-negative gene expression data[75]. The NMF function implemented in RcppML v.0.3.7, was selected for its computational efficiency by minimizing reconstruction error and optimizations matrix factorization[76]. RcppML operates directly on raw count matrices and incorporates L1 regularization with reproducible factor scaling, enabling robust handling of ambiguous zeroes in single-cell data. NMF iteratively decomposing the data matrix into two lower-dimensional non-negative matrices, a basis matrix representing gene programmes and a coefficient matrix capturing the effect of each gene programmes on each cell. To incorporate prior biological knowledge, the supervised framework from Tagore et. al was adapted[77]. NMF was run on each sample to identify latent gene programmes, followed by rank-factor. Genes were pre-filtered to retain the top 7,000 genes based on total counts from the filtered RNA assay, serving as input.

Metaprogrammes (also referred to as consensus factors) were identified through co-correlation analysis using Spearman’s correlation applied to sample-specific factors generated by NMF. The optimal number of consensus factors was determined using silhouette and distortion scores. Ward’s clustering was then performed on individual factors, with the optimal number of consensus factors setting the clustering thresholds to define metaprogrammes[77]. This analysis was conducted on a per-cohort basis.

Metaprogramme correlation with annotated hallmarks of tumour heterogeneity

To annotate the biological functions underlying each metaprogramme, gene signatures were defined based on the top genes with the highest normalized contributions to each metaprogramme. Functional characterization of these gene signatures was performed through GSEA using recently described transcriptional hallmarks of tumour heterogeneity[46]. To assess the similarity between gene signatures, the Jaccard similarity index and a derived correlation metric were calculated. The Jaccard similarity index quantifies overlap between two sets as the size of their intersection divided by the size of their union. To facilitate interpretation in a heatmap, pairwise Jaccard similarity scores were scaled linearly to a correlation-like metric ranging from 0 to 1. The resulting heatmap highlights pairwise relationships between gene signatures, with metaprogrammes exhibiting the highest correlation with a given metaprogramme annotated as representing biologically related functions.

Normalized gene contribution towards metaprogrammes

For each metaprogramme, genes were ranked based on a weighted Stouffer-integrated expression value. The top 50 genes per metaprogramme, determined by this ranking, were selected for downstream analyses. Within each sample, cell barcodes from the H matrix generated by NMF were assigned to a specific metaprogramme based on their highest association with a corresponding factor. These assignments linked individual barcodes to metaprogrammes according to their factor membership. To evaluate the gene contributions to each metaprogramme, an expectation-maximization Gaussian mixture model (EM-GMM) was applied to the normalized gene expression matrices[77]. This model assessed the modality of gene expression distributions, enabling the identification of peaks that correspond to the ‘normalized gene contribution’ for each metaprogramme. These contributions were subsequently scaled between 0 and 1 across all metaprogrammes to ensure comparability.

GSEA across metaprogrammes

The top 50 ranked genes for each metaprogramme, based on the weighted Stouffer-integrated expression values, were used for functional enrichment analysis. GSEA was performed using EnrichR v.3.1, leveraging the Hallmarks of MSigDB v.7.4.1 and GO Biological Process 2021 databases[78]. Enrichment results were aggregated across cohorts, with the top-ranked gene sets identified for each metaprogramme. The P values and q scores returned by EnrichR were scaled and visualized to highlight significant functional associations for each metaprogramme.

Sample preparation and image acquisition for electron microscopy

NSG mice were engrafted with either 16T-mGFP or NCI-H446-GFP cells into mouse hippocampi. Three weeks after engraftment, mice were sacrificed by transcardial perfusion with Karnovsky’s fixative: 2% glutaraldehyde (EMS 16000) and 4% paraformaldehyde (PFA) (EMS 15700) in 0.1 M sodium cacodylate (EMS 12300), pH 7.4. The samples were then post-fixed in 1% osmium tetroxide (EMS 19100) for 1 h at room temperature, washed 3 times with ultrafiltered water, then en bloc stained for 2 h at room temperature. Samples were dehydrated in graded ethanol (50%, 75% and 95%) for 15 min each at 4 °C; the samples were then allowed to equilibrate to room temperature and were rinsed in 100% ethanol 2 times, followed by acetonitrile for 15 min. Samples were infiltrated with EMbed-812 resin (EMS 14120) mixed 1:1 with acetonitrile for 2 h followed by 2:1 EMbed-812:acetonitrile for 2 h. The samples were then placed into EMbed-812 for 2 h, then placed into TAAB capsules filled with fresh resin, which were then placed into a 65 °C oven overnight. Sections were taken between 40 and 60 nm on a Leica Ultracut S (Leica) and mounted on 100-mesh Ni grids (EMS FCF100-Ni). For immunohistochemistry, microetching was done with 10% periodic acid and eluting of osmium with 10% sodium metaperiodate for 15 min at room temperature on parafilm. Grids were rinsed with water three times in between and followed by 0.5 M Glycine quench. Grids were incubated in blocking solution (0.5% BSA, 0.5% Ovalbumin in PBST) at room temperature for 20 min. Primary rabbit anti-GFP (1:300; MBL International) was diluted in the same blocking solution and incubated overnight at 4 °C. The following day, grids were rinsed in PBS three times, and incubated in secondary antibody (1:10 10 nm Gold conjugated IgG TED Pella 15732) for 1 h at room temperature and rinsed with PBST followed by water. For each staining set, samples that did not contain any GFP-expressing cells were stained simultaneously to control for any non-specific binding. Grids were contrast stained for 30 s in 3.5% uranyl acetate in 50% acetone followed by staining in 0.2% lead citrate for 90 s. Samples were imaged in the tumour mass within the CA1 region of the hippocampus or within the contralateral normal hippocampus using a JEOL JEM-1400 transmission electron microscope at 120 kV and images were collected using a Gatan Orius digital camera.

Electron microscopy data analysis

Sections from the allografted hippocampi of mice were imaged as above using transmission electron microscopy. Here, 36 sections of 16T-mGFP across 6 mice were analysed. Electron microscopy images were taken at 6,000× with a field of view of 15.75 μm^2. Synapses were inspected by two individual investigators. SCLC cells were counted and analysed after unequivocal identification of immunogold particle labelling with four or more particles. For identification of synaptic structures, all three of the following criteria had to be clearly met: (1) visually apparent synaptic cleft; (2) presence of synaptic vesicle clusters in a cell on one side of the cleft; and (3) identification of clear post-synaptic density on the cell on opposite side of cleft. For identification of neuron-to-tumour synapses, the post-synaptic cell had to exhibit clear immunogold particle labelling. Tumour cells in perisynaptic positions were identified when an immunogold-particle-positive SCLC cell was seen apposed to or surrounding the synaptic structures between two other immunogold-particle-negative cells. Density of both types of synaptic relationships (neuron-to-tumour synapses, neuron-to-tumour perisynaptic connections) were quantified as the number of connections per number of SCLC cells identified within the region.

Histology

Mice with intracranial tumour allografts were anaesthetized with intraperitoneal avertin (tribromoethanol), then transcardially perfused with 20 ml of ice-cold PBS. Brains were fixed in 4% PFA overnight at 4 °C, then cryoprotected in 30% sucrose, embedded in Tissue-Tek O.C.T. (Sakura) and sectioned in the coronal plane at 40 μm using a sliding microtome. Lung tumour mice were perfused as above but also with 10 ml ice-cold 4% PFA. The lungs were inflated with 1–3 ml 2% UltraPure Low Melting Point Agarose (Invitrogen). Lungs and livers were fixed overnight at 4 °C on a shaker, then transferred to 70% ethanol and sectioned at 15 μm for H&E and immunohistochemistry, or cryoprotected in 30% sucrose and sectioned at 150 μm on a cryotome for visualizing tumour innervation.

For immunofluorescence staining, the coronal sections were incubated in blocking solution (3% normal donkey serum, 0.3% Triton X-100 in TBS) at room temperature for 45 min, followed by an overnight incubation with primary antibodies in antibody diluent solution (1% normal donkey serum in 0.3% Triton X-100 in TBS) at 4 °C. On the next day, after a 5-min rinse in TBS, sections were incubated with DAPI (1 μg ml−1 in TBS, Thermo Fisher) for 5 min, and rinsed again with TBS for 5 min. Afterwards, slices were incubated in secondary antibody solution at 4 °C overnight, then washed thrice in TBS and mounted with ProLong Gold Mounting medium (Life Technologies). All images were acquired with Zen 3.4 and analysed using Fiji ImageJ 2.1.0. For quantifying SCLC proliferation, average number of Ki67+ SCLC cells divided by total number of SCLC cells labelled by GFP was calculated within regions of interest either in axon-rich/axon-poor areas of the tumour or in response to various optogenetic manipulations. For quantification of SCLC spread, average distance from the core was measured as longest distance from the initial site of injection to outer core of the tumour. For quantification of tumour invasion, average number of SCLC cells that migrated out of the circumscribed tumour edge over ~500 μm was calculated.

For visualizing lung and liver tumour innervation with immunofluorescence, 150-μm-thick sections were processed as above, except primary antibody incubation time was extended to 72 h. Streptavidin Alexa Fluor 594 conjugate (Invitrogen) was used to visualized airway epithelium[79]. To quantify lung and liver tumour burden, five equidistant H&E sections from each organ were evaluated by a pathologist blinded to the experimental conditions to estimate percent of section area occupied by the tumour. Any section with <5% of tissue estimated to be occupied by tumour was given a score of 0, 5–25% was given a score of 1, 25–50% was given a score of 2, 50–75% was given a score of 3, and >75% was given a score of 4. The score for each mouse was then generated as an average across the five sections.

The following primary antibodies were used: chicken anti-GFP (Aves Labs, 1:500), rabbit anti-MAP2 (EMD Millipore, 1:500), mouse anti-NeuN (EMD Millipore, 1:500), rabbit anti-Ki67 (Abcam, 1:500), guinea pig anti-synapsin (Synaptic Systems, 1:500), rabbit anti-HOMER1 (Synaptic Systems, 1:500), mouse anti-neurofilament (Abcam, 1:500), mouse anti-nestin (Abcam, 1:1,000), guinea pig anti-VChAT (Synaptic Systems, 1:200), mouse anti-TH (Abcam, 1:200), and rat anti-MBP (Abcam, 1:200). The following secondary antibodies were used (all Jackson Immuno Research, 1:500): DyLight 405 Donkey Anti-Mouse IgG, Alexa Fluor 488 Donkey Anti-Chicken IgG, Alexa Fluor 488 Donkey Anti-Guinea Pig IgG, Alexa Fluor 488 Donkey Anti-Rabbit IgG, Alexa Fluor 594 Donkey Anti-Rabbit IgG, Alexa Fluor 647 Donkey Anti-Mouse IgG, Alexa Fluor 647 Donkey Anti-Rabbit IgG, Alexa Fluor 647 Donkey Anti-Rat IgG.

Cell culture

The mouse 16T SCLC line was derived from a primary tumour from the lungs of an Rb/p53 mutant mouse[44]. 16T-GFP cells were generated by transducing 16T cells with pLV-CMV-GFP followed by FACS selection[28]. These cells are grown as neurospheres (unless otherwise stated) in 10% FBS medium consisting of DMEM (Invitrogen) and 1× liquid antibiotic-antimycotic (Invitrogen). The spheres were dissociated using TrypLE (Gibco) for seeding of in vitro experiments. For human cell lines, SCLC22H, H69, CORL47, and H526 were generously provided by M. Oser. SCLC22H was cultured in DMEM plus 10% FBS (Cytiva) and 1× Glutamax. H69, CORL47, H446 and H526 were cultured in RPMI with 10% FBS and 1× Glutamax. H1048 was cultured in RPMI plus 10% FBS, 1× Glutamax, and 1× Insulin transferrin selenium (Fisher Scientific). Primary small airway epithelial cells (HSAECs) were purchased from ATCC and grown in airway epithelial cell basal media supplemented with bronchial epithelial cell growth components (ATCC). All cultures were monitored by short tandem repeat fingerprinting for authenticity throughout the culture period and mycoplasma testing was routinely performed.

Co-culture of SCLC cells with primary mouse neurons

Neurons were isolated from the brains of CD1 mice using the Neural Tissue Dissociation Kit - Postnatal Neurons (Miltenyi), followed by the Neuron Isolation Kit, Mouse (Miltenyi) per the manufacturer’s instructions. After isolation, 300,000 neurons were plated onto circular glass coverslips (Electron Microscopy Services) pre-treated for 20 min at 37 °C with poly-l-lysine (Sigma) and then 3 h at 37 °C with 5 μg ml−1 mouse laminin (Thermo Fisher). Neurons were cultured in BrainPhys neuronal medium (Stemcell Technologies) supplemented with 1× Glutamax (Invitrogen), pen/strep (Invitrogen), B27 supplement (Invitrogen), BDNF (10 ng ml−1; Shenandoah), and GDNF (5 ng ml−1; Shenandoah), TRO19622 (5 μM; Tocris), β-mercaptoethanol (1×, Gibco) and 2% fetal bovine serum. Half of the medium was replenished on day in vitro (DIV) 1 and 5-fluoro-2'-deoxyuridine (UFDU) was added at 1 μM. This was repeated at DIV 3. On DIV 5, half of the medium was replaced with serum-free medium in the morning. In the afternoon, the medium was again replaced with half serum-free medium containing 75,000 SCLC cells dissociated from neurospheres or attached cultures with TrypLE. Tumour cells were cultured with neurons for 24 h and then fixed with 4% PFA for 20 min at room temperature and stained for immunofluorescence analysis as described below.

Co-culture of SCLC cells with human iPS cell-derived glutamatergic neurons

iPS cell lines were obtained from the Brigham and Women’s Hospital NeuroHub Core Facility and all permissions were received for use (from BWH NeuroHub Core and Rush Alzheimer’s Disease Center’s Biospecimen Distribution Committee). Induced neurons were generated from BR33 iPS cells as described[80]. In brief, iPS cells were plated in mTeSR1 medium at a density of 95,000 cells per cm^2 on Matrigel-coated plates for viral transduction. Viral plasmids were obtained from Addgene (plasmids #19780, #52047 and #30130). FUdeltaGW-rtTA was a gift from K. Hochedlinger (Addgene plasmid #19780; http://n2t.net/addgene:19780; RRID: Addgene_19780). TetO-FUW-EGFP was a gift from M. Wernig (Addgene plasmid #30130; http://n2t.net/addgene:30130; RRID: Addgene_30130). pTet-O-Ngn2-puro was a gift from M. Wernig (Addgene plasmid #52047; http://n2t.net/addgene:52047; RRID: Addgene_52047). Lentiviruses were obtained from Alstem with ultrahigh titres (~1 × 10^9) and used at the following concentrations: pTet-ONGN2-puro: 0.13 µl, 50,000 cells; TetO-FUW-eGFP: 0.13 µl, 50,000 cells; FUdelta GW-rtTA: 0.13 µl, 50,000 cells. Transduced cells were dissociated with 3:1 DPBS: Accutase (Stemcell Technologies) + ROCKi (10 µM, Stemcell Technologies) and plated onto Matrigel-coated plates at 200,000 cells per cm^2 in StemFlex medium + ROCKi (10 µM, Stemcell Technologies) (day 0). On day 1, medium was changed to KSR medium with doxycycline (2 µg ml−1, Sigma). Doxycycline was maintained in the medium for the remainder of the differentiation. On day 2, medium was changed to 1:1 KSR: N2B medium with puromycin (5 µg ml−1, GIBCO). Puromycin was maintained in the medium throughout the differentiation. On day 3, medium was changed to N2B medium + 1:100 B27 supplement (Life Technologies). From day 4 on, cells were cultured in NBM medium + 1:50 B27 + puromycin (5 µg ml−1) + BDNF, GDNF, CNTF (10 ng ml−1, Peprotech). Half medium changes were performed every 2–3 days. Around D10, puromycin was removed from medium. Around D12-D-15, SCLC cells were added at a ratio of 1:3 (cancer cells:neurons). Cultures were monitored over the next ten days in the case of MEA recordings. For histological analyses, SCLC cells were added in the presence of 1 μM TTX (Tocris), 50 μM MK801 (Selleck Chemicals), 50 μM CNQX (Tocris), or vehicle. Co-cultures were then fixed and analysed 24 h later in the case of proliferation assays, and 5 days after co-culture in the case of synapse quantification.

Co-culture of SCLC cells with human iPS cell-derived GABAergic neurons

Induced pluripotent stem cell lines were obtained from the Brigham and Women’s Hospital NeuroHub Core Facility and all permissions were received for use (from BWH NeuroHub Core and Rush Alzheimer’s Disease Center’s Biospecimen Distribution Committee). In brief, iPS cells were plated in mTeSR1 medium at a density of 95,000 cells per cm^2 on Matrigel-coated plates for viral transduction. Viral plasmids were obtained from Addgene (plasmids #19780, #97329 and #97330). FUdeltaGW-rtTA was a gift from K. Hochedlinger (Addgene plasmid #19780). TetO-Ascl1-puro was a gift from M. Wernig (Addgene plasmid #97329; http://n2t.net/addgene:97329; RRID: Addgene_97329). DLX2-hygro was a gift from M. Wernig (Addgene plasmid #97330; http://n2t.net/addgene:97330; RRID: Addgene_97330). Lentiviruses were obtained from Alstem with ultrahigh titres (~1 × 10^9) and used at the following concentrations: TetO-Ascl1-T2A-Puro: 0.13 µl, 50,000 cells; DLX2-hygro: 0.13 µl, 50,000 cells: FUdelta GW-rtTA: 0.13 µl, 50,000 cells. Transduced cells were dissociated with 3:1 DPBS: Accutase + ROCKi (10 µM) and plated onto Matrigel-coated plates at 200,000 cells per cm^2 in StemFlex medium + ROCKi (10 µM) (day 0). On day 1, medium was changed to N2B medium with doxycycline (2 µg ml−1) and forskolin (10 µM, Sigma). Doxycycline and forskolin were maintained in the medium for the remainder of the differentiation. On day 2, medium was replaced with fresh N2B medium. On day 3, medium was changed to N2B medium + puromycin (10 µg ml−1, GIBCO). On day 4 onwards, cells were cultured in N2B medium + puromycin (8 µg ml−1). From day 5 onwards, cells were cultured with NBM medium + 1:50 B27 + puromycin (5 µg ml−1) + BDNF, GDNF, CNTF (10 ng ml−1) + doxycycline (2 µg ml−1) + forskolin (10 µM) + AraC (2 µM, Sigma). Half medium changes were performed every 2–3 days. Around day 14, puromycin, doxycycline, AraC, and forskolin were removed from medium. Around day 20 to day 25, SCLC cells were added at a ratio of 1:3 (cancer cells:neurons). For histological analyses, SCLC cells were added in the presence of 1 μM TTX (Tocris), 20 μM gabazine (Tocris) or vehicle. Co-cultures were then fixed and analysed 24 h later in the case of proliferation assays, and 5 days after co-culture in the case of synapse quantification.

Induced neuron protocol medium

KSR medium: Knockout DMEM, 15% KOSR, 1× MEM-NEAA, 55 µM β-mercaptoethanol, 1× Glutamax (Life Technologies). N2B medium: DMEM/F12, 1× GlutaMAX (Life Technologies), 1× N2 supplement B (StemCell Technologies), 0.3% dextrose (d-(+)-glucose, Sigma). NBM medium: neurobasal medium, 0.5× MEM-NEAA, 1× GlutaMAX (Life Technologies), 0.3% dextrose (d-(+)-glucose, Sigma).

Conditioned media assays

For conditioned media assays, either glutamatergic or GABAergic iPS cell-derived neurons were cultured as above. On day 17–18, neurons were replenished with a full medium change with or without the presence of 1 μM TTX. Conditioned medium was collected 24 h later for immediate use. Mouse 16T cells were then plated (30,000 cells in a 48 well plate) with 500 μL neuronal conditioned medium (again in the absence or presence of TTX) with the addition of 10 μM EdU to each well. After 24 h, cells were fixed cells with 4% PFA and stained using Click-iT 594 EdU kit and protocol (Invitrogen). All conditioned media assays were performed alongside direct co-culture assays described above.

EdU incorporation assay

EdU staining was performed on glass coverslips in 24-well plates which were precoated with poly-l-lysine (Sigma) and 5 μg ml−1 mouse laminin (Thermo Fisher). Neurosphere cultures were dissociated with TrypLE and plated onto coated slides with 10 μM of EdU. After 24 h the cells were fixed with 4% PFA in PBS for 20 min and then stained using the Click-iT 594 EdU kit and protocol (Invitrogen) with or without additional antibody staining and mounted using Prolong Gold mounting medium (Life Technologies). Proliferation index was determined by quantifying the fraction of EdU-labelled cells/GFP-labelled cells using confocal microscopy at 40× magnification.

Synaptic puncta staining and visualization

For immunohistochemistry, fixed coverslips were incubated in blocking solution (3% normal donkey serum, 0.3% Triton X-100 in TBS) at room temperature for 1 h. Primary antibodies guinea pig anti-synapsin1/2 (1:500; Synaptic Systems), rabbit anti-HOMER1 (1:500; Synaptic Systems), rabbit anti-gephyrin (1:300, Cell Signaling Technologies), or mouse anti-neurofilament (1:500; Abcam) in 0.3% Triton X-100 in TBS and incubated overnight at 4 °C. Samples were then rinsed 3 times in TBS and incubated in secondary antibody solution (Alexa 488 donkey anti-guinea pig IgG; Alexa 594 donkey anti-rabbit IgG, and Alexa 647 donkey anti-mouse IgG, all at 1:500 (Jackson Immuno Research)) in antibody diluent solution at 4 °C overnight. Coverslips were rinsed three times in TBS and mounted with ProLong Gold Mounting medium (Life Technologies). Images were collected using a 63× oil-immersion objective on a Zeiss LSM800 confocal microscope and processed with Airyscan. Colocalization of puncta was quantified as described[2].

MEA recordings

All MEA recordings were taken and analysed using the Axion Biosystems platform. Prior to culturing, 6-well Axion plates were coated with poly-l-lysine and laminin. Day 4 iNs (created as described[80]) were then thawed and plated in neurobasal medium, at 100,000 cells per well. For the initial week prior to co-culture, iNs were subjected to a half medium change every 3–4 days. Doxycycline and puromycin treatment were stopped after day 10 to allow for the addition of tumour cells. On day 14–15, SCLC cells were added at 30,000 cells per well MEA plates. MEA plates were recorded every day for 10 min for up to 2 weeks after co-culture. All spike numbers and amplitude were assessed using proprietary Axion Software.

Electrophysiology

For all electrophysiology experiments, 35,000 16T-GFP cells were allografted into the CA1 hippocampus of 4–6 week old NSG mice. Three weeks after allograft, brain slices were obtained using standard techniques. Mice were anaesthetized by isoflurane inhalation and perfused transcardially with ice-cold ACSF containing (in mM) 125 NaCl, 2.5 KCl, 25 NaHCO_3, 2 CaCl_2, 1 MgCl_2, 1.25 NaH_2PO_4 and 25 glucose (295 mOsm kg−1). Brains were blocked and transferred into a slicing chamber containing ice-cold ACSF. Coronal slices of hippocampus were cut at 300-μm thickness with a Leica VT1000s vibratome in ice-cold ACSF, transferred for 10 min to a holding chamber containing choline-based solution consisting of (in mM) 110 choline chloride, 25 NaHCO_3, 2.5 KCl, 7 MgCl_2, 0.5 CaCl_2, 1.25 NaH_2PO_4, 25 glucose, 11.6 ascorbic acid, and 3.1 pyruvic acid at 34 °C then transferred to a secondary holding chamber containing ACSF at 34 °C for 30 min and subsequently maintained at room temperature (20–22 °C) until use. All recordings were obtained within 5 h of slicing. Both choline solution and ACSF were constantly bubbled with 95% O_2/5% CO_2.

Individual brain slices were transferred into a recording chamber, mounted on an upright microscope (Olympus BX51WI or Scientifica SliceScope Pro 1000) and continuously superfused (2–3 ml min−1) with ACSF and bubbled with 95% O_2/5% CO_2 warmed to 32–34 °C by passing it through a feedback-controlled in-line heater (SH-27B; Warner Instruments). Cells were visualized through 40× or 60× water immersion objectives with either infrared differential interference contrast optics or epifluorescence to identify GFP+ cells. For whole-cell voltage clamp recording, patch pipettes (2–4 MΩ) pulled from borosilicate glass (Sutter Instruments) were filled with internal solution containing (in mM) 135 CsMeSO_3, 10 HEPES, 1 EGTA, 3.3 QX-314 (Cl− salt), 4 Mg-ATP, 0.3 Na-GTP, 8 sodium phosphocreatine (pH 7.3 adjusted with CsOH; 295 mOsm·kg−1). sEPSCs and spontaneous inhibitory post-synaptic currents (sIPSCs) of GFP+ cells were recorded for 5 min at –70 or 0 mV holding potential. For membrane current variance, 10 consecutive sweeps, each 10 s long, recorded in voltage clamp at −70 mV holding potential were analysed by calculating the s.d. of the current after applying a 3-point median filter. To record evoked excitatory post-synaptic currents (eEPSCs) and evoked inhibitory post-synaptic currents (eIPSCs), the membrane voltages were clamped at –70 mV or 0 mV. For perforated-patch recording eIPSCs (gramicidin D, 40–60 μg ml−1) with access resistance 40–60 MΩ, variable holding membrane voltages were applied to generate an I–V curve and identify chloride’s equilibrium potential. Extracellular stimulation was performed with a stimulus isolation unit (MicroProbes, ISO-Flex), bipolar electrodes (75 μm apart, PlasticOne or MicroProbes) which were placed away 100–400 μm along the CA1–CA2 axis from the recorded cells. Stimuli were delivered at 10 s intervals with 0.1 ms duration and 100–200 μA amplitude. In some recordings, to confirm whether sEPSCs or eIPSCs were glutamatergic or GABAergic, 10 μM NBQX and CPP (Sigma) or 10 μM gabazine (Tocris) were added to bath ACSF. For perforated patched recordings, 10 μM NBQX and CPP were in bath continuously. Pyramidal cells in allografted and contralateral sites of hippocampus were recorded in whole-cell current clamp to study intrinsic properties. Patch pipettes were filled with internal solution containing (in mM) 135 potassium methanesulfonate, 10 HEPES, 1 EGTA, 4 Mg-ATP, 0.4 Na-GTP, 8 sodium phosphocreatine (pH 7.3 adjusted with KOH; 295 mOsm kg−1). From a resting potential of −70 mV, currents were injected for 1,000 ms at 50 pA steps from −100 to 1,000 pA. Intrinsic properties of hippocampus pyramidal cells were analysed with software in Matlab.

Extracellular local field potentials (fEPSPs) were recorded from hippocampal allograft and contralateral sites under current clamp (I = 0 mode). A bipolar electrode (75 μm spacing, MicroProbes) was placed in stratum radiatum near the allograft or in the correspondent contralateral site. Recording glass electrodes (2–3 MΩ) filled ACSF were place 100–200 μm perpendicularly away from the stimulating bipolar electrode at a depth of 50 100 μm. The stimulation currents were delivered at 20 s intervals with 0.1 ms duration and variable intensities from 20 μA to 140 μA using 20 μA steps and repeated 10 times. The average slopes of fEPSPs for each current intensity were measured.

The resting membrane potential of SCLC was assessed through cell-attached configuration (Rseal > 1 GΩ) using the procedure previously described in Antel et al.[81]. Glass pipettes were filled with a solution containing (in mM) 145 KCL, 2 MgCl2, 5 HEPES (pH was adjusted to 7.3 with KOH). A voltage protocol consisting of hold at 0 mV, stepping to 100 mV and ramping back to −120 mV over 137.5 ms was applied to GFP+ cells. Five to ten consecutive traces were recorded and averaged. All data were acquired with a MultiClamp 700B amplifier (Molecular Devices) and digitized at 3 kHz with a National Instruments data acquisition device (NI USB- 6343).

Calcium imaging

For calcium imaging, the genetically encoded calcium indicator GCaMP6s was lentivirally transduced into mouse SCLC 16T (pLV-ef1-GCaMP6s-P2A-nls-tdTomato). In this case, SCLC cells containing the GCaMP6s reporter can be identified using the tdTomato nuclear tag. These cells were isolated and grafted into the CA1 region of the hippocampus as described above. Two-photon calcium imaging experiments were performed using Prairie Ultima XY upright two-photon microscope for tissue slices equipped with an Olympus LUM Plan FI W/IR-2 40× water immersion objective. The temperature of the perfusion medium, ACSF as described above, was kept at 30 °C, and perfused through the system at rate of 2 ml min−1. Excitation light was provided at a wavelength of 920 nm through a tunable Ti:Sapphire laser (Spectra Physics Mai Tai DeepSee) to allow for excitation of both tdTomato and GCaMP6s. The actual laser power reaching the scanhead for each scope is dynamically controlled by Pockels cells via software interface. Pockels cell were set at 10 for all experiments, and photomultiplier tubes (PMTs) were set at 800 for each channel. For these settings, power at back aperture of the objective was approximately 30 mW at 920 nm. The wavelength ranges for the emission filters were PMT1: 607 nm centre wavelength with 45 nm bandpass (full-width at half-maximum) and PMT2: 525-nm centre wavelength with 70-nm bandpass (full-width at half-maximum). Recordings were made at 0.65 frames per second (~1.5 Hz) for about 30 min in the case of spontaneous activity and 10 min in the case of response to periodic electrical stimulation. Cells were identified via the expression for the nuclear tdTomato tag and were only imaged in the area of interest, specifically in the CA1 region of the hippocampus. Similar to the electrophysiology paradigm, for neuronal stimulation experiments, the electrode was placed in the hippocampus to stimulate the neuronal inputs originating in CA3. For electrical stimulation, approximately 20 µA over 200 µs was delivered to local axons using a stimulating bipolar microelectrode. For inhibitor experiments, TTX was directly diluted into the ACSF perfusion medium at 500 nM, oxygenated, and delivered to the slices through the perfusion system.

Calcium imaging analysis

Quantitative fluorescence intensity analysis was done on calcium transients that were reliably evoked by axonal stimulation. To determine the effect of TTX on the calcium transients in response to electrical stimulation of the CA3 Schaffer collaterals, the field of cells were stimulated three times in 1-min intervals to ensure synaptic connectivity. TTX (500 nM) was then perfused into the slices and the stim was repeated on the same field of cells to gauge direct effect of TTX on stimulation response. For analysis, regions of interest of each responding nucleus were set and ΔFmax/F_0 (maximum difference in fluorescence intensity normalized to background fluorescence) measurements were determined before and after TTX treatment.

Visualization of human SCLC lung tumour gene expression

Expression data from 81 patients with SCLC primary tumours[38] as FPKM values was accessed and plotted for genes of interest.

Unilateral cervical vagotomy

Adult RPR2-luc mice (4–7 months old, 2–5 months from virus administration) weighing more than 17 g were anaesthetized with 1–4% isoflurane through a nose cone in a supine position. Skin of the ventral surface of the neck was shaved and aseptically prepared according to IACUC guidelines. Under a dissection microscope, a 1 cm midline skin incision was made, and salivary glands revealed were separated with blunt dissection to expose the airways. The right vagal nerve was then carefully dissected from the carotid sheath and cut at the cervical level posterior to the pharyngeal branch. Sham mice underwent the same surgical procedure for blunt dissection of the vagus nerve but the latter was left intact. The mice were monitored until recovery from anaesthesia for changes in heart rate and respiration and after that monitored bi-weekly for changes in weight, eating, drinking, and general activity. RPM mice were handled the same way, except vagotomy was performed within 1 week of virus administration.

Bioluminescence imaging

For in vivo monitoring of tumour growth, bioluminescence imaging was performed using an IVIS imaging system (Xenogen). Mice were placed under 1–4% isofluorane anaesthesia and injected with luciferin substrate, then imaged in pronated position for intracranial allograft experiments or supine position for lung tumour mice. Baseline bioluminescence was used to randomize mice by a blinded investigator so that experimental groups contained an equivalent range of tumour sizes. For vagotomy experiments, each cohort of mice was imaged weekly for a total of 6 months.

Survival studies

For survival studies, morbidity criteria used were either reduction of weight by 15% of initial weight, sickness behaviour (such as dyspnea, abnormal gait or posturing, or ill-groomed fur), or severe neurological motor deficits consistent with brainstem dysfunction (that is, hemiplegia or an incessant stereotyped circling behaviour seen with ventral midbrain dysfunction). Kaplan–Meier survival analysis using log-rank testing was performed to determine statistical significance.

Mouse drug treatment studies

For all drug studies, NSG mice were xenografted as above with either 16T-GFP or NCI-H446-GFP cells and randomized to treatment group by a blinded investigator. One week post-engraftment, tumour-bearing mice were treated with systemic administration of levetiracetam (20 mg kg−1; Selleck Chemicals; formulated in a sterile saline solution) via intraperitoneal injection daily for 2 weeks. Controls were treated with an identical volume of vehicle.

Statistical analyses

Statistical tests were conducted using Prism v.9.3.1 (GraphPad) software unless otherwise indicated. Gaussian distribution was confirmed by the Shapiro–Wilk normality test. For parametric data, unpaired two-tailed Student’s t-test or one-way ANOVA with Tukey’s post hoc tests to examine pairwise differences were used as indicated. Paired two-tailed Student’s t-tests were used in the case of same-cell or same-animal experiments (as in electrophysiological recordings and vagotomy experiments). For non-parametric data, a two-sided unpaired Mann–Whitney test was used as indicated, or a one-tailed Wilcoxon matched pairs signed rank test was used for same-cell experiments. Two-tailed log-rank analyses were used to analyse statistical significance of Kaplan–Meier survival curves. In all box-and-whiskers plots, whiskers indicate minimum and maximum values, the box extends to the 25th and 75th percentile, and the centre line is plotted at the median. In all violin plots, lines are drawn at the median and quartiles.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Sequencing data for SCLC cells isolated from neuronal co-cultures and human brain-metastatic SCLC are available at the Gene Expression Omnibus (GEO) under accessions GSE262422 and GSE303152, respectively. All other data are available in the manuscript or from the corresponding author upon reasonable request. Source data are provided with this paper.

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Acknowledgements

Research reported in this publication was supported by the National Institutes of Health (NIH) (grants R00CA252001 and DP2CA290968 to H.S.V.; R01NS092597, DP1NS111132, P50CA165962, R01CA258384 and U19CA264504 to M.M.; CA231997 to J.S.; R37NS046579 to B.L.S.; R37CA258829, R01CA266446, R01CA280414 and U54CA274506 to B.I.), Glaucoma Research Foundation (to H.S.V.), Charles Hood Foundation (to H.S.V.), the Sontag Foundation (to H.S.V.), the McKnight Foundation (to H.S.V.), The Searle Scholars Research Program (to H.S.V.), the Damon Runyon Foundation (to H.S.V.), The V Foundation (to H.S.V.), the Pew Foundation (to H.S.V.), ChadTough Defeat DIPG (to M.M. and H.S.V.), Virginia and D.K. Ludwig Fund for Cancer Research (to M.M.), Cancer Research UK (to M.M.), Robert J. Kleberg Jr and Helen C. Kleberg Foundation (to M.M.), McKenna Claire Foundation (to M.M.), Gatsby Charitable Foundation (to M.M.), Damon Runyon Cancer Research Foundation fellowship (F.Q.), Stanford Medical Scholars Research Program (to S.S.) and the Pershing Square Sohn Cancer Research Alliance Award (to B.I.). B.I. is a CRI Lloyd J. Old STAR (CRI5579). This work was additionally supported by the Herbert Irving Comprehensive Cancer Center (HICCC) Human Tissue Immunology and Immunotherapy Initiative (to B.I.) NIH/NCI Cancer Center Support Grant P30CA013696, the Molecular Pathology Shared Resource and its Tissue Bank, and the Human Immune Monitoring Core at Columbia University. The authors thank J. Ko for providing RPM mice, M. Oser for providing human SCLC cell lines, and K. Casey for reviewing mouse lung and liver histology.

Author information

Author notes

1. These authors contributed equally: Solomiia Savchuk, Kaylee M. Gentry

Authors and Affiliations

1. Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA

Solomiia Savchuk, Belgin Yalçın, Lijun Ni, Richard Drexler, Lehi Acosta-Alvarez, Pamelyn J. Woo, Yoon Seok Kim & Michelle Monje

2. Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA

Kaylee M. Gentry, Elana Carleton, Hannah C. Farnsworth, Rachel A. Davis, Elisa C. Pavarino & Humsa S. Venkatesh

3. Department of Neurobiology, Harvard Medical School, Boston, MA, USA

Wengang Wang & Bernardo L. Sabatini

4. Department of Pediatric Oncology, Dana-Farber Boston Children’s Cancer and Blood Disorders Center, Boston, MA, USA

Carlos A. O. Biagi-Junior, Jenna LaBelle & Mariella G. Filbin

5. Department of Medicine, Division of Hematology/Oncology, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA

Karan Luthria, Johannes C. Melms & Benjamin Izar

6. Department of Biochemistry, Stanford University, Stanford, CA, USA

Yin Liu & Mark A. Krasnow

7. Departments of Pediatrics and Genetics, Stanford University, Stanford, CA, USA

Griffin G. Hartmann, Fangfei Qu & Julien Sage

8. Department of Pathology, Stanford University, Stanford, CA, USA

Angus M. Toland

9. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Mark A. Krasnow & Michelle Monje

10. Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA

Keith L. Ligon

11. Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA

Bernardo L. Sabatini

12. Department of Neurology, Harvard Medical School, Boston, MA, USA

Humsa S. Venkatesh

Contributions

S.S., K.M.G., M.M. and H.S.V. designed experiments. S.S., K.M.G. and H.S.V. conducted and analysed experiments. S.S. and K.M.G. contributed to all in vitro and in vivo data collection and analysis. W.W. performed the in situ electrophysiological recordings and analysed the data with help from B.L.S. E.C. and E.C.P. contributed to co-culture and MEA experiments and co-culture assays. C.A.O.B.-J. contributed to single-cell RNA sequencing analysis. L.N. contributed to electron microscopy imaging. K.L.L. and A.M.T. performed histological staining of human tissue. B.Y., R.D. and P.J.W. contributed to in vivo optogenetic surgeries. K.L, J.C.M. and B.I. contributed all human RNA sequencing data and analysis. Y.L. and M.A.K. contributed to the vagotomy surgeries and strategy. H.C.F. and R.A.D. contributed to brain and lung histology. F.Q., G.G.H. and J.S. provided SCLC mice. Y.S.K. contributed to validation of ChR2-expressing SCLC cells. J.L. and M.G.F. contributed to bioinformatic analyses. All authors contributed to manuscript editing. M.M. and H.S.V. wrote the manuscript and supervised all aspects of the work. H.S.V. conceived of the project.

Corresponding authors

Correspondence to Michelle Monje or Humsa S. Venkatesh.

Ethics declarations

Competing interests

M.M. holds equity in MapLight Therapeutics and Stellaromics, and stock in CARGO Therapeutics. M.M. was previously on the scientific advisory board for Cygnal Therapeutics. J.S. has equity in and is an advisor for DISCO Pharmaceuticals. B.I. has received consulting fees and honoraria from Volastra Therapeutics Inc., Merck, AstraZeneca, Novartis, Eisai and Janssen Pharmaceuticals and has received research funding to Columbia University from Alkermes, Arcus Biosciences, Checkmate Pharmaceuticals, Compugen, Immunocore, Merck, Regeneron and Synthekine. B.I. is a founder of Basima Therapeutics, Inc.

Peer review

Peer review information

Nature thanks Frank Winkler 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 Denervation in genetic model of spontaneously forming SCLC.

a, Quantification of neurotransmitter receptor gene expression in human samples of primary SCLC (n = 81, for gene list see Supplementary Table 1). b, Visualization of tumor innervation in primary lung tumor samples taken from the genetic mouse model of spontaneously forming SCLC. Streptavidin (SA, green) used to mark airways[79]; proliferative cells are labeled with Ki67 (red) to help identify tumors. Vesicular acetylcholine transporter (VAChT), tyrosine hydroxylase (TH), and myelin basic protein (MBP) (yellow) are used to visualize parasympathetic, sympathetic, and myelinated sensory populations of nerve fibers in left, middle and right image, respectively. Scale bar = 100 µm. c, Visualization of tumor innervation of liver metastasis in murine RPR2-luc model of SCLC. Streptavidin (SA, green) used to mark epithelial cells and help identify airways[79]; proliferative cells are labeled with Ki67 (red) to help identify tumors; MBP (yellow) is used to visualize subpopulation of nerve fibers. Scale bar = 50 µm. d, Body mass surveillance of animals up to 4 weeks following vagotomy or sham procedure (n = 5 animals per group). e, IVIS bioluminescence analysis of overall tumor growth in SCLC primary tumors measured weekly after vagotomy procedure (n = 11 sham and n = 9 vagotomy animals from 2 independent cohorts, n = 0.0041). Data represented as raw values of total flux. Arrowheads demonstrate animal endpoints. f, Quantification of percent of lung occupied by tumor in sham and denervated animals (n = 7 sham, n = 9 vagotomy, p = 0.0430). g, As in f, but for livers (n = 7 sham, n = 9 vagotomy, p = 0.0192). h, Gross image of livers harvested at the endpoint of the experiment from the sham operated (left) and denervated (right) animals. Scale bar = 1 cm. i, Representative immunofluorescence of nerve fibers (VAChT, yellow) around the early lung lesions (marked via Ki67, red) in RPR2-luc animals (left, 2–3 months since Cre administration, n = 4) compared to late-stage tumors (right, 8–9 months since Cre administration, n = 3). Streptavidin (SA, green) used to delineate airways. Scale bar = 100 µm. j, Quantification of data in i demonstrating decrease in innervation in late-stage tumors. k, Time course of tumor growth as measured by IVIS bioluminescence imaging in sham operated (grey) and denervated (vagotomy; red) animals that underwent surgery after initial tumor formation (n = 7 sham and n = 9 vagotomy animals from 2 independent cohorts). l, As in k for onset of liver metastasis as detected by IVIS imaging. m, Kaplan–Meier survival curve for sham operated (grey) and denervated (vagotomy; red) animals that underwent surgery after initial tumor formation (n = 7 sham and n = 9 vagotomy). Data are box plots for a (box defined by 25th percentile, median, and 75th percentile, whiskers extend to min/max, dots represent outliers), median +/- I.Q.R for f,g,j. Analysis with two-way ANOVA for e, two-sided unpaired Mann-Whitney test for f, g, j, Gehan-Breslow-Wilcoxon test for k, Log-rank (Mantel-Cox) test for l, m. All tests are two-tailed. *P < 0.05.

Source data

Extended Data Fig. 2 Denervation does not offer survival advantage for MYC-driven SCLC.

a, Kaplan–Meier survival curve for sham operated (grey) and denervated (vagotomy; red) animals of RPM-CMV-Cre model (n = 3 sham and n = 3 vagotomy). b, Quantification of tumor burden by percent of lung occupied by tumor for animal cohort in a. c, As in a for RPM-CGRP-Cre model animals (n = 4 sham and n = 4 vagotomy). d, Quantification of tumor burden by percent of lung occupied by tumor for animal cohort in c. Data are mean ± s.e.m. for b, d. Analysis with Log-rank (Mantel-Cox) test for a,c, two-sided unpaired t-test for b,d.

Source data

Extended Data Fig. 3 Microenvironmental dynamics within intracranial SCLC tumors.

a, Representative neurofilament (top) and Ki67 (bottom) immunohistochemistry of two human samples of SCLC brain metastasis in regions within (red box, zoomed in on right panel) and outside (yellow box, zoomed in on middle panel) 100 µm distance from neurofilament. Scale bar on left = 100 µm; scale bar on middle and right = 50 µm. b, Comparison of local nuclear density as a measure of proliferative history in human SCLC brain metastases for n = 9 patients. Regions are grouped by distance to axons immunostained for neurofilament (less than or greater than 100 µm, p = 0.0044). c, Representative immunofluorescent images of murine SCLC brain allografts (GFP, green) with neurons labeled with MAP2 (red) and NeuN (white). Scale bar = 50 µm (left), 20 µm (right). d, Representative images of proliferation rate (Ki67, red) of murine SCLC allografts (GFP, green) assessed in regions deprived of (left) or enriched for (right) neurons. Scale bar = 50 µm. e, Quantification of data in d, illustrating increased proliferation in neuron-rich compared to neuron-poor areas (n = 6 animals, p < 0.0001). Analysis with two-tailed paired t-test for b, e. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

Source data

Extended Data Fig. 4 Single-cell RNA sequencing of SCLC cells after co-culture with neurons, either with or without addition of tetrodotoxin, reflects distinct subpopulations of cells that cluster separately from SCLC cells in monoculture.

a, Representative immunofluorescence of neuronal populations present in primary neuronal co-cultures demonstrating presence of glutamatergic (VGLUT2, white) and GABA-ergic (GAD65, red) neurons and absence of astrocytes (GFAP, green). Scale bar = 100 µm. Data from 2 independent experiments. b, Quantification of data in a, demonstrating approximately equal proportions of glutamatergic and GABA-ergic neurons. c, Expression of membrane GFP (mGFP) in 16T SCLC cells grown in monoculture or isolated from co-culture with primary murine neurons. d, Composition of the 16 clusters depicted in Fig. 2g as percentage of cells isolated from the monoculture (grey) or co-culture (black) conditions. e, Gene Ontology (GO) enrichment analysis for the top pathways expressed by cells in cluster 14. f, UMAP embedding of single cell RNA-seq profiles of murine 16T SCLC cells isolated from monoculture (grey) or co-culture (black) with primary murine neurons in the presence of 1 µM TTX. 44,562 cells were analyzed from 3 biological replicates. g, Expression of membrane GFP (mGFP) in cells from f. h, Composition of the 18 clusters depicted in Fig. 2k as percentage of cells isolated from the monoculture (grey) or co-culture (black) in the presence of TTX conditions.

Extended Data Fig. 5 Human brain metastatic SCLC contains a subpopulation enriched for neural gene signatures.

a, Integrated UMAP embeddings of all cells from a cohort of n = 12 patients with SCLC brain metastases, colored by their assigned cell-types (left) or patient (right). b, as in a, but of cells from n = 16 patients with lung primary, recurrent, and non-brain-metastatic SCLC lesions (data made publicly available by Chan et al., 2021). c, Spearman correlation of cell programs classifying malignant cells in a, grouped into 6 distinct metaprograms (indicated by boxes within the co-correlation plot). d, as in c, but for malignant cells in b grouped into 9 distinct metaprograms. e, Normalized gene contribution of top defining genes (rows) for each respective metaprogram in the brain-metastatic cohort (columns). Annotated biological functions corresponding to these genes, individual metaprograms, or groups of metaprograms are shown on the right. Metaprogram 3 is defined by genes driving neural-like differentiation. f, Scaled correlation of brain-metastatic cohort metaprograms (columns) with annotated transcriptional hallmarks of tumor heterogeneity derived by Gavish et al.[46] (rows). Metaprogram 3 demonstrates a strong correlation with neural-like hallmarks. g, as in e for the primary, recurrent, and non-brain metastatic cohort. h, as in f for the primary, recurrent, and non-brain metastatic cohort.

Extended Data Fig. 6 Electrical currents in SCLC pathogenesis.

a, Immuno-electron microscopy of H446-GFP SCLC allografted to mouse hippocampus. Black dots represent immunogold particles labeling GFP (tumor cells). Post-synaptic density in GFP+ tumor cells (pseudo-colored green), synaptic cleft, and clustered synaptic vesicles in apposing presynaptic neuron (pseudo-colored blue) identify synapses (white arrowheads). Scale bar = 300 nm. b, Immuno-electron microscopy of 16T-GFP SCLC allografted to mouse hippocampus to demonstrate perisynaptic SCLC connections. Post-synaptic density in GFP-negative neuron (pseudocolored blue), synaptic cleft, and clustered synaptic vesicles in apposing presynaptic neuron identify neuron-to-neuron synapses (black arrowheads). Tumor cells in perisynaptic position are pseudocolored green. Scale bar = 200 μm. c, as in b, but with H446-GFP SCLC cells. Scale bar = 300 μm. d, Quantification of synaptic and perisynaptic neuron-to-tumor connections in murine 16T and human H446 SCLC cells. e, Experimental paradigm for acute slice electrophysiology. GFP + SCLC cells (grey) allografted into the mouse hippocampal CA1 region with CA3 Schaffer collateral afferent stimulation. Created in BioRender. Savchuk, S. (2025) https://BioRender.com/5fwotqm. f, Biocytin-filled (red) SCLC cell allografted to CA1 region of mouse hippocampus. Scale bar = 20 μm. g, Representative recordings of spontaneous currents in allografted SCLC cells at baseline (black) or after addition of 10 µM glutamatergic inhibitors NBQX and CPP (red). h, Cumulative probability density function (CDF) of membrane current of SCLC cells recorded at baseline (n = 8) and after application of glutamate receptor antagonists (n = 15). i, Expression of K+/Cl− co-transporter genes in SCLCs cell lines (n = 4 samples). j, Expression of K+/Cl− co-transporter genes in human samples of primary SCLC (n = 81 patients). Data are plotted as mean ± s.e.m. for d, box-and-whisker plots for i, j, (box defined by 25th percentile, median, and 75th percentile, whiskers extend to min/max, dots represent outliers). Analysis with 2-sided t-test, Wilcoxon rank sum, and Kolmogorov-Smirnov for h.

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Extended Data Fig. 7 Human iPSC-derived glutamatergic neurons drive activity-dependent proliferation of multiple SCLC subtypes.

a, Expression of ASCL1 in 16T SCLC cells grown in monoculture or isolated from co-culture with primary murine neurons and processed for single cell RNA sequencing (see Fig. 2f). b, As in a for NEUROD1. c, Quantification of ASCL1 and NEUROD1 in 16T SCLC cells grown in monoculture or neuron co-culture (data from a, b, p < 0.0001). d, Quantification of proliferative index of murine 16T SCLC-A subtype cells co-cultured with human iPSC-derived glutamatergic neurons reveals increased proliferation in co-culture, abrogated by the addition of 1 µM TTX (n = 4 coverslips per condition, p < 0.0001). e, As in d, but human H69 SCLC-A subtype cells were used (n = 3 coverslips per condition, p = 0.0110). f, As in d, but human CORL47 SCLC-A subtype cells were used (n = 3 coverslips per condition, p = 0.0006). g, As in d, but human H446 SCLC-N subtype cells were used (n = 3 coverslips per condition, p = 0.0030). h, As in d, but human SCLC22H SCLC-N subtype cells were used (n = 3 coverslips per condition, p = 0.0002). i, As in d, but human H1048 SCLC-P subtype cells were used (n = 3 coverslips per condition, p = 0.0003). Data are violin plot for c, mean ± s.e.m for d-i. Analysis with Wilcoxon rank sum test for c, 2-way ANOVA for d-i. All tests are two-tailed. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

Extended Data Fig. 8 Glutamatergic and GABA-ergic signaling drives proliferation of multiple SCLC subtypes via neurotransmitter-mediated signaling pathways.

a, Quantification of proliferative index of human SCLC-A subtype H69 cells co-cultured with human iPSC-derived glutamatergic neurons with or without addition of 50 µM MK801 (inhibitor of NMDA receptor for glutamate) reveals increased proliferation in co-culture abrogated by MK801 (n = 3 coverslips per condition, p = 0.0002). b, As in a, but 50 µM CNQX (inhibitor of AMPA receptor for glutamate) was used (n = 3 coverslips per condition, p = 0.0017). c, As in a, but human SCLC-N subtype H446 cells were used (n = 3 coverslips per condition, p = 0.0005). d, As in a, but human SCLC-P subtype H1048 cells were used (n = 3 coverslips per condition, p = 0.0001). e, Quantification of proliferative index of human SCLC-N subtype H446 cells co-cultured with human iPSC-derived GABA-ergic neurons with or without addition of 1 µM TTX or 20 µM Gabazine (GABA-A receptor inhibitor) reveals increased proliferation in co-culture abrogated by TTX and Gabazine (n = 4–6 coverslips per condition, p < 0.0001). f, As in e, but human SCLC-P subtype H1048 cells were used (n = 3–5 coverslips per condition, p < 0.0001). Data are mean ± s.e.m. Analysis with 2-way ANOVA. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

Extended Data Fig. 9 Neuron-conditioned media increases proliferation of SCLC, but not to the extent of direct co-culture.

a, Quantification of proliferative index of murine 16T SCLC cells exposed to conditioned media (CM) from or directly co-cultured with iPSC-derived glutamatergic neurons with or without addition of 1 µM TTX (n = 3 coverslips per condition, p < 0.0001). Conditioned media alone renders a partial increase in SCLC proliferation compared to baseline, but not to the full effect of direct co-culture (grey). b, As in a, but human H69 SCLC cells were used (n = 3–5 coverslips per condition, p = 0.0003). c, As in a, but iPSC-derived GABA-ergic neurons were used (n = 3–6 coverslips per condition, p < 0.0001). d, As in c, but human H69 SCLC cells were used (n = 3–4 coverslips per condition, p = 0.0007). e, Quantification of proliferative index of murine 16T SCLC cells exposed to conditioned media (CM) from lung epithelial cells (n = 3 coverslips per condition, p = 0.9010. f, Quantification of proliferative index of murine 16T SCLC cells at baseline or when treated with 100 nM neuroligin 3 (NLGN3) or 100 nM brain derived neurotrophic factor (BDNF). 10% FBS condition is used as positive control (n = 4 coverslips per condition, p = 0.9403 for NLGN3, p = 0.8969 for BDNF). g, As in f but for human H446 SCLC cells (n = 4 coverslips per condition, p = 0.9387 for NLGN3, p = 0.9969 for BDNF). h, Quantification of proliferative index of patient derived glioma cells (SU-DIPGVI) at baseline or treated with 100 nM NLGN3 (n = 4 coverslips per condition, p = 0.0483). i, as in h for glioma cells treated with 100 nM BDNF (n = 4 coverslips per condition, p = 0.0096). Data are mean ± s.e.m. Analysis with one-way ANOVA for a, b, c, d, f, g, two-tailed unpaired t-test e, h, i. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

Extended Data Fig. 10 Neuronal circuit activity drives invasion of SCLC cells.

a, Immunofluorescent images demonstrating cells expanding beyond the invasive edge of the tumor in SCLC brain allografts (GFP, green) of WT (left) or optogenetically stimulated ChR2 animals (right). Scale bar = 100 µm. b, Immunofluorescent images demonstrating increased tumor area in SCLC brain allografts of WT or ChR2 animals. Scale bar = 150 µm. c, Quantification of data in b, illustrating increased tumor spread from core after optogenetic stimulation (n = 7 WT, n = 8 ChR2 animals, p = 0.0267). Data are mean ± s.e.m for c. Analysis with two-tailed unpaired t-test for c. *P < 0.05.

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Extended Data Fig. 11 Membrane depolarization drives SCLC pathogenesis.

a, Representative calcium imaging of GCaMP6s-expressing 16T SCLC cells at baseline (left) and after application of 1 mM GABA. Scale bar = 100 μm. b, Quantification of GCaMP6s fluorescence in individual SCLC cells (from a) in response to administration of 1 mM GABA (n = 44 cells, p < 0.0001). c, As in b, but with administration of 1 mM glutamate (n = 19 cells, p < 0.0001). d, Quantification of the percent of cells exhibiting spontaneous calcium transients as depicted in Fig. 4i in SCLC tumors at baseline or with the addition of 0.5 µM TTX (p = 0.0002). e, Whole-cell patch-clamp voltage trace from ChR2-expressing SCLC cells (16T-ChR2) in response to blue light-induced depolarization. The lower panel represents the timing of blue light delivery. f, as in e, but demonstrating whole-cell patch-clamp current trace from ChR2-expressing SCLC cells (16T-ChR2). g, Quantification of proliferation index of 16T-ChR2 SCLC cells in mock-stimulated tumors or membrane depolarized tumors (n = 5 mock, n = 6 depolarized mice, p = 0.0460). Data are plotted as mean ± s.e.m. for d, g. Analysis with two-tailed paired t-test for b-c, two-tailed unpaired t-test for d, g. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

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Extended Data Fig. 12 Bidirectional neuron-SCLC interactions drive peritumoral hyperexcitability.

a, Quantification of the number of colocalized pre- and post-synaptic inhibitory puncta (quantified via colocalized synapsin-gephyrin staining) on neuronal processes at baseline vs. co-cultured with SCLC cells defined per 10 µm of neurofilament length (n = 10 coverslips in neuron baseline condition, n = 9 coverslips in SCLC co-culture condition). b, Quantification of the number of synapses per number of neurons within the region of murine 16T SCLC hippocampal allografts compared to contralateral normal hippocampi, demonstrating increased synaptic density in the tumor region compared to contralateral control hippocampus. Only regions of abundant (>5% cells) perisynaptic SCLC cells were considered (n = 3 mice, p = 0.0158). c, As in b, but for human H446 SCLC cells (n = 4 mice, p = 0.0037). d, Quantification of spike frequency in iPSC-derived glutamatergic neurons co-cultured with murine 16T SCLC cells (n = 4, p = 0.0296). e, as in d, but with the addition of human H446 SCLC cells (n = 3, p = 0.0005). f, Quantification of spike number and amplitude of iPSC-derived glutamatergic neurons at baseline or with the addition of conditioned media (CM) collected from SCLC cells (n = 91 spikes at baseline, n = 430 spikes in co-culture, n = 236 spikes in CM-treated condition, p < 0.0001). CM-treated neurons exhibit some elevation in spike depth but not to the degree of those recorded from neurons grown in direct co-culture with SCLC cells. g, Quantification of spike number and amplitude of SCLC cultured alone. h, Biocytin (red)-filled pyramidal neuron in the area of the tumor cells in situ. Scale bar = 100 μm. i, Representative current clamp currents and induced action potentials measured in pyramidal neurons in response to varying current injections (−100 pA, black; 100 pA, blue; 200 pA, red). j, Current to voltage relationship of action potentials in neurons from either the allograft (tumor-bearing) hippocampus or control contralateral hippocampus (n = 65 SCLC-associated, 32 control neurons). k, Current to action potential firing frequency relationship in neurons from either the allograft (tumor-bearing) hippocampus or control contralateral hippocampus (n = 65 SCLC-associated, 32 control neurons). l, Table listing cell-intrinsic properties of pyramidal neurons from either the allograft (tumor-bearing) hippocampus or control contralateral hippocampus (n = 65 SCLC-associated, 32 control neurons). m, Expression of astrocyte-related gene signature (Supplementary Table 1) taken from scRNAseq analysis of cells isolated from patient lung primary, recurrent or non-brain-metastatic lesions (n = 16, data made publicly available by Chan et al. 2021) vs. cells from patient SCLC brain metastases (n = 12). n, Representative immunohistochemistry imaging of overall tumor burden in animals allografted with 16 T SCLC cells (green) and treated with vehicle or levetiracetam (LEV). Scale bar = 200 μm. o, Quantification of data in n (n = 6 vehicle, n = 7 levetiracetam, p = 0.0309). p, As in o, but for animals xenografted with human SCLC-N subtype H446 (n = 3 vehicle, n = 5 levetiracetam, p = 0.0472). Data are mean ± s.e.m for a, d, e, o, p, violin plot for f, g, violin and box blot for m (box defined by 25th percentile, median, and 75th percentile, whiskers extend to min/max, dots represent outliers). Analysis with two-tailed unpaired t-test for a, d, e, o, p, two-tailed paired t-test for b, c, Kruskal-Wallis test for f. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

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Supplementary information

Reporting Summary

Supplementary Table 1

Gene expression signatures. Each tab labels the specific gene signatures queried in single-cell and single-nucleus datasets.

Supplementary Table 2

Statistical analysis for gene expression signatures. Statistical analyses provided for expression of astrocytic and synaptic gene signatures across distinct clusters in the mouse scRNA-seq dataset.

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Source Data Extended Data Figs. 1–3, 6 and 10–12

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Savchuk, S., Gentry, K.M., Wang, W. et al. Neuronal activity-dependent mechanisms of small cell lung cancer pathogenesis. Nature 646, 1232–1242 (2025). https://doi.org/10.1038/s41586-025-09492-z

- Received: 02 December 2022

- Accepted: 04 August 2025

- Published: 10 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09492-z

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Vignesh Sakthivelu,

Anna Schmitt,

Franka Odenthal,

Kristiano Ndoci,

Marian Touet,

Ali H. Shaib,

Abdulla Chihab,

Gulzar A. Wani,

Pascal Nieper,

Griffin G. Hartmann,

Isabel Pintelon,

Ilmars Kisis,

Maike Boecker,

Naja M. Eckert,

Manoela Iannicelli Caiaffa,

Olta Ibruli,

Julia Weber,

Roman Maresch,

Christina M. Bebber,

Ali Chitsaz,

Anna Lütz,

Mira Kim Alves Carpinteiro,

Kaylee M. Morris,

Camilla A. Franchino,

Jonas Benz,

Laura Pérez-Revuelta,

Jorge A. Soriano-Campos,

Maxim A. Huetzen,

Jonas Goergens,

Milica Jevtic,

Hannah M. Jahn-Kelleter,

Hans Zempel,

Aleksandra Placzek,

Alexandru A. Hennrich,

Karl-Klaus Conzelmann,

Hannah L. Tumbrink,

Pascal Hunold,

Joerg Isensee,

Lisa Werr,

Felix Gaedke,

Astrid Schauss,

Marielle Minère,

Marie Müller,

Henning Fenselau,

Yin Liu,

Alena Heimsoeth,

Gülce S. Gülcüler Balta,

Henning Walczak,

Christian Frezza,

Ron D. Jachimowicz,

Julie George,

Marcel Schmiel,

Johannes Brägelmann,

Tim Hucho,

Silvia von Karstedt,

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Alessandro Annibaldi,

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Roland Rad,

Roman K. Thomas,

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…

Filippo Beleggia

Nature volume 646, pages 1243–1253 (2025)

A Publisher Correction to this article was published on 19 September 2025

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Abstract

Small cell lung cancer (SCLC) is a highly aggressive type of lung cancer, characterized by rapid proliferation, early metastatic spread, frequent early relapse and a high mortality rate1,2,3. Recent evidence has suggested that innervation has an important role in the development and progression of several types of cancer4,5. Cancer-to-neuron synapses have been reported in gliomas6,7, but whether peripheral tumours can form such structures is unknown. Here we show that SCLC cells can form functional synapses and receive synaptic transmission. Using in vivo insertional mutagenesis screening in conjunction with cross-species genomic and transcriptomic validation, we identified neuronal, synaptic and glutamatergic signalling gene sets in mouse and human SCLC. Further experiments revealed the ability of SCLC cells to form synaptic structures with neurons in vitro and in vivo. Electrophysiology and optogenetic experiments confirmed that cancer cells can receive NMDA receptor- and GABAA receptor-mediated synaptic inputs. Fitting with a potential oncogenic role of neuron–SCLC interactions, we showed that SCLC cells derive a proliferation advantage when co-cultured with vagal sensory or cortical neurons. Moreover, inhibition of glutamate signalling had therapeutic efficacy in an autochthonous mouse model of SCLC. Therefore, following malignant transformation, SCLC cells seem to hijack synaptic signalling to promote tumour growth, thereby exposing a new route for therapeutic intervention.

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Main

SCLC constitutes approximately 15% of all lung cancer cases1,2,3. Frontline treatment, consisting of cisplatin, etoposide and immune-checkpoint blockade plus optional prophylactic cranial irradiation, induces response rates of greater than 60%2,3. However, these responses are largely transient, resulting in a median overall survival of around 12 months1,2.

SCLC is characterized by nearly universal biallelic loss of TP53 and RB1 (refs. 8,9). Several studies have shown that pulmonary neuroendocrine cells (PNECs) are a permissive cell type of origin for SCLC, but other cell types can also give rise to SCLC in mice following Trp53 and Rb1 loss, especially when Myc is concomitantly overexpressed10,11,12,13. These non-neuroendocrine lung epithelial cells acquire a PNEC-like phenotype and express neuroendocrine markers[11]. PNECs develop from lung epithelial progenitors of endodermal lineage and are innervated by different types of nerve fibres originating from the nodose, jugular and dorsal root ganglia14,15,16,17.

Three molecular subtypes of SCLC, driven by the transcription factors ASCL1 (SCLC-A), NEUROD1 (SCLC-N) or POU2F3 (SCLC-P), have been described. A fourth subtype is variably described as inflamed (SCLC-I) or YAP1 expressing (SCLC-Y)18,19,20. SCLC-A and SCLC-N contain electrically active cells that can fire action potentials[21], whereas SCLC-P and SCLC-Y show lower neuroendocrine differentiation[18].

Recent evidence has suggested that innervation impacts tumour initiation and plasticity4,5,22,23. For instance, glutamate spillover from the synaptic cleft of neuron-to-neuron synapses was reported to stimulate breast cancer cells located in a perisynaptic position[24]. Direct synaptic contacts between presynaptic neurons and postsynaptic glioma cells were also reported to increase proliferation and invasion5,6,7. By contrast, no bona fide synapses have thus far been described between neurons and cancers that arise outside the central nervous system.

Synaptic genes influence mouse SCLC

To search for genes and pathways that contribute to SCLC tumorigenesis in vivo, we performed a piggyBac insertional mutagenesis screen in the Rb1fl/flTrp53fl/fl (RP) SCLC model[25]. Expression of the piggyBac transposase in Rosa26LSL.PB (L) mice[26] is prevented by a loxP-STOP-loxP cassette (LSL) (Extended Data Fig. 1a,b). We crossed RPL mice with ATP1-S2 (S) mice (carrying 20 transposon copies on chromosome 10) or ATP-H39 (H) mice (carrying 80 transposon copies on chromosome 5). Depending on the integration site, the transposons can intercept and block transcription or activate expression of different isoforms through the CAG promoter[26]. SCLC was induced by intratracheal instillation with Ad-CMV-Cre adenovirus25,27 (Extended Data Fig. 1c,d). We sequenced genomic DNA from 106 tumours derived from 14 untreated mice, 117 tumours from 24 mice treated with cisplatin and etoposide, and 90 tumours from 20 mice treated with the anti-PD-1 antibody RMP1-14 (Extended Data Fig. 1e).

Initial examination did not reveal any gene with a significantly different number of insertions between untreated, cisplatin + etoposide-exposed and anti-PD1-treated tumours or between primary and metastatic tumours (Supplementary Tables 1–3). Therefore, all samples were pooled for subsequent analyses. The significantly transposon-targeted genes in our piggyBac screen were essentially distributed across the entire genome (Fig. 1a and Supplementary Table 4), and our screen returned genes with known roles in SCLC, such as Crebbp, Pten, Nfib and Trp73 (refs. 8,28,29,30; Extended Data Fig. 1f–i). Unexpectedly, we also identified several genes associated with the formation of synapses, such as Nrxn1, Nlgn1, Dcc and Reln31,32,33,34 (Extended Data Fig. 1j–m).

Fig. 1: Genome-wide analysis of SCLC across species.

a, Circos plot displaying the transposon integration pattern of an unbiased piggyBac insertional mutagenesis screen in 303 mouse tumours. The chord plot in the centre shows the transpositions from the donor loci (empty triangles) on chromosomes 5 and 10 to the 100 genes with the most significant enrichment in transposon insertions. The middle layer shows the chromosome labels. The scatterplot in the outer layer includes all genes with a significant enrichment in transposon insertions (q < 0.1, Poisson distribution with false discovery rate (FDR) correction). Selected genes are annotated, and genes previously linked to SCLC have label boxes. b, Top 20 most significantly enriched GO terms in the piggyBac dataset and human genetic data. Significance was determined by two-sided Fisher’s exact test with FDR correction. Mod., modulation; reg., regulation. c, Force-directed graph of GO analysis, showing gene sets enriched for genes upregulated in SCLC compared with other types of cancer from the TCGA dataset and with healthy tissue types from the GTEx dataset. Significance was determined by two-sided Fisher’s exact test with FDR correction. d, Scatterplot of the gene sets in c. On the y axis is the RB–E2F score, calculated using ChIP–seq data from the CISTROME database. A high score indicates strong ChIP–seq signal in experiments with antibodies against RB1, RBL2, E2F1, E2F2, E2F3, E2F4 or E2F5 near the promoter of the upregulated genes included in the gene set. On the x axis is the fold change in expression on the log_2 scale for PNECs versus other lung cell types in published scRNA-seq data. A high fold change indicates that the upregulated genes in the gene set are also upregulated in healthy PNECs. Significance was determined by two-sided Fisher’s exact test with FDR correction.

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Synaptic genes are mutated in human SCLC

To cross-validate the hits derived from our piggyBac screen, we re-analysed sequencing data from 456 human SCLC samples8,35,36,37,38,39,40. The specimens included cell lines, primary tumours and metastases from both chemotherapy-naive and chemotherapy-exposed patients (Extended Data Fig. 2a). These different SCLC samples were similar in their mutation profiles (Extended Data Fig. 2b–d) and had aberrations in genes with known roles in SCLC, such as TP53, RB1, CREBBP and PTEN (Extended Data Fig. 2e–h and Supplementary Table 5). We also identified a significant number of mutations in several genes that were recurrently targeted by piggyBac transposon integration, including NRXN1, NLGN1, DCC and RELN (Extended Data Fig. 2i–l and Supplementary Table 5). Overall, the piggyBac and human datasets were highly overlapping (P = 7.2 × 10−37, Fisher’s exact test).

Notably, the rate of transposon insertions in mice and the rate of mutations in human samples showed an opposite correlation to gene expression, suggesting that these two datasets ideally complement each other (Extended Data Fig. 3a). In agreement with this notion, mutations in genes that were significantly mutated in human samples but not identified in the piggyBac screen were enriched for non-conserved nucleotides, whereas genes that were identified in both datasets had mutations that were depleted of non-conserved nucleotides, suggesting a functional role for the genes identified in both datasets (Extended Data Fig. 3b). We further confirmed the validity of our screen using the Rb1fl/flTrp53fl/flRbl2fl/flR26LSL-tdTomatoH11LSL-Cas9 SCLC model[41], combined with lentiviral delivery of Cre and single-guide RNAs (sgRNAs) against Reln, a gene identified in both datasets. Fully in line with our cross-species discovery approach, two distinct sgRNAs targeting Reln resulted in significantly larger tumours compared with non-targeting control sgRNAs (Extended Data Fig. 3c–g).

We next asked which Gene Ontology (GO) gene sets were significantly enriched in the human sequencing datasets and in the piggyBac screen (Fig. 1b, Extended Data Fig. 3h and Supplementary Tables 6 and 7). Unexpectedly, the vast majority of the enriched terms were related to neuronal phenotypes and synaptic functions, such as ‘synaptic membrane’, ‘glutamatergic synapse’, ‘glutamate receptor activity’, ‘GABAergic synapse’ and ‘transsynaptic signalling’. Therefore, the only clear genetic signal we identified at the network level in 456 human and 313 mouse tumours was related to neuronal and synaptic functions.

Expression of synaptic genes in SCLC

To probe the relevance of these synaptic genes, we analysed transcriptome data derived from tumour specimens and normal tissue. We collected raw expression data from the datasets in refs. 8,40 and re-analysed them using The Cancer Genome Atlas (TCGA) transcriptome pipeline, to identify gene sets with expression that was enriched in SCLC compared with 33 distinct cancer entities. We similarly deployed the Genotype-Tissue Expression (GTEx) pipeline to ask which gene sets were specifically enriched in SCLC transcriptomes compared with those derived from 27 healthy tissue types (Fig. 1c, Extended Data Fig. 4a–h and Supplementary Tables 8–11). Using this approach, we identified several gene sets involved in DNA replication, cell cycle checkpoint signalling, chromosome organization and the DNA damage response (Fig. 1c). Individual genes identified in the piggyBac and human genetic datasets, such as NRXN1, NLGN1, DCC and RELN, were highly expressed (Extended Data Fig. 4e–h). Notably, we also identified several of the same gene sets that were enriched at the genetic level, such as ‘synaptic membrane’, ‘glutamatergic synapse’, ‘chemical synaptic transmission’ and ‘neuron differentiation’, among others (Fig. 1c).

To further characterize the gene sets that are enriched in SCLC tumours, we derived an RB–E2F score, using chromatin immunoprecipitation and sequencing (ChIP–seq) data from the CISTROME database[42]. A high score indicates a strong, ChIP–seq-verified presence of RB1, RBL2, E2F1, E2F2, E2F3, E2F4 or E2F5 near the promoter of the upregulated genes included in the gene set. We also plotted gene expression profiles derived from PNECs versus other lung-resident cell populations on a log_2 scale, deploying a previously published dataset[43], with a high fold change indicating that the upregulated genes in a given gene set are specifically upregulated in PNECs. This analysis indicated that the SCLC-specific expression of neuronal and synaptic gene sets is part of the PNEC-like SCLC phenotype, whereas the high expression of genes associated with cell cycle regulation and genome maintenance seems to be largely driven by RB–E2F signalling (Fig. 1d).

To confirm that expression of the SCLC-specific gene sets was driven by cancer cells, we performed single-nucleus RNA sequencing (snRNA-seq) on six tumours collected from RP mice and re-analysed available human single-cell RNA sequencing (scRNA-seq) data[44]. In both species, the gene sets specifically enriched in cancer cells were dominated by cell proliferation and neuronal gene sets, resulting in a nearly identical pattern to our analysis of bulk RNA (Fig. 1c and Extended Data Fig. 4i–o).

Therefore, two signals are evident in human and mouse SCLC at the expression level: (1) the high expression of cell cycle gene sets downstream of the RB–E2F axis and (2) the high expression of neuronal and synaptic gene sets, which are part of the PNEC-like phenotype of SCLC cells and substantially overlap with the GO terms we identified at the genetic level.

Neuronal processes contact SCLC cells

The observation that neuronal and synaptic gene sets constituted the strongest and most consistent signal in our piggyBac screen and in human SCLC prompted us to investigate a physical interaction between SCLC cells and neurons. We first asked whether neuron–cancer contacts could be detected in lung sections isolated from tumour-bearing RP mice. Interestingly, vesicular glutamate transporter 1 (VGluT1)-, P2X purinoceptor 3 (P2X3)- and growth-associated protein 43 (GAP43)-positive nerve fibres were detectable in a subset of healthy PNECs, clustered into neuroepithelial bodies (NEBs; Fig. 2a and Extended Data Fig. 5a–c), and in small SCLC tumours (Fig. 2a,b and Extended Data Fig. 5d). Conversely, larger tumours mostly lacked intralesional nerve fibres (Extended Data Fig. 5e) and, when present, GAP43- and synaptophysin (SYP)-positive fibres were observed at the tumour border (Fig. 2c). Calcitonin gene-related peptide (CGRP)-positive, substance P (SP)-positive and SYP-positive fibres were also profusely present near, but not within, tumours (Fig. 2a,b and Extended Data Fig. 5d–h). Using RP mice that additionally carried an enhanced green fluorescent protein (eGFP)-marked allele (Rb1fl/flTrp53fl/flRosa26Cas9-EGFP; RPC mice), we detected VGluT1-positive fibres arborizing between neuroendocrine cells from the initial stages of transformation up to the formation of small and medium-sized tumours (Fig. 2d–f). The presence of nerve fibres within small RP-derived SCLC tumours was corroborated through electron microscopy, where vesicle-enriched axon-like fibres appeared in close proximity to tumour cells (Extended Data Fig. 5i–k). We also detected nerve fibres immunoreactive for neurofilaments and SYP at the border or in the vicinity of human SCLC tumours (Extended Data Fig. 5l–p).

Fig. 2: Detection of nerve fibres in mouse SCLC tumours.

a, Confocal image of an intrapulmonary airway from an RP mouse. Two small tumours (ST) and a normal NEB (open arrowhead) are visualized with CGRP (green) and can be observed to bulge in the airway lumen (L). VGluT1-immunoreactie nerve terminals (red) are detected contacting the NEB and arborizing (arrowhead) in one of the tumours. CGRP-positive nerve fibres (open arrows) can be observed at the base of the tumours and NEB. E, epithelium. b, P2X3-positive nerve terminals (red, arrowheads) can be seen to arborize between the CGRP-positive (green) neuroendocrine cells of a small tumour. CGRP-positive nerve fibres (open arrows) can be observed at the base of the tumour. c, Immunolabelling of a CGRP-positive (green) large tumour (LT). The connective tissue between the tumour and the epithelium harbours many GAP43-positive nerve fibres (red, arrows), which do not appear to penetrate the tumour mass. d, Confocal image of an NEB in an RPC mouse. Two cells are positive for eGFP (blue), indicating successful recombination and incipient transformation. VGluT1-positive fibres (green) arborize between the neuroendocrine cells (red). e, Initial proliferation of eGFP-positive neuroendocrine cells (blue) in an NEB. VGluT1-positive fibres (green) arborize between the transforming cells. f, Small SCLC tumour positive for eGFP (blue) and CGRP (red). VGluT1-positive fibres (green) arborize between the tumour cells. g, Immunolabelling of SCLC cells (expressing DsRed) transplanted into the hippocampus of Thy1-eGFP mice. The inset shows that the core of the tumour is devoid of eGFP-positive fibres. h, 3D reconstruction of SCLC cells located in the tumour periphery surrounded by eGFP-positive axonal varicosities. i, Co-localization analysis of eGFP- and VGluT1-positive boutons contacting a DsRed-expressing SCLC cell.

To assess the formation of contacts between SCLC cells and neurons in vivo, we transplanted DsRed-expressing RP tumour cells into the hippocampus of recipient Thy1-eGFP transgenic mice, in which excitatory neuronal subsets express eGFP. Using confocal microscopy, we found that by 10–12 days after transplantation the cancer cells located in the periphery of the tumour were profusely contacted by eGFP-positive boutons and axonal bundles (Fig. 2g–i). Most of these eGFP-positive boutons were strongly immunoreactive for the excitatory presynaptic marker VGluT1 (Fig. 2i).

We next established co-culture experiments of human SCLC cells with mouse cortical neurons. Human COR-L88 cells, of the SCLC-A subtype, were profusely contacted by VGluT1-positive neuronal processes (Extended Data Fig. 6a). Lastly, we demonstrated that these points of contact on cancer cells mostly occurred with neuronal axons marked by phosphorylated neurofilaments (anti-SMI-312 antibody) and not with dendrites immunoreactive for MAP2 (Extended Data Fig. 6b–d).

These data show that SCLC cells have the ability to form contacts with neurons, both in vivo and in vitro.

Neuron-to-cancer synapses in SCLC

To investigate the nature of these contacts, we performed confocal and stimulated emission depletion (STED) microscopy of SCLC cells in five distinct experimental settings. First, in co-cultures of SCLC cells and cortical neurons, immunostaining for glutamatergic vesicles (anti-VGluT1) and the postsynaptic protein HOMER1 revealed co-localizing formations at the contacts between neurons and cancer cells (Fig. 3a,b). Second, we identified similar contacts in co-cultures with human induced pluripotent stem (iPS) cell-derived cortical neurons, which were characterized by expression of the presynaptic protein Bassoon in neurons and HOMER1 in cancer cells (Extended Data Fig. 6e). Third, in co-cultures with mouse nodose ganglia, which physiologically innervate PNECs and are the most likely origin of the VGluT1-positive fibres observed in tumours in vivo[14] (Fig. 2a,d–f), we again identified juxtaposition of HOMER1 and VGluT1 on cancer cells (Extended Data Fig. 6f–i). Fourth, we detected HOMER1-positive postsynaptic structures in cancer cells in close proximity to eGFP-positive axonal boutons in brain allografts (Extended Data Fig. 7a). Lastly, we detected HOMER1–VGluT1 proximity at the interface of Cre-exposed, recombined eGFP-positive cancer cells in lung sections from autochthonous RP mice (Extended Data Fig. 7b–d).

Fig. 3: Structural evidence for bona fide synapses in SCLC cells.

a, 3D STED images of SCLC (expressing mNeonGreen)–neuron co-cultures stained for presynaptic VGluT1 and postsynaptic HOMER1. The magnified views on the right show regions of marker co-localization. b, Analysis of the number of VGluT1 and HOMER1 single-positive and double-positive puncta per SCLC cell. n = 29 cells derived from three independent cultures and two x10ht experiments. Kruskal–Wallis one-way ANOVA test, ****P < 0.0001. NS, not significant. c, Overview of a representative 3D-reconstructed confocal image of an SCLC cell in a neuronal co-culture subjected to x10ht. Bottom panels depict magnified regions of contact between the neuron (VGluT1 positive) and SCLC cell (HOMER1 positive). d, Two-colour 3D ONE image of region 3 in c. e, Three-colour 2D ONE image of a representative putative synapse showing presynaptic (VGluT1-positive) and postsynaptic (HOMER1-positive) markers at points of contact between neurons and SCLC cells. f, Line scan of the neuron–SCLC contact in e showing the distance between VGluT1- and HOMER1-positive puncta. g, VGluT1–HOMER1 apparent distance measured in neuron–SCLC cell contacts. n = 15 contacts. h, VGluT1–HOMER1 apparent distance measured in neuron–neuron contacts. n = 20 contacts. i, CLEM of SCLC cells (expressing tdTomato) grafted into the mouse hippocampus. The left two panels depict the registered overlay between the fluorescence signal and electron microscopy (EM) image. The third panel shows the electron tomogram of an identified synaptic contact. The tomogram (single slice) depicts a presynaptic bouton (yellow pseudocolour) filled with vesicles contacting a tdTomato-positive cancer cell (red pseudocolour). Blue pseudocolour indicates the nucleus. The rightmost panel shows an enlarged view of the synaptic cleft and a pool of vesicles located within 20 nm of the plasma membrane (green pseudocolour).

Source data

We next conducted tenfold expansion microscopy (x10ht), reaching approximately 25-nm resolution[45]. Three-dimensional (3D) reconstruction of cortical neuron co-cultures showed a spatial organization consistent with synaptic structures, with VGluT1-positive puncta outside cancer cells juxtaposed to HOMER1 immunoreactivity in cancer cells (Fig. 3c). To visualize synapses in even greater detail, we used one-step nanoscale expansion (ONE) microscopy[46]. 3D and two-dimensional (2D) ONE images showed clear separation of the pre- and postsynaptic elements, with their localization resembling that in canonical synapses between neurons (Fig. 3d,e). Notably, the distance between the VGluT1- and HOMER1-positive puncta was comparable to that observed for neuron-to-neuron synapses in the same cultures (Fig. 3e–h).

We further characterized these synaptic contacts through electron microscopy and correlative light electron microscopy (CLEM) in brain allografts and co-cultures. Electron tomograms and 3D reconstructions of DsRed- or tdTomato-positive SCLC cells confirmed the presence of synaptic boutons filled with vesicles contacting the plasma membrane of cancer cells (Fig. 3i). Detailed examination of 280 cell perimeters located at the periphery of the allografts in ultrathin sections revealed that an average of 8.2% of the cancer cells exhibited synapses with axonal boutons (Extended Data Fig. 7e). We also identified additional ultrastructural hallmarks of stereotypical synapses, including the presence of a synaptic cleft and a pool of vesicles close to the presynaptic membrane (Fig. 3i and Extended Data Fig. 7f).

Neuron-to-cancer neurotransmission

To assess the functionality of cancer–neuron synapses, we next conducted electrophysiological recordings of cancer cells in co-culture with cortical neurons. Although whole-cell patch-clamp recordings of COR-L88 monocultures did not show any spontaneous inputs, the same cells developed spontaneous postsynaptic currents (sPSCs) when co-cultured with neurons (Extended Data Fig. 8a,b). These currents were reduced when the co-cultures were treated with the voltage-gated sodium channel blocker tetrodotoxin (TTX), with the AMPA receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX), with the NMDA receptor antagonist d-2-amino-5-phosphonopentanoate (d-AP5) or with the glutamate release inhibitor riluzole[47], but not with the GABA receptor inhibitor bicuculline (Extended Data Fig. 8c). Similar to COR-L88 cells, the H524 cell line (SCLC-N) exhibited no sPSCs in monoculture (Extended Data Fig. 8d). However, we detected sPSCs in H524 cells co-cultured with cortical neurons when measuring with a holding potential of +40 mV. The majority of these currents could be inhibited with d-AP5 (Fig. 4a,b and Extended Data Fig. 8e). In two cells, a small fraction of the currents remained after d-AP5 exposure, presented a shape consistent with GABAA receptor-mediated currents and could be inhibited with the GABAA receptor blocker gabazine (Gbz; Fig. 4a,b). We also identified examples of synaptic events when measuring at 0 mV, a voltage at which mainly GABAA-mediated chloride currents are observable (Extended Data Fig. 8f). Notably, optogenetic stimulation of co-cultured neurons expressing channelrhodopsin-2 (ChR2) elicited postsynaptic events in SCLC cells measured at +40 mV, which could be abolished with d-AP5, further corroborating the existence of direct synaptic glutamatergic transmission (Fig. 4c,d). In one cell, we identified the presence of both evoked NMDA receptor- and GABAA receptor-mediated currents, further suggesting that cancer cells in co-culture are able to form functional synaptic contacts with both glutamatergic and GABAergic neurons (Extended Data Fig. 8g).

Fig. 4: Neuron-to-SCLC synapses are functional.

a, Whole-cell voltage-clamp traces in artificial cerebrospinal fluid (aCSF; control) and following treatment with NMDA receptor (d-AP5) and GABAA receptor (Gbz) blockers. Representative of seven cells across three experiments. Red asterisks or numbers mark individual events. b, Frequency of currents in H524 cells co-cultured with cortical neurons (untreated or exposed to d-AP5 alone or together with Gbz). Current frequency is compared before and after addition of d-AP5 (paired two-sided Wilcoxon test, n = 6 treated cells). Inset, example of a patched H524 cell. c, Whole-cell voltage-clamp traces of SCLC cells (grey) after a blue-light pulse (5 ms) to stimulate ChR2–enhanced yellow fluorescent protein (eYFP)-expressing neurons. The effects of d-AP5 (n = 12/13) or d-AP5 + Gbz (n = 1/13), compared to aCSF, are shown. d, Amplitude of evoked currents in H524 cells co-cultured with ChR2–eYFP-expressing cortical neurons after optogenetic stimulation. The amplitude before and after addition of d-AP5 is compared (two-sided paired Wilcoxon test, n = 13). Inset, example of a patched tdTomato-expressing H524 cell. e, Retrograde tracing of neurons monosynaptically connected to SCLC cells expressing DsRed, G and TVA after addition of EnvA-pseudotyped (ΔG) RABV-GFP. Lower panels, magnified views of double-positive SCLC starter cells (arrowheads). f, Quantification of RABV-GFP-mediated neuronal labelling following SCLC transduction with virus encoding TVA alone or together with G (n = 5 biological replicates). All conditions are compared to the full experimental system (RABV, DsRed, G, TVA). q values were obtained by two-sided Mann–Whitney test with FDR correction. g, Connectivity ratio per COR-L88 and DMS273 starter cell (n = 4–5 biological replicates). h, Retrograde tracing of neurons monosynaptically connected to G-TVA- and DsRed-expressing SCLC cells grafted into the mouse hippocampus. Right panels, magnified views of GFP-positive presynaptic excitatory neurons. GFP-only-positive axonal fibres contacting SCLC cells are indicated (arrowheads). CA1, cornus ammonis; DG, dentate gyrus; Sub, subiculum. i, Connectivity ratio per SCLC starter cell in mice grafted with TVA- or G-TVA-expressing SCLC cells (n = 6–7 mice per condition), P value obtained by two-sided Mann–Whitney test. j, Number of traced GFP-positive neurons classified as excitatory or inhibitory (n = 7 mice).

Source data

Furthermore, ex vivo patch-clamp recordings in slices from brain allografts revealed detectable biphasic sPSCs in a fraction of recorded SCLC cells (Extended Data Fig. 8h,i). Treatment of the slices with TTX, CNQX, d-AP5 or a combination of CNQX, d-AP5 and bicuculline reduced the occurrence of sPSCs, although the difference did not reach statistical significance (Extended Data Fig. 8i). Nevertheless, these data indicate that SCLC cells engage in synaptic transmission in brain tissue.

To substantiate these findings, we performed retrograde monosynaptic rabies virus (RABV) tracing experiments in SCLC cells using a replication-incompetent EnvA-pseudotyped G-gene-deficient virus, ΔG RABV-GFP, which can only infect cells expressing the avian viral receptor TVA. Following initial infection, cells that complement expression of the RABV glycoprotein (G) are able to transmit the virus to their first-order presynaptic partners[48]. After transduction of SCLC cells with a DsRed retrovirus encoding G and TVA (G-TVA), we co-cultured them with cortical neurons and added RABV-GFP to the cultures (Fig. 4e). In line with retrograde RABV-GFP spread from DsRed-positive SCLC ‘starter cells’ to synaptically connected neurons, we detected DsRed and GFP double-positive SCLC cells surrounded by clusters of GFP-positive neurons, which also displayed strong VGluT1 immunoreactivity (Fig. 4e and Extended Data Fig. 9a–c). Time-lapse experiments of these co-cultures showed that neurons acquired GFP fluorescence within 48 h of the appearance of DsRed and GFP double-positive SCLC starter cells (Extended Data Fig. 9d). Assessment of GFP-positive neurons in co-cultures with SCLC cells lacking any prior retroviral transduction or expressing only TVA and/or DsRed (but not G) as negative controls revealed a low and quantifiable level of spurious labelling by RABV under our conditions (Fig. 4f). By contrast, co-expression of G in SCLC cells resulted in a net increase in neuronal labelling of tenfold or more, corroborating the reliability of this transsynaptic approach (Fig. 4f). Analysis of co-cultures with either COR-L88 (SCLC-A) or DMS273 (SCLC-N) cells identified a connectivity ratio of 3 to 12 neurons per starter cancer cell (Fig. 4g).

We next conducted transsynaptic tracing experiments in brain allografts in vivo, by stereotactically co-injecting G- and TVA-expressing or TVA-only-expressing SCLC cells and EnvA-pseudotyped ΔG RABV-GFP into the mouse hippocampus (Fig. 4h). In animals injected with G-TVA-encoding virus, DsRed and GFP double-positive SCLC starter cells were typically surrounded by GFP-positive axonal fibres (Fig. 4h and Extended Data Fig. 9e). In line with this, GFP-positive neurons were found in the regions (hippocampus and subiculum) adjacent to grafted G-TVA-expressing SCLC cells, whereas, in control experiments with cancer cells expressing exclusively TVA, neuronal labelling was absent or minor (Fig. 4i and Extended Data Fig. 9f). Classification of neurons according to their morphology and layer positioning in traced anatomical regions near the injection area identified both putative excitatory and inhibitory neurons, further indicating that SCLC cells can be innervated by distinct neuronal subtypes in vivo (Fig. 4h,j and Extended Data Fig. 9e). These experiments indicate that SCLC cells are capable of forming functional synapses with neurons in vitro and in vivo.

Neurons stimulate SCLC proliferation

To test whether SCLC cells derive a growth advantage when kept in co-culture with neurons, we compared the proliferative capacity of DsRed-expressing human COR-L88 cells seeded at low density and maintained either alone (monoculture) or in co-culture with cortical neurons, followed by 5-ethynyl-2′-deoxyuridine (EdU) labelling 2 h before analysis. While only a few scattered EdU-positive SCLC cells were found in monocultures, SCLC cells in co-cultures frequently appeared as larger proliferating clusters (Fig. 5a,b). This effect was significantly, but not completely, reduced when the co-cultures were treated with TTX, suggesting that the proliferative advantage is mediated by both neuronal activity-dependent and neuronal activity-independent mechanisms (Fig. 5c,d).

Fig. 5: Glutamatergic signalling constitutes an actionable target in SCLC.

a,b, DsRed-expressing COR-L88 SCLC cells cultured for 3 days with (a) or without (b) cortical neurons. c,d, Fold change in total (c) and EdU-positive (d) COR-L88 cells cultured for 3 days with or without neurons and/or TTX (n = 3; two-sided paired t test; centre, mean; error bars, s.d.). e, Growth curves of COR-L88 cells cultured with or without cortical neurons from one experiment (n = 10 and n = 30 wells). f, Quantification of live-cell imaging of SCLC cell lines (n = 8; in order: H526, H1836, H146, H69, COR-L88, DMS273, H211, H524). Cancer cells were cultured with cortical neurons or cortical neuron-conditioned medium. Proliferation was normalized to the growth of monocultures in the same plates. P value were obtained by two-sided Wilcoxon signed-rank test. AUC, area under the curve. g, Growth quantification of NSCLC cell lines (n = 4; in order: HOP62, HCC44, H2291, H1975) in co-culture with cortical neurons, normalized to the growth of monocultures. h, Growth curves of COR-L88 cells cultured with or without nodose ganglia from two experiments (n = 4 and n = 16 wells). i, Growth quantification of SCLC cell lines (n = 3; in order: DMS273, H211, COR-L88) co-cultured for 5 days with nodose ganglion explants, relative to monocultures. j, Response of tumours in mice treated with vehicle (n = 102), DCPG (n = 54) or riluzole (n = 57), expressed as percentage of the initial volume. q values were obtained by two-sided Mann–Whitney test with FDR correction. k, Overall survival of RP mice treated with DCPG (n = 12), riluzole (n = 12) or the relative control (n = 33). q values were obtained by two-sided log-rank test with FDR correction. l, Response of tumours in mice treated with etoposide + cisplatin alone (n = 45) or combined with DCPG (n = 38) or riluzole (n = 36), expressed as percentage of the initial volume. q values as in j. m, Overall survival of RP mice treated with etoposide + cisplatin alone (n = 11) or combined with riluzole (n = 13) or DCPG (n = 10). q values as in k. See Extended Data Fig. 10 for individual replicates of f, g and i.

Source data

To evaluate whether neuronal co-culture stimulates proliferation in all SCLC subtypes, we monitored the growth of eight distinct cell lines with live-cell imaging: COR-L88, H1836, H69 and H146 (all SCLC-A), DMS273 and H524 (SCLC-N), and H211 and H526 (SCLC-P). All lines, including the SCLC-P lines, derived a significant proliferation advantage when co-cultured with cortical neurons (Fig. 5e,f and Extended Data Fig. 10a). Some cell lines (COR-L88, H69, DMS273, H524 and H211) also derived a minor proliferation advantage when cultured in conditioned medium derived from neuronal cultures. However, for all cell lines, physical presence of the neurons conferred a significantly stronger proliferation advantage (Fig. 5f and Extended Data Fig. 10a). The proliferative advantage appeared to be specific for neurons, as it vastly exceeded that observed in high-density monocultures (Extended Data Fig. 10a) and it was even stronger in four of the cell lines than that conferred by co-culture with fibroblasts, which have been shown to strongly promote SCLC growth[49] (Extended Data Fig. 10a). The effects seemed to be largely specific for SCLC, as four non-small cell lung cancer (NSCLC) lines (H1975, HCC44, HOP62 and H2291) derived only a minor proliferation advantage when co-cultured with neurons (Fig. 5g and Extended Data Fig. 10b). Finally, increased proliferation also occurred when co-culturing SCLC cells with mouse nodose ganglia (Fig. 5h,i and Extended Data Fig. 10c–e). These data indicate that all major SCLC subtypes derive a growth benefit when co-cultured with neurons. This advantage is at least partially dependent on neuronal activity and physical proximity.

Targeting glutamate signalling in SCLC

Given the formation of functional synapses between SCLC cells and glutamatergic neurons in vitro and in vivo (Figs. 3 and 4), we next sought to target the glutamatergic system therapeutically. The SCLC samples that we analysed at the expression level can be broadly classified into classic SCLC with strong neuroendocrine features and variant SCLC with lower expression of neuroendocrine features, using a lung-specific neuroendocrine score[50] (Extended Data Fig. 10f). Expression of genes in the GO term ‘glutamatergic synapse’ was particularly high in the ASCL1- and NEUROD1-expressing subtypes (Extended Data Fig. 10g), suggesting that these subtypes in particular might benefit from interference with the glutamatergic system.

Among the possible molecular targets in this system are the glutamate receptors, which we also identified as individual genes targeted by transposon insertion in our piggyBac screen (Grid1, Grik2, Grin3a, Grm1, Grm3, Grm5 and Grm8), in human mutation data (GRIA1, GRIA2, GRIA3, GRIA4, GRID2, GRIK2, GRIK3, GRIK4, GRIN2A, GRIN2B, GRIN3A, GRM1, GRM3, GRM5 and GRM8) and at the expression level in human samples (GRIA2, GRIN3A, GRIK3, GRIK5, GRM2, GRM4 and GRM8). Prominent among them was GRM8, a gene encoding an inhibitory metabotropic glutamate receptor that has been shown to counteract glutamate signalling by negatively regulating cyclic AMP-dependent sensitization of inositol 1,4,5-trisphosphate receptors, thereby limiting glutamate-induced calcium release from the endoplasmic reticulum[51]. GRM8 has been identified as an ASCL1 and NEUROD1 ChIP–seq target in human SCLC cell lines[52], and expression of GRM8 correlates with the expression of ASCL1 in cell lines reported in SCLC-CellMiner[53] and is reduced in autochthonous SCLC mice in which Ascl1 is deleted specifically in cancer cells[12]. In our datasets, GRM8 showed expression specifically in SCLC and a few other tumour types (Extended Data Fig. 10h), a statistically significant enrichment of both non-synonymous mutations and more severe loss-of-function mutations (Extended Data Fig. 10i), and a significant number of piggyBac insertions (Extended Data Fig. 10j). Re-analysis of the scRNA-seq data from ref. [44] confirmed that GRM8 is specifically expressed in SCLC cells (Extended Data Fig. 10k,l). We also found specific expression of Grm8 in our mouse SCLC snRNA-seq dataset, although at a substantially reduced fraction compared with the human dataset (Extended Data Fig. 10m). This specific expression suggests that GRM8 can be targeted, while the enrichment in loss-of-function mutations suggests that the activity of GRM8 is detrimental to SCLC tumours. On the basis of these data, we selected (S)-3,4-dicarboxyphenylglycine (DCPG) and riluzole, two compounds with predicted anti-glutamatergic effects, for preclinical testing. DCPG is a potent and selective agonist of GRM8 (ref. [54]), while riluzole is a US Food and Drug Administration-approved inhibitor of glutamate release that inhibited sPSCs in our co-culture experiments (Extended Data Fig. 8c).

To evaluate the efficacy of DCPG and riluzole in vivo, we exposed tumour-bearing RP mice to DCPG, riluzole or vehicle. Responses were evaluated every 2 weeks by magnetic resonance imaging (MRI). Whereas all tumours from vehicle-treated mice progressed during treatment, the response was significantly improved both in DCPG- and in riluzole-treated animals, with several tumours showing short-term stable disease or slower growth and a small subset of tumours showing modest shrinkage (Fig. 5j and Extended Data Fig. 11a–j). Mice treated with DCPG and riluzole also showed significantly improved survival, with a median survival of 66 days (DCPG) and 71.5 days (riluzole), compared to 54 days in the control group (Fig. 5k). To further confirm these findings, we tested the efficacy of riluzole and DCPG in a second cohort of mice using CGRP-driven expression of Cre, which has been shown to more selectively induce transformation in PNECs10,11,12,13. In this cohort, DCPG did not significantly improve response or survival, whereas treatment with riluzole resulted in significantly improved response and a significant survival advantage, compared with vehicle control (Extended Data Fig. 11k,l).

We then compared mice with CMV-induced expression of Cre treated with cisplatin and etoposide with mice that received this chemotherapy plus DCPG or riluzole. Tumours exposed to chemotherapy alone showed a mixed response, which included shrinkage, stable disease and progressive disease. In the context of chemotherapy, inclusion of DCPG did not significantly improve the response of SCLC tumours (Fig. 5l and Extended Data Fig. 12a–d,g–j). By contrast, chemotherapy in combination with riluzole resulted in a significantly improved response, with almost all tumours showing partial response or stable disease and slower growth for more than 2 months (Fig. 5l and Extended Data Fig. 12a,b,e–j). Similarly, inclusion of DCPG in the frontline chemotherapy regimen in our mice did not result in significantly improved survival, whereas addition of riluzole resulted in a significant improvement in survival of 21 days (Fig. 5m).

Thus, targeting glutamatergic signalling has preclinical activity against SCLC both alone and in combination with frontline chemotherapy in vivo.

Discussion

We performed an in vivo insertional mutagenesis screen in a mouse model of SCLC and cross-validated our findings through the re-analysis of genetic and expression data from human SCLC. Unexpectedly, almost all gene sets we identified at the genetic level were related to a neuron-like phenotype in general and to synapses and glutamatergic signalling in particular.

Our co-culture and transplantation experiments revealed a striking ability of SCLC cells to form synapses and receive direct neurotransmitter-mediated inputs. These data reveal that functional, bona fide synapses can form between neurons and cancer cells of extracranial origin.

We speculate that the ability to form synapses is part of the PNEC-like phenotype of SCLC. In line with this notion, we detected fibres known to innervate PNECs, such as VGluT1- and P2X3-positive vagal fibres[14], in a subset of small SCLC tumours in mouse lungs. Here we also detected co-localization of presynaptic VGluT1 and postsynaptic HOMER1 in cancer cells, suggesting that synapses may also form in this primary setting. The precise nature and functionality of these contacts in the lung, as well as a potential role in cancer initiation, remain to be determined.

All SCLC cell lines we tested derived a growth advantage when co-cultured with neurons. This advantage was at least partially dependent on direct neuronal innervation and neuronal activity. However, the advantage was not fully abolished by TTX, suggesting the presence of additional, action potential-independent contributions to the proliferation of cancer cells. For example, we detected a small effect of neuron-conditioned medium in vitro. We also cannot exclude a paracrine contribution in vivo, as we observed several CGRP-positive, SP-positive and GAP43-positive nerve fibres around autochthonous RP tumours, which could potentially engage in paracrine communication with the tumours. Similarly, although we focused mainly on glutamatergic contacts, the ‘GABAergic synapse’ GO term was also identified in our genetic screen and the potential for GABAergic communication between neurons and SCLC cells was corroborated by our electrophysiological and tracing experiments.

As SCLC is characterized by a high degree of inter- and intratumoral heterogeneity and plasticity2,13,55,56, the general exploitability of glutamate-targeting strategies and potential therapy sequencing algorithms remain to be defined. Our data indicate that SCLC may be capable of hijacking neuronal programmes, such as the ability to form synapses, to derive a growth advantage. As we show for anti-glutamatergic drugs, investigation of these neuronal phenotypes may hold the key to finally providing more effective therapies to patients with SCLC.

Methods

Mice

This study was performed in accordance with FELASA recommendations and with European Union and German guidelines. The experiments were approved by the local ethics committee on animal experiments (Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen). Mice were housed in groups of up to five animals per cage and supplied with standard pellet food and water ad libitum with a 12-h light/12-h dark cycle, while the temperature was controlled to 21–22 °C with a relative humidity of 45–65%. Animals were regularly examined for body condition, body weight, accelerated breathing, behaviour, tumour size (<1.5 cm in diameter) and neurological symptoms. In compliance with the respective animal permissions, animals were killed before or immediately after reaching a severe burden. Mice of both sexes were included. For animal experiments performed at Stanford University, mice were maintained according to practices approved by the US National Institutes of Health, the Stanford Institutional Animal Care and Use Committee and the Association for Assessment and Accreditation of Laboratory Animal Care. The study protocol was approved by the Stanford Administrative Panel on Laboratory Animal Care (protocol 13565).

Cell lines

Mouse cell lines (AVR424.3 and RP1462) were isolated from mouse tumours in the RP line and identified by genotyping. Human cell lines (COR-L88, H1836, H69, H146, DMS273, H524, H211, H526, H1975, HCC44, HOP62, H2291 and HEK293T) were gifts from R. Thomas, University Hospital of Cologne, and identified through STR profiling. All cell lines were tested for mycoplasma contamination.

Statistics and reproducibility

The statistical tests used are reported in the figure legends and specific methods sections. No measurements were performed more than once on the same sample. Statistical analyses were performed with Python v3.8, v3.9 and v3.10 with the packages pandas v1.1.4 and numpy v1.20. Whenever necessary, correction for multiple testing was performed with the FDR using the Python package statsmodel v0.12.2 with the method ‘Benjamini/Hochberg’. Pearson and Spearman correlation coefficients and the corresponding P values were calculated using scipy v1.6.3. Statistical analysis of survival was performed with lifelines v0.25.6. The packages matplotlib v3.4.2 and seaborn 0.11.0 were used for visualization. The micrographs depicted are representative of repeated experiments, as detailed in the figure legends or as follows: Fig. 2a, 10 experiments; Fig. 2b, 2 experiments; Fig. 2c, 9 experiments; Fig. 2d–i, 3 experiments; Fig. 3i, 3 experiments; Fig. 4e, 4 experiments; Fig. 4h, 7 experiments; Fig. 5a,b, 3 experiments; Extended Data Fig. 1c,d, 3 experiments; Extended Data Fig. 3c,d, 7 experiments; Extended Data Fig. 3e, 5 experiments; Extended Data Fig. 5a, 10 experiments; Extended Data Fig. 5b, 2 experiments; Extended Data Fig. 5c,d, 7 experiments; Extended Data Fig. 5e,f, 3 experiments; Extended Data Fig. 5g, 5 experiments; Extended Data Fig. 5h, 3 experiments; Extended Data Fig. 5i–k, 2 experiments; Extended Data Fig. 6a, 3 experiments; Extended Data Fig. 6e, 2 experiments; Extended Data Fig. 6f–i, 4 experiments; Extended Data Fig. 7a, 2 experiments; Extended Data Fig. 7b–d, 3 experiments; Extended Data Fig. 7f, 2 experiments; Extended Data Fig. 8a, 40 experiments; Extended Data Fig. 9a, 4 experiments; Extended Data Fig. 9b,c, 4 experiments; Extended Data Fig. 9d, 3 experiments; Extended Data Fig. 9e, 7 experiments; Extended Data Fig. 9f, 6 experiments; Extended Data Fig. 10c,d, 9 experiments.

SCLC tumour induction

To induce lung tumour formation and, when present, activation of the piggyBac transposition system or the Cas9-EGFP allele, 8- to 12-week-old mice of both sexes were anaesthetized with ketavet (100 mg kg–1) and xylazine (20 mg kg–1) by intraperitoneal injection, followed by intratracheal instillation of replication-deficient adenovirus encoding Cre recombinase (Adeno-Cre, 2.5 × 10^7 plaque-forming units (PFU)). Viral vectors were provided by the University of Iowa Viral Vector Core (http://www.medicine.uiowa.edu/vectorcore).

MRI

An Achieva 3.0-T clinical MRI system (Philips Healthcare) in combination with a dedicated mouse solenoid coil (Philips Healthcare) was used for imaging. Animals were anaesthetized using isoflurane (2.5%), and T2-weighted MR images were acquired in the axial plane using a turbo-spin echo sequence (repetition time, 3,819 ms; echo time, 60 ms; field of view, 40 × 40 × 20 mm^3; reconstructed voxel size, 0.13 × 0.13 × 1.0 mm^3; number of average, 1). MR images (DICOM files) were analysed in a blinded fashion by determining and calculating regions of interest (ROIs) using Horos software v3.0 with the package Export Rois v2.0.

PiggyBac transposition system in SCLC

For activation of transposition in an SCLC mouse model, we used the following alleles, as detailed in Extended Data Fig. 1: Rosa26LSL-PB, ATP1-S2, ATP1-H39, Rb1flox and Trp53flox (refs. 25,26). Mice were kept on a mixed C57BL/6–Sv/129 background. The Trp53flox allele was genotyped with primers Trp53fw (CACAAAAACAGGTTAAACCCAG) and Trp53rv (AGCACATAGGAGGCAGAGAC). The Rb1flox allele was genotyped with primers RB1_F3 (GAAGCCATTGAAATCTACCTCCCTTGCCCTGT), RB1_F_4 (ACTCATGGACTAGGTTAAGT), RB1_R_1 (TGCCATCAATGCCCGGTTTAACCCCTGT) and RB1_R_2 (AGCATTTTATATGCATTTAATTGTC). The ATP1 alleles were genotyped using primers ATP-F (CTCGTTAATCGCCGAGCTAC) and ATP-R (GCCTTATCGCGATTTTACCA). The Rosa26LSL.PB knock-in allele was genotyped using primers BpA5F (GCTGGGGATGCGGTGGGCTC) and Rosa3R (GGCGGATCACAAGCAATAATAACCTGTAGTTT). The wild-type Rosa26 allele was detected with primers Rosa5F (CCAAAGTCGCTCTGAGTTGTTATCAG) and Rosa3R (GGCGGATCACAAGCAATAATAACCTGTAGTTT). To study SCLC formation, all four mouse lines were imaged following adenoviral instillation, as described above. After reaching the termination criteria, mice were killed and single tumour nodules were isolated and used for DNA extraction. Analysis of transposon mobilization at the donor locus and splinkerette-PCR amplification of transposon insertion sites were performed as previously described26,57.

Treatment of piggyBac mice

Starting 5 months after tumour induction, tumour growth was monitored through biweekly MRI as described above until termination criteria were reached. Following tumour detection (minimum tumour size of 3 mm^3), RPLS and RPLH mice were treated with either a combination of cisplatin and etoposide or the anti-PD-1 antibody RMP1-14. Compound solutions were prepared and injected as follows: etoposide (Hexal) was administered on days 1, 2 and 3 of a 14-day cycle, intraperitoneally, at a concentration of 10 mg kg–1. Cisplatin (Accord) was administered intraperitoneally on day 1 of a 14-day cycle at a concentration of 5 mg kg–1. The anti-PD-1 antibody RMP1-14 (BioXCell) was administered intraperitoneally 2 days per week (250 μg per administration).

Deletion of Reln in the RPR2 model of SCLC

Generation of the Rb1fl/flTrp53fl/flRbl2fl/flRosa26LSL-tdTomato/LSL-tdTomatoH11LSL-Cas9/LSL-Cas9 (RPR2;TC) mice used in this study has been described previously[41]. Forty-eight hours before lentivirus delivery, naphthalene (Sigma-Aldrich, 184500) was dissolved in corn oil vehicle (Sigma-Aldrich, C8267) at a concentration of 50 mg ml–1 and administered to mice (8- to 12-week-old males and females) through intraperitoneal injection at a dosage of 200 mg kg–1. Mice were then instilled with Lenti-sgRNA/Cre viruses (1.5 × 10^6 PFU for each condition) through intratracheal delivery to generate lung tumours. Five months after tumour induction, tissues were dissected from mice after they were killed and perfused with 10% neutral-buffered formalin (NBF). Lungs were inflated with 10% NBF and fixed in 10% NBF overnight. Tissues were transferred to 70% ethanol before paraffin embedding and processing. Quantification of lung tumour number and area on sections stained with haematoxylin and eosin was performed in a blinded fashion using ImageJ v1.54h. sgRNAs targeting Reln (Reln_a756, GACCCCATCTAAGCCAAACGG; Reln_a894, GAACTGGACATACATAGTAT) and a non-targeting guide (GCGAGGTATTCGGCTCCGCG) were cloned into the pLL3 backbone[58] (https://www.addgene.org/browse/article/15541/). Each Lenti-sgRNA/Cre virus was packaged separately in HEK293T cells through cotransfection with polyethylenimine alongside pCMV-VSV-G (Addgene, 8454) envelope plasmid and pCMV-dR8.2 dvpr (Addgene, 8455) packaging plasmid. The medium was replaced 24 h after transfection. Virus-containing supernatant was collected at 48- and 72-h time points following transfection, filtered using 0.45-µm syringe filters, concentrated by ultracentrifugation at 25,000 RPM for 90 min at 4 °C, resuspended in PBS and titered using LSL-YFP mouse embryonic fibroblasts as previously described[59].

Reference genomes and gene definitions

The reference genome used for all human analyses was TCGA GRCh38.d1.vd1, with the exception of the comparison of human RNA-seq data to GTEx data, which was performed using the GTEx v8 reference (Homo_sapiens_assembly38_noALT_noHLA_noDecoy_ERCC.fasta). The reference genome used for all mouse analyses was Ensembl version GRCm38.102, with the exception of the analysis of snRNA-seq data, which was performed using Ensembl reference GRCm39.110. The gene annotation for analyses of human genetic data was GENCODE annotation v22, while the gene annotation for analyses of mouse data was GENCODE annotation vM23 (ref. [60]). Both GENCODE annotations were filtered first to include only transcripts marked as ‘protein coding’ and subsequently to include only the 17,153 genes for which a one-to-one orthologue could be identified between mouse and human using the HCOP 15-column orthology table (downloaded on 6 January 2020 from the HGNC database[61]). The gene annotation for analysis of TCGA expression data was GENCODE annotation v22, and the gene annotation used for analysis of GTEx expression data was GENCODE annotation v26.

Analysis of piggyBac insertions

Sequencing reads that contained internal transposon sequences were excluded, and the remaining reads were aligned against the GRCm38 reference using BWA v0.7.15 and samtools v1.3.1. Aligned reads that did not align to the consensus TTAA target sequence were excluded. At each TTAA locus in each sample, reads derived from the same fragment, identified by the identical position of the read ends, were collapsed. TTAA loci were kept if five or more different fragments were identified. Germline insertions were identified by the presence of ten or more different fragments at a TTAA locus in tail or ear samples. These TTAA loci were excluded from analysis in the whole cohort and the sequences 1 Mb upstream and downstream were masked from analysis of tumours from the affected mice. The 10-Mb regions encompassing the donor loci were also masked from analysis (chromosome 5:50000000–70000000 for the RPLH line and chromosome 10:0–10000000 for the RPLS line). Insertions detected in more than one tumour were assigned to the tumour with the highest number of fragments. For each of the 17,153 protein-coding genes present in both the human and mouse genomes, we defined the included genomic range as the union of all the transcripts for the gene from the transcription start site (TSS) to the stop codon. The statistical analysis included two steps. First, at the sample level, the Poisson distribution was used to calculate the one-sided probability of seeing at least as many transposon fragments as were actually present. The rate used for the Poisson distribution was based on the total insertion rate within genes on each chromosome of each sample, on the total number of TTAA sites within genes on the chromosome and on the number of TTAA sites within each gene. We then calculated FDR-corrected q values for each sample and each gene. We obtained a total of 11,208 genes (an average of 37 genes per sample) that were significant at a cutoff of q < 0.05 at the sample level. To calculate the statistical significance of genes at the cohort level, we again used the Poisson distribution with a rate derived from distributing the 11,208 hits evenly across all non-masked genes of all samples and calculated the one-sided probability of seeing at least as many insertions in a given gene. We then calculated the FDR-corrected q value at the cohort level for each gene.

Analysis of piggyBac subcohorts

We used a two-sided permutation test to compare the distribution of the transposon insertions in different subcohorts: untreated versus chemotherapy, untreated versus immunotherapy and lung tumours versus metastatic tumours. For each comparison, the union of samples included in the comparison was shuffled 1,000,000 times, while maintaining the same number of samples from each mouse line in each subcohort (RPLH and RPLS). For each gene, we then counted the number of iterations in which the absolute difference in the fraction of samples carrying an insertion was greater than in the real configuration. We calculated the FDR-corrected q value for each gene.

Simulation and annotation of possible human mutations

For each gene included in the filtered GENCODE annotation v22, all possible single-nucleotide substitutions were simulated, annotated using ANNOVAR v2018Apr16 (ref. [62]) with the filtered GENCODE annotation v22 and divided into three categories: synonymous (no predicted change in the protein sequence), severe (causing a premature stop, loss of the starting ATG site, a frameshift or a nucleotide change in one of the two intronic bases flanking each side of an exon) and non-synonymous (any other predicted change in the protein sequence). For each simulated variant in each gene, only the most severe consequence among all the transcripts associated with the gene was kept. All simulated variants were also annotated using the total population frequency in non-cancer samples from the gnomAD v2.1.1 GRCh38 liftover exome and the gnomAD v3 genomes and excluded if they were found in more than 1 in 10,000 samples. On the basis of this simulation, the number of possible non-synonymous or severe variants for each gene was used for calculation of the expected number of mutations in each gene.

Data collection of human somatic mutations

Sample information and mutations were downloaded from the supplementary tables of the respective papers or from the Cancer Cell Line Encyclopedia (CCLE) website (Cell_lines_annotations_20181226.txt and CCLE_DepMap_18q3_maf_20180718.txt; https://portals.broadinstitute.org/ccle/). Where needed, the mutations were mapped to the TCGA GRCh38 reference (GRCh38.d1.vd1.fa) using the liftOver v385 tool from the UCSC database (http://genome.ucsc.edu; ref. [63]). The resulting 177,983 mutations were annotated as described above for the simulated variants; 613 mutations were excluded from analysis (517 mapped to mitochondrial genes and 96 could not be mapped to primary chromosomes in h38). The remaining 177,370 variants were left-aligned using GATK LeftAlignAndTrimVariants v4.1.3.0 (ref. [64]). A total of 28 samples were excluded from analysis because they shared five or more mutations with a sample from a more recent study, leaving 456 samples.

Statistical analysis of the human cohort

Samples sharing five or more mutations were merged (e.g., samples sequenced both before and after treatment). In total, 439 samples and 117,353 non-synonymous mutations were used for analysis. We used the Poisson distribution to estimate the one-sided probability of observing at least as many mutations by chance in each gene. To obtain the rate for the Poisson distribution for each gene, we divided the total number of non-synonymous mutations, counting each sample at most twice per gene, by the total number of possible non-synonymous mutations within the 17,153 protein-coding genes present in both the human and mouse genomes. For each gene, we then multiplied this value by the number of non-synonymous mutations that were theoretically possible in the gene (see simulations above). The rate therefore represented the expected number of non-synonymous mutations under a uniform distribution model. For each gene, we then calculated the probability of observing at least as many mutations as were actually present. We corrected the resulting P values for multiple testing using the FDR to derive the q value for each gene. Finally, we repeated this analysis but included only severe mutations (stop gain, start loss, frameshift and canonical splicing) to derive the probability of observing at least as many severe mutations as were actually present. The same analysis was performed on subsets of the whole cohort to compare the statistical significance across subcohorts. Mutations in selected genes were plotted on the corresponding proteins with annotations derived from the UniProt Knowledgebase (v2022_5; https://www.uniprot.org/; accessed 14 June 2022)[65].

Analysis of evolutionary conservation of mutated nucleotides

PhyloP conservation tracks across 470 mammalian genomes were downloaded from UCSC63,66. Genes were divided into those that were non-significant (q > 0.1 in the human mutation dataset), significant in human data only (q < 0.1 in the human mutation dataset but q > 0.1 in the piggyBac dataset) and significant in both (q < 0.1 in both datasets). For each gene in the three groups, the median of the phyloP scores for all mutated nucleotides was calculated. The significant groups were compared with the non-significant group using a two-sided Mann–Whitney test, followed by FDR correction.

Comparison of expression data to the TCGA database

SCLC RNA-seq data from two different studies8,40 and RNA-seq data from neuroblastoma samples[67] were re-analysed using the TCGA pipeline. In brief, STAR v2.4.2a was used to align reads to the GRCh38 reference using GENCODE annotation v22. HTSeq v0.6.1p1 was then used to quantify expression at the gene level. Raw counts were converted to transcripts per million (TPM) using the median length of all transcripts for each gene, as reported in GENCODE annotation v22. TPM + 1 values were then log scaled and used for further analysis. Expression data were downloaded from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov). The TPM values of SCLC samples were compared with the TPM values of individual types of tumours in TCGA using a two-sided Mann–Whitney test, and the fold change for each gene was calculated as the median of the SCLC log_2(TPM + 1) values minus the median of the TCGA cohort log_2(TPM + 1) values.

Comparison of expression data to the GTEx database

SCLC RNA-seq data from two different studies8,40 were re-analysed using GTEx pipeline v8. In brief, STAR v2.5.3a was used to align reads to the GRCh38 reference using GENCODE annotation v26. RNA-SeQC v1.1.9 was then used to quantify expression at the gene level. Raw counts were converted to TPM using the median length of all transcripts for each gene, as reported in GENCODE annotation v26. TPM + 1 values were then log scaled and used for further analysis. Expression data were downloaded from the GTEx database (https://gtexportal.org). Tissues with fewer than 30 samples were excluded (fallopian tube, bladder, cervix uterus). The TPM values of SCLC samples were compared with the TPM values of the individual tissues in GTEx using a two-sided Mann–Whitney test, and the fold change for each gene was calculated as the median of the SCLC log_2(TPM + 1) values minus the median of the GTEx tissue log_2(TPM + 1) values.

snRNA-seq

Sucrose buffer (1 M; 1 M sucrose, 10 mM Tris-HCl (pH 8) and 3 mM magnesium acetate), lysis buffer 1 (5 mM CaCl_2, 3 mM magnesium acetate, 2 mM of 0.5 M EDTA (pH 8, RNase-free), 0.5 mM EGTA (ThermoFisher), 1× cOmplete, EDTA-free protease inhibitor cocktail (Sigma), 1 mM dithiothreitol (Roth), 0.1 mM phenylmethylsulfonyl fluoride (Roth) and 1.6 U ml–1 mouse RNase inhibitor (NEB)), lysis buffer 2 (lysis buffer 1, 0.4% (v/v) Triton X-100 (Sigma) and 4 U ml–1 mouse RNase inhibitor), lysis buffer 3 (lysis buffer 1 and lysis buffer 2 in a 1:1 ratio and 5.7 U ml–1 mouse RNase inhibitor) and resuspension buffer (D-PBS with MgCl_2 and CaCl_2 plus 12 U ml–1 mouse RNase inhibitor) were prechilled on ice for at least 1 h before isolation. Snap-frozen RP tumours were thawed in a 60-mm dish on ice and sharply minced with a precooled scalpel. Subsequently, the minced tissue was transferred to a gentleMACS M-tube (Miltenyi) and the 60-mm dish was rinsed with lysis buffer 1, which was then added to the gentleMACS M-tube. The tissue was dissociated using programme ‘Protein-M-tube 1.0’ of gentleMACS (Miltenyi). Lysis buffer 2 was added to the M-tube, followed by inversion. The lysed tissue was filtered through a 40-µm cell strainer prewetted with lysis buffer 1. Centrifugation (5 min, 450g, 4 °C, with breaks; Eppendorf) was conducted to pellet the nuclei. Next, the supernatant was discarded and the nuclei were resuspended in lysis buffer 3 and kept on ice. Sucrose buffer was drawn into a 25-gauge needle and syringe and ejected underneath the nuclear suspension, followed by centrifugation (5 min, 450g, 4 °C, with breaks). The upper phase was removed, and the nuclei were gently resuspended in resuspension buffer and filtered through a 15-µm cell strainer. Fixation of the nuclei, barcoding of single nuclei, amplification of barcoded cDNA and preparation of sequencing libraries were carried out according to the Evercode WT Mega v2.1.1 user manual (Parse Biosciences). Libraries were sequenced at the Cologne Center for Genomics using an Illumina NovaSeq 6000 instrument at an average depth of 216,310,743.5 reads per sample.

Processing of mouse snRNA-seq data

Raw sequencing data were processed using Parse scRNA-seq pipeline v1.1.1, which included alignment to the GRCm39.110 reference using STAR v2.7.10b and demultiplexing of cells to the corresponding samples based on the first barcode. The resulting raw count matrices and cell annotation files, together with the Ensembl GRCm39.110 gene annotations, were assembled into an Anndata object using scanpy v1.9.3. Cell detection and background removal were performed using Cellbender v0.3.0 with standard settings. Doublet filtering was performed using doubletdetection v4.2 with a voter threshold of 0.5 and a P-value threshold of 0.001. Low-quality cells were filtered out using a two-step protocol. First, we excluded cells that had fewer than 25 protein-coding genes with at least 3 raw counts. Then, we log scaled four quality-control metrics and calculated the median and the median absolute deviation (MAD) for each. These metrics included the percentage of counts mapped to mitochondrial transcripts, the percentage of counts mapped to ribosomal transcripts, the percentage of counts included in the top ten most expressed genes and the total number of protein-coding genes. We excluded cells that had a value greater than 3 MAD from the median for each of these metrics, as well as cells with a value lower than 3 MAD from the median for the total number of genes. We also excluded genes that were not protein coding and genes that were not expressed in any cell. For clustering and visualization, the remaining counts were converted to transcripts per 10,000 (tp10k) by dividing by the median length of the transcripts for each gene and normalizing to 10,000 using scanpy.pp.normalize_total with the option to exclude highly expressed genes. The tp10k values were converted to a log_10(tp10k + 1) scale, and the most variable genes were selected using scanpy.pp.highly_variable_genes with standard settings. Principal-component analysis was performed using 100 components, and these were batch corrected with Harmony using scanpy.pp.harmony_integrate with standard settings. Neighbours were calculated using scanpy.pp.neighbors with 100 neighbours and using the cosine distance. Leiden clusters were calculated using scanpy.tl.leiden with a resolution of 0.5. Coarse connectivity of the manifold was calculated using PAGA with scanpy.tl.paga and used as the starting point for uniform manifold approximation and projection (UMAP) embedding with scanpy.umap using standard settings. Markers of expected cell types were identified in the literature and used for cell type calling at the cluster level.

UMAP visualization of gene sets in mouse scRNA-seq data

The sum of the log_10(tp10k + 1) values was calculated for included genes in the gene sets ‘glutamatergic synapse’ and ‘synaptic membrane’ and normalized and clipped to the range 0–1, with 0 being the mean score of the cluster with the lowest score and 1 being the mean score of the cluster with the highest score.

Re-analysis of scRNA-seq data from patients with SCLC

Published scRNA-seq data[44] were obtained from https://cellxgene.cziscience.com/collections containing preprocessed gene expression values, annotations of cell types, SCLC subtypes and UMAP embeddings. Samples marked as NSCLC were excluded. Individual cells marked as neuroendocrine or NSCLC were also excluded.

Gene set analysis with GO

The GO architecture and annotations were downloaded from the GO website (v2020-09-10; http://geneontology.org)68,69. For each term annotation of each gene, the gene was additionally annotated with all its parent terms. For each dataset of interest, the identified genes were compared to all GO terms that included at least 10 and at most 1,000 genes using a two-sided Fisher’s exact test and FDR correction. PiggyBac hits (n = 504) and human mutation hits (n = 991) were included if they had a q value of less than 0.1 and at least one GO annotation. Genes highly expressed in SCLC were first filtered to include only genes with at least one GO annotation and with a q value of less than 0.1 in at least 90% of the comparisons (30/33 tumours or 25/27 healthy tissue samples). The remaining genes were then ranked by the median log_2-transformed fold change across all comparisons and only the top 1,000 genes were included. Genes from the mouse snRNA-seq and re-analysed human scRNA-seq datasets[44] were selected by comparing the pseudobulk counts of SCLC cells to the pseudobulk counts of other cells using a two-sided Fisher’s exact test followed by FDR correction. Genes were included if they had a q value of less than 0.1 in the majority of the samples and a median fold change of at least 2. The remaining genes were ranked by median fold change and the top 1,000 genes were included in the analysis. Force-directed graphs were generated with datashader v0.12.1 using the ForceAtlas2 layout. For this analysis, up to 100 GO terms were included as nodes if they were significantly enriched (q < 0.1) for genes in the datasets and if they were not a perfect subset or overset of a GO term with a more significant overlap. An edge was present between two GO terms if the genes included in the terms significantly overlapped (q < 0.1 by two-sided Fisher’s exact test and FDR correction). GO terms identified at the expression level both versus cancer types and versus healthy tissue types were further cross-referenced with ChIP–seq data downloaded from the CISTROME database (http://cistrome.org/db; accessed 27 November 2019)[42] and with scRNA-seq data from healthy human lung samples from ref. [43]. The ChIP–seq peaks from experiments using antibodies against RB1, RBL2, E2F1, E2F2, E2F3, E2F4 and E2F5 were downloaded from the CISTROME database to derive an RB–E2F score. For each gene, the peaks were merged across samples and replicates and their fold change over background was added across samples and replicates. Peaks with a total fold change of at least 10 were included in the analysis. The regulatory potential was calculated for all target genes whose TSS was within 100 kb of the peak, using the distance between the peak and the TSS, as described in ref. [70]. The regulatory potential was multiplied by the total fold change, and this score was added for all peaks near a TSS. For each target gene, the transcript with the highest score was kept. The scores for each transcription factor were normalized between 0 and 1 and then added together to derive the RB–E2F score for each target gene. The score for each enriched GO term was calculated as the mean score across genes included in the GO term and in the SCLC dataset. scRNA-seq data, as well as the corresponding metadata from ref. [43], were downloaded from Synapse (Synapse:syn21560406). Cell type annotations were obtained from the metadata. Cells were divided into two groups: cells annotated as neuroendocrine and all others. Unique molecular identifiers in each group were added and converted to TPM. The TPM + 1 values were then log scaled, and the log_2-transformed fold change between PNECs and other lung-resident cells was calculated as the difference in the two log-scaled values for each gene. For each GO term, we calculated the mean fold change for genes included in the GO term and in the SCLC dataset.

Neuroendocrine and ‘glutamatergic synapse’ expression scores

The neuroendocrine score was calculated using the 50 marker genes identified in ref. [50] as the correlation between the log ratio described in the publication and the expression levels of the genes in individual SCLC samples. To calculate the expression score for genes in the GO term ‘glutamatergic synapse’, the log-scaled expression values of the genes in the gene set were first normalized between the median of the tumour type or tissue with the highest expression (normalized to 1) and the median of the tumour type or tissue with the lowest expression (normalized to 0). The score was then calculated as the mean of the normalized expression for all genes in the gene set.

Virus production

Retroviruses encoding DsRedExpress2 and those encoding the RABV glycoprotein and TVA800 (the glycosylphosphatidylinositol-anchored form of the TVA receptor), as well as GFP-encoding EnvA-pseudotyped RABV, were described previously[71].

Cell line maintenance

SCLC and NSCLC cell lines were maintained in culture in RPMI 1640 (Life Technologies) supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin/streptomycin (Gibco).

Isolation of mouse cortical neurons

Mouse embryos (embryonic day 13.5–16.5) were isolated following cervical dislocation of the anaesthetized pregnant mother as previously described[72]. In brief, cortices were dissected in Hank’s buffered salt solution (Gibco) supplemented with HEPES (10 mM; Gibco), and dissociated by means of enzymatic digestion for 15 min at 37 °C by incubating the tissue in DMEM high-glucose GlutaMAX (Gibco) containing papain (20 U ml–1; Merck) and cysteine (1 μg ml–1; Merck), followed by mechanical trituration in medium supplemented with 10% FBS (Gibco).

Generation of human cortical neurons

Neurons were grown for at least 4 weeks before using them in co-cultures. Human cortical neurons were derived from the WTC11 human iPS cell line carrying a doxycycline-inducible Ngn2 transgene[73] and were cultivated as previously described[74]. In brief, iPS cells were cultured on GelTrex-coated plates (1×; ThermoFisher Scientific) in StemMACS iPS-Brew XF (Miltenyi). When reaching confluence, the cultures were passaged with Versene passaging solution (ThermoFisher Scientific) and seeded in thiazovivine (Axon Medchem)-supplemented iPS-Brew for 1 day. Cells were grown at 37 °C and 5% CO_2 in a humidified incubator. Differentiation into cortical neuronal cultures was performed by seeding iPS cells at high density onto GelTrex-coated plates using predifferentiation medium supplemented with thiazovivine. The predifferentiation medium was replaced daily for the following 2 days with thiazovivine-free predifferentiation medium. Cells were then seeded onto poly(d-lysine) (Sigma-Aldrich)- and laminin (Trevigen)-coated plates using maturation medium supplemented with 1:100 GelTrex. Half of the medium was exchanged once per week until analysis.

Nodose ganglion explant cultures

Wild-type C57BL/6 mice (3–5 weeks old) were killed by cervical dislocation, and nodose ganglia were isolated using an intracranial approach[75]. The top of the skull was removed, followed by extraction of the brain and brainstem to expose the base of the skull. Under stereomicroscopic visualization, a midline incision was made into the occipital bone plate, extending rostrally from the foramen magnum. The occipital bone plate was then detached from the temporal bone to expose the vagus nerve and its associated nodose ganglion. Following isolation, surrounding tissues and the ganglion capsule were carefully removed using fine scissors and forceps. The isolated ganglia were then plated in 96-well plates (Sarstedt) precoated with collagen I (Ibidi) and containing Neurobasal-A medium (Gibco), supplemented with 2% FBS (Gibco), 2% B27 supplement (Gibco), 1% penicillin/streptomycin (Gibco), 0.5 mM GlutaMAX (Gibco), 25 µM l-glutamate (Sigma), 50 ng ml–1 nerve growth factor (Alomone Labs), 20 ng ml–1 glial cell line-derived neurotrophic factor (PeproTech) and 20 ng ml–1 brain-derived neurotrophic factor (PeproTech). Explant cultures were maintained at 37 °C and 5% CO_2 throughout the experiment. Medium changes were performed once per week. Twelve days after plating, explant cultures were examined under a microscope to evaluate attachment and extension of neurites. Only explants exhibiting neurite outgrowth were used for subsequent co-culture experiments.

Monitoring of proliferation in cell culture

Proliferation was assessed using IncuCyte live-cell imaging. Cancer cells were stably transduced using lentiviral vectors carrying an EF1α-tdTomato-IRES-G418 transgene. For co-culture experiments, 30,000 fibroblasts or neurons were plated per well. For analysis of high-density monocultures, 3,000 non-fluorescent cells were plated per well. Conditioned medium was collected from neuronal cultures and filtered through 0.2-µm filters. Two thousand cancer cells were added to each well and transferred into the IncuCyte system 1 day after initiation of co-culture. Whole-well images (×4 objective) were captured every 6 hours for a total of 6 days. Bright-field and fluorescence channels were acquired (557 nm, 607 nm). The captured images were analysed using IncuCyte analysis software (Sartorius) to quantify total integrated intensity as a measure of cell proliferation. As a control, cells in monoculture were plated in separate wells on the same plate and were maintained under identical culture conditions. The intensity was normalized to the intensity of the first scan, and the AUC was calculated over 5 days of culture. The AUC was further normalized to the AUC of monocultures in the same plate. In Extended Data Fig. 10a, all conditions were compared to the co-cultures with cortical neurons using a two-sided Mann–Whitney test followed by FDR correction. In Fig. 5, the median normalized AUC values of SCLC cell lines were compared between co-cultures with cortical neurons and cultures in neuron-conditioned medium using a two-sided Wilcoxon signed-rank test. For visualization of nodose fibres, the ganglia were transduced with a peripheral nervous system-specific AAV encoding tdTomato (AAV-PHP.S-hSyn-tdTomato-P2A-APEX2-V5; VectorBuilder) and seeded into 96-well plates. Two thousand COR-L88 cells were added once neurites started to form, and cultures were monitored for 5 days at ×4 resolution over the whole well.

RABV tracing

Neurons were plated at a density of 60,000–70,000 cells per coverslip (24-well plate) on poly(l-lysine) (0.1 mg ml–1; Merck)-coated glass coverslips. After 4 h, the medium was replaced with Neurobasal serum-free medium (Gibco) containing B27 supplement (1%; Gibco) and GlutaMAX (0.5 mM; Merck). Neurons were then maintained at 37 °C and 5% CO_2 throughout the experiment and semi-feeding was performed once per week. SCLC cells were transduced with retroviruses encoding DsRed alone, DsRed and TVA, or DsRed, TVA and G and preconditioned in Neurobasal medium for 48 h before seeding of 500 cells onto the neuronal layer. At neuronal division 12 (DIV12)–DIV13, EnvA-pseudotyped (ΔG) RABV encoding GFP was added to the medium. Analysis was performed after an additional 3 days in culture, at which time samples were fixed in paraformaldehyde (PFA; 4% in PBS; Sigma) and the number of GFP-only-positive presynaptic neurons was quantified and normalized to the number of double-positive starter SCLC cells. For quantification, a confocal Stellaris microscope (Leica Microsystems) equipped with a ×10 air objective was used to acquire two distinct ROIs of 3 × 3 tiles randomly chosen within the coverslip, and two coverslips for each condition were examined per biological replicate. One embryo preparation obtained from a pregnant mouse was considered to be a biological replicate. Within each ROI, GFP-positive neurons were manually counted using the plugin Cell Counter v3.0.0 for ImageJ v1.54h, and their numbers were normalized to those of DsRed and GFP double-positive starter cancer cells. A minimum of four biological replicates per condition were used for quantification. The q value was calculated by comparing each condition to the full experimental system (DsRed, TVA, G) using a two-sided Mann–Whitney test followed by FDR correction. For time-lapse imaging, COR-L88 cancer cells were transduced with a DsRed retrovirus encoding G and TVA, followed by addition of RABV-GFP 2–3 days later. After 24 h, cancer cells were washed thoroughly with PBS, trypsinized and plated onto DIV12 neuronal cultures. The resulting co-cultures were placed in an IncuCyte live-cell analysis system (Sartorius) for the subsequent 72 h. For monosynaptic tracing experiments of grafted cancer cells in vivo, a suspension of retrovirally transduced mouse SCLC cells derived from the RP model and freshly added EnvA-pseudotyped (ΔG) RABV-GFP were infused into the hippocampus of mice as previously described[76]. After mice were killed, GFP-positive neurons and cancer starter cells double positive for DsRed and GFP were quantified to obtain a connectivity ratio per starter cell. Neurons were classified according to their morphology and location within the layers of the hippocampus, dentate gyrus and subiculum. Occasional glial cells exhibiting morphological hallmarks of astrocytes or oligodendrocytes were excluded from the analysis. The P value was calculated using a two-sided Mann–Whitney test.

EdU chase assays

To compare the proliferation rates of monocultures and co-cultures with immunofluorescence staining, cultures of COR-L88 cells were treated with 20 µM EdU and incubated for 2 h before fixation with 4% PFA prewarmed at room temperature for 10 min. Cells were washed three times with 1× PBS followed by EdU staining using the Click-iT EdU imaging kit (Invitrogen). P values were calculated with a paired two-sided t test.

Transplantation experiments

Thy1-GFP-M mice[77] were anaesthetized by intraperitoneal injection of a ketamine/xylazine mixture (100 mg kg–1 ketamine and 10 mg kg–1 xylazine), injected subcutaneously with carprofen (5 mg kg–1) and fixed in a stereotactic frame provided with a heating pad. A portion of the skull covering the somatosensory cortex (from bregma: caudal, −2.0; lateral, 1.5) was thinned with a dental drill, avoiding disturbing the underlying vasculature, and a small craniotomy sufficient to allow penetration of a glass capillary was performed. A finely pulled glass capillary containing a suspension of mouse SCLC cells derived from the RP model in sterile PBS was then inserted through the dura to reach the hippocampus, and an estimated total of about 30,000–50,000 cells (corresponding to a total injected volume of 0.8–1.0 µl) were slowly infused using a manual syringe (Narishige) in multiple vertical steps spaced by 50 µm (−1.9 to −1.3 from bregma) over a total duration of 10–20 min. After capillary removal, the scalp was sutured and mice were placed on a warm heating pad until fully recovered. The physical condition of the animals was monitored daily before they were killed 10–12 days after surgery.

Fluorescence immunostaining of brain slices and co-cultures

Immunostaining of fixed brain slices and cultures (Figs. 2 and 4 and Extended Data Figs. 6a,e, 7a and 9) was performed using conventional procedures described previously[72]. The following primary antibodies were used: chicken anti-GFP (1:500; Aves Labs, GFP-1020), rabbit anti-RFP (1:500; Rockland, 600401379), chicken anti-MAP2 (1:500; Abcam, ab5392), mouse anti-VGluT1 (1:500; Synaptic Systems, 135311). rabbit anti-Homer (1:500; Synaptic Systems, 160003) and mouse anti-BSN (1:500; Synaptic Systems, 141111). The following secondary antibodies were used (raised in donkey): Alexa Fluor 488-conjugated secondary antibody anti-chicken (1:1,000; Jackson ImmunoResearch, 703-545-155), Alexa Fluor 546-conjugated secondary antibody anti-rabbit (1:1,000; ThermoFisher Scientific, A10040), Alexa Fluor 647-conjugated secondary antibody anti-rabbit (1:500; Jackson ImmunoResearch, 711-605-152), Alexa Fluor 647-conjugated secondary antibody anti-mouse (1:1,000; Jackson ImmunoResearch, 715-605-150) and DyLightTM 405-conjugated secondary antibody anti-rabbit (1:100; Jackson ImmunoResearch, 711-475-152). Images were acquired using an SP8 confocal microscope (Leica) equipped with a ×20 (NA 0.75), ×40 (NA 1.3), ×63 (NA 1.4) or ×100 (NA 1.3) oil-immersion objective and further processed with Fiji v2.14.0.

Imaging of co-cultures with cortical neurons

Co-cultures of mouse cortical neurons and human SCLC cells (Fig. 3 and Extended Data Fig. 6b–d) were fixed with 4% PFA and quenched with 50 mM glycine for 10 min and were immunostained as described in ref. [78]. In brief, samples were permeabilized and blocked with 0.2% Triton X-100, 2.5% bovine serum albumin (BSA), 2.5% normal goat serum (NGS) and 2.5% donkey serum in PBS for 30 min and then washed with 2.5% NGS in PBS. Samples were incubated with primary antibodies (chicken anti-MAP2 (1:1,000; Novus Biologicals, NB300-213), mouse anti-mNeonGreen (1:500; ChromoTek, 32F6), rabbit anti-HOMER1 (1:500; Synaptic Systems, 160003), mouse anti-BSN (1:500; Enzo, ADI-VAM-PS003-F, SAP7F407), mouse anti-SMI-312 (1:1,000; HISS Diagnostics, SMI-312R), guinea pig anti-VGluT1 (1:500; Synaptic Systems, 135304), rabbit anti-VGluT1 (1:500; Synaptic Systems, 135308) and mouse anti-VGluT1 (1:500; Synaptic Systems, 135011)) for 1.5 h at room temperature, washed with 2.5% NGS in PBS and incubated with secondary antibodies (Alexa Fluor 405-conjugated anti-chicken (1:500; Abcam, ab175674), Alexa Fluor 568-conjugated anti-mouse (1:1,000; Life Technologies, A-11004), STAR 635P-conjugated anti-rabbit (1:1,000; Abberior, 1002-500UG) and Alexa Fluor 750-conjugated anti-guinea pig (1:500; Abcam, ab175758)) for 45 min. Samples were washed five times with 0.2% Triton X-100, 2.5% BSA, 2.5% NGS and 2.5% donkey serum in PBS with gentle shaking and two times with PBS. They were then washed with double-distilled water, before mounting with Prolong Glass for non-expanded specimens. A Stellaris 8 PP STED Falcon microscope (Leica Microsystems) was used for confocal, 3D STED, and 2D and 3D ONE microscopy imaging[46]. Confocal overview images were acquired using the navigator function spiral mode scan, and tiles were stitched with 12% overlap. An HC PL apo ×100/1.4 NA oil STED W objective was used for all imaging modalities. The white-light laser was used as the main excitation source, tuned to the best excitation wavelength for each fluorophore, at a pulse frequency of 80 MHz. Blue-shifted dyes were excited using a separate 405-nm DMOD laser. 3D STED images were acquired using a theoretical pixel size set between 20 and 37 nm. Three STED depletion beams, at 775, 660 and 592 nm, were used at a repetition rate of 80 MHz with more than 1.5 W of output power together with a 50-nm xy vortex donut and an 130-nm z donut. Near-infrared and/or far-red emissions were detected using a Power HyD R SP detector, red-shifted emissions were detected using Power HyD S SP Core Unit detectors, green-shifted emissions were detected using Power HyD X SP detectors and blue-shifted emissions were detected using HyD S SP detectors, in the presence of the respective notch filter set STED 3X. The detectors were set to either counting intensity or counting τSTED mode. 3D reconstructions were done through the 3D viewer in LAS-X. ONE microscopy images were acquired using a 12-kHz tandem scanner with and without dynamic signal enhancement (between 5 and 11, weighing of 0.4). The theoretical acquired pixel size was set to 92 nm, which yielded a final computed pixel size of 0.92 nm after computation with 32-bit image depth. Two thousand frames per channel were acquired for 2D ONE images. Three hundred to 500 frames per channel were acquired for 3D ONE images. The resultant images were processed with TRAC4, radiality magnification of 25 or TRA mode, as described in refs. 46,79. A TCS SP5 STED microscope (Leica Microsystems) was used for 2D ONE microscopy, using 488-, 561- and 633-nm laser lines and an HCX Plan apo STED ×100/1.4 NA oil-immersion objective. Images were acquired using an 8-kHz resonant scanner in unidirectional line scan mode, and emission was detected using HyD and PMT detectors. The theoretical pixel size was set to 98 nm and the image bit depth was set to 8 bits, with a line format of 128 × 128 and a frame count ranging between 1,000 and 2,000 frames.

Expansion microscopy

Samples were expanded following the x10ht protocol as described in ref. [80]. In brief, specimens were anchored overnight at 4 °C with 0.3 mg ml–1 Acryloyl-X (SE; ThermoFisher Scientific, A-20770) in PBS (pH 7.4). Gel monomer solution was added onto the samples, which were later homogenized by application of disruption buffer and autoclaving for 60 min at 110 °C. The samples were then expanded by adding double-distilled water to 22 ×22 cm^2 square culture dishes.

Imaging of mouse nodose ganglion neuron and human SCLC co-cultures

Co-cultures of mouse nodose ganglia and human COR-L88 cells (Extended Data Fig. 6f–i) were fixed with 4% PFA and quenched for 30 min with 100 mM ammonium chloride. They were then permeabilized and blocked with 0.2% Triton X-100, 2.5% BSA, 2.5% NGS and 2.5% donkey serum in PBS for 30 min, before washing with 2.5% NGS in PBS. Samples were incubated with primary antibodies (chicken anti-MAP2 (1:1,000; Novus Biologicals, NB300-213), mouse anti-mNeonGreen (1:500; ChromoTek, 32F6), rabbit anti-HOMER1 (1:500; Synaptic Systems, 160003), alpaca anti-VGluT1 nanobody (1:500; Nanotag, N1602-AF568-L, conjugated to AZDye 568), mouse anti-SMI-312 (1:1,000; HISS Diagnostics, SMI-312R) and mouse anti-SMI311 (1:1,000; Biozol, BLD-837801)) for 1.5 h at room temperature, washed with 2.5% NGS in PBS and incubated with secondary antibodies (Alexa Fluor 405-conjugated anti-chicken (1:500; Abcam, ab175674), STAR 635P-conjugated anti-rabbit (1:1,000; Abberior, ST635P, 1002-500UG) and Alexa Fluor 750-conjugated anti-mouse (1:1,000; ThermoFisher, A21037)) for 45 min. The samples were washed five times with 0.1% Triton X-100, 2.5% BSA, 2.5% NGS and 2.5% donkey serum in PBS with gentle shaking and two times with PBS. They were then washed with double-distilled water, before mounting with Prolong Glass. A Stellaris 8 PP STED Falcon microscope was used for confocal and 3D τSTED Xtend imaging.

Imaging of mouse autochthonous SCLC tumours in tissue sections

For Extended Data Fig. 7b–d, the lungs of tumour-bearing RPC mice induced with CGRP-driven Cre were snap frozen in liquid nitrogen using OCT medium and sectioned at a thickness of 10 µm with a Leica CM3050 S cryotome. Slices were fixed in 4% PFA and quenched for 30 min with 50 mM glycine. They were then washed three times with PBS + iT-Fx image signal enhancer for 20 min. Samples were permeabilized and blocked with 0.3% Triton X-100, 2.5% BSA, 2.5% NGS and 2.5% donkey serum in PBS for 45 min and washed twice in 2.5% NGS in PBS for 5 min each. Specimens were stained with rabbit anti-HOMER1 (1:500; Synaptic Systems, 160003), alpaca anti-VGluT1 nanobody (1:500; Nanotag, N1602-AF568-L, conjugated to AZDye 568), mouse anti-SMI-312 (1:1,000; HISS Diagnostics, SMI-312R), mouse anti-SMI311 (1:1,000; Biozol, BLD-837801) and anti-GFP nanobody Alexa Fluor 488 (1:500; Nanotag, N0301) for 3 h and 45 min in 2.5% NGS in PBS. Samples were washed three times with 0.1% Triton X-100, 2.5% BSA, 2.5% NGS and 2.5% donkey serum in PBS for 10 min and then stained with secondary antibodies for 1.5 h using STAR 635P-conjugated anti-rabbit (1:1,000; Abberior, ST635P, 1002-500UG), Alexa Fluor 750-conjugated anti-mouse (1:1,000; ThermoFisher, A21037). The samples were then washed five times with 0.1% Triton X-100, 2.5% BSA, 2.5% NGS and 2.5% donkey serum in PBS for 10 min each and twice with PBS for 15 min each. To label the tissues with pan-NHS-ester labelling, the specimens were washed with sodium bicarbonate buffer, stained with NHS-ester Pacific Blue (1:5,000, BroadPharm, 215868-33-0) in sodium bicarbonate buffer for 15 min, washed four times with PBS and once with double-distilled water, and mounted using Aqua-Poly Mount, before imaging with the microscope indicated in the previous section.

Automated analysis of HOMER1–VGluT1 co-localization

For the analysis in Extended Data Figs. 6h,i and 7c,d, 3D stacks were reduced to 2D summed images. HOMER1 images were subjected to an automatic threshold, equal to the mean and the standard deviation of the fluorescence signal, which found the spots above background. Signals corresponding to background noise were removed using an automated erosion procedure. The centres of mass of the remaining signals (true HOMER1 spots) were determined, and vertical and horizontal line scans were generated through the centres of mass. The vertical and horizontal line scans were averaged for every spot. The correlation of the scans was determined using the Pearson correlation coefficient, obtained using MATLAB (MathWorks, version 2023b).

Fluorescence immunostaining of lung cryostat sections

RP and RPC mice were killed 2, 4 or 8 months after tumour induction. Lungs were fixed by intratracheal instillation with 4% PFA in 0.1 M phosphate buffer and processed to collect cryostat sections[14]. Immunostaining was performed as previously described for mouse lungs[14]. The following primary antibodies were used: goat anti-CGRP (1:1,000; Abcam, ab36001), rabbit anti-GAP43 (1:2,000; Novus Biologicals, NB300-143), chicken anti-GFP (1:500; Abcam, 13970), rabbit anti-PGP9.5 (1:2,000; Abcam, ab108986), rabbit anti-P2X3 (1:1,000; Chemicon, AB5895), rat anti-SP (1:200; Biogenesis, 8450-0505), guinea pig anti-SYP (1:4,000; Synaptic Systems, 101002) and rabbit anti-VGluT1 (1:250; Synaptic Systems, 135303). The following secondary antibodies were used (raised in donkey): Alexa Fluor 647-conjugated anti-chicken (1:400; Jackson ImmunoResearch, 703-605-155), Cy3-conjugated anti-rabbit (1:2,000; Jackson ImmunoResearch, 711-167-003), Cy3-conjugated anti-goat (1:400; Jackson ImmunoResearch, 705-165-147), Cy3-conjugated anti-guinea pig (1:400; Jackson ImmunoResearch, 706-165-148), FITC-conjugated anti-rabbit (1:500; Jackson ImmunoResearch, 711-095-152) and FITC-conjugated anti-goat (1:500; Jackson ImmunoResearch, 705-095-147). To enhance staining intensity, biotinylated secondary antibodies (1:500; Jackson ImmunoResearch, 711-065-152 and 1:200; Jackson ImmunoResearch, 712-065-150) were combined with FITC (1:1,000; Jackson ImmunoResearch, 016-010-084)- or Cy3 (1:6,000; Jackson ImmunoResearch, 016-160-084)-conjugated streptavidin. Confocal images were acquired using a microlens-enhanced dual-spinning-disk confocal microscope (UltraVIEW VOX, PerkinElmer) equipped with 488-nm, 561-nm and 640-nm diode lasers for excitation of FITC, Cy3 and Alexa Fluor 647, respectively.

Immunohistochemistry of human samples

Patients consented to the use of their tissue specimens, and approval was obtained by the ethics committee of the University of Cologne (Biomasota 13-091, 2016). Formalin-fixed and paraffin-embedded tissue sections of human SCLC tumours were deparaffinized and immunohistochemically stained according to standard protocols using an automated immunostainer and a horseradish peroxidase-based detection system with diaminobenzidine as the chromogen. Primary mouse monoclonal antibodies were directed against SYP (1:100; Leica Biosystems, PA0299), neurofilament, 200-kDa subunit (NF-H) (1:500; Sigma, N0142) and neurofilament, 70-kDa subunit (NF-L) (1:500; Agilent, M0762). Immunostained sections were counterstained with haemalum.

Transmission electron microscopy

Anaesthetized mice were transcardially perfused with a fixative solution containing 4% formaldehyde and 2.5% glutaraldehyde in 0.1 M cacodylate buffer. The lungs were isolated and cut into 1-mm-thick sagittal sections, and the examined area was dissected according to the location of the tumour mass. Epon embedding was performed and ultrathin sections were prepared using standard procedures[81]. Electron micrographs were taken with a JEM-2100 Plus transmission electron microscope (JEOL) equipped with a OneView 4K 16-bit camera (Gatan) and DigitalMicrograph v3.32.2403.0 software (Gatan). For analysis, electron micrographs were acquired with a digital zoom of ×5,000 or ×6,000.

CLEM in vivo and in vitro

Mice were perfusion fixed with electron microscopy-grade 4% formaldehyde in PBS. For brain tissue, 100-µm sections (obtained with a Leica vibratome) were stained for nuclei with DAPI (ThermoFisher; 3 µM) and placed into imaging dishes with a glass bottom (Ibidi) filled with PBS. Co-cultures were directly grown in glass-bottom dishes (MatTek, P356-1.5-14-C) coated with a carbon finder pattern using a mask (Leica, 16770162) and an ACE 200 carbon coater (Leica). Cells were fixed for 10 min with electron microscopy-grade 4% formaldehyde in PBS. z stacks of the ROI showing reporter-positive tumour cells (DsRed or tdTomato) were acquired using an SP8 confocal microscope (Leica). After confocal and bright-field imaging, samples were prepared for transmission electron microscopy using standard protocols. In brief, post-fixation was applied using 1% osmium tetroxide (Science Services) and 1.5% potassium hexacyanoferrate (Merck) for 30 min at 4 °C. After three 5-min washes with double-distilled water, samples were dehydrated using an ascending ethanol series (50%, 70%, 90%, 100%) with 10 min at each step. Infiltration was carried out with a mixture of 50% epon in ethanol for 1 h, 70% epon in ethanol for 2 h and 100% epon overnight (Merck). After incubation with fresh epon for 4 h, vibratome sections were mounted onto empty polymerized epon blocks and covered with Aclar foil to provide a flat surface. For co-cultures, TAAB capsules (Agar Scientific) filled with epon were placed upside down onto the glass bottom. Samples were cured for 48 h at 60 °C. Aclar foil was removed by peeling it off, and the glass bottom was removed by alternating between putting the dish in boiling water and liquid nitrogen. Samples were trimmed to the ROI, which was previously acquired by confocal microscopy, using a diamond 90° trimming tool (Diatome). For orientation, stereotypic shapes of the hippocampus including the granule cell and molecular layers as well as the vasculature were used and matched to measurements obtained from confocal images of the same region. For cell culture, the carbon pattern was used to find the back ROI. Serial sections (300 nm) were cut using an UC6 ultramicrotome (Leica) and collected onto pioloform (Plano)-coated slot grids. Post-staining was performed with 1.5% uranyl acetate (Agar Scientific) for 15 min and Reynolds lead citrate (Roth) solution for 3 min. Electron micrographs were acquired using a JEM-2100 Plus transmission electron microscope (JEOL) operating at 200 kV and equipped with a OneView 4K camera (Gatan). Tomograms of ROIs were acquired using SerialEM v3.7.11 and reconstructed using IMOD v4.11.7 (ref. [82]). Registration of images obtained by light (confocal) and electron microscopy was done using nuclei (with nucleoli) as fiducials with the plugin EC-CLEM v1.1.0.0 from ICY v2.5.2.0 software[83]. 3D reconstruction of identified synaptic contacts was performed using Imaris v10.2.0 (Oxford Instruments). 3D-rendered volumes were generated from masks created through manual segmentation of pre- and postsynaptic compartments and synaptic vesicles using Microscopy Image Browser (MIB) software v2.84 (ref. [84]).

Electrophysiology of COR-L88 cells and allograft slices

Acutely isolated brains were sectioned into coronal slices (300-μm thick) by using a vibrating microtome (HM-650 V, ThermoFisher Scientific) filled with ice-cold carbogenated (95% O_2 and 5% CO_2) aCSF cutting solution (125.0 mM NaCl, 2.5 mM KCl, 1.25 mM sodium phosphate buffer, 25.0 mM NaHCO_3, 25.0 mM d-glucose, 1.0 mM CaCl_2 and 6.0 mM MgCl_2, adjusted to pH 7.4 and 310 to 330 mOsm). The obtained brain slices were transferred to a chamber containing aCSF recording solution (125.0 mM NaCl, 2.5 mM KCl, 1.25 mM sodium phosphate buffer, 25.0 mM NaHCO_3, 25.0 mM d-glucose, 4.0 mM CaCl_2 and 3.5 mM MgCl_2, adjusted to pH 7.4 and 310 to 320 mOsm). Slices were stored for at least 30 min to allow recovery before performing recording. All recordings were performed using a microscope stage equipped with a fixed recording chamber and a ×20 water-immersion objective (Scientifica). For ex vivo experiments, recordings were performed in aCSF recording solution. For in vitro experiments, SCLC cells (day 3–6 in mono- or co-culture) were used, and recordings were performed in extracellular solution (124.0 mM NaCl, 10.0 mM d-glucose, 10.0 mM HEPES-KOH (pH 7.3), 3 mM KCl, 2 mM CaCl_2 and 1 mM MgCl_2, adjusted to pH 7.4). Patch pipettes with a tip resistance of 5 to 10 MΩ were made from borosilicate glass capillary tubing (GB150-10, 0.86 mm × 1.5 mm × 100 mm; Science Products) with a horizontal pipette puller (P-1000, Sutter Instruments). The patch pipette was filled with internal solution (4.0 mM KCl, 2.0 mM NaCl, 0.2 mM EGTA, 135.0 mM potassium gluconate, 10.0 mM HEPES, 4.0 mM ATP(Mg), 0.5 mM guanosine triphosphate (GTP)(Na) and 10.0 mM phosphocreatine, adjusted to pH 7.25 and 290 mOsm (sucrose)). Recordings were performed with an ELC-03XS patch-clamp amplifier (npi electronic) controlled with Signal software (v6.0; Cambridge Electronic). Experiments were recorded with a sampling rate of 12.5 kHz. The signal was filtered with two short-pass Bessel filters that had cut-off frequencies of 1.3 and 10 kHz. Capacitance of the membrane and pipette was compensated by using the compensation circuit of the amplifier. All experiments were performed under visual control using an Orca-Flash 4.0 camera (Hamamatsu) controlled with Hokawo software (v2.8; Hamamatsu). SCLC cells were identified by expression of the cytosolic fluorescent protein DsRed or tdTomato. Cells were clamped at a holding potential of −30 mV after rupturing the membrane, and spontaneous activity was recorded for 5 min in whole-cell voltage-clamp mode. Synaptic inputs were isolated by adding the following blockers to the recording solution: CNQX (Sigma, C127; 10 µM), d-AP5 (Sigma, A8054; 20 µM), bicuculline (bicuculline-methiodide; Sigma, 14343; 100 µM), riluzole (Sigma; 10 µM) and TTX (1 µM). sPSCs were identified and measured with Igor Pro (v32 7.01; WaveMetrics) using a semiautomatic identification script.

Electrophysiology of H524 cells

For in vitro experiments, SCLC cells (on day 3 in mono- or co-culture; see also above) were used, and recordings were performed under constant superfusion of oxygenated aCSF (130 mM NaCl, 1.25 mM NaH_2PO_4, 10 mM NaHCO_3, 10 mM d-glucose, 3.5 mM KCl, 2 mM CaCl_2 and 1 mM MgCl_2, adjusted to pH 7.4).

Patch pipettes with a tip resistance of 6–8 MΩ were made from borosilicate glass 3.3 capillary tubing with filament (outer diameter, 1.5 mm; inner diameter, 1.2 mm; length, 100 mm; Hilgenberg). The patch pipette was filled with a caesium-based internal solution (125 mM CH_3CsO_3S, 16 mM KHCO_3, 2.0 mM NaCl, 1 mM EGTA, 4.0 mM ATP(Mg), 0.3 mM GTP(Na) and 10 mM QX-314-Cl, adjusted to an osmolarity of 295 mOsm).

Voltage-clamp recordings were performed with an Axon MultiClamp 700B amplifier (Molecular Devices) and digitized by an Axon Digidata 1550B (Molecular Devices). Electrophysiological recordings were acquired using Clampex (v10.7.0.3; Molecular Devices). Experiments were recorded with a sampling rate of 20 kHz for spontaneous event recordings or 50 kHz for optogenetic experiments. The signal was filtered at 3 kHz. Pipette capacitance was compensated using the compensation circuit of the amplifier.

All experiments were performed under visual control using a Teledyne Moment camera (Teledyne Technologies) controlled with Micro-Manager software (v2.0). SCLC cells were identified by expression of the cytosolic fluorescent protein tdTomato.

Pharmacological receptor blockade was performed by bath application of d-AP5 (Sigma, A8054; 20 µM) and/or Gbz (MCE, HY-103533; 12.5 µM). The event frequency of spontaneous synaptic events was analysed using Clampfit v11.2.2.17 (Molecular Devices).

For optogenetic experiments, neuronal cultures were first transduced at DIV2 with a mixture of CMV-driven Cre-expressing AAV (Addgene, 105537-AAV9) and double-loxP-flanked ChR2–eYFP AAV (Addgene, 20298-AAV1). For optogenetic stimulation of co-cultured neurons expressing ChR2–eYFP, the microscope was equipped with a SOLIS-470C LED (470-nm peak; Thorlabs), triggered for 5 ms at approximately 50% peak power every 120 s. An average trace of three recordings was calculated and subsequently analysed. Synaptic event peak amplitude was analysed using Clampfit v11.2.2.17 (Molecular Devices). In cases where synaptic events were abolished, the peak current in the corresponding temporal region of the previous synaptic event was used.

Preclinical SCLC mouse model

For preclinical experiments, we used the RP genetically engineered mouse model for SCLC, in which tumour formation is driven by Cre-inducible conditional Rb1 and Trp53 knockout, as previously described[25]. Tumours were induced and monitored with MRI as described above. Following tumour detection (minimum tumour size of 5 mm^3), mice were randomly assigned to the treatment cohorts, with sample size determined by power analysis. Compound solutions were prepared and injected as follows: etoposide (Hexal) was administered on days 1, 2 and 3 of a 14-day cycle, intraperitoneally, at a concentration of 8 mg kg–1. Cisplatin (Accord) was administered on day 1 of a 14-day cycle, intraperitoneally, at a concentration of 4 mg kg–1. Riluzole (15 mg kg–1) was dissolved in 10% DMSO, 40% PEG-300, 5% Tween-80 and 45% PBS and administered 5 days per week. DCPG (60 mg kg–1) was dissolved in PBS and administered intraperitoneally for 5 days per week. Best response was calculated as the lowest percentage change measured from the last MRI scan before treatment, including only mice that had evaluable tumours at the first follow-up. The burden per mouse was calculated as the total sum of the volumes of individual tumours. Growth curves of the total burden of individual mice were linearly interpolated between scans. The median value of the interpolated curves was plotted for each day, as long as at least seven mice were alive in the treatment cohort. The interpolated value at five times the initial burden was used to calculate the time to fivefold burden, excluding mice that did not reach a fivefold burden. Data from preclinical experiments were analysed with blinding.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Reference genomes were downloaded from the Genomic Data Commons (TCGA GRCh38.d1.vd1, https://api.gdc.cancer.gov/data/254f697d-310d-4d7d-a27b-27fbf767a834), from Ensembl (https://www.ensembl.org, GRCm38.102 and GRCm39.110) and from GTEx (https://www.gtexportal.org, Homo_sapiens_assembly38_noALT_noHLA_noDecoy_ERCC.fasta). Gene annotations were downloaded from GENCODE (vM23 and v22, https://www.gencodegenes.org/). Orthology mapping was downloaded from the HGNC database (https://www.genenames.org/; downloaded 6 January 2020). Mutation data were downloaded from the supplementary tables of the referenced publications or from the CCLE website (Cell_lines_annotations_20181226.txt and CCLE_DepMap_18q3_maf_20180718.txt, https://portals.broadinstitute.org/ccle/). TCGA expression data were downloaded from the Genomic Data Commons Data Portal (v27, https://portal.gdc.cancer.gov). GTEx expression data were downloaded from the GTEx database (v8, https://gtexportal.org). GO data were downloaded from the GO website (v2020-09-10, http://geneontology.org). ChIP–seq data were downloaded from the CISTROME database (v2, http://cistrome.org/db; accessed 27 November 2019). scRNA-seq data, as well as the corresponding metadata, were downloaded from Synapse (Synapse:syn21560406, https://www.synapse.org/) and CZ CellXGene (https://datasets.cellxgene.cziscience.com/7a30310a-2239-4d84-b99e-a12456c2fe19.h5ad). PhyloP conservation tracks across 470 mammalian genomes were downloaded from UCSC (hg38.470way.phyloP, https://genome.ucsc.edu/). UniProt Knowledgebase annotations were downloaded from the UniProt website (v2022_5, https://www.uniprot.org). Raw sequencing data from the piggyBac screen and mouse snRNA-seq are available through the Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) under accessions PRJNA1275653 and PRJNA1276342, respectively. A scanpy data object of the snRNA-seq dataset is available at Zenodo (https://doi.org/10.5281/zenodo.15647008)[85]. The full analysed data from our whole-genome analyses are available in the supplementary information tables. Source data are provided with this paper.

Code availability

Python scripts generated in this study are available from GitHub (https://github.com/beleggia-lab/neuron-to-SCLC-synapses) and Zenodo (https://doi.org/10.5281/zenodo.15667860)[86].

Change history

- 19 September 2025

A Correction to this paper has been published: https://doi.org/10.1038/s41586-025-09638-z

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Acknowledgements

We are indebted to our patients, who provided primary material. We thank B. Göbel, J. Knufer, A. Florin, M. Müller, U. Rommerscheidt-Fuß, G. Piper and J. Klimek from the University Hospital Cologne for their outstanding technical support; M. Krasnow from Stanford University for scientific input and suggestions; the CECAD In Vivo Facility and the CECAD Imaging Facility in Cologne for use of the electron and light microscopes and the Cologne Center for Genomics for sequencing; the IT Center University of Cologne (ITCC) for use of the CHEOPS and RAMSES computing clusters. Stem cell work was performed at the iPS cell laboratory of the Center for Molecular Medicine Cologne (CMMC). This work was funded through the German Research Foundation (DFG) (SFB1399–grant no. 413326622 to H.C.R., J. George, R.K.T., M.L.S., S.v.K., M.P., M.F., H.G., R.B., F.B., C.F., L.W., H.W., J. Brägelmann and R.H.-H; SFB1218–grant no. 269925409 to M. Bergami and E.M.; SFB1430–grant no. 424228829 to H.C.R.; SFB1403–grant no. 414786233 to S.v.K. and H.W.; SFB1310–grant no. 325931972 to S.v.K. and J. Brägelmann; SFB1530–grant no. 455784452 to H.C.R., H.W., R.D.J. and S.v.K.; CECAD EXC 2030–grant no. 390661388 to M. Bergami and E.M.; SFB1451–grant no. 431549029 to M. Bergami; Co 260/6-1 to A.A.H. and K.-K.C.; SFB1286/A03 and RTG 2824 to S.O.R.; HA 8562/4-1 to R.H.-H.; FOR5504 HA 8562/5-1 to R.H.-H.; SFB1678 INST 216/1317-1 to R.H.-H.; SO1155/5-1 and GRK2338 (P13) to M.L.S.; grant nos. 496650118 JA 2439/5-1 and 496874193 JA2439/4-1 to R.D.J.; SFB1588–grant no. 493872418 to M.F.; grant no. 497777992 to J. George and M.P.; and GRK 3110/1 to J. George, M.P. and S.v.K.), the Deutsche Krebshilfe (1117240 to H.C.R.; 70113041 to H.C.R. and F.B.; 70116707 to F.B.; and the Mildred Scheel Nachwuchszentrum grant 70113307 to F.B. and J. Brägelmann), the German-Israeli Foundation for Research and Development (I-65-412.20-2016 to H.C.R.), the Else Kröner-Fresenius Stiftung (EKFS-2014-A06 to H.C.R. and 2016_Kolleg.19 to H.C.R. and R.D.J.), the Boehringer Ingelheim Stiftung (exploration grant to M. Bergami and E.M.), the European Research Council (ERC-StG-2015, grant no. 677844 to M. Bergami and grant agreement no. 835102 to S.O.R.), the Alzheimer Forschung Initiative (24019R) with kind support of the Stiftung Alzheimer Initiative (SAI) to M. Bergami, the German Ministry of Education and Research (BMBF e:Med Consortium InCa, grant nos. 01ZX1901 and 01ZX2201A to R.K.T., J. George, H.C.R., S.v.K., M.L.S. and M.P.), the Chan Zuckerberg Foundation (iNano) to S.O.R., the Alexander von Humboldt Foundation (professorship award to H.W.), Cancer Research UK (A27323 to H.W.), the Wellcome Trust (214342/Z/18/Z to H.W.), the Medical Research Council (MR/S00811X/1 to H.W.), the Fritz Thyssen Foundation (10.22.1.010MN to R.H.-H.), the CMMC (JRG X to R.H.-H.), the Bruno-Helene-Joester Foundation (J. George and M.P.) and the Jean Uhrmacher Foundation (J. George). Additional funding was received from the programme ‘Netzwerke 2021’, an initiative of the Ministry of Culture and Science of the state of North Rhine-Westphalia for the CANTAR project to H.W., H.C.R., J. George, R.K.T., M.P., S.v.K., J. Brägelmann, R.D.J., M.F. and R.H.-H. We are grateful for support by the translational cancer programme of the German Cancer Aid TACTIC, project no. 70115201. The results shown here are in part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga). The data used for the analyses described in this article were obtained in part from the GTEx portal on 28 August 2020.

Funding

Open access funding provided by Universität zu Köln.

Author information

Author notes

1. These authors contributed equally: Vignesh Sakthivelu, Anna Schmitt, Franka Odenthal, Kristiano Ndoci, Marian Touet, Ali H. Shaib, Abdulla Chihab

2. These authors jointly supervised this work: Max Anstötz, Silvio O Rizzoli, Matteo Bergami, Elisa Motori, Hans Christian Reinhardt, Filippo Beleggia

3. Deceased: Karl-Klaus Conzelmann

Authors and Affiliations

1. Department of Translational Genomics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Vignesh Sakthivelu, Pascal Nieper, Ilmars Kisis, Maike Boecker, Naja M. Eckert, Manoela Iannicelli Caiaffa, Olta Ibruli, Christina M. Bebber, Ali Chitsaz, Anna Lütz, Mira Kim Alves Carpinteiro, Hannah L. Tumbrink, Pascal Hunold, Alena Heimsoeth, Gülce S. Gülcüler Balta, Julie George, Johannes Brägelmann, Silvia von Karstedt, Martin Peifer, Robert Hänsel-Hertsch, Martin L. Sos, Roman K. Thomas & Filippo Beleggia

2. Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Vignesh Sakthivelu, Anna Schmitt, Marian Touet, Pascal Nieper, Ilmars Kisis, Maike Boecker, Manoela Iannicelli Caiaffa, Olta Ibruli, Ali Chitsaz, Anna Lütz, Mira Kim Alves Carpinteiro, Maxim A. Huetzen, Jonas Goergens, Gülce S. Gülcüler Balta, Ron D. Jachimowicz & Filippo Beleggia

3. Mildred Scheel School of Oncology Aachen Bonn Cologne Düsseldorf (MSSO ABCD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Vignesh Sakthivelu, Ilmars Kisis, Maike Boecker, Manoela Iannicelli Caiaffa, Ali Chitsaz, Anna Lütz, Mira Kim Alves Carpinteiro, Gülce S. Gülcüler Balta, Johannes Brägelmann & Filippo Beleggia

4. Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Anna Schmitt

5. Institute of Biochemistry, University of Cologne, Cologne, Germany

Franka Odenthal, Kaylee M. Morris, Camilla A. Franchino & Elisa Motori

6. Cologne Excellence Cluster on Cellular Stress Response in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany

Kristiano Ndoci, Abdulla Chihab, Gulzar A. Wani, Christina M. Bebber, Jonas Benz, Laura Pérez-Revuelta, Jorge A. Soriano-Campos, Maxim A. Huetzen, Jonas Goergens, Milica Jevtic, Hannah M. Jahn-Kelleter, Aleksandra Placzek, Felix Gaedke, Astrid Schauss, Marielle Minère, Marie Müller, Henning Fenselau, Henning Walczak, Ron D. Jachimowicz, Silvia von Karstedt, Robert Hänsel-Hertsch, Matteo Bergami & Elisa Motori

7. Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Essen, Germany

Marian Touet & Hans Christian Reinhardt

8. Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany

Ali H. Shaib & Silvio O. Rizzoli

9. Department of Pediatrics, Stanford University, Stanford, CA, USA

Griffin G. Hartmann & Julien Sage

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

Griffin G. Hartmann & Julien Sage

11. Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium

Isabel Pintelon, Dirk Adriaensen & Inge Brouns

12. Antwerp Centre for Advanced Microscopy (ACAM), University of Antwerp, Antwerp, Belgium

Isabel Pintelon

13. Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technical University of Munich, Munich, Germany

Julia Weber, Roman Maresch & Roland Rad

14. Max Planck Institute for Biology of Ageing, Cologne, Germany

Maxim A. Huetzen, Jonas Goergens & Ron D. Jachimowicz

15. Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Maxim A. Huetzen, Jonas Goergens, Hans Zempel, Pascal Hunold, Alena Heimsoeth, Ron D. Jachimowicz, Johannes Brägelmann, Silvia von Karstedt, Martin Peifer, Alessandro Annibaldi, Robert Hänsel-Hertsch, Matthias Fischer, Matteo Bergami & Elisa Motori

16. Institute of Human Genetics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Hans Zempel & Robert Hänsel-Hertsch

17. Virology, Max von Pettenkofer Institute, Faculty of Medicine and Gene Center, Ludwig Maximilians University, Munich, Germany

Alexandru A. Hennrich & Karl-Klaus Conzelmann

18. Molecular Pathology, Institute of Pathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Hannah L. Tumbrink & Alena Heimsoeth

19. Translational Pain Research, Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Joerg Isensee & Tim Hucho

20. Pain Center, Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Joerg Isensee & Tim Hucho

21. Department of Experimental Pediatric Oncology, University Children’s Hospital of Cologne, Medical Faculty, University of Cologne, Cologne, Germany

Lisa Werr & Matthias Fischer

22. Research Group Synaptic Transmission in Energy Homeostasis, Max Planck Institute for Metabolism Research, Cologne, Germany

Marielle Minère, Marie Müller & Henning Fenselau

23. Policlinic for Endocrinology, Diabetes and Preventive Medicine, University Hospital Cologne, Cologne, Germany

Henning Fenselau

24. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

Yin Liu

25. Cell Death, Inflammation and Immunity Laboratory, Institute of Biochemistry I, Center for Biochemistry, Faculty of Medicine, University of Cologne, Cologne, Germany

Henning Walczak

26. Centre for Cell Death, Cancer and Inflammation, UCL Cancer Institute, University College London, London, UK

Henning Walczak

27. Institute for Metabolomics in Ageing, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Cologne, Germany

Christian Frezza

28. Institute of Genetics, University of Cologne, Faculty of Mathematics and Natural Sciences, Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Cologne, Germany

Christian Frezza

29. Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Cologne, Cologne, Germany

Julie George

30. Institute of Pathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Marcel Schmiel, Reinhard Büttner & Roman K. Thomas

31. Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Thorsten Persigehl & Holger Grüll

32. Department of Translational Oncology, German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and Ludwig-Maximilians-Universität München (LMU), Munich, Germany

Martin L. Sos

33. Department of Medicine III, LMU University Hospital Munich, Munich, Germany

Martin L. Sos

34. Institute of Neuropathology, Medical Faculty, Heinrich Heine University and University Hospital Düsseldorf, Düsseldorf, Germany

Guido Reifenberger

35. Institute of Anatomy II, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany

Max Anstötz

36. Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany

Silvio O. Rizzoli

37. Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells’ (MBExC), University of Göttingen, Göttingen, Germany

Silvio O. Rizzoli

38. Institute of Genetics, University of Cologne, Cologne, Germany

Matteo Bergami

39. Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

Matteo Bergami

40. West German Cancer Center, University Hospital Essen, Essen, Germany

Hans Christian Reinhardt

41. DKTK, partner site Essen, University Hospital Essen, Essen, Germany

Hans Christian Reinhardt

42. Center for Molecular Biotechnology, University Hospital Essen, Essen, Germany

Hans Christian Reinhardt

Contributions

M. Bergami, E.M., H.C.R. and F.B. designed and coordinated the overall study, with scientific advice from R.K.T., M.A. and S.O.R. H.C.R. is the lead corresponding author. R.R. and H.C.R. designed the piggyBac screen. A. Schmitt, J.W., R.M., C.M.B. and F.B. performed the piggyBac screen. R.R., H.C.R. and F.B. analysed piggyBac screen data. A. Schmitt, G.R. and R.B. performed immunohistochemical analysis. M.P., J. George, R.K.T. and F.B. designed the analysis of human genetic data. M.P., J. George and F.B. analysed human genetic data. M.F., R.K.T. and F.B. designed the analysis of human expression data. M.P., J. George and F.B. analysed human expression data. J.S. and F.B. designed the Reln knockout experiment. M.T., N.M.E. and G.S.G.B. designed and cloned CRISPR guides for the Reln experiment. G.G.H. performed the Reln knockout experiments. G.G.H., J.S. and F.B. analysed data from the Reln knockout experiment. M.S., J. Brägelmann, J. George and F.B. analysed human scRNA-seq data. R.H.-H., H.C.R. and F.B. designed snRNA-seq experiments. V.S., P.N., I.K. and P.H. performed snRNA-seq experiments. Y.L., P.N. and F.B. analysed snRNA-seq data. D.A., I.B., A.H.S., S.O.R., H.C.R. and F.B. designed immunofluorescence experiments in mouse lung tumours. A.H.S., I.P., I.B. and S.O.R. performed immunofluorescence analysis of mouse lung tumours. A.H.S., D.A., I.B. and S.O.R. analysed immunofluorescence data from mouse lung tumours. M. Bergami and E.M. designed immunofluorescence experiments with brain-engrafted cancer cells. F.O., K.N., M. Bergami and E.M. performed immunofluorescence experiments with brain-engrafted cancer cells. A.H.S., S.O.R., M. Bergami, E.M., H.C.R. and F.B. designed immunofluorescence experiments with cultured cells. F.O., K.N., A.H.S., G.A.W. and E.M. performed immunofluorescence experiments of cultured cells. F.O., A.H.S., K.M.M., S.O.R. and E.M. analysed the immunofluorescence data from experiments with cultured cells. A. Schauss, M. Bergami, E.M. and F.B. designed electron microscopy experiments with cultured cells, brain-engrafted cells and mouse lungs. K.N., A. Chihab, J. Benz, L.P.-R., J.A.S.-C. and F.G. performed electron microscopy experiments of cultured cells, brain-engrafted cells and mouse lungs. V.S., M.T., K.N., A. Chihab and M. Bergami analysed electron microscopy data. H.F., M.A., M. Bergami and F.B. designed electrophysiology experiments. K.N., M. Minère, M. Müller, M.A. and M. Bergami performed electrophysiological measurements. K.N., M.A. and M. Bergami analysed electrophysiology data. H.M.J.-K. and M. Bergami designed rabies tracing experiments. A.A.H. and K.-K.C. generated rabies virus. M.J. generated retroviruses. F.O., K.N., A. Chihab and M. Bergami performed rabies tracing experiments in vitro and in vivo. K.N., F.O. and A. Chihab analysed rabies tracing data. V.S., A. Schmitt, K.N., M.T., M. Boecker, A.H.S., I.P., I.B., M. Bergami and F.B. prepared ex vivo samples. M.T., C.F., T.H., M.L.S., M. Bergami, E.M., H.C.R. and F.B. designed neuronal and nodose ganglion co-culture experiments. V.S., M.T., G.A.W., A.P., M.J., J.I. and E.M. established neuronal and nodose ganglion co-cultures. H.Z. established induced human neuronal cultures. V.S., F.O., M.T., A.H.S., G.A.W., M. Boecker, C.A.F., A.L., M.J., H.Z., J.I. and A.H. prepared co-cultures and co-culture samples. F.O., M.T., R.D.J., S.v.K., A.A., M. Bergami, E.M., H.C.R. and F.B. designed cell viability experiments. V.S., F.O., M.T., M.A.H. and J. Goergens performed cell viability experiments under different conditions. F.O., P.N., M.K.A.C., E.M. and F.B. analysed data from cell viability experiments. H.W., T.P., H.G., M.L.S., H.C.R. and F.B. designed preclinical experiments. V.S., A. Schmitt, M.T., M. Boecker, M.I.C., O.I., H.L.T., L.W. and F.B. performed preclinical experiments. A. Schmitt, M.T., M. Boecker, M.I.C., O.I., A. Chitsaz, T.P. and F.B. analysed preclinical and MRI data. A. Schmitt, F.O., K.N., I.P., J. Brägelmann, A.H.S., S.O.R., G.R., G.G.H., I.B., M.A., M. Bergami, E.M. and F.B. generated the figures. M. Bergami, E.M., H.C.R. and F.B. wrote the manuscript. All authors revised the manuscript.

Corresponding authors

Correspondence to Max Anstötz, Silvio O. Rizzoli, Matteo Bergami, Elisa Motori, Hans Christian Reinhardt or Filippo Beleggia.

Ethics declarations

Competing interests

H.C.R. received consulting and lecture fees from Abbvie, AstraZeneca, Vertex and Merck. H.C.R. received research funding from AstraZeneca and Gilead Pharmaceuticals. H.C.R. is a co-founder of CDL Therapeutics. R.K.T. is a founder of PearlRiver Bio (now part of Centessa), a shareholder of Centessa, a founder and shareholder of Epiphanes and a consultant to PearlRiver Bio and Epiphanes. R.K.T. has received research support from Roche. R.K.T. and J.S. are co-founders and shareholders of DISCO Pharmaceuticals. M.L.S. is a co-founder and was an advisor of PearlRiver Bio (now part of Centessa). M.L.S. received research funding from PearlRiver Bio (now part of Centessa). S.O.R. is a shareholder of NanoTag Biotechnologies. H.W. was a co-founder of Apogenix. J. George is a consultant to DISCO Pharmaceuticals and received honoraria from MSD and Boehringer Ingelheim. J. Brägelmann has received research funding from Bayer and travel grants from Merck and Bicycle Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Frank Winkler 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 piggyBac insertional mutagenesis screen.

a) Alleles included in the mouse model b) Mouse lines included in the screen carry the Rb1fl/fl and Tp53fl/fl alleles with the addition of the conditional allele to express the piggyBac transposase (Rosa26LSL-PB). The RPLH line (blue) additionally carries the donor allele ATP1-H39, with 80 copies of the ATP1 transposon on chromosome 5. The RPLS line (orange) additionally carries the donor allele ATP1-S2, with 20 copies of the ATP1 transposon on chromosome 10. c, d) Tumors derived from RPLH and RPLS mice display typical SCLC morphology, including scant cytoplasm, salt and pepper chromatin and positivity for NCAM1 and SYP. e) Tumors harvested from 31 RPLH (blue) and 27 RPLS (orange) mice include lung and metastatic samples and derive from untreated mice, from mice treated with etoposide and cisplatin and from mice treated with anti-PD1 antibody RMP1-14. PB, piggyBac inverted terminal repeat (ITR); SB, Sleeping Beauty ITR; SA, splicing acceptor; pA, polyadenylation signal; CAG, CMV enhancer and chicken beta-actin promoter; SD, splicing donor; NEO, neomycin resistance; iPBase, piggyBac transposase. f-m) Transposon insertions (red arrows) identified in selected genes (horizontal blue lines). The orientation of the exons (vertical obtuse blue angles) point to the direction of transcription. f) Insertions in Crebbp g) Insertions in Pten. h) Insertions in Nfib. i) Insertions in Trp73. j) Insertions in Nrxn1. k) Insertions in Nlgn1. l) Insertions in Dcc. m) Insertions in Reln.

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Extended Data Fig. 2 Overview of genetic data from human SCLC patients.

a) Origin and characteristics of human samples from different studies. b) Similar genes are identified in tumor and cell line samples. c) Similar genes are identified in primary and metastatic samples. d) Similar genes are identified in treated and untreated samples. e-l) Selected genes are shown with the corresponding proteins annotated with UniProt Knowledgebase annotations. Mutations identified in SCLC samples are shown as a lollipop chart above the protein. Severe mutations (stop, frameshift, start-loss, and canonical splice-site mutations) are shown in red. Nonsynonymous mutations (amino-acid substitutions, non-frameshift indels) are shown in light blue. e) Mutations in TP53 are either severe or clustered in the DNA-binding domain. f) Mutations in RB1 are almost exclusively severe. g) Mutations in CREBBP are severe or clustered in the HAT domain. h) Mutations in PTEN are severe or clustered on the active site. i) Mutations in NRXN1 are mainly nonsynonymous. j) Mutations in NLGN1 are exclusively nonsynonymous. k) Mutations in DCC are mainly nonsynonymous. l) Mutations in RELN are nonsynonymous or severe.

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Extended Data Fig. 3 Cross-validation of genetic datasets.

a) The background rate of genetic events in the piggyBac and human mutation datasets have opposite correlations to expression levels. Genes are binned into equal-sized bins based on their expression level. On the x axis, the bins are plotted on the mean expression of their genes. On the y axis, the bins are plotted on the ratio of total observed/total expected events for the bin. p-value: two sided Spearman correlation test. b) Mean conservation of mutated nucleotides for genes not identified in the human mutation datasets, for genes identified only in the human mutations dataset and for genes identified in both the human mutations and piggyBac datasets. q-values: two-sided Mann-Whitney test with FDR correction, both compared to non-significant genes c-e) Hematoxylin-eosin stains of individual RPR2TC mice induced with lentiviral vectors carrying a non-targeting sgRNA or sgRNAs targeting Reln. Representative of 7, 5 and 5 mice, respectively. f) The mean area of tumors identified in mice induced with sgRNAs targeting Reln is significantly larger than the area of tumors induced with the non-targeting sgRNA. N = 7 mice for non-targeting sgRNA and sgReln-1, n = 5 mice for sgReln-2. q-values: Mann-Whitney test with FDR correction, both compared to sgNT controls. g) The size of individual tumors from the mice in f is significantly greater in mice induced with sgRNAs targeting Reln. q-values as in f. h) Force-directed graph of gene ontology analysis, showing gene sets enriched in both the piggyBac dataset and the analysis of human genetic data. Most gene sets are related to synaptic and neuronal functions (light blue).

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Extended Data Fig. 4 Expression of synaptic gene sets in SCLC.

a-h) Selected genes highly expressed in SCLC. The expression levels of individual SCLC samples are shown on the left of each panel. The median expression levels in cancer types included in TCGA and Neuroblastoma as positive control are depicted in the middle. The median expression levels of healthy tissues are on the right. a, b, c, d) The expression levels of TOP2A, CCNE2, RRM2 and UBE2C, representative of genes involved in cell-proliferation, are higher in SCLC than in any other cancer or healthy tissue. e, f, g, h) The expression levels of NRXN1, NLGN1, DCC and RELN, representative of synaptic and neuronal genes, are higher in SCLC than in most other cancers and tissues. i) Leiden clustering of snRNA-seq data from six murine tumors derived from Rb1fl/fl;Trp53fl/fl mice. j) The leiden clusters from panel i show markers of SCLC cells (Calca, Chga, Syp, Ncam1) or of one of four broad cell types expected in the lung (Ptprc for immune cells, Col1a2 for stromal cells, Sftpb for epithelial cells and Cdh5 for endothelial cells). k) Visualization of cell types based on markers identified in panel j. l) Genes within the Synaptic Membrane GO term are enriched in the cancer cells. m) Genes within the Glutamatergic Synapse GO term are enriched in the cancer cells. n) Comparison of murine SCLC cells to other lung cell types revealed an enrichment in neuronal and cell proliferation GO terms, with striking resemblance to the analysis of bulk human RNA-seq data (Fig. 1c). o) Comparison of SCLC cells to other cell types in published human lung scRNA-seq data revealed an enrichment in neuronal and cell proliferation GO terms, with striking resemblance to the analysis of bulk human RNA-seq data (Fig. 1c).

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Extended Data Fig. 5 Nerve fibers in the SCLC microenvironment.

a-h) Confocal images of lung cryostat sections of RP mice. L: lumen of the airways. E: airway epithelium. a) Intraepithelial VGluT1+ nerve terminals (arrowheads) branch between the CGRP+ PNECs. b) Intraepithelial P2X3+ nerve terminals (arrowheads) protruding between the CGRP+ (green) neuroendocrine cells of a NEB. c) GAP43+ nerve fibers (arrows) branch and protrude (arrowheads) between the CGRP+ PNECs. d) A GAP43+ nerve fiber (arrow) branches (arrowheads) between the CGRP + SCLC cells (green). CGRP+ nerve fibers (open arrows) are seen close to the base of the tumor. e) Subepithelial SYP+ and CGRP+ nerve terminals (arrows) innervate a NEB. Remarkable is that the subepithelial area adjacent to a large tumor appears devoid of nerve fibers. f) Small tumor (ST) from an RPC mouse, with no visible innervation from VGluT1+ fibers. Varicose CGRP+ fibers are visible below the tumor (open arrowheads). g) Large tumor (LT) surrounded by varicose CGRP+ and substance P+ (SP) nerve fibers. h) SYP+ (arrows) and CGRP+ (open arrows) nerve fibers can be seen in the epithelium at the base of a small tumor (ST). i) Electron micrographs showing a cancer cell surrounded by long axon-like fibers near the periphery of a tumor in the lung of an RP mouse. j, k) Magnifications showing the presence of enlarged structures along identified fibers (yellow pseudocolor) containing multiple vesicles and mitochondria (M) near the cancer cell (red pseudocolor). l-p) DAB staining of biopsies from three SCLC patients. All sections are counterstained with hemalum. l) NF-H-positive nerve fibers near an intratumoral vessel in the biopsy from the first patient. m, n) NF-H-positive nerve fibers at the borders of a SYP-positive tumor in a biopsy from the second patient. o, p) NF-H-positive nerve fibers at the borders of a SYP-positive tumor in a biopsy from a third patient.

Extended Data Fig. 6 Cancer-to-neuron contacts in vitro.

a) Co-culture of murine cortical neurons (immunolabeled against MAP2) and SCLC cells (COR-L88, expressing DsRed) showing the appearance of dense VGLUT1-positive puncta onto SCLC cells contacted by neuronal terminals b-d) Different views of a 3D-reconstruction of 3D-STED for co-cultures immunolabeled against axonal marker SMI-312, mNeonGreen to mark SCLC cells, dendritic marker MAP2, and postsynaptic marker HOMER1, showing that that the contacts on cancer cells are predominantly axonal. Representative of 3 experiments. e) Co-culture of human iPSC-derived cortical neurons and SCLC cells (COR-L88, expressing tdTomato), immunostained for the pre- and post-synaptic markers BSN and HOMER1. Right panels show a single confocal stack (top) and 3D reconstruction (bottom) of an SCLC cell contacted by a GFP-positive axonal fiber (white arrowheads) exhibiting BSN and HOMER1 co-localizing puncta (yellow arrowheads) located outside and inside the SCLC cell surface. f) Confocal overview of SCLC cells (mNeonGreen + , shown in white) co-cultured with murine nodose ganglia. g) A detailed view of the boxed region from panel f, followed by individual magnified regions, which indicate the arrangements of VGluT1 (presynaptic, neuronal) and HOMER1 (postsynaptic, within SCLC cell) molecules. h) Line scans were drawn automatically across HOMER1 spots, starting in their intensity maxima, and moving towards the periphery. The signal drops, as expected; a similar drop is seen in the VGluT1 signal, confirming their close apposition (N = 4 independent experiments, n = 782 line scans. Colocalization tested using a two-sided Pearson correlation test. Error bars: standard error of the mean). i) Correlative intensity scatter plot of SCLC mNeonGreen signal vs HOMER1 signal (N = 4 independent experiments, N = 782) indicates that a substantial proportion of the HOMER1-marked spots are formed on SCLC cells, and therefore show a measurable mNeonGreen signal (62.02%).

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Extended Data Fig. 7 Cancer-to-neuron contacts in vivo.

a) Confocal imaging of grafted DsRed-expressing SCLC cells in the hippocampus of a Thy1-GFP mouse (top-left), depicting GFP-positive fibers contacting SCLC cells in the tumor periphery (lower-right). On the right and below are orthogonal views on a point of contact between a putative axonal bouton and a DsRed/HOMER1 double-positive punctum in the SCLC cell (arrowheads). b) 3D-STED image of a lung section immunolabeled against the presynaptic marker VGluT1, the postsynaptic marker HOMER1, and an axonal marker (SMI312/SMI311 epitopes). The right panels show magnifications of putative synapses on cancer cells in the marked regions. c) Automatic line scans from the intensity maxima of HOMER1 spots towards the periphery. The signal drops and a similar drop is seen in the VGluT1 signal (N = 3 independent experiments, n = 609 line scans, of which 213 represented putative synapses. Two-sided Pearson correlation test. Error bars: standard error of the mean). d) Correlative intensity scatter plot of SCLC mNeonGreen signal vs HOMER1 signal (N = 3 independent experiments, n = 609 measurements) indicates that most HOMER1 spots in these regions are within SCLC cells. e) Quantification of synapses contacting tdTomato-positive cancer cells in brain allografts. For each mouse (n = 3), 90-96 perimeters in 12-14 consecutive ultrathin sections were examined, for a total of 280 cell perimeters. f) CLEM of COR-L88 SCLC cells (expressing DsRed) co-cultured with cortical neurons. The left panels depict the registered overlay between fluorescent and EM images. The third panel shows the electron tomogram of a synapse, with a presynaptic bouton (yellow pseudocolor) filled with vesicles, contacting the cancer cell (red pseudocolor). The right panels show a 3D reconstruction of the tomogram (250 nm thick), depicting cancer cell (red), axonal bouton (yellow) with vesicles (white), and vesicles located within 20 nm from the plasma membrane (PM) (green).

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Extended Data Fig. 8 Electrophysiology of SCLC cells.

a) Example of a patched DsRed-expressing SCLC cell (COR-L88) under whole-cell configuration in cortical neuron-SCLC co-cultures. b) Whole-cell, voltage-clamp traces of sPSCs in SCLC cells (COR-L88) in the presence or absence of neurons. c) Quantification of sPSC frequency in co-culture in the presence or absence of the indicated blockers (TTX, CNQX, D-AP5, Riluzole and Bicuculline) (n = 7-30 cells per condition). All conditions are compared to untreated co-cultures. q values: two-sided Mann-Whitney with FDR correction d-g) Whole-cell voltage-clamp traces of H524 cells d) Traces recorded at three different voltages (−70 mV, 0 mV, and +40 mV) in mono-culture. e) Traces recorded at +40 mV in co-culture with cortical neurons. The synaptic events (red stars and numbers) can be completely abolished by the application of the NMDA receptor blocker D-AP5 and display a long decay time lasting several hundred milliseconds. f) Traces recorded at −70 mV and 0 mV in co-culture with cortical neurons. Note the occurrence of synaptic events at 0 mV, indicating a GABA-A-mediated chloride inward current. g) Traces recorded at +40 mV in co-culture with Channelrhodopsin 2-eYFP expressing (ChR2-eYFP, green) cortical neurons after a short blue light pulse (5 ms). Note the partial decrease in event amplitude during NMDA receptor blockade with D-AP5 (orange), followed by complete abolishment after additional GABA-A receptor blockade with Gbz (lower trace). h) Whole-cell, voltage-clamp recording in an acute hippocampal slice of grafted DsRed-expressing murine SCLC cells. i) Quantification of sPSCs in grafted cancer cells in acute slices in the absence or presence of the indicated blockers (TTX, CNQX, D-AP5 and Bicuculline). All conditions are compared to untreated slices. q values: two-sided Mann-Whitney with FDR correction, n = 8-17 cells per condition.

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Extended Data Fig. 9 RABV-tracing of SCLC cells to presynaptic neurons.

a) RABV-GFP-based tracing of neurons monosynaptically connected to DMS273 SCLC cells expressing DsRed. Right panels show enlarged views of the boxed area containing double-positive starter SCLC cells (arrowheads). b) 3D reconstruction of double-positive starter cells in a cluster of DsRed-expressing SCLC cells (COR-L88) following RABV-GFP-based tracing. c) Magnification of the panel boxed in b, showing the profuse expression of VGluT1-positive puncta in GFP-positive neuronal fibers (yellow arrowheads) contacting starter SCLC cells. d) Time-lapse of RABV-traced neurons in neuron-SCLC co-cultures over 48 h. Selected frames at the indicated time points show the initial presence of starter cancer cells (double-positive for the retrovirally-encoded DsRed and the RABV-encoded GFP, yellow arrowheads), which proliferate over time, and the emergence of GFP+ neurons at 48 h (white arrowheads). e) Example of RABV-GFP-based tracing of morphologically identified inhibitory GABAergic neurons located in the stratum oriens (SO) and pyramidale (SP) of CA1, following transplantation of G-TVA-expressing murine SCLC cells (dashed area). Right panels show zooms of the boxed areas depicting identified GFP+ neurons (1) and starter SCLC cell (2), contacted by varicosities of a passing axon (arrowheads). SR, stratum radiatum. f) Example of RABV-GFP-based tracing following transplantation of TVA-only-expressing murine SCLC cells (dashed area), showing the virtual absence of GFP-positive presynaptic neurons. Right panels show zooms of the boxed area depicting an identified DsRed/GFP double-positive SCLC cell (arrowhead).

Extended Data Fig. 10 neuron-promoted SCLC proliferation and Grm8.

a) Growth of SCLC cell lines monitored via live cell imaging under different conditions. Each dot represents an individual well. All conditions are compared to the growth in co-culture with cortical neurons. q-value: two-sided Mann-Whitney test with FDR correction. n ≥ 20 wells / condition, n ≥ 4 neuron batches. Red q-values indicate faster growth than neuronal co-cultures. b) Growth of NSCLC cell lines monitored via live cell imaging in mono-culture or co-culture. Each dot represents an individual well. p-values: two-sided Mann-Whitney test. n ≥ 20 wells / condition, n ≥ 4 neuron batches.c, d) Individual wells containing COR-L88 SCLC cells in mono-culture (c) or in co-culture with nodose ganglia (d). e) Quantification of the growth of SCLC cell lines via live cell imaging with and without nodose ganglia. Each dot represents an individual well. p-value: two-sided Mann-Whitney test as in b. n = 4-29 wells/condition, n ≥ 4 individual ganglia. f) SCLC samples are separated into classic and variant subtypes based on the expression of neuroendocrine features. g) SCLC samples of the SCLC-A and SCLC-N subtypes express higher level of genes included in the GO term Glutamatergic Synapse. h) The expression of GRM8 is higher in SCLC than in most other cancers and tissues and is especially high in classic SCLC with strong neuroendocrine features. i) GRM8 protein with annotations from the UniProt Knowledgebase. Mutations in SCLC samples are shown as a lollipop chart. j) Transposon insertions identified in Grm8. k) UMAP plot of published human SCLC and normal lung scRNA-seq. The cells are grouped into differentiation groups. l) GRM8 is specifically expressed in SCLC cells from panel k. m) UMAP plot of snRNA-seq samples from murine RP tumors, characterized in Extended Data Fig. 4. Grm8 is specifically expressed in SCLC cells.

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Extended Data Fig. 11 Response of SCLC tumor-bearing mice under anti-glutamatergic treatment.

a-f) Representative MRI scans of tumor-bearing Rb1fl/fl;Trp53fl/fl mice under different treatments. Tumors are pseudocolored in red. a, b) Mouse treated with PBS, showing a large increase in tumor size after one month. c, d) Mouse treated with DCPG, showing a minor increase in tumor size after one month. e, f) Mouse treated with riluzole, showing a minor increase in tumor size after one month. g) Median tumor burden for RP mice treated with riluzole (n = 12), DCPG (n = 12) or vehicle controls (n = 33). q-values: two-sided Mann-Whitney test with FDR correction. h) Waterfall chart, showing the best response of individual tumors, grouped by mouse. The mice are sorted based on the total best response. i) Time required for tumors to reach a size five fold greater than the size at inclusion for mice treated with riluzole (n = 12), DCPG (n = 11) or the relative controls (n = 32). q-values: two-sided Mann-Whitney test with FDR correction. j) Best response achieved throughout treatment, calculated based on the total tumor burden for each mouse for mice treated with DCPG (n = 12), riluzole (n = 12) or the relative controls (n = 28). q-values: two-sided Mann-Whitney test with FDR correction. k) Best response of individual tumors from RP mice induced with CGRP-Cre. The mice were treated with DCPG (31 tumors from 18 mice), riluzole (19 tumors from 13 mice), or the relative controls (23 tumors from 17 mice for PBS plus 20 tumors from 12 mice for riluzole vehicle). l) Survival of RP mice induced with CGRP-Cre. Riluzole treatment (n = 14) results in significantly longer survival compared to control mice (n = 18 for PBS plus n = 14 for riluzole vehicle). The benefit provided by DCPG (n = 20) is not statistically significant. q-values: two-sided Mann-Whitney test with FDR correction.

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Extended Data Fig. 12 Combination treatment with chemotherapy and anti-glutamatergc drugs.

a-f) Representative MRI scans of tumor-bearing Rb1fl/fl;Trp53fl/fl mice under different treatments. Tumors are pseudocolored in red. a, b) Mouse treated with etoposide and cisplatin (EC), showing the increase in tumor size after two months. c, d) Mouse treated with EC and DCPG (ECD), showing the increase in tumor size after two months. e, f) Mouse treated with EC and riluzole (ECR), showing a stable disease after two months. g) Median tumor burden for RP mice treated with EC (n = 11), ECR (n = 13) or ECD (n = 10). q-values: two-sided Mann-Whitney test with FDR correction. h) Waterfall chart, showing the best response of individual tumors, grouped by mouse. The mice are sorted based on the total best response. i) Time required for tumors to reach a size five fold greater than the size at inclusion for mice treated with EC (n = 9), ECD (n = 9) or ECR (n = 10). q-values: two-sided Mann-Whitney test with FDR correction. j) Best response achieved throughout treatment, calculated based on the total tumor burden for each mouse for mice treated with EC (n = 10), ECR (n = 9) and ECD (n = 10). q-values: two-sided Mann-Whitney test with FDR correction.

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Supplementary information

Supplementary Tables

Supplementary Tables 1–11: comparisons of subcohorts of the piggyBac screen, gene-level and Gene Ontology analysis of piggyBac insertions, human somatic mutations and expression data.

Reporting Summary

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Sakthivelu, V., Schmitt, A., Odenthal, F. et al. Functional synapses between neurons and small cell lung cancer. Nature 646, 1243–1253 (2025). https://doi.org/10.1038/s41586-025-09434-9

- Received: 20 December 2022

- Accepted: 18 July 2025

- Published: 10 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09434-9

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Engineered prime editors with minimal genomic errors

Vikash P. Chauhan,

Phillip A. Sharp &

Robert Langer

Nature volume 646, pages 1254–1260 (2025)

Abstract

Prime editors make programmed genome modifications by writing new sequences into extensions of nicked DNA 3′ ends[1]. These edited 3′ new strands must displace competing 5′ strands to install edits, yet a bias towards retaining the competing 5′ strands hinders efficiency and can cause indel errors[2]. Here we discover that nicked end degradation, consistent with competing 5′ strand destabilization, can be promoted by Cas9-nickase mutations that relax nick positioning. We exploit this mechanism to engineer efficient prime editors with strikingly low indel errors. Combining this error-suppressing strategy with the latest efficiency-boosting architecture, we design a next-generation prime editor (vPE). Compared with previous editors, vPE features comparable efficiency yet up to 60-fold lower indel errors, enabling edit:indel ratios as high as 543:1.

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Main

Prime editors are advanced CRISPR tools that enable replacement of targeted DNA with programmed sequences[3]. A prime editor comprises a Cas9 nickase (Cas9n) fused to a reverse transcriptase and paired with an extended guide RNA (pegRNA) that encodes both the genomic target sequence and the intended edit[1]. Editing initiates with the prime editor binding its genomic target and forming a single-strand DNA break (nick; Fig. 1a). The nicked 3′ DNA end is released to anneal to the pegRNA template, priming the reverse transcriptase to write the template sequence into an extension of the 3′ DNA end. This resulting edited 3′ new strand can displace the competing 5′ strand to install the intended edit. This process can be adapted to a wide variety of edit types, including substitutions, insertions and deletions1,4,5,6,7,8,9. Substantial efforts have been made to increase prime-editing efficiency, including inhibition of cellular mismatch repair (MMR)2,10, stabilization of pegRNAs11,12,13 and engineering the reverse transcriptase domain2,14,15,16. These advances have led to highly effective and versatile prime-editing systems.

Fig. 1: Mutations that relax prime editor nick positioning suppress error generation.

a, Schematic of the prime-editing process, depicting two competing structures that can produce edits or indel errors. PAM, protospacer-adjacent motif; RT, reverse transcriptase. b, Schematic of mechanisms through which indel errors are produced by end joining of edited 3′ new strands. c, Model depicting how Cas9 variants with different break stabilities are proposed to either protect non-target strand 5′ ends (left) or promote their degradation (right). d, Positions of most frequently shifted non-target strand nicks for Cas9 variants screened. e, Screen of different Cas9 variants to quantitate the frequency of shifted nicks (top) compared with the relative frequency (middle) and extent (bottom) of cut DNA end degradation. f, Screen of engineered PE variants to detect the relative frequency of nicked-end flap degradation (top) determined using a sensor assay (bottom). g, Screen of engineered PE variants to suppress indel errors with quantification of edit and indel frequencies (bottom), indel classes (middle) and edit:indel ratios (top). h–j, Correlations comparing PE nicked-end flap degradation with Cas9 nick shift frequencies (h), PE edit:indel ratios with Cas9 nick shift frequencies (i) and PE edit:indel ratios with PE nicked-end flap degradation (j) for variants in the screen with the same mutations. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons with Cas9 (e) or to PE (f,g). Correlations were determined by Pearson coefficients (h–j). All data were analysed by deep sequencing and represent means of n = 6 (e) or n = 3 (f,g) independent replicates with standard errors.

A key remaining challenge is the elimination of errors that occur as byproducts of prime editing. These errors are insertion and deletion (indel) mutations generated in lieu of the intended edit within a fraction of targeted cells, resulting in DNA sequences that are unpredictable and possibly deleterious2,17. Previous work has identified major drivers of indel error formation, although the mechanisms have not been fully elucidated. First, the prime editor may extend the edited 3′ new strand past the pegRNA template and into the scaffold1,2. This can be addressed by recoding the pegRNA scaffold to limit its homology with the genomic sequence[2]. Second, errant double-strand breaks (DSBs) can be generated, sometimes as a consequence of MMR converting nicks into DSBs, and can induce indels consistent with DSB repair2,18. These indels can be addressed through inhibition or avoidance of MMR2,10. Third, the edited 3′ new strand generated by the prime editor can end join at unintended positions[2]. This often produces large deletions or tandem duplication-like insertions, and there are presently no strategies for addressing these errors.

In the prime-editing process, the edited 3′ new strand is disfavoured in displacing the competing 5′ strand due to the former being mismatched to the complementary strand[1] (Fig. 1a). We reasoned that this bias against annealing of the edited 3′ new strand can limit editing efficiency and promote errors (Fig. 1b). It is known that the 3′ end of a nicked DNA substrate can escape from bound Cas9 complex, whereas the 5′ end remains stably bound19,20. We hypothesized that destabilizing positioning of the 5′ end might enable its degradation (Fig. 1c). We recently discovered that mutations in the Cas9–DNA interface can relax nick positioning[21], which encouraged our exploration here of whether competing 5′ strands formed by prime editors can be destabilized. In this study, we examined mutations that relax Cas9 nick positioning for induction of nicked end degradation. Using these mutations, we engineered prime editors to discover an unexpectedly large influence of nick relaxation on indel error generation. Through rational design, we created efficient prime editors that rarely produce indel errors.

Relaxing nick positioning reduces errors

To determine whether the DNA ends at Cas9-induced nicks can be destabilized, we characterized nick positioning and DNA end degradation for engineered Cas9 variants. We assessed nick location through inference from indirect measurements of paired DSB junctions in cells21,22. In this assay, paired gRNAs produce deletion junctions between their DSBs, and retained sequences within these junctions indicate shifted nicks (Extended Data Fig. 1a). We screened Streptococcus pyogenes Cas9 alanine substitutions in the DNA-binding clefts, combining sequencing data for paired gRNA junctions at the CXCR4 locus with previously generated data at the EMX1 locus[21] (Extended Data Fig. 1b,c). Several Cas9 variants promoted retention of sequences within these junctions (Extended Data Fig. 1c), which we interpreted as evidence of relaxed nick positioning (Fig. 1d and Extended Data Fig. 1d). As these nick positions are inexact due to extensive processing of DNA ends, we reasoned that nick relaxation could be quantitated using the frequency of nicks shifted from the canonical position, which we termed ‘nick shift frequency’ (Fig. 1e). We further assayed DNA end perturbations using analysis of paired DSB junctions, reasoning that additional deletions on the PAM and non-PAM sides of these junctions would indicate degradation of their respective DNA ends (Extended Data Fig. 2a). We quantitated the length and frequency of deletions as metrics of degradation for these Cas9 variants (Fig. 1e and Extended Data Fig. 2b). Our analysis indicated that wild-type Cas9 produced fewer deletions on the PAM side versus the non-PAM side, mostly 1 bp but rarely larger, which we interpreted to indicate minimal end degradation with stable PAM-side ends (Extended Data Fig. 2c). We similarly observed minimal PAM-side deletions for several Cas9 variants without relaxed nicks. By contrast, mutations that relaxed nick positioning (R780A, K810A, K848A, K855A, R976A and H982A) increased PAM-side deletions without affecting non-PAM-side ends. In particular, R976A and H982A generated dominant deletions several base pairs in length, which we interpreted as evidence of significant PAM-side end degradation (Extended Data Fig. 2c). We postulated that this bias towards removal of PAM-side ends indicates destabilization of the non-target strand nicked 5′ end promoted by relaxed nick positioning.

As our findings suggested Cas9 mutations that relaxed nick positioning also promoted DNA end degradation, we explored whether prime editors with these mutations might show enhanced editing. We began with PEmax, composed of Cas9n(H840A) with R221K–N394K mutations[2]. For direct comparison to the screened Cas9 variants, we reverted these to R221–N394 and named this prime editor PE. To probe how nick-relaxing mutations affect editing outcomes, we introduced the 14 mutations from our Cas9 screen into PE. We developed an assay for prime editor nicked end degradation at the AAVS1 locus, where paired nicks produce homology deletions by annealing of nicked non-target strand flaps, with an included activity marker edit (Extended Data Fig. 2d,e). In this assay, stable nicked ends enable flap homology deletions while degraded nicked ends inhibit deletions. We quantitated the ratio of the activity marker edit to the flap homology deletion, which we termed ‘flap degradation’, to infer nicked end degradation for these 15 PE variants in HEK293T cells (Fig. 1f and Extended Data Fig. 2f). We also screened these PE variants targeting edits at the TGFB1 and KRAS loci in HEK293T cells with the pegRNA + nicking gRNA (ngRNA) mode (Fig. 1g and Extended Data Fig. 3a). We further screened these variants for editing in a negative position at the TGFB1 locus (Extended Data Fig. 3b), indicating relaxation of the nick position to shift the +1 start of the editing window, and quantitated nick relaxation using the ratio of negative versus positive edit efficiencies (Extended Data Fig. 3c). Several PE variants (R780A, K810A, K848A, K855A, R976A and H982A) increased negative:positive edit ratios and flap degradation up to 22-fold, which we interpreted as supporting non-target strand nick relaxation and nicked end degradation. These same PE variants decreased indel errors up to 20-fold, with improved edit:indel ratios up to 10-fold. We observed similar suppression of four different indel classes (deletions and insertions, without or with the edit). Of note, there was a strong correlation between nick shift frequency, flap degradation and edit:indel ratios for Cas9 and PE variants bearing the same mutations (Fig. 1h–j). This supported a connection between nicked strand degradation and indel error suppression, suggesting this as a new mechanism for enhancing prime-editing fidelity.

We next combined these mutations to create double-mutant PE variants and similarly tested them (Fig. 1f,g and Extended Data Fig. 3a–c). These variants demonstrated greatly increased negative:positive edit ratios, up to 42-fold, and dramatically reduced indel errors, up to 118-fold lower than PE. One variant, K848A–H982A, nearly eliminated errors, reducing them 36-fold versus PE and improving the edit:indel ratio 28-fold. We named this variant precise prime editor (pPE). Comparing pPE with PEmax across six loci (CXCR4, EMX1, GFP, MYC, STAT1 and TGFB1) in HEK293T cells revealed consistent indel error suppression. For pegRNA-only editing versus PEmax, pPE reduced indels 7.6-fold (range of 1.1–13-fold) and increased the edit:indel ratio 6.3-fold (range of 0.4–10-fold; Extended Data Fig. 4a–d). These improvements versus PEmax were more dramatic for pegRNA + ngRNA editing, in which pPE decreased indels 26-fold (range of 7.7–36-fold) and improved the edit:indel ratio 20-fold (range of 6.6–39-fold; Extended Data Fig. 4e–h). Examination of different indel classes demonstrated significant reductions of each (Extended Data Fig. 4c,g). For pegRNA-only editing, these reductions were 2.5–25-fold, and for pegRNA + ngRNA editing, they were 7.8–28-fold. We observed similar improvements in the presence of MMR inhibition (Extended Data Fig. 4i–p). These gains for pPE enabled edit:indel ratios of up to 361:1 (Extended Data Fig. 4d,h). Therefore, the effects of nick-relaxing mutations on error generation extended to diverse edit types and prime-editing modes.

Further design improves efficiency

Although pPE suppressed errors versus PEmax, we observed a moderate reduction in editing efficiency (Extended Data Fig. 4). Similar reductions in Cas9 activity were associated with efforts to improve on-target specificity, expand targeting space and alter repair outcomes21,23,24,25,26,27. Accordingly, we reasoned that decreased efficiency might be addressed by incorporating mutations previously found to enhance Cas9 activity into pPE21,26,27,28. We tested eight mutations that introduce charged residues near the nuclease positions in Cas9 to possibly rescue reduced activity. We screened these pPE variants using edits at the MYC and STAT1 loci in HEK293T cells in the pegRNA-only mode (Fig. 2a and Extended Data Fig. 5a). Several mutants demonstrated increased efficiency up to 1.2-fold with modestly improved edit:indel ratios. Of note, two mutations (R221K and L244Q) are known to non-specifically increase activity for Cas9 (ref. [28]), whereas the other two (G1104K and N1317R) were near A982 and increase Cas9 activity in the context of nearby mutations21,27. We then assessed combinations of these mutations to identify variants with maximized efficiency (Fig. 2a and Extended Data Fig. 5a). We observed increased efficiency up to 1.7-fold, again with improved edit:indel ratios. Comparison with mutations that reduce potential DSB formation[18], N854A and N863A, showed superior editing for our variants, and inclusion of N863A did not improve edit:indel ratios, suggesting that our variants caused minimal DSBs (Extended Data Fig. 5b–g). The most efficient variant, R221K–K848A–H982A–N1317R, also improved edit:indel ratios from 276:1 for pPE to 354:1. We named this variant extra-precise prime editor (xPE).

Fig. 2: Error suppression synergizes with efficiency-enhancing designs.

a, Screen of engineered pPE variants to enhance efficiency with quantification of edit and indel frequencies (bottom), indel classes (middle) and edit:indel ratios (top). b, Architectures of different prime editors, highlighting the components of the standard editors PEmax and PE7 versus the engineered editors xPE and vPE. MMLV, Moloney murine leukaemia virus; NLS, nuclear localization signal. c, Edit and indel frequencies comparing vPE with PE7 using pegRNA-only editing, with fold reductions in indel rates marked. d, Means of edit and indel frequencies comparing PEmax, xPE, PE7 and vPE using pegRNA-only editing (from panel c and Extended Data Fig. 6a), with each point representing an individual edit and replicate. e, Means of edit:indel ratios comparing PEmax, xPE, PE7 and vPE using pegRNA-only editing (from panel c and Extended Data Fig. 6a), with each point representing an individual edit and replicate. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons with PE7 or PEmax. All data were analysed by deep sequencing and represent means of n = 3 independent replicates with standard errors.

We investigated the capabilities of these engineered prime editors to understand the mechanisms by which they improved editing. We compared xPE with PEmax across six loci (CXCR4, EMX1, GFP, MYC, STAT1 and TGFB1) in HEK293T cells. For pegRNA-only editing, we observed a decrease in indel rates from 0.59% (range of 0.095–1.2%) for PEmax to 0.11% (range of 0.072–0.14%) for xPE with slightly lower rates of intended edits (Extended Data Fig. 6a,b). Subclassifying indels showed similar reductions for deletions without or with the intended edit, whereas the decrease in insertions was less for xPE versus PEmax (Extended Data Fig. 6c). This corresponded to broad improvements in edit:indel ratios (Extended Data Fig. 6d). Summarizing our findings for pegRNA-only editing, xPE decreased indels 5.0-fold (range of 1.3–9.2-fold) and increased the edit:indel ratio 4.2-fold (range of 0.7–8.2-fold) versus PEmax. These effects of xPE were more dramatic for pegRNA + ngRNA editing, in which we observed a reduction in indels from 16% (range of 14–19%) for PEmax to 1.8% (range of 0.52–3.1%) for xPE (Extended Data Fig. 6e,f). Analysing indel subclasses showed that xPE decreased all indel types compared with PEmax, most dramatically for deletions (Extended Data Fig. 6g). These effects for xPE led to large increases in the edit:indel ratio versus PEmax (Extended Data Fig. 6h). For pegRNA + ngRNA editing, xPE significantly reduced indels 12.7-fold (range of 5.0–27.5-fold) and improved the edit:indel ratio 9.4-fold (range of 4.0–14.1-fold) versus PEmax. These improvements for xPE led to edit:indel ratios of up to 199:1 (Extended Data Fig. 6d,h).

RNA protection boosts low-error editing

Although xPE gained efficiency over pPE while maintaining low error rates, we still observed slightly lower efficiency versus PEmax. Because the mutations in xPE are not known to reduce Cas9 activity21,24,28, we reasoned that this reduced efficiency might be related to nick repositioning. Previous work showed that pegRNA 3′ ends are unstable, decreasing efficiency by eliminating binding to nicked DNA 3′ ends[11]. We hypothesized that prime editors with repositioned nicks might be particularly susceptible to pegRNA instability due to reduced overlap between their shortened nicked DNA 3′ ends and pegRNAs. To evaluate this, we swapped our error-suppressing Cas9n from xPE (R221K–K848A–H982A–N1317R) into a recent efficiency-boosting prime-editing architecture (PE7) that stabilizes pegRNAs using the La poly-U RNA-binding protein[13]. We named this new editor very-precise prime editor (vPE; Fig. 2b). We compared vPE with PE7 across six loci (CXCR4, EMX1, GFP, MYC, STAT1 and TGFB1) in HEK293T cells using pegRNA-only editing. Compared with PE7, vPE reduced indel rates 8.6-fold (range of 1.3–16-fold), with similar efficiency, and increased the edit:indel ratio 8.2-fold (range of 1.0–15-fold; Fig. 2c). Comparing vPE and PE7 with xPE and PEmax for the same edits (Extended Data Fig. 6a,b), we observed substantially increased editing efficiency (Fig. 2d). For PE7 versus PEmax, both composed of the same Cas9n (R221K–N394K), the increase in efficiency was 2.7-fold (range of 1.4–5.3-fold). Yet for vPE versus xPE, the gain in efficiency was 3.2-fold (range of 1.5–6.5-fold), suggesting that xPE was somewhat more restrained by pegRNA instability than PEmax. This increased efficiency coincided with similarly increased indel error rates for PE7 but not for vPE (Fig. 2d). Correspondingly, PE7 featured an edit:indel ratio of 138:1, slightly better than PEmax at 91:1, whereas vPE increased the edit:indel ratio to 465:1 (Fig. 2e).

To clarify the potential of vPE over earlier prime editors, we edited a larger set of targets for both pegRNA-only and pegRNA + ngRNA-editing modes. As previous optimization of pegRNAs and paired ngRNAs enabled low indel error rates[1], we tested these optimized designs to evaluate whether further suppression was achievable[2]. These edits encompassed all transition and transversion point mutations, spanning +1 to +7 positions, at well-studied loci (DNMT1, EMX1, FANCF, HEK3/LINC01509, RNF2, RUNX1 and VEGFA) in HEK293T cells. For pegRNA-only editing, we measured mean efficiencies of 34% (range of 25–55%) for PE7 and 32% (range of 15–48%) for vPE (Fig. 3a,b). For these same edits, we observed mean indel errors of 0.50% (range of 0.15–1.1%) for PE7 and 0.14% (range of 0.039–0.23%) for vPE (Fig. 3a,b). Analysing indel subtypes revealed that vPE reduced all classes versus PE7 (Fig. 3c). This high efficiency for vPE coupled with significant error suppression led to large increases in edit:indel ratios versus PE7 (Fig. 3d). We next evaluated pegRNA + ngRNA editing, an important editing mode that boosts efficiencies but is error prone[1], at these same loci and edits. Here we measured mean editing efficiencies of 28% (range of 15–60%) for PE7 and 31% (range of 15–44%) for vPE (Fig. 3e,f). For pegRNA + ngRNA editing, we observed higher mean indel error rates of 16% (range of 1.6–33%) for PE7 and 1.9% (range of 0.069–7.9%) for vPE (Fig. 3e,f). We again found that vPE reduced all indel classes when compared with PE7 (Fig. 3g). As vPE dramatically reduced errors and increased efficiency for pegRNA + ngRNA editing versus PE7, we observed large increases in edit:indel ratios (Fig. 3h). To evaluate whether vPE could also improve larger edits, we tested larger substitution, insertion and deletion edits at the same loci in HEK293T cells[16]. For these larger edits, we measured mean editing efficiencies of 35% (range of 19–53%) for PE7 and 29% (range of 7–43%) for vPE (Fig. 3i,j). We further observed mean indel error rates of 3.5% (range of 0.14–18%) for PE7 and 0.20% (range of 0.017–0.78%) for vPE (Fig. 3i,j). We found that vPE significantly reduced all indel classes versus PE7 (Fig. 3k). As vPE greatly reduced errors for larger edits versus PE7, we observed large increases in edit:indel ratios (Fig. 3l).

Fig. 3: Engineered prime editors suppress error formation for diverse edit types.

a, Edit and indel frequencies comparing vPE with PE7 using pegRNA-only editing, with fold reductions in indel rates marked. b, Means of edit and indel frequencies comparing vPE with PE7 using pegRNA-only editing, with each point representing an individual edit and replicate. c, Means of different indel class frequencies comparing vPE with PE7 using pegRNA-only editing, with each point representing an individual edit and replicate. d, Means of edit:indel ratios comparing vPE with PE7 using pegRNA-only editing, with each point representing an individual edit and replicate. e, Edit and indel frequencies comparing vPE with PE7 using pegRNA + ngRNA editing, with fold reductions in indel rates marked. f, Means of edit and indel frequencies comparing vPE with PE7 using pegRNA + ngRNA editing, with each point representing an individual edit and replicate. g, Means of different indel class frequencies comparing vPE with PE7 using pegRNA + ngRNA editing, with each point representing an individual edit and replicate. h, Means of edit:indel ratios comparing vPE with PE7 using pegRNA + ngRNA editing, with each point representing an individual edit and replicate. i, Edit and indel frequencies comparing vPE with PE7 for larger edits, with fold reductions in indel rates marked. j, Means of edit and indel frequencies comparing vPE with PE7 for larger edits, with each point representing an individual edit and replicate. k, Means of different indel class frequencies comparing vPE with PE7 for larger edits, with each point representing an individual edit and replicate. l, Means of edit:indel ratios comparing vPE with PE7 for larger edits, with each point representing an individual edit and replicate. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons with PE7. All data were analysed by deep sequencing and represent means of n = 3 (most samples) or n = 2 (VEGFA edits) independent replicates with standard errors.

We next examined the generalizability of enhanced editing with our engineered prime editors. Summarizing editing in HEK293T cells, vPE increased the edit:indel ratio to 543:1 for pegRNA-only editing and 102:1 for pegRNA + ngRNA editing (Fig. 4a,b). As prime editors sometimes install edits at off-target loci, we also measured editing at known off-target positions in HEK293T cells2,13 (Fig. 4c,d). We observed that vPE reduced off-target edits up to 14-fold compared with PE7, probably due to inclusion in vPE of mutations known to suppress off-target breaks[24]. Next, we explored editing in additional cell models, including A549 and HeLa cells, and broadly observed suppression of indel errors with vPE versus PE7 (Extended Data Fig. 7a–h). To further assess whether engineered prime editors can yield efficient and functional edits in important cell types, we evaluated a prime edit in mouse embryonic stem cells (ESCs) that converts a GFP transgene to BFP (Fig. 4e). Analysis by flow cytometry revealed that PE7 had an editing efficiency of 9.3% with an indel error rate of 2.8%, whereas vPE edited 15% of cells with effectively no errors (Fig. 4f,g). We further analysed the allelic identities of edited loci for several edits in HEK293T to determine whether errors were dominated by any particular sequence (greater than 0.05% of sequenced reads). Whereas PE7 produced several dominant indel-containing alleles for each edit for pegRNA-only or pegRNA + ngRNA editing, vPE resulted in no significant indel-containing alleles (Extended Data Figs. 8a–d and 9a–c). Of note, the fold reductions in errors were largest for edits where PE7 made the most indels. Thus vPE minimized errors, correcting numerous edits that were highly error prone with earlier prime editors.

Fig. 4: Engineered prime editors suppress target and off-target errors for efficient functional edits.

a, Summary of edit:indel ratios comparing vPE with PE7 using pegRNA-only editing for all experiments in this study, with each point representing an individual edit and replicate. b, Summary of edit:indel ratios comparing vPE with PE7 using pegRNA + ngRNA editing for all experiments in this study, with each point representing an individual edit and replicate. c, Edit frequencies at target and off-target sites comparing vPE with PE7 using pegRNA-only editing, with fold reductions in off-target edit rates marked. d, Means of target:off-target edit ratios comparing vPE with PE7 for pegRNA-only editing, with each point representing an individual edit and replicate. e, Assay for functional editing by conversion of a GFP transgene to BFP in ESCs. f, Flow cytometry for populations of GFP+, GFP− and BFP+ ESCs comparing vPE with PE7 for pegRNA + ngRNA editing. g, Edit and indel frequencies comparing vPE with PE7 for pegRNA + ngRNA editing using flow cytometry for ESCs. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons with PE7. All data were analysed by deep sequencing (a–d) or flow cytometry (f,g) and represent means of n = 3 independent replicates with standard errors.

Discussion

We described the surprising discovery that prime-editing errors can be greatly suppressed through engineering the CRISPR nuclease. We propose that this finding is explained by relaxation of Cas9-induced nick positioning that generates degradation of competing 5′ strands to reduce their competition with edited 3′ new strands (Extended Data Fig. 10a,b). We routinely observed several-fold decreases in indels with our engineered prime editors coupled with high editing efficiencies, resulting in significant increases in edit:indel ratios. This insight suggests how other engineered or natural Cas9 variants and orthologues could be tested for alternative break structures[29], which may also promote non-target strand 5′ end degradation following cleavage. Our engineering strategy could potentially be applied to related genome editor classes that similarly utilize polymerases to introduce edits, which also produce significant errors30,31. Indeed, we propose that modulating DNA substrate stability to enhance editing and suppress errors is a design paradigm that can yield superior genome editors.

Uncertainty in editing outcomes is a major concern as errors might propagate with harmful consequences, for example, in multiplexed editing, gene drives, molecular recording and gene therapy. The unpredictability of errors is a significant design challenge as efficiency must be maximized while indel errors are minimized, necessitating extensive pegRNA and ngRNA optimization32,33,34. The engineered prime editors described here appear to eliminate one of these constraints by suppressing errors to minimal levels. We observed that pegRNA-only editing with vPE reduces error rates to near-uniformly low levels, in contrast to a large range of error rates for PE7. We speculate that variations in local sequences and cellular factors controlling relative annealing rates of the competing 3′ and 5′ strands influence the edit:indel ratio. This is supported by our observation that vPE consistently suppresses error rates to low levels, regardless of how frequent those errors were for PE7. Sequence differences may also explain why different edits at the same locus can yield very different editing efficiencies for vPE versus PE7. Because these adjacent edits utilize nearly identical pegRNAs, this observation could be explained by vPE being sensitive to sequence-specific misfolding that is known to occur for gRNAs[35]. This suggests that although vPE may simplify pegRNA design, some optimization to maximize efficiency will likely be necessary.

The design of prime editors that produce minimal errors shows that protein engineering can address a key challenge for precise genome editing. This approach is minimally invasive to cells, requiring no manipulation of cellular states, modulation of DNA repair processes or addition of exogenous factors. Use of these engineered prime editors is straightforward, enabling their facile substitution into existing and future genome-editing applications. The prime editors described here exhibit a uniquely high level of editing precision at many loci and could potentially form the basis for a range of advanced tools and applications.

Methods

Mammalian cell culture

All mammalian cell cultures were maintained in a 37 °C incubator at 5% CO_2. HEK293T human embryonic kidney (Thermo Fisher), A549 human lung cancer (a gift from S. Garg) and HeLa human cervical cancer (a gift from M. Stewart) cells were maintained in Dulbecco’s modified eagle’s medium with high glucose, sodium pyruvate and GlutaMAX (DMEM; 10569, Thermo Fisher) supplemented with 10% fetal bovine serum (FBS; 10438, Thermo Fisher) and 100 U ml−1 penicillin–streptomycin (15140, Thermo Fisher). V6.5 mouse ESCs (a gift from R. Jaenisch) were maintained in DMEM with high glucose and sodium pyruvate (11995, Thermo Fisher) supplemented with 15% FBS (SH30070, GE Healthcare), 1 mM HEPES (15630, Thermo Fisher), 2 mM l-glutamine (25030, Thermo Fisher), 1X MEM non-essential amino acids (NEAAs; 11140, Thermo Fisher), 0.0008% 2-mercaptoethanol (M6250, Millipore-Sigma), 1,000 U ml−1 leukemia inhibitory factor (LIF; ESG1107, Millipore-Sigma) and 100 U ml−1 penicillin–streptomycin (30-002-Cl, Corning). V6.5 cells were grown on plates coated with 0.2% gelatin (G1890, Millipore-Sigma). HEK293T cells without or with a GFP transgene insertion were used as previously described[21]. ESCs with a GFP transgene insertion were created by infection of V6.5 cells with pLX TRC209 lentivirus and isolation of single-cell clones expressing GFP. All cell lines were tested for mycoplasma contamination and confirmed mycoplasma free. Cell lines were not authenticated. No commonly misidentified cell lines were used.

Mutagenesis and cloning

PE2 and PEmax prime editors were obtained from pCMV-PE2 and pCMV-PEmax, and a cloning backbone for pegRNA expression was obtained from pU6-pegRNA-GG-acceptor, which were gifts from D. R. Liu. PE7 prime editor was obtained from Lenti-PE7-P2A-Puro, which was a gift from F. J. Sanchez-Rivera. PE was created by restriction cloning of Cas9n(H840A) from pCMV-PE2 into pCMV-PEmax using NotI and SacI digestion. PE mutagenesis was performed using PCR-driven splicing by overlap extension using primers listed in Supplementary Table 1. In brief, one fragment was amplified by PCR from PE using the pe-FWD or pe-mid-FWD and mutant-BOT primers, and a second fragment was amplified using the mutant-TOP and pe-mid-REV or pe-rt-REV primers for each mutant. Each pair of fragments was then spliced by overlap extension PCR using the pe-FWD and pe-mid-REV or pe-mid-FWD and pe-rt-REV primers to create a PE gene fragment with a single-residue mutation. These PE gene fragments were then each cloned back into PE using unique NotI, SacI and BamHI restriction sites to replace the PE sequence with the mutant sequence. Additional mutants (double, triple and quadruple mutants) were made iteratively starting from these single-mutant plasmids. PE7 and vPE were created by restriction cloning of a fragment containing La amplified from Lenti-PE7-P2A-Puro into pCMV-PEmax, for PE7, or into xPE, for vPE, using BamHI and BshTI digestion. The pegRNA oligos, listed in Supplementary Table 2, were cloned into pU6-pegRNA-GG-acceptor by Golden Gate cloning with Eco31I digestion.

Cas9 variants were generated as previously described[21]. A custom gRNA cloning backbone vector was created by PCR amplification from pX330 using the gRNA-scaffold-NheI-FWD and gRNA-scaffold-EcoRI-REV primers and restriction cloning into pUC19 (Thermo Fisher) using NheI and EcoRI digestion. The nicking gRNA spacer sequence oligos, listed in Supplementary Table 2, were phosphorylated with T4 polynucleotide kinase (NEB) and cloned into gRNA cloning backbone by Golden Gate cloning with BpiI digestion.

Primers were synthesized by IDT. Restriction enzymes were obtained from Thermo Fisher. T7 DNA ligase was obtained from NEB. Plasmids were transformed into Stbl3 chemically competent Escherichia coli (Thermo Fisher). Sequences for the PEmax, PE, pPE, xPE, PE7 and vPE vectors are presented in the Sequences section in the Supplementary Information.

Structure analysis

Crystal structures of Cas9 with substrate DNA bound (5F9R) or without substrate DNA bound (4ZT0) were analysed using PyMol (Schrödinger).

Cell transfection

Cells were seeded in the maintenance medium (without penicillin–streptomycin for HEK293T, A549 and HeLa cells) into 48-well plates at 50,000 cells per well. Transfections with dual gRNAs were carried out 24 h after seeding using 200 ng Cas9 expression vector and 72 ng of each gRNA expression vector formulated with 0.86 µl Lipofectamine 2000 (Thermo Fisher) at a total volume of 34.4 µl in OptiMEM I (Thermo Fisher) per well. Transfections with prime-editing vectors were carried out 24 h after seeding using 238 ng PE expression vector, 57 ng pegRNA expression vector and 72 ng nicking gRNA expression vector (for pegRNA + ngRNA editing) formulated with 0.74–0.92 µl (equal volume per DNA) Lipofectamine 2000 at a total volume of 29.5–36.7 µl (equal DNA concentration) in OptiMEM I per well. For sequencing assays, genomic DNA was extracted 72 h after transfection using QuickExtract (Epicentre). For flow cytometry assays, cells were transferred to 10-cm dishes 72 h after transfection and harvested 9 days after transfection in PBS with 5% FBS (Thermo Fisher).

High-throughput sequencing

The targeted loci were amplified from extracted genomic DNA by PCR using Herculase II polymerase (Agilent). The PCR primers included Illumina sequencing handles as well as replicate-specific barcodes. These PCR products were then tagged with sample-specific barcodes and sequenced on an Illumina MiSeq. Primers, listed in Supplementary Table 3, were synthesized by IDT. A sequencing file listing and sequencing depth data are available in Supplementary Table 4.

Flow cytometry

Flow cytometry analysis was performed on an LSR Fortessa analyser and data were collected using FACSDiva (BD Biosciences). Cells were first gated comparing side scatter (SSC) and forward scatter (FSC) parameters, starting with SSC-A and FSC-A, then SSC-H and SSC-W, then FSC-H and FSC-W parameters to select for single cells (Supplementary Fig. 1). To assess editing frequencies, cells were gated for GFP (488-nm laser excitation, 530/30-nm filter detection) and BFP (405-nm laser excitation, 450/50-nm filter detection). Flow cytometry data were analysed using FlowJo (FlowJo). Intended edit rates were quantified as the fraction of cells gated as BFP+ for a prime-edited sample minus the fraction gated as BFP+ for the unedited control sample. Indel rates were quantified as the fraction of cells gated as GFP− and BFP− for a prime-edited sample minus the fraction gated as GFP− and BFP− for the unedited control sample.

Genome-editing analysis

To measure editing outcomes, the high-throughput sequencing data were analysed using CRISPResso2 (ref. [36]). Data for prime-editing experiments were processed using the ‘prime editing’ mode in CRISPResso2 by including sequence values for the parameters ‘prime_editing_pegRNA_spacer_seq’, ‘prime_editing_pegRNA_extension_seq’, ‘prime_editing_pegRNA_scaffold_seq’ and ‘prime_editing_nicking_guide_seq’ (for pegRNA + ngRNA modes). Editing window parameters ‘prime_editing_pegRNA_extension_quantification_window_size’ and ‘w’ were set to 5. The ‘ignore_substitutions’ option was used to account for small sequence variations that occur due to PCR and sequencing errors. Intended edit rates were quantified as the fraction of reads marked as prime edited out of total sequencing reads. Indel rates were quantified as the fraction of reads marked as indels out of total sequencing reads. Frequencies of specific indel sizes were quantified as the fraction of reads containing these sizes out of all indel reads or out of total sequencing reads, as noted, and were averaged over three independent replicates. Mean indel sizes were calculated as the mean of the absolute values of indel sizes weighted by their indel fractions. Depletion of specific indel sizes was quantified as the fractional reduction in the frequency of that indel size, comparing different editors. Plots of insertion and deletion positions were produced from data generated in CRISPResso2 and averaged over three independent replicates.

Plots of editing outcome alleles were processed using the standard mode in CRISPResso2. The editing window parameter ‘w’ was set to 30. The plot size parameter ‘plot_window_size’ was set to 30, the minimum allele frequency parameter ‘min_frequency_alleles_around_cut_to_plot’ was set to 0.05 and the allele number parameter ‘max_rows_alleles_around_cut_to_plot’ was set to 30. Accordingly, the top 30 alleles were displayed regardless of frequency.

DNA nick shift frequency and end degradation analysis

To quantitate nick shift frequencies and nicked end degradation for Cas9 variants in cells, editing outcomes for dual-gRNA cutting of genomic DNA were analysed21,22. In these datasets, HEK293T cells were edited with pairs of gRNAs separately targeting either the EMX1 or CXCR4 locus. The CXCR4 dataset was generated from new experiments, whereas the EMX1 dataset was reanalysed from previously published data[21]. The gRNA pairs were complementary to the same strand at each locus and were expected to make cuts 84 bp apart, resulting in junctions. The loci were amplified and sequenced by high-throughput sequencing. The high-throughput sequencing data were analysed using CRISPResso2 with the expected junction as a reference sequence. To assess DNA nick position, sequencing reads aligned to the junction reference were analysed for retained sequences perfectly matching the sequences between the expected gRNA cut sites. The lengths of these retained sequences in the junctions were used to infer shifts in nick positioning leading to each read. The most frequent cut position with a frequency of 5% of reads or greater was presented as the dominant shifted nick position. The nick shift frequency was quantified as the fraction of reads containing a retained sequence indicating shifted nicks out of the total sequencing reads containing either a retained sequence or a perfect junction sequence. To assess DNA nicked end degradation, sequencing reads aligned to the junction reference were analysed for deletion sequences. The deletions to the PAM side of the junction were inferred to correspond to lengths of non-target strand 5′ end degradation, whereas the deletions to the non-PAM side of the junction were inferred to correspond to lengths of non-target strand 3′ end degradation. The degradation lengths were quantified as the median length of these degradation products. The degradation frequencies were quantified as the fraction of reads containing deletions on the PAM or non-PAM sides of the junction out of all reads containing either a deletion sequence or a perfect junction sequence, and were presented as the ratio between degradation on the PAM side versus non-PAM side.

To estimate nicked end degradation for prime editor variants, editing outcomes for a dual-nick genomic DNA degradation sensor were analysed. In these datasets, HEK293T cells were edited with a paired pegRNA and ngRNA targeting the AAVS1 locus. The gRNA pair was complementary to opposite strands and was expected to make nicks 43 bp apart, resulting in annealing of homologous sequences flanking the nick position. The frequency of these flap homology deletions could then be used to quantify the degradation of the nicked ends for each prime editor variant, as degradation would reduce deletion frequency. The pegRNA also generated a larger edit 8 bp from the nick that could be used as an activity marker, such that the effects of a mutation on overall activity of the prime editor variant could be determined in the same assay. The loci were amplified and sequenced by high-throughput sequencing. The high-throughput sequencing data were analysed using CRISPResso2 as described above for prime-editing data. Flap degradation was quantified as the number of reads containing the activity marker edit divided by the number of reads containing the flap homology deletions, normalized to this ratio for the standard prime editor PE.

To estimate nick shift frequencies for prime editor variants, editing outcomes comparing edits at a positive and negative position relative to a nick were analysed. In these datasets, HEK293T cells were edited with pegRNAs targeting the TGFB1 locus. The pegRNAs targeted the same sequence, but installed an edit either at the +6 or −1 position. The frequency of the −1 edit could then be used to quantify the nick shift frequency, as the nick would have to shift from the canonical position between −1 and +1 to enable a −1 edit. The loci were amplified and sequenced by high-throughput sequencing. The high-throughput sequencing data were analysed using CRISPResso2 as described above for prime-editing data. Negative:positive edit ratios were quantified as the intended edit rate for the −1 edit divided by the total editing rate for the +6 edit, normalized to this ratio for the standard prime editor PE.

Off-target editing

To measure off-target editing, two to three of the most commonly cleaved Cas9 off-target sites for given gRNAs were analysed as previously described2,13. Analysis was performed for off-target editing by pegRNAs targeting the EMX1, FANCF and HEK3 loci at known off-target sites (OT2 and OT3 for EMX1; OT1, OT3 and OT4 for FANCF; and OT1, OT2 and OT4 for HEK3). High-throughput sequencing was performed on these amplified sequences and data were processed using CRISPResso2. The quality filtering parameter ‘q’ was set to 30, whereas the editing window centre parameter ‘wc’ was set to 0 and the editing window size parameter ‘w’ was set to 3. The ‘discard_indel_reads’ option was used to remove reads containing deletions or insertions from the analysis. For each off-target locus, the sequence on the PAM side of the off-target nick was compared with the sequence encoded by the pegRNA template on the PAM side of the target nick to identify the first nucleotide on the off-target locus where these sequences differ. Sequencing reads at the off-target locus that matched the pegRNA template sequence from the nick to this first differing nucleotide position were considered off-target edit reads. Off-target-editing efficiencies were quantified as the fraction of reads marked as off-target edits out of total sequencing reads.

Edit notation

Edits were denoted based on the position where the edit begins relative to expected gRNA nick position for wild-type Cas9, denoting position +1 as 3 bp upstream of the first PAM position. Substitution edits were noted using a ‘>’ mark, deletions were noted by a ‘del’ mark, and insertions were noted by an ‘ins’ mark. The base identities of the strand containing the gRNA spacer sequence were used in all cases.

Statistical analysis

Specific statistical comparisons are indicated in the figure legends. Error bars indicate the standard error for independent replicates as noted. Significance where noted was assessed using unpaired, two-tailed Student’s t-tests. Correlations were determined by Pearson coefficients. Figures and analysis were produced using Graphpad Prism and Microsoft Excel software.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Raw next-generation sequencing data are available on the National Center for Biotechnology Information Sequence Read Archive database under accession PRJNA1272011. Plasmids and their sequences are available on Addgene (https://www.addgene.org/browse/article/28248655/).

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Acknowledgements

We thank D. R. Liu for helpful suggestions and critical reading of the manuscript and A. Jiang, D. B. Mahat, F. J. Sanchez-Rivera and J. Witten for helpful discussions. This research was supported by a grant from the Life Sciences Research Foundation, grants (R01-EB000244 and R01-EB027717) from the National Institute of Biomedical Imaging and Bioengineering, grants (P01-CA042063, R01-CA226898 and R01-CA248393) from the National Cancer Institute, and partially by a Cancer Center Support (core) grant (P30-CA14051) from the National Cancer Institute. V.P.C. is a Fellow of the Life Sciences Research Foundation.

Author information

Authors and Affiliations

1. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Vikash P. Chauhan, Phillip A. Sharp & Robert Langer

2. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

Vikash P. Chauhan & Phillip A. Sharp

3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Robert Langer

Contributions

V.P.C., P.A.S. and R.L. conceived or designed the work. V.P.C., P.A.S. and R.L. acquired, analysed or interpreted the data. V.P.C., P.A.S. and R.L. drafted the manuscript or substantively revised it.

Corresponding authors

Correspondence to Vikash P. Chauhan, Phillip A. Sharp or Robert Langer.

Ethics declarations

Competing interests

V.P.C., P.A.S. and R.L. are inventors on a patent filed by the Massachusetts Institute of Technology (US application no. 18/069,387) that covers engineered nucleases and their uses relating to this work. V.P.C. reports no other potential competing interests. P.A.S. is or has been recently involved, compensated or uncompensated, with Alnylam Pharmaceuticals, Danaher, Dewpoint Therapeutics, Fidelity Biosciences, Gairdner Foundation, GreenLight Biosciences, Longwood Fund, Polaris Partners and Skyhawk Therapeutics. For a list of entities with which R.L. is, or has been, recently involved with, compensated or uncompensated, see Supplementary Information.

Peer review

Peer review information

Nature thanks Shaohua Yao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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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 Assay for determining nick shift frequencies for nuclease variants.

a, Assay schematic, where paired gRNAs induce DNA breaks that lead to perfect junctions for nuclease variants that cut at the canonical position between the +1 and −1 nucleotides. Shifts in the cut position lead to retention of additional sequences within the junction, and the frequencies of these retained sequences are used to determine the nick shift frequencies for each nuclease variant. b, Design of gRNA pairs and expected sequences of the junction products at the CXCR4 and EMX1 loci, with the expected retained sequences due to shifts in nick position indicated. c, Rates of the top genomic sequences resulting from editing with different Cas9 variants at the junction for the CXCR4 locus. Only reads with the reference sequence or retained sequences (insertions boxed in red) are depicted. The most frequent allelic sequences above 0.05% of reads are displayed along with sequencing reads and percentages out of all reads. d, DNA nick position frequencies for nuclease variants averaged at the CXCR4 and EMX1 loci. The relative frequencies of retained sequences in the junction products are used to estimate the frequencies of nick shifts of corresponding length. Data were analysed by deep sequencing and represent a single replicate (in c) or means of n = 6 independent replicates (in d).

Extended Data Fig. 2 Assays for measuring end degradation for nuclease and prime editor variants.

a, Assay schematic for end degradation comparing nuclease variants, where paired gRNAs induce DNA breaks that lead to perfect junctions for non-degraded DNA ends and deletions in the junction products indicate degradation of the DNA ends. The length of each deletion to the non-PAM-side or PAM-side of the junction indicates the extent of degradation, and the frequencies of these deletions indicate the frequency of degradation. b, Design of gRNA pairs and expected sequences of the junction products at the EMX1 and CXCR4 loci, with the direction of degradation-associated deletions indicated. c, Rates of the top genomic sequences resulting from editing with different Cas9 variants at the junction for the EMX1 locus. Only reads with the reference sequence or deletions are depicted. The most frequent allelic sequences above 0.05% of reads are displayed along with sequencing reads and percentages out of all reads. d, Assay schematic for end degradation comparing prime editor variants, where a paired pegRNA and ngRNA induce DNA nicks that lead to large deletions between homologies in the flaps flanking the nicks for non-degraded ends but cannot produce deletions if the nicked end flaps are degraded. The pegRNA can also install a larger edit that is used as an activity marker for the prime editor, and degradation is quantitated as the ratio of the activity marker edit to the flap homology deletion. e, Design of the pegRNA and ngRNA pair at the AAVS1 locus, with the extensive GC-rich homologies indicated. f, Deletion frequencies by position within the targeted locus comparing several prime editor variants, indicating dominant deletions between homologous sequences flanking the nicks. Data were analysed by deep sequencing and represent a single replicate (in c) or means of n = 3 independent replicates (in f).

Extended Data Fig. 3 Effects of prime editor mutations on editing precision.

a, Screen of engineered PE variants to suppress indels with quantification of edit and indel frequencies (bottom), indel classes (middle), and edit:indel ratios (top). b, Screen of engineered PE variants to suppress indels in a negative position edit with quantification of edit and indel frequencies (bottom), indel classes (middle), and edit:indel ratios (top). c, Screen of engineered PE variants to detect the relative frequency of shifted nicks by the ratio of editing at negative to positive positions. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons to PE. All data were analysed by deep sequencing and represent means of n = 3 independent replicates with standard errors.

Extended Data Fig. 4 Comparison of edit and indel generation for pPE and PEmax.

a, Edit and indel frequencies comparing pPE to PEmax using pegRNA-only editing, with fold-reductions in indel rates marked. b, Means of edit and indel frequencies comparing pPE to PEmax using pegRNA-only editing, with each point representing an individual edit and replicate. c, Means of different indel class frequencies comparing pPE to PEmax using pegRNA-only editing, with each point representing an individual edit and replicate. d, Means of edit:indel ratios comparing pPE to PEmax using pegRNA-only editing, with each point representing an individual edit and replicate. e, Edit and indel frequencies comparing pPE to PEmax using pegRNA+ngRNA editing, with fold-reductions in indel rates marked. f, Means of edit and indel frequencies comparing pPE to PEmax using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. g, Means of different indel class frequencies comparing pPE to PEmax using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. h, Means of edit:indel ratios comparing pPE to PEmax using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. i, Edit and indel frequencies comparing pPE to PEmax using pegRNA-only editing with inhibition of mismatch repair, with fold-reductions in indel rates marked. j, Means of edit and indel frequencies comparing pPE to PEmax using pegRNA-only editing with inhibition of mismatch repair, with each point representing an individual edit and replicate. k, Means of different indel class frequencies comparing pPE to PEmax using pegRNA-only editing with inhibition of mismatch repair, with each point representing an individual edit and replicate. l, Means of edit:indel ratios comparing pPE to PEmax using pegRNA-only editing with inhibition of mismatch repair, with each point representing an individual edit and replicate. m, Edit and indel frequencies comparing pPE to PEmax using pegRNA+ngRNA editing with inhibition of mismatch repair, with fold-reductions in indel rates marked. n, Means of edit and indel frequencies comparing pPE to PEmax using pegRNA+ngRNA editing with inhibition of mismatch repair, with each point representing an individual edit and replicate. o, Means of different indel class frequencies comparing pPE to PEmax using pegRNA+ngRNA editing with inhibition of mismatch repair, with each point representing an individual edit and replicate. p, Means of edit:indel ratios comparing pPE to PEmax using pegRNA+ngRNA editing with inhibition of mismatch repair, with each point representing an individual edit and replicate. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons to PEmax. All data were analysed by deep sequencing and represent means of n = 3 independent replicates with standard errors.

Extended Data Fig. 5 Further engineering of pPE to rescue editing efficiency.

a, Screen of engineered pPE variants to enhance efficiency with quantification of edit and indel frequencies (bottom), indel classes (middle), and edit:indel ratios (top). b, Edit and indel frequencies comparing xPE to PE, PE-N854A, and PE-N863A using pegRNA+ngRNA editing. c, Means of different indel class frequencies comparing xPE to PE, PE-N854A, and PE-N863A using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. d, Means of edit:indel ratios comparing xPE to PE, PE-N854A, and PE-N863A using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. e, Edit and indel frequencies comparing xPE to xPE-N863A using pegRNA-only editing. f, Means of different indel class frequencies comparing xPE to xPE-N863A using pegRNA-only editing, with each point representing an individual edit and replicate. g, Means of edit:indel ratios comparing xPE to xPE-N863A using pegRNA-only editing, with each point representing an individual edit and replicate. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons to pPE. All data were analysed by deep sequencing and represent means of n = 3 independent replicates with standard errors.

Extended Data Fig. 6 Comparison of edit and indel generation for xPE and PEmax.

a, Edit and indel frequencies comparing xPE to PEmax using pegRNA-only editing, with fold-reductions in indel rates marked. b, Means of edit and indel frequencies comparing xPE to PEmax using pegRNA-only editing, with each point representing an individual edit and replicate. c, Means of different indel class frequencies comparing xPE to PEmax using pegRNA-only editing, with each point representing an individual edit and replicate. d, Means of edit:indel ratios comparing xPE to PEmax using pegRNA-only editing, with each point representing an individual edit and replicate. e, Edit and indel frequencies comparing xPE to PEmax using pegRNA+ngRNA editing, with fold-reductions in indel rates marked. f, Means of edit and indel frequencies comparing xPE to PEmax using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. g, Means of different indel class frequencies comparing xPE to PEmax using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. h, Means of edit:indel ratios comparing xPE to PEmax using pegRNA+ngRNA editing, with each point representing an individual edit and replicate. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons to PEmax. All data were analysed by deep sequencing and represent means of n = 3 independent replicates with standard errors.

Extended Data Fig. 7 Comparison of edit and indel generation for vPE and PE7 in additional cell models.

a, Edit and indel frequencies comparing vPE to PE7 using pegRNA-only editing in A549 cells, with fold-reductions in indel rates marked. b, Means of edit and indel frequencies comparing vPE to PE7 using pegRNA-only editing in A549 cells, with each point representing an individual edit and replicate. c, Means of different indel class frequencies comparing vPE to PE7 using pegRNA-only editing in A549 cells, with each point representing an individual edit and replicate. d, Means of edit:indel ratios comparing vPE to PE7 using pegRNA-only editing in A549 cells, with each point representing an individual edit and replicate. e, Edit and indel frequencies comparing vPE to PE7 using pegRNA-only editing in HeLa cells, with fold-reductions in indel rates marked. f, Means of edit and indel frequencies comparing vPE to PE7 using pegRNA-only editing in HeLa cells, with each point representing an individual edit and replicate. g, Means of different indel class frequencies comparing vPE to PE7 using pegRNA-only editing in HeLa cells, with each point representing an individual edit and replicate. h, Means of edit:indel ratios comparing vPE to PE7 using pegRNA-only editing in HeLa cells, with each point representing an individual edit and replicate. *P < 0.05 by two-tailed unpaired Student’s t-test for comparisons to PE7. All data were analysed by deep sequencing and represent means of n = 3 independent replicates with standard errors.

Extended Data Fig. 8 Sequencing reads showing pegRNA-only editing products for PE7 and vPE.

a-d, Rates of the top genomic sequences resulting from editing with PE7 or vPE at the a, FANCF, b, HEK3, c, RNF2, and d, VEGFA loci. Substitutions, insertions, and deletions are depicted. The most frequent allelic sequences above 0.05% of reads are displayed along with sequencing reads and percentages out of all reads. Data were analysed by deep sequencing and represent a single replicate.

Extended Data Fig. 9 Sequencing reads showing pegRNA+ngRNA editing products for PE7 and vPE.

a-c, Rates of the top genomic sequences resulting from editing with PE7 or vPE at the a, RNF2, b, RUNX1, and c, VEGFA loci. Substitutions, insertions, and deletions are depicted. The most frequent allelic sequences above 0.05% of reads are displayed along with sequencing reads and percentages out of all reads. Data were analysed by deep sequencing and represent a single replicate.

Extended Data Fig. 10 Model for how engineered prime editors suppress error formation.

a, Previous prime editors produce edited 3’ new strands that are disfavoured in displacing competing 5’ strands, leading to a structure that promotes indel errors. b, Engineered prime editors with relaxed nicks enable degradation of competing 5’ strands, eliminating the barrier to edited 3’ new strand binding and favouring a structure that produces the intended edit.

Supplementary information

Supplementary Information

Supplementary Figure 1, Prime Editor Sequences List and Supplementary Competing Interests.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–4. Supplementary Table 1. Oligodeoxynucleotide sequences used for mutagenesis and cloning. Materials used for generating plasmids expressing prime editor variants. Supplementary Table 2. Oligodeoxynucleotide sequences used for pegRNA and ngRNA cloning. Materials used for generating plasmids expressing guide RNAs for prime editing. Supplementary Table 3. Oligodeoxynucleotide sequences used for next-generation sequencing. Materials used for amplifying targeted genomic DNA for amplicon sequencing. Supplementary Table 4. Sequencing read data. Read counts for all amplicon sequencing data generated and analyzed.

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Chauhan, V.P., Sharp, P.A. & Langer, R. Engineered prime editors with minimal genomic errors. Nature 646, 1254–1260 (2025). https://doi.org/10.1038/s41586-025-09537-3

- Received: 14 August 2024

- Accepted: 14 August 2025

- Published: 17 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09537-3

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Spatial joint profiling of DNA methylome and transcriptome in tissues

Chin Nien Lee,

Hongxiang Fu,

Angelysia Cardilla,

Wanding Zhou &

…

Yanxiang Deng

Nature volume 646, pages 1261–1271 (2025)

Abstract

The spatial resolution of omics analyses is fundamental to understanding tissue biology1,2,3. The capacity to spatially profile DNA methylation, which is a canonical epigenetic mark extensively implicated in transcriptional regulation4,5, is lacking. Here we introduce a method for whole-genome spatial co-profiling of DNA methylation and the transcriptome of the same tissue section at near single-cell resolution. Applying this technology to mouse embryogenesis and the postnatal mouse brain resulted in rich DNA–RNA bimodal tissue maps. These maps revealed the spatial context of known methylation biology and its interplay with gene expression. The concordance and distinction in spatial patterns of the two modalities highlighted a synergistic molecular definition of cell identity in spatial programming of mammalian development and brain function. By integrating spatial maps of mouse embryos at two different developmental stages, we reconstructed the dynamics that underlie mammalian embryogenesis for both the epigenome and transcriptome, revealing details of sequence-, cell-type- and region-specific methylation-mediated transcriptional regulation. This method extends the scope of spatial omics to include DNA cytosine methylation, enabling a more comprehensive understanding of tissue biology across development and disease.

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Main

DNA methylation is a key epigenetic mechanism that modulates gene expression and lineage specification by regulating chromatin structure and accessibility to transcriptional machinery4,5. This epigenetic modification varies dynamically across cell types, developmental stages and environmental conditions[6]. Abnormal cytosine-methylation patterns are associated with various diseases, including cancer[7], autoimmune diseases[8] and inflammation[9]. Furthermore, altered DNA-methylation levels have been observed in ageing tissues[10], contributing to biomarkers and predictive clock models for chronological and biological age[11].

Despite advances in single-cell methylome analysis5,12, the lack of spatial information regarding intact tissues limits our understanding of gene regulation during tissue development and disease progression. Emerging spatial multi-omics technologies now enable profiling of molecular features in native tissue microenvironments, significantly enhancing our understanding of biological complexity in situ. However, current spatial methods are limited to histone modifications, chromatin accessibility, transcriptomes and selected protein panels1,2,3. Direct spatial mapping of DNA methylation has not been available, leaving this crucial epigenetic layer underexplored.

Here we introduce a technology for spatial joint profiling of the DNA methylome and transcriptome (spatial-DMT) on the same tissue section at near single-cell resolution. We used spatial-DMT to profile mouse embryos and postnatal mouse brains. The spatial maps uncovered intricate spatiotemporal regulatory mechanisms of gene expression in a native tissue context. Spatial-DMT investigates interactive molecular hierarchies in development, physiology and pathogenesis in a spatially resolved manner.

Spatial-DMT design and workflow

Spatial-DMT combines microfluidic in situ barcoding[1], cytosine deamination conversion[12] and high-throughput next-generation sequencing to achieve spatial methylome profiling directly in tissue. The experimental workflow of spatial-DMT is illustrated in Fig. 1a,b, with a step-by-step protocol provided in Extended Data Fig. 1 and detailed reagent information provided in Supplementary Tables 1–4.

Fig. 1: Overview of co-profiling of DNA methylation and transcriptome in tissues.

a, Spatial-DMT workflow. A tissue section is fixed, permeabilized, treated with HCl treatment, and subjected to Tn5 transposition and reverse transcription (RT). Spatial barcodes are sequentially ligated, after which cDNA and gDNA are separated using streptavidin beads. cDNA is processed using template switching, after which a cDNA library is prepared; gDNA is processed using enzymatic methyl-seq (EM-seq) conversion and splint ligation, after which a library is constructed. b, Chemistry workflow of DNA methylation and RNA library preparation. BCA, barcode A; BCB, barcode B; TSO, template switch oligo. c, Comparison of the number of CpGs per tissue pixel per cell across spatial-DMT and other single-cell DNA-methylation datasets5,10,12,14 (number of cells: mouse muscle stem cells[10], n = 260; mouse brain[5], n = 103,560; human brain LA[12], n = 1,049; human brain SL[12], n = 1,920; mouse brain[12], n = 491; number of pixels: E11 embryo (10 µm), n = 2,493; E11 embryo 1 (50 μm), n = 1,954; E11 embryo 2 (50 µm), n = 1,947; E13 embryo (50 µm), n = 1,699; P21 brain (20 µm), n = 2,235). The solid line indicates the maximum number of CpGs in the mouse genome, large dashed line indicates 10% of the total number of CpGs and small dashed line indicates 1% of the total number of CpGs. d, Box plots showing CG/CH retention rates in E11 and E13 embryos and P21 brain tissues. Number of pixels are as stated in part c for these datasets. e, Number of genes (left) and UMIs (right) per pixel. For data from previous studies1,2, the numbers of pixels are as follows (left to right): ref. [1], n = 901, 1,789, 1,837, 1,840; ref. [2], n = 2,373, 2,187. For the other datasets, number of pixels are as stated in part c. The box plots show the median (centre line), first and third quartiles (box limits) and 1.5× interquartile range (whiskers). f, Spatial clusters (top) and UMAP analysis (bottom) of DNA methylation (DNAm, left), RNA transcription (RNA, middle) and integrated data (spatial DNAm + RNA (WNN), right) in E11 mouse embryo (pixel size, 50 μm; ROI (red dashed box), 5 × 5 mm^2; technical replicates, n = 2). Integrated analysis reveals more-refined spatial clusters and distinct anatomical regions, including brain, spinal cord, heart and craniofacial regions. Scale bars, 500 µm. D, DNA; R, RNA; W, WNN. g, Spatial mapping and joint clustering of DNA methylation and RNA data in E11 facial and forebrain regions (pixel size, 10 μm; ROI (red dashed box), 1 × 1 mm^2). Scale bar, 500 µm. h, Spatial mapping of GABAergic cortical interneurons and telencephalon cells on the basis of deconvolution of transcriptomic pixels using a scRNA-seq reference[25]. Cell-type distributions align with the anatomic references from the Allen developing mouse atlases[55]. Pallm, mantle zone of the pallium; Pallv, ventricular zone of the pallium; POA, preoptic alar plate; POHv, ventricular zone of preopto-hypothalamic band; POm, mantle zone of preoptic area; POv, ventricular zone of preoptic area; PThEm, mantle zone of prethalamic eminence; PThEv, ventricular zone of prethalamic eminence; p2A, alar plate of prosomere 2; p3A, alar plate of prosomere 3; p3R, roof plate of prosomere 3; SPallm, mantle zone of the subpallium; SPallv, ventricular zone of the subpallium; TelA, alar plate of evaginated telencephalic vesicle; TelR, roof plate of evaginated telencephalic vesicle; TH, thalamus; THyA, alar part of terminal hypothalamus; THyB, basal part of terminal hypothalamus; TPaAv, ventricular zone of TPaA (terminal paraventricular area of THyA); TSPaAv, ventricular zone of TSPaA (terminal subparaventricular area of THyA).

Specifically, hydrochloric acid (HCl) was applied to fixed frozen tissue sections to disrupt the nucleosome structures and remove histones to improve Tn5 transposome accessibility for DNA-methylation profiling[13]. Next, Tn5 transposition was performed and adapters containing a universal ligation linker were inserted into genomic DNA (gDNA). To further reduce the size of the large gDNA fragment and improve yield, we adopted a multi-tagmentation strategy as previously described[13]. Specifically, we implemented two rounds of tagmentation to balance DNA yield with experimental time and minimize the risk of RNA degradation. mRNAs were then captured by the biotinylated reverse transcription primer (poly-biotinylated deoxythymidine (dT) primer with unique molecular identifiers (UMIs) and a universal linker sequence; Supplementary Table 1), followed by reverse transcription to synthesize the complementary DNA (cDNA). Both genomic fragments and cDNA in the tissue were then ligated to spatial barcodes sequentially. In brief, two sets of spatial barcodes (barcodes A1–A50 and B1–B50; Supplementary Tables 2 and 3) flowed perpendicularly to each other in the microfluidic channels. They were covalently conjugated to the universal linker through the templated ligation. This results in a two-dimensional grid of spatially barcoded tissue pixels in the region of interest (ROI), each defined by the unique combination of barcodes Ai and Bj (i = 1–50, j = 1–50; barcoded pixels, n = 2,500). Barcoded gDNA fragments and cDNA were then released after reverse crosslinking. Afterwards, the biotin-labelled cDNA was enriched by the streptavidin beads and separated from the gDNA-containing supernatant. Subsequently, cDNA was subjected to a template switching reaction and the cDNA library was constructed (Fig. 1b, right, and Extended Data Fig. 1). gDNA was treated with enzymatic methyl-sequencing (EM-seq) conversion, splint ligation and DNA library construction (Fig. 1b, left, Extended Data Fig. 1 and Methods).

To minimize DNA damage during methylome profiling, we used enzymatic methyl-seq, an enzyme-based alternative to bisulfite conversion (Fig. 1b and Extended Data Fig. 1). In this process, modified cytosines (the sum of 5-methylcytosine and 5-hydroxymethylcytosine) were both oxidized by the ten–eleven translocation methylcytosine dioxygenase 2 (TET2) protein and protected from deamination by the APOBEC protein, whereas unmodified cytosines were deaminated to uracil. After the conversion, a SLP5 adapter with ten random H (A, C or T) nucleotides was ligated to the 3′ end of the gDNA, adding the other PCR handle[12]. The gDNA fragments were then amplified using uracil-literate VeraSeq Ultra DNA polymerase. Because C residues on the PCR handle of barcode B were changed to T residues after deamination and PCR, we modified the P7 primer (N70X-HT) by replacing C residues with T residues to complement the A residues on the PCR handle and amplify the fragments. Barcodes have been designed such that no crosstalk under C-to-T deamination is possible.

To demonstrate the ability of spatial-DMT to co-profile DNA methylation and transcription in complex tissues, we profiled mouse embryos on embryonic days 11 (E11) and 13 (E13) and mouse brain on postnatal day 21 (P21). Mouse embryos were profiled at two pixel resolutions: 50 μm for the head and upper body and 10 μm for zoomed-in mapping of the facial and forebrain region. Postnatal mouse brains were profiled at 20 μm pixel resolution. We developed a computational pipeline to preprocess and analyse the data (Supplementary Fig. 1).

Data quality of spatial-DMT

Spatial-DMT is reproducible. DNA methylation and RNA expression had high concordance (Pearson’s r = 0.9836 for DNA methylation and r = 0.9752 for RNA expression; Methods) between the replicate E11 embryo maps with matched body parts (Extended Data Fig. 2a). Uniform manifold approximation and projection (UMAP) co-embedding of the two replicate spatial maps revealed co-localization of pixels from similar body parts but from different datasets (Extended Data Fig. 2b,c). Notably, marker genes, including Frem1 (face), Ank3 (brain) and Trim55 (heart), showed spatial expression patterns consistent with their known tissue-specific expression profiles (Extended Data Fig. 2d–f).

DNA-methylation analysis generated 2.8–3.9 billion raw reads per sample for E11 (10 μm and 50 μm), E13 (50 μm) and P21 brain (20 μm) samples (Supplementary Table 5). Low-signal pixels were filtered on the basis of the knee-plot cut-off threshold (Extended Data Fig. 3a). After stringent quality control, 32.2–65.7% of reads (887,671,712–1,882,630,968) were retained, yielding 355,069–753,052 reads per pixel across 1,699–2,493 pixels in the E11, E13 and P21 samples (Supplementary Table 5). On average, 136,639–281,447 CpGs were covered per pixel across E11, E13 and P21 samples (Supplementary Table 5), comparable to previous single-cell DNA-methylation studies of mouse embryos and brain samples5,10,12,14 (Fig. 1c). Duplication rates ranged from approximately 20 to 53% across samples (Extended Data Fig. 3b). The retention rate, defined as the percentage of unconverted cytosines owing to methylation or incomplete conversion[12], of mitochondrial DNA was minimal (below 1%; Extended Data Fig. 3c). The CpG retention rates were consistently between 70% and 80% across all samples, whereas the methylation level of non-CpG sites (mCH; H denotes A, C or T) remained low (mCA < 1% in embryos; mCA ≈ 3–4% in the postnatal brain; Fig. 1d and Extended Data Fig. 3d). The mCA level was higher in the P21 mouse brain, consistent with the known increase in non-CpG methylation in postnatal neuronal tissues5,15. Analysis of the methylation-free linker sequences showed that more than 99% of cytosines were successfully converted, further confirming conversion efficiency (Extended Data Fig. 3e). Furthermore, no evidence of RNA contamination, such as poly(A), poly(T) or template switching oligonucleotide sequences, was detected in the DNA-methylation libraries (Extended Data Fig. 3f).

We further assessed the genomic distribution of CpG coverage and found it to be uniformly distributed across genomic regions (Extended Data Fig. 4a). Furthermore, methylation levels across various chromatin states were consistent with known biology and comparable to those reported in published databases5,10,12,14,16 (Extended Data Fig. 4b). For instance, DNA-methylation levels were low at transcription start sites but increased upstream and downstream of these regions (Extended Data Fig. 4c). Collectively, our approach yielded accurate and unbiased profiling of DNA methylation across the genome.

Simultaneously, we generated high-quality RNA data from the same tissue sections, enabling direct comparisons between transcriptional activity and epigenetic states. Specifically, we identified expression of 23,822–28,695 genes in our spatial maps (Supplementary Table 5). At the pixel level, the average number of detected genes per pixel ranges from 1,890 (E11 embryo, 10 μm; 3,596 UMIs) to 4,626 (E11 embryo, 50 μm; 16,709 UMIs), comparable to previous spatial transcriptomic studies of the mouse embryo and brain1,2 (Fig. 1e and Supplementary Table 5). Lower-resolution pixels (for example, 50 μm) captured more UMIs and expressed genes, probably owing to the inclusion of more cells in each pixel.

Spatial co-profiling of mouse embryos

Mammalian embryogenesis is an intricately programmed process with complex gene-expression dynamics regulated by epigenetic mechanisms, including DNA methylation, at spatiotemporal scales[17]. We applied spatial-DMT to produce spatial tissue maps of the DNA methylation and gene expression of the E11 mouse embryo. The DNA methylome and transcriptome define cell identity independently, with pixels clustered by methylation read-outs sampled from variably methylated regions (VMRs)[18] and expression levels from variably transcribed genes (Fig. 1f, left and middle, and Extended Data Fig. 4d). The two modalities can be integrated to achieve improved discrimination of intercellular and spatial diversity using a weighted nearest neighbour (WNN) method[19] (Fig. 1f, right). Superimposing the WNN clusters over histological images suggests clear correspondence with anatomical structures, for example, craniofacial region (W0), hindbrain–spinal cord (W2) and embryonic heart (W6), consistent with the tissue histology (Fig. 1f, bottom left).

The correlations between single-modality (RNA and DNA methylation) clusters and their integrated WNN clustering results were evaluated (Fig. 1f and Extended Data Fig. 4e,f). Each modality captured distinct yet complementary aspects of cellular identity and their integration through WNN analysis yielded clusters with enhanced resolution. RNA-expression profiles had a broader dynamic range, which may result in distinct clustering granularity across modalities. To quantify the relative contributions of each modality to the integrated clusters, we computed modality weights for individual spatial pixels (Extended Data Fig. 4g). This analysis revealed that some clusters were defined predominantly by gene expression (for example, W6, cardiac tissue), whereas others by DNA methylation (for example, W11, craniofacial region). Collectively, these results highlight the advantage of spatial multi-omics integration in resolving cellular heterogeneity missed by single-modality analyses.

We examined VMR methylations specific to the brain (W2), craniofacial (W0) and heart (W6) regions (Fig. 1f) and neighbouring gene expression (Fig. 2a,b, cluster W2; Extended Data Fig. 6a,b, cluster W0; and Extended Data Fig. 7a,b, cluster W6). Spatial cluster-specific gene expression is frequently associated with low DNA methylation at the neighbouring VMRs, as exemplified by signature genes Runx2, Mapt, and Trim55, which mark the craniofacial regions (jaw and upper nasal), the brain and spinal cord, and the heart, respectively (Fig. 2c and see Extended Data Figs. 5a, 6c and 7c for additional signature gene examples). Testing the epigenetically regulated genes from each cluster for Gene Ontology (GO) enrichment identified the developmental process related to the anatomical structures (Extended Data Figs. 5b, 6d and 7d). Although the canonical negative correlations between DNA methylation and RNA expression have been observed in many genes, we also identified genes, for example, Ank3, Atp11c, Cyfip2, Lmln and Khdrbs2, for which expression is positively correlated with the methylation levels of the associated VMRs (Fig. 2d). For instance, Ank3, primarily located at the axonal initial segment and nodes of Ranvier in neurons of the central and peripheral nervous systems[20], had high levels of expression and DNA methylation in the brain region (Fig. 2c). The positive correlations between DNA methylation and RNA expression have been documented at enhancers[21], gene bodies[22] and Polycomb targets[23], suggesting a complex mechanism of DNA methylation in transcriptional regulation, contingent on its interaction with transcriptional factor (TF) binding and histone modifications.

Fig. 2: Analysis of DNA methylation and gene expression in E11 embryo.

a, Heat map of DNA methylation levels for the top ten differentially methylated genomic loci in brain and spinal cord (cluster W2) of E11 embryo. Each row represents a specific genomic locus and each column represents a different cluster. The colour scale indicates the z scores of DNA-methylation levels. b, Heat map of expression levels for nearby genes corresponding to the genomic loci in a. Each row represents a specific gene and each column represents a different cluster. The colour scale indicates the z scores of gene-expression levels. c, UMAP visualization and spatial mapping of DNA methylation (methylation percentage) and RNA-expression levels (log-normalized expression) for selected marker genes across different clusters. U1, UMAP 1; U2, UMAP2. d, Correlation analysis between DNA methylation and RNA expression of nearby genes. Negative correlations (blue) indicate a repressive effect of methylation, whereas positive correlations (orange) indicate activation. Correlation coefficient and P value were calculated using the two-sided Pearson correlation test and P values were adjusted for multiple comparisons using the Benjamini–Hochberg method. e, Scatterplots of enriched TF motifs in the respective clusters. The y axis shows the enrichment P values from Homer one-sided hypergeometric test of TF motifs and the x axis shows the average gene expression of the corresponding TFs.

To directly assess the influence of cell-type-specific methylation on TF binding, we performed motif enrichment analysis on differentially hypomethylated VMRs and examined the gene expression of each TF gene. Our findings uncovered a relationship between the expression of TFs and the DNA methylation at their binding sites. For example, TFs associated with heart development, Hand2, Tbx20 and Meis1, were expressed in cluster W6, the corresponding hypomethylated VMRs of which were enriched in the binding motifs of these TFs (Fig. 2e, right). Similar sets of tissue-specific TFs are identified for other embryo structures, for example, Ebf1 and Pbx1 in the brain and spinal cord and Sox9, Ebf1 and Zeb2 in the craniofacial region (Fig. 2e). Notably, Ebf1 was expressed and its binding motif was also enriched across all three tissue regions (Fig. 2e). EBF1 was reported as an interaction partner for TET2 and was suggested as a sequence-specific mechanism for regulating DNA methylation in cancer[24].

To precisely resolve the fine structure of transcriptional regulation in the craniofacial and forebrain regions of the E11 embryo, we used a 10 μm pixel size microfluidic chip to produce a spatial map with near single-cell resolution (Fig. 1g,h and Extended Data Fig. 8a). Integration of our spatial dataset with a single-cell reference[25] revealed a strong concordance between the two datasets (Extended Data Fig. 8b,c). For example, W11 maps to neuroectoderm and glial cells in the single-cell reference, W7 to CNS neurons, W5 to olfactory sensory neurons and W10 to epithelial cells (Extended Data Fig. 8b,c). Cell-type deconvolution using the single-cell RNA-sequencing (scRNA-seq) reference[25] identified distinct clusters that correspond precisely to known anatomical structures of the developing mouse brain. Notably, two spatially defined clusters, W7 and W11, captured key telencephalic compartments. W11 was enriched for telencephalon progenitors in the ventricular zone of the pallium, a neurogenic niche characterized by active cell division and proliferation, whereas W7 corresponded to γ-aminobutyric-acid-releasing (GABAergic) cortical interneurons localized in the mantle zone, where newborn neurons migrate, accumulate and differentiate to establish cortical architecture[26] (Fig. 1h and Extended Data Fig. 8d). GO enrichment analysis further supported these regional identities, highlighting biological processes associated with neurogenesis and progenitor proliferation in W11, and neuron projection and migration in W7 (Extended Data Fig. 8e). Moreover, the cell-type lineage tree constructed from the scRNA-seq reference confirmed the developmental trajectory, positioning telencephalon progenitors as direct precursors to GABAergic cortical interneurons[25]. Beyond the forebrain, our spatial analysis also resolved refined sensory structures in the developing olfactory system (cluster W5 and W10; Fig. 1g and Extended Data Fig. 8a). Sensory neurons were notably enriched in W5 (Extended Data Fig. 8f,g), spatially localized adjacent to the forebrain. This spatial pattern aligns closely with the established developmental trajectory of the olfactory system, in which olfactory sensory neurons progressively form connections with the forebrain as embryogenesis progresses[27]. Together, these findings provide a high-resolution view of neural and sensory system formation, underscoring the power of spatial-DMT in resolving intricate anatomical structures and capturing the spatiotemporal dynamics.

Methylation and transcription dynamics

Mammalian embryogenesis is a precisely timed process with dynamic DNA methylation and gene expression underlying cell differentiation and tissue development[16]. By using pseudotime and spatial-DMT analyses on embryos of two different gestational ages (E11 and E13), we can investigate the dynamics of DNA methylation, gene expression and their interactions at both spatial and temporal scales. We first analysed the developing brain, focusing on the differentiation trajectory from oligodendrocyte progenitors to premature oligodendrocytes. Spatial mapping of the pseudotime of each pixel revealed the organized migration of oligodendrocyte progenitor cells from the subpallium to the pallium during oligodendrogenesis (Fig. 3a and Extended Data Fig. 5c). This pseudotemporal process is associated with coupled DNA methylation and gene expression in different patterns. For example, loss of DNA methylation can be both associated with gene activation (for example, Nrg3, an oligodendrocyte marker[28]) and silencing (for example, Pdgfra, an oligodendrocyte precursor marker[29]; Fig. 3b, red boxes). The presence of both the positive and the negative couplings between DNA methylation and gene expression is aligned with the above comparisons across spatial pixels, reinforcing the regulatory diversity of mouse embryogenesis and oligodendrogenesis[29].

Fig. 3: Spatiotemporal dynamics of DNA methylation and RNA transcription during embryogenesis.

a, Spatial mapping of oligodendrocyte progenitors (left) and premature oligodendrocytes (middle) identified by label transfer from mouse embryo scRNA-seq data[17] to spatial-DMT, with pseudotemporal reconstruction of the oligodendrogenesis plotted in space (right). b, Pseudotime heat maps of VMRs that become demethylated (left) and expression changes of nearby genes from oligodendrocyte progenitors to premature oligodendrocytes (right). Each row represents a specific genomic locus (left) and nearby genes (right), with columns representing tissue pixels ordered by pseudotime. The colour scale indicates the z scores of DNA methylation and gene expression. c,d, UMAP visualization (c) and spatial distribution (d) of integrated E11 and E13 WNN analysis. Spatial tissue pixels from different developmental stages conform well and match with the tissue types. e,f, Comparative analysis of RNA-expression levels (log-normalized expression) with two-sided Wilcoxon rank-sum test, unadjusted P = 8.47 × 10−36, P = 7.62 × 10−54, P = 4.00 × 10−37 from top to bottom (e) and DNA methylation (methylation percentage) with two-sided Wilcoxon rank-sum test, unadjusted P = 2.07 × 10–82, P = 5.45 × 10−82, P = 8.55 × 10−80 from top to bottom (f) for upregulated genes in E13 brain and spinal cord. g, GO enrichment analysis from one-sided hypergeometric test of biological processes related to demethylated and upregulated genes in the brain and spinal cord from E11 to E13 stages. h, Spatial mapping of the expression levels (log-normalized expression) of DNA-methylation-related enzymes in the brain and spinal cord regions across E11 and E13 stages.

To further illustrate the temporal molecular dynamics of embryonic development, we performed spatial mapping of the E13 mouse embryo (Extended Data Fig. 9a). First, we validated our RNA dataset by integrating it with a published spatial ATAC–RNA (ATAC, assay for transposase-accessible chromatin) reference[2], which revealed a strong concordance (Supplementary Fig. 2a). Co-clustering the two datasets identified 11 distinct cluster populations, each corresponding to the histological location in both samples (Supplementary Fig. 2b,c). By integrating spatial data from E11 and E13 embryos (Fig. 3c,d), we identified genes upregulated in E13 (Fig. 3e and Extended Data Fig. 9b,d, left) associated with notable loss of DNA methylation (Fig. 3f and Extended Data Fig. 9c,d, right). These genes are implicated in the corresponding tissue functions. For example, Usp9x, Ank3 and Shank2 are critically involved in neuronal development, synaptic organization, morphogenesis and transmission30,31,32 (Fig. 3e,f). Ctnna1, Pecam1 and Lamb1 are pivotal for maintaining cardiac tissue integrity[33], vascular development[34] and cardiac tissue structuring[35], respectively (Extended Data Fig. 9b,c). Functional analysis of upregulated genes further corroborated their association with biological processes, for example, synapse organization, neuron projection organization and dendritic spine morphogenesis in the brain (Fig. 3g), and sphingolipid metabolic processes in the heart (Extended Data Fig. 9e). Our tissue map data more precisely timed these gene and pathway activations to a specific stage of embryo development, tissue location and shed light on their epigenetic regulatory mechanisms. Notably, besides regulators of tissue development, some DNA methylation writers[36], readers[37] and eraser enzymes[38], for example, Dnmt1, Dnmt3a, Mecp2 and Tet1, showed higher expression in the E13 embryo, suggesting elevated biochemical activity that drove the global DNA methylation dynamics (Fig. 3h).

Co-mapping of mCH–mCG–RNA in mouse brain

mCH methylation, particularly mCA, is uniquely abundant in the brain5,15. To evaluate the spatial heterogeneity of non-CpG cytosine methylation, we applied spatial-DMT to the cortical and hippocampal regions of a P21 mouse brain (Fig. 4a). Initial analysis of global mCA and mCG levels revealed relatively low methylation in the dentate gyrus (DG), cornu ammonis (CA)1/2 and CA3 regions, compared with the cortex (Extended Data Fig. 3d).

Fig. 4: Spatial DNA-methylation and RNA-transcription analyses in the P21 mouse brain.

a, Bright-field image of the P21 mouse brain section (pixel size, 20 μm; ROI area, 2 × 2 mm^2) showing the analysed regions, including the DG, CA1, CA2, CA3 and cortex (n = 1). Scale bar, 500 µm. b, Spatial distribution of clusters identified in DNA-methylation data (left), RNA-transcription data (middle) and integrated DNA and RNA data using WNN analysis (right). c–e, Scatter plot displaying the relationship between CpG and CpA methylation changes (log-transformed fold change) and gene-expression changes (log-transformed fold change; gene exp. (log fold)) for marker genes in DG (c), CA1/2 (d) and CA3 (e). Each dot represents a specific gene, coloured by the log-fold change in gene expression, with red indicating upregulation and blue indicating downregulation. These plots demonstrate how differential methylation at CpG and CpA sites correlates with changes in gene expression, illustrating the complex regulatory landscape across different brain regions. f–h, Spatial mapping of CpG (left) and CpA (middle) methylation and RNA-expression levels (right) for selected genes in different brain regions.

The spatial distribution of methylome and transcriptome clusters matched anatomical structures shown by the histology image, reflecting the arealization of this brain region (Fig. 4b and Extended Data Fig. 10a). Expression of known cell-specific marker genes is spatially distributed in regions in which these cell types were enriched. For instance, Prox1, a TF crucial for neurogenesis and the maintenance of granule cell identity[39], was prominently expressed in the DG (Fig. 4f, right). Satb1, which has a role in cortical neuron differentiation and layer formation[40], was strongly enriched in the cerebral cortex (Extended Data Fig. 10b, right). Bcl11b, a TF essential for neuronal progenitor cell differentiation[41], demonstrated elevated expression in the hippocampus (Extended Data Fig. 10c, right).

To elucidate the regulatory roles of mCG and mCA on gene transcription, we compared the two modifications for the signature genes of each cluster against all other clusters combined. By correlating the modification levels with gene expression, we identified genes potentially regulated by mCG, mCA or both (Fig. 4c–e). Prox1 and Bcl11b expression was significantly associated with both mCG and mCA (Fig. 4c,f and Extended Data Fig. 10c), broadly marking the DG and CA1/2 regions, respectively. By contrast, Ntrk3, a receptor tyrosine kinase crucial for nervous system function[42], was highly expressed in the hippocampal CA1/2 and DG regions, correlating primarily with mCG levels but not with mCA (Fig. 4d,g). Similarly, Satb1 expression in the cortex was strongly correlated with mCG but not with mCA levels (Extended Data Fig. 10b). The silencing of Cux1, a TF involved in neuronal development and function[43], had a negative correlation with CA and CG hypermethylation in the CA3 region. By contrast, in the CA1/2 region, Cux1 expression showed a negative correlation only with CA hypermethylation and seemed independent of mCG levels (Fig. 4e,h). Across both sequence contexts, negative correlations between DNA methylation and gene expression are more prevalent than positive ones, highlighting the predominantly repressive nature of these epigenetic modifications (Extended Data Fig. 10d). Collectively, mCG and mCA regulate transcription in a gene-specific manner, jointly defining the cell identity across cell types and brain regions.

Further analysis of neuronal and glial populations revealed cell-type- and region-specific transcriptomic and epigenetic variation. For example, Syt1 and Rbfox3 are broadly expressed across neurons in all cortical layers (Supplementary Fig. 3a). By contrast, Cux2, Cux1 and Satb2 were highly expressed in upper-layer neurons, whereas Bcl11b was enriched in the deeper cortical layers (Supplementary Fig. 3b,c). Oligodendrocytes (Mbp and Plp1) and fibrous astrocytes (Gfap) were specifically enriched in the corpus callosum and hippocampal regions (Supplementary Fig. 3d,e). To further resolve cell identities, we integrated our spatial-DMT dataset with a reference scRNA-seq dataset[44] to associate spatial clusters with previously defined cell types. For instance, W0, W3 and W5 were identified as oligodendrocytes, DG granule neurons and telencephalon-projecting excitatory neurons, respectively (Supplementary Fig. 4a–c). Aggregated gene-expression and DNA-methylation levels across pixels in each cluster were highly correlated with the corresponding scRNA-seq transcriptomes[44] (Supplementary Fig. 4d) and single-cell DNA methylome profiles[5] (Supplementary Fig. 4e), as illustrated by W3 (DG) and W5 (CA1) (Fig. 4b, right). Cell-type deconvolution using the same scRNA-seq reference[44] revealed spatial organization of diverse cell types, consistent with known brain anatomy (Supplementary Fig. 4f). For example, cortical excitatory neurons showed expected laminar distribution: TEGLU7, TEGLU8, TEGLU4 and TEGLU3 were enriched in layers 2/3, 4, 5 and 6, respectively. In the hippocampus, TEGLU24 and TEGLU23 mapped to CA1/2 and CA3 regions, whereas in granule neurons, DGGRC2 localized to the DG. Furthermore, DEGLU1 was enriched in the thalamic region. All cell types were mapped to expected brain anatomical regions in agreement with the scRNA-seq reference[44]. These observations underscore the robustness of spatial-DMT in resolving complex tissue architectures and simultaneously profiling the epigenome and transcriptome with their spatial context, which remains challenging for conventional anatomical and single-cell approaches.

Resolving regional epigenetic variations

The spatially resolved methylation landscape enables the identification of subtle, region-specific epigenetic variations that may otherwise be undetected. To illustrate this, we performed regional differential methylation analysis, comparing the ventricular and mantle zone of the hindbrain–spinal cord (Fig. 5a). This analysis identified differential methylation at promoters and enhancers specific to neural progenitor cells, as shown by the enrichment of corresponding methylated histone H3 K4 (H3K4me1). These differential methylated loci co-localized with enhancers functionally implicated in brain and neural tube development (mEnhA9), which are associated with TF binding sites crucial for neuronal differentiation, including for the TFs FOXO4 (ref. [45]), NEUROG2 (ref. [46]) and HOXC9 ref. [47]).

Fig. 5: Spatial-DMT resolves mitotic history and subtle region-specific epigenetic variations.

a, Differential region analysis between adjacent WNN cell clusters (left). Chromatin features enriched at hypomethylated genome loci in the blue region (middle) and those enriched in the red region (right) are shown. A summary of the ChromHMMfullStack (full-stack chromatin hidden Markov model) annotation is provided in Supplementary Table 6. HM, histone modification; TFBSrm, transcription factor binding sites from roadmap dataset. b, Differential region analysis in the same WNN (W7) cell cluster but across spatially distant locations (left). Chromatin features enriched at hypomethylated loci in the blue (middle) and red (right) regions are shown. False discovery rate (FDR) from one-sided Fisher’s exact test. c–e, Methylation levels at PMDs in the E11 mouse embryo (c), E13 mouse embryo (d) and P21 mouse brain (e). f, Enriched TF motifs from Homer one-sided hypergeometric test q value (on the basis of Benjamini–Hochberg procedure) in differentially hypomethylated VMRs between D0 and D4 cluster pixels derived from cluster R3 in the E11 embryo (two-sided Wilcoxon rank-sum test, adjusted P value on the basis of Bonferroni correction). g, Differentially expressed genes corresponding to the D0 and D4 pixels originated from the R3 cluster in the E11 embryo (Wilcoxon rank-sum test (two-sided), adjusted P value on the basis of Bonferroni correction).

Spatial methylation profiles may reveal subtle differences between distantly located pixels in the same WNN cell cluster. We examined two W7 pixel groups located in the forebrain and spinal cord, respectively (Fig. 5b). In the forebrain, regions with loss of methylation were enriched for binding sites of FOXI1, TFs crucial for auditory development[48]. By contrast, spinal-cord-specific hypomethylation correlated with occupancy by TLX1, a TF essential for spinal cord maturation and neuronal differentiation[49]. These findings demonstrate that spatial-DMT can resolve subtle epigenetic heterogeneity across distinct tissue microenvironments.

Elucidating mitotic history differences

Besides capturing regional TF-mediated cell state differences, spatial-DMT enables the analysis of diverse cell traits through other methylation-based features. For example, partially methylated domains (PMDs), which lose methylation over successive mitotic divisions, serve as indicators of mitotic activity[50]. By spatially mapping PMD methylation in E11 and E13 embryos (Fig. 5c,d), we identified distinct regional patterns. For instance, regions such as the forebrain and hindbrain–spinal cord had higher PMD methylation, whereas embryonic heart tissue demonstrated lower PMD methylation levels, reflecting active cardiogenesis at these developmental stages[51] (Fig. 5c,d). Notably, we observed spatial gradients in PMD methylation, decreasing from the centre to the periphery in the heart, and from the mantle to the ventricular zone in the forebrain and hindbrain–spinal cord. These gradients potentially reflect the spatial organization of progenitor cells and their differentiation trajectories. In the P21 brain (Fig. 5e), cortical layers displayed higher PMD methylation, consistent with reduced proliferative capacity typical of differentiated neurons[52]. By contrast, the DG has comparatively lower PMD methylation, consistent with the presence of neural stem and progenitor cells in the subgranular zone that continue to undergo mitotic division and neurogenesis[53].

Finally, the spatially resolved variations in the methylome and transcriptome can complement each other in distinguishing differentiated cell states (Fig. 1f, left and middle). Despite originating from the same RNA-defined clusters (R3), DNA methylation-defined cluster 0 (D0) and 4 (D4) cells had distinct subpopulations when stratified by their VMRs. Motif enrichment analysis of low-methylation sites in D0 and D4 identified regulatory elements associated with facial and cardiac morphogenesis (Fig. 5f), whereas corresponding gene-expression changes were limited (Fig. 5g). This may reflect epigenetically primed subpopulations that share similar transcriptional states. Together, these findings highlight the power of spatial-DMT in delineating cell identity beyond the resolution of transcriptomic analysis alone.

Discussion

Understanding how epigenetic regulation is spatially organized in tissues remains a major challenge. Although single-cell DNA methylation and transcriptomic technologies have revealed diverse regulatory architectures in both health and disease4,5,16, they require tissue dissociation, disrupting the native context. Here we developed spatial-DMT, a pioneering spatial multi-omics technology that enables simultaneous profiling of DNA methylation and RNA expression in the same intact tissue sections, preserving cellular and anatomical architectures.

Spatial-DMT generated high-quality data, with strong reproducibility across technical replicates and concordance with established single-cell and spatial transcriptomic references. When applied to mouse embryos at E11 and E13, it revealed methylome dynamics during embryogenesis. In the postnatal brain, it resolved the neuronal mCH landscape, consistent with elevated mCA levels in mature neurons[5]. With 10 μm resolution, spatial-DMT approaches single-cell granularity, enabling precise delineation of fine tissue structures and region-specific epigenetic features that are typically obscured by tissue dissociation. Our data indicate that gene expression may be differentially influenced by mCG and mCA for different cell types in a spatially restricted manner, potentially owing to the distinct genomic distribution of these methylation marks. Although the observed associations are correlative, they offer valuable insights into the complex interplay between regulatory elements on gene expression. This integrative data analysis also demonstrated that each modality captures distinct yet complementary aspects of cellular states, enhancing cell state differentiation beyond what is achievable with single-modality approaches. This may reflect regulatory redundancy, in which distinct epigenetic mechanisms converge to produce similar transcriptional outcomes. Conversely, the opposite scenario is equally plausible—transcriptional states may diverge despite similar epigenetic landscapes, as epigenetic regulation represents only one layer influencing gene expression. As a result, spatial‐DMT offers a cost-effective strategy for high-resolution, unbiased genome-wide methylome analysis.

Future development of spatial-DMT may incorporate additional molecular modalities, including chromatin conformation (HiC), accessibility (ATAC-seq), histone modifications (CUT&Tag), metabolome (mass spectrometry imaging) and surface proteins (CITE-seq), to enhance the resolution of gene regulatory mechanisms in situ. The current enzymatic approach does not distinguish between 5-methylcytosine and 5-hydroxymethylcytosine, this limitation may be overcome by using emerging methods that enable simultaneous measurement of the full spectrum of cytosine base modifications[54]. Coupling our method with long-read sequencing technologies, such as Nanopore or PacBio, could simplify detection and improve coverage of epigenetic modifications. Adaptation of the protocol to formalin-fixed paraffin-embedded (FFPE) specimens would broaden its clinical use, whereas optimizing tagmentation and data-processing pipelines will further improve sensitivity and interpretability. The development of advanced spatial computational deconvolution strategies is essential to resolve signals from lower-resolution pixels, minimize potential biases and enhance biological interpretability. Broader application across tissues, developmental stages and species will further establish the generalizability of spatial-DMT for spatial multi-omics profiling in both basic and translational research.

In summary, spatial-DMT advances spatial omics analyses by enabling the simultaneous mapping of DNA methylation and transcription in the native tissue context. This approach provides a powerful framework for exploring epigenetic regulation, facilitates the discovery of methylation biomarkers, deepens our understanding of disease mechanisms and benefits both fundamental and translational research.

Methods

Tissue slide preparation

Mouse C57 embryo sagittal frozen sections (MF-104-11-C57 and MF-104-13-C57) were purchased from Zyagen. Freshly collected E11 or E13 mouse embryos were snap frozen in optimal cutting temperature (O.C.T.) compounds and sectioned at 7–10 μm thickness. Tissue sections were collected on poly-l-lysine-coated glass slides (Electron Microscopy Sciences, 63478-AS).

Juvenile mouse brain tissue (P21) was obtained from the C57BL/6 mice housed in the University of Pennsylvania Animal Care Facilities under pathogen-free conditions. All procedures used were approved by the Institutional Animal Care and Use Committee.

Mice were euthanized at P21 using CO_2 inhalation, followed by transcranial perfusion with cold Dulbecco’s PBS (DPBS). After isolation, brains were embedded in Tissue-Tek O.C.T. compound and snap frozen on dry ice and a 2-methylbutane bath. Coronal cryosections of 8–10 μm were mounted on the back of Superfrost Plus microscope slides (Fisher Scientific, 12-550-15).

Preparation of transposome

Unloaded Tn5 transposome (C01070010) was purchased from Diagenode and the transposome was assembled following the manufacturer’s guidelines. The oligonucleotides used for transposome assembly were: Tn5ME-B, 5′-/5Phos/CATCGGCGTACGACTAGATGTGTATAAGAGACAG-3′; Tn5MErev, 5′-/5Phos/CTGTCTCTTATACACATCT-3′.

DNA barcode sequences, DNA oligonucleotides and other key reagents

DNA oligonucleotides used for PCR and library construction are shown in Supplementary Table 1. All DNA barcode sequences are provided in Supplementary Tables 2 (barcode A) and 3 (barcode B) and all other chemicals and reagents are listed in Supplementary Table 4.

Fabrication of the polydimethylsiloxane microfluidic device

Chrome photomasks were purchased from Front Range Photomasks, with a channel width of either 20 or 50 μm. The moulds for polydimethylsiloxane (PDMS) microfluidic devices were fabricated using standard photolithography. The manufacturer’s guidelines were followed to spin-coat SU-8-negative photoresist (Microchem, SU-2025 and SU-2010) onto a silicon wafer (WaferPro, C04004). The heights of the features were about 20 and 50 μm for 20- and 50-μm-wide devices, respectively. PDMS microfluidic devices were fabricated using the SU-8 moulds. We mixed the curing and base agents in a 1:10 ratio and poured the mixture onto the moulds. After degassing for 30 min, the mixture was cured at 66–70 °C for 2–16 h. Solidified PDMS was extracted from the moulds for further use. The detailed protocol for the fabrication and preparation of the PDMS device can be found in our previous research[24].

Spatial joint profiling of DNA methylation and RNA transcription

Frozen tissue slides were quickly thawed for 1 min in a 37 °C incubator. The tissue was fixed with 1% formaldehyde in PBS containing 0.05 U ml−1 RNase inhibitor (Enzymatics) for 10 min and quenched with 1.25 M glycine for another 5 min at room temperature. After fixation, tissue was washed twice with 1 ml of DPBS–RNase inhibitor and cleaned with deionized H_2O.

The tissue was subsequently permeabilized with 100 μl of 0.5% Triton X-100 plus 0.05 U ml−1 RNase inhibitor for 30 min at room temperature, then washed twice with 200 μl DPBS–RNase inhibitor for 5 min each. After permeabilization, the tissue was treated with 100 μl of 0.1 N HCl for 5 min at room temperature to disrupt histones from the chromatin, then washed twice with 200 μl of wash buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl_2, 1% BSA and 0.1% Tween 20) plus 0.05 U ml−1 RNase inhibitor for 5 min at room temperature. Next, 50 μl of transposition mixture (5 μl of assembled transposome, 16.5 μl of 1× DPBS, 25 μl of 2× Tagmentation buffer, 0.5 μl of 1% digitonin, 0.5 μl of 10% Tween 20, 0.05 U ml−1 RNase inhibitor (Enzymatics) and 1.87 μl nuclease-free water) was added and incubated at 37 °C for 60 min. After 60 min incubation, the first round of transposition mixture was removed and a second round of 50 μl of fresh transposition mixture was added and incubated for another 60 min at 37 °C. To stop the transposition, 200 μl of 40 mM EDTA with 0.05 U ml−1 RNase inhibitor was added with incubation for 5 min at room temperature. After that, 200 μl 1× NEB3.1 buffer plus 1% RNase inhibitor was used to wash the tissue for 5 min at room temperature. The tissue was then washed again with 200 μl of DPBS–RNase inhibitor for 5 min at room temperature before proceeding with the in situ reverse transcription reaction.

In situ reverse transcription

For the in situ reverse transcription, the following mixture was added: 12.5 μl 5× reverse transcription buffer, 4.05 μl RNase-free water, 0.4 μl RNase inhibitor (Enzymatics), 1.25 μl 50% PEG-8000, 3.1 μl 10 mM dNTPs, 6.2 μl 200 U μl−1 Maxima H Minus Reverse Transcriptase, 25 μl 0.5× DPBS–RNase inhibitor and 10 μl 100 μM reverse transcription primer (biotinylated-dT oligo). The tissue was incubated for 30 min at room temperature, then at 45 °C for 90 min in a humidified container. After the reverse transcription reaction, tissue was washed with 1× NEB3.1 buffer plus 1% RNase inhibitor for 5 min at room temperature.

In situ barcoding

For in situ ligation with the first barcode (barcode A), the first PDMS chip was covered at the tissue ROI. For alignment purposes, a 10× objective (KEYENCE BZ-X800 fluorescence microscope, BZ-X800 Viewer Software) was used to take the bright-field image. The PDMS device and tissue slide were clamped tightly with a custom acrylic clamp. Barcode A was first annealed with ligation linker 1 by mixing 10 μl of 100 μM ligation linker, 10 μl of 100 μM individual barcode A and 20 μl of 2× annealing buffer (20 mM Tris-HCl pH 7.5–8.0, 100 mM NaCl_2 and 2 mM EDTA). For each channel, 5 μl of ligation master mixture was prepared with 4 μl of ligation mixture (27 μl T4 DNA ligase buffer, 0.9 μl RNase inhibitor (Enzymatics), 5.4 μl 5% Triton X-100, 11 μl T4 DNA ligase and 71.43 μl RNase-free water) and 1 μl of each annealed DNA barcode A (A1–A50, 25 μM). Vacuum was applied to flow the ligation master mixture into the 50 channels of the device and cover the ROI of the tissue, followed by incubation at 37 °C for 30 min in a humidified container. Then the PDMS chip and clamp were removed after washing the tissue with 1× NEB 3.1 buffer for 5 min. The slide was then washed with deionized water and dried using compressed air.

For in situ ligation with the second barcode (barcode B), the second PDMS chip was covered at the ROI and a bright-field image was taken with the 10× objective. An acrylic clamp was applied to clamp the PDMS and tissue slide together. Annealing of barcode B (B1–B50, 25 μM) and preparation of the ligation mixture are the same as barcode A. The whole device was incubated at 37 °C for 30 min in a humidified container. The PDMS chip and clamp were then removed, and the slide was washed with deionized water and dried using compressed air. A bright-field image was then taken for further alignment.

Reverse crosslinking

For tissue lysis, the ROI was digested with 100 μl of the reverse crosslinking mixture (0.4 mg ml−1 proteinase K, 1 mM EDTA, 50 mM Tris-HCl pH 8.0, 200 mM NaCl and 1% SDS) at 58–60 °C for 2 h in a humidified container. The lysate was then collected in a 0.2-ml PCR tube and incubated on a 60 °C shaker overnight.

gDNA and cDNA separation

For gDNA and cDNA separation, the lysate was purified with Zymo DNA Clean and Concentrator kit and eluted with 100 μl nuclease-free water. Next, 40 μl of Dynabeads MyOne Streptavidin C1 beads were used and washed three times with 1× B&W buffer containing 0.05% Tween 20 (50 μl 1 M Tris-HCl pH 8.0, 2,000 μl 5 M NaCl, 10 μl 0.5 M EDTA, 50 μl 10% Tween 20 and 7,890 μl nuclease-free water). After washing, beads were resuspended in 100 μl of 2× B&W buffer (50 μl 1 M Tris-HCl pH 8.0, 2,000 μl 5 M NaCl, 10 μl 0.5 M EDTA and 2,940 μl nuclease-free water) containing 2 μl of SUPERase In RNase inhibitor, then mixed with the gDNA–cDNA lysate and allowed to bind for 1 h with agitation at room temperature. A magnet was then used to separate the beads, which bind to the cDNA that contains dT, from the supernatant that contains the gDNA.

gDNA library generation

Supernatant (200 μl) was collected from the above separation process for further methylated gDNA detection and library construction. Next, 1 ml of DNA binding buffer was added to the 200 μl supernatant and purified with the Zymo DNA Clean and Concentrator kit again, then eluted in 84 μl (3 × 28 μl) nuclease-free water. The NEBNext enzymatic methyl-seq conversion module (EM-seq) was then used to detect methylated DNA in the sample by converting unmethylated cytosines to uracil; the manufacturer’s guidelines were followed. Then, 28 μl of DNA sample was aliquoted into each PCR tube, TET2 reaction mixture (10 μl TET2 reaction buffer containing reconstituted TET2 reaction buffer supplement, 1 μl oxidation supplement, 1 μl DTT, 1 μl oxidation enhancer and 4 μl TET2) was added to the DNA sample on ice. In brief, 5 μl of diluted 1:1,300 of 500 mM Fe (II) solution was added to the mixture and incubated for 1 h at 37 °C in a thermocycler. After the reaction, the sample was transferred to ice and 1 μl of stop reagent from the kit was added. The sample was then incubated for another 30 min at 37 °C. TET2 converted DNA was then purified with 90 μl of solid-phase reversible immobilization (SPRI) beads and eluted with 16 μl nuclease-free water. The thermocycler was preheated to 85 °C, 4 μl formamide was added to the converted DNA and incubated for 10 min at 85 °C in the preheated thermocycler. After the reaction, the heated sample was immediately placed on ice to maintain the open chromatin structure, then reagents from the kit were added (68 μl nuclease-free water, 10 μl APOBEC reaction buffer, 1 μl BSA and 1 μl APOBEC) to deaminate unmethylated cytosines to uracil for 3 h at 37 °C in a thermocycler. Deaminated DNA was then cleaned up using 100 μl (1:1 ratio) of SPRI beads and eluted in 20 μl nuclease-free water.

Splint ligation

The gDNA tube was heat-shocked for 3 min at 95 °C and immediately put on ice for 2 min. Then, 10 μl of 0.75 μM pre-annealed Splint Ligate P5 (SLP5) adapter was added. This adapter was diluted from a 12 μM stock, which contained 6 μl of 100 μΜ SLP5RC oligo, 8.4 μl of 100 μΜ SLS5ME-A-H10 oligo, 5 μl of 10× T4 RNA Ligase Buffer and 30.6 μl nuclease-free water in a PCR tube that was incubated at 95 °C for 1 min, then gradually cooled by −0.1 °C s−1 to 10 °C on a thermocycler. Next, 80 μl of ligation master mixture was added to the gDNA tube at room temperature. The mixture contained 40 μl preheated 50% PEG-8000, 12.5 μl SCR buffer (666 mM Tris-HCl pH 8.0 and 132 mM MgCl_2 in nuclease-free water), 10 μl of 100 mM DTT, 10 μl of 10 mM ATP, 1.25 μl of 10,000 U ml−1 T4 PNK and 6.25 μl of 400,000 U ml−1 T4 ligase. The splint ligation mixture was then splinted into five 0.2-ml PCR tubes, 20 μl per tube. The tubes were shaken at 1,000 rpm for 10 s and spun down, then incubated for 45 min at 37 °C, followed by 20 min at 65 °C to inactivate the ligase. For splint ligation indexing PCR, 80 μl of the PCR reaction mixture was mixed in each splint ligated tube. The mixture contained 20 μl 5× VeraSeq GC Buffer, 4 μl 10 mM dNTPs, 3 μl VeraSeq Ultra Enzyme, 5 μl 20× EvaGreen dye, 2 μl of 10 μM N501 primer and 2 μl of 10 μM N70X-HT primer (Supplementary Table 1). The mixture was then aliquoted into a clean PCR tube with 50 μl volume and run on a thermocycler with the setting below, 98 °C for 1 min, then cycling at 98 °C for 10 s, 57 °C for 20 s and 72 °C for 30 s, for 13–19 cycles, followed by 72 °C for 10 s. The reaction was removed once the quantitative PCR (qPCR) signal began to plateau. The amplified PCR products were pooled and purified with a 0.8× volume ratio of SPRI beads (bead-to-sample ratio) and the completed DNA library was eluted in 15 μl nuclease-free water.

cDNA library generation

The separated beads containing cDNA were used for cDNA library generation. In brief, 400 μl of 1× B&W buffer with 0.05% Tween 20 was used to wash the beads twice. Then, the beads were washed once with 400 μl of 10 mM Tris-HCl pH 8.0 containing 0.1% Tween 20 for 5 min at room temperature. Streptavidin beads with bound cDNA molecules were placed on a magnetic rack and washed once with 250 μl nuclease-free water before being resuspended in a template switching oligonucleotide solution (44 μl 5× Maxima reverse transcription buffer, 44 μl of 20% Ficoll PM-400 solution, 22 μl of dNTPs, 5.5 μl of 100 mM template switch oligo, 11 μl Maxima H Minus reverse transcriptase, 5.5 μl of RNase inhibitor (Enzymatics) and 88 μl nuclease-free water). Resuspended beads were then incubated for 30 min with agitation at room temperature and for 90 min at 42 °C, with gentle agitation. After the reaction, beads were washed with 400 μl of 10 mM Tris pH 8.0 containing 0.1% Tween 20 and washed without resuspension in 250 μl nuclease-free water. Water was removed on the magnetic rack and the beads were resuspended in the PCR solution (100 μl of 2× Kappa Master mix, 8.8 μl of 10 μM primers 1 and 2 and 92.4 μl nuclease-free water). Next, the beads were mixed well and 50 μl of the PCR mixture was split into four 0.2-ml PCR tubes. The PCR programme was run as follows: 95 °C for 3 min and cycling at 98 °C for 20 s, 65 °C for 45 s and 72 °C for 3 min, for a total of five cycles, followed by 4 °C on hold. After five cycles of PCR reaction, four PCR tubes were placed on a magnetic rack and 50 μl of the clear PCR solution was transferred to four optical-grade qPCR tubes, adding 2.5 μl of 20× Evagreen dye to each tube. The sample was run on a qPCR machine with the following conditions: 95 °C for 3 min, cycling at 98 °C for 20 s, 65 °C for 20 s and 72 °C for 3 min, for 13–17 cycles, followed by 72 °C for 5 min. The reaction was removed once the qPCR signal began to plateau. The amplified PCR product was purified with a 0.8× volume ratio of SPRI beads and eluted in 20 μl nuclease-free water.

A Nextera XT DNA Library Prep Kit was used for cDNA library preparation. In brief, 2 μl (2 ng) of purified cDNA (1 ng μl−1), 10 μl Tagment DNA buffer, 5 μl Amplicon Tagment mix and 3 μl nuclease-free water were mixed and incubated at 55 °C for 5 min. Then, 5 μl NT buffer was added to stop the reaction with incubation at room temperature for 5 min. PCR master mix (15 μl 2× N.P.M. Master mix, 1 μl of 10 μM P5 primer (N501) and 1 μl of 10 μM indexed P7 primer (N70X) and 8 μl nuclease-free water) was added. The PCR reaction was run with the following programme: 95 °C for 30 s, cycling at 95 °C for 10 s, 55 °C for 30 s, 72 °C for 30 s and 72 °C for 5 min, for a total of 12 cycles. The PCR product was then purified with a 0.7× ratio of SPRI beads and eluted in 15 μl nuclease-free water to obtain the cDNA library.

Library quality check and next-generation sequencing

An Agilent Bioanalyzer D5000 ScreenTape was used to determine the size distribution and concentration of the library before sequencing. Next-generation sequencing was conducted on an Illumina NovaSeq 6000 sequencer and NovaSeq X Plus system (150 bp paired-end mode).

Data preprocessing

For RNA-sequencing data, Read 2 was processed to extract barcode A, barcode B and the UMIs. Using the STARsolo pipeline[56] (v.2.7.10b), these processed data were mapped to the mouse genome reference (mm10). This step generated a gene matrix that captures both gene-expression and spatial-positioning information, encoded through the combination of barcodes A and B. The gene matrix was then imported into R for downstream spatial transcriptomic analysis using Seurat package (v.5.1.0)[57].

For DNA-methylation data, adaptor sequences were trimmed before demultiplexing the FASTQ files using the combination of barcodes A and B. We used the BISulfite-seq CUI Toolkit (BISCUIT) (v.0.3.14)[58] to align the DNA sequences to the mouse reference genome (mm10). Methylation levels at individual CG and CH sites were stored as continuous values between 0 and 1, representing the fraction of methylated reads after quality filtering. These processed CG–CH files were then analysed independently using the MethSCAn pipeline[18] to identify VMRs[59], defined as fused genome intervals with methylation-level variance in the top 2%. We used default parameter settings when running MethSCAn, and the MethSCAn filter min-sites parameter was determined from the read coverage knee plot (Extended Data Fig. 3a). The methylation levels and residuals of VMRs were then imported into R for downstream DNA-methylation analysis.

Clustering and data visualization

We mapped the exact location of pixels on the bright-field tissue image using a custom Python script (https://github.com/zhou-lab/Spatial-DMT-2024/tree/main/Data_preprocess/Image), before removing additional empty barcodes on the basis of read-count thresholds determined by the knee plot (Extended Data Fig. 3a). Clustering and data visualization were conducted using R in RStudio.

For RNA data, we used the SCTtransform function in the Seurat package (v.5.1.0), built using a regularized negative binomial model, for normalization and variance stabilization. Dimensionality reduction was performed using RunPCA function with the SCTtransformed assay. We then constructed the nearest-neighbour graph using the first 30 principal components with the FindNeighbors function and identified clusters with the default Leiden method in the FindClusters function. Finally, a UMAP embedding was computed using the same principal components with RunUMAP function.

Owing to the inherent sparsity of DNA-methylation data, it is impractical to analyse methylation status solely at the individual CpG level. Binary information at sparse loci cannot be used directly to construct a feature matrix suitable for downstream analysis. In our study, we adopted the VMR framework, which divides the genome into variable-sized tiles and calculates the average methylation level across CpGs in each tile for each pixel[18]. This approach results in a continuous-valued matrix, in which rows correspond to pixels and columns represent genomic tiles, with values ranging from 0 to 1. VMR methylation levels and residuals were then imputed using the iterative principal component analysis approach as suggested in the MethSCAn instructions. Initially, missing residual values were replaced with zero and missing methylation levels were replaced with the average values for that VMR interval. The principal component analysis approach was iteratively applied until updated values stabilized to a threshold. The imputed residual matrix for VMRs was then imported into the existing Seurat object as another modality. Similar to the RNA-clustering pipeline, dimensionality was reduced using the RunPCA function. The first ten principal components from the residual matrix were used for clustering and UMAP embedding.

To visualize clusters in their spatial locations, the SpatialDimPlot function was used after clustering on the basis of gene expression or VMR residuals. UMAP embedding was visualized with the DimPlot function. The FindMarkers function was applied to select genes and VMRs that were differentially expressed or methylated for each cluster. For spatial mapping of individual VMR methylation levels or gene expression, we applied the smoothScoresNN function from the FigR package[60]. The SpatialFeaturePlot function was then used to visualize VMR methylation levels and gene expression across all pixels. To illustrate the relationships between clustering results from different modalities, we generated the confusion matrix and alluvial diagram using the pheatmap and ggalluvial R package[61].

Integrative analysis of DNA methylation and RNA data

To integrate spatial DNA methylation and RNA data, WNN analysis in Seurat was applied using the FindMultiModalNeighbors function[19]. On the basis of the WNN graph, clustering, UMAP embedding and spatial mapping of identified clusters were performed for integrated visualization.

For the integration of spatial transcriptomics data of E11 and E13 mouse embryos, the top 3,000 integration features were selected, followed by the use of PrepSCTIntegration and IntegrateData functions to generate an integrated dataset. Similarly, to integrate with public single-cell transcriptomic data25,44, we first identified anchors using the FindIntegrationAnchors function in Seurat, followed by data integration using the IntegrateData function. To integrate DNA-methylation data, common VMRs between both developmental stages were obtained and the integrated CCA method from the IntegrateLayers function was used to join the methylation data from the two developmental stages. A Wilcoxon signed-rank test was performed to compare the methylation levels and gene-expression differences between the two time points.

TF motif enrichment

To perform TF motif enrichment, we first used the MethSCAn diff function on distinct groups of cells to identify differentially methylated VMRs on the basis of the clustering assignment. The HOMER[62] findMotifsGenome function was then applied to analyse the enrichment of known TF motifs using its default database. We followed the same parameter settings used in MethSCAn, with motif lengths of 5, 6, 7, 8, 9, 10, 11 and 12.

CpGs enrichment analysis

Enrichment analysis of individual CpGs in the differential regions (Fig. 5a,b) was performed using knowYourCG (https://github.com/zhou-lab/knowYourCG), which provides a comprehensive annotation database for each CpG, including chromatin states, TF binding sites, motif occurrences, PMD annotations and more. To avoid inflated odds ratios for high-coverage data, genomic uniformity was quantified using fold enrichment, defined as the ratio of observed overlaps to expected overlaps. The expected number of overlaps was calculated as: (number of CpGs sequenced × number of CpGs in the chromatin state feature)/total number of CpGs in the genome.

Correlation and GO enrichment analysis

Correlation analysis was performed for different clusters. We first used the findOverlaps function in GenomicRanges package (v.4.4)[63] to map VMRs to overlapped genes. Then, the Pearson correlation test was applied to obtain the correlation between mapped genes and corresponding VMRs. The Benjamini–Hochberg procedure was used to adjust all P values.

GO enrichment analysis was conducted using the enrichGO function from clusterProfiler package[64] (v.4.2). For GO enrichment in the comparative analysis of E11 and E13 mouse embryos, the FindMarkers function in Seurat package was used to find differential genes and VMRs in the same cluster from integrated data across two developmental stages. Differentially upregulated genes (false discovery rate ≤ 0.05) with demethylated VMRs were used for the GO analysis.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Raw and processed data reported in this paper are deposited in the Gene Expression Omnibus (GEO) with accession code GSE270498. Resulting FASTQ files were aligned to the mouse reference genome (mm10). Published data for data quality comparison and integrative data analysis include a single-cell atlas of mouse embryos (https://oncoscape.v3.sttrcancer.org/atlas.gs.washington.edu.mouse.rna/downloads and https://omg.gs.washington.edu/), a mouse brain atlas (http://mousebrain.org/adolescent/downloads.html) and the Allen Mouse Brain Atlas (https://developingmouse.brain-map.org/).

Code availability

The data analysis pipeline and code to reproduce analyses are available at GitHub (https://github.com/zhou-lab/Spatial-DMT-2024/)[65] and Zenodo (https://doi.org/10.5281/zenodo.15843594)[66].

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Acknowledgements

We acknowledge support from the Packard Fellowship for Science and Engineering (to Y.D.), the pilot award from the Epigenetics Institute at the University of Pennsylvania (to Y.D.), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number DP2AI177913 (to Y.D.) and the National Institute of Health and National Institute of General Medical Sciences (R35-GM146978) (to W.Z.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. C.N.L. was supported in part by the Institute for RNA Innovation of the Perelman School of Medicine at the University of Pennsylvania.

Author information

Author notes

1. These authors contributed equally: Chin Nien Lee, Hongxiang Fu

Authors and Affiliations

1. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

Chin Nien Lee, Hongxiang Fu, Angelysia Cardilla, Wanding Zhou & Yanxiang Deng

2. Institute of RNA innovation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Chin Nien Lee

3. Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

Hongxiang Fu & Wanding Zhou

4. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

Hongxiang Fu & Angelysia Cardilla

5. Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Yanxiang Deng

6. Institute of Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Yanxiang Deng

Contributions

C.N.L. and Y.D. were responsible for the methodology. C.N.L. conducted all spatial joint profiling of DNA methylation and transcription experiments in E11 (50 μm), E11 (10 μm), E11 replicate (50 μm), E13 embryos and P21 mouse brain. A.C. assisted with experiments, including reagent preparation for E11 (50 μm), E13 embryos and P21 mouse brain. C.N.L., H.F., W.Z. and Y.D. performed computational analysis for E11 (50 μm), E11 (10 μm), E11 replicate (50 μm), E13 embryos and P21 mouse brain DNA methylation and RNA data. The original draft was prepared by C.N.L., H.F., W.Z. and Y.D. All authors reviewed, edited and approved the paper.

Corresponding authors

Correspondence to Chin Nien Lee, Wanding Zhou or Yanxiang Deng.

Ethics declarations

Competing interests

Y.D. and C.N.L. are inventors on a provisional patent application related to this work (application number: 63/809,054). Y.D. is a scientific adviser at AtlasXomics. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Chengqi Yi 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 Chemistry workflow of Spatial-DMT.

The diagram illustrates the chemistry workflow for simultaneous DNA methylation and RNA library preparation in Spatial-DMT. Initially, tissue sections are fixed and permeabilized to preserve tissue architecture and molecular integrity while allowing reagent penetration. Following permeabilization, sections undergo HCl treatment to disrupt the protein structure and remove nucleosome histones to improve Tn5 transposome accessibility. Next, Tn5 transposition integrates adapters into genomic DNA, and in situ reverse transcription (RT) converts mRNA into complementary DNA (cDNA) directly within the tissue. Subsequently, Barcode A and Barcode B are sequentially ligated to label and spatially encode DNA and mRNA molecules. Streptavidin bead separation then facilitates the differential enrichment and processing of nucleic acids: cDNA molecules undergo a template switching reaction, followed by Nextera XT library preparation, while genomic DNA (gDNA) is subjected to NEBNext EM-seq conversion for methylation detection, splint ligation, and subsequent library construction.

Extended Data Fig. 2 Reproducibility of Spatial-DMT.

a, Correlation analysis between replicates based on DNA methylation (left) and RNA transcription (right) data from E11 embryo samples with two-sided Pearson correlation test. b, c, Integrative analysis (b) and spatial distribution (c) of clusters from two independent Spatial-DMT experiments using E11 embryo samples. d-f, Spatial mapping of DNA methylation and RNA expression levels for selected marker genes in facial (d), brain (e), and heart (f) regions from two independent Spatial-DMT experiments using E11 embryo samples.

Extended Data Fig. 3 Quality control metrics for Spatial-DMT datasets.

a, Barcode rank plots showing the distribution of reads per spatial barcode for E11, E13 embryos, and P21 brain samples. Valid barcodes are shown in blue, and filtered barcodes are shown in orange. b, Box plots, where the central line represents the median, the lower and upper hinges indicate the first and third quartiles, and the whiskers extend to the most extreme data points within 1.5 times the interquartile range (IQR) from the hinges, showing the duplication rate for E11, E13 embryos, and P21 brain samples, and other single-cell DNA methylation datasets12,67. The y-axis represents the percentage of duplicated reads (n = 278 cells for Cabernet Embryo, n = 927 cells for SciMETv2 LA Brain, n = 1619 cells for SciMETv2 SL Brain, n = 2,493 for E11 embryo (10 μm), n = 1,954 for E11 embryo 1 (50 μm), n = 1,947 for E11 embryo 2 (50 μm), n = 1,699 for E13 embryo (50 μm), and n = 2,235 for P21 brain (20 μm)). c, Box plots showing the mitochondrial retention rate for E11, E13 embryos, and P21 brain samples. The y-axis represents the percentage of reads mapped to the mitochondrial genome. d, Spatial distribution heatmaps of global DNA methylation levels in E11, E13 embryos (mCG), and P21 mouse brain (mCG and mCA). e, The conversion rate of unmethylated cytosine in the linker sequence in E11, E13 embryos, and P21 mouse brain. f, Percentage of reads with poly A (≥ 30 adenine), poly T (≥ 30 thymine), and TSO sequence (AAGCAGTGGTATCAACGCAGAGTACATGGG) in the DNA libraries of E11, E13 embryos, and P21 mouse brain.

Extended Data Fig. 4 Further analysis of DNA methylation data quality of Spatial-DMT.

a, Genomic uniformity analysis showing the fold enrichment (observed overlaps divided by expected overlaps) for different genomic features, comparing E11, E13 embryos, and P21 brain samples with reference datasets5,10,12,14,16 (n = 32 cells for 2021_Shareef, n = 103560 cells for 2021_Liu, n = 107 samples for 2020_He, n = 491 cells for 2022_Nichols_mm10, n = 260 cells for 2019_Hernando, n = 2,493 for E11 embryo (10 μm), n = 1,954 for E11 embryo 1 (50 μm), n = 1,947 for E11 embryo 2 (50 μm), n = 1,699 for E13 embryo (50 μm), and n = 2,235 for P21 brain (20 μm), same for b). b, DNA methylation levels under different chromatin states, comparing E11, E13 embryos, and P21 brain samples with reference datasets5,10,12,14,16. The y-axis represents the methylation levels, with different chromatin states on the x-axis. These chromatin states include active states such as active transcription start site (TSS)-proximal promoter states (TssFlink), actively-transcribed states (Tx, TxWk), enhancer states (Enh, EnhG, EnhLo, EnhPois, EnhPr). Inactive states consist of constitutive heterochromatin (Het), quiescent states (Quies, Quies3, Quies4, QuiesG), repressed Polycomb states (ReprPC, ReprPCWk), and bivalent regulatory states (TssBiv). Boxplots in a and b contain the central line, which represents the median, the lower and upper hinges indicate the first and third quartiles, and the whiskers extend to the most extreme data points within 1.5 times the interquartile range (IQR) from the hinges. c, Line plots showing the mean DNA methylation levels (%) across different genomic regions for E11, E13 embryos, and P21 brain samples. The x-axis represents the genomic regions, and the y-axis represents the mean methylation levels. Error bands represent mean values ± 1 standard deviation. d, Histogram and pie chart showing the size distribution and genomic annotation of variably methylated regions (VMRs) identified in the E11 embryo. The pie chart indicates the percentage of VMRs in different genomic features, such as promoters, exons, and intergenic regions. e, Confusion matrix illustrating the correspondence between clustering results derived from RNA transcription and DNA methylation data in the E11 embryo. f, Alluvial diagram showing the relationships among clusters identified by DNA methylation, RNA, and WNN integration in the E11 embryo. g, Modality weights indicating the relative contributions of gene expression and DNA methylation for each WNN cluster in the E11 embryo.

Extended Data Fig. 5 Spatial mapping of DNA methylation and RNA expression in the brain and spinal cord region of E11 mouse embryo.

a, Heatmaps illustrating the spatial distribution of DNA methylation levels (top rows) and RNA expression levels (bottom rows) for marker genomic loci and corresponding genes in the brain and spinal cord regions (W2) of E11 mouse embryo. b, GO enrichment analysis from one-sided hypergeometric test for differentially expressed genes in clusters W2. The dot plots show the top GO terms for biological processes enriched in each cluster. The x-axis represents the GeneRatio, and the y-axis lists the GO terms. The size of the dots indicates the count of genes associated with each term, and the color represents the adjusted p-value (p.adjust). c, In situ hybridization (ISH) images from the Allen Developing Mouse Brain Atlas[55] show the spatial distribution of markers of oligodendrocyte progenitors and premature oligodendrocytes in the E11 mouse embryo. The images display the expression patterns of markers Olig2 and Nkx2-1 for oligodendrocyte progenitors, and Emx1 and Cdon for premature oligodendrocytes. These ISH images highlight the regions where these progenitor and premature cells are localized within the tissue.

Extended Data Fig. 6 Spatial mapping of DNA methylation and RNA expression in the craniofacial region of E11 mouse embryo.

a, Heatmap of DNA methylation levels for the top 10 differentially methylated genomic loci in the craniofacial region (cluster W0) of E11 mouse embryo. Each row represents a specific genomic locus, and each column represents a different cluster. The color scale indicates the Z-scores of DNA methylation levels. b, Heatmap of expression levels for nearby genes corresponding to the genomic loci in a. Each row represents a specific gene, and each column represents a different cluster. The color scale indicates the Z-scores of gene expression levels. c, Spatial mapping of DNA methylation and RNA expression levels for selected marker genes in the craniofacial region (W0) of E11 mouse embryo. d, GO enrichment analysis from one-sided hypergeometric test for differentially expressed genes in clusters W0. The dot plots show the top GO terms for biological processes enriched in each cluster. The x-axis represents the GeneRatio, and the y-axis lists the GO terms. The size of the dots indicates the count of genes associated with each term, and the color represents the adjusted p-value (p.adjust).

Extended Data Fig. 7 Spatial mapping of DNA methylation and RNA expression in the heart region of E11 mouse embryo.

a, Heatmap of DNA methylation levels for the top 10 differentially methylated genomic loci in the heart region (cluster W6) of E11 mouse embryo. Each row represents a specific genomic locus, and each column represents a different cluster. The color scale indicates the Z-scores of DNA methylation levels. b, Heatmap of RNA expression levels for nearby genes corresponding to the genomic loci in a. Each row represents a specific gene, and each column represents a different cluster. The color scale indicates the Z-scores of gene expression levels. c, Spatial mapping of DNA methylation and RNA expression levels for selected marker genes in the heart region (W6) of E11 mouse embryo. d, GO enrichment analysis from one-sided hypergeometric test for differentially expressed genes in clusters W6. The dot plots show the top GO terms for biological processes enriched in each cluster. The x-axis represents the GeneRatio, and the y-axis lists the GO terms. The size of the dots indicates the count of genes associated with each term, and the color represents the adjusted p-value (p.adjust).

Extended Data Fig. 8 Further analysis of DNA methylation and RNA expression in E11 facial and forebrain regions (pixel size, 10 μm).

a, Spatial distribution of all clusters for DNA methylation (left) and RNA transcription (right). b-c, Integration of Spatial-DMT RNA data with scRNA-seq data from mouse embryo[25]. Cells are colored according to cell annotations from scRNA-seq data[25] (b) and unsupervised clustering results from Spatial-DMT (c). d, Spatial mapping of DNA methylation, RNA expression levels, and in situ hybridization (ISH) images from the Allen Developing Mouse Brain Atlas[55] show the spatial distribution of selected markers in clusters W7 and W11. e, GO enrichment analysis from one-sided hypergeometric test for differentially expressed genes in clusters W7 and W11. The dot plots show the top GO terms for biological processes enriched in each cluster. The x-axis represents the GeneRatio, and the y-axis lists the GO terms. The size of the dots indicates the count of genes associated with each term, and the color represents the adjusted p-value (p.adjust). f, Spatial distribution of olfactory epithelial cells and olfactory sensory neurons revealed by cell type decomposition of spatial transcriptomic pixels using a scRNA-seq reference[25]. g, Spatial mapping of DNA methylation and gene expression level of Fstl5 (olfactory sensory neurons marker).

Extended Data Fig. 9 Comparative analysis of DNA methylation and RNA expression between E11 and E13 mouse embryos.

a, Spatial distribution and UMAP visualization of clusters identified from spatial DNA methylation data (left), spatial RNA transcription data (middle), and integrated DNA and RNA data using WNN analysis (right) for the E13 embryo. Each color represents a different cluster, illustrating the spatial distribution of methylation patterns, gene expression profiles, and the integrated data across different regions such as the forebrain, midbrain, hindbrain or spinal cord, heart, and face, nose and jaw region. Scale bars, 500 µm. b, c, Comparative analysis of RNA expression (b) and DNA methylation levels (c) for upregulated genes in E13 heart region, indicating significant changes in methylation and gene expression patterns during embryonic development with two-sided Wilcoxon test. d, Heatmaps comparing global RNA expression (left) and DNA methylation levels (right) in brain and spinal cord regions between E11 and E13 embryos. e, GO enrichment analysis from one-sided hypergeometric test for biological processes related to demethylated and upregulated genes in the heart region of E13 embryo. The size of the dots indicates the count of genes associated with each term, and the color represents the adjusted p-value (p.adjust).

Extended Data Fig. 10 Analysis of DNA methylation and RNA expression in the P21 mouse brain.

a, UMAP visualization of clusters identified from spatial DNA methylation data (left), spatial RNA transcription data (middle), and integrated DNA and RNA data using WNN analysis (right) for the P21 brain. b, Spatial mapping of CpG (left) and CpA (middle) methylation levels, and RNA expression levels (right) for Satb1 in the P21 brain. c, Spatial mapping of CpG (left) and CpA (middle) methylation levels, and RNA expression levels (right) for Bcl11b in the P21 brain. d, Density plot showing the correlation of gene expression with CpA and CpG methylation in the P21 mouse brain. The plot illustrates the distribution of correlation coefficients, with red indicating CpA methylation and green indicating CpG methylation.

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Lee, C.N., Fu, H., Cardilla, A. et al. Spatial joint profiling of DNA methylome and transcriptome in tissues. Nature 646, 1261–1271 (2025). https://doi.org/10.1038/s41586-025-09478-x

- Received: 01 July 2024

- Accepted: 30 July 2025

- Published: 03 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09478-x

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Structure and mechanism of the mitochondrial calcium transporter NCLX

Minrui Fan,

Chen-Wei Tsai,

Jinru Zhang,

Jianxiu Zhang,

Aswini R. Krishnan,

Tsung-Yun Liu,

Yu-Lun Huang,

Deniz Aydin,

Siyuan Du,

Briana L. Sobecks,

Madison X. Rodriguez,

Andrew H. Reiter,

Carolyn R. Bertozzi,

Ron O. Dror,

Ming-Feng Tsai &

…

Liang Feng

Nature volume 646, pages 1272–1280 (2025)

Abstract

As a key mitochondrial Ca2+ transporter, NCLX regulates intracellular Ca2+ signalling and vital mitochondrial processes1,2,3. The importance of NCLX in cardiac and nervous-system physiology is reflected by acute heart failure and neurodegenerative disorders caused by its malfunction4,5,6,7,8,9. Despite substantial advances in the field, the transport mechanisms of NCLX remain unclear. Here we report the cryo-electron microscopy structures of NCLX, revealing its architecture, assembly, major conformational states and a previously undescribed mechanism for alternating access. Functional analyses further reveal an unexpected transport function of NCLX as a H+/Ca2+ exchanger, rather than as a Na+/Ca2+ exchanger as widely believed[1]. These findings provide critical insights into mitochondrial Ca2+ homeostasis and signalling, offering clues for developing therapies to treat diseases related to abnormal mitochondrial Ca2+.

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Main

Mitochondrial Ca2+ transport—which is critical for tuning cell bioenergetics, shaping cytosolic Ca2+ transients and regulating cell death pathways10,11—is mediated by (1) the mitochondrial Ca2+ uniporter (referred to hereafter as uniporter), a highly selective Ca2+ channel that delivers cytoplasmic Ca2+ into the mitochondrial matrix12,13,14, and (2) Ca2+ transporters that catalyse H+- or Na+-dependent Ca2+ efflux15,16,17. Although substantial progress has been made in mechanistic studies on the uniporter2,11,18, our understanding of the mechanisms and molecular identities of Ca2+-efflux transporters remains limited.

Mitochondrial Ca2+ efflux is essential for preventing mitochondrial Ca2+ overload, a detrimental condition that leads to cell death, and for protecting against pathological conditions such as heart failure, neuromuscular diseases and neurodegeneration[2]. The prevailing view is that NCLX—a member of the Ca2+/cation antiporter (CaCA) superfamily—mediates mitochondrial Na+/Ca2+ exchange (mito-NCX)1,2,3, extruding matrix Ca2+ into the cytoplasm[17]. The activity of NCLX is critical for normal physiology in numerous cell types, including cardiomyocytes[4], neurons and astrocytes19,20,21, brown adipose tissue[22], pancreatic β-cells[23] and B lymphocytes[24]. Dysfunction of NCLX has been linked to heart failure4,5, neurodegenerative disorders6,7,8,9 and tumour progression25,26. Thus, elucidating NCLX’s structural and functional mechanisms will fundamentally improve our knowledge of physiology and disease.

There are currently two major knowledge gaps in NCLX mechanisms. First, the structural basis for its substrate recognition and transport remains unknown. Ca2+/cation antiporter proteins fall into five distinct families: Na+/Ca2+ exchangers (NCX), K+-dependent Na+/Ca2+ exchangers (NCKX), H+/Ca2+ exchangers (CAX), prokaryotic-specific YRBG and cation/Ca2+ exchangers (CCX), the family to which NCLX belongs[27]. Structures of NCX-family (archaeal NCX from Methanococcus jannaschii, MjNCX, and recent human NCX1) and CAX-family (yeast Vcx1, archaeal CAX from Archaeoglobus fulgidus, AfCAX, and bacterial YfkE)28,29,30,31,32 transporters have provided important mechanistic insights. Nonetheless, the utility of these structures for understanding NCLX is limited due to their low sequence identity with NCLX and the presence of substantial structural elements unique to NCLX.

Moreover, the inward- and outward-facing conformations of CaCA proteins were captured from distant homologues28,29,30,31,32. The structural divergence associated with sequence and function divergence, combined with challenges in precisely aligning structural elements, hampers efforts to pinpoint exact conformational changes underlying the alternating-access mechanism. Although recent molecular dynamics simulations of MjNCX have provided insights into its conformational cycle[33], experimental evidence for such conformational changes remains unavailable. In this regard, obtaining both inward- and outward-facing structures of the same transporter could shed light on the molecular basis of ion exchange and translocation in NCLX and, more broadly, in CaCA proteins.

The second knowledge gap concerns NCLX function. Although proposed[1] to mediate mito-NCX, NCLX, puzzlingly, lacks multiple Na+-coordinating residues conserved in NCX proteins28,34. Moreover, although NCLX knockout (KO) has been achieved in mouse models and cell lines, there has been no unambiguous demonstration that NCLX KO abolishes mito-NCX. Various studies using intact cells have assessed the impact of NCLX KO on mitochondrial Ca2+ efflux, which is mediated not only by mito-NCX but also by other Ca2+ transport mechanisms; however, the results have been conflicting, with effects ranging from profound to marginal4,6,19,22,25,35,36. There is therefore clearly a need to define NCLX’s function using more quantitative approaches.

Here we report the cryogenic electron microscopy (cryo-EM) structures of rat NCLX in cytosol-facing (intermembrane space) and matrix-facing conformations, with and without Ca2+. Together with functional investigations using quantitative flux assays in heterologous systems, our work provides a framework for understanding NCLX’s architecture, ion selectivity, transport mechanism and physiological functions.

Overall structure

We selected rat NCLX (83% sequence identity with human NCLX; Extended Data Fig. 1) for cryo-EM due to its favourable biochemical behaviour (Extended Data Fig. 2a,b). Cryo-EM studies on NCLX under Ca2+-bound (2 mM Ca2+, pH 7.4) or Ca2+-free (5 mM ethylene glycol tetraacetic acid (EGTA), without adding Ca2+, pH 5.5 and 7.4) conditions produced maps with resolutions of up to 2.15 Å (Extended Data Figs. 2–4), enabling unambiguous modelling of NCLX (Fig. 1 and Extended Data Figs. 2–4).

Fig. 1: Structure of an NCLX protomer.

a, Structure of an NCLX protomer. The ribbon representations of the NCLX protomer (in a matrix open conformation) are viewed from membrane and intermembrane space, respectively. b, The overall architecture of an NCLX protomer. c, The topology of NCLX.

Two-dimensional classifications showed that NCLX adopts a trimeric assembly in both Ca2+-bound and -free conditions (Extended Data Figs. 2–4), consistent with its relatively early elution volume on size-exclusion chromatography (Extended Data Fig. 2a,b). All three protomers are in the same orientation and are at the same plane level, compatible with a trimeric assembly in the membrane (Fig. 2a,b). Moreover, the same trimer assembly is maintained regardless of the protomer conformational states (Fig. 2c). The trimer interface is formed through interactions between transmembrane (TM) helices 11 and 12 of each subunit (Fig. 2b), with prominent crevices between protomers filled with non-protein densities, presumably lipids or detergent molecules.

Fig. 2: Assembly of NCLX.

a, The density map of an NCLX trimer. The density map (Ca2+ bound, cytosol-facing conformation) is coloured by subunit. b, The structure of an NCLX trimer. The Ca2+ bound, cytosol-facing conformation of NCLX is shown as a ribbon from the cytosolic and matrix sides, respectively, with TM11 and TM12 labelled at the trimer interfaces. c, The conformational state assembly of NCLX trimers. The transport domain is coloured cyan, whereas the gate domain of each protomer is coloured according to the conformational state. The conditions leading to the 3D reconstructions with specific conformational state assembly are indicated by the coloured lines below.

The orientation of NCLX in the inner mitochondrial membrane (IMM) is currently unknown. We performed protease digestion on human NCLX in HEK293 cell mitoplasts (submitochondrial vesicles with ruptured outer membranes) to investigate this. Two key observations support the conclusion that NCLX’s N and C termini—located on the same side of the protein—reside in the matrix. First, a C-terminal 1D4 tag is protected from proteinase K digestion (Extended Data Fig. 5a). Second, engineered tobacco etch virus (TEV) protease sites on the same side as the N and C termini are protected from TEV protease, whereas those on the opposite side are susceptible (Extended Data Fig. 5a–d). As a control, solubilizing the IMM exposes all previously inaccessible sites to digestion (Extended Data Fig. 5c). Together, these results define the side of NCLX containing the N and C termini as matrix-facing and the opposite side as cytosol-facing.

Each NCLX protomer contains twelve transmembrane helices and two sets of helical hairpins/bundles roughly parallel to the membrane (Fig. 1). Two transmembrane helices (TM6 and TM7) and five membrane-parallel helices (α1–5) are unique to NCLX, giving rise to a unique overall architecture. The structure comprises a transport domain, which mediates NCLX trimer formation at the centre, and a gate domain, located on the periphery without contacting other protomers. The transport domain includes an inner, intertwined four-helix bundle (TM2, TM3, TM9 and TM10), which contains the signature α-repeats critical for Ca2+ recognition in CaCA transporters, and a peripheral layer of TM4, TM5, TM11 and TM12. This domain shares a similar structural organization with corresponding regions28,29,30,31,32 in prokaryotic and yeast CaCAs.

The gate domain, composed of TM1, TM6, TM7 and TM8, and α1–5 (Fig. 1b,c), is topologically more complex, constituting a unique structure. Two long anti-parallel helices, TM1 and TM8, pack against each other and are highly tilted in the membrane (~45°). On each side near the membrane surface, this two-helix structure interacts with helical hairpins roughly parallel to the membrane. On the matrix side, two disulfide bridges stabilize a small N-terminal three-helix motif, in which α2 and α3 form a helical hairpin. On the cytosol-facing side, α4 and α5 are parallel to the membrane and interact with each other. Transmembranes 6 and 7 are short, and the loop connecting them interacts with the loop between α2 and α3 within the membrane. These four elements together constitute an expanded structure.

Structural basis of alternating access

We observed multiple classes of particles in distinct conformational states under both Ca2+-bound and -free conditions (Fig. 2c). When comparing trimers in different classes, the three transport domains superimposed well without significant conformational differences (Extended Data Fig. 5e,f), likely defining the membrane plane. By contrast, the gate domains can adopt an up (matrix-facing) or down (cytosol-facing) position relative to the transport domain. In the Ca2+-bound condition, we observed three distinct classes (Extended Data Fig. 3): (1) all three protomers in cytosol-facing conformation; (2) two protomers in cytosol-facing and one in matrix-facing conformation; (3) and one protomer in cytosol-facing and two in matrix-facing conformation. Under Ca2+-free conditions, we also observed distinct assemblies (Extended Data Figs. 2 and 4), including all three protomers in matrix-facing; one or two in cytosol-facing; or all three in cytosol-facing conformation. Comparing matrix- and cytosol-facing conformations reveals the mechanisms underlying alternating access of the substrate-binding pocket in the NCLX transport cycle (Fig. 3).

Fig. 3: Conformational states of NCLX.

a, NCLX in matrix- and cytosol- facing conformations. The transport domains are represented by the surface, whereas the gate domain helices are shown as cylinders. b, Superposition of the transport domain in matrix- and cytosol- facing conformations. c, Superposition of the gate domain in matrix- and cytosol- facing conformations. d, Superposition of NCLX in matrix- and cytosol- facing conformations. The arrow indicates the direction of the movement of the gate domain from the matrix- to the cytosol-facing conformation. e, The slab view of NCLX in the matrix-facing conformation. f, The slab view of NCLX in a cytosol-facing conformation. Ca2+ is shown as a magenta sphere in e and f.

In the matrix-facing conformation, a cavity opens to the matrix and extends to near the membrane centre (Fig. 3e). This cavity is mainly lined by TM2, TM8, TM9, part of TM3, and the loop between α2 and α3. The Ca2+-binding site resides near the end of the cavity and is exposed to the interface between the transport and gate domains. The cavity’s negative electrostatic surface may facilitate cation transport. The highly tilted TM1 and TM8 (within the gate domain) interact with TM2 and TM9 (within the transport domain), respectively, blocking cytosolic access to the substrate translocation pathway mainly by the interacting F122TM1–L145TM2 residue pair, as well as the side chains of I433TM8 and N434TM8. Other TM1 residues also form conformation-specific interactions with residues on TM2 and TM9 to stabilize this matrix-facing conformation, including Y483TM9–S127TM1, L478TM9–C123TM1 and L144TM2–L126TM1.

In the cytosol-facing conformation, the Ca2+-binding site is accessible from the cytosol through a large cavity ending near the membrane centre (Fig. 3f); TM1 is packed against TM2 and TM9, with residues L114TM1, P152TM2, V441TM8, N467TM9 and F111TM1 coming together to seal the end of the substrate passageway. The solvent-accessible cavities in the cytosol- and matrix-facing conformations overlap near their ends. This overlapped region defines a pocket that contains the Ca2+-binding site, which can alternately open to either side of the membrane, supporting the alternating-access transport mechanism.

From the cytosol- to the matrix-facing conformations, both the gate and transport domains retain similar configurations with only local changes (Fig. 3b,c). The main structural transition is the relative movement (~11 Å in distance) between these two domains (Fig. 3a,d). During this drastic movement, the gate domain moves upward by ~9 Å while also shifting towards TM9 relative to TM2. Furthermore, the cytosolic side of the gate domain is brought closer to the transport domain. Accordingly, the N-terminal parts of TM2 and TM9, through which the transport domain connects with the gate domain, tilt inward and outward, respectively (Fig. 3b), resulting in a ~9 Å movement of their N-terminal ends to accommodate the large movement of the gate domain.

During the transition between cytosol- and matrix-facing states, the long, tilted anti-parallel TM1 and TM8—together with the rest of the gate domain—slide along the surface of the transport domain right next to where Ca2+ is bound, moving from below to above the Ca2+-binding site (Fig. 3a). These two transmembrane helices seal the translocation pathway from either the matrix or cytosol side, depending on NCLX’s conformation, thus functioning as a barrier that enables alternating access. In the transport domain, TM1 and TM8 interact with TM2 and TM9, where P152 and G148 (TM2), and G466 and G470 (TM9)—located mid-helix at the domain interface—surround the Ca2+-binding site. They form a relatively flat surface without side-chain extrusion, which may facilitate TM1 and TM8 sliding during conformational transitions. Consistently, the domain-interface residues on TM1 and TM8 are lined with hydrophobic residues, which may facilitate their movement along the transport domain.

Ca2+-binding sites

The cation-binding sites of CaCA transporters are typically located between the α1 and α2 repeats28,29,30,31,37. With 2 mM Ca2+ present, a strong sphere-like non-protein density was observed in all protomers within the trimer, at a site analogous to the Ca2+-binding site of MjNCX (Fig. 4a,b). These strong, ion-like densities were assigned to Ca2+, given their absence in Ca2+-free structures. The bound Ca2+ is clamped between the main-chain oxygens of N149 and N467 and the side-chains of two negatively charged residues, D153 and D471, which are conserved across NCLX homologues[27]. The high-resolution map (cytosol-open) at 2.15 Å allowed us to define the coordination geometry of Ca2+. The binding site adopts a classic Ca2+-coordination configuration with seven oxygen atoms[38] (Fig. 4a), including the D153 and D471 side-chain oxygens; the N149 and N467 backbone oxygens; and water molecules stabilized by these residues. This seven-oxygen coordination, including carboxyl groups from acidic residues, is arranged in a (pseudo)pentagonal bipyramidal geometry—a configuration that is commonly observed in Ca2+-binding sites and probably underlies the Ca2+ selectivity of NCLX38,39. Furthermore, the side chains of S468 and N498, conserved across species (Extended Data Fig. 1), form hydrogen bonds with Ca2+-coordinating water molecules, indirectly contributing to Ca2+ binding (Extended Data Fig. 5g). We observed similar Ca2+ coordination in both cytosol- and matrix-open conformations (Extended Data Fig. 5h), indicating that the Ca2+ site is maintained during conformational transitions.

Fig. 4: The Ca2+-binding site of NCLX.

a, Coordination of Ca2+ at the Ca2+-binding site in the experimental structure (Ca2+ shown as a magenta sphere in the zoomed-in view on the right and as a green sphere on the left for contrast). Ca2+ is coordinated by the D153 and D471 side-chain oxygens; the N149 and N467 backbone oxygens; and water molecules. b, The density map of the Ca2+-binding site of NCLX. The densities are shown as blue meshes (contoured at 14σ), with Ca2+ displayed as a magenta sphere and the coordinating water molecules as red spheres. c, Superposition of matrix-facing NCLX in the presence and absence of Ca2+. d, Superposition of cytosol-facing NCLX in the presence and absence of Ca2+. A magnified view of TM2 and TM9 coordinated by Ca2+ is shown on the right.

We performed all-atom molecular dynamics simulations to further validate the Ca2+ site. For both matrix- and cytosol-facing conformations, simulations were initiated with a Ca2+ ion placed at the position identified by cryo-EM. In all such simulations (five simulations for each conformation; 2 µs each), the charged side chains of D153 and D471 form direct coordination with Ca2+, and the Ca2+ remained stably bound at this location throughout the simulations (Extended Data Fig. 6), with a similar overall coordination geometry as observed in the high-resolution experimental structure (Extended Data Fig. 7b). The coordination of Ca2+, including the salt bridges formed by the carboxyl oxygens of D153 and D471, is dynamic. Interestingly, in the cytosol-facing conformation, Ca2+ is more frequently coordinated by the N467 backbone oxygen, whereas the N149 backbone oxygen indirectly interacts through water molecules (Extended Data Fig. 7a). In comparison, in the matrix-facing conformation, Ca2+ is more frequently coordinated by the N149 backbone oxygen, whereas the N467 backbone oxygen indirectly interacts through water molecules. These results demonstrate the plasticity of the Ca2+ coordination and suggest potential differences in Ca2+-binding affinity according to the conformational state of NCLX.

We also initiated simulations without Ca2+ in the binding pocket (Extended Data Fig. 6). In the majority of these simulations, we observed Ca2+ diffusion into the Ca2+ site, initially interacting with D153 or D471. Once in this site, Ca2+ remained there throughout each simulation. These results further supported the Ca2+-binding site assigned on the basis of the cryo-EM structure.

NCLX exhibits similar overall structures with and without Ca2+ (both at pH 7.4) (Fig. 4c,d). In the matrix-facing conformation, the Ca2+-bound and -free structures superimpose well (Fig. 4c). Notably, in the cytosol-facing conformation, local conformational changes were observed in the N-terminal half of TM2, which tilts ~10° around the Ca2+-coordinating site (Fig. 4d). This movement brings TM2’s N terminus closer to TM9. Given that the conformational switch between cytosol- and matrix-facing states also involves tilting of the N-terminal halves of TM2 towards TM9 (Fig. 3b), these Ca2+-induced structural changes in the cytosol-facing conformation may potentially represent a crucial step linked to the conformational isomerization that drives substrate exchange.

Functional characterization

Sequence alignment shows that the Na+-coordinating serine residues in the MjNCX structure (S51 and S236 in Sint; S77 in Sext) are replaced by glycine or alanine in NCLX, raising the question of how NCLX can bind three Na+ ions to catalyse mito-NCX, which is thought to operate in a 3:1 stoichiometry40,41,42. Structural comparison indeed reveals that NCLX lacks the Na+-binding sites identified in MjNCX (Sext and Sint). Moreover, no obvious Na+ ion densities were observed in NCLX structures solved with 150 mM Na+ under Ca2+-free conditions (at the same pH of 7.4), raising the possibility that Na+ is a poor ligand for NCLX.

To directly assess the contributions of NCLX to mito-NCX, we produced NCLX KO HeLa and Chinese hamster ovary (CHO) cells. Surprisingly, a standard mitochondrial Ca2+ flux assay (Fig. 5a) showed that, after mitochondrial Ca2+ uptake was abolished by a uniporter inhibitor Ru360, adding Na+ ions elicited Ca2+ efflux, which was as robust as in wild-type (WT) cells, and was strongly suppressed by a well-established mito-NCX inhibitor CGP-37157 (ref. [43]). These results demonstrate that mito-NCX remains intact in NCLX KO cells. To further verify this surprising observation, we tested NCLX KO HEK293 and HCT116 cells that were independently generated by the Trebak laboratory[25] and again observed strong mito-NCX (Fig. 5a).

Fig. 5: NCLX functional determination.

a, Mito-NCX activity in indicated cell lines. Cells were digitonin-permeabilized with calcium green 5N (CG5N) for reporting extra-mitochondrial Ca2+. Ca2+ (10 µM) increases CG5N fluorescence, followed by a signal reduction reflecting mitochondrial Ca2+ uptake. After Ru360 inhibits Ca2+ uptake, 10 mM Na+ induces mito-NCX, abolished by 5 µM CGP-37157. Mito-NCX rates are summarized in the bar chart. b, Effect of expressing NCLX constructs on mito-NCX in HEK cells. Ca2+ efflux traces (top left), efflux rates (bottom left) and Western blots (right) compare activity and expression levels. Control: no NCLX overexpression. c, Mitochondrial Ca2+ transport in Sf9 cells. Top: 10 mM Na+ fails to elicit mito-NCX with or without human NCLX overexpression in permeabilized Sf9 cells. Bottom: an MCU–EMRE fusion protein (hME) with a D261A substitution induces Ru360-insensitive mitochondrial Ca2+ uptake. Ru360 was also added before Ca2+ addition to inhibit native uniporter activity. d, 45Ca2+ influx into Xenopus oocytes expressing indicated human NCLX constructs. Solid lines indicate linear fits used to obtain Ca2+ uptake rates. 2DA, D153A–D471A. e, Impact of CGP-37157 or NCLX substitutions on Ca2+ uptake into oocytes. Expression levels of mutants are 80–120% of WT. Uninjected, no RNA injection; 2DE, D153E–D471E. P values were obtained by comparing with the WT. f,g, Sensitivity of WT NCLX activity to external pH, Na+ (100 mM) or K+ (100 mM). The Ca2+-uptake rate summary and pH-dependence raw data are shown in f and g, respectively. Solid lines represent linear fits. h, H+-coupled Ca2+ flux. Oocytes expressing WT NCLX and preloaded with 45Ca2+ were exposed to the indicated external pH. Intracellular 45Ca2+ measured at various time points were normalized to the average count at t = 0. CPM, count per minute. i, NICE in HeLa cells. Following CG5N addition (initial signal jump) and digitonin-induced slow signal decline, Ru360 addition inhibits the uniporter and reveals NICE, as also shown in the zoomed-in traces (bottom left) and quantified in the bar chart. RES, NICE rescue by expressing WT NCLX in NCLX KO cells. Numbers in parentheses indicate independent biological repeats. The molecular mass marker unit is kilodaltons. Data show the mean ± s.e.m. Statistics was performed using an unpaired, two-tailed t-test (significant at P < 0.05). Refer to Supplementary Fig. 1 for gel source data. a.u., arbitrary unit.

Source Data

We then overexpressed either NCLX(WT) or an NCLX(S468T) mutant previously proposed to be dominant-negative[1]. Both proteins properly localized to mitochondria (Extended Data Fig. 8a), but neither affected mito-NCX (Fig. 5b). Interestingly, insect Sf9 cell lines showed rapid mitochondrial Ca2+ uptake but lacked mito-NCX activity (Fig. 5c). Expressing human uniporter subunits MCU (a Ru360-insensitive variant) and EMRE in Sf9 cells produced Ru360-insensitive mitochondrial Ca2+ uptake (Fig. 5c). By contrast, expressing similar levels of human NCLX in Sf9 mitochondria (Extended Data Fig. 8b) failed to yield mito-NCX (Fig. 5c and Extended Data Fig. 8c). Altogether, these mitochondrial Ca2+ flux experiments (Fig. 5a–c), along with sequence and structural analyses, strongly suggest that NCLX does not mediate mito-NCX.

To establish an assay for studying NCLX function, we first attempted to reconstitute rat NCLX into liposomes; however, the proteoliposomes exhibited a large non-specific Ca2+ leak, making it impossible to isolate NCLX-specific signals. This leak probably stems from the lauryl maltose neopentyl glycol (LMNG) detergent, which is used to purify NCLX but is difficult to remove from liposomes due to its low critical micelle concentration. We then turned to the Xenopus oocyte expression system, widely employed to study NCX proteins44,45,46. A fraction of human NCLX was directed to the oocyte plasma membrane through codon optimization and targeting sequence modification (Extended Data Fig. 8d). As in the initial characterization of NCX1 (ref. [44]), we quantified NCLX activity by measuring 45Ca2+ influx into oocytes. Results show that NCLX exhibited robust Ca2+ transport activity, which was absent in oocytes lacking heterologous NCLX expression (Fig. 5d,e). This Ca2+ transport was unaffected by CGP-37157 but was strongly suppressed by (1) mutating NCLX’s Ca2+-coordinating residues D153 and D471; (2) Ala substitution of Y483, a critical residue that seals the cytoplasmic entrance in the matrix-facing conformation; or (3) introducing a positive charge into the Ca2+-binding pocket via an S468K substitution (Fig. 5d,e). These results demonstrate that NCLX is a Ca2+ transporter and highlight the functional importance of critical NCLX residues observed in the structures.

If NCLX is an exchanger like other CaCA proteins, its Ca2+ transport should be sensitive to the electrochemical gradients of coupled ions. Imposing inward Na+ or K+ gradients across the oocyte membrane did not affect NCLX Ca2+ transport (Fig. 5f); however, Ca2+ transport was enhanced by increasing the external pH and then suppressed by acidification (Fig. 5f,g), suggesting potential H+ coupling. A H+/Ca2+ exchange mechanism demands that NCLX uses a H+ gradient to drive energetically uphill movement of Ca2+. Indeed, reducing external pH in NCLX-expressing oocytes—preloaded with 45Ca2+—causes Ca2+ efflux against the gradient (Fig. 5h), establishing NCLX as a H+/Ca2+ exchanger.

It has long been recognized that mitochondria can extrude matrix Ca2+ via Na+-independent H+/Ca2+ exchange (NICE)[17]. Consistent with NCLX being a H+/Ca2+ exchanger, NCLX KO reduced the rate of NICE in HeLa cells—an effect restored by expressing WT NCLX (Fig. 5i). Moreover, WT NCLX expression in Sf9 cells induced robust NICE, whereas functionally impaired NCLX mutants—NCLX(D153A–D471A) or NCLX(S468K)—elicited minimal NICE (Extended Data Fig. 8e,f). Altogether, these mitochondrial Ca2+ flux assays further support our proposed NCLX function.

As our structural, functional and molecular dynamics simulation data all suggest that D153 and D471 are critical for Ca2+-binding, we propose that H+/Ca2+ exchange occurs when H+ binds to the side chains of D153 and/or D471, reducing Ca2+ affinity to cause Ca2+ release. This mechanism is analogous to those proposed for the Saccharomyces cerevisiae H+/Ca2+ exchanger Vcx1 and Archaeoglobus fulgidus H+/Ca2+ exchanger AfCAX (refs. 29,30), and is consistent with the MjNCX structure solved at a low pH of 4, where protonation of E54 and E213—corresponding to NCLX(D153) and NCLX(D471)—prevents Ca2+ or Na+ binding[37]. Indeed, a D153N substitution reduced NCLX Ca2+ transport activity, presumably due to compromised Ca2+-binding (Fig. 5d,e; the NCLX(D471N) mutant was not analysed due to much lower expression). By contrast, a double D153E–D471E substitution, which preserves side-chain charges, retained transport function (Fig. 5e). Furthermore, molecular dynamics simulations revealed that protonation of D153 and D471 led to a decrease in stability of the bound Ca2+, with Ca2+ exiting the transporter entirely in several of these simulations (Extended Data Fig. 9), providing further support for the proposed H+/Ca2+ exchange mechanism.

Discussion

Here we elucidated the structural and functional mechanisms of the key mitochondrial transporter NCLX. Structural analyses of oligomer formation and substantial vertical movement of the gate domain in the membrane reveal an intriguing parallel to transporters that operate via an elevator alternating-access mechanism[47], including the sodium–aspartate co-transporter GltPh (ref. [48]), sodium–proton antiporter NhaA (ref. [49]), bile acid transporters50,51 and sodium–nucleoside co-transporter CNT (ref. [52]). In both NCLX and elevator transporters, the substrate-binding site resides in the transport domain, whereas a pair of tilted anti-parallel transmembrane helices separate the solvent from either side of the membrane. The substrate-binding site becomes either above or below the barrier formed by these two transmembrane helices during an elevator-like movement of one domain relative to the other, thus giving rise to alternating access.

However, NCLX seems to fundamentally differ from elevator transporters. First, in NCLX, the transport domain mediates oligomerization, whereas this interaction is provided by the scaffold domain in elevator transporters. Second, whereas the transport domain moves considerably to carry substrates across the membrane in elevator transporters, the transport domain and substrate-binding site in NCLX seem to remain stationary during conformational transitions (Cα r.m.s.d. = 0.54 Å for the transport domain, excluding the N-terminal part of TM2 and TM9 that connect to the gate domain). NCLX seems to use an ‘elevator-gate’ that traverses the membrane to control alternating access. This distinct transport mechanism may give rise to relative movement between the transport domain and the domain that contains the hydrophobic barrier for alternating access. Notably, the bacterial CAX transporter YfkE also adopts a trimeric architecture, with the transport domain mediating trimer formation and the hydrophobic barrier portion located on the periphery[31]. This hints that its transport domains, like NCLX, remain stationary in the membrane.

Some other families of CaCA transporters were thought to operate via a rocker-switch mechanism[47], which is distinct from the elevator-gate mechanism of NCLX. Interestingly, comparing inward- and outward-facing conformations of those CaCA transporters from different families28,29,30,31,32, along with molecular dynamics simulations of archaeal MjNCX (ref. [33]), suggests similarly large movements of the transmembrane regions equivalent to NCLX’s TM1–TM8 pair. For those CaCA transporters, it is unclear whether the TM1–TM8 pair constitutes a separate structural domain and how these transporters move within the membrane without oligomerization to define the stationary part. Nonetheless, those structural comparisons and simulations suggest a critical role for TM1–TM8 in the transport cycle, lending support to the conformational changes observed within a protomer of NCLX.

The CaCA protein superfamily contains transporters that exchange Ca2+ with various cations, including H+, Na+ or K+. A key finding in this work is that NCLX functions as a H+/Ca2+ exchanger rather than a Na+/Ca2+ exchanger. Indeed, phylogenetic analysis suggests that NCLX is distant from canonical NCX proteins and more closely related to CCX-family proteins in plants[27]. The exact exchange mechanism for these plant proteins is unclear, but they also lack multiple Na+-coordinating residues in MjNCX, suggesting that Na+ is unlikely to be the coupled ion for Ca2+ transport. A H+/Ca2+ exchange mechanism similar to that in NCLX would seem more plausible, given that pH gradients—not Na+ gradients—are the primary driving force for secondary active transport in plants[53].

Our structure determined at a low pH of 5.5 without Ca2+ presumably represents proton-loaded NCLX, given the ~100-fold higher proton concentration compared with the transporter’s typical operating conditions. Notably, under this condition, the structure exhibits cytosol-open conformations across all protomers within a trimer, suggesting that this conformation is predominant at low pH. By contrast, the structure determined at pH 7.4 without Ca2+ displays a combination of cytosol- and matrix-open, or all matrix-open, conformations within a trimer, compatible with NCLX potentially in mixed protonated states. This pH-dependent shift in conformational state distribution aligns with our finding that NCLX operates a H+-coupled transport process. Furthermore, we found that, for each protomer, the overall structure of this putative proton-bound NCLX is similar to the Ca2+-bound form, with the N-terminal half of TM2 rotating around the Ca2+-binding site along the interface with the gate domain (Extended Data Fig. 5i). This rotation is presumably caused by local changes in the Ca2+-binding site configuration, whereas the domain-interface residues of TM2 remain staying on the interface. It is conceivable that these local conformational differences may potentially influence key steps in a transport cycle. Together, our Ca2+-bound and putative proton-loaded structures provide a structural framework for dissecting NCLX transport. The exact stoichiometry and exchange mechanism still require future investigation.

For decades, excitable cells have been known to exhibit much stronger mito-NCX than non-excitable cells[54]; however, after the identification of NCLX, its expression was found to be homogeneous across tissues55,56. This discrepancy can now be resolved in light of our finding that NCLX does not mediate mito-NCX. We note that our results do not contradict most previous studies analysing how NCLX KO impacts mitochondrial Ca2+ transport4,6,19,22,25,35,36. In particular, most studies report that in intact cells, loss of NCLX reduces the rate of mitochondrial Ca2+ efflux to varying degrees. However, this reduction may reflect a decrease in NICE, to which NCLX contributes (Fig. 5i), or may arise indirectly from altered Ca2+ electrochemical gradients following NCLX KO. Our results are not inconsistent with previous efforts to identify mitochondrial H+/Ca2+ exchangers57,58,59,60. In our hands, NCLX KO does not fully abolish NICE in HeLa cells, raising the possibility that more than one transporter might underlie mitochondrial NICE.

There is no doubt that NCLX is critical for normal physiology, as evidenced by the wide range of phenotypes and pathologies induced by NCLX ablation; however, how exactly NCLX regulates mitochondrial processes must now be understood in the context of its role in H+/Ca2+ rather than Na+/Ca2+ exchange. In mitochondria, the proton motive force across the IMM, produced by proton pumping via the electron transport chain, is harvested by the FoF_1-ATPase to synthesize ATP. Moreover, matrix [Ca2+] regulates oxidative phosphorylation by modulating the activity of TCA cycle enzymes. Thus, a H+/Ca2+ exchange machinery linking the proton motive force to mitochondrial Ca2+ homeostasis could create a feedback mechanism to regulate mitochondrial bioenergetics. Such a scenario might help to explain why NCLX KO often causes severe metabolic phenotypes. Clearly, further investigation is needed to link NCLX’s molecular properties to its critical roles in health and disease.

Methods

Protein expression and purification

The rat NCLX gene with a Strep tag at the C terminus was cloned to the BacMam vector[61]. The NCLX virus was generated and used to infect HEK293 cells at a cell density of 3 × 10^6 cells per millilitre at 37 °C. After 12 h, the cells were cultured in the presence of 10 mM sodium butyrate at 30 °C. After another 48 h, cells were harvested and stored at −80 °C for further use.

To purify the NCLX protein under the high-Ca2+ condition (2 mM Ca2+), the cells were dounce-homogenized in buffer A (50 mM HEPES pH 7.4, 150 mM NaCl, 2 mM CaCl_2) with protease inhibitors and ribonuclease. The membranes were solubilized at 4 °C for 2 h with the addition of 1.5% LMNG (Anatrace) and cholesteryl hemisuccinate (Anatrace) mixture. Following spinning at 18,000 r.p.m. for 45 min, the supernatant was mixed with Strep-Tactin resin (IBA) and incubated for 2 h at 4 °C. After extensive washing using buffer B (150 mM NaCl, 2 mM CaCl2, 20 mM HEPES pH 7.4) containing 0.05% glyco-diosgenin (GDN, Anatrace), the protein was eluted using buffer B with 0.01% GDN and 10 mM desthiobiotin. To polish the protein, the eluted sample was concentrated and subjected to further purification using a Superose 6 increase column (Cytiva) in buffer B containing 0.006% GDN. The peak fractions of NCLX were concentrated to 15 mg ml–1 for preparing cryo-EM grids. To purify the NCLX protein in a Ca2+-free state at pH 7.4, the 2 mM CaCl_2 was replaced with 5 mM EGTA. To purify the protein in a calcium-free state at low pH, the protein was initially extracted in the same Ca2+-free pH 7.4 conditions as above and gradually exchanged into buffer C (20 mM buffer with sodium acetate-acetic acid pH 5.5, 150 mM NaCl, 5 mM EGTA) containing 0.006% GDN for preparing cryo-EM grids.

Cryo-EM sample preparation and data collection

Protein sample (3 µl) was used for each cryo-EM grid. The glow discharged grids (300 mesh, R2/1, Au, holey carbon, Quantifoil) with the sample were blotted using a Vitrobot Mark IV for 3 s (4 °C and 100% humidity) or a Leica EM GP2 for 2 s (4 °C and 96% humidity), before being plunge frozen in liquid ethane.

For the NCLX sample without calcium (pH 7.4), datasets were collected on Titan Krios (300 keV; with GIF-Quantum Energy Filter) at cEMc/Stanford or S2C2/Stanford. Images were recorded using K3 Summit detector (Gatan) (super-resolution counting mode; 105,000× magnification; 0.86 Å physical pixel size) by serialEM. The Multiple Record method was used for data collection (image shift; 3 × 3). A total of 50 video frames were collected.

For the NCLX sample with calcium, and the NCLX low-pH sample without calcium, datasets were acquired using EPU software, similarly on Titan Krios (cEMc/Stanford or S2C2/Stanford) with Falcon 4 detector (Thermo Fisher Scientific; counted mode) at 96,000× nominal magnification (0.82 Å physical pixel size) or 130,000× nominal magnification (0.946 Å or 0.95 Å physical pixel size). Forty movie frames were collected in MRC format, with a dose of ~50 electrons per Å^2.

Cryo-EM data processing

For NCLX without calcium data (pH 7.4), a total of 4,264 movies were processed by cryoSPARC live[62] v4.2 (Patch motion correction, Patch CTF estimation and template picking). Then, 821,498 particles were extracted with 80-pixel box and 4 × 4 binning from 3,241 images (with <8 Å CTF estimates). Heterogeneous refinement was performed over three rounds, using accurate and biased maps. This resulted in 95,304 particles for subsequent steps. Furthermore, 57,247 particles from 1,304 images were selected for Topaz (v.0.2.4)[63] training. Topaz repicking subsequently yielded 659,059 particles (extracted with 80-pixel box and 4 × 4 binning), which underwent 3D classification using a seed-facilitated multi-reference strategy[64]. The reference sets included (1) non-uniform refinement accurate map; (2) maps with a resolution gradient (an accurate map along with low-pass filtered maps at 10 Å and 20 Å); (3) biased maps via ab initio reconstruction; and (4) maps with noise re-weight (accurate map and maps with micelles scaled down by 0.3 and 0.7). After removing duplicated particles, 208,064 particles were re-extracted with a 320-pixel box. Two rounds of heterogeneous refinement were performed with noise reweighted and noise accurate maps, respectively. Afterwards, 86,447 selected particles gave rise to a 3.29 Å map (class 1) through successive rounds of non-uniform, Local CTF and Local refinements. The map was sharpened by DeepEMhancer[65]. Similarly, 34,185 particles underwent non-uniform refinement, yielding a 3.93 Å map (class 2) and 26,871 particles yielded a 4.29 Å map (class 3). The local resolution estimate was performed by BlocRes.

For NCLX with calcium data, datasets A and B were collected and processed via the same processing strategy; thus, dataset A is described in the following steps. For dataset A, 13,255 videos were motion corrected using MotionCorr2 (ref. [66]). CryoSPARC (v.4.2) was used to process the dose-weighted images, including Patch CTF estimation. Topaz picking resulted in 3,180,107 particles (80-pixel box and 4 × 4 binning) from 12,725 images (with <8 Å CTF estimates). Heterogeneous refinement was performed over three rounds with accurate and biased maps. This resulted in 390,406 seed particles for subsequent processing. Furthermore, the previous 3,180,107 particles underwent 3D classification using the seed-facilitated multi-reference strategy with similar multiple-references as described above. After removing duplicates, the resulting 948,917 particles were combined with the seed particles, yielding 1,021,442 particles with duplicate removal. Two rounds of heterogeneous refinement were performed using noise reweighted maps. Afterwards, the re-extracted particles (with 320-pixel box) were subjected to two rounds of heterogeneous refinement with noise reweighted and accurate maps, respectively. The outcome was two classes with 334,585 particles and 191,077 particles, which were combined with 155,219 and 230,218 particles from dataset B using the same processing strategy as dataset A, respectively. The combined particles underwent heterogeneous, non-uniform, local CTF and non-uniform refinements. Refinement of a high-quality subset of 150,233 particles then yielded a 3.31 Å map (class 3a), and 145,777 particles yielded a 2.97 Å map (class 2a). The selected 132,072 particles were refined, yielding a 3.10 Å map (class 4a) after 265,702 particles underwent 3D classification without alignment. All maps were sharpened by DeepEMhancer for improved density. For dataset C, 10,373 videos were motion corrected using MotionCorr2. The dose-weighted images were imported and estimated by Patch CTF estimation using cryoSPARC v4.4. After Blob picking from 500 images, 158,465 selected particles were subjected to 2D classification and ab initio refinement, yielding 97,913 particles as seeds. Simultaneously, following template picking for whole images, 9,112,846 particles (extracted with 80-pixel box, 4 × 4 binning) underwent 3D classification using seed-facilitated multi-references. After duplicate removal, 1,423,457 re-extracted particles (with 320-pixel box) underwent three rounds of heterogeneous refinement (one with accurate and biased maps, followed by two with accurate map). The outcome was two classes with 686,084 and 205,206 particles, respectively. Refinement of the selected subset of 686,084 particles then yielded a 2.15 Å map with C3 symmetry (class 4a). The other 205,206 particles were processed via heterogeneous refinement, yielding 115,207 particles, which were further refined to produce a 2.60 Å map (class 3a). Classes 3a and 4a exhibit higher resolution compared to those in datasets A and B. Consequently, these higher-resolution maps derived from dataset C are retained for use in the paper. The local resolution estimate was performed using BlocRes in cryoSRARC.

For NCLX at low-pH (without calcium) sample, a total of 9,653 videos were imported into cryoSPARC live v4.4 and processed through steps similar to those described above for the NCLX dataset without calcium at pH 7.4, including particle extraction with 80-pixel box and 4 × 4 binning. This resulted in 5,574,228 particles from 9,593 images with CTF estimate better than 8 Å. Three-dimensional classification using the seed-facilitated multi-reference strategy yielded 816,107 particles, following duplicate removal. In parallel, following topaz picking, 2,221,595 particles underwent 2D classification (two rounds) and heterogeneous refinement (three rounds), resulting in 115,569 particles as seeds. These 2,221,595 particles underwent 3D classification using the seed-facilitated multi-reference strategy, which resulted in 805,025 particles after duplicate removal. These two sets of particles were combined, resulting in 1,117,206 particles after duplicate removal. Following one round of heterogeneous refinement using noised-reweighted map, 683,191 particles were re-extracted with 320-pixel box. Finally, these particles were refined with C_3 symmetry, yielding a 2.62 Å map (class 4b). The local-resolution estimate was performed using BlocRes in cryoSRARC.

Model building and refinement

The initial model used for NCLX structure was obtained through AlphaFold2[67] prediction. Chimera was then used to fit the initial model into the cryo-EM map, which was manually rebuilt using Coot[68]. The rebuilt model was then subjected to refinement in Phenix[69] to optimize its geometry and stereochemistry and was assessed by MolProbity. The two structures, including class 1 from NCLX without calcium, and class 2a from NCLX with calcium, were manually adjusted and refined, the other structures used classes 1 and 2a as initial model and were manually adjusted and fitted into the corresponding map. Compared with other classes, density maps for classes 2 and 3 (in the Ca2+-free, pH 7.4 condition) are at lower resolution. In these two classes, side chains were removed in regions where the corresponding maps lack sufficient information, and their register was based on corresponding models in the Ca2+ bound forms. Figures were generated using PyMOL (Schrödinger)[70], UCSF Chimera[71] and ChimeraX[72].

System set-up for molecular dynamics simulations

We performed simulations of NCLX under five conditions: (1) simulations of cytosol-open NCLX initiated with a Ca2+ ion in the binding pocket, with residues D153 and D471 deprotonated; (2) simulations of matrix-open NCLX initiated with a Ca2+ ion in the binding pocket and residues D153 + D471 deprotonated; (3) simulations of matrix-open NCLX initiated with no Ca2+ ion in the binding pocket and residues D153 + D471 deprotonated; (4) simulations of cytosol-open NCLX initiated with a Ca2+ ion in the binding pocket and residues D153 + D471 protonated (neutral); and (5) simulations of matrix-open NCLX initiated with a Ca2+ ion in the binding pocket and residues D153 + D471 protonated. We initiated all simulations from the cryo-EM structures of NCLX (based on an earlier version of the cryo-EM maps): simulation sets (1) and (4) were initiated using chain A of the Ca2+-bound NCLX structure; simulation sets (2) and (5) were initiated using chain C of the Ca2+-bound NCLX structure; and simulation set (3) was initiated using the chain A of the NCLX structure with no Ca2+ bound. The Ca2+ ion in the Ca2+-bound NCLX structure was preserved for simulation sets (1), (2), (4) and (5). We performed five independent simulations for each simulation condition, each at least 1.4 µs in length. Initial atom velocities were assigned randomly and independently for each simulation.

For all simulation conditions, the protein structure was aligned with the Orientations of Proteins in Membranes[73] entry for 3V5U (NCX from Methanocaldococcus jannaschii[28]) using PyMOL, and water molecules from 3V5U were incorporated. Prime (Schrödinger)[74] was used to add capping groups to protein chain’s termini. Protonation states of all titratable residues other than D153 and D471 were assigned at pH 7. Histidine residues were modelled as neutral, with a hydrogen atom bound to either the delta or epsilon nitrogen depending on which tautomeric state optimized the local hydrogen-bonding network. Using Dabble[75], the prepared protein structures were inserted into a pre-equilibrated palmitoyl-oleoyl-phosphatidylcholine bilayer, the system was solvated, and calcium and chloride ions were added to neutralize each system at a calcium concentration of 75 mM and a chloride concentration of 150 mM. The final systems comprised approximately 135,000 atoms, and system dimensions were approximately 120 × 140 × 110 Å (Supplementary Table 2).

Molecular dynamics simulation and analysis protocols

The simulation protocol was similar to that used in previous work[76], as those simulations proved sufficient to describe atomic-level interactions between a membrane transporter and its substrate. We used the CHARMM36m force-field for proteins, the CHARMM36 force-field for lipids and ions, and the TIP3P model for water molecules77,78,79. All simulations were performed using the Compute Unified Device Architecture version of particle-mesh Ewald molecular dynamics in AMBER20 on graphics processing units.

Systems were first minimized using three rounds of minimization, each consisting of 500 cycles of steepest descent followed by 500 cycles of conjugate gradient optimization. Harmonic restraints (10.0 and 5.0 kcal mol−1 Å−2) were applied to the protein and lipids for the first and second rounds of minimization, respectively. Then, 1 kcal mol−1 Å−2 harmonic restraints were applied to the protein for the third round of minimization. Systems were then heated from 0 K to 100 K in the NVT ensemble over 12.5 ps, and then from 100 K to 310 K in the NPT ensemble over 125 ps, using 10.0 kcal mol−1 Å−2 harmonic restraints applied to protein heavy atoms. Subsequently, systems were equilibrated at 310 K and 1 bar in the NPT ensemble, with harmonic restraints on the protein non-hydrogen atoms tapered off by 1.0 kcal mol−1 Å−2 starting at 5.0 kcal mol−1 Å−2 in a stepwise fashion every 2 ns for 10 ns, and then by 0.1 kcal mol−1 Å−2 every 2 ns for 20 ns. Production simulations were performed without restraints at 310 K and 1 bar in the NPT ensemble using the Langevin thermostat and the Monte Carlo barostat, and using a timestep of 4.0 fs with hydrogen mass repartitioning[80]. Bond lengths were constrained using the SHAKE algorithm[81]. Non-bonded interactions were cut off at 9.0 Å, and long-range electrostatic interactions were calculated using the particle-mesh Ewald method with an Ewald coefficient of approximately 0.31 Å−1, and fourth-order B-splines. The particle-mesh Ewald grid size was chosen such that the width of a grid cell was approximately 1 Å. Trajectory frames were saved every 200 ps during the production simulations. The AmberTools17 CPPTRAJ package was used to reimage trajectories[82]. Simulations were visualized and analysed using Visual Molecular Dynamics (VMD)[83] and PyMOL[70]. The D153–Ca2+ distance is the minimum distance between a side-chain oxygen of D153 and a Ca2+ ion. The D471–Ca2+ distance is the minimum distance between a side-chain oxygen of D471 and a Ca2+ ion. The N467–Ca2+ distance is the minimum distance between the backbone oxygen of N467 and a Ca2+ ion. The N149–Ca2+ distance is the minimum distance between the backbone oxygen of N149 and a Ca2+ ion. In Extended Data Fig. 7a, to construct the probability distributions for these distance metrics, we used trajectory frames from all simulations under each condition and applied a Gaussian kernel density estimator.

Proteomic sample preparation for mass spectrometry analysis

The mass spectrometry (MS) sample preparation, data collection and data analyses follow similar protocols to those previously reported[84]. To prepare NCLX for liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis, human NCLX with a C-terminal Strep-tag II was cloned into the BacMam vector and expressed and purified as described above for rat NCLX. Purified NCLX was then processed for proteomics analysis using an S-Trap Micro Column (C02-micro-80, ≤100 µg, Protifi). Briefly, purified proteins were reduced by incubating with 10 mM dithiothreitol at 95 °C for 10 min and subsequently alkylated using 40 mM 2-chloroacetamide at room temperature for 60 min. Phosphoric acid (final concentration = 1.2%) was added to acidify the denatured, non-digested proteins before loading the samples onto S-trap columns. The columns were washed three times with 100 mM triethylammonium bicarbonate (TEAB) in 90% methanol. To digest the bound proteins, each column was incubated with Trypsin (Promega, V5113) in 50 mM TEAB at a 1:20 trypsin-to-substrate ratio (w/w) at 37 °C overnight. Elutions were performed in succession using 50 mM TEAB (40 μl), 0.2% formic acid (FA) (40 μl), and then 50% acetonitrile (ACN) + 0.2% FA (40 μl) to elute the peptides from the column. The combined eluate was dried using a vacuum concentrator and resuspended in 10 μl of 0.2% FA, 3% ACN.

To analyse the presence of NCLX in WT and KO HeLa cells, the mitochondrial fraction was enriched by resuspending cells in mitochondrial resuspension buffer (5 mM HEPES-KOH pH 7.2, 250 mM sucrose). Cells were lysed using dounce homogenization, and large cellular debris were pelleted by centrifuging the lysate at 1,500 g for 10 min. The supernatants were then collected and further centrifuged at 13,000 g for 10 min, and the pellet was resuspended in mitochondrial resuspension buffer. This resuspension was then centrifuged at 13,000 g for 10 min to pellet mitochondria. To solubilize NCLX, the mitochondria were incubated with 1.5% m/v LMNG/cholesteryl hemisuccinate in 20 mM HEPES-NaOH, 150 mM NaCl, pH 7.4 supplemented with protease inhibitors and ribonuclease for 2 h at 4 °C. The detergent-solubilized mixture was then centrifuged at 18,000 r.p.m. (39,191 g) for 40 min to pellet any insoluble material. The resulting supernatant was collected and either directly run on sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), or the supernatant was fractionated by size-exclusion chromatography on a Superose 6 column to isolate the fractions corresponding to the elution volume of NCLX, which were then run on SDS-PAGE. The gel was visualized with AcquaStain (Bulldog Bio, AS001000) and the gel fractions in the expected molecular mass range of NCLX were excised for MS analysis. The excised gel pieces were washed with H_2O and 100% ACN, reduced using 50 mM ammonium bicarbonate pH 8.5 (ABC) with 6.5 mM dithiothreitol for 60 min at room temperature, alkylated using 50 mM ABC with 54 mM iodoacetamide for 30 min in the dark at room temperature, and washed with alternating 100% ACN and ABC solutions. To digest the peptides, the dehydrated gel pieces were pre-incubated with digestion buffer (3 ng µl–1 trypsin in ABC) for 60 min on ice. The excess digestion buffer was then replaced with an equivalent volume of ABC, and the samples were incubated overnight at 37 °C for protein digestion. The gel pieces were treated with 100% ACN to extract the peptides, which were dried in a vacuum concentrator and resuspended in 10 μl of 0.2% FA, 3% ACN for LC–MS/MS analysis with parallel reaction monitoring (PRM).

LC–MS/MS analysis

Data-dependent acquisition (DDA) analysis of purified, tryptic, human NCLX peptides was performed to build a spectral peptide library and to generate the PRM inclusion list yielding expected m/z and retention time windows for twelve unique NCLX peptides, which was subsequently used to examine the presence of NCLX in WT and KO HeLa samples. 2 µl of reconstituted peptides (~0.57 µg) were loaded onto a Dionex Ultimate 3000 RPLC nano system (Thermo Fisher Scientific) for liquid chromatography using mobile phases A (0.2% FA in water) and B (0.2% FA in ACN). The samples were first processed on an Acclaim PepMap 100 C18 trap column (Thermo Fisher Scientific, 164213) using 100% A at 5 µl min–1 flow rate. The peptides were then separated using a nanoflow ultra-high-performance liquid chromatography column (Aurora Ultimate 25 cm, IonOpticks, AUR3-25075C18) with an elution gradient of 2–28% over 66.5 min at 300 nl min–1. An Orbitrap Fusion Tribrid MS system (Thermo Fisher Scientific) was used to analyse eluted peptides. A Nanospray Flex Ion Source (Thermo Fisher Scientific) held at +2.2 kV was used to ionize precursors, with an inlet capillary temperature of 275 °C. The MS1 scans for both DDA and PRM methods were collected over 350–1,350 m/z (resolution = 60,000 at 200 m/z; automatic gain control (AGC) target = 400,000; RF lens = 30%; maximum injection time = 118 ms; normalized AGC target = 100%). The MS2 scans for both DDA and PRM methods were collected over 140–1,400 m/z (isolation window = 1.6 m/z; AGC target = 50,000; maximum injection time = 22 ms; Orbitrap resolution = 15,000 at 200 m/z; higher-energy collisional dissociation collision energy = 30%). The DDA method used a top 20 precursor pick for MS2 acquisition. The MS2 scans for the PRM method were collected using a targeted mass-triggered scan function using an inclusion list containing reference m/z and corresponding retention time windows (Supplementary Table 1).

LC–MS/MS data analysis

Datasets were imported into Skyline (v.23.1.1.520; MacCoss Lab software). The confidence of the peptide identity was assessed by the number of fragment ions, and their retention time compared with the purified NCLX peptides. Confidently identified peptides were determined by the presence of at least three fragment ions (<10 ppm error) within a 4 min retention time window from the purified NCLX peptides. Furthermore, transition ions were compared to library spectrum for the same peptide. Mass spectra for peptides, SLGVVFR and ALNPLDYMK, were obtained using FreeStyle 1.8 SP2 (Thermo Fisher Scientific).

Cell culture and molecular biology

Chinese hamster ovary, HEK293 and HeLa cells were cultured in DMEM with 10% fetal bovine serum. HCT116 cells were cultured in McCoy’s 5A medium with 10% fetal bovine serum. All of these vertebrate cell lines were incubated at 37 °C with 5% CO_2. Sf9 cells were cultured in the Sf-900 III SFM medium at 27 °C.

To express human NCLX in HEK cells, a gene encoding a C-terminally 1D4 (TETSQVAPA)-tagged full-length NCLX was cloned into the pcDNA3.1(+) vector. Site-directed mutagenesis was performed using the QuickChange II kit (Agilent) and verified using Sanger sequencing. Transient expression was achieved using Lipofectamine 3000 (Thermo Fisher Scientific) following the manufacturer’s instructions. HEK cells were harvested for experiments two days after transfection. The Bac-to-Bac baculovirus expression system (Thermo Fisher Scientific) was used to express proteins in Sf9 cells. Briefly, C-terminally 1D4-tagged human NCLX or an MCU–EMRE fusion protein[85] was cloned into the pFastBac1 vector, which was transformed into DH10Bac competent cells to produce bacmids. Sf9 cells were transfected with bacmids using the Cellfectin II reagent to generate the P1 baculovirus, which was then used to infect Sf9 cells to produce the P2 virus. Sf9 cells were harvested for experiments three days after being infected with the P2 virus.

To express human NCLX in Xenopus oocyte plasma membranes, the first 28 amino acids in the protein’s N-terminal mitochondrial targeting sequence were substituted with an MAGRQHGSGRLWALGG sequence from the mouse TMIE protein, which was found to show very high levels of oocyte plasma membrane expression. The DNA sequence was then optimized for Xenopus expression and cloned into a pOX vector for in vitro RNA synthesis using the mMESSAGE mMACHINE T3 transcription kit (Thermo Fisher Scientific) as described in our previous work[86].

CRISPR KO was performed as described in a past work[87]. The sgRNA sequences used for HeLa and CHO cells are 5′-CCTGGATCTACCAACGGCAA-3′ and 5′-GGCTACCTGGACTACCTCGA-3′, respectively. To confirm gene KO, we cloned (1) the NCLX gene and (2) the NCLX complementary DNA produced by reverse transcription of the NCLX mRNA into pcDNA3.1(+) for Sanger sequencing to identify the exact gene modifications. qPCR was also performed in these NCLX KO lines, showing reduced NCLX mRNA levels, consistent with nonsense-mediated mRNA decay caused by CRISPR-induced premature stop codons (Extended Data Fig. 8g). Forward and reverse primers for HEK and HeLa cells: GGCTTCACTGGCTC TTTGCTT and CGAGAAGGCATCTCCAATGCTGTT, respectively; forward and reverse primers for CHO cells: GCATCATTTTCAATATCCTGGTGG and TGAGTTGGAAACACTGAAGC, respectively. In our hands, and as also observed in other laboratories35,88, there are currently no useful antibodies against native NCLX. We were therefore unable to further verify NCLX KO using Western blot. As an alternative approach to validate NCLX KO at the protein level, NCLX peptides were detected by LC–MS/MS (Supplementary Fig. 2), appearing only in WT but not NCLX KO HeLa cells.

Mitochondrial extraction, protease digestion and Western blot

Protein expression in HEK or Sf9 cells was analysed via Western blot detection of proteins in mitochondrial lysates. HEK cell mitochondria extraction was performed using differential centrifugation as described before[85]. To obtain Sf9 mitochondria, 2 × 10^6 of Sf9 cells were spun down, resuspended in 1 ml of an Sf9 mitochondria isolation buffer (SMIB, 200 mM sucrose, 10 mM Tris, 1 mM EGTA, pH 7.5), and then lysed by passing through a 27-gauge needle for 25 times on ice in the presence of a protease inhibitor cocktail (Thermo Fisher Scientific, PIA32955). The lysate was spun down at 600 g for 10 min. The supernatant was then transferred to a new tube, and spun down at 7,000 g for 10 min. Finally, the pellet was resuspended in 500 μl of SMIB, and spun down again at 7,000 g for 10 min to obtain mitochondrial samples. NCLX expression in Xenopus oocytes was quantified by Western blot analysis of oocyte plasma membranes, which were isolated using our published protocols[86].

To conduct protease digestion experiments, mitochondria were extracted from HEK cells expressing NCLX constructs in a 10-cm dish, and were treated with 800 µl of a hypotonic shock buffer (5 mM sucrose, 5 mM HEPES, 1 mM EGTA, pH 7.2 KOH) on ice for 10 min to produce mitoplasts. After adding 200 µl of a high-salt buffer (750 mM KCl, 100 mM HEPES, 2.5 mM EGTA, pH 7.2 KOH), mitoplasts were pelleted at 17,000 g for 10 min. The isolated mitoplasts were resuspended in tris-buffered saline (TBS), with a small portion used for protein quantification using the bicinchoninic acid assay (Thermo Fisher Scientific, 23227). The samples were treated with 3 µg of proteinase K (Sigma, 70663) or TEV protease (produced in house) per 10 µg of mitoplast proteins. Protease digestion was performed at room temperature for 5 min, and was terminated by adding 0.5 mM of phenylmethylsulphonyl fluoride (Sigma, P7626) and 2 µg ml–1 of leupeptin (Sigma, LEU-RO) and pepstatin (Sigma, PEPS-RO) at room temperature for 5 min. The samples were then denatured with an SDS loading buffer for subsequent SDS-PAGE.

To perform Western blot, 10 μg of mitochondrial or mitoplast proteins or membranes from 20 oocytes were separated using SDS-PAGE and transferred to low-fluorescence PVDF membranes (LI-COR), which were blocked in the LI-COR Intercept blocking buffer. The membranes were then incubated with primary antibodies diluted in TBST (that is, TBS + 0.075% Tween-20) at 4 °C overnight, followed by 1 h incubation with fluorescent secondary antibodies, diluted in TBST, at room temperature. Western blot signals were acquired using a LI-COR Odyssey CLx imager, and quantified using the ImageStudio software (v.5.0). Antibodies and dilutions: anti-1D4 (homemade, 100 ng ml–1); anti-Tim23 (Santa Cruz, sc-514463, 1:1,000); anti-MCU (Cell Signalling, D2Z3B, 1:10,000); anti-actin (Santa Cruz, sc-68979, 1:2,000); anti-COX2 (Abcam, ab110258, 1:10,000); anti-Histone H3 (Millipore, 05-928, 1:10,000); IRDye 680RD goat anti-mouse secondary antibody (LI-COR, 925-68070, 1:15,000); and IRDye 800CW goat anti-rabbit secondary antibody (LI-COR, 926-32211, 1:10,000).

Mitochondrial and oocyte Ca2+ transport assays

Each mitochondrial Ca2+ flux experiment was performed using 2 × 10^7 of HEK, HeLa, CHO or HCT116 cells, or 2.8 × 10^7 of Sf9 cells. Cells were suspended in 10 ml of a wash buffer (120 mM KCl, 25 mM HEPES, 2 mM K_2HPO_4, 1 mM MgCl_2, 50 μM EGTA, pH 7.2 KOH), pelleted at 1,500 g for 3 min, and then resuspended in 2.2 ml of a recording buffer (120 mM KCl, 25 mM HEPES, 2 mM K_2HPO_4, 5 mM succinate, 1 mM MgCl_2, pH 7.2 KOH); 2 ml of the cell suspension was then transferred into a stirred quartz cuvette in a Hitachi F-7100 spectrophotometer (excitation = 508 nm; excitation-slit = 2.5 nm; emission = 531 nm; emission-slit = 5 nm; sampling rate = 2 Hz), with 250 nM of CG5N (Thermo Fisher Scientific, C3737) used to report extra-mitochondrial [Ca2+], and 30 μM of digitonin (Sigma, D141) used to permeabilize cells. Inhibitors used in these assays include Ru360 (synthesized in house) and CGP-37157 (Cayman, 1561110).

For oocyte Ca2+ uptake assays, stage V–VI oocytes were injected with 50 ng of NCLX mRNA, and incubated in an ND96 solution (96 mM NaCl, 2 mM KCl, 2 mM CaCl_2, 0.5 mM MgCl_2, 5 mM HEPES, pH 7.4 NaOH) for 3 to 4 days. To measure 45Ca2+ uptake (Fig. 5d–g), oocytes were washed three times in an oocyte recording buffer (ORB, 100 mM NMG, 5 mM HEPES, pH 7.4 HCl), and finally placed in a density of ten oocytes per 400 µl ORB. To begin the assay, 400 µl of the oocyte-containing solution was mixed well with 100 µl of ORB containing 5 µCi of 45Ca2+ (Perkin Elmer, NEZ013001MC). The assay was performed at room temperature. At desired time points, the reaction was terminated by transferring ten oocytes into 30 ml of ORB. After washing the oocytes two more times in fresh 30 ml ORB, each oocyte was lysed individually via pipetting and vigorous shaking in 10 ml of a scintillation cocktail for radioactivity measurements using a Beckman LS6500 scintillation counter. To obtain a data point, we first measured 45Ca2+ in ten individual oocytes, and obtained the median reading. We then excluded oocytes with readings >5-fold higher than the median, which probably reflect sick oocytes with compromised membranes, as well as those with readings <20% of the median, which probably reflect oocytes with failed mRNA injection. The remaining readings, usually from 5–8 oocytes, were averaged and presented as an independent measurement. The results were discarded if there were fewer than five useful readings from ten oocytes.

To test H+-coupled 45Ca2+ transport (Fig. 5h), oocytes one day after mRNA injection were incubated in a counter flux buffer containing 96 mM NaCl, 2 mM KCl, 0.5 mM CaCl_2, 2 mM MgCl_2, 2.5 mM HEPES, 5 µCi ml–1 45Ca2+, pH 7.4 NaOH at a density of ten oocytes per 1 ml. After two days of equilibration, oocytes were transferred into microcentrifuge tubes, with the external solution reduced to 20 oocytes per 800 µl. To begin the reaction, 800 µl of the oocyte-containing solution was mixed well with 200 µl of counter flux buffer that contains high concentrations of pH buffers (200 mM MOPS for pH 6.8, 200 mM HEPES for pH 7.4, or 200 mM Tris for pH 8.2) to adjust the pH, BAPTA to reduce free [Ca2+] to 250 nM, and 5.2 µCi ml–1 45Ca2+. Reaction termination and data analyses were performed as above, but with 20 oocytes used for each data point (experiments with fewer than ten useful oocyte readings were discarded).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The NCLX maps have been deposited in the Electron Microscopy Data Bank under codes EMD-71819, EMD-71820, EMD-71821, EMD-71822, EMD-71823, EMD-71824 and EMD-71826. The corresponding models have been deposited in the Protein Data Bank (PDB) under IDs 9PS1, 9PS2, 9PS3, 9PS4, 9PS5, 9PS6 and 9PS8. Mass spectrometry data have been deposited in MassIVE under MSV000098428. Simulation trajectories generated in this study are available at https://doi.org/10.5281/zenodo.15793477 (ref. [89]). The PDB and OPM files for 3V5U used in this study are available from the PDB (ID: 3V5U) and the OPM database (https://opm.phar.umich.edu/proteins/1933), respectively. Source Data are provided with this paper.

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Acknowledgements

We thank L. Montabana at Stanford cEMc for help with electron microscopy data collection. Some of this work was conducted at the Stanford-SLAC Cryo-EM Center (S2C2), which is supported by the National Institutes of Health Common Fund Transformative High-Resolution Cryo-Electron Microscopy program (U24 GM129541). We thank M. Trebak for sharing NCLX KO cell lines and D. Roberts and J. Hevler for helpful discussions. This work was made possible by support from Stanford University, the Harold and Leila Y. Mathers Charitable Foundation, and NIH R35GM153424 (L.F.) and NIH R01-GM144485 and R35-GM151958 (M.-F.T).

Author information

Author notes

1. These authors contributed equally: Minrui Fan, Chen-Wei Tsai, Jinru Zhang, Jianxiu Zhang, Aswini R. Krishnan

Authors and Affiliations

1. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA

Minrui Fan, Jinru Zhang, Jianxiu Zhang, Aswini R. Krishnan, Deniz Aydin, Siyuan Du, Briana L. Sobecks, Ron O. Dror & Liang Feng

2. Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Chen-Wei Tsai, Tsung-Yun Liu, Yu-Lun Huang, Madison X. Rodriguez & Ming-Feng Tsai

3. Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA

Chen-Wei Tsai, Tsung-Yun Liu, Yu-Lun Huang & Ming-Feng Tsai

4. Department of Computer Science, Stanford University, Stanford, CA, USA

Deniz Aydin, Siyuan Du, Briana L. Sobecks & Ron O. Dror

5. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA

Deniz Aydin, Siyuan Du, Briana L. Sobecks & Ron O. Dror

6. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA

Deniz Aydin, Siyuan Du, Briana L. Sobecks & Ron O. Dror

7. Department of Chemistry, Stanford University, Stanford, CA, USA

Siyuan Du & Carolyn R. Bertozzi

8. Department of Chemical Engineering, Stanford University, Stanford, CA, USA

Briana L. Sobecks

9. Department of Biology, Stanford University, Stanford, CA, USA

Andrew H. Reiter

10. Stanford Sarafan ChEM-H, Stanford University, Stanford, CA, USA

Carolyn R. Bertozzi

Contributions

M.F., Jinru Zhang, Jianxiu Zhang and A.R.K. performed biochemical and cryo-EM studies. C.-W.T., T.-Y.L. and Y.-L.H. performed functional studies. D.A., S.D. and B.L.S. performed and analysed molecular dynamics simulations under the guidance of R.O.D. M.X.R. determined NCLX orientation. A.R.K. and A.H.R. performed and analysed mass spectrometry studies under the guidance of C.R.B. M.F. contributed to functional studies. L.F. and M.-F.T. directed the studies. M.F. prepared part of the initial draft. M.-F.T. and L.F. wrote the manuscript, with input from all authors.

Corresponding authors

Correspondence to Chen-Wei Tsai, Ming-Feng Tsai or Liang Feng.

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The authors declare no competing interests.

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Nature thanks Diego De Stefani 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 Sequence alignment of NCLX.

The sequences of NCLX from Rattus norvegicus (Rn; NCBI-ProteinID: NP_001017488), Homo sapiens (Hs; ID: NP_079235), Gallus gallus (Gg; ID: XP_004934554), Xenopus laevis (Xl; ID: NP_001128697), Carcharodon carcharias (Cc; ID: XP_041058292), Ciona intestinalis (Ci; ID: XP_002130159), Caenorhabditis elegans (Ce; ID: NP_499146), Arabidopsis thaliana (At; ID: NP_197288) are aligned. For each organism, the protein with the highest sequence similarity to rat NCLX (identified as the top BLASTP hit) was selected for sequence alignment. These homologues all exhibit similarity across the full length of the protein. Subcellular localization of these homologues remains to be determined. Some organisms, such as C. elegans, possess multiple potential NCLX homologues, including ncx-9, which has been suggested to localize to mitochondria[90]. The two Ca2+-coordinating aspartate residues, D153 and D471, are indicated with blue circles.

Extended Data Fig. 2 Purification and biochemical characterization of rat NCLX and cryo-EM data processing of NCLX without calcium (pH 7.4).

a, Gel filtration profile of NCLX. b, SDS-PAGE gel of the purified NCLX (representative of six independent experiments with similar results). The gel source data are included in Supplementary Fig. 1. c, A flowchart of NCLX without calcium data processing. Conformational states of protomers: Class 1, three in matrix-facing conformation; Class 2, two in matrix-facing and one in cytosol-facing conformation; Class 3, one in matrix-facing and two in cytosol-facing conformation. Final map gold-standard FSC curves and angular particle distribution are shown next to the local resolution map for each class. d, A representative cryo-EM image (from 4,264 micrographs with similar results). e, Typical 2D class averages. f, Map vs. model FSC. g, Cryo-EM density corresponding to model.

Extended Data Fig. 3 Cryo-EM data processing of NCLX with calcium.

a, A flowchart of NCLX with calcium data processing. Conformational states of protomers: Class 2a, two in matrix-facing and one in cytosol-facing conformation; Class 3a, one in matrix-facing and two in cytosol-facing conformation; Class 4a, three in cytosol-facing conformation. Final map gold-standard FSC curves and angular particle distribution are shown next to the local resolution map for each class. b, A representative cryo-EM image (from 24,443 micrographs with similar results). c, Typical 2D class averages. d, Map vs. model FSC. e, Cryo-EM density corresponding to model.

Extended Data Fig. 4 Cryo-EM data processing of NCLX without calcium at low pH.

a, A flowchart of NCLX without calcium at low pH data processing. Conformational states of protomers: Class 4b, three in cytosol-facing conformation. Final map gold-standard FSC curves and angular particle distribution are shown next to the local resolution map. b, A representative cryo-EM image (from 9,653 micrographs with similar results). c, Typical 2D class averages. d, Map vs. model FSC. e, Cryo-EM density corresponding to model.

Extended Data Fig. 5 Determination of NCLX orientation and the comparison of NCLX assembly and the Ca2+-binding site in different conformational states.

a, Protease digestion of NCLX. After isolating mitoplasts from HEK cells expressing C-terminally 1D4-tagged human NCLX, adding proteinase K (Pro-K) causes a downward shift of the NCLX band, as detected by an anti-1D4 antibody (image on the left). This suggests that (1) the C-terminal 1D4 tag dwells within the matrix and is therefore protected from Pro-K digestion, and (2) Pro-K cuts an NCLX area in the intermembrane space to cause the observed band shift. As the loop between TM5 and TM6 is large and unstructured, we hypothesized that this loop is digested by Pro-K. Accordingly, we introduced a TEV protease (TEVP) site after V278 in the TM5-6 loop and digested this construct in mitoplasts using TEVP. This manoeuvre produces a band at a similar location as the band generated by Pro-K digestion of WT NCLX, suggesting that Pro-K and TEVP both can cut the TM5-6 loop and that this loop is in the intermembrane space. Two subunits in the mitochondrial Ca2+ uniporter complex, MCU and EMRE, were used as controls. The green bands represent native MCU proteins. As most of MCU’s protein mass is in the matrix, it is not affected by Pro-K or TEVP. The EMRE protein has its C-terminal end exposed to the intermembrane space. Therefore, Pro-K was able to digest a 1D4 tag attached to EMRE’s C-terminus, as reflected by the disappearance of the Western blot signal created by the anti-1D4 antibody (image on the right). The experiment was performed with four independent biological replicates, all yielding similar results. Molecular weight marker unit: kDa. For gel source data, see Supplementary Fig. 1. b, A schematic of NCLX orientation and transmembrane topology. Key residues in panels a and c are highlighted in blue. The orange dotted lines surrounding M196 indicate linkers that were engineered to make M196 more exposed. c, TEVP digestion of NCLX. TEVP sites were introduced into NCLX in positions after the indicated residues. Those sites, whose digestion by TEVP is unaffected by DDM (e.g., D350 and M196), are located in the intermembrane space. By contrast, those sites, which require DDM to be fully digested by TEVP (e.g., M584 and V51), are inside the matrix. In the presence of DDM, cleavage of the TEVP site after M584 causes the disappearance of the band (lane 9 from the left). This is because the fragment that contains the 1D4 tag is too small (~10 amino acids) and would migrate out of the gel. Digestion of the TEVP site after V51 causes a band shift (lane 12 from the left). This is because the digested fragment that contains NCLX residue 52–584 and the C-terminal 1D4 tag is smaller than the undigested NCLX (lanes 10 and 11). For gel source data, see Supplementary Fig. 1. d, A summary of TEVP digestion results. The Western signal ratios of digested NCLX with or without DDM in panel c are presented. A ratio close to 1 indicates that DDM does not have effects on proteolysis, while a ratio close to 0 indicates that the TEV site is in the matrix, protected by the inner mitochondrial membrane. Data are shown as means ± s.e.m. Numbers in parentheses indicate the number of independent biological replicates. e, The superposition of three classes of Ca2+-free NCLX. The transport domains that mediate the oligomerization superimpose well. f, The superposition of three classes of NCLX with Ca2+. The transport domains that mediate the oligomerization superimpose well. g, The coordination environment around the Ca2+ binding site (cytosol-facing conformation). Ca2+ is depicted as a green sphere, and the water molecules are shown as red spheres. h, Superposition of Ca2+-coordinating residues in cytosol- and matrix- facing conformations. i, Superposition of cytosol-facing NCLX with Ca2+ bound or at low pH without Ca2+ (comparison of the protomer on the left and the transport domain on the right).

Source Data

Extended Data Fig. 6 Time series of key distances in the Ca2+-binding site.

Plots shown are for five independent simulations for each of three simulation sets: (1) simulations of cytosol-open NCLX initiated with a Ca2+ ion in the binding pocket (blue traces); (2) simulations of matrix-open NCLX initiated with a Ca2+ ion in the binding pocket (orange traces); (3) simulations of matrix-open NCLX initiated with no Ca2+ ions in the binding pocket (black traces). All carboxylic acidic side chains, including those of D153 and D471, are deprotonated (negatively charged) in all simulations shown. Unsmoothed traces (thin lines) and smoothed traces (thick lines) are shown for all simulations. Time traces were smoothed using a moving average with a window size of 20 ns. See Methods.

Extended Data Fig. 7 Frequency distributions of key distances in the Ca2+-binding site and a comparison between experimental structures and MD simulations.

a, Distributions are computed across five independent simulations for each of two simulation sets: (left) simulations of cytosol-facing NCLX initiated with a Ca2+ ion in the binding pocket(blue curves); (right) simulations of matrix-facing NCLX initiated with a Ca2+ ion in the binding pocket (orange curves) (see Methods). All aspartate and glutamate residues are charged in all simulations shown. b, Comparison of Ca2+-coordinating residues in cryo-EM structures and MD simulations. Left: superposition of the cryo-EM structure of NCLX in a cytosol-facing conformation onto a representative frame from MD simulations. Right: superposition of the cryo-EM structure of NCLX in a matrix-facing conformation onto a representative frame from MD simulations.

Extended Data Fig. 8 Functional analysis of NCLX.

a, Subcellular localization of overexpressed NCLX in HEK cells. NCLX in whole-cell lysates (WCL), mitochondria (Mito), and post-mitochondrial supernatant (PMS), prepared from equal numbers of cells, was analysed. As a control to demonstrate the robustness of the subcellular fractionation, we show that MCU and a mitochondrial protein COX2 are predominantly present in the Mito fraction, while β-actin is mostly in the cytosol. Results show that overexpressed, C-terminally 1D4-tagged WT or mutant NCLX, detected by the 1D4 antibody, mostly travels to mitochondria, with only a small fraction present outside of mitochondria as summarized in the bar chart. b, A representative result showing the presence of NCLX in Sf9-cell mitochondria. Experiments were performed as in a. The absence of Histone H3 in the mitochondrial fraction confirms minimal nuclear contamination. c, Mitochondrial Ca2+ efflux in Sf9 cells. The trace supplements the upper panel in Fig. 5c to show that adding Ru360 to inhibit the uniporter reveals mitochondrial Ca2+ efflux in Sf9 cells. d, NCLX in Xenopus oocyte membranes. The plasma membranes of Xenopus oocytes expressing 1D4-tagged WT or mutant NCLX were isolated, as described in Materials and Methods, and were analysed with Western blot using the 1D4 antibody. e, NCLX-induced NICE in Sf9 mitochondria. WT, 2DA (D153A-D471A), or S468K NCLX was expressed to similar levels in sf9 cells, as shown in the Western blot image. Mitochondrial Ca2+ flux experiment, performed in the absence of Na+, shows that blocking the uniporter with Ru360 reveals NICE mediated by NCLX. The rate of NICE in Sf9 mitochondria is ~10-fold faster than that in HeLa cells (Fig. 5i), likely because higher NCLX expression in Sf9 cells. The p value in the bar chart was obtained by comparing with WT NCLX. Control: uninfected Sf9 cells. f, The effect of functionally impaired NCLX mutants on WT NCLX. WT NCLX was co-expressed with 2DA NCLX or an unrelated protein EMRE as a control in Sf9 cells. 2DA NCLX does not affect NICE mediated by WT NCLX, suggesting that a functionally impaired mutant would not affect the function of WT subunits in the NCLX trimer. g, NCLX mRNA levels in various NCLX-KO cell lines. The bar chart presents NCLX mRNA levels in NCLX-KO cell lines as compared with their corresponding WT cells. Note that CRISPR KO would not eliminate NCLX mRNA, because it works by introducing insertion or deletion into the NCLX gene to cause frameshifts, instead of targeting mRNA for degradation as in the case of shRNA. Throughout the entire figure, data are presented as means ± s.e.m., with values in parentheses denoting the number of independent biological replicates, and statistics performed using unpaired, two-tailed t-test. Experiments in panels b-d were performed with three independent biological replicates with consistent results. Molecular weight marker unit: kDa. For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 9 Time series of Ca2+ position in simulations with D153 and D471 deprotonated or protonated.

Plots are shown for five independent simulations under each of four conditions: (1) simulations of cytosol-open NCLX initiated with a Ca2+ ion in the binding pocket, with D153 and D471 deprotonated (blue traces); (2) simulations of cytosol-open NCLX initiated with a Ca2+ ion in the binding pocket, with D153 and D471 protonated(gray traces); (3) simulations of matrix-open NCLX initiated with a Ca2+ ion in the binding pocket, with D153 and D471 deprotonated (orange traces); (4) simulations of matrix-open NCLX initiated with a Ca2+ ion in the binding pocket, with D153 and D471 protonated (brown traces). The z-axis is the direction perpendicular to the lipid bilayer. The z-coordinate is 0 at the initial Ca2+ position, with values increasing in the direction of the matrix and decreasing in the direction of the cytosol. Red arrows mark times at which the Ca2+ exits the transporter; subsequently, it either interacts with charged residues at the protein surface or diffuses freely through the solvent. Unsmoothed traces (thin lines) and smoothed traces (thick lines) are shown for all simulations. Time traces were smoothed using a moving average with a window size of 20 ns.

Extended Data Table 1 Cryo-EM data collection, refinement, and validation statistics

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, and Supplementary Tables 1 and 2.

Reporting Summary

Source data

Source Data Fig. 5 and Source Data Extended Data Figs. 5 and 8

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Fan, M., Tsai, CW., Zhang, J. et al. Structure and mechanism of the mitochondrial calcium transporter NCLX. Nature 646, 1272–1280 (2025). https://doi.org/10.1038/s41586-025-09491-0

- Received: 28 August 2023

- Accepted: 04 August 2025

- Published: 10 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09491-0

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Amendments & Corrections

- Author Correction: Crystal structure of heliorhodopsin

- Author Correction: Prophylactic TNF blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy

- Author Correction: Ribonucleotide incorporation into mitochondrial DNA drives inflammation

- Author Correction: EBI2 augments Tfh cell fate by promoting interaction with IL-2-quenching dendritic cells

- Publisher Correction: Psilocybin’s lasting action requires pyramidal cell types and 5-HT2A receptors

Author Correction Published: 30 September 2025

Author Correction: Crystal structure of heliorhodopsin

Wataru Shihoya,

Keiichi Inoue,

Manish Singh,

Masae Konno,

Shoko Hososhima,

Keitaro Yamashita,

Kento Ikeda,

Akimitsu Higuchi,

Tamaki Izume,

Sae Okazaki,

Masanori Hashimoto,

Ritsu Mizutori,

Sahoko Tomida,

Yumeka Yamauchi,

Rei Abe-Yoshizumi,

Kota Katayama,

Satoshi P. Tsunoda,

Mikihiro Shibata,

Yuji Furutani,

Alina Pushkarev,

Oded Béjà,

Takayuki Uchihashi,

Hideki Kandori &

…

Osamu Nureki

Nature volume 646, page E28 (2025)

The Original Article was published on 25 September 2019

Correction to: Nature https://doi.org/10.1038/s41586-019-1604-6 Published online 25 September 2019

In the version of this article originally published, due to a figure preparation error following peer review, information in Extended Data Fig. 2e was a duplicate of Extended Data Fig. 6a. Extended Data Fig. 2e and Extended Data Fig. 6a both show the maximum conserved residues around retinal and their conservation ratios in HeR family members, while Extended Data Fig. 2e should instead have shown the conserved residues and their conservation ratios in the HeR family members. Upon finding this error, we also noted that the transmembrane helix labelling in Fig. 1 is incorrect, with the current labels TM4 (lower) and TM6 (higher) incorrectly switched.

Due to the age of the paper, these figures cannot be updated directly. The Supplementary Information accompanying this amendment contains the corrected Extended Data Fig. 2e and Fig. 1, and serves to correct the article.

Supplementary information is available in the online version of this amendment.

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Authors and Affiliations

1. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan

Wataru Shihoya, Keitaro Yamashita, Akimitsu Higuchi, Tamaki Izume, Sae Okazaki & Osamu Nureki

2. Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Nagoya, Japan

Keiichi Inoue, Manish Singh, Masae Konno, Shoko Hososhima, Masanori Hashimoto, Ritsu Mizutori, Sahoko Tomida, Yumeka Yamauchi, Rei Abe-Yoshizumi, Kota Katayama, Satoshi P. Tsunoda & Hideki Kandori

3. OptoBioTechnology Research Center, Nagoya Institute of Technology, Showa-ku, Nagoya, Japan

Keiichi Inoue, Kota Katayama, Yuji Furutani & Hideki Kandori

4. The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan

Keiichi Inoue

5. PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan

Keiichi Inoue & Satoshi P. Tsunoda

6. RIKEN SPring-8 Center, Sayo-gun, Japan

Keitaro Yamashita

7. School of Mathematical and Physical Sciences, Graduate School of Natural Science & Technology, Kanazawa University, Kanazawa, Japan

Kento Ikeda

8. Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kanazawa, Japan

Mikihiro Shibata

9. High-speed AFM for Biological Application Unit, Institute for Frontier Science Initiative, Kanazawa University, Kanazawa, Japan

Mikihiro Shibata

10. Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, Japan

Yuji Furutani

11. Department of Structural Molecular Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Japan

Yuji Furutani

12. Faculty of Biology, Technion–Israel Institute of Technology, Haifa, Israel

Alina Pushkarev & Oded Béjà

13. Department of Physics, Nagoya University, Nagoya, Japan

Takayuki Uchihashi

14. Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Japan

Takayuki Uchihashi

Corresponding authors

Correspondence to Hideki Kandori or Osamu Nureki.

Supplementary information

Corrected Extended Data Fig. 2e, Fig. 1.

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Shihoya, W., Inoue, K., Singh, M. et al. Author Correction: Crystal structure of heliorhodopsin. Nature 646, E28 (2025). https://doi.org/10.1038/s41586-025-09669-6

- Published: 30 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09669-6

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Author Correction Published: 01 October 2025

Author Correction: Prophylactic TNF blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy

Elisabeth Perez-Ruiz,

Luna Minute,

Itziar Otano,

Maite Alvarez,

Maria Carmen Ochoa,

Virginia Belsue,

Carlos de Andrea,

Maria Esperanza Rodriguez-Ruiz,

Jose Luis Perez-Gracia,

Ivan Marquez-Rodas,

Casilda Llacer,

Martina Alvarez,

Vanesa de Luque,

Carmen Molina,

Alvaro Teijeira,

Pedro Berraondo &

…

Ignacio Melero

Nature volume 646, pages E29–E30 (2025)

The Original Article was published on 01 May 2019

Correction to: Nature https://doi.org/10.1038/s41586-019-1162-y Published online 1 May 2019

In the version of the article initially published, in Extended Data Fig. 5d, the “DSS+ ICB + Etanercept” flow cytometry panel was a duplicate of the “DSS + ICB + anti-TNF” plot due to a mistake in figure preparation. The correct flow cytometry panel is now in the amended version of the Extended Data Fig. 5, included below as Fig. 1. This notice serves to correct the error.

Fig. 1: Corrected Extended Data Fig. 5

.

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Author notes

1. These authors jointly supervised this work: Pedro Berraondo, Ignacio Melero

Authors and Affiliations

1. Program of Immunology and Immunotherapy, Cima Universidad de Navarra, Pamplona, Spain

Elisabeth Perez-Ruiz, Luna Minute, Itziar Otano, Maite Alvarez, Maria Carmen Ochoa, Virginia Belsue, Maria Esperanza Rodriguez-Ruiz, Carmen Molina, Alvaro Teijeira, Pedro Berraondo & Ignacio Melero

2. Navarra Institute for Health Research (IDISNA), Pamplona, Spain

Elisabeth Perez-Ruiz, Luna Minute, Itziar Otano, Maite Alvarez, Maria Carmen Ochoa, Virginia Belsue, Carlos de Andrea, Jose Luis Perez-Gracia, Carmen Molina, Alvaro Teijeira, Pedro Berraondo & Ignacio Melero

3. Department of Oncology, Clínica Universidad de Navarra, Pamplona, Spain

Elisabeth Perez-Ruiz, Maria Esperanza Rodriguez-Ruiz, Jose Luis Perez-Gracia & Ignacio Melero

4. Department of Oncology, Hospital Costa del Sol, Marbella, Spain

Elisabeth Perez-Ruiz

5. Instituto de Investigación Biomédica de Málaga (IBIMA), Hospitales Universitarios Regional y Virgen de la Victoria, Málaga, Spain

Elisabeth Perez-Ruiz, Martina Alvarez & Vanesa de Luque

6. Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain

Maria Carmen Ochoa, Jose Luis Perez-Gracia, Ivan Marquez-Rodas, Alvaro Teijeira, Pedro Berraondo & Ignacio Melero

7. Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain

Carlos de Andrea

8. Department of Oncology, Hospital General Universitario Gregorio Marañon, Madrid, Spain

Ivan Marquez-Rodas

9. Department of Oncology, Hospital Universitario Virgen de la Victoria, Málaga, Spain

Casilda Llacer

10. Laboratorio de Biología Molecular del Cáncer, Centro de Investigaciones Médico-Sanitarias (CIMES), Universidad de Málaga, Málaga, Spain

Martina Alvarez & Vanesa de Luque

11. Department of Pathology, Faculty of Medicine, Universidad de Málaga, Málaga, Spain

Martina Alvarez

12. Department of Immunology and Immunotherapy, Clínica Universidad de Navarra, Pamplona, Spain

Ignacio Melero

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Correspondence to Pedro Berraondo or Ignacio Melero.

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Perez-Ruiz, E., Minute, L., Otano, I. et al. Author Correction: Prophylactic TNF blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy. Nature 646, E29–E30 (2025). https://doi.org/10.1038/s41586-025-09672-x

- Published: 01 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09672-x

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Author Correction Open access Published: 03 October 2025

Author Correction: Ribonucleotide incorporation into mitochondrial DNA drives inflammation

Amir Bahat,

Dusanka Milenkovic,

Eileen Cors,

Mabel Barnett,

Sadig Niftullayev,

Athanasios Katsalifis,

Marc Schwill,

Petra Kirschner,

Thomas MacVicar,

Patrick Giavalisco,

Louise Jenninger,

Anders R. Clausen,

Vincent Paupe,

Julien Prudent,

Nils-Göran Larsson,

Manuel Rogg,

Christoph Schell,

Isabella Muylaert,

Erik Larsson,

Hendrik Nolte,

Maria Falkenberg &

…

Thomas Langer

Nature volume 646, page E31 (2025)

The Original Article was published on 24 September 2025

Correction to: Nature https://doi.org/10.1038/s41586-025-09541-7 Published online 24 September 2025

In the version of the article initially published, Erik Larsson’s surname was incorrect and has now been amended in the HTML and PDF versions of the article.

Author information

Author notes

1. Thomas MacVicar

Present address: The CRUK Scotland Institute, Glasgow, UK

2. These authors contributed equally: Amir Bahat, Dusanka Milenkovic

Authors and Affiliations

1. Max Planck Institute for Biology of Ageing, Cologne, Germany

Amir Bahat, Dusanka Milenkovic, Eileen Cors, Mabel Barnett, Sadig Niftullayev, Athanasios Katsalifis, Marc Schwill, Petra Kirschner, Thomas MacVicar, Patrick Giavalisco, Hendrik Nolte & Thomas Langer

2. Institute for Biomedicine, University of Gothenburg, Gothenburg, Sweden

Louise Jenninger, Anders R. Clausen, Isabella Muylaert, Erik Larsson & Maria Falkenberg

3. Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK

Vincent Paupe & Julien Prudent

4. Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

Nils-Göran Larsson

5. Institute of Surgical Pathology, Faculty of Medicine, Medical Center—University of Freiburg, Freiburg, Germany

Manuel Rogg & Christoph Schell

6. Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany

Thomas Langer

Corresponding author

Correspondence to Thomas Langer.

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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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Bahat, A., Milenkovic, D., Cors, E. et al. Author Correction: Ribonucleotide incorporation into mitochondrial DNA drives inflammation. Nature 646, E31 (2025). https://doi.org/10.1038/s41586-025-09682-9

- Published: 03 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09682-9

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Author Correction Published: 02 October 2025

Author Correction: EBI2 augments Tfh cell fate by promoting interaction with IL-2-quenching dendritic cells

Jianhua Li,

Erick Lu,

Tangsheng Yi &

…

Jason G. Cyster

Nature volume 646, page E32 (2025)

The Original Article was published on 05 May 2016

Correction to: Nature https://www.nature.com/articles/nature17947 Published online 5 May 2016

In the version of the article initially published, due to an error during figure preparation, the flow cytometry plot shown in the top-right panel of Extended Data Fig. 6j was an inadvertent duplicate of the bottom-left plot. Due to the age of the article, the figure cannot be replaced directly. The Supplementary information accompanying this amendment serves to update the article.

Supplementary information is available in the online version of this amendment.

Author information

Author notes

1. Tangsheng Yi

Present address: Department of Discovery Immunology, Genentech, South San Francisco, 94143, California, USA

Authors and Affiliations

1. Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, 94143, California, USA

Jianhua Li, Erick Lu, Tangsheng Yi & Jason G. Cyster

2. Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, 94143, California, USA

Jianhua Li, Erick Lu, Tangsheng Yi & Jason G. Cyster

3. Department of Medical Microbiology, Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China

Jianhua Li

Corresponding author

Correspondence to Jason G. Cyster.

Supplementary information

Updated Extended Data Fig. 6j

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Li, J., Lu, E., Yi, T. et al. Author Correction: EBI2 augments Tfh cell fate by promoting interaction with IL-2-quenching dendritic cells. Nature 646, E32 (2025). https://doi.org/10.1038/s41586-025-09691-8

- Published: 02 October 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09691-8

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Publisher Correction Published: 29 September 2025

Publisher Correction: Psilocybin’s lasting action requires pyramidal cell types and 5-HT2A receptors

Ling-Xiao Shao,

Clara Liao,

Pasha A. Davoudian,

Neil K. Savalia,

Quan Jiang,

Cassandra Wojtasiewicz,

Diran Tan,

Jack D. Nothnagel,

Rong-Jian Liu,

Samuel C. Woodburn,

Olesia M. Bilash,

Hail Kim,

Alicia Che &

…

Alex C. Kwan

Nature volume 646, page E33 (2025)

The Original Article was published on 02 April 2025

Correction to: Nature https://doi.org/10.1038/s41586-025-08813-6 Published online 2 April 2025

In the version of this article originally published, there were errors in the Fig. 3e,h,k,n and Extended Data Fig. 7b–e x-axis labels, where the text now reading “(after – before)/before (%)” originally read “(before – after)/before (%).” The figures are updated in the HTML and PDF versions of the article.

Author information

Author notes

1. These authors contributed equally: Ling-Xiao Shao, Clara Liao

Authors and Affiliations

1. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA

Ling-Xiao Shao, Clara Liao, Pasha A. Davoudian, Neil K. Savalia, Quan Jiang, Cassandra Wojtasiewicz, Diran Tan, Jack D. Nothnagel, Samuel C. Woodburn, Olesia M. Bilash & Alex C. Kwan

2. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

Ling-Xiao Shao, Rong-Jian Liu, Alicia Che & Alex C. Kwan

3. Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA

Clara Liao, Pasha A. Davoudian & Neil K. Savalia

4. Medical Scientist Training Program, Yale University School of Medicine, New Haven, CT, USA

Pasha A. Davoudian & Neil K. Savalia

5. Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea

Hail Kim

6. Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA

Alex C. Kwan

Corresponding author

Correspondence to Alex C. Kwan.

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Shao, LX., Liao, C., Davoudian, P.A. et al. Publisher Correction: Psilocybin’s lasting action requires pyramidal cell types and 5-HT2A receptors. Nature 646, E33 (2025). https://doi.org/10.1038/s41586-025-09671-y

- Published: 29 September 2025

- Issue date: 30 October 2025

- DOI: https://doi.org/10.1038/s41586-025-09671-y

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Amendments & Corrections

Author Correction: Crystal structure of heliorhodopsin

Author Correction: Prophylactic TNF blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy

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