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EDITORIAL 06 January 2026

Defossilize our chemical world

Achieving net zero means eliminating fossil fuels, not carbon — the chemical element has a crucial part to play in powering the modern world.

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Biofuels, such as rapeseed, are not an ideal alternative to non-fossil carbon.Credit: Krisztian Bocsi/Bloomberg/Getty

There’s a relatively new word doing the rounds in sustainability research and policy: defossilization. Beyond expert circles, it isn’t necessarily obvious that phasing out fossil fuels does not mean phasing out carbon. Under net-zero scenarios, carbon-based fuels are still needed, to provide power, for example, and for aviation. Carbon, currently often derived from fossil hydrocarbons, is also integral to everyday consumer products such as soaps and detergents, as well as medicines, fertilizers and plastics.

Worldwide, demand for ‘embedded’ carbon — that found in chemicals — is expected to double by 2050, according to the nova-Institute, a green-energy research institute in Hürth, Germany (see go.nature.com/4jpx6qi). But this carbon cannot come from the usual sources, such as coal, natural gas and oil. These must remain in the ground, and this is where defossilization comes in.

Defossilization means finding sustainable ways to make carbon-based chemicals. Alternative sources of carbon include the atmosphere and plants, as well as carbon in existing biological or industrial waste, such as used plastics or agricultural residue. In some cases, these chemicals will eventually return carbon dioxide to the atmosphere through burning or biodegradation. In principle, this will occur as part of a circular process, rather than one that has added greenhouse gases.

The subject of defossilization is of increasing research interest — as it needs to be — despite signs that some governments, including a number in Europe and that of the United States, are backsliding on their climate commitments. In this two-part Editorial, we describe some of the challenges faced by researchers, in both academia and industry, that scientists and policymakers need to solve to enable defossilization to happen on the scale required. In this first instalment, we focus on Europe. In the next, we explore advances under way in China.

Biomass from crops is a key source of non-fossil carbon, and one that can be obtained at scale. One driver of large-scale production is the European Union’s biofuels strategy. This mandates that transport fuels include biomass-derived products. Examples include biodiesel, which can be made from oils such as sunflower and palm, and bioethanol, which is synthesized from crops such as maize (corn) and wheat. But clearing existing cropland or converting uncultivated land to grow biofuels can’t be the alternative of choice, not least because of the attendant risk to biodiversity and soil health, and the demand it puts on water resources. There’s also some evidence that, by encouraging farmers to convert land previously used to grow food crops, the directive has pushed up food prices.

The extraction of carbon from lignocellulose — tough plant matter — in crop waste is an alternative with potential that remains mostly untapped. One major advantage is the fact that it can be produced without the use of extra land. But it is expensive to extract, and production timelines are long, both of which hinder scalability[1].

Other potential sources of waste carbon include municipal and industrial waste, with used plastic among this. More than 40% of plastic produced in the EU is already recycled. This recycling rate could be increased if technical challenges can be surmounted[2]. Current recycling methods break waste plastics into flakes through shredding or melting, then form pellets that can be used to make new products. For higher recycling rates to be achieved, chemical recycling methods will need to be further developed and scaled up. These methods break down plastics into smaller molecules that can be used to rebuild new, larger ones.

Carbon dioxide captured from fossil-fuel burning or the air offers one of the largest potential avenues for defossilization. The global chemicals industry could obtain one-third of its carbon needs from this source by 2050, according to the nova-Institute. That compares with 22% from biomass. By one estimate, there are almost 900 gigatonnes of carbon in the atmosphere, nearly double the 450 gigatonnes of carbon contained in vegetation[3]. But the scenarios for 2050 vary widely. Some say CO_2 will become the main feedstock for chemicals, whereas others say its contribution will be negligible.

To make useful carbon-based molecules, CO_2 must first be transformed into other molecules. Usually, it is reacted with hydrogen, either to form hydrocarbons or to remove an oxygen atom. Because CO_2 is highly stable, a considerable amount of energy is needed to overcome the thermodynamic barrier to these reactions. This must be powered renewably for the process to be truly sustainable. Capturing atmospheric CO_2 is difficult and expensive, in part because of the compound’s stability. As a result, the technology has not been a priority for European governments. This must change.

In May, Elisa Morgera, the United Nations Special Rapporteur on human rights in the context of climate change, published a report urging governments to defossilize economies as part of the fossil-fuel phase-out. In the United Kingdom, the Royal Society and the Institution of Chemical Engineers have urged the government to support research on defossilization. They have a strong case, because such research, which is intended to boost the chemical industry, aligns with government policies to invest in science that supports economic growth. The EU and China also have a joint research programme called the EU–China Bridge, which is focused on decarbonization, but this is set to expire next year. This not only needs to be renewed, it needs a renewed focus — on defossilization.

Nature 649, 267 (2026)

doi: https://doi.org/10.1038/d41586-026-00005-0

References

1. Sulis, D. B. et al. Nature Commun. 16, 1244 (2025).

2. Joseph, T. M. et al. Case Stud. Chem. Environ. Eng. 9, 100673 (2024).

3. Erb, K. H. et al. Nature 553, 73–76 (2018).

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News in Focus

- AI and quantum science take centre stage under Trump — but with little new proposed funding

- AI is saving time and money in research — but at what cost?

- More than half of researchers now use AI for peer review — often against guidance

- Scientists skip key US meetings — and seize on smaller alternatives

- NSF softens grant-review rules to cope with backlog

- Restoring youth to old immune cells: mRNA therapy turns back the clock

- ‘I rarely get outside’: scientists ditch fieldwork in the age of AI

- Why cancer can come back years later — and how to stop it

NEWS 19 December 2025

AI and quantum science take centre stage under Trump — but with little new proposed funding

The US administration is banking on public–private partnerships and an expanded workforce to deliver progress, but critics say that this strategy could be offset by other US policies.

By

Jenna Ahart

A quantum computer. The administration of US President Donald Trump has announced sizable spending prorammes for quantum information science and artificial intelligence.Credit: Kent Nishimura/Bloomberg/Getty

For much of US science, 2025 was a year of cancelled grants and budget anxiety — but a few fortunate fields came out ahead. Since President Donald Trump took office in January, artificial intelligence and quantum-information science have sat at the top of his administration’s scientific priorities. And they don’t seem to be leaving any time soon.

Trump has ordered his advisers to ensure that the nation is “the unrivaled world leader” in AI and quantum information. The subjects are listed first and second on the administration’s list of research and development priorities. And on 11 December, Trump signed an executive order preventing US states from regulating AI, with the goal of speeding up the technology’s development.

But some researchers question the effectiveness of the administration’s programmes to promote quantum science and AI. And some argue that the administration’s strategies might be hampered by some of its other policy changes.

Here Nature examines three ways in which the administration aims to move quantum science and AI forwards.

Funding opportunities

Trump’s budget proposal for the National Science Foundation (NSF) in 2026 largely spared quantum science and AI, with a 3% increase in AI funding and a 0.4% increase in spending on quantum science — in contrast to cancelled research grants and proposals for steep cuts in many other areas. And throughout 2025, both the NSF and the Department of Energy (DoE) have announced new investments in AI and quantum science, including $100 million for AI projects at five US universities.

Steven Rolston, a quantum physicist at the University of Maryland in College Park and chair of the university’s physics department, says that the disparity between fields is visible there: faculty members in fields such as particle astrophysics face deep uncertainty about how much funding they’ll receive and how often they’ll have to reapply for awards. For quantum research, however, the biggest change is that the grant cycle has slowed down because of a US government shutdown and agency staffing changes. “I sort of have survivor’s guilt here,” Rolston says.

But quantum science and AI have not been immune to grant cuts. According to data from the non-profit website Grant Witness, the NSF has cancelled 101 grants that mention ’artificial intelligence’ in their abstracts, and 68 grants that mention ‘quantum’ in their abstracts.

And although AI and quantum science escaped massive cuts, the administration’s funding increases for these areas “are negligible in scope”, says David Schatsky, an AI-policy researcher at Harvard University in Cambridge, Massachusetts. “The 2026 budget doesn’t reflect — in dollar terms — an increase in focus or commitment to AI and quantum.” The NSF declined Nature’s request for comment, and the White House Office of Science and Technology Policy (OSTP) did not respond.

Joining forces

To pursue its priorities, the administration is also promoting both new and existing public–private partnerships, most prominently through an initiative, called the Genesis Mission, that was announced in November. The project, headed by the DoE, will make scientific data sets from the 17 US national laboratories available to private companies and academic researchers, who will use those data to build AI models to accelerate scientific research. Among the companies that have signed up to collaborate with federal researchers are Microsoft, IBM and OpenAI.

The administration has promoted public–private partnerships in quantum science as well. Under the National Quantum Initiative, which began during the first Trump administration, quantum-information research centres at five national labs partner with private quantum companies to create prototypes and test new technology. The DoE made an extra $625-million investment in the programme this year, and an executive order outlining the administration’s overarching approach to quantum science is expected soon, says Constanza Vidal Bustamante, a quantum-technology policy researcher at the Center for a New American Security, a think tank in Washington DC.

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Nature 649, 271-272 (2026)

doi: https://doi.org/10.1038/d41586-025-04108-y

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NEWS 05 December 2025

AI is saving time and money in research — but at what cost?

Artificial-intelligence tools are boosting researchers’ productivity, but some worry about the effect of a growing reliance on them.

By

Rachel Fieldhouse

More than 60% of researchers surveyed about AI say they use it for work.Credit: MD Abu Sufian Jewel/NurPhoto via Getty

Scientists are increasingly using artificial intelligence (AI) to do their work. Many say the tools are saving them time and money, but others have seen the negative effects that such tools can have on research.

In a survey of more than 2,400 researchers released in October by the publishing company Wiley, 62% of respondents said they used AI for tasks related to research or publication — up from 45% in 2024, when there were 1,043 respondents. Early-career scientists and researchers in physical sciences were the most likely to use AI tools in their work, and were more likely to be early adopters of AI than were later-career researchers or those working in humanities, mathematics or statistics.

Researchers are using AI tools to help with writing, editing and translating. They are also using them to detect errors or bias in their writing, and to summarize large volumes of studies. In a sample of 2,059 respondents, 85% said that AI helped with efficiency, 77% that it helped to increase the quantity of work completed, and 73% that it improved the quality of their work.

Matthew Bailes, an astrophysicist at Swinburne University of Technology in Melbourne, Australia, says AI tools are popular among astronomers, helping them to process massive data sets. His team has been using AI for about a decade to identify neutron-star signatures in their data. “When you’ve got 10,000 candidates, it’s handy to just be able to whip through it in a few seconds, rather than manually looking at everything.”

His team is also developing a virtual simulation of the Universe. The project uses a plug-in version of the generative AI model Claude, developed by Anthropic in San Francisco, California, to display data alongside visualizations. Bailes hopes to use it as a ‘co-teacher’. It could show a simulation of a globular cluster — a collection of thousands to millions of stars — against graphs showing how many black holes or neutron stars develop over time. “The opportunities for education there are phenomenal,” he adds.

Productivity boost

AI is also having an impact on scientists’ outputs and their careers. A 2024 preprint[1] published on arXiv reports that scientists who used AI published more papers, had more citations and became team leaders four years earlier than those who did not use AI.

The researchers used a large language model to identify more than one million AI-assisted papers among 67.9 million studies published in six fields between 1980 and 2024. The authors note that “AI accelerates work in established, data-rich domains”. That suggests that, although AI might enhance the productivity of individual scientists, it could reduce scientific diversity, they say.

Many researchers worry about other detrimental effects of AI on research. The survey by Wiley, based in Hoboken, New Jersey, found that 87% of people were concerned about AI making errors, called hallucinations, and about data security, ethics and a lack of transparency around training. In last year’s survey, the figure was 81%.

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Nature 649, 272-273 (2026)

doi: https://doi.org/10.1038/d41586-025-03936-2

References

1. Hao, Q., Xu, F., Li, Y. & Evans, J. Preprint at arXiv https://doi.org/10.48550/arXiv.2412.07727 (2025).

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NEWS 15 December 2025 Correction 16 December 2025

More than half of researchers now use AI for peer review — often against guidance

A survey of 1,600 academics found that more than 50% have used artificial-intelligence tools while peer reviewing manuscripts.

By

Miryam Naddaf

Survey results suggest that peer reviewers are increasingly turning to AI.Credit: Panther Media Global/Alamy

More than 50% of researchers have used artificial intelligence while peer reviewing manuscripts, according to a survey of some 1,600 academics across 111 countries by the publishing company Frontiers.

Nearly one-quarter of respondents said that they had increased their use of AI for peer review over the past year. The findings, posted on 11 December by the publisher, which is based in Lausanne, Switzerland, confirm what many researchers have long suspected, given the ubiquity of tools powered by large-language models such as ChatGPT.

“It’s good to confront the reality that people are using AI in peer-review tasks,” says Elena Vicario, Frontiers’ director of research integrity. But the poll suggests that researchers are using AI in peer review “in contrast with a lot of external recommendations of not uploading manuscripts to third-party tools”, she adds.

Source: Unlocking AI’s untapped potential, Frontiers

Some publishers, including Frontiers, allow limited use of AI in peer review, but require reviewers to disclose it. Like most other publishers, Frontiers forbids reviewers from uploading unpublished manuscripts to chatbot websites because of concerns about confidentiality, sensitive data and compromising authors’ intellectual property.

The survey report calls on publishers to respond to the growing use of AI across scientific publishing and implement policies that are better suited to the ‘new reality’. Frontiers itself has launched an in-house AI platform for peer reviewers across all of its journals. “AI should be used in peer review responsibly, with very clear guides, with human accountability and with the right training,” says Vicario.

“We agree that publishers can and should proactively and robustly communicate best practices, particularly disclosure requirements that reinforce transparency to support responsible AI use,” says a spokesperson for the publisher Wiley, which is based in Hoboken, New Jersey. In a similar survey published earlier this year, Wiley found that “researchers have relatively low interest and confidence in AI use cases for peer review,” they add. “We are not seeing anything in our portfolio that contradicts this.”

Checking, searching and summarizing

The Frontiers’ survey found that, among the respondents who use AI in peer review, 59% use it to help write their peer-review reports. Twenty-nine per cent said they use it to summarize the manuscript, identify gaps or check references. And 28% use AI to flag potential signs of misconduct, such as plagiarism and image duplication (see ‘AI assistance’).

Mohammad Hosseini, who studies research ethics and integrity at Northwestern University Feinberg School of Medicine in Chicago, Illinois, says the survey is “a good attempt to gauge the acceptability of the use of AI in peer review and the prevalence of its use in different contexts”.

Some researchers are running their own tests to determine how well AI models support peer review. Last month, engineering scientist Mim Rahimi at the University of Houston in Texas designed an experiment to test whether the large language model (LLM) GPT-5 could review a Nature Communications paper[1] he co-authored.

He used four different set-ups, from entering basic prompts asking the LLM to review the paper without additional context to providing it with research articles from the literature to help it to evaluate his paper’s novelty and rigour. Rahimi then compared the AI-generated output with the actual peer-review reports that he had received from the journal, and discussed his findings in a YouTube video.

His experiment showed that GPT-5 could mimic the structure of a peer-review report and use polished language, but that it failed to produce constructive feedback and made factual errors. Even advanced prompts did not improve the AI’s performance — in fact, the most complex set-up generated the weakest peer review. Another study found that AI-generated reviews of 20 manuscripts tended to match human ones but fell short on providing detailed critique.

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Nature 649, 273-274 (2026)

doi: https://doi.org/10.1038/d41586-025-04066-5

Updates & Corrections

- Correction 16 December 2025: An earlier version of this article incorrectly stated that Frontiers’ AI tool for peer reviewers offers only a limited set of prompts.

References

1. Hassan, A., Afshari, M. & Rahimi, M. Nature Commun. 16, 6333 (2025).

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NEWS 16 December 2025

Scientists skip key US meetings — and seize on smaller alternatives

Researchers are devising creative ways to get together as Trump’s policies curb travel to US gatherings.

By

Alexandra Witze

Organizers of the American Geophysical Union’s annual meeting (pictured, a past meeting), which started 15 December, expect it to draw fewer visitors than it did in 2024.Credit: Marek Uliasz/imageBROKER/Alamy

Attendance at several of the biggest science conferences held in the United States either fell this year compared with last year or is expected to fall in 2026. There are many reasons for the changes, but at least some researchers are curbing their travel to the United States because of policies put in place by the administration of US President Donald Trump.

The obstacles have galvanized some meeting organizers to hatch alternative plans to bring the international research community together.

Earlier this month, for instance, the artificial-intelligence conference NeurIPS hosted not only its main meeting in San Diego, California, but also its first-ever alternative location, in Mexico City, with the goal of alleviating travel challenges. Meanwhile, a group of AI researchers in Europe organized an independent spin-off conference, dubbed EurIPS, in Copenhagen.

“Our main focus was on giving a home to people who felt intellectually homeless this year,” says Søren Hauberg, a machine-learning and computer-vision researcher at the Technical University of Denmark in Lyngby who helped to organize EurIPS.

Smaller crowds

Nature asked the organizers of large conferences that were scheduled to take place in US cities in the second half of 2025 or the first quarter of 2026 for details of their attendance trends. Of the six that responded, three had seen or were expecting a drop in attendance compared with the previous year.

In some cases, the observed decrease was slight, and the reasons were complex. For instance, attendance at the Society for Neuroscience’s annual meeting last month was down 6%, from 22,359 people in 2024 to 21,093 this year. The number of countries those attendees represented also fell, from 88 in 2024 to 73 in 2025. Organizers of the conference, one of the biggest in the United States, say that attendance has fluctuated since the start of the COVID-19 pandemic in 2020, making it difficult to pinpoint the effect Trump’s policies have had.

Organizers of the American Geophysical Union (AGU) conference, another of the nation’s largest meetings, say that they also are seeing a drop in numbers at the 2025 meeting, which began on Monday, compared with 2024. More than 30,000 people attended last year’s AGU conference; more than 20,000 were registered for this year’s as of Monday, conference organizers say. The lighter attendance was notable in the poster and exhibit halls, which were slightly less jam-packed than usual.

Barriers to entry

Scientists hoping to travel to the United States in 2025 faced a raft of new restrictions. As part of Trump’s focus on immigration and visa enforcement, border officials have tightened up scrutiny of incoming travellers, turning some away at airports and other entry points. In June, Trump’s administration banned or limited entry for citizens of 19 countries, including Iran and Venezuela, citing national-security concerns. And on 9 December, the administration proposed requiring visitors from dozens of countries to provide their social media posts for the past five years before entering the United States.

Other policies have also had an impact on scientists’ willingness or ability to attend US meetings. This week’s Pacifichem meeting in Honolulu, Hawaii, which around 11,000 people have registered to attend, has seen significantly fewer attendees than usual from Canada, says meeting chair Laurel Schafer, a chemist at the University of British Columbia in Vancouver. That aligns, she says, with overall drops in tourism from Canada to the United States after Trump imposed tariffs on Canadian goods and said he would make it “the 51st state”.

For Tanja Junkers, a chemist at Monash University in Melbourne, Australia, the deciding factor on attending Pacifichem was Trump’s attacks on trans and non-binary people; on his first day in office he issued an executive order declaring that there are “two sexes, male and female” and that the government will “enforce all sex-protective laws to promote this reality”. “Being of a gender-diverse background, I do not feel I am able to safely travel to the US, even to a more liberal state such as Hawaii,” Junkers says. She helped to arrange a symposium at the meeting from a distance, but did not participate in it, even remotely.

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Nature 649, 274-275 (2026)

doi: https://doi.org/10.1038/d41586-025-04083-4

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NEWS 15 December 2025

NSF softens grant-review rules to cope with backlog

Agency staff members say that the changes are reasonable, but they are worried about the potential effects on review.

By

Dan Garisto

The National Science Foundation, one of the world’s largest funders of basic science, has reduced the minimum number of external reviews that grant proposals must undergo.Credit: Briscoe Savoy for Nature

The US National Science Foundation (NSF) is relaxing some of its requirements for the review of grant proposals in an effort to cope with a backlog of applications and reduced staff numbers. Staff members at the agency, one of the world’s leading funders of basic science, say that the new strategy could have benefits, but raises the risk of grant applications being inadequately reviewed.

Previously, proposals for research grants were generally required to undergo at least three external reviews by scientists from outside the agency. Now, two reviews are enough and, under some circumstances, one can be an internal review by a scientist at the NSF.

The change in policy was described in an update of the agency’s Proposal and Award Policies and Procedures Guide that was published on 8 December. Nature also obtained the more detailed internal guidance supplied to NSF programme officers, who are in charge of the review process. To get a better understanding of the changes, Nature spoke to five NSF staff members. All requested anonymity because of concerns about retaliation.

Substituting an internal review for an external review could save time, programme officers say. “I’m glad to have a little more flexibility,” one says, especially when they are handling proposals that do not meet basic criteria for funding. “But I would hate to see two reviews becoming the norm for the proposals that are legitimate candidates for funding.” Programme officers say that they value a range of external expert perspectives.

Variety of views

Like other science agencies, the NSF relies heavily on external expertise to assess grant proposals. Proposals are sent to peer-review panels of between three and ten independent specialists, each of whom produces reviews of a handful of proposals. To supplement panellists’ evaluations, programme officers also solicit other external experts for ad hoc reviews.

During day-long discussion sessions, panels consider panellists’ reviews and any ad hoc reviews, then produce assessments of proposals. Programme officers use these assessments to make recommendations to division directors, who decide which proposals are funded.

Programme officers also write summaries of these assessments, which can provide feedback for researchers. Until now, those summaries were highly detailed and multiple paragraphs long. But new internal guidance limits summaries to three to five sentences. It is a shame to limit the summaries, which are “a more helpful synthesis” than other documents from the review process, says Laurel Yohe, a bioinformatics researcher at the University of North Carolina, Charlotte.

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Nature 649, 275-276 (2026)

doi: https://doi.org/10.1038/d41586-025-04067-4

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NEWS 17 December 2025

Restoring youth to old immune cells: mRNA therapy turns back the clock

Testing in mice suggests that rejuvenating T cells could make vaccines and some cancer therapies more effective.

By

Heidi Ledford

As people age, immune cells called T cells tend to decline in both quantity and quality. Credit: National Institutes of Health, NIAID/SPL

A twice-weekly cocktail of three messenger RNAs can rejuvenate the weary immune systems of aged mice and boost responses to vaccination and cancer treatments, a study has found[1].

The treatment provides a needed boost to immune cells called T cells, which coordinate immune responses and kill infected cells. As people age, their ability to produce T cells wanes, and the ones they have become less effective.

T-cell ageing helps to explain why vaccines are sometimes less effective in older people than in young adults, and why cancer treatments that unleash the immune system against tumours don’t work as well in older adults, says María Mittelbrunn, an immunologist at the Spanish National Research Council in Madrid. Flagging T-cell immunity is also linked to the chronic inflammation associated with many age-related diseases, including some forms of cardiovascular disease.

“T cells, in particular, are one of the cell types that change the most during ageing,” says Mittelbrunn, who was not involved in the study. “To rejuvenate them could have immense consequences.”

The work was reported in Nature on 17 December and earlier this month at the American Society of Hematology annual meeting in Orlando, Florida.

Targeting T cells

T cells are produced in the bone marrow and then travel to a tiny gland called the thymus to mature. In the thymus, they learn to recognize and respond to pathogens such as bacteria or viruses. They also learn not to attack the body’s own healthy cells.

But the thymus degrades with age: it begins to shrink and is gradually replaced by fatty tissue. Attempts to reverse this using hormone treatments and other drugs have not worked, says Mirco Friedrich, a haematologist and oncologist at the German Cancer Research Center in Heidelberg, and first author of the study.

So, Friedrich and his colleagues decided to take a different approach: rather than treating the thymus directly, they targeted T cells by delivering an experimental therapy to the liver. “Most T cells are in the blood,” Friedrich says. “And the liver receives the body’s whole blood volume.”

The team began by characterizing the effects of ageing on T cells in mice, cataloguing differences in gene activity and molecular signalling pathways from shortly after birth until the animals were old and frail, at about 20 months of age.

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Nature 649, 276 (2026)

doi: https://doi.org/10.1038/d41586-025-04082-5

References

1. Friedrich, M. J. et al. Nature https://doi.org/10.1038/s41586-025-09873-4 (2025).

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NEWS FEATURE 07 January 2026

‘I rarely get outside’: scientists ditch fieldwork in the age of AI

In the race to embrace new technologies, some ecologists fear their field is losing touch with nature.

By

Aisling Irwin

A climber affixes a wildlife camera to a tree in French Guiana. Credit: Philippe Psaila/Science Photo Library

Tadeo Ramirez-Parada studied the timing of plant flowering for his PhD — but he didn’t touch a single petal. Instead, he developed a machine-learning algorithm to analyse the digitized captions of one million herbarium specimens, which showed him how flowering times are changing with rising temperatures.

Ramirez-Parada’s work has helped to solve an important mystery in ecology — showing that as temperatures change, plants shift their flowering times to cope with the heat, rather than adapting through natural selection[1]. Yet his work so far has been almost entirely computer-based. “I have had to do very little experimental or field work,” says Ramirez-Parada, who did his PhD at the University of California, Santa Barbara.

Ramirez-Parada’s work is typical of a change that is reaching into every part of ecology. Whatever scientists are analysing — digitized specimens, images of the natural world, DNA samples, or data streaming in from sensors — many are doing it indoors.

The technologies are creating a world that can be monitored at times, places and scales that were previously unimaginable. We are moving towards the “fully automated monitoring of ecological communities”, wrote Marc Besson, a marine scientist at the Sorbonne University Ocean Observatory in Banyuls-sur-Mer, France, in a 2022 paper[2].

Many ecologists say this revolution offers huge potential for understanding the biodiversity crisis and discerning patterns of global change.

But some ecologists are dismayed. They feel that the discipline is losing intimacy with its subject matter. They argue that field experience is in decline, and that this loss could lead to error, bias and oversimplification of results.

“If it becomes a world where you don’t actually have to go out in order to become an ecologist, we kind of lose sight of what the actual world is like,” says Bill Sutherland, who studies conservation biology at the University of Cambridge, UK.

Always on

Like scientists everywhere, ecologists are grappling with how to make the most of a torrent of data.

Natural-history museums and herbariums around the world have digitized more than one billion specimens over the past few decades, some with accompanying DNA records.

A unique plant specimen is digitized at the Royal Botanic Gardens in Kew, UK, as part of an effort to make plant and fungal data available to researchers worldwide.Credit: Chris Jackson/Getty Images for RBG Kew

Meanwhile, citizen scientists and researchers alike have been feeding databases such as iNaturalist with hundreds of millions of observations, which are absorbed into the Global Biodiversity Information Facility (GBIF), a central database for natural history.

There is also a stream of data from sensors such as camera traps — which take pictures when activated by movement — microphones, animal-tracking devices, drones, satellites and DNA samplers.

Such sensors can run for years without intervention. Once, a remotely planted camera trap would eventually run out of power: now, the energy consumption of such a device is minimal and it can rely on solar or wind energy. Bandwidth is no longer an obstacle to data being transmitted 24 hours a day.

And computer science is more than keeping up[3]. Artificial-intelligence systems are already identifying species from these data; they are also being used for more complicated tasks such as building species-distribution models and ancestry trees. Some ecologists predict that generative AI, which creates new content based on learning from huge data sets, will soon be able to make more complex models, leading the way to understanding ecological processes and forecasting how species will respond to environmental changes.

There are already at least 100 laboratories that would label their work as ‘AI for nature’, according to Tanya Berger-Wolf, a computational ecologist at the Ohio State University in Columbus.

The approach is starting to bear fruit. One European project, called CamAlien, is tracking invasive species using high-resolution cameras with machine-learning processing power, affixed to cars, boats and trains. As they speed along, they rapidly photograph the sides of roads and tracks, analyse the images in situ and upload alerts about alien invasive plants to a Europe-wide online map.

The system shows how, just in the past few years, new technologies combined with AI have “gone from mostly demonstrating potential to actually beginning to deliver real implementations”, says Toke Thomas Høye, an ecologist at Aarhus University in Denmark, who co-developed CamAlien. Some 16 European countries are trying out the technology to assess the distribution of invasive alien species.

A solar-powered recording device on a coffee farm in the Alishan Mountains, Taiwan, enables real-time monitoring of the impact of agriculture on migratory birds.Credit: Sarab Sethi

Similarly, amid the steep declines in some insects, a consortium of researchers has finessed camera-trap technology, originally designed to spot mammals, so that it can identify and monitor insect species, which are much more numerous. Automated insect monitoring didn’t exist five years ago, says Høye. Thanks to developments in AI, scientists can distinguish between thousands of species.

“It’s opening up a door to part of our natural world that is so much more diverse compared to what camera traps have been used for previously,” says Høye. He and his group think that making insect monitoring easier and less labour-intensive will shed light on the state of insect populations around the globe.

Another group has deployed a system of microphones in search of a more detailed understanding of migration as birds fly across Europe from Norway to the Mediterranean coast of Spain. Known as the TABMON project, it is now streaming real-time soundscape data, day and night. An AI tool analyses the data and converts them into commonly used biodiversity indicators.

“Having standardized ecological data on continental scales is extremely rare,” says Sarab Sethi, who studies ecosystem sensing at Imperial College London, and led the design of the microphones, “especially when it’s on the fine-scale temporal resolution that acoustics gives, across a wide range of species, and across multiple years”. The project has yet to report its first results.

Extinction of experience

Few would dispute the benefits of more data and detail, but there is an ominous side effect, says Kevin Gaston at the University of Exeter, UK, who studies people’s relationship with nature: field experience is on the wane.

Gaston and his co-author Masashi Soga, who studies the loss of human–nature interactions at the University of Tokyo, argued in a March 2025 paper[4] that there has been an ‘extinction of experience’: a widespread decline in fieldwork-based research and education, with knock-on effects on the depth of ecological understanding. They also flagged other dangers, such as reduced engagement with local communities — a practice known to be crucial for successful conservation.

Others have expressed concern about ‘AI colonialism’, a practice in which data, collected remotely in poorer countries, are siphoned off for analysis in well-equipped labs elsewhere.

There are few quantitative data available to support or challenge Gaston and Soga’s argument. One analysis[5] of ecological studies published between 1980 and 2014 found that fieldwork-based studies decreased by 20% (as a proportion of the total), whereas modelling and data analyses increased by 600% and 800%, respectively. But these are relative changes, rather than absolute numbers, and the data set ends more than a decade ago.

Anecdotally, however, Gaston and Soga’s paper struck a nerve. Since publication, a number of groups have cited it while warning that a lack of outdoor research is hindering studies on subjects ranging from solitary bees to dinosaur fossils.

There’s also anecdotal evidence that more computer scientists have entered ecology, excited about what they can offer, but lacking field experience. That was the case for Berger-Wolf, considered a founder of computational ecology. She completed her PhD in theoretical computer science, but, being married to an ecologist, says she would chat to others in the ecology community and walk away “with a feeling like, oh, there’s got to be a different way of answering this question”.

Marc Besson and a colleague monitor juvenile and larval fishes along the coast of southern France.Credit: Pascal Romans

Berger-Wolf changed tack in 2003, and by 2005 was developing algorithms for dynamic network analysis to depict the social interactions of zebras in the Kenyan Serengeti. Field colleagues urged her to go and see her data but she always refused: “I’m a city girl. And I don’t like dust and bugs. And my answer was: ‘no, my data looks beautiful on my screen.’”

Sethi is another convert to ecology, having arrived in the field with an engineering background. In 2016, he decided to apply acoustic monitoring to ecology for his PhD — but the self-confessed metrophile quickly found himself out of his depth in a rainforest in Malaysian Borneo.

“I did what I now realize was the extremely dumb thing of trying to develop a new technology and for its first deployment to be in a tropical forest on the other side of the world,” Sethi grins. On the first night, he lay under a mosquito net in a pitch-dark hut on stilts, wide awake, while his ecologist colleagues dozed comfortably amid the sounds of the rainforest. He remembers thinking: “My God, is this just like a joke that’s gone a bit too far?” Now he values his field experiences but works mostly from the lab.

Some ecologists have gone the other way, coming in from outdoors to embrace big data. Laura Pollock at McGill University in Montreal, Canada, began her career as a field ecologist, first in the swamps of New Orleans, Louisiana, and then in isolated mountain regions in Australia. She saw a need for ecologists to do better data analysis, and now she uses machine learning to do predictive modelling of biodiversity across landscapes.

“I rarely get outside,” she says. “I’m trying, but it’s really hard because there’s so much technology creating so much data that we need people who have these data-science skills to analyse this.”

But Besson has embraced technology without diminishing his hours in the field. He says that he is spending as much time outside as he did before automation arrived. “Cameras and hydrophones can capture things in addition to my own eyes and ears, and they can stay in the field when I need to go back to the lab ... and when I need to sleep.”

Perfect storm

There are also many systemic forces driving ecologists indoors, argues Gaston.

There’s a widespread perception that funding for field studies is in decline — although the data are not often differentiated into grants for fieldwork versus those for lab-based projects. Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.

Other contributing forces include the fact that research institutes are increasingly in urban areas; that more scientists have childcare responsibilities that deter them from doing long or far-flung trips; that many feel the need to reduce their carbon footprint and that others want to avoid ‘helicoptering’ in and out of a country to do fieldwork that local scientists could do.

Another major issue, says Sutherland, is that the fast track to career-boosting publications is to analyse, rather than physically collect, data.

“Supposing you do your PhD and you spend all your time doing fieldwork,” he says. “And the person sitting next to you has been extracting data [from day one]”. After three years, he says, they might have published in increasingly highly ranked journals, while “you’re still in the Amazon catching fish”.

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Nature 649, 278-281 (2026)

doi: https://doi.org/10.1038/d41586-025-04150-w

References

1. Ramirez-Parada, T. H. et al. Nature Ecol. Evol. 8, 467–476 (2024).

2. Besson, M. et al. Ecol. Lett. 25, 2753–2775 (2022).

3. Reynolds, S. A. et al. Trends Ecol. Evol. 40, 191–207 (2025).

4. Soga, M. & Gaston, K. J. Trends Ecol. Evol. 40, 212–215 (2025).

5. Ríos-Saldaña, C. A., Delibes-Mateos, M. & Ferreira, C. C. Glob. Ecol. Conserv. 14, e00389 (2018).

6. Rafiq, K. et al. Trends Ecol. Evol. 39, 1059–1062 (2024).

7. Sethi, S. S. et al. Nature Ecol. Evol. 7, 1373–1378 (2023).

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NEWS FEATURE 06 January 2026

Why cancer can come back years later — and how to stop it

Researchers are targeting dormant tumour cells that might explain why some cancers reappear long after successful treatment.

By

Amanda Heidt

Cancer cells can reawaken after a period of dormancy, triggering a relapse. Credit: Science History Images/Alamy

When Lisa Dutton was declared free of breast cancer in 2017, she took a moment to celebrate with family and friends, even though she knew her cancer journey might not be over. As many as one-third of people whose breast tumours are cleared see the disease come back, sometimes decades later. Many other cancers are known to recur in the years following an initial treatment, some at much higher rates.

“It’s always in the back of your mind, and that can be stressful,” says Dutton, a retired health-care management consultant living in Philadelphia, Pennsylvania.

As part of her treatment, Dutton had enrolled in a clinical trial called SURMOUNT. This would monitor her for sleeping cancer cells, which many researchers now think might explain at least some cancer recurrence[1]. These dormant tumour cells evade initial treatment and move to other parts of the body. Instead of multiplying to form tumours right away — as is typical for metastatic cancer, in which cells spread from the main tumour — the dormant cells remain asleep. They are hidden from the immune system and not actively dividing. But later, they can reawaken and give rise to tumours.

Even though Dutton understood that her treatment might not have removed all signs of cancer, she says she was floored in 2020 when dormant cells were found in her bone marrow for the first time.

Researchers are discovering dormant tumour cells, also known as disseminated cancer cells, in association with breast, prostate, lung, colon[2] and other cancers, and these cells are increasingly implicated in some metastatic cancers. An estimated 30% of people who have been successfully treated for cancer might harbour these cells, although unpublished work suggests they could be even more common.

Over the past decade, a flurry of efforts have attempted to identify and understand dormant cells, with the ultimate goal of treating them. A handful of clinical trials are now under way to test potential therapies.

Although the first trial Dutton enrolled in only monitored the cells, she has since enrolled in a second, called CLEVER, that aims to eliminate them[3]. As such trials move ahead, open questions about the sleeper cells, including what induces dormancy and how to fight it, are drawing more researchers into the field.

“We’re starting to see multiple groups converging on some of the same ideas, which is always very affirming,” says Cyrus Ghajar, a cancer biologist at the Fred Hutchinson Cancer Center in Seattle, Washington. The trials under way are “a testament to just how much progress has been made”.

A silent threat

The existence of dormant tumour cells was proposed as early as the 1930s, when Australian pathologist Rupert Willis attributed some secondary cancer growths to such cells[4]. As people who had been treated for cancer began to live longer, he and others noticed that the disease sometimes returned much later, and was often even more aggressive. Despite this early proposal, the idea of dormancy didn’t catch on for decades.

Lewis Chodosh, a physician-scientist at the University of Pennsylvania in Philadelphia, recalls facing resistance when he began discussing the idea with colleagues more than 20 years ago. No one wanted to believe that cancer-killing drugs might be leaving something behind, he says, and drug companies weren’t interested in developing therapies for people who seemed to have been cured. Many scientists at the time said that recurrent cancers must instead be new, not linked to any past diagnoses.

“It’s only when enough evidence accumulates that you get pushed out of this way of thinking,” says Chodosh, who is a co-investigator on the SURMOUNT and CLEVER studies alongside Angela DeMichele, a medical oncologist at the University of Pennsylvania’s Perelman School of Medicine.

Angela DeMichele (left) and Lewis Chodosh are investigating ways to target dormant tumour cells.Credit: Peggy Peterson for Penn Medicine

Using a handful of cellular markers, researchers have now identified dormant tumour cells in many parts of the body[5]. These markers can tell scientists not only whether the cells are growing and dividing, but also where the cells originated and therefore which type of cancer they’re associated with. The methods aren’t perfect, however, and researchers are still trying to determine whether certain cells are more likely to go dormant than others, and which features define these cells.

Ghajar and others have found that dormant cells leave the primary tumour early in a cancer’s progression, often before the disease has been diagnosed[6]. How and why these cells break away isn’t entirely clear, but after spending just minutes in circulation, they exit the bloodstream and concentrate in certain parts of the body, such as the bone marrow and lymph nodes. Even in these niches, dormant cells are extremely rare, amounting to just a handful among millions of healthy cells, he says. Their state of suspended animation shields them from conventional treatments, such as chemotherapy, that target rapidly dividing cells.

Petros Tsantoulis, a medical oncologist at the University of Geneva in Switzerland, says that dormancy differs from other known states such as senescence, in which old cells stop dividing as they prepare to die. Given the right conditions, dormant cells can begin dividing anew. Once reawakened, dormant cells multiply into tumours that replicate the full complexity of the original tumour.

This has led some researchers to suggest that dormant tumour cells might be cancer stem cells, a type of cell that through renewal and differentiation might give rise to the tumour — or that, at least, they might be cancer cells with stem-like features.

Dormant tumour cells have some characteristics that are commonly associated with stem cells, such as the overexpression of certain genes. Cancer biologist Joan Massagué, director of the Sloan Kettering Institute in New York City, says that stem cells spend most of their time dormant, waking only after an injury or illness — making them obvious candidates. Still, the existence of cancer stem cells is a contentious idea.

Scientists seem poised to resolve some of these open questions soon. With advanced laboratory techniques that give researchers the ability to study individual cells more closely, it’s now possible to identify, isolate and enrich dormant tumour cells for further study. Chodosh and DeMichele’s team, for example, is developing an assay to identify dormant cells. Chodosh says it is much more sensitive than existing approaches, and might ultimately improve estimates of how many people harbour dormant cells.

Ghajar, meanwhile, is settling on a different way of thinking about these cells. If a dormant cell from a breast tumour ends up in the bone marrow, for example, it might be expected to retain many features of a breast cancer cell that would enable its identification. “But what we’re finding is that these expectations don’t hold up,” Ghajar says, noting that once a cancer cell spreads, it often changes its shape, size and behaviour. “We’re going to have to go beyond a definition based on unifying features, and instead map the mutations in these cells to mutations in the originating tumour — to define a disseminated cell not by what we think it should look like, but what its genome tells us it is.”

Sleep–wake signals

Beyond defining dormancy, researchers want to understand how and why cells go dormant, and what sorts of triggers reawaken them.

According to Judith Agudo, an immunologist at the Dana-Farber Cancer Institute in Boston, Massachusetts, cells probably enter dormancy as a protective measure. As part of a tumour, individual cells might be insulated from attack by the immune system, but once on their own, “they can easily be picked off without taking steps to hide themselves”, she says. In addition, the journey through the body to a new niche is a stressful one that kills the vast majority of cells that break away. Dormancy is one way of persisting in a harsh environment.

Research has shown that while cells are asleep, they continue to engage in crosstalk with their microenvironment[7] and modify themselves to actively maintain dormancy. For example, dormant cells seem to alter the expression patterns of genes involved in cell survival, including a central regulator of cell metabolism and growth called the mTOR pathway[8]. The cells also exploit a form of self-recycling called autophagy — literally ‘self-eating’ — that allows dormant cells to repurpose internal resources and survive with little input from their surroundings[9].

The cells seem to have a complex relationship with their external environment, too, including the immune system. Julio Aguirre-Ghiso, founding director of the Cancer Dormancy Institute at the Montefiore Einstein Comprehensive Cancer Center in New York City, says that the immune response is implicated not only in inducing dormancy, but also in maintaining and ending it.

He and his team have shown that macrophages in the lungs produce a particular protein that binds to dormant breast cancer cells and reinforces dormancy[10]. Other research has demonstrated how dormant cells can evade surveillance by immune-system cells, including T cells[11] and natural killer cells[12].

Taken together, the research suggests that cells most often remain dormant until the immune landscape is disrupted in some way, shifting the balance enough for cells to reawaken safely. These shifts might involve injury or illness, with studies in the past few years linking cellular damage[13] and COVID-19 and influenza infections[14] to an escape from dormancy. Ageing, fibrosis[15], chronic stress or lifestyle choices might also contribute to reawakening.

Flu infections can force dormant tumour cells to start growing and dividing (green).Credit: Bryan Johnson

For the cells, “it’s an odds game”, says Shelly Peyton, a biomedical engineer at Tufts University in Medford, Massachusetts. Cells are constantly attempting to exit dormancy after small perturbations, only to be killed. “But in moments when the balance is disrupted, that’s when we often see metastasis taking off,” she says.

Peyton’s work focuses on fibrosis, the build-up of fibrous connective tissue at a damaged site. This is often associated with cancer, because stiffness helps the tumour to grow and aids in signalling between cells. But the bone marrow, where dormant cells often reside, is soft, and Peyton is interested in whether that soft environment might be one feature reinforcing dormancy. She says it’s possible that natural, age-related loss of bone density (osteoporosis) or hormonal changes in women who previously had breast cancer could trigger fibrosis, and potentially a reawakening of their cancer.

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Nature 649, 282-284 (2026)

doi: https://doi.org/10.1038/d41586-025-04149-3

References

1. Phan, T. G. & Croucher, P. I. Nature Rev. Cancer 20, 398–411 (2020).

2. Aguirre-Ghiso, J. A. Nature Rev. Cancer 7, 834–846 (2007).

3. DeMichele, A. et al. Nature Med. 31, 3464–3474 (2025).

4. Willis, R. A. The Spread of Tumours in the Human Body (J. & A. Churchill, 1934).

5. Boydell, E., Borgeaud, M. & Tsantoulis, P. Onco 5, 3 (2025).

6. Páez, D. et al. Clin. Cancer Res. 18, 645–653 (2012).

7. Di Martino, J. S. et al. Nature Cancer 3, 90–107 (2022).

8. Aleksandrova, K. V., Vorobev, M. L. & Suvorova, I. I. Cell Death Dis. 15, 176 (2024).

9. Vera-Ramirez, L., Vodnala, S. K., Nini, R., Hunter, K. W. & Green, J. E. Nature Commun. 9, 1944 (2018).

10. Dalla, E. et al. Cell 187, 6631–6648 (2024).

11. Park, S.-Y. & Nam, J.-S. Exp. Mol. Med. 52, 569–581 (2020).

12. Bushnell, G. G. et al. Cancer Res. 84, 3337–3353 (2024).

13. Ganesan, R. et al. PLoS Biol. 21, e3002275 (2023).

14. Chia, S. B. et al. Nature 645, 496–506 (2025).

15. Turrell, F. K. et al. Nature Cancer 4, 468–484 (2023).

16. Goddard, E. T. et al. Cancer Cell 42, 119–134 (2024).

17. Calvo, V. et al. Clin. Cancer Res. 29, 5155–5172 (2023).

18. Dudek, A. Z. et al. J. Clin. Oncol. 42 (16 Suppl.), e15118 (2024).

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Books & Arts

- These women helped to shape quantum mechanics — it’s time to recognize them

- Living water and whispering rocks: Books in brief

BOOK REVIEW 05 January 2026

These women helped to shape quantum mechanics — it’s time to recognize them

An astute book redresses our collective perception of a field that became known as ‘boys’ physics’.

By

Indianara Lima Silva

Physicist Chien-Shiung Wu was the first to confirm quantum entanglement experimentally. Credit: Science Source/SPL

Women in the History of Quantum Physics: Beyond Knabenphysik Edited by Patrick Charbonneau et al. Cambridge Univ. Press (2025)

Have you ever doubted your knowledge or expertise? Noticed, if you’re a woman, that you receive less recognition than your male colleagues do, that your ideas were unheard in a discussion until they were echoed by a man — who then received credit for them? Have you observed a gendered division of labour in your workplace; a pay gap; gender, racial or class prejudices? Have you felt pressured to choose between being a wife, a mother and a scientist? Most women in science have.

One such scientist was Scottish astronomer Williamina Fleming. She had moved to Massachusetts with her husband, James Fleming, in 1878. Soon after, he left her — pregnant and alone in a foreign land. To survive, she found work in the household of Edward Pickering, director of Harvard College Observatory in Cambridge, Massachusetts. His wife, Lizzie Sparks Pickering, recognized Fleming’s scientific aptitude. The observatory employed Fleming in 1881 as one of its ‘computers’ — a role only women could have, under the institution’s strict gender division of labour. The women performed extensive calculations and difficult spectral classifications that provided insights into the physical nature and composition of stars.

Rather than directing her own studies, Fleming performed repetitive tasks, prepared research by her male colleagues for publication and edited the observatory’s reports and articles. Nevertheless, she discovered a set of spectral lines from a helium ion found in the spectrum of hot stars that later became instrumental evidence for extending Danish physicist Niels Bohr’s model of the atom beyond neutral hydrogen. Yet, rather than bearing her name, the set is known as the ‘Pickering series’. Fleming died of pneumonia in 1911. An immigrant, a woman, a mother and an astronomer, she deserves a place in the history of quantum theory and astronomy.

Williamina Fleming (right) was instrumental in the discovery of several types of star.Credit: Science History Images/Alamy

Fleming is one of 16 pioneers recognized in Women in the History of Quantum Physics, an insightful, meticulously researched collection of essays edited by physicists Patrick Charbonneau and Margriet van der Heijden, science writer Michelle Frank and historian of science Daniela Monaldi. In highlighting the contributions of women from diverse backgrounds and nationalities, this bold anthology rewrites the history of quantum physics and challenges its image of Knabenphysik — or boys’ physics, as it became known as in the 1920s because of the prominent work of a small group of young men, including Paul Dirac, Werner Heisenberg, Pascual Jordan and Wolfgang Pauli. The name reflects the view that took hold in a generation of physicists, regardless of their gender.

The people behind the science

The book invites readers to explore what I call a situated–relational history of physics. Situated because researchers viewed themselves, scientific contributions and the field of quantum physics through the lens of their own subjectivity, experience and social position. And relational because knowing how physicists interacted with people, experiments, theories, objects and institutions can help us to understand them and their contributions.

For example, the book shows that to fully understand the research that earned US physicist John Clauser the 2022 physics Nobel prize, one needs to also understand the work of Chinese–US experimental physicist Chien-Shiung Wu. In the 1970s, Clauser performed experiments on entangled photons, in which particles of light become inextricably linked. But in 1950, together with her graduate student Irving Shaknov, Wu had already published what later became recognized as the first documented experimental evidence of entanglement between photons. In the 1970s, Wu turned to experimental philosophy and, together with her student Leonard Kasday at Columbia University in New York City, performed a technically improved version of the 1950 experiment to test local hidden-variable theories in quantum mechanics. Clauser and Wu’s paths intersected in the quantum foundations. Clauser’s criticism of the Columbia group regarding their assumptions and experimental design reveals the greater complexity Wu and her team brought to quantum philosophy. Yet quantum history remembers Clauser more prominently than Wu.

Hurdles and perseverance

As historians, we too need to acknowledge our involvement in the histories we write. We must consider what we might have overlooked because of who we are and our situated knowledge.

The book gives the field of science history a challenge. How do the experiences of these quantum women support or upset our previous knowledge and our historical, sociological and philosophical theses? Until now, we have only had a partial history of quantum physics. This anthology invites historians, and readers, to keep searching for a more complex and situated–relational picture.

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Nature 649, 286-288 (2026)

doi: https://doi.org/10.1038/d41586-025-04151-9

Competing Interests

The author declares no competing interests.

BOOK REVIEW 18 December 2025

Living water and whispering rocks: Books in brief

Andrew Robinson reviews five of the best science picks.

By

Andrew Robinson

The Whispers of Rock

Anjana Khatwa Bridge Street Press (2025)

In 1902, on the Caribbean island of Saint Vincent, a group of women reported that water was boiling in the crater of the La Soufrière volcano, accompanied by booming sounds and shaking, but they were ignored by the colonial authorities. The subsequent eruption killed 1,680 islanders. This is one of many global examples of the public’s appreciation of nature in Earth scientist Anjana Khatwa’s enticing book. “Awakening yourself to the whispers of rock”, she says, “can transform the way you connect with and understand the world”.

Lo—TEK Water

Julia Watson Taschen (2025)

Water is “not a resource to be extracted or managed, but a living relative, a system of memory, intelligence, and reciprocity”, says landscape designer Julia Watson. This view underlies the Lo—TEK movement that she spearheads, which draws on Indigenous philosophy that water is a regenerative substance. Her pioneering, lavishly illustrated book contains contributions from many communities, from Bangladesh’s floating farms to China’s dike-pond systems and Micronesia’s traps to catch fish using tide flows.

Surviving the Twenty-First Century

Noam Chomsky & José Mujica Verso (2025)

In 2017 and 2022, filmmaker Saúl Alvídrez recorded conversations between linguist Noam Chomsky and political activist José Mujica, former president of Uruguay. Their discussions range from the risks of climate change to neo-fascism and the wisdom of nature, and make up this brief, thought-provoking book. Despite mutual admiration, the two do disagree at times. For example, Chomsky says that automation will “free people to undertake truly creative and satisfying work”, but Mujica says that “robots work only for their masters!”

Warhead

Nicholas Wright St. Martin’s Press (2025)

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Nature 649, 288 (2026)

doi: https://doi.org/10.1038/d41586-025-04139-5

Opinion

Work

- Pandemic PhDs: graduates anxious, but optimistic, about the future

- I see Mozambique’s baboons as windows into hominid evolution

CAREER FEATURE 05 January 2026

Pandemic PhDs: graduates anxious, but optimistic, about the future

Five early-career scientists from around the world share how lingering COVID-19 pandemic shadows and ripples from US academic disruptions have affected them.

By

Amanda Heidt

Find a new job

Yunhee Kim is finishing a PhD investigating why some tumours respond to immunotherapy better than others do. Credit: Yunhee Kim/Seoul National University, Institute of Molecular Biology and Genetics

Last year, Nature’s careers team spoke to five recent PhD students in North America, Israel and the United Kingdom to understand how disruptions, such as the COVID-19 pandemic, had affected their career plans. At the time, these students expressed anxiety about their prospects as they prepared to enter the job market — citing concerns around substantial cuts to US scientific funding, the war in Gaza and lingering delays to PhD projects caused by the pandemic — but also a strong desire to remain in science.

Now, to get a better sense of the global forces shaping the decisions of recent PhD students, Nature has cast a wider net, inviting people in Australia, South Korea, Germany, South Africa and China to share their career journeys. Five students explain how the shadow of COVID-19 continues to shape their careers, and how the chaos of US President Donald Trump’s second administration has reverberated across academia, disrupting plans and shifting priorities far beyond the United States. Still, these students remain optimistic about their prospects, with several pursuing opportunities abroad before they return to their home countries to build careers and, ultimately, support those of other early-career researchers.

YUNHEE KIM: Supporting Korea’s next generation

Will earn her PhD in stem-cell and cancer biology in August from Seoul National University in South Korea.

I started my PhD in stem-cell and cancer biology about six years ago at Pohang University of Science and Technology in South Korea, but, in 2022, my adviser moved to Seoul National University, and I followed him there. Fortunately, the impacts of COVID-19 were not as serious in South Korea as they were in other countries because we didn’t have a heavy lockdown. And because I was living on campus, I was free to go to the laboratory. It would have been disastrous if things had been different. There was a mandatory two-week quarantine for people who became ill, and back then I had to culture stem cells and change their medium every day at the same time. Just one day of delayed experiments would have set me back by six months.

I started studying cancer because I have many family members who have had it. I think immunotherapy is one of the most powerful treatments, but the response rate varies among people, and so I’m looking at the differences between people who do and don’t respond. Why is it that certain tumours do not respond to immunotherapy, and is there a way to alter this type of cancer by changing the tumour microenvironment so that it is more similar to that of someone who does respond?

I only have one semester left before I graduate. I’m excited because it’s been seven years since I started to discuss this project with my adviser as an undergraduate student. When I first joined his lab, I was not used to reading and writing in English, so it was hard to even read articles to learn more. I’m proud to see the results, after years of struggling.

In my field, clinical translation is important, and so I was interested in exploring how my work can be turned into a real therapy. But because the underlying mechanism is more interesting to me, I decided to do postdoctoral basic research instead. I’d like to go abroad, ideally to the United States, because many of the big names in my field are there and there is a better culture of working with clinicians than there is in Korea. I’d also like to experience a different culture.

In Korea, visible outputs such as publications are strongly tied to securing research funding. Because of this, short-term goals and measurable metrics often become a priority. I sometimes feel this structure limits our ability to pursue more-difficult, long-term or time-intensive scientific questions, or to grow as researchers by tackling more-fundamental challenges. I wonder how other countries deal with these kinds of problems.

Eventually, I’d like to return to Korea to run my own group and help students like me. During my research, I often had concerns about whether it was right for me, and if I could see myself doing it for the rest of my life. Under different circumstances, I might easily have left science. Such concerns are sometimes shared more easily with someone who is close to you, or someone who resembles you, and so I want to work with our country’s students so that they know they have a place in science.

Manan Shah wants to work in research support services with scientists in India.Credit: Lisa Wolany on behalf of Manan Shah

MANAN SHAH: Seeking a research support role

Defended his PhD in biodiversity at the University of Duisburg-Essen in Germany in February 2025.

I experimented with the corporate world after my master’s degree in 2015. But in India, where I’m from, people with a master’s usually end up working in quality assurance. God knows the number of nights I’ve spent resetting passwords and debugging data banks. I soon realized that research is better, and after a few years working in a research position, in 2020 I decided to take a leap and start a PhD at the University of Duisburg-Essen in Germany.

My dissertation focused on analysing water quality in 250 lakes across Europe. With my colleagues, I extracted the complete set of genetic material from the environment, known as the metagenome, to see how varying conditions affect microbial communities. We also did experiments in the lab using artificial rivers to test specific hypotheses. In India, most of the drinking water is not very good quality, and I wanted to do something that would alleviate that problem.

During my PhD, I started thinking ahead to what I wanted to do next. In 2022, a collaborative research centre opened at my university, and my supervisor gave me the opportunity to become a data steward. I interact with different projects to see what type of data researchers are generating, help them to analyse the information and ensure that they’re storing it in a manner that conforms to funding-agency rules. The partnerships I made there allowed me to secure a postdoc with another lab head to develop methods for extracting DNA using a new liquid-handling robot.

Because I’m on a visa that is tied to my employment and I want to stay in Germany, navigating the visa system has been challenging. I’ve been hesitant to leave the country because I’m worried I won’t be allowed back in — which means I haven’t seen my family much.

My goal is to spend five years as a postdoc in Germany. When I come back to India, hopefully with a stronger work portfolio, I’d like to provide research services to scientists there. There are a lot of researchers in India, but not a lot of support staff, and I enjoy jumping into projects, learning about them and problem solving. That type of freedom, in which I get to have a say in what type of research I want to do, is very important for me.

Dezhou Cao is pursuing a physics PhD.Credit: Dezhou Cao

DEZHOU CAO: Pressured to progress

Will graduate in March with a PhD in physics from the Harbin Institute of Technology in China.

I started my PhD in China in 2021, and I’m now finishing my last year as a visiting student at the University of Barcelona in Spain. I applied for a fellowship that supports academic exchanges between China and Spain, and it has been a unique experience. In China, I focused on chemically active colloids — mixtures in which microscopic particles are suspended in a substance — in micro- and nanorobotics. I studied how those particles self-propel, rotate and self-assemble. In Barcelona, I’m learning about condensed matter that relates to how much force is generated when micromotors rotate.

Experiencing a different culture has also been good. In China, we work the whole day, sometimes more than 12 hours. But in Barcelona, I’m more relaxed. I start work at 10:00 and finish around 16:00.

Soon, I will go back to China to write my dissertation, and I’m preparing a two-year postdoctoral research plan in the lab in which I did my PhD. Afterwards, I’d like to find another position in Europe, because physics research is much stronger there than in China. Eventually, I want to run my own lab in China because I’m already adapted to the fast-paced research environment. I would also like to stay close to my parents because I’m an only child, so it’s up to me to look after them.

I am hopeful about starting a lab in China, but there’s a huge pressure. When you are hired, you undergo a three-year assessment period, during which you need to publish a certain number of papers and successfully apply for at least one national research project or grant. If an assistant professor fails to meet these targets, they risk being dismissed. That’s stressful for younger researchers. In China, researchers are also subject to age restrictions that usually don’t exist in Europe and the United States. Some universities set an explicit upper age limit of 35 (and in some cases, 32) for hiring young faculty or research staff. I’m 29, so I feel a lot of pressure to keep making progress.

Nxalati Mkhombo studies in South Africa.Credit: Nxalati

NXALATI MKHOMBO: Keeping an open mind for next steps

Plans to submit her dissertation in molecular and cell biology at the University of Cape Town in South Africa this month.

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Nature 649, 515-517 (2026)

doi: https://doi.org/10.1038/d41586-025-04152-8

These interviews have been edited for length and clarity.

WHERE I WORK 05 January 2026

I see Mozambique’s baboons as windows into hominid evolution

In Gorongosa National Park in Mozambique, Rassina Farassi studies how humans came to walk on two legs.

By

Patricia Maia Noronha

Rassina Farassi is a primatologist at the Paleo-Primate Project in Gorongosa National Park, Mozambique. Credit: Kang-Chun Cheng for Nature

“Gorongosa National Park in Mozambique has fossil sites that, as recently as 2016, had never been explored. It is here that the Paleo-Primate Project (PPP) studies fossil evidence and living primates to understand human evolution.

In 2017, during the final year of my bachelor’s degree in archaeology and cultural heritage at Eduardo Mondlane University in Maputo, I had the opportunity to work on that project. It was my first encounter with primatology, and it changed the course of my life. Today, I spend most of my time observing grey-footed Chacma baboons (Papio ursinus griseipes) in the park — the best workplace I can imagine.

In this photo, taken in October 2025 in the park’s palaeontology laboratory, I’m holding the skull of the extinct mammal Arsinoitherium, a large herbivore that once lived in Africa’s swampy and coastal regions. The lab is where fossil remains from ongoing excavations are curated. Discoveries such as this suggest that Gorongosa was once part of the coastal forests of East Africa. It might have been a refuge that allowed some species to survive here longer than anywhere else in Africa.

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Nature 649, 520 (2026)

doi: https://doi.org/10.1038/d41586-025-04153-7

This interview has been edited for length and clarity.

Research

- A young progenitor for the most common planetary systems in the Galaxy

- Surface optimization governs the local design of physical networks

- Aharonov–Bohm interference in even-denominator fractional quantum Hall states

- Programmable on-chip nonlinear photonics

- Towards fibre-like loss for photonic integration from violet to near-infrared

- Land-use change undermines the stability of avian functional diversity

- Mapping the genetic landscape across 14 psychiatric disorders

- Adenosine signalling drives antidepressant actions of ketamine and ECT

- Ecology and spread of the North American H5N1 epizootic

- Gene-drive-capable mosquitoes suppress patient-derived malaria in Tanzania

- Parity and lactation induce T-cell-mediated breast cancer protection

- SIGLEC12 mediates plasma membrane rupture during necroptotic cell death

- Tumour-reactive heterotypic CD8 T cell clusters from clinical samples

- Lymph node environment drives FSP1 targetability in metastasizing melanoma

- Targeting FSP1 triggers ferroptosis in lung cancer

- An RNA splicing system that excises DNA transposons from animal mRNAs

Article Open access Published: 07 January 2026

A young progenitor for the most common planetary systems in the Galaxy

John H. Livingston,

Erik A. Petigura,

Trevor J. David,

Kento Masuda,

James Owen,

David Nesvorný,

Konstantin Batygin,

Jerome de Leon,

Mayuko Mori,

Kai Ikuta,

Akihiko Fukui,

Noriharu Watanabe,

Jaume Orell Miquel,

Felipe Murgas,

Hannu Parviainen,

Judith Korth,

Florence Libotte,

Néstor Abreu García,

Pedro Pablo Meni Gallardo,

Norio Narita,

Enric Pallé,

Motohide Tamura,

Atsunori Yonehara,

Andrew Ridden-Harper,

Allyson Bieryla,

Alessandro A. Trani,

Eric E. Mamajek,

David R. Ciardi,

Varoujan Gorjian,

Lynne A. Hillenbrand,

Luisa M. Rebull,

Elisabeth R. Newton,

Andrew W. Mann,

Andrew Vanderburg,

Guðmundur Stefánsson,

Suvrath Mahadevan,

Caleb Cañas,

Joe Ninan,

Jesus Higuera,

Kamen Todorov,

Jean-Michel Désert &

…

Lorenzo Pino

Nature volume 649, pages 310–314 (2026)

Abstract

The Galaxy’s most common known planetary systems have several Earth-to-Neptune-size planets in compact orbits[1]. At small orbital separations, larger planets are less common than their smaller counterparts by an order of magnitude. The young star V1298 Tau hosts one such compact planetary system, albeit with four planets that are uncommonly large (5 to 10 Earth radii)2,3. The planets form a chain of near-resonances that result in transit-timing variations of several hours. Here we present a multi-year campaign to characterize this system with transit-timing variations, a method insensitive to the intense magnetic activity of the star. Through targeted observations, we first resolved the previously unknown orbital period of the outermost planet. The full 9-year baseline from these and archival data then enabled robust determination of the masses and orbital parameters for all four planets. We find the planets have low, sub-Neptune masses and nearly circular orbits, implying a dynamically tranquil history. Their low masses and large radii indicate that the inner planets underwent a period of rapid cooling immediately after dispersal of the protoplanetary disk. Still, they are much less dense than mature planets of comparable size. We predict the planets will contract to 1.5–4.0 Earth radii and join the population of super-Earths and sub-Neptunes that nature produces in abundance.

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Main

V1298 Tau is a young (10–30 Myr), approximately solar-mass star (1.10 ± 0.05 M⊙) in the Taurus star-forming region2,4,5,6,7,8. Observations by NASA’s Kepler space telescope in its extended K2 mission[9] revealed transits of the star by four different planets, each larger than Neptune2,3. The V1298 Tau planets occupy a sparsely populated region of the observed exoplanet period versus radius plane. As a young system of large planets, it provides a crucial snapshot of planetary architecture just after formation, serving as the ‘missing link’ between protoplanetary disks and the mature systems found by Kepler[3]. Measuring their masses and orbits is, therefore, a key test of planet formation theories and allows us to witness early evolutionary processes, such as atmospheric mass loss, that sculpt planetary systems over billion year timescales.

Between 2019 and 2024, we observed 43 other transits of all four planets using both space- and ground-based telescopes. This campaign successfully recovered the previously lost outermost planet, V1298 Tau e, and resolved a long-standing period ambiguity[10] (Methods). We performed homogeneous and self-consistent modelling of all transit data from 2015 to 2024. After determining the transit shape parameters, we fit the midpoint of each transit individually (Methods). The transit-timing variations (TTVs) are shown in Fig. 1. All planets exhibit significant TTVs with amplitudes ranging from  approximately 50 min to 100 min. Moreover, the TTVs of the c–d pair are anticorrelated, as are those of the b–e pair. This indicates that the c–d and b–e interactions dominate over other pairwise interactions.

Fig. 1: TTVs in the V1298 Tau system.

Top left, points show the transit times of planet c measured against a reference linear ephemeris; error bars represent 1σ uncertainties. Grey curves show credible transit times drawn from the N-body models described in the text. Bottom left, same as above but for planet d. The interactions between c and d are nearly sinusoidal and anticorrelated. Top and bottom right, the same but for planets b and e. The TTVs of b and e are also sinusoidal and anticorrelated.

Previous works have developed analytic models of TTVs applicable to certain orbital configurations11,12. Other works have developed N-body TTV models based on a dynamical integration of the star–planet system subject to Newtonian gravity (for example, refs. 13,14,15). Analytic models are generally faster to evaluate, have fewer free parameters and offer a clearer connection between the system properties and the TTV waveform. N-body models, with more free parameters, are slower to evaluate but can completely describe any star–planet system.

Most TTV studies in the literature treat planets from the 4-year Kepler mission. These studies had the benefit of near-continuous sampling over a full TTV period. The sparse sampling of our dataset presents different challenges. We, therefore, used analytic models to build our intuition of the system dynamics before undertaking a full N-body analysis. The nature of TTV interactions depends on the proximity to resonance Δ, defined as \(\varDelta =\frac{{P}_{2}}{{P}_{1}}\frac{j-1}{j}-1\), where P_1 and P_2 are the orbital periods of the inner and outer planets, respectively, and j is a positive integer defining the resonance, with smaller Δ associated with larger TTVs. In this system, Δcd = 0.2%, Δdb = −2.7% and Δbe = 0.8%. Given the strength of the c–d and b–e interactions over d–b interactions, we used analytic models to treat the c–d and b–e interactions separately. The analytic models indicated low masses and low eccentricities (Methods).

Guided by our analytic results, we then performed a full N-body dynamical fit to the transit times to derive a final, robust set of planet parameters. This model accounts for all gravitational interactions in the system simultaneously, including subtle, higher-order TTVs that can help break degeneracies inherent in our analytic models. Details of our N-body model, Bayesian statistical framework and Markov chain Monte Carlo sampling are provided in Methods.

Figure 1 shows a selection of credible models drawn from our posterior samples, along with the timing data. The models fit the data well, with increased scatter where observations are sparse. The credible range of each planetary parameter is listed in Table 1. We find masses Mc = 4.7 ± 0.6 M⊕, Md = 6.0 ± 0.7 M⊕, Mb = 13.1 ± 5.3 M⊕ and Me = 15.3 ± 4.2 M⊕ (the uncertainties correspond to the 68% highest density intervals of the marginal posteriors). In addition, the planetary eccentricities are all  less than about 1%. A detailed dynamical analysis (Methods) confirms that this solution corresponds to a long-term stable and non-resonant orbital architecture. The N-body results are consistent with the analytic results at 2σ or better with smaller uncertainties. The N-body model is a more complete description of the planetary dynamics than our analytic models and includes effects like synodic chopping[16] and d–b interactions. It exhibits root mean square (r.m.s.) values of 4–11 min, consistent with our analytic models. Henceforth, we adopt and interpret the N-body results. The masses of planets b and e have substantial fractional uncertainties but are distinct from zero; they are larger than 4.8 M⊕ and 7.8 M⊕ to 95% confidence. The broad uncertainties stem from the well-known mass versus eccentricity degeneracy[11]. Extremely low masses and high eccentricities would produce b–d interactions that are inconsistent with the data.

Table 1 Planetary parameters

The combination of low planet masses with the youth of the host star makes Doppler mass measurements challenging. The expected semi-amplitudes of the radial velocity are  approximately 1–2 m s−1, which are two orders of magnitude smaller than the stellar activity signal. For comparison, the measured r.m.s. values of the radial velocity in existing datasets for V1298 Tau are 260 m s−1, 197 m s−1 and 195 m s−1 for HARPS-N, CARMENES VIS and CARMENES NIR, respectively[7]. The challenge of this high stellar activity has been a central theme in recent studies of the system17,18,19,20. Reference [7] simultaneously modelled planetary and activity radial velocity variations, reporting masses of 203 ± 60 M⊕ and 367 ± 95 M⊕ for planets b and e, an order of magnitude larger than our TTV results. However, ref. [21] found that the planet-activity model is biased towards over-predicting planet masses when stellar activity dominates. Given these challenges and the risk of systematic bias, a TTV-only analysis provides, at present, the most robust and unbiased mass constraints for this system. Notably, our dynamical mass for planet b is consistent with independent atmospheric constraints. A recent analysis of transmission spectra captured by the James Webb Space Telescope by ref. [22] inferred a mass from the atmospheric scale height that is in excellent agreement with our TTV result. That two independent methods—one based on gravitational dynamics and the other on atmospheric structure—yield such consistent results provides a powerful validation of our measurement.

The planetary densities that we measured in the V1298 Tau system are among the lowest exoplanet densities recorded. The only known multi-planet system exhibiting comparably low densities is, perhaps not coincidentally, the young (approximately 300 Myr) transiting system Kepler-51, for which mass measurements were also made through TTVs23,24,25,26,27, although V1298 Tau is significantly younger and more compact. Figure 2 places the V1298 Tau planetary system in the context of the broader, mature exoplanet population. Figure 2a shows these young planets positioned above the radius gap[28]. To trace their future evolution, we overplot the ‘fluffy’ planet scenarios from ref. [29], which are the most relevant analogues. These models bracket a range of possibilities by assuming two different core masses (5 M⊕ and 10 M⊕) and two stellar extreme-ultraviolet activity levels that result in different degrees of atmospheric mass loss. The 5 M⊕ scenario is a particularly strong analogue, as our own interior structure modelling (Methods) constrains the core masses of planets c and d to be 4–6 M⊕ (1σ). The resulting tracks indicate that some planets will contract across the gap to become super-Earths, whereas others will become sub-Neptunes, thus directly tracing the formation of the bimodal radius distribution observed by Kepler. Figure 2b reveals substantial H/He envelopes[30], although their final evolved states may have densities that are degenerate with water worlds[31].

Fig. 2: Planetary radius versus orbital period and planetary mass.

a,b, Planetary radius versus orbital period (a) and planetary radius versus planetary mass (b) for the V1298 Tau system (red filled circles); error bars represent 1σ uncertainties. The low-density planets of the Kepler-51 system are shown for comparison (purple squares), along with kernel density estimates of the distributions of well-characterized exoplanets (shaded contours), drawn from the NASA Exoplanet Archive (n = 624 planets with mass and radius uncertainties less than 20%, P < 150 days and host Teff = 4,500–6,500 K to exclude M dwarfs). The parameters of the Kepler-51 planets were sourced from the ‘outside 2:1’ solution in Table 6 of ref. [27]. Theoretical radius evolution tracks from ref. [29] are shown as vertical dashed lines. The terminal radii at 5 Gyr from that work are shown as open triangles. The colour indicates the assumed core mass (red for 5 M⊕ and black for 10 M⊕). The orientation represents the stellar extreme-ultraviolet activity level (upwards for high activity and downwards for low activity). The black dashed line in a depicts the observed location of the radius valley[28]. Theoretical mass–radius relations for different planet compositions from ref. [31] are shown in b as dashed lines. Grey dotted lines indicate theoretical mass–radius relations for Earth-like cores with H/He envelopes with various mass fractions from ref. [30], calculated for an age of 100 Myr and an insolation of 10 F⊕.

The low masses and densities of the V1298 Tau planets have significant ramifications for planet formation theory. Theoretical modelling indicated that planet c (Mc = 4.7 ± 0.6 M⊕) was one of the best targets for constraining its formation history: a mass higher than 10 M⊕ would be consistent with standard core-accretion models, whereas a mass lower than 6 M⊕ would require a ‘boil-off’ phase during protoplanetary disk dispersal[32]. Such a phase occurs when the pressure support of the disk is removed swiftly, triggering profuse atmospheric mass loss through a Parker wind and rapid cooling, leaving behind an envelope with lower entropy and a longer Kelvin–Helmholtz timescale compared with predictions from standard core-accretion models33,34.

To explore the possible formation channels for the V1298 Tau planets, we modelled the planets as two-layer objects consisting of an Earth composition rocky core ensheathed in a H/He envelope. The initial envelope entropy is parameterized by its Kelvin–Helmholtz contraction timescale. We ran a dense grid of models spanning core mass, initial envelope mass fraction and initial envelope entropy at the location of each planet in the system and evolved them to the current age of the system.

Figure 3 shows posterior distributions for the initial properties of all four planets, providing a deeper insight into the system architecture. The right panel confirms that the inner planets c and d require low-entropy initial states (much greater than 30 Myr Kelvin–Helmholtz cooling times), whereas the less-irradiated outer planets b and e remain unconstrained. The left panel, however, reveals a notable uniformity: all four planets are consistent with having similar core masses (approximately 4–6 M⊕) and initial envelope mass fractions (approximately 0.1–0.2). This indicates that the system is an exemplar of the ‘peas in a pod’ phenomenon at formation[35], implying its present-day size diversity is a transitory phase driven by different levels of photoevaporation.

Fig. 3: Posterior distributions for the initial properties of the V1298 Tau planets.

The posteriors were derived by applying the planetary evolution and mass loss framework of ref. [32] to our measured masses and radii for planets c (red), d (orange), b (green) and e (blue). Left, initial envelope mass fraction versus core mass. Right, initial Kelvin–Helmholtz cooling timescale versus core mass. Contours show the 1σ and 2σ credible regions. (Note that the jagged appearance of some contours is a numerical artefact of the discrete core mass grid used in our analysis; see Methods for more details). The vertical dotted line in the right panel at 10 Myr marks the approximate upper limit for standard high-entropy formation models. These models are strongly disfavoured for the inner planets c and d, whereas for the less-irradiated outer planets b and e, the method lacks the statistical power to distinguish between high- and low-entropy scenarios.

Extended Data Fig. 1 shows that the measured masses and radii of planets c and d lie outside the region of parameter space accessible to standard, high-entropy core-accretion models. As the illustrative tracks in the figure demonstrate, only lower-entropy (boil-off) models can simultaneously satisfy both the mass and radius constraints after accounting for 23 Myr of evolution and mass loss. During boil-off, the planetary envelope becomes over-pressurized and expands hydrodynamically, carrying away significant internal energy and leaving behind a cooler interior33,34,36. Although our measurements support boil-off for the inner planets, recent atmospheric retrievals indicating a high internal temperature for planet b[37] present a possible tension that merits further investigation.

Theoretical modelling of the system under the influence of extreme-ultraviolet- and X-ray-driven photoevaporation indicates that these planets will continue to lose mass over the next 100 million years8,29, even though they have already experienced significant atmospheric loss. For our measured masses, standard evolutionary models predict that all the planets will retain a small fraction of their initial atmospheres, although the inner two could become stripped, depending on the future spin evolution of the star[29]. Interestingly, observational searches for continuing atmospheric escape have so far yielded inconclusive results5,38,39,40, possibly because strong stellar winds act to suppress planetary outflows41,42,43.

Methods

Transit observations and analysis

We analysed a heterogeneous dataset of light curves from space- and ground-based telescopes (Supplementary Table 1) to measure transit times for the ultimate purpose of modelling TTVs. We used PyMC3[44], exoplanet (https://docs.exoplanet.codes/en/stable/)[45] and starry[46] to fit the light curve, incorporating tailored models for correlated noise and instrumental systematics appropriate for each dataset.

Our analysis of the K2 and Transiting Exoplanet Survey Satellite (TESS) light curves involved two distinct approaches with different noise models. For the joint analysis of all transits in both light curves (described below), we modelled stellar variability as a Gaussian process[47]. By contrast, for measuring individual transit times (see below), a third-order basis spline was sufficient to model the local correlated noise.

To account for systematics in the Spitzer data, we used pixel-level decorrelation (PLD)[48], which uses a linear model with a design matrix formed by the PLD basis vectors (see below for more details). For the ground-based datasets, we included a linear model with a design matrix formed by airmass, pixel centroids, and the pixel response function peak and width covariates, when available.

The limb-darkening coefficients were calculated using stellar parameters from ref. [2] by interpolation of the parameters tabulated by refs. 49,50. These were fixed for individual transit fits but sampled with uninformative priors in the joint K2 and TESS analysis described below.

We used Broyden–Fletcher–Goldfarb–Shanno optimization[51] as implemented in scipy.optimize for initial parameter estimates, followed by posterior sampling with the No-U-Turn Sampler[52], an efficient gradient-based Hamiltonian Monte Carlo sampler implemented in PyMC3. The chains were well mixed (Gelman–Rubin statistic  less than about  1.01) with negligible sampling error.

We first performed a joint fit of K2 and TESS data assuming a linear ephemeris (see below). We then measured all individual transit times uniformly using Gaussian priors from the joint fit for Rp/R⋆, b (the transit impact parameter), and T_14 (the total transit duration), and uniform priors for Tc (the transit centre time) centred on predicted times. We verified that Tc posteriors were Gaussian and isolated well from prior edges.

In all individual transit fits, we assumed Gaussian independent and identically distributed noise and included a jitter parameter σjit to account for underestimated photometric uncertainties. The log-likelihood was, thus,

$$\text{ln}{\mathcal{L}}=-\frac{1}{2}\text{ln}| \varSigma | -\frac{1}{2}{{\bf{r}}}^{{\rm{T}}}{\varSigma }^{-1}{\bf{r}}+{\rm{const.}},$$

where Σ is the diagonal covariance matrix with entries equal to the total variance (that is, the ith entry is \({\sigma }_{{\rm{tot,}}i}^{2}={\sigma }_{{\rm{obs,}}i}^{2}+{\sigma }_{{\rm{jit}}}^{2}\), where σobs,i is the observational uncertainty of the ith data point), and r is the residual vector (\({\bf{r}}=[{\widehat{y}}_{1}-{y}_{1},{\widehat{y}}_{2}-{y}_{2},\ldots ,{\widehat{y}}_{n}-{y}_{n}]\), where \(\widehat{y}\) is the model and y is the data consisting of n measurements). When a given transit event was observed by several telescopes (for example, at Las Cumbres Observatory (LCO))[53], or several band-passes from the same instrument (for example, from MuSCAT3)[54], we jointly fitted all light curves covering the same event.

We obtained ground-based follow-up transit observations from a variety of facilities spanning several observing seasons. Early in the project, observations were distributed diversely among a half-dozen telescopes, but later we focused almost exclusively on the LCO telescope network, which enabled both the acquisition of data and its analysis to be conducted more uniformly. The individual dates, facilities, band-passes and exposure times of these observations are listed in chronological order in Supplementary Table 1. The measured transit times are provided in Supplementary Table 2.

Joint analysis of the K2 and TESS light curves

V1298 Tau (EPIC 210818897) was observed between 7 February and 23 April 2015 during campaign 4 of the K2 mission[9]. We analysed the K2 light curve produced by the EVEREST pipeline (https://github.com/rodluger/everest)55,56, which is available at the Mikulski Archive for Space Telescopes (MAST) (https://archive.stsci.edu/hlsp/everest).

V1298 Tau (TIC 15756231) was observed at 2-min cadence in Sectors 43 and 44 (16 September to 6 November 2021) of the TESS mission[57] as part of the Director’s Discretionary Time (DDT) programme 036 (PI T. David).

We conducted a joint fit to the K2 and TESS light curves assuming a linear ephemeris, using a Gaussian process to account for correlated noise arising from a combination of stellar variability and instrumental systematics (based on the tutorial available at https://gallery.exoplanet.codes/tutorials/lc-multi/). We used a simple-harmonic-oscillator covariance function with a power spectral density given by:

$$S(\omega )=\sqrt{\frac{2}{{\rm{\pi }}}}\frac{{S}_{0}{\omega }_{0}^{4}}{{({\omega }^{2}-{\omega }_{0}^{2})}^{2}+{\omega }_{0}^{2}{\omega }^{2}/{Q}^{2}},$$

where ω is the angular frequency, ω_0 is the undamped angular frequency of the oscillator and S_0 is a scale factor that sets the amplitude of the variability. This was re-parameterized by the undamped period of the oscillator ρ (defined as ρ = 2π/ω_0), the standard deviation of the process σ (defined as \(\sigma =\sqrt{{S}_{0}{\omega }_{0}Q}\)) and the quality factor Q (fixed to 1/3). Like our model for individual transits, we included a photometric jitter term (σjit), the square of which was added to the diagonal of the covariance matrix. The likelihood was, thus, identical to that shown for individual transits above, but the covariance matrix contained non-zero off-diagonal elements determined by the covariance function. The results of this fit are shown in Extended Data Fig. 2 and the posteriors are summarized in Extended Data Table 1.

Individual K2 and TESS transits

To create a uniform transit-timing dataset, we analysed individual transits from the long-baseline K2 and TESS light curves in the same manner as our short-duration follow-up observations. We constructed individual datasets from windows of three times the transit duration centred on each transit event. When there were overlapping transits, we used the longest transit duration and centred the window on the approximate midpoint of the dimming event. Unlike follow-up datasets, which are often partial transits, stellar variability is typically nonlinear on the timescale of these datasets. To account for this, we included a third-order basis spline with five evenly spaced knots. The transits of V1298 Tau c on 10 and 26 October 2021 ut resulted in poor-quality fits, probably due to the presence of short-timescale red noise close to ingress or egress or a low signal-to-noise ratio; as the timing posteriors from these fits were highly non-Gaussian, we discarded them from subsequent analyses.

Spitzer

We used the ephemeris derived from the K2 observations[2] to predict transits of V1298 Tau b within Spitzer visibility windows in 2019. Subsequently, we did the same for V1298 Tau c,d using the ephemerides from ref. [3]. Another transit of V1298 Tau b was scheduled in early 2020 using an updated ephemeris based on ref. [2] and the first Spitzer observation of that planet. The Spitzer data and best-fitting transit models are shown in Extended Data Fig. 3.

The first epoch of Spitzer observations of V1298 Tau were acquired as part of the DDT programme 14227 (PI E. Mamajek) and executed on 1 June 2019 ut. The second epoch of Spitzer observations were acquired as part of the target of opportunity programme 14011 (PI E. Newton) and executed on 28 December 2019 ut. In both epochs, data were acquired with channel 2 of the infrared array camera (IRAC) onboard Spitzer (with effective wavelength λeff = 4.5 μm) in the subarray mode using 2-s exposures. A third epoch of Spitzer observations were acquired in IRAC channel 1 (λeff = 3.6 μm) as part of DDT 14276 (PI K. Todorov) and executed on 4 January 2020 ut.

We extracted photometry following ref. [58] and modelled the instrumental systematics using PLD, which combines normalized pixel light curves as basis vectors in a linear model:

$${M}_{{\rm{PLD}}}^{t}({\boldsymbol{\alpha }})=\frac{{\sum }_{i=1}^{9}{c}_{i}{P}_{i}^{t}}{{\sum }_{i=1}^{9}{P}_{i}^{t}},$$

where Pi is the ith pixel light curve, the superscript t denotes the value at a specific time step, and α = {c_1, …, c_9} are the coefficients of the PLD basis vectors. The first epoch, which captured a partial transit of V1298 Tau b over approximately 11.5 h, was fitted well by including a linear trend in addition to PLD. The second epoch, which contained transits of both planets c and d over an approximately 14-h baseline, exhibited significant nonlinear variability that required the inclusion of a basis spline. Similarly, the third epoch, containing a full transit of planet b over approximately 12.5 h, also warranted the inclusion of a basis spline; although IRAC1 systematics are typically larger than those of IRAC2, PLD performed well and we attribute this to stellar variability. We validated our approach of selecting the baseline model by inspecting the fit residuals for the longest and most complex observation (the second epoch). A quantitative comparison confirmed that a basis spline was strongly preferred over a simple linear trend by the Bayesian information criterion[59].

Ground-based observations

Most of our follow-up transit observations were obtained from 2020 to 2024 using LCO. We primarily used the Sinistro[53] and MuSCAT3 instruments on the 1-m and 2-m telescopes, respectively.

In addition to LCO, we used data from a variety of other facilities, including Apache Point Observatory (APO)/Astrophysical Research Consortium Telescope Imaging Camera (ARCTIC)[60], Fred Lawrence Whipple Observatory/KeplerCam[61], WIYN/half degree imager[62], Three-hundred MilliMeter Telescope[63], MuSCAT[64], MuSCAT2[65] and Araki/ADLER. Data were obtained using a variety of filters and reduced using standard pipelines and methods66,67,68,69,70,71. See Supplementary Information for more details.

Recovering planet e

The outermost planet, V1298 Tau e, transited only once during the K2 mission. TESS recovered transits of all four planets, including a second transit of planet e[10]. It was not clear how many transits occurred between the K2 and TESS observations given the 6.5-year gap between the two campaigns. Thus, a discrete comb of periods was allowed, such that P = Δt/n, where Δt is the measured time between transit midpoints and the integer \(n=1,2,3,\ldots ,{n}_{\max }\). The upper bound on n, and thus, the lower bound on a period of 42.7 days, was provided by the absence of other transits by planet e within the K2 and TESS time series[10].

By the summer of 2022, a preliminary version of our timing dataset had revealed large TTVs of planet b that we assumed were dominated by interactions with planet e. We ran a suite of TTV models at each of the possible Pe between 42.7 days and 120 days. Few trial periods yielded good fits to the timing dataset, and dynamical simulations revealed that only a fraction of those were stable over \({\mathcal{O}}(1{0}^{6})\) years. One of the stable solutions with Pe = 48.7 days corresponded to a near 2:1 commensurability for the b–e pair, a common configuration among the Kepler planets exhibiting large and detectable TTVs. With this prediction, we recovered a partial transit of planet e from the ground on 18 October 2022. LCO datasets used to recover planet e and confirm its orbital period are shown in Extended Data Fig. 4.

Datasets containing flares

Several observations were affected by stellar flares and were excluded from our TTV analyses to avoid potential timing measurement biases. These datasets were modelled using our standard approach, augmented with a parametric flare model[72] (Extended Data Fig. 5). Significant flares were observed in ARCTIC data (12 October 2020; see also ref. [38]), KeplerCam data (24 September 2023) and LCO data (18 December 2023), with amplitudes ranging from 6 parts per thousand (ppt) to 42 ppt and timescales of 14 min to 21 min. The parameters of these flares are detailed in the Supplementary Information, and they may prove valuable for future studies of the activity of V1298 Tau.

Mass constraints from analytic TTV modelling

To build intuition for the system dynamics, we first performed a preliminary analysis using analytic models of TTVs. Based on the foundational analytic frameworks for TTVs11,12,73, we determined that the system dynamics can be effectively decoupled into two pairs of planets: c–d and b–e.

To quantify the TTV behaviour, we fitted a multi-harmonic sinusoidal model to the transit time series (see Supplementary Information for the model equations). We explored the posterior distributions of the 16 model parameters using a Markov chain Monte Carlo sampler, like approaches used by other public TTV analysis codes74,75. The posteriors of these parameters are listed in Supplementary Table 3, and the model fits are shown in Extended Data Fig. 6.

The results for the c–d pair are consistent with the planets being in a near-resonant regime. The TTVs are described well by a single sinusoid with a period Pcd = 1,604 ± 12 days and an r.m.s. of the residuals of only 11 min. This sinusoidal signal is dominated by variations in the planetary mean longitudes (λ), characteristic of systems very close to resonance. The ratio of the TTV amplitudes is sensitive to the planetary mass ratio, indicating nearly equal masses (Md/Mc ≈ 1.2). From the full fit, we derived preliminary masses \({M}_{{\rm{c}}}\approx {2.7}_{-0.8}^{+1.7}\) M⊕ and \({M}_{{\rm{d}}}\approx {3.2}_{-1.0}^{+2.1}\) M⊕.

By contrast, the b–e pair is described well by a simpler, linear TTV model, as it is further from resonance. The TTVs arising from variations in mean longitude and eccentricity have the same frequency in this regime. In this case, a well-known degeneracy exists between the planet masses and their orbital eccentricities16,76, leading to broader initial constraints of \({M}_{{\rm{b}}}=3{1}_{-17}^{+14}\) M⊕ and \({M}_{{\rm{e}}}=2{4}_{-8}^{+4}\) M⊕. A full theoretical treatment and a discussion of strategies for breaking the mass–eccentricity degeneracy, such as measuring secondary eclipse times[77], can be found in the Supplementary Information.

Mass constraints from N-body TTV modelling

Guided by our analytic models, our primary analysis relies on a full N-body dynamical model to derive the final planet parameters. We fitted the model to the observed transit times using a Bayesian framework. To be robust against outlier measurements, we adopted a log-likelihood function based on Student’s t-distribution78,79, with priors as listed in Extended Data Table 2. The posterior probability distribution was sampled using the No-U-Turn Sampler80,81. The model is implemented in JAX to enable automatic differentiation and is available as part of the jnkepler package27,82. The full mathematical details of the model implementation, the log-likelihood equation and the sampler set-up are provided in the Supplementary Information. The resulting mass and eccentricity posterior distributions are shown in Extended Data Fig. 7.

To verify the physical plausibility of our solution, we performed a detailed dynamical analysis of the posterior. We investigated both the long-term stability and the resonant state of the system using several complementary methods. First, to assess stability, we used a probabilistic classifier (SPOCK)[83] on 1,000 samples from our posterior, which yielded a median stability probability of 95% over 10^9 orbits. We confirmed this with direct N-body integrations of 128 samples for 1 Myr, which showed that the system is deeply stable and regular (minimum separation over 12RH (mutual Hill radii), maximum semimajor axis drift of less than 0.01%, and MEGNO (Mean Exponential Growth factor of Nearby Orbits) = 2.000). As a final check, we integrated 32 posterior samples for 4 Myr, all of which were found to be stable. Second, to characterize the resonant state, our integrations show that all classical resonant angles are circulating, which we confirmed by projecting our solution onto the resonant representative plane[12]. The solution lies clearly outside the resonant island where libration would occur, confirming the non-resonant nature of the system.

Initial thermal state and planetary evolution

Young planets with hydrogen-dominated atmospheres contract over time due to mass loss and thermal evolution. Reference [32] showed that young planets with measured masses and radii can be used to constrain their initial entropies. Planets with a measured mass and radius have a degeneracy between their hydrogen envelope mass fraction and their thermal state. The hydrogen envelope mass of the planet can be reduced and compensated for by an increase in its entropy. However, this can only go so far; the envelope mass cannot be reduced arbitrarily to the point where it is too small to survive mass loss. Thus, one can place a bound on the initial entropy of the planet such that it survives until today. To perform this calculation, we computed a grid of MESA evolutionary models that include photoevaporation (a comparison with ref. [84] indicates that these planets will be undergoing photoevaporation rather than core-powered mass loss). This model grid comprised 36 core masses, 128 initial mass fractions and 96 initial entropies. We used an identical method to that in ref. [32]. We then compared this model grid with the observed masses and radii of the V1298 Tau planets to derive posterior distributions of the core masses, initial envelope mass fractions and initial entropies (which we encode as the initial Kelvin–Helmholtz cooling timescale of the planets). Our results indicate that all the planets had initial envelope mass fractions and core masses that are consistent with typical sub-Neptunes at billion year ages. Furthermore, the initial cooling timescales are constrained to require boil-off for planets c and d, whereas an evolution without boil-off cannot be ruled out for the outer planets. Extended Data Fig. 8 shows the models that best reproduce the present-day masses and radii of planets c and d. Interestingly, they require an initial low entropy; that is, an initial Kelvin–Helmholtz contraction time that is longer than the age of the system. Furthermore, if one considers only models with a high initial entropy, one can match the current mass or radius, but not both.

Data availability

The transit times that form the primary dataset for this study are provided in Supplementary Table 2. K2 and TESS photometry are publicly available at MAST (https://archive.stsci.edu). Images from the Spitzer Space Telescope are available at the Spitzer Heritage Archive (https://sha.ipac.caltech.edu), and images from LCO are available at the LCO Science Archive (https://archive.lco.global). All archival data can be found by searching for the stellar identifier V1298 Tau. Photometry from Spitzer, LCO and other ground-based observations is available from the corresponding authors upon reasonable request.

Code availability

The transit light-curve analyses were conducted using the exoplanet package available at GitHub (https://github.com/exoplanet-dev/exoplanet). The stability analysis used the SPOCK package, which is also publicly available at GitHub (https://github.com/dtamayo/spock). The N-body TTV analysis was performed using the publicly available jnkepler package at GitHub (https://github.com/kemasuda/jnkepler), which includes an example notebook demonstrating the analysis for this system upon publication.

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Acknowledgements

This work is supported by JSPS (KAKENHI Grant Nos. JP24H00017, JP24H00248, JP24K00689, JP24K17082, JP24K17083, JP25KJ0091 and JP25K17450), a JSPS Grant-in-Aid for JSPS Fellows (Grant No. JP24KJ0241) and the JSPS Bilateral Program (JPJSBP120249910). This work is based in part on observations made with the Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory (JPL), California Institute of Technology (Caltech) under a contract with NASA. Support for this work was provided by NASA through an award issued by JPL, Caltech. This paper includes data collected by the Kepler mission and obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the Kepler mission is provided by the NASA Science Mission Directorate. STScI is operated by the Association of Universities for Research in Astronomy (AURA) under NASA contract NAS 5-26555. This paper includes data collected with the TESS mission, also obtained from the MAST data archive. Funding for the TESS mission is provided by the NASA Explorer Program. These results are based on observations obtained with the 3.5-m telescope at APO. This telescope is owned and operated by the Astrophysical Research Consortium. We wish to thank the APO 3.5-m telescope operators for their assistance in obtaining these data. This Article is based in part on observations made at the Kitt Peak National Observatory, NSF’s NOIRLab, managed by AURA under a cooperative agreement with the National Science Foundation (NSF). The WIYN 0.9-m telescope is operated by WIYN Inc. on behalf of a consortium of ten partner universities and organizations. WIYN is a joint facility of the University of Wisconsin–Madison, Indiana University, NSF’s NOIRLab, the Pennsylvania State University, Purdue University, the University of California, Irvine, and the University of Missouri. We are honoured to be permitted to conduct astronomical research on Iolkam Du’ag (Kitt Peak), a mountain with particular significance to the Tohono O’odham. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among Caltech, the University of California and NASA. The observatory was made possible by the generous financial support of the W. M. Keck Foundation. We wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the Indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This work makes use of observations from the LCO Global Telescope Network. This paper is based on observations made with the MuSCAT3 instrument, which was developed by the Astrobiology Center and is financially supported by JSPS (KAKENHI Grant No. JP18H05439) and JST PRESTO (JPMJPR1775), at Faulkes Telescope North on Maui, HI, operated by LCO. The research was carried out at JPL, Caltech, under a contract with NASA (80NM0018D0004). C.C. acknowledges support from NASA Headquarters through an appointment to the NASA Postdoctoral Program at the Goddard Space Flight Center, administered by ORAU through a contract with NASA. E.A.P. acknowledges support from Heising-Simons Foundation grant 2022-3833.

Author information

Authors and Affiliations

1. Astrobiology Center, Mitaka, Japan

John H. Livingston, Mayuko Mori & Motohide Tamura

2. National Astronomical Observatory of Japan, Mitaka, Japan

John H. Livingston, Mayuko Mori & Motohide Tamura

3. Department of Astronomical Science, The Graduate University for Advanced Studies, Hayama, Japan

John H. Livingston

4. Department of Physics & Astronomy, University of California Los Angeles, Los Angeles, CA, USA

Erik A. Petigura

5. Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA

Trevor J. David

6. Department of Earth and Space Science, Osaka University, Osaka, Japan

Kento Masuda

7. Astrophysics Group, Department of Physics, Imperial College London, London, UK

James Owen

8. Department of Earth, Planetary, and Space Sciences, UCLA, Los Angeles, CA, USA

James Owen

9. Department of Space Studies, Southwest Research Institute, Boulder, CO, USA

David Nesvorný

10. Division of Geological and Planetary Sciences, Caltech, Pasadena, CA, USA

Konstantin Batygin & Lynne A. Hillenbrand

11. Komaba Institute for Science, University of Tokyo, Tokyo, Japan

Jerome de Leon, Kai Ikuta, Akihiko Fukui, Noriharu Watanabe & Norio Narita

12. Instituto de Astrofísica de Canarias, La Laguna, Spain

Jaume Orell Miquel, Felipe Murgas, Hannu Parviainen, Florence Libotte, Néstor Abreu García, Pedro Pablo Meni Gallardo & Enric Pallé

13. Observatoire astronomique de l’Université de Genève, Versoix, Switzerland

Judith Korth

14. Sabadell Astronomical Society, Sabadell, Spain

Florence Libotte, Néstor Abreu García & Pedro Pablo Meni Gallardo

15. Department of Astronomy, University of Tokyo, Tokyo, Japan

Motohide Tamura

16. Department of Astrophysics and Atmospheric Sciences, Kyoto Sangyo University, Kyoto, Japan

Atsunori Yonehara

17. Las Cumbres Observatory Global Telescope Network, Goleta, CA, USA

Andrew Ridden-Harper

18. Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA

Allyson Bieryla

19. The Niels Bohr International Academy, Niels Bohr Institute, Copenhagen, Denmark

Alessandro A. Trani

20. National Institute for Nuclear Physics, Trieste, Italy

Alessandro A. Trani

21. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

Eric E. Mamajek & Varoujan Gorjian

22. NASA Exoplanet Science Institute, IPAC, Pasadena, CA, USA

David R. Ciardi & Luisa M. Rebull

23. Department of Physics and Astronomy, Dartmouth College, Hanover, NH, USA

Elisabeth R. Newton

24. Department of Physics and Astronomy, UNC Chapel Hill, Chapel Hill, NC, USA

Andrew W. Mann

25. Department of Physics, Kavli Institute for Astrophysics and Space Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Andrew Vanderburg

26. Anton Pannekoek Institute for Astronomy, University of Amsterdam, Amsterdam, The Netherlands

Guðmundur Stefánsson, Kamen Todorov & Jean-Michel Désert

27. Department of Astronomy & Astrophysics, The Pennsylvania State University, University Park, PA, USA

Suvrath Mahadevan

28. Center for Exoplanets and Habitable Worlds, The Pennsylvania State University, University Park, PA, USA

Suvrath Mahadevan

29. NASA Goddard Space Flight Center, Greenbelt, MD, USA

Caleb Cañas

30. Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Mumbai, India

Joe Ninan

31. NSF National Optical-Infrared Astronomy Research Laboratory, Tucson, AZ, USA

Jesus Higuera

32. INAF-Osservatorio Astrofisico di Arcetri, Florence, Italy

Lorenzo Pino

Contributions

J.H.L. planned and executed the observations, modelled the light curves and performed the dynamical simulations. E.A.P., J.H.L. and K.M. analysed the TTVs. J.H.L., T.J.D. and E.A.P. led the writing of the paper. J.O. performed the theoretical atmospheric evolution calculations. D.N., K.B. and A.A.T. conducted the dynamical analyses. J.d.L., M.M., K.I., N.W., J.O.M., F.M., H.P., J.K., F.L., N.A.G., P.P.M.G. and E.P. conducted observations with MuSCAT and MuSCAT2. N.N., A.F. and M.T. contributed LCO guaranteed time observation, with which J.H.L. conducted the MuSCAT3 observations. A.Y. contributed Araki/ADLER observations. A.R.-H. contributed LCO observations. A.B. contributed KeplerCam observations. E.E.M., D.R.C., V.G., L.A.H., L.M.R., E.R.N., A.W.M., A.V., K.T., J.-M.D. and L.P. contributed Spitzer observations. G.S. and S.M. contributed APO observations. G.S., S.M., C.C., J.N. and J.H. contributed half degree imager observations. J.d.L. performed the photometric extractions of the MuSCAT, MuSCAT2 and MuSCAT3 data. G.S. performed the photometric extractions of the APO, half degree imager and Three-hundred MilliMeter Telescope data. All authors reviewed and approved the final paper.

Corresponding authors

Correspondence to John H. Livingston, Erik A. Petigura or Trevor J. David.

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Extended data figures and tables

Extended Data Fig. 1 Mass-radius diagram for V1298 Tau c (left) and d (right) at the system’s age of 23 Myr.

The gray shaded region marks the boundary of parameter space accessible by standard, high-entropy formation models (initial Kelvin-Helmholtz cooling timescale τKH ≲ 10 Myr), with the 1-σ and 2-σ uncertainty on its location shown in dark and light gray. Illustrative model tracks are shown for comparison: the green and orange lines demonstrate that high-entropy models fail to simultaneously match the measured mass and radius, while the blue line shows a successful low-entropy model (τKH ~ 300 Myr) consistent with the data. The positions of planets c and d firmly in the lower-right region provide direct evidence that their formation required a low-entropy initial state, such as that produced by a ‘boil-off’ phase.

Extended Data Fig. 2 K2 and TESS data with models from the joint fit.

The top two rows show the variability-corrected K2 light curves centered on the transit of each planet with the best-fit transit model over-plotted, with the residuals from the fit shown below; dark error bars show the measured photometric uncertainties, while the lighter error bars denote the error bars including the jitter value from the fit. The bottom two rows show the same, but for the TESS data.

Extended Data Fig. 3 Spitzer observations used in this work with best-fit transit models.

The photometry has been corrected for stellar variability and systematics.

Extended Data Fig. 4 Recovery of V1298 Tau e from ground-based transit observations.

The top left panel shows the partial transit recovered on 2022 October 18, which resolved the period ambiguity. The remaining panels show additional follow-up observations.

Extended Data Fig. 5 Datasets containing flares: a transit of planet c from ARCTIC (left); a transit of planet e from KeplerCam (middle); a transit of planet d from LCO (right).

The residuals from the fit to the KeplerCam data have a feature that may be due to a smaller, secondary flare.

Extended Data Fig. 6 Multi-harmonic model of V1298 Tau TTVs described in Methods.

The top left panel shows the measured deviations of planet c’s transit times from the best-fitting linear ephemeris. The red lines show draws from credible models. The second panel shows the difference between measured times and the model predictions. Other panels show the same quantities for planets d, b and e. The TTVs of planets c and d are dominated by interactions between the two planets. They are well-described by a sinusoid with an amplitude of ~100 min and a period of Pcd = 1604 ± 12 days. The TTVs of planets b and e are dominated by interactions between the two planets. Here, the TTVs are well-described by a sinusoid with an amplitude of ~50 min and a period of 2852 ± 50 days.

Extended Data Fig. 7 Mass and eccentricity posterior distributions.

Left: joint and marginal posterior distributions of mass and eccentricity for planets c and d. Right: same as left but for planets b and e. The joint posterior panels show 1- and 2-σ contours for each planet.

Extended Data Fig. 8 Planetary evolution models for V1298 Tau c and d.

Left: plausible and implausible mass/radius evolution tracks of planet c. We ran a grid of planetary models with different masses, initial envelope fractions, and envelope entropies. The black line best matches the present-day mass and radius together, but requires low entropy. If we require high entropy (orange curve) we may match the radius, cannot match the mass. Similarly, we may match mass (blue curve), but cannot match the radius. Therefore a self-consistent formation model requires a low initial entropy. The planet evolves into a sub-Neptune of ≈2.5R⊕. Right: same as left, but for planet d, which matures into a slightly larger sub-Neptune.

Extended Data Table 1 Results of K2 -TESS joint modeling

Extended Data Table 2 Results of N-body TTV modeling

Supplementary information

Supplementary Information

A single PDF containing supplementary notes, including details about observations and TTV analyses, as well as Tables 1 and 3.

Supplementary Table 2

Transit-timing measurements. A machine-readable table (CSV format) containing the primary dataset used in this work, consisting of the measured transit times and their 1σ uncertainties for planets V1298 Tau b, c, d and e.

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Livingston, J.H., Petigura, E.A., David, T.J. et al. A young progenitor for the most common planetary systems in the Galaxy. Nature 649, 310–314 (2026). https://doi.org/10.1038/s41586-025-09840-z

- Received: 12 March 2025

- Accepted: 31 October 2025

- Published: 07 January 2026

- Version of record: 07 January 2026

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09840-z

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Surface optimization governs the local design of physical networks

Xiangyi Meng,

Benjamin Piazza,

Csaba Both,

Baruch Barzel &

…

Albert-László Barabási

Nature volume 649, pages 315–322 (2026)

Abstract

The brain’s connectome1,2,3 and the vascular system[4] are examples of physical networks whose tangible nature influences their structure, layout and, ultimately, their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organization. Here we empirically explore the local branching geometry of a wide range of physical networks, uncovering systematic violations of the long-standing predictions of wiring minimization. This leads to the hypothesis that predicting the true material cost of physical networks requires us to account for their full three-dimensional geometry, resulting in a largely intractable optimization problem. We discover, however, an exact mapping of surface minimization onto high-dimensional Feynman diagrams in string theory5,6,7, predicting that, with increasing link thickness, a locally tree-like network undergoes a transition into configurations that can no longer be explained by length minimization. Specifically, surface minimization predicts the emergence of trifurcations and branching angles in excellent agreement with the local tree organization of physical networks across a wide range of application domains. Finally, we predict the existence of stable orthogonal sprouts, which are not only prevalent in real networks but also play a key functional role, improving synapse formation in the brain and nutrient access in plants and fungi.

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Main

The vascular system and the brain are examples of physical networks that differ from the networks typically studied in network science owing to the tangible nature of their nodes and links, which are made of material resources and constrain their layout. The importance of these material factors has been noted in many disciplines: as early as 1899, Ramón y Cajal suggested that we must consider the laws conserving the ‘wire’ volume to explain neuronal design[8] and in 1926, Cecil D. Murray applied volume minimization principles to vascular networks, deriving the branching principles known as Murray’s law[9]. Today, wiring optimization is used to account for the morphology and the layout of a wide range of physical systems10,11, from the distributions of neuronal branch sizes[12] and lengths[13] to the morphology of plants[14], the structure[15] and flow[16] in transport networks, the layout of supply networks[17], the wiring of the Internet[18] or the shape of inter-nest trails built by Argentine ants[19] and the design of 3D-printed tissues with functional vasculature[20].

The premise of wiring economy approaches is the optimal wiring hypothesis, which conceptualizes physical networks as a set of connected one-dimensional wires whose total length is minimized21,22,23. The optimal wiring in this case is exactly predicted by the Steiner graph24,25,26,27. However, the lack of high-quality data on physical networks has limited the systematic testing of the Steiner predictions to single neuron branches[28] and ant tunnels[19] and offered at best mixed evidence of their validity28,29. Yet, data availability has substantially improved in the past few years, thanks to advances in microscopy and three-dimensional reconstruction techniques, offering access to the detailed three-dimensional structure of physical networks ranging from high-resolution layouts of brain connectomes1,2,3 to vascular networks[4] or the structure of coral trees[30]. Here we take advantage of these experimental advances to explore in a quantitative manner the role of wiring optimization in shaping the local morphology of physical networks. We begin by documenting systematic deviations from both the Steiner predictions[24] and volume optimization9,28,29, failures that we show to be rooted in the hypothesis that approximates the cost of physical networks as the sum of their link lengths21,22,23 or as simple cylinders28,29. Indeed, the links of real physical networks are inherently three-dimensional, prompting us to suggest that their true material cost must also consider surface constraints. Building on previous analyses that introduced volumetric constraints9,28,29, here we successfully account for the local surface morphology, ensuring that, when links intersect, they morph together continuously and smoothly, free of singularities, as dictated by the physicality of their material structure. To achieve this, we map the local tree structure of physical networks into two-dimensional manifolds, arriving at a numerically intractable surface and volume minimization problem. We discover, however, a formal mapping between surface minimization and high-dimensional Feynman diagrams, which allows us to take advantage of a well-developed string-theoretical toolset5,6,7 to predict the basic characteristics of minimal surfaces. We find that surface minimization can not only account for the empirically observed discrepancies from the Steiner predictions but offers testable predictions on the degree distribution and the angle asymmetry of physical networks, which we can falsify, offering a crucial window into the design principles of physical networks.

Steiner graphs

The Steiner graph problem[24] begins with M spatially distributed nodes (Fig. 1a), with the task of connecting these nodes through the shortest possible links. The key insight of the Steiner solution is that, by adding intermediate nodes to serve as branching points (Fig. 1b), the obtained link length can be shorter than any attempt to connect the nodes directly[24] (Fig. 1a). Although for arbitrary M the Steiner problem is NP-hard, for M = 4, we can get an exact solution, resulting in a globally optimal Steiner graph that is characterized by three strict local rules (Fig. 1b). (1) Bifurcation only. All branching instances represent bifurcations, in which a single link splits into two daughter links. Consequently, all intermediate nodes have degree k = 3 and higher-degree nodes (k > 3) are forbidden. (2) Planarity. At a bifurcation, all three links are embedded in the same plane (Ω = 2π). (3) Angle symmetry. All three branches of a bifurcation form the same angle θ = 2π/3 with each other.

Fig. 1: Real physical networks versus length and volume optimization predictions.

a, Physical networks aim to connect spatially distributed nodes (coloured) with physical links in three dimensions. If we connect nodes directly, the wiring cost (total link length) is about 26.1. b, The Steiner graph minimizes the wire length by permitting intermediate nodes (green), resulting in the total wire length of approximately 22.0. The Steiner graph offers three predictions. Rule 1: all branching instances are bifurcations with degree k = 3. Rule 2: bifurcations are all planar, having a solid angle of Ω = 2π. Rule 3: the angles between adjacent links are θ = 2π/3. Volume optimization, which generalizes links as simple cylinders of varying thickness, preserves rules 1 and 2 and predicts a broader distribution for θ, peaked around 2π/3. c, A neuron of the human connectome, demonstrating the violations of the Steiner rules. In the top inset, we highlight a trifurcation (k = 4) violating rule 1. We also highlight a non-symmetric branching angle, in which links sprout out perpendicularly (right inset), breaking rule 3. d, The percentage of k = 4 nodes across our six empirical locally tree-like physical networks. We observe roughly 15% of the nodes violating Steiner rule 1. e, The probability density P(Ω) versus Ω as obtained from all bifurcations (k = 3) in our empirical network ensemble (coloured solid lines). The observed density functions are more prone to Steiner rule 2 (thin grey line) than to random branching without optimization (thick grey line). f, The probability density P(θ) versus θ as obtained from all bifurcations (coloured solid lines). Once again, we observe a clear discrepancy from Steiner (thin grey line) and a tendency towards random branching (thick grey line) or volume optimization of cylindrical links with random thickness (dashed grey line).

To test the validity of the local predictions of the Steiner solution, we collected three-dimensional resolved data of six classes of physical networks (Supplementary information Section 1): (1) human neurons[1] (also in Fig. 1c); (2) fruit fly neurons[31]; (3) human vasculature[4]; (4) tropical trees from moist forests[32]; (5) corals of several species[30]; (6) arabidopsis at different growth stages[33]. As wiring optimization relies on the skeleton representations of physical networks, we confirmed that our test of Steiner’s prediction is not sensitive to the choice of the particular skeletonization algorithm (Supplementary information Section 1). To examine the validity of rule 1 (bifurcation only), we extracted the degree distribution of each skeletonized network. In agreement with the Steiner principle (an outcome also predicted by volume optimization of simple cylinders28,29), we observe a prevalence of k = 3 nodes, accounting, for example, for 79% of the nodes in the human neurons and for 94% in arabidopsis. Yet, we also observe a substantial number of trifurcations (k = 4) and several even higher degree (k = 5, 6) nodes (Fig. 1d), violating the Steiner and volume optimization prediction34,35. Note that, because of errors in skeletonizing a physical motif, two closely spaced bifurcations may be mistakenly identified as a trifurcation or, conversely, a trifurcation may be incorrectly perceived as two bifurcations[36]. We therefore verified that the observed high-degree nodes (as demonstrated in Fig. 1c) cannot be attributed to resolution limits (Supplementary information Section 1).

To examine the validity of rule 2 (planarity), which is predicted by both Steiner and volume optimization, we quantified the planarity for each bifurcation (k = 3) by measuring the probability P(Ω) that the three links span a solid angle Ω. We find that, in all of the studied networks, P(Ω) is strongly peaked at a solid angle that is smaller than the expected Ω = 2π, which is necessary (and sufficient) for planarity (Fig. 1e). Finally, to test the validity of rule 3 (angle symmetry), we extracted the pairwise angles (θ_1, θ_2, θ_3) between the links at each bifurcation, measuring the probability density P(θ). As Fig. 1f indicates, none of the six classes of real networks have a peak at the predicted θ = 2π/3 but instead the branching angles are broadly distributed, an asymmetry violating the Steiner prediction. Note that P(θ) predicted by volume optimization is also peaked around θ = 2π/3 but it can account for a broader range of branching angles thanks to the fact that links can have varying thickness28,29.

Taken together, although we see the signature of the Steiner theorem and volume optimization in the prevalence of k = 3 nodes, the optimal wiring hypothesis is unable to account for the existence of k > 3 nodes, the prevalence of non-planar bifurcations and the lack of θ = 2π/3 symmetry, results that question the validity of the optimal wiring hypothesis for physical networks.

Beyond wires—physical networks as manifolds

The Steiner problem relies on the hypothesis that nature aims to minimize the total length of the links, solving an inherently global problem. However, real physical networks have rich local geometries (Fig. 1c), characterized by varying diameters[9] and non-cylindrical surface morphologies. Over the past century, beginning with Murray’s 1926 work, researchers have combined geometry-based volume optimization calculations9,28,29 with algorithmic approximations to identify network configurations that satisfy the inherent system-specific constraints and align with experimental data in specific domains37,38,39. However, these approaches cannot account for either the smoothness of the joints that characterize real physical networks or for the cost associated with deviations from a simple linear or cylindrical solution. Indeed, to account for the true cost of building and maintaining these networks, we must capture the full morphology of a locally tree-like system, which is best described as a manifold \({\mathcal{M}}({\mathcal{G}})\) assigned to the graph \({\mathcal{G}}\). Formally, a manifold is a series of charts representing local coordinate systems that, when patched together, define a global coordinate system, or an atlas[40]. Previous advances related graphs to discrete manifolds through the use of simplicial complexes, assembled to form an atlas of connected, discrete coordinates41,42,43. Here, however, we aim to build smooth manifolds by formally describing each chart as a continuous surface embedded in three dimensions, whose shape is described by three-dimensional coordinates X = (x, y, z), in which x(σ), y(σ) and z(σ) are two-variable functions of a local, two-dimensional coordinate system, σ = (σ^0, σ^1) (Fig. 2a). This formalism replaces the total link length in the Steiner graph (Supplementary information Section 2) with the total surface area \({S}_{{\mathcal{M}}({\mathcal{G}})}\) (Supplementary information Section 3):

$${S}_{{\mathcal{M}}({\mathcal{G}})}=\mathop{\sum }\limits_{i=1}^{L}\int {{\rm{d}}}^{2}{{\boldsymbol{\sigma }}}_{i}\sqrt{\det {\gamma }_{i}}.$$

(1)

Here γi is given by \({\gamma }_{i,\alpha \beta }\equiv (\partial {{\bf{X}}}_{i}/\partial {\sigma }_{i}^{\alpha })\cdot (\partial {{\bf{X}}}_{i}/\partial {\sigma }_{i}^{\beta })\) (ref. [40]), characterizing the infinitesimal surface area elements of each link i. Hence, equation 1 sums over the surfaces of sleeve-like charts \({{\bf{X}}}_{i}({{\boldsymbol{\sigma }}}_{i})\) dressed over the links \(i=1,\ldots ,{L}\) of graph \({\mathcal{G}}\) (Fig. 2). To ensure that the sleeves form a smooth manifold (Supplementary information Section 4) and describe a compact physical object, they must obey several strict conditions: (1) to avoid non-physical cusps when two (or more) sleeves are sewn together, the ends of the sleeves must be perfectly aligned (Fig. 2b); (2) in principle, surface minimization can collapse a link, predicting that the minimum solution requires a thinning out at mid-point (Supplementary information Section 5). However, many real physical networks must support material flux, which requires a minimum circumference w everywhere, hence surface minimization is also subject to the functional constraint

$${\oint }_{{\rm{circumference}}}{\rm{d}}{l}_{i}\ge w,$$

(2)

in which the arc length is given by \({{\rm{d}}{l}_{i}}^{2}={\sum }_{\alpha ,\beta }{\gamma }_{i,\alpha \beta }{\rm{d}}{\sigma }_{i}^{\alpha }{\rm{d}}{\sigma }_{i}^{\beta }\).

Fig. 2: Physical network manifold.

a, In a physical network, the links are represented by charts, with a manifold morphology Xi(σi). Each chart i is described by its local coordinate system σi. The natural parametrization of a surface is provided by the longitudinal (\({\sigma }_{i}^{0}\), red) and azimuthal (\({\sigma }_{i}^{1}\), blue) coordinates. The minimum circumference around a link is denoted by w, measured along a path in the azimuthal direction. b, The intersections between the links define the geometry around the nodes. The local charts must be stretched and expanded to ensure a smooth and continuous patching at their boundaries (blue lines), guaranteeing that \({{\boldsymbol{\sigma }}}_{i}=({\sigma }_{i}^{0},{\sigma }_{i}^{1})\) match perfectly with \({{\boldsymbol{\sigma }}}_{j}=({\sigma }_{j}^{0},{\sigma }_{j}^{1})\) at the i, j intersection. c, A Feynman diagram (top) describes the interactions between elementary particles in field theory. In string theory, Feynman diagrams are smooth and continuous manifolds in higher dimensions (bottom), known as a worldsheet, that translate the discrete diagram at the top into the integrable object at the bottom. An exact mapping of the surface minimization problem (equations (1) and (2)) to these higher-dimensional worldsheets allows us to map abstract geometry into a structurally consistent physical network.

We, therefore, arrive at our final optimization problem: given a set of terminals (predetermined nodes), we seek the smooth and continuous surface manifold that links all terminals through finite paths, whose circumference exceeds the predefined threshold w and minimizes the cost \({S}_{{\mathcal{M}}({\mathcal{G}})}\) (equation (1)). At first glance, this optimization problem is intractable, as we must compare an uncountably infinite set of circumferences, known as non-contractable closed curves[44], ensuring that none of them violate equation (2) while minimizing equation (1). Our key methodological advance is the discovery of a direct equivalence between the network manifold minimization problem defined above and higher-dimensional Feynman diagrams (known as pants decomposition) in string theory5,6,7. The traditional Feynman diagram is a graph \({\mathcal{G}}\) that views particle trajectories as links and collisions as nodes (Fig. 2c). String (field) theory generalizes Feynman diagrams to two-dimensional surfaces, called the ‘worldsheets’, which represent the paths that strings sweep through in spacetime5,6,7. The smoothness of this surface guarantees that the path integral does not diverge, making it renormalizable[45], resulting in the Nambu–Goto action[45] that is formally identical to equation (1). The classical solution of the Nambu–Goto action, obtained in the absence of quantum fluctuations but subject to the constraint of equation (2), is exactly the manifold \({\mathcal{M}}({\mathcal{G}})\) we seek. According to Strebel’s theorem, in the absence of boundary conditions, this minimal surface is exactly cylindrical. With boundary conditions added, we can simplify equation (2) to a local constraint (Supplementary information Section 5), allowing us to construct local trees with discrete surfaces that are optimized for both smoothness and minimality. Numerically, this is performed by the min-surf-netw package, described in Supplementary information Section 6 and shared on GitHub.

Degree distribution

We start from a symmetric configuration of four terminals, laid out on the corners of a regular tetrahedron (Fig. 3a) and construct the minimal-surface network motif, represented by a tree that links these four nodes, with minimal link circumference w (Fig. 3b). We define the dimensionless weight parameter, χ = w/r, in which r is the distance between the intermediate nodes. In the χ → 0 limit, we have a quasi-one-dimensional configuration with long and thin links. In this case, the surface minimization predictions converge to the Steiner rules 1–3 (Fig. 1b), linking the four terminal nodes through two intermediate bifurcations with degree k = 3 (Fig. 3c,d). Yet, the optimal solution also predicts that, for higher χ (thicker links), the two k = 3 nodes gradually approach each other and that, at χ ≈ 1, they merge into a single k = 4 node, resulting in a trifurcation (Fig. 3e,f). In other words, surface minimization[7] predicts a transition from a Steiner bifurcation to a stable trifurcation at χ ≈ 1, an outcome that eluded volume optimization as well28,29.

Fig. 3: Emergence of trifurcations.

a, We consider four nodes forming a perfect tetrahedral configuration with spatial length scale r, capturing the radius of the tetrahedron. b, We construct a physical network to link these four nodes under surface minimization with circumference constraint w (link thickness). c,d, When χ = w/r → 0, the sleeves behave as one-dimensional links and the resulting manifold is well approximated by the Steiner solution, the network featuring two k = 3 bifurcations. e,f, As χ increases, the intermediate link l becomes shorter, until, beyond a certain thickness, the separation parameter λ = l/w → 0, indicating that the two intermediate bifurcations unite into a single trifurcation with k = 4. g, To examine the predicted transition, we plot λ versus χ for the minimal surface (green). For small χ, we have λ > 0, following a pattern also predicted by Steiner (grey line). This captures the two-bifurcation scenario predicted by length minimization. However, at χ ≈ 0.83, we observe a sudden decrease to λ = 0, capturing the transition from double bifurcations to a single trifurcation. h, We examined a series of random four-node configurations within a unit cube and implicitly constructed for each a Steiner graph and a minimal-surface manifold (w = 1). We then extracted P(λ), capturing the probability density to observe λ. Under Steiner optimization, P(λ) vanishes as λ → 0 (grey curve), capturing the fact that trifurcations are forbidden. By contrast, for surface minimization (green curve), we have P(λ → 0) > 0, describing a finite likelihood to observe trifurcations. i–n, P(λ) versus λ obtained from real physical networks. In each network, we collected all tetrahedral motifs in which the four external nodes are linked through two intermediate nodes and extracted λ between these intermediaries. Compared with Steiner’s predictions (grey lines), the empirically observed P(λ) (distinct colours) follows the green pattern in h, capturing a coexistence of bifurcations (λ > 0) and trifurcations (λ = 0), as predicted by surface minimization.

To quantify this transition, we use the dimensionless separation λ = l/w as an order parameter, in which l is the length of the link between the two k = 3 nodes, and using min-surf-netw (Supplementary information Section 6), we numerically generate the connecting minimal surface, allowing us to measure λ(χ) as a function of χ. For small χ, we have λ > 0, predicting that the two k = 3 nodes are separated, in line with the Steiner prediction (Fig. 3g). Yet, at χ ≈ 0.83, we observe a sudden drop to λ = 0, when the one-dimensional Steiner approximation breaks down and instead surface minimization predicts the emergence of a trifurcation (k = 4). This transition represents our first key prediction, indicating that the empirically observed k = 4 nodes in locally tree-like physical networks represent a stable configuration predicted by local surface optimization.

To generalize our approach, we place the four terminals randomly in a unit cube and run several configurations to extract the probability density P(λ). For χ = 0 (corresponding to w = 0, which reduces to the Steiner problem), we find that P(λ) → 0 for small separation λ (Fig. 3h, grey line), confirming the absence of trifurcations. By contrast, for large χ (for example, w = 1), we find that P(λ → 0) does not vanish (Fig. 3h, green line). Rather, we observe a finite probability for trifurcations with λ = 0 (Supplementary information Section 7). Figure 3h indicates that the density function P(λ) offers an empirically falsifiable fingerprint of surface minimization. We therefore divided each physical network into local groups of four connected links and extracted P(λ). We find that each locally tree-like network exhibits a non-vanishing P(λ → 0) (Fig. 3i–n, coloured lines), representing a clear deviation from the Steiner prediction (green line) and offering direct evidence that, in real networks, the cost function is not linear in the link length but is better described by surface minimization.

Angle asymmetry

To understand the origin of the observed angle diversity, a violation of rule 3 (Fig. 1f), we assume that each link i is characterized by its unique circumference constraint wi. Without a loss of generality, we set w_1 = w_2 = w and w_3 = w′, and vary the ratio ρ = w′/w, to obtain the minimal manifold that connects nodes 1, 2 and 3 (Fig. 4a,b). Although Steiner’s solution posits a constant steering angle Ω1→2 ≈ 0.3π, surface minimization predicts two distinct regimes separated by a threshold value ρth (Supplementary information Section 7). (1) For ρ > ρth, we predict the steering angle Ω1→2 ≈ k(ρ − ρth) (Fig. 4e,f), that is, a linear dependence on ρ (Fig. 4g). This regime can therefore account for the wide range of angles observed in Fig. 1f. (2) For ρ < ρth, surface minimization makes an unexpected prediction: if links 1 and 2 have comparable diameters, they are expected to form a straight path (that is, continue with solid angle of Ω1→2 = 0), whereas the thinner link 3 is predicted to emerge perpendicularly at Ω1→3 ≈ Ω2→3, consistent with an orthogonal sprouting behaviour (Fig. 4c,d). Note that a geometric approach predicted as early as 1976 (refs. 28,29) that the branch angles converge to 90° in the ρ → 0 limit (Supplementary information Section 7). By contrast, our framework predicts that the 90° solution is optimal for any ρ < ρth (Fig. 4g). Hence, orthogonal sprouts are not singular solutions that emerge only in the ρ → 0 limit28,29. Rather, they are stable solutions of surface minimization that remain minimal for a wide range of parameter values and hence they should be not only observable but prevalent in real physical networks.

Fig. 4: Branching versus sprouting bifurcations.

a, We start from a triangular node configuration, with w_1 = w_2 = w and w_3 = w′. b, We construct the minimal-surface manifold connecting the three nodes. c,d, For small ρ = w′/w, the link of node 3 is thin and the optimal manifold favours a sprouting structure: nodes 1 and 2 linked through a straight line and node 3 by means of a perpendicular link. e,f, For large ρ, we find a linear relation between ρ and the three-dimensional steering angle, Ω1→2, related to the branching angle θ (Fig. 1f) through Ω1→2 = 4πsin^2((π − θ)/4). As ρ increases, the bifurcation point approaches the triangle centre and the bifurcation gradually resembles a symmetric branching. g, Ω1→2 versus ρ. We observe a transition from sprouting (Ω = 0) to branching (Ω > 0) at ρ ≈ 0.6. The symmetric branching observed by Steiner appears near ρ = 1. h, In the human connectome, 92% of the observed sprouts end on synapses, suggesting that neuronal systems use surface minimization to form direct synaptic connections to adjacent neurons with minimal material cost. i–n, According to g, cumulative \(| {\int }_{\rho }^{{\rho }_{{\rm{th}}}}\varOmega (\rho ){\rm{d}}\rho | \) should follow approximately (ρth − ρ)1 for ρ < ρth and approximately (ρ − ρth)2 for ρ > ρth, predictions closely followed by real physical networks. Band thickness represents one standard error of the fitting.

To test these predictions, we identified all bifurcation motifs in each network in our database and then searched for branches that satisfy w_1 = w_2 = w. We then measured Ω(ρ) = Ω1→2 as a function of the empirically observed ρ, finding that almost all bifurcations for ρ < ρth are sprout-like, characterized by small Ω(ρ) (Supplementary information Section 7). In Fig. 4i–n, we show the cumulative value of the observed angles in the two regimes, offering evidence that the cumulative \(| {\int }_{\rho }^{{\rho }_{{\rm{th}}}}\varOmega (\rho ){\rm{d}}\rho | \) follows approximately (ρth − ρ)1 for ρ < ρth and a quadratic behaviour approximately (ρ − ρth)2 for ρ > ρth, in line with the predictions of Fig. 4g.

The key outcome of surface minimization is the predicted prevalence of the orthogonal sprouts, expected to emerge each time ρ < ρth. To falsify this prediction, we ask: are such sprouts really present in physical networks? Note that the excess of sprouts over the expectations of length or volume optimization was already noted in arterial systems as early as 1976 (ref. [29]). This abundance remained unanswered and it also remains unclear whether sprouts represent a generic feature across all physical networks or are unique to blood vessels. To address this, we first identified all bifurcations with w_1 ≈ w_2 in blood vessels, confirming that, in 25.6% of the cases, the third branch, independent of ρ, is perpendicular to the main branches, representing an abundant sprouting behaviour. Yet, we find that sprouts are not limited to the circulatory system but are present in all studied networks, representing 12.9% of the w_1 ≈ w_2 cases in the tropical trees, 52.8% in corals, 11.2% in arabidopsis, 13.8% in the fruit fly neurons and 18.4% in the human neurons. Most importantly, some systems have learned to turn sprout behaviour to their advantage, assigning it a functional role. Indeed, in the human connectome, we identified 4,003 sprouts, finding that 3,911 of these (98%) end with a synapse (Fig. 4h). In other words, neuronal systems have adapted to rely on surface minimization by using orthogonal sprouts as dendritic spines that allow them to form synapses with nearby neurons with minimal material cost. Similarly, roots in plants[46] and hyphae branches in fungi[47] are known to sprout perpendicularly, allowing plants and fungi to explore a larger volume of soil for water and nutrients with minimal material expenditure.

The predicted relation between Ω(ρ) and ρ in Fig. 4g leads to further falsifiable predictions for the P(Ω) angle distributions, conditioned on the empirically observed ρ values. In the sprouting regime (ρ < ρth), we predict Ω = 0, independent of ρ, hence we anticipate a sharp peak of P(Ω) at Ω = 0, in agreement with the empirical data (left side, sprouting regime in Fig. 5a–f). In the branching regime (ρ > ρth), however, P(Ω) is predicted to exhibit a broad distribution with high variance, rooted in the linear behaviour of Fig. 4g. The empirical data support this prediction as well (right side, branching regime in Fig. 5a–f). By comparison, the Steiner prediction posits a sharp peak of P(Ω) independent of ρ (thin grey lines in both sprouting and branching regimes in Fig. 5a–f).

Fig. 5: Sprouting in physical networks.

We predicted and measured the branching angle distribution across six physical networks. a–f, The relation of Ω1→2 versus ρ in Fig. 4 predicts distinct distributions P(Ω) based on the observed ρ values in the sprouting (dashed lines) and branching (solid lines) regimes. Both distributions align with our predictions (green), violating the Steiner predictions (grey).

Discussion

The three-dimensional layout of physical networks is subject to several, often evolutionary-induced, constraints. For example, brain wiring is governed by developmental programs[48] and locally guided by a complex inventory of chemoattractants and repellents that govern the journey of an individual neuron across the brain. Similarly, the vascular system must transport nutrients to all cells and is subject to several optimization goals, from flow efficiency to material cost[49]. Given the diversity of the processes that govern the development of physical networks, we would expect that minimization principles are ultimately overwritten by global and functional needs50,51. By contrast, here we find that physical networks observed in a wide range of systems follow common quantifiable morphological branching characteristics that are well predicted by a local surface minimization process. The robustness of our results across several systems indicates that cost minimization is a stereotypical principle that is not overwritten by functional or global need; rather, development and selection probably rely on these local minimization processes to add function to a network. As local optimization does not necessarily dictate the global optimum[28], functional demands may exert greater influence at larger scales20,38. For example, we find that wiring optimization fails to correctly predict the total length of physical networks, which are, on average, 25% longer than Steiner’s prediction across all six datasets (Supplementary information Section 8).

More empirical studies are needed to validate surface minimization predictions across more complex network structures[52]. Indeed, although here we focused on the universal branching characteristics of locally tree-like structures, construction of larger-scale structures could reveal whether specific network types exhibit unique geometrical adaptations, such as varying link thickness and curvature, owing to the unique functional pressures of the networks, such as flow conservation in vascular systems[9] or neuron placement constraints[48]. These features are beyond the scope of our present surface minimization framework, which predicts straight, uniform cylinders far from the branching points. Furthermore, loops—which we find to be absent in our datasets (Supplementary information Section 8) but ubiquitous in engineered networks such as traffic and power grids—represent a departure from simple wiring efficiency, hence requiring an extended analytical framework. Such advances will open avenues to integrate crowding10,53, knotting11,54 or bundling[55] of physical links, exploring their influence on the global layout. Such extensions could offer further insights into how networks balance efficiency with functional demands[56] and help us understand how a global and functional organization can emerge from local processes. They may also offer insight into differences between classes of physical networks, helping us understand which features are governed by optimization principles and which require further functional considerations.

Future work could also compare the predicted manifold geometries directly to the observed geometric features, such as surface geodesics, curvatures and other fine details, helping reveal the degree to which the surface minimization model reproduces the observed local geometry beyond skeletons. Indeed, we find that trifurcation junctions are consistently smooth and that their shapes strongly prefer symmetric morphology, features predicted by surface minimization (Supplementary information Section 9). This validation at the level of fine-grained geometry reinforces the empirical foundation of our framework and opens avenues for more detailed comparison with the predictions.

Physical networks in the three-dimensional Euclidean space can be described as either two-dimensional manifolds \({\mathcal{M}}({\mathcal{G}})\) subject to surface minimization or three-dimensional objects subject to volume optimization. Although in vascular networks the material investment is limited to the surface area of the blood vessels, for neurons, corals and trees, an accurate accounting of the material cost must also consider the volume of the branches. The existing literature on volume optimization assumes cylindrical links28,29 and fails to account for non-trivial topologies emerging at the intersections. As the min-surf-netw algorithm exploits the string-theoretic solution, it is limited to surface minimization. Yet, the two problems are not independent: our numerical simulations indicate that, for the branching processes, suboptimal surfaces also increase the volume, suggesting that the minimal surfaces correspond to close-to-optimal volumes as well (Supplementary information Section 10). However, further work is needed to understand whether a self-consistent volume optimization could offer new solutions and morphologies that are not predicted by our present framework, hence can further enrich our understanding of physical networks.

Data availability

The dataset is available at https://physical.network.

Code availability

The code used for this manuscript is available at https://github.com/Barabasi-Lab/min-surf-netw.

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Acknowledgements

We are grateful to U. H. Danielsson and F. Ruehle for helpful discussions on the string theory approach. A.-L.B. was partially supported by the NSF award no. 2243104 - COMPASS and by the European Union’s Horizon 2020 research and innovation programme no. 810115 - DYNASNET. B.B. was supported by the Israel Science Foundation grant no. 499/19, the Israel-China ISF-NSFC joint research programme grant no. 3552/21, and by the VATAT grant for data science research.

Author information

Authors and Affiliations

1. Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, USA

Xiangyi Meng

2. National Institute for Theory and Mathematics in Biology, Northwestern University and The University of Chicago, Chicago, IL, USA

Xiangyi Meng

3. Network Science Institute, Northeastern University, Boston, MA, USA

Xiangyi Meng, Benjamin Piazza, Csaba Both, Baruch Barzel & Albert-László Barabási

4. Department of Physics, Northeastern University, Boston, MA, USA

Xiangyi Meng, Benjamin Piazza, Csaba Both, Baruch Barzel & Albert-László Barabási

5. Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel

Baruch Barzel

6. The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel

Baruch Barzel

7. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Albert-László Barabási

8. Department of Network and Data Science, Central European University, Budapest, Hungary

Albert-László Barabási

Contributions

All authors contributed to the research. X.M. and A.-L.B. conceived the research. X.M., B.P. and C.B. collected and cleaned the data. X.M. and B.P. analysed the data. X.M. conducted theoretical analysis, designed the algorithm and performed the simulation. B.B., X.M. and A.-L.B. wrote the manuscript. X.M., C.B. and A.-L.B. reviewed and edited the manuscript.

Corresponding author

Correspondence to Albert-László Barabási.

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A.-L.B. is the scientific founder of Scipher Medicine, Inc., which applies network medicine to biomarker development. The other authors declare no competing interests.

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Nature thanks Bernat Corominas-Murtra, Shakti N. Menon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

This Supplementary Information contains 24 Supplementary Figures, 5 Supplementary Tables and Supplementary References, and the following sections: 1. Physical network datasets; 2. One-dimensional cost minimization—the Steiner graph; 3. Two-dimensional links—charts; 4. The unified coordinate system—atlas; 5. Surface minimization; 6. Numerical implementation; 7. Empirical analysis of branching; 8. Empirical analysis of link lengths and loops; 9. Empirical analysis of node morphology; and 10. Surface versus volume minimization.

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Meng, X., Piazza, B., Both, C. et al. Surface optimization governs the local design of physical networks. Nature 649, 315–322 (2026). https://doi.org/10.1038/s41586-025-09784-4

- Received: 19 November 2024

- Accepted: 21 October 2025

- Published: 07 January 2026

- Version of record: 07 January 2026

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09784-4

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Aharonov–Bohm interference in even-denominator fractional quantum Hall states

Jehyun Kim,

Himanshu Dev,

Amit Shaer,

Ravi Kumar,

Alexey Ilin,

André Haug,

Shelly Iskoz,

Kenji Watanabe,

Takashi Taniguchi,

David F. Mross,

Ady Stern &

…

Yuval Ronen

Nature volume 649, pages 323–329 (2026)

Abstract

Position exchange of non-Abelian anyons affects the quantum state of their system in a topologically protected way[1]. Their expected manifestations in even-denominator fractional quantum Hall (FQH) systems offer the opportunity to directly study their unique statistical properties in interference experiments[2]. Here we present the observation of coherent Aharonov–Bohm interference at two even-denominator states in high-mobility bilayer-graphene-based van der Waals (vdW) heterostructures by using the Fabry–Pérot interferometry technique. Operating the interferometer at a constant filling factor, we observe an oscillation period corresponding to two flux quanta inside the interference loop, ΔΦ = 2Φ_0, at which the interference does not carry signatures of non-Abelian statistics. The absence of the expected periodicity of ΔΦ = 4Φ_0 may indicate that the interfering quasiparticles carry the charge \({e}^{* }=\frac{1}{2}e\) or that interference of \({e}^{* }=\frac{1}{4}e\) quasiparticles is thermally smeared. Notably, at two hole-conjugate states, we also observe oscillation periods of half the expected value, indicating interference of \({e}^{* }=\frac{2}{3}e\) quasiparticles instead of \({e}^{* }=\frac{1}{3}e\). To investigate statistical phase contributions, we operated the Fabry–Pérot interferometer (FPI) with controlled deviations of the filling factor, thereby introducing fractional quasiparticles inside the interference loop. The resulting changes to the interference patterns at both half-filled states indicate that the extra bulk quasiparticles carry the fundamental charge \({e}^{* }=\frac{1}{4}e\), as expected for non-Abelian anyons.

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Main

For more than four decades, quasiparticles carrying fractional charge and obeying fractional statistics have captivated the condensed matter physics community[1]. Their most prevalent types are the Abelian anyons, which exhibit quantized exchange phases lying between those of bosons and fermions. Even more notable are non-Abelian anyons that can fundamentally transform the many-body wavefunction through particle exchanges, processing quantum information in a topologically protected manner[2]. The FQH systems have emerged as a leading platform for realizing and manipulating these exotic quasiparticles, owing to high electron mobility, long coherence times and exceptional controllability[3]. Fractional charge was first observed by means of shot-noise measurements at odd-denominator filling factors expected to host Abelian states4,5 and later at the even-denominator filling \(\nu =\frac{5}{2}\) in GaAs (refs. 6,7), a leading candidate for non-Abelian topological order[8].

Direct measurements of anyonic exchange statistics require phase-sensitive techniques such as quantum Hall interferometry in the Aharonov–Bohm regime, in which Coulomb interactions are sufficiently weak for the interferometer area to remain constant as B is varied[9]. Seminal works by Nakamura et al. demonstrated Aharonov–Bohm interference of fractionally charged quasiparticles using a GaAs FPI at filling \(\nu =\frac{1}{3}\) (ref. [10]), and braiding (double-exchange) phases in a subsequent study[11]. These findings were generalized to different filling factors[12] and also observed in alternative platforms, first in integer13,14,15 and then in fractional fillings16,17,18, and interferometer architectures19,20. In parallel, time-domain braiding experiments21,22,23,24,25,26 also support anyonic quasiparticle statistics in Abelian FQH states[27]. At even-denominator fillings, Fabry–Pérot interferometry studies at \(\nu =\frac{5}{2}\) in GaAs have reported signatures consistent with non-Abelian statistics[28]. However, the interpretation of those experiments remains challenging, primarily because of the absence of robust Aharonov–Bohm interference.

Even-denominator states have been observed in several FQH platforms, including GaAs (ref. [29]), ZnO (ref. [30]), graphene[31], bilayer graphene32,33,34,35 and WSe_2 (ref. [36]). In GaAs narrow quantum wells, thermal-transport measurements37,38 consistently support a non-Abelian topological order known as PH-Pfaffian[39]. Distinct non-Abelian orders known as Moore–Read Pfaffian[40] and anti-Pfaffian[41] are indicated by daughter states[42] in bilayer graphene35,43 and GaAs wide quantum wells[44]. Specifically, bilayer graphene realizes quantized plateaus at seven half-integer filling factors in the zeroth Landau level. Moreover, the presumed topological orders alternate between Pfaffian and anti-Pfaffian, offering a rich playground for interference studies of non-Abelian anyons.

In this work, we report the observation of robust Aharonov–Bohm oscillations at two even-denominator FQH plateaus in bilayer graphene. Using a gate-defined FPI in a high-mobility bilayer-graphene-based vdW heterostructure, we perform a detailed study of the interference patterns as a function of magnetic field, area and density. At both fillings, we observe the unexpected Aharonov–Bohm periodicity ΔΦ = 2Φ_0 when the magnetic field and the density are varied together to maintain contact filling. The most conservative interpretation of these measurements is the interference of quasiparticles with charge \({e}^{* }=\frac{1}{2}e\), twice the charge expected theoretically[40] and observed in earlier shot-noise and single-electron transistor measurements in GaAs (refs. 6,7). However, this frequency could also originate from \({e}^{* }=\frac{1}{4}e\) quasiparticles performing an even number of loops.

This finding prompted us to study the nearby odd-denominator states at Landau-level fillings of \(\nu =\frac{1}{3}\) and \(\frac{2}{3}\), for which we found Aharonov–Bohm periodicities corresponding to interference of quasiparticles with charges \({e}^{* }=\frac{1}{3}e\) and \(\frac{2}{3}e\), respectively. Across the three fillings, the interfering charge follows e* = νe instead of the minimal charges of bulk quasiparticles, which are \(\frac{1}{3}e\), \(\frac{1}{4}e\) and \(\frac{1}{3}e\) for these states. All even-denominator and odd-denominator states for this study are indicated in the phase diagram of bilayer graphene (see Supplementary Information Section 1). We note that, in GaAs, shot-noise measurements at hole-conjugate states also find a partitioned charge of νe (ref. [45]) but interference at \(\nu =\frac{2}{3}\) shows e* = e (ref. [10]). Finally, by tuning the electron density independently of the magnetic field, we deviate from the fixed-filling constraint, thereby introducing localized bulk quasiparticles[46]. Unlike the integer case, we observe a statistical contribution to the interference phase of fractional fillings, supporting their anyonic character.

Design and measurement phase-space of an FPI

The FPI device is constructed on a high-mobility vdW heterostructure, with bilayer graphene as the active 2D layer, which is encapsulated between hexagonal boron nitride (hBN) dielectric layers, whereas conductive graphite layers on the top and bottom serve as gates. The heterostructure design and nanofabrication techniques follow those detailed in our previous study[16], with measurements conducted under a perpendicular magnetic field up to B = 12 T and at a base temperature of T = 10 mK.

A false-colour scanning electron microscopy image of the FPI is shown in Fig. 1a. The top graphite layer is divided into eight distinct regions by 40-nm-wide etched trenches, with each region contacted by means of air bridges. Together with a global graphite back gate, these eight top gates enable capacitive tuning of the potential and displacement fields across various regions of the bilayer graphene. The filling factor inside the interferometer is controlled by the centre gate, whereas the left and right gates set the outer fillings. The two quantum point contacts (QPCs) are formed by the left and right split gates, which set the filling underneath to zero, thereby guiding the counter-propagating edge modes on opposite sides into close proximity and introducing tunnelling between them. An extra plunger gate (denoted PG) allows fine control over the area enclosed by the interfering quantum Hall edge mode. Figure 1b provides a zoomed-in view of the interfering region, lithographically defined to be 1 μm^2 (see Supplementary Information Section 2). Two air bridges, denoted LBG and RBG, positioned 200 nm above the QPC regions, act as gates, fine-tuning the transmission tR, tL of each QPC independently. In the measurements presented in this study, tR,L are set to 0.5–0.7 for the interference at the integer states and 0.6–0.9 for the interference at the fractional states (see Supplementary Information Section 3).

Fig. 1: FPI based on the bilayer graphene.

a, Measurement configurations depicted as a false-colour scanning electron microscopy image of the FPI in a bilayer graphene heterostructure (see inset). The top graphite layer (purple) is divided into eight separate regions by means of etched trenches, better seen in b. Each region acts as a gate, electrostatically tuned by means of air bridges (blue) to define the interferometer. The device dimensions are indicated by the white scale bar of length 3 μm. In the quantum Hall regime, current ISD applied through an ohmic contact (yellow) propagates by means of edge modes and is partitioned by two QPCs formed by the left and right split gates (LSG and RSG), resulting in oscillating diagonal resistance \({R}_{{\rm{D}}}=({V}_{{\rm{D}}}^{+}-{V}_{{\rm{D}}}^{-})/{I}_{{\rm{SD}}}\). b, Magnification of the interfering region near the centre gate (CG). Left and right air bridges (LBG and RBG, shown in green) are suspended 200 nm above each QPC region, fine-tuning the saddle-point potential. The lithographic interference area, determined by CG area, is 1 μm^2. Scale bar, 0.3 µm. c, Measurement phase space defined by B, VPG and VCG. RD is measured along planes defined by \(\alpha =\frac{\partial B}{\partial {V}_{{\rm{CG}}}}\) and shown as 2D B∣α–VPG pajamas.

We inject a bias current ISD, which propagates along the FQH edge modes with an anticlockwise (clockwise) chirality for electron (hole) carriers, impinging on the FPI as illustrated in Fig. 1a. Current is collected on the other side of the interferometer by a single ground while measuring the diagonal resistance \({R}_{{\rm{D}}}=\frac{{V}_{{\rm{D}}}^{+}-{V}_{{\rm{D}}}^{-}}{{I}_{{\rm{SD}}}}\) to reveal interference. In the low backscattering regime, RD includes an oscillatory contribution ΔRD ∝ cosθ, in which the interference phase θ is composed of both Aharonov–Bohm (denoted ‘AB’) and statistical phases, that is9,47,48,

$$\theta ={\theta }_{{\rm{AB}}}+{\theta }_{{\rm{stat}}}=2{\rm{\pi }}\frac{{e}^{* }}{e}\frac{AB}{{\varPhi }_{0}}+{N}_{{\rm{qp}}}{\theta }_{{\rm{anyon}}},$$

(1)

in which A is the interfering area, Nqp is the integer number of localized quasiparticles within the interference loop and θanyon is the braiding phase. For non-Abelian quasiparticles, RD is predicted to follow a more intricate pattern that differs for even and odd Nqp (refs. 49,50).

We perform measurements of RD in the 3D parameter space spanned by the magnetic field B, the plunger gate voltage VPG and the centre gate voltage VCG; see Fig. 1c. To disentangle the two terms in θ of equation (1), we follow lines of different slopes \(\alpha =\frac{\partial B}{\partial {V}_{{\rm{CG}}}}\) in the B–VCG plane. The Aharonov–Bohm contribution is isolated at the critical trajectories αc, for which charges are continuously added to the interference loop to maintain constant fillings. Along α ≠ αc trajectories, the filling factor deviates from the rational value defining the quantum Hall plateau through the Hall conductance. All such trajectories are measured within the incompressible region, as shown in Supplementary Information Section 4.

Consequently, RD follows the well-known ‘pajama pattern’ in the B–VPG plane with a flux periodicity set by the interfering quasiparticle charge e*. Deviations from this trajectory introduce bulk quasiparticles, Nqp, which are expected to manifest individually through phase slips and which alter the average flux periodicity. Other notable trajectories include constant density, α = ∞, and constant magnetic field, α = 0, illustrated in Fig. 1c.

Even-denominator Aharonov–Bohm interference

We begin the Fabry–Pérot interferometry study of even-denominator states at the filling factor \(\nu =-\,\frac{1}{2}\) owing to its simple edge structure, which consists only of fractional modes. Figure 2a shows the longitudinal resistance Rxx and Hall resistance Rxy measured at 11 T on the right side of the FPI. The data clearly reveal fully developed integer and fractional quantum Hall states at ν = −1, \(-\frac{2}{3}\), \(-\frac{1}{2}\) and \(-\frac{1}{3}\). Figure 2b presents an Rxx fan diagram, which we use to extract the constant-filling-factor trajectories. We define \({\alpha }_{{\rm{c}}}=\frac{{\varPhi }_{0}}{\nu e}C\), with \(C=\frac{1}{A}\frac{{\rm{d}}Q}{{\rm{d}}{V}_{{\rm{RG}}}}\) the capacitance per unit area between the right gate and the bilayer graphene underneath, extracted from the Streda formula for each fractional state as the centre of the incompressible region, whose boundaries are indicated by dashed red lines (see Supplementary Information Section 5).

Fig. 2: Even-denominator Aharonov–Bohm interference.

a, Longitudinal resistance Rxx and Hall resistance Rxy measured at 11 T on the right side of the FPI, clearly showing fully developed even-denominator and odd-denominator quantum Hall states at \(\nu =-\,\frac{2}{3}\), \(-\frac{1}{2}\) and \(-\frac{1}{3}\). b, Rxx fan diagram performed on the right side of the FPI between 10.5 and 11.5 T. Dashed red lines indicate the boundaries for each quantum Hall state. c, ΔRD at \(\nu =-\,\frac{1}{2}\) shown as a \(B{| }_{{\alpha }_{{\rm{c}}}}-{V}_{{\rm{PG}}}\) pajama plot, showing clear Aharonov–Bohm oscillations. Inset, 2D-FFT analysis used to extract the magnetic-field periodicity \(\frac{{\varPhi }_{0}}{\Delta B}\) shown on the lower-right side of the pajama. d,e, Same as a,b for the electron-doped filling factors \(\nu =\frac{4}{3}\), \(\frac{3}{2}\) and \(\frac{5}{3}\). f, Same as c for \(\nu =\frac{3}{2}\) with partitioning of the fractional inner mode. a.u., arbitrary units.

Focusing on the \(-\frac{1}{2}\) state, Fig. 2c shows the interference pattern as a function of VPG and \(B{| }_{{\alpha }_{{\rm{c}}}}\), in which the αc constraint indicates that VCG is adjusted to maintain constant filling. Specifically, we present the data as ΔRD = RD − ⟨RD⟩, subtracting the average value at each magnetic field. The positive slope of the pajama indicates Aharonov–Bohm-dominated interference, because increasing VPG decreases the interference area for hole-doped states. The measured visibility, defined as Visibility = (Gmax − Gmin)/(Gmax + Gmin − 2Gouter), in which Gmax and Gmin are the maximum and minimum diagonal conductance values, respectively, and Gouter represents the conductance of any fully transmitted outer edge modes, is around 1.9%, comparable with that at integers and odd-denominator states (see Supplementary Information Section 6), and the corresponding edge mode velocity of vedge = 7.95 × 10^3 m s−1, approximately an order of magnitude smaller than that observed at integer filling, is extracted from source-drain bias VSD-dependent RD (see Supplementary Information Section 7). To extract the flux periodicity, we perform a 2D fast Fourier transform (2D-FFT), shown in the inset of Fig. 2c as a function of \(\frac{{\varPhi }_{0}}{\Delta B}\) and \(\frac{1}{\Delta {V}_{{\rm{PG}}}}\). From the magnetic-field periodicity, we extract \(A\frac{{\varPhi }_{0}}{\Delta \varPhi }\approx -\,0.53\,{{\rm{\mu }}{\rm{m}}}^{2}\). The lithographic area A ≈ 1 μm^2 agrees with that extracted from interference at ν = −1 to within 2% (see Supplementary Information Section 8). Using the same area at \(\nu =-\,\frac{1}{2}\) yields the unexpected flux periodicity ΔΦ = (1.89 ± 0.26)Φ_0 ≈ 2Φ_0. We found that this unexpected 2Φ_0 flux periodicity was robust against changes in the compressibility of the bulk, magnetic field and in plunger-gate spectroscopy[13] (see Supplementary Information Sections 10, 11 and 12). Furthermore, our transmission study (Supplementary Information Section 9) shows qualitatively similar interference patterns over the experimentally available tR,L range. At very low t, at which we expect electron-dominated tunnelling, the visibility is lost.

As shown in Supplementary Fig. 3, nearly all QPCs show resonances, probably because of tunnelling by means of localized states near the saddle-point potential. Such resonances can affect the leading tunnelling channel and the total inference phase[51]. Nevertheless, irrespective of the precise tunnelling mechanism across the QPC, the magnetic-field periodicity of the Aharonov–Bohm oscillations at a constant filling provides a direct and robust measure of the fractional charges that dominate tunnelling and interference processes. As long as the QPC environment is approximately constant, owing to low cross-capacitance with the plunger gate and small magnetic-field variations, the total variations in the interference phase owing to Aharonov–Bohm effect and bulk quasiparticles remain unaffected.

Following the first term in equation (1), this periodicity suggests an interfering quasiparticle charge of \({e}^{* }=\frac{1}{2}e\), which tunnels across the QPCs to form an interference loop. Quasiparticles with this charge exist as bulk excitations at half-filling, arising from the fusion of two fundamental quasiparticles carrying charge \(\frac{1}{4}e\), in all Abelian or non-Abelian FQH candidate states. Alternatively, this periodicity could also arise in a scenario in which non-Abelian \(\frac{1}{4}e\) quasiparticles interfere. In that case, when a non-zero number of non-Abelian quasiparticles are localized in the bulk, there are several degenerate ground states. Fluctuations between these ground states on the timescale of the measurement could suppress the 4Φ_0 periodicity that arises from a single winding of \(\frac{1}{4}e\) quasiparticles while not affecting the 2Φ_0 periodicity arising from double windings or \(\frac{1}{2}e\) quasiparticles49,50.

To test the generality of these findings, we investigated the \(\nu =\frac{3}{2}\) plateau (on the electron side), which exhibits a gap comparable with \(\nu =-\,\frac{1}{2}\) (refs. 33,34). Similar to the previous case, Fig. 2d shows Rxx and Rxy measured at 11 T, revealing well-developed FQH states at \(\nu =\frac{4}{3}\), \(\frac{3}{2}\) and \(\frac{5}{3}\). Figure 2e presents an Rxx fan diagram, which we use to extract αc as before. Figure 2f shows the interference pattern as a function of VPG and \(B{| }_{{\alpha }_{{\rm{c}}}}\) that arises when the QPCs are tuned to partition the fractional inner edge mode (see Supplementary Information Section 13 for the interference of the integer outer edge). The slope of the pajama pattern with 5.6% visibility is opposite to \(\nu =-\,\frac{1}{2}\), indicating Aharonov–Bohm-dominated interference for electron-doped states (see Supplementary Information Section 14). The 2D-FFT, shown in the inset of Fig. 2f, yields the magnetic-field periodicity \(A\frac{{\varPhi }_{0}}{\Delta \varPhi }\approx 0.42\,{{\rm{\mu m}}}^{2}\). Estimating the interfering area based on the integers ν = 1 and 2, we find A = 0.99 ± 0.10 μm^2, consistent with the lithographic area (see Supplementary Information Section 15). Using this area at \(\nu =\frac{3}{2}\), we conclude ΔΦ = (2.35 ± 0.78)Φ_0 ≈ 2Φ_0. Temperature-dependence measurements showed a reduction in visibility with increasing temperature, whereas the magnetic-field periodicity remained constant (see Supplementary Information Sections 16 and 17).

These two measurements consistently show periodicities close to 2Φ_0 and not the expected 4Φ_0. The observations reflect the interference of \(\frac{1}{2}e\) quasiparticles at \(\nu =-\,\frac{1}{2}\) and \(\frac{3}{2}\). We note that the topological orders of electrons at both fillings are believed to be Pfaffians35,43 but their edge structures at the boundary to ν = 0 are qualitatively different. In particular, a Pfaffian order of electrons at \(\nu =-\,\frac{1}{2}\) is equivalent to an anti-Pfaffian of holes. Consequently, it exhibits an edge state with three upstream Majorana fermions at a ν = 0 boundary. Insofar as the identification of these states is accurate, our experiment effectively examines two distinct non-Abelian topological orders.

Comparing fractional quasiparticle interference

At both even-denominator filling factors, the observed Aharonov–Bohm periodicity is consistent with an interfering quasiparticle charge that matches the Landau-level filling factor \({\nu }_{{\rm{LL}}}=\frac{1}{2}\). In this study, νLL is the filling factor of the partially filled Landau level νLL = ν − ⌊ν⌋. The interfering charge at \(\nu =\frac{1}{3}\) also follows the filling factor10,17,18. We extended the study to states at \(\nu =-\,\frac{2}{3}\) and \(\frac{5}{3}\) to determine whether their interfering charge is also set by the filling or by the minimal bulk excitation. We refer to these states as ‘hole-conjugate’ based on their presumed topological orders and edge structures, which are related to the ‘particle-like’ states at \(\nu =-\,\frac{1}{3}\) and \(\frac{4}{3}\) by a hole conjugation in the partially occupied Landau level. As there is no particle–hole symmetry, the microscopic wavefunctions at these fillings are not related by hole conjugation.

Figure 3a,b shows the extracted flux periodicities for all six fractional fillings in constant-filling measurements. The values for the odd denominators are extracted from the pajama patterns in Fig. 3c–f through the 2D-FFTs shown in Fig. 3g–j, assuming the same interference areas for hole-doped and electron-doped states as before. The results confirm the interference of e* = νLLe quasiparticles in all states included in our study. A recent experiment on the hole-conjugate states \(\nu =\frac{2}{3}\), \(\frac{3}{5}\) and \(\frac{4}{7}\) in GaAs using a Mach–Zehnder interferometer also observed interference of e* = νLLe quasiparticles[20]. It is not understood why non-fundamental quasiparticles should dominate the interference, as our measurements at half-filled and hole-conjugate states indicate. We point out that previous interference experiments at the hole-conjugate \(\nu =\frac{2}{3}\) state in GaAs reported the periodicity ΔΦ = Φ_0 corresponding to the interference of electrons[10]. Moreover, Mach–Zehnder interference of the higher particle-like Jain states \(\nu =\frac{2}{5}\) and \(\frac{3}{7}\) observed ΔΦ = 5Φ_0 and 7Φ_0 (ref. [19]), corresponding to the fundamental quasiparticle charge instead of νLLe.

Fig. 3: Interference of e* = νLLe quasiparticles in various FQH states.

a, Magnetic-field periodicities \(\frac{{\varPhi }_{0}}{\Delta B}\) at constant filling extracted from 2D-FFT analyses at \(\nu =-\,\frac{2}{3}\), \(-\frac{1}{2}\) and \(-\frac{1}{3}\). Error bars correspond to the variance of the Gaussian fit obtained by fitting the 2D-FFT with a Gaussian function (see Supplementary Information Section 8). b, Same for \(\nu =\frac{4}{3}\), \(\frac{3}{2}\) and \(\frac{5}{3}\) with partitioning of the fractional inner mode. Error bars correspond to the variance of the Gaussian fit obtained by fitting the 2D-FFT with a Gaussian function (see Supplementary Information Section 15). c–f, ΔRD shown as \({V}_{{\rm{PG}}}-B{| }_{{\alpha }_{{\rm{c}}}}\) pajamas for \(\nu =-\,\frac{2}{3}\), \(-\frac{1}{3}\), \(\frac{4}{3}\) and \(\frac{5}{3}\), respectively. At \(\nu =\frac{5}{3}\), α deviated from αc by 3%. g–j, Corresponding 2D-FFTs, along with the extracted magnetic-field periodicities. We show all 2D pajama plots measured with partitioning of all of the fractional modes, before subtracting the average value at each magnetic field to discuss how changes in B and VCG affect the QPC transmission (see Supplementary Information Section 18). a.u., arbitrary units.

Theoretically, the question of which type of quasiparticle tunnel is addressed on the basis of the renormalization of bare tunnelling amplitudes by the interactions intrinsic to fractional edge modes. The bare tunnelling amplitudes for different quasiparticles are non-universal and hard to calculate reliably. Their renormalization, encoded by means of a scaling dimension of tunnelling operators, is the same for fundamental and \({e}^{* }=\frac{2}{3}e\) quasiparticles at the \(\nu =\frac{2}{3}\) edge[52]. It is possible that interactions across the QPC tip the balance in favour of \({e}^{* }=\frac{2}{3}e\) tunnelling. Alternatively, when both tunnelling processes occur with comparable probabilities, the 3Φ_0 periodicity expected for \(\frac{1}{3}e\) quasiparticles could be thermally suppressed because it requires exciting a neutral mode, which propagates with a much smaller velocity than the charge mode. At half-filling, the scaling dimensions of \({e}^{* }=\frac{1}{4}e\) quasiparticles depend on which topological state is realized but their numerical values are generally close to those of \({e}^{* }=\frac{1}{2}e\) tunnelling. Different interactions across the QPC could favour either and a similar thermal suppression may affect the \(\frac{1}{4}e\) quasiparticle, which also excites a neutral mode. We also warn that the scaling dimensions extracted from experiments often deviate substantially from theoretical expectations.

Statistical phase from bulk anyons in FPI

Interference of fractional quasiparticles fundamentally differs from that of electrons by quantum statistical effects, that is, the second term in equation (1). Interfering quasiparticles acquire a quantized phase change for each localized anyon in the interferometer bulk. For electron interference, this extra phase is an unobservable multiple of 2π independent of the bulk anyon type. To observe such contributions, we operate the FPI at α ≠ αc, such that tuning the magnetic field or VCG causes the filling factor to deviate slightly from the rational value (\(\nu =\frac{p}{q}\)) reflected by the bulk Hall conductivity. These deviations introduce excess charge carriers in the form of quasiparticles inside the interference loop. Each well-isolated quasiparticle in the bulk is expected to result in a sharp phase jump in the interference pattern. Similar to previous studies11,16,18,19, we observe such discrete phase slips at \(\nu =-\,\frac{1}{3}\), which are analysed in Supplementary Information Sections 19 and 20. Introducing quasiparticles at a constant rate along a fixed α trajectory alters the overall slope of the constant-phase lines in the pajama pattern. This change in slope is more robust against fluctuations in the number of bulk quasiparticles around its mean than individual phase slips. Such fluctuations can broaden and even entirely wash out phase slips (see Supplementary Information Section 21). We attribute the absence of phase slips at both half-fillings and hole-conjugate states to such fluctuations and proceed with a careful analysis of the slope.

The change of the slope provides crucial insights into which quasiparticles enter the interference loop as the filling factor varies. In Supplementary Information Section 21, we show theoretically that the slope can even distinguish between different bulk quasiparticle types being introduced as ν changes. For the case in which interfering quasiparticles entering equation (1) carry charge e* = νLLe and fundamental quasiparticles are introduced into the bulk, we find

$$\begin{array}{ll}\text{Integer edge modes}: & \frac{{\varPhi }_{0}}{\Delta B}=A=\text{constant},\\ \text{Fractional edge modes}: & \frac{{\varPhi }_{0}}{\Delta B}=({\nu }_{{\rm{LL}}}-\nu )A+\nu \frac{{\alpha }_{{\rm{c}}}}{\alpha }A.\end{array}$$

(2)

Because the mutual statistics with the \({e}^{* }=\frac{1}{2}e\) quasiparticles with all other quasiparticles is Abelian, equation (2) holds for all paired states. The second line matches the phase θ = 2π⟨N⟩, with N the number of electrons in the loop, generalizing the result in ref. [46]. If filling-factor deviations introduce quasiparticles other than the fundamental ones into the bulk, the slope on the right-hand side (ναcA) changes. We warn that strong bulk–edge coupling would also affect the slope and lead to an α dependence of the Aharonov–Bohm pattern even at integer fillings. We analyse this scenario in detail in Supplementary Information Sections 22 and 23 and show that it does not apply to our observations in the fractional case (see Supplementary Information Section 24).

We extract \(\frac{{\varPhi }_{0}}{\Delta B}\) from the 2D-FFT for the fillings \(\nu =\frac{4}{3}\), \(\frac{3}{2}\) and \(\frac{5}{3}\), for partitioning of both the fractional inner modes and the integer outer modes. Figure 4 shows our results for each α, with \(\frac{{\varPhi }_{0}}{\Delta B}\) obtained from the 2D-FFT of the corresponding pajama patterns. In Supplementary Information Section 25, we show all 2D pajama images and corresponding 2D-FFT analyses for all fractional filling factors discussed in this study. For all of the integer outer modes, ΔB is independent of the α, as expected. By contrast, for the fractional modes, all measurements collapse into a single linear dependence on \(\frac{1}{\alpha }\) as in the second line of equation (2). Their slope deviates by 15% from the numerical value expected on the basis of the bulk capacitance C, obtained through the Streda formula for the region to the right of the FPI. This discrepancy can arise from boundary effects of the comparatively small centre gate, small changes in the interference area with VCG and bulk–edge couplings (see Supplementary Information Section 26). At \(\nu =\frac{3}{2}\) with an interfering inner mode, the observed slope matches the theoretically expected statistical contribution of bulk \({e}^{* }=\frac{1}{4}e\) quasiparticles to the interference of Abelian \({e}^{* }=\frac{1}{2}e\) quasiparticles.

Fig. 4: Statistical contribution to the interference of fractional quasiparticles.

Magnetic-field periodicities \(\frac{{\varPhi }_{0}}{\Delta B}\) obtained along different trajectories α from 2D-FFTs for the fractional inner and integer outer modes of \(\nu =\frac{4}{3}\), \(\frac{3}{2}\) and \(\frac{5}{3}\). Error bars correspond to the variance of the Gaussian fit obtained by fitting the 2D-FFT with a Gaussian function (see Supplementary Information Sections 15, 25).

Conclusions

Our results mark two notable advancements towards the long-standing goal of observing non-Abelian statistics, bringing this objective within reach. The first essential condition—quasiparticle coherence in candidate non-Abelian states—is demonstrated by the observation of Aharonov–Bohm interference at two even-denominator FQH states. The second condition—interference contributions from localized bulk quasiparticles—was also observed by tuning the magnetic field and density to deviate from constant filling. Notably, our observations indicate that the localized bulk quasiparticles exhibit a charge of \(\frac{1}{4}e\), as expected for non-Abelian quasiparticles.

The observed flux periodicity of 2Φ_0 is consistent with the non-Abelian double-winding scenario or with a scenario in which it arises from the interference of Abelian \(\frac{1}{2}e\) quasiparticles. Distinguishing between these two possibilities is crucial for conclusively identifying non-Abelian behaviour and could be achieved through shot-noise measurements at a single QPC. If the Abelian \(\frac{1}{2}e\) quasiparticles are indeed responsible for the interference signal, developing techniques to facilitate the tunnelling of fundamental quasiparticles at the QPC will be essential. Adjusting the saddle-point potential or screening of the interedge interactions could affect the characteristics of the QPCs in this way. Our observation of an apparent interfering charge being twice the fundamental one at \(\nu =-\,\frac{2}{3}\) and \(\frac{5}{3}\) indicates that Abelian hole-conjugate states can provide valuable insights on how to control tunnelling of different quasiparticle types. Resolving this question in bilayer graphene could permit direct observation of non-Abelian statistics at several distinct FQH phases.

Methods

Stack preparation

In this study, we use vdW heterostructures in which a bilayer graphene layer is encapsulated between hBN and graphite layers. To prepare flakes from bulk graphite crystals, SiO_2/Si substrates are cut into 10-mm × 10-mm pieces. These pieces are placed on tape, which is used to exfoliate the bulk crystals. The exfoliated flakes are then transferred onto the SiO_2/Si substrate and heated on a hotplate at 170–180 °C for 90 s. When the tape has cooled, these pieces are removed to search for the desired bilayer graphene and graphite flakes. After cooling, the tape is removed and the flakes are inspected to identify suitable bilayer graphene and graphite layers. hBN flakes are prepared in a similar manner by exfoliating bulk hBN crystals using thin polydimethylsiloxane (PDMS). The stack is prepared using polycarbonate stamps held with Kapton tape, placed on a diamond-shaped PDMS layer on top of a glass slide. To ensure strong adhesion between the polycarbonate film and the PDMS, the stamps are placed on a hotplate at 170–180 °C for 2 h. The transfer stage is heated to 130–131 °C, allowing the sequential pickup of all vdW layers in the following order: top graphite, top hBN, bilayer graphene, bottom hBN and bottom graphite. A thickness of 29 (27) nm for the top (bottom) hBN and 5 nm for the top and bottom graphite is used in the fabricated device. The estimated bulk-gate capacitance Cb is approximately 2.2 × 10−15 F, yielding the bulk charging energy Ec = 36 μeV. The prepared stack is transferred at 180 °C on a clean SiO_2/Si substrate and left for roughly 15 min at 180 °C to melt the polycarbonate and detach it from the PDMS. The polycarbonate film is dissolved by placing the sample in chloroform for 3–4 h and subsequently cleaned with isopropyl alcohol and deionized water. Subsequently, the stack on the SiO_2/Si substrate undergoes thermal annealing in an ultrahigh vacuum (about 10−9 torr) at 400 °C for 4 h and 30 min to remove residual contaminants and bubbles. Last, we use atomic force microscopy (AFM) ironing, for which contact-mode AFM is used with 100-nN mild force applied on the stack, to flatten the stack on an atomic scale by suppressing the random strain fluctuations induced while stacking. This ironing with AFM can improve the quality of the bulk, resulting in an increase in the gap of quantum Hall states.

Device fabrication

The bilayer-graphene-based electronic FPI is fabricated on a five-layer vdW heterostructure placed on a highly p-doped Si substrate with a 280-nm SiO_2 oxide layer, using standard nanofabrication and lithography techniques. The process begins with creating alignment markers for the electron-beam lithography and bonding pads, using Ti (10 nm)/Au (60 nm)/Pd (20 nm). The device geometry is defined through reactive ion etching with poly(methyl methacrylate) (PMMA) resist serving as the etch mask. To etch out two main materials used in the heterostructure, hBN and few-layer graphite layers, O_2/CHF_3 mixture with the volume ratio of 1:10, and O_2 are used as etching gas for hBN and graphite, respectively. After defining the geometry, the sample undergoes thermal annealing in ultrahigh vacuum (about 10−9 torr) at 350 °C for 2 h and 30 min for resist residual removal on the stack. A trench approximately 40 nm wide is etched into the top graphite using mild O_2 plasma conditions to minimize damage to the top hBN layer, dividing the top graphite into eight sections. Finally, bridges are fabricated to independently tune the potential of each graphite section. This is achieved using PMMA/MMA/PMMA trilayer resists, followed by a 20-s mild O_2 plasma etch and the subsequent evaporation of Cr (5 nm)/Au (320 nm). To achieve highly transparent contacts between the metal and the bilayer graphene, the edge contacts are fabricated in the last step by etching the top hBN layer with O_2/CHF_3, followed by angled evaporation of Cr (2 nm)/Pd (20 nm)/Au (60 nm), avoiding that the contacts are exposed to lots of heat during device fabrication.

Measurements

The device is measured in a highly filtered dilution refrigerator at the base temperature of 10 mK using the standard low-frequency lock-in amplifier technique. An SRS 865A lock-in amplifier generates an alternating voltage at 13.7 Hz and measures the voltage difference between two contacts. A 100-MΩ load resistor is included in series with the lock-in amplifier, allowing the system to source an alternating current ranging from 50 to 0.5 nA. A QDAC, an ultralow-noise 24-channel digital-to-analogue converter (Qdevil-QM), is used to tune the voltages applied to all graphite gates and the two air bridges. Furthermore, a Keithley 2400 voltage source is used to apply a voltage to the highly p-doped Si substrate, doping the contact region and improving the contact resistance.

Plunger-gate spectroscopy

Plunger-gate spectroscopy[53] (Supplementary Figs. 6 and 15) reveals a divergence of the interference frequency for integer and most of the particle-like fractional states as the edge goes beneath the gate, consistent with a rapidly increasing gate–edge capacitance and enabling extraction of the edge–gate distance13,14. For hole-conjugate and even-denominator states, the oscillation visibility is suppressed as the edge approaches the gate, precluding direct resolution of the divergence within the accessible range. We attribute this contrast to a more complex edge structure and greater sensitivity to local disorder near the etched trenches. From the plunger-gate dispersion of the interference frequency (Supplementary Fig. 13), we extract the gate–edge separation. For integer and particle-like fractional states, the inferred distance decreases from  about 70 nm and  about 150 nm, respectively, to nearly zero as the edge moves under the plunger gate. These values are consistent with previous integer-regime measurements13,14 and extend the approach to odd-denominator fractional fillings (see Supplementary Information Section 12). For hole-conjugate and even-denominator states, the oscillations lose visibility as the edge nears the gate, obscuring the expected frequency divergence and preventing a reliable distance extraction over the accessible range. In these cases, QPC characterization (Supplementary Fig. 3f–i) identifies the participating edge by means of step-like transmission with plateaus, consistent with a fully transmitted integer outer mode.

Data availability

The data supporting the plots in this paper, as well as all custom analysis code written in Igor for data fitting and visualization, are available from the online depository Zenodo at https://doi.org/10.5281/zenodo.17454013 (ref. [54]).

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Acknowledgements

It is a pleasure to thank M. Heiblum, B. Halperin and P. Kim for the illuminating discussions. Funding: J.K. acknowledges support from the Dean of the Faculty and the Clore Foundation. Y.R. acknowledges the support from the Quantum Science and Technology Program 2021, the Schwartz Reisman Collaborative Science Program, supported by the Gerald Schwartz and Heather Reisman Foundation, supported by a research grant from the Goldfield Family Charitable Trust, the Minerva Foundation with funding from the Federal German Ministry for Education and Research, the European Research Council Starting Investigator Grant Anyons, 101163917 and the support by a research grant from the Estate of Hermine Miller, the Sheba Foundation and Dweck Philanthropies, Inc. D.F.M. acknowledges support from the ISF (grant 2572/21), from the Minerva Foundation with funding from the Federal German Ministry for Education and Research and the DFG (CRC/Transregio 183). A. Stern acknowledges support from the ISF, ISF Quantum Science and Technology (grant 2074/19) and the DFG (CRC/Transregio 183).

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Author notes

1. These authors contributed equally: Jehyun Kim, Himanshu Dev

Authors and Affiliations

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

Jehyun Kim, Himanshu Dev, Amit Shaer, Ravi Kumar, Alexey Ilin, André Haug, Shelly Iskoz, David F. Mross, Ady Stern & Yuval Ronen

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

Kenji Watanabe

3. International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan

Takashi Taniguchi

Contributions

H.D. and S.I. prepared the stacks. K.W. and T.T. provided the hBN crystals. J.K., H.D., A.I. and R.K. improved the quality of the device. J.K. fabricated the device. A.H. and R.K. developed the measurement circuit and a dilution refrigerator. J.K. performed the measurements. J.K., H.D., A. Shaer, A. Stern, D.F.M. and Y.R. analysed the measured data. A. Shaer, A. Stern and D.F.M. developed the theoretical aspect. J.K., H.D., A. Shaer, A. Stern, D.F.M. and Y.R. wrote the paper, with input from all authors. Y.R. supervised the overall work carried out on the project.

Corresponding author

Correspondence to Yuval Ronen.

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Kim, J., Dev, H., Shaer, A. et al. Aharonov–Bohm interference in even-denominator fractional quantum Hall states. Nature 649, 323–329 (2026). https://doi.org/10.1038/s41586-025-09891-2

- Received: 02 January 2025

- Accepted: 10 November 2025

- Published: 07 January 2026

- Version of record: 07 January 2026

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09891-2

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Programmable on-chip nonlinear photonics

Ryotatsu Yanagimoto,

Benjamin A. Ash,

Mandar M. Sohoni,

Martin M. Stein,

Yiqi Zhao,

Federico Presutti,

Marc Jankowski,

Logan G. Wright,

Tatsuhiro Onodera &

…

Peter L. McMahon

Nature volume 649, pages 330–337 (2026)

Abstract

Nonlinear optics[1] plays a central role in many photonic technologies, both classical2,3,4,5 and quantum6,7,8. However, the function of a nonlinear-optical device is typically determined during design and fixed during fabrication[9], restricting the use of nonlinear optics to scenarios in which this inflexibility is tolerable. Here we present a photonic device with highly programmable nonlinear functionality: an optical slab waveguide with an arbitrarily reconfigurable two-dimensional distribution of χ(2) nonlinearity. The nonlinearity is realized using electric-field-induced χ(2) (refs. 10,11,12,13,14,15,16), and the programmability is engineered by massively parallel control of the electric-field distribution within the device using a photoconductive layer and optical programming with a spatial light pattern. To showcase the versatility of our device, we demonstrate spectral, spatial and spatio-spectral engineering of second-harmonic generation by tailoring arbitrary quasi-phase-matching grating structures[1] in two dimensions. The programmability of the device makes it possible to perform inverse design of grating structures in situ, as well as real-time feedback to compensate for fluctuations in operating and environmental conditions. Our work shows that we can break from the conventional one-device–one-function paradigm, potentially expanding the applications of nonlinear optics to situations in which fast device reconfigurability is desirable—such as in programmable optical quantum gates and quantum light sources7,17,18,19, all-optical signal processing[20], optical computation[21] and adaptive structured light for sensing22,23,24.

Similar content being viewed by others

Main

Nonlinear optics (NLO)[1] encompasses a diverse range of processes, including sum-frequency, difference-frequency and parametric generation, and has become a central tool in many optical technologies2,3,4,5,6,7,8. The NLO processes used in nonlinear photonics are usually not accessible in raw materials because, in their natural state, raw materials do not satisfy the conditions for phase-matching, which is essential for an NLO process to be efficient[1]. A particularly efficacious and flexible technique for achieving phase-matching artificially is quasi-phase matching (QPM). In QPM, a periodic spatial modulation of χ(2) optical nonlinearity, which is referred to as a QPM grating, compensates for phase mismatch among interacting light waves25,26. A simple QPM grating enables highly efficient coherent-wave mixing, whereas more complicated ones can realize highly nontrivial nonlinear-optical functions. Many exotic QPM grating structures have been explored, enabling a wide range of functions including high-harmonic generation27,28, arbitrary pulse shaping[29], quantum-pulse gates[17] and holographic generation of structured light30,31,32.

These functions of an NLO device, including engineering phase-matching, often require special structures to be ‘sculpted’ in the raw nonlinear-optical material, and this is usually done by means of nanofabrication processes[9]. For instance, a QPM grating can be formed by periodically inverting the crystal axis of a nonlinear material. Fabricating such a structure requires sophisticated techniques—for example, epitaxial growth of semiconductors on orientation-patterned wafers[33] or domain inversion of ferroelectric materials by means of lithographically patterned electrodes[34]. Although this sculpting-based model has driven decades of progress in nonlinear photonics, it restricts the design and use of photonic devices because each device is typically optimized for one function that is fixed when the device is made. The performance of the device can also be very sensitive to fabrication imperfections as well as operating and environmental conditions that deviate from those that the device was designed for[35], which lowers the yield of correctly functioning devices.

We present an approach that avoids many of these disadvantages: NLO based on a programmable nonlinear waveguide. Our proposed device is a planar optical waveguide whose two-dimensional distribution of χ(2) optical nonlinearity, χ(2)(x, z), can be arbitrarily programmed, that is, dynamically set and updated (x and z are the transverse and longitudinal dimensions of the waveguide, respectively; see Fig. 1a). Such programmable χ(2) nonlinearity allows flexible engineering of QPM gratings to perform various NLO functions with a single device. The programmable χ(2) nonlinearity is induced by biasing the χ(3) nonlinearity with an electric field Ebias(x, z), leading to χ(2)(x, z) = 3χ(3)Ebias(x, z). Electric-field-induced χ(2) nonlinearity36,37,38 has recently been used to engineer a variety of NLO processes10,11,12,13,14,15,16 in which the bias field was applied by means of lithographically patterned electrodes10,11 or all-optically12,13,14,15,16. The all-optical approach reconfigures the spatial pattern of nonlinearity depending on how the device is optically pumped. However, completely arbitrary spatial patterns of χ(2) nonlinearity have not been realized, and this challenge also applies to other approaches to tuning χ(2) nonlinearity, such as using ferroelectric nematic liquid crystals39,40. Reconfigurable QPM structures have also been limited to one-dimensional geometries.

Fig. 1: Illustration of the working principle and capabilities of a programmable nonlinear waveguide.

a, Structured light projected on the surface of a planar waveguide plays the role of a programming illumination I(x, z), inducing the same pattern of χ(2) nonlinearity, χ(2)(x, z), which allows versatile control of broadband SHG by means of QPM. Here x and z are the transverse and longitudinal positions on the waveguide, respectively. b, The structure and physical working mechanism of a programmable waveguide. The device is composed of a SiN waveguide (2.05-μm-thick SiN core and 1-μm-thick SiO_2 cladding at the top and bottom), a photoconductor layer (7.5-μm-thick SRN) and a transparent electrode (20-nm-thick ITO). The photoconductor, when illuminated by green (532-nm) laser light, becomes locally conductive, letting the external bias electric field through to the waveguide core. The resulting spatially shaped Ebias(x, z) induces a spatially programmable χ(2) nonlinearity according to χ(2)(x, z) = 3χ(3)Ebias(x, z). See Methods and Supplementary Information for details on the device fabrication and electrical characterization of the device, respectively. c, Varying the longitudinal and transverse structure of QPM gratings enables spectral and spatial control, respectively, of NLO. By programming the full two-dimensional structure of χ(2)(x, z), we can simultaneously engineer the spectral and spatial structure of the generated output light.

In our planar waveguide device, we use lithography-free photoconductive electrodes and patterned optical illumination to program arbitrary spatial patterns into the bias field, Ebias(x, z). This programmable bias field produces a corresponding programmable χ(2)(x, z). Patterned optical illumination has previously been used to program the real[41] and imaginary[42] parts of the refractive-index distribution of planar waveguides. Here we demonstrate a programmable χ(2)(x, z) nonlinearity with a dynamic range (that is, contrast) of 0.47 pm V−1, a spatial resolution of 7.5 μm and a functional area (z × x) of approximately 0.7 × 0.4 cm, with updates possible every second (Supplementary Information). Using this full two-dimensional programmability, we experimentally realized complex QPM structures and demonstrated flexible control over the spectral, spatial and spatio-spectral dynamics of broadband second-harmonic generation (SHG). Moreover, the real-time reconfigurability of the device enables in situ inverse design and optimization of QPM grating structures, allowing us to engineer very unusual optical spectral and spatial shapes in a way that is robust to experimental imperfections.

Design and operating principle of the device

Our programmable nonlinear waveguide and how we realized arbitrary two-dimensional distributions of nonlinearity χ(2)(x, z) is illustrated in Fig. 1. Our programmable nonlinear waveguide comprised several layers (Fig. 1b; see Methods). The waveguide was made on a conductive silicon substrate. On top of the substrate was a silicon nitride (SiN) optical waveguide comprising silicon dioxide (SiO_2) cladding layers and a SiN core layer. On top of the upper cladding layer was a layer of photoconductive material—silicon-rich silicon nitride (SRN). Finally, a transparent electrode was deposited on the photoconductor layer. During operation, a bias electric field was applied across the entire stack by connecting a voltage source to the substrate and the top electrode.

To realize a programmable χ(2)(x, z), we shone programming illumination with a spatial intensity pattern I(x, z) onto the top of the device. The photoconductor layer became conductive where light intensity was highest and let the electric field from the bias voltage through to the SiN core layer (Supplementary Information). Consequently, the pattern of the programming illumination I(x, z) resulted in a pattern of the bias field Ebias(x, z) inside the core. The third-order nonlinear polarization induced by the sum of the bias field Ebias and the optical field Eopt (the field inside the waveguide, travelling in the z direction; not the programming illumination field) can be expanded as

$${P}_{{\rm{NL}}}={\chi }^{(3)}{({E}_{{\rm{bias}}}+{E}_{{\rm{opt}}})}^{3}={\chi }^{(3)}({E}_{{\rm{opt}}}^{3}+3{E}_{{\rm{bias}}}{E}_{{\rm{opt}}}^{2}+3{E}_{{\rm{bias}}}^{2}{E}_{{\rm{opt}}}+{E}_{{\rm{bias}}}^{3}).$$

(1)

Here an effective quadratic nonlinearity arises as a term proportional to \({E}_{{\rm{opt}}}^{2}\), whose coefficient χ(2)(x, z) = 3χ(3)Ebias(x, z) is proportional to the bias field. The central operating principle of our device can be summarized as: patterned programming illumination I(x, z) on the photoconductor layer induces a spatial pattern of electric field Ebias(x, z) inside the waveguide core, which then induces a spatial pattern of optical nonlinearity χ(2)(x, z).

The specific design we used for our device (choice of materials and layer thicknesses) constrained the χ(2)(x, z) we could realize (Supplementary Information). The first constraint was that fringing of the electric fields blurred the mapping from I(x, z) to Ebias(x, z), limiting the smallest programmable feature size to about 7.5 μm. The second constraint was that the resistor–capacitor (RC) time constant of the device limits the update speed of χ(2)(x, z) to about 20 Hz. The slow speed of the projector used in the set-up limited the update speed even more, to about 1 Hz.

The nonlinear-optical functionality of our device can be tailored by modifying the structure of χ(2)(x, z) (Fig. 1c). For example, consider narrowband SHG, in which light at frequency ω_1 is converted to light at frequency ω_2 = 2ω_1. Efficient frequency conversion requires that the momenta of interacting waves be matched. This requirement is quantified by the native phase mismatch,

$$\Delta k={k}_{2}-2{k}_{1},$$

(2)

in which kj is the wavenumber of light at frequency ωj (j ∈ {1, 2}). Efficient SHG can be achieved when this phase mismatch is compensated by a QPM grating—that is, by a periodic modulation of χ(2) with a period designed to offset Δk, for example, \({\chi }^{(2)}(x,z)\approx \sin (\Delta kz)\) (ref. [1]).

We performed experiments demonstrating three types of use of our device. First, we engineered the longitudinal (that is, z) structure of χ(2)(x, z) to control which wavelengths interact efficiently, enabling spectral-domain engineering of NLO. Second, we tailored the transverse (that is, x) structure of χ(2)(x, z), enabling spatial-domain engineering. For instance, if we set \({\chi }^{(2)}(x,z)\approx \sin (\Delta kz+\phi (x))\), with a spatially varying phase term ϕ(x), the generated second-harmonic (SH) light acquires a corresponding spatial phase profile, eiϕ(x), thereby shaping the output field in the transverse (x) direction. Finally, we used the full two-dimensional programmability of χ(2)(x, z)—in both the longitudinal and transverse directions—enabling simultaneous control of the light in both the spectral and spatial domains, giving rise to spatio-spectral engineering. Below, we present the results of our experiments.

Real-time programmable periodic poling

First we characterized the basic nonlinear-optical properties of the device by programming canonical QPM gratings with different poling periods Λ and measuring the power of the SHG when the device was pumped with a continuous-wave (CW) laser that had a tunable wavelength between λ = 1,500 and 1,630 nm (Fig. 2a). For this initial characterization, we fixed the poling period Λ and scanned the wavelength of the pump laser to measure the SHG conversion efficiency. Depending on the value of Λ, different wavelengths of pump light undergo phase-matched SHG (Fig. 2b). The nonlinearity of the device is proportional to the bias electric field Ebias, which should not be set higher than the breakdown field of the material. With the highest Ebias we applied, we found the induced χ(2) nonlinearity of χ(2) = 0.47 pm V−1 (Supplementary Information).

Fig. 2: Real-time programmable periodic poling with a programmable nonlinear waveguide.

a, Experimental set-up. We pumped a prototype programmable nonlinear waveguide using a CW laser with a tunable wavelength λ. A grating pattern with period Λ was projected on the waveguide, realizing QPM for a SHG process. The output SH power was measured by a photodetector and the measurements could be used to update Λ. b, Nonlinear-optical characterization of the device. (i) For various choices of Λ, we scanned λ and measured the SHG conversion efficiency, which we report as an efficiency normalized by input power. (ii) The optimal pump wavelength λ for each poling period Λ. The quadratic fit between Λ and an optimal λ yielded a nominal poling period of 16.65 μm and group-velocity mismatch (GVM) of −92 fs mm−1 between the fundamental and second harmonic at 1,560 nm. The colours of the markers serve as legends for Λ in (i). c, Real-time feedback control of Λ to compensate for a random walk of λ shown in (i). To compensate for these fluctuations, we dithered Λ and used the measured SHG signal to update Λ in a way that maximizes the signal. The evolution of Λ is shown as a solid green line in (ii). In (iii), the SHG efficiency with and without such real-time feedback control is shown as blue solid and grey dashed lines, respectively. For all of the measurements, the bias voltage was 1,000 V. The nominal on-chip pump power, inferred from the transmission, ranged between 2.2 and 4.5 mW, depending on the wavelength, primarily because of the wavelength-dependent loss of the core. See Supplementary Information for experimental details.

We took advantage of the ability to reprogram the poling period in our initial device characterization, but in these experiments, the programming did not have to take place quickly. To showcase the ability of our device to be programmed in real time, we performed an experiment to show that it is possible to compensate for environmental noise and drifts by adjusting the poling period on a timescale of approximately 1 s (Fig. 2c). To emulate large noise, we artificially modulated the pump wavelength λ so that it followed a Gaussian random walk. The compensation task was to dynamically change the poling period Λ to maximize the SHG efficiency, without being given information about the random changes in λ. We used a feedback scheme in which Λ was dithered to obtain an error signal and the error signal was used to update Λ. The data clearly show that, when the feedback controller was on, Λ closely followed the evolution of the pump wavelength λ, maintaining a high level of SHG efficiency. On the other hand, when the feedback controller was off, the SHG efficiency dropped to near zero relatively quickly.

Spectral engineering

Next we show how the programmable nonlinear waveguide can be used to manipulate the spectral shape of the generated SH light by programming χ(2)(x, z) in the longitudinal (z) direction. In each of the experiments, we pumped a prototype device with an ultrashort pulse laser and measured the output SH spectrum using a spectrometer.

We measured the SHG spectra for various manually designed QPM grating structures (Fig. 3a) to verify that our device could reproduce well-known results in NLO. As a reference case, we programmed a grating pattern with a single period Λ, which phase-matched SHG for a particular pump wavelength (Fig. 3a (i)). This manifested as a single, narrow peak in the SH spectrum. Beyond such a simple grating pattern, we programmed a summation of several grating patterns with different periods (Fig. 3a (ii)). This super-grating pattern can simultaneously phase-match various SHG processes and can generate several wavelengths of SH, which are visible as peaks in the recorded spectrum. Finally, we programmed a chirped grating—a grating in which the period is changed as a function of the longitudinal position—and observed broadband SHG output (Fig. 3a (iii)), consistent with previous non-programmable demonstrations of adiabatic SHG[43]. For more general spectral features, refs. 44,45 presented and demonstrated frameworks for analytically designing QPM grating structures, which could be used to design patterns for the programmable device we present.

Fig. 3: Spectral engineering of SHG.

a, Output SH spectrum of broadband SHG pumped by ultrashort pump pulses for various illumination patterns. The bias voltage was 500 V. (i) Periodic grating with a period Λ = 16.64 μm. (ii) Superposition of four grating patterns with different periods. (iii) An adiabatically chirped grating pattern. Owing to the rapid spatial oscillations of these QPM gratings, showing the raw illumination patterns is not visually informative. Instead, in the green patterns shown above the results plots, we present the QPM grating patterns downsampled to a spatial period of 17 μm in the longitudinal direction. The same applies to the patterns shown in b. See Supplementary Information for the original (non-downsampled) illumination patterns. b, By constructing a feedback loop based on the measured SH spectrum, we optimized the illumination pattern to obtain various target SH spectra. Dashed lines represent the target spectrum. The bias voltage was 800 V. c, The illumination pattern was updated in real time to output a sequence of SH spectra, using pre-recorded illumination patterns. We show the results for drawing ‘CORNELL’ in the SH spectrum, with time as the horizontal axis of the image. The bias voltage was 800 V. See Supplementary Information for experimental details. For all of the measurements, we used a pulse laser with 60-fs pulse duration and 100-MHz repetition rate. The average on-chip pump power inferred from the transmission was 6 mW.

Up to this point, the illumination patterns we used to program the waveguide were designed manually, in that we designed them on the basis of standard knowledge of NLO. To demonstrate the ability to shape the SHG spectrum in ways that are probably impractical using conventional NLO devices, which do not support real-time reconfiguration, we arbitrarily shaped and dynamically updated the SHG spectrum. We achieved this by constructing a real-time feedback loop between the broadband SH spectral measurement and update of the programming illumination patterns (Fig. 3b) (Supplementary Information). This approach enables spectral engineering in a way that is robust against imperfections and miscalibrations in the device and experimental set-up.

The illumination patterns that are optimized for in inverse design can be stored and later retrieved to program a sequence of nonlinearity distributions in real time. We demonstrated this by drawing ‘CORNELL’ in the SH spectrum as a function of time (Fig. 3c); the programming illumination pattern was updated every few seconds, for which the update speed was limited by that of the spatial light modulator (about 1 s) we used to pattern the illumination.

Spatial engineering

In this section, we show the ability to engineer the spatial structure of light generated using our programmable nonlinear waveguide by controlling phase-matching conditions in the transverse dimension, as has been demonstrated previously in non-programmable NLO30,32,46,47. Here we pumped the programmable nonlinear waveguide with a pulse with a fixed Gaussian spatial beam shape (beam waist: 132 μm) and imaged the output SHG profile for various programming illumination patterns, that is, different distributions of χ(2)(x, z) (Fig. 4a).

Fig. 4: Spatial engineering of SHG.

a, Experimental set-up. The waveguide was pumped with a pulse pump laser with fixed Gaussian spatial profile, and the spatial distribution of the generated SH on a camera was measured. b, A part of the normalized programming illumination pattern (left column), simulated SHG dynamics within the waveguide (middle column) and a comparison between the experimentally measured and simulated SH profiles (right column). (i) Monotonic grating pattern. (ii) Quadratically chirped grating pattern. (iii) Superposition of nine quadratically chirped grating patterns with transverse offsets. (iv) Cubically chirped grating pattern. For all of the measurements, we used a pulse laser with 60-fs pulse duration and 100-MHz repetition rate. The average on-chip pump power inferred from the transmission was 6 mW. The bias voltage was 600 V. See Supplementary Information for experimental details.

As a reference, we first projected a simple, flat (that is, constant in the transverse dimension) grating pattern with a period of 16.75 μm, corresponding to phase-matched SHG near 790 nm. The output SHG also had a Gaussian profile with a large beam waist of 94 μm (Fig. 4b (i)). Then we performed experiments in which the phase of the QPM grating was spatially varied and observed that the generated SH light inherited the phase of the grating, which allowed us to engineer the spatial profiles of the SHG. For instance, by quadratically chirping the phase of the grating, we were able to focus the generated SH light to the output facet, resulting in a substantially narrower beam waist of 16 μm (Fig. 4b (ii)).

More complex patterns can be produced by superimposing several grating structures; for example, nine quadratically curved grating patterns, evenly spaced in the transverse direction, focused SHG into nine distinct peaks (Fig. 4b (iii)). This approach can, in principle, be used to generate arbitrary superpositions of Gaussian peaks.

Diffraction-free beams that maintain their spatial profiles during propagation are used in microscopy and imaging[47]. NLO can generate Airy beams—one-dimensional non-diffracting beams—by applying a cubic chirp to a QPM grating in the transverse direction. We reproduced the seminal demonstration from ref. [30] with our programmable platform: our spatially resolved measurement of the waveguide output (Fig. 4b (iv)) clearly shows the characteristic asymmetric interference fringes of an Airy beam.

The SH output profiles we recorded are in excellent agreement with theoretical simulations across all measurements; the simulations used only a single fitting parameter, for the overall amplitude. The match between experiment and theory makes it possible to optimize illumination patterns for engineering spatial features entirely in silico.

Spatio-spectral engineering

So far, we have shown independent control of spectral and spatial features of SHG by tailoring the longitudinal and transverse structure of QPM gratings, respectively. In this section, we show that it is possible to use the full two-dimensional programmability of the χ(2) nonlinearity to simultaneously tailor the spatial and spectral profiles of the generated light. The experimental set-up is illustrated in Fig. 5a. We projected patterns of light onto the programmable waveguide and pumped it with broadband optical pulses. The output SH light was measured by spectrally resolved one-dimensional imaging, in which the spectrum was recorded for each transverse spatial position by using a diffraction grating and a camera.

Fig. 5: Spatio-spectral engineering of SHG.

a, Experimental set-up. We combined a reflective grating with a 4f imaging set-up to record spectrally resolved one-dimensional spatial profiles of the output SH light. The waveguide was pumped by pulses with a fixed Gaussian spatial profile. b, Results for an illumination pattern designed to generate various numbers of spatial peaks at five different wavelengths. c, Results for an illumination pattern designed to generate oppositely chirped Airy beams at two different wavelengths. In each of b and c, the left inset shows a part of the projected grating pattern. The bottom-right inset shows the spatial distribution of the SH light at various wavelengths, marked with dashed lines in the top-right inset. For all of the measurements, we used a pulse laser with 60-fs pulse duration and 100-MHz repetition rate. The average on-chip pump power inferred from the transmission was 40 mW. The bias voltage was 600 V. See Supplementary Information for experimental details.

We aimed to obtain a spatio-spectral hologram in which the SHG output has a spatial profile that is a function of the output wavelength. To do this, we superimposed various QPM grating patterns with different longitudinal periods. In our first spatio-spectral experiment, we designed the grating structure to generate one, two, three, four and five spatial peaks at five different wavelengths (Fig. 5b). As shown in the one-dimensional hyperspectral image captured by the camera, we observed clearly separated Gaussian peaks localized in both space and wavelength. In our second experiment, we took inspiration from the SHG-based hologram proposed in ref. [30] as a means to generate different Airy beams for different wavelengths. Here we show that oppositely chirped Airy beams can be generated by combining two grating patterns with different longitudinal periods and opposite cubic spatial chirps. We clearly observed the characteristic asymmetric interference fringes of the Airy beams but in opposite directions for two separate wavelengths (Fig. 5c).

Discussion and outlook

Summary of the results

We developed a programmable nonlinear waveguide with an arbitrarily reconfigurable two-dimensional distribution of χ(2) nonlinearity. By engineering QPM gratings—both conventional and exotic, inverse-designed ones—we demonstrated versatile control over broadband SHG across the spectral, spatial and spatio-spectral domains. The programmability of our device enabled real-time in situ optimization of QPM grating structures using feedback from experimental measurements and the real-time playback of pre-designed gratings. Notably, all of the results reported in this paper were obtained using a single programmable nonlinear waveguide design and the same pulse laser (except in Fig. 2, in which we used a tunable CW laser). This highlights the flexibility and multifunctionality of programmable nonlinear waveguides.

Limitations and potential for improvements

In this study, we demonstrated a broad range of functions using programmable nonlinearity in a planar waveguide. However, our experimental prototype has several practical drawbacks that, although not fundamental, need to be addressed if the programmable-waveguide approach is to be widely used. First and foremost is the weak optical nonlinearity. On our prototype device, we estimated the maximum programmable χ(2) nonlinearity to be 0.47 pm V−1. Although this lies on the higher side of the reported values for electric-field-induced χ(2) in SiN nanophotonics (0.03–0.50 pm V−1)12,13,14,15, it is still low compared with conventional nonlinear-optical materials[6]. Fortunately, there are known methods to increase this value, improve the device speed and eliminate the need for AC operation (Supplementary Information). Second, we observed relatively large optical loss, ranging from a nominal value of about 1 dB cm−1 for wavelengths >1,550 nm to a peak value of roughly 5 dB cm−1 around 1,520 nm, owing to residual hydrogen in plasma-enhanced chemical vapour deposition (PECVD)-deposited SiN. This limits the use of the device for wavelengths near 1,520 nm. High-temperature annealing is known to substantially reduce such absorption by driving out hydrogen impurities, and optical loss as low as 0.4 dB m−1 has been demonstrated in SiN nanophotonic devices fabricated with low-pressure chemical vapour deposition48,49. Third, the conversion efficiency of the device was lower than that of other demonstrations in SiN nanophotonics, primarily because of the weak field confinement of the planar waveguide geometry12,13,14,15. To verify that it is possible to apply our approach to nanophotonic structures with tight field confinement, we fabricated and tested a programmable nonlinear channel waveguide. As shown in Methods, we observed a normalized efficiency of ηnorm = 2 × 10−3% W−1, representing a 40-fold improvement over our planar waveguide result of ηnorm = 5 × 10−5% W−1. Forming a resonant structure with a waveguide could further boost the conversion efficiency by recirculating light. Assuming state-of-the-art loss values for SiN and the performance of the channel waveguide we demonstrated, we estimate that a programmable microring resonator could possibly reach ηnorm > 1 × 10^7% W−1, which exceeds the present state of the art in integrated platforms (Supplementary Information).

Prospective applications

The ability to realize arbitrary χ(2)(x, z) distributions makes our device platform very versatile, particularly in enabling devices that must seamlessly switch between several functions. As shown in the demonstration of the programmable channel waveguide (Methods), our approach is, in principle, compatible with a wide range of device geometries beyond planar waveguides and could be seamlessly co-integrated with conventional photonic systems. Concretely, we have quantitatively analysed the potential for our approach to be applied in four example application areas: (1) on-chip arbitrary pulse shapers; (2) reconfigurable quantum frequency converters; (3) widely wavelength-tunable integrated light sources; and (4) quantum light sources with programmable entanglement structure (Supplementary Information). For these applications, we find that future SiN programmable nonlinear photonics could potentially achieve competitive or even state-of-art performance assuming the χ(2) nonlinearity demonstrated in this work, all while allowing the flexibility enabled by programmability. In several of our application analyses, a crucial feature enabling programmable SiN to deliver competitive performance is the exceptionally low material loss of SiN, which enables snaking channel waveguides to be metres long and still have low loss49,50. The long path lengths possible in SiN can fully compensate for the low χ(2) nonlinear coefficient and increase the programmable degrees of freedom. We expect that these four applications are not the only future possibilities: more speculatively, all-optical signal processing for optical communications[20] could benefit from reconfigurable nonlinear processes, as could classical optical computation[21] and sensing with structured light22,23,24. The scope of applications could further expand with the use of materials with higher inducible χ(2) nonlinearities[10]. Finally, in situ inverse design may enable quantitative control over nonlinear-optical processes for which we do not have accurate simulation models at present.

In conclusion, the ability to programmably control nonlinearity has the potential to circumvent the limitations of the conventional one-device–one-function paradigm. The programmable nonlinear waveguide we have proposed and the demonstrations of reconfigurable SHG we have reported with a prototype device take a step into this new frontier of NLO.

Methods

Fabrication of programmable nonlinear planar waveguides

In this section, we describe the fabrication process for the programmable nonlinear planar waveguide. As illustrated in Extended Data Fig. 1a, the device was composed of a stack of several material layers. Silicon Valley Microelectronics (SVM) supplied the substrates, including the bottom cladding and the core layer. The substrate was a conductive, boron-doped Si wafer with a resistivity in the range 0.01–0.02 Ω cm. The bottom cladding consisted of a 1-μm-thick wet thermal oxide layer, onto which approximately 2 μm of low-stress SiN was deposited by means of PECVD. Using a Metricon prism coupler, we measured the film thickness as dcore = 2.05 μm, with a thickness variation of approximately 50 nm across a 4-inch wafer. The refractive index of the film was specified as 1.98 at a wavelength of 632.8 nm.

We performed rapid thermal annealing (RTA) on the wafers acquired from SVM at 650 °C for 3 min. This RTA process reduced the refractive index of the film and eliminated undesired fluorescence in the near-infrared region when the waveguide was pumped near 800 nm. Because the results presented in the main text did not depend on this pumping wavelength, the RTA process shifted the phase-matching conditions without causing notable adverse effects.

Next we deposited a 1-μm-thick layer of SiO_2 as the top cladding using our in-house PECVD system (Oxford Instruments PlasmaPro 100 PECVD), thereby forming the planar SiN waveguide. To make the waveguide programmable, we further deposited a 7.5-μm-thick layer of SRN using PECVD. The SRN was deposited at an RF power of 200 W with gas flows of SiH_4: 8 sccm, H_2: 40 sccm and N_2: 2,000 sccm. We note that no NH_3 was used.

At this stage, we cleaved the wafer into rectangular pieces of approximately 1.0 × 1.5 cm. Although cleaving typically produces facets that are sufficiently clean for light coupling, more polishing can further improve the beam profile quality. Finally, we deposited a 20-nm-thick layer of indium tin oxide (ITO) as a transparent electrode using a physical vapour deposition (PVD) system (PVD 75, Kurt J. Lesker). It was important to leave several millimetres of space between the electrode and the chip edge to prevent electrical breakdown of the air at the boundary. A picture of the resultant programmable nonlinear planar waveguide is shown in Extended Data Fig. 1b.

Fabrication and characterization of programmable nonlinear channel waveguides

In the main text, we primarily focus on a programmable planar waveguide. Although this design allowed us to engineer spatial features of light, it came with the drawback of lower light confinement, which reduced the conversion efficiency. One possible solution to this challenge is to fabricate a channel waveguide structure to prevent spatial diffraction, which enables light propagation over longer distances while maintaining tight transverse confinement. To demonstrate the compatibility of our approach with this channel waveguide geometry, we fabricated a prototypical programmable channel waveguide using SiN and characterized its performance.

The device was fabricated with the following steps. First, SVM provided a Si substrate with a 1-μm bottom oxide cladding and a 2-μm PECVD-grown SiN layer, identical to the substrate type used for the programmable planar waveguides. Next we deposited a 500-nm-thick SiO_2 layer using PECVD, followed by sputtering a 200-nm-thick Cr layer. For photolithography, we spin-coated a deep ultraviolet (DUV) anti-reflective coating and photoresist. Exposure was performed using a DUV stepper (PAS 5500, ASML), after which we developed the photoresist and wet-etched the Cr to form a hard mask. We then etched through the SiO_2 and SiN layer using CHF_3/O_2/N_2 gases in a plasma etcher (PlasmaPro 100 RIE, Oxford Instruments). We then deposited 3 μm of SiO_2 with PECVD and subsequently etched away 2.5 μm of oxide using CHF_3/O_2 gases in the same plasma etcher, which added 500 nm of oxide on top of the waveguide. Notably, 1.5 μm of oxide remained on the waveguide sidewalls owing to the conformal oxide deposition and anisotropic oxide etching. This extra oxide on the sidewall reduces loss into the photoconductor layer. At this stage, we performed RTA at 800 °C for 5 mins. Finally, we deposited a 12-μm layer of SRN and sputtered a 20-nm layer of ITO as a transparent electrode. A cross-sectional scanning electron microscope (SEM) image of the fabricated channel waveguide is shown in Extended Data Fig. 2a. The SiN core was approximately 4 μm wide and 2 μm high. The waveguide had an overall length of 1.5 cm, with the programmable region limited to approximately 7 mm owing to constraints of the imaging set-up.

We then conducted a nonlinear-optical characterization of the device. We coupled CW pump light with various wavelengths between 1,540 and 1,600 nm to the waveguide. The pump light is polarized in the vertical (that is, y) direction, which is expected to mainly excite the fundamental transverse magnetic (TM) mode, and the SHG takes place to the fundamental TM mode of the SH wavelength. We show the mode profiles of these modes in Extended Data Fig. 2b. We measured the generated SHG while scanning the period of the monotonic QPM grating. We calculated the normalized SHG conversion efficiency by dividing the detected SHG power by the pump power squared measured after the waveguide, including appropriate corrections for propagation losses and collection inefficiencies (Supplementary Information). The measurement results are shown in Extended Data Fig. 3, which clearly demonstrate the capability to achieve phase-matching at a desired pump wavelength by adjusting the QPM grating period.

Note that our waveguide core measures approximately 2 × 4 μm in cross-section, supporting several spatial modes even at the pump wavelength. Moreover, the skewed aspect ratio of the core made it difficult to couple the pump light exclusively into the fundamental mode. Consequently, the pump power measured after the waveguide could include contributions from higher-order modes that do not contribute to SHG, which explains the wavelength-dependent variations in the normalized conversion efficiency. Thus, the efficiencies shown in Extended Data Fig. 3 should be considered a lower bound of the device performance. The maximum normalized conversion efficiency observed was ηnorm = 2 × 10−3% W−1, corresponding to a normalized slope conversion efficiency of η_0 = 4 × 10−3% W−1 cm−2, showing an approximately 40-fold improvement over the programmable nonlinear planar waveguide results presented in the main text. We note that reducing the core thickness below the micron scale to achieve single-mode operation causes the first-order poling period to fall below the smallest programmable feature size. If single-mode operation is given priority, higher-order QPM can be used, albeit with reduced conversion efficiency. At the same time, the tighter field confinement increases nonlinear coupling and could compensate for the reduction of conversion efficiency.

Data availability

Experimental data and scripts to replicate the figures in this paper are available at https://doi.org/10.5281/zenodo.17074707 (ref. [51]).

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52. Bolla, L. EMpy - Electromagnetic Python. GitHub https://github.com/lbolla/EMpy (2017).

Acknowledgements

We thank T. Chervy, J. Clark, N. Flemens, R. Hamerly, O. Jaramillo, G. Marega, J. Moses, R. Nehra, E. Ng, T. Takahashi, T. Wang, A. Willner and Y. Yamamoto for helpful comments and discussions. We thank C. Bacon and Springer Nature Author Services for English language editing. We thank NTT Research for their financial and technical support. We gratefully acknowledge the Air Force Office of Scientific Research for funding under award number FA9550-22-1-0378 and the National Science Foundation for funding under award number CCF-1918549. This work was performed in part at the Cornell NanoScale Facility, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (grant NNCI-2025233). P.L.M. gratefully acknowledges financial support from a David and Lucile Packard Foundation Fellowship.

Author information

Author notes

1. Martin M. Stein & Logan G. Wright

Present address: Department of Applied Physics, Yale University, New Haven, CT, USA

2. Federico Presutti

Present address: Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA

Authors and Affiliations

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

Ryotatsu Yanagimoto, Benjamin A. Ash, Mandar M. Sohoni, Martin M. Stein, Yiqi Zhao, Federico Presutti, Logan G. Wright, Tatsuhiro Onodera & Peter L. McMahon

2. Physics & Informatics Laboratories, NTT Research, Inc., Sunnyvale, CA, USA

Ryotatsu Yanagimoto, Marc Jankowski, Logan G. Wright & Tatsuhiro Onodera

3. E. L. Ginzton Laboratory, Stanford University, Stanford, CA, USA

Marc Jankowski

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

Peter L. McMahon

Contributions

R.Y., T.O., L.G.W. and P.L.M. conceived the project. R.Y., B.A.A., M. M. Stein, T.O. and L.G.W. designed the devices. R.Y., T.O. and P.L.M. designed the experiments. R.Y., B.A.A. and Y.Z. fabricated the device, with aid and recipe development from M. M. Stein and T.O. R.Y. and M. M. Sohoni designed and built the imaging set-up to program the χ(2) nonlinearity pattern. R.Y. designed and built the set-up for nonlinear-optical experiments, with aid from F.P. and M.J. R.Y. and M. M. Sohoni wrote the code for real-time optimization of the QPM grating. R.Y. performed the experiments, analysed the results and produced the figures. R.Y. and P.L.M. wrote the manuscript, with input from all authors. P.L.M. supervised the project.

Corresponding authors

Correspondence to Ryotatsu Yanagimoto or Peter L. McMahon.

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Competing interests

M. M. Stein, L.G.W., T.O. and P.L.M. are listed as inventors on a patent application (WO2023220401A1) on 2D-programmable waveguides.

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Extended data figures and tables

Extended Data Fig. 1 Structure of a programmable nonlinear planar waveguide.

a, Illustration of the stack structure of a programmable nonlinear planar waveguide. b, Photograph of a programmable nonlinear planar waveguide that was fabricated and used to produce parts of the results in the main text. The rectangular line visible on the surface of the chip is the edge of the transparent electrode.

Extended Data Fig. 2 Structure of a programmable nonlinear channel waveguide.

a, SEM image of the cross-section of a programmable nonlinear channel waveguide. We highlight the boundaries between different materials with dashed lines. Scale bar, 2 μm. b, Numerically simulated distribution of the vertical electric field Ey of the fundamental TM mode at wavelengths of 1,560 nm (left) and 780 nm (right). Scale bar, 1 μm. The simulation was performed with EMpy (ref. [52]).

Extended Data Fig. 3 Tunable SHG on a programmable nonlinear channel waveguide.

Normalized SHG conversion efficiency of the programmable nonlinear channel waveguide for various pump wavelengths and programmed QPM grating periods. Approximately 4 mW of pump power was detected after the waveguide. We used a bias voltage of 1,500 V with frequency of 10 Hz.

Supplementary information

Supplementary Information

This file contains Supplementary Sections 1–13, including Supplementary Tables 1–3, Supplementary Figs. 1–40 and Supplementary references.

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Yanagimoto, R., Ash, B.A., Sohoni, M.M. et al. Programmable on-chip nonlinear photonics. Nature 649, 330–337 (2026). https://doi.org/10.1038/s41586-025-09620-9

- Received: 07 April 2025

- Accepted: 11 September 2025

- Published: 08 October 2025

- Version of record: 08 October 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09620-9

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Towards fibre-like loss for photonic integration from violet to near-infrared

Hao-Jing Chen,

Kellan Colburn,

Peng Liu,

Hongrui Yan,

Hanfei Hou,

Jinhao Ge,

Jin-Yu Liu,

Phineas Lehan,

Qing-Xin Ji,

Zhiquan Yuan,

Dirk Bouwmeester,

Christopher Holmes,

James Gates,

Henry Blauvelt &

…

Kerry Vahala

Nature volume 649, pages 338–344 (2026)

Abstract

Over the past decades, remarkable progress has been made in reducing the loss of photonic integrated circuits (PICs) within the telecom band1,2,3,4, facilitating on-chip applications spanning low-noise optical[5] and microwave synthesis[6], to lidar[7] and photonic artificial intelligence engines[8]. However, several obstacles arise from the marked increase in material absorption and scattering losses at shorter wavelengths9,10, which prominently elevate power requirements and limit performance in the visible and near-visible spectrum. Here we present an ultralow-loss PIC platform based on germano-silicate—the material underlying the extraordinary performance of optical fibre—but realized by a fully CMOS-foundry-compatible process. These PICs achieve resonator Q factors surpassing 180 million from violet to telecom wavelengths. They also attain a 10-dB higher quality factor without thermal treatment in the telecom band, expanding opportunities for heterogeneous integration with active components[11]. Other features of this platform include readily engineered waveguide dispersion, acoustic mode confinement and large-mode-area-induced thermal stability—each demonstrated by soliton microcomb generation, stimulated Brillouin lasing and low-frequency-noise self-injection locking, respectively. The success of these germano-silicate PICs can ultimately enable fibre-like loss onto a chip, leading to an additional 20-dB improvement in waveguide loss over the current highest performance photonic platforms. Moreover, the performance abilities demonstrated here bridge ultralow-loss PIC technology to optical clocks[12], precision navigation systems[13] and quantum sensors[14].

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Main

At shorter wavelengths (400–1,100 nm), waveguide losses surge because of two fundamental limitations9,10. First, scattering losses increase as the optical wavelength approaches the scale of surface roughness (surface Rayleigh scattering). Second, absorption losses increase as photon energy enters the Urbach tail of amorphous or crystalline dielectrics. However, many important photonic applications operate in these wavelengths14,15, such as optical clocks, quantum computing and networks, bioimaging, astronomical observation, underwater and data centre communications, compact lidar, and atomic physics studies in general (Fig. 1a). Silica (SiO_2) and germanium-doped silica (germano-silicate or Ge-silica) have been widely adopted in optical fibres for short-wavelength operation because of their exceptionally low material absorption[16]. Nevertheless, because of the need for suspended geometries in the case of silica17,18, or the lack of well-developed fabrication processes for germano-silicate[19], the promise of fibre-like loss in a photonic integrated circuit (PIC) platform remains untapped.

Fig. 1: Ultrahigh-Q germano-silicate device from violet to near IR.

a, Spectrum coverage of photonics application in the visible and short-NIR regions[14]. QD, quantum dot; YDFA, Yb-doped fibre amplifier. b, Waveguide propagation loss across broadband spectrum of state-of-the-art integrated platforms compared with the present work. Annealed Ge-silica waveguide losses are 0.49 dB m−1 at 458 nm, 0.32 dB m−1 at 532 nm, 0.32 dB m−1 at 685 nm, 0.19 dB m−1 at 780 nm, 0.14 dB m−1 at 965 nm and 0.08 dB m−1 at 1,064 nm, 0.09 dB m−1at 1,550 nm. Unannealed Ge-silica waveguide losses are 1.76 dB m−1 at 458 nm, 1.02 dB m−1 at 532 nm, 0.56 dB m−1 at 685 nm, 0.29 dB m−1 at 780 nm, 0.21 dB m−1 at 965 nm, and 0.19 dB m−1 at 1,064 nm, 0.15 dB m−1 at 1,550 nm. Data from refs. 9,36,37,38 for ULL Si_3N_4; refs. 10,15,39,40 for Si_3N_4; refs. 41,42,43 for LiNbO_3; ref. [16] for the material limit of Ge-silica (standard modern fibre with a GeO_2 content around 3.5 mol%). c, Schematic of high-performance visible PICs with multi-material integration for on-chip atom/ion control, consisting of blocks of III–V lasers, Ge-silica microresonators, LiNbO_3 electro-optic modulators and vertical grating couplers. d, Microscopy image of a Ge-silica microring device. e, SEM image of annealed waveguide. f, Transmission spectra and corresponding intrinsic Q factors (Q_0) of Ge-silica microring resonators at seven wavelengths spanning from violet to NIR. The observed double-dip features at 780 nm and 1,064 nm arise from mode splitting induced by backscattering in the microcavity, whereas the Fano lineshape at 458 nm originates from multimode interference (see detailed analysis in the Supplementary Information). Scale bars, 500 μm (d); 2 μm (e).

In this work, we present the first step towards using the materials of fibre optics in achieving planar integrated photonic circuits with fibre-like loss. As in fibre optics, GeO_2 doping elevates the refractive index of the Ge-silica core, enabling optical confinement within a silica cladding. A deep-ultraviolet (DUV) stepper lithography-based manufacturing process is developed to fabricate planar integrated waveguide circuits on silicon wafers. Leveraging the famously low material loss of Ge-silica[16] and the unique low-viscosity reflow properties[20] at standard furnace temperatures, we achieve sub-dB m−1 waveguide losses spanning the violet to telecom bands (Fig. 1b). Notably, the waveguide loss in the violet band is 13 dB lower than any current integrated platforms, whereas the lowest loss of 0.08 dB m−1 at 1,064 nm reaches a loss level close to the first low-loss optical fibre produced by Corning in 1970 (0.02 dB m−1) (ref. [21]). Crucially, ultralow losses without post-processing thermal annealing can be achieved, which is an important enabler for monolithic/heterogeneous integration with temperature-sensitive materials[11].

Beyond its record-low loss performance, several material and practical advantages of this platform are demonstrated. First, the DUV-stepper-defined waveguides are readily dispersion-engineered as required for soliton microcomb generation (demonstrated by soliton microcomb). Second, GeO_2 doping reduces the acoustic velocity relative to silica, enabling simultaneous confinement of an optical and acoustic mode within the waveguide core (demonstrated by Brillouin lasing). Finally, it can support thicknesses over 4 μm, enabling low thermorefractive noise (TRN) by the generation of a large mode area (LMA) circuit (demonstrated by low-frequency-noise self-injection locking (SIL)). Overall, this platform incorporates essential ingredients of high-performance multi-functionalized visible PICs, including ultralow loss, multi-material integration ability and high noise suppression, thereby paving the way for high-complexity system-level applications such as on-chip atom and ion control[22] (Fig. 1c).

Broadband ultrahigh-Q microresonators

To evaluate performance across the broadband spectrum, microring resonators are fabricated. For this work, air-cladded microring resonators with a 3-mm diameter (corresponding to a 21.2 GHz free spectral range) are used mostly for convenience in coupling and measurement, while also completely eliminating substrate leakage and bending losses (for details, see Supplementary Figs. 1 and 2). The microscopy image of a typical microring resonator is shown in Fig. 1d. The ring and pulley coupler are colorized in green, with ridge waveguides fabricated by etching trenches around the ridges to increase etch efficiency. Unlike Si_3N_4 photonic circuits, these PICs do not require stress release patterns even at 4 μm of thickness. The inherent low viscosity of Ge-silica can be accessed at standard furnace annealing temperatures, thereby allowing for surface-tension-induced smoothing. By choosing the appropriate temperature, the resonators can achieve atomic-scale smoothness without deformation of the waveguide shape (Fig. 1e). This smoothness overcomes the typical scattering limitations at short wavelengths in integrated microresonators.

U-shape tapered-fibre couplers[23] are used to measure Q factors from 458 nm to 1,550 nm. Intrinsic Q factors were determined from transmission spectra scans in the undercoupled regime (for some stronger coupled modes in the visible band, see Supplementary Fig. 5), in which resonant transmitted power and linewidth were used to infer loaded, coupled and intrinsic optical Q factors. A series of tunable external cavity lasers, each calibrated by a separate interferometer, were used to measure Q at each wavelength. Seven resonator transmission spectra at different wavelengths are shown in Fig. 1f, demonstrating intrinsic Q factors exceeding 180 million across a broad wavelength range from 458 nm to 1,550 nm, with the highest Q of 463 million at 1,064 nm, corresponding to a waveguide loss of 0.08 dB m−1. The waveguide loss α is calculated from resonators Q_0 by \(\alpha ({\rm{dB}}\,{{\rm{m}}}^{-1})=10\times {\log }_{10}({\rm{e}})\times \frac{2{\rm{\pi }}{n}_{{\rm{eff}}}}{{Q}_{0}\,\lambda }\). Here, neff is the effective index of optical modes and λ is the wavelength. The atomic-scale smoothness, along with the broad ultralow-material-loss window of Ge-silica, enables it to break the short-wavelength limitation and achieve the lowest waveguide propagation losses among all integrated platforms within the visible and short-NIR ranges (Fig. 1b). Notably, the loss at 458 nm is 0.49 dB m−1, which is a 13-dB improvement over previous records.

CMOS-compatible fabrication

The fabrication process for this work is shown in Fig. 2a. In the devices studied, a 4-μm-thickness germano-silica layer (25 mol% GeO_2, corresponding to an approximately 2% refractive index difference) is deposited around 270 °C (setting the anneal-free thermal budget for the fabrication flow) by plasma-enhanced chemical vapour deposition (PECVD) on a thick (15 μm) layer of thermal oxide on silicon wafer. The Ge-silica layer is then processed into ridge waveguides through ruthenium (Ru) and silica hard masking[24], deep-ultraviolet (DUV) lithography and inductively coupled plasma etching. The Ru mask provides high selectivity in fluorine-based etches enabling high-fidelity deep etching of Ge:silica. To reduce the roughness-induced scattering loss limitation and achieve ultrahigh Q across the broadband spectrum, the entire wafer is subjected to furnace annealing at 1,000 °C for 12–18 h. As shown in Fig. 2b (bottom left), the sidewalls of the Ge-silica waveguides undergo reflow, removing the etch-induced roughness, whereas the thermally grown oxide (THOX) substrate remains unaffected.

Fig. 2: CMOS compatibility and process flow of Ge-silica ultralow-loss PICs.

a, Schematic of fabrication workflow for ultrahigh-Q Ge-silica resonators. ALD, atomic layer deposition. b, Photograph of Ge-silica PICs on silicon at wafer scale, SEM image showing the sidewall of an annealed waveguide (intentionally overetched to show the contrast in reflow compared with non-reflowed material) and cross-section of cladded waveguides. c, Anneal-free waveguide loss (C band) compared with temperature for state-of-the-art low-loss PICs11,44,45,46,47,48,49,50 compared with the present work. CVD, chemical vapour deposition; PVD, physical vapour deposition. d, Examples of monolithic/heterogeneous integration applications of ultralow-loss anneal-free Ge-silica PICs, including co-integration with III–V materials, organic electronics/photonics, thin-film lithium niobate, thermal-engineered quartz substrates and Ge-on-silicon photodetectors. Scale bars, 500 nm (b, bottom left); 5 μm (b, bottom right).

From here, an optional upper silica cladding layer can be deposited. Two cladding deposition methods are investigated in this work and applied to different devices. First, a 14-μm thick slightly P-doped (1.5 mol% P_2O_5) silica cladding is used for full acoustic confinement (Fig. 2b, bottom right). Second, a higher-quality inductively coupled plasma PECVD (ICP-PECVD) is used to protect the devices from contamination and degradation due to long-term atmospheric exposure. With the addition of this ICP-PECVD >6 μm cladding (Extended Data Fig. 2a), the Ge-silica PICs have been confirmed to maintain ultralow loss (optical Q factors >10^8, as shown in Extended Data Fig. 2b), when exposed to the atmosphere for over several months.

Owing to the high quality of the PECVD Ge-silica layer, high precision of DUV lithography and the well-developed etching process[24], the Ge-silica microresonators can achieve ultrahigh Q even before annealing. Air-clad resonators without the reflow smoothing anneal are measured and attain ultrahigh Q of nearly 200 million and maintain ultrahigh Q across a similarly broad spectrum (Extended Data Fig. 1). This corresponds to a lowest waveguide loss of 0.15 dB m−1 at 1,550 nm. Figure 2c compares anneal-free waveguide loss (C band) of state-of-the-art integrated platforms with the present work, which exhibits a more than 10-fold reduction over the previous record. This anneal-free improvement in waveguide loss will enhance the performance of applications requiring co-integration with temperature-sensitive material such as III–V devices, organic electronics/photonics, thin-film lithium niobate, thermal-engineered quartz and Ge-on-silicon photodetectors (Fig. 2d).

Advantages and demonstrations

Beyond the ultralow loss across a broadband spectrum, the Ge-silica PICs have three more key advantages in dispersion engineering, acoustic confinement and thermal noise mitigation. To demonstrate them, soliton microcombs, stimulated Brillouin lasing (SBL) and SIL laser experiments are performed using the new platform. As these applications do not require monolithic or heterogeneous integration with other materials, annealed Ge:silica devices are used.

Single-ring soliton generation

Soliton microcombs represent the main new application area of microcavities and enable transfer of large-scale frequency comb technology to an integrated photonic chip. The Q factor of the microcavity determines the microcomb pumping power required as well as its coherence. However, at this time, the only ultrahigh-Q integrated platform—thin Si_3N_4—has a limited dispersion-engineering ability because of its thin waveguide thickness. This platform exhibits only normal dispersion and requires coupled-ring structures to generate soliton microcombs[25]. Here, an integrated Ge-silica resonator is used to demonstrate a new ability for an integrated design: soliton generation in a single ultrahigh-Q microring with anomalous dispersion. The microring resonator was designed to have anomalous dispersion and single-mode transmission as required for soliton generation (Supplementary Figs. 3 and 4). And the soliton mode family dispersion is characterized by measuring the frequency of all modes between 1,520 nm and 1,630 nm using an external cavity laser calibrated by a Mach–Zehnder interferometer (Fig. 3a). There is no observable mode-crossing-induced distortion of the mode family, therefore making the mode family well suited for soliton formation.

Fig. 3: Demonstration applications using the germano-silicate platform.

a, Dispersion of a Ge-silica single-ring resonator, Dint = ωμ − ω_0 − D_1μ, where μ = 0 is assigned to the mode at 1,550 nm, D_1/2π is the mean free spectral range and D_2 is the group velocity dispersion (GVD) (positive for anomalous GVD). A fitting gives D_1/2π =  21.2 GHz and D_2/2π = 8.9 kHz. Inset, soliton step in transmission. b, Optical spectrum of generated soliton microcomb (left) and radiofrequency spectrum of microcomb beatnote (right). RBW, resolution bandwidth. c, Measured SBS gain spectrum of a fully cladded Ge-silica waveguide. The simulation result is overlaid on the measurement response. Insets, electric-field mode profile of the fundamental TE mode (left) and Brillouin-scattering induced displacement response (right). d, Optical spectrum of stimulated Brillouin laser (left) and radiofrequency spectrum of SBL beatnote (right). a.u., arbitrary units.

Soliton triggering and stabilization are performed using the frequency kick and capture-lock[26] technique. A measured soliton spectrum generated using a 3-mm-diameter ring is shown in Fig. 3b (for Q characterization, see Supplementary Fig. 6). The spectral envelope exhibits a well-defined sech^2 envelope. To confirm a stable repetition rate, the soliton pulse stream was detected and analysed using an electrical spectrum analyser. The electrical spectrum in Fig. 3b (right) gives a repetition rate near 21.2 GHz, and the resolution bandwidth of 1 kHz confirms pulse stream stability.

Acoustic confinement and Brillouin lasing

Apart from soliton microcombs, the stimulated Brillouin laser (SBL) is another device that has attracted considerable interest27,28. A key challenge for SBL operation is achieving simultaneous optical and acoustic waveguiding so as to enhance photon–phonon interactions—a feat hindered in conventional platforms by the low acoustic impedance of the silica cladding. Ge-silica PICs overcome this barrier through GeO_2 doping, which reduces the longitudinal acoustic velocity of the waveguide core relative to the silica cladding, thereby enabling a fully transverse confined acoustic mode.

In the experiment, an approximately 25-mm-long waveguide is used to characterize the stimulated Brillouin scattering (SBS) gain spectrum and verify acoustical confinement by comparison to simulation. The waveguide core is 4 μm × 6 μm Ge-silica, with 15-μm thermal silicon oxide bottom cladding and 14-μm P_2O_5-doped silica upper cladding (the same cladding as shown in Fig. 2b, bottom right). Simulations of the normalized electric field and mechanical displacement distributions, shown in Fig. 3c, demonstrate simultaneous optical and acoustic wave confinement. Applying a pump–probe method (Methods), the SBS gain spectrum is obtained and agrees well with the simulation. The gain peak at 9.55 GHz has a full width at half maximum of 44.7 MHz, corresponding to a mechanical quality factor of  about 210.

As a device demonstration, integrated Ge-silica resonators are used to generate a high-coherence Brillouin laser. To achieve phase matching for the Brillouin process near 1,550 nm, air-clad devices with diameters of approximately 20 mm were fabricated (for Q characterization, see Supplementary Fig. 7). Figure 3d (left) shows the optical spectrum of the lasing Stokes wave. The weaker pump signal peak in the spectrum arises from the need to collect the lasing Stokes wave in the propagation direction opposite to the pumping direction. Its strength is determined by residual reflection and backscattering in the measurement. The Brillouin lasing frequency shift is 9.68 GHz, which is lower than the typical 10.9 GHz shift observed in silica resonators[18]. Figure 3d (right) shows the microwave beatnote between the pump and Stokes waves, revealing its high coherence, as indicated by the high signal-to-noise ratio. This synergy of ultralow optical loss and engineered acoustic confinement unlocks low-noise Brillouin lasers for high-performance on-chip gyroscopes[13], integrated microwave photonics and temperature/strain sensors.

LMA-enhanced hybrid-integrated low-noise laser

Narrow-linewidth lasers are pivotal for applications ranging from precision metrology to coherent optical communications. The recent integration of ultrahigh-Q microresonators with semiconductor diode lasers has markedly reduced the frequency noise of on-chip lasers and soliton microcombs by SIL[4]. The operational principle is shown in Fig. 4a. A resonant mode of the ultrahigh-Q microresonator acts as a frequency reference for stabilization, whereas backscattered light from the resonator provides optical feedback to narrow the linewidth of the diode laser. White frequency noise suppression scales quadratically with the quality factor (Q^2) of the microresonator, whereas TRN scales inversely with the cavity mode volume. Here, TRN in the resonator mode is simulated using the commercial finite element analysis software COMSOL Multiphysics (Methods). The results, plotted in Fig. 4b, compare Ge-silica with low- and high-confinement Si_3N_4 resonators of identical diameters. Calculated mode areas are 28.06 μm^2 (Ge-silica), 7.71 μm^2 (thin Si_3N_4) and 1.33 μm^2 (thick Si_3N_4). Owing to its substantially larger mode area, the Ge-silica platform has a greatly reduced TRN among the three integrated photonic systems.

Fig. 4: Hybrid integration of diode laser and germano-silicate device.

a, Schematic of SIL of the diode laser using external ultrahigh-Q microring. b, Simulated TRN spectra. Mode areas are 28.06 μm^2, 7.71 μm^2 and 1.33 μm^2 for Ge-silica, thin Si_3N_4 and thick Si_3N_4, respectively. c, Single sideband frequency noise of SIL laser, indicating a Hz-level fundamental linewidth (FL). Inset, the image of a 1,550-nm DFB laser and integrated Ge-silica microring (indicated in false colour). d, Narrow-linewidth lasers at red, green and violet wavelengths, realized by SIL of Fabry–Pérot (FP) diode lasers to microrings. The fundamental linewidths are 15 Hz at 632 nm, 12 Hz at 512 nm and 90 Hz at 444 nm. e, Comparison of the fundamental linewidths of integrated lasers with state-of-the-art Si_3N_4 platforms30,31.

As a demonstration of this hybrid-integrated low-noise laser based on our platform, a commercial C-band DFB laser is endfire-coupled to the bus waveguide of a Ge-silica resonator chip with Q factor exceeding 100 million (Fig. 4c, inset, in which the microring resonator and bus waveguide are indicated in false colour). The laser chip, which is mounted on a thermoelectric cooler to avoid long-term drift, is able to deliver power of >100 mW at 1,548 nm. Optical feedback is provided to the laser by backward Rayleigh scattering in the microresonator, which spontaneously aligns the laser frequency to the nearest resonator mode. In experiments, as the phase accumulated in the feedback is critical to determining the stability of injection locking, the feedback phase is controlled by adjusting the air gap between the chips. At a specific phase detuning, a soliton crystal comb[29] is generated (Supplementary Fig. 8a). The pump line of the comb output is filtered and directed to a self-heterodyne setup for frequency-noise characterization. The SIL operation boosts the laser coherence, with the frequency noise spectrum shown in Fig. 4c and achieves a Hz-level fundamental linewidth resulting from a 46-dB noise reduction compared with the free-running DFB laser. In the data, the increasing frequency noise at high-offset frequency is an artefact of spontaneous noise from the DFB laser and can be eliminated by out-coupling from a drop waveguide[4].

The narrow-linewidth lasers are further extended into the visible spectrum by SIL of commercially available multimode Fabry–Pérot diode lasers to high-Q microrings. Under SIL, the devices become single mode with high side-mode-suppression ratios (Supplementary Fig. 8b). Also, fundamental linewidths of 15 Hz at 632 nm, 12 Hz at 512 nm and 90 Hz at 444 nm are achieved (Fig. 4d). Compared with state-of-the-art integrated lasers30,31, the Ge:silica platform provides more than 20 dB improvement in fundamental linewidth across the visible spectrum (Fig. 4e), holding the potential to redefine performance benchmarks in integrated visible photonics[14].

Discussion

In summary, we have developed a Ge-silica ultralow-loss platform that markedly advances integrated photonics (see detailed comparison between Ge-silica and the state-of-the-art low-loss Si_3N_4 in Supplementary Table 1). This new platform has achieved a  >10 dB improvement in both the violet wavelength range and anneal-free Q. The unique material and geometric properties of these waveguides enable dispersion engineering for soliton microcomb generation, simultaneous confinement of acoustic and optical modes, and suppression of thermal refractive noise. They also maintain ultrahigh Q factors in both polarizations, and enhance power-handling abilities for high-power mode-locked lasers. Moreover, the photosensitivity of Ge-silica[32] allows UV-written optical gratings for applications forming the basis of fibre Bragg gratings in photonic systems. By contrast, the small index contrast of Ge:silica on silica leads to larger bending losses, which limit integration density. These can be mitigated by using 3D integration, increasing Ge doping or operating at shorter wavelengths. On account of the quadratic[33] and cubic dependence[34] of nonlinearity on optical Q factor, the low nonlinearity of silica can be readily compensated by exploiting ultrahigh-Q devices. Moreover, electro-optic tuning can be enabled through heterogeneous integration with lithium niobate or lithium tantalate.

The surface-tension-induced smoothness of the waveguides bypasses Rayleigh scattering loss limitations, greatly extending integrated photonics at shorter operational wavelengths. Specifically, by integrating visible diode lasers with ultralow-loss Ge-silica resonators, Hz-level, narrow-linewidth lasers[31] and microcombs[35] are possible in the visible band. With further development in deposition and fabrication techniques, the Ge-silica PICs hold the potential to reach their material-limited waveguide loss of 0.2 dB km−1 (corresponding to a microresonator Q factor of >100 billion). This fibre-like optical loss for PICs would revolutionize certain fibre-based technologies by moving their manufacture to CMOS foundries, including solid-state gyroscopes, advanced frequency comb technology for precision portable clocks, large-scale low-loss circuits for quantum computing, high-power amplifiers and mode-locked lasers.

Methods

High-quality upper silica cladding deposition

The upper cladding of these germano-silicate resonators was deposited by ICP-PECVD (Unaxis VLR) at 250 °C using a deuterated silane based precursor and oxygen plasma. To reduce the losses due to the stress optic effect of the cladding, while also repairing the solarization of the germano-silicate due to direct plasma exposure, the devices studied underwent a 20-min 1,000 °C rapid thermal anneal between each deposition of 500 nm of cladding. As a test, we deposited a 6-μm thickness upper cladding (Extended Data Fig. 2a), observing complete filling of the coupling gap. This encapsulation effectively protects the device from environmental contamination, enabling the maintenance of ultrahigh Q up to 160 million (Extended Data Fig. 2b) over several months. It is noted that the original Q factor before cladding deposition was measured at approximately 250 million. This decrease in Q factor with upper cladding, partial Q recovery through thermal treatment, yet never fully recovering the original Q values, is consistent with what was previously observed in ULL silicon nitride in ref. [51], in which the same ICP-PECVD system was used. The possible solutions to this would be to switch to a deposition type that avoids exposure to a plasma or using better precursors, such as low-pressure chemical vapour deposition or tetraethoxysilane PECVD[52].

Brillouin gain spectrum measurements and simulations

To achieve high sensitivity, we use a dual-intensity-modulation pump–probe technique[53]. The test device has a 14-μm-thick 1.5 mol% P_2O_5-doped silica upper cladding, designed to fully confine both optical and acoustic fields. This slightly phosphorus-doped silica layer was deposited by PECVD, chosen for its ability to rapidly produce thick, stress-free films. Post-cladding characterization shows waveguide propagation loss below 0.5 dB m−1 and edge coupling loss of 1.4 dB per facet. The SBS gain characterization uses counterpropagating pump and probe lasers injected through facet couplers, with both lasers operating near 1,560 nm. The pump laser is intensity-modulated at a frequency of 10 MHz and the probe laser is intensity-modulated at a frequency of 10.075 MHz. The transmission signal of the probe laser is routed to a lock-in amplifier for gain measurement, using a 75-kHz reference beatnote of the two modulation frequencies. The SBS gain spectrum is obtained by frequency-scanning the probe laser over a 20 GHz range from red- to blue-detuning relative to the fixed pump frequency. Both optical and acoustic fields are calculated using the finite element method based on the model described in ref. [54]. The material properties used in the simulation are as follows: refractive index of 1.478, 1.451 and 1.449; density of 2,750, 2231 and 2,200 kg m−3; Poisson’s ratio of 0.180, 0.166 and 0.170; Young’s modulus of 64.8, 72.6 and 73.0 GPa; Brillouin linewidth of 59 MHz, 28 MHz and 17 MHz; and photoelastic coefficient p_12 of 0.2373, 0.2264 and 0.2260, for Ge-silica (core), P-silica (upper cladding) and thermally grown silica (bottom cladding), respectively.

TRN simulations

The TRN in Fig. 4b of Ge-silica, thin SiN and thick SiN are numerically simulated in a finite element solver (COMSOL Multiphysics) using a model based on the fluctuation-dissipation theorem[55]. For each of the three materials, the simulation is based on a ring microresonator with a diameter of 3 mm. The rectangular waveguide dimensions of the Ge-silica, thin SiN and thick SiN microresonators are 12 μm × 4 μm, 8 μm × 0.1 μm and 2 μm × 0.8 μm, respectively (width × height). These dimensions of thin SiN and thick SiN are taken from refs. 4,56. The Ge-silica waveguide is air-cladded, whereas the thin and thick SiN waveguides have silica cladding. Material parameters used in the simulation are as follows: for Ge-silica, the thermo-optic coefficient 1.27 × 10−5 K−1, heat conductivity 1.38 W m−1 K−1, heat capacity 740 J kg−1 K−1, density 2.2 × 10^3 kg m−3; for SiN, the thermo-optic coefficient 2.45 × 10−5 K−1, heat conductivity 30 W m−1 K−1, heat capacity 800 J kg−1 K−1, density 3.29 × 10^3 kg m−3; for the silica cladding of the SiN waveguide, the thermo-optic coefficient 1.2 × 10−5 K−1, heat conductivity 1.38 W m−1 K−1, heat capacity 740 J kg−1 K−1, density 2.2 × 10^3 kg m−3. The ambient temperature is set at 300 K.

Data availability

The data presented in the main text are available from Zenodo[57]. All other data are available from the corresponding authors upon request.

Code availability

The codes used in the study are available from the corresponding authors upon request.

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Acknowledgements

This work was funded by DARPA (HR0011-22-2-0009 and HR00112420383), the Air Force Research Laboratory (FA9453-20-2-0001) and the EPSRC grant (EP/Z531169/1). K.C. thanks the NSF for graduate fellowship support (2139433). We acknowledge the support of the entire UCSB Nanofabrication facility and its staff (especially D. John, B. Thibeault, B. Mitchell and D. Read), the Caltech KNI staff (K. McKenzie), as well as I. Hedgepeth, B. Moog, K. Liu, Q.-F. Yang, J. Guo, M. Li, L. Wu and J. Bowers. We thank the helpful discussion with X. Yin from S. H. Xinguang Optoelectronics Technology, Y. Huang from Henan Shijia Photons Technology and X. Shi and X. Fu from Shanghai Precilasers Technology.

Author information

Author notes

1. These authors contributed equally: Hao-Jing Chen, Kellan Colburn

Authors and Affiliations

1. T. J. Watson Laboratory of Applied Physics, California Institute of Technology, Pasadena CA, USA

Hao-Jing Chen, Kellan Colburn, Peng Liu, Hongrui Yan, Hanfei Hou, Jinhao Ge, Jin-Yu Liu, Phineas Lehan, Qing-Xin Ji, Zhiquan Yuan, Henry Blauvelt & Kerry Vahala

2. Department of Physics, University of California Santa Barbara, Santa Barbara CA, USA

Dirk Bouwmeester

3. Huygens-Kamerlingh Onnes Laboratory, Leiden University, Leiden, The Netherlands

Dirk Bouwmeester

4. Optoelectronics Research Centre, University of Southampton, Southampton, UK

Christopher Holmes & James Gates

Contributions

H.-J.C., K.C., H.B. and K.V. conceived the idea and designed the experiments. H.-J.C. and K.C. developed, fabricated and characterized the devices. P. Lehan assisted in the fabrication of the devices. P. Liu, H.Y., H.H. and J.-Y.L. simulated and/or designed the circuits. H.-J.C., K.C., H.H., P. Liu and J. Ge performed the measurements and experiments, with assistance from Q.-X.J., Z.Y. and H.Y.; C.H. and J. Gates provided valuable discussions on the Ge:silica platform development. D.B. hosted and contributed instruments for part of the experiments. H.-J.C., K.C. and K.V. wrote the paper with assistance from all other authors.

Corresponding authors

Correspondence to Hao-Jing Chen, Kellan Colburn or Kerry Vahala.

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Extended data figures and tables

Extended Data Fig. 1 Anneal-free microresonator characterization.

Resonator transmission spectra and corresponding intrinsic Q factors (Q_0) of anneal-free Ge-silica microring resonators at 7 wavelengths, which are 50M at 458 nm, 74M at 532 nm, 104M at 685 nm, 178M at 780 nm, 195M at 980 nm, 194M at 1064 nm, and 173M at 1550 nm, respectively. Corresponding waveguide loss of 1.76 dB/m at 458 nm, 1.02 dB/m at 532 nm, 0.56 dB/m at 685 nm, 0.29 dB/m at 780 nm, 0.21 dB/m at 980 nm, 0.19 dB/m at 1064 nm, 0.15 dB/m at 1550 nm.

Extended Data Fig. 2 Cladded germano-silicate PIC.

a, SEM image of a cladded device deposited by 6 μm thickness ICP-PECVD silica. b, Transmission spectrum of a fully cladded resonator, indicating an intrinsic Q factor (Q_0) of 160M at 1550 nm.

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1–8, Supplementary Table 1 and Supplementary References.

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Chen, HJ., Colburn, K., Liu, P. et al. Towards fibre-like loss for photonic integration from violet to near-infrared. Nature 649, 338–344 (2026). https://doi.org/10.1038/s41586-025-09889-w

- Received: 15 April 2025

- Accepted: 10 November 2025

- Published: 07 January 2026

- Version of record: 07 January 2026

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09889-w

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Land-use change undermines the stability of avian functional diversity

Thomas L. Weeks,

Patrick A. Walkden,

David P. Edwards,

Alexander C. Lees,

Alexander L. Pigot,

Andy Purvis &

…

Joseph A. Tobias

Nature volume 649, pages 381–387 (2026)

Abstract

Land-use change causes widespread shifts in the composition and functional diversity of species assemblages. However, its impact on ecosystem resilience remains uncertain. The stability of ecosystem functioning may increase after land-use change because the most sensitive species are removed, which leaves more resilient survivors1,2,3. Alternatively, ecosystems may be destabilized if land-use change reduces functional redundancy, which accentuates the ecological impacts of further species loss4,5. Current evidence is inconclusive, partly because trait data have not been available to quantify functional stability at sufficient scale. Here we use morphological measurements of 3,696 bird species to estimate shifts in functional redundancy after recent anthropogenic land-use change at 1,281 sites worldwide. We then use extinction simulations to assess the sensitivity of these altered assemblages to future species loss. Although the proportion of disturbance-tolerant species increases after land-use change, we show that this does not increase stability because functional redundancy is reduced. This decline in redundancy destabilizes ecosystem function because relatively few additional extinctions lead to accelerated losses of functional diversity, particularly in trophic groups that deliver important ecological services such as seed dispersal and insect predation. Our analyses indicate that land-use change may have major undetected impacts on the resilience of key ecological functions, hindering the capacity of natural ecosystems to absorb further reductions in functionality caused by ongoing perturbations.

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Main

Anthropogenic land-use change is the primary driver of biodiversity decline and turnover[6]. At a global scale, natural and semi-natural habitats are undergoing complex and accelerating changes, including agricultural expansion, industrial development and urbanization7,8. These landscape transformations are a defining feature of the Anthropocene and have led to substantial shifts in the composition of species assemblages9,10,11,12. However, the impacts of these compositional changes on ecosystem function are difficult to measure or predict[13].

A standard approach to inferring changes to ecosystem function involves estimating the diversity of functional traits in species assemblages. This is based on strong evidence that species traits provide information about functional roles14,15,16,17. A growing number of studies have also shown a correlation between trait diversity and ecological processes (Supplementary Information), which provides support for the widespread use of functional diversity (FD) metrics to assess the impacts of land-use change on ecosystem function9,18,19. Such analyses often conclude that land-use change has relatively minor effects on FD after accounting for species turnover20,21 and that high levels of functionality are therefore retained in human-modified landscapes22,23. Nonetheless, most studies focus on overall trait diversity of assemblages, an approach with two main limitations. First, FD estimated for whole assemblages does not provide information about the integrity of particular ecological functions, some of which are less resilient than others (Fig. 1). Second, standard FD metrics reflect a snapshot in time and do not tell us anything about the stability of ecosystem function in the face of further environmental perturbation[5], which suggests that the long-term impacts of land-use change may be underestimated.

Fig. 1: Impacts of environmental disturbance on the functional stability of species assemblages.

a–h, Conceptual diagram illustrating how species loss may influence FD and resilience. Resilience (the ability to withstand disturbance) is a function of resistance (the amount of change after a disturbance) and recovery (the ability to return to equilibrium after a disturbance). Note that functional resistance—the potential for assemblages to absorb extinctions without declines in FD—is boosted by redundancy. That is, declines in FD are slower when there are more surplus species with similar function. a, FD of an intact species assemblage visualized as the volume of functional trait space occupied by all species present, with the red subset showing FD of a key function (for example, pollination). b, FD of the same assemblage after various potential disturbances, illustrated by a colour gradient from weak to strong red, reflecting the intensification of anthropogenic pressures on the environment. These hypothetical future disturbances will remove sensitive species from the assemblage (X indicates extinction). Note that standard FD assessments may overlook changes to stability because surplus species can be lost with minimal effect on the total area of occupied trait space. c, Functional resistance is more clearly expressed by an extinction curve that describes FD loss until species richness declines to zero. d, Human-modified habitats may have lower FD with uncertain effects on functional resistance. e,f, Hypothetically, if most sensitive species of pollinators (red subset) have already been filtered from the assemblage (e), functional resistance may increase if surviving species are more tolerant to disturbance, thereby slowing FD loss (f). g,h, Alternatively, functional resistance may be undermined if functionally unique species are not disproportionately tolerant (g), thereby leading to rapid losses of key functions (h). AUC, area under the extinction curve.

Functional redundancy—and its flipside, functional uniqueness—are dimensions of FD that focus on the supply of species to deliver each function. Redundancy metrics achieve this parameter by estimating the number of co-occurring species with overlapping functionality[24]. If multiple species in an assemblage provide similar functions, surplus species are functionally redundant[25]. In ecological terms, functional redundancy is a positive attribute[26] because surplus species increase resilience and stability, which facilitates the continuity of ecological processes when conditions change4,5,27,28. This ‘insurance effect’ is widely reported in empirical studies29,30,31, suggesting that functional redundancy is a core feature of resilient ecosystems. In general, assemblages with many surplus species that perform similar roles will have increased levels of functional resistance—one of the two major components of overall resilience[32] (Fig. 1)—therefore ensuring that ecological functionality is maintained when species are lost from the assemblage33,34,35.

Under random species loss, functional redundancy is equivalent to functional resistance. However, the effects of land-use change are non-random because species with particular combinations of traits are more extinction-prone and tend to be filtered from the new environment36,37. Moreover, these sensitive species are distributed non-randomly, often clustering in distinct functional groups. These groups may undergo increased rates of local extinction, which in turn lead to increased risks of ecological collapse and vulnerability of ecological processes38,39,40. Indeed, if land-use change drives non-random species gains in some tolerant functional groups in parallel with species losses in more sensitive groups, the functional stability of an assemblage can be impaired despite no overall loss of functional redundancy. This may occur, for instance, when ecological specialists are replaced by disturbance-tolerant or generalist species in anthropogenic habitats2,3. We are left with a key conundrum: whether anthropogenic land-use change leads to new species assemblages that are more resilient to future shocks (because sensitive species are already lost and resilient species increase in abundance) or to assemblages that become more fragile and sensitive to further collapse.

To examine this question, we quantified the impacts of land-use change on functional trait diversity and redundancy of bird assemblages (Extended Data Fig. 1a–d). As redundancy and resistance can be decoupled if highly sensitive species provide unique functions in the assemblage, we also quantified the vulnerability of each assemblage to functional losses. Previous trait-based analyses have made progress in identifying which response traits predict species sensitivity to land-use change36,37, with an emphasis on the first element of standard response–effect frameworks[41]. In this study, we shifted the focus onto the second element—effect traits—to estimate the impacts of species loss on the functioning of future ecosystems[42]. Birds provide an ideal opportunity to quantify the functional effects of environmental change with high resolution because they have been intensively surveyed. Moreover, comprehensive trait data with well-established links to key ecological and trophic processes16,43 are now available for all bird species[44].

In total, we examined 3,696 bird species in 1,281 focal assemblages worldwide, sampled across land-use gradients from primary vegetation to urban habitats (Fig. 2a and Supplementary Table 1). For each species, we compiled 8 morphometric traits from AVONET[44], representing averages calculated from a mean of 11 individuals per species, then used a two-step principal component analysis (PCA) to account for collinearity among traits (Methods and Extended Data Fig. 2). We estimated the FD of each assemblage as functional richness, which was defined as the total volume of the occupied trait space in a probabilistic hypervolume generated from the PCA axes and dietary information for all species in the assemblage[45] (Fig. 1 and Methods). We first compared 177 assemblages in primary vegetation—including 152 (86%) in forests and 25 (14%) in non-forest vegetation (mainly grasslands and shrublands)—with 1,104 assemblages in nearby human-modified landscapes. Anthropogenic land-uses drove the removal of species with larger body size, lower dispersal ability and narrower geographical, climatic and dietary niches (Extended Data Fig. 3), a result that is in line with previous studies36,37,46. In tandem, low-intensity human activity drove minor but significant increases in FD, as detected in disturbed primary vegetation (\(\hat{\beta }\) = 0.150, P = 0.006). Mature secondary forests recovered similar levels of FD to intact primary vegetation (\(\hat{\beta }\) = –0.35, P = 0.144). However, substantial reductions in FD were consistently observed across other, more heavily disturbed land-use types (Fig. 2b), particularly in highly urbanized landscapes (\(\hat{\beta }\) = –0.463, P < 0.001).

Fig. 2: Sampling and impacts of land-use change on avian assemblages.

a, Circles show the geographical location of 98 field surveys that provided data for 1,281 avian assemblages. The circle size is proportional to the number of assemblages surveyed in each study landscape. Colours indicate major biomes[69] projected onto a world map. b–d, Outputs from univariate mixed-effects models assessing the impact of land-use change on three assemblage-level metrics calculated from all assemblages (n = 1,281): FD (measured as functional richness) (b); functional redundancy (c); and functional vulnerability (d). All metrics are compared with a pristine primary vegetation baseline (including forests, grasslands, shrublands and wetlands; dashed red line). Functional vulnerability was calculated using Spearman’s rank correlation coefficient between species-level redundancy and trait-based sensitivity scores (Methods). To aid comparison, response variables were converted to z scores by square-root transformation before analysis and then scaled by their standard deviation. Points shown are z score estimates for each functional metric and error bars indicate 95% confidence intervals. Note that a negative functional vulnerability implies greater resistance to further loss of FD. World map outline in a adapted from Natural Earth (https://www.naturalearthdata.com) and ArcGIS under a Creative Commons licence CC BY 4.0.

Functional redundancy

To evaluate these patterns in FD from the perspective of ecosystem resilience, we estimated functional redundancy for each assemblage using the position of all species in the trait hypervolume. We also estimated their intraspecific variation generated using a standard kernel density estimator[47] (Methods). These values of intraspecific variation provide an estimate of niche breadth in the context of trait space, which enabled us to calculate functional redundancy as the average number of species that could be removed from each cell of the hypervolume without reducing the functional volume occupied by the assemblage as a whole (Supplementary Information). The results revealed that in addition to its effects on FD, land-use change altered assemblage-level functional redundancy (Fig. 2c), with roughly matching patterns reported from plant communities[48]. Specifically, trait redundancy initially increased after the switch from pristine to disturbed primary vegetation (\(\hat{\beta }\) = 0.200, P = 0.010), with minor, nonsignificant reductions in both mature (\(\hat{\beta }\) = −0.087, P = 0.394) and intermediate-age (\(\hat{\beta }\) = −0.145,  P = 0.089) secondary vegetation. However, more intensive land-use showed significant decreases in redundancy, with particularly sharp declines in cropland (\(\hat{\beta }\) = −0.506, P < 0.001) and intensively urbanized landscapes (\(\hat{\beta }\) = −0.386, P < 0.001). Results were similar when we calculated intraspecific variation based on direct measurements of multiple individuals per species (Methods and Extended Data Fig. 4).

To disentangle the effects of land-use change on different ecological processes regulated by trophic interactions, we modelled changes in FD and functional redundancy within trophic guilds (Extended Data Fig. 5). In dietary generalists and granivores, FD and redundancy either remained constant or increased in agricultural and urban landscapes, which reflected an influx of open-country and urban-tolerant species, some with distinctive traits[12]. By contrast, FD and redundancy declined steeply in frugivores (which are involved in seed dispersal) and invertivores (with roles in controlling insect populations). For these analyses, we used a standard classification that defines trophic specialists as species that consume the relevant food type across most (>60%) of their diet[44]. However, trophic generalists may contribute to the same ecological functions; therefore we ran sensitivity analyses with broader trophic guilds (>25% of diet; Methods). The results were similar, which suggests that declines in FD and redundancy are much steeper in components of avian diversity that contribute to seed dispersal and insect predation (Extended Data Figs. 5 and 6). These findings indicate that whole-assemblage FD and redundancy should be treated with caution because they are averaged across multiple ecological processes with widely diverging sensitivity to land-use change. Specifically, when diversity increases in disturbance-tolerant guilds, this can obscure substantial declines in disturbance-sensitive guilds and mask the reduced capacity of anthropogenic assemblages to maintain important ecosystem functions.

Functional vulnerability

Although functional redundancy patterns imply that land-use change can limit the capacity of ecosystems to withstand further species losses, the link between functional redundancy and stability is not clear-cut. Assemblages with low redundancy can be stable if the remaining species are well adapted to human-modified landscapes. Moreover, ecological functions can be unstable even in highly redundant assemblages if many species are densely packed into only a few functional groups to leave other areas of trait-space under-represented[49]. In such a scenario, the delivery of rarer functions can be unstable if the species responsible are disproportionately sensitive to land-use change or persist in small population sizes (Methods).

To examine the question of stability more closely, we devised two metrics of functional vulnerability that incorporated the amount of unique function provided by species, along with their probable sensitivity to anthropogenic pressures. Specifically, we calculated a species-level redundancy value based on the relative contribution of each species to total assemblage functional redundancy and a general sensitivity score estimating the vulnerability of each species to future threats. We estimated sensitivity based on general response traits (Supplementary Table 2) or population size (rarity) to represent the likelihood that each species would undergo local extinction in response to a broad range of disturbances (Methods and Supplementary Information). To generate functional vulnerability values, we calculated the covariance between the sensitivity scores for all species occurring in the assemblage and their functional redundancy. High functional vulnerability values indicate a negative covariance between sensitivity and redundancy, which implies that species with increased extinction risk also provide a large proportion of unique function.

Our global-scale models revealed that functional vulnerability is reduced in all anthropogenic land-use types whether the sensitivity of species to disturbance is estimated as a function of general response traits (trait-based functional vulnerability; Extended Data Fig. 5i–l) or abundance (rarity-based functional vulnerability; Extended Data Fig. 5m–p). However, the only significant declines were detected for trait-based functional vulnerability in young secondary vegetation and agricultural landscapes. These findings support the hypothesis that intensive land-use change removes the most sensitive species, which results in lower assemblage vulnerability because most of the species surviving in and colonizing anthropogenic landscapes tend to be less prone to extinction2,50.

Functional stability

By showing that functional redundancy and functional vulnerability both decline after land-use change, our analyses suggest that human impacts have opposing effects on ecosystem stability. In anthropogenic environments, ecological functions are delivered by a reduced set of species. However, these species have lower extinction risk because they are more tolerant of further anthropogenic pressures. To disentangle these effects, we calculated two functional-resistance values for each assemblage under realistic species-loss scenarios that targeted the most extinction-prone or rarest species. We simulated these scenarios by removing species in order of sensitivity (high to low) using the same trait-based and rarity-based sensitivity scores devised to calculate functional vulnerability values (Fig. 3a and Methods). This approach enabled us to track the rate at which FD declines when species are sequentially removed from the assemblage (Supplementary Fig. 1), thereby quantifying how land-use change may influence the functional stability of assemblages undergoing future stressors33,34.

Fig. 3: Land-use change reduces functional stability.

a, Schematic of the procedure used to quantify functional stability of assemblages (n = 1,281). As a first step, all species in an assemblage were ranked by extinction risk based on four general response traits (Methods). Arrows are coloured to illustrate the gradient from low sensitivity (blue) to high sensitivity (red). Using these sensitivity gradients, simulated extinction curves were generated by removing species sequentially in order of their sensitivity score (high to low). The impact of species loss was then quantified by calculating functional trait diversity as a proportion of the starting FD before any species was removed, which provides an index of stability (functional resistance). b, Using this approach, we plotted average extinction curves for each land-use type predicted using a cubic smooth spline algorithm. The shaded region shows 95% confidence intervals. To aid visualization, we use the total number of species removed, whereas our functional vulnerability and resistance analyses use the proportion of species remaining in the assemblage (to avoid our results being driven by species richness). c, The impacts of land-use change on functional resistance in different trophic groups were visualized by calculating the predicted change in functional resistance as the AUC (Supplementary Fig. 1). Dashed red lines indicate the standardized predicted functional resistance (set to 0) for pristine primary vegetation (forests, shrublands, grasslands and wetlands). Metrics were calculated and compared across five subsets: all species (n = 1,281 assemblages); trophic generalists (n = 1,281 assemblages); and the three key trophic guilds granivores (n = 1,271 assemblages), frugivores (n = 944 assemblages) and invertivores (n = 1,274 assemblages). Points shown are coefficient estimates from five separate linear mixed-effects models and error bars indicate 95% confidence intervals. Silhouettes are adapted from PhyloPic (https://www.phylopic.org) under a CC0 1.0 Universal Public Domain licence, unless otherwise stated. Campephilus magellanicus created by Edwin Price; Chionis minor created by Alexandre Vong; Geranium maculatum created by Mason McNair; Malus pumila created by T. Michael Keesey under a Public Domain Mark 1.0 licence; Ploceidae created by lucy_the_bob_man under a Public Domain Mark 1.0 licence; Popillia japonica created by Andy Wilson; Riparia riparia created by Bruno Maggia; Xenicus gilviventris created by Ferran Sayol; Ramphastos created by Edwin Price under a Creative Commons licence CC BY 4.0.

We did not find evidence that lower functional vulnerability in disturbed habitats acts as a buffer to initial losses of function. Instead, under both trait-based and rarity-based extinction, functional resistance for whole assemblages followed a similar pattern to functional redundancy. That is, substantial declines occurred in human-modified landscapes, particularly in agricultural and urban settings (Fig. 3b,c). The trait-based scores we used in simulated extinctions were generated from a basket of general response traits that reflect sensitivity to unknown future threats, which provides little direct insight into the likely interaction between land-use change and other explicit stressors, such as climate change[51]. Moreover, we actively removed the most vulnerable species at each time step until species richness dropped to zero, which may not reflect the longer-term persistence of disturbance-tolerant species in anthropogenic environments.

To evaluate the robustness of our results in the context of methods, we ran three sensitivity analyses (Methods). First, we selected a different set of response traits associated with species sensitivity to climate change. Second, we generated extinction curves with passive (probability-weighted) species loss, wherein tolerant species with lower sensitivity scores were allowed to remain in the assemblage for longer periods. Third, we re-ran our main analyses with an alternative functional resistance metric, the half-life (t1/2) of each extinction curve, defined as the proportion of species that need to be removed for FD to decline by 50%[5] (Extended Data Fig. 1e and Supplementary Fig. 1). These analyses produced similar results (Extended Data Fig. 7), which suggests that functional stability consistently declines with increasing land-use intensity. In effect, any additional stability conferred by lower functional vulnerability in disturbed habitats seems to be counteracted by an absence of surplus species across the whole assemblage, leading resistance to decline because there is less redundancy per species (Extended Data Fig. 8). Our simulations also support the view that land-use change creates species assemblages with reduced resilience to synergistic threats, including climate change[51] (Extended Data Fig. 7a). Finally, removal of all spatially autocorrelated studies from our analyses did not change the results. This finding indicates that our conclusions are not influenced by spatial autocorrelation (Extended Data Fig. 9).

Interpreting variation in resilience

Functional stability calculated at the assemblage level reflects the combined stability of different trophic groups with varying responses to land-use change36,37 (Extended Data Fig. 5). Accordingly, functional resistance in assemblages of dietary generalists and granivores tended to remain stable or even to increase across human-modified landscapes. Such changes reflected the proliferation of different food resources such as domestic waste, carrion and seed-bearing grasses in the borders of agriculture and human settlements. By contrast, functional resistance was highly unstable in more sensitive guilds, including frugivores and invertivores (Fig. 3c and Extended Data Figs. 5 and 6). Declines in ecosystem stability after land-use change are therefore unevenly distributed, with the largest losses concentrated in key trophic guilds that mediate ecological services, such as seed dispersal and pest control, which are vulnerable to rapid future collapse in human-modified landscapes. In particular, the substantial increase in functional vulnerability of frugivores in disturbed habitats (Extended Data Figs. 5 and 6) suggests that few surplus species survive in the most at-risk regions of functional space. This scenario implies that there would be accelerated declines in seed dispersal services under further species loss. The high sensitivity of key trophic guilds was found in all species-removal simulations (Extended Data Fig. 7), a result consistent with previous studies showing that frugivorous and insectivorous birds are susceptible to local extirpation in disturbed tropical forests40,52. Indeed, the response of tropical seed dispersers and insect predators to land-use change across all extinction scenarios drove a more general pattern of accentuated declines in FD and redundancy at lower latitudes (Extended Data Fig. 10), where many bird species are sensitive to habitat loss and fragmentation46,53.

Implications for land-use management

The widespread decline we observed in avian FD is consistent with numerous studies reporting similar patterns in response to agricultural expansion, land-use intensification and urbanization9,12,54,55. This outcome reflects the loss of species maladapted to highly modified environments, including ecological specialists that occupy unique regions of functional trait space[56]. It can be argued that the reduction in FD simply reflects reduced ecological demand for services provided by these functionally unique species in agricultural and urban assemblages. Nonetheless, a wide variety of trophic interactions are still required for human-modified ecosystems to function efficiently, and a diverse baseline of predators, pollinators and seed dispersers is needed to maintain the potential for ecosystem recovery and restoration40,57 (Supplementary Information).

In addition to lower FD, we detected substantial reductions in trait redundancy and assemblage-level functional vulnerability. This finding indicates that human-modified assemblages are dominated by fewer, typically generalist species as landscapes become more intensively transformed[3]. The lower functional vulnerability of post-disturbance assemblages implies that they are more resilient, perhaps because extinction filters have removed the most sensitive species1,50. However, the results of simulated extinctions suggest the opposite (Fig. 3). Instead, as redundancy declines, the insurance effect provided by the rich diversity of undisturbed bird assemblages is eroded, which accentuates the adverse impacts of further species losses5,33. In other words, the minor positive effects of land-use change on functional vulnerability are outweighed by reductions in trait redundancy, which potentially leaves ecosystems susceptible to much larger declines in functionality if further species are lost.

High functional vulnerability detected in pristine habitats reflects an increased number of disturbance-sensitive species with unique trait combinations. This finding highlights the role of intact ecosystems as safe harbours for rare, functionally distinct and extinction-prone species58,59,60. It may seem logical to conclude that ecosystem functionality is least stable in natural primary vegetation where so many disturbance-sensitive species are important to ecological function[61]. However, we showed that greater instability arises from widespread reductions in trait redundancy that is occurring throughout the entire assemblage in moderately to heavily disturbed environments, consistent with theoretical predictions and experimental evidence29,30,31. Undisturbed habitats support much higher levels of redundancy throughout the entire assemblage, thereby promoting functional stability. Notably, well-developed secondary vegetation and lightly disturbed habitats had similar levels of redundancy to those found in pristine primary vegetation. This result highlights the importance of retaining and restoring semi-natural and disturbed vegetation to boost the resilience of ecosystem functions[62].

Caveats and limitations

Our analyses are subject to multiple limitations and unavoidable sampling biases. The use of space-for-time comparisons to estimate impacts of land-use change introduces uncertainty, not least because most primary habitats sampled have a long history of human disturbance. Widespread defaunation of ecosystems worldwide, including Pleistocene megafaunal extinctions, mean that even primary vegetation supports much-depleted levels of FD compared with a historical baseline63,64. Moreover, any dataset derived from bird surveys is prone to error because survey-detection probabilities vary across species and land-use types, with rates of detection increasing in open or disturbed habitats where birds are visible at longer range (Supplementary Information). Our dataset nonetheless provides a reasonable estimate of recent land-use-change impacts given that most bird species are identifiable and relatively detectable even in dense habitats because of their songs and other acoustic signals. Moreover, the main effect of shifting baselines and imperfect detection is to reduce estimates of species richness, abundance and redundancy in primary habitats (Supplementary Information). Therefore, improved detection rates would most probably accentuate our main results by boosting FD values in undisturbed landscapes and steepening the estimated decline in resilience after land-use change.

Another source of uncertainty lies in our simulation of future extinctions. Although it is not possible to know which species will drop out of a local assemblage and over what time frame, we used species traits and rarity to define the most likely sequence of extinctions. We also resampled many extinction sequences, under varying levels of extinction probability, to provide an estimate of uncertainty. Future studies should explore different ways of simulating extinctions, with refined estimates of species sensitivity and turnover. Finally, the ecological trait diversity of bird assemblages can only provide limited insight into the functioning of whole ecosystems. For example, our analyses did not consider variation in activity patterns and physiological rates nor the extent to which particular functions are replaceable by mammals, insects and other taxa. Further research is needed to integrate additional species traits across a wider set of taxonomic groups and to quantify the connection between FD and ecosystem resilience. Nonetheless, the abundance and trait diversity of birds provides a useful starting point for understanding the impacts of environmental change. That is, a global framework for estimating trophic processes and associated energy flows that can be strongly mediated by birds even at the ecosystem scale40,65.

Conclusions

By integrating species traits into biodiversity metrics, our analyses revealed that land-use change drives pervasive declines in functional resistance and stability of bird species assemblages. The impacts were most severe in heavily modified environments and concentrated in key ecological groups with prominent roles in seed dispersal (frugivores) and pest control (invertivores). Overall, anthropogenic landscapes support fewer surplus species in these regions of trait space, exposing them to future dysfunction if additional species are removed. The consistent pattern detected in birds confirms and extends the findings of local-scale studies showing reduced functional resistance in invertebrate assemblages66,67. An important implication of these findings is that standard approaches to estimating the effects of land-use change on ecosystem function may underestimate longer-term impacts. Specifically, they may suggest that species assemblages in human-modified landscapes are more resilient, whereas a detailed appraisal using trait hypervolumes reveals that they are actually more fragile and primed for further declines in functionality if biodiversity losses continue unchecked[68]. Conservation efforts should therefore focus on maintaining and restoring the functional resilience of species assemblages to reduce the risk of future ecological collapse.

Methods

Survey data

To assess impacts of land-use change on bird diversity, we began by collating surveys from the PREDICTS database, a repository of species occurrence and abundance data sampled across multiple land-use types[70]. We removed 30 datasets because they lacked abundance data (n = 12) or were incomplete (n = 18), usually because sampling was limited to particular guilds or methods, such as camera traps (Supplementary Information). This process produced a baseline of 72 datasets that we augmented by conducting a systematic literature review using Web of Science to identify further published bird surveys that targeted land-use gradients (Supplementary Information). After contacting authors for data, we received 29 suitable datasets that we added to the PREDICTS database. Our sample in this study contains data from these 29 surveys, along with 5 additional datasets released in the latest version of PREDICTS[71]. Finally, to improve sampling in megadiverse regions, we integrated further independent datasets generated by intensive surveys in Bornean[72] and Amazonian rainforests[73]. Geographical location and sources for all published surveys used in our analyses are presented in Fig. 2, Supplementary Table 1 and Supplementary Data 1.

Most survey data in the PREDICTS database are organized as a hierarchy. Survey sites are nested in study blocks, and blocks are nested in study landscapes. Study blocks are spatially segregated but not always temporally defined[70]. We found 16 datasets in PREDICTS that contain surveys sampled in different years or seasons; therefore, we subdivided the data into separate study blocks partitioned by location and time of survey. To ensure consistency, we also collapsed 17 surveys in PREDICTS into 7 studies by combining all data extracted from the same original source publications. We then partitioned these seven studies into distinct study blocks representing geographical and temporal subsets. After restructuring, our final dataset consisted of 98 study landscapes (Fig. 2), each with numerous survey sites clustered into study blocks. Subdividing our data in this way enabled us to account for spatial, annual and seasonal effects across studies by including study block as a random effect in our models (Supplementary Information).

We converted survey data into species assemblages to enable comparisons across land-use types. In some study landscapes, species assemblages reflect the total number of species identified in a study site, usually pooled across a series of transects or point-counts conducted at intervals between dawn and midday. Other published studies focused at finer resolution, sometimes defining each point-count as a separate survey site. Single point-counts generally undersample species richness, and neighbouring sites may be very close together, which caused problems for our analyses. In these cases, to facilitate calculation of functional metrics and to minimize the risk of pseudo-replication, we grouped species into larger assemblages by aggregating survey sites with similar land-use types in the same study block (Supplementary Information). Species assemblages were therefore defined as all species encountered in a restricted, spatially and temporally segregated area, largely confined to the same land-use type. We do not use the term community because we do not have direct data confirming species interactions[74].

Land-use classification

To classify land-use types for each survey site, we used PREDICTS data to estimate the predominant type and stage of vegetation and the intensity of human use. First, we assigned sites to one of six vegetation classes: primary vegetation, secondary vegetation, plantation forests, pasture, cropland and urban. We then classified lightly or intensively used primary vegetation sites as disturbed primary vegetation, whereas minimal-use primary vegetation sites were classified as pristine primary vegetation (a proxy for undisturbed natural vegetation). Based on previous analyses showing reduced avian FD in intensely urbanized areas[55], we also split minimal-use urban sites from sites with more intensive urbanization. Finally, to account for the effects of vegetation structure at different successional stages[75], we partitioned secondary vegetation according to age class (mature, intermediate, young, indeterminate; Supplementary Information). Indeterminate age secondary vegetation was removed from our dataset.

Our final dataset consisted of 98 study landscapes distributed across 6 continents (Fig. 2a and Supplementary Table 1), representing a total of 1,281 avian assemblages in 10 distinct land-use types: pristine primary vegetation (n = 177); disturbed primary vegetation (n = 281); mature secondary vegetation (n = 44); intermediate age secondary vegetation (n = 77); young secondary vegetation (n = 86); plantation forest (n = 218), pasture (n = 184); cropland (n = 107); and urban, including both minimal-use (n = 46) and intense-use (n = 61) urban landscapes.

Functional trait data

Species traits can provide information about sensitivity to perturbations (response traits) and the impacts of species presence or absence on ecological function (effect traits). In both cases, data availability is often patchy for major taxonomic groups at a global scale[76]. We obtained morphometric measurements for all 3,696 species reported in our study landscapes from the AVONET trait database[44]. Species means were compiled for seven traits: beak length (culmen), beak length (tip-to-nares distance), beak depth, beak width, tail length, tarsus length and wing length (Supplementary Data 1). These traits have been shown to predict a range of key ecological niche axes, including diet and foraging strategy[16] (Supplementary Table 3). We also included data on the hand–wing index (HWI), a metric of wing elongation that predicts aerial lifestyle and dispersal distance in birds[77]. HWI is widely used as a proxy for dispersal ability[78]. Species mean values for all traits used in our study were calculated from an average of 11 individuals per species (41,515 individual birds measured in total).

Avian morphological traits are often intercorrelated because of an underlying association with body size[16]. Accordingly, all traits in our dataset were strongly correlated with the body size axis (Extended Data Fig. 2a), apart from HWI (R = 0.22). Following previous studies40,79, we removed the association with body size through a two-step PCA, which reduced our seven linear morphometric traits into three niche axes related to ecological functions (Extended Data Fig. 2b). We performed two separate PCAs on trophic traits (related to beak morphology) and locomotory traits using all species in our dataset. In both cases, the first principal component (PC) was strongly correlated with body size; therefore, we used the second PC to represent the dominant axis of variation, which is effectively independent from body size (Extended Data Fig. 2c). We then performed a third PCA on the first PC scores from both the trophic and locomotory PCAs, taking the resultant first PC to represent the body size axis.

We use this metric of body size because it correlates strongly with body mass while also reflecting trophic niche differences. As the body size axis is extracted from linear measurements of beak, wing, tail and tarsus, it more closely reflects trophic and locomotory niches than conventional body-mass estimates. For example, our estimates of body size will distinguish between hummingbirds with equal body mass but different beak lengths, thereby reflecting associations with different foraging niches linked to pollination. Finally, we supplemented these three derived trait axes (trophic, locomotory and size) with a fourth morphological trait axis consisting of the log-transformed HWI (related to dispersal ability).

Taxonomic matching

Bird species names and classifications vary over time and between different taxonomic treatments. This is problematical for global datasets based on published field surveys because different authors use a variety of taxonomic approaches, including English or local names. We converted all species names into a single taxonomy using published cross-walks[44] and verified taxonomic assignments with geographical range maps[80] (Supplementary Information). This enabled accurate alignment with species trait data. Some taxa reported in survey data were impossible to assign directly to species because they were only identified to the genus level. Deleting these taxa would result in missing data, which can reduce the accuracy of FD estimates[81]. Instead, we created pseudo-species representative of the genus. Given that avian life history and morphological traits tend to be highly conserved within genera[44], we assigned trait values to pseudo-species by averaging the trait values of all congeners potentially occurring at the locality. To generate trait data for averaging, we used geographical range maps to provide a list of all members of the focal genus with geographical distributions overlapping the site location (Supplementary Information). We synthesized data for 73 pseudo-species in 133 of our 1,281 study assemblages.

Dietary data

Birds mediate a wide range of ecological processes and services depending on their trophic interactions, including seed dispersal by frugivores and pest control by invertivores82,83. The morphological trait dataset used in this study was strongly correlated with avian diets and associated foraging behaviours16,43. However, the connection between morphology and dietary niche was noisy and weak in some taxonomic groups (Supplementary Information). Therefore, we also included standard diet classifications in functional metric calculations. We used published estimates of the proportion of species diets across nine major resource types: herbivore (aquatic), herbivore (terrestrial), nectarivore, granivore, frugivore, invertivore, vertivore (aquatic), vertivore (terrestrial) and scavenger. The data were extracted from a previous publication[16] and were primarily based on the EltonTraits dataset[84] with extensive updates and reorganization based on subsequent literature.

To define dietary groups for analyses, we used published data that classified species into trophic guilds according to their primary food source, with any species obtaining >60% of their diet from a single food type defined as a trophic specialist[44]. Species that obtained resources more equally across different food types were classed as omnivores16,85. Our main analyses focused on trophic guilds rather than omnivores because bird species with more specialized diets have a higher certainty of contributing to particular ecological roles and services[40]. However, generalists are often abundant, which suggests that they may contribute substantially at the population level to ecological processes such as seed dispersal and insect predation. Thus, non-specialist omnivores may help to stabilize ecosystems by providing additional redundancy. To assess whether our results were sensitive to trophic guild classification and inclusion of generalists, we generated broader dietary groupings containing all species that obtained >25% of their diet from a single food source. We then repeated our main analyses on these expanded groups (Extended Data Fig. 6 and Supplementary Information).

Calculating FD and redundancy

To calculate functional metrics, we created trait probability densities (TPDs) using the TPD package in R[86]. The TPD approach uses species mean trait-values and intraspecific trait variation to calculate probabilistic hypervolumes in which the potential position and extent of occupancy for each species can be predicted along multiple trait axes. By using axes of trait variation to define ecological niche axes, TPD hypervolumes represent a Hutchinsonian niche[45]. We constructed species hypervolumes on the basis of their diet proportion data across nine major resource types and their distribution along four derived morphological trait axes (locomotory, trophic, dispersal and size). We then estimated FD for each assemblage as the total cumulative volume occupied in trait space by all species in the assemblage. Functional redundancy was calculated as the proportion of this total volume shared by multiple species, weighted by the relative abundance of species occupying the same regions of trait space[47] (Supplementary Information).

To create TPDs, we first calculated distance matrices using the R package gawdis, which is specifically designed to combine compositional data (such as our proportional diet data) into a single axis of variation[87] (Supplementary Information). We calculated distance matrices for each of our 98 study landscapes using diet and morphological data for all species present. Following previously described methods[88], we back-transformed our distance matrices into three-dimensional coordinates representing the relative position of each species in functional trait space (Supplementary Information).

Calculation of TPDs requires the estimated position of species means in trait space and the square-root of intraspecific variability. The latter is required to generate a probability kernel around each species-mean position. This intraspecific variation kernel (IV kernel) is taken as the niche of each species, represented in functional trait space. Ideally, the IV kernel is estimated by directly comparing measurements of many conspecific individuals and calculating the standard deviation across each dimension in functional trait space. However, as bird diet data are only available as species-mean estimates, we were unable to use this method for our main analysis. Thus, following previous studies47,89,90, we approximated the IV kernel using a bandwidth estimator, which calculates an equally sized density kernel around each species mean based on the distances between co-occurring species (Supplementary Information). As the volume covered by the IV kernel can vary between assemblages, we calculated the dimensions of the kernel for each assemblage and took the square-root of the mean dimensions as our common IV kernel for each species (Supplementary Information). For each of our 98 study landscapes, we calculated a landscape-level TPD using the TPDsMean function, which generates a TPD using the species-mean positions in each assemblage, alongside the IV kernel dimensions. The functional trait space of each TPD was divided into 125,000 equally sized grid cells, with a value reflecting the likelihood of occupancy.

For each assemblage in each study landscape, we calculated FD and functional redundancy by running the in-built REND and redundancy functions from the TPD package across respective landscape-level TPDs[47]. Both functions filter the landscape-level TPDs for the species that occur in each assemblage. FD was then calculated as the number of grid cells with a likelihood of occupancy >0. Redundancy was calculated as the average number of species that could be removed from each grid cell without reducing the total occupied area of functional trait space[47] (Supplementary Information). By setting a minimal threshold for occupancy (>0), the FD value reflects the volume of occupied trait space independent of species abundance. Conversely, functional redundancy calculations are shaped by abundance and highly sensitive to the number of species that share a similar area of trait space (Supplementary Information).

To analyse the effect of land-use change on specific ecological roles, we calculated functional metrics across five species subsets to assess how functional trait structure changed within different trophic guilds in each assemblage. We focused on the entire assemblage (n = 1,281) and all generalist species (n = 1,281), as well as three key dietary guilds sampled across all land-use types: granivores (n = 1,271), frugivores (n = 944) and invertivores (n = 1,274). Sample sizes varied between guilds as TPDs cannot be created when three or fewer members of the guild are recorded in a given study landscape.

We did not standardize functional metrics by the number of species present and instead allowed both FD and redundancy to correlate with species richness. We assumed that each additional species added to an assemblage will either increase the number of trophic processes performed or increase the probability that multiple species deliver a particular function, or both[5]. We therefore allowed increased species richness to drive greater FD and redundancy values in larger assemblages. To examine how this decision influenced our results, we also conducted a supplementary analysis in which we modelled the relationship between functional resistance and a species richness standardized redundancy metric (Extended Data Fig. 8 and Supplementary Information).

To address whether our method of estimating the dimensions of our IV kernel affected our conclusions, we recreated our TPDs using intraspecific variation calculated from multiple measurements of conspecific individuals, using morphological measurements extracted from AVONET[44] (Supplementary Information). The results of the sensitivity analyses based on these revised TPDs were similar, which showed the same general patterns of decline in FD or redundancy with land-use change (Extended Data Fig. 4).

As overall assemblage redundancy reflects the total amount of shared trait space in the assemblage, declines in redundancy are driven by either species losses or reduced amount of niche overlap per species (that is, niche differentiation). To decipher whether the effect of redundancy losses on functional resistance was driven by declines in species richness or reduced niche overlap per species, we also calculated the relative redundancy for each assemblage using in-built functions in the TPD package (Extended Data Fig. 8).

Calculating sensitivity scores

To calculate functional vulnerability and functional resistance metrics, we began by scoring sensitivity to disturbance, which reflects the likelihood that a particular species would be removed from the assemblage by future perturbations. For each species in each assemblage, we generated two forms of sensitivity score: (1) trait-based and (2) rarity-based. Our main trait-based sensitivity score was based on four general response traits associated with extinction risk: geographical range size, body size, diet specialism and dispersal limitation91,92 (Supplementary Information). This is a broad bandwidth score that uses traits associated with any form of disturbance (for example, fire, storms, drought, habitat loss, pollution or human exploitation) to reflect uncertainty regarding the source and severity of future perturbations. To provide a more explicit test of sensitivity associated with a known stressor, we calculated a secondary trait-based score using response traits associated with sensitivity to climate change: higher elevational distributions, narrower temperature niche, longer generations and dispersal limitation[93]. To calculate our rarity-based sensitivity scores, we extracted the inverse (that is, negative) abundance of each species in the assemblage based on the assumption that rarer species are more likely to be removed from an environment by population fluctuations[94]. All sensitivity scores were scaled by their standard deviation and centred to have a mean of zero.

Functional vulnerability

Previous studies have associated functional stability with the distribution of unique functional traits[95] and have linked assemblage vulnerability to the variation in disturbance sensitivity of species traits[60]. We devised a new metric of functional vulnerability to encapsulate both these concepts. To estimate the relationship between trait redundancy and species sensitivity, we quantified the amount of redundancy provided by each species. Species redundancy was estimated as the change in assemblage redundancy after removal of the focal species. To calculate this value, we separately removed each species from the full assemblage and recalculated the assemblage redundancy. We then calculated two functional vulnerability scores as the covariance between species redundancy and either trait-based or rarity-based sensitivity scores using Spearman’s rank correlation coefficient. We reversed the direction of the covariance so that a high positive correlation indicates that the most sensitive species in the assemblage tend to be the least redundant (that is, most unique).

Functional resistance

To estimate functional resistance, we ran species extinction simulations using sequential removals and quantified the associated decline in FD. The sensitivity of species to environmental change is strongly influenced by their functional response traits, with certain trait combinations predicting the likelihood of local extinction36,41. Moreover, species abundances are often a strong indicator of local extinction risk, as rarer species are typically more sensitive to environmental perturbations96,97. Therefore, for each assemblage, we ran two extinction simulation scenarios by sequentially removing each species according to both sensitivity scores (trait-based and rarity-based).

Following previous methods47,98,99,100, we plotted extinction curves to quantify how FD declines as the proportion of species occurring in the original assemblage is reduced to zero. Using a standard approach, we then measured the AUC as an estimate of functional resistance99,100. If an assemblage maintains constant FD when species are removed, the AUC remains large, which indicates high levels of functional resistance. Conversely, when extinctions drive declines in FD, the AUC decreases (Extended Data Fig. 1 and Supplementary Fig. 1). For each extinction curve, the AUC was measured using the MESS package in R[101].

As we were specifically interested in quantifying the pace of FD decline, we standardized the extinction curves by scaling the FD values between 1 and 0, where 1 equals the FD calculated for the full assemblage before any species was removed. This standardization prevented the magnitude of the FD value from influencing the AUC, which ensured that AUC values reflect variation in the shape of the extinction curve only[98]. For each assemblage, we calculated our functional resistance metric across the same five subsets of species as in the preceding FD and redundancy analyses: all species, all dietary generalists, granivores, frugivores and invertivores.

One drawback of the AUC approach is its sensitivity to the order in which species are lost from an assemblage. For example, when a single morphologically unique species is lost before other more redundant species, this causes a steep initial decline in FD. Therefore, following the same methods used to generate functional vulnerability values, we also estimated functional resistance as the half-life (t1/2) of each extinction curve (Supplementary Information).

In our main simulations, we implemented extinction scenarios based on a set of traits associated with general disturbance. To assess the robustness of our results to methods and choice of traits, we conducted a series of sensitivity analyses. First, we calculated extinction curves generated using trait-based sensitivity scores based on a different combination of response traits specifically related to climate change tolerance. Second, we re-ran analyses under passive (probability-weighted) species loss scenarios, in which 0–2 species were removed at each time step, and the probability of a species being removed at each step was equal to its sensitivity score (Supplementary Information). Under this procedure, highly tolerant species were allowed to remain in the assemblage longer, and assemblages with a high proportion of tolerant species did not necessarily lose species at each time step, thereby increasing the AUC. Results of these sensitivity analyses were similar to our main analyses (Extended Data Fig. 7).

Statistical analyses

To assess how the distribution of key functional traits were affected by land-use change, we subdivided assemblages (n = 1,281) into four categories: (1) primary vegetation, (2) secondary vegetation, (3) agriculture (including plantation forests) and (4) urban. We then constructed a set of univariate linear mixed-effects models with land-use as a single predictor variable and nine separate response variables covering all response and effect traits analysed in this study (see Supplementary Information for the rationale). Models were assessed by comparing coefficient estimates for each land-use type against primary vegetation. When coefficient estimates were positive, land-use change was inferred to filter species with low functional trait values.

To assess the impact of land-use on functional structure, we first constructed three univariate linear mixed-effects models. In all three models, land-use was the sole predictor variable and each model analysed the effect of land-use change on FD, functional redundancy or functional vulnerability. Each model was conducted across all species and separately across four different data subsets related to dietary guild (generalists, granivores, frugivores and invertivores), and two subsets related to climatic region: tropical study landscapes and non-tropical study landscapes. For these models, land-use was split into ten distinct categories: pristine-primary vegetation, disturbed primary vegetation, mature secondary vegetation, intermediate age secondary vegetation, young secondary vegetation, plantation forests, pasture, cropland, minimal urban and intense urban.

To address how land-use change affects the functional resistance of bird assemblages, we ran six additional sets of univariate mixed-effects models. These analyses modelled the effects of land-use change on the functional resistance of each assemblage under both of our main species-loss scenarios (general trait-based AUC and rarity-based AUC) and our four alternative functional stability (general trait-based t1/2, rarity-based t1/2, climate trait-based AUC and passive AUC). For these final analyses, we also split land-use into four categories: (1) primary vegetation, (2) secondary vegetation, (3) agriculture (including plantation forests) and (4) urban. In line with FD and redundancy analyses, we conducted our functional resistance models separately across all species and our four dietary guild subsets (generalists, granivores, frugivores and invertivores).

All models were conducted using the lme4 package in R[102]. We added study landscape to account for among-study differences in sampling methods and study block to account for confounding variables related to temporal and geographical distinctions in each study. Models were interpreted by comparing the change in estimated regression coefficients for each land-use type using the functional metric value calculated for the least disturbed land-use category as our reference. Our hierarchical modelling approach did not require all land uses to be present in each study landscape to produce standardized regression coefficients. For most models, our least disturbed category was termed pristine primary vegetation, which accounted for 177 (13.8%) of our survey sites and was present in 50 out of our 98 study landscapes (51%). Although no primary vegetation landscape is entirely unaffected by human disturbance, this term is used to differentiate between more heavily disturbed vegetation types. For our functional stability models, we combined all primary vegetation sites into a single land-use type and used this broader classification as our least-disturbed land-use category.

To account for seasonal effects, we ensured that assemblages in the same study block were surveyed in the same season. Therefore, by including study block as a random effect, we removed detection biases arising from variation in sampling season. Moreover, incomplete sampling owing to undetected rare or cryptic species was reduced by aggregating survey sites into larger species assemblages to increase overall sampling depth (Supplementary Information).

We assessed all models for normality of residuals and heteroskedasticity and did not find that any of our models violated the assumptions of a linear model. Owing to the hierarchical structure of our data, it was difficult to incorporate covariance structures that account for spatial autocorrelation between local survey sites in the same study landscape into our global models. However, following a previously described method[11], we assessed spatial autocorrelation in study landscapes and study blocks separately using Moran’s I and found that it did not affect our results (Extended Data Fig. 9).

Reporting summary

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

Data availability

All data are available at Zenodo (https://zenodo.org/records/17184411)[103]. Original survey datasets are available from PREDICTS (https://doi.org/10.5519/JG7I52DG). Bird traits for all study species are available from AVONET at Figshare (https://figshare.com/s/b990722d72a26b5bfead)[104].

Code availability

The code to conduct analyses and replicate figures is available at Zenodo (https://zenodo.org/records/17184411)[103].

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Acknowledgements

This research was supported by grants from the Natural Environment Research Council UK (NE/I028068/1 and NE/K016385/1 to J.A.T., NE/Z50404X/1 to D.P.E. and NE/X015262/1 to A.C.L.) and the UKRI Global Challenges Research Fund (ES/P011306/1 to J.A.T.). T.L.W. and P.A.W. were funded by Natural Environment Research Council studentships through the Science and Solutions for a Changing Planet Doctoral Training Programme. We thank J. Borer, S. Contu, X. Huang and G. Needler for compiling and processing survey data.

Author information

Authors and Affiliations

1. Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK

Thomas L. Weeks, Patrick A. Walkden, Andy Purvis & Joseph A. Tobias

2. Department of Life Sciences, Natural History Museum London, London, UK

Thomas L. Weeks, Patrick A. Walkden & Andy Purvis

3. Department of Plant Sciences, University of Cambridge, Cambridge, UK

David P. Edwards

4. Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK

Alexander C. Lees

5. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK

Alexander L. Pigot

Contributions

This study was conceived and developed by J.A.T. and T.L.W., with input from A.P. and A.L.P. T.L.W. integrated datasets and ran all analyses with support from P.A.W. and A.P. D.P.E. and A.C.L. provided additional datasets. T.L.W. wrote the manuscript and designed all figures with extensive input from J.A.T. All authors contributed to subsequent drafts and gave final permission for publication.

Corresponding author

Correspondence to Joseph A. Tobias.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature thanks Daniel Karp, Peter Manning, Carlos Perez Carmona 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 Hypothetical impacts of land-use change on functional structure of species assemblages.

Panels show diagrams clarifying concepts and terms used in this study. Disturbance drives non-random shifts in functional diversity (FD) because species with traits ill-suited to the modified environment are removed (pale orange circles), more tolerant species are retained (dark orange circles), and species with advantageous trait combinations are gained (grey circles). This turnover alters the functional trait space (orange polygon) and the assemblage’s internal functional structure. The total volume of this space (functional richness) tends to decline as primary habitats (a) are converted into human-modified ones (b). To assess the effects of species loss and turnover, we use two primary metrics. Functional redundancy (c) reflects the amount of functional trait space shared by multiple species; high redundancy is identified when species are clustered in trait space. In contrast, functional vulnerability (d) is based on the relationship between how sensitive and how unique a species is within the assemblage. Assemblages are highly vulnerable when numerous sensitive species occupy unique areas of trait space. Functional resistance (e) describes the rate at which functional richness declines as species are removed from the assemblage. Assemblages with high redundancy can absorb the loss of more species with minimal effect on the total functional trait space, whereas low vulnerability means the most sensitive species are unlikely to provide a unique function. Both high redundancy and low vulnerability contribute to high resistance, allowing the assemblage to absorb more species losses with a slower decline in overall function. We quantify functional resistance as either the Area Under the Curve (AUC) or half-life of the extinction curve (see Supplementary Fig. 1).

Extended Data Fig. 2 Using traits to generate independent niche dimensions.

Raw measurements of trait dimensions reflect overall size of the measured specimen and are strongly correlated to body mass (a). Only hand-wing index is uncorrelated with body mass as it is calculated as a ratio of two separate traits[44]. To avoid functional metrics simply reflecting variation in overall body mass, we use a 2-step principal component to generate three distinct trait axes (b). We separated seven morphological traits correlated with body mass into a trophic axis based on beak measurements (culmen length, tip-to-nares, depth, width) and a locomotory axis (wing length, tail length, tarsus length). We then performed two separate PCAs on trophic and locomotory traits using all species in the dataset. The first principal components (PCs) are correlated with body mass, so we used the second principal component to represent the dominant axis of variation, independent of body mass, for beak shape and locomotion, respectively (c). After taking these second PCs as our trophic and locomotory trait axes, we performed a third PCA using both first PCs of the original PCAs, then used the first PC of this final PCA to reflect the size axis. Traits were extracted from AVONET[44] for all species in this dataset (n = 3696), log-transformed and scaled to their standard deviation. R-values are Pearson’s correlation coefficients; red line indicates the slope of the correlation coefficient estimate. Beak illustration was created by the authors. Bird outline adapted from PhyloPic (https://www.phylopic.org). Cariama cristata created by George Edward Lodge (vectorized by T. Michael Keesey) under a Public Domain Mark 1.0 licence (Supplementary Table 4).

Extended Data Fig. 3 Impacts of land-use change on species traits associated with sensitivity to disturbance.

Results of global models (n = 3696 bird species; n = 1281 assemblages). a, Estimated change in community mean (CM) trait-values for response traits related to biogeography and demography: Range limitation = inverse (negative) total geographical area (km^2) of breeding and non-breeding ranges; Elevation = minimum elevation recorded per species[105]; Climate limitation = inverse (negative) mean annual temperature seasonality across the species range[106]; Generation rate = age of first breeding, longevity and adult survival rate[107]. b, Estimated change in CM for ecological traits: body size, body shape and beak shape calculated from morphological traits[44] (see Methods; Extended Data Fig. 2a); dispersal limitation = inverse (negative) Hand-wing Index; diet specialism = proportion of diet obtained from the primary food-source. Higher values for body shape indicate longer wing/tail and shorter tarsus; for beak shape indicate thinner, more elongated beaks; for diet specialism indicate greater limitation to single food source. Rationale for trait selection in Supplementary Tables 2–3. CMs for each land-use category are compared against a standardized baseline value (set to 0, dashed red line) calculated for pristine primary vegetation. To aid comparison, all traits were standardized by their standard deviation prior to analyses. Points shown are coefficient estimates from nine separate linear mixed effects models; error-bars indicate 95% confidence intervals.

Extended Data Fig. 4 Functional metrics of bird assemblages using alternative method for calculating intraspecific variation.

We estimated intraspecific variation using individual-level measurements for species traits extracted from AVONET[44]. Panels show outputs from univariate mixed effects models estimating shifts in functional metrics: (a-e) functional diversity (FD; measured as functional richness); (f-j) functional redundancy (FRed); and (k-o) Functional vulnerability (FV; calculated using general sensitivity scores). Models were run separately on the full sample (n = 1281, 1281) and dietary subsets of generalists (n = 1281, 1185), granivores (n = 1271, 775), frugivores (n = 944, 506), and invertivores (n = 1274, 1224). Two sample sizes are given because FD and FRed are calculated for all assemblages, whereas FV can only be measured when >1 species of a single guild is present. Species are assigned to a trophic guild if they consume >60% of their diet from a single food source; all other species are defined as generalists. We omit dietary data from these calculations due to a lack of individual-level estimates. FV is calculated using Spearman’s rank correlation coefficient between species-level redundancy and general trait-based sensitivity scores (see Methods). Functional metrics are compared against a standardized baseline value (set to 0, dashed red line) calculated for pristine primary vegetation (forests, grasslands, shrublands). Points shown are coefficient estimates; error-bars indicate 95% confidence intervals. Response variables were squareroot transformed prior to analysis and scaled by their within-group standard deviation to aid comparison.

Extended Data Fig. 5 Land-use change alters functional diversity and redundancy of avian assemblages.

Panels show outputs from univariate mixed effects models estimating shifts in functional metrics: (a-d) functional diversity (FD; measured as functional richness); (e-h) functional redundancy (FRed); and functional vulnerability (FV) calculated using general trait-based sensitivity scores (i-l) and rarity-based sensitivity scores (m-p). Models were run separately on samples of generalists (n = 1281, 1185), granivores (n = 1271, 775), frugivores (n = 944, 506), and invertivores (n = 1274, 1224). Two sample sizes are given because FD and FRed are calculated for all assemblages, whereas FV can only be measured when >1 species of a single guild is present. Species are assigned to a trophic guild if they consume >60% of their diet from a single food source; all other species are defined as generalists. Functional metrics are compared within trophic guilds against a standardized baseline value (set to 0, dashed red line) calculated for pristine primary vegetation (including forests, grasslands and shrublands). Points shown are coefficient estimates; error-bars indicate 95% confidence intervals. Response variables were squareroot transformed prior to analysis and scaled by their within-group standard deviation to aid comparison.

Extended Data Fig. 6 Impacts of land-use change on expanded trophic guilds associated with key ecological functions.

Panels show outputs from univariate mixed effects models estimating shifts in functional metrics after land-use change: (a-c) functional diversity (FD; measured as functional richness); (d-f) functional redundancy (FRed); and functional vulnerability (FV) calculated using general trait-based sensitivity scores (g-i) and rarity-based sensitivity scores (j-l). Models were run separately on samples of granivores (n = 1271, 1095), frugivores (n = 1271, 1033), and invertivores (n = 1274, 1269). Two sample sizes are given because FD and FRed are calculated for all assemblages, whereas FV can only be measured when >1 species of a single guild is present. Samples are larger than in Extended Data Fig. 4 because a wider set of species are assigned to a trophic guild if they consume >25% of their diet from a single food source. Functional metrics are compared within trophic guilds against a standardized baseline value (set to 0, dashed red line) calculated for pristine primary vegetation (including forests, grasslands and shrublands). Points shown are coefficient estimates; error-bars indicate 95% confidence intervals. Response variables were squareroot transformed prior to analysis and scaled by their within-group standard deviation to aid comparison.

Extended Data Fig. 7 Functional resistance of bird assemblages under alternative projections of future extinction.

Results show predicted functional resistance of species assemblages for each land-use type in response to different extinction scenarios and calculations: climate change (a-e), passive (probability-weighted) species loss (f-j), half-life (k-o). The climate change scenario uses a set of response traits linked to climate sensitivity; the passive scenario assigns a probability of extinction based on general response traits (see Methods). In both simulations, functional resistance is calculated as the area under the extinction curve (AUC; Extended Data Fig. 1e; Supplementary Fig. 1) for each assemblage. Functional resistance calculations calculated using the half-life (t1/2; Extended Data Fig. 1e; Supplementary Fig. 1) is measured using extinction curves generated under trait-based (circles) and rarity-based (triangles) species-loss scenarios. Under the trait-based scenario, species are removed based on general response traits; under the rarity-based scenario, species are removed in reverse order of abundance (see Methods). Functional resistance is compared within all species (n = 1281 assemblages) and generalists (n = 1281 assemblages), as well as for trophic guilds defined as standard dietary specialists (see Methods): granivores (n = 1271 assemblages), frugivores (n = 944 assemblages), and invertivores (n = 1274 assemblages). Dashed red line indicates the standardized predicted functional resistance to species losses (set to 0) for assemblages in pristine primary vegetation (forests, grasslands, shrublands). Points shown are coefficient estimates, and error-bars indicate 95% confidence intervals.

Extended Data Fig. 8 Functional resistance as a product of functional trait space overlap per species.

To understand whether functional resistance arises from total species richness or from the degree of trait overlap among species, we decomposed functional redundancy into two components: species richness and the average trait space overlap per species (relative redundancy). To test whether niche overlap alone can explain variation in assemblage resistance, we modelled the relationship between relative redundancy and functional resistance. a-c, Panels show the results of linear models assessing the relationship between relative redundancy and functional resistance when extinction curves are generated by general trait-based sensitivity scores (a), climate sensitivity scores (b), and through a passive (probability-weighted) species loss procedure (c; see Methods). Under scenarios a and b, a species is removed at each time step; in c, this procedure is relaxed so that species with low sensitivity can survive indefinitely. Each point represents a species assemblage (n = 1281); colour scale indicates level of overlap from low density (purple) to high density (yellow). Results shown are the coefficient estimate from three separate linear models (red line) alongside the 95% confidence intervals (shaded area). Functional resistance and relative redundancy are positively related under each scenario suggesting that functional resistance is driven by relative redundancy per species rather than species richness alone.

Extended Data Fig. 9 Functional metrics of bird assemblages after removal of spatially autocorrelated data.

Within each study-landscape we measured the spatial auto-correlation across survey sites for each functional metric using Moran’s I (see Methods). Results are outputs from univariate mixed effects models exploring effects of land-use change on each of our assemblage-level functional metrics (a) after removing study landscapes that showed clear spatial autocorrelation between functional metrics calculated at different survey sites. Sample sizes vary as the number of spatially auto-correlated study landscapes is differs according to functional metric; Functional diversity (measured as functional richness; n = 1177 assemblages), functional redundancy (n = 899 assemblages) & functional vulnerability (n = 973 assemblages). We repeated this process for the functional resistance across different dietary-groups (b) calculated as the Area Under the Curve (AUC; see Methods) of extinction curves generated from two different species-loss scenarios: trait-based (circles; removal on basis of species response traits) and rarity-based (triangles; removal in reverse order of abundance). Results are shown for the entire-assemblage (n = 1269 assemblages), trophic generalists (n = 1242 assemblages), and three separate trophic guilds: granivores (n = 1025 assemblages), frugivores (n = 630 assemblages), and invertivores (n = 1247 assemblages). Dashed red line indicates the standardized predicted functional resistance (set to 0) for assemblages in pristine primary vegetation (forests, grasslands, shrublands). Results shown are coefficient estimates from two separate linear mixed-effects models with 95% confidence intervals. All response variables were squareroot transformed prior to analysis and then scaled by their standard deviation to aid comparison.

Extended Data Fig. 10 Impacts of land-use change on structure and function of avian assemblages vary by latitude.

Results are outputs from univariate mixed effects models exploring effects of land-use change on (a, b) assemblage functional diversity (FD; measured as functional richness); (c, d) functional redundancy (FR), and (e, f) functional vulnerability (FV). Metrics are calculated and analyzed separately for Tropical (n = 613), and Non-tropical (n = 668) survey sites. FV is calculated using Spearman’s rank correlation coefficient between species-level redundancy and trait-based general sensitivity scores (see Methods). In all cases, metrics are compared to the baseline value (dashed red line) for pristine primary vegation (forests, grasslands, shrublands and wetlands). Points shown are coefficient estimates, and error-bars indicate 95% confidence intervals. Response variables were squareroot transformed prior to analysis and then scaled by their standard deviation to aid comparison (the transformation step was not possible for FV).

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Discussion, Supplementary Tables 1–4, Supplementary Fig. 1 and Supplementary References. Supplementary Database 1 (hosted separately at https://zenodo.org) contains all the source data and code required to reproduce results and figures. The separate data files and code are available at Zenodo (https://zenodo.org/records/17184411).

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Weeks, T.L., Walkden, P.A., Edwards, D.P. et al. Land-use change undermines the stability of avian functional diversity. Nature 649, 381–387 (2026). https://doi.org/10.1038/s41586-025-09788-0

- Received: 17 January 2024

- Accepted: 21 October 2025

- Published: 26 November 2025

- Version of record: 26 November 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09788-0

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Mapping the genetic landscape across 14 psychiatric disorders

Andrew D. Grotzinger,

Josefin Werme,

Wouter J. Peyrot,

Oleksandr Frei,

Christiaan de Leeuw,

Lucy K. Bicks,

Qiuyu Guo,

Michael P. Margolis,

Brandon J. Coombes,

Anthony Batzler,

Vanessa Pazdernik,

Joanna M. Biernacka,

Ole A. Andreassen,

Verneri Anttila,

Anders D. Børglum,

Gerome Breen,

Na Cai,

Ditte Demontis,

Howard J. Edenberg,

Stephen V. Faraone,

Barbara Franke,

Michael J. Gandal,

Joel Gelernter,

Alexander S. Hatoum,

John M. Hettema,

Emma C. Johnson,

Katherine G. Jonas,

James A. Knowles,

Karestan C. Koenen,

Adam X. Maihofer,

Travis T. Mallard,

Manuel Mattheisen,

Karen S. Mitchell,

Benjamin M. Neale,

Caroline M. Nievergelt,

John I. Nurnberger,

Kevin S. O’Connell,

Roseann E. Peterson,

Elise B. Robinson,

Sandra S. Sanchez-Roige,

Susan L. Santangelo,

Jeremiah M. Scharf,

Hreinn Stefansson,

Kari Stefansson,

Murray B. Stein,

Nora I. Strom,

Laura M. Thornton,

Elliot M. Tucker-Drob,

Brad Verhulst,

Irwin D. Waldman,

G. Bragi Walters,

Naomi R. Wray,

Dongmei Yu,

Anxiety Disorders Working Group of the Psychiatric Genomics Consortium,

Attention-Deficit/Hyperactivity Disorder (ADHD) Working Group of the Psychiatric Genomics Consortium,

Autism Spectrum Disorders Working Group of the Psychiatric Genomics Consortium,

Bipolar Disorder Working Group of the Psychiatric Genomics Consortium,

Eating Disorders Working Group of the Psychiatric Genomics Consortium,

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium,

Nicotine Dependence GenOmics (iNDiGO) Consortium,

Obsessive-Compulsive Disorder and Tourette Syndrome Working Group of the Psychiatric Genomics Consortium,

Post-Traumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium,

Schizophrenia Working Group of the Psychiatric Genomics Consortium,

Substance Use Disorders Working Group of the Psychiatric Genomics Consortium,

Phil H. Lee,

Kenneth S. Kendler &

…

Jordan W. Smoller

Nature volume 649, pages 406–415 (2026)

Abstract

Psychiatric disorders display high levels of comorbidity and genetic overlap1,2, challenging current diagnostic boundaries. For disorders for which diagnostic separation has been most debated, such as schizophrenia and bipolar disorder[3], genomic methods have revealed that the majority of genetic signal is shared[4]. While over a hundred pleiotropic loci have been identified by recent cross-disorder analyses[5], the full scope of shared and disorder-specific genetic influences remains poorly defined. Here we addressed this gap by triangulating across a suite of cutting-edge statistical and functional genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders (1,056,201 cases). Using genetic association data from common variants, we identified and characterized five underlying genomic factors that explained the majority of the genetic variance of the individual disorders (around 66% on average) and were associated with 238 pleiotropic loci. The two factors defined by (1) Schizophrenia and bipolar disorders (SB factor); and (2) major depression, PTSD and anxiety (Internalizing factor) showed high levels of polygenic overlap[6] and local genetic correlation and very few disorder-specific loci. The genetic signal shared across all 14 disorders was enriched for broad biological processes (for example, transcriptional regulation), while more specific pathways were shared at the level of the individual factors. The shared genetic signal across the SB factor was substantially enriched in genes expressed in excitatory neurons, whereas the Internalizing factor was associated with oligodendrocyte biology. These observations may inform a more neurobiologically valid psychiatric nosology and implicate targets for therapeutic development designed to treat commonly occurring comorbid presentations.

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Main

Half of the population will meet criteria for at least one psychiatric disorder during their lifetime[7], with many meeting criteria for multiple disorders[1]. High levels of psychiatric comorbidity complicate efforts to differentiate among psychiatric disorders. These challenges are heightened because psychiatric disorders are defined by signs and symptoms, as the underlying pathophysiologies remain largely unclear. Rapid progress in psychiatric genomics has identified hundreds of associated loci (genetic variants), many of which exhibit pleiotropic (shared) associations across disorders, and revealed high correlations in genetic liability across disorders[8].

The present analyses represent the third major study from the Psychiatric Genomics Consortium Cross-Disorder working group[9] (CDG3). Here we examined the shared and unique influences of common genetic variants across 14 psychiatric disorders. Triangulating across multiple, complementary analytic approaches, we dissected the genetic architecture across disorders at the genome-wide, regional, functional and individual genetic variant levels. Our results have implications for refining clinical nosology and repurposing and developing novel treatments.

GWAS data for 14 psychiatric disorders

A summary of the datasets is provided in Extended Data Table 1. Psychiatric disorders were included if described in a psychiatric diagnostic manual10,11 and power was sufficient to interpret genetic correlations[4]. This reflects a major update relative to previous CDG1 (ref. [12]) and CDG2 (ref. [5]) analyses (average case increase of around 165% above CDG2; Supplementary Fig. 1), with new genome-wide association studies (GWASs) for all eight disorders from CDG2: attention-deficit/hyperactivity disorder (ADHD), anorexia nervosa (AN), autism spectrum disorder (ASD), bipolar disorder (BIP), major depression (MD), obsessive–compulsive disorder (OCD), schizophrenia (SCZ) and Tourette’s syndrome (TS)13,14,15,16,17,18,19,20. We added six additional disorders: alcohol-use disorder (AUD)[21], anxiety disorders (ANX)[22], post-traumatic stress disorder (PTSD)[23], nicotine dependence assessed using the Fagerström test for nicotine dependence (NIC)[24], opioid-use disorder (OUD)[25] and cannabis-use disorder (CUD)[26]. The three substance-use disorders (SUDs) are novel relative to a more recent cross-disorder analysis[27], and sample size increases were significant for previously included disorders (average case increase of around 287%). The sample sizes, and therefore the power of the disorder GWAS, differed (Extended Data Table 1 (Neffective)).

Owing to an uneven representation of ancestral groups, the full set of cross-disorder analyses was restricted to GWAS summary statistics from a single genetic ancestry group—European-like (EUR-like)—defined on the basis of genetic similarity to European descent in global reference panels[28]. We also report bivariate results for MD[29] and SCZ[30] in East-Asian-like (EAS-like) genetic ancestry groups and AUD[31], CUD[26], OUD[25] and PTSD[23] in African-like (AFR-like) genetic ancestry groups similarly defined based on reference panels.

Genome-wide genetic correlations

Genetic correlations (rgs) estimated using linkage disequilibrium (LD) score regression (LDSC)[4] revealed pervasive genetic overlap across disorders at the genome-wide level, with clusters of disorders demonstrating particularly high genetic overlap in individuals of EUR-like genetic ancestry (Fig. 1; Supplementary Table 1; see Supplementary Figs. 2–4 for consideration of high rg across PTSD and MD). The LDSC estimates within AFR-like participants were not significant, due to limited power (Supplementary Table 4). The rg between MD and SCZ in EAS-like participants (rg = 0.45, s.e. = 0.09) was double that observed in EUR-like participants (rg = 0.22, s.e. = 0.04), which has been shown[29] to be driven by a single cohort of severe and recurrent MD[32].

Fig. 1: Genome-wide structural models.

a, Heatmap of rgs across the 14 disorders as estimated using LDSC on the lower diagonal and the correlations among the psychiatric factors as estimated using GenomicSEM above the diagonal. Two-sided P values were derived from the Z-statistics, calculated as the point estimate of the rg divided by its s.e. Cells depicted with an asterisk reflect values that were significant at a Bonferroni-corrected threshold for multiple comparisons. Exact values are reported in Supplementary Table 1. Disorders that load on the same factor are shown in the same colour. Per the legend at the bottom of the panel, darker blue shading indicates larger, positive rgs. LDSC estimates were used as the input to genomic SEM to produce the results in b and c. b, Estimates from the five-factor model along with standard errors in parentheses. Estimates are standardized relative to SNP-based heritabilities, where this is equal to the sum of the squared factor loading (the single-headed arrow(s) from the factor to the disorder) and the residual variance (the values on the double-headed arrows on the single-colour circles with text labels that begin with u). Disorders are shown as pie charts; the proportion of residual variance is shaded in purple and the variance explained by the psychiatric factors is shaded in the colour of the corresponding factor. c, Standardized estimates from the p-factor model. The disorders are colour coded as in b, and the first-order factors (F1–F5) are also colour coded to show variance explained by the second-order p-factor in yellow.

As the majority of analyses were restricted to participants of EUR-like genetic ancestry, we sought to gauge how generalizable our findings were across ancestral groups. We achieved this using Popcorn[33], which can estimate rgs for the same trait across ancestral groups. We estimated the genetic impact correlation (ρgi), which considers different allele frequencies across populations by calculating the correlation between the population-specific, allele-variance-normalized single-nucleotide polymorphism (SNP) effect sizes. The results were underpowered for many comparisons, but included a strong EAS–EUR correlation for SCZ (ρgi = 0.85, s.e. = 0.04), followed by lower correlations between EAS-like and EUR-like for MD (ρgi = 0.67, s.e. = 0.16) and for AFR-like and EUR-like PTSD (ρgi = 0.59, s.e. = 0.27; Supplementary Table 4). While these results suggest that the findings that follow for EUR-like ancestry groups may generalize better for some disorders (such as SCZ) than for others (for example, PTSD and MD), that conclusion awaits replication in more highly powered analyses.

MiXeR reveals pervasive genetic overlap

Genome-wide rgs from LDSC indicate shared genetic risk across psychiatric disorders. However, LDSC may underestimate the extent of genetic overlap if shared causal variants reflect a mixture of directionally concordant and discordant associations. We applied bivariate causal mixture modelling (MiXeR) to quantify the degree of genome-wide polygenic overlap reflecting the total number of shared causal variants regardless of magnitude or directionality[6]. Cross-trait analyses were limited to MD, SCZ, BIP, ANX, ADHD, PTSD, AUD and AN, because other disorders were underpowered (Methods; results for univariate MiXeR are reported in Supplementary Table 5 and Extended Data Fig. 1). Supplementary Fig. 5 displays cross-trait MiXeR results for pairwise overlap across four particularly well-powered disorder samples: ADHD, SCZ, BIP and MD (complete results are shown in Supplementary Figs. 6–9 and Supplementary Table 6). There was greater polygenic overlap across psychiatric disorders than suggested by the rgs from LDSC. Overall, MiXeR results suggested that the shared genetic signal for psychiatric disorders primarily reflects variants with concordant effects across disorders, while differentiation in genetic risk is driven by fewer shared discordant or unique variants.

Genomic SEM identifies five factors

We used genomic structural equation modelling (genomic SEM)27,34 in the EUR-like genetic ancestry datasets to model genetic overlap from LDSC across 14 disorders as latent factors representing dimensions of shared genetic risk (Methods). A five-factor model (Supplementary Tables 2 and 3) provided the best fit to the data (comparative fix index (CFI) = 0.971, standard root mean square residual (SRMR) = 0.063). These five latent genomic factors (capitalized throughout, to distinguish them from the psychiatric disorders that define them) (Fig. 1) comprised: F1, a Compulsive disorders factor defined by AN, OCD and, more weakly, TS and ANX; F2, a SB factor defined by SCZ and BIP; F3, a Neurodevelopmental factor defined by ASD, ADHD and, more weakly, TS; F4, an Internalizing disorders factor defined by PTSD, MD and ANX; and F5, a SUD factor defined by OUD, CUD, AUD, NIC and, to a lesser extent, ADHD.

Within this five-factor model, Internalizing disorders and SUD factors displayed the highest interfactor correlation (rg = 0.60; s.e. = 0.02). The median residual genetic variance unexplained by the latent factors was 33.5%, indicating that most genetic risk was shared among disorder subsets. TS displayed the most unique genetic signal, with 87% of its genetic variance unexplained by the factors. The structure of the first four factors was similar to that found by genomic SEM applied to subsets of these disorders in previous work5,27, indicating stability in the underlying factor structure, even as sample sizes and the number of disorders have increased. The newly added SUD traits formed the fifth factor.

Evidence of moderate rg between factors suggests that a higher-order factor may explain common variance across the correlated factors. Consistent with this observation, a hierarchical model also fit the data well (CFI = 0.959, SRMR = 0.074). We refer to this as the p-factor model, which included a higher-order general psychopathology factor defined by the five lower-order psychiatric factors (such as SUD). Internalizing loaded most strongly on p (0.95), with the other 4 factors having moderate loadings (0.50–0.63).

As some of the underlying data were obtained using brief, self-reported diagnoses, we performed a sensitivity analysis in which those data were excluded (Supplementary Note 1, Supplementary Tables 7–11 and Supplementary Figs. 10–18). The rg matrix was largely unchanged; the five-factor model identified in the full sample continued to provide good fit to the data and produced similar point estimates, and downstream GWAS analyses (detailed below) identified similar loci.

Genetic correlations with factors

We estimated rgs between the five correlated factors, hierarchical p-factor and 31 complex traits (Supplementary Table 12) to place shared genetic liability indexed by the factors in a broader clinical context. These factors vary in their use for capturing shared genetic signal; accordingly, we used the QTrait heterogeneity statistic to assess this use at the genome-wide level. When QTrait is significant, this indicates a trait’s rg deviates from the factor structure. For example, if trait X is negatively correlated with SCZ but unrelated to BIP, QTrait would probably be significant, suggesting that trait X lies outside the shared signal captured by the factor. Significant correlations were defined at a Bonferroni-corrected threshold of P < 2.68 × 10−4, while not significant for QTrait at this same threshold. This QTrait exclusion criteria was relaxed for the p-factor if that trait was significantly associated with the majority (≥3) of the five correlated factors, as this indicates the trait is capturing transdiagnostic associations the p-factor is intended to index.

The Internalizing disorders and SUD factors were the only factors associated with household income (rg_Internalizing = −0.40, s.e. = 0.02; rg_SUD = −0.41, s.e. = 0.03; Fig. 2) and were the most pervasively associated with different cognitive outcomes, including childhood intelligence (rg_Internalizing = −0.27, s.e. = 0.05; rg_SUD = −0.40, s.e. = 0.07). Only the SUD factor was associated with adult intelligence (rg_SUD = −0.40, s.e. = 0.03) and verbal numerical reasoning (rg_SUD = −0.41, s.e. = 0.03). This was compared to more circumscribed cognitive associations for the Compulsive disorders and SB factors, including a large negative correlation with the pairs matching test (potentially indexing memory; rg_Compulsive = −0.33, s.e. = 0.03; rg_SB = −0.34, s.e. = 0.03). The SB and SUD factors were the only ones associated with risk tolerance (rg_SB = 0.31, s.e. = 0.03; rg_SUD = 0.38, s.e. = 0.03). The Neurodevelopmental factor was uniquely associated with childhood BMI (rg_Neurodevelopmental = 0.26, s.e. = 0.06) and showed high genetic overlap with childhood aggression (rg_Neurodevelopmental = 0.94, s.e. = 0.10). As would be expected, the five traits significantly associated with all five correlated factors were also among the top correlations for the p-factor, reflecting stress sensitivity (rg_p = 0.50, s.e. = 0.02), loneliness (rg_p = 0.62, s.e. = 0.02), neuroticism (rg_p = 0.64, s.e. = 0.02), self-harm (rg_p = 0.74, s.e. = 0.04) and suicide attempts (rg_p = 0.87, s.e. = 0.03). The full set of correlations is shown in Supplementary Table 13; comparison across factors is shown in Extended Data Fig. 2; and comparison across traits within each factor is shown in Extended Data Fig. 3.

Fig. 2: External trait genetic correlations for psychiatric factors.

Point estimates for the rgs between 14 external traits and the 5 psychiatric factors from the correlated factors model and the p-factor from the hierarchical model. These traits were selected as they were significantly correlated with at least one factor at >0.35 or <−0.35. Bars depicted with a dashed outline were significant for the QTrait heterogeneity statistic, which indicates that the pattern of rgs for that trait did not fit the factor structure. Bars depicted with an asterisk reflect values that were significant at a Bonferroni-corrected threshold for multiple comparisons, that were also not significant at this same Bonferroni corrected threshold for QTrait. This is with the exception that the p-factor is depicted with an asterisk even if it is significant for the QTrait, provided that the same trait was significantly correlated with the majority (at least three) of the five other factors. The two-sided P values used to evaluate significance were derived from the Z-statistics, calculated as the point estimate of the rg divided by its s.e. Error bars are ±1.96 s.e., centred around the point estimate of the rgs. Traits are ordered by the point estimate for the p-factor. The implied sample size for the psychiatric factors was: Compulsive (\(\hat{n}\) = 54,100), SB (\(\hat{n}\) = 127,202), Neurodevelopmental (\(\hat{n}\)  = 84,760), Internalizing (\(\hat{n}\) = 1,637,337), SUD (\(\hat{n}\) = 313,395) and p-factor (\(\hat{n}\) = 2,168,621). Sample sizes for the external traits are reported in Supplementary Table 12 and exact P values are reported in Supplementary Table 13.

LAVA finds regional hotspots of overlap

Global estimates of pleiotropy, such as the genome-wide rgs from LDSC, provide an average of the degree of shared signal across the genome. However, as genetic overlap is unlikely to be constant across genomic regions, we segmented the genome into 1,093 LD-independent regions, and applied local analysis of (co)variant association (LAVA[35]; Methods) to assess the rg between disorders within these regions. In addition to capturing heterogeneity in genetic overlap and pinpointing relevant regions, LAVA identifies potential rg hotspots shared among several disorders, thereby providing further insight into genetic architecture.

We restricted analyses to loci with sufficient SNP-based heritability for the disorders analysed (P < 4.6 × 10−5 = 0.05/1,093; Methods). Correcting for the number of bivariate tests performed across all regions and disorder pairs, we detected 458 significant pairwise local rgs (P < 2.1 × 10−6 = 0.05/24,273). The pairs of disorders with the greatest number of local rg hits were MD and ANX (113 regions), MD and PTSD (88 regions), and BIP and SCZ (40 regions), accounting for over half of all significant local rgs detected (Fig. 3a). This is consistent with the genome-wide levels of overlap indicated through the LDSC global rg (Fig. 1), the polygenic overlap estimated with MiXeR (Supplementary Figs. 5–9), and the multivariate genetic structure identified by genomic SEM. Both global and local rgs tended to be positive, with significant negative rgs identified in only three instances (Supplementary Fig. 19). This indicates that the genetic risk for one disorder typically increases the risk for another (Supplementary Fig. 20).

Fig. 3: Local genetic correlations.

a, An overview of the average patterns of local rgs across the genome for all pairs of disorders, shown as a heatmap (below diagonal) and a network plot (above diagonal). The colours of the heatmap represent the average local rgs across all evaluated loci, with darker red and blue shading indicating more negative and positive rg, respectively; the dot size reflects the strengths of average associations; and the numbers indicate how many of the local rgs were significant. These results are mirrored in the network plot, where the width or the edges reflect the number of significant associations, meaning that only disorders with at least one significant local rg are connected, and the edge opacity reflects the strength of the average local rg across tested loci. Note that label colours are concordant with the genomic SEM factor structure from Fig. 1 and, as shown, disorders of similar colours also tend to be proximally located within the network. b, The local rg structure within the top rg hotspot on chromosome (chr.) 11 (112755447–114742317, GRCh37 reference genome), that is, the region where the greatest number of significant rgs were found across all disorder pairs. Here, the network plot illustrates all significant rgs detected in this region, with both edge width and opacity reflecting the strength of the association. The region plot in the middle displays the genes contained within the hotspot, and the table below shows the rg estimates (Rho), 95% confidence intervals (CIlower, CIupper), variance explained (R^2) and P values for all significant pairwise local rgs in this region. Label colours are again concordant with those used for the genomic SEM factor structure in Fig. 1.

We detected 101 regions that contained significant local rgs between several disorder pairs, which we call rg hotspots (see Supplementary Tables 14–23 for local rgs across disorders in the top 10 hotspots). The most pleiotropic of these hotspots was on chromosome 11, which contained 17 positive and significant local rgs involving 8 of the 14 analysed disorders (Fig. 3b). This region also stands out as the most significantly associated with 8 of these 17 disorder pairs, while ranking in the top 25% of associated loci for 12 of them (Supplementary Fig. 21). Notably, this region contains the NCAM1–TTC12–ANKK1–DRD2 gene cluster that has been frequently associated with psychiatric phenotypes36,37,38,39, and flagged as a likely pleiotropy hotspot for a range of cognitive and behavioural outcomes related to, for example, intelligence, personality, substance use and sleep35,40,41,42.

Risk loci for psychiatric factors

We used multivariate GWAS within genomic SEM[34] to identify SNPs associated with the factors from the five-factor model or the p-factor in the hierarchical model. Similar to the QTrait metric, we estimated factor-specific QSNP heterogeneity statistics. This indexes SNPs that deviate strongly from the factor structure, due to either disorder-specific or directionally discordant effects. We defined genomic hits for the factors as those that were significant after Bonferroni correction (P < 5 × 10−8/6 genomic factors) and did not overlap with QSNP hits for that factor (Methods). Most hits were identified for the SB (n = 102) and Internalizing (n = 150) factors. After merging overlapping loci across the five correlated factors, 238 unique hits remained, including 27 broadly pleiotropic loci associated with two or more factors. The hierarchical model identified 160 hits for the p-factor (Fig. 4, Supplementary Fig. 22 and Supplementary Tables 24–36), 57 of which were not identified in the five-factor model (295 unique hits across both models). Forty-eight hits were novel relative to the univariate GWAS, of which 38 have been described in previous GWAS for a broad range of outcomes, and 10 are entirely novel (Supplementary Table 37).

Fig. 4: Locus-level results.

a, Heatmap of CC-GWAS loci below the diagonal across pairwise combinations of disorders; the darker orange shading indicates a higher number of CC-GWAS hits. CC-GWAS results are not shown for the Internalizing disorders as their rgs were too high, or for nicotine dependence as this is a continuously measured trait. Genomic SEM results (number of hits and mean χ^2 for each factor and factor-specific QSNP estimate) are reported above the diagonal. Results for the p-factor are shown above the plot along with a Venn diagram of the overlap between p-factor, p-factor QSNP and overall CC-GWAS hits. The disorders are ordered and coloured according to the genomic SEM factor structure from Fig. 1. b,c, The Miami and QQ-plots for the p-factor (b) and SBs factors (c), respectively. These panels show the results for the −log_10-transformed two-tailed P values for the factor on the top half of the Miami plot and the log_10-transformed one-tailed P values for QSNP on the bottom half. Factor hits that were within 100 kb of univariate hits are shown as black triangles, novel hits for the factors that were not within 100 kb of a univariate or QSNP hit are shown as red triangles and QSNP hits are shown as purple diamonds. d, The two-tailed −log_10[P] in a Manhattan plot for the CC-GWAS comparison across MD and SCZ, which produced the most hits (orange diamonds), as well as the scatterplot of standardized case–control effect sizes of MD (x axis) versus SCZ (y axis), with CC-GWAS significant SNPs labelled in red. For b–d, the grey dashed lines indicate the significance threshold, which was defined using Bonferroni correction for multiple comparisons.

We identified 33 unique hits with significant QSNP effects across the factors from the five-factor model. By comparison, we identified 117 QSNP hits from the p-factor model that showed significantly divergent effects across the five, lower-order psychiatric factors (Supplementary Table 36). These p-factor QSNP hits also included the chromosome 11 LAVA hotspot, where this region was found not to confer transdiagnostic risk due to an absence of signal for the Neurodevelopmental factor. For the SUD factor, highly significant QSNP hits were driven by variants in the genes involved in biological pathways specific to particular psychoactive substances, including the alcohol dehydrogenase genes (ADH1A, ADH1B and ADH1C) for AUD and the CHRNA3–CHRNA5–CHRNB4 nicotinic receptor subunit gene cluster for NIC. More QSNP loci for the p-factor model relative to the five-factor model indicates that many shared genetic relationships are better captured by the five factors (Supplementary Figs. 23 and 24).

A phenome-wide association study conducted in the Mayo Clinic Biobank revealed that factor hits were associated with multiple psychiatric disorders, especially those that loaded on the factor (Supplementary Table 38 and Supplementary Fig. 25). The Internalizing disorders (Supplementary Fig. 25d) and p-factor (Supplementary Fig. 25f) loci were also associated with a range of medical outcomes (for example, chronic pain and hypertension).

Divergent loci across disorders

In more fine-grained analyses of disorder pairs, case–case GWAS (CC-GWAS)[43] was used to identify loci with different allele frequencies across cases of different disorders. Such loci may reflect distinctive genetic effects across disorder pairs. CC-GWAS was applied to 75 disorder pairs, comparing 13 disorders. NIC was excluded because it is a continuous trait, and the pairs ANX–MD, ANX–PTSD and MD–PTSD were excluded because all had an rg estimate of >0.8, thereby risking an inflated type I error rate (Methods). The genome-wide significance threshold was defined at 5.5 × 10−10 (that is, 5 × 10−8/91 pairwise comparisons). An overview of CC-GWAS input parameters is provided in Supplementary Table 39.

In total, 412 loci showed significantly different effects across the 75 disorder pairs (Supplementary Tables 40 and 41); most (294 out of 412) were in comparisons that included SCZ, possibly reflecting either greater power for the SCZ GWAS or more distinctive biology for this disorder. Owing to overlap among the hits, the 412 loci comprised 109 LD-independent loci (Supplementary Table 42). Five of these were CC-GWAS specific, implying that they were not significantly associated with case–control status in either of the disorders in the respective disorder pair. CC-GWAS also computes a genome-wide genetic distance between the cases of two disorders (FST,causal), indicating how genetically dissimilar the cases are on average. As expected, these genetic distances were inversely correlated (r = −0.79, s.e. = 0.07) with rg (Supplementary Table 43). In support of the five-factor model, >99% of the CC-GWAS hits were identified for disorder pairs that loaded on separate factors (Supplementary Tables 44 and 45). Disorders that cluster on the same factor from the five-factor model are, apparently, largely indistinguishable at the level of individual genetic variants.

Functional annotation

Enrichment analyses

To understand biological functions influenced by the risk loci, we prioritized candidate risk genes implicated by the multivariate GWAS loci using expression quantitative trait loci (eQTL)44,45 and Hi-C44,46 datasets collected from fetal and adult brain samples (Methods and Supplementary Tables 46 and 47). Owing to the limited number of variants associated with other factors, analyses were restricted to the p-factor, the SB and Internalizing disorders factors and QSNP for these latter two factors. We first compared the target gene expression along the temporal trajectory of human brain development, finding that genes associated with the three factors were expressed at higher levels than QSNP target genes across the lifespan, with the largest difference observed at fetal stages and early life (Fig. 5 and Supplementary Fig. 26). This suggests that pleiotropic variants are involved in early, fundamental neurodevelopmental processes. We next examined biological processes using Gene Ontology (GO) enrichment analysis[47]. The target genes of the p-factor were primarily enriched in broader biological processes related to gene regulation (Fig. 5). To enhance the specificity of the gene sets, we removed Internalizing disorders and SB target genes that also appeared for the p-factor. SB (minus p-factor) target genes were enriched in more specific terms related to neuron development. No significant results were identified for the Internalizing disorders factor, probably reflecting the large proportion of target genes overlapping with the p-factor. Results from MAGMA[48] (Supplementary Methods) provided convergent support for the role of early neurodevelopmental processes in transdiagnostic psychiatric risk. Specifically, genetic signal for the five correlated factors and p-factor showed enrichment in genes identified from rare variant studies of ASD49,50,51, neurodevelopmental delay[49] or both (Supplementary Fig. 27).

Fig. 5: Functional annotation of factor variants.

a, GO enrichment analysis of predicted target genes with transdiagnostic associations (that is, variants associated with the p-factor), or those target genes associated with the SB factor that were not overlapping with p-factor target genes. Depicted −log_10-transformed P values are one-sided, calculated using a χ^2 test; false-discovery rate (FDR) correction was applied for multiple comparisons. b, The averaged and normalized expression levels of target genes of the indicated classes along the temporal trajectory of human brain development. Shading around the lines reflects 95% CIs. pcw10, post-conception week 10. c,d, Average log_10[P] values across EWCE and MAGMA enrichment for genes associated with the indicated factors in fetal brain cell types using two independent single-cell RNA-sequencing (scRNA-seq) datasets53,54 (c) or adult brain cell types using three independent single-nucleus RNA-seq (snRNA-seq) datasets55,56,57 (d). The P values from EWCE and MAGMA were two-sided and each had an FDR correction applied for multiple comparisons before averaging the two sets of results. EWCE P values were empirically derived using a permutation test; MAGMA P values were calculated using an F-test. Int, Internalizing disorders factor. The implied sample size for the three depicted psychiatric factors was: SB (\(\hat{n}\) = 127,202), Internalizing (\(\hat{n}\) = 1,637,337) and p-factor (\(\hat{n}\) = 2,168,621). CycProg, cycling progenitor; Endo/BBB, endothelial/blood brain barrier; ExNeu, excitatory neuron; InNeu, interneurons; IP, intermediate progenitor; OPC, oligodendrocyte progenitor cell; RG, radial glia; Astro, astrocyte; MSN, medium spiny neuron; ODC/Oligo, oligodendrocyte.

Averaged results across expression-weighted cell type enrichment (EWCE)[52] and MAGMA were used to evaluate enrichment within neuronal cell types in fetal and adult single-cell datasets53,54,55,56,57 (Supplementary Tables 48 and 49). Genes associated with the SB factor were significantly enriched in fetal data in interneurons and seven excitatory neuron subtypes, the strongest of which was for excitatory maturing neurons53,54 (Fig. 5). The SB factor was also uniquely enriched for deep-layer excitatory neurons in the adult brain[57]. Internalizing disorder genes were enriched within four excitatory neuron subtypes in fetal data[53], although the signal was not as strong or pervasive as for the SB factor. In adult data, the Internalizing factor was enriched for medial ganglionic eminence (MGE) interneurons[56] and different glial cells, specifically oligodendrocytes and Bergmann glia56,57. The p-factor was enriched for five excitatory neuron subtypes in fetal data and oligodendrocyte precursor cells in adult data[56]. A significant proportion of these genes is expressed during both fetal and adult stages; cell type enrichment was largely driven by genes that are not expressed in a particular developmental stage (Supplementary Fig. 28). We also tested enrichment for loci specific to MD and SCZ identified from CC-GWAS. MD-specific signal was enriched for cycling and intermediate progenitors in fetal brain. SCZ-specific signal was enriched for endothelial, vascular and upper rhombic lip cells in adult brain (Supplementary Fig. 28).

Stratified genomic SEM

We used stratified genomic SEM[27], a multivariate corollary of partitioned LDSC[58], to characterize the functional signals captured by the psychiatric factors in the five-factor and p-factor models, estimating enrichment for 162 functional annotations that passed quality control (Methods and Supplementary Table 50). Enrichment of the factor variances in the five-factor or p-factor models reflects groups of genetic variants that index a disproportionate concentration of genetic risk sharing. For the p-factor model, we also examined the enrichment of the residual (unique) variances of the five lower-order factors. Annotations significant for a factor in the p-factor model are therefore likely to capture signal specific to that factor. Enrichment was also calculated for a recent GWAS of height[59] to evaluate the specificity of the psychiatric findings. We used a Bonferroni-corrected significance threshold of P < 2.81 × 10−5 (Methods). We focus here on results for the better-powered SB, Internalizing and p-factor, and do not discuss annotations that lacked psychiatric specificity, as indicated by significant enrichment for height (for example, evolutionarily conserved annotations).

We identified 34 annotations that were significant for the SB factor in both models and are thereby likely to be specific to the neurobiology of the SB factor. This included the intersection between protein-truncating-variant-intolerant (PI) genes and several neuronal subtypes, including excitatory CA1 and CA3 hippocampal neurons (Extended Data Fig. 4 and Supplementary Table 50). In total, 51 significant annotations were identified for the Internalizing disorders factor, including PI-oligodendrocyte precursor annotations. We also found strong enrichment for an annotation reflecting neural progenitor biology[60], further implicating early neurobiological processes in shared psychiatric risk. No annotations remained significant for the Internalizing disorders factor’s residual variance (that is, independent of the p-factor), as would be expected given that only 10% of the genetic variance in the Internalizing disorders factor was separate from p. Finally, 64 significant annotations were detected for the p-factor, the strongest of which were fetal male brain H3K4me1 histone mark and PI-GABAergic neuron annotations.

Discussion

Our analyses characterized the landscape of shared and divergent genetic influences of common variants on 14 psychiatric disorders. At the genome-wide level, we confirmed pervasive genetic overlap across 14 clinically distinguished psychiatric disorders, as indicated by large pairwise rg within the EUR-like genetic ancestry group and even greater overlap when including loci that are shared, but have divergent directional effects. This overlap was parsimoniously captured by five genomic factors (Compulsive, SB, Neurodevelopmental, Internalizing and SUD), which explained the majority of the genetic variance of the individual disorders. We identified 101 regions with correlated effects, including a hotspot on chromosome 11 with associations for 8 disorders. We found that broadly pleiotropic variants are primarily involved in early neurobiological processes, while also identifying different brain cell types that uniquely confer risk to more circumscribed subsets of disorders. At the individual-variant level, we identified 238 loci associated with at least one of the five correlated psychiatric factors, along with 412 loci that distinguished disorders that primarily belong to different factors.

The SB (defined by SCZ and BIP) and Internalizing disorders (defined by major depression, PTSD and anxiety) factors offered a particularly useful way to understand shared risk across sets of disorders. For these factors, a diverse set of methods produced convergent results across genome-wide, regional and locus-level results, indicating that the disorders within these factors are characterized by overlapping genetic signal. A replicated finding across functional methods reflected enrichment for the SB factor in excitatory neuron annotations, including CA1 and CA3 hippocampal neurons, deep-layer neurons from adult data, and maturing, migrating, prefrontal and visual cortex excitatory neurons in fetal data. The Internalizing factor also showed enrichment in excitatory neurons, but was more consistently enriched in different glial cells in adult data, including oligodendrocytes and their precursor cells and Bergmann glia.

At the genome-wide level, the p-factor was strongly related to the Internalizing disorders factor and evinced the largest rgs with external traits reflecting broad clinical characteristics, such as neuroticism, stress sensitivity and loneliness. These results are consistent with conceptualizations of the p-factor as reflecting a general tendency towards negative emotionality[61]. In support of the p-factor, LAVA identified pleiotropic hotspots characterized by widespread local rg across disorders and multivariate GWAS yielded 160 hits for this factor alone. However, the p-factor also had more hits for the QSNP heterogeneity metric (117) than all five-factors from the correlated factors model (33), indicating that the p-factor alone is insufficient for explaining cross-disorder risk. The p-factor was largely enriched for broad biological categories, such as gene regulation. These results suggest a conceptual model in which there is a partial, broadly transdiagnostic component of genetic vulnerability to psychiatric disorders that primarily captures Internalizing genetic signals, with subsequent levels of more canalized and neurobiologically meaningful subdomains of psychopathology captured by the five factors.

Our study has several limitations. Analyses were restricted primarily to EUR-like genetic ancestry populations due to the limited availability of GWAS data for other groups and the limitations of methods requiring more genetically homogeneous groups[62]. The sample sizes for GWASs of non-EUR-like populations are still orders of magnitude smaller and not currently powered for more precise cross-ancestry assessments; this emphasizes the need for future research including the generation of additional ancestrally representative data, which will enable well-powered studies and the examination of cross-disorder genetic architecture across regional and cultural differences. Cross-ancestry rgs should be interpreted in light of findings that show considerably smaller within-disorder, within-ancestry rgs across cohorts for PTSD (rg = 0.73, s.e. = 0.21)[63] and MD (rg = 0.76, s.e. = 0.03)[64] relative to SCZ (rg = 0.95, s.e. = 0.03)[65]. This suggests that cross-ancestry rgs for PTSD and MD could drop below 1 for reasons independent of ancestry-specific signal, such as environmental moderation of genetic effects or increased phenotypic heterogeneity. Another limitation reflects potential inflation in rg estimates by cross-trait assortative mating[66], diagnostic misclassification[67] or the use of super-normal controls[68]. However, the high genetic overlap observed among subclusters of psychiatric disorders is unlikely to be explained by cross-trait assortment alone[69] and current sensitivity analyses using stricter case definitions suggested that impact of diagnostic misclassification was modest. Wide ranges in sample sizes across the univariate psychiatric GWAS used as input should also be considered when evaluating relative levels of significant findings, particularly for locus discovery.

The current investigation into the genetic structure of psychopathology reflects a comprehensive genomic examination of cross-disorder psychiatric risk. It extends previous cross-disorder psychiatric genetics analyses5,27 using updated datasets, new disorders and triangulation across different methodological approaches to produce a robust set of findings[70]. We identified subsets of disorders with particularly high genetic overlap and characterized the biological processes implicated by their shared risk. This evidence should contribute substantially to the ongoing debates regarding diagnostic boundaries between disorders such as BIP and SCZ. Certain pharmacological interventions have proven to be effective across a range of disorders (for example, selective serotonin reuptake inhibitors)[71], indicating that future work could build on our findings to identify new or repurposed therapeutics that target the shared signal captured by the factors. While much remains to be done, cross-disorder genetics continues to fill in critical gaps in our understanding of shared and unique psychiatric risk factors with implications for the future of psychiatric research, therapeutics and nosology.

Methods

Quality control of summary statistics

A standard set of quality-control filters was applied to all univariate GWAS summary statistics before conducting cross-disorder analyses. Any additional quality-control filters applied by a method are noted in its corresponding section below. These quality-control filters included removing strand ambiguous SNPs, restricting to SNPs with an imputation score (INFO) > 0.6 and with a minor allele frequency > 1% when this information was available in the GWAS data. We also restricted analyses to SNPs with an SNP-specific sum of the effective sample that is >50% of the total sum of the effective sample or, when this SNP-specific information was not available, to SNPs for which >50% of the cohorts contributed information, as indexed by the direction column in the GWAS summary statistics. The MHC region was excluded from all summary statistics before the analysis. Base pair location is given in genome build GRCh37/hg19 throughout the Article and its Supplementary Information.

Genomic SEM

Genome-wide models

All GWAS summary statistics were run through the munge function before running the multivariable version of LDSC used as input to genomic SEM[7]. The munge function aligns GWAS effects to the same reference allele and restricts to HapMap3 SNPs and SNPs with INFO > 0.9. LDSC was estimated using these munged summary statistics, applying a liability threshold model for all case–control psychiatric disorders (that is, all disorders except for the NIC outcome, which reflects a GWAS of the continuous Fagerström test for nicotine dependence[24]). For comparability, population prevalence was chosen to match what was used in the corresponding manuscript that introduced the GWAS of each trait. The ascertainment correction was performed using the sum of effective sample sizes across contributing cohorts for each disorder[72]. We note that, for CUD[26], we used the recently described formula[72] for estimating the sum of effective sample size directly from the GWAS data. This is because, in this instance, we found that the implied sum of effective sample size was much smaller than the value computed from the reported sample sizes, which is probably attributable to the complex familial structure in the included deCODE sample.

The two primary estimates from multivariable LDSC are the genetic covariance matrix and the corresponding sampling covariance matrix. The genetic covariance matrix contains SNP-based heritabilities on the diagonal and the co-heritabilities (genetic covariances) across every pairwise combination of included disorders on the off-diagonal. The sampling covariance matrix contains squared standard errors (sampling variances) on the diagonal, which allows genomic SEM to appropriately account for differences in the precision of GWAS estimates for disorders with unequal power. The off-diagonal contains sampling dependencies, which will arise in the presence of sample overlap across GWAS phenotypes. As these sampling dependencies are estimated directly from the data, summary statistics can be included with varying and unknown levels of sample overlap. We note that study overlap between disorders is not expected to affect the findings, as study overlap affects only the covariance of error terms of the GWASs resulting in increased intercepts of cross-trait LDSC with no expected impact on the estimates of rg4,43. To guard against model overfitting, an exploratory factor analysis (EFA) was performed on even chromosomes and used to inform the fitting of an confirmatory factor analysis (CFA) in odd chromosomes. The EFA was performed using the factanal R package for 2–5 factors using both promax (correlated) and varimax (orthogonal) rotations. Disorders were specified to load on a factor in the CFA when the standardized EFA loadings were >0.3, with disorders allowed to cross-load (for example, TS on the Compulsive and Neurodevelopmental factors) if this was the case for multiple factors. Models specified based on varimax EFA results still allowed for interfactor correlations, as allowing only subsets of disorders to load on each factor will induce genetic overlap. A common-factor model was also modelled to test a single-latent-factor model predicting all 14 disorders. We did not evaluate models with more than five factors as these caused issues with model convergence. Results revealed that a five-factor model specified based on the promax EFA results (Supplementary Table 3) fit the data best in odd chromosomes (CFI = 0.973, SRMR = 0.073; Supplementary Table 2). This model also fit the data well in all autosomes, and was subsequently carried forward for all analyses, along with the p-factor model described in the main text. Considering the high rg across PTSD and MD, we also evaluated a model (in odd autosomes) that estimated the residual genetic covariance across these two disorders; however, we found that this did not significantly improve model fit (model χ^2_1 difference = 2.86, P = 0.094).

Stratified genomic SEM

Stratified genomic SEM proceeds in two stages[27]. In stage 1, the s_ldsc function in genomic SEM, a multivariable implementation of stratified LDSC (S-LDSC)[58], was used to estimate the stratified genetic covariance and sampling covariance matrices within each functional annotation. We specifically used the zero-order estimates for these analyses. In stage 2, the enrich function was used to estimate the enrichment of the factor variances and residual genetic variances unique to the indicators. This is achieved by first estimating the model in the genome-wide annotation including all SNPs. The factor loadings from these genome-wide estimates are then fixed and the (residual) variances of the factors and disorders are freely estimated within each annotation. These reflect the within-annotation estimates for each variance component that are scaled to be comparable to the genome-wide estimates. This cumulative set of results is used to calculate the enrichment ratio of ratios. The numerator reflects the ratio of the estimate of the factor variance within an annotation over the genome-wide estimate. The denominator is the ratio of SNPs in the annotation over the total number of SNPs examined. Enrichment estimates greater than the null of 1 are therefore observed when an annotation explains a disproportionate level of genetic variance relative to the annotation’s size.

Functional annotations used to estimate the stratified matrices were obtained from a variety of data resources. This included: (1) the baseline annotations from the 1000 Genomes Phase 3 BaslineLD (v.2.2)[73] from the S-LDSC developers[58]; (2) tissue-specific gene expression annotation files created using data from GTEx[74] and DEPICT[75]; (3) tissue-specific histone marks from the Roadmap Epigenetics project[76]; (4) annotations that we created[27] from data in GTEx[74] and the Genome Aggregate Database (gnomAD)[77] that index protein-truncating-variant-intolerant (PI) genes, genes expressed in different types of brain cells in the human hippocampus and prefrontal cortex, and their intersection; (5) 11 neuronal cell type annotations defined by peaks from single-cell assay for transposase accessibility by sequencing (scATAC–seq) in the human forebrain[54]; (6) an annotation defined by peaks from ATAC–seq data with greater accessibility in neural progenitor enriched regions encompassing the ventricular, subventricular and intermediate zones (GZ) over neuron-enriched regions within the subplate, marginal zone and cortical plate (CP; GZ > CP), and a second CP > GZ annotation reflecting the converse[60]; and (7) a fetal and an adult annotations defined by eQTLs identified using high-throughput RNA-seq[45]. We excluded 22 annotations that produced stratified genetic covariance matrices that were highly non-positive definite to examine a total of 162 annotations. We corrected for multiple testing by using a strict Bonferroni correction for the 162 annotations analysed that passed quality control across the 11 factors examined (the factors from the five-factor factor model and the p-factor and residuals of the five factors from the p-factor model) of P < 2.81 × 10−5.

Multivariate GWAS

The sumstats function in genomic SEM was used to align SNP effects across traits to the same reference allele and standardize the effects and their corresponding s.e. values relative to the total variance in the predicted phenotype. The s.e. values were additionally corrected for uncontrolled confounds by taking the product of s.e. values and the LDSC univariate intercept when this value was >1. After removing 136 SNPs that produced highly non-positive definite matrices when combined with the genetic covariance matrix, the final listwise deleted set consisted of 2,795,800 SNPs present across all 14 disorders. The userGWAS function was used to estimate the multivariate GWAS for SNP effects on the five factors from the five-factor model and the p-factor. We used a significance threshold of P < 8.33 × 10−9, reflecting the standard genome-wide threshold of 5 × 10−8 with a Bonferroni correction for the six factors. As a quality-control check, we confirmed that the attenuation ratio[32] was near 0 for all factors (Supplementary Table 17), suggesting that the factor signal is not due to uncontrolled confounds (such as population stratification).

The QSNP heterogeneity metric is a χ^2-distributed test statistic produced through a nested-model comparison of a common pathway model, in which the SNP predicts a latent factor, to an independent pathways model, where the SNP directly predicts the factor indicators. Factor-specific QSNP estimates for the five-factor model were estimated using five independent pathways models that consisted of the SNP predicting both the indicators for one factor and the remaining four factors. For the p-factor model, the SNP predicted the five, first-order factors to obtain QSNP estimates for the second-order, p-factor.

Cross-ancestry analyses

We applied the cross-ancestry Popcorn[33] method to estimate genetic impact correlation (ρgi metric) across EUR-like, EAS-like and AFR-like genetic ancestry groups. Six disorders were included in the analysis, including EAS-like summary statistics for MD and SCZ and AFR-like summary statistics for OUD, AUD, PTSD and CUD. The reference panel for the EAS dataset was based on 504 individuals from EAS population of the 1000 Genomes Phase3 data[78]. For AFR-like genetic ancestry, we performed the Popcorn analysis using three alternative references from 1000 Genomes Phase3 data: (1) the African Ancestry in the southwest United States subgroup (n = 61); (2) the African population (n = 661); and (3) a reference panel created to capture the admixed ancestral background of some AFR-like individuals reflecting the combination across the EUR-like and AFR-like sample (n = 1,164). Cross-ancestry results and within-ancestry LDSC results for the AFR-like and EAS-like populations are reported in Supplementary Table 4. We acknowledge that using LDSC with admixed ancestry may violate its assumptions; thus, our results for AFR-like ancestry should be interpreted with caution. With this in mind, we performed LDSC for AFR-like datasets using two different LD reference panels for AFR-like ancestry or admixed American ancestry from Pan UK Biobank to assess their impact on results (Supplementary Table 4). The results in Extended Data Table 1 report liability-scale heritabilities for AFR-like datasets using the admixed LD scores, as these produced more sensible results.

MiXeR

MiXeR (v.1.3) was applied using the procedure outlined in the original publication[6]. We performed additional simulations to evaluate appropriate threshold for inclusion of a GWAS study in cross-trait MiXeR analysis. In previous simulations, we demonstrated that MiXeR cannot produce reliable estimates for analyses using low-powered input[79]. Specifically, as statistical power increases, the Akaike information criterion (AIC) differences indicate that MiXeR-modelled estimates become increasingly more distinguishable from the minimum and maximum overlap, corresponding to the increasing precision of MiXeR estimates. This demonstrates that AIC differences are sensitive to the input power of the summary statistics and can be used to support the reliability of MiXeR estimates. On the basis of these previous simulations, psychiatric disorders were brought forward for cross-trait MiXeR analysis when the product of NEff and MiXeR \({h}_{\mathrm{SNP}}^{2}\) estimates were >12,000, where this cut point reflects the product of NEff ≥ 100,000 and \({h}_{{\rm{SNP}}}^{2}\ge 0.12\). As a result, we excluded OUD, TS, NIC, OCD, ASD and CUD. As AN was very close to this threshold and had a high AIC in univariate analysis, it was brought forward for cross-trait analyses along with the seven remaining psychiatric disorders. For the NIC summary statistics, we excluded two loci defined as a 2 Mb window around either the CHRNA3–CHRNA5–CHRNB4 gene cluster or the CHRNA4 gene, which is known to have such a large effect on the phenotype that it would skew results. We note that, for PTSD, ANX and MD, the rgs were so high that there was little room for additional overlap beyond correlation, given MiXeR’s modelling assumptions. Specifically, the range in size of the putative shared component is too small to allow for an accurate model fit in this situation, as demonstrated by the range on the respective x axes (Supplementary Fig. 7). There is also a considerable uncertainty of polygenicity estimates for PTSD and ANX. Thus, cross-trait MiXeR results for PTSD, ANX and MD should be interpreted with caution.

LAVA

Local rg analyses were conducted using LAVA v.0.1.0[35]. To avoid evaluating local rgs in regions in which there is a low amount of genetic signal (which could lead to unstable or uninterpretable estimates) for all phenotype pairs and loci separately, we used the univariate test in LAVA as a filtering step, computing bivariate local rgs only in loci where both analysed phenotypes have a \({h}_{\mathrm{SNP}}^{2}\) significant at P < 4.6 × 10−5 = 0.05/1,093 (where 1,093 represents the total number of analysed loci). Given this filtering step, we performed 24,273 local rg tests across all loci and phenotype pairs, resulting in a Bonferroni corrected P value threshold of P < 2.1 × 10−6 = 0.05/24,273 for the bivariate, local rg analyses.

Genomic loci used for the regional rg analyses were defined by segmenting the genome into approximately equal-sized, semi-independent blocks using the LAVA partitioning algorithm (https://github.com/cadeleeuw/lava-partitioning). This algorithm works by iteratively splitting the chromosomes into smaller chunks, creating break points at regions where the LD between SNPs is the lowest (see the program manual for more details). To achieve a balance between block size and correlations between adjacent blocks, we ran the algorithm with the default parameters, changing only the minimum size requirement (in the number of SNPs) to 5,000, based on the 1,000 genomes data. Sample overlap was accounted for by obtaining the estimated intercepts from bivariate LDSC and providing these to LAVA.

CC-GWAS

CC-GWAS[43] was applied to identify loci with different allele frequencies across cases of different disorders, contrasting cases one disorder pair at a time. CC-GWAS is based on estimating a weighted difference of the CC-GWAS results of the disorders considered, thereby avoiding the necessity to match cases across disorders at individual level. CC-GWAS combines two components. The first component (CC-GWASOLS) optimizes power and protects against type I error rate at null–null SNPs (SNPs that affect neither of both disorders), based on analytical expectations of genetic differences between cases and controls of both diseases. The second component (CC-GWASExact) controls type I error rate at ‘stress test’ SNPs (SNPs affecting both disorders resulting in no allele frequency difference across cases of both disorders). A SNP is significantly associated with case–case status when the P value of the OLS component reaches genome-wide significance and when the P value of the exact-component is <10−4 (there is an upper bound on the number of stress test SNPs as these are causal SNPs). Importantly, CC-GWAS also filters false-positive associations that may arise due to (subtle) differential tagging of a stress test SNP in the respective CC-GWAS, which are present even in within-ancestry analysis[43]. CC-GWAS excludes analyses of any disorder pair with an rg > 0.8 because these have a small genetic distance between cases with increased risk of type-I error at stress test SNPs.

Locus definition and cross-locus overlap

The same locus definition (also referred to as a hit in the main text) was used for CC-GWAS and genomic SEM. Significant loci were identified using the clumping functionality in PLINK v.1.9 with an r^2 threshold of 0.1 and a 3,000 kb window. Physically proximal loci (including when comparing loci both within and across factors from genomic SEM and for CC-GWAS and univariate GWAS results), were additionally collapsed into a single locus when the locus windows were within 100 kb of one another on either side. For the univariate results, we use the same locus definition applied to the complete GWAS summary statistics for each disorder (that is, without our quality-control filters applied), along with a more liberal genome-wide significance threshold of P < 5 × 10−8 without a Bonferroni correction. These more liberal quality-control and significance thresholds were used for univariate loci to benchmark whether genomic SEM and CC-GWAS loci could be considered strictly novel. The 1000 Genomes Phase 3 reference files[78] were used for LD pruning for each respective genetic ancestry group (that is, EUR-like, EAS-like, AFR-like).

Functional annotation

To predict the target genes of the variants (Supplementary Fig. 17), we first expanded the variants by including any variants within the LD block (r^2 > 0.6) based on the EUR population using LDProxy from the LDlink R package[80]. We began by curating the genes of which the promoters (±500 bp from the transcription start site) or exons overlap with the variants of interest. Conversely, to map target genes that are not near the variants, we first filtered the variants for those localized in either human fetal brain open-chromatin regions[60] or human adult brain H3K27ac ChIP–seq regions[44], both of which indicate enhancer activity, but during different stages of brain development. Next, we assigned target genes to each filtered variant using eQTL44,45 or HiC loops44,46 generated from samples from the corresponding stages. We also assigned variants present in promoter or exonic regions to the corresponding genes (Supplementary Fig. 17). Finally, we filtered all of the target genes for those expressed (RNA-seq count > 0) in the corresponding tissues. In this way, we obtained 715 and 572 target genes in fetal and adult brains, respectively (Supplementary Tables 40 and 41). Notably, there is a prominent overlap between the two sets of genes, which is a result of the shared, positional mapping of genes to promoters or exons (Supplementary Fig. 17). Both the fetal and adult target genes were enriched in GO terms related to neuron or brain development, suggesting the biological relevance of the genetic variants.

To plot the temporal expression trends of the predicted target genes, we used gene expression datasets from the BrainSpan. We plotted the averaged gene expression (reads per million kb) of the selected genes over all samples collected from the cortex at the available stages of development, then generated a smoothened curve with the loess method. We performed GO enrichment analysis using the ToppGene suite[36]. We filtered the enriched terms by containing at least 10% of the input list of genes, then displayed up to top 5 terms by adjusted P values under the indicated category.

EWCE[52] was used to assess the cell type enrichment of target genes for the variants using a size-biased averaging method. This method uses single-cell datasets to compute the average expression of a set of genes (in this case, genes assigned to variants for each factor) and compares this to the average expression levels for 100,000 permuted gene lists of the same size that are randomly sampled from a background set of genes. Annotations were taken from publicly available datasets53,54,55,56,57, but simplified to provide cell-type-level instead of cluster-level enrichments. For example, several upper-layer clusters in the dataset of ref. [57] were combined into ‘ExcNeu superficial’ and so on. For the ref. [56] dataset, EWCE objects were processed for each brain region separately. This included the hippocampal formation, cortex, cerebral nuclei (dissections including basal nuclei, amygdaloid complex, basal forebrain, claustrum), midbrain (including tissues from thalamic complex, hypothalamus, and midbrain) and hindbrain (including tissues from spinal cord, pons, myelencephalon and cerebellum) and non-neuronal cells across regions. For superclusters that were present in multiple regions, enrichment was tested only for regions with the highest abundance of that supercluster (for example, MGE interneuron supercluster is most abundant in cortex, so this cell type was dropped from enrichment analyses in the midbrain) to prevent excess multiple comparisons. P values were FDR-corrected based on the number of cell types × gene lists within brain region and dataset.

MAGMA gene-set enrichment analyses were performed using the MAGMA.Celltyping package in R[81]. Rather than considering only the top associated genes, as done in EWCE, MAGMA relies on the genome-wide signals to competitively evaluate enrichment through linear regression[48]. We used the European subset of the 1000 Genomes[78] as LD reference data, and mapped SNPs to genes based on their genomic location (GRCh37/hg19). To allow the inclusion of nearby regulatory variants, we considered all SNPs within a 35 kb upstream and 10 kb downstream window of the gene transcription region. As signed effect-size estimates are not available for the QSNP results, these analyses were restricted to the factors. The FDR corrected P values from MAGMA and EWCE were averaged together to produce the results reported in the main text (but see Supplementary Tables 48 and 49 for P values from the individual methods).

Reporting summary

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

Data availability

The data supporting the findings of this study are all publicly available or can be requested for access. Specific download links for various datasets are directly below. Psychiatric disorder GWAS summary statistics for data from the PGC can be downloaded or requested online (https://www.med.unc.edu/pgc/download-results/). Links to the LD scores and reference panel data for GenomicSEM analyses can be found at GitHub (https://github.com/GenomicSEM/GenomicSEM/wiki). Links to the BaselineLD v.2.2 annotations can be found online (https://data.broadinstitute.org/alkesgroup/LDSCORE). Gene expression datasets from Brainspan can be found online (https://brainspan.org/static/download.html). Multivariate GWAS summary statistics for the latent psychiatric factors in GenomicSEM, including the sensitivity GWAS results, are available online (https://www.med.unc.edu/pgc/download-results/).

Code availability

Genomic SEM analyses were implemented using publicly available code (v.0.5.0, https://github.com/GenomicSEM/GenomicSEM). Factanal was conducted using publicly available code within the stats R package (v.3.6.2, https://www.rdocumentation.org/packages/stats/versions/3.6.2). MiXeR was conducted using publicly available code (v.1.3; https://github.com/precimed/mixer). LAVA was conducted using publicly available code (v.0.1.0, https://github.com/josefin-werme/LAVA). CC-GWAS was conducted using publicly available code (v.0.1.0, https://github.com/wouterpeyrot/CCGWAS). LDlink was conducted using publicly available code (v.1.4.0, https://cran.r-project.org/web/packages/LDlinkR/vignettes/LDlinkR.html). ToppGene suite was conducted using publicly available code (v.0.1.0, https://toppgene.cchmc.org/). EWCE was conducted using publicly available code (v.1.16.0, https://nathanskene.github.io/EWCE/). MAGMA was conducted using publicly available code (v.2.0.15, https://neurogenomics.github.io/MAGMA_Celltyping/index.html).

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Acknowledgements

We acknowledge the work of the members of individual Psychiatric Genomics Consortium working groups, the iNDiGO consortium and the MVP who contributed summary statistics to these analyses. GWAS summary statistics for the MVP used in this study were obtained from the database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v9.p1.c1 under approved project 30159. We thank the MVP staff, researchers and volunteers, who have contributed to MVP, and especially participants who previously served their country in the military and now agreed to enrol in the study (https://www.research.va.gov/mvp/). This work was made possible by the contributions of the many investigators who comprise these working groups and the numerous grants from governmental and charitable bodies, as well as philanthropic donation. We acknowledge the Mayo Clinic Biobank (MCB) research team, as well as the patient participants who consented to participate in this research program; the Mayo Clinic Center for Individualized Medicine for support of the MCB, and Regeneron Genetics Center for providing genetic data for MCB participants for the analysis. In particular, we thank the research participants worldwide who shared their life experiences and biological samples to make work like this possible. The PGC has been supported by the following grants: MH085508, MH085513, MH085518, MH085520, MH094411, MH094421, MH094432, MH096296, MH109499, MH109501, MH109514, MH109528, MH109532, MH109536, MH109539, MH124871, MH124851, MH124839, MH124847, MH124873, MH124875 and DA054869. Specific investigators were supported by the following grants: R01MH120219 and R01AG073593 (A.D.G.); European Union Horizon 2020 grant agreement 964874 (RealMent) (J.W.); The Amsterdam Cohort Hub, which is part of the Sector Plan ‘Accelerating Health’ of the Dutch Ministry of Education, Culture and Science (W.J.P.); Autism Speaks Postdoctoral Fellowship (Q.G.); DP1DA054394 and T32IR5226 (S.S.S.-R.); ERC-2018-ADG 834057 (C.d.L.); European Union Horizon grant agreement 965381, U01AR076092, R01MH116037, 1R01NS128535, R01MH131685, 1R01MH130899, U01MH135970 and Supernus (S.V.F.); F30MH135712 (M.P.M.); K08MH135343 (T.T.M.); Lundbeck Foundation (R102-A9118, R155-2014-1724, and R248-2017-2003), NIH/NIMH (1R01MH124851-01) and EU’s Horizon Europe program under grant agreement no. 101057385 (R2D2-MH) (A.D.B.); R01MH124839-02, Research Council of Norway (RCN) 334920 (K.S.O.); U54GM115516 (S.L.S.); R01MH124847 (J.M.H.); R01DA054869 (H.J.E.); R01MH106595 (A.X.M.); R01MH106595 (K.C.K., C.M.N. and M.B.S.); R01MH124847 (C.M.N.); R01MH112904, R01MH123775, U24MH068457, R01MH104964 and R01MH123451 (J.A.K.); R01MH119243 and R01GM148494 (P.H.L.); R01MH120219, R01AG073593, P30AG066614 and P2CHD042849 (E.M.T.-D.); R01MH121924 (B.J.C., A.B., V.P. and J.M.B.); R01MH123922 and R01MH121521 (M.J.G.); R01MH124851 (B.F.); R01MH136149 and R01120170 (L.M.T.); R21MH123908 and K08MH122673 (K.G.J.); the Novo Nordisk Foundation (NNF20OC0065561, NNF21SA0072102), the Lundbeck Foundation (R344-2020-1060), the European Union’s Horizon 2020 research and innovation program under grant agreement no. 965381 (TIMESPAN) (D.D.); R01NS102371, R01NS105746 and R01MH124851 (J.M.S.); U01MH125050 (CIHR) and PJT-180339 (M.M.); NIHR Biomedical Research Centre (IS-BRC-1215-20018) (G.B.); K01DA051759 (E.C.J.); AA030083 (A.S.H.); and R01MH125938, R01MH137208, P50AA022537 and The Brain & Behavior Research Foundation NARSAD grant 28632P&S Fund (R.E.P.).

Author information

Author notes

1. These authors contributed equally: Andrew D. Grotzinger, Josefin Werme, Wouter J. Peyrot, Oleksandr Frei

2. These authors jointly supervised this work: Kenneth S. Kendler, Jordan W. Smoller

Authors and Affiliations

1. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA

Andrew D. Grotzinger

2. Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA

Andrew D. Grotzinger

3. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Josefin Werme, Wouter J. Peyrot & Christiaan de Leeuw

4. Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands

Wouter J. Peyrot

5. Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway

Oleksandr Frei

6. NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

Oleksandr Frei, Ole A. Andreassen & Kevin S. O’Connell

7. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

Lucy K. Bicks & Qiuyu Guo

8. Discovery Biomarker, Amgen, Thousand Oaks, CA, USA

Qiuyu Guo

9. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Michael P. Margolis

10. Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Michael P. Margolis

11. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

Brandon J. Coombes, Anthony Batzler, Vanessa Pazdernik, Joanna M. Biernacka & Anthony J. Batzler

12. Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA

Joanna M. Biernacka

13. KG Jebsen Centre for Neurodevelopment, University of Oslo, Oslo, Norway

Ole A. Andreassen

14. Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

Verneri Anttila

15. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Verneri Anttila

16. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Verneri Anttila, Benjamin M. Neale, Elise B. Robinson, Clement C. Zai, Daniel P. Howrigan, Calwing Liao, Raymond K. Walters, Nikolaos P. Daskalakis & Bart PF Rutten

17. Center for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark

Anders D. Børglum & Ditte Demontis

18. Department of Biomedicine, Aarhus University, Aarhus, Denmark

Anders D. Børglum, Ditte Demontis, Manuel Mattheisen, Isabell Brikell & Jakob Grove

19. The Lundbeck Foundation Initiative for Integrative Psychiatric Research—iPSYCH, Aarhus, Denmark

Anders D. Børglum, Ditte Demontis, Jakob Grove, Jonas Bybjerg-Grauholm & David M. Hougaard

20. Social Genetic and Developmental Psychiatry Centre, King’s College London, London, UK

Gerome Breen

21. Computational Health Centre, Helmholtz Munich, Neuherberg, Germany

Na Cai

22. Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland

Na Cai

23. Department of Medicine, Technical University of Munich, Munich, Germany

Na Cai

24. Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany

Na Cai

25. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Ditte Demontis

26. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA

Howard J. Edenberg

27. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

Howard J. Edenberg

28. Department of Neuroscience and Physiology, Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA

Stephen V. Faraone

29. Department of Psychiatry, Norton College of Medicine, SUNY Upstate Medical Universitiy, Syracuse, NY, USA

Stephen V. Faraone

30. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands

Barbara Franke

31. Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands

Barbara Franke, Marieke Klein, Nina R. Mota, Yingjie Shi & Giuseppe Fanelli

32. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

Barbara Franke, Marieke Klein, Nina R. Mota & Yingjie Shi

33. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Michael J. Gandal

34. Lifespan Brain Institute at Penn Med and the Children’s Hospital of Philadelphia, Philadelphia, PA, USA

Michael J. Gandal

35. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

Joel Gelernter

36. VA Connecticut Healthcare Center, West Haven, CT, USA

Joel Gelernter

37. Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA

Alexander S. Hatoum & Emma C. Johnson

38. Department of Psychiatry and Behavioral Sciences, Texas A&M University, College Station, TX, USA

John M. Hettema & Brad Verhulst

39. Department of Psychiatry & Behavioral Health, Stony Brook University, New York, NY, USA

Katherine G. Jonas

40. Department of Genetics, Rutgers University, Piscataway, NJ, USA

James A. Knowles

41. Human Genetics Institute of New Jersey (HGINJ), Rutgers University, Piscataway, NJ, USA

James A. Knowles

42. Broad Institute of MIT and Harvard, Boston, MA, USA

Karestan C. Koenen & Jordan W. Smoller

43. Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA

Karestan C. Koenen

44. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

Karestan C. Koenen

45. Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

Adam X. Maihofer & Caroline M. Nievergelt

46. Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA, USA

Adam X. Maihofer, Caroline M. Nievergelt, Sandra S. Sanchez-Roige & Murray B. Stein

47. Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

Adam X. Maihofer & Caroline M. Nievergelt

48. Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

Travis T. Mallard

49. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

Travis T. Mallard, Alysa E. Doyle, Erin C. Dunn & Jordan W. Smoller

50. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Travis T. Mallard, Jeremiah M. Scharf, Dongmei Yu, Phil H. Lee & Jordan W. Smoller

51. Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm, Sweden

Manuel Mattheisen & Nora I. Strom

52. Department of Community Health and Epidemiology and Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

Manuel Mattheisen

53. Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany

Manuel Mattheisen, Nora I. Strom, Janos L. Kalman & Eva C. Schulte

54. Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA

Karen S. Mitchell & Kelly M. Harrington

55. National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA

Karen S. Mitchell

56. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

Benjamin M. Neale, Daniel P. Howrigan, Calwing Liao & Raymond K. Walters

57. Department of Medical and Molecular Genetics, Department of Psychiatry, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA

John I. Nurnberger

58. Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, New York, NY, USA

Roseann E. Peterson

59. Center for Genomic Medicine and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

Elise B. Robinson

60. Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA

Sandra S. Sanchez-Roige

61. Institute for Genomic Medicine, University of California San Diego, San Diego, CA, USA

Sandra S. Sanchez-Roige

62. Center for Clinical and Translational Science, Maine Health Institute for Research, Portland, ME, USA

Susan L. Santangelo

63. Department of Psychiatry, Tufts University School of Medicine, Boston, MA, USA

Susan L. Santangelo

64. Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Jeremiah M. Scharf

65. Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Jeremiah M. Scharf, Daniel A. Geller & Erica L. Greenberg

66. Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA

Jeremiah M. Scharf & Dongmei Yu

67. Amgen—deCODE Genetics, Reykjavik, Iceland

Hreinn Stefansson, Kari Stefansson & G. Bragi Walters

68. Faculty of Medicine, University of Iceland, Reykjavík, Iceland

Kari Stefansson & G. Bragi Walters

69. Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

Murray B. Stein

70. VA San Diego Healthcare System, San Diego, CA, USA

Murray B. Stein

71. Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany

Nora I. Strom, Norbert Kathmann & Julia Klawohn

72. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Laura M. Thornton & Cynthia M. Bulik

73. Center on Aging and Population Sciences, University of Texas at Austin, Austin, TX, USA

Elliot M. Tucker-Drob

74. Department of Psychology, University of Texas at Austin, Austin, TX, USA

Elliot M. Tucker-Drob

75. Population Research Center, University of Texas at Austin, Austin, TX, USA

Elliot M. Tucker-Drob

76. Department of Psychology, Emory University, Atlanta, GA, USA

Irwin D. Waldman

77. Department of Psychiatry, University of Oxford, Oxford, UK

Naomi R. Wray

78. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

Naomi R. Wray

79. Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA

Anna R. Docherty, Bradley T. Webb, Anna R. Docherty & Kenneth S. Kendler

80. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA

Kenneth S. Kendler

81. Department of Sociology, University of Utah, Salt Lake City, UT, USA

Daniel E. Adkins

82. Graduate Program in Statistics, University of Utah, Salt Lake City, UT, USA

Daniel E. Adkins

83. Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany

Georg W. Alpers

84. PROMENTA research center, University of Oslo, Oslo, Norway

Helga Ask

85. PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway

Helga Ask, Elizabeth C. Corfield & Alexandra Havdahl

86. Department of Morphology and Genetics, Universidade Federal de Sao Paulo, Sao Paulo, Brazil

Sintia I. Belangero & Vanessa K. Ota

87. Laboratory of Integrative Neuroscience, Universidade Federal de Sao Paulo, Sao Paulo, Brazil

Sintia I. Belangero, Carolina M. Carvalho, Vanessa K. Ota, Pedro M. Pan & Marcos L. Santoro

88. Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway

Ottar Bjerkeset & Eystein Stordal

89. Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway

Ottar Bjerkeset

90. Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway

Sigrid Børte, Bendik S. Winsvold & John-Anker Zwart

91. HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway

Sigrid Børte, Anne Heidi Skogholt, Laurent Thomas, Bendik S. Winsvold & John-Anker Zwart

92. Institute of Clinical Medicine, University of Oslo, Oslo, Norway

Sigrid Børte & John-Anker Zwart

93. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

Sandra A. Brown, Abraham A. Palmer & Tamara L. Wall

94. Department of Psychology, University of California San Diego, La Jolla, CA, USA

Sandra A. Brown

95. Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

Enrique Castelao, Giorgio Pistis & Martin Preisig

96. Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA

Hilary Coon, Anna R. Docherty, Andrey A. Shabalin & Anna R. Docherty

97. Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA

Hilary Coon

98. Department of Psychiatry, University of Vermont, Burlington, VT, USA

William E. Copeland

99. Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway

Elizabeth C. Corfield

100. Max-Planck-Insitute of Psychiatry, Department Genes and Environment, Munich, Germany

Darina Czamara

101. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany

Jürgen Deckert, Angelika Erhardt-Lehmann & Heike Weber

102. Center for Genomic Medicine, Salt Lake City, UT, USA

Anna R. Docherty & Anna R. Docherty

103. Huntsman Mental Health Institute, Salt Lake City, UT, USA

Anna R. Docherty, Andrey A. Shabalin & Anna R. Docherty

104. Department of Psychiatry and Psychotherapy, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany

Katharina Domschke & Ludger Tebartz van Elst

105. Department of Psychiatry, Sørlandet Hospital, Kristiansand/Arendal, Norway

Ole Kristian Drange

106. NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

Ole Kristian Drange & Morten Mattingsdal

107. Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

Thalia C. Eley, Jonna Kuntsi, Jonathan RI Coleman, Cathryn M. Lewis, Christopher Hübel, Gursharan K. Kalsi, Marta Di Forti & Diego Quattrone

108. Max-Planck-Institute for Psychiatry, Munich, Germany

Angelika Erhardt-Lehmann & Peter Falkai

109. Centre for Human Genetics, University of Marburg, Marburg, Germany

Andreas J. Forstner & Johannes Schumacher

110. Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany

Andreas J. Forstner, Eva C. Beins, Friederike S. David, Per Hoffmann & Eva C. Schulte

111. Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany

Andreas J. Forstner & Sven Cichon

112. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

Miguel Garcia-Argibay, Henrik Larsson, Isabell Brikell, Mikael Landén, Andreas Birgegård & Cynthia M. Bulik

113. School of Medical Sciences, Örebro University, Faculty of Medicine and Health, Örebro, Sweden

Miguel Garcia-Argibay & Henrik Larsson

114. Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

Scott D. Gordon, Nicholas G. Martin, Brittany L. Mitchell, Sarah E. Medland, Penelope A. Lind, Eske M. Derks, Zachary F. Gerring & Jackson G. Thorp

115. Brain and Mind Centre, The University of Sydney Australia, Sydney, New South Wales, Australia

Ian B. Hickie

116. Department of Psychology and Logopedics, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland

Iiris Hovatta, Jari Lahti & Marius Lahti-Pulkkinen

117. SleepWell Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland

Iiris Hovatta & Elisa Tasanko

118. Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK

Matthew H. Iveson

119. Department of Psychiatry, Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada

James L. Kennedy, Clement C. Zai, Gwyneth Zai, Jennifer Crosbie, Russell J. Schachar & Paul Sandor

120. Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

Daniel F. Levey

121. Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA

Daniel F. Levey & Hang Zhou

122. SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa

Christine Lochner

123. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

Michelle K. Lupton

124. School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia

Michelle K. Lupton, Penelope A. Lind, Zachary F. Gerring, Jackson G. Thorp & Charles P. Morris

125. Virginia Institute for Psychiatric and Behavioral Genetics, Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA

Hermine HM Maes

126. Department of Psychiatry, University of Tartu, Tartu, Estonia

Eduard Maron

127. Faculty of Medicine, Department of Medicine, Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, UK

Eduard Maron

128. Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada

Sandra M. Meier, Martin Alda & Alessio Squassina

129. Department of Clinical Psychology, Experimental Psychopathology and Psychotherapy, University of Marburg, Marburg, Germany

Christiane A. Melzig

130. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

Teemu Palviainen, Jaakko Kaprio & Stephanie Zellers

131. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University Duisburg-Essen, Essen, Germany

Börge Schmidt

132. SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa

Dan J. Stein

133. Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway

Eystein Stordal

134. Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité–Universitätsmedizin Berlin, Berlin, Germany

Andreas Ströhle

135. Department of Psychology, Faculty of Medicine, University of Helsinki, Helsinki, Finland

Elisa Tasanko

136. BioCore—Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway

Laurent Thomas

137. Clinic of Laboratory Medicine, St.Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Laurent Thomas

138. Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway

Laurent Thomas

139. Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands

Henning Tiemeier

140. Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Medicine, Boston, MA, USA

Henning Tiemeier

141. Department of Neurology, Oslo University Hospital, Oslo, Norway

Bendik S. Winsvold

142. Tanenbaum Centre for Pharmacogenetics, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

Clement C. Zai

143. Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

Gwyneth Zai

144. Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain

Silvia Alemany, Marta Ribasés & María Soler Artigas

145. Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain

Silvia Alemany, Marta Ribasés, María Soler Artigas & Raquel Rabionet

146. Department of Mental Health, Hospital Universitari Vall d’Hebron, Barcelona, Spain

Silvia Alemany, Marta Ribasés & María Soler Artigas

147. Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain

Silvia Alemany, Marta Ribasés & María Soler Artigas

148. ADHD and Developmental Psychiatry Programs, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Claiton HD Bau & Eugenio H. Grevet

149. Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Claiton HD Bau

150. Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands

Dorret I. Boomsma & Jouke-Jan Hottenga

151. Netherlands Twin Register, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Dorret I. Boomsma

152. Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Spain

Rosa Bosch & Josep Antoni Ramos-Quiroga

153. SJD MIND Schools Program, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain

Rosa Bosch & Miquel Casas

154. Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway

Isabell Brikell

155. Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada

Christie L. Burton, Jennifer Crosbie, Russell J. Schachar & Cathy L. Barr

156. Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain

Miquel Casas

157. Fundació Privada d’Investigació Sant Pau (FISP), Barcelona, Spain

Miquel Casas

158. Department of Genetics, Microbiology, and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain

Bru Cormand

159. Biomedical Network Research Centre on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain

Bru Cormand

160. Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain

Bru Cormand

161. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Alysa E. Doyle, Susan S. Kuo & Erin C. Dunn

162. Department of Pediatrics, Nemours Children’s Health Delaware, Sydney Kimmel School of Medicine, Philadelphia, PA, USA

Josephine Elia

163. Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

Joseph T. Glessner

164. Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA

Joseph T. Glessner

165. Department of Psychiatry and Legal Medicine, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Eugenio H. Grevet

166. Department of Biomedicine, University of Bergen, Bergen, Norway

Jan Haavik

167. Division of Psychiatry, Haukeland University Hospital, Bergen, Norway

Jan Haavik & Gerd Kvale

168. Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia

Ziarih Hawi

169. Center for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany

Anke Hinney

170. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Duisburg, Germany

Anke Hinney

171. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Henry R. Kranzler

172. Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA

Henry R. Kranzler

173. Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden

Kate Langley & Mikael Landén

174. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Wuerzurg, Wuerzburg, Germany

Klaus-Peter Lesch & Karin M. Egberts

175. Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands

Klaus-Peter Lesch, Jurjen J. Luykx & Bart PF Rutten

176. UCLA Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA

Sandra K. Loo & James J. McGough

177. David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

James J. McGough

178. School of Psychology and Counselling, Queensland University of Technology, Brisbane, Queensland, Australia

Sarah E. Medland

179. School of Psychology, University of Queensland, Brisbane, Queensland, Australia

Sarah E. Medland & Lucia Colodro-Conde

180. Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK

Michael C. O’Donovan, Michael J. Owen & James TR Walters

181. Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Danielle Posthuma

182. Department of Child and Adolescent Psychiatry, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands

Danielle Posthuma

183. Department of Mental Health, Vall Hebron University Hospital, Barcelona, Spain

Josep Antoni Ramos-Quiroga

184. Group of Psychiatry, Mental Health and Addictions, Vall Hebron Research Institute, Barcelona, Spain

Josep Antoni Ramos-Quiroga

185. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany

Andreas Reif

186. Psychosomatic Medicine and Psychotherapy, Department of Psychiatry, University Hospital, Goethe University Frankfurt, Frankfurt, Germany

Andreas Reif

187. Department of Physiology and Biophysics, Instituto de Ciencias Biomedicas Universidade de Sao Paulo, Sao Paulo, Brazil

Diego L. Rovaris

188. Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Institute of Biomedical Sciences, University of Sao Paulo, Sao Paulo, Brazil

Diego L. Rovaris

189. Department of Molecular Genetics and McLaughlin Centre, University of Toronto, Toronto, Ontario, Canada

Stephen W. Scherer & Jacob AS Vorstman

190. The Centre for Applied Genomics and Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada

Stephen W. Scherer

191. Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Edmund JS Sonuga-Barke

192. School of Medicine, Aarhus University, Aarhus, Denmark

Edmund JS Sonuga-Barke

193. School of Psychology, University of Hong Kong, Hong Kong, China

Edmund JS Sonuga-Barke

194. Department of Child and Adolescent Psychiatry, University of Zurich, Zurich, Switzerland

Hans-Christoph Steinhausen

195. Department of Mental Health and Suicide, Norwegian Institute of Mental Health, Oslo, Norway

Martin Tesli

196. Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

Stephanie H. Witt

197. Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY, USA

Yanli Zhang-James & Stephen J. Glatt

198. University of Illinois, Chicago, IL, USA

Edwin H. Cook

199. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

Jakob Grove

200. Center for Genomics and Personalized Medicine, Aarhus, Denmark

Jakob Grove

201. Broad Institute of MIT and Harvard, Cambridge, MA, USA

Susan S. Kuo

202. Carolina Institute for Developmental Disabilities, Carrboro, NC, USA

Joseph Piven

203. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Joseph Piven

204. Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, USA

Stephan J. Sanders

205. Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK

Stephan J. Sanders

206. New York Genome Center, New York, NY, USA

Stephan J. Sanders

207. Center for Applied and Translational Genomics (CATG), MBRU, Dubai Health, Dubai, UAE

Mohammed Uddin

208. GenomeArc, Mississauga, Ontario, Canada

Mohammed Uddin

209. Department of Psychiatry and Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada

Jacob AS Vorstman

210. Departments of Psychiatry and Psychology, University of Cambridge, Cambridge, UK

Varun Warrier

211. Institute for Human Genetics, Department of Psychiatry and Behavioral Science, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

Lauren A. Weiss

212. National Institute of Mental Health, Klecany, Czech Republic

Martin Alda

213. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

Lars Alfredsson

214. Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany

Till F. M. Andlauer

215. Division of Psychiatry, University College London, London, UK

Nicholas Bass, Andrew McQuillin, Tracey van der Veen & Glyn Lewis

216. Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia

Bernhard T. Baune

217. Department of Psychiatry, University of Münster, Münster, Germany

Bernhard T. Baune

218. Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, Department of Epidemiology and Biostatistics, State University of New York Downstate Health Sciences University, New York, NY, USA

Tim B. Bigdeli

219. Department of Veterans Affairs (VA) New York Harbor Healthcare System, New York, NY, USA

Tim B. Bigdeli

220. Department of Biomedicine, University of Basel, Basel, Switzerland

Sven Cichon

221. Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland

Sven Cichon

222. Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany

Sven Cichon

223. NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK

Jonathan RI Coleman & Gursharan K. Kalsi

224. Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA

Alfredo B. Cuellar-Barboza & Victor M. Karpyak

225. Department of Psychiatry, Universidad Autonoma de Nuevo Leon, Monterrey, Mexico

Alfredo B. Cuellar-Barboza

226. Institute for Translational Psychiatry, University of Münster, Münster, Germany

Udo Dannlowski

227. Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany

Friederike S. David

228. Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

Josef Frank, Marcella Rietschel, Lea Sirignano, Fabian Streit, Jana Strohmaier, Lea Zillich & Jerome C. Foo

229. Neuroscience Research Australia, Randwick, New South Wales, Australia

Janice M. Fullerton

230. University of New South Wales, Faculty of Medicine, School of Biomedical Science, Kensington, New South Wales, Australia

Janice M. Fullerton

231. Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA

Fernando S. Goes, Peter P. Zandi, James B. Potash, Oscar J. Bienvenu & Marco A. Grados

232. Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania

Maria Grigoroiu-Serbanescu

233. Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania

Maria Grigoroiu-Serbanescu

234. Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany

Janos L. Kalman, Eva C. Schulte, Peter Falkai & Peter Zill

235. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia

Kristi Krebs, Lili Milani & Kelli Lehto

236. Janssen Research and Development, Titusville, NJ, USA

Qingqin S. Li

237. Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, The Netherlands

Jurjen J. Luykx

238. GGZ inGeest Mental Health Care, Amsterdam, The Netherlands

Jurjen J. Luykx

239. Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy

Mirko Manchia

240. Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada

Mirko Manchia

241. Unit of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy

Mirko Manchia

242. Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Rud, Norway

Morten Mattingsdal

243. Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

Andrew M. McIntosh & Mark J. Adams

244. Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy

Anna Meloni, Claudia Pisanu & Alessio Squassina

245. Discipline of Psychiatry and Mental Health, School of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia

Philip B. Mitchell

246. Centre for Neuroimaging, Cognition and Genomics (NICOG), University of Galway, Galway, Ireland

Derek W. Morris

247. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Niamh Mullins

248. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Niamh Mullins, Sarah MC Colbert & Alison M. Goate

249. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Niamh Mullins

250. Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seoul, South Korea

Woojae Myung

251. Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea

Woojae Myung

252. Department of Psychiatry, Department of Medical and Molecular Genetics, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA

John I. Nurnberger Jr

253. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

Roel A. Ophoff

254. Clinical Bioinformatics Unit, Department of Pathology, Faculty of Medicine and Health Sciences, Erasmus MC, Rotterdam, The Netherlands

George P. Patrinos

255. Department of Genetics and Genomics, United Arab Emirates University, College of Medicine and Health Sciences, Abu Dhabi, UAE

George P. Patrinos

256. Zayed Center for Health Sciences, United Arab Emirates University, Abu Dhabi, UAE

George P. Patrinos

257. Department of Pharmacy, University of Patras School of Health Sciences, Patras, Greece

George P. Patrinos

258. Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA

James J. Prisciandaro

259. Division of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria

Eva Z. Reininghaus

260. Department of Psychiatry, Washington University in Saint Louis, St Louis, MO, USA

John P. Rice

261. Department of Psychiatry and Psychotherapy, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany

Eva C. Schulte

262. Department of Psychiatry, University Hospital, Faculty of Medicine, University of Bonn, Bonn, Germany

Eva C. Schulte

263. German Center for Mental Health (DZPG) partner site Munich-Augsburg, Munich, Germany

Eva C. Schulte

264. Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany

Eva C. Schulte

265. Department of Medicine and Surgery, Kore University of Enna, Enna, Italy

Alessandro Serretti

266. Oasi Research Institute—IRCCS, Troina, Italy

Alessandro Serretti

267. Human Genetics Branch, Intramural Research Program, National Institute of Mental Health, NIH, US Department of HHS, Bethesda, MD, USA

Tracey van der Veen

268. Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA

Marquis P. Vawter

269. Brain Molecular Science, Centre for Addiction & Mental Health, Toronto, Ontario, Canada

John B. Vincent

270. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

John B. Vincent

271. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

John B. Vincent

272. Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada

John B. Vincent

273. Department of Translational Neuroscience, UMC Utrecht, Utrecht, The Netherlands

Roger A. Adan & Martien J. H. Kas

274. Rintveld Eating disorder clinic, Altrecht GGZ, Zeist, The Netherlands

Roger A. Adan

275. Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Cynthia M. Bulik

276. l’institut du thorax, INSERM, CNRS, Nantes Université, Nantes, France

Christian Dina

277. Department of Psychiatric Genetics, Department of Psychiatry, University of Medical Sciences, Poznan, Poland

Monika Dmitrzak-Weglarz

278. Department of Human Genetics, University Hospital of Liège, Liège, Belgium

Elisa Docampo

279. Rheumatology Department, University Hospital of Liège, Liège, Belgium

Elisa Docampo

280. Ciber Physiopathology of Obesity and Nutrition (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain

Fernando Fernandez-Aranda & Susana Jimenez-Murcia

281. Department of Clinical Psychology, University Hospital of Bellvitge-IDIBELL, Barcelona, Spain

Fernando Fernandez-Aranda & Susana Jimenez-Murcia

282. Department of Clinical Sciences, Shool of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain

Fernando Fernandez-Aranda & Susana Jimenez-Murcia

283. Psychoneurobiology of Eating and Addictive Behaviors Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain

Fernando Fernandez-Aranda & Susana Jimenez-Murcia

284. Center for Excellence in Eating Disorders Tübingen (KOMET), Tübingen, Germany

Katrin E. Giel

285. German Center for Mental Health (DZPG), Tübingen, Germany

Katrin E. Giel

286. Department of Psychosomatic Medicine & Psychotherapy, Medical University Hospital Tübingen, Tübingen, Germany

Katrin E. Giel

287. Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (INSERM U1266), Paris, France

Philip Gorwood, Nicolas Ramoz & Boris Chaumette

288. National Centre for Register-based Research, Aarhus University, Aarhus, Denmark

Christopher Hübel, Janne T. Larsen & Liselotte V. Petersen

289. Department of Psychiatry, McLean Hospital, Harvard Medical School, Boston, MA, USA

James I. Hudson & Nikolaos P. Daskalakis

290. Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

Jennifer Jordan & Joseph M. Boden

291. Te Whatu Ora—Waitaha (Health New Zealand), Christchurch, New Zealand

Jennifer Jordan

292. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

Leila Karhunen

293. Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands

Martien J. H. Kas

294. Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand

Martin A. Kennedy

295. Department of Clinical Psychology, College of Professional Psychology, The Chicago School, Washington, DC, USA

Lisa R. Lilenfeld

296. Department of Psychiatry, University of Campania L. Vanvitelli, Naples, Italy

Alessio Maria Monteleone

297. Department of Community, Family, and Addiction Sciences, Texas Tech University, Lubbock, TX, USA

Melissa A. Munn-Chernoff

298. Department of Neuroscience, Psychology, Drug Research and Child Health University of Florence, Florence, Italy

Benedetta Nacmias & Sandro Sorbi

299. IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy

Benedetta Nacmias & Sandro Sorbi

300. Department of Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Dalila Pinto

301. Department of Psychiatry, Division of Adolescent Psychiatry, Helsinki University Hospital, Helsinki, Finland

Anu Raevuori

302. Faculty of Medicine, Clinicum, University of Helsinki, Helsinki, Finland

Anu Raevuori

303. Department of Health Science, University of Florence, Firenze, Italy

Valdo Ricca

304. Department of General Practice & Primary Healthcare, Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand

Marion E. Roberts

305. Department of Adult Psychiatry, University of Medical Sciences, Poznan, Poland

Filip Rybakowski

306. Department of Psychological Medicine, Centre for Research in Eating and Weight Disorders, King’s College London, London, UK

Ulrike H. Schmidt

307. Department of Psychiatry and Psychotherapy, Medical University Vienna, Vienna, Austria

Alexandra Schosser

308. Faculty of Medicine, Sigmund Freud University Vienna, Vienna, Austria

Alexandra Schosser

309. Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience & Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

Michael A. Strober

310. Department of Psychiatry, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Hunna J. Watson

311. Discipline of Psychology, School of Population Health, Curtin University, Perth, Western Australia, Australia

Hunna J. Watson

312. Division of Paediatrics, School of Medicine, The University of Western Australia, Perth, Western Australia, Australia

Hunna J. Watson

313. Centre of Excellence for Eating Disorders Tuebingen Germany (KOMET), Tuebingen, Germany

Stephan Zipfel

314. Department of Psychosomatic Medicine and Psychotherapy, University Medical Hospital University Tuebingen, Tuebingen, Germany

Stephan Zipfel

315. German Centre for Mental Health (DZPG) Tuebingen, Tuebingen, Germany

Stephan Zipfel

316. Copenhagen Research Centre for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark

Michael E. Benros

317. Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

Michael E. Benros, Merete Nordentoft & Thomas Werge

318. Department of Psychiatry, Universidade Federal de Sao Paulo, Sao Paulo, Brazil

Rodrigo A. Bressan, Ary Gadelha & Cristiano Noto

319. Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia

Enda M. Byrne

320. Department of Psychiatry and Medical Psychology, Universidade Federal de São Paulo (UNIFESP), Sao Paulo, Brazil

Carolina M. Carvalho

321. Department of Psychiatry, McGill University, Montreal, Quebec, Canada

Boris Chaumette

322. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy

Chiara Fabbri & Giuseppe Fanelli

323. Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany

Hans J. Grabe & Sandra Van der Auwera

324. Department of Psychiatry, Washington University, St Louis, MO, USA

Andrew C. Heath, Pamela A. Madden, Laura J. Bierut, Arpana Agrawal, Karhleen K. Bucholz, Sarah M. Hartz & Alex P. Miller

325. Department of Psychiatry, Stanford University, Stanford, CA, USA

Douglas F. Levinson

326. Department of Biological Sciences, Purdue University, West Lafayette, IN, USA

Peristera Paschou

327. Departments of Psychiatry and Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA

Brien P. Riley

328. Child Mind Institute, New York, NY, USA

Giovanni A. Salum

329. Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Giovanni A. Salum

330. Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil

Giovanni A. Salum

331. National Institute of Developmental Psychiatry, Sao Paulo, Brazil

Giovanni A. Salum

332. Department of Biochemistry, Universidade Federal de Sao Paulo, Sao Paulo, Brazil

Marcos L. Santoro

333. GenOmics and Translational Research Center, RTI International, Durham, NC, USA

Bradley T. Webb, Nathan C. Gaddis, Eric O. Johnson, Jesse A. Marks & Bryan C. Quach

334. Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Timothy B. Baker

335. Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers—The State University of New Jersey, New Brunswick, NJ, USA

Danielle M. Dick

336. Department of Psychiatry, Rutgers Robert Wood Johnson School of Medicine, Rutgers University, Piscataway, NJ, USA

Danielle M. Dick

337. Drichel Analytics, Bonn, Germany

Dmitriy Drichel

338. Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA

Lindsay A. Farrer & Richard Sherva

339. RTI International, Research Triangle Park, NC, USA

Dana B. Hancock

340. Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

John E. Hokanson & Kendra Young

341. Fellow Program, RTI International, Durham, NC, USA

Eric O. Johnson

342. Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Mary L. Marazita

343. Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA

Daniel W. McNeil

344. Cologne Center for Genomics, University of Cologne, Cologne, Germany

Michael Nothnagel

345. Department of Genetics, Washington University School of Medicine, St Louis, MO, USA

Nancy L. Saccone

346. Division of Biostatistics, Washington University School of Medicine, St Louis, MO, USA

Nancy L. Saccone

347. National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

Nancy YA Sey

348. Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA

Richard Sherva

349. Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA

Scott Vrieze & Stephanie Zellers

350. Biobot Analytics, Cambridge, MA, USA

Alex Waldrop

351. PI Pharmaimage Biomarker Solutions, Cambridge, MA, USA

Georg Winterer

352. Departments of Psychiatry and Physiology, University of Toronto, Toronto, Ontario, Canada

Cathy L. Barr

353. Division of Experimental and Translational Neuroscience, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada

Cathy L. Barr

354. Department of Molecular Biology, Semmelweis University, Budapest, Hungary

Csaba Barta

355. Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany

Katharina Bey & Michael Wagner

356. Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden

Julia Boberg, David Mataix-Cols & Elles de Schipper

357. Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

Jonas Bybjerg-Grauholm & David M. Hougaard

358. Duke University Hospital, Department of Surgery, Durham, NC, USA

Adrian Camarena

359. Department of Pharmacogenetics, Instituto Nacional de Psiquiatría Ramon de la Fuente Muñiz, Mexico City, Mexico

Beatriz Camarena

360. Department of Psychiatry, UMCG & RUG, Groningen, The Netherlands

Danielle C. Cath

361. GGZ Drenthe, Assen, The Netherlands

Danielle C. Cath

362. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

James J. Crowley

363. Accare Child Study Center, Groningen, The Netherlands

Andrea Dietrich & Pieter J. Hoekstra

364. University of Groningen, University Medical Center Groningen, Department of Child and Adolescent Psychiatry, Groningen, The Netherlands

Andrea Dietrich & Pieter J. Hoekstra

365. Child Study Center and Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

Thomas V. Fernandez

366. Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland

Edna Grünblatt

367. Neuroscience Center Zurich, University of Zurich and the ETH Zurich, Zurich, Switzerland

Edna Grünblatt

368. Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland

Edna Grünblatt

369. Bergen Center for Brain Plasticity (BCBP), Haukeland University Hospital, Bergen, Norway

Kristen Hagen & Bjarne Hansen

370. Department of Psychiatry, Helse Møre and Romsdal Hospital Trust, Molde, Norway

Kristen Hagen

371. Norwegian University for Science and Technology, Trondheim, Norway

Kristen Hagen

372. Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA

Gregory L. Hanna

373. Centre for Crisis Psychology, University of Bergen, Bergen, Norway

Bjarne Hansen

374. Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA

Gary A. Heiman & Christopher P. Walker

375. Rutgers University, Piscataway, NJ, USA

Gary A. Heiman & Christopher P. Walker

376. Department of Medicine, MSB Medical School Berlin, Berlin, Germany

Julia Klawohn

377. Department of Clinical Psychology, University of Bergen, Bergen, Norway

Gerd Kvale

378. Carracci Medical Group, Mexico City, Mexico

Nuria Lanzagorta & Humberto Nicolini

379. Department of Clinical Science, University of Bergen, Bergen, Norway

Stephanie Le Hellard

380. Department of Psychiatry, University of California, Irvine, Irvine, CA, USA

Fabio Macciardi

381. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Brion S. Maher & Stephen J. Glatt

382. Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA

Irene A. Malaty & Michael S. Okun

383. Department of Clinical Sciences, Lund University, Lund, Sweden

David Mataix-Cols

384. Department of Psychiatry and Genetics Institute, University of Florida, Gainesville, FL, USA

Carol A. Mathews

385. UF Center for OCD, Anxiety and Related Disorders, Gainesville, FL, USA

Carol A. Mathews

386. Alpert Medical School of Brown University, Providence, RI, USA

Nicole CR McLaughlin

387. Butler Hospital, Providence, RI, USA

Nicole CR McLaughlin

388. Faculdade de Medicina, Universidade de São Paulo, Sao Paulo, Brazil

Euripedes C. Miguel

389. Department of Psychiatry, Socialpsychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany

Kirsten R. Müller-Vahl

390. Laboratory of Genomics of Psychiatric and Neurodegenerative Diseases, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico

Humberto Nicolini

391. Department of Mental Health, Greater Los Angeles VA Healthcare, Los Angeles, CA, USA

Erika L. Nurmi

392. Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA

Erika L. Nurmi

393. Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, Madrid, Spain

Raquel Rabionet

394. Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain

Raquel Rabionet

395. Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain

Raquel Rabionet

396. Cologne Excellence Cluster for Stress Responses in Ageing-Associated Diseases (CECAD), University of Cologne, Cologne, Germany

Alfredo Ramirez

397. Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany

Alfredo Ramirez & Michael Wagner

398. Department of Psychiatry, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, TX, USA

Alfredo Ramirez

399. Department of Psychiatry and Psychotherapy, Division of Neurogenetics and Molecular Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany

Alfredo Ramirez

400. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

Alfredo Ramirez & Michael Wagner

401. Department of Clinical and experimental Medicine, Child and Adolescent Neuropsichiatry, Catania University, Catania, Italy

Renata Rizzo

402. Genomics and Bioinformatics, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Compostela, Spain

Cristina Rodriguez-Fontenla

403. Department of Neurology and the Kennedy Krieger Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Harvey S. Singer

404. Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA

Eric A. Storch

405. Departments of Psychiatry and Pediatrics, Columbia University, New York, NY, USA

Jeremy Veenstra-VanderWeele

406. New York State Psychiatric Institute, New York, NY, USA

Jeremy Veenstra-VanderWeele

407. Imaging Future Aps, Tåstrup, Denmark

Søren B. Andersen

408. Biogen, Cambridge, MA, USA

Chia-Yen Chen

409. Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Boston, MA, USA

Seth G. Disner

410. Minneapolis VA Health Care System, Boston, MA, USA

Seth G. Disner

411. Case Western Reserve University, Boston, MA, USA

Norah C. Feeny

412. Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Boston, MA, USA

Lana R. Grasser

413. Cohen Veterans Bioscience, Boston, MA, USA

Magali Haas

414. Million Veteran Program (MVP) Coordinating Center, VA Boston Healthcare System, Boston, MA, USA

Kelly M. Harrington

415. Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA

Victor M. Hesselbrock

416. Department of Psychology, Eberhard Karls Universität Tübingen, Tubingen, Germany

Mohammed H. Ibrahim

417. Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA

Seyma Katrinli & Alicia K. Smith

418. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA

Nathan A. Kimbrel

419. Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA

Nathan A. Kimbrel

420. VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA

Nathan A. Kimbrel

421. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA

Jessica L. Maples-Keller, Barbara O. Rothbaum & Alicia K. Smith

422. Department of Psychiatry, State University of New York Downstate Medical Center, New York, NY, USA

Jacquelyn L. Meyers

423. Connecticut VA Healthcare Center, Orange, CT, USA

Janitza L. Montalvo-Ortiz & Robert H. Pietrzak

424. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

Janitza L. Montalvo-Ortiz, Renato Polimanti, Joseph D. Deak & Hang Zhou

425. Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA

Richard J. Rosenblum

426. South African Medical Research Council Genomics of Brain Disorders Research Unit, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

Soraya Seedat

427. Department of Human Genetics, Emory University, Atlanta, GA, USA

Alicia K. Smith

428. Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA

Ralph E. Tarter

429. Mental Health Nuroscience Research Department, Division of Psychiatry, UCL, London, UK

Muhammad Ayub

430. Centre Hospitalier du Rouvray, Rouen, France

Dominique Campion & Olivier Guillin

431. INSERM U1245, Rouen, France

Dominique Campion & Olivier Guillin

432. Centre de Référence des Maladies Rares à Expression Psychiatrique, Department of Child and Adolescent Psychiatry, AP-HP Sorbonne Université, Hôpital Universitaire de la Pitié-Salpêtrière, Paris, France

David Cohen, Angel Consoli, Marianna Giannitelli & Claudine Laurent-Levinson

433. Faculté de Médecine Sorbonne Université, Groupe de Recherche Clinique n°15 - Troubles Psychiatriques et Développement (PSYDEV), Department of Child and Adolescent Psychiatry, Hôpital Universitaire de la Pitié-Salpêtrière, Paris, France

David Cohen, Angel Consoli, Marianna Giannitelli & Claudine Laurent-Levinson

434. Institut des Systèmes Intelligents et de Robotique (ISIR), CNRS UMR7222, Sorbonne Université, Campus Pierre et Marie Curie, Faculté des Sciences et Ingénierie, Paris, France

David Cohen

435. Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland

Johan G. Eriksson

436. Department of Obstetrics and Gynecology, National University of Singapore, Singapore, Singapore

Johan G. Eriksson

437. Mental Health Research Institute, Tomsk National Research Medical Center, Tomsk, Russia

Olga YU Fedorenko & Svetlana A. Ivanova

438. School of Non-Destructive Testing, Tomsk Polytechnic University, Tomsk, Russia

Olga YU Fedorenko

439. Department of Psychiatry, University of Colorado Denver School of Medicine, Aurora, CO, USA

Robert Freedman & Christian J. Hopfer

440. Institute of Translational Biomedicine, Saint Petersburg State University, St Petersburg, Russia

Raul R. Gainetdinov

441. Saint Petersburg University Hospital, Saint Petersburg State University, St Petersburg, Russia

Raul R. Gainetdinov

442. Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria

Ina Giegling

443. Departments of Psychiatry and Human and Molecular Genetics, INSERM, Institut de Myologie, Hôpital de la Pitiè-Salpêtrière, Paris, France

Stephanie Godard

444. UFR Santé, Université de Rouen Normandie, Rouen, France

Olivier Guillin

445. Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria

Annette M. Hartmann, Bettina Konte & Dan Rujescu

446. Psychiatry, Addictology and Psychotherapy Department, Siberian State Medical University, Tomsk, Russia

Svetlana A. Ivanova

447. Department of Psychiatric Genomics, Bekhterev National Medical Research Center for Psychiatry and Neurology, St Petersburg, Russia

Alexander O. Kibitov

448. Valdman Institute of Pharmacology, First St. Petersburg Pavlov State Medical University, St Petersburg, Russia

Alexander O. Kibitov & Evgeny M. Krupitsky

449. Saint Petersburg State University, St Petersburg, Russia

Anastasia Levchenko

450. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Robin Murray

451. Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark

Merete Nordentoft

452. Généthon, Evry, France

Safaa Saker-Delye

453. Faculty of Science, Medicine and Health, School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia

SIbylle G. Schwab

454. Illawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia

SIbylle G. Schwab

455. INSERM U1266, Institut de Psychiatrie et de Neurosciences, Paris, France

Florence Thibaut

456. Université de Paris, Faculté de Médecine, Hôpital Cochin-Tarnier, Paris, France

Florence Thibaut

457. Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, Denmark

Thomas Werge

458. School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Perth, Western Australia, Australia

Dieter B. Wildenauer

459. Department of Psychological & Brain Sciences, Washington University, St Louis, MO, USA

David AA Baranger & Ryan Bogdan

460. Institute of Behavioral Science and Department of Sociology, University of Colorado, Boulder, CO, USA

Jason D. Boardman

461. Department of Molecular Pharmacology and Experimental Theraeputics, Mayo Clinic College of Medicine and Science, New York, NY, USA

Doo-Sup Choi

462. Department of Psychiatry and Psychology, Mayo Clinic College of Medicine and Science, New York, NY, USA

Doo-Sup Choi

463. Department of Psychiatry, VeteransAffairs Connecticut Healthcare Center, West Haven, CT, USA

Joseph D. Deak

464. Office of the Clinical Director, NIAAA, NIH, Bethesda, MD, USA

Nancy Diazgranados

465. Department of Psychiatry, Virginia Commonwealth University School of Medicine, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA

Alexis C. Edwards

466. Office of the Clinical Director and Lab of Neurogenetics, NIAAA, NIH, Rockville, MD, USA

David Goldman

467. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA

Laura M. Hack

468. Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA

Laura M. Hack

469. Department of Sociology and the Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Kathleen Mullan Harris

470. MRC Human Genetics Unit, IGC, University of Edinburgh, Edinburgh, UK

Caroline Hayward

471. Institue for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA

John K. Hewitt

472. Department of Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, CO, USA

Kenneth S. Krauter

473. Department of Addictions, Bekhterev National Medical Research Center for Psychiatry and Neurology, Edinburgh, UK

Evgeny M. Krupitsky

474. Department of Psychiatry, University of Iowa Carver College of Medicine, Edinburgh, UK

Samuel Kuperman

475. Folkhälsan Research Center, Helsinki, Finland

Jari Lahti

476. Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK

Marius Lahti-Pulkkinen

477. Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland

Marius Lahti-Pulkkinen

478. Department of Medical & Molecular Genetics, Indiana University, Indianapolis, IN, USA

Dongbing Lai, Robbee Wedow & Leah Wetherill

479. Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA

Abraham A. Palmer

480. Department of Psychology, Emory University, Atlanta, GA, USA

Rohn H. Palmer

481. The Jackson Laboratory, Bar Harbor, ME, USA

Rohn H. Palmer

482. Providence VA Medical Center, Providence, RI, USA

Rohn H. Palmer

483. Department of Medicine, University of Otago, Christchurch, New Zealand

John F. Pearson

484. Department of Psychiatry, State University of New York Downstate Health Science University, New York, NY, USA

Bernice Porjesz

485. Department of Psychiatry, Psychotherapie and Psychosomatics, Martin-Luther University Halle-Wittenberg, Newark, NJ, USA

Ulrich W. Preuss

486. RKH Ludwigsburg, Psychiatrie, Psychotherapie und Psychosomatische Medizin, Newark, NJ, USA

Ulrich W. Preuss

487. Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA

Daniel M. Rosenblum & Stanley H. Weiss

488. Department of Psychiatry and Psychotherapy, LVR-University Hospital Essen, University of Duisburg-Essen, Duisburg-Essen, Germany

Norbert Scherbaum

489. Departments of Pharmaceutical Sciences, Psychiatry, and Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA

Michael Vanyukov

490. Department of Sociology, Purdue University, Regensburg, Germany

Robbee Wedow

491. Department of Psychiatry and Psychotherapy, Center of Addiction Medicine, University Hospital Regensburg at the Bezirksklinikum, Regensburg, Germany

Norbert Wodarz

492. Epidemiology and Biometry Branch, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA

Haitao Zhang

493. Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

Hongyu Zhao

494. Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA

Hang Zhou

Consortia

Anxiety Disorders Working Group of the Psychiatric Genomics Consortium

- Daniel E. Adkins

- , Georg W. Alpers

- , Helga Ask

- , Sintia I. Belangero

- , Ottar Bjerkeset

- , Sigrid Børte

- , Gerome Breen

- , Sandra A. Brown

- , Enrique Castelao

- , Hilary Coon

- , William E. Copeland

- , Elizabeth C. Corfield

- , Darina Czamara

- , Jürgen Deckert

- , Anna R. Docherty

- , Katharina Domschke

- , Ole Kristian Drange

- , Thalia C. Eley

- , Angelika Erhardt-Lehmann

- , Andreas J. Forstner

- , Miguel Garcia-Argibay

- , Scott D. Gordon

- , John M. Hettema

- , Ian B. Hickie

- , Iiris Hovatta

- , Matthew H. Iveson

- , James L. Kennedy

- , Henrik Larsson

- , Daniel F. Levey

- , Christine Lochner

- , Michelle K. Lupton

- , Hermine HM Maes

- , Eduard Maron

- , Nicholas G. Martin

- , Manuel Mattheisen

- , Sandra M. Meier

- , Christiane A. Melzig

- , Brittany L. Mitchell

- , Teemu Palviainen

- , Roseann E. Peterson

- , Giorgio Pistis

- , Martin Preisig

- , Börge Schmidt

- , Johannes Schumacher

- , Andrey A. Shabalin

- , Anne Heidi Skogholt

- , Dan J. Stein

- , Murray B. Stein

- , Eystein Stordal

- , Andreas Ströhle

- , Nora I. Strom

- , Elisa Tasanko

- , Laurent Thomas

- , Henning Tiemeier

- , Brad Verhulst

- , Heike Weber

- , Bendik S. Winsvold

- , Clement C. Zai

- , Gwyneth Zai

- & John-Anker Zwart

Attention-Deficit/Hyperactivity Disorder (ADHD) Working Group of the Psychiatric Genomics Consortium

- Silvia Alemany

- , Claiton HD Bau

- , Sintia I. Belangero

- , Dorret I. Boomsma

- , Rosa Bosch

- , Isabell Brikell

- , Christie L. Burton

- , Miquel Casas

- , Elizabeth C. Corfield

- , Bru Cormand

- , Jennifer Crosbie

- , Ditte Demontis

- , Alysa E. Doyle

- , Josephine Elia

- , Stephen V. Faraone

- , Barbara Franke

- , Miguel Garcia-Argibay

- , Joseph T. Glessner

- , Eugenio H. Grevet

- , Jan Haavik

- , Alexandra Havdahl

- , Ziarih Hawi

- , Anke Hinney

- , Daniel P. Howrigan

- , Marieke Klein

- , Henry R. Kranzler

- , Jonna Kuntsi

- , Kate Langley

- , Henrik Larsson

- , Klaus-Peter Lesch

- , Calwing Liao

- , Sandra K. Loo

- , Hermine HM Maes

- , James J. McGough

- , Sarah E. Medland

- , Nina R. Mota

- , Benjamin M. Neale

- , Michael C. O’Donovan

- , Roseann E. Peterson

- , Danielle Posthuma

- , Josep Antoni Ramos-Quiroga

- , Andreas Reif

- , Marta Ribasés

- , Diego L. Rovaris

- , Russell J. Schachar

- , Stephen W. Scherer

- , Yingjie Shi

- , María Soler Artigas

- , Edmund JS Sonuga-Barke

- , Hreinn Stefansson

- , Kari Stefansson

- , Hans-Christoph Steinhausen

- , Ludger Tebartz van Elst

- , Martin Tesli

- , G. Bragi Walters

- , Raymond K. Walters

- , Stephanie H. Witt

- & Yanli Zhang-James

Autism Spectrum Disorders Working Group of the Psychiatric Genomics Consortium

- Anders D. Børglum

- , Edwin H. Cook

- , Elizabeth C. Corfield

- , Jakob Grove

- , Alexandra Havdahl

- , Susan S. Kuo

- , Joseph Piven

- , Danielle Posthuma

- , Elise B. Robinson

- , Stephan J. Sanders

- , Susan L. Santangelo

- , Stephen W. Scherer

- , Ludger Tebartz van Elst

- , Mohammed Uddin

- , Jacob AS Vorstman

- , Varun Warrier

- & Lauren A. Weiss

Bipolar Disorder Working Group of the Psychiatric Genomics Consortium

- Martin Alda

- , Silvia Alemany

- , Lars Alfredsson

- , Till F. M. Andlauer

- , Ole A. Andreassen

- , Nicholas Bass

- , Anthony J. Batzler

- , Bernhard T. Baune

- , Eva C. Beins

- , Joanna M. Biernacka

- , Tim B. Bigdeli

- , Rosa Bosch

- , Gerome Breen

- , Miquel Casas

- , Sven Cichon

- , Jonathan RI Coleman

- , Brandon J. Coombes

- , Alfredo B. Cuellar-Barboza

- , Udo Dannlowski

- , Friederike S. David

- , Andreas J. Forstner

- , Josef Frank

- , Janice M. Fullerton

- , Fernando S. Goes

- , Scott D. Gordon

- , Maria Grigoroiu-Serbanescu

- , Jakob Grove

- , Per Hoffmann

- , James A. Knowles

- , Janos L. Kalman

- , James L. Kennedy

- , James A. Knowles

- , Kristi Krebs

- , Mikael Landén

- , Phil H. Lee

- , Cathryn M. Lewis

- , Qingqin S. Li

- , Calwing Liao

- , Penelope A. Lind

- , Christine Lochner

- , Jurjen J. Luykx

- , Mirko Manchia

- , Nicholas G. Martin

- , Morten Mattingsdal

- , Andrew M. McIntosh

- , Andrew McQuillin

- , Sarah E. Medland

- , Sandra M. Meier

- , Anna Meloni

- , Lili Milani

- , Brittany L. Mitchell

- , Philip B. Mitchell

- , Derek W. Morris

- , Niamh Mullins

- , Woojae Myung

- , John I. Nurnberger Jr

- , Kevin S. O’Connell

- , Michael C. O’Donovan

- , Roel A. Ophoff

- , Michael J. Owen

- , George P. Patrinos

- , Claudia Pisanu

- , Danielle Posthuma

- , James J. Prisciandaro

- , Josep Antoni Ramos-Quiroga

- , Andreas Reif

- , Eva Z. Reininghaus

- , Marta Ribasés

- , John P. Rice

- , Marcella Rietschel

- , Eva C. Schulte

- , Alessandro Serretti

- , Lea Sirignano

- , María Soler Artigas

- , Jordan W. Smoller

- , Alessio Squassina

- , Dan J. Stein

- , Fabian Streit

- , Jana Strohmaier

- , Martin Tesli

- , Tracey van der Veen

- , Marquis P. Vawter

- , John B. Vincent

- , Stephanie H. Witt

- , Peter P. Zandi

- & Lea Zillich

Eating Disorders Working Group of the Psychiatric Genomics Consortium

- Roger A. Adan

- , Lars Alfredsson

- , Helga Ask

- , Andreas Birgegård

- , Gerome Breen

- , Cynthia M. Bulik

- , Jonathan RI Coleman

- , Christian Dina

- , Monika Dmitrzak-Weglarz

- , Elisa Docampo

- , Karin M. Egberts

- , Fernando Fernandez-Aranda

- , Katrin E. Giel

- , Scott D. Gordon

- , Philip Gorwood

- , Alexandra Havdahl

- , Anke Hinney

- , Christopher Hübel

- , James I. Hudson

- , Susana Jimenez-Murcia

- , Jennifer Jordan

- , Gursharan K. Kalsi

- , Jaakko Kaprio

- , Leila Karhunen

- , Martien J. H. Kas

- , James L. Kennedy

- , Martin A. Kennedy

- , Mikael Landén

- , Janne T. Larsen

- , Qingqin S. Li

- , Lisa R. Lilenfeld

- , Jurjen J. Luykx

- , Nicholas G. Martin

- , Sarah E. Medland

- , Alessio Maria Monteleone

- , Karen S. Mitchell

- , Melissa A. Munn-Chernoff

- , Benedetta Nacmias

- , Roel A. Ophoff

- , Liselotte V. Petersen

- , Dalila Pinto

- , Anu Raevuori

- , Nicolas Ramoz

- , Valdo Ricca

- , Marion E. Roberts

- , Filip Rybakowski

- , Ulrike H. Schmidt

- , Alexandra Schosser

- , Sandro Sorbi

- , Michael A. Strober

- , Laura M. Thornton

- , Hunna J. Watson

- & Stephan Zipfel

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

- Mark J. Adams

- , Till F. M. Andlauer

- , Helga Ask

- , Bernhard T. Baune

- , Sintia I. Belangero

- , Michael E. Benros

- , Tim B. Bigdeli

- , Ottar Bjerkeset

- , Dorret I. Boomsma

- , Gerome Breen

- , Rodrigo A. Bressan

- , Enda M. Byrne

- , Na Cai

- , Carolina M. Carvalho

- , Enrique Castelao

- , Boris Chaumette

- , Sven Cichon

- , Jonathan RI Coleman

- , Lucia Colodro-Conde

- , Hilary Coon

- , William E. Copeland

- , Elizabeth C. Corfield

- , Darina Czamara

- , Udo Dannlowski

- , Eske M. Derks

- , Anna R. Docherty

- , Katharina Domschke

- , Erin C. Dunn

- , Chiara Fabbri

- , Giuseppe Fanelli

- , Jerome C. Foo

- , Andreas J. Forstner

- , Josef Frank

- , Ary Gadelha

- , Zachary F. Gerring

- , Fernando S. Goes

- , Scott D. Gordon

- , Hans J. Grabe

- , Jakob Grove

- , Jan Haavik

- , Andrew C. Heath

- , Matthew H. Iveson

- , James A. Knowles

- , Jaakko Kaprio

- , James L. Kennedy

- , James A. Knowles

- , Henry R. Kranzler

- , Kristi Krebs

- , Mikael Landén

- , Kelli Lehto

- , Daniel F. Levey

- , Douglas F. Levinson

- , Cathryn M. Lewis

- , Glyn Lewis

- , Qingqin S. Li

- , Penelope A. Lind

- , Jurjen J. Luykx

- , Hermine HM Maes

- , Eduard Maron

- , Nicholas G. Martin

- , Andrew M. McIntosh

- , Sarah E. Medland

- , Lili Milani

- , Brittany L. Mitchell

- , Woojae Myung

- , Michael C. O’Donovan

- , Vanessa K. Ota

- , Michael J. Owen

- , Teemu Palviainen

- , Pedro M. Pan

- , Peristera Paschou

- , Roseann E. Peterson

- , Giorgio Pistis

- , Danielle Posthuma

- , James B. Potash

- , Martin Preisig

- , Andreas Reif

- , John P. Rice

- , Marcella Rietschel

- , Brien P. Riley

- , Giovanni A. Salum

- , Marcos L. Santoro

- , Eva C. Schulte

- , Alessandro Serretti

- , Andrey A. Shabalin

- , Jordan W. Smoller

- , Lea Sirignano

- , Dan J. Stein

- , Murray B. Stein

- , Fabian Streit

- , Jana Strohmaier

- , Martin Tesli

- , Jackson G. Thorp

- , Henning Tiemeier

- , Sandra Van der Auwera

- , Bradley T. Webb

- , Stephanie H. Witt

- & Naomi R. Wray

Nicotine Dependence GenOmics (iNDiGO) Consortium

- Timothy B. Baker

- , Dorret I. Boomsma

- , Danielle M. Dick

- , Dmitriy Drichel

- , Lindsay A. Farrer

- , Nathan C. Gaddis

- , Dana B. Hancock

- , John E. Hokanson

- , Jouke-Jan Hottenga

- , Eric O. Johnson

- , Jaakko Kaprio

- , Henry R. Kranzler

- , Pamela A. Madden

- , Mary L. Marazita

- , Jesse A. Marks

- , Daniel W. McNeil

- , Michael Nothnagel

- , Teemu Palviainen

- , Bryan C. Quach

- , Marcella Rietschel

- , Nancy L. Saccone

- , Nancy YA Sey

- , Richard Sherva

- , Scott Vrieze

- , Alex Waldrop

- , Georg Winterer

- , Kendra Young

- & Stephanie Zellers

Obsessive-Compulsive Disorder and Tourette Syndrome Working Group of the Psychiatric Genomics Consortium

- Silvia Alemany

- , Helga Ask

- , Cathy L. Barr

- , Csaba Barta

- , Katharina Bey

- , Oscar J. Bienvenu

- , Julia Boberg

- , Rosa Bosch

- , Christie L. Burton

- , Jonas Bybjerg-Grauholm

- , Enda M. Byrne

- , Adrian Camarena

- , Beatriz Camarena

- , Miquel Casas

- , Danielle C. Cath

- , Edwin H. Cook

- , Jennifer Crosbie

- , James J. Crowley

- , Eske M. Derks

- , Andrea Dietrich

- , Katharina Domschke

- , Peter Falkai

- , Thomas V. Fernandez

- , Daniel A. Geller

- , Zachary F. Gerring

- , Fernando S. Goes

- , Hans J. Grabe

- , Marco A. Grados

- , Erica L. Greenberg

- , Jakob Grove

- , Edna Grünblatt

- , Jan Haavik

- , Kristen Hagen

- , Gregory L. Hanna

- , Bjarne Hansen

- , Gary A. Heiman

- , Pieter J. Hoekstra

- , David M. Hougaard

- , James A. Knowles

- , Jaakko Kaprio

- , Norbert Kathmann

- , Julia Klawohn

- , Gerd Kvale

- , Nuria Lanzagorta

- , Stephanie Le Hellard

- , Daniel F. Levey

- , Christine Lochner

- , Jurjen J. Luykx

- , Fabio Macciardi

- , Brion S. Maher

- , Irene A. Malaty

- , David Mataix-Cols

- , Carol A. Mathews

- , Manuel Mattheisen

- , Nicole CR McLaughlin

- , Euripedes C. Miguel

- , Kirsten R. Müller-Vahl

- , Humberto Nicolini

- , Erika L. Nurmi

- , Michael S. Okun

- , Peristera Paschou

- , Danielle Posthuma

- , Raquel Rabionet

- , Alfredo Ramirez

- , Josep Antoni Ramos-Quiroga

- , Marta Ribasés

- , Renata Rizzo

- , Cristina Rodriguez-Fontenla

- , Paul Sandor

- , Jeremiah M. Scharf

- , Elles de Schipper

- , Harvey S. Singer

- , María Soler Artigas

- , Dan J. Stein

- , Murray B. Stein

- , Eric A. Storch

- , Nora I. Strom

- , Jackson G. Thorp

- , Jeremy Veenstra-VanderWeele

- , Michael Wagner

- , Christopher P. Walker

- , Dongmei Yu

- & Gwyneth Zai

Post-Traumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium

- Søren B. Andersen

- , Helga Ask

- , Sintia I. Belangero

- , Laura J. Bierut

- , Gerome Breen

- , Rodrigo A. Bressan

- , Sandra A. Brown

- , Carolina M. Carvalho

- , Chia-Yen Chen

- , Jonathan RI Coleman

- , Lucia Colodro-Conde

- , Nikolaos P. Daskalakis

- , Jürgen Deckert

- , Seth G. Disner

- , Anna R. Docherty

- , Norah C. Feeny

- , Ary Gadelha

- , Scott D. Gordon

- , Lana R. Grasser

- , Magali Haas

- , Kelly M. Harrington

- , Victor M. Hesselbrock

- , Mohammed H. Ibrahim

- , Seyma Katrinli

- , James L. Kennedy

- , Nathan A. Kimbrel

- , Karestan C. Koenen

- , Kristi Krebs

- , Kelli Lehto

- , Daniel F. Levey

- , Jurjen J. Luykx

- , Adam X. Maihofer

- , Jessica L. Maples-Keller

- , Sarah E. Medland

- , Jacquelyn L. Meyers

- , Janitza L. Montalvo-Ortiz

- , Charles P. Morris

- , Caroline M. Nievergelt

- , Vanessa K. Ota

- , Pedro M. Pan

- , Robert H. Pietrzak

- , Renato Polimanti

- , Richard J. Rosenblum

- , Barbara O. Rothbaum

- , Bart PF Rutten

- , Nancy L. Saccone

- , Giovanni A. Salum

- , Marcos L. Santoro

- , Soraya Seedat

- , Andrey A. Shabalin

- , Alicia K. Smith

- , Dan J. Stein

- , Murray B. Stein

- , Ralph E. Tarter

- , Clement C. Zai

- & Gwyneth Zai

Schizophrenia Working Group of the Psychiatric Genomics Consortium

- Muhammad Ayub

- , Nicholas Bass

- , Bernhard T. Baune

- , Sintia I. Belangero

- , Tim B. Bigdeli

- , Rodrigo A. Bressan

- , Dominique Campion

- , Boris Chaumette

- , Sven Cichon

- , David Cohen

- , Angel Consoli

- , Marta Di Forti

- , Johan G. Eriksson

- , Olga YU Fedorenko

- , Josef Frank

- , Robert Freedman

- , Janice M. Fullerton

- , Ary Gadelha

- , Raul R. Gainetdinov

- , Marianna Giannitelli

- , Ina Giegling

- , Stephen J. Glatt

- , Stephanie Godard

- , Jakob Grove

- , Olivier Guillin

- , Annette M. Hartmann

- , Svetlana A. Ivanova

- , Katherine G. Jonas

- , James A. Knowles

- , Kenneth S. Kendler

- , James L. Kennedy

- , Alexander O. Kibitov

- , James A. Knowles

- , Bettina Konte

- , Claudine Laurent-Levinson

- , Phil H. Lee

- , Anastasia Levchenko

- , Douglas F. Levinson

- , Qingqin S. Li

- , Jurjen J. Luykx

- , Brion S. Maher

- , Morten Mattingsdal

- , Andrew McQuillin

- , Sandra M. Meier

- , Robin Murray

- , Merete Nordentoft

- , Cristiano Noto

- , Michael C. O’Donovan

- , Roel A. Ophoff

- , Vanessa K. Ota

- , Michael J. Owen

- , Danielle Posthuma

- , Diego Quattrone

- , Marcella Rietschel

- , Brien P. Riley

- , Dan Rujescu

- , Bart PF Rutten

- , Safaa Saker-Delye

- , Marcos L. Santoro

- , SIbylle G. Schwab

- , Alessandro Serretti

- , Jordan W. Smoller

- , Fabian Streit

- , Jana Strohmaier

- , Florence Thibaut

- , Marquis P. Vawter

- , James TR Walters

- , Bradley T. Webb

- , Thomas Werge

- , Dieter B. Wildenauer

- , Stephanie H. Witt

- & Clement C. Zai

Substance Use Disorders Working Group of the Psychiatric Genomics Consortium

- Daniel E. Adkins

- , Arpana Agrawal

- , Silvia Alemany

- , David AA Baranger

- , Anthony J. Batzler

- , Joanna M. Biernacka

- , Laura J. Bierut

- , Tim B. Bigdeli

- , Jason D. Boardman

- , Joseph M. Boden

- , Ryan Bogdan

- , Sandra A. Brown

- , Karhleen K. Bucholz

- , Doo-Sup Choi

- , Sarah MC Colbert

- , Brandon J. Coombes

- , William E. Copeland

- , Joseph D. Deak

- , Marta Di Forti

- , Nancy Diazgranados

- , Danielle M. Dick

- , Anna R. Docherty

- , Howard J. Edenberg

- , Alexis C. Edwards

- , Jerome C. Foo

- , Josef Frank

- , Raul R. Gainetdinov

- , Joel Gelernter

- , Ina Giegling

- , Alison M. Goate

- , David Goldman

- , Laura M. Hack

- , Dana B. Hancock

- , Kathleen Mullan Harris

- , Annette M. Hartmann

- , Sarah M. Hartz

- , Alexander S. Hatoum

- , Caroline Hayward

- , Andrew C. Heath

- , John K. Hewitt

- , Per Hoffmann

- , Christian J. Hopfer

- , Daniel P. Howrigan

- , Emma C. Johnson

- , Eric O. Johnson

- , Jaakko Kaprio

- , Victor M. Karpyak

- , Martin A. Kennedy

- , Alexander O. Kibitov

- , Bettina Konte

- , Henry R. Kranzler

- , Kenneth S. Krauter

- , Evgeny M. Krupitsky

- , Samuel Kuperman

- , Jari Lahti

- , Marius Lahti-Pulkkinen

- , Dongbing Lai

- , Anastasia Levchenko

- , Daniel F. Levey

- , Penelope A. Lind

- , Jurjen J. Luykx

- , Pamela A. Madden

- , Hermine HM Maes

- , Brion S. Maher

- , Nicholas G. Martin

- , Sarah E. Medland

- , Jacquelyn L. Meyers

- , Alex P. Miller

- , Janitza L. Montalvo-Ortiz

- , John I. Nurnberger Jr

- , Abraham A. Palmer

- , Rohn H. Palmer

- , Teemu Palviainen

- , John F. Pearson

- , Roseann E. Peterson

- , Renato Polimanti

- , Bernice Porjesz

- , Ulrich W. Preuss

- , James J. Prisciandaro

- , Diego Quattrone

- , Josep Antoni Ramos-Quiroga

- , Marta Ribasés

- , John P. Rice

- , Brien P. Riley

- , Daniel M. Rosenblum

- , Richard J. Rosenblum

- , Dan Rujescu

- , Nancy L. Saccone

- , Sandra S. Sanchez-Roige

- , Norbert Scherbaum

- , Andrey A. Shabalin

- , Richard Sherva

- , María Soler Artigas

- , Fabian Streit

- , Ralph E. Tarter

- , Michael Vanyukov

- , Tamara L. Wall

- , Raymond K. Walters

- , Bradley T. Webb

- , Robbee Wedow

- , Stanley H. Weiss

- , Leah Wetherill

- , Stephanie H. Witt

- , Norbert Wodarz

- , Stephanie Zellers

- , Haitao Zhang

- , Hongyu Zhao

- , Hang Zhou

- , Peter Zill

- & Lea Zillich

Contributions

Note that the lists of working group members are organized alphabetically by last name. We also highlight that these lists are not exhaustive with respect to the members of the working groups; rather, they reflect individual members of the working groups who approved the contents of this Article. A.D.G., J.W., W.J.P. and O.F. conducted the primary analyses presented in the paper for GenomicSEM, LAVA, CC-GWAS and MiXeR, respectively. L.K.B., Q.G., M.P.M. and J.W. ran the functional follow-up analyses. O.F. and R.E.P. ran and provided feedback on cross-ancestry and diverse ancestry genome-wide analyses. B.J.C., A.B., V.P. and J.M.B. conducted the phenome-wide association study analyses. C.d.L., E.M.T.-D. and P.H.L. provided additional feedback on the analyses and included data. A.D.G., J.W., W.J.P., O.F., K.S.K. and J.W.S. wrote the initial draft of the manuscript. K.S.K. and J.W.S. jointly supervised the research. All of the named authors provided iterative feedback on the manuscript, including O.A.A., V.A., A.D.B., G.B., N.C., D.D., H.J.E., S.V.F., B.F., M.J.G., J.G., A.S.H., J.M.H., E.C.J., T.T.M., M.M., K.S.M., B.M.N., C.M.N., J.I.N., K.S.O., E.B.R., S.S.S.-R., S.L.S., J.M.S., H.S., K.S., M.B.S., N.I.S., L.M.T., B.V., I.D.W., G.B.W., N.R.W. and D.Y. All of the collaborators within the listed working group banners approved the contents of the manuscript.

Corresponding authors

Correspondence to Andrew D. Grotzinger or Jordan W. Smoller.

Ethics declarations

Competing interests

J.W.S. is a member of the scientific advisory board of Sensorium Therapeutics (with stock options) and has received grant support from Biogen. K.G.J. is a consultant for Allia Health. A.D.B. has received a speaker fee from Lundbeck. In the past year, S.V.F. received income, potential income, travel expenses continuing education support and/or research support from Aardvark, Aardwolf, AIMH, Akili, Atentiv, Axsome, Genomind, Ironshore, Johnson & Johnson/Kenvue, Kanjo, KemPharm/Corium, Noven, Otsuka, Sky Therapeutics, Sandoz, Supernus, Tris and Vallon. With his institution, S.V.F. has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. S.V.F. also receives royalties from books published by Guilford Press (Straight Talk about Your Child’s Mental Health), Oxford University Press (Schizophrenia: The Facts) and Elsevier (ADHD: Non-Pharmacologic Interventions) and is program director of www.ADHDEvidence.org and www.ADHDinAdults.com. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Univariate MiXeR Results.

Power curves estimating the sample size of a GWAS study are needed to saturate the yield of genome-wide significant loci. The legend shows the current effective sample size of today’s GWAS, followed by the projected effective sample size needed for the GWAS yield to saturate.

Extended Data Fig. 2 External trait genetic correlations: Comparison across psychiatric factors.

Bar graphs depict genetic correlations with the 31 complex traits for the five psychiatric factors from the correlated factors model and the second-order, p-factor from the hierarchical model. Panels are separated by the different groupings of traits (e.g., cognitive; socioeconomic). Bars depicted with a dashed outline were significant at a Bonferroni-corrected threshold for the QTrait heterogeneity metric that flags traits whose patterns of genetic correlations from LDSC do not conform to those implied by the factor model. Error bars are +/− 1.96 SE that are centred around the point estimate of the genetic correlations. Bar depicted with a * reflect values that were significant at a Bonferroni corrected threshold for multiple comparisons, that were also not significant at this same Bonferroni corrected threshold for QTrait. This is with exception of the p-factor, which is depicted with a ‘*’ even if it is significant for the QTrait, as long as that same trait was significantly correlated with the majority (at least three) of the five other factors. The two-sided P-values used to evaluate significance were derived from the Z-statistics, calculated as the point estimate of the genetic correlation divided by its standard error. Correlations are ordered according to the point estimate for the p-factor. The implied sample size for the psychiatric factors was: Compulsive (\(\hat{n}\) = 54,100); Schizophrenia/Bipolar (\(\hat{n}\) = 127,202); Neurodevelopmental (\(\hat{n}\)  = 84,760); Internalizing (\(\hat{n}\) = 1,637,337); Substance Use (\(\hat{n}\) = 313,395); p-factor (\(\hat{n}\) = 2,168,621). See Suppl. Table 12 for sample sizes for the external traits and Suppl. Table 13 for exact P-values.

Extended Data Fig. 3 External trait genetic correlations: Comparison within factors.

Bar graphs depict genetic correlations with the 31 complex traits that are ordered by magnitude within each factor for the five psychiatric factors from the correlated factors model and the second-order, p-factor from the hierarchical model. Bars depicted with a dashed outline for the QTrait heterogeneity metric. Bar depicted with a * reflect values that were significant at a Bonferroni corrected threshold for multiple comparisons, that were also not significant at this same Bonferroni corrected threshold for QTrait. This is with exception of the p-factor, which is depicted with a ‘*’ even if it is significant for the QTrait, as long as that same trait was significantly correlated with the majority (at least three) of the five other factors. The two-sided P-values used to evaluate significance were derived from the Z-statistics, calculated as the point estimate of the genetic correlation divided by its standard error. Error bars are +/− 1.96 SE that are centred around the point estimate of the genetic correlations. The implied sample size for the psychiatric factors was: Compulsive (\(\hat{n}\) = 54,100); Schizophrenia/Bipolar (\(\hat{n}\) = 127,202); Neurodevelopmental (\(\hat{n}\)  = 84,760); Internalizing (\(\hat{n}\) = 1,637,337); Substance Use (\(\hat{n}\) = 313,395); p-factor (\(\hat{n}\) = 2,168,621). See Suppl. Table 12 for sample sizes for the external traits and Suppl. Table 13 for exact P-values.

Extended Data Fig. 4 Stratified Genomic SEM results.

Bar graph depicts the enrichment results for different brain cell types, protein-truncating variant intolerant (PI) genes, and the intersection across PI genes and brain cell types. Results are shown only for the SB, Internalizing, and p-factor due to the limited signal for the other factors. Enrichment for height is depicted in purple to benchmark results and evaluate specificity in signal for the psychiatric factors relative to another human complex trait. Error bars are +/- 1.96 SE that are centred around the enrichment point estimate. Enrichment estimates that were significant at a strict Bonferroni corrected threshold for multiple comparisons are shown with a *. The one-sided P-values used to evaluate significance were derived from the Z-statistics, calculated as the enrichment point estimate divided by its standard error. Exact P-values are reported in Suppl. Table 50. The implied sample size for the psychiatric factors was: Compulsive (\(\hat{n}\) = 54,100); Schizophrenia/Bipolar (\(\hat{n}\) = 127,202); Neurodevelopmental (\(\hat{n}\)  = 84,760); Internalizing (\(\hat{n}\) = 1,637,337); Substance Use (\(\hat{n}\) = 313,395); p-factor (\(\hat{n}\) = 2,168,621).

Extended Data Table 1 Summary of Psychiatric Disorder Datasets

Supplementary information

Supplementary Information

Supplementary Note 1, including Supplementary Figs. 1–28.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–50.

Peer Review File

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Grotzinger, A.D., Werme, J., Peyrot, W.J. et al. Mapping the genetic landscape across 14 psychiatric disorders. Nature 649, 406–415 (2026). https://doi.org/10.1038/s41586-025-09820-3

- Received: 05 December 2024

- Accepted: 28 October 2025

- Published: 10 December 2025

- Version of record: 10 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09820-3

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Adenosine signalling drives antidepressant actions of ketamine and ECT

Chenyu Yue,

Na Wang,

Haojiang Zhai,

Zhengwei Yuan,

Yuting Cui,

Jing Quan,

Yu Zhou,

Xiaofeng Fan,

Hongshuang Wang,

Zhaofa Wu,

Huijie Mi,

Wooping Ge,

Yulong Li,

Xiaohui Wang &

…

Minmin Luo

Nature volume 649, pages 423–431 (2026)

Abstract

Ketamine and electroconvulsive therapy (ECT) achieve rapid remission in treatment-resistant depression. However, their mechanisms of action—the understanding of which is essential for refining therapeutic precision—remain unclear1,2,3. Here, using mouse models, we identify adenosine signalling as a central pathway that underlies the antidepressant effects of these interventions. Results from genetically encoded adenosine sensor experiments and real-time optical recordings reveal that both therapies induce strong adenosine surges in key mood-regulatory regions, including the medial prefrontal cortex and the hippocampus. Genetic or pharmacological disruption of A_1 and A2A adenosine receptors abolishes their therapeutic effects, which establishes the essential role of adenosine signalling in antidepressant efficacy. Notably, adenosine signalling specifically in the medial prefrontal cortex drives antidepressant actions. Ketamine increases adenosine by modulating cellular metabolism to increase intracellular adenosine levels without causing neuronal hyperactivity. Leveraging this mechanism, we develop ketamine derivatives that enhance adenosine signalling and exhibit improved antidepressant efficacy with reduced side effects at therapeutic doses. Furthermore, acute intermittent hypoxia, a non-pharmacological intervention involving controlled reductions in oxygen levels, increases brain adenosine levels and produces antidepressant effects, paralleling the actions of ketamine and ECT. Our findings establish adenosine as a pivotal mediator of rapid-acting antidepressants and a tractable target for scalable, noninvasive therapeutics in major depressive disorder.

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Main

A single subanaesthetic dose of ketamine induces rapid, robust and enduring antidepressant effects that often manifest within hours and persist for days1,2. This efficacy has galvanized efforts to uncover the molecular and cellular mechanisms that underlie its therapeutic action, with the goal of developing safer and more effective treatments4,5,6,7,8,9,10,11. Originally identified as an NMDA receptor (NMDAR) antagonist[12], ketamine is now understood to influence a network of neuromodulators.Ketamine alters neuronal activity and promotes synaptic plasticity in brain regions implicated in major depressive disorder (MDD), such as the medial prefrontal cortex (mPFC) and the hippocampus9,13,14. Electroconvulsive therapy (ECT), another rapid-acting antidepressant intervention, has also been shown to engage multiple neuromodulators in the brain. The underlying mechanisms of ECT also remain incompletely understood3,15.

To identify the neuromodulator that underlies these therapies, the following key criteria should be met: both ketamine and ECT should change neuromodulator levels in the brain, and activation or inhibition of the associated signalling pathways should either replicate or abolish antidepressant effects. Moreover, insights into these mechanisms should guide the development of new therapeutic strategies. Although numerous candidates have been proposed as the key neuromodulator9,13,14, many do not fully satisfy these stringent criteria, which underscores the need for continued exploration to identify a central player.

Emerging evidence suggests that adenosine, a purine nucleoside with high-affinity A_1 (encoded by Adora1) and A2A (encoded by Adora2a) receptors16,17, is a promising but underexplored candidate. Adenosine regulates neuronal excitability, synaptic plasticity and inflammatory responses[16], all of which are implicated in MDD[18]. Dysregulated adenosine metabolism has been linked to depressive symptoms[19], and interventions that enhance adenosine signalling, such as sleep deprivation20,21 and ketogenic diets[22], have shown antidepressant-like effects in humans and animal models. Despite the therapeutic potential of ketamine and ECT, studying the role of adenosine in these treatments has been technically challenging owing to the transient and dynamic nature of its signalling and its brain-region-specific and receptor-specific actions16,23,24. Notably, although adenosine generally exhibits neuroprotective and antidepressant properties, chronic A2A receptor activation in certain brain regions may counteract these benefits19,25, which highlights the complexity of its spatiotemporal regulation.

Here we use genetically encoded adenosine sensors to reveal rapid adenosine surges in mood-regulatory circuits after ketamine administration or ECT. Notably, systemic or specific depletion or blockade of A_1 and A2A receptors in the mPFC abolishes the antidepressant efficacy of these therapies. Moreover, the activation of these two receptors recapitulates therapeutic effects, thereby demonstrating the pivotal role of adenosine. Building on these insights, we identify ketamine derivatives that potentiate adenosine signalling to produce enhanced antidepressant efficacy and reduced side effects. We further demonstrate that acute intermittent hypoxia (aIH), a noninvasive method that involves controlled oxygen reduction, is a potent adenosine-dependent antidepressant strategy. These findings establish adenosine signalling as the central mechanism that unifies the actions of ketamine and ECT and open new therapeutic avenues for MDD.

Ketamine triggers brain adenosine surges

We first investigated the effects of ketamine on extracellular adenosine dynamics. To that end, we used the GPCR-based adenosine probe GRABAdo1.0 with multichannel fibre photometry to enable real-time monitoring of adenosine levels in the mouse brain (Extended Data Fig. 1a). We first validated the fidelity of this sensor in vivo. Acute hypoxia, a potent physiological stimulus for adenosine release[26], induced a rapid increase in the GRABAdo1.0 signal. By contrast, a functionally inactive mutant sensor (GRABAdo1.0-mut) did not produce any effects. This result confirms the specificity of GRABAdo1.0 for detecting changes in extracellular adenosine levels (Extended Data Fig. 1b).

Systemic administration of a subanaesthetic, antidepressant dose of ketamine (10 mg kg–1, intraperitoneal (i.p.) injection) caused a rapid and sustained increase in extracellular adenosine in the mPFC—an area comprising the prelimbic (PrL), infralimbic (IL) and anterior cingulate (ACC) cortices—and in the hippocampus (HPC), but not in the nucleus accumbens (NAc) (Fig. 1a,b and Extended Data Fig. 1c). The adenosine surge featured peak amplitudes of about 15% change in fluorescence (ΔF/F), onset times of 100–150 s after injection, peak times of about 500 s and decay time constants of 500–600 s after the peak (Extended Data Fig. 1d–h). Two-photon imaging confirmed that this release was spatially diffuse (Extended Data Fig. 1i–k). Notably, ketamine induced a similar increase in adenosine levels in mice subjected to chronic restraint stress (CRS) (a mouse model of depression). These results demonstrate that this neurochemical response is reliable across physiological and pathological states (Fig. 1c,d and Extended Data Fig. 1l).

Fig. 1: Ketamine induces adenosine surges in the brain.

a, Representative images of GRABAdo1.0 expression (green) in the ACC, the HPC and the PrL and IL of the mPFC. Red dashed lines indicate tracks of the optical fibre. Scale bars, 1 mm. b, Time course of extracellular adenosine levels in the mPFC (red; n = 12 mice), ACC (green; n = 8 mice) and HPC (blue; n = 8 mice) after ketamine administration (10 mg kg–1, i.p. injection; arrow). c,d, Sucrose preference in mice subjected to CRS versus naive mice (c) and the corresponding time course of extracellular adenosine levels in the mPFC after ketamine administration (10 mg kg−1, i.p. injection) (d). e–g, Dose–response effects of ketamine on extracellular adenosine levels in the mPFC. e, Time course after ketamine injections (5, 10, 20, 30 or 50 mg kg−1, i.p. injection) and saline control. Image shows the recording site. Cg1, cingulate cortex 1, which is part of the ACC. Scale bar, 500 µm. f,g, Adenosine (Ado) peak levels (f) and area under the curve (AUC; normalized to saline) (g) after drug administration. Data are the mean ± s.e.m. (shading in b, d and e; error bars in c, f and g). Statistics: two-tailed unpaired t-tests (c,f,g). **P < 0.01, ***P < 0.001. See Supplementary Table 1 for detailed statistics.

Source data

The ketamine-induced adenosine increase in the mPFC was dose-dependent. A low dose (5 mg kg–1) produced a modest signal, whereas the response amplitude and duration increased substantially at 10 and 20 mg kg–1. Higher doses (30 and 50 mg kg–1) did not further augment the peak amplitude but prolonged the signal decay time (Fig. 1e–g and Extended Data Fig. 1m,n). This effect reflected endogenous adenosine release, as ketamine did not directly activate the GRABAdo1.0 sensor (Extended Data Fig. 1o,p). The kinetics of the ketamine response were distinct from those of acute hypoxia, inducing a signal with a smaller peak amplitude (around 35% for high ketamine doses compared with about 60% ΔF/F for hypoxia) but a markedly slower decay (>500 s compared with about 50 s) (Fig. 1e and Extended Data Fig. 1b). These results demonstrate that the modulation of adenosine by ketamine operates within the dynamic range of the sensor and aligns with its therapeutically relevant subanaesthetic doses.

We next investigated whether the adenosine surge is triggered by ketamine itself or by its metabolites (Extended Data Fig. 2a). Systemic administration of two primary metabolites, norketamine (NK) and (2R,6R)-HNK[10], at an equivalent dose (10 mg kg–1, i.p. injection) did not trigger a detectable adenosine response in the mPFC (Extended Data Fig. 2b,c). To corroborate this finding, we pharmacologically inhibited key enzymes responsible for ketamine metabolism in vivo[27]. Pretreatment with the CYP3A4 inhibitors ketoconazole or ritonavir significantly potentiated the amplitude and duration of the ketamine-induced adenosine signal (Extended Data Fig. 2d–g). By contrast, inhibition of CYP2B6 with ticlopidine had no effect (Extended Data Fig. 2h,i). Because the inhibitors alone did not alter baseline adenosine levels (Extended Data Fig. 2j–l), these results suggest that the parent ketamine molecule is directly responsible for triggering adenosine release, the magnitude of which is regulated by CYP3A4-mediated metabolism.

Adenosine drives the action of ketamine

To determine whether adenosine signalling is required for the antidepressant action of ketamine, we first confirmed its efficacy in wild-type (WT) mice subjected to CRS and then to forced swim tests (FSTs) or sucrose preference test (SPTs), which are behavioural assays for despair and anhedonia, respectively (Fig. 2a). Given the established roles of adenosine A_1 and A2A receptors in central adenosine signalling24,28,29, we evaluated ketamine in Adora1–/– and Adora2a–/– mice (Supplementary Fig. 1). The antidepressant effects of ketamine, assessed at both 1 h (acute) and 24 h (sustained) after administration, were abolished in both knockout mouse lines (Fig. 2b–e).

Fig. 2: Adenosine signalling drives the antidepressant action of ketamine.

a, Experimental timeline for assessing the antidepressant action of ketamine at 1 and 24 h in WT, Adora1–/– and Adora2a–/– mice subjected to CRS. b–e, FSTs (b,d) and SPTs (c,e) were performed after CRS in WT, Adora1–/– and Adora2a–/– mice 1 h (b,c) or 24 h (d,e) after ketamine administration (10 mg kg−1, i.p. injection). f–i, Optogenetic release of adenosine in the mPFC produces rapid antidepressant-like effects. f, Schematic of optogenetic astrocyte activation that leads to adenosine production via ectonucleotidase activity (for example, CD73). ER, endoplasmic reticulum. g, Schematic and representative image of optogenetic activation. Scale bar, 500 µm. h,i, Immobility times in FSTs (h) and sucrose preference in SPTs (i) were measured 1 h after optogenetic activation in mice expressing the light-sensitive opsin cOpn5 or a mCherry control in the mPFC and subjected to CRS. Data are the mean ± s.e.m. (error bars in b–e,h,i). Statistics: two-tailed unpaired t-tests (b–e,h,i). *P < 0.05, **P < 0.01, ***P < 0.001. See Supplementary Table 1 for detailed statistics. The schematics in a, f and g were created using BioRender (https://www.biorender.com).

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The loss of therapeutic action in the knockout mice was not attributable to motor confounds and occurred despite the preservation of ketamine-induced hyperlocomotion, which was comparable between WT and mutant mice (Extended Data Fig. 3a–c). These results therefore dissociate the antidepressant properties of the drug from its psychostimulant side effects. We also confirmed that the behavioural deficits in knockout mice resulted from receptor absence rather than a developmental impairment in adenosine release. That is, the ketamine-induced adenosine surge remained intact in both Adora1–/– and Adora2a–/– mice (Extended Data Fig. 3d).

Complementing these genetic data, acute pharmacological blockade in WT mice subjected to CRS revealed a pivotal role for both receptors (Extended Data Fig. 3e–i). Pretreatment with a selective A_1 receptor antagonist completely abolished the antidepressant-like effects of ketamine in FSTs, whereas A2A receptor blockade produced a reduced attenuation 1 h after ketamine administration (Extended Data Fig. 3f,h). This result confirms the necessity of adenosine receptor activity for reducing behavioural despair30,31. A similar dependency was observed for anhedonia, whereby A_1 and, to a lesser extent, A2A receptor antagonism prevented ketamine from reversing deficits in sucrose preference (Extended Data Fig. 3g,i). This requirement for adenosine receptor signalling was not specific to the CRS model, as inhibition of both receptors also blunted the efficacy of ketamine in a mouse model of depression induced by lipopolysaccharide (LPS)[32] (Extended Data Fig. 3j).

Collectively, these converging data—derived from complementary genetic and pharmacological approaches in both stress-induced and inflammation-induced depression models—establish that the efficacy of ketamine in reversing core depressive-like behaviours, including anhedonia and behavioural despair, depends on adenosine signalling.

mPFC adenosine signalling is crucial

We asked whether adenosine signalling is sufficient to induce antidepressant effects. Intracerebroventricular injection of adenosine or systemic delivery of selective agonists of the A_1 receptor (N^6-cyclohexyladenosine (CHA)) and the A2A receptor (CGS21680) replicated the immediate therapeutic effects of ketamine in mice subjected to CRS (Extended Data Fig. 4a–c). Notably, 24 h after administration, only CHA—but not CGS21680—retained efficacy in behavioural assays (Extended Data Fig. 4d,e). This lasting therapeutic action occurred despite the agonist being cleared from the brain by 24 h (Extended Data Fig. 4f), which indicated that transient A_1 receptor activation triggers a durable antidepressant state, whereas A2A receptor signalling contributes primarily to acute effects.

To test whether localized adenosine signalling is sufficient to produce an antidepressant effect, we focused on the mPFC, a key region implicated in both mood regulation and the therapeutic action of ketamine. Direct stereotaxic infusion of adenosine into the mPFC produced a robust antidepressant response (Extended Data Fig. 5a–c). To corroborate this finding with a more physiologically relevant approach, we used optogenetics to stimulate astrocytes expressing cOpn5, which triggers localized, adenosine release that is mediated by CD73 (encoded by Nt5e)[33] (Fig. 2f). This light-induced adenosine production, specifically in the mPFC, was also sufficient to alleviate depressive-like behaviours in WT mice, an effect that was absent in Nt5e–/– knockout mice (Fig. 2g–i and Extended Data Fig. 5d). The behavioural rescue was associated with changes in a key molecular pathway, as ketamine-induced upregulation of brain-derived neurotrophic factor (BDNF)—a canonical downstream effector34,35—was prevented in Adora1–/– and Adora2a–/– mice, in which these receptors are globally knocked out (Extended Data Fig. 5e and Supplementary Fig. 2a). In support of the circuit-specificity of this mechanism, neither local adenosine infusion nor optogenetic stimulation in the dorsal hippocampus produced any antidepressant actions (Extended Data Fig. 5f–j).

Having established that adenosine signalling in the mPFC is sufficient, we next investigated whether this circuit is also necessary for the therapeutic effects of systemic ketamine. To that end, we used the adeno-associated virus (AAV)-mediated CRISPR–Cas9 approach and delivered single-guide RNAs (sgRNAs) into the mPFC of adult mice to genetically deplete A_1 and A2A receptor expression (Extended Data Fig. 5k and Supplementary Fig. 1). This circuit-restricted knockout also abolished the antidepressant behavioural effects of systemic ketamine in both the FSTs and SPTs (Extended Data Fig. 5l,m). Together, these results establish that adenosine signalling in the mPFC is a critical node that mediates rapid antidepressant efficacy.

Metabolic basis for adenosine efflux

To investigate how ketamine increases extracellular adenosine concentrations in the brain, we first considered whether adenosine is generated through the hydrolysis of extracellular adenine nucleotides by CD73 (ref. [36]) (Extended Data Fig. 6a). Using GRABATP1.0, a sensor capable of detecting both ATP and ADP, we monitored purine dynamics in the mPFC. Ketamine administration did not increase extracellular ATP or ADP levels; instead, it caused a modest reduction (Extended Data Fig. 6b–d). Furthermore, genetic depletion of CD73 did not have an effect on ketamine-induced adenosine signals (Extended Data Fig. 6e–g and Supplementary Fig. 2b). These findings exclude extracellular nucleotide hydrolysis as the primary source of ketamine-induced increases in extracellular adenosine levels.

An alternative source of extracellular adenosine originates from the activity of the equilibrative nucleoside transporters ENT1 and ENT2 (ENT1/2), which regulate the balance between intracellular and extracellular adenosine levels23,36 (Fig. 3a). We proposed that blocking the activity of ENT1/2 would increase ketamine-induced adenosine signals if adenosine was produced outside cells, whereas the opposite would occur if adenosine was produced inside cells and then released through ENT1/2 activity. To test this hypothesis, we first inhibited ENT1/2 using intracranial administration of dipyridamole, an ENT1/2 inhibitor. This treatment significantly attenuated ketamine-induced adenosine signals (Fig. 3b,c), whereas dipyridamole alone did not cause rapid increases in extracellular adenosine levels (Extended Data Fig. 6h,i). These results suggest that ketamine disrupts the intracellular–extracellular adenosine equilibrium, probably by increasing intracellular adenosine and subsequently transporter-mediated efflux.

Fig. 3: Ketamine modulates cellular metabolism and promotes adenosine efflux via ENT1/2.

a, Schematic of intracellular adenosine generation and its efflux through ENT1/2. b,c, Inhibition of ENT1/2 blocks ketamine-induced adenosine release. b, Time course of extracellular adenosine in the mPFC after ketamine administration with or without pretreatment with the ENT1/2 inhibitor dipyridamole (1 µg, intracerebroventricular injection). c, Quantification of peak adenosine and AUC (normalized to vehicle). d, Ketamine decreases the intracellular ATP/ADP ratio in mPFC excitatory neurons. Left, representative image of PercevalHR expression in CaMKII+ neurons. Scale bar, 200 µm. Middle, time course of the ATP/ADP ratio after ketamine (10 mg kg−1, i.p. injection) or saline treatment. Right, corresponding AUC (normalized to saline). e,f, Intracellular ATP/ADP ratios in mPFC GABAergic neurons (e) and astrocytes (f). Panels show representative images, time courses and saline-normalized AUCs as in d. g, Experimental workflow for mitochondrial isolation and subsequent metabolic flux analyses in the mouse prefrontal cortex. h, PCA of the metabolomic profiles, illustrating separation of the treatment groups along the first two principal components. Ellipses represent 95% confidence intervals calculated using the multivariate t-distribution. Data are the mean ± s.e.m. (shading in b, d–f; error bars in c–f). Statistics: two-tailed paired t-test (c) or two-tailed unpaired t-tests (d–f). **P < 0.01, ***P < 0.001. See Supplementary Table 1 for detailed statistics. The schematics in a and g were created using BioRender (https://www.biorender.com).

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Intracellular adenosine accumulation is closely associated with shifts in cellular metabolic activity, particularly reductions in the ATP/ADP ratio23,37,38. To assess whether ketamine modulates ATP/ADP dynamics in neurons and glia, we used PercevalHR—a genetically encoded sensor of the intracellular ATP/ADP ratio—in the mPFC[37]. This method enabled us to track metabolic changes in excitatory neurons, inhibitory neurons and astrocytes (Fig. 3d–f). Ketamine administration rapidly decreased the ATP/ADP ratio across all cell types, with the largest reduction in excitatory CaMKII-expressing pyramidal neurons (Fig. 3d). GABAergic neurons showed an initial reduction followed by a rebound, whereas astrocytes exhibited a sustained decrease (Fig. 3e,f). Analysis of GRABAdo1.0 and PercevalHR temporal response profiles revealed that reduction in the intracellular ATP/ADP ratio preceded the extracellular adenosine surge (Extended Data Fig. 6p,q). This temporal sequence strongly supports the hypothesis that ketamine-induced metabolic alterations drive adenosine efflux, which then lead to the observed increase in extracellular adenosine levels.

Adenosine build-up in cells typically arises from increased cellular metabolic activity23,37,38, which is often triggered by excessive excitability in neurons, as seen during epileptic states. To determine whether ketamine induces neuronal hyperactivity, we monitored intracellular Ca2+ dynamics using the Ca2+ indicator GCaMP8s expressed in mPFC neurons. In pyramidal neurons, ketamine significantly reduced Ca2+ signalling at a high dose (20 mg kg–1) but not at the standard antidepressant dose (10 mg kg–1) (Extended Data Fig. 6j–l). Moreover, GABAergic neurons exhibited a decrease in Ca2+ activity at the dose of 10 mg kg−1 (Extended Data Fig. 6m–o). Thus, ketamine induces adenosine signals by altering cellular metabolism and promoting transporter-mediated efflux while avoiding neuronal hyperactivity.

To determine whether ketamine directly targets mitochondria to modulate cellular metabolism, we performed metabolic flux analyses on purified brain mitochondria. By incubating isolated mitochondria from the mPFC with [13C_3]pyruvate, we traced the entry and metabolism of this key substrate through the TCA cycle in the presence of ketamine (Fig. 3g and Extended Data Fig. 7a). At therapeutically relevant concentrations (≥2 µM)39,40, ketamine exposure caused an accumulation of [13C]pyruvate while dose-dependently suppressing the 13C enrichment of downstream TCA cycle intermediates, including fumarate, malate and aspartate (Extended Data Fig. 7b,c). The metabolic changes were reflected in a global dose-dependent shift in the mitochondrial metabolome, as shown by principal component analysis (PCA) (Fig. 3h). This inhibitory effect on pyruvate utilization was consistent with a decrease in the ATP/ADP ratio. This result provides a mechanistic link between the action of ketamine at the mitochondrion and the observed surge in extracellular adenosine. Crucially, as these metabolic alterations occur in an isolated organelle system that lacks NMDARs, our findings indicate that subanaesthetic ketamine may directly engage and reprogram mitochondrial function.

Ketamine-derived antidepressants

To identify new compounds with enhanced therapeutic profiles, we used a phenotypic drug discovery approach using adenosine dynamics in the mPFC as a functional biomarker. We synthesized and screened 31 ketamine-derived compounds by systematically modifying the chloro substituent (-Cl) on the aromatic ring, the methylamino group attached to the cyclohexanone ring and the sixth position of the cyclohexanone ring—a key site for hydroxylation during metabolism (Fig. 4a, Extended Data Fig. 8a and Supplementary Methods). By leveraging fibre photometry, we assessed the capacity of these compounds to enhance extracellular adenosine levels in the mPFC. Although most compounds produced weak or no adenosine responses, dechlorinated derivatives, particularly deschloroketamine (DCK) and deschloro-N-ethyl-ketamine (2C-DCK), induced significantly stronger and longer-lasting adenosine surges than ketamine at the same doses of 5 and 10 mg kg−1 (Fig. 4b–d and Extended Data Fig. 8b), indicating their potential as improved therapeutic agents. Further dose mapping revealed that even at a low dose of 2 mg kg−1, DCK significantly increased extracellular adenosine levels (Fig. 4e–g). This result highlights the strong adenosine-modulating properties of DCK and its potential efficacy at lower doses.

Fig. 4: Adenosine-based screening identifies ketamine analogues with potent antidepressant-like effects.

a, Left, schematic of the ketamine analogue synthesis strategy. Right, scatter plot of peak adenosine amplitude compared with the AUC from an in vivo screen of synthesized compounds (10 mg kg−1, i.p. injection), normalized to ketamine responses. b, Time course of extracellular adenosine levels in the mPFC after administration of DCK (n = 7 mice), 2C-DCK (n = 8 mice), 3′-Cl-ketamine (n = 6 mice) or ketamine (n = 13 mice; 10 mg kg–1, i.p. injection; arrow). c,d, Comparison of peak adenosine levels (c) and AUC (d; normalized to saline) for DCK versus ketamine at an equivalent dose. e–g, Dose–response of DCK on adenosine release in the mPFC. e, Time course following injections of DCK (2, 5 and 10 mg kg−1, i.p. injection) or saline. f, Peak adenosine levels. g, AUC, normalized to saline. h,i, Rapid antidepressant-like effects of lead analogues. Immobility time in FSTs (h) and sucrose preference in SPTs (i) were measured in mice subjected to CRS 1 h after i.p. administration of ketamine, DCK, 2C-DCK or 3′-Cl-ketamine (3′-Cl-K). Data are the mean ± s.e.m. (shading in b and e; error bars in c, d and f–i). Box plots (c,d) show the median (centre line), first and third quartiles (box bounds), and 1.5× the interquartile range (whiskers). Statistics: two-tailed unpaired t-tests (c,d,f–i). *P < 0.05, **P < 0.01, ***P < 0.001. In h, significance is relative to the saline control. See Supplementary Table 1 for detailed statistics.

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We investigated whether the strong adenosine signals induced by DCK and 2C-DCK translate to antidepressant efficacy. In FSTs of mice subjected to CRS, ketamine at 10 mg kg−1 significantly decreased immobility time, whereas 5 mg kg−1 showed marginal effects, and lower doses were ineffective. By contrast, DCK displayed substantial efficacy at substantially lower doses: 2 mg kg−1 significantly alleviated depressive behaviours, and 5 mg kg−1 produced even greater improvements (Fig. 4h). Similarly, in SPTs, 2 mg kg−1 DCK demonstrated antidepressant effects equivalent to 10 mg kg−1 ketamine (Fig. 4i). These results highlight the enhanced potency of DCK compared with ketamine. In parallel, 2C-DCK, which induced similarly strong adenosine responses in the mPFC, exhibited significant antidepressant effects at 5 mg kg−1, whereas 3′-Cl-ketamine, which produced negligible adenosine responses, did not lead to improvements in depressive phenotypes at 10 mg kg−1 (Fig. 4h). These findings confirm that adenosine dynamics serve as a predictive biomarker for identifying ketamine-derived compounds with antidepressant efficacy.

We also used hyperlocomotion to evaluate the dissociative side effects of DCK. At a dose of 2 mg kg−1, which effectively alleviated depressive-like behaviours, DCK induced only mild locomotor effects, which was in contrast to the significant hyperlocomotion observed with 10 mg kg−1 ketamine (Extended Data Fig. 8c,d). Higher doses of DCK caused more conspicuous motor activity, but the ability to achieve antidepressant effects at lower doses with minimal side effects underscores its favourable therapeutic window.

To determine whether NMDAR antagonism is the primary driver of adenosine release, we systematically compared the in vivo adenosine-releasing capacity of ketamine and six derivatives with their respective NMDAR inhibitory potencies (IC_50) and pharmacokinetics in the brain (Extended Data Fig. 9a–h). Our findings revealed a dissociation between these two properties. Notably, after integrating ex vivo potency with in vivo pharmacokinetics to estimate the effective NMDAR target engagement for each compound, we did not find a correlation between the degree of NMDAR blockade and the amplitude of the adenosine surge (Extended Data Fig. 9i). This was exemplified by compounds such as 3′-Cl-ketamine, which blocked NMDARs but did not trigger adenosine release, and 3C-DCK, which produced a much stronger release despite having comparable NMDAR affinity (Extended Data Fig. 9a,d,f). This dissociation between NMDAR antagonism and adenosine release, combined with our evidence that ketamine directly modulates mitochondrial metabolism (Extended Data Fig. 7), suggests that a non-NMDAR mechanism mediates the adenosine surge.

Adenosine mediates ECT and aIH action

Although ketamine represents a pharmacological intervention for rapid antidepressant effects, ECT offers a non-pharmacological approach with similarly rapid efficacy3,41. ECT induces epileptic and convulsive behaviours, characterized by neuronal hyperactivity, often linked to extracellular adenosine release as a protective mechanism against overexcitation. However, real-time adenosine dynamics during ECT remain unexplored. We monitored extracellular adenosine in the mPFC of anaesthetized mice undergoing ECT-induced seizures. ECT induced an increase in extracellular adenosine levels in the mPFC, comparable in magnitude to those produced with a 10 mg kg−1 intraperitoneal injection of ketamine, but with a faster onset and decay (Fig. 5a,b). An ECT regimen significantly alleviated depressive-like behaviours in mice subjected to CRS, as assessed by FSTs and SPTs, effects that were abolished in Adora1–/– and Adora2a–/– mice (Fig. 5c–e). These results highlight the essential role of adenosine signalling in ECT.

Fig. 5: Adenosine signalling is essential for the antidepressant effects of ECT.

a, Adenosine dynamics following ECT. Left, experimental setup for ECT in mice, showing ear-clamp electrodes for stimulation with concurrent GRABAdo1.0 monitoring. Right, extracellular adenosine time course in the mPFC after a single ECT cycle, ketamine injection (10 mg kg−1, i.p. injection; replotted from Fig. 1e for comparison purpose) or sham ECT. Arrow indicates ECT or drug administration. b, Quantification of adenosine peak levels, AUC (normalized to sham ECT) and time to peak. c–e, Experimental paradigm for assessing ECT antidepressant efficacy (c), FST immobility time (d) and sucrose preference (e) of WT, Adora1–/– and Adora2a–/– mice subjected to CRS. Data are the mean ± s.e.m. (shading in a; error bars in b, d and e). Statistics: two-tailed unpaired t-tests (b,d,e). **P < 0.01, ***P < 0.001. See Supplementary Table 1 for detailed statistics. The schematics in a and c were created using BioRender (https://www.biorender.com).

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The connection between adenosine signalling and the antidepressant effects of ketamine and ECT points to the potential of non-pharmacological strategies such as aIH, which has demonstrated neuroprotective and anxiolytic benefits in both preclinical and clinical settings42,43. To replicate the adenosine surges observed with ketamine and ECT, we developed a controlled aIH protocol consisting of five 5-min exposures to 9% hypoxia, interspersed with 5-min normoxic intervals (21% O_2), administered once daily for three consecutive days. Fibre photometry revealed that aIH generated a rapid and significant increase in extracellular adenosine levels in the mPFC (Extended Data Fig. 10a,b). Behavioural assays demonstrated that this 3-day aIH regimen alleviated depression-like behaviours in mice subjected to CRS. These mice exhibited reduced immobility in FSTs and increased sucrose preference in SPTs, effects that were not seen in mice in which A_1 or A2A receptors were genetically disrupted (Extended Data Fig. 10c–e). Notably, aIH treatment did not impair motor function or exercise capacity, which provides support for the safety of this strategy (Extended Data Fig. 10f). These findings indicate that aIH is a promising non-pharmacological therapy for depression, leveraging adenosine signalling to deliver rapid antidepressant effects with minimal side effects.

Discussion

Our study establishes adenosine signalling as a convergent mechanism for rapid-acting antidepressants that meets three key criteria: elevation in mood-regulatory circuits by ketamine and ECT; the necessity of A_1 and A2A receptor activity for efficacy; and direct translatability to therapeutic design. Real-time monitoring revealed that these interventions induced adenosine surges in the brain (Figs. 1 and 5). Notably, this pathway is not merely correlational; receptor blockade abolished antidepressant effects, whereas receptor activation led to comparable responses (Figs. 2 and 5). Capitalizing on this mechanism, we engineered adenosine-enhancing ketamine derivatives that retained efficacy while attenuating psychotomimetic side effects (Fig. 4). We also demonstrated that aIH—a noninvasive strategy[42]—recapitulates antidepressant actions through adenosine signalling (Extended Data Fig. 10). This unified adenosine-centric framework positions adenosine signalling as a tractable target for mechanism-driven antidepressant development.

Ketamine, ECT and aIH produce their antidepressant effects through adenosine signalling, which selectively engages high-affinity, nanomolar-range A_1 and A2A receptors over the low-affinity, micromolar-range A2B and A_3 subtypes24,28,29. Optical recordings using the GRABado1.0 sensor (EC_50 of around 60 nM) revealed that the adenosine signals induced by ketamine and ECT were less than half the amplitude of those induced by transient hypoxia, which suggests that the unsaturated sensor responses correspond to local adenosine concentrations in the range of 100 nM. This level is sufficient to engage A_1 and A2A receptors but remains substantially below the activation threshold for A2B and A_3 receptors24,28,29. The causal necessity of this pathway was demonstrated by our findings that genetic or pharmacological disruption of either A_1 or A2A receptor signalling abolished their therapeutic efficacy (Figs. 2 and 5 and Extended Data Figs. 3 and 10). A_1 receptor activation, which was sufficient to produce sustained antidepressant-like effects on its own, is thought to suppress neuronal hyperactivity via Gi-coupled signalling24,28,29. In parallel, Gs-coupled A2A signalling promotes synaptic plasticity, in part by upregulating BDNF44,45. This dual-receptor signalling may provide a complementary upstream mechanism for previously implicated pathways, linking the action of ketamine to both mTOR activation and the induction of HOMER1A5,20.

Our model also helps to reconcile the seemingly paradoxical role of the A2A receptor, the chronic activity of which in the hippocampus and lateral septum is often linked to pro-depressive states19,25. Our findings indicate that the chronic, pathophysiological function of the A2A receptor differs from its acute, instrumental role as a necessary component of the specific signalling cascade initiated by ketamine and ECT. Furthermore, this adenosine pathway is engaged by ketamine but not its metabolites (Extended Data Fig. 2), pointing towards a distinct, adenosine-independent mechanism for the potential effects of the latter. Thus, although the baseline activity of A_1 and A2A receptors is context-dependent, their acute, coordinated activation is essential to initiate and sustain the neuroplastic changes that underlie recovery.

Circuit-level analyses identified the mPFC as the critical hub for these effects. Ketamine induced adenosine release selectively in the mPFC and hippocampus, but not the NAc (Fig. 1a,b and Extended Data Fig. 1c). Notably, adenosine signalling in the mPFC was both necessary and sufficient for antidepressant efficacy (Fig. 2f–i and Extended Data Fig. 5). Local adenosine infusion or optogenetic astrocyte activation in this region alleviated depression-like behaviours through CD73-dependent ATP-to-adenosine conversion, whereas mPFC-specific knockout of A_1 or A2A receptors abolished the effects of ketamine. Although many studies suggest that the hippocampus contributes to the antidepressant action of ketamine4,10,14,46,47, local adenosine signalling in the dorsal subregion was insufficient to drive this effect on its own (Extended Data Fig. 5f–j). This may reflect the known functional heterogeneity along the dorsoventral axis of the hippocampus, a complexity that warrants further investigation. Nevertheless, our findings establish the mPFC as a pivotal hub for this adenosine-mediated antidepressant efficacy.

Our findings further reveal that ketamine triggers this cascade by acting as a metabolic neuromodulator. Rather than causing neurotoxic ATP release from stressed cells, ketamine decreases the intracellular ATP/ADP ratio and directly attenuates mitochondrial TCA cycle activity (Fig. 3 and Extended Data Figs. 6 and 7). Owing to the large concentration gradient between millimolar intracellular ATP and nanomolar adenosine23,36,37, a controlled brake on cellular metabolism can be amplified into potent adenosine signals. This mechanism informed our phenotypic drug discovery strategy, in which we engineered derivatives with enhanced adenosine release, greater efficacy and reduced side effects (Fig. 4). The clear dissociation between adenosine release and NMDAR inhibition provides an actionable blueprint for decoupling therapeutic benefits from psychomimetic actions, as demonstrated by the separation of antidepressant effects from hyperlocomotion (Extended Data Figs. 3b,c and 9).

The discovery that aIH also mediates its antidepressant effects through adenosine signalling (Extended Data Fig. 10) provides a promising non-pharmacological avenue. Unlike ECT, which induces seizures and carries risks of cognitive impairment3,15, or repetitive transcranial magnetic stimulation, which has limited efficacy in treatment-resistant cases48,49, aIH is a noninvasive strategy with an established safety profile in humans[42]. Its ability to elevate brain adenosine presents an opportunity to repurpose this protocol for treatment-resistant depression.

In summary, adenosine signalling emerges as a conserved mediator of rapid antidepressant efficacy across pharmacological, electrical and physiological interventions. Our findings highlight key clinical considerations, such as the potential for dietary caffeine to interfere with these treatments49,50, and provide a unified framework to accelerate the development of safer, mechanism-targeted antidepressants with broad clinical applicability.

Methods

Mice

All animal procedures were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee of the Chinese Institute for Brain Research, Beijing (CIBR), and complied with the national guidelines for the housing and care of laboratory animals set by the Ministry of Health, China. Mice were housed in a specific pathogen-free facility on a 12-h light–dark cycle with ad libitum access to food and water. All experiments were conducted on male and female mice aged 8–16 weeks.

The study used WT C57BL/6J mice, Adora1−/− mice (NM-KO-225140, Shanghai Model Organisms Center), Adora2a−/− mice (NM-KO-200018, Shanghai Model Organisms Center), Nt5e−/− mice (provided by J. Chen, Wenzhou Medical University) and Rosa26-Cas9-GFP mice (Gt(ROSA)26Sortm1.1(CAG-cas9*, -EGFP) Fezh/J; The Jackson Laboratory, 024858). All mouse strains were subjected to CRS to induce depression, and WT, Nt5e–/–, Adora1−/− and Adora2a−/− mice were used for fibre photometry experiments.

Chemical reagents

Ketamine analogues were synthesized and provided by the Changchun Institute of Applied Chemistry, CAS, China (see below). Additional key chemicals were purchased from commercial sources, including norketamine hydrochloride (Tocris, 1970), (2R,6R)-HNK (Tocris, 6094), ticlopidine (Selleck, S0721), ketoconazole (Selleck, S1353), ritonavir (Selleck, S1185), dipyridamole (Selleck, S1895), LPS from Escherichia coli O127:B8 (LPS; Sigma, L3129), PSB36 (MCE, HY-103175), ZM241385 (Selleck, S8105), CHA (MCE, HY-18939), CGS21680 hydrochloride (MCE, HY-13201A), adenosine (MCE, HY-B0228), sodium [13C_3]pyruvate (MCE, HY-W015913S), ADP (MCE, HY-W010918) and wheat germ agglutinin (Alexa Fluor 555; Thermo Scientific, W32464).

Compound synthesis and characterization

Full experimental procedures, compound characterization data (1H NMR and 13C NMR) and analytical spectra are provided in the Supplementary Information. A summary of the synthesis for the two key compounds (DCK and 2C-DCK) is presented below.

For the general procedure for the synthesis of 2-aryl-2-bromo-cycloketones, N-bromosuccinimide (1.5 equiv.) and dimethyl sulfoxide (2.0 equiv.) were added to a solution of 2-arylcyclohexan-1-one (1.0 equiv.) in CHCl_3. The reaction mixture was stirred at room temperature and monitored by thin-layer chromatography. After completion, the reaction was quenched with saturated aqueous Na_2S_2O_3 and water. The aqueous phase was extracted with CH_2Cl_2 (3 times). The combined organic layers were dried over anhydrous Na_2SO_4, filtered and concentrated under reduced pressure. The crude product was purified by flash column chromatography (petroleum ether/ethyl acetate) to afford the desired 2-aryl-2-bromo-cycloketone.

For the general procedure for the synthesis of ketamine derivatives (DCK and 2C-DCK), a solution of the appropriate 2-aryl-2-bromo-cycloketone (1.0 mmol) in anhydrous THF was cooled to the specified temperature (−25 °C) under a nitrogen atmosphere. The corresponding amine (methylamine for DCK; ethylamine for 2C-DCK; 4.0 equiv.) was added, and the reaction was stirred until thin-layer chromatography indicated complete consumption of the starting material. The reaction was quenched by the addition of saturated aqueous Na_2CO_3 and water. The mixture was extracted with CH_2Cl_2 (3 times), and the combined organic layers were dried over anhydrous Na_2SO_4. The solvent was removed in vacuo, and the residue was treated with diethyl ether and aqueous HCl. The aqueous layer was washed with diethyl ether, neutralized with saturated aqueous Na_2CO_3 and extracted with CH_2Cl_2 (3 times). The final organic layers were combined, dried over anhydrous Na_2SO_4 and concentrated to dryness under vacuum to produce the final product.

Below are the characterizations of DCK and 2C-DCK using NMR spectroscopy.

Compound 2 (DCK): 2-(Methylamino)−2-phenylcyclohexan-1-one

1H NMR (300 MHz, CDCl_3): δ 7.45–7.34 (m, 2H), 7.33–7.19 (m, 3H), 3.01–2.79 (m, 1H), 2.48–2.19 (m, 3H), 2.03 (s, 3H), 2.01–1.91 (m, 1H), 1.90–1.61 (m, 4H). 13C NMR (75 MHz, CDCl_3): δ 211.7, 138.9, 128.9, 127.6, 127.2, 70.0, 39.9, 35.5, 29.0, 27.9, 22.4.

Compound 3 (2C-DCK): 2-(ethylamino)−2-phenylcyclohexan-1-one

1H NMR (300 MHz, CDCl_3) δ 7.42–7.33 (m, 2H), 7.31–7.20 (m, 3H), 2.90 (d, J = 11.0 Hz, 1H), 2.50–2.22 (m, 3H), 2.16 (s, 1H), 2.11–2.01 (m, 1H), 2.01–1.60 (m, 5H), 0.99 (t, J = 7.1 Hz, 3H). 13C NMR (75 MHz, CDCl_3): δ 211.5, 139.5, 128.9, 127.5, 127.1, 69.9, 39.8, 36.6, 36.1, 27.8, 22.4, 15.7.z.

AAV vectors

The following AAV vectors were produced in-house (M.L’s laboratory) by co-transfecting HEK293T cells with the following respective AAV plasmid and helper plasmids: AAV2/9-hSyn-GRABAdo1.0, AAV2/9-EF1a-DIO-GCaMP8s, AAV2/8-GfaABC1D-GRABATP1.0 and AAV2/8-GfaABC1D-cOpsin5-T2A-mCherry. Viral particles were purified by cesium chloride density gradient ultracentrifugation, dialysed into PBS and titred by quantitative PCR (qPCR) to 5–15 × 10^12 viral genomes per ml[51].

The following additional vectors were obtained: AAV-hSyn-GRABAdo1.0-mut was generated in Y.L’s Laboratory; AAV-EF1α-DIO-PercevalHR and AAV2/5-GfaABC1D-PercevalHR were produced in Z.W’s laboratory; and AAV2/8-GfaABC1D-mCherry was purchased from Taitool Bioscience.

For conditional knockout in the mPFC, sgRNAs targeting Adora1 (5′-GTGTAGCGGTAGCCAGCTGA-3′, 5′-CCGGAACTTGTGGATTCGGA-3′ and 5′-GATCAAGTGTGAGTTCGAGA-3′) and Adora2a (5′-TCGCCATCCGAATTCCACTC-3′, 5′-TCTGGCGGCGGCTGACATCG-3′ and 5′-AGCACACAAGCACGTTACCC-3′) were designed. Non-targeting control sgRNAs (5′-GCGAGGTATTCGGCTCCGCG-3′, 5′-GCTTTCACGGAGGTTCGACG-3′ and 5′-ATGTTGCAGTTCGGCTCGAT-3′) were also used. Each sgRNA, driven by a U6 promoter, was co-packaged with a CMV-driven saCas9-3×HA into either AAV2/9 (for neuronal targeting) or AAV2/8 (for astrocytic targeting) vectors.

Validation of global Adora1 and Adora2a knockout and mPFC-specific knockdown was performed using qPCR on cDNA from brain tissues with the following primer pairs: Adora1 knockout (F: 5′-TGTGCCCGGAAATGTACTGG-3′, R: 5′-TCTGTGGCCCAAATGTTGATAAG-3′); Adora2a knockout (F: 5′-GTGCTGTCATTCGCCATCGG-3′, R: 5′-GGGAGCAACACAAAAGCGAAG-3′); Adora1 sgRNA for conditional knockdown (F: 5′-GCCAGAAACCCAGCATCCTC-3′, R: 5′-CAGAAAGGTGACCCGGAACT-3′); and Adora2a sgRNA (F: 5′-GCCATCCCATTCGCCATCA-3′, R: 5′-GCAATAGCCAAGAGGCTGAAGA-3′). All primers were designed to span critical exon junctions or CRISPR–Cas9-targeted regions, with reaction specificity confirmed by melt curve analysis and agarose gel electrophoresis. qPCR was carried out in triplicate using SYBR Green chemistry under standard cycling conditions.

Surgical procedures

Mice were anaesthetized with avertin (250 mg kg−1, i.p. injection) and secured in a stereotaxic apparatus (RWD). Following skull exposure, a small craniotomy was made above the target region. AAV injections were performed using a microsyringe pump (Nanoliter 2010 Injector, WPI) at a rate of 46 nl min–1 via a glass pipette. Injection coordinates (anterior–posterior (AP), medial–lateral (ML), dorsal–ventral (DV) in mm from bregma) were as follows: PrL and IL in the mPFC (+1.78, ±0.88, −2.15, respectively) with a 15° lateral-to-medial angle; ACC (+1.15, ±0.70, −1.47, respectively) with a 15° lateral-to-medial angle; HPC (−2.54, ±2.00, −1.60, respectively) and NAc (+1.20, ±1.20, −4.65, respectively).

For fibre photometry and optogenetic stimulation experiments52,53, optical fibre implantation was performed after viral injection. Optical fibres (FT200UMT, Thorlabs) mounted in ceramic ferrules were positioned above the mPFC, ACC, HPC or NAc, with the tip located 0.1 mm above the injection site. For intracranial adenosine injection, a cannula (62004, RWD) was implanted into the lateral ventricle (AP: −0.45, ML: −1.84, DV: −2.45 DV) at a 15° lateral-to-medial angle.

For in vivo two-photon imaging, mice were imaged 14 days after viral injection to allow time for virus expression. Subsequently, under anaesthesia, a 3-mm diameter skull aperture was drilled at the injection site and covered with a glass window. A stainless-steel head-restraining bar integrated with an imaging chamber was affixed with dental cement. Mice recovered for 1 week before imaging, which was conducted in the awake state.

Fibre photometry

In vivo fluorescence signals from GRABAdo1.0, GRABAdo1.0-mut, GRABATP1.0 and GCamp8s signals were recorded using a multichannel fibre photometry system54,55 (ThinkerTech). A 470-nm blue LED provided sensor excitation, with the intensity adjusted to minimize photobleaching. The resulting emission was passed through a dichroic mirror (MD498, Thorlabs) and a bandpass filter (525 ± 19.5 nm; MF525–39, Thorlabs) before detection.

Fibre photometry of PercevalHR signals was conducted on a separate two-colour multichannel optical fibre photometry system (Optical Imaging Facility, CIBR). To measure the intracellular ATP/ADP ratio, the PercevalHR sensor was alternately excited at 405 nm and 470 nm, with emission collected at 525 nm, as previously described37,56. The ratio of fluorescence intensity from 470 nm excitation to that from 405 nm excitation (F_470/F_405) was calculated to represent changes in the ATP/ADP ratio.

For pharmacological studies, mice implanted with an optical fibre were habituated for 15–20 min in a behaviour chamber (20 × 20 × 35 cm) to establish a baseline signal. Following habituation, the mice given an i.p. injection of the drug of interest, and fluorescence signals were continuously recorded. Animal behaviour was monitored using a top-mounted camera.

For acute hypoxia experiments, mice were placed in a cylindrical chamber with ports for the optical fibre, gas flow and an oxygen sensor (ST8100A, Smart Sensor). After a 20-min acclimation period with a continuous flow of air (21% O_2), hypoxia was induced by mixing the room air with 100% N_2 via a three-way valve. GRABAdo1.0 and GRABAdo1.0-mut signals were continuously recorded during acclimation, hypoxia and subsequent reoxygenation with air. After the trial, the chamber was cleaned with 70% ethanol and dried.

Fibre photometry data were analysed using custom Matlab scripts. Fluorescence changes were calculated as ΔF/F = (F – F_0)/F_0, where F_0 represents the mean fluorescence during a baseline period before drug administration. For recordings longer than 30 min, photobleaching was corrected by subtracting a ‘blank’ signal, which was recorded from the same animals on a separate day without any drug or saline administration. From the resulting ΔF/F traces, the following parameters were quantified: peak amplitude, the maximum signal intensity reached after stimulation or drug administration; time to peak, the latency from administration to the peak amplitude; onset time, the time for the signal to reach 20% of the peak amplitude; rise time, the interval during which the signal increased from 20% to 90% of its peak; and decay time, the time taken for the signal to decrease to 50% of its peak amplitude.

In vivo two-photon imaging

During imaging sessions, mice remained awake and were gently restrained using a custom-built head-fixation device. Images were acquired 100–150 µm below the dura mater using a Stellaris 8 Dive multiphoton microscope (Leica, ×25 water-immersion lens with NA 1.05). The microscope was calibrated for consistent illumination and exposure settings across all imaging sessions. A 20-min baseline was recorded before ketamine (10 mg kg−1, i.p. injection) or an equivalent volume of saline as a control was given. Following the injection, imaging continued for an additional 20 min to capture changes in neural activity.

To correct for lateral shifts in two-photon images, we used the Image Stabilizer plugin in ImageJ (Fiji, v.2.14.0). For time trace analyses of fluorescence signals based on the region of interest, the Time Series Analyzer V3 plugin in ImageJ was used. ΔF/F values were calculated using customized Matlab scripts (MathWorks), and heatmaps or time traces were generated accordingly. Statistical significance was assessed using Prism 9 (GraphPad Software). All data are reported as the mean ± s.e.m. in the figures.

Confocal imaging of GRABAdo1.0 in cultured cells

HEK293T cells were cultured on 35-mm poly-d-lysine-coated glass-bottom dishes (NEST, 801002). At 60–70% confluency, cells were transfected with the GRABAdo1.0 plasmid using Neofect DNA transfection reagent (Neofect Biotech). Imaging was performed 48 h after transfection on a Zeiss LSM 880 inverted confocal microscope (Carl Zeiss) using a ×20/0.8 NA Plan-Apochromat objective. The GRABAdo1.0 sensor was excited with a 488-nm argon laser.

Behavioural assays

All behavioural assays were performed on animals 12–16 weeks old. Most behavioural assays were performed during the light phase, except for the SPT, which was performed during the dark phase to maximize the consumption of solution. Behavioural analyses were performed blinded to experimental conditions.

CRS assay

Mice were subjected to an environmental acclimation period of 3 days preceding initiation of the experiment. Subsequently, the mice were immobilized utilizing custom-fabricated restraining tubes (50 ml centrifuge tubes) with ventilation apertures to ensure the maintenance of normal respiration. The restraint protocol was implemented for a duration ranging from 4 to 6 h per day over a consecutive 14-day period[57]. After completion of the modelling phase, a behavioural experiment was conducted for the purpose of assessment.

LPS-induced inflammatory depression model

The LPS-induced depression model is a well-established paradigm for rapidly inducing depressive-like behaviours in mice[32]. In brief, i.p. administration of a low dose of LPS (0.83 mg kg–1, E. coli O127:B8, Sigma-Aldrich) induces a mild inflammatory response and triggers transient sickness behaviour within 24 h. Subsequently, between 24 and 72 h after injection, mice develop persistent depression-like phenotypes, including anhedonia, behavioural despair and anxiety-like responses.

FST assay

Mice were individually placed into Plexiglass cylinders (26.5 cm high × 18 cm in diameter) containing 14 cm of water maintained at 25 ± 1 °C. The test lasted for 6 min under standard illumination, with a digital video camera recording from the side. Immobility time during the final 4 min of the test, defined as the period during which mice floated passively with only minimal movements necessary to maintain balance, was scored by a trained observer blinded to the experimental treatments[58].

SPT assay

Mice were habituated to two bottles of drinking water in their home cages for 2 days, followed by exposure to two bottles containing 2% sucrose solution for an additional 2 days. After habituation, mice were deprived of water for 24 h and then presented with one bottle of 1% sucrose solution and one bottle of water for 2 h during the dark phase. The positions of the bottles were switched after 1 h to control for side preference. Sucrose preference was calculated as the percentage of sucrose intake relative to the total fluid intake (sucrose and water combined)[59].

Open-field test

Locomotor activity was assessed using an infrared open-field system (Med Associates; 50 × 50 × 30 cm). Baseline activity was measured in WT, Adora1–/ and Adora2a–/– mice during a 10-min session. Mice were placed at the centre of the arena, and total travel distance was recorded using an automated tracking system. To evaluate the effects of ketamine and DCK, mice were acclimated for 15 min in the arena, followed by drug administration. Locomotor activity was monitored for 75 min after treatment, with total travel distance recorded over the 90-min session. In a separate experiment, WT, Adora1–/– and Adora2a–/– mice underwent the same acclimation period (15 min) followed by ketamine and saline administration, with activity recorded for 30 min after treatment.

ECT in mice

Following CRS, mice were anaesthetized with avertin, and their ears were cleaned with 70% ethanol. ECT was delivered via ear-clip electrodes using a YC-3 Bipolar Programmable Stimulator, applying an electrical current of 40 mA (100 Hz, 10-s duration, 0.5-ms pulse width)[60]. This stimulation induced a tonic–clonic seizure lasting approximately 10 s. Sham-treated animals underwent identical handling, including electrode attachment, but no current was delivered.

aIH treatment

Mice subjected to CRS were exposed to aIH using an interval conditioning regimen[42]. The regimen comprised five cycles of a 5-min hypoxia period at 9% O_2, each followed by a 5-min normoxic interval (21% O_2), repeated daily for three consecutive days. The oxygen concentration was monitored in real time using an integrated sensor (ST8100A, Smart Sensor). The antidepressant effects of aIH were assessed using established models of depressive-like behaviour, including the FST and SPT. For fibre photometry recordings of adenosine dynamics, a 15-min baseline recording was performed before aIH exposure to monitor signal changes in response to the interval training protocol.

Local drug infusion

Bilateral 26-gauge guide cannulae (RWD Life Science) were stereotaxically implanted to target the mPFC (AP: +1.78 mm, ML: ±0.5 mm, DV: −2.05 mm from bregma) and the HPC (AP: −2.54 mm, ML: ±2.00 mm, DV: −1.60 mm). Following a 7-day postoperative recovery period, during which dummy cannulae maintained patency, mice were subjected to a 2-week CRS paradigm. For microinfusions, adenosine (0.1 µg µl–1) were dissolved in sterile 0.9% saline. Solutions were delivered bilaterally (1 µl per side) at a rate of 0.2 µl min–1 via 33-gauge injectors connected to a microsyringe pump. The injectors remained in place for 7 min after infusion to allow for diffusion. Behavioural testing, using either the FST or SPT, was conducted 24 h after ketamine administration. At the conclusion of all experiments, cannula placement was histologically verified following the infusion of wheat germ agglutinin conjugated to Alexa Fluor 555 (WGA-555; 1 µl per side; Thermo Fisher Scientific).

Optogenetics

WT and Nt5e–/– mice previously subjected to CRS were used for optogenetic experiments. Animals were injected with AAVs expressing either AAV-GfaABC1D-cOpn5-T2A-mCherry (cOpn5 group) or AAV-GfaABC1D-mCherry (control group) in the target brain region. For stimulation, blue light (473 nm; MBL-III-473, Changchun New Industries Optoelectronics) was delivered through the implanted optical fibre at 20 Hz for 10 min (peak power at fibre tip: 0.75 mW)[61]. Stimulation timing was controlled by a Master-8 pulse generator (AMPI). FSTs and SPTs were performed 1 h after the cessation of stimulation to assess antidepressant-like effects.

LC–MS quantification of drug concentrations

WT mice were administered either adenosine receptor agonists or ketamine derivatives. At specified time points after injection (10 min for ketamine derivatives; 30 min or 24 h for agonists), mice were deeply anaesthetized with isoflurane. Whole blood was collected via retro-orbital bleeding, and mice were subsequently transcardially perfused with ice-cold PBS. Brain tissue was rapidly dissected, weighed and flash-frozen in liquid nitrogen. Blood samples were allowed to clot at room temperature and then centrifuged at 3,500g for 10 min at 4 °C to separate the serum. For analysis, serum proteins were precipitated by adding four volumes of acetonitrile to one volume of serum, followed by vortexing and centrifugation. Brain tissue was homogenized in 80% acetonitrile using a bead-based homogenizer, and the resulting lysate was clarified by centrifugation. Supernatants from both serum and brain preparations were diluted 100-fold with 80% acetonitrile. Drug concentrations were then quantified using a SCIEX 7500 triple quadrupole mass spectrometer.

Mitochondrial metabolic flux analysis

Mitochondrial isolation

Mitochondria were isolated from the prefrontal cortex of adult mice using a commercial kit (EpiZyme, PC205) with minor modifications. In brief, dissected tissue was homogenized, and crude mitochondria were pelleted by differential centrifugation. Highly purified mitochondria were then obtained by density gradient centrifugation according to the manufacturer’s protocol, washed and resuspended for downstream assays.

Metabolic modulation and metabolite extraction

Purified mitochondria were incubated for 30 min at 37 °C in an intracellular buffer containing sodium [13C_3]pyruvate and ADP, with or without ketamine at various concentrations. Following incubation, mitochondria were pelleted, and metabolites were extracted using cold (–40 °C) 50% aqueous methanol solution followed by phase separation with chloroform. The resulting aqueous phase, containing polar metabolites, was collected for analysis.

LC–MS-based metabolomics

Metabolite profiling was performed using hydrophilic interaction chromatography (HILIC) on an XBridge BEH Amide column (Waters) coupled to a Q Exactive Plus Orbitrap mass spectrometer (Thermo Fisher Scientific). Samples were separated using a gradient of aqueous ammonium acetate/hydroxide (pH 9.4) and acetonitrile. Mass spectrometry was operated in negative ion mode at a resolution of 140,000 (m/z 200), with an AGC target of 1 × 10^6 and a scan range of m/z 75–1,000. Metabolite identification, quantification and isotopic tracing were conducted using El-MAVEN software, with correction for natural isotope abundance. We performed PCA of the metabolomic data and defined ellipse 95% confidence intervals based on the multivariate t-distribution (Fig. 3h). The corresponding source data and analysis code are provided in the source files.

Ex vivo electrophysiology

Mice (C57BL/6J mice, 7–8 weeks old) were anaesthetized with an overdose of avertin and transcardially perfused with ice-cold, oxygenated choline-based slicing solution (in mM: 110 choline chloride, 2.5 KCl, 0.5 CaCl_2, 7 MgCl_2, 1.3 NaH_2PO_4, 25 NaHCO_3, 10 glucose, 1.3 sodium ascorbate and 0.6 sodium pyruvate). Coronal brain slices (200 µm) containing the mPFC were prepared using a vibratome (Leica VT1200). Slices were first recovered at 34 °C for 40 min in oxygenated artificial cerebrospinal fluid (ACSF; in mM: 125 NaCl, 2.5 KCl, 2 CaCl_2, 1.3 MgCl_2, 1.3 NaH_2PO_4, 1.3 sodium ascorbate, 0.6 sodium pyruvate, 10 glucose and 25 NaHCO_3) and then maintained at room temperature for at least 1 h before recording.

Whole-cell patch-clamp recordings were performed in Mg2+-free ACSF to relieve the voltage-dependent block of NMDARs. Recording pipettes (4–6 MΩ) were filled with a caesium-based internal solution (in mM: 115 CsMeSO_3, 20 CsCl, 10 HEPES, 2.5 MgCl_2, 4 sodium ATP, 0.4 sodium GTP, 10 sodium phosphocreatine, 0.6 EGTA and 5 QX-314; pH 7.25–7.30). To isolate NMDAR-mediated excitatory postsynaptic currents (eEPSCs), recordings were performed in the presence of NBQX (10 µM, MCE) and picrotoxin (100 µM, Tocris). eEPSCs were evoked by local stimulation (0.4-ms pulse, every 20 s) while holding the cell at −65 mV. Following a 4-min stable baseline, various concentrations of test compounds were bath-applied for 16 min. The degree of blockade was quantified as the eEPSC amplitude during the final minute (15–16 min) of drug application, normalized to the baseline.

Western blotting

The mPFC total protein for BDNF and CD73 detection were performed in WT, Adora1−/−, Adora2a−/− and Nt5e−/− mice. Animals were anaesthetized with isoflurane, and the mPFC tissue was quickly dissected from the brain and homogenized in lysis RIPA buffer (50 mM Tris HCl, pH 7.4 (Sigma), 150 mM NaCl, 1% Triton X-100 (Sigma) and protease inhibitor cocktail (Sigma)) on ice. After determining the protein concentration with the bicinchoninic acid assay, 35 mg of total protein from each mPFC sample was loaded onto a 4–20% SDS–PAGE gel for separation. Proteins were then transferred to a polyvinylidene fluoride membrane for western blot analyses. Rabbit anti-BDNF (1:1,000; Abcam, ab108319), rabbit anti-CD73 (1:1,000; Cell Signaling Technology, 13160), rabbit anti-GAPDH (1:5,000; Cell Signaling Technology, 2118), rabbit anti-HSP90 (1:1,000; Cell Signaling Technology, 4874) and HRP-conjugated antibody goat anti-rabbit IgG (1:30,000; Sigma-Aldrich, AP156P) were used, along with high-sensitivity ECL reagent (Perkin Elmer). All bands were analysed using ImageJ software.

Histology and immunohistochemistry

For tissue preparation, mice were anaesthetized with an overdose of pentobarbital and intracardially perfused with PBS followed by 4% paraformaldehyde in PBS. Brains were postfixed in 4% paraformaldehyde for 4 h at room temperature or overnight at 4 °C, then cryoprotected in 30% sucrose until they sank. Coronal sections (35 µm) were prepared using a cryostat (Leica CM1950). Virus expression and fibre implantation sites were verified in brain sections using an Olympus VS120 slide scanner with a ×10 objective.

Statistics and reproducibility

All experiments were independently performed ≥3 times with mice randomly assigned to each group, and investigators were blinded to allocation during behavioural experiments and outcome assessment. No statistical methods were used to predetermine the sample sizes.

Reporting summary

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

Data availability

All data supporting the findings of this study are provided within the Article and its Supplementary Information. Any additional information required to reproduce analyses is available from the corresponding authors. Source data are provided with this paper.

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Acknowledgements

We thank J. Chen for providing Nt5e–/– mice; J. Li, X. Han and G. Wang for drug reagents; X. Qi and Y. He for advice on medicinal chemistry; B. Li for advice on statistical analysis; and staff at the Vector Core, Laboratory Animal Resource Center and at the Optical Imaging Core at CIBR for technical support. This study was supported by CAMS Innovation Fund for Medical Sciences (CIFMS 2024-I2M-ZD-012 to M.L.), a China Brain Initiative grant (STI2030-Major Projects 2021ZD0202803 to M.L.), the Research Unit of Medical Neurobiology at the Chinese Academy of Medical Sciences (2019RU003 to M.L.), the New Cornerstone Investigator Program (M.L. and Y.L.), the Beijing Municipal Government (M.L.), the National Key R&D Program of China (2022YFE0108700 to Y.L.), the National Natural Science Foundation of China (32400820 to Z.Y., 32422031 to Z.W., T2350008 to X.W., and 22207103 to H.W.) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0450102 to X.W.).

Author information

Author notes

1. These authors contributed equally: Chenyu Yue, Na Wang

Authors and Affiliations

1. Beijing Institute for Brain Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

Chenyu Yue, Zhengwei Yuan, Yuting Cui, Jing Quan, Yu Zhou, Huijie Mi, Wooping Ge & Minmin Luo

2. Chinese Institute for Brain Research, Beijing, China

Chenyu Yue, Na Wang, Zhengwei Yuan, Yuting Cui, Jing Quan, Yu Zhou, Xiaofeng Fan, Huijie Mi, Wooping Ge & Minmin Luo

3. National Institute of Biological Sciences, Beijing, China

Chenyu Yue & Minmin Luo

4. Research Unit of Medical Neurobiology, Chinese Academy of Medical Sciences, Beijing, China

Chenyu Yue, Zhengwei Yuan, Yuting Cui, Jing Quan, Yu Zhou & Minmin Luo

5. Peking University–Tsinghua University–NIBS Joint Graduate Program, NIBS, Beijing, China

Chenyu Yue

6. School of Basic Medical Sciences, Capital Medical University, Beijing, China

Na Wang

7. Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China

Haojiang Zhai, Hongshuang Wang & Xiaohui Wang

8. Department of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, China

Haojiang Zhai & Xiaohui Wang

9. Laboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China

Zhaofa Wu

10. State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China

Yulong Li

11. New Cornerstone Science Laboratory, Shenzhen, China

Yulong Li & Minmin Luo

Contributions

M.L., C.Y. and N.W. conceived the study. C.Y., N.W., Z.Y. and X.F. performed the behavioural assays. C.Y., N.W. and Z.Y. performed fibre photometry. Y.C. and J.Q. conducted early exploration of the project and advised on western blotting. Y.C. and Y.Z. conducted ex vivo electrophysiology. H.Z. and H.W. synthesized the ketamine analogue compounds under X.W.’s supervision. Y.L. and Z.W. provided the GRABAdo1.0, GRABAdo1.0-mut, GRABATP1.0 and PercevalHR AAV vectors. H.M. conducted metabolomic analysis; W.G. advised on data interpretation. C.Y. and M.L. analysed the data and wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Xiaohui Wang or Minmin Luo.

Ethics declarations

Competing interests

M.L. is the scientific founder of ThinkerTech Biotech, GenAns Biotech and Neucyber Neurotech. This relationship did not influence this study. The other authors declare no competing interests.

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Nature thanks Susan Masino 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 and validation of ketamine-induced adenosine signals.

a, Schematic of fiber photometry setup for monitoring GRABAdo1.0 signals. b, Schematic diagram of acute hypoxia chamber (left) and real-time fluorescence changes of GRABAdo1.0 and GRABAdo1.0-mut probes in the mPFC during acute hypoxia (right; red line). Blue dashed line represents oxygen levels. c, Adenosine signals were not detected in the nucleus accumbens (NAc) following ketamine administration (10 mg·kg−1, i.p.) compared to saline control. Scale bar, 1 mm. d-h, Quantification of GRABAdo1.0 signals induced by ketamine (10 mg·kg−1, i.p.) in the mPFC, ACC, and HPC, showing adenosine peak amplitude (d), average time to peak (e), onset time (f), rise time (g), and decay time (h). i, Two-photon images show fluorescence changes of the GRABAdo1.0 probe in the prefrontal cortex at 0 s (top) and 200 s (middle) following ketamine and saline administration. Bottom, corresponding time-course heatmaps for each region of interest (ROI). Scale bar, 50 µm. j, Heatmaps showing extracellular adenosine changes in the cortex at different time points after ketamine and saline administration. Scale bar, 50 µm. k, Time course of mean adenosine levels in prefrontal cortex ROIs following ketamine or saline administration. l, Area under the curve (AUC) of adenosine levels in the mPFC of CRS and naive mice after ketamine administration (10 mg·kg−1, i.p.), normalized to the naive group. m, n, Rise time (m) and decay time (n) of GRABAdo1.0 signals in the mPFC in response to different ketamine doses. o, p, Specificity of the GRABAdo1.0 probe validated in cultured HEK293T cells. o, Heatmaps and p, time course of fluorescence changes following application of adenosine and ketamine (n = 15 cells per group). Scale bar, 10 µm. Data are mean ± s.e.m. (shading in b, c, p; error bars in d-h, l-n). Statistical analyses used a two-tailed unpaired t-test (c, l) and one-way ANOVA (m, n) (***P < 0.001). See Supplementary Table 1 for detailed statistics. The schematics in a and b were created using BioRender (https://www.biorender.com).

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Extended Data Fig. 2 Effect of ketamine metabolites and metabolism inhibitors on adenosine release in the mPFC.

a, Schematic of ketamine metabolism pathways. b, c, Time course of extracellular adenosine levels in the mPFC (b) and and corresponding AUC (c) following administration of ketamine, norketamine (NK), (2 R,6 R)-HNK (all at 10 mg·kg−1, i.p.), or saline. Area under the curve (AUC, normalized to saline) (c) was calculated post-administration. d, e, Effect of ritonavir pretreatment (50 mg·kg−1, i.p.) on ketamine-induced adenosine release. d, Adenosine time course with and without ritonavir. e, Quantification of adenosine peak (ΔF/F %), time to peak, and AUC (normalized to vehicle). Recordings were performed on separate days in the same cohort of mice. f, g, Effect of ketoconazole pretreatment (50 mg·kg−1, i.p.) on ketamine-induced adenosine signals. Same conventions as in (d, e). h, i, Effects of ticlopidine pretreatment (20 mg·kg−1, i.p.) on ketamine-induced adenosine signals, displayed as in d, e. j-l, Time course and AUC of extracellular adenosine in the mPFC after administration of ritonavir (j), ketoconazole (k), or ticlopidine (l) alone, compared to vehicle. Data are mean ± s.e.m. (shading in b, d, f, h, j-l; error bars in c, j-l). Statistical analyses used a two-tailed paired t-test (e, g, i) and two-tailed unpaired t-tests (c, j-l) (**P < 0.01, ***P < 0.001). See Supplementary Table 1 for detailed statistics.

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Extended Data Fig. 3 Adenosine receptor activity is critical for ketamine’s antidepressant effects without altering locomotion or adenosine release.

a, Total distance traveled in the open-field test for WT, A1 KO and A2A KO mice. b, c, Time-course (b) and quantification (c) of locomotor activity in WT, A1 KO and A2A KO mice following ketamine (10 mg·kg−1, i.p.) and saline administration. Mouse sample sizes (n) are shown in b. d, Time course of extracellular adenosine levels in the mPFC of WT, A1 KO and A2A KO mice after equal dose of ketamine, quantification of area under the curve (AUC, normalized to WT). e, Experimental timeline for assessing the impact of adenosine receptor antagonists on the antidepressant effects of ketamine. f-i. Effects of pretreatment with adenosine receptor antagonists (PSB36 for A1 and ZM241385 for A2A; 1 mg·kg−1, i.p.) on ketamine’s antidepressant actions (forced swimming immobility: f, h; sucrose preference: g, i) 1 h (f, g) and 24 h (h, i) after ketamine administration. j, upper, Experimental timeline for testing the effect of adenosine receptor antagonists on the antidepressant-like action of ketamine in a lipopolysaccharide (LPS)-induced depression model. lower, Effects of pretreating an A1 antagonist (PSB36, 1 mg·kg−1, i.p.) and A2A antagonist (ZM241385, 1 mg·kg−1, i.p.) on ketamine’s antidepressant actions in a FST test of LPS-challenged mice. Data are mean ± s.e.m. (error bars in a–c, f-j; shading in d). Two-tailed unpaired t-tests were used for a, c, d, f-j (*P < 0.05, **P < 0.01, ***P < 0.001). See Supplementary Table 1 for detailed statistics. The schematics in e and j were created using BioRender (https://www.biorender.com).

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Extended Data Fig. 4 Adenosine receptor activation produces antidepressant-like effects.

a, Experimental timelines for assessing the antidepressant-like effects of direct adenosine administration or systemic delivery of specific adenosine receptor agonists in mice subjected to chronic restraint stress (CRS). b, c, Rapid antidepressant-like effects. b, Immobility time in the FST 30 min after intracerebroventricular (i.c.v.) injection of adenosine (1 µg per mouse). c, Immobility time in the FST 30 min after intraperitoneal (i.p.) injection of an A1 receptor agonist (CHA; 0.1 mg·kg−1) or an A2A receptor agonist (CGS21680; 0.1 mg·kg−1). d, e, Sustained antidepressant-like effects. Immobility time in the FST (d) and sucrose preference (e) were measured 24 h after i.p. administration of CHA or CGS21680 in CRS mice. f, Concentrations of CHA and CGS21680 in serum (top) and brain tissue (bottom) at 30 min and 24 h after i.p. injection, measured by LC-MS. Data are mean ± s.e.m. (error bars in b–f). Two-tailed unpaired t-tests were used for b–e (*P < 0.05, **P < 0.01, ***P < 0.001). See Supplementary Table 1 for detailed statistics. The schematic in a was created using BioRender (https://www.biorender.com).

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Extended Data Fig. 5 Regional specificity of adenosine-mediated effects.

a-c, Direct infusion of adenosine into the mPFC produced sustained antidepressant-like effects. a, Experimental timeline and representative image of the bilateral infusion site in the mPFC of CRS. Scale bar, 500 µm. b,c, Immobility time in the forced swim test (FST; b) and sucrose preference (c) were measured 24 h after infusion of adenosine (0.1 µg per side). d, FST immobility time in CRS-exposed WT and CD73 knockout (KO) mice following cOpn5-mediated optogenetic stimulation in the mPFC. e, Ketamine-induced BDNF upregulation in the mPFC requires A1/A2A receptor signaling. Top, representative western blots. Bottom, quantified BDNF protein in WT, A1 KO and A2A KO mice 30 min after receiving saline or ketamine (10 mg·kg−1, i.p.). f-j, Adenosine signaling in the dorsal HPC (dHPC) does not elicit antidepressant-like effects. f, images showing adenosine infusion (upper) and optogenetically induced adenosine production (lower) within the dHPC. Scale bar, 500 µm. g, h, FST immobility (g) and sucrose preference (h) in CRS mice 24 h post-infusion (0.1 µg per side). i, j, FST immobility (i) and sucrose preference (j) measured 1 h post-stimulation (cOpn5 vs mCherry) in CRS mice. k-m, Adenosine receptor signaling in the mPFC is necessary for the rapid antidepressant-like effects of ketamine. k, Representative image of saCas9-HA expression (yellow) in the mPFC. Scale bar, 1 mm. l, m, Immobility time in the FST (l) and sucrose preference (m) were measured 1 h after ketamine administration (10 mg·kg−1, i.p.) in CRS-exposed Cas9 mice expressing sgRNAs targeting the A1 receptor, the A2A receptor or a non-targeting control. Data are mean ± s.e.m. (error bars in b–e, g–j, l, m). Two-tailed unpaired t-tests were used for b–e, g–j, l, m (*P < 0.05, **P < 0.01). See Supplementary Table 1 for detailed statistics. The schematic in a was created using BioRender (https://www.biorender.com).

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Extended Data Fig. 6 Ketamine-induced adenosine surges are independent of extracellular ATP hydrolysis and broad neuronal hyperactivation.

a, Schematic of extracellular adenosine generation via ATP hydrolysis. b-d, Ketamine does not alter extracellular ATP levels in the mPFC. b, Representative image of GRABATP1.0 expression (green). Scale bar, 500 µm. c, Time course of extracellular ATP following ketamine (10 mg·kg−1, i.p.) or saline administration. d, Corresponding AUC, normalized to saline. e-g, Ketamine-induced adenosine release does not depend on the ectonucleotidase CD73. e, Western blot validation of CD73 knockout (KO). f, Time course of extracellular adenosine in WT (n = 13 mice) and CD73 KO mice (n = 10 mice) after ketamine administration. g, Quantification of adenosine peak and AUC, normalized to WT. h, i, Time course (h) and AUC (i) of extracellular adenosine in the mPFC following the administration of dipyridamole (i.c.v., 1 µg) or vehicle. j-l, Ketamine inhibited Ca²+ signaling in pyramidal neurons. j, Representative images show GCaMP8s expression (green) in mPFC pyramidal neurons of CaMKII-Cre mice. k, Time course of Ca²+ signals in mPFC pyramidal neurons in response to ketamine (10 and 20 mg·kg−1, i.p.) and saline. l, AUC (normalized to saline) quantification of Ca²+ signals in pyramidal neurons. Scale bar, 200 µm. m-o, Ketamine inhibited Ca²+ signaling in inhibitory interneurons. m, Representative images showing GCaMP8s expression in GABAergic neurons of VGAT-Cre mice. n, Time course of Ca²+ signals in mPFC GABAergic neurons in response to ketamine (10 mg·kg−1, i.p.) and saline. o, AUC quantification of Ca²+ signals in GABAergic neurons. Scale bar, 200 µm. p, q, Temporal dynamics of intracellular energy and extracellular adenosine. p, Aligned time courses of PercevalHR (ATP/ADP) and GRABAdo1.0 (adenosine) signals following ketamine injection (10 mg·kg−1; adenosine data replotted from Fig. 1e). q, Quantification of the time to peak for each signal. Data are mean ± s.e.m. (shading in c, f, h, k, n, p; error bars in d, g, i, l, o, q). The box plot in g shows the median (center line), first and third quartiles (box bounds), and 1.5 × IQR (whiskers). Statistical analyses used a two-tailed paired t-test (i) and two-tailed unpaired t-tests (d, g, l, o, q) (*P < 0.05, **P < 0.01, ***P < 0.001). See Supplementary Table 1 for detailed statistics. The schematic in a was created using BioRender (https://www.biorender.com).

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Extended Data Fig. 7 Ketamine directly modulates mitochondrial TCA cycle flux.

a, Schematic illustrating the incorporation of ¹³C atoms from pyruvate-13C_3 into key tricarboxylic acid (TCA) cycle intermediates. b, Heatmap showing the relative ¹³C-enrichment of TCA cycle metabolites in isolated brain mitochondria incubated with increasing concentrations of ketamine (3 replicates for each concentration). Data are Z-score normalized to the vehicle control group for each metabolite. c, Quantification of ¹³C-labeling for representative TCA cycle intermediates across ketamine concentrations. Data are mean ± s.e.m. (error bars in c). One-way ANOVA was used (*P < 0.05, **P < 0.01). See Supplementary Table 1 for detailed statistics.

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Extended Data Fig. 8 In vivo characterization of ketamine analogues: adenosine release and locomotor activity.

a, Chemical structures of the synthesized ketamine analogues. b, Heatmap of peak adenosine signal (upper) and AUC (lower) in the mPFC following administration of each analogue (10 mg·kg−1, i.p.). All values are normalized to the response induced by an equivalent dose of ketamine. c, Locomotor activity time course after ketamine or DCK administration. Baseline recorded for 15 min; i.p. injection at dashed line; activity monitored for 75 min post-injection. Mouse sample sizes (n) are shown near the data series. d, Dose-dependent increase in locomotor activity induced by ketamine and DCK. Data are mean ± s.e.m. (c, d). Two-tailed unpaired t-tests between drugs and saline control (*P < 0.05, ***P < 0.001). See Supplementary Table 1 for detailed statistics.

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Extended Data Fig. 9 Comparative analysis of NMDA receptor blockade and adenosine modulation by ketamine analogues.

a–g, Normalized NMDA receptor-mediated excitatory postsynaptic currents (NMDAR-eEPSCs; left) and adenosine responses (right) for each analogue. The numbers in the left panels indicate the sample size (n, cells) for each concentration (increasing left to right). Those in the right panels indicate mouse sample size. h, Relative brain tissue concentrations of ketamine analogues (n = 6, 3, 3, 3, 3, 3, 3 mice, respectively) 10 min after intraperitoneal injection (i.p.), measured by LC–MS and normalized to ketamine. i, Lack of significant correlation between estimated NMDAR inhibition in brain tissue and adenosine modulation in the mPFC by ketamine analogues (Pearson correlation, P = 0.097). Data are mean ± s.e.m. (error bars in a–h; shading in a–g). See Supplementary Table 1 for detailed statistics.

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Extended Data Fig. 10 Acute intermittent hypoxia (aIH) alleviates depressive-like phenotypes through adenosine signaling.

a, Experimental timeline for acute intermittent hypoxia (aIH) exposure in mice. b, Oxygen-dependent adenosine dynamics in the mPFC, monitored with GRABAdo1.0 sensor during graded hypoxia (9–21% O_2; n = 11 mice). c-e, Experimental paradigm for assessing aIH antidepressant efficacy (c), FST immobility time (d), and sucrose preference (e) of CRS-challenged WT, A1 KO and A2A KO mice. f, Open-field locomotor activity in WT, adenosine receptor A1 KO and A2A KO mice before and after aIH following CRS. Data are mean ± s.e.m. (shading in b; error bars in d–f). Two-tailed unpaired t-tests were used for d–f (***P < 0.001). See Supplementary Table 1 for detailed statistics. The schematics in a and c were created using BioRender (https://www.biorender.com).

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Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1 and 2 and the Supplementary Methods.

Reporting Summary

Supplementary Table 1

Statistical analyses and sample sizes.

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Yue, C., Wang, N., Zhai, H. et al. Adenosine signalling drives antidepressant actions of ketamine and ECT. Nature 649, 423–431 (2026). https://doi.org/10.1038/s41586-025-09755-9

- Received: 27 January 2025

- Accepted: 14 October 2025

- Published: 05 November 2025

- Version of record: 05 November 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09755-9

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Ecology and spread of the North American H5N1 epizootic

Lambodhar Damodaran,

Anna S. Jaeger &

Louise H. Moncla

Nature volume 649, pages 432–441 (2026)

Abstract

Since late 2021, a panzootic of highly pathogenic H5N1 has devastated wild birds, agriculture and mammals. Here an analysis of 1,818 haemagglutinin sequences from wild birds, domestic birds and mammals reveals that the North American panzootic was driven by around nine introductions into the Atlantic and Pacific flyways, followed by rapid dissemination through wild, migratory birds. Transmission was primarily driven by Anseriformes, while non-canonical species acted as dead-end hosts. In contrast to the epizootic of 2015 (refs. 1,2), outbreaks in domestic birds were driven by around 46–113 independent introductions from wild birds that persisted for up to 6 months. Backyard birds were infected around 9 days earlier on average than commercial poultry, suggesting potential as early-warning signals for transmission upticks. We pinpoint wild birds as critical drivers of the epizootic, implying that enhanced surveillance in wild birds and strategies that reduce transmission at the wild–agriculture interface will be key for future tracking and outbreak prevention.

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Main

Highly pathogenic avian influenza (HPAI) viruses pose persistent challenges for human and animal health. Since emerging in 1996, highly pathogenic H5N1 viruses of the A/goose/Guangdong lineage have spread globally through enzootic transmission in domestic poultry in Asia and Africa, paired with occasional cross-continental movement by wild birds of the Anseriformes (ducks, geese, swans) and Charadriiformes (shorebirds) orders3,4,5,6,7,8,9. In 2005, introduction of poultry-derived H5N1 viruses into wild birds in China led to viral dispersal across Northern Africa and Asia, establishing new lineages of endemic circulation in poultry10,11. In 2014, wild migratory birds carried highly pathogenic H5N8 viruses from Europe to North America, sparking an outbreak in which over 50.5 million commercial birds were culled in the USA4,12. As these viruses did not establish persistently within wild birds, the outbreak was extinguished by aggressive culling, and North America remained free of HPAI for years.

In December 2021, clade 2.3.4.4b HPAI H5N1 viruses were introduced and spread across the Americas13,14,15, causing a panzootic of considerable morbidity and mortality in wild and domestic animals. In contrast to past North American epizootics, domestic bird culling has not halted detections, and morbidity and mortality has been widespread across wild avian and mammal species not usually impacted by HPAI15,16,17,18,19,20, raising the possibility that new reservoir hosts could be established that should be actively surveilled. In Europe, clade 2.3.4.4b virus incursions into wild and domestic birds has led to seasonal outbreaks[21], frequent reassortment[22] and a broader range of affected wild bird species since 2020, and recent analyses suggest that wild birds may now have a greater role in global viral maintenance and dissemination8,23. In North America, the broad affected host range and continued agricultural outbreaks suggest that patterns of transmission since 2022 may be distinct from past epizootics. However, the role of wild versus domestic birds in driving transmission in North America has not been robustly or comprehensively studied, limiting informed surveillance and outbreak control.

Viral phylodynamic approaches are emerging as critical tools for outbreak reconstruction. We used Bayesian phylogeographical approaches to trace the introduction and spread of highly pathogenic H5N1 viruses during the first 18 months in North America. We identify multiple incursions into the continent and subsequent spread by wild, migrating birds that drove repeated introductions into agriculture. These data pinpoint wild birds as important drivers of epizootic spread, and implicate enhanced wildlife surveillance and interventions at the wild–domestic interface as key for future viral tracking and spillover prevention.

Sequences reflect HPAI cases over time

The first detection of HPAI H5N1 in North America was reported in migratory gulls in Newfoundland and Labrador Canada in November 2021 (ref. [13]). From January to May 2022, a total of 2,510 total detections was reported across 43 US states and 91 species (Extended Data Fig. 1), followed by a larger epizootic wave from August 2022 to March 2023 (8,001 detections, 48 contiguous US states and Alaska). During the time period analysed (November 2021 to September 2023), most US detections were reported in wild birds (Supplementary Fig. 1a). Case detections peaked in the fall and spring, coinciding roughly with seasonal migration timing for birds migrating between North and South America24,25. Continued monitoring is necessary to determine whether these patterns persist in future years.

Although sequencing data from North America are heavily skewed towards the USA and the first 6 months of the outbreak (Supplementary Fig. 2), case detections were modestly correlated with viral effective population size (Ne) (highest Spearman rank correlation = 0.65, P = 4.4 × 10−11) (Extended Data Fig. 1c and Supplementary Figs. 3 and 4), a measure of genetic diversity mathematically related to disease transmission and prevalence[26]. Peaks in Ne preceded peaks in detections by around 1 week (Supplementary Fig. 5), probably reflecting the lag between viral transmission and case detection. Thus, despite uneven sampling, sequence diversity roughly reflects the amplitude of sampled cases over time.

Repeated incursions drove the epizootic

Most North American sequences descend from a single introduction from Europe in late 2021 (95% highest posterior density (HPD), 9 September to 7 October 2021; Fig. 1a), consistent with previous reports13,14,15 that these viruses may have been introduced as early as 1 to 2 months before the first detection. We recapitulate a second, short-lived introduction from Europe in 2022 (ref. [27]), and seven additional (median = 7, 95% HPD = 6–8) introductions between February and September 2022 from Asia (Fig. 1b,c). These introductions persisted briefly (0.024–6.9 months) and represent infections sampled in Alaska, Oregon, California, Wyoming and British Columbia, suggesting introduction through the Pacific flyway[28] (Extended Data Fig. 2). Although none of these Pacific introductions had sampled descendants in the time period analysed, data at the time of writing indicate that one re-emerged in late 2024 as the D1.1 lineage[29] (Fig. 1b). Although it remains unclear why this HA lineage was not detected from mid-2023 to 2024, the novel introductions documented here and the eventual outgrowth of one of these lineages highlight the importance of surveillance in the Pacific region for capturing viral importations. These data suggest that H5N1 viruses were introduced into North America at least nine times, and that viral flow into the Pacific coast may be far more common than previously documented.

Fig. 1: H5N1 viruses were introduced repeatedly from Europe and Asia.

a, Bayesian phylogenetic reconstruction of n = 1,927 globally sampled sequences of HPAI clade 2.3.4.4b coloured according to the continent of isolation. The opacity of branches corresponds to posterior support for the discrete trait inferred for a given branch, and the thickness corresponds to the number of descendent tips the given branch produces. The major Atlantic introduction is annotated. b, A magnified view of the starred section of the tree in a, focusing on introductions from Asia. The introduction that resulted in the D1.1 lineage is also noted. c, We inferred the number of transitions from Asia to North America across the posterior set of 9,000 trees. The x axis represents the number of introductions, and the y axis represents the proportion of trees across the posterior set with that number of inferred transitions.

H5N1 spread across migratory flyways

Recent data from Europe and Asia suggest that wild birds may be increasingly important sources of clade 2.3.4.4b virus evolution and transmission[8]. In the Americas, wild birds migrate across four major flyways: the Atlantic, Mississippi, Central and Pacific[30]. We assigned avian sequences to the migratory flyway matching the US state of sampling and modelled the diffusion between flyways as a proxy for viral movement. To determine whether sequences clustered more strongly by flyway than expected by chance, we calculated the association index (AI)—a measure of how strongly a trait is associated with a phylogenetic tree[31]. To determine whether movement between flyways was better supported than movement across other adjacent geographical regions, we quantified transitions between four North American regions stratified by latitude.

Introductions from viruses circulating in Asia (Fig. 1b) form a basal clade inferred in the Pacific flyway (posterior probability (PP) = 0.98). The primary introduction from Europe entered through the Atlantic flyway, and subsequently spread rapidly across North America (Fig. 2a,c). From the inferred time of introduction in the Atlantic coast (9 September to 7 October 2021), viruses descending from this introduction had been sampled in every other flyway within approximately 4.8 months. Sequences clustered strongly by flyway (AI = 10.563, P = 0.00199), grouping most closely with those sampled within the same or geographically adjacent flyway (Fig. 2a and Extended Data Table 1). Transitions (inferred as Markov jumps) between adjacent flyways were about 10 times more frequent (mean = 239, 95% HPD = 216–262) than those between distant flyways (mean = 24, 95% HPD = 12–33; Fig. 2d), and 2.8 times more frequent between adjacent latitudinal regions (Extended Data Fig. 4 and Supplementary Table 2), indicating a strong signal of dissemination through geographical proximity. Transitions were predominantly inferred from east to west (Fig. 2c,d and Supplementary Table 1); east to west jumps were inferred around 4.4 times more frequently (mean = 214, 95% HPD = 196–232) than west to east jumps (mean = 49, 95% HPD = 38–57) (Fig. 2d), and 2.3–3.8 times more frequently than jumps along the north–south axis (Extended Data Fig. 4 and Supplementary Table 2).

Fig. 2: Wild migratory birds drove rapid dissemination across continental migratory flyways.

a, Phylogenetic reconstruction of n = 1,000 sequences coloured by migratory flyway. Inset: the results of the PACT analysis quantifying persistence in each flyway (measured as the length of time a tip takes to leave its sampled location, going backwards on the tree), excluding the Pacific clade. b, The mean and 95% HPD for the number of Markov jumps per year between US Fish and Wildlife Service (USFWS) flyways. The colour of the bar on the right of each jump pair corresponds to the source population and the height of the bar corresponds to the BF support. c, USFWS waterfowl flyways map; arrows are annotated to represent rates with BF support of at least 100. The size of the arrow corresponds to the magnitude of the mean transition rate. d, The posterior distribution of the number of Markov jumps between flyways in the eastward or westward direction and between adjacent and distant flyways. e, Chord diagram of discrete trait diffusion based on migratory status going from the source population on the left to the sink population on the right. The chord thickness represents the mean transition rate and the colour represents the BF support. D, domestic; M, migratory; MA, mammal; PM, partially migratory; S, sedentary.

Transitions were inferred most frequently from the Mississippi to Central flyway (56.301 Markov jumps per year; 95% HPD = 47.85–64.33), Atlantic to Mississippi flyway (37.34 Markov jumps per year; 95% HPD = 30.84–43.065) and Central to Pacific flyway (13.127 Markov jumps per year; 95% HPD = 7.975–18.077; Fig. 2b, Extended Data Fig. 3 and Supplementary Table 1). Although the Pacific flyway experienced the highest number of introductions, transitions originating from the Pacific flyway were inferred with low magnitude and weak support, with only one statistically supported rate (Pacific to Central, 11.236 Markov jumps per year; 95% HPD = 7.975–13.292). Viral lineages persisted for the longest in the Atlantic and Pacific flyways, although estimates were variable (Fig. 2a). We speculate that this pattern could reflect higher habitat and species richness within coastal flyways[32], or that coastal flyways each only border 1 other flyway.

The strong clustering by flyways is consistent with long-range transmission by wild migratory birds. We next classified sequences into five categories and modelled diffusion among them: wild migratory birds (most ducks and geese), wild partially migratory birds (some ducks, raptors and vultures), wild sedentary birds (owls crows), domestic birds and non-human mammals. Migratory and partially migratory wild birds are inferred at the root far more frequently than expected from sampling alone (Supplementary Fig. 6 and Extended Data Table 2), indicating a role for these species in sustained transmission across the epizootic. Transitions from wild migratory birds were inferred with the highest number and most strongly supported transition rates (Bayes factor (BF) > 3,000), indicating that migrating wild birds were critical sources of infections in other species (Fig. 2e and Supplementary Table 3). By contrast, transitions from non-migratory wild birds were inferred with low magnitudes and weak support (Fig. 2e and Supplementary Table 3). These results suggest that wild, migratory birds played a pivotal part in transmission, and highlight their capacity to rapidly disseminate novel viral incursions across continental North America.

Transmission driven by canonical hosts

Previous outbreaks of highly pathogenic H5N1 viruses have been facilitated by wild Anseriformes (waterfowl) and Charadriiformes (shorebirds), and domestic species (Galliformes and Anseriformes)1,33,34,35,36. While domestic ducks have been critical for bridging wild and domestic populations in Asia, domestic ducks account for only 2% of all detections in the USA, with most cases reported in wild birds and Gallinaceous poultry (turkeys and chickens)[37]. In the current panzootic, die-offs have occurred across a range of wild, non-canonical hosts, including Accipitriformes (raptors, condors, vultures), Strigiformes (owls) and Passeriformes (including sparrows, crows, robins)15,19,20, raising the possibility that these new species could establish as reservoirs that merit surveillance. To determine whether particular host groups had outsized roles in driving transmission in the epizootic, we classified sequences into seven host order groups (Anseriformes, shorebirds, Strigiformes, Passeriformes, Raptors, Galliformes and non-human mammals), calculated the AI for each group (Extended Data Table 1) and modelled transmission between them. To control for variation in case and sequence acquisition across groups, we performed these analyses under two subsampling regimes (proportional and equal), each with three replicates and report results that were concordant. We also formulated a modified tip-shuffle test to measure the impact of sampling on the inferred host at the root[38] (further details are provided in the Methods).

The first introduction into North America comprised infections from gulls and harbour seals from New England, consistent with migratory shorebirds facilitating transmission from Europe and seeding mammal outbreaks19,39 (Fig. 3a). Tip-shuffle results indicate mixed evidence for the role of shorebirds in transmission. However, shorebird sequences were highly clustered with each other (AI = 8.008, null = 2.324, P = 0.00999), supporting some degree of separation between viruses circulating in shorebirds and other species[3]. Beyond this early cluster of infections, multiple deep, internal nodes across the phylogeny are inferred in Anseriformes with high posterior support (PP = 0.99), indicating that Anseriformes played an important role in driving sustained transmission and dispersal across North America. Across all replicates in both sampling regimes, Anseriformes are inferred at the root 2–3 times more frequently than in null, shuffled datasets (Extended Data Table 2), providing strong support for Anseriformes as critical drivers of epizootic transmission. We infer Anseriformes as the predominant hosts seeding infections into other species (Fig. 3b,d, Supplementary Fig. 7 and Supplementary Tables 4–11), with the highest rates to Galliformes (17.81 Markov jumps per year; 95% HPD = 9.27–26.02, BF = 1,691, PP = 0.99) and Strigiformes (13.51 Markov jumps per year; 95% HPD = 5.35–22.87, BF = 232, PP = 0.99). Each of these patterns was preserved in each independent subsample in both sampling regimes, indicating high robustness to sampling (Supplementary Figs. 8 and 9).

Fig. 3: Epizootic transmission was sustained by canonical host species.

a, Bayesian phylogenetic reconstruction of 655 sequences, sampled evenly across host groups. The phylogeny with the highest posterior support is shown; all other replicates are provided in Supplementary Fig. 8 and Supplementary Tables 4–11. Colour represents the taxonomic order of the source host. b, The mean number of Markov jumps per year and the 95% HPD from the host group on the left (labelled ‘from’) to the host on the right (labelled ‘to’) as inferred from the combined results of three equal sampling replicates. The dot represents the mean, and the lines (whiskers) represent the 95% HPD. The corresponding bar plot shows the BF support for each jump pair, with colour representing the ‘from’ host. The bar height represents BF support. Values at 100 indicate support of greater than or equal to 100. c, Inference of phylogenetic persistence in each host order for the phylogeny shown in a. d, For each host, we computed the proportion of Markov jumps involving that host order in which that host was inferred as a source (jump coming from that order) or as a sink (jump going to that order). The bars represent the variability across the three replicates of equal orders subsamples.

We also infer support for transmission originating from Galliformes, suggesting that transmission from domestic birds back to wild birds and mammals may have occurred. However, lineages in Galliformes tended to be short-lived, persisting for 0.26 years on average (95% HPD = 0.07–0.33 years). Galliformes were inferred at the root less frequently than expected for their sampling frequency (Extended Data Table 2), and were highly clustered (P = 0.0099; Extended Data Table 1), consistent with transmission confined to localized agricultural outbreaks. By contrast, viral lineages persisted for the longest in Anseriformes and shorebirds (Fig. 3c). These data suggest that, while Anseriformes, shorebirds and Galliformes may all have contributed to infections in other species, Anseriformes were the predominant drivers of longer-term persistence and spread to other hosts.

In the ongoing panzootic, raptors represent the third most prevalent group in wild bird detections in Europe (12% of detections) and the second most detected group in North America (20.3%)18,40. Notably, raptors were inferred as a low-frequency but statistically well supported source population to Anseriformes (5.18 Markov jumps per year; 95% HPD = 0.36–9.27, BF = 39,PP = 0.87). Tip-shuffle results indicate that raptors are less probable at the root than expected based on their frequency, supporting a limited role for epizootic transmission. Future work to better establish the reasons for high case numbers among raptors will be necessary for formulating wildlife management strategies.

We found limited support for non-canonical host groups (songbirds, owls and non-human mammals) in seeding infections in other species. Passeriformes (songbirds), Strigiformes (owls) and mammals each primarily served as sinks for viral diversity (Fig. 3b,d), with transitions inferred with low-magnitude and weak support (Fig. 3b). Summing the number of jumps originating from wild canonical (Anseriformes, shorebirds), wild non-canonical (Passeriformes, Strigiformes, raptors, mammals) and Galliforme (domestic) hosts confirm that non-canonical hosts primarily acted sinks that were far likelier to receive virus than propagate it onward (Extended Data Fig. 5), supporting short, terminal transmission chains that did not lead to long-term persistence (Fig. 3c and Supplementary Fig. 10). Mammal sequences cluster across the entire diversity of the phylogeny (Fig. 3a) and are not associated with one particular cluster of viruses, indicating that mammal infections were not confined to a particular viral lineage, supporting very short persistence times of 0.22 years (95% HPD = 0.088–0.328), and only one strongly supported transition rate to Anseriformes (BF = 53, PP = 0.89). Instead, these findings are most compatible with a model in which wild mammals and other non-canonical species are infected by direct interaction with wild birds, possibly related to scavenging and predation behaviour[41]. Taken together, these data suggest that despite high case numbers in several unusual wild hosts, non-canonical species generally had minor roles in transmission. Instead, epizootic transmission was most strongly supported in Anseriformes, supporting surveillance in these species for capturing trends in viral diversity and spread.

Repeated introductions into agriculture

From 2022 to mid-2025, the USA culled over 160 million domestic birds, with agricultural losses estimated between US$2.5 to US$3 billion[42]. Understanding the extent of agricultural transmission driven by repeated introductions from wild birds versus between-premise spread is critical for formulating biosecurity practices, but challenged by differences in sampling between wild and domestic birds. Domestic birds represent 23.2% of sequences, but only 11% of detections, while wild birds are probably undersampled owing to technical challenges20,43. While each detection in wild birds represents a single infection, domestic detections usually represent a single infected farm, with an unknown number of infected animals. To measure the impact of varied sampling on transmission inference between wild and domestic birds, we designed a titration analysis. We first generated a dataset with equal domestic and wild bird sequences, therefore forcing the inference to be driven by the sequencing data rather than sampling. Next, we added in progressively more wild bird sequences until we reached a final ratio of domestic to wild sequences that approximates the ratio of detections (1:3), generating five datasets in total (ratios of domestic to wild bird sequences of 1:1, 1:1.5, 1:2, 1:2.5 and 1:3). For each dataset, we inferred transmission between wild and domestic birds using a discrete trait diffusion model. This analysis was designed to determine whether domestic or wild birds would be inferred as the primary source population, and whether that inference would vary across sampling regimes. Moreover, we hoped to assess whether the inferred number of transitions between hosts stabilized at a certain ratio as a measure of whether currently available data are sufficient for inferring transmission dynamics within this time period.

When domestic/wild sequences were included in equal proportions, wild birds are inferred as the primary source in the outbreak (Supplementary Fig. 11a). Wild birds were inferred at the root of the tree at a far higher probability than expected from their sampling (PP = 0.895 in empirical data versus 0.482 in tip-shuffled data), while domestic birds were under-represented (Extended Data Table 2). This pattern is consistent with higher genetic diversity among wild bird sequences, supporting a large, source population. Within the background of wild bird sequences, domestic bird sequences form highly clustered groups (AI = 23.096, P = 0.0019; Extended Data Table 1), consistent with some transmission between them. However, as wild sequences were progressively added into the tree, most domestic-only clusters became smaller, broken up by wild sequences that interspersed within these clades (Supplementary Fig. 11a–e). The ‘breaking up’ of these domestic clusters results in more inferred transitions from wild to domestic birds, and fewer transitions from domestic to wild birds (Fig. 4b,c and Extended Data Fig. 6a). The largest changes in the inferred transitions occurred between the 1:1 and 1:2.5 titrations, with minimal to no changes observed between transitions inferred in the 1:2.5 and 1:3 datasets, suggesting stability in the inferred transitions at the end of the experiment (Supplementary Table 12). The phylogeny of the final dataset (1:3 ratio of domestic to wild sequences) shows 106 introductions into domestic birds, and 4 from domestic to wild (Fig. 4a,b, Supplementary Figs. 12 and 13 and Supplementary Table 12). While domestic bird lineages persisted for around 4.5 months on average (95% HPD = 2.7–5.63), viral lineages in wild birds persisted for over twice as long (around 10 months, 95% HPD = 5.7–14.07; Fig. 4c).

Fig. 4: Outbreaks in domestic birds were seeded by repeated introductions from wild birds, with some onward transmission.

a, Phylogenetic reconstruction of transmission between wild and domestic birds. Taxa and branches are coloured by wild or domestic host status containing a 1:3 ratio of domestic to wild bird sequences. n = 1,080. b, The number of transitions from a given trait to another trait inferred through ancestral state reconstruction for each titration. c, The results of the PACT analysis for persistence in domestic and wild birds for each titration.

Commercial turkey operations have been heavily impacted during the epizootic, comprising 53.7% of all detections on commercial farms[44]. To determine whether excluding turkey sequences (Methods) may have biased our results, we assigned any turkey sequence not labelled as ‘wild turkey’ as ‘domestic’ and reran the titration analysis. Turkey sequences did not substantially change the inferred transition rates between wild and domestic birds (Extended Data Fig. 6a and Supplementary Table 12). In both titration experiments, the final number of inferred transmission events from domestic to wild birds was 4 (Supplementary Table 12), indicating minimal transmission back to wild species, regardless of whether turkeys were included (Supplementary Fig. 14 and Supplementary Table 12). Inclusion of turkey sequences did result in a slightly longer inferred domestic bird persistence (1.29 and 1.54 months; Extended Data Fig. 7e) as well as some turkey-only clusters on the tree (Extended Data Fig. 6c–e). Reconstruction using a dataset with equal turkey and domestic (non-turkey) sequences showed that, while most introductions into turkey populations stemmed from wild birds (42 transitions), transmission events between turkeys and other domestic birds were frequent. We infer around 38 introductions from turkeys to other domestic birds, and 18 in the opposite direction (Extended Data Fig. 7a–d and Supplementary Table 13), suggesting a putative role for turkeys in mediating transmission between wild birds and other poultry production types.

These data suggest a few important conclusions. First, wild birds are inferred as the major source of transmission even when heavily downsampled, and independent of whether turkeys were included in the analysis. Second, regardless of sampling regime, we find that outbreaks in agricultural birds were driven by repeated, independent introductions from wild birds, with some onward transmission between domestic operations. While the exact number of inferred introductions vary across analyses (Supplementary Tables 12 and 13), we infer no fewer than 46, and as many as 113 independent introductions into domestic birds. When allowing sampling frequencies to approximate detections (the 1:3 dataset), we resolve a higher number of introductions into domestic birds with shorter transmission chains, although lineages still persisted for 4–6 months. Together, these results indicate that—while the epizootic of 2014/2015 was started by a small number of introductions that rapidly propagated between commercial operations2,12—intensive and persistent transmission among wild birds since 2022 resulted in continuous incursions into domestic birds. Thus, wild birds had a critical role in agricultural outbreaks in North America from 2021–2023, marking an important departure from past epizootics that may necessitate updates to biosecurity, surveillance and outbreak control.

Spillovers to backyard/commercial birds

The 2014/2015 H5Nx epizootic in the USA was driven by extensive transmission in commercial poultry[2], prompting a series of biosecurity updates for commercial poultry farms12,45. However, not all domestic birds are raised in commercial settings. Rearing domesticated poultry in the home setting has become increasingly popular in the USA, with an estimated 12 million Americans owning ‘backyard birds’ in 2022 (ref. [46]). These birds have been heavily impacted during the ongoing epizootic, with some evidence for distinct transmission chains circulating in backyard birds versus commercial poultry[15]. As backyard birds generally experience less biosecurity than commercial birds and are more likely to be reared outdoors[47], we hypothesized that spillovers into backyard birds may be more frequent than spillovers directly into commercial poultry.

To test this hypothesis, we used a subset of sequences sampled between January and May of 2022, with additional metadata specifying whether they were collected from commercial poultry or from backyard birds. We built a tree with equal sequences from domestic and wild birds, with domestic sequences split between commercial and backyard birds (commercial birds = 85, backyard bird = 85, wild birds = 193). As previously, we infer wild birds as the primary source population, with multiple introductions into commercial and backyard birds (Extended Data Fig. 8a). However, backyard bird sequences clustered more basally than commercial poultry sequences, sometimes falling directly ancestral to clusters of commercial poultry sequences (Extended Data Fig. 8a). While all backyard bird clusters descended from wild birds, 10 out of 26 commercial poultry introductions were inferred from backyard birds (Supplementary Fig. 15a). This pattern was reproducible across multiple independent subsamples, indicating robustness to the exact subset of sequences in the tree. Given the debated link between backyard birds and commercial poultry[48], we further explored two hypotheses that could explain this pattern. The first is that backyard birds mediated transmission between wild birds and commercial birds. Under this model, spillovers into backyard birds (possibly through outdoor rearing) could be spread to commercial populations through shared personnel, clothing or equipment, resulting in backyard bird sequences clustering between wild and commercial bird sequences. Alternatively, backyard birds could have been infected earlier than commercial birds. If backyard birds have a higher risk of exposure (possibly due to lessened biosecurity and increased interactions with wildlife), then a successful spillover event may take less time to occur and be detected in backyard birds, resulting in clustering that is more basal in the tree.

To differentiate between these hypotheses, we performed a second titration analysis. We started with the phylogeny including equal numbers of sequences from commercial and backyard birds, enabling us to directly compare introduction patterns in these two groups. We then added progressively more wild bird sequences into the tree until all available wild bird sequences were added and, for each dataset, inferred the number and timings of transmission events between wild birds, commercial birds and backyard birds. If backyard birds mediated outbreaks in commercial birds (hypothesis 1), then the relationship between backyard birds and commercial birds should remain unchanged. If backyard birds and commercial birds were infected independently (hypothesis 2), then wild bird sequences should intersperse between commercial and backyard bird sequences, resulting in more independent introductions that occur earlier in backyard birds.

Throughout the experiment, wild bird sequences attached throughout the phylogeny, disrupting nearly every backyard bird-commercial bird cluster originally observed (Extended Data Fig. 8). The final tree with all available wild bird sequences resulted in inference of around 82 independent introductions from wild birds to domestic birds, with most clusters containing only commercial (39 clusters) or backyard bird (43 clusters) sequences (Fig. 5a,b, Extended Data Fig. 8 and Supplementary Fig. 15), suggesting that outbreaks in these groups were mostly seeded independently. Of the initial ten transmission events inferred from backyard birds to commercial birds, only two remained undisturbed in the final tree (Fig. 5b and Supplementary Fig. 15), representing outbreaks in the same state and week, which could be plausibly linked. However, all of the other clusters were disrupted. As wild bird sequences were added into the tree, the number of inferred introductions into backyard birds and commercial birds diverged across the posterior trees for each titration (Extended Data Fig. 8), with backyard birds experiencing slightly more introductions (mean = 42 introductions, 95% HPD = 35–49) than commercial poultry (mean = 39 introductions, 95% HPD = 32–44) (Fig. 5c).

Fig. 5: Backyard birds were infected independently, and earlier on average than commercial birds.

a, Phylogenetic reconstruction of sequences collected between Jan 2022 and May 2023, with all available wild bird sequences and equal proportions of commercial and backyard birds. n = 942. Taxa and branches are coloured by host domesticity status. b, Exploded tree view of the phylogeny showing the branches of transmission in each domestic bird type after transmission from wild birds; subtrees represent the traversal of a tree from the root to the tip, whereby the state is unchanged from the initial state (given by the large dot on left) to the tips represented by the smaller dots representing continuous chains of transmission within a given state. c, The proportion of trees from the posterior tree set with a given number of transitions from wild birds to backyard birds and commercial birds (100% available wild sequences). d, The Markov reward trunk proportion for domesticity status showing the waiting time for a given status across branches of the phylogeny over time. e, Cumulative Markov jumps from a given bird type to another over time; each line represents a single phylogeny from the posterior sample of trees.

To determine whether spillovers into backyard birds occurred earlier than those into commercial poultry, we estimated the number of transitions between hosts across the phylogeny (Markov jumps) and the amount of time that is spent in each host between transitions (Markov rewards)49,50. Early in the epizootic, transmission in backyard birds slightly preceded transmission in commercial poultry (Fig. 5d and Supplementary Fig. 15). Enumeration of the cumulative number of transitions between hosts (Markov jumps), showed that backyard birds experienced slightly more jumps than commercial poultry (backyard birds = 43 introductions, 95% HPD = 36–50; commercial birds = 39 introductions, 95% HPD = 32–44), and that these introductions occurred around 9.6 days earlier on average (Fig. 5e). Comparison of detections and sequence availability show no apparent skewing in samples for commercial and backyard birds in that time period, suggesting that this pattern is not simply due to excess earlier cases in backyard birds at that time (Supplementary Fig. 16). Data on testing turnarounds and enrolment in the US indemnity payment register show that commercial and backyard bird farms have nearly identical lag times between case reporting and confirmation (2.15 days for commercial birds, 2.4 days for backyard birds)[51], with testing and depopulation in commercial poultry that is efficient[52] and slightly earlier than in backyard birds. While 511 out of 168,048 commercial operations (0.3%) reported cases and received indemnity payments (a proxy for enrolment in testing programs), only 656 out of around 12 million backyard bird owners (0.0055%) were enrolled46,51,53. Thus, the earlier spillovers that we observe cannot be readily explained by systematically earlier case detection, testing or reporting. Future studies using expanded datasets across future epizootic waves are necessary to confirm this pattern more broadly.

Discussion

Our study collectively supports wild birds as critical sources of the North American H5N1 epizootic. By directly modelling transitions between host groups based on domestic/wild classification, taxonomic order and migratory behaviour, paired with strong dispersal across flyways, we show that wild birds were key drivers of epizootic transmission and introductions into agriculture. These results imply that continuous surveillance in wild birds, particularly Anseriformes[54], may now be critical for viral tracking and outbreak reconstruction. As the primary source of transmission shifts from poultry to wild migratory birds, the ecology of clade 2.3.4.4b viruses in North America may now follow patterns unfolding globally, whereby evolution is increasingly governed by wild bird movement, ecology and reassortment. Recent modelling of HPAI risk in Europe identified Anatinae and Anserinae Anseriformes prevalence as consistent predictors of HPAI detection[54], supporting wildlife surveillance for outbreak forecasting and risk assessment. Future work investigating the use of real-time tracking of wild bird abundance and movement for forecasting outbreaks may be useful for formulating new approaches to prevention.

Our study highlights the capacity of migratory birds to rapidly disseminate highly pathogenic H5N1 viruses across North America. We speculate that rapid geographical spread from east to west could be explained by the high inherent transmissibility of clade 2.3.4.4b viruses in wild birds, rapid avian migration or exponential spread among immunologically naive wild birds during early epizootic expansion55,56. We infer five incursions[57] into the Pacific that mostly persisted transiently, suggesting frequent viral flow between Asia and the Pacific coast of North America. Limited transmission from the Pacific flyway could be explained by differential fitness of the lineages introduced into the Pacific versus Atlantic flyways, ecological isolation of the Pacific flyway58,59,60, differences in host distributions at the locations and times of these incursions or simply due to chance. While future work is necessary to differentiate among these hypotheses, these data support the Pacific coast as an important region for capturing viral transmission between Asia and North America.

We find that outbreaks in agriculture were seeded by repeated introductions from wild birds, a pattern that held true regardless of sampling regime, and that aligns with global observations that clade 2.3.4.4b viruses are increasingly spread by wild birds8,61. These findings contrast with the epizootic in 2014/2015, in which a small number of introductions spread efficiently between commercial poultry operations2,12. As the viruses circulating in 2014/2015 did not establish in local wild bird populations, that epizootic subsided following aggressive culling. Since 2014/2015, biosecurity plans have improved12,45 and depopulation occurs more rapidly12,52, potentially contributing to the shorter domestic persistence and limited transmission back to wild birds we observe. Despite these improvements, efficient transmission in wild birds probably allowed for rapid dispersal and continuous outbreak reseeding, making this epizootic far more challenging to control. US and Canadian policy currently classifies H5N1 as a foreign animal disease, meaning that biosecurity to reduce spread between farms and rapid culling62,63 are prioritized for outbreak control. Although these control measures will probably remain important, our results suggest that reducing future spillovers into agriculture may now necessitate changes in management priorities. The repeated spillovers that we identify suggest that gaps in farm biosecurity remain that could be enhanced to reduce outbreak risk. Finally, layered approaches, including enhanced wild bird monitoring, new methods to separate wild and domestic birds, and potentially domestic animal vaccination, may necessitate exploration.

Using a small dataset from the first 6 months of the epizootic, we find phylogenetic evidence that spillovers into backyard birds may have occurred slightly earlier and more frequently than those into commercial farms. A large survey of backyard bird populations from 2004 showed that backyard bird flocks often contain multiple species, usually have outdoor access, and that 60–75% regularly interact with wild birds[47]. Biosecurity precautions tend to be much more limited in backyard populations, with 88% of backyard flocks using no precautions (shoe covers, footbaths, clothing changes) at all[47]. Given the enhanced exposure of backyard birds to wild birds, expanded studies to determine whether the patterns of earlier spillovers in these populations hold true more broadly are necessary to investigate backyard birds as potential sentinel species for transmission in wild birds.

Sampling bias is pervasive across viral outbreak datasets, and no modelling approach can completely overcome biases in data acquisition. In the USA, only wild Anseriformes are sampled live or hunter harvested, while all other host groups are sampled sick or dead. Detections in domestic birds depend on producer reporting and testing, which probably varies across production types, locations and premises. To account for this variability, we used multiple subsampling approaches, reported results that were consistent and carried out statistical tests to measure the impact of sampling on our results. The titration tests that we used show that the precise number of transitions between wild and domestic birds depends on sampling numbers, providing a clear argument for continuous surveillance in wildlife, and a warning for overconfidence in estimating the transitions between groups. Still, all phylodynamic inferences are limited by the availability of sequencing data, and the results could change if future data become available. Our analyses use only HA sequences, meaning that differences between reassortants could not be compared[23]. Finally, although we retain data from across North America for all analyses, our results are probably most informative of transmission within the USA during the first 6 months of the epizootic.

Taken together, we show that wild birds played the central role in dispersal of the 2021–2023 H5N1 epizootic. Transmission in wild birds provides an explanation for the rapid cross-continental spread and continued agricultural outbreaks despite aggressive culling. Our results highlight the utility of wild-bird surveillance for accurately distinguishing hypotheses of epizootic spread, and suggest that continuous surveillance is critical for preventing and dissecting future outbreaks. Our data underscore that continued establishment of H5N1 in North American wildlife may necessitate a shift in risk management and mitigation, with interventions focused on reducing risk within the context of enzootic circulation in wild birds. At the time of writing, outbreaks in dairy cattle highlight the critical importance of modelling ecological interactions that drive spillovers between wildlife and domestic production to inform biosecurity, outbreak response and vaccine strain selection.

Methods

Dataset collection and processing

Information on case detections in North America

In this study, a detection is defined as a positive PCR test from a collected sample. In Canada, year-round surveillance in wild and domestic populations is coordinated by the Canadian Food Inspection Agency, Environment Canada, the Public Health Agency of Canada and the Canadian Wildlife Health Centre[64]. In the USA, the United State Department of Agriculture Animal and Plant Health Inspection Service (APHIS) manages HPAI surveillance and testing in wild birds through investigation of reported morbidity and mortality events, hunter-collected game birds/waterfowl, sentinel species/live bird collection, and environmental sampling of water bodies and surfaces43,65. USDA APHIS also surveilles domestic birds using several reporting methods: mandatory testing through the National Poultry Improvement Plan, coordination with state agricultural agencies, routine testing in high-risk areas and backyard flock surveillance[66].

Data on detections of HPAI in the USA used in analyses for this study were collected from USDA APHIS. Reports for mammals, wild birds and domestic poultry were all downloaded in November 2023 (download date: 25 November 2023)[40]. During the time period analysed in this study (November 2021-September 2023), most HPAI detections in the USA were reported in wild birds (Supplementary Fig. 1a). Data on domestic bird detections are reported with information on poultry type (such as duck, chicken) and by whether the farm is classified as a commercial operation or backyard flock. Backyard flocks are categorized by the USDA as operations with fewer than 1,000 birds47,67 and by the World Organization for Animal Health (WOAH) as any birds kept in captivity for reasons other than for commercial production[68]. Among domestic birds, detections (1,177 total) came predominantly from commercial chickens (9.3%), commercial turkeys (28.5%), commercial breeding operations (species unspecified) (15.3%) and birds designated WOAH non-poultry, which refers to backyard birds (42.3%) (Supplementary Fig. 1b). Other domestic bird detections occurred in game bird raising operations (2.5%) and commercial ducks (2.0%). The North American epizootic has impacted a broad range of mammalian hosts, with detections (399) reported in red foxes (24.3%), mice (24.1%), skunks (12.2%) and domestic cats (13.2%). Other mammalian hosts (26.2%) represent a wide range of species including harbour seals, bobcats, fishers and bears (Supplementary Fig. 1c).

Genomic data processing and initial phylogenetics

We downloaded all available nucleotide sequencing data and associated metadata for the haemagglutinin protein of all HPAI clade 2.3.4.4b H5Nx viruses from the GISAID database on 25 November 2023 (ref. [69]). For each subset of the data described for further phylodynamic modelling, the following process was followed. We first aligned sequences using MAFFT v.7.5.20, sequence alignments were visually inspected using Geneious and sequences causing significant gaps were removed and nucleotides before the start codon and after the stop codon were removed70,71. We deduplicated identical sequences collected on the same day (retaining identical sequences that occurred on different days). We identified and removed temporal outliers for all genomic datasets by performing initial phylogenetic reconstruction in a maximum-likelihood framework using IQtree v.1.6.12 and the program TimeTree v.0.11.2 was used to remove temporal outliers and to assess the clockliness of the dataset before Bayesian phylogenetic reconstruction72,73. This resulted in a dataset of 1,824 sequences that were used in further analyses (Supplementary Fig. 17).

Biases in genomic data and N e inference

Sequencing data sampled in North America are heavily skewed toward sequences from the USA (USA, 1,590; Canada, 224; Central America, 8), and from the first 6 months of the outbreak, with 74% of all available sequences sampled from January to July 2022 (Supplementary Fig. 2). To evaluate whether sequencing data reflect case detections, we inferred the viral Ne—a measure of viral genetic diversity shown to be mathematically related to disease prevalence and the disease transmission rate[26]. We inferred Ne using a nonparametric population model (Skygrid), which captures relative changes in genetic diversity and the variability of growth rate in the virus population over time, providing a proxy for epidemic dynamics as previously described. Ne is modestly correlated with detections (highest Spearman rank correlation: 0.65, P = 4.4 × 10−11) (Fig. 1c and Supplementary Figs. 3 and 4), with peaks in Ne preceding peaks in detections by about 1 week (Supplementary Fig. 5), probably reflecting the lag between viral transmission and case detection. We interpret these results to suggest that, despite uneven sequence acquisition across time, the diversity of sampled sequences roughly reflect the amplitude of H5N1 cases. Given these results, we opted to use sequencing data for the entire sampling period for broad inferences on introductions and geographical spread, but supplement these analyses with controls for sampling differences between groups. For more-intensive reconstructions of transmission patterns between wild birds, commercial poultry and backyard birds, we focus on the initial 6-month period with the most densely sampled data, coupled with experiments to assess the impacts of sampling on results. Finally, although we retained data from Canada and Central America for all subsequent analyses, our results are probably most informative about transmission within the USA due to the heavy skewing of data towards the USA.

AVONET database

We downloaded the AVONET database for avian ecology data and merged it to available host metadata from GISAID for each sequence[74]. We used the species if provided to match the species indicated in the AVONET database. If host metadata in GISAID was defined using common name for a bird, we determined the taxonomic species name and used that for further merging with the AVONET data (for example, ‘mallard’ was replaced with Anas platyrhynchos) for the given region to match the species to its respective ecological data. Domesticity status (whether a sequence was isolated from a wild host or a domestic host) was determined using available metadata downloaded from GISAID using the ‘Note’ and ‘Domestic_Status’ fields in sequence associated metadata. Moreover, if a given sequence strain name (in the field ‘Isolate_Name’) indicated domestic status (for example, A/domestic_duck/2022) these sequences were labelled as belonging to domestic hosts.

Phylodynamic analysis

The following Bayesian phylogenetic reconstructions and analyses were performed using BEAST (v.1.10.4)[75].

Empirical tree set estimation and coalescent analysis

We performed Bayesian phylogenetic reconstruction for each dataset before discrete trait diffusion modelling to estimate a posterior set of empirical trees. The following priors and settings were used for each subset of the sequencing data. We used the HKY nucleotide substitution model with gamma-distributed rate variation among sites and log-normal relaxed molecular clock model76,77. The Bayesian SkyGrid coalescent was used with the number of grid points corresponding to the number of weeks between the earliest and latest collected sample (for example, for a dataset collected between 4 November 2021 and 11 August 2023, we would set 92 grid points)[78]. We initially ran four independent MCMC chains with a chain-length of 100 million states logging every 10,000 states. We diagnosed the combined results of the independent runs diagnosed Tracer v1.7.2. to ensure an adequate effective sample size (ESS > 200) and reasonable estimates for parameters[75]. If ESS was inadequate additional independent MCMC runs were run increasing chain length to 150 million states, sampling every 15,000 states were performed. We combined the tree files from each independent MCMC run removing 10–30% burn-in and resampling to get a tree file with between 9,000 and 10,000 posterior trees using Logcombiner v.1.10.4. A posterior sample of 500 trees was extracted and used as empirical tree sets in discrete trait diffusion modelling.

Discrete trait modelling framework

For each discrete trait dataset, we used an asymmetric continuous time Markov chain discrete trait diffusion model and implemented the Bayesian stochastic search variable selection (BSSVS) to determine the most parsimonious diffusion network[79]. We inferred the history of changes from a given trait to another across branches of the phylogeny, providing a rate of transitions from A to B per year for each pair of trait states. When reporting these results, we refer to state A as the source population/state and B as the sink population/state. We implemented the BSSVS, which enables us to determine which rates have the highest posterior support by using a stochastic binary operator which turns on and off rates to determine their contribution to the diffusion network. In addition to the discrete trait diffusion rate, we used a Markov Jump analysis to observe the number of jumps between discrete states across the posterior set of trees and estimated the Markov rewards to determine the waiting time for a given discrete trait state in the phylogeny49,50. The Markov reward proportion is calculated as the proportion of the phylogeny at a given time being a given discrete state. By looking at the proportion of a given state over time across the phylogeny, we can provide a proxy for how long transmission has occurred in each group between transition events. We calculate the transition rate as a realization of the CTMC process by dividing the number of Markov jumps by the tree height (branch length from the earliest tip to the root of the tree), and separately, by tree length (sum of all branch lengths). For each pairwise transition rate, we calculate the level of BF support that the given rate has. The BF represents the support of a given rate, and is calculated as the ratio of the posterior odds of the given rate being non-zero divided by the equivalent prior odds, which is set as a Poisson prior with a 50% prior probability on the minimal number of rates possible[79]. We use the support definitions by Kass and Rafferty to interpret the BF support where BF > 3 indicates little support, a BF between 3 and 10 indicates substantial support, a BF between 10 and 100 indicates strong support, and a BF of greater than 100 indicates very strong support[80].

Empirical tree sets were used with the discrete traits defined for each sequence to perform discrete trait diffusion modelling. Each discrete trait model was implemented using three independent MCMC chains with a chain length of 10 million states, logging every 1,000 states. Runs were combined using LogCombiner v.1.10.4, subsampling a posterior sample of 10,000 trees/states. The BF support for transition rates were calculated using the program SPREAD3 (ref. [81]). Maximum clade credibility trees were constructed using TreeAnnotator v.1.10.4.

Extraction of phylogenetic metrics

We calculated the transitions between states across branches of phylogenies estimated from ancestral state reconstructions using the Baltic python package[82]. To calculate the persistence of a given discrete trait, we used the program PACT v.0.9.5, which calculates the persistence of a trait by traversing the phylogenetic tree backwards and measuring the amount of time that a tip takes to leave its sampled state[83].

Dataset subsampling and definition of discrete traits

Geographical introductions analysis

We characterized the geographical introduction of HPAI into North America by randomly sampling 100 sequences from Europe and Asia for each year between 2021 and 2023 (total, 300 non-North American) and all available North American sequences across the study period. After removal of temporal outliers, this resulted in a dataset of n = 1,927 sequences annotated by continent of origin. The sequencing data available from North America broken down by country are as follows: USA (1,590), Canada (224), Honduras (2), Costa Rica (5) and Panama (1).

Migratory flyways analysis

To characterize geographical transmission within North America after introduction, we constructed a dataset of sequences subsampled based on migratory flyway. We used place-of-isolation data to match the US state or Canadian province that the sequence was collected from with the respective US Fish and Wildlife Service Migratory Bird Program Administrative Flyway[30]. We subsampled 250 sequences for each flyway (Atlantic, Mississippi, Central and Pacific) to create a dataset of 1,000 sequences collected between November 2021 and August 2023. In addition to USFWS flyways, we defined four geographical regions going north to south based on latitude lines, with the following delineations for each group. We divided North America into four regions segregated by latitude, with the northernmost group above the 49° N parallel and the southernmost group below the 36° N parallel. We then sampled 916 sequences uniformly across these categories and inferred transitions between these regions.

Host order analysis

We classified sequences by host taxonomic order, inferring the host species using designations in the strain name and/or metadata to match species records in AVONET[74]. To ensure that each discrete trait had an adequate number of samples for the discrete trait analysis of host orders, we combined orders in two instances based on taxonomic and behavioural similarity. The order Falconiformes (n = 14), representing falcons, was added to Accipitriformes (n = 363), which includes other raptors such as eagles, hawks and vultures. Pelecaniformes (n = 34), including pelicans, were grouped with Charadriiformes (n = 74, shorebirds and waders) due to their similar aquatic lifestyles and behaviours. Mammals were kept as a broad non-human classification as most samples were of the order carnivora (foxes, skunks, bobcats), apart from samples of dolphins (Artiodactyla) and Virginia opossum (Didelphimorphia). The following orders were omitted due to a low number of sequences: Rheaforimes (n = 2), Casuariiformes (n = 1), Apodiformes (n = 2), Suliformes (n = 7), Gaviiformes (n = 1), Gruiformes (n = 1) and Podicipediformes (n = 1).

Discrete trait approaches assume that the number of sequences in a dataset are representative of the underlying distribution of cases in an outbreak, resulting in faulty inference when this assumption is violated27,39,53 and bias when groups are unevenly sampled82,84,85. To account for differential sampling among these host order groups, we considered two distinct subsampling approaches. The first is a proportional sampling regime in which sequences are sampled proportional to the detections in each host group each month. This common sampling regime assumes that case detections in each group are the closest proxy for the case distribution in the outbreak, and attempts to align sampling with underlying model assumptions. However, this approach may not be appropriate if case detection is heavily biased between groups. For HPAI H5N1 in North America, detections in wild birds are primarily identified when humans report sick or dead birds to wildlife health authorities or wildlife rescues (Supplementary Fig. 1a), which may skew detections towards birds with dedicated rescue services or birds that reside in closer proximity to humans. For example, Anseriformes and raptors comprised 50.2% and 20.3% of all sequences, respectively, which could arise from high case intensity or a higher rate of case acquisition. A second, complementary subsampling approach is to sample sequences equally, meaning that sequences are sampled from each group in perfectly equal numbers. By forcing the number of sequences from each group to be equal, the transmission inference must be driven by the underlying sequence diversity in each group rather than by sampling differences. Given the high variation among detections within each host group, we opted to pursue both sampling regimes and focus on results that were concordant in both. We first performed an AI test to confirm that clustering was sufficient for discrete trait inference (Extended Data Table 1). Next, for each regime, we performed three independent subsamples, where the dataset was sampled either proportional to cases or equally. For the equal sampling regime, each dataset included 100 randomly sampled sequences per host group, except for Passeriformes, for which only 57 sequences were available. To account for variation across subsampled datasets, we combined the results for the three independent subsamples to summarize statistical support (Supplementary Fig. 12 and Supplementary Tables 4 and 5). Owing to similar tree topologies across replicates, we visualized the phylogeny of the dataset with the highest posterior support (equal order subsample 1) in the main text and make the results of all analyses available in supplement (Supplementary Fig. 8 and Supplementary Tables 4–11). Finally, to measure the effects of potential sampling bias on the inferred transition rates, we performed a modified tip-shuffle analysis. We generated 100 datasets in which the host tip assignments were randomly shuffled, re-inferred the host group at internal nodes and infer a mean root state probability for each host across the 100 shuffled datasets. We then compared the root state probability in the empirical data to that inferred in the shuffled data as a measure of the impact of sampling on the results as previously described (see the ‘Tip-shuffle analysis’ section for further details)[38].

For the equal sampling regime, we randomly subsampled 100 sequences for each host order between 4 November 2021 and 11 August 2023, resulting in a dataset of n = 655 sequences whereby all isolates for host orders with less than 100 samples, Passeriformes (n = 57) and Strigiformes (n = 99) (removing one temporal outlier), were used (Supplementary Fig. 18). We repeated this random subsampling three times, resulting in three separate datasets. For the proportional sampling regime, we performed three subsamples of sequences based on the proportion of detections in each host order group, which were collected between 4 November 2021 and 11 August 2023. Three random proportional samples were taken each with the following number of sequences for each group: Accipitriformes (133), Anseriformes (342), Passeriformes (12), non-human-mammal (16), Galliformes (83), Charadriiformes (40), Strigiformes (29) (total n = 655 sequences).

Migratory behaviour analysis

We defined discrete traits for use in discrete trait diffusion modelling based on the available sequence metadata and merged AVONET data. In addition to taxonomic order, we defined migratory behaviour. Birds were classified as sedentary (staying in each location and not showing any major migration behaviour), partially migratory (for example, small proportion of population migrates long distances, or population undergoes short-distance migration, nomadic movements, distinct altitudinal migration) or migratory (the majority of population undertakes long-distance migration). We subsampled sequences based on migratory behaviour including non-human-mammals and domestic birds to create a subsample of 500 sequences with equal sampling across behaviour groups.

Rationale for inclusion of turkeys as domestic birds

While commercial turkey operations represent 53.7% of all detections on commercial farms[44], the presence of wild turkeys throughout North America makes categorizing turkey sequences as domestic or wild status ambiguous. 98% of all turkey sequences are not associated with metadata on domestic/wild status, and were therefore excluded from the first analysis of domestic/wild bird diffusion. However, epidemiological data suggest that most deposited turkey sequences probably stem from domestic outbreaks. Among case detections during the study period, only 139 were reported in wild turkeys, representing 1.5% of all wild bird detections. By contrast, commercial turkey outbreaks comprised 28.5% of all domestic detections in the study period, suggesting that unlabelled turkey sequences are most likely to have come from domestic birds. While these data are not conclusive, we opted to perform an additional analysis to determine whether our exclusion of turkey sequences (that are most likely domestic) may have biased our results. In the analyses detailed below, turkeys are assumed to be domestic.

Domestic/wild titration analysis

To study the impact of sampling of wild birds on the estimation of rates between domestic and wild birds, we created five separate datasets with varying numbers of wild birds for sequences collected between 2021 and 2023. We randomly sampled 270 domestic sequences and 270 wild sequences as the initial 1:1 ratio dataset. We then made four more datasets increasing the number of wild sequences by a factor of 0.5 (adding 135 wild sequences), resulting in a final titration of 1:3 domestic to wild sequences (n = 1,080). We applied a two-state asymmetric CTMC discrete trait diffusion model in which sequences were labelled as domestic or wild. All priors and model parameters selected are the same as those described in the empirical tree set description above. To study the impact of the inclusion of turkeys in the transmission between domestic and wild populations, we annotated all unannotated sequences collected from turkeys as domestic (see the rationale in section above). We then created three datasets starting with 525 domestic and 525 wild bird sequences, adding 263 sequences to successive titrations resulting in 1:1, 1:1.5 and 1:2 (domestic:wild) sequencing datasets with a final titration size of 1,575 sequences. We again applied a two-state asymmetric CTMC discrete trait diffusion model in which sequences were labelled as domestic or wild, and all priors and model parameters selected are the same as those described in the empirical tree set description above. To determine whether the proportion of turkeys to other domestic birds would impact the results of the previously described titration analysis we built a dataset in which the domestic bird group had equal numbers of turkey and domestic (non-turkey) sequences. This dataset included 173 turkey, 173 domestic bird and 692 wild bird sequences, totalling 1,038 sequences. Given that turkeys comprised 53.7% of commercial poultry outbreaks in the study period, this sampling regime conforms to both equal and proportional sampling regimes. We applied an asymmetric CTMC discrete trait diffusion model using a BSSVS for a three-trait model with the following states: wild birds, domestic birds (not turkey) and turkey. We performed three independent runs of this analysis using the models and parameters described in the empirical tree analysis section above. All titration replicates were performed using an MCMC chain length of 100 million states sampling every 10,000 states.

Commercial, backyard, wild-bird titration analysis

Metadata and annotated sequences were made available describing sequences as being from backyard birds for sequences collected in early 2022 which distinguished them from commercial poultry (previously all sequences being determined domestic)[15]. We used these metadata to create a dataset with equally sampled backyard birds and commercial birds (n = 85 for each bird type) and then added all available wild birds (n = 722) in 25% increments creating four separate datasets for sequences collected between Jan 2022 and June 2022. This resulted in a final dataset of n = 942 sequences. We performed discrete trait diffusion modelling using an asymmetric CTMC diffusion model described in the previous section for sequences labelled as backyard bird, commercial bird and wild bird. Calculation of the lag time between the cumulative Markov Jumps for backyard birds and commercial birds was calculated as the average length of time between points where cumulative Markov jumps are equal between backyard birds and commercial birds. This was calculated for each tree in the posterior.

Assessment of sampling bias

BaTs analysis

To determine whether the discrete traits analysed correlated with shared ancestry in the phylogeny, we employed tip trait association tests implemented in the Bayesian Tip-Association Significance (BaTs) program (v.1.0)[31]. This program assesses the phylogenetic structure of discrete traits across viral lineages using three metrics: the AI, parsimony score (PS) and maximum monophyletic clade size (MC). The AI measures the imbalance of internal nodes of a phylogeny for a given set of traits. The PS calculates the number of state changes in the phylogeny. The MC measures the maximum number of tips belonging to a monophyletic clade for each discrete trait of interest. These metrics are calculated for the phylogeny as tips are randomly swapped to create a null distribution to compare against. Taken together, these metrics quantify the degree of clustering within the phylogeny, with lower AI and PS values indicating stronger phylogenetic structure, suggesting that closely related taxa tend to share the same trait, whereas higher values indicate weaker structure and more-frequent transitions between trait states. Statistical significance was assessed by comparing observed values against a null distribution generated through randomization, with P values reported for each test. All discrete trait groupings showed evidence for clustering by trait, supporting the use of trait modelling across the tree. The results of BaTS analyses for each discrete trait in this study are provided in Extended Data Table 1.

Tip-shuffle analysis

To assess the sensitivity of each of our discrete trait reconstructions to differences in sampling between groups, we implemented a modified version of a tip-swap analysis[38]. As originally developed, a tip swap analysis attempts to assess the impact of trait sampling on discrete trait measurements. An operator is implemented within the MCMC chain that randomly picks pairs of tips and swaps their trait values, thus generating a posterior set of trees among which pairs of trait assignments have been randomly swapped. The probability of each state at the root is then computed, and compared to the inferred root state probabilities in the empirical data. As the root state probabilities in randomized datasets should primarily reflect the frequency of each trait in the analysis, empirical results that differ substantially from this null distribution are interpreted as evidence that the sequencing data are informing the analysis beyond what is expected based on trait frequencies alone. Thus, traits for which the root state probability differs considerably from the root state probability in the null data are frequently interpreted as being informed by the data, rather than sampling bias. While this approach has been shown to perform well on small phylogenies1,86, the strategy of swapping single pairs of tips poses challenges for larger trees. In our flyways dataset, which includes around 1,000 tips, we found that, even with extremely high operator values (4,000), the traditional tip-swap analysis resulted in a posterior set of trees in which the majority of tips (93.3%) remained assigned to their true state at least 50% of the time, resulting in a null dataset that was only partially randomized. We believe that this is due to the high number of tips in our analysis, resulting in only an extremely small fraction of tips randomized at any given step in the MCMC chain. To overcome this limitation, we instead performed a randomized tip-shuffle analysis. Using the empirical set of trees inferred for each discrete trait analysis, we generated 100 null datasets in which we shuffled the trait assignments randomly across the tips. In this approach, we preserve the phylogenetic tree topology and the ratio of samples from each group, but shuffle their assignments at the tips. For each discrete trait analysis, we generated 100 distinct shuffled versions of the empirical trees, reran the analysis and summarized the resulting posterior distribution by inferring a maximum clade credibility tree. We then computed the root state probabilities for each trait for each MCC tree, and computed the mean root state probability across all 100 replicates. This computed mean is reported in Extended Data Table 2, in the column labelled ‘mean root state probability across 100 datasets with randomly shuffled tip states’. We then compared the root state probabilities in the empirical data (reported as ‘root state probability in empirical data’ in Extended Data Table 2) to the shuffled data as a measure of the impact of sampling on the results. As expected, the root state probabilities inferred in the shuffled datasets are proportional to the number of sequences included for each group. For the analyses using an equal sampling regime (migration, flyway, host orders equal and initial titration tests), this leads to approximately equal expected root state probabilities across groups. By contrast, the root state probabilities in the empirical data generally differ significantly from expectation, suggesting that the phylogenetic results are informed by the genetic data rather than from sampling alone. The results of tip shuffling analyses for each discrete trait in this study are provided in Supplementary Figs. 19–26 and Extended Data Table 2.

Reporting summary

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

Data availability

All data that were used in this analysis were sourced from public databases. The acknowledgement table for GISAID isolates used in this analysis is provided in Supplementary Table 18, which can also be found at GitHub (https://github.com/moncla-lab/North-American-HPAI). Several of the analyses presented have also been publicly made available using a maximum-likelihood framework through the Nextstrain pipeline and a narrative of this work can be found online (https://nextstrain.org/community/narratives/moncla-lab/nextstrain-narrative-hpai-north-america@main/HPAI-in-North-America).

Code availability

All analytical scripts, metadata annotations and BEAST XMLs used in this analysis are available at GitHub (https://github.com/moncla-lab/North-American-HPAI). This code is also tracked and freely available at Zenodo[87] (https://doi.org/10.5281/zenodo.17259872).

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Acknowledgements

We thank M. K. Torchetti for her feedback and discussion about our results. This work was supported by NIH R00-AI147029-05 and by funding from the Centers of Excellence for Influenza Research and Response (CEIRR), funded by NIH 75N93021C00015. L.H.M. is a Pew Biomedical Scholar and is supported by NIH R00-AI147029-05. L.D. is supported by NIH 75N93021C00015 and A.S.J. is supported by NIH R00-AI147029-05.

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Authors and Affiliations

1. Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA

Lambodhar Damodaran, Anna S. Jaeger & Louise H. Moncla

Contributions

L.D. and L.H.M. conceived the project. L.D. and A.S.J. curated data and performed analyses. L.D. and L.H.M. wrote the manuscript. L.D., A.S.J. and L.H.M. edited the manuscript text and figures. All of the authors discussed and approved the manuscript.

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Correspondence to Louise H. Moncla.

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Nature thanks Justin Bahl Vijaykrishna Dhanasekaran 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 Detections of HPAI in North America over time show distinct epidemic waves following introduction events in late 2021.

A) Detections of HPAI in wild birds, domestic birds, and non-human mammals over time. B) The Log-scaled Effective population size (Ne) estimates estimated in BEAST using the Bayesian SkyGrid coalescent for sequences collected between Sep 2021 and Aug 2023. C) Correlation plot of log(Ne) vs HPAI detections by week, spearman correlation displayed.

Extended Data Fig. 2 Multiple introductions of HPAI into North America from Asia.

An enlarged view of the clade associated with Asian introductions of HPAI into North America is shown, with taxa labelled.

Extended Data Fig. 3 Cumulative Markov jumps over time between USFWS flyways.

The cumulative number of markov jumps over time for each transition pair between each flyway with the three largest transition rate pairs labelled. Each line represents a single posterior sample.

Extended Data Fig. 4 Markov Jumps based on direction and adjacency.

A) Mean number of Markov jumps across the posterior distribution of trees between adjacent (directly next to each other) and distant geographic (not next to each other) groups based on latitude. B) Mean number of Markov jumps across the posterior distribution of trees based on direction of jump (North or South between geographic groups based on latitude). C) Map of North America with states coloured by latitude groups.

Extended Data Fig. 5 Source and sink behaviour by host type.

The top panel shows density plots for the mean number of Markov jumps across the posterior distribution of trees for jumps where the host acts as source. The bottom panel shows density plots for the mean number of Markov jumps across the posterior distribution of trees for jumps where the host acts as sink.

Extended Data Fig. 6 Two state rarefaction analysis with turkey sequences.

A) Violin plots of discrete trait transition rates from domestic to wild birds and from wild to domestic birds for two state titration test. B) Violin plots of discrete trait transition rates from domestic to wild and wild to domestic for the two state titration test that includes turkey sequences. All transition rates had a posterior probability of 0.99. C-E) MCC trees of the ratios of domestic (including turkey) to wild sequences across the titration test, shown in increasing order based on the number of wild bird sequences. Tips and branches are coloured by the state (wild or domestic) of the sample and inferred state respectively with turkey sequences coloured red.

Extended Data Fig. 7 Final titration of three state rarefaction analysis with turkey sequences.

A) MCC tree of three state analysis of 1:2 (domestic:wild) sequence dataset with equal numbers of turkey and domestic bird sequences. B) Exploded tree view of the three state 1:2 MCC tree for transitions into domestic. C) Exploded tree view of the three state 1:2 MCC tree for transitions into turkey. D) Exploded tree view of the three state 1:2 MCC tree for transitions into wild birds. E) Combined results of PACT analysis for each titration in the 2-state rarefaction and 2-state rarefaction including turkey. Colours correspond to analysis.

Extended Data Fig. 8 Three state rarefaction analysis between wild, domestic, and backyard birds.

A-D) MCC trees for each tree in the three-state titration analysis. Percentage refers to the percentage of all available wild sequences used in the given analysis. Colours correspond to the host domesticity status (wild, backyard bird, commercial bird). E-H) Proportion of transitions from wild to backyard birds and commercial birds across the posterior set of trees for each titration. Ratio represents the ratio (backyard:commercial:wild bird) of sequences in the given analysis.

Extended Data Table 1 Results of BaTs analysis for each discrete trait set used

Extended Data Table 2 Root state probabilities for discrete traits of each analysis in the study using the discrete trait shuffling test

Supplementary information

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Supplementary Figs. 1–26 and Supplementary Tables 1–18.

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Damodaran, L., Jaeger, A.S. & Moncla, L.H. Ecology and spread of the North American H5N1 epizootic. Nature 649, 432–441 (2026). https://doi.org/10.1038/s41586-025-09737-x

- Received: 11 January 2025

- Accepted: 09 October 2025

- Published: 12 November 2025

- Version of record: 12 November 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09737-x

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Gene-drive-capable mosquitoes suppress patient-derived malaria in Tanzania

Tibebu Habtewold,

Dickson Wilson Lwetoijera,

Astrid Hoermann,

Rajabu Mashauri,

Fatuma Matwewe,

Rehema Mwanga,

Prisca Kweyamba,

Gilbert Maganga,

Beatrice Philip Magani,

Rachel Mtama,

Moze Ally Mahonje,

Mgeni Mohamed Tambwe,

Felista Tarimo,

Pratima R. Chennuri,

Julia A. Cai,

Giuseppe Del Corsano,

Paolo Capriotti,

Peter Sasse,

Jason Moore,

Douglas Hudson,

Alphaxard Manjurano,

Brian Tarimo,

Dina Vlachou,

Sarah Moore,

Nikolai Windbichler &

…

George K. Christophides

Nature volume 649, pages 442–448 (2026)

Abstract

Gene drive technology presents a transformative approach to combatting malaria by introducing genetic modifications into wild mosquito populations to reduce their vectorial capacity. Although effective modifications have been developed, these efforts have been confined to laboratories in the global north. We previously demonstrated that modifying Anopheles gambiae to express two exogenous antimicrobial peptides inhibits the sporogonic development of laboratory-cultured Plasmodium falciparum, with models predicting substantial contributions to malaria elimination in Africa when integrated with gene drive1,2,3. However, the effectiveness of this modification against genetically diverse, naturally circulating parasite isolates remained unknown. To address this critical gap, we adapted our technology for an African context by establishing infrastructural and research capacity in Tanzania, enabling the engineering of local A. gambiae under containment. Here we report the generation of a transgenic strain equipped with non-autonomous gene drive capabilities that robustly inhibits genetically diverse P. falciparum isolates obtained from naturally infected children. These genetic modifications were efficiently inherited by progeny when supplemented with Cas9 endonuclease provided by another locally engineered strain. Our work brings gene drive technology a critical step closer to application, providing a locally tailored and powerful tool for malaria eradication through the targeted dissemination of beneficial genetic traits in wild mosquito populations.

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Main

Malaria remains a major public health concern, with many African nations being far from meeting their malaria elimination targets4,5. Vector control methods including indoor residual spraying and long-lasting insecticide-treated bed nets have played a pivotal role in reducing malaria incidence, but the emergence of insecticide-resistant mosquitoes has impeded further progress[6]. In addition, Africa’s rapidly growing population and persistent malaria receptivity make these interventions increasingly unsustainable as standalone solutions. This highlights the urgent need for innovative, self-sustaining and cost-effective technologies to complement existing efforts in malaria elimination. Gene drive technology, which enables the biased inheritance of selected traits and can spread through populations at rates exceeding those predicted by Mendelian genetics, has emerged as a promising new paradigm7,8.

Gene drive can offer a transformative solution for malaria elimination by spreading genetic modifications that can either suppress mosquito populations or render them unable to transmit the disease2,9,10. Our work focuses on the latter approach known as mosquito population modification or replacement, whereby antiparasitic effectors introduced into the mosquito genome are spread to fixation within populations using a Cas9 endonuclease-based synthetic gene drive. In our design, the transmission-blocking effector and gene drive functions are separated into distinct genetic traits and strains3,11,12,13. This separation offers several advantages: it allows the development, testing and optimization of effector constructs in endemic settings independently of a full gene drive system; it facilitates rigorous risk assessment and community engagement before introducing self-propagating elements and it provides a safer, more modular pathway towards deployment[12]. Crucially, evaluating non-autonomous effector strains helps address elevated regulatory and containment requirements associated with autonomous gene drive systems.

Genetic modification of mosquitoes to reduce their vectorial capacity was first attempted more than two decades ago, and dozens of transgenic strains have been described in the literature to date9,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30. However, no effector has ever been evaluated against parasites other than laboratory strains many of which were established in the early 1980s[31]. For this reason, their propensity to block the transmission of genetically diverse Plasmodium isolates now in circulation is unknown.

We previously demonstrated the efficacy of one such A. gambiae effector modification in inhibiting the NF54 strain of laboratory-cultured P. falciparum. This modification, termed MM-CP, involves two antimicrobial peptides, magainin 2 from the African clawed frog and melittin from the European honeybee[32], integrated into and expressed from within the endogenous zinc carboxypeptidase A1 gene (CP)[33]. This minimal genetic modification that harbours no fluorescent markers interferes with oocyst development causing a significant delay in the release of infectious sporozoites. It also reduces the lifespan of homozygous female mosquitoes, further minimizing their potential to transmit malaria. Predictive models suggest that gene-drive-mediated population-wide propagation of MM-CP could disrupt disease transmission across various settings, offering promise for malaria elimination even in scenarios in which resistance to the effector or the drive eventually emerge. Here we adapted this technology for an African context to evaluate its ability to suppress P. falciparum parasites naturally circulating among humans.

The implementation of gene drive technologies in malaria-endemic regions faces substantial challenges, including limited access to appropriate containment infrastructure, regulatory uncertainty, insufficient local capacity for genetic engineering and biosafety, and the imperative for community trust and public transparency. To enable our work, we developed an integrated Modular Portable Laboratory and Containment Level 3 (MPL/CL3) insectary facility, specifically designed for generating, housing and studying genetically modified mosquitoes within an African context (Fig. 1a). The MPL/CL3 was designed to address some of these constraints by offering a high-security and standardized facility tailored to local environmental and regulatory conditions. It incorporates climate and illumination control systems, rearing chambers, microbiologically safety cabinets, water management and waste disposal systems, an autoclaving unit and a fully equipped laboratory. The facility was constructed within two intermodal shipping containers in Spain and transported and installed at the Bagamoyo campus of the Ifakara Health Institute (IHI) in Tanzania (Fig. 1b). By embedding cutting-edge vector biology capacity within endemic settings, the MPL/CL3 supported local research leadership, regulatory readiness and public engagement, laying essential groundwork for responsible development and evaluation of gene drive technologies. Detailed specifications and technical plans are presented in the Methods and Supplementary Note. All protocols involving the generation and study of transgenic mosquitoes were reviewed and approved by the relevant institutional and national regulatory authorities in Tanzania.

Fig. 1: Infrastructure capacity building and malaria surveillance sites in northeastern Tanzania.

a, Integrated MPL/CL3 facility. Architectural design plans for the (left) and a detailed view of the integrated laboratory and insectary container unit (right) are shown. The laboratory comprises a lobby, an incubator room for mosquito husbandry, a molecular biology laboratory and a dedicated space for P. falciparum DMFAs and housing of infected mosquitoes. The second container unit houses systems that regulate and maintain optimal environmental conditions, including a negative pressure system for biosecurity, water purification and waste treatment. An external electricity generator supports these operations. b, Field sites for parasitological surveys and gametocyte carrier recruitment. Locations of villages in the Pwani region where parasitological surveys were conducted in children are shown in relation to the IHI Bagamoyo campus (housing the MPL/CL3 facility), the capital Dodoma, the major port city Dar es Salaam and the town of Chalinze, where meteorological data were recorded. The map is modified to highlight sites mentioned in the paper. Tanzania road map in b adapted from OnTheWorldMap.com (https://ontheworldmap.com/tanzania/tanzania-road-map.html).

The first A. gambiae transgenic line developed onsite within the MPL/CL3, named zpg-CC, was designed to streamline all transgenesis processes by expressing both Cre recombinase and Cas9 endonuclease under the control of the zero-population growth (zpg) gene promoter. This dual helper strain enables the efficient removal of sequences such as transgenesis markers flanked by loxP sites and establishment of transgene homozygosity through homing. The initial development and characterization of the zpg-CC line were conducted at Imperial, before the line was recreated in Tanzania.

The zpg-CC construct includes a dominant DsRed transgenesis marker, integrated into the kynurenine hydroxylase (kh) gene locus (Extended Data Fig. 1a). Disruption of both copies of the gene results in white-eyed mosquitoes, serving as a recessive phenotypic marker. Although the zpg-CC helper line was robust and fertile, it showed reduced overall fitness, probably due to the disruption of the kh locus and/or the effects of germline-specific or leaky expression of both Cre and Cas9. Compared with wild-type (wt) females, sugar-fed transgenic homozygous zpg-CC females showed a small decline in survival over time (Extended Data Fig. 1b), consistent with previous observations in other mosquitoes34,35. They also laid significantly fewer eggs after blood feeding, with a lower proportion hatching, indicating a reduction in reproductive fitness (Extended Data Fig. 1c).

To assess the efficiency of the zpg-CC helper line in inducing homing when combined with a non-autonomously driveable transgene expressing guide RNA (gRNA), we crossed heterozygous zpg-CC males with females of a previously generated CP knockout (CP-KO) line that harbour a green fluorescent protein (GFP) expression cassette and a gRNA expression module inserted within and targeting the CP gene[36] (Extended Data Fig. 1a). Heterozygous offspring expressing both GFP and DsRed were sib-mated, and the resulting larvae were screened for green fluorescence. All 623 larvae screened were GFP positive, compared with the 75% expected from a Mendelian intercross of hemizygotes. This indicates 100% Cas9-mediated homing, induced by Cas9 provided by the zpg-CC helper line (Extended Data Fig. 1d).

Next, we assessed the capacity of the zpg-CC helper line to excise a loxP-flanked GFP expression cassette through the expression of Cre recombinase. As a tester line we used the zpg-Cas9GFP strain, in which a Cas9 coding sequence was inserted within the zpg gene to encode Cas9 linked to the zpg C terminus through an E2A ribosome-skipping peptide sequence. An intron harbouring the excisable GFP expression cassette flanked by loxP sites and a gRNA module is located within the E2A sequence (Extended Data Fig. 1a). We crossed zpg-Cas9GFP males with heterozygous zpg-CC females, selecting GFP and DsRed positive males for subsequent crosses with wt females (Extended Data Fig. 1e). Among 417 offspring larvae, only 13 showed green fluorescence, indicating efficient Cre-mediated excision of the GFP cassette (97%).

These experiments confirmed efficient Cas9 and Cre expression by the zpg-CC helper strain. We therefore recreated the zpg-CC line in Tanzania, by microinjecting embryos of the A. gambiae Ifakara strain[37] with the zpg-CC plasmid together with independent Cas9 and gRNA helper plasmids. G_0 larvae showing transient fluorescence were allowed to mature into adults that were then crossed with wt mosquitoes in separate male and female crosses (Fig. 2). These crosses produced 28 G_1 transgenic larvae expressing DsRed in the nervous tissue, of which 25 (16 males, 9 females) reached adulthood. After four rounds of backcrossing with wt mosquitoes, a pure and stable colony was established by continuously selecting larvae showing DsRed fluorescence and kh locus disruption, indicated by the absence of eye pigments. Despite Cas9 expression, these mosquitoes cannot autonomously propagate their modification through gene drive, as they lack genomic integration of a gRNA gene.

Fig. 2: Schematic representation of the strategy for the generation of a markerless, homozygous MM-CP line.

This approach involved a series of breeding and selection steps, detailed as follows in a clockwise progression. Top left, tabular summary of the processes for generating the zpg-CC and MM-CPGFP transgenic lines. Top middle, each transgenic line was outcrossed with the wt Ifakara strain to enhance line vigour. Top right, strains were maintained through sibling mass crossing to preserve genetic stability. Bottom right, MM-CPGFP females were crossed with zpg-CC males, and double-positive individuals (GFP and DsRed) were selected and mass-bred. Bottom middle, this process enabled Cre-mediated removal of the GFP expression cassette (floxing) and Cas9-driven homozygosity of the MM-CP transgene (homing). Bottom left, non-fluorescent individuals were selected for pupal case genotyping to identify homozygous MM-CP individuals. These homozygous mosquitoes were then mass-bred to establish the MM-CP line, subsequently used in homing and P. falciparum DMFA infection assays. eGFP, enhanced GFP.

Next, we generated the MM-CP line by microinjecting A. gambiae wt Ifakara embryos with the MMGFP-CP plasmid, containing a gRNA for integration into the CP locus and a Cas9 source[38]. This resulted in a single G_1 transgenic male expressing GFP in the eyes and ganglia, which was then backcrossed with wt mosquitoes for four generations to establish a stable MMGFP-CP precursor line (Fig. 2). To achieve transgene homozygosity and remove the GFP marker cassette, MMGFP-CP females were crossed with zpg-CC males and double fluorescent progeny were sib-mated. The resulting colony carried the antimalarial MM-CP transgene as a minimal, markerless modification. Molecular genotyping confirmed that most mosquitoes in the colony were homozygous for the transgene (Fig. 2).

To quantify MM-CP inheritance rates in the presence of Cas9, we crossed MM-CP females with zpg-CC males (which express Cas9) and vice versa (Fig. 3a). Equivalent crosses with wt mosquitoes served as controls. The resulting F_1 zpg-CC;MM-CP and +;MM-CP heterozygotes from these experimental crosses were then separately crossed with wt mosquitoes, with male and female G_1 individuals crossed independently. We conducted three replicates for each cross and genotyped 20 F_2 progeny per replicate to assess the presence of the MM-CP transgene in a heterozygous state (Extended Data Table 1). Both male and female MM-CP;zpg-CC crosses with wt mosquitoes showed high inheritance rates of the MM-CP transgene to F_2 progeny, averaging 94.2 ± 4.9%, compared with the control crosses (MM-CP females crossed to wt males), which showed near-Mendelian segregation at 48.3 ± 4.1% (Fig. 3a). These results indicate high rates of non-autonomous gene drive of the MM-CP transgene when combined with germline Cas9 expression.

Fig. 3: Transmission efficiency and life history traits of MM-CP Ifakara mosquitoes.

a, Schematic of the non-autonomous homing assay used to evaluate transmission efficiency of the MM-CP transgene. Homozygous MM-CP females were crossed with either zpg-CC males (Cas9 source) or wt Ifakara males (control). F_1 progeny were sexed and reciprocally crossed with wt Ifakara mosquitoes. Twenty F_2 progeny per cross (in triplicate) were genotyped by PCR to detect the MM-CP transgene. The bar graph (far right) shows inheritance rates, with significantly higher MM-CP transmission in zpg-CC crosses, confirming efficient non-autonomous homing. Each row represents a biological replicate, and each box denotes one mosquito (5% rate). Note that only 17 mosquitoes were genotyped in 1 replicate. b, Fecundity of MM-CP (n = 23 and 25) and wt (n = 25 and 24) females, measured as the number of eggs laid per mosquito in two independent biological replicates. MM-CP females showed significantly reduced egg output compared with wt controls (P = 0.0005, two-sided Mann–Whitney U-test). Boxplots show median, interquartile range (25th to 75th percentiles) and full data range (whiskers) for each group; dots outside boxplots are outliers. Source data are provided in Source Data Sheet 1. c, Mean fertility of female mosquitoes used for the fecundity assays (b), measured as hatching rate (% of eggs developing into larvae), did not differ significantly between lines (P = 0.95, two-sided Mann–Whitney U-test). Error bars show the range of fertility rates across the two replicates. Source data are provided in Source Data Sheet 1. d, Survival curves postemergence. Left, females post-bloodmeal (BF); right, sugar-fed males. MM-CP females showed markedly reduced survival after blood feeding (P < 0.0001, log-rank test); males also showed reduced survival under sugar-only conditions (P < 0.0001), although to a lesser extent. Data points represent the mean of two independent biological replicates (MM-CP, n = 378 and 242; wt, n = 368 and 350), each comprising three cages of mosquitoes reared from separate aquatic trays. Error bars indicate the range between replicates. Source data are provided in Source Data Sheet 2.

MM-CP mosquitoes originally generated in a mixed KIL/G3 genetic background showed reduced fitness, including lower fecundity and decreased survival, particularly in females[38]. KIL and G3 are two genetically distinct A. gambiae laboratory strains colonized from northern Tanzania and The Gambia in the 1970s, respectively. Life history assays with the new MM-CP line in the A. gambiae Ifakara background yielded similar results, confirming that these phenotypes are consistent across genetic backgrounds, an important consideration for gene drive deployment. Specifically, MM-CP females laid significantly fewer eggs than controls (Fig. 3b), although hatching rates were comparable (Fig. 3c). Survival was reduced in both sexes, with the most pronounced effect observed in females following a bloodmeal (Fig. 3d). Although some survival effects may reflect inbreeding from the transgenesis process and are unlikely to persist under gene drive conditions involving continuous outcrossing, the sharp post-bloodmeal decline in female survival is probably driven by strong antimicrobial peptide expression at the bloodmeal-inducible CP locus or perturbations of CP expression due to the antimicrobial peptide integration. This phenotype is modelled to enhance the efficacy of the intervention by reducing the likelihood that infected females survive long enough to transmit the disease[38]. Despite these fitness costs, multi-generational cage experiments have demonstrated that MM-CP can still be driven efficiently to near-fixation when combined with a self-propagating Cas9 source, supporting the robustness of MM-CP under gene drive rconditions[39].

Next, we proceeded to assess the efficacy of the locally developed MM-CP strain in inhibiting parasites circulating among infected children. We conducted surveys in primary school pupils in Wami Mkoko and Miono Kikalo villages to determine the levels of parasitaemia and gametocytaemia (Fig. 2b). These surveys were later expanded to include children aged 6–14 in Kibindu village. Malaria infection was determined using rapid diagnostic testing (RDT), with parasites quantified through thick blood smear microscopy. Written informed consents were obtained from parents or guardians, and oral assent was secured from children. These activities were conducted alongside structured community engagement to promote transparency, address concerns and foster public trust. The results indicated year-round malaria transmission, with parasitaemia and gametocytaemia present in roughly 25–30% and 2–5% of screened children, respectively (Fig. 4a). The high malaria prevalence throughout the survey period may be linked to the 2023–2024 El Niño-Southern Oscillation that occurred between July 2023 and April 2024 and was associated with significantly more rainfall in this part of the country[40]. To ensure that a diverse array of parasite genotypes were circulating in these three villages, we sequenced four genes (CSP, AMA1, SERA2 and TRAP) known to provide a robust measure of P. falciparum diversity[41]. The results confirmed that our sampling strategy captured a representative diversity of genotypes in these communities (Fig. 4b).

Fig. 4: P. falciparum epidemiology and phylogenetic analysis.

a, Epidemiological data on P. falciparum parasitaemia and gametocytaemia among children in three villages within the Pwani region. The bottom x axis indicates screening dates, the top x axis indicates the number of children screened each day, and the y axis shows the percentage of parasitaemic and percentage of gametocytaemic children among the total number of children screened on each date. The total number of children screened per date is shown above the graph. Dates when gametocytaemic blood samples (one per date) were used for successful mosquito infections are indicated, with corresponding results reported in referenced figure panels. The blue gradient in the background represents the 60-day cumulative rainfall (mm) before each survey, recorded at the Chalinze meteorological station, and the gradient below the graph (yellow to blue) shows the difference in rainfall (mm) compared with the same period 1 year earlier. Screening dates when gametocytaemic blood was collected for the mosquito infection replicates (r1–5) presented in Fig. 5 are indicated. Source data are provided in Source Data Sheet 3. b, Phylogenetic tree of P. falciparum isolates obtained from 1–2 gametocytaemic children on most screening days. Consensus sequences of the CSP, AMA1, SERA2 and TRAP genes were concatenated and aligned to assess genetic relatedness. Each tip label shows the sample ID, collection date and percentage sequence identity to the P. falciparum NF54 reference genome. Coloured labels correspond to isolates used for mosquito infection experiments and are matched to their respective village of origin as shown in a. The scale bar represents sequence divergence, expressed as the square root of percentage sequence divergence. Raw sequencing data are available under BioProject accession PRJNA1299763 (NCBI SRA).

Children with high gametocyte densities were invited to provide blood samples for mosquito direct membrane feeding assays (DMFAs). Previous studies have shown that infection outcomes from DMFAs correlate closely with those from direct skin feeding, supporting their biological relevance42,43. Of numerous DMFAs conducted, five produced significant oocyst numbers and were processed further. The first three infections were used to assess oocyst counts and sizes at 9 days postfeeding, whereas the last two replicates served to quantify sporozoite loads in mosquito midgut and head and/or thorax tissues using real-time quantitative PCR (qPCR) at 13–15 days postfeeding. The parasite genotypic analysis confirmed that isolates used for the first three experiments diverge from the NF54 reference genome and from each other (Fig. 4b). Isolates used in the last two infection experiments were not sequenced.

Microscopy showed that most MM-CP mosquito midguts contained notably smaller oocysts (Fig. 5a), consistent with what was previously observed in KIL/G3 MM-CP mosquitoes infected with laboratory P. falciparum[38]. Quantitative measurements indicated a median oocyst diameter of 22.2 µm in MM-CP compared with 57.3 µm in wt midguts (Fig. 5b). However, some MM-CP mosquitoes also contained larger oocysts, similar in size to those in wt mosquitoes. Molecular genotyping of mosquito carcasses revealed that these midguts derived from heterozygous MM-CP or non-transgenic mosquitoes present in the MM-CP colony (Fig. 5c).

Fig. 5: Effect of MM-CP transgenic mosquitoes on P. falciparum oocyst growth and sporozoite output.

a, Representative images of mercurochrome-stained midguts from wt and MM-CP transgenic mosquito lines at 9 days post-infectious bloodmeal (dpi), showing marked differences in oocyst size. Insets show magnified regions of oocyst clusters. b, Quantification of oocyst diameters in midguts from wt (n = 19, n = 37 and n = 17, respectively) and MM-CP (n = 25, n = 29 and n = 21, respectively) mosquitoes from three independent infections experiments using P. falciparum gametocytes from infected children. The leftmost plot shows pooled data from all replicates (r1–3) and the remaining plots show data from each replicate individually. Boxplots show median, interquartile range and full data range (whiskers) for each group; dots outside the boxplots are outliers. Significance was tested using the Kruskal–Wallis H-test (****P < 0.0001; effect size η^2 = 0.355). Note that some variability in oocyst size is also visible in wt midguts, reflecting natural variation commonly observed in wt infections. Source data are provided in Source Data Sheet 4. c, Oocyst diameters in midguts of the MM-CP line classified by genotype: homozygous MM-CP (orange circles), heterozygous MM-CP/+ (yellow squares) or wt+/+ (grey circles). Note that all oocysts in wt mosquitoes originate from only two midguts, cautioning against any interpretations of size differences relative to heterozygous mosquitoes. Source data are provided in Source Data Sheet 4. d, Sporozoite data in wt and MM-CP mosquitoes from the fourth infection replicate (r4) assayed at 13–15 dpi. Left, prevalence of midgut sporozoites. Middle, prevalence of sporozoites in head and/or thorax tissues (used as salivary (sal.) glands proxy). Each shaded box represents 5% prevalence. Total numbers assayed and positives are shown. Right, dot plot showing relative sporozoite abundance in head and/or thorax samples. Source data are provided in Source Data Sheet 5. e, Same analyses as in d, shown for the fifth replicate (r5). Note that between the third and fourth replicates, the colony was further cleaned using pupal case genotyping to enrich for MM-CP homozygotes. Source data are provided in Source Data Sheet 5. Scale bars, 100 µm.

To improve the consistency of phenotypic analyses, we enriched the MM-CP transgene in the colony by implementing a pupal case genotyping strategy to identify and select homozygous individuals, thereby eliminating wt alleles at the CP locus and increasing the proportion of MM-CP homozygotes. In the fourth and fifth replicates, mosquitoes were dissected 13–15 days post-infective bloodmeal, and parasite detection was conducted through qPCR targeting the 18S ribosomal subunit in genomic DNA extracted from midguts and separately from head and/or thorax tissues for those mosquitoes that tested positive for midgut parasites.

Results from the fourth replicate indicated that although 36 (85%) of 42 MM-CP mosquitoes had detectable parasites in their midguts, none (0%) tested positive for parasites in their head and/or thorax, a proxy for salivary gland infection (Fig. 5d). By contrast, 82 (57%) of 143 wt control mosquitoes were midgut positive and 50 (35%) had head and/or thorax parasite presence, showing variable sporozoite loads. Similarly, in the fifth replicate, 56 (54%) of 104 MM-CP mosquitoes tested positive for midgut infection, but only 7 (7%) tested positive for salivary gland infection, all with very low infection levels (Fig. 5e). This contrasts with 104 wt mosquitoes of which 66 (63%) were midgut positive and 57 (55%) showed salivary gland infection.

These findings demonstrate that our earlier observations from a different A. gambiae MM-CP genetic background infected with the laboratory P. falciparum NF54 strain[38], characterized by reduced and delayed sporozoite development, impaired oocyst maturation and limited salivary gland invasion, are recapitulated when MM-CP mosquitoes are challenged with genetically diverse P. falciparum isolates from malaria patients. This highlights the robustness of the MM-CP phenotype across parasite genotypes and reinforces its potential for impact under real-world transmission settings. Although we cannot fully exclude the possibility that low-level sporozoite development may eventually lead to transmission, the combination of delayed parasite maturation and reduced post-bloodmeal mosquito survival is projected to severely constrain the likelihood of onwards transmission under field conditions.

Conclusion

Our study is breaking new ground towards the trialling and application of new genetic technologies to interrupt malaria transmission in Africa with the successful generation of genetically modified A. gambiae mosquitoes in Tanzania. By inhibiting P. falciparum oocyst growth, this engineered strain causes delayed sporozoite migration to the mosquito salivary glands, creating a barrier against malaria transmission. Its efficacy against field-derived parasites ensures that the findings are directly applicable to real-world transmission settings, providing a strong foundation for field testing. To ensure robustness across the continent, further validation is essential across a range of A. gambiae genetic backgrounds and environmental contexts. This must be accompanied by a comprehensive risk assessment framework that includes entomological and environmental evaluations to consider any potential or unintended affects. Monitoring for resistance in both mosquitoes and parasites is also key to ensure long-term effectiveness.

This research was executed within purpose-built, high-biosafety facilities in Tanzania, emphasizing the importance of African-led research infrastructure and expertise in advancing locally relevant innovation. Beyond enabling the present study, these facilities now serve as a scalable, sustainable platform for regional stakeholder engagement, training and future technology evaluation across the region. Our study illustrates the potential of gene drive technologies in malaria elimination and the critical need for continued investment in local capacity to ensure their safe, effective and equitable implementation.

Methods

Design and construction of the MPL/CL3 laboratory

Our design intent was to build a containerized insectary facility including a derogated BSL-3 laboratory with rearing areas for the culturing and manipulation of transgenic and infected mosquitoes completely within transportable ISO 668 series 1AAA (40 ft high cube) intermodal shipping containers, enabling cost-effective research in a disease endemic location. The project stages included the completion of the technical design, manufacture and/or construction, shipping to host site and finally the facilities in use. To enable reuse and modification for open malaria research the technical design plans are shared and accessible as part of this publication (Supplementary Note). Host facility provision of essential electrical and plumbing site service requirements and builders works to accept, connect to and operate the facility was incorporated into the design plan.

Characteristics of the MPL/CL3 facility include two self-contained modules comprising one laboratory module container and a second plant container (PCR) for the provision of all mechanical, electrical and general services. Notably all systems and equipment were shipped fully assembled and built in for delivery as a single package, to a concrete base prepared at the host site for connection to local services and infrastructure. Services connections between the laboratory container and plant container modules are specified in the technical plans (Supplementary Note).

The general layout of the laboratory module (drawing 2, layout) comprises fully sealed and insulated internal panels, partitions and doors to form four separate rooms making efficient use of space and functionality while ensuring biosafety and security. The laboratory module container layout incorporates (1) an entrance lobby; (2) a colony rearing room; (3) an infection, dissection and imaging room and (4) an infected mosquito room BSL-3 (drawing 10, sections laboratory module). All doors are self-closing and interlocked with access control on the main entry door. An emergency only exit panel is located to the rear. Laboratory benching, storage solutions and sinks are distributed within the four rooms. Supplied equipment include a glass washer, fridge freezer, autoclave and a Class 2 recirculating microbiological cabinet. Rooms (2) and (4) are equipped with self-contained incubators complete with lighting, temperature and humidity control. Room (3) is equipped with the Class 2 recirculating microbiological cabinet and an autoclave (drawing 3, equipment). The ventilation system serving the laboratory provides controlled airflow (drawing 12, HVAC (heating, ventilation and air conditioning) ducts) and a controlled air pressure regime (drawing 4, room pressures). Small power (drawing 20, power sockets), lighting (drawing 19, lighting) fire detection and alarm provision is incorporated throughout (drawing 8, fire protection). The PCR module consists of all essential mechanical, electrical and public health infrastructure to service the laboratory (drawing 11, sections PCR module). Ventilation systems include run and standby chillers, a dedicated air handling unit, general supply and extract air distribution including high efficiency particulate air-filtered extract (drawing 13, HVAC P&ID (piping and instrumentation diagram) schema; drawing 14, cooling and heating P&ID schema). Plumbing systems including hot and cold water services (drawing 15, cold and hot water P&ID schema) and purified water production and storage. A laboratory effluent treatment system comprising dosing tanks and pipework (drawing 16, waste effluent treatment P&ID). An electrical distribution cabinet serving the laboratory lighting, equipment and socket outlets; ventilation, plumbing and effluent systems and controls (drawing 24, modular laboratory general cabinet and HVAC). Uninterruptable power supply equipment provides back-up power to electrical systems including lighting, access control, fire alarm and controls (drawing 25, modular laboratory general cabinet uninterruptable power supply). The host facility assumed responsibility for the design and provision of essential site service requirements to support the containerized insectary facility including a 64-kW electricity supply (drawing 23, electrical installation general schema), plumbing comprising cold water and drainage services and a structural support or base. Host facility solutions for electricity included electrical supply cabling with connection to the national energy grid plus a back-up electrical generator and fuel storage. A purpose-built electrical installation building near to the plant container provides electrical supply cabling directly into the plant container. Piped cold water is provided into the plant container through a 7-metre elevated water tank with a soak away pit in place to receive piped treated wastewater from the plant container. In addition to the construction of a concrete base (drawing 29, MPL support; drawing 33, PCR module foundation), an insect moat, ramp, stairs and corrugated iron roof covering completed the site-specific building works.

Mosquito husbandry

We used the A. gambiae sensu stricto Ifakara strain, derived from mosquitoes collected in Njage, Tanzania, in 1996 (ref. [37]). Mosquitoes were maintained under optimized conditions: 27  ±  1 °C temperature, 70  ±  5% humidity and a 12 h/12 h dark/light cycle. Before floating, mosquito eggs were treated with a 1% bleach solution for 60 s. Larvae were fed with TetraMin fish flakes and reared at a density of 200 larvae per litre of deionized water from the L2 stage, ensuring a healthy mosquito population.

Mosquito transgenesis

For the zpg-CC helper line, freshly laid embryos were microinjected with a mixture of donor plasmid pD-zpg-Cre-Cas9 (400 ng µl−1) and helper plasmid p165-KMO[44] (200 ng µl−1). For the MM-CPGFP line, embryos were injected with donor plasmid pD-Mag-Mel-CP (400 ng µl−1) and helper plasmid p155-vasa-Cas9 (200 ng µl−1). To establish a homozygous markerless MM-CP mosquito line, we developed a pupal case genotyping protocol. Freshly shed pupal cases were collected individually, and genomic DNA was extracted using 20 µl of dilution buffer from the Phire Tissue Direct PCR Master Mix kit (Thermo Scientific). Multiplex PCR was performed with primers: HA5′ CP (GGGTTAAGCTGGGCTCGTTG), Mag-R (AGTTCATGATCTCGCCCACG) and HA3′ CP (CTCCTTCGGATGCTCACTGG). The wt alleles yielded a 670-bp band, whereas MM-CP alleles produced a 357-bp band. Confirmed homozygotes were used to propagate the colony.

Survival assays

For the zpg-CC A. gambiae line, triplicate groups of 15 female mosquitoes for each of the transgenic and wt lines were set up in standard insectary conditions. Cumulative mortality was recorded daily, and survival data were collected until all individuals had either died or were censored at the end of 25 days. Kaplan–Meier survival analysis was performed to generate survival curves, and differences between the transgenic and wt lines were evaluated using the log-rank test. For the MM-CP Ifakara line, 150 pupae from each of the MM-CP and wt controls were placed in separate BugDorm-4H3030 cages and allowed to emerge. Adults were maintained on a constant supply of 10% sugar solution. Mosquitoes were monitored daily, with dead individuals removed every 24 h, sexed and counted. Monitoring continued until all individuals in at least one cage had died. The assay was conducted in two biological replicates, each with a minimum of two technical replicates. Survival curves were compared between strains using the log-rank (Mantel–Cox) test in Prism v.10.

Reproductive fitness assays

For the zpg-CC A. gambiae line, relative fecundity (number of eggs laid) and fertility (hatch rate) were assessed by placing single, mated and blood-fed female mosquitoes in individual cups 1 day after blood feeding. Each mosquito was allowed to lay eggs that were subsequently allowed to hatch. The total number of eggs and larvae were counted under a microscope to determine fecundity and fertility. For the MM-CP Ifakara line, 100 5-day-old females from each MM-CP and wt control lines were blood-fed through membrane feeders. Only fully engorged females were retained. Forty-eight hours post-bloodmeal, 25 females per strain were individually housed in oviposition cups lined with moist filter paper. After 72 h, eggs laid were counted. Cups were then topped with water to facilitate hatching. On day six, the number of larvae per cup was recorded. Data on the number of eggs per female and the hatch rate (larvae-to-egg ratio) were analysed using the Kolmogorov–Smirnov test in Prism v.10.

Parasitological surveys

Participants were recruited from primary schools in Wami Mkoko and Miono villages during the school term, and children aged 6–14 were sampled in Kibindu village during the school closure period. Finger-prick blood samples were collected from children in good health who assented to the procedure and had written consent from a parent or guardian. A drop was used for a malaria RDT to identify infected participants, and another drop was used to prepare thick smears. Thick smears from participants who were RDT-positive were examined microscopically to detect and quantify gametocytes (per 500 white blood cells) by a certified microscopist using an OLYMPUS light microscope. Children with gametocyte density of ≥16 gametocytes per microlitre were candidates for blood drawing for DMFAs. All participants infected with malaria received treatment at the local clinic within 24 h, following the recommended protocol of oral Artemether-Lumefantrine.

Informed consent

Parents of study participants or their legal guardians provided written informed consent before enrolment. The consent process was conducted in accordance with approved ethical guidelines and included a detailed explanation of the study aims, procedures (including blood sampling and mosquito feeding assays), potential risks (such as minor discomfort from blood draws) and expected benefits (including free malaria diagnosis and treatment for positive cases). Participants were informed of their right to withdraw at any time. Assent was also obtained from participating children, and a witness was present for consent procedures involving illiterate participants. The informed consent form was reviewed and approved by the IHI Institutional Review Board and the Tanzania National Institute for Medical Research.

Phylogenetic profiling

Blood from malaria-infected patients was collected on QIAcard FTA Classic cards (Qiagen). Plasmodium genomic DNA was extracted from the dried blood spots using the DNeasy Blood & Tissue kit (Qiagen). Four genes were amplified from each sample using KAPA HiFi HotStart ReadyMix (Takara) as previously described[41], and samples were library sequenced by use of Oxford Nanopore sequencing by Full Circle Laboratories (UK). Adaptor sequences were removed from raw read sequences with Cutadapt[45] and mapped to the P. falciparum 3D7 genome (PlasmoDB v68) with BWA-MEM. Genetic variants were called and consensus sequences for each of the four genes assayed in each sample were generated with BCFtools[46]. Consensus sequences of each gene were concatenated for each sample, aligned against each other using the ‘msa’ package[47] and were incorporated into a phylogenetic tree using the ‘ape’ package[48] in RStudio (RStudio Team). Pairwise percentage identity values were calculated by aligning each concatenated sequence to the NF54 reference genome using pwalign in Bioconductor, applying the formula: 100 × (number of identical positions)/(aligned positions + internal gap positions). Sequence data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under BioProject accession number PRJNA1299763.

Blood processing and mosquito DMFAs

Study participants with high gametocyte counts were invited, along with at least one parent or guardian, to the MPL/CL3 laboratory for blood donation, which was then used in mosquito DMFAs. Blood collected in lithium-heparin coated vacutainers were processed by centrifugation to separate cellular components from the serum that was then replaced with commercially available AB human serum at half the original volume. The prepared blood mixture was transferred to membrane feeders, and mosquitoes were allowed to feed on blood for 15 min. Cages were then moved to an incubator set at 27 °C and 75% relative humidity. After 48 h, mosquitoes were provided with a 10% sucrose solution changed daily, and dead, unfed females were removed.

Mosquito dissection and oocyst detection

Parasite development, infection intensity and prevalence were analysed through systematic procedures across three independent infections. Midguts were dissected on day 9 post-blood feeding and stained with a 0.1% mercurochrome to facilitate oocyst identification, followed by microscopic examination. The diameter of oocysts was measured using ImageJ software. Dissections on day 9 assessed oocyst development, whereas dissections between days 13 and 15 evaluated sporozoite development in the salivary glands.

qPCR for sporozoite detection

Genomic DNA was used in 20-μl qPCR with reverse transcription reactions with the Fast SYBR Green Master Mix kit (ThermoFisher) to quantify the P. falciparum 18S ribosomal RNA (rRNA) gene fragment, using primers and methods as described in ref. [49]. Standard curves for both the Pf18S rRNA and the A. gambiae S7 reference gene were created through serial dilution of nucleic acid templates. Cycle threshold (Ct) values were converted using these standard curves, and Pf18S rRNA Ct values were normalized to those of S7.

Inclusion and ethics

This study was conducted with a commitment to inclusion, ethics and local engagement. Local researchers played a leading role throughout the process, including study design, implementation and data ownership, with roles and responsibilities agreed on in advance. The epidemiological and parasitological study was reviewed for posing no risks of stigmatization, discrimination or harm to participants. Written informed consent was obtained from parents or guardians, and oral assent was secured from children. No biological materials, cultural artefacts or traditional knowledge were transferred out of the country, unless specific transfer agreements were obtained. All research adhered to ethical guidelines and regulations, including the Declaration of Helsinki, ensuring that the rights of participants to withdrawal and privacy were protected throughout the study. Environmental and biorisk-related standards were carefully considered in constructing the infrastructure and carrying out the research. Approvals to carry out the research were obtained by IHI Institutional Review Board and the Tanzania Commission of Science and Technology. The study protocol was reviewed and approved by the IHI Institutional Biosafety Committee and the Tanzania National Institute for Medical Research.

Reporting summary

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

Data availability

The datasets generated and analysed during this study are available in the paper, Extended Data Fig. 1, Extended Data Table 1 and the Supplementary Information. Raw sequencing data are available under BioProject accession PRJNA1299763 (NCBI SRA).

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Acknowledgements

We extend our sincere gratitude to the pupils, principals and teachers at the participating primary schools for their invaluable involvement in this study. We are also grateful to the guardians of the pupils for their consent and collaboration, as well as the doctors and staff at the health centres for their assistance in P. falciparum gametocyte screening. We gratefully acknowledge T. Kebede for his technical support with mosquito maintenance. This research was made possible by the support of the Gates Foundation under grant nos. OPP1158151 and INV-058071 to the Transmission Zero project.

Author information

Author notes

1. Tibebu Habtewold

Present address: Liverpool School of Tropical Medicine, Liverpool, UK

Authors and Affiliations

1. Department of Life Sciences, Imperial College London, London, UK

Tibebu Habtewold, Astrid Hoermann, Pratima R. Chennuri, Julia A. Cai, Giuseppe Del Corsano, Paolo Capriotti, Douglas Hudson, Dina Vlachou, Nikolai Windbichler & George K. Christophides

2. Environmental Health and Ecological Sciences, Ifakara Health Institute, Bagamoyo, Tanzania

Tibebu Habtewold, Dickson Wilson Lwetoijera, Rajabu Mashauri, Fatuma Matwewe, Rehema Mwanga, Prisca Kweyamba, Gilbert Maganga, Beatrice Philip Magani, Rachel Mtama, Moze Ally Mahonje, Mgeni Mohamed Tambwe, Felista Tarimo, Peter Sasse, Jason Moore, Brian Tarimo & Sarah Moore

3. Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland

Jason Moore & Sarah Moore

4. University of Basel, Basel, Switzerland

Jason Moore & Sarah Moore

5. National Institute for Medical Research, Mwanza Centre, Mwanza, Tanzania

Alphaxard Manjurano

Contributions

T.H.: conceptualization, methodology, investigation, supervision, formal analysis and writing—review and editing. D.W.L.: conceptualization, project administration and resources. A.H.: methodology and investigation. R. Mashauri: investigation. F.M.: investigation. R. Mwanga: investigation. P.K.: investigation and project administration. G.M.: investigation. B.P.M.: investigation. R. Mtama: investigation. M.A.M.: investigation. M.M.T.: project administration and supervision. F.T.: project administration. P.R.C.: investigation. J.A.C.: investigation. G.D.C.: investigation. P.C.: investigation. P.S.: methodology. J.M.: methodology. D.H.: methodology. A.M.: project administration. B.T.: methodology and project administration. D.V.: conceptualization and supervision. S.M.: methodology and supervision. N.W.: conceptualization, project administration, funding acquisition, formal analysis, visualization and writing—review and editing. G.K.C.: conceptualization, supervision, project administration, funding acquisition, formal analysis, visualization and writing—original draft preparation.

Corresponding authors

Correspondence to Tibebu Habtewold, Dickson Wilson Lwetoijera, Nikolai Windbichler or George K. Christophides.

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Extended data figures and tables

Extended Data Fig. 1 Design and characterisation of the An. gambiae zpg-CC transgenic line.

a, Schematic representation of the molecular designs of transgenic lines used in this study (not to scale). zpg-CC: Inserted in the kh locus and contains a 3xP3-DsRed-SV40T marker, Cre recombinase and Cas9 under zpg promoter (zpgP)/terminator(zpgT) control, with Cre and Cas9 separated by a furin cleavage site and F2A ribosome skipping peptide (fuF2A). CP-KO: Includes a 3xP3-GFP-SV40T cassette and a gRNA under a U6 promoter (open red circle) inserted within the CP gene. zpg-Cas9GFP: Features Cas9 at the C-terminus of the zpg gene, separated by an E2A peptide split by the An. gambiae gambicin intron containing a 3xP3-GFP-RGT cassette flanked by loxP sites, and a U6-driven gRNA. RGT corresponds to the sequence of the rabbit global terminator cloned from the 3′UTR of the pTriEx7 expression system plasmid (Novagen). b, Kaplan-Meier survival analysis of zpg-CC and wt female mosquitoes (n = 15) maintained on sugar (Mantel-Cox/log-rank test) conducted in triplicates. c, Fecundity and fertility of individual zpg-CC females (n = 8) compared to wt controls (n = 11). Box plots show median (line), interquartile range (box), and full data range (whiskers); outliers are plotted individually. Statistical analysis was performed with two-sided Wilcoxon signed-rank tests. Left: wt vs zpg-CC, **p = 0.0017 (**). Right: wt, p = 0.022 (*); zpg-CC, p = 0.0038 (**). d, Mating scheme for testing the ability of zpg-CC mosquitoes to induce homing of the CP-KO transgenic cassette within the CP locus. The bar chart shows the observed and default (if Cas9 was not functional) percentage of green-fluorescent larvae. e, Mating scheme assessing the capacity of zpg-CC mosquitoes to cause Cre-mediated excision of the GFP expressing cassette in zpg-Cas9GFP mosquitoes. The bar chart shows the observed and default (if Cre was not functional and assuming 100% homing catalysed by a trans source of Cas9 as observed in panel 2d) percentage of green-fluorescent larvae.

Extended Data Table 1 Inheritance rates of the MM-CP transgene in the presence or absence of Cas9

Supplementary information

Supplementary Information

MPL/CL3 specifications and technical plans.

Reporting Summary

Supplementary Data

Source Data Sheet 1. Raw data on fecundity and fertility of MM-CP and wt female mosquitoes. Individual-level data from two independent replicates assessing the reproductive output of MM-CP and wt females. Each row corresponds to a single female monitored for oviposition (fecundity) and larval hatching (fertility). For each sample, the total number of eggs laid and the number of larvae that successfully hatched were recorded. Zero values indicate females that did not lay eggs or whose eggs failed to hatch. This data sheet contains the raw data used in Fig. 3b,c. Source Data Sheet 2. Daily survival data of MM-CP and wt mosquitoes over 17 days. Raw data from two biological replicates (replicates 1 and 2), each containing three technical replicates per strain, to assess mosquito survival under insectary conditions. For each day, the number of dead female and male mosquitoes was recorded separately for each strain. Each entry indicates a single observed death event on the corresponding day and replicate. This data sheet contains the raw data used in Fig. 3d. Source Data Sheet 3. Prevalence of parasitaemia and gametocytaemia in children from three Tanzanian villages. Data from malaria surveys conducted in schoolchildren across Wami Mkoko, Miono Kikalo and Kibindu villages in coastal Tanzania. Surveys involved RDTs to identify malaria infections and thick blood smear microscopy to quantify parasitaemia and gametocytaemia. Data are disaggregated by village and survey round. This data sheet contains the raw data used in Fig. 4a. Source Data Sheet 4. Quantification and genotyping of oocysts in MM-CP and wt mosquitoes infected with patient-derived P. falciparum isolates. Detailed measurements of oocyst size and corresponding mosquito genotypes from infection experiments using field-derived P. falciparum. Midguts were dissected at day 9 post-infection, stained and imaged to quantify oocyst area, diameter and radius. Each midgut was individually linked to a mosquito carcass, from which selective genotyping was performed to determine the presence and zygosity of the MM-CP transgene. The experimental strain (condition), biological replicate, mosquito/gut ID, oocyst ID within a given gut, the area of the oocyst in micrometres square, the estimated radius and diameter of the oocyst in micrometres, and the gut genotype are identified. ND, not determined. This data sheet contains the raw data used in Fig. 5b,c. Source Data Sheet 5. Quantification of parasite dissemination to mosquito head and/or thorax tissues in MM-CP and wt mosquitoes. qPCR results for P. falciparum 18S rRNA gene quantification in head and/or thorax samples of MM-CP and wt mosquitoes at 13–15 days post-blood feeding during two independent transmission-blocking experiments (replicates 4 and 5). A value of 0.00 indicates no detectable parasite signal. Non-zero values reflect varying degrees of parasite load in the salivary glands. This data sheet contains the raw data used in Fig. 5d,e.

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Habtewold, T., Lwetoijera, D.W., Hoermann, A. et al. Gene-drive-capable mosquitoes suppress patient-derived malaria in Tanzania. Nature 649, 442–448 (2026). https://doi.org/10.1038/s41586-025-09685-6

- Received: 29 November 2024

- Accepted: 29 September 2025

- Published: 10 December 2025

- Version of record: 10 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09685-6

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Parity and lactation induce T-cell-mediated breast cancer protection

Balaji Virassamy,

Franco Caramia,

Peter Savas,

Michael A. Harris,

Jia-Wern Pan,

Jianan Wang,

Emmaline Brown,

Megan M. R. O’Malley,

Courtney T. van Geelen,

Michael Hun,

Thomas N. Burn,

Sneha Sant,

Jamieson D. Ballan,

Jasmine Kay,

Luis E. Lara Gonzalez,

Kylie Clarke,

Han Xian Aw Yeang,

Rejhan Idrizi,

Metta Jana,

Damon J. Challice,

Roberto Salgado,

Heather Thorne,

kConFab consortium,

Cathie Poliness,

Sophie Nightingale,

Soo-Hwang Teo,

Terence P. Speed,

Jane Visvader,

Paul J. Neeson,

Phillip K. Darcy,

Laura K. Mackay &

…

Sherene Loi

Nature volume 649, pages 449–459 (2026)

Abstract

Parity and breastfeeding reduce the risk of breast cancer, particularly triple-negative breast cancer (TNBC)1,2, yet the immunological mechanisms underlying this protection remain unclear. Here we show that parity is associated with increased numbers of CD8+ T cells, including cells with a tissue-resident-memory-like phenotype within human normal breast tissue. In mouse models, pregnancy followed by lactation and involution drove the accumulation of CD8+ T cells in the mammary gland, coinciding with reduced tumour growth and increased intratumoural immune cell infiltration, effects that were abrogated by CD8+ T cell depletion. Importantly, this CD8+ T-cell-dependent tumour control was observed only after a complete cycle of lactation and involution. Consistent with this, primary triple-negative breast cancers from parous women exhibited greater T cell infiltration and improved clinical outcomes. Together, these findings, spanning preclinical models and over 1,000 patient samples, provide insights into how reproductive history shapes breast immunity, positioning CD8+ T cells as key mediators of parity-associated protection and informing strategies for both the prevention and the treatment of breast cancer.

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It is widely recognized that parity and lactation are associated with a reduced long-term risk of breast cancer1,2. Parity is thought to protect by reshaping mammary epithelial cell differentiation and growth pathways that occur during pregnancy, lactation and involution, thereby reducing susceptibility to malignant transformation over time3,4,5. While parity has been associated with a decreased risk of hormone-receptor-positive breast cancer, breastfeeding appears to confer a more specific reduction in the risk of triple-negative breast cancer (TNBC)1,6. The cellular and molecular mechanisms underlying this association are unclear. Deeper understanding could enable new subtype-specific prevention and treatment strategies for breast cancer. This could be particularly relevant for women at higher risk of TNBC, such as those who carry germline pathogenic variants, or for groups with poorer outcomes even after adjusting for key prognostic factors, such as certain ethnic groups[7].

The prognostic role of CD8+ tumour-infiltrating lymphocytes (TILs) is well established in both early- and advanced-stage TNBC8,9 and T cell checkpoint inhibitors are now part of standard-of-care therapy, underscoring the critical role of host immunity. Our previous research demonstrated that a subpopulation of CD8+ T cells with a tissue-resident-memory (TRM)-like phenotype mediates robust anti-tumour responses and is associated with reduced breast cancer recurrence8,10,11. We also showed that TRM-like cells were present in both breast tumours and cancer-unaffected normal breast tissue, implicating them in treatment response and long-term immune surveillance of the breast.

Post-partum, immune cell populations in mammary tissue undergo considerable modulation: pregnancy and lactation are followed by widespread apoptosis and tissue remodelling in the process known as involution12,13. Changes in immune subsets, such as increased natural killer T (NKT) cells in the post-involution mammary gland, suggest a parity-induced immune surveillance mechanism[14]. Furthermore, circulating T cells increases in parous women have been observed to persist for months post-partum and are thought to contribute to autoimmunity14,15,16,17,18. Collectively, these observations suggest long-lasting immunological breast remodelling after pregnancy, although the functional importance of these populations in tumour surveillance remains unclear. Here we tested the hypothesis that parity and post-lactational involution favourably reshape the mammary microenvironment by recruiting and retaining CD8+ T cells that persist long-term in the breast, to support immune surveillance and protect against tumorigenesis.

Parity associations with T cell quantity

To assess how reproductive history influences the immune landscape of normal breast tissue, we first compared the quantity and phenotype of CD8+ T cells in cancer-unaffected breast tissue from parous and nulliparous women[11]. We analysed four single-cell transcriptomic datasets comprising 985,662 single cells (including more than 35,000 CD8+ T cells) from normal breast tissue of 170 adult women at average breast cancer risk, collated from a previous study[16]. We observed that parous women (at least one full-term pregnancy) had a significantly higher proportions of CD3+, CD8+ and CD8+ TRM-like cells than nulliparous women (Fig. 1a). Other cell subsets analysed did not differ significantly (Extended Data Fig. 1a–e).

Fig. 1: Parity is associated with a significantly increased T cells in cancer unaffected human breast.

a, The immune cell abundance in normal breast from single-cell RNA-seq (scRNA-seq; y axis, cell type frequency) data, calculated as the indicated immune cells over total epithelial cells in women with average breast cancer risk. n = 170. The violin plots show the data distribution; the box plots show the interquartile range (IQR) extending from Q1 to Q3 (box limits) and the median (white bar), and the whiskers extend to the most extreme dot point within 1.5 × IQR and the points beyond show outliers. Statistical analysis was performed using two-sided Wilcoxon rank-sum tests; unadjusted P values are shown. Total cells, 985,662; CD45+, 120,467 from nulliparous (N, n = 53) versus parous (P, n = 117); CD3+ (n = 72,288 cells) in nulliparous (n = 53) versus parous (n = 115); CD8+ (n = 38,834 cells) in nulliparous (n = 53) versus parous (n = 114); and CD8+ TRM (n = 18,963 cells) in nulliparous (n = 53) versus parous (n = 114). b, The proportions of CD45+, CD3+ and CD8+ T cells of total cells and CD69+CD103– and CD69+CD103+ T cells of total CD45RA−CCR7−CD8+ T cells were determined by flow cytometry (nulliparous, n = 25; and parous, n = 65) analysis of normal breast in women with a high risk of breast cancer. Parous was defined as 1 or more full-term pregnancies (Methods). Statistical analysis was performed using two-sided Wilcoxon rank-sum tests, unadjusted for multiple comparisons. c, Representative multiplex OPAL images of nulliparous versus parous normal breast stained for DAPI, pan-cytokeratin (AE1/AE3), CD3, CD8, CD69 and CD103. Green indicates representative CD8+ T cell and white indicates merged CD69+CD103+CD8+CD3+ T cell (Methods). Scale bars, 100 μm. d, Persistence of CD69+CD103– versus CD69+CD103+CD8+ T cells over time (x axis; years from last live birth to human breast donation). n = 65. Datapoints are coloured according to age <50 years or >50 years for menopausal status. Unadjusted two-sided P values are shown. e, Volcano plot of breast CD69+CD103+CD8+ versus autologous circulating CD8+ T cells from non-cancerous parous women. n = 3. Significantly upregulated genes in CD69+CD103+CD8+ (limma test, false-discovery rate (FDR) < 1%, log[fold change (FC)] > 1) are referred to as the PB-TRM signature, f, Gene set enrichment analysis (GSEA) of the PB-TRM signature in normal breast (n = 109) from nulliparous women (n = 30) versus parous women (n = 79) from a previous study[21]. Exact P values are shown.

Source Data

We next evaluated immune infiltration using flow cytometry in cancer-unaffected healthy breast tissue from women undergoing prophylactic mastectomy including parous (n = 65; >1 child) and nulliparous (n = 25) individuals at high risk of breast cancer (Supplementary Table 1). Compared with nulliparous women, the breast tissue of parous women showed increased proportions of CD45+, CD3+ and CD8+ lymphocytes and, notably, significantly increased proportions of CD8+ T cells expressing CD69 and CD103 canonical markers of tissue residency (TRM-like cells; Fig. 1b). Multiplex OPAL staining further confirmed increased numbers of CD3+ and CD8+ T cells, including a CD69+CD103+CD8+ TRM-like cell population in breast tissue from parous compared with nulliparous women, with localization predominantly in intraepithelial or periductal pan-cytokeratin-positive regions (Fig. 1c and Extended Data Fig. 2a,b). This dataset also enabled us to examine the long-term persistence of CD8+ TRM-like cells over a wide range of age intervals between last live birth and tissue donation, in both pre- and post-menopausal women. CD69+CD103+CD8+ T cells were stably maintained for over 30 years after pregnancy and showed superior long-term retention compared with their CD69+CD103−CD8+ T cell counterparts, consistent with previous observations that CD103+ TRM-like cells predominate over time19,20 (Fig. 1d).

To investigate the qualitative differences in breast-associated CD8+ T cells related to parity and lactation, we performed bulk RNA-sequencing (RNA-seq) analysis of TRM-like cells isolated from breast tissue from parous women unaffected by cancer who had breast fed for at least 6 months, comparing the gene expression profiles with those of autologous circulating CD8+ T cells. We identified 347 differentially expressed genes in the parous breast TRM-like cells (Supplementary Table 2), including upregulation of ITGAE, ITGA1 and CXCR6, hereafter the parous breast-associated-TRM (PB-TRM) signature (Fig. 1e). Analysis of an independent dataset comprising 109 normal breast tissue samples from cancer-unaffected women[21] revealed significant positive enrichment of the signature in parous compared with nulliparous women, confirming parity-associated transcriptional differences (Fig. 1f). We also confirmed that the PB-TRM signature was significantly enriched in the CD8+ TRM compared with CD8+ TEM annotated single cells in the collated transcriptomic datasets[16] (Extended Data Fig. 2c).

Increased CD8+ T cells in healthy mouse MFP

We next investigated immune cell populations in the mouse mammary fat pad (MFP) after pregnancy, lactation and involution in C57BL/6 mice under three conditions: (1) a complete cycle of natural lactation (21 days) followed by pup weaning, with tissue analysed at 28 days after involution when the mammary gland had returned to a pre-pregnancy state (hereafter, d28-inv mice); (2) early force-weaned (FW) involution, whereby pups were removed within 12–24 h of birth with minimal lactation and tissues were analysed 10 days after involution (d10-FW mice); and (3)  respective age-matched controls to parous mice (virgin mice) (Fig. 2a).

Fig. 2: Complete lactation and involution significantly increased CD8+ T cells in healthy mouse mammary tissue.

a, Experimental schematic of parity. b, Immune cell frequencies (%) among CD45+ cells in the MFP were analysed using flow cytometry analysis of virgin (n = 9) and d28-inv (n = 12) C57BL/6 healthy mice; CD8α+CD44high T cells are highlighted in green. c, The numbers of the indicated immune cell subpopulations per gram of MFP from virgin (n = 9) and d28-inv (n = 12) C57BL/6 mice. d, The numbers of the indicated immune cell subpopulations per gram of MFP from virgin (n = 8) and d28-inv (n = 8) BALB/c mice. e, The numbers of the indicated immune cell subpopulations per gram of MFP from virgin (n = 8) and d10-FW (n = 8) C57BL/6 mice. f, Uniform manifold approximation and projection (UMAP) analysis of CD8α+ T cells generated from flow cytometry data from the MFP of C57BL/6 virgin (n = 8) and d28-inv (n = 9) mice; three enriched are clusters indicated. Each dot represents an individual cell. g, Feature plots of the indicated markers on CD8α+ T cells projected on UMAP. Data are mean ± s.e.m. The results represent two combined biologically independent experiments. Statistical significance was determined using two-sided (c–e) and one-sided (f) Mann–Whitney U-tests. Exact P values are shown, or P < 0.0001 in cases in which P values are very small.

Source Data

Immune profiling of the MFP at these timepoints revealed a selective increase in the frequency of CD44highCD8+ T cells among CD45+ populations in d28-inv mice (Fig. 2b and Extended Data Fig. 3a). Total CD45+ and CD8+ T cell numbers were also significantly elevated compared with virgin mice, including CD8+ T cell subpopulations expressing CD69+CD103− and CD69+CD103+ (Fig. 2c and Extended Data Fig. 3b). A similar enrichment of CD8+ T cell subsets was observed in parous BALB/c mice at 28 days after involution, reinforcing that CD8+ T cell responsiveness is a strain-independent feature of the post-involution mammary gland (Fig. 2d). By contrast, CD8+ T cells and TRM-like cell populations were not significantly increased in d10-FW C57BL/6 mice compared with the virgin controls, and only modestly in BALB/c mice (Fig. 2e and Extended Data Fig. 3c). However, d10-FW mice showed a marked increase in ductal macrophages, consistent with previous reports13,22 (Fig. 2e).

To further characterize parity-associated CD8+ T cells, we next performed high-dimensional immunoproteomic analyses. This revealed three clusters that were significantly enriched in d28-inv mice compared with the virgin controls (Fig. 2f). All three clusters expressed canonical residency markers such as CD69, CD103 and CD49a along with differential expression of granzyme A, NK1.1 and CXCR3 (Fig. 2g and Extended Data Fig. 4a). Supporting the presence of TRM-like cells, the d28-inv MFP also showed increased expression of Tgfb2, Tnf and Cxcl16 (Extended Data Fig. 5a), factors that are associated with CD8+ TRM development23,24,25. Consistent with our human breast tissue findings, multiplex OPAL fluorescence microscopy revealed a significant increase in CD3+CD8+ TRM-like cells in the MFP of d28-inv mice compared with the virgin control mice (Extended Data Fig. 5b–d), with these cells located closer to E-cadherin+ epithelial cells (Extended Data Fig. 5e). Together, these data show that a complete cycle of lactation and involution promotes the accumulation and epithelial association of CD8+ T cells in mouse mammary tissue, paralleling observations in the human normal breast.

Lactation and involution restrain tumour growth

Accelerated tumour growth during early involution has been reported in preclinical models and associated with increased myeloid cell infiltration12,26. To further explore how different stages of involution affect tumour progression, we examined tumour growth in the MFP of d28-inv and d10-FW mice, compared with in the age-matched virgin controls using syngeneic orthotopic mouse models of TNBC. To this end, AT3-OVA TNBC cells were injected into the MFP of C57BL/6 mice at the timepoints described above (Fig. 2a). Tumour growth in d10-FW mice was comparable to virgin controls (Fig. 3a), consistent with previous reports[12]. By contrast, tumour growth was significantly reduced in d28-inv mice (Fig. 3b). This protective effect was also observed in BALB/c mice using the D2A1 breast cancer cells, which lack engineered antigens such as ovalbumin (OVA), providing an additional model of parity-induced tumour suppression (Fig. 3c,d).

Fig. 3: Lactation and involution restrain tumour growth and are significantly associated with increased mouse mammary CD8+ TILs.

a, Tumour growth (left) curves and end-point tumour volume (right) of AT3-OVA TNBC cells injected into the fourth MFP of d10-FW (n = 7) and age-matched virgin control (n = 7) C57BL/6 mice. b, Tumour growth (left) and end-point tumour volume (right) of AT3-OVA cells in d28-inv (n = 8) and age-matched virgin control (n = 8) C57BL/6 mice. c, Tumour growth (left) and end-point tumour volume (right) of D2A1 cells injected into d10-FW (n = 9) and age-matched virgin control (n = 10) BALB/c mice. d, Tumour growth (left) of D2A1 cells in d28-inv (n = 10) and age-matched virgin (n = 11) control mice and the end-point tumour volume (right) of d28-inv (n = 5) and age-matched virgin (n = 6) BALB/c mice. e, Immune cell frequencies (%) were analysed by flow cytometry among all CD45+ cells in AT3-OVA tumours from virgin (n = 9) and d28-inv (n = 10) mice 23–25 days after breast cancer tumour cell injection. f, The ratio of CD8+ to CD4+ T cells and the frequency of CD8+ T cells among all CD45+ cells AT3-OVA tumours from virgin (n = 9) and d28-inv (n = 10) mice. g, The numbers of the indicated immune cell subpopulations per gram of AT3-OVA tumour from virgin (n = 9) and d28-inv (n = 10) C57BL/6 mice. Data are mean ± s.e.m. Results represent two combined independent experiments. Statistical significance was determined using two-sided Mann–Whitney U-tests. Exact P values are shown, or P < 0.0001 in cases in which P values are very small.

Source Data

Concomitant with reduced tumour outgrowth, we observed a significant increase in CD8+ T cells in d28-inv AT3-OVA tumours compared with in those from virgin mice (Fig. 3e,f), including OVA-specific tetramer+CD8+ T cells and CD69+CD103+ TRM-like subsets (Fig. 3g). Monocytes, CD4+ T helper type 1 cells, dendritic cells and XCR1+ type I dendritic cells were also elevated in d28-inv tumours, while B cells, natural killer (NK) cells and T cell receptor γδ (TCRγδ)+ cells were not significantly changed (Fig. 3g and Extended Data Fig. 6a). By contrast, CD8+ T cells were not increased in d10-FW AT3-OVA tumours compared with those from the virgin controls (Extended Data Fig. 6b). Similarly, in D2A1 tumours, CD45+ and CD8+ T cell numbers were higher in d28-inv mice, but not in d10-FW mice, relative to the virgin controls (Extended Data Fig. 6c,d).

CD8+ T cells mediate parity-induced protection

To investigate whether reduced tumour growth in parous mice was T cell mediated, we inoculated virgin and d28-inv Rag2−/−Il2rg−/−mice (lacking NK, T and B cells) with AT3-OVA cells and observed no difference in tumour growth (Fig. 4a). To assess whether CD8+ T cells could restore protection, we transferred naive CD8+ TCR transgenic OT-I cells (specific for OVA) into Rag2−/−Il2rg−/−mice before mating or into virgin controls. Tumour growth was significantly reduced in d28-inv mice compared with the virgin mice (Fig. 4b), but not when tumour cells were inoculated at 10 days after force-weaned involution (Extended Data Fig. 7a). Activated OT-I cells transferred before mating accumulated in greater numbers in the MFP at 28 days after involution compared with in virgin mice (Extended Data Fig. 7b), indicating that the post-lactation mammary microenvironment promotes the recruitment and differentiation of CD8+ TRM-like cells. In established tumours, CD45+ and TCR-vα2+CD8+ T cells were elevated in d28-inv mice compared with virgin mice, but not in d10-FW (Fig. 4c and Extended Data Fig. 7c). Adoptive transfer of activated gBT-I cells (irrelevant specificity) into Rag1−/− mice bearing AT3-OVA tumours had no effect on tumour growth (Extended Data Fig. 7d).

Fig. 4: Parity-induced protection against breast cancer is mediated by CD8+ T cells.

a, AT3-OVA tumour growth (left) and end-point tumour burden (right) in the fourth MFP of Rag2−/−Il2rg−/− d28-inv (n = 13) and age-matched virgin control (n = 14) mice. b, AT3-OVA tumour growth (left) and end-point tumour burden (right) in the fourth MFP of Rag2−/−Il2rg−/− d28-inv (n = 11) and age-matched virgin control (n = 10) mice pre-inoculated with OT-I cells 7 days before mating. c, The numbers of the indicated immune cells per gram of AT3-OVA tumour from virgin (n = 6) and d28-inv (n = 6) Rag2−/−Il2rg−/− mice at 4 weeks after tumour development. d, AT3-OVA tumour growth in the fourth MFP of virgin (left) and d28-inv (right) C57BL/6 mice treated with anti-CD4 (virgin, n = 12; d28-inv, n = 13), anti-CD8α (n = 9), anti-CD8β (virgin, n = 12; d28-inv, n = 14) and combined anti-CD4/CD8α (n = 14) or isotype control (n = 16). e, End-point AT3-OVA tumour weight in virgin (left) and d28-inv (right) C57BL/6 mice depleted with anti-CD4 (virgin, n = 12; d28-inv, n = 13), anti-CD8α (n = 9), anti-CD8β (virgin, n = 12; d28-inv, n = 14) and combined anti-CD4/CD8α (n = 14) or isotype control (n = 16). f, The numbers of CD3+ T cells per μl blood in virgin and d28-inv C57BL/6 mice treated with FTY720 (n = 8) or vehicle (n = 8). g, AT3-OVA tumour growth after treatment with FTY720 at the indicated timepoints (virgin, n = 11; d28-inv, n = 15) or treatment with vehicle (virgin, n = 12; d28-inv, n = 15). h, End-point AT3-OVA tumour weight in virgin and d28-inv C57BL/6 mice treated with FTY720 (virgin, n = 11; d28-inv, n = 15) or vehicle (virgin, n = 12; d28-inv, n = 15). i, The number of OVA-tetramer+ T cells per gram of AT3-OVA tumour (virgin, n = 10; d28-inv, n = 13) or vehicle (virgin, n = 10; d28-inv, n = 15) analysed 23–25 days after breast cancer tumour cell injection. Data are mean ± s.e.m. from two combined independent experiments. Statistical analysis was performed using two-sided Mann–Whitney U-tests (a–c), two-way analysis of variance (ANOVA) at the end point (d and g), Kruskal–Wallis tests (f and i) and one-way ANOVA (e and h). Exact P values are shown, or P < 0.0001 in cases in which P values are very small.

Source Data

We next tested the requirement for CD8+ T cells in parity-induced protection. For this, virgin and d28-inv mice were depleted of CD8α+, CD8β+ or CD4+ T cells before tumour inoculation and throughout the experiment (Extended Data Fig. 8a,b). As expected, isotype-treated d28-inv mice had lower tumour burdens than virgins, accompanied by increased CD8α+CD44high T cell numbers in tumours (Extended Data Fig. 8c,d). Critically, we found that tumour protection in d28-inv mice was significantly reduced by either CD8α or CD8β depletion alone, identifying CD8αβ+ as the key mediators and excluding a dominant role for CD8αα+ T cells (Fig. 4d,e). In virgin mice, only combined CD8α+ and CD4+ T cell depletion increased tumour growth. Notably, CD4+ T cell depletion alone enhanced tumour control in d28-inv mice. High-dimensional proteomic analysis of CD45+ cells from tumours revealed an increased abundance of two clusters (C1 and C2) enriched for CD8+ T cells and cDC1 markers in CD4+ depleted d28-inv mice (Extended Data Fig. 8e–k).

To assess whether local CD8+ T cells were sufficient for tumour control or whether circulating T cell recruitment was required, we treated virgin and d28-inv mice with the S1PR1 agonist FTY720 to block T cell egress before tumour inoculation (Fig. 4f). Tumour protection was lost in FTY720-treated d28-inv mice, accompanied by reduced tumour-specific CD8+ T cells (Fig. 4g–i). Collagen abundance did not differ in between virgin and d28-inv tumours (Extended Data Fig. 8l,m), suggesting that stromal differences are unlikely to explain T cell recruitment patterns. Together, these findings demonstrate that parity-induced tumour protection is mediated by conventional CD8αβ+ T cells. After lactation and involution, tumour challenge drives both expansion of the mammary-resident T cells and recruitment of circulating tumour-specific effectors, enhancing local immune surveillance and tumour clearance.

Breastfeeding and tumour immune infiltration

Having established that reduced tumour growth after lactation and involution in mice is dependent on CD8+ T cells, we next examined whether a similar association exists in human TNBC patients, in which high T cell infiltrate is well known to correlate with improved prognoses[8]. Epidemiological studies also suggest that, while parity may transiently increase breast cancer risk, breastfeeding attenuates the risk of developing TNBC[1]. To explore this, we examined the Malaysian MyBrCa cohort, which includes 934 women with early-stage breast cancer and detailed clinicopathological and genomic information. We focused on the subset of 734 patients with recorded parity status and 656 with breastfeeding history27,28. In this cohort, the median maternal age at birth of first child was 27 years (range, 14–43 years), the median number of children per woman was 3 (range, 1–11) and the median lifetime breastfeeding duration in women who breastfed at all was 4 months (range, 0.5–156; Supplementary Table 3). Using the gene-expression-derived ESTIMATE immune infiltrate score[29], our analysis revealed significantly higher tumoural immune content in basal-like (TNBC) in parous women overall, compared with in the nulliparous women who subsequently developed breast cancer post-partum (Fig. 5a). A similar increase in the ESTIMATE score was seen in parous women who breastfed compared with nulliparous women (Fig. 5b). We next performed differential expression analysis between these basal-like tumours in parous versus nulliparous patients and parous-breastfeeding versus parous-non-breastfeeding patients. We found that the PB-TRM signature generated in Fig. 1f was highly enriched with both parity and breastfeeding status (Fig. 5c,d), highlighting the specificity of the PB-TRM signature to breast involution by parity and lactation. Furthermore, parous women who had breastfed had a significantly greater intratumoural CD8+ T cell density in basal-like tumours and when compared with other breast cancer subtypes (Fig. 5e and Extended Data Fig. 9f).

Fig. 5: Parity and breastfeeding are associated with increased TILs in women with primary TNBC.

a, The effect of parity on subsequent tumour immune infiltration, as calculated using the RNA-seq-based ESTIMATE score in the MyBrCa cohort. Statistical analysis was performed using a linear model for ESTIMATE score with parity and sequencing batch as covariates. β is the estimated non-standardized coefficient for parity with 95% CI. b, The immune (ESTIMATE) score from the MyBrCa cohort with known breastfeeding (BF) status before breast cancer. Statistical analysis was performed using ANOVA with breastfeeding status and sequencing batch as covariates; two-sided, 95% CI; the mean difference was compared using Tukey’s honest significant difference test. P values were adjusted for intrasubtype comparisons only. c, GSEA of the PB-TRM signature in parous versus nulliparous women in the MyBrCa basal cohort. d, GSEA of the PB-TRM signature in breastfed compared with non-breastfed (no-BF) parous women in the MyBrCa basal cohort. e, The intratumoural T cell density in basal-like breast cancer was determined through immunohistochemistry analysis of CD8+ and CD3+ in the MyBrCa cohort, comparing nulliparous versus parous women by breastfeeding durations before breast cancer. The percentage CD8+ and CD3+ T cell density was quantified as the ratio of stain-positive pixels to all pixels within the tumour margins. Modelled with β regression adjusting for covariates of age at diagnosis and tumour grade. Unadjusted P values are shown with the average marginal effect (AME) for each group compared with nulliparous women. f, Kaplan–Meier survival analysis of OS in women (n = 270) diagnosed post-partum with primary hormone-receptor-negative breast cancer, stratified by breastfeeding status (yes versus no). Statistical analysis was performed using univariate Cox regression hazard ratio with 95% CIs. g, Quantification of stromal TILs on standard H&E-stained digital slides from primary hormone-receptor-negative women with high familial risk (n = 136). Age-adjusted percentage infiltration (y axis) by breastfeeding duration (x axis; Supplementary Table 4). The horizontal lines indicate the median values, and the vertical lines show the IQR. Group differences were tested using Kruskal–Wallis (K-W) tests. For a, b and e, the box plots show the median (horizontal line), IQR (box limits), the whiskers extend to the most extreme dot point within 1.5 × IQR and the points beyond show outliers. Exact P values are shown.

Source Data

We further examined an independent of cohort of 270 Australian women who carry a germline pathogenic variant and had been diagnosed with hormone-receptor-negative early breast cancer after at least one full-term pregnancy, with available breastfeeding history and survival outcomes (Fig. 5f and Supplementary Table 4). After adjustment for known prognostic factors, we found that patients who breastfed exhibited significantly longer overall survival (OS) after breast cancer diagnosis compared with patients who did not breastfeed (hazard ratio = 0.39; 95% confidence interval (CI) = 0.19–0.79, P = 0.009; Extended Data Fig. 9a). Using an established method to assess immune infiltration on haematoxylin-and-eosin-stained (H&E) digital slides[30], we found increased lymphocytic quantity in individuals with longer breastfeeding duration (Fig. 5g). Although precise separation of parity and breastfeeding effects in human datasets is challenging, our analyses of two independent cohorts suggest that breastfeeding may contribute additional immune-mediated protection beyond parity alone.

To further support the general clinical relevance of the PB-TRM signature, we examined associations of the PB-TRM signature with TILs and survival outcomes in the available early-stage breast cancer cohort31,32. We observed a strong correlation between increased TIL quantities[30] and higher levels of the PB-TRM signature in patients with TNBC in the TCGA Breast Cancer dataset[32] (Extended Data Fig. 9b,c). The PB-TRM signature was also enriched in basal-like tumours and associated with improved disease specific and OS in the METABRIC dataset (Extended Data Fig. 9d,e).

Discussion

The mammary gland undergoes profound structural and immunological changes across reproductive stages. While ductal macrophages in mammary gland biology are well studied, evidence increasingly supports a critical role for adaptive immunity, particularly during puberty and pregnancy13,33. Various immune cell subsets populate the ductal epithelium, and progesterone has been shown to influence T cell polarization during pregnancy12,33. Moreover, breast cancers that develop in the immediate post-partum period have a poorer prognosis34,35. However, the mechanisms by which parity and breastfeeding confer long-term protection from TNBC have remained unclear.

We show that normal breast tissue from parous women has significantly more CD8+ T cells, particularly those with a TRM-like phenotype, compared with nulliparous women across diverse backgrounds[16]. Given that CD8+ T cell quantity is associated with improved TNBC outcomes, we hypothesized that parity-driven CD8+ T cell responses could enhance long-term cancer immunosurveillance. Supporting this, CD8+ TRM-like cells in normal breast tissue from parous women were observed to persist for decades post-partum.

In mouse models, completion of a full reproductive cycle was associated with reduced tumour growth and a sustained increase in T cells in the MFP, including those with a TRM-like phenotype. These cells localized to the mammary epithelium under homeostatic conditions and were more frequently found within tumours after lactation, supporting their role in tumour immune surveillance. Depletion of conventional CD8αβ+ T cells abrogated parity-induced protection, confirming their essential functional role. Similarly, FTY720-mediated blockade of lymphocyte egress impaired tumour protection, suggesting that the parous mammary gland not only maintains resident CD8+ T cells, but also facilitates the recruitment of circulating tumour-reactive T cells from the periphery. This was accompanied by increased numbers of conventional type 1 dendritic cells the tumours of parous mice, consistent with enhanced CD8+ T cell priming and maintenance[36]. Previous studies reporting expansion of mammary intraepithelial lymphocytes and NKT cells through epithelial CD1d expression support the concept of immune remodelling after lactation, but our results indicate that these populations are not the primary mediators of tumour protection in our mouse models14,17,37. Instead, our data complement these studies by demonstrating a durable, CD8+ T-cell-mediated layer of tumour protection in the mammary tissue after lactation. These findings were paralleled in human TNBC, in which tumours from parous women exhibited greater T cell infiltration, which was also correlated with longer breastfeeding duration. Such associations may reflect cumulative antigen exposure, cytokine signalling, in addition to the involution-associated tissue changes that promote long-term T cell retention.

Given known population-level variation in immune infiltration and breast cancer risk, it is important to consider how these findings may generalize across populations. For example, higher TIL content in primary TNBCs from Asian compared with European populations has been previously reported, with exploratory substudies from phase III trials suggesting a numerically greater magnitude of benefit from checkpoint inhibition, although these comparisons remain hypothesis generating[38]. Although our findings focus on the potential preventive role of parity-induced CD8+ T cells that are resident in the breast, they suggest that the immune contexture of the parous breast could influence TNBC therapeutic responsiveness. This is particularly relevant given the increasing use of immune checkpoint blockade in early-stage disease. However, we acknowledge that this remains the subject of future investigation.

Tumour-extrinsic factors, such as the mammary microbiome and dietary influences, may further modulate immune infiltration and tumour development[39]. Moreover, post-partum tissue remodelling, including clearance of mutant clones and epigenetic changes3,40, might act synergistically with CD8+ T-cell-mediated surveillance to eliminate premalignant cells. Similar immune-mediated protective mechanisms have been observed in other tissues, such as commensal skin human papilloma viruses promoting local CD8+ T cell populations; immune-mediated regression of early lung neoplastic conditions; and HER2-specific T cells linked to parity-associated protection of ductal carcinoma in situ41,42,43. These examples underscore the importance of physiological immune health in normal tissue as a mechanism for cancer prevention—a concept that we demonstrate here specifically for TNBC.

Our study has limitations. Although we focused on TNBC, reproductive history might also influence immune-mediated protection in other breast cancer subtypes, although this remains to be determined. We recognize that we did not assess hormone-receptor-positive models due to limitations in preclinical systems, even though parity and breastfeeding is associated with reduced risk of all breast cancer subtypes. Oestrogen signalling has been shown to have diverse effects on immune cell function, with context- and receptor-specific outcomes. Although oestrogen can suppress certain immune responses through ERα signalling, ERβ activation in immune cells has been associated with enhanced anti-tumour activity[44]. Moreover, our human cohorts varied in reproductive histories, population backgrounds, time from tissue donation and last child, potentially introducing heterogeneity. Lastly, antigen specificity of the CD8+ T cells identified remains undefined, warranting future research.

In conclusion, our findings reveal that lactation-associated remodelling of the mammary gland confers durable CD8+ T-cell-mediated protection against TNBC. By integrating data from mouse models and diverse human populations, this work provides a new framework for understanding how reproductive history shapes long-term immune surveillance and influences breast cancer risk. These insights support the need for tailored prevention and treatment strategies that consider parity status and the importance of systematically capturing reproductive history in future immunotherapy clinical trials. This is particularly critical given the rising global burden of breast cancer[45].

Methods

Patient samples

This project was approved by the Human Research Ethics Committee of the Peter MacCallum Cancer Centre (project approval number ‘SEGMENT’ 13/123 and 97/27 for the Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab) with project approval numbers 129 and 150). All of the participants provided written informed consent before tissue and blood collection. Ethics approvals for the human MyBrCa cohort can be found in the original publication[28].

Tissue processing of human normal breast

Cancer-unaffected normal breast tissues were collected from prophylactic mastectomies from women from high-risk familial cancer families. Mammary tissues were divided into segments and either placed in neutral-buffered formalin for processing to formalin-fixed paraffin-embedded (FFPE) blocks, or a single-cell suspension was created for further processing. In brief, adipose tissue was removed from the mammary glands and the associated connective tissues were then finely diced into smaller fragments in RPMI1640 containing 1 mg ml−1 collagenase type 4 (Worthington Biochemical), 30 U ml−1 DNase (Roche Diagnostics) and incubated for 30 min at 37 °C. Digested tissue fragments were teased through a 70-μm sterile cell strainer, the sieve was irrigated with Dulbecco’s PBS and the homogenized cells were collected into multiple 50 ml conical tubes. Pelleted cells were stained with monoclonal antibodies for immunophenotyping and resuspended in 2% paraformaldehyde (PFA) buffer for flow cytometry spectral analysis.

Antibody labelling of human cells for FACS

Homogenized cells in suspension were labelled with monoclonal antibodies for 30 min at 4 °C in fluorescence-activated cell sorting (FACS) buffer (2% FBS in Dulbecco’s PBS), and washed twice in FACS wash buffer. The antibody panel included CD45 and HLA-ABC to discriminate between lymphocytes from other stromal and epithelial cell compartments in suspension, T cell markers CD3, CD4 and CD8, and the T cell differentiation markers CCR7, CD45RA, CD69 and CD103. Viable cells were revealed using the fixable zombie red, PE-Tex-red or fixable yellow viability dyes (BioLegend). Compensation controls were prepared for each antibody using UltraComp beads (BD Biosciences). Multiparameter flow cytometry data were acquired on the BD FACS Symphony A5 or LSR Fortessa X-20 instrument (BD Biosciences) and data were analysed using FlowJo v.10 (BD Biosciences). A representative flow cytometry gating strategy of immune cells in human normal breast tissue is shown in Extended Data Fig. 10a.

Human normal breast data analysis

Two-sided Wilcoxon rank-sum tests were used to compare parous and nulliparous groups across different T cell subpopulations. β regression was used to model the interval from last birth to tissue donation and how this affected CD8+CD103− and CD8+CD103+ T cells, as implemented in the betareg R package (v.3.2-1). Regression lines were fitted using β regression, controlling for age at tissue donation, with the 95% CIs shaded. The AME for trend over time, representing the change in the percentage abundance per 1 year increase in birth to donation interval (Fig. 1d). Age at tissue donation was included as a continuous covariate. The AME of the interval since last pregnancy was estimated using the ‘margins’ function from the margins R package (v.0.3.28), interpreted as the average change in cell proportion per year increase in the interval, averaged over the empirical distribution of age at tissue donation. To represent model fit on plot and calculate CIs, the fitted β-regression model was supplied to the ggpredict function of the ggeffects R package (v.2.2.1).

Mouse tissue processing and FACS

Spleens were mashed through a 70-μm cell strainer and incubated with red blood cell lysis buffer for 5 min at room temperature before staining. Mouse mammary tissue with the lymph nodes removed or tumour were minced into fragments and incubated in RPMI1640 containing 1 mg ml−1 collagenase type 4 and DNase II at 0.2 mg ml−1 for 30 min at 37 °C with shaking (300 rpm). Cells were serially passed and washed through 70-μm strainers followed by viability staining in PBS then Fc receptor (FcR) blockade antibody (clone 2.4G2) at 1:500 dilution for 10 min at room temperature. Flow cytometry staining was then performed with cell surface antibodies in FACS wash buffer (4% FCS, 2 mM EDTA) containing BD Horizon Brilliant Stain Buffer Plus (BD). After staining with an antibody cocktail for 30 min at room temperature, the samples were washed twice with FACS buffer and fixed with FOXP3/transcription factor staining buffer set (Invitrogen) according to the manufacturer’s instructions. The samples were either resuspended in 2% PFA or underwent intracellular staining in FOXP3/transcription factor permeabilization buffer followed for 30 min at room temperature. Intracellular stained samples were washed twice and resuspended in 2% PFA. The samples were analysed on the BD Symphony FACS analyser or 5-laser Cytek Aurora. Data were analysed using FlowJo v.10 or OMIQ cloud-based platform.

OMIQ was used for high dimensional analysis. Data were scaled using hyperbolic arcsine (asinh) transformation and clustered using Phenograph. For high dimensional analysis of immune cells in the MFP, TCRγδ+ and CD1d+ cells were excluded and CD45+TCRβ+CD8α+ cells were clustered based on the expression of granzyme B, CD38, CD62L, TCF-1, CD49a, NK.1, CD8β, SLAMF6, CD103, CD44, Ly6C, KLRG1, CXCR3, CD39, PD1, CD244, TBET, CD69, CXCR6, CD8α and granzyme A. For high-dimensional analysis of immune cells in AT3-OVA tumours, CD45+ cells were clustered based on the expression of CD19, NK1.1, CD3, TCRb, TCRγδ, CD8α, CD8β, CD4, CD44, CD62L, CD69, CD103, CD49a, CD11b, CD11c, F480, MHC-II, SIGLEC-F, XCR1, SIRPα, CX3CR1, CD64, CD39, OVA-tetramer, PD-1, PD-L1, LAG3, KLRG1, CD38, FOXP3, TBET, TCF-1 and CTLA-4.

Bulk RNA-seq gene expression analysis

Cancer-unaffected normal-breast-tissue-associated CD69+CD103+CD8+ T cells and autologous blood derived CD8+ circulating T cells (5 × 10^3–1 × 10^4 cells) were FACS purified under sterile conditions using the BD FACSAria II Cell Sorter (BD Biosciences). For reanalysis of isolated CD8+ T cell subpopulations after FACS, each individual sample was assessed for >90% purity. Total RNA was extracted from the indicated T cell subpopulations from n = 3 pooled cancer-unaffected normal breast tissue samples using the RNeasy mini kit (Qiagen) according to the manufacturer’s instructions. RNA Tape Station (Agilent) analysis was performed according to the manufacturer’s instructions to assess the quantity and quality of RNA present in the sample. Then, 2–5 ng was used for RNA library preparation according to the manufacturer’s instructions with ribo-depletion NEB. The library was then amplified with 3′ PCR primers containing sample indices and the Illumina clustering guides. 10× lysis Buffer is a cell lysis buffer that can be used together with the SMARTer Ultra Low Input RNA Kit for Sequencing v3 and the SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing. Indexed libraries were pooled and sequenced on the Illumina NextSeq HO 75SE (Illumina) system. Two to five million single-end reads were generated as an output. Adaptor trimming was performed, and reads were aligned to the mm10 reference genome using HISAT2 (v.2.2)[46]. Aligned reads were quantified using HTSeq (v.2.0.3). Counts were normalized and unwanted variance that was introduced by library size differences was removed using RUV-III (v.0.9.7.1) in R. Differential expression analysis was performed using limma (v.3.60.3). Volcano plots were produced with custom code using ggplot2 (v.3.5.1). The PB-TRM signature was derived from differential expression analysis by filtering the genes by FDR < 1% and log[FC] > 1. A gene expression dataset of premenopausal parous and nulliparous human breast tissue was accessed at Gene Expression Omnibus (GEO: GSE112825). We used limma[47] to analyse the previously normalized expression and perform differential expression analysis of parous against nulliparous women. To detect enrichment of PB-TRM gene signature, we performed GSEA using the differential expression results with the fgsea (v.1.30.0) library in R (v.4.4.1)[48].

Single-cell cohort

The processed and annotated single-cell normal breast tissue atlas presented previously[16] was used; only cells annotated as ‘not sorted’ or ‘live sorted’ were collated from several datasets of normal human breast tissue. We used samples from women annotated as ‘average risk’ or ‘unknown risk’ of breast cancer, that is, normal breast samples from reduction mammoplasties and not cancer mastectomies or prophylactic mastectomies, and where parity status was known.

To calculate and compare the abundance of normal breast cell types between parous and nulliparous women, we calculated ratios of CD3+ T cells, CD8+ T cells and CD8+ TRM-like cells over total epithelial cells per sample, compared using unpaired Wilcoxon tests; Violin plots shown were produced in R (v.4.4.1). We conducted a focused analysis on the CD8+ T cells, performing integration with Harmony (v.1.2.3)[49] using the default parameters (using dataset and donor as covariates), and dimensionality reduction and visualization with Seurat (v.5.2.1)[50]. We used the scaled gene counts and calculated single-cell signature enrichment of the PB-TRM signature using the AUCell (v.1.28.0) package in R[51]. We compared single-cell enrichments of two different previously annotated CD8+ T cell subpopulations (TEM and TRM-like phenotype) using the unpaired Wilcoxon rank-sum test.

Mouse cell lines

AT3-OVA cells were provided by P. Darcy. D2A1 cells[52] were provided by K. Britt. AT3-OVA TNBC cells were generated by transducing the parental AT3 cell line with a retroviral vector pMIG/MSCV-IRES-eGFP plasmid encoding membrane-bound chicken OVA cDNA (model antigen) protein, tagged with GFP as previously described[53]. Both cell lines were well established from Peter MacCallum Cancer Centre laboratory stocks and published. Breast cancer cells were cultured with complete DMEM medium supplemented with 10% FCS. For in vivo experiments, 2.5 × 10^5 AT3-OVA or 1 × 10^4 D2A1 cells at an early passage state were resuspended in 50 µl PBS solution at neutral pH and injected orthotopically into the right fourth MFP of mice. Cell lines were verified to be mycoplasma negative at the Victorian Infectious Diseases References Lab and were maintained at 37 °C in a humidified incubator under 5% CO_2.

Mouse tumour and parity models

All animal experiments conducted in this study were approved by the relevant Peter MacCallum Cancer Centre Animal Experimentation Ethics Committee (E648, 2025-05) or by The University of Melbourne Animal Ethics Committee (21938) and conducted in accordance with the National Health and Medical Research Council Australian Code of Practice for the Care and Use of Animals for Scientific Purposes.

C57BL/6J wild type (WT), BALB/c WT, Rag2−/−Il2rg−/−, Rag1−/−, gBT-I and OT-I CD45.2 WT female mice were obtained from the Walter and Eliza Hall Institute of Medical Research or bred in-house (Victoria, Australia). Mice were allocated to experiments at 6–8 weeks of age. For tumour models, volume (length × width^2 × 0.5) was calculated by vernier callipers after development of palpable tumours, and measurements were taken every 3–4 days. Mice were euthanized when MFP tumours reached an ethical limit of up to 1,500 mm^3, or if the animals showed any signs of adverse health indications as per Peter MacCallum cancer centre institutional guidelines. For parity experiments, dams were whitened before the introduction of stud males to establish breeding pairs/trios. Pregnancy was confirmed by plug formation and studs were removed. After littering, dams underwent complete lactation and with pups for 21 days and, for forced wean experiments, the pups were removed at birth within 12–24 h from the dams post-partum and euthanized (d10-FW timepoints). Dams that underwent early pup removal were immediately co-housed with other female mice and did not exhibit changes in social behaviour. Housing conditions for the mice were followed according to institutional guidelines, including dark–light cycle, ambient temperature and humidity conditions.

Adoptive transfer of transgenic T cells

OT-I cells were isolated using the negative immunomagnetic cell-separation method, MACS (Stem Cell Technologies), from the spleen of WT OT-I CD45.2 donor mice. A total of 2.5 × 10^5 OT-I cells was intravenously transferred to recipient Rag2−/−Il2rg−/− mice, 7 days before mating and in house breeding began. OT-I cells were immunophenotyped by flow cytometry to assess the purity of OT-I cell isolation (>90–95%) with the use of CD45.2, TCR-vα2, CD8α, CD44 and CD62L cell surface markers. For experiments with effector OT-I cells, T cells were activated in culture for 4 days with OVA257–264 (SIINFEKL) peptide-pulsed, irradiated (50 Gy) splenocytes in the presence of recombinant human IL-2 (25U ml−1; PeproTech) at 37 °C and 5% CO_2 and 1 × 10^4 cells were injected intravenously per mouse.

For the gBT-I experiment, HSV-specific gBT-I transgenic T cells were activated in culture for 4 days with gB498–505 (SSIEFARL) peptide-pulsed, irradiated (50 Gy) splenocytes in the presence of recombinant human IL-2 (25U ml−1; PeproTech) at 37 °C and 5% CO_2. The 2 × 10^7 activated gBT-I cells were injected intravenously into naive Rag1−/− mice. Then, 56–62 days after gBT-I cell transfer, mice were subsequently inoculated orthotopically with 5 × 10^5 AT3-OVA cells into the fourth MFP as described above.

T cell depletion experiments

C57BL/6 mice were mated as described above before being allocated to tumour experiments. Seven days before AT3-OVA tumour challenge, non-randomized mice were injected intraperitoneally with 200 µg IgG2 isotype control combined with 200 µg anti-CD4 (GK1.5, Bio X Cell Leganon), anti-CD8α (YTS, Bio X Cell), anti-CD8b (53-5.8, Bio X Cell) or 400 µg IgG2 isotype control (Bio X Cell) twice then once weekly.

Fingolimod (FTY720) in vivo treatments

C57BL/6 mice were mated as described above before being allocated to tumour experiments. In brief, 3 days before AT3-OVA challenge, the mice were injected intraperitoneally with 1 mg per kg FTY720 compound reconstituted in 2% 2-hydroxypropyl-β-cyclodextrin (Merck) or vehicle control (2% 2-hydroxypropyl-β-cyclodextrin) with daily injections separated by one day breaks every 5 days.

Mouse histology and OPAL analysis

Intact fourth MFPs from C57BL/6 mice were fixed in 10% neutral-buffered formalin for 24 h and then processed and embedded into FFPE blocks. Then, 4-µm-thick sections were cut from blocks and mounted onto SuperFrost+ slides. m-IHC staining using OPAL fluorophores was then performed according to the manufacturer’s instructions (Akoya Bioscience). In brief, the reagents were loaded into the Bond Rx Autostainer (Leica Biosystems) system for an entire cycle of approximately 12 h. The tissue sections were baked, dewaxed and incubated in a 3% hydrogen peroxide (H_2O_2) solution (Merck EMSURE) to block endogenous peroxidases in the tissues. Subsequently, the slides were incubated with 1% BSA in Tris-buffered saline to block non-specific background binding. Slides were incubated iteratively with the primary antibodies including CD8 (1:4,000, pH 9), CD3e (1:600, pH 6) and CD103 (1:2,000 pH 9), for 30 min, the secondary antibody (OPAL polymer HRP mouse + rabbit) for 10 min, and the tyramide-conjugated OPAL fluorophores (570 and 690 (1:150) and 780 (1:25)) for 10 min. Finally, the slides were incubated with spectral DAPI for 10 min. All antigen retrieval was performed at 97 °C for 20 min and all of the other steps were performed at ambient temperature. After the completion of staining, the slides were briefly rinsed in milliQ water and then manually cover slipped using Prolong Glass Antifade Mountant (Invitrogen).

The stained 4-plex OPAL m-IHC slides were imaged on the Phenoimager HT imaging system (AKOYA Biosciences). The whole tissue section was spectrally imaged with a ×20 objective. The optimal exposure settings were manually set for each tissue. Whole-slide images were then unmixed to subtract the autofluorescence signal and images were exported as component .tiff files using InForm software (v.2.6.0, AKOYA Biosciences). In brief, the spectral library was created using multispectral images obtained from single stained slides for each marker and the associated fluorophore (DAPI, OPAL 570, 690 and 780) on InForm software (v.2.6.0, AKOYA Biosciences) for downstream image analysis workflow. For Masson’s trichrome staining, 4-µm-thick sections were cut from FFPE blocks and incubated in Bouin’s fixative for 60 min at 60 °C, washed in water and stained with haematoxylin. The slides were washed and stained with 1% Ponceau 2R and 1% acid fucshin for 2 min. Slides were washed with water stained with 2% light green in 1% acetic acid for 5 min, washed with water, dehydrated and cover slips were mounted.

Human histology and OPAL analysis

Human breast tissue preparation

Immune cells were examined to visualize the expression of CD3, CD8, CD69, CD103 and pan-cytokeratin (AE1/AE3) in cancer-unaffected normal breast tissue, using the OPAL serial immunostaining protocol. In brief, FFPE tissue sections were cut at a thickness of 4 μm and melted at 60 °C for 45 min, followed by dewaxing in three changes of histolene for 11 min and 3 changes of 100% ethanol for 1 min each and 70% alcohol for 1 min. The Leica Bond auto-stainer protocol was performed according to the manufacturer’s instructions.

Antigen-retrieval process

Heat-induced antigen retrieval was achieved using a microwave. Tissue sections were placed into a plastic hellendahl jar (Trajan Scientific Australia) in EDTA (pH 8) antigen-retrieval buffer and citrate buffer (pH 6) antigen-retrieval buffer for CD3 staining and brought to the boil at a 100% power for 50 s and at 10% power for 15 min. The tissue sections were then left to cool for 30 min and washed in 0.02% TBST three times for 7 min each with gentle agitation.

Primary antibody staining

The sections were blocked in blocking buffer (Dako, X0909) for 10 min at room temperature before incubation with primary antibodies or isotype controls. For primary antibody staining, tissue sections were incubated for 30 min at room temperature with mouse anti-human pan-cytokeratin (AE1/AE3; 1:1,000, Opal 480; isotype: mouse IgG1), rabbit anti-human CD3e (SP7; 1:500, Opal 690; isotype: rabbit IgG), mouse anti-human CD8 (4B11; Leica; 1:800, 570–1:500, Opal 570; isotype: mouse IgG2b), rabbit anti-human CD69 (EPR21814; Abcam, Opal 520; isotype: rabbit IgG), rabbit anti-human CD103 (EPR416602; Abcam; 1:1,000, Opal 780; isotype: rabbit IgG) and cell-permeable DNA-binding dye (DAPI) obtained from Abcam. After primary incubation, the sections were washed five times in 0.02% TBST for 5 min each. The tissue sections were then incubated in 0.3% H_2O_2 for 10 min after the first primary antibody incubation and washed three times in 0.02% TBST for 5 min each. Respective isotype antibody control expression was examined under similar conditions and was tested on matched human normal breast and in human tonsil lymphatic tissues.

Secondary antibody detection

All sections were incubated with secondary-HRP conjugated antibodies (Leica Bond autostainer protocol) at a dilution of 1:1,000 for 10 min at room temperature. The sections were washed three times for 5 min each in 0.02% TBST followed by signal amplification using 100 μl of TSA Plus working solution per slide at a dilution of 1:50 in 1× amplification diluent, incubated at room temperature for 10 min as specified by the manufacturer (Opal 6-Plex Manual Detection Kit for Whole Slide Imaging, AKOYA Biosciences). Nuclei were counterstained with DAPI according to the manufacturer’s instructions diluted from 10× spectral DAPI (AKOYA Biosciences) for 2 min at room temperature. The sections were then washed twice in 0.02% TBST for 2 min each and mounted in Vectashield hard-set medium (Vector) and left to dry flat for 20 min in the absence of light.

Microscopy image analysis

OPAL m-IHC slides were imaged on the Pheno Imager HT (Polaris) (AKOYA Biosciences). The acquired images were spectrally unmixed using inform v.2 (AKOYA Biosciences) into individual component .tiff files. Whole-slide images were then stitched using the HALO Image Analysis Platform v.3.6.4134 (Indica Labs). For human tissue, quantification of immune cells, specifically the CD8+ T cell subpopulations in the mammary epithelial-dense regions after excluding adipose-dense regions across the entire tissue was performed using the HighPlex FL v.4.2.3 module. For mouse tissue, quantification of immune cell subsets was performed using the HighPlex FL v.4.2.3 module after excluding lymph node regions within the surrounding tissues of interest. For the distance between ECAD+ epithelial cells and CD3+CD8+CD103+ T cells, nearest neighbour was calculated on the HALO Image Analysis Platform v.3.6.4134. Masson’s-trichrome-positive staining was quantified using QuPath (v.0.6)[54].

RNA extraction and qPCR with reverse transcription analysis

MFP tissue from C57BL/6 mice was manually minced and then homogenized in 500 µl TRIzol using metal bead lysing matrix (MP Biomedicals) on the FastPrep-24 bead-beating grinder and lysis system (MP Biomedicals). RNA was extracted from the homogenized tissue using the PureLink RNA Mini Kit (Invitrogen) according to the manufacturer’s instructions. Total RNA was then quantified using the NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific). First-strand complementary DNA (cDNA) was synthesized from extracted RNA using the SuperScript IV VILO Master Mix with ezDNase enzyme (Invitrogen) according to the manufacturer’s instructions. This was performed on a T100 Thermal Cycler (Bio-Rad).

Quantitative PCR (qPCR) was performed on the synthesized cDNA to assess relative gene expression using the TaqMan Gene Expression Assay with primers targeting Gapdh, Mm99999915_g1; Tgfb2, Mm00436955_m; Tnf, Mm00443258_m1; and Cxcl16, Mm00469712_m1. All qPCR reactions were performed on the StepOnePlus Real-Time PCR System (Thermo Fisher Scientific). Gapdh was used as the housekeeping gene. The \({2}^{-{\rm{\Delta \Delta }}{C}_{{t}}}\) method was used for calculating relative gene expression[55].

Human and mouse reagents and antibodies

Flow cytometry antibodies used for normal breast immunophenotyping include CD45-BV510, CD3-APCH7, CD103-BV421, CD8-BV605, PD-1-BV785, CCR7-BV711, CD3-BV711, CD4-BV650, CD69-PerCP-Cy5-5, Zombie red-PE-Tex-Red (BioLegend), HLA-ABC-PECy5, CD45-FITC, CD8-PE and CD45RA-FITC (BD Biosciences).

Flow cytometry antibodies used for mouse healthy MFP analysis and mammary tumour immunophenotyping include CD45-APCCY7, TCRβ-BB700, TCRβ-BV786, CD8-BV711, CD8-BUV395, CD4-BUV805, CD44-BV605, CD62L-PECY7, CD62L-BUV737, CD69-BUV395, CD69-PE, CD103-FITC, CD103-PE, CD11B-BV711, CD11C-BV786, F4/80-EF450, MHC-II APC and Fixable dyes Fix-Yellow-BV510, Zombie NIR or Zombie red PE-Tex-Red (BioLegend).

Antibodies used for spectral flow cytometry immunophenotyping were as follows: CD45-PerCP, CD19-AF488, TCRγδ-PE-Cy7, NK1.1-BV570, CD3-Spark NIR685, TCRβ-BB700, CD8α-BUV395, CD4-BUV805, CD44-APCR700, CD62L-BUV737, CD69-PE, CD103-BV421, CD11b-BV711, CD11c-BV785, F480-APC, Ly6G-BV650, Ly6C-BV605, MHC-II-Spark Blue 550, SIGLEC-F-PerCP-Fire 806, XCR1-Spark UV387 an CD64-PE-Fire 744, and H-2Kb-OVA-tetramer-PE or H-2Kb-OVA-tetramer-APC were used to detect and track CD8+ T cells specific to SIINFEKL (OVA257–264) peptide from OVA-expressing tumour cells. Antibodies used to generate the CD8α+ T cell UMAP were as follows: CD49a-BUV395, NK1.1-BUV563, CD8α-RB545, CD8b-BUV661, CD4-BUV805, SLAMF6-BV421, CD103-BV480, CD44-BV510, Ly6C-BV570, KLRG1-BV605, CXCR3-BV650, CD39-BV711, PD1-BV750, CD244-BV785, TCRγδ-BB700, CD1D-tetramer-PE, CXCR6-PeDazzle594, CD69-PE-Cy7, CD45.2-SparkNIR685, CD62L-APCR700, TCRβ-APC-Cy7, CD38-APC-Fire810, granzyme-A-e450, TCF1-AF488, granzyme-B-APC, TBET-PE/Fire810. Antibodies used to generate the AT3-OVA CD45+ UMAP were as follows: CD45-PerCP, CD19-AF488, TCRγδ-PE-Cy7, NK1.1-BV570, CD3-Spark NIR685, TCRβ-BB700, CD8α-BUV395, CD8β-BUV615, CD4-BUV805, CD44-APCR700, CD62L-BUV737, CD69-PE, CD103-BV421, CD11b-BV711, CD11c-BV785, F480-BUV661, Ly6G-BV650, Ly6C-BV605, MHC-II-Spark Blue 550, SIGLEC-F-PerCP-Fire 806, XCR1-Spark UV387, CD64-PE-Fire 744, H-2Kb-OVA-tetramer-APC, CD39-PE-Dazzle594, PD1-RY586, PD-L1-RB780, LAG3-RB744, KLRG1-BV510, CD38-APCFire810, FOXP3-e450, TBET-PE/Fire810, TCF1-RB705 and CTLA-4-PE-Fire-640. A representative flow cytometry gating strategy of immune cells in murine tissue is shown in Extended Data Fig. 10b.

Human MyBrCa clinical dataset

The full cohort described previously[28] was filtered to cases with available RNA-seq data, complete parity information and stage 1–3 diseases. Where patients had multiple synchronous tumours that underwent RNA-seq, one sample per patient was selected giving preference to more complete clinical metadata and basal-like subtyping. Parity was defined as at least one live birth, and pregnancy-associated cancers were not excluded. The ESTIMATE immune infiltration scores calculated in the MyBrCa publication were used unmodified. An exploratory analysis of the associations of sequencing technical factors and known clinicopathological predictors influencing immune infiltration was conducted. An effect of sequencing batch on the ESTIMATE score was observed. There was otherwise variable impact of age at diagnosis, grade and stage on the ESTIMATE score that was not consistent across subtypes. Linear models were fit for each PAM50 subtype with the ESTIMATE score as the response (non-standardized) and parity (yes or no), age at diagnosis (continuous), grade (grade 1/2 or grade 3) and stage (I, II or III) as covariates, using the lm function in R. Next, stepwise model selection using the akaike information criterion (AIC) was performed using the step function. Three cases without PAM50 and 20 cases that contained normal-like classification were excluded from the analysis. Selected covariates differed across subtypes, with parity being the only selected covariate for the basal-like subtype. The most parsimonious model was therefore considered to include parity (yes or no) and sequencing batch (1 or 2) as covariates, and this was used for the final analysis for all subtypes. The 95% CIs for the estimated co-efficient were calculated using the confint function in R (v.1.11.0). Use of the AIC selected models with more covariates specific to each subtype did not alter the conclusion about the influence of parity on immune infiltration in basal-like cases.

For the breastfeeding analysis, cases were divided into those with any history of breastfeeding regardless of duration (BF), no history of breastfeeding (no-BF) and nulliparous cases (N). Three-way ANOVA models were fit for each subtype with the ESTIMATE score as the response (non-standardized), breastfeeding status as the grouping variable (BF, no-BF or N) and sequencing batch (1 or 2) as a covariate as above using the aov function in R (v.1.11.0). Homogeneity of variances between groups in each subtype was confirmed with Bartlett’s test using the bartlett.test function in R. Departure from normality was tested using the Shapiro–Wilk test and examination of QQ plots. This showed a minor departure from normality for the basal-like group ESTIMATE scores, which was not considered detrimental as there are at least 30 cases in each group. Between group mean differences were tested with Tukey’s honest significant difference method using the TukeyHSD function in R, also reporting a 95% CI for the mean difference and P value adjusted for multiple comparisons with the Tukey–Kramer method.

For GSEA in the MyBrCa dataset, RNA-seq was conducted and processed to raw counts as described previously[28]. The RNA-seq counts were normalized using RUVIII[56] to remove batch effects and other unwanted variations. We next performed differential expression analysis comparing parous and nulliparous basal samples as well as breastfeeding parous and non-breastfeeding parous samples using the R package limma[47]. GSEA was performed using the fgsea (v.1.30.0) library in R to detect gene set enrichment of the TRM gene signatures, outputting a normalized enrichment score (NES) and FDR.

The P values reported for subtype-specific analyses were not corrected for multiple testing. The rationale for this is that these subtypes display clearly distinct characteristics, and there is a large body of literature supporting the differential biology of these subtypes and the variable significance of immune infiltration between them. There is no reasonable expectation of equipoise regarding the influence of parity on immune infiltration across subtypes.

Human MyBrCa Immunohistochemistry

IHC-stained FFPE tissue sections were prepared according to a standard diagnostic workflow. Four slides per tumour sample were stained with primary antibodies targeting specific antigens: anti-CD3 (2GV6, predilute; Ventana Medical Systems), anti-CD8 (SP57, predilute; Ventana Medical Systems), with an additional slide stained with H&E. The antibody staining was conducted using a Roche Ventana Bench Mark XT Autostainer, and the stained slides were digitized using the Aperio AT2 whole-slide scanner (Leica Biosystems).

Semi-automated scoring of immune markers

For CD3+ and CD8+ T cell IHC scoring, Aperio Imagescope (Leica Biosystems) was used to view the scanned images and 50 annotation boxes, each of size 0.7 mm × 0.4 mm, were drawn within the borders of the invasive tumour region. The borders of the invasive tumour region were determined with reference to the H&E-stained slides. Next, the Pixel Positive v.9 algorithm implemented in Aperio Imagescope was used to calculate the number of positively stained pixels within the annotation boxes at a 0.16 colour saturation threshold. The number of positive pixels was then divided by the total number of pixels within the annotation boxes to obtain the proportion of the annotated area that was positively stained for CD3+ and CD8+ immune cell types.

Human MyBrCa data analysis

β regression was used to model the relationship between T cell density (represented as a proportion of stained to total pixels) and parity/breastfeeding, as implemented in the R package betareg (v.3.2-1). Exploratory analysis determined that age at diagnosis of cancer and tumour grade were associated with T cell density, and these were used as covariates in addition to parity/breastfeeding as predictor variable. The emmeans R package (v.1.11.0) was used to calculate the AME and CIs between groups.

Breastfeeding patient cohort

Patient data were acquired from kConFab[57], including women diagnosed with breast cancer over the age of 18, with a known germline pathogenic variant who were diagnosed between the years of 1980 and 2022. Only patients who had hormone-receptor-negative primary breast cancers, had at least one full-term pregnancy and confirmed breastfeeding status (yes/no) were included for survival analysis. Patients who had subsequent pregnancies after breast cancer diagnosis were not included in the cohort. All relevant clinical data are shown in Supplementary Table 4. Data were not blinded. Breastfeeding duration was recorded based on total length across one or more children, where available.

Stromal TIL scoring

TILs were scored in accordance with the guidelines from TILs International Working Group as the percentage of infiltration of the tumoural stroma[30] from digitized H&E slides. Slides were acquired where possible (n = 136 out of 270).

To account for potential confounding effects of patient age on stromal TIL percentages, we performed an age-adjusted analysis. Stromal TIL data were obtained from the updated dataset containing clinical annotations and pathological assessments of breast cancer patients. The remaining patients were stratified into defined breastfeeding duration groups (0, <6 months, 6–12 months and >12 months).

We adjusted the stromal TIL percentages for patient age using a linear regression model, fitting stromal TIL percentage as the dependent variable and age at diagnosis as the independent predictor. Adjusted stromal TIL values were derived from the residuals of this linear model, standardized to the mean intercept value, effectively removing linear age-related variation. A Kruskal–Wallis test was used to evaluate differences in age-adjusted stromal TIL percentages across breastfeeding groups.

Breastfeeding patient survival analysis

The analysis examined the association between breastfeeding and survival outcomes using both univariate and multivariate Cox proportional hazards models. Survival data were censored at 15 years, resulting in 45 observed events. Univariate analyses evaluated the direct effect of breastfeeding status on OS, while multivariate analyses controlled for potential confounders including patient age at breast cancer diagnosis, years from breast cancer diagnosis from last live birth (≤10 years versus >10 years), chemotherapy treatment (yes/no), and mastectomy (yes/no). Robust variance estimates were applied and the proportional hazards assumption was tested using the Schoenfeld residuals test.

PB-TRM signature and TILs correlation

We accessed the ht-seq gene counts from TCGA using the Broad Institute Firehose platform. The counts were normalized to remove library size effects using edgeR[58]. The PB-TRM signature scores were computed using the genefu package[46] in R. Stromal TILs were scored for each TCGA case by a pathologist (R.S.) using whole-slide H&E images according to established methods[30]. PB-TRM signature scores and TILs were correlated using the Spearman’s correlation coefficient in R.

PB-TRM signature score and survival

We accessed the normalized microarrays from the METABRIC study[31] using cBioportal[59] and computed the PB-TRM signature scores using genefu (v.2.36.0)[46]. The samples were stratified into two groups around the median signature score (high versus low) and Kaplan–Meier survival estimates were compared between groups using the log-rank test. Hazard ratios were also calculated in R using the PB-TRM signature scores by fitting a Cox proportional-hazards regression model.

Statistics, codes and reproducibility

All statistical analyses and the associated information relating error bars, box plots and sample size used to determine statistical significance are provided in the figure legends, and the statistical tools used are given in respective methods section. For Fig. 1b, the proportions of CD45+, CD3+ and CD8+ T cells over total cells were provided, and CD69+CD103– and CD69+CD103+ T cells over total CD45RA−CCR7−CD8+ T cells (y axes) were determined by flow cytometry analysis of normal breast of women with high breast cancer risk. For box plots, the horizontal bar shows the median, the box limits show the IQR and the whiskers extend to the most extreme dot point within 1.5 × IQR, and points beyond show outliers. Multiplex OPAL microscopy data associated with representatives in Fig. 1c were also verified by flow cytometry and OPAL verification cohort were repeated n = 5 per group in independent (N versus P) human breast tissue samples with comparable results; single-colour-stained images with markers and the collated data are provided in Extended Data Fig. 2a,b. All experiments were reproduced twice, and the results represent two combined biologically independent experiments unless specified. Breastfeeding patient cohort statistical information and statistical analyses of human data were conducted using R v.4. For mouse data, we used GraphPad Prism v.9. Violin plots with error bars representing the IQR were used to compare immune cell proportions. No codes or software tools were specifically developed for this study. Plots were produced using the ggplot2 R package (v.3.5.1), and data input and processing were performed with dplyr v.1.1.2, tidyr v.1.3.0, readxl v.1.4.3, readr v.2.1.4 and magrittr v.2.0.3.

Reporting summary

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

Data availability

Processed and raw bulk RNA-seq counts resulting from FACS-sorted CD69+CD103+CD8+ T cells used to generate the PB-TRM signature can be accessed at the GEO (GSE271307). scRNA-seq data and all associated cell annotations are available from the original publication[16]. Code associated with analyses can be accessed at Zenodo[60] (https://doi.org/10.5281/zenodo.17120517). Data associated with human normal breast and murine experimental analyses are available as source data. Clinical metadata and sequencing data for the human MyBrCa dataset are accessible with permission as per the original publication[28]. Human breastfeeding data with associated OS and TIL scores are provided by kConFab. As this is an ongoing prospective cohort, the ethics pertaining to this data requires that data access be approved via the process described at https://www.kconfab.org/Data%20Access/AppProcess.html. using the contact details provided at https://www.kconfab.org/contact_us.html. The corresponding author (S.L.) can assist requesting parties in facilitating applications to access the data. Responses are provided within a 2-month time frame. All other data supporting the findings of this study are provided in the Supplementary Information. Source data are provided with this paper.

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Acknowledgements

We thank the staff at the Peter MacCallum Cancer Centre Flow Cytometry Facility, A. Lee, S. Bencraig and T. Semple from the Molecular Genomics Core, the Animal Core Facility and N. Anthony from the Centre for Advanced Histology and Microscopy at the Peter MacCallum Cancer Centre; members of the Australian genome research facility (AGRF) for their assistance with RNA-seq; R. Young and D. Byrne for the OPAL microscopy reagents and histology sectioning support; C. Mintoff, M. Neeson, T. Tan and V. Setiono for assistance with human breast tissue processing; H. Makmur and M. Khairi from Cancer Research Malaysia for their assistance with compiling the clinical and digital pathology data from the human MyBrCa cohort; M. Pereira for providing D2A1 cells; the Kconfab team members G. Glavich, A. Smith, L. Mokdsi and E. Niedermayr for their ongoing support; and J. Parrodi for her administrative support. This work was supported by the Mark Foundation ASPIRE II Award (5820526561), Australia, S.L. is supported by the National Breast Cancer Foundation of Australia (NBCF) (EC-17-001), the Breast Cancer Research Foundation, New York (BCRF-21-102) and a National Health and Medical Council of Australia (NHMRC) Investigator Grant (1162318), and the Peter MacCallum Cancer Centre Foundation; P.S. by a National Health and Medical Council of Australia (NHMRC) Investigator Grant (2017477) and the Peter MacCallum Cancer Centre Foundation; B.V. by a Postdoctoral Research Fellowship from Cancer Council Victoria and the Department of Health, Victoria, Australia; and P.K.D. by a National Health and Medical Research Council Principal Research Fellowship (1136680). L.K.M. is a Senior Medical Research Fellow supported by the Sylvia and Charles Viertel Charitable Foundation.

Author information

Author notes

1. These authors contributed equally: Balaji Virassamy, Franco Caramia, Peter Savas, Michael A. Harris

Authors and Affiliations

1. Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

Balaji Virassamy, Franco Caramia, Peter Savas, Michael A. Harris, Jianan Wang, Emmaline Brown, Megan M. R. O’Malley, Courtney T. van Geelen, Michael Hun, Sneha Sant, Jamieson D. Ballan, Jasmine Kay, Luis E. Lara Gonzalez, Kylie Clarke, Han Xian Aw Yeang, Rejhan Idrizi, Metta Jana, Damon J. Challice, Roberto Salgado, Heather Thorne, Yoland Antill, Ian Campbell, Sarah-Jane Dawson, Andrew Fellows, Stephen Fox, Marion Harris, Sherene Loi, Jia-Min Pang, Kelly Phillips, Anita Skandarajah, Peter Savas, Heather Thorne, Paul J. Neeson, Phillip K. Darcy & Sherene Loi

2. The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia

Balaji Virassamy, Franco Caramia, Peter Savas, Michael A. Harris, Jianan Wang, Megan M. R. O’Malley, Courtney T. van Geelen, Michael Hun, Jamieson D. Ballan, Jasmine Kay, Luis E. Lara Gonzalez, Han Xian Aw Yeang, Rejhan Idrizi, Metta Jana, Heather Thorne, Sherene Loi, Paul J. Neeson, Phillip K. Darcy & Sherene Loi

3. Cancer Research Malaysia, Subang Jaya, Malaysia

Jia-Wern Pan & Soo-Hwang Teo

4. Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia

Thomas N. Burn & Laura K. Mackay

5. ZAS Hospitals, Antwerp, Belgium

Roberto Salgado

6. Department of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

Cathie Poliness & Sophie Nightingale

7. Bioinformatics and Computational Biology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

Terence P. Speed

8. Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

Geoff Lindeman, Clare Scott, Jane Visvader & Jane Visvader

9. Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia

Jane Visvader

10. Genetic Health Services Victoria, Royal Children’s Hospital, Melbourne, Victoria, Australia

David Amor

11. Hereditary Cancer Clinic, Prince of Wales Hospital, Sydney, New South Wales, Australia

Lesley Andrews, Leon Botes, Michael Friedlander, Jessica Koehler, Bettina Meiser, Mona Saleh, Elizabeth Salisbury, Alison Trainer, Kathy Tucker, Rachael Williams & Milita Zaheed

12. Westmead Hospital and Westmead Institute for Cancer Research, Sydney, New South Wales, Australia

Rosemary Balleine, Anna deFazio, Judy Kirk, Graham Mann, Edwina Rickard, Abi Ragunathan & Shweta Srinivasa

13. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

Jonathan Beesley, Georgia Chenevix-Trench, Nick Hayward, Jason Lee, Amanda Spurdle & Logan Walker

14. Department of Surgery, The University of Queensland, Brisbane, Queensland, Australia

Ian Bennett

15. Familial Cancer Centre and Genomic Medicine, The Royal Melbourne Hospital/VCCC, Melbourne, Victoria, Australia

Michael Bogwitz, Laura Forrest, Clara Gaff, Cass Hoskins, Nick Pachter, Ellen Pieper, Adrienne Sexton, Jessica Taylor & Ingrid Winship

16. Austin Health, Melbourne, Victoria, Australia

Simon Bodek & Martin Delatycki

17. NSW Breast Cancer Institute, Sydney, New South Wales, Australia

Meagan Brennan

18. The University of Queensland, Brisbane, Queensland, Australia

Melissa Brown, Stacey Edwards, Juliet French, Jodi Saunus & Peter Simpson

19. Molecular and Cytogenetics Unit, Prince of Wales Hospital, Sydney, New South Wales, Australia

Michael Buckley

20. ICON Cancer Care, Hobart, Tasmania, Australia

Jo Burke

21. Medical Psychology Unit, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia

Phyllis Butow

22. Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Sydney, New South Wales, Australia

Liz Caldon, David Gallego-Ortega & Mary-Ann Young

23. Tasmanian Clinical Genetics Service, Royal Hobart Hospital, Hobart, Tasmania, Australia

Michelle Cao & Briony Patterson

24. Specialist Breast Cancer Surgery, Melbourne, Victoria, Australia

Anannya Chakrabarti

25. School of Psychology, The University of Sydney, Sydney, New South Wales, Australia

Deepa Chauhan, Liz Lobb & Joanne Shaw

26. St Vincent’s Hospital Cancer Genetics Clinic, The Kinghorn Cancer Centre, Sydney, New South Wales, Australia

Manisha Chauhan

27. Central Regional Genetics Service, Wellington Hospital, Wellington, New Zealand

Alice Christian, Joanne Dixon & Alexa Kidd

28. St John of God Subiaco Hospital/Genetic Services of Western Australia, Subiaco, Western Australia, Australia

Paul Cohen, Sarah O’Sullivan & Gargi Pathak

29. Department of Clinical Genetics, Liverpool Hospital, Liverpool, New South Wales, Australia

Alison Colley

30. Department of Clinical Genetics, Royal North Shore Hospital, Sydney, New South Wales, Australia

Ashley Crook, Eliza Courtney, Rebecca Dickson & Michael Field

31. Monash University, Melbourne, Victoria, Australia

James Cui, Philip Harraka, Mitchell Lawrence & Renea Taylor

32. Centre for Clinical Research, The University of Queensland, Brisbane, Queensland, Australia

Margaret Cummings

33. SA Pathology, Royal Adelaide Hospital, Adelaide, South Australia, Australia

Gelareh Farshid & James Kollias

34. South West Family Cancer Clinic, Liverpool Hospital, Liverpool, New South Wales, Australia

Georgina Fenton

35. Imperial College London, London, UK

James Flanagan

36. Auckland City Hospital/University of Auckland, Auckland, New Zealand

Peter Fong & Andrew Shelling

37. Queensland Clinical Genetic Service/Royal Brisbane and Women’s Hospital, Brisbane, Queensland, Australia

Mike Gattas & Sunil Lakhani

38. Canterbury Health Laboratories, Christchurch, New Zealand

Peter George

39. Illawarra Cancer Centre, Wollongong Hospital, Wollongong, New South Wales, Australia

Sian Greening

40. Southern Health Familial Cancer Centre, Monash Medical Centre, Melbourne, Victoria, Australia

Stewart Hart, Clare Hunt & Shona O’Connell

41. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

John Hopper, Mark Jenkins, Shuai Li & Melissa Southey

42. Genetic Health Services, Monash Medical Centre, Melbourne, Victoria, Australia

Paul James

43. Department of Surgery, St Vincent’s Hospital, Melbourne, Victoria, Australia

Jocelyn Lippey

44. Medical Oncology and Clinical Haematology Unit, Western Health, Melbourne, Victoria, Australia

Lara Lipton

45. School of Women’s and Children’s Health/Lowy Cancer Research Centre, UNSW Sydney, Sydney, New South Wales, Australia

Deborah Marsh & Susan Ramus

46. Department of Oncology, St Vincent’s Hospital, Melbourne, Victoria, Australia

Sue-Anne McLachlan

47. Department of Surgery, The Royal Melbourne Hospital, Parkville, Victoria, Australia

Roger Milne & Christobel Saunders

48. Department of Surgery, University of Western Australia, Perth, Western Australia, Australia

Roger Milne & Christobel Saunders

49. The Gene Council, Perth, Western Australia, Australia

Amy Pearn

50. Christchurch Hospital, Christchurch, New Zealand

Bridget Robinson

51. Hunter Area Pathology Service, John Hunter Hospital, Newcastle, New South Wales, Australia

Rodney Scott

Consortia

kConFab consortium

- David Amor

- , Lesley Andrews

- , Yoland Antill

- , Rosemary Balleine

- , Jonathan Beesley

- , Ian Bennett

- , Michael Bogwitz

- , Simon Bodek

- , Leon Botes

- , Meagan Brennan

- , Melissa Brown

- , Michael Buckley

- , Jo Burke

- , Phyllis Butow

- , Liz Caldon

- , Ian Campbell

- , Michelle Cao

- , Anannya Chakrabarti

- , Deepa Chauhan

- , Manisha Chauhan

- , Georgia Chenevix-Trench

- , Alice Christian

- , Paul Cohen

- , Alison Colley

- , Ashley Crook

- , James Cui

- , Eliza Courtney

- , Margaret Cummings

- , Sarah-Jane Dawson

- , Anna deFazio

- , Martin Delatycki

- , Rebecca Dickson

- , Joanne Dixon

- , Stacey Edwards

- , Gelareh Farshid

- , Andrew Fellows

- , Georgina Fenton

- , Michael Field

- , James Flanagan

- , Peter Fong

- , Laura Forrest

- , Stephen Fox

- , Juliet French

- , Michael Friedlander

- , Clara Gaff

- , Mike Gattas

- , Peter George

- , Sian Greening

- , Marion Harris

- , Stewart Hart

- , Philip Harraka

- , Nick Hayward

- , John Hopper

- , Cass Hoskins

- , Clare Hunt

- , Paul James

- , Mark Jenkins

- , Alexa Kidd

- , Judy Kirk

- , Jessica Koehler

- , James Kollias

- , Sunil Lakhani

- , Mitchell Lawrence

- , Jason Lee

- , Shuai Li

- , Geoff Lindeman

- , Jocelyn Lippey

- , Lara Lipton

- , Liz Lobb

- , Sherene Loi

- , Graham Mann

- , Deborah Marsh

- , Sue-Anne McLachlan

- , Bettina Meiser

- , Roger Milne

- , Sophie Nightingale

- , Shona O’Connell

- , Sarah O’Sullivan

- , David Gallego-Ortega

- , Nick Pachter

- , Jia-Min Pang

- , Gargi Pathak

- , Briony Patterson

- , Amy Pearn

- , Kelly Phillips

- , Ellen Pieper

- , Susan Ramus

- , Edwina Rickard

- , Abi Ragunathan

- , Bridget Robinson

- , Mona Saleh

- , Anita Skandarajah

- , Elizabeth Salisbury

- , Christobel Saunders

- , Jodi Saunus

- , Peter Savas

- , Rodney Scott

- , Clare Scott

- , Adrienne Sexton

- , Joanne Shaw

- , Andrew Shelling

- , Shweta Srinivasa

- , Peter Simpson

- , Melissa Southey

- , Amanda Spurdle

- , Jessica Taylor

- , Renea Taylor

- , Heather Thorne

- , Alison Trainer

- , Kathy Tucker

- , Jane Visvader

- , Logan Walker

- , Rachael Williams

- , Ingrid Winship

- , Mary-Ann Young

- & Milita Zaheed

Contributions

B.V., F.C., P.S. and M.A.H. performed primary human and mouse experiments, analysed results, coordinated the project and wrote the research manuscript. F.C., P.S., J.W. and J.-W.P. generated programming scripts and analysed the bioinformatics results. E.B. and D.J.C. performed mouse experiments, dosing, tumour cell injections, associated procedures and maintenance of mouse models. M.M.R.O., C.T.v.G., M.H., T.N.B. and S.S. supported with interpretation of results and performed experiments. J.K., J.D.B., L.E.L.G., K.C., S.T., H.X.A.Y., R.I. and M.J. supported with experiments. kConFab Investigators contributed to the recruitment of participants and coordinated biospecimen acquisition, clinical data curation and governance. H.T. from KConFab contributedto recruitment of participants, biospecimen acquisition and clinical data curation. R.S., H.T., C.P., S.N., T.P.S., J.V., P.J.N., P.K.D. and L.K.M. provided scientific expertise. All of the authors approved the final research manuscript. S.L. as senior author conceived, led and coordinated the project, and provided the project funding.

Corresponding authors

Correspondence to Laura K. Mackay or Sherene Loi.

Ethics declarations

Competing interests

P.S. has acted as a consultant (not compensated) to Roche-Genentech and received research funding to his institution from Roche-Genentech. S.L. receives research funding to her institution from Novartis, Bristol Meyers Squibb, Merck, Puma Biotechnology, Eli Lilly, Nektar Therapeutics, Astra Zeneca, Roche-Genentech and Seattle Genetics; has acted as a consultant to Seattle Genetics, Novartis, Bristol Meyers Squibb, Merck, AstraZeneca, Eli Lilly, Roche-Genentech, Aduro Biotech, GlaxoSmithKline, Silverback Therapeutics, G1 Therapeutics, PUMA Biotechnologies, Pfizer, Gilead Therapeutics, Daiichi Sankyo, Amuni and Tallac Therapeutics. P.K.D. receives research funding from Myeloid Therapeutics, Prescient Therapeutics and Bristol-Myers Squibb. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Sandra Demaria, Matteo Lambertini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Cell types by parity status in cancer unaffected human breast tissue.

a-e, Abundance of immune cell types quantified from scRNAseq by parity status in human normal breast tissue shown as ratio of cell type in women of average breast cancer risk. Cell types are annotated as per ref. [16]. Y-axes indicate cell type frequency as calculated by cell subpopulations as indicated over total epithelial cells. a, Macrophages, n = 170 women (N, n = 53) vs (P, n = 117), M1 and M2 macrophage cell sub-populations, n = 105 women (N, n = 30) vs (P, n = 75). b, Monocytes, n = 105 women (N, n = 30) vs (P, n = 75). c, Dendritic cells n = 124 women (N, n = 37) vs (P, n = 87). d, B cells n = 123 women (N, n = 37) vs (P, n = 86). e, Fibroblasts, n = 170 women (N, n = 53) vs (P, n = 117), Fibroblast matrix associated cells, n = 102 women, (N, n = 27) vs (P, n = 75) and Fibroblast SFRP4+ subpopulations, n = 90 women (N, n = 25) vs (P, n = 65). Violin plots show data distribution with boxplots indicate IQR extension from Q1 to Q3 and median as a white bar, whiskers show minimum and maximum within 1.5 times the IQR and datapoints show outliers. Two-sided Wilcoxon rank sum test, unadjusted exact p-values are shown.

Source Data

Extended Data Fig. 2 T cells in cancer unaffected human normal breast.

a, Representative OPAL multiplex immunofluorescence single-colour and merged images of cancer unaffected normal breast tissue from nulliparous and parous women stained for DAPI, pan-cytokeratin (AE1/AE3) CD3, CD8, CD69, and CD103. Scale bar is defined in the panels. b, Quantification of OPAL immunofluorescence in cancer unaffected human normal breast (n = 5 per group) for T cells subpopulations in mammary regions indicating density of CD3+, CD8+ and CD69+CD103+CD8+CD3+ T cells per mm^2 from nulliparous (N) and parous(P) women. Data in graphs represent mean ± s.e.m, two-sided Wilcoxon rank-sum test and dots points represent indicated immune cell quantities from independent normal breast tissue samples. c, Immune cell abundance (right) and enrichment of PB-TRM gene signature (left) in the TEM and TRM-like cell clusters as annotated by originating publication, ref. [16]. Effector CD8+ T cells (n = 26,333 cells) from nulliparous vs parous women. Wilcoxon rank-sum test (two-sided) and unadjusted p-value is shown.

Source Data

Extended Data Fig. 3 Immune cell subsets in healthy murine mammary tissue.

a, Frequency of indicated immune cell subsets among all CD45+ cells from the MFP of virgin (n = 9) and d28-inv (n = 12) C57BL/6 mice. b, Number of indicated immune cell subsets per gram of MFP from virgin (n = 9) and d28-inv (n = 12) C57BL/6 mice. c, Number of indicated immune cell subpopulations per gram of MFP from virgin (n = 8) and d10-FW (n = 8) BALB/c mice. Data in graphs represent mean ± s.e.m, results represent two combined independent experiments. Statistical significance determined by two-sided Mann-Whitney test and exact p values are shown.

Source Data

Extended Data Fig. 4 CD8+ T cell sub-populations in healthy murine MFP.

a, Top: UMAP of merged CD8α+ T cells generated by flow cytometry data from the MFP of virgin (n = 8) and d28-inv (n = 9) C57BL/6 mice with three enriched clusters (C1, C2, C3) as indicated. Each dot represents an individual cell. Feature plots showing expression of indicated markers projected on UMAP of CD8α+ T cells are presented in the bottom layout.

Source Data

Extended Data Fig. 5 CD8+TRM-like cells in healthy murine MFP.

a, Relative expression of indicated expressed gene transcripts relative to GAPDH control isolated from MFP from virgin (n = 10) vs d28-inv (n = 9) timepoints from C57BL/6 mice. b, Representative OPAL multiplex images indicating the presence of DAPI+, CD3+, CD8+, CD103+ cells (white dots, depicts the co-expression of CD3, CD8 and CD103 surface markers on T cells) in the MFP of virgin, d10-FW and D28-inv C57BL/6 mice. Blue regions indicate local mammary draining lymph node and yellow represent mammary fat pad tissue boundary. c, Total number of CD3+CD8+CD103+ T cells in the 4th MFP of virgin (n = 7), d10-FW (n = 4) and d28-inv (n = 6) C57BL/6 mice measured by OPAL microscopy per mammary fat pad section. d, Representative OPAL images indicate the single-colour staining of DAPI, E-cadherin (ECAD), CD3, CD8 and CD103 on T cells in the MFP of virgin, d10-FW and D28-inv C57BL/6 mice. e, Mean distance between ECAD+ cells and the closest CD3+ CD8+ CD103+ T cells of virgin (n = 7), d10-FW (n = 4) and d28-inv (n = 6) C57BL/6 mice. Data in graphs represent mean ± s.e.m, results represent two combined independent experiments. Statistical significance determined by two sided Mann-Whitney test (a) or Kruskal-Wallis test (c, e). Exact p values are shown.

Source Data

Extended Data Fig. 6 Immune cell infiltration in TNBC murine mammary tumours.

a, Numbers of indicated immune cell subpopulations per gram of AT3-OVA tumour from virgin (n = 9) and d28-inv (n = 10) C57BL/6 mice. b, Numbers of indicated immune cell subpopulations per gram of AT3-OVA tumour from virgin (n = 8) and d10-FW (n = 8) C57BL/6 mice. c, Numbers of indicated immune cell subpopulations per gram of D2A1 tumour from virgin (n = 8) and d10-FW (n = 8) BALB/c mice. d, Numbers of indicated immune cell subpopulations per gram of D2A1 tumour from virgin (n = 6) and d28-inv (n = 7) BALB/c mice. Data in graphs represent mean ± s.e.m, results represent two combined independent experiments. Statistical significance determined by two-sided Mann-Whitney test. Exact p values are shown.

Source Data

Extended Data Fig. 7 TNBC tumour growth and immune infiltration in murine models.

a, Tumour growth (left) and endpoint tumour volume (right) of AT3-OVA cells in the 4th MFP of RAG2−/−γc−/− mice pre-inoculated with naive OT-I cells seven days prior to mating in d10-FW (n = 4) and age-matched virgin (n = 4) control mice. b, Numbers of indicated immune cell populations in RAG2−/−γc−/− mice pre-inoculated with effector OT-I cells seven days prior to mating in d28-inv (n = 10) and age-matched virgin (n = 10) control mice. c, Numbers of indicated immune cell subpopulations per gram of AT3-OVA tumour from virgin (n = 6) and d10-FW (n = 6) RAG2−/−γc−/− mice at four weeks post tumour cell injection. d, Tumour growth (left) and endpoint tumour burden (right) of AT3-OVA cells in the 4th MFP of RAG−/−1−/− mice. Mice were injected with 20 × 10^6 effector gBT-I cells or PBS control eight weeks prior to injection of AT3-OVA cells (n = 5 per group for tumour growth and 10 for endpoint weight). Data in graphs represent mean ± s.e.m. Results are representative of n = 2 independent experiments (a, d left) or two combined independent experiments (b-d right). Statistical significance determined by two-sided Mann-Whitney test. Exact p values are shown.

Source Data

Extended Data Fig. 8 T cell depletions and TIL differences in TNBC murine models.

a, Number of indicated immune cells per gram of AT3-OVA tumour from virgin mice treated with isotype control(n = 16), anti-CD4 (n = 12), anti-CD8α (n = 9), anti-CD8β(n = 12) or anti-CD4/CD8a (n = 14). b, Number of indicated immune cells per gram of AT3-OVA tumour from d28-inv mice treated with isotype control (n = 16), anti-CD4 (n = 13), anti-CD8α (n = 9), anti-CD8β (n = 14) or combined anti-CD4/CD8α (n = 14). c, AT3-OVA endpoint tumour weight in C57BL/6 mice in virgin (n = 16) and d28-inv (n = 16) isotype treated controls. d, Number of indicated immune cells per gram of AT3-OVA tumour from d28-inv virgin (n = 16) and d28-inv (n = 16) mice treated with isotype control. e, Representative UMAP of 20,000 CD45+ immune cells by flow cytometry from AT3-OVA tumours of C57BL/6 d28-inv isotype control and CD4 depleted mice with two enriched clusters (C1,C2) indicated and CD4+ T cells highlighted. Each dot represents an individual cell. f, UMAP plots of indicated CD45+ cells from AT3-OVA tumours in d28-inv treated with isotype, anti-CD4, anti-CD8α, anti-CD8β or anti-CD4/CD8α. g, Frequency of cluster 1 among all CD45+ cells in AT3-OVA tumours from d28-inv isotype (n = 8) and CD4 depleted (n = 7) C57BL/6 mice. h, Relative expression of indicated markers in cluster 1 relative to all CD45+ cells from d28-inv isotype (n = 8). i, Frequency of cluster 2 among all CD45+ in AT3-OVA tumours from d28-inv isotype (n = 8) and CD4 depleted(n = 7) C57BL/6 mice. j, Relative expression of indicated markers in cluster 2 relative to all CD45+ from d28-inv isotype control mice (n = 8). k, Number of indicated immune cells per gram of AT3-OVA tumour from d28-inv mice treated with isotype (n = 16) or anti-CD4 (n = 13). l, Representative images of AT3-OVA tumours stained with Masaon’s trichrome. Arrows indicate positive green staining of collagen in virgin (left) and d28-inv (right) tissue. m, Proportion of Masson’s trichrome staining in AT3-OVA tumours from virgin (n = 6) and d28-inv (n = 5) C57BL/6 measured by Masson’s trichrome staining. Two combined independent experiments (a-c,k-m) representative of two independent experiments (e-j). mean ± s.e.m, two-sided Mann-Whitney test. Exact p values are shown.

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Extended Data Fig. 9 Parity, breastfeeding and TIL associations in human breast cancer subtypes.

a, Breastfeeding cohort multivariate analysis with clinical prognostic factors shown (n = 270). OS events were censored at 15 years follow up and adjusted for breastfeeding status (BF), age at diagnosis (Age.dx), years since last live birth to BC diagnosis (time since last birth), and treatment with chemotherapy (treatment_chemo) or mastectomy (treatment_mastectomy). Hazard ratios are presented with 95% confidence intervals, shown as squares and horizontal lines, respectively. b, PB-TRM signature correlation with tumour infiltrating lymphocyte (TIL) counts in TNBC (Basal subtype, n = 136) from the TCGA dataset. R value indicate Spearman’s correlation coefficient (two-sided p value from correlation test). c, Enrichment of the PB-TRM signature in the indicated BC subtypes from the Metabric dataset, Basal (n = 329), HER2 (n = 240), LumA (n = 718) and LumB (n = 488). Kruskal Wallis test, p-value shown is unadjusted, d, Kaplan–Meier survival analysis indicates disease-free survival and e, overall survival from (n = 329) primary basal-like/TNBCs with prognostic separation according to PB-TRM signature from parous normal breast tissue. Log-rank, p-values and hazard ratios are shown. f, Intratumoural T cell density in HER2 and Luminal BC subtypes determined by immunohistochemistry for CD8+ and CD3+ in MyBrCa dataset, comparing nulliparous (N) and parous (P) women with differing breastfeeding histories prior to cancer diagnosis (bf: any recorded breastfeeding activity). CD8+ and CD3+ T cell density quantified as ratio of stain-positive pixels to all pixels within tumour margins, represented as a percentage. Modelled with beta regression adjusting for covariates of age at diagnosis and tumour grade. Unadjusted p values with the AME for each group compared to N are presented. Box plots: horizontal bar shows median, hinges represent IQR, and whiskers extend to the most extreme dot point within 1.5xIQR, and points beyond show outliers. Individual points show data from each case, coloured according to the status shown on the x-axis. Exact p values are shown.

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Extended Data Fig. 10 Representative flow cytometry gating strategies.

a, Immunophenotyping of cancer-unaffected human normal breast tissue (Fig. 1b). b, Immunophenotyping of healthy C57BL/6 murine mammary tissue are shown from cells gated based on morphology (FSC-A, SSC-A) and singlets (FSC-H, FSC-A) (Fig. 2).

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Virassamy, B., Caramia, F., Savas, P. et al. Parity and lactation induce T-cell-mediated breast cancer protection. Nature 649, 449–459 (2026). https://doi.org/10.1038/s41586-025-09713-5

- Received: 02 August 2024

- Accepted: 06 October 2025

- Published: 20 October 2025

- Version of record: 03 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09713-5

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SIGLEC12 mediates plasma membrane rupture during necroptotic cell death

Hyunjin Noh,

Zeena Hashem,

Elena Boms &

…

Ayaz Najafov

Nature volume 649, pages 460–466 (2026)

Abstract

Necroptosis is a form of lytic cell death that is overactivated during infections and in inflammatory pathologies[1]. NINJ1 was recently found to be a mediator of plasma membrane rupture (PMR) during pyroptosis, toxin-induced necrosis, apoptosis, and ferroptosis2,3, but the mediator of PMR during necroptotic cell death remained unknown. Here, using a CRISPR–Cas9-based genome-wide knockout approach, we identify SIGLEC12 as a key mediator of necroptosis downstream of MLKL at the PMR step. Cells with knockdown or knockout of SIGLEC12 are defective in necroptosis-induced PMR and demonstrate ballooning morphology. During necroptosis, SIGLEC12 undergoes dephosphorylation, interacts with MLKL, forms cytosolic puncta and assembles into fibrils. Notably, SIGLEC12 is cleaved by TMPRSS4 during necroptosis to produce a 20-kDa fragment highly homologous to NINJ1, and this cleavage event is required and sufficient to induce PMR during necroptosis. A SIGLEC12 variant associated with cancer (Ser458Phe) and a variant found in the general human population (Arg528Trp) attenuate SIGLEC12 cleavage by TMPRSS4. Knockout of Siglec12 in mouse cells does not affect PMR, suggesting a species-specific role. Our identification of SIGLEC12 as a mediator of PMR expands our understanding of how programmed necrosis is executed and offers new approaches for targeting this proinflammatory form of cell death in human diseases.

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Main

Necroptosis is mediated by a signalling cascade culminating in the oligomerization of a pseudokinase called MLKL, which forms pore-like structures that allow water influx, leading to PMR[4]. Execution of PMR during pyroptosis, toxin-induced necrosis, apoptosis and ferroptosis requires NINJ1, but this transmembrane protein is not required for PMR during necroptosis downstream of MLKL2,5,6. Thus, our mechanistic understanding of the molecular events occurring downstream of MLKL is incomplete.

SIGLEC12 mediates necroptosis downstream of MLKL

To determine mechanisms of necroptosis execution downstream of MLKL, we used a doxycycline-inducible expression system in HEK293 cells (FlpIn-Trex)[7] to express a constitutively active form of MLKL (MLKLQ356A)8,9 on the background of a genome-wide CRISPR–Cas9-based knockout library[10] (Fig. 1a). Strikingly, the outlier hit whose knockout protected cells from cell death induced by MLKLQ356A expression was a transmembrane protein called SIGLEC12 (Fig. 1b). This observation was confirmed in our follow-up experiments using further single-guide RNA (sgRNA) sequences (Fig. 1c), with a similar observation made for the constitutively active MLKLT357E/S358D phosphomimetic[11] (Extended Data Fig. 1a).

Fig. 1: SIGLEC12 is a mediator of necroptosis.

a, Outline of the genome-wide CRISPR–Cas9-based knockout screen performed in HEK293-FlpIn-Trex (293FT)-MLKLQ356A cells to identify mediators of necroptosis. FC, fold change. b, Volcano plot summarizing the screening results. c, 293FT-MLKLQ356A cells with or without SIGLEC12 knocked out were treated with 0.1 μg ml−1 doxycycline for 24 h, and cell death was quantified using CellToxGreen or Toxilight (P = 3.54 × 10−7 (left), 9.01 × 10−13 (right)). Data are plotted as the mean ± s.d. (n = 6 (left) or n = 9 (right)) from three independent experiments. d, Predicted structures of NINJ1 and SIGLEC12. The NISI (NINJ1/SIGLEC12 homology) motif is indicated in red. Statistical analyses were performed using two-tailed t-test; ****P < 0.0001. V-type, V-type immunoglobulin; C2-type, C2-type immunoglobulin; ITIM, immunoreceptor tyrosine-based inhibitory motif; ITSM, immunoreceptor tyrosine-based switch motif; TM, transmembrane. Panels a and d were created using BioRender (https://biorender.com).

SIGLEC12 (sialic-acid-binding Ig-like lectin 12) is a member of the SIGLEC family of immunoglobulin-like lectins, which are primarily expressed on immune cells and play key parts in the regulation of immune responses through recognition of sialic-acid-containing glycans12,13,14. SIGLEC12 has two V-type sialic-acid-binding immunoglobulin domains and two C2-type immunoglobulin domains in its extracellular region, as well as an immunoreceptor tyrosine-based inhibitory motif and an immunoreceptor tyrosine-based switch motif in its intracellular region[14] (Fig. 1d).

Unlike other members of the SIGLEC family, human SIGLEC12 has lost its sialic-acid-binding activity owing to several mutations in its V-type immunoglobulin domains12,14,15. SIGLEC12 is expressed in the gastrointestinal tract, epithelial cells and macrophages, as well as certain cancers, in which it may contribute to tumour progression13,14,15. The inability of SIGLEC12 to bind to sialic acid in humans suggests a potential species-specific role in the homeostasis of biological processes native to human physiology.

SIGLEC12 mediates PMR downstream of MLKL

Consistent with the screen finding, SIGLEC12 knockout and knockdown protected cells from necroptotic cell death induced by (1) expression of the constitutively active form of MLKL (MLKLQ356A), (2) the necroptosis-inducing TSE (TNF, SM-164, emricasan) cocktail or (3) the necroptosis-inducing TRAIL + SE (TRAIL, SM-164, emricasan) cocktail (Fig. 2a–d and Extended Data Figs. 1b–i and 2a). Notably, SIGLEC12 loss protected cells from becoming positive for CellToxGreen (a membrane-impermeant fluorescent DNA probe that stains cells only when plasma membrane is ruptured), as well as protecting them from LDH release, but not from cell death, as judged by loss of ATP levels using a CellTiter-Glo assay, suggesting that the role of SIGLEC12 is likely to involve mediation of PMR (Fig. 2a–e (left), Extended Data Figs. 1b–i and 2a and Supplementary Videos 1–4). Loss of SIGLEC12 did not alter activation of the necroptosis mediators RIPK1, RIPK3 and MLKL, as judged by their autophosphorylation markers and MLKL oligomerization (Fig. 2d). The inability of SIGLEC12-deficient cells to rupture plasma membrane during necroptosis resulted in a ‘bubble’ phenotype observed by light and electron microscopy (Figs. 2e (right) and 2f and Supplementary Videos 5 and 6). Finally, the defective PMR in SIGLEC12-deficient cells resulted in diminished release of cytosolic proteins and HMGB1 (Fig. 2g) and production of the proinflammatory cytokines IL-1β, IL-8, CXCL1 and TNF[16] (Extended Data Fig. 2b).

Fig. 2: PMR downstream of MLKL is mediated by SIGLEC12.

a–d, HT-29 cells with the indicated knockouts (a) or knockdowns (b–d) were treated with TSE (30 ng ml−1 hTNF, 0.2 µM SM-164, 5 µM emricasan) or TRAIL + SE (50 ng ml−1, 0.2 µM SM-164, 5 µM emricasan) for 8 h, and cell death or cell viability was quantified using the indicated assays (P = 1.39 × 10−13 (a), P < 1 × 10−15 (b), P < 1 × 10−15 (c) and P = 6.82 × 10−12, P = 1.23 × 10−11 (d)). Cell lysates were analysed using reducing or non-reducing gel electrophoresis with the indicated antibodies. e, HT-29 cells with indicated stable short hairpin RNA (shRNA)-mediated knockdowns were treated with TSE for the indicated times, and cell death was assessed using CellToxGreen and time-lapse fluorescence confocal microscopy. f, As in e, except cell morphology was visualized by scanning electron microscopy. g, As in e, except the release of HMGB1 was analysed by immunoblotting. Data are plotted as the mean ± s.d., representative of n = 4 (c), n = 9 (a,d) or n = 12 (b); three independent experiments. Statistical analyses were performed using two-tailed t-tests and two-way analyses of variance; ****P < 0.0001. Data are representative of three independent experiments (d, middle and right, g). Scale bars, 25 μm (e), 10 μm (f).

These findings are analogous to those of Kayagaki et al.[2] with respect to the role of NINJ1 in PMR during pyroptosis, toxin-induced necrosis and apoptosis, suggesting that whereas NINJ1 mediates PMR during pyroptosis and apoptosis, SIGLEC12 mediates PMR during necroptosis. Consistently, we found striking amino acid similarity between SIGLEC12 and NINJ1, indicating possible functional homology (Fig. 1d and Extended Data Fig. 3). Owing to the high degree of similarity, we propose that this sequence be called the NINJ1/SIGLEC12 homology motif or NISI motif. Notably, mutating the first four residues of the SIGLEC12 NISI motif (ISLS) to four alanine residues blocked PMR during necroptosis, confirming the functionality of this motif in SIGLEC12 (Extended Data Fig. 4).

In our analysis of human pan-tissue RNA sequencing (RNA-seq) data curated in ProteinAtlas, SIGLEC12 expression was enriched in the bone marrow and lymphoid tissue cluster, which contained the gene encoding MLKL and other necroptosis-related genes, including those coding for FasL, TNFR2, TRAIL-R2, the IFNα, IFNβ and IFNγ receptors, TNFAIP3 (A20), CIAP2, CFLAR, PELI1, TBK1 and ZBP1 (Extended Data Fig. 5a). Similarly, cell subtype analysis of human single-cell RNA sequencing (scRNA-seq) data showed that SIGLEC12 expression was enriched in the monocyte cluster, together with necroptosis-related genes including TNFRSF1B (encoding TNFR2), TNFRSF10B (encoding TRAIL-R2), TNFAIP3 (encoding A20), IFNB1, IFNGR2, CFLAR, PELI1 and TBK1, as well as NINJ1 (Extended Data Fig. 5b). These observations further suggest a functional connection between SIGLEC12 and NINJ1.

SIGLEC12 is regulated during necroptosis

As NINJ1 was found to be important for PMR in different cell death contexts2,3,17 but dispensable for PMR during necroptosis2,5,6, we tested whether SIGLEC12 was important for PMR in cell death contexts other than necroptosis. Under the conditions tested, SIGLEC12 was dispensable for PMR during extrinsic apoptosis (TNF + SM-164), intrinsic apoptosis (etoposide), pyroptosis (α-ketoglutarate and LPS + nigericin) and ferroptosis (erastin) (Fig. 3a,b). These results indicated that the role of SIGLEC12 in mediation of PMR could be necroptosis-specific.

Fig. 3: Regulation of SIGLEC12 during necroptosis.

a,b, HT-29 cells (a) and THP-1 cells (b) with indicated stable shRNA-mediated knockdowns were treated with TSE (necroptosis, 8 h), TS (extrinsic apoptosis, 12 h), etoposide (intrinsic apoptosis, 24 h), α-ketoglutarate (pyroptosis, 24 h), LPS + nigericin (pyroptosis, 8 h) or erastin (ferroptosis, 24 h). Cell death was quantified using the indicated assays (P = 3.96 × 10−13, P = 3.24 × 10−2, P = 3.83 × 10−8, P = 0.46, P = 3.96 × 10−4 (a); and P = 2.9 × 10−10, P = 0.196 (b)). c, HeLa-RIPK3–HA cells were transfected with SIGLEC12–FLAG for 16 h and treated with TSE for 4 h. Cells were analysed by confocal microscopy following immunofluorescence for FLAG. SIGLEC12 was localized to the plasma membrane and cytosolic puncta during necroptosis. d, Human normal colon and colon tumour tissue biopsies from patients were stained with SIGLEC12 (n = 3). e, The HT-29-SIGLEC12-KO-SIGLEC12–FLAG stable cell line was treated with TSE, and SIGLEC12–FLAG was immunoprecipitated and analysed by transmission electron microscopy. f,g, HT-29 cells (f) and HT-29-SIGLEC12-KO-SIGLEC12–FLAG stable cells (g) were treated with TSE for the indicated times. Cell lysates and indicated immunoprecipitation samples were immunoblotted with the indicated antibodies. 0* indicates the IgG control antibody lane. h, The HT-29-SIGLEC12-KO-SIGLEC12–FLAG stable cell line was treated with TSE for 4 h with or without NSA (2 μM) cotreatment. SIGLEC12–FLAG was immunoprecipitated, eluted with FLAG peptide and analysed by immunoblotting. i, The HT-29-SIGLEC12-KO-SIGLEC12–FLAG stable cell line was treated with TSE for 4 h, and SIGLEC12–FLAG was immunoprecipitated. Phosphomapping was performed using mass spectrometry. Data are plotted as the mean ± s.d., representative of n = 9 (a,b); three independent experiments. Statistical analyses were performed using two-tailed t-tests; NS, not significant; *P < 0.05; ***P < 0.001; ****P < 0.0001. Data are representative of three independent experiments (f–h). Scale bars, 10 μm (c), 100 μm (d), 100 nm (e).

Similar to NINJ1 (during pyroptosis[2]), SIGLEC12 formed puncta during necroptosis and translocated to the plasma membrane (Fig. 3c and Extended Data Fig. 6a). Notably, SIGLEC12 formed puncta in human colon tumour biopsies but not normal colon tissue (Fig. 3d and Extended Data Fig. 6b–d), consistent with necroptosis being induced in the intratumour microenvironment18,19. Electron microscopy observations showed that SIGLEC12 formed fibrils during necroptosis (Fig. 3e), an assembly feature that was recently shown to be critical for PMR induction by NINJ1 (ref. [20]). Consistent with its role in mediation of necroptosis downstream of MLKL, SIGLEC12 interacted with MLKL during necroptosis but not with RIPK1 or RIPK3 (Fig. 3f–h and Extended Data Fig. 6e), and this interaction was inhibited by treatment with the MLKL inhibitor necrosulfonamide (NSA) (Fig. 3h and Extended Data Fig. 6f), which blocks binding of MLKL to the plasma membrane21,22.

Mass spectrometry-based phosphomapping showed that SIGLEC12 was dephosphorylated at Thr519, Ser532;Ser539 and Thr555 during necroptosis (Fig. 3i and Extended Data Fig. 7a). Thr519 and Thr555 are not conserved in rodents, whereas Ser532 is conserved in mouse but not in rat, and Ser539 is conserved in both mouse and rat orthologues (Extended Data Fig. 3b). Mutation of these four residues to phosphorylation-resistant alanine to mimic the dephosphorylated status of SIGLEC12 did not induce PMR (Extended Data Fig. 7b), suggesting that the dephosphorylation event that occurs during necroptosis is not sufficient for activation of the PMR-inducing function of SIGLEC12.

We did not find any inhibition of PMR during necroptosis in Siglec12 knockout mouse cell lines (Extended Data Fig. 8a,b), suggesting that the pro-PMR activity of SIGLEC12 downstream of MLKL could be species-specific or possibly even human-specific. Notably, rodent amino acid sequences of SIGLEC12 orthologues substantially diverged from the primate sequences (Extended Data Fig. 3). Moreover, the human-specific SIGLEC12 mutations that abolish its sialic-acid-binding property12,14,15,23 further reinforce the notion that the molecular role of SIGLEC12 in human cells could be unique to Homo sapiens. Consistently, the NISI motifs of human and mouse SIGLEC12 have sequence differences (Extended Data Fig. 3). Finally, constitutively active human MLKLQ356A and its mouse counterpart MLKLQ343A could both induce PMR in a SIGLEC12-independent manner in mouse cells (Extended Data Fig. 8c).

It is important to note that SIGLEC family conservation is convoluted and complex, and many species have completely different numbers of SIGLECs[24]. For example, the human genome has 16 family members, the macaque has 10, the marmoset has 6 and mice have 9. In mice, Siglec12 is the closest orthologue of human SIGLEC12 on the basis of sequence alignment[24]. The remaining eight mouse Siglecs are non-homologous to the human SIGLEC12, and none of the nine mouse orthologues has lost sialic-acid-binding capacity, unlike human SIGLEC12 (refs. 12,25). We could not find any literature or evidence for SIGLEC12 in Carnivora, which also do not have MLKL[26]. Thus, our results indicate that the human, but not mouse, plasma membrane requires SIGLEC12 to undergo PMR during necroptosis, revealing a potential uniqueness of human cells in this context.

Overall, these results indicate that SIGLEC12 is critical for PMR during necroptosis but dispensable for apoptosis, pyroptosis and ferroptosis, which are known to require NINJ1 for PMR; they thus suggest that the induction of PMR by SIGLEC12 downstream of MLKL is likely to be human-specific.

SIGLEC12 is a membrane-rupturing molecule

To test the hypothesis that SIGLEC12 could directly induce PMR, we overexpressed full-length SIGLEC12 in 293T cells, but we did not detect significant cell death (Fig. 4a and Extended Data Fig. 9b). Following this, we proposed the hypothesis that SIGLEC12 activation during necroptosis could be required for its PMR-inducing capability to be unleashed. Indeed, we found that a small cleavage fragment of approximately 20 kDa was generated during necroptosis (Fig. 4b,c). This fragment was detected with an antibody against a carboxy-terminal FLAG tag, indicating that the extracellular region of SIGLEC12 was cleaved during necroptosis. Amino acid analysis of the region for trypsin-like protease sites revealed a conserved Arg410 (which was notably not conserved in rodents) that fit this motif, with cleavage at this residue predicted to yield the observed 20-kDa fragment; notably, this fragment contained the NISI motif (Extended Data Figs. 3 and 9a).

Fig. 4: SIGLEC12 is cleaved by TMPRSS4 to induce PMR during necroptosis.

a, 293T cells were transfected with full-length (SIGLEC12-FL–FLAG) or [22–410 amino acid]-truncated SIGLEC12 (SIGLEC12-ΔN–FLAG) for 40 h. Cell death was quantified using Toxilight (P = 0.183, P = 5.16 × 10−7), and protein expression levels were assessed using immunoblotting for FLAG. b,c, The HT-29-SIGLEC12-KO-SIGLEC12–FLAG stable cell line (b) or HeLa-RIPK3–HA cells (c) were treated with TSE for the indicated times (b) or for 4 h (c). Cell lysates and anti-FLAG immunoprecipitation samples were immunoblotted with the indicated antibodies. d, 293T cells were transiently transfected with SIGLEC12-ΔN–FLAG for 24 h, followed by anti-FLAG immunoprecipitation and analysis by transmission electron microscopy. e, HT-29 cells with the indicated knockdowns were treated with TSE for 8 h. Cell death or cell viability was quantified using the indicated assays (P = 2.77×10−10, P = 1.2 × 10−9, P = 6.59 × 10−9, P = 1.38×10−7). f,g,i, 293T cells were cotransfected with the indicated SIGLEC12-FL–FLAG plasmids with or without TMPRSS4–HA for 24 h. Cell lysates and anti-FLAG immunoprecipitation samples were immunoblotted with the indicated antibodies. The effect of TMPRSS4–HA expression on wild-type SIGLEC12 cleavage (f), cleavage of Arg cleavage site mutants (g), and cancer and general human population mutants (i) was detected. h, HT-29-SIGLEC12-KO-SIGLEC12-FL-WT or cleavage mutant (R410A) stable cell lines were treated with TSE, and cell death was assessed by green fluorescence intensity every hour for the indicated times using Incucyte S3 (P ≤ 1 × 10−15, P = 4 × 10−13, P = 3.7 × 10−6). Data are plotted as the mean ± s.d., representative of n = 3 (h, left), n = 6 (e, middle), n = 7 (a) or n = 9 (e, left and right, h, middle and right). Statistical analyses were performed using two-tailed t-tests and two-way analyses of variance; ****P < 0.0001. Data are representative of three independent experiments (a–c, f, g and i). Scale bars, 100 nm (d).

We generated a truncation mutant (SIGLEC12-ΔN) that lacked the amino acids between the signal peptide and Arg410, which would mimic SIGLEC12 cleavage at that residue by a hypothetical trypsin-like extracellular or transmembrane protease (Extended Data Fig. 9a). Notably, expression of SIGLEC12-ΔN but not the full-length SIGLEC12 (SIGLEC12-FL) resulted in significant PMR induction (Fig. 4a). This PMR was not blocked by NSA, suggesting that SIGLEC12-ΔN-induced PMR is not MLKL-dependent; this was consistent with SIGLEC12 cleavage during necroptosis downstream of MLKL being a key step in the activation of its ability to induce PMR (Extended Data Fig. 9b). Finally, expression of the cleavage-mimetic SIGLEC12-ΔN was sufficient to form both cytosolic and membrane-associated puncta (Extended Data Fig. 9c) and produce fibrils (Fig. 4d), suggesting that the cleavage event is a critical and probably sufficient step in the activation of SIGLEC12 that drives fibril assembly.

SIGLEC12 is activated by TMPRSS4 during necroptosis

To determine the mechanism of SIGLEC12 cleavage during necroptosis, we mined the NCBI Gene database for reported interactors with SIGLEC12 that possess protease activity. Notably, an interaction with a transmembrane protease called TMPRSS4 has previously been reported in a yeast two-hybrid screen[27]. TMPRSS4 is a type II serine protease that has been linked to viral infections and cancer28,29,30,31. Its role in necroptosis is not known. We proposed that TMPRSS4 could be responsible for cleavage of the extracellular region of SIGLEC12, leading to its activation during necroptosis. Indeed, TMPRSS4 knockdown inhibited PMR during necroptosis without altering the upstream signalling (Fig. 4e and Extended Data Fig. 9d); in addition, TMPRSS4 physically interacted with SIGLEC12 (Fig. 4f,g), and coexpression of the wild-type but not the protease-dead S387A mutant of TMPRSS4 with SIGLEC12–FLAG induced SIGLEC12 cleavage and PMR (Fig. 4f and Extended Data Fig. 9e,f). Finally, TMPRSS4-induced SIGLEC12 cleavage and PMR were inhibited by mutation of the R410 residue (Fig. 4g,h and Extended Data Fig. 9g–i) and by supplementation of the cell culture medium with a protease inhibitor cocktail (Extended Data Fig. 9j,k).

In summary, these results demonstrate that TMPRSS4 is key for extracellular cleavage and activation of SIGLEC12 and consequent PMR during necroptosis, as it converts SIGLEC12 from its precursor form to its active form, allowing induction of PMR by SIGLEC12.

SIGLEC12 mutations in cancer and the human population

Loss of necroptosis potential is frequently found in cancer cells[32]. According to The Cancer Genome Atlas (TCGA), 444 missense and 63 truncating mutations have been reported in the exons of SIGLEC12 in 67,030 individuals with cancer (somatic mutation frequency of 0.7%). We found that the top missense SIGLEC12 mutation reported in TCGA (Ser458Phe) inhibited the ability of SIGLEC12 to be cleaved by TMPRSS4 (Fig. 4i). Notably, the Ser458Phe mutation is located in the NISI motif of SIGLEC12, suggesting that this motif may have a role in the cleavage and activation of SIGLEC12 (Extended Data Figs. 3 and 9a). These findings indicate that inactivation of the pro-PMR role of SIGLEC12 may contribute to resistance to necroptotic cell death in cancer.

Functional human MLKL mutations that are present in 2–3% of the world population have been previously reported[33]. We mined the NCBI dbSNP database34,35 for SIGLEC12 variants found in the normal human population and found three top variants in the cytosolic region of the protein: Val516Met, Arg528Trp and Pro546Leu (Extended Data Fig. 10a). Notably, similar to the cancer-associated Ser458Phe variant, the Arg528Trp mutation blocked SIGLEC12 cleavage by TMPRSS4 (Fig. 4i). The ramifications of these SIGLEC12 sequence variations on human physiology remain to be elucidated.

Furthermore, an insG mutation has been suggested as a frameshift mutation (Ala66fs) in exon 1 of SIGLEC12 that is present in the general population at a substantial frequency—ranging from 38% to 86% (depending on the region)—and at various zygosities[14]. As this insertion mutation is in exon 1 of SIGLEC12, it remains to be determined whether this exon is spliced out in a cell-specific or tissue-specific manner, or whether SIGLEC12 expression occurs through multiple transcriptional start sites, allowing the rest of the open reading frame of SIGLEC12 to be expressed, especially when such a mutation is present. Such alternative transcription or splicing could also be triggered during necroptosis-activating conditions, such as infection, allowing expression of SIGLEC12 that misses the exon 1 residues (1–142), which we have shown are part of the SIGLEC12 region that is cleaved away during necroptosis. However, an isoform missing exon 1 would require a cryptic signal peptide, as exon 1 contains the signal peptide. Notably, a SIGLEC12 isoform missing exon 1 has been reported[36]. However, given the high frequency of the frameshift mutation, it is conceivable that defects in PMR because of this mutation could provide selective fitness owing to the inability of the infected cells to release the pathogens that rely on cell lysis for propagation.

Our data show that MLKL and SIGLEC12 interact before SIGLEC12 is cleaved and that inhibition of MLKL by NSA attenuates SIGLEC12 cleavage (Fig. 3h and Extended Data Fig. 6f). These findings point to a model in which the role of MLKL upstream of SIGLEC12 is to promote cleavage of SIGLEC12 by TMPRSS4. Future studies will identify the molecular mechanisms that enable SIGLEC12 cleavage by TMPRSS4 downstream of MLKL activation during necroptosis.

The residues in the NISI motif of NINJ1 (I^84SISLVLQ^91 in human NINJ1) have been shown to be critical for its PMR function: I84F and Q91A mutations reduced its ability to homo-oligomerize and mediate PMR, whereas I86F and L90W reduced its ability to mediate PMR20,37,38. These findings further highlight the functional importance of this motif. Whether SIGLEC12 is the sole mediator of PMR during necroptosis or whether NINJ1 also contributes to this process, possibly in a tissue-specific, cell-type-specific and/or necroptosis-inducing trigger-specific manner, remains to be determined. It is worth noting that a recent study showed that MLKL could target mitochondrial membranes[39]; this could partially or fully explain the loss of ATP levels during necroptosis without loss of plasma membrane integrity in the absence of SIGLEC12 (Fig. 2a,d), as mitochondrial membrane permeabilization would significantly affect cellular ATP production.

Overall, our work identifies SIGLEC12 as a protein that is critical for PMR during necroptosis, with feature and sequence similarities to NINJ1 (Extended Data Fig. 10b). Whether cancer-associated mutations in SIGLEC12 or its variants found in the general population play an important part in the pathogenesis of human diseases, such as sensitivity to infections, in which necroptosis has been implicated, remains to be determined. Our discovery of SIGLEC12 as a species-specific and possibly human-specific mediator of PMR during necroptosis sheds light on the complexity of how programmed necrosis is executed and offers both SIGLEC12 and TMPRSS4 as druggable targets for modulating proinflammatory cell death in human diseases.

Methods

Antibodies and reagents

The following antibodies were used in this study: RIPK1 (Cell Signaling Technology, catalogue no. 3493); p-hRIPK1 (Cell Signaling Technology, catalogue no. 65746); hRIPK3 (Cell Signaling Technology, catalogue no.13526); p-hRIPK3 (Cell Signaling Technology, catalogue no. 93654); hMLKL (Cell Signaling Technology, catalogue no. 14993; Abcam, ab183770; Thermo Fisher Scientific, PA5-34733); p-hMLKL (Cell Signaling Technology, catalogue no. 91689); anti-SIGLEC12 (Boster, A10550-1; Invitrogen, PA5-110369; Invitrogen, PA5-31457); TMPRSS4 (Cell Signaling Technology, catalogue no. 84382); anti-HMGB1 (Cell Signaling Technology, catalogue no. 6893); anti-β-actin (Sigma-Aldrich, A5316); anti-FLAG (Sigma-Aldrich, F1804); anti-FLAG–HRP (Cell Signaling Technology, catalogue no. 86861); anti-mouse IgG (Cell Signaling Technology, catalogue no. 7074); anti-rabbit IgG (Cell Signaling Technology, catalogue no. 7076), anti-mouse IgG (H + L) and F(ab′)2 Fragment (Alexa Fluor 488 Conjugate) (Cell Signaling Technology, catalogue no. 4408). All primary antibodies were used at 1:2,000 dilution. All secondary antibodies were used at 1:5,000 dilution.

The following reagents were used in this study: SM-164 (S7089), Nec-1s (S8641), GSK′872 (S8465), NSA (S8251), emricasan (S7775) and erastin (S7242) from SelleckChem; etoposide (E1383), α-ketoglutarate (349631), lipopolysaccharide (L4391), luminol (A8511), p-coumaric acid (C9008) and anti-FLAG M2 agarose beads (M8823) from Sigma-Aldrich; nigericin (11437) from the Cayman Chemical Company; Lipofectamine 3000 (L3000015), SuperSignal West Atto Ultimate Sensitivity Substrate (A38556) and Prolong Diamond Antifade Mountant with DAPI (P36966) from Thermo Fisher Scientific; polyethylenimine (PEI; 24765-100) from Kyfora Bio; polybrene (TR-1003) from EMD Millipore; and 10X Tris/Glycine/SDS Electrophoresis Buffer (1610772), Tween 20 (1610781), Stacking Gel Buffer for PAGE (1610799), Resolving Gel Buffer for PAGE (1610798), Precision Plus Protein Dual Color Standards (1610394) and nitrocellulose membrane (1620115) from Bio-Rad. Autoradiography films were from MTC Bio (A8815).

Cell lines and growth media

The 293T, HEK293-FlpIn-TREx, HeLa and MEF cells were grown in DMEM (Cytiva, SH30243.FS), with l-glutamine, 4.5 g l−1 glucose and pyruvate; HT-29 cells were grown in McCoy’s 5A medium (Gibco, 16600-082, with l-glutamine); and A549, Jurkat and THP-1 cells were grown in RPMI 1640 medium (Fisher Scientific, SH30255.01, with HEPES and l-glutamine). All media were supplemented with 10% fetal bovine serum (GeminiBio), 1× non-essential amino acids (Cytiva, SH30238.01) and 1× antibiotic/antimycotic solution (Sigma-Aldrich, A5955). All cell lines were regularly tested for mycoplasma contamination using Lonza’s MycoAlert Kit (catalogue no. LT07-318) and tested negative for mycoplasma.

Human tumour and normal tissue samples

Deidentified human benign (normal) lung and colon and lung and colon adenocarcinoma tumour specimens were obtained from the University of Texas Southwestern Tissue Management Shared Resource. Patients were enrolled and consented to a protocol approved by the institutional review board. Formalin-fixed tissues were processed as in the ‘Immunohistochemistry of tissue slices’ section.

Molecular cloning and plasmids

Molecular cloning was performed using New England Biolabs restriction enzymes and T4 DNA ligase. Plasmids were transformed into chemically competent NEB Stable (for lentiviral plasmids, at 30 °C) and DH5α Escherichia coli cells (for non-lentiviral plasmids at 37 °C). Plasmid purification and extraction were performed using a QIAprep Spin Miniprep Kit (Qiagen, 27106) and QIAquick Gel Extraction Kit (Qiagen, 28704). sgRNAs targeting SIGLEC12 were cloned into pSpCas9(BB)-2A-GFP (pX458) (Addgene, catalogue no. 48138). Cloned plasmids were amplified and purified using a ZymoPURE II Plasmid Midiprep Kit (Zymo Research, D4201).

Generation of knockout cell lines using CRISPR–Cas9

Cells were transfected with the pSpCas9(BB)-2A-GFP (pX458) plasmids for 24 h using Lipofectamine 3000. Cells expressing the highest levels of GFP were then sorted into 96-well plates containing 150 µl of high-glucose DMEM or McCoy’s medium supplemented with 16% fetal bovine serum, 1× non-essential amino acids and 1× antibiotic/antimycotic. All flow cytometry experiments were performed at the UT Southwestern Cell Sorting Facility. Fluorescence-activated cell sorting was performed with a BD FACSAria II flow cytometer under sterile conditions using a 100 nozzle at 37 °C. About 2–3 weeks later, the clones were expanded into 12-well plates. Cells were lysed in 2× SSB (150 mM Tris-HCl, pH 6.8, 4% SDS, 0.01% bromophenol blue, 20% glycerol) + 2% β-mercaptoethanol (β-ME), and lysates were analysed for knockout screening using immunoblotting.

Generation of knockdown cell lines using lentiviral short hairpin RNA

MISSION short hairpin RNA plasmids targeting SIGLEC12, TMPRSS4 and MLKL were from Sigma-Aldrich. For generation of lentiviral particles, plasmids were transfected into 293T cells using PEI (3 μl per 1 μg DNA). Pseudoviral particles were collected 72 h after transfection, and cells were transduced in the presence of polybrene (8 μg ml−1). Cells were selected with puromycin (2 μg ml−1) for 48 h after transduction, and knockdown of target proteins was confirmed by immunoblotting after all cells in the untransduced control plates had been selected out.

Transfection experiments

Cells were transfected with a total of 1 µg of plasmid DNA or 3 µg of plasmid DNA in a 24-well plate or 100 mm dish, respectively. The ratio of pcDNA5-SIGLEC12–FLAG to pLenti-III-EF1-TMPRSS4–HA plasmid DNA was 1:1, and the total amount was adjusted using an empty vector. HeLa-RIPK3–HA cells were transfected using Lipofectamine 3000 (1.5 μl per 1 μg plasmid DNA), whereas 293T cells were transfected using PEI (3 μl per 1 μg plasmid DNA).

Cell death and viability assays

Necroptosis was induced with TSE cocktail (TSE; T: 30 ng ml−1 hTNF, S: 0.2 µM SM-164, E: 5 µM emricasan; 1 h pretreatment for S + E) for 8 h. Extrinsic apoptosis was induced with T and S (1 h pretreatment with S). Intrinsic apoptosis was induced with 100 µM etoposide. Pyroptosis was induced with α-ketoglutarate (15 mM) or LPS (1 μg ml−1, 4 h pretreatment) + nigericin (20 μM). Ferroptosis was induced with erastin (20 μM). CellToxGreen (Promega, G8731), ToxiLight Non-destructive Cytotoxicity BioAssay (Lonza, LT07-117) and CytoTox 96 Non-Radioactive Cytotoxicity Assay (LDH assay, Promega, G1780) were used for detection of cell death. Cell viability was measured using a CellTiter-Glo Luminescent Cell Viability Assay (Promega, G7570). For CellToxGreen, dye (used at 1:3,000 dilution) was added to the wells immediately before the fluorescence reads. For the Toxilight assay, 12.5 µl of culture medium was collected in triplicate and mixed with an equal volume of Toxilight reagent in 384-well plates. LDH assay was performed according to the manufacturer’s instructions, and absorbance was measured at 490 nm. For the cell survival assay, CellTiter-Glo reagent was added directly to the medium. Cells were lysed for 10 min in the dark at 25 °C, and luminescence was measured. Cell survival was normalized to that of an untreated control.

Live-cell imaging of cell death using Incucyte S3

Cells were seeded into 24-well plates and treated the following day. Plates were imaged using an Incucyte S3 Live-Cell Analysis System (Sartorius) with scans every 1 h using a ×10 objective, capturing phase contrast under the AI Scan module. Quantification of cell death was performed using the integrated AI Cell Health Analysis module, which applies a deep learning model to distinguish living and dead cells on the basis of morphology. Cell death was expressed as the percentage of dead cells per total cell count of cumulative cell counts over time.

Sample preparation for immunoblotting

Cells were lysed in 200 µl of 2× SSB + β-ME buffer. The plates were heated at 90 °C for 3 min, then cooled to room temperature for 5 min, and 1–2 µl of 1× benzonase (1:10 diluted with 50% glycerol from 10× supplier stock to obtain 1×; Santa Cruz Biotechnology, sc-202391) was added to the lysates to degrade genomic DNA for 5 min at room temperature, on a rocker. Total protein levels were normalized using a BCA kit (Thermo Fisher Scientific, 23225) or reducing-agent-compatible BCA kit (Thermo Fisher Scientific, 23250). For non-reducing sample preparation, cells were lysed in NLB buffer (NP-40 lysis buffer: 25 mM HEPES (pH 7.5), 0.2% NP-40, 120 mM NaCl, 0.27 M sucrose, 5 mM EDTA, 5 mM EGTA, 50 mM NaF, 10 mM b-glycerophosphate, 5 mM sodium pyrophosphate, 1 mM Na3VO4 (fresh), 0.1% β-ME, 1 mM phenylmethylsulfonyl fluoride (fresh), 2× complete protease inhibitor cocktail (Roche, 80024400)). Cell lysates were mixed with 4× SDS sample buffer without β-ME (non-reducing). Equal amounts of protein were resolved by SDS–PAGE and analysed using the indicated antibodies.

Immunoblotting

Total cell lysates or pull-down samples were heated at 90 °C for 5 min or 10 min in 2× or 4× SSB buffer, respectively, then subjected to 10% or 15% SDS–PAGE. Proteins were electrotransferred on to nitrocellulose membranes for 1.5 h at 0.4 A with the wet transfer tank submerged in an ice bath. The membranes were blocked for 1 h in TBST buffer containing 5% (w/v) skimmed milk and then incubated with the primary antibodies in TBST containing 5% (w/v) bovine serum albumin + 0.05% NaN_3 overnight at 4 °C. Detection was carried out using HRP-conjugated secondary antibodies and a homemade chemiluminescence reagent (2.5 mM luminol, 0.4 mM p-coumaric acid, 100 mM Tris-HCl, pH 8.6, 0.018% H_2O_2).

Immunoprecipitation

Cells were seeded into a 100-mm dish at 50% confluence. After 24 h, cells were transfected with total 3 µg of plasmid DNA using 9 μl of PEI or 4.5 µl of lipofectamine 3000. After 14–24 h, cells were lysed in NRP buffer. Cell lysates were incubated and precipitated with specific primary antibody for overnight at 4 °C and then incubated with protein A/G-magnetic beads (Thermo Fisher Scientific, 88802) or anti-FLAG agarose beads (Sigma-Aldrich, M8823) for 4 h at 4 °C. Bound proteins were removed by boiling in 2× SSB + β-ME buffer for 10 min at 90 °C, separated by SDS–PAGE and immunoblotting, and visualized using a homemade chemiluminescence reagent.

Time-lapse live-cell imaging microscopy

Cells were seeded on a 24-well plate (Cellvis, P24-1.5 P). After 48 h, cells were treated with TSE with added CellToxGreen. Live-cell imaging was performed using a Nikon Spinning Disk Confocal CSU-W1 with a ×40 air objective. Both bright-field and GFP fluorescence channels were captured every 30 min for 24 h. During the live-cell imaging process, cells were maintained at 37 °C and 5% CO_2.

Immunofluorescence microscopy

Cells were seeded on 70% ethanol-sterilized glass coverslips (AmScope, CS-R18-100). After 24 h, cells were transfected with total 0.5 µg of plasmid DNA using 0.75 µl of lipofectamine 3000. Cells were treated with TSE and then fixed in 4% paraformaldehyde for 12 min. Cells were washed twice with phosphate-buffered saline (PBS), with or without permeabilization using 0.05% Triton X-100 for 5 min. After incubation in a blocking buffer (1% bovine serum albumin in PBS) for 1 h, the cells were incubated overnight at 4 °C with the following primary antibodies: anti-FLAG or anti-HA. They were then incubated with the following Alexa Fluor secondary antibodies for 1 h at room temperature. Cells were mounted using Prolong Diamond Antifade Mountant with DAPI. Images were obtained with Laser scanning confocal Zeiss LSM880 inv. + Airyscan confocal, magnification 63x. Images were analysed using Fiji/ImageJ. Images are representative of at least ten fields of view per each sample.

Immunohistochemistry of tissue slices

Immunohistochemical analyses were conducted using a Dako Autostainer Link 48 system. Initially, the slides were baked at 60 °C for 20 min, followed by deparaffinization and hydration. Heat-induced antigen retrieval was performed using the Dako PT Link. The tissue samples were treated with a peroxidase block, and antibody incubations were carried out at a 1:200 dilution. Staining was visualized with a Nikon Widefield Epi-scope, magnification ×60. For Extended Data Fig. 4c, images were quantified using Fiji. In brief, the total tissue area was manually outlined, not considering large blood vessels. Then, the raw data was colour-deconvolved using the Colour Deconvolution2 plugin and thresholded manually to include the darkest SIGLEC12 staining. Results were expressed as the percentage area of SIGLEC12 relative to total tissue area.

Cell plug preparation

Cells were seeded in 100-mm culture dishes at approximately 80% confluence. After 24 h, cells were rinsed with 10 ml of cold PBS and fixed in 10 ml of 4% paraformaldehyde for 20 min at room temperature in the dark. Cells were then washed again with cold PBS and gently scraped into 1 ml of PBS. The suspension was centrifuged at 300g for 5 min at room temperature, and the pellet was resuspended in 100 μl of PBS and kept on ice. Separately, 20 ml of 1% (w/v) agarose in PBS was prepared by boiling and then cooled. A 500-μl aliquot of molten agarose was transferred to a sterile 2-ml microcentrifuge tube, and 100 μl of the cell suspension was added. The mixture was gently inverted 20 times to ensure even distribution, then centrifuged at 300g for 5 min at room temperature. The resulting cell plugs were maintained on ice for 1 h to solidify and stored at 4 °C for up to 2 days before processing for sectioning, haematoxylin and eosin staining, and preparation of unstained slides.

Electron microscopy

For transmission electron microscopy, carbon grids with a 400-mesh size (Electron Microscopy Sciences, CF-400-Cu-50) were subjected to glow discharge using a PELCO easiGlow Discharge Cleaning System for 25 s. Then, 5 µl of the purified fibril sample was applied to the grid and left to incubate for 1 min before being removed with filter paper. The grid was then stained with 5 µl of filtered aqueous 2% uranyl acetate solution (Electron Microscopy Sciences, catalogue no. 22400) for 1 min; excess stain was absorbed using filter paper. After drying, the grid was imaged using a JEM-1400 Plus transmission electron microscope, equipped with a LaB6 source operating at 120 kV and an AMT-BioSprint 16M CCD camera. For scanning electron microscopy, samples were fixed with 2.5% (v/v) glutaraldehyde in 0.1 M sodium cacodylate buffer overnight at 4 °C. After three rinses in 0.1 M sodium cacodylate buffer, they were postfixed with 2% osmium tetroxide in 0.1 M sodium cacodylate buffer for 2 h. The samples were then rinsed with water and dehydrated with increasing concentrations of ethanol, followed by increasing concentrations of hexamethyldisilazane in ethanol. Cells on coverslips were air-dried under a hood, mounted on scanning electron microscopy stubs with carbon tape, and sputter-coated with gold/palladium using a Cressington 108 auto sputter coater. Images were acquired with a field-emission scanning electron microscope (Zeiss Sigma) at an accelerating voltage of 3 kV and a 15-degree tilt.

Genome-wide CRISPR-based knockout screens

Human Brunello sgRNA library (Addgene, catalogue no. 73178-LV) was used[40]. This library contains 76,441 sgRNAs targeting 19,114 genes (4 sgRNAs per gene), with 1,000 non-targeting controls[40]. HEK293-FlpIn-TREx-MLKLQ356A cells were transduced at a multiplicity of infection of 0.5 using 8 µg ml−1 polybrene, and the plates were spun at 1,000g for 60 min at room temperature (spinfection). Two days later, cells were expanded and selected with 2 µg ml−1 puromycin for 2 weeks. The knockout library was treated with 0.1 µg ml−1 doxycycline to induce MLKLQ356A expression and necroptotic cell death for 3 days (with daily feeding for 3 days to remove dead cells). Genomic DNA from the surviving cells was isolated (using Tissue/Blood DNeasy kit from Qiagen; catalogue no. 69504), and PCR was performed using Emerald Taq (Takara Bio, catalogue no. RR310B) with P7 and P5 primers to amplify the sgRNA sequences, which were then subjected to next-generation sequencing on an Illumina NextSeq 500 with a read configuration of 100 bp, single-end. All the fastq files underwent routine quality checks using FastQC (v.0.11.2; http://www.bioinformatics.babraham.ac.uk/projects/fastqc) and FastQ Screen (v.0.4.4; http://www.bioinformatics.babraham.ac.uk/projects/fastq_screen). The trimmed fastq files were mapped to the reference sgRNA library with a mismatch option set to 0 using MAGeCK. Read counts for each sgRNA were generated, and median normalization was performed to adjust for library sizes. Positively and negatively selected sgRNAs and genes were identified using the default parameters of MAGeCK.

Quantitative real-time PCR

Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s instructions. Cells were lysed directly in TRIzol, and RNA was purified by chloroform phase separation and isopropanol precipitation. The RNA pellet was washed with 75% ethanol, air-dried and resuspended in RNase-free water. RNA concentration and purity were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific). cDNA was synthesized from 1 μg total RNA using EcoDry Premix with Random Hexamers (Takara Bio, catalogue no. 639547). The RNA was added directly to the lyophilized premix, incubated at 42 °C for 1 h and heat-inactivated at 85 °C for 5 min. Quantitative PCR was performed using SYBR Green PCR Master Mix (Applied Biosystems) on a QuantStudio 5 Real-Time PCR System (Thermo Fisher Scientific). Each reaction was run in triplicate with the following cycling conditions: 95 °C for 2 min, followed by 40 cycles of 95 °C for 10 s, 60 °C for 30 s and 72 °C for 30 s. Gene expression was normalized to that of ACTB using the ΔΔCt method, and data were expressed as fold change relative to control samples. Expression of inflammatory cytokines and chemokines was assessed using gene-specific primers as follows: CXCL1 (forward 5′-AGGGAATTCACCCCAAGAAC-3′, reverse 5′-TGGATTTGTCACTGTTCAGCA-3′); TNFA (forward 5′-CAGAGGGCCTGTACCTCATC-3′, reverse 5′-GGAAGACCCCTCCCAGATAG-3′); IL1B (forward 5′-AAGTACCTGAGCTCGCCAGTGA-3′, reverse 5′-TGCTGTAGTGGTGGTCGGAGAT-3′); CXCL8 (forward 5′-TCTGCAGCTCTGTGTGAAGG-3′, reverse 5′-AATTTCTGTGTTGGCGCAGT-3′).

Proteomics

Samples were digested overnight with trypsin (Pierce) following reduction and alkylation with DTT and iodoacetamide (Sigma-Aldrich). Following solid-phase extraction cleanup with an Oasis HLB µElution Plate (Waters), the resulting peptides were reconstituted in 10 µl of 2% (v/v) acetonitrile (ACN) and 0.1% trifluoroacetic acid in water. Then, 2 µl of each sample was injected into an Orbitrap Fusion Lumos mass spectrometer (Thermo Electron) coupled to an Ultimate 3000 RSLC-Nano liquid chromatography system (Dionex). Samples were injected into a 75 μm i.d., 75-cm-long EasySpray column (Thermo) and eluted with a gradient from 0–28% buffer B over 90 min. Buffer A contained 2% (v/v) ACN and 0.1% formic acid in water, and buffer B contained 80% (v/v) ACN, 10% (v/v) trifluoroethanol and 0.1% formic acid in water. The mass spectrometer was operated in positive ion mode with a source voltage of 1.5 kV and an ion transfer tube temperature of 300 °C. Mass spectrometry scans were acquired at 120,000 resolution in the Orbitrap, and up to ten tandem mass spectra spectra were obtained in the Orbitrap for each full spectrum acquired using higher-energy collisional dissociation for ions with charges 2–7. Dynamic exclusion was set for 25 s after an ion had been selected for fragmentation. Raw mass spectrometry data files were analysed using Proteome Discoverer v.2.4 SP1 (Thermo), with peptide identification performed using Sequest HT searching against the human reviewed protein database from UniProt[41]. Fragment and precursor tolerances of 10 ppm and 0.6 Da were specified, and three missed cleavages were allowed. Carbamidomethylation of Cys was set as a fixed modification, and oxidation of Met was set as a variable modification. The false discovery rate cutoff was 1% for all peptides.

Statistical and bioinformatics analysis

For all experiments, unless otherwise indicated, n was at least 3. Statistical analyses were performed using Prism (GraphPad Software). Data were analysed using one-way analysis of variance with Bonferroni post-test. Student’s t-test was used for paired datasets. Data points indicate the mean ± s.d. Alignments were done using Clustal Omega[42] and visualized using Jalview[43]. Secondary structure predictions were obtained from UniProt[41].

ProteinAtlas data analysis

The clustering data for the gene expression sets from the deep sequencing of RNA from 40 different normal tissue types (https://www.proteinatlas.org/ENSG00000254521-SIGLEC12/tissue) and scRNA-seq clustering data from 31 human tissue types (https://www.proteinatlas.org/ENSG00000254521-SIGLEC12/single+cell) were obtained from the ProteinAtlas database (v.22)[44]. UMAP plots display gene clusters from Louvain clustering of gene expression across all tissue types or single cell types.

Human mutation analyses

To identify the most prevalent SIGLEC12 point mutations in the general population, we conducted a comprehensive analysis using the dbSNP database. We specifically targeted missense mutations owing to their potential impact on protein function. The search involved filtering for missense mutations in the specified genes and extracting the relevant data. We isolated the necessary columns and ranked the mutations in descending order on the basis of allele frequency aggregator (ALFA) values. The ALFA value represents the aggregation of allele frequency across diverse populations, providing a comprehensive measure of mutation prevalence. From this analysis, we identified the top ten mutations with the highest ALFA values, implicating the most significant missense mutations in the general population. To identify the most frequent SIGLEC12 mutations associated with cancer, TCGA was analysed using cBioportal[45].

Reporting summary

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

Data availability

All data reported in this paper are available on request. Data from the NCBI dbSNP database (https://www.ncbi.nlm.nih.gov/snp/?term=SIGLEC12) and ProteinAtlas database (https://www.proteinatlas.org/ENSG00000254521-SIGLEC12/tissue; https://www.proteinatlas.org/ENSG00000254521-SIGLEC12/single+cell) were used in this study.

Code availability

All code reported in this manuscript are available on request.

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Acknowledgements

This work was supported in part by the National Institutes of Health (NIH), National Institute of General Medical Sciences (NIGMS) grant R35 GM146861 awarded to A.N. We thank C. Lewis, A. Lemoff, P. Doss and M. Mettlen for crucial support with tissue biopsy management and immunohistochemistry, proteomics, electron microscopy and confocal microscopy, respectively; and Y. Kim (Ajou University School of Medicine, Republic of Korea) for the A549-RIPK3 stable cell line. We acknowledge the University of Texas Southwestern Tissue Management Shared Resource at the Simmons Comprehensive Cancer Center, which is supported in part by the National Cancer Institute under award number P30 CA142543; the UT Southwestern Electron Microscopy Core Facility and its supporting NIH grants (S10 OD021685-01A1 and S10 OD020103-01); and the Quantitative Light Microscopy Core and its supporting NIH grants (1S10OD021684-01 and 1S10OD028630-01).

Author information

Authors and Affiliations

1. Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA

Hyunjin Noh, Zeena Hashem, Elena Boms & Ayaz Najafov

Contributions

H.N., Z.H. and E.B. generated reagents, designed and performed experiments, collected data, interpreted the data, prepared the figures, and contributed to writing of the manuscript. A.N. conceived and supervised the project, generated reagents, designed and performed experiments, collected data, interpreted the data, and wrote the manuscript.

Corresponding author

Correspondence to Ayaz Najafov.

Ethics declarations

Competing interests

A.N. and H.N. are listed as inventors for UT Southwestern’s intellectual property disclosure related to interventions related to necroptosis blockade that is pending patent application review and/or preparation. 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 Plasma membrane rupture downstream of MLKL is mediated by SIGLEC12.

a, 293 T cells were transfected with a constitutively active human MLKLT357E/S358D mutant for 24 h. Cell death was quantified using Toxilight (p-value = 2.67 × 10−4), and protein expression levels were assessed using immunoblotting for FLAG-tagged MLKL. b, Stable shRNA-mediated knockdowns were validated in the indicated cells by immunoprecipitation with anti-SIGLEC12 antibody, followed by immunoblotting for SIGLEC12. c, 293FT-MLKLQ356A cells with indicated knockouts were treated with 0.1 μg/ml doxycycline, and cell death was quantified using CellToxGreen. Green fluorescence intensity was measured every hour for 14 h using Incucyte S3 (p-value < 1 × 10−15). d, 293FT-MLKLQ356A cells with indicated stable shRNA-mediated knockdowns were treated with 0.1 μg/ml doxycycline for 24 h, and cell death (CellToxGreen) or cell viability (CellTiterGlo) was quantified (p-value = 8.73 × 10−7). e-i, Jurkat, SK-BR-3, A549-RIPK3 cells (e, f) or HT-29 cells (g-i) with indicated shRNA-mediated stable knockdowns were treated with TSE or TRAIL + SE for 8 h and cell death (CellToxGreen, Toxilight, or LDH release) or cell viability (CellTiterGlo) were quantified (p-value = <1 × 10−15 (e), 3.2 × 10−14, 2.73 × 10−12 (f), <1 × 10−15, 4.4 × 10−9 (g), 2.96 × 10−10, 5.6 × 10−9 (h), and 1.7 × 10−7, 1.54 × 10−7 (i)). Green fluorescence intensity was measured every hour for the indicated times using Incucyte S3. Data were plotted as the mean ± s.d., representative of n = 4 (c and e, left and middle), n = 6 (a and e, right), n = 9 (d, g-i) or n = 12 (f); three independent experiments. Statistical analyses were performed using two-tailed t-test and two-way ANOVA; ***p < 0.001, ****p < 0.0001. Data are representative of three independent experiments (a, b).

Extended Data Fig. 2 SIGLEC12-dependent plasma membrane rupture downstream of MLKL and cytokine production during necroptosis.

a, Cell death was assessed by CellToxGreen fluorescence in HT-29-SIGLEC12-WT, HT-29-SIGLEC12-KO, and HT-29-SIGLEC12-KO-SIGLEC12-FLAG stable cell lines treated with TSE for 8 h (p-value = 1.48 × 10−10). b, Jurkat cells with indicated stable shRNA-mediated knockdowns were treated with TSE for 7 h. IL-1β, IL-8, CXCL1, and TNF mRNA levels were measured by qRT-PCR (p-value = 1.21 × 10−5, 2 × 10−4, 2.24 × 10−6, 2.96 × 10−10, 3.15 × 10−6). Data were plotted as the mean ± s.d., representative of n = 9 (a, b); three independent experiments. Statistical analyses were performed using two-tailed t-test and two-way ANOVA; ***p < 0.001, ****p < 0.0001. Data are representative of three independent experiments (a).

Extended Data Fig. 3 Amino acid sequence alignment of SIGLEC12 and NINJ1 across species.

a, Alignment for SIGLEC12 versus NINJ1 for the indicated species. b, Alignment for SIGLEC12 for the indicated species. The NISI (NINJ1/SIGLEC12 homology motif) motifs are highlighted with red boxes.

Extended Data Fig. 4 The role of the NISI motif in PMR during necroptosis.

a, HT-29-SIGLEC12-KO-SIGLEC12-FL-WT or NISI mutant (NISI-4A) stable cell lines were treated with TSE, and cell death was measured by green fluorescence intensity every hour for the indicated times using Incucyte S3 (p-value = <1 × 10−15, 5.99 × 10−9, 1.53 × 10−6). b, c, 293 T cells were co-transfected with indicated SIGLEC12-FL-FLAG plasmids ± TMPRSS4-HA for 40 h (b). 293FT-MLKLQ356A-SIGLEC12-KO cells were transfected with the indicated SIGLEC12-FL-FLAG plasmids and treated with 0.1 μg/ml doxycycline for 24 h to induce the expression of MLKL-Q356A (c). Cell death was measured using the Toxilight assay (p-value = 2.91 × 10−7 (b) and 8.26 × 10−8, 3.74 × 10−7 (c)). Data were plotted as the mean ± s.d., representative of n = 3 (a, left) or n = 9 (a, middle and right, b and c); three independent experiments. Statistical analyses were performed using two-tailed t-test and two-way ANOVA; ****p < 0.0001.

Extended Data Fig. 5 SIGLEC12 expression is enriched in the same clusters as that of NINJ1.

a, b, The clustering data for the gene expression sets from the deep sequencing of RNA (RNA-seq) from 40 different normal tissue types (https://www.proteinatlas.org/ENSG00000254521-SIGLEC12/tissue) (a) and single-cell RNA sequencing (scRNAseq) (b) clustering data from 31 human tissue types (https://www.proteinatlas.org/ENSG00000254521-SIGLEC12/single+cell). The data was obtained from the ProteinAtlas.org database, version 22. UMAP plots display gene clusters from Louvain clustering of gene expression across all tissue types or single cell types.

Extended Data Fig. 6 Regulation of SIGLEC12 during necroptosis.

a, HeLa-RIPK3-HA cells were transfected with SIGLEC12-FLAG for 16 h and treated with TSE for 4 h. Cells were analyzed by confocal microscopy following immunofluorescence for FLAG. SIGLEC12 is localized to the plasma membrane and cytosolic puncta during necroptosis. Scale bars, 10 μm. b, Cell plugs for HT-29-SIGLEC12-WT or KO cells were immunostained for endogenous SIGLEC12 and imaged using brightfield microscopy at 20x magnification. Scale bars, 100 μm. c, SIGLEC12 IHC signal intensity for slides shown in Fig. 3d was quantified using Fiji/ImageJ (p-value = 8.61×10−3). d, Human colon tumor tissue biopsies from patients were stained with SIGLEC12 (n = 3). Scale bars, 100 μm. e, HeLa-RIPK3-HA cells were transfected with SIGLEC12-FLAG for 16 h and treated with TSE for the indicated times. Cell lysates and indicated immunoprecipitation samples were immunoblotted with the indicated antibodies. f, HT-29-SIGLEC12-KO-SIGLEC12-FLAG stable cell line was treated with TSE for the indicated time points with or without NSA co-treatment. SIGLEC12-FLAG was immunoprecipitated and analyzed by immunoblotting. Data were plotted as the mean ± s.d., representative of n = 3 (c); three independent experiments. Statistical analyses were performed using two-tailed t-test and and two-way ANOVA; **p < 0.01. Data are representative of three independent experiments (e, f).

Extended Data Fig. 7 Mass spectrometry-based analysis of SIGLEC12 phosphorylation during necroptosis and its effect of SIGLEC12-mediated PMR.

a, A large-scale IP-FLAG of SIGLEC12 from HT-29-SIGLEC12-KO-SIGLEC12-FLAG stable cell line following indicated treatments was resolved on SDS-PAGE and stained with Coomassie brilliant blue. Boxes indicate the bands that were analyzed by mass spectrometry to perform phosphomapping. LC-MS/MS ion fragmentation graphs of peptides containing phospho-T519, phospho-S532/S539, and phospho-T555, respectively. pT and pS indicate the phosphorylated residues. b, 293FT-SIGLEC12-FL-WT, 293FT-SIGLEC12-FL-4TS/A, and 293FT-MLKLQ356A cells were treated with 0.1 μg/ml doxycycline, and cell death was quantified using Incucyte S3. Data were plotted as the mean ± s.d., representative of n = 4 (b); three independent experiments. Data are representative of three independent experiments (a, b).

Extended Data Fig. 8 Comparison of human and mouse constitutively active MLKL mutants for their requirements for SIGLEC12 to induce PMR.

a, Cell lysates and SIGLEC12 immunoprecipitation samples from MEFs with CRISPR-induced knockout of SIGLEC12 were immunoblotted with the indicated antibodies. b, Cells were treated with TSE for 3 h, and cell death (CellToxGreen) or cell viability (CellTiterGlo) was quantified. c, Cells were transiently transfected with constitutively active mouse MLKLQ343A or human MLKLQ356A mutants for 24 h. Data were plotted as the mean ± s.d., representative of n = 9 (b, left and c) or n = 12 (b, right); three independent experiments. Statistical analyses were performed using two-tailed t-test. Data are representative of three independent experiments (a).

Extended Data Fig. 9 SIGLEC12 is cleaved by TMPRSS4 to induce plasma membrane rupture during necroptosis.

a, Predicted structure of the [22-410aa]-truncated SIGLEC12 (SIGLEC12-ΔN). The red box shows the NINJ1/SIGLEC12 homology motif (NISI motif). b, 293 T cells were transfected with full-length (SIGLEC12-FL-FLAG) or [22-410aa]-truncated SIGLEC12 (SIGLEC12-ΔN-FLAG) for 40 h with or without NSA co-treatment. Cell death was measured using the Toxilight assay (p-value = 9.62 × 10−8). c, HeLa cells were transiently transfected with signal-peptide-HA-SIGLEC12-ΔN plasmid, and immunofluorescence was performed with or without cell permeabilization as indicated. Scale bars, 10 μm. d, HT-29 cells with shRNA-mediated stable knockdown of TMPRSS4 (two different shRNA sequences) were immunoblotted with the indicated antibodies following necroptosis induction by TSE. e, f, 293 T cells were co-transfected with indicated SIGLEC12-FLAG plasmids ± TMPRSS4-HA for 24 h. At 24 h post-transfection, cell lysates and anti-FLAG immunoprecipitation samples were immunoblotted with the indicated antibodies (e). Cell death was quantified at 40 h post-transfection by Toxilight assay (p-value = 9.69 × 10−7, 8.32 × 10−7) (f). g, HeLa-RIPK3-HA cells were transfected with indicated SIGLEC12-FLAG plasmids for 16 h and treated with TSE for 4 h. Cell lysates and anti-FLAG immunoprecipitation samples were analyzed by immunoblotting. h, i, 293 T cells were co-transfected with indicated SIGLEC12-FLAG plasmids ± TMPRSS4-HA for 40 h (h). 293FT-MLKLQ356A-SIGLEC12-KO cells were transfected with the indicated SIGLEC12-FLAG plasmids and treated with 0.1 μg/ml doxycycline for 24 h to induce MLKL-Q356A expression (i). Cell death was measured using the Toxilight assay (p-value = 9.74 × 10−9, 1.43 × 10−10 (h) and 7.79 × 10−10, 7.55 × 10−8 (i)). j, k, 293 T cells were co-transfected with SIGLEC12-FL-FLAG and TMPRSS4-HA for 24 h (j), and HT-29-SIGLEC12-KO-SIGLEC12-FLAG stable cell line was treated with TSE for 4 h with or without co-treatment with a protease inhibitor cocktail (PIC, cOmplete™, 1x final concentration) (k). Cell lysates and anti-FLAG immunoprecipitation samples were immunoblotted with the indicated antibodies. Data were plotted as the mean ± s.d., representative of n = 6 (b), n = 7 (i) or n = 9 (f and h); three independent experiments. Statistical analyses were performed using two-tailed t-test; ****p < 0.0001. Data are representative of three independent experiments (d, e, g, j and k). Panel a was created using Biorender (https://biorender.com).

Extended Data Fig. 10 SIGLEC12 mutations and the proposed model of TMPRSS4-mediated SIGLEC12 activation during necroptosis.

a, Summary of the SIGLEC12 mutation frequencies found in the general population (graphs) and their location in the SIGLEC12 domains. Green circles: extracellular mutations; Red circle: cancer-associated mutation S458F; Yellow circles: intracellular mutations; TM: transmembrane region. Blue boxes: V-type Ig domains; Green boxes: C2-type Ig domains; Orange boxes: ITIM and ITSM domains; Red box: NISI motif. b, A proposed model of SIGLEC12 activation and plasma membrane rupture induction during necroptosis. Panels a and b were created using Biorender (https://biorender.com).

Supplementary information

Supplementary Fig. 1

Reporting Summary

Supplementary Video 1

Time-lapse live-cell imaging confocal microscopy of PMR in HT-29-shGFP stable cell line during necroptosis (bright-field and CellToxGreen channels). Stills are shown in Fig. 2d (left panel). HT-29-shGFP stable shRNA-mediated knockdown cell line was treated with TSE for 24 h, and cell death was assessed using CellToxGreen and time-lapse fluorescence confocal microscopy. A merge of bright-field and green fluorescence channels is shown. Representative of three independent experiments.

Supplementary Video 2

Time-lapse live-cell imaging confocal microscopy of PMR in HT-29-shGFP stable cell line during necroptosis (CellToxGreen channel). Stills are shown in Fig. 2d (left panel). HT-29-shGFP stable shRNA-mediated knockdown cell line was treated with TSE for 24 h, and cell death was assessed using CellToxGreen and time-lapse fluorescence confocal microscopy. The green fluorescence channel is shown. Representative of three independent experiments.

Supplementary Video 3

Time-lapse live-cell imaging confocal microscopy of PMR in HT-29-shSIGLEC12 stable cell line during necroptosis (bright-field and CellToxGreen channels). Stills are shown in Fig 2d (left panel). HT-29-shSIGLEC12 stable shRNA-mediated knockdown cell line was treated with TSE for 24 h, and cell death was assessed using CellToxGreen and time-lapse fluorescence confocal microscopy. A merge of bright-field and green fluorescence channels is shown. Representative of three independent experiments.

Supplementary Video 4

Time-lapse live-cell imaging confocal microscopy of PMR in HT-29- shSIGLEC12 stable cell line during necroptosis (CellToxGreen channel). Stills are shown in Fig. 2d (left panel). HT-29-shSIGLEC12 stable shRNA-mediated knockdown cell line was treated with TSE for 24 h, and cell death was assessed using CellToxGreen and time-lapse fluorescence confocal microscopy. The green fluorescence channel is shown. Representative of three independent experiments.

Supplementary Video 5

Time-lapse live-cell imaging confocal microscopy of PMR in HT-29-shGFP stable cell line during necroptosis (high magnification of the bright-field channel). Stills are shown in Fig. 2d (right panel). HT-29-shGFP stable shRNA-mediated knockdown cell line was treated with TSE for 24 h, and time-lapse confocal microscopy was performed. The bright-field channel is shown. Representative of three independent experiments.

Supplementary Video 6

Time-lapse live-cell imaging confocal microscopy of PMR in HT-29-shSIGLEC12 stable cell line during necroptosis (high magnification of the bright-field channel). Stills are shown in Fig. 2d (right panel). HT-29-shSIGLEC12 stable shRNA-mediated knockdown cell line was treated with TSE for 24 h, and time-lapse confocal microscopy was performed. The bright-field channel is shown. Representative of three independent experiments

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Noh, H., Hashem, Z., Boms, E. et al. SIGLEC12 mediates plasma membrane rupture during necroptotic cell death. Nature 649, 460–466 (2026). https://doi.org/10.1038/s41586-025-09741-1

- Received: 13 December 2024

- Accepted: 10 October 2025

- Published: 12 November 2025

- Version of record: 12 November 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09741-1

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Tumour-reactive heterotypic CD8 T cell clusters from clinical samples

Sofía Ibáñez-Molero,

Johanna Veldman,

Juan Simon Nieto,

Joleen J. H. Traets,

Austin George,

Kelly Hoefakker,

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Su Min Pack,

Liselotte Tas,

Nils L. Visser,

Susan E. van Hal-van Veen,

Paula Alóndiga-Mérida,

Maartje Alkemade,

Iris M. Seignette,

Renaud Tissier,

Marja Nieuwland,

Martijn van Baalen,

Joanna Poźniak,

Erik Mul,

Simon Tol,

Sofia Stenqvist,

Lisa M. Nilsson,

Jonas A. Nilsson,

John B. A. G. Haanen,

Winan J. van Houdt &

…

Daniel S. Peeper

Nature volume 649, pages 467–476 (2026)

Abstract

Emerging evidence suggests a correlation between CD8+ T cell–tumour cell proximity and anti-tumour immune response1,2. However, it remains unclear whether these cells exist as functional clusters that can be isolated from clinical samples. Here, using conventional and imaging flow cytometry, we show that from 21 out of 21 human melanoma metastases, we could isolate heterotypic clusters, comprising CD8+ T cells interacting with one or more tumour cells and/or antigen-presenting cells (APCs). Single-cell RNA-sequencing analysis revealed that T cells from clusters were enriched for gene signatures associated with tumour reactivity and exhaustion. Clustered T cells exhibited increased TCR clonality indicative of expansion, whereas TCR-matched T cells showed more exhaustion and co-modulation when conjugated to APCs than when conjugated to tumour cells. T cells that were expanded from clusters ex vivo exerted on average ninefold increased killing activity towards autologous melanomas, which was accompanied by enhanced cytokine production. After adoptive cell transfer into mice, T cells from clusters showed improved patient-derived melanoma control, which was associated with increased T cell infiltration and activation. Together, these results demonstrate that tumour-reactive CD8+ T cells are enriched in functional clusters with tumour cells and/or APCs and that they can be isolated and expanded from clinical samples. Typically excluded by single-cell gating in flow cytometry, these distinct heterotypic T cell clusters are a valuable source to decipher functional tumour–immune cell interactions and may also be therapeutically explored.

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Main

An increasing body of evidence suggests that, in addition to the type, density and state of immune cells in the tumour microenvironment (TME), their proximity to cancer cells also influences immunotherapy outcomes1,2,3,4,5. For example, in two melanoma studies, favourable responses to immune checkpoint inhibitors are associated with either higher densities of CD8+ tumour-infiltrating lymphocytes (TILs) within a distance of 20 μm of melanoma cells[5] or a higher proportion and closer proximity of (proliferating) antigen-experienced CD8+ T cells to the tumour cells[6]. Similarly, upon anti-PD-1 and chemoradiotherapy, a higher proportion of on-treatment PD-1+CD4+ and CD8+ T cells within 100 μm of tumour cells predicts longer overall survival in oesophageal cancer[7], while, in locally advanced cervical cancer, progression-free patients show closer proximity of CD3+ TILs to PD-L1+ tumour cells[8]. Furthermore, an automated image classifier characterizing interactions between TILs and non-TILs can predict immunotherapy outcome[9].

These notions are consistent with the understanding that, after specific antigen recognition, cytotoxic T cells physically engage their target cells through their TCRs, followed by immunological synapse formation[10]. The structural and functional avidity of cytotoxic CD8+ T cells are important parameters for infiltration into and activity against tumours[11]. It takes successive interactions and dynamic contacts for T cells to effectively eliminate cancer cells12,13. The importance of direct interactions between cytotoxic T cells and tumour cells has been confirmed by single-cell sequencing analyses, which have been instrumental for our understanding of TME complexity14,15,16,17,18,19. Additional information can be extracted when cells are isolated if clustered with neighbouring cells14,16,17,18,20. For example, relative to unconjugated tumour cells, mouse circulating tumour cells (CTCs) associated with neutrophils show increased cell cycle activity and metastatic potential[21]. Physically interacting cells have also been analysed by integrated single-cell sequencing and computational modelling (PIC-seq), showing specific gene signatures in interacting myeloid and CD4+ T cells in the TME22,23.

As described below in detail, in defined co-cultures, we noted that human antigen-specific CD8+ T cells outcompeted non-specific T cells in forming heterotypic clusters with matched antigen-expressing tumour cells. This result, together with the observations described above, prompted us to investigate whether tumour-specific CD8+ T cells could be isolated from clinical cancer specimens as heterotypic clusters, and whether they show a distinct biological phenotype and anti-tumour activity.

Antigen-specific T cell competitiveness

To study functional interactions between human T cells and tumour cells, we used a matched co-culture model that we established previously24,25. We engineered melanoma cells to express both HLA-A*02:01 and the MART-1 tumour antigen, as well as an mPlum fluorescent marker (Extended Data Fig. 1a). CD8+ T cells were isolated from healthy donors, retrovirally transduced with a MART-1-specific TCR and labelled with CellTrace Violet (CTV). On the basis of flow cytometry analysis performed after co-culture for 4 h, we observed single tumour cells and single T cells. However, we also noticed a cell population that was positive for both the tumour and T cell labels, suggestive of the formation of heterotypic clusters (Fig. 1a and Extended Data Fig. 1b,c). Using imaging flow cytometry (ImageStream Mark II) we visualized these cell clusters and their immunological synapses (as judged by the significant relocalization of HLA-A*02, ICAM1 and CD58 specifically to the T cell–tumour cell interface; Fig. 1b, Extended Data Fig. 1d–g and Supplementary Table 1). This observation was not limited to melanoma, but was also made for four other cancer indications (Fig. 1c).

Fig. 1: Antigen-specific T cell competitiveness.

a, FM6 melanoma cells co-cultured for 4 h with T cells were analysed by flow cytometry. b, D10 melanoma cells co-cultured for 4 h with T cells were stained and visualized using imaging flow cytometry. The white arrows indicate immunological synapse marker relocalization. Numbers indicate cell identifiers. c, The percentage of clustered T cells after 4 h co-culture with different cancer cell lines using 40% MART-1-transduced T cells. n = 5 biological replicates (different T cell donors). Data are mean ± s.d. NSCLC, non-small-cell lung cancer. d, Diagram of the competition assay: tumour cells were co-cultured with a mixture of MART-1-specific and non-specific T cells and subsequently analysed using flow cytometry. e, A875 melanoma cells were co-cultured for 4 h with a 40:60 mix of MART-1-specific:non-specific T cells (input, I). The percentage of MART-1-specific and non-specific T cells in clusters (C) and singlets (S) was assessed using flow cytometry. The average fold change (avg. FC) in MART-1-specific T cells in clusters over singlets was calculated. P values were calculated using paired t-tests. n = 5 biological replicates. Data are mean ± s.d. f, A875 melanoma cells were co-cultured for 4 h with different mixtures of MART-1-specific:non-specific T cells; the experiment was performed and analysed as described in e. g, Co-culture of different cancer cell lines with a 5:95 mixture of MART-1-specific:non-specific T cells; the experiment was performed and analysed as described in e. h, Diagram of the in vivo experiment: BLM melanoma cells were co-cultured for 4 h with a 20:80 mixture of MART-1-specific:non-specific T cells, and subsequently sorted using FACS. T cells from singlets or clusters were expanded using a rapid expansion protocol (REP). A total of 1.0 × 10^7 T cells was intravenously injected at day 7 and day 9 into BLM melanoma-bearing mice (NSG), and tumour growth was evaluated. i, Tumour growth after ACT with T cells from singlets, T cells from clusters, all T cells or PBS (control). P values were calculated using two-way analysis of variance (ANOVA) followed by Tukey’s multiple-comparison test. Significance is indicated compared with the control. n = 4 mice per group. Data are mean ± s.e.m. NS, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source Data

These results led us to investigate whether non-specific and antigen-specific T cells differentially engage with tumour cells to form cell–cell conjugates. We admixed non-specific (approximately 60%) and MART-1-specific (approximately 40%) T cells to compete for association with tumour cells. After co-culture for 4 h, we evaluated the contribution of each T cell group to the clusters (Fig. 1d). We observed a 2.7-fold enrichment of MART-1-specific T cells in heterotypic tumour cell clusters compared with singlets (Fig. 1e). We next challenged the system to mimic a more physiological setting such as the TME, in which tumour-reactive T cells are probably under-represented15,18,26,27. In all titrations, MART-1-specific T cells outcompeted their non-specific counterparts for cluster formation. Even when specific T cells accounted for only 1% of all T cells, they were up to 11-fold enriched in clusters with tumour cells (Fig. 1f and Extended Data Fig. 1h–i). This competitive advantage of antigen-specific T cells was not limited to melanoma, but reproduced across different cancer indications (Fig. 1g). In all cases, conjugation with tumour cells led to increased activation of antigen-specific T cells, as judged by CD69 induction, compared with non-specific T cells (Extended Data Fig. 1j). To determine the specificity of the system, we also inverted these titrations: when 95% of MART-1-specific T cells were mixed with 5% of non-specific T cells, the latter were depleted from (rather than enriched in) tumour cell clusters (Extended Data Fig. 1k).

To begin exploring preclinical translation of these findings, we determined the relative tumour-controlling potential of single and clustered T cells. After a rapid-expansion protocol (REP), we performed two rounds of adoptive cell transfer (ACT) with the different T cell populations in human-melanoma-bearing immunodeficient NOD-scid Il2rg-null (NSG) mice (Fig. 1h). Whereas T cells expanded from singlets showed no tumour control, T cells derived from heterotypic clusters with tumour cells significantly suppressed tumour growth (Fig. 1i and Extended Data Fig. 1l,m). Together, these results indicate that, in defined co-cultures, matched T cells and tumour cells form heterotypic clusters that can form immunological synapses, in which antigen-recognizing T cells outcompete non-specific T cells. When transplanted into mice, T cells from clusters show enhanced tumour control.

Clinical heterotypic CD8+ T cell clusters

These observations, together with the reported correlations between CD8+ T cell–tumour cell proximity and immunotherapy response described above, prompted us to investigate whether heterotypic clusters between CD8+ T cells and tumour cells can also be isolated directly from clinical cancer specimens. We analysed a cohort of 21 melanoma metastases from various anatomical sites, including lymph nodes (Supplementary Table 2). After surgical removal, the tissue was cut into small fragments and enzymatically digested for a maximum of 30 min, after which the samples were analysed by flow cytometry using antibodies specific for melanoma (CD146 and NGFR) and T cells (CD8). Owing to the prevalence of APCs in lymph nodes, we also included an APC marker (CD11c). As expected, we identified single cells for each of these populations: CD8+ T cells, melanoma cells and APCs. Importantly, we also observed heterotypic CD8+ T cell–melanoma cell clusters and CD8+ T cell–APC clusters from all patient samples (Fig. 2a–c and Extended Data Fig. 2a). The percentage of heterotypic CD8+ T cell clusters within live cells significantly correlated with the degree of T cell infiltration into tumours and was not affected by a freeze–thaw cycle (Extended Data Fig. 2b,c).

Fig. 2: Clinical heterotypic CD8+ T cell clusters.

a, Diagram of tumour sample collection and processing: fresh tumour samples were obtained, cut into small pieces, briefly enzymatically digested, stained and analysed using flow cytometry to identify tumour cells, T cells and APCs. b, Representative flow cytometry plots of a melanoma tumour digest (patient 1, P1) processed as in a. The tumour digest was stained for the tumour cell markers CD146 and NGFR, the T cell marker CD8 and APC marker CD11c. Plots were obtained from live cells gated as shown in Extended Data Fig. 2a. c, The percentage of T cell–tumour cell (T–Tum) and T cell–APC (T–APC) clusters within the total CD8+ T cell population. Each coloured point represents an individual patient (n = 21). Data are mean ± s.e.m. d, The tumour digest (patient 1) from b was visualized using imaging flow cytometry. Representative single cells (top) and heterotypic clusters with different compositions (bottom) are shown. The white arrows indicate relocalization of markers to the immunological synapse in T cell–APC clusters. e, Tumour digest (patient 6, P6) visualized using imaging flow cytometry. Representative clusters are shown. The white arrows indicate relocalization of immunological synapse markers. f, Tumour digest (patient 17, P17) visualized using imaging flow cytometry. Representative heterotypic clusters containing CD4+ T cells are shown. In d–f, numbers indicate cell identifiers. g, Multiplex immunofluorescence staining of tissue sections of patient 2 (lymph node metastasis (LN met)) and patient 6 (non-lymph node metastasis). The sections were stained for the tumour cell markers SOX10 and HMB45, T cell marker CD8 and APC marker CD11c. DAPI was included as a nuclear marker. In the top rows, merged images are shown (DAPI is not included for clarity). The white boxes indicate magnified areas. In the bottom rows, channels are separated and correspond to the second images on the top row. n = 11 patients; representative patients are shown. Scale bars, 500 μm (g, left) and 100 μm (g, right).

Source Data

ImageStream imaging flow cytometry confirmed these heterotypic cell clusters, comprising one or more CD8+ T cells conjugated to either one or more tumour cells and/or one or more APCs (Fig. 2d). The immune synapse markers CD11c, HLA-ABC and CD58 were significantly relocalized to the cell–cell interface (Fig. 2d,e, Extended Data Fig. 2d and Supplementary Table 1). We also identified clusters comprising CD4+ T cells, tumour cells and APCs (Fig. 2f). The presence of CD8+ clusters was corroborated by multiplex immunofluorescence analysis of the same clinical samples (Fig. 2g and Extended Data Fig. 2e,f). We noted common niches comprising CD8+ T cells, tumour cells and APCs in <10 μm vicinity. Together, these results confirm that heterotypic CD8+ and CD4+ T cell clusters can be detected in, and isolated from, clinical cancer specimens.

Tumour-reactive CD8+ T cells from clusters

Next, we used combined single-cell RNA-sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq) for in-depth comparisons between singlets and clusters for CD8+ T cells, tumour cells and APCs. Melanoma specimens were again digested briefly, but this time were separated by fluorescence-activated cell sorting (FACS) to obtain CD8+ T cell, tumour cell and APC singlets and clusters (Fig. 3a). The sorting caused most clusters to dissociate into single cells, which were captured into gel beads in emulsion droplets and subjected to sequencing. Combined analysis of all patient specimens (n = 5) confirmed three distinct cell types in the clusters as expected: T cells, tumour cells and APCs, consistent with our flow and imaging analyses (Extended Data Fig. 3a,b).

Fig. 3: Tumour-reactive CD8+ T cells from clusters.

a, Diagram of the workflow of the scRNA-seq and scTCR-seq analysis. b, scRNA-seq UMAP of CD8+ T cells, highlighting the main cell states (left) and the average frequencies (right). n = 5 patients. Each patient was weighted equally. Tc17, IL-17-producing T cells; MAIT, mucosa-associated invariant; ISG+, interferon-stimulated-gene positive cells. Bonferroni-adjusted P values were calculated using generalized linear mixed-effects models; significantly enriched cell states in clustered versus single T cells are indicated. c, The average frequencies of the top 15 TCR clonotypes in single or clustered T cells analysed as in b. d, The average tumour- and virus-reactivity gene signature scores per TCR clonotype (≥10 cells) from single or clustered T cells; rows are z scored. e, CD8+ T cell frequencies in a cohort of patients with melanoma treated with TILs30,31 by cluster 30 signature-derived tertiles (high, medium and low) in responder (R) and non-responder (NR) baseline tumours (n = 13 patients; n = 6 (R), n = 7 (NR)). CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease. P values were calculated using unpaired t-tests. f, The average signature scores for exhaustion, CD28 and CTLA4 signalling in TCR-matched clonotypes from clustered T cells. The top 10 clonotypes per patient are shown (≥10 cells per cluster group); n = 37 matched clonotypes from 4 patients. Each patient (P) is represented by a different colour. P values were calculated using paired Wilcoxon signed-rank tests. In e and f, all datapoints are shown. g, The top 50 inferred ligands and their receptor interactions. The arrow transparency reflects inferred ligand signalling activity. ‘Unspecific’ indicates shared ligands or receptors. h, scRNA-seq UMAP from melanoma tumour cells, highlighting the main cell states (left) and average frequencies (right). n = 5 patients. Analysed as in b. i, scRNA-seq UMAP of all APC types. n = 5 patients. Mono/mac, monocytes and macrophages; DCs, dendritic cells. j, scRNA-seq UMAP from monocytes and macrophages, highlighting the main cell states (left) and the average frequencies (right). n = 5 patients. Analysed as in b. NS, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source Data

We analysed the CD8+ T cell population derived from both singlets and clusters in detail to determine their cell states and TCR clonality, corresponding to two key characteristics of T cell activity. CD8+ T cells were annotated according to their respective cell states based on RNA expression profiles of T-cell-related genes and cross-labelling with external single-cell datasets of human CD8+ TILs14,15,20,28. We identified 14 cell states, showing similarities to cell states described previously14,15,20,28 (Fig. 3b and Extended Data Fig. 3c–f), including naive (Tn, expressing SELL and IL-7R), (early) effector memory (Tem, expressing GZMH and GZMK), exhausted (Tex, expressing PDCD1, TOX, CXCL13, LAG3) and proliferating (Tprol, expressing MKI67) CD8+ T cells. Notably, two subpopulations of proliferating T cells that we identified also expressed exhaustion markers and were therefore termed Tex/Tprol cells.

We next compared the cell states of T cells derived from singlets and clusters. T cell singlets were enriched for naive and (early) effector memory T cells (Tn, Tn/Tmem, Tem), whereas T cells derived from both tumour and APC clusters were enriched for exhausted and proliferative cell states (Tex, Tex/Tprol) (Fig. 3b, Extended Data Fig. 3g and Supplementary Table 3). We also determined the expansion of the top 15 most-frequent TCR clonotypes17,26 (defined as one or more cells with a unique paired α- and β-TCR sequence). This revealed that, relative to single T cells, T cells from both tumour cell and APC clusters were enriched for clonal TCRs (Fig. 3c, Extended Data Fig. 3h and Supplementary Table 3). These results raised the possibility that T cells from clusters are expanded and enriched for tumour-reactive clonotypes.

We also assessed the expression of several gene signatures specific for T cell reactivity against tumour cells15,18,29. T cell clones originating from clusters showed increased expression of tumour-reactivity signatures (Fig. 3d, Extended Data Fig. 4a,b and Supplementary Table 4). By contrast, single T cell clones exhibited higher expression of a virus reactivity signature, characteristic of bystander T cells[15]. Furthermore, we generated a gene signature derived from clustered T cells that showed both unique and shared features with other tumour-reactivity T cell signatures (Extended Data Fig. 4c and Supplementary Table 4), as well as enrichment for T cell activation, cytotoxicity and cell–cell adhesion gene sets (Extended Data Fig. 4d and Supplementary Table 5). We then projected these cluster T cell (30- and 100-gene) signatures onto an external dataset of melanoma TIL therapy responders and non-responders30,31. We observed that the frequency of CD8+ TILs with a high cluster signature score at baseline was significantly predictive of therapy response (Fig. 3e and Extended Data Fig. 4e), corroborating the association between T cells from clusters and tumour reactivity. Together, these data indicate that CD8+ T cells from clusters, compared with single T cells, show a more exhausted and tumour-reactive phenotype and increased TCR clonality, while they have a distinct RNA profile that predicts TIL therapy response.

Differential tumour and APC conjugation

We next determined whether CD8+ T cells show differential exhaustion profiles after conjugation with APCs compared with tumour cells. To avoid confounding effects, we performed this analysis taking advantage of our scTCR-seq data. Gene signature analysis revealed that T cells with identical TCRs from top-expanded clonotypes were more exhausted when conjugated to APCs than when conjugated to tumour cells[32] (Fig. 3f and Supplementary Tables 4 and 5). Moreover, while several ligand–receptor pairs were shared between CD8+ T cell–tumour cell and T cell–APC conjugates (including HLA-CD8), the latter clusters showed more co-modulatory interactions, such as between CD80/CD86 and CTLA-4/CD28 and between PD-L1/2 and PD-1 (Fig. 3f,g). For T cell–tumour cell interactions, we observed several adhesion interactions, including between CD58 and CD2 and between ICAM1 and ITGAL (Fig. 3g). These results reveal that TCR-matched T cells show more exhaustion and co-modulation when conjugated to APCs than to tumour cells. Notably, exhausted T cells in the TME usually contain the largest fraction of tumour-reactive T cells that can be reinvigorated after treatment15,18,29,30.

Distinct APCs and tumour cells in clusters

After this characterization of clustered T cells, we next investigated whether there is any preferential conjugation of CD8+ T cells to specific tumour cell and APC subpopulations. We annotated tumour cell and APC subtypes on the basis of existing gene signatures and marker genes4,31,33,34,35,36,37,38,39,40,41,42. In agreement with previous studies on melanoma heterogeneity4,33,35, we observed a broad spectrum of melanoma cell states, including melanocytic and neural-crest like (Fig. 3h, Extended Data Fig. 5a–e and Supplementary Table 5). When analysing their representation in clusters, we observed that specific melanoma subpopulations were enriched, particularly those associated with immune response (for example, antigen presentation and interferon signalling) and stress/hypoxia response (for example, HIF signalling) (Fig. 3h, Extended Data Fig. 5f–i and Supplementary Table 3). Cell–cell communication analysis revealed that specifically the T-cell-interacting, immune-response-associated melanoma subpopulation showed higher expression of ligands mediating T cell attraction (for example, CCL5–CCR5, CXCL9/10–CXCR3), immune synapse formation (for example, HLA-CD8 and ICAM1–ITGAL) and immune modulation (for example, PD-L1–PD-1) (Extended Data Fig. 5j).

We performed a similar enrichment analysis for APCs, based on the identification of monocytes/macrophages, dendritic cells (DCs) and B/plasma cells isolated from the same clinical samples (Fig. 3i), all of which we annotated on the basis of previous studies31,36,37,38,39,40,41,42 (Fig. 3j and Extended Data Fig. 6a–e). The monocytes/macrophages comprised a range of phenotypes, including CD16high monocyte-like cells and C1qhigh macrophages (Fig. 3j). We observed that, among all states, specifically the C1qhigh lipid-associated and C1qhigh inflammatory macrophages were enriched in CD8+ T cell clusters (Fig. 3j, Extended Data Fig. 6f and Supplementary Table 3). These subpopulations showed higher expression of ligands mediating T cell attraction (for example, CCL4–CCR5, CXCL9/10–CXCR3) and co-modulation (for example, PD-L1–PD-1 and CD80–CTLA-4/CD28) (Extended Data Fig. 6g). Likewise, we found enrichment of subpopulations of DCs (particularly plasmacytoid DCs and mature DCs enriched in regulatory molecules, also known as mregDCs) and B cells (particularly plasma cells) in T cell clusters. These enriched DC groups were associated with similar predicted ligand–receptor interactions (such as CCL4–CCR5, CXCL9–CXCR3 and PD-L1–PD-1) (Extended Data Fig. 6g). Collectively, these results show that CD8+ T cells preferentially bind to specific subpopulations of both APCs and tumour cells, communicating through specific ligand–receptor pairs.

Enhanced killing by T cells from clusters

The results above show that CD8+ T cell clusters can be isolated from clinical samples and that they harbour several features predicting enhanced tumour reactivity, which we put to the test. We again isolated T cell singlets and clusters from melanoma digests using FACS but, this time, the sorting step was followed by a REP. After a resting period, the T cells were used to treat autologous melanoma cells ex vivo (Fig. 4a and Supplementary Tables 6 and 7). After 4 h exposure to tumour cells, production of the cytokines IFNγ and TNF was significantly increased in T cells from clusters compared with in T cells from singlets, indicative of higher activation (Fig. 4b,c and Extended Data Fig. 7a). We next determined the tumour-killing potential of T cells from singlets and clusters. Autologous melanoma cells were established from the clinical samples and co-cultured with T cells for several days. As a measure of cell killing, we performed CellTiter-Blue assays, and untreated tumour cells were used as controls. The killing capacity of CD8+ T cells derived from tumour cell clusters was increased in 9 out of 11 patients compared with T cell singlets; for T cells from APC clusters, this was seen in 11 out of 11 patients. Relative to T cell singlets, T cells from tumour cell clusters showed over eightfold higher killing activity and T cells from APC clusters were more than ninefold more active (Fig. 4d and Extended Data Fig. 7b).

Fig. 4: Enhanced killing by T cells from clusters.

a, Schematic of the ex vivo and in vivo experiments: sorted singlet and clustered T cells were expanded using REP, rested and co-cultured with autologous tumour cells or injected into matched PDX-bearing mice. b, Representative flow cytometry plots showing cytokine production by T cells from singlets or clusters after 4 h co-culture with tumour cells (patient 2, P2). c, Cytokine production by T cells from singlets or clusters after 4 h co-culture with tumour cells. n = 12 patients. The mean of at least two technical replicates is shown. P values were calculated using unpaired t-tests versus singlets. d, Tumour killing by T cells from singlets or clusters (n = 11 patients), normalized to untreated tumour cells. The points represent technical replicates. P values were calculated using two-way ANOVA followed by Dunnett’s multiple-comparison test. Significantly increased killing and average fold change compared with singlets is shown. Data are mean ± s.d. e, Representative immunohistochemistry for CD8, CD137 and PD-L1 in PDX tumours from hIL-2-NOG mice (patient 8, P8), 2 weeks after ACT. n = 5 mice per group. Scale bar, 500 μm. f, Flow cytometry analysis of T cell activation in PDXs from e, measured as the percentage of CD137+ and CD39+PD1+ cells. P values were calculated using unpaired t-tests. n = 5 mice per group; 3 mice were not included owing to insufficient material. Data are mean ± s.d. g, PDX tumour growth (patient 8) in NSG mice receiving ACT with T cells from singlets, clusters or PBS (control). P values were calculated using two-way ANOVA followed by a Tukey’s multiple-comparison test. Significance was calculated versus the control. n = 10 mice per group, except for T cells from tumour clusters, for which n = 9. Data are mean ± s.e.m. h, The percentage of singlet and clustered T cells lost after single-cell gating (n = 7 patients). The points represent patients. Statistical analysis was performed using paired t-tests versus singlets. Data are mean ± s.e.m. i, Tumour killing by T cells from singlets, clusters or CD39+ cells (n = 4 patients) normalized as in d. P values were calculated using one-way ANOVA, followed by Tukey’s multiple-comparison test. The black lines show singlet versus clustered T cells; and the blue lines show clustered versus CD39+ T cells. Data are mean ± s.d. In c, d and i, each patient is indicated with P and a number. NS, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Source Data

We next set out to examine the therapeutic potential of these clustered T cells in two independent mouse experiments and in a clinical TIL REP protocol. First, we performed ACT with patient TILs that were expanded using a REP. Expanded TILs were subsequently inoculated into hIL-2-NOG mice[43] (for optimal in vivo T cell support) carrying a matched patient-derived melanoma xenograft (PDX). ACT was performed with either T cell singlets or T cells from tumour or APC clusters, which were characterized for in vivo T cell infiltration and activation. We observed that T cells from tumour cell clusters showed increased infiltration, while a similar trend was observed for T cells from APC clusters (Fig. 4e and Extended Data Fig. 8a,b). Moreover, corroborating our ex vivo data, we found significantly increased activation of T cells from clusters relative to T cell singlets, as judged by upregulation of CD137, PD-1, CD39 and increased PD-L1 expression by tumour cells (indicative of IFNγ secretion by active neighbouring T cells) (Fig. 4e,f and Extended Data Fig. 8a–c). As the T cell toxicity observed in this model due to the high IL-2 levels precluded analysis of tumour growth, we also set up an independent mouse experiment using PDX-bearing NSG mice. Whereas adoptively transferred T cell singlets had no effect, T cells from both tumour and APC clusters significantly delayed tumour growth (Fig. 4g and Extended Data Fig. 8d). Thus, T cells from clusters are strongly enriched for tumour killing activity also in vivo.

Second, we adjusted our REP to make it more compatible with the current clinical TIL REP[44]. We found that T cells derived from clusters retained their functionality and power to significantly outperform singlets in tumour-killing activity (Extended Data Fig. 8e–g). We also benchmarked our cluster-enriched TIL product to current methods using cell-surface markers to enrich for tumour-reactive single T cells, particularly PD-1 and CD3916,17,26,27,45,46,47,48,49. However, the use of single-cell gates caused an almost complete loss of T cell–tumour cell clusters (average 91% loss) and a profound decrease in T cell–APC clusters (average 36% loss) (Fig. 4h and Extended Data Fig. 9a,b), highlighting that these clusters represent a unique cell population. Most cell clusters were positive for both CD39 and PD-1 (Extended Data Fig. 9c). Notably, CD39 is not a unique T cell marker, as it is expressed also by tumour cells and/or APCs (Extended Data Fig. 9d). Whereas sorting for CD8+CD39+ T cells also enriched for tumour-reactive T cells, in 4 out of 4 patients, at least one of the cluster-derived T cell groups outperformed them in tumour cell killing (Fig. 4i and Extended Data Fig. 9e).

Although T cells from clusters contained CD39+ cells, we noted that they also included a CD39− T cell population (ranging from 7 to 51%) (Extended Data Fig. 10a,b). These CD39− T cells were even observed in expanded clonotypes (ranging from 28 to 76% within CD39− T cells from clusters), suggesting tumour reactivity. Using single-cell analysis, we next compared the cell states between sorted single CD8+CD39+ T cells and CD8+ T cells from clusters. We observed that T cells from clusters were enriched for persistence-associated50,51,52, memory-like and early dysfunctional cell states (including TCF7+ stem-like Tex cells, which are more frequently CD39−), whereas sorted CD39+ T cells were enriched for terminally exhausted cells (LAG3high Tex cells) (Extended Data Fig. 10c–f and Supplementary Tables 3 and 4). This was also true for TCR-matched T cells (Extended Data Fig. 10g). Analysis of an annotated external melanoma dataset[31] revealed that the cluster-enriched TCF7+ stem-like Tex cell state was significantly associated with clinical TIL response (Extended Data Fig. 10h,i).

We conclude from these analyses together that CD8+ T cells derived from cell clusters exert significantly greater anti-tumour activity than single T cells both ex vivo and in vivo. Moreover, they retain their potential in an expansion protocol resembling clinical TIL REP and are enriched in a favourable TCF7+ stem-like exhausted cell state compared with other enrichment strategies.

Discussion

To study the TME, much effort has focused on the analysis of single cells by flow cytometry and sequencing, which has greatly advanced our understanding of its composition and complexity18,19,20,36. However, cells can engage in stable homotypic and heterotypic interactions in vivo21,22,23,53,54,55. This study shows that heterotypic CD8+ T cell clusters containing tumour cells and/or APCs can be retrieved directly from clinical cancer specimens from various anatomical sites, including lymph nodes. We demonstrate that, compared with single T cells, these clustered T cells possess biologically distinct features and are strongly enriched for tumour reactivity.

Using flow and imaging analyses, we show that CD8+ T cells are conjugated to several types of APCs, including monocytes/macrophages, DCs and B cells. These clusters are stable enough to withstand a freeze–thaw cycle and, although some interactions may result from dissociations and (re)associations ex vivo, our single-cell RNA analysis revealed that the cell–cell associations do not occur in a random manner. Indeed, for all these APC types, we observe enrichment of specific subpopulations in clusters. For example, for monocytes/macrophages, we find enrichment of particularly C1qhigh lipid-associated and C1qhigh inflammatory subtypes in CD8+ T cell conjugates, which are similar to macrophage subtypes associated with clinical responses to TIL therapy[31]. Likewise, among different melanoma cell states present in the TME, there is significant enrichment of those characterized by high antigen presentation, IFN signalling and stress/hypoxia-response signalling, which could be a cause or consequence of T cell interaction. Furthermore, both APC and tumour subpopulations enriched in clusters exhibit higher expression of ligands that are critical for immune synapse formation, T cell attraction and immune modulation upon conjugation with T cells.

In-depth analysis of clustered CD8+ T cells themselves also revealed several unique features. First, compared with single T cells, we observed that clustered CD8+ T cells are more exhausted and proliferative, while they also show clonal expansion of their TCRs. Notably, exhausted T cells within the TME typically have the largest proportion of tumour-reactive T cells, which can be reactivated through treatment15,18,29,30. Second, whereas single T cells show features consistent with virus specificity typical of bystander cells, clustered T cells are strongly enriched for tumour-reactive signatures. Third, a T cell signature that we derived from clusters is predictive in baseline samples for patient response to TIL therapy30,31. Fourth, taking advantage of our scTCR-seq analysis, we observed that T cells carrying identical top clonal TCRs show more exhaustion and co-modulation when conjugated to APCs than when conjugated to tumour cells. All of these results are in agreement with, and extend, previous observations on cellular conjugates, for example, homotypic and heterotypic circulating-tumour-cell clusters with different properties[21], interacting CD4+ T cell–APC clusters characterized by PIC-seq22,23 and the importance of spatial positioning of CD4+–CD8+ T cell–DC triads56,57.

The characterization of the biology of tumour–immune cellular conjugates described above revealed that clustered CD8+ T cells display many features predictive of tumour-reactivity in other studies14,15,18,20,29,51,58. These include competitive engagement with tumour cells, enrichment of exhausted phenotypes, specific tumour-reactive gene expression programs, increased TCR clonality and association with response to TIL therapy. This was corroborated in several different preclinical models. First, CD8+ T cells from clusters expanded in a REP show on average ninefold increased ex vivo killing activity compared with single T cells, which was associated with increased production of IFNγ and TNF. Second, ACT with CD8+ T cells from clusters into two models of autologous PDX-bearing mice showed significantly more T cell infiltration, T cell activity and tumour control compared to treatment with single T cells. Third, we corroborated these results in a REP resembling the current clinical TIL REP[44].

Benchmarking these results to single CD8+CD39+ T cells16,26,27,46,47,48,49,59, we demonstrate that clusters also contain considerable numbers of CD39− T cells, even in expanded clonotypes, and that they are enriched in favourable memory-like and TCF7+ stem-like exhausted cell states. Putting this into context, it was recently shown in TIL products that the presence of memory-progenitor CD39− stem-like cells within neoantigen-specific TILs is associated with clinical response and TIL persistence, in contrast to a terminally differentiated CD39+ cell state[50]. This is consistent with previous reports demonstrating that the presence of less-differentiated memory-like, early dysfunctional or stem-like TILs at baseline is associated with improved outcomes after immune checkpoint blockade or TIL therapy, as well as prolonged response duration3,20,51,52,60. Together, these results demonstrate that T cells from clusters differ substantially from single CD39+ T cells and possess features related to persistence that are relevant for the development of enhanced TIL therapy.

In conclusion, we demonstrate that heterotypic CD8+ T cell clusters represent a cell population with distinct biological characteristics and a marked enrichment for tumour reactivity. We propose that these clusters, which are often excluded from cell sorting and therefore neglected in single-cell sequencing procedures, represent a unique cell population allowing for better understanding of functional tumour–immune cell interactions and warranting preclinical exploration. Our findings not only support the potential for improving TIL therapy, but also merit exploring therapeutic strategies based on the isolation of TCRs from clustered T cells.

Methods

Cell lines

Human cancer cell lines were obtained from the Peeper lab repository. They were short-tandem-repeat profiled to confirm identity and tested mycoplasma-negative at the start of in vitro experiments. Cell lines were transduced with lentivirus to express HLA-A*02:01-MART1-mPlum plasmid as described previously[25]. D10, FM6, BLM, A875, M063 and MDA-MB-231 (referred to as MDA-231) cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM; 41966052, Gibco) with 10% FBS (3101120, Sigma-Aldrich) and 100 U ml−1 penicillin–streptomycin (15140122, Invitrogen). LCLC-103H, EBC-1, DU-145 and SW480 cells were cultured in RPMI (21875034, Thermo Fisher Scientific) with 10% FBS and 100 U ml−1 penicillin–streptomycin. For REP, the suspension cell line EBV-JY was used, which was cultured in IMDM (CA IMDM-A, Capricorn Scientific) supplemented with 10% FBS and 100 U ml−1 penicillin–streptomycin.

Primary human CD8+ T cell isolation, transduction and culture

Primary CD8+ T cells used in in vitro experiments with cell lines were isolated from healthy donor blood (from buffy coats). In brief, PBMCs were isolated by density centrifugation using Ficoll (11743219, Thermo Fisher Scientific) (2,500 rpm, 15 min, no break). CD8+ T cells were positively isolated with Dynabeads (11333D, Invitrogen) and activated for 48 h in a precoated plate with anti-hCD3 and anti-hCD28 (16-0037-85/16-0289-85, eBioscience), 5 mg per well in 24-well plates at 10^6 cells per ml. CD8+ T cells were then retrovirally transduced in retronectin-coated (T100B, Takara) plates with the MART-1-specific TCR (2,000g, 1.5 h, no break). For the first 2 days after activation, primary CD8+ T cells were cultured in RPMI with 10% human serum (H3667, Sigma-Aldrich) and 100 U ml−1 penicillin–streptomycin, with IL-2, IL-7 and IL-15 (100 IU ml−1, 10 ng ml−1, 10 ng ml−1 respectively) (Proleukin, Novartis; 11340075, Immunotools; 11340155, Immunotools). T cells were then refreshed three times a week with RPMI containing 10% FBS, 100 U ml−1 penicillin–streptomycin and 100 IU ml−1 IL-2.

Patient samples

Resected tumour material was collected from patients with melanoma undergoing surgery at the Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital (NKI-AvL) (Supplementary Table 2). The study was approved by the Medical Ethical Review Board of the NKI-AvL (under studies B16MEL, IRBm23-029) and performed in compliance with the ethical regulations. All of the patients provided prior informed consent to use their anonymized data and tumour material for research, including publication of the results in a manuscript.

Patient tumour digestion

To obtain tumour digests, freshly obtained patient tumours were cut in small pieces and incubated in prewarmed RPMI medium supplemented with pulmozyme (12.6 µg ml−1; Roche), collagenase (1 mg ml−1; 17104-019, Thermo Fisher Scientific) and a pan-caspase inhibitor (Q-VD-Oph, 50 μM; or Z-VAD, 5 μg ml−1; S7311, Selleckchem; sc-3067, Santa Cruz Biotechnology) at 37 °C in a spinning rotor for a maximum of 30 min. The sample was then passed through a 100-μm filter, washed with RPMI containing 10% FBS and frozen in FBS + 10% DMSO until further processing.

In vitro T cell–tumour cell line co-cultures

Before the start of the co-culture, primary CD8+ T cells were labelled with CTV (C34557, Invitrogen) or carboxyfluorescein succinimidyl ester (CFSE; C34554, Invitrogen) according to the manufacturer’s instructions. Tumour cell lines and pre-labelled CD8+ T cells were counted and seeded in a non-tissue-culture-treated 96-well V-bottom plate (781601, Brand) at a 2:1 tumour:T cell ratio for standard flow cytometry and at a 1:1 ratio for image-based flow cytometry assays (50,000 tumour and 25,000 or 50,000 T cells, respectively). Co-culturing was performed in 100 μl per well with 50 μl of tumour cell medium and 50 μl of T cell medium with IL-2. In standard assays, cells were co-cultured for 4 h and subsequently analysed by flow cytometry. For competition assays, non-specific and MART-1-specific T cells were mixed at the indicated ratios before the start of co-culture, based on the measured transduction efficiency. After most co-cultures, the percentage of MART-1-specific T cells in the populations of interest was determined by staining for the mouse TCR β-chain. For the experiment in which the 5:95 and 95:5 ratios (MART-1-specific:non-specific) were studied together (Extended Data Fig. 1k), the T cells were sorted after transduction to obtain a pure MART-1-specific T cell population. Before the co-culture, MART-1-specific T cells were stained with CTV and non-specific T cells with CFSE, after which they were mixed at the ratios described above to perform the co-culture.

Flow cytometry and cell sorting

For flow cytometry, the culture medium was removed and cells were washed with 0.1% BSA in PBS. For surface staining, cells were stained with the indicated antibodies diluted in 0.1% BSA in PBS for 30 min on ice in the dark. For intracellular staining, cells were stained using the FOXP3 kit (00-5523-00, Invitrogen) according to the manufacturer’s instructions. A list of the antibodies used is provided in Supplementary Table 8. After staining, cells were washed twice with 0.1% BSA in PBS and measured using a BD LSRFortessa, BD LSR-II SORP or BD FACSymphony A5 SORP flow cytometer with the FACSDiva (v.8 or v.9) acquisition software. Data were analysed using Flowjo (v.10.8.1). For primary human tumour samples, previously frozen tumour digests were thawed and washed twice with RPMI, supplemented with 10% FBS and 1:1,000 benzonase nuclease (purity, >90%) (70746-3, VWR). Cells were washed an additional time with 0.1% BSA in PBS after which they were stained with antibody mix for 30 min on ice in the dark. After staining, cells were washed twice with 0.1% BSA in PBS before flow cytometry or sorting. When indicated, the samples were washed and stained with 2% BSA in PBS and sorted in 2% FBS in PBS. Cell sorting was performed using a BD FACSAria Fusion cell sorter with an 85, 100 or 130 µM nozzle depending on the size of cells and clusters sorted. Sorted cells were collected in RPMI supplemented with 20% FBS, before proceeding to downstream processing. To prevent mislabelling of non-interacting cells as clusters, we ran the samples at a low cell concentration and measured at a low event rate. Moreover, the Fusion cell sorter has several quality-control measures to prevent sorting of these events (for example, electronic aborts and precision mode). As described previously61,62,63, cell sorting disrupted physical connections between cells in clusters, which was confirmed by microscopy, with the vast majority of cells being singlets post-sort, allowing further downstream single-cell analyses.

ImageStream analysis

For ImageStream analysis, samples were processed following the flow cytometry staining procedure described above and diluted to 1.0 × 10^7 cells per ml in 0.1% BSA in PBS after the final wash. Cells were analysed using ImageStream Mark II system with INSPIRE acquisition software (v.200.1.681.0). Obtained data were processed using IDEAS software (v.6.3 or v.6.4). Data were exported as individual OME .tiff[64] files and combined into multichannel stack .tiff files using the custom made program ImageStreamCombiner. Image analysis workflows were developed in FIJI (v.2.14)[65] with the steps performed using CLIJ (v.2.5)[66] for GPU processing. Cellpose (v.2 or v.3)[67] was used for cell segmentation as follows. For the in vitro samples, a nuclear and membranous signal served as the input, whereby the membranous signal was obtained by applying a variance filter (radius 2 pixels) on the bright-field image and the nuclear signal was obtained by combining the normalized signals from the T cell and tumour marker channels (Extended Data Fig. 1d). For patient-derived samples, cellpose was performed on a single cytosolic/membranous input channel: a combination of all normalized fluorescence channels and the normalized variance-filtered bright-field channel. After segmentation the resulting labels were contracted with 2 pixels. Cell types (tumour cell, T cell and/or APC) were separated by k-means clustering (IJ-Plugins toolkit v.2.3), with the intensities of the fluorescence channels and the cell area as input. The clusters were then classified as cell types by comparing their average marker intensity. Further analysis was focused on 1:1 clusters of two different cell types (larger clusters and non-interacting cells were excluded). The membrane was estimated as the outer 3 pixels of the segmented cells. Touching regions between two different cell types were regarded as interfaces, while the rest of the membrane was considered ‘not an interface’. The intensity of the marker of interest in or outside the interface was measured as the mean of the region. Details on ImageStream experiments are provided in Supplementary Table 1. Scripts for image analysis are available at GitHub (https://github.com/BioImaging-NKI/ImageStreamCombiner and https://github.com/BioImaging-NKI/ImageStreamAnalysis).

Multiplex staining and analysis

Automated multiplex staining on the Discovery Ultra Stainer

Before multiplex staining, 3-µm slides were cut on TOMO slides. The slides were then dried overnight and stored at 4 °C. Before a run was started, the slides were baked for 30 min at 70 °C in an oven. Staining was performed on the Ventana Discovery Ultra automated stainer, using the Opal 6-Plex Detection Kit (50 slides kit, Akoya Biosciences, NEL871001KT). The protocol starts with baking for 28 min at 75 °C, followed by dewaxing with Discovery Wash using the standard setting of 3 cycles of 8 min at 69 °C. Pretreatment was performed using Discovery CC1 buffer for 64 min at 95 °C, after which Discovery Inhibitor was applied for 8 min to block endogenous peroxidase activity. Specific markers were detected consecutively on the same slide using the following antibodies: anti-CD8 (C8/144B, M7103, DAKO, 1:50, 2 h at room temperature), anti-CD4 (SP35, 104R-16, Cell Marque, 1:25, 2 h at room temperature), anti-CD69 (EPR21814, ab233396, Abcam, 1:100, 1 h at room temperature), anti-CD11c (D3V1E, CST45581S, Cell Signaling, 1:50, 1 h at room temperature), anti-SOX10 (BC34, BCARACI3099C, Biocare Medical, 1:20, 2 h at room temperature), anti-HMB45 (PMEL/melanoma gp100, 38815, Cell Signaling, 1:400, 2 h at room temperature) and anti-HLA-A (EP1395Y, ab52922, Abcam, 1:2,000, 2 h at room temperature). Anti-SOX10 and anti-HMB45 were incubated at the same time by making a mixture of the two antibodies. Each staining cycle was composed of four steps: primary antibody incubation, secondary antibody mouse (PI-2000-1, Vector laboratories, 1:100, 32 min at room temperature) or rabbit (31460, Invitrogen, 1:250, 32 min at room temperature), OPAL dye incubation (OPAL480, OPAL520, OPAL570, OPAL620, OPAL690, OPAL780, 1:40 or 1:50 dilution as appropriate for 32 min or 1 h at room temperature) and an antibody denaturation step using CC2 buffer for 20 min at 95 °C. Cycles were repeated for each new antibody to be stained. DAPI (FP1490, Akoya, 1:10, 12 min at room temperature) was stained manually afterwards. After the run was finished, slides were washed with demineralized water and mounted with Fluoromount-G (Southern Biotech, 0100-01) mounting medium.

Scanning of multiplexed slides with PhenoImager HT

After staining, the slides were imaged using the PhenoImager HT automated imaging system (Akoya). Scans were made with the MOTIF unmixing protocol, using the InForm software v.3.0. The MOTIF images were unmixed into eight channels: DAPI, OPAL480, OPAL520, OPAL570, OPAL620, OPAL690, OPAL780 and autofluorescence.

Image analysis using HALO software

The HALO software (v.4.0.5107.357, Indica Labs) was used for image analysis. Analysis was focused on DAPI, CD8, CD11c and SOX10/HMB45. On the basis of tumour area, regions of interest were selected together with a pathologist using the annotation tool. The Indica Labs HighPlex FL v.4.2.14 analysis algorithm was used for analysis using AI nuclei segmentation. Regions of interest were analysed and both the summary data and cell object data were exported in comma-separated value files using the export manager in HALO. Value files were imported into Python (v.3.12) using Pandas (v.2.2.3). Values included the classification and centroid position. Some cells were triple or double positive and needed to be reclassified for further analysis. SOX10/HMB45+CD8+ double-positive and SOX10/HMB45+CD8+CD11c+ triple-positive cells were changed to unclassified. SOX10/HMB45+CD11c+ double-positive cells were reclassified as SOX10/HMB45+, as the CD11c is often present on membranes that protrude into SOX10/HMB45-positive tissue and cause false-positive classification for CD11c. CD8+CD11c+ double-positive cells were reclassified as CD8+ for the same reason. Nearest-neighbour analysis was performed using scikit-learn (v.1.5.2). For each cell the distance to the nearest SOX10/HMB45-, CD11c- and CD8-positive cell was determined. CD8+ cells were counted based on their vicinity to SOX10/HMB45- and CD11c-positive cells. A cut-off of 10 µm was used to define direct proximity as the size of the cells is approximately 10 µm. For downstream analysis, CD8+ T cells within <10 μm of SOX10/HMB45-positive or <10 μm of both SOX10/HMB45- and CD11c-positive cells were defined as T cell–tumour cell clusters, similar to our flow cytometry gating strategy in Extended Data Fig. 2a. CD8+ T cells within <10 μm to CD11c+ cells were defined as T cell–APC clusters.

scRNA-seq and scTCR-seq

Tumour digests were thawed, stained and sorted as described above. Five populations were sorted from live cells: tumour singlets (NGFR/CD146+); tumour–CD8+ T cell clusters (NGFR/CD146+CD8+); APC–CD8+ T cell clusters (NGFR−CD146−CD11c+CD8+), CD8+ T cell singlets (NGFR−CD146−CD11c−CD8+) and APC singlets (NGFR−CD146−CD11c+CD8−). For two patients, CD8+CD39+ T cells were sorted separately from single live cells. Singlets were pooled together during sorting at a ratio of 1:1:1. If the number of clusters was low, they were kept as separate samples. If sufficient numbers of clusters were sorted (>40,000 clusters), they were hashtagged with TotalSeq-C0251 (T cell–tumour clusters, 394661, BioLegend) or with TotalSeq-C0252 (T cell–APC clusters, 394663, BioLegend) and subsequently pooled 1:1. Both CD8+CD39+ single T cell samples were also hashtagged using the same antibodies and pooled 1:1. For hashtagging, sorted cells were washed once with 2% BSA in PBS and incubated with the hashtagging antibody for 30 min on ice. After hashtagging, cells were washed an additional two times with 0.04% BSA in PBS, after which they were pooled. Cells that did not need hashtagging were washed twice with 0.04% BSA in PBS, before proceeding to single-cell 5′ sequencing library preparation.

The Chromium Controller and Chromium X platform of 10x Genomics were used for single-cell partitioning and barcoding. Each cell’s transcriptome was barcoded during reverse transcription, pooled cDNA was amplified and single-cell 5′ gene expression (GEX), V(D)J and feature barcode (FB) Libraries were prepared according to the manufacturer’s protocols (CG000330 and CG000331, 10x Genomics). All libraries were quantified and normalized based on library QC data generated on the Bioanalyzer system according to the manufacturer’s protocols (G2938-90321 and G2938-90024, Agilent Technologies). On the basis of the expected target cell counts, a balanced library subpool of samples was composed for SC5′ GEX, V(D)J and FB libraries. Library subpools were quantified by quantitative PCR (qPCR), according to the KAPA Library Quantification Kit Illumina Platforms protocol (KR0405, KAPA Biosystems). Based on the qPCR results, a final sequencing pool was composed. Paired-end sequencing was performed on the NovaSeq 6000 Instrument (Illumina) using the NovaSeq 6000 Reagent Kits v1.5 100 cycles (20028401, 20028319, 20028316 Illumina), using 28 cycles for read 1, 10 cycles for read i7, 10 cycles for read i5 and 90 cycles for read 2.

Processing and analysis of scRNA-seq and scTCR-seq data

Processing of single-cell data

Sequence alignment was performed with CellRanger (v.7.0.1) using the human genome GRCh38 as a reference to obtain gene expression and TCR sequence data from the samples. For patients 2 and 8, the CD8+ T cell–tumour cell clusters and CD8+ T cell–APC clusters were pooled and sequenced with Totalseq-C hashtags as described above and processed together using the Cell Ranger multi-run functionality.

For all samples, the gene expression data from the CellRanger output was loaded using Seurat (v.4.4.0)[68]. The pooled samples from patients 2 and 8 are separated using the antibody capture matrix. We generated density plots of hashtag expression, determined the local minimum and identified hashtag-positive cells. Cells expressing both hashtags were filtered out. Moreover, cells containing <200 gene counts, >8,000 gene counts and a percentage of mitochondrial gene expression >15% were filtered out for quality reasons. A total of 71,867 cells passed quality control.

Annotation of main cell types

Objects of different patients and samples were merged, log-normalized and integrated per patient using Harmony (v.1.2.1)[69]. Different cell types were identified looking at the expression of relevant tumour, T cell and APC marker genes on gene-weighted kernel density plots (Extended Data Fig. 3a,b). For downstream analyses, specific cell types were selected, reintegrated and reclustered.

Annotation and analyses within CD8+ T cells

The Seurat clusters expressing CD3D and/or CD8A were selected as T cells and reintegrated using Harmony. During the principal component analysis (PCA) calculation, genes related to mitochondrial function, non-coding RNA, immunoglobulins, TCR genes, stress-related genes and ribosomal genes were filtered out. Clustering was performed using the default Louvain algorithm. Seurat clusters expressing no CD8A and high levels of CD4, ITGAX and/or FOXP3 were removed. Together, 28,372 CD8+ T cells were identified and reintegrated again (Fig. 3b). CD8+ Seurat clusters were then annotated using a panel of T-cell-related genes and cross-labelling with reference gene signatures from external single-cell datasets of human TILs14,15,20,28. Ultimately, 14 CD8+ T cell states were identified and annotated (Extended Data Fig. 3c). Next, CD8+CD39+ sorted single T cells of two matched patients (P8 and P15) were included in a follow-up analysis (Extended Data Fig. 10c) and processed according to the above-described pipeline. In total, 34,466 CD8+ T cells were annotated into 14 cell states. Notably, we observed a restructuring of exhausted T cell states. Previously annotated TCF7+ stem-like Tex cells were largely subdivided, with one cluster retaining stem-like characteristics; another, termed CD137high early Tex cells, was characterized by high TNFRSF9 and XCL1/2 expression. Moreover, a new subpopulation emerged marked by expression of HSP genes. Both previously annotated natural-killer-like clusters were redistributed across multiple other clusters. CD39− status was determined using each cell’s ENTPD1 expression and the average of its ten closest neighbours to avoid false negatives due to dropouts, common in scRNA-seq.

The Gene Expression Omnibus (GEO) GSE221553 dataset[31] was processed to extract CD8+ T cells, which were then annotated by label transfer, using Seurat’s functions FindTransferAnchors and MapQuery. Cells with a low predicted.celltype.score (≤0.4) were removed from subsequent analyses.

scTCR-seq data were integrated using the scRepertoire v.2.0.4 package[70]. A TCR clonotype was defined as an individual cell or group of cells with a unique paired α and β TCR sequence (the same CDR3 amino acid sequence). CD8+ T cells with multiple α or β TCR chains were included and considered as a unique TCR. Cells with missing α or β chains were not included in TCR analyses.

A CD8+ T cell cluster signature was established after differential gene expression analysis between T cells from clusters and single T cells. A MAST test[71] was used and patient of origin was used as a latent variable. Genes with negative log_10-adjusted P > 150 and expressed in >30% of cells from clusters were preselected. These preselected genes were reordered based on average log_2-transformed fold change and the top 30 and top 100 genes were used to build the respective cluster 30 and cluster 100 signatures (Supplementary Table 4). All over-representation and gene set enrichment analyses shown were performed with fgsea v.1.28.0.

Annotation and analysis within tumour cells

The Seurat clusters expressing MCAM and PMEL were selected as tumour cells and anchor-based integration per patient was performed. Seurat clusters expressing CD8A, CD4 and ITGAX were filtered out. SCTransform was performed by regressing the percentage of mitochondrial genes and gene counts, after which remaining tumour cells were reintegrated with anchor based CCA integration and reclustered. We used the tool infercnv v1.20.0 to confirm the malignant nature of selected tumour cells. APC and T cells were used as a reference (Extended Data Fig. 5a). The infercnv was run with 0.1 cut-off for minimum average read counts per gene.

Together, 25,009 tumour cells were processed. Tumour Seurat clusters were annotated based on melanoma phenotype-specific markers and on cross-labelling with reference gene signatures from external single-cell datasets of melanoma tumour cells4,33,34,35. Tumour cells were scored for each of these gene signatures using AUCell (v.1.24.0)[72]. The scores were aggregated and scaled across the Seurat clusters. Each Seurat cluster was annotated with the highest scoring phenotype. Clusters with the same annotation were combined (Extended Data Fig. 5b). We identified nine tumour cell states (Fig. 3h). The Seurat cluster defined by low gene counts was excluded from downstream analysis.

Analyses and annotation of APCs

The Seurat clusters expressing ITGAX and/or CD19 were selected as APCs and reintegrated using Harmony. Seurat clusters expressing PMEL, MCAM or CD8A were removed. Together, 11,382 APCs were included and reintegrated. The resulting subset was then split across three APC types; monocytes/macrophages (7,911), DCs (2,405) and B cells/plasma cells (1,066), based on scGate (v.1.6.2)[73] analysis. One of the Seurat clusters was reintegrated, reclustered and subdivided because it contained proliferating cells of all APC types (Fig. 3i). During PCA calculation, the same features as for CD8+ T cells were filtered out.

APC types were then annotated for specific cell states using a panel of APC-related genes and cross-labelled with reference gene signatures from external single-cell datasets of human TILs31,36,37,38,39,40,41,42. We identified 21 APC cell states. In follow-up analyses, only single APCs and APCs from T cell–APC clusters were taken into account. Analyses on specific APC types included only patients with at least 20 APCs in T cell clusters.

Cell–cell communication analysis to compare CD8+ T cell interactions with tumour cells or APCs

We created a curated list of ligand–receptor pairs using Nichenet’s weighted network ligand–receptor file, including only pairs with a weight of >0.75 (weighted_networks_nsga2r_final.rds). The list was further selected by including only pairs that also met one of the following criteria: (1) present in CellChat’s (CellChatDB.human.rda) curated database for annotations[74]; (2) present in CellChat protein–protein interaction experimental data (PPI.human.rda); (3) Nichenet[75] database weight >0.9 or (4) Nichenet database weight >0.8 and present in CellTalk[76] (human_lr_pair.txt) or SingleCellSignalR[77] (data_LRdb.rda) curated databases. Finally, only the pairs with receptors with subcellular localizations encompassing the key terms ‘cell membrane’ or ‘surface’ in UniProtKB were considered.

Ligands and receptors that were expressed in <10% of senders or receivers in clusters were filtered out. Ligands were ranked based on their predicted activity using nichenetr (v.2.2.0)[75]. Geneset parameter was set to upregulated genes in the interacting versus non-interacting CD8+ T cell population (p_val_adj<0.05, avg_log2FC > 0.1 and pct.1 > 0.05). Ligand and receptors were traced back to specific cell types or states based on expression across all senders or receivers. Receptors were associated to one of the following T cell states: (1) Tex, merging TOXhigh Tex, GZMKhigh Tex, LAG3high Tex and TCF7+ stem-like Tex cells; (2) Tprol, merging MKI67high Tex/Tprol, MKI67+ Tex/Tprol and MKI67+ Tem-NK like/Tprol cells; (3) Tn/Tmem, merging Tn and Tn/Tmem cells; (4) Tem, merging early Tem, Tem and Tem-NK like cells; (5) ISG+ and (6) Tc17 MAIT. If the average expression of a receptor in one subgroup exceeded the mean plus one s.d. of the average expressions across all subgroups, and this occurred exclusively in that subgroup, the receptor was assigned to it. If multiple subgroups or none met this threshold, the receptor was categorized as unspecific, which means it is shared between multiple or all T cell states. Ligands were associated with APCs or tumours using the same criteria, only considering cells from clusters. CD8+ T cells were taken into account for the average expression levels but were not accounted for in the ligand classification.

Cell–cell communication analysis focused on cluster-enriched tumour or APC cell states

In a second cell communication analysis, we focused on interactions between T cells and tumour cells or between T cells and APCs separately. For this, we used our previously curated database and prioritized ligands expressed in the tumour or APC cell states enriched in T cell clusters. As possible senders, we considered the tumour cells or APCs for each identified cell state. The minimum percentage for ligand expression was set at 35% in the cells from clusters at any cell state. Receivers were defined as all interacting CD8+ T cells (from APC or tumour clusters) and the threshold was set at 10%. Ligands were then associated to the cluster-enriched cell states if their averaged expression exceeded that of the mean plus s.d. across groups. If the condition was met exclusively in one of the cluster-enriched groups, the ligand was labelled as specific. Receptors were classified as described above. Interacting and non-interacting cells were included in the analysis. The geneset parameter was defined by comparing T cells from tumour or APC clusters to those in singlets.

REP of TILs from patient material

Tumour digests were thawed, stained and sorted as described above. Four populations were sorted from live cells: tumour singlets, tumour–CD8+ T cell clusters, APC–CD8+ T cell clusters and CD8+ T cell singlets. For some experiments, single CD8+CD39+ T cells were also sorted from live cells. The research-REP (R-REP) was performed according to a protocol adjusted from a previous study[78]. In brief, sorted CD8+ T cell populations were plated at 100–150 cells per well in round-bottom tissue-culture-treated 96-well plates (650-180, Greiner) in 100 μl RPMI medium supplemented with 10% human serum, 5% FBS, 100 U ml−1 penicillin–streptomycin, 300 IU ml−1 IL-2, 10 ng ml−1 IL-7, 10 ng ml−1 IL-15, 0.8 μg ml−1 phytohemagglutinin (PHA, R30852801, Thermo Fisher Scientific) and 50,000 irradiated feeder cells. Feeder cells consisted of 45,000 35-Gray-irradiated allogeneic PBMCs (mix of two donors) and 5,000 50-Gray-irradiated EBV-JY cells. After 7 days, 100 μl of medium without PHA was added. Then, after 10–11 days, T cells were collected and rested for at least 3 days in RPMI medium supplemented with 10% FBS, 100 U ml−1 penicillin–streptomycin and 100 IU ml−1 IL-2, before functional tests were performed. For the clinical-REP (C-REP), the same populations were sorted, but cells were collected in RPMI supplemented with 20% human serum. Sorted CD8+ T cell populations were plated at 10,000 cells per well in flat-bottom tissue-culture-treated 24-well plates in 2 ml 20/80 AIM V/RPMI medium (AIM V, 12055083, Thermo Fisher Scientific) supplemented with 10% human serum, 100 U ml−1 penicillin–streptomycin, 3,000 IU ml−1 IL-2 and 30 ng ml−1 anti-hCD3 (OKT3) and 2 × 10^6 irradiated feeder cells. Feeder cells consisted of a mix of two PBMC donors that were irradiated with 40 Gy. After 7 days, 1 ml of medium was refreshed with medium without anti-hCD3 antibodies. After 10–11 days, T cells were collected and rested for at least 3 days in 20/80 AIM V/RPMI medium with 10% human serum, 100 U ml−1 penicillin–streptomycin and 100 IU ml−1 IL-2, before functional tests were performed. Sorted melanoma tumour cells were cultured in tissue-culture-treated flat-bottom plates in DMEM or Ham’s F-10 medium (11550043, Gibco) supplemented with 10% FBS and 100 U ml−1 penicillin–streptomycin and adherent cells were split when reaching confluency.

Secondary co-cultures after REP

Details on secondary co-cultures are provided in Supplementary Tables 6 and 7. To assess cytokine production, CTV-labelled CD8+ T cells were co-cultured with autologous melanoma tumour cells for 4 h at the indicated ratios. After 2 h 1:1,000 diluted Golgiplug (555029, BD) was added to the culture. After co-culture, an intracellular staining protocol was performed as described above and cytokine production was measured by flow cytometry. For killing assays, melanoma tumour cells were seeded into tissue-culture-treated 96-well flat-bottom plates, after which unlabelled T cells were added at the indicated ratios. At the end of co-cultures, T cells were removed from the plates and tumour cell viability was determined using CellTiter-Blue (G8081, Promega) according to the manufacturer’s instructions.

Mouse experiments

ACT of primary human T cells in tumour-bearing NSG mice

Animal work procedures performed in NSG mice were approved by the animal experimental committee (Instantie voor Dierenwelzijn) of the NKI according to Dutch law and performed in accordance with ethical and procedural guidelines established by the NKI and Dutch legislation. All animals are housed in disposable cages in the laboratory animal centre (LAC) of the NKI, minimizing the risk of cross-infection, improving ergonomics and obviating the need for a robotics infrastructure for cage-washing. The mice were kept under specific-pathogen-free conditions under a controlled filtered air humidity (55–65%), temperature (21 °C) and light–dark cycle from 07:00 to 19:00. For all mouse experiments, mice were randomized into treatment groups by tumour size on the day of ACT. Randomization ensured that the treatment groups were balanced with respect to mean tumour size and s.d. at the baseline.

Primary human T cells were isolated and transduced with the MART-1-specific TCR as described above. For the experiment, a mixture of 20:80 MART-1-specific:non-specific T cells was made and this mixture was co-cultured with a MART-1-expressing BLM cell line in a tumour:T ratio of 2:1 for 4 h. After 4 h, the cells were stained for CD8, NGFR/CD146 and msTCRβ, after which all T cells (CD8+), T cell singlets (NGFR−CD146−CD8+) and CD8+ T cell–tumour cell clusters (NGFR/CD146+CD8+) were sorted. These populations were then expanded using the R-REP protocol described above. Then, 7 days before the end of the REP, 1 × 10^6 MART-1-expressing BLM cells in culturex BME Type III were subcutaneously (s.c.) injected into the right flanks of NSG mice (Jax, bred at NKI). On days 7 and 9 after tumour injection, mice were intravenously injected through the tail vein with PBS (control) or 1.0 × 10^7 T cells from the respective groups. T cells were in vivo stimulated with an intraperitoneal injection of 1 × 10^5 U hIL-2 (Proleukin, Novartis) between days 7–11. The tumour size was monitored three times a week with callipers by measuring tumour length (L) and width (W) and calculating volume using the formula LW^2/2. All experiments ended for individual mice when the tumour volume exceeded 1,500 mm^3. Male mice were used for the experiment at an age of 10–12 weeks at the start of the experiment.

ACT of patient TILs in PDX-bearing hIL-2-NOG mice

Animal experiments in (hIL-2) NOG mice were conducted in conformity with EU directive 2010/63 (regional animal ethics committee of Gothenburg approvals 4684/23). All animals in Gothenburg are housed in sterile air-ventilated cages in the laboratory animal centre (EBM). The mice were kept under specific-pathogen-free conditions under controlled filtered air humidity (45–70%), temperature (19–21 °C) and a light–dark cycle from 07:00 to 19:00.

CD8+ T cell singlets, CD8+ T cell–tumour cell clusters and CD8+ T cell–APC clusters from a patient digest were sorted and expanded using the R-REP as described above. After REP, T cells were frozen. PDX material (passage 2), generated from the same patient material, was digested and 0.5 × 10^6 tumour cells were s.c. injected into the flank of immunocompromised, severe combined immune deficient interleukin-2 chain receptor-γ knockout (NOG, Taconic, controls) mice or NOG mice transgenic for human IL-2 (hIL-2-NOG, Taconic, ACT groups). Tumour growth and weights of the mice were monitored twice a week throughout the experiment. Tumour growth was measured using callipers. When tumours showed consistent growth on repeated measurements (day 19 after tumour injection), TILs of the respective groups were thawed and 5 × 10^6 TILs were intravenously injected through the tail vein into the hIL-2-NOG mice. Then, 2 weeks later (day 33 after tumour injection), all mice were euthanized due to body weight loss and material was collected for flow cytometry and immunohistochemistry analysis. Female mice were used for the experiment at an age of 6–8 weeks at the start of the experiment.

Flow cytometry was performed as described above, with a panel staining CD3, NGFR, CD146, CD137, PD1 and CD39. For immunohistochemistry, tissue from the PDX-bearing mice was fixed in 4% formalin, dehydrated and embedded in paraffin. Sections of 4 µm were mounted onto positively charged glass slides and dried overnight at 37 °C. The slides were stained using an autostainer (Autostainer Link 48, Dako). Primary antibodies were against CD3 (IR503, Dako, ready to use), CD8 (C8/144B, IR623, Dako, ready to use), CD137 (E6Z7F XP, 19541, Cell Signaling Technology, 1:250) and PD-L1 (E1L3N XP, 13684, Cell Signaling Technology, 1:200). The slides were finally counterstained with haematoxylin, dehydrated and mounted with Pertex. Stained slides were scanned using the Olympus VS200 slide scanner system. Positive cell detection of CD3+, CD8+ and CD137+ cells was performed in Qupath (v.0.5.1)[79]. The RGB signal was first split into two separate stains with the stain vector [0.65111 0.70119 0.29049] for hematoxylin and [0.26917 0.56824 0.77759] for DAB. The positive cell detection plugin was set to detect cells for which the DAB optical density in the whole cell was higher than 0.01. The script for automation of this workflow is available on request. To quantify PD-L1 expression, we used the pixelwise H-score as previously described[80]. The method was implemented in QuPath and the resulting score can range between 0 (no expression) and 300 (maximum expression).

ACT of patient TILs in PDX-bearing-NSG mice

The same PDX material and TILs as described in the ‘ACT of patient TILs in PDX-bearing hIL-2-NOG mice’ section was used. In total, 0.5 × 10^6 tumour cells in culturex BME Type III were s.c. injected into the right flanks of NSG mice (Jax, bred at NKI). When tumours reached an average size of between 20 and 50mm^3 (day 19 after injection), they were treated with 1.0 × 10^7 thawed T cells from the respective groups. T cells were thawed 1 day before ACT. Then, 1 × 10^5 U hIL-2 was injected intraperitoneally once daily after ACT as described before[43]. Tumour size was monitored three times a week with callipers as described in the ‘ACT of primary human T cells in tumour-bearing NSG mice’ section. All experiments ended for individual mice when the tumour volume exceeded 1,000 mm^3. Male mice were used for the experiment at an age of 8 weeks at the start of the experiment.

Statistics and reproducibility

Throughout the paper, different statistical tests were used as indicated in each figure legend. Two-sided tests were used unless stated otherwise. For average cell state/TCR analyses (Fig. 3b,c,h,j and Extended Data Figs. 6b and 10e), statistical significance was assessed using Bonferroni-adjusted P values from generalized linear mixed-effects models with a binomial distribution. For each cluster, the proportion of events was modelled using interaction status as a fixed effect and patient origin as a random effect. In Fig. 4d, statistical analysis was performed using two-way ANOVA followed by a Dunnett’s multiple-comparison test versus singlets, including all T cell–tumour cell co-culture ratios tested (visualized in Extended Data Fig. 7b). For all box plots, the box limits represent the interquartile range, the centre lines indicate the median, and the whiskers extend to the furthest point above the third quartile or below the first quartile within 1.5× the interquartile range. For in vivo experiments, the investigator measuring the tumours was blinded to the treatment. For other experiments, the investigators were not blinded. To ensure reproducibility, multiple biological and technical replicates were included. Technical replicates were generated during the same period in time and biological replicates were obtained during different moments in time. Complex bioinformatic analyses were always verified by a second researcher. Analyses were performed using GraphPad (v.10.4.1) and R (v.4.3.3).

Reporting summary

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

Data availability

Plotted data and statistical output supporting this study are provided in Supplementary Tables 1–8 and the source data. Processed scRNA-seq and scTCR-seq data are publicly available at the NCBI GEO (GSE283942). The raw scRNA-seq and TCR-seq files have been deposited at the European Genome–Phenome Archive under study accession code EGAS50000000785 and dataset ID EGAD50000001155. Owing to the privacy sensitivity of the raw data, requests for the data need to be made through https://ega.nki.nl, and will be reviewed by the NKI IRB and the principal investigator of the study. The request should include the research goal, specific names and email addresses of the people requesting access to the EGA data, privacy and governance aspects and intended use of the EGA data. Time from request to approval will take up to 2 weeks. Data are available on condition that no attempt is made to reidentify patients, the data are used for the requested goal, the data will not be transferred to a third party and are used in accordance with all applicable laws and regulations. After approval, the researcher will need to sign a common data access agreement with the NKI. We also used the UniProt database (https://www.uniprot.org); gene sets for GSEA (https://www.gsea-msigdb.org/gsea/index.jsp); human genome reference GRCh38 and human V(D)J reference (https://www.10xgenomics.com/support/software/cell-ranger/downloads); and reprocessed data from GEO (GSE221553)[31]. Moreover, we downloaded and used for downstream analyses files from Nichenet (https://github.com/saeyslab/nichenetr)[75], SingleCellSignalR (https://github.com/SCA-IRCM/SingleCellSignalR)[77], CellTalkDB (https://github.com/ZJUFanLab/CellTalkDB)[76] and CellChat (https://github.com/jinworks/CellChat)[74]. Source data are provided with this paper.

Code availability

Code is available at GitHub (https://github.com/PeeperLab/HeterotypicClustersR).

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Acknowledgements

We thank our colleagues in the laboratory and department for their valuable input and discussion, particularly T. Schumacher, D. Thommen, I. Jedema, N. Slingerland, M. Hancock and M. Machuca-Ostos; C. Marine, S. Zander, A. van Vliet, T. Arnoldus, A. de Groot and J. Pruijs for their contributions; C. Karlsson for immunohistochemistry on PDX tumours; the staff at the NKI Genomics Core facility, Flow Cytometry Facility, BioImaging Facility and the Core Facility Molecular Pathology & Biobanking (CFMPB) for their technical support, in particular, C. Chen, J. Peters-Sieljes, A. Velds, F. van Diepen, G. de Roo, S. Vig, A. Broeks and D. Peters.

Author information

Author notes

1. These authors contributed equally: Sofía Ibáñez-Molero, Johanna Veldman

Authors and Affiliations

1. Division of Molecular Oncology and Immunology, Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands

Sofía Ibáñez-Molero, Johanna Veldman, Juan Simon Nieto, Austin George, Kelly Hoefakker, Anita Karomi, Su Min Pack, Liselotte Tas, Nils L. Visser, Susan E. van Hal-van Veen, Paula Alóndiga-Mérida, John B. A. G. Haanen & Daniel S. Peeper

2. Division of Tumor Biology and Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands

Joleen J. H. Traets

3. BioImaging Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands

Rolf Harkes & Bram van den Broek

4. Core Facility Molecular Pathology & Biobanking, Netherlands Cancer Institute, Amsterdam, The Netherlands

Maartje Alkemade

5. Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands

Iris M. Seignette

6. Biostatistics Unit, Netherlands Cancer Institute, Amsterdam, The Netherlands

Renaud Tissier

7. Genomic Core Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands

Marja Nieuwland

8. Flow Cytometry Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands

Martijn van Baalen

9. Laboratory for Molecular Cancer Biology, Department of Oncology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium

Joanna Poźniak

10. Flow Cytometry Facility, Sanquin, Amsterdam, The Netherlands

Erik Mul & Simon Tol

11. Sahlgrenska Center for Cancer Research, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

Sofia Stenqvist, Lisa M. Nilsson & Jonas A. Nilsson

12. Harry Perkins Institute of Medical Research and University of Western Australia, Perth, Western Australia, Australia

Lisa M. Nilsson & Jonas A. Nilsson

13. Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands

Winan J. van Houdt

Contributions

J.S.N., J.J.H.T. and A.G. share second authorships. S.I.-M., J.V. and D.S.P. conceived the study and designed the experiments. S.I.-M., J.V., K.H., S.M.P., P.A.-M. and A.K. performed the experiments. J.S.N., J.J.H.T., A.G. and J.P. performed bioinformatics analyses. R.T. assisted in statistical analyses. S.E.v.H.-v.V., N.L.V. and S.S. performed mouse experiments coordinated by J.A.N., L.M.N. and D.S.P.; M.A., I.M.S. and R.H. performed multiplex staining and analysis. L.T., J.B.A.G.H. and W.J.v.H. coordinated clinical patient studies. B.v.d.B. and R.H. performed image analysis. M.N. performed single-cell sequencing. M.v.B. performed flow cytometry cell sorting. E.M. and S.T. assisted in imaging flow cytometry. S.I.-M., J.V. and D.S.P. wrote the manuscript. All of the authors read and approved the manuscript. The project was supervised by D.S.P.

Corresponding author

Correspondence to Daniel S. Peeper.

Ethics declarations

Competing interests

S.I.-M., J.V. and D.S.P. are named as inventors on patent P097110NL based on this work. J.B.A.G.H. has provided consultation, attended advisory boards and/or provided lectures for BMS, CureVac, GSK, Imcyse, Iovance Bio, Instil Bio, Immunocore, Ipsen, Merck Serono, MSD, Molecular Partners, Novartis, Pfizer, Roche/Genentech, Sanofi, Scenic and Third Rock Ventures; has participated in the scientific advisory board of Achilles Tx, BioNTech US, Instil Bio, PokeAcell, T-Knife, Scenic and Neogene Therapeutics; and through this the NKI has received grant support from Amgen, Asher Bio, BioNTech, BMS, MSD, Novartis, Sastra Cell Therapy, has stock options in Neogene Tx. D.S.P. is co-founder, shareholder and advisor of Flindr Therapeutics, which is unrelated to this study. J.V. is funded by a Veni-fellowship from the Dutch Research Council (NWO): 09150162210069. J.A.N. is funded by the Swedish Cancer Society, Vetenskapsrådet, the Erling-Persson and Sjöberg Foundations. D.S.P. is funded by the Oncode Institute, the Dutch Cancer Society KWF and by an ERC Advanced grant 101054465—ReverT. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Florian Mair, Paul Robbins, Caroline Robert 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 Antigen-specific T cell competitiveness.

a, Percentage mPlum+ and mPlum+HLA-A*02:01+ cells in the cancer cell line panel, at start and end of experiments in Fig. 1, measured by flow cytometry. b, Gating strategy for identifying clusters after 4 h co-culture of tumour cells (mPlum+) and CD8+ T cells (CTV-stained). Cells were gated based on FSC-A and SSC-A, after which live cells were identified as nearIR-negative. Within live cells, clusters were identified based on mPlum/CTV double-positive cells (also visualized in Fig. 1a). c, FSC-A, SSC-A plot showing location of T cell singlets, tumour singlets and T cell:tumour clusters as identified in (b). d, Schematic representation of image analysis. ImageStream images were analysed by FIJI. Cellpose was used for segmentation based on nuclear and membranous signals. The membranous signal was obtained by applying a variance filter on the brightfield image. The nuclear signal was obtained by combining the normalized signal from the nuclear markers. From the segmented regions membrane label maps were created, with the interface between cells identified as pixels where different labels touch. Mean intensity of interface versus no interface was measured in the fluorescence channel of interest. e, 4 h co-culture of A875 melanoma cells (mPlum+) with CD8+ T cells (CD8 or CTV-stained) visualized by imaging flow cytometry. White arrows indicate relocalization of indicated markers to the immunological synapse. f, 4 h co-culture of D10 melanoma cells with CD8+ T cells as in (e). White arrows indicate relocalization of the indicated markers to the immunological synapse. g, Quantification of ICAM-1, CD58 and HLA-A*02 mean fluorescence intensity at the T cell:tumour cell interface versus rest of the membrane (not-interface). Representative T cell donor and two melanoma cell lines (D10 and A875) shown. Each data point indicates a tumour cell interacting with a CD8+ T cell. Paired t-test was used. n = 3 biological replicates (different T cell donors, see Supplementary Table 1). h, Measured (y-axis) compared to expected (x-axis) percentage MART-1-specific T cells in input mixes. n = 5 biological replicates. Mean ± S.D. i, 4 h co-culture of D10 melanoma cells with different mixtures of MART-1-specific:non-specific T cells. Percentage MART-1-specific T cells in clusters (C) versus singlets (S) and Avg. FC were determined. Paired t-test was used. n = 5 biological replicates. Mean ± S.D. j, Quantification of CD69+ percentage on MART-1-specific (blue) or non-specific (grey) T cells in clusters. Average CD69+ background shown as dotted line. Paired t-test was used. n = 5 biological replicates. Mean ± S.D. k, 4 h co-culture of A875 (left) or D10 (right) melanoma cells with 5:95 or 95:5 mixture MART-1-specific:non-specific T cells. Percentage MART-1-specific or non-specific T cells in clusters versus singlets and Avg. FC were determined. Points represent technical replicates. Paired t-test was used. n = 3 biological replicates. Mean ± S.D. l, Percentage MART-1-specific and non-specific T cells in all T cells (A), singlets (S) and clusters (C) before/after REP. These different populations were used for ACT in BLM melanoma-bearing NSG mice of Fig. 1i. Avg. FC comparing clusters versus singlets or all T cells. m, In vitro killing of BLM melanoma cells by the different T cell populations used for ACT. Tumour killing was normalized to untreated tumour cells. Two-way ANOVA, followed by Dunnett’s multiple comparisons test. Significantly increased killing compared to T cells from singlets shown. Mean ± S.D. Mean of three technical replicates. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 2 Clinical heterotypic CD8+ T cell clusters.

a, Gating strategy for identification of clusters in clinical melanoma specimens. Cells were gated based on FSC-A and SSC-A, after which live cells were identified as nearIR-negative. From all T cells, first T cell:tumour cell clusters were gated. Second, from the tumour-negative population T cell:APC clusters were identified. Plots in Fig. 2b are both derived from the live cells gate to also visualize tumour and APC singlets. b, Percentage CD8+ T cell infiltration within live cells (n = 21 patients, top) and correlation between percentage infiltration and percentage T:tumour cell (bottom left) or T:APC (bottom right) clusters within live cells. Pearson correlation coefficients. Bars (top) and points (bottom) represent individual patients. c, Percentage CD8+ T cell infiltration within live cells and percentage T:tumour cell or T:APC clusters within total CD8+ T cells comparing fresh and frozen tumour digests. Paired t-test was used. Points represent individual patients (n = 7), bar represents mean. d, Quantification of CD11c, HLA-ABC and CD58 mean fluorescence intensity at the T:APC (CD11c, CD58) or T:tumour cell (HLA-ABC) interface versus rest of the membrane (not-interface). Paired t-test was used. Each data point indicates a T:tumour cell or T:APC cluster. n = 2-5 patients (see Supplementary Table 1 for all markers and cluster types analysed). e, Percentage of CD8+ T cells within 10 μM proximity of a tumour cell (T:Tum) or APC (T:APC) in multiplex analyses (n = 11 patients). Each coloured point represents an individual patient. Mean ± S.E.M. f, Multiplex immunofluorescence stainings on tissue sections of patient 4 (non-lymph node metastasis), patient 8 (lymph node metastasis) and patient 12 (lymph node metastasis). Sections were stained for tumour cell markers SOX10 and HMB45, T cell marker CD8 and APC marker CD11c. DAPI was included as a nuclear marker. In the top row, merged images are shown, DAPI is not included for clarity reasons. White boxes indicate zoom-in areas. In the bottom row, channels are separated and correspond to the second pictures on the top row. n = 11 patients, representative patients are shown. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 3 CD8+ T cell states and TCR clonality in melanoma clinical samples.

a, scRNA-seq UMAP of all sequenced cells from melanoma lymph node metastases (n = 5 patients), coloured by cell type. b, Expression of representative tumour cell, T cell and APC markers projected on UMAP of (a). Colour scale indicates gene-weighted density. c, Dotplot showing gene expression of a panel of T cell-related genes indicating different CD8+ T cell states. Rows labelled by annotated cell states of Fig. 3b. Colours, average expression of gene; dot size, percentage of cells expressing the gene. d, Absolute number of CD8+ T cells per annotated cell state subdivided by patient. e, Absolute number of CD8+ T cells per annotated cell state stratified by origin: single T cells, T cells from tumour clusters or T cells from APC clusters. f, Distribution of single T cells and T cells from tumour or APC clusters on the UMAP of sequenced T cells in Fig. 3b. g, Frequency of CD8+ T cell states in single and clustered T cells per patient (n = 5). FDR-adjusted Fisher’s exact test was used. Significantly enriched cell states in clustered versus single T cells indicated. h, Frequency of top 15 TCR clonotypes in single or clustered T cells per patient (n = 5) analysed as in (g). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 4 Tumour-reactive CD8+ T cells from clusters.

a, Violin plots of average tumour- and virus-reactivity gene signature scores per TCR clonotype (n = 7102 clonotypes from 5 patients) for single or clustered T cells. Used gene signatures can be found in Supplementary Table 415,18,29. Unpaired Wilcoxon signed-rank test was used. Data points beyond the boxplot whiskers are shown as dots. b, Heatmap showing log2 fold-change (FC) in average tumour- and virus-reactivity gene signature scores per TCR clonotype for clustered versus single T cells per patient. Unpaired Wilcoxon signed-rank test was used (n = 5 patients). c, Overlap of the genes from the cluster top 30 signature with the three external tumour-reactivity gene signatures from (a). d, Over-representation analysis using the genes from the cluster top 30 signature. Top 10 identified pathways according to significance are visualized. FDR-adjusted one-sided hypergeometric test was used. e, Frequency of CD8+ T cells with high (top-tertile) signature scores in baseline tumours of responders (R) and non-responders (NR) from an external melanoma TIL-treated patient cohort30,31 (n = 13 patients; R = 6, NR = 7). Cluster top 100 and the three tumour-reactivity gene signatures from (a) were used. CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease. Unpaired t-test was used. All data points are shown. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 5 Tumour cell states in melanoma clinical samples.

a, InferCNV-estimated copy number variations (CNVs) in tumour cells and as a reference T cells and APCs, shown per patient. Gains and losses of specific regions of the DNA are indicated in red and blue, respectively. b, Heatmap of AUCell scores for several external melanoma gene signatures4,33,34,35 across annotated tumour cell states. Columns labelled by annotated cell states of Fig. 3h. Rows are z-scored. c, Heatmap showing AUCell scores of enriched pathways across annotated tumour cell states. Visualized as in (b). Used pathways in Supplementary Table 5. d, Distribution of single tumour cells and tumour cells from T cell clusters on the UMAP of sequenced tumour cells in Fig. 3h. e, Absolute number of tumour cells per annotated cell state subdivided by patient (left) or stratified by origin (right): single tumour cells or tumour cells from T cell clusters. f, Frequency of cell states in single tumour cells or tumour cells from T cell clusters per patient (n = 5). FDR-adjusted Fisher’s exact test was used. Significantly increased cell state frequencies of tumour cells from T cell clusters versus single tumour cells are indicated. g, Volcano plot comparing tumour cells from T cell clusters with single tumour cells. Blue, genes enriched in single tumour cells; purple, genes enriched in tumour cells from T cell clusters. Representative selection of genes labelled. Bonferroni-adjusted MAST test was used, with patient of origin as latent variable (n = 5 patients). h, Gene set enrichment analysis using differentially expressed genes in tumour cells from clusters and single tumour cells. Top seven most upregulated and downregulated pathways shown. Significant pathways (FDR-adjusted P-value < 0.05) are indicated with an asterisk. Used gene sets in Supplementary Table 5. i, For the two most enriched pathways in tumour cells from T cell clusters in (h), the gene signature score of each cell was plotted per patient (n = 23,772 cells from 5 patients). Unpaired Wilcoxon signed-rank test was used. Data points beyond the boxplot whiskers are shown as dots. j, Circos plot of top 30 inferred ligands and their receptor interactions focusing on differential signalling of different tumour cell states towards T cells. Arrow transparency reflects predicted ligand signalling activity. Ligands or receptors were assigned to specific tumour or T cell states respectively based on expression level differences. Ligands for tumour cell states enriched in T cell clusters are highlighted. If the ligand is coloured and bold, it is uniquely associated with that cluster enriched group. Ligands labelled as “other” are not associated with the cluster enriched groups. Receptors shared between all or multiple T cell states are labelled unspecific. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 6 APC cell types and states in melanoma clinical samples.

a, Dotplot of UCell scores for a set of defined APC types to discriminate APC types in Fig. 3i. Rows labelled by annotated cell states. Colours, average UCell score; dot size, percentage of cells expressing the UCell geneset. b, scRNA-seq UMAP of sequenced DCs and B/plasma cells highlighting the main cell states (left) and average frequencies (right) (n = 5 patients). Each patient was weighted equally (n = 3 for DCs and n = 2 for B/plasma cells). DC and B/plasma cell states were annotated based on marker genes and signatures31,36,37,38,40,41,42. Bonferroni-adjusted P values from generalized linear mixed-effects models; significant increase in cell state frequency of APCs in T cell clusters versus single APCs is indicated. Patients were excluded if <20 cells were detected in the respective APC type from T cell clusters. c, Dotplot showing gene expression of a panel of APC-related genes discriminating different APC cell states. Rows labelled by annotated cell states of Fig. 3j and (b). Colours, average expression of gene; dot size percentage of cells expressing the gene. d, Absolute number of APCs per annotated cell state subdivided by patient (left) or stratified by origin (right): single APCs or APCs from T cell clusters. e, Distribution of single APCs and APCs from T cell clusters on the UMAP of sequenced monocytes and macrophages, DCs and B/plasma cells as provided in Fig. 3j or (b) respectively. f, Frequency of cell states in single APCs or APCs from T cell clusters per patient (n = 5). FDR-adjusted Fisher’s exact test was used. Significantly increased cell state frequencies of APCs in T cell clusters versus single APCs are indicated. Patients were excluded if <20 cells were detected in the respective APC type from T cell clusters. g, Circos plots of top 30 inferred ligands and their receptor interactions focusing on the differential signalling of different APC states towards T cells. Mono/macrophages and DCs were analysed separately. Arrow transparency reflects predicted ligand signalling activity. Ligands or receptors were assigned to specific APC or T cell states respectively based on expression level differences. Ligands for APC states enriched in T cell clusters are highlighted. If the ligand is coloured and bold, it is uniquely associated with that cluster enriched group. Ligands labelled as “other” are not associated with the cluster-enriched groups. Receptors shared between multiple or all T cell states are labelled unspecific. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 7 Enhanced killing by T cells from clusters.

a, Quantification of cytokine production by expanded CD8+ T cells derived from singlets (blue), tumour cell clusters (purple) or APC clusters (green) after a 4 h co-culture with autologous tumour cells (n = 12 patients). Points represent technical replicates. Unpaired t-test versus T cells from singlets was used. Mean ± S.D. b, Tumour killing by expanded CD8+ T cells derived from either singlets, tumour cell clusters or APC clusters (n = 11 patients), normalized to untreated tumour cells. Points represent technical replicates. On the x-axis, the T cell:tumour cell ratios are shown. Two-way ANOVA, followed by Dunnett’s multiple comparisons test was used. Significantly increased killing compared to T cells from singlets from the same ratio is indicated. Mean ± S.D. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 8 CD8+ T cells from clusters retain their tumour-reactive capacity in vivo and after a clinical rapid expansion protocol.

a, Representative IHC for CD8, CD137 and PD-L1 in PDX tumours from hIL2-NOG mice (patient 8), 2 weeks post-ACT with T cells from singlets, tumour cell clusters or APC clusters. One additional mouse in Fig. 4e. n = 5 mice/group. b, Quantification of IHC shown in Fig. 4e and (a). The whole slide was quantified using Qupath. CD8/CD137: percentage of positive cells within all detected cells. PD-L1: pixelwise H-score (0, no expression; 300, maximum expression). Unpaired t-test was used. n = 5 mice/group. Mean ± S.D. c, Flow cytometry analysis of T cell activation in PDXs from Fig. 4e and (a), measured as percentage CD39+ or PD1+ positive cells, for T cells from singlets, tumour cell clusters and APC clusters. Unpaired t-test was used. n = 5 mice/group; 3 mice not included due to insufficient material for flow cytometry (two from singlet, one from tumour cell cluster group). Mean ± S.D. d, Spider plots of PDX tumour growth in ACT-treated NSG mice receiving T cells from singlets (blue), T cells from tumour clusters (purple), T cells from APC clusters (green) or PBS (control, black). Each line is an individual mouse. n = 10 mice/group; except T cells from the tumour clusters n = 9. e, Tumour killing by CD8+ T cells expanded with research-REP (R-REP) or clinical REP (C-REP) derived from either singlets, tumour cell clusters or APC clusters (n = 4 patients). Tumour killing was normalized to untreated tumour cells. Points represent technical replicates. Unpaired t-test was used. Significantly increased killing and average fold-change (Avg. FC) compared to T cells from singlets is indicated. Mean ± S.D. f, Heatmaps showing cytokine production by R-REP or C-REP expanded CD8+ T cells derived from either singlets, tumour cell clusters or APC clusters after 4 h co-culture with autologous tumour cells (n = 5 patients). Mean of 3 technical replicates shown. Unpaired t-test versus T cells from singlets. Significant increases in cytokine production compared to T cells from singlets is indicated. g, Bar plots of technical replicates from (f), showing cytokine production. Mean ± S.D. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 9 Heterotypic CD8+ T cell clusters are a distinct population compared to single CD8+CD39+ T cells.

a, Gating strategy for identification of single CD8+CD39+ T cells in clinical melanoma specimens. Cells were gated based on FSC-A and SSC-A, after which single cells were identified based on FSC-W against FSC-H and SSC-W against SSC-H. From single cells, nearIR-negative live CD8+ T cells were identified in which CD39+ T cells were gated. PD1 was included as an additional marker in this staining panel. b, Projection of single T cells (blue), T:Tum clusters (purple) and T:APC clusters (green) on the first three gating plots of (a). Cluster populations identified in the same sample using the gating strategy shown in Extended Data Fig. 2a. The cells gate in the first plot was increased in size to accommodate clusters. c, Frequency of CD39+ (left) or PD-1+ (right) cells in single T cells, T:Tum clusters and T:APC clusters. Total CD8+ T cells, identified using the gating strategy in (a), included as a reference. Points represent patients (n = 7). Paired t-test was used. Mean ± S.D. d, Representative flow cytometry histogram of CD39 expression in single APCs, single tumour cells, single T cells, T:Tum clusters and T:APC clusters. Dotted line separates negative (left) and positive (right) cells. Values indicate percentage CD39+ cells. e, Quantification of cytokine production by expanded CD8+ T cells derived from either singlets, tumour cell clusters, APC clusters or CD39+ cells after a 4 h co-culture with autologous melanoma cells (n = 4 patients). One-way ANOVA, followed by Tukey’s multiple comparisons test. Points represent technical replicates. Black lines: singlet versus clustered T cells; blue lines: clustered versus CD39+ T cells. Mean ± S.D. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 10 Heterotypic CD8+ T cell clusters contain CD39− T cells and are enriched with favourable TCF7+ stem-like exhausted T cells compared to single CD8+CD39+ T cells.

a, Imaging flow cytometry of tumour digest (patient 18) showing: (i) a CD39+ T cell in a cluster with a CD39− tumour cell (top row); (ii) CD39− T cells in a cluster with a CD39+ tumour cell or APC (middle and bottom rows, respectively). b, Frequency of CD39+ (grey) and CD39− (pink) T cells in clusters across patients used for single cell analyses. CD39 status was determined from scRNA-seq data, using each cell’s ENTPD1 expression and the average of its ten closest neighbours. Expanded ( ≥ 3 cells/clonotype) and non-expanded (<3 cells) CD39+ and CD39− T cells were visualized. Expanded subdivided into two groups: ≥3 and <10 or ≥ 10 cells. Dotted line: average background of predicted CD39− T cells in CD8+CD39+-sorted samples. c, scRNA-seq UMAP of CD8+ T cells from Fig. 3b (n = 5 patients), supplemented with CD8+CD39+-sorted single T cells from two of those patients (P8 and P15). Main CD8+ T cell states are highlighted, including two new states: CD137hi early Tex and HSP (Heat Shock Proteins). d, Dotplot showing gene expression of a panel of T cell-related genes indicating different CD8+ T cell states. Rows labelled by annotated cell states provided in (c). Colours, average expression of gene; dot size, percentage of cells expressing the gene. e, Pie charts visualizing enriched and depleted cell states in CD8+ T cells from clusters compared to sorted single CD8+CD39+ T cells or single CD8+ T cells (n = 2 patients). Cell states enriched/depleted in both comparisons are separately grouped. Each pie chart contains all 14 cell states defined in (c). Bonferroni-adjusted P values from generalized linear mixed-effects models. f, Frequency of CD39− T cells (left) or the CD39−CD69− signature score (right) in TCF7+ stem-like Tex and LAG3hi Tex CD8+ T cells (n = 5 patients). This plot does not include sorted CD8+CD39+ samples. The used gene signature for CD39−CD69− T cells was obtained from Krishna et al. [50] and can be found in Supplementary Table 4. Paired t-test was used. Quantification of CD39 status as in (b). g, Frequency of TCF7+ stem-like Tex and LAG3hi Tex cell states in TCR-matched expanded clonotypes across single CD8+ T cells, CD8+ T cells from clusters and sorted single CD8+CD39+ T cells (n = 14 matched clonotypes from 2 patients). Expanded clonotypes contain ≥ 10 cells per respective subpopulation. Each connected line represents a different TCR clonotype. Paired Wilcoxon signed-rank test was used. h, UMAP visualization of label-transferred CD8+ T cells from Barras et al. (GSE221553)[31]. The annotated cell states and embedding visualized in (c) were used as a reference. i, Frequency of TCF7+ stem-like Tex within neoTCRhi CD8+ T cells from Barras et al. (GSE221553)[31] in TIL responder (R) and non-responder (NR) baseline tumours (n = 13 patients, R = 6, NR = 7). Annotated label-transferred cells from (h) were used. Cells within the top-tertile of the neoTCR score were considered neoTCRhi. CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease. Unpaired t-test was used. For (f), (g) and (i) all data points are shown as dots. *P < 0.05; **P < 0.01; ***P < 0.001; P < 0.0001.

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Ibáñez-Molero, S., Veldman, J., Simon Nieto, J. et al. Tumour-reactive heterotypic CD8 T cell clusters from clinical samples. Nature 649, 467–476 (2026). https://doi.org/10.1038/s41586-025-09754-w

- Received: 02 August 2023

- Accepted: 14 October 2025

- Published: 19 November 2025

- Version of record: 19 November 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09754-w

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Lymph node environment drives FSP1 targetability in metastasizing melanoma

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Milena Chaufan,

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Sebastian Müller,

Marie Sabatier,

Cameron S. Fraser,

Krystina J. Szylo,

Mahsa Yavari,

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Eikan Mishima,

Toshitaka Nakamura,

Bettina Proneth,

Marcos Labrado,

Yanshan Liang,

Nicole Cayting,

Lan Zheng,

Tatiana Cañeque,

Ludovic Colombeau,

Adam Wahida,

José Pedro Friedmann Angeli,

Alpaslan Tasdogan,

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Abstract

Ferroptosis has emerged as an actionable target to eliminate therapy-resistant and metastatic cancers[1]. However, which ferroptosis surveillance systems may offer a therapeutic window to leverage redox maladaptation in cancer remains unclear. In melanoma, glutathione peroxidase 4 (GPX4) impedes ferroptosis during haematogenous metastasis, but is dispensable during lymphatic metastasis[2]. Here, using a metastatic mouse melanoma model selected for lymph node metastasis, we show that lymph-node-derived metastatic cells exhibit markedly diminished expression of glutamate–cysteine ligase (GCLC) and reduced glutathione (GSH) levels relative to their parental counterparts. This metabolic shift occurs within the hypoxic lymphatic niche. Under comparable low-oxygen conditions, GPX4 undergoes ubiquitination and proteasomal degradation. In response, lymph node metastatic cells acquire increased reliance on ferroptosis suppressor protein 1 (FSP1), which is localized with perinuclear lysosomes. These findings reveal that the reduced reliance on the GPX4 axis enables melanoma cells to shift toward FSP1 dependency. Notably, intratumoural monotherapy with selective FSP1 inhibitors (viFSP1 and FSEN1) effectively suppresses melanoma growth in lymph nodes, but not in subcutaneous tumours, emphasizing a microenvironment-specific dependency on FSP1. Thus, targeting FSP1 in the lymph nodes holds strong potential for blocking melanoma progression.

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Main

Unrestrained iron-dependent lipid oxidation triggers ferroptosis—a non-apoptotic cell death modality with far-reaching implications for human disease, including neurodegeneration, ischaemia–reperfusion injury and cancer3,4. GPX4 is the guardian of ferroptosis and uses GSH to detoxify and reduce lipid oxidation5,6. Thus, depletion of cellular cysteine or GPX4 inhibition, among other processes, can result in the lethal accumulation of oxidized lipids predominantly on specific polyunsaturated-fatty-acid-containing phospholipids, eventually leading to rupture of the plasma membrane[7].

Inducing ferroptosis has emerged as a possible strategy to target therapy-resistant and metastatic cancers[8]. Nonetheless, cancer cells adeptly leverage effective oxidation–reduction adaptations to mitigate uncontrolled lipid oxidation, including the FSP1–ubiquinone pathway, among others9,10,11,12. In the context of cancer metastasis to lymph nodes (LNs), melanoma cells are transiently protected from ferroptosis by incorporating high levels of oleic acid into their phospholipid membranes[2]. Notably, whereas metastasizing melanoma cells in the blood are GPX4 dependent and undergo death by oxidative stress2,13,14, metastasizing melanoma cells in the lymph are GPX4 independent but remain protected against ferroptosis[2].

Comparing distinct microenvironmental contexts in which cancer cells display differential sensitivity or dependency on GPX4 provides a route to elucidate the complex and still poorly understood mechanisms governing ferroptosis vulnerability and resistance. To explore this, we used an in vivo mouse model of melanoma metastasis selected for LN metastasis compared with subcutaneous (s.c.; primary tumour) lines[15]. This model revealed a mechanism of context-dependent regulation of ferroptosis resistance mediated by the LN microenvironment and highlighted potential therapeutic opportunities to inhibit tumour growth in LNs.

LN colonization shifts ferroptosis defences

To investigate how LN colonization influences ferroptosis surveillance dependencies in melanoma, we used an in vivo mouse model of melanoma metastasis, generated by selecting for LN metastases across nine generations using a strategy adapted from other organ-specific metastasis models[15]. C57BL/6J mice were s.c. implanted with the syngeneic melanoma cell line B16-F0, hereafter, the parental line. Spontaneous LN metastases were isolated, expanded ex vivo and reimplanted into naive mice over nine generations, generating nearly 300 unique LN metastatic lines[15] (Fig. 1a). Analysis of metastatic incidence in 30 cell lines across different generations revealed that the late-generation LN metastatic lines (LN7, LN8 and LN9, from generations 7 to 9) exhibit an increased incidence of spontaneous LN metastases compared with the parental lines[15].

Fig. 1: LN metastatic lines exhibit elevated FSP1 and reduced GCLC, GPX4 and ACSL4 expression.

a, Schematic of the generation of LN metastatic melanoma lines through serial in vivo selection across nine generations. The diagram was created using BioRender. b,c, The transcript levels of Gclc (b) and Fsp1 (c) across LN generations. TPM, transcripts per million. d, Immunoblot of ACSL3, ACSL4, GCLC, FSP1, xCT and GPX4 in B16-F0 (parental), early (LN1) and late (LN7–9) generations of LN metastatic lines. e–l, Individual (e,g,i,k) and grouped (f,h,j,l) quantifications of GCLC (e,f), FSP1 (g,h), GPX4 (i,j) and ACSL4 (k,l) protein expression levels across LN generations. Each replicate represents an independent experiment. For e–l, data are mean ± s.d. Statistical analysis was performed using one-way analysis of variance (ANOVA) followed by Dunnett’s test (e, g, i and k) and two-sided unpaired Student’s t-tests with Welch’s correction (f, h, j and l).

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To understand how LN colonization altered the propensity of melanoma cells to undergo cell death, we conducted RNA-sequencing (RNA-seq) analyses of parental and late-generation LN metastatic lines (Extended Data Fig. 1a). Analysis comparing transcript expression of key cell death genes between the late (LN7–9) versus early (parental, LN1 and LN2) generations of LN metastatic lines revealed differences in ferroptosis, necroptosis, autophagy and apoptosis-associated genes. Notably, the most downregulated gene among all of these cell death pathways was Gclc (Extended Data Fig. 1b), which is required for de novo GSH synthesis and can contribute to protection from ferroptosis by replenishing GSH, a co-factor of GPX4, among other actions16,17. By contrast, Fsp1 (also known as Aifm2) was upregulated among ferroptosis-related genes in late-generation LN metastatic lines (Extended Data Fig. 1b).

Gene expression analysis showed a progressive decrease in Gclc (Fig. 1b), and a progressive increase in Fsp1 across LN line generations (Fig. 1c), corroborated by analysis using quantitative PCR (qPCR; Extended Data Fig. 1c). Protein levels demonstrated lower levels of GCLC and higher levels of FSP1 in late generations compared with the parental line (B16-F0) and early generation 1 (LN1-18IL) (Fig. 1d–h). These correlative changes suggest that serial passaging through the LNs confers decreases in Gclc and increases in Fsp1 expression. No significant differences were detected in the mRNA levels of Gpx4, Acsl4, Acsl3 or Slc7a11 (the gene that encodes the functional subunit of system \({{\rm{x}}}_{{\rm{c}}}^{-}\) or xCT) (Extended Data Fig. 1d–h). However, protein levels of GPX4 and ACSL4 were significantly reduced, along with modest reductions in ACSL3 and system \({{\rm{x}}}_{{\rm{c}}}^{-}\), in late-generation lines compared with in parental lines (Fig. 1d,i–l and Extended Data Fig. 1i,j).

Next, to assess the role of the LN environment in the regulation of FSP1, GCLC and GPX4, B16-F10 WT cells were injected either s.c. or i.n. (intranodal) into C57BL/6J mice. The LN environment is characterized by low oxygen availability, with oxygen concentrations ranging from approximately 1 to 3%, and transient reductions to as low as 0.5% (ref. [18]). Accordingly, we used HIF-1α as a marker to assess tumour hypoxia and found that i.n. tumours exhibited higher HIF-1α levels compared with s.c. tumours, consistent with lower oxygen availability in the LN environment (Extended Data Fig. 1k–m). Consistent with LN metastatic lines, i.n. tumours exhibited reduced GCLC and GPX4 protein levels compared with s.c. tumours (Extended Data Fig. 1k,m–o), along with a non-significant trend toward increased FSP1 expression in WT cells (Extended Data Fig. 1k,p). A similar reduction in GCLC and GPX4 was observed in B16-F10 Fsp1-knockout (KO) lines injected i.n. (Extended Data Fig. 1l). Notably, both B16-F10 WT and Fsp1-KO cells formed tumours with 100% incidence. These findings indicate that the LN microenvironment induces decreases in GCLC and GPX4 independently of FSP1.

FSP1 contributes to ferroptosis resistance in a GPX4-independent manner by reducing coenzyme Q10 (ubiquinone) to ubiquinol, thereby neutralizing lipid radicals9,10. While its role in metastasis is unclear, FSP1 is overexpressed in several cancers, including melanoma9,10. TCGA data in metastatic melanoma demonstrated a negative correlation between FSP1 and GCLC expression (Extended Data Fig. 1q).

To further investigate this relationship, we analysed FSP1, GCLC and GPX4 protein levels using immunohistochemistry (IHC) in a human melanoma tissue microarray (TMA) comprising primary tumours and LN metastases. While the correlations observed were modest and these IHC analyses do not distinguish between expression tumour cell expression and immune cell expression, FSP1 trended toward a positive correlation with GCLC in primary tumours but trended toward a negative correlation with both GCLC and GPX4 in LN metastases (Extended Data Fig. 1r,s,u). By contrast, GCLC and GPX4 remained positively correlated in both the s.c. and LN contexts (Extended Data Fig. 1t,u). However, these modest correlations should be cautiously interpreted, as total expression levels may not necessarily reflect FSP1 functional activity.

Epigenetic and NRF2 influences on GCLC and FSP1

We next examined potential epigenetic and transcriptional influences on GCLC and FSP1 expression in the LN metastatic lines. Epigenetic dysregulation is a common feature of human cancers, which contributes to tumorigenesis and maintenance of malignant phenotypes[19]. Previously, it has been described that LN colonization induces significant epigenetic changes in melanoma LN metastatic cells[15] (Extended Data Fig. 2a). Assay for transposase-accessible chromatin using sequencing (ATAC–seq) analysis revealed reduced chromatin accessibility at the Gclc transcription start site and the promoter in LN metastatic lines compared with the parental line (Extended Data Fig. 2b). By contrast, no differences in chromatin accessibility at the Fsp1 promoter were observed in LN metastatic lines compared with the parental line, but there was an increase in chromatin accessibility at distant regions (putative enhancers) (Extended Data Fig. 2c). Given the modest changes in chromatin accessibility, we further evaluated potential transcriptional regulation, initially focusing on NRF2 due to its established role in oxidative stress.

NRF2 is a key regulator of the oxidative stress response and controls the expression of several genes involved in ferroptosis regulation, including Gclc, Slc7a11 and Lrp8[20]. NRF2 has also been shown to influence Fsp1 expression in specific contexts[21]. We therefore next measured the expression of NRF2 and its primary downstream targets in parental versus LN metastatic lines. Although Nrf1, Nrf2 and Keap1 mRNA levels were not significantly different across LN generations (Extended Data Fig. 2d–g), key NRF2-target genes associated with ferroptosis were differentially changed in late versus early LN metastatic lines (Extended Data Fig. 2h). Protein levels of NRF2 were also decreased in the late LN metastatic lines compared with in the parental line (Extended Data Fig. 2i–k). Moreover, NRF2 overexpression in parental cells significantly increased GCLC, GPX4 and FSP1 levels, albeit to a lesser extent (Extended Data Fig. 2l,m).

Together, these results indicate that reduced Gclc expression in LN metastatic cells may result from epigenetic modification at the Gclc locus and reduced NRF2 expression and activity. By contrast, elevated Fsp1 mRNA and reduced GPX4 protein levels in LN metastatic lines are probably driven by NRF2-independent mechanisms, involving epigenetic and post-translational regulation, respectively.

GPX4 dependency in vitro versus in vivo

We next sought to understand how sensitivities to ferroptosis inducers changed across the LN generations. To test this, we evaluated the ferroptosis sensitivity of these lines in vitro and, in this context, LN metastatic lines exhibited greater sensitivity than their parental counterparts to GPX4 inhibitors RSL3 and ML210, as well as to the system xc− inhibitor erastin-2 (Extended Data Fig. 3a–h). Consistently, LN metastatic lines showed elevated lipid oxidation (as detected by BODIPY-C11) after RSL3 treatment in vitro (Extended Data Fig. 3i). By contrast, LN metastatic cells (LN7-1134BL) isolated from spontaneous LN metastases compared with those isolated from the s.c. tumour were less sensitive to erastin-2 or GPX4 inhibition ex vivo (Extended Data Fig. 3j), confirming that the LN-mediated protection from ferroptosis persists in these cell lines, consistent with our previous work[2].

Also consistent with our previous findings[2], pretreatment of parental cells with albumin-bound oleic acid in vitro fully rescued viability after RSL3 treatment (Extended Data Fig. 3k). However, oleic acid pretreatment only partially rescued cell viability in LN metastatic lines (Extended Data Fig. 3k), which correlated with a trend toward reduced ACSL3 protein levels in some LN metastatic lines and reduced sensitivity under pharmacological inhibition of ACSLs (Extended Data Fig. 3l,m). This model therefore provides a valuable system to investigate the durable cellular adaptations arising during LN colonization, which are distinct yet complementary to the transient metabolic responses mediated by oleic acid protection[2].

Reduced GSH in LN metastatic cells

Ferroptosis is a metabolic form of cell death in which GSH has a critical protective role as a key co-substrate required for GPX4 activity[22]. To investigate whether metabolic changes contribute to the increased sensitivity of LN metastatic lines to GPX4 or system \({{\rm{x}}}_{{\rm{c}}}^{-}\) inhibition in vitro, we performed unbiased metabolomics, revealing distinct clustering between the parental and LN metastatic lines (Fig. 2a). Over 50 metabolites, including several mitochondrial-associated metabolites, were significantly altered in LN metastatic cells (P < 0.001; Fig. 2b). However, we did not observe significant differences between parental and LN metastatic cell lines using the Seahorse assay (Extended Data Fig. 4a). Metabolomic analysis identified significant differences in metabolites involved in GSH synthesis between parental and LN metastatic lines (Fig. 2b,c), including reduced glutamate (Fig. 2d,e), and reduced and oxidized GSH (Fig. 2f–i). These alterations were confirmed by both liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) and an independent luminescence-based assay (Fig. 2j). Although GCLC expression was reduced in LN metastatic lines (Fig. 1b,d–f), the expression of other GSH synthesis enzymes, such as GCLM and GSS, was not significantly altered (Extended Data Fig. 4b,c).

Fig. 2: De novo GSH synthesis is reduced in LN metastatic melanoma cells.

a, Principal component analysis (PCA) of metabolomic profiles from the B16-F0 (parental) and LN1, 7–9 lines. b, The top 25 differentially altered metabolites in LN7–9 compared with in the LN1 and parental lines. c, Diagram of the GSH-synthesis pathway. The diagram was created using BioRender. d,f,h, LC–MS/MS quantification of glutamate (d), GSH (f) and GSSG (h). e,g,i, Grouped quantification of d, f and h, respectively. j, Luminescence-based GSH quantification with or without cysteine depletion. For d–j, n = 3 independent experiments. For d–j, data are mean ± s.d. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s test (d, f, h and j) and two-sided unpaired Student’s t-tests with Welch’s correction (e, g and i). GSSG, glutathione disulfide.

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Given the cysteine requirement for de novo GSH synthesis through GCLC, we tested GSH levels under cysteine-depleted conditions. Depletion of L-cysteine reduced GSH levels in both the parental and LN metastatic lines, with a greater reduction observed in LN metastatic cells (Fig. 2j), indicating impaired cysteine-dependent GSH synthesis. Integration of transcriptomic and metabolomic profiles further confirmed that GCLC expression and GSH metabolism were among the most differentially regulated genes and pathways in LN metastatic cells compared with in the parental cells (Extended Data Fig. 4d,e). These findings suggest that reduced GCLC expression in LN metastatic lines may contribute to impaired GSH synthesis.

Oxygen modulates GPX4 protein levels

The lymph and LN microenvironment contains several ferroptosis-modulating factors, including low free iron, elevated oleic acid and reduced oxygen levels (1–3%)2,18. We next examined whether oleic acid, oxygen or GSH levels modulate GCLC, GPX4 and FSP1 expression in parental melanoma cells to assess their contributions to ferroptosis resistance. Oleic acid supplementation did not alter GPX4, GCLC or FSP1 protein levels under standard culture conditions (21% O_2) (Extended Data Fig. 5a). Notably, exposure to 1% O_2 levels reduced GPX4 protein levels, independent of oleic acid (Extended Data Fig. 5a), suggesting that oxygen may contribute to the decreased GPX4 expression observed in the LN metastatic lines.

Given that GSH levels are elevated in lymph relative to plasma[2], we next tested whether exogenous GSH (GSH-ethyl ester, GSHee) could recapitulate the protein expression patterns observed in LN metastatic lines. GSHee increased GPX4 levels under 21% O_2 levels but did not alter GCLC or FSP1 expression (Extended Data Fig. 5b,c). Under 1% O_2 levels, GPX4 was also reduced but partially rescued by GSHee (Extended Data Fig. 5b,c). However, GCLC overexpression did not restore GPX4 levels in parental and LN metastatic lines under 21% or 1% O_2 levels (Extended Data Fig. 5d–g), and GCLC inhibition through L-BSO further decreased GPX4 only under hypoxia, with no effect under 21% O_2 (Extended Data Fig. 5h,i). Similarly to the GCLC overexpression, Gclc-KO lines did not exhibit reduced GPX4 under 1% O_2 compared to Gclc-WT lines (Extended Data Fig. 5j,k).

A time-course experiment at 1% O_2 confirmed that GPX4 protein levels decreased progressively over time under 1% O_2 (Fig. 3a,b), which was rapidly reversed after reoxygenation (Fig. 3c,d), Similarly, CoCl_2 treatment—a chemical inducer of hypoxia—led to similar effects, including HIF-1α stabilization and reduction of GPX4 protein levels (Extended Data Fig. 5l). Decreases in GPX4 protein levels under lower oxygen availability was observed in both parental and LN metastatic cells (Extended Data Fig. 5m,n) and in multiple mouse and human melanoma lines (Extended Data Fig. 5o). Moreover, 5% O_2 levels also reduced GPX4 protein levels in parental and LN metastatic lines compared with 21% O_2 (Extended Data Fig. 5p–s). Together, these findings reinforce our previous observations, highlighting oxygen availability as a critical regulator of the GPX4 surveillance axis in melanoma.

Fig. 3: Oxygen levels regulate GPX4 protein levels and sensitivity to GPX4 inhibition.

a, Immunoblot of HIF-1α and GPX4 in B16-F0 under 1% O_2 for 16, 24 and 48 h. b, Quantification of GPX4 from a. c, Immunoblot of HIF-1α and GPX4 after reoxygenation. B16-F0 cells were cultured under 1% O_2 for 24 h, then re-exposed to 21% O_2 for 2, 4 or 8 h. d, Quantification from c. e, Confocal microscopy analysis of GPX4 (green) and mitochondria (MitoView; magenta) under 21% and 1% O_2 for 24 h. Scale bars, 50 μm. f, Subcellular fractionation of GPX4 under 21% and 1% O_2 for 24 h. g,i, GPX4 protein levels in B16-F0 and LN7-1134BL cells treated with proteasome inhibitors (BTZ (10 nM) (g) or MG-132 (0.5 μM) (i)) under 21% or 1% O_2 for 24 h. h,j, Quantification of the experiments in g and i, respectively. k, Gpx4 mRNA levels with or without BTZ (10 nM) under 21% or 1% O_2 for 24 h. l, GPX4 immunoprecipitation and ubiquitination in the LN7-1134BL line under 21% or 1% O_2 for 16 h. LE, long exposure; SE, short exposure. m, The cell viability of B16-F0 and LN metastatic lines treated with ML-210 under 21% or 1% O_2 for 48 h. n, The total GSH levels in parental and LN metastatic lines under 21% or 1% O_2 for 24 h. n = 3 (a–e, g–l and n) and n = 2 (f) independent experiments. For m, n = 3 technical replicates, representative of 1 of 3 independent experiments. For b, d, h, j, k and n, data are mean ± s.d. Statistical analysis was performed using Kruskal–Wallis tests followed by Dunn’s multiple-comparison test (b, d, h and j) and one-way ANOVA followed by Tukey’s (k) or Šidák’s (m and n) post hoc test.

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Low oxygen promotes GPX4 degradation

Although GPX4 is mainly cytoplasmic, it has been reported in the nucleus, mitochondria and at additional subcellular sites23,24. Confocal immunofluorescence analyses and subcellular fractionation revealed that, under 1% O_2, GPX4 protein levels were reduced primarily in the cytoplasm while nuclear and mitochondrial GPX4 levels were maintained (Fig. 3e,f). To understand the underlying molecular mechanism by which oxygen availability may regulate GPX4 cytoplasmic levels in melanoma, we first examined the involvement of known GPX4 regulators, including antioxidants, selenium[25], autophagic GPX4 degradation26,27 and ubiquitin-proteosome system (UPS)-mediated degradation of GPX428,29,30. Treatment with antioxidants (N-acetyl cysteine) and the ferroptosis inhibitor liproxstatin-1 did not rescue GPX4 protein levels under 1% O_2 conditions (Extended Data Fig. 6a,b), whereas selenium supplementation and inhibition of autophagy by chloroquine (CQ) partially rescued GPX4 levels in these conditions (Extended Data Fig. 6c–e). By contrast, the proteosome inhibitors bortezomib (BTZ) and MG-132 significantly rescued GPX4 protein levels in the parental (B16-F0) and LN metastatic lines (LN7-1134BL) under low O_2 levels (Fig. 3g–j). Similarly, inhibition of the ubiquitin-activating enzyme (E1) using NSC 624206 rescued GPX4 protein levels under 1% O_2, therefore implicating UPS-mediated degradation of GPX4 (Extended Data Fig. 6f,g). To exclude transcriptional effects, we measured Gpx4 and Nrf2 mRNA levels and observed no significant changes after 1% O_2 or BTZ treatment (Fig. 3k and Extended Data Fig. 6h). To confirm that GPX4 is ubiquitinated and regulated by UPS under lower O_2 levels, we evaluated the ubiquitination of GPX4 under 21% and 1% O_2. Exposure to 1% O_2 induced HIF-1α and reduced GPX4 protein levels; however, immunoprecipitation of endogenous GPX4 revealed increased ubiquitination specifically under 1% but not under 21% O_2 (Fig. 3l and Extended Data Fig. 6i). Together, these results demonstrate that oxygen availability regulates GPX4 protein stability through UPS-mediated degradation, contributing to the reduced GPX4 levels observed in LN metastatic melanoma cells.

Oxygen modulates ferroptosis sensitivity

Given that LN metastatic lines are more sensitive to RSL3, ML-210 and erastin-2 in standard culture conditions (21% O_2) compared with the parental line in vitro (Extended Data Fig. 3a–h) and that GPX4 protein levels are reduced by low oxygen availability, we next examined whether oxygen availability modulates the sensitivity to GPX4 and system \({{\rm{x}}}_{{\rm{c}}}^{-}\) inhibitors. Under 1% O_2, both parental and LN metastatic lines exhibited reduced sensitivity to GPX4 inhibition (ML-210 or RSL3) and system \({{\rm{x}}}_{{\rm{c}}}^{-}\) inhibition (erastin-2) (Fig. 3m and Extended Data Fig. 6j,k). Moreover, GSH levels were reduced under 1% O_2 in both the parental lines and the LN metastatic lines (Fig. 3n). These findings highlight the importance of considering LN microenvironmental factors, such as oxygen availability, that significantly modulate the GCLC–GSH–GPX4 axis and melanoma cell susceptibility to ferroptosis. Given the observed upregulation of FSP1 in LN metastatic lines, we next investigated its regulation and therapeutic potential in melanoma cells within the LNs.

Lysosomal FSP1 in LN metastatic lines

FSP1 has an important protective role neutralizing lipid oxidation9,10. However, the regulation and the pathophysiological contexts in which FSP1 may be targeted in cancer are still unclear. FSP1 undergoes N-myristylation and membrane localization for its anti-ferroptotic action9,10; we therefore first characterized the localization of FSP1 in the LN metastatic lines. FSP1 was significantly located perinuclearly in LN metastatic cells compared to the parental line (Fig. 4a,b). Protein N-myristoylation is a fatty acylation catalysed by N-myristoyltransferases (NMTs). The myristoyl group added to the protein is crucial for cellular localization and signal transduction[31]. FSP1 perinuclear association was reduced by IMP-1088, a potent N-myristoyltransferase inhibitor, supporting the relevance of FSP1 myristoylation in the association with perinuclear endomembranes in LN metastatic lines (Fig. 4a,b).

Fig. 4: FSP1 localizes to lysosomes in LN metastatic lines in an N-myristoylation-dependent manner.

a, Confocal images of FSP1–OFP (green) in B16-F0 (parental), LN7-1134BL, LN8-1194BR and LN9-1315BL cells with or without IMP-1088 (0.1 μM) for 24 h. Nuclei (N; DAPI; blue) are outlined with a dotted line. b, Quantification of perinuclear FSP1–OFP in the B16-F0, LN7, LN8 and LN9 lines. B16-F0: n = 194 (−IMP-1088), n = 193 (+IMP-1088); LN7-1134BL: n = 187 (−IMP-1088), n = 221 (+IMP-1088); LN8-1194BR: n = 211 (−IMP-1088), n = 193 (+IMP-1088); LN9-1315BL: n = 148 (−IMP-1088), n = 162 (+IMP-1088). c, Co-localization of FSP1–OFP (green) with lysosomal (LAMP1; magenta), Golgi (RCAS1; red) or ER (ERp72; red) markers. Fluorescence intensity profiles were measured along the arrows shown in the images. Nuclei (DAPI; blue) are outlined with a dotted line. d, Orthogonal immunofluorescence view of FSP1–OFP (green) and LAMP1 (magenta) stack images. Nuclei (DAPI; blue) are outlined with a dotted line. e, Immunoblot of lysosome-enriched and whole-cell extracts. LAMP1, LAMP2 and LIMPII were used as lysosomal markers and γ-tubulin served as a marker of whole-cell extract. f, Quantification of FSP1 protein levels in whole-cell (n = 9), lysosomal-enriched (n = 5), Golgi-enriched (n = 4) and ER-enriched (n = 4) extracts. g, Quantification of FSP1–OFP (green) and LAMP1 (magenta) co-localization in B16-F0, LN7-1134BL, LN8-1194BR and LN9-1315BL cells with or without IMP-1088 (0.1 μM) for 24 h. B16-F0: n = 105 (−IMP-1088), n = 68 (+IMP-1088); LN7-1134BL: n = 104 (−IMP-1088), n = 101 (+IMP-1088); LN8-1194BR: n = 94 (−IMP-1088), n = 76 (+IMP-1088); LN9-1315BL: n = 63 (−IMP-1088), n = 64 (+IMP-1088). h, Confocal microscopy of FSP1–OFP (green) and Lysotracker (magenta) in LN8-1194BR with or without IMP-1088 (0.1 μM) for 24 h. Nuclei (Hoechst; blue) are outlined with a dotted line. i, Quantification of FSP1–OFP and Lysotracker co-localization from h. n = 92 (−IMP-1088) and n = 94 (+IMP-1088). j, Confocal images of LN7-1134BL cells expressing FSP1–OFP WT or G2A mutant, co-stained with LAMP1 (magenta) and DAPI (blue). n = 3 (a–d, g and j) and 4 (h and i) independent experiments. For g and i, data are shown as violin plots with all points; the median is indicated by a dashed line. For b and f, data are mean ± s.d. Scale bars, 50 μm (a), 10 μm (c) and 5 μm (d, h and j). Statistical analysis was performed using one-way ANOVA with Šidák’s multiple-comparison test (b), Tukey’s test (g), Kruskal–Wallis with Dunn’s post-test (f) or two-sided unpaired Student’s t-tests with Welch’s correction (i).

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FSP1 has been shown to localize at the plasma membrane, lipid droplets, perinuclear structures and mitochondria9,10. In the LN metastatic cells, perinuclear FSP1 did not co-localize with lipid droplets or mitochondria (Extended Data Fig. 7a). Given that FSP1 was located with the perinuclear region of LN metastatic lines, we further examined its association with perinuclear organelles, including endoplasmic reticulum (ER), Golgi and lysosomes. Immunofluorescence analysis revealed that FSP1 localized primarily with perinuclear lysosomes in the LN metastatic lines (Fig. 4c). Immunofluorescence stack analysis of FSP1 and LAMP1 confirmed that FSP1 is distributed throughout the entire lysosome compartment (Fig. 4d). Biochemical fractionation and enrichment of lysosomes, Golgi and ER confirmed a significant increase in lysosomal FSP1 in the LN metastatic lines compared with in the parental cell lines, whereas FSP1 levels in the Golgi and ER were comparable between the parental and LN metastatic lines (Fig. 4e,f and Extended Data Fig. 7b,c).

FSP1 co-localized with perinuclear lysosomes under both 21% (Fig. 4g–i and Extended Data Fig. 7d,e) and 1% O_2 (Extended Data Fig. 7f) conditions. NMT1 and NMT2 activity were required for the FSP1 association with lysosomes (Fig. 4g–i and Extended Data Fig. 7d–f). Indeed, the FSP1 G2A mutant, which cannot undergo N-myristoylation, confirmed that N-myristoylation is essential for lysosomal localization of FSP1 in LN metastatic lines (Fig. 4j and Extended Data Fig. 7g). FSP1 association with lysosomes was also observed in SK-MEL5 and MeWo cells—two human metastatic melanoma lines isolated from axillary LNs[32]—as well as in A-375 cells (isolated from primary tumour) (Extended Data Fig. 7h). Thus, FSP1 association with perinuclear lysosomes is conserved in mouse and human (Extended Data Fig. 7i) and is generalizable to contexts beyond the LN environment, as observed in the A-375 cell line.

To investigate whether lysosomal activity influences FSP1 protein levels, we used bafilomycin A (BafA1) and CQ to inhibit lysosomal activity. FSP1 protein levels remained unchanged after BafA1 treatment in the parental line and the LN metastatic lines (Extended Data Fig. 8a–d). Similar results were observed with CQ treatment (Extended Data Fig. 8e,f). Together, these data suggest that the lysosomal association of FSP1 does not contribute to its degradation.

Lysosomes have recently been recognized as an organelle involved in the regulation and execution of ferroptosis33,34. Recent evidence indicates that exposure to hypoxic conditions leads to increased lysosomal pH[35]. Notably, FSP1 enzymatic activity remains largely unaffected by pH fluctuations in a concentration-dependent manner, maintaining its functionality even under the acidic environment typical of lysosomes or other organelles (Extended Data Fig. 8g,h). A recent study developed fentomycins—a synthetic family of small molecules[34]. Fentomycin is composed of a lysosome-targeting moiety conjugated to an iron-activating ligand, enabling activation of lysosomal iron and induction of ferroptosis. Indeed, sublethal doses of fentomycin-1 increased FSP1 mRNA levels in HT-1080 cells[34]. Given the lysosomal localization of FSP1 in the LN metastatic cells, we hypothesized that the absence of FSP1 would sensitize lysosomes to lipid oxidation induced by fentomycins. Indeed, LN7-1134BL Fsp1-KO cells (Extended Data Fig. 8i,j) showed increased lipid oxidation after fentomycin-3 treatment compared with wild-type cells (Extended Data Fig. 8k). These data suggest that the LN environment not only induces FSP1 expression, but also functionally engages FSP1 at lysosomal membranes to suppress lipid oxidation under ferroptotic stress.

FSP1 and GCLC inhibition impairs viability in vitro

To determine whether FSP1 is a targetable vulnerability in LN lines, we first compared ferroptosis resistance in WT and Fsp1-KO parental and LN metastatic lines under RSL3 or ML-210 treatment at 21% and 1% O_2. As expected, Fsp1-KO lines were more sensitive to RSL3 treatment under 21% O_2 (Extended Data Fig. 9a–d). Under 1% O_2, although the overall sensitivity to GPX4 inhibition was reduced, Fsp1-KO lines compared with WT lines exhibited significantly diminished protection (Fig. 5a,b and Extended Data Fig. 9b). Notably, RSL3 protection was decreased across LN generations in Fsp1-KO lines, with LN9 Fsp1-KO cells exhibiting the highest sensitivity under 1% O_2 (Fig. 5b). A similar trend was observed with ML-210 treatment, in which only Fsp1-KO LN metastatic lines remained sensitive under 1% O_2 (Extended Data Fig. 9e,f).

Fig. 5: FSP1 and GCLC inhibition reduces LN metastatic line viability, and FSP1 monotherapy reduces i.n. tumour growth.

a,b, Cell viability of B16-F0 and LN7-1134BL WT and Fsp1-KO lines (a) or LN9-1315BL WT and Fsp1-KO lines (b) treated with RSL3 under 1% O_2 for 48 h. c, Lipid oxidation levels (BODIPY-C11ox/red) of B16-F0 WT, B16-F0 Fsp1-KO, LN7-1134BL WT and LN7-1134BL Fsp1-KO lines under 21% (n = 4) or 1% O_2 (n = 4) with or without liproxstatin-1 (1 μM) (n = 3) for 24 h. d, Cell viability of the B16-F0 and LN8-1194BR lines treated with viFSP1 (30 μM), BSO (1 mM), liproxstatin-1 (1μM) or combinations of which under 1% O_2 for 48 h. e,f, Cell viability of MeWo (e) and SK-MEL5 (f) cells treated with FSP1 inhibitors (iFSP1 and FSEN1, 10 μM; icFSP1 and viFSP1, 15 μM) with or without BSO (100 μM) and with or without liproxstatin-1 (1 μM) under 21% (e and f) or 1% O_2 (f) for 24 h. g, The end-point SK-MEL5 tumour volume after intratumoural treatment with vehicle (n = 19), icFSP1 (n = 8), viFSP1 (n = 10), BSO (n = 14), or combinations of BSO with icFSP1 (n = 8) or viFSP1 (n = 10). Data are normalized to the vehicle treatment. h, End-point SK-MEL5 tumour volumes in mice treated intratumourally with vehicle (n = 9) or FSEN1 (n = 8) normalized to the vehicle treatment. i, End-point tumour volumes comparing i.n. versus s.c. injections of LN7-1134BL WT and Fsp1-KO cells treated with vehicle or viFSP1. i.n.: WT: vehicle (n = 7), viFSP1 (n = 10); Fsp1 KO: vehicle (n = 9), viFSP1 (n = 8). s.c.: WT: vehicle (n = 7), viFSP1 (n = 8); Fsp1 KO: vehicle (n = 8), viFSP1 (n = 5). j, Regulation of GCLC, GPX4 and FSP1 in melanoma within LNs. The diagram was created using BioRender. For a, b and d–f, n = 3 technical replicates, representative of 1 of 3 independent experiments. For g–i, n = 2 independent experiments. For a–c, data are mean ± s.d. For g–i, the box and whisker plots show all points, with the whiskers showing the minimum to maximum values. The box limits represent the first and third quartiles (Q1–Q3), and the centre line indicates the median (Q2). Statistical significance was determined using Kruskal–Wallis tests followed by Dunn’s post hoc test (c), one-way ANOVA followed Tukey’s multiple-comparison test (d) with Šidák’s multiple-comparison test (e–g and i) and two-sided unpaired Student’s t-tests with Welch’s correction (h).

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To confirm the relevance of FSP1 under lower O_2 availability contexts, we measured lipid oxidation in both WT and Fsp1-KO cells from parental and LN7 lines under both 21% and 1% O_2 conditions. Exposure to 1% O_2 increased BODIPY-C11 staining in all lines (Fig. 5c), but to an even greater extent in the LN7-1134BL Fsp1-KO line (Fig. 5c), therefore emphasizing the functional importance of FSP1 under reduced O_2 availability.

Several small-molecule inhibitors of FSP1 have recently been developed, including iFSP1[10], FSEN1[36] and icFSP1[37], that primarily target human FSP1 through different mechanisms. viFSP1 is the first cross-species inhibitor for FSP1 effective against mouse and human FSP1[38]. viFSP1 in combination with lower doses of RSL3 significantly reduced the viability of LN metastatic lines, but not in the parental line under 1% O_2 levels (Extended Data Fig. 9g). However, FSP1 inhibition alone was insufficient to trigger ferroptosis in vitro[8], including in LN metastatic lines (Extended Data Fig. 9h).

Although GCLC expression and GSH levels are reduced in LN metastatic lines, they are not entirely absent, suggesting a targetable opportunity in combination with FSP1 inhibition. GCLC has gained attention as a ferroptosis target17,39. However, the contexts in which GCLC inhibition is effective remain unclear. To assess the impact of dual targeting, we tested genetic and pharmacologic inhibition of FSP1 combined with pharmacologic GCLC inhibition using L-BSO. This combination significantly decreased LN8-1194BR viability under 1% O_2 compared to the parental cells (Fig. 5d). Fsp1 KO in parental B16-F0 cells had no effect on viability after treatment with L-BSO, whereas Fsp1-KO in LN7-1134BL led to reduced viability under 1% O_2, highlighting a selective FSP1 dependency (Extended Data Fig. 9i).

Similar to mouse LN metastatic lines, human melanoma lines derived from LNs, including MeWo and SK-MEL5[32], exhibited increased sensitivity to L-BSO and FSP1 inhibitors (iFSP1, FSEN1, icFSP1, viFSP1) compared with the primary tumour-derived A-375 line (Fig. 5e,f and Extended Data Fig. 9j,k). Notably, SK-MEL5 was sensitive to combined FSP1 and GCLC inhibition under both 21% and 1% O_2—an effect rescued by liproxstatin-1 (Fig. 5f). These in vitro findings support co-targeting FSP1 and GCLC in vivo to reduce tumour growth in LNs.

FSP1 inhibition reduces LN tumour growth

To examine the pharmacological relevance of targeting of FSP1 and/or GCLC in melanoma LN tumours, SK-MEL5 cells were injected i.n. into the popliteal LN of NSG mice. Once palpable, mice were treated daily with intratumoural administration of vehicle, L-BSO, icFSP1 or icFSP1 + L-BSO for 14 days (Extended Data Fig. 10a). Notably, treatment with BSO, icFSP1 or their combination did not result in a significant reduction in tumour size (Fig. 5g and Extended Data Fig. 10c–f). By contrast, viFSP1 monotherapy significantly reduced i.n. tumour burden (Fig. 5g and Extended Data Fig. 10c–f). However, co-treatment with BSO and viFSP1 did not produce an enhanced effect compared with viFSP1 alone in vivo, despite the combined treatment showing greater efficacy in vitro. These results further highlight the context-dependent differences in FSP1 targetability, therefore emphasizing the importance of evaluating the effects of FSP1 inhibitors both in vitro and in vivo.

To further validate the specificity and therapeutic potential of FSP1 inhibition in vivo, we tested FSEN1, a chemically distinct and human-specific FSP1 inhibitor36,40. FSEN1 was administered using the same formulation and dosing regimen as viFSP1 (Extended Data Fig. 10a). Notably, FSEN1 also significantly reduced i.n. tumour growth (Fig. 5h and Extended Data Fig. 10g,h) and extended overall survival in NSG mice (Extended Data Fig. 10i). These findings further support the in vivo efficacy of pharmacological FSP1 inhibition, particularly within the LN microenvironment, using structurally distinct compounds.

To examine potential off-target effects, we used the syngeneic models with LN7-1134BL Fsp1 WT and KO lines injected i.n. or s.c. into immunocompetent C57BL/6J mice (Extended Data Fig. 10b). Daily local administration of viFSP1 significantly reduced i.n. tumour growth in WT tumours at both the experimental end point (and at timepoints matched to the end point of the s.c. experiments), while no response was observed in Fsp1-KO tumours (Fig. 5i and Extended Data Fig. 10j–m), confirming that viFSP1 activity is on-target. Fsp1-KO tumours exhibited slower growth and, in some cases, regression, suggesting that genetic deletion of Fsp1 sensitizes LN-derived cells in the LNs (Fig. 5i and Extended Data Fig. 10j–m).

To assess whether this dependency is specific to the LN microenvironment, we compared s.c. tumour growth in the same model. In this context, viFSP1 treatment resulted in only a modest reduction in tumour growth in WT tumours and had no effect in Fsp1-KO tumours (Fig. 5i and Extended Data Fig. 10n–q). Indeed, no reduction in the tumour growth was observed in the Fsp1-KO tumours treated or not with viFSP1 (Fig. 5i and Extended Data Fig. 10n–q). These findings indicate that FSP1 dependency is markedly enhanced within the LN environment compared to s.c. tumours.

Given the relevance of FSP1 in LN tumours, we evaluated the contribution of FSP1 to metastatic colonization. Experimental metastasis through intravenous injection of LN7 Fsp1-WT or -KO cells resulted in a modest increase in lung colonization in mice injected with the Fsp1-KO lines (Extended Data Fig. 10r), indicating that the loss of FSP1 does not decrease overall survival of metastasizing melanoma cells in the bloodstream. However, in spontaneous models of metastasis, although there were no differences in primary tumour growth of mice implanted s.c. with LN7 Fsp1-WT or -KO cells (Extended Data Fig. 10s; consistent with Fig. 5i), mice bearing LN7 Fsp1-KO compared with WT tumours had a significantly decreased incidence of tumour-draining LN metastasis (Extended Data Fig. 10t,u). These findings suggest that melanoma cells in the LNs represent a context in which targeting FSP1 holds potential for limiting metastatic progression.

Discussion

Here we identify a vulnerability of LN metastatic melanoma cells to pharmacological inhibition of FSP1 as a monotherapy in vivo. In LN metastatic lines, we show that GCLC and GSH are reduced (Figs. 1 and 2) and GPX4 undergoes oxygen-dependent ubiquitin–proteasome-mediated degradation (Fig. 3). We show that the release of melanoma cell dependence on the GPX4 surveillance axis in LNs leads to increased functional reliance on FSP1, which remains intact and accumulates with perinuclear lysosomes through N-myristoylation (Fig. 4).

Notably, FSP1 inhibition is insufficient to reduce the viability of LN lines in vitro (Fig. 5). By contrast, both pharmacological inhibition and genetic deletion of FSP1 significantly impair melanoma growth within LNs, but not in s.c. sites (Fig. 5), emphasizing FSP1 dependencies of cancer cells that arise in vivo. Consistent with this finding, a complementary study demonstrates that FSP1 inhibition in vivo, but not in vitro, significantly reduced lung cancer survival, further indicating that the dependency of FSP1 inhibition differs between the in vitro and in vivo contexts[41].

Our findings identify a distinct, durable adaptation that emerges during LN colonization: a shift from GPX4 to FSP1 dependence. The extent to which intratumoural heterogeneity influences this shift in FSP1 dependency remains to be determined. Additional limitations of this work include understanding why FSP1 small-molecule inhibitors have increased efficacy in vivo compared with in vitro. One possibility is that the low oxygen availability, high oleic acid levels in the lymph environment and/or lower GSH levels in the LN metastatic cells drive increased dependency on FSP1 beyond what can be achieved in vitro. Another possibility is that the in vivo environment allows favourable pharmacokinetics of the FSP1 small-molecule inhibitors. The differential efficacies between icFSP1 and viFSP1/FSEN1 may reflect compartmentalized mechanisms of action of these small molecules in vivo, which could account for differences in efficacy; this remains an area of active investigation. Furthermore, FSP1’s association with lysosomes raises numerous yet-to-be explored questions regarding the mechanisms underlying this location, including how FSP1 may protect lysosomes from lipid oxidation.

Our findings indicate that there are contexts that offer promise for FSP1 targetability in circumstances in which GPX4 is endogenously downregulated. Furthermore, our findings hold considerable opportunity for understanding and therapeutically targeting the nuanced physiological context-dependency of ferroptosis[42], which has implications for pathophysiological disease states that extend beyond the scope of cancer, such as neurodegeneration and ischaemia–reperfusion injury characterized by heightened ferroptosis vulnerabilities[1]. Regulation of FSP1 activity and changes in FSP1 subcellular distribution emerge as promising targets to sensitize melanoma cells in LNs to ferroptosis, thereby reducing cancer progression.

Methods

Cell lines

B16-F0 (ATCC; CRL-6322) and its LN metastatic derivatives: NBF0-LN1-18IL, NBF0-LN7-1112AR, NBF0-LN7-1120BL, NBF0-LN7-1134BL, NBF0-LN8-1194BR, NBF0-LN8-1198AR, NBF0-LN8-1205BL, NBF0-LN9-1315BL and NBF0-LN9-1358IR—were provided by the Reticker-Flynn Laboratory. For simplicity, these cell lines are referred to throughout the manuscript as: B16-F0, LN1-18IL, LN7-1112AR, LN7-1120BL, LN7-1134BL, LN8-1194BR, LN8-1198AR, LN8-1205BL, LN9-1315BL and LN9-1358IR, respectively. B16F10 wild-type (WT), B16F10 Fsp1-KO and B16F10 Gpx4-KO cells were obtained from the Conrad Laboratory. B16-F0 Fsp1-KO, LN7-1134BL Fsp1-KO, LN9-1315BL Fsp1-KO, B16-F0 Gclc-overexpression, LN7-1134BL Gclc-overexpression, B16-F0 Gclc-KO and B16-F0 Nrf2-overexpression lines were generated in this study. Human melanoma cell lines MeWo, SK-MEL-5, A375, murine melanoma lines Yale University Melanoma Model (YUMM) 3.3 and YUMM 5.2, and HEK293T cells were purchased from ATCC. All cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Thermo Fisher Scientific, 11885076) supplemented with 10% FBS (Thermo Fisher Scientific, 26400044) and 1% penicillin–streptomycin (Thermo Fisher Scientific, 15140122). All of the other lines were authenticated by ATCC using STR profiling. Cells were routinely tested for mycoplasma contamination using MycoStrip (InvivoGen, rep-mys-50).

Chemicals

RSL3 (HY-100218A), erastin-2 (HY-139087), iFSP1 (HY-136057), BTZ (HY-10227) and PEG300 (HY-Y0873) were purchased from MedChemExpress. ML-210 (S0788), MG-132 (S2619) and icFSP1 (E1535) were acquired from Selleck Chemicals. Rotenone (R8875), oligomycin (75351), antimycin A (A8674), L-BSO (B2515), N-acetyl cysteine (A9165), Na_2SeO_3 (S5261), CQ (C6628) and PEG400 (202398) were obtained from Sigma-Aldrich. FCCP (15218), MTT (21795), GSHee (14953), liproxstatin-1 (17730), IMP-1088 (25366), NSC 624206 (20569), FSEN1 (38025), viFSP1 (39927) and triacsin C (10007448) were obtained from Cayman Chemical Company. MitoView Fix 640 (70082) and LipidSpot 488 (70065) were sourced from Biotium. Lipofectamine 3000 (L3000015), Bodipy 581/591 C11 (D3861), SYTOX Green (S7020), Lysotracker Deep Red (L12492) and NucBlue Live ReadyProbes Reagent (R37605) were from Thermo Fisher Scientific.

Plasmids

pCMV3-FSP1-OFP plasmid (MG52065-ACR) was obtained from Sino Biological. Lenti-luciferase-P2A-neo (Addgene, 105621), psPAX2 (Addgene, 12260), pMD2.G (Addgene, 12259) and PX458 (Addgene, 48138) were obtained from Addgene. Custom constructs including pTWIST-mFSP1-G2A-OFP, pLVX-EF1α-GCLC-IRES-Hygro, and pLVX-EF1α-NRF2-IRES-Hygro were synthesized by Twist Bioscience and cloned into expression vectors using Gibson Assembly.

Generation of stable cell lines

Stable cell lines expressing luciferase, GCLC or NRF2 were generated through lentiviral transduction followed by antibiotic selection. Lentivirus was produced by co-transfecting HEK293T cells with 5 µg of either Lenti-luciferase-P2A-neo, pLVX-EF1α-GCLC-IRES-Hygro or pLVX-EF1α-NRF2-IRES-Hygro, combined with 5 µg psPAX2 and 0.5 µg pMD2.G using Lipofectamine 3000. Virus-containing supernatants were collected every 24 h for 48 h, filtered and supplemented with 8 µg ml−1 Polybrene (Sigma-Aldrich, H9268). Target cells were infected and subsequently selected with either 1,500 µg ml−1 G418 or 1,000 µg ml−1 hygromycin B for 6 days to establish stable populations.

CRISPR–Cas9-mediated gene KO

To generate Fsp1- or Gclc-KO cell lines in B16-F0 and its LN metastatic derivatives, sgRNAs were designed with BbsI-compatible overhangs and cloned into the PX458 Cas9-GFP vector. The sgRNA sequences were as follows: Fsp1 (CACCGGCGGCTGCCAGCCAGCTGC) and Gclc (CACCGGGGAGTTACATGATCGA). sgRNA insertion was confirmed by whole-plasmid sequencing. Cells were transfected with PX458-sgRNA constructs using Lipofectamine 3000 and GFP-positive cells were sorted by flow cytometry and expanded. Transfection and cell sorting was repeated a second time to generate a pure population for expansion prior to validation. KOs were validated by western blotting and Sanger sequencing (Extended Data Fig. 8i,j for FSP1 and Extended Data Fig. 5j for GCLC).

LN9-1315BL Fsp1-KO cell lines were generated by lentiviral transduction using the LCv2_Blast vector containing mouse Fsp1 sgRNA 1 (sequence: CACCGCCGTGCACGTGGTGATCGT), previously validated[43]. Transduced cells were selected with 5 µg ml−1 blasticidin. KO validation is shown in Extended Data Fig. 9c.

Western blot analysis

Cell lysates (15–20 μg protein) were separated by SDS–PAGE, transferred onto PVDF membranes (Bio-Rad, 1620177), blocked with 5% non-fat milk in TBS-T or PBS-T, and incubated with primary antibodies overnight at 4 °C in 5% non-fat milk in PBS-T. After washes, the membranes were incubated with HRP-conjugated secondary antibodies and proteins detected by enhanced chemiluminescence (Thermo Fisher Scientific, 32106). The following antibodies were used: ACSL3 (Abcam, ab151959, 1056272-1, WB,1:5,000, Ms), ACSL4 (Santa Cruz Biotechnology, A-5, I1222, WB,1:200, Ms), actin (MP Biomedical, 691001, 0101008716, WB, 1:20,000, Ms and Hu), FSP1 (Proteintech, 20886-1-AP, 00111298, WB,1:2,000, KD validated in-house, Ms and Hu), anti-mouse IgG HRP (Cell Signaling, 7076S, 36, WB, 1:5,000), anti-rabbit IgG HRP (Cell Signaling, 7074S, 33, WB, 1:5,000), COX IV (Cell Signaling, 4850, 11, WB, 1:1000, Ms), GAPDH (Santa Cruz Biotechnology, 6C5, J2523, WB, 1:20,000, Ms), GCLC (Santa Cruz Biotechnology, H-5, J0621, WB, 1:2,000, KO validated in-house), GPX4 (Abcam, ab125066, lot 1000287-43, WB, 1:2,000, KO validated in-house), HIF-1α (Cell Signaling, 36169, 5, WB, 1:1,000), LAMP1 (Abcam, ab24170, GR3235630-1, WB, 1:1,000, Ms), LAMP2A (Abcam, ab18528, 1029399-1, WB, 1:1,000, Ms), LC3 (Cell Signaling, 3868, 14, WB, 1:1,000), LIMPII (Proteintech, 27102-1-AP, WB), NRF2 (Proteintech, 16396-1-AP, 00116728, WB, 1:5,000), NRF2 (Proteintech, 80593-1-RR, 23013625, WB, 1:1,000), PDIA3 (AMAB90988, WB, 1:200), RCAS1 (Cell Signaling, 12290S, D2B6N, 6, WB, 1:1,000), SCL7a11/xCT (Cell Signaling, 98051, 1, WB, 1:300), ubiquitin (Cell Signaling, 43124T, 4, WB, 1:1,000), γ-tubulin (Cell Signaling, T5326, WB, 1:1,000).

Immunoprecipitation and ubiquitination detection

B16-F0 and LN7 1134BL cells were incubated under normoxic (21% O_2) or hypoxic conditions (1% O_2) for 16 h. Proteins were extracted with RIPA buffer plus protease and phosphatase inhibitors. For denatured immunoprecipitation, lysates were heated to 95 °C for 5 min. Both native and denatured lysates were incubated with anti-GPX4 antibody (Proteintech, 67763-1-Ig, 10027815) or mouse IgG control (Proteintech, B900620) overnight at 4 °C, followed by incubation with anti-mouse IgG Sepharose beads (Cell Signaling, 5946) for 6 h at 4 °C. Beads were washed with RIPA buffer and analysed by immunoblotting using the anti-ubiquitin antibodies (Cell Signaling, 43124T, 4, WB, 1:1,000).

IHC analysis

A TMA containing primary cutaneous melanoma and LN metastases (ME551; TissueArray.com) was used to assess the expression of GCLC, GPX4 and FSP1. The sections were stained with antibodies against GPX4 (Abcam, ab125066, 1:500), GCLC (Santa Cruz, sc-390811, 1:500) and FSP1 (Proteintech, 68049-1-Ig, 1:500) using the Zytomed Permanent AP Red Kit (ZUC001-125) according to the manufacturer’s instructions, followed by counterstaining with haematoxylin. The slides were scanned with an Axio Scan.Z1 slide scanner (Zeiss). Quantification of AP Red signal intensity was performed using QuPath (v.0.5) with uniform thresholding parameters across all samples.

FSP1 enzyme activity

NADH consumption assays were performed in PBS (Gibco, 14190094) containing 15 or 25 nM recombinant non-myristoylated human FSP1, 100 μM menadione (Sigma-Aldrich, M5625) and 200 μM NADH[43]. The pH of the final reaction was adjusted from 4.0 to 9.0 by titrating PBS with HCl or NaOH. After the addition of FSP1, the absorbance at 340 nm was recorded every 20 s at 37 °C using the SpectraMax M5 microplate reader (Molecular Devices). Reactions lacking NADH or enzyme were included for background correction. Data were normalized and fitted using GraphPad Prism 10.

Confocal fluorescence microscopy

Cells plated on coverslips were transfected with FSP1-OFP using Lipofectamine 3000. After 16 h, cells were treated with IMP-1088 (0.1 μM) for 24 h. Cells were fixed (4% paraformaldehyde), permeabilized (0.1% Triton X-100), and incubated overnight with primary antibodies in 3% BSA/PBS and then with by Alexa-Fluor-conjugated secondary antibodies. For live-cell imaging, cells were plated on 30-mm glass-bottom dishes, transfected as described above, and incubated with Lysotracker (50 nM) and NucBlue Live ReadyProbes reagent during the final 30 min of IMP-1088 treatment. Images were captured with a Nikon Eclipse Ti confocal microscope using consistent settings for comparisons and analysed with Fiji software. Antibodies and stains used included Alexa Fluor 647 donkey anti-rat (Thermo Fisher Scientific, A48272, YK388772, IF, 1:500), Alexa Fluor 488 goat anti-rabbit (Thermo Fisher Scientific, A32731, YI374177, IF, 1:500), Alexa Fluor 546 goat anti-rabbit (Thermo Fisher Scientific, A11010, 2570547, IF, 1:500), ERp72 (Cell Signaling, 5033, 4, IF, 1:200) GPX4 (Abcam, ab125066, 1000287-7, IF, 1:100, KO validated in-house) from Abcam; LAMP1 (Thermo Fisher Scientific, 14-1071-82, 2698949, IF, 1:50), RCAS1 (Cell Signaling, 12290, 6, IF, 1:200), MitoView Fix640 (70082-50 μg, 23M0201-1215003) and LipidSpot 488 (70065, 22L0820) from Biotium.

Lipid oxidation assays

Cells (60,000 per well) were seeded in 12-well plates one day before treatment. Cells were treated with 0.5 µM RSL3 for 4 h or 1% O_2 for 24 h, washed with PBS, trypsinized and resuspended in PBS containing 1.5 µM C11-BODIPY 581/591 (Invitrogen, D3861). After 30 min incubation at 37 °C, cells were washed, incubated with DAPI, filtered through a 70-µm strainer and analysed on the BD LSR Fortessa flow cytometer. Excitation was performed at 488 nm, detecting oxidized BODIPY (FITC, 525/40 nm) and reduced BODIPY (PE, 585/42 nm). At least 10,000 events were analysed per sample. Data were processed using FlowJo software, and the lipid oxidation ratio (FITC/PE ratio) was calculated as (median FITC-A − median FITC-A unstained)/(median PE-A − median PE-A unstained). The flow cytometry gating strategies for the lipid oxidation assays are presented in Supplementary Fig. 2.

Cell viability and cell death assays

Cells (2,500–3,000 per well) were seeded into 96-well plates. Viability was measured using MTT assay 24 h (erastin-2) or 48 h (RSL3, ML-210, viFSP1 + BSO and Triacsin C) after treatment. Cell death was monitored every 3 h using SYTOX Green (25 nM) in the Incucyte S3 (Sartorius) system.

Isolation of lysosome-enriched fractions

Lysosome-enriched fractions were isolated using the Lysosome Isolation Kit (Abcam, ab234047) according to the manufacturer’s protocol. In brief, 2 × 10^7 cells were washed and centrifuged at 600g for 10 min and the supernatant was removed. Cells were resuspended in Lysosome Isolation Buffer, vortexed and incubated on ice for 2 min. Complete cell disruption was obtained using a dounce homogenizer. After adding Lysosome Enrichment Buffer, the homogenate was centrifuged at 500g for 10 min at 4 °C. The supernatant was added to the top of a discontinuous gradient density and an ultracentrifugation at 145,000g for 2 h at 4 °C was performed. The lysosome-enriched fraction was present in the top 10% of the gradient volume. For western blot analyses, the protein content of the lysosomal-enriched gradient supernatant was quantified using the Qbit 1 fluorometer (Thermo Fisher Scientific) and a protein quantification kit (Thermo Fisher Scientific, Q33212). Equal total protein amounts of total cell extracts and lysosome-enriched extracts were loaded for comparison for western blot analyses.

Isolation of Golgi-enriched fractions

Golgi-enriched fractions were isolated using the Golgi enrichment extraction kit (Invent, GO-037) according to the manufacturer’s instructions. In brief, filter cartridges were placed and cooled on ice for several minutes. Then, 2 × 10^7 cells were trypsinized and collected by centrifugation at 500g, washed with 1× PBS and centrifuged again at 500g. The pellet was resuspended in buffer A with vigorous shaking. The filter cartridge was capped, the tube inverted several times and centrifuged at 16,000g for 30 s. The tube was then centrifuged at 4 °C at 5,000g for 5 min without removing the filter. The filter was then removed and the supernatant transferred to a fresh tube and centrifuged at 4 °C at 16,000g for 30 min. The supernatant was then transferred to a fresh tube. An equivalent in volume of buffer B was added to the supernatant, the resulting mixture incubated on ice for 15 min and then centrifuged at 8,000g for 5 min. The pellet was then resuspended in buffer A and mixed by pipetting up and down 50 times and subsequently centrifuged at 8,000g for 5 min. The supernatant was then transferred to a fresh tube and ice old buffer C was added, mixed by vortexing for 20 s and incubated on ice for 20 min. The tube was then centrifuged at 8,000g for 10 min and the supernatant removed. The pellet was resuspended Laemmli buffer for subsequent western blot analysis. For western blot analyses, the protein content of the Golgi-enriched extracts was quantified using the Qbit 1 fluorometer (Thermo Fisher Scientific) and a protein quantification kit (Thermo Fisher Scientific, Q33212). Equal total protein amounts of total cell extracts and lysosome-enriched extracts were loaded for comparison for western blot analyses.

Isolation of ER-enriched fraction

ER were isolated using the ER enrichment extraction kit (Novus Biologicals, NBP2-29482) according to the manufacturer’s instructions. In brief, 500 µl of 1 × isosmotic homogenization buffer followed by 5 µl of 100× PIC were added to a pellet of 2 × 10^7 cells. The resulting suspension was centrifuged at 1,000g for 10 min at 4 °C. The supernatant was transferred to a clean centrifuge tube and centrifuged at 12,000g for 15 min at 4 °C. The floating lipid layer was discarded. The supernatant was centrifuged in a clean centrifuge tube using an ultracentrifuge at 90,000g for 1 h. The resulting pellet contained the total ER fraction (rough and smooth). The pellet was resuspended Laemmli buffer for subsequent western blot analysis. For western blot analyses, the protein content of the ER-enriched extracts was quantified using the Qbit 1 fluorometer (Thermo Fisher Scientific) and a protein quantification kit (thermo Fisher Scientific, Q33212). Equal total protein amounts of total cell extracts and lysosome-enriched extracts were loaded for comparison for western blot analyses.

Mitochondrial/cytoplasmic fractionation

Mitochondrial and cytoplasmic fractions were obtained using a mitochondria isolation kit for mammalian cells (89874) from Thermo Fisher Scientific according to the manufacturer’s instructions.

RNA-seq analyses

RNA-seq data were generated and analysed as described previously[15]. Raw sequencing reads were trimmed and quality-filtered using Trimmomatic and FastQC, respectively. Transcript abundance was quantified with Salmon v.0.7.2 using quasi-mapping mode and corrected for sequence, GC and positional biases, using the mouse genome GRCm38 GENCODE release M11. TPM values were computed using tximport and renormalized after removing mitochondrial transcripts. Differential expression analysis was performed using DESeq2 with regularized log-transformed counts. Hierarchical clustering and PCA analyses used Spearman correlations from the top 1,000 highly variable genes. Heat maps (Extended Data Fig. 1a) were generated using heatmap3 from the top 200 differentially expressed genes. Data have been deposited in the Gene Expression Omnibus (GEO: GSE117529).

ATAC–seq analyses

ATAC–seq analyses were conducted as described previously[15]. In brief, cells were permeabilized and DNA was transposed using Tn5 transposase. Libraries were purified, amplified and sequenced (NovaSeq, 2 × 100 cycles, around 50 million paired reads per sample). Reads were mapped to mm10 (hisat2), duplicates removed (Picard) and peaks were called using MACS2. Normalized coverage was visualized in IGV. Transcription factor activity and motif enrichment were assessed with Chromvar and HOMER, respectively. Data were deposited at the GEO (GSE117529).

RNA isolation and qPCR analyses

RNA was extracted using the RNeasy Plus Mini Kit (Qiagen, 74134), and cDNA was synthesized using the iScript Reverse Transcription Supermix (Bio-Rad, 1708841). qPCR was performed using the iTaq Universal SYBR Green Supermix (Bio-Rad, 1725121) on the BioRad CFX96 system. The primers used were as follows: mNRF2_F: AACGACAGAAACCTCCATCTAC; mNRF2_R: AGTAAGGCTTTCCATCCTCATC; mFSP1_F: GCAATGAGTATCGGGAGTACAT; mFSP1_R: GTAGGCAGAGCTGTTGATCTT; mGPX4_F: ACTGACGTAAACTACACTCAGC; mGPX4_R: GGAAGGCCAGGATTCGTAAA; RNA pol II_F: ACTGTGCGGAACTCCATCAA; RNA pol II_R: AGCCAGGTTCTGGAACTCAA; mPPIB_F: CATCAAGGACTTCATGATCCA; mPPIB_R: ATAGATGCTCTTTCCTCCTGTG. RNA pol II and PPIB amplification were used as reference genes. PPIB was used as a housekeeping gene for qPCR analyses of parental and LN metastatic lines, while RNA Pol II was used for qPCR analyses of BTZ treatment under 21% and 1% O_2 conditions.

Metabolite extraction and LC–MS analysis

For metabolite extraction, 5 × 10^5 cells were seeded into 6-well plates and cultured for 24 h. The medium was then aspirated, and cells were washed with cold normal saline (9 g l−1 sodium chloride). Immediately, 400 µl of extraction buffer (methanol:acetonitrile:water, 40:40:20, with 0.5% formic acid) was added per well, and the plates were incubated on ice for 5–10 min. The samples were neutralized with 35 µl of 15% ammonium bicarbonate (NH_4HCO_3), cells were scraped and lysates were transferred to 1.5 ml tubes and centrifuged at 16,000 rpm for 15 min. A total of 80 µl of supernatant was transferred to LC–MS vials, and 20 µl from each sample was pooled to generate a quality control sample. All of the extracts were stored at –80 °C until analysis.

Metabolites were analysed using a Q Exactive HF mass spectrometer (Thermo Fisher Scientific) coupled to hydrophilic interaction chromatography (HILIC). Separation was performed using an XBridge BEH Amide XP column (2.5 µm, 2.1 × 150 mm) with a guard column (2.5 µm, 2.1 × 5 mm; Waters). Mobile phase A consisted of water:acetonitrile (95:5) and mobile phase B comprised water:acetonitrile (20:80), both containing 10 mM ammonium acetate and 10 mM ammonium hydroxide. The gradient was as follows: 0–3 min, 100% B; 3.2–6.2 min, 90% B; 6.5–10.5 min, 80% B; 10.7–13.5 min, 70% B; 13.7–16 min, 45% B; 16.5–22 min, 100% B. The flow rate was 0.3 ml min−1. The autosampler was maintained at 4 °C and the column at 30 °C. The injection volume was 5 µl. Needle washes were performed between injections using acetonitrile:methanol:water (4:4:2, v/v/v).

MS1 scans were acquired from m/z 70 to 1,000 with polarity switching and a resolution of 120,000 (at m/z 200). Other MS parameters were as follows: sheath gas, 40; auxiliary gas, 10; sweep gas, 2; spray voltage, 3.5 kV; capillary temperature, 300 °C; S-lens RF level, 45; maximum injection time, 500 ms; AGC target, 3 × 10^6.

Raw data were converted to mzXML format using msConvert and analysed in El-Maven (Elucidata) for targeted metabolite identification based on accurate mass and retention time, using an in-house standard library. Data were normalized to protein content and analysed in MetaboAnalyst 6.0 (https://www.metaboanalyst.ca).

GSH measurements

Cells (5,000 per well) were seeded into 96-well plates, and GSH levels were assessed using the GSH/GSSG-Glo assay (Promega, V6611). Parallel cell viability assessments were used for data normalization.

Seahorse assay

Cells (5,000 per well) were seeded in 96-well plates and analysed using the Seahorse XF24 system. Oxygen consumption rates were measured sequentially after oligomycin (1 μM), FCCP (1 μM) and rotenone/antimycin A (0.5 μM each). Data were normalized to protein content.

s.c. and i.n. tumour models

Mice were housed under sterile conditions with sterilized standard chow and water provided ad libitum and maintained under a 12 h–12 h light–dark cycle and 22 ± 2 °C, 55 ± 5% humidity. Animals were allocated randomly to treatment groups, and the samples were processed in an arbitrary order. No formal randomization or blinding was applied. The maximum permitted tumour diameter of 2.0 cm was not exceeded in any of the experiments. All procedures complied with institutional ethical guidelines and were approved by the Institutional Animal Care and Use Committee of the Harvard T.H. Chan School of Public Health (protocol IS00003460) or the Stanford University Institutional Animal Care and Use Committee (protocol APLAC-34518).

For s.c. injections, 2 × 10^5 B16-F10 WT Luc, B16-F10 Fsp1-KO Luc, or LN7 1134BL WT or Fsp1-KO cells were suspended in 100 µl of DMEM without phenol red and injected into either the right or left flank of 6–8-week-old male or female C57BL/6J or C57BL/6N mice[44].

For i.n. injections, 1 × 10^4 SK-MEL5 or LN7 1134BL WT or Fsp1-KO cells were injected into the popliteal LN of 6–8-week-old NSG or C57BL/6J mice. To visualize the lymphatics, 2% Evans Blue dye (Sigma-Aldrich, E2129) was injected into the footpad 5 min before the procedure. Mice were injected with buprenorphine and anesthetized with isoflurane, and a 5–10 mm incision was made in the region of the right popliteal LN. The node was identified by Evans Blue staining, immobilized with forceps and 1 × 10^4 cells in 10 µl of 1× PBS were injected into the LN using a 27 G Hamilton syringe. Successful injection was confirmed by visible swelling of the node. Incisions were closed with surgical glue (VetBond Tissue Adhesive, 3M, 1469SB) and the mice were monitored for signs of pain or distress for 5 days[45].

Once tumours were palpable in ≥50% of mice (around 1 week after injection), 10 µl of vehicle or drug solution was administered daily through intratumoural (i.n. or s.c.) injection into tumour-bearing sites. Treatment groups included: L-BSO (1 mM; Thermo Fisher Scientific, 235520050), icFSP1 (0.025 mg (2.5 mg ml−1); Selleckchem, E1535), L-BSO + icFSP1 (1 mM + 0.025 mg (2.5 mg ml−1)), viFSP1 (0.025 mg (2.5 mg ml−1); MedChemExpress, HY-163002), L-BSO + viFSP1 (1 mM + 0.025 mg (2.5 mg ml−1)) and FSEN1 (0.025 mg (2.5 mg ml−1); MedChemExpress, HY-153629). L-BSO was dissolved in 0.9% sodium chloride (saline; Quality Biology, 114-055-101). icFSP1 was formulated in 55% PBS (Corning, VWR45000-430) and 45% PEG300 (MedChemExpress, HY-Y0873). viFSP1 and FSEN1 were formulated in 20% DMA, 40% PEG400 and 40% of 50% 2-hydroxypropyl-β-cyclodextrin (2HPβCD) in water.

Tumour diameters were measured daily using callipers until any tumour reached around 1.5 cm in its largest dimension, which defined the experimental end point. At the end point, all of the mice in the cohort were euthanized in accordance with approved protocols. Tumour diameters and weights were recorded, and tissues were collected and frozen for downstream analyses.

Experimental lung metastasis was evaluated through intravenous delivery of cancer cells in the lateral tail vein of tumour-naive mice. A total of 2 × 10^6 LN7-1134BL WT or Fsp1-KO cells was resuspended in 200 µl of DMEM without phenol red and injected into the lateral tail vein of 8-week-old female C57BL/6N mice using a 27-gauge needle[44]. Mice were euthanized 14 days after injection, and the lungs were inflated with PBS using a 25-gauge needle inserted into the trachea, and the lungs were removed for visible counting of metastatic nodules identified by melanin.

For LN spontaneous metastasis assays, 2 × 10^5 LN7 1134BL WT or Fsp1-KO cells were suspended in 100 µl DMEM (without phenol red) and injected s.c. into the right or left flank of 6–8-week-old male or female C57BL/6J or C57BL/6N mice. Mice were euthanized 24 days after injection and the draining LNs were collected and classified as metastatic (LN+) or non-metastatic (LN−) based on the presence of melanin-containing melanoma cells[44].

Bioinformatics analysis

Correlation analyses used tools available online (https://hgserver1.amc.nl/). Metabolomic data were analysed using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/).

Joint pathway analysis transcriptomic and metabolomic datasets showing significant alterations (P < 0.05, |Fold Change| > 1) between parental (B16-F0) and LN (LN8) clones underwent joint pathway enrichment analysis using MetaboAnalyst. Parameters included integrated metabolic pathways, hypergeometric test, degree centrality topology and pathway-level P-value combination. Pathways were considered significant at P < 0.05 and impact > 0.2 (normalized degree centrality), with at least two significantly altered metabolites.

Correlation analysis gene–metabolite correlations were calculated using the cor.test function (R stats package v.3.6.2). Analysis focused on highly interconnected genes and metabolites within the KEGG glutathione metabolism pathway modules (glutathione biosynthesis and ferroptosis protection), obtained using the MetaboSignal package (v.1.32.1) and the cluster_walktrap algorithm from the igraph package (v.2.0.2). Only late LN tumour generations were included due to sample size limitations.

Bayesian inference of directed acyclic graphs (DAGs) was used to identify cause–effect networks among genes and metabolites across tumour generations (early: B16-F0, F018IL; late: LN7, LN8, LN9). DAG networks were inferred using the BiDAG package (v.2.1.4) with Bayesian Gaussian equivalent scoring and order Markov Chain Monte Carlo structure learning. Networks were averaged over 100 iterations to account for inference variability, assigning edge probabilities based on inference frequency.

Software for Illustrations

Illustrations were generated using FIJI (2.0.0-rc-69/1.52n), Prism (10.5.0) and BioRender (http://biorender.com). Figures created using BioRender include Figs. 1a, 2c and 5j and Extended Data Figs. 7i and 10a,b.

Statistical analysis

Data are presented as mean ± s.d. Statistical analyses were performed using GraphPad Prism v.10.5.0 (GraphPad Software) and included unpaired two-sided Student’s t-tests with Welch’s correction, one-way ANOVA with Dunnett’s, Tukey’s or Šidák’s multiple-comparisons tests, Kruskal–Wallis tests followed by Dunn’s post hoc test, log-rank (Mantel–Cox) tests for survival analyses and contingency analysis using χ^2 with Fisher’s exact test. P < 0.05 was considered to be statistically significant. Sample sizes (n) refer to biological or technical replicates as defined in individual figure legends. Numbers independent biological replications are indicated in the figure legends, with the exception of Fig. 1, for which replicates are noted here: for Fig 1e,g, B16-F0 (n = 30), LN1-18IL (n = 30), LN7-1112AR (n = 9), LN7-1120BL (n = 9), LN7-1134BL (n = 9), LN8-1194BR (n = 12), LN8-1198AR (n = 12), LN8-1205BL (n = 12), LN9-1315BL (n = 6), LN9-1358IR (n = 6); Fig. 1f,h, parental (n = 30), LN (n = 75); Fig. 1i, B16-F0 (n = 7), LN1-18IL (n = 7), LN7-1112AR (n = 4), LN7-1120BL (n = 3), LN7-1134BL (n = 4), LN8-1194BR (n = 3), LN8-1198AR (n = 3), LN8-1205BL (n = 3), LN9-1315BL (n = 7), LN9-1358IR (n = 7); Fig. 1j, parental (n = 7), LN (n = 34); (k) B16-F0 (n = 15), LN1-18IL (n = 15), LN7-1112AR (n = 6), LN7-1120BL (n = 6), LN7-1134BL (n = 6), LN8-1194BR (n = 6), LN8-1198AR (n = 6), LN8-1205BL (n = 6), LN9-1315BL (n = 6), LN9-1358IR (n = 6); Fig. 1l, parental (n = 15), LN (n = 48).

Reporting summary

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

Data availability

Metabolomic data are presented in Supplementary Table 1. RNA-seq and ATAC–seq raw data have been deposited at the GEO (GSE117529). Source data and uncropped blot scans are provided in Supplementary Fig. 1. Source data are provided with this paper.

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Acknowledgements

This work was supported by the Melanoma Research Program Department of Defense Team Science Award (J.M.U. and N.E.R.-F.), the Ludwig Center at Harvard (J.M.U.), the Melanoma Research Foundation Young Career Investigators Award (J.M.U.), R01CA282202 (J.M.U.), NIH T32DK128781 Predoctoral Fellowship (C.S.F. and A.C.), Find the Cause Breast Cancer Foundation (J.M.U.), the METAvivor Early Career Investigator Award (N.E.R.-F.), NIH DP2 AI177915 (N.E.R.-F.), Arc Institute Fellowship and NCI F31CA301857 (C.B.B.). S.H. acknowledges support from R56DK140139. R.R. is supported by Ligue Contre le Cancer (Equipe Labellisée), Fondation Charles Defforey-Institut de France, Fondation Bettencourt Schueller, Institut National du Cancer, Agence Nationale de la Recherche and the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 647973). M. Conrad is supported by DFG Priority Program SPP 2306 (CO 291/9-1, 461385412; CO 291/10-1, 461507177; CO 291/9-2, CO 291/10-2, CO 291/14-1) and the CRC TRR 353 (CO 291/11-1; 471011418), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. GA 884754). A.T. acknowledges the support of an Emmy Noether Award from the German Research Foundation (DFG, 467788900), the Ministry of Culture and Science of the State of North Rhine-Westphalia (NRW-Nachwuchsgruppenprogramm) and an ERC starting grant (METATARGET, 101078355). A.T. holds the Peter Hans Hofschneider endowed Professorship of Molecular Medicine from the Stiftung Experimentelle Biomedizin. J.P.F.A. acknowledges the EU-H2020 (ERC-Consolidator, DeciFERR) and the support of the Deutsche Forschungsgemeinschaft (DFG) SPP2306.

Author information

Authors and Affiliations

1. Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Mario Palma, Milena Chaufan, Marie Sabatier, Cameron S. Fraser, Krystina J. Szylo, Mahsa Yavari, Alanis Carmona, Mayher Kaur, Luiza Martins Nascentes Melo, June Monge-Lorenzo, Midori Flores, Yanshan Liang, Nicole Cayting, Adam Wahida, Sheng Hui & Jessalyn M. Ubellacker

2. Department of Otolaryngology–Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA

Cort B. Breuer, Marcos Labrado, Lan Zheng & Nathan E. Reticker-Flynn

3. Program in Immunology, Stanford University, Stanford, CA, USA

Cort B. Breuer & Marcos Labrado

4. Institut Curie, CNRS, INSERM, PSL Research University, Paris, France

Sebastian Müller, Tatiana Cañeque, Ludovic Colombeau & Raphaël Rodriguez

5. Department of Dermatology, University Hospital Essen & German Cancer Consortium (DKTK), Essen, Germany

Luiza Martins Nascentes Melo, Feyza Cansiz & Alpaslan Tasdogan

6. Institute of Metabolism and Cell Death, Molecular Targets and Therapeutics Center, Helmholtz Munich, Neuherberg, Germany

Eikan Mishima, Toshitaka Nakamura, Bettina Proneth, Adam Wahida & Marcus Conrad

7. Molecular Signaling and Cell Death Unit, VIB-UGent Center for Inflammation Research, Flanders Institute for Biotechnology, Ghent, Belgium

Adam Wahida

8. Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium

Adam Wahida

9. Rudolf Virchow Center for Integrative and Translational Bioimaging, Chair of Translational Cell Biology, University of Würzburg, Würzburg, Germany

José Pedro Friedmann Angeli

10. Translational Redox Biology, Technical University of Munich (TUM), TUM Natural School of Sciences, Garching, Germany

Marcus Conrad

11. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

Nathan E. Reticker-Flynn

12. Ludwig Center at Harvard, Boston, MA, USA

Jessalyn M. Ubellacker

Contributions

M.P., N.E.R.-F. and J.M.U. conceived the study and supervised the research. M.P., C.B.B., N.E.R.-F. and J.M.U. designed the experiments. M.P. performed experiments with support from M. Chaufan and K.J.S.; T.N. and B.P. generated the B16F10 Fsp1-KO cells. C.B.B. generated the B16-F0 and LN7 1134BL Fsp1-KO lines and the Gclc-KO and Gclc-overexpressing lines. M.P. generated the LN9-1315BL Fsp1-KO and Nrf2-overexpression lines. S.M. performed lysosomal, Golgi and ER fractionation and assessed lipid oxidation under fentomycin-3 treatment. T.C. and L.C. synthesized fentomycin-3. A.T., F.C. and L.M.N.M. designed and performed IHC analyses on the TMA. E.M. performed hFSP1 in vitro assays. In vivo studies were conducted by M.P., M. Chaufan, C.B.B., M.S., K.J.S., C.S.F., M.K., M.Y., A.C., M.L., L.Z. and M.F. Data analysis was conducted by M.P., C.S.F., M. Chaufan, S.M., M.K., J.M.-L., Y.L., M.S., M.F., N.C., S.H., S.M., A.T., L.M.N.M., R.R. and J.M.U.; M.P. and J.M.U. wrote the manuscript with input from S.M., J.P.F.A., A.W., M. Conrad, R.R. and N.E.R.-F.

Corresponding author

Correspondence to Jessalyn M. Ubellacker.

Ethics declarations

Competing interests

M. Conrad, B.P., T.N., R.R., S.M., T.C. and L.C. are listed as inventors for some of the compounds described here, and M. Conrad and B.P. are co-founders and shareholders of ROSCUE Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Hozumi Motohashi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 Expression of ferroptosis-related genes in LN metastatic melanoma lines.

a, Heat map of differentially expressed genes (log2(Fold change) in parental (B16-F0), early (LN1), and late (LN7-9) generations of LN metastatic lines (RNA-seq from ref. [15]). b, Volcano plot of cell death–related genes in late (LN7–9) vs. early (Parental, LN1–2) generations. c,d, qPCR of Fsp1 (c) and Gpx4 (d) mRNA levels in B16-F0 (Parental), LN7, LN8, and LN9 lines. e–h, Transcript levels of Gpx4 (e), Acsl4 (f), Acsl3 (g), and Slc7a11/xCT (h) across LN generations (R^2 < 0.2 indicate negligible correlations). i,j, Quantification of ACSL3 (i) and xCT (j) immunoblots from Fig. 1d. k,l, Immunoblots of HIF-1α, GCLC, FSP1, and GPX4 in SubQ vs. IN tumours from B16-F10 WT (k) and FSP1 KO (l), (validation of FSP1 KO lines shown in Extended Data Fig. 8i). m–o, Quantification of HIF-1α (m), GCLC (n), and GPX4 (o) from (k–l). n = 9 mice for SubQ tumours; n = 10 mice for IN tumours. p, Quantification of FSP1 from (k). n = 4 mice for SubQ tumours; n = 5 mice for IN tumours. q, Correlation of FSP1 and GCLC mRNA expression in metastatic melanoma (Matta dataset, n = 198, R2 platform). r–t, Correlation of FSP1 and GCLC (r), FSP1 and GPX4 (s), and GCLC and GPX4 (t) protein levels assessed by immunohistochemistry (IHC) of a human melanoma tissue microarray (ME551, TissueArray), including primary tumours (n = 25) and lymph node (LN) metastases (n = 22). u, Representative IHC images from samples analysed in panels r-t. PT: primary tumours. Data in c, d, i, j, m-p show mean ± s.d. n = 3 independent experiments for c, d, i, j. Statistical significance was determined by Kruskal-Wallis followed by Dunn’s post hoc test (i, j); one-way ANOVA followed Dunn’s post hoc test (c, d), or two-sided unpaired Student’s t-test with Welch’s correction (m-p).

Source data

Extended Data Fig. 2 Epigenetic and transcriptional regulation of Gclc and Fsp1 in LN metastatic cells.

a, Heat map of chromatin accessibility in B16-F0 (parental) (n = 4) and LN7 (n = 4) lines. b,c, Chromatin accessibility at Gclc (b) and Fsp1 (c) loci; green shading marks peaks at promoter (b) or putative enhancer (c). d–f, Transcript levels of Nrf1 (d), Nrf2 (e), and Keap1 (f) across LN generations. g, qPCR of Nrf2 mRNA levels in B16-F0, LN7, LN8, and LN9 lines. h, Heat map of canonical NRF2 target genes in late (LN7–9) vs. early (Parental, LN1–2) lines. *indicates significantly changed genes. i, Immunoblot of NRF2 expression across parental, LN1, and LN7–9 lines. j,k, Quantification of immunoblots in (i). l, Immunoblot of NRF2, GCLC, FSP1, and GPX4 in B16-F0 WT and NRF2-overexpressing (NRF2 O.E) lines. m, Quantification of immunoblot from (l). n = 3 independent experiments for g, i-m. Data in g, j, k and m shown mean ± s.d. Statistical analysis by one-way ANOVA followed by Dunnett’s test (g, j); two-sided unpaired Student’s t-test for grouped comparisons (k, m).

Source data

Extended Data Fig. 3 GPX4 dependency differs in vitro versus in vivo.

a,b, Cell viability of B16-F0 and LN metastatic lines treated with increasing concentrations of RSL3 (a) or 0.5 μM (b) for 48 h. c, Cell death assays using Sytox Green in B16-F0, LN7, and LN9 cells treated with RSL3 (0.5 or 1 μM) for 16 h. d,e, Cell viability of B16-F0 and LN metastatic lines treated with increasing concentrations of ML-210 (d) or 2.5 μM (e) for 48 h. f,g, Cell viability of B16-F0 and LN metastatic lines treated with increasing concentrations of Erastin-2 (f) or 0.5 μM (g) for 24 h. h, Cell death assays in B16-F0, LN8, and LN9 lines treated with Erastin-2 (0.5 or 1 μM) for 16 h. i, Lipid oxidation levels (BODIPY-C11 ox/red) in B16-F0, LN7, LN8, and LN9 lines treated with RSL3 (0.5 μM) for 4 h. j, Ex vivo treatment of LN7 1134BL cells isolated from primary tumours (PT) or spontaneous lymph node (LN) metastases with DMSO, Erastin-2 (1 μM), RSL3 (1 μM), or ML-210 (2.5 μM) for 24 h. k, Cell viability heat map of B16-F0, LN7, LN8, and LN9 lines ± RSL3 (1 μM), ± oleic acid (OA, 125 μM), or combinations for 24 h. l,m, Cell viability of B16-F0 and LN metastatic lines treated with increasing concentrations of Triacsin C (l), or 0.5 μM (m) for 24 h. n = 3 technical replicates, representative of 1 of 3 independent experiments for a-i, k-m; n = 9 (3 technical replicates) from 3 independent experiments for j. Data in a-j and l,m shown mean ± s.d. Statistical analysis by one-way ANOVA followed by Dunnett’s test (b, e, g, m) or Sidak’s multiple comparisons test (c, h, j, k); two-way ANOVA followed by Tukey’s test (i).

Source data

Extended Data Fig. 4 Mitochondrial stress profiling with integrated transcriptomic and metabolomic analysis in parental and LN metastatic cells.

a, Mitochondrial stress test (Seahorse) of B16-F0, LN1, and late LN metastatic lines. mean ± s.d; n = 4 technical replicates, representative of 1 of 2 independent experiments. b,c, Transcript levels of Gclm (b) and Gss (c) across LN generations. d, KEGG glutathione metabolism pathway showing fold changes (LN8 vs. B16-F0); red and blue circles indicate up- and downregulated elements. e, Average directed acyclic graphs (DAGs) showing glutathione-gene/metabolite relationships for early (B16-F0, LN1) and late (LN7–9) lines. Beige, metabolites; teal, genes; edge weight = inference strength.

Source data

Extended Data Fig. 5 Oxygen regulates GPX4 independently of oleic acid and glutathione.

a, Immunoblot of GCLC, FSP1, GPX4 in B16-F0 ± oleic acid (125 μM) under 21% or 1% O_2 for 24 h. b, Immunoblot of HIF-1α, GCLC, FSP1, GPX4 ± GSHee (10 mM) under 21% or 1% O_2 for 24 h. c, GPX4 quantification from (b). d,f, Immunoblots of HIF-1α, GCLC, GPX4 in B16-F0 (d) or LN7 1134BL (f) WT and GCLC-OE cells under 21% or 1% O_2 for 24 h. e,g, Quantification of GPX4 from (d) and (f). h,i, Immunoblot and quantification of GPX4 in cells ± L-BSO under 21% or 1% O_2 for 24 h. j,k, Immunoblots and quantification of GPX4 in B16-F0 WT and GCLC KO cells under 21% or 1% O_2 for 24 h. l, Immunoblot of HIF-1α and GPX4 in B16-F0 and LN metastatic lines ± CoCl_2 (200 μM) for 24 h. m, Immunoblot of HIF-1α, NRF2, ACSL3, GCLC, and GPX4 in B16-F0 and LN metastatic lines under 21% or 1% O_2 for 24 h. n, Immunoblot of GCLC, and GPX4 in B16-F0 and LN metastatic lines under 21% or 1% O_2 for 48 h. o, Immunoblots of HIF-1α, and GPX4 from mouse and human melanoma lines under 21% or 1% O_2 levels. p-r, Immunoblot of HIF-1α, GCLC, FSP1, and GPX4 from B16-F0 (p), LN7 1134BL (q), and LN9 1315BL (r) under 21% O_2 and 5% O_2 for 24 h. s, Quantification of GPX4 protein levels from p,q, and r. n = 3 independent experiments for a-s. Data in c, e, g, i, k and s shown mean ± s.d. Statistical analysis by one-way ANOVA with Tukey’s test (c, e, g, i, k) or Sidak’s test (s).

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Extended Data Fig. 6 GPX4 is downregulated by UPS under low oxygen levels, independent of antioxidants, selenium, or autophagy.

a-c, Immunoblots of GPX4 from B16-F0 treated with NAC (1 or 5 mM) (a), Liproxstatin-1 (1 μM) (b) or selenium (15 or 50 nM) (c) under 21% or 1 % O_2 for 24 h. d, Immunoblot of GPX4 from B16-F0 treated with chloroquine (CQ) (20 μM) under 21% or 1 % O_2 for 24 h. e, Quantification from (d). f, Immunoblot of GPX4 from B16-F0 treated with NSC 624206 (10 μM) under 21% or 1 % O_2 for 24 h. g, Quantification from (f). h, Nrf2 mRNA levels under 21% and 1% O_2 ± BTZ (10 nM). i, GPX4 immunoprecipitation and ubiquitination in B16-F0 under 21% or 1% O_2 for 16 h. j,k, Cell viability heatmaps following treatment with RSL3 (j) for 48 h or Erastin-2 (k) for 24 h under 21% or 1% O_2. n = 3 independent experiments for a-i; n = 3 technical replicates, representative of 1 of 3 independent experiments for j, k. Data in e, g, and h shown mean ± s.d. Statistical analysis was performed using the Kruskal–Wallis test followed by Dunn’s post hoc test (e, g), or one-way ANOVA followed by Tukey’s test (h) or Sidak’s test (j, k).

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Extended Data Fig. 7 FSP1 preferentially localizes to perinuclear lysosomes in LN metastatic lines.

a, Left, representative confocal microscopy of FSP1-OFP (Green) with lipid droplets (LipidSpot: Red), mitochondria (Mitoview: Magenta), and nuclei (Hoechst; Blue) in LN7 and LN8 lines. Right, histogram of the fluorescence intensity profile across the arrow. b,c, Immunoblots of Golgi- (b) and ER-enriched (c) fractions. RCAS1 and PDI3A were used as Golgi and ER markers, respectively and γ-tubulin served as a whole-cell extract control. ER extract (b), n = 4; Golgi extract (c), n = 4 independent experiments. d, Representative confocal microscopy of FSP1-OFP (Green) and LAMP1 (Magenta) ± IMP-1088 (0.1 μM) in B16-F0, LN7, LN8, and LN9 lines for 24 h. e, Representative confocal microscopy of FSP1-OFP (Green) and Lysotracker (Magenta) in B16-F0, LN7, LN8, and LN9 lines. f, Representative confocal microscopy of FSP1-OFP (Green) and Lysotracker (Magenta) localization in LN8 1194BR ± IMP-1088 (0.1 μM) under 1% O_2 for 24 h. g, Representative confocal microscopy of FSP1-OFP WT and FSP1 OFP G2A (Green) localization with LAMP1 (Magenta) in LN9 1315BL. h, Representative confocal microscopy of FSP1-OFP (Green) and Lysotracker (Magenta) in SK-MEL5, MeWo and A-375 cells. i, FSP1 subcellular localization in LN metastatic lines. The diagram was created using BioRender. 3 independent experiments for a, d-h. Scale bar 10 μm (a, d, e, f, h) and 5 μm (g).

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Extended Data Fig. 8 FSP1 protects lysosomes from lipid oxidation without undergoing lysosomal degradation.

a, c, Immunoblots of FSP1 and LC3I/II ± Bafilomycin A1 (10 nM) in B16-F0 (a) and LN7 (c) under 21% or 1 % O_2 for 24 h. b,d, Quantification of FSP1 from (a, c). e,f, Immunoblot and quantification of FSP1 and LC3I/II ± chloroquine (20 μM) under 21% or 1 % O_2 for 24 h. g,h, In vitro enzymatic activity of recombinant human FSP1 at 15 nM (g) or 25 nM (h) across a range of pH values. i, Immunoblot analysis of FSP1 protein levels in B16-F0 and LN7 1134BL WT and FSP1 KO lines (top) and B16-F10 WT and FSP1 KO (bottom). j, Analysis of the histogram from the sequencing of the LN7 1134BL WT and LN7 1134BL FSP1 KO lines. In green, the deletion observed in the KO models. The sequence and position of the sgRNA is at the top. k, Lipid oxidation levels (BODIPY-C11 ox/red) from LN7 1134BL WT, and LN7 1134BL FSP1 KO lines ± Fentomycin-3 (7 μM) for 6 h. n = 3 independent experiments for a-f; 1 experiment for g, h; n = 5 independent experiments for k. Data in b, d, f, k shown mean ± s.d. Statistical analysis by Kruskal–Wallis test with Dunn’s multiple comparisons (b, d, f), and one-way ANOVA with Sidak’s test (k).

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Extended Data Fig. 9 FSP1 and GCLC inhibition reduces LN cell viability under 21% and 1% O[2].

a, Cell viability of B16-F0 and LN7 1134BL WT and FSP1 KO lines treated with RSL3 under 21% O_2 for 48 h. b, Cell viability of B16-F0 WT, B16-F0 FSP1 KO, LN7 1134BL WT, and LN7 1134BL FSP1 KO lines ± RSL3 (0.1 μM and 0.25 μM) under 21% or 1% O_2 for 48 h. c, Immunoblot of FSP1 protein levels in LN9 1315BL WT and LN9 1315BL FSP1 KO clones. d, Cell viability of LN9 1315BL WT and FSP1 KO lines treated with RSL3 under 21% O_2 for 48 h. e, Cell viability of B16-F0 and LN7 1134BL WT and FSP1 KO lines treated with ML-210 under 1% O_2 for 48 h. f, Cell viability assay of B16-F0 WT, B16-F0 FSP1 KO, LN7 1134BL WT, and LN7 1134BL FSP1 KO lines ± ML-210 (2.5 μM) under 1% O_2 for 48 h. g, Cell viability of B16-F0 and LN7 1134BL cells ± RSL3 (0.25 μM), viFSP1 (15 μM or 30 μM), or combinations under 21% and 1% O_2 for 48 h. h, Cell viability of B16-F0 and LN metastatic lines treated with viFSP1 under 21% O_2 for 48 h. i, Cell viability of B16-F0, LN7 1134BL WT and FSP1 KO lines ± BSO (1 mM and 3 mM) under 1% O_2 for 48 h. j,k, Cell viability of A-375 cells ± iFSP1 (10 μM) (j) or FSEN1 (10 μM) (k) ± BSO (1 mM) or combinations. n = 3 technical replicates for a, b, d-f, h-k or n = 6 technical replicates for g, representative of 1 of 3 independent experiments. Data in a,b, d-k shown mean ± s.d. Statistical significance was determined using one-way ANOVA followed by Sidak’s multiple comparisons test (b, f, g, i–k).

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Extended Data Fig. 10 Targeting FSP1 or its genetic deletion reduces intranodal tumour growth and LN metastasis.

a, Schematic of the experimental design for intranodal implantation of SK-MEL5 cells into NSG mice, followed by treatment with FSP1 inhibitors, BSO, or their combinations. The diagram was created using BioRender. b, Schematic of the experimental design for intranodal or subcutaneous implantation of LN7 1134BL WT or FSP1 KO cells into C57BL/6J mice treated with viFSP1. The diagram was created using BioRender. c–h, Representative tumour growth curves from one of the two independent experiments corresponding to Fig. 5g (c–f) and Fig. 5h (g,h). i, Kaplan–Meier survival curves of NSG mice treated intranodally with vehicle (n = 9) or FSEN1 (n = 8). j–q, Representative tumour growth curves from one of the two independent experiments from Fig. 5i showing intranodal (j–m) and subcutaneous (n–q) tumour growth of LN7 1134BL WT and FSP1 KO lines ± viFSP1. r, Quantification of lung metastases (number of nodules) from intravenous injection of LN7 1134BL WT (n = 19) and FSP1 KO cells (n = 21). Data combined 2 independent experiments (Exp1: white dots; Exp2: grey dots). s, Subcutaneous tumour growth curves for LN7 1134BL WT and FSP1 KO cells. Representative curves from 1 of 2 independent experiments. t, Quantification of spontaneous lymph node metastases in draining lymph nodes from 2 independent experiments including subcutaneous LN7 1134BL WT (n = 11) or FSP1 KO tumours (n = 13). u, Representative images of LNs from 1 of 2 independent experiments. Statistical significance was determined using the log-rank (Mantel–Cox) test for survival curve comparisons (i), Mann-Whitney test (r) and contingency analysis using Chi-square with Fisher’s exact test (t).

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Supplementary information

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Reporting Summary

Supplementary Table 1

Raw metabolomic data pertaining to Fig. 2.

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Palma, M., Chaufan, M., Breuer, C.B. et al. Lymph node environment drives FSP1 targetability in metastasizing melanoma. Nature 649, 477–486 (2026). https://doi.org/10.1038/s41586-025-09709-1

- Received: 21 August 2024

- Accepted: 06 October 2025

- Published: 05 November 2025

- Version of record: 05 November 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09709-1

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Targeting FSP1 triggers ferroptosis in lung cancer

Katherine Wu,

Alec J. Vaughan,

Jozef P. Bossowski,

Yuan Hao,

Aikaterini Ziogou,

Seon Min Kim,

Tae Ha Kim,

Mari N. Nakamura,

Ray Pillai,

Mariana Mancini,

Sahith Rajalingam,

Mingqi Han,

Toshitaka Nakamura,

Lidong Wang,

Suckwoo Chung,

Diane Simeone,

David Shackelford,

Yun Pyo Kang,

Marcus Conrad &

…

Thales Papagiannakopoulos

Nature volume 649, pages 487–495 (2026)

Abstract

Emerging evidence indicates that cancer cells are susceptible to ferroptosis, a form of cell death that is triggered by uncontrolled lipid peroxidation1,2,3. Despite broad enthusiasm about harnessing ferroptosis as a novel anti-cancer strategy, whether ferroptosis is a barrier to tumorigenesis and can be leveraged therapeutically remains unknown4,5. Here, using genetically engineered mouse models of lung adenocarcinoma, we performed tumour-specific loss-of-function studies of two key ferroptosis suppressors, GPX46,7 and ferroptosis suppressor protein 1 (FSP1)8,9, and observed increased lipid peroxidation and robust suppression of tumorigenesis, suggesting that lung tumours are highly sensitive to ferroptosis. Furthermore, across multiple pre-clinical models, we found that FSP1 was required for ferroptosis protection in vivo, but not in vitro, underscoring a heightened need to buffer lipid peroxidation under physiological conditions. Lipidomic analyses revealed that Fsp1-knockout tumours had an accumulation of lipid peroxides, and inhibition of ferroptosis with genetic, dietary or pharmacological approaches effectively restored the growth of Fsp1-knockout tumours in vivo. Unlike GPX4, expression of FSP1 (also known as AIFM2) was prognostic for disease progression and poorer survival in patients with lung adenocarcinoma, highlighting its potential as a viable therapeutic target. To this end, we demonstrated that pharmacologic inhibition of FSP1 had significant therapeutic benefit in pre-clinical lung cancer models. Our studies highlight the importance of ferroptosis suppression in vivo and pave the way for FSP1 inhibition as a therapeutic strategy for patients with lung cancer.

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Main

Ferroptosis is a non-apoptotic, oxidative stress-dependent mechanism of cell death that is uniquely distinguished by lipid peroxidation of polyunsaturated fatty acids of membrane phospholipids (PUFA-PLs)1,10. Lipid peroxidation involves the formation of lipid and lipid peroxyl radicals, which, in auto-oxidative propagation reactions, generate lipid hydroperoxides. Aberrant, unrestricted lipid peroxidation results in altered membrane integrity, cell swelling, and ultimately membrane rupture11,12. In recent years, growing interest in ferroptosis has revealed that cells depend on several enzymes to protect against ferroptosis6,7,8,9,13,14,15, of which GPX4 and FSP1 have key roles. GPX4 catalyses the reduction of PUFA-PL hydroperoxides to non-toxic alcohols. FSP1 catalyses the reduction of extramitochondrial coenzyme Q_10 (CoQ), a highly potent lipid radical-trapping antioxidant (RTA). Conversely, cells can be sensitized to ferroptosis through PUFA-PL composition[16] and biosynthesis by ACSL417,18,19. In addition to these cellular pathways, exogenous lipid RTAs, such as LIP1, FER1, vitamin E and vitamin K, have also been shown to specifically inhibit ferroptosis1,6,20,21,22.

Emerging evidence indicates that cancer cells, including drug-resistant cells that have adopted a mesenchymal state2,3, are highly sensitive to lipid peroxidation in vitro, suggesting a potential for harnessing ferroptosis as a novel anti-cancer strategy. Although there have been extensive studies characterizing GPX4 and more recently FSP1 in various diseases4,23, whether ferroptosis constitutes a barrier to tumorigenesis and the functional roles of GPX4 and FSP1 in in vivo cancer models remain poorly characterized. To date, FSP1 has been shown to be a critical ferroptosis suppressor only in the absence of GPX48,9,24. However, given the high toxicity, poor selectivity and low-to-limited bioavailability of GPX4 inhibitors in vivo25,26, as well as the only recent development of FSP1 inhibitors, the majority of which do not have in vivo efficacy9,24,27,28,29,30, much work remains to demonstrate that ferroptosis induction could indeed be a therapeutic strategy for cancer[5].

Here we show that genetic knockout of either Gpx4 or Fsp1 (also known as Aifm2) in genetically engineered mouse models (GEMMs) of autochthonous KRAS-driven lung adenocarcinoma (LUAD)31,32,33,34,35 results in marked restriction of lung tumorigenesis. We demonstrate that disruption of FSP1, often considered a backup axis of ferroptosis suppression, is sufficient to trigger exhibit enhanced lipid peroxidation in lung tumours. We confirm that Fsp1 deletion across several human cancer cell lines with various oncogenic drivers, co-mutations and tissue lineages consistently lead to in vivo tumour restriction. As ferroptosis can be functionally defined as a modality of cell death resulting from increased lipid peroxides and rescued only with inhibitors of lipid peroxidation, we demonstrate that Fsp1-knockout (Fsp1KO) tumours have increased oxidized PUFA-PLs and that administration of lipid RTAs, as well as genetic loss of Acsl4, effectively restores lung tumorigenesis. Finally, we report that FSP1 is upregulated as LUAD tumours progress and show that FSP1 inhibition suppresses tumour growth and prolongs survival in multiple pre-clinical cancer models. Thus, our work utilizes novel in vivo models to characterize and test ferroptosis induction, particularly via FSP1 perturbation, as a therapeutic approach for lung cancer.

GPX4 prevents ferroptosis in lung tumours

GPX4 is considered the primary mediator of ferroptosis suppression, and has been shown to be essential across multiple mouse and human cancer cell types7,26,36,37. Therefore, we sought to interrogate whether GPX4, and more broadly ferroptosis suppression, is a requirement for lung tumorigenesis in vivo. Using a well-established GEMM of LUAD (KrasLSL-G12D/+; Tp53fl/fl; Rosa26LSL-Cas9/LSL-Cas9), we initiated KrasG12D/+; Tp53−/− (KP) tumours by intratracheal delivery of lentiviruses expressing Cre recombinase and dual single guide RNAs (sgRNAs)[38] targeting Gpx4 (sgGpx4) or non-targeting control sgRNAs (sgNeo), enabling CRISPR–Cas9-mediated Gpx4-knockout (Gpx4KO) or wild-type control tumours, respectively. Concurrently, we treated a cohort of mice with each tumour genotype with LIP1, a potent lipid RTA and ferroptosis inhibitor, throughout the entire course of tumorigenic progression (Fig. 1a). Tumour-specific Gpx4 deletion (sgGpx4) led to a significant decrease in lung tumour burden that was not observed in sgGpx4 tumour-bearing mice treated with LIP1 (Fig. 1b–d). Efficient Gpx4 deletion was verified by immunohistochemistry, with less than 15% of tumours in the sgGpx4 group staining positive for GPX4 (Extended Data Fig. 1a). Immunohistochemistry for 4-hydroxy-2-noneal (4-HNE), a marker for lipid peroxidation in tissues[20] (Extended Data Fig. 1b), revealed significantly higher levels of lipid peroxidation in sgGpx4 tumours compared with control sgNeo lung tumours, which was blunted with LIP1 treatment (Fig. 1e,f).

Fig. 1: Gpx4 loss triggers ferroptosis in lung tumours.

a, Schematic of KP LUAD GEMMs intratracheally infected with pUSEC lentiviruses containing dual sgRNAs targeting Neo (control; n = 13) or Gpx4 (n = 13). Mice were dosed with LIP1 (sgNeo: n = 6, sgGpx4: n = 7) or vehicle (sgNeo: n = 7, sgGpx4: n = 6) every other day, starting from tumour initiation to experiment endpoint. b, Tumour burden of KP LUAD tumours with control (sgNeo) or Gpx4 (sgGpx4) knockout, treated with vehicle (Veh) or LIP1. c, Representative MRI of KP LUAD tumours with control (sgNeo) or Gpx4 (sgGpx4) knockout, treated with vehicle or LIP1. d, Representative haematoxylin and eosin (H&E) staining of KP LUAD tumours with control (sgNeo) or Gpx4 (sgGpx4) knockout, treated with vehicle or LIP1. Scale bars, 2,000 µm. e, Immunohistochemical detection of 4-HNE in KP LUAD tumours with control (sgNeo) or Gpx4 (sgGpx4) knockout, treated with vehicle or LIP1. f, Representative images of 4-HNE immunohistochemical staining from e. Scale bars, 20 µm. Data are mean ± s.e.m. One-way ANOVA with multiple comparisons (b,e). Drawing in a created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

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Similar to our in vivo observations, CRISPR–Cas9-mediated Gpx4KO in KP LUAD cells resulted in a near complete loss of viability and clonogenicity that was fully rescued by the addition of LIP1 (Extended Data Fig. 1c). Targeted lipidomics to profile oxidized phosphatidylcholine and phosphatidylethanolamine species in KP LUAD cells treated with RSL3, a covalent GPX4 inhibitor and inducer of ferroptosis, revealed a higher abundance of oxidized phospholipids in RSL3-treated cells that was decreased in cells treated with concomitant LIP1 (Extended Data Fig. 1d). GPX4 overexpression in KP LUAD cells had no effect on cell viability or clonogenicity basally (Extended Data Fig. 1e), but did confer greater resistance to ferroptosis in vitro (Extended Data Fig. 1f). Similarly, supplementation of KP LUAD cells with sodium selenite (Na_2SeO_3) to increase translation of GPX4[39], a selenoprotein, resulted in a corresponding increase in GPX4 expression and enhanced ferroptosis resistance in vitro (Extended Data Fig. 1g). Together, these results demonstrate that GPX4 is essential for lung tumours to evade ferroptosis and provide evidence that ferroptosis induction may be an effective anti-cancer strategy.

FSP1 is upregulated in LUAD

Given our observation that GPX4 was required for lung tumorigenesis in a GEMM that closely recapitulates human LUAD development and progression, we next explored whether there was clinical evidence suggesting that ferroptosis regulators in general are altered in lung cancer[40]. Although GPX4 expression was modestly increased in KRAS-mutant LUAD tumours compared with normal lung tissue, there was no correlation with tumour stage or overall survival (Extended Data Fig. 1h,i). Concordantly, GPX4 protein levels also did not change during autochthonous LUAD progression in KP GEMM tumours (Extended Data Fig. 1j).

Since GPX4 expression did not appear to correlate with any clinical prognostic factors in patients with KRAS-mutant LUAD, we next investigated FSP1, a second axis of ferroptosis surveillance (Fig. 2a). We found that FSP1 expression was robustly increased in tumours from patients with KRAS-mutant LUAD compared with normal lung, and exhibited a positive correlative trend with higher tumour stages (Extended Data Fig. 2a). Additionally, high FSP1 expression correlated with poor survival in patients with KRAS-mutant LUAD (Fig. 2b). Unlike GPX4, FSP1 protein levels increased temporally as lung tumours progressed from adenomas to adenocarcinomas in KP GEMMs (Fig. 2c,d). Together, these findings suggest that FSP1 may constitute a major suppressor of ferroptosis in LUAD and is potentially a more viable therapeutic target than GPX4 for patients with lung cancer.

Fig. 2: Fsp1 knockout robustly restricts lung tumorigenesis.

a, Schematic depicting mechanisms of FSP1- and GPX4-mediated buffering against cellular lipid peroxidation and ferroptosis. GSH, reduced glutathione; GSSG, glutathoine disulfide; PUFA, polyunsaturated fatty acid; α-TOH, tocopherol (vitamin E). b, Overall survival of patients with KRAS-mutant LUAD (n = 280) from The Cancer Genome Atlas (TCGA), stratified by high versus low primary tumour FSP1 expression. Median survival times for FSP1-high and FSP1-low tumours were 1,215 days and 1,498 days, respectively (hazard ratio 1.51 [1.01–2.25]). c, Representative immunohistochemistry for FSP1 and GPX4 in KP LUAD GEMM adenomas versus adenocarcinomas (AdenoCA). Scale bars, 100 µm. d, Immunohistochemical detection of FSP1 expression in KP LUAD GEMM tumours at 10 weeks and 14 weeks post-tumour initiation. e, Schematic of KP LUAD GEMMs intratracheally infected with pUSEC lentiviruses containing double sgRNAs targeting control (Neo; n = 4), Fsp1 (n = 7) or Gpx4 (n = 6). f, Tumour burden of KP LUAD tumours with knockout of either control (Neo), Fsp1 or Gpx4. g, Representative H&E staining of KP LUAD tumours with knockout of either control (Neo), Fsp1 or Gpx4. Scale bars, 2,000 µm. h, Relative proportion of KP adenomas and adenocarcinomas with knockout of either control (Neo), Fsp1 or Gpx4. i, Representative TUNEL staining of KP, Fsp1KO and Fsp1WT orthotopic lung tumours. Scale bars, 50 µm j, Schematic depicting generation of isogenic Fsp1WT versus Fsp1KO cells for paralleled in vitro assays and in vivo syngeneic orthotopic transplantation studies. k, Representative images of crystal violet clonogenic growth assay in isogenic KP, Fsp1KO and Fsp1WT cells treated with RSL3 (0.5 µM) with or without LIP1 (100 nM). l, Tumour burden of KP, Fsp1KO (n = 8) and Fsp1WT (n = 7) orthotopic lung tumours. m, Representative H&E staining of KP, Fsp1KO and Fsp1WT orthotopic lung tumours. Scale bars, 2,000 µm. Data are mean ± s.e.m. Two-sided Student’s t-test (d,l), one-way ANOVA with multiple comparisons (f) or Kaplan–Meier simple survival analysis (b). Drawings in a,e,j created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

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Since prior studies have suggested that FSP1 may be regulated by KRAS signalling[41], to further determine whether FSP1 upregulation was specific to KRAS-mutant LUAD, we stratified patients with LUAD by major oncogenic mutations. Again, FSP1 expression was robustly increased in tumours relative to normal lung tissue, and this upregulation was observed irrespective of driver mutation or KRAS variant (Extended Data Fig. 2b,c). Although FSP1 expression was more robustly upregulated in KRAS-mutant tumours compared with EGFR-mutant tumours, LUAD tumours with neither mutation also exhibited upregulation of FSP1 to the same extent as KRAS-mutant tumours. Next, we investigated whether the MAPK–ERK pathway regulates FSP1 expression. Both in vitro pathway inhibition with RMC-042, a pan-RAS inhibitor, in a panel of KRAS-mutant LUAD cell lines, as well as immunofluorescence of FSP1 and phosphorylated ERK in KP GEMM tumours revealed a lack of correlation between FSP1 expression and ERK activation (Extended Data Fig. 2d,e). Thus, these data indicate that FSP1 expression, at least in the context of LUAD, may not be exclusively dependent on KRAS-mediated oncogenic signalling.

Given that patients with KRAS-mutant LUAD tumours often have concomitant mutations in STK11 and KEAP1, which indicate more advanced disease and poorer prognosis34,42, we also tested whether FSP1 expression was impacted by these mutations. We observed that FSP1 was significantly increased in LUAD tumours with co-mutation of either STK11 or KEAP1 (Extended Data Fig. 2f). Additionally, treatment of a panel of human LUAD cell lines with the NRF2 activator KI696 led to significantly increased FSP1 gene expression (Extended Data Fig. 2g). Of note, these findings are in accordance with recent studies that describe a role for NRF2, which is activated by KEAP1 mutations, in FSP1 upregulation[43].

FSP1 is required for lung tumorigenesis

To systematically investigate the functional role of FSP1 in lung cancer, we initiated autochthonous KP LUAD tumours, this time with tumour-specific CRISPR–Cas9-mediated deletion of either Fsp1 or Gpx4 (Fig. 2e). Of note, we found that genetic deletion of Fsp1 (sgFsp1) in KP LUAD tumours strongly suppressed lung tumorigenesis, resulting in significantly decreased tumour burden to the same extent as Gpx4KO (sgGpx4) tumours (Fig. 2f,g). Efficient Fsp1 deletion was verified by immunohistochemistry, with less than 5% of tumours in the in sgFsp1 group still staining positive for FSP1 (Extended Data Fig. 3a). We did not observe significant changes to the total number or size of tumours formed across the three biological groups (Extended Data Fig. 3b,c), which suggested that loss of Fsp1 or Gpx4 primarily affects tumour progression rather than tumour initiation. We also observed that Fsp1 or Gpx4 deletion resulted in higher proportions of adenomas to adenocarcinomas compared with control tumours (Fig. 2h), indicating that FSP1 and GPX4 are functionally important for malignant disease progression. Additionally, there were no notable changes to tumour cell proliferation or apoptotic cell death (Extended Data Fig. 3d,e) in sgFsp1 or sgGpx4 tumours. By contrast, TUNEL staining of lung tumours demonstrated increased cell death with loss of FSP1 (Fig. 2i), suggesting that the decreased lung tumour burden with Fsp1 or Gpx4 genetic deletion is likely to be due to increased tumour cell ferroptosis. Perhaps most notable is the extent to which Fsp1 deletion phenocopied Gpx4 loss in vivo, as this phenotype, to our knowledge, has not previously been reported in any other cell lines, tissues or disease models.

Fig. 3: Sensitivity to FSP1 loss is not dependent on tumour mutations or lineage.

a–g, Longitudinal tumour growth and endpoint tumour mass from indicated cell lines with CRISPR–Cas9-mediated knockout of FSP1 (sgFSP1) or control (sgTOM, sgNeo), transplanted as subcutaneous xenograft tumours into NSG mice. a, H2009 cells. sgFSP1: n = 8, control: n = 8. b, H1299 cells. sgFSP1: n = 9, control: n = 9. c, PC9 cells. sgFSP1: n = 9, control: n = 9. d, H1975 cells. sgFSP1: n = 8, control: n = 9. e, A549 cells. sgFSP1: n = 9, control: n = 10. f, 16645 cells. sgFsp1: n = 7, control: n = 9. g, KPC7 (mouse PDAC cells). sgFsp1: n = 9, control: n = 10. Data are mean ± s.e.m. Two-sided Student’s t-test (a–g).

Source data

FSP1 is required uniquely in vivo

The identification of FSP1 as a key regulator of ferroptosis arose from genetic screens to identify ferroptosis suppressors in the absence of functional GPX48,9. Thus, FSP1 is generally thought to control a secondary anti-ferroptotic axis whose function, at least in vitro, is masked when GPX4 is intact. Accordingly, we observed that CRISPR–Cas9-mediated Fsp1 deletion in KP LUAD cells had no effect on cell viability and clonogenicity in vitro (Extended Data Fig. 4a). As expected, Fsp1KO cells were more sensitive to RSL3 treatment than isogenic Fsp1-wild-type (Fsp1WT) cells and were protected from killing following RSL3 administration by LIP1 (Fig. 2k). Despite the remarkable restriction of KP LUAD tumorigenesis by Fsp1 loss in vivo, our in vitro studies indicated that the anti-ferroptotic function of FSP1 was not required by KP LUAD cells in the presence of GPX4.

We sought to dissect this dichotomy and better understand the distinct in vivo dependency of lung tumours on FSP1. We first performed a series of syngeneic transplantation experiments using isogenic KP, Fsp1KO and Fsp1WT cell lines with no proliferation differences in vitro to systematically characterize the impact of FSP1 loss on tumour growth and further interrogate whether FSP1-mediated tumour differences were specific to in vivo growth conditions (Fig. 2j and Extended Data Fig. 4b). Consistently, we found that Fsp1KO cells formed significantly smaller tumours in both immunocompetent subcutaneous xenografts (Extended Data Fig. 4c,d) as well as orthotopic lung tumours (Fig. 2l,m and Extended Data Fig. 4e,f). Robust suppression of Fsp1KO orthotopic lung tumour growth was similarly observed in immunodeficient athymic (NU/J) mice (Extended Data Fig. 4g), as well as in an immunogenic mouse model[42] (Extended Data Fig. 4h). We also observed the same phenotype in age- and sex-matched Fsp1WT versus Fsp1KO mice (Extended Data Fig. 4i). These studies collectively provide strong evidence that the pro-tumorigenic effect of FSP1 is primarily cell intrinsic. Fsp1 overexpression (Fsp1OE) in KP LUAD cells exhibited no in vitro growth advantage (Extended Data Fig. 4j) but had enhanced resistance to killing by RSL3 (Extended Data Fig. 4k) and accelerated growth of xenograft tumours in vivo (Extended Data Fig. 4l,m). Altogether, these results suggest that FSP1 is essential for survival of LUAD cells in vivo and that increased FSP1 expression is sufficient to promote Kras-mutant tumorigenesis in vivo.

FSP1 dependency is not mutation-specific

To more thoroughly interrogate the functional importance of FSP1 for tumorigenesis and the translational potential for targeting FSP1 in cancer, we assessed the requirement of FSP1 for growth of xenograft tumours from human LUAD cell lines with a variety of driver mutations, including KRAS, NRAS, EGFR, TP53, STK11 and KEAP1. We found that CRISPR–Cas9-mediated FSP1 genetic deletion consistently and markedly decreased tumour growth in all models tested. Specifically, knockout of FSP1 (sgFSP1) in H2009 (KRAS, TP53 mutant), H1299 (NRAS, TP53 mutant), PC9 and H1975 (EGFR, TP53 mutant) LUAD cells led to robust suppression of subcutaneous tumour growth (Fig. 3a–d), but, analogous to aforementioned studies, did not affect proliferation or viability of cells in vitro unless they were treated with RSL3 (Extended Data Fig. 5a–d). To assess whether KRAS-mutant tumours with STK11 (LKB1) and/or KEAP1 mutations also depend on FSP1, we performed CRISPR–Cas9-mediated FSP1 deletion in A549 (KRAS, KEAP1, STK11 mutant) and 16645 (Kras mutant, Stk11-null) LUAD cells. Again, we observed a dependency for FSP1 in vivo (Fig. 3e,f) but not in vitro (Extended Data Fig. 5e,f). Together these data provide further evidence that FSP1 is indeed required for in vivo LUAD tumour growth irrespective of driver and co-mutations.

Finally, we tested whether KRAS-driven tumours of a different lineage also exhibited a requirement for FSP1 in vivo. Transplantation of pancreatic adenocarcinoma (PDAC) Kras, Tp53 mutant (KPC7) cells with CRISPR–Cas9-mediated Fsp1 deletion also led to a robust tumour suppression in vivo (Fig. 3g and Extended Data Fig. 5g), mirroring the phenotype observed consistently in LUAD models and suggesting that FSP1 dependency in vivo may extend to other tissue lineages.

Ferroptosis inhibition rescues Fsp1 loss

We hypothesized that defective regulation of lipid peroxidation and subsequent induction of ferroptosis was the mechanism underlying Fsp1KO tumorigenic suppression. To assess this, we first performed epilipidomic analysis of Fsp1WT and Fsp1KO orthotopic lung tumours and observed that Fsp1KO tumours exhibited increased abundance of oxidized and truncated phosphatidylcholine and phosphatidylethanolamine species, indicative of increased cellular lipid peroxidation (Fig. 4a). We then explored whether ectopic overexpression of FSP1 or GPX4 (Extended Data Fig. 6a,b) was sufficient to restore the growth of Gpx4KO or Fsp1KO tumours, which would shed light on the capacity for each ferroptosis defence arm to compensate for the loss of the other. Indeed, we found that overexpression of FSP1 and GPX4 effectively restored the growth of Gpx4KO and Fsp1KO tumours, respectively (Fig. 4b,c and Extended Data Fig. 6c,d). These data implicated that tumours require extensive buffering capacity against lipid peroxidation, and that although GPX4 and FSP1 act through distinct pathways, each can compensate for the loss of the other.

Fig. 4: FSP1 is required for the suppression of ferroptosis in vivo.

a, Heat map of indicated oxidized phosphatidylethanolamine (oxPE) and oxidized phosphatidylcholine (oxPC) lipid species detected via LC–MS from KP, Fsp1KO and Fsp1WT orthotopic lung tumours. b, Longitudinal growth of KP, Gpx4KO subcutaneous xenograft tumours with Fsp1OE (n = 10) versus control (empty vector (emptyOE); n = 9) in C57BL/6 J mice. c, Longitudinal growth of KP, Fsp1KO subcutaneous xenograft tumours with either Fsp1 restoration (Fsp1WT, n = 9), GPX4 overexpression (Gpx4OE; n = 8) or controls (emptyOE: n = 9; Fsp1KO: n = 8) in C57BL/6 J mice. d, Ratio of CoQ_9H_2/CoQ_9 detected via LC–MS in KP, Fsp1KO and Fsp1WT orthotopic lung tumours and cells treated with DMSO or RSL3 (0.5 µM) for 8 h. e, Schematic depicting the pro-ferroptotic function of ACSL4. VitE, vitamin E. f, Tumour burden in C57BL/6 J mice with KP, Fsp1KO and Fsp1WT orthotopic lung tumours with CRISPR–Cas9-mediated Acsl4 or control (Neo) deletion (wild type: n = 5, Acsl4KO: n = 4, Fsp1KO: n = 7, Fsp1KOAcsl4KO: n = 7). g, Schematic depicting dietary vitamin E manipulation studies in h,i. h, Longitudinal lung tumour growth (measured via bioluminescence normalized to first timepoint) in C57BL/6 J mice orthotopically transplanted with KP, Fsp1KO cells and receiving high (n = 8) or low (n = 7) vitamin E diets ad libitum 5 days before tumour initiation. i, Longitudinal lung tumour growth (measured via bioluminescence) in C57BL/6 J mice orthotopically transplanted with KP, Fsp1KO cells and placed on high (n = 6) or low (n = 6) vitamin E diets ad libitum on day 18 after tumour establishment. j, Schematic of KP LUAD GEMMs intratracheally infected with pUSEC lentiviruses containing double sgRNAs targeting Neo (control; n = 12) or Fsp1 (n = 12). Mice were dosed with LIP1 (n = 6 per genotype) or vehicle (n = 6 per genotype) daily starting 5 weeks after tumour initiation. k, Tumour burden of mice described in j. l, Representative H&E staining of tumours from experiment in j. Scale bars, 100 µm. Data are mean ± s.e.m. Two-sided Student’s t-test (b,d,f,h,i,k) or two-way ANOVA with Tukey’s multiple comparison test (c). Drawings in e,g,j created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

Source data

Mechanistically, FSP1 has been shown to suppress ferroptosis by reducing the endogenous RTA CoQ, and this function is dependent on its localization to the plasma membrane. We utilized a liquid chromatography–mass spectrometry (LC–MS)-based, quantitative method to determine the abundance of CoQ in tumours, which revealed that Fsp1KO tumours had a significantly decreased ratio of reduced CoQ_9H_2 to oxidized CoQ_9 (Fig. 4d). This decreased ratio was not present in genotypically matched cells at baseline in vitro, but upon treatment with RSL3, Fsp1KO cells mimicked the decreased CoQ_9H_2/CoQ_9 observed in tumours. Previous studies have described that mutation of the myristoylation sequence that targets FSP1 to the plasma membrane renders FSP1 unable to protect against lipid peroxidation in vitro8,9, which we also observed in KP LUAD cells (Extended Data Fig. 6e,f). We anticipated that expression of this mutant FSP1 (Fsp1mut) would be similarly unable to restore Fsp1KO tumour growth. Indeed, whereas Fsp1WT-restored tumours grew quickly, Fsp1mut and Fsp1KO tumours exhibited decreased growth (Extended Data Fig. 6g). These data suggest that tumours require Fsp1 enzymatic activity and subcellular localization in vivo to specifically buffer against ferroptosis.

Next, we explored whether ferroptosis suppression, using several orthogonal approaches including tumour-specific Acsl4 knockout, dietary vitamin E supplementation and LIP1 treatment would restore Fsp1KO tumour growth. Loss or inhibition of ACSL4, which limits the supply of PUFA-PL and restricts lipid peroxidation (Fig. 4e), has previously been shown to be protective against ferroptosis in Gpx4KO cells in vitro17,18,19. We therefore sought to determine whether genetic deletion of Acsl4 would suppress ferroptosis and rescue the growth of Fsp1KO tumours in vivo. We performed CRISPR–Cas9-mediated genetic deletion of Acsl4 (sgAcsl4) in isogenic KP, Fsp1KO and Fsp1WT cells. We observed that loss of Acsl4 did not affect cell viability but robustly rescued RSL3-induced ferroptosis in vitro, in both Fsp1WT and Fsp1KO cells (Extended Data Fig. 6h). Of note, Acsl4 deletion significantly restored Fsp1KO lung tumour growth in vivo (Extended Data Fig. 6i) and mice with Acsl4- and Fsp1-double-knockout tumours were found to have increased endpoint disease burden (Fig. 4f and Extended Data Fig. 6j) and decreased overall survival (Extended Data Fig. 6k). Furthermore, tumour-specific knockout of Acsl4 led to a basal acceleration of tumour growth (Extended Data Fig. 6i) and decreased overall survival (Extended Data Fig. 6k), suggesting that decreased accumulation of PUFA-PL in lung tumours may also protect against ferroptosis in vivo.

Vitamin E is nature’s primary lipophilic antioxidant and is specifically able to scavenge lipid radicals and protect against ferroptosis (Fig. 4e). Therefore, we tested whether dietary vitamin E supplementation could rescue the growth of Fsp1KO lung tumours. Mice were fed diets that were high or low in vitamin E ad libitum either before or after tumour initiation (Fig. 4g). We found that increased dietary vitamin E accelerated Fsp1KO tumour growth (Fig. 4h,i), with no effect on growth of Fsp1WT tumours (Extended Data Fig. 6l).

Finally, since LIP1 suppressed tumour lipid peroxidation and entirely mitigated the impact of Gpx4 loss on KP LUAD tumorigenesis (Fig. 1b–d), we conducted parallel studies to investigate whether LIP1 also blunts ferroptosis in and restores Fsp1KO tumorigenesis. In the orthotopic lung tumour model, we observed a tendency for Fsp1KO tumours to grow more quickly with daily LIP1 administration (Extended Data Fig. 6m). In the KP LUAD GEMM (Extended Data Fig. 6n), which was conducted in accordance with experimental parameters for Gpx4KO GEMM studies, LIP1 treatment significantly increased autochthonous Fsp1KO (sgFsp1) tumour burden (Extended Data Fig. 6o). Moreover, sgFsp1 tumours stained positively for 4-HNE, which was effectively suppressed by LIP1 treatment (Extended Data Fig. 6p). As observed with vitamin E supplementation, timing of LIP1 treatment did not appear to have a differential impact, as LIP1 treatment after tumours were already established was equally effective at restoring sgFsp1 lung tumorigenesis (Fig. 4j–l). Together, these studies provide clear evidence that LUAD tumours require FSP1 to protect against ferroptosis in vivo and suggest that increased lipid peroxidation is a barrier for tumour growth and progression.

FSP1 inhibition extends overall survival

As a result of the growing interest in harnessing ferroptosis to kill tumour cells, several FSP1 inhibitors have been developed9,28, although the majority are effective only in vitro against human FSP1 in the context of GPX4 loss or inhibition (Extended Data Fig. 7a–d). icFSP1 was recently developed as the first inhibitor of human FSP1 with in vivo stability and efficacy, albeit solely in tumours with concomitant GPX4 loss[24]. Accordingly, we observed that icFSP1 treatment of KP LUAD cells in vitro did not affect cell viability (Extended Data Fig. 7e) unless RSL3 was added (Extended Data Fig. 7f). Given that mouse and human FSP1 are highly conserved in sequence and structure, we generated a hybrid tumour model in which human FSP1 was expressed in mouse KP, Fsp1KO tumours. We observed that expression of either mouse or human FSP1 resulted in similar acceleration of tumour growth compared with Fsp1KO tumours (Extended Data Fig. 7g,h), which indicated that human FSP1 can functionally compensate for mouse FSP1 in vivo.

Using this hybrid tumour model, we next tested whether icFSP1 had therapeutic benefit against lung cancer. We found that FSP1 inhibition as a monotherapy improved overall survival of mice bearing lung tumours, almost to the same extent as genetic Fsp1 deletion (Fig. 5a). To determine whether the therapeutic effect of icFSP1 was indeed due to the induction of ferroptosis, we tested whether LIP1, which we expected to restrict lipid peroxidation induced by FSP1 inhibition, would rescue tumour suppression in icFSP1-treated mice. In correlation with the survival data, we observed that icFSP1 treatment significantly decreased tumour growth compared with vehicle-treated mice, and concomitant LIP1 treatment abrogated the tumour-suppressive effect of icFSP1 treatment (Fig. 5b and Extended Data Fig. 7i). Further, to test whether icFSP1 was exerting an on-target, tumour-specific effect, we repeated the treatment study with an internal control—lung tumours expressing FSP1(Q319K), a mutant that is resistant to icFSP1 but maintains anti-ferroptotic function (Extended Data Fig. 7a,b). We found that icFSP1 treatment specifically extended overall survival of mice bearing tumours expressing wild-type human FSP1 but not those bearing tumours expressing FSP1(Q319K) (Fig. 5c), indicating that indeed the tumour suppression seen with icFSP1 treatment was due to inhibitor activity directly on tumour cells.

Fig. 5: FSP1 is a viable therapeutic target for KRAS-mutant LUAD.

a, Overall survival of C57BL/6 J mice with orthotopic lung tumours with Fsp1KO (n = 7) or re-expression of human FSP1, treated with either icFSP1 (n = 8) or vehicle (n = 7). b, Longitudinal lung tumour growth (measured via bioluminescence) in C57BL/6 J mice orthotopically transplanted with FSP1WT cells and treated with either vehicle (n = 8), LIP1 (n = 7), icFSP1 (n = 8) or icFSP1 plus LIP1 (n = 8). c, Overall survival of C57BL/6 J mice with Fsp1KO (n = 5) tumours expressing wild-type human FSP1 (FSP1WT) or human FSP1(Q319K) (FSP1Q319K), treated with icFSP1 (FSP1WT: n = 5, FSP1Q319K: n =7) or vehicle (FSP1WT: n = 7; FSP1Q319K: n = 7). d, Longitudinal growth of PDX LX465 tumours treated with icFSP1 (n = 10) or vehicle (n = 10) in NSG mice. Data are mean ± s.e.m. Two-way ANOVA with Tukey’s multiple comparisons test (b), two-sided Student’s t-test (d) or Kaplan–Meier simple survival analysis (a,c). Drawing in d created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

Source data

We considered whether icFSP1 treatment also affected the tumour microenvironment, but we found no differences in the proportion of total T cells, neutrophils, alveolar or interstitial macrophages between the treatment groups (Extended Data Fig. 7j). These data further suggest that icFSP1 primarily exerts on-target, tumour-specific effects, although more granular immune profiling is needed to determine the potential effects, and thus potential side effects, of FSP1 inhibition on the tumour microenvironment. Finally, we utilized a patient-derived xenograft (PDX) model (LX465; KRASG12D, TP53-mutant) to further assess the therapeutic potential of FSP1 inhibition. We found that icFSP1 treatment significantly decreased PDX tumour growth (Fig. 5d), further bolstering the translational potential of targeting FSP1 in patients with LUAD. These pre-clinical studies with icFSP1 are the first to demonstrate that FSP1 is a promising therapeutic target, and our work highlights FSP1 inhibition as a novel therapeutic strategy for improving disease outcome in patients with lung cancer.

Discussion

Cancer is a disease of hyperproliferation, and cancer cells increase their metabolic output to support sustained growth. This occurs at the cost of increased production of reactive oxygen species that can damage macromolecules such as lipids and have deleterious effects44,45. One such effect is excessive lipid peroxidation of membrane-associated PUFA-PLs, which can lead to ferroptosis, a unique non-apoptotic mechanism of cell death. Through our systematic interrogation of the key mechanisms that regulate aberrant lipid peroxidation in vivo, we demonstrated that ferroptosis constitutes a barrier to lung cancer and that lung tumours rely on GPX4 and uniquely, FSP1, to overcome ferroptosis and sustain tumorigenesis (Extended Data Fig. 7k,l).

Our work specifically highlights the dependence of lung tumours on FSP1 in vivo, even in the presence of functional GPX4 (Extended Data Fig. 7k). The companion study by Palma et al.[46] reveals that this dependency extends beyond lung tumours, as they show that metastasizing melanoma cells in the lymph also require FSP1 expression to buffer ferroptosis. These studies are the first reports of a context in which FSP1 and GPX4 are independently required for ferroptosis suppression and therefore either can affect disease outcome. Moreover, analysis of GEMM and human lung tumours demonstrated that FSP1 levels increase during tumour progression and correlate with poor survival. By contrast, GPX4 is ubiquitous in tumours and most tissues. Given that germline Gpx4KO mice are not viable[47], whereas Fsp1KO mice are viable with no notable physiological defects20,48,49, the therapeutic window for targeting FSP1 with fewer toxic side effects is expected to be much greater than for GPX4. Furthermore, loss of GPX4 is toxic to T cells, which would suppress any anti-tumour immune responses50,51. Leveraging this unique requirement for FSP1 in lung cancer, our studies demonstrate the therapeutic benefit of FSP1 inhibition in promoting ferroptosis, suppressing tumour growth and extending survival as a monotherapy in aggressive pre-clinical lung cancer models. Similarly, the therapeutic potential of FSP1 inhibition in suppressing melanoma lymph node metastases is demonstrated in the accompanying Article[46]. Finally, our studies also underscore the value of in vivo pre-clinical models in capturing physiologically relevant metabolic dependencies that may be missed in vitro and pave the way for development of FSP1 inhibitors for clinical testing in patients with lung cancer.

Methods

Cell lines

KP LUAD cell lines were obtained from the laboratory of T. Jacks. KP, Fsp1KO cell lines were generated by transient transfection of PX458 (Addgene #48138) expressing an sgRNA targeting Fsp1. Single GFP-positive clones were selected and FSP1 loss was validated by western blot. The 16645 cell line was developed from KrasG12D Stk11−/− GEMM as previously described[52]. KPC7 cells were obtained from the laboratory of D. Simeone. All cell lines were maintained in DMEM or RPMI 160 (Corning) supplemented with 10% FBS (Sigma Aldrich) and gentamicin (Invitrogen) and were tested for mycoplasma regularly (PlasmoTest, InvivoGen). All mouse cell lines were authenticated by PCR genotyping. All human cell lines used were purchased from ATCC and were authenticated by short-tandem-repeat profiling. Genetic manipulation of cell lines was performed via lentiviral transduction of plasmids, detailed in next section, followed by either puromycin (7 μg ml−1) or hygromycin (800 μg ml−1) selection for 1 week.

Cloning/lentivirus generation

CRISPR–Cas9-mediated knockout of target genes was achieved by cloning sgRNAs into pLenti-USEC or lentiCRISPRv2-puro vectors, as previously described[31]. In short, backbones were digested with Esp3I (New England Biosciences) and purified with a gel extraction kit (QIAGEN). sgRNAs were designed using CRISPick (Broad Institute), obtained from Integrated DNA Technologies (Coralville), annealed, phosphorylated, Esp3I-digested, and ligated into the purified digested backbones using Quick Ligase (New England Biosciences). Double sgRNA ultramers were designed and generated as previously described[38]. In brief, ultramers were Gibson-assembled to digested pDonor_sU6 (Addgene #69351), Esp3I-digested, and ligated to purified digested backbones. Guide RNA sequences used to make gene knockouts can be found in Supplementary Data 4. GPX4, mouse FSP1 and human FSP1 expression plasmids were generated using Gibson assembly of the respective cDNA into pLenti-v2-filler.

Lentivirus was generated by co-transfection of HEK293 cells with a viral vector and packaging plasmids psPAX2 (Addgene #12260) and pMD2.G (Addgene #12259) using PEI transfection reagent. Cell supernatant containing lentivirus was collected 72 h after transfection and filtered through 0.45-µm PVDF filters. For in vivo experiments, lentivirus was concentrated by ultracentrifugation at 25,000 rpm for 2 h at 4 °C. The viral pellet was resuspended in PBS and stored at −80 °C until use. Viral titre was quantified with the use of a Cre-dependent GreenGo reporter cell line. For in vitro experiments, medium containing virus was collected, filtered, and added directly to recipient cells with polybrene at 8 µg ml−1 for 48 h before selection.

Mouse models

All mouse experiments described in this study were approved by the NYU Institutional Animal Care and Use Committee (IACUC). Mice were housed according to IACUC guidelines in ventilated caging in a specific pathogen-free animal facility. For all mouse studies, ≥4 mice were used for each experimental condition. KrasLSL-G12D/+; Tp53fl/fl; Rosa26LSL-Cas9/LSL-Cas9 (KPC GEMMs) mice were bred as previously described31,32,33,34,35. C57BL/6 J (JAX strain 000664) mice with the appropriate genotype, aged 8 to 12 weeks were randomly selected to begin tumour initiation studies with pUSEC lentivirus. Care was taken to ensure each experimental arm had an equal number of male and female mice. Mice were intratracheally infected with lentiviruses as described and monitored until experimental endpoint. Tumour burden was quantified by H&E staining and analysed using QuPath software as a measurement of total tumour area/total lung lobe area. All quantifications were done with investigator blinded to the respective sample genotypes. All transplantation experiments were performed using nude (JAX strain 002019), NOD SCID Gamma (NSG; JAX strain 005557 F), C57BL/6 J Fsp1-knockout (Conrad group[16]) or C57BL/6 J wild-type (JAX strain mice aged 8 to 12 weeks old). For mouse cell xenograft experiments, 100,000 cells in 100 µl of phosphate-buffered saline (PBS) was injected subcutaneously into each flank of the mouse. For the xenograft studies in Fig. 3 the number of cells injected per flank and whether they were injected with 50:50 PBS and Matrigel (Corning) are indicated as follows: H2009 (2 million cells + Matrigel), H1299 (1 million cells), PC9 (1 million cells), H1975 (2 million cells + Matrigel), A549 (1 million cells), 16645 (500,000 cells), KPC7 (250,000 cells). All human cell line xenograft experiments were carried out in male NSG mice unless specified. For the PDX experiment, tumours were implanted subcutaneously in male NSG mice as previously described[34]. Tumours were measured with callipers, and volume was calculated based on 0.5 × length × width^2. The maximum tumour diameter permitted by our IACUC protocol was 2 cm, and this was not exceeded in any experiment. For orthotopic lung tumour experiments, 100,000 luciferase-expressing cells in 200 μl of PBS were injected intravenously into tail vein of male mice unless specified in the legend. Tumour growth was measured by bioluminescence (PerkinElmer IVIS Spectrum In Vivo Imaging System; D-luciferin, PerkinElmer 122799). Data were analysed using Living Image software.

Antioxidant and drug treatments

For LIP1 treatment, mice were dosed with 10 mg kg−1 LIP1 (BOC Sciences) or vehicle (2% DMSO + 40% PEG300 + 2% Tween 80 in sterile H_2O) by intraperitoneal injection for frequency and duration indicated in figure schematics. For icFSP1 treatment, mice were dosed with 50 mg kg−1 icFSP1 (WuXi LabNetwork) or vehicle (45% PEG300 in sterile PBS) by intraperitoneal injection twice daily. High (TD.2108412) and low (TD.210841) irradiated vitamin E diets were obtained from Inotivco and provided ad libitum for length of time indicated in figure legends. In all experiments, mice were randomly assigned to treatment group.

Cell clonogenic and viability assays

Cell clonogenic assays were conducted by seeding 2,000 cells per well into 12-well dishes (BD/Falcon) in RPMI-1640 medium. After 12–16 h, medium containing RSL3 and/or LIP1 was added to wells. After 5 days of growth, plates were washed twice with PBS and stained with 0.5% crystal violet (Fisher Scientific) solution in 20% methanol. Plates were dried, scanned, and crystal violet was quantified by solubilization with 10% acetic acid and measurement of absorbance at 592 nm by spectrometer (Molecular Devices). Cell viability assays were conducted by seeding 2,000 cells per well into white-walled, clear-bottom, 96-well plates (Corning) in RPMI-1640 medium. After 12–16 h, medium containing RSL3, LIP1 and/or FSEN1, iFSP1 or icFSP1 was added. After three days, cell viability was assessed by CellTiter-Glo (Promega) and luminescence was measured by spectrometer (Molecular Devices).

Immunoblotting

Cells were plated to 75% confluency in a 6-well dish, and the following day cells were lysed on ice with Pierce RIPA buffer (ThermoScientific) containing 1× protease/phosphatase inhibitor cocktail (Thermo Fisher Scientific). Samples were sonicated in cooled 4 °C water (12 rounds, 30 s on and 30 s off) and then centrifuged at 14,000 rpm at 4 °C for 15 min. The supernatant was collected, and protein was quantified using the DC Rad Protein Assay kit. Protein was diluted to 1 μg μl−1 with water and 4× NuPage LDS sample buffer, then boiled at 95 °C for 10 min. Twenty micrograms of protein per well was loaded into Invitrogen 4–12% Bis-Tris gels and then transferred onto PVDF membranes using a standard protocol. PVDF membranes were then blocked using 5% BSA in TBST for 60 min at room temperature and incubated with primary antibodies in 5% BSA overnight at 4 °C. Primary antibodies were obtained as follows: GPX4 (Abcam), FSP1 (Proteintech), ACSL4 (Santa Cruz Biotechnologies), phospho-ERK (Cell Signaling), ERK (Cell Signaling) and Hsp90 (BD Bioscences). The following day, membranes were washed in TBST and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies for 1 h at room temperature. Enhanced chemiluminescent horseradish peroxidase substrate (ThermoScientific SuperSignal West PICO Plus) was added to the membrane for 1 min, and the resulting membrane was imaged using the General Electric Amersham Imager 680. For gel source data, see Supplementary Data 1.

Oxidized lipidomics

For the Gpx4-knockout experiment, cells were plated to 75% confluency in 6-well dishes. The following day, cells were treated with DMSO, RSL3 (0.5 µM), or RSL3(0.5 µM) plus LIP1(100 nM). After 8 h, cells were collected and washed in an antioxidant solution (PBS containing dibutylhydroxytoluene (100 µM) and diethylenetriamine pentaacetate (100 µM)) and centrifuged. Supernatent was discarded and cell pellets were immediately frozen in liquid nitrogen and stored at –80 °C. Frozen samples were sent on dry ice to Wayne State Lipidomics Core for metabolite extraction and LC–MS analysis.

Fsp1 wild-type and knockout tumour and cell lipidomic analysis were performed in the laboratory of Y.P.K. Fsp1 wild-type and knockout cells were plated at 75% confluency in 10 cm plates and treated as stated above. After euthanasia, mice were perfused with the antioxidant solution (+3.8% trisodium citrate), orthotopic tumours were microdissected, frozen in liquid nitrogen and stored at –80 °C. Mouse lung cancer tissues were cryopulverized and extracted with chloroform:methanol (2:1, v/v) at a tissue concentration of 25 mg mL−1. For the cells, 1.0 × 10^7 cells were extracted by 1 ml of chloroform:methanol (2:1, v/v). EquiSPLASH LIPIDOMIX internal standard (Avanti Polar Lipids) was added to the extraction solvent at 0.1 µg ml−1 per lipid class. Samples were sonicated on ice for 1 min using a VCX 130 probe sonicator (5 s on/off cycles), incubated for 30 min, and centrifuged at 17,000 × g for 20 min at 4 °C. The supernatant was was dried under vacuum (EZ-2 Elite, Genevac), reconstituted in 10% of the original volume with isopropanol, and transferred to glass autosampler vials for LC–MS analysis. Chromatographic separation was performed using a Waters ACQUITY UPLC CSH C18 column (100 × 2.1 mm, 1.7 µm) with a VanGuard precolumn (5 × 2.1 mm, 1.7 µm). Mobile phase A consisted of acetonitrile/water (60:40, v/v), and mobile phase B of isopropanol/acetonitrile (90:10, v/v), both containing 5 mM ammonium formate and 0.1% formic acid. The column was maintained at 65 °C with a flow rate of 0.6 ml min−1. The gradient was as follows: 0–2 min, 15–30% B; 2–2.5 min, 30–48% B; 2.5–11 min, 48–82% B; 11–11.5 min, 82–99% B; 11.5–12 min, 99% B; 12–12.1 min, 99–15% B; and 12.1–16 min, hold at 15% B for re-equilibration. Mass spectrometry was performed on a Q Exactive Plus Quadrupole-Orbitrap (Thermo Fisher Scientific) equipped with a heated electrospray ionization (HESI) source and operated in negative ion mode. Source settings were: sheath gas, 60 a.u.; auxiliary gas, 25 a.u.; sweep gas, 2 a.u.; spray voltage, 3.0 kV; capillary temperature, 320 °C; S-lens RF level, 50%; and auxiliary gas heater temperature, 370 °C. Parallel reaction monitoring (PRM) was carried out with the following parameters: resolution, 17,500 at m/z 200; AGC target, 2 × 10^5; maximum injection time, 50 ms; isolation window, m/z 1.2; and stepped normalized collision energies of 20, 30, and 40. The oxidized lipid analysis was adapted by previous PRM based analysis[53]. To generate the PRM inclusion list, pooled sample extracts were first analysed in DDA mode to identify the most abundant polyunsaturated phosphatidylcholine and phosphatidylethanolamine species using Lipostar2[54]. These precursors were subjected to in silico oxidation using LPPtiger2 to predict candidate oxidized lipids. A semi-targeted DDA experiment was then conducted to confirm precursor detectability and finalize the inclusion list[53]. Data were analysed using Skyline (v24.1)[55]. Quantification was based on fragment anions derived from oxidized fatty acyl chains, with peak areas normalized to phosphatidylcholine(15:0/18:1(d7)) or phosphatidylethanolamine(15:0/18:1(d7)) internal standards from the EquiSPLASH LIPIDOMIX mixture.

CoQ_9 and CoQ_9H_2 analysis

CoQ_9 and CoQ_9H_2 analysis was conducted by modification of previous study[56]. The D_9-CoQ_10 standard was purchased from IsoSciences (Ambler, PA, USA). The D_6-CoQ_10H_2 standard was synthesized from D_6-CoQ_10, which was obtained from Good Laboratory Practice Bioscience (Montclair, CA, USA). The CoQ_9 standard was purchased from Tokyo Chemical Industry (Tokyo, Japan), and CoQ_9H_2 was synthesized from CoQ_9. D_6-CoQ_10H_2 and D_9-CoQ_10 were used as internal standards for the CoQ_9H_2 and CoQ_9, respectively. Chloroform: methanol (2:1, v/v), containing internal standards of 0.5 µM of D_6-CoQ_10H_2 and D_6-CoQ_10 was used to extract mouse lung cancer tissue at a tissue concentration of 25 mg ml−1. For the cells, 1.0 × 10^7 cells were extracted by 1 ml of same extraction solvent. Samples were then sonicated on ice for 1 min using a VCX 130 probe sonicator (5 s on/off cycles), incubated for 30 min, and centrifuged at 17,000g for 20 min at 4 °C. A 100 µl aliquot of the supernatant was transferred to glass autosampler vials for LC–MS/MS analysis. The liquid chromatography conditions were identical to those used for oxidized lipid analysis. Q Exactive Plus MS was operated in positive ion mode. Source settings were as follows: sheath gas, 60 a.u. (arbitrary units); auxiliary gas, 25 a.u.; sweep gas, 2 a.u.; spray voltage, 3.0 kV; capillary temperature, 320 °C; S-lens RF level, 50%; and auxiliary gas heater temperature, 370 °C. The mass range was m/z 120–1,200; resolution, 70,000 at m/z 200; AGC target, 1 × 10^6; and maximum injection time, 100 ms. By using EL–MAVEN (v0.12.0), the LC–MS peaks of CoQ_9, CoQ_9H_2, D_6-CoQ_10H_2, D_6-CoQ_10, and D_9-CoQ_10 were identified by matching with standard library and their peak areas were extracted with 10 ppm error range. Standard curves were generated using known concentrations of CoQ_9H_2 and CoQ_9, and used to calculate the concentrations of CoQ_9H_2 and CoQ_9 in the samples following an algorithm described in a previous study[56].

Immunohistochemistry

Tumour-bearing mice were euthanized by carbon dioxide asphyxiation, after which the lungs were dissected and fixed in 4% PFA solution overnight. Fixed lungs were washed with PBS 2 times, transferred, and stored in 70% ethanol, until cassette loading and paraffin embedding. Sections were cut and stained with H&E. For immunohistochemistry with the exception of TUNEL staining, sections were immunostained on a Leica BondRX automated stainer according to the manufacturer’s instructions. In brief, tissues underwent deparaffinization online, followed by epitope retrieval for 20 min at 100° with Leica Biosystems ER2 solution (pH9, AR9640), endogenous peroxidase activity blocking with H_2O_2, and non-specific binding site blocking with Rodent Block M (Biocare, RBM961L) and Bond Primary Antibody Diluent (Leica Biosystems, AR9352). Sections were then incubated with primary antibodies against GPX4 (Abcam), FSP1 (obtained from M. Conrad), 4-HNE (JaICA), Ki67 and cleaved caspase-3 for 60 min at room temperature. Primary antibodies were detected with anti-rat HRP-conjugated polymer (Biocare, BRR4016H), 3,3′-diaminobenzidine (DAB) substrate (provided in the Leica BOND Polymer Refine Detection System, DS9800), and for 4-HNE staining Bond DAB Enhancer (Leica Biosystems, AR9432). Following counter-staining with haematoxylin, slides were scanned at 40× on a Hamamatzu Nanozoomer (2.0HT). TUNEL staining was performed by Histowiz according to their protocol.

For OPAL imaging, coronal 5-µm sections were immunostained on a Leica BondRx auto-stainer according to the manufacturer’s instructions. In brief, sections were deparaffiinized online and then treated with 3% H_2O_2 to inhibit endogenous peroxidases, followed by antigen retrieval with either ER1 (Leica, AR9961; pH6) or ER2 (Leica, AR9640; pH9) retrieval buffer at 100 °C for 20 min. After blocking with either Rodent Block M (Biocare, RBM961L) or Primary Antibody Diluent (Leica, AR93520), slides were incubated with the first primary antibody (FSP1 (obtained from M. Conrad); phospho-ERK1/2, CST; GPX4, Abcam) and secondary HRP polymer pair, followed by HRP-mediated tyramide signal amplification with a specific Opal fluorophore. Once the Opal fluorophore was covalently linked to the antigen, primary and secondary antibodies were removed with a heat retrieval step. This sequence was repeated three more times with subsequent primary and secondary antibody pairs, using a different Opal fluorophore with each primary antibody (see table below for primary antibody sequence and reagent details). After antibody staining, sections were counterstained with spectral DAPI (Akoya Biosciences, FP1490) and mounted with ProLong Gold Antifade (ThermoFisher Scientific, P36935). Semi-automated image acquisition was performed on an Akoya Vectra Polaris (PhenoImagerHT) multispectral imaging system. Slides were scanned at 20× magnification using PhenoImagerHT 2.0 software in conjunction with Phenochart 2.0 and InForm 3.0 to generate unmixed whole slide qptiff scans. All image files were uploaded to the NYUGSoM’s OMERO Plus image data management system (Glencoe Software).

Flow cytometry

Lung tissue was processed into a single-cell suspension for flow cytometry as previously described42,57. In brief, prior to euthanasia, mice were injected with 2 μg of Anti-mouse CD45-APC conjugated antibody (Biolegend, Clone 30-F11, 103111) retro-orbitally. Lungs were harvested and digested with Collagenase (Sigma-Aldrich, C5138) and deoxyribonuclease I (Sigma-Aldrich, DN25) followed by red blood cell lysis. Single cells were then resuspended in fluorescence-activated cell sorting (FACS) buffer and stained using the following antibodies: CD45 (Biolegend, 103132), CD11b (Biolegend, 101216), CD11c (Biolegend, 117324), Ly6G (Biolegend, 127622), MHCII (BD, 748708), CD103 (Biolegend, 121433), CD64 (Biolegend, 139309), SiglecF (BD, 740956), MertK (R&D, BAF591), CD45 (BD, 748371), CD3e (BD, 740854), CD4 (Invitrogen, MCD0428), CD8a (EBioscience, 563152) and Secondary (Streptavidin) (BD, 564176). Samples were run on the BD LSRFortessa and analysed on FlowJo version 10. Gating strategy is presented in Supplementary Data 2.

TCGA analyses

Gene expression profiles of primary tumours and relevant clinical data of 515 patients with LUAD were obtained from The Cancer Genome Atlas6 (TCGute.org). GPX4 and FSP1 (AIFM2) mutational status of TCGA tumour samples was retrieved from cBioPortal151 using the TCGA PanCancer Atlas collection (https://gdc.cancer.gov/about-data/publications/pancanatlas). For survival data, patients were stratified based on GPX4 or FSP1 expression and overall survival rates were plotted to compare patients with high-GPX4 or FSP1 expression (top 50% above median expression) with the rest of the cohort (n = 464 patients). All survival analyses were conducted using the survival curve analyses in GraphPad Prism v9.

Statistics and reproducibility

Statistical analysis was performed using GraphPad Prism v9. All data are expressed as mean plus standard error of the mean, unless otherwise specified. Data were analysed by statistical tests as indicated in figure legends. All tests were two-tailed and replicates are biological unless otherwise stated. All western blots were replicated at least three times with results reproducible of data shown in figures. All in vitro assays were replicated at least three times with a minimum of n = 3 biological replicates per group for statistical power. For all in vivo experiments, the minimum sample size was four independent mice or tumours and respective sample size per genotype or condition is further specified in the figure legend. Sample size was not calculated but was chosen in each experiment based on previous experience with various models and to ensure that there were enough samples for statistical power. All in vivo experiments were replicated at least twice with results reproducible of data shown in figures. When representative images are shown, a minimum of three samples from the larger cohort were stained from each group. In the case of representative MRI images, all mice from the cohort were imaged. During sample processing and analysis for the lipidomic the samples were given numeric IDs which after analysis, were unblinded and graphed. For histological and immunohistochemistry analysis, researcher was blinded to the sample condition. The investigators were not blinded during most other data collection or analysis.

Reporting summary

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

Data availability

All data and raw gel images are included with the paper. Raw lipidomic data have been deposited in the MassIVE data base (https://massive.ucsd.edu/) under accession number MSV000098883. Analysed lipidomic data are available in Supplementary Data 3. Gene expression profiles of primary tumours and relevant clinical data of 515 patients with LUAD were obtained from The Cancer Genome Atlas (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). GPX4, FSP1 (AIFM2), KRAS, EGFR, KEAP1 and STK11 mutational status of TCGA tumour samples was retrieved from cBioPortal151 using the TCGA PanCancer Atlas collection (https://gdc.cancer.gov/about-data/publications/pancanatlas). All other materials are available upon request from T.P. Source data are provided with this paper.

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Acknowledgements

We thankG. Ward and C. Loomis from the Experimental Pathology Core (RRID:SCR_017928) at NYU Langone Health, which is partially supported by the Cancer Center Support Grant P30CA016087 at NYU Langone’s Laura and Isaac Perlmutter Cancer Center, for support with immunohistochemistry staining and imaging. The PhenoImagerHT Mutlispectral imaging system was initially purchased through a Shared Instrumentation Grant S10OD021747. We thank the Wayne State University School of Medicine Lipidomics Core Facility, which is supported in part by National Center for Research Resources, National Institutes of Health Grants S10RR027926 and S10OD032292, for support with LC–MS. The analysis of FSP1-oxidized lipids and CoQ_9 were conducted by the Proteomics and Metabolomics Core at the College of Pharmacy, Seoul National University, South Korea. We thank R. Possemato and S. Kotschi for critical reading of this manuscript. We acknowledge J. Teixeira for supporting and motivating this work. T.P. is supported by NIH grants (R37CA222504, R01CA227649, R01CA283049 and R01CA262562) and an American Cancer Society Research Scholar Grant (RSG-17-20001–TBE). K.W. is supported by the Ruth L. Kirschstein Individual Predoctoral NRSA fellowship (F30CA275258) and NIH training grants (T32GM136573 and T32GM136542). A.J.V. is supported by the NIH training grant (T32GM136542). M.C. received funding from the Deutsche Forschungsgemeinschaft (DFG) (CO 291/7-1), the Priority Program SPP 2306 (CO 291/9-1, 461385412 and CO 291/10-1, 461507177) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement GA 884754).

Author information

Author notes

1. These authors contributed equally: Katherine Wu, Alec J. Vaughan

Authors and Affiliations

1. Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA

Katherine Wu, Alec J. Vaughan, Jozef P. Bossowski, Yuan Hao, Aikaterini Ziogou, Mari N. Nakamura, Ray Pillai, Mariana Mancini, Sahith Rajalingam, Suckwoo Chung & Thales Papagiannakopoulos

2. Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, USA

Katherine Wu & Alec J. Vaughan

3. Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA

Jozef P. Bossowski, Yuan Hao, Aikaterini Ziogou, Mari N. Nakamura, Mariana Mancini, Sahith Rajalingam, Suckwoo Chung & Thales Papagiannakopoulos

4. College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Korea

Seon Min Kim, Tae Ha Kim & Yun Pyo Kang

5. Division of Pulmonary and Critical Care Medicine, Department of Medicine, VA New York Harbor Healthcare System, New York, NY, USA

Ray Pillai

6. Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA

Ray Pillai

7. Pulmonary and Critical Care Medicine, David Geffen School of Medicine (DGSOM), University of California Los Angeles (UCLA), Los Angeles, CA, USA

Mingqi Han & David Shackelford

8. Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA

Mingqi Han & David Shackelford

9. Institute of Metabolism and Cell Death, Molecular Targets and Therapeutics Center, Helmholtz Munich, Neuherberg, Germany

Toshitaka Nakamura & Marcus Conrad

10. Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA

Lidong Wang & Diane Simeone

11. Translational Redox Biology, Technical University of Munich (TUM), TUM Natural School of Sciences, Garching, Germany

Marcus Conrad

Contributions

T.P., K.W. and A.J.V. designed and directed the study. K.W., A.J.V., J.P.B., A.Z., M.N.N., and M.M. performed various in vitro and in vivo experiments reported in the study. R.P. performed FACS for lung tumour immune profiling. Y.H. performed TCGA patient data analyses. S.M.K. performed FSP1 epilipidomics and CoQ measurements, T.H.K. assisted with data analysis, and Y.P.K. supervised. L.W. and D. Simeone provided guidance for the pancreatic cancer experiment. S.C. performed unbiased tumour burden quantification. M.M. and S.R. maintained and genotyped all experimental mice. M.C., T.N., M.H. and D. Shackelford provided conceptual advice. T.P., K.W. and A.J.V. wrote the manuscript with comments and input from all authors.

Corresponding author

Correspondence to Thales Papagiannakopoulos.

Ethics declarations

Competing interests

T.P. received funding from Pfizer Medical Education Group, Dracen Pharmaceuticals, Kymera Therapeutics, Bristol Myers Squibb and Agios Pharmaceuticals not related to the submitted work. M.C. is a co-founder and shareholder of ROSCUE Therapeutics GmbH. M.C. and T.N. have filed a patent application (WO2024115673A1) for some of the FSP1 inhibitor compounds described here. The other authors declare no competing interests.

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Nature thanks Jiyeon Kim, Hozumi Motohashi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Gpx4 is required by lung cancer cells.

a, Quantification and representative images of Gpx4 IHC in KP LUAD GEMM tumours with knockout of either control (Neo, n = 11) or Gpx4 (n = 12). Scale bars: 200 µm. b, Representative 4-hydroxy-2-noneal (4-HNE) IHC of liver tissue from conditional Gpx4-knockout mice. Scale bars: 100 µm. c, Top: Western blot of KP LUAD cells with CRISPR/Cas9-mediated genetic deletion of Gpx4 with either two individual or duplexed sgRNAs. Bottom: representative images of crystal violet clonogenic assay of KP LUAD cells with knockout of either control (Neo) or Gpx4. Cells were treated with 100 nM LIP1. d, Heatmap of LC-MS detection of oxidized phospholipids in KP LUAD cells treated with DMSO control, RSL3 (0.5 µM), and RSL3 (0.5 µM) + LIP1 (100 nM) for 8 h. e, Schematic of Gpx4 ectopic overexpression (OE) method in KP LUAD cells. Western blot and representative images of crystal violet clonogenic assay of KP LUAD cells with wildtype (WT) or OE of Gpx4. f, CellTiter-Glo Luminescence viability assay of KP, Gpx4WT or Gpx4OE cells upon increasing concentrations of RSL3 (n = 5 per group). g, Western blot of KP LUAD cells treated with 20 nM Na_2SeO_3 or DMSO. CellTiter-Glo Luminescence viability assay of KP LUAD cells treated with 20 nM Na_2SeO_3 or DMSO with increasing RSL3 addition (n = 5 per group). h, GPX4 expression in KRAS-mutant primary LUAD tumours from TCGA, divided into early and late tumour stages (normal lung, n = 54; stage I/II, n = 354; stage III/IV, n = 98). i, Overall survival of KRAS-mutant LUAD patients (n = 464) from TCGA, stratified by high vs low tumour GPX4 expression. j, Tumour Gpx4 IHC quantification of KP LUAD GEMM tumours at 10 weeks (n = 4) vs 14 weeks (n = 4) post-tumour initiation. Box plots indicate median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers). Data are represented as mean values, error bars represent SEM, significance determined via one-way ANOVA with multiple comparisons (panel h), two-sided student’s t-test (panel j) or Kaplan-Meier simple survival analysis (panel i). For gel source data, see Supplementary Data 1. Drawing in d created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

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Extended Data Fig. 2 FSP1’s anti-ferroptotic function is not dependent on oncogenic signalling.

a, FSP1 (AIFM2) expression of KRAS-mutant primary LUAD tumours from TCGA, divided into early and late tumour stages (normal lung, n = 54; stage I/II, n = 354; III/IV, n = 98). b, FSP1 (AIFM2) expression of KRAS-mutant primary LUAD tumours from TCGA, separated by the KRAS mutation (G12C n = 51; G12V n = 32; G12D n = 17; G12A n = 16; other n = 20). c, FSP1 (AIFM2) expression of primary LUAD tumours from TCGA, separated by oncogenic driver mutation (normal lung, n = 59; EGFR, n = 71; KRAS, n = 135; other n = 307). d, Western blot of KP LUAD human cell lines treated with 50 nM RMC-042 for indicated durations. e, representative multi-IF images of KP tumours for markers indicated. Panels are 10X, scale bars: 50 µm; insets are 20X, scale bars: 20 µm. f, FSP1 (AIFM2) expression of KRAS-mutant primary LUAD tumours from TCGA, separated by tumour co-mutation status (KEAP1/STK11 WT n = 88; STK11 mutant n = 20; KEAP1 mutant n = 16; KEAP1/STK11 mutant n = 11). g, FSP1 (AIFM2) expression in KP LUAD human cell lines treated with an Nrf2 activator, KI696, for 5 days (n = 3 per group). Box plots indicate median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers). Data are represented as mean values, error bars represent SEM, significance determined via one-way ANOVA with multiple comparisons (panels a–c, f, g). For gel source data, see Supplementary Data 1.

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Extended Data Fig. 3 Neither Fsp1 nor Gpx4 have an impact on tumour initiation, proliferation, or apoptosis.

a, Quantification and representative images of tumour Fsp1 IHC in KP LUAD GEMMs with knockout of either control (Neo, n = 11) or Fsp1 (n = 14). Scale bars: 100 µm. b, Tumour number quantification in KP LUAD GEMMs with tumour-specific knockout of either control (Neo, n = 4), Fsp1 (n = 7), or Gpx4 (n = 6). c, Individual area of KP LUAD GEMM tumours with knockout of either control (Neo, n = 465), Fsp1 (n = 453), or Gpx4 (n = 384). d, Quantification of Ki67 IHC of KP LUAD GEMM tumours with knockout of either control (Neo, n = 355), Fsp1 (n = 268), or Gpx4 (n = 291). e, Quantification of cleaved caspase-3 IHC of KP LUAD GEMM tumours with knockout of either control (Neo, n = 46), Fsp1 (n = 22), or Gpx4 (n = 52). Data are represented as mean values, error bars represent SEM, significance determined via one-way ANOVA with multiple comparisons (panels b–e).

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Extended Data Fig. 4 Fsp1 is required for cell-autonomous tumour growth in vivo.

a, Top: Western blot of KP LUAD cells with CRISPR/Cas9-mediated genetic deletion of Fsp1 with either two individual or duplexed sgRNAs. Bottom: representative images of crystal violet clonogenic assay of KP LUAD cells with knockout of either control (Neo) or Fsp1. Cells were treated with 100 nM LIP1. b, Schematic depicting all transplantation models performed using isogenic KP, Fsp1KO and Fsp1WT. c,d, Longitudinal growth and endpoint tumour weights of KP sgFsp1 (n = 8) versus control (sgNeo n = 10) subcutaneous (subQ) xenograft tumor transplanted into C57BL/6 J male mice. e, Longitudinal lung tumour growth (measured via bioluminescence) in C57BL/6 J male mice orthotopically transplanted with KP LUAD cells with CRISPR/Cas9-mediated knockout of Fsp1 (n = 8) or control (n = 7). f, Longitudinal lung tumour growth (measured via bioluminescence normalized to first timepoint (day 7)) in C57BL/6 J male mice orthotopically transplanted with isogenic KP, Fsp1KO (n = 6) and Fsp1WT (n = 7) cells. g, Longitudinal lung tumour growth (measured via absolute bioluminescence) in NU/J immunocompromised mice orthotopically transplanted with isogenic KP, Fsp1KO (n = 6) and Fsp1WT (n = 7) cells. h, Longitudinal lung tumour growth (measured via absolute bioluminescence) in C57BL/6 J female mice with orthotopic transplantation of isogenic KP, Fsp1KO (n = 8) and Fsp1WT (n = 8) cells. i, Longitudinal tumour growth and endpoint tumour weights of either isogenic KP, Fsp1KO or Fsp1WT subcutaneous (subQ) xenograft tumours transplanted in either C57BL/6 J WT (Fsp1KO, male n = 10 female n = 10; Fsp1WT, male n = 8, female n = 10) or C57BL/6 J Fsp1-knockout mice (Fsp1KO, males n = 6, females n = 6; Fsp1WT, male n = 4,  female n = 6). j, Western blot of KP LUAD cells with wildtype (WT) or overexpression (OE) of Fsp1. Representative images of crystal violet clonogenic assay of KP LUAD cells with Fsp1WT or Fsp1OE. k, CellTiter-Glo Luminescence viability assay of KP, Fsp1WT or Fsp1OE cells (5 biological replicates per group) with increasing concentrations of RSL3. l,m, Longitudinal growth and endpoint weights of KP Fsp1WT (n = 10) or Fsp1OE (n = 10) subcutaneous (subQ) xenograft tumour transplanted into C57BL/6 J male mice. Data are represented as mean values, error bars represent SEM, significance determined via one-way ANOVA with multiple comparisons (panel i), two-way ANOVA with Tukey’s multiple comparisons test (panels c, e–h, l), or two-sided student’s t-test (panels d, m). For gel source data, see Supplementary Data 1. Drawings in b,c,e–h,l created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

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Extended Data Fig. 5 FSP1 loss in vitro does not induce ferroptosis without RSL3.

a, Top: Western blot; bottom: baseline growth (normalized to control); and right: CellTiter-Glo Luminescence viability assay of H2009 cells with the lentiviral addition of Cas9 and guide RNAs targeting FSP1 or a non-targeting control (TOM) (at least 4 biological replicates per group). Viability assay is upon increasing concentrations of RSL3. b, As in a, but with H1299 cells. c, As in a, but with PC9 cells. d, As in a, but with H1975 cells. e, As in a, but with A549 cells. f, As in a, but with 16645 cells. g, As in a, but with KPC7 cells. Data are represented as mean values, error bars represent SEM, significance determined via two-sided student’s t-test (panels a–g). For gel source data, see Supplementary Data 1.

Extended Data Fig. 6 Fsp1 loss promotes tumour cell ferroptosis.

a, Western blot of KP, Gpx4KO LUAD cells with either empty vector (—) or Fsp1OE. b, Western blot of KP, Fsp1KO LUAD cells with re-expression of either empty vector (—), Fsp1WT, Gpx4OE. c, CellTiter-Glo Luminescence viability assay of KP, Fsp1KO LUAD cells in b upon increasing concentrations of RSL3. d, Endpoint tumour weights of subcutaneous (subQ) xenograft tumours from indicated cell lines transplanted in C57BL/6 J female mice (control n = 10; Fsp1WT, n = 9; Gpx4OE; n = 8). e, Top: Western blot of KP, Fsp1KO LUAD cells with re-expression of either empty vector (—), wildtype (WT) Fsp1, or mutant (mut) Fsp1. Bottom: representative images of crystal violet clonogenic assay. f, CellTiter-Glo Luminescence viability assay of KP, Fsp1KO LUAD cells with re-expression of either empty vector (Fsp1KO), Fsp1WT, or Fsp1mut upon increasing concentrations of RSL3. LIP1 used at 100 nM. g, Longitudinal tumour growth (measured via bioluminescence normalized to initial imaging timepoint (day14)) in C57BL/6 J male mice orthotopically transplanted with isogenic KP, Fsp1KO LUAD cells with re-expression of either empty control vector (Fsp1KO, n = 7), Fsp1WT (n = 7), or Fsp1mut (n = 7). h, Representative images of crystal violet clonogenic assay of KP, Fsp1WT or Fsp1KO LUAD cells with CRISPR/Cas9- mediated knockout of control (Neo) or Acsl4. i, Longitudinal tumour growth (measured by bioluminescence) of C57BL/6 J male mice with orthotopic transplantation of isogenic KP, Fsp1KO and Fsp1WT cells with CRISPR/Cas9-mediated knockout of control (Neo) or Acsl4 (WT, n = 5; Acsl4KO, n = 4; Fsp1KO, n = 7; Fsp1KOAcsl4KO, n = 7). j, Representative H&E of experiment in i. Scale bars: 1000 µm. k, Overall survival of C57BL/6 J male mice with orthotopic transplantation of isogenic KP, Fsp1KO and Fsp1WT cells with CRISPR/Cas9-mediated knockout of control (Neo) or Acsl4 (n = 5 per group). l, Longitudinal tumour growth (measured via bioluminescence normalized to first imaging timepoint (day7)) of C57BL/6 J mice orthotopically transplanted with KP, Fsp1WT cells and receiving high (n = 6) or low (n = 5) VitE diets ad libitum 5 days pre-tumor initiation. m, Tumour bioluminescence signal normalized to first imaging timepoint (day7) of C57BL/6 J mice orthotopically transplanted with KP, Fsp1KO cells and receiving Liproxstatin-1 (LIP1, n = 7) or vehicle (Veh, n = 5) daily after tumour establishment. n, Schematic of KP LUAD GEMMs intratracheally infected with pUSEC lentiviruses containing dual sgRNAs targeting Fsp1. Mice were dosed with LIP1 (n = 6) or Vehicle (Veh, n = 6) every other day starting from tumour initiation. o, Tumour burden of KP LUAD GEMMs with CRISPR/Cas9-mediated knockout of Fsp1 and treated with Veh or LIP1. p, Representative H&E and 4-HNE IHC of KP LUAD GEMM tumours with knockout of Fsp1 and treated with Veh or LIP1. Scale bars: 1000 µm. Data are represented as mean values, error bars represent SEM, significance determined via one-way ANOVA (panel d), two-way ANOVA with Tukey’s multiple comparisons (panels g, i, l, m), two-sided student’s t-test (panel o), or Kaplain-Meier simple survival analysis (panel k). For gel source data, see Supplementary Data 1. Drawing in n created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

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Extended Data Fig. 7 icFSP1 exerts on-target tumor-suppressive effect.

a, Top: Western blot of KP, Fsp1KO LUAD cells with re-expression of either empty vector (—), wildtype (WT) hFSP1, or Q319K-mutant hFSP1. Bottom: representative images of crystal violet clonogenic assay. b, Cell-TiterGlo Luminescence viability assay of KP, Fsp1KO LUAD cells with re-expression of either empty vector (—), WT hFSP1, or Q319K-mutant hFSP1. c, CellTiter-Glo Luminescence viability assay of human or mouse Fsp1WT-expressing cells upon RSL3 (0.2 µM) plus increasing concentrations of iFSP1. d, CellTiter-Glo Luminescence viability assay as in c but with FSEN1. e, CellTiter-Glo Luminescence viability assay of human or mouse Fsp1WT-expressing cells upon increasing concentrations of icFSP1. f, CellTiter-Glo Luminescence viability assay of WT or Q319K-mutant hFSP1-expressing cells upon RSL3 (0.2 µM) plus increasing concentrations of icFSP1. g, Longitudinal tumour growth (measured via bioluminescence normalized to first imaging timepoint (day7)) in C57BL/6 J mice orthotopically transplanted with isogenic KP, Fsp1KO cells with re-expression of either human FSP1 (hFSP1, n = 5), mouse Fsp1 (mFsp1, n = 6), or control (n = 6) vector. h, Tumour burden and representative H&E of KP, Fsp1KO (n = 4) and hFSP1WT (n = 4) orthotopic lung tumours. Scale bars: 1000 µm. i, Representative H&E of KP, hFSP1WT orthotopic lung tumours from indicated treatment groups. Scale bar: 2000 µm. j, Percent of T cells, alveolar macrophages, interstitial macrophages, and neutrophils of total CD45+ cells in tumour bearing lungs from mice treated with the indicated compounds for two weeks (Veh, n = 8; LIP1, n = 7; icFSP1, n = 9; icFSP1 + LIP1, n = 6). k, Graphical summary depicting ferroptosis as a barrier to lung cancer, as loss of either Fsp1 or Gpx4 induces tumour cell ferroptosis and restricts disease progression. l, Summary table of cell lines, their respective tumour lineage, and mutations where Fsp1 loss resulted in tumour suppression in vivo. For all Cell-TiterGlo assays, n = 5 biological replicates per group. Data are represented as mean values, error bars represent SEM, significance determined via one-way ANOVA with multiple comparisons (panels g, j) or student’s t test (panel h). For gel source data, see Supplementary Data 1. Drawings in k created in BioRender. Vaughan, A. (2025) https://BioRender.com/99qhixq.

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Supplementary information

Supplementary Data 1 and 2

Supplementary Figure 1: Gel source data. All uncropped western blot images with protein ladder markers. Supplementary Figure 2: Representative gating strategy. Figure from FlowJo that indicates the separation of cells using the various antibodies and fluorophores in the FACS experiment shown in Extended Data Fig. 7j.

Reporting Summary

Supplementary Data 3

Analysed lipidomic results. Sheet 1: Results of the oxidized lipidomic experiment of Fsp1 wild-type versus knockout tumours. Values are normalized peak areas that were converted to z-scores for the heat map shown in Fig. 4a. Sheet 2: CoQ_9 concentration measurements from LC–MS experiment of Fsp1 wild-type and knockout tumours and cell lines treated with RSL3 or DMSO. Data includes raw measurements and those after correction algorithm as described in the methods section.

Supplementary Data 4

sgRNA sequences used to knock out indicated genes

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Wu, K., Vaughan, A.J., Bossowski, J.P. et al. Targeting FSP1 triggers ferroptosis in lung cancer. Nature 649, 487–495 (2026). https://doi.org/10.1038/s41586-025-09710-8

- Received: 25 August 2024

- Accepted: 06 October 2025

- Published: 05 November 2025

- Version of record: 05 November 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09710-8

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An RNA splicing system that excises DNA transposons from animal mRNAs

Long-Wen Zhao,

Christopher Nardone,

Cindy Chang,

Joao A. Paulo,

Stephen J. Elledge &

…

Scott Kennedy

Nature volume 649, pages 496–504 (2026)

Abstract

All genomes have mobile genetic segments called transposable elements (TEs)[1]. Here we describe a system, which we term SOS splicing, that protects Caenorhabditis elegans and human genes against DNA-transposon-mediated disruption by excising these TEs from host mRNAs. SOS splicing, which seems to operate independently of the spliceosome, is a pattern-recognition system triggered by the base-pairing of inverted terminal repeat elements, which are a defining feature of DNA transposons. We identify three factors required for SOS splicing in both C. elegans and human cells: AKAP17A, which binds TE-containing mRNAs; the RNA ligase RTCB; and CAAP1, which bridges RTCB and AKAP17A to allow RTCB to ligate mRNA fragments generated by TE excision. We propose that SOS splicing is a previously undescribed conserved and RNA-structure-directed mode of mRNA splicing, and that an identified function of SOS splicing is to genetically buffer animals from the deleterious effects of DNA-transposon-mediated gene perturbation.

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Main

TEs that integrate into or near genes often disrupt gene function. To limit such disruptions, organisms have epigenetic systems that inhibit TE expression and replication1,2,3. Despite these systems, TEs constitute a substantial (3–80%) fraction of extant genomes[4], which indicates that TE-silencing systems are not infallible. Whether organisms also have systems that protect genes from disruption—after TE-silencing systems fail and TEs mobilize into genes—is not well understood.

DNA transposons are TEs that typically have a transposase-encoding gene flanked by inverted terminal repeat elements (ITRs)[5]. Insertion of DNA transposons into plant or animal genes does not always destroy host-gene function, despite the TE-based introduction of premature stop codons (PTCs) into these genes6,7,8,9. A probable explanation for this paradox was suggested by the observation that DNA transposons can be imprecisely excised from mRNAs in animals[6]. We wondered whether the excision of TEs from mRNA is an active, host-mediated process that perhaps evolved to protect genes from TE-mediated disruption. Therefore, we set out to explore this possibility.

RSD-3 is an epsin N-terminal homology (ENTH) domain protein required for RNA interference (RNAi) in C. elegans[10]. RNAi-mediated depletion of dpy-6 mRNA causes a RSD-3-dependent Dumpy (Dpy) phenotype (Fig. 1a). Tc1 is the most active and abundant DNA transposon in C. elegans[11]. A Tc1 insertion in the first coding exon of rsd-3 (rsd-3(pk2013), henceforth Tc1::rsd-3) does not abolish rsd-3 function in RNAi[9] (Fig. 1a) despite the introduction of PTCs. This is because Tc1 is excised from rsd-3 mRNA[7]. We first used nanopore long-read sequencing to verify that Tc1 is excised from rsd-3 mRNA (Fig. 1b). We also sequenced RNA from animals in which Tc1 was mobilized into coding exons of five other C. elegans genes to assess the generality of this process ((Extended Data Fig. 1)). To capture all Tc1 excision events, including those that create out-of-frame mRNAs, RNA from smg-2(–);Tc1::rsd-3 animals, which are defective for nonsense-mediated decay[12], was sequenced (Fig. 1b). Finally, in vitro synthesized rsd-3 RNAs, which did or did not contain Tc1, were included to ensure that Tc1 excision was not an in vitro artefact of library preparation or sequencing (Fig. 1b). The analysis showed that in all cases, Tc1 was efficiently excised from 90–100% of its host mRNAs in vivo (Fig. 1b and Extended Data Fig. 1). Moreover, PCR with reverse transcription (RT–PCR) analysis did not detect evidence of Tc1 backsplicing to generate circular Tc1 RNA (Extended Data Fig. 1). Also, in all cases, Tc1, which exhibits a low rate of transposition from DNA[13], was present in DNA but not mRNA in these animals (Fig. 1c and Extended Data Fig. 1), which indicated that Tc1 excision occurred at the level of RNA. TE excision from mRNA exhibited the following properties, results that support and extend previous studies6,7: (1) excision occurs at multiple sites in or near Tc1 ITRs (Fig. 1d); (2) a subset of the Tc1-excised mRNAs are in-frame (Fig. 1d); (3) excision sites only rarely map to consensus spliceosomal GU-AG[14] splice sites (Fig. 1e); and (4) excision leaves short insertions or deletions (indels) in repaired mRNAs (Fig. 1d). Henceforth, we refer to the removal of a TE from its host mRNA as SOS splicing.

Fig. 1: SOS splicing excises DNA transposons from C. elegans mRNAs.

a, Left, schematic of wild-type (WT), rsd-3-null (–) or animals with Tc1 insertions treated with dpy-6 dsRNA (RNAi). Right, the percentage of DPY animals caused by dpy-6 RNAi (mean ± s.d., n = 3 biological replicates). One-way analysis of variance (ANOVA; two-tailed) with Tukey’s test was used to compare each group with the WT. b, Nanopore long-read sequencing of in vitro-synthesized rsd-3 RNAs with or without Tc1, and RNA from animals of indicated genotypes. PCR amplicons were generated with primers flanking the Tc1 insertion site. SOS splicing efficiency and borders of 5′ and 3′ splice sites (SS) are shown, with common SOS SS underlined. Tc1::rsd-3 is rsd-3(pk2013). c, SOS splicing visualized by automated DNA electrophoresis via Agilent TapeStation (henceforth, TapeStation) of amplicons from gDNA (left) or cDNA (RT–PCR) (right). WT (N2) (–Tc1) served as the negative control. Unspliced and spliced amplicons are indicated. d, Summary of SOS splicing events in Tc1::rsd-3 (rsd-3(pk2013)), smg-2(–);Tc1::rsd-3 and five other exonic Tc1 insertions (Extended Data Fig. 1). In-frame, out-of-frame isoforms and total in-frame percentage is shown. e, Left, SOS splice sites for C. elegans smg-2(–);Tc1::rsd-3 and Tc3::unc-22(r750) (this figure), Tc4::ced-4(n1416) (Extended Data Fig. 1), Tc1-traΔ::ITRscr::rsd-3 (Fig. 2) and HSMAR2–GFP in human HEK293T cells (Fig. 4). A total of 3,028, 4,030, 5,100, 160 and 759 reads were analysed. Right, spliceosomal splice sites for 10,000 random C. elegans introns. f, Nanopore sequencing of RNA from Tc3::unc-22(r750) animals with SOS splicing efficiency indicated. g, TapeStation analysis of Tc3::unc-22(r750) gDNA (left) or cDNA (RT–PCR) (right). WT (N2) (–Tc3) served as the negative control. h, SOS splicing isoforms from f, ranked by abundance. Isoforms with >1% of total reads are shown. In-frame percentage is indicated. Tc3::unc-22 is Tc3::unc-22(r750). Tc3::unc-22(r750) animals exhibit an Unc phenotype[15], consistent with the low (2%) percentage of in-frame Tc3::unc-22(r750) RNAs.

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The analysis of additional TE insertions revealed further features of SOS splicing. First, Tc1 located in the 3′ untranslated region (UTR) of a mRNA was subjected to SOS splicing. However, two intronic Tc1 insertions were not (Extended Data Fig. 2). Second, two additional DNA transposons (Tc3 and Tc4), which do not share homology with Tc1, were also subjected to SOS splicing when present in exons of C. elegans genes (Fig. 1f–h and Extended Data Fig. 1). For the Tc3::unc-22 allele, 98% of SOS spliced RNAs were out-of-frame (Fig. 1h), which may explain why Tc3::unc-22 animals exhibit an uncoordinated (Unc) phenotype despite SOS splicing[15]. Finally, when Tc1 was inserted into eight different coding sites in the rsd-3 gene, all eight insertions were subjected to SOS splicing. However, SOS splicing only rescued rsd-3 function in RNAi when Tc1 was inserted into regions of rsd-3 that are poorly conserved (that is, not the ENTH domain) (Fig. 1a and Extended Data Fig. 3). Together, these data suggest that SOS splicing operates in C. elegans only on DNA transposons located in mRNA. However, SOS splicing will only rescue gene function when SOS splicing generates in-frame mRNAs, and indels left by SOS splicing do not disrupt essential protein functions.

We wondered how C. elegans might identify transposon-containing RNAs to initiate SOS splicing. We engineered a mScarlet gene that contains a Tc1 element (Tc1::NmScarlet, where NmScarlet is nuclear mScarlet) that, if subjected to SOS splicing, would produce mScarlet signals localized to the nucleus (Fig. 2a). Injection of this reporter gene into the germline of adult C. elegans resulted in progeny that expressed mScarlet, which indicated that SOS splicing had occurred (Fig. 2a). RNA sequencing confirmed that the Tc1::NmScarlet mRNA was SOS spliced (Fig. 2a). Deleting all sequences, including transposase, located between the ITRs of Tc1::NmScarlet did not interfere with Tc1::NmScarlet expression, which indicated that these sequences are not necessary for triggering SOS splicing (Fig. 2b). Similarly, deletion of the transposase gene from a genomic Tc1::rsd-3 locus (Tc1-traΔ::rsd-3) did not affect SOS splicing, as indicated by nanopore sequencing (Fig. 2c), RT–PCR analysis (Fig. 2d and Extended Data Fig. 4) and RSD-3 functional analyses (Extended Data Fig. 4). However, deleting one or both 54-nucleotide ITRs (ITRΔ) from Tc1::NmScarlet abrogated mScarlet expression (Fig. 2b). Moreover, deleting an ITR element from chromosomally integrated Tc1::rsd-3, Tc1-traΔ::rsd-3 or Tc1::unc-54::Ngfp (henceforth Tc1::Ngfp) genes prevented SOS splicing of Tc1 from its host mRNAs (Fig. 2d and Extended Data Fig. 4). We conclude that ITRs are necessary for SOS splicing.

Fig. 2: SOS splicing is a pattern-recognition system triggered by the RNA structure.

a, Top, schematic of the assay to explore SOS splicing in C. elegans. Plasmids with Tc1::NmScarlet (NmSc) were injected into adult C. elegans (P0). Middle, SOS splicing was visualized using nanopore sequencing of RNA isolated from F_1 progeny. Total in-frame percentage is shown. Bottom, SOS splicing was associated with mScarlet expression in F_1 progeny. b, Indicated constructs were injected and the percentage of F_1 progeny expressing mScarlet signal was quantified. Left, schematic of injected constructs, which all contain NmScarlet. Variants retaining the ability to engage in ITR base-pairing are indicated by hairpin schematics. Data are the mean ± s.d. with all data points shown. n = 3 biologically independent experiments. One-way ANOVA (two-tailed) with Tukey’s test was used to compare each group with Tc1::NmScarlet. c, Nanopore sequencing of RNA isolated from Tc1-traΔ::rsd-3 animals. In vitro-synthesized RNAs were included as controls. d, SOS splicing of chromosomally integrated Tc1-traΔ reporter variants detected with TapeStation. Top, schematic of variants. RT–PCR amplicons for indicated Tc1-traΔ variants are shown. Unspliced and spliced amplicons are indicated with arrowheads. Variants undergoing SOS splicing are indicated with dashed rectangles. Scr, scrambled ITR. e, Nanopore sequencing of RNA isolated from Tc1-traΔ::ITRscr::rsd-3 animals. In vitro-synthesized Tc1-traΔ::ITRscr::rsd-3 RNA is shown. Common SOS splice sites (SS) are underlined. f, SOS splicing isoforms observed in e, ranked by abundance. Isoforms representing >2% of total reads are shown. The total in-frame percentage is indicated. The diagram in a was created using BioRender (https://www.biorender.com).

Source Data

ITRs are able to base-pair to form double-stranded RNA (dsRNA) hairpin structures, which might be the signal that induces SOS splicing. Consistent with this idea, we observed adenosine to inosine editing of transposon ITRs, which supports the idea that ITRs base-pair in vivo[16] (Extended Data Fig. 4). Moreover, SOS splicing of Tc1::NmScarlet required that ITRs are inverted with respect to each other, which suggests that base-pairing of ITRs is needed for SOS splicing to occur (Fig. 2b). To test this idea, we scrambled the Tc1 ITR sequence (ITRscr) and replaced the ITR elements of Tc1::NmScarlet and Tc1-traΔ::rsd-3 with ITRscr in an inverted orientation. Thus, in these ITRscr genes, the ITR sequence is scrambled; however, scrambled ITRs retain the ability to base-pair with each other. mScarlet expression was observed in Tc1-ITRscr::NmScarlet animals (Fig. 2b). Moreover, nanopore sequencing (Fig. 2e,f) and RT–PCR analysis (Fig. 2d) revealed that SOS splicing occurred on Tc1-traΔ::ITRscr::rsd-3 mRNA, albeit with reduced efficacy compared with wild-type ITRs. These data suggest that SOS splicing is a pattern-recognition system triggered by base-pairing of inverted repeats, which are a defining feature of DNA transposons.

If SOS splicing is an active host response to DNA transposons interrupting an mRNA, then a genetic screen could identify host factors that mediate SOS splicing. We generated C. elegans that express two SOS splicing reporter genes: Tc1::Ngfp and Tc1::rsd-3. Tc1::Ngfp;Tc1::rsd-3 animals expressed GFP and were competent for RNAi (see below), because both TE-containing mRNAs were, as expected, subjected to SOS splicing (Fig. 1b and Extended Data Fig. 4). utp-20 RNAi causes developmental arrest at the larval stage[17]. We mutagenized Tc1::Ngfp;Tc1::rsd-3 animals and screened around 100,000 haploid genomes to identify 20 mutant animals that did not exhibit arrested development when subjected to utp-20 RNAi and did not express GFP (Fig. 3a). Genome sequencing identified candidate SOS splicing genes in the 20 mutants. We identified 8 mutations in the gene C07H6.8, 11 mutations in sut-2 and 1 mutation in F15D4.2 (Fig. 3b). The chromosomal locations of C07H6.8, sut-2 and F15D4.2 were consistent with positional mapping data (Extended Data Fig. 5). CRISPR–Cas9-based introduction of stop codons into C07H6.8, sut-2 or F15D4.2 resulted in animals that did not express GFP or RSD-3 from Tc1::Ngfp or Tc1::rsd-3, respectively (Fig. 3c). These mutants were also defective for SOS splicing of ITR-containing mRNAs (Fig. 3d and Extended Data Fig. 6), a result that confirmed that C07H6.8, SUT-2 and F15D4.2 are required for rescuing the function of TE-interrupted genes through SOS splicing. We refer to C07H6.8 and F15D4.2 henceforth as akap-17 and caap-1, respectively, for reasons outlined below. akap-17 encodes a protein with an arginine-rich and serine-rich domain (RS) domain, two protein kinase A-anchoring domains (PBDs) and an RNA recognition motif (RRM) (Fig. 3b). The putative mammalian orthologue of AKAP-17 is A-kinase anchoring protein 17 A (AKAP17A; also known as XE7), which is a nuclear speckle-localized protein linked to alternative splicing of reporter minigenes in human cells18,19. The putative mammalian orthologue of CAAP-1 is CAAP1, which promotes chemotherapeutic resistance20,21,22 and may22,23 or may not[24] regulate apoptosis. The putative mammalian orthologue of SUT-2 is MSUT2 (also known as ZC3H14 and NAB2), which is a poly(A) RNA-binding and RNA-regulating protein25,26 linked to tauopathy resistance27,28 and circular RNA biogenesis[29]. The role of SUT-2 in SOS splicing is not explored further here because SUT-2 was not linked to SOS splicing until after most of this work had been completed. We conclude that AKAP-17, SUT-2 and CAAP-1 are conserved proteins required for SOS splicing in C. elegans.

Fig. 3: Identification of three C. elegans factors required for SOS splicing.

a, Schematic of the genetic screen used to identify SOS splicing factors. Animals with Tc1::rsd-3;Tc1::Ngfp (nuclear GFP) were mutagenized with ethyl methanesulfonate (EMS) and F_2 progeny were treated with utp-20 RNAi. Mutants that did not arrest at the developmental stage were isolated, and lineages lacking GFP expression were identified. b, Alleles of akap-17, caap-1 and sut-2 identified in a screen (red arrowhead) and generated by CRISPR–Cas9 (black arrowhead). Asterisks indicate stop codons. M1X, initiating methionine mutation; NLS, nuclear localization signal; S2NH, SUT-2 N-terminal homology; SAV, splice acceptor variant; SDV, splice donor variant; ZFD, zinc finger domain. c, RNAi responsiveness (left) and GFP expression (right) in animals with the indicated genotypes. Boxed regions are magnified at the far right. Scale bar, 50 μm. Data are the mean± s.d. n = 3 biological replicates. One-way ANOVA (two-tailed) with Tukey’s test was used to compare each group with WT. d, TapeStation analysis showing defective SOS splicing in akap-17(gg911), caap-1(gg981) and sut-2(gg1074) animals. Unspliced and spliced amplicons are indicated. e, Confocal images of L4 C. elegans with mScarlet-tagged AKAP-17 or CAAP-1. These animals produce nuclear GFP (NGFP) from the Tc1::Ngfp SOS reporter. Scale bar, 10 μm. f, RIP–seq analysis of endogenous SOS splicing targets. Normalized fold change (log_2) versus reproducibility scores from two biological repeats. Results show non-enriched RNAs (blue), reporter RNAs and validated endogenous RNAs (red) and unvalidated endogenous RNAs (pink; fold change > 2, reproducibility > 0.55). g, Nanopore sequencing of W07G4.3 3′ UTR, containing Tc5A-derived inverted repeats, using RNA from WT animals. SOS splicing isoforms are ranked by abundance, with isoforms > 2% of total reads shown. h, TapeStation detection of W07G4.3 3′ UTR SOS splicing. AKAP-17 is required for W07G4.3 3′ UTR SOS splicing. Unspliced and spliced amplicons are indicated. The diagram in a was created using BioRender (https://www.biorender.com).

Source Data

We introduced amino-terminal mScarlet tags to C. elegans akap-17 and caap-1. The tags did not affect AKAP-17 or CAAP-1 function, as evidenced by proficient SOS splicing in these animals (Extended Data Fig. 7). mScarlet::AKAP-17 and mScarlet::CAAP-1 fluorescence was observed in nuclei of all to most C. elegans cells in both the soma and germline and at all stages of development (Fig. 3e and Extended Data Fig. 7). These data suggest that SOS splicing is a nuclear process that may be active in all C. elegans cell types.

Illumina-based RNA sequencing of RNA from wild-type or akap-17(–) animals identified SOS spliced Tc1::Ngfp RNAs, which depended on AKAP-17 for their production (Extended Data Fig. 7). No obvious defects in canonical splicing, however, were observed in akap-17 mutants (Extended Data Fig. 7), which suggests that the role of AKAP-17 in RNA splicing is focused largely on SOS splicing. We performed RNA immunoprecipitation with sequencing (RIP–seq) of AKAP-17 to try and identify endogenous targets of SOS splicing. RIP–seq identified Tc1::Ngfp and Tc1::rsd-3 as AKAP-17-associated mRNA (Fig. 3f). Directed RT–qPCR analyses confirmed that AKAP-17 co-precipitated with Tc1-containing mRNAs but not with control mRNAs that lack Tc1 (Extended Data Fig. 6). RIP of AKAP-17 also identified about 20 other AKAP-17-associated RNAs (Fig. 3f). We tested three of these RNAs, for which published nanopore sequencing data suggested might be targets of SOS splicing[30]. We observed complete (Fig. 3g,h) or partial (Extended Data Fig. 6) AKAP-17-dependent SOS splicing in all three RNAs, which occurred near inverted repeat elements. In these RNAs, SOS splicing removed part of a presumed pseudogene exon or parts of 3′ or 5′ UTR elements of protein-coding mRNAs (Fig. 3g,h and Extended Data Fig. 6). In two cases, the SOS-spliced inverted repeats were derived from non-autonomous DNA transposons (Fig. 3g and Extended Data Fig. 6), whereas in the third case, no link between the SOS spliced inverted repeats and transposons could be identified (Extended Data Fig. 6). These data show that AKAP-17 associates with ITR-containing mRNAs, which implies that AKAP-17 could be directly involved in the SOS splicing process. The data also showed that C. elegans mRNAs, which contain TE-derived or non-TE-derived inverted repeats, are subjected to SOS splicing, thereby suggesting that SOS splicing has a potential role in gene regulation during growth and development. Incidentally, akap-17(–) animals exhibited an increased rate of excision of Tc1 from chromosomes, which indicated that SOS splicing may also have a role in limiting transposon activity (Extended Data Fig. 6).

Given that AKAP-17, SUT-2 and CAAP-1 are conserved in mammals, we wondered whether SOS splicing might also occur in human cells. To address this question, we constructed plasmids that express a GFP gene interrupted by either C. elegans Tc1 (Tc1–GFP) or the human hsMar2 DNA transposon (HSMAR2–GFP). These reporter genes would not be expected to produce GFP owing to introduced PTCs, unless a SOS splicing system was operational in human cells. Transfection of either plasmid into HEK293T cells resulted in cells that expressed GFP, albeit at lower levels than a control construct lacking a transposon (Fig. 4a). RT–PCR analysis (Extended Data Fig. 8) and nanopore sequencing (Fig. 4b,c) showed that both transposons were excised from their host mRNAs in human cells. The molecular hallmarks of TE excision in human cells resembled those of SOS splicing in C. elegans. That is, TE excision was efficient and was not restricted to spliceosome-associated GU-AG splice sites (Fig. 4c and Extended Data Fig. 8). Notably, although excision sites for both Tc1 and HSMAR2 occurred near their respective ITRs, the precise sites of excision differed. Tc1 excision occurred in Tc1 ITRs, whereas most of the HSMAR2 excision sites occurred 100–393 nucleotides distal to HSMAR2 ITRs (Fig. 4b). Swapping the ITRs of Tc1–GFP and HSMAR2–GFP did not alter these patterns of SOS splicing (Extended Data Fig. 8), which indicated that sequences located outside ITR elements can influence where SOS splicing will occur. Taken together, these data suggest that a SOS splicing-like system is active in human cells.

Fig. 4: SOS splicing in human cells.

a, Representative fluorescence micrographs of HEK293T cells transfected with indicated human SOS splicing reporter plasmids. Left, schematic of transfected reporter constructs. Short and long exposure images show that SOS splicing can restore GFP expression, but only to a low level, which is probably due to the low percentage of in-frame SOS splicing. Scale bar, 20 μm. b, Nanopore sequencing of RNA isolated from AKAP17A+ (WT) or AKAP17A– HEK293T cells transfected with the Tc1–GFP SOS splicing reporter (top) or the HSMAR2–GFP reporter (bottom). See Extended Data Fig. 8 for data demonstrating that cells are AKAP17A–. Bottom, the source of the small deletion seen in HSMAR2–GFP RNAs in AKAP17A– cells is not known. c, SOS splicing isoforms detected in Tc1–GFP mRNA from WT HEK293T cells, ranked by abundance. Isoforms representing >0.2% of total reads are shown, with those >0.5% highlighted. Total in-frame percentage is indicated. d, Flow cytometry of AKAP17A+ and AKAP17A– HEK293T cells transfected with the indicated Tc1–GFP variant constructs. mCherry-expressing plasmid was co-transfected, and mCherry-positive cells were selected for GFP expression analysis. Non-transfected cells served as the negative control. Tc1–GFP constructs that retain ITR base-pairing capability are indicated. Left, schematic of variant reporters. e, Flow cytometry of HEK293T cells with a chromosomally integrated Tc1–GFP SOS splicing knock-in reporter gene. Top, AKAP17A+ or AKAP17A– cells, and AKAP17A– cells complemented with AKAP17A+ via lentiviral transformation. Bottom, CAAP1+ cells and two independently derived clones (1 and 2) of CAAP1– HEK293T cells. See Extended Data Fig. 8 for data showing that cells are CAAP1–.

To explore this idea further, we asked whether ITR elements are necessary and sufficient for TE excision from reporter mRNAs in human cells, similar to C. elegans. Indeed, structure–function analyses of the Tc1–GFP reporter gene showed that the ITRs of Tc1–GFP were necessary and sufficient to induce TE excision from Tc1–GFP. Moreover, excision occurred independently of the underlying ITR sequence (Fig. 4d). We next asked whether the putative mammalian orthologues of AKAP-17 or CAAP-1 are needed for TE excision in human cells. We inserted Tc1–GFP or HSMAR2–GFP reporter genes into the AAVS1 safe-harbour site[31] in HEK293T cells. We confirmed that Tc1–GFP and HSMAR2–GFP expressed GFP using flow cytometry (Fig. 4e and Extended Data Fig. 8). We then deleted all copies of AKAP17A or CAAP1 from Tc1–GFP or HSMAR2–GFP cells (Extended Data Fig. 8) and observed that homozygous AKAP17A– and CAAP1– cells no longer expressed GFP (Fig. 4e and Extended Data Fig. 8). Reintroduction of a wild-type copy of AKAP17A into AKAP17A– cells using lentiviral transduction rescued GFP expression from Tc1–GFP and HSMAR2–GFP cells (Fig. 4e and Extended Data Fig. 8). These data show that SOS splicing occurs in human cells and that orthologous proteins and related RNA structures mediate SOS splicing in humans and nematodes.

SOS splicing was recalcitrant to the spliceosome inhibitor pladienolide B (PladB)[32] (Fig. 5a). This result, along with the diversity and non-GU-AG nature of many SOS splices (Fig. 1), suggests that SOS splicing is not mediated by the spliceosome. Note that in Extended Data Fig. 9, we present evidence that SOS splicing and the spliceosome can, in specific contexts, act cooperatively to splice RNAs. To further understand the mechanism of SOS splicing, we conducted immunoprecipitation with mass spectrometry (IP–MS) on human CAAP1. IP–MS of CAAP1 from HEK293T cells identified AKAP17A and MSUT2 and the RNA ligase RTCB as candidate CAAP1-interacting proteins (Fig. 5b). Some tRNAs possess introns that are excised by the endonucleases TSEN2 and TSEN34 (ref. [33]). The resultant tRNA fragments are ligated by RTCB to generate mature tRNAs[34]. RTCB also ligates XBP1 mRNA fragments generated during the unfolded protein response[35]. We wondered whether RTCB might contribute to SOS splicing, perhaps by ligating mRNA fragments created by TE excision. Directed co-immunoprecipitation analyses confirmed the CAAP1 IP–MS results, whereby CAAP1–RTCB and CAAP1–AKAP17A co-immunoprecipitated in both C. elegans and in human cells (Fig. 5c,d and Extended Data Fig. 10). AlphaFold3 simulations showed that CAAP1–RTCB and CAAP1–AKAP17A may interact directly (Extended Data Fig. 10; ipTM precision of prediction values of 0.23–0.47). We used the tripartite split-GFP system to assess where in the cell RTCB might interact with the other SOS splicing factors[36]. We co-expressed the following constructs: (1) AKAP17A fused to the 10th β-strand of GFP (GFP10); (2) RTCB fused to the 11th β-strand of GFP (GFP11); and (3) β-strands 1–9 of GFP (GFP1–9) in HEK293T cells. In these cells, we observed GFP fluorescence in nuclear foci, which suggested that AKAP17A–RTCB interact in these foci (Fig. 5e). GFP fluorescence was not observed when GFP β-strands were not fused to AKAP17A or RTCB (Fig. 5e). Because CAAP1 associates with both RTCB and AKAP17A, we wondered whether CAAP1 might be necessary for RTCB and AKAP17A to interact. Indeed, in CAAP1– cells, RTCB and AKAP17A did not interact via split-GFP (Fig. 5e). Moreover, transiently transfected AKAP17A and RTCB could be co-immunoprecipitated from HEK293T cells, but only when CAAP1 was co-expressed in these cells (Fig. 5f). Thus, AKAP17A and RTCB interact in the nucleus and CAAP1 promotes this interaction. Moreover, because AKAP17A localizes to nuclear speckles[18], the interaction between RTCB and AKAP17A probably occurs in nuclear speckles. Related split-GFP analyses showed that CAAP1–AKAP17A interact in nuclear foci and that RTCB–CAAP1 interact throughout cells (Fig. 5e). Taken together, these data suggest that SOS splicing is likely to occur in nuclear speckles in human cells and that CAAP1 recruits the RNA ligase RTCB to these foci.

Fig. 5: The RNA ligase RTCB is required for SOS splicing.

a, TapeStation analysis of amplicons from cDNA (RT–PCR) isolated from HEK293T cells transfected with plasmids for Tc1–GFP, HSMAR2–GFP or DNAJB1. Two days after transfection, cells were treated with 100 nM of the splicing inhibitor PladB, and total RNA was collected at 0, 1, 2 and 3 h after treatment. Splicing of endogenous (endo) DNAJB1 and BRD2 is shown. Unspliced and spliced amplicons are indicated, and intron retention changes compared with 0 h are indicated. b, STRING network analysis of CAAP1 IP–MS results from HEK293T cells. Factors identified in the genetic screen (Fig. 3) and the RNA ligase RTCB are highlighted in bold. c,d, Co-immunoprecipitation (co-IP) assay showing interactions between AKAP17A and CAAP1 (c) and between RTCB and CAAP1 (d) in HEK293T cells transfected with plasmids expressing the indicated tagged proteins. GAPDH served as the loading control. Asterisk indicates a nonspecific band. e, Top, schematic of tripartite split-GFP system. Bottom, representative images showing interactions between AKAP17A, CAAP1 and RTCB. Nuclei were stained with Hoechst 33342. Scale bar, 10 μm. f, Co-IP assay showing AKAP17A–RTCB interactions, observed only with CAAP1 co-transfection. GAPDH served as the loading control. Asterisk indicates a nonspecific band. g, Tc1::Ngfp SOS splicing reporter expression in WT, RTCB-1(C122A) or RTCB-1(H428A) C. elegans. Boxed regions are magnified on the right. Scale bar, 50 μm. h, Flow cytometry of GFP expression in Tc1–GFP reporter knock-in HEK293T cells treated with esiRNA targeting RTCB (siRTCB) or Renilla luciferase (siNC). i, TapeStation analysis of amplicons from RNA extracted from HEK293T cells treated with esiRNA targeting RTCB (KD) or Renilla Luciferase (NC) and transfected with Tc1–GFP reporter plasmid. Unspliced and spliced amplicons are indicated.j, Model for SOS splicing. ITR hairpins initiate SOS splicing, and an unknown process (represented by scissors) excises TEs. The AKAP17A–CAAP1–RTCB complex seals the SOS spliced mRNA.

Finally, we asked whether RTCB is required for SOS splicing. Note that RTCB is essential for viability owing to its role in tRNA splicing; therefore, it probably would not have been identified by our genetic screen. We used CRISPR–Cas9 to alter two residues in C. elegans RTCB ((RTCB-1(C122A,H428A)), which are required for RTCB-based RNA ligation37,38. Heterozygous rtcb-1(C122A/+) and rtcb-1(H428A/+) animals were isolated and their RTCB-1(C122A) or RTCB-1(H428A) homozygous progeny were arrested at larval stage three (L3) of development (Extended Data Fig. 10). Homozygous RTCB-1(C122A) or RTCB-1(H428A) progeny, which have the Tc1::Ngfp SOS splicing reporter gene, did not express GFP as L2/L3 animals, which suggests that RTCB-1-based RNA ligation is required for SOS splicing (Fig. 5g). Similar results were obtained with animals that expressed the Tc1::NmScarlet SOS splicing reporter gene and were homozygous for a deletion allele of rtcb-1 (Extended Data Fig. 10). Animals with mutations in the catalytic sites of TSEN-2 or TSEN-34 (tRNA intron endonucleases) exhibited arrested development similar to animals with rtcb-1 mutations (L4 stage) (Extended Data Fig. 10). However, these animals did not exhibit defects in SOS splicing (Extended Data Fig. 10), which suggests that the SOS splicing defects observed in animals with rtcb-1 mutations are not an indirect consequence of failing to splice tRNAs,  and that the TE excision step of SOS splicing is mediated by another, unknown process (Discussion). Introduction of mutations into the dsRNA endonucleases Dicer or Drosha also did not abrogate SOS splicing (Extended Data Fig. 10). Finally, knockdown of RTCB using endoribonuclease-prepared short interfering RNA (esiRNA) in HEK293T cells decreased the expression of GFP from Tc1–GFP and HSMAR2–GFP cells (Fig. 5h and Extended Data Fig. 10). Moreover, RT–PCR analysis showed that SOS splicing of Tc1–GFP and HSMAR2–GFP became inefficient when RTCB was depleted (Fig. 5i and Extended Data Fig. 10). Together, these data show that the RNA ligase RTCB promotes SOS splicing in C. elegans and human cells. The data suggest that, in human cells, CAAP1 recruits RTCB to nuclear speckles, where it interacts with AKAP17A to promote SOS splicing by ligating the mRNA fragments generated during the TE-excision step of SOS splicing.

Discussion

Here we described a new mode of mRNA splicing, which we term SOS splicing. We showed that SOS splicing is a conserved pattern-recognition system that detects inverted repeats in mRNAs and excises them. We identified four proteins required for SOS splicing: the TE::mRNA-binding AKAP17A; the RNA ligase RTCB; CAAP1, which recruits RTCB to AKAP17A in nuclear foci; and MSUT2, the role of which in SOS splicing has not yet been investigated (Fig. 5j). Finally, we showed that a biological function of SOS splicing is to remove DNA transposons from mRNAs to restore function to some TE-interrupted genes.

We propose that RTCB is the enzyme that ligates mRNA fragments generated during SOS splicing because RTCB-based RNA ligation is required for SOS splicing and because RTCB interacts physically with the other SOS splicing factors. We do not yet understand how TEs are excised from mRNAs before RTCB ligation. The tRNA intron endonucleases TSEN2 and TSEN34, and the dsRNA endonucleases Dicer and Drosha, are not required for SOS splicing. It is possible that a currently unknown endonuclease excises TEs from mRNAs or that TE excision during SOS splicing does not depend on a protein endonuclease but, rather, is mediated by RNA, similar to the chemistry of spliceosome-mediated splicing39,40. ITR base-pairing is necessary and sufficient to trigger SOS splicing, which suggests that a dsRNA-binding protein may contribute to SOS splicing. The nuclear dsRNA-binding protein SON[41], which was identified by IP–MS analysis of CAAP1, is an attractive candidate for such a factor.

Although SOS splicing can be >99% efficient, it is not precise. Moreover, even though all SOS splice sites occur near the 3′ or 5′ termini of ITRs, the exact site of splicing can differ, which causes many SOS spliced mRNAs to be out-of-frame and/or have indels. Thus, SOS splicing is an imperfect solution to the problem of genic TEs. Indeed, recent insertions of DNA transposons in C. elegans exons are rare, which suggests that these alleles are purged over evolutionary time despite SOS splicing. Thus, the gene-protective benefits of SOS splicing could be evolutionarily short-lived or SOS splicing could have evolved for another currently unknown role in cis or trans RNA splicing, which was later co-opted for TE mitigation. Notably, two out of three endogenous C. elegans SOS spliced mRNAs that we identified have DNA transposon-derived ITRs. Moreover, other wild isolates of Caenorhabditae have DNA-transposon-interrupted exons, yet do not exhibit phenotypes associated with gene inactivation[42]. These latter observations indicate that SOS splicing may, despite its imprecision, be capable of protecting genes from DNA transposons over evolutionary time scales and, therefore, could have evolved for this purpose. RTCB and DNA transposons are present in all three domains of life38,43,44. Assessing whether archaea or bacteria possess RTCB-dependent SOS splicing systems will be a strong test of these ideas.

We have not yet identified the RNA targets of SOS splicing in human cells. Although DNA transposons are not currently active in the human genome[45], relics of DNA transposons constitute 2–3% of the genome[46]. It will be of interest to ask whether these TE relics, or other transcribed inverted repeats, which are common in most genomes, are cis-spliced or trans-spliced by SOS splicing, and whether such splicing creates new RNAs with biological functions. Moreover, many viral genomes have inverted repeats, which could be recognized and processed by SOS splicing47,48. Identifying the mammalian targets of SOS splicing should help clarify the biological reason for SOS splicing. Determining the exact ITR characteristics needed to trigger SOS splicing, and local sequence contributions to sites of SOS splicing, could reveal strategies to trigger SOS splicing in human mRNA for therapeutic benefit.

Organisms have sophisticated systems to prevent TE expression and replication1,2,3. This study, along with other recent studies49,50, show that organisms also have fail-safe systems that enable coexistence with TEs when TE-silencing systems fail and TEs mobilize into genes. Given the diversity and near-ubiquity of TEs in genomes, and across evolutionary time, additional TE-coexistence systems are likely to await discovery.

Methods

C. elegans husbandry

All C. elegans strains (Supplementary Table 1) were maintained at 20 °C on nematode growth medium (NGM) plates seeded with E. coli OP50 according to previously described methods[51], unless otherwise specified. Strains obtained from the Caenorhabditis Genetics Center are listed in Supplementary Table 1. WT refers to the N2 Bristol strain. All strains are available upon reasonable request. All experiments were performed with age-matched C. elegans hermaphrodites. Sample sizes were not predetermined by statistical methods. Randomization was not applied, as experiments were designed according to genotype or condition. Investigators were not blinded to allocation, but control and experimental samples were processed in parallel under identical conditions. No ethical approval is required to work with C. elegans.

Cell lines

HEK293T cells (CRL-3216; sourced and authenticated by the American Type Culture Collection) and HEK293T-derived reporter and knockout cell lines (Supplementary Table 1) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (Gibco, 11995-065) supplemented with 10% fetal bovine serum (FBS) (Gibco, 10437-028). Cells were maintained at 37 °C in a humidified atmosphere with 5% CO_2. All cells used in this study were tested negative for mycoplasma contamination, and no commonly misidentified cell lines were used.

RNAi

Feeding RNAi

Gravid adults were treated with alkaline-bleach solution (20% commercial bleach, 0.5 M NaOH) to isolate embryos (egg preparation). The embryos were then transferred to RNAi plates (NGM plates containing 1 mM IPTG and 25 mg ml–1 carbenicillin) seeded with E. coli HT115 expressing either control dsRNA (L4440 empty vector) or dsRNAs targeting specific genes (utp-20 or dpy-6). The utp-20 and dpy-6 RNAi colonies are from the Ahringer library and all plasmid identities were confirmed by Sanger sequencing and whole plasmid sequencing (Supplementary Table 1).

esiRNA treatment

Dried esiRNA oligonucleotides targeting RTCB (Sigma Aldrich, EHU009301-20UG) were resuspended in TE buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA). esiRNA targeting RLUC (Sigma Aldrich, EHURLUC-50UG) was used as a negative control in all RNAi experiments. HEK293T cells were transfected with esiRNAs using Lipofectamine RNAiMAX transfection reagent (Invitrogen, 13778075), following manufacturer’s protocols. Transfections were performed at a final concentration of 10 pmol in a 24-well plate. Knockdown efficiency of target genes was confirmed by immunoblot analysis.

In vitro RNA synthesis

To prepare in vitro RNAs as controls for nanopore long-read sequencing experiments, RNAs were synthesized using a MEGAscript T7 Transcription kit (Invitrogen, AM1334). DNA fragments were cloned into a pDONR221 plasmid (Tc1::rsd-3, rsd-3, Tc1::Ngfp) containing the T7 promoter using Gateway BP Clonase II enzyme mix (Invitrogen, 11789-100) or pUC57 plasmid (Tc1-traΔ::rsd-3, Tc1-traΔ::ITRscr::rsd-3) using AccI (NEB, R0161S) and AvaI (NEB, R0152S), linearized and purified using phenol–chloroform extraction to serve as DNA templates for in vitro transcription. Transcription reactions were incubated overnight at 37 °C. Resulting RNA was purified using RNA Clean & Concentrator-5 (Zymo Research, R1014) and stored at −80 °C.

Long-read nanopore sequencing and SOS splicing analysis

Total RNA was extracted from animals using RNA Clean & Concentrator-5 (Zymo Research, R1014), followed by DNase treatment to deplete gDNA contamination. For cDNA synthesis, 1 μg of in vivo RNA (or 1 ng of in vitro RNA) was reverse-transcribed using Induro reverse transcriptase (NEB, M0681L) with random primer mix (60 μM) (NEB, N0447). In brief, RNA was mixed with random primer mix and dNTPs, denatured at 65 °C for 5 min and then immediately chilled on ice. Induro RT reaction buffer, RNase inhibitor and Induro reverse transcriptase were added to the mixture. The reaction was incubated in a thermocycler with the following conditions: 2 min at 25 °C, followed by 30 min at 60 °C and a final hold at 95 °C for 2 min.

SOS splicing isoforms were amplified using primers flanking transposon insertion sites. In brief, 200 ng cDNA (from in vivo RNA) or 10−5 pg cDNA (from in vitro RNA) was used for PCR amplification. Reactions were performed in a 200 μl reaction volume with 20–23 cycles using Q5 High-Fidelity DNA polymerase (NEB, M0491L). PCR products were then purified using 0.7× to 1× AMPure XP reagent (Beckman Coulter, A63881) depending on the amplicon size and analysed by automated electrophoresis using a 4200 TapeStation system (Agilent, G2991BA). PCR sequencing was performed by Plasmidsaurus using Oxford Nanopore Technologies followed by custom analysis and annotation.

Reads were mapped to the indicated reference genomes (Supplementary Table 2) using Minimap2 (v.2.22, --r1109dirty)[52], with specific alignment parameters optimized for SOS splicing events (minimap2 -ax splice -C 0) and for hybrid spliceosome–SOS splicing events and the F19B2.5.2 5′ UTR locus (minimap2 -ax splice). Isoform identification was performed using IsoQuant (v.3.5.0)[53] with the following parameters: isoquant.py --data_type assembly --check_canonical --keep_tmp --stranded none --report_canonical all --splice_correction_strategy none --model_construction_strategy sensitive_ont --report_novel_unspliced false. Nanopore sequencing coverage for specified genomic regions was normalized to the highest value, and splicing isoforms and splice sites were identified based on detected splicing events. Spliceosomal splice sites were identified from 10,000 randomly selected C. elegans introns using the annotation file WBcel235.gtf (Ensembl Release 104, WormBase WS276). Results were visualized using R (v.4.4.2).

Automated electrophoresis using TapeStation

Total RNA was extracted from animals using a RNA Clean & Concentrator-5 Kit (Zymo Research, R1014) and reverse-transcribed with Induro reverse transcriptase (NEB, M0681L) using a random primer mix, as described above. For PCR amplification, 50–200 ng of total DNA (to detect transposons in gDNA) or 25 ng of cDNA (to detect transposons in mRNA) was used as templates. Amplification was performed using Q5 High-Fidelity DNA polymerase (NEB, M0491L) for 30 cycles. PCR amplicons were analysed using a 4200 TapeStation system (Agilent, G2991BA) with D1000 ScreenTape (Agilent, 5067–5582) or D5000 ScreenTape (Agilent, 5067–5588) following the manufacturer’s instructions. All primers used in this study are listed in Supplementary Table 1. Uncropped TapeStation results are provided in Supplementary Fig. 1.

SOS splicing reporter microinjection

The Tc1::NmScarlet SOS splicing reporter plasmid (Prpl-28::Tc1::NmScarlet::unc-54 3′ UTR) or variant constructs were microinjected at 50 ng μl–1, along with 2 ng μl–1 of the co-injection marker pCFJ421 plasmid (Pmyo-2::gfp::h2b; Addgene, 34876). Pharyngeal GFP expression (co-injection marker) was used to detect successful microinjection. P0 animals were microinjected into the germline and allowed to lay a brood at 20 °C. F_1 animals with pharyngeal GFP expression were scored for mScarlet expression.

EMS screening and whole-genome sequencing

Tc1::rsd-3 (that is, pk2013);Tc1::Ngfp-containing L4 stage animals were washed twice with M9 buffer and then mutagenized with 47 mM EMS by rotating at 20 °C for 4 h. Mutagenized animals were grown on 100 mm NGM plates seeded with E. coli OP50 until most F_1 animals had reached the young adult stage. F_2 embryos were obtained via egg preparation from gravid adult hermaphrodites and placed on RNAi plates seeded with E. coli HT115 bacteria expressing utp-20 dsRNA. Animals that did not respond to utp-20 RNAi (that is, they developed to young adults) were transferred to NGM plates seeded with E. coli OP50. Lineages established from these animals were tested for GFP expression. All potential mutants resistant to utp-20 RNAi and that did not express GFP were further examined for reporter SOS splicing patterns using RT–PCR as described above.

A mapping strain was established from an animal mutagenized with EMS but exhibited a WT response to utp-20 RNAi (larval arrest) and normal GFP expression. To map mutants, males from the mapping strain were crossed with mutant hermaphrodites, and eggs from F_1 adult animals were collected and grown on utp-20 RNAi plates. F_2 animals that did not respond to utp-20 RNAi were scored for GFP expression. F_2 animal lineages that exhibited SOS splicing defects were pooled (>20 F_2 animal lineages per mutant) in nearly equal proportions and processed for gDNA extraction and whole-genome sequencing at the Biopolymers Facility at Harvard Medical School or BGI Genomics. Reads were aligned to the C. elegans genome (WBcel235/ce11) using BWA-MEM (v.0.7.17-r1188), and variants were identified using Samtools (v.1.3.1) and bcftools (v.1.13). Mapping single-nucleotide polymorphisms were genotyped using GATK (v.4.1.9.0), and unique mutations absent in the mapping strains were plotted using R (v.4.4.2).

Microscopy imaging of C. elegans

For live-worm imaging, worms were collected from NGM plates and washed once with M9 buffer. To restrain worm movement, 2% low melting agarose (Invitrogen, 16520050) was mixed with animals and then seeded onto glass slides. After the agarose solidified, imaging was performed using a Nikon Ti2 W1 Yokogawa spinning disk confocal microscope equipped with a Plan Apo λ ×20/0.8 DIC I or a Plan Apo λD ×60/1.42 oil DIC objective lens.

RIP and RT–qPCR

Approximately 5,000 young adult animals were washed 3 times with PBS–TX (PBS containing 0.01% (v/v) Triton X-100) and crosslinked with 1.8% formaldehyde by rotating at room temperature for 30 min. The reaction was neutralized by adding 125 mM glycine and rotated for 5 min at room temperature. Crosslinked animals were washed with PBS–TX and resuspended in Pierce IP lysis/wash buffer (Thermo Scientific, 1861603) containing 80 U ml–1 RNaseOUT and protease inhibitor cocktail without EDTA (Roche, 11836170001) and sonicated (3 s on, 10 s off, 30% output for 2 min, 2 cycles). Lysates were rotated for an additional 15 min and clarified by centrifuging at 14,000 rpm for 15 min at 4 °C. The concentration of the supernatant was determined using Pierce BCA Protein Assay kits (Thermo Scientific, 23225), and equal amounts of proteins from each sample were used for IP. Anti-Flag M2 magnetic beads (used for 3×Flag::AKAP-17; Millipore Sigma, M8823) were pre-blocked by 1% BSA with rotating 1 h at room temperature and then incubated with supernatants at 4 °C overnight. Beads were washed with Pierce IP lysis buffer for 6 times. Associated RNAs were reverse-crosslinked by incubating at 70 °C for 1 h and extracted with RNA Clean & Concentrator-5 (Zymo Research, R1014). cDNA was synthesized from isolated RNA using SuperScript IV Reverse Transcriptase (Invitrogen, 18090010). Next, 5 ng of input cDNA per reaction was used for analyses. iTaq Universal SYBR Green Supermix (Bio-Rad, 1725120) was used for RT–qPCR analysis. Fold changes were normalized to inputs. All primers used in this study are listed in Supplementary Table 1.

RIP–seq

RIP was performed as described above. To identify potential RNA targets of AKAP-17, approximately 7,000 young adult C. elegans animals expressing 3×Flag::AKAP-17 (tagged group) or AKAP-17 (non-tagged control) were collected for RIP experiments. RIP–seq libraries were prepared following a previously described protocol[54], with modifications for C. elegans samples.

In brief, RNA extracted from IP was dephosphorylated using FastAP (Thermo Scientific, EF0651), and cyclic phosphates were removed using T4 polynucleotide kinase (PNK) (NEB, M0201S). RNA was then ligated to an adapter containing a reverse transcription (RT) primer binding site. For input samples, an additional DNase treatment was performed using TURBO DNase (Thermo Scientific, AM2238) to ensure complete removal of gDNA contamination. The RNA was reverse-transcribed into single-stranded cDNA, which was then hydrolysed with NaOH. Following RT, a second adapter was ligated to the ssDNA. PCR amplification was carried out using Illumina-barcoded primers targeting the ligated adapters.

The molarity of PCR-amplified libraries was measured using Agilent TapeStation (G2991BA) DNA ScreenTape (Agilent, 5067–5582). All samples were pooled at equal molarity and subjected to paired-end sequencing (150 bp) on an Illumina NovaSeqX/X+ platforms with 25B flowcells (Genewiz).

Reads were processed using a Snakemake workflow based on the CLAPAnalysis pipeline[54], with modified alignment parameters. Paired-end RNA sequencing reads were trimmed to remove adaptor sequences using TrimGalore! (v.0.6.2) and assessed for quality using FastQC (v.0.11.8). Read pairs were first aligned to a combined reference that contained repetitive and structural RNA sequences (ribosomal RNAs, snRNAs, snoRNAs, 45S pre-rRNAs and tRNAs) using Bowtie2 (v.2.5.1). Unaligned reads were subsequently aligned to the C. elegans genome (WBcel235/ce11) using STAR (v.2.7.9a) with the following parameters: --readFilesCommand zcat --alignEndsType Local --outFilterMatchNmin 100 --outFilterScoreMin 100 --outFilterIntronMotifs RemoveNoncanonical --outFilterMultimapNmax 1 --outFilterType BySJout --outSAMunmapped Within --outReadsUnmapped Fastx. PCR duplicates were removed using the MarkDuplicates function in Picard (v.2.18.7).

For each reference gene, we divided the gene body into 100-nucleotide windows and counted the number of reads overlapping each window. Read counts were normalized to the total number of sequenced reads. Gene-level enrichment was calculated by summing the normalized counts across all windows for a gene, and IP enrichment was defined as the ratio of normalized IP to normalized input values. To assess reproducibility between two biological replicates, we defined a reproducibility score based on the difference in enrichment values between IP samples, ranging from 0 (low) to 1 (perfect). Genes with a tag/no-tag fold change greater than 2 and a reproducibility score above 0.55 were defined as AKAP-17-targeted mRNAs. Significance (P values) was assessed using the limma (v.3.62.2) linear modelling framework with an empirical Bayes approach, based on log_2-transformed normalized enrichment values. Results are summarized in Supplementary Table 3.

Tc1 excision qPCR assay

Single L4 stage C. elegans animals were transferred onto 6 cm NGM plates and grown at 20 °C until the progeny reached adulthood. After 4 days, worms were collected by washing plates with M9 buffer, and whole-plate lysates were prepared in worm lysis buffer (50 mM KCl, 10 mM Tris-HCl, pH 8.3, and 2.5 mM MgCl_2) supplemented with proteinase K (0.2 mg ml–1). Lysates were used directly as DNA templates for qPCR. Reactions were performed with iTaq Universal SYBR Green Supermix (Bio-Rad, 1725121) according to the manufacturer’s instructions, and amplification was carried out on a CFX Connect Real-Time PCR detection system (Bio-Rad, 1855201). Cycling parameters were as follows: initial denaturation at 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s and 61 °C for 30 s (annealing–extension with plate read). eft-3 served as the internal control gene. A nhj-1-null allele (gg875), in which somatic Tc1 excision is suppressed[55], was used as a negative control. All primers used in this study are provided in Supplementary Table 1.

CRISPR

C. elegans

All CRISPR deletion or insertion experiments were done using CRISPR-targeted genome-editing techniques[56]. crRNAs were designed using the IDT online guide RNA design tool (https://www.idtdna.com/site/order/designtool/index/CRISPR_SEQUENCE). For oligonucleotide-mediated homology-directed repair (HDR), RNP complexes containing gene-specific crRNA (Supplementary Table 1), tracrRNA (IDT, 1072532) and Alt-R S.p.HiFi Cas9 nuclease V3 (IDT, 1081060) were assembled at 37 °C then mixed with ssODN (IDT, standard desalting; 4 nmol Ultramer) and the co-injection marker PRF4::rol-6(su1006) following manufacturer’s protocols. For dsDNA-mediated HDR (akap-17::mScarlet, caap-1::mScarlet), repair templates that contained 35 bp homologous arms were amplified with PCR, gel-purified and cleaned with 1× to 1.5× AMPure XP reagent (Beckman Coulter, A63881). Diluted repair template (100 ng μl–1) was melted (95 °C 2 min, 85 °C 10 s, 75 °C 10 s, 65 °C 10 s, 55 °C 1 min, 45 °C 30 s, 35 °C 10 s, 25 °C 10 s, 4 °C 10 s, with a 1 °C s–1 ramp down at each step) and mixed with RNPs before microinjection. The injection mix was microinjected into gonads of P0 animals and maintained at 20 °C. Rolling animals, which indicate successful injection, were isolated 4 days later and screened for successful genome deletion or insertion.

For integration of the Tc1::Ngfp SOS splicing reporter (Peft-3::Tc1::unc-54::Ngfp::unc-54 3′ UTR), individual fragments were fused to generate the final genetic constructs using the SapTrap cloning method as previously described[57]. Engineered transgenes were subcloned into the pDD379 vector and integrated into the genome using CRISPR–Cas9.

Generation of HEK293T knockout cell lines

To generate AKAP17A and CAAP1 knockout cell lines in HEK293T cells, guide RNA (gRNA) sequences targeting AKAP17A or CAAP1 were designed using the CRISPick tool[58]. cDNA oligonucleotides encoding gRNA sequences (Supplementary Table 1) were synthesized from IDT, annealed and cloned into BsmBI-digested pLentiCRISPR-v2-BFP backbone59,60 using a Quick Ligation kit (NEB, M2200S). HEK293T cells were transfected with plasmids encoding gRNAs using Lipofectamine 3000 transfection reagent (Invitrogen, L3000008). Forty-eight hours after transfection, single BFP-positive cells were isolated by FACS and cultured for 10–14 days for clonal expansion. Clonal populations were screened for knockout validation by immunoblotting, which confirmed the absence of protein expression from targeted genes.

Generation of SOS reporter knock-in cell lines

To generate HEK293T cells expressing Tc1–GFP or HSMAR2–GFP reporters, SOS splicing reporters were cloned into donor plasmid AAVS1-tdTomato targeting vector (Addgene, 194728) using NEBuilder HiFi DNA assembly master mix (NEB, E2621S) with the following modifications: (1) tdTomato was replaced by Tc1–GFP or HSMAR2–GFP reporter cassettes; and (2) the PuroR selection marker was replaced with the BlastR selection marker. The donor plasmids AAVS1-BlastR-Tc1-GFP or AAVS1-BlastR-HSMAR2-GFP, along with a Cas9-expressing plasmid targeting the AAVS1 insertion site (pX458-AAVS1-sg; Addgene, 194721), were co-transfected into HEK293T cells using Lipofectamine 3000 transfection reagent (Invitrogen, L3000008). Three days after transfection, 15 μg ml–1 blasticidin HCl (InvivoGen, ant-bl-05) was added to select for edited cells. After 7 days of selection, surviving cells were collected, and single GFP-positive cells were isolated in a 96-well plate by FACS. Resulting single-cell colonies were expanded, and knock-in cell lines were validated by Sanger sequencing and confirmed by observation of GFP expression.

Lentivirus production and transduction

For lentiviral packaging, HEK293T cells cultured in plain DMEM were transfected with 2.5:1:1.5 ratio of the transfer plasmid pHAGE-2×Flag-AKAK17A, VSV-G envelope-expressing plasmid pMD2.G (Addgene, 12259) and lentiviral packaging psPAX2 (Addgene, 12260) using a CalPhos Mammalian Transfection kit (Takara, 631312). After 12 h, medium was replaced with DMEM containing 10% FBS and the cells were incubated for 48 h to produce lentiviral particles. Virus-containing supernatant was collected at 24 h, 36 h and 48 h after transfection and filtered through a 0.45 μm syringe filter. Lentiviral particles were used to transduce the target cells with 10 μg ml–1 polybrene transfection reagent (Sigma Aldrich, TR-1003-G). Following transduction, cells were selected with 2 μg ml–1 puromycin dihydrochloride (Gibco, A1113803) for 14 days. Puromycin-resistant cells were pooled for downstream analysis.

Flow cytometry

HEK293T cells were rinsed once with PBS and detached from plates using TrypLE Express enzyme (Gibco, 12605028) and passed through a 35 μm nylon mesh strainer (Corning, 352235). Approximately 10,000 individual cells were analysed for B525-FITC-A (green) using a CytoFLEX S flow cytometer. Flow cytometry data were collected using CytExpert software (Beckman Coulter), and figures were created using FlowJo (v.10.7.1) software. A Sony MA900 was used for FACS to isolate isogenic single clones. Flow cytometry gating strategies are provided in Supplementary Fig. 2.

SDS–PAGE and immunoblotting

For cell lysate preparation, HEK293T cells were washed once with ice-cold PBS and lysed on ice for 5 min in RIPA lysis and extraction buffer (Thermo Scientific, 89900) supplemented with PhosSTOP (Roche, 4906845001). Lysates were clarified by centrifugation at 20,000g for 10 min at 4 °C. The supernatants were mixed with 4× SDS sample buffer (Millipore, 70607-3) supplemented with 4% β-mercaptoethanol (Sigma, M6250), denatured at 95 °C for 15 min and resolved using NuPAGE Bis-Tris mini protein gels (Invitrogen, NP0322B0X) in 1× NuPAGE MOPS SDS running buffer (Invitrogen, NP0001).

After SDS–PAGE, proteins were transferred to nitrocellulose membranes (Bio-Rad, 1620112) using a semi-dry transfer method with a Power Blotter station (Invitrogen, PB0010). Membranes were blocked for 1 h at room temperature with Intercept (TBS) blocking buffer (Li-Cor Bio, 927–60001). Primary antibody (Supplementary Table 1) incubation was carried out at 4 °C overnight in TrueBlack WB antibody diluent (Biotium, 23013B-1L). After washing with TBS-T (0.1% Tween 20), membranes were incubated with IRDye secondary antibodies (Li-Cor Bio; Supplementary Table 1) for 1 h. Membranes were washed with TBS-T and imaged using an Odyssey DLx Imaging System (Li-Cor Bio). Uncropped gel images are provided in Supplementary Fig. 1.

IP–MS

A total of 5 × 10^8 HEK293T cells were seeded 1 day before transfection. HEK293T cells were transfected with plasmid expressing HA-CAAP1 (pGCS-3×HA-CAAP1) using a CalPhos Mammalian transfection kit (Takara, 631312). After 24 h, cells were lysed in 5 ml lysis buffer (40 mM HEPES pH 7.4, 100 mM NaCl and 0.05% CHAPS) with PhosSTOP (Roche, 4906845001) and sonicated as described above. Cell lysates were further rotated for 20 min at 4 °C and cleared by centrifugation at 21,000g for 12 min. Supernatants were incubated with Pierce anti-HA magnetic beads (Thermo Scientific, 88836) at 4 °C for 2 h on a rotator to immunoprecipitate HA-tagged CAAP1 protein complexes. Beads were washed 4 times with lysis buffer, and proteins were eluted using elution buffer (50 mM Tris-HCl pH 7.5, and 10% SDS) by boiling at 95 °C for 4 min.

The eluted proteins were digested with sequencing-grade modified trypsin (Promega, V5113) on S-Trap Micro columns (Protifi, C02-micro-10) according to the manufacturer’s instructions. First, proteins were reduced using 5 mM Tris (2-carboxyethyl) phosphine hydrochloride (TCEP) (Sigma Aldrich, C4706-2G) at 55 °C for 15 min, followed by alkylation with 20 mM iodoacetamide (Sigma Aldrich, I6125) at room temperature in the dark for 30 min. After alkylation, samples were acidified with phosphoric acid (Sigma Aldrich, 345245-100 ml) to a final concentration of 2.5% (v/v). To assist in protein trapping, 10 volumes of 100 mM Tris (pH 7.55) in 90% methanol and 10% water (v/v) were added, and the solution was passed through the S-Trap column by centrifugation at 4,000g for 30 s. Multiple centrifugation rounds were performed to ensure columns were fully loaded. Once proteins were trapped, the column was washed 3 times with 100 mM Tris (pH 7.55) in 90% methanol and 10% water (v/v) and spun dry. Proteins were digested by adding 2 µg trypsin in 20 μl of 50 mM ammonium bicarbonate (pH 8) (Sigma Aldrich, A6141-25G). Digestion occurred overnight at 37 °C in a humidified environment. After digestion, peptides were eluted from the column in three steps, each by centrifugation at 4,000g for 1 min: 40 μl ammonium bicarbonate (pH 8), 40 μl 0.2% formic acid in water and 40 μl 50% acetonitrile (Sigma Aldrich, 34851) in water. Eluted peptides were pooled, dried under reduced pressure using a SpeedVac (Eppendorf, 22820109) and re-suspended in 30 μl of 0.1% formic acid in water. LC–MS/MS data were acquired as previously described[61].

A protein database from the Human UniProt SwissProt proteome was used to identify proteins that co-immunoprecipitated with 3×HA–CAAP1. The FragPipe graphical user interface (v.18.0) was used to search data with MSFragger search engine and to post-process results. Tryptic peptides with up to two missed cleavages were included. Carbamidomethylation of cysteine was set as a fixed modification, and oxidation of methionine was allowed as a variable modification, with a maximum of four variable modifications per peptide. The allowed mass tolerances were 10 ppm for precursor ions and 0.04 Da for product ions. Peptide hits were filtered to a 1% false discovery rate using PeptideProphet in FragPipe. Peptide counts are provided in Supplementary Table 4.

Co-immunoprecipitation

C. elegans co-IP

Approximately 5,000 young adult animals were collected per sample and resuspended in Pierce IP lysis/wash buffer (Thermo Scientific, 1861603) containing protease inhibitor cocktail without EDTA (Roche, 11836170001) and sonicated using a probe sonicator (10 s on, 10 s off, 50% output for 2 min with probe sonication). Lysates were rotated for 20 min at 4 °C and cleared by centrifugation at 21,000g for 10 min. The protein concentration of supernatant was determined using the BCA method. For each co-IP experiment, equal amounts of proteins from each sample were incubated with Myc-Trap magnetic agarose (ChromoTek, ytma-20) for 2 h at 4 °C. Beads were washed four times with Pierce IP lysis/wash buffer (Thermo Scientific, 1861603). Proteins were eluted by adding 1× SDS sample buffer (Millipore, 70607-3) supplemented with 1% 2-mercaptoethanol (Sigma, M6250), followed by boiling at 95 °C for 15 min. Input and eluted proteins were separated by SDS–PAGE and detected by immunoblotting as described above.

Cell-based co-IP

Co-IP experiments were performed using 2 × 10^6 HEK293T cells, seeded in 60 mm dishes 1 day before transfection. Cells were transfected with the required plasmids using Lipofectamine 3000 transfection reagent (Invitrogen, L3000008). Forty-eight hours after transfection, whole-cell lysates were prepared by adding 600 μl nuclear lysis buffer (1× PBS, 300 mM NaCl, 1% Triton X-100 and 0.1% Tween 20) supplemented with PhosSTOP (Roche, 4906845001). Lysates were cleared by centrifugation at 20,000g for 10 min at 4 °C and then incubated with anti-Flag M2 magnetic beads (for Flag–AKAP17A; Millipore Sigma, M8823), Pierce anti-HA magnetic beads (for HA–RTCB; Thermo Scientific, 88836). IP was performed by incubating lysates with beads on a tube rotator at 4 °C for 2 h. Beads were washed 3 times with nuclear lysis buffer and once with nuclear dialysis buffer (50 mM Tris-HCl pH 7.4, 100 mM NaCl and 0.1% Tween 20). Proteins were eluted by adding 1× SDS sample buffer, followed by boiling at 95 °C for 15 min. Both input and elution fractions were collected and analysed by immunoblotting.

To capture RTCB–CAAP1 and RTCB–AKAP17A interactions, dithiobis (succinimidyl propionate) (DSP) crosslinking was performed as previously described[62]. Before cell lysis, cells were washed twice with PBS, and protein complexes were crosslinked using 0.1 mM DSP (Thermo Scientific, 22586) in PBS at 37 °C for 30 min. Crosslinking reactions were quenched by adding 20 mM Tris-HCl in PBS (pH 7.4) for 15 min at room temperature. Cells were lysed and proceeded with co-IP protocol described above.

Tripartite split-GFP reporter

To monitor protein–protein interactions for AKAP17A, CAAP1 and RTCB, 5 × 10^4 HEK293T WT or CAAP1 knockout cells were seeded into 24-well glass-bottom plates (Cellvis, P24-1.5H-N) 1 day before transfection. The next day, cells were co-transfected using using Lipofectamine 3000 transfection reagent with the following encoding plasmids: GFP1–9 (Addgene, 182244), GFP10 (pGCS-HA–GFP10) and GFP11 (pGCS-Flag–GFP11) (negative control); AKAP17A–GFP10, GFP10 and GFP1–9 (negative control for AKAP17A); CAAP1–GFP10, GFP11 and GFP1–9 (negative control for CAAP1); RTCB–GFP11, GFP10 and GFP1–9 (negative control for RTCB); AKAP17A–GFP10, RTCB–GFP11 and GFP1–9 (AKAP17A and RTCB interaction); CAAP1–GFP10, AKAP17A–GFP11 and GFP1–9 (CAAP1 and AKAP17A interaction); CAAP1–GFP10, RTCB–GFP11 and GFP1–9 (CAAP1 and RTCB interaction). Twenty-four hours after transfection, cells were washed with PBS and the culture medium was replaced with live-cell imaging medium. Imaging was performed 30 min after medium exchange using a Nikon Ti2 W1 Yokogawa spinning disk confocal microscope equipped with Plan Apo λD ×60/1.42 oil DIC objective lens.

Illumina sequencing and alternative splicing analysis

Total RNA was extracted using TRIzol reagent (Invitrogen, 15596026) followed by DNase I (Thermo Scientific, EN0521) treatment to remove contaminating DNA. RNA concentrations were determined by Nanodrop 2000, and RNA quality was assessed by gel electrophoresis. rRNA and mtRNA were depleted using 50-nucleotide DNA oligonucleotides complementary to C. elegans rRNA and mtRNA sequences, followed by Thermostable RNase H (NEB, M0523S) treatment, as previously described[63]. Ribosomal-depleted RNA was then analysed using TapeStation and qPCR, following quality control methods outlined in manufacturer instructions. Libraries were prepared using a KAPA mRNA HyperPrep kit (KR1352-v.4.17) and sequenced with 150-nucleotide paired-end reads, which generated 30 million read pairs on the Illumina NovaSeq 6000 (Biopolymers Facility, Harvard Medical School).

Reads were processed using the Nextflow (v.24.04.4)-based nf-core/rnasplice pipeline[64] (v.1.0.4), primarily for downstream rMATS analysis[65] (v.4.1.2). Read quality was assessed using FastQC (v.0.12.1), and low-quality reads were trimmed or removed using TrimGalore! (v.0.6.7). Adapter sequences and low-quality bases were clipped. Remaining reads were aligned to the C. elegans genome (WBcel235/ce11) using STAR (v.2.7.9a)[66]. The resulting alignment was subjected to rMATS for annotation of alternative splicing events. Differential alternative splicing events were identified with the following cut-off criteria: ΔPSI ≥ 0.05 or ≤ −0.05, P value < 0.05. Results were visualized using boxplots.

In vitro protein synthesis and pull-down

HA::RTCB-1 protein was synthesized using a PURExpress In Vitro Protein Synthesis kit (NEB, E6800S) for C. elegans HA::RTCB-1 pull-down experiments. C. elegans rtcb-1 was codon-optimized for E. coli expression and synthesized by Twist Bioscience. The codon-optimized rtcb-1 sequence was cloned into the pDFCI vector with an N-terminal 3×HA tag. Protein synthesis reactions were performed according to manufacturer’s instructions. Following protein synthesis, the reaction was stopped by dilution in 500 μl of Pierce IP lysis/wash buffer (Thermo Scientific, 1861603) and incubated overnight at 4 °C with Pierce anti-HA magnetic beads (Thermo Scientific, 88836) to capture HA::RTCB-1.

For pull-down, worm lysates were prepared as described above and incubated with HA::RTCB-1-bound beads at 4 °C for 2 h. Beads were washed four times with Pierce IP lysis/wash buffer, and bound proteins were eluted using 1× SDS sample buffer. Both input and elution fractions were collected and analysed by immunoblotting.

AlphaFold3 multimer prediction

For each AlphaFold3 protein–protein interaction and docking prediction, the full sequence of proteins was used as input[67]. AlphaFold3 multimer prediction was performed on the AlphaFold Server (https://alphafoldserver.com) to generate four predicted unrelaxed docking structures with default parameters. For each docking prediction, the highest scoring predicted structure is shown and illustrated using PyMOL (v.2.5.8).

Statistics and reproducibility

For all assays, data are presented as the mean ± s.d. unless otherwise specified in the figure legends. Statistical analyses were performed using GraphPad Prism (v.10). Comparisons between two groups were assessed with unpaired two-tailed Student’s t-tests. For multiple-group comparisons, two-way ANOVA (two-tailed) followed by Tukey’s post hoc test was applied. A P value <  0.05 was considered significant. Exact P values are reported in the figures. All statistical tests were performed on data derived from at least three independent biological replicates. Sample sizes were not predetermined by statistical methods. Randomization was not applied because experiments were designed on the basis of genotype or experimental condition. Investigators were not blinded to allocation; however, control and experimental samples were processed in parallel and handled equally to ensure consistency.

Reporting summary

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

Data availability

RNA sequencing data supporting the findings of this study have been deposited into the NCBI’s Gene Expression Omnibus under accession numbers GSE288884 (nanopore long-read sequencing) and GSE288885 (Illumina RNA sequencing). The MS proteomic data are included in Supplementary Table 4. Other databases used in this work included UniProt (https://www.uniprot.org) and WormBase (https://wormbase.org). Source data are provided with this paper.

Code availability

Descriptions of the analyses of nanopore long-read and Illumina RNA sequencing data are provided in the Methods. Custom scripts used in this study are available at GitHub (https://github.com/loz104/SOS-splicing).

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Acknowledgements

We thank D. Lowe and D. Wassarman for discussions; staff at the Taplin Mass Spectrometry, Biopolymers, Harvard Chan Bioinformatics Core and MicRoN facilities for support; M. Hammarlund for sharing the rtcb-1 deletion strain; and H. R. Horvitz for sharing the Tc4 strain. Some strains were provided by the Caenorhabditis Genetics Center (P40 OD010440). This work was supported by NIH grants R35GM148206 (to S.K.) and AG11085 (to S.J.E.), and the Howard Hughes Medical Institute. In May of 2025, R35GM148206 and AG11085 were terminated by the US government. S.K. thanks Harvard University, Harvard Medical School and the Department of Genetics at Harvard Medical School for support following grant termination.

Author information

Authors and Affiliations

1. Department of Genetics, Harvard Medical School, Boston, MA, USA

Long-Wen Zhao, Christopher Nardone, Cindy Chang, Stephen J. Elledge & Scott Kennedy

2. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Christopher Nardone & Stephen J. Elledge

3. Howard Hughes Medical Institute, Boston, MA, USA

Christopher Nardone & Stephen J. Elledge

4. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

Christopher Nardone & Joao A. Paulo

Contributions

L.-W.Z. designed and performed all experiments and generated all figures. C.N. and S.J.E. assisted with cell line work and contributed to Figs. 4d,e and 5b,h and Extended Data Figs. 8f,g and 10i. C.C. generated the C. elegans Tc1::Ngfp strain and contributed to Fig. 3a. J.A.P. assisted with MS experiments and contributed to Fig. 5b. S.K. assisted with the genetic screen and contributed to Fig. 3a,b. S.K. and L.-W.Z. conceived the project. S.K. supervised the study and wrote the manuscript with edits from all authors.

Corresponding author

Correspondence to Scott Kennedy.

Ethics declarations

Competing interests

S.J.E. is a founder of TSCAN Therapeutics, MAZE Therapeutics, ImmuneID and Mirimus, serves on the scientific advisory boards of Homology Medicines, ImmuneID, MAZE Therapeutics, X-Chem, and TSCAN Therapeutics, and is an advisor for MPM Capital. The other authors declare no competing interests.

Peer review

Peer review information

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 SOS splicing removes mariner-type DNA transposons from host mRNAs.

Nanopore sequencing of RNA isolated from rsd-3::pk2010 (a), bli-1::gg881 (b), glc-1::pk54 (c), lin-41::ma104 (d), msh-2::ev679 (e), ced-4::n1416 (f) animals. Tc1 insertions (a-e) and Tc4 insertions (f) are shown. PCR amplicons were generated using primers flanking the transposon insertion site. Primer sites are indicated. Transposon ITR regions are highlighted. Protein domain for each gene, and transposon insertion sites, are also indicated. SOS splicing visualized by TapeStation. PCR amplicons were generated from genomic DNA (left panel) or cDNA (RT-PCR) (right panel) isolated from rsd-3::pk2010 (g), bli-1::gg881 (h), glc-1::pk54 (i), lin-41::ma104 (j), msh-2::ev679 (k), ced-4::n1416 (l) animals (w/ transposon). Wild-type (N2) animals (w/o transposon) serve as negative controls. PCR products generated from genomic DNA using primers spanning/flanking Tc4, with the amplified regions indicated in blue (l). Unspliced and spliced amplicons are indicated. SOS splicing efficiency is indicated. SOS splicing isoforms detected in rsd-3::pk2013 (referred to as Tc1::rsd-3 in main text) (m), smg-2(-);rsd-3::pk2013 (n), rsd-3::pk2010 (o), bli-1::gg881 (p), glc-1::pk54 (q), lin-41::ma104 (r), msh-2::ev679 (s), ced-4::n1416 (t), are shown and ranked by abundance. Isoforms representing > 2% of total reads are shown. In-frame percentages are indicated for each case. u, Circular RNA detection for Tc1, pqn-14, and zip-2 visualized by TapeStation. PCR amplicons were generated from cDNA isolated from animals expressing SOS splicing reporters. Primer pairs amplifying either circular RNA (top) or linear RNA (bottom) were illustrated accordingly. Expected amplicon sizes are indicated. The data show that while circular RNAs can be detected under these conditions, no circular Tc1 RNA is detected. The data suggest that SOS splicing is not caused by spliceosomal backsplicing and that SOS splicing does not lead to circular transposon RNA products, which are detectable in this assay.

Extended Data Fig. 2 SOS splicing of Tc1 from a 3’ UTR, but not from introns.

a,c, Nanopore sequencing of RNA isolated from (a) cct-1::pk58 (Tc1 in 3′ UTR) and (c) Y37D8A.6::pkP516 animals (Tc1 in intron). b,d,e, SOS splicing of (b) cct-1::pk58, (d) Y37D8A.6::pkP516, and (e) a Tc1 derivative (Tc1-traΔ), which lacks 98% of the Tc1 transposase gene but is, nonetheless, still subjected to SOS splicing when placed in exons (see Fig. 2) was inserted into the ninth intron of rsd-3 and visualized by RT-PCR and TapeStation. PCR amplicons were generated from genomic DNA or cDNA (RT-PCR), with no-RT serving as a negative control. Unspliced and spliced amplicons with percentage are indicated.

Extended Data Fig. 3 Position dependent rescue of gene function by SOS splicing.

a, C. elegans RSD-3 protein and its orthologous sequences from S. cerevisiae, A. thaliana, D. melanogaster, D. rerio, X. laevis, M. musculus, and H. sapiens were aligned using Clustal Omega multiple sequence alignment program. Conservation scores generated from ConSurf analysis are represented by a colour gradient. The ENTH domain is well-conserved across species. b, RSD-3 protein sequence showing locations of transposon insertions within the rsd-3 gene. Full-length Tc1 elements (Tc1), or a Tc1 derivative (Tc1-traΔ), which lacks 98% of the Tc1 transposase gene but is, nonetheless, subjected to SOS splicing when inserted into exons (see Fig. 2), were inserted into the indicated eight locations in rsd-3. Amino acids encoded by different exons are demarcated with different colours. The ENTH (epsin N-terminal homology) domain, which is the only portion of RSD-3 that is conserved in other animals, is highlighted. c, SOS splicing visualized by RT-PCR and TapeStation analysis. PCR amplicons were generated from genomic DNA (left panel) or cDNA (RT-PCR) (right panel) isolated from the specified Tc1-traΔ insertions. Wild-type (N2) animals (w/o reporter) serve as negative controls. Unspliced and spliced amplicons are indicated. d, DIC images of animals harboring the indicated Tc1-traΔ knock-in alleles from b and Extended Data Fig. 2e, treated with dpy-6 dsRNA. rsd-3(-) animals, which harbour a rsd-3 deletion, serve as positive controls for loss of RSD-3 function. Scale bar = 200 μm. e, Left, quantification of RNAi responsiveness to dpy-6 dsRNA. % of animals exhibiting dumpy (Dpy) phenotypes is indicated. Right, sites of Tc1-traΔ insertion relative to RSD-3, and the RSD-3 ENTH domain, are shown. Data are presented as mean ± SD with all data points shown. N = 3 biologically independent experiments. One-way ANOVA (two-tailed) with Tukey’s test was used to compare each group with E1.

Source Data

Extended Data Fig. 4 ITR base-pairing triggers SOS splicing.

a, TapeStation analysis showing a lack of Tc1-traΔ excision from genomic DNA from animals harbouring the indicated Tc1-traΔ variants. These data are a control for results shown in Fig. 2d in the main text. Top, schematic of variants tested. Wild-type (N2) animals (w/o Tc1-traΔ) serve as negative controls. Scr, scrambled ITR. b, In vitro transcribed Tc1-traΔ or Tc1-traΔ::ITRscr RNAs were analysed by RT-PCR and TapeStation. The results show that SOS splicing does not occur during library preparation or sequencing for Tc1-traΔ or Tc1-traΔ::ITRscr RNAs, establishing that SOS splicing events shown in Fig. 2d of the main text occur in vivo. Wild-type (N2) animals (w/o Tc1-traΔ) serve as negative controls. c, SOS splicing isoforms detected by nanopore sequencing in Tc1-traΔ::rsd-3 mRNA, ranked by abundance. Isoforms representing > 2% of total reads are shown. Total in-frame percentage is indicated. d, DIC images of animals harbouring the indicated Tc1-traΔ variant alleles treated with dpy-6 dsRNA. Bottom right corner, schematic of variants. Scale bar = 200 μm. e, Quantitation of RNAi responses to dpy-6 dsRNA. % of animals exhibiting dumpy (Dpy) phenotypes is shown. Note that Tc1-traΔ::ITRscr::rsd-3 animals were defective for RNAi (indicated by red arrowhead), which is not the expected result based upon our ITR base-pairing triggers SOS splicing model. Lack of RSD-3 rescue in Tc1-traΔ::ITRscr animals is not due to a failure of base-paired ITRscr to trigger SOS splicing in this RNA (see main text, Fig. 2e,f, which show that SOS splicing does occur) but, rather, we suspect because of the relatively poor efficiency of SOS splicing for this mRNA (see main text, Fig. 2e) and because relatively few SOS splice events, which do occur in this RNA, happen to be in-frame (see main text, Fig. 2f). Data are mean ± SD with all data points shown. N = 3 biologically independent experiments. One-way ANOVA (two-tailed) with Tukey’s test was used to compare each group with Tc1-traΔ allele. f, TapeStation-based detection of SOS splicing in RNA isolated from Tc1::rsd-3 and Tc1-5’ ITRΔ::rsd-3 animals. Unspliced and spliced amplicons are indicated. g, TapeStation-based detection of SOS splicing in RNA isolated from Tc1::Ngfp and Tc1-5’ ITRΔ::Ngfp animals. Unspliced and spliced amplicons are indicated. Tc1::Ngfp is Tc1::unc-54::Ngfp. h, A-to-I editing of Tc3 ITRs detected by cDNA-PCR based nanopore sequencing. ADAR-based A-to-I RNA editing by adenosine deaminases results in A-to-G mismatches in cDNAs derived from edited RNAs. For each A nucleotide in the Tc3 reference sequence, the frequency of A or G was calculated from normalized read counts. Stacked bar plots show the relative proportion of A (blue) and G (pink). Note, A-to-I editing was not observed in Tc1 ITRs (not shown), which could be because Tc1 ITRs do not form long enough dsRNA structures (54 nts in Tc1 vs. 466 nts in Tc3) to trigger editing.

Source Data

Extended Data Fig. 5 Positional mapping of SOS splicing mutations.

Mutants were outcrossed and >20 F2 progeny exhibiting SOS splicing defects were isolated. Genomic DNA from pooled F2s was sequenced. Chromosomal regions linked to the SOS splicing genes should be enriched for EMS-based mutations in these F2 animals. Allele frequencies for EMS-induced mutations in F2 progeny of gg886 (a), gg935 (b), and gg1024 (c) crosses are shown for the six C. elegans chromosomes. Red line denotes the genomic location of the indicated candidate SOS splicing factor. Chromosome positions are shown on the x-axis (in Mb), and SNP allele frequencies are shown on the y-axis. The dotted line indicates EMS SNP allele frequency greater than 75%. SNP allele frequencies were calculated using the bcftools algorithm.

Extended Data Fig. 6 akap-17 is required for SOS splicing.

a,b, TapeStation analysis showing that SOS splicing of Tc1-traΔ::rsd-3 and Tc1-traΔ::ITRscr::rsd-3 RNAs requires AKAP-17. (Top) As expected, DNA from animals harbouring (a) Tc1-traΔ::rsd-3 or (b) Tc1-traΔ::ITRscr::rsd-3 does not show Tc1 excision. Bottom, a, RT-PCR followed by TapeStation analysis shows that the Tc1-traΔ::rsd-3 and b, Tc1-traΔ::ITRscr::rsd-3 RNA are SOS spliced and that this splicing depends upon a wild-type copy of akap-17. For all panels in this figure, akap-17(−) is akap-17(gg911). Wild-type (N2) animals (w/o Tc1-traΔ) serve as negative controls. Scr, scrambled ITR. Unspliced and spliced amplicons are indicated. c. akap-17(+) or (−) animals that express Tc1-traΔ::rsd-3 were treated with dpy-6 dsRNA. % of animals exhibiting dumpy (Dpy) phenotypes is indicated. The data show that AKAP-17 is required for rescuing RSD-3 functionality in Tc1-traΔ::rsd-3 animals. Data are presented as mean ± SD with all data points shown. N = 3 biologically independent experiments. The two-tailed unpaired student’s t-test. Scale bar = 200 μm. d, Tc1::NmScarlet SOS splicing reporter was injected into P0 adult animals of the indicated genotypes, and the percentage of F1 progeny expressing mScarlet signal was quantified. The data show that AKAP-17 is required for rescuing mScarlet expression from Tc1::NmScarlet SOS splicing reporter. Data are presented as mean ± SD with all data points shown. N = 3 biologically independent experiments. The two-tailed unpaired student’s t-test. e, TapeStation-based detection of Tc1::NmScarlet SOS splicing in F1 progeny animals of the indicated genotypes. The data show that AKAP-17 is required for SOS splicing of Tc1::NmScarlet SOS splicing reporter. Unspliced and spliced amplicons are indicated. f, AKAP-17 RNA immunoprecipitation (RIP) analysis. FLAG::AKAP-17 was immunoprecipitated using anti-FLAG. AKAP-17-bound RNAs were quantified by qRT-PCR. Data were normalized to RIP signals from animals expressing AKAP-17 without FLAG tag. Data are presented as mean ± SD. All data obtained, after optimizing RIP protocol, are shown. N = 3 biologically independent experiments. Two-tailed unpaired student’s t-test. Tc1::Ngfp is Tc1::unc-54::Ngfp. g,h, Nanopore sequencing analysis of (g) wago-2 RNA, which contains inverted repeats from a non-autonomous Tc5 DNA transposon, and (h) the F19B2.5.2 5’ UTR, which contains an inverted repeat without homology to known DNA transposons. PCR amplicons were generated using primers flanking the wago-2 or F19B2.5.2 ITR regions. SOS splicing isoforms ranked by abundance. Isoforms representing > 1% of total reads are shown. wago-2 is annotated as a pseudogene because the mRNA predicted to be made from this locus contains premature termination codons. The data presented in g raise the possibility that wago-2 could be an active gene, due to the ability of SOS splicing to generate in-frame mRNAs. It will be of interest, once the cellular targets of SOS splicing have been comprehensively identified, to determine if other genes, currently annotated as pseudogenes, have biological function(s) because of SOS splicing. i,j, TapeStation-based SOS splicing detection of wago-2 (i) or F19B2.5.2 5’ UTR (j) in animals of the indicated genotypes. The data show that AKAP-17 is partially required for wago-2 and F19B2.5.2 splicing. Unspliced and spliced amplicons are indicated. k, Quantification of transposon excision from two endogenous Tc1 insertion sites (Wormbase ID WBTransposon00000020, WBTransposon00000033) via qPCR. Fold change was normalized to w/o reporter group. An nhj-1 null allele (gg875) was used as a negative control. Data are presented as mean ± SD with all data points shown. N = 3 biologically independent experiments. One-way ANOVA (two-tailed) with Tukey’s test was used to compare each group with w/ reporter.

Source Data

Extended Data Fig. 7 AKAP-17 and CAAP-1 are ubiquitously expressed nuclear proteins, and AKAP-17 is not required for most spliceosomal splicing events.

a,b, Top, mScarlet was inserted into the N-terminus of the akap-17 (a) or caap-1 (b) genes. Bottom, spinning disc confocal images of mScarlet::AKAP-17 (a) or mScarlet::CAAP-1 (b) in C. elegans of the indicated developmental stages. Scale bar = 50 μm for larval stage worms; scale bar = 10 μm for (60 x) images of germ cells. c, Left, SOS splicing isoforms detected by nanopore sequencing in Tc1::Ngfp mRNA, ranked by abundance. Isoforms representing more than 2% of total reads are shown. Total in-frame percentage is indicated. Right, the corresponding percentages of each splicing isoform detected by Illumina-seq in akap-17(+) and akap-17(−) animals are shown, respectively. Both spliced and unspliced isoforms were identified through SOS splicing junction searches conducted on raw read data. Seed sequences used for junction identification are listed in Supplementary Table 2. Percentages were calculated based on total junction-matched reads. Data are presented as mean ± SD with all data points shown. N = 3 biologically independent experiments. The two-tailed unpaired student’s t-test. d, PSI analysis for five types of alternative splicing events analysed by rMATS from Illumina-based RNA sequencing data of rRNA-depleted total RNA from animals of the indicated genotypes. n, number of alternative splicing events detected. A3SS, alternative 3’ splice sites; A5SS, alternative 5’ splice sites; MXE, mutually exclusive exons; RI, retained introns; SE, skipped exons. Box and whisker plots represent the median extending from the 25th to 75th percentiles (box) or min and max values (whiskers) with outliers (outside of 1.5x interquartile range) also shown. The Wilcoxon rank-sum test (two-tailed) was performed to assess statistical differences, with all P-values shown.

Source Data

Extended Data Fig. 8 The SOS splicing system is conserved in human cells.

a,b, TapeStation analysis of amplicons generated from RNA extracted from HEK293T cells of the indicated genotypes, transfected with Tc1-GFP (a) or HSMAR2-GFP (b) SOS splicing reporter plasmid. The data show that AKAP17A is needed for SOS splicing of transposons from their respective mRNAs in human cells. Unspliced and spliced amplicons are indicated with arrows. c, SOS splicing isoforms detected after nanopore-based sequencing of the HSMAR2-GFP mRNA, ranked by abundance. Isoforms representing > 0.5% of total reads are shown. Total in-frame percentage is indicated. d, Nanopore sequencing analysis comparing ITR swap effects of SOS splicing reporters. HEK293T cells transfected with, top, Tc1-GFP, HSMAR2 ITR-Tc1-GFP SOS splicing reporters, and bottom, HSMAR2-GFP, or Tc1 ITR-HSMAR2-GFP SOS splicing reporters. PCR amplicons were generated using primers flanking the transposon insertion site. e, TapeStation analysis of amplicons generated from DNA or from cDNA (RT-PCR) isolated from HEK293T cells transfected with indicated SOS splicing reporters. Unspliced and spliced amplicons are indicated. f,g, Flow cytometry of HEK293T cells harbouring the chromosomally integrated HSMAR2-GFP SOS splicing “knock-in” reporter gene. (f) AKAP17A(+) or (-) cells, and AKAP17A(-) cells complemented with AKAP17A(+), are shown. (g) CAAP1(+) cells and two independently derived clones (1# and 2#) of CAAP1(-) HEK293T cells are shown. The data show that SOS splicing of the HSMAR2-GFP mRNA requires AKAP17A and CAAP1. h, Western blot analysis showing that AKAP17A(-) cells lack AKAP17A. And that lentiviral-based complementation with wild-type AKAP17A(+) was successful. GAPDH serves as a loading control. i, Western blot analysis showing that CAAP1(-) cells lack CAAP1. GAPDH serves as a loading control.

Extended Data Fig. 9 Hybrid spliceosome-SOS splicing.

a, Nanopore sequencing of RNA isolated from Tc1::Ngfp animals. In vitro synthesized RNAs were included as controls. In Tc1::Ngfp animals, Tc1 is located 3 nucleotides from a canonical 5’ host-gene spliceosome splice site. Sequence surrounding observed splice sites are shown, with common splice sites underlined. The data show that Tc1 is excised from Tc1::Ngfp via 5’ SOS splice sites in or near the 5′ ITR of Tc1 and a canonical spliceosome 3’ splice site in a downstream host-gene exon. The existence of these splices suggests that, remarkably, the two splicing systems (SOS and spliceosome) can, in certain contexts, coordinate their splicing efforts (see below). This splicing is henceforth referred to as hybrid SOS-spliceosome splicing. Interestingly, for both examples of hybrid splicing described in this figure (Tc1::Ngfp and Tc1::dpy-19(n1347), see below), Tc1 is integrated near a host gene spliceosome acceptor or donor splice site, hinting that proximity to a canonical splice site may trigger hybrid SOS-spliceosome splicing. Assessing how and when the SOS and spliceosomal splicing systems interact could reveal additional biological function(s) of SOS splicing. b, Because Tc1::Ngfp is one of the two reporter genes used for the genetic screen presented in Fig. 3 of the main text, a more detailed description of Tc1 excision patterns from Tc1::Ngfp is presented in panels b,c. b, Tc1::Ngfp splice isoforms visualized by TapeStation analysis are shown. PCR amplicons were generated from genomic DNA (left panel) or cDNA (RT-PCR) (right panel) isolated from animals of indicated genotypes. AKAP-17 is required for 90–99% of SOS splicing events that occur at most loci in which Tc1 is integrated in an exon at a distance >11nt from a host-gene splice site (Fig. 3). The data in panels b,c show that the production of a subset of Tc1::Ngfp splice isoforms depend upon AKAP-17 for their production. The AKAP-17 dependence of these splices support the model put forth in a that these RNAs are hybrid splices produced via the concerted action of the spliceosome and the SOS splicing machinery. A subset of the Tc1 excision isoforms shown in panel a are GU-AG splices and one of these splices does not require AKAP-17 for its biogenesis (b and Extended Data Fig. 7c). Thus, TE excision from the Tc1::Ngfp RNA is a complex process involving AKAP-17-dependent SOS-spliceosome hybrid splices as well as AKAP-17-independent splices, which are not mediated by SOS splicing. We hypothesize that the GU-AG AKAP-17-independent Tc1::Ngfp splices shown in b and Extended Data Fig. 7c are spliceosome-derived. Transposons do not benefit when they harm their hosts. For this reason, transposons such as Tc1 might be expected to evolve in multiple ways that limit their interference with host gene expression. Given these considerations, we speculate that Tc1, and perhaps other transposons, have evolved spliceosomal 5’ or 3’ splice sites in their ITR elements, which can be used by host cells to excise TEs via the spliceosome, independently of SOS splicing. And it is possible that this spliceosome-mediated TE removal pathway becomes active when Tc1 lands very near a host donor (a-c) (or acceptor, see panels d,e) splice site, which could prevent recognition of canonical splice sites. When taken together with other data presented in this work, the data leading us to the following model: C. elegans has both primary and backup systems to remove TEs from host mRNAs; 1) SOS splicing, which is the primary pathway and the subject of this work, excises most TEs from mRNAs independently of the spliceosome; and 2) back-up pathways that uses either SOS splicing and the spliceosome (hybrid, non GU-AG, AKAP-17 dependent), or just the spliceosome (GU-AG, AKAP-17 independent splices), to remove TEs when TEs disrupt host donor or acceptor splice sites. Tc1::Ngfp was chosen as one of the two reporter genes for our genetic screen before we understood the remarkable complexity of splicing at this locus. A genetic screen that uses reporter genes that report solely on hybrid SOS-spliceosome splicing biogenesis might identify cellular machinery needed specifically for hybrid spliceosome-SOS splicing. c, qRT-PCR analysis quantifying efficiency of Tc1 excision from Tc1::Ngfp in animals of the indicated genotypes. Splicing efficiency was determined by calculating the percentage of spliced mRNA relative to total Tc1::Ngfp reporter mRNA, with splicing efficiency of wild-type animals serving as a normalization standard (set to 1). Tc1 excision from Tc1::Ngfp in three independent akap-17 mutant strains is decreased 2-3x. The data support the idea that most, but not all, Tc1 excision events from Tc1::Ngfp depend on the SOS splicing machinery, which is consistent with data in panel b. We suspect that the residual Tc1 excision observed from Tc1::Ngfp in akap-17(−) animals represents GU-AG spliceosomal splicing, as outlined in panel b. Data are presented as mean ± SD with all data points shown. N = 3 biologically independent experiments. One-way ANOVA (two-tailed) with Tukey’s test was used to compare each group with akap-17(+). d, Nanopore sequencing of RNA isolated from Tc1::dpy-19(n1347) animals. In Tc1::dpy-19 animals, Tc1 is located 11 nucleotides from a canonical 3’ host-gene spliceosome splice acceptor site. Sequences bordering splice sites (SS) are shown, with common splice sites underlined. Hybrid SOS-spliceosome isoforms are detected in Tc1::dpy-19 animals, which connect apparent SOS splice sites in or near the 3’ ITR of Tc1 to a neighboring spliceosome 5’ donor site. The data also show that in Tc1::dpy-19(n1347) animals, similar to what was observed for the Tc1::Ngfp gene (a), about half of the splice isoforms generated by Tc1::dpy-19 are GU-AG splices and about half are not. We have not yet tested the AKAP-17 (SOS splicing) dependence of these isoforms. We predict that in SOS splicing defective animals the non-GU-AG Tc1::dpy-19 splice isoforms will be missing- because these RNAs are hybrid SOS-spliceosome splices- while the GU-AG Tc1::dpy-19 splice isoforms will persist, because they derive exclusively from spliceosomal activity. e, Splicing isoforms of Tc1::dpy-19 visualized by RT-PCR and TapeStation analysis. Spliced amplicons with percentage are indicated.

Source Data

Extended Data Fig. 10 The RNA ligase RTCB is required for SOS splicing.

a, Co-immunoprecipitation (Co-IP) assay showing interactions between C. elegans AKAP-17 and CAAP-1. GAPDH serves as a loading control. b, Pull-down experiment demonstrating a physical interaction between C. elegans AKAP-17, CAAP-1 and RTCB-1. C. elegans HA::RTCB-1 protein was synthesized using PURExpress® In Vitro Protein Synthesis Kit, and incubated with lysates generated from animals expressing FLAG::AKAP-17 and MYC::CAAP-1. HA::RTCB-1 was IP’ed with Pierce™ anti-HA magnetic beads and HA::RTCB-1 co-precipitating proteins were detected by Western blot using the indicated antibodies. c, AlphaFold Multimer predictions. Left panel, upper, C. elegans AKAP-17 (pink) and CAAP-1 (gold) (ipTM = 0.47). Left panel, bottom, H. sapiens AKAP17A (pink) and CAAP1 (gold) (ipTM = 0.36). Right panel, upper, C. elegans RTCB-1 (blue) and CAAP-1 (gold) (ipTM = 0.3). Right panel, bottom, H. sapiens RTCB (blue) and CAAP1 (gold) (ipTM = 0.23). PAE matrix for each prediction is shown. Dashed black lines indicate chain boundaries. d, DIC images of wild-type, rtcb-1(C122A), and rtcb-1(H428A) animals, after 65 h of development at 20 °C. The data show that rtcb-1 mutant animals arrest development at the L3 stage. Scale bar = 200 μm. Genotypes were established by PCR analysis of arrested and non-arrested animals. e, rtcb-1(gk451) contains a 370 bp deletion spanning the promoter and the first exon of the rtcb-1 locus. Tc1::NmScarlet SOS splicing reporter was injected into rtcb-1(gk451)/hT2 [bli-4(e937) let-?(q782) qIs48] P0 adult animals, and the percentage of F1 progeny expressing mScarlet signal was quantified. qIs48 is an insertion of ccEx9747 with markers: MYO-2::GFP expressed brightly in the pharynx throughout development, PES-10::GFP expressed in embryos, and a gut promoter driving GFP in the intestine. GFP marks the wild-type copy of rtcb-1. Therefore, lack of GFP expression in progeny indicates animals are rtcb-1(gk451) and, therefore, animals not expressing GFP are (rtcb-1 M( + )Z(-)). Representative images of NmScarlet expression in animals expressing GFP ((rtcb-1 M( + )Z(+)) and not expressing GFP (rtcb-1 M( + )Z(-)) are shown. The data show that animals homozygous for a deletion allele of rtcb-1 are defective for SOS splicing of Tc1::NmScarlet SOS splicing reporter. Data are presented as mean ± SD with all data points shown. N = 3 biologically independent experiments. The two-tailed unpaired student’s t-test. Scale bar = 50 μm. f, Alleles of tsen-2, tsen-34, drsh-1 and dcr-1 generated by CRISPR-Cas9 are indicated in red. Predicted protein domains are shown. g, Left, DIC images of wild-type, tsen-2(gg1115::H197A), tsen-34(gg1150::5 bpΔ), drsh-1(gg1152::RIBOcΔ), and dcr-1(gg1153::S1377*, 1 bpΔ) animals, after 65 h of development at 20 °C. The data show that tsen-2, tsen-34, drsh-1, and dcr-1 mutant animals arrest development at the L3 or L4 stage. Scale bar = 200 μm. Genotypes were established by PCR analysis of arrested and non-arrested animals. Right, fluorescence micrographs showing Tc1::Ngfp SOS splicing reporter expression in wild-type, tsen-2(gg1115::H197A), tsen-34(gg1150::5 bpΔ), drsh-1(gg1152::RIBOcΔ), and dcr-1(gg1153::S1377*, 1 bpΔ) animals. Boxed regions are magnified to the right. Scale bar = 50 μm. h, Western blot analysis showing RTCB knockdown following siRTCB transfection into HEK293T cells. GAPDH serves as a loading control. i, Flow cytometry analysis detecting GFP in HEK293T HSMAR2-GFP reporter knock-in cells treated with esiRNA targeting RTCB (siRTCB) or Renilla Luciferase (siNC). j, TapeStation analysis of amplicons generated from RNA extracted from HEK293T cells treated with esiRNA targeting RTCB (KD) or Renilla Luciferase (NC) and co-transfected with HSMAR2-GFP reporter plasmid. Unspliced and spliced amplicons are indicated with arrows.

Source Data

Supplementary information

Supplementary Figs. 1 and 2

Supplementary Fig. 1. Uncropped gel images and TapeStation results. Supplementary Fig. 2. Flow cytometry gating strategies for the main figures and Extended Data figures.

Reporting Summary

Supplementary Table 1

Oligonucleotides, C. elegans strains, cell lines, antibodies and RNAi constructs used in this study.

Supplementary Table 2

SOS splicing reference sequences and Illumina seed sequences used in this study.

Supplementary Table 3

Results of AKAP-17 RIP–seq analysis. AKAP-17 RIP–seq targets (log_2[fold change] > 1), with normalized enrichment, significance and reproducibility scores.

Supplementary Table 4

Results of CAAP1 IP–MS analysis and CAAP1-associated proteins identified by IP–MS, with corresponding peptide counts.

Peer Review File

Source data

Source Data Fig. 1–3 and Source Data Extended Data Fig. 3, 4, 6, 7, 9, 10

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Zhao, LW., Nardone, C., Chang, C. et al. An RNA splicing system that excises DNA transposons from animal mRNAs. Nature 649, 496–504 (2026). https://doi.org/10.1038/s41586-025-09853-8

- Received: 12 February 2025

- Accepted: 03 November 2025

- Published: 10 December 2025

- Version of record: 10 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09853-8

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Amendments & Corrections

- Author Correction: Unravelling cysteine-deficiency-associated rapid weight loss

- Author Correction: Distinct fibroblast subsets drive inflammation and damage in arthritis

- Author Correction: Cancer SLC43A2 alters T cell methionine metabolism and histone methylation

- Author Correction: Conservation and alteration of mammalian striatal interneurons

Author Correction Open access Published: 12 December 2025

Author Correction: Unravelling cysteine-deficiency-associated rapid weight loss

Alan Varghese,

Ivan Gusarov,

Begoña Gamallo-Lana,

Daria Dolgonos,

Yatin Mankan,

Ilya Shamovsky,

Mydia Phan,

Rebecca Jones,

Maria Gomez-Jenkins,

Eileen White,

Rui Wang,

Drew R. Jones,

Thales Papagiannakopoulos,

Michael E. Pacold,

Adam C. Mar,

Dan R. Littman &

…

Evgeny Nudler

Nature volume 649, page E1 (2026)

The Original Article was published on 21 May 2025

Correction to: Nature https://doi.org/10.1038/s41586-025-08996-y Published online 21 May 2025

In the version of the article initially published, there was a copy-paste error in Extended Data Fig. 2f,g. Specifically, the muscle H&E panel for Day 3 Het No Cys was identical to the Day 7 KO Ctrl diet, and the Day 3 KO Ctrl panel was identical to the Day 7 KO No Cys. These errors involved the incorrect panels for Day 3 Het No Cys and Day 3 KO Ctrl. The figure has now been updated with the correct panels in both the HTML and PDF versions of the article. This error did not affect the conclusions of the figure or the overall paper.

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Author notes

1. These authors contributed equally: Alan Varghese, Ivan Gusarov

Authors and Affiliations

1. Department of Cell Biology, NYU Grossman School of Medicine, New York, NY, USA

Alan Varghese, Daria Dolgonos, Yatin Mankan, Mydia Phan & Dan R. Littman

2. Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA

Alan Varghese, Ivan Gusarov, Ilya Shamovsky, Drew R. Jones & Evgeny Nudler

3. Department of Neuroscience and Physiology, Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA

Begoña Gamallo-Lana & Adam C. Mar

4. Division of Advanced Research Technologies, NYU Grossman School of Medicine, New York, NY, USA

Rebecca Jones

5. Rutgers Cancer Institute, Rutgers University, New Brunswick, NJ, USA

Maria Gomez-Jenkins & Eileen White

6. Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ, USA

Maria Gomez-Jenkins & Eileen White

7. Ludwig Princeton Branch, Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA

Eileen White

8. Department of Biology, York University, Toronto, Ontario, Canada

Rui Wang

9. Department of Pathology, Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA

Thales Papagiannakopoulos

10. Department of Radiation Oncology and Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA

Michael E. Pacold

11. Howard Hughes Medical Institute, New York, NY, USA

Dan R. Littman & Evgeny Nudler

Corresponding authors

Correspondence to Dan R. Littman or Evgeny Nudler.

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Varghese, A., Gusarov, I., Gamallo-Lana, B. et al. Author Correction: Unravelling cysteine-deficiency-associated rapid weight loss. Nature 649, E1 (2026). https://doi.org/10.1038/s41586-025-09780-8

- Published: 12 December 2025

- Version of record: 12 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09780-8

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Author Correction Published: 12 December 2025

Author Correction: Distinct fibroblast subsets drive inflammation and damage in arthritis

Adam P. Croft,

Joana Campos,

Kathrin Jansen,

Jason D. Turner,

Jennifer Marshall,

Moustafa Attar,

Loriane Savary,

Corinna Wehmeyer,

Amy J. Naylor,

Samuel Kemble,

Jenefa Begum,

Kerstin Dürholz,

Harris Perlman,

Francesca Barone,

Helen M. McGettrick,

Douglas T. Fearon,

Kevin Wei,

Soumya Raychaudhuri,

Ilya Korsunsky,

Michael B. Brenner,

Mark Coles,

Stephen N. Sansom,

Andrew Filer &

…

Christopher D. Buckley

Nature volume 649, page E2 (2026)

The Original Article was published on 29 May 2019

Correction to: Nature https://doi.org/10.1038/s41586-019-1263-7 Published online 29 May 2019

In the version of this article initially published, in the legend of Extended Data Fig. 6, “SL fibroblasts correspond to F1–F4 fibroblast subsets and are PDPN+THY1−” should have read “SL fibroblasts correspond to F1–F4 fibroblast subsets and are PDPN+THY1+”. Due to the age of the article, the caption cannot be updated directly; this amendment serves to correct the article.

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1. These authors contributed equally: Stephen N. Sansom, Andrew Filer

Authors and Affiliations

1. Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK

Adam P. Croft, Joana Campos, Jason D. Turner, Jennifer Marshall, Loriane Savary, Corinna Wehmeyer, Amy J. Naylor, Samuel Kemble, Jenefa Begum, Kerstin Dürholz, Francesca Barone, Helen M. McGettrick, Andrew Filer & Christopher D. Buckley

2. Versus Arthritis Centre of Excellence in the Pathogenesis of Rheumatoid Arthritis, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK

Adam P. Croft, Andrew Filer & Christopher D. Buckley

3. The Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

Kathrin Jansen, Moustafa Attar, Mark Coles, Stephen N. Sansom & Christopher D. Buckley

4. Musculoskeletal Medicine, University of Muenster, Muenster, Germany

Corinna Wehmeyer

5. Department of Internal Medicine 3, Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany

Kerstin Dürholz

6. Department of Medicine, Division of Rheumatology, Northwestern University, Feinberg School of Medicine Chicago, Evanston, IL, USA

Harris Perlman

7. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

Douglas T. Fearon

8. Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Kevin Wei, Soumya Raychaudhuri, Ilya Korsunsky & Michael B. Brenner

9. University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

Andrew Filer

10. MRC and Versus Arthritis Centre for Musculoskeletal Ageing Research (CMAR), College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK

Andrew Filer & Christopher D. Buckley

Corresponding author

Correspondence to Christopher D. Buckley.

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Croft, A.P., Campos, J., Jansen, K. et al. Author Correction: Distinct fibroblast subsets drive inflammation and damage in arthritis. Nature 649, E2 (2026). https://doi.org/10.1038/s41586-025-09783-5

- Published: 12 December 2025

- Version of record: 12 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09783-5

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Author Correction Published: 12 December 2025

Author Correction: Cancer SLC43A2 alters T cell methionine metabolism and histone methylation

Yingjie Bian,

Wei Li,

Daniel M. Kremer,

Peter Sajjakulnukit,

Shasha Li,

Joel Crespo,

Zeribe C. Nwosu,

Li Zhang,

Arkadiusz Czerwonka,

Anna Pawłowska,

Houjun Xia,

Jing Li,

Peng Liao,

Jiali Yu,

Linda Vatan,

Wojciech Szeliga,

Shuang Wei,

Sara Grove,

J. Rebecca Liu,

Karen McLean,

Marcin Cieslik,

Arul M. Chinnaiyan,

Witold Zgodziński,

Grzegorz Wallner,

Iwona Wertel,

Karolina Okła,

Ilona Kryczek,

Costas A. Lyssiotis &

…

Weiping Zou

Nature volume 649, page E3 (2026)

The Original Article was published on 02 September 2020

Correction to: Nature https://doi.org/10.1038/s41586-020-2682-1 Published online 3 September 2020

In the version of the article initially published, due to a figure preparation mistake, in Extended Data Fig. 1j, the representative flow cytometry dot plot for condition “Sup+Ser” was mistakenly a duplicate image of condition “A375 sup.” The figure has been updated in the HTML and PDF versions of the article.

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Author notes

1. These authors contributed equally: Yingjie Bian, Wei Li

Authors and Affiliations

1. Department of Surgery, University of Michigan School of Medicine, Ann Arbor, MI, USA

Yingjie Bian, Wei Li, Shasha Li, Joel Crespo, Houjun Xia, Jing Li, Peng Liao, Jiali Yu, Linda Vatan, Wojciech Szeliga, Shuang Wei, Sara Grove, Ilona Kryczek & Weiping Zou

2. Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, University of Michigan School of Medicine, Ann Arbor, MI, USA

Yingjie Bian, Wei Li, Shasha Li, Joel Crespo, Houjun Xia, Jing Li, Peng Liao, Jiali Yu, Linda Vatan, Wojciech Szeliga, Shuang Wei, Sara Grove, Ilona Kryczek & Weiping Zou

3. Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA

Daniel M. Kremer, Peter Sajjakulnukit, Zeribe C. Nwosu, Li Zhang & Costas A. Lyssiotis

4. Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA

Shasha Li & Marcin Cieslik

5. Department of Virology and Immunology, Maria Curie-Skłodowska University, Lublin, Poland

Arkadiusz Czerwonka

6. First Chair and Department of Oncological Gynecology and Gynecology, Medical University of Lublin, Lublin, Poland

Anna Pawłowska, Iwona Wertel & Karolina Okła

7. Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA

J. Rebecca Liu & Karen McLean

8. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

Marcin Cieslik, Arul M. Chinnaiyan & Weiping Zou

9. Department of Urology, University of Michigan, Ann Arbor, MI, USA

Arul M. Chinnaiyan

10. Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA

Arul M. Chinnaiyan

11. Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA

Arul M. Chinnaiyan

12. Second Department of General, Gastrointestinal Surgery and Surgical Oncology of the Alimentary Tract, Medical University of Lublin, Lublin, Poland

Witold Zgodziński & Grzegorz Wallner

13. Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA

Costas A. Lyssiotis

14. Graduate Program in Immunology, University of Michigan School of Medicine, Ann Arbor, MI, USA

Costas A. Lyssiotis & Weiping Zou

15. Graduate Program in Cancer Biology, University of Michigan School of Medicine, Ann Arbor, MI, USA

Costas A. Lyssiotis & Weiping Zou

Corresponding author

Correspondence to Weiping Zou.

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Bian, Y., Li, W., Kremer, D.M. et al. Author Correction: Cancer SLC43A2 alters T cell methionine metabolism and histone methylation. Nature 649, E3 (2026). https://doi.org/10.1038/s41586-025-09845-8

- Published: 12 December 2025

- Version of record: 12 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09845-8

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Author Correction Open access Published: 11 December 2025

Author Correction: Conservation and alteration of mammalian striatal interneurons

Emily K. Corrigan,

Michael DeBerardine,

Aunoy Poddar,

Miguel Turrero García,

Sean de la O,

Siting He,

Harsha Sen,

Mariana Duhne,

Shanti Lindberg,

Mengyi Song,

Matthew T. Schmitz,

Karen E. Sears,

Ricardo Mallarino,

Joshua D. Berke,

Corey C. Harwell,

Mercedes F. Paredes,

Fenna M. Krienen &

…

Alex A. Pollen

Nature volume 649, page E4 (2026)

The Original Article was published on 05 November 2025

Correction to: Nature https://doi.org/10.1038/s41586-025-09592-w Published online 5 November 2025

In the version of the article initially published, Mengyi Song’s name appeared incorrectly (as Menygi) and has now been amended in the HTML and PDF versions of the article.

Author information

Author notes

1. These authors contributed equally: Michael DeBerardine, Aunoy Poddar, Miguel Turrero García

Authors and Affiliations

1. Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA

Emily K. Corrigan, Aunoy Poddar, Miguel Turrero García, Sean de la O, Mengyi Song, Corey C. Harwell, Mercedes F. Paredes & Alex A. Pollen

2. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

Emily K. Corrigan, Aunoy Poddar, Miguel Turrero García, Mariana Duhne, Mengyi Song, Joshua D. Berke, Corey C. Harwell, Mercedes F. Paredes & Alex A. Pollen

3. Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

Michael DeBerardine, Siting He & Fenna M. Krienen

4. Medical Scientist Training Program, University of California San Francisco, San Francisco, CA, USA

Aunoy Poddar

5. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

Harsha Sen & Ricardo Mallarino

6. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA

Shanti Lindberg & Karen E. Sears

7. Allen Institute for Brain Science, Seattle, WA, USA

Matthew T. Schmitz

8. Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA

Karen E. Sears

9. Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

Joshua D. Berke, Corey C. Harwell, Mercedes F. Paredes & Alex A. Pollen

10. Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA

Joshua D. Berke

11. Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, USA

Joshua D. Berke & Alex A. Pollen

12. Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA

Corey C. Harwell & Mercedes F. Paredes

Corresponding authors

Correspondence to Mercedes F. Paredes, Fenna M. Krienen or Alex A. Pollen.

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Corrigan, E.K., DeBerardine, M., Poddar, A. et al. Author Correction: Conservation and alteration of mammalian striatal interneurons. Nature 649, E4 (2026). https://doi.org/10.1038/s41586-025-09993-x

- Published: 11 December 2025

- Version of record: 11 December 2025

- Issue date: 08 January 2026

- DOI: https://doi.org/10.1038/s41586-025-09993-x

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Amendments & Corrections

Author Correction: Unravelling cysteine-deficiency-associated rapid weight loss

Author Correction: Distinct fibroblast subsets drive inflammation and damage in arthritis

Author Correction: Cancer SLC43A2 alters T cell methionine metabolism and histone methylation

Author Correction: Conservation and alteration of mammalian striatal interneurons