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EDITORIAL 04 August 2026

What’s missing from the White House plan for the future of US science

To realize ‘a new golden age for science’, the United States needs open borders and must properly fund studies in the social sciences, public health and the humanities — not just research in technology and engineering.

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A 1945 report by Vannevar Bush, director of the US Office of Scientific Research and Development, was seminal to the growth of US research.Credit: AP/Alamy

Last month, Michael Kratsios, head of the US Office of Science and Technology Policy (OSTP), released a report called Science: A New Golden Age. The 123-page document is a response to a letter from US President Donald Trump tasking Kratsios with ushering in “a new golden age of American innovation” (see go.nature.com/8tyfk4k). But how likely is it that the report will help the country to achieve this aim?

Such a letter from the president to the OSTP head is not uncommon. It is a signal of the government’s interests and priorities. However, Trump’s letter and the response come amid a systematic effort to reshape US research.

There’s a lot to take in. The report asks hard questions about the future of US science, including about how to support the research that provides the foundations for technological advances and how to enhance crucial scientific infrastructure. There are timely questions about the role of artificial intelligence in science and around the process of issuing research grants. The report proposes changes, too, to how the US federal research-funding portfolio is balanced, for instance. It addresses the persistent concern that grant managers and reviewers are too cautious, and proposes fresh ways to fund people who are willing to take risks and to individuals outside universities. The details need time for reflection and we plan to return to some of the report’s themes in greater depth in future articles.

Before diving deeper into the report’s content, it’s important to consider its precedents and the context in which it was produced. A key predecessor is a 1945 report called Science: The Endless Frontier, which was submitted to then-president Harry Truman by Vannevar Bush, director of the Office of Scientific Research and Development at the time (see go.nature.com/4g7nufa). Produced in the aftermath of the Second World War, this fundamental document in US science policy led to the establishment of a new public funding agency for basic research: the National Science Foundation. The report and its implementation took several years and was a highly inclusive exercise with inputs from academia, industry and policymakers.

Science: A New Golden Age has also been written during a tumultuous period, but of a different kind. It was released after 18 months of executive orders, rule changes, abrupt funding cuts, delays to grants being awarded and political interference in many scientific matters that should be the domain of experts, ranging from vaccine safety to climate change. However, unlike the 1945 report, the current one does not name the members of scientific and technical communities who were consulted. This follows a pattern: Trump’s administration has substantially reduced the flow of advice from independent specialists to the federal government, as we documented in an April News Feature (Nature 652, 1106–1109; 2026).

Science: A New Golden Age, at its core, is a set of priorities from Trump’s administration for scientific leaders to focus on. The fields named include “AI, quantum information science, semiconductors, advanced communications, robotics, advanced manufacturing, nuclear fission and fusion, and space systems”. Such large-scale instructions to the research community, without transparent consultation with its stakeholders, is rare for a democratic country — let alone for a powerhouse of research and the home of many globally respected universities and science-based corporations.

But the details of these instructions are not straightforward to find. They are included in an annex in the main document. This part contains a memorandum for the heads of departments and executive agencies co-signed by Kratsios and Russell Vought, the director of the Office of Management and Budget. The annex includes a list of what the document calls “foundational research to drive scientific breakthroughs for emerging technologies”.

Yes minister

The priorities focus mainly on research in physics, materials science, chemistry, computer science, engineering, space science and mathematics, and especially on “national security-relevant research portfolios”. Federal agencies with annual research budgets of at least US$3 billion have 90 days to tell the White House how they will “prioritize both the absolute level and the relative share of funding”. Furthermore, to support the administration’s priorities in biotechnology and biomanufacturing, agencies are being asked to “prioritize foundational research in the biological sciences over the life sciences, a broader category not focused on foundational research”. Details on which fields will be prioritized for funding extend over several pages.

But the people who can assess foundational science accurately are those closest to it: the researchers and their representative societies. The report says that parties “across the country’s scientific enterprise” have been consulted, but doesn’t specify who has provided input. Which scientists or organizations in fields such as physics, computer science, space science and chemistry were asked for feedback? Who is saying that life sciences does not focus on foundational research?

One glaring omission in this proposal for overhauling science is direct evidence of input from the National Academies of Sciences, Engineering, and Medicine and from scholarly societies representing individual disciplines. These communities are the modern equivalents of professional guilds. They organize conferences and publish research in their fields; they act as stewards for their disciplines, by setting quality standards, for instance. They represent the field’s interests to policymakers, and protect and defend scholarly integrity.

Missing in action

The report’s outlook for the future of science also does not mention some key research areas, such as climate change and public health. Neither is research in social sciences included, although metascience is mentioned. These are surprising omissions, considering that national security is an objective. Climate change and emerging infectious diseases will have huge consequences, for both the United States and the world. Studies in the social sciences and humanities are just as important as those in science and engineering, and will be necessary to help societies to navigate the transformational impacts of AI.

Other surprising omissions from the report’s annex are phrases such as world, global and international cooperation. This is in keeping with Trump’s decision to withdraw funding for, and the country’s participation in, international scientific networks such as the Intergovernmental Panel on Climate Change and the World Health Organization. There is an assumption that US science will achieve its golden age all by itself — cut off from researchers in other countries. That idea goes against the evidence of the past 75 years or so. Moreover, the broader literature of the history of science shows what can be achieved by civilizations that welcome people from all parts of the world. Science is not restricted to national borders, nor should it be.

Time machine

The latest report seeks to establish itself as a successor to Science: An Endless Frontier — and mentions the words frontier or frontiers more than 50 times. But the two documents are very different. The 1945 report provided a clear path for the United States to make groundbreaking discoveries and inventions — continuing on from the global flurry of advances, such as radar, nuclear weapons and the mass production of penicillin, that emerged during the Second World War. In the process of producing the report, Bush formed four scientific committees, representing medicine, science and public welfare, scientific talent development and the publication of scientific information. Their advice was included in the final report. Science: An Endless Frontier was an inclusive discussion that offered hope for a better future.

The current White House plan could have spoken to the moment: the world is dealing with both planetary and societal challenges, including ones posed by AI. The report could have been inclusive, spanning borders and disciplines, not to complete a box-ticking exercise, but because a consultative approach provides the best chance for the world to emerge out of crisis. It could have said, let’s learn from the best, regardless of who they are; let’s learn what works, regardless of where the ideas come from; let’s ensure that scientists are valued partners in those conversations. But it doesn’t say any of these things and that’s a missed opportunity.

Nature 656, 7-8 (2026)

doi: https://doi.org/10.1038/d41586-026-02384-w

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WORLD VIEW 04 August 2026

Why the need for the WHO has never been greater

Diseases don’t stop at borders. Pandemics, climate change and conflicts demand a collective approach.

By

Tedros Adhanom Ghebreyesus

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This July marked 80 years since representatives of 61 states signed the constitution of the World Health Organization (WHO) in New York City. The founders understood that diseases do not respect borders, and that progress depends on countries working towards a common goal.

Over the past eight decades, working with more than 190 member states and other partners, the WHO has helped to eradicate smallpox, driven progress towards wiping out polio, expanded access to life-saving vaccines and led the global fight against HIV. It has strengthened health-care systems, set health standards and helped to save millions of lives through efforts to improve tobacco control, water and air quality and sanitation.

As a result, people today live more than 25 years longer, on average, than they did 80 years ago. Far fewer women die during pregnancy and childbirth, and many more children survive and thrive past their fifth birthday.

These health gains come down to one choice: cooperation over isolation. Events this year show that the vision behind the WHO constitution is just as necessary today.

The current Ebola epidemic in northeastern Democratic Republic of the Congo, cases of measles surging across the Americas and the now-ended hantavirus outbreak are reminders that health threats can emerge anywhere and affect anyone. The COVID-19 pandemic, the most severe health crisis in a century, showed that no country can tackle such health threats alone.

The WHO’s strength lies in its ability to bring together health leaders, scientists and the global health community to tackle shared challenges and find solutions, to set global standards and coordinate action in times of crisis. That value was demonstrated again last year, when member states adopted the WHO Pandemic Agreement. This treaty applies the lessons of COVID-19 to make the world more equitable and better prepared for future pandemics.

But cooperation isn’t enough: countries also need greater capacity to respond to health crises. The WHO’s Universal Health and Preparedness Review helps countries to identify gaps in their ability to respond to health emergencies. Through the Pandemic Fund, the WHO and the World Bank have supported projects in 70 countries to strengthen disease surveillance, laboratory networks, health workforces and coordination across sectors.

Last year also tested global multilateralism, science and solidarity like never before. Sudden, severe cuts to international aid disrupted essential health services in many countries, threatening to reverse decades of progress.

The WHO is supporting these nations to build more sustainable, self-reliant health-care systems. In the short term, we are helping them to design affordable packages of essential health services, introducing or raising health taxes on tobacco, alcohol and sugary drinks, and investing in domestic manufacturing. In the long term, countries can strengthen risk-sharing mechanisms, such as publicly funded health insurance, to better protect people financially.

In 2025, the United States gave notice of its intent to leave WHO. For years, the organization leant too heavily on voluntary funding from a few donors. In 2017, it began a finance reform to broaden its sources of revenue. In 2022, member states agreed to raise their membership fees from 16% to 50% of the core budget over time, a key step towards financial independence. Two increases have been approved since then, with three more due over the next five years.

The reform of the WHO’s financing was part of a wider institutional transformation to sharpen the WHO’s focus on what it does best: providing high-quality science, data and standards to help countries deliver health for all. Before 2019, the way that the WHO developed its guidance and standards was fragmented. We created a science division to modernize how we develop guidelines, expand global research networks and increase investment in digital technology and data systems, including artificial intelligence. In 2015, 85 member states had a national digital health strategy; today, 129 do. The WHO also serves as a hub for health data and digital services, through platforms such as the World Health Data Hub, the Global Health Observatory and the Global Digital Health Certification Network.

As the organization enters its ninth decade, digital technologies will be crucial as we adapt to meet the world’s fast-changing needs. The world’s population has more than tripled in 80 years, placing strain on natural and urban environments with consequences for people’s health. Outbreaks and epidemics keep emerging. The health effects of conflict, climate change and displacement are growing. Non-communicable diseases, such as heart disease and cancer, are causing more illness and premature death in every country. Antimicrobial resistance threatens to undo a century of medical progress. Technological advances are creating both opportunities and risks for health.

In short, the need for a science-led, evidence-based, impartial and globally representative health organization has never been greater. Eighty years on, the WHO constitution remains a living promise, and a reminder of why the world still needs a strong WHO.

Nature 656, 9 (2026)

doi: https://doi.org/10.1038/d41586-026-02358-y

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Competing Interests

The author declares no competing interests.

News in Focus

- This El Niño is set to be the largest on record by a ‘mind-blowing margin’

- Humanity spoke thousands of languages at the dawn of farming — now many of them are lost

- First volunteer gets Ebola vaccine — three months after the outbreak began

- This AI Raygun can shrink and supersize proteins — opening the door to easy editing

- Rising stars of mathematics awarded prestigious 2026 Fields Medal

- White House rolls out AI funding — and signals a new era for US science

- Exclusive: the science papers that patents cite the most

- Could mending damaged DNA prolong life?

NEWS 23 July 2026

This El Niño is set to be the largest on record by a ‘mind-blowing margin’

Forecasters predict that the monster climate pattern will combine with global warming to push global temperatures in 2027 to new heights.

By

James Dinneen

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California (Mondos Beach shown here in 2016) often experiences increased storms — and storm waves — during El Niños that can lead to flooding. Credit: Mark Ralston/AFP via Getty

The El Niño climate pattern that is strengthening in the Pacific Ocean is on track to be the largest one in at least 150 years, researchers said today during a press briefing. If the monster forecasts they presented pan out, the surge of heat that the El Niño will unleash from the ocean, combined with warming from human-caused climate change, will probably make next year the hottest on record — and cue up extreme weather in many parts of the world.

According to the projections from a suite of climate models, the peak of this El Niño is set to surpass the previous record-holder by “truly, a mind-blowing margin”, says Zeke Hausfather, a climate scientist at Berkeley Earth, a non-profit research organization in California that hosted the briefing.

It’s “not outside the realm of possibility” that the event could combine with climate change to raise global average temperatures in 2027 as high as 1.7 °C above the pre-industrial average, he said at the briefing. This would bring the planet right to the edge of breaching the 1.5 °C threshold that governments pledged to avoid under the Paris agreement.

The fact that an El Niño is occurring now isn’t surprising. The El Niño–Southern Oscillation (ENSO) is a natural fluctuation in sea surface temperature and winds in the tropical Pacific Ocean. Historically, it has swung between hot El Niños and cool La Niñas every two to seven years. The last El Niño took place in 2023–24 and, after forecasters had warned of a big event looming, this year’s emerged in June.

But the latest predictions suggest that this El Niño could become even bigger than anticipated. According to Justin Mankin, a climate researcher at Dartmouth College in Hanover, New Hampshire, it looks a lot like the ones that scientists had projected being amplified by human-caused climate change later this century.

Extreme forecasts

The elevated Pacific sea surface temperatures and wind patterns that are the hallmark of an El Niño have rapidly strengthened since June. In its latest advisory, the US National Oceanic and Atmospheric Administration (NOAA) gave a four in five chance of a “very strong” event developing by the end of the year, with near certainty that El Niño conditions will persist until at least April 2027.

“Right now, we’re seeing all the markers of very vigorous atmosphere–ocean coupling across the equatorial Pacific Ocean, which is giving us more confidence we will ultimately see a ‘very strong’ El Niño that would rank among the strongest El Niños in our historical record,” says Michelle L’Heureux, a meteorologist at NOAA who leads the agency’s ENSO prediction team in College Park, Maryland.

The NOAA forecast could be conservative, however. In a blog post last week and during the briefing today, Hausfather compared the projections from 14 climate models for this El Niño, and showed that the median estimate for peak sea surface temperature in the Pacific is about 0.8 °C higher than the previous record set in 2015–16 (see ‘Record breaker’). “It’s by no means a foregone conclusion, but the likelihood has risen month over month,” he said.

Source: Zeke Hausfather/Berkeley Earth

In addition to the El Niño event raising global average temperatures to record highs in 2027, Hausfather says that there’s a fair chance that it will boost temperatures enough in the next few months to make 2026 a record hot year, too. The pattern of heatwaves, droughts, flooding and wildfires associated with El Niño will also have consequences for economies. Mankin and his colleagues have estimated[1] that changes in weather attributed to a strong El Niño in 1997–98 shrank global economic growth by US$5.7 trillion in the years that followed.

If the forecasts are anywhere near correct, the current El Niño could cause “$10 trillion in economic losses by 2032”, Mankin says. Beyond impacts on human society, this El Niño is likely to stress ecosystems and oceans, driving declines in the net amount of carbon they take up from the atmosphere. Elevated ocean temperatures are also bad news for coral reefs bleached by the heat.

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Nature 656, 11-12 (2026)

doi: https://doi.org/10.1038/d41586-026-02293-y

References

1. Callahan, C. W. & Mankin, J. S. Science 380, 1064–1069 (2023).

2. Abram, N. J. et al. Nature 579, 385–392 (2020).

3. Coquereau, A., Sévellec, F., Huck, T. & Fedorov, A. V. Geophys. Res. Lett. 52, e2025GL116541 (2025).

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NEWS 23 July 2026

Humanity spoke thousands of languages at the dawn of farming — now many of them are lost

A shift from nomadic lifestyles could have triggered linguistic diversity before it then declined rapidly.

By

Freda Kreier

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The Rosetta Stone helped experts learn to read Egyptian hieroglyphs.Credit: Juan Naharro Gimenez/Getty

Humanity might once have spoken many more languages than it does today, with tens of thousands in use between 3,000 and 1,000 years ago. This far exceeds the more than 7,500 unique languages now used worldwide.

The findings, published on 23 July in Science[1], suggest that the rise of agriculture spurred an initial language boom as people shifted from a nomadic, hunter-gatherer lifestyle and settled into small, isolated communities. If accurate, these results indicate that language extinction is not a uniquely modern phenomenon, says Heidi Harley, a linguist at the University of Arizona in Tucson.

The true historical diversity of languages suggested by this analysis is “mind-blowing”, she says. “The sheer number of languages that they projected to have existed is like, ‘What? That’s crazy’.”

Farm of Babel

Aside from written sources, evidence of language in the archaeological record is sparse — meaning that researchers often know “surprisingly little” about historical language diversity, says Joe Salmons, a historical linguist at the University of Wisconsin–Madison.

This has led researchers to propose widely differing accounts of how language diversity has evolved over time. For the most part, those calculations are “back-of-the-envelope figures”, says co-author Claire Bowern, a historical linguist at Yale University in New Haven, Connecticut.

The transition to farming settlements in the Neolithic, like this one in Çayönü, Turkey, helped spur a boom in linguistic diversity.Credit: Omer Yasin Ergin/Anadolu Agency/Getty

In their study, Bowern and her colleagues aimed to track the global trajectory of language diversity over the past 12,000 years. They began by estimating how many languages were spoken 12,000 years ago, which was around the end of the last glacial period. To do so, they divided the approximate total global population at the time by the average size of hunter-gatherer groups from the same ethnic and cultural background. For this model, the researchers worked on the assumption that these groups of around 800 people spoke the same language — an inference based on twentieth-century research into native Alaskan groups and global historical language diversity.

Their calculation suggested that between 3,300 and 7,800 languages were spoken 12,000 years ago.

The team then aimed to estimate how language diversity has evolved over time, using statistical models. Because the structure of many societies changed at the end of the last glacial period, shifting from nomadic hunter-gatherer groups to agricultural communities and expanding into nation states, the model assumed that the groups speaking the same language eventually became larger.

Despite this, the analysis suggests that language diversity rose over the next few millennia as agriculture become more widespread globally, and peaked around 2,000 years ago, when tens of thousands of languages were in use — about ten times as many as were spoken at the end of the last ice age.

Why this happened is unclear, but Bowern suspects that one factor was the transition from nomadic to more-permanent settlements. This is supported by research in New Guinea and Vanuatu, which suggests that the dialects and languages of small farming settlements diverge more starkly from those of their neighbours than do those of hunter-gatherers in the same region.

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Nature 656, 12-13 (2026)

doi: https://doi.org/10.1038/d41586-026-02279-w

References

1. Blasi, D. E., Hamilton, M. J., Gray, R. D. & Bowern, C. L. Science https://doi.org/10.1126/science.adx4343 (2026).

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NEWS Q&A 28 July 2026

First volunteer gets Ebola vaccine — three months after the outbreak began

The University of Oxford has already vaccinated its first clinical-trial participant. The study’s lead scientist, Teresa Lambe, tells Nature how the team was able to move so fast.

By

Dhruv Shenai

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Researchers are aiming to test the vaccine in outbreak scenarios in Uganda later in 2026.Credit: Glody Murhabazi/AFP via Getty

Four days ago, the first volunteer was vaccinated against Bundibugyo ebolavirus. That is just 68 days after the World Health Organization declared the current Ebola outbreak in the Democratic Republic of the Congo (DRC) and Uganda a public-health emergency of international concern. By 27 July, officials had confirmed 3,200 cases and 1,405 deaths in the DRC alone.

The vaccine, called ChAdOx1, is being tested in a phase I clinical trial being run by the Oxford Vaccine Group at the University of Oxford, UK. ChAdOx1 uses the same vector platform as that in the Oxford–AstraZeneca COVID-19 vaccine, but carries genetic instructions for Bundibugyo ebolavirus protein. Around 620,000 doses of the vaccine have already been manufactured.

The trial aims to assess the safety of the vaccine and the immune response it generates before larger trials are rolled out in Uganda later this year. Two other vaccines are also in development: an mRNA vaccine developed by Moderna in Cambridge, Massachusetts, which is expected to go into clinical trials next month; and a candidate being developed by the International AIDS Vaccine Initiative that is similar to the WHO-recommended one for the Zaire ebolavirus species. All three vaccines are being funded by the Coalition for Epidemic Preparedness Innovations.

Teresa Lambe, head of vaccine immunology at Oxford, is the lead scientific investigator for the ChAdOx1 vaccine study. She also co-developed the Oxford–AstraZeneca COVID-19 vaccine.

Nature spoke to Lambe about how the ChAdOx1 vaccine made it into clinical trials so quickly and what comes next.

What was the secret to your speed?

We have been working on vaccines against Ebola virus for quite some time. Several people in the team were involved in the 2013 outbreak, and since then, we’ve been making candidate vaccines against Ebola and other filoviruses.

When the Bundibugyo outbreak was declared a public-health emergency, we actually had in the freezer the right type of genetic sequence to let us make the Bundibugyo virus-specific vaccine really quickly.

Teresa Lambe is head of vaccine immunology at Oxford, and the lead scientific investigator for the ChAdOx1 vaccine study.Credit: University of Oxford/Matt GREETHAM

To get a vaccine from concept all the way through to licensure or use in the field at speed, there are several bodies of work that need to be done at the same time, and not sequentially. There is the non-clinical work, the clinical work and the chemistry, manufacturing and controls framework used to ensure drug safety and quality that all need to be done concurrently.

We were generating the supportive non-clinical data to demonstrate that the vaccine was immunogenic while we were scaling up [manufacturing]. We worked with our onsite clinical biomanufacturing facility and the Serum Institute of India in Pune to make the starting material and doses for the clinical trials. The advantage of working with the serum institute is that they know how to rapidly scale up vaccine production — this know-how is built on years of knowledge. While the vaccine doses were being made, we started our non-clinical studies onsite and worked with trusted partners in the United Kingdom and United States. In parallel, we worked on the clinical protocol and engaged with regulators to ensure we could rapidly open the trial for recruitment. It goes without saying that you can’t do this without a dedicated team, global partnerships and meaningful collaborations.

What will the phase I trial assess?

We are looking in the first instance to recruit about 50 adults, 18- to 55-year-olds, in the United Kingdom. We’re looking for an indication of safety and any physical signs of the vaccine causing inflammation and immune responses. And with those data, we are hopeful that we will be able to start a trial with our partners in the MRC Uganda Virus Research Institute. This will be done in healthy volunteers who are not necessarily exposed to the disease. And we are working to enable the phase II and III trials in outbreak scenarios as rapidly as possible. Hopefully this will be in autumn or winter, but we need the phase-I trial data first.

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Nature 656, 14-15 (2026)

doi: https://doi.org/10.1038/d41586-026-02278-x

This interview has been edited for length and clarity.

NEWS 29 July 2026

This AI Raygun can shrink and supersize proteins — opening the door to easy editing

The model can modify existing proteins using a probability approach.

By

Gemma Conroy

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The protein haemoglobin (artist’s illustration) can be miniaturized by an AI tool called Raygun.Credit: Evan Oto/SPL

A sci-fi weapon has entered the AI age. An artificial-intelligence tool dubbed Raygun can miniaturize or supersize a natural protein without disrupting its shape or ability to function[1].

Scientists have developed a host of AI ‘protein language models’ that can create proteins from scratch. But Raygun can modify existing proteins using some of the same steps as natural evolution: adding or deleting single protein subunits or substituting one protein subunit for another.

Raygun is a “meaningful and creative” first step towards editing existing proteins, says Fajie Yuan, a computational biologist who works on protein language models at Westlake University in Hangzhou, China, who is not involved in the work. “For many real applications, that is exactly what researchers want — not a completely new protein, but a better, smaller, larger or more adaptable version of one they already trust,” he says.

The study is published today in Nature.

Protein revolution

Protein-building models are now so advanced that they can design antibodies with clinical potential. But scientists would also like to resize natural proteins — a tantalizing prospect for biotechnology applications, says study co-author Rohit Singh, a computational biologist at Duke University in Durham, North Carolina. “Sometimes a smaller protein can do things or fit into places where a larger protein cannot,” he says.

But efforts to use AI systems to modify existing proteins have been limited, because they tend to change the structure or function.

Singh and his colleagues turned to ESM-2, a large language model for protein design. Instead of text, ESM-2 and other AI-based protein models are trained on millions of protein sequences. After learning the evolutionary ‘grammar’ or patterns of these sequences, these models can generate new proteins.

Most protein language models create representations of proteins as amino-acid sequences of various lengths. But this system makes it difficult to create proteins that keep their overall structure and function at different sizes. So Raygun, which is based on ESM-2, takes a more mathematical approach: it divides each protein into pieces and translates the information in each piece into numerical patterns. The tool then uses these patterns to create a standardized version of the protein that follows particular rules. The model learns those rules, allowing it to generate the protein at various sizes without compromising its structural integrity.

Mini molecules

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Nature 656, 15-16 (2026)

doi: https://doi.org/10.1038/d41586-026-02335-5

References

1. Devkota, K. et al. Nature https://doi.org/10.1038/s41586-026-10842-8 (2026).

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NEWS 23 July 2026

Rising stars of mathematics awarded prestigious 2026 Fields Medal

Two of the four are the first Chinese-born winners of the prize since 1982.

By

Davide Castelvecchi

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The four Fields Medal recipients are, from left to right, Hong Wang, Jacob Tsimerman, John Pardon and Yu Deng.Credit: Simons Foundation

Mathematicians Yu Deng, John Pardon, Jacob Tsimerman and Hong Wang have won the 2026 Fields Medal — one of the most coveted awards in their discipline. Their names were revealed at the International Congress of Mathematicians (ICM) today in Philadelphia, Pennsylvania.

The four winners represent a range of subfields of maths, from number theory to mathematical physics. They had all been rumoured to be favourites to win the medal, which is awarded every four years to up to four mathematicians under the age of 40.

The winners all work in North America, but two — Deng and Wang — were born and raised in China. They are only the second and third Chinese nationals to have earned a Fields Medal: Shing-Tung Yau, who is now at Tsinghua University in Beijing, won his in 1982, before either Deng or Wang were born. Wang is also only the third woman to win in the award’s 90-year history, after the late Maryam Mirzakhani in 2014 and Maryna Viazovska in 2022.

Irreversible fluids

Deng, who is 37 and grew up in Shenzhen, did his PhD at Princeton University in New Jersey. He is now at the University of Chicago in Illinois, where he specializes in differential equations, which are often used to describe physical phenomena. He says that hearing he had won the medal made him “really happy, not just for myself, but for the field I’m representing”.

Deng’s most celebrated achievement was a breakthrough on one of the problems posed by German mathematician David Hilbert in a historic talk at the ICM in 1900: he challenged mathematicians to reconcile the behaviour and smooth appearance of fluids with the idea (still not broadly accepted at that time) that they were made of multitudes of atoms or molecules.

Together with two collaborators[1], Deng found a rigorous proof that the microscopic jostling of many constituent particles — acting like tiny billiard balls continuously bouncing off one another — produces as a necessary consequence a differential equation formulated in the late 1800s to describe fluids.

This helped to reconcile an apparent conundrum. Microscopic physics works just as well when time is reversed — meaning that it might be impossible to tell whether a video of two molecules bouncing off each other is being played forwards or backwards. But when many molecules form a fluid, they have an unavoidably irreversible behaviour: if you mix a cold gas with a hot one, say, the mixture will never spontaneously separate back into hot and cold.

String contact

Pardon, who is at Stony Brook University in New York, was born in Chapel Hill, North Carolina, and is also 37. He made his first original contribution to mathematics — on a deceptively simple problem about the geometry of loops on a flat surface[2] — while at secondary school. He went on to publish several research papers during his undergraduate degree at Princeton, where he also studied Chinese and performed as a cellist in a university orchestra.

During his PhD at Stanford University in California, Pardon partially solved another of Hilbert’s problems[3], but mostly switched to symplectic and contact geometry — fields that arose from the mathematical description of physical systems, such as the motion of planets. Symplectic ‘spaces’ always have an even number of dimensions, and contact spaces are their odd-dimensional counterparts that often sit inside a symplectic space.

Pardon’s PhD thesis was a technical tour-de-force in which he developed techniques for telling two contact spaces apart. Later, he applied those tools to cracking major problems in symplectic and contact geometry, including some that arose from string-theory physics[4] — a speculative framework to interpret all elementary particles and fundamental forces as vibrations of loops called strings. Mathematicians often model such strings as loops moving from one contact space to another within a symplectic space.

Resonating with equations

Tsimerman’s passion for solving mathematical puzzles began to show as early as the age of three, he says. “I had an interest in math as early as I can remember,” says Tsimerman, who is 38 and a Russian-born Canadian at the University of Toronto, Canada.

His main focus is on the theory of numbers, in particular whether, and how, certain equations — known as Diophantine equations — can have whole numbers as solutions. Such equations are intimately connected to algebraic geometry, including to the structure of objects with more than three dimensions, called Shimura varieties. (These also play an essential part in the proof of Fermat’s last theorem, one of the most celebrated maths breakthroughs of the late twentieth century.)

One of Tsimerman’s favourite facts is that if Shimura varieties could vibrate like physical objects, each of their resonant frequencies would correspond to a specific Diophantine equation. Tsimerman and his collaborators proved one of the central statements in the theory of Shimura varieties in 2021[5], called the André–Oort conjecture after the two mathematicians who formulated it, and another major conjecture stated by Phillip Griffiths at the Institute for Advanced Study in Princeton[6].

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Nature 656, 16-17 (2026)

doi: https://doi.org/10.1038/d41586-026-02169-1

References

1. Deng, Y., Hani, Z. & Ma, X. Preprint at arXiv https://doi.org/10.48550/arXiv.2408.07818 (2024).

2. Pardon, J. Trans. Amer. Math. Soc. 361, 1749–1764 (2009).

3. Pardon, J. J. Amer. Math. Soc. 26, 879–899 (2013).

4. Pardon, J. Preprint at arXiv https://doi.org/10.48550/arXiv.2308.02948 (2023).

5. Pila, J., Shankar, A. N., Tsimerman, J. Esnault, H. & Groechenig, M. Preprint at arXiv https://doi.org/10.48550/arXiv.2109.08788 (2021).

6. Bakker, B., Brunebarbe Y. & Tsimerman, J. Invent. Math. 232, 163–228 (2023).

7. Wang, H. & Zahl, J. Preprint at arXiv https://doi.org/10.48550/arXiv.2502.17655 (2025).

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NEWS 24 July 2026 Correction 27 July 2026

White House rolls out AI funding — and signals a new era for US science

Chief US science adviser calls for a shake-up in funding while handing out grants to accelerate research with AI.

By

Dan Garisto &

Kaia Glickman

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Michael Kratsios, chief science adviser to US President Donald Trump, released a report this week that reimagines how science in the country is funded. Credit: Daniel Heuer/Bloomberg via Getty

Washington DC

It was a big week for science in the administration of US President Donald Trump. On Tuesday, the president’s chief science adviser, Michael Kratsios, released a report called Science: A New Golden Age that proposes substantial changes to federally funded research. The next day saw him help to unveil some of the first winners of research grants from the Genesis Mission, the administration’s flagship artificial-intelligence initiative.

For the past year and a half, the Republican-led administration has made headlines for attempting to cut funding for US science that it disfavours. This week’s events are notable because they move forward the science that it does favour — in particular, AI.

Some 5,000 teams applied for funding from the Genesis Mission, which the administration billed as a programme “comparable in urgency and ambition to the Manhattan Project” to accelerate US science with AI. At a summit on 22 July, officials announced about 280 winning projects in a first phase of the programme. Teams will apply AI to problems such as improving the US power grid and automating laboratories, and will receive a 9-month grant for between US$500,000 and $750,000. Researchers in these teams can also submit applications for a second phase: a three-year programme involving millions in annual funding.

“I was very, very, very happy” to be chosen “because it was incredibly competitive,” winner Jeremy Zucker told Nature at the summit. Zucker, a computational scientist at Pacific Northwest National Laboratory in Richland, Washington, is leading a project that will use AI to optimize the growth of algae for use in products such as fertilizers and biofuels.

Kratsios’s report got a more mixed reception at a hearing in Washington DC that he attended on the same day. In the report, he calls for shifting US science funding away from “legacy institutions” such as universities. “Too much of our research enterprise has come to serve itself rather than the scientists within it,” he wrote. Under this new vision, individual scientists would be supported by programmes such as ‘golden tickets’, which give grant reviewers the ability to override the decisions of their colleagues to fund high-risk proposals. “We should be testing other ways to make breakthroughs than what we’ve been doing for so many years,” he said at the hearing of the US House of Representatives Committee on Science, Space and Technology. He argued that US science is stagnating and needs to be rethought.

Members of the US Congress, who decide what science to fund, had mixed reactions. Republicans on the committee broadly expressed support for Kratsios’s vision for US science; Democrats, however, criticized the Trump administration’s cuts to science budgets and agencies, as well as its push to take political control of grant awards. “You aren’t launching us into ‘the golden age of innovation’. You’re sending us back to the Stone Age,” said Gabe Amo, a Democrat representing Rhode Island.

Some scientists have been quick to criticize the report. Carl Bergstrom, an evolutionary biologist at the University of Washington in Seattle, wrote on the social-media platform Bluesky that the administration seems determined to “destroy” US universities. (Trump and his Republican allies have accused US research institutions of being discriminatory towards conservative views, and the administration has withheld funding from some universities, citing antisemitism.)

Grant challenges

Lawmakers also questioned Kratsios about media reports, including one from Nature, that the US National Science Foundation (NSF) plans to claw back about $500 million from its grant-making divisions and funnel the money towards a new White House initiative. Democrat Zoe Lofgren of California queried the chief science adviser about this. He responded: “I’m not familiar with those details. I would refer you to NSF,” but made the case that the United States should “be more deliberative and specific about trying to tackle grand challenges”.

Nature has learnt that the NSF has now pulled back $300 million from two of its divisions: mathematics and physical sciences, and computer and information science and engineering. Programme officers, who handle grant proposals at the NSF, have pulled back 150 proposals that have already passed peer review to free up the money, which was allotted to the agency by the US Congress. Some proposals have been rescinded, others have had funding cuts and still others have been held until the next fiscal year.

Staff members at the NSF, who spoke to Nature on the condition of anonymity out of fear of reprisals, say the money is probably going to a programme called Grand Research Challenges, organized by the White House Office of Science and Technology Policy, which Kratsios runs. A preliminary document shown to Nature that appears to outline the challenges lists four areas: the future of intelligence, the frontier of quantum phenomena, advanced materials and matter and the foundations of biological design.

A spokesperson for the NSF said that the agency would not comment on internal budget deliberations. They said that funds for research activities in fiscal year 2026 could be awarded that year or the next one, and that “NSF revises budgets and manages portfolios as needed”.

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Nature 656, 17-18 (2026)

doi: https://doi.org/10.1038/d41586-026-02332-8

Updates & Corrections

- Correction 27 July 2026: An earlier version of this story said that Zucker’s project would maximize biofuel production. It will optimize algae growth for use in a range of products, including biofuels.

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NEWS FEATURE 05 August 2026

Exclusive: the science papers that patents cite the most

Nature reveals the studies that are mentioned most often in patent filings — and why.

By

Richard Van Noorden,

Heidi Ledford &

Edward Chen

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Illustration: Chiara Vercesi

Half a century ago, biochemists Georges Köhler and César Milstein kicked off a revolution in medicine. Their 1975 paper described how to mass-produce monoclonal antibodies: proteins that can be designed to recognize and bind specifically to almost any protein target[1]. These antibodies have since become a workhorse of biological research and the basis of blockbuster treatments for cancer, autoimmune conditions and infectious diseases. “Such cultures could be valuable for medical and industrial use,” the paper concludes.

That was right on the money. The influence of Köhler and Milstein’s paper in academia and industry is clear from its citations. It has been cited in more than 17,000 academic papers, putting it in the world’s top 1,000 most-cited studies.

Even more strikingly, with more than 13,000 mentions in patent applications, it ranks fifth among the scholarly articles that are most cited in patents — that is, the documents filed to gain legal protection for potentially money-making inventions.

Patent citations are one way to examine how academic research flows into commercial innovation. So, with an eye to getting a sense of the most industrially influential work, Nature has charted the scholarly papers that patents cite the most.

One might expect this list to be a parade of commercially important research. Köhler and Milstein’s paper is one such example: it netted them a share of the 1984 Nobel Prize in Physiology or Medicine, and other Nobel-prizewinning work appears in the list.

César Milstein (left) and Georges Köhler received a share of the Nobel Prize in Physiology or Medicine in 1984.Borje Thuresson/Keystone/Getty

In fact, the list of top-cited studies in patents presents some surprising results, because of the vagaries of the patenting process and its conventions about what to reference. Although many papers have obvious importance, some studies in the list are rarely mentioned in academic journals, and even their authors don’t know why they are cited in so many patents.

“Causality in knowledge flows between scholarly work and patents is not a simple matter,” says Richard Jefferson, the founder and former director of The Lens Project, a free database of patents and scholarly works that was one source for Nature’s analysis. (Jefferson is chief scientist at the non-profit social enterprise Cambia in Canberra, Australia.)

Nonetheless, the exercise does reveal insights about the influence of academia on industry: not least, that the majority of papers most cited in patents are the product of publicly funded research.

That was the case for Köhler and Milstein’s study, which was supported by the Medical Research Council (MRC) Laboratory of Molecular Biology (LMB) in Cambridge, UK, where the researchers worked. Ironically, however, their invention was not patented in the United Kingdom, notes Arthur Lesk, a biochemist at Pennsylvania State University in University Park, who was working at the LMB at the time. Milstein “spent a lot of time” trying to persuade the MRC to pursue a patent, Lesk says. “They refused.” In the end, it was US researchers who patented the work and took the royalties.

Why patents cite papers

References to scholarly papers in patents serve a different function from those in academic research, explains economist Adam Jaffe, a specialist in technological innovation who has retired from Brandeis University in Waltham, Massachusetts.

To gain a patent, an inventor must show, among other things, that their work is genuinely new and wouldn’t be ‘obvious’ given the state of existing research. So it is a legal requirement for the inventor — or the lawyers or technology-transfer specialists who file the patent application — to list studies and patents that summarize the current state of the art. And a patent examiner, who decides whether to grant or reject an application, can add more references to the list.

This produces an ordered list of references, sometimes known as the front page of the patent, that should be a paper trail of relevant sources of knowledge leading up to an invention (see ‘How patents cite papers’). But it means that well-written reviews or commentaries that are not themselves breakthrough works can feature heavily.

Source: Patent no. US 5583013 A

For instance, second in the list of top-cited works in the front pages of patents, according to The Lens, is a 1977 review[2] by researchers at the multinational drug firm Pfizer, on how to convert drugs into their salt forms. As of March, the review had more than 16,000 references in patents, but only a few hundred academic mentions.

More confusingly, a 1994 review[3] about antibodies, by biochemist Peter Colman, has just 5 citations in academic papers, but a whopping 4,467 mentions in patents, according to The Lens — placing it 30th on the most-cited list. “I have no solid explanation for this puzzling statistic,” says Colman, who is retired and most recently worked at the Walter and Eliza Hall Institute of Medical Research (WEHI) in Melbourne, Australia.

The paper explains how changing an amino acid in an antibody’s protein sequence can have unpredictable effects on the antibody’s function. That is an important point for patent examiners, suggests Kevin Noonan, a patent lawyer and partner at the law firm MBHB in Chicago, Illinois. Applicants used to be able to claim legal protections around not just the antibodies they disclosed in a patent, but also many variants of that structure. But as it became understood that small variations can change function, examiners now tend to allow for only narrow legal protection around precisely what’s disclosed. Colman’s article became a favoured reference for examiners to add to patents, Nature’s analysis suggests.

Such quirks mean it can be dangerous to draw inferences about the commercial influence of individual papers that are heavily cited in patents, notes Jaffe. However, large-scale analyses of papers cited in patents can pull out a meaningful signal from the noise. For instance, Jaffe worked with The Lens team to introduce a ranking of the influence of academic research on innovation, called In4M. In a 2018 study, the team used the ranking to report that the Scripps Research Institute in La Jolla, California (now Scripps Research), had the most patent citations per article, when normalized for research discipline[4].

The dominance of antibodies

Most of the top-cited scholarly articles in patents relate to the life sciences. This reflects not just the enormous size of the biotechnology industry, but also the fact that some fields are simply more prone to citing research articles in patents than are others, Jaffe says. In a list of the top 100 cited works, downloaded from The Lens, more than a dozen of the papers relate to methods for making or modifying antibodies, which sprung up after Köhler and Milstein’s work (see ‘The papers most cited in patents — front pages’ and Supplementary information). Lesk co-authored a 1987 paper[5] that is seventh on the list; he and his colleagues’ guidelines help scientists to modify the sequence of small portions of an antibody so that it binds more strongly to a target protein.

Source: lens.org

It took decades to resolve other barriers to making antibodies usable as drugs. Köhler and Milstein used antibody-producing cells from mice, and papers that were the first to ‘humanize’ an antibody — by grafting the portion of a mouse antibody that recognizes its protein target onto a human antibody backbone — are ninth and 11th on the list[6],[7]. Two papers that report how to create a simpler version of an antibody, known as a single-chain antibody, are ranked 12th and 16th[8],[9]. And two 1991 papers that describe how to screen large amounts of antibodies to select for the ones that bind best to a target are 20th and 21st[10],[11].

This screening technique, called phage display, won biochemist Greg Winter, who was also at the MRC LMB, a share of the 2018 Nobel Prize in Chemistry. “At the time, of course, I didn’t know exactly how far therapeutic antibodies would go,” says Winter, who is now retired. He founded the company that developed Humira (adalimumab), an injectable antibody-based drug to treat inflammatory diseases, which has earned more than US$200 billion in sales; he has also in the past earned royalties from the anti-cancer antibody Keytruda (pembrolizumab), which is the world’s top-selling drug. Winter is one of the most-mentioned authors in The Lens top-100 list; he is a co-author of 9 of the papers.

Today, says Paul Carter, a biochemist at Genentech in South San Francisco, California, there are more than 200 distinct approved drugs, 400,000 papers and 16,000 patents that rely on antibodies. One of the drugs is his own invention: the anti-cancer treatment now sold as Herceptin entered the scientific literature in 1992, in a paper[12] that is 41st on the list.

“It’s stunning,” says Carter. “There aren’t many technologies which are so profoundly impactful in a relatively short period of time.”

Comparing sequences

Scattered through The Lens list are eight papers that describe methods for comparing DNA or protein sequences. Such analyses are crucial for demonstrating whether a particular protein or piece of DNA constitutes a new discovery, as well as for tracing its evolutionary history and providing clues to its function.

The 1990 publication[13] of a software tool for comparing such sequences, the Basic Local Alignment Search Tool (BLAST), is the most frequently cited paper in the front pages of patents — and is also among the top-cited papers in academic research.

Users feed in a sequence and BLAST rapidly fishes out similar sequences from genetic databases and aligns them with the query. “It’s like Google for DNA and protein,” says Stephen Altschul, a bioinformatician who has retired from the US National Institutes of Health in Bethesda, Maryland, and a co-author of the paper. Two other papers for comparing sequences, including a 1997 update to BLAST, are third and fourth on the list[14],[15].

Molecular biology has come a long way since early versions of BLAST were shipped out to users on CD-ROM, says Altschul. In 1990, the biotechnology industry was young but on the verge of explosion, and companies had already started to patent naturally occurring genes — a practice that was later abolished by the US Supreme Court. “It was clear there was a gold rush,” says David Lipman, another author of the early BLAST papers and the former director of the US National Center for Biotechnology Information in Bethesda.

By 2023, the global market for drugs based on biological molecules, including antibodies, had climbed above $400 billion. Amid all the changes, BLAST remained an important tool for characterizing DNA and proteins. “It’s still being used over 35 years later,” says Altschul. “I’m happy about that.”

Plant science and ‘in-text’ citations

The front page isn’t the only place where papers are cited in patents. Mentions of scientific works are also scattered through a patent’s body text — known as in-text citations. These tend to be added by inventors themselves: sometimes to explain their inspiration, or as part of disclosures that enable a skilled reader to reproduce the patent’s methods. But they are usually embedded in sentences rather than in an ordered reference list, and are sometimes alluded to cryptically (“Smith 1992” or “Palmer and Watkins”, for example), so are hard to analyse.

In the past decade, however, researchers have used machine-learning algorithms to fish out many (although not all) of these cryptic references, and to link them to papers. One such project, Reliance on Science, is led by Matt Marx at Cornell University in Ithaca, New York, who studies entrepreneurship and innovation, and computer scientist Aaron Fuegi at Boston University in Massachusetts. They found that just over 30% of scientific citations appear only in the body text of patents; these would be missed by just examining the front-page references[16].

Not considering in-text citations “can result in a substantial understatement of the commercial impact of academic science”, Marx and Fuegi noted in a 2021 paper[16]. For instance, they referred to a previous study that suggested 31% of all US National Institutes of Health grants had some commercial impact, as measured by studying how many scholarly articles listing such grants are mentioned in patents. But the Reliance on Science data push this up to 44%.

For this article, Nature re-analysed a publicly shared Reliance on Science data set that goes up to 2023, producing lists to show the works most often mentioned overall in patents, and breaking that down by front-page and in-text citations. (A newer data set goes up to 2025, but contains references from US patent filings only.) Nature then compared the Reliance on Science list with that from The Lens.

The two data sources don’t give the same results because their source material and data-extraction methods differ. For instance, tens of thousands of references to various editions of a book first published in 1982, Molecular Cloning: A Laboratory Manual, mean that it tops the Reliance on Science list once all references to its editions are combined (see ‘The papers most cited in patents — including body text’ and Supplementary information). But it doesn’t appear high up in The Lens list, apparently because most of its mentions were not mapped to a scholarly-work record. Despite this, the sources mainly agree on the top-cited papers overall, with antibody research and biological software topping the lists. This is in part because popular work cited on the front page of patents also appears as in-text citations.

Source: Nature analysis/relianceonscience.org

Differences emerge lower down the lists, however. For instance, only two plant-biology papers make it to the list of top 100 papers mentioned in the front pages of patents. But including in-text citations pushes 28 plant papers into the Reliance on Science list. Some of them describe methods and concepts that are important for plant breeding or making transgenic plants. Others describe discoveries in plant metabolism, or mechanisms of disease resistance.

The popularity of such papers among patent filers does not surprise Chris Holly, a patent attorney and partner at the international law firm Cooley in San Francisco, California. Agriculture generates a steady stream of patents, he notes. As an example, he points to patents involving the gene-editing technique CRISPR. Although the method’s potential medical applications garner more media attention than its agricultural uses do, the firm with the most US patents on CRISPR technology is Corteva Agriscience in Indianapolis, Indiana (a spin-off formed after the merger of the giant chemical firms Dow and DuPont).

Noonan and Holly both suggest that plant-science papers that lay out biological techniques will often be cited to point readers to key methods in the field. That, perhaps, is why they turn up so frequently in a patent’s body text.

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Nature 656, 19-23 (2026)

doi: https://doi.org/10.1038/d41586-026-02386-8

Additional reporting by Davide Castelvecchi.

References

1. Köhler, G. & Milstein, C. Nature 256, 495–497 (1975).

2. Berge, S. M., Bighley, L. D. & Monkhouse, D. C. J. Pharm. Sci. 66, 1–19 (1977).

3. Colman, P. M. Res. Immunol. 145, 33–36 (1994).

4. Jefferson, O. A. et al. Nature Biotechnol. 36, 31–39 (2018).

5. Chothia, C. & Lesk, A. M. J. Mol. Biol. 196, 901–917 (1987).

6. Riechmann, L., Clark, M., Waldmann, H. & Winter, G. Nature 332, 323–327 (1988).

7. Jones, P. T., Dear, P. H., Foote, J., Neuberger, M. S. & Winter, G. Nature 321, 522–525 (1986).

8. Huston, J. S. et al. Proc. Natl Acad. Sci. USA 85, 5879–5883 (1988).

9. Bird, R. E. et al. Science 242, 423–426 (1988).

10. Marks, J. D. et al. J. Mol. Biol. 222, 581–597 (1991).

11. Clackson, T., Hoogenboom, H. R., Griffiths, A. D. & Winter, G. Nature 352, 624–628 (1991).

12. Carter, P. et al. Proc. Natl Acad. Sci. USA 89, 4285–4289 (1992).

13. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. J. Mol. Biol. 215, 403–410 (1990).

14. Needleman, S. B. & Wunsch, C. D. J. Mol. Biol. 48, 443–453 (1970).

15. Altschul, S. F. et al. Nucleic Acids Res. 25, 3389–3402 (1997).

16. Marx, M. & Fuegi, A. J. Econ. Mgmt Strateg. 31, 369–392 (2021).

17. Jones, D. A., Thomas, C. M., Hammond-Kosack, K. E., Balint-Kurti, P. J. & Jones, J. D. G. Science 266, 789–793 (1994).

18. Kraft, T., Hansen, M. & Nilsson, N.-O. Theor. Appl. Genet. 101, 323–326 (2000).

19. Nomura, K. et al. Nature 432, 488–492 (2004).

20. Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A. & Charpentier, E. Science 337, 816–821 (2012).

21. He, K., Zhang, X., Ren, S. & Sun, J. IEEE Conf. Comput. Vis. Pattern Recognit. 2016, 770–778 (2016).

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Supplementary Information

1. Methodology and full data for the papers most cited in patents (Excel file)

NEWS FEATURE 04 August 2026

Could mending damaged DNA prolong life?

Inspired by long-lived animals — and human centenarians — researchers are hunting for ways to enhance DNA repair and extend healthspan.

By

Elizabeth Quill

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Your DNA is under constant assault. Ultraviolet light, environmental toxins, reactive molecules made during run-of-the-mill metabolism and many other disruptors muck with the instructions that keep life humming along. Thankfully, repair crews are at the ready.

A typical cell can acquire up to a whopping 100,000 lesions each day. “The vast, vast majority are repaired,” says Morten Scheibye-Knudsen, a translational geroscientist at the University of Copenhagen. “We have very, very efficient repair.”

That’s a good thing for a couple of reasons. First, unrepaired or poorly repaired damage can introduce mutations, which can contribute to cancer. And second, DNA damage seems to be one of the main drivers of ageing.

Researchers are amassing evidence that this type of damage underlies many of the hallmarks of ageing, including chronic inflammation, metabolic malfunctions and protein-folding problems[1]. Such damage triggers cellular alarm bells that can promote inflammation, force cells into an ‘undead’ state known as senescence and even kill them. These responses help the body to grow and thrive, but they become more problematic as we age. The accumulation of beleaguered cells over time is associated with many age-related conditions, including cardiovascular disease, osteoporosis and Alzheimer’s.

That raises a question: if DNA damage is at the root of ageing, can boosting DNA repair slow the process, keeping people healthy for longer? For the first time, this is starting to look like a promising approach, say researchers who study DNA repair[2].

Their new optimism comes from studying relatively long-lived species, such as bowhead whales (Balaena mysticetus)[3] and naked mole rats (Heterocephalus glaber)[4], and looking at the genetics of human centenarians. These studies are pointing to the existence of a great variety of molecular maintenance workers that make for a long and healthy life. A ‘master regulator’ of repair, discovered in 2023, also suggests that these fix-it systems could be enhanced in unison[5].

Such findings come alongside a booming interest in longevity more generally, propelled by biotechnology companies, health influencers and governments overseeing ageing populations.

“If you can reduce DNA damage, you would probably have a dramatic effect on the ageing process,” says Paul Robbins, who directs the Nathan Shock Center on Genome Integrity and Aging, which opened last year at the University of Minnesota in Minneapolis. “There are tricks that we can do. But it’s not simple.”

Impressive toolkit

Despite having such a big job, DNA is remarkably fragile. Left unrepaired, its many breaks, kinks, lost bases and crosslinks can physically block the processes necessary to make proteins or to form new cells. That means that for life to get anywhere with DNA as a blueprint, maintenance is essential.

“DNA damage has been the fundamental problem at the origin of life,” says Björn Schumacher, a geroscientist at the University of Cologne in Germany.

Cells have an ancient and varied toolkit. There are six major DNA-repair systems, a few smaller ones and probably some that haven’t been discovered, Schumacher says. Different systems respond to different forms of damage. If a single DNA base, such as guanine, gets oxidized — an oxygen atom gets added to its structure — then a process known as base-excision repair can make the fix. This removes and replaces one base at a time. Nucleotide-excision repair, by comparison, removes a couple of dozen nucleotides along a single strand at once — a heftier fix often triggered by UV damage.

Scientists are studying centenarians to find out what the secret to a long life is.Credit: freemixer/Getty

The repair systems tend to require several steps. They call on many proteins and overlap with one another, with one system jumping in if another is not active. Some are sloppier than others, prone to introducing errors as they make their fixes. One way to repair a break that spans both strands of the DNA double helix, for example, is homologous recombination. This uses an intact DNA strand to serve as a template and is generally accurate. Nonhomologous end joining, however, another way to fix double-strand breaks, doesn’t require a template and does a more slapdash job by fusing broken ends together. This sometimes introduces errors that can lead to cancer-driving mutations, but a repair with a small risk of mutation is better than no repair at all.

Many genes are involved in these repair systems; some researchers suggest that 10% of the genome plays a part in genome maintenance. In searching for ways to enhance repair, “this complexity has always been a limiting factor”, says Shumacher. When researchers have tried to enhance repair directly, by switching on one repair system or overexpressing a repair enzyme, the effect has often been limited — or, worse, it has thrown the whole process out of balance.

One of the big challenges is that there are so many repair pathways, says Agnel Sfeir, a molecular geneticist at the Memorial Sloan Kettering Cancer Center in New York City who studies DNA repair in the context of cancer. “At the moment, we do not know which DNA repair can be boosted or should be boosted,” she says.

Animal inspiration

The animal kingdom might provide clues. Scientists are studying species with long, relatively cancer-free lives, including naked mole rats, Greenland sharks (Somniosus microcephalus), elephants, bats and lobsters.

For one of her latest projects, biologist Vera Gorbunova at the University of Rochester, New York, and her team chose bowhead whales. These marine mammals weigh in at more than 80,000 kilograms and glide and dive through frigid Arctic waters year-round. Although the whales can live for more than 200 years and have 1,000 times as many cells growing and dividing as humans do, cancer rarely creeps in.

In a study published last year[3], Gorbunova and her colleagues went looking for reasons why bowhead whales are resistant to cancer. They thought they might find extra copies of genes that help to suppress and kill cancers. But instead they found that whale cells had very accurate double-strand break repair. “The whales don’t need to kill the cells; they just don’t let cells mutate as far,” Gorbunova says.

A protein, called CIRBP (cold-inducible RNA-binding protein), that helps cells to survive cold-related stress seems to have a role. When expressed in human cells, the whale protein increased two types of double-strand break repair.

The bowhead whale (Balaena mysticetus) can live for more than 200 years.Credit: Tony Wu/NaturePL

That finding chimes with a study from 2019, in which Gorbunova and her colleagues looked at 18 rodent species with varying lifespans. They found a strong link between the maximum lifespan and the accuracy and efficiency of double-strand break repair in skin and lung cells[6]. That superior repair was explained in large part by one member of a family of enzymes called sirtuins, which are known to have roles in ageing, metabolism and the stability of the genome. The overexpression of the sirtuin SIRT6 had already been linked to extended lifespan in mice. In the 2019 study, the team identified five amino acids that differ between the beaver and mouse versions of SIRT6 and seem to make the beaver version more effective. Beavers live for 10–12 years in the wild, whereas mice typically live for a few years at most.

Genetic studies suggest that some human centenarians[7] might also carry a superior variant of the gene SIRT6, says geneticist Jan Vijg at the Albert Einstein College of Medicine in New York City. Vijg co-leads a multiteam effort to identify important genes and pathways in centenarians, validate them and develop drugs that target them.

The team has identified a group of compounds called fucoidans, which occur naturally in brown seaweed and activate the SIRT6 protein, as potential therapeutics. Studies by Robbins, Gorbunova and others[8],[9] show that supplementing mouse diets with fucoidans improves the animals’ DNA repair, reduces senescence and extends their healthspan and lifespan.

Clinician-geroscientist Andrea Maier, director of the National University of Singapore’s Academy for Healthy Longevity, is now leading a study that gives fucoidans to men aged 50 to 80. The study is looking at cellular markers of ageing and clinical outcomes, measuring as directly as possible how fucoidans affect biology.

The ins and outs of DNA repair don’t just differ from organism to organism; they also differ from cell to cell. Sperm and egg cells seem to accumulate much less DNA damage than other cell types do; DNA repair is energetically costly, so it makes sense that it would be prioritized in cells that must pass genetic information down the generations. “Our germ cells are in a sense immortal,” Schumacher says.

He and his colleagues have identified a possible key to this immortality. In 2023, the team reported from experiments in the roundworm Caenorhabditis elegans that a protein complex already known for its role in cell proliferation represses many DNA-repair genes in non-reproductive, or somatic, cells[5]. This ‘DREAM complex’ is found across species. When the team turned it off in a mouse model of a premature-ageing syndrome, the mice showed less DNA damage. Switching it off in human cells boosted the expression of DNA-repair genes.

In the study, Schumacher’s team suppressed the DREAM complex by inhibiting an enzyme that helps to build it. The enzyme, called DYRK1A, is already a potential drug target: it is overexpressed in people with Down’s syndrome, and is tied to cognitive impairments and neurodegeneration associated with the condition.

Schumacher sees DREAM as a game changer for the field because it seems to act as a master regulator of repair, affecting many systems. “For the first time, we could really boost the overall capacity to repair,” he says.

Robbins agrees that the finding is exciting, calling the study “beautiful work”. But he says that there’s a lot more research to do to work out whether the complex could be targeted and how best to do it.

Staying pristine

Studying other types of cell with different apparent levels of repair could inform future strategies. There is evidence, for example, that stem cells, which also need to maintain their genome across many divisions, have lower mutation rates than do other somatic cells. If stem cells are better at DNA repair, then partially reprogramming somatic cells into a stem-like state could, among other advantages, improve their repair.

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Nature 656, 24-26 (2026)

doi: https://doi.org/10.1038/d41586-026-02385-9

References

1. Schumacher, B., Pothof, J., Vijg, J. & Hoeijmakers, J. H. J. Nature 592, 695–703 (2021).

2. Bujarrabal-Dueso, A., Garinis, G. A., Robbins, P. D., Vijg, J. & Schumacher, B. Nature Rev. Drug Discov. 24, 785–807 (2025).

3. Firsanov, D. et al. Nature 648, 717–725 (2025).

4. Chen, Y. et al. Science 390, eadp5056 (2025).

5. Bujarrabal-Dueso, A. et al. Nature Struct. Mol. Biol. 30, 475–488 (2023).

6. Tian, X. et al. Cell 177, 622–638 (2019).

7. Simon, M. et al. EMBO J. 41, EMBJ2021110393 (2022).

8. Biashad, S. A. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.03.24.645072 (2025).

9. Robbins, P. et al. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-6613032/v1 (2025).

10. Brazhnik, K. et al. Sci. Adv. 6, eaax2659 (2020).

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Books & Arts

- The secret relationships that govern life on Earth

- Why pain hurts more when we’re lonely, and the myth of original sin: Books in brief

BOOK REVIEW 03 August 2026

The secret relationships that govern life on Earth

Books on parasitism and symbiosis inspire wonder at the strange ways in which species’ lives intertwine.

By

Richard Schiffman

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Clownfish help to keep anemones clean and healthy. In exchange, anemones protect clownfish from predators. Credit: Getty

Hidden Creatures: Luscious Leeches, Bashful Botflies, and the Wondrous, History-Shaping World of Parasites Dino Martins Knopf (2026)

Togetherness: How Symbiosis Can Change the Way We Understand Our World, Ourselves and Our Future Rowan Hooper Fern (2026)

Humans often fancy that we are independent creatures, living sealed off from other species. Yet, that false sense of separation vanishes when bacteria make us sick or a bear raids the rubbish bin. Whether we like it or not, we are interconnected with the living world. Two books explore the ties that bind all living things.

In Togetherness, biologist and science journalist Rowan Hooper proposes that interspecies cooperation is the key driver of life’s evolutionary journey. Hidden Creatures, by entomologist and evolutionary biologist Dino Martins, takes a darker view. Life lives off life, the author explains. “Half of all living things are mooching off others!”

Nature’s villains

Take parasites, which, Martins notes, have afflicted humans since the beginning of recorded history. Inscribed in ancient Canaanite on an ivory comb are the words: “May this tusk root out the lice of the hair and the beard.” It is the oldest known sentence written using an alphabet. Lice continue to prey on humans nearly 4,000 years later — as do countless other parasites.

Martins invites us to take a second look at these oft-villainized creatures that are, nevertheless, integral to life. Many today who profess to ‘love nature’ tend to edit out the less savoury and less inspiring parts. But that is misguided, the author says. You must “seek to understand all of nature”, he insists, or “you will end up loving and understanding none of it”.

His book is not for the squeamish. Martins introduces us to caterpillars that are eaten alive by squirming wasp pupae. He devotes a chapter to a rogues’ gallery of worms; one species (Stephanurus dentatus) lives in the urinary tract of pigs, another (Rhabditis orbitalis) in the eyes of mice and voles, and even more species live in the lungs of birds.

Others dwell in the human body. Today, one-third of the human population is infected with nematode worms, often with grisly consequences. Hookworms, once called the “vampires of the south” owing to their prevalence in the southern United States, live for years in the intestine, sapping millions of their strength and vitality.

Tsetse flies in Africa carry the protozoan parasites that cause sleeping sickness. Brain worms infest moose, deer and elk. Thirty-metre-long tapeworms have been found in the bellies of whales. The list goes on.

Wasps can lay eggs inside caterpillars, which the larvae consume after hatching.Credit: Jim Lane/Education Images/UIG/Getty

Parasites can even drive their hosts to kill themselves, when it is to the parasite’s advantage to do so. Take the Gordian worm, which infects the brain of praying mantises and alters gene expression in a way that makes them jump into a body of water and drown. Only then can the worm fulfil its biological destiny by laying its eggs, which hatch in the water.

But there is a silver lining to the parasitic cloud. Some creatures have come up with ingenious ways to combat their parasites, such as birds that evict itchy parasites from their feathers by rubbing themselves with formic acid secreted by the ants that bite them.

Leech therapy shows promise in treating osteoarthritis. Leeches secrete substances with anti-inflammatory and anaesthetic properties, which relieve pain and reduce swelling. They are sometimes used in specialized microsurgeries and skin grafts: the anti-clotting agent that they secrete prevents the surgeon’s incision from prematurely closing.

Martins’s final verdict on the parasites that he so exuberantly describes? They are crucial actors that clean up Earth by decomposing dead flesh and recycling it. Without this invaluable service, ecosystems could not function.

Productive partners

Hooper’s book focuses on the more harmonious side of life. Symbiotic relationships in which two or more species live in partnerships that benefit all parties have been essential in the evolution of life — yet scientists have taken a long time to fully recognize this. “Cooperation is much harder to see than competition,” Hooper explains. “Fights are easier to measure than favors.”

Lichen, Hooper says, provided the first evidence that individuals are not as delineated as we had once thought. What looks like a single organism is in fact a partnership in which algae feed fungi with photosynthesized sugars, and fungi provide algae with water, minerals and a secure structure in which to live. Lichens have been known since ancient times, but it was not until the nineteenth century that scientists recognized their nature as a composite organism.

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Nature 656, 28-30 (2026)

doi: https://doi.org/10.1038/d41586-026-02387-7

Competing Interests

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BOOK REVIEW 17 July 2026

Why pain hurts more when we’re lonely, and the myth of original sin: Books in brief

Andrew Robinson reviews five of the best science picks.

By

Andrew Robinson

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Original Sin

Kathryn Paige Harden Weidenfeld & Nicolson (2026)

A 2021 analysis of genetic data from 1.5 million people, co-published by psychologist Kathryn Paige Harden, concluded that some DNA sequences were more common in people with an addiction than in those without. “This conflation of genetics with incorrigibility is yet another legacy of the doctrine of original sin,” Harden remarks in her intriguing study of what causes human wrongdoing, informed by a Christian upbringing that she later rejected. DNA, as with original sin, “cannot definitely say that you will commit a crime”.

Tell Me Where It Hurts

Rachel Zoffness Grand Central (2026)

Pain is a complex phenomenon. It “hurts more when we’re lonely and sad than when we’re happy and surrounded by people we love”, writes pain scientist Rachel Zoffness. Her fascinating book abounds in vivid, complex examples of pain. For instance, a construction worker jumped off a plank onto an upright 18-centimetre nail that pierced his entire boot, causing him agony. But, after doctors sedated him and removed the boot, they saw that the nail had miraculously passed through a space between his toes with “not a scratch to be found”.

The Information State

Jacob Siegel Henry Holt (2026)

Experience as a US Army officer serving in Afghanistan allowed journalist Jacob Siegel to recognize the techniques of information warfare. His well-informed book accuses US government–technology partnerships, established after 2001 to fight the global war on terror, of now targeting Americans through a war on disinformation. Rather than reforming the Internet’s infrastructure, they make it serve their own interests — as revealed by the archives of the social-media company Twitter (now X).

Inescapable

F. Marina Schauffler Johns Hopkins Univ. Press (2026)

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Nature 656, 30 (2026)

doi: https://doi.org/10.1038/d41586-026-02264-3

Competing Interests

The author declares no competing interests.

Opinion

- Obituary: Susumu Tonegawa, Japan’s first Nobel prizewinner in physiology or medicine

- Plans for nuclear-powered merchant ships must confront risks

- Europe can become a bioengineering powerhouse with these three changes

OBITUARY 29 July 2026

Obituary: Susumu Tonegawa, Japan’s first Nobel prizewinner in physiology or medicine

Fearless molecular biologist who revealed the workings of antibodies and memories.

By

Adrian Hayday

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Credit: Allen Green/Photo Researchers History/Getty

Susumu Tonegawa was a renowned neuroscientist who transformed our understanding of learning and memory. He also had an immense impact on immunology, discovering the genetic mechanism behind the immense diversity of antibodies that target and provide immunity against myriad infections and cancers. This revelation led to Tonegawa becoming the sole winner of the 1987 Nobel Prize in Physiology or Medicine — and the first Japanese person to win the prize.

When asked how he transformed two fields, Tonegawa’s answer evoked a contemporary conundrum. Is it better to ask big questions and consider what technologies would be needed to answer them, or to think about what fresh information the latest technologies can unearth? With his characteristic wry smile, he would bluntly state that the answer lies between the two: scientists should ask how an emerging technology might offer a fresh approach to a really big question. And that’s exactly what he did — twice.

Tonegawa, who has died aged 86, was born in Nagoya, Japan, in 1939. His father, an engineer who was regularly assigned to different cities, sent Tonegawa to live with his uncle near Tokyo so that he could attend the prestigious Hibiya High School. The value of education stayed with him: throughout his life, he remained an irrepressible student, thrilled with any opportunity to learn.

Another influence was a sense of national defeatism felt by many people in Japan in the early 1960s, when he was studying chemistry at Kyoto University. He wanted to embody a fresh start. After reading the game-changing work on gene regulation by François Jacob and Jacques Monod, he was excited by an opportunity to study molecular genetics at the University of California, San Diego.

Like many scientists, Tonegawa struggled, producing a PhD thesis that he was “not particularly proud of”. Nonetheless, it enabled him to become a postdoc at the Salk Institute in San Diego under Renato Dulbecco, studying eukaryotic molecular biology.

Tonegawa was an eternally grateful person, who acknowledged crucial support from his family, friends and colleagues. Dulbecco was an enormous influence on him and shaped his views of mentorship. In 1969, Dulbecco advised him that immunology was ripe for the application of molecular biology and that, given the imminent expiry of Tonegawa’s US visa, the best place might be the newly founded Basel Institute for Immunology in Switzerland.

A brilliant experimentalist, Tonegawa thought that big scientific questions would yield their secrets if they were approached rationally and unrelentingly. By the 1970s, the diversity of antibodies was well known but unexplained. In 1976, Tonegawa reported that the origin of this diversity lies in the capacity of a type of antibody-producing white blood cell called a B cell to rearrange its genes, thereby expanding the number of antibody proteins that humans can synthesize from about 25,000 to billions (N. Hozumi and S. Tonegawa Proc. Natl Acad. Sci. USA 73, 3628–3632; 1976).

He did this by applying molecular-biology methods to immunology, saying later that he didn’t know why scientists were trying to understand antibody diversity by studying the antibodies rather than the genes encoding them. The answer was that few (if any) other researchers had worked out how to do so — by using serum biochemistry methods and newly identified DNA-restriction enzymes.

Tonegawa’s results, together with studies of colour variegation in the kernels of maize (corn) by Barbara McClintock, showed that genome stability was not sacrosanct, as most scientists assumed. They laid the groundwork for the idea that genomes contain myriad mobile genetic elements and viral DNA segments that have the potential to influence the physiology and evolution of organisms profoundly.

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Nature 656, 31 (2026)

doi: https://doi.org/10.1038/d41586-026-02340-8

Competing Interests

The author declares no competing interests.

COMMENT 03 August 2026

Plans for nuclear-powered merchant ships must confront risks

Vessels propelled by atomic reactors have low emissions but create other hazards that need clear regulations.

By

Jihong Chen &

Zheng Wan

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A Russian nuclear-powered icebreaker, Rossiya, operates in Arctic waters. Credit: Patrick Landmann/Science Photo Library

The shipping industry needs low-carbon fuels if it is to reach net-zero greenhouse-gas emissions by 2050. But options are limited. Batteries can power ferries and vessels for use in harbours, but are impractical over long distances[1]. Methanol is hard to produce sustainably[2]. Ammonia is toxic and its supply chain is polluting[3]. Hydrogen is too bulky and difficult to store safely at sea[4].

Nuclear reactors are being eyed as a solution. Atomic energy produces no direct carbon dioxide emissions and avoids the need to store several thousand cubic metres of fuel on board, freeing up space for cargo. Using nuclear power could shorten voyage times by eliminating refuelling stops and attaining higher speeds. It could save a ship owner tens of millions of dollars per ship per year in marine fuel costs and carbon taxes (see go.nature.com/4hs7u3r).

A new generation of nuclear-powered commercial ships could be ploughing through the waves in the next decade. Interest from industry and governments is growing. In May, the US Maritime Administration sought public input on a commercially viable, scalable small modular reactor system for marine use. In 2028, London-based Core Power plans to open an order book for mass-produced floating nuclear power plants, with full commercialization in the mid-2030s and nuclear civil ships to follow. In China, the state-owned Jiangnan Shipyard, based in Shanghai, plans to build the world’s first thorium-powered container vessel by 2035.

A 2023 US report estimates that several hundred nuclear-powered merchant vessels (mostly container ships,large bulk carriers and oil tankers) could be operating worldwide by 2050, assuming a 2–5% market share[5] (see ‘Commercial shipping goes atomic’). But a merchant ship fitted with a reactor is a mobile nuclear facility that needs to be treated with great care. It could be hijacked, run aground or even come under missile attack.

Source: Ref. 5

Before nuclear-powered ships reach the seas, the world needs to develop robust regulations around them. Engineers, regulators, ports and shipping companies must make these vessels safe and controllable. But current rules are outdated and fragmented.

Two major efforts are under way. In late August, at a ministerial-level event in Washington DC, the International Atomic Energy Agency (IAEA) plans to bring nuclear and maritime regulators together at the launch of its Atomic Technologies Licensed for Applications at Sea (ATLAS) initiative. In June 2025, the International Maritime Organization (IMO) began revising its 1981 Code of Safety for Nuclear Merchant Ships, and expects to adopt a revised code in 2030.

Here we set out what is at stake and call on the IAEA and IMO to link reactor licensing, ship certification, port entry and emergency response into one framework, lest companies, ports and countries implement incompatible systems and a nuclear accident at sea ensues.

From military to commercial use

Nuclear power has a long history at sea, driving submarines, aircraft carriers and icebreakers. Compact reactors with robust shielding, detailed maintenance plans and regular crew training have operated for at least 70 years in tough maritime environments, even under polar ice.

The US Navy runs one of the world’s largest nuclear fleets. By December 2024, it was operating 77 nuclear-powered warships — 66 submarines and 11 aircraft carriers — making up roughly half of the global fleet. Since 1955, US nuclear-powered vessels have racked up distances of some 300 million kilometres.

China, France, India, Russia and the United Kingdom also operate nuclear submarines and Russia has civil nuclear icebreakers. Today’s global fleet is smaller than it was at the end of the cold war — in 1989, more than 400 nuclear submarines were in operation or being built, compared with around 150 in service today.

Yet this same record exposes the difficulty of commercial application. Nuclear use in a navy is part of a system of sovereign capabilities: one state (or several in close partnership) can design and construct the reactor, build the infrastructure for maintaining or replacing it, train the crew and assume responsibility for safety.

By contrast, most commercial ships are designed in one nation, built in another, flagged in a third, insured in a fourth, enter a variety of foreign ports, and often recycled at the end of their lives in countries with lax safety procedures[6].

Early civil nuclear-powered merchant ships failed to achieve broad commercial success. Just four have been built. Launched in 1959, the NS Savannah was part of US president Dwight Eisenhower’s Atoms for Peace programme. It was able to travel the world and dock in many ports. But the complicated procedure for obtaining permits for visits and berths made it unviable as a cargo ship.

West Germany’s Otto Hahn, launched in 1964, carried ore around the world for 9 years, travelling 1.2 million kilometres and visiting 33 ports in 22 countries, before its reactor was removed and the ship converted to conventional propulsion in 1982.

Launched in 1969, Japan’s Mutsu faced a public-relations disaster in 1974, after a shielding flaw caused neutrons to leak during a reactor test. Although not catastrophic, the incident bred local opposition, which kept the ship out of operation for years.

The fourth, Russia’s Sevmorput, entered service in 1988 and spent decades carrying cargo along the northern sea route in the Arctic. It was the only nuclear-powered merchant ship still operating in the 2020s, having been kept on as a state-owned ice-capable cargo vessel.

New technologies

Most early ships ran on low-enrichment uranium fuel, much as a civil nuclear power station does. But current propulsion options are more diverse[7]. They include technologies developed from long experience with icebreakers and small modular reactor programmes, such as light-water, high-temperature gas-cooled, lead-cooled fast, molten-salt and heat-pipe reactors. Other experimental designs are on the table.

However, an expanded technology menu only poses more safety questions. Any reactor-powered ship requires licensing, insurance, crewing, upkeep, port acceptance, emergency preparedness and response and support for decommissioning. Even Savannah’s final disposition remains a regulatory issue, yet to be solved. Some business models assume leased reactor modules and lower operating costs through remote monitoring, centralized refuelling and decommissioning. But those choices do not assure ship safety.

One of two nuclear reactors on board the Russian ship Rossiya. Credit: Patrick Landmann/Science Photo Library

Military records show where stress and poor planning can lead. Soviet nuclear-powered submarines have experienced loss-of-coolant accidents, unexpected power surges and damage to the reactor core. On the K-19 submarine in 1961, emergency repairs to restore cooling exposed sailors to lethal radiation. On K-27 in 1968, a liquid-metal-cooled reactor accident led again to fatal radiation exposures. On K-431 in 1985, a power surge during refuelling at Chazhma Bay, near Vladivostok, caused a steam explosion, killing several workers and contaminating the surrounding area.

Newer reactor designs can reduce some of the hazards, especially when they operate at low pressure and have sealed cores, systems for removing excess heat and robust containment structures[8]. But advanced approaches mainly shift problems rather than eliminating them. A molten-salt reactor might avoid high-pressure coolant accidents, but regulators still need to know whether salt corrosion can be managed after prolonged exposure to vibration. They will also be concerned about management of tritium and volatile fission products, adequate shielding and reliable removal of heat from radioactive decay after shutdown.

A port licence is also a safety licence

A nuclear-powered ship will require a reactor licence, ship certificates and port authorizations. Each involves separate judgements and permissions.

For the nuclear regulator, the core questions surround reactor safety, radiological protection and nuclear security. For the flag state, they include seaworthiness and crew safety. The port authority is concerned mainly with whether the vessel can enter the harbour, berth, discharge cargo, take refuge or be held in port without risk to the surrounding community.

For a conventional ship, these decisions are standard. For a nuclear-powered vessel, the relationships are more complex. If the harbour master, pilot, terminal operator, underwriter or emergency services are unable to understand the safety assurances provided by the reactor licence, then the document has little practical meaning.

Savannah’s port calls, for example, required extensive negotiations about berthing and transit as well as procedures to deal with emergencies and actions to reassure the public. Mutsu showed that, once confidence is lost, no rational argument is of any use; the ship won’t be welcome.

The war-risk gap

Threats of war and terrorism also pose risks that must be taken seriously. The cost–benefit analysis for nuclear shipping assumes orderly marine traffic and peacetime cooperation between ports. Current events in the Red Sea and the Strait of Hormuz since US and Israeli strikes on Iran challenge this assumption. Iranian-supported Houthi militias in Yemen have repeatedly used drones, missiles and unmanned boats against commercial shipping. In the Strait of Hormuz, restrictions and attacks have disrupted traffic and stranded thousands of seafarers.

If a nuclear-powered ship were to transit such waters, it could face the same missiles, drones, terrorist boarding attempts and electronic disruption as do conventional tankers, but with much greater consequences.

A marine reactor cannot explode like a nuclear weapon; the fuel and geometry of a civilian reactor make that impossible. But other risks cannot be ignored: damage to fuel, loss of shielding or coolant, hydrogen fires or steam explosions can release radioactive material and contaminated firefighting water, and create a shipwreck that no port is willing to receive.

Would a missile attack make a maritime reactor unsafe? It depends. A strike that hits the bow or a container stack might not reach a reactor located near the middle of the ship. However, a strike that hits the engine room, switchboard, emergency generator, control cables or cooling-water intakes could disable the systems that support a safe shutdown.

A container fire could burn for days and block rescue attempts. A hole below the waterline could compromise the vessel’s stability and flood compartments that are assumed to stay dry in the reactor design.

Follow-up strikes might hit rescuers. A boarding party could evacuate the crew after the reactor is shut down, but decay heat still needs to be managed.

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Nature 656, 33-36 (2026)

doi: https://doi.org/10.1038/d41586-026-02388-6

References

1. Kistner, L., Bensmann, A. & Hanke-Rauschenbach, R. Energy Convers. Mgmt X 21, 100507 (2024).

2. Fasihi, M. & Breyer, C. Energy Environ. Sci. 17, 3503–3522 (2024).

3. European Maritime Safety Agency. Potential of Ammonia as Fuel in Shipping (EMSA, 2022; updated 2023).

4. European Maritime Safety Agency. Potential of Hydrogen as Fuel for Shipping (EMSA, 2023).

5. Dowling, M. Report No. INL/RPT-24-82274-Rev000 (Idaho National Laboratory, 2023).

6. Wan, Z., Wang, L., Chen, J., & Sperling, D. Mar. Policy 130, 104542 (2021).

7. Holbert, K. E. J. Nucl. Eng. 6, 5 (2025).

8. Emblemsvåg, J. et al. Nucl. Technol. https://doi.org/10.1080/00295450.2025.2575544 (2025).

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COMMENT 04 August 2026

Europe can become a bioengineering powerhouse with these three changes

Sustaining the region’s strong position in the life-sciences sector will require investment and education in bioengineering.

By

Vasilis Ntziachristos,

Thomas Schwarz-Romond &

Fiona M. Watt

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Bioengineering can produce tools for use in cutting-edge cell therapies, among other applications.Credit: Philippe Lopez/AFP via Getty

Europe is losing ground in one of the most innovation-intensive sectors in biomedicine and health care: bioengineering[1]. Many advances in biomedicine are technologically driven, for example, the use of artificial intelligence to predict protein structures and rapid genome sequencing for precision medicine.

Bioengineering produces technologies that aim to solve medical problems, by merging methods from mathematics, computation, engineering and biomedicine. Many countries, including the United States[1], China, Switzerland and the United Kingdom, understand the power of such research. Yet, the European Union has still not caught on.

The EU’s 27 member states (EU27) host fewer than ten fully autonomous bioengineering departments, compared with more than 100 each in the United States and China. Not one EU27 university made it into the 2025 ShanghaiRanking’s top-30 list of biomedical engineering departments, compared with 18 from Asia.

Investment in medical technologies in the EU27 is low (see ‘Untapped potential’). In 2023, biotechnology companies in Europe attracted US$11.5 billion in funding, compared with about $57 billion in the United States and $21 billion China (see go.nature.com/3twrfgr and ref. 2).

Source: European Commission

Concerned about this widening gap, in March, 25 specialists from around the world — including us and our co-signatories (see Supplementary information) — came together at Europe’s life-sciences organization EMBO in Heidelberg, Germany, to find ways to address it. Here, we call on the European Council and relevant EU directorates to adopt a European bioengineering agenda that focuses on three themes.

Build academic excellence

The EU can build on its vibrant life-sciences sector, which contributes €1.5 trillion to its economy and supports 29 million jobs (see go.nature.com/4fr475p). The region has a strong culture of collaborative research, academic–industry partnerships and funding programmes that encourage the development of technologies. For decades it has excelled in other engineering disciplines, from civil to computational. And the EU’s open society, social-security systems, health-care and education infrastructures attract global talent.

What’s needed now is coordinated European investment in bioengineering education, research and innovation ecosystems, to create the conditions for globally competitive health-care technology and biotech companies to flourish.

The EU should incentivize national governments to establish dedicated bioengineering departments at their leading universities. Funding programmes should be used to promote bioengineering excellence.

Universities should recruit dedicated bioengineering staff members with a range of expertise to establish world-class curricula. This should be done in a similar way to how electrical and computer-engineering faculties were established originally, by bringing together top researchers from physics, mathematics and other engineering and technology fields.

New bioengineering institutions should teach students how to develop a problem-solving mindset[3]. This skill can be applied across life-sciences research, including to methodologies that advance biomedical discovery and technologies that improve clinical care.

Curricula should also emphasize medical ethics and regulation, to help students embrace the processes involved in developing successful biomedical technologies. This will help future bioengineers to navigate the rules and market forces that are specific to technology acceptance in medicine, which are different from other engineering products and pipelines.

Having several such top-level departments will build homegrown expertise and attract medical technology and biotechnology companies. Evidence for this can be found in Boston, Massachusetts, and San Francisco, California. These areas are home to biotechnology and medical-technology hubs, the foundations of which were built on a reputation for education and research excellence, including top-ranking bioengineering departments.

Recognition and celebration of high-achieving bioengineers can enhance the visibility and reputation of the field, initiating a virtuous cycle that makes entering the bioengineering industry more desirable. As the number of bioengineers in Europe grows, these researchers could establish dedicated societies that can present scientific awards, elect fellows, recognize early-career achievements and establish distinguished lectureships, as well as inspire established funders, societies and philanthropists to support bioengineering.

Invest in bioengineers

The EU invests billions of euros annually in biomedical and life-sciences research, including through research funding from the European Research Council and the European Innovation Council. Yet, dedicated review panels for assessing bioengineering grant proposals are absent, and funding proposals are typically assessed by researchers in neighbouring disciplines, such as biology, medicine, physics and engineering. Although reviewers might have relevant expertise, they are likely to evaluate proposals through the lens of their own disciplines.

The EU should establish dedicated funding schemes and review panels for bioengineering research. Experts in the field should also be involved in shaping future initiatives. These include Framework Programme 10 — the EU’s next research and innovation funding programme, which is currently being developed and is scheduled to run from 2028 to 2034.

Tissue bioengineering techniques could improve treatments for congenital heart conditions.Credit: Christophe Archambault/AFP via Getty

To rapidly translate research into marketable products, Europe needs policies that actively engage industry, venture-capital and investment firms. The region cannot establish global leadership in bioengineering while much of its talent is moving abroad and its innovations are being commercialized elsewhere. Dedicated funding programmes, public–private co-investment schemes, tax incentives for investments in technology and reduced administrative barriers to technology transfer are essential at both the EU and national levels. These efforts should be complemented by regulatory pathways that support safe but efficient early approval of technology for use in clinical settings.

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Nature 656, 36-38 (2026)

doi: https://doi.org/10.1038/d41586-026-02389-5

References

1. Biomedical Engineering Society. Securing the Pipeline: How Biomedical Engineering Drives American Leadership (BMES, 2025).

2. Organisation for Economic Co-operation and Development. Boosting Biotechnology Innovation Through Agile Regulation and Finance Instruments (OECD, 2025).

3. Miller, M. I. et al. Ann. Biomed. Eng. 4, 0001 (2023).

4. MedTech Europe. MedTech Europe Survey Report (MedTech Europe, 2022).

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1. List of co-signatories

Competing Interests

The authors declare no competing interests.

Work

- Science-backed tips on how to avoid distractions

CAREER FEATURE 03 August 2026

Science-backed tips on how to avoid distractions

Struggling to stay focused in the lab? Attention researchers offer some advice.

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Nikki Forrester

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Illustration by Claire Welsh/Nature. Adapted from image by Roman Samborskyi/Shutterstock

Smartphones and other digital technologies are a constant potential source of distraction. According to a 2025 survey by Reviews.org, an online resource that reviews mobile services and Internet providers, on average, US adults check their phones 186 times per day, or roughly once every five minutes (see go.nature.com/4b4zgje). They also spend 5 hours each day using smartphones, which amounts to 76 days of screen time per year, with Generation Z (those born between 1997 and 2012) and Millennials (those born between 1981 and 1996) spending the most time scrolling on their phones.

Over the past two decades, these has been increased interest in whether people’s ability to focus on a single task has deteriorated. People often report that they think their attention span has shortened (see go.nature.com/4dfz8yc). But data that definitively show a drop in attention are sparse.

“Every year that passed, things that are so important to me, like reading books, having proper long conversations, even watching films, were getting harder and harder to do,” says Johann Hari, whose 2022 book Stolen Focus, outlines 12 reasons why people’s attention spans have declined, and offers tips to help people regain their focus. “I felt like I was running up a down escalator that was going faster and faster every year,” adds Hari about his declining ability to pay attention.

Staying focused is crucial for short-term memory, problem solving and creativity, whereas frequent distractions lead to more errors and higher levels of stress and frustration. “Attention is at the heart of all human achievement,” says Hari, who lives in London and Los Angeles, California. “Anything you’ve ever achieved in your life that you’re proud of, whether it’s starting a business, getting a PhD, being a good parent or learning to play the guitar, required a huge amount of sustained focus and attention.”

Nature’s careers team asked six attention researchers and educators for tips on how to mitigate disruptions and improve concentration.

Defining attention

“There are two rather separate attention systems,” says Michael Posner, a psychologist at the University of Oregon in Eugene, whose research career of more than 50 years led to the discovery that there are different forms of attention that are associated with different regions of the brain. One form is called executive attention, which describes the ability to focus on one thing. The other is the orienting system, which is guided by disruptions, including changes in lighting, unexpected sounds and other environmental influences.

“Powerful external stimuli are pretty hard to resist,” Posner says. “That’s because orienting is important for being aware of potential predators.” If someone is deeply involved in a task, he says, it’s nevertheless helpful to know if a tiger is sneaking up on them.

Today, distractions often come in the form of interruptions such as text messages, e-mails, in-person requests, meetings and sudden noises. But people also actively and frequently interrupt themselves. “The problem is not that we get distracted,” says Gloria Mark, a psychologist and attention researcher at the University of California, Irvine. “The problem is: what do we do about it?”

The human brain isn’t able to consciously pay attention to more than one thing at a time, says Mark. Instead, it switches rapidly between tasks, which comes at a cost. “A lot of people think that multitasking is this badge of honour — it’s not,” says Mark. Generally, people who multitask make more errors and take longer to complete a single task because they have to reformulate a mental plan for completing that task every time their attention switches.

Cultivating focus

Individuals have their own peaks and valleys for when they focus best, says Mark. These personal rhythms vary depending on factors such as a person’s natural tendency to sleep and wake at certain times and how much sleep they get. In general, people have a period of peak focus mid-morning, then a dip around lunchtime and a second peak in the afternoon. “That’s on average,” Marks adds, noting that some people have focus peaks early in the morning, whereas others focus best later in the day or during the evening.

“For scientists, it’s really important for them to understand that they have these limited cognitive resources,” says Mark. “They should understand when their peak periods are and use those times to do their most important work.” For instance, instead of a researcher using their peak focus times to check their e-mail or attend meetings, they can allot that time to analysing data or writing a grant proposal. If someone isn’t aware of their personal rhythm of attention, Mark recommends keeping a diary and taking notes every half an hour or hour, to record how focused they feel.

One of the most effective ways to stay focused during peak periods is by putting away the major source of distraction — the mobile phone. On average, people in the United States spend 10 seconds looking at their phone each of the 200 or so times they check it daily[1]. Every interruption breaks focus and can make it harder to complete a task. “People find it difficult to work with their smartphone sitting next to them,” says Stefan van der Stigchel, a cognitive psychologist at Utrecht University in the Netherlands. “You won’t go on a diet with a pack of cookies next to your laptop, right? There’s one moment where you’re bored or when your will is not as strong, and all of the sudden you’re picking up your phone or you’re eating that cookie.”

Researchers can try putting their phones in a drawer, in another room or in a safe with a timer to keep it locked away for a set time. They can also set digital well-being limits on the phone itself. Apps such as Freedom and Cold Turkey Blocker can be used to block specific websites, apps or Internet access. Nova Corciega, a science instructor and education researcher at Negros Oriental State University Mabinay Campus in the Philippines, uses the digital well-being settings on her phone to limit her use of the social-media site Instagram to 35 minutes per day and the platform YouTube to one hour per day. “This gets me to think that I only have 35 minutes for this application per day, so I need to use it wisely,” she says.

In the moment, it’s easy to press ‘ignore’ on a timer, but over a longer period, setting digital limits can help researchers to establish mental routines that support their ability to focus and become more introspective about their attention. “We do so many things automatically and unconsciously, like picking up our phones, swiping them open, going to check e-mail,” Mark says. If researchers recognize when they feel an urge to check their phone or their e-mail inbox, they can then ask themselves whether they really need to. “It’s a way of counteracting habits and being intentional about what we do,” she adds.

When scientists start to feel their attention spans waning, it’s time to take a break. “We prefer not to have lunch meetings because those are times when you can recharge your battery,” says van der Stigchel, who often takes walks outside (without his phone) during the working day. It can be difficult to take breaks, especially if someone pops into the laboratory to ask a question, but van der Stigchel says that it’s important to be strict about taking time to recharge. “I always say I’m never late for a meeting, including a meeting with myself,” he says. “Not working is part of what allows me to focus and be productive.”

Goal setting is another way that researchers can improve their attention spans. According to a 2023 study[2] of 108 university students in the United States, those who set specific and difficult but achievable goals had shorter lapses in attention and stayed engaged with a task longer than did those who set vague goals.

However, goal setting improves attention only when a person is already committed to that goal: you can’t just write something down and expect that to be enough. Mark recommends that researchers write down their goals the night before work and reaffirm them the next morning. “Create a visualization of where you want to be at the end of the day,” she says. “I want to be sitting on my couch. I want to be reading. I want to be relaxing. I don’t want to be working on that paper.”

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Nature 656, 261-263 (2026)

doi: https://doi.org/10.1038/d41586-026-01850-9

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

References

1. Brinberg, M. et al. Comput. Hum. Behav. 114, 106570 (2021).

2. Strayer, D. L., Robison, M. K. & Unsworth, N. Atten. Percept. Psychophys. 86, 1594–1608 (2023).

3. Oh, Y.-A., Kim, S.-O. & Park, S.-A. Int. J. Environ. Res. Public Health 16, 796 (2019).

4. Mark, G., Voida, S. & Cardello, A. In CHI ‘12: Proc. SIGCHI Conf. Human Factors Comput. Syst. 555–564 (Association for Computing Machinery, 2012).

5. Lentoor, A. G. NeuroSci. 3, 376–386 (2022).

6. Wang, S., Zhang, J., Yao, J. & Du, M. Pol. J. Environ. Stud. 25, 1791–1797 (2016).

Research

- An entangling gate for dual-rail erasure qubits

- Imaging of nanoscale polar textures in quantum paraelectric SrTiO3

- Family of magnetic field-boosted superconductors in rhombohedral graphene

- Ligand-enabled distal desaturative lactonization of aliphatic acids

- High-fidelity modular skeletons authenticate a Cambrian origin for Bryozoa

- Cell-type-resolved genetic variation shapes inflammatory bowel disease risk

- Mechanism of age-related accumulation of mtDNA mutations in human blood

- Cortical development dynamics across autism spectrum disorder mouse models

- Universal cell embedding provides a foundation model for cell biology

- Disparate privacy risks from medical AI

- LASER couples damage sensing to ESCRT assembly for lysosome repair

- Spatial distribution of the proteome in the human body and in cancers

- Zero-shot design of drug-binding proteins via neural iterative selection−expansion

Article Open access Published: 05 August 2026

An entangling gate for dual-rail erasure qubits

D-Wave Quantum Inc.

Nature volume 656, pages 47–53 (2026)

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Abstract

Quantum error correction (QEC) will likely be required to realize the full potential of quantum computing, but comes with daunting hardware overheads and demands low gate errors on the physical qubits1,2,3,4. These requirements can be eased by engineering qubits with a strong error hierarchy, in which the most common noise channels are also the easiest to correct. Erasure qubits can achieve this when detectable leakage errors out of the computational subspace dominate over the residual Pauli errors5,6,7,8,9,10,11, resulting in higher thresholds and improved scaling with code distance5,12,13. In practice, these advantages come to fruition only if the error hierarchy is preserved as much as possible throughout all gates and operations. Here we design and realize a two-qubit entangling gate for dual-rail cavity qubits, a type of erasure qubit encoded in a pair of superconducting microwave cavities[7]. Our experimental demonstration confirms that the error hierarchy is largely preserved during the gate. The gate is fast (about 500 ns duration) and shows low erasure rates of approximately 0.5% per gate, remaining Pauli errors below 0.1%, and a strong bias towards dephasing errors, in which bit-flips are practically non-existent at the 10−6 level. These results enable a faster path to error-corrected systems that rapidly suppress errors as they scale; a claim we support with our detailed surface code simulations.

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Main

To progress from the present-day era of NISQ machines[14] to the era of effective quantum error correction (QEC), the performance of physical qubits and their fundamental operations must improve markedly. A useful approach to relax the requirements for QEC is to engineer qubits with structured noise, in which certain types of errors occur much more frequently than others, and to adapt the QEC scheme accordingly to take advantage of this noise. This strategy works best when the error structure is preserved as much as possible throughout all gates and measurements.

Stabilized cat qubits are an example of this model15,16,17,18,19,20, in which bit flips can be greatly suppressed relative to phase flips then allows us to concatenate these physical qubits with a repetition[18] or thin rectangular surface code8,21,22,23,24 for a substantially improved QEC encoding rate.

A second way to realize this model is with erasure qubits, in which most of the errors can be detected as they occur at the hardware level[10]. After resetting these faulty qubits, the resulting error channel resembles a Pauli error at known spacetime location—an erasure error. QEC codes are generally more tolerant to erasures and can generally correct for twice as many erasures as Pauli errors at a given code distance[5]. They also possess higher thresholds as demonstrated by the surface code, which has a threshold close to 25% for phenomenological erasure noise12,13.

Erasure qubits are most often realized by detecting loss events that take us out of the computational subspace. For dual-rail qubits, such as those in photonic25,26,27,28 or superconducting platforms, this is loss of a photon or excitation that takes us to the vacuum state. In other platforms, such as neutral atoms or trapped ions, leakage to non-computational states or loss of the atom from the trap can also be detected and converted to an erasure10,29,30, with similar benefits9,31.

In superconducting circuit platforms, dual-rail qubits can be realized with pairs of transmons6,32,33,34 or microwave cavities7,35,36. When idling, dual-rail cavity qubits exhibit a strong hierarchy of errors that make them a promising erasure qubit. In this hierarchy, erasures dominate by a factor of 5–10 compared with phase flips, whereas the remaining bit flips are exceedingly rare. So far, superconducting dual-rail cavity qubits have demonstrated fast, high-fidelity single-qubit gates37,38, good state preparation and measurement (SPAM)[36], and efficient, non-destructive detection of erasures39,40. However, a vital missing ingredient is an entangling operation between two dual-rail qubits, which must simultaneously have low error rates and preserve the favourable error hierarchy.

Here, we propose and experimentally demonstrate a controlled-Z (CZ) operation that satisfies these requirements for dual-rail cavity qubits. Our so-called ‘Swap–Wait–Swap’ (SWS) gate uses high-fidelity parametric operations to temporarily swap an excitation from one of the cavities (that comprise the dual-rail ‘control’ qubit) to a transmon coupler connecting the two dual-rail qubits, similar to the scheme in ref. [41]. By temporarily populating the coupler, we are able to use the strong (MHz level) dispersive shift between the coupler and a cavity of the other (‘target’) dual-rail qubit. Swapping the excitation back into the control cavity completes the gate, allowing us to realize a CZ entangling gate in about 500 ns.

Importantly, our gate is bias-preserving in several aspects. The excitation-preserving nature of the gate preserves the erasure-to-Pauli noise bias on both qubits, with erasure rates below 1%. After erasure detection, we find the residual dephasing errors are at or below 0.1% per gate, as characterized by quantum state tomography (QST) and interleaved randomized benchmarking (IRB). Moreover, we also observe a strong Pauli noise bias in our gate with bit-flip errors at the order of 10−6 per CZ gate, an advantageous bias first explored theoretically in ref. [11]. Moreover, target qubit erasures and dephasing rates are a factor of 3–4 times lower than those for the control qubit. This asymmetry can be carefully managed when detecting error syndromes in a QEC context (J.D.T., manuscript in preparation) to ensure errors on data qubits are minimized. Finally, we study the error channel when one of the qubits suffers from photon loss during or before the gate, and show both theoretically and experimentally that this amounts to a conditional-dephasing error on the other qubit. All of these properties can be exploited by properly designed error correction codes, as we show with simulations in the surface code (Methods). The performance levels we demonstrate are well past the predicted surface code thresholds obtained from simulations[10] (J.D.T., manuscript in preparation) and should thus allow significant logical error suppression with increasing code distance.

The SWS CZ gate

Our two-qubit gate makes use of a dispersive interaction to implement a CZ gate between two dual-rail qubits, each comprising a pair of nominally linear microwave cavities. For dual-rail cavity qubits, enacting a CZ gate in the Fock basis between two cavity modes (one from each dual-rail qubit) also enacts a CZ gate in the dual-rail basis.

Thus, nearest-neighbour coupling of the cavities is sufficient to implement a dual-rail CZ gate. The physical system shown in Fig. 1a realizes this by using a coupler c to enact a CZ gate on the adjacent a_2 and b_1 cavities. The dispersive interaction we use for this purpose acts between the coupler and cavity mode b_1 in the target qubit, \({{\mathcal{H}}}_{{\rm{int}}}/h={\chi }_{bc}{\widehat{b}}_{1}^{\dagger }{\widehat{b}}_{1}{\widehat{c}}^{\dagger }\widehat{c}\), where χbc/2π = −1.51 MHz in our system.

Normally, this static interaction term does not affect the system dynamics in any way, and is effectively ‘switched off’ since the coupler mode c is in its ground state. The interaction can be effectively switched on by attempting to swap a photon between the cavity a_2 and coupler c, temporarily redefining the control dual-rail qubit between the non-local elements a_1 and c (Fig. 1b). Such a swap here is implemented using a parametric sideband interaction between the coupler and cavity: \({\mathcal{H}}/\hbar =\frac{{g}_{ac}}{2}({\hat{a}}_{2}^{\dagger }\hat{c}+{\hat{a}}_{2}{\hat{c}}^{\dagger })\), similar to the interaction used to affect single-qubit gates in dual-rails[38]. In our hardware, gac/2π = 4.23 MHz. The dispersive interaction is then allowed to act for a time t ≈ π/χbc, thus entangling the two qubits. A final swap between the coupler and cavity a_2 returns the excitation back to the control cavity. The entangling phase ϕe = χbctwait has thus been imparted only to the |0,1,1,0⟩ initial basis state (using the notation |a_1,a_2,b_1,b_2⟩), which results in a control-phase gate with phase set by the wait time. We adjust this wait time to realize a CZ gate.

Our gate construction allows us to effectively realize a large ‘switchable’ dispersive interaction between cavity modes a_2 and b_1, without needing to modulate the strength of the underlying static dispersive interactions in our system, which often requires complex Hamiltonian engineering[42]. From this construction, we also expect our gate to exhibit highly structured noise, which is advantageous for QEC syndrome extraction circuits (see the Methods for an example with the surface code and J.D.T., manuscript in preparation).

We now describe this noise structure in detail. The first observation we make is that the total number of excitations are preserved throughout the entire operation. Both control and target dual-rails never leave the single-excitation manifold (redefining the control dual-rail as modes a_1 and c during the gate). This property ensures that any photon loss, even in the coupler mode, leads to a loss error that can be detected after our CZ gate. As an added benefit, we also avoid detrimental effects due to cavity self-Kerr, as no mode is populated with more than one photon at any point during the gate. A caveat, however, is that errors on the target and control qubits can have significant asymmetry. The control qubit, which partially occupies the coupler mode during the gate, experiences increased loss (with probability t/T_1 ≈ 1%) and dephasing (t/Tϕ ≈ 0.1%) compared with the target qubit (t/T1,cav ≈ 0.1%, t/Tϕ,DR ≈ 0.01%), owing to the lower coherence times of the coupler mode compared with the cavities. We term this property of our gate as an error asymmetry. Another property that arises naturally is the strong suppression of bit-flip errors, as observed in the idling state of the dual-rails[36]. This is because the outer cavities a_1 and b_2 and their couplers remain completely idle during the gate. As such, our gate is bias-preserving to dephasing errors. Finally, we expect well-behaved propagation of leakage errors. The dominant leakage channel is decay to the vacuum state, which switches off the CZ-generating cross-Kerr interaction. Thus, if either of the dual-rails relaxes to vacuum, subsequent CZ gates simply do not occur, as if they were omitted from the quantum circuit.

Fig. 1: Device hardware schematic and CZ gate sequence.

Full size image

a, Schematic of a two dual-rail cavity qubit system, similar to ref. [36], in which the control and target qubits are realized using pairs of three-dimensional λ/4 coaxial microwave cavities (red and blue). Three SQUID transmon couplers (grey and purple) each dispersively couple to pairs of cavity modes. These couplers are flux-pumped[38] to actuate parametric beamsplitter interactions for single-qubit gates within the dual-rails, and also to implement the dual-rail two-qubit gate, which is achieved by the middle coupler. Each cavity is coupled to an additional transmon qubit (green) and readout resonator (blue lines) that are used for SPAM. b, Diagram of the SWS gate sequence between two dual-rail cavity qubits. This gate consists of three steps: (1) performing a Swap operation between the control qubit (a_2 cavity) and the coupler c; (2) allowing the dispersive interaction between coupler and target qubit (b_1 cavity) to acquire phase and entangle the two dual-rail qubits; and (3) effectively switching off this dispersive interaction by performing another Swap between control qubit (a_2 cavity) and coupler, restoring population to the a_2 cavity. c, Schematic of photon population during the gate sequence for the four basis states.

Our experimental two-qubit system also exhibits high-fidelity SPAM and single-qubit gate fidelities that aid in the detailed characterization of our two-qubit gate. Single-qubit gates are performed by driving a beamsplitter interaction within the dual-rail for durations of about 200 ns, achieving erasure rates of approximately 0.1% and post-selected randomized benchmarking error of about 0.01%. By using end-of-the-line erasure-detected measurements[36], we are able to obtain low post-selected SPAM errors of about 0.02%. We provide additional results in Supplementary Information section I. A detailed explanation of these parameters and the calibration protocol can be found in Supplementary Information sections II and XIII.

Gate benchmarking

We now present several methods for characterizing the fidelity and error structure of the SWS gate. As with all operations on erasure qubits, we must carefully quantify both the erasure rate and the residual Pauli errors. We use the SWS gate to generate an entangled Bell state, which we analyse using QST. We also perform IRB to evaluate gate performance in arbitrary circuits.

For our first demonstration of the SWS operation, we show high-fidelity entanglement between two dual-rail qubits by implementing the circuit in Fig. 2a with a single CZ gate (N = 1). As shown in Fig. 2, we generate a high-fidelity Bell state and, after post-selection of erasures, extract an end-to-end circuit state fidelity of 99.60(1)% and a state purity of 99.46(3)%. We highlight that this infidelity of about 0.4% includes errors aggregated from SPAM, single-qubit gates and the CZ gate.

Next, we perform repeated CZ gates that repeatedly entangle and disentangle the two qubits up to N = 103 to establish a more precise metric for the error per CZ gate, using the sequence shown in Fig. 2a. The dependence on the number of gates is shown in Fig. 2c, in which we present state fidelity, state purity and post-selected fraction as a function of the number of repeated CZ gates. First, we can consider the behaviour at lower gate repeats. As shown in Fig. 2c (inset), our data show a linear decrease in fidelity and purity out to N = 15 repeated gates, from which we extract a post-selected infidelity of 0.029(6)% per gate.

We also analyse the results for larger gate depths up to N = 103. First, we extract the post-selected fraction (Fig. 2c, bottom), defined to be the fraction of experimental shots for which no erasures were detected. From fitting the results to an exponential decay, we extract a total erasure probability per CZ gate of 0.53(2)%. As the number of gates increases, both the fidelity and purity of the final state (Fig. 2c, top) exhibit an unexpected approximately quadratic decrease. We propose the cause to be drift in calibration parameters or fluctuations in the frequency of the coupling transmon on timescales of experimental shot averaging. Our results indicate that dynamical decoupling by adding more X gates between CZ gates does not alleviate this effect. Understanding the root cause of this effect will require further investigation. As a conservative bound for the gate error, we consider the error at N = 103 and show the corresponding QST results in Fig. 2b and extract an end-to-end circuit state fidelity of 87.5(1)% and state purity of 78.4(2)%. From this, we infer a bound of 0.12(1)% error per CZ gate, which is among the best reported for superconducting qubits.

As an alternative method for determining the post-selected gate fidelity, we perform IRB to test our CZ gate (Fig. 2d). Because of the finite erasure probability of 0.69(2)% per gate, the usable sequence depth is limited to only a few hundred gates (Fig. 2d, bottom). This complicates the fitting to the typical exponential form expected for sequence yield (Fig. 2d, top). A detailed analysis of gate error extraction can be found in Supplementary Information section IV. We, therefore, use a linear fit at shorter gate depths (Fig. 2d, inset), obtaining an estimate for the error per CZ gate of 0.108(5)% in reasonable agreement with the result from repeated Bell state tomography. See Supplementary Information section V for an analysis on nuanced effects of leakage and seepage errors (leakage states silently re-entering codespace)43,44. In Supplementary Information section XII, we performed repeated IRB benchmarking for up to 15 h on seven different two-qubit systems, with similar properties to the one characterized here.

Fig. 2: Benchmarking a dual-rail qubit CZ gate.

Full size image

a, Gate sequence used to quantify CZ gate fidelity by QST. Applying an odd number N of repeated CZ gates ideally generates a Bell state. For all cases except N = 1, X gates are applied to each qubit midway through the sequence, after the first (N − 1)/2 CZ gates. This serves to echo out low-frequency dephasing noise and to eliminate effects of no-jump-back action (Supplementary Information section VII). By changing the single-qubit gates, Rc and Rt (using an overcomplete gateset spanning \(\{I,{X}_{\pm \frac{{\rm{\pi }}}{2}},{Y}_{\pm \frac{{\rm{\pi }}}{2}},{X}_{{\rm{\pi }}}\}\)), we can extract all 16 joint Pauli correlators for the final state. b, QST after applying the gate sequence for N = 1 CZ gates (dark) and N = 103 CZ gates (light), represented by joint Pauli correlations. The ideal Bell state is defined by ⟨II⟩ = ⟨XX⟩ = 1 and ⟨YZ⟩ = ⟨ZY⟩ = −1, with the residuals shown below. c, Repeated CZ gate Bell state tomography showing the increase in gate error with increasing gate repeats. We measure Bell state fidelity (top, purple circles), state purity (top, yellow squares), and post-selected fraction, the fraction of shots for which no erasure errors are detected (bottom, grey), as a function of the number of CZ gates. The insets show data up to N = 15 CZ gates. Each tomography setting is averaged 10^6 times. Error bars are generated from bootstrapped resampling of the data and are smaller than the size of the markers. d, Two-qubit IRB showing reference (blue squares) and interleaved (purple circles) experiments. Each Clifford sequence, generated from the gateset \(\{{X}_{\frac{{\rm{\pi }}}{2}},{Z}_{\pm \frac{{\rm{\pi }}}{2}},\mathrm{CZ}\}\), is randomized 20 times and averaged 40,000 times, and the mean and standard deviation over random seeds are plotted. The fraction of shots measured in the two-qubit codespace is plotted in the bottom panel.

CZ gate noise structure

Having benchmarked the performance of the CZ gate, we now investigate the structure of gate errors on the control and target qubit and show we preserve the error hierarchy when performing CZ gates. In the following, we measure the error asymmetry for leakage, dephasing and bit-flip errors.

First, we characterize the fraction of detected leakages for our repeated gate Bell state tomography, extracting assigned erasures for the control qubit only, target qubit only, and both qubits, shown in Fig. 3a. As expected, we observe a higher fraction of erasures on the control qubit compared with the target qubit, extracting 0.400(4)% and 0.096(4)% per CZ gate for control and target qubit, respectively. Taken together, this erasure asymmetry of about 4 is consistent with the relative T_1 between the coupler and the average T_1 of the target qubit cavities.

Next, we study the relative fraction of dephasing errors on the control and target qubit. Analysing the reconstructed quantum state from our Bell state tomography, we extract Z errors on the target and control qubit individually as well as correlated ZZ dephasing on both qubits. Our results are shown in Fig. 3b, showing a clear asymmetry in Z errors between the control and target qubit, and small ZZ errors that affect both qubits. From these results, we extract target qubit Z errors of 0.0112(9)% per CZ gate. The control qubit Z errors, however, show the same type of nonlinear behaviour observed for the QST results, suggesting that the root cause of this behaviour could be attributed to some characteristic of the coupler. To quantify the control qubit error, we evaluate at N = 27 gate repeats and extract an averaged Z-error of 0.039(1)% per CZ gate. Using the same analysis, we extract ZZ errors of 0.007(1)% per CZ gate. Overall, we observe a dephasing ratio of about 3.5, which is consistent with what we expect from coupler and dual-rail Tϕ dephasing times. Detailed simulations of the CZ gate error structure can be found in Supplementary Information section XIV.

Idle dual-rail cavity qubits exhibit extremely low bit-flip errors[36], because they require the loss of a photon in one cavity and the gain of a photon in the other (a double error). We expect our CZ gate to also exhibit this property and experimentally confirm only a small increase in bit-flip errors when executing the CZ gate, relative to the idle case, as shown in Fig. 3d. We also further characterize bit-flips on the control and target qubits separately, as summarized in Fig. 3e and Supplementary Information section XI. The total probability of any bit-flip is bounded at a few parts per million per gate, which is probably not limited by ‘true’ bit-flip errors but rather a photon loss event followed by misassigning a leakage error as being in the codespace in the final. Once again, the asymmetry in observed bit-flips on the control and target qubits is in line with what we expect from the lower T_1 of the coupler.

Fig. 3: Quantifying CZ error structure.

Full size image

a, Extracted erasure fraction.b, Z-error fraction after applying a variable number of CZ gates, showing a breakdown for the following categories of dephasing errors: control only (red circles), target only (blue squares), both control and target (purple triangles) and total error (yellow diamonds). For both results, a strong asymmetry between the control and target erasures is observed. Error bars are generated from bootstrapped resampling of the data and are smaller than the size of the markers. c, Example circuit to measure control qubit bit-flip errors using a Ramsey-like sequence with a series of N CZ gates or an equivalent delay. The control (target) qubit is initialized in either |01⟩ or |10⟩ to avoid having dephasing errors that could convert into bit-flip errors. Unlike the QST circuit, this experiment is performed without an echo pulse to eliminate a source of extraneous bit-flip errors from the single-qubit gate. d, Measured control qubit bit-flip fraction when initialized in |01⟩ as a function of the number of applied CZ gates (circles) and as a function of the equivalent idle (squares). From a linear fit to the data, we infer a total bit-flip error per CZ gate of 2.8(4) × 10−6 and 0.5(2) × 10−6 when simply idling. Error bars are 95% confidence intervals generated using the Clopper–Pearson method. e, Extracted bit-flip error fractions per CZ gate for the control qubit (red, left) and target qubit (blue, right). CZ bit-flip errors are compared against idling errors, showing low bit-flip errors at the level of about 10−6 per gate. Each measurement setting is averaged 10^6 times.

CZ leakage propagation

Finally, we investigate how the dominant |00⟩ leakage propagates through the CZ gate while awaiting detection by erasure check measurements. Leakage is typically considered to be a particularly damaging error in a QEC context. If not properly controlled, leakage may propagate without bound, resulting in correlated errors and diminished effective code distance.

In our system, we consider two possible leakage scenarios: photon loss to |00⟩, which occurs before a CZ gate and leakage that occurs midway through the CZ gate at an unknown time. We study the more likely scenario of leakage affecting only the control qubit, although due to the symmetry of a CZ gate, our results are expected to apply similarly to the case of leakage on the target qubit. Although other dual-rail leakage states are possible due to cavity heating, such as |02⟩ or |11⟩, these occur at least 1,000 times less frequently in our hardware and are not considered here.

We can first consider what happens if the control qubit is in the |00⟩ state before the CZ gate. As there is only one excitation in the system (in the target qubit), the dispersive interaction does not affect any system dynamics, resulting in an identity operation on the target qubit. The exact same scenario occurs if the control qubit begins in |10⟩ and suffers from photon loss in mode a_1 during the CZ gate: as no photons were ever swapped into the coupler, the dispersive interaction was never activated, which again results in an identity operation on the target.

The case when the control qubit begins in |01⟩ and swaps into the coupler is more involved. Should excitation loss occur at any time during the gate, the end result is equivalent to performing a CPHASE gate with an unknown rotation angle, and the control qubit ending in vacuum. Averaging over all possible loss times results in what we term a ‘CZ error’, a correlated error in which the target qubit dephases, but only if the control leaks from the initial |01⟩ state.

This type of ‘benign’ leakage propagation can be incredibly useful for QEC, by enabling fewer and delayed erasure checks. For instance, in the surface code, we may perform all erasure checks at the end of a round of syndrome extraction rather than after every two-qubit gate (Methods), without significant loss of threshold or effective code distance. A more detailed and theoretical treatment of this leakage error model can be found in Supplementary Information section VI.

We use quantum process tomography to verify and measure these leakage properties, as shown in Fig. 4c. We prepare the control qubit in the known states |01⟩, |10⟩ and |00⟩ and perform N repeated CZ gates followed by process tomography on just the target qubit. In all cases, we require the control qubit to end in the state |00⟩ and post-select accordingly. Our results (Fig. 4d,e for N = 1 and N = 27 CZ gates) show the expected trivial behaviour when the control qubit begins in |00⟩ or |10⟩ state, and at N = 1, we measure the deviation from the ideal identity channel to be Pauli errors on the target qubit with strength 0.19(2)% and 0.18(2)%, when preparing |00⟩ or |10⟩ in the control qubit, respectively. Repeating the gate many times allows us to reduce the contribution from SPAM errors. At N = 27, we estimate the fraction of CZ erasures to be roughly two-thirds of total erasures, and measure the target qubit Pauli errors to be 0.012(1)% and 0.013(1)%, respectively, per CZ gate.

Finally, tomography for when the control qubit begins in |01⟩ is shown in Fig. 4f. Visually, the process matrix on the target qubit closely matches a pure dephasing channel. More quantitatively, we extract the deviation from the pure dephasing channel: for N = 1 gates, the extracted residual X and Y errors total 0.7(1)%. Similarly, at N = 27, we determine this error to be 0.094(2)% per CZ gate. This can be interpreted as an upper bound on the X and Y Pauli errors on the target when the control qubit leaks from |01⟩ during the gate.

Fig. 4: Leakage propagation properties of the CZ gate.

Full size image

a, Leakage on the control qubit to |00⟩ before the CZ gate causes the gate to be switched off, and instead the identity process is performed on both qubits. b, If photon loss occurs during the gate, the error channel can be accurately modelled as a mixture of leakage occurring before the gate, or after the gate, resulting in what we term a ‘CZ error’. c, Circuit diagram used to test the leakage propagation properties of the CZ gate. For different preparations on the control qubit, quantum process tomography is performed on the target qubit after applying N CZ gates on the target qubit. To test for control qubit leakage properties, the tomography is conditioned on the control qubit being found in the leakage |00⟩ state. d–f, Conditioned process tomography on target qubit when the control qubit is prepared in |00⟩, |10⟩ and |01⟩, respectively. To quantify the error channel, we plot the leakage-conditioned quantum process tomography using the χ-error matrix[46] against a reference process, here, the identity channel (Supplementary Information section VI). For each case, the real (top) and imaginary (bottom) part of the χ-error matrices are plotted for N = 1 CZ gates (left) and N = 27 CZ gates (right). The colour scale is clipped to have a maximum value of 0.1, to make deviations from the expected error channel more visually apparent in the plots. For control qubit initialized in |00⟩ and |10⟩, the expected identity process is observed, indicating that a leakage error turns off the gate; while a dephasing-like error is observed on the target qubit when the control qubit is initialized in |01⟩ indicated by II and ZZ components in the real part of χ-error matrix. This error channel is discussed in detail in Supplementary Information section VI, along with the significance of the IZ and ZI components in the imaginary part of the χ-error matrix. Quantum process tomography is averaged over 10,000 repetitions when starting in |00> and 100,000 times when starting in |01> and |10> states.

Discussion and conclusion

The addition of a fast and high-fidelity entangling operation completes the toolbox of gates and operations for dual-rail cavity qubits[7]. We have experimentally confirmed several properties important for erasure qubits. It preserves (Table 1) the desirable error hierarchy by exhibiting both a strong erasure-to-Pauli noise bias and a dephasing-to-bit-flip noise bias. Furthermore, we observe a significant asymmetry in the errors induced on the control compared with the target qubit. Finally, the gate has a simple error channel in the event that one of the qubit is erased before or during the gate, which is useful for delaying erasure checks in QEC contexts.

Our results present numerous opportunities for operating dual-rail cavity qubits at scale with high-performance: from enabling high-circuit performance for short-depth circuits, to new types of error mitigation through flagging erasures, to—perhaps most excitingly—a viable path for implementing QEC using erasure qubits. As a preview for how well we expect our dual-rail cavity qubits to perform for QEC, in the Methods, we present some preliminary results simulating the performance of a surface code implemented using dual-rail cavity qubits with a more comprehensive treatment in an upcoming publication (J.D.T., manuscript in preparation). We find that even with current CZ gate performance, the error rates are already a factor of ten below the respective erasure and dephasing thresholds. Further improvements in physical error rates can also be straightforwardly achieved through improvements in beamsplitter rates to increase gate speed and through improving the coherences of, primarily, the coupler. In the presence of both kinds of errors, detailed numerical simulations (J.D.T., manuscript in preparation) are needed to estimate the actual performance, but the results described here provide a path to realize large error correction gains of 10 or more with an increase in code distance in the near future. All of these results highlight that superconducting dual-rail cavity qubits have quickly become an attractive path for achieving fault-tolerant quantum computing. During the completion of this paper, we became aware of related work on implementing the two-qubit gates using tunable dual-rail transmon qubits[45].

Table 1 Error hierarchy and asymmetry for the SWS gate

Methods

Quantum error correction benefits of structured noise in our CZ gate

Our SWS gate possesses an error structure that can be leveraged to reduce errors during syndrome extraction in stabilizer codes such as the surface code. Here, we show simulations using Stim[47] that support and quantify this claim by studying the reduction in logical error rate compared with code distance in a surface code memory. More complete simulations results and methodology will be the subject of an upcoming, separate publication (J.D.T., manuscript in preparation), which solely focuses on QEC scaling in a dual-rail cavity qubit surface code.

There are four aspects to the SWS gate error structure that are expected to improve syndrome extraction circuits in general:

- Erasures (leakages to |00⟩) occur much more frequently than Pauli errors.

- Pauli errors are mostly dephasing errors; bit flips are rare.

- We can designate the ‘control’ qubit to be the ancilla, which then picks up most of the erasure and dephasing errors, leaving the data qubits mostly error-free.

- Once a qubit has leaked to |00⟩, subsequent CZ gates are skipped, which allows us to perform less frequent erasure checks.

We quantify error correction performance by extracting Λ, the factor by which the logical error rate (per syndrome extraction round) decreases when the surface code distance is increased by 2 (refs. 3,4). We compare two noise models. The first is a noise model in which only the CZ gates are error prone, and the error rates for all CZ gates are identical to the values shown in the paper at low repeated gate depth, that is, ancilla erasure = 0.4%, data erasure = 0.1%, ancilla dephasing = 0.04%, data dephasing = 0.01%, joint dephasing = 0.01%, ancilla bit flip = 0.0003%, data bit flip = 0.00005%. We also include perfect erasure checks, which are delayed to the end of the syndrome extraction round (see next section in Methods as to why we make this choice). The second noise model uses only a two-qubit depolarizing noise channel after each CZ gate as the only source of error, a well-studied noise model with a threshold close to 1%. For this noise model, we set the error rate p = 0.1% to reflect state-of-the-art performance in other two-qubit gates with ‘unstructured’ errors. Both simulations to extract Λ are performed using Stim. For the first error model, leakage errors cause subsequent CZ gates to be omitted from the simulation (a property confirmed experimentally in Fig. 4), until the qubit is detected and reset, which is crucial for accurately modelling the propagation of leakage errors with delayed erasure checks. The results shown in Extended Data Fig. 1 show Λ ≈ 14 for the depolarizing noise model and Λ ≈ 27 for our SWS gate error model, demonstrating the benefits of structured errors. More detailed analysis can be found in our upcoming publication (J.D.T., manuscript in preparation). We reiterate that these noise models are chosen to be simplistic, to allow direct comparison between the two-qubit gate errors. More realistic simulations must include errors in the single-qubit gates, all measurements (including erasure checks) and idling errors, which reduce the value of Λ.

Leakage propagation and delayed erasure checks in a dual-rail surface code

A key property of our SWS gate is that attempting to perform the gate when one of the qubits has leaked to |00⟩ before the gate results in identity being performed on both qubits, and excitation loss at any point in time during the gate can be accurately modelled as a CPHASE gate of unknown angle (between 0 and π) followed by photon loss on one of the dual-rail qubits. These properties were experimentally verified in Fig. 4. Here, we show with simulations that this property can be leveraged to delay all mid-circuit erasure checks to the end of the syndrome extraction round, without significant loss of code distance or threshold we expect from erasure qubits.

Performing erasure checks once every four CZ gates (rather than after every CZ gate) makes us much less prone to errors in the erasure checks themselves, which is vital for achieving good error correction performance in practice. This also, in principle, enables us to avoid mid-circuit erasure detection entirely in a surface code, by instead detecting erasures by 1-bit Knill-style teleportation in between syndrome extraction rounds[48] (that is, teleporting back and forth between data and ancilla qubits, and performing a ‘destructive’ erasure check whenever a qubit is measured out in an end-of-the-line measurement). As such, the importance of a high-fidelity, fast mid-circuit erasure check may have been previously overemphasized for erasure qubitsthat possess this ‘leakage skips subsequent gates’ property.

To verify these claims, we perform Stim simulations in which the data qubit and ancilla qubit each have probability p/2 of leaking to the |00⟩ state after every CZ gate, and subsequent CZ gates are skipped (that is, omitted from the quantum circuit) if any one of the qubits in the CZ pair are in |00⟩. We then perform perfect erasure checks at the end of each round. From Extended Data Fig. 2, we extract a threshold of 5.6% and verify preservation of the effective code distance by fitting the logical error rate to the model pL ∝ pαd. We find α = 0.93, close to the value of 1 expected for erasure-like errors. For Pauli-like errors, α = 0.5 is expected instead.

The threshold we find is slightly higher than that obtained in ref. [10], in which a threshold of 5.13% was obtained for an error model with perfect erasure checks after every CZ gate, but also assumed leakage during a CZ gate caused a depolarizing error on the other qubit, which we believe introduces more entropy (and more possible syndromes to decode) than our error model.

When delaying erasure checks to the end of the syndrome extraction round, there is an increased uncertainty with regard to the exact time location of the leakage event in the syndrome extraction gate sequence. However, we find that this added uncertainty does not lead to any bad hook errors. This property can be verified by exhaustively sampling detector flip patterns for all possible locations of a single leakage event in the previous syndrome extraction round. This lack of hook errors is also thought to be the reason why we obtain α close to 1, preserving the scaling with code distance expected for typical erasures. Delayed erasure checks will be studied more extensively in an upcoming publication (J.D.T., manuscript in preparation).

Data availability

The data that support the findings of this study are available on Zenodo at https://doi.org/10.5281/zenodo.20433754.

Code availability

The code used to generate the QEC simulation results will be presented in an upcoming publication (J.D.T., manuscript in preparation).

References

1. Shor, P. W. Scheme for reducing decoherence in quantum computer memory. Phys. Rev. A 52, R2493(R)–R2496(R) (1995).

2. Steane, A. M. Overhead and noise threshold of fault-tolerant quantum error correction. Phys. Rev. A 68, 042322 (2003).

3. Google Quantum AI and Collaborators. Quantum error correction below the surface code threshold. Nature 638, 920–926 (2025).

4. Google Quantum AI and Collaborators.Suppressing quantum errors by scaling a surface code logical qubit. Nature 614, 676–681 (2023).

5. Grassl, M., Beth, T. & Pellizzari, T. Codes for the quantum erasure channel. Phys. Rev. A 56, 33–38 (1997).

6. Kubica, A. et al. Erasure qubits: overcoming the T_1 limit in superconducting circuits. Phys. Rev. X 13, 041022 (2023).

7. Teoh, J. D. et al. Dual-rail encoding with superconducting cavities. Proc. Natl Acad. Sci. USA 120, e2221736120 (2023).

8. Xu, Q. et al. Tailored XZZX codes for biased noise. Phys. Rev. Res. 5, 013035 (2023).

9. Sahay, K., Jin, J., Claes, J., Thompson, J. D. & Puri, S. High-threshold codes for neutral-atom qubits with biased erasure errors. Phys. Rev. X 13, 041013 (2023).

10. Wu, Y., Kolkowitz, S., Puri, S. & Thompson, J. D. Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays. Nat. Commun. 13, 4657 (2022).

11. Aliferis, P. & Preskill, J. Fault-tolerant quantum computation against biased noise. Phys. Rev. A 78, 052331 (2008).

12. Barrett, S. D. & Stace, T. M. Fault tolerant quantum computation with very high threshold for loss errors. Phys. Rev. Lett. 105, 200502 (2010).

13. Delfosse, N. & Zémor, G. Linear-time maximum likelihood decoding of surface codes over the quantum erasure channel. Phys. Rev. Res. 2, 033042 (2020).

14. Preskill, J. Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018).

15. Puri, S., Boutin, S. & Blais, A. Engineering the quantum states of light in a Kerr-nonlinear resonator by two-photon driving. npj Quantum Inf. 3, 18 (2017).

16. Puri, S. et al. Bias-preserving gates with stabilized cat qubits. Sci. Adv. 6, eaay5901 (2020).

17. Grimm, A. et al. Stabilization and operation of a Kerr-cat qubit. Nature 584, 205–209 (2020).

18. Putterman, H. et al. Hardware-efficient quantum error correction via concatenated bosonic qubits. Nature https://doi.org/10.1038/s41586-025-08642-7 (2025).

19. Mirrahimi, M. et al. Dynamically protected cat-qubits: a new paradigm for universal quantum computation. New J. Phys. 16, 045014 (2014).

20. Leghtas, Z. et al. Confining the state of light to a quantum manifold by engineered two-photon loss. Science 347, 853–857 (2015).

21. Tuckett, D. K. et al. Tailoring surface codes for highly biased noise. Phys. Rev. X 9, 041031 (2019).

22. Tuckett, D. K., Bartlett, S. D., Flammia, S. T. & Brown, B. J. Fault-tolerant thresholds for the surface code in excess of 5% under biased noise. Phys. Rev. Lett. 124, 130501 (2020).

23. Darmawan, A. S., Brown, B. J., Grimsmo, A. L., Tuckett, D. K. & Puri, S. Practical quantum error correction with the XZZX code and Kerr-Cat qubits. PRX Quantum 2, 030345 (2021).

24. Claes, J., Bourassa, J. E. & Puri, S. Tailored cluster states with high threshold under biased noise. npj Quantum Inf. 9, 9 (2023).

25. Chuang, I. L. & Yamamoto, Y. Simple quantum computer. Phys. Rev. A 52, 3489–3496 (1995).

26. Chuang, I. L. & Yamamoto, Y. Quantum bit regeneration. Phys. Rev. Lett. 76, 4281–4284 (1996).

27. Knill, E., Laflamme, R. & Milburn, G. J. A scheme for efficient quantum computation with linear optics. Nature 409, 46–52 (2001).

28. Slussarenko, S. & Pryde, G. J. Photonic quantum information processing: a concise review. Appl. Phys. Rev. 6, 041303 (2019).

29. Ma, S. et al. High-fidelity gates and mid-circuit erasure conversion in an atomic qubit. Nature 622, 279–284 (2023).

30. Kang, M., Campbell, W. C. & Brown, K. R. Quantum error correction with metastable states of trapped ions using erasure conversion. PRX Quantum 4, 020358 (2023).

31. Gu, S., Vaknin, Y., Retzker, A. & Kubica, A. Optimizing quantum error-correction protocols with erasure qubits. PRX Quantum https://doi.org/10.1103/985g-58gd (2025).

32. Shim, Y.-P. & Tahan, C. Semiconductor-inspired design principles for superconducting quantum computing. Nat. Commun. 7, 11059 (2016).

33. Campbell, D. L. et al. Universal nonadiabatic control of small-gap superconducting qubits. Phys. Rev. X 10, 041051 (2020).

34. Levine, H. et al. Demonstrating a long-coherence dual-rail erasure qubit using tunable transmons. Phys. Rev. X 14, 011051 (2024).

35. Zakka-Bajjani, E. et al. Quantum superposition of a single microwave photon in two different ‘colour’ states Nat. Phys. 7, 599–603 (2011).

36. Chou, K. S. et al. A superconducting dual-rail cavity qubit with erasure-detected logical measurements. Nat. Phys. 20, 1454–1460 (2024).

37. Chapman, B. J. et al. High-on-off-ratio beam-splitter interaction for gates on bosonically encoded qubits. PRX Quantum 4, 020355 (2023).

38. Lu, Y. et al. High-fidelity parametric beamsplitting with a parity-protected converter. Nat. Commun. 14, 5767 (2023).

39. Koottandavida, A. et al. Erasure detection of a dual-rail qubit encoded in a double-post superconducting cavity. Phys. Rev. Lett. 132, 180601 (2024).

40. de Graaf, S. J. et al. A mid-circuit erasure check on a dual-rail cavity qubit using the joint-photon number-splitting regime of circuit QED. npj Quantum Inf. 11, 1 (2025).

41. Rosenblum, S. et al. A CNOT gate between multiphoton qubits encoded in two cavities. Nat. Commun. 9, 652 (2018).

42. Rosenblum, S. et al. Fault-tolerant detection of a quantum error. Science 361, 266–270 (2018).

43. Chen, Y.-H. & Baldwin, C. H. Randomized benchmarking with leakage errors. Phys. Rev. Res. https://doi.org/10.1103/j8sz-6ws9 (2025).

44. Wood, C. J. & Gambetta, J. M. Quantification and characterization of leakage errors. Phys. Rev. A 97, 032306 (2018).

45. Huang, W. et al. Logical multi-qubit entanglement with dual-rail superconducting qubits. Nat. Phys. 22, 591–597 (2026).

46. Korotkov, A. N. Error matrices in quantum process tomography. Preprint at arxiv.org/abs/1309.6405 (2013).

47. Gidney, C. Stim: a fast stabilizer circuit simulator. Quantum 5, 497 (2021).

48. Baranes, G. et al. Leveraging qubit loss detection in fault-tolerant quantum algorithms. Phys. Rev. X 16, 011002 (2026).

Acknowledgements

We thank L. Frunzio, A. Kubica, S. Xue, J. Garmon, and T. Tsunoda for their feedback on the manuscript.

Funding

All authors declare no relevant funding.

Author information

Author notes

1. Taewan Noh

Present address: Q-CTRL, Los Angeles, CA, USA

2. Anthony Cabrera

Present address: NVIDIA Corporation, Santa Clara, CA, USA

3. Jonathan Chen

Present address: Atom Computing, Berkeley, CA, USA

4. Tzu-Chiao Chien & Anirudh Narla

Present address: Microsoft Quantum, Redmond, WA, USA

5. Bassel Heiba Elfeky

Present address: IBM Quantum, Yorktown Heights, NY, USA

6. Nishaad Khedkar

Present address: Departments of Applied Physics and Physics and Yale Quantum Institute, Yale University, New Haven, CT, USA

7. Tali Shemma

Present address: Department of Condensed Matter Physics, Weizmann Institute of Science, Rehovot, Israel

8. Teng Zhang

Present address: Rigetti Computing, Berkeley, CA, USA

9. Olivier Boireau

Present address: Alice & Bob, Paris, France

10. These authors contributed equally: Nitish Mehta, James D. Teoh, Taewan Noh

Authors and Affiliations

1. D-Wave Quantum Inc., New Haven, CT, USA

Nitish Mehta, James D. Teoh, Taewan Noh, Ankur Agrawal, Amos Anderson, Beau Birdsall, Avadh Brahmbhatt, Winfred Byrd, Anthony Cabrera, Marc Cacioppo, Leo Carroll, Jonathan Chen, Tzu-Chiao Chien, Richard A. Chamberlin, Jacob C. Curtis, Doreen Danso, Sanjana Renganatha Desigan, Francesco D’Acounto, Bassel Heiba Elfeky, S. M. Farzaneh, Chase Foley, Benjamin Gudlewski, Hannah Hastings, Robert Johnson, Nishaad Khedkar, Trevor Keen, Anup Kumar, Cihan Kurter, Kamila Krawczuk, Eric Langstengel, Richard D. Li, Gangqiang Liu, Hanyi Lu, Pinlei Lu, Luke Mastalli-Kelly, Adam Maines, Michael Maxwell, Heather McCarrick, Mona Mirzaei, Anirudh Narla, Omar Rashad, Erik Reikes, Mizanur Rahman, Rurik Primiani, Michael Schwaller, Ali Sabbah, Tali Shemma, Ruby A. Shi, Sitakanta Satapathy, Dean Stolpe, Jonathan Strenczewilk, Doug Szperka, Iu-Wei Sze, David Sweeney, Preetham Tikkireddi, Chin-Lun Tsung, Daren Vet Sam, Daniel K. Weiss, Zhibo Yang, Liuqi Yu, Teng Zhang, Olivier Boireau, Stephen Horton, Sean Weinberg, José Aumentado, Bryan Cord, Chan U. Lei, Joseph O. Yuan, Shantanu O. Mundhada, Kevin S. Chou, S. Harvey Moseley Jr & Robert J. Schoelkopf

2. Departments of Applied Physics and Physics, Yale University, New Haven, CT, USA

Robert J. Schoelkopf

3. Yale Quantum Institute, Yale University, New Haven, CT, USA

Robert J. Schoelkopf

Consortia

D-Wave Quantum Inc.

- Nitish Mehta

- , James D. Teoh

- , Taewan Noh

- , Ankur Agrawal

- , Amos Anderson

- , Beau Birdsall

- , Avadh Brahmbhatt

- , Winfred Byrd

- , Anthony Cabrera

- , Marc Cacioppo

- , Leo Carroll

- , Jonathan Chen

- , Tzu-Chiao Chien

- , Richard A. Chamberlin

- , Jacob C. Curtis

- , Doreen Danso

- , Sanjana Renganatha Desigan

- , Francesco D’Acounto

- , Bassel Heiba Elfeky

- , S. M. Farzaneh

- , Chase Foley

- , Benjamin Gudlewski

- , Hannah Hastings

- , Robert Johnson

- , Nishaad Khedkar

- , Trevor Keen

- , Anup Kumar

- , Cihan Kurter

- , Kamila Krawczuk

- , Eric Langstengel

- , Richard D. Li

- , Gangqiang Liu

- , Hanyi Lu

- , Pinlei Lu

- , Luke Mastalli-Kelly

- , Adam Maines

- , Michael Maxwell

- , Heather McCarrick

- , Mona Mirzaei

- , Anirudh Narla

- , Omar Rashad

- , Erik Reikes

- , Mizanur Rahman

- , Rurik Primiani

- , Michael Schwaller

- , Ali Sabbah

- , Tali Shemma

- , Ruby A. Shi

- , Sitakanta Satapathy

- , Dean Stolpe

- , Jonathan Strenczewilk

- , Doug Szperka

- , Iu-Wei Sze

- , David Sweeney

- , Preetham Tikkireddi

- , Chin-Lun Tsung

- , Daren Vet Sam

- , Daniel K. Weiss

- , Zhibo Yang

- , Liuqi Yu

- , Teng Zhang

- , Olivier Boireau

- , Stephen Horton

- , Sean Weinberg

- , José Aumentado

- , Bryan Cord

- , Chan U. Lei

- , Joseph O. Yuan

- , Shantanu O. Mundhada

- , Kevin S. Chou

- , S. Harvey Moseley Jr

- & Robert J. Schoelkopf

Contributions

J.D.T. and S.O.M. contributed to the proposal and concept of the SWS gate. S.O.M. led the design of the dual-rail hardware architecture. J.O.Y. led hardware deployment, experimental setup and system validation. B.C. led the device fabrication. C.U.L. led the cavity design, preparation and testing. K.S.C. led two-qubit gate testing, measurements and analysis. N.M., T.N., P.L., B.H.E., A. Agrawal, A.N., K.S.C, J.O.Y. and J.D.T. contributed to data acquisition and data analysis. T.N., N.M., P.L., A. Agrawal, K.S.C. and J.D.T. contributed to the SWS gate calibration procedure. J.D.T. and K.S.C. contributed to numerical simulations of the SWS gate. P.L., N.M., J.O.Y., T.N., A. Agrawal, B.H.E., G.L., K.S.C. and N.K. contributed to system bring-up and calibration. S.O.M., R.A.C., S.M.F., T.Z., R.D.L., H.M., D.K.W. and J.O.Y. contributed to quantum hardware design and simulation. E.L., B.B., M.C., C.F. and M.R. contributed to mechanical design. S.O.M. and R.A.C. contributed to coupler design and flux-pump delivery. C.U.L., C.K., J.O.Y., B.H.E., A.S. and N.K. contributed to cavity preparation and testing. J.S., A.B., F.D., H.H. and D.V.S. contributed to device assembly and integration. D.D., J.O.Y., M. Maxwell and J.S. contributed to laboratory infrastructure. S.S., M. Mirzaei, D. Stolpe, B. Gudlewski, B.C., H.L., L.Y. and T.Z. performed device fabrication. M. Maxwell, E.R., A.C., S.R.D., R.J., R.P. and O.R. designed, tested and built the radiofrequency control system. J.O.Y., S.O.M., G.L., N.K. and R.A.S. contributed to RF wiring and implementation of the readout chain. J.C.C., P.L., N.M., D.K.W., K.S.C. and T.K. contributed to data analysis of randomized benchmarking results and supporting simulations. J.D.T., S.O.M., A.N., N.M., T.N., K.S.C., H.M., T.-C.C., N.K. and T.S. contributed to an earlier version of the SWS gate. M.S., S.W., D. Sweeney, A. Anderson, W.B., J.C., L.C., A.K., K.K., T.K., A.M., L.M.-K., D. Szperka, I.-W.S., P.T. and Z.Y. contributed to software infrastructure and calibration tools used in this project. K.S.C., N.M., T.N., J.D.T. and R.J.S. wrote the paper. R.J.S., S.H.M., J.A., S.H., O.B. and C.-L.T. managed the project.

Corresponding authors

Correspondence to Nitish Mehta, Kevin S. Chou or Robert J. Schoelkopf.

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Competing interests

Authors affiliated with D-Wave Quantum Inc. have financial interest in the company. R.J.S. is a shareholder and consultant for D-Wave Quantum Inc. J.D.T., S.O.M., N.M., K.S.C., T.N., S.H.M. and R.J.S. have filed a provisional patent (application no. PCT/US2025/054681) for the SWS entangling operation.

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Extended data figures and tables

Extended Data Fig. 1 Simulated surface code memory scaling vs code distance, to compare structured noise in our SWS gate (blue) vs. unstructured CZ gate noise in the form of two-qubit depolarizing noise (orange) with strength 0.1%.

Fits to a simple exponential shown with dashed lines are used to extract Λ, the reduction factor in per-cycle logical error rate when the code distance increases by two. For the blue plot, the error rate per CZ gate are chosen to be the same as the values reported in Fig. 3. Having predominantly erasure errors allows us to simulate at even code distance. Despite our total CZ gate error being ~0.5%, around 5 times higher than other state-of-the-art two-qubit gates, we obtain a value of Λ that is roughly double that of simple depolarizing noise. This provides strong evidence that the structured noise in our SWS gate can be leveraged to improve quantum error correction and is on-par with, or better than current state-of-the-art two qubit gates with 0.1% Pauli errors when used in a surface code.

Extended Data Fig. 2 Simulated surface code memory under erasure errors with delayed, perfect erasure checks.

Results are averaged over the X and Z basis, under an error model that includes only erasures in the native CZ gates with total erasure rate p per gate, and perfect erasure checks that are delayed to only occur at the end of each stabilizer round. Dashed lines are fits from which we extract a threshold of 5.6% and α = 0.93 (from fitting the function \({p}_{L}\propto {(p/{p}^{* })}^{\alpha d}\) below threshold), showing that delaying erasure checks to the end of each stabilizer round still preserves the favorable error-correction properties of erasures, under our ‘leakage skips subsequent gates’ error model. Showing these favorable QEC properties in the presence of imperfect erasure checks, and with many other noise sources accounted for is the subject of an upcoming publication (J.D.T., manuscript in preparation).

Supplementary information

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This file contains Supplementary Sections 1–15, including Supplementary Tables 1–5 and Supplementary Figs. 1–10; see the Contents for details.

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D-Wave Quantum Inc.. An entangling gate for dual-rail erasure qubits. Nature 656, 47–53 (2026). https://doi.org/10.1038/s41586-026-10822-y

- Received: 06 May 2025

- Accepted: 17 June 2026

- Published: 05 August 2026

- Version of record: 05 August 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10822-y

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Imaging of nanoscale polar textures in quantum paraelectric SrTiO_3

Yang Zhang,

Suk Hyun Sung,

Nishkarsh Agarwal,

Maya Gates,

Cong Li,

Pu Yu,

Robert Hovden &

…

Ismail El Baggari

Nature volume 656, pages 54–59 (2026)

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Abstract

When cooling towards a ferroelectric phase transition, collective atomic motions (phonons) slow down (soften) until a static atomic displacement pattern forms, giving rise to spontaneous polarization throughout the material[1]. However, in quantum paraelectrics such as strontium titanate (SrTiO_3), long-range ferroelectric order does not develop at low temperatures due to persistent quantum fluctuations of ionic positions2,3. In SrTiO_3, quantum paraelectricity emerges below Tq ≈ 40 K refs. 4,5,6 and is preceded by anomalous phonon dynamics: a transverse acoustic phonon mode partially softens at a finite wavevector, hinting at a modulated state at the nanoscale7,8,9,10,11,12. The precise real-space structure of SrTiO_3 at low temperature, however, has remained unresolved despite decades of study. Here we directly image the low-temperature polar structure of a SrTiO_3 lamella using cryogenic scanning transmission electron microscopy down to 20 K. High-resolution imaging reveals a spatially fluctuating landscape of nanoscale domains. Below about 105 K, short-range polar domains initially self-organize into a periodic structure extending over tens of nanometres; however, upon entering the quantum paraelectric regime below Tq, the process reverses and the periodically ordered polar nanodomains fragment into smaller clusters. Quantum paraelectricity in SrTiO_3 underlies remarkable properties, including large dielectric permittivity13,14, proximity to ferroelectricity15,16, multiferroicity[17] and unconventional superconductivity18,19,20. Our visualizations suggest that these phenomena may be linked to complex ordering and disordering of polar nanodomains at low temperature.

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Main

Bulk SrTiO_3 hosts distinct structural phases (Fig. 1a). In the high-temperature cubic phase, the titanium ions sit at the centre of the oxygen octahedron and strontium corner atoms. A so-called antiferrodistortive (AFD) structural transition occurs below TAFD ≈ 105 K and involves rotations of oxygen octahedra in opposite directions[21], leading to a tetragonal phase and a doubling of the unit cell along the rotational axis. This AFD transition maintains centrosymmetry of the crystal. Further cooling is accompanied by a large rise in the dielectric function, indicating proximity to a polar, ferroelectric transition4,5,6. However, in pure SrTiO_3, quantum paraelectricity manifests as deviation from classical Curie–Weiss behaviour below the temperature Tq ≈ 40 K, and long-range ordering of polar displacements does not set in, purportedly due to quantum fluctuations of opposite polarization states.

Fig. 1: Structural transitions in the SrTiO_3 lamella.

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a, Structural phases of bulk SrTiO_3. From the cubic paraelectric to the AFD tetragonal phase, TiO_6 octahedra undergo out-of-phase rotations, lowering the symmetry to tetragonal. In the ferroelectric phase, titanium atoms shift away from the centre of the TiO_6 octahedra, leading to an electric dipole moment. b, Phase diagram of SrTiO_3 as a function of temperature and strain. The stabilization of a long-range ferroelectric phase can also be realized using calcium doping or oxygen isotope substitution. The parent SrTiO_3 phase transition follows the grey dashed line, entering the AFD phase at around TAFD ≈ 105 K followed by a quantum paraelectric regime at lower temperatures. The phase diagram is based on previously published data[53]. c, Schematic of polar nanodomains in quantum paraelectric SrTiO_3, with spatial fluctuations of polarization orientations at the nanoscale (10 × 10 nm^2 field of view). d, Electron diffraction patterns collected at 23 K (left), 91 K (middle) and 172 K (right). Black arrows mark the AFD superspots located at half-integer positions. Scale bars, 0.1 Å–1. a.u., arbitrary units. e, Quantification of AFD superspot intensity, IAFD/IBragg (top), and the lattice parameter along the AFD-rotation axis (bottom). Two anomalies near TAFD and Tq are highlighted by black dashed lines. The SrTiO_3 lamella used for STEM imaging thus retains the bulk-like structural transitions.

SrTiO_3 has further anomalous dynamics that precede and influence this quantum regime. A transverse acoustic phonon mode partially softens at a finite wavevector, q, hinting at a possible new state with spatial correlations at the nanometre scale7,8,9,10,11,12. Pinpointing the precise structure—both below and above Tq—could unveil the nature of quantum paraelectricity in this prototypical system. Here we leverage liquid helium cryogenic scanning transmission electron microscopy (STEM) to directly image local polarity in a SrTiO_3 lamella down to very low temperatures (about 20 K), revealing a spatially fluctuating and evolving landscape of nanoscale polar textures (Fig. 1c).

Structural transitions in SrTiO_3

The quantum paraelectric regime in SrTiO_3 lies in proximity to long-range ferroelectricity, which can be accessed under large enough external tuning parameters, including strain, oxygen vacancies or isotope substitution (Fig. 1b)15,16,17,22,23,24,25,26,27,28,29. Quantum paraelectricity is thus confined within a finite window in the phase diagram, before the onset of long-range ferroelectricity. We verify that our sample lamella prepared for STEM imaging retains the key transitions associated with parent SrTiO_3 (inset of Fig. 2a). The potential influence of thermal mismatch strain and oxygen deficiency is examined and excluded (Extended Data Fig. 1, Supplementary Fig. 1 and Supplementary Note 1). Figure 1d shows the emergence of AFD superspots at half-integer positions in electron diffraction patterns (marked by the black arrow) due to the out-of-phase rotation of TiO_6 octahedra, which doubles the unit cell. The superspot intensity, initially low at elevated temperatures (Fig. 1d; refer to Extended Data Fig. 2 for the complete datasets), reveals a pronounced increase near TAFD, confirming entry into the AFD state. Rather than appearing suddenly at TAFD, weak superspot intensity persists above TAFD, which is in agreement with the documented precursor effects and the diffuse intensity reported in neutron and X-ray measurements30,31,32. In addition to the AFD order, the lattice parameter along the AFD rotation axis elongates upon crossing TAFD (Fig. 1e), which is in agreement with the symmetry change into a tetragonal phase expected for the bulk AFD transition.

Fig. 2: Imaging polar textures in cryogenic SrTiO_3.

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a, Experimental set-up for liquid helium cryogenic STEM with nanobeam 4D-STEM for mapping polarization in SrTiO_3. The inset illustrates the geometry of SrTiO_3 lamellar sample prepared by focused ion beam. b, Structures of tetragonal SrTiO_3 with polarization along \([00\bar{1}]\) (P-down) and [001] (P-up) directions, viewed along [011]. Only titanium and oxygen atoms are shown for simplicity. c, Simulated nanobeam electron diffraction based on two opposite polarization states. The antisymmetric component in the diffraction pattern, defined as A(k) = |k|2(I(k) – I(–k)), reveals polarization-induced intensity asymmetry in the Kikuchi bands. Asymmetry along the ky direction is apparent and switches between P-up and P-down structures. The black dashed box is selected to extract intensity profiles (right panel). d, Real-space map of centre-of-mass shifts of Kikuchi bands, Δk(r), at 69 K. The field of view is 40 × 80 nm^2 and the temperature is 69 K. The arrows reflect the amplitude, whereas the colours represent the direction. e, Averaged diffraction patterns from two boxed regions (I, left; II, right) in d. Consistent with simulations, A(k) reveals polarization-induced intensity asymmetry in the diffraction pattern. The right panel shows line profiles of A(k) for regions of opposite polarization. Scale bars, 2 Å–1 (c,e), 10 nm (d).

At lower temperatures, we observe further anomalies in these parameters; the AFD superspot intensity drops below Tq, and the lattice constant along the AFD rotation axis contracts. This behaviour represents independent evidence of the elastic anomaly associated with the quantum paraelectric phase33,34,35. These experimental signatures indicate the lamellar sample qualitatively retains the structural transitions at Tq and TAFD (Supplementary Note 2); however, we cannot rule out slight few-degree changes in the transition temperatures due to the coarse temperature sampling.

Imaging polar order in cryogenic SrTiO_3

Beyond AFD and lattice order, maps of polarization directly reveal a complex local landscape in the low-temperature phases of SrTiO_3. Polarization and inversion symmetry breaking lead to asymmetries in dynamical electron scattering and can be extracted from momentum (k)-resolved electron diffraction36,37,38. In so-called four-dimensional scanning transmission electron microscopy (4D-STEM), an electron nanobeam (here using a probe size of about 1.2 nm) is scanned across the material, and two-dimensional electron diffraction patterns, I(k), are recorded at every position (Fig. 2a) using a pixelated detector with single electron sensitivity39,40,41. In addition to intense Bragg reflections, the two-dimensional electron scattering distribution contains Kikuchi bands, which arise from multiple scattering processes within the crystal lattice. Asymmetry between Kikuchi bands related by inversion (Friedel pairs at ±k momentum space coordinates) encodes polarization in the sample. By scanning across a sample, real space maps of polarization textures in ferroelectrics can be formed42,43,44,45.

In SrTiO_3, the connection between polarity and asymmetry in Kikuchi band intensity is confirmed using dynamical scattering multislice simulations (Methods). Figure 2b,c shows simulated diffraction patterns with opposite polarization added to tetragonal SrTiO_3. The antisymmetric component in the diffraction pattern, defined as A(k) = |k|2(I(k) – I(–k)), reveals polarization-induced intensity asymmetry in the Kikuchi bands (Methods and Extended Data Fig. 3). Intensity differences between the Friedel pairs (highlighted by the black dashed box) emerge when polarization is present (right panel of Fig. 2c). The direction of polarization is also reflected in the asymmetry: downward polarization leads to Kikuchi band asymmetry along the same direction. The asymmetry reverses when the polarization is upward. This intensity asymmetry is absent in the cubic, non-polar structure (Extended Data Fig. 4).

Mapping the low-temperature phases in SrTiO_3 requires stable cryogenic temperatures that can reach the quantum paraelectric phases[46] below TAFD and ideally below Tq. The recent development of a low-vibration liquid-helium-cooled STEM sample stage with stable variable temperature control[47] enables access to the low-temperature phases in SrTiO_3. Figure 2a shows a simplified experimental set-up, which involves 4D-STEM imaging of a SrTiO_3 single crystal at cryogenic temperatures down to about 20 K.

Nanoscale polar textures

Experimental maps reveal that SrTiO_3 is locally polar, hosting finite polarization below the AFD transition. Polarization is extracted by mapping the centre-of-mass (COM) shift of Kikuchi band intensity in the raw diffraction pattern, Δk(r), at each scan position r (Extended Data Fig. 5 and Methods). No polarization is detected in the room temperature cubic phase (Extended Data Fig. 6), as expected for the non-polar paraelectric phase. Following cooling below TAFD, however, local polar textures emerge in Δk(r) over the same field of view. Figure 2d shows a map of Δk(r) in SrTiO_3 at 69 K. The direction and colour of the arrows reflect the local polar direction. The 40 × 80 nm^2 field of view contains multiple small domains with opposite polarization directions. Domains with opposite polarity (regions I and II) show a reversal in A(k), the intensity asymmetry in the Kikuchi band (Fig. 2e and Extended Data Fig. 3), in agreement with dynamical scattering multislice simulations. Other experimental effects of mask positions, sample mis-tilt, noise and electron beam influence on the polarization measurement are systematically evaluated (Supplementary Figs. 2–8 and Supplementary Notes 3–6). Maps of Δk(r) reveal that, below TAFD, polar order emerges in the form of spatially fluctuating nanoscale regions, making it locally non-centrosymmetric.

The spatially fluctuating landscape involves polar nanodomains with a characteristic length of about 20 nm. Figure 3a displays a real space map of Δk(r) in SrTiO_3 at 69 K over a larger 140 × 140 nm^2 field of view. The colour and transparency represent the direction and amplitude of Δk(r), respectively (shown without the arrows for simpler visualization). The map represents static configurations over the minute-timescale of image acquisition; fast temporal fluctuations are averaged over. The real space map has a highly textured arrangement of spatially fluctuating polar nanodomains, involving multiple polarization directions over tens of nanometres. The spatial organization of polar order is then quantified through the correlation function, C(r) = < Δk(r)·Δk(r + r′) > r′, where < … > r′ denotes the spatial average. Figure 3b shows C(r) and the radial profile, C(r). The correlation function first has a rapid exponential decay, indicative of short-range polar order. The short-distance decay is described by C(r) ≈ C_0exp[–r/ξ], where ξ characterizes the domain size or correlation length. Fitting yields ξ ≈ 20 ± 0.6 nm at this temperature (Methods), establishing the characteristic size of the emergent polar nanodomains in SrTiO_3.

Fig. 3: Nanoscale polar textures in quantum paraelectric SrTiO_3.

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a,c, Large field-of-view map of Δk(r) at 69 K (a) and 35 K (c). The field of view is 140 × 140 nm^2. The colour and transparency represent the direction and amplitude of Δk(r), respectively. b,d, The inset is a 2D map of autocorrelation function of Δk(r), C(r). The plots show the radial profile, C(r). Scale bars, 20 nm.

The local polar nanodomains in quantum paraelectric SrTiO_3 also develop a periodic spatial ordering. In the correlation function, a further peak emerges at a length scale of λ ≈ 25 nm, indicating the presence of longer-range spatial correlations beyond the typical size of individual nanodomains. This extra peak suggests that the small polar nanodomains are not randomly distributed; instead, they have periodic spatial order among themselves over tens of nanometres. Such behaviour points to an unusual intermediate level of polar order below TAFD, where nanodomains have a degree of spatial modulation. Inelastic scattering measurements on SrTiO_3 reveal a polar-acoustic anomaly, involving an incomplete softening of a transverse acoustic mode at a finite wavevector, q (refs. 9,10,11). A full softening would indicate a phase transition into an incommensurate modulation of atomic positions with a period λ = 2π/q. However, the acoustic phonon dispersion shows only a slight and local minimum at q—a behaviour analogous to the ‘roton’ minimum in helium9,10,11. Images of the polar nanodomains show that the periodic length scale λ represents spatial correlations between the emergent polar nanodomains, rather than a true incommensurate structural modulation.

Cooling below Tq ≈ 40 K reveals that the quantum paraelectric phase undergoes a transition akin to a polar glass transition; the polar nanodomains fragment at lower temperatures. Typically, spatial correlations and order increase when cooling. However, instead of a growth or coalescence of domains at low temperature, the quantum paraelectric regime below Tq is characterized by smaller and more disordered polar clusters. Figure 3c shows a real space map of Δk(r) at 35 K, below Tq where SrTiO_3 enters the quantum paraelectric regime. Domains are much smaller and more fragmented, with C(r) displaying a more rapid decay, and ξ ≈ 14 ± 0.4 nm (Fig. 3d). The smoothly evolving background along the lateral direction arises from a small thickness gradient across the field of view (Supplementary Figs. 9 and 10, and Supplementary Note 7). Beyond domain size, the periodic ordering between the polar nanodomains is also suppressed and the extra peak in C(r) weakens substantially.

Order and disorder of polar nanodomains

Tracking the temperature dependence reveals the unusual evolution of the spatially fluctuating polar landscape of quantum paraelectric SrTiO_3. Figure 4a shows real space maps of Δk(r) across temperatures; from 111 K to 23 K, the polar textures have clear changes (refer to Extended Data Fig. 8 for the complete datasets). At 111 K (around TAFD), tiny and weak polar nanodomains exist. At temperatures down to around 69 K, the polar nanodomains grow in size, and show spatial fluctuations over length scales of around 20 nm. Below 69 K, and especially below 40 K, quantum paraelectricity emerges as fragmented clusters of smaller and more disordered polar nanodomains. Further cooling to 23 K leads to even greater fragmentation, with the polar nanodomains shrinking further.

Fig. 4: Evolution of a spatially fluctuating polar landscape at low temperature.

Full size image

a, Large field-of-view map of Δk(r) in SrTiO_3 at 23 K, 35 K, 69 K, 91 K and 111 K (from left to right). The field of view is 140 × 140 nm^2. The colour and transparency represent the direction and amplitude of Δk(r). Dashed rectangles denote matching features across temperatures over roughly the same field of view. Scale bars, 20 nm. b, Fourier transforms of Δk(r) maps. The inset is the radial profile of the Fourier transform. The black arrows highlight a superlattice peak (q ≈ 0.04 nm−1, corresponding to a wavelength λ ≈ 25 nm in real space) at intermediate temperatures. Scale bars, 0.1 nm–1. c, Superlattice peak intensity as a function of temperature (raw data in Extended Data Fig. 8 and analysis in Supplementary Fig. 11). d, Schematic of the polar landscape in quantum paraelectric SrTiO_3 based on our real-space observations. Polar nanodomains form below TAFD ≈ 105 K and develop periodic spatial correlations. The nanodomains fragment when SrTiO_3 enters the quantum paraelectric regime below Tq ≈ 40 K.

The transition to the quantum paraelectric regime also involves a loss of the periodic order between nanodomains. The Fourier transform of Δk(r) encodes the periodic spatial ordering of polar nanodomains. As shown in Fig. 4b, superlattice peaks (highlighted by arrows) at 69 K and 91 K reflect periodic ordering of the polar nanodomains. Radial profiles of the peaks indicate a wavevector q ≈ 0.04 nm−1, consistent with the λ ≈ 25 nm length scale associated with the periodic ordering of polar nanodomains found in real space. The intensity of the superlattice peaks increases up to 69 K (Fig. 4c and Supplementary Fig. 11). It then reverses and drops in the quantum paraelectric phase, indicating a loss of the periodic ordering of polar nanodomains.

The polar structure of quantum paraelectric SrTiO_3 thus has a rich spatial landscape at the nanoscale. Figure 4d summarizes the unusual evolution of the fluctuating polar ordering. Polar nanodomains form around TAFD and self-organize into an emergent periodic texture, down to around 69 K. Below this temperature, quantum fluctuations play a more dominant role, leading to a reversal of the trend, with a decrease in the periodic correlation and the change of polar nanodomains into smaller clusters. Upon entering the quantum regime below Tq ≈ 40 K, the polar nanodomains dissolve into fragmented polar nanodomains, featuring smaller and more random domain patterns without long-range correlation. The loss of order at lower temperature is reminiscent of re-entrant spin glass transitions[48] or inverse melting[49]. Quantum paraelectricity in SrTiO_3 is thus characterized not by a complete absence of polarization but by the evolution of existing polar nanodomains through a re-entrant disordering at low temperatures.

Discussion and outlook

SrTiO_3 has elastic anomalies and unusual lattice dynamics at low temperature, first below the AFD transition and then upon entering the quantum paraelectric regime. The low-temperature anomalies could possibly originate in AFD domain walls that become polar and mobile below Tq (refs. 50,51,52); however, our direct real-space visualizations show that SrTiO_3 has intrinsic polar order throughout the sample, rather than being confined to AFD domain walls. Inelastic neutron measurements find a polar-acoustic regime with intrinsic spatial fluctuations of polar order9,11. A slight softening of the transverse acoustic phonon branch at a small, finite wavevector below TAFD is followed by a loss of intensity below Tq. The precise structure of these spatial fluctuations in SrTiO_3 remains unresolved as it could correspond to several real-space ordering patterns, including polarization density waves[11], lamellar order[12] or long-wavelength transverse deformations[9]. Further, the nature of the transition below Tq, defined by a loss of the transverse acoustic branch, has likewise remained unclear. Our direct imaging reveals that SrTiO_3 below TAFD hosts polar nanodomains that organize into a modulated structure over length scales of tens of nanometres. The nanodomains then fragment into small, uncorrelated polar textures in the quantum paraelectric regime.

The observed real-space polar textures and their evolution are reminiscent of the theoretical proposal of melted lamellar order[12]. The coupling of polarization to a secondary mode (such as strain gradient or flexoelectricity) could allow for instabilities at a finite wavevector, giving rise to incommensurate periodic ordering of polarization—a state dubbed the lamellar phase. Crystalline anisotropy further helps stabilize this modulation into long-range unidirectional stripes at low temperatures. However, the lamellar phase is easily suppressed by thermal fluctuations even at low temperatures, leaving only traces of a melted modulated order defined by decaying oscillatory correlations[12]. Our visualizations of the real space textures and their spatial correlations provide a more concrete picture of the nature of the polar-modulated lamellar state.

The spatial ordering and disordering of polar nanodomains raise further questions on the mechanisms through which external tuning parameters induce new orders in quantum paraelectrics15,16,17,23,24,25,26,27,28,29. Possibly, external stimuli such as mid-infrared pulses or strain cause a long-range ferroelectric state through emergent correlations between the preformed polar nanodomains, similar to order–disorder transitions in ferroelectrics. This should also motivate the exploration of the link between unconventional superconductivity in doped SrTiO_3 (refs. 18,19,20) and the spatially fluctuating polar state in the quantum paraelectric regime. The evolution of polar nanodomains as a function of electron doping could reveal the nature of the parent fluctuating polar phase from which superconductivity and quantum criticality emerge.

Methods

Sample preparation

Commercial SrTiO_3 single crystals (MTI) were used for this study. The SrTiO_3 lamella was prepared using standard gallium focused ion beam (FEI Helios 660) lift-out and thinning. The samples were thinned down using an accelerating ion voltage of 30 kV with a decreasing current from 100 pA to 40 pA, and then with a fine polishing process using an accelerating voltage of 5 kV and 2 kV, and a current of 41 pA and 23 pA.

The STEM lamella sample is supported on a copper post of the focused ion beam grid on only one side, making the sample free to naturally contract with temperature (Extended Data Fig. 1). This mounting geometry ensures minimal strain imparted to the lamella while maintaining thermal coupling for stable cryogenic experiment. The thickness of the sample within the imaging region is estimated using the zero-loss peak in electron energy loss spectroscopy data (Supplementary Fig. 10). Thickness varies from about 53 to 69 nm across the relatively large 140 × 140 nm^2 field of view.

Liquid helium cryogenic electron microscopy

Cryogenic STEM experiments were performed using an h-Bar Instruments ULT holder which uses continuous flow through a heat exchanger that is mechanically decoupled from the sample to reduce vibrations47,54. Variable temperatures are enabled by an integrated heater and proportional–integral–derivative (PID) control. The sample drift rate at low temperature is measured as 0.37 Å s–1 at base temperature through image tracking, lower than the usual range (0.6–1.0 Å s–1) for collecting a high-resolution image in conventional side-entry liquid nitrogen cryogenic holders55,56. For each temperature step during heating, we realign the sample location to the same region after waiting for temperature to stabilize and sample drift to become negligible. A complete 4D-STEM dataset is acquired in about 2 min, so drift during acquisition produces at most about 4 nm, far smaller than the observed domain size scale.

Four dimensional scanning transmission electron microscopy

Advances in pixelated detectors, particularly direct electron detectors with high dynamic range41,57, have enabled the simultaneous capture of both intense Bragg reflections and more subtle features such as Kikuchi bands (more than 10^4 weaker than Bragg spots), which arise from multiple scattering processes in the crystal lattice. 4D-STEM measurements were performed in an aberration-corrected scanning transmission electron microscope (Thermo Fisher Scientific Spectra 300) operated at 300 kV. 4D-STEM datasets were collected by a first-generation electron microscopy pixel array detector, using a 1 mrad convergence semi-angle. The probe current was 30 pA. The real-space sampling was 140 × 140 nm^2 for each dataset, with about 0.69 nm pixel size. The reciprocal space area spans 14 × 14 Å−2, with a pixel size of around 0.1 Å−1. Each diffraction pattern was acquired with 1 ms dwell time, with an additional 0.86 ms for readout time.

Visualization of Friedel-pair asymmetry in Kikuchi bands

Polarity-induced asymmetry in nanobeam electron diffraction is revealed by plotting the antisymmetric component of the diffraction pattern. We first establish the asymmetry in simulated data. Extended Data Fig. 3a shows two simulated diffraction patterns, I(k), with P-down and P-up structures (the same simulated patterns in Fig. 2c). We define the antisymmetric component d(k) = I(k) – I(–k), in the 2D diffraction pattern which is sensitive to polarity. The 2D pattern contains intense features at small k. Multiplying diffraction intensity by |k|2 reduces the dominance of the very strong low-k signal and amplifies high-k features of the Kikuchi bands, allowing structural information in the outer parts of reciprocal space to become visible. To visualize the differences in the high-k Kikuchi bands, we thus plot A(k) = |k|2d(k) = |k|2(I(k) – I(–k)). Extended Data Fig. 3c shows clear asymmetry along the ky direction when comparing P-up and P-down diffraction patterns. Line profiles (Extended Data Fig. 3d) further help visualize this asymmetry.

The connection between polarity and intensity asymmetry is revealed in the experimental data as well. To better visualize the intensity asymmetry in the experimental data, we average diffraction patterns from 3 × 3 nm^2 regions (5 × 5 pixels), which is smaller than the typical domain size of about 20 nm, to improve the signal-to-noise ratio. We reproduce results consistent with the simulations by plotting A(k) from regions with P-up and P-down regions (Extended Data Fig. 3e–h).

Diffraction patterns from multiple regions reproducibly show this intensity asymmetry due to polarity. As shown in Extended Data Fig. 7, the Kikuchi band intensity asymmetry consistently matches the direction of polarity determined from the independent method of COM shift mapping. The A(k) is only used for visualizing intensity asymmetry in the Kikuchi bands, whereas the COM shift measurement is performed directly on the raw diffraction pattern, I(k).

Experimental data on the non-polar structure of SrTiO_3 at room temperature shows no asymmetry in A(k) (Extended Data Fig. 4). The simulated pattern for the cubic paraelectric SrTiO_3 shows the same result. This is consistent with the disappearance of polar textures and COM shifts in the paraelectric phase (Extended Data Fig. 6).

Measuring local polarity via Kikuchi band in 4D-STEM dataset

To measure local polar order, we quantify the intensity asymmetry of opposing Kikuchi bands (Friedel pairs at ±k momentum space coordinates) in the raw diffraction pattern, I(k). We isolate the relevant Kikuchi bands (along kx and ky direction) and measure the COM of each (four in total) (Extended Data Fig. 5c). For each diffraction pattern, we then compute the COM shift of isolated Kikuchi bands relative to the central spot position along kx and ky direction, yielding Δk,x and Δk,y, respectively.

We apply analysis for each raw diffraction pattern at each single scan position (40,000 in total), and plot Δk,x and Δk,y in real space (Extended Data Fig. 5d,e). From Δk,x and Δk,y, the amplitude and direction of Δk is determined as (Δk,y^2 + Δk,x^2)1/2 and arctan(Δk,y/Δk,x), respectively. The direction and amplitude are finally rendered as colour and transparency in Extended Data Fig. 5f. For all temperatures, the amplitudes are normalized to the room temperature data to allow comparison across datasets.

Multislice simulation

Dynamical scattering multislice simulations were carried out using abTEM[58] and custom Python scripts. The slice thickness was set to 0.5 nm. The frozen phonon method was used to account for thermal diffuse scattering, with a total of 30 configurations per slice. The convergence semi-angle is 1 mrad, similar to the experimental conditions. The acceleration voltage was set to 300 kV and defocus to 0 nm. For the nanobeam diffraction patterns shown in Extended Data Figs. 3 and 4, and Supplementary Fig. 5, an about 8 × 8 nm^2 cell with 60 nm thickness (close to sample thickness) was constructed. The B-factor (√<u^2>) in the frozen phonon is set to 0.1 and 0.12 Å for tetragonal and cubic SrTiO_3 to mimic the temperature effect on diffuse scattering. A series of thicknesses ranging from 5–70 nm along the beam direction was used for the simulations in Supplementary Fig. 9.

Autocorrelation function

To characterize the size and periodicity of polar domains, we computed the 2D autocorrelation function of Δk(r), derived from a complex-valued 2D field Δ(r)exp(iϕ(r)), where Δ(r) and ϕ(r) denote the amplitude and angle of Δk(r), respectively. The autocorrelation function was then obtained by computing the inverse Fourier transform of the summed power spectra of each component:

$$C({\bf{r}})={{\mathcal{F}}}^{-1}[|{\mathcal{F}}\,[{\boldsymbol{\Delta }}({\bf{r}})\exp (i\phi ({\bf{r}}))]{|}^{2}]$$

where \({\mathcal{F}}\) and \({{\mathcal{F}}}^{-1}\) denote the forward and inverse 2D Fourier transforms, respectively.

The 2D autocorrelation map (inset of Fig. 3b,d) was then radially averaged to obtain the radial profile, C(r), as a function of radial distance r (Fig. 3b,d). C(r) was used for quantification of the correlation length, (ξ), a measure of the characteristic domain size and short range order. As the autocorrelation function follows exponential decay, we fit it using the standard form

$$y(r)=\Delta \exp (-\frac{r}{\xi })$$

To obtain ξ, we converted the radial profile to a logarithmic scale and then performed linear fitting using the transformed expression to get ξ.

$$\log (y)=\log (\Delta )-\frac{r}{\xi }$$

Data availability

Electron microscopy data corresponding to the figures in the manuscript are available on Zenodo at https://doi.org/10.5281/zenodo.20300700. Further data are available from the corresponding authors on reasonable request.

Code availability

Analysis codes for the center-of-mass measurement and spatial map are available on Zenodo at https://doi.org/10.5281/zenodo.20300700.

References

1. Cochran, W. Crystal stability and the theory of ferroelectricity. Adv. Phys. 9, 387–423 (1960).

2. Müller, K. A. & Burkard, H. SrTiO_3: an intrinsic quantum paraelectric below 4 K. Phys. Rev. B 19, 3593 (1979).

3. Rowley, S. E. et al. Ferroelectric quantum criticality. Nat. Phys. 10, 367–372 (2014).

4. Shirane, G. & Yamada, Y. Lattice-dynamical study of the 110 K phase transition in SrTiO_3. Phys. Rev. 177, 858 (1969).

5. Weaver, H. E. Dielectric properties of single crystals of SrTiO_3 at low temperatures. J. Phys. Chem. Solids 11, 274–277 (1959).

6. Yamanaka, A. et al. Evidence for competing orderings in strontium titanate from hyper-Raman scattering spectroscopy. Europhys. Lett. 50, 688 (2000).

7. Salje, E. K. H., Aktas, O., Carpenter, M. A., Laguta, V. V. & Scott, J. F. Domains within domains and walls within walls: evidence for polar domains in cryogenic SrTiO_3. Phys. Rev. Lett. 111, 247603 (2013).

8. Frenkel, Y. et al. Imaging and tuning polarity at SrTiO_3 domain walls. Nat. Mater. 16, 1203–1208 (2017).

9. Fauqué, B. et al. Mesoscopic fluctuating domains in strontium titanate. Phys. Rev. B 106, L140301 (2022).

10. Müller, K. A., Berlinger, W. & Tosatti, E. Indication for a novel phase in the quantum paraelectric regime of SrTiO_3. Zeit. Für Phys. B 84, 277–283 (1991).

11. Orenstein, G. et al. Observation of polarization density waves in SrTiO_3. Nat. Phys. 21, 961–965 (2025).

12. Guzmán-Verri, G. G., Liang, C. H. & Littlewood, P. B. Lamellar fluctuations melt ferroelectricity. Phys. Rev. Lett. 131, 046801 (2023).

13. Sakudo, T. & Unoki, H. Dielectric properties of SrTiO_3 at low temperatures. Phys. Rev. Lett. 26, 851 (1971).

14. Neville, R. C., Hoeneisen, B. & Mead, C. A. Permittivity of strontium titanate. J. Appl. Phys. 43, 2124–2131 (1972).

15. Haeni, J. H. et al. Room-temperature ferroelectricity in strained SrTiO_3. Nature 430, 758–761 (2004).

16. Lee, D. et al. Emergence of room-temperature ferroelectricity at reduced dimensions. Science 349, 1314–1317 (2015).

17. Basini, M. et al. Terahertz electric-field-driven dynamical multiferroicity in SrTiO_3. Nature 628, 534–539 (2024).

18. Schooley, J. F., Hosler, W. R. & Cohen, M. L. Superconductivity in semiconducting SrTiO_3. Phys. Rev. Lett. 12, 474 (1964).

19. Koonce, C. S., Cohen, M. L., Schooley, J. F., Hosler, W. R. & Pfeiffer, E. R. Superconducting transition temperatures of semiconducting SrTiO_3. Phys. Rev. 163, 380 (1967).

20. Collignon, C., Lin, X., Rischau, C. W., Fauqué, B. & Behnia, K. Metallicity and superconductivity in doped strontium titanate. Annu. Rev. Condens. Matter Phys. 10, 25–44 (2019).

21. Fleury, P. A., Scott, J. F. & Worlock, J. M. Soft phonon modes and the 110 K phase transition in SrTiO_3. Phys. Rev. Lett. 21, 16 (1968).

22. Jang, H. W. et al. Ferroelectricity in strain-free SrTiO_3 thin films. Phys. Rev. Lett. 104, 197601 (2010).

23. Aschauer, U. & Spaldin, N. A. Competition and cooperation between antiferrodistortive and ferroelectric instabilities in the model perovskite SrTiO_3. J. Phys. Condens. Matter 26, 122203 (2014).

24. Xu, R. et al. Strain-induced room-temperature ferroelectricity in SrTiO_3 membranes. Nat. Commun. 11, 3141 (2020).

25. Salmani-Rezaie, S., Ahadi, K., Strickland, W. M. & Stemmer, S. Order–disorder ferroelectric transition of strained SrTiO_3. Phys. Rev. Lett. 125, 087601 (2020).

26. Mitsui, T. & Westphal, W. B. Dielectric and X-Ray Studies of CaxBa1–xTiO_3 and CaxSr1–xTiO_3. Phys. Rev. 124, 1354 (1961).

27. Itoh, M. et al. Ferroelectricity induced by oxygen isotope exchange in strontium titanate perovskite. Phys. Rev. Lett. 82, 3540 (1999).

28. Li, X. et al. Terahertz field–induced ferroelectricity in quantum paraelectric SrTiO_3. Science 364, 1079–1082 (2019).

29. Nova, T. F., Disa, A. S., Fechner, M. & Cavalleri, A. Metastable ferroelectricity in optically strained SrTiO_3. Science 364, 1075–1079 (2019).

30. Riste, T., Samuelsen, E. J., Otnes, K. T. & Feder, J. Critical behaviour of SrTiO_3 near the 105 K phase transition. Solid State Commun. 9, 1455–1458 (1971).

31. Shapiro, S. M., Axe, J. D., Shirane, G. & Riste, T. Critical neutron scattering in SrTiO_3 and KMnF_3. Phys. Rev. B 6, 4332 (1972).

32. Darlington, C. N. W. & O’Connor, D. A. The central mode in the critical scattering of X-rays by SrTiO_3. J. Phys. C 9, 3561 (1976).

33. Tsunekawa, S., Watanabe, H. F. J. & Takei, H. Linear thermal expansion of SrTiO_3. Phys. Status Solidi 83, 467–472 (1984).

34. Neumann, H. B., Poulsen, H. F., Rütt, U., Schneider, J. R. & Zimmermann, M. V. Investigation of structural phase transitions in perovskites using high energy synchrotron radiation. Phase Transit. 55, 17–35 (1995).

35. Kityk, A. V. et al. Low-frequency superelasticity and nonlinear elastic behavior of SrTiO_3 crystals. Phys. Rev. B 61, 946 (2000).

36. Spence, J. C. H. On the accurate measurement of structure-factor amplitudes and phases by electron diffraction. Found. Cryst. 49, 231–260 (1993).

37. Zuo, J. M. & Spence, J. C. H. Advanced Transmission Electron Microscopy, Imaging and Diffraction in Nanoscience (Springer, 2017).

38. Deb, P. et al. Imaging polarity in two dimensional materials by breaking Friedel’s law. Ultramicroscopy 215, 113019 (2020).

39. Cowley, J. M. Configured detectors for STEM imaging of thin specimens. Ultramicroscopy 49, 4–13 (1993).

40. Pennycook, T. J. et al. Efficient phase contrast imaging in STEM using a pixelated detector. Part 1: experimental demonstration at atomic resolution. Ultramicroscopy 151, 160–167 (2015).

41. Tate, M. W. et al. High dynamic range pixel array detector for scanning transmission electron microscopy. Microsc. Microanal. 22, 237–249 (2016).

42. Tsuda, K., Sano, R. & Tanaka, M. Nanoscale local structures of rhombohedral symmetry in the orthorhombic and tetragonal phases of BaTiO_3 studied by convergent-beam electron diffraction. Phys. Rev. B 86, 214106 (2012).

43. Shao, Y. T. & Zuo, J. M. Lattice-rotation vortex at the charged monoclinic domain boundary in a relaxor ferroelectric crystal. Phys. Rev. Lett. 118, 157601 (2017).

44. Shao, Y. T. et al. Emergent chirality in a polar meron to skyrmion phase transition. Nat. Commun. 14, 1355 (2023).

45. Caretta, L. et al. Non-volatile electric-field control of inversion symmetry. Nat. Mater. 22, 207–215 (2023).

46. Mun, J. et al. Atomic resolution scanning transmission electron microscopy at liquid helium temperatures for quantum materials. Ultramicroscopy 267, 114039 (2024).

47. Rennich, E. et al. Ultracold cryogenic TEM with liquid helium and high stability. Proc. Natl Acad. Sci. USA 122, e2509736122 (2025).

48. Dho, J., Kim, W. S. & Hur, N. H. Reentrant spin glass behavior in Cr-doped perovskite manganite. Phys. Rev. Lett. 89, 027202 (2002).

49. Zhang, Y., Sung, S. H., Clement, C. B., Cheong, S. W. & El Baggari, I. Inverse melting of polar order in chemically substituted BaTiO_3. Phys. Rev. Lett. 134, 256801 (2025).

50. Wang, R., Zhu, Y. & Shapiro, S. M. Electron diffraction studies of phonon and static disorder in SrTiO_3. Phys. Rev. B 61, 8814 (2000).

51. Scott, J. F., Salje, E. K. H. & Carpenter, M. A. Domain wall damping and elastic softening in SrTiO_3: evidence for polar twin walls. Phys. Rev. Lett. 109, 187601 (2012).

52. Pesquera, D., Carpenter, M. A. & Salje, E. K. H. Glasslike dynamics of polar domain walls in cryogenic SrTiO_3. Phys. Rev. Lett. 121, 235701 (2018).

53. Li, Y. L. et al. Phase transitions and domain structures in strained pseudocubic (100) SrTiO_3 thin films. Phys. Rev. B 73, 184112 (2006).

54. Sung, S. H. et al. Liquid helium cryogenic TEM below 1 Å. Preprint at https://arxiv.org/abs/2509.12475 (2025).

55. El Baggari, I. et al. Nature and evolution of incommensurate charge order in manganites visualized with cryogenic scanning transmission electron microscopy. Proc. Natl Acad. Sci. USA 115, 1445–1450 (2018).

56. Goodge, B. H., Bianco, E., Schnitzer, N., Zandbergen, H. W. & Kourkoutis, L. F. Atomic-resolution cryo-STEM across continuously variable temperatures. Microsc. Microanal. 26, 439–446 (2020).

57. Philipp, H. T. et al. Very-high dynamic range, 10,000 frames/second pixel array detector for electron microscopy. Microsc. Microanal. 28, 425–440 (2022).

58. Madsen, J. & Susi, T. The abTEM code: transmission electron microscopy from first principles. Open Res. Eur. 1, 24 (2021).

Acknowledgements

We acknowledge insightful discussions with J. Heron, H. Sha and Y.-Tsun Shao.

Funding

Y.Z., S.H.S. and I.E. were supported by the Rowland Institute at Harvard. R.H. and liquid helium TEM instrument time were supported by the US Department of Energy, Basic Energy Sciences, under award DE-SC0024147. N.A. was supported by the National Science Foundation through the Materials Research Science and Engineering Center at the University of Michigan (award no. DMR-2309029). M.G. was supported by a National Science Foundation SBIR grant (grant no. 2507716) for cryogenic hardware development. C.L. and P.Y. were financially supported by the National Natural Science Foundation of China (NSFC grant nos. 52025024, 52388201 and 12421004), and the National Key R&D program of China (grant no. 2023YFA1406400). Liquid helium cryogenic electron microscopy experiments were performed at the Michigan Center for Materials Characterization ((MC)2, RRID: SCR_026770) at the University of Michigan. Focused ion beam sample preparation was performed at the Harvard University Center for Nanoscale Systems (CNS); a member of the National Nanotechnology Coordinated Infrastructure Network (NNCI), which is supported by the National Science Foundation under NSF award no. ECCS-2025158.

Author information

Authors and Affiliations

1. The Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA

Yang Zhang, Suk Hyun Sung & Ismail El Baggari

2. Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA

Yang Zhang

3. Michigan Center for Materials Characterization, University of Michigan, Ann Arbor, MI, USA

Suk Hyun Sung

4. Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA

Nishkarsh Agarwal & Robert Hovden

5. h-Bar Instruments, Ann Arbor, MI, USA

Maya Gates

6. State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China

Cong Li & Pu Yu

7. Department of Physics, University of Michigan, Ann Arbor, MI, USA

Robert Hovden

8. Department of Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada

Ismail El Baggari

Contributions

Y.Z. and I.E. conceived the study. Y.Z., S.H.S., N.A., M.G., R.H. and I.E. performed microscopy experiments. C.L. and P.Y. helped prepare samples. Y.Z., S.H.S., R.H. and I.E. analysed data and wrote the manuscript with contributions from all authors.

Corresponding authors

Correspondence to Yang Zhang or Ismail El Baggari.

Ethics declarations

Competing interests

I.E. and R.H. are inventors on a patent application (US18/845,264) related to the liquid helium sample holder invention. I.E., R.H. and M.G. have a financial interest in h-Bar Instruments which is commercializing the invention. The remaining authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Sample mounting for liquid helium STEM experiment.

(a-b) Schematic illustration of mounting geometry. A Cu FIB half-grid with SrTiO_3 lamella mounted on one side to a post. The Cu-grid is attached to the Mo grid. (c) Optical microscope image of the mounted Mo and Cu grids attached to the tip. (d) Low-magnification TEM image showing the SrTiO_3 lamella supported by the Cu post on one end.

Extended Data Fig. 2 Quantification of AFD superspot intensity across temperatures.

(a) Electron diffraction patterns collected from 23 to 215 K. Black arrows mark the AFD superspots located at half-integer position. (b) Line profiles (left) and zoom-in views of the normalized superspot peak (right), extracted along the dashed line in (a). The blue line represents the fit.

Extended Data Fig. 3 Visualization of Friedel-pair asymmetry in Kikuchi bands.

(a) Simulated diffraction pattern with P-up and P-down tetragonal SrTiO_3. (b) Plot of |k|2I(k) highlights how the Kikuchi bands become more visible relative to the very intense scattering at low k. (c) Antisymmetric component, A(k)=|k|2(I(k)-I(-k)), isolates asymmetry in the intensity in the high-k Kikuchi bands. Antisymmetric intensity along the ky direction is apparent. Further, it reverses between P-up and P-down structures. (d) Line profiles extracted from dashed rectangles in (c) confirm the asymmetry of Friedel pairs reverses signs between P-up and P-down structures. (e-h) The same data analysis applied to experimental diffraction patterns extracted from P-down and P-up regions (I and II in Fig. 2d). The diffraction pattern is averaged over 5 × 5 pixels (3 × 3 nm^2) regions.

Extended Data Fig. 4 Intensity symmetry in Kikuchi bands of cubic SrTiO[3].

(a-d) The same data analysis methods are applied to simulated diffraction pattern of cubic (paraelectric) SrTiO_3. (e-f) Repeated analysis of experimental data on SrTiO_3 at room temperature. The diffraction pattern is averaged over 5 × 5 pixels (3 × 3 nm^2) regions.

Extended Data Fig. 5 Structure of the 4D-STEM dataset and data analysis.

(a-b) Representative 4D-STEM dataset acquired using a pixelated direct electron detector. (a) The real-space scan pixel density is 200 × 200, over a 140 × 140 nm^2 field of view. At each scan position, a full 2D diffraction pattern is collected with a 128 × 128 pixel sampling. (b) Representative raw diffraction pattern collected from a single scan position. (c) Masks used to extract the center of mass (COM) of Kikuchi bands along the kx and ky directions. (d-e) Spatial maps of COM shifts Δk,x(r) and Δk,y(r), measured from the raw diffraction patterns. (f) Vector field of COM shift direction and magnitude, derived from Δk,x(r) and Δk,y(r). Colour represents the direction and the transparency represents the amplitude. The dataset shown here was collected at 69 K.

Extended Data Fig. 6 Polar maps at room and cryogenic temperature.

The colour and transparency represent the direction and amplitude of Δk(r). The direction ranges from 0 to 2π, and the amplitude scales from 0 to 0.2 Å−1. No polar textures are detected at room temperature, as expected for the non-polar paraelectric phase of SrTiO_3 at room temperature. When cooling down below TAFD, finite polarization and polar textures develop over the same field of view.

Extended Data Fig. 7 Consistency of polarity-driven Friedel-pair asymmetry across regions.

(a) Real space Δk,y(r) map collected at 69 K. (b) Additional examples of antisymmetric component (similar to Extended Data Fig. 3) of diffraction patterns extracted from multiple regions. (c) Corresponding line profiles extracted from dashed rectangles in (b), demonstrating consistent reversal of Kikuchi-band intensity asymmetry between P-up and P-down regions.

Extended Data Fig. 8 Temperature evolution of polar textures.

(a) Real-space Δk(r) maps across temperatures. The color and transparency represent the direction and amplitude of Δk(r). Dashed rectangles denote matching features within the same region across temperatures. (b) Fourier transform of Δk(r) shown in (a). (c) Radial profile extracted from Fourier transform. The arrow highlights the appearance of superspots.

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Zhang, Y., Sung, S.H., Agarwal, N. et al. Imaging of nanoscale polar textures in quantum paraelectric SrTiO_3. Nature 656, 54–59 (2026). https://doi.org/10.1038/s41586-026-10823-x

- Received: 26 September 2025

- Accepted: 17 June 2026

- Published: 22 July 2026

- Version of record: 22 July 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10823-x

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Henok Weldeyesus,

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Zhengguang Lu,

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Abstract

In some unconventional superconductors, time-reversal symmetry can be broken apart from the gauge symmetry[1], resulting in superconductivity that can be enhanced or induced by magnetic fields[2]. However, field-enhanced superconductors are more vulnerable to impurities than their Bardeen–Cooper–Schrieffer counterparts[3]. Crystalline rhombohedral multilayer graphene is a promising platform to explore them because of its superior material quality and gate-tunable strong correlation effects4,5. Here we report transport measurements of rhombohedral tetralayer and pentalayer graphene, demonstrating a spectrum of clean-limit superconductivities. We found three different types of field-enhanced and field-induced superconductivities in the pentalayer. They are all robust against an in-plane field up to 8.5 T, exceeding the Pauli limit by tens of times. Compared with Bernal bilayer graphene showing only in-plane field-enhancement[6], pentalayer graphene features superconductors enhanced by out-of-plane as well as in-plane fields. They also reside at much lower gate electric fields owing to the intrinsically flatter band dispersion—facilitating their study and further engineering. Moreover, we observed that proximitized spin–orbit coupling generates multiple new superconductors without introducing additional disorder effects. Our work establishes a new family of magnetic field-boosted superconductors in rhombohedral graphene. Using the high accessibility with moderate gate voltages, this will pave the way for realizing non-Abelian quasiparticles through interfacial engineering[7] in the extreme clean limit, in that proximitized spin–orbit coupling leads to topological states[8] and maintains the ultrahigh quality of crystalline graphene.

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Main

In a strongly correlated system, the normal state of a superconductor is subject to diverse instabilities. This can result in unconventional superconductivity (SC) breaking a symmetry on top of global gauge symmetry[1]. Unconventional SCs play a key part in understanding correlated electron physics and can enable fault-tolerant quantum computations[9]. Despite its importance, it remains one of the biggest open questions in condensed matter physics owing to the intricate interplay between various broken symmetries and their effects on pairing mechanisms. In bulk materials10,11,12,13,14,15,16, studying doping-dependent behaviour relies on comparing different crystals, which can be complicated by varied defect levels. By contrast, two-dimensional materials allow for in situ tunable doping by electrostatic gating, which enables comparing hundreds of samples effectively in a reliable fashion. Especially, unconventional SC has been actively explored in moiré superlattices17,18,19,20,21,22,23 because of flexibility in interlayer twist angles. Nevertheless, the requirement of a precise twist angle and its spatial variation[24] have posed challenges to reproducing phase diagrams from different devices[25].

Rhombohedral N-layer graphene (RNG) has recently emerged as an ideal platform to study unconventional SC. It features a simple chemical ingredient and homogeneous crystalline structure, facilitating the search of unconventional SCs vulnerable to impurities[3]. Moreover, it hosts a flat low-energy band dispersion (E ∝ kN) that can be further fine-tuned by gate voltages[26]. These features foster enhanced electron correlation effects and various isospin-symmetry-broken states4,5, which can lead to SCs mediated by mechanisms other than phonons27,28,29. Although SCs in rhombohedral bilayer graphene (R2G)6,30,31 and trilayer graphene (R3G)32,33,34 have been studied, unconventional SCs in thicker RNG remain less explored. The latter, however, offers several advantages over its thinner counterparts. First, RNGs with N > 3 provide richer isospin-symmetry-broken states, such as the layer-antiferromagnet4,5 and valley-polarized half-metal[35], in which exotic SCs can emerge. Second, isospin-symmetry-broken parent states are induced by lower gate electric fields for N > 3, reducing the risk of gate leakage. Third, RNGs with N > 3 have shown a rich family of quantum anomalous Hall (QAH) states8,36,37,38 that can lead to non-Abelian quasiparticles when combined with SC7,39,40.

Here, we report transport measurements on hole-doped rhombohedral tetralayer (R4G) and pentalayer graphene (R5G) without proximitized spin–orbit coupling (SOC) and moiré effects. We observed multiple superconducting states, highlighting several unconventional ones in R5G that are enhanced or induced by magnetic fields: (1) SC2 is strengthened by an in-plane magnetic field B∥; (2) SC4 is induced by a high B∥; (3) SC3 is enhanced by a small out-of-plane magnetic field B⊥ and is robust against B∥. These observations imply their unconventional nature. From a separate R4G/WSe_2 device, we observed several new SCs induced by proximitized SOC effects. These states are located at relatively low displacement fields, and their parent states preserve the long mean free path of bare graphene—both facts will facilitate unconventional SC through interface engineering. The main findings in this work were reproduced in two R4G and five R5G devices and in two independent fridges from two different labs.

SC in rhombohedral multilayer graphene

Figure 1a,b shows the Rxx maps of R5G and R4G at the mixing chamber temperature T of 7 mK, highlighting several regions with vanishing resistance. We label each state as SC1–SC3. Figure 1c,d shows Rxx linecuts at varied temperatures. Rxx drops to zero as T decreases in a range of charge densities. The insets show dVxx/dI as a function of source–drain current I and B⊥. Peaks in dVxx/dI are visible at zero B⊥ and get suppressed by nonzero B⊥. Fermiology analyses (Methods) demonstrate that SC1 is developed at the phase boundary between a full-metal with an annular Fermi surface and a partially isospin-polarized phase (Fig. 1e,f), identically to SC1 in R3G[32]. Temperature-dependent Rxx, dVxx/dI and fermiology analyses for SC2 are shown in Extended Data Figs. 1 and 2.

Fig. 1: SC in hole-doped bare RNG.

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a,b, Rxx as a function of n and D/ε_0 for devices R5G-1 (a) and R4G-1 (b), respectively, at zero magnetic field. The inset of a shows a magnified map corresponding to the black box. Several regions with vanishing Rxx are observed, including the SC1 akin to that observed in R3G and three new SC states labelled as SC2 and SC3. c,d, Rxx as a function of n at varied temperatures and fixed D/ε_0 for SC1 in devices R5G-1 (c) and R4G-1 (d), respectively. The inset shows differential resistance as a function of d.c. current and out-of-plane magnetic field in R5G (n = −3.315 × 10^12 cm−2, D/ε_0 = 0.20 V nm−1) and R4G (n = −2.58 × 10^12 cm−2, D/ε_0 = 0.28 V nm−1). Vanishing Rxx, nonlinear dVxx/dI and the modulation of Ic by B⊥ suggest that these are superconducting states. e,f, Fast Fourier transform of Rxx(1/B⊥) as a function of n and the normalized frequency fν along the dashed lines in a and b, respectively. The relation f_1 − f_2 = 1/4 holds for SC1 in both R5G and R4G (grey range in the bottom panel), suggesting an annular-shaped full-metal as the parent state. The partially isospin-polarized phase (PIP) appears next to SC1. FFT, fast Fourier transform.

The decrease in Rxx, nonlinear dVxx/dI and response to B⊥ indicate that SC1 and SC2 in R5G and R4G are superconductors. Their phenomenology resembles SCs in moiré graphene17,18,19,20,21, twisted WSe_2 (refs. 22,23) and thinner rhombohedral graphene6,30,31,32,33,34. However, the coherence length ξ and mean free path l of these SCs and their normal state (Methods) put themselves in the clean limit (ξ/l ≪ 1), in that ξ ≈ 200 nm, l ≈ 1.6 μm and ξ ≈ 300 nm, l ≈ 1 μm for SC1 in R5G and R4G, respectively. This is contrary to twisted graphene and WSe_2 superconductors, in which the normal-state resistance is higher and ξ/l > 1. Rhombohedral graphene in the clean limit provides an ideal playground for exploring unconventional SCs, which are discussed in the following sections.

SC2 enhanced and SC4 induced by B ∥

Having SCs established in R5G and R4G, we investigate R5G under B∥. Figure 2a shows Rxx maps at B∥ = 0 and 8 T. We observe two differences comparing these maps: (1) SC2 is expanded in the n–D space at B∥ = 8 T; (2) a new low-resistance state indicated as SC4 is induced by high B∥. SC4 is well separated from SC2 and SC3 in the map, suggesting it is distinct from the superconductors existing at B∥ = 0 T. SC3 also survives at B∥ = 8 T and is eventually connected to SC2 at B∥ = 8.5 T in the other two devices (Fig. 3d and Extended Data Fig. 4g).

Fig. 2: SC2 and SC4 in R5G, respectively, enhanced and induced by B∥.

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a, Rxx maps at B∥ = 0 T (top) and 8 T (bottom). Whereas SC3 survives, SC2 is enlarged, and SC4 is induced by B∥. For comparison, the boundary of SC2 at B∥ = 0 T is drawn as white dashed curves. b, Rxx as functions of T at the (n, D) point (indicated as circles in a) with the minimal resistance at each field. The transition temperature is increased by applying B∥. The inset shows differential resistance as a function of I and B⊥ for SC2 at B∥ = 6.5 T. Fraunhofer-like modulations are observed. c, Fast Fourier transform of Rxx(1/B⊥) as a function of n and fν. Around SC2, f_2 and f_3 have merged and the relation f_1 − 2f_3 ≈ 1/2 holds, suggesting its normal state is a half-metal with an annular-shaped Fermi surface. d, Rxy as a function of B⊥ in the forward and backward sweeping directions in the normal state of SC2 (n = −1.07 × 10^12 cm−2, D/ε_0 = 58 mV nm−1, B∥ = 0 T, T = 160 mK). The absence of the anomalous Hall effect indicates zero valley polarization. e, Rxx maps magnifying into SC4 at B∥ = 8.0 T (top) and 8.835 T (bottom). The resistance of SC4 decreases as applying higher B∥. f, Rxx as a function of n at varied temperatures and D/ε_0 = 140.1 mV nm−1 for SC4 at B∥ = 8.835 T. A clear decrease in Rxx is observed as T decreases. The inset shows differential resistance as a function of I and B⊥ for SC4 at B∥ = 8.835 T and the n value marked by the dashed arrow. Fraunhofer-like modulations are observed. All the data were taken from device R5G-2. FFT, fast Fourier transform.

Fig. 3: SC3 in R5G enhanced by B⊥.

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a, Rxx as a function of n at D/ε_0 = 49.61 mV nm−1 and varied temperatures. The resistance vanishes as T decreases. b, Fast Fourier transform of Rxx(1/B⊥) as a function of n and fν, suggesting SC3 is developed from a half-metal with an annular-shaped Fermi surface. c, Rxy as a function of B⊥ in the forward and backward scanning directions in the normal state of SC3 (T = 160 mK). The absence of the anomalous Hall effect indicates zero valley polarization. d, Rxx map at B⊥ = 0 mT and B∥ = 8.5 mT. SC3 features ultrahigh PVR of >34. e, Rxx maps at B⊥ = 0 mT (top) and 1.8 mT (bottom). The SC3 region is enlarged by B⊥. f, Differential resistance as a function of I and B⊥ for n = −1.135 × 10^12 cm−2 and D/ε_0 = 57 mV nm−1. Ic is increased by small B⊥. g, Differential resistance as a function of I and T for n = −1.135 × 10^12 cm−2 and D/ε_0 = 57 mV nm−1 under B⊥ = 0 mT (top) and 1.8 mT (bottom). Both Ic and transition temperature increased with the small B⊥. Data in e–g show a rare enhancement of SC by B⊥. h, Description of a theoretical model. When B⊥ much larger than B∥ is applied, the spin vectors are steered along the z-axis, resulting in the valley-contrasting energy ±λ⟨σz⟩/2. This induces the valley imbalance and enhanced valley DOS, which can strengthen intravalley pairing. i, Critical currents (extracted from peaks in dVxx/dI from Extended Data Fig. 9f–k) as functions of B⊥ for B∥ tuned around a few mT. The enhancement of SC3 by B⊥ gets weaker under B∥ of only a few mT. The data in b–g are from device R5G-1 and the data in a,i are from device R5G-2. FFT, fast Fourier transform.

We now focus on the comparison of SC2 at B∥ = 0 and 8 T. First, the area of SC2 in the n–D space increased at B∥ = 8 T. This can be seen in the contour of SC2 at B∥ = 0 T, indicated as a white dashed curve, being smaller than SC2 at B∥ = 8 T. Second, Rxx dropped to lower values across the SC2 at B∥ = 8 T. Finally, Rxx starts to drop from the normal state at higher temperatures under B∥ = 8 T (Fig. 2b). Here, we compared the evolution of Rxx under varied temperatures at the (n, D) point featuring the minimal resistance at each field, because SC2 shifts to a different location in the n–D space by B∥. The (n, D) points where the data in Fig. 2b were taken are denoted by yellow and green circles in Fig. 2a.

The expansion of the phase in the n–D space, with the increase in the transition temperature and critical current Ic (Extended Data Fig. 1e) suggests that SC2 is enhanced by B∥. We performed fermiology analyses to understand its isospin structure. Figure 2c shows the fast Fourier transform of Rxx(1/B⊥) as a function of n and fν (Methods). Two branches f_2 and f_3 obey the relation f_1 − (f_2 + f_3) ≈ 1/2, and they are merged around the emergence of SC2. This suggests the normal state of SC2 is an annular half-metal. Combining this with the absence of anomalous Hall effect in its normal state (Fig. 2d), we conclude that SC2 arises from a valley-unpolarized annular half-metal parent state.

Contrary to SC2 existing at zero magnetic field, SC4 is observed only at high B∥. Figure 2e shows the Rxx map around SC4 at B∥ = 8.0 T and 8.835 T, featuring a stripe-shaped region with low resistance. The resistance decreases as applying higher B∥, which was limited by the magnet used. Figure 2f shows Rxx linecuts at varied temperatures with Vxx–I characteristics. Nonlinear dVxx/dI is observed at the base temperature, and the reduction of Rxx disappears as temperature increases. Peaks in dVxx/dI disappear at B⊥ ≈ 6 mT.

The suppression of Rxx, nonlinear dVxx/dI and responses to B⊥ for SC4 are consistent with SC phenomenology. Although the fragility of SC4 complicates detailed analyses, we may take 70 mK at which Rxx starts decreasing as a conservative estimate of the transition temperature. This gives the Pauli-limit violation ratio (PVR) of about 68 at the highest field available, when compared with a zero-field Bardeen–Cooper–Schrieffer superconductor with the same transition temperature.

SC3 enhanced by B ⊥

Figure 3a shows Rxx for SC3, featuring vanishing resistance at the base temperature and a sharp rise as T increases. Together with nonlinear dVxx/dI (Fig. 3f), this suggests that SC3 is a superconductor.

We perform fermiology analyses to understand its Fermi surface structure. Figure 3b shows the fast Fourier transform of Rxx(1/B⊥) as a function of n and fν. The branch f_1 spans across the whole carrier density, and the low-frequency feature is broken into two segments, f_2 and f_3. Although the low-frequency branch in the density range of SC3 is unclear, f_2 and f_3 at its two ends follow relations f_1 − f_2 ≈ 1/2 and f_1 − f_3 ≈ 1/2, suggesting a half-metal with an annular Fermi surface. Figure 3c shows Rxy as B⊥ is swept back and forth in the normal state of SC3, showing no anomalous Hall effect. SC3 is still observable at B∥ = 8.5 T (Fig. 3d).

These observations suggest that SC3 is developed from a valley-unpolarized half-metal with annular Fermi surface. Especially, SC3 exceeds the Pauli limit (about 0.24 T) by a factor of >34 (see Extended Data Fig. 7j for the estimation of the Berezinskii–Kosterlitz–Thouless (BKT) transition temperature TBKT). This ultrahigh PVR supports the unconventional nature of SC3.

Figure 3e shows Rxx maps at B⊥ = 0 and 1.8 mT. Unlike SC2 and most other SCs, SC3 is expanded by B⊥. Figure 3f shows dVxx/dI as a function of I and B⊥. The critical current is doubled by B⊥ = 1.8 mT before decreasing at higher fields. TBKT also increases from about 47 mK to 76 mK at this (n, D) point, as shown in comparison between maps at the two field values (Fig. 3g). Similar enhancements of Ic and TBKT are observed across the SC3 region (Extended Data Fig. 7a–f) and disappear under nonzero B∥ (Extended Data Fig. 8a,b).

The enhancement of SC by small B⊥ adds more exoticness to SC3. As explained in the Methods, mechanisms based on spin or valley magnetism, or natural Josephson junctions with the phase difference of π (that is, 0–π junction) cannot explain this observation. We sketch one possible scenario that consistently explains all of our observations (see Methods for details). Under zero magnetic fields, the spin-polarized and valley-unpolarized normal state possesses the spin vectors canted with the angle φ ≈ 90° (ref. [33]) (Fig. 3h), because Hund’s coupling EH (about 2 meV) is much stronger than the intrinsic SOC λ of graphene41,42 (approximately 50 μeV). For nonzero B⊥ with B∥ = 0, the spin vectors can be steered along the z-axis even when the spin Zeeman energy (about 0.2 μeV for 1.8 mT) is much smaller than λ, because of the interplay with Hund’s coupling. The nonzero spin component ⟨σz⟩ leads to the valley-contrasting energy ±λ⟨σz⟩/2 and the valley imbalance in the hole occupation, as described in Fig. 3h. The detailed model calculations are presented in the Methods and Extended Data Fig. 9. This will result in an increase in the density of states (DOS) for one valley and a decrease for the other valley. Assuming intravalley pairing dominates SC3, the increased DOS for one valley can strengthen the superconductivity.

Transport measurements under carefully adjusted B⊥ and B∥ provide further evidence for the proposed scenario, although more experiments are required to fully test it. The spin vectors aligned to the z-axis by B⊥ will tend to be steered back to their easy plane (φ = 90°) when B∥ comparable to B⊥ is applied. As B⊥ of approximately 1.8 mT maximizes the strength of SC3, B∥ around the same value will weaken the enhancement. This hypothesis is confirmed in Fig. 3i, in which the maximum of Ic at B⊥ ≈ 1.8 mT is suppressed by B∥ of merely a few mT. Moreover, when stronger SOC is imposed, the spin vectors will be pinned more robustly and accordingly the same B⊥ will induce a smaller spin polarization and valley imbalance. This suggests the enhancement of SC3 is suppressed when strong SOC is proximitized to R5G. This prediction is consistent with the absence of the increase in Ic by B⊥ for SC3 in R5G/WS_2 (Extended Data Fig. 9o).

New SCs induced by SOC

Finally, we examine the impact of proximitized SOC on SC in rhombohedral graphene. Figure 4a shows the Rxx map of device R4G/WSe_2. Compared with bare R4G (Fig. 1b), we observe additional states with vanishing Rxx labelled as SC3–7. Figure 4b–g shows dVxx/dI as a function of I and B⊥ for these states. For each SC, dVxx/dI vanishes at small I and exhibits peaks at zero B⊥. These features are suppressed as B⊥ increases. Extended Data Fig. 10 shows temperature-dependent Rxx and dVxx/dI, which are aligned with SC phenomenology.

Fig. 4: Proliferation of SC in R4G by proximitized SOC effects.

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a, Rxx map of a R4G/WSe_2 device. Five new states (SC3–7) emerge while SC2 is suppressed. The names of SCs follow the convention of Fig. 1b. b–g, Differential resistance as a function of I and B⊥ for SC3 (n = −2.356 × 10^12 cm−2, D/ε_0 = 0.18 V nm−1), SC4 (n = −1.482 × 10^12 cm−2, D/ε_0 = 0.072 V nm−1), SC5 (n = −1.242 × 10^12 cm−2, D/ε_0 = 0.072 V nm−1), SC6 (n = −1.00 × 10^12 cm−2, D/ε_0 = 0.026 V nm−1), SC7 (n = −0.38 × 10^12 cm−2, D/ε_0 = − 0.24 V nm−1) and SC1 (n = −3.25 × 10^12 cm−2, D/ε_0 = − 0.42 V nm−1). Vanishing Rxx, nonlinear dVxx/dI (Extended Data Fig. 10) and their suppression by B⊥ suggest SC3–7 are superconductors. h, Square resistance as a function of n in the normal state of the SC1 of bare R4G (top, D/ε_0 = 0.42 V nm−1), R4G/WSe_2 at negative D (middle, D/ε_0 = − 0.42 V nm−1) and R4G/WSe_2 at positive D (bottom, D/ε_0 = 0.469 V nm−1). Little change in Rs implies the preserved high quality of graphene with WSe_2 at proximity.

These observations indicate SC3–7 are superconductors. Phenomenologically, the introduction of SOC proliferates regions with different isospin symmetries and renders more SCs at their phase boundaries. Simultaneously, SC1 is weakened when the holes are polarized to the WSe_2-proximal side of R4G, evidenced by decreases in Tc, Ic and Bc,⊥ (Extended Data Fig. 10). Together with SC2 missing in R4G/WSe_2, the diverse impacts of SOC on SC in R4G are similar to observations in R3G[34].

We note that engineering SC states by WSe_2 proximity keeps the high quality of bare graphene. Figure 4h compares normal-state resistance for SC1 in bare R4G, the WSe_2-distant and WSe_2-proximal side of R4G/WSe_2 at comparable D fields. Although the strength of SC1 varies, all the normal-state square resistance is about 25 Ω/□ for the hole density where SC1 emerges. This suggests that the mean free path and the cleanliness of graphene are mostly unaffected by the WSe_2 layer.

Discussions and outlook

In summary, we observed lots of superconductors in R4G and R5G, featuring three unconventional ones (SC2–4 in R5G) with unusual responses to magnetic fields. Especially, their remarkably high PVRs put them among superconductors with the highest PVR value reported2,43. These new SCs show several distinct characteristics compared with SCs in the literature. First, in-plane critical field exceeding the Pauli limit has been reported in transition-metal dichalcogenides (TMDs)43,44,45,46 and SOC-proximitized graphene31,37. There, strong Ising SOC pins the electron spins to the out-of-plane direction and makes the SC robust against B∥. However, these Ising SCs are killed by high B∥ before being fully spin-polarized. By contrast, SC2–4 are robust against B∥ with ultrahigh PVR and arise from half-metals. As the intrinsic SOC of graphene is much weaker than Hund’s coupling33,41,42 favouring the spin polarization of half-metals, this suggests SC2–4 are spin-polarized differently from Ising SC. Second, notable PVR has been reported in twisted trilayer graphene[21] and R3G[32] without proximitized SOC. However, their demonstrated PVRs are smaller than the lower bound of SC3 in R5G that is not even enhanced by B∥. Third, field-enhanced and field-induced SCs have been observed in uranium-based compounds47,48, organic superconductors49,50 and EuxSn1−xMo_6S_8 (ref. [51]). Spin fluctuations or the Jaccarino–Peter effect[52] has been proposed to explain these phenomena. However, bare graphene lacks magnetic elements and the aforementioned mechanisms cannot be directly applied. Considering the alteration of adjacent states under a high in-plane field, we propose that change in the normal states by B∥ may enhance SC2 and induce SC4 (Methods). This might also be the case for SC in R2G[6] appearing only under nonzero B∥. Further efforts are necessary to fully understand these field-enhanced and field-induced SCs.

Rhombohedral multilayer graphene also provides a platform for further engineering SCs. A promising direction is proximations with QAH states to engineer non-Abelian quasiparticles7,39,40 in a lateral junction. QAH states with a wide range of Chern numbers and fractional QAH states have been discovered in rhombohedral graphene neighboured by TMDs[8] or with moiré effects36,37,38. The coexistence of SCs and topological states alleviates complexities in combining them from different materials. Moreover, many superconductors in our work can be reached by low gate electric fields, compared with other graphene SCs6,30,31,53,54. This reduces the risk of gate leakage when accessing these SCs, which will facilitate realizing non-Abelian anyons and topological quantum computations.

Methods

Device fabrications

Rhombohedral graphene and hexagonal boron nitride (hBN) flakes were prepared onto SiO_2/Si substrates by mechanical exfoliation. The rhombohedral domains of graphene were identified and confirmed with an infrared camera[55], near-field infrared nanoscopy[56] and Raman spectroscopy[57], and subsequently isolated by cutting with a femtosecond laser. Van der Waals heterostructures were made following a dry transfer procedure. We picked up the top hBN, graphite, middle hBN and graphene using poly(bisphenol A carbonate) film on polydimethylsiloxane, and landed it on a prepared bottom stack consisting of an hBN and graphite bottom gate. The device was then etched into a multi-terminal structure using electron-beam lithography and reactive ion etching. Cr/Au was thermally evaporated for electrical connections to the source, drain and gate electrodes.

Electrical transport measurements

The devices were measured in either a Bluefors LD250 dilution refrigerator at MIT or a Leiden MNK126-700 dilution refrigerator at the University of Basel. In the setup at MIT, the d.c. and a.c. currents were generated by Keysight 33210A function generator with an a.c. frequency of 17.77 Hz. Stanford Research Systems SR830 lock-in amplifiers and Basel Precision Instruments (BASPI) SP1004 voltage preamplifiers were used to measure the longitudinal and Hall resistance Rxx and Rxy. Keithley 2400 source meters were used to apply top- and bottom-gate voltages, VTG and VBG. Twisted phosphorus bronze wires or thermocoax cables thermalized at each stage of the fridge went through two thermal meanders (one on the mixing chamber plate and the other on the cold finger), ceramic radiofrequency filters (DigiKey), four-stage RC filters, and silver epoxy filters (MFT25 from BASPI and a custom-made one) to cool down electrons. In the setup at the University of Basel, MFLI Zurich Instrument lock-in amplifiers were used. The same lock-in amplifier generated the a.c. current, whereas the d.c. current was provided by a BASPI DAC SP927. Moreover, the drain–source current was pre-amplified to a voltage signal by an SP983c I to V converter. Thermocoax cables thermalized at each stage of the fridge were used with two sets of silver epoxy filter[58] (one on the mixing chamber plate and the other right next to the sample holder). Keithley 2400 or BASPI DAC SP927 was used to apply the gate voltages. The applied VTG and VBG were swept to adjust carrier density n = (CTVTG + CBVBG)/e and displacement field D = (CTVTG − CBVBG)/2, where CT and CB are top-gate and bottom-gate capacitance per unit area obtained from the Landau fan diagram. The longitudinal resistance Rxx is defined as four-terminal differential resistance dVxx/dI at zero d.c. current.

Shubnikov–de Haas oscillations and fermiology analyses

Shubnikov–de Haas oscillations were obtained by measuring Rxx as a function of B⊥ at the base temperature. For fermiology analyses, Rxx was Fourier-transformed as a function of 1/B⊥. The Fourier-transformed data are a function of the frequency f1/B. Then, f1/B is normalized by the frequency that corresponds to the full carrier density as fν = f1/B/(|n|h/e).

Extraction of coherence length and mean free path

The coherence length ξ of a superconductor and the mean free path l of its normal state are extracted as ξ \(=\sqrt{\frac{h/(2e)}{2\pi {B}_{c,\perp }}}\) and \(l\cong \frac{h}{{e}^{2}}\frac{1}{4{k}_{F}{R}_{s}}\), respectively. Here, Bc,⊥ is the out-of-plane critical field, \({R}_{s}={R}_{n}W/L\) is the square resistance of normal-state resistance Rn, \({k}_{{\rm{F}}}\approx \sqrt{\pi n}\) is the Fermi wavevector, L and W are the length and width of the sample, h is Planck’s constant and e is the elementary charge[32].

Measurements under high B ∥

Devices R5G-1, R5G-2 and R5G-3 were measured in the fridges at MIT and the University of Basel, and SC1-3 appearing at zero magnetic fields were observed in both of the fridges. The fridge at the University of Basel is equipped with a Cryomagnetics two-axis magnet providing the magnetic fields both in-plane and perpendicular to the sample. Here, the quench boundaries limited the highest available fields. As the perpendicular component of the in-plane field due to a sample misalignment could be compensated with the perpendicular magnet using the sample as a magnetometer, we obtained the data for R5G under B∥ from the fridge at the University of Basel. The data for R4G/WSe_2 under B∥ (Supplementary Fig. 1) were obtained from the fridge at MIT using a three-axis superconducting magnet (American Magnetics).

Fraunhofer-like interference patterns and nonzero residual resistance

The residual resistance relative to the normal-state resistance in some of the superconducting states, such as SC2 and SC4 in R5G, is relatively large compared with the others. The ratios of residual resistance to normal-state resistance for superconductors reported in this work are presented in Supplementary Table 1. This nonzero residual resistance has been observed in several graphene SCs30,31,32,59. Fraunhofer-like interference patterns that arise from phase-coherent transport can serve as additional evidence for SC. There can be non-SC islands in a μm-size device because of sample inhomogeneity, even when (n, D) are tuned to a superconducting phase. Phase-coherent charge transport is affected by the Aharonov–Bohm phase, and thus the interference pattern appears. We found these interference patterns and periodic modulations of SC by B⊥ in most of the superconducting states reported here: SC1 (Extended Data Fig. 6c) in R4G; SC1 (Extended Data Figs. 3d and 5f), SC2 (Fig. 2b, Extended Data Figs. 3e and 5g,h), SC3 (Extended Data Figs. 5i and 7i) and SC4 (Fig. 2f and Extended Data Fig. 3f) in R5G; SC1 (Fig. 4g), SC3 (Fig. 4b), SC4 (Fig. 4c) and SC6 (Fig. 4e) in R4G/WSe_2. We note that a limited number of crystalline rhombohedral graphene devices32,33 have demonstrated Fraunhofer-like patterns in their superconducting states. This is in contrast to moiré graphene devices17,18,19,20,60,61 in which Fraunhofer-like patterns could be observed more universally and were more pronounced. Possible reasons are (1) less inhomogeneity in a crystalline graphene system and (2) a lower out-of-plane critical field that kills SC even before the interference pattern is developed. For example, SC1 at negative D in R4G/WSe_2 shows the first side-peak at B⊥ ≈ 1.1 mT (Fig. 4g). However, SC5 (Fig. 4d) and SC1 at positive D (Extended Data Fig. 10s) are already killed at this value of B⊥ and the interference patterns do not appear.

Ruling out possible mechanisms for the enhancement of SC3 by B ⊥

In summary, SC3 is enhanced by a small out-of-plane magnetic field when B∥ is zero. Similar values of B∥ do not enhance the SC3. The enhancement by B⊥ also disappears in the presence of nonzero B∥. Here, we discuss several scenarios that cannot fully explain these observations. First, B⊥ can couple to orbital ferromagnetic moments and lower the energy of the SC state compared with its competing states. This is the case for chiral SC developed at high displacement fields[53]. However, unlike chiral SC, the absence of magnetic hysteresis in the normal (Fig. 3c) and superconducting (Extended Data Fig. 8c) states of SC3 suggests the vanishing orbital ferromagnetism. Second, even without a spontaneous valley polarization, the valley-contrasted orbital magnetization can be generated by finite B⊥ through the valley Zeeman effect and induce the valley polarization. The DOS in one of the valleys may increase, and thus SC3 is enhanced. This is, however, contradictory to the disappearance of the enhancement under nonzero B∥, because it is unlikely that the in-plane field affects the valleys. Third, spin Zeeman energy can shift the Fermi level to a van Hove singularity whose enhanced DOS can develop stronger SC. However, the absence of the increase in Ic under B∥ (Extended Data Fig. 8f) rules out this scenario. Finally, for a percolative superconductor, natural junctions formed by superconducting and normal-state islands in a sample can lead to an increase in Ic under tiny B⊥. This occurs when a π phase difference across the junction, which is called a 0–π junction, is compensated by the B⊥-induced Aharonov–Bohm phase. This phenomenon has been observed in engineered Josephson junctions62,63,64,65. The anomalous SC phenomenology under B⊥ observed in some twisted bilayer graphene devices18,60 and twisted MoTe_2 (tMoTe_2) nanojunctions[66] has also been understood as coming from a 0–π junction. For the tMoTe_2 nanojunctions, it was proposed that both spin-singlet and spin-triplet condensates exist across the Pd_7MoTe_2–tMoTe_2–Pd_7MoTe_2 junction (where Pd_7MoTe_2 is a superconductor without anomalous behaviour), because tMoTe_2 breaks the inversion symmetry. Provided these two condensates have a phase difference of π, a 0–π junction is formed across the nanojunction and the SC transport phenomena may be enhanced by applying B⊥. Regarding SC3 in R5G, if there are 0–π junctions in the devices due to sample inhomogeneity, the optimal B⊥ value resulting in the strongest enhancement will notably depend on (n, D). This is because the normal-state area connecting two superconducting regions in the junction will expand as the SC gets weaker. However, the value of B⊥ showing the strongest SC3 is independent of (n, D) in our data (Extended Data Fig. 7a–f).

Proposed mechanism for the enhancement of SC3 by B ⊥ based on SOC

Here, we provide a physical picture compatible with all our observations on SC3 in R5G. The normal state of SC3 is a spin-polarized and valley-unpolarized half-metal. Owing to the intrinsic SOC strength λ much smaller than the Hund’s coupling EH in bare R5G, the spins from the two valleys are largely polarized in-plane with a small canting to the out-of-plane direction33,67. When a large-enough B⊥ is applied and the spins are polarized out of plane, the SOC projected to one layer of R5G (suggested by the fermiology analysis), \({H}_{\mathrm{SOC}}=-\frac{\lambda }{2}{\tau }_{z}{\sigma }_{z}\), where τz is the valley, and σz is the z-component of the spin, induces a valley-contrasting shift in the Fermi energy. Then, the DOS of one valley increases, whereas that of the other valley decreases. Assuming the dominant pairing mechanism is intravalley and the two valleys are mostly decoupled[68] owing to the large momentum difference between them, one of the intravalley pairing channels can be enhanced by the increased DOS[69]. As the global transport of a superconductor is dominated by the stronger pairing channel, SC can be enhanced by B⊥. The SOC-induced shift in the Fermi energy is weaker when the spin is mostly aligned in-plane by B∥, and thus the enhancement by B⊥ is not observed.

We provide calculations supporting the aforementioned scenario. We consider the free energy density F (refs. 33,67,70):

$$\begin{array}{c}F=\frac{\kappa }{2}({n}_{+}^{2}+{n}_{-}^{2})-{J}_{{\rm{H}}}{{\bf{n}}}_{+}{\boldsymbol{\cdot }}{{\bf{n}}}_{{\boldsymbol{-}}}-\frac{\lambda }{2}({n}_{+}^{z}-{n}_{-}^{z})\\ \,-\,{h}_{\perp }({n}_{+}^{z}+{n}_{-}^{z})-{h}_{\Vert }({n}_{+}^{x}+{n}_{-}^{x})-{h}_{V}({n}_{+}-{n}_{-}).\end{array}$$

Here, n+ and n− describe the total spin polarization vectors whose magnitude is the hole density and direction corresponds to the spin direction for each valley (+ for K, − for K′). The first term features the largest energy scale from the band dispersion and long-range Coulomb interaction. The second term describes Hund’s coupling, where JH ≈ 2 meV/(10^12 cm−2) (ref. [33]). The third term accounts for the SOC of the system. For bare R5G, this term corresponds to the intrinsic Kane–Mele SOC projected to one layer of the graphene. We take λ = 50 μeV for bare R5G33,41,42,71. When the SOC is proximitized to graphene by TMD, λ is of the order of 1 meV depending on the relative angle between graphene and TMD[31]. The fourth and fifth terms are the spin Zeeman energy for B⊥ and B∥, respectively. Here, h⊥ = gSμBB⊥ and h∥ = gSμBB∥ with the spin g-factor gS = 2 and Bohr magneton μB. The final term accounts for the valley Zeeman effect, where hV = gVμBB⊥. The two remaining parameters, κ and gV, are affected by the band structure. We used a 10-band Slonczewski–Weiss–McClure-type tight-binding model and calculated the average energy per hole ⟨Eh⟩ and gV. We assumed a half-metal state with n = −1.1 × 10^12 cm−2, D/ε_0 = 50 mV nm−1, which corresponds to where the SC3 was observed. Two sets of tight-binding parameters taken from refs. [53],[72] gave consistent results of ⟨Eh⟩ ≈ 1.4 meV and gV ≈ 6. The value of ⟨Eh⟩ is used to estimate κ as \(\frac{\kappa }{2}({n}_{0}^{2}+{n}_{0}^{2})=\frac{\kappa {n}^{2}}{4}=\langle {E}_{{\rm{h}}}\rangle |n|\) and thus \(\kappa =4\langle {E}_{{\rm{h}}}\rangle /|n|\) ≈ 5 meV/(10^12 cm−2).

We can calculate the spin canting angle (Fig. 3h) and the valley imbalance of hole occupations by minimizing F as a function of n±. We write the spin polarization vectors as

$${{\bf{n}}}_{+}=({n}_{0}+\delta )(\sin {\theta }_{+},0,\cos {\theta }_{+}),\,{{\bf{n}}}_{-}=({n}_{0}-\delta )(\sin {\theta }_{-},0,\cos {\theta }_{-}).$$

Here, θ+ and θ− are the spin canting angles and δ is the valley imbalance. The total hole density is |n| = 2n_0. For zero magnetic fields, the solution is \({\theta }_{+}=\pi -{\theta }_{-}={\cos }^{-1}\left(\frac{\lambda }{2{J}_{{\rm{H}}}|n|}\right)\) and δ = 0 (ref. [33]). When external magnetic fields are applied, we cannot assume θ+ = π − θ− and δ = 0, but can numerically find the solution.

We first focus on the case when B∥ = 0 but B⊥ is nonzero. The calculated spin polarization along the z-axis, \({s}_{z}=({n}_{+}^{z}+{n}_{-}^{z})/|n|\), and the valley imbalance δ normalized by n_0 are shown in Extended Data Fig. 9a,b. The spin polarization is saturated to unity at around B⊥ = 1.1 mT, regardless of the valley Zeeman effect. This field value is reasonably close to the optimal B⊥ (1.4–1.8 mT) found in experiments. The valley imbalance also rapidly increases up to the same field value and shows the cusp regardless of gV. This increase leads to the substantial change in the DOS of each valley and thus the enhancement of SC3 as explained previously. When the valley Zeeman term is turned on, the valley imbalance keeps increasing slowly above the optimal field, although sz is saturated to unity. Nevertheless, this increase in δ is much slower than that driven by the spin canting, and the orbital depairing effect will probably become comparable to or more important than the valley Zeeman effect in experiments.

We can expect that the spins are steered ineffectively when B∥ comparable to B⊥ is applied. This can be directly shown in the calculation, including the spin Zeeman effect from B∥, as shown in Extended Data Fig. 9c. The increase in the valley imbalance by B⊥ becomes much slower under B∥ of a few mT. This is manifested in the SC3 phenomenology as the B⊥-induced enhancement disappearing by a tiny B∥ of a few mT, which is observed in Fig. 3i and Extended Data Fig. 9f–k. We note that there has been no example showing that graphene SCs are affected by this tiny B∥, and thus this peculiar observation well supports the validity of our model.

The theory can also be checked by increasing the SOC strength through the proximation to a TMD. This is because the spin vectors are pinned more robustly when stronger SOC is imposed, as the Ising SOC serves as a valley-contrasting effective magnetic field. This is explicitly shown in Extended Data Fig. 9d, where sz was computed with λ = 0.05 and 1.0 meV. The spin polarization decreases substantially in the strong-SOC case. Accordingly, the valley imbalance arising from the valley-contrasting energy ±λsz/2 is suppressed. This is demonstrated in Extended Data Fig. 9e, where the valley imbalance at the optimal B⊥ is reduced by an order of magnitude (from 0.66% to 0.05%).

To test the scenario with varied SOC strength, we characterized device R5G/WS_2 shown in Extended Data Fig. 9l. SC3 was weak (Extended Data Fig. 9n), but nonlinear Vxx–I relations and peaks in dVxx/dI were observed. Extended Data Fig. 9o shows the evolution of dVxx/dI under B⊥ for SC3, featuring no enhancement behaviour. This is consistent with the prediction from our theory as discussed above.

Effects of B ∥ on a superconducting state

The in-plane magnetic field affects a superconducting state in two ways. First, it affects the free-energy difference between the SC and normal state, ΔE, depending on the spin configuration of a Cooper pair. For a spin-singlet Cooper pair, ΔE decreases as B∥ is applied due to spin Zeeman energy. For a spin-triplet Cooper pair, ΔE is mostly unaffected by B∥. Second, B∥ modifies the details of a parent state. For instance, both superconducting and normal states move in the n–D space by high B∥ (Fig. 2a). The modified parent state can give rise to a larger ΔE and thus a more stable superconductivity. As noted, when a Cooper pair is spin-triplet, the superconducting state does not gain energy from the normal state by B∥. In a naïve picture, therefore, B∥ will not enhance SC by the first effect. This leads to the hypothesis that the second effect (that is, modification of normal state) may result in the enhancement of SC2 and induction of SC4 by B∥. Further efforts are needed to separate the two coexisting effects here and understand the mechanism of the unusual responses of SC2 and SC4 under B∥.

Ruling out other mechanisms for nonlinear dV xx/dI

Nonlinear Vxx–I characteristics, one of the manifestations of superconductivity, can result from other mechanisms. We discuss how to exclude such possible mechanisms other than SC. First, nonlinear dVxx/dI can occur by the Joule heating effect from the drain–source current[73]. In this case, dVxx/dI monotonically increases by increasing the d.c. current, because the Joule heating power increases with the d.c. current. Our dVxx/dI data show peaks at a certain threshold current, which is inconsistent with this scenario. Second, nonlinear dVxx/dI can arise from a Schwinger-like effect74,75,76,77. The analogy of the Schwinger effect in quantum electrodynamics can come from the displacement of the Fermi surface by a large drain–source current. However, this current-induced displacement does not disappear by a small B⊥, which is inconsistent with our observations where nonlinear dVxx/dI is killed by a small B⊥. The same logic has been applied for distinguishing two different origins of nonlinear dVxx/dI in moiré graphene superconductors76,77.

Recent reports on unusual SCs in rhombohedral graphene

During the preparation and after the submission of this work, there have been several recent reports on unusual SCs in rhombohedral graphene70,78,79,80,81,82,83.

Data availability

The data shown in the figures are available from https://doi.org/10.7910/DVN/H3LOEA (ref. [84]). Other data that support the findings of this study are available from the corresponding authors on request.

References

1. Stewart, G. R. Unconventional superconductivity. Adv. Phys. 66, 75–196 (2017).

2. de Visser, A. Magnetic field-boosted superconductivity. Phys. Today 73, 44–50 (2020).

3. Anderson, P. W. Theory of dirty superconductors. J. Phys. Chem. Solids 11, 26–30 (1959).

4. Han, T. et al. Correlated insulator and Chern insulators in pentalayer rhombohedral-stacked graphene. Nat. Nanotechnol. 19, 181–187 (2024).

5. Liu, K. et al. Spontaneous broken-symmetry insulator and metals in tetralayer rhombohedral graphene. Nat. Nanotechnol. 19, 188–195 (2024).

6. Zhou, H. et al. Isospin magnetism and spin-polarized superconductivity in Bernal bilayer graphene. Science 375, 774–778 (2022).

7. Alicea, J. & Stern, A. Designer non-Abelian anyon platforms: from Majorana to Fibonacci. Phys. Scr. 2015, 014006 (2015).

8. Han, T. et al. Large quantum anomalous Hall effect in spin-orbit proximitized rhombohedral graphene. Science 384, 647–651 (2024).

9. Lian, B. et al. Topological quantum computation based on chiral Majorana fermions. Proc. Natl Acad. Sci. USA 115, 10938–10942 (2018).

10. Steglich, F. et al. Superconductivity in the presence of strong Pauli Paramagnetism: CeCu_2Si_2. Phys. Rev. Lett. 43, 1892–1896 (1979).

11. Fisher, R. A. et al. Specific heat of UPt_3: evidence for unconventional superconductivity. Phys. Rev. Lett. 62, 1411–1414 (1989).

12. Ran, S. et al. Nearly ferromagnetic spin-triplet superconductivity. Science 365, 684–687 (2019).

13. Bednorz, J. G. & Müller, K. A. Possible high Tc superconductivity in the Ba-La-Cu-O system. Z. Phys. B Cond. Matt. 64, 189–193 (1986).

14. Wu, M. K. et al. Superconductivity at 93 K in a new mixed-phase Y-Ba-Cu-O compound system at ambient pressure. Phys. Rev. Lett. 58, 908–910 (1987).

15. Kamihara, Y. et al. Iron-based layered superconductor La[O_1−xFx]FeAs (x = 0.05−0.12) with Tc = 26 K. J. Am. Chem. Soc. 130, 3296–3297 (2008).

16. Chen, X. H. et al. Superconductivity at 43 K in SmFeAsO_1−xFx. Nature 453, 761–762 (2008).

17. Cao, Y. et al. Unconventional superconductivity in magic-angle graphene superlattices. Nature 556, 43–50 (2018).

18. Yankowitz, M. et al. Tuning superconductivity in twisted bilayer graphene. Science 363, 1059–1064 (2019).

19. Lu, X. et al. Superconductors, orbital magnets and correlated states in magic-angle bilayer graphene. Nature 574, 653–657 (2019).

20. Hao, Z. et al. Electric field-tunable superconductivity in alternating-twist magic-angle trilayer graphene. Science 371, 1133–1138 (2021).

21. Cao, Y., Park, J. M., Watanabe, K., Taniguchi, T. & Jarillo-Herrero, P. Pauli-limit violation and re-entrant superconductivity in moiré graphene. Nature 595, 526–531 (2021).

22. Xia, Y. et al. Superconductivity in twisted bilayer WSe_2. Nature 637, 833–838 (2025).

23. Guo, Y. et al. Superconductivity in 5.0˚ twisted bilayer WSe_2. Nature 637, 839–845 (2025).

24. Uri, A. et al. Mapping the twist-angle disorder and Landau levels in magic-angle graphene. Nature 581, 47–52 (2020).

25. Lau, C. N., Bockrath, M. W., Mak, K. F. & Zhang, F. Reproducibility in the fabrication and physics of moiré materials. Nature 602, 41–50 (2022).

26. Zhang, F., Sahu, B., Min, H. & MacDonald, A. H. Band structure of ABC-stacked graphene trilayers. Phys. Rev. B 82, 035409 (2010).

27. Chatterjee, S., Wang, T., Berg, E. & Zaletel, M. P. Inter-valley coherent order and isospin fluctuation mediated superconductivity in rhombohedral trilayer graphene. Nat. Commun. 13, 6013 (2022).

28. Dong, Z., Levitov, L. & Chubukov, A. V. Superconductivity near spin and valley orders in graphene multilayers. Phys. Rev. B 108, 134503 (2023).

29. Vituri, Y., Xiao, J., Pareek, K., Holder, T. & Berg, E. Incommensurate intervalley coherent states in ABC graphene: collective modes and superconductivity. Phys. Rev. B 111, 075103 (2025).

30. Li, C. et al. Tunable superconductivity in electron- and hole-doped Bernal bilayer graphene. Nature 631, 300–306 (2024).

31. Zhang, Y. et al. Twist-programmable superconductivity in spin-orbit-coupled bilayer graphene. Nature 641, 625–631 (2025).

32. Zhou, H., Xie, T., Taniguchi, T., Watanabe, K. & Young, A. F. Superconductivity in rhombohedral trilayer graphene. Nature 598, 434–438 (2021).

33. Patterson, C. L. et al. Superconductivity and spin canting in spin-orbit-coupled trilayer graphene. Nature 641, 632–638 (2025).

34. Yang, J. et al. Impact of spin-orbit coupling on superconductivity in rhombohedral graphene. Nat. Mater. 24, 1058–1065 (2025).

35. Han, T. et al. Orbital multiferroicity in pentalayer rhombohedral graphene. Nature 623, 41–47 (2023).

36. Lu, Z. et al. Fractional quantum anomalous Hall effect in multilayer graphene. Nature 626, 759–764 (2024).

37. Choi, Y. et al. Superconductivity and quantized anomalous Hall effect in rhombohedral graphene. Nature 639, 342–347 (2025).

38. Xie, J. et al. Tunable fractional Chern insulators in rhombohedral graphene superlattices. Nat. Mater. 24, 1042–1048 (2025).

39. Lindner, N. H., Berg, E., Refael, G. & Stern, A. Fractionalizing Majorana fermions: non-Abelian statistics on the edges of Abelian quantum Hall states. Phys. Rev. X 2, 041002 (2012).

40. Clarke, D. J., Alicea, J. & Shtengel, K. Exotic non-Abelian anyons from conventional fractional quantum Hall states. Nat. Commun. 4, 1348 (2013).

41. Sichua, J. et al. Resonance microwave measurements of an intrinsic spin-orbit coupling gap in graphene: a possible indication of a topological state. Phys. Rev. Lett. 122, 046403 (2019).

42. Arp, T. et al. Intervalley coherence and intrinsic spin-orbit coupling in rhombohedral trilayer graphene. Nat. Phys. 20, 1413–1420 (2024).

43. Lu, J. et al. Full superconducting dome of strong Ising protection in gated monolayer WS_2. Proc. Natl Acad. Sci. USA 115, 3551–3556 (2018).

44. Lu, J. M. et al. Evidence for two-dimensional Ising superconductivity in gated MoS_2. Science 350, 1353–1357 (2015).

45. Saito, Y. et al. Superconductivity protected by spin-valley locking in ion-gated MoS_2. Nat. Phys. 12, 144–149 (2016).

46. Xi, X. et al. Ising pairing in superconducting NbSe_2 atomic layers. Nat. Phys. 12, 139–143 (2016).

47. Lévy, F., Sheikin, I., Grenier, B. & Huxley, A. D. Magnetic field-induced superconductivity in the ferromagnet URhGe. Science 309, 1343–1346 (2005).

48. Ran, S. et al. Extreme magnetic field-boosted superconductivity. Nat. Phys. 15, 1250–1254 (2019).

49. Uji, S. et al. Magnetic-field-induced superconductivity in a two-dimensional organic conductor. Nature 410, 908–910 (2001).

50. Balicas, L. et al. Superconductivity in an organic insulator at very high magnetic fields. Phys. Rev. Lett. 87, 067002 (2001).

51. Meul, H. W. et al. Observation of magnetic-field-induced superconductivity. Phys. Rev. Lett. 53, 497–500 (1984).

52. Jaccarino, V. & Peter, M. Ultra-high-field superconductivity. Phys. Rev. Lett. 9, 290–292 (1962).

53. Han, T. et al. Signatures of chiral superconductivity in rhombohedral graphene. Nature 643, 654–661 (2025).

54. Morissette, E. et al. Superconductivity, anomalous Hall Effect, and stripe order in rhombohedral hexalayer graphene. Preprint at https://arxiv.org/html/2504.05129v2 (2025).

55. Lu, Z. et al. Extended quantum anomalous Hall states in graphene/hBN moiré superlattices. Nature 637, 1090–1095 (2025).

56. Ju, L. et al. Topological valley transport at bilayer graphene domain walls. Nature 520, 650–655 (2015).

57. Lui, C. H. et al. Imaging stacking order in few-layer graphene. Nano Lett. 11, 164–169 (2011).

58. Scheller, C. P. et al. Silver-epoxy microwave filters and thermalizers for millikelvin experiments. Appl. Phys. Lett. 104, 211106 (2014).

59. Holleis, L. et al. Nematicity and orbital depairing in superconducting Bernal bilayer graphene. Nat. Phys. 21, 444–450 (2025).

60. Arora, H. S. et al. Superconductivity in metallic twisted bilayer graphene stabilized by WSe_2. Nature 583, 379–384 (2020).

61. Park, J. M., Cao, Y., Watanabe, K., Taniguchi, T. & Jarillo-Herrero, P. Tunable strongly coupled superconductivity in magic-angle twisted trilayer graphene. Nature 590, 249–255 (2021).

62. Weides, M. et al. 0-π Josephson tunnel junctions with ferromagnetic barrier. Phys. Rev. Lett. 97, 247001 (2006).

63. Frolov, S. M., Van Harlingen, D. J., Bolginov, V. V., Oboznov, V. A. & Ryazanov, V. V. Josephson interferometry and Shapiro step measurements of superconductor-ferromagnet-superconductor 0-π junctions. Phys. Rev. B 74, 020503(R) (2006).

64. Ai, L. et al. Van der Waals ferromagnetic Josephson junctions. Nat. Commun. 12, 6580 (2021).

65. Kang, K. et al. van der Waals π Josephson junctions. Nano Lett. 22, 5510–5515 (2022).

66. Jia, Y. et al. Anomalous superconductivity in twisted MoTe_2 nanojunctions. Sci. Adv. 11, eadq5712 (2025).

67. Dong, Z., Lantagne-Hurtubise, É & Alicea, J. Superconductivity from spin-canting fluctuations in rhombohedral graphene. Phys. Rev. X 16, 011055 (2026).

68. Wang, K. & Levin, K. Kekulé superconductivity in twisted magic angle bilayer graphene. Preprint at https://doi.org/10.48550/arXiv.2510.06451 (2026).

69. Chou, Y.-Z., Zhu, J. & Sarma, S. D. Intravalley spin-polarized superconductivity in rhombohedral tetralayer graphene. Phys. Rev. B 111, 174523 (2025).

70. Kumar, M. et al. Pervasive spin-triplet superconductivity in rhombohedral graphene. Preprint at https://doi.org/10.48550/arXiv.2511.16578 (2026).

71. Banszerus, L. et al. Observation of the spin-orbit gap in bilayer graphene by one-dimensional ballistic transport. Phys. Rev. Lett. 124, 177701 (2020).

72. Zhou, H. et al. Half- and quarter-metals in rhombohedral trilayer graphene. Nature 598, 429–433 (2021).

73. He, M. et al. Symmetry breaking in twisted double bilayer graphene. Nat. Phys. 17, 26–30 (2021).

74. Berdyugin, A. I. et al. Out-of-equilibrium criticalities in graphene superlattices. Science 375, 430–433 (2022).

75. Schmitt, A. et al. Mesoscopic Klein-Schwinger effect in graphene. Nat. Phys. 19, 830–835 (2023).

76. Tian, H. et al. Evidence for Dirac flat band superconductivity enabled by quantum geometry. Nature 614, 440–444 (2023).

77. Liu, L. et al. Electric field tunable coupling strength and quantum metric hot spots in a moiré flatband superconductor. Nat. Commun. https://doi.org/10.1038/s41467-026-74848-6 (2026).

78. Kumar, M. et al. Superconductivity from dual-surface carriers in rhombohedral graphene. Nat. Phys. 22, 862–869 (2026).

79. Deng, J. et al. Superconductivity and ferroelectric orbital magnetism in semimetallic rhombohedral hexalayer graphene. Preprint at https://doi.org/10.48550/arXiv.2508.15909 (2025).

80. Yang, J. et al. Magnetic field-enhanced graphene superconductivity with record Pauli-limit violation. Preprint at https://doi.org/10.48550/arXiv.2510.10873 (2025).

81. Guo, Y. et al. Flat band surface state superconductivity in thick rhombohedral graphene. Preprint at https://doi.org/10.48550/arXiv.2511.17423 (2025).

82. Xie, J. et al. Magnetic-field-driven insulator-superconductor transition in rhombohedral graphene. Phys. Rev. Lett. 136, 176505 (2026).

83. Deng, J. et al. Magnetic-field-induced superconductivity in hexalayer rhombohedral graphene. Preprint at https://doi.org/10.48550/arXiv.2603.13498 (2026).

84. Seo, J. Data for: family of magnetic field-boosted superconductors in rhombohedral graphene. Harvard Dataverse https://doi.org/10.7910/DVN/H3LOEA (2026).

Acknowledgements

We acknowledge A. V. Chubukov, C. Yoon, F. Zhang, T. Senthil, J. Park, Z. Dong and L. Levitov for their discussions.

Funding

This project was mainly supported by ONR through grant no. N00014-25−1-2294. J.S. was supported by the Global Partnership Program of Leading Universities in Quantum Science and Technology (N01260111) and NSF grant no. DMR-2414725. The device fabrication and transport measurements at MIT were supported by the Nano and Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2024-004447252), and the MIT Portugal Program. T.H. acknowledges a Mathworks Fellowship. The device fabrication for this work was carried out at the Harvard Center for Nanoscale Systems and MIT.nano. Work in Basel was supported by the H2020 Marie Skłodowska–Curie Actions (MSCA) co-funded by the Quantum Science and Technologies at the European Campus (QUSTEC) grant no. 847471, the Swiss National Science Foundation (grant no. 215757), the Georg H. Endress Foundation, the WSS Research Center for Molecular Quantum Systems (molQ) of the Werner Siemens Foundation and the UpQuantVal InterReg. The National High Magnetic Field Laboratory (MagLab) is supported by the National Science Foundation through NSF/DMR-2128556 and by the State of Florida. R.G. acknowledges discussions with Mark Meisel. K.W. and T.T. acknowledge support from the JSPS KAKENHI (grant nos. 21H05233 and 23H02052), the CREST (JPMJCR24A5), the JST and World Premier International Research Center Initiative (WPI), MEXT, Japan.

Author information

Author notes

1. These authors contributed equally: Junseok Seo, Armel A. Cotten, Shenyong Ye

Authors and Affiliations

1. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

Junseok Seo, Shenyong Ye, Tonghang Han, Zhengguang Lu, Zhenghan Wu, Wei Xu, Jixiang Yang, Emily Aitken, Prayoga P. Liong, Phatthanon Pattanakanvijit, Zach Hadjri & Long Ju

2. Department of Physics, University of Basel, Basel, Switzerland

Armel A. Cotten, Mingchi Xu, Omid Sharifi Sedeh, Henok Weldeyesus & Dominik M. Zumbühl

3. Department of Physics, Florida State University, Tallahassee, FL, USA

Zhengguang Lu

4. Department of Physics, University of Florida, Gainesville, FL, USA

Rasul Gazizulin

5. National High Magnetic Field Laboratory High B/T Facility, University of Florida, Gainesville, FL, USA

Rasul Gazizulin

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

Kenji Watanabe

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

Takashi Taniguchi

8. Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Mingda Li

Contributions

L.J. and D.M.Z. supervised the project. S.Y., T.H., Z.W., W.X., E.A., P.P.L. and P.P. fabricated the devices. K.W. and T.T grew bulk hBN crystals. J.S. performed measurements at MIT with assistance from T.H., Z.L. and J.Y.; J.S. and A.A.C. carried out measurements at the University of Basel with assistance from M.X., O.S.S., H.W., Z.H. and D.M.Z.; R.G. and M.L. helped with the transport measurements. J.S. and L.J. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Dominik M. Zumbühl or Long Ju.

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Competing interests

D.M.Z. is a co-founder of BASPI. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Additional data for SC1 and SC2 in R5G.

a, Optical image of Device R5G-1 with a representative measurement configuration. Scale bar: 3 μm. b, Voltage Vxx versus current I at varied temperatures for the SC1 of R5G. c, Rxx as a function of n and B⊥, covering the density range of the SC1. The data in c was used for the Fourier transform in Fig. 1e. d, Rxx maps at B║ = 0.0 (top) and 8.0 (bottom) T from Device R5G-1. While SC3 is robust, SC2 is enlarged and SC4 is induced by B║. e, Differential resistance as a function of I and T at the orange and green dot positions in d. Both Ic and TBKT are enhanced by B║. f, Rxx as a function of n at varied temperatures along the dashed lines in d. The Rxx dip corresponding to SC2 persists to higher temperatures under finite B║. g, Differential resistance as a function of I and B⊥ at SC2 (n = −1.07 × 10^12 cm−2, D/ε_0 = 58 mV nm) in Device R5G-1. The disappearance of nonlinear dVxx/dI by B⊥ is consistent with SC phenomenology. In addition, Bc,⊥ ≈ 1.5 mT and Rn ≈ 30 Ω at this (n, D) point, which puts the SC2 around the clean limit (ξ ≈ 470 nm and l ≈ 1.1 μm). h, Landau fan diagrams taken at D/ε_0 = 63 mV/nm as a function of n. This data was used for the Fourier transform in Fig. 2e. i, Rxx map in a large n-D space at B║ = 8.0 T. SC1 is fully killed at this magnetic field.

Extended Data Fig. 2 Additional data for SC1 and SC2 in R4G.

a, Optical image of Device R4G-1 with a representative measurement configuration. Scale bar: 3 μm. b, Voltage Vxx versus current I at varied temperatures for the SC1 of R4G. c, Rxx as a function of n and B⊥, covering the density range of the SC1. The data in c was used for the Fourier transform in Fig. 1f. d, Rxx as a function of n at varied temperatures for the SC2 of R4G. e, Voltage Vxx versus current I at varied temperatures for the SC2. The dashed line denotes when Vxx is proportional to I^3, corresponding to the BKT transition temperature TBKT ≈ 19 mK. f, Differential resistance as a function of I and B⊥ at n = −0.836 × 10^12 cm−2 and D/ε_0 = 5 mV/nm. Vanishing Rxx, nonlinear dVxx/dI, and their quick suppression by B⊥ suggest SC2 in R4G is a superconductor. In addition, Bc,⊥ ≈ 3 mT and Rn ≈ 60 Ω at this (n, D) point, putting the SC2 near the clean limit (ξ ≈ 330 nm and l ≈ 660 nm). g, Rxx as a function of n and B⊥ at D/ε_0 = 5 mV/nm. h, Landau fan diagrams taken at D/ε_0 = 5 mV/nm as a function of n. i, Fourier transform of Rxx(1/B⊥) as a function of n and fν. There is a peak at fν = 1/12, which suggests the normal state of SC2 in R4G is a trigonally warped full metal.

Extended Data Fig. 3 Reproducibility of SC in R5G from Device R5G-2.

a, Optical image of Device R5G-2 with a representative measurement configuration. Scale bar: 3 μm. b, Rxx as a function of n and D/ε_0. All three SCs in R5G at zero magnetic fields are reproduced. c, Temperature-dependent Rxx of SC3 (n = −1.100 × 10^12 cm−2, D/ε_0 = 49.61 mV/nm). SC3 approaches 0 Ω below the transition temperature. d, Differential resistance as a function of I and B⊥ for SC1 (n = −3.38 × 10^12 cm−2, D/ε_0 = 0.214 V/nm). Clear interference patterns are observed. e,f, Rxx under varied B⊥ for SC2 (VTG = − 0.2 V, e) and SC4 (D/ε_0 = 140.1 mV/nm, f). Modulations of SC2 and SC4 by B⊥ were observed, demonstrating Fraunhofer-like interferences.

Extended Data Fig. 4 Reproducibility of SC in R5G from Device R5G-3.

a, Optical image of Device R5G-3 with a representative measurement configuration. Scale bar: 3 μm. b, Rxx as a function of n and D/ε_0. All three SCs in R5G at zero magnetic fields are reproduced. c, Rxx as a function of n and D/ε_0 taken at B⊥ = 0.0 (top) and 1.4 mT (bottom). Area in the n-D space occupied by SC3 is expanded by a nonzero B⊥, which is the opposite from the case of SC2. d, Differential resistance as a function of I and B⊥ for SC3 (n = −1.11 × 10^12 cm−2, D/ε_0 = 50.6 mV/nm), showing the increase in the critical current by small B⊥. e, Rxx as a function of n and D/ε_0 taken at B║ = 8.0 T. SC4 that did not appear at zero field is observed. f, Zoomed-in view of Rxx around SC4 at B║ = 8.5 T. g, Zoomed-in view of Rxx around SC3 at B║ = 8.5 T. SC3 is connected to SC2 in the n-D space.

Extended Data Fig. 5 Reproducibility of SC in R5G from Device R5G-4 and R5G-5.

a,b, Optical image of Device R5G-4 (a) and R5G-5 (b) with representative measurement configurations. Scale bar: 3 μm. c, Rxx as a function of n and D/ε_0 for Device R5G-4. All three SCs in Fig. 1a are reproduced. d, Rxx as a function of n and D/ε_0 focusing on SC2 and SC3 at B⊥ = 0.0 (top) and 1.4 mT (bottom). Area in the n-D space occupied by SC3 increased by a nonzero B⊥. e, Differential resistance as a function of I and B⊥ for SC3 (n = −1.16 × 10^12 cm−2, D/ε_0 = 55 mV/nm), showing the increase of critical current by small B⊥. f-i, Differential resistance as a function of I and B⊥ for SC1 (n = −3.57 × 10^12 cm−2, D/ε_0 = 0.225 V/nm) (f), SC2 (n = −1.17 × 10^12 cm−2, D/ε_0 = 67 mV/nm) (g) and SC3 (n = −1.16 × 10^12 cm−2, D/ε_0 = 55 mV/nm) (i) from Device R5G-4. The differential resistance at zero DC current for SC2 in its normal state (top) and superconducting state (bottom) is plotted in h. The modulation of dVxx/dI by B⊥ are observed in SC1-SC3. This modulation in SC2 is highlighted with yellow arrows in g and h. j, Rxx as a function of n and D/ε_0 for Device R5G-5. All three SCs in Fig. 1a are reproduced. k, Rxx as a function of n and D/ε_0 focusing on SC2 and SC3 at B⊥ = 0.0 (top) and 1.4 mT (bottom). Area in the n-D space occupied by SC3 increased by a nonzero B⊥. l, Differential resistance as a function of I and B⊥ for SC3 (n = −1.143 × 10^12 cm−2, D/ε_0 = 55.9 mV/nm), showing the increase of critical current by small B⊥.

Extended Data Fig. 6 Reproducibility of SC in R4G from Device R4G-2.

a, Optical image of Device R4G-2 with a representative measurement configuration. Scale bar: 3 μm. b, Rxx as a function of n and D/ε_0. Two SCs in Fig. 1b in the main text are reproduced. c,d, Differential resistance as a function of I and B⊥ for SC1 (n = −2.68 × 10^12 cm−2, D/ε_0 = 0.28 V/nm) (c) and SC2 (n = −0.836 × 10^12 cm−2, D/ε_0 = 0.00 V/nm) (d). SC1 shows a clear Fraunhofer-like pattern. e,f, Rxx as a function of n at varied temperatures and fixed D/ε_0 for SC1 (e) and SC2 (f). g,h, Differential resistance dVxx/dI as a function of I at varied temperatures for SC1 (n = −2.68 × 10^12 cm−2, D/ε_0 = 0.28 V/nm, g) and SC2 (n = −0.836 × 10^12 cm−2, D/ε_0 = 0.00 V/nm, h). Vanishing Rxx, nonlinear dVxx/dI, and the modulation of critical current by B⊥ suggest these are superconducting states.

Extended Data Fig. 7 T- and B⊥-dependent measurements on SC3 in Device R5G-1.

a-f, Differential resistance as a function of I and B⊥ at different (n, D) points. The enhancement of SC3 by B⊥ is observed globally across its phase space. g-i, Differential resistance as a function of I and B⊥ at different (n, D) points for a wider range of the magnetic field. Fraunhofer-like patterns arising from quantum interference are observed in i which is adjacent to the phase boundary of SC3, and they become fainter as D/ε_0 decreases. j, Vxx versus current I at T = 8, 88, 115, 126, 134 and 156 mK for n = −1.104 × 10^12 cm−2 and D/ε_0 = 52 mV/nm, where SC3 was the strongest in this device. The dashed line is the prediction from the BKT theory, giving TBKT ≈ 134 mK. k,l, Differential resistance as a function of I and T at n = −1.119 × 10^12 cm−2 and D/ε_0 = 55 mV/nm under B⊥ = 0 and 1.8 mT. TBKT increases from 99 to 110 mK by applying B⊥ of 1.8 mT, which demonstrates the increase in TBKT happens globally.

Extended Data Fig. 8 Additional data for SC3 in R5G.

a,b, Differential resistance as a function of I and B⊥ for B║ = 0.4 T (a) and B║ = 0.8 T (b). B⊥-induced enhancement for SC3 disappeared under a high enough in-plane field. c, Rxx as a function of B⊥ in the forward and backward scanning directions for SC3 (n = −1.132 × 10^12 cm−2, D/ε_0 = 54 mV/nm) at the base temperature. There is no hysteretic behavior observed, implying the absence of orbital magnetism in the superconducting state of SC3. d, Landau fan diagrams taken at D/ε_0 = 54 mV/nm as a function of n covering SC3. This data was used for the Fourier transform in Fig. 3b. e,f, Dependence of the critical current of SC3 on small B⊥ and B║. The critical current Ic increases with showing maxima only when the out-of-plane field is applied. These data were measured for n = −1.16 × 10^12 cm−2 and D/ε_0 = 55 mV/nm.

Extended Data Fig. 9 Calculations for the enhancement of SC3 and supporting experimental results.

a,b, Spin polarization sz (a) and valley imbalance normalized by n_0 (b) as functions of a perpendicular magnetic field for bare R5G with gV = 0 and 6. The optimal value of B⊥, calculated to be 1.1 mT, is mostly independent of gV. c, Valley imbalance normalized by n_0 under varied B║ as functions of B⊥. The increase in the valley imbalance by B⊥ gets slower by applying B║, which is consistent with the experimental observations. d,e, Effect of the proximitized SOC on the spin polarization (d) and valley imbalance (e). When the SOC is strong, the valley imbalance is reduced by an order of magnitude and the cusp in the valley imbalance at optimal B⊥ disappears. f-k, Differential resistance as a function of I and B⊥ in SC3 from Device R5G-3 at B║ = 0.0 (f), 0.5 (g), 1.0 (h), 1.5 (i), 2.5 (j) and 4.5 (k) mT. l, Optical image of the Device R5G/WS_2 and the representative measurement configuration. m, Rxx map of the device at the WS_2-proximal side. n, Rxx map in the yellow dashed box in m. A weak SC3 is observed. o, Differential resistance as a function of I and B⊥ for SC3 (n = −1.133 × 10^12 cm−2, D/ε_0 = 55.7 mV/nm). No enhancement by B⊥ was observed from SC3 in R5G/WS_2.

Extended Data Fig. 10 Additional data for SC in R4G/WSe[2].

Differential resistance as a function of I at varied temperatures for a, SC1 at negative D (n = −3.25 × 10^12 cm−2, D/ε_0 = − 0.42 V/nm), c, SC1 at positive D (n = −3.385 × 10^12 cm−2, D/ε_0 = 0.469 V/nm), e, SC3 (n = −2.356 × 10^12 cm−2, D/ε_0 = 0.18 V/nm), g, SC4 (n = −1.482 × 10^12 cm−2, D/ε_0 = 0.072 V/nm), i, SC5 (n = −1.242 × 10^12 cm−2, D/ε_0 = 0.072 V/nm), k, SC6 (n = −1.00 × 10^12 cm−2, D/ε_0 = 0.026 V/nm) and m, SC7 (n = −0.38 × 10^12 cm−2, D/ε_0 = − 0.24 V/nm). The dashed lines indicate when Vxx is proportional to I^3, corresponding to the BKT transition. Rxx as a function of n at varied temperatures for b, SC1 at negative D (D/ε_0 = − 0.42 V/nm), d, SC1 at positive D (D/ε_0 = 0.469 V/nm), f, SC3 (D/ε_0 = 0.18 V/nm), h, SC4 (D/ε_0 = 0.072 V/nm), j, SC5 (D/ε_0 = 0.072 V/nm), l, SC6 (D/ε_0 = 0.026 V/nm) and n, SC7 (D/ε_0 = − 0.24 V/nm). o, Optical image of Device R4G/WSe_2 and the representative measurement configuration. Scale bar: 3 μm. p,q, Rxx as a function of n and B⊥ for the SC1 on the WSe_2-distant side (p) and the WSe_2-proximal side (q). r,s, Differential resistance as a function of I and B⊥ for the SC1 on the WSe_2-distant side (r) and the WSe_2-proximal side (s). These data show the range of SC1 along the n axis, Ic, and Bc,⊥ all decrease when SC1 is placed to the WSe_2-proximal side.

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Seo, J., Cotten, A.A., Ye, S. et al. Family of magnetic field-boosted superconductors in rhombohedral graphene. Nature 656, 60–66 (2026). https://doi.org/10.1038/s41586-026-10815-x

- Received: 03 September 2025

- Accepted: 16 June 2026

- Published: 29 June 2026

- Version of record: 29 July 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10815-x

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Ligand-enabled distal desaturative lactonization of aliphatic acids

Tanay Pal,

Md Saimuddin Sk,

Yazhinimuthu C M,

Somnath Kar,

Animesh Ghosh &

…

Debabrata Maiti

Nature volume 656, pages 98–105 (2026)

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Abstract

Transition-metal catalysis is a fundamental strategy for transforming inert C−H bonds into valuable functional motifs1,2. However, achieving regioselective activation of remote C−H bonds remains challenging, particularly in unbiased hydrocarbon frameworks2,3. In this context, distal C(sp^3)−H bonds are especially difficult to functionalize, as conformational flexibility favours proximal C−H activation3,4. In this study, we demonstrate a ligand-enabled strategy using designed O-allyl amido ester ligands for palladium-catalysed activation of γ-methylene and methine C−H sites in unbiased aliphatic carboxylic acids, derived from readily available fatty and cyclic acid feedstocks. This protocol enables direct transformation of aliphatic carboxylic acid substrates into distal desaturated γ-lactones and double-dehydrogenated γ-spirolactones. Mechanistic studies are consistent with a pathway involving Pd(II)-mediated γ-C(sp^3)−H activation, followed by dehydrogenation and intramolecular cyclization. These lactones, with an unsaturated arm, serve as key intermediates for the formation of complex natural products and pharmaceuticals. For instance, muricatacin (from soursop/Laxman phal) and its analogue were rapidly assembled in three steps from margaric acid using this strategy and evaluated for anticancer activity, thereby demonstrating the potential of our approach for providing a rapid access to biologically relevant frameworks for traditional medicine. The introduced distal desaturation further opens up new avenues for remote functionalization, streamlining access to diverse bioactive molecules with improved step and atom economy.

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Main

Fatty acids constitute an important class of aliphatic carboxylic acids, in particular, long-chain saturated fatty acids are prevalent in nature and widely used in pharmaceuticals, polymers, lubricants and surfactants[5]. Their structural simplicity, commercial availability and natural abundance make them attractive starting materials for sustainable synthesis. Direct and selective functionalization of these readily available, simple hydrocarbon frameworks could provide streamlined access to structurally complex molecules. Among these structurally valuable motifs, γ-lactones, including dehydrogenated butyrolactones and spirolactones, are widespread in bioactive natural products, fragrances and therapeutic agents4,6,7,8,9,10,11. The introduction of remote unsaturation into aliphatic frameworks represents a valuable transformation that can further promote late-stage diversification12,13. However, executing these transformations in linear-chain carboxylic acids remains challenging because these substrates possess electronically and sterically unbiased sp^3 C−H bonds with similar reactivity profiles, limiting catalyst-controlled differentiation and site-selective activation. Strategies that can promote catalyst-controlled regioselective C–H activation[11], therefore, remain highly desirable in these systems. Although elegant metal-mediated desaturation strategies at proximal positions have been explored, these transformations are largely directed by electronically or conformationally activated sites near polar functionalities14,15,16,17,18,19,20,21. On a complementary spectrum, distal unsaturation in long-chain aliphatic carboxylic acids remains less developed, particularly at δ or ε positions owing to the absence of any substrate bias. Existing examples often rely on pre-installed olefins or structural constraints such as the Thorpe–Ingold effect, generally requiring tedious multistep substrate preparation20,21,22,23,24,25,26,27,28. Based on our previous findings in cyclic acid systems[25], we believed that γ-C−H activation of linear-chain carboxylic acids could generate a γ,δ-unsaturated acid intermediate that undergoes intramolecular oxypalladation and π-migration to furnish a δ,ε-unsaturated γ-lactone, thereby combining γ-lactonization and distal desaturation in a single sequence.

Implementation of this pathway requires overcoming of several intrinsic challenges associated with γ-C−H activation in unbiased systems. Most notably, β-C−H bonds are generally more accessible for functionalization because their activation involves the formation of kinetically favoured five-membered palladacycles, consequently leading to an elevated bar of difficulty in γ-methylene activation, which proceeds through six-membered metallacycle formation20,21 (Fig. 1a). Moreover, several additional factors further complicate γ-activation in unbiased acids. First, the absence of Thorpe–Ingold constraints disfavours γ-activation25,26,27,28,29,30,31,32. Second, flexible alkyl chains hinder the formation of defined transition states. Another factor is weak carboxylate coordination, which can lead to poor metal binding or side reactions. Compared with γ-methylene sites, γ-methine C−H activation poses additional challenges arising from increased steric hindrance, lower C−H acidity and less favourable metal–carbon bond formation. Intrigued by these limitations, we questioned whether a suitably designed ligand framework could enable catalyst-controlled remote site selectivity in flexible aliphatic systems. Recent advances in ligand-enabled C−H activation suggest that tuning ligand electronics and binding environment can help in overcoming subtle steric and energetic limitations20,21.

Fig. 1: Ligand-enabled distal dehydrogenative γ-lactonization of unbiased fatty acids.

Full size image

a, Challenges associated with selective γ-C(sp^3)−H activation in unbiased aliphatic carboxylic acids. b, Conceptual ligand-design framework for distal γ-methylene and methine C(sp^3)−H activation. OAAE, O-allyl amido ester.

Guided by these considerations, we sought to develop ligand frameworks that can enable remote γ-C−H activation in saturated fatty acids to promote distal desaturation and lactonization. We prepared a series of N,O-protected amino acid (NOPA) ligands, among which an O-allyl amido ester (OAAE) variant bearing an amidate donor, a π-functional side chain and a sterically encumbered iso-butyl substituent emerged as particularly effective (Fig. 1b). This architecture was visualized to provide a flexible coordination environment that can promote site-selective γ-methylene and γ-methine activation in the absence of strong intrinsic substrate bias. Using this strategy, we have developed a ligand-enabled palladium-catalysed dehydrogenative lactonization of unbiased aliphatic carboxylic acids to access δ,ε-desaturated γ-lactones. This methodology provides efficient access to diverse distal desaturated lactone frameworks, including dehydrogenated butyrolactones and spirolactones, which can be further transformed into bioactive scaffolds such as muricatacin analogues and L-factor, highlighting the utility of this strategy for C−H activation and valorization of renewable feedstocks.

Optimization of the reaction conditions

We proposed that lactonization proceeds through an initial γ-C(sp^3)−H activation followed by sequential dehydrogenation and cyclization steps, analogous to previously reported Pd-catalysed unsaturated lactonization[25]. Realizing this sequence at γ-methylene and γ-methine positions in unbiased aliphatic acids demands ligand frameworks capable of promoting remote C−H activation, stabilizing key Pd-intermediates, and suppressing competitive pathways (Fig. 2). Initial optimization with undecylic acid using Pd(OAc)2 (10 mol%), Na_2HPO_4 (2 equiv.), Ag_2CO_3 (2 equiv.) and a range of pyridone-based and mono-protected amino acid (MPAA) ligands in hexafluoroisopropanol (HFIP) at 110 °C afforded low yields (<4%) (Supplementary Information section 3 and Supplementary Table 1). Quinoline–pyridone ligands promoted α,β-dehydrogenation of the corresponding acids (Supplementary Information section 10b), indicating that these ligand classes are not suited for the desired transformation. We, therefore, sought to design ligand frameworks that can electronically influence multiple elementary steps of the Pd catalytic cycle, including C−H activation, β-hydride elimination and reductive elimination, while also stabilizing key organopalladium intermediates. Guided by previous studies demonstrating that π-coordinating motifs can modulate transition-metal reactivity through electronic perturbation of the metal centre and transient ligand–metal interactions1,33,34,35, we proposed that incorporation of π-coordinating side handles into ligand scaffolds could tune both the coordination environment and electronic properties of Pd. This design, together with a strongly coordinating amidate motif, was expected to promote transient ligand–metal organization that enables selective remote C(sp^3)−H activation in aliphatic carboxylic acids while disfavouring competing pathways through combined electronic and steric modulation of the metal centre.

Fig. 2: Ligand design, optimization and proposed mechanistic pathway for Pd-catalysed distal desaturative γ-lactonization.

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a, Ligand optimization studies for Pd-catalysed δ,ε-dehydrogenative γ-lactonization. Reaction conditions for ligand optimization studies: acid (0.05 mmol), Pd(OAc)2 (10 mol%), ligand (20 mol%), Ag_2CO_3 (2 equiv.), Na_2HPO_4 (2 equiv.), HFIP, 110 °C, 36 h. Final optimized condition: aacid (0.05 mmol), Pd(OAc)2 (10 mol%), ligand (30 mol%), Ag_2CO_3 (2.5 equiv.), Na_2HPO_4 (3 equiv.), NaTFA (0.5 equiv.), HFIP, 120 °C, 36 h. Yield determined by 1H-NMR of crude reaction mixture using mesitylene as internal standard. n.r., no reaction; n.d.p., no desired product. For L25 and L26, α,β-dehydrogenated carboxylic acid product formation had been observed. See Supplementary Information sections 3 and 10.b for details. b, Proposed catalytic pathway and schematic ligand–Pd organization.

To test this hypothesis, we prepared a series of NOPA ligands by incorporating side chains with varying π-character into MPAA scaffolds, including alkyl, aryl, benzyl, alkynyl and allyl substituents (Supplementary Information section 3 and Supplementary Table 1), to systematically evaluate the influence of side-chain structure on catalytic performance. These ligands were selected to provide varying combinations of π-character, steric environment and conformational flexibility. Allyl substituents were expected to provide a conformationally flexible and accessible π-functional framework with moderate steric demand, whereas aryl groups provide comparatively rigid and sterically demanding π-rings. Benzyl substituents retain aromatic π-character while offering greater spatial adaptability through a flexible methylene linker, and alkynes contribute a linear π-system with reduced conformational flexibility. By contrast, alkyl substituents lack π-functionality altogether.

Among the ligands examined, allyl-containing systems exhibited the most pronounced enhancement in reactivity. Introduction of an allyl substituent into an N-acetyl-valine (L10) increased the yield sevenfold (29%), and subsequent optimization identified N-acetyl-leucine allyl ester (L14) as optimal ligand, affording the product in 42% yield (Fig. 2a). We designate this allyl-containing NOPA motif as an OAAE ligand. By contrast, benzyl-, aryl- and alkynyl-substituted analogues (L15–L17) delivered diminished yields, indicating that increased rigidity or altered π-distribution is not beneficial in this transformation. Ligands lacking π-functional side chains were largely ineffective, showing a strong dependence of reactivity on side-chain π-functionality. Further modification of the allyl side arm showed that perturbation of its spatial arrangement or electronic environment by substitution at either the terminal olefin or adjacent methylene position (L18–L20) or by relocation to a homoallylic framework (L21–L23) led to a marked decrease in reactivity. Altogether, these structure–activity relationships highlight the importance of both the identity and spatial disposition of the allyl-derived side handle for catalytic performance.

To probe the origin of the allyl effect, the interaction between L14 and Pd(OAc)2 was examined by 1H NMR spectroscopy in the presence of base at different temperatures. Base addition attenuated and perturbed the amide NH signal, consistent with amidate involvement during interaction with Pd species. Small but reproducible upfield shifts were observed for the allylic −OCH_2− and terminal olefinic protons (Δδ ≈ 0.03–0.07 ppm), whereas the internal olefinic proton remained largely unaffected (Supplementary Information section 10a). Time-dependent spectral changes indicate interconverting species converging towards a dominant equilibrium state with minor coexisting components. Subtle differences between the terminal and internal olefinic resonances are consistent with non-uniform perturbation[33] of the allyl π-system, and may reflect transient, spatially differentiated interactions between the allyl moiety and the Pd centre rather than a discrete coordination mode. In line with these observations, the ligand–Pd ensemble may adopt a flexible ‘puckered’ or tub-shaped conformation in solution (Fig. 1b), in which ligand flexibility brings the π-functional side chain into proximity with the Pd centre without implying a single well-defined static structure. Together with precedent in the literature for metal–alkene interactions and alkene-mediated modulation of Pd reactivity1,33,34,35, these observations suggest that the allyl-derived side chain may influence the electronic environment of Pd(II), potentially affecting its electrophilic character. Beyond these putative ligand–metal interactions, the amide unit likely facilitates base-assisted concerted metalation–deprotonation, whereas the sterically encumbered isobutyl side chain seems important for the observed reactivity. Although the present data support a role for the allyl-derived side chain in modulating catalyst behaviour, the precise nature of the underlying ligand–Pd interactions remains under investigation. Collectively, these observations suggest that the OAAE ligand creates a catalytic environment conducive to the proposed C−H activation and downstream reaction sequence.

Mechanistic studies were then conducted to validate the plausible dehydro-lactonization sequence. The transformation is proposed to proceed by initial γ-C(sp^3)−H activation followed by β-hydride elimination to generate a γ,δ-unsaturated acid intermediate (Int-C), which can subsequently undergo intramolecular oxypalladation and a second β-hydride elimination to furnish the distal desaturated γ-lactone (Fig. 2b). To evaluate the competence of this intermediate, control experiments were performed (Supplementary Information section 10). The β,γ-unsaturated acid failed to furnish the desired product and instead produced α,β-unsaturated γ-lactone, thereby excluding β-activation pathways, whereas the γ,δ-unsaturated acid furnished the desired γ-lactone in near-quantitative yield, strongly supporting its role as a catalytically relevant intermediate (Supplementary Information section 10).

These mechanistic insights, together with the observed ligand structure–activity relationships, collectively defined the behaviour of L14, enabling distal selectivity and improved formation of the desaturated γ-lactone under the optimized conditions. The pronounced ligand dependence observed here highlights the central role of ligand architecture in governing catalytic outcome. Under optimized conditions of Pd(OAc)2 (10 mol%), L14 (30 mol%), Ag_2CO_3 (2.5 equiv.), Na_2HPO_4 (3 equiv.) and NaOCOCF_3 (0.5 equiv.) in HFIP at 120 °C for 36 h, the desired lactone (1) was obtained in 60% isolated yield.

Substrate scope

Having established the optimized OAAE-enabled reaction conditions, we next examined a range of naturally occurring fatty acids spanning the C9–C22 chain-length range, which furnished δ,ε-unsaturated γ-lactones (1–7) by regioselective γ-C(sp^3)−H activation and dehydro-lactonization in moderate to good yields (Fig. 3a). Although substrates across the entire chain-length range were compatible, longer-chain fatty acids generally afforded somewhat reduced yields, probably reflecting solubility limitations in HFIP. Additional fatty acids of varying chain lengths exhibited similar reactivity (Supplementary Information and Supplementary Fig. 1). The direct conversion of abundant fatty-acid feedstocks into γ-vinylic lactones highlights the synthetic utility of the method.

Fig. 3: Expanding the chemical space of distal dehydrogenative γ-lactones.

Full size image

a, Examples of saturated fatty acids that undergo desaturative lactonization. b, Substrate scopes with unnatural carboxylic acids. c, Access to γ-vinylic-spirolactones by acyclic γ-methylene C−H activation. d, γ-Methine C(sp^3)−H activation for the synthesis of unsaturated γ-lactones. Standard conditions: aacid (0.1 mmol), Pd(OAc)2 (10 mol%), L14 (30 mol%), Ag_2CO_3 (2.5 equiv.), Na_2HPO_4 (3 equiv.), NaTFA (0.5 equiv.), HFIP (1 mL), 120 °C, 36 h. bHFIP (0.95 ml) and DCE (0.05 ml) were used as a mixture of solvents. cAg_2CO_3 (3 equiv.), NaOAc (1.5 equiv.) and Na_2HPO_4 (0.5 equiv.) were used. dAg_2CO_3 (3 equiv.) and K_2HPO_4 (2 equiv.) were used. eProduct 30 formed with accompanying side products (Supplementary Information). Isolated yields are reported. d.r., diastereomeric ratio; r.r., regio-isomeric ratio. For detailed information on reaction set-up, see Supplementary Information section 5. Additional desaturated lactones as part of the extended scopes are provided in Supplementary Information section 5.e and Supplementary Fig. 1.

We next examined substrates bearing functional groups, conformational bias and aryl substitution (Fig. 3b). Ester- and maleimide-containing acids were well tolerated, affording products (8 and 9) with good chemoselectivity. β-Substituted acids furnished γ-lactones 10 and 11 by preferential γ-methylene activation over competitive methyl sites (10), including selective formation of an internal olefin in 11, consistent with thermodynamic alkene stabilization. An aryl-containing substrate underwent selective transformation without perturbation of aryl C−H bonds affording lactone (12). Notably, this dehydrogenated lactone has been reported to exhibit cytotoxic activity against leukemic T-cell lines[36]. Cyclohexylbutanoic acid afforded desaturated γ-lactone (13), a potential intermediate for hexahydronaphthalene carboxylate synthesis[37]. Complex terpenoid-derived substrates prepared from α-ionone and farnesol also proved compatible, affording γ-lactones 14 and 15 bearing distal olefins with varying degrees of substitution, demonstrating compatibility of the protocol with structurally elaborate frameworks.

We next investigated substrates containing competing acyclic γ- and cyclic γ′-methylene sites within cyclohexylacetic acid frameworks (Fig. 3c). In contrast to previously reported MPAA-based systems[25], the present OAAE ligand framework preferentially activated alternative acyclic long-chain γ-methylene positions over cyclic methylene sites, affording complementary distal desaturated spirolactone products. This reversal of the typical selectivity pattern[25] highlights the role of ligand architecture in controlling regioselective C(sp^3)−H activation when multiple methylene sites compete. Cyclohexane substrates bearing ring substituents were well tolerated (16–20), whereas the unsubstituted analogue produced a mixture of spirocyclic and bicyclic lactones (21), indicating diminished selectivity in the absence of ring substitution. Expansion to larger cyclic frameworks afforded [7,5]-, [15,5]-spirolactones (22 and 23), respectively. Additional substrates exhibited similar reactivity and selectivity trends (Supplementary Information and Supplementary Fig. 1). Consistent with this distinct selectivity profile relative to previous MPAA systems[25], no H_2 evolution was detected under the reaction conditions, supporting a Pd(II)/Pd(0) manifold in which Ag(I) mediates reoxidation of Pd(0) to Pd(II) during the catalytic cycle. Deuterium-labelling experiments using d_2-HFIP showed no deuterium incorporation at the γ-position, indicating that γ-C−H activation is irreversible under this protocol and probably contributes to observed regioselectivity. These observations further distinguish the present catalytic system from the previously reported MPAA system[25] (Supplementary Information section 10).

Finally, we introduced greater steric demand by replacing one methylene hydrogen with an alkyl substituent, thereby generating more challenging γ-methine C−H sites, which are sterically inaccessible, least acidic and kinetically disfavoured relative to methylene and methyl C−H bonds (Fig. 3d). An acyclic carboxylic acid bearing a tertiary γ-methine C−H bond proved to be a competent substrate under the optimized conditions, affording mixtures of mono- and di-dehydrogenated lactones (24). We next investigated cyclic substrates of greater steric complexity. Cyclohexyl propanoic acid derivatives furnished γ-methine activation followed by tandem double dehydrogenation and lactonization to form spirocyclic unsaturated lactones (25–28). Substrates bearing substituents at different ring positions, including electron-donating and electron-withdrawing groups, were generally well tolerated. Variations in substitution pattern influenced regioselectivity, probably reflecting differences in preferred β-hydride elimination pathways during the dehydro-lactonization sequence. Benzylic methine substrates required modified conditions to selectively furnish mono-dehydrogenated lactone (29). Moreover, a substrate bearing a fully substituted β-site, leaving only a γ-methine C−H bond available for activation, afforded the mono-dehydrogenated lactone (30), further supporting γ-methine C−H activation and indicating that secondary dehydrogenation requires an accessible β-hydrogen (Supplementary Information section 10). Across the substrate scope, incomplete conversion and recovery of starting material suggest partial catalyst deactivation (Supplementary Information section 9). Nevertheless, these results demonstrate that the Pd-OAAE system can engage challenging γ-methine C−H bonds and enable access to diverse spirocyclic and unsaturated γ-lactone frameworks.

Applications

To demonstrate the synthetic utility of this methodology, we applied it to the synthesis of pharmaceutically and industrially relevant γ-lactone scaffolds (Fig. 4), typically prepared through multistep routes4,6,7,8. Soursop (Laxman phal)-derived muricatacin (31), a δ-hydroxy-γ-lactone motif found in annonaceous acetogenins with reported antimalarial, cytotoxic and pesticidal activities, has inspired numerous elegant syntheses, including asymmetric approaches38,39. In this context, the present strategy enables a concise three-step synthesis from readily available margaric acid, albeit as racemate (Fig. 4a), providing a complementary entry to this framework. Motivated by the biological relevance of the muricatacin scaffold, we prepared a focused library of structurally related δ-hydroxy-γ-lactone analogues (41–46) for computational and biological evaluation.

Fig. 4: Synthetic utility of the developed method in bioactive compound and natural product synthesis and late-stage product modification.

Full size image

a, Total synthesis of muricatacin (an anti-proliferative, cytotoxic, antimalarial, immunosuppressant). b, Total synthesis of L-factor (autoregulator of anthracycline biosynthesis). c, Synthesis of cognac lactone. d, Synthesis of dodecalactone. e, Synthesis of dl-NBP. f, Synthesis of EFBL. g, Synthesis of peach lactone. h, Access to γ-hydroxylated δ,ε-dehydrogenated aliphatic carboxylate. i, Synthesis of macrolide natural product. j, Late-stage lactonization of mycophenolic acid. d.r., diastereomeric ratio. For detailed reaction conditions, see Supplementary Information section 6.

The generality of this approach was further demonstrated through the synthesis of diverse γ-lactone derivatives. L-factor (32), an autoregulator[38], was accessed in three steps from capric acid. Cognac lactone (33), widely used in flavour and fragrance applications[40], and dodecalactone (34), a bioactive compound in insect secretions, were each obtained from simple fatty acids in fewer steps compared with reported routes40,41. The method was also applied to the preparation of dl-3-n-butylphthalide (35), a clinically used drug for ischaemic stroke[42], synthesized in two steps from ortho-alkylated benzoic acids using our protocol. This contrasts with the traditional four-step synthesis from phthalic anhydride[43]. γ-ethyl-γ-phenyl-butyrolactone (36), a scaffold of pharmacological interest44,45, was synthesized through step-economic γ-C−H activation. Moreover, γ-decalactone (37), an industrially important flavour compound[46], was prepared under standard conditions. Further transformations highlight the versatility of products: base-mediated γ-lactone ring opening afforded γ-hydroxylated δ,ε-unsaturated carboxylates (38), whereas conversion to hydroxy-oxolane (39) provides access to intermediates relevant to macrolide synthesis[47]. Finally, late-stage functionalization of mycophenolic acid formed the corresponding distal desaturated lactone (40), illustrating compatibility with structurally complex molecules.

Beyond these applications, δ,ε-desaturated γ-lactones obtained through this protocol serve as versatile platforms for further diversification (Extended Data Fig. 1a). The distal olefin readily undergoes epoxidation (41 and 44) and oxidative dihydroxylation (42 and 45), while the resulting epoxides can be opened by nucleophiles to generate functionalized derivatives (43, 46 and 47). Reductive manipulations further expand the accessible chemical space, enabling the formation of 1,4-diols (48), hydroxylated tetrahydrofuran derivatives (49) or long-chain alcohols (50). Additional transformations, including Grignard addition (51) and electrophilic bromination (52), further underscore the synthetic flexibility of these desaturated γ-lactones (Supplementary Information section 8).

In silico studies

Docking studies identified several analogues with improved binding affinities relative to muricatacin (Supplementary Information section 11). For COX-2, compounds 46 and 42 exhibited stronger binding (−9.3 and −8.8 kcal mol−1) than muricatacin (−7.2 kcal mol−1), with compound 46 forming hydrogen bonds with Arg-106 and Tyr-341 (Extended Data Fig. 1b). Molecular dynamics simulations confirmed stable compound–protein complexes (Extended Data Fig. 1c). For caspase-3, compounds 42 and 46 also exhibited enhanced binding compared with muricatacin. Based on these results, selected analogues were advanced for further biological evaluation (Supplementary Information section 11).

MTT assay

Compounds 42, 43, 45 and 46, together with muricatacin (31), were evaluated against HT-29 and HCT-116 colorectal carcinoma cells using an MTT assay (Supplementary Information section 11). Compounds 42 and 43 exhibited dose-dependent growth inhibition, whereas compounds 45 and 46 showed limited activity (Extended Data Fig. 1d,e). Compound 43 exhibited potent cytotoxicity, with IC_50 values of 55.11 μM (HT-29) and 60.83 μM (HCT-116), comparable to muricatacin (53.14 μM and 53.60 μM, respectively), whereas compound 42 was less active. Notably, compound 43 was synthesized in only three steps from margaric acid, highlighting the efficiency of the developed strategy for rapid analogue generation.

Conclusion

We have developed a general strategy for the synthesis of three distinct classes of distal desaturated γ-lactones from readily available aliphatic carboxylic acids, including butyrolactones and spiro-lactones. These results address the longstanding challenge of regioselective γ-C−H activation in unbiased aliphatic carboxylic acids. The OAAE ligand framework is proposed to modulate the Pd coordination environment, enabling selective activation of remote methylene and methine sites. The versatility of this ligand system is further demonstrated by complementary γ-methylene selectivity in cyclic acetic acids. Mechanistic studies, supported by control experiments and deuterium-labelling investigations, indicate irreversible γ-C(sp^3)−H activation. The synthetic utility of the method is illustrated through concise access to bioactive molecules, including EFBL, cognac lactone, dodecalactone, peach lactone, dl-NBP, muricatacin and L-factor. Furthermore, a muricatacin analogue prepared in three steps exhibited anticancer activity comparable to that of muricatacin. Overall, this work expands the scope and wider application of aliphatic C(sp^3)−H activation and highlights its potential for the valorization of renewable feedstocks in drug discovery and natural product synthesis.

Data availability

All data supporting the findings of this study, including experimental procedures and compound (starting materials, ligands, products) characterization, are available in the Supplementary Information.

References

1. Hartwig, J. F. Organotransition Metal Chemistry: From Bonding to Catalysis (Univ. Science Books, 2010).

2. Grover, J., Sebastian, A. T., Maiti, S., Bissember, A. C. & Maiti, D. Unified approaches in transition metal catalyzed C(sp^3)–H functionalization: recent advances and mechanistic aspects. Chem. Soc. Rev. 54, 2006–2053 (2025).

3. Mal, S. & van Gemmeren, M. Transition metal-catalyzed direct C(sp^3)–H functionalization reactions of aliphatic carboxylic acids. Chem. Rev. 126, 4900–4944 (2026).

4. Premkumar, E. et al. Synthesis of lactones and lactams via C(sp^3)–H bond functionalization. Chem. Soc. Rev. 54, 6238–6281 (2025).

5. Saturated Fatty Acid Market Size, Share & Industry Analysis, By Type (Stearic acid, Palmitic acid, Lauric acid, Myristic acid, and Others), By Application (Food & Beverages, Cosmetics and Personal Care, Pharmaceutical, and Others), and Regional Forecast, 2026-2034. Report No. FBI114043 https://www.fortunebusinessinsights.com/saturated-fatty-acid-market-114043 (Fortune Business Insights, 2026).

6. Silva, R., Coelho, E., Aguiar, T. Q. & Domingues, L. Microbial biosynthesis of lactones: gaps and opportunities towards sustainable production. Appl. Sci. 11, 8500 (2021).

7. Quintavalla, A. Spirolactones: recent advances in natural products, bioactive compounds and synthetic strategies. Curr. Med. Chem. 25, 917–962 (2018).

8. Goethe, O. F. et al. C–H functionalization as a powerful method for direct synthesis of lactones. ACS Catal. 15, 9013–9034 (2025).

9. Kurganskiy, V. I. et al. Manganese-catalyzed regioselective C–H lactonization and hydroxylation of fatty acids with H_2O_2. Org. Lett. 24, 8764–8768 (2022).

10. Kurganskiy, V. I., Bryliakova, A. A., Medvedev, A. G., Shashkov, M. V. & Bryliakov, K. P. Mn-catalyzed regio- and stereoselective C(sp^3)–H lactonization of carboxylic acids with H_2O_2. ACS Catal. 15, 14938–14954 (2025).

11. Chan, H. S. S., Yang, J.-M. & Yu, J.-Q. Catalyst-controlled site-selective methylene C–H lactonization of dicarboxylic acids. Science 376, 1481–1487 (2022).

12. Maji, R. et al. A catalytic asymmetric hydrolactonization. J. Am. Chem. Soc. 145, 8788–8793 (2023).

13. Kennemur, J. L., Maji, R., Scharf, M. J. & List, B. Catalytic asymmetric hydroalkoxylation of C–C multiple bonds. Chem. Rev. 121, 14649–14681 (2021).

14. Wang, Z. et al. Ligand-controlled divergent dehydrogenative reactions of carboxylic acids via C–H activation. Science 374, 1281–1285 (2021).

15. Pal, T. et al. Tandem dehydrogenation-olefination-decarboxylation of cycloalkyl carboxylic acids via multifold C–H activation. Nat. Commun. 15, 5370 (2024).

16. Meng, G., Hu, L., Chan, H. S. S., Qiao, J. X. & Yu, J.-Q. Synthesis of 1,3-dienes via ligand-enabled sequential dehydrogenation of aliphatic acids. J. Am. Chem. Soc. 145, 13003–13007 (2023).

17. Diao, T. & Stahl, S. S. Synthesis of cyclic enones via direct palladium-catalyzed aerobic dehydrogenation of ketones. J. Am. Chem. Soc. 133, 14566–14569 (2011).

18. Xu, Y., Zhang, R., Zhou, B. & Dong, G. Iridium-catalyzed oxidant-free transfer dehydrogenation of carboxylic acids. J. Am. Chem. Soc. 146, 22899–22905 (2024).

19. Pradhan, S. et al. A formal γ-C–H functionalization of carboxylic acids guided by metal-nitrenoids as an unprecedented mechanistic motif. J. Am. Chem. Soc. 145, 28251–28263 (2023).

20. Li, Y.-H., Ouyang, Y., Yan, J.-L., Chekshin, N. & Yu, J.-Q. From mono-N-protected amino acids to pyridones: a decade of evolution of bifunctional ligands for Pd(II)-catalyzed C–H activation. Acc. Chem. Res. 58, 2910–2926 (2025).

21. Moradell, J., Bohan, C., Pop, A. & Urriolabeitia, E. P. Advances in ligand-driven Pd-catalyzed C─H functionalizations: recent insights and updates. ChemCatChem 17, e00664 (2025).

22. Larock, R. C., Leuck, D. J. & Harrison, L. W. Mercury in organic chemistry. 35. Synthesis of vinylic lactones from vinylmercurials and alkenoic acids via intramolecular π-allylpalladium displacement. Tetrahedron Lett. 28, 4977–4980 (1987).

23. Xiong, H. & Rieke, R. D. Synthesis of spiro γ-lactones from conjugated dienes. J. Org. Chem. 57, 7007–7008 (1992).

24. Larock, R. C. & Hightower, T. R. Synthesis of unsaturated lactones via palladium-catalyzed cyclization of alkenoic acids. J. Org. Chem. 58, 5298–5300 (1993).

25. Das, J. et al. Access to unsaturated bicyclic lactones by overriding conventional C(sp^3)–H site selectivity. Nat. Chem. 15, 1626–1635 (2023).

26. Yan, J.-L., Hu, L., Lu, Y. & Yu, J.-Q. Catalyst-controlled chemoselective γ-C(sp^3)–H lactonization of carboxylic acid: methyl versus methylene. J. Am. Chem. Soc. 146, 29311–29314 (2024).

27. Hu, L., Yan, J.-L., Lin, Y.-K., Strassfeld, D. A. & Yu, J.-Q. Regiocontrollable [2 + 2] benzannulation of γ,δ-C(sp^3)–H bonds with dihaloarenes using palladium catalysis. Nat. Synth. 4, 1556–1564 (2025).

28. Ghosh, A., Chauhan, R. S., Das, N., Ali, W. & Maiti, D. Pd-catalyzed distal dehydrogenative δ-arylation and γ-lactonization of acyclic free carboxylic acids. ACS Catal. 15, 20786–20795 (2025).

29. Dolui, P., Das, J., Chandrashekar, H. B., Anjana, S. S. & Maiti, D. Ligand-enabled PdII-catalyzed iterative γ-C(sp^3)−H arylation of free aliphatic acid. Angew. Chem. Int. Ed. 58, 13773–13777 (2019).

30. Ghiringhelli, F., Uttry, A., Ghosh, K. K. & van Gemmeren, M. Direct β- and γ-C(sp^3)−H alkynylation of free carboxylic acids. Angew. Chem. Int. Ed. 59, 23127–23131 (2020).

31. Xu, T., Mal, S. & van Gemmeren, M. The direct Pd-catalyzed γ-lactonization of aliphatic carboxylic acids. ACS Catal. 15, 2735–2741 (2025).

32. Li, B., Elsaid, M. & Ge, H. Transition-metal-catalyzed site-selective γ- and δ-C(sp^3)–H functionalization reactions. Chem 8, 1254–1360 (2022).

33. Zhao, H., Ariafard, A. & Lin, Z. In-depth insight into metal–alkene bonding interactions. Inorganica Chim. Acta 359, 3527–3534 (2006).

34. Fairlamb, I. J. S. π-Acidic alkeneligand effects in Pd-catalysed cross-coupling processes: exploiting the interaction of dibenzylidene acetone (dba) and related ligands with Pd(0) and Pd(II). Org. Biomol. Chem. 6, 3645–3656 (2008).

35. Zhu, C., Yang, B., Jiang, T. & Bäckvall, J.-E. Olefin-directed palladium-catalyzed regio- and stereoselective oxidative arylation of allenes. Angew. Chem. Int. Ed. 54, 9066–9069 (2015).

36. Mohideen, M. et al. Design, synthesis, in vitro cytotoxicity evaluation and structure-activity relationship of goniothalamin analogs. Arch. Pharm. Res. 36, 812–831 (2013).

37. Li, Z., Alameda-Angulo, C., Quiclet-Sire, B. & Zard, S. Z. A flexible approach to hexahydronaphthalene-1-carboxylates. Tetrahedron 67, 9844–9852 (2011).

38. Fernandes, R. A., Gangani, A. J., Kumari, A. & Kumar, P. A decade of muricatacin synthesis and beyond. Eur. J. Org. Chem. 2020, 6845–6858 (2020).

39. Wang, W. et al. Antitumour activity of muricatacin isomers and its derivatives in human colorectal carcinoma cell HCT116. Anticancer Agents Med. Chem. 20, 254–263 (2020).

40. Menna, T. J. & Kerr, M. A. Synthesis of the (±)-trans-whiskey and cognac lactones via a donor–acceptor cyclopropane hemimalonate. Synlett 36, 504–507 (2024).

41. Ugurchieva, T. M., Lozanova, A. V., Zlokazov, M. V. & Veselovsky, V. V. Synthesis of (±)−4-alkanolides from pent-4-enoic acid. Russ. Chem. Bull. 57, 657–659 (2008).

42. Jung, H.-Y., Chang, S. & Hong, S. Strategic approach to the metamorphosis of γ-lactones to NH γ-lactams via reductive cleavage and C–H amidation. Org. Lett. 21, 7099–7103 (2019).

43. Beijing Collab Pharma. Preparation method of butylphthalide. Chinese patent CN111943921A (2020).

44. Rasgado, L. A. V., Villanueva, I. & Díaz, F. V. Effect of γ-ethyl-γ-phenyl-butyrolactone (EFBL), anticonvulsant and hypnotic drug, on mouse brain catecholamine levels. Acta Pharm. 67, 215–226 (2017).

45. Bao, X., Yokoe, T., Ha, T. M., Wang, Q. & Zhu, J. Copper-catalyzed methylative difunctionalization of alkenes. Nat. Commun. 9, 3725 (2018).

46. Aguedo, M., Beney, L., Waché, Y. & Belin, J.-M. Interaction of an odorant lactone with model phospholipid bilayers and its strong fluidizing action in yeast membrane. Int. J. Food Microbiol. 80, 211–215 (2003).

47. Ma, Q. et al. Biocatalytic enantioselective γ-C–H lactonization of aliphatic carboxylic acids. Nat. Synth. 3, 123–130 (2024).

Acknowledgements

We acknowledge the financial support from the Nandan Nilekani Foundation and Poonawalla Fincorp Limited. We also thank PMRF-India (T.P. and M.S.S.) and CSIR-India (A.G.) for providing fellowship. We thank A. Prasannan, P. Manna and A. Pal for scientific assistance. We also thank the DM lab for their support.

Funding

D.M. discloses support for the research of this work from the Anusandhan National Research Foundation (ANRF) (DST-SERB: SPR/2022/000234).

Author information

Author notes

1. These authors contributed equally: Md Saimuddin Sk, Yazhinimuthu C M

Authors and Affiliations

1. Department of Chemistry, Center for Climate Studies, Center for ML-driven Traditional Medicine and Catalysis (CMTMC), Indian Institute of Technology Bombay, Mumbai, India

Tanay Pal, Md Saimuddin Sk, Yazhinimuthu C M, Animesh Ghosh & Debabrata Maiti

2. Radiation Medicine Centre, Bhabha Atomic Research Centre, Mumbai, India

Somnath Kar

Contributions

T.P., M.S.S., Y.C.M., A.G. and D.M. conceived the concept and designed the project. T.P., M.S.S., Y.C.M. and A.G. performed the reactions and analysed the products. S.K. designed and performed all the biological studies. A.G. performed the X-ray diffraction analysis and analysed the structure. T.P., M.S.S., Y.C.M., S.K., A.G. and D.M. wrote the paper.

Corresponding author

Correspondence to Debabrata Maiti.

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Extended data figures and tables

Extended Data Fig. 1 Synthetic diversification and biological evaluation of distal-desaturated γ-lactones.

A. Distal-desaturated γ-lactone, a versatile building block to different synthons: a. Epoxidation of distal olefin; b. δ,ε-Di-hydroxylated-γ-lactone formation; c. Synthesis of ε-tosylated-δ-hydroxy-γ-lactone; d. δ,ε-Hetero-di-functionalized-γ-lactone formation; e. Lactone ring cleavage leading to di-hydroxylation; f. Selective reduction of desaturated lactone carbonyl; g. Complete reduction of the olefin and lactone motif; h. Formation of unsaturated-1,4-diol; i. Synthesis of δ,ε-dibrominated-γ-lactone; aCompound 49 has been isolated as a mixture of multiple diastereomers. d.r. = diastereomeric ratio. For detailed reaction conditions, see Supplementary Information, Section 8. B. Protein-ligand (i.e. protein-compound 46) binding interaction diagram for compounds 46 bound to COX-2 (PDB: 3LN1), illustrating multiple stabilizing interactions. C. MD simulation trajectories (250 ns) of COX-2 complex with compound 46, indicating stable binding. D, E. In-vitro cytotoxicity profiles of selected analogues against human colorectal cancer cell line HT-29 and HCT-116 respectively, determined by MTT assay.

Supplementary information

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This file contains Supplementary Information sections 1–17, including Supplementary Tables 1–9, Supplementary Figs. 1–27, X-ray crystallographic data and NMR spectra data; see the Contents for details.

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Pal, T., Sk, M.S., C M, Y. et al. Ligand-enabled distal desaturative lactonization of aliphatic acids. Nature 656, 98–105 (2026). https://doi.org/10.1038/s41586-026-10826-8

- Received: 23 September 2025

- Accepted: 18 June 2026

- Published: 25 June 2026

- Version of record: 29 July 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10826-8

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High-fidelity modular skeletons authenticate a Cambrian origin for Bryozoa

Baopeng Song  (宋宝鹏),

Zhifei Zhang  (张志飞),

Luke C. Strotz,

Timothy P. Topper,

Andrej Ernst,

Junye Ma  (马俊业),

Zhiliang Zhang  (张志亮),

Mei Luo  (罗梅),

Lars E. Holmer,

Yue Liang  (梁悦),

Yazhou Hu  (胡亚洲),

Caibin Zhang  (张彩彬),

Yanlong Chen  (陈延龙) &

…

Glenn A. Brock

Nature volume 656, pages 123–128 (2026)

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Abstract

The major animal body plans originated during the Cambrian explosion, yet the phylum Bryozoa has remained a conspicuous exception to this pattern[1]. The initial discovery of Protomelission gatehousei[2] provided compelling evidence for a Cambrian origin for the Bryozoa, together with other major metazoan phyla and compatible with independent molecular clock estimates3,4,5,6,7. Nevertheless, the scarcity of definitive soft-tissue anatomy and diagnostic skeletal microstructure has left its phylogenetic affinities ambiguous and debated8,9. Here we report exquisite fossils of P. gatehousei and a new taxon, Dayingomelission hexaclitia gen. et sp. nov., from the early Cambrian Xiannüdong Formation of China. These specimens preserve in situ phosphatized soft tissues in modular skeletons, revealing critical anatomical structures, including styles, annular muscles, membranous sacs and ring septa. This suite of traits provides definitive evidence that these taxa belong to the Bryozoa. Phylogenetic analysis incorporating these new features identifies them as crown group stenolaemates. These results confirm a Cambrian origin for the phylum and reveal an unexpected early disparity in colonial architecture, demonstrating that bryozoan diversification was an integral component of the Cambrian radiation. Moreover, the early appearance of a differentiated stenolaemate crown group indicates a still deeper origin for the bryozoan stem lineage than was first apparent.

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Main

The origin of the colonial lophotrochozoan phylum Bryozoa has often been considered an evolutionary enigma, because a diverse fossil record comprising six of the eight recognized bryozoan orders appears suddenly in the Early Ordovician Epoch[10]. The timing of this event is in stark contrast to molecular clock analyses, which have consistently indicated an origin for bryozoans in the early Cambrian period (Terreneuvian Epoch)3,4,5,6 and is inconsistent with almost all other animal phyla, which first appeared during the Cambrian evolutionary radiation6,7,11. Several putative Cambrian bryozoans have been previously proposed (such as Pywackia12,13, Archaeotrypa[14] and Marcusodictyon[15]) but have generally been discredited16,17,18,19,20.

Resolution of this conundrum seemingly came in the form of the modular, bilaminate colony of P. gatehousei, described from the lower Cambrian of South China and South Australia[2] and recognized as the first well-supported candidate for a stem-group bryozoan on the basis of its mosaic of well-defined character traits shared with both gymnolaemates and mineralized stenolaemates. The bryozoan affinities of P. gatehousei have been widely accepted21,22,23,24,25,26, aligning the origins of the Bryozoa with other skeletonized clades and integrating bryozoans into the broader context of the Cambrian radiation. However, this interpretation has been subsequently challenged by alternative hypotheses that have relied on the absence of definitive bryozoan soft-tissue anatomy and diagnostic skeletal microstructure to suggest that the phylogenetic position of P. gatehousei may lie outside the Bryozoa8,9.

Here we describe new specimens from the early Cambrian Xiannüdong Formation (southern Shaanxi, China; Extended Data Fig. 1), representing two bryozoan morphotypes: the modular bilaminate colony of P. gatehousei (Fig. 1) and a unilaminate colony ascribed to a new genus, Dayingomelission hexaclitia gen. et sp. nov. (Fig. 2). Crucially, these specimens preserve not only the skeletal traits previously used to establish the bryozoan affinities of P. gatehousei but also soft-tissue features of exceptional fidelity, including internal moulds of membranous sacs in the zooid chambers (Fig. 3). This combined set of characters unequivocally validates the assignment of these two taxa to the Bryozoa, reaffirming a Cambrian origin for the phylum. The presence of two distinct genera indicates that bryozoans were already diversifying during the Cambrian radiation.

Fig. 1: Specimen of P. gatehousei from the Xiannüdong Formation in which the membranous sacs are preserved (ELI DYCX 8-001).

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a, Front side of the colony. The outlined area is magnified in h. b, Back side of the colony. The outlined area is magnified in j. c, Lateral view of the bifoliate colony. d, Oblique lateral view of the bifoliate colony showing the hollow arched mesotheca (arrow). e, Partial enlargement of c showing the staggered budding pattern. f,g, X-ray tomographic microscopy images showing the longitudinal section of the colony and the orifice of autozooids (arrowheads) (f, oblique lateral view; g, lateral view). h, Quincuncial arrangement of sub-hexagonal membranous sacs with elliptical orifice. Note the 10-μm gap present between adjacent membranous sacs, indicating the loss of skeletal walls during the taphonomic processes. The outlined area is the membranous sac magnified in i. i, Enlarged view of a membranous sac showing the orifice (asterisk), circular fibres (arrow) and longitudinal fibres (arrowhead). These features suggest muscle preservation in the membranous sac. j, Enlarged view of a zooid. Note that the aperture is coated with secondary phosphate (the energy-dispersive spectroscopy analysis of this aperture is shown in Extended Data Fig. 5). k, Enlarged view of a zooid. Note that the secondary phosphate coating of the aperture is partially stripped away. l,m, Enlarged view of the membranous sac showing the longitudinal fibres (l, arrowhead) and circular fibres (m, arrow). These features suggest muscle preservation in the membranous sac. Scale bars, 500 μm (a–d), 50 μm (e,i–k), 200 μm (f), 150 μm (g), 100 μm (h) and 30 μm (l,m).

Fig. 2: Specimens of D. hexaclitia gen. et sp. nov. from the Xiannüdong Formation showing the colony and cystids.

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a,b, ELI ZJBX 10-001 (holotype). a, Oblique view of the front side of a unilaminate colony form clearly showing the regular hexagonal, compactly arranged, honeycomb-shaped cystids. The outlined area is shown in b. b, Hexagonal cystid with vertical wall and ring septa clearly evident (arrow). c–e, ELI ZJBX 10-002. c, Front side of a unilaminate colony form. The bottom outlined area shows the cystids magnified in d; whereas the top outline shows the cystids magnified in e. d, Enlarged view of adjacent cystids. Note the hexagonal vertical wall (arrow) and the basal exterior wall of cystids (arrowheads). e, Row bifurcation showing change in zooid width along rows. f–i, ELI ZJBX 3-001. f, Front side of a unilaminate colony form with styles indenting the zooidal chambers. g, Oblique view showing hexagonal cystids with styles. h, Oblique view of colony surface. Note that the styles arise in the endozone and extend through most of exozone. i, Enlarged view of the vertical wall with planar spherulitic fabric. Scale bars, 500 μm (a,c), 80 μm (b), 100 μm (d), 200 μm (e), 300 μm (f,g), 100 μm (h) and 25 μm (i).

Fig. 3: Membranous sacs preserved in situ in the autozooid cystids of P. gatehousei and D. hexaclitia gen. et sp. nov. and colonial growth reconstruction of P. gatehousei.

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a,b, P. gatehousei ELI DYCX 8-016. a, Front side of a bifoliate colony showing the eight series of zooids. The outlined area is magnified in b. b, Enlarged view of a zooid. Note that the membranous sac (arrow) is preserved in the cystid (arrowhead). c–g, D. hexaclitia ELI DYCX 8-004. c, Front side of a unilaminate colony, with ten series of zooids, all with membranous sacs and cystids. The outlined area is magnified in g. d, Back side of the colony showing the membranous sacs of the zooids and the gap between the sacs. The outlined area is magnified in e. e, Enlarged view showing capsule-like membranes and gaps. f, X-ray tomographic microscopy image showing the longitudinal section of zooids with membranous sacs and cystids. g, Enlarged view highlighting that the membranous sacs (arrow) are captured in the cystids (arrowhead), and the membranous sacs are in contact with the cystids 20 μm from the apertures (ligamentous attachment, asterisks). h, Three-dimensional reconstruction of a bryozoan zooid with protruding lophophore. i, Longitudinal section of reconstructed bryozoan zooid. Greyish white, cystid; translucent white, membranous sac and tentacles; pink, polypide excluding tentacles. j, Reconstruction of P. gatehousei, front surface view. Illustration in j adapted from ref. [2], under a Creative Commons licence CC BY 4.0. Scale bars, 500 μm (a,c,d), 40 μm (b), 200 μm (e,f) and 100 μm (g).

A collection of 38 modular fossils (see Methods section ‘Materials and occurrences’ for a full catalogue of specimens) are preserved as millimetric, secondarily phosphatized colonies (Figs. 1a–d, 2a–c,f–g and 3a–d). P. gatehousei has an erect, bilaminate, oligoserial colony form, 1–2 mm in width and up to 3 mm in height, tapering distally (Figs. 1a–d and 3j and Extended Data Fig. 2a–c). Each colony consists of two curved sheets of zooids arranged back to back, comprising six to eight rows of autozooids per side, with budding originating from a planar mesotheca (Extended Data Fig. 3a–c) or a flattened axial cylinder (Fig. 1d). D. hexaclitia is characterized by a unilaminate colony (Fig. 2a,c) (see ‘Systematic palaeontology’ section). Its autozooid chambers align within a single plane, growing almost perpendicular to the basal exterior wall (Fig. 2c–e). The colony surface displays a distinct zooidal arrangement, with colony growth proceeding through bifurcation (Fig. 2e). One of the unilaminate specimens (ELI ZJBX 3-001) preserves elongated, elliptical–cylindrical styles that originate in the endozone and extend prominently throughout the exozone (Fig. 2f–h).

All zooids of both taxa are hexagonal in outline and box-shaped in profile, with a phosphatized or silicified skeleton (cystid) present in all colony forms (Fig. 2a–e and Extended Data Fig. 3a–c). On the frontal surface, these autozooids have an average width of 208.4 μm and a length of 121.7 μm (Extended Data Fig. 4 and Supplementary Data 1). The cystids are uniform in size, short and box-shaped, angled at 30–75° to the median lamina (mesotheca) (Extended Data Fig. 3a–c,f) or basal exterior wall (Fig. 2d). The skeleton is composed of non-porous basal and vertical walls. Although the vertical walls of adjacent zooids form a double wall (Fig. 2a–e and Extended Data Fig. 3d–f), the frontal (exterior) skeleton walls are structurally absent. The internal cystid walls of D. hexaclitia have a planar spherulitic fabric (Fig. 2i), whereas its vertical walls preserve ring septa (Fig. 2b).

The zooids of P. gatehousei and D. hexaclitia contain phosphatized structures interpreted as membranous sacs, featuring thin walls and smooth outlines (Figs. 1a,e,h,i and 3b,g). The distal end of each membranous sac consists of a long, elliptical orifice surrounded by an undulating fold (Fig. 1h,i and Extended Data Fig. 5i). A consistent, uniform, 10–20-µm-wide gap separates adjacent sacs (Fig. 1h,i). The densely packed, well-developed circular and longitudinal fibres on the membranous sac surfaces represent annular and longitudinal muscles (Fig. 1i,l,m). The sac membranes are composed of crystalline apatite particles (Extended Data Figs. 6 and 7). In ELI DYCX 8-001, the phosphatized sacs and secondarily filled orifices (Extended Data Fig. 5) are overgrown by a subsequent phosphate coating, forming smooth bulges (Fig. 1b,j,k), indicating a multiphase phosphatization history. Longitudinally aligned cylindrical structures, possibly representing protective shields or a broad operculum, are preserved on both sides of the secondary orifice coatings (Extended Data Fig. 2a–f). The exceptional preservation of specimens ELI DYCX 8-016 and ELI DYCX 8-004 reveals that the membranous sacs are preserved in situ in the autozooid cystids (Fig. 3a–c,g–j). The sac is attached to the cystid wall in the distal apertural area, with a consistent spacing of 10–20 µm (Fig. 3g), matching the thickness interval of adjacent sacs in specimen ELI DYCX 8-001 (Fig. 1h,i). Transmission electron microscopy confirms the absence of a replacement relationship between the phosphatized sac and the silica-replaced cystid wall in ELI DYCX 8-004 (Extended Data Fig. 6), indicating their independent preservation pathways.

Discussion

P. gatehousei and D. hexaclitia satisfy almost all key diagnostic criteria for Palaeozoic bryozoans and share several traits with Class Stenolaemata27,28,29 (Extended Data Table 1). These shared traits include aspects of colony morphology30,31,32 (Fig. 1a,b and Supplementary Videos 1–6), skeletal architecture10,29,32,33 (Fig. 2 and Extended Data Fig. 3) and the presence of soft-tissue structures (Fig. 1 and Extended Data Fig. 2), such as membranous sacs24,28 (Fig. 1h), as well as annular and longitudinal musculature (Fig. 1i,l,m). Crucially, assignment to the Stenolaemata is strongly supported by the presence of several apomorphic palaeostomate features, including styles32,34 (Fig. 2f–h) and a free-walled colony organization10,35 (Figs. 2 and 3). A phylogenetic analysis incorporating these new traits now robustly identifies these early Cambrian forms both as crown group bryozoans and as part of the Stenolaemata (Fig. 4 and Supplementary Data 2–4). This pivotal revision moves beyond the previous stem-group hypothesis for P. gatehousei[2]. With both Cambrian bryozoan taxa now considered to be stenolaemates, they were conceivably biomineralized[36], although on the basis of the available evidence (Extended Data Figs. 5–7) their primary skeletal composition remains unknown. This establishes the possibility that the first of several independent biomineralization events in the Bryozoa[36] occurred during the early Cambrian, a scenario consistent with molecular analyses23,37. These results also imply that the common ancestor of the organic-walled Gymnolaemata and the mineralized Stenolaemata probably originated in the early Cambrian (Terreneuvian) or even perhaps in the Ediacaran period21,23,38.

Fig. 4: Phylogenetic relationships of bryozoans.

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A 50% majority-rule consensus phylogenetic tree inferred using morphological characters and Bayesian analysis based on a matrix of 22 taxa and 50 characters (see Methods and Supplementary Data 2–4 for source data and further information). Node values are Bayesian posterior probability support values. Coloured areas indicate the three taxonomic classes that comprise the Bryozoa along with outgroups, with Protomelission and Dayingomelission belonging to Stenolaemata.

These newly described specimens directly address and refute alternative interpretations that have suggested P. gatehousei should not be considered a bryozoan. A previous study[8] proposed that P. gatehousei represents a dasycladalean green alga, relying heavily on comparisons with larger, highly compressed Xiaoshiba fossils. These specimens differ critically from both the original specimens of P. gatehousei[2] and our own material. First, P. gatehousei specimens are small (2–3 mm), phosphatized, three-dimensional and bilaminate, with a tapering outline and a regular hexagonal chamber arrangement[2] (Fig. 1a–d and Extended Data Table 1). By contrast, the Xiaoshiba fossils are more than 20 times larger, clavate and have a single-layer wall[8]. Second, P. gatehousei possesses a suite of definitive bryozoan traits absent in the Xiaoshiba material, including zooidal budding from an endozone that produces overlapping zooids at an acute angle to a well-defined mesotheca2,39, (a characteristic commonly seen in ptilodictyine bryozoans[40]; Extended Data Fig. 3). Conversely, the Xiaoshiba modules have non-overlapping units and a dual-aperture structure more similar to archaeocyath bracts and pores[41]. These fundamental discrepancies indicate that the Xiaoshiba fossils are fundamentally different and unrelated to P. gatehousei. The alternative hypothesis that P. gatehousei represents the sclerites of Cambroclavus[9] is also inconsistent with our evidence. In complete specimens of P. gatehousei, the zooidal aperture either has a distinct, elongated–elliptical orifice (Fig. 1h,i) or is occluded by secondary phosphatic coatings, with only residual clay minerals marking its original position (Fig. 1b,j, Extended Data Fig. 5 and Supplementary Videos 1 and 2). By contrast, Cambroclavus is characterized by isolated sclerites with solid, circular spines in cross-section. Cambroclavus sclerites preserved in the mesotheca-subtended cylindrical cavities of P. gatehousei are disordered infills, not in situ structures2,9. Both anatomical and taphonomic evidence thus unequivocally establish that P. gatehousei and D. hexaclitia represent Cambrian bryozoans.

The presence of two bryozoan taxa reported here from the Xiannüdong Formation along with P. gatehousei from the lower Wirrealpa Limestone of South Australia[2] and a possible mineralized bryomorph from the lower Cambrian of Nevada[22] indicates that Cambrian bryozoans were more prevalent and widespread in early Cambrian shelf seas than previously assumed10,35, perhaps particularly so in archaeocyath reef-associated carbonate platform settings[42]. The exquisite preservation of these taxa not only validates their bryozoan affinities but also reveals an unexpected early disparity in colony architecture. Furthermore, the exceptional preservation of soft tissues alongside skeletal structures provides critical morphological evidence for reconstructing the evolution of colonial complexity in metazoans24,29,32 and, combined with evidence for other metazoan clades43,44,45, highlights that hierarchical colonial organization evolved as a fundamental innovation during the Cambrian radiation.

Systematic palaeontology

Phylum Bryozoa Ehrenberg, 1831

Class Stenolaemata Borg, 1926

Order and Family uncertain

Dayingomelission gen. nov. Song, Zhang & Ernst

Diagnosis. The colony is encrusting and unilaminate. Autozooecia are short and box-shaped, with hexagonal apertures. Vertical walls are straight or slightly inclined proximally. Adjacent autozooecia are separated by double-walled structures. Ring septa are present. Basal wall is planar to gently curved. Styles are occasionally present.

Type species. D. hexaclitia gen. et sp. nov. Song, Zhang & Ernst (Figs. 2a–i and 3c–g and Extended Data Fig. 6a,b).

Etymology. The generic name Dayingomelission combines the locality Daying with the Greek melission (honeycomb), alluding to the structure of the colony.

D. hexaclitia gen. et sp. nov. Song, Zhang & Ernst.

Holotype. ELI ZJBX 10-001 (Fig. 2a,b).

Paratypes. ELI ZJBX 10-002 (Fig. 2c–e), ELI DYCX 8-004 (Fig. 3c–g) and ELI ZJBX 3-001 (Fig. 2f–i).

Etymology. The species epithet hexaclitia is derived from the Greek hexa (six) and klitia (slope), describing the sloped, hexagonal apertures of the autozooids.

Diagnosis. Genus diagnosis is by monotypy.

Description. The colony is unilaminate, forming a sheet-like growth on the substrate (Fig. 2a,c). The available specimens are incompletely preserved, with the largest fragment exceeding 4 mm in maximum dimension and comprising more than ten zooidal rows (Fig. 2a). Astogeny occurs through linear budding, with branching observed where a single row of zooids diverges to form two new rows. Autozooids are short and box-like, arranged in a regular pattern (Fig. 2b). Their apertures are distinctly hexagonal, ranging from 200 µm to 400 µm in diameter (Extended Data Fig. 4). The orifice is incompletely preserved. The morphology of autozooids may show local deformation or reduction in size, probably resulting from spatial constraints during growth (Fig. 2c). The vertical walls are straight to very slightly inclined in the proximal direction. Adjacent autozooids are separated by a double-walled structure. The vertical walls are double-layered throughout (Fig. 2a–e). They are characterized by a planar spherulitic fabric in the endozone and bear 12–15 styles per autozooid aperture in the exozone of some specimens (Fig. 2f–h). These styles exhibit an elliptical cross-section, with the long axis measuring approximately 50 µm. Transverse ridges are developed on the vertical walls in the exozone (Fig. 2g). Internally, ring septa are consistently present (Fig. 2b). The basal wall is predominantly planar (Fig. 2i), with an occasional gentle curvature observed in some specimens.

Remarks. D. hexaclitia and P. gatehousei have similar hexagonal zooid apertures, and the arrangement of zooecial series is oligoserial in both taxa. In P. gatehousei, this oligoserial arrangement is expressed as a bilaminate colony with distally tapering frontal surfaces, which is a common growth pattern among early stenolaemate bryozoans, particularly in certain cryptostome lineages. However, D. hexaclitia can be clearly distinguished from P. gatehousei by its unilaminate colony arrangement (Figs. 1 and 2) and its larger aperture width (Extended Data Fig. 4). Any differences between the orifices remain unknown owing to the incomplete preservation of this feature in D. hexaclitia. The combination of a unilaminate colony, short box-like autozooids with hexagonal apertures and a distinctive double-walled structure confirms the bryozoan affinities of D. hexaclitia and serves to clearly distinguish it from other known bryozoan genera. The simple morphology of D. hexaclitia, particularly the lack of extra internal structures such as hemisepta, suggests a phylogenetic position among the early-diverging bryozoan groups.

The taphonomy of early Cambrian fossils preserved in carbonates is governed by complex, differential mineralization processes. In D. hexaclitia, for example, preservation varies from exclusively phosphatized skeletal walls (Fig. 2) to a mosaic of phosphatized membranous sacs and silicified skeletal walls (Fig. 3c–g). Conversely, exceptionally preserved P. gatehousei specimens exhibit robustly phosphatized soft tissues despite the near-total loss of vertical skeletal walls (Fig. 1h,i). Although such preservational biases, probably driven by localized geochemical microenvironments during diagenesis, can obscure vertical skeletal walls, their presence in both Cambrian bryozoan taxa is unequivocally supported by specimens in which soft tissues remain in situ in distinct skeletal chambers (Fig. 3b,g).

Terminology. In accordance with established bryozoan literature[29], we define orifice as referring to the opening bordered by soft tissues (the membranous sac opening), and aperture refers to the skeletal opening.

Stratigraphic occurrence. The specimens originate from the Xiannüdong Formation, Cambrian Stage 3, southern Shaanxi, China. A total of 14 specimens were examined, with the following specimen numbers: ELI ZJBX 10-001–002, ELI ZJBX 3-001, ELI DYCX 5-002, ELI DYCX 8-004, ELI DYCX 8-015, ELI DGWX 10-020, ELI DGWX 10-022, ELI DGWX 10-023, ELI DGWX 10-025, ELI DGWX 10-030, ELI DGWX 10-031, ELI DGWX 10-046 and ELI DGWX 10-048.

Methods

Terminology

We adopted the morphological terminology used in previous studies of fossils and extant bryozoans2,10,33,34,46,47.

Materials and occurrences

All fossils described in this study were collected from bioclastic and reef limestone outcrops of the Xiannüdong Formation (Cambrian Stage 3) in southern Shaanxi, South China. Carbonate rock samples were immersed in 5–10% acetic acid and washed twice weekly until all matrix material was completely dissolved, with insoluble residues collected after each treatment. Specimens were then picked from the washed and dried residues under a ZEISS binocular stereo microscope (Stemi 305). Measurements of fossil length, width and angle were obtained from X-ray computed tomography and scanning electron microscopy (SEM) images using ImageJ v.1.54f. Of the specimens, 19 are from the Dayingcun Section (ELI DYCX 5-001–002, ELI DYCX 8-001–016 and ELI DYCX 9-001), 3 are from the Zhujiaba Section (ELI ZJBX 3-001 and ELI ZJBX 10-001–002), 2 are from the Yangjiagou Section (ELI XX 4-001–002) and 14 are from the Donggouwan Section in Hanzhong, southern Shaanxi, South China (ELI DGWX 10-013, ELI DGWX 10-020, ELI DGWX 10-022, ELI DGWX 10-023, ELI DGWX 10-025, ELI DGWX 10-030, ELI DGWX 10-031, ELI DGWX 10-043, ELI DGWX 10-046, ELI DGWX 10-047, ELI DGWX 10-048, ELI DGWX 10-049, ELI DGWX 10-051 and ELI DGWX 10-052) (Extended Data Fig. 1b,c). The geological and geographical setting has been previously described in detail42,48. Among these specimens, those assigned to P. gatehousei include ELI DYCX 5-001, ELI DYCX 8-001–003, ELI DYCX 8-005–014, ELI DYCX 8-016, ELI DYCX 9-001, ELI XX 4-001–002, ELI DGWX 10-013, ELI DGWX 10-049, ELI DGWX 10-051 and ELI DGWX 10-052. Those classified as D. hexaclitia include ELI ZJBX 10-001–002, ELI ZJBX 3-001, ELI-DYCX 5-002, ELI DYCX 8-004, ELI DYCX 8-015, ELI DGWX 10-020, ELI DGWX 10-022, ELI DGWX 10-023, ELI DGWX 10-025, ELI DGWX 10-030, ELI DGWX 10-031, ELI DGWX 10-046 and ELI DGWX 10-048, with two indeterminate specimens ELI DGWX 10-043 and ELI DGWX 10-047. All specimens are deposited at Northwest University (NWU).

SEM

The well-preserved specimens were gold-coated and examined using SEM at the State Key Laboratory of Continental Evolution and Early Life at NWU (model and parameters: FEI Quanta 450-FEG with 20.0 kV, 60 Pa and working distance of 8–10 mm; Phenom XL G2 with 5–15 kV, high vacuum mode and working distance of 6–8 mm). Further semi-quantitative analyses were performed using the energy spectrometer attached to the above SEM system.

Focused ion beam–transmission electron microscopy

The fossils were directionally encapsulated by epoxy filling under vacuum and polished using a Cross Section Polisher (SEMPrep2 with 12.0 kV, 4.2°–4.4°, 1.09 × 10^4 mbar and 3.8 mA) until the test surface was exposed. Ultrathin foils (15 μm in length, 10 μm in width and less than 100 nm in thickness) were prepared using the ZEISS Auriga Compact dual-beam focused ion beam–SEM (FIB–SEM) instrument equipped with an OmniProbe 200 micromanipulator (high voltages, 5–30 kV; beam currents, 50 pA–2 nA) at the Institute of Geology and Geophysics, Chinese Academy of Sciences (IGGCAS). High-angle annular dark-field imaging, selected area electron diffraction imaging and scanning transmission electron microscopy–energy dispersive X-ray spectroscopy (STEM–EDXS) mapping were performed using the JEOL JEM-2100 transmission electron microscope at the IGGCAS and the JEM-F200 at NWU.

X-ray computed tomography

Five well-preserved specimens were scanned using a ZEISS Xradia CrystalCT, with the source operating at 80 kV and 125 µA over a 180° sample rotation (−92° to 92°) at NWU. The X-ray attenuation coefficients for each voxel were calculated from the scanned information and arranged into a digital matrix to reconstruct the image. X-ray computed tomography images were visualized and segmented through thresholding using ORS Dragonfly v.2022.2.0.1367. To help with differentiation, the morphological characteristics of interest were coloured differently from one another. Supplementary Videos 1–6 provide three-dimensional information (ELI DYCX 8-001, ELI DYCX 8-004, ELI DYCX 8-005, ELI DYCX 8-013 and ELI XX 4-001).

Phylogenetic analysis

Fifty characters were coded for Protomelission and Dayingomelission, 18 bryozoan genera and 2 outgroup genera (a total of 22 genera). The resulting phylogenetic data matrix was analysed using the same methods outlined in previous work[2]. The 18 bryozoan genera are exemplars of the eight major bryozoan orders, and the fossil genera chosen are all from the Ordovician period (except for Fenestrapora, which is from the Devonian period). The two outgroup taxa (Eoobolus and Phoronis) correspond to the two major non-bryozoan clades in the Lophophorata. Character codings were based on previously published data (Supplementary Data 2–4). All character codings are provided in NEXUS format, along with a full list of the characters used in Supplementary Data 2–4. Phylogenetic trees were inferred using Bayesian methods. Bayesian analyses used MrBayes (v.3.2.7) with the Mkv model, incorporating gamma-distributed rate variation and variable coding. This analysis featured a sampling frequency of 1,000, two concurrent runs and four Metropolis-coupled chains. It was conducted over ten million generations. A 25% relative burn-in was applied for all summary statistics, and the resulting phylogenetic tree is shown in Fig. 4.

Statistics and reproducibility

The SEM images and micrographs presented in this study were selected from all available materials to best illustrate the diagnostic features described. As this is a palaeontological study, it uses unique fossil specimens that are part of the evolutionary record, and, therefore, the exact fossils cannot be replicated. However, detailed locality information and complete methodological descriptions have been provided to enable independent resampling of the source horizons and repetition of all analytical procedures.

Reporting summary

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

Data availability

All data analysed in this study, including the phylogenetic datasets, are available within this paper and in Extended Data Figs. 1–7, Extended Data Table 1 and the Supplementary Information. Raw datasets are available at Figshare (https://doi.org/10.6084/m9.figshare.31995942)[49]. Computed tomography scans of specimens in this publication can be accessed in the MorphoSource Repository (https://doi.org/10.17602/M2/M851384, https://doi.org/10.17602/M2/M851389, https://doi.org/10.17602/M2/M851394, https://doi.org/10.17602/M2/M851399, https://doi.org/10.17602/M2/M851404 and https://doi.org/10.17602/M2/M851409) and the affiliated project (https://www.morphosource.org/projects/000850335?locale=en). The nomenclature of D. hexaclitia gen. et sp. nov. is registered in ZooBank (urn:lsid:zoobank.org:act:B5030E29-675D-4F12-9840-9999D64E52EB for genus and urn:lsid:zoobank.org:act:58073D95-64BE-4D1B-832C-1A5223E37837 for species). The Life Sciences Identifier for this publication is urn:lsid:zoobank.org:pub:D8979F9C-502B-41BE-9889-4510AB5664CD.

References

1. Budd, G. E. & Mann, R. P. Survival and selection biases in early animal evolution and a source of systematic overestimation in molecular clocks. Interface Focus 10, 20190110 (2020).

2. Zhang, Z. et al. Fossil evidence unveils an early Cambrian origin for Bryozoa. Nature 599, 251–255 (2021).

3. Erwin, D. H. et al. The Cambrian conundrum: early divergence and later ecological success in the early history of animals. Science 334, 1091–1097 (2011).

4. Wray, G. A. Molecular clocks and the early evolution of metazoan nervous systems. Philos. Trans. R. Soc. B 370, 20150046 (2015).

5. Budd, G. E. & Jackson, I. S. C. Ecological innovations in the Cambrian and the origins of the crown group phyla. Philos. Trans. R. Soc. B 371, 20150287 (2016).

6. Erwin, D. H. The origin of animal body plans: a view from fossil evidence and the regulatory genome. Development 147, dev182899 (2020).

7. Zhang, X. & Shu, D. Current understanding on the Cambrian Explosion: questions and answers. PalZ 95, 641–660 (2021).

8. Yang, J., Lan, T., Zhang, X. & Smith, M. R. Protomelission is an early dasyclad alga and not a Cambrian bryozoan. Nature 615, 468–471 (2023).

9. Xiang, K., Yin, Z., Liu, W., Zhao, F. & Zhu, M. Early Cambrian Cambroclavus is a scleritomous eumetazoan unrelated to bryozoan or dasyclad algae. Geology 52, 130–134 (2024).

10. Ernst, A. in Phylum Bryozoa (eds Helmcke, J. G. et al.) 11–56 (De Gruyter, 2020).

11. Landing, E. et al. Early evolution of colonial animals (Ediacaran Evolutionary Radiation–Cambrian Evolutionary Radiation–Great Ordovician Biodiversification Interval). Earth Sci. Rev. 178, 105–135 (2018).

12. Landing, E., English, A. & Keppie, J. D. Cambrian origin of all skeletalized metazoan phyla—discovery of Earth’s oldest bryozoans (Upper Cambrian, southern Mexico). Geology 38, 547–550 (2010).

13. Landing, E. et al. Distinguishing Earth’s oldest known bryozoan (Pywackia, late Cambrian) from pennatulacean octocorals (Mesozoic–recent). J. Paleontol. 89, 292–317 (2015).

14. Fritz, M. A. Cambrian Bryozoa. J. Paleontol. 21, 434–435 (1947).

15. Bassler, R. S. Taxonomic notes on genera of fossil and recent Bryozoa. J. Wash. Acad. Sci. 42, 381–385 (1952).

16. Rowland, S. M. Earliest cavity-dwelling organisms (coelobionts), Lower Cambrian Poleta Formation, Nevada: discussion. Can. J. Earth Sci. 20, 1348–1349 (1983).

17. Taylor, P. D. Marcusodictyon Bassler from the Lower Ordovician of Estonia: not the earliest bryozoan but a phosphatic problematicum. Alcheringa 8, 177–186 (1984).

18. Xia, F., Zhang, S. & Wang, Z. The oldest bryozoans: new evidence from the Late Tremadocian (Early Ordovician) of East Yangtze Gorges in China. J. Paleontol. 81, 1308–1326 (2007).

19. Taylor, P. D., Berning, B. & Wilson, M. A. Reinterpretation of the Cambrian ‘bryozoan’ Pywackia as an octocoral. J. Paleontol. 87, 984–990 (2013).

20. Hageman, S. J. & Vinn, O. Late Cambrian Pywackia is a cnidarian, not a bryozoan: insights from skeletal microstructure. J. Paleontol. 97, 990–1001 (2023).

21. Orr, R. J. et al. Paleozoic origins of cheilostome bryozoans and their parental care inferred by a new genome-skimmed phylogeny. Sci. Adv. 8, eabm7452 (2022).

22. Pruss, S. B., Leeser, L., Smith, E. F., Zhuravlev, A. Y. & Taylor, P. D. The oldest mineralized bryozoan? A possible palaeostomate in the lower Cambrian of Nevada, USA. Sci. Adv. 8, eabm8465 (2022).

23. Saadi, A. J. et al. Phylogenomics reveals deep relationships and diversification within phylactolaemate bryozoans. Proc. R. Soc. Lond. B 289, 20221504 (2022).

24. Ernst, A. Fossilized soft tissues in Palaeozoic bryozoans. Pap. Palaeontol. 9, e1483 (2023).

25. McMenamin, M. A. S. The Cambrian Explosion: macroevolution and biomineralization. Acad. Biol. https://doi.org/10.20935/AcadBiol6036 (2023).

26. Reid, C. M., Wyse Jackson, P. N. & Key, M. M. Latitudinal influences on bryozoan calcification through the Paleozoic. Paleobiology 49, 271–283 (2023).

27. Nielsen, C. & Pedersen, K. J. Cystid structure and protrusion of the polypide in Crisia (Bryozoa, Cyclostomata). Acta Zool. 60, 65–88 (1979).

28. Schwaha, T. F., Ostrovsky, A. N. & Wanninger, A. Key novelties in the evolution of the aquatic colonial phylum Bryozoa: evidence from soft body morphology. Biol. Rev. 95, 696–729 (2020).

29. Schwaha, T. in Phylum Bryozoa, Handbook of Zoology (ed. Schwaha, T.) (De Gruyter, 2020).

30. Hageman, S. J. et al. Bryozoan growth habits: classification and analysis. J. Paleontol. 72, 418–436 (1998).

31. Taylor, P. D. & James, N. P. Secular changes in colony-forms and bryozoan carbonate sediments through geological history. Sedimentology 60, 1184–1212 (2013).

32. Taylor, P. D. Bryozoan Paleobiology (Wiley, 2020).

33. Ma, J., Taylor, P. D., Xia, F. & Zhan, R. The oldest known bryozoan: Prophyllodictya (Cryptostomata) from the lower Tremadocian (Lower Ordovician) of Liujiachang, south-western Hubei, central China. Palaeontology 58, 925–934 (2015).

34. Blake, D. B. Acanthopore morphology and function in the bryozoan family Rhabdomesidae. J. Paleontol. 47, 421–435 (1973).

35. Ma, J., Buttler, C. J. & Taylor, P. D. Cladistic analysis of the ‘trepostome’ suborder Esthonioporina and the systematics of Palaeozoic bryozoans. Studi Trent. Sci. Nat. 94, 153–161 (2014).

36. Taylor, P. D., Lombardi, C. & Cocito, S. Biomineralization in bryozoans: present, past and future. Biol. Rev. 90, 1118–1150 (2015).

37. Wernström, J. V., Gąsiorowski, L. & Hejnol, A. Brachiopod and mollusc biomineralisation is a conserved process that was lost in the phoronid–bryozoan stem lineage. EvoDevo 13, 17 (2022).

38. Erwin, D. H. Early metazoan life: divergence, environment and ecology. Philos. Trans. R. Soc. B 370, 20150036 (2015).

39. Gorjunova, R. V. New cryptostomide bryozoans from the Upper Devonian of Transcaucasia and some aspects of the evolution of the Order Cryptostomida. Paleontol. J. 41, 600–613 (2007).

40. Karklins, O. L. in Treatise on Invertebrate Paleontology, Part G, Revised, Bryozoa (ed. Robison, R. A.) 453–488 (Geological Society of America and Univ. Kansas Press, 1983).

41. Yang, A. et al. Niche expansion of archaeocyaths during their palaeogeographic migration: evidence from the Chengjiang Biota. Palaeogeogr. Palaeoclimatol. Palaeoecol. 653, 112419 (2024).

42. Song, B. et al. Small skeletal fossils associated with archaeocyath reefs from the Xiannüdong Formation (Cambrian Series 2), southern Shaanxi, China. Palaeobio. Palaeoenv. https://doi.org/10.1007/s12549-025-00677-y (2026).

43. Chang, S. et al. New probable cnidarian fossils from the lower Cambrian of the Three Gorges area, South China, and their ecological implications. Palaeogeogr. Palaeoclimatol. Palaeoecol. 505, 150–166 (2018).

44. Park, T. et al. A stem-group cnidarian described from the mid-Cambrian of China and its significance for cnidarian evolution. Nat. Commun. 2, 1–6 (2011).

45. Manzuk, R. A., Maloof, A. C., Kaandorp, J. A. & Webster, M. Branching archaeocyaths as ecosystem engineers during the Cambrian radiation. Geobiology 21, 66–85 (2022).

46. McKinney, F. K. & Jackson, J. B. C. Bryozoan Evolution (Univ. Chicago Press, 1989).

47. Taylor, P. D. & Waeschenbach, A. Phylogeny and diversification of bryozoans. Palaeontology 58, 585–599 (2015).

48. Li, G., Zhu, M., Van Iten, H. & Li, C. Occurrence of the earliest known Sphenothallus Hall in the Lower Cambrian of southern Shaanxi Province, China. Geobios 37, 229–237 (2004).

49. Song, B. et al. Supplementary Data 1–4 from: High-fidelity modular skeletons authenticate a Cambrian origin for Bryozoa. Figshare https://doi.org/10.6084/m9.figshare.31995942 (2026).

Acknowledgements

We thank J. Zhai, R. Fu, Z. Wang, J. Zhao and Q. Zhang for their fieldwork and laboratory support; J. Sun and J. Dai for their assistance with micro-computed tomography reconstruction and STEM–EDXS mapping, respectively; J. Li and his team for their help with FIB–SEM; and X. Song for his advice and discussion.

Funding

This study was financially supported by the National Key Research and Development Program of China (grant 2023YFF0803601 to Zhifei Zhang), the National Natural Science Foundation of China (grantW2441016 to L.C.S.), the Department of Science and Technology of Shaanxi Province (2022TD-11 to Zhifei Zhang) and 111 Project (D17103).

Author information

Authors and Affiliations

1. State Key Laboratory of Continental Evolution and Early Life, Shaanxi Key Laboratory of Early Life and Environments, Department of Geology, Northwest University, Xi’an, China

Baopeng Song  (宋宝鹏), Zhifei Zhang  (张志飞), Luke C. Strotz, Timothy P. Topper, Zhiliang Zhang  (张志亮), Mei Luo  (罗梅), Lars E. Holmer, Yue Liang  (梁悦), Yazhou Hu  (胡亚洲), Caibin Zhang  (张彩彬), Yanlong Chen  (陈延龙) & Glenn A. Brock

2. Department of Earth Sciences, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands

Luke C. Strotz

3. Department of Palaeobiology, Swedish Museum of Natural History, Stockholm, Sweden

Timothy P. Topper

4. Institut für Geologie, Universität Hamburg, Hamburg, Germany

Andrej Ernst

5. State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences, Nanjing, China

Junye Ma  (马俊业) & Zhiliang Zhang  (张志亮)

6. Department of Earth Sciences, Palaeobiology, Uppsala University, Uppsala, Sweden

Lars E. Holmer

7. School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia

Glenn A. Brock

Contributions

Zhifei Zhang conceived the research. Zhifei Zhang led and supervised the fieldwork, assisted by B.S., M.L. and Y.C. B.S. prepared all specimens and SEM photographs, conducted FIB–TEM and STEM–EDXS analyses, performed visualization with micro-computed tomography data and produced the figures. L.C.S., A.E., J.M., G.A.B. and Zhiliang Zhang undertook phylogenetic analyses. Zhifei Zhang and B.S. wrote the early drafts. Zhifei Zhang, B.S., L.C.S. and T.P.T. wrote the final draft and all authors approved the results and final submitted version of the paper.

Corresponding author

Correspondence to Zhifei Zhang  (张志飞).

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Nature thanks Débora Iturra, Juan López-Gappa, Catherine Reid and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Geographic map of fossil localities.

a, Map of China, modified from the Standard Map of China (No. GS (2023)2767). b, Locality details of the Zhujiaba, Dayingcun, Yangjiagou, Donggouwan and Xiaoyangba sections, southern Shaanxi Province, South China. The Xiaoyangba locality represents the lowest stratigraphic occurrence of Protomelission gatehousei[2]. c, Bubble diagram showing the distribution of specimens of P. gatehousei and D. hexaclitia gen. et sp. nov. in the Xiannüdong Formation stratigraphic succession.

Extended Data Fig. 2 Soft-tissue preservation of Protomelission gatehousei.

a–e, ELI DYCX 8-005. a, Front side of the colony, box corners indicate the area shown in d. b, The back side of the colony. c, Lateral view of the bifoliate colony. d,e, Enlarged view of elongated hexagonal zooids. Note the longitudinally neatly arranged cylindrical structures on the both sides of the ridge-like orifice, which are possible secondary coatings of protective shields. f, Protective shields developed in an extant cheilostome Valdemunitella sp. photographed by Dennis Gordon (Wellington).

Extended Data Fig. 3 X-ray tomographic microscopy images of Protomelission gatehousei and Dayingomelission hexaclitia gen. et sp. nov.

a–c, P. gatehousei. ELI DYCX 8-016. a,b, CT-reconstructed longitudinal section with false-colour delineating the distribution of the frontal surface (purple), vertical wall (red) and basal wall (yellow). Boxed region in a indicates the areas enlarged in b and c. c, Enlarged CT-reconstructed longitudinal section rendered using a bone colour palette. d, ELI DYCX 8-005, transverse section of D. hexaclitia gen. et sp. nov., note the regular hexagonal compactly arranged cystids in original greyscale. e,f, P. gatehousei rendered using a bone colour palette. ELI DYCX 8-013. e, Front side of colony, showing the hexagonal cystids with vertical walls. f, longitudinal section of e, showing phosphatized skeleton preserved in high fidelity (in contrast to other specimens, e.g. Extended Data Fig. 6) and indicating the presence of a double wall. Z, zooids; tailed arrow, mesotheca; arrows, vertical walls.

Extended Data Fig. 4 Scatter and violin plots of zooid size of Protomelission gatehousei and Dayingomelission hexaclitia gen. et sp. nov.

Note in a that P. gatehousei and D. hexaclitia gen. et sp. nov. show distinct differences in zooid width. Values for boxplots are provided in Supplementary Data 1. N = 646 independent measurements of zooid size. Zooid length and width are defined in b (ELI DYCX 8-001). Length refers to the longitudinal axis of zooids (parallel to the growth axis), while width refers to the transverse axis measured perpendicular to the growth direction.

Extended Data Fig. 5 Elemental mapping of the zooids of Protomelission gatehousei in Fig. 1j, showing the orifice is coated by secondary phosphate, ELI DYCX 8-001.

a, SEM image of zooids. b, Elemental map of Ca. c, Elemental map of P. d, Elemental map of Si. e, Elemental map of Al. f, Elemental map of C. g, Elemental map of Fe. h, Elemental map of O. i, Elemental map of O, Ca and P, Si and Fe concentrations, noting the orifice is filled with clay minerals.

Extended Data Fig. 6 TEM characterization of the mineralogy and chemistry of the vertical skeletal wall and its relationship with the preserved membranous sacs in Dayingomelission hexaclitia gen. et sp. nov., ELI DYCX 8-004.

a, Enlarged view of c in Fig. 3, b, Enlarged view of a, showing the cystids and membranous sacs at the boundary of the three zooids, box corners indicate the area shown in c. c, SEM image after encapsulating and polishing to the test exposure surface, note the cystids are compact and no particles visible, sac consists of small particles tightly packed. White box showing FIB cutting area. d, SEM image of the FIB ultra-thin foid sample prepared by FIB-SEM, white box indicates the area shown in e. Note the apatite particles are embedded in cystids. e, HAADF image of FIB ultra-thin foid, showing no obvious replacement relationship between cystid and membranous sac, cystids consist of amorphous SiO_2 and membranous sac consists of crystalline apatite nanoparticles. f, HRTEM image of apatite nanoparticle in membranous sac, with Miller indices and the corresponding d-spacing values, bottom left corner indicates SAED pattern of apatite nanoparticles in membranous sac. g, SAED pattern of cystid, revealing its amorphous silicon composition. h,i, EDXS spectra of membranous sac (h) and cystid (i) derived from the microregions (<50 nm) in d, respectively. Cy, cystid; Ms, membranous sac.

Extended Data Fig. 7 STEM-EDXS elemental maps of cystid and membranous sac of Dayingomelission hexaclitia gen. et sp. nov., ELI DYCX 8-004.

a, SEM image of the FIB ultra-thin foil sample prepared by FIB-SEM, white box indicates the STEM-EDXS elemental map area. Cy, cystid; Ms, membranous sac. b, STEM-HAADF image of elemental map area. c, Elemental map of C. d, Elemental map of Ca. e, Elemental map of P. f, Elemental map of O. g, Elemental map of Si. h, Elemental map of P, Ca and Si.

Extended Data Table 1 Character traits expected in ancestral Cambrian bryozoans

Supplementary information

Supplementary Data 1–4 (download PDF )

Supplementary Data 1 contains measurements of P. gatehousei and D. hexaclitia gen. et sp. nov. Supplementary Data 2 contains a character taxon matrix in NeXus format. Supplementary Data 3 contains character traits and morphological states for bryozoans. Supplementary Data 4 contains a character trait dataset matrix.

Reporting Summary (download PDF )

Peer Review File (download PDF )

Supplementary Video 1 (download MP4 )

Three-dimensional reconstruction of computed tomography data for ELI DYCX 8-001, showcasing the bilaminate colony of P. gatehousei.

Supplementary Video 2 (download MP4 )

Three-dimensional reconstruction of computed tomography data for ELI DYCX 8-001, with sections showing the details of the longitudinal sections of zooids and colony.

Supplementary Video 3 (download MP4 )

Three-dimensional reconstruction of computed tomography data for ELI DYCX 8-013, showing the double-walled structure of the colony on one side of P. gatehousei.

Supplementary Video 4 (download MP4 )

Three-dimensional reconstruction of computed tomography data for ELI DYCX 8-004, showcasing the unilaminate morphology of D. hexaclitia gen. et sp. nov. colony.

Supplementary Video 5 (download MP4 )

Three-dimensional reconstruction of computed tomography data for ELI DYCX 8-004, with sections showing the details of the longitudinal sections of membranous sacs.

Supplementary Video 6 (download MP4 )

Three-dimensional reconstruction of computed tomography data for ELI DYCX 8-005, showcasing the bilaminate morphology of P. gatehousei colony.

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Song, B., Zhang, Z., Strotz, L.C. et al. High-fidelity modular skeletons authenticate a Cambrian origin for Bryozoa. Nature 656, 123–128 (2026). https://doi.org/10.1038/s41586-026-10590-9

- Received: 10 February 2026

- Accepted: 23 April 2026

- Published: 03 June 2026

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- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10590-9

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Abstract

Most genetic variants associated with complex diseases lie in non-coding regions[1], complicating efforts to identify effector genes and relevant cell types. Here we map cis-expression quantitative trait loci (eQTLs) across 2.2 million single cells using intestinal biopsies and blood from 421 individuals, including 125 with inflammatory bowel disease (IBD). Cell-type-level eQTLs were more distal to transcription start sites, enriched in enhancers, less likely to regulate the nearest gene, and more than 3.5-fold more likely to colocalize with IBD loci detected in genome-wide association studies (GWASs) than eQTLs detected at tissue-level resolution. We nominate effector genes at more than half of known IBD loci, including MAML2, PSEN2 and ZMIZ1 in myeloid cells, implicating reduced Notch signalling in intestinal immune dysfunction. We also identify Wnt-regulated genes, including MYC, in epithelial stem and progenitor cells, suggesting that impaired renewal contributes to barrier breakdown. Our results provide a mechanistic map that links genetic risk to specific genes and cell types in IBD, and a generalized framework for interpretation of GWAS loci using single-cell eQTL mapping of disease-relevant tissues in complex diseases.

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Main

Common complex diseases cause substantial morbidity and mortality worldwide, yet the biological mechanisms that underlie susceptibility remain incompletely understood. Genetic variation contributes considerably to disease susceptibility, and GWASs have identified hundreds of thousands of associations between genetic variants and complex diseases and traits[2]. However, more than 90% of GWAS signals map to non-coding regions of the genome[1], complicating efforts to identify dysregulated genes, pathways and cell types.

Non-coding variants are thought to influence disease risk by altering expression of nearby genes. This has prompted large-scale efforts to map cis-eQTLs and integrate them with GWAS findings. Although these studies have advanced our understanding of disease biology3,4,5,6,7, most GWAS loci remain unannotated. This lack of functional resolution poses a major challenge for therapeutic development, as drug targets supported by human genetic evidence are substantially more likely to progress successfully through clinical trials8,9,10,11. One possible explanation for this annotation gap is that eQTL and GWAS signals often have different genomic contexts: eQTLs are more frequently found in promoters, whereas GWAS signals are enriched in enhancers[12]. However, this divergence may reflect methodological biases in current eQTL discovery efforts, which often rely on low-resolution bulk RNA sequencing of heterogeneous tissues or coarsely sorted cell populations13,14,15,16. These approaches may favour detection of regulatory effects that are shared across multiple cell types, which are more likely to reside in promoters than enhancers[17].

Single-cell RNA sequencing (scRNA-seq) can capture gene expression variation at much higher cellular resolutions, enabling the identification of cell-type-specific eQTLs that may be inaccessible to bulk tissue studies7,18,19,20,21,22,23. However, it remains unclear to what extent eQTLs identified at different cellular resolutions improve effector gene nomination at GWAS loci.

We hypothesized that GWAS signals would be preferentially enriched among cell-type-specific single-cell eQTLs (sc-eQTLs) as contextual restriction of their regulatory effects may mean they face weaker selective pressures. To test this, we established the IBDverse project, generating large-scale scRNA-seq datasets from blood and intestinal biopsies of the terminal ileum and rectum—the sites that are most commonly affected in Crohn’s disease (CD) and ulcerative colitis (UC), respectively. These diseases represent the two main forms of IBD, a debilitating group of gastrointestinal disorders affecting more than 4.9 million people worldwide. Using nearly 2.2 million single-cell transcriptomes from more than 400 individuals, including 125 with IBD, we comprehensively mapped eQTL effects across a broad range of cellular resolutions, from individual cell types to tissue-level groupings. Our analyses enabled the nomination of candidate effector genes and cell types for more than half of the 321 known IBD loci, including 74 loci where an effector gene is nominated for the first time. Furthermore, we systematically quantified the contribution of eQTLs detected at different cellular resolutions to GWAS association signals, demonstrating that cell-type-level eQTLs are substantially enriched among IBD GWAS loci and preferentially reside in distinct genomic contexts compared to eQTLs detected across broader cell groupings. These findings highlight the power of single-cell resolution for interpreting complex disease genetics and provide a general framework for effector gene discovery across diverse diseases.

A cellular atlas of intestine and blood

To enable large-scale sc-eQTL mapping at sites relevant to IBD, we created IBDverse, comprising scRNA-seq data generated from 275 healthy rectal biopsies, 119 CD terminal ileal biopsies, 243 healthy terminal ileal biopsies and 95 blood samples from individuals with CD (Fig. 1a). After stringent quality control, we obtained more than 1.8 million high-quality cells, which we clustered into 86 transcriptionally distinct groups across 9 major cell populations (Supplementary Fig. 1 and Extended Data Fig. 1). Samples from all anatomical sites were analysed jointly to ensure consistent cell-type assignment and to capture both site-specific and shared cell states. Following training and annotation with CellTypist[24] (Methods), our final sc-eQTL mapping cohort comprised nearly 2.2 million cells from 396 individuals with matched genome-wide genotype data (Extended Data Figs. 1c and 2).

Fig. 1: Distinct eQTLs are observed at different cellular resolutions.

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a, scRNA-seq data were generated from blood, rectal and terminal ileal biopsies from 421 healthy individuals and patients with CD. After joint quality control, integration and clustering, a multi-site single-cell atlas of IBD was constructed (Methods and Supplementary Fig. 1). Genome-wide germline genotypes were obtained from the same individuals. Within each anatomical site, eQTLs were mapped at three resolutions: cell type (highest resolution; n = 86); major population (coarse resolution; n = 9); and the entire sample (‘all cells’; lowest resolution; n = 1). Samples were also combined across sites (cross-site), generating 4 site-level annotations, 96 cell-level annotations, and up to 384 unique groupings. Disease effector genes were nominated by colocalizing eQTLs with IBD GWAS signals. Panel created in BioRender; Harris, B. https://BioRender.com/61lbk6w (2026). b, Relationship between the mean number of cells per donor and the number of eGenes detected per cell type in the cross-site analysis. c, Sharing of eGenes across anatomical sites. d, Identification of eGenes (left) and eQTLs (right) across resolutions. Genes and eQTLs are grouped by the minimum resolution at which they were detected, with percentages shown for each level. e, Capture of resolution-specific eQTLs in 49 GTEx tissues using the π1 statistic (higher values indicate greater sharing; Methods). Each point represents comparison with one GTEx tissue. P values were derived from paired Wilcoxon tests. f, Distance between each eQTL lead variant and the TSS of its eGene. Median distances and the percentage located less than 10 kb from the TSS are shown above the plots. Total number of eQTLs is 31,307 (‘all cells’), 31,261 (major population) and 25,980 (cell type). g, Relative enrichment of eQTLs from each resolution in FANTOM and ENCODE enhancer annotations versus promoters, estimated from 1,000 bootstraps against matched variants (Methods). All pairwise two-sided t-tests had P < 2.2 × 10−16. Horizontal bars in e–g indicate the 25th, 50th and 75th percentiles.

Cell-type resolution reveals distinct eQTLs

Leveraging the single-cell and multi-site nature of IBDverse, we performed cis-eQTL mapping across a broad range of cellular resolutions. Specifically, we mapped eQTLs at the level of 86 transcriptionally defined cell types, within 9 major cell populations, and across all cells combined from each sample (‘all cells’ resolution). This was done within each anatomical site separately (rectum, terminal ileum or blood), or by combining expression across sites (cross-site) (Fig. 1a), resulting in a theoretical maximum of 384 unique cellular annotations. Where eQTL effects were detected, we mapped conditionally independent signals and grouped lead variants based on linkage disequilibrium to enable direct comparison of eQTL effects across annotations and resolutions (Methods).

We identified 88,548 eQTLs associated with the expression levels of 20,389 out of the 24,761 genes tested (82.2%) (genome-wide false discovery rate (FDR) < 0.05; Methods). Because the germline remains fixed from conception, this represents genetic variation that confers a regulatory effect on gene expression level. In total, 252 out of the possible 384 cellular annotations had a sufficient number of samples for testing and at least 1 eQTL was detected in 251 out of these 252 annotations (Methods), with the number of eGenes (genes with at least 1 significant eQTL) being positively correlated with the number of constituent cells, the number of donors, and the average number of genes expressed per cell (all P < 0.001; Fig. 1b and Extended Data Fig. 3). Most eGenes were shared across sites, most frequently between the rectum and terminal ileum (5,861, 28.7%), followed by all three sites (5,613, 27.5%) (Fig. 1c). However, many eGenes (1,945, 9.5%) were only detected when combining sites, displaying the value of aggregating common cell types across different tissues.

The majority of eGenes (15,713, 77.1%) were detected at the ‘all cells’ resolution, with additional eGenes identified at the major population (3,565, 17.5%) and cell-type levels (1,111, 5.4%) (Fig. 1d). By contrast, nearly one-third (25,980, 29.3%) of eQTLs were detected at only the cell-type level, with even more detected at the major population but not ‘all cells’ level (31,261, 35.3%). The number of eQTLs per eGene also declined at coarser resolutions—averaging 3.1 at the cell-type level, 2.8 at the major population level and 2.0 at the ‘all cells’ level (all comparisons P < 2.2 × 10−16; Extended Data Fig. 4a). Although many of our eQTLs are also captured by bulk eQTLs from GTEx[4] or peripheral blood sc-eQTLs from Yazar et al.[18] (Methods and Supplementary Note), our cell-type eQTLs were much less likely to be found previously than major population or ‘all cells’ eQTLs (median π1 with GTEx tissues = 0.2, 0.25 and 0.4, respectively, maximum P value = 1.1 × 10−14) (Fig. 1e). Together, these findings demonstrate a key distinction: although the ‘all cells’ resolution identifies more eGenes overall, cell-type-level mapping reveals distinct and diverse regulatory variants that are masked in bulk analyses.

Given these differences in detection, we next tested whether the genomic location of eQTLs also varied by resolution. eQTLs detected only at the cell-type level were located significantly further from the transcription start site (TSS) of their associated eGene (median distance = 200 kb) than those detected at the major population (median = 114 kb) or ‘all cells’ level (median = 52 kb; both comparisons P < 2.2 × 10−16; Fig. 1f). Correspondingly, the nearest gene was the eGene for 35.5% of eQTLs detected at the ‘all cells’ level, compared to 27% and 19.8% for those at the major population and cell-type levels, respectively (Extended Data Fig. 4b).

Functionally, eQTLs identified at only the cell-type level were more enriched in FANTOM and ENCODE enhancer annotations rather than promoters, compared with those at the ‘all cells’ or major population level (P < 2.2 × 10−16 for all comparisons) (Fig. 1g). Because GWAS signals also tend to be found in enhancers[12], these findings suggest that gene expression changes driven by eQTL signals found only at a cell-type resolution are more likely to mediate phenotypic differences in complex traits and diseases.

We next explored whether genetic effects on gene expression are modulated by individual-level traits. We performed interaction eQTL (ieQTL) mapping using genotype–expression models that included interaction terms for age, sex and smoking status as well as CD diagnosis and macroscopic degree of ileal inflammation at the time of sampling, where possible (Methods). This analysis could uncover effects that are exacerbated by, specific to, or diminished during disease onset or progression. We identified 1,051 ieQTLs regulating the expression of 977 genes (ieGenes), and 764 (78.2%) of these ieGenes were modulated by gut inflammation status (adjusted P value < 0.05; Methods). Of note, the majority of these inflammation-responsive ieGenes (501, 65.6%) were detected in enterocytes, suggesting that genetic effects on gene expression in this major population are highly sensitive to inflammatory context (Extended Data Fig. 5).

Although most ieGenes (941, 96.3%) were also eGenes, only 53 ieQTLs (5.0%) were in strong linkage disequilibrium with eQTLs (r^2 > 0.5) for the same gene. Even at a much lower threshold of r^2 > 0.1, only 149 (14.3%) of ieQTLs overlapped with eQTLs (Supplementary Fig. 2), indicating that these effects are largely distinct from those observed in conventional eQTL analyses. To better understand the pathways disrupted by the interaction between inflammation and genetics in enterocytes we performed gene set enrichment analysis, which returned four pathways (coefficient > 0; FDR < 0.05; Supplementary Information). Notably, the most enriched pathway was ‘response to metal ion binding’ in enterocytes (coefficient = 2.04). Leading edge genes encode many isoforms of metallothionein proteins that bind metal ions[25], such as MT2A, MT1H and MT3, genes that are also associated with protective effects against inflammation across several tissues including the skin[26], blood[27], liver[28], lung[29] and intestine30,31. Together, these findings support the value of mapping genetic regulation under different disease-relevant conditions to uncover context-dependent mechanisms that would be missed in steady-state or healthy tissue analyses.

Cell-type eQTLs better nominate disease effector genes

Next, we reasoned that cell-type-specific sc-eQTLs, being more distal to TSSs and enriched in enhancer elements, would be more likely to colocalize with GWAS signals than eQTLs identified at coarser resolutions. We therefore performed colocalization analyses between IBD GWAS signals and our full set of eQTL and ieQTL maps, using summary statistics for CD, UC and the more general phenotype of IBD[3]. This method tests the likelihood that two traits, in this case eQTL and GWAS associations, share a causal variant at a given genetic locus[32]. To benchmark our findings, we compared them to colocalizations that were previously reported for IBD loci in the Open Targets genetics portal, including those based on expression, splicing and other molecular QTLs[33].

Among the 321 IBD loci reported to date[34], we observed colocalization (posterior probability of a shared causal variant (PPh4) > 0.75) with either eQTLs or ieQTLs at 180 loci (56%), implicating 419 distinct effector genes (Extended Data Table 1 and Supplementary Information). This includes 74 loci where no effector gene had previously been nominated by colocalization in the Open Targets genetics portal (Fig. 2a) and 257 novel disease effector genes (101 at newly colocalized loci). Even compared with an sc-eQTL study of peripheral blood mononuclear cells from 1,925 healthy individuals[35], our study nominates 173 novel effector genes for IBD, including 88 across 49 loci where one is not otherwise nominated (PPh4 > 0.8), leaving 169 (67.3%) out of the 251 effector genes that Cuomo et al.[35] nominate to be specific to their dataset. Despite a considerably smaller sample size, 18 loci have effector genes nominated only in our gastrointestinal immune populations. The likelihood that we identify a novel disease effector gene compared with this dataset was negatively associated with the number of cells within each major population that was derived from the blood (slope = −0.194, P = 0.014) (Supplementary Fig. 3), indicating a considerable benefit to the inclusion of samples from the primary site of disease. Notably, at 2 of these 180 loci, colocalization was observed only with ieQTLs, illustrating that context-dependent regulatory effects can reveal additional candidate effector genes that are not detected under baseline conditions.

Fig. 2: Detection and distribution of colocalization events with IBD, CD and UC GWAS.

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a, The number of IBD GWAS loci[34] coloured by the proportion that previously colocalized (posterior probability of hypothesis 4 (PPh4) > 0.75) with existing bulk quantitative trait loci (QTLs) in Open Targets genetics (Methods) or do so for the first time with IBDverse sc-eQTLs. The total number of loci (321) is shown on top, along with the cumulative number of loci for which disease effector genes are nominated by each dataset. b, Comparison between the proportion of eGenes detected and the OR between disease effector gene and eGene detection. Values greater than one indicate an enrichment of disease effector genes among eGenes; values less than one indicate underrepresentation of disease effector genes among eGenes. c, Relationship between the proportion of CD samples for each cellular annotation of the terminal ileum and log_10-transformed OR between disease effector gene and eGene detection. The horizontal line indicates an OR of 1 and therefore no enrichment, as in b. Univariate linear regression slope = 3.98, P = 4.68 × 10−9. Colour scheme as in d,e. d,e, The number of IBD (d) or subtype-specific (e) GWAS loci that colocalize with IBDverse eQTLs (PPh4 > 0.75), coloured by the major population (or ‘all cells’) where the variance in disease effector gene expression explained by the colocalizing variant was greatest. f, The number of loci where variance in disease effector gene expression explained by the colocalizing variant was greatest across cell types. Bars for each annotation are coloured by the major population they belong to. Colour scheme as in d,e. CRM, central resident memory; EC, endothelial cell; EM, effector memory; GD, gamma-delta; LP, lamina propria; NK1 and NK2, natural killer cell subsets 1 and 2; Treg, regulatory T cell; TRM, tissue resident memory T cell.

Our eQTLs and ieQTLs also colocalized with 104 out of the 137 (75.9%) GWAS loci that were already reported to colocalize with molecular QTLs in the Open Targets genetics portal (Supplementary Information). At 78 of these loci, at least 1 effector gene colocalized in both our data and the Open Targets analysis. Notably, 103 of the previously implicated genes had only been supported by colocalization in non-gut tissues, whereas our data revealed colocalization in gut tissue for the first time. Together, these findings significantly extend effector gene nomination at IBD loci and enable more targeted hypotheses about the cellular contexts in which genetic risk of IBD is exerted.

We next tested whether eGenes identified at the cell-type, major population or tissue level differed in their likelihood of colocalizing with IBD GWAS loci. GWAS signals were significantly more likely to colocalize with eQTLs identified at the cell-type level (odds ratio (OR) between eGene detection and disease effector gene nomination = 2.68), with more modest enrichment for major population-level eQTLs (OR = 1.57) and negative enrichment for those identified at the ‘all cells’ level (OR = 0.75) (Fig. 2b). This pattern was not explained by cell-type-specific expression of genes at these loci, as expression specificity did not differ between eGenes that colocalized at different resolutions (minimum Wilcoxon P value = 0.4; Supplementary Fig. 4). These findings support the hypothesis that disease-relevant regulatory variation is often active in restricted cellular contexts, and that mapping eQTLs at cell-type resolution increases power to detect the effector genes underlying GWAS associations.

To determine whether the inclusion of diseased samples aided disease effector gene discovery, we compared the proportion of CD samples that contributed to eQTL mapping in each cell type of the terminal ileum and the likelihood that an eGene detected in this cell type was a disease effector gene (Fig. 2c). Broadly, this shows that cell types enriched in CD samples are more likely to carry regulatory effects that colocalize with disease (univariate linear model slope = 3.98, P = 4.68 × 10−9). The greatest enrichment of disease effector genes was observed in ADGRL3+ myofibroblasts, proliferating macrophages and KLRG1+ γδ T cells, each of which have established roles in IBD susceptibility36,37,38,39,40. Together, this suggests that the inclusion of CD samples has a general positive effect on the discovery of disease effector genes.

To refine hypotheses for the annotations in which colocalized effects may be active, we identified, for each colocalized signal, the annotation in which the lead eQTL variant explained the greatest proportion of variance in gene expression (Methods), and refer to these as effector cell types. This approach mitigates power-related biases across eQTL maps that would affect interpretation if based solely on the cell type with the highest posterior probability of colocalization. By focusing on variance explained rather than detectability, we aimed to better prioritize cellular contexts that are likely to mediate disease risk. Across all IBD loci with colocalizing eQTLs, myeloid cell populations were most frequently the cell type in which the lead variant explained the largest proportion of gene expression variance (47 loci, 26.4%), followed by T cells and innate lymphoid cells (T/ILC cells) (34 loci, 19.1%) and colonocytes (32 loci, 18%) (Fig. 2d and Supplementary Information). Broadly, enrichment in these three major populations held when stratifying by disease subtype; at CD-specific loci, the most likely effector cell types were myeloid cells and colonocytes (11 loci each, 17.5%) and T/ILC cells (10 loci, 15.9%), whereas in UC, they were most often myeloid cells (14 loci, 36%) and colonocytes (9 loci, 23.1%) (Fig. 2e). The variance in gene expression explained by the colocalizing variant was correlated with overall expression level for only 103 effector genes (27.2%, P < 0.05), indicating that expression level can sometimes contribute, but is not the predominant driver of discovery.

Dendritic cell Notch signalling in IBD

Among myeloid cell types, dendritic cells (type 2 conventional dendritic cells (cDC2s), type 1 conventional dendritic cells (cDC1s) and plasmacytoid dendritic cells (pDCs)), were three of the top four most frequently nominated effector cell types, with a total of 18 IBD loci showing the highest proportion of gene expression variance explained in these populations (Fig. 2f). In humans, conventional dendritic cells (cDCs) originate from CD34+ bone marrow progenitors and differentiate into transcriptionally and functionally distinct subsets, including cDC1 and cDC2. Although both contribute to antigen presentation, they differ in pathogen sensing, cytokine production and T cell priming, particularly in mucosal tissues. These functions are unique to cDCs and are essential for oral tolerance and T cell priming, placing them at a critical interface between innate and adaptive immunity in the gut. Their recurrent nomination as likely effector cell types at IBD loci suggests a central role for dendritic cell-intrinsic pathways in shaping disease risk.

Out of the 13 IBD loci where the strongest colocalizing regulatory effects were observed in cDCs, disease effector genes at 2 are associated with Notch signalling. Whereas previously nominated effector genes by Open Targets genetics and Cuomo et al.[35] include only 4 members of this pathway, we nominate 6 novel effector genes that encode Notch regulators, making it over-represented among our disease effector genes (P = 0.026). One of these is MAML2, which is widely expressed across major populations (Fig. 3a) and encodes a nuclear co-activator essential for canonical Notch pathway activation41,42. In our data, a MAML2 eQTL colocalized with a UC GWAS signal in cDC2s from the terminal ileum (PPh4 = 0.98), and was the only gene supported at that locus (Fig. 3b). This represents a novel colocalization that is not reported in the Open Targets genetics portal. Although we identified 8 distinct eQTLs for MAML2 across cell types and tissues (Supplementary Fig. 5), colocalization with UC was observed for a single eQTL effect that was in low linkage disequilibrium with the others (maximum r^2 = 0.3), suggesting that the disease association arises from a distinct regulatory effect that is active in dendritic cells.

Fig. 3: Novel colocalization between MAML2 dysregulation and IBD highlights susceptibility-associated dysregulation of Notch signalling in conventional dendritic cells.

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a, Expression of MAML2 (log(1 + counts per 10,000)) across all major populations. b, Significance (−log_10(P value)) of variant associations with MAML2 expression and UC susceptibility. Lead variants for each dataset are presented as a diamond. The lead variant for the eQTL is coloured by the minimum resolution at which the eQTL is detected. All other variants are coloured by their in-sample linkage disequilibrium (r^2) with the index variant from each dataset. Lead variants for other eQTLs are outlined by the colour of the minimum resolution at which they were detected. TI, terminal ileum. c, Left, median expression of MAML2 and the amount of variance in normalized MAML2 expression explained by genotype at the colocalizing variant for each annotation with a nominal eQTL effect (P < 0.05) and median expression greater than zero. Annotations where colocalization is detected (PPh4 > 0.75) are outlined. Right, inverse normal transformed expression levels of MAML2 in the three annotations where the greatest variance in gene expression is explained by this variant. Box plots indicate the 25th, 50th and 75th percentiles and whiskers extend to 1.5 times the interquartile range.

Although colocalization was detected in cDC2s, the lead variant also explained substantial variance in MAML2 expression in cDC1s (Fig. 3c). We interpret this as probably reflecting reduced power to detect colocalization in cDC1s, which are both rarer in intestinal tissue and less well represented in our dataset (Extended Data Fig. 2). As eQTL effect sizes were not correlated with MAML2 expression (Spearman’s R = –0.29; P = 0.21), the disease association is likely driven by a cell-type-specific regulatory effect, not by general gene expression levels. Together, these results support the conclusion that dysregulation of MAML2 expression by this specific sc-eQTL in cDCs contributes to UC risk.

An eQTL for an additional Notch pathway gene, ZMIZ1, also colocalized with IBD GWAS signals in dendritic cells (Extended Data Fig. 6a). ZMIZ1 encodes a transcriptional co-regulator of STAT and Notch pathways, and its deletion in mice phenocopies disruption of Notch ligand activity[43]. In our data, a variant associated with reduced ZMIZ1 expression colocalized with CD risk (PPh4 = 0.99), with the strongest effect observed in cDC2s from the rectum (23% variance explained) (Extended Data Fig. 6b). Although we refer to these cells as cDC2s based on their transcriptional profile, we note that monocyte-derived dendritic cells (mo-DCs) are indistinguishable from cDC2s in our atlas. Together with findings at MAML2, this result suggests that disruption of Notch pathway activity across dendritic cell subsets may have a protective role in maintaining intestinal homeostasis potentially by promoting tolerance or limiting pro-inflammatory signalling at the mucosal surface.

Epithelial Wnt signalling in IBD risk

Next, we examined colocalizations that implicate epithelial cells, where barrier dysfunction represents an established mechanism of IBD susceptibility. Colonocyte populations were among the most frequently nominated effector cell types, particularly the RBFOX1-expressing and CEACAM7, KRT20 and IFI27-expressing subsets (6 loci each), along with the broader colonocyte compartment at the major population level (13 loci) (Fig. 2f). At one such locus, we replicate a previously reported colocalization between IBD risk and an eQTL associated with reduced expression of FERMT144,45,46 (PPh4 = 0.96; Extended Data Fig. 7e,f). Null variants in FERMT1 are the most common defect in Kindler syndrome, a rare condition of epithelial fragility caused by lack of FERMT1 (also known as kindlin-1), which anchors the actin cytoskeleton to the plasma membrane, and is an important cause of monogenic IBD47,48,49. Our single-cell analysis refines this colocalization to epithelial populations, and CEACAM7, KRT20 and IFI27-expressing colonocytes in particular (Supplementary Information).

Several novel colocalization events also implicated genes involved in epithelial differentiation and renewal. An eQTL for RASGRP1 colocalized with CD risk (PPh4 = 0.97), and the risk allele was associated with increased RASGRP1 expression in colonocytes (Fig. 4a and Extended Data Fig. 7a). RASGRP1 encodes a Ras guanine nucleotide exchange factor that modulates MAPK signalling and epithelial proliferation50,51, and its depletion results in colitis phenotypes in mice[52], suggesting a role in maintaining epithelial turnover. In addition, an eQTL for LPIN3 colocalized with IBD risk (PPh4 = 0.89), with the strongest effect observed in CEACAM7, KRT20 and IFI27-expressing colonocytes (Supplementary Information). LPIN3 is a phosphatidic acid phosphatase that is involved in lipid metabolism and also regulates Wnt signalling[53], which is essential for maintaining epithelial stem cell renewal and mucosal integrity. Previously nominated effector genes by Open Targets genetics and Cuomo et al.[35] include only 6 Wnt regulators, however we nominate an additional 14, making this pathway over-represented among our disease effector genes (P = 0.015). Disruption of this pathway can impair epithelial regeneration, promote barrier permeability, and drive intestinal inflammation54,55.

Fig. 4: Wnt-associated genes RASGRP1, MYC and RNF14 colocalize with IBD susceptibility loci.

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a–c, Significance (–log_10(P value)) of variant associations with RASGRP1 (a), MYC (b) and RNF14 (c) expression and CD susceptibility. Lead variants for each dataset are presented as a diamond. The lead variant for the eQTL is coloured by the minimum resolution at which the eQTL is detected. All other variants are coloured by their in-sample linkage disequilibrium (r^2) with the index variant from each dataset. Lead variants for other eQTLs are outlined by the colour of the minimum resolution at which they were detected. d, The interaction between genotype at the colocalizing variant for RNF14 expression in enterocytes from the top of the villus and disease severity. Box plots indicate the 25th, 50th and 75th percentiles and whiskers extend to 1.5 times the interquartile range.

Further supporting this mechanism, an eQTL regulating increased MYC expression colocalized with CD risk in OLFM4 and LGR5-expressing intestinal stem cells (PPh4 = 0.92, Fig. 4b and Extended Data Fig. 7b). MYC is a canonical Wnt target and master regulator of epithelial proliferation and metabolism, and its dysregulation perturbs intestinal homeostasis56,57,58. Notably, MYC is a well-established oncogenic transcription factor that drives tumorigenesis through both somatic amplification and germline regulatory variation59,60,61,62. A role for MYC expression in gastrointestinal inflammation is further supported by additional colocalization between CD risk and an eQTL for FUBP1 (PPh4 = 1.00, Extended Data Fig. 7c,d), a regulator of MYC and Wnt components such as DVL163,64.

An inflammation-dependent ieQTL regulating RNF14 colocalized with CD susceptibility (PPh4 = 0.86, Fig. 4c). RNF14 encodes ring finger 14 protein, a E3 ubiquitin ligase that enhances Wnt signalling and promotes the expression of downstream targets, including MYC65,66. In enterocytes from inflamed samples, the CD risk allele was associated with reduced RNF14 expression (Fig. 4d), supported by fine-mapping (Supplementary Note), and suggests that Wnt signalling and epithelial regeneration may be impaired in the presence of inflammation. However, because our power to detect inflammation-dependent eQTLs outside of enterocytes is limited, we cannot exclude the possibility of larger effects in other cell types.

Together, these findings nominate dysregulation of epithelial renewal and Wnt-dependent proliferation as a potential mechanism contributing to barrier dysfunction in IBD, complementary to established defects in adhesion. Failure to maintain or regenerate the epithelial barrier may be an important and under-recognized contributor to intestinal inflammation.

Effector gene mapping for drug prioritization

To explore how our effector gene nominations might inform therapeutic strategies, we first explored whether any of the colocalizing genes were established or clinically relevant drug targets for IBD. We observed colocalization between IBD risk and eQTLs for ITGA4 and JAK2, which encode direct molecular targets of vedolizumab and tofacitinib, respectively (Extended Data Fig. 8a–d). The strongest colocalizing regulatory effects for these genes indicate their expression is positively associated with IBD susceptibility, consistent with the hypothesis that their inhibition would be therapeutically effective. We also identified colocalizations with ITGAL, TNFSF15 and TNFRSF14, genes that, although not directly targeted by existing therapies, participate in immune pathways that are modulated by current IBD drugs. Notably, the strongest regulatory effects for these genes were distributed across diverse tissues and cell types, including myeloid cells from the blood (TNFSF15), Plasma B CD38hi IgG IgA from the rectum (ITGAL), and intermediate monocytes from the blood (ITGA4) (Supplementary Information). These findings are consistent with understanding that IBD treatments target a number of different immunological pathways, many of which are active across multiple cell types and tissues[67].

We next investigated whether our colocalized genes included targets of drugs approved or in clinical trials for other diseases, that were potentially suitable for repurposing. Using the ChEMBL database[68], we identified several such candidates (Fig. 5). We observed colocalization between UC risk and an eQTL for PRKCB (PPh4 = 0.95; Supplementary Information), which encodes PKCβ, a druggable kinase with inhibitors approved for oncology. In our data, the IBD risk allele was associated with increased PRKCB expression in classical and intermediate monocytes, suggesting that increased activity of this kinase may drive pathogenic inflammatory responses. Notably, PRKCB inhibitors are already undergoing early-phase clinical trials for IBD, and our findings provide human genetic support for their mechanism of action in relevant immune cell populations.

Fig. 5: Identifying therapeutic repurposing opportunities by colocalization.

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Comparison between the disease effector genes identified by colocalization in IBDverse and the maximum stage of clinical trials therapeutic modifiers of each gene are described to be at in the CHEMBL database (Methods). Hollow circles correspond to a maximum trial phase of 0.5–3, and circles with central dot indicate that a drug is approved for use or in trial phase 4. Associations for genes that are previously colocalized in Open Targets genetics (OTG) are represented in grey, genes that colocalize only in IBDverse are dark blue. Drugs are grouped by their broad mechanism of action (y axis) and diseases are grouped into related families (x axis).

We also explored whether our data could offer insights into potential on-target safety concerns. For example, we found colocalization between a UC risk locus and an eQTL for NDUFAF1 (PPh4 = 0.88), which encodes a mitochondrial complex I assembly factor targeted by metformin, which is approved for treatment of diabetes (Extended Data Fig. 8e). The UC risk allele decreases NDUFAF1 expression across many cell types, but the greatest explained variance in expression was in colonocytes (Extended Data Fig. 8f), which may explain the common metformin side effects of diarrhoea, nausea, weight loss and abdominal discomfort69,70. Another notable example was PSEN2, where we observed colocalization between an eQTL and UC (PPh4 = 0.91; Extended Data Fig. 8g,h). PSEN2 encodes presenilin 2, a catalytic component of the γ-secretase complex that is required for Notch receptor cleavage and activation. Mutations in PSEN2 cause autosomal dominant early-onset Alzheimer’s disease by destabilizing fidelity of cleavage by γ-secretase, leading to increased production of aggregation-prone Aβ42 peptides71,72. Although γ-secretase inhibitors were developed to reduce this pathogenic cleavage, their clinical use was curtailed by dose-limiting side effects such as weight loss and gastrointestinal bleeding73,74. Notably, mouse Psen2-knockout models also show susceptibility to colitis[75], suggesting that PSEN2 dysregulation may compromise epithelial homeostasis and barrier integrity. Thus, colocalization of eQTLs across diverse single-cell atlases could potentially be used to anticipate context-specific effects of targeted therapies.

Discussion

IBDverse represents the largest single-cell atlas of IBD-relevant tissues to date, encompassing nearly 2.2 million high-quality single-cell transcriptomes from 421 individuals, including 125 with CD. This size and diversity was critical for mapping eQTLs and nominating probable effector genes and cell types that shape disease susceptibility. By including patients with CD with and without active inflammation, we captured regulatory effects in cell types that expand during disease but are rare in healthy individuals—most notably myeloid subsets76,77,78,79—which carried more IBD-colocalizing eQTLs than any other major cell population. We also identify 1,051 ieQTLs, including 2 modulated by inflammation, that colocalize with IBD GWAS at loci where an eQTL did not. These context-dependent regulatory effects could only be uncovered by sampling inflamed tissue from individuals with active disease. At the same time, many GWAS-colocalizing eQTLs were identified in cell types abundant in healthy tissue, including epithelial and stromal populations. This underscores the power of generating cell-type-resolved eQTL maps in disease-relevant tissues even in the absence of disease. Given that disease-affected tissues may not be accessible for some complex diseases, our findings reinforce the value of tissue atlases from non-diseased individuals for biological interpretation of GWAS loci, while also demonstrating the added insight gained by integrating samples from individuals with disease and relevant sub-phenotypic data.

A key insight from this study is the critical impact of cell-type resolution on eQTL discovery. Although most eGenes were detected at the ‘all cells’ level (77.1%), many distinct regulatory variants (29.3%) were identified at the cell-type level. Consistent with previous work[12], eQTLs found at low resolutions were more proximal to TSSs and were relatively enriched in promoters. However, cell-type level eQTLs were distal and more frequently located in enhancer regions—genomic features characteristic of GWAS loci. They were also less likely to regulate the nearest gene, suggesting more distal or context-specific modes of action. Previous studies have shown that cell-type-specific eQTLs tend to be more distal from the TSS23,80,81. Here we extend this observation by analysing eQTLs across hierarchical levels of resolution within the same tissue (‘all cells’→Major population→cell type), showing that distance to TSS systematically increases with cellular resolution. In line with these properties, GWAS signals were substantially more likely to colocalize with cell-type-level eQTLs than those detected at coarser resolutions. Our findings therefore imply that the genetic architecture of complex diseases is shaped by regulatory effects active in restricted cellular contexts—effects that are often missed in bulk analyses.

These properties are consistent with an evolutionary model in which regulatory variants that broadly affect the expression of disease-relevant genes are more likely to be deleterious and subject to negative selection. As a result, such variants would remain at very low frequencies in the population, limiting their detection in both GWAS and eQTL studies. By contrast, variants whose regulatory effects are restricted to specific cellular contexts may evade such selective pressures, allowing them to persist at higher frequencies and be more readily detected. This model provides a potential evolutionary explanation for the observed enrichment of GWAS loci among eQTLs mapped at the cell-type level.

Our findings inform future eQTL mapping for GWAS effector genes. Although cell-type resolution is clearly advantageous, detection power remains restricted by per-annotation cell counts. Despite analysing 2.2 million cells, 57% of our cell types (for example, all stromal populations) yielded fewer than 100 cells per donor, limiting discovery in rare or disease-specific contexts that are often missed by bulk mapping. Consequently, although we nominated effector genes for more than 50% of known IBD loci, larger single-cell diseased cohorts will improve discovery and more precisely map disease-driving cellular dysregulation. Furthermore, adopting multi-variant colocalization methods (for example, coloc-SuSiE[82]) to relax our single-causal-variant assumption is likely to uncover additional independent signals and novel effector genes.

Larger and more deeply powered single-cell eQTL studies will also enable more precise interpretation of regulatory mechanisms at individual loci, particularly where risk variants affect pleiotropic pathways across multiple cell types. Several of the loci that we identified converge on the Notch signalling pathway, but do so through distinct mechanisms and in different cellular compartments. MAML2 and ZMIZ1 encode regulators of Notch-dependent gene transcription, and at both loci, reduced expression in dendritic cells that colocalized with increased IBD risk. By contrast, at the PSEN2 locus, the IBD risk allele was associated with increased expression across epithelial and endothelial cells, which is likely to enhance Notch activity. This duality may help explain the mixed outcomes of γ-secretase inhibition in clinical trials, where beneficial anti-inflammatory effects could be counterbalanced by epithelial or vascular side effects.

A complementary mechanism suggested by our data involves disrupted epithelial renewal and stem cell maintenance. Although barrier dysfunction is a well-established feature of IBD, prior models have largely focused on defects in epithelial adhesion. Our results point to an additional axis of risk involving impaired regeneration, with IBD risk alleles colocalizing with eQTLs affecting genes such as RASGRP1, MYC, LPIN3, RPS14 and FUBP1 that regulate Wnt signalling, epithelial proliferation and mucosal repair. Many of these colocalized signals were assigned to colonocytes and motivate the hypothesis that disruption of epithelial renewal and stem cell maintenance in the absorptive epithelial cells of the rectum may be particularly relevant to IBD pathogenesis—consistent with work highlighting that Wnt signalling is reduced in patients with CD, independent of inflammation[83]. Regulators of both Notch and Wnt signalling also harbour rare variants that predispose to monogenic forms of IBD, such as FERMT1, XIAP, ADAM17, IL2RA, ITCH and TGFBR284,85,86,87,88,89. By more than doubling the number of Notch and Wnt pathway IBD GWAS effector genes, we strengthen evidence that these pathways have a role in IBD, underscoring the value of cell-type-level resolution for dissecting the roles of pleiotropic pathways in complex disease. Further experimental investigation is now required to test these hypotheses and establish causality.

This study demonstrates the power of single-cell eQTL mapping in disease-relevant tissues to resolve the cellular and regulatory architecture of complex diseases and traits. By systematically comparing genetic effects across cellular resolutions, we show that disease-associated variants are disproportionately enriched among eQTLs detected at the cell-type level, and often act through context-restricted regulatory mechanisms. Although true evidence for the causality of our colocalizations can only be obtained from experimental, genetic knockout of nominated genes in the relevant cellular contexts, these findings provide a framework for interpreting non-coding GWAS loci with greater precision, constructing novel hypotheses about disease pathogenesis, and highlight the importance of sampling both healthy and inflamed tissues. As single-cell technologies continue to scale, the increased ability to map genetic effects at cell-type resolution will enable more complete, mechanistically informed and therapeutically relevant interpretations of complex disease association signals.

Methods

Patient recruitment and single-cell sequencing

This study was approved by the National Health Service (NHS) Research Ethics Committee (Cambridge South, REC ID 17/EE/0338). Written informed consent was given by all participants.

296 individuals without IBD and 125 individuals with CD were recruited at Addenbrooke’s Hospital, Cambridge, UK. Patients with CD were required to have a history of ileal disease involvement. During routine endoscopy, gastrointestinal biopsies were collected from the rectum and terminal ileum, and blood samples were drawn after the procedure. Terminal ileal biopsies were obtained from 243 individuals without IBD and 119 individuals with CD. Ileal biopsies from patients with CD included both inflamed and uninflamed tissue. Rectal biopsies were collected from 275 individuals without IBD. Where possible, rectal and ileal samples were collected from the same healthy individuals, and blood and ileal samples were collected from the same patients. For gastrointestinal biopsies, we have developed a gentle single-cell dissociation protocol that minimizes transcriptional changes and cell stress or death during cell isolation. Early samples were processed using version 1 of the protocol, whereas most of the samples were processed using version 290,91. Whole blood was collected for scRNA-seq from 95 individuals with CD. Peripheral blood mononuclear blood cells (PBMCs) were isolated from the whole blood using the EasySep Direct Human PBMC Isolation Kit (StemCell Technologies).

Because IBD tissue displays increased cellular heterogeneity due to immune infiltration, we targeted 6,000 and 3,000 cells for CD and healthy biopsies, respectively. This is to ensure adequate representation of diverse cell populations and to maintain statistical power for expression quantification and eQTL mapping across annotations. scRNA-seq was performed using 3′ 10X Genomics Chromium kits (v3.0 and v3.1) according to the manufacturer’s instructions. Libraries were sequenced to a target depth of 50,000 reads per cell. CellRanger (v7.2.0) was used to align reads to the GRCh38 human genome reference with Ensembl v93 transcript definitions (GRCh38-3.0.0 reference file provided by 10X Genomics), and to generate cell-by-gene count matrices. The greater number of cells targeted for patients with CD is confirmed in the terminal ileum samples from the ‘atlasing’ dataset (see ‘Single-cell RNA sequencing quality control and cell clustering’) (Wilcoxon P value < 2.2 × 10−16), with no discernable influence on sequencing depth, as indicated by median number of counts per sample (P = 0.27) (Supplementary Fig. 6).

Single-cell RNA-sequencing quality control and cell clustering

Cell count matrices were initially subset to retain droplets likely to contain cells and corrected for ambient RNA using CellBender v2.1[92], as previously described[76]. Scrublet v0.2.1[93] was then used to identify and remove potential multiplets. All subsequent quality control, normalization, and clustering steps were performed with Scanpy v1.9.6[94], including normalizing to library sizes of 10,000 total counts using the function sc.pp.normalise_per_cell and log transformation using sc.pp.log1p to produce the ln[cp10k + 1] count matrix.

A lineage-sensitive, relative quality control strategy was applied to account for technical and biological variation across tissues and lineages (Supplementary Fig. 1). First, the input dataset (4,099,804 cells) was aggregated and subjected to an initial round of quality control. Cells were removed if they had fewer than 250 genes expressed, fewer than 500 total counts, or more than 20% or 50% of reads originating from the mitochondrial genome, for blood-derived and non-blood-derived cells, respectively. This left a total of 2,725,568 cells. Genes were then removed if they were not expressed in at least five cells. A total of 5,000 highly variable genes were then classified within each sample and merged using sc.pp.highly_variable_genes and flavour = seurat. Ribosomal, mitochondrial and immunoglobulin genes were then excluded from the highly variable set.

These data were then subject to an initial round of integration and clustering. Data were integrated using the scVI v0.16.3[95] function scvi.model.SCVI on raw count data of highly variable genes. This was trained on 2 layers with 30 latent variables and gene likelihood modelled as a negative binomial distribution. A total of 50 nearest neighbours was calculated per cell using the function sc.pp.neighbors to generate an adjacency matrix, which was used for calculation of both an initial uniform manifold approximation and projection (UMAP) embedding using function sc.tl.umap with both a minimum distance and spread of 0.5, and Leiden clusters at a resolution of 0.03 using sc.tl.leiden. Initial clusters were then grouped into cell lineages, based on the expression of canonical marker genes (Supplementary Fig. 7), including: B cells, epithelial cells, mast cells, mesenchymal cells, myeloid cells, platelets and T/ILC cells. Markers for these annotations are described in ‘Annotation of cell clusters’.

Within each cell lineage (B cells, epithelial cells, mesenchymal cells, myeloid cells (not including platelets or mast cells) and T/ILC cells), we then further subset cells based on relative thresholds for quality control parameters. Cells with number of unique genes expressed, total counts and percentage of genes originating from the mitochondrial genome that lay outside the median ± 2.5 median absolute deviations were removed. For the epithelial lineage, the median deviation was far greater than that for non-epithelial, and so a window of 2 median absolute deviations was utilized. Additionally, epithelial cells from samples with lower overall depth (indicated by median number of genes expressed per cell <2,000) were removed from the epithelial population only, and any infiltration of blood-derived cells were also removed. Highly variable genes were then re-selected from the full initial set within each lineage independently, and re-integrated using scVI as before, computing 20 nearest neighbours for non-epithelial cells and 50 nearest neighbours for epithelial cells.

Cell types were then identified by clustering within each lineage independently. As described previously[76], the resolution at which final clusters were derived was determined in a data-driven manner. Cells from each lineage were clustered at resolutions of 0.1 to 1.5 (interval of 0.1) using the sc.tl.leiden function. To determine the reproducibility of the clusters detected at each resolution, two-thirds of the data were then used to train a single layer dense neural network model using keras v2.4.3, and the per-cluster matthews correlation coefficient (MCC) of the fit was assessed in the remaining one-third of data. After clusters that were either comprising >40% cells from a single sample or showed outlying quality control parameters were removed, we selected the resolution at which all remaining clusters had a MCC > 0.75. Additionally, reproducible (MCC > 0.75) clusters demarcated by the expression of genes indicative of poor quality cells, such as MALAT1[96], and cross-lineage marker genes were also removed. The resolutions utilized for the final cell clusters were: epithelial cells, 0.9; mesenchymal cells, 1.0; myeloid cells, 0.7; B cells, 0.8; and T/ILC cells, 1.1.

Finally, the additionally filtered and clustered per-lineage clusters were then recombined with the mast and platelet cells. This population, known as the ‘atlasing’ dataset, comprised 1,852,681 cells and was re-integrated as described above. With scope to auto-annotate cells with reliable transcriptional profiles that may have been removed by the strict, within-lineage quality control, a CellTypist v1.6.2[24] model was trained on the atlasing dataset. This was assessed by quantifying the proportion of cells that were correctly assigned to the same annotations in the initial, manual annotation. Overall, this was achieved at a rate of 97% (median 98%), with only 4 clusters achieving an accuracy <90% and only one <80% (tissue plasma B CD38hi IgG IgA, 87.8%; tissue plasma B CD38low, 79%; blood atypical B cells, 88.7%; tissue memory B HIVEP3+, 80%). This model was then used to auto-annotate the full set of cells that passed the first round of quality control, and filtered for those with confidence <0.5, resulting in a final dataset that after intersection with genotyped individuals, results in 2,196,874 cells.

Annotation of cell clusters

As described above, cells from all tissues were initially clustered into cell lineages after the first round of quality control and integration. These were grouped and annotated on the basis of canonical marker genes (Supplementary Fig. 8); Epithelial cells were defined by high expression of EPCAM, CDH1 and KRT19; mesenchymal by expression of COL1A1, COL1A2, COL6A2 and VWF; B cells by expression of CD79A, MS4A1 and CD79B; plasma B cells by expression of MZB1 and JCHAIN; T and T/ILC cells by expression of CD3D, CD3E, CD3G, CCR7, IL7R, TRAC and NCAM1; myeloid cells by expression of ITGAM, CD14, CSF1R and TYROBP; mast cells by expression of TPSAB1, TPSB2 and CPA3; and platelets by expression of RUNX1, GATA2, HDC and SLC24A3 (but low expressors of mast cell genes). For within-lineage clustering, B cells and plasma B cells were combined into a single lineage, with mast cells and platelets excluded.

Following clustering within lineage, cell types were first annotated by major population using a series of canonical markers, detailed in subsections below. In the case of T/ILC and mesenchymal populations, this matched their clustering lineage, however; epithelial cells were further subdivided into enterocyte, colonocyte, stem cells and secretory cells; the B cell lineage was re-divided into B cells and plasma B cells; and the myeloid cell lineage were combined with mast and platelet cells. To interpret individual cell clusters, marker genes were calculated within major populations or across targeted subsets of clusters using scanpy’s sc.tl.rank_genes_groups function (method = ‘wilcoxon’). The significant results (FDR < 0.05), along with a discriminative subset that were filtered using scanpy’s sc.tl.filter_rank_genes_groups (min_in_group_fraction=0.5, max_out_group_fraction=0.3, min_fold_change=1.5), were used to annotate and label clusters using expert knowledge. Enrichment of specific populations in a single tissue, or combination of tissues, was determined by the overall proportion of cells from each site (Extended Data Fig. 2).

Epithelial cell lineages

Genes used to demarcate epithelial lineages are presented in Supplementary Fig. 8a. The stem cell major population was defined by expression of LGR5, OLFM4, ASCL2, SMOC2 and RGMB. One of these populations was specific to the rectum, while two were specific to the terminal ileum, and thus are described as ‘rectal’ and ‘intestinal’, respectively. Within the intestinal subsets, one showed greater expression of MKI67, and the other had greater expression of OLFM4 and LGR5, which were thus used to discriminate these populations.

Absorptive epithelial cells were also largely terminal ileum- or rectum-specific (Extended Data Fig. 2). The majority show clear separation in dimensionality reduced space (Fig. 1b), and were grouped into the enterocyte (high expression of RBP2, ANPEP and FABP2) and colonocyte (high expression of CA2, SLC26A2 and FABP1) categories, respectively. The one exception was a population composed of cells from both predominantly diseased terminal ileum and rectal origin, with high expression of OLFM4, thus referred to as ‘epithelial progenitor OLFM4hi’, grouped into the colonocyte major population due to their embedding in dimensionality reduced space (Fig. 1b). Both enterocyte and colonocyte populations were characterized by the relative expression of marker genes, with discriminative ones being added to their label, including OLFM4 and MKI67 (indicative of proliferation), BEST4, CEACAM7, KRT20, IFI27, ALDOB and RBFOX1.

Secretory cells were broadly more shared across the terminal ileum and rectum, but with some site enriched clusters (Extended Data Fig. 2); goblet cells were identified by expression of MUC2, CLCA1 and FCGBP; tuft cells by expression of POU2F3, SH2D6, LRMP and TRPM5; Paneth cells by expression of DEFA5 and DEFA6; enteroendocrine cells by expression of NTS, PYY, GCG, ISL1; and enterochromaffin cells by expression of TPS1 and CES1.

Mesenchymal cell lineages

Markers used to demarcate mesenchymal cell subsets are displayed in Supplementary Fig. 8b. Fibroblast clusters were identified by their expression of ADAMDEC1, PDGFRA, BMP4, LUM, COL1A2, COL3A1 and COL1A1, subsets of which were then delineated by their expression of PI16, C3, CPM, NPY, GRIN2A and ADAMDEC1 (lamina propria fibroblast), and tissue specificity. Glial cells were identified by expression of NRXN1; smooth muscle cells by expression of ACTA2; pericytes by expression of PDGFRB, RGS5 and NDUFA4L2; endothelial cells by expression of PECAM1, VWF, PLVAP, ACKR1 and CD36. Endothelial cells were further divided into inferred residency by expression of high TLL1, ACKR1 and PECAM1 (venous), modest TLL1 and PECAM1 (lymphatic) and high CD36 greatest PECAM1 and PLVAP (capillary). Myofibroblasts were identified by expression of ACTA2 and MYH11, which were then delineated by relative expression of GREM2 and ADGRL3.

B cell lineages

Markers calculated within plasma B cells were not very discriminatory, however these clusters show a gradient of CD38 expression, which is therefore outlined in the label (Supplementary Fig. 8c). Each of these include a majority of IgA-expressing cells, with one cluster containing a minority of high IgG-expressing cells. For the non-plasma B cells; atypical B cells were identified by their expression of FCRL2, ITGAX and TBX21; naive B cells by expression of IGHD and FCER2; germinal centre/plasmablast B cells by expression of CD19 and TCL1A (for which we also identified a MKI67-expressing, proliferative subset); memory B cells by expression of BANK1 and FCRL2. The several clusters of memory B cells were also distinguished by the relative expression of marker genes; CD27, HIVEP3, PTPRJ, PDE4D and TEX9.

Myeloid cell lineages

Across the myeloid cell lineage, we identified several distinct clusters of monocytes, macrophage and dendritic cells (Supplementary Fig. 8d). Monocytes were defined by high expression of S100A8 and S100A9 and were broadly divisible by anatomical location. Blood monocytes were distinguished by expression of CD14 and FCGR3A (encoding CD16) and tissue monocytes by expression of SOD2, CXCL9 and CXCL10.

Macrophages were predominantly identified in the terminal ileum and rectum and were identified by high expression of ITGA4, CSF1R and MAF. Macrophage subpopulations could thereafter be distinguished by relative expression of C1QA/B/C, CD163, MRC1, SELENOP and HLA-DRA.

Three separate dendritic cell subsets were identified: cDC1s were identifiable from expression of CLEC9A and XCR1, cDC2s were identified by expression of CLEC10A, and pDCs identified by IL3RA.

T cell and ILC lineages

Within the T cell and ILC lineage, T cells were defined by expression of CD3D, CD3E, CD3G, TRAC, TRBC1 and CD96, refined by CD4, CD8A, CD8B, CD3 and GZMK expression (Supplementary Fig. 8e). Naive subsets were identified by high expression of SELL; γδ (GD) by expression of TRGC2 and TRDC (for which we also defined gut- and blood-enriched subsets referred to as ‘GD T KLRG1’ and ‘Tissue resident GD T’; see Extended Data Fig. 2); Treg cells by expression of FOXP3and CTLA4; Effector memory subsets divided by expression of TIGIT and THEMIS; tissue resident memory with high expression of TRGC2. Two T cell clusters that did not express CD8 or CD4 were also identified, one of which strongly expressed TRDC, and the other that strongly expressed GZMK. NK1 cells were identified by expression of CX3CR1 and FCGR3A, and NK2 cells by XCL1, XCL2 and CD44. ILCs were identified by expression of IL1R1, ALDOC, LST1 and RORC.

Pseudobulking

Pseudobulk expression data were obtained by calculating the mean ln[cp10k + 1] expression across cells of a given annotation, as per previous sc-eQTL mapping benchmarks[97]. This was performed at ‘all cells’, major population and cell-type resolutions, both within and across anatomical sites (Fig. 1a). Expression data were limited to samples with 5 or more cells in the given annotation and only taken forward for further analysis if there were 30 or more individuals contributing to the annotation. In addition, the genes present in each annotation matrix were limited to those with a total count greater than 0 in at least 20% of pseudobulked samples, or 40% of pseudobulked samples for ieQTL mapping. Pseudobulked expression matrices were then inverse normal transformed at the gene level prior to eQTL mapping.

DNA collection and quality control

On the day of the endoscopy, blood (or gastrointestinal tissue, where blood was unavailable) was collected from individuals for DNA array genotyping. DNA was extracted from blood or tissue samples and genotyped with the Applied Biosystems UK Biobank Axiom Array in five batches of samples by YourGene Health. Genotypes were called jointly across all five batches of samples to mitigate potential batch effects. Genotype data were filtered to remove low missingness, duplicate samples, variants not in Hardy–Weinberg equilibrium, individuals with non-European ancestry (as predicted by KING[98]) and first-degree relatives. Variants were lifted over from human genome build 37 to human genome build 38 with LiftOver prior to imputation being performed using the Michigan Imputation Server with the TOPMed reference panel99,100. Imputed variants with imputation quality lower than 0.3 were removed.

cis-eQTL mapping

eQTL mapping was carried out using TensorQTL[101] permutation and nominal modes with default parameters. cis-eQTLs were mapped for variants with minor allele frequency > 0.05 that were within a 2 Mb window (1 Mb in either direction) of the TSS of a given gene. The following model was used for mapping cis-eQTLs:

$${\mathrm{Ex}}_{G,C} \sim \mathrm{GT}+{\mathrm{PC}}_{\mathrm{GT}}+{\mathrm{PC}}_{{\mathrm{Ex}}_{C}}$$

where Ex is gene expression, G denotes a gene and C is an annotation. GT denotes the genotype alternative allele dosage of the individual (typed or imputed) and PC refers to principal components of genotype and annotation expression. To detect conditionally independent signals, a forwards regression framework was applied, as described previously[13].

For interaction cis-eQTL mapping, a stricter minor allele frequency filter of 0.1 was applied to each variable level and an expanded model was used:

$${\mathrm{Ex}}_{G,C} \sim \mathrm{GT}+I+\mathrm{GT}:I+{\mathrm{PC}}_{\mathrm{GT}}+{\mathrm{PC}}_{{\mathrm{Ex}}_{C}}$$

where I is the interaction variable and GT:I indicates the coefficient for the interaction between genotype and the interaction variable, reported in results. We tested for interactions with sex, age (in bins of 5 years), disease status (CD or healthy), smoking status, and inflammation status (TI-SES-CD score, as defined previously[76]). The reported sex of each sample was verified by Y chromosome carriage and pseudobulk expression of XIST versus all genes on the Y chromosome.

Five genetic principal components were included to control for population structure between individuals. The number of expression principal components to include was determined by repeating the analysis several times, increasing numbers of principal components from 2 to 100 in intervals of 2. The optimal model was selected based on the number of expression principal components that returned the greatest number of eGenes (genome-wide FDR < 0.05), with ties broken by the model with fewest covariates (see ‘Multiple hypothesis testing correction’). For the interaction analysis, any expression principal components which were correlated with the interaction variable were removed from the model (Pearson’s R > 0.25).

Multiple hypothesis testing correction

Correction of P values to account for multiple hypothesis testing in eQTL mapping was done within each cellular annotation independently. This varied slightly for primary, conditional and interaction effects and is detailed below.

Multiple testing correction of primary signals

1. 1)
Within each gene, multiple variants are being tested for their association with expression. To account for this, expression is permuted 1,000 times, and associations with each variant are recalculated. This procedure allows estimation of a null distribution of associations, and calculation of beta-approximated empirical P values (beta-P). The variant with the smallest beta-P value is then selected for each gene.

2. 2)
Multiple genes are being tested for eQTL associations within each annotation. A q-value correction[102] is then applied to the variants with the smallest beta-P value per gene, allowing estimation of a genome-wide FDR. We then apply a threshold of FDR < 0.05 to account for both the number of variants being tested per gene and the number of genes, while accommodating for the varied underlying null distributions.

Multiple testing correction of conditionally independent effects

For genes with a significant eQTL association in a cellular annotation (genome-wide FDR < 0.05), subsequent signals are then tested in a forward regression framework by iteratively incorporating variants into the tested model. Firstly, the minimum beta-P value with genome-wide FDR < 0.05 is defined across all genes (min-beta-P). During each round of conditional analysis, associations between variants and gene expression level are re-tested and permuted, and the variant with the smallest beta-P value is selected for each gene. If this variant has a beta-P < min-beta-P from the primary analysis, it is declared a conditionally independent signal and included into the model being tested for that gene in the next round. Because the beta-P must always be less than min-beta-P, conditional signals are iteratively mapped per gene until they would fail to pass the genome-wide FDR < 0.05 from the primary analysis.

Multiple testing correction of interaction effects

Following ieQTL testing, the number of effectively independent variants being tested is estimated for each gene using eigenMT[103]. Nominal P values for ieQTL effects are then adjusted using the Bonferroni method to account for the number of effectively independent tests being performed (emt-p). The variant with the minimum emt-p value is then selected for each gene, and a Benjamini-Hochberg adjustment is then applied to account for the number of genes being tested (adjusted P). Reported ieQTL effects all have adjusted P < 0.05.

Aggregation of QTL effects

Lead variants for the same genetic association may differ across conditions or QTL types (primary, conditional or interaction) due to minor changes in significance. To group lead variants across all tests and define comparable QTL signals, linkage disequilibrium clumping was performed using the ‘–clump’ flag in plink v1.90b6.27[104] and a threshold of r^2 > 0.5 for variants within 1 Mb with no additional significance filter. This was performed using lead variants from conditionally independent eQTLs (genome-wide FDR < 0.05), ieQTLs (adjusted P < 0.05) and lead variants after intersection with GWAS summary statistics to help map colocalization events to eQTLs.

Inferring capture of our eQTLs in other studies

Harmonized nominal summary statistics for eQTLs from every GTEx v8 bulk RNA sequenced tissue(GTEx Consortium 2020) and sc-eQTLs for peripheral blood mononuclear cells of Yazar et al.[18] were downloaded from eQTL catalogue (https://www.ebi.ac.uk/eqtl/, study IDs QTS000015 and QTS000038, respectively). For eQTLs detected in each of our annotations, we then calculated the enrichment of these associations among low P values, π1 (ref. [102]), in each external dataset using qvalue v2.34.0[105]. This was also performed between eQTLs detected at each resolution in our dataset with eQTLs from GTEx tissues. For comparison with Yazar et al.[18], cell types were grouped into major populations based on nomenclature (B - intermediate, memory, naive; Myeloid - CD14_Mono, CD16_Mono, Platelet, cDC2, pDC; T - CD4_CTL, CD4_Naive, CD4_TCM, CD4_TEM, CD8_Naive, CD8_TCM, CD8_TEM, MAIT, NK, NK_CD56bright, NK_Proliferating, Treg, dnT, gdT; unknown - HSPC, Haematopoietic stem; Plasma - Plasmablast). A detailed summary of these comparisons is shown in the Supplementary Note.

Colocalization

Colocalization was performed in a pairwise fashion between sc-eQTL summary statistics and GWAS summary statistics using coloc v5.2.2 and the coloc.abf function[32]. GWAS summary statistics from de Lange et al.[3] were downloaded from GWAS Catalog (https://www.ebi.ac.uk/gwas/home) (see ‘Data availability’ and ‘Code availability’). Analyses were conducted in 2 Mb cis windows centred around the lead single-nucleotide polymorphism (SNP) of each eGene with a permuted FDR-corrected P value < 0.05 and using GWAS loci which were significant at the genome-wide suggestive threshold (P value < 5 × 10−5). Analysis was conducted using default prior probabilities of p1 = p2 = 10−4 and p12 = 10−5 where p1 and p2 are the prior probabilities of true genetic associations in each input dataset and p12 is the probability of true associations in both datasets. For colocalization between GWAS and ieQTLs, approximate Bayes factors were computed from the effect sizes and standard errors of the GT:I term, and the prior standard deviation on the true effect size was estimated as 0.3 based on the distribution of GT:I effect sizes across all nominal results on chromosome 21. A posterior probability of colocalization (PPh4) greater than 0.75 was considered to be sufficient evidence for colocalization of genetic signals. Colocalizations which fell within the HLA on chromosome 6 (build 38, positions 28510120–33480577) were discarded as likely false positives due to the complicated patterns of linkage disequilibrium in this region. Colocalization events were assigned to the closest existing IBD locus, if the lead colocalized GWAS variant was within 500 kb of a known IBD locus as defined by the interval comprising ±500 kb containing: (1) the most likely causal variant from published fine-mapping analysis3,106; or alternatively, if the locus has now been previously fine mapped, (2) from the GWAS lead variant as defined in refs. 3,34.

We used the R package otargen (v1.1.5) to download all colocalizations between genes with molecular QTLs and IBD, CD and UC GWAS loci from the Open Targets genetics portal (accessed 22 January 2025). We retained any colocalizations with PPh4 > 0.75 and assigned to IBD loci as previously described for our colocalizations. For comparison of disease effector genes nominated in this work with that of Cuomo et al.[35], as their colocalization is limited to IBD and with a threshold of PPh4 > 0.8, we applied the same filtration to our own associations.

To check if disease effector genes identified at high resolutions were more specifically expressed, genes were grouped into the lowest resolution at which a colocalization was detected on their expression. The maximum specificity of gene expression at the cell-type level was then calculated using CELLEX[107], and compared across resolutions using a Wilcoxon test.

Detection of colocalization events between two phenotypes is highly dependent on the significance of those effects. This is therefore underpinned by the statistical power in those tests, driven by the total number of observations. As this varies substantially for sc-eQTL tests (Fig. 1b and Supplementary Fig. 3), inherent power to determine eQTL and colocalization events also varies. To circumvent the influence of varied statistical power in the nomination of cell types within which colocalization events are active, for each colocalization, we quantified the proportion of variance in disease effector gene expression (r^2) that was explained by the lead eQTL variant after intersection with the GWAS variants across each condition. This was calculated by linear regression of disease effector gene expression in the inverse normal transformed pseudobulk data on genotype.

To assess the evidence for enrichment of Notch and Wnt pathway regulators in our disease effector genes, we performed a gene ontology over-representation test of ‘Notch signalling pathway’ (GO:0007219) and ‘Wnt signalling pathway’ (GO:0016055) in our disease effector genes against a background of all eGenes using clusterProfiler v4.10.1[108]. This was performed using default parameters except minGSSize=3, pvalueCutoff = 1, qvalueCutoff = 1. These gene sets were also used to quantify the number of disease effector genes previously and presently nominated in these pathways.

Enrichment of eQTLs in genomic annotations

To calculate the enrichment of lead variants from eQTLs in ENCODE and FANTOM genomic annotations, we followed previous approaches[12], available on GitHub https://github.com/hakha-most/gwas_eqtl. In brief, eQTLs were first grouped by the minimum resolution at which they were detected, and their presence in functional elements was calculated. To calculate bootstrapped confidence intervals, we then sampled with replacement from independent linkage disequilibrium blocks (calculated previously[109]) that contained eQTLs detected at each resolution one thousand times. Enrichment values in different annotations were then first normalized to that of random variants, before values in enhancers being normalized to those in promoters, in order to identify relative enrichment in these annotations specifically. Distal and proximal enhancer-like annotations from ENCODE were grouped together. P values were calculated by two-tailed t-tests comparing the bootstrapped distributions, with the 25th, 50th and 75th percentiles displayed in the enrichment plot.

Therapeutic evidence analysis

We downloaded data on existing therapeutics and their clinical approval stage from ChEMBL and Open Targets68,110. Therapeutics were grouped into broad disease terms using the Ontology Lookup Service[111] and filtered for those which directly targeted genes which we had colocalized with UC, CD or IBD (PPh4 > 0.75, within 0.5 Mb of existing IBD locus) but had not yet been trialed as IBD treatments according to ChEMBL.

Reporting summary

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

Data availability

We have centralized all interactive data exploration and file access at our project website: https://www.ibdverse.info/. We deposited public processed data (expression data from the atlasing and eQTL mapping cohorts, summary statistics from all eQTL, ieQTL and colocalization analyses, the CellTypist prediction model, and metadata) in the BioStudies database (http://www.ebi.ac.uk/biostudies) under accession numbers S-BSST2922 and E-MTAB-16986. scRNA-seq reads and genotype data are available in the European Genome-phenome Archive (EGA) under accession numbers EGAD00001015692 and EGAD00001016069, respectively, which are accessible following approval by the Sanger Data Access Committee.

Code availability

Code used to perform all analyses is available at https://github.com/andersonlab/IBDVerse-sc-eQTL-code  (ref. [112]). GWAS data is available from GWAS Catalog using accession numbers GCST004132 (CD), GCST004133 (UC) and GCST004131 (IBD).

References

1. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

2. Sollis, E. et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51, D977–D985 (2023).

3. de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).

4. The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

5. Panousis, N. I. et al. Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases. Nat. Commun. 16, 7204 (2025).

6. de Klein, N. et al. Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nat. Genet. 55, 377–388 (2023).

7. Soskic, B. et al. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. Nat. Genet. 54, 817–826 (2022).

8. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

9. Minikel, E. V., Painter, J. L., Dong, C. C. & Nelson, M. R. Refining the impact of genetic evidence on clinical success. Nature 629, 624–629 (2024).

10. Trajanoska, K. et al. From target discovery to clinical drug development with human genetics. Nature 620, 737–745 (2023).

11. Razuvayevskaya, O., Lopez, I., Dunham, I. & Ochoa, D. Genetic factors associated with reasons for clinical trial stoppage. Nat. Genet. 56, 1862–1867 (2024).

12. Mostafavi, H., Spence, J. P., Naqvi, S. & Pritchard, J. K. Systematic differences in discovery of genetic effects on gene expression and complex traits. Nat. Genet. 55, 1866–1875 (2023).

13. GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

14. Schmiedel, B. J. et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701–1715.e16 (2018).

15. Bossini-Castillo, L. et al. Immune disease variants modulate gene expression in regulatory CD4+ T cells. Cell Genom. 2, 100117 (2022).

16. Alasoo, K. et al. Genetic effects on promoter usage are highly context-specific and contribute to complex traits. eLife 8, e41673 (2019).

17. Dimas, A. S. et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246–1250 (2009).

18. Yazar, S. et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 376, eabf3041 (2022).

19. Nathan, A. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 606, 120–128 (2022).

20. Oelen, R. et al. Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure. Nat. Commun. 13, 3267 (2022).

21. Bryois, J. et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat. Neurosci. 25, 1104–1112 (2022).

22. Tian, Y., Wu, L., Huang, C.-C. & Wang, L. Identify regulatory eQTLs by multiome sequencing in prostate single cells. Preprint at bioRxiv https://doi.org/10.1101/2024.06.19.599704 (2024).

23. Natri, H. M. et al. Cell-type-specific and disease-associated expression quantitative trait loci in the human lung. Nat. Genet. 56, 595–604 (2024).

24. Domínguez Conde, C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).

25. Atrian, S. & Capdevila, M. Metallothionein–protein interactions. Biomol. Concepts 4, 143–160 (2013).

26. Sirvent, S. et al. Impaired expression of metallothioneins contributes to allergen-induced inflammation in patients with atopic dermatitis. Nat. Commun. 14, 2880 (2023).

27. Giacconi, R. et al. Novel -209A/G MT2A polymorphism in old patients with type 2 diabetes and atherosclerosis: relationship with inflammation (IL-6) and zinc. Biogerontology 6, 407–413 (2005).

28. Zhang, F., Guo, S., Zhong, W., Huang, K. & Liu, Y. Integrative analysis of metallothioneins identifies MT1H as candidate prognostic biomarker in hepatocellular carcinoma. Front. Mol. Biosci. 8, 672416 (2021).

29. Takano, H. et al. Protective role of metallothionein in acute lung injury induced by bacterial endotoxin. Thorax 59, 1057–1062 (2004).

30. Tsuji, T. et al. Role of metallothionein in murine experimental colitis. Int. J. Mol. Med. 31, 1037–1046 (2013).

31. Inoue, K.-I. et al. Role of metallothionein in coagulatory disturbance and systemic inflammation induced by lipopolysaccharide in mice. FASEB J. 20, 533–535 (2006).

32. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

33. Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311–D1320 (2021).

34. Liu, Z. et al. Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries. Nat. Genet. 55, 796–806 (2023).

35. Cuomo, A. S. E. et al. Impact of rare and common genetic variation on cell type-specific gene expression. Preprint at medRxiv https://doi.org/10.1101/2025.03.20.25324352 (2025).

36. Lin, S.-N. et al. Human intestinal myofibroblasts deposited collagen VI enhances adhesiveness for T cells - A novel mechanism for maintenance of intestinal inflammation. Matrix Biol. 113, 1–21 (2022).

37. Okuno, T. et al. Interleukin-1β and tumor necrosis factor-alpha induce chemokine and matrix metalloproteinase gene expression in human colonic subepithelial myofibroblasts. Scand. J. Gastroenterol. 37, 317–324 (2002).

38. Garrido-Trigo, A. et al. Macrophage and neutrophil heterogeneity at single-cell spatial resolution in human inflammatory bowel disease. Nat. Commun. 14, 4506 (2023).

39. Uo, M. et al. Mucosal CXCR4+ IgG plasma cells contribute to the pathogenesis of human ulcerative colitis through FcγR-mediated CD14 macrophage activation. Gut 62, 1734–1744 (2013).

40. Hu, M. D. et al. Γδ intraepithelial lymphocytes facilitate pathological epithelial cell shedding via CD103-mediated granzyme release. Gastroenterology 162, 877–889.e7 (2022).

41. Zhou, B. et al. Notch signaling pathway: architecture, disease, and therapeutics. Signal Transduct. Target. Ther. 7, 95 (2022).

42. Shang, Y., Smith, S. & Hu, X. Role of Notch signaling in regulating innate immunity and inflammation in health and disease. Protein Cell 7, 159–174 (2016).

43. Pinnell, N. et al. The PIAS-like coactivator Zmiz1 is a direct and selective cofactor of Notch1 in T cell development and leukemia. Immunity 43, 870–883 (2015).

44. Rognoni, E. et al. Kindlin-1 controls Wnt and TGF-β availability to regulate cutaneous stem cell proliferation. Nat. Med. 20, 350–359 (2014).

45. Momozawa, Y. et al. IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes. Nat. Commun. 9, 2427 (2018).

46. Liu, C.-C. et al. FERMT1 mediates epithelial–mesenchymal transition to promote colon cancer metastasis via modulation of β-catenin transcriptional activity. Oncogene 36, 1779–1792 (2017).

47. Herz, C. et al. Kindlin-1 is a phosphoprotein involved in regulation of polarity, proliferation, and motility of epidermal keratinocytes. J. Biol. Chem. 281, 36082–36090 (2006).

48. Sadler, E. et al. Novel KIND1 gene mutation in Kindler syndrome with severe gastrointestinal tract involvement. Arch. Dermatol. 142, 1619–1624 (2006).

49. Kern, J. S. et al. Chronic colitis due to an epithelial barrier defect: the role of kindlin-1 isoforms. J. Pathol. 213, 462–470 (2007).

50. Depeille, P. et al. RasGRP1 opposes proliferative EGFR-SOS1-Ras signals and restricts intestinal epithelial cell growth. Nat. Cell Biol. 17, 804–815 (2015).

51. Hsu, T. C. et al. A focused review of Ras guanine nucleotide-releasing protein 1 in immune cells and cancer. Int. J. Mol. Sci. 24, 1652 (2023).

52. Wang, C. et al. RasGRP1 promotes the acute inflammatory response and restricts inflammation-associated cancer cell growth. Nat. Commun. 13, 7001 (2022).

53. Zhang, X. et al. LPIN3 promotes colorectal cancer growth by dampening intratumoral CD8+ T cell effector function. Cancer Immunol. Immunother. 74, 135 (2025).

54. Gehart, H. & Clevers, H. Tales from the crypt: new insights into intestinal stem cells. Nat. Rev. Gastroenterol. Hepatol. 16, 19–34 (2019).

55. Flanagan, D. J., Austin, C. R., Vincan, E. & Phesse, T. J. Wnt signalling in gastrointestinal epithelial stem cells. Genes 9, 178 (2018).

56. Clevers, H., Loh, K. M. & Nusse, R. Stem cell signaling. An integral program for tissue renewal and regeneration: Wnt signaling and stem cell control. Science 346, 1248012 (2014).

57. Muncan, V. et al. Rapid loss of intestinal crypts upon conditional deletion of the Wnt/Tcf-4 target gene c-Myc. Mol. Cell. Biol. 26, 8418–8426 (2006).

58. Dang, C. V., Le, A. & Gao, P. MYC-induced cancer cell energy metabolism and therapeutic opportunities. Clin. Cancer Res. 15, 6479–6483 (2009).

59. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

60. Kalkat, M. et al. MYC deregulation in primary human cancers. Genes 8, 151 (2017).

61. Wokolorczyk, D. et al. A range of cancers is associated with the rs6983267 marker on chromosome 8. Cancer Res. 68, 9982–9986 (2008).

62. Tuupanen, S. et al. The common colorectal cancer predisposition SNP rs6983267 at chromosome 8q24 confers potential to enhanced Wnt signaling. Nat. Genet. 41, 885–890 (2009).

63. Avigan, M. I., Strober, B. & Levens, D. A far upstream element stimulates c-myc expression in undifferentiated leukemia cells. J. Biol. Chem. 265, 18538–18545 (1990).

64. Yin, H. et al. FUBP1 promotes colorectal cancer stemness and metastasis via DVL1-mediated activation of Wnt/β-catenin signaling. Mol. Oncol. 15, 3490–3512 (2021).

65. Wu, B. et al. Ring Finger Protein 14 is a new regulator of TCF/β-catenin-mediated transcription and colon cancer cell survival. EMBO Rep. 14, 347–355 (2013).

66. Thomas, J. J. et al. RNF4-dependent oncogene activation by protein stabilization. Cell Rep. 16, 3388–3400 (2016).

67. Graham, D. B. & Xavier, R. J. Pathway paradigms revealed from the genetics of inflammatory bowel disease. Nature 578, 527–539 (2020).

68. Zdrazil, B. et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 52, D1180–D1192 (2024).

69. Bonnet, F. & Scheen, A. Understanding and overcoming metformin gastrointestinal intolerance. Diabetes Obes. Metab. 19, 473–481 (2017).

70. Subramaniam, K., Joseph, M. P. & Babu, L. A. A common drug causing a common side effect at an uncommon time: Metformin-induced chronic diarrhea and weight loss after years of treatment. Clin. Diabetes 39, 237–240 (2021).

71. Jayne, T. et al. Evidence for and against a pathogenic role of reduced γ-secretase activity in familial Alzheimer’s disease. J. Alzheimers Dis. 52, 781–799 (2016).

72. Cruchaga, C. et al. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer’s disease families. PLoS ONE 7, e31039 (2012).

73. Hur, J.-Y. γ-secretase in Alzheimer’s disease. Exp. Mol. Med. 54, 433–446 (2022).

74. Doody, R. S. et al. A phase 3 trial of semagacestat for treatment of Alzheimer’s disease. N. Engl. J. Med. 369, 341–350 (2013).

75. Erkert, L. et al. Alzheimer’s disease-related presenilins are key to intestinal epithelial cell function and gut immune homoeostasis. Gut 73, 1618–1631 (2024).

76. Krzak, M. et al. Single-cell RNA sequencing of terminal ileal biopsies identifies signatures of Crohn’s disease pathogenesis. Nat. Genet. https://doi.org/10.1038/s41588-026-02634-7 (2026).

77. Anderson, A. et al. Monocytosis is a biomarker of severity in inflammatory bowel disease: analysis of a 6-year prospective natural history registry. Inflamm. Bowel Dis. 28, 70–78 (2022).

78. Kwon, Y., Kim, Y. Z., Choe, Y. H. & Kim, M. J. Increased monocyte abundance as a marker for relapse after discontinuation of biologics in inflammatory bowel disease with deep remission. Front. Immunol. 13, 996875 (2022).

79. Maddipatla, S. C. et al. Assessing cellular and transcriptional diversity of ileal mucosa among treatment-naïve and treated Crohn’s disease. Inflamm. Bowel Dis. 29, 274–285 (2023).

80. Gutierrez-Arcelus, M. et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet. 11, e1004958 (2015).

81. Brown, C. D., Mangravite, L. M. & Engelhardt, B. E. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genet. 9, e1003649 (2013).

82. Wallace, C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet. 17, e1009440 (2021).

83. Wehkamp, J. et al. The Paneth cell α-defensin deficiency of ileal Crohn’s disease is linked to Wnt/Tcf-4. J. Immunol. 179, 3109–3118 (2007).

84. Blaydon, D. C. et al. Inflammatory skin and bowel disease linked to ADAM17 deletion. N. Engl. J. Med. 365, 1502–1508 (2011).

85. Uhlig, H. H. et al. Clinical genomics for the diagnosis of monogenic forms of inflammatory bowel disease: a position paper from the paediatric IBD Porto group of European Society of Paediatric Gastroenterology, Hepatology and Nutrition. J. Pediatr. Gastroenterol. Nutr. 72, 456–473 (2021).

86. Bolton, C. et al. An integrated taxonomy for monogenic inflammatory bowel disease. Gastroenterology 162, 859–876 (2022).

87. Ouahed, J. et al. Very early onset inflammatory bowel disease: a clinical approach with a focus on the role of genetics and underlying immune deficiencies. Inflamm. Bowel Dis. 26, 820–842 (2020).

88. Pazmandi, J., Kalinichenko, A., Ardy, R. C. & Boztug, K. Early-onset inflammatory bowel disease as a model disease to identify key regulators of immune homeostasis mechanisms. Immunol. Rev. 287, 162–185 (2019).

89. Nambu, R. et al. A systematic review of monogenic inflammatory bowel disease. Clin. Gastroenterol. Hepatol. 20, e653–e663 (2022).

90. McIntyre, R. E. Single cell dissociation of intestinal gut tissue for scRNAseq of all cell types. protocols.io https://doi.org/10.17504/protocols.io.bq94mz8w (2021).

91. Ghouraba, M. & McIntyre, R. E. Single cell dissociation of intestinal gut tissue for scRNAseq_v2. protocols.io https://doi.org/10.17504/protocols.io.e6nvwwwrdvmk/v1 (2026).

92. Fleming, S. J. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods 20, 1323–1335 (2023).

93. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).

94. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

95. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

96. Clarke, Z. A. & Bader, G. D. MALAT1 expression indicates cell quality in single-cell RNA sequencing data. Preprint at bioRxiv https://doi.org/10.1101/2024.07.14.603469 (2024).

97. Cuomo, A. S. E. et al. Optimizing expression quantitative trait locus mapping workflows for single-cell studies. Genome Biol. 22, 188 (2021).

98. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

99. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

100. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

101. Taylor-Weiner, A. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol. 20, 228 (2019).

102. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

103. Davis, J. R. et al. An efficient multiple-testing adjustment for eQTL studies that accounts for linkage disequilibrium between variants. Am. J. Hum. Genet. 98, 216–224 (2016).

104. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

105. Storey, J. D., Bass, A. J., Dabney, A. & Robinson, D. qvalue: Q-value estimation for false discovery rate control. R package version 2.44.0 https://doi.org/10.18129/B9.bioc.qvalue (2026).

106. Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).

107. Timshel, P. N., Thompson, J. J. & Pers, T. H. Genetic mapping of etiologic brain cell types for obesity. eLife 9, e55851 (2020).

108. Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

109. Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).

110. Buniello, A. et al. Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Res. 53, D1467–D1475 (2025).

111. Jupp, S., Burdett, T., Leroy, C. & Parkinson, H. A new Ontology Lookup Service at EMBL-EBI. In CEUR Workshop Proceedings vol. 1546, 118–119 (of Gesellschaft für Informatik, 2015).

112. Alegbe, T. & Harris, B. andersonlab/IBDVerse-sc-eQTL-code: v0.1-alpha (v0.1). Zenodo https://doi.org/10.5281/zenodo.19300999 (2026).

Acknowledgements

We thank all the individuals who kindly donated samples and their time to the study. We also thank B. Cakir and M. Prete for setting up the cellxgene interactive data explorer; P. Bevan, S. Robinson, M. Waller and H. Banks for their contributions to the IBDverse website and graphics; R. Piovani and K. Venkat for their administrative support; and E. Hidari for his assistance with software and reproducibility. The views expressed are those of the authors and not necessarily those of the funding bodies. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Funding

T.A. discloses support for the research of this work from the Wellcome Trust (grant number 222547/Z/21/Z). C.A.A. and T.R. disclose support for the research of this work from the NIHR Cambridge Biomedical Research Centre (grant number BRC-1215-20014), the Wellcome Trust (grant numbers 206194, 220540/Z/20/A and 108413/A/15/D), The Crohn’s Colitis Foundation Genetics Initiative (grant numbers 612986 and 997266) and Open Targets (grant number OTAR2057).

Author information

Author notes

1. These authors contributed equally: Tobi Alegbe, Bradley T. Harris

2. These authors jointly supervised this work: Tim Raine, Carl A Anderson

Authors and Affiliations

1. Wellcome Sanger Institute, Hinxton, UK

Tobi Alegbe, Bradley T. Harris, Laura Fachal, Lucia Ramirez-Navarro, Marcus Tutert, Monika Krzak, Mennatallah Ghouraba, Michelle Strickland, Matiss Ozols, Celeste E. Cohen, Saniya Khullar, Eleonora Khabirova, Noor Wana, May Xueqi Hu, Jason Skelton, Jasmin Ostermayer, Kimberly Ai Xian Cheam, D. Leland Taylor, Yong Gu, Steven Leonard, Vivek Iyer, Rebecca E. McIntyre, Cristina Cotobal Martin & Carl A. Anderson

2. Open Targets, Hinxton, UK

Tobi Alegbe, David Ochoa, Tim Raine & Carl A. Anderson

3. EMBL-EBI, Hinxton, UK

Tobi Alegbe & David Ochoa

4. GlaxoSmithKline, Stevenage, UK

Nikolaos I. Panousis

5. Addenbrooke’s Hospital, Cambridge, UK

Claire Dawson, Tina Thompson, Kenneth Arestang, Nilanga Nishad, Biljana Brezina, Charry Queen Caballes, Wendy Garri, Miles Parkes & Tim Raine

6. Department of Medicine, University of Cambridge, Cambridge, UK

Chris Wallace

7. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

Chris Wallace

8. University of Edinburgh Centre for Inflammation Research, Queens Medical Research institute, Edinburgh, UK

Gareth-Rhys Jones

Contributions

Manuscript drafting: T.A. and B.T.H. Sample collection: K.A., W.G., B.B., C.Q.C., C.D., T. T., T.R. and M.P. Sample processing: M.G., M.S., N.W., J.S., J.O., M.X.H., K.A.X.C., C.C.M. and R.E.M. Clinical information: N.N. Critical discussion: L.F., L.R.-N., M.K., S.K., C.W., N.I.P., C.C.M., D.O., and G.-R.J. Data processing: T.A., B.T.H., M.T., M.O., E.K., L.R.-N., D.O., D.L.T., Y.G., S.L., C.E.C. and V.I. Statistical analysis: T.A. and B.T.H. Writing—review and editing: G.-R.J., T.R. and C.A.A. Conceptualization and supervision: R.E.M., T.R. and C.A.A.

Corresponding authors

Correspondence to Tim Raine or Carl A. Anderson.

Ethics declarations

Competing interests

B.T.H. has received speaker fees from BridgeBio. C.A.A. has received research grants or consultancy/speaker fees from Genomics plc, BridgeBio, GSK and AstraZeneca. T.R. has received research/educational grants and/or speaker/consultation fees from AbbVie, Arena, Aslan, AstraZeneca, Boehringer-Ingelheim, BMS, Celgene, Ferring, Galapagos, Gilead, GSK, Heptares, LabGenius, Janssen, Mylan, MSD, Novartis, Pfizer, Sandoz, Takeda and UCB. R.E.M. and. M.T. are employees of Relation Therapeutics. N.I.P. is an employee at GSK. C.W. receives funding from MSD and GSK and is a part-time employee of GSK. The other authors declare no competing interests.

Peer review

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Nature thanks Subra Kugathasan and the other, 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 Cell types identified in the IBDverse dataset.

Uniform Manifold Approximation and Projection (UMAP) of the 9 major populations and 86 cell types identified after quality control and clustering.

Extended Data Fig. 2 Cell type-level metadata.

Total number (top) and contribution of different sites, disease status, and ages of sample to each cell-type in the 1,852,681 cells in the atlasing cohort.

Extended Data Fig. 3 Factors influencing eGene detection.

Association between the number of eGenes detected in the cross-site analysis and (a) the mean number of genes per pseudobulked sample or (b) the number of individuals.

Extended Data Fig. 4 Number of eQTLs per eGene and likelihood of targeting the nearest gene across resolutions.

(a) Comparison of the number of eQTLs (determined by linkage disequilibrium clumping, see Methods) that were found for each eGene at each resolution. Black line connects the means of each resolution, ‘n = ’ indicates the total number of annotations per resolution, p-values calculated by Wilcoxon test. (b) Proportion of eQTLs detected at each resolution for which the nearest gene is the eGene.

Extended Data Fig. 5 Number of ieGenes detected for each variable.

Absolute number of eGenes detected by interaction (ieGenes) with sample level phenotypes, grouped by the interaction variable and the major population in which ieGenes were detected.

Extended Data Fig. 6 Identification and contextualisation of NOTCH signalling colocalization target ZMIZ1.

(a) Regional association pattern between genetic dysregulation of ZMIZ1 and CD susceptibility. Point fill and colour key follows that of 4b. (b) Comparison of the variance of disease effector gene expression explained by the colocalizing variant across conditions where a nominal (p < 0.05) eQTL effect was detected (left), and boxplots showing effect in the three annotations with the most variance explained (right). Boxplots indicate the 25th, 50th and 75th percentiles and whiskers extend to 1.5 times the interquartile range.

Extended Data Fig. 7 IBD colocalizations may impact epithelial barrier function and Wnt signalling.

(a/b/d/f) Comparison of the variance of disease effector gene expression explained by the colocalizing variant across conditions where a nominal (p < 0.05) eQTL effect was detected for each gene (left), and boxplots showing effect in the three annotations with the most variance explained (right). (c/e) Regional association pattern between genetic dysregulation of FUBP1 and CD susceptibility or FERMT1 and IBD susceptibility, respectively. Point fill and colour key follows that of 4b. Boxplots indicate the 25th, 50th and 75th percentiles and whiskers extend to 1.5 times the interquartile range.

Extended Data Fig. 8 Colocalization of IBD with dysregulation of existing therapeutic targets ITGA4, JAK2, NDUFAF1 and PSEN2.

Regional association pattern between genetic dysregulation of ITGA4 (a), JAK2 (c), NDUFAF1 (e) and PSEN2 (g) and IBD types. Comparison of the variance of disease effector gene expression explained by the colocalizing variant across conditions where a nominal (p < 0.05) eQTL effect was detected for each gene (b/d/f/h, left), and boxplots showing effect in the three annotations with the most variance explained (right). Boxplots indicate the 25th, 50th and 75th percentiles and whiskers extend to 1.5 times the interquartile range.

Extended Data Table 1 Novel disease effector genes of high confidence

Supplementary information

Supplementary Information (download PDF )

Supplementary Methods, Supplementary Figs. 1–11 and Supplementary Notes.

Reporting Summary (download PDF )

Supplementary Table 1

Gene set enrichment analysis results for ieGenes detected with each interaction covariate in each cell type.

Supplementary Table 2

Summary of genes nominated by colocalization in IBDverse at each IBD/CD/UC GWAS locus (PPh4 > 0.75), compared with the genes nominated by Open Targets genetics.

Supplementary Table 3

Summary of the variance in disease effector gene expression that is explained by the colocalizing QTL variant across cell types.

Peer Review File (download PDF )

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Alegbe, T., Harris, B.T., Fachal, L. et al. Cell-type-resolved genetic variation shapes inflammatory bowel disease risk. Nature 656, 129–139 (2026). https://doi.org/10.1038/s41586-026-10627-z

- Received: 24 June 2025

- Accepted: 06 May 2026

- Published: 03 June 2026

- Version of record: 03 June 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10627-z

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Mechanism of age-related accumulation of mtDNA mutations in human blood

Rahul Gupta,

Timothy J. Durham,

Grant Chau,

Masahiro Kanai,

Md Mesbah Uddin,

Wenhan Lu,

M. Austin Argentieri,

Konrad J. Karczewski,

Daniel Howrigan,

Pradeep Natarajan,

Wei Zhou,

Benjamin M. Neale &

…

Vamsi K. Mootha

Nature volume 656, pages 140–148 (2026)

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Abstract

Accumulation of mutant mitochondrial DNA (mtDNA) heteroplasmy is among the strongest signatures of ageing[1]. Here we investigated the underlying mechanism by calling mtDNA sequence, mtDNA abundance and mtDNA heteroplasmic variants in human blood using whole-genome sequences from approximately 750,000 individuals. We observed that mtDNA single-nucleotide variants (mtSNVs) accumulate sharply at age 60 years, occur at low levels of heteroplasmy, exhibit little evidence of positive selection and are likely to be predominantly neutral. The mutational spectrum of mtSNVs does not reflect oxidative lesions, as is commonly invoked, but is more consistent with mtDNA replication errors. To understand why mtSNVs become detectable with age, we performed a genome-wide association study for heteroplasmic mtSNV burden, identifying germline variants near TERT, TCL1A and SMC4, all of which have been linked to clonal haematopoiesis (CH)[2]. Rare-variant analysis also showed that high mtSNV burden is associated with mutations in numerous CH driver genes. These genetic associations persisted even after exclusion of individuals with known CH driver mutations. Our results support a model in which ‘cryptic’ mtDNA mutations initially arise randomly as replication errors but are undetectable in bulk. They then become apparent only through age-related expansion of cellular clones in blood. We propose that the high copy number and mutation rate of mtDNA make it a sensitive blood-based marker of somatic mosaicism due to CH. Our work mechanistically unifies three prominent signatures of ageing: common germline variants in TERT, CH and observed accrual of mtDNA mutations.

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- Mitochondrial genome

- Genetic variation

- Genome-wide association studies

- Ageing

- Mutation

Main

Mitochondrial DNA (mtDNA) heteroplasmy arises when a cell or tissue contains a mixture of two or more different mtDNA alleles. Heteroplasmy dynamics tend to be complex, varying across generations, during development, in disease and with ageing. Historically, most studies of mtDNA heteroplasmy in humans have focused on rare, maternally transmitted disorders, which are typically driven by loss-of-function mtDNA mutations at high levels of heteroplasmy. However, there is growing evidence that low levels of mtDNA heteroplasmic variants are found in nearly all humans[3]. Using biobank-scale genomics, we previously reported that nearly everyone harbours two different classes of such variants in blood[4]. ‘Length heteroplasmies’ (insertion/deletion (indel) mutations within polypyrimidine tracts) do not accumulate with age, tend to be maternally transmitted and, once inherited, exhibit levels of heteroplasmy under nuclear genetic control. The associated nuclear DNA (nucDNA) loci tend to implicate mitochondria-localized proteins with established roles in mtDNA replication and maintenance. By contrast, heteroplasmic mtSNVs tend not to be inherited but rather seem to be somatic in origin and accumulate with age[4].

The mechanism of this age-related accrual of heteroplasmic mtSNVs in blood is unknown. Classically, oxidative damage to mtDNA from reactive oxygen species has been invoked as a part of a ‘vicious cycle’ in which mtDNA mutations lead to further generation of reactive oxygen species and more mutations5,6,7. More recent studies of ageing brains[8] and tumour samples[9] have questioned the role of oxidative damage[10] in mutation generation. Once individual mutations arise, it is unclear how they become abundant enough to detect.

Here we investigated why mtSNVs accumulate with age in blood. We report an analysis of mtDNA using a callset of approximately 750,000 individuals across the UK Biobank (UKB) and All of Us (AoU). We resolved the mutational spectrum of age-accumulating blood mtDNA and then performed a genome-wide association study (GWAS) for the burden of mtSNVs to identify mechanisms of control by rare and common germline nuclear genetic variants. Unexpectedly, the loci we identified were related not to mitochondrial homeostasis but rather to CH. Our analyses support a model in which individual blood cells randomly accumulate low levels of neutral mtDNA variation (that is, cryptic mutations), probably owing to replication-related errors, that are not detectable in bulk. However, with age-related expansion of individual cellular clones (by means of CH), these low-level cryptic variants become detectable and give rise to the observed accumulation with age.

mtDNA sequences from 736,038 individuals

We applied mtSwirl[4], a pipeline for calling mtDNA abundance and heteroplasmic variation (Methods), to whole-genome sequencing (WGS) data from 736,038 diverse individuals in AoU and UKB, representing an approximately 3× increase in sample size compared with ref. [4]. mtSwirl reconstructs each individual’s mtDNA to serve as a ‘self-reference’ against which low levels of heteroplasmic variants can be more accurately called[4]. After quality control, we called a total of 19,051,526 variants (Methods) from 620,385 individuals. Of the 1,151,297 heteroplasmic variants (Supplementary Note 1), 754,108 were indels and 397,189 were SNVs (Extended Data Fig. 1).

To benchmark our callset, we performed GWAS of mtDNA copy number (mtCN) and individual common heteroplasmic variants and compared them with the results of our previous study[4]. GWAS for blood-composition-adjusted mtCN in UKB (mtCNadj; Supplementary Note 2) across 398,250 individuals identified 107 loci (Extended Data Fig. 2c and Methods), many of which were corroborated by fine-mapping and gene-based rare-variant testing (Extended Data Fig. 2d,e). We replicated 44 of 46 loci from our previous mtCNadj GWAS[4] (n = 163,372) at genome-wide significance (GWS) and discovered 63 further associations (for instance near LIG3 and TBRG4). As in previous work4,11, adjustment for blood cell composition eliminated or reversed the direction of most associations between reduced mtCN and increased rates of age-related diseases (Extended Data Fig. 2f). GWAS for heteroplasmy of each of 78 common mtDNA variants, most of which were indels (Extended Data Fig. 1), identified 163 associations across 60 nuclear loci (Extended Data Fig. 3a). We replicated virtually all previously identified associations[4] and identified many more hits. For example, we report associations between chrM:302:A:AC (the most common heteroplasmy in humans) and nucDNA variants near TSFM, TOP3A, PANK1 and UQCRC1; UQCRC1 is supported by a fine-mapped missense variant (Extended Data Fig. 3b). Across mtDNA sites, DGUOK and PNP were associated with most heteroplasmies (N < 42,000) except for chrM:302 (n = 244,879) (Extended Data Fig. 3a–f), implying differences in nuclear control of heteroplasmy across mtDNA sites beyond the effects of power (Supplementary Note 3). Recessive GWAS revealed no new loci (Extended Data Fig. 3g,h).

Heteroplasmic mtSNVs reflect replication error

Focusing on heteroplasmic mtSNVs in AoU, we observed mutation accrual particularly after the age of 60 years (Fig. 1). We found a similar mtSNV accumulation in UKB (Extended Data Fig. 4a). This accumulation occurred regardless of smoking status; however, heteroplasmic SNVs were more abundant in smokers, as described previously[12] (Extended Data Fig. 4b).

Fig. 1: Age accumulation of mtSNVs in AoU (n = 236,749 individuals).

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Points represent means, error bars represent 95% confidence intervals. See Extended Data Fig. 4a for the corresponding analysis in UKB.

We first visualized mtDNA mutations by variant type and strand. mtDNA contains a ‘heavy strand’ with more purines and a ‘light strand’ with more pyrimidines. Replication begins on the light strand at the origin of replication (Ori). We excluded Ori, as it has been shown to have a different mutational spectrum from the coding mtDNA[9]. Outside Ori, most variants were transitions (Fig. 2a,b and Extended Data Fig. 5a,b) with a striking strand bias: C>T and A>G variants occurred far more frequently on the heavy strand than on the light strand.

Fig. 2: Age-accumulating mtSNVs show a stark strand bias, tend to occur at lower heteroplasmy and are more neutral than other variants.

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a, Normalized mean mtSNV count as a function of variant location, strand, class and age. b, Normalized mean mtSNV count as a function of strand and variant class. c, Mean mtSNV count as a function of age for all variants, age-accumulating class variants (C>T heavy and A>G heavy and light) and other variants. d, Heteroplasmy distribution across variants and individuals for age-accumulating classes only and all variants. e, Normalized mean mtSNV count for coding variants as a function of strand, class, age and consequence. f, Heteroplasmy distributions for missense (green) versus synonymous (black) variants within mtDNA protein coding genes, stratified by age-accumulating class (dashed versus solid). Insets are corresponding sample sizes. g, dN/dS estimates (dots) as a function of heteroplasmy, gene and mutation class for age-accumulating class variants in mtDNA protein coding genes. Dashes indicate the median dN/dS of null draws; error bars are 95% confidence intervals under null, computed non-parametrically as the 2.5–97.5% range of null draws. In a–c and e, error bars indicate ±1 s.e. For a, c and e, total n = 236,749 individuals. For b and g, total n = 237,500 individuals. All panels in this figure use AoU data; see Extended Data Fig. 5 for UKB.

Classically, mutations in mtDNA have been thought to arise owing to oxidative stress[7]; however, growing evidence over the past decade8,9,13 has suggested that replication-linked errors may better explain heavy-strand-biased transition mutations. Under the strand displacement model, once replication of the light strand begins at Ori, the non-template heavy strand persists in a single-stranded state until replication progresses through approximately two-thirds of the molecule[14]. A bias towards heavy strand C>T and A>G mtSNVs has been observed in human brain and tumour samples and attributed to deamination from the protracted time that the heavy strand spends in a single-stranded state9,15. By contrast, oxidative damage predominantly produces C>A transversions due to guanosine oxidation16,17,18. Consistent with findings in human brain and tumours8,9, our observation of heavy-strand-biased C>T and A>G mutations with little C>A in blood suggests replication-related errors with little oxidative damage.

Although mechanisms of mtDNA and nucDNA replication are distinct, we briefly explored whether our observed mtDNA mutation spectrum had parallels with nucDNA. Our observed pattern of mtSNVs was most correlated with known nuclear single-base substitution signatures attributed to nucDNA damage repair defects (for instance, mismatch repair) (Extended Data Figs. 6a,b and 7a,b). On the other hand, nuclear mutation signatures attributed to oxidative damage19,20,21 were not correlated with our observed mtDNA mutational signature.

Only C>T mutations (on the heavy strand) and A>G mutations (on both strands) accumulated with age (Fig. 2a and Extended Data Fig. 5a), whereas heteroplasmic SNVs outside these classes did not (Fig. 2c and Extended Data Fig. 5c). In contrast to A>G and C>T heavy strand mutations, for which all trinucleotide contexts showed age accumulation (Extended Data Figs. 6c,d and 7c,d), only a subset of A>G light strand contexts showed age accumulation. A contributing mechanism may be polymerase error, which can produce transitions[22] and could be context-specific and strand independent.

We previously showed that mtSNV heteroplasmy tends to be somatic and not maternally transmitted[4]. Here we wanted to know whether this observation extended to age-accumulating class variants (that is, A>G on both strands and C>T on heavy strand). SNVs with high heteroplasmy (higher than 0.2) showed a greater than 75% chance of being found in both siblings, whereas low-heteroplasmy (0.2 or lower) variants had a less than 30% chance of being shared between siblings (Extended Data Fig. 4c). To control for the possibility that sibling-sharing could have been present but below our detection threshold, we repeated this analysis for chrM:302:A:AC, which tends to be maternally inherited[4]; we found that more than 60% of variants at heteroplasmy less than 0.2 showed sibling-sharing. Most age-accumulating class variants had heteroplasmy less than 0.2, substantially less than that of non-age-accumulating class variants (Fig. 2d and Extended Data Figs. 4d,e and 5d). Overall, these results indicate that age-accumulating class mtSNVs may have lower heteroplasmy and are more likely to be somatic than non-age-accumulating class variants.

Age-accumulating mtSNVs appear neutral

We next sought to determine whether positive selection shaped the observed pattern of heteroplasmic mtSNV accrual. We observed a similar strand bias among age-accumulating class variants regardless of whether the variant was missense or synonymous (Fig. 2e and Extended Data Fig. 5e), arguing against selection as a central driver. Missense variants had left-shifted heteroplasmy distributions relative to synonymous variants for all genes, suggesting selection against coding variants at higher levels of heteroplasmy (Fig. 2f and Extended Data Fig. 5f). A metric traditionally used to quantify the degree of selection acting on variation is the normalized ratio between non-synonymous and synonymous variation (dN/dS), where a ratio less than 1 suggests negative selection, approximately 1 suggests neutrality and greater than 1 suggests positive selection. In mtDNA, this metric must account for the underlying mutational process (which is strand- and mutation-class-specific) and codon composition. Accordingly, we used two methods to estimate and interpret dN/dS: first, a non-parametric approach recently used in mtDNA[23]; second, a parametric approach accounting for strand and mutation class[24] (Methods). We found that dN/dS among age-accumulating variants was closest to neutral at low heteroplasmy and fell as heteroplasmy rose in all genes except MT-ATP6, MT-ATP8 and MT-ND6 (Fig. 2g and Extended Data Figs. 5g, 6e and 7e). These results indicate that purifying selection against deleterious alleles may increase with higher levels of heteroplasmy, consistent with the ‘heteroplasmy threshold effect’ in which high levels of heteroplasmy are required before phenotypic effects of the mutation are observed25,26,27,28. The variants that accumulate with age, however, tend to be low-heteroplasmy and are probably functionally neutral.

GWAS of mtSNV burden implicates CH

Next, we sought to define the mechanism driving the age-dependent accumulation of mtSNVs. We performed GWAS using mtSNV count as the phenotype (n = 547,947). Surprisingly, we found genome-wide significant associations near TERT, TCL1A, THRB, SMC4, VSIG4 and others (Fig. 3a), almost none of which corresponded to nuclear-encoded mitochondrial proteins. This was in stark contrast to our GWAS for mtCN and mtDNA heteroplasmic indels, which implicated mtDNA replication machinery. The associated variants tended to fall near genes involved in nucDNA repair (for instance, TERT, RAD52 and SMC4) and haematopoietic cell proliferation (such as TCL1A, MTCP1 and THRB). Most have been found to be associated with CH, and TERT and SMC4 have been found to have the most significant genetic associations with CH[2]. TCL1A has been linked to heterogenous effects on DNMT3A-, TET2-, and ASXL1-driven CH[2], and the exact TCL1A variant we identified has been implicated in the haematopoietic clonal expansion rate[29]. Similar to those identified in our mtSNV GWAS, very few of the genes implicated in CH GWAS encoded mitochondrial proteins[2]. We identified ‘CH carriers’ in UKB and AoU on the basis of the presence of putative somatic variants within known nuclear driver genes (Methods) and found that most of the identified GWS associations for mtSNV count persisted after removal of 17,504 individuals labelled as CH carriers, albeit with slightly reduced significance (Fig. 3b). We next performed Mendelian randomization between mtSNV burden and CH. Across virtually all variants associated with CH at GWS[2], we observed a positive correlation with corresponding effect sizes for mtSNV burden (Fig. 3c and Methods), indicating that CH increases mtSNV burden. The converse was not true (Extended Data Fig. 8a), suggesting that mtSNV burden does not affect CH risk. As our method for quantification of mtSNVs relies on a cutoff dependent on mtDNA coverage (Methods and Supplementary Note 1), as a sensitivity analysis we repeated our GWAS in UKB and AoU including mtCN as a covariate. We found minimal changes to the observed genetic architecture (Extended Data Fig. 8b–e).

Fig. 3: Nuclear genetic analysis of mtSNV burden identify drivers and correlates of CH.

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a, Common variant GWAS meta-analysis between AoU and UKB for age-accumulating heteroplasmic mtSNV burden (maximum n = 547,947 individuals). Labelled genes identified as nearest or through curation. b, GWAS as in a but excluding any sample with detected CH (maximum n = 530,443 individuals). In a and b, red lines correspond to the 5 × 10−8 GWS threshold. c, Comparison of GWAS effect size estimates for mtSNV count trait versus those for CH (maximum n = 363,475 individuals) as part of bidirectional Mendelian randomization. Inset represents zoom near origin. Dotted line indicates the inverse-variance weighted linear regression line, with P values from two-sided test for a non-zero beta. Points represent effect sizes. CH effect sizes are log-odds ratios, and mtSNV accrual effect sizes are linear regression betas. Alleles were set such that lead variants for CH were risk-increasing. Error bars are 95% confidence intervals. d, Quantile–quantile plots for gene-based SKAT-O tests of association between variants of various classes (colours) and MAF cutoffs (panels) and SNV count. Genes labelled with large text passed Bonferroni GWS; smaller-text labels correspond to genes passing a 10% false discovery rate cutoff using the Benjamini–Hochberg procedure. †Gene nearby fine-mapped variant with PIP > 0.1.

CH driver mutations predict mtSNV burden

To complement our common variant analysis, we performed a rare-variant association study (RVAS) to identify rare variants in the nuclear genome (Methods) that co-occurred in samples with a high burden of heteroplasmic mtSNVs. We found that predicted loss-of-function and missense variants in several genes, including ASXL1, DNMT3A, TET2, SRSF2, JAK2, CHEK2 and NEMF (Fig. 3d), tended to correlate with high mtSNV burden. Most of the genes identified through RVAS were classical CH driver genes in which somatic variants are used to identify individuals with CH[2]. Recent work has identified evidence of positive selection in CHEK2 in CH, suggesting that it can act as a non-classical driver as well[30]. Our results indicated that CH may contribute to changes in detected mtSNV burden, in turn suggesting that our gene-based tests were, in many cases, identifying somatic driver mutations in nucDNA associated with mtSNV burden. ASXL1 and JAK2 also showed singleton single-variant associations with mtSNV burden (Fig. 3a); the associated nuclear variants were identified only by WGS, raising the possibility that these associations were driven by somatic nucDNA mutations. Although many identified genes are known drivers, NEMF is involved in translation but has yet to be linked to CH; thus, it is a potential novel driver gene candidate. Again, removal of individuals labelled as having CH caused a decline in the gene-based test association significance; however, RVAS associations with DNMT3A, TET2, ASXL1, CHEK2 and C10orf35 persisted (Extended Data Fig. 8f).

TERT variants link mtDNA, telomeres and CH

Given the rich literature linking the TERT gene product telomerase with telomere length and ageing, we next evaluated the evidence for colocalization of the GWAS signal in the TERT locus for mtCN (Extended Data Fig. 2c) and mtSNV burden (Fig. 3a). The rs7705526 variant (chr5: 1285859:C:A) had a posterior inclusion probability (PIP) (from fine-mapping) of approximately 1 for mtSNV burden and 0.39 for mtCN, suggesting colocalization (colocalization posterior probability of approximately 0.39). This colocalization was also reported in recent GWAS of CH[2] (colocalization posterior probability = 1) and telomere length[31] (colocalization posterior probability = 1). Thus, on the basis of our current work and previous studies, this inherited TERT variant: (1) increases mtCNadj, (2) increases mtSNV burden, (3) increases CH risk and (4) increases telomere length.

We next explored the phenotypic correlation between key telomere and mtDNA traits in UKB (Methods). After correction for covariates including age, we observed a strong positive correlation between mtCNadj and telomere length (Extended Data Fig. 9a). By contrast, mtSNV burden was not correlated with telomere length (Extended Data Fig. 9b,c). This was not entirely unexpected, as recent work has shown that genetic predisposition to longer telomere length is associated with higher CH, whereas CH subsequently results in a decline in telomere length[32]. Consistent with this, our fine-mapping indicated that the probable causal variant at TERT increased telomere length and CH risk. This same variant increased mtSNV count. We speculate that a genetic predisposition for longer telomeres may increase mtSNV count by increasing CH predisposition; however, this effect is masked by the fact that CH itself results in an increase in cell replications, which reduces telomere length but increases heteroplasmic mtSNV count.

We also evaluated the correlation of mtDNA phenotypes with circulating levels of age-associated markers (GDF15, FGF21) and inflammatory cytokines (IL-1A, IL-6, TNF). Even after correction for age, we observed a significant inverse relationship between GDF15 and mtCNadj (Extended Data Fig. 9a), which may indicate a shared underlying mechanism of variation; notably, GDF15 was identified as a correlate of an inborn mitochondrial disease[33]. We also observed that higher mtCNadj was correlated with lower IL-6 and TNF, probably owing to residual effects of inflammatory pathways in blood mtCNadj despite compositional correction. These protein biomarkers did not show significant correlation with mtSNV burden, which probably indicates an independent process (that is, CH due to nucDNA variation rather than expansion due to inflammation).

mtSNV burden is correlated with blood cancer

Whether mtSNV burden is associated with disease independent of age is an important question. We focused on 28 common diseases spanning different organ systems. After filtering to never-smokers and applying corrections for age, sex and genetic ancestry group (Methods), we found a strong association for heteroplasmic mtSNV burden only with haematologic cancer; we also found a weak but significant association with chronic kidney disease (Fig. 4a). Notably, we did not observe any associations with other common diseases (Supplementary Table 8). We then extracted cancer-related phenotypes (Methods) and tested for associations with heteroplasmic mtSNV burden. The most substantial effect size was observed for myelodysplastic syndrome, followed by several leukaemia phenotypes (Fig. 4b, Extended Data Fig. 10 and Supplementary Table 9). Ample evidence has linked CH directly to blood cancer risk34,35 and even kidney disease[36]. Taken together, our genetic and phenotypic results link the observed age accumulation in mtSNVs in blood with CH.

Fig. 4: Age-accumulating SNV burden predicts increased risk of haematologic cancer and chronic renal failure when smokers are removed.

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a, Survey of associations with major common diseases. b, Significant associations between SNV burden and cancers. Results for all cancers are shown in Extended Data Fig. 10. Points represent odds ratios across both panels. Red indicates an association with Bonferroni-corrected P < 0.05. Odds ratios were computed using logistic regression including covariates for sex, age, ancestry and haplogroup. Error bars indicate 95% confidence intervals. Per-test sample sizes and test statistics can be found in Supplementary Tables 8 and 9 for panels a and b, respectively. Bonf., Bonferroni-corrected; LoF, loss of function.

mtSNV burden is a sensitive marker of CH

Two lines of evidence demonstrate that mtSNV burden may represent an early and sensitive biomarker of CH. First, GWAS and RVAS for mtSNV burden indicated nuclear germline variants that predisposed individuals to CH, even after removal of the 17,504 individuals with documented CH (Fig. 3a,b). Second, when we removed these individuals from the UKB data (n = 11,465), the association between mtSNV burden and haematologic cancer persisted, with an odds ratio of 1.27 (P = 3.06 × 10−47) versus 1.29 (P = 2.33 × 10−59) for all samples.

Finally, we directly examined heteroplasmic mtSNV burden among individuals with and without detected CH. Individuals with CH had a similar strand bias for A>G and C>T variants to that of controls, and a concordant mutational spectrum with trinucleotide context (Extended Data Figs. 11a and 12a). This suggested that the underlying mutational mechanism was the same for individuals with and without detected CH. Individuals with CH had significantly more age-accumulating class mtDNA variants and even seemed to have a faster rate of mtSNV accumulation with age (Fig. 5a and Extended Data Fig. 12b) relative to age- and sex-matched individuals without detected CH. As discussed above, only a subset of trinucleotide contexts for A>G light strand mutations showed age accumulation; notably, in A>G light strand contexts, only those that accumulated with age were more abundant in samples with CH versus those without (Extended Data Figs. 6d, 7d, 11a and 12a). Among individuals with CH, we observed a similar increase in the number of age-accumulating class variants for both missense and synonymous variants (Fig. 5b and Extended Data Fig. 12c), arguing against the notion that functional mtDNA variants influence expansion of haematopoietic clones. To evaluate this directly, we used dN/dS and the mtDNA local constraint score, a recently developed positional metric of mtDNA constraint[37]. Neither metric showed strong evidence of positive selection in individuals with CH (Extended Data Figs. 11b,c and 12d,e) relative to controls; dN/dS estimates were substantially greater than 1 for MT-ATP6, which is known to have limited constraint[37]. Taken together, these results indicate that mtSNVs may be most likely to be passenger mutations in the setting of an independently arising cellular clonal process in blood.

Fig. 5: Individuals with identified CH tend to have increased accumulation of age-accumulating mtSNVs relative to the general population and do not show evidence of positive selection.

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a, Normalized mean mtSNV count per person as a function of CH status, strand, variant class and age. b, Normalized mean mtSNV count per person as a function of variant consequence and age-accumulating status for CH carriers versus controls. In both panels, error bars correspond to ±1 s.e.. AoU was used for both panels; see Extended Data Fig. 12 for the corresponding analyses in the UKB. Only variants outside the Ori region are shown. For both panels, n = 8,266 individuals with CH; these individuals were compared with control individuals, who were selected by identifying 500 random samples of 8,266 age- and sex-matched individuals without CH (Methods).

Discussion

Age accumulation of human mtSNVs was observed as early as 1999 (ref. [38]), but both the mutational process underlying this accumulation and why it becomes more apparent later in life have been debated. Here we use large-scale human genetics to elucidate this mechanism in blood (Fig. 6). First, in each cell, mtDNA replication errors generate low levels of ‘cryptic’ variation in the mtDNA. These variants are likely to be functionally ‘silent’ given their low-level heteroplasmy. Then, age-related CH causes expansion of cells carrying somatic mtDNA variants as passengers, rendering them detectable as a function of age. These findings reconcile the mechanistic relationships between observed accrual of age-associated mtDNA mutation and two other prominent signatures of ageing biology, namely CH and TERT variants.

Fig. 6: Two-step mechanism by which mtSNV heteroplasmy burden accumulates with ageing.

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In the first step, cryptic mtDNA variation is generated owing to errors that arise randomly during replication. In the second step, independently generated somatic nucDNA variation drives clonal expansion, carrying cryptic mtDNA variation to detection. Ovals represent cells; large circles represent nuclei.

We found that heteroplasmic age-accumulating mtSNVs in human blood tended to be transitions with C>T and A>G variants biased towards the heavy strand, consistent with the known mutational signature of mtDNA replication errors. Under the mtDNA strand displacement model, replication of the light strand starts first, leaving the parent heavy strand in a single-stranded state until replication reaches approximately two-thirds of the way through the molecule, and the heavy strand is copied in reverse[14]. This exposed heavy strand is prone to cytosine or adenosine deamination, producing heavy strand C>T and A>G, respectively[9], in agreement with our data and as seen in Drosophila[13], human tumours[9] and human brain[8]. The origin of age-accumulating A>G light strand mutations is less obvious. Unlike that of A>G and C>T heavy strand mutations, age accumulation of A>G light strand mutations seems to be trinucleotide context-specific. One possibility is polymerase-γ error, for example, due to proofreading errors or base misincorporation, which tends to produce transition mutations22,39,40,41. By contrast, oxidative stress is thought to predominantly produce C>A transversions through oxidation of deoxyguanosine16,18. A>C transversions are also possible if oxidized guanosine is directly incorporated into DNA[17]. In agreement with results obtained in tumour samples[9] and brain[8], these transversions were very rare in our data and did not accumulate with age.

Our proposed mechanism (Fig. 6), in which CH acts as an amplifier and allows bulk detection of low-level neutral heteroplasmic mtSNVs with age, is supported by several lines of evidence: (1) individuals with detectable CH tend to have an elevated mtSNV burden; (2) common germline variants in genes such as TCL1A and TERT that are strongly implicated in CH are also associated with mtSNV accrual; and (3) gene-based analyses identify coding mutations in numerous known CH driver genes as co-occurring in those with high mtSNV burden. These coding mutations probably represent somatic driver mutations for CH that we have now discovered using mtSNV burden as a trait. Previous work in brain required ultradeep sequencing to identify extremely low-heteroplasmy variants that accumulated with age[8]. In whole blood, CH seems to unmask latent variation in mtDNA that is otherwise cryptic and below the threshold of detection. Although the mutation-generating process is likely to be related to mtDNA replication, our analyses suggest that CH heavily shapes the resulting observed pattern of age-associated mtSNV accrual. Our results are compatible with those obtained in models in which somatic mtDNA mutations arise during embryogenesis, early in life[42] and/or with ageing and are subsequently carried up to detection through CH in older age.

Several lines of evidence indicate that the low-level heteroplasmic mtSNVs in those with detected CH are likely to be passenger mutations rather than drivers. First, CH is identified by detection of known somatic mutations in nuclear driver genes. Given the established mechanisms by which these nuclear driver mutations lead to clonal expansion, it is unnecessary to additionally invoke a separate contribution from these mtDNA variants. Second, we found that both missense and synonymous mtSNVs were similarly increased in CH carriers relative to controls, and we saw no evidence of positive selection for functional mtDNA variation. Third, we found that age-accumulating variants, which are enriched in CH carriers, were strongly enriched for low-heteroplasmy mtSNVs (Fig. 2d). Low-heteroplasmy variation was more likely to be neutral, with a higher dN/dS; by contrast, high heteroplasmy variants were depleted for non-synonymous variation, implying more purifying selection at higher heteroplasmy (Fig. 2f,g). This is consistent with the notion that selective forces on mtDNA variants at the level of the cell increase in strength as a function of increasing heteroplasmy. The heteroplasmy ‘threshold effect’25,26 is an established phenomenon in which pathogenic variants do not produce pathologic phenotypes until a sufficiently high heteroplasmy is achieved owing to complementation by wild-type mtDNA molecules. Indeed, in recent work in which dividing cells were engineered to carry a nonsense mutation in MT-ND4, fitness effects were dependent on the heteroplasmy level, with low-heteroplasmy cells seeming similar to wild type[27]. Although purifying selection could occur at the level of the organelle even if cell-wide heteroplasmy is low, recent work has indicated a more prominent effect of cell fitness in shaping heteroplasmy[27]. Overall, this suggests that mtSNV burden is unlikely to drive age-related disease (Fig. 4) despite being an excellent ageing biomarker.

Our study focused on age-accumulating passenger mtSNVs, which seem to be neutral and are distinct from pathogenic mtDNA mutations, which cause mitochondrial disease and are under negative selection[4]. Disease-causing mtDNA mutations, which can arise somatically or be maternally transmitted, tend to exhibit high levels of heteroplasmy and disrupt protein expression. Recent single-cell analysis of peripheral blood mononuclear cells from patients with maternally transmitted mtDNA diseases demonstrated purifying selection against high levels of mutant heteroplasmy in some (but not all) cell lineages[43]. An interesting possibility arising from our work is that in some instances of mtDNA disease, high levels of mutant heteroplasmy may be maintained owing to the balancing effects of nuclear genomic driver mutations. Joint sequencing of nuclear and mtDNA genomes in patients with maternally transmitted or sporadic mtDNA mutations could be highly informative.

We propose that mtSNV count in bulk sequencing data could be a sensitive marker of somatic mosaicism that can arise from processes such as CH. Even after removal of all individuals with detected CH, the common germline genetic architecture for mtSNV count was largely unchanged (Fig. 3b), rare variants in CH driver genes still co-occurred with high SNV count, and mtSNV count was still correlated with haematologic cancer. Further, the mtDNA mutational spectrum observed in CH carriers was also seen in those without detected CH (Fig. 5). These findings suggest that there are individuals with undetected CH (for instance, due to unidentified driver mutations) that can be identified on the basis of elevated mtSNV burden, consistent with work showing that CH is universally present at low fraction44,45. We suspect the high depth of mtDNA sequencing drives this improved detection of CH, potentially explaining literature suggesting that mtSNV burden is independently associated with blood cancer risk beyond detectable CH[46]. Rare-variant associations with mtSNV burden identified many canonical CH driver genes, non-canonical drivers (such as CHEK2) and genes that were otherwise unlinked to CH (for instance, NEMF and KCNIP3). NEMF and KCNIP3 may represent putative driver genes; NEMF is a potential tumour suppressor in lung cancer[47] and is thought to regulate the DNA damage response[48] in Drosophila melanogaster. Larger sample sizes could enable discovery of more drivers. Notably, a recent method[29] that used nuclear passenger mutations in CH carriers as a measure of clonal expansion rate also identified the TCL1A locus that we report here. This suggests that mtSNV burden, reflecting passenger mtDNA mutations, may integrate information from both the presence or absence of CH and its expansion rate.

Although we have focused on the mechanism underlying the age-related increase in measured mtSNV burden in blood and the role of CH in driving this increase, whether an analogous process (Fig. 6) operates in other tissues is an open question. Mutant mtDNA heteroplasmy has been shown to accumulate with age in other dividing tissues, such as in colon, where functional mtDNA mutations that disrupt electron transport chain activity in colonic crypt stem cells arise and spread throughout the crypt with age[49]. Expansion of mtDNA mutations across crypts through crypt fission into clonal ‘patches’ has also been observed[50]. It is possible that colonic crypt stem cell dynamics resembling CH, in combination with a ‘permissive environment’, contribute to accumulation of missense mtSNVs. In fact, recent work in dividing cells[27] has shown that certain environments (notably hypoxia) can be permissive for accumulation of high missense or nonsense heteroplasmy. Somatic mtDNA mutations have also been reported to accumulate in non-dividing tissues such as skeletal muscle51,52, and it is possible that similar clonal dynamics could occur in stem cell populations[53].

Our study has some potential limitations. First, there is a risk of contamination of mtSNV count by mtDNA pseudogenes in the nucDNA (NUMTs), which can result in spurious low-heteroplasmy variant calls due to read mismapping. To address this, we constructed heteroplasmy filters to account for the impact of mtCN on NUMT risk, eliminated suspicious variants (Methods) and confirmed that there were no known mtDNA pseudogenes around associated nuclear loci. Long-read sequencing or mtDNA isolation would further reduce contamination. Second, we could not definitively prove whether each variant is inherited or somatic; contaminating germline mutations have been described to bias dN/dS estimates[24] and would reduce power for detection of nuclear loci that influence somatic mtSNV burden. We used related individuals to show that low-heteroplasmy age-accumulating mtSNVs are predominantly de novo. In addition, dN/dS estimates are unlikely to be differentially biased in individuals with CH versus control individuals. Further, the increase in mtSNV count with CH and age suggests that clone-specific rather than inherited variants increase in heteroplasmy, allowing detection. However, we recognize that, owing to bottleneck effects, some inherited SNVs could exist in heteroplasmy in only some cells, such that they are initially undetectable until clonal expansion[3]. Third, because all analyses occurred in bulk samples, we were unable to evaluate the heteroplasmy distribution of a particular variant across cells in each sample or to determine whether individuals with multiple mtSNVs had a single CH clone carrying all SNVs or multiple clones carrying individual mtSNVs. Single-cell analysis would provide further resolution to elucidate this. Our core results showing the overall mutational spectrum, our observation that higher heteroplasmy variants are subject to higher levels of negative selection, and our nuclear genetic results would not be affected. We anticipate that population-scale, single-cell analysis is likely to be required to further extend our work, identify whether low-heteroplasmy variants in bulk are also low-heteroplasmy in single cells, and uncover interactions between the selective forces on somatic mtDNA variants and CH driver mutations.

Methods

Cohorts

UK Biobank

The UKB is a prospective cohort of approximately 500,000 individuals from the UK aged 40–70 years at enrolment, described in more detail elsewhere[54]. The resource includes a vast array of information on each individual, including hospital inpatient diagnoses, questionnaires and surveys collected at the time of enrolment, and several aliquots of peripheral blood. These blood samples are used to obtain estimates of blood cell composition, quantitative levels of blood biomarkers and genomic data. The final tranche of WGS data was recently released, covering virtually everyone in the cohort. This is in addition to whole-exome sequencing (WES) and genotyping array data that also exist for each participant. Pertinent to the present study, the final tranche of WGS data was generated using Illumina NovaSeq 6000 sequencing machines across Sanger and the deCODE facility to an average coverage of 32.5×[55]. All WGS and WES data were accessed using the UKB Research Analysis Platform (RAP) under application 31063.

All of Us

AoU is a large longitudinal cohort intended to capture the diversity of people living in the USA and includes physical measurements, biospecimen collection and survey data on enrolment, as well as capture of electronic health records[56]. For this study, participant data were accessed from the ‘Genetic determinants of mitochondrial DNA phenotypes (v7)’, ‘Genetic determinants of mitochondrial DNA phenotypes (v7) second 40k’, and ‘Genetic determinants of mitochondrial DNA phenotypes (v7) finish pipeline’ workspaces, all of which use the Controlled Data Repository v.7. This data release incorporated WGS data from 245,394 individuals obtained from peripheral blood and sequenced at Baylor College of Medicine, the Broad Institute and the University of Washington. DNA extraction was performed using one of two kits: Autogen or Chemagen.

mtSwirl v2: an updated mtDNA coverage estimation and variant-calling approach

To infer mtCN and accurately call variation on mtDNA, we used mtSwirl, a pipeline we have previously described in more detail[4]. In brief, mtSwirl is a scalable pipeline intended for deployment on cloud platforms for analysis of hundreds of thousands of whole-genome sequences. The pipeline involves initial extraction of unmapped reads, as well as a set of 385 nuclear regions in the GRCh38 reference genome with high homology to mtDNA obtained previously through BLAST (putative nuclear regions of mitochondrial homology, NUMT)[4]. These reads are used to perform an initial pass of variant calling using Mutect2 and HaplotypeCaller for mtDNA and nucDNA, respectively, with GATK v.4.2.6.0. High-confidence homoplasmic and homozygous variation, respectively, are then used to construct a per-individual consensus sequence to which all extracted reads are realigned. Alignment is repeated to a shifted version of the consensus sequence to avoid artificial coverage depression along the edges of the mtDNA sequence. mtDNA variant calling is then repeated using Mutect2, and a custom pipeline is used to map all variant calls and per-base coverage estimates back to GRCh38 for output. In parallel, Haplogrep[57] is used to perform haplogroup inference.

mtSwirl is intended to be usable on any platform that supports the WDL pipeline language. Owing to substantial limitations of parallelization on UKB RAP, we previously released mtSwirlMulti, which runs multiple samples on individual larger machines. As part of this study, to boost efficiency in UKB, we implemented parallelization within each machine for the most computationally intensive tasks in mtSwirl (SubsetBam, ProcessBamAndRevert, HaplotypeCaller, AlignToMtRegShiftedAndMetrics and LiftoverVCFAndGetCoverage). In effect, our updated version of mtSwirlMulti now implements two layers of parallelization, distributing sets of samples across machines and then using multicore machines to efficiently process the sets of samples in parallel. We ran this new version of mtSwirlMulti on approximately 2,000 samples from the previous release and found that the results were completely identical to those obtained previously. mtSwirlSingle is unchanged from before. We also developed new submission pipelines to efficiently automate job submissions in the UKB RAP and AoU. More details are available at our GitHub repository (https://github.com/rahulg603/mtSwirl).

When used on platforms with a Google Cloud backend (such as AoU Researcher Workbench), the pipeline accesses only the portions of the WGS file relevant for the analysis. Despite this, if WGS data are stored in cold storage (for instance, Nearline), data access charges can be substantial.

We deployed mtSwirlMulti on UKB RAP, processing all of the approximately 300,000 new samples in around 3 weeks given the limit of 100 concurrent machines. We ran mtSwirlSingle across approximately 150,000 new samples in AoU over about 1 week. Within each biobank, new results were merged with mtDNA callsets generated in our previous effort across approximately 250,000 individuals[4], producing a final, per-biobank mtDNA callset of approximately 500,000 individuals in UKB and approximately 250,000 individuals in AoU.

Sample-level quality control

After variant calls had been completed, samples were removed if:

1. (1)
the sample showed evidence of abnormally overlapping homoplasmic mutations;

2. (2)
the sample showed evidence of contamination, assessed by testing whether the sample met any of the following criteria: (a) mtDNA contamination estimate > 2% according to HaploCheck 0124 run by mtSwirl[58], (b) nucDNA contamination estimate > 2% (obtained as a freemix percentage from the genomic metrics table in AoU and quality control (QC) metrics table from UKB) or (c) multiple haplogroup-defining variants identified at relatively low heteroplasmy;

3. (3)
the sample was from UKB and was collected during the 2006 pilot analysis.

Derivation of mtCN

The following formula was used to compute raw mtCN:

$${\rm{mtC}}{{\rm{N}}}_{{\rm{raw}}}=\frac{2\times {\rm{mean}}\,{\rm{mtDNA}}\,{\rm{coverage}}}{{\rm{mean}}\,{\rm{nucDNA}}\,{\rm{coverage}}}.$$

Mean mtDNA coverage was computed and emitted from the mtSwirl pipeline and was generated from alignment of reads to the per-individual consensus sequence using picard CollectWgsMetrics. In AoU, we obtained mean nucDNA coverage from the genomic metrics file provided in the Controlled Tier v.7. For UKB, as this information was not provided, we computed mean nucDNA coverage using the following formula as described previously[4]:

$${\rm{mean}}\,{\rm{nucDNA}}\,{\rm{coverage}}=\frac{{\rm{total}}\,{\rm{mapped}}\,{\rm{reads}}-{\rm{singletons}}-{\rm{mate}}\,{\rm{different}}\,{\rm{chr}}-{\rm{duplicates}}}{{\rm{nucDNA}}\,{\rm{genome}}\,{\rm{length}}}\times {\rm{read}}\,{\rm{length}}.$$

mtDNA variant QC

For mtDNA variant calls but not mtCN, before variant QC, we also removed any samples with mtCN < 50, as we have previously shown that mtDNA variant count rises in low mtCN samples. This is indicative of potential increased NUMT contamination[4].

All variants called by Mutect2 were emitted from mtSwirl along with filters obtained using FilterMutectCalls. During merging and annotation, several steps of variant QC were performed once sample filtration had been completed to ensure that our callset was of high quality. Briefly, the steps were as follows.

1. (1)
Any genotype with a QC filter produced by FilterMutectCalls was removed.

2. (2)
If heteroplasmy (HL) < 0.01, it was set to homoplasmic recessive.

3. (3)
If there was no genotype at a site, it was considered homoplasmic recessive provided coverage at that site was at least 100.

4. (4)
We then removed variants on the basis of heteroplasmy as follows.

1. (a)
For analysis of individual common heteroplasmic mutations, we conservatively removed any variants with heteroplasmy less than 0.05 as done previously[4];

2. (b)
For analysis of mtSNV burden, we adopted a more lenient filter to better capture high-confidence low-heteroplasmy variation. which tends to be enriched for age accumulation (Supplementary Note 1). We identified a coverage depth for each person such that 95% of nuclear genomic sites would be expected to have less coverage under a Poisson model. We then removed SNV heteroplasmies with alternative allele depth less than this threshold.

1. (i)
As part of the generation of a variant callset for mtSNV burden, we also removed any variants that (in either UKB or AoU) increased by more than 500% relative to an HL > 0.05 filter and were detected more than 30 times. This resulted in exclusion of five suspicious variants: chrM:11467:A:G, chrM:12684:G,A, chrM:12705:C,T, chrM:13052:A,G and chrM:13095:T,C.

mtCN covariate correction

Covariate correction was performed for mtCN as described previously[4]. Briefly, we obtained all blood cell composition percentages within fields 30000–30300, as well as white blood cell count (field 30,000). We (1) excluded lymphocyte percentage owing to collinearity and (2) excluded nucleated RBC percentage as most individuals had zero values. To address outliers, we removed any blood cell measurements that differed from the mean by more than four standard deviations. We also obtained several technical covariates: assessment centre, median blood draw time, fasting time, assessment date and assessment month. To model the nonlinear relationships between mtCN and draw time as well as mtCN and the seasonal effects of assessment date, we used natural splines with five degrees of freedom and seasonally placed knots (3-month increments). Assessment centre and month were modelled as indicator variables. Fasting times between 1 h and 18 h were modelled as indicators as well, with a fasting time of 0 relabelled as 1 and that greater than 18 relabelled as 18 h. All of these variables were included in a joint model:

$$\log [({\mathrm{mtCN}}_{\mathrm{raw}})] \sim \mathrm{ns}(\mathrm{blood}\,\mathrm{draw}\,\mathrm{time},\,5)+\mathrm{assessment}\,\mathrm{centre}+\mathrm{fasting}\,\mathrm{time}+\mathrm{ns}(\mathrm{assessment}\,\mathrm{date},\,\mathrm{seasonal}\,\mathrm{knots})+\mathrm{assessment}\,\mathrm{month}+\mathrm{blood}\,\mathrm{cell}\,\mathrm{variables},$$

where ns corresponds to the natural spline function. mtCNadj was obtained by taking the residuals from the above model. To visualize this residual quantity, we rescaled this value by adding the preadjustment mean of log[mtCNraw] and then exponentiated to return to absolute scale. As aforementioned, samples obtained during the pilot study in 2006 were excluded.

Construction of mtDNA variant classes

To visualize mtDNA variation as a function of strand, location and mutation type, we considered variants that were between positions 16,172 and 210 to be in the ‘Ori’ region, with all other variants in the ‘Other’ region. As the reference mtDNA genome was the light strand, we considered any variant with a reference allele of C or A to be a light strand variant. For variants with a reference allele of G or T, we took the complement and considered the complement allele to be a heavy strand variant. We considered ‘age-accumulating’ variant classes to comprise those variants that were in the ‘other’ region and were either A>G on either strand or C>T on the heavy strand.

Assessment of mean mtDNA mutation count per person

We performed several analyses in which we considered the mean mtSNV count per person within subcategories of variants (for instance, variant classes or heteroplasmy categories). For these analyses, we counted the number of QC-pass SNVs identified (through the allele-depth-based filter) for each person within each subcategory. The total number of individuals included in this analysis was the number of individuals that passed QC for mtDNA variation assessment. For analyses that considered the mean count per person within subcategories of people (for instance, within age strata) we computed means within defined groups of individuals after quantifying SNV count per person as above within variant subcategories.

In cases where we compared mean SNV counts across variant classes (for instance, that shown in Fig. 2a), we normalized the estimates of mean SNV count across people by the number of possible variants in that variant class to account for differences in the base abundance across mtDNA strand or region. For instance, when comparing the mean variant count per person in the C>T other region on the light versus the heavy strand, we divided the mean observed count in the C>T other region light strand by the total number of possible C>T variants in the other region on the light strand and performed the analogous process for the C>T other region heavy strand.

For all analyses in which relationships with age were assessed, only individuals ages 18–90 years (AoU) or 40–70 years (UKB) were included to ensure sufficient samples were retained in age strata.

Analysis of single-base substitution signatures with trinucleotide context

For each variant, trinucleotide context was obtained using the bases immediately 5′ and 3′ to the variant of interest in a strand-specific way. To perform correlations with nucDNA-based single-base substitution signatures, we obtained release v.3.4 of the COSMIC human cancer single-base signatures (SBS) collection[19] (https://cancer.sanger.ac.uk/signatures/downloads/). For each SBS signature, a linear model was fitted to predict the observed mutational spectrum using the SBS signature. P values were reported from the test of significance for the beta coefficient from this model and adjusted for multiple comparisons using the Bonferroni method (two-sided P values, 86 total signatures).

mtDNA variant-based phenotype construction

For analysis of individual common heteroplasmies, we identified all variants that showed QC-pass heteroplasmy (0.05 ≤ HL ≤ 0.95) in 500 or more individuals across both biobanks. This resulted in 78 common heteroplasmic variants selected for analysis. These common mtDNA variants were extracted in each of UKB and AoU, and ‘case-only’ phenotypes were constructed for each heteroplasmy by, for each heteroplasmic variant, marking any individual who failed QC or had a HL of 0 as missing and performing analysis only among those with QC-pass heteroplasmy.

For analysis of heteroplasmic SNVs, we extracted all mtSNVs passing QC with HL ≤ 0.95. We computed SNV burden by counting the number of QC-pass heteroplasmic SNVs per person, retaining people without any recorded QC-pass heteroplasmic SNVs as 0. We also computed an SNV burden trait consisting of the count of age-accumulating class variants only; this was the phenotype on which we performed the bulk of our genetic association analyses.

Family-based analysis in UKB

Relatedness information was obtained for UKB from previous work[59]. All analyses involving related samples were performed using SNVs only. When performing transmission analysis, we only included variants that were identified in at least five individuals to ensure exclusion of artefactual variant calls. When evaluating the degree of heteroplasmic SNV sharing between siblings, we considered a variant to be shared if it was found in both siblings at any heteroplasmy. For each heteroplasmy category, we computed the proportion of variants in sibling 1 (chosen arbitrarily) that were also found in sibling 2.

mtDNA variant consequence annotation and dN/dS estimation

Variant consequences for mtDNA mutations were inferred using VEP v.101 as implemented in Hail. For analysis of mtSNVs, we considered variants labelled as ‘synonymous_variant’, ‘stop_retained_variant’, and ‘non_coding_transcript_exon_variant’ as synonymous; ‘stop_lost’, ‘start_lost’, ‘missense_variant’ as non-synonymous, and ‘stop_gained’ as predicted loss of function.

To estimate dN/dS, we used two approaches. We generated a table of all possible mtSNVs (49,704) and used VEP v.101 to assign mutational consequence. Then, for a given region (such as a gene boundary), we estimated dN as the number of observed non-synonymous variants divided by the number of possible non-synonymous variants within the region and dS as the number of observed synonymous variants divided by the number of possible synonymous variants within the region. The ratio of these two quantities was dN/dS. For all such analyses, we restricted variants to the age-accumulating classes (C>T heavy strand and A>G both strands), as this is the mutational process we were investigating; only this subset of mutations was used for both the observed variants and the count of possible variants (that is, the denominator). In cases in which we generated a variant class- and strand-specific estimate of dN/dS, we restricted variants to the class of interest when counting both observed variants and the count of possible variants. This method was also used in the construction of per-gene dN/dS estimates for mutations found in individuals with detected CH versus in randomly sampled matched controls.

To construct a null distribution under a model of neutrality, we performed random sampling inspired by an approach described elsewhere[23]. For each gene and heteroplasmy bin, for each individual i and mutation class c, we randomly sampled \({N}_{i}^{c}\) variants from the set of all possible variants in that gene without replacement, where \({N}_{i}^{c}\) was the number of variants observed for that individual of a specific class within the gene/heteroplasmy bin. This generated a ‘callset’ under the null for which dN/dS was estimated as above. This procedure was repeated 1,000 times to generate a null distribution for dN/dS for each gene, variant class and heteroplasmy range.

As independent validation, we also used the dNdScv package[24] to estimate dN/dS for age-accumulating variants. The dndscv command was used with default parameters, including trinucleotide and stranded context, for the mtDNA reference genome using sequence code 2. A 192-rate parameter substitution model was used. The command was run for all age-accumulating class mutations within coding regions within each heteroplasmy bin (that is, it was run separately for each heteroplasmy bin), and gene-specific dN/dS estimates were subsequently obtained from the model outputs.

The genes MT-ATP6 and MT-ATP8 share coding DNA sequence from chrM:8527 to chrM:8572. This meant there was a risk of introducing bias into dN/dS estimates, as mutations within this region face selection forces from two different genes and codon reading frames. Thus, for all dN/dS estimation methods, we corrected for sequence sharing between MT-ATP6 and MT-ATP8 by excluding this region from all stages of the analysis.

UKB disease and smoking phenotype curation

To assess the association of mtDNA phenotypes and common diseases in UKB, we used a previously curated set of 28 common disease phenotypes based on ICD-10 codes, categorical traits curated by UKB and phecodes that spanned several different disease domains[60]. To simplify the cancer terms included in this phenotype set, we constructed two new ICD-10-based traits: haematologic cancer, which involved a diagnosis of C81–C96 or D45–D47; and solid tumour, which involved a diagnosis of C00–C75.

To further evaluate the associations between mtSNV burden and cancer-related phenotypes, we extracted all phecodes with the category of ‘neoplasm’ as curated in previous work[60]. We manually excluded traits corresponding to benign conditions (such as lipoma) or otherwise non-specific traits (for instance, malignant neoplasm other). We filtered to only phenotypes that had more than 140 observed cases in UKB. We obtained smoking status from UKB by obtaining the ID 20116.

Correlation of mtCN and SNV count with disease traits

We computed odds ratios for the associations between mtDNA traits (mtCN, SNV burden) and categorical disease traits using logistic regression as implemented in R in UKB. For all mtDNA traits and disease phenotypes we included genetic ancestry assignment, age, age × sex, age^2 and age^2 × sex as covariates, as well as haplogroups as indicator variables. Genetic ancestry groups with fewer than three individuals with any given defined mtDNA trait or disease phenotype were excluded. For analyses in which the independent variable was mtDNA heteroplasmic SNV burden, only age-accumulating variants were used in the burden metric. Individuals who were described as ever having smoked were excluded for all analyses with mtSNV burden.

Correlation of mtCN and SNV count with protein levels and telomere length

We assessed the correlations of mtCN and mtSNV burden with levels of several proteins (FGF21, GDF15, IL1A, IL6, TNF) and telomere length. Proteomics data were obtained as generated and processed by the UKB Pharma Proteomics Project team[61]. In brief, UKB plasma samples were obtained and processed through Olink Explore 3072, which provides a platform capable of targeting 2,923 unique proteins. Results were normalized to sample- and plate-specific controls and then corrected for batch. More information can be found using resources 4654–4658 in the UKB Showcase. Field 30900 from UKB contains the normalized proteomics data. For this study, proteomics data were obtained from the 50k release, which was a random subsample of the total UKB population. In terms of further postprocessing, missing data were imputed using the miceforest package as described previously[62]. Telomere length data were also obtained from UKB from field 22192 (‘Z-adjusted T/S log’). These measurements were obtained as described previously[63]. In brief, DNA from virtually the entire UKB cohort was assayed for telomere length at the University of Leicester by using robot-assisted multiplex qPCR to measure the ratio of telomere repeat copy number relative to a single copy gene. Extensive QC was performed, including removal of samples with a high coefficient of variation and abnormal PCR melt curves. Raw telomere-to-single copy gene ratios were adjusted for several technical factors including batch, machine, temperature, time of day and time-to-collection. Samples were then Z-standardized and log-transformed. As suggested in previous literature[63], we adjusted telomere length for white blood cell count using a linear model. This adjusted telomere length was subsequently used for analysis.

Associations between processed telomere length and protein phenotypes and mtCN and mtSNV burden were computed similarly to those done for disease traits. Namely, a linear regression model was used to identify the effect size of changes in mtCN or mtSNV count with respect to telomere length and protein levels while correcting for genetic ancestry assignment, age, age × sex, age^2, age^2 × sex and haplogroup. For associations with mtSNV count, we restricted analysis to never-smokers.

Variant-based testing and meta-analysis

In UKB, we performed GWAS using a similar approach to that described previously4,60. In brief, GWAS was run on GRCh37-based imputed array data using SAIGE v.1.3.6[64] within previously defined genetic ancestry groups[60]. SAIGE was run using a full genetic relatedness matrix (GRM) in the null model step with leave-one-chromosome-out enabled. All phenotypes were inverse-rank normalized before genetic analysis.

In AoU, we performed GWAS on GRCh38-based WGS data using SAIGE v.1.4.3. We performed extensive sample and variant QC before analysis, in an approach similar to that used by the AoU All-by-All initiative (see https://support.researchallofus.org/hc/en-us/articles/27049847988884-Overview-of-the-All-by-All-tables-available-on-the-All-of-Us-Researcher-Workbench for more details). Unimputed genotype data and WES and WGS data were all used in the generation of files to run SAIGE. Across all data modalities, sample and variant QC were performed as follows. For sample QC, genetic ancestry assignment and ancestry outliers for removal were obtained from the All-by-All effort. Samples were also removed if they were flagged by the AoU DRC. For variant QC, variants were removed if they failed ‘adj’ criteria[59] or if their ancestry-specific call rate was less than 90%. Ancestry-specific principal components were computed after sample QC on unrelated individuals with projection of related individuals on to the computed principal component space.

To generate the sparse GRM in AoU for each ancestry group, we performed linkage disequilibrium pruning using PLINK on post-QC autosomal genotype data. Linkage disequilibrium pruning was performed using a step size of 1, window size of 10,000 kb, and r2 of 0.1. We excluded the HLA locus and the chromosome 8 inversion locus. Linkage-disequilibrium-pruned genotype data were then exported to PLINK format, and sparse GRMs were constructed within each ancestry with 2,000 markers, a minimum allele frequency of 0.01 and a relatedness cutoff of 0.125 using SAIGE.

Next, to generate files relevant for generation of null models in AoU, we subsampled both WGS and WES data. To ensure that we captured the full range of allele frequencies (given that WGS data were only provided for variants that had allele frequency > 0.01 in at least one ancestry group, that is, ACAF WGS), we obtained 50,000 common autosomal variants at random (allele frequency > 0.01) from WGS data and appended randomly sampled rare variants from WES data. Rare variants were sampled within the following categories: minor allele count (MAC) 1, 2, 3, 4, 5, 6–10, 11–20; minor allele frequency (MAF) < 0.001, MAF < 0.01 and MAF > 0.01. Two-thousand variants were extracted from each MAC category and 10,000 variants were obtained from each MAF category. This was done separately for each population. Linkage disequilibrium pruning was then performed on the combined table of subsampled rare and common variants after removal of HLA and the chromosome 8 inversion locus using r2 0.1 and a window size of 10,000 kb. The resulting pruned variants were exported to PLINK format.

Finally, to efficiently run SAIGE tests, we obtained QC-pass WGS data and then exported BGEN files tiled across the genome separately for each population. This greatly increased parallelism. In AoU, as SAIGE was run with a sparse GRM for improved computational efficiency, leave-one-chromosome-out was disabled.

In both biobanks, all GWAS included ‘baseline’ covariates including the ancestry-specific principal components (10 in UKB, 20 in AoU), age, age × sex, age^2, and age^2 × sex. Ancestry groups were only included in the analysis if the analysis had a sample size of 50 or greater. For all mtDNA variant-based analyses (that is, heteroplasmy and SNV burden), we included indicator variables for top-level haplogroup assignments obtained from mtSwirl. We removed any individuals belonging to haplogroups that had fewer than 30 people in either biobank; this resulted in removal of individuals from haplogroups L5, P, Q and S. These were not included for analyses of mtCN. In AoU, a further covariate corresponding to the sequencing site was included. GWAS was always run on phenotypes after inverse-rank normal transformation. Covariate offset was disabled.

For mtDNA heteroplasmy GWAS, we ran analyses using both additive and recessive encoding. In the case of the recessive encoding, BGEN files were re-exported in both biobanks after recoding of heterozygous individuals as homozygous reference. No changes were made in inputs to the null model step.

When GWAS were complete, we performed summary statistics QC. This involved removal of any low-confidence variants (defined as MAC < 20 or call rate < 0.9) and dropping of phenotypes with lambda GC < 1.5. We then performed fixed-effects meta-analysis with inverse-variance weighting within each cohort to meta-analyse populations, followed by another round of fixed-effects meta-analysis with inverse-variance weighting across cohorts. As UKB GWAS was performed in GRCh37, we used a previously generated liftover file to transfer UKB variants to GRCh38 for meta-analysis[4].

Fine-mapping of UKB GWAS

To identify putative causal variants, we conducted statistical fine-mapping of mtDNA traits in UKB as previously described4,60,65. Briefly, we used FINEMAP-inf and SuSiE-inf, which model infinitesimal effects[66], with cross-ancestry meta-analysis summary statistics from UKB and a covariate-adjusted in-sample dosage linkage disequilibrium matrix. To reduce computational burden due to the increased number of genome-wide significant loci, we defined fine-mapping regions on the basis of a 1-Mb window (rather than the previously described 3 Mb4,60,65) around each lead variant and merged overlapping regions. This resulted in 802 genome-wide significant regions for fine-mapping, of which only 5 loci exceeded 3 Mb, with a maximum length of 5.2 Mb. This approach properly handles long-range linkage disequilibrium regions[67] while keeping fine-mapping region sizes manageable. We excluded the major histocompatibility complex region (chr6: 25–36 Mb) from analysis, owing to its extensive linkage disequilibrium structure. Allowing up to ten causal variants per region, we derived PIPs using each method with a uniform prior probability of causality and computed the minimum PIP across methods. For 95% credible sets, we used those reported by SuSiE-inf.

Identification of loci and assignment of genes

Owing to the multiancestry and multibiobank nature of our cohort, we used a proximity-based approach to identify separate loci. Specifically, for each GWAS, we identified initial SNVs with P < 5 × 10−5 and then constructed a window of 100 kb up and down from each such variant. We then iterated through variants in order and merged variants with overlapping windows. A locus was defined as a merged set of variants non-overlapping with any other set, and lead nucDNA variants for a given locus were identified by that with the most significant P value. This process was repeated for published GWAS for CH to obtain lead variants.

For gene assignment, where fine-mapped variants were identified with PIP > 0.1, gene assignment was performed by a combination of nearest gene and manual curation in the genomic region proximal to the fine-mapped variant. Genes in which a protein coding change was predicted by the fine-mapped variant were prioritized. If no fine-mapped variants with PIP > 0.1 were detected, a combination of nearest gene and manual curation was used.

One-sided Mendelian randomization between CH and mtSNV burden

Summary statistics from a recent well-powered case-control analysis of aggregate CH were obtained (GWAS catalogue ID GCST90165261)[2]. Location-based lead variant identification was performed as described above. Odds ratios for lead variants were transformed to log-odds scale, and allele direction was set such that all effect sizes were risk-increasing. Effect sizes and standard errors for these variants from heteroplasmic mtSNV count GWAS were then obtained, ensuring that allele direction was concordant. To determine the statistical significance of the correlation between effect sizes, inverse-variance weighted linear regression was used with weights given by \(1/S{E}_{x}^{2}\times 1/S{E}_{y}^{2}\).

Gene-based testing and meta-analysis

We performed gene-based testing in UKB using the OQFE 500k whole-exome data release. Owing to limited sample sizes for non-European ancestry assigned groups, we performed our analyses using the 500k exome data from individuals of European ancestry only. We used SAIGE-GENE+[64] v.1.4.3 with identical covariates and phenotypes to those used for variant-based GWAS in UKB. To produce null models, we extracted genotyped variants in the following categories: MAC 1, 2, 3, 4, 5, 6–10, 11–20; MAF < 0.001, MAF < 0.01 and MAF > 0.01. Two-thousand variants were extracted from each MAC category, and 10,000 variants were obtained from each MAF category at random. PLINK was used to perform linkage disequilibrium pruning to obtain a linkage-disequilibrium-independent set of variants for variance ratio estimation. Variance ratios were estimated separately by MAC category, given by MAC 1, 2, 3, 4, 5, 6–10, 11–15, 16–20 and 21+. Assignments of variants to genes and consequence annotations were obtained from previous work[64].

To perform gene-based testing in AoU, we used the same approaches for variant and sample QC as for variant-based testing. The same sparse GRM was used as well. Analyses were performed in all six ancestry groups. To generate the variant annotation group file, we used the variant annotation table provided by AoU (v.7.1). For each gene, the worst consequence across all transcripts was used to assign each variant to either synonymous, missense or predicted loss of function. Variants with ancestry-wide call rate less than 0.9 were excluded.

To generate null models for rare-variant analysis in AoU, the same input genotypes were used as for common variant analysis, namely a subsampled set of variants with AF > 0.01 using ACAF WGS data combined with subsampled variants with MAC 1, 2, 3, 4, 5, 6–10, 11–20; MAF < 0.001, MAF < 0.01, MAF > 0.01. SAIGE was run with the same parameters as for AoU common variant analysis except with the ‘—isCateVarianceRatio’ option enabled, generating variance ratio estimates for MAC 1, 2, 3, 4, 5, 6–10, 11–15, 16–20 and 21+. These were the same categories used for gene-based testing in UKB.

For parallelism in AoU, we generated BGEN files comprising QC-pass WES-based variant calls tiled across the genome for each population. We adjusted the cut points of these tiles to ensure that gene boundaries were not split across multiple files. These files were used to run SAIGE tests. In AoU, as SAIGE was run with a sparse GRM for improved computational efficiency, leave-one-chromosome-out was disabled.

In both biobanks, a total of 15 tests were performed per gene: maximum MAF values of 0.01, 0.001, and 0.0001; missense/LoF/synonymous, missense/LoF, LoF, missense and synonymous annotations; and a Cauchy combination test combining evidence across annotations. SKAT-O[68] was primarily used for assessment of gene-based associations. To minimize comparisons, we used the Cauchy test to nominate phenotypes for further evaluation and then evaluated associated variant groups only for those phenotypes. Our genome-wide threshold was set using the Bonferroni method with a threshold of 0.05 divided by ~18,000 corresponding to the number of tested genes, and our ‘suggestive’ threshold was a false discovery rate of 0.1 using the Benjamini–Hochberg procedure.

To conduct cross-ancestry and cross-biobank meta-analysis of gene-based tests, we used both inverse-variance weighted meta-analysis and Stouffer’s weighted P value combination method. Inverse-variance weighting was used to combine burden tests to produce effect size estimates. Stouffer’s weighted P value combination method[69] was used to combine Burden, SKAT, SKAT-O and Cauchy P values. Weights were constructed as \(\sqrt{N}\), where N is the sample size of a given ancestry or biobank. This meta-analysis procedure was first performed in AoU to combine per-ancestry results and then repeated to combine UKB summary statistics with AoU summary statistics.

Identification of individuals with CH in UKB and AoU

Individuals with CH were obtained from both UKB and AoU as described in detail elsewhere[70]. In brief, CH was identified by assessing for putative somatic variants in WGS data within a curated list of known driver genes for CH by identifying fractional variants with Mutect2, followed by extensive QC and removal of suspected germline variants.

Age- and sex-matching in analyses of individuals with CH

CH is known to be strongly associated with age[70]. Given this, in analyses comparing individuals with detected CH with those without detected CH, we performed age- and sex- matching to minimize the risk of confounding by these variables. We obtained ages and sexes of individuals with known CH and then constructed 500 random samples of people without diagnosed CH of the same size as the CH group. Each of these random samples consisted of people with the same sex and age counts as the CH group. We then computed statistics within each of these control groups (for instance, mean variant count per person within a variant class and age group) and then computed the mean across all random subsamples as the point estimate and the standard deviation of the means across subsamples as the standard error.

For the analysis comparing the normalized mean mutation count between CH and age- and sex-matched controls, we also computed a difference statistic within each random sample; this was the difference in the mean mutation count for a mutation in a particular trinucleotide context between the CH sample and then random sample. We then obtained the point estimate and standard error of the difference statistic using the mean and standard deviation of the difference statistic across all 500 samples, respectively. Significance tests were performed with a null hypothesis of difference of 0 and a two-sided alternative of difference not equal to 0, and P values were corrected using the Bonferroni method assuming 192 comparisons (96 trinucleotide contexts × 2 strands).

Reporting summary

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

Data availability

The source data analysed in this study were obtained from UKB and AoU. The UKB data were accessed under application 31063. UKB WGS data were accessed and analysed within the UKB Researcher Analysis Platform (https://ukbiobank.dnanexus.com/landing). AoU data were accessed as part of three custom workspaces: ‘Genetic determinants of mitochondrial DNA phenotypes (v7)’, ‘Genetic determinants of mitochondrial DNA phenotypes (v7) second 40k’ and ‘Genetic determinants of mitochondrial DNA phenotypes (v7) finish pipeline’. All analysis of AoU data was performed within the AoU Researcher Workbench (https://workbench.researchallofus.org/) using the workspaces described above. Summary statistics from mtCN and heteroplasmy GWAS were obtained from previous work[4]. Summary statistics from CH GWAS were obtained from work by Kessler et al.[2] (GWAS catalogue accession GCST90165261). GRCh37 and GRCh38 reference genomes and other reference sequence-related data were obtained from the GATK resource bundle (https://gatk.broadinstitute.org/hc/en-us/articles/360035890811-Resource-bundle). mtDNA local constraint scores were obtained from work by Lake et al.[37]. The previously curated set of common disease traits in UKB was obtained from previous work[60]; more information can be found online at https://pan.ukbb.broadinstitute.org. COSMIC SBS signatures were obtained from https://cancer.sanger.ac.uk/signatures/downloads/. Sample sizes for cross-biobank meta-analysis of heteroplasmy GWAS and SNV count GWAS are provided in Supplementary Tables 1 and 2 respectively. Lead SNPs from proximity-based clumping at a P value threshold of 5 × 10−5 for mtCNadj, heteroplasmy GWAS and SNV count GWAS are included in Supplementary Tables 3, 4 and 5 respectively. Gene-based test results for SNV count are included for genes with P < 5 × 10−5 in Supplementary Table 6. All variants with fine-mapped PIP > 0.1 in any tested trait in UKB are included in Supplementary Table 7. Raw effect sizes and P values from tests of association between mtDNA traits and disease are available in Supplementary Tables 8–10. Association P values between CH and mutation levels in various mutation classes are provided in Supplementary Tables 11 and 12. All of our largest sample size GWAS summary statistics (cross-biobank meta-analysis when possible, single biobank otherwise) are available on GWAS Catalog (study accession GCP001670). We have made AoU calls and full meta-analysis summary statistics available on the AoU Workbench under a workspace titled ‘Mechanism of age-related accumulation of mtDNA mutations in human blood’. We have made UKB meta-analysis summary statistics available on Google Cloud Platform (see GitHub repository in Code Availability). We are in the process of returning our mtDNA variant and copy number calls to UKB for broader use.

Code availability

All code related to the updated version of mtSwirl (v.2) and to generate the callset is available at on GitHub (https://github.com/rahulg603/mtSwirl). Software versions used within the mtSwirl pipeline have not changed and can be found elsewhere[4]. This includes GATK v.4.2.6.0 (same as previously), which includes Mutect2. The mtSwirl repository includes code used to perform GWAS in UKB. Code written to generate phenotypes for downstream analysis in AoU, perform GWAS and/or RVAS in AoU, and perform meta-analysis (cross-ancestry and cross-biobank) can also be found at GitHub (https://github.com/rahulg603/mtdna_750k_gwas). To perform downstream analyses, Hail was used for parallelized analyses (v.0.2.120 in UKB and v.0.2.130-post1 in AoU; v.0.2.119 used in pipelines), with single-machine data processing, statistical testing and figure generation done using R v.4.4.0 (most analysis) and v.4.2.1 (munging of UKB phenotypes). Tasks were parallelized using Hail Batch in UKB (https://hail.is/docs/batch/index.html) and Cromwell in AoU (https://cromwell.readthedocs.io). Linkage disequilibrium pruning was performed using PLINK v.1.9 (https://www.cog-genomics.org/plink/). GWAS were performed using SAIGE v.1.3.6 (UKB GWAS) or v.1.4.3 (UKB RVAS and AoU analyses) (https://saigegit.github.io/SAIGE-doc/). As part of our analyses, we used VEP v.101 as implemented in Hail to assign consequence to mtDNA variation. dndscv v.0.0.1.0 was used to quantify dN/dS in mtDNA. Plots were generated using ggplot2 v.3.5.2; rounded rectangle annotations were generated using ggchicklet v.0.5.2, and certain combined plots were generated using cowplot v.1.1.3.

References

1. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194 (2013).

2. Kessler, M. D. et al. Common and rare variant associations with clonal haematopoiesis phenotypes. Nature 612, 301–309 (2022).

3. Payne, B. A. I. et al. Universal heteroplasmy of human mitochondrial DNA. Hum. Mol. Genet. 22, 384–390 (2013).

4. Gupta, R. et al. Nuclear genetic control of mtDNA copy number and heteroplasmy in humans. Nature 620, 839–848 (2023).

5. Cooke, M. S., Evans, M. D., Dizdaroglu, M. & Lunec, J. Oxidative DNA damage: mechanisms, mutation, and disease. FASEB J. 17, 1195–1214 (2003).

6. Harman, D. The biologic clock: the mitochondria?. J. Am. Geriatr. Soc. 20, 145–147 (1972).

7. Wallace, D. C. Mitochondrial diseases in man and mouse. Science 283, 1482–1488 (1999).

8. Kennedy, S. R., Salk, J. J., Schmitt, M. W. & Loeb, L. A. Ultra-sensitive sequencing reveals an age-related increase in somatic mitochondrial mutations that are inconsistent with oxidative damage. PLoS Genet. 9, e1003794 (2013).

9. Ju Y. S. et al. Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer. eLife 3, e02935 (2014).

10. Stumpf, J. D. & Copeland, W. C. Mitochondrial DNA replication and disease: insights from DNA polymerase γ mutations. Cell. Mol. Life Sci. 68, 219–233 (2011).

11. Zaidi A. A. et al. The genetic and phenotypic correlates of mtDNA copy number in a multi-ancestry cohort. Hum. Genet. Genom. Adv. 4, 100202 (2023).

12. Hong, Y. S. et al. Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality. Nat. Commun. 14, 6113 (2023).

13. Itsara, L. S. et al. Oxidative stress is not a major contributor to somatic mitochondrial DNA mutations. PLoS Genet. 10, e1003974 (2014).

14. Shadel, G. S. & Clayton, D. A. Mitochondrial DNA maintenance in vertebrates. Annu. Rev. Biochem. 66, 409–435 (1997).

15. Clayton, D. A. Replication of animal mitochondrial DNA. Cell. 28, 693–705 (1982).

16. Croteau, D. L. & Bohr, V. A. Repair of oxidative damage to nuclear and mitochondrial DNA in mammalian cells. J. Biol. Chem. 272, 25409–25412 (1997).

17. Suzuki, T. & Kamiya, H. Mutations induced by 8-hydroxyguanine (8-oxo-7,8-dihydroguanine), a representative oxidized base, in mammalian cells. Genes Environ. 39, 2 (2016).

18. Pinz, K. G., Shibutani, S. & Bogenhagen, D. F. Action of mitochondrial DNA polymerase γ at sites of base loss or oxidative damage. J. Biol. Chem. 270, 9202–9206 (1995).

19. Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).

20. Tomkova, M., Tomek, J., Kriaucionis, S. & Schuster-Böckler, B. Mutational signature distribution varies with DNA replication timing and strand asymmetry. Genome Biol. 19, 129 (2018).

21. Jin, S. G., Meng, Y., Johnson, J., Szabó, P. E. & Pfeifer, G. P. Concordance of hydrogen peroxide–induced 8-oxo-guanine patterns with two cancer mutation signatures of upper GI tract tumors. Sci. Adv. 8, eabn3815 (2022).

22. Zheng, W., Khrapko, K., Coller, H. A., Thilly, W. G. & Copeland, W. C. Origins of human mitochondrial point mutations as DNA polymerase γ-mediated errors. Mutat. Res. Mol. Mech. Mutagen. 599, 11–20 (2006).

23. An, J. et al. Mitochondrial DNA mosaicism in normal human somatic cells. Nat. Genet. 56, 1665–1677 (2024).

24. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell. 171, 1029–1041 (2017).

25. Shoffner, J. M. et al. Myoclonic epilepsy and ragged-red fiber disease (MERRF) is associated with a mitochondrial DNA tRNALys mutation. Cell. 61, 931–937 (1990).

26. Rossignol, R. et al. Mitochondrial threshold effects. Biochem. J. 370, 751–762 (2003).

27. Kotrys, A. V. et al. Single-cell analysis reveals context-dependent, cell-level selection of mtDNA. Nature 629, 458–466 (2024).

28. Chomyn, A. et al. MELAS mutation in mtDNA binding site for transcription termination factor causes defects in protein synthesis and in respiration but no change in levels of upstream and downstream mature transcripts. Proc. Natl Acad. Sci. USA 89, 4221–4225 (1992).

29. Weinstock, J. S. et al. Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis. Nature 616, 755–763 (2023).

30. Bernstein, N. et al. Analysis of somatic mutations in whole blood from 200,618 individuals identifies pervasive positive selection and novel drivers of clonal hematopoiesis. Nat. Genet. 56, 1147–1155 (2024).

31. Burren, O. S. et al. Genetic architecture of telomere length in 462,666 UK Biobank whole-genome sequences. Nat. Genet. 56, 1832–1840 (2024).

32. Nakao, T. et al. Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential. Sci. Adv. 8, eabl6579 (2022).

33. Sharma, R. et al. Circulating markers of NADH-reductive stress correlate with mitochondrial disease severity. J. Clin. Invest. 131, 1–16 (2021).

34. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

35. Stacey, S. N. et al. Genetics and epidemiology of mutational barcode-defined clonal hematopoiesis. Nat. Genet. 55, 2149–2159 (2023).

36. Vlasschaert, C. et al. Clonal hematopoiesis of indeterminate potential is associated with acute kidney injury. Nat. Med. 30, 810–817 (2024).

37. Lake, N. J. et al. Quantifying constraint in the human mitochondrial genome. Nature 635, 390–397 (2024).

38. Michikawa, Y., Mazzucchelli, F., Bresolin, N., Scarlato, G. & Attardi, G. Aging-dependent large accumulation of point mutations in the human mtDNA control region for replication. Science 286, 774–779 (1999).

39. Trifunovic, A. et al. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature 429, 417–423 (2004).

40. Larsson, N. G. Somatic mitochondrial DNA mutations in mammalian aging. Annu. Rev. Biochem. 79, 683–706 (2010).

41. Lee, H. R. & Johnson, K. A. Fidelity of the human mitochondrial DNA polymerase. J. Biol. Chem. 281, 36236–36240 (2006).

42. Greaves, L. C. et al. Clonal expansion of early to mid-life mitochondrial DNA point mutations drives mitochondrial dysfunction during human ageing. PLoS Genet. 10, e1004620 (2014).

43. Walker, M. A. et al. Purifying selection against pathogenic mitochondrial DNA in human T cells. N. Engl. J. Med. 383, 1556–1563 (2020).

44. Acuna-Hidalgo, R. et al. Ultra-sensitive sequencing identifies high prevalence of clonal hematopoiesis-associated mutations throughout adult life. Am. J. Hum. Genet. 101, 50–64 (2017).

45. Jaiswal, S. & Ebert, B. L. Clonal hematopoiesis in human aging and disease. Science 366, eaan4673 (2019).

46. Hong, Y. S. et al. Mitochondrial heteroplasmy improves risk prediction for myeloid neoplasms. Nat. Commun. 15, 10133 (2024).

47. Carbonnelle, D. et al. Up-regulation of a novel mRNA (NY-CO-1) involved in the methyl 4-methoxy-3-(3-methyl-2-butenoyl) benzoate (VT1)-induced proliferation arrest of a non-small-cell lung carcinoma cell line (NSCLC-N6). Int. J. Cancer 92, 388–397 (2001).

48. Wang, Y. et al. The tumor suppressor Caliban regulates DNA damage-induced apoptosis through p53-dependent and -independent activity. Oncogene. 32, 3857–3866 (2013).

49. Taylor, R. W. et al. Mitochondrial DNA mutations in human colonic crypt stem cells. J. Clin. Invest. 112, 1351–1360 (2003).

50. Greaves, L. C. et al. Mitochondrial DNA mutations are established in human colonic stem cells, and mutated clones expand by crypt fission. Proc. Natl Acad. Sci. USA 103, 714–719 (2006).

51. Wanagat, J., Cao, Z., Pathare, P. & Aiken, J. M. Mitochondrial DNA deletion mutations colocalize with segmental electron transport system abnormalities, muscle fiber atrophy, fiber splitting, and oxidative damage in sarcopenia. FASEB J. 15, 322–332 (2001).

52. Wang, Y. et al. Muscle-specific mutations accumulate with aging in critical human mtDNA control sites for replication. Proc. Natl Acad. Sci. USA 98, 4022–4027 (2001).

53. Tierney, M. T., Stec, M. J., Rulands, S., Simons, B. D. & Sacco, A. Muscle stem cells exhibit distinct clonal dynamics in response to tissue repair and homeostatic aging. Cell Stem Cell. 22, 119–127 (2018).

54. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e200226 (2015).

55. The UK Biobank Whole-Genome Sequencing Consortium. Whole-genome sequencing of 490,640 UK Biobank participants. Nature 645, 692–701 (2025).

56. The All of Us Research Program Investigators. The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019)

57. Weissensteiner, H. et al. HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing. Nucleic Acids Res. 44, W58–W63 (2016).

58. Weissensteiner, H. et al. Contamination detection in sequencing studies using the mitochondrial phylogeny. Genome Res. 31, 309–316 (2021).

59. Karczewski, K. J. et al. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. Cell Genom. 2, 100168 (2022).

60. Karczewski, K. J. et al. Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects. Nat. Genet. 57, 2408–2417 (2025).

61. Sun, B. B. et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 622, 329–338 (2023).

62. Argentieri, M. A. et al. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nat. Med. 30, 2450–2460 (2024).

63. Codd, V. et al. Measurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank. Nat. Aging 2, 170–179 (2022).

64. Zhou, W. et al. SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests. Nat. Genet. 54, 1466–1469 (2022).

65. Kanai M. et al. Insights from complex trait fine-mapping across diverse populations. Preprint at medRxiv https://doi.org/10.1101/2021.09.03.21262975 (2021).

66. Cui, R. et al. Improving fine-mapping by modeling infinitesimal effects. Nat. Genet. 56, 162–169 (2024).

67. Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135 (2008); author reply 135–139.

68. Lee, S. et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am. J. Hum. Genet. 91, 224–237 (2012).

69. Whitlock, M. C. Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach. J. Evol. Biol. 18, 1368–1373 (2005).

70. Vlasschaert, C. et al. A practical approach to curate clonal hematopoiesis of indeterminate potential in human genetic data sets. Blood 141, 2214–2223 (2023).

Acknowledgements

We thank L. Hu, C. A. Walsh, M. Feany, N. Perrimon, R. Mitchell and C. Winter for helpful conversations; L. Lichtenstein for helpful conversations regarding the AoU biobank; and T. Issa for design assistance. This research was conducted using the UKB Resource under application number 31063. The AoU Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA no.: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the AoU Research Program would not be possible without the partnership of its participants. V.K.M. discloses support for this work by the National Institute on Aging of the National Institutes of Health (1R01AG095944). V.K.M. is also an Investigator of the Howard Hughes Medical Institute and is supported by the Freedom Together Foundation. B.M.N. discloses support by the National Institute of Mental Health of the National Institutes of Health (5R37MH107649). R.G. discloses support by the National Institute on Aging of the National Institutes of Health (5F30AG074507). W.Z. discloses support through the National Human Genome Research Institute of the National Institutes of Health (K99/R00HG012222 and R01HG014518).

Author information

Authors and Affiliations

1. Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, MA, USA

Rahul Gupta, Timothy J. Durham & Vamsi K. Mootha

2. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

Rahul Gupta, Grant Chau, Wenhan Lu, M. Austin Argentieri, Konrad J. Karczewski, Daniel Howrigan & Benjamin M. Neale

3. Harvard Medical School, Boston, MA, USA

Rahul Gupta, Pradeep Natarajan & Vamsi K. Mootha

4. Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Rahul Gupta, Timothy J. Durham & Vamsi K. Mootha

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

Rahul Gupta, Grant Chau, Masahiro Kanai, Md Mesbah Uddin, Wenhan Lu, M. Austin Argentieri, Konrad J. Karczewski, Daniel Howrigan, Pradeep Natarajan, Wei Zhou & Benjamin M. Neale

6. Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA

Rahul Gupta, Timothy J. Durham & Vamsi K. Mootha

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

Grant Chau, Wenhan Lu, M. Austin Argentieri, Daniel Howrigan, Wei Zhou & Benjamin M. Neale

8. Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA

Masahiro Kanai

9. Heart and Vascular Institute, Mass General Brigham, Boston, MA, USA

Md Mesbah Uddin & Pradeep Natarajan

10. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Md Mesbah Uddin & Pradeep Natarajan

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

Konrad J. Karczewski & Benjamin M. Neale

12. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Wei Zhou

Contributions

R.G., B.M.N. and V.K.M. conceived the project. G.C. and R.G. implemented updates to the mtSwirl pipeline. T.J.D. and G.C. deployed mtSwirl across WGS data. T.J.D. and R.G. merged calls into a final mtDNA callset. M.M.U. and P.N. provided CH calls. M.A.A. performed UKB proteomic data QC and imputation. R.G. performed all downstream data analysis, statistical testing and data visualization. M.K. performed fine-mapping in UKB. W.Z., W.L. and K.J.K. provided guidance on genetic analyses and SAIGE implementation in AoU. R.G. and W.L. performed data export. R.G. T.J.D., M.M.U., D.H., P.N., W.Z., B.M.N. and V.K.M. designed analyses. R.G. and V.K.M. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Rahul Gupta, Benjamin M. Neale or Vamsi K. Mootha.

Ethics declarations

Competing interests

V.K.M. is a paid advisor to 5am Ventures and Falcon Bio. B.M.N. is a member of the scientific advisory board at Deep Genomics, Camp4 and Vesalius. R.G. is a consultant for Marea Therapeutics. K.J.K. is a consultant for Tome Biosciences, AlloDx and Vor Biosciences, and a member of the scientific advisory board of Nurture Genomics. P.N. reports research grants from Allelica, Amgen, Apple, Boston Scientific, Genentech/Roche and Novartis; personal fees from Allelica, Apple, AstraZeneca, Bain Capital, Blackstone Life Sciences, Bristol Myers Squibb, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly & Co, Esperion Therapeutics, Foresite Capital, Foresite Labs, Genentech/Roche, GV, HeartFlow, Magnet Biomedicine, Merck, Novartis, Novo Nordisk, TenSixteen Bio and Tourmaline Bio; equity in Bolt, Candela, Mercury, MyOme, Parameter Health, Preciseli, and TenSixteen Bio; and spousal employment at Vertex Pharmaceuticals (all unrelated to the present work). The remaining authors declare no competing interests.

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Peer review information

Nature thanks Jonathan Alder, Brett Kaufman, Guillaume Lettre and Jyoti Nangalia 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 Landscape of mtDNA insertion/deletion and SNV mutations across UKB and AoU.

Upper plot corresponds to SNVs, lower plot corresponds to indels, and middle scheme shows genomic annotations. C1-C5 correspond to OXPHOS genes, HV are the non-coding hypervariable regions, RIB are the ribosomal RNA genes, and TRNA are the mtDNA-encoded tRNA genes. Arrows correspond to the most commonly occurring mtDNA variants located near poly-C tracts. Y-axis of SNV and indel plots correspond to the number of variant-sample pairs observed at each site across both biobanks. Dotted line corresponds to 500 instances of the observed variant.

Extended Data Fig. 2 Nuclear genetic and phenotypic correlates of mtDNA copy number across UKB.

A. Distribution of mtCNraw across AoU (aggregate and stratified by DNA extraction kit) and UKB. B. mtCNadj as a function of age and sex in UKB. Each point represents mean mtCNadj per bin. Error bars represent +/− 1SE. n = 401,385 individuals. C. GWAS of mtCNadj across 398,250 individuals in UKB. Gene assignments were made based on a combination of manual curation, fine-mapping, and nearest gene. Asterisks above chromosomes 1, 18, 19, 20, 21 represent DOCK7, BCL2, GP6, SNX5, RUNX1 respectively. Grey annotations correspond to loci that decreased in significance after correction for technical variables and blood composition. Arrows point to genes thought to produce protein products that localize to mitochondria. † = gene nearby fine-mapped variant with PIP > 0.1 and ‡ = gene with fine-mapped variant at PIP > 0.1 causing a protein sequence change. D. Gene-based testing for association between ultra-rare (AF ≤1 * 10−4) missense and LoF variation and mtCNadj. E. Gene-based testing for association with mtCNadj using the Cauchy combination method. F. Impact of adjustment of mtCN for technical and blood composition covariates on correlations with 29 common curated case/control disease traits. Each point represents an odds ratio (OR) for association. OR is computed using logistic regression including covariates for sex, age, ancestry, and haplogroup. Error bars represent 95% CI. Per-test sample sizes and test statistics can be found in Supplementary Table 10. P-values are two sided and adjusted using the Bonferroni method.

Extended Data Fig. 3 Heterogeneity in nuclear genetic influences on common heteroplasmic mtDNA sites.

A. Case-only GWAS for common heteroplasmies for all traits that show >=1 genome-wide significant association. B. GWAS for chrM:302:A,AC heteroplasmy (n = 244,879). Genes were identified as nearest or via manual curation. Red genes correspond to those not identified with the previous analysis in ~68,500 individuals. In both A and B, † = gene nearby fine-mapped variant with PIP > 0.1 and ‡ = gene with fine-mapped variant at PIP > 0.1 causing a protein sequence change. Comparison of square root sample size with association Z-scores for C. TFAM, D. SSBP1, E. DGUOK, and F. PNP locus. In C-F, orange corresponds to chrM:302 traits and green are other common heteroplasmies (567, 955, 5894, 16179–16183). G. 2D histogram to compare between recessive and additive p-values from the cross-ancestry and cross-biobank meta-analysis for heteroplasmy traits. H. Zoomed in view of panel G. Dotted lines are 5e-8 p-value threshold. Diagonal line is y = x.

Extended Data Fig. 4 Correlates of age accumulation of mtSNVs.

A. Age accumulation of mtSNVs in UKB (n = 343,266 individuals). Error bars represent 95% CI. See Fig. 1 for a similar analysis in AoU. B. Relationship between age and mtDNA heteroplasmy count as a function of smoking status (n = 373,823 individuals). C. Degree of variant sharing between siblings as a function of heteroplasmy in UKB. Each bar represents the proportion of one sibling’s variants found in the other sibling at any level. Color represents SNVs that are in age accumulating classes (yellow) versus those that are not. Results computed using n = 2,821 variants. Mean heteroplasmic SNV count as a function of age broken down by variant heteroplasmy range in D. AoU (n = 236,749 individuals) and E. UKB (n = 343,266 individuals). For panels D and E, color corresponds to heteroplasmy range legend next to panel E. For panels B-E, error bars are +/− 1SE.

Extended Data Fig. 5 Mutational spectrum and markers of selection for age-accumulating mtSNVs in UKB.

A. Normalized mean mtSNV count as a function of variant location, strand, class, and age. B. Normalized mean mtSNV count as a function of strand and variant class. C. Mean mtSNV count as a function of age for all variants, age-accumulating class variants (C > T heavy and A > G heavy and light), and other variants. D. Heteroplasmy distribution across variants and individuals for age-accumulating classes only and all variants. E. Normalized mean mtSNV count for coding variants as a function of strand, class, age, and consequence. F. Heteroplasmy distributions for missense (green) versus synonymous (black) variants within mtDNA protein coding genes, stratified by age accumulating class (dotted versus solid). Insets are corresponding sample sizes. G. dN/dS estimates (dots) as a function of heteroplasmy for age-accumulating class variants in mtDNA protein coding genes. Dashes are median of null draws; error bars are 2.5%–97.5% range of null. For panels A, B, C, E, error bars are +/− 1SE. For panels A, C, E, total n = 343,266 individuals. For panels B, G, total n = 382,505 individuals. All panels of this figure use UKB data; see Fig. 2 for the corresponding analyses in AoU.

Extended Data Fig. 6 Mutational signature and selection analysis of mtSNVs in AoU.

A. Normalized mean mtSNV count as a function of variant class, strand, and tri-nucleotide context. Context is determined by the 5’ and 3’ base in a strand-specific way. n = 237,500 individuals. B. Z-scores from p-values obtained via linear regression of mtDNA mutation spectrum onto all cancer nuclear single base substitution signatures. Upper plot represents heavy strand, lower represents light strand. Line corresponds to Bonferroni-corrected significance threshold at p < 0.05. Triangles represent significantly-associated SBS spectra thought to be related to impaired DNA damage repair and deamination. Age-accumulation of normalized mean mtSNV burden as a function of strand and trinucleotide context for C. C > T and D. A > G variants. For panels A, C, and D, error bars correspond to +/− 1 SE. For panels C and D, n = 236,749 individuals. E. dN/dS point estimates stratified by gene and heteroplasmy bin. Only age-accumulating class variants were used as input to dN/dS estimation, and estimation was performed separately for each heteroplasmy range and biobank. A 192-rate parameter substitution model was used as part of the dNdScv package. All panels of this figure use AoU data; see Extended Data Fig. 7 for the corresponding analyses in UKB.

Extended Data Fig. 7 Mutational signature and selection analysis of mtSNVs in UKB.

A. Normalized mean mtSNV count as a function of variant class, strand, and tri-nucleotide context. Context is determined by the 5’ and 3’ base in a strand-specific way. n = 382,505 individuals. B. Z-scores from p-values obtained via linear regression of mtDNA mutation spectrum onto all cancer nuclear single base substitution signatures. Upper plot represents heavy strand, lower represents light strand. Line corresponds to Bonferroni-corrected significance threshold at p < 0.05. Age-accumulation of normalized mean mtSNV burden as a function of strand and trinucleotide context for C. C > T and D. A > G variants. For panels A, C, and D, error bars correspond to +/− 1 SE. For panels C and D, n = 343,266 individuals. E. dN/dS point estimates stratified by gene and heteroplasmy bin. Only age-accumulating class variants were used as input to dN/dS estimation, and estimation was performed separately for each heteroplasmy range and biobank. A 192-rate parameter substitution model was used as part of the dNdScv package. All panels of this figure use UKB data; see Extended Data Fig. 6 for the corresponding analyses in AoU.

Extended Data Fig. 8 Evaluation of the common and rare variant landscapes of mtSNV accrual.

A. Detection of reverse causation between CH (max n = 363,475 individuals) and mtSNV accrual (max n = 547,947 individuals) using MR. Dotted line is inverse variance weighted linear regression line with p-value representing a two-sided test for a non-zero beta. Points represent effect sizes. CH effect sizes are log-odds ratios and mtSNV accrual effect sizes are linear regression betas. Alleles are set such that lead variants for mtSNV accrual are risk-increasing. Error bars are 95% CI. Common variant GWAS of age accumulating heteroplasmic SNV burden in UKB EUR with B. the usual covariates (sex, age, PCs, haplogroups) and C. with an additional covariate for mtCNadj. Common variant GWAS with cross-ancestry meta-analysis of age accumulating heteroplasmic SNV burden in AoU with D. the usual covariates (sex, age, PCs, haplogroups, site) and E. with additional covariates for mtCN and extraction kit. F. Quantile-quantile plots showing SKAT-O p-values from gene-based association testing of heteroplasmic SNV count after individuals with identified CH were removed. Analysis shows a meta-analysis between UKB and AoU cross-ancestry meta-analyses. Various variant classes and AF cutoffs are denoted by colors and panels respectively. See Fig. 3d for corresponding analysis with all samples retained.

Extended Data Fig. 9 Correlations between mtDNA traits and CH and biomarkers of aging and inflammation.

Regression betas of inflammatory biomarkers (upper panel: IL1A, IL6, TNF as measured by proteomics) and aging / reductive stress biomarkers (FGF21 and GDF15 via proteomics and telomere length via PCR) onto A. mtCNadj, B. mtSNV count, C. mtSNV count with CH carriers excluded, and D. CH. In all analyses, corrections were performed by including age, age^2, sex, age*sex, age^2*sex, genetic ancestry, and haplogroup in the association model. Each square point represents a linear regression effect size. Red line is beta = 0. Red points indicate associations that pass Bonferroni multiple testing correction (n = 6 tests per analysis). Error bars correspond to 95% CI.

Extended Data Fig. 10 Among all identified cancers, age-accumulating heteroplasmic mtSNV burden is associated with increased risk for a variety of hematologic cancers.

Odds ratio is computed using logistic regression including covariates for sex, age, ancestry, and haplogroup. Points represent odds ratios (OR) across both panels. Error bars are 95% CI. Right panel excludes anyone with any recorded smoking history. Red indicates Bonferroni-corrected p-value < 0.05. Per-test sample sizes and test statistics can be found in Supplementary Table 9.

Extended Data Fig. 11 Individuals with CH have more mtSNVs in specific tri-nucleotide contexts relative to the general population with no detected difference in functional consequence.

A. Normalized mean mtSNV count per person as a function of CH status, variant class, and tri-nucleotide context. * = p < 0.05 for the two-sided test of difference between CH and controls, corrected via Bonferroni method (192 comparisons). P-values can be found in Supplementary Table 11. B. dN/dS estimates with non-parametric error estimates for CH carriers versus controls in each coding mtDNA gene among age-accumulating variants. Each bar represents a dN/dS estimate. C. Mean MLC metric for age-accumulating variants in UKB for CH carriers versus controls in each coding mtDNA gene. In all panels, error bars correspond to +/− 1SE. All panels of this figure use AoU; see Extended Data Fig. 12 for the corresponding analyses in UKB. Only variants outside the Ori region are shown. For all panels, n = 8,266 individuals with CH who are compared to controls, which were selected by identifying 500 random samples of 8,266 age- and sex-matched individuals without CH (Methods).

Extended Data Fig. 12 Individuals with identified CH show increased accumulation of mtDNA age accumulating variants relative to the general population in UKB.

A. Normalized mean mtSNV count per person as a function of CH status, variant class, and tri-nucleotide context. * = p < 0.05 for the two-sided test of difference between CH and controls, corrected via Bonferroni method (192 comparisons). P-values can be found in Supplementary Table 12. B. Normalized mean mtDNA variant count per person as a function of CH status, strand, variant class, and age. C. Normalized mean mtSNV count per person as a function of variant consequence and age-accumulating status for CH carriers versus controls. D. dN/dS estimates for CH carriers versus controls in each coding mtDNA gene among age-accumulating variants. Each bar represents a dN/dS estimate. E. Mean MLC metric for age-accumulating variants in UKB for CH carriers versus controls in each coding mtDNA gene. In all panels, error bars correspond to +/− 1SE. All panels of this figure use UKB data; see Fig. 5 and Extended Data Fig. 11 for the corresponding analyses in AoU. Only variants outside the Ori region are included. For all panels, n = 10,544 individuals with CH who are compared to controls, which were selected by identifying 500 random samples of 10,544 age- and sex-matched individuals without CH (Methods).

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Supplementary Tables 1–12, containing sample sizes, lead variants, significant gene-based association test results, and fine-mapping results from GWAS/RVAS of all traits tested in this study.

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Gupta, R., Durham, T.J., Chau, G. et al. Mechanism of age-related accumulation of mtDNA mutations in human blood. Nature 656, 140–148 (2026). https://doi.org/10.1038/s41586-026-10569-6

- Received: 06 May 2025

- Accepted: 20 April 2026

- Published: 27 May 2026

- Version of record: 27 May 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10569-6

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Cortical development dynamics across autism spectrum disorder mouse models

Lena A. Schwarz,

Christoph P. Dotter,

Sergey Isaev,

Michela Lisi,

Daniel Malzl,

Christoph Büschl,

Sabrina Ladstätter,

Bárbara Oliveira,

Matteo Barel,

Bernadette Basilico,

Chaitanya Chintaluri,

Sarah Gorkiewicz,

Mohammad Goudarzi,

Tereza Belinova,

Stephan Reichl,

Gintarė Sendžikaitė,

Satish Arcot Jayaram,

Peter Koppensteiner,

Christoph Sommer,

Tim P. Vogels,

Jörg Menche,

Igor Adameyko,

Peter V. Kharchenko,

Christoph Bock &

…

Gaia Novarino

Nature volume 656, pages 159–172 (2026)

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Abstract

Despite the functional diversity of over 100 causal genes1,2,3, phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD.

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Main

Several studies have uncovered the heterogeneous genetic landscape of ASD, identifying numerous monogenic forms4,5. Although these discoveries are crucial for understanding ASD biology, fundamental questions remain: how mutations that are scattered across many genes lead to similar disorders; and how similar these genetically defined ASDs are to one another.

Here we used single-nucleus multi-omics sequencing to study 251 samples from mouse models of high-risk genes linked to ASD, spanning developmental stages, sexes and two brain regions (Fig. 1a). These data comprehensively represent cell states across ASD-associated mutants, uncovering converging as well as diverging phenotypes.

Fig. 1: Large-scale multi-omics profiling of high-risk genes associated with ASD.

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a, Simultaneous transcriptome and chromatin accessibility profiling of brain samples from 11 monogenic mouse lines, each carrying a mutation in a high-risk gene linked to ASD. An overview of the experimental design is shown, indicating total samples and cells analysed per time point. Illustration of the multi-omics approach created by J. Kirchner. b–d, Uniform manifold approximation and projection (UMAP) visualization of integrated snRNA-seq and snATAC-seq data from E15.4 (b), P4 (c) and P14 (d). Cell clusters are coloured and numbered by cell type. ET, extratelencephalic-projecting neurons; NP, near-projecting; PN, projection neuron; SVZ, subventricular zone; Vend, vascular endothelial cells; VLMC, vascular leptomeningeal cell. e–g, Changes in cell-type abundances in individual mutants linked to ASD from E14.5 (e), P4 (f) and P14 (g). The arrowheads indicate relative percentage differences compared with WT. Statistical significance was assessed in edgeR using a two-tailed quasi-likelihood F-test. Nominal P values are shown. h, Pseudotime analysis of the RG lineage at E14.5. The solid line indicates the trajectory inferred from gene expression embedding (Methods). The dashed arrow indicates the trajectory from the RG (root) towards cortical plate cells (left). Two RG subpopulations were distinguished: RG1 (G1/S phase) and RG2 (G2 phase) (centre and right). i, Pseudotime analysis of RG cells indicating that mutants linked to ASD tend to remain in the proliferating RG1 subpopulation. The vertical line marks the boundary between two modes of the pseudotime distribution. j, Median pseudotime analysis of RG cells indicates a consistent developmental delay in mutants linked with ASD compared with WT. The centre line represents the median, the box limits show the 25–75% interquartile range (IQR), the whiskers extend to the furthest data points within 1.5× the IQR from the box edges, and the data points outside this range are represented as dots. The dashed line indicates the median of the WT per-sample averages. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/− and Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/− and colony-matched controls) and 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/− and colony-matched controls), 4 (Kmt5b+/−) and 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/− and Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/− and Wac+/−) and 3 (Ptchd1−/y).

Single-nucleus multi-omics profiling

Droplet-based single-cell multi-omic technologies enable matched gene expression and chromatin accessibility profiling in thousands of single cells in one experiment. However, existing methods remain costly and limited in scalability. Here we optimized a multiplexing strategy for combined single-nucleus RNA sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq). We utilized a multiplexing approach using cholesterol-modified oligos (CMOs)[6] as sample-specific barcodes (Fig. 1a and Supplementary Table 1), enabling pooled processing. In this way, we profiled 45 conditions comprising 251 samples (Supplementary Table 1), including mutants and controls, allowing us to assess transcriptional and chromatin states at single-cell resolution across a broad range of genetic ASD mouse models (Fig. 1a; Methods).

In total, we collected the molecular profiles of 200,787 nuclei (66,969 at embryonic day 14.5 (E14.5), 56,260 at postnatal day 4 (P4) and 59,974 at P14 for the forebrain; 17,584 at P4 for the cerebellum). We detected an average of approximately 3,900 transcripts, about 2,000 genes and approximately 4,200 fragments per cell at a median sequencing depth of about 25,000 reads per cell (Supplementary Figs. 1d and 3a), aligning with standard recommendations for these types of analyses.

We identified 12 (E14.5), 22 (P4), 26 (P14; cortex) and 16 (P4; cerebellum) major cell types (Figs. 1b–d and 6a; Methods). All samples and genotypes were similarly distributed across clusters (Extended Data Fig. 1a–f and Supplementary Table 1). Each cluster was annotated based on canonical marker gene expression and established mouse brain cell-type classifications (Extended Data Fig. 1g–j).

At E14.5 (Fig. 1b), the cell types comprised primarily cortical plate neurons (expressing Bcl11b and Satb2), radial glial (RG) cells (Pax6 and Gas1) and intermediate progenitors (Unc5d), with a small fraction of medial ganglionic eminence (MGE)-derived interneurons (Nxph1). At postnatal stages (Fig. 1c,d), we differentiated between upper (layers II–IV and layer II/III; Cux1, Cux2 and Bcl11a) and lower (layer V/VI deep-layer cortical projection neurons (DLCPN); Bcl11b, Ldb2 and Pex5l) cortical layers, including corticothalamic projection (layer V/VI corticothalamic projection neuron (CThPN) and layers IV–VI; Gse1, Tle4 and Zfpm2) and near-projecting neurons (layer V/VI; Epha6 and Lingo2). As samples included the hippocampus, we distinguished between hippocampal cells (Cacna2d3, Nr3c2 and Epha6) and adjacent CA1–subiculum projection neurons (Tshz2 and Tshz3). Glial cells were first observed at P4, including oligodendrocyte precursor cells (OPCs; Pdgfra and Cspg4) and their maturation stages (Itpr2 and Plp2 for postmitotic; and Mpb for pre-myelinated), astrocytes (Ptprz1, Slc1a3 and Apoe) and microglia (Hexb, Cx3cr1 and Csf1r).

Consistent with the timely cortical positioning of interneurons, we identified several interneuron populations from P4 onwards, including caudal ganglionic eminence (Int. CGE (Vip) and Int. Sncg (Vip); Adarb2, Gad2, Vip and Synpr) and MGE-derived interneurons (Int. MGE (Pvalb), Int. Sst and Int. Lamp5; Erbb4, Lamp5, Nxph1 and Sst).

Our dataset thus provides a comprehensive view of the cellular landscape of the developing mouse cerebral cortex (Fig. 1b–d), consistent with previous reports7,8,9,10.

RG progression defects in mutants linked to ASD

To explore how mutations associated with ASD affect cortical development, we initially compared the relative proportion of each cell type in all mutants combined versus wild-type (WT) controls (Extended Data Fig. 2a–c). Overall proportions were similar; however, cortical plate cells and OPCs were significantly depleted in mutants at E14.5 and P4 (Extended Data Fig. 2a,b), respectively. Stratifying by genotype confirmed that these changes were consistent across mutants (Fig. 1e–g), with stronger trends in Cul3, HnrnpU, Kdm6b and Ptchd1. The reduction in cortical plate cells at E14.5 coincided with an increased proportion of RG cells (Fig. 1e). At P4, neuronal numbers were similar between mutants and WT; however, mutants displayed a reduction in the number of immature astrocytes and OPCs (Fig. 1f). These reductions were independently confirmed in two selected mutants, HnrnpU+/− and Kdm6b+/−, by in situ hybridization for Slc1a3 (immature astrocytes) and Pdgfra (OPCs; Extended Data Fig. 2d). These observations independently suggest a delay in the RG cell lineage progression, potentially affecting the gliogenic switch. This delay was pronounced in mutants exhibiting larger differences in RG and cortical plate cell numbers at E14.5, such as Cul3, HnrnpU, Kdm6b, Ptchd1 and Setd5 mutants. At P14, OPC numbers remained lower across mutants linked with ASD, whereas the number of other cell types had normalized (Fig. 1g).

To identify the source of these alterations, we investigated RG lineage progression by performing pseudotime analysis on the E14.5 dataset (Fig. 1h). By analysing the trajectories from RG cells towards cortical plate neurons, we identified two RG subpopulations (RG1 and RG2; Fig. 1h,i). RG1 cells were characterized by the expression of proliferation markers (for example, MKi67), indicating active cell division. Pseudotime analysis revealed that mutant RG cells tend to remain in the proliferating RG1 state (Fig. 1h–j) at the cost of delayed differentiation.

To validate alterations in RG cell proliferation, we performed EdU labelling at E14.5 in Kdm6b+/− and HnrnpU+/− embryos and WT littermates (Extended Data Fig. 2e,f′ and Supplementary Video 1). Consistent with the snRNA-seq results, both HnrnpU+/− and Kdm6b+/− mutants showed increased numbers of EdU-positive cells, indicative of prolonged proliferation and/or an expanded pool of proliferative RG cells at E14.5 (Extended Data Fig. 2e,f′).

We next analysed differentially expressed genes (DEGs) in RG1 cells shared across mutants by comparing all mutants, collectively, to WT RG1 cells. Only two genes, Rsrp1 and Galnt17, were significantly downregulated (Extended Data Fig. 2g). Rsrp1 encodes a protein implicated in splicing and cell cycle regulation[11]. Galnt17 is an N-acetylgalactosaminyltransferase that has been linked to developmental delay and neural differentiation defects[12]. Although these two genes may represent biomarkers for neurodevelopmental delay, they are unlikely to fully account for the observed RG progression phenotype. The analyses of each mutant separately showed variable DEG numbers (false discovery rate (FDR) < 0.1) in RG1 cells, with some mutants showing few or no DEGs, suggesting that in some cases, delays may originate earlier during development. HnrnpU was the most affected (more than 150 DEGs; Supplementary Table 2). Although other mutants showed fewer DEGs, several DEGs were detected in multiple mutants. For example, HnrnpU, Kdm6b and Kmt5b mutants showed downregulation of Setbp1, a gene linked with ASD implicated in neural differentiation[13]. Nrcam, an ASD-associated cell-adhesion gene[14], was downregulated in Kdm6b+/− and Kmt5b+/− RG1 cells (Supplementary Table 2).

Together, our data indicate a shared delay in RG lineage progression across mutants associated with ASD, but limited convergence in the potential underlying mechanisms.

Convergent early postnatal changes in mutants linked with ASD

To investigate the effect of mutations linked with ASD across development and cell types, we performed pseudobulk differential gene expression analysis within each cell type, comparing all mutants collectively to WT controls, thereby identifying shared transcriptional changes (Fig. 2a, all mutants; and Supplementary Table 3). This analysis revealed numerous DEGs at P4 in layer II/IV (715 DEGs, adjusted P < 0.05) and layer V/VI DLCPN (600 DEGs) neurons, most of which were downregulated in mutants. Few or no DEGs were detected in other cell types and time points when considering all mutants together. Aggregating cell types into broader cell subclasses (for example, aggregating all excitatory neurons; Extended Data Fig. 3a and Supplementary Table 4), confirmed that P4 excitatory neurons showed the strongest shared DEG signal (Extended Data Fig. 3a′).

Fig. 2: Differential gene expression highlights stage-specific convergent mechanisms.

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a, Number of DEGs (adjusted P < 0.05) across mutants and cell types. ‘All mutants’ denotes mutants analysed collectively compared with colony-matched controls. Total DEG counts are summarized per time point across cell types, counting repeated DEGs only once. Statistical significance was assessed using a two-sided Wald test in DESeq2 with Benjamini–Hochberg correction for multiple testing. b, Number of Gene Ontology (GO) terms identified by GSEA (FDR < 0.05) at each time point. The colours indicate the direction of the normalized enrichment score. Gene Ontology terms were simplified to remove redundancy; all three sub-ontologies (Biological Processes, Molecular Function and Cellular Component) are included. GSEA was performed using clusterProfiler, with a Kolmogorov–Smirnov-like statistic, permutation-based testing, and Benjamini–Hochberg correction (Methods). c, For each genotype, the magnitude (that is, effect size) and significance of expression changes in network clusters representing biological processes in excitatory neurons are shown. Processes mentioned in the text are highlighted in bold. Coefficients were tested against zero using two-sided Student’s t-tests and Benjamini–Hochberg multiple comparison test. Those with adjusted P < 0.1 were considered significant and marked with a black outline in the plot. AA, amino acid; ECM, extracellular matrix; ER, endoplasmic reticulum. Grey bar colours are as in panel d. d, Pairwise distances between DEG network localizations in excitatory neurons across genotypes and developmental stages. Smaller distances denote greater similarity (convergence). Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/− and Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/− and colony-matched controls) and 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/− and colony-matched controls), 4 (Kmt5b+/−) and 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/− and Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/− and Wac+/−) and 3 (Ptchd1−/y).

Next, we analysed each mutant separately against WT. Most genotypes showed more than 700 DEGs across cell subclasses and time points, counting repeated DEGs once (Extended Data Fig. 3b). At E14.5, changes were limited, except in HnrnpU+/− and Kdm6b+/−, which exhibited numerous DEGs affecting cortical plate, RG and subventricular zone migrating cells (Fig. 2a, left). Conversely, P4 showed widespread transcriptional changes across genotypes and cell types (Fig. 2a, centre), with excitatory neurons most affected. Several mutants also showed many DEGs in inhibitory neurons and glia. By P14, DEG numbers declined (Fig. 2a, right). Individual mutants (aggregating replicates per genotype) consistently showed more DEGs than all genotypes together, indicating that many changes are not shared across genotypes. The distribution of upregulated and downregulated DEGs was more balanced in individual mutants than in the collective analysis. However, some mutants showed few DEGs across all analysed time points, including mice with mutations in histone modifier genes, such as Kmt5b, Setd5 and Wac (Fig. 2a and Extended Data Fig. 3b). This suggests that mutations in this gene class may, at these stages and under baseline conditions, have subtle effects on gene expression, affecting few genes or causing small expression changes that are difficult to detect.

To control for potential effects of sequencing depth and enable comparisons of DEG counts across mutants, cell types and time points, we downsampled each group to a matched sequencing depth. This confirmed previous trends: excitatory and inhibitory neurons were similarly affected, whereas glial cells showed weaker transcriptional changes, except for Cul3 mutants, where glial cells showed a stronger transcriptional response than neurons (Extended Data Fig. 3c).

Mutants exhibited significant DEG overlap across time points (Extended Data Fig. 3d), independently validating these DEGs and indicating persistent molecular alterations. For example, HnrnpU+/− had DEGs associated with the cell cycle (for example, Ccnd2, Cdk4 and Mef2c), axonogenesis (for example, Dscam2, Sema5a and Robo2) and transmembrane transporters (for example, Cacnb3, Gabra4 and Gria4) overlapping across early and perinatal development. Trip12+/− shared DEGs across time related to metabolic processes (for example, Eef1a1 and Nceh1) and synaptic signalling (for example, Nrxn3, Syt5 and Rims1) at P4 and P14 (Extended Data Fig. 3d).

Neuronal subclasses shared DEGs across mutants, particularly in P4 excitatory neurons, where approximately 10% of DEGs were shared by at least four mutants (Extended Data Fig. 3e). By contrast, the overlap between neuronal and non-neuronal cells within the same mutant was minimal. To assess whether overlaps exceeded random expectation, we compared each mutant pair against distinct sets of control samples randomly divided into two disjoint groups (Methods). The results indicated stage-dependent, non-random convergence on shared transcriptional responses across genotypes (Extended Data Fig. 3f).

Next, we compiled the most frequent DEGs across mutants and cell types (Extended Data Fig. 3g and Supplementary Table 1). This set included a large proportion of genes associated with ASD (34%) and was enriched for genes encoding synaptic proteins (q = 1.03 × 10−15; Extended Data Fig. 3h). These proteins localize to presynaptic and postsynaptic compartments and are involved in synaptic translation, actin cytoskeleton, synapse organization, and membrane channel and receptor function (Extended Data Fig. 3g,h). The directionality of these alterations was largely consistent across mutants.

Although similarities in gene expression patterns and pathway alterations were evident, genotype-restricted changes were also prominent, with at least 50% of DEGs in each cell type identified only in a single genotype (Extended Data Fig. 3e).

To validate the snRNA-seq results, we performed bulk RNA-seq on a subset of mutants and time points and, despite methodological differences, found significant DEG overlap, with concordant effect directions and magnitudes (Extended Data Fig. 3i).

These results pinpoint critical periods and highlight variations in developmental trajectories across mutants linked with ASD, with some mutants having greater effects earlier and others later. We also identified shared DEGs across mutants associated with ASD, underscoring convergent mechanisms that probably reflect stage-specific developmental processes.

Functional pathway disruptions in mice with mutations linked with ASD

To identify biological processes affected by mutations associated with ASD, we used gene set enrichment analysis (GSEA), which captures coordinated gene set changes beyond DEG cut-offs and provides a broader perspective on cell states.

We performed GSEA in individual mutants at the cell-subclass level. Consistent with the DEG analysis, the effects were most pronounced at P4, although GSEA also revealed differences at E14.5 and P14 (Fig. 2b, Extended Data Figs. 4–6a and Supplementary Table 5). For example, we identified enrichment of gene sets in neural lineage cells at E14.5 in Cul3, Kmt5b and Setd5, in addition to HnrnpU and Kdm6b mutants (Supplementary Table 5).

At E14.5, several mutants exhibited changes in pathways associated with cell cycle regulation, chromosome segregation and neuronal projection development (Extended Data Figs. 4a and 6b). At P4, mutants displayed changes in pathways related to synaptic and ion channel functions. Synapse-related transcripts were also affected at P14 (Extended Data Fig. 4b,c).

Inhibitory neurons showed fewer enriched gene sets than excitatory neurons, but were qualitatively similarly affected, with terms related to synaptic transmission, intracellular signalling, ion channels and transmembrane transporters (Extended Data Fig. 5a,b). Glial cells were generally less affected than neurons, except in Cul3 and Ptchd1 mutants (Extended Data Fig. 6a and Supplementary Table 5). Certain gene sets in neurons were oppositely regulated at P4 and P14 (Extended Data Fig. 5). For instance, synaptic transmission and organization were downregulated in P4 Kdm6b-mutant neurons but upregulated at P14.

We refined GSEA to identify cell-type-specific features (Supplementary Table 6). For example, RG and cortical plate cells shared disruptions in neurogenesis-related terms but also showed distinct features. HnrnpU and Kdm6b mutations altered cell-cycle-relevant transcripts in RG cells, probably contributing to the developmental delay observed through pseudotime analysis. Conversely, synaptic signalling and developmental processes were predominantly affected in cortical plate cells (Extended Data Fig. 6b). GSEA also revealed more enriched terms in the excitatory neurons of layer II/IV, corticothalamic and near-projecting layer V/VI, and parvalbumin-expressing (PV+) interneurons, whereas hippocampal and somatostatin-expressing (SST+) neurons showed fewer terms (Supplementary Table 6).

Several mutants showed deregulation of axonogenesis and dendrite morphogenesis-related genes. In most mutants, DEGs associated with these processes were upregulated in PV+, SST+ and vasoactive intestinal peptide-expressing (VIP+) interneurons, but downregulated in excitatory layer II/IV neurons. Cul3+/− and Ptchd1−/y were exceptions, showing upregulation of DEGs in the latter. Cul3 and Ptchd1 mutants also exhibited increased synaptic gene expression in immature astrocytes and VIP+ interneurons (Supplementary Table 6). Ptchd1−/y and Setd5+/− had minimal effects on synaptic gene expression in excitatory neurons across cortical layers, aligning with previous findings[15]. Protein secretion-related processes were upregulated in hippocampal neurons and interneurons, but downregulated in cortical excitatory neurons (Supplementary Table 6).

To identify genotype-associated processes, we compared each mutant against the aggregate of the other ten mutants (Extended Data Fig. 7a and Supplementary Table 7). The number of affected processes yielded a pattern similar to the mutant–WT comparison, with most changes occurring at P4. We then confirmed that processes identified in the cross-mutant comparison were also affected in the mutant–WT comparison. For example, Cul3 mutants showed enrichment for genes related to cell migration (Extended Data Fig. 7b), aligning with previous results[16]. Bckdk−/− and Trip12+/− showed upregulation of metabolism and energy production genes. Consistent with Bckdk function[17], amino acid-related genes (for example, Bcat1, Cars and Slc7a5; Supplementary Tables 3 and 4) were altered across multiple cell types and time points in Bckdk−/− but not other mutants. Our data suggest that these changes predominantly affect neurons rather than glial cells (Extended Data Fig. 3c). Trip12 mutation affected processes related to cellular respiration and energy production, including glycolysis (for example, Pdk3 and Mpc2), the tricarboxylic acid cycle (for example, Mdh1 and Mdh2) and lipid metabolism (for example, Lactb; Extended Data Fig. 7b and Supplementary Table 7). HnrnpU mutants showed changes in transcripts related to secretion and cilium development (Extended Data Fig. 7b and Supplementary Table 7), consistent with previous literature[18]. Finally, Kdm6b mutants exhibited differential expression of WNT signalling-related genes, often associated with ASD19,20 (Extended Data Fig. 7b and Supplementary Table 7).

In summary, these results show convergent and genotype-associated changes across ASD models. Although most mutants exhibited dysregulation of synaptic transmission and ion channel pathways, some alterations appeared enriched within particular genotypes, including alterations in cytoskeleton dynamics, cilium function and metabolism.

Network analysis reveals convergent phenotypes

As GSEA revealed commonalities across mutants, we evaluated the extent of overlaps across genotypes and developmental stages using network colocalization analysis on a protein–protein interaction network. Functionally related proteins were grouped into clusters via averaged random walks, and each cluster assessed for functional enrichment (Methods).

For each genotype, we assessed the magnitude (effect size) and significance of expression changes across clusters, mapping the network-wide effect of each mutation on biological processes, with emphasis on stage-restricted effects (Fig. 2c and Extended Data Fig. 8a).

Consistent with the GSEA, at E14.5 (Fig. 2c, top), we observed enrichment of actin cytoskeleton, cell cycle and proliferation pathways in excitatory neurons, particularly in HnrnpU and Kdm6b mutants, but also in Cul3 mutants, consistent with earlier reports[16]. Network-level analysis also highlighted changes in WNT signalling, particularly in HnrnpU and Kdm6b. At postnatal stages (Fig. 2c, centre and bottom), the network highlighted effects on synaptic processes, ion transport and metabolism, reflecting the transition towards neuronal specialization. Compared with the other mutants, Kmt5b mutants showed an inverse effect size pattern at P4, suggesting counteractive or compensatory influences within shared biological systems, potentially reflecting an altered regulatory role. At P14, metabolism-related processes emerged, with Bckdk, HnrnpU and Trip12 mutants exhibiting larger effect sizes (Extended Data Fig. 7b).

Next, we compared network localization profiles of top-ranked DEGs across genotypes and developmental time points to evaluate the similarity of affected gene systems. Network localization and corresponding similarity scores were primarily shaped by developmental stage (Fig. 2d and Extended Data Fig. 8b). Within each developmental stage, mutants were more similar than expected by chance, indicating that their DEGs cluster within comparable network regions. By P14, however, mutants were less similar than at earlier stages, particularly in excitatory neurons. Thus, mutations associated with ASD influence common, stage-specific processes but progressively diverge into distinct genotype-associated transcriptional programs.

Synaptic and ion channel changes in ASD models

Given the link between ASD and excitation–inhibition imbalances21,22,23,24, we explored ion channel alterations, focusing on calcium, potassium and sodium channels (Supplementary Tables 3 and 4). Scn1a was among the most commonly affected genes. Scn1a encodes a sodium channel involved in the generation of action potentials and mutated in Dravet syndrome[25]. Mutants showing Scn1a downregulation often also exhibited reduced Scn8a and increased Scn9a expression. Scn9a physically and genetically interacts with Scn1a26,27, potentially reflecting compensatory mechanisms. Similarly, several potassium and calcium channels were affected in excitatory neurons, including Kcnv1, which encodes a modifier subunit of voltage-gated potassium channels, and Kcnq3, which encodes a mediator of neuronal M-currents and is mutated in familial epilepsy syndromes28,29,30.

We explored synaptic functions using SynGO (Extended Data Fig. 7c). Most mutants disrupted both postsynaptic and presynaptic processes, including synapse organization and cytoskeleton, vesicle release, signalling and neurotransmitter receptor organization (Extended Data Fig. 7c′ and Supplementary Table 8). None of the mutants showed enrichment for transcripts related to axon or dendritic transport. Metabolic and synaptic translation processes were affected in Bckdk, Cul3 and Trip12 mutants, consistent with their functions related to proteostasis16,31,32,33,34. Both Ash1l and Cul3 mutants showed enrichment for synaptic actin cytoskeleton genes, with Ash1l mutants affecting actin cytoskeleton dynamics, and Cul3 mutants affecting actin assembly. In addition, Ash1l mutants showed enrichment for synaptic vesicle priming and release, terms absent in the SynGO analysis of Cul3 mutants.

Commonly altered transcripts related to neurotransmitter receptors and synaptic transmission included upregulated Cacng4, an AMPA receptor modulator, and downregulated Camk2a, a calcium/calmodulin-dependent kinase involved in neuronal and glutamatergic synaptic functions. Rims1 and Rims2, encoding key organizers of the presynaptic compartment, were downregulated across multiple genotypes and time points. Stxbp5l, encoding a syntaxin-binding protein relevant for vesicular trafficking and neurotransmitter release[35], was also downregulated in most mutants.

Overall, ion channel and synaptic gene alterations represent a convergent molecular theme across mutants associated with ASD. Yet, each genotype exhibits a distinct combination of affected pathways, underscoring shared biology layered on pronounced heterogeneity.

Electrophysiology correlates with transcriptomics

To test whether transcriptional changes in ion channel and synaptic genes translate into functional abnormalities, we selected a subset of mutants (Bckdk, HnrnpU, Trip12 and Usp7) and performed whole-cell patch-clamp recordings from layer II/III somatosensory cortical pyramidal neurons at P7, the earliest stage near P4 with robust synaptic activity (Fig. 3). We quantified passive membrane and action potential properties (frequency–current curves; Fig. 3a,b), then blocked action potentials to isolate miniature excitatory and inhibitory postsynaptic currents (mEPSCs and mIPSCs, respectively) in mutants and controls (Fig. 3e,f and Extended Data Fig. 9a).

Fig. 3: Correlation of electrophysiological and transcriptomic changes in mutants associated with ASD.

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a, Frequency–current curves for Bckdk−/−, HnrnpU+/−, Trip12+/− and Usp7+/− mutants (top to bottom) versus pooled controls (colony matched and littermate; two-way analysis of variance (ANOVA) and Sidak’s multiple comparison test). AP, action potential. b, Radar plots summarizing membrane and action potential properties; significant features are highlighted in red; values are normalized to matched controls. Colours are as in panel a. The dashed grey line indicates normalized WT properties. AHP, action potential afterhyperpolarization; Amp, amplitude; Fmax, maximal firing rate; FWHM, full-width at half-maximum; I, current; Rin, input resistance; RMP, resting membrane potential; Tau, membrane time constant. c, Radar plots of DEGs encoding ion channels involved in action potential generation (log_2FC; non-significant channels set to 0). Colours are as in panel a. d, Spearman correlation between Euclidean distances of electrophysiological properties (b) and channel expression profiles (c); weighted by −log_10(adjusted P). The identity line for reference is shown. e,f, mEPSCs (e) and mIPSCs (f) in mutants and controls. Statistically significant values are shown in red. Animals and cells used for electrophysiology analysis: n (animals; Bckdk−/−) = 5, n (HnrnpU+/−) = 4, n (Usp7+/−) = 7 and n (Trip12+/−) = 7; n (pooled controls: colony-matched controls n = 11, littermate controls n = 7) total = 18; n (cells; detailed cell counts per experiment are provided in the Source Data); unpaired two-tailed t-test for normally distributed data, and unpaired two-tailed Mann–Whitney test for non-normally distributed data. g, Heatmap of synaptic DEGs in layer II/IV neurons at P4, showing convergence across mutants. DEGs are ordered by the degree of shared differential expression; risk genes for ASD are highlighted and coloured by SFARI score. Data are mean ± s.e.m. (a,e,f).

Source data

Three of the four models showed altered intrinsic excitability and/or input–output (frequency–current) relationships (Fig. 3a,b). The exception was the Trip12 mutant, which, consistent with its subtle transcriptional changes in layer II/IV excitatory neurons at P4 (Fig. 3c), exhibited no obvious differences in passive or active membrane properties (Fig. 3a,b).

To link molecular and cellular phenotypes, we computed Euclidean distances between each mutant and controls based on the expression of ion channels linked to the measured properties (Fig. 3c) and compared them with distances in a multivariate space of intrinsic and action potential features (Fig. 3a,b). These distances correlated (Fig. 3d), indicating that greater transcriptional divergence corresponds to larger physiological deviations.

Thus, although each genotype presented a distinct combination of molecular and functional alterations, the overall coupling between transcriptional and physiological changes was robust. To assess this relationship, we generated a minimal conductance-based model of a P7 layer II/III pyramidal neuron recapitulating the passive properties and the frequency–current relationship of a WT neuron. We then parametrically adjusted the ion channel conductances of the model according to the direction and magnitude of expression changes inferred from the transcriptomic data. This framework enabled testing whether small shifts in channel composition could reproduce the observed diversity of action potential and frequency–current curves. Indeed, tuning channel activities within transcriptomically informed ranges allowed us to reproduce the experimentally observed electrophysiological changes and recapitulate the recorded responses (Extended Data Fig. 9c).

Miniature synaptic recordings similarly reflected transcriptional signatures of synaptic dysfunction. mEPSC and mIPSC frequencies were reduced in three mutants (Bckdk, Usp7 and Trip12; Fig. 3e,f), matching downregulated presynaptic release machinery genes36,37,38,39,40 (for example, Rims1/2, Syn2 and Dnm1l; Fig. 3g and Supplementary Table 8). Changes in mIPSC frequency (Fig. 3f) were comparable with those in mEPSCs. Trip12 mutants were the outlier with a small but significant excitation–inhibition frequency imbalance (Extended Data Fig. 9d). Bckdk mutants also showed reduced mEPSC amplitude (Fig. 3e), accompanied by selective downregulation of Gria1 (Fig. 3g), a determinant of quantal amplitude[41]. Our data support a dose-like coupling: the greater the transcriptional deviation of synaptic-related and ion channel-related genes, the larger the intrinsic and synaptic physiological effects.

An exception were HnrnpU mutants, which, despite broad transcriptomic alterations, showed mild miniature synaptic phenotypes. DEG (adjusted P < 0.01) annotation with SynGO revealed downregulation of a core set of synaptic changes but also displayed genotype-associated upregulated DEGs (Fig. 3g and Extended Data Fig. 7c′), suggesting combinatorial effects. In HnrnpU mutants, several synaptic and signalling genes were upregulated and plausibly buffer presynaptic deficits (Fig. 3g), thereby helping to maintain near-control mEPSC or mIPSC rates. For example, Ap3b2 promotes synaptic vesicle biogenesis and recycling[42], and its upregulation could sustain vesicle supply and oppose frequency drops. Tprgl1 modulates presynaptic release probability[43], and Diaph1 can stabilize dendritic spines and presynaptic endocytic scaffolds[44], preserving transmission efficacy. Akap12, a regulator of synaptic plasticity[45], was strongly upregulated in excitatory neurons in HnrnpU and Usp7 mutants (Fig. 3g), both of which lacked changes in mEPSC frequency. By contrast, Akap12 was not differentially regulated in Bckdk and Trip12 mutants, which showed reduced mEPSC frequency, consistent with a potential compensatory role for Akap12 in maintaining excitatory transmission. These changes may offset presynaptic deficits in HnrnpU, explaining the absence of pronounced mEPSC or mIPSC frequency or amplitude changes. More broadly, although electrophysiological and transcriptomic changes correlate at the pathway level, the net phenotype probably reflects multiple small, opposing effects and possible post-transcriptional buffering, making one-to-one attributions difficult.

Chromatin accessibility reveals regulatory dynamics

Although snRNA-seq revealed transcriptional variations across developmental stages and mutants, it offers limited insight into upstream regulatory elements. Our single-nucleus multi-omics approach links chromatin accessibility (snATAC-seq) to matched transcriptomes (snRNA-seq) within the same nuclei, enabling investigation of the gene-regulatory landscape underlying the observed expression changes.

As with snRNA-seq, we analysed snATAC-seq across all mutants and by genotype. Accessibility patterns varied from transcriptional profiles. We detected the highest number of differentially accessible regions (DARs) at E14.5. At this stage, all cell types showed DARs, with RG cells showing the greatest number (Fig. 4a and Supplementary Table 9). DARs declined at later stages, except in P4 excitatory neurons, which retained comparable high counts. Overall, the collective mutant-versus-WT comparison (Fig. 4a, all mutants) highlighted decreased chromatin accessibility (Fig. 4b), mirroring shared transcriptional downregulation (Fig. 2a).

Fig. 4: Chromatin accessibility changes in mutants associated with ASD.

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a, Number of DARs (P < 0.001) across cell types and mutants. All mutants indicates pooled mutants compared with colony-matched controls. Five comparisons exceeded the plot scale; thus, exact values are shown. b, Comparison of DARs in individual mutants versus WT reveals both decreased and increased accessibility, whereas the aggregate of all mutants shows decreased accessibility. c, Enriched biological processes of differentially accessible genes in RG cells of HnrnpU and Kdm6b mutants. Gene Ontology enrichment analysis was performed using clusterProfiler with a one-sided hypergeometric test and Benjamini–Hochberg correction. d, Heatmaps of transcription factor motif enrichment analysis in RG DARs from HnrnpU and Kdm6b mutants (adjusted P < 0.05), including regulators associated with neurogenesis and progenitor differentiation. Transcription factors are sorted by ascending adjusted P values. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/− and Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/− and colony-matched controls) and 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/− and colony-matched controls), 4 (Kmt5b+/−) and 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/− and Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/− and Wac+/−) and 3 (Ptchd1−/y). Statistical significance was assessed using two-tailed Wald test without multiple comparisons testing as implemented in DESeq2 (a,d).

Next, we correlated log_2 fold change (log_2FC) values from snRNA-seq with the snATAC-seq data by aggregating peaks across gene bodies. We found a strong correlation in immature astrocytes and layer II/IV neurons at P4 (Extended Data Fig. 10), probably reflecting the high number of shared transcriptomic changes in these cells.

Next, we analysed snATAC-seq data stratified by genotype. Fewer DARs were detected in the single-mutant analyses than in the collective analysis (Fig. 4a,b), with HnrnpU and Kdm6b mutants showing the highest numbers of DARs. At E14.5, HnrnpU+/− and Kdm6b+/− RG cells showed 801 and 335 DARs, respectively. Although HnrnpU and Kdm6b mutants had fewer DEGs in RG cells at E14.5, RG cells exhibited the greatest differences in chromatin accessibility in these mutants. Although other mutants had fewer DARs overall, most showed more DARs in cortical plate and RG cells at E14.5 than in any other cell type at later stages. The Bckdk−/− mutant was an exception, with the largest number of DARs in layer II/IV at P4, matching the mutant-specific and cell-type-specific transcriptomic profile. The general decrease in DARs over time aligns with reports showing that chromatin gets locked in specific states with neural differentiation46,47.

Comparison of snATAC-seq and snRNA-seq revealed strong log_2FC correlations in HnrnpU+/− RG and cortical plate cells (Extended Data Fig. 10). Gene Ontology enrichment of differentially accessible genes in RG cells overlapped terms identified at the transcript level (Fig. 4c), including progenitor proliferation and forebrain development. WNT signalling and BMP activity emerged as altered processes in both HnrnpU+/− and Kdm6b+/− RG cells. Although we observed changes in WNT-related transcripts in Kdm6b mutants, they became apparent at the chromatin level in HnrnpU+/− progenitors as well. Kdm6b+/− RG cells also showed responses to BMP activity, which promotes neural progenitor division during neurogenesis48,49.

To identify gene-regulatory elements, we performed motif enrichment analysis of regions with decreased accessibility (Fig. 4d). DARs in HnrnpU+/− RG cells were enriched for multiple transcription factors (adjusted P < 0.01) linked to neurodevelopment and RG lineage progression50,51,52,53. Among the top hits was Sp2, which regulates cell cycle length in neural progenitor cells[54]. Several of these transcription factors were similarly enriched in Kdm6b mutants, suggesting shared regulatory mechanisms.

Sex-specific gene expression alterations

An open question in ASD research is its different presentation in female and male individuals. As the selected genes associated with ASD are found mutated in both sexes, we tested whether their mutations manifest similarly between sexes. Aside from sex-chromosome genes, we observed few DEGs between female and male controls (Extended Data Fig. 11a and Supplementary Table 10). We also found no sex differences in gene expression variability, transcript numbers or cell-type clustering (Extended Data Fig. 11b,c).

Next, we explored the effect of ASD-related mutations on gene transcript levels by splitting sexes (Fig. 5 and Supplementary Fig. 2). Gene expression changes in mutants relative to sex-matched controls differed markedly by sex. At P4, Ash1l, Bckdk, HnrnpU, Trip12, Usp7 and Wac mutants showed more DEGs in females than in males (Fig. 5a), whereas the other strains showed minimal sex-dependent effects. For example, combined data from males and females indicated comparable DEG numbers in Cul3 and Trip12 mutants, but sex-stratified analysis revealed that Trip12 haploinsufficiency predominantly affected females, whereas Cul3 mutants showed similar DEG numbers in the two sexes. Sex differences were generally weaker at E14.5 and P14, except for HnrnpU+/− and Trip12+/− females, which consistently had more DEGs at E14.5 and P14, respectively. Conversely, at P14, Bckdk−/− and Kdm6b+/− displayed stronger responses in males.

Fig. 5: Sex-specific effects across mutants linked to ASD.

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a, Number of DEGs (adjusted P < 0.05) in female and male mutants versus sex-matched colony-matched controls. Statistical significance was assessed using two-sided Wald test in DESeq2 with Benjamini–Hochberg correction for multiple testing. b, Number of Gene Ontology terms identified by GSEA (FDR < 0.05) across time points, split by sex. The colours indicate the normalized enrichment score direction. Gene Ontology terms were simplified to remove redundancy; all sub-ontologies (Biological Processes, Molecular Function and Cellular Component) are shown. GSEA was performed using clusterProfiler, with a Kolmogorov–Smirnov-like statistic, permutation-based testing, and Benjamini–Hochberg correction (Methods). c,d, Heatmaps of fold changes for representative merged DEGs (c) and fbDEGs (d) in P4 excitatory neurons. e, Effect sizes for merged DEGs and fbDEGs in females and males. fbDEGs show larger effect sizes in females, whereas merged DEGs show comparable effect sizes between sexes (one-way ANOVA and Sidak’s multiple comparison test). Statistically significant values are shown in red. Black lines indicate the median. f, Shared fsDEGs across mutants in P4 excitatory and inhibitory neurons. ASD risk genes are highlighted by the SFARI gene score. Animals used for analyses: n (females; E14.5) = 4 (Cul3+/−, Kdm6b+/− and Trip12+/−) and 3 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/− and colony-matched controls); n (males; E14.5) = 3 per genotype; n (females and males; P4) = 3 per genotype; n (females; P14) = 4 (Ash1l+/−, Cul3+/− and colony-matched controls), 3 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Trip12+/−, Usp7+/− and Wac+/−) and 2 (Setd5+/−); n (males; P14) = 4 (Ash1l+/−, Cul3+/− and Setd5+/−) and 3 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Ptchd1−/y, Trip12+/−, Usp7+/−, Wac+/− and colony-matched controls).

Source data

Next, we performed a sex-stratified GSEA, mitigating variance-driven differences in P values. At E14.5, most mutants showed changes in cell adhesion, chemotaxis and neuronal maturation in ganglionic eminence cells in males but not in females (Fig. 5b and Supplementary Tables 11 and 12). At P4, GSEA confirmed stronger effects in females, especially in neurons. However, differences were less pronounced than at the DEG level, as most mutants also showed numerous affected Gene Ontology terms in males (Fig. 5b). Exceptions at P4 were Ash1l, HnrnpU and Wac mutants, which consistently exhibited more affected processes in females. Cul3+/− animals displayed balanced patterns in neurons, whereas glial cells were more affected in males (Fig. 5b). At P14, sex differences were minimal, with overall balanced effects.

To investigate the origins of the sex differences at P4, we first analysed transcripts identified as DEGs in the combined-sex analysis, but not in sex-stratified analyses (approximately 61% of DEGs). We called these genes ‘merged DEGs’. Although not significant in the sex-specific analysis, these genes showed comparable trends in both sexes (Fig. 5c), suggesting shared effects that failed to reach statistical significance when analysing sexes separately due to insufficient statistical power. Examples include mutant-associated genes, such as Bcat1 and Slc7a5 in Bckdk, Ccnd2 and Sema5a in HnrnpU, and Mdh1 and Lactb in Trip12.

Subsequently, we explored overlaps of DEGs in female and male mice. In most instances, DEGs in males were also DEGs in females of the same genotype and cell type, independently confirming these changes. Conversely, many of the DEGs in female mice were not significant in male mice. We categorized these genes into two groups: (1) DEGs significant only in females with similar expression trends in both sexes, termed female-biased DEGs (fbDEGs; Fig. 5d), and (2) DEGs significant only in females but exhibiting no or opposite expression changes in males, referred to as female-specific DEGs (fsDEGs; Supplementary Table 13).

Next, we compared standardized effect sizes (Cohen’s d values) of fbDEGs between females and males (Fig. 5e). Unlike merged DEGs, which showed similar effect sizes in both sexes, fbDEGs consistently exhibited higher effect sizes in females. In addition, fbDEGs showed larger average effect sizes in females than merged DEGs in either sex, indicating that sex differences are driven by stronger effects in females (Fig. 5e and Supplementary Note).

Finally, we examined fsDEGs (Fig. 5f). Wac mutants showed the highest proportion of fsDEGs. Of note, WAC has recently been identified as a female-specific candidate gene[55], suggesting sex-specific effects and potentially greater disease penetrance in female individuals.

Several fsDEGs were shared across mutants linked to ASD and cell subclasses (Fig. 5f). These were enriched for synaptic functions (q = 6.5 × 10−7), ion channels (q = 4.8 × 10−6) and cell adhesion (q = 7.9 × 10 −3). Some fsDEGs have been previously implicated in sex-specific effects. For example, deletion of Grik1, an fsDEG in inhibitory neurons of Bckdk and HnrnpU mutants, results in sex-divergent phenotypes in mice[56]. ApoE was downregulated specifically in female interneurons in Ash1l, Bckdk, Trip12 and Usp7 mutants. ApoE, an Alzheimer’s disease risk gene, has been associated with sex differences in ASD57,58,59,60.

In summary, mutations in several genes associated with ASD elicit sex-specific effects, with generally higher effect sizes in female mutant mice.

Restricted cerebellar effects in mutants linked with ASD

To investigate whether mutations associated with ASD lead to changes in other brain regions, we analysed gene expression in the cerebellum (Fig. 6a and Supplementary Note), a region associated with motor coordination and cognitive functions that has been shown to be affected in individuals who have autism61,62.

Fig. 6: Multi-omics sequencing of perinatal cerebellar tissue.

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a, UMAP of integrated snRNA-seq and snATAC-seq data from male P4 cerebellum, coloured by cell-type annotation. MLI, molecular layer interneuron; PLI, Purkinje layer interneuron; UBC, unipolar brush cell. b, Changes in cell-type abundance across mutants. The arrowheads represent relative percentage differences versus colony-matched controls (WT). Statistical significance was assessed in edgeR using a two-tailed quasi-likelihood F-test. Nominal P values are shown. n = 3 per genotype. c, Number of DEGs (adjusted P < 0.05) across mutants and cell types. All mutants denotes mutants analysed collectively compared with colony-matched controls. Total DEGs per cell type per time point, counting repeated DEGs once, are shown. DEGs (adjusted P < 0.05) identified by comparing each mutant to all other mutants combined is also displayed (right). Statistical significance was assessed using two-sided Wald test in DESeq2 with Benjamini–Hochberg correction for multiple testing. d, Overlap of DEGs between the Bckdk-mutant cortex and cerebellum at P4 (1,023 shared genes between indicated cell types). e, Schematic of gait patterns in juvenile (P13–15) Bckdk and Kmt5b mutants and littermate controls. f, Altered gait in Bckdk−/− mice, including reduced normalized stride and step length of forepaws and hindpaws (top), reduced stride speed and mild forepaw coordination deficits assessed as cross-correlation during walking (bottom; n = 3 (Bckdk+/+), 4 (Bckdk−/−) and 7 (Kmt5b+/+ and Kmt5b+/−). Data are mean ± s.e.m.; one-way ANOVA with Sidak’s multiple comparison test. Statistically significant values are shown in red. g, Abnormal hindpaw positioning during stride cycles in Bckdk−/− mice versus littermate controls. Scale bar, 1 cm.

Source data

First, we assessed cerebellar cell-type abundance to assess lineage progression in mutants. Compared with WT, only Cul3 mutants exhibited differences, with a trend towards increased Golgi cell and reduced Granule cell numbers (Fig. 6b).

We then investigated cerebellar gene expression at P4. Pseudobulk analysis revealed no shared transcriptional changes across all mutants. However, analysing each mutant separately revealed widespread gene expression changes in Bckdk mutants, affecting glial cells as well as excitatory and inhibitory neurons (Fig. 6c and Supplementary Tables 3 and 4). To exclude potential control-specific effects, we compared each mutant against all other mutants collectively (Fig. 6c, right). This confirmed the distinct transcriptional profile of Bckdk mutants, with limited changes in the others. Transcripts affected in the cerebellum of Bckdk mutants largely matched those altered in the cortex, suggesting consistent pathway disruption across regions (Fig. 6d).

GSEA confirmed significant shifts in amino acid and metabolic pathways in Bckdk mutants, particularly affecting neurons and, more specifically, Golgi cells, consistent with the cerebral cortex. Other genotypes displayed only few changes, with some showing variations in synaptic protein expression (Supplementary Table 6).

Given the role of the cerebellum in motor coordination, we examined motor behaviour in juvenile (P13–15) Bckdk−/− mice and compared them with Kmt5b-mutant mice (Fig. 6e), which exhibited only one DEG in the cerebellum. Bckdk mutants had motor impairments, including reduced speed, shorter stride and step lengths, and a trend towards reduced cross-correlation of forepaw movements during the walking phase (Fig. 6f and Supplementary Videos 2 and 3). These mice also showed abnormal hindpaw positioning during stride cycles compared with WT littermates (Fig. 6g). By contrast, Kmt5b mutants showed no motor behaviour abnormalities at the same age.

Overall, mutations linked with ASD did not broadly alter cerebellar gene expression at P4, except in Bckdk mutants, which also showed motor deficits at this stage. In the cerebellum, Bckdk mutation effects mirror those in the cerebral cortex, predominantly influencing metabolic and synaptic functions in neuronal cells.

Discussion

By testing multiple ASD models across development, our study highlights the dynamic nature of ASD-related changes and outlines a coherent trajectory in which diverse mutations linked with ASD impinge on common, stage-bound programs, such as delayed RG progression and early postnatal downregulation of ion channel or synaptic modules, while preserving genotype-associated signatures that intensify with maturation. Network localization confirms that convergence is strongest within early development but weakens by P14. Functionally, transcriptome–electrophysiology coupling and minimal conductance modelling suggest that small shifts in ion channel expression are sufficient to produce a diverse range of phenotypes. Collectively, our results motivate stage-aware and sex-aware intervention strategies, while also raising the question of whether intervention targeting shared early developmental changes can redirect later disorder trajectories (Supplementary Discussion).

Methods

Animals

All animals used in this study were approved by the Institutional Animal Care and Use Committee at ISTA and by the Bundesministerium für Bildung, Wissenschaft und Forschung, Austria (approval numbers BMWFW−66.018/0008-WF/II/3b/2014, BMWFW-66.018/0012-WF/V/3b/2015, BMWFW-66.018/0012-WF/V/3b/2017, BMWFW-66.018/0015-V/3b/2019, BMWFW-66.018/0032-V/3b/2019, BMBWF-V/3b/2020-0.342.159, BMBWF-V/3b/2020-0.148.791, BMBWF-V/3b/2021-0.291.172, BMBWF-V/3b/2021-0.291.177, BMBWF-V/3b/2022-0.292.788 and BMBWF-V/3b/2022-0.121.445). All our mutant lines were maintained in a C57BL/6J background. To maintain this background, colonies are continuously refreshed by mating mutants with C57BL/6J animals drawn from a continuously refreshed central C57BL/6J colony. All experiments were performed on mice ranging from E14.5 to P4 and P14. Embryonic time points were determined by plug checks after timed matings, defining E0.5 as the morning post-coitum. Animals were kept in the Preclinical Facility at ISTA, housed in commercially available individually ventilated cages under defined standard laboratory conditions (room temperature 22 ± 1 °C, relative humidity 55 ± 10%) on a 12-h light–dark cycle (lights on at 07:00). Animals were housed in groups of 3–4 animals per cage, with food and water available ad libitum. Experiments were carried out under specific pathogen-free conditions, and the health status of the mouse lines was routinely checked by a veterinarian. All transgenic mouse lines were backcrossed into a C57BL/6J background a minimum of two times. Both females and males were used for experiments.

Generation of constitutive heterozygous mouse lines

Constitutive heterozygous mice (that is, Ash1l+/−, Kdm6b+/−, Kmt5b+/−, Trip12+/−, Usp7+/− and Wac+/−) were generated within the Transgenic Unit at IST Austria in a C57BL/6J background by zygote electroporation of the Cas9 protein (634621, TakaraBio) and custom-made sgRNAs. To generate Ash1l+/− mice, one sgRNA (CAATCACCATTCCGCTATCC AGG) targeting exon 12 of the Ash1l gene was used. For Kdm6b+/−, two sgRNAs (CCCAAGGACCCGAGGTGATA GGG and TACGTATGAGGAGCGAACCC TGG) targeting intronic regions flanking exon 19 of the Kdm6b gene were used. For Kmt5b+/−, two sgRNAs (CCCTCACGACCCTACTACTG TGG and TTTACGTCTCAAGTCACACT GGG) targeting intronic regions flanking exon 9 of the Kmt5b gene were used. Similarly, to generate Trip12+/− mice, two sgRNAs (AGCGTATTCTCACTTTGATC TGG and TAAATTTGTAGAGACAGTTA AGG) targeting intronic regions flanking exon 3 of the Trip12 gene were used. Similarly, for Usp7+/−, two sgRNAs (CTCTCCCGAACATTATGAAG AGG and TGCCACGTCTTATACACATT AGG) targeting intronic regions flanking exon 6 of the Usp7 gene were used. To generate Wac+/− mice, one sgRNA (ATGTCGGAGGCGCAGATGTA GGG) targeting exon 8 of the Wac gene was used.

Superovulation was performed to produce large numbers of zygotes. In brief, 3–4-week-old donor females (in-house) were hormone primed by intraperitoneal injection of 5 IU of pregnant mare serum gonadotropin. Of human chorionic gonadotropin, 5 IU was administered at 46–48 h after pregnant mare serum gonadotropin treatment, followed by 1:1 mating with stud males (in-house). Zygotes were isolated from the oviduct of mated females at 0.5 days post-coitum. M2 with hyaluronidase (MR-051-F, Sigma) was used to dissociate cumulus cells. Zygotes were then cultured in small drops of KSOM (MR-121-D, Sigma) under mineral oil (M-5310, Sigma) at 37 °C and 5% CO_2, until the electroporation. Electroporation of mouse embryos was performed similarly as previously described[63] with the adaptation of using Opti-MEM (31985062, Thermo Fisher) instead of PBS. Finally, F0 founder mice were genotyped using standard PCR amplification with primers 500 bp to 1 kb away from guide RNA-binding sites. F1 mice were generated by backcrossing founder mice with C57BL/6J animals. The F1 mice were again genotyped by PCR, and positive amplicons covering the deleted sequence were Sanger sequenced to confirm precise targeting. All transgenic mouse lines were back-crossed into a C57BL/6J background a minimum of two times, and predicted off-targets were checked. A list of oligonucleotides used is provided in Supplementary Table 1. The constitutive knockout mouse line (HnrnpU+/−) was obtained by mating HnrnpUWT/flox (Hnrnpu<tm1.1Tman>/J, strain: #032187, RRID:IMSR_JAX:032187) mice with a CMV-Cre line (B6.C-Tg(CMV-cre)1Cgn/J; strain #:006054, RRID:IMSR_JAX:006054) and subsequent backcrossings to C57BL/6J WT animals. Crossing of HnrnpUWT/flox mice with homozygous animals of the CMV-CreCre/Cre line results in ubiquitous deletion of one allele of HnrnpU spanning exons 4–14.

Single-nucleus multi-omics profiling of gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) from the same nucleus (snMultiome-seq)

Sample preparation and nuclei isolation

All the sequenced mutant animals were generated by mating a C57BL/6J female from the continuously refreshed central C57BL/6J colony with a mutant male maintained on the same C57BL/6J background (for example, C57BL/6J female × Ash1l+/− male = litter sequenced). The only exceptions were homozygous Bckdk mice whose heterozygous breeders were regularly refreshed with the same central C57BL/6J colony. Control animals were C57BL/6J × C57BL/6J offspring sourced from the same central colony. Dams used for mutant and control litters were drawn from the same central-colony cohorts and often littermates; at minimum, they were not separated by generations of inbreeding.

Brain tissue (forebrain and cerebellum) from E14.5, P4 and P14 mice was used for snMultiome-seq experiments. Animals coming from at least three separate litters were collected to avoid potential litter-specific biases. For the embryonic time point (that is, E14.5; n = 3 per genotype + sex), timed-pregnant females were decapitated, and embryos were rapidly dissected on ice. Cortical tissue was dissected in ice-cold PBS, meninges were removed, and forebrain structures were snap-frozen in liquid nitrogen and stored at −80 °C until nuclei isolation.

For postnatal time points (that is, P4 and P14; n = 3 per genotype + sex + time point), mice were decapitated, the brain was rapidly dissected on ice and subcortical structures were removed. The remaining forebrain (cortex + hippocampus) and cerebellum were separately snap-frozen in liquid nitrogen and stored at −80 °C until nuclei isolation.

Frozen tissue samples were dissociated to generate single-nuclei gene expression libraries following a modified protocol from 10X Genomics (Chromium Nuclei Isolation Kit CG000505, Rev A). Buffers were supplemented with RNase inhibitors (provided by 10X Genomics and 3335402001, Sigma). In brief, 200 µl of lysis buffer was added to the sample and dissociated with pestles provided by the vendor. After adding additional 300 µl of lysis buffer, the tissue was homogenized by pipette mixing and incubated on ice for 10 min. For the P14 forebrain, an additional 500 µl of EZ lysis buffer (NUC101, Sigma) was added to the samples to prevent clogging of the nuclei isolation columns. Afterwards, 500 µl of the cell suspension was loaded onto nuclei isolation columns and centrifuged for 20 s at 16,000 rcf at 4 °C, followed by vortexing the flow-through for 10 s and centrifugation for 3 min at 500 rcf at 4 °C in a swinging bucket rotor. The supernatant was removed, and the pellet was gently resuspended in 500 µl of debris removal buffer before centrifugation for 10 min at 700 rcf at 4 °C. After removal of the supernatant, the pellet was washed with 1 ml of wash buffer followed by centrifugation for 5 min at 500 rcf at 4 °C. For multiplexing, six samples were pooled after barcoding with CMOs (sequences of all CMOs used are provided in Supplementary Table 1). Each sample pellet was resuspended in a total of 180 µl of wash buffer containing 0.2 µM unique CMO anchor–barcode, vortexed for 3 s and incubated on ice for 5 min. CMO co-anchor was added to all samples of a multiplexing group with a final concentration of 0.2 µM, vortexed for 3 s and incubated on ice for 5 min. After adding 1 ml of wash buffer, samples were centrifuged for 5 min at 500 rcf at 4 °C. Depending on the tissue size, one (embryonic, P4 cortex and cerebellum) or two (P14 cortex) additional washing steps were performed. After removing the remaining supernatant, the pellet was resuspended in 50 µl of resuspension buffer. Nuclei were counted by flow cytometry (BD LSRFortessa) with Precision Count Beads (424902, BioLegend), and samples were diluted to obtain approximately 4,000 nuclei per sample, resulting in a total of 20,000 nuclei per lane of a 10X Chromium Controller (PN-1000204). GEMs were generated using Chromium Next GEM Chip J Single Cell kit (PN-1000230), Chromium Next GEM Single Cell Multiome GEM Kit A (PN-1000232) and Chromium Next GEM Single Cell Multiome Gel Bead Kit A (PN-1000231) as instructed by the manufacturer.

Library preparation and snMultiome sequencing

Library preparation was carried out according to the manufacturer’s protocol (Chromium Next GEM single cell multiome ATAC + gene expression, CG000338, Rev F) with the following modifications regarding the multiplexing reactions: during cDNA amplification, 0.2 µM ADT additive primer (5′-CCTTGGCACCCGAGAATTCC-3′) was added to the reaction. During cDNA cleanup, 120 µl of supernatant was saved to purify Hash-DNA using a 2.0× ratio of SPRIselect bead volume (B23319, Beckman Coulter) to sample volume. Hashtag libraries were generated using 5 ng of Hash-DNA, 1X KAPA2G Fast HotStart Readymix (7958935001, Roche) and unique combinations of 0.4 µM i5 and i7 indices (sequences are provided in Supplementary Table 1). Following amplification (98 °C for 2 min, 10 times (98 °C for 20 s, 54 °C for 30 s, 72 °C for 20 s), 72 °C for 5 min, 4 °C hold), libraries were cleaned up using a 1.2× ratio of SPRIselect bead volume (B23319, Beckman Coulter) to sample volume. Library quality and size were assessed with the High Sensitivity DNA Analysis Kit (5067-4626, Agilent Technologies) and the Bioanalyzer 2100. Libraries were sequenced by the Biomedical Sequencing Facility at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, using the Illumina NovaSeq 6000 platform. For gene expression, libraries were pooled in a ratio of 83% RNA:17% HTO and sequenced on Illumina NovaSeq6000 systems using the sequencing format: 28 cycles for read 1, 10 cycles for i7, 10 cycles for i5 and 90 cycles for read 2. For snATAC-seq, libraries were sequenced on Illumina NovaSeq6000 systems using the sequencing format: 50 cycles for read 1N; 8 cycles for i7, 24 cycles for i5 and 49 cycles for read 2N.

Single-nucleus muli-omics data analysis

Pre-processing and demultiplexing, initial analysis and clustering

Samples used for multi-omics analyses were demultiplexed, and the raw sequencing data were converted to FASTQ format using bcl2fastq (v2.20.0.422). Joint processing of the gene expression and ATAC modalities was performed using the cellranger-arc software (v2.0.0). The mm10 reference genome was used for the alignment (refdata-cellranger-arc-mm10-2020-A-2.0.0, obtained from https://cf.10xgenomics.com/supp/cell-arc/refdata-cellranger-arc-mm10-2020-A-2.0.0.tar.gz). To capture CMO reads, the gene expression modality was separately processed using the cellranger count pipeline (v7.0.0 and v7.1.0), aligning it against the mm10 reference genome (refdata-gex-mm10-2020-A, obtained from https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-mm10-2020-A.tar.gz). Each nucleus was assigned to the sample of origin as part of further demultiplexing, for which a Gaussian mixture model was fitted to the CMO count distributions. The probability that a given CMO read count was derived from background or positive signal was then inferred (as previously described[64]). The resulting multiome data was further analysed in R (v4.3.0) or Python (v3.10.5) using Seurat[65] (v4.3.0.9012) and Signac[66] (v1.10.0). For reproducibility, the analysis was implemented as targets[67] (v1.2.2) pipeline using renv (v1.0.2).

Pre-processing was done per 10X lane (that is, per multiplexing group of 4–6 samples). Peaks for the snATAC-seq modality were called for each run separately using MACS2 (v2.2.7.1) within Signac. A set of consensus peaks for the snATAC-seq analysis was created according to Signac. CMO demultiplexing information was taken from Cellranger output and added as metadata.

Doublets were identified using scDblFinder[68] (v1.14.0), separately for snRNA-seq and snATAC-seq modality. snRNA-seq parameters for the scDblFinder function were dims = 50, cluster = TRUE and knownUse = ‘discard’. snATAC-seq features were first aggregated (aggregateFeatures function, k = 500) and parameters for scDblFinder were dims = 50 and aggegateFeatures = TRUE. Cells identified as doublets based on CMO data were included as known doublets (knownDoublets parameter). Quality control metrics such as the percentage of mitochondrial reads, nucleosome signal and transcription start site enrichment were calculated per run. After removing known and estimated doublets, outlier thresholds for snRNA-seq read counts (nCount_RNA) or snATAC-seq read counts (nCount_ATAC) were derived using the isOutlier function from the scuttle package[69] (v1.10.2) with parameters nmads = 3, type = ‘both’ and log = TRUE. Nuclei falling outside these thresholds or with more than 5% of mitochondrial reads were removed.

Integration into combined analysis

Complete datasets for each time point or tissue were created by integrating individual Seurat objects representing separate 10X lanes (that is, multiplexing groups). snRNA-seq data were normalized using SCTransform70,71 from Seurat with parameter vst.flavor = ‘v2’. Integration of normalized snRNA-seq data was performed according to Seurat using the reciprocal principal component analysis (PCA) method with the following non-default parameters: SelectIntegrationFeatures(nfeatures = 2,500), FindIntegrationAnchors(reduction = ‘rpca’) and IntegrateData(dims = 1:50). Integration of snATAC-seq data was performed at the level of the LSI embedding using the IntegrateEmbeddings(dims.to.integrate = 1:50) function with the integration anchors derived from snRNA-seq data. Identification of cell types was performed using the combination of both modalities: snRNA-seq and snATAC-seq. A PCA embedding was derived for the integrated snRNA-seq data, the weighted nearest neighbour graph was calculated using the FindMultimodalNeighbors function with the integrated LSI and integrated PCA reductions as input and dims_atac = 2:50 and dims_rna = 1:50. The resulting weighted nearest neighbour graph was used for UMAP visualization of the dataset and identification of clusters using the FindClusters function with parameters algorithm = 3 (smart local moving algorithm), resolution = 0.5 (forebrain and cortex) or 0.3 (cerebellum). Cells designated as outliers per cell cluster were designated as such and removed using the Mahalanobis distance (more than 100) calculated from genes expressed in more than 80% of the cells.

Label transfer and cell-type annotation

To support cluster annotation, reference datasets for each tissue and time point were used (a list of respective references used is provided in Supplementary Table 1). Seurat objects were created from all datasets except where data were provided in this form. Normalization and dimensionality reduction were performed in alignment with the published studies where possible. Cell-type annotation of clusters was done in multiple steps. Label transfer from multiple published datasets (see above) was used for automated annotation of clusters using Seurat. Seurat’s FindMarkers was used to determine marker genes for each cluster. Cluster annotations were manually validated afterwards. Here information from different reference datasets, in addition to the expression of canonical marker genes, was validated. Clusters annotated as striatal cell types were excluded from further analyses due to sample preparation variabilities, that is, brain region adjacent to the forebrain, and the presence or absence thereof is highly variable. To reduce the complexity of downstream analyses, clusters belonging to the same cell-type annotation were merged. To avoid merging disparate clusters, differential expression (using FindMarkers of subgroups of clusters) was used as a reference.

Cell variability

Single-cell gene expression heterogeneity within each cell-type cluster and mutant was tested and visualized using coefficient of variation histogram plots and permutation testing. Permutation testing refers to the number of genes designated as significantly different between (1) cells of the same transgenic mouse line or the C57BL/6J WT mice, or (2) cells of different transgenic mouse lines or the C57BL/6J WT mice. Twelve cells were randomly sampled from the respective population (or populations) two times, and differences in expression values were obtained. Any difference value that was obtained from less than 10 cells with an expression value was discarded. Then, two such differences in expression value sets (either for (1) or (2) were compared with a t-test and deemed to have a high variability when their P value was below 0.05. This was repeated ten times. Furthermore, differences were also calculated between cell-type clusters of one transgenic mouse line and the C57BL/6J WT mice.

Differential abundance analysis

Differential compositional analysis of cell abundances between all mutants linked with ASD collectively and WT mice was conducted using the scCODA Python package[72] (v0.1.8), using Hamiltonian Monte Carlo sampling. The reference cell types used were as follows: layer I (Cajal–Retzius) neurons for the cerebral cortex at E14.5, CGE interneurons (interneurons CGE (Vip)) for the cerebral cortex at P4, interneurons Sncg (Vip) for the cerebral cortex at P14 and choroid plexus cells for the cerebellum at P4. The credibility of the effects was evaluated at two FDR thresholds: 0.05 and 0.01. Differential abundance of cell-type clusters across individual mutants linked with ASD was also tested with edgeR[73] (v3.42.4) using absolute cluster abundances in cells per sample as input. For cortical time points, the model included sex as a covariate.

Pseudotime analysis

To reconstruct the differentiation trajectory from RG to the cortical plate cells at E14.5, we created an embedding based on the transcriptomic profiles using the Python package Harmony[74] (harmonypy, v0.0.5) for batch correction. After that, Palantir[75] (v1.0.1) was used to generate a latent representation that preserves the pseudotime ordering of cells. Utilizing the first five components of this representation, the SimplePPT algorithm76,77 was applied to learn the principal tree that captures the differentiation process. Subsequently, the scFates tool[78] (v1.0.6) was used to map this principal tree back onto the constructed UMAP using Harmony-corrected PCA coordinates. Pseudotime values for each cell were assigned by projecting the cells onto the principal tree. The RG cell cluster was defined as the root of the trajectory, progressing towards cells of the cortical plate. The pseudotime analysis included RG cells, subventricular zone migrating cells, intermediate progenitors and cortical plate cells.

Pseudobulk differential gene expression analysis

Differential gene expression between genotypes within cell-type clusters was performed on pseudobulk data. Either (1) all female mice only, (2) all male mice only, or (3) all female and male mice without sex indicators were used for testing between one transgenic mouse line and the C57BL/6J WT mice.

Pseudobulk profiles per cluster and per sample were derived using the Libra package (v1.0.0). Mitochondrial genes were removed, and only genes with at least 15 counts in at least two samples (single sex) or 30 counts in at least two samples (pooled sexes) were included. DESeq2 (ref. [79]) (v1.40.2) was used to test for differential gene expression using test = ‘Wald’, fitType = ‘local’ and an adjusted P value threshold of 0.05. The model included sex as a covariate for the analysis of the forebrain and cortex. Shrunken fold changes for downstream rank-based analyses were calculated using the apeglm method[80] implemented in the lfcShrink function from DESeq2. The tests involved three comparison types: (1) comparing each single mutant genotype to WT controls, (2) comparing each of the single mutant genotypes to all other mutant genotypes (excluding WT controls), and (3) comparing the WT mice to all mutants collectively. Composite differential expression analysis was carried out by comparing gene names designated as differently expressed (DESeq2) and organizing these as Venn diagrams. Furthermore, a leave-one-out cross-validation strategy, which systematically removed one of the C57BL/6J WT mice from the DESeq2 comparison, was used to investigate whether any of the WT mice were a strong outlier. To assess differences between female and male WT controls, WT controls were also compared with a similar statistical setup as described above using pseudobulk differential gene expression analysis. Here, instead of adjusted P values, nominal P values were used to assess the extent of the sex differences with a critical α-threshold of 0.05.

To evaluate the statistical significance of overlap in differential gene expression patterns between mutants, each mutant pair was compared against distinct sets of C57BL/6J control samples. Control samples (including both male and female samples) were randomly divided into two disjoint groups. For each mutant pair, differential expression analysis was performed in DESeq2 using the same model as described above (‘~group + sex’), except for Ptchd1 mutants, where all samples were male and the model ‘~group’ was used. Comparison of each mutant to a distinct control set ensured statistical independence between the two comparisons. Overlap between each mutant pair was assessed by comparing the top 300 most DEGs (ranked by P value), and the observed-to-expected ratio and statistical significance of the overlap were determined using Fisher’s exact test. Only genes with a minimal expression level (baseMean ≥ 1) in at least one mutant of a given pair were included in the analysis.

To compare the extent of transcriptional differences across mutants and cell types, we used a downsampling-based differential expression analysis aimed at balancing statistical power. Pseudobulk profiles were generated for each genotype–cell type combination and downsampled to 10,000 molecules per profile. Profiles with fewer than 95% of this target were discarded, and only combinations with at least five valid samples were included in the analysis. Differential expression testing was performed with DESeq2 using the design formula (‘~genotype + sex’). To mitigate stochastic effects from subsampling, we conducted 100 independent downsampling iterations, and for each mutant–cell type comparison, we reported the average number of genes with an adjusted P < 0.05.

Gene Ontology analysis and GSEA

Gene Ontology term analysis was performed using the enrichGO function from the clusterProfiler package[81] (v4.8.2) using the list of DEGs using a cut-off P < 0.01 and cut-off q < 0.05.

GSEA was performed as previously described[82], using the gseGO function from the clusterProfiler package[81] (v4.8.2) with default parameters except minGSSize = 100 and ont = ‘ALL’ using a cut-off P < 0.05. All genes were used as input for clusterProfiler and the list of genes was sorted by log_2FC using shrunk fold changes using the lfcShrink function. Enrichment was assessed using a Kolmogorov–Smirnov-like statistic with permutation-based significance testing. Benjamini–Hochberg FDR correction of P values was used for both analyses.

To remove redundancy in enriched gene ontologies, the simplify-method function from clusterProfiler was used to visualize enriched gene sets (Figs. 2b and 5b).

We performed Gene Ontology term enrichment analysis on genes associated with differential accessibility at the gene-body level. DAR (P < 0.01) per cell type were enriched with Gene Ontology terms using the clusterProfiler R package (using the Biological Process ontology, using a cut-off P < 0.01 and a cut-off q < 0.05. Enriched terms were illustrated using the enrichplot R package (v1.20.1) with the dotplot function.

Constructing enrichment maps using Cytoscape

To visualize the results of enriched gene sets and pathways for mutants linked with ASD, the EnrichmentMap plugin[83] (v3.4.0) for Cytoscape (v3.10.1) was used. Enriched gene sets for excitatory neurons were loaded into the EnrichmentMap plugin with an FDR cut-off value of 0.1. Gene sets were not filtered, setting no minimum experimental cut-off (Extended Data Fig. 4).

Summarizing of Gene Ontology terms using REVIGO

REVIGO[84] was used to consolidate GSEA terms into a reduced set of parent terms. For this, a comprehensive list of all GSEA terms, irrespective of time point or genotype, was constructed and uploaded. The parameters ‘small (0.5)’ and ‘remove obsolete GO terms’ were used, resulting in robust and comprehensive clustering. Subsequently, all detected GSEA terms were mapped to their new parents. The parent terms were organized in subclusters of circular heatmaps, which were implemented with the circlize (v0.4.15) R package[85]. Representative terms were highlighted in the corresponding figures showing circular heatmaps.

Synaptic function and localization analysis using SynGO

Synaptic Gene Ontology (SynGO)[86] (v1.2) was used to identify types of known synaptic proteins (in presynaptic and postsynaptic compartments) and different functional classes. Lists of DEGs of the respective mutants linked with ASD of P4 and P14 excitatory neurons were used as input to investigate overrepresented synaptic terms. Biological processes were selected for enrichment.

Differential accessibility

We conducted pseudobulk testing using the DESeq2 R package[79] (v1.34.0) to identify peaks that differ between ASD mutant and WT mice. Given the low signal-to-noise ratio in scATAC-seq experiments[87] and the high number of comparisons, we used an uncorrected P value threshold of 0.001 to include peaks in the list of DARs. Motif enrichment analysis was performed on a set of downregulated peaks using the SnapATAC2 Python package (v2.3.0), with binomial testing and Benjamini–Hochberg FDR correction of P values. We tested for correlations between the gene-body differential accessibility P values and the corresponding gene lengths. Our analysis revealed no significant association (<0.1), demonstrating that gene length is not a systematic confounding factor.

Network-based analyses

Generation of reference protein–protein interaction network

Several sources were merged to generate the protein–protein interaction network (PPI) used in this study. In brief, interaction data for mouse was downloaded from MIPPIE[88], IntAct[89], STRING[90], Reactome[91] and BioGRID[92]. For STRING, only physical interactions were used. The BioGRID database was downloaded separately, as newer releases were not integrated in the MIPPIE database. In addition, we incorporated mouse-brain-specific interaction data from Skinnider et al.[93] to ensure coverage of target tissue-specific interactions. The resulting PPI comprises 18,921 proteins with 417,767 undirected interactions.

Network colocalization of DEGs

Raw count data were log normalized using a library size normalization \(\log (\frac{x}{\mathrm{libary}}\times 10,000+1)\). DEGs from pseudobulk differential gene expression analysis of all 11 mutant lines were evaluated for similarity in their underlying PPIs using a modified version of a previously published method[94] applied to our reference PPI network. Key modifications include the use of a symmetric random walk, which inherently corrects for degree distribution[95], and an extension of the original tied diffusion algorithm[96] to handle more than two random walks. In brief, for each genotype and time point, we performed symmetric random walks using the pseudobulk DEGs as seed nodes. For each walk, network nodes were ranked in descending order according to their visiting probability. An aggregated rank for each node was then computed as the geometric mean of its ranks across all walks, referred to as the rank product[97]. Only nodes with a rank product below 3,000 were retained for further analysis. This approach builds on NetColoc, originally designed to compare pairs of diseases using the tied diffusion algorithm introduced by Paull et al.[96] to identify shared components of molecular networks between two sets of molecules. Although NetColoc was only demonstrated for pairwise comparisons, we argue that the method is not inherently restricted to two sets, as described below. In brief, TieDIE begins by performing a random walk on a common reference interaction network for each set of molecules of interest. Each random walk assigns weights to network nodes according to their proximity to the respective molecules. The shared neighbourhood among the resulting walks is then determined by applying a threshold to a weighting function \(z=f(R)\), where R denotes the set of random walk outputs and \(f(.)\) is an arbitrary function that prioritizes nodes with consistently high weights across walks. To accommodate an arbitrary number of random walks, we adopted a previously published ranking approach developed to identify highly ranked DEGs across multiple differential expression analyses[97]. Specifically, our ranking function is the rank product across all random walks in R, calculated as the geometric mean of the rank for each node across the walks.

Computing gene systems from colocalized genes

Colocalized gene sets were grouped into clusters of genes with similar molecular functions, referred to as gene systems, using HiDeF[98]. We applied HiDeF to cluster colocalized genes separately for each time point. To focus on more specific systems, we excluded clusters containing more than 250 genes, as such large clusters are often nonspecific artefacts of the hierarchical clustering used by HiDeF. We further removed clusters with a persistence score below 20 or without any overlap with the corresponding seed gene sets. For the remaining clusters, we assessed functional enrichment in GO_BP_2023 (Biological Processes), GO_MF_2023 (Molecular Function), GO_CC_2023 (Cellular Component) and MGI_Mammalian_Phenotype_Level_4_2024 using the ENRICHR API via gseapy[99]. To avoid redundant gene systems across time points, we merged clusters based on their enrichment profiles using hierarchical clustering. The similarity between two clusters was calculated as the jaccard index of the tokenized enrichment terms. If no enrichment was available for either cluster, similarity was computed from the overlap with genes in the system.

Gene system–genotype associations

Associations between gene systems and genotypes were assessed using a regression-based method described in Jin et al.[100]. In brief, we first filtered the gene systems to retain only those with at least four genes present in our snRNA-seq dataset, removing any system that fell below this threshold after excluding genes absent from the data. For each time point, we then assessed the association between gene systems and genotypes by fitting regression models of the genotype variables on the module eigengene scores of each system, calculated per cell in the corresponding subset of the snRNA-seq data. The full model was fit in statsmodels (v0.14.3) using the WT as reference, and is specified as follows:

module eigengene score ~ genotype + nFeature_RNA

The resulting regression coefficients represent the associations between each genotype and gene system relative to WT. To further reduce the number of systems for visualization, we retained only those that (1) were associated with more than 20% of genotype–time point combinations, and (2) exhibited substantial variation across genotypes and time points, defined as having a maximum absolute difference from the median greater than 0.2 across all combinations (genotype + time point).

Network overlap computation

We computed pairwise gene set overlaps on the PPI as introduced in Menche et al.[101]. Gene sets were defined for each genotype and time point by ranking genes within the PPI network according to their absolute log_2FC values from pseudobulk differential expression analysis. Excitatory and inhibitory neurons were analysed separately. Network overlap analyses were then performed for all pairs of genotype–time point combinations following Menche et al.[101]. For each comparison, overlaps of 1,000 randomly drawn gene set pairs were computed to generate a reference distribution. To ensure comparability, we implemented a degree-matched randomization procedure by binning PPI nodes into 10 equally sized bins according to their degree and drawing random genes from these bins based on the degree distribution of the actual gene sets. Statistical significance of observed overlaps was assessed by computing empirical P values, defined as the proportion of random overlaps more extreme than the observed overlap. Resulting P values were corrected for multiple testing using the Benjamini–Hochberg procedure as implemented in the statsmodels package[102] (v0.14.3). Coefficients different from zero were assessed using two-sided t-tests with Benjamini–Hochberg correction. Coefficients with adjusted P < 0.1 were considered significant and indicated with a black outline in the plot.

Bulk RNA-seq

Sample preparation

Brain tissue from Bckdk−/−, Cul3+/−, HnrnpU+/− and C57BL/6J colony-matched controls (n = 3 per sex + genotype) across time points (E14.5, P4 and P14), as well as from E16.5 HnrnpU and WT littermate mice (n = 3 per sex + genotype) were used for the bulk RNA-seq experiment. Animals coming from at least three separate litters were collected to avoid potential litter-specific biases. Timed-pregnant females were decapitated, and embryos were rapidly dissected on ice. Cortical tissue was dissected in ice-cold PBS, for embryonic tissue, the meninges were removed, and the tissue (whole brain for E16.5 HnrnpU animals, forebrain for mutants linked with ASD at E14.5, P4 and P14) was snap-frozen in liquid nitrogen and stored at −80 °C. RNA extraction for the E16.5 HnrnpU dataset was performed using TRIzol–chloroform extractions as described in the Methods section for RNA isolation. For the dataset using multiple mutants linked with ASD at E14.5, P4 and P14, the RNAeasy Mini RNA isolation kit (74104, Qiagen) was used according to the manufacturer’s protocol. One microgram of RNA obtained was used for cDNA library preparation using the NEBNext Ultra II Directional RNA Library Prep kit (E7765, NEB) with unique dual indexing primers (NEBNext Multiplex Oligos for Illumina, E6440S, NEB) and poly(A) selection with the NEBNext Poly(A) mRNA Magnetic Isolation Module (E7490, NEB), according to the manufacturer’s protocol. RNA quality, library quality and size were assessed with the RNA 6000 Nano Kit (5067-1511, Agilent) and High Sensitivity DNA Analysis Kit (5067-4626, Agilent) and the Bioanalyzer 2100. Libraries were sequenced on the Illumina NextSeq 2000 and NovaSeq X platforms by the Vienna Bio Center NGS facility.

Pre-processing and analysis of RNA-seq

For the analysis of E16.5 HnrnpU mutant and WT animals, demultiplexed FASTQ files were filtered and trimmed using Cutadapt[103] (v4.1) and aligned to the mm10 reference genome using STAR[104]. Similarly, this method was used to obtain read counts. All further downstream analyses were performed in R. The same pipeline used for pseudobulk DGE analysis of the snRNA dataset as described above was used. However, the pseudobulking step was omitted, and only the mutant genotype was compared with WT littermate mice. One sample (HET genotype, female) was removed due to an increased number (approximately 10%) of duplicated sequences. For the analysis of mutants linked with ASD (Bckdk−/−, Cul3+/− and HnrnpU+/−) and C57BL/6J colony-matched controls, data were analysed using R (v4.5.0). Demultiplexed FASTQ files were filtered and trimmed using Cutadapt[103] (v4.1). Samples were aligned to the GRCm39 reference genome using STAR (v2.7.11b), and gene-level counts were obtained using FeatureCounts (v2.1.1)[105]. Mutant samples were compared with WT C57BL/6J colony-matched controls. Unwanted variation was corrected using the RUVSeq package (v1.42.0)[106]. To define empirical control genes, an initial DESeq2 analysis was performed, and all genes were ranked by P value. The top 5,000 DEGs were excluded, and the remaining genes were used as empirical controls for RUV (remove unwanted variation) correction. The value of k was selected as the lowest number that effectively reduced unwanted variation, based on visual inspection of correction effects in the PCA space, following the recommendations of Risso et al.[106].

RNA isolation and quantitative real-time PCR

Brain tissue from male and female Ash1l+/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Trip12+/−, Usp7+/− and Wac+/− and their WT littermates were used to validate the expression of targeted genes. For RNA extraction of cortical samples, 700 µl TRIzol (Invitrogen) and 150 µl chloroform (Sigma) were used for homogenization, followed by centrifugation at 12,000g for 15 min at 4 °C. For cerebellar samples, 600 µl TRIzol and 130 µl chloroform were used.

The upper aqueous phase was transferred to a new tube, and 1.5 volumes of 100% ethanol (EtOH) were added. RNA was extracted using Zymo-Spin IC columns (Zymo Research). The aqueous phase–ethanol mixture was loaded onto the column and washed with 400 µl of 70% EtOH following treatment with RQ1 DNAseI (Promega; 5 µl + 5 µl reaction buffer + 40 µl 70% EtOH) for 15 min at room temperature. After two washes with 70% EtOH, the sample was eluted from the column with DEPC-treated H_2O. RNA concentration was measured using the NanoDrop spectrophotometer (Thermo Scientific). Of RNA, 1 µg was used for cDNA preparation with the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher). cDNA was diluted 1:5 and used for quantitative PCR analysis with Lightcycler 480 Sybr green master mix (Roche) on a Real-Time PCR Roche Lightcycler 480 machine. Samples were run in triplicates for target genes, and a list of all oligonucleotides used is provided in Supplementary Table 1. ΔCq expression levels (relative mRNA) were calculated upon normalization to housekeeper genes and plotted.

Cytometry, imaging and analysis

Sample preparation and imaging of EdU-injected and CUBIC-cleared brains

Timed-pregnant female mice were injected with 0.05 mg g−1 of 5-ethynyl-2′-deoxyuridine (EdU) at E14.5 and euthanized 6 h later; embryos were decapitated, rapidly dissected on ice and immersion fixed in 4% paraformaldehyde overnight at 4 °C, followed by a washing step in 1X PBS. Cells in S-phase that incorporated EdU were detected using click chemistry (BCK-EdU555IM100+IV-M, Baseclick). Embryonic heads were cleared using the CUBIC clearing protocol[107] with adaptations coupled with EdU staining. In brief, fixed, washed samples were incubated for 4 h at 37 °C on a rotating shaker in CUBIC-L/R1a in mH_2O. Samples were incubated in CUBIC-L/R1a solution for 48 h at 37 °C on a rotating shaker with a change of solution after 24 h. Afterwards, samples were washed with PBS and incubated overnight at 37 °C with PBS–0.2% Triton X-100–20% DMSO–0.3 M glycine. Before incubating the samples with the EdU reaction solution according to the manufacturer’s protocol overnight, samples were incubated for 2 h in PBS–0.2% Triton X-100 and briefly washed twice with PBS. After incubating with the EdU reaction, samples were again washed twice with PBS and 2 h in PBS–0.2% Triton X-100. To match the refractive index, samples were stepwise incubated for 2 h at 37 °C in ascending concentrations of CUBIC-R + (N) in mH_2O. In the case that embryonic heads were not fully transparent, they were incubated overnight in CUBIC-L/R1a at 37 °C on a rotating shaker, followed by stepwise refractive index (RI) matching. Cleared heads were imaged in oil (Gelest #PDM-7040, RI of 1.556; and J62592.AP, Thermo Fisher, RI of 1.467). Both oils were mixed to match the refractive index (RI ~ 1.52) of the cleared heads. Imaging was performed on a Zeiss Lightsheet 7 microscope equipped with dual-side illumination and two camera detection (PCO edge 4.2 sCMOS) using ZEN Black imaging software (v3.10) using 5×/0.1foc illumination and 5×/0.16 EC-Plan Neofluar detection objectives with the following settings: filter set: band pass 505-530/LP560/LP585, Zoom 1.4, lightsheet thickness of 7.76 µm, scaling xyz of 0.67 µm × 0.67 µm × 1.5 µm. Imaging was performed using single-side illumination to achieve optimal sample illumination and to prevent potential duplication of nuclei in the images. Whenever possible, both the left and right brain hemispheres were imaged, and damaged or incompletely cleared samples were excluded from the analysis. Images of EdU-stained, cleared embryo heads were analysed automatically using Zeiss Arivis Pro software (v4.2.0). A semi-automated analysis pipeline was optimized to count EdU-positive cells within one cortical hemisphere, defined as a region of interest, using the Blob Finder function implemented in Zeiss Arivis Pro (v4.2.0).

Sample preparation and analysis of EdU-injected brains by cytometry

Timed-pregnant female mice were injected with 0.05 mg g−1 EdU at E14.5 and euthanized 2 h later; embryos were rapidly dissected on ice. Cortical tissue was isolated in ice-cold HBSS (14175053, Thermo Fisher), meninges were removed and forebrain structures were dissociated using an established papain-DNAse protocol[108]. Following dissociation, S-phase cells that incorporated EdU were detected by click chemistry (BCK-EdU488FC50+IV-S, Baseclick). In brief, washed samples were fixed for 15 min at room temperature and permeabilized using the supplied 1X saponin-based permeabilization and wash buffer. Two washes (1% BSA in PBS, 300g for 10 min each) were performed between fixation and permeabilization. Following fixation and permeabilization, the click reaction was prepared according to the manufacturer’s protocol, and cells were incubated for 30 min at room temperature, protected from light. Samples were washed twice with supplied 1X saponin-based permeabilization and wash buffer and centrifuged at 300g for 10 min per wash. A control sample was processed identically up to the permeabilization step and used as negative control for cytometry analysis. Samples were analysed on a Beckman Coulter Cytoflex LX. Cell populations were gated based on forward scatter versus side scatter, and doublets were excluded via forward scatter area versus height gating. Fluorescence of EdU in gated single cells was measured in the B525-FITC-A channel (excitation of 488 nm and emission of 525/40 band pass. Total events (n = 50,000) were acquired per sample, and gating and analysis were performed using Beckman Coulter’s CytExpert software (v2.7).

RNAscope assay

Spatial gene expression analysis was performed using the RNAscope94 Multiplex Fluorescent v2 Assay (323110, ACD) kit including specific probes targeting Pdgfra and Slc1a3 mRNA (480661 and 430781-C3, ACD). For sample preparation, P4 mice were decapitated, and the brain was dissected rapidly on ice. The cerebellum was removed, and the brain was embedded in pre-cooled Tissue-Tek OCT Compound (Sakura Finetek) and stored at −80 °C until further use. Tissue was sliced at 20 μm at a CryoStar NX70 cryostat (Thermo Scientific) and directly mounted on SuperFrost Plus Adhesion slides (Epredia). Sections were stored at −80 °C until use. The assay was performed according to the instructions provided by the RNAscope Multiplex Fluorescent v2 Assay kit. In brief, sections were fixed in 4 °C cold 4% paraformaldehyde and further pre-treated using a H_2O_2 and protease digestion. After pre-treatment, sections were incubated with the probes targeting the mRNAs of interest. Probes were further hybridized to a cascade of signal amplification molecules, followed by a hybridization with TSA vivid fluorophore 520 for Pdgfra and TSA vivid fluorophore 570 for Slc1a3 (323271 and 323272, ACD). After the hybridization steps, sections were stained with a nuclear counterstain and mounted using ProLong Gold Antifade Mountant (P36930, Invitrogen). Images were acquired using a LSM900 inverted confocal microscope using 10× objective and analysed in ImageJ. To assess the number of astrocytes and OPCs, Slc1a3+ and Pdgfra+ cells were manually quantified in blinded images of coronal brain sections of similar size in at least three images per mouse.

Electrophysiology

Brain slices were obtained from P7 Bckdk−/−, HnrnpU+/−, Trip12+/−, Usp7+/−, littermate and colony-matched C57BL/6J control mice. Acute coronal slices (300 µm) were prepared from the primary somatosensory cortex. Animals were decapitated and whole brains were rapidly removed from the skull and sectioned using a VT 1200S vibratome (Leica Microsystems). Slices were then allowed to recover in in regular artificial cerebrospinal fluid in 1 mM Ca2+ at 35 °C for 10 min, followed by a cool-down to room temperature for at least 20 min in artificial cerebrospinal fluid, containing: 118 mM NaCl, 2.5 mM KCl, 1.5 mM MgSO_4 × 7H_2O, 1.25 mM NaH_2PO_4, 2 mM CaCl_2, 20 mM sucrose, 3 mM myo-inositol, 10 mM glucose and 26 mM NaHCO_3 (320 mOsm l−1, pH 7.35–7.40).

Patch pipettes (3–5 MΩ; Sutter Instrument) were pulled on a P-1000 puller (Sutter Instrument) and filled with the intracellular recording solution, containing: 120 mM K-gluconate, 20 mM KCl, 2 mM MgCl_2, 10 mM HEPES, 0.5 mM EGTA, 2 mM MgATP, 0.2 mM NaGTP, 23.4 mM sucrose and 0.3% w/v biocyin; adjusted to a pH of 7.35, and osmolarity adjusted to 302 mOsm l−1.

Excitability (current versus action potential frequency) was measured in current clamp mode by applying current steps from −10 to +50 pA in 5-pA increments (1-s duration and 8-s inter-sweep interval). Input resistance (Rin) was calculated as the linear regression of the current–voltage relationship from −10 to 0 pA. Membrane time constant tau was measured by an exponential fit of the initial voltage change in response to a −10-pA current injection. Membrane capacitance was calculated by dividing tau by Rin. Resting membrane potential was measured as the average voltage at the 0-pA current injection sweep. Action potential properties were measured in the first evoked action potential, with threshold potential measured at the onset of the steep rising phase. Action potential amplitude was measured as the difference between threshold and peak potential. Action potential full-width at half-maximum was measured as the time between rising and falling phase at half of the maximal amplitude. Action potential afterhyperpolarization was measured as the voltage 50 ms after the peak relative to the threshold potential. mEPSCs and mIPSCs were recorded for 3–5 min in voltage clamp mode at a holding potential of −60 mV and in the presence of 1 µM tetrodotoxin + 10 µM bicuculline methiodide (mEPSCs) or 1 µM tetrodotoxin + 10 µM DNQX + 50 µM D-AP5 (for mIPSCs). Access resistance (Ra) was monitored throughout all recordings, and recordings with Ra exceeding 20 MΩ were excluded. Miniature neurotransmission was manually analysed using Mini Analysis Program (Synaptosoft, v6.0.7). Passive and active membrane properties were analysed using Clampfit (v11.3.0.02). All recordings and analyses were performed with the experimenter blind to the animal experimental condition.

Minimal conductance-based model

To model the electrophysiological properties of WT neurons, we utilized the biophysical model of a layer II/III pyramidal neuron from the mouse visual cortex provided by the Allen Brain Institute (https://modeldb.science/184162). Membrane properties and morphology were modified to match the developmental stage of P7 and electrophysiological characteristics (Rin and membrane time constant) observed in our data. The soma and dendrites were scaled to 70% of their original radius and length, and the axon was reduced to 50% of its original dimensions. To match the observed frequency–current relationship, the membrane capacitance was set to 0.38× and the passive membrane conductance to 0.5× of the original values in their respective compartments. Reversal potentials were set to −75 mV for passive current, +30 mV for Na+ and −110 mV for K+ ions. Simulations were performed in Python 3 using the NEURON simulator[109]. Following the described parameter adjustments, the model exhibited a net input resistance of approximately 988 MΩ, a membrane time constant of 63 ms and a resting membrane potential of −61 mV, approximately matching the mean passive properties of a WT control neuron.

The model includes multiple ion channel types: persistent potassium (Kp), delayed rectifier potassium (Kt), A-type potassium (Kv3.1), Ca2+-activated potassium (SK), non-inactivating potassium (M-channel), persistent sodium, transient sodium, hyperpolarization-activated cyclic nucleotide-gated (Ih), Im channels, high-voltage-activated calcium and low-voltage-activated calcium channels.

Behavioural analysis of Bckdk-mutant and Kmt5b-mutant animals

Gait recordings

Bckdk-mutant and WT littermate animals (P13–15) were acclimated to the testing room 1 h before behavioural recording and were not handled before testing due to their young age. Animals were led to explore an empty open-field arena (50 × 50 × 40 cm) constructed from IR-transparent acrylic covered with a lid, creating a dark environment during testing. Animals were recorded from below using an IR-camera (Basler acA3088-57µm) ensuring continuous visibility of the paw of the animals. Video acquisition was performed with OBS Studio (v31.0.1). Each recording lasted a minimum of 15 min to capture a sufficient number of stride cycles. The experimenter was blind to the genotype during recordings. Pairs of control-mutant littermates were randomly tested in the morning and in the afternoon to control for circadian rhythm.

Analysis of mouse pose estimation and gait

Body parts of mice were tracked using SLEAP[110] (v1.4.1) by defining a SLEAP skeleton containing 14 body parts. Four are at the head of the mouse (nose, ear left, ear right and neck base), the four paws (forepaw left, forepaw right, hindpaw left and hindpaw right), at the centre and sides of the mouse body (spine centre, left side, right side) and three parts for the tail (tail base, tail middle and tail tip).

After annotating 364 frames of a single mouse across WT and mutant conditions, we trained both a centroid and a centred instance model using the top-down pipeline in SLEAP. In the centroid model, which detects the central body region, we used an input scaling of 0.5, an output stride of 2, and defined spine centre as the anchor point with σ = 2.5 px. All other network parameters were kept at their default values. The model was trained for 128 epochs with early stopping enabled (plateau patience = 32 epochs).

The centred instance model, which detects all body parts within a bounding box centred on the anchor point predicted by the centroid model, was configured with an input scaling of 1.0, output stride of 2, maximum stride of 32, number of filters of 32 and the filter rate of 2. We used a σ = 1.75 for the body parts. This network was trained for 500 epochs allowing for early stopping (plateau patience = 84 epochs).

Both models used a UNet backbone and were trained with an Adam optimizer (initial learning rate = 0.0001). Data augmentation included random rotations, horizontal flips and random size variations between a scale of 0.95 and 1.05. The validation dataset comprised 10% of the total annotated frames. Training was performed on a NVIDIA RTX 4080 SUPER GPU with 16 GB of VRAM.

The centroid model achieved a detection precision and recall of 1 on the validation set. The centred instance model reached a body-part recall of 0.992 and precision of 0.996, with an average pixel error of 2.59 px. To track the detected mouse poses, the default centroid tracker in SLEAP using the Hungarian method for instance matching in a frame window of size 5 was used.

To analyse mouse gait, we implemented mouse gait and posture metrics as previously described[111]. To extract strides, we first egocentrically aligned each mouse pose by centring the mouse to its central part (that is, spine centre) and rotating the coordinate system such that the neck base pointed upwards along the y axis. In this egocentrically aligned coordinate system, local maxima of the y location of paws (for example, forepaw left) were identified as the time point of foot-strike events, marking the start of the stance phase. Similarly, local minima determine the toe-off event, marking the beginning of the swing phase.

Before detecting extrema, paw trajectories were smoothed with a one-dimensional Gaussian filter (σ = 3 frames, corresponding to 0.05 s). Maxima and minima were then extracted using the scipy.signal.find_peaks function, requiring a minimum peak prominence of 11 px (0.6 cm).

Strides extracted from one paw part were validated by the corresponding opposite paw. A stride was considered valid if, during its stance phase, the opposite paw exhibited a foot-strike event. Both forepaw and hindpaw strides were validated and included in downstream stride analyses.

To focus on periods of active locomotion, valid strides were filtered to include only those occurring during forwards movement. Forwards motion was quantified by projecting the displacement vector (instantaneous speed) of the mouse onto its body axis (defined by the unit vector from spine centre to neck base). This resulting forwards speed was smoothed using a Gaussian kernel (σ = 60 frames; 1 s) and thresholded with 0.5 cm s−1 to define periods of forwards movement. Only strides occurring during forwards movement were used for gait metric computation. All raw stride, step and speed metrics were normalized to the body length of the mouse, defined by the distance from tail base to nose. All resulting stride metrics were averaged per movie.

Stride extraction and metric computation were implemented using the TadPose library[112] (v0.4.0), a Python library for the analysis of SLEAP pose-estimation data. Mouse forwards movement was calculated in custom Python Jupyter notebook using the scipy library (v0.15.3). Left and right forepaws and hindpaws were analysed separately but averaged to obtain overall gait metrics distinguishing between forepaw and hindpaw movements.

Statistics

Information on the statistical analyses is provided in the corresponding Methods sections. Blinding and randomization were not applied unless otherwise specified. Each n value refers to the number of biological replicates (that is, animals or cells) used per condition.

Reporting summary

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

Data availability

Single-nucleus multiomics data are available from the Gene Expression Omnibus (GSE328363). The mm10 reference genome was used for the alignment (refdata-cellranger-arc-mm10-2020-A-2.0.0, obtained from https://cf.10xgenomics.com/supp/cell-arc/refdata-cellranger-arc-mm10-2020-A-2.0.0.tar.gz). Single-cell data can be accessed and visualized through a CELLxGENE database (https://adameykolab.hifo.meduniwien.ac.at/cellxgene_public/filecrawl/.2026_Nature_Schwarz). Source data are provided with this paper.

Code availability

Scripts and analyses that support the main findings of this study are accessible in a GitHub repository (https://git.ista.ac.at/research-sofware/mouseome).

References

1. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

2. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

3. Pinto, D. et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94, 677–694 (2014).

4. Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).

5. Wamsley, B. et al. Molecular cascades and cell type-specific signatures in ASD revealed by single-cell genomics. Science 384, eadh2602 (2024).

6. McGinnis, C. S. et al. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16, 619–626 (2019).

7. Di Bella, D. J. et al. Molecular logic of cellular diversification in the mouse cerebral cortex. Nature 595, 554–559 (2021).

8. Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241.e26 (2021).

9. Loo, L. et al. Single-cell transcriptomic analysis of mouse neocortical development. Nat. Commun. 10, 134 (2019).

10. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e20 (2020).

11. Li, Y. et al. Spliceosome-regulated RSRP1-dependent NF-κB activation promotes the glioblastoma mesenchymal phenotype. Neuro-Oncology 23, 1693–1708 (2021).

12. Chen, C.-Y. et al. Galnt17 loss-of-function leads to developmental delay and abnormal coordination, activity, and social interactions with cerebellar vermis pathology. Dev. Biol. 490, 155–171 (2022).

13. Cardo, L. F., de la Fuente, D. C. & Li, M. Impaired neurogenesis and neural progenitor fate choice in a human stem cell model of SETBP1 disorder. Mol. Autism 14, 8 (2023).

14. Kurolap, A. et al. Bi-allelic variants in neuronal cell adhesion molecule cause a neurodevelopmental disorder characterized by developmental delay, hypotonia, neuropathy/spasticity. Am. J. Hum. Genet. 109, 518–532 (2022).

15. Deliu, E. et al. Haploinsufficiency of the intellectual disability gene SETD5 disturbs developmental gene expression and cognition. Nat. Neurosci. 21, 1717–1727 (2018).

16. Morandell, J. et al. Cul3 regulates cytoskeleton protein homeostasis and cell migration during a critical window of brain development. Nat. Commun. 12, 3058 (2021).

17. Novarino, G. et al. Mutations in BCKD-kinase lead to a potentially treatable form of autism with epilepsy. Science 338, 394–397 (2012).

18. Mastropasqua, F. et al. Deficiency of the heterogeneous nuclear ribonucleoprotein U locus leads to delayed hindbrain neurogenesis. Biol. Open 12, bio060113 (2023).

19. Caracci, M. O. et al. Wnt/β-catenin-dependent transcription in autism spectrum disorders. Front. Mol. Neurosci. 14, 764756 (2021).

20. Kalkman, H. O. A review of the evidence for the canonical Wnt pathway in autism spectrum disorders. Mol. Autism 3, 10 (2012).

21. Rubenstein, J. L. R. & Merzenich, M. M. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav. 2, 255–267 (2003).

22. Oliveira, B., Mitjans, M., Nitsche, M. A., Kuo, M.-F. & Ehrenreich, H. Excitation-inhibition dysbalance as predictor of autistic phenotypes. J. Psychiatr. Res. 104, 96–99 (2018).

23. Howell, B. W. & Smith, K. M. Synaptic structural protein dysfunction leads to altered excitation inhibition ratios in models of autism spectrum disorder. Pharmacol. Res. 139, 207–214 (2019).

24. Sohal, V. S. & Rubenstein, J. L. R. Excitation-inhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders. Mol. Psychiatry 24, 1248–1257 (2019).

25. Catterall, W. A. Dravet syndrome: a sodium channel interneuronopathy. Curr. Opin. Physiol. 2, 42–50 (2018).

26. Singh, N. A. et al. A role of SCN9A in human epilepsies, as a cause of febrile seizures and as a potential modifier of Dravet syndrome. PLoS Genet. 5, e1000649 (2009).

27. Guerrini, R. et al. Variable epilepsy phenotypes associated with a familial intragenic deletion of the SCN1A gene. Epilepsia 51, 2474–2477 (2010).

28. Charlier, C. et al. A pore mutation in a novel KQT-like potassium channel gene in an idiopathic epilepsy family. Nat. Genet. 18, 53–55 (1998).

29. Miceli, F. et al. A novel KCNQ3 mutation in familial epilepsy with focal seizures and intellectual disability. Epilepsia 56, e15–e20 (2015).

30. Fister, P., Soltirovska-Salamon, A., Debeljak, M. & Paro-Panjan, D. Benign familial neonatal convulsions caused by mutation in KCNQ3, exon 6: a European case. Eur. J. Paediatr. Neurol. 17, 308–310 (2013).

31. Genschik, P., Sumara, I. & Lechner, E. The emerging family of CULLIN3-RING ubiquitin ligases (CRL3s): cellular functions and disease implications. EMBO J. 32, 2307–2320 (2013).

32. Kajiro, M. et al. The E3 ubiquitin ligase activity of Trip12 is essential for mouse embryogenesis. PLoS ONE 6, 6–13 (2011).

33. Park, Y., Yoon, S. K. & Yoon, J.-B. TRIP12 functions as an E3 ubiquitin ligase of APP-BP1. Biochem. Biophys. Res. Commun. 374, 294–298 (2008).

34. Biswas, D. et al. Inhibiting BCKDK in triple negative breast cancer suppresses protein translation, impairs mitochondrial function, and potentiates doxorubicin cytotoxicity. Cell Death Discov. 7, 241 (2021).

35. Kumar, R. et al. Homozygous mutation of STXBP5L explains an autosomal recessive infantile-onset neurodegenerative disorder. Hum. Mol. Genet. 24, 2000–2010 (2015).

36. Kaeser, P. S., Deng, L., Fan, M. & Südhof, T. C. RIM genes differentially contribute to organizing presynaptic release sites. Proc. Natl Acad. Sci. USA 109, 11830–11835 (2012).

37. Wu, S. et al. The role of RIM in neurotransmitter release: promotion of synaptic vesicle docking, priming, and fusion. Front. Neurosci. 17, 1123561 (2023).

38. Medrihan, L. et al. Synapsin II desynchronizes neurotransmitter release at inhibitory synapses by interacting with presynaptic calcium channels. Nat. Commun. 4, 1512 (2013).

39. Chung, C., Barylko, B., Leitz, J., Liu, X. & Kavalali, E. T. Acute dynamin inhibition dissects synaptic vesicle recycling pathways that drive spontaneous and evoked neurotransmission. J. Neurosci. 30, 1363–1376 (2010).

40. Boumil, R. M. et al. A missense mutation in a highly conserved alternate exon of dynamin-1 causes epilepsy in fitful mice. PLoS Genet. 6, e1001046 (2010).

41. Chater, T. E. & Goda, Y. The role of AMPA receptors in postsynaptic mechanisms of synaptic plasticity. Front. Cell. Neurosci. 8, 401 (2014).

42. Newell-Litwa, K., Seong, E., Burmeister, M. & Faundez, V. Neuronal and non-neuronal functions of the AP-3 sorting machinery. J. Cell Sci. 120, 531–541 (2007).

43. Körber, C. et al. Modulation of presynaptic release probability by the vertebrate-specific protein mover. Neuron 87, 521–533 (2015).

44. Deguchi, Y. et al. mDia and ROCK mediate actin-dependent presynaptic remodeling regulating synaptic efficacy and anxiety. Cell Rep. 17, 2405–2417 (2016).

45. Havekes, R. et al. Gravin orchestrates protein kinase A and β2-adrenergic receptor signaling critical for synaptic plasticity and memory. J. Neurosci. 32, 18137–18149 (2012).

46. Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020).

47. Meléndez-Ramírez, C. et al. Dynamic landscape of chromatin accessibility and transcriptomic changes during differentiation of human embryonic stem cells into dopaminergic neurons. Sci. Rep. 11, 16977 (2021).

48. Bond, A. M., Bhalala, O. G. & Kessler, J. A. The dynamic role of bone morphogenetic proteins in neural stem cell fate and maturation. Dev. Neurobiol. 72, 1068–1084 (2012).

49. Segklia, A. et al. Bmp7 regulates the survival, proliferation, and neurogenic properties of neural progenitor cells during corticogenesis in the mouse. PLoS ONE 7, e34088 (2012).

50. Qin, S. & Zhang, C.-L. Role of Krüppel-like factor 4 in neurogenesis and radial neuronal migration in the developing cerebral cortex. Mol. Cell. Biol. 32, 4297–4305 (2012).

51. Luo, Z. et al. Zic2 is an enhancer-binding factor required for embryonic stem cell specification. Mol. Cell 57, 685–694 (2015).

52. Hong, W. et al. Krüppel-like factor 7 deficiency disrupts corpus callosum development and neuronal migration in the developing mouse cerebral cortex. Brain Pathol. 33, e13186 (2023).

53. Inoue, T., Ota, M., Ogawa, M., Mikoshiba, K. & Aruga, J. Zic1 and Zic3 regulate medial forebrain development through expansion of neuronal progenitors. J. Neurosci. 27, 5461–5473 (2007).

54. Johnson, C. A. & Ghashghaei, H. T. Sp2 regulates late neurogenic but not early expansive divisions of neural stem cells underlying population growth in the mouse cortex. Development 147, dev186056 (2020).

55. Zhang, Y. et al. Genetic evidence of gender difference in autism spectrum disorder supports the female-protective effect. Transl. Psychiatry 10, 4 (2020).

56. Ojanen, S. et al. Interneuronal GluK1 kainate receptors control maturation of GABAergic transmission and network synchrony in the hippocampus. Mol. Brain 16, 43 (2023).

57. Kissel, L. T. & Werling, D. M. Neural transcriptomic analysis of sex differences in autism spectrum disorder: current insights and future directions. Biol. Psychiatry 91, 53–60 (2022).

58. Hsu, M., Dedhia, M., Crusio, W. E. & Delprato, A. Sex differences in gene expression patterns associated with the APOE4 allele. F1000Research 8, 387 (2019).

59. Altmann, A., Tian, L., Henderson, V. W., Greicius, M. D. & Alzheimer’s Disease Neuroimaging Initiative Investigators. Sex modifies the APOE-related risk of developing Alzheimer disease. Ann. Neurol. 75, 563–573 (2014).

60. Holland, D., Desikan, R. S., Dale, A. M. & McEvoy, L. K. Higher rates of decline for women and apolipoprotein E ε4 carriers. Am. J. Neuroradiol. 34, 2287–2293 (2013).

61. Marko, M. K. et al. Behavioural and neural basis of anomalous motor learning in children with autism. Brain 138, 784–797 (2015).

62. Mosconi, M. W., Wang, Z., Schmitt, L. M., Tsai, P. & Sweeney, J. A. The role of cerebellar circuitry alterations in the pathophysiology of autism spectrum disorders. Front. Neurosci. 9, 296 (2015).

63. Remy, S. et al. Generation of gene-edited rats by delivery of CRISPR/Cas9 protein and donor DNA into intact zygotes using electroporation. Sci. Rep. 7, 16554 (2017).

64. Krausgruber, T. et al. Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation. Immunity 56, 289–306.e7 (2023).

65. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

66. Stuart, T., Srivastava, A., Madad, S., Lareau, C. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

67. Landau, W. M. The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing. J. Open Source Softw. 6, 2959 (2021).

68. Germain, P.-L., Lun, A., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research 10, 979 (2021).

69. McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Willis, Q. F. Scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

70. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

71. Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 23, 27 (2022).

72. Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. scCODA is a Bayesian model for compositional single-cell data analysis. Nat. Commun. 12, 6876 (2021).

73. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

74. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

75. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

76. Mao, Q., Yang, L., Wang, L., Goodison, S. & Sun, Y. SimplePPT: a simple principal tree algorithm. In Proc. 2015 SIAM International Conference on Data Mining (SDM) (eds Venkatasubramanian, S. & Ye, J.) 792–800 (Society for Industrial and Applied Mathematics, 2015).

77. Soldatov, R. et al. Spatiotemporal structure of cell fate decisions in murine neural crest. Science 364, eaas9536 (2019).

78. Faure, L., Soldatov, R., Kharchenko, P. V. & Adameyko, I. scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data. Bioinformatics 39, btac746 (2023).

79. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

80. Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2019).

81. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R Package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

82. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

83. Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment Map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5, e13984 (2010).

84. Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6, e21800 (2011).

85. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

86. Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234.e4 (2019).

87. Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).

88. Alanis-Lobato, G., Möllmann, J. S., Schaefer, M. H. & Andrade-Navarro, M. A. MIPPIE: the mouse integrated protein–protein interaction reference. Database 2020, baaa035 (2020).

89. Orchard, S. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358–D363 (2014).

90. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

91. Milacic, M. et al. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 52, D672–D678 (2024).

92. Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47, D529–D541 (2019).

93. Skinnider, M. A. et al. An atlas of protein-protein interactions across mouse tissues. Cell 184, 4073–4089.e17 (2021).

94. Rosenthal, S. B. et al. Mapping the common gene networks that underlie related diseases. Nat. Protoc. 18, 1745–1759 (2023).

95. Volchenkov, D. Random walks and flights over connected graphs and complex networks. Commun. Nonlinear Sci. Numer. Simul. 16, 21–55 (2011).

96. Paull, E. O. et al. Discovering causal pathways linking genomic events to transcriptional states using tied diffusion through interacting events (TieDIE). Bioinformatics 29, 2757–2764 (2013).

97. Breitling, R., Armengaud, P., Amtmann, A. & Herzyk, P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83–92 (2004).

98. Zheng, F. et al. HiDeF: identifying persistent structures in multiscale ‘omics data. Genome Biol. 22, 21 (2021).

99. Fang, Z., Liu, X. & Peltz, G. GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics 39, btac757 (2023).

100. Jin, X. et al. In vivo Perturb-seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370, eaaz6063 (2020).

101. Menche, J. et al. Uncovering disease-disease relationships through the incomplete human interactome. Science 347, 1257601 (2015).

102. Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python Sci. Conf. 2010 (2010).

103. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

104. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

105. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

106. Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).

107. Matsumoto, K. et al. Advanced CUBIC tissue clearing for whole-organ cell profiling. Nat. Protoc. 14, 3506–3537 (2019).

108. Amberg, N., Cheung, G. & Hippenmeyer, S. Protocol for sorting cells from mouse brains labeled with mosaic analysis with double markers by flow cytometry. STAR Protoc. 5, 102771 (2024).

109. Carnevale, N. T. & Hines, M. L. The NEURON Book (Cambridge Univ. Press, 2006).

110. Pereira, T. D. et al. SLEAP: a deep learning system for multi-animal pose tracking. Nat. Methods 19, 486–495 (2022).

111. Sheppard, K. et al. Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation. Cell Rep. 38, 110231 (2022).

112. Sommer, C. sommerc/tadpose: tadpose v0.4.0: Added: mouse gait analysis. Zenodo https://doi.org/10.5281/zenodo.17532669 (2025).

Acknowledgements

We thank F. Freeman, V. Voronin and M. Ladron de Guevara for technical assistance; A. Stichelberger and S. Liegenfeld for the management of our animal colony; M. Schunn, C. Gold and the Preclinical Facility team for technical assistance; C. Jansen and the Scientific Computing Facility for bioinformatics support and technical assistance; the Biomedical Sequencing Facility at CeMM for assistance with next-generation sequencing; and J. Lin and T. Krausgruber in the laboratory of C. Bock for support with flow cytometry; J. Kirchner for illustrating the multi-omics approach depicted in Fig. 1; and all members of the laboratory of G.N. for their support and discussions. This study was supported by the Scientific Service Units of ISTA through resources provided by the Imaging & Optics Facility and the Laboratory Support Facility. Bulk RNA-seq was performed by the Next Generation Sequencing Facility at Vienna BioCenter Core Facilities, member of the Vienna BioCenter.

Funding

This work was supported by a European Research Council Consolidator Grant (PR1028ERC02), by SFARI (PR1028SIM02) and by the Austrian Science Fund (PE1028W1232 and PR1028FG1803) to G.N. Open access funding provided by Institute of Science and Technology (IST Austria).

Author information

Authors and Affiliations

1. Institute of Science and Technology Austria, Klosterneuburg, Austria

Lena A. Schwarz, Christoph P. Dotter, Michela Lisi, Christoph Büschl, Bárbara Oliveira, Matteo Barel, Bernadette Basilico, Chaitanya Chintaluri, Sarah Gorkiewicz, Mohammad Goudarzi, Tereza Belinova, Gintarė Sendžikaitė, Satish Arcot Jayaram, Peter Koppensteiner, Christoph Sommer, Tim P. Vogels, Peter V. Kharchenko & Gaia Novarino

2. Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, Vienna, Austria

Sergey Isaev & Igor Adameyko

3. Ludwig Boltzmann Institute for Network Medicine, University of Vienna, Vienna, Austria

Daniel Malzl & Jörg Menche

4. Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna, Austria

Daniel Malzl & Jörg Menche

5. Department of Structural and Computational Biology, Center for Molecular Biology, University of Vienna, Vienna, Austria

Daniel Malzl & Jörg Menche

6. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

Daniel Malzl, Sabrina Ladstätter, Stephan Reichl, Jörg Menche & Christoph Bock

7. Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria

Stephan Reichl & Christoph Bock

8. Faculty of Mathematics, University of Vienna, Vienna, Austria

Jörg Menche

9. Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden

Igor Adameyko

Contributions

L.A.S. and C.P.D. designed the experimental and downstream analysis. L.A.S. conducted sample preparations and processing. L.A.S., G.N., C.P.D., S.I., M.L., D.M., C. Büschl, T.B., S.R., P.K., C.S. and P.V.K. analysed the data. S.L. optimized the method for multiplexing samples for combined snRNA-seq and snATAC-seq, and performed nuclei isolation together with L.A.S. B.O., M.B., B.B., S.G., M.G. and G.S. performed the experiments. C.C. generated a conductance-based model. S.A.J. generated the knockout mouse lines used in this paper. T.P.V. supervised the generation of a neuronal model. J.M. supervised the network-based analyses. I.A. supervised the analysis of the snATAC-seq data. C. Bock supervised the preparations for the multi-omics sequencing. G.N. conceived and supervised the study. G.N. wrote the paper together with L.A.S. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Gaia Novarino.

Ethics declarations

Competing interests

G.N. is a founder and scientific advisor of Neurolentech, Solgate and Cellgate. C. Bock is a cofounder and scientific advisor of Myllia Biotechnology and Neurolentech. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Cell type classifications and distribution across time points.

a-c, Average deviation from the median expression of individual cell types of the ASD mutants across time points. Each tile in the heatmap shows the average expression deviation of each replicate relative to the median expression of all samples per cell type expressed as a percentage. d-f, Relative proportion of cell types by genotype, time point, and sex. g-i, UMAP visualization of cells collected at each time point, showing expression levels of canonical molecular markers. j, Box plots show marker abundance of selected cell type markers across time points. Center lines represent medians, the box limits show the 25-75% interquartile range (IQR), whiskers extend to the furthest data points within 1.5 × IQR from the box edges. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y).

Extended Data Fig. 2 Quantification of cell-type abundance by snRNA-seq, in situ labeling, cytometry and EdU labeling.

a-c, scCODA analysis from individual time points quantifying compositional cell type changes in the aggregated ASD mutants compared to colony-matched controls. Center lines represent medians, the box limits show the 25−75% interquartile range (IQR), whiskers extend to the furthest data points within 1.5 × IQR from the box edges, and data points outside this range are represented as dots. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y). d, In situ hybridization for Pdgfra (OPCs; green), Slc1a3 (immature astrocytes; red) and Hoechst (nuclear counterstain; blue) in HnrnpU+/− and Kdm6b+/− animals compared to their littermate controls (n = 4 animals per condition, at least 3 cortical slices/ animal were analyzed; unpaired one-tailed t-test; unpaired one-tailed Mann-Whitney test for Pdgfra+ cells in Kdm6b+/−). d’, Representative image of in situ hybridization. e, EdU-labeling combined with CUBIC clearing showing increased numbers of proliferative cells in HnrnpU+/− animals (n = 6 (HnrnpU+/+), 5 (HnrnpU+/−), unpaired two-tailed t-test). f, Representative cell gates and EdU populations of Kdm6b mutant and littermate controls. Sequential gating was performed according to a defined population hierarchy. f’ EdU-labeling combined with cytometry analysis showing increased numbers of proliferative cells in Kdm6b+/− animals (n = 15 (Kdm6b+/+), 18 (Kdm6b+/−), unpaired two-tailed t-test). Center lines represent medians, the box limits show the 25-75% IQR, whiskers extend to the minimum and maximum values. g, Differential gene expression analysis between all mutants collectively and control RG1 cells highlights two significant downregulated genes, associated with cell cycle, neuronal maturation, and differentiation. Data are mean ± SEM in d and e. Scale bars: 100 µm in d’, 500 µm in e.

Source data

Extended Data Fig. 3 Analysis of the cellular composition and transcriptome correlations across mutants and datasets.

a, Scheme of the hierarchy between cell classes, subclasses, and types with examples. a’, Number of differentially expressed genes (p-adj. < 0.05) in cell subclasses highlights excitatory neurons at P4 as the main cell subclass sharing DEGs across mutants. “All mutants” refers to the mutants analyzed collectively compared to colony-matched controls. b, Overview of the number of DEGs combined across cell subclasses and time points, with repeated DEGs counted only once. c, Down-sampling differential expression analysis to match sequencing depth across mutants, cell types, and time points. “X” marks combinations that did not meet minimal coverage requirements. d, UpSet R plot showing the number of DEGs identified in the excitatory neuron lineage across time points and mutants, showing that many DEGs are identified multiple times over time. Representative overlaps are shown. e, Proportion of DEGs identified at P4 overlapping across a given number of mutants. f, Overlap of differential gene expression between mutants across time in excitatory and inhibitory neurons. Each mutant pair was compared vs. distinct colony-matched control sets to ensure independent contrasts. g, Heatmap of fold changes of a representative subset of the most shared DEGs across cell types at P4. DEGs were organized into functional groups such as cytoskeleton, channels/receptors, and synaptic processes. ASD risk genes are highlighted with their respective SFARI gene score. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y). h, SynGO analysis of DEGs found in the highest number of ASD mutants, affecting at least one cell type, reveals enrichment for genes encoding synaptic-related proteins at P4. i, Correlations of log_2FC of DEGs (p-adj. < 0.05) from the present snRNA-seq data (log_2FC_A) with bulk RNA sequencing (log_2FC_bulk) of Bckdk, Cul3 and HnrnpU mutants vs. colony-matched controls. Spearman correlation was applied when data violated normality assumptions (HnrnpU+/−at E14.5, Bckdk−/−, Cul3+/− and HnrnpU+/− at P4, Bckdk−/− at P14); otherwise, Pearson’s r was used (HnrnpU+/− at P14). Identity line is shown for reference. Overlap p-value obtained from one-tailed Fisher’s exact test (enrichment). Animals used for analyses: n = 6 per genotype and time point.

Source data

Extended Data Fig. 4 Network of the biological processes affected in ASD mutant mice.

a-c, Networks of GO terms identified in the excitatory neuronal lineage at E14.5 (a), P4 (b), and P14 (c). GO terms were automatically clustered using the EnrichmentMap plugin of Cytoscape. Nodes represent functionally related gene sets. Cluster size (circles) corresponds to the total number of gene sets. Clusters in grey indicate processes shared by at least 50% of the mutants. Clusters are colored to match the color of an ASD mutant and to indicate processes unique to that mutant. Shared processes are highlighted by colored contours representing the respective ASD mutants. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y).

Extended Data Fig. 5 Biological processes impacted in excitatory and inhibitory neurons at postnatal stages.

a, b, Circular heatmap of enriched biological processes at P4 and P14 in excitatory (a) and inhibitory neurons (b). Representative terms have been selected, and processes mentioned in the results are highlighted in bold. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y).

Extended Data Fig. 6 Biological processes affected in glial cells at postnatal stages, and neural lineage cells at E14.5.

a, b, Circular heatmap of enriched biological processes at P4 and P14 in glial cells (a) and neural lineage cells, including RG and CP cells at E14.5 (b). Representative terms have been selected, and processes mentioned in the results are highlighted in bold. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y).

Extended Data Fig. 7 Distinct transcriptional profiles and biological processes identified in ASD mutant mice.

a, Number of GO terms identified by the Gene Set Enrichment Analysis (GSEA, FDR < 0.05) across the analyzed time points when comparing each mutant with all the other ASD mutants collectively. Colors indicate the direction of the normalized enrichment score (NES). GO terms were simplified to remove redundancy; all three sub-ontologies (BP, MF, CC) are shown. GSEA was performed using clusterProfiler, with a Kolmogorov–Smirnov–like statistic, permutation-based testing, and Benjamini–Hochberg correction (Methods). b, Volcano plots of the biological processes enriched in the selected ASD mutants across the analyzed time points. Volcano plots are based on GO enrichment analysis using clusterProfiler, with a one-sided hypergeometric test and Benjamini–Hochberg correction. Colored GO terms were identified specifically in one ASD mutant. The circle size represents the gene count ratio. E14,5, colored filled circles; P4, gray filled circles; P14, black filled circles. c, Annotation of synaptic processes based on SynGO. c’, Representation of the synaptic-related DEGs identified in excitatory neurons of each ASD mutant. All the DEGs were used as input for the SynGO analysis. Only SynGO terms identified as significantly enriched in the respective mutant were retained for the generation of the heatmap. Heatmaps are colored based on the SynGO terms. Each ASD mutant shows a unique combination of affected synaptic processes. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y).

Extended Data Fig. 8 Network colocalization in inhibitory neurons.

a, For each genotype, effect sizes and significance of expression changes mapped onto protein–protein interaction (PPI) network clusters (biological processes) in inhibitory neurons. Coefficients were tested against zero using two-sided t-tests and Benjamini–Hochberg multiple comparison test. Those with p-adj. <0.1 were considered significant and marked with a black outline in the plot. b, Pairwise distances between DEG network localizations of inhibitory neurons across genotypes and developmental stages. Smaller distances denote greater similarity (convergence). Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y).

Extended Data Fig. 9 Electrophysiological recordings and minimal conductance model.

a, Recorded membrane and action potential properties of Bckdk−/−, HnrnpU+/−, Trip12+/−, and Usp7+/− mutant animals alongside pooled colony-matched and littermate controls (top to bottom). b, Both colony-matched and littermate controls were tested. Given there were no overt differences between the control groups, data were pooled for analyses. c, Minimal conductance-based model of a P7 Layer II/III pyramidal neuron, parametrically adjusted according to the direction and magnitude of observed ion channel expression changes to reproduce the diversity of AP and F–I curves. By tuning channel activities within transcriptomically informed ranges, the model approximated the experimentally observed electrophysiological changes and recapitulated recorded responses. The F–I curve shows mean wild-type recordings (solid line) and model output (dashed line; top left). Example voltage traces illustrate firing responses of the simulated neuron to increasing current injections during a 1-second voltage response (top right). d, Excitation/Inhibition (E/I) ratio derived from AP frequency. Data are mean ± SEM. n (animals; Bckdk−/−)= 5, n (HnrnpU+/−)= 4, n (Usp7+/−)= 7, n (Trip12+/−)= 7, n (pooled controls)= 18, n (colony-matched controls)= 11, n (littermate controls)= 7; n(cells; detailed cell counts per experiment are provided in the Source Data); unpaired two-tailed t-test for normally distributed data, unpaired two-tailed Mann-Whitney test for non-normally distributed data in a, b and d.

Source data

Extended Data Fig. 10 Correlation of snRNA- and snATAC-seq.

Pearson correlation between fold changes of genes (p-value < 0.1) of pseudobulk chromatin accessibility and gene expression across cell types and time points. Center lines represent medians, the box limits show the 25-75% interquartile range (IQR), whiskers extend to the furthest data points within 1.5 × IQR from the box edges, and data points outside this range are represented as dots. Statistical significance was assessed using two-tailed Wald test without multiple comparisons testing. Animals used for analyses: n (E14.5) = 7 (Cul3+/−, Kdm6b+/−, Trip12+/−), 6 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls), 3 (Ptchd1−/y); n (P4) = 6 (Ash1l+/−, Bckdk−/−, Cul3+/−, HnrnpU+/−, Kdm6b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls), 4 (Kmt5b+/−), 3 (Ptchd1−/y); n (P14) = 8 (Ash1l+/−, Cul3+/−), 7 (colony-matched controls), 6 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Setd5+/−, Trip12+/−, Usp7+/−, Wac+/−), 3 (Ptchd1−/y).

Extended Data Fig. 11 Differential gene expression analysis and quality control of female and male mice.

a, Differential gene expression analysis of control female and male mice exhibits very little transcriptional differences. DEGs with a nominal p-value < 0.05 are shown. Statistical significance was assessed using two-tailed Wald test without multiple comparisons testing. b, Gene expression differences observed through pairwise comparison of random cells between females and males of the same genotype suggest minimal variability in gene expression across cells. Animals used for analyses: n (females and males; E14.5) = 3; n (females and males; P4) = 3; n (females; P14) = 4; n (males; P14) = 3. c, Quality metrics for number of genes sequenced in snRNA- and snATAC-seq in females and males across time points. The dotted lines represent the median values. Animals used for analyses: n (females; E14.5) = 4 (Cul3+/−, Kdm6b+/−, Trip12+/−), 3 (Ash1l+/−, Bckdk−/−, HnrnpU+/−, Kmt5b+/−, Setd5+/−, Usp7+/−, Wac+/−, colony-matched controls); n (males; E14.5) = 3 per genotype; n (females and males; P4) = 3 per genotype; n (females; P14) = 4 (Ash1l+/−, Cul3+/−, Controls), 3 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Trip12+/−, Usp7+/−, Wac+/−), 2 (Setd5+/−); n (males; P14) = 4 (Ash1l+/−, Cul3+/−, Setd5+/−); 3 (Bckdk−/−, HnrnpU+/−, Kdm6b+/−, Kmt5b+/−, Ptchd1−/y, Trip12+/−, Usp7+/−, Wac+/−, colony-matched controls).

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Supplementary Notes, Supplementary Discussion, Supplementary Figs. 1–3 and descriptions for Supplementary Data Tables 1–13.

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Supplementary Data Tables 1–13.

Supplementary Video 1 (download MOV )

Cleared embryo head combined with EdU-labelling. EdU-labelling at E14.5 was combined with CUBIC clearing and imaged on a Zeiss Lightsheet microscope.

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Example video of Bckdk+/+ animal during gait recordings. Recording of a Bckdk+/+ animal used for gait analysis, with stride renderings overlaid on the video frame. Video recorded at 60 fps.

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Example video of Bckdk−/− animal during gait recordings. Recording of a Bckdk−/− animal used for gait analysis, with stride renderings overlaid on the video frame. Video recorded at 60 fps.

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Schwarz, L.A., Dotter, C.P., Isaev, S. et al. Cortical development dynamics across autism spectrum disorder mouse models. Nature 656, 159–172 (2026). https://doi.org/10.1038/s41586-026-10679-1

- Received: 24 July 2024

- Accepted: 18 May 2026

- Published: 17 June 2026

- Version of record: 17 June 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10679-1

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Universal cell embedding provides a foundation model for cell biology

Yanay Rosen,

Yusuf Roohani,

Ayush Agrawal,

Leon Samotorčan,

Tabula Sapiens Consortium,

Stephen R. Quake &

…

Jure Leskovec

Nature volume 656, pages 183–191 (2026)

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Abstract

Developing a universal representation space for cells that encompasses the tremendous molecular diversity of cell types across species would be transformative for cell biology. Recent work using single-cell transcriptomic approaches to create molecular definitions of cell types in the form of cell atlases has provided the necessary data for such an endeavour1,2,3. Here we present the universal cell embedding (UCE) foundation model. UCE was trained on a large corpus of cell data using self-supervision, creating a unified biological latent space that can represent cells across diverse tissues and species. This latent space captures important biological variation despite the presence of experimental noise. UCE’s universality means that new cells can be embedded with no data labelling, model training or fine-tuning. We used UCE to create the Integrated Mega-scale Atlas, embedding 36 million cells, with more than 1,000 uniquely named cell types, from hundreds of experiments, dozens of tissues and eight species. We gain insights into the organization of cell types and tissues within the space. UCE’s embedding space exhibits emergent behaviour, identifying biology that it was never trained for, such as identifying developmental lineages and embedding data from species that were not included in the training set. Overall, by enabling a universal representation for every cell state and type, UCE is a valuable tool for analysis, annotation and hypothesis generation over single-cell data.

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Main

Cells are the fundamental unit of life and biologists have long conceptualized cells as members of different universal landscapes4,5,6,7. A notable example of this is the Waddington landscape, which presents a theoretical framework for the developmental lineages of cells as they transition from pluripotent stages such as stem cells to more terminally differentiated end points[8]. Broadly, the field of cell biology has sought to map the range of phenotypes that cells might exhibit, their interrelationships, and the shifts between these states during development and disease9,10,11,12,13.

The substantial growth in the size of single-cell RNA sequencing (scRNA-seq) datasets presents a fresh opportunity to revisit these questions. Detailed transcriptomic snapshots of cells are now widely available from a range of timepoints, tissues, donors and species1,2,3. These rich, high-dimensional states are typically distilled into low-dimensional vectors or embeddings to facilitate computational analysis14,15. However, existing computational approaches struggle to jointly analyse these diverse datasets. The unified representations that they produce are often unable to extend to new datasets due to species-specific constraints or the presence of dataset-specific artifacts (or batch effects), which can obscure the underlying biological signal16,17.

Some computational methods for scRNA-seq data have managed to overcome these limitations, but at the cost of requiring model tuning for each new dataset, therefore rendering the representations non-universal15,18,19. As a result, whenever a new experiment is performed and new data are collected, it requires dedicated, resource-intensive data labelling and model training to perform even the most standard analyses, such as clustering or annotation. This process is time consuming and inefficient, and results in suboptimal analyses based on small, limited and private datasets.

Recent advances in the field of artificial intelligence have enabled general-purpose foundation models (such as ChatGPT20,21, PaLM[22] and SAM[23]) that can learn universal representations that are then applied to diverse downstream tasks and analyses. These foundation models are not specifically trained for these downstream tasks, therefore presenting clear instances of emergent capabilities[24]. This foundation model strategy has also found valuable applications in biological contexts such as learning representations of DNA and protein sequences25,26. Although recent work has applied foundation model architectures to single-cell genomic data, the unique characteristics of these data require a specialized modelling approach to fully realize their potential27,28. Directly modelling gene expression as text in the form of a sequence of genes is both inefficient from a learning perspective and often relies on inaccurate biological assumptions.

Here we present UCE, a foundation model for single-cell gene expression that is designed to address questions in cellular and molecular biology. UCE is uniquely able to create representations of new single-cell gene expression datasets with no model fine-tuning or retraining while still remaining robust to dataset and batch-specific artifacts. Moreover, it does so while requiring no cell type annotation and no input dataset preprocessing, such as gene selection. UCE can be applied to any set of protein-coding genes from any species, even if they are not homologues of genes seen during training. UCE learns a universal, intrinsically meaningful representation of cell biology that enables insights extending beyond experimentally observed data.

The representations learned by UCE display an emergent organization of cell types and states that is consistent with known biology. These cell embeddings can be used to accurately predict cell types/states without additional model retraining, showing improved performance in dataset integration against existing atlas-scale integration methods. UCE also enables the mapping of new data into a universal embedding space, already populated with annotated reference states. This strategy addresses issues such as noisy measurements that limit data alignment between different experiments and reduces the reliance on small sets of marker genes to translate insights between studies[29]. UCE can foster cross-dataset discoveries and overcome the limitations currently faced when working with small, isolated datasets. For example, a cell type classifier trained to predict specific immune cell types can be seamlessly applied to a completely new dataset.

Bio-informed cell foundation model

Integrating scRNA-seq datasets is challenging for two primary reasons: scRNA-seq data do not always contain the same genes or features, and those features are plagued by dataset-specific experimental artifacts or batch effects, which means that models have to be built separately for each dataset. UCE overcomes these challenges by abstracting cells as ‘bags of RNA’[30]. UCE (Fig. 1a) converts the RNA gene expression of a single cell into an expression-weighted sample of its corresponding genes. Next, UCE represents the sample’s genes by their protein products, using a protein language model. This enables UCE to meaningfully represent any protein-coding gene, from any species based on sequence alone, regardless of whether the species had appeared in the training data. After integrating additional metadata on gene chromosomal locations, the representation is processed by a large transformer model[31]. UCE can then map any cell—from any tissue or species—into a shared universal space without further training.

Fig. 1: The UCE model is a large foundation model for single-cell biology.

Full size image

a, Overview of the UCE model. UCE has a unique, biologically motivated representation of cells (blue) and training scheme (purple). Given the gene expression for a single cell, UCE samples with replacement genes that were expressed, weighted by their level of expression. Each gene is represented using a token corresponding to its protein product. Gene tokens are represented numerically by using ESM2 protein embeddings, a 15-billion-parameter protein language model that takes amino acid sequences as an input. The gene tokens are sorted by genomic location and grouped by chromosome. Chromosome groups are delineated by specific chromosome start tokens and end tokens, joined and then passed into a transformer neural network. The embedding of the cell is determined by taking the final layer output of a special CLS token that is appended before all the other tokens. To train the UCE model, a portion of genes that were expressed is masked. The model next combines the protein embeddings corresponding to each of these genes with the embedding of the cell, and passes this joint representation through a neural network that predicts if a given gene was expressed in the cell or not. This objective function is then used to update the weights of the model. b, Uniform manifold approximation and projection (UMAP) visualizations of the UCE space. We apply UCE to embed 36 million cells, with more than 1,000 uniquely named cell types, from hundreds of datasets, dozens of tissues and eight species (Supplementary Fig. 9), creating an IMA spanning the universe of cell biology. The diagram in a was created using BioRender; Rosen, Y. https://biorender.com/hyo5pz3 (2026).

UCE takes as input (1) scRNA-seq count data; and (2) the corresponding protein embeddings, generated by a protein language model, ESM2 (ref. [32]), for the genes in the dataset. The ESM2 protein language model takes amino acid sequences as an input and produces a numerical representation called a protein embedding. Given the expression count data for a cell, UCE takes a weighted and normalized sample, with replacement, of the cell’s genes. This sample can only contain genes with non-zero expression and can contain multiple copies of each gene. These genes are then tokenized by converting them to the protein embedding representation of the protein that they encode[33]. Genes belonging to the same chromosome are grouped together by placing them between special tokens and then sorted by genomic location. A special token representing the entire cell, the CLS token, is attached to the beginning of the cell representation[34]. This combined representation is passed into a transformer neural network. The embedding of a cell is taken as the embedding of the CLS token at the final layer of the transformer (Fig. 1a).

UCE is trained in a fully self-supervised manner, without using any cell type or dataset-based annotations. While incorporating cell type labels into the model design or training could seem advantageous, it is preferable for the organization of cells within UCE to emerge organically, independent of human labelling.

UCE is a 33-layer model consisting of over 650 million parameters. UCE was trained across more than 300 datasets that are largely collected from the CellXGene corpus[35] comprising over 36 million cells, for 40 days across 24 A100 80 GB GPUs (Methods and Supplementary Tables 2 and 16). We also performed extensive ablation experiments to validate UCE architecture choices, understand training behaviour and quantify the benefits of ESM2 protein embeddings (Supplementary Tables 3, 4, 8 and 9 and Supplementary Figs. 7 and 8). The model’s weights and implementation are freely available and the model is hosted as an openly available resource for the community.

Creating the IMA

We used UCE to generate the Integrated Mega-scale Atlas (IMA) of 36 million cells sampled from diverse biological conditions, demonstrating the emergent organization of UCE cell representations (Fig. 1b). We find that cells within the UCE space naturally cluster under biological conditions such as cell type, while mixing among experimental conditions such as batch (Fig. 1b). As UCE is trained using only unlabelled data, this organization represents an emergent behaviour of the model.

We inspect how tissue residency can influence the state of cell types. Cells in the IMA have been prelabelled by their cell type. As these labels were never used for training the UCE model, we use them to validate the quality of the learned representation. Although macrophages found in different tissues are characterized by diverse transcriptional identities[36], they align closely in the UCE space (Supplementary Table 15). For example, human macrophages are found in 73 different tissues and among these tissues, 72% (53) of tissue-specific macrophage centroids were embedded closest to a macrophage centroid from another tissue. Similar cross-tissue homogeneity can also be identified in other prolific cell types, like endothelial cells or neurons. This shows that UCE, without explicit training or labels, identifies that macrophages have a unique cellular identity that is shared between tissues. More broadly, it is an example of a UCE’s emergent organization that is consistent with known biology, even though not explicitly trained for.

Zero-shot embedding of new datasets

We evaluated the quality of UCE representations by directly mapping new datasets that were not part of the training set into the embedding, without any additional training or refinement of the UCE model. This is called a ‘zero shot’ ability, as the model was never trained on any samples from the new dataset (Fig. 2a).

Fig. 2: Zero-shot cell embedding capabilities of UCE.

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a, Comparison of zero-shot and fine-tuned single-cell embedding models. A zero-shot embedding model maps new data directly to the representation space, with no additional model training. By contrast, fine-tuned models must first be retrained on a given dataset, and only then can be applied on that dataset, fundamentally altering the model’s representation space. b, UMAP embeddings of UCE and other methods for Tabula Sapiens v.1 and v.2, coloured by cell type. UCE zero-shot embeddings outperform other methods when scored using metrics from the single cell integration benchmark[16] (Supplementary Table 1). c, UMAP of cells from a novel species, green monkey, coloured by cell type. UCE is able to generate high-quality zero-shot embeddings of novel species that were never seen during training. The UCE embedding for green monkey mediastinal lymph node[40] recaptures cell type clusters. Notably, a population of cells annotated as B cells (blue) clusters nearby to T cells. This breakaway cluster of putative T cells expresses canonical T cell markers including Cd3, Fyb1, Il7r, Hcst and Gzmk (Extended Data Fig. 3). d, Green monkey lymph node cells can be accurately annotated using the IMA. A logistic classifier is first trained to predict cell types based on UCE embeddings of human lymph node cells. The classifier is then directly applied on green monkey cells to predict the cell types. Predicted cell types have high agreement with the original cell type annotations, demonstrating that UCE can be used to transfer cell type annotations to novel species. DC, dendritic cell; ILC, innate lymphoid cell; NK cell, natural killer cell; NKT cell, natural killer T cell; pDC, plasmacytoid dendritic cell. The diagrams in a and c were created using BioRender; Rosen, Y. https://biorender.com/hyo5pz3 and https://biorender.com/vpbhnzf (2026).

While a variety of deep learning models has been proposed for this cell embedding task, we choose to compare the performance of UCE to other self-supervised transformer-based methods. This is because they do not rely on cell-type annotation and are trained on large datasets. In particular, we compare against Geneformer[27] and scGPT[28]. Geneformer represents cells as lists of genes sorted by their expression, while scGPT represents cells as lists of genes with binned expression values.

We assessed the performance of these methods on a new and unpublished dataset (at the time of training the model), Tabula Sapiens v.2, which contains diverse human data from 581,430 cells, 27 tissues, 167 batches and 162 unique cell types. Tabula Sapiens is collected in a highly standardized manner and represents one of the largest gold-standard expert-annotated cell atlases available. We use established metrics for embedding quality that measure the conservation of cell type information and the correction of batch effects (Methods). We found that UCE substantially outperforms the next-best Geneformer method by 13.9% in the overall score, 16.2% in the biological conservation score and 10.1% in the batch correction score (Supplementary Table 1). UCE embeddings distinctly separate cell types more effectively than other methods (Extended Data Figs. 1 and 2). To further evaluate the value of UCE’s zero-shot embeddings, we compared them to fine-tuned methods that rely on cell-type labels and dataset-specific training. Notably, UCE performs slightly better than these established approaches, including scVI[15] and scArches[18] (Supplementary Table 1).

Tabula Sapiens v.2 includes cells measured using both droplet- or plate-based sequencing methods. Thus, correcting technology-based batch effects is a difficult task. UCE zero-shot embeddings of Tabula Sapiens v.2 ovary tissue, which contains 45, 757 cells profiled using 10x 3′ v3.1 and 3, 610 cells profiled using Smart-seq2.5, successfully correct batch effects at the same level as fine-tuned methods, while more accurately representing cell types. When scored using the single-cell integration benchmark, UCE’s batch correction scores are close to that of scVI and scArches, while its bio conservation scores are higher (Supplementary Table 5).

Furthermore, we demonstrate how UCE can simply transfer cell-type labels. We trained a simple logistic classifier on the UCE embeddings of the Immune Cell Atlas[37], and then applied the classifier to cell embeddings from Tabula Sapiens v.2. We observed that the classifier accurately classifies Tabula Sapiens v.2 cells as memory and naive B cells (Extended Data Fig. 2b), which is confirmed by marker gene analysis (Extended Data Fig. 2c).

Beyond Tabula Sapiens v.2, we evaluated UCE on held-out (not seen during training) complex datasets with hundreds of cell types2,38 and on an additional large-scale benchmark that measures how well embedding spaces capture cell-type relationships across samples in million-cell datasets with hundreds of cell types and batches1,2,39, finding that UCE outperforms Geneformer and scGPT and matches the performance of fine-tuned methods such as scVI (Supplementary Note 6 and Supplementary Tables 10 and 12).

Zero-shot embedding of new species

UCE can also align datasets from previously unseen species without additional training. This ability arises because UCE is genome agnostic: each gene is translated into its corresponding protein sequence and embedded within a universal protein space. As a result, the representation is species independent and does not rely on identifying homologous gene pairs. As UCE can analyse cell atlas data from species outside its training set, its performance on this task provides a stringent test of how well its emergent representations capture underlying biological principles.

UCE’s training data comprise datasets from eight species: human, mouse, mouse lemur, zebrafish, pig, rhesus macaque, crab-eating macaque and western clawed frog (Supplementary Fig. 9). We used UCE to embed datasets from three species that were not included in the training set (novel-species dataset). For each species, we generated a zero-shot embedding and then determined the nearest cell-type centroid from the IMA for each of the dataset’s existing annotated cell types.

Within a dataset of green monkey lymph node and lung cells[40], for 13 out of the 17 cell type centroids, the closest centroid from another species corresponds to the same cell type in the green monkey. This match extends to all 17 centroids when considering the three nearest centroids (Fig. 2c,d and Supplementary Table 15). Moreover, a population of lymph node cells that was originally labelled as B cells, form a distinct cluster in UCE space (Extended Data Fig. 3b). Differential expression analysis revealed that this cluster predominantly expresses the T cell markers Cd3d, Fyb1, Il7r, Hcst and Gzmk41,42 (Extended Data Fig. 3a,c).

In the case of naked mole rat spleen and circulating immune cells[43], for 17 out of 24 cell types, the nearest cross-species centroid matches the naked mole rat cell type (Supplementary Table 15 and Extended Data Fig. 4b). In the case of chicken, we embed two different chicken datasets, the chick retina[44] and the developing chick heart[45] (Extended Data Fig. 5a,b). Different eye-specific neurons within the chick retina map to mouse lemur neurons, such as chick oligodendrocytes, which are closest to mouse lemur oligodendrocytes (Supplementary Table 15). In chicken heart, 12 out of 15 cell type centroids are matched within the nearest two cross-species centroids (Supplementary Table 15). No bird species were included when training UCE.

We compared UCE’s performance to a state-of-the-art supervised method for cross-species label transfer, SATURN33,46 and SAMap[19]. For each of the four datasets, we integrated them with a paired human dataset from the same tissue from Tabula Sapiens v.1, except for chicken retina, which was paired with retina data from ref. [47]. In comparison to UCE, which is zero-shot and unsupervised, SATURN is a supervised method: it is aware of each species’ cell type annotations individually. Both SATURN and SAMap are fine-tuned methods, trained from scratch on the human and novel-species dataset and designed explicitly to map cell types together. Still, UCE outperforms both methods for cell type label transfer between the human and the novel species for three out of four datasets (Supplementary Note 5 and Supplementary Table 14). UCE produces coherent zero-shot embeddings even for highly divergent species such as Drosophila[3] (Supplementary Fig. 10). At the same time, fine-grained cell-type transfer across such large evolutionary distances remains challenging, reflecting both reduced cross-species alignment performance and inherent ambiguity in defining reliable ‘ground-truth’ correspondences between distantly related cell-type ontologies (Supplementary Table 18).

Organization of cell types in new data

Moving beyond metrics focused on individual cell type clusters, we also examined the structure of the universal embedding space as a whole, through the relative positioning of different cells within it. A meaningful arrangement of cell types emerges after embedding of all cells from the Tabula Sapiens v.2 dataset from lung tissue (Fig. 3a). Not only do distinct cell types like T cells, monocytes and endothelial cells cluster together, but higher-level categories, such as immune cells and epithelial cells, are also clearly distinguished. When compared with the cell hierarchy derived using the Cell Ontology[48], UCE-identified clusters showed greater similarity (measured using the adjusted Rand index) than other zero-shot embedding methods (Supplementary Fig. 4).

Fig. 3: UCE learns meaningful organization of cell types.

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a, The UCE space generated for new, previously unseen data shows a meaningful arrangement of cell types. Lung data were used from new donors from the Tabula Sapiens Consortium. A dendrogram of hierarchical clustering of all annotated cell types in the UCE embedding space is shown. Closely connected cell types in the dendrogram show meaningful relationships both at finer- and coarser-scale resolutions. b, Evaluation of the organization of cell types in the embedding space when compared to Cell Ontology. The x axis shows the density of Euclidean distances between all pairs of cells across all tissues for these new donors from the Tabula Sapiens Consortium. The y axis shows the corresponding tree distance between cell types as found in the Cell Ontology. Asterisks denote statistical significance, determined using one-sided t-tests: from bottom to top, P = 10−106, P = 10−201, P = 10−25, P = 10−131. c, Mapping data from new donors to the IMA across multiple lung datasets. The red labels correspond to data from new donors; grey labels correspond to data from IMA datasets. All cell type labels from multiple datasets are displayed as is, with no modifications or reformatting of text. Accurate alignment between the new dataset and IMA is observed at finer resolution. Four different subtypes of endothelial cells are shown to correctly map to their corresponding counterparts in the complete mega-scale atlas. In the case of lung ciliated cells, they map more closely to their matching counterpart as compared to all other ciliated cell subtypes also present in the IMA. d, Quantification of cell type alignment between new dataset and IMA. Accuracy in three-nearest centroid matches between new dataset and IMA cell types at the finest level of original annotation. Results are measured across all 27 tissues in Tabula Sapiens v.2 for both the UCE space and the original gene expression space. Tissues are ordered by accuracy in the UCE space.

We also noted substantial colocalization among cells originating from the same developmental lineages, in particular from the mesoderm, endoderm and ectoderm germ layers. For Tabula Sapiens v.2, 90 out of 97 of the centroids for mesoderm-derived cell types had other mesoderm-derived cell type centroids as their closest neighbours. A similar pattern was observed for 46 out of the 56 endoderm-derived cell types and 22 out of the 30 ectoderm-derived cell types (Extended data Fig. 7a). A neural network classifier trained to predict the germ layer of origin for individual held-out cell types using their universal embeddings showed an accuracy of over 80% (Extended data Fig. 7b). When mapping unseen data from the bone marrow, we observed a clustering of cell types that aligns well with both developmental stage—in which early progenitors are closer to each other in the embedding space—and lineage, with more developed cell types in the lymphoid and myeloid lineages clustering distinctly (Supplementary Fig. 6).

To further assess the organization of all cells within the embedding, we compared distances between pairs of cell types across all tissues in the embedding space to their distances in the Cell Ontology tree (Fig. 3b). We hypothesized that cells that are known to be similar based on the Cell Ontology would likely also be closer together in the embedding space, and that the degree of closeness would be correlated with ontological similarity. The results validate this relationship: at each additional unit of separation between cell types in the Cell Ontology tree, there is a significant increase in the embedding distance in UCE between those cell types (Fig. 3b). We observed this trend up to five hops in the ontology tree, after which it plateaued (Extended Data Fig. 6), probably due to the curse of dimensionality and variations in ontological refinement across branches (Supplementary Note 3).

The precision of the organization of cell types in the Tabula Sapiens v.2 lung dataset was evaluated by comparing it with other lung datasets in the IMA (Fig. 3c and Extended Data Fig. 8). Four different endothelial cell subtypes are observed to map correctly to their corresponding counterparts in the IMA (Fig. 3c). Similarly, lung ciliated cells correctly map to their counterpart in the larger corpus despite the presence of four different subtypes of ciliated cells. Further analysis of the alignment of cell-type centroids between Tabula Sapiens v.2 and the IMA in all tissues showed an exact alignment for 41% of cell types (Methods). This alignment, based on the three nearest-neighbour cell-type centroids, is 65% more accurate compared with that measured in the original gene expression space (Fig. 3d). When focusing on the nearest centroid, the alignment accuracy improves by 92%. Many inexact matches are caused by differences in the labelling resolution of different datasets in the IMA (Supplementary Tables 6, 7 and 21). These results demonstrate that UCE can effectively learn a universal representation of cell biology that not only enables discrimination between individual cell types but also captures their relative similarities across scales with the potential to reveal deeper insights into development and function.

Decoding the function of new cell types

UCE’s zero-shot embedding capabilities enable computational analyses of scRNA-seq data and aid in hypothesis generation. UCE differs from other methods in that the same cell type can also be compared against all previously assayed cells across tissues, disease states and species. UCE is not biased by existing annotations, opening the identification of new functions (Fig. 4a). With existing fine-tuning-based methods, every searched dataset would need to be integrated, requiring repeated model retraining. Thus, UCE enables a new workflow for scRNA-seq data analysis that performs an unbiased search across the universe of cell biology.

Fig. 4: A Norn cell case study shows that UCE enables further analyses of single-cell datasets.

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a, Overview of the single-cell analysis workflow that UCE facilitates. (1) Analysis begins with the identification of a novel cell type (circled) within the embedding space, using methods such as clustering and confirmation using marker gene analysis. (2) Next, the novel cell type can be easily identified in other datasets profiled from the same tissue (for example, kidney). A simple classifier, such as a logistic classifier, is trained to predict cell types from UCEs, and is then applied to embeddings from other datasets of the same tissue (kidney), to confirm the cell type’s existence and improve its characterization. (3) Finally, the same simple classifier can be applied to the embeddings of cells from any other tissue, to find cell types with similar biological functions or patterns of gene expression. b, Identification of novel Norn cells in mouse kidney. UMAP visualization of zero-shot embedding of mouse renal cells from ref. [49]. Norn cells form a distinct cluster within the embedding space (circled), and a logistic cell type classifier trained on the dataset recaptures Norn cell labels 98.3% of the time. c, Identification of Norn cells and Norn-like cells across tissues. A logistic classifier is trained to predict Norn cells from UCEs, and is then applied to other kidney datasets (left) and datasets from lung and heart (right). The log-transformed fold change of known Norn marker genes between cells predicted to be Norn cells and the remaining cells within each dataset is visualized. Cells that are predicted to be Norn-like preferentially express Norn markers in kidney, as well as in lung and heart. Notably, CXCL14 has a mixed pattern of expression among some datasets. d, Cells predicted to be Norn-like cells within a lung disease dataset[51] express known Norn markers, as demonstrated by log-transformed fold change. e, Pseudobulk differential gene expression (Supplementary Table 13) in predicted Norn-like cells, grouped by disease status. Norn markers have similar expression levels across disease status.

We present an example of this analysis by using the recently identified kidney Norn cell as a case study. The kidney Norn cell is the long-sought erythropoietin-producing (encoded by Epo) cell in the kidney, and is characterized as fibroblast like. We performed a zero-shot embedding of mouse renal cells from ref. [49], which produces a cluster of cells corresponding to Norn cells (Fig. 4b).

Using a simple logistic classifier trained on the embedding of mouse renal cells, we predict the existence of Norn cell clusters in many kidney datasets. As this classifier takes UCEs as an input, we can directly apply it to all 36 million cells in the IMA, in a manner that is unbiased by cell-type annotations ascribed by previous studies. To evaluate the model’s predictions that these are Norn or Norn-like cells, we investigate the expression of canonical Norn cell marker genes. Cells classified as Norn cells in the top 13 kidney datasets by Norn abundancy demonstrate preferential expression of the Norn markers Dcn, Lpar1, Col1a1, Cxcl12 and Cfh (Supplementary Table 17). Notably, Epo transcripts, which are often missing from datasets and are lowly expressed, are not typically differentially expressed in these cells. Epo cannot readily be used as a marker gene for Norn cells owing to a variety of factors, including low overall expression, even in hypoxic Norn cells, the bursty nature of Epo transcription, and the fast degradation of Epo mRNA after reoxygenation[49]. Cxcl14, another marker of Norn cells, displays mixed expression patterns in these predicted Norn cells (Fig. 4c). The same pattern of marker gene expression is also found in cells from other tissues, including lung and heart datasets (Fig. 4c). Furthermore, these predicted cells also share a common set of genes with low expression in mouse renal Norn cells (Extended Data Fig. 9). The tissues with the highest number of predicted Norn cells were gonad, heart and lung. Finally, we also identified differences in gene expression specific to each tissue (Extended Data Fig. 10). While Epo expression has been previously observed in the heart and lung tissue, the mechanisms and cell types associated with this expression and their relationship to kidney Norn cells have not been previously determined[50].

UCE analysis of lung disease outcomes

Lastly, we used UCE and our Norn-cell classifier to investigate Norn-like cells in lung diseases. We generated an embedding of lung cells sampled from patients with idiopathic pulmonary fibrosis (IPF) or chronic obstructive pulmonary disease (COPD) or patients from a control group[51]. We identified Norn-like lung cells that preferentially express Norn markers in all three groups (Fig. 4d). For these Norn-like lung cells, we identified differences across disease groups (Fig. 4e). COPD and IPF are both associated with elevated bloodstream Epo, but COPD has higher levels than IPF. Furthermore, in patients with IPF, secondary erythrocytosis is absent or reduced compared with patients with COPD52,53,54. Given the identification of Norn-like cells in the lung, and Norn cell’s production of Epo, it is possible that this difference in disease prognosis could be related to disease-associated differences in Norn-like cells.

IPF predicted Norn-like cells do not have significantly different expression of Norn marker genes compared with patients with COPD, except for Cxcl14, which they express at a significantly higher rate. However, they do preferentially express collagen genes (Col1a1, Col5a1, Col1a2 and Col3a1), and have significantly lower expression of Egln1, encoding the oxygen-sensing enzyme PHD2 (ref. [49]) (Fig. 4e and Supplementary Table 13).

Taken together, these results indicate that cells with similar transcriptional states to Norn cells can be found in other tissues in the body, and such cells may have a previously undescribed role in disease. UCE greatly facilitates an analysis of this scale and diversity because it is a universal model that is agnostic to tissue, species or disease state. To ground this analysis, the results were confirmed based on the expression of canonical marker genes (Extended Data Fig. 9).

Discussion

UCE is a single-cell foundation model that is built from the ground up to represent cell biology across the wide array of single-cell datasets. We envision UCE as an embedding approach that enables researchers to map any new data, including entire atlases, into an accurate, meaningful and universal space. The embedding space that emerges from UCE is highly structured and diverse and aligns cell types across tissues and species. Moreover, these cell types organize themselves in a pattern that reflects existing biological knowledge.

By building UCE, we enable further analyses of scRNA-seq data. UCE’s zero-shot embeddings are an important ability because they enable an intrinsically meaningful representation that can extend insights beyond the data that have already been observed and annotated experimentally. Our results demonstrate that UCE can achieve such a generalizable representation across different datasets while maintaining accuracy comparable to methods that require retraining for each specific dataset. To ensure that UCE’s embeddings, or any downstream models trained on them, remain universally shareable, we have designed UCE to be used as is, without any model fine-tuning. This enables UCE representations to serve as a universal method for connecting any single-cell gene expression dataset.

Although the model represents a considerable advance towards universal embeddings, several caveats temper its promise and highlight directions for improvement. Current benchmarks primarily evaluate UCE’s ability to recover expert-annotated cell types, but these tasks are inherently limited by the granularity and subjectivity of available cell labels. As such, they may not fully capture the model’s ability to represent subtler biological processes, such as cellular responses to perturbations or cross-modality integration. Moreover, like other large-scale biological foundation models, UCE operates largely as a black box, making it difficult to interpret how or why certain embeddings arise. Addressing this opacity will require the development of interpretability tools that connect learned representations to mechanistic biological understanding. UCE’s training data, while extensive, are biased toward mammalian species—particularly human and mouse—and to specific tissues such as brain, raising concerns about its generalizability to under-represented species and contexts. Methodological improvements in dataset construction could help to alleviate these biases while reducing computational burden. While UCE’s model size and hyperparameters are based both on heuristics derived from existing models and results showing improved performance when training for longer, on more data and with more parameters, these hyperparameter choices can surely be improved. Future work optimizing both model architecture and data mixtures is a critical line of research. Finally, although UCE improves alignment across datasets by capturing shared biological signal, it does not replace traditional batch effect correction methods, which remain useful for handling known experimental confounders. Similarly, its reliance on gene sampling based on expression means that some fine-grained quantitative variation is lost, potentially limiting the resolution of certain cell state distinctions.

In 2002, Nobel laureate Sydney Brenner identified many of the core motivations for the creation of cell atlases and virtual cells. Virtual cells should be the goal of biological foundation modelling, because cells are the “real units of function and structure in an organism”[55]. Sydney Brenner also identified the need for such models to be computationally efficient, predictive and able to generate new cell types. We believe that UCE represents a considerable advancement in the progress towards a virtual cell[56]. Through learning a universal representation of every cell state and type, we expect that UCE will be a valuable tool for analysis, annotation and hypothesis generation as the scale and diversity of single-cell datasets continues to grow.

Methods

Overview of UCE

UCE is a machine learning model for mapping single-cell gene expression profiles into a universal embedding space, denoted as \({\mathcal{U}}\). In this space, each cell ci is represented as a demb-dimensional vector, where demb = 1,280. For all analyses we use the 1,280 dimensional UCE representation and not a two-dimensional embedding such as that generated from t-distributed stochastic neighbour embedding or UMAP. Users of UCE should rely on the full 1,280-dimensional representations to judge distances between cells, rather than data visualization using these methods, especially when they expect to see strong biological differences.

The model takes as input a dataset \({\mathcal{D}}\) with N cells \({\{{{\bf{c}}}_{i}\}}_{i=1}^{N}\). Cells in \({\mathcal{D}}\) can be drawn from one or more distinct scRNA-seq experiments. Each cell ci in \({\mathcal{D}}\) is described by a gene expression vector \({{\bf{x}}}^{i}\in {{\mathbb{N}}}^{{K}_{i}}\), where Ki is the number of genes measured in ci and can differ across \({\mathcal{D}}\). The gene expression vectors \({{\bf{x}}}^{i}\in {{\mathbb{N}}}^{{K}_{i}}\) are not subset to those with high variance. UCE defines a function \({f}_{u}:{\{{{\rm{{\mathbb{N}}}}}^{{K}_{i}}\to {{\rm{{\mathbb{R}}}}}^{{d}_{\mathrm{emb}}}\}}_{i=1}^{N}\) that maps each gene expression vector xi to its cell embedding vector hi.

Model input: gene representation

The expression of gene g in cell ci is denoted by \({x}_{g}^{i}\), where g represents any protein-coding gene. The corresponding token embedding pg is a pretrained embedding for the protein(s) encoded by the gene g. These embeddings are derived from a pretrained protein language model that takes an amino acid sequence as input and returns a dp-dimensional embedding vector as output. To create Pg, we take the average of all proteins coded by gene g. In the context of UCE, we can formulate this as a dictionary that maps each gene g to a dp-dimensional protein embedding vector. Specifically, we used the ESM2 model, which yields embeddings of size dp = 5,120 (refs. 32,33).

Protein language models are chosen for gene representation because they can generate universal representations of any protein sequence. Thus, for new species, all that is required is to have the amino acid sequences of that species’ protein coding genes. These genes do not need to have solved structures and orthology does not need to be calculated between them and the existing training data. Model training ablations demonstrate that, among a class of different protein language models, UCE models trained with ESM2-15B performed the best (Supplementary Fig. 8). Model ablation experiments further demonstrate that the use of protein embeddings to tokenize genes has substantial benefits for the more poorly represented species in the training corpus, as a small ablation model trained with protein embeddings outperformed a model with randomly initialized embeddings on all species except for human (Supplementary Fig. 8). Moreover, the use of protein embeddings enables a unique capability: embedding new species not found in the training data (Fig. 2).

Model input: cell representation

For each cell ci in the input dataset \({\mathcal{D}}\), we identify two distinct sets of protein-coding genes: the expressed genes \({G}_{i}^{+}\) and the non-expressed genes \({G}_{i}^{-}\). These sets are defined as follows:

$${G}_{i}^{+}=\{g| {x}_{g}^{i} > 0\}$$

(1)

$${G}_{i}^{-}=\{g| {x}_{g}^{i}=0\}$$

(2)

For producing the cell embedding, a multi-set of 1,024 non-unique genes \({G}_{i}^{s}\) are sampled from the expressed genes \({G}_{i}^{+}\), with replacement. The probability of sampling a gene \(g\in {G}_{i}^{+}\) is weighted by the log-normalized expression of that gene, which can be formulated as:

$$P(g| {c}_{i})=\frac{\log ({x}_{g}^{i}+1)}{{\sum }_{j\in {G}_{i}^{+}}\log ({x}_{j}^{i}+1)}$$

(3)

where \({x}_{g}^{i}\) is the expression count of gene g in cell ci, and the sum in the denominator is over all genes in \({G}_{i}^{+}\).

Note that, for a given cell, different random seeds can yield different sampled gene subsets and therefore slightly different representations (Supplementary Table 11); however, using a fixed seed selects the same genes each time and produces an identical embedding for repeated runs on the same cell.

Once the multi-set \({G}_{i}^{s}\) is compiled for each cell ci, we arrange the genes within each chromosome according to their genomic positions. Different chromosomes are specified using special chromosome start and end tokens. Start tokens are unique to each chromosome and species. Every chromosome group is combined into a single sequence, with chromosome order randomly determined. A cell-level CLS token is appended to the start of the sequence. It is designed to capture the cell-level embedding after training the model. The final sequence of genes ordered by genomic location and separated by chromosome is referred to as the cell sentence Si for cell ci.

Ordering the genes using this information does appear to have a positive effect on model performance (Supplementary Note 4 and Supplementary Table 3). Transformer models do not require any ordering of their inputs, and these metadata were added to the model only to enable potential applications, such as examining chromosomal aberrations or synteny between species. Future models of this type may choose to exclude this metadata, or include it using other methods besides positional encodings.

Transformer architecture

Each cell sentence Si is fed into a transformer that consists of nlay layers. Each layer contains a multi-head self-attention mechanism with nhead attention heads and a feedforward network operating over a hidden space of dimensionality dhid. We also initialize sinusoidally-varying positional embeddings. Gene token embeddings are compressed using a single layer MLP to demb-dimensional vectors before passing through the transformer.

While the transformer architecture is highly performative, it requires substantial computational cost to train and evaluate. One important aspect of this cost is the quadratic increase in the runtime of transformers’ attention operations proportional to the number of tokens. Such a relationship requires that the number of genes sampled by UCE, 1,024, be relatively limited, especially given that individual cells might have more than 1,024 uniquely expressed genes. Future model architectures could explore increasing this context length, or using models with subquadratic scaling such as state space models[57].

Model output: cell embedding

The final output from the model is the cell embedding vector \({{\bf{h}}}_{\mathrm{cell}}^{i}\in {\mathcal{U}}\), which corresponds to the demb-dimensional embedding of the CLS token in the final layer of the model following decoding with an additional MLP.

When cells with unrealistic random gene expression patterns, such as those generated by shuffling the expression of real cells, are inputted, the resulting cell embeddings default to a heterogeneous, out of distribution output (Supplementary Fig. 1).

Model training: cell representation

At the time of training, we generate a set \({G}_{i}^{M+}\subset {G}_{i}^{+}\) by randomly selecting a certain percentage (rmask) of genes from \({G}_{i}^{+}\), without replacement. This set is used for computing the loss during training, and is masked from the cell representation.

The probability of sampling a gene \(g\in {G}_{i}^{+}\backslash {G}_{i}^{M+}\) (equation 3) is then updated to be:

$$P(g| {c}_{i})=\frac{\log ({x}_{g}^{i}+1)}{{\sum }_{j\in {G}_{i}^{+}\backslash {G}_{i}^{M+}}\log ({x}_{j}^{i}+1)}$$

(4)

We also establish two additional gene sets to be used for loss computation: \({G}_{i}^{L+}\in G\) and \({G}_{i}^{L-}\in G\). \({G}_{i}^{L+}\) and \({G}_{i}^{L-}\) are randomly selected from the masked set of expressed genes \({G}_{i}^{M+}\) and the set of unexpressed genes \({G}_{i}^{-}\), respectively. Both \({G}_{i}^{L+}\) and \({G}_{i}^{L-}\) are of equal size, specifically Nloss/2. In the case of \({G}_{i}^{L-}\), the sampling is done without replacement unless \(| {G}_{i}^{-}| < {N}_{\mathrm{loss}}/2\). Similarly \({G}_{i}^{L+}\), is also sampled without replacement unless \(| {G}_{i}^{M+}| < {N}_{\mathrm{loss}}/2\). In this case, \({G}_{i}^{M+}\) is used as is along with additional samples drawn with replacement from the full set of expressed genes \({G}_{i}^{+}\).

Model training: loss function

UCE is trained to reconstruct the binarized expression of genes in a cell, when those genes are masked from the model by setting their expression to 0. To calculate the loss function for a given cell ci, the cell embedding vector \({{\bf{h}}}_{\mathrm{emb}}^{i}\) is individually concatenated with every gene g within both \({G}_{i}^{L+}\) and \({G}_{i}^{L-}\). These concatenated vectors then serve as input to a feedforward multilayer perceptron (MLP), which computes the probability that gene g is expressed within cell ci.

\({{\bf{h}}}_{\mathrm{cell}}^{i}\) represents the embedding vector for cell ci and pg represents the token embedding for gene g. Then the concatenated vector \({{\bf{z}}}_{g}^{i}\) that serves as input to the MLP for cell ci and gene g is:

$${{\bf{p}}}_{g}^{{\prime} }={\rm{M}}{\rm{L}}{\rm{P}}({{\bf{p}}}_{g})$$

(5)

$${{\bf{z}}}_{g}^{i}=[{{\bf{h}}}_{{\rm{c}}{\rm{e}}{\rm{l}}{\rm{l}}}^{i}||{{\bf{p}}}_{g}^{{\prime} }]$$

(6)

where ∣∣ denotes the concatenation operation and \({{\bf{p}}}_{g}^{{\prime} }\) is the compressed protein embedding.

The MLP then processes this concatenated input to produce the predicted probability that gene g is expressed:

$$p({y}_{g}^{i})={\rm{M}}{\rm{L}}{\rm{P}}({{\bf{z}}}_{g}^{i})$$

(7)

This probability is then used in the binary cross-entropy loss function. The true classification labels for each gene’s expression status in cell ci are represented by the vector yi. UCE is trained to accurately predict the expression of genes in \({G}_{i}^{L+}\) and the lack of expression in \({G}_{i}^{L-}\). The model is trained using a binary cross-entropy loss, which is averaged across all Nloss genes and all N cells in the minibatch as follows:

$$L=-\frac{1}{N}\mathop{\sum }\limits_{i=1}^{N}\frac{1}{{N}_{\mathrm{loss}}}\mathop{\sum }\limits_{j=1}^{{N}_{\mathrm{loss}}}[{y}_{j}^{i}\log (p({y}_{j}^{i}))+(1-{y}_{j}^{i})\log (1-p({y}_{j}^{i}))]$$

(8)

Further details on hyperparameter choices are provided in Supplementary Table 2. Different hyperparameter choices for this masked loss can impact final model quality. Reducing the masking percentage from 20% to 10% or increasing it to 40% did not improve model performance (Supplementary Note 4 and Supplementary Table 4). In future models, an alternate loss, for example, a generative decoder style loss, could be used, and might improve performance as no genes would be masked during training.

Creating IMA and dataset preprocessing

The IMA that was used to train UCE was created by combining scRNA-seq datasets from multiple publicly available sources. The majority of IMA data (33.9 million cells and 285 datasets) are human and mouse data downloaded from CZI Cell X Gene (CxG) Census[35] v.2023-07-10 (10 July 2023). Duplicate cells were removed by selecting primary cells only. The remainder of the IMA is composed of 2.3 million cells from 28 datasets, from eight different species: human, mouse, zebrafish, rhesus macaque, crab-eating macaque, mouse lemur, frog and pig.

For datasets from the CxG Census, preprocessing involved only filtering cells by minimum gene counts (200) and genes by a minimum cell counts of 10. No highly variable gene selection was applied. For datasets collected from other sources, preprocessing was not uniform (Supplementary Note 1).

For visualization of the IMA (Fig. 1b), predicting green monkey cell types (Fig. 2d), matching new species centroids (Supplementary Table 15) and prediction of Norn-like cells (Fig. 4 and Extended Data Fig. 9), a representative sample of the IMA was used in place of the full 36 million cells. This representative sample was used to speed up computationally intensive tasks like UMAP calculation. The sample was created by randomly choosing 10,000 cells from each dataset, without replacement. For datasets with fewer than 10,000 cells, the entire dataset was included. In total, this representative sample has 2,969,114 cells. The average number of cells per dataset in the sample is 9486. For visualization and centroid calculation, cell types in the sample were coarsened by mapping them to a set of 51 coarse cell types (Supplementary Table 19).

Model Evaluation

- Zero-shot embedding quality and clustering: for evaluating the quality of embeddings, we used metrics from the single-cell integration benchmark[16].

- Cell type organization: for each cell type dendrogram the Euclidean distance was used to perform hierarchical clustering across all cells.

- Comparison to cell ontology: here, we used the tree distance between any two cell types in Cell Ontology[48]. We used the Cell Ontology from release date 16 August 2016. To determine the Euclidean distance distribution, we sampled 100,000 random pairs of cells from Tabula Sapiens v.2.

- Zero-shot cell type alignment to IMA: for each cell type θ, a centroid was identified separately for data from Tabula Sapiens v.2 (TSv2) \({c}_{\theta }^{T}\) and from IMA \({c}_{\theta }^{I}\). For each cell type that is present in both TSv2 and the IMA, the three nearest-neighbour cell type centroids \({{\bf{N}}}_{\theta }^{T}\) to the centroid in Tabula Sapiens \({c}_{\theta }^{T}\) were identified. These neighbours could be either from Tabula Sapiens or from the IMA. If this set of neighbours \({{\bf{N}}}_{\theta }^{T}\) to the anchor centroid from TSv2 data \({c}_{\theta }^{T}\) contains the centroid for the same cell type in IMA data \({c}_{\theta }^{I}\), then this was counted as a correct match. This analysis was performed per tissue, both in the UCE embedding space as well as in the original expression space (after log-normalization). In the case of the original data representation, the set of 5,704 shared genes across all 184 human datasets in the IMA was used to represent each cell. The expression counts in each cell are normalized by total counts over all genes, so that every cell has the same total count after normalization. This is followed by a log transformation. This follows standard Scanpy data preprocessing guidelines[58].

- Scanpy analyses and package versions: unless otherwise stated, all analyses were performed using Python v.3.7+ and scanpy v.1.9.3+. The default settings were always used unless otherwise specified. For differential expression analyses, the scanpy function sc.pp.highly_variable_genes was used with the default settings with the flavour seurat_v3. Scanpy was also used to generate all UMAPs. First, sc.pp.neighbours was run, using the default settings except for setting use_rep to the correct embedding value, such as the embedding from UCE. Then sc.tl.umap was used with the default settings to generate the UMAP. The evaluation framework for UCE is released with the following versions: numpy v.1.26.4, scipy v.1.14.1, pandas v.2.2.2, tqdm v.4.66.5, torch v.2.1.1, scanpy v.1.10.2, accelerate v.0.24.0, and for downloading model files: requests v.2.25.1, urllib3 v.1.26.6.

Description of other models

- Geneformer[27] is a transformer-based foundation model. Geneformer represents a cell as a list of genes sorted by their expression. Geneformer was trained using masked language modelling on 30 million cells. For all analyses, the GF-12L-30M-i2048 model was used.

- tGPT[59] is a transformer-based foundation model. tGPT represents cells, like Geneformer, as a list of genes sorted by expression. However, tGPT is trained as an autoregressive language model, rather than a masked language model. tGPT was trained on 22.3 million cells.

- scGPT[28] is a transformer-based foundation model. scGPT represents cells as a list of the expression values of its genes. scGPT is trained to iteratively decode those genes’ expressions when some are masked, using generative pretraining. scGPT was trained on 33 million cells. For all analyses, the whole-human model was used.

- scVI[15] is a variational autoencoder. scVI is trained to reconstruct gene expression values using a zero-inflated negative binomial loss. scVI was used with the default parameters for all experiments. scVI v.1.01+ was used. scVI input datasets were transformed only by taking the highly variable gene subset using sc.pp.highly_variable_genes with the flavour seurat_v3, and then the scVI model was trained with the default settings (number of hidden variables, 10).

- scArches builds on the scVI architecture to enable transfer learning of cell types. As used in this work, scArches first trains an scVI model on a reference dataset, fine-tunes a cell-type-aware scANVI model using the cell types from the reference dataset and then further fine-tunes this model on a transfer dataset using architecture surgery. scArches was used with the default parameters for all experiments. scArches v.0.5.1 was used, and was trained using the same scVI model for the dataset as a starting point.

- For comparisons to PCA, the default scanpy settings and best practices were used. The dataset was normalized using sc.pp.normalize_total, which normalized cell counts to the median count value for the dataset. Expression values were then log normalized using sc.pp.log1p, and PCA was calculated with sc.pp.pca, which defaults to the top 50 principal components.

Norn cell differential expression

A logistic classifier was trained to predict cell types from UCE embeddings on mouse kidney cells. This classifier was then applied to UCE embeddings from the representative sample of IMA datasets. The datasets were then split by tissue, and the datasets with the most predicted Norn cells in each tissue were used for differential expression analysis. The top 13 kidney datasets, top 6 lung and top 6 heart datasets were chosen.

For each individual (full) dataset, RNA counts were log normalized, and then differential expression was run using default settings as implemented in Scanpy[58], comparing predicted Norn cells with all other cells in the dataset. The results of these differential expression tests were used to determine the log-transformed fold change of marker genes in predicted Norn cells (Fig. 4c and 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

The full list of datasets used to train UCE are provided in Supplementary Table 16. Most of these datasets are available to download from CellXGene[35], for those that are not, we provide a link to where they were downloaded from. All datasets analysed in the paper are publicly available to download. The green monkey lung and lymph node dataset is available under accession code GSE156755. The naked mole rat dataset is available under accession code GSE132642. The chicken retina dataset is available under accession code GSE159107. The chicken heart dataset is available under accession code GSE149457. The mouse kidney dataset is available under accession code GSE193321. The human lung disease dataset is available under accession code GSE136831. Processed datasets used for analysis with their corresponding cell embeddings, including Tabula Sapiens v.2, have been deposited at Zenodo[60] (https://doi.org/10.5281/zenodo.19462110).

Code availability

UCE was written in Python using the PyTorch library. The source code is available at GitHub (https://github.com/snap-stanford/uce). Code for reproducing figures and analyses is available at GitHub (https://github.com/yhr91/uce_reproduce/tree/master).

References

1. The Tabula Sapiens Consortium. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).

2. Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023).

3. Li, H. et al. Fly cell atlas: a single-nucleus transcriptomic atlas of the adult fruit fly. Science 375, eabk2432 (2022).

4. Vaishnav, E. D. et al. The evolution, evolvability and engineering of gene regulatory DNA. Nature 603, 455–463 (2022).

5. Onuchic, J. N., Luthey-Schulten, Z. & Wolynes, P. G. Theory of protein folding: the energy landscape perspective. Ann. Rev. Phys. Chem. 48, 545–600 (1997).

6. Moris, N., Pina, C. & Arias, A. M. Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet. 17, 693–703 (2016).

7. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

8. Waddington, C. H. The Strategy of the Genes (Routledge, 1957).

9. The Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a tabula muris. Nature 562, 367–372 (2018).

10. Regev, A. et al. The human cell atlas. elife 6, e27041 (2017).

11. Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the human cell atlas on medicine. Nat. Med. 28, 2486–2496 (2022).

12. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

13. Plass, M. et al. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science 360, eaaq1723 (2018).

14. Eraslan, G., Avsec, Ž, Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).

15. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

16. Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).

17. Argelaguet, R., Cuomo, A. S., Stegle, O. & Marioni, J. C. Computational principles and challenges in single-cell data integration. Nat. Biotechnol. 39, 1202–1215 (2021).

18. Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40, 121–130 (2022).

19. Tarashansky, A. J. et al. Mapping single-cell atlases throughout metazoa unravels cell type evolution. eLife 10, e66747 (2021).

20. Brown, T. et al. Language models are few-shot learners. Adv. Neural Inform. Process. Syst. 33, 1877–1901 (2020).

21. OpenAI. GPT-4 Technical Report. Report No. 2303.08774 (OpenAI, 2023).

22. Anil, R. et al. Palm 2 technical report. Preprint at arXiv https://doi.org/10.48550/arXiv.2305.10403 (2023).

23. Kirillov, A. et al. Segment anything. In Proc. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) 3992–4003 (IEEE, 2023).

24. Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at arXiv https://doi.org/10.48550/arXiv.2108.07258 (2021).

25. Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021).

26. Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).

27. Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

28. Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470–91480 (2024).

29. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

30. Quake, S. R. The cell as a bag of RNA. Trends Genet. 37, 1064–1068 (2021).

31. Vaswani, A. et al. Attention is all you need. In Proc. 31st Conference on Neural Information Processing Systems (eds Guyon, I. et al.) (NIPS, 2017).

32. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

33. Rosen, Y. et al. Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN. Nat. Methods 21, 1492–1500 (2024).

34. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: pre-training of deep bidirectional transformers for language understanding. In Proc. 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019) 4171–4186 (Association for Computational Linguistics, 2019).

35. CZI Cell Science Program. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. Nucleic Acids Res. 53, D886–D900 (2025).

36. Gordon, S., Plüddemann, A. & Martinez Estrada, F. Macrophage heterogeneity in tissues: phenotypic diversity and functions. Immunol. Rev. 262, 36–55 (2014).

37. Conde, C. D. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).

38. Schroeder, M. E. et al. A transcriptomic atlas of astrocyte heterogeneity across space and time in mouse and marmoset. Neuron 113, 3942–3965.e19 (2025).

39. Wang, H., Leskovec, J. & Regev, A. Limitations of cell embedding metrics assessed using drifting islands. Nat. Biotechnol. 44, 574–577 (2026).

40. Speranza, E. et al. Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of african green monkeys. Sci. Transl. Med. 13, eabe8146 (2021).

41. Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

42. Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).

43. Hilton, H. G. et al. Single-cell transcriptomics of the naked mole-rat reveals unexpected features of mammalian immunity. PLoS Biol. 17, e3000528 (2019).

44. Yamagata, M., Yan, W. & Sanes, J. R. A cell atlas of the chick retina based on single-cell transcriptomics. eLife 10, e63907 (2021).

45. Mantri, M. et al. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat. Commun. 12, 1771 (2021).

46. Zhong, H. et al. Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life. Nucleic Acids Res. 53, gkae1316 (2025).

47. Orozco, L. D. et al. Integration of eQTL and a single-cell atlas in the human eye identifies causal genes for age-related macular degeneration. Cell Rep. 30, 1246–1259 (2020).

48. Bard, J., Rhee, S. Y. & Ashburner, M. An ontology for cell types. Genome Biol. 6, R21 (2005).

49. Kragesteen, B. K. et al. The transcriptional and regulatory identity of erythropoietin producing cells. Nat. Med. 29, 1191–1200 (2023).

50. Haine, L. et al. Cytoprotective effects of erythropoietin: what about the lung? Biomed. Pharmacother. 139, 111547 (2021).

51. Adams, T. S. et al. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. Sci. Adv. 6, eaba1983 (2020).

52. Tassiopoulos, S. et al. Erythropoietic response to hypoxaemia in diffuse idiopathic pulmonary fibrosis, as opposed to chronic obstructive pulmonary disease. Respir. Med. 95, 471–475 (2001).

53. Abdel-Aziz, C., Okaily, N. & Kasem, A. Erythropoietin: role in idiopathic pulmonary fibrosis revisited. Egypt. J. Chest Dis. Tuberc. 69, 716 (2020).

54. Tsantes, A. E. et al. Red cell macrocytosis in hypoxemic patients with chronic obstructive pulmonary disease. Respir. Med. 98, 1117–1123 (2004).

55. Brenner, S. Nature’s gift to science (Nobel Lecture). Chembiochem 4, 683–687 (2003).

56. Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024).

57. Gu, A., Goel, K. & Ré, C. Efficiently modeling long sequences with structured state spaces. In Proc. 10th International Conference on Learning Representations (ICLR, 2022).

58. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

59. Shen, H. et al. Generative pretraining from large-scale transcriptomes for single-cell deciphering. iScience 26, 106536 (2023).

60. Rosen, Y. et al. Universal cell embeddings provides a foundation model for cell biology. Zenodo https://doi.org/10.5281/zenodo.19462110 (2026).

61. Morgan, D. & Tergaonkar, V. Unraveling b cell trajectories at single cell resolution. Trends Immunol. 43, 210–229 (2022).

62. Subbannayya, Y., Pinto, S. M., Bösl, K., Prasad, T. S. K. & Kandasamy, R. K. Dynamics of dual specificity phosphatases and their interplay with protein kinases in immune signaling. Int. J. Mol. Sci. 20, 2086 (2019).

63. Ziegler, A., Marti, E., Summerfield, A. & Baumann, A. Identification and characterization of equine blood plasmacytoid dendritic cells. Dev. Comp. Immunol. 65, 352–357 (2016).

64. Travaglini, K. J. et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature 587, 619–625 (2020).

65. Litviňuková, M. et al. Cells of the adult human heart. Nature 588, 466–472 (2020).

66. Lake, B. B. et al. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619, 585–594 (2023).

67. Safran, M. et al. In Practical Guide to Life Science Databases (eds Abugessaisa, I. & Kasukawa, T.) 27–56 (Springer, 2021).

68. Stelzer, G. et al. The genecards suite: from gene data mining to disease genome sequence analyses. Curr. Protoco. Bioinform. 54, 1.30.1–1.30.33 (2016).

Acknowledgements

We thank R. Sosič, K. Huang, C. Bunne, H. Wang, M. Yasunaga, M. Moor, M. Xu, M. Jain, G. Crowley, M. Brbić, K. Pang, M. Bereket, Z. Piran, M. Schaefer, M. Roed, L. Nair, C. Kanatsoulis, J. Cool, N. Sofroniew, A. Tolopko, I. Jelic, A.-M. Istrate, P. Garcia-Nieto and O. Rozenblatt-Rosen for discussions and for providing feedback on our manuscript. We acknowledge support from R. C. Jones for help with accessing and analysing the Tabula Sapiens v.2 dataset. Icons were created using BioRender.

Funding

We acknowledge support from the Chan Zuckerberg Initiative, including help with accessing and processing CxG datasets; DARPA under no. N660011924033 (MCS); NSF under no. OAC-1835598 (CINES), CCF-1918940 (Expeditions); Stanford Data Science Initiative; the Wu Tsai Neurosciences Institute; Amazon; Genentech; GSK; Hitachi; Juniper Networks; and KDDI. Y. Roohani acknowledges funding support from GlaxoSmithKline. L.S. was supported by the American Slovenia Education Foundation.

Author information

Author notes

1. These authors contributed equally: Yanay Rosen, Yusuf Roohani

2. A full list of members and their affiliations appears in the Supplementary Information

Authors and Affiliations

1. Department of Computer Science, Stanford University, Stanford, CA, USA

Yanay Rosen, Ayush Agrawal, Leon Samotorčan & Jure Leskovec

2. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

Yusuf Roohani

3. Biohub, San Francisco, CA, USA

Stephen R. Quake

4. Department of Bioengineering, Stanford University, Stanford, CA, USA

Stephen R. Quake

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

Stephen R. Quake

6. Chan Zuckerberg Initiative, Redwood City, CA, USA

Stephen R. Quake

Consortia

Tabula Sapiens Consortium

- Stephen R. Quake

Contributions

Y. Rosen, Y. Roohani, S.R.Q. and J.L. conceived the study. Y. Rosen, Y. Roohani, S.R.Q. and J.L. performed research, contributed new analytical tools, designed algorithmic frameworks, analysed data and wrote the manuscript. Y. Rosen and Y. Roohani performed experiments and developed the software. A.A. and L.S. contributed to code and performed analyses. The members of the Tabula Sapiens Consortium provided annotated data. A full list of Tabula Sapiens Consortium members is available in the Supplementary Information.

Corresponding authors

Correspondence to Stephen R. Quake or Jure Leskovec.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Jinmiao Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 UCE outperforms existing methods for integrating Tabula Sapiens v2.

UMAP embeddings of Tabula Sapiens v2 and v1 donor coloured by tissue, cell type, sample and donor group (v1 or v2). UCE’s zero-shot embedding of Tabula Sapiens outperforms existing methods using comprehensive benchmarking metrics of cell embedding quality. For UCE, Geneformer, tGPT and log normalized expression, no additional gene selection was done. For scGPT and scArches, highly variable gene selection was performed on Tabula Sapiens v1, selecting the top 3000 highly variable genes, and the same gene set was used for generating embeddings for Tabula Sapiens v2. For scVI, 3000 highly variable genes were selected from cells from both Tabula Sapiens v1 and v2. scArches and scVI were trained using default settings, taking the sample as categorical covariate. For scArches, the cell type column (“cell_ontology_class”) was used during SCANVI training.

Extended Data Fig. 2 UCE embeddings recapture B cell identities in Tabula Sapiens v2.

(a) UMAP and B cell silhouette width scores of Tabula Sapiens v2 embeddings generated by Geneformer (left), scGPT (middle) and UCE (right), coloured by cell type. The silhouette width score of B cells is 93% higher in UCE versus scGPT and 25% higher versus Geneformer (SI Table 20). Cells annotated as B cells, coloured in red, are distinctly clustered in the UCE space. In the Geneformer UMAP, B cells do form a large cluster, but a large number of other small clusters as well. In the scGPT UMAP, B cells are widely distributed. (b) UMAP of Tabula Sapiens v2 B cells, generated by UCE, coloured by predicted cell type. A logistic classifier is trained on the UCE embeddings from the cross-tissue immune cell atlas[37], and then applied to predict cell types for Tabula Sapiens v2 B cells. Tabula Sapiens v2 B cells are indeed accurately classified as B cells of two categories, memory B cells and naive B cells. (c) Differential gene expression analysis between predicted memory B cells and predicted naive B cells. Known naive B cell markers Ighm and Ighd are preferentially expressed in the predicted naive B cells versus the predicted memory B cells[61]. Memory B cell markers Tnfrsf13b and Cd27 are also preferentially expressed in the predicted memory B cells[37].

Extended Data Fig. 3 UCE captures Cd3 expression in green monkey lymph node.

UMAP embeddings of cells originally labelled as B cells from green monkey lymph node, coloured by (a) log-normalized expression of Cd3d and by (b) predicted human cell type. A population of cells, originally identified as B cells, forms a separate cluster, visually identifiable in the UMAP. This cluster preferentially expresses a T cell marker, Cd3d. A logistic classifier is trained to predict cell types from UCE embeddings for human lymph node cells from the IMA. The classifier is then applied to predict cell types for green monkey cells. (c) Differential expression for predicted T and dendritic cells versus predicted B cells, for cells originally annotated as B cells. Predicted T cells preferentially express Cd3, Fyb1, Il7r, Hcst and Gzmk, which are known markers for Cd4 and Cd8 T cells[42]. Supplementary RNA expression data from the human protein atlas confirms that these genes have higher RNA expression in T cells versus B cells[41]. Additionally, cells predicted to be dendritic cells preferentially express dendritic cell markers Dusp1[62] and Plac8[63].

Extended Data Fig. 4 UCE can embed novel species that it was not trained on (green monkey, naked mole rat) in a zero-shot setting.

UMAP embeddings of green monkey lung and lymph node[40] (a), and naked mole rat spleen and circulating immune cells[43] (b) coloured by cell type annotations. UCE is able to embed datasets from species it was not trained on in a zero-shot setting. UCE embeddings accurately recapture cell type information, and cell types in the new species map closely to their counterparts from other species in the IMA (Fig. 2d, SI Table 15).

Extended Data Fig. 5 UCE can embed novel species that it was not trained on (chicken) in a zero-shot setting.

UMAP embeddings of chicken (a) retina[44] and (b) heart[45], coloured by cell type annotations. UCE is able to embed datasets from species it was not trained on in a zero-shot setting. UCE embeddings accurately recapture cell type information, and cell types in the new species map closely to their counterparts from other species in the IMA (SI Table 15).

Extended Data Fig. 6 Relationship between Embedding Distance and Ontological Similarity.

Relative organization of cell types in the embedding space compared to Cell Ontology. The x-axis depicts the Euclidean distance between all cell pairs across all tissues from Tabula Sapiens Consortium v2. The y-axis shows the corresponding tree distance between these cell types as found in the Cell Ontology.

Extended Data Fig. 7 Cell types show organization in embedding space based on developmental lineage.

(a) For each cell type centroid in the Tabula Sapiens v2 dataset, we determined the germ layer from which the closest neighbouring cell type centroid originated. This information is presented in a stacked bar chart, summarizing the data for all cell types. On the x-axis, each bar represents a cell type associated with a different germ layer. The height of each segment within a stacked bar indicates the proportion of nearest centroids belonging to a specific germ layer. For instance, the leftmost bar shows that over 90% of the nearest centroids to mesoderm lineage cell types also originated from the mesoderm lineage. (b) We also present confusion matrices to demonstrate the accuracy of a classifier in predicting the germ layer of origin for cell types not included in the training set. The matrix on the left displays results from a classifier trained by excluding one cell type at a time. The matrix on the right, however, shows results from a model trained by excluding groups of related cell types. These related cell types were identified based on coarse annotations and were grouped for exclusion during the training process.

Extended Data Fig. 8 Mapping lung data from Tabula Sapiens v2 to multiple lung datasets from the Integrated Mega-scale Atlas (IMA).

Red labels correspond to data from new donors (Tabula Sapiens v2), black are from IMA datasets. All cell type labels from multiple datasets are displayed as-is, with only small modifications to abbreviate longer labels.

Extended Data Fig. 9 Predicted Norn cells express canonical Norn Markers.

Identification of Norn cells and Norn-like cells across tissues. A logistic classifier is trained to predict Norn cells from universal cell embeddings for mouse kidney cells[49], and is applied to kidney datasets (top) and datasets from lung and heart (bottom). The log fold change of kidney cell type marker genes is visualized between cells predicted to be Norn cells and the remaining cells within each dataset. Cells which are predicted to be Norn-like indeed have high expression of Norn marker genes like Col1a1 and Dcn[49] (SI Table 17).

Extended Data Fig. 10 Differential Expression Between Norn-like cells Across Tissues.

Results of differential expression analysis between predicted Norn-like cells in datasets from three tissues, lung[64], heart[65] and kidney[66]. Predicted Norn-like cells in the lung preferentially express ferritin components Fth1 and Ftl67,68. Norn-like cells in the heart preferentially express Cdh19, which is broadly expressed in heart tissues41,67,68.

Supplementary information

Supplementary Information (download PDF )

Supplementary Notes 1–8, Supplementary Figs. 1–10, Supplementary Tables 1–14 and Supplementary References.

Reporting Summary (download PDF )

Supplementary Table 15 (download XLSX )

IMA centroids: cross-species cell type annotations for the four new species datasets (sheets 1–4).

Supplementary Table 16 (download XLSX )

Training datasets: information about the training datasets.

Supplementary Table 17 (download XLSX )

Norn marker genes differential expression: the differential gene expression results for Norn-positive marker genes, as well as all marker genes from ref. [49] for each of the datasets from kidney, lung and heart. For each tissue, the top datasets, as shown in Fig. 4 and Extended Data Fig. 9 are listed.

Supplementary Table 18 (download XLSX )

Fly predicted cell types: the predicted fly cell types for Tabula Drosophilae.

Supplementary Table 19 (download XLSX )

IMA sample counts: information about the IMA sample used for analyses in the paper.

Supplementary Table 20 (download XLSX )

Tabula Sapiens silhouette scores: zero-shot model silhouette scores for each cell type in Tabula Sapiens v.2.

Supplementary Table 21 (download XLSX )

IMA and Tabula Sapiens alignments: nearest-neighbour alignments between IMA and Tabula Sapiens by tissue.

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Rosen, Y., Roohani, Y., Agrawal, A. et al. Universal cell embedding provides a foundation model for cell biology. Nature 656, 183–191 (2026). https://doi.org/10.1038/s41586-026-10689-z

- Received: 03 December 2023

- Accepted: 21 May 2026

- Published: 08 July 2026

- Version of record: 08 July 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10689-z

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Disparate privacy risks from medical AI

Moritz A. Knolle,

Martin J. Menten,

Friederike Jungmann,

Felix Meissen,

Ben Glocker,

Daniel Rueckert &

…

Georgios Kaissis

Nature volume 656, pages 192–198 (2026)

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Abstract

Medical artificial intelligence (AI) models hold the promise to improve global access to high-quality diagnostics[1]. However, the training data underlying these models often contain sensitive patient information that may be exposed through privacy attacks2,3,4,5,6,7. Previous research has primarily quantified the success of these attacks in aggregate, across all records in a dataset. Thus, the privacy risk faced by individual patients, who often contribute multiple similar records to a training dataset, is poorly understood. Here we present one of the first patient-level privacy audits of AI models for medical diagnostic applications. We focus on membership inference attacks2,3,4 (MIAs), which seek to determine whether the data of a given individual were used to train a model. Across a diverse range of medical datasets, we show that MIAs can achieve near-perfect success rates for individual patients, even when the aggregate performance does not substantially deviate from random guessing. We further find that the number of patients with high attack success increases substantially with model capacity, and that underrepresented groups—stratified by disease status, self-reported race, insurance, sex or imaging protocol—face disproportionately high attack success. Together, our findings show that aggregate privacy metrics can severely underestimate individual privacy risk. Whether the disparate risk profiles we observe extend to attacks beyond MIAs remains an open question, motivating the further development of risk assessment and mitigation techniques that cater to all data-contributing patients.

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Main

Medical artificial intelligence (AI) has immense potential to improve health outcomes, particularly in regions in which specialized medical expertise is scarce[1]. At the same time, AI also poses new challenges and risks, including security vulnerabilities that arise when models are deployed. Untrusted users with access to an AI model may, by merely observing its predictions, steal its parameters8,9 or perform privacy attacks2,3,4,5,6,7, which can extract sensitive details about the data used for model training.

Privacy attacks against an AI model can enable detailed inferences about the individuals who contributed to its training data. For example, a membership inference attack (MIA)[2] attempts to determine whether the data of a specific patient were included in the training dataset of a model. The extent to which this constitutes a privacy violation is nuanced and depends on factors such as the underlying training population and the deployment context of the model. Although inferring membership for a model trained on a general population may be benign, doing so for a model trained on a narrow, disease- or centre-specific cohort acts as a direct proxy for sensitive medical information. For example, a successful MIA against the model in ref. [10], which predicts anti-cancer immunotherapy efficacy from routine blood test data, reveals that an individual has cancer.

The accelerating deployment of medical AI models trained on sensitive patient data[11] calls for rigorous privacy risk assessments. However, previous studies primarily quantified the success rate of MIAs, in aggregate, across all records in a training dataset. This implicitly averages risk across records, thereby obscuring important information on record- and patient-level attack success. Consequently, the risk that an individual faces by contributing their personal data (often multiple records) to an AI training dataset is poorly understood. Given that medical data are a key target for cybercriminals12,13, and pseudonymization alone is increasingly recognized as insufficient to prevent the re-identification of individuals in large, high-dimensional datasets14,15,16, there is a need to improve our understanding of the threat that AI privacy attacks pose to individual patients.

Here we show that deploying medical AI models without protective measures can pose substantial privacy risks to individual data-contributing patients. These risks are particularly acute when membership in a training population itself reveals sensitive medical information. Our privacy audit of AI models trained to perform standard diagnostic (supervised classification) tasks quantifies state-of-the-art MIA success3,4 at the resolution of individual data contributors. Using seven large datasets comprising real-world clinical data, including various types of medical images, electrocardiograms and electronic health records, we demonstrate that the success of a MIA is unequally distributed among data-contributing patients. We show that this disparity exists at two levels: (1) the individual patient level, at which some patients experience near-perfect attack success, whereas others remain essentially unaffected; and (2) the group level, at which patient groups underrepresented in a training dataset are often overrepresented among records most vulnerable to MIAs.

Together, our results indicate that privacy attacks against AI models may be much more effective at compromising the privacy of individual data contributors than previously thought. This suggests that current AI privacy risk reporting practices may underestimate individual-level risk and thus motivates the integration of mathematically verifiable risk mitigation strategies such as differential privacy (DP) into medical AI model development workflows.

Attacking AI by simple hypothesis tests

A popular deployment strategy for AI models gives users access to a model through a prediction interface, which, for a given input (for example, the chest radiograph of a patient), returns a corresponding prediction (for example, a 78% chance of pneumonia). This black-box access to a model can be exploited by an untrusted user to conduct a MIA that shows the membership status of a target record, that is, whether the target record was a member of the training dataset of a model or not (Fig. 1a). To infer membership status, MIAs typically make use of the fact that AI models are often slightly more confident about their predictions on training than on non-training data.

Fig. 1: MIA and evaluation strategies.

Full size image

a, Schematic of a MIA, in which an untrusted user, only by observing the predictions of a model, aims to infer whether a specific target record was part of the training dataset. The attack is considered successful if the untrusted user can reliably distinguish between model A and model B, which are identical except for the inclusion and exclusion of the target record in the respective training dataset. b,c, Attack success can be measured either, in aggregate, across all records in the dataset (b) or, more granularly, for each record individually across many target models (c).

Likelihood-ratio MIAs (LR-MIAs)3,4, the current state-of-the-art in MIAs, frame membership inference as a simple vs. simple hypothesis testing problem on the prediction confidence provided by the target model. In essence, LR-MIAs compare the likelihood of the predicted confidence of the target model for the target record under the null (the target record was not a member) and the alternative hypothesis (the target record was a member). Here, the parameters of the distributions under the two hypotheses are specified by parametric fitting of sample confidence values obtained from reference models. Reference models are models assumed to be trained by the attacker and are ideally, but not necessarily, of similar architecture as the target model and trained on data similar to the training dataset of the target model.

Note that objectively larger threats are posed by privacy attacks with stronger assumptions on a potential attacker, such as access to model parameters[17], access to parameter updates during model training[18] or, furthermore, the ability to modify the model architecture19,20. However, we do not consider them in this study as their strong assumptions are not realistic for careful, practical deployment scenarios. By contrast, the type of attack we consider here requires querying the target model only once (to obtain a prediction for the target record) and may thus be executed by any attacker posing as a real user of an AI system. Notably, as the attacks we study are executed against fully trained models, data-governance-preserving techniques such as federated/swarm-learning[21] provide no protection.

From aggregate to patient-level risk

MIA performance is evaluated through a receiver operating characteristic (ROC) analysis[22] on numerous repetitions of the MIA game scenario, in which an untrusted user is challenged to guess the membership status of a given record (Fig. 1a). In practice, owing to the computational cost of training AI models, attack success is typically evaluated using a single target model. More specifically, a target model is trained on a random subset of the training dataset, and subsequently, an ROC analysis is performed on the aggregated membership predictions for all records in the dataset (Fig. 1b). Although practical, this approach has a key shortcoming: it provides no indication of the performance of the attack for individual records or patients.

To address this issue, we propose a simple technique for estimating record-, and by extension, patient-level vulnerability to LR-MIAs (Fig. 1c). In brief, using a large set of target models (N = 200) trained on random patient subsets, we estimate, for each training record, sampling distributions of the confidence of the target model under the null and alternative hypotheses in LR-MIAs. In other words, we estimate empirical distributions of confidence values as provided by target models, partitioned into models trained and not trained on the target record. Because these distributions are assumed to take Gaussian form in LR-MIAs3,4, record-level attack success, as measured by the area under the ROC curve (AUC), can be calculated in closed form (Methods). A high AUC score, close to the maximum value of 1.0, suggests high privacy risk: a MIA for this record could achieve high sensitivity with little to no false positives. Notably, the record-level MIA AUC also offers a probabilistic interpretation[22]: the record-level MIA AUC is the probability that a confidence score from a target model trained on the target record is larger than a score from a target model not trained on the target record.

Correctly determining the membership status for one of the records contributed by an individual patient reveals the membership status of the patient. Thus, we compute patient-level scores by taking the maximum across all record-level scores for a given patient. The raw record-level scores and the average patient-level scores can be found in Extended Data Fig. 1.

Notably, our technique for measuring record-level attack success reduces to estimating the bi-normal AUC from sample statistics and thus has desirable statistical properties. Its standard error at the record level can be computed in closed form[23] (Methods). As expected, using a total of N = 200 target models evenly split between null and alternative hypotheses for each record, the standard error of the record-level MIA AUC is small across all records in the investigated datasets (Extended Data Fig. 2).

Attacking open-source models

Recent advances in attack design have made LR-MIAs much more practical. To illustrate the practical feasibility of conducting MIAs, we demonstrate attacks against two chest radiograph models from the TorchXrayVision[24] library. We used the Robust Membership Inference Attack[4] (RMIA), an improved LR-MIA that requires only one or two reference models, compared with more than 100 for the Likelihood Ratio Attack[3] (LiRA). RMIA achieves this efficiency gain by effectively using reference data (data similar to the target record) alongside the target record to query the target model. Crucially, the attack does not require knowledge of the membership status of the reference data.

We simulated a realistic attack setting in which an attacker lacks access to the training dataset of the target model to train reference models and is further constrained by computational resources. Specifically, we used only a single pre-trained PadChest[25] model as a reference model to perform attacks against the CheXpert[26] and MIMIC-CXR[27] models of the library. In this setting, also known as an offline attack, an attacker incurs no computational cost in training the reference model. Instead, they simply need to obtain predictions from the reference model for both the target record and the reference data. This can be done efficiently on commodity hardware without a graphics processing unit (GPU). To conduct the attack, we queried the target model once to collect confidence values for all target records. Using this collection, we then computed RMIA test statistics for each target record by randomly selecting, independent of membership status, N = 500 confidence values from the other targets in this collection as reference data. This strategy would effectively conceal the additional reference data queries to the target model in a real attack.

We evaluated attack success on a combined dataset of records from CheXpert and MIMIC-CXR (N = 25, 000 each), which were, respectively, labelled as members and non-members for the CheXpert model (v.v. for the MIMIC-CXR model). In this setting, RMIA achieved substantial aggregate success with respective AUC scores of 0.61 and 0.65 (Fig. 2a). Note that owing to the distribution shift between members and non-members, results from this evaluation setting are not directly comparable to the standard evaluation protocol in which members and non-members are sampled at random from the training dataset. Notably, however, such a distribution shift is expected in a real attack, and this setting is thus of high interest.

Fig. 2: MIAs pose substantial privacy risks to individual data-contributing patients.

Full size image

a, Aggregate MIA success for a realistic attack against open-source CheXpert and MIMIC-CXR models (RMIA offline, R = 1 reference model pre-trained on PadChest). b, eSF analysis of patient-level MIA AUC scores computed using N = 200 target models for each dataset (residual networks, about 1.5 million parameters each). Patient-level scores are computed as the maximum record-level score for a given patient. c, ROC analysis of aggregate attack success (LiRA online, vertical-average mean for N = 10 target models, R = 190 reference models). d,e, eSF plots of patient-level MIA AUC scores alongside diagnostic performance (macro-average AUC) on unseen test data for N = 200 target models with varying levels of record-level (ε, δ)-DP privacy protection: PTB-XL (d) and EMBED (e). δ was kept constant at 1/D, where D is the dataset size. f,g, eSF plots of patient-level MIA AUC scores alongside diagnostic performance (macro-average AUC) on unseen test data for N = 200 target models of increasing model capacity: Fitzpatrick-17k (f) and CheXpert (g). Error bars indicate s.d., round brackets indicate aggregate attack AUC; square brackets indicate AUC upper bound implied by record-level DP accounting; dashed grey lines in ROC curve plots indicate random-guessing performance; and dashed lines in eSF plots indicate 95% Greenwood CI.

Near-perfect success for some patients

After demonstrating realistic attacks against two open-source models, we next investigated how effectively MIAs can compromise the privacy of individual patients. To this end, we measured patient-level MIA success across a diverse range of medical datasets using, for each, a large set of target models. Notably, we used state-of-the-art model training techniques (for example, data augmentation, weight decay and learning rate schedules) and furthermore, took explicit countermeasures to prevent overfitting, which is known to exacerbate privacy risks2,28. As a result, the investigated target models, despite being trained on roughly half of the available data each, provide high diagnostic performance within a few percentage points of published baselines (Methods).

Across all investigated datasets and models, we identified a small subset of patients who are highly vulnerable to LR-MIAs. This is indicated by empirical survival functions (eSF) of patient-level MIA AUC scores, which, for a given score, show the proportion of patients with this score or higher (Fig. 2b). By contrast, ROC curves of aggregate attack success and their corresponding AUC scores do not deviate substantially from the random-guessing baseline, thus incorrectly indicating a low attack vulnerability (Fig. 2c and Extended Data Fig. 1c–e). This suggests that average-case metrics of attack success, as used in the standard evaluation protocol, are unsuitable measures of privacy risk. They do not accurately reflect that some records or patients may be highly vulnerable, whereas the vast majority are not.

For the two non-imaging datasets, MIMIC-IV-ED[29] (electronic health records) and PTB-XL[30] (electrocardiograms), we simulated attack settings in which an attacker only has partial access to the target record (Extended Data Fig. 3). Although MIA success generally decreases under partial data access, a subset of patients retain high AUC scores, even in settings in which the attacker has access to only basic clinical information—such as a patients’ age, sex, chief complaints and vital signs (MIMIC-IV-ED), or only the lead I signal from a 12-lead electrocardiogram (PTB-XL).

We verified how resolvable the discovered vulnerabilities are by training models with different levels of record-level (ε, δ)-DP protection (Fig. 2d,e). As expected, we find that patient-level MIA risk decreases with stronger levels of privacy protection (smaller ε values). Moreover, in most scenarios, we observe no violation of the record-level DP guarantee (indicated by the square brackets in the panel legend), although many patients contributed multiple records. Violations are observed only for a subset of patients under strong privacy protection (ε = 1), in which some patients have MIA AUC scores exceeding the upper bound on the MIA AUC implied by the record-level DP guarantee. This behaviour is expected and could be mitigated by implementing patient-level DP accounting.

Larger models, greater risks

Many of the recent AI success stories have been driven not by methodological advances but by scaling up model and dataset sizes[31]. In light of this scaling trend, we next investigated the impact of model capacity on MIA success. For Fitzpatrick 17k[32] and CheXpert, we trained models with increasing capacity, including wide residual networks[33] (WRN-28-2 and WRN-40-4) and vision transformers[34] (ViT-B/16 and ViT-L/16). Where computationally feasible, vision transformers were trained on images of different sizes: 64 × 64 and 128 × 128 pixels; this is indicated by a trailing number behind the model name (for example, ViT-B/16-64 and ViT-B/16-128).

We find that MIA success (both at the aggregate and patient levels) increases with model capacity. We observe that the relative share of patients highly vulnerable to MIAs increases greatly for larger models, often by an order of magnitude (Fig. 2f,g). For the dermatology dataset (Fitzpatrick 17k), increasing model capacity yields large gains in diagnostic performance with a pronounced increase between WRN-40-4 and ViT-B/16-128, which was pre-trained on a large dataset of more than 14 million natural images. However, simultaneously, the number of patients with near-perfect attack success (AUC score of 0.95 or higher) increases substantially: 0 (WRN-28-2), 1 out of 10,000 (WRN-40-4), 1 out of 1,000 (ViT-B/16-64) and 1 out of 10 (ViT-B/16-128). We observe a similar trend in the much larger dataset CheXpert, although attack success is generally lower. Notably, for CheXpert, vision transformer models do not achieve diagnostic performance competitive with WRN-based models. This is probably because of the diminished utility of natural-image pre-training for medical greyscale images[35].

Attack success varies by subgroup

Motivated by recent findings36,37, which revealed that the diagnostic performance of AI models can differ across patient subgroups, we investigated whether differences in privacy risk exist between subgroups. To this end, we focused our analysis on the most vulnerable records (99th MIA AUC percentile) and compared how frequently a subgroup appears in this extreme-risk tail compared with the overall dataset. We did not consider differences in aggregate attack success, as we previously identified this metric as an unsuitable measure of privacy risk.

We find that extreme MIA risk is unequally distributed across patient subgroups when stratifying by disease status, self-reported race, sex, imaging protocol or health insurance. More precisely, for most comparisons, we observe significant differences in subgroup composition between the most vulnerable records and the overall dataset (Fig. 3 and Extended Data Fig. 4). For example, in MIMIC-IV-ED, records from Black patients, patients with Medicaid insurance or patients diagnosed with cancer were observed more frequently than expected among the most vulnerable records (+31%, +126%, and +18% relative change to the overall dataset, respectively). Raw data on the composition of the extreme MIA risk tails as well as the overall datasets are provided in Supplementary Tables 3–16. To find factors that could explain the observed differences, we performed a post hoc test analysis and computed Pearson residuals for all subgroup comparisons (Fig. 3 and Extended Data Fig. 4).

Fig. 3: Significant differences in extreme MIA risk between patient subgroups.

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a–d, The panel rows show two-sided χ^2-test results for subgroup counts among the 99th record-level MIA AUC percentile for CheXpert (a), MIMIC-CXR (b), EMBED (c) and MIMIC-IV-ED (d). Pearson residuals measure the contribution of a group to the test statistic. A large, positive value indicates that more records were observed for this group than expected. A negative value indicates the opposite. The column titles show stratification variables; bars are coloured according to the relative share of records of a group in the training dataset. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 and NS, not significant. Multiple comparison correction was applied row-wise using the Bonferroni method; statistical significance could not be tested for CheXpert and MIMIC-CXR disease label groups as the categorization is not mutually exclusive; race subgroup comparisons in EMBED are likely confounded by breast density differences. Left to right, CheXpert/MIMIC-CXR disease labels refer to no finding (NF), enlarged cardiomediastinum (EC), cardiomegaly (Cm), lung opacity (LO), lung lesion (LL), oedema (Ed), consolidation (Co), pneumonia (Pn), atelectasis (At), pneumothorax (Px), pleural effusion (PE), pleural other (PO), fracture (Fr) and support devices (SD). Imaging protocol abbreviations refer to anteroposterior (AP), posteroanterior (PA), lateral (L), lateral left (LL), mediolateral oblique (MLO) and craniocaudal (CC). BI-RADS indicates breast cancer assessment: incomplete (BI-RADS-0) to biopsy-proven malignancy (BI-RADS-6) and breast density: mostly fatty (BI-RADS-A) to extremely dense (BI-RADS-D). Left to right, adjusted P values are as follows. CheXpert: 8.1 × 10−9, 9.7 × 10−2, 2.1 × 10−5; MIMIC-CXR: 0.1, 2.2 × 10−23, 1.6 × 10−29; EMBED: 1.0 × 10−68, <1.0 × 10−100, 1.0 × 10−8, 1.0, 2.7 × 10−7. MIMIC-IV-ED: 0.61, 4.1 × 10−24, 2.3 × 10−9, 6.3 × 10−20, 1.8 × 10−2, 2.3 × 10−53.

We primarily observe large, positive Pearson residuals for underrepresented groups in the datasets, suggesting that relative group size influences MIA risk. Consider, for example, EMBED[38], a mammography dataset comprising mostly negative findings, that is, unremarkable mammograms of healthy breasts with no indication of a tumour. Models for this dataset are trained to predict breast density, and thus never have direct access to tumour findings. Despite this, benign tumour findings (BI-RADS-2) and tumour findings suspicious of malignancy (BI-RADS-4) account for a disproportionately large share of the most vulnerable records (+60% and +1,179% relative change to the overall dataset, respectively). Similarly, otherwise relatively uncommon images of almost entirely fatty (BI-RADS-A) or extremely dense (BI-RADS-D) breasts also occur disproportionately frequently (+90% and +755%, respectively).

To further investigate the relationship between group size and MIA risk, we conducted a meta-analysis of all computed Pearson residuals (Extended Data Fig. 5). Confirming previous observations, we find that large positive Pearson residuals occur mostly for small groups (those that contribute less than 20% of the records of a dataset). Moreover, we observe a weak to moderate negative correlation between group size and Pearson residuals. This suggests that the observed differences in MIA risk may, at least in part, be driven by group-size differences in the training data.

Discussion

We present data from the first patient-level privacy audit of medical AI models. Our findings confirm early observations of MIA risk heterogeneity39,40,41,42 and, at the same time, substantially advance previous AI privacy auditing efforts along three key dimensions. First, our work marks a shift towards patient-level risk assessment, which is crucial for real-world clinical datasets, in which individuals often contribute multiple, similar records. Second, we demonstrate that aggregate success rates, as used in the standard evaluation protocol and previous subgroup analyses41,42, underestimate true privacy risks. Third, we confirm that MIA vulnerabilities previously observed on low-dimensional benchmark datasets2,3,4,39,40,41,42 are present, and arguably more critical, in large representative clinical datasets. Below, we briefly discuss our findings and their implications.

The fact that MIAs can achieve near-perfect success rates for individual patients is not adequately captured by the standard evaluation protocol, which measures attack success in aggregate across records. This remains true even when evaluating aggregate attack success at very low false-positive rates (for example, 10−4), which is the current standard practice. Thus, reporting standards for AI privacy audits need to change. Audits should report the success of privacy attacks at the level of individual data contributors or, if the necessary patient- or person-level identifiers are unavailable, at the record level.

We observed that the number of patients highly vulnerable to MIAs increases drastically for larger models. Although the magnitude of this change in patient-level risk was previously unknown, other works have also reported greater attack success against larger, more performant models3,5,7,43. This observation that privacy risks grow with model size and predictive performance is explained by theoretical research44,45, which postulates that, for long-tailed data distributions, fitting atypical records from the tail is necessary to achieve optimal performance on unseen data at test time. Our results provide further empirical support for this theory and, together, suggest that a trade-off between patient privacy and model performance is inevitable, particularly for rare diseases. Generally, as we found that the number of patients highly vulnerable to MIAs increases by orders of magnitude with larger models, we recommend carefully evaluating the need for the performance improvements they offer.

We found substantial differences in the frequency with which patients from different subgroups experience extreme MIA risk. The fact that some of these groups (for example, self-reported race subgroups in chest radiographs) are not readily distinguishable by human experts raises concerns that MIA risk differences, which probably exist beyond the stratification variables we investigated, may pass unnoticed in practice. We found that the observed risk differences are driven, at least in part, by group-size differences in the training data. Groups of patients that are underrepresented in a model training dataset are often overrepresented among the records most susceptible to MIAs. By contrast, the opposite often holds for majority groups. This finding—that a disproportionately large share of the AI privacy risk burden rests on underrepresented groups—complements the existing literature on health inequalities, which has reported worse health outcomes and life expectancy for marginalized and minority groups[46]. Our findings suggest that current trends in medical AI development and deployment could exacerbate these health inequalities. Previous research has shown that the diagnostic performance of AI models, which typically increases with the amount of suitable training data, can be significantly lower for underrepresented (minority) groups36,37,47. Thus, there is a possibility of a vicious cycle in which minority groups place decreasing levels of trust in AI model performance and security, leading to a decreased willingness to contribute to model training datasets.

MIAs facilitate data extraction attacks against generative AI models5,6,7. Thus, our findings have potentially far-reaching implications for generative AI privacy risk assessments. Extraction attacks allow for high-fidelity reconstruction of full individual records from the training dataset of a model and have been demonstrated for large language models[5], diffusion-based image generation models[6] and recently, aligned, production-level large language models[7]. Although our study focused on discriminative (diagnostic) AI models, the type of attack we studied is generally applicable and can be used against generative models with little to no modification. We thus see the exploration of our proposed methodology for estimating record- and patient-level MIA success against generative models as an interesting direction for future research. Given the substantial computational resources this would require, exploring scalable approximation techniques is another valuable avenue to investigate.

Unlocking the full potential of medical AI will require training models on vast medical datasets; this depends on gaining and upholding the trust of data-contributing patients. To this end, mathematically verifiable approaches to risk mitigation, such as DP[48], are emerging as the most promising solution. DP, by carefully perturbing parameter updates with white noise during model training or fine-tuning[49], limits the contribution of the data of any individual to the parameter update and, by extension, to the final model. This provably protects the privacy of any data-contributing patient, no matter how unique or atypical their data may be. Our experimental data confirmed that stronger levels of DP protection effectively reduce MIA success for all data-contributing patients. However, we also observed that mitigating MIAs requires stronger levels of DP protection than previously thought3,50. Specifically, our results indicate that fully mitigating MIAs for all data-contributing patients requires implementing DP protection at the patient level rather than at the record level. Recent research has demonstrated that, in practice, AI models can be trained with strong privacy guarantees while incurring minimal degradation in predictive performance compared with a non-private model51,52,53,54. We are thus optimistic that medical AI models protected by DP will have a significant positive impact on health outcomes globally without endangering the privacy of any data-contributing patient.

In summary, we present evidence that MIAs can be highly effective at compromising the privacy of individual data-contributing patients. Given this vulnerability, medical AI models and their deployment contexts should be assessed for the sensitive information that attackers could obtain by successfully inferring training dataset membership. To prevent privacy harm, we recommend that vulnerable models be protected by verifiable risk mitigation strategies and/or strict access controls.

Methods

CheXpert processing and model training details

CheXpert[26] is a large chest radiograph dataset from Stanford Hospital comprising 224,313 chest radiographs from 64,539 patients. CheXpert comes with structured labels that were extracted automatically from free-text radiology reports and indicate the presence of 14 common thoracic conditions. The dataset provides demographic data that were obtained from the electronic data storage system of the hospital and includes information on the sex and age of the patient. Race (self-reported) and ethnicity (self-reported) data of the patient were released separately and can be matched by cross-linking patient IDs. Models for this dataset were trained with data augmentation (random horizontal flipping, random pixel shifts and random rotations) to detect the presence of all 14 conditions. We combined official CheXpert test and validation sets to accurately evaluate diagnostic model performance on a large set of expert-consensus-verified labels (total of N = 668 records). On this unseen data, a WRN-28-2 with around 1.5 million parameters trained on the records from a random 50% patient subset achieves a macro-average AUC score of 0.892 across the CheXpert competition classes: cardiomegaly, oedema, consolidation, atelectasis and pleural effusion.

MIMIC-CXR processing and model training details

MIMIC-CXR[27] is a large chest radiograph dataset consisting of 377,110 chest radiographs from 65,379 patients at the Beth Israel Deaconess Medical Center. We use MIMIC-CXR-JPG[55], a subsequent re-release of the original dataset that contains images in ‘.jpg’ format and structured labels derived from free-text radiology reports. The structured labels indicate the presence of 14 common thoracic conditions and are in an identical format to the CheXpert dataset. The dataset comprises demographic data that were obtained from the electronic data storage system of the hospital and includes patients’ age, sex, race (self-reported), ethnicity (self-reported), marital status, insurance and mortality information. Some of the demographic data were released separately and can be matched by cross-linking patient identifiers. Models for this dataset were trained with data augmentation (random horizontal flipping, random pixel shifts and random rotations) to detect the presence of all 14 conditions. As expert-verified labels are not available for MIMIC-CXR, we evaluate model performance, as we did for CheXpert, on the combined set of records from the official CheXpert validation and test set (N = 668 records). On this unseen data, a WRN-28-2 with around 1.5 million parameters trained on the records from a random 50% patient subset achieves a macro-average AUC score of 0.851 across all classes.

Fitzpatrick 17k processing and model training details

Fitzpatrick 17k[32] is a dermatology dataset consisting of 16,523 RGB (red, green and blue) images from an unknown number of patients. Images in the dataset were initially sourced from two open-source dermatology atlases and complemented with Fitzpatrick skin-type labels, which were provided by trained non-experts. Owing to the noise expected in these labels, we grouped together types I/II, III/IV and V/VI, respectively. The dataset contains no demographic information apart from the Fitzpatrick skin-type labels. Models for this dataset were trained with data augmentation (random horizontal flipping and random pixel shifts) to classify images into one of three mutually exclusive classes (non-neoplastic, benign and malignant). Diagnostic performance was evaluated on the expert-verified test set (N = 348 records). On this unseen data, a WRN-28-2 with around 1.5 million parameters trained on the records from a random 50% patient subset achieves an accuracy of 0.746 and a macro-average AUC score of 0.872 across the three classes.

Note that patient identifiers are not available for Fitzpatrick 17k. Thus, to compute patient-level MIA AUC, we assume that each record belongs to a unique patient.

FairVision processing and model training details

FairVision[56] is an ophthalmoscopy dataset from the Department of Ophthalmoscopy at Harvard Medical School consisting of 30,000 scanning laser ophthalmoscopy fundus images from 30,000 unique patients. The dataset comprises demographic data that were obtained from the electronic data storage system of the hospital and includes patients’ age, gender, race (self-reported), ethnicity (self-reported), preferred language and marital status. The dataset consists of three patient cohorts, each with 10,000 patients or images and their respective disease labels indicating the presence or severity of age-related macular degeneration (AMD), diabetic retinopathy (DR) and glaucoma. To combine all available data into a single dataset for a seven-class classification problem, we merged the disease labels across the three cohorts under the assumption of mutual exclusivity (no patient from one cohort had a disease from another). The glaucoma label ‘no’, and AMD label ‘normal’ were merged to create the ‘healthy’ label. A basic image quality screening was performed, in which dark, low-contrast images with image-wise mean and s.d. below 1.0 and 3.0, respectively, were discarded. This left 28,542 images from 28,542 patients for model training. Models for this dataset were trained with data augmentation (random horizontal flipping, random pixel shifts) to classify images into one of seven mutually exclusive classes: healthy, non-vision-threatening DR, vision-threatening DR, glaucoma, early AMD, intermediate AMD and late AMD. Diagnostic performance was evaluated on N = 500 randomly chosen records from the official test set; the remaining images were used for model training. On these unseen data, a WRN-28-2 with around 1.5 million parameters trained on the records from a random 50% patient subset achieves a macro-average AUC score of 0.817 across all classes.

EMBED processing and model training details

EMBED[38] is a large mammography dataset consisting of 480,323 mammograms from 23,253 patients who underwent breast cancer screening or diagnosis at four hospital sites of Emory University in Atlanta. The dataset comprises demographic data that were obtained from electronic health records and includes patients’ age, race (self-reported), ethnicity (self-reported) and insurance status. We considered only craniocaudal and mediolateral oblique (MLO) views from screening exams and excluded any non-female patients, thus reducing the dataset size to 360,159 mammograms from 19,429 female patients. Following ref. [57], we train models to predict breast density, an important clinical risk factor for breast cancer. Models for this dataset were trained with data augmentation (random horizontal flipping and random pixel shifts) to classify images into one of the four BI-RADS breast density categories. Diagnostic performance was evaluated on a subset of 500 randomly picked patients (N = 11,121 images). On this unseen data, a WRN-28-2 with around 1.5 million parameters trained on the records from a random 50% patient subset achieves a macro-average AUC score of 0.936 across all classes.

PTB-XL processing and model training details

PTB-XL[30] is a cardiology dataset comprising 21,799 electrocardiograms from 18,869 patients. The dataset contains 12-lead electrocardiograms sampled at 100 Hz and 10 s long paired with their corresponding diagnostic label (multi-hot encoded), indicating the presence of common cardiac conditions. The dataset comprises demographic data obtained from the electronic data storage system of the hospital, including patients’ age and sex. Models for this dataset were trained without data augmentation to perform multi-label classification for the superclasses: normal, myocardial infarction, conduction disturbance, ST/T-changes and hypertrophy. Diagnostic performance was evaluated on the official test set. On these unseen data, a convolutional residual network[58] with around 1.5 million parameters trained on the records from a random 50% patient subset achieves a macro-average AUC score of 0.916 across all classes.

MIMIC-IV-ED processing and model training details

MIMIC-IV-ED[29] is a large electronic health record dataset comprising data from 418,007 records from 201,213 patients at the emergency department of the Beth Israel Deaconess Medical Center. The dataset contains demographic data that were obtained from the electronic data storage system of the hospital and includes patients’ age, sex, race (self-reported), ethnicity (self-reported), marital status, insurance and mortality information. We followed the pre-processing steps in ref. [59] and trained models without data augmentation to predict hospitalization outcome (binary classification) based on 64 clinical features (discrete and continuous). Diagnostic performance was evaluated on the official test set in ref. [59]. On these unseen data, a tabular residual network[60] with around 1.5 million parameters trained on the records from a random 50% patient subset achieves an AUC score of 0.815 across all classes.

General model training details

All investigated models were trained using stochastic gradient descent with momentum, exponential moving parameter average (EMA), weight decay, data augmentation and a learning rate schedule in the form of cosine decay with linear warm-up. Hyperparameter values were determined by a random search to maximize diagnostic performance (macro-average AUC) on unseen data. Specifically, for each dataset, a random search with N = 50 trials over suitable values for learning rate, weight decay, momentum and EMA decay was conducted (Supplementary Table 1). Training and test data were rescaled to have values within [−1, 1]. To prevent overfitting, model checkpointing was used, ensuring that only the model with the best generalization performance was retained after the training process terminated. We estimate that reproducing all of our experimental data would require around 900 h on a single A100 GPU.

Strategies to improve training efficiency

Improving training efficiency was crucial because of the limited computational resources available to us. Thus, we resized all images to a spatial resolution of 64 × 64 pixels unless otherwise stated in the main text. Moreover, to further improve training efficiency, we used compilation caching with Keras[61] JAX[62] backend and used mixed-precision training with the default Keras policy: ‘mixed_float16’. Supplementary Table 1 shows training times of residual networks with about 1.5 million parameters for each of the investigated datasets using a single A100 GPU. Notably, the training runs required for our analysis are independent of one another, so the process can be easily accelerated by parallelizing them across multiple GPUs, with no need for inter-device communication during training.

Adapting LR-MIAs to multi-label classification

LR-MIAs were designed for multi-class classification and are thus not directly compatible with multi-label classification settings in which multiple classes (diseases) may be present in a single input. We adapt LR-MIAs to the multi-label classification setting by using a softmax activation function on the raw network output (instead of the sigmoid function typically used). Then, to obtain a scalar quantity, we take the sum over the confidence values for all present classes (conditions). Note that our strategy does not change the assumed threat model. An attacker with access to the post-sigmoid activation confidence values can obtain the values necessary for our adapted attack by applying the inverse sigmoid (logit) function. Empirically, we found that our strategy performed better than estimating multivariate Gaussians for the confidence values of all present classes (the more natural adaptation of LR-MIAs to multi-label settings). This is probably because accurately estimating multivariate Gaussians requires more samples than their univariate counterparts.

Estimating record-level LR-MIA success

Quantifying record-level MIA vulnerability requires measuring MIA performance independently for each target record across many target models. To do this with a manageable amount of computational resources, we leverage the fact that LR-MIAs are essentially hypothesis tests and directly estimate the empirical sampling distributions of the confidence of the target model under the null and alternative hypotheses. As these distributions are assumed to take Gaussian form in both LiRA[3] and RMIA[4], the record-level MIA AUC for a target record x admits a closed-form solution:

$$\hat{\theta }(x)=\varPhi \left(\frac{{\mu }_{x}-{\mu }_{{x}^{{\prime} }}}{\sqrt{{\sigma }_{x}^{2}+{\sigma }_{{x}^{{\prime} }}^{2}}}\right).$$

(1)

Here, Φ denotes the CDF of the standard normal and μx, μx′, \({\sigma }_{x}^{2}\), \({\sigma }_{{x}^{{\prime} }}^{2}\) denote sample means and variances of the logit-transformed confidence values provided by target models partitioned into models trained on x and models not trained on x (denoted by x and x′, respectively). To obtain a scalar quantity from the vector of predicted confidences for all possible classes, we follow the standard protocol3,4 and index the vector at the correct class indicated by the corresponding label. Multi-label confidence values were transformed, as explained in the previous section. Note that, for the Gaussian assumption of LR-MIAs to hold, confidence values need to be logit-transformed (\({\rm{logit}}(p)\,=\,{\rm{ln}}\,\frac{p}{1-p}\)). For models trained with data augmentation, we use N = 16 augmented queries to the target model; confidence values are then averaged across augmentations.

We calculate the standard error of \(\widehat{\theta }\) (the bi-normal AUC) as described in ref. [23]:

$$\mathrm{SE}(\hat{\theta }(x))=\sqrt{\frac{\hat{\theta }(x)(1-\hat{\theta }(x))+({N}_{{x}^{{\prime} }}-1)({Q}_{1}-\hat{\theta }(x))+({N}_{x}-1)({Q}_{2}-\hat{\theta }{(x)}^{2})}{{N}_{{x}^{{\prime} }}\times {N}_{x}}},$$

(2)

where \(\widehat{\theta }(x)\) is the estimated AUC score from equation (1), \({Q}_{1}=\frac{\widehat{\theta }(x)}{1-\widehat{\theta }(x)}\) and \({Q}_{2}=\frac{2\widehat{\theta }{(x)}^{2}}{1+\widehat{\theta }(x)}\). Nx and \({N}_{{x}^{{\prime} }}\) refer to the number of target models trained and not trained on the target record, respectively. To ensure that each target model effectively reduces the standard error of the estimated AUC across all records, it is important to train the target models on random data subsets sampled in an unbiased fashion (see ref. [3] for details). This guarantees that Nx and \({N}_{{x}^{{\prime} }}\) are equal for all x.

Note that it is important to perform the subset sampling at the patient- rather than the record level to accurately estimate patient-level MIA vulnerability. This is to ensure that either all or none of a given patients’ records are included in a target model’s training dataset. In initial experiments, we sampled subsets at the record level and found that this led to underestimating MIA success for patients who contributed multiple similar records.

Model scaling and differential privacy experiments

All models in the model scaling experiments were trained with the same hyperparameter values found during the initial hyperparameter tuning procedure described above. This decision was made because of the limited computational resources available to us.

Models for the DP experiments were trained with DP-Adam49,63 using the jax-privacy library[64]. Concretely, hyperparameters for these models were determined for a fixed privacy budget of ε = 100 using a random search with N = 20 trials over suitable values for the learning rate (see Supplementary Table 2 for results); EMA and weight decay were not used. The clipping threshold for DP-Adam was determined using the procedure described in ref. [65]. Note that the diagnostic performance of the models could likely be improved by performing a separate hyperparameter search for each privacy budget and adapting the other engineering techniques proposed in ref. [53]. Bounds on the MIA AUC implied by the record-level (ε, δ)-DP guarantee, where calculated from the trade-off curve obtained by converting the privacy profile of the sub-sampled Gaussian mechanism[66] to f-DP[67].

Attacking open-source chest radiograph models

We follow the standard protocol as described in the original publication[4] to conduct RMIA on the CheXpert and MIMIC-CXR models from TorchXrayVision. We use offline RMIA with a single pre-trained reference model (PadChest[25], also from TorchXrayVision). Offline approximation parameter a was calculated as described in ref. [4]; a = 0.857 and 0.918 for CheXpert and MIMIC-CXR, respectively. We did not use augmented queries for the target model; multi-label confidence values were transformed as described previously.

Notably, the open-source implementation of RMIA relies on a large outer product, which requires prohibitive amounts of memory when conducting the attack in parallel for many target records. Our adapted implementation greatly improves on this by holding only relevant entries of the large outer product matrix in memory, thereby easily scaling to large numbers of target records.

Measuring MIA success with partial target records

We simulate settings in which MIAs are executed using only partial access to target records. Specifically, for MIMIC-IV-ED and PTB-XL, we train target models on random subsets of the full datasets and subsequently obtain confidence scores for these models when queried on partial target records, in which we redacted increasing amounts of information. We then compute record- and patient-level MIA AUC scores from confidence values as described previously. To redact information from target records, we simply set all relevant feature values to 0. For MIMIC-IV-ED, we investigate a setting we term ‘realistic subset’, in which all except the following 20 features are redacted: age, gender_binarized, triage_temperature, triage_heartrate, triage_resprate, triage_o2sat, triage_sbp, triage_dbp, triage_pain, triage_acuity, chiefcom_chest_pain, chiefcom_abdominal_pain, chiefcom_headache, chiefcom_shortness_of_breath, chiefcom_back_pain, chiefcom_cough, chiefcom_nausea_vomiting, chiefcom_fever_chills, chiefcom_syncope and chiefcom_dizziness. These features represent patients’ age, sex, chief complaints and vital signs at triage that we deem realistically obtainable for an adversary. We refer to Extended Data Table 1 from ref. [59] for a full description of the features in MIMIC-IV-ED. For MIMIC-IV-ED, we investigate another setting ‘most predictive’, in which all features except the following 10 features that ref. [59] identified as most predictive for hospitalization are redacted: age, triage_acuity, triage_sbp, triage_heartrate, triage_dbp, triage_temperature, triage_pain, triage_o2sat, triage_resprate and n_hosp_365d. For PTB-XL, we investigate settings in which an attacker has access to only temporal snippets of the 12-lead ECG signal and another setting in which only the signal from lead I is available. As for MIMIC-IV-ED, relevant information was redacted by replacing all signal values with zeros.

Subgroup analysis

Our subgroup analysis compares the composition of the records most vulnerable to MIAs to that of the overall dataset. We measure O, the number of records observed for a given subgroup in the 99th record-level MIA AUC percentile and compare it with E, the expected number of records for this subgroup in the 99th percentile based on the composition of the overall training dataset. To this end, we compute the χ^2 test statistic and Pearson residuals, which measure the contribution of a subgroup to the test statistic:

$$\mathrm{Pearson}\,\mathrm{residual}=\frac{O\,-\,E}{\sqrt{E}}.$$

(3)

The relative change between the observed and expected counts in the 99th percentile, as reported in the main body text, is calculated as

$${\varDelta }_{\mathrm{relative}}( \% )=\frac{(O\,-\,E)}{E}\times 100.$$

(4)

Note that we excluded records with incomplete (other/unknown) subgroup information from the subgroup analysis. Small discrepancies between Pearson residuals and data in Supplementary Tables 3–16 are thus to be expected.

Reporting summary

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

Data availability

All datasets used in this study are publicly available for research purposes. See GitHub (https://github.com/moritzknolle/leakoscope) for access instructions.

Code availability

Code and instructions to reproduce our results are available at GitHub (https://github.com/moritzknolle/leakoscope) and are permanently archived on Zenodo[68] (https://doi.org/10.5281/zenodo.20124226).

Change history

- 06 July 2026

In the version of this aritcle initially published, Ben Glocker (Department of Computing, Imperial College London, London, UK) was listed with an incorrect affiliation; the affiliation is now amended in the HTML and PDF versions of the article.

References

1. Fleming, K. A. et al. The Lancet Commission on diagnostics: transforming access to diagnostics. Lancet 398, 1997–2050 (2021).

2. Shokri, R., Stronati, M., Song, C. & Shmatikov, V. Membership inference attacks against machine learning models. In Proc. 2017 IEEE Symposium on Security and Privacy (SP) 3–18 (IEEE, 2017).

3. Carlini, N. et al. Membership inference attacks from first principles. In Proc. 2022 IEEE Symposium on Security and Privacy (SP) 1897–1914 (IEEE, 2022).

4. Zarifzadeh, S., Liu, P. & Shokri, R. Low-cost high-power membership inference attacks. In Proc. 41st International Conference on Machine Learning 58244–58282 (PMLR, 2024).

5. Carlini, N. et al. Extracting training data from large language models. In Proc. 30th USENIX Security Symposium 2633–2650 (USENIX, 2021).

6. Carlini, N. et al. Extracting training data from diffusion models. In Proc. 32nd USENIX Security Symposium 5253–5270 (USENIX, 2023).

7. Nasr, M. et al. Scalable extraction of training data from aligned, production language models. In Proc. Thirteenth International Conference on Learning Representations (ICLR, 2025).

8. Tramèr, F., Zhang, F., Juels, A., Reiter, M. K. & Ristenpart, T. Stealing machine learning models via prediction APIs. In Proc. 25th USENIX Security Symposium 601–618 (USENIX, 2016).

9. Carlini, N. et al. Stealing part of a production language model. In Proc. 41st International Conference on Machine Learning 5680–5705 (ICML, 2024).

10. Yoo, S.-K. et al. Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data. Nat. Med. 31, 869–880 (2025).

11. FDA. Artificial intelligence-enabled medical devices. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices (2024).

12. Seh, A. H. et al. Healthcare data breaches: insights and implications. Healthcare 8, 133 (2020).

13. Albert Haro Abad, S. C. Health Threat Landscape: ENISA Report 2023. Technical report (European Union Agency for Cybersecurity, 2023).

14. Narayanan, A. & Shmatikov, V. Robust de-anonymization of large sparse datasets. In Proc. 2008 IEEE Symposium on Security and Privacy (sp 2008) 111–125 (IEEE, 2008).

15. Gadotti, A., Rocher, L., Houssiau, F., Creţu, A.-M. & de Montjoye, Y.-A. Anonymization: the imperfect science of using data while preserving privacy. Sci. Adv. 10, eadn7053 (2024).

16. Rocher, L., Hendrickx, J. M. & de Montjoye, Y.-A. A scaling law to model the effectiveness of identification techniques. Nat. Commun. 16, 347 (2025).

17. Suri, A., Zhang, X., Evans, D. Do parameters reveal more than loss for membership inference? In Proc. 2nd Workshop on High-dimensional Learning Dynamics (HiLD, 2024).

18. Geiping, J., Bauermeister, H., Dröge, H. & Moeller, M. Inverting gradients - how easy is it to break privacy in federated learning? In Proc. 34th International Conference on Neural Information Processing Systems 16937–16947 (Curran Associates, 2020).

19. Fowl, L. H., Geiping, J., Czaja, W., Goldblum, M. & Goldstein, T. Robbing the fed: directly obtaining private data in federated learning with modified models. In Proc. International Conference on Learning Representations (ICLR, 2022).

20. Feng, S. & Tramèr, F. Privacy backdoors: stealing data with corrupted pretrained models. In Proc. 41st International Conference on Machine Learning 13326–13364 (PMLR, 2024).

21. Warnat-Herresthal, S. et al. Swarm Learning for decentralized and confidential clinical machine learning. Nature 594, 265–270 (2021).

22. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006).

23. Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982).

24. Cohen, J. P. et al. TorchXRayVision: a library of chest X-ray datasets and models. In Proc. 5th International Conference on Medical Imaging with Deep Learning 231–249 (PMLR, 2022).

25. Bustos, A., Pertusa, A., Salinas, J.-M. & de la Iglesia-Vayá, M. PadChest: a large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020).

26. Irvin, J. et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In Proc. Thirty-Third AAAI Conference on Artificial Intelligence 590–597 (PKP Publishing, 2019).

27. Johnson, A. E. W. et al. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6, 317 (2019).

28. Yeom, S., Giacomelli, I., Fredrikson, M. & Jha, S. Privacy risk in machine learning: analyzing the connection to overfitting. In Proc. 2018 IEEE 31st Computer Security Foundations Symposium (CSF) 268–282 (IEEE, 2018).

29. Johnson, A. et al. MIMIC-IV-ED (v.1.0) (PhysioNet, 2021).

30. Wagner, P. et al. PTB-XL, a large publicly available electrocardiography dataset. Sci. Data 7, 154 (2020).

31. Kaplan, J. et al. Scaling laws for neural language models. Preprint at https://doi.org/10.48550/arXiv.2001.08361 (2020).

32. Groh, M. et al. Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset. In Proc. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 1820–1828 (CVPRW, 2021).

33. Zagoruyko, S. & Komodakis, N. Wide residual networks. In Proc. British Machine Vision Conference (BMVC) (eds Wilson, R. C. et al.) 87.1–87.12 (BMVA Press, 2016).

34. Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 45–67 (ICLR, 2021).

35. Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: understanding transfer learning for medical imaging. In Proc. 33rd International Conference on Neural Information Processing Systems 3347–3357 (NIPS, 2019).

36. Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182 (2021).

37. Daneshjou, R. et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci. Adv. 8, eabq6147 (2022).

38. Jeong, J. J. et al. The EMory BrEast imaging Dataset (EMBED): a racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images. Radiol. Artif. Intell. 5, e220047 (2023).

39. Long, Y. et al. A pragmatic approach to membership inferences on machine learning models. In Proc. 2020 IEEE European Symposium on Security and Privacy (EuroS&P) 521–534 (IEEE, 2020).

40. Aerni, M., Zhang, J. & Tramèr, F. Evaluations of machine learning privacy defenses are misleading. In Proc. 2024 on ACM SIGSAC Conference on Computer and Communications Security 1271–1284 (CCS, 2024).

41. Kulynych, B., Yaghini, M. & Cherubin, G., Veale, M., Troncoso, C. Disparate vulnerability to membership inference attacks. In Proc. Privacy Enhancing Technologies 460–480 (sciendo, 2022).

42. Chang, H. & Shokri, R. On the privacy risks of algorithmic fairness. In Proc. 2021 IEEE European Symposium on Security and Privacy (EuroS&P) 292–303 (IEEE, 2021).

43. Carlini, N. et al. Quantifying memorization across neural language models. In Proc. Eleventh International Conference on Learning Representations (ICLR, 2023).

44. Feldman, V. Does learning require memorization? a short tale about a long tail. In Proc. 52nd Annual ACM SIGACT Symposium on Theory of Computing 954–959 (STOC, 2020).

45. Feldman, V. & Zhang, C. What neural networks memorize and why: discovering the long tail via influence estimation. In Proc. 34th International Conference on Neural Information Processing Systems 2881–2891 (NIPS, 2020).

46. World Health Organization et al. World Report on Social Determinants of Health Equity, 2025 (WHO, 2025).

47. Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).

48. Dwork, C. & Roth, A. The algorithmic foundations of differential privacy. Foundations Trends Theoret. Comput. Sci. 9, 211–487 (2014).

49. Abadi, M. et al. Deep learning with differential privacy. In Proc. 2016 ACM SIGSAC Conference on Computer and Communications Security 308–318 (CCS, 2016).

50. Nasr, M., Songi, S., Thakurta, A., Papernot, N. & Carlini, N. Adversary instantiation: lower bounds for differentially private machine learning. In Proc. 2021 IEEE Symposium on Security and Privacy (SP) 866–882 (IEEE, 2021).

51. Ziller, A. et al. Reconciling privacy and accuracy in AI for medical imaging. Nat. Mach. Intell. 6, 764–774 (2024).

52. Berrada, L. et al. Unlocking accuracy and fairness in differentially private image classification. Preprint at https://doi.org/10.48550/arXiv.2308.10888 (2023).

53. De, S., Berrada, L., Hayes, J., Smith, S. L. & Balle, B. Unlocking high-accuracy differentially private image classification through scale. Preprint at https://doi.org/10.48550/arXiv.2204.13650 (2022).

54. Mckenna, R. et al. Scaling laws for differentially private language models. In Proc. 42nd International Conference on Machine Learning 43375–43398 (PMLR, 2025).

55. Johnson, A. E. W. et al. MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. Preprint at https://doi.org/10.48550/arXiv.1901.07042 (2019).

56. Luo, Y. et al. FairVision: equitable deep learning for eye disease screening via fair identity scaling. Preprint at https://doi.org/10.48550/arXiv.2310.02492 (2023).

57. Khara, G. et al. Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race. Commun. Med. 4, 21 (2024).

58. Wang, Z., Yan, W. & Oates, T. Time series classification from scratch with deep neural networks: a strong baseline. In Proc. 2017 International Joint Conference on Neural Networks (IJCNN) 1578–1585 (IEEE, 2017).

59. Xie, F. et al. Benchmarking emergency department prediction models with machine learning and public electronic health records. Sci. Data 9, 658 (2022).

60. Gorishniy, Y., Rubachev, I., Khrulkov, V. & Babenko, A. Revisiting deep learning models for tabular data. In Proc. 35th International Conference on Neural Information Processing Systems (eds Ranzato, M. et al.) 18932–18943 (NIPS, 2021).

61. Chollet, F. et al. Keras. https://keras.io (2015).

62. Bradbury, J. et al. JAX: composable transformations of Python+NumPy programs. GitHub http://github.com/google/jax (2026).

63. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations (ICLR, 2015).

64. Balle, B. et al. JAX-Privacy: algorithms for privacy-preserving machine learning in JAX. GitHub http://github.com/google-deepmind/jax_privacy (2026).

65. Ponomareva, N. et al. How to DP-fy ML: a practical guide to machine learning with differential privacy. J. Artif. Intell. Res. 77, 1113–1201 (2023).

66. Balle, B., Barthe, G. & Gaboardi, M. Privacy profiles and amplification by subsampling. J. Priv. Confid. https://doi.org/10.29012/jpc.726 (2020).

67. Dong, J., Roth, A. & Su, W. J. Gaussian differential privacy. J. R. Stat. Soc. Ser. B. 84, 3–37 (2022).

68. moritzknolle. moritzknolle/leakoscope: V1 (v.v1). Zenodo https://doi.org/10.5281/zenodo.20124226 (2026).

Acknowledgements

We thank A. Ziller, L. Lux and many others from the AIM Lab for their feedback and support. We acknowledge computational resources provided by the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities.

Funding

This work was partially funded by the Konrad Zuse School of Excellence in Reliable AI (relAI), ERC Grant Deep4MI (grant no. 884622) and the German Research Foundation (project no. 532139938). B.G. received support from the Royal Academy of Engineering as part of his Research Chair in Safe Deployment of Medical Imaging AI. Open access funding provided by Technische Universität München.

Author information

Author notes

1. These authors contributed equally: Ben Glocker, Daniel Rueckert

Authors and Affiliations

1. Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany

Moritz A. Knolle, Martin J. Menten, Friederike Jungmann, Felix Meissen & Daniel Rueckert

2. Munich Center for Machine Learning, Munich, Germany

Martin J. Menten & Daniel Rueckert

3. Department of Computing, Imperial College London, London, UK

Martin J. Menten, Ben Glocker & Daniel Rueckert

4. Institute for Diagnostic and Interventional Radiology, TUM University Hospital, Technical University of Munich, Munich, Germany

Friederike Jungmann

5. Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany

Georgios Kaissis

Contributions

M.A.K. and G.K. conceptualized the study with input from M.J.M., B.G. and D.R.; M.A.K. created the core technical implementation and conducted the experiments. G.K. and M.A.K. contributed to the differential privacy experiments. F.J. provided clinical inputs to the research. F.M. provided feedback and helped frame the initial draft of the paper. All authors contributed to the drafting of the manuscript. M.A.K., M.J.M., F.J., B.G., D.R. and G.K. contributed to revising the manuscript.

Corresponding author

Correspondence to Moritz A. Knolle.

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Competing interests

B.G. is a part-time employee at DeepHealth. G.K. is a part-time employee at Google DeepMind.

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Nature thanks Sana Tonekaboni 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 Additional MIA success data.

a, eSF plots of raw record-level MIA AUC scores computed using N = 200 target models. b, eSF plots of mean patient-level MIA AUC scores computed using N = 200 target models. c–e, ROC analysis of aggregate MIA success (R = 190 reference models, online) for LiRA on a linear scale (c) and RMIA on linear (d) and log-log scale (e). ROC curve plots show mean and standard deviation (vertical average) aggregate attack success for N = 10 target models; on the log-log scale, the standard deviation was omitted to improve visibility. Dashed grey lines in ROC curve plots indicate random-guessing performance, dashed lines in eSF plots indicate 95% Greenwood CI.

Extended Data Fig. 2 Ablation study on the number of target models used to estimate record-level MIA AUC scores.

Panels show box plots of the standard error values for record-level MIA AUC scores in a given dataset when varying the number of target models used for record-level MIA AUC estimation. White box plots show data for all records in a given dataset, red box plots show data for the 99th record-level MIA AUC percentile. In box plots, the centre line indicates the median, box bounds represent 25th and 75th percentiles, whiskers extend to 1.5 times the interquartile range from the box bounds, and triangles indicate largest outlier value.

Extended Data Fig. 3 MIAs remain highly successful with partial target record access.

a, Patient- and record-level MIA AUC scores for MIMIC-IV-ED. We simulate an attacker with access to: all clinical features (F = 64), a realistic subset of features related only to a patient’s current stay (F = 20) and the ten most predictive features for hospitalisation (F = 10). b, Patient- and record-level MIA AUC scores for PTB-XL. We simulate an attacker with access to: the full twelve-lead ECG signal, Lead I only, and a range of temporal snippets, each 1 to 5 seconds long. Dashed lines indicate 95% Greenwood confidence intervals; record-level MIA AUC scores were computed using N = 200 target models for each experimental setting.

Extended Data Fig. 4 Subgroup differences in extreme MIA risk for FairVision, Fitzpatrick17k and PTB-XL.

Panel rows show two-sided χ^2-test results and Pearson residuals for subgroup counts in the set of highly vulnerable records (99th record-level MIA AUC percentile) for FairVision (a), Fitzpatrick 17k (b) and PTB-XL (c). Column titles show stratification variables; bars are coloured according to group size in the training dataset; *** and n.s. denote p ≤ 0.001 and not significant, respectively; multiple comparison correction was performed row-wise using the Bonferroni method. From left to right, the FairVision disease abbreviations refer to healthy, non-vision-threatening diabetic retinopathy; vision-threatening diabetic retinopathy; glaucoma; early age-related macular degeneration (AMD); intermediate AMD; and late AMD. From left to right, the Fitzpatrick 17k disease abbreviations refer to non-neoplastic, malignant, benign, inflammatory, malignant epidermal, genodermatoses, benign dermal, benign epidermal, malignant melanoma, benign melanocyte, malignant cutaneous lymphoma, and malignant dermal. Fitzpatrick skin type is a clinical categorisation of human skin colour that describes sensitivity to sun exposure, ranging from sensitive, always burns, never tans (Type I) to sun-insensitive, never burns, deeply pigmented (Type VI). From left to right adjusted p values read. Fairvision: < 1 × 10−100, 0.53, 0.85. Fitzpatrick 17k: < 1 × 10−100, < 1 × 10−100, 0.74. PTB-XL: 0.67, 0.53.

Extended Data Fig. 5 Underrepresented groups experience disproportionally high MIA risk.

Scatter plot of Pearson residuals against group size (relative number of records contributed) for all subgroup comparisons. Across the investigated datasets, group size is weakly/moderately inversely correlated with Pearson residual as indicated by Spearman’s rank correlation coefficient ρ and Pearson’s correlation coefficient r.

Supplementary information

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This file contains Supplementary Tables 1–16. Supplementary Tables 1 and 2 report hyperparameters for general model training and model training with differential privacy. The remaining tables report characteristics of the investigated datasets and the extreme MIA risk tails.

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Knolle, M.A., Menten, M.J., Jungmann, F. et al. Disparate privacy risks from medical AI. Nature 656, 192–198 (2026). https://doi.org/10.1038/s41586-026-10688-0

- Received: 18 March 2025

- Accepted: 21 May 2026

- Published: 24 June 2026

- Version of record: 24 June 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10688-0

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LASER couples damage sensing to ESCRT assembly for lysosome repair

Claire S. Goul,

Aakriti Jain,

Samira Yitiz,

Zahra E. Soltani,

Serim Yang,

Simon Rapp,

Martina Spacci,

Scot Federman,

James Sacco,

Huinan Li,

Lauren D. Enriquez,

Nalan Liv,

Laralynne Przybyla &

…

Roberto Zoncu

Nature volume 656, pages 216–226 (2026)

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Abstract

Lysosomal membrane integrity is essential for cell survival, but how damage sensing is spatiotemporally coupled to repair remains poorly understood. Recruitment and assembly of endosomal sorting complex required for transport (ESCRT) I–III rapidly counteracts membrane damage, but it is unclear how ESCRT-I recognizes defective lysosomal membranes. Here, leveraging genome-wide CRISPRi screens in a damage-sensitized genetic background, we identified LC3/GABARAP-assisted stimulator for ESCRT recruitment (LASER), a multicomponent protein assembly that forms rapidly upon calcium release from damaged lysosomes and couples sensing of lysosomal membrane damage to ESCRT-dependent repair. At the core of LASER is TFG, an endoplasmic reticulum exit-site-resident protein that translocates to damaged lysosomes by binding to ATG8 family proteins (LC3 and GABARAP) conjugated to lysosomal phospholipids. ATG8-bound TFG forms oligomeric assemblies that directly recruit the essential ESCRT-I subunit TSG101 via conserved motif recognition enhanced by avidity-driven interactions. TFG binding to TSG101 stimulates sequential ESCRT-I–II–III polymerization and promotes membrane repair. TFG mutations that drive hereditary spastic paraplegia disrupt its oligomerization and impair lysosomal ESCRT recruitment and membrane resealing, implicating defective repair as a driver of TFG-associated neurodegeneration. Thus, LASER promotes ESCRT polymerization at damaged lysosomes and couples damage sensing to membrane repair.

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Lysosomal membrane integrity is essential for cellular homeostasis and survival, yet lysosomes are continuously exposed to stresses that threaten membrane stability, including lipid oxidation, lipid asymmetry and the accumulation of aggregation-prone luminal cargo[1]. In response to membrane disruption, cells activate a multi-tiered repair system that monitors and restores lysosomal integrity, preventing leakage of luminal contents and the resulting cell death[1]. Lysosomal membrane damage occurs along a continuum, ranging from subtle loss of ion gradients and lipid packing defects to overt membrane rupture, raising the possibility that distinct damage-sensing and repair mechanisms operate at early versus advanced stages of disruption2,3,4,5,6.

The membrane remodelling ESCRT-I, -II and -III complexes provide one of the earliest-acting repair mechanisms, undergoing sequential assembly and oligomerization on damaged lysosomes that help to prevent, stabilize or reseal membrane tears7,8,9,10. Accordingly, depletion of the essential ESCRT-I subunit tumor susceptibility gene 101 (TSG101) compromises ESCRT polymerization and leads to accumulation of severely damaged lysosomes7,8. However, despite its obligate role in membrane repair, how sensing of lysosomal damage is coupled to ESCRT recruitment remains unclear.

In contrast to intraluminal vesicle formation, in which ESCRT-0 recognizes ubiquitylated endocytosed proteins to initiate sequential ESCRT-I–II–III polymerization[11], ESCRT-0 is dispensable for lysosomal membrane repair, and ubiquitylation is instead detected during bulk removal of irreversibly damaged lysosomes (lysophagy)2,7,12,13. The calcium sensor apoptosis linked gene-2 (ALG2) was proposed to recruit ESCRT to damaged lysosomes in a calcium-dependent manner14,15. However, in vitro and cell-based evidence suggests that the requirement for calcium-ALG2 in ESCRT polymerization at damage sites may be insult-specific and not universal14,15,16.

Recently, recruitment of ESCRT subunits and of the accessory ESCRT assembly factor ALG2-interacting protein X (ALIX) to damaged lysosomes was shown to require conjugation of ATG8 proteins to single membranes (CASM). During CASM, ATG8 proteins are conjugated to lysosomal membrane phospholipids by the E3-like ATG5–ATG12 complex, which is recruited to damaged lysosomes either by ATG16L1, bound to stalled vacuolar ATPases, or by tectonin β-propeller repeat-containing 1 (TECPR1), bound in turn to inner leaflet sphingomyelin that becomes exposed to the cytosol15,17,18,19,20,21. Despite evidence for both ATG16L1- and TECPR1-dependent CASM contributing to ESCRT-I recruitment, the specific molecular links between lysosome-bound ATG8 and ESCRT-I subunits have not been identified.

Another unresolved question is the relationship between ESCRT assembly and the extensive endoplasmic reticulum (ER)–lysosome membrane contact sites that are established upon lysosomal damage22,23,24. The proximity created by these contacts enable the ER-to-lysosome transfer of phospholipids by oxysterol binding protein-related proteins (ORPs) and bridge-like lipid transport protein (BLTP) family proteins, which together help restore the optimal topology, surface area and composition of the lysosomal limiting membrane20,22,23,24. However, whether ER–lysosome contacts have additional repair-promoting roles, specifically whether and how they contribute to the regulation of ESCRT assembly on damaged lysosomes, is currently unknown.

Neuronal lysosomes operate close to a damage threshold and are therefore uniquely reliant on efficient membrane repair3,25. Excess or aberrant proteolytic cargo, compounded by genetic or environmental perturbations in cholesterol and sphingolipid metabolism, accelerates membrane injury26,27,28. Increasing evidence suggests that even healthy neurons undergo constitutive, albeit transient, lysosomal permeabilization3,5,29. Mutations in several components of the lysosomal repair machinery are increasingly linked to neurodegenerative conditions. Most notably, the ESCRT-III subunit charged multivesicular body protein 2B (CHMP2B) is mutated in amyotrophic lateral sclerosis and frontotemporal degeneration11,30,31, whereas mutations in the ESCRT-I subunit ubiquitin associated protein 1 (UBAP1) underlie hereditary spastic paraplegia (HSP)[32]. However, given the pleiotropic roles of ESCRT in membrane remodelling, the extent to which defective ESCRT assembly in response to lysosomal damage drives neurodegeneration remains unclear.

Here we identify LASER and its core component Trk-fused gene (TFG) as a key molecular link between lysosomal damage sensing and ESCRT-mediated membrane repair, acting through cooperative, motif-driven interactions that promote ESCRT polymerization. By uncovering a requirement for VAMP-associated proteins (VAPs) in LASER assembly, we further reveal how ER–lysosome contact formation is coordinated with ESCRT activation during repair. Finally, we demonstrate that TFG mutations linked to HSP cause profound defects in ESCRT recruitment and lysosomal membrane resealing. Together, these findings define a molecular pathway that integrates damage detection, ESCRT assembly and membrane contact site remodelling and implicate defective lysosomal repair as a mechanistic driver of TFG-associated neurodegeneration.

TFG is a modifier of lysosomal damage response

In cells that lack the lysosomal cholesterol transporter Niemann–Pick type C1 (NPC1), lysosomes have increased propensity to damage upon treatment with the lysosomotropic reagent, l-leucyl-l-leucine methyl-ester (LLOMe)26,33. Consistent with increased damage, NPC1-knockout HEK293T cells are more sensitive than control cells to LLOMe-mediated growth inhibition (Extended Data Fig. 1a). Thus, NPC1-knockout cells represent a sensitized background in which to identify genetic modifiers of lysosome membrane damage and repair. To this end, we designed parallel, growth-based, whole-genome CRISPR inhibition (CRISPRi) screens in control and NPC1-deleted K562 cells to identify genes that modify survival in the presence of an LLOMe challenge (Fig. 1a and Extended Data Fig. 1b). Control and NPC1-knockout K562 cells stably expressing CRISPRi–dCas9 machinery were transduced with a genome-wide single guide RNA (sgRNA) library with coverage of five guides per gene. Both cell populations were challenged for 8 days with LLOMe at a concentration resulting in half-maximal growth inhibition, followed by next-generation sequencing (Fig. 1a). After thresholding for statistically significant gene hits across control and NPC1-knockout cells, we identified 389 genes that, when knocked down, sensitize cells to LLOMe (sensitizing hits, indicating a damage-protective gene function) and 322 desensitizing gene hits. Consistent with their role in membrane repair, depletion of multiple components of the ESCRT-I and ESCRT-III complexes was strongly sensitizing in both genotypes (Fig. 1b, Extended Data Fig. 1c,d and Supplementary Table 1). Notably, the NPC1-knockout cells yielded the majority of both sensitizing and desensitizing hits (65% and 77%, respectively), consistent with the damage-prone state of this genetic background.

Fig. 1: Genome wide CRISPRi screen identifies TFG as a modifier of ESCRT-mediated lysosome membrane repair.

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a, Genome-wide CRISPRi screen design. Cells underwent an 8-day regimen of two 4-day cycles: 2 days of LLOMe (0.5 mM) followed by 2 days without drug. b, Scatter plot of hits (P < 0.05, blue/black) with lipid transport (OSBP), ESCRT-0 (STAMBP), ESCRT-I (UBAP1), ESCRT-III (CHMP2A, CHMP3, CHMP6) genes labelled. sgNPC1, sgRNA targeting NPC1; sgNT, non-targeting control sgRNA. c, Network analysis pipeline to identify functional modules. d, ESCRT-0, ESCRT-I and ESCRT-II subnetwork containing hits (blue) and coessential genes (grey), with protein–protein interactions (dark grey edges) and neurodegenerative mutations (red outline). e,f, Representative images (e; t = 90 min) and quantification (f) of mCherry–Gal3 (mCh–Gal3) recruitment to lysosomes upon pulse–chase with 0.125 mM LLOMe following treatment with shTFG or luciferase control (shLuc). Each point shows the mean number of mCh–Gal3 puncta per cell (n = 8 fields of view (FOVs)) and the shaded region indicates s.d. g,h, Representative images (g; t = 5 min) and quantification of the maximum (max) number (h) of CHMP4B–mNG puncta per cell across the time course in 293T cells treated with DMSO (vehicle) or 0.25 mM or 0.5 mM LLOMe following treatment with shTFG (pink), shTSG101 (blue), shALIX (green) or shLuc (black). Each point represents one FOV (n = 10). ALIX is also known as PDCD6IP. i,j, Representative images (i; t = 60 min) and quantification of the maximum number (j) of eGFP–UBAP1 puncta per cell across the time course in 293T cells treated with DMSO or 0.5 mM LLOMe following treatment with shTFG (pink) or shLuc (black). Each point represents one FOV (n = 9). Comparison of groups by two-sided Mann–Whitney test (b) or two-way ANOVA (h,j). h, 0.25 mM, shLuc versus shTFG: P = 8.5 × 10−6; shLuc versus shTSG101: P = 1.2 × 10−6; 0.5 mM, shLuc versus shTFG: P = 7.0 × 10−6; shLuc versus shTSG101: P = 5.8 × 10−3. j, P = 9.4 × 10−12. Scale bars, 10 µm. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; NS, not significant (P ≥ 0.05).

Source data

To prioritize gene hits for functional characterization, we built a bioinformatic pipeline that clusters hits into functionally related networks based on coessentiality, overlaid with evidence for physical protein–protein interaction and for neurodegenerative disease association (Fig. 1c). Inspection of the sensitizing networks revealed several functional modules including lysosomal membrane repair (ESCRTs and autophagy), vesicle trafficking (transport protein particle (TRAPP), conserved oligomeric Golgi (COG) complex), and cholesterol and phospholipid homeostasis (Extended Data Figs. 1e and 2). By contrast, the desensitizing hits included growth regulation (mitochondrial translation, tRNA processing and HRI (also known as eIF-2α kinase) signalling) and endosome and lysosome biogenesis (COMMD and homotypic fusion and protein sorting (HOPS) complex) (Extended Data Figs. 1e and 2).

The network analysis revealed novel candidate protective genes within networks of known function. In particular, the ESCRT-I–II network contained TFG, a protein not previously associated with lysosomal repair (Fig. 1d). We proceeded to validate TFG as protective against lysosome damage by measuring the growth of TFG-depleted HEK293T cells under LLOMe challenge. In both control and NPC1-knockout genotypes, TFG knockdown with small hairpin RNA (shRNA) led to decreased growth rate upon LLOMe treatment compared with shRNA targeting luciferase (shLuc) (Extended Data Fig. 1f,g).

To test whether the reduced growth of TFG-depleted cells was due to failure to repair damaged lysosomes, we assessed LLOMe-induced recruitment of mCherry–galectin 3 (Gal3), a readout for lysosomal membrane permeabilization7,8. In both control and NPC1-knockout genotypes, we found increased Gal3 recruitment in cells treated with shRNA targeting TFG (shTFG) compared with shLuc-treated cells (Fig. 1e,f and Extended Data Fig. 1h–l).

Loss-of-function mutations in TFG drive several neurological disorders, including HSP, Charcot–Marie–Tooth disease, hereditary motor and sensory neuropathy (HMNS-P) and amyotrophic lateral sclerosis[34]. Consistent with a neuroprotective function of TFG, its CRISPRi-mediated depletion in induced pluripotent stem cell (iPSC)-derived neurons led to increased damage upon LLOMe treatment, as assessed by higher numbers of mCherry–Gal3 puncta compared with control neurons (Extended Data Fig. 1m–o). It is worth emphasizing that although TFG scored as a hit only in the NPC1-knockout arm of the CRISPRi screen (Extended Data Fig. 1c,d), its depletion in follow-up experiments sensitized both control and NPC1-knockout HEK293T cells to LLOMe, highlighting the general requirement for TFG in lysosomal membrane homeostasis.

TFG is required for ESCRT recruitment to damaged lysosomes

TFG was shown to localize to ER exit sites (ERES), where it regulates COPII vesicle assembly and turnover34,35. Notably, TFG has also been linked to the sorting of internalized plasma membrane receptors into multivesicular bodies (MVBs), an ESCRT-dependent process[36]. The presence of TFG in an ESCRT network prompted us to investigate whether TFG is required for ESCRT recruitment to damaged lysosomes. We generated HEK293T, HeLa and U2OS cells that stably expressed the ESCRT-III subunit CHMP4B–mNeonGreen (CHMP4B–mNG), which, as expected, accumulated on LAMP2-positive lysosomes upon LLOMe-induced damage[7] (Extended Data Fig. 3a,b). Depletion of TFG impaired lysosomal CHMP4B–mNG recruitment to a similar degree to depletion of the obligate ESCRT-I component TSG101, whereas depleting ALIX did not affect CHMP4B–mNG recruitment (Fig. 1g,h and Extended Data Fig. 3c–j). TFG depletion similarly reduced CHMP4B–mNG recruitment following treatment with a distinct lysosomotropic agent, glycyl-L-phenylalanine 2-naphthylamide (GPN)[14], indicating a broad role of TFG in ESCRT-III assembly at damaged lysosomes (Extended Data Fig. 3k,l).

We next investigated the role of TFG in earlier ESCRT assembly steps. ESCRT-II recruits ESCRT-III subunits, initiating ESCRT-III filament polymerization and subsequent membrane remodelling[11]. Consistent with a requirement in ESCRT-II assembly, TFG depletion impaired recruitment of the ESCRT-II subunit EAP30 (also known as VPS22)–mNG to damaged lysosomes, to a degree comparable to the effect of TSG101 depletion (Extended Data Fig. 4a,b). Accumulation of ALIX at damaged lysosomes was also TFG-dependent (Extended Data Fig. 4c,d). The requirement for TFG extended to ESCRT-I, as shown by the lack of lysosomal recruitment of eGFP–UBAP1 in TFG-depleted HEK293T and U2OS cells (Fig. 1i,j and Extended Data Fig. 4e,f).

We next examined the TFG requirement in HEK293T cells in which TSG101, VPS25 and CHMP2B (ESCRT-I, ESCRT-II and ESCRT-III, respectively) are tagged at the endogenous loci with mNG. mNG-tagged TSG101, VPS25 and CHMP2B accumulated at damaged lysosomes and, consistent with the exogenous ESCRT reporters, their recruitment was abolished by TFG depletion (Extended Data Fig. 4g–p). Increasing the LLOMe concentration reduced the requirement for TFG in the recruitment of these ESCRT subunits, as well as the dependency on TSG101 for CHMP2B recruitment, possibly pointing to alternative modes of ESCRT-III recruitment as the extent of damage increases1,15,18.

To further validate the requirement for TFG in ESCRT-III polymerization on damaged lysosomes, we carried out immunofluorescence co-staining for endogenous CHMP1A (ESCRT-III) and the lysosomal marker TMEM192. In both HeLa cells and U2OS cells, shRNA-mediated depletion of TFG resulted in decreased accumulation of CHMP1A on damaged lysosomes, to a similar extent to TSG101 depletion (Extended Data Fig. 4r–u).

Similar to wild-type cells, and consistent with the identification of TFG in the sgNPC1 background, NPC1-knockout cells exhibited strong defects in ESCRT recruitment to damaged lysosomes upon TFG or TSG101 depletion (Extended Data Fig. 5a–g).

Collectively, these data identify TFG as a critical factor upstream of ESCRT-I and thus as having a role early in the ESCRT assembly cascade that enables lysosomal membrane repair.

TFG translocates from ERES to damaged lysosomes

Our finding that TFG promotes ESCRT recruitment to damaged lysosomes motivated us to test whether TFG physically associates with these organelles. At baseline, immunofluorescence staining in U2OS and HeLa cells revealed endogenous TFG as discrete spots, about 80% of which colocalized with the ERES marker SEC31A (Extended Data Fig. 6a,b).

Remarkably, within 5 min of LLOMe treatment in multiple cell lines, TFG extensively colocalized with lysosomes (Fig. 2a–d and Extended Data Fig. 6c,d), which, as expected, also accumulated eGFP–TSG101 (Extended Data Fig. 6e,f). This result was supported by immunogold staining coupled with electron microscopy (EM–IGS) in HeLa cells, which showed TFG-positive gold particles clustered around damaged lysosomes (Fig. 2e). TFG accumulation could reflect the formation of membrane contact sites between ERES and damaged lysosomes or, alternatively, the translocation of TFG itself from ERES to the lysosomal membrane. The following observations support the second scenario: (1) there was no increase in SEC31A colocalization with lysosomes upon LLOMe treatment (Extended Data Fig. 6g–j); and (2) TFG colocalization with SEC31A partially decreased in LLOMe-treated conditions, while most SEC31A spots retained some TFG signal, indicating that lysosomal TFG is at least in part derived from ERES (Extended Data Fig. 6k–p).

Fig. 2: TFG translocates from ERES to damaged lysosomes.

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a–d, Representative images (a,c) and quantification (b,d) of colocalization ratio of TFG and LAMP2 in HeLa (a,b) or U2OS (c,d) cells treated with DMSO (vehicle control) or 0.5 mM LLOMe. Colocalization ratio is measured as area of colocalized signals divided by the area of LAMP2 signal. Each point represents one FOV (n = 9). Images are shown for 15 min of treatment. Scale bars, 5 µm. b, 0 versus 5 min: P = 1.5 × 10−6; 0 versus 10 min: P = 9.1 × 10−6; 0 versus 15 min: P = 7.3 × 10−5. d, 0 versus 5 min: P = 4.9 × 10−6; 0 versus 10 min: P = 2.9 × 10−6; 0 versus 15 min: P = 5.8 × 10−6. e, Representative EM–IGS images of TFG in HeLa cells treated with DMSO or 0.5 mM LLOMe. Images are shown for 15 min of treatment. Pink arrows indicate immuno-gold labeled TFG. Scale bars, 150 nm. Experiment was repeated three times. f,g, Representative confocal images of TFG and LAMP2 immunofluorescence staining (f) and quantification of colocalization ratio between TFG and LAMP2 (g) in U2OS cells. Cells were treated with DMSO or 20 µM BAPTA-AM, followed by treatment with either 4 µM ML-SA5 or 0.5 mM LLOMe for 15 min. Each point represents one FOV (n = 5). Scale bars, 10 µm. Comparison of groups carried out by one-way ANOVA (b,d,g). Data are mean ± s.d. g, Untreated versus ML-SA5, P = 8.2 × 10−11; Untreated versus LLOMe, P = 7.8 × 10−9.

Source data

EM–IGS showed enlarged lysosomes wrapped by ER positive for the ER retrieval amino acid motif KDEL, consistent with previous reports22,23,24 (Extended Data Fig. 7a). ER–lysosome membrane contact sites are largely mediated by VAPA and VAPB, which have been implicated in lysosomal repair22,23. Of note, co-depletion of VAPA and VAPB by shRNA strongly suppressed LLOMe-induced accumulation of TFG on lysosomes (Extended Data Fig. 7b–d). Together, these data point to a model in which lysosomal damage triggers the relocalization of TFG from ERES to lysosomes, and VAP-mediated physical proximity between ER and lysosomes is required for this translocation to occur.

TFG binds to ATG8 proteins to form LASER

We next dissected the mechanism that specifically recruits TFG to damaged lysosomes. Release of luminal calcium is an early event during lysosomal membrane damage[20]. The calcium chelator BAPTA-AM blocked both TFG translocation and CHMP4B–mNG accumulation on damaged lysosomes, when co-administered with LLOMe (Fig. 2f,g and Extended Data Fig. 7e–h). Conversely, inducing lysosomal calcium release with ML-SA5, a chemical activator of the lysosomal calcium channel transient receptor potential mucolipin 1 (TRPML1), was sufficient to induce lysosomal accumulation of TFG and CHMP4B–mNG in the absence of LLOMe and in a BAPTA-AM sensitive manner (Fig. 2f,g and Extended Data Fig. 7e–h). We therefore interrogated calcium-dependent processes implicated in lysosomal repair.

ALG2, a calcium-binding protein associated with ERES, has been implicated in several membrane repair processes[14]. However, shRNA-mediated depletion of ALG2 had no effect on TFG translocation from ERES to damaged lysosomes (Extended Data Fig. 7i–k) or on CHMP4B–mNG accumulation (Extended Data Fig. 7l–q).

CASM is a lysosomal repair process that is linked, at least in part, to calcium efflux15,19,20. Consistent with CASM induction, LLOMe treatment led to accumulation of endogenous LC3B, mCherry-tagged LC3B and GABARAP on LAMP2-positive lysosomes that also contained TFG (Extended Data Fig. 8a–f). In line with its calcium dependence, lysosomal LC3B conjugation was inhibited by BAPTA-AM and was induced by the TRPML1 agonist ML-SA5 in the absence of LLOMe (Extended Data Fig. 8e,f).

Lysosomal LC3B conjugation was abolished by shRNA-mediated knockdown of the obligate E3-like component ATG5 but not of ATG13, which is required for canonical macroautophagy but dispensable for CASM[15] (Extended Data Fig. 8g–j). Notably, TFG was not required for LLOMe-induced lysosomal LC3B conjugation (Extended Data Fig. 8g–j). We then tested whether, conversely, CASM could be responsible for the recruitment of TFG to damaged lysosomes. Consistent with this possibility, depletion of ATG5, but not ATG13, abolished LLOMe-induced relocalization of TFG to LAMP2-positive lysosomes (Fig. 3a,b and Extended Data Fig. 8k–m). Moreover, in line with the requirement for TFG in ESCRT assembly and lysosomal repair, depleting ATG5 abolished accumulation of eGFP–UBAP1 and CHMP4B–mNG on damaged lysosomes (Extended Data Fig. 9a–h) and inhibited repair to a similar degree as depleting TFG or TSG101, as assessed by dextran–fluorescein isothiocyanate (FITC) unquenching (Extended Data Fig. 9i,j).

Fig. 3: TFG binds to the ATG8 proteins to form LASER on damaged lysosomal membranes.

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a,b, Representative images (a; t = 15 min) and quantification (b) of TFG–LAMP2 colocalization in HeLa cells treated with shATG5 or shLuc and 0.5 mM LLOMe. Each point represents one FOV (n = 9). Scale bars, 5 µm. b, shLuc 0 versus 5 min: P = 2 × 10−6; shLuc 0 versus 10 min: P = 8.3 × 10−5. c, Top, multiple sequence alignment of TFG LIRs (conserved, blue; non-conserved, grey). Bottom, schematic of TFG, with oligomerization domains (Phox and Bem1 (PB1) and CC) in pink and PQ-rich domains in grey. d,e, Representative images (d; t = 20 min) and quantification (e) of TFG–LAMP2 colocalization in U2OS cells treated with 0.5 mM LLOMe and shTFG or shLuc, reconstituted with wild-type (WT) or LIR2-mutant (LIR2-mut; SAFDPL>SAADPA) TFG. Each point represents one FOV (n = 14). Scale bars, 5 µm. e, Wild type versus LIR2-mut, P = 1 × 10−10. f,g, Representative images (f; t = 20 min) and quantification of fold change in maximum number (g) of CHMP4B–mNG puncta per cell across the time course in 293T cells treated with 0.25 mM LLOMe and shLuc or shTFG, reconstituted with wild-type, LIR2-mut or LIR3-mut (PGFTSL>PGATSA) TFG. Each point represents one FOV (n = 6). Scale bars, 10 µm. g, None versus WT: P = 2.24 × 10−8; WT versus LIR2: P = 1.0 × 10−10. h,i, Representative images (h) and quantification (i) of fluorescence intensity (FITC- versus Alexa Fluor 647-labelled dextran) in U2OS cells treated with 0.25 mM LLOMe and shLuc or shTFG, reconstituted with wild-type or LIR2-mutant TFG. Each point represents one FOV (n = 12). Scale bars, 10 µm. i, shLuc versus shTFG: P = 2.8 × 10−4, shLuc versus shTFG plus LIR2: P = 7.0 × 10−7. Comparison of groups by one-way ANOVA (b,e,g,i). Data are mean ± s.d.

Source data

Inspection of the amino acid sequence of TFG revealed three predicted, highly conserved LC3-interacting regions (LIRs); LIR2 and LIR3 are in exposed, unstructured domains and were therefore selected for testing[37] (Fig. 3c). Fluorescence polarization experiments demonstrated binding of a peptide encompassing LIR2 of TFG to recombinant LC3B and GABARAP, with apparent dissociation constants of 60 μM and 100 μM, respectively (Extended Data Fig. 9k–n). By contrast, peptides containing a mutated LIR2 (FDPL>ADPA, LIR2-mut) or encompassing LIR3 (or mutant LIR3 (FTSL>ATSA, LIR3-mut)) did not bind to either LC3B or GABARAP (Extended Data Fig. 9m,n).

We next assessed whether TFG and LC3B could directly interact in an equilibrium binding assay. We purified truncated wild-type and LIR2-mutant mCherry–TFG1–210 (full length TFG, including the C-terminal intrinsically disordered region, could not be purified owing to aggregation and precipitation), and incubated these with agarose beads coated with Flag–GFP–LC3B. Recombinant wild-type TFG exhibited detectable association with bead-bound LC3B (Extended Data Fig. 9o–q). Identical amounts of the LIR2-mutant TFG1–210 did not exhibit any detectable binding to LC3B (Extended Data Fig. 9o–q).

In line with the lower affinity of the TFG-LIR2 peptide versus that of p62-LIR peptide for LC3B (approximately 60 µM for TFG-LIR2 versus 2 µM for p62-LIR) (Extended Data Fig. 9k–n), the binding of wild-type mCherry–TFG1–210 to LC3B-containing beads was weaker than binding of the same amount of mCherry–p62 (Extended Data Fig. 9r,s).

To determine whether LIR2 is functionally required for TFG assembly at damaged lysosomes, we reintroduced wild-type (WT), LIR2-mutant and LIR3-mutant TFG (coupled to mCherry via IRES or P2A to monitor expression levels) into U2OS cells depleted of endogenous TFG. As with the endogenous protein, exogenous wild-type TFG localized to lysosomes upon LLOMe challenge (Fig. 3d,e). Underscoring the essential role of the LIR2–ATG8 interaction, TFG(LIR2-mut), but not TFG(LIR3-mut), did not cluster on LAMP2-positive lysosomes upon LLOMe treatment (Fig. 3d,e and Extended Data Fig. 9t).

Consistent with the critical role for LIR2 in the lysosomal translocation of TFG, TFG-depleted HEK293T and U2OS cells reconstituted with TFG(LIR2-mut) did not recruit CHMP4B–mNG to damaged lysosomes, whereas TFG(LIR3-mut) supported CHMP4B–mNG recruitment as efficiently as the wild-type protein (Fig. 3f,g and Extended Data Fig. 9u,v). Furthermore, TFG(LIR2-mut) did not support lysosomal repair to a similar degree as complete TFG depletion, as assessed by dextran–FITC unquenching (Fig. 3h,i). Thus, a LIR2-mediated interaction of TFG with ATG8 proteins promotes ESCRT assembly on damaged lysosomal membranes, leading to their repair.

On the basis of these data, we propose that TFG and lysosome-conjugated ATG8 proteins together form a functional complex, LASER, that is essential for damage-induced ESCRT polymerization.

TFG–TSG101 binding drives ESCRT assembly

We next sought to determine the molecular mechanism through which LASER promotes ESCRT polymerization on the lysosomal membrane. On the basis of previous literature, we hypothesized that the ubiquitin E2 variant (UEV) domain of TSG101, which is known to bind to both ubiquitin and Pro-Ser/Thr-Ala-Pro (PS/TAP) motifs, may interact with TFG[38]. We expressed and purified recombinant glutathione S-transferase (GST)-tagged UEV domains (wild-type, PSAP-binding and ubiquitin-binding mutants), incubated them with cell lysates, and evaluated binding to endogenous TFG (Extended Data Fig. 10a). Wild-type and ubiquitin-binding mutant (N45A) UEV domains bound to TFG, whereas the PSAP-binding mutant (M95A) did not (Fig. 4a). This pattern mirrored the binding between the UEV domain and the ESCRT-0 protein HRS[11], indicating a putative PSAP-dependent interaction (Fig. 4a).

Fig. 4: TFG binds the UEV domain of TSG101 to initiate ESCRT assembly on damaged lysosomes.

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a, Western blot analysis of immunoprecipitated recombinant GST fusion with the UEV domain of TSG101 incubated with lysate from 293T cells. TSG101-UEV binding experiments were repeated three times. b, Multiple sequence alignment of TFG-PSAP (196–199; conserved, blue; non-conserved, grey). Oligomerization domains are in pink; PQ-rich domain are in grey; solvent-accessible LIRs (LIR2 and LIR3) are in blue. c,d, Representative images (c) and quantification (d) of binding of recombinant wild-type or PSAP>AAAA mutant mCherry–TFG1–210 to GST beads coated with wild-type or M95A-mutant TSG101-UEV domain. Each point represents one bead. Quantification of n = 32 beads (TSG101-UEV(WT)), n = 25 beads (TSG101-UEV(M95A)). d, Comparison of groups by two-sided unpaired t-test; P < 1 × 10−15. e,f, Representative images of CHMP4B–mNG (e; t = 20 min) and quantification of fold change in the maximum number of CHMP4B–mNG puncta per cell (f) in 293T cells treated with 0.25 mM LLOMe. Cells were treated with shLuc or shTFG and reconstituted with TFG(WT) or TFG(PSAP>AAAA). Each point represents one FOV (n = 8). Scale bars, 10 µm. f, One-way ANOVA; none versus WT: P = 2.4 × 10−8; WT versus PSAP: P = 6.5 × 10−6. g,h, Representative images (g) and quantification of total fluorescence intensity ratio (h) of internalized dextran (FITC- versus Alexa Fluor 647-labelled) puncta following treatment with 0.25 mM LLOMe. U2OS cells were treated with shLuc or shTFG and reconstituted with TFG(WT) or TFG(PSAP>AAAA). Each point represents one FOV (n = 9). Scale bars, 10 µm. h, One-way ANOVA; shTFG versus shTFG + TFG(WT): P = 3.2 × 10−4; shTFG + TFG(WT) versus shTFG + TFG(PSAP): P = 5.3 × 10−3. Data are mean ± s.d.

Source data

By inspecting the primary amino acid sequence of TFG, we noted the presence of a highly conserved PSAP motif following the coiled-coil (CC) region (Fig. 4b). Consistent with this motif being the primary UEV-interacting region, when we incubated mCherry-tagged fragments encompassing the N-terminal region of TFG with recombinant UEV immobilized on agarose beads, only the PSAP-containing region (TFG161–210, or fragment 4) bound to the beads (Extended Data Fig. 10b–e). Moreover, unlike wild-type recombinant TFG1–210, TFG1–210 in which the PSAP motif was mutated to AAAA did not bind to the UEV domain (Fig. 4c,d and Extended Data Fig. 10f–i). Consistent with a critical role for the interaction between TSG101-UEV and TFG-PSAP in ESCRT recruitment, the PSAP>AAAA mutant of TFG did not support CHMP4B–mNG clustering on damaged lysosomes in HEK293T cells (Fig. 4e,f), despite its unaltered recruitment to damaged lysosomes, as predicted by an intact LIR2 (Extended Data Fig. 10j,k). In agreement with decreased ESCRT recruitment, TFG-depleted U2OS cells reconstituted with the PSAP>AAAA mutant displayed a comparable repair defect to non-rescued, TFG-depleted cells, as shown by dextran–FITC unquenching (Fig. 4g,h).

Thus LASER recruits ESCRT-I, at a minimum, through a specific interaction between the PSAP motif of TFG and the UEV domain of TSG101.

TFG oligomerization enables ESCRT recruitment

Within the ESCRT pathway, oligomerization-driven cooperativity enables efficient assembly, despite low-affinity interactions among the various ESCRT components10,11. TFG is also thought to exist in a highly assembled state. It undergoes oligomerization via its N-terminal PB1 and CC domains, whereas a PQ-rich, low-complexity region encompassing the C-terminal half of TFG drives its liquid–liquid phase separation (LLPS)39,40,41.

Of note, TFG161–210, which lacks all oligomerization domains, displayed substantially reduced binding compared with wild-type TFG1–210, despite having an intact PSAP motif (Extended Data Fig. 10h,i). This finding was consistent with previous reports that the PSAP motif of HRS binds weakly to the UEV of TSG101 and requires oligomerization-driven avidity for stable association. We thus hypothesized that TFG oligomerization, mediated by its PB1 and CC domains[40], enhances TSG101 binding through avidity-based stabilization. Consistent with this possibility, deleting the CC domain from the recombinant TFG1–210 completely abolished interaction with recombinant UEV, whereas deleting the PB1 domain significantly weakened it, despite the presence of an intact PSAP in both constructs (Extended Data Fig. 10g,l,m).

Consistent with the reduced in vitro binding, in TFG-depleted HEK293T cells reconstituted with exogenous TFG constructs, both PB1-deleted and CC-deleted TFG failed to support lysosomal CHMP4B–mNG recruitment upon LLOMe treatment (Extended Data Fig. 10n,o), despite localizing correctly to lysosomes (Extended Data Fig. 10p,q). Thus, TFG oligomerization mediated by the PB1 and CC domains enhances its PSAP-dependent interaction with TSG101, supporting a model in which oligomeric TFG within LASER accelerates ESCRT assembly on damaged lysosomes.

The pathogenic R106C mutation—located in the CC domain of TFG—has been linked to autosomal recessive, early-onset HSP34,40. In vitro, this mutation distorts the ring-like structures formed by the PB1 and CC portions of TFG[34]. Consistent with a critical role for oligomerization, recombinant TFG(R106C) exhibited strongly reduced binding to the UEV domain of TSG101 (Fig. 5a,b and Extended Data Fig. 10g). Despite localizing correctly to lysosomes in LLOMe-treated conditions, and as predicted by the TSG101-binding defect, TFG(R106C) did not support lysosomal CHMP4B–mNG recruitment in knockdown–reconstitution experiments (Fig. 5c,d and Extended Data Fig. 10p,q).

Fig. 5: TFG(R106C) impairs ESCRT recruitment and sensitizes cells to lysosome damage.

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a,b, Representative images (a) and quantification (b) of binding between mCherry–TFG1–210 (wild type or R106C mutant) and GST–TSG101-UEV. Each point represents one bead (WT: n = 65 (0.6 µM), n = 77 (1.25 µM), n = 88 (2.5 µM), n = 29 (5 µM); R106C: n = 25 (0.6 µM), n = 44 (1.25 µM), n = 61 (2.5 µM), n = 40 (5 µM)). 5 µM, 2.5 µM, 1.25 µM: P < 1 × 10−15; 0.6 µM: P = 1.1 × 10−7. c,d, Representative images (c; t = 20 min) and quantification of fold change in the maximum number (d) of CHMP4B–mNG puncta across the time course. 293T cells expressing shRNA-resistant TFG(WT) or TFG(R106C) were treated with shTFG or shLuc, followed by treatment with 0.25 mM LLOMe. Each point represents one FOV (n = 14). d, None versus WT: P = 4.8 × 10−4; WT versus R106C: P = 2.5 × 10−6. e,f, Representative immunofluorescence images (e; t = 7 min) and quantification (f) of CHMP1A–TMEM192 colocalization in fibroblasts from individuals with HSP (TFGR106C/R106C) reconstituted with wild-type, R106C or LIR2-mutant (SAFDPL>SAADPA) TFG and treated with 0.5 mM LLOMe. Each point represents one FOV (n = 6). f, WT versus R106C: P = 2.3 × 10−8; WT versus LIR2-mut: P = 2.0 × 10−6; WT versus none: P = 1.5 × 10−8; g,h, Representative images (g) and quantification (h) of FITC versus Alexa Fluor 647 dextran fluorescence intensity following treatment with 0.5 mM LLOMe. Fibroblasts from individuals with HSP (TFGR106C/R106C) were reconstituted with wild-type, R106C or LIR2-mutant TFG. Each point represents one FOV (n = 11). h, P < 1 × 10−15 for all comparisons. i, Model of LASER-dependent ESCRT recruitment. Lysosomal damage triggers calcium leakage, CASM and ER–lysosome contacts. TFG dissociates from ERES and binds ATG8 proteins, forming LASER. TFG directly binds TSG101 to initiate ESCRT-mediated repair. Comparison of groups by two-way (b,h) or one-way (d,f) ANOVA. Data are mean ± s.d. Scale bars, 10 µm.

Source data

We further dissected the role of the CC domain in skin fibroblasts from individuals with HSP who are homozygous for the R106C mutation. In agreement with the knockdown–reconstitution experiments in HEK293T cells, TFGR106C/R106C fibroblasts exhibited defective LLOMe-dependent recruitment of endogenous CHMP1A to damaged lysosomes, which was rescued by re-expression of wild-type TFG but not of TFG(R106C) or TFG(LIR2-mut) (Fig. 5e,f). Notably, the repair defect occurred despite unchanged recruitment of TFG(R106C) to damaged lysosomes relative to wild-type TFG (Extended Data Fig. 10r,s). Defective ESCRT recruitment was paralleled by impaired ability of TFG(R106C) to support lysosomal repair, as shown by dextran–FITC unquenching (Fig. 5g,h).

Together, these data support the possibility that defective TFG oligomerization within LASER compromises its ability to recruit ESCRT and maintain lysosomal integrity and may underlie, at least in part, the neuropathological effects of the R106C mutation in TFG.

Discussion

How detection of lysosomal membrane damage is coupled to its ESCRT-mediated repair has not been fully elucidated. Here we define LASER as a rapid, early-acting molecular assembly that bridges damage sensing to ESCRT-I activation, thereby resolving a central mechanistic gap in lysosomal membrane repair. Our results support a model in which ATG8 proteins, conjugated to lysosomal membrane phospholipids upon onset of damage and luminal calcium release, act as a physical anchor for oligomeric TFG. Within LASER, TFG binds to ATG8 proteins via LIR2, leading to its association with damaged lysosomes (Fig. 5i). Oligomeric TFG at sites of lysosomal membrane damage acts as a hub that helps to recruit TSG101 via combined affinity (PSAP-dependent) and avidity (CC-dependent) effects, thus accelerating the downstream assembly of ESCRT-II and ESCRT-III (Fig. 5i). In this way, LASER converts local damage signals into cooperative ESCRT polymerization, ensuring spatially restricted membrane repair.

It was previously shown that, upon CASM induction, binding between GABARAPL2 and ALIX promotes recruitment of ALIX to damaged lysosomes, thus leading to ESCRT assembly[18]. However, our data and other reports point to ALIX having a conditional, non-essential role in ESCRT recruitment to damaged lysosomes7,8. A recent report supports the strict requirement for ATG5–ATG12 in the recruitment of ESCRT subunits, while hinting that this complex may have additional roles, independent of ATG8 conjugation, in promoting ESCRT assembly[15]. However, the biochemical mechanisms through which CASM recruits ESCRT-I, and particularly TSG101, to damaged lysosomes were not established. Our data identify a crucial consequence of CASM induction: the recruitment of ERES-derived, oligomeric TFG to damaged lysosomes, forming LASER as a dedicated platform for TSG101 recruitment to trigger ESCRT assembly. As CASM supports multiple lysosomal repair and quality control pathways, including lipid transfer and membrane remodelling15,18,42, an important follow-up question will be how LASER is coordinated with other CASM-dependent repair pathways.

Lysosomal membrane damage occurs along a continuum, ranging from subtle lipid packing defects and ion leakage to progressive membrane ruptures and, eventually, irreversible permeabilization1,6. In this study, we primarily relied on LLOMe as a tractable and temporally precise model of lysosomal membrane damage, as LLOMe-derived poly-Leu oligomers recapitulate key biophysical features of progressive lysosomal injury, from induction of lipid packing defects to nanoscale membrane lesions and eventual permeabilization[6]. Our data support a model in which LASER activates ESCRT rapidly upon damage onset. Alternative, TFG-independent mechanisms for ESCRT recruitment (for example, ALG2–ALIX) may become increasingly important at more severe stages of damage14,15,18. However, our findings position LASER as a principal early trigger of ESCRT assembly at reversible stages of lysosomal injury.

LASER expands the repertoire of mechanisms that locally activate ESCRT polymerization on membranes. Similar to ubiquitin-dependent ESCRT-0-mediated activation during intraluminal vesicle formation, or CEP55- and LEMD2-driven activation during cytokinesis and nuclear envelope closure, LASER relies on low-affinity interactions amplified by oligomerization[11]. For example, the PSAP motif of HRS has relatively weak affinity for the UEV domain of TSG101, necessitating cooperativity via ESCRT-0 oligomerization[43]. Similarly, CC-dependent oligomerization was essential for TFG to bind and recruit TSG101 to lysosomes. Thus, LASER obeys a unifying principle of ESCRT regulation: spatially confined polymerization driven by cooperative, multivalent interactions that amplify weak motif recognition into membrane remodelling events. In addition to PSAP-mediated interactions, the ESCRT-I subunits TSG101 and UBAP1 bind directly to ubiquitin via their UEV and UBA domains, respectively[38]. It remains to be determined whether any ESCRT subunits engage in PSAP-independent interactions with TFG.

TSG101 can be found in an autoinhibited state mediated by an intramolecular UEV–PTAP interaction, which is displaced by the PSAP motif of HRS, triggering ESCRT-I dependent recruitment of ESCRT-II[11]. It remains to be determined whether, analogous to HRS, oligomeric TFG may help to relieve the autoinhibited state of TSG101, possibly in response to its engagement of ATG8 proteins within LASER. If so, LASER may not only recruit ESCRT-I but actively prime it for polymerization. Finally, the functional partnership between TFG and ATG8 proteins may extend to other membrane remodelling processes involving ESCRT, including LC3-associated phagocytosis[44], phagophore closure[45] and microautophagy-dependent ERES degradation[11].

Although our data establish a requirement for TFG oligomerization via its PB1 and CC domains, whether TFG phase separation contributes to ESCRT recruitment remains unresolved. Phase-separated stress granules have been shown to accumulate rapidly at damaged lysosomes to stabilize the ruptured lysosomal membrane or to trigger specific signalling responses46,47. Conversely, phase separation of ESCRT-recruiting proteins has been shown to facilitate nuclear envelope reformation in mammalian cells, as well as MVB formation in plants11,48. TFG condensates with a hollow morphology were recently reconstituted and proposed to act as ‘molecular sieves’ that may concentrate COPII coat subunits within their internal space to regulate COPII vesicle formation[41]. In the context of LASER, such higher-order assemblies could further enhance local concentration and cooperativity of ESCRT subunits, reinforcing their rapid and spatially restricted activation at damaged lysosomal membranes.

The calcium-dependent mobilization of TFG and ALG2 from ERES suggests that ERES function as regulated reservoirs of membrane repair factors, which are released upon detection of specific alarm signals. TFG localization at ERES, mediated by interactions with SEC23[35], may prevent its premature or aberrant activation of ESCRT polymerization, which could have deleterious effects on lysosomal membrane homeostasis. An important aspect of this model is the requirement for physical proximity between ERES and lysosomes. This proximity relies on VAPA and VAPB-dependent ER–lysosome membrane contact sites, which have been extensively shown to become expanded in the context of lysosomal membrane damage22,23,24, and may be critical for sensing of lysosome-derived calcium at ERES.

The requirement for VAP proteins may also reflect coordination between LASER and repair-promoting mechanisms for lipid exchange at ER–lysosome contacts22,23,24. ER–lysosome contacts enable directional lipid transfer by ER-bound OSBP-related proteins and VPS13, which are anchored to the lysosomal membrane by PI4P and RAB7, respectively22,23,24. The physical proximity between ER and damaged lysosomes may enable TFG to recruit ESCRT in close spatial proximity to the sites of phospholipid delivery, a mechanism reminiscent of the cooperation between ESCRT and VPS13 in MVB formation[49]. Thus, these findings establish ER–lysosome contacts as regulatory platforms that coordinate lysosomal damage sensing with ESCRT-dependent membrane remodelling and with lipid replenishment.

TFG has a role in spatially organizing ERES, and TFG loss of function has been linked to secretory defects35,40. The observation that TFG translocates from ERES to lysosomes upon damage suggests that the roles of TFG in secretory traffic and lysosomal membrane repair are separable. Moreover, our results with TFGR106C mutant cells suggest that defective maintenance of lysosomal integrity could synergize with secretory defects in driving the neuropathic manifestations of TFG-linked HSP. Additional studies are required to fully elucidate the relationship between secretory and lysosome-repairing functions of TFG in both normal physiology and in the context of HSP.

The identification of LASER as a lysosomal repair-promoting factor was made possible by the use of a damage-sensitized genetic background, NPC1-deleted cells. Our genome-wide screen and follow-up studies suggest that defective lysosomal repair may be a common driver in many neurodegenerative diseases linked to lipid imbalance or defective ESCRT assembly. More broadly, our work establishes lysosomal membrane resealing as a multi-layered process in which damage sensing, membrane contact site remodelling and ESCRT polymerization are molecularly integrated.

Methods

Antibodies and chemicals

Reagents were obtained from the following sources: antibodies against HA (3724S, clone C29F4; 1:1,000 dilution for western blot), LC3B (3868S, clone D11; 1:1,000 dilution for western blot), VINCULIN (13901S, clone E1E9V; 1:1,000 dilution for western blot), LAMTOR4 (13140S, clone D4P60; 1:300 dilution for immunofluorescence), HRS (15087S, clone D7T5N; 1:1,000 dilution for western blot), TOM20 (42406S, clone D8T4N; 1:1,000 dilution for western blot), ATG5 (2630S; 1:1,000 dilution for western blot), ATG13 (13468S, clone E1Y9V; 1:1,000 dilution for western blot) from Cell Signaling Technology; LAMP2 (sc-18822, clone H4B4; 1:300 dilution for immunofluorescence), CHMP1A (sc-271617, clone B-5; 1:300 dilution for immunofluorescence) from Santa Cruz Biotechnology; TFG (ab156866, clone EPR8766; 1:300 dilution for immunofluorescence, 1:1,000 dilution for western blot) and NPC1 (ab36983; 1:1,000 dilution for western blot) from Abcam; SEC31A (612350, clone 32; 1:300 dilution for immunofluorescence) from BD Biosciences; SEC31A (17913-1-AP;1:300 dilution for immunofluorescence), TSG101 (67381-Ig, clone 2B7G8; 1:1,000 dilution for western blot), ALIX (67715-Ig, clone 1H9D9; 1:1,000 dilution for western blot), PDCD6/ALG2 (12303-1-AP; 1:1,000 dilution for western blot) from Proteintech; TFG (NBP2-62212, clone TFG-03; 1:300 dilution for immunofluorescence) from Novus; LLOMe (16008) from Cayman; DMSO (D2650-100mL) from Sigma-Aldrich; Pierce Protease Inhibitor Tablets (A32965) from Thermo Fisher; DMEM (11965), DMEM-Phenol Red (31053-028), 0.25% Trypsin-EDTA (25300062), 0.05% Trypsin-EDTA (25300054) from Gibco; U1866A (1638) from Tocris Bioscience; Puromycin (Gibco, A1113803) and Blasticidin (Gibco, A1113903).

CRISPRi cell line generation and validation

K562 sgNT and K562 sgNPC1 cell lines were generated using Cas9–sgRNA ribonuclear protein transfection (sgNPC1 sgRNA sequence: GGACGATCCTTGGCTTGGAC) as previously described[26], and knockout was validated by western blot (Extended Data Fig. 1b). In total, 2 × 10^5 cells were transduced with Ef1a-dCas9-KRAB-BFP construct lentivirus in cell culture medium supplemented with 1 μl ml−1 Polybrene (Millipore Sigma, TR-1003-G). Cells were spinfected at 1,000g for 1 h at room temperature and cultured overnight. Viral supernatant was removed next day and replaced with fresh K562 medium. Cells were expanded and then sorted for BFP+ population on a BD FACS Aria and maintained as a pooled cell line.

To validate CRISPRi efficiency, cells were transduced with lentivirus for guides targeting Gal4 (negative control) or CD55 (cell surface marker). Cells were selected with puromycin (Gibco, A1113803) to obtain a pool of guide containing cells. Cells were incubated with Fc block (Biolegend, 422302) in FACS Buffer (0.5% BSA in PBS) and stained for CD55 (Biolegend, 311312). Knockdown efficiency was analysed by flow cytometry on the Thermo Fisher Attune NxT Flow Cytometer and analysed on FlowJo.

sgRNA guide library plasmid production

The lentiviral expression vector used for CRISPR guide expression is available on Addgene as pLGR1002 (188320). The sgRNA sequences as well as the sgRNA library cloning procedure performed is as described50,51. sgRNA library representation was confirmed by next-generation sequencing.

Guide library lentivirus production and titration

Guide library virus was prepared using the Mega Lentivirus protocol from the Weissman laboratory with modifications (https://weissman.wi.mit.edu/resources/Large_scale_lentivirus_production.pdf). A total of 18 million Lenti-X 293T cells (Takara Bio, 632180) were plated on a 15 cm plate. The next day, transfection mixture was prepared using 125 μl Mirus LT1 transfection reagent (Mirus Bio, MIR2304), 1.5 ml DMEM (Gibco, 11965-092), 18 μg psPax2 (packaging vector), 4 μg MD2G (env expression plasmid, VSVG) and 18 μg of lentiviral plasmid (sgRNA library). Transfection mixture was incubated for 15–20 mins at room temperature. Transfection mix was added to the 293T cells and incubated for 6 h. After 6 h the medium was replaced with fresh 293T medium supplemented with 2 μg ml−1 ViralBoost (ALSTEM, VB100). Lentiviral supernatant was collected 48 h after transfection and filtered (0.45 μm). Lentivirus was stored at −80 °C until use.

Lentivirus titration was performed in K562 cells prior to the screen. Multiplicity of infection (MOI) was determined by percent infection shown by BFP signal via flow cytometry. K562 sgNT and K562 sgNPC1 cells (2 × 10^5) were transduced with guide library lentivirus in a range of 5–250 μl in K562 medium supplemented with 1 μl ml−1 Polybrene (Millipore Sigma, TR-1003-G). Cells were left in virus for 24 h and then changed to regular medium. Forty-eight hours after infection, cells were analysed on the Attune NXT flow cytometer to determine BFP fluorescence and MOI.

Lentivirus for individual guide RNA experiments was produced in 6-well plates with adjusted protocol. Lenti-X 293T cells (0.8 × 10^6) per well were seeded the day before transfection. Transfection mix consists of 7.5 μl Mirus transfection reagent (MIR 2700), 100 μl DMEM (Gibco, 11965-092), 1.2 μg psPAX2, 0.3 μg MD2G and 1.2 μg guide plasmid. The protocol is as described above.

Lysosomal integrity survival screen

sgNT K562 dCas9-KRAB and sgNPC1 K562 dCas9-KRAB cells were infected with genome-wide sgRNA library lentivirus to target 1,000× coverage of all guides at a low MOI to ensure roughly 1 guide per cell. Cells from each cell line (around 550 million) were pelleted and resuspended in pure virus supplemented with 1 μl ml−1 Polybrene (Millipore Sigma, TR-1003-G). Cells with virus were plated into 6-well plates and spinfected at 1,000g for 1 h. Cells were then supplemented with complete medium and cultured in flasks in shaking incubators at 100g. Cells were maintained in shaking conditions throughout the screen. After ~24 h, virus was removed and cells were resuspended in fresh medium. The next day BFP infection percentage was assessed (34% for sgNT, 45% for sgNPC1) and selection was started with 1 μg ml−1 puromycin (Gibco, A1113803). Cells were selected with 1 μg ml−1 puromycin for 6 days until population reached ~80–90% BFP+. Cells were grown for an additional three days to reach numbers necessary for the screen.

On day T0 of the screen, samples were split for 850× coverage of the library to each replicate. Two replicates from each cell line were frozen in medium with 10% DMSO as backup. Two replicates from each cell line were pelleted and stored at −80 °C for T0 gDNA samples. Two replicates from each cell line were seeded at 0.25 × 10^6 cells per ml and treated with 0.5 mM LLOMe hydrochloride (Cayman Chemicals, 16008) or 0.01% DMSO mock treatment for another arm of the screen (not shown) and cultured for 2 days. Drug treatment was then removed from cells and replaced with fresh medium for two days. This cycle of drug treatment and recovery was repeated, after which cells were expanded for 6 days until cell numbers were sufficient to end the screen. Multiple cell pellets representing 850× coverage of the library were stored at −80 °C for each sample for gDNA processing.

Genomic DNA processing, library preparation and sequencing

Cell pellets from the screen were processed using the NucleoSpin Blood XL Kit (Macherey Nagel, 740950) according to manufacturer’s directions and extracted DNA was quantified with DeNovix Nanodrop.

After DNA extraction, samples were processed for NGS sequencing using the protocol described[50]. DNA samples were amplified by PCR using NEBNext Ultra II Q5 MasterMix (NEB, M0544) with a common reverse primer (CAAGCAGAAGACGGCATACGAGATATGCTGTTTCCAGCTTAGCTCTT) and a unique forward primer per sample containing a 6 bp barcode (AATGATACGGCGACCACCGAGATCTACACGATCGGAAGAGCACACGTCTGAACTCCAGTCACnnnnnnGCACAAAAGGAAACTCACCCT) (sequences listed in Supplementary Table 1). For each sample total gDNA was amplified in multiple PCRs with an input of 10 μg per reaction. PCRs were run with the following cycling conditions: 98 °C for 30 s, followed by 24 cycles of 98 °C for 10 s and 62.6 °C for 75 s, and a final elongation at 65 °C for 5 mins. Reactions were pooled and purified with double-sided SPRI beads (Beckman Coulter, A63882). Samples were quantified on the Agilent TapeStation and pooled to generate a library with equal representation of each sample. Library was sequenced on the Illumina NextSeq 550 using a NextSeq High Output kit (75 cycles) (Illumina, 20024906). A custom sequencing primer (GTGTGTTTTGAGACTATAAGTATCCCTTGGAGAACCACCTTGTTGG) was added to the standard Illumina sequencing primer. Library runs were supplemented with PhiX DNA to increase sequence diversity.

Computational analysis of screens

Two integrated analysis pipelines for pooled CRISPR screens were used to generate and process sgRNA read counts. The first pipeline was a branched version of ScreenProcessing (https://github.com/ucsf-lgr/ScreenProcessing) developed by the Weissman laboratory[52] using hitThreshold of 4. The second was MAGeCK-Vispr53,54.

Network analysis of screen hits

Significant (P < 0.05) sensitizing (score < 0) hits from sgNT and sgNPC1 screens were combined (and likewise for protective (score > 0) hits). Coessentiality data from DepMap (22Q2, Achilles_gene_effect.csv) was pre-processed to obtain locus-adjusted correlations (Mendillo laboratory; https://github.com/mendillolab/fireworks/tree/master/generate_corr_matrices)[55]. A custom Python script (https://github.com/claire-goul/local_DepMapandBioGrid) was written to obtain the top correlated genes to gene hits (using the default parameters of threshold > 0.2 and num = 3). Physical protein–protein interactions for gene hits were obtained from the BioGrid website (Biogrid_MV-Physical_4.4.243_Human; https://downloads.thebiogrid.org/BioGRID/Release-Archive/BIOGRID-4.4.243/), using default parameters (minimum number of citations; that is, numcitations parameter, equal to 2)[56]. Biogrid interactions were then mapped onto the correlated genes obtained from the gene hits, and networks containing at least 2 gene hits and at least 3 nodes per network were plotted in Cytoscape (v3.10.3). Genes implicated in neurodegenerative disease were downloaded from the Monarch Initiative (https://monarchinitiative.org/MONDO:0005559#causal-gene)[57] and overlaid manually onto the Cytoscape networks by importing the downloaded file into Cytoscape. A custom Python script (Python v3.9.6) was written to cluster networks in communities using the Python Louvain package (https://github.com/taynaud/python-louvain), using a resolution value of 2. Communities and the top Gene Ontology term (https://geneontology.org/) for each community were manually annotated on Cytoscape networks.

Growth curves

Cells were seeded in 96-well plates at 10,000 cells per well in 100 μl complete medium, and the following day they were treated with 100 μl of LLOMe (2× concentration) prior to imaging, with images of each well taken every 4 h, on Incucyte S3 with a 10× objective (Sartorius).

Confocal imaging of live cells

Live imaging of eGFP–UBAP1, EAP30–mNeonGreen, PDCD6IP–mNeonGreen, CHMP4B–mNG and mCherry–Gal3, as well as endogenously tagged mNG lines, was performed on OperaPhenix (Perkin Elmer). Two days prior to imaging, 96-well plates were coated overnight in fibronectin, and cells were seeded the following day with 30K–40K cells per well onto 96-well CellVis (P96-1.5H-N) or Greiner Screenstar COC plates (655866) in 100 μl phenol red-free DMEM. The next day, 50 μl of Hoescht dye was added to cells to stain nuclei 30 min before the start of the experiment. Imaging of non-overlapping frames (at minimum 4 per well) were taken; first, the nuclei were imaged, then cells were imaged at baseline, followed by addition of the indicated concentrations of either LLOMe (or DMSO vehicle control), and imaged every 4−6 min. Temperature and CO_2 levels were maintained at 37 °C and 5%, respectively, during the experiment. All images were taken with a 40× water objective. Image analysis of number of spots per cell was performed on Harmony 3D analysis software (v5.3) for nuclei and spot selection and masking and quantification of nuclei/spot number and spot relative intensity for all frames and time points collected.

For i3 neurons: At day 4 post-differentiation, i3 neurons plated in 24-well glass bottom plates (CellVis, P24-1.5H-N) were transduced with concentrated mCherry–Gal3 or CHMP4B–mNG lentivirus resuspended in BrainPhys neuronal medium, and 24 h later changed into fresh BrainPhys medium. Live imaging was performed 72–96 h post-transduction on OperaPhenix (Perkin Elmer). Prior to live imaging, medium was changed into BrainPhys neuronal medium lacking Phenol Red.

Immunofluorescence staining and confocal imaging

Cells were seeded on fibronectin-coated glass coverslips in 12-well plates the day before the experiment and treated with the indicated DMSO or LLOMe concentrations. Cells were then rinsed one time with PBS, permeabilized with 0.1% (w/v) saponin in PBS for 10 min at room temperature (or, for LC3B immunofluorescence, at −20 °C in ice-cold 100% methanol for 5 min) and rinsed once with PBS. Primary antibodies were diluted at 1:300 concentration in 5% normal donkey serum (Jackson ImmunoResearch, 017-000-121) and incubated for 1 h at room temperature. Coverslips were rinsed 3 times with DPBS and then labelled with fluorescently conjugated secondary antibodies (diluted 1:400 in 5% normal donkey serum in DPBS) for 1 h at room temperature, protected from light. Coverslips were rinsed with PBS four times and mounted on glass slides using VECTASHIELD Antifade Mounting Medium with or without DAPI (Vector Laboratories, H-1200 or H-1000). Confocal microscopy was performed on a spinning-disk Nikon Ti-E inverted microscope (Nikon Instruments) with Andor Zyla-4.5 cMOS camera (Andor Technology) using iQ3 acquisition (v3.6) and Andor Fusion software (v1.1.1) (Andor Technology). All images were taken with a 60× oil objective. Colocalization analysis from immunofluorescence was performed by importing raw, unprocessed, non-overlapping images to FIJI (v2.1.0/1.53c) or ImageJ (v2.16). Images of individual channels were thresholded independently to exclude background and converted to binary mask. Colocalization between lysosomes (LAMP2) and marker of interest (TFG or SEC31A) or TFG and ERES (SEC31A) was determined using the AND function of the image calculator. Confocal images of contact sites between TFG and LAMP were acquired using a 100× oil objective on a BC43 Andor Spinning-disk confocal microscope, and images shown in figures were pre-processed using deconvolution with Imaris Quant software (v10.2).

Immuno-electron microscopy

Cells in culture dishes were initially fixed by incubation for 5 min with an equal volume of 2% paraformaldehyde in 0.1 M phosphate buffer, mixed with the culture medium to minimize osmotic stress. The solution was then replaced with freshly prepared 4% paraformaldehyde in the same buffer, and fixation was continued for 2 h at room temperature. After fixation, samples were washed three times in PBS, and residual aldehydes were quenched by incubation with 0.15% glycine in PBS for 10 min. Cells were scraped in PBS supplemented with 1% gelatin and transferred to microcentrifuge tubes. Next, gelatin concentration was increased to 12%, and samples were incubated for 10 min at 37 °C. Cells were collected by centrifugation, and the gelatin-embedded cell pellets were allowed to solidify on ice for 30 min. Solidified pellets were trimmed into small blocks and cryoprotected by infiltration with 2.3 M sucrose overnight at 4 °C. The blocks were then mounted onto specimen pins and stored in liquid nitrogen until further processing.

For cryo-ultramicrotomy, sucrose-infiltrated, gelatin-embedded samples were sectioned at a nominal thickness of 80 nm at −110 °C using a diamond knife (DiATOME) on an ultracryomicrotome (Leica). Ultrathin sections were collected on Formvar- and carbon-coated TEM grids using a pickup solution consisting of a 1:1 mixture of 2.3 M sucrose and 1.8% methylcellulose.

For immunogold labelling, sections on the grids were first incubated in PBS at 37 °C for approximately 30 min to remove residual embedding material, followed by washing in PBS containing 0.15% glycine. Nonspecific binding was blocked by incubation in PBS supplemented with 0.5% fish skin gelatin (FSG; Sigma) and 0.1% BSA (BSA-c; Aurion). Sections were incubated with a primary antibody against TFG (ab156866; 1:30 dilution), followed by with Protein A conjugated to 10-nm colloidal gold particles (Cell Microscopy Core, UMC Utrecht). Post-labelling fixation was performed using 1% glutaraldehyde. Grids were rinsed six times in Milli-Q water and contrasted for 5 min with 2% uranyloxalate-acetate (pH 7), and 10 min with uranylacetate/methylcellulose (pH 4) on ice. Finally, grids were picked up using the loop-out method with a rhenium wire loop and air-dried for 30 min prior to imaging.

Imaging was performed on a TECHNAI12 transmission electron microscope (Thermo Fisher) operated at 80 kV. For each condition, images were acquired from two independent grids. A more detailed description of the immunolabelling procedure has been reported previously[58].

Protein purification

Human codon-optimized truncated 6×His-TEV-mCherry TFG (WT, ΔPB1, LIR2-mut, ΔCC, PSAP, R106C mutant; residues 1–210) was expressed in Escherichia coli Rosetta (DE3) pLysS cells. Bacteria were grown in Luria broth (LB) medium until OD_600 ≈ 0.8–1, induced with 0.5 mM isopropylthiogalactoside at 20 °C for 18 h. Cell pellets from 0.5 l of culture were frozen in liquid nitrogen, and subsequently resuspended in lysis buffer (50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) at pH 7.5, 500 mM KCl, 10 mM imidazole, 2 mM MgCl_2, complete protease inhibitor (Roche)) followed by lysis with a tip sonicator (4× 50 mA; 30 s on/1 min off). Lysates were cleared by ultracentrifugation at 24,000g for 30 min at 4 °C (Beckman, Ti45 rotor). Cleared supernatants were applied to Ni-NTA resin (Qiagen, 30230) pre-washed in lysis buffer and incubated with rotation at 4 °C for 1 h, and the mixture was applied to a gravity column. Following washes with 15 bead volumes of lysis buffer, 6×His-tagged TFG constructs were rapidly eluted in 4 bead volumes of lysis buffer containing 300 mM imidazole. Elution was evaluated by Coomassie staining, flash frozen in liquid nitrogen, and stored at −80 °C.

Human 6×His-TEV-mCherry-P62 (Addgene #199780) was purified using the same protocol as for TFG, with additional separation on a HiLoad 16/600 Superdex column (Cytiva). Protein was eluted in buffer containing 25 mM HEPES pH 7.5, 500 mM KCl, and 1 mM DTT, protein containing fractions were pooled and concentrated with an Ultra Centrifugal Filter (50 kDa MWCO, Amicron), and frozen in liquid nitrogen.

Human 6×His–TSG101 UEV domain (WT, M95A, N45A or M95A/N45A) was expressed in E. coli strain BL21(DE3) Star cells, and bacteria were grown in Luria broth (LB) medium until OD600 ≈ 0.5, induced with 1 mM isopropylthiogalactoside at 37 °C for 4 h. Cell pellets from 0.5 l of culture were frozen in liquid nitrogen, and subsequently resuspended in lysis buffer (50 mM HEPES at pH 7.5, 150 mM NaCl, 2 mM MgCl_2, complete protease inhibitor (Roche)), followed by lysis with a tip sonicator (4× 50 mA; 30 s on/1 min off). Lysates were cleared by ultracentrifugation at 24,000g for 30 min at 4 °C (Beckman, Ti45 rotor). Cleared supernatant was applied to GST resin pre-washed in lysis buffer and incubated with rotation at 4 °C for 30 min. Following washes with 10 bead volumes of lysis buffer, 6×His-tagged TFG constructs were eluted in 2 bead volumes of elution buffer (lysis buffer plus 20 mM glutathione, freshly made to pH 8) with 1× 10-min elution and 1× overnight elution. Elutions were evaluated by Coomassie staining, pooled, concentrated on an Amicron 30 kDA MWCO column, flash frozen in liquid nitrogen, and stored at −80 °C.

Plasmids and cloning

Human codon-optimized truncated 6×His-TEV-mCherry TFG (WT, deltaPB1, LIR2MUT, deltaCC, PSAP, R106C mutant; residues 1–210) was cloned into pETDuet-His-TEV-mCherry (Addgene #199780). Human pGST2-TSG101 UEV plasmid was obtained from Addgene (Addgene #184810) and cDNA for M95A, N45A, M95A/N45A (Twist Biosciences) was subcloned into the same plasmid. pCDH-CHMP4B–mNG-3×HA IRES-Blast and pCDH-mCherry-GAL3-IRES-Blast were received from the laboratory of H. Stenmark, and human codon-optimized ALIX or EAP30 cDNA (Twist Biosciences) was subcloned into the digested pCDH-mNeonGreen-3×HA IRES Blast vector. Human codon-optimized UBAP1 cDNA (Twist Biosciences) was subcloned into the pLJM1 eGFP Hygro vector. For rescue construct cloning, TFG human codon-optimized cDNA (WT, R22W, R106C, P285L, LIR2 mutant, LIR3 mutant, double LIR mutant, deltaPB1 and deltaCC; IDT and Twist Biosciences) were cloned into pLJM1 Hygro plasmid using Gibson assembly along with an IRES-mCherry cDNA fragment.

Short-hairpin oligonucleotides (shRNAs) directed against TFG (TRCN0000078660), TSG101 (TRCN0000007563), PDCD6IP (TRCN0000029395), ATG5 (TRCN0000151474), PDCD6 (TRCN0000053388, TRCN0000303757, TRCN0000303818), ATG13 (TRCN0000172801), FIP200 (TRCN0000315347) and luciferase (TRCN0000072243, used as a non-targeting control), were cloned into the pLKO.1 lentiviral vector (The RNAi Consortium, Broad Institute) according to the manufacturer’s instructions. TFG sgRNA sequences (sg1: TCAGCGGAGACCTGCGGAGG; sg2: CTCGCGTCCGCCCATTCAGG) were cloned into the pLG15 vector (Kampmann laboratory).

Peptide binding and fluorescence polarization

A binding curve of FAM-p62 LIR peptide (SGGDDDWTHLSSKEVDl; 12.5 nM final) with recombinant LC3B or GABARAP was generated by incubating FAM-p62 LIR peptide with serially twofold-diluted recombinant LC3 or GABARAP (starting at 50 nM), in triplicate. Fluorescence polarization of FAM-peptide was measured on a Tecan Spark Multimode Microplate reader. Recombinant LC3B or GABARAP (5 μM final) was incubated with FAM-p62 LIR peptide (12.5 nM final) for 30 min, followed by incubation with 3 mM or serially twofold-diluted unlabelled p62-LIR peptide, TFG-LIR2 wild-type (AASMSAFDPLKNQDEI) or LIR3 wild-type (YQPRPGFTSLPGSTMT), TFG LIR2-mutant (AASMSAADPAKNQDEI) or LIR3-mutant (YQPRPGATSAPGSTMT) peptides, in triplicate for 15 min, followed by fluorescence polarization measurement. All peptides were ordered from GenScript. Curves were plotted in Prism v10.2.0 (GraphPad), and Kis were calculated using the Prism One site fit ki algorithm, while Kds were calculated using the Prism One site total and nonspecific binding algorithm.

Mammalian cell culture

Human 293T sgNT and sgNPC1 knockout cell lines were generated using Cas9/sgRNA ribonuclear protein transfection (sgNPC1 sgRNA sequence: GCTGCTACTGTGTCCAGCGC) as previously described[26]. HEK293T, HeLa and U2OS cells and their derivatives, including HEK293T cells with ESCRT components tagged at their endogenous loci with mNG[59], were cultured in DMEM base medium with 10% fetal bovine serum supplemented with 2 mM glutamine, penicillin and streptomycin. All cell lines were maintained at 37 °C and 5% CO_2. The cells were free of mycoplasma contamination and routinely checked using mycoplasma PCR Detection kit (abm, G238) and/or DAPI staining. All wild-type parental cell lines, with the exception of the patient fibroblasts, were obtained from and authenticated by the UC Berkeley cell culture facility.

All K562 cell lines were maintained in Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco, 11875085) supplemented with 10% FBS (Avantor Seradigm, 97068-085), 1% Penicillin-Streptomycin (Gibco, 15140122), and 1% Glutamax (Gibco, 35050-061) All cells were cultured at 37 °C in a humidified atmosphere containing 5% CO_2. Cells were incubated in shaking conditions for the duration of the screen.

Human i3 neurons were obtained from M. Ward and cultured and differentiated according to the described protocol[60]. CRISPRi-i3 iPSCs containing NGN2-inducible cassette and dCas9-BFP-KRAB were a gift from M. Ward61,62. CRISPRi-i3 iPSCs were differentiated as described[60]. In brief, iPSCs were dissociated with Accutase (Gibco, A1110501) and seeded onto Matrigel (Corning, 354277) coated six-well plates. iPSCs were infected with lentiviruses concentrated in essential-8 medium (Gibco, A1516901) expressing sgNT or sgTFG. After 24 h, medium was replaced with fresh medium containing puromycin (1 μg ml−1). Medium was replaced daily with fresh medium containing puromycin for 72 h. After selection, cells were expanded for one passage before dissociating and seeding cells into induction medium containing N2 Supplement (Gibco, 17502048) in Knockout DMEM/F:12 (Thermo Fisher, 12660012) with non-essential amino acids (Gibco, 11140050), GlutaMax (Gibco, 35050-061) and doxycycline (2 μg ml−1, 35050-061). Medium was replaced with fresh induction medium for 72 h, before dissociating cells and seeding onto PLO (Sigma, P3655)/Laminin (R&D Systems, 3446-005-01) coated plates (prepared as described previously[60]) containing BrainPhys neuronal differentiation medium (StemCell Technologies, 5790) supplemented with B27+ (Gibco, A3582801) containing BDNF (Preprotech, 450-02), NT-3 (Preprotech, 450-03) and GDNF (Preprotech, 450-10). i3Neurons were given 60% medium changes (remove 40%, add back 60%) every 3 days until collected for experiments.

Patient fibroblasts were obtained from E. Lamantea[63] and cultured in DMEM with 10% fetal bovine serum supplemented with 2 mM Glutamine, penicillin and streptomycin.

Lentivirus production and infection

Lentiviruses were made by co-transfecting pLJM1, pCDH, pLenti-sgRNA or pLKO constructs with psPAX2 and pMD2G packaging plasmids into HEK293T cells using PEI transfection reagent. Viral supernatant was collected 48–72 h post-transfection and filtered using a 0.45-µm syringe filter. The virus was then concentrated using Lenti-X concentrator (Clontech 631231) according to manufacturer’s protocol and stored at −80 °C. For lentivirus infection, target cells were seeded in 6-well plates and infected the following day with lentivirus and 10 µg ml−1 Polybrene (Millipore TR-1003-G). After 24 h, virus was removed, and cells were changed into fresh medium containing either puromycin (1 μg ml−1), blasticidin (5 μg ml−1) or hygromycin B (200 μg ml−1) for selection. Experiments on shRNA infected cells were performed 4–5 days post-transduction. For neuron experiments, mCherry–Gal3 virus was concentrated and resuspended in BrainPhys neuronal differentiation medium containing the described supplements, and neurons were infected 3 days prior to the experiment with concentrated virus (no Polybrene), and medium was changed into fresh BrainPhys (containing no antibiotics) the day following infection.

Immunoprecipitation

For GST–UEV immunoprecipitations with cell lysate, cells were rinsed once with PBS and lysed in lysis buffer (PBS, 1% NP-40, 1 mM DTT, 5 mM EDTA, EDTA-free protease inhibitor tablet) rotating for 15 min at 4 °C. Cell lysates were cleared by centrifugation in a microcentrifuge at 13,000 rpm for 10 min at 4 °C. Well mixed glutathione sepharose 4B resin slurry (Cytiva, 17-0756-05) was washed once in lysis buffer, then incubated with recombinant UEV domain (12 μl of GST slurry with lysis buffer containing 100 μg of UEV domain) for 30 min rotating at 4 °C. GST–UEV or empty beads were washed 3 times with lysis buffer, then incubated with cleared supernatant for 1 h at 4 °C, followed by 3 washes with lysis buffer and resuspended in a 1:1 ratio of 5× sample buffer and lysis buffer. Following incubation at 95 °C for 5 min, immunoprecipitated proteins were resolved by SDS–PAGE and analysed by immunoblotting.

Dextran assay for lysosomal deacidification

For dextran experiments in patient fibroblasts, cells were incubated with fluorescently labelled dextrans 16–20 h prior to imaging. Specifically, 10 kDa dextran conjugated to Alexa Fluor 647 (dextran−647, Thermo Fisher D22914) and 40 kDa dextran conjugated to fluorescein isothiocyanate (dextran-Fluorescein, Thermo Fisher D1845) were added to the culture medium at a final concentration of 25 μg ml−1 each. Two hours before imaging, cells were washed two times with phosphate-buffered saline (PBS) to remove extracellular dextran, then incubated in phenol red-free Dulbecco’s modified Eagle’s medium (DMEM, Thermo Fisher 31053-028) for the chase period.

Equilibrium binding assay using visual immunoprecipitation

For TSG101-UEV visual immunoprecipitation experiments, 90 μl glutathione sepharose 4B resin slurry (Cytiva, 17-0756-05) was washed with excess buffer (500 mM KCl, 50 mM HEPES), and beads were resuspended in 90 μl buffer. Ten micrograms recombinant GST-tagged UEV protein per μl of bead suspension was added and incubated with beads for 15 min nutating at 4 °C. Beads were spun down (1,200g for 2 min), washed once in excess buffer, and resuspended in 90 μl buffer.

For Flag–GFP–LC3B visual IP experiments, an 80–100% confluent plate of HEK293T cells expressing Flag–GFP–LC3B were resuspended in lysis buffer (50 mM HEPES at pH 7.3, 500 mM KCl, 1% Triton X-100 and protease inhibitor cocktail). Lysates were nutated for 15 min at 4 °C, after which unlysed precipitates were removed by spinning the lysates at 13,000 rpm for 10 min at 4 °C. Meanwhile, 30 μl of GFP-Trap agarose beads were washed with 500 μl of lysis buffer. Incubate washed beads with lysate for 30 min at 4 °C. After incubation, wash coated beads once with bulk lysis buffer and twice with wash buffer (50 mM HEPES at pH 7.3, 500 mM KCl) and resuspend beads in 60 μl wash buffer.

Three microlitres of UEV-coated GST beads or LC3B-coated agarose beads were incubated with 10 μl of recombinant TFG protein (at indicated concentration of 5 μM or lower) and samples were spotted onto glass slides and covered with glass coverslips. Confocal microscopy was performed on a spinning-disk Nikon Ti-E inverted microscope (Nikon Instruments) with Andor Zyla-4.5 cMOS camera (Andor Technology) using iQ3 acquisition software (Andor Technology). All images were taken with a 10× objective.

Statistical analysis and graphs

Statistical analyses were performed using unpaired two-tailed Student’s t-tests for comparisons of two groups, one-way analysis of variance (ANOVA) or two-way ANOVA for group comparisons using Prism (v10.2.0, GraphPad). Details of each statistical test are given in the legend accompanying each figure. Curve fitting for enzyme kinetics and other statistical analyses for peptide binding and polarization experiments are detailed in the appropriate methods. Graphs were plotted with Prism (v10.2.0, Graphpad), or RStudio (v1.4.1717). All data are displayed as mean ± s.d. All t-tests were two-tailed, unpaired; one-way ANOVAs used Dunnett’s multiple comparisons test with a single pooled variance for comparison to control sample, or for comparisons among all pairs, Tukey’s test; all two-way ANOVAs used used Dunnett’s multiple comparisons test with a single pooled variance for comparison to control sample, or for comparisons among all pairs, Šidák multiple comparisons test with a single pooled variance.

Reporting summary

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

Data availability

CRISPRi screen data are provided in Supplementary Table 1 and raw data are available at NCBI Gene Expression Omnibus (GEO) under accession GSE328484. Full versions of all blots and gels can be found in Supplementary Information Fig. 1. Data obtained from publicly available sources used in this paper were coessential genes from DepMap (22Q2, Achilles_gene_effect.csv), protein interactions (Biogrid_MV-Physical_4.4.243_Human; https://downloads.thebiogrid.org/BioGRID/Release-Archive/BIOGRID-4.4.243/), neurodegenerative causal genes from Monarch Initiative (https://monarchinitiative.org/MONDO:0005559#causal-gene) and Gene Ontology terms (https://geneontology.org/). Source data are provided with this paper.

Code availability

The Python script for gene correlation analysis can be found at https://github.com/claire-goul/local_DepMapandBioGrid and enabled identification of relevant hits from the CRISPRi screen.

References

1. Xun, J. & Tan, J. X. Lysosomal repair in health and disease. J. Cell. Physiol. 240, e70044 (2025).

2. Gahlot, P. et al. Lysosomal damage sensing and lysophagy initiation by SPG20-ITCH. Mol. Cell 84, 1556–1569.e10 (2024).

3. Sanyal, A. et al. Neuronal constitutive endolysosomal perforations enable α-synuclein aggregation by internalized PFFs. J. Cell Biol. 224, e202401136 (2025).

4. Kaur, N. et al. TECPR1 is activated by damage-induced sphingomyelin exposure to mediate noncanonical autophagy. EMBO J. 42, e113105 (2023).

5. Rose, K. et al. Tau fibrils induce nanoscale membrane damage and nucleate cytosolic tau at lysosomes. Proc. Natl Acad. Sci. USA 121, e2315690121 (2024).

6. Li, D. et al. Cathepsin-dependent amyloid formation drives mechanical rupture of lysosomal membranes. Preprint at bioRxiv https://doi.org/10.64898/2026.01.17.700056 (2026).

7. Radulovic, M. et al. ESCRT-mediated lysosome repair precedes lysophagy and promotes cell survival. EMBO J. 37, e99753 (2018).

8. Skowyra, M. L., Schlesinger, P. H., Naismith, T. V. & Hanson, P. I. Triggered recruitment of ESCRT machinery promotes endolysosomal repair. Science 360, eaar5078 (2018).

9. Shukla, S., Larsen, K. P., Ou, C., Rose, K. & Hurley, J. H. In vitro reconstitution of calcium-dependent recruitment of the human ESCRT machinery in lysosomal membrane repair. Proc. Natl Acad. Sci. USA 119, e2205590119 (2022).

10. Pfitzner, A.-K. et al. An ESCRT-III polymerization sequence drives membrane deformation and fission. Cell 182, 1140–1155.e18 (2020).

11. Hurley, J. H., Coyne, A. N., Miączyńska, M. & Stenmark, H. The expanding repertoire of ESCRT functions in cell biology and disease. Nature 642, 877–888 (2025).

12. Maejima, I. et al. Autophagy sequesters damaged lysosomes to control lysosomal biogenesis and kidney injury. EMBO J. 32, 2336–2347 (2013).

13. Eapen, V. V., Swarup, S., Hoyer, M. J., Paulo, J. A. & Harper, J. W. Quantitative proteomics reveals the selectivity of ubiquitin-binding autophagy receptors in the turnover of damaged lysosomes by lysophagy. eLife 10, e72328 (2021).

14. Chen, W., Motsinger, M. M., Li, J., Bohannon, K. P. & Hanson, P. I. Ca2+-sensor ALG-2 engages ESCRTs to enhance lysosomal membrane resilience to osmotic stress. Proc. Natl Acad. Sci. USA 121, e2318412121 (2024).

15. Corkery, D. P. et al. The ATG8 E3-like ligases sense lysosomal damage and initiate ESCRT-mediated membrane repair. EMBO J. 45, 930–952 (2026).

16. Yim, W. W.-Y., Yamamoto, H. & Mizushima, N. Annexins A1 and A2 are recruited to larger lysosomal injuries independently of ESCRTs to promote repair. FEBS Lett. 596, 991–1003 (2022).

17. Timimi, L. et al. The V-ATPase/ATG16L1 axis is controlled by the V1H subunit. Mol. Cell 84, 2966–2983.e9 (2024).

18. Ogura, M. et al. Microautophagy regulated by STK38 and GABARAPs is essential to repair lysosomes and prevent aging. EMBO Rep. 24, e57300 (2023).

19. Corkery, D. P., Castro-Gonzalez, S., Knyazeva, A., Herzog, L. K. & Wu, Y.-W. An ATG12–ATG5–TECPR1 E3-like complex regulates unconventional LC3 lipidation at damaged lysosomes. EMBO Rep. 24, e56841 (2023).

20. Cross, J. et al. Lysosome damage triggers direct ATG8 conjugation and ATG2 engagement via non-canonical autophagy. J. Cell Biol. 222, e202303078 (2023).

21. Boyle, K. B. et al. TECPR1 conjugates LC3 to damaged endomembranes upon detection of sphingomyelin exposure. EMBO J. 42, e113012 (2023).

22. Tan, J. X. & Finkel, T. A phosphoinositide signalling pathway mediates rapid lysosomal repair. Nature 609, 815–821 (2022).

23. Radulovic, M. et al. Cholesterol transfer via endoplasmic reticulum contacts mediates lysosome damage repair. EMBO J. 41, e112677 (2022).

24. Wang, X. et al. The bridge-like lipid transport protein VPS13C/PARK23 mediates ER–lysosome contacts following lysosome damage. Nat. Cell Biol. 27, 776–789 (2025).

25. Chou, C.-C. et al. Proteostasis and lysosomal repair deficits in transdifferentiated neurons of Alzheimer’s disease. Nat. Cell Biol. 27, 619–632 (2025).

26. Davis, O. B. et al. NPC1–mTORC1 signaling couples cholesterol sensing to organelle homeostasis and is a targetable pathway in Niemann-Pick type C. Dev. Cell 56, 260–276.e7 (2021).

27. Yong, J. et al. Impairment of lipid homeostasis causes lysosomal accumulation of endogenous protein aggregates through ESCRT disruption. eLife 12, RP86194 (2024).

28. van der Kant, R. et al. Cholesterol metabolism is a druggable axis that independently regulates Tau and amyloid-β in iPSC-derived Alzheimer’s disease neurons. Cell Stem Cell 24, 363–375.e9 (2019).

29. Sanyal, A. et al. Tau seeding in neurons enabled by transient endolysosomal perforations are confined within endolysosomes. Preprint at bioRxiv https://doi.org/10.1101/2025.11.02.686178 (2025).

30. Lee, J.-A., Beigneux, A., Ahmad, S. T., Young, S. G. & Gao, F.-B. ESCRT-III dysfunction causes autophagosome accumulation and neurodegeneration. Curr. Biol. 17, 1561–1567 (2007).

31. Filimonenko, M. et al. Functional multivesicular bodies are required for autophagic clearance of protein aggregates associated with neurodegenerative disease. J. Cell Biol. 179, 485–500 (2007).

32. Bourinaris, T. et al. Identification of UBAP1 mutations in juvenile hereditary spastic paraplegia in the 100,000 Genomes Project. Eur. J. Hum. Genet. 28, 1763–1768 (2020).

33. Liu, E. A. et al. Fbxo2 mediates clearance of damaged lysosomes and modifies neurodegeneration in the Niemann-Pick C brain. JCI Insight 5, e136676 (2020).

34. Slosarek, E. L. et al. Pathogenic TFG mutations underlying hereditary spastic paraplegia impair secretory protein trafficking and axon fasciculation. Cell Rep. 24, 2248–2260 (2018).

35. Hanna, M. G. et al. TFG facilitates outer coat disassembly on COPII transport carriers to promote tethering and fusion with ER–Golgi intermediate compartments. Proc. Natl Acad. Sci. USA 114, E7707–E7716 (2017).

36. Perez Verdaguer, M. et al. Time-resolved proximity labeling of protein networks associated with ligand-activated EGFR. Cell Rep. 39, 110950 (2022).

37. North, B. J., Fracchiolla, D., Ragusa, M. J., Martens, S. & Shoemaker, C. J. The rapidly expanding role of LC3-interacting regions in autophagy. J. Cell Biol. 224, e202504076 (2025).

38. Schlundt, A. et al. Proline-rich sequence recognition: II. Proteomics analysis of Tsg101 ubiquitin-E2-like variant (UEV) interactions. Mol Cell Proteomics 8, 2474–2486 (2009).

39. Jakobi, A. J. et al. Structural basis of p62/SQSTM1 helical filaments and their role in cellular cargo uptake. Nat. Commun. 11, 440 (2020).

40. Zhang, Z. et al. Multiple roles for TFG ring complexes in neuronal cargo trafficking. Preprint at bioRxiv https://doi.org/10.1101/2024.11.05.621662 (2024).

41. Bogus, S. M. et al. A hollow TFG condensate spatially compartmentalizes the early secretory pathway. Nat. Commun. 16, 3715 (2025).

42. Hanna, M. G. et al. BLTP3A is associated with membranes of the late endocytic pathway and is an effector of CASM. EMBO J. 44, 6168–6195 (2025).

43. Banjade, S., Zhu, L., Jorgensen, J. R., Suzuki, S. W. & Emr, S. D. Recruitment and organization of ESCRT-0 and ubiquitinated cargo via condensation. Sci. Adv. 8, eabm5149 (2022).

44. Hooper, K. M. et al. V-ATPase is a universal regulator of LC3-associated phagocytosis and non-canonical autophagy. J. Cell Biol. 221, e202105112 (2022).

45. Carinci, M. et al. TFG binds LC3C to regulate ULK1 localization and autophagosome formation. EMBO J. 40, e103563 (2021).

46. Bussi, C. et al. Stress granules plug and stabilize damaged endolysosomal membranes. Nature 623, 1062–1069 (2023).

47. Jia, J. et al. Stress granules and mTOR are regulated by membrane atg8ylation during lysosomal damage. J. Cell Biol. 221, e202207091 (2022).

48. Wang, Y. et al. Biomolecular condensates mediate bending and scission of endosome membranes. Nature 634, 1204–1210 (2024).

49. Suzuki, S. W. et al. A role for Vps13-mediated lipid transfer at the ER-endosome contact site in ESCRT-mediated sorting. J. Cell Biol. 223, e202307094 (2024).

50. Heo, S.-J. et al. Compact CRISPR genetic screens enabled by improved guide RNA library cloning. Genome Biol. 25, 25 (2024).

51. Horlbeck, M. A. et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5, e19760 (2016).

52. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

53. Li, W. et al. Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR. Genome Biol. 16, 281 (2015).

54. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

55. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

56. Oughtred, R. et al. The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 30, 187–200 (2021).

57. Putman, T. E. et al. The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Res. 52, D938–D949 (2024).

58. van der Beek, J. et al. Ultrastructural localization of endogenous LC3 by on-section correlative light-electron microscopy. J. Vis. Exp. 193, e65067 (2023).

59. Cho, N. H. et al. OpenCell: endogenous tagging for the cartography of human cellular organization. Science 375, eabi6983 (2022).

60. Fernandopulle, M. S. et al. Transcription factor-mediated differentiation of human iPSCs into neurons. Curr Protoc. Cell Biol. 79, e51 (2018).

61. Tian, R. et al. CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons. Neuron 104, 239–255.e12 (2019).

62. Ramos, D. M., Skarnes, W. C., Singleton, A. B., Cookson, M. R. & Ward, M. E. Tackling neurodegenerative diseases with genomic engineering: a new stem cell initiative from the NIH. Neuron 109, 1080–1083 (2021).

63. Catania, A. et al. R106C TFG variant causes infantile neuroaxonal dystrophy ‘plus’ syndrome. Neurogenetics 19, 179–187 (2018).

Acknowledgements

This work is supported by the Laboratory for Genomics Research established by GSK, UCSF and UC Berkeley. The authors thank H. Stenmark for sharing plasmids; M. Ward for sharing i3 iPSC cells; M. Leonetti for sharing endogenously tagged mNG (TSG101, VPS25 and CHMP2B) lines; V. S. Tiranti for sharing TFGWT/WT and TFGR106C/R106C patient fibroblasts; A. Ernst and A. Audhya for valuable discussions and insights; the Cell Microscopy Core, UMCU and C. de Heus for help with sectioning and immunogold staining; M. West and the High-throughput Screening Facility, UC Berkeley, for support with Opera Phoenix imaging; members of the Zoncu laboratory for useful discussions; and Y. R. Citron for providing reagents.

Funding

R.Z. discloses funding from the National Institutes of Health (grant: 1R01NS133575-01) and the Ara Parseghian Medical Research Foundation. C.S.G. discloses funding from a Cancer Research Coordinating Committee predoctoral fellowship.

Author information

Author notes

1. Aakriti Jain

Present address: Department of Physiology, University of Texas Southwestern Medical Center, Dallas, TX, USA

2. These authors contributed equally: Claire S. Goul, Aakriti Jain

Authors and Affiliations

1. Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA

Claire S. Goul, Aakriti Jain, Serim Yang, Simon Rapp, Martina Spacci & Roberto Zoncu

2. Laboratory for Genomics Research, San Francisco, CA, USA

Samira Yitiz, Scot Federman, James Sacco, Huinan Li, Lauren D. Enriquez & Laralynne Przybyla

3. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA

Samira Yitiz, Scot Federman, James Sacco, Huinan Li, Lauren D. Enriquez & Laralynne Przybyla

4. Center for Molecular Medicine, University Medical Center Utrecht, Institute of Biomembranes, Utrecht University, Utrecht, The Netherlands

Zahra E. Soltani & Nalan Liv

Contributions

C.S.G., A.J. and R.Z. conceived the study. C.S.G., A.J., L.P. and R.Z. designed and supervised experiments. A.J. and C.S.G. performed experiments, analysed the data and generated the figures. C.S.G. wrote the code and performed the network analysis. Z.E.S. performed and analysed EM–IGS experiments, supervised by N.L. S.R. generated and validated K562 sgNPC1 and sgNT cell lines and performed growth assays. M.S. and S. Yang assisted with immunoprecipitations and immunofluorescence experiments, respectively, during revisions. S. Yitiz conducted pilot growth curves and performed the CRISPRi screen, supervised by L.P. and with input from H.L. S.F., J.S. and L.D.E. analysed the raw CRISPRi screen data, supervised by L.P. A.J., C.S.G. and R.Z. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Aakriti Jain or Roberto Zoncu.

Ethics declarations

Competing interests

R.Z. is a co-founder and advisor to Frontier Medicine Corp. C.S.G., A.J., S. Yang, S. Yitiz, Z.E.S., S.R., M.S., S.F., J.S., H.L., N.L., L.P. and L.D.E. declare no competing interests.

Peer review

Peer review information

Nature thanks Liang Ge, Jay Xiaojun Tan and the other, anonymous reviewers for their contribution to the peer review of this work. Peer review reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Genome wide CRISPRi screen identifies TFG as a new modifier of lysosome membrane repair.

(A) Growth rate of wild-type (WT) or NPC1 knockout (KO) 293 T cells treated with shRNAs against Luciferase (Luc) control or TFG. Cells were grown in the presence of DMSO (vehicle control) or 0.25, 0.5, or 1 mM LLOMe. Quantification of 3 replicates per group. (B) Western blot of WT, sgNPC1 polyclonal, or sgNPC1 clonal K562 cell lines. Growth curves with sgNPC1 clonal K562 lines were repeated 3 times. (C, D) Individual waterfall plot of screen hits (significant, ρ < 0.05, in blue/black) from sgNT (C) and sgNPC1 (D) screens. (E) Bubble plot showing the top 9 unique enriched subnetworks identified from Louvain algorithm among sensitizing (blue) or desensitizing (yellow) hits, ordered by mean score of screen hits in community. Comparison of groups carried out by two-way ANOVA (A). (F, G) Growth rate of WT (F) or NPC1-KO (G) 293 T cells treated with shRNAs against Luc control or TFG. Cells were grown in the presence of DMSO (vehicle control) or 0.125, 0.25, 0.5, or 1 mM LLOMe. Quantification of 3 replicates per group. (H, I) Representative images (H) of mCherry-Galectin-3 (Gal3) and LAMTOR4 (lysosomal marker) immunofluorescence staining and quantification (I) of colocalization ratio between mCherry-Gal3 puncta and LAMTOR4 in 293 T cells. Cells were treated with vehicle control (DMSO) or 0.25 mM LLOMe for 30 mins. Quantification of 18 individual cells. (J) Western blot analysis of WT or NPC1-KO mCherry-Gal3 expressing 293 T cells following knockdown with shRNAs against Luc control or TFG. TFG knockdown experiments in Gal3-mCherry cells were repeated at least 3 times. (K, L) Representative images (K) and quantification (L) of mCherry-Gal3 expressing NPC1-KO 293 T cells treated with shRNAs against Luc control or TFG and treated with a pulse of 0.125 mM LLOMe followed by a chase. Shaded region within dotted lines is standard deviation. Quantification of 8 fields of view (FOVs). Scale bars, 10 µm. (M) Western blot analysis i3 neurons treated with single guide RNAs against TFG (sgTFG) or non-against control (sgNT). Experiments with sgNT and sgTFG in i3neurons were repeated 2 times. (N, O) Representative images (N) and quantification (O) of mCherry-Gal3 puncta formation upon treatment with DMSO vehicle control or 0.25 mM or 0.5 mM LLOMe in i3 neurons following knockdown of TFG (sgTFG) or non-against control (sgNT). Each point is one FOV (n = 41). Images are shown for 45 min of treatment. Scale bars, 20 µm. Comparison of groups carried out by unpaired two sided t-test (I) or two-way ANOVA (A, F, G, L, O) (A: 0.25 mM, p = 7.6 × 10−3; 0.5 mM, p = 4.8 × 10−8; 1 mM, p = 3.9 × 10−5; F: 0 vs 0.5 mM, p = 5.5 × 10−3; G: 0 vs 0.25 mM: p = 6.3 × 10−5; I: p = 5.3 × 10−10; L: p = 1 × 10−15 (t = 91), p = 3.3 × 10−14 (t = 84.5), p = 1.7 × 10−12 (t = 78), p = 6.8 × 10−11 (t = 71.5), p = 4.2 × 10−9 (t = 65), p = 2.9 × 10−7 (t = 58.5), p = 4.3 × 10−5 (t = 52); O: 0.25 mM: p = 3.2 × 10−2, 0.5 mM: p = 1.7 × 10−6). Error bars represent mean +/− standard deviation. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05.

Source data

Extended Data Fig. 2 Coessentiality networks of CRISPRi screen hits.

Coessentiality networks of sensitizing (top) or desensitizing (bottom) hits (blue or yellow nodes, respectively) from sgNT and sgNPC1 screens. Functional subnetworks are annotated (gray circles) with corresponding top Gene Ontology terms (mean scores of hit nodes in the screen per subnetwork was calculated, and the top 9 scoring subnetworks are annotated in blue or orange text and bolded gray circles).

Extended Data Fig. 3 TFG loss results in decreased ESCRT-III recruitment to damaged lysosomes.

(A, B) Representative images (A) and quantification (B) of CHMP4B-mNeonGreen (mNG) and LAMP2 colocalization in 293 T cells treated with vehicle (DMSO) or 0.25 mM LLOMe for 45 min (n = 20 cells). (C-E) Western blots of 293 T (C), U2OS (D), or HeLa (E) expressing CHMP4B-mNeonGreen treated with shRNA against Luc control, TFG, or TSG101. Experiments with CHMP4B-mNeonGreen cells were repeated at least 3 times. (F) CHMP4B-mNG puncta formation in 293 T treated with shRNAs against Luc, TFG, or TSG101 and 0.25 mM LLOMe. Each point is the mean. Quantification of 10 FOVs. (G, H) Representative images (G) and quantification (H) of maximum CHMP4B-mNG puncta across the time course in U2OS treated with DMSO, 0.25 mM or 0.5 mM LLOMe and shRNA against TFG (pink), TSG101 (blue), or Luc (black). Images are of 15 min of treatment. Each point is one FOV (n = 12, except n = 9 for shTSG101 0.5 mM). (I, J) Representative images (I) and quantification (J) of maximum CHMP4B-mNG puncta across the time course in HeLa treated with DMSO, 0.25 mM or 0.5 mM LLOMe and shRNA against TFG (pink), TSG101 (blue), or Luc (black). Images are of 20 min of treatment. Each point is one FOV (n = 12, except n = 6 for shTSG101 0.5 mM and n = 9 for shTFG 0.25 mM), Scale bars, 10 µm. (K, L) Representative images (K) and quantification (L) of maximum CHMP4B-mNG puncta in 293 T treated with DMSO or 0.4 mM GPN following shRNA against TFG (pink) or Luc (black). Images are of 5 min of treatment. Each point is one FOV (n = 8). Comparisons by two-sided unpaired t-test (B) or two-way ANOVA (H, J, L) (B: p = 7.4 × 10−6, H: 0 mM shLuc vs shTFG: p = 4.7 × 10−10, shLuc vs shTSG101: p = 1.9 × 10−6; 0.25 mM shLuc vs shTFG: p = 6.6 × 10−9, shLuc vs shTSG101, p = 1.0 × 10−4; 0.5 mM shLuc vs shTFG: p = 9 × 10−15, shLuc vs shTSG101, p = 5.0 × 10−2; J: 0.25 mM shLuc vs shTFG: p = 1.6 × 10−8, shLuc vs shTSG101: p = 2.3 × 10−12; 0.5 mM shLuc vs shTFG: p = 3.8 × 10−6, shLuc vs shTSG101 p = 5.8 × 10−6; L: 0.4 mM GPN, p = 1.2 × 10−12). Error bars are mean +/− standard deviation. ****p < 0.0001, *p < 0.05. Scale bars, 10 µm.

Source data

Extended Data Fig. 4 TFG loss results in decreased ESCRT-I, II, III, and ALIX recruitment to damaged lysosomes.

(A, B) Representative images (A) and quantification (B) of maximum EAP30-mNG puncta formed across the time course in 293 T cells treated with DMSO (vehicle control), 0.25 mM or 0.5 mM LLOMe. Cells were treated with shRNA against TFG (pink) or TSG101 (blue) compared to Luc control (black). Images are shown for 60 min of treatment. Each point is one FOV (n = 8). (C, D) Representative images (C) and quantification (D) of maximum Alix-mNG puncta formed in 293 T cells treated with DMSO, 0.25 mM or 0.5 mM LLOMe following treatment with shRNA against TFG (pink) or TSG101 (blue) compared to Luc control (black). Images are shown for 15 min of treatment. Each point is one FOV (n = 10). (E, F) Representative images (E) and quantification (F) of maximum eGFP-UBAP1 puncta formed in U2OS cells treated with DMSO, 0.25 mM or 0.5 mM LLOMe following treatment with shRNA against TFG (pink) compared to Luc control (black). Images are shown for 60 min of treatment. Each point is one FOV (n = 12). (G) Western blot analysis of 293 T cells tagged at the endogenous locus for CHMP2B, TSG101, or VPS25 with mNG following knockdown with shRNAs against Luc control, TFG, or TSG101. Experiments in these cell lines with Luc, TFG, or TSG101 knockdown were repeated 2 times. (H-P) Representative images (H, K, N) and quantification (I, J, L, M, O, P) of maximum mNG-CHMP2B (H-J), VPS25-mNG (K-M), or mNG-TSG101 (N-P) puncta formed in endogenously tagged 293 T cells treated with either 0.25 mM (H, I, K, L, N, O) or 0.5 mM (J, M, P) LLOMe following treatment with shRNA against TFG (pink) or TSG101 (blue) compared to Luc control (black). Images are shown for 20 min of treatment. Each point is one FOV (n = 12). (Q) Western blot analysis of Hela and U2OS cells following knockdown with shRNAs against Luc control, TFG, or TSG101. HeLa and U2OS ESCRT colocalization experiments were repeated 2 times. (R-U) Representative immunofluorescence images (R, T) and quantification (S, U) of colocalization between CHMP1A and TMEM192 in HeLa (R, S) or U2OS (T, U) cells treated with vehicle control (DMSO) or 0.5 mM LLOMe. Cells were treated with shRNAs against Luc control, TFG, or TSG101. Each point is one FOV. Quantification of n = 3 FOVs. Comparison of groups carried out by two-way ANOVA (B, D, F) or one-way ANOVA (S, U). ****p < 0.0001, ***p < 0.001. Scale bars, 10 µm. Error bars represent mean +/− standard deviation (I, L, M, J, O, P) (B: 0.25 mM shLuc vs shTFG, p = 4.3 × 10−5; shLuc vs shTSG101, p = 6.5 × 10−12; 0.5 mM shLuc vs shTFG, p < 1 × 10−15, shLuc vs shTSG101 p < 1 × 10−15; D: 0.25 mM shLuc vs shTFG, p = 2.9 × 10−7, shLuc vs shTSG101, p < 1 × 10−15; 0.5 mM shLuc vs shTFG, p = 9.3×10−7; shLuc vs shTSG101, p < 1 × 10−15; F: 0 mM p = 1.7 × 10−11, 0.25 mM p = 1.1 × 10−11, 0.5 mM p = 1.6 × 10−11; S: LLOMe shLuc vs shTFG, p = 3.5 × 10−3, shLuc vs shTSG101 p = 2.7 × 10−4; U: shLuc vs shTFG LLOMe p = 3.9 × 10−4, shLuc vs shTSG101 p = 6.7 × 10−5).

Source data

Extended Data Fig. 5 TFG loss results in decreased ESCRT recruitment to damaged lysosomes in NPC1-KO cells.

(A) Western blot analysis of NPC1-KO 293 T cells following knockdown with shRNAs against Luc control, TFG, TSG101, or ALIX. Experiments with CHMP4B-mNG NPC1 KO cells were repeated at least 3 times. (B, C) Representative images (B) and quantification (C) of maximum CHMP4B-mNG puncta per cell across the time course in NPC1-KO 293 T cells treated with DMSO (vehicle control), 0.25 mM or 0.5 mM LLOMe. Cells were treated with shRNA against Luc control (black), TFG (pink) or TSG101 (blue), or ALIX (green). Each point is one FOV (n = 10, except n = 5 for 0.5 mM LLOMe). Images are shown for 20 min of treatment. (D, E) Representative images (D) and quantification (E) of maximum EAP30-mNG puncta per cell across the time course in NPC1-KO 293 T cells treated with DMSO (vehicle control), 0.25 mM or 0.5 mM LLOMe. Cells were treated with shRNA against Luc control (black), TFG (pink) or TSG101 (blue). Each point is one FOV (n = 8). Images are shown for 60 min of treatment. (F, G) Representative images (F) and quantification (G) of maximum ALIX-mNG puncta per cell across the time course in NPC1-KO 293 T cells treated with DMSO (vehicle control), 0.25 mM or 0.5 mM LLOMe. Cells were treated with shRNA against Luc control (black), TFG (pink) or TSG101 (blue). Each point is one FOV (n = 10). Images are shown for 15 min of treatment. Comparison of groups carried out by two-way ANOVA (C, E, G). ****p < 0.0001, ***p < 0.001, **p < 0.01 (C: 0.25 shLuc vs shTFG p = 4.8 × 10−10, shLuc vs shTSG101 p = 7.8 × 10−14, shLuc vs shALIX p = 4.8 × 10−5; 0.5 mM shLuc vs shTFG p = 3.4 × 10−6, shLuc vs shTSG101 p = 6.9 × 10−5, shLuc vs shALIX p = 9.1 × 10−3, E: 0.25 mM shLuc vs shTFG p = 1.0 × 10−6, shLuc vs shTSG101 p = 3.7 × 10−6; 0.5 mM shLuc vs shTFG p < 1 × 10−15, shLuc vs shTSG101 p = 4.5 × 10−12; G: 0.25 mM shLuc vs shTFG p < 1 × 10−15, shLuc vs shTSG101 p < 1 × 10−15; 0.5 mM shLuc vs shTFG p = 1.1 × 10−11, shLuc vs shTSG101 p < 1 × 10−15). Scale bars, 10 µm.

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Extended Data Fig. 6 TFG translocates from ERES to damaged lysosomes.

(A, B) Representative images of TFG and SEC31A immunofluorescence staining (A) and quantification (B) of the fraction of TFG colocalized with SEC31A in U2OS and HeLa cells. Each point is one FOV (n = 5). Scale bars, 10 µm. (C, D) Representative images of TFG and TMEM192 immunofluorescence staining (A) and quantification (B) of the fraction of TFG colocalized with TMEM192 in RPE-1 cells. Each point is one FOV (n = 6). Scale bars, 10 µm. (E, F) Representative images (E) of TFG, eGFP-TSG101, and LAMP2 immunofluorescence staining and quantification (F) of colocalization ratio between TFG and eGFP-TSG101 in U2OS cells expressing eGFP-TSG101. Cells were treated with DMSO or 0.5 mM LLOMe for 15 min. Quantification of 8 FOVs. Scale bars, 5 µm. (G-J) Representative images of LAMP2 and SEC31A immunofluorescence staining (G, I) and quantification (H, J) of the fraction of SEC31A colocalized with LAMP2 in U2OS (G, H) or HeLa (I, J) cells following treatment with DMSO (vehicle control) or 0.5 mM LLOMe for 10 min. Each point is one FOV (n = 10). Scale bars, 10 µm. (K-P) Representative images in U2OS (K-M) or RPE-1 (N-P) cells of TFG and SEC31A immunofluorescence staining (K, N) and quantification of the fraction of TFG colocalized with SEC31A (L, O) or the fraction of SEC31A colocalized with TFG (M, P) following treatment with DMSO or 0.5 mM LLOMe for 10 min. Each point is one FOV (n = 6 for U2OS and n = 8 for RPE-1). Scale bars, 10 µm. Comparison of groups carried out by one-way ANOVA (D, H, J) or two-sided Welch’s unpaired t-test (F, L, M, O, P) (D: DMSO vs 5 min, p = 3.1 × 10−3; DMSO vs 15 min, p = 5.2 × 10−4; F: p = 1.0 × 10−2, L: p = 7.2 × 10−4; O: p = 1.6 × 10−4). Error bars represent mean +/− standard deviation. ****p < 0.0001, ***p < 0.001, *p < 0.05, ns = not signficant.

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Extended Data Fig. 7 ER-lysosome contacts and calcium release during lysosome damage facilitate TFG translocation to lysosomes.

(A) KDEL Immunogold-EM of DMSO or 0.5 mM LLOMe treated HeLa cells. Images are shown for 15 min of treatment. Scale bar, 200 nm. Experiment was repeated 2 times. (B) Western blot analysis of HeLa cells following knockdown with shRNAs targeting Luc or VAPA and VAPB. VAP knockdown experiments in HeLa were repeated 2 times. (C, D) Representative images of TFG and LAMP2 immunofluorescence staining (C) and quantification (D) of the fraction of LAMP2 colocalized with TFG in HeLa cells following treatment with shRNAs targeting Luc or VAPA and VAPB followed by treatment with DMSO (vehicle control) or 0.5 mM LLOMe for 15 min. Each point is one field of view (FOV; n = 5). Scale bars, 10 µm. (E, F) Representative images (E) and quantification (F) of maximum CHMP4B-mNG puncta in U2OS cells upon treatment with DMSO or 4 µM ML-SA5, with or without pre-treatment for 1 h with 20 µM BAPTA-AM. Images are shown for 10 min of treatment. Each point is one FOV (n = 21). Scale bars, 10 µm. (G, H) Representative images (G) and quantification (H) of maximum CHMP4B-mNG puncta in U2OS cells upon treatment with LLOMe, with or without pre-treatment for 1 h with 20 µM BAPTA-AM. Images are shown for 10 min of treatment. Each point is one FOV (n = 24). Scale bars, 10 µm. (I) Western blot analysis of HeLa cells following knockdown with shRNAs targeting Luc control or ALG2. ALG2 knockdown experiments in HeLa were done 2 times. (J, K) Representative images (J) of TFG and LAMP2 immunofluorescence staining and quantification (K) of colocalization ratio between TFG and LAMP2 in HeLa cells. Cells were transduced with shRNAs targeting ALG2 or Luc, followed by treatment with vehicle control (DMSO) or 0.5 mM LLOMe for 5 or 15 min. Images are shown for 15 min of treatment. Each point is one FOV (n = 5). Scale bars, 10 µm. (L, M) Western blot analysis of CHMP4B-mNG expressing 293 T (L) or U2OS (M) cells following knockdown with shRNAs targeting Luc control or ALG2. ALG2 knockdown experiments in U2OS and 293 T were done 2 times. (N-Q) Representative images (N, P) and quantification (O, Q) of maximum CHMP4B-mNG puncta formed in 293 T (N, O) or U2OS (P, Q) cells treated with DMSO (vehicle control), 0.25 mM, or 0.5 mM LLOMe following treatment with shRNA targeting ALG2 (green) compared to Luc control (black). Images are shown for 15 min of treatment. Each point is one FOV (n = 4 for 293 T; n = 15 for U2OS). Scale bars, 10 µm. Comparison of groups carried out by two-way ANOVA (D, K, O, Q), one-way ANOVA (F) or two-tailed Welch’s unpaired t-test (H). ****p < 0.0001, ***p < 0.001, **p < 0.01, ns = not significant (D: t = 15 min, p = 2.7 × 10−7, F: DMSO vs ML-SA5, p < 1 × 10−15; BAPTA + DMSO vs ML-SA5, p < 1 × 10−15; ML-SA5 vs BAPTA + ML-SA5, p = 2.0×10−9; H: p = 1.1 × 10−8; O: 0.5 mM shLuc vs shALG2#1, p = 5.5 × 10−3).

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Extended Data Fig. 8 LLOMe treatment or TRPML1-mediated calcium release result in ATG-8ylation of lysosomes.

(A-D) Representative images (A, C) of TFG, mCherry-LC3B/GABARAP, and LAMP2 immunofluorescence staining and quantification (B, D) of colocalization ratio between TFG and mCherry-LC3B (A, B) or mCherry-GABARAP (C, D) in U2OS cells expressing mCherry-LC3B (A, B) or mCherry-GABARAP (C, D). Cells were treated with DMSO or 0.5 mM LLOMe for 15 min. Each point is one FOV (n = 7 for B and n = 8 for D). Scale bars, 5 µm. (E, F) Representative images (E) of LC3B and LAMP2 immunofluorescence staining and quantification (F) of colocalization ratio between LC3B and LAMP2 in U2OS cells. Cells were treated with vehicle control (DMSO) or 20 µM BAPTA-AM, followed by treatment with either 4 µM MLSA5 or 0.5 mM LLOMe for 15 min. Each point is one FOV (n = 5). Scale bars, 10 µm. (G-J) Representative images (G, I) of LC3B and LAMP2 immunofluorescence staining and quantification (H, J) of colocalization ratio between LC3B and LAMP2 in HeLa (G, H) or U2OS (I, J) cells. Cells were treated with shRNAs against ATG5, ATG13, TFG, or Luc control, followed by treatment vehicle control (DMSO) or 0.5 mM LLOMe. Each point is one field of view (FOV; n = 9). Images are shown for 15 min of treatment. Scale bars, 10 µm. (K) Western blot analysis of HeLa cells following knockdown with shRNAs targeting Luc control, ATG5, or ATG13. HeLa ATG5 and ATG13 knockdown experiments were repeated 2 times. (L, M) Representative images (L) of TFG and LAMP2 immunofluorescence staining and quantification (M) of colocalization ratio between TFG and LAMP2 in HeLa cells. Cells were transduced with shRNAs targeting ATG5, ATG13, or Luc, followed by treatment with DMSO (vehicle control) or 0.5 mM LLOMe for 5 or 15 min. Each point is one FOV (n = 6). Images are shown for 15 min of treatment. Scale bars, 10 µm. Comparison of groups carried out by two-sided Welch’s unpaired t-test (B, D) or one-way ANOVA (F, H, J, M). ****p < 0.0001, ***p < 0.001, *p < 0.05, ns=not significant. Error bars represent mean +/− standard deviation (B, D, F) (B: p = 1.9 × 10−3; D: p = 1.1 × 10−7; F: Untreated vs ML-SA5, p = 6.1 × 10−7; Untreated vs LLOMe, p = 1.4 × 10−4; H: shLuc 0 min vs shLuc 5 min, p = 4.4 × 10−6; shLuc 0 min vs shLuc 15 min, p = 8 × 10−15; shTFG 0 min vs shTFG 5 min, p = 3.6 × 10−6; shTFG 0 min vs shTFG 15 min, p < 1 × 10−15; shATG13 0 min vs shATG13 5 min, p = 4.5 × 10−2, shATG13 0 min vs shATG13 15 min, p = 3.4 × 10−12; J: shLuc 0 min vs shLuc 5 min, p = 1.6 × 10−3; shLuc 0 min vs shLuc 15 min, p < 1 × 10−15; shATG5 0 min vs shATG5 15 min, p = 1.9 × 10−7; shTFG 0 min vs shTFG 5 min, p = 1.3 × 10−6; shTFG 0 min vs shTFG 15 min, p < 1 × 10−15; shATG13 0 min vs shATG13 5 min, p = 2.0 × 10−5, shATG13 0 min vs shATG13 15 min, p < 1 × 10−15; M: shLuc 0 min vs shLuc 5 min, p = 2.4 × 10−7; shLuc 0 min vs shLuc 15 min, p = 9.9 × 10−9; shATG13 0 min vs shATG13 5 min, p = 1.2 × 10−4; shATG13 0 min vs shATG13 15 min, p = 7.0 × 10−5).

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Extended Data Fig. 9 CASM and the LIR2-dependent recruitment of TFG to damaged lysosomes is required for ESCRT recruitment.

(A-F) Representative images (A, C, E) and quantification (B, D, F) of fold-change in maximum CHMP4B-mNG puncta per cell across the time course in U2OS (A, B), HeLa (C, D) or 293 T (E, F) cells following treatment with vehicle control (DMSO), 0.25 mM, or 0.5 mM LLOMe and knockdown with shRNAs against Luc or ATG5. Each point is one FOV (n = 6). Images are shown for 15 min of treatment. (G, H) Representative images (G) and quantification (H) of fold-change in maximum eGFP-UBAP1 puncta per cell across the time course in U2OS cells upon treatment with vehicle control (DMSO), 0.25 mM, or 0.5 mM LLOMe and knockdown with shRNAs against Luc or ATG5. Each point is one FOV (H: n = 6). Images are shown for 40 min of treatment. (I, J) Representative images (I) and quantification (J) of total fluorescence intensity ratio between internalized dextran (FITC vs AlexaFluor−647) punctae following treatment with 0.25 mM LLOMe. U2OS cells were transduced with shRNAs targeting Luc, TFG, ATG5, or TSG101. Each point is one FOV (n = 24). (K, L) Binding curve, measured by fluorescence polarization of FAM-p62-LIR peptide incubated with increasing concentrations of recombinant human LC3B (K) or GABARAP (L) protein. Quantification of n = 3 replicates. (M, N) Peptide competition, measured by fluorescence polarization, in which FAM-p62-LIR peptide was pre-incubated with recombinant human LC3B (M) or GABARAP (N), followed by addition of increasing concentrations of unlabeled TFG peptides (LIR2, LIR2-mutant, LIR3, LIR3 mutant). Quantification of n = 3 replicates (O) Coomassie staining of purified recombinant 6xHis-mCherry-TFG1-210 WT versus mutant (ΔPB1 and LIR2 mutant) proteins. TFG purifications were done at least 3 times and experiments with recombinant TFG were repeated at least 2 times. (P, Q) Representative images (P) and quantification (Q) of binding between recombinant mCherry-TFG1-210 WT (left) or LIR2-mutant (SAFDPL > SAADPA) (right) and GFP-trap beads coated with Flag-GFP-LC3B. Quantification of 16–30 individual beads. (R, S) Representative images (R) and line graph quantification (S) of bead-bound mCherry-p62 (top) versus mCherry-TFG1-210 WT (bottom) recombinant proteins. GFP-trap beads were coated with Flag-GFP-LC3B. (T) Representative images of TFG and LAMP immunofluorescence staining in 0.5 mM LLOMe treated U2OS cells following treatment with shRNA against TFG and reconstitution with LIR3-mutant (PGFTSL > PGATSA) TFG protein (related to Fig. 3d). Images are shown for 15 min of treatment. Scale bars, 5 µm. (U, V) Representative images (U) and quantification (V) of fold-change in maximum CHMP4B-mNG per cell across the time course upon treatment with 0.25 mM LLOMe in U2OS cells reconstituted with either WT, LIR2-mutant (SAFDPL > SAADPA), or LIR3-mutant (PGFTSL > PGATSA) TFG and treated with shRNAs against endogenous TFG or Luc control. Each point is one FOV (n = 7). Images are shown for 30 min of treatment. Comparison of groups carried out by two-way ANOVA (B, D, F, H) or one-way ANOVA (J, Q, V). ****p < 0.0001, ***p < 0.001, **p < 0.01, ns=not significant. Scale bars, 10 µm, unless otherwise stated. Error bars represent mean +/− standard deviation (K, L, M, N) (B: DMSO, p = 9.8 × 10−4; 0.25 mM LLOMe, p = 8.1 × 10−5; 0.5 mM LLOMe, p = 5.6 × 10−6; D: 0.25 mM LLOMe, p = 6.6 × 10−11; 0.5 mM LLOMe, p = 4.6 × 10−3; F: 0.25 mM LLOMe, p = 1.5 × 10−3; 0.5 mM LLOMe, p = 1.2 × 10−9; H: DMSO: p = 1.3 × 10−10; 0.25 mM LLOMe: 2.7 × 10−13; 0.5 mM LLOMe 3.9 × 10−10; J: p < 1 × 10−15 for all comparisons plotted; Q: TFGWT vs TFGΔPB1 p = 1.4 × 10−5; TFGWT vs TFGWT + empty bead, p = 1.4 × 10−12; V: None vs WT, p = 1.8 × 10−5; WT vs LIR2, p = 6.0 × 10−3).

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Extended Data Fig. 10 TFG PSAP motif and oligomerization domains are required for ESCRT recruitment.

(A) Coomassie staining of purified recombinant GST-tagged human TSG101 UEV domains: WT; N45A (Ubiquitin binding mutant); M95A (PSAP binding mutant; NMAA (N45A/M95A double mutant). UEV purifications were done at least 3 times and experiments with recombinant UEV domain were repeated at least 2 times. (B) Overview of recombinant 6xHis-mCherry-TFG1-210 fragment proteins. (C) Coomassie staining of purified recombinant 6xHis-mCherry-TFG1-210 fragments. TFG1-210 purifications were done at least 2 times and experiments with recombinant TFG fragments were repeated at least 2 times. (D, E) Representative confocal images (D) and quantification (E) of recombinant mCherry-TFG1-210 fragments, binding to either wild-type (WT; left) or M95A-mutant (right) TSG101-UEV domain coated GST beads. Each point represents one bead. Each point represents one individual bead (F1: n = 25; F2: n = 42; F3: n = 23; F4: n = 36). (F) Overview of recombinant 6xHis-mCherry-TFG1-210 WT versus mutant (PSAP > AAAA, ΔPB1, and ΔCC) proteins. (G) Coomassie staining of purified recombinant 6xHis-mCherry-TFG1-210 WT and mutant proteins. TFG1-210 mutant purifications were done at least 2 times and experiments with recombinant TFG mutants were repeated at least 2 times. (H, I) Representative confocal images (H) and quantification (I) of binding between recombinant mCherry-TFG1-210, WT or mutants (F4 or PSAP > AAAA), and GST beads coated with the TSG101-UEV domain. Each point represents one individual bead (in decreasing order of concentration, WT: n = 5, 9, 6, 9, 12, 3; F4: n = 10, 12, 17, 25, 13, 3; PSAP: n = 11, 14, 5, 9, 15, 3). (J, K) Representative confocal images (J) of TFG and LAMP2 immunofluorescence staining and quantification (K) of colocalization ratio between TFG and LAMP2 in U2OS cells. Cells were transduced with shRNAs targeting Luc control or endogenous TFG and reconstituted WT or PSAP > AAAA mutant TFG. Cells were treated with vehicle control (DMSO) or 0.5 mM LLOMe. Each point represents one field of view (FOV). Quantification of n = 14 FOVs. Images are shown for 15 min of treatment. Scale bars, 5 µm. (L, M) Representative images (L) and quantification (M) of binding between recombinant mCherry-TFG1-210, WT or mutants lacking the PB1 (ΔPB1) or CC (ΔCC) domains and GST beads coated with the TSG101-UEV domain. Quantification of 8–20 individual beads. (N, O) Representative images (N) and quantification (O) of fold-change in maximum CHMP4B-mNG puncta in 293 T cells upon treatment with vehicle control (DMSO) or 0.25 mM LLOMe and following knockdown with shRNAs against Luc or TFG and reconstituted with wild-type TFG or TFG lacking the PB1 or coiled-coil (CC) domains. Each point is one field of view (FOV; n = 15). Images are shown for 20 min of treatment. Scale bars, 10 µm. (P, Q) Representative images (P) of TFG and LAMP2 immunofluorescence staining and quantification (Q) of colocalization ratio between TFG and LAMP2 in U2OS cells transduced with hairpins targeting Luc control or TFG reconstituted with wild-type TFG, R106C mutant TFG, or TFG lacking the PB1 or coiled-coil (CC) domains. Cells were treated with 0.5 mM LLOMe for 15 min. Each point is one FOV (n = 10). Scale bars, 5 µm. (R, S) Representative images (R) of TFG and LAMP2 immunofluorescence staining and quantification (S) of colocalization ratio between TFG and LAMP2 in TFGR106C/R106C or control (TFGWT/WT) patient fibroblast cells treated with 0.5 mM LLOMe for 15 min. Each point is one FOV (n = 8). Scale bars, 5 µm. Comparison of groups carried out by one-way (E, O, Q) or two-way (I, M, S) ANOVA, or two-sided unpaired t-test (K). Error bars represent mean +/− standard deviation. ****p < 0.0001, **p < 0.01, ns = not significant (E: p = 2.2 × 10−14 for all comparisons; I: 5 µM TFG: WT vs PSAP p < 1 × 10−15; 2.5 µM TFG: WT vs PSAP p < 1 × 10−15, WT vs F4 p = 1.6 × 10−5; 1.25 µM TFG: WT vs PSAP p = 2.3 × 10−10, WT vs F4 p < 1 × 10−15; M: 5 µM TFG WT vs ΔPB1, p = 4.0 × 10−2, TFG WT vs ΔCC, p = 8.3 × 10−14; 5 µM TFG WT vs ΔPB1, p = 2.4 × 10−4, TFG WT vs ΔCC, p = 8.8 × 10−14; 2.5 µM WT vs ΔPB1, p = 1.3 × 10−12, WT vs ΔCC p = 1.1 × 10−13; O: WT vs None, p = 2.4 × 10−13; WT vs ΔCC, p = 8 × 10−15; WT vs ΔPB1, p = 1.1 × 10−13; S: WT/WT DMSO vs WT/WT LLOMe: p = 2.6 × 10−3; R106C/R106C DMSO vs R106C/R106C LLOMe, p = 8.5 × 10−5).

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All statistically significant hits from the genome-wide CRISPRi screen in sgNT or sgNPC1 cells

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Goul, C.S., Jain, A., Yitiz, S. et al. LASER couples damage sensing to ESCRT assembly for lysosome repair. Nature 656, 216–226 (2026). https://doi.org/10.1038/s41586-026-10604-6

- Received: 26 August 2025

- Accepted: 28 April 2026

- Published: 03 June 2026

- Version of record: 03 June 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10604-6

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Spatial distribution of the proteome in the human body and in cancers

Liang Yue,

Wenhao Jiang,

Sainan Li,

Meng Luo,

Ning Fan,

Xiaolu Zhan,

Rui Sun,

Honghan Cheng,

Zhangzhi Xue,

Tong Liu,

Qianhe Zhou,

Kexin Chen,

Tian Lu,

Fang Guo,

Dongwei Li,

Weigang Ge,

Zongxiang Nie,

Mengge Lyu,

Jun A,

Yingrui Wang,

Yingdan Chen,

Zhenhai Fu,

Nan Xiang,

Lu Li,

Fengchao Yu,

Guo Ci Teo,

Alexey I. Nesvizhskii,

Meng Wang,

Michael P. Snyder,

Ben C. Collins,

Qi Xiao,

Ruedi Aebersold,

Fei Xu,

Hui Yang,

Sijia Zhang,

Yi Han,

Yi Zhu,

Yong Ji,

Yan Li &

…

Tiannan Guo

Nature volume 656, pages 227–236 (2026)

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Abstract

A detailed, spatially resolved quantitative map of the human proteome is essential for a deeper understanding of human biology and disease1,2,3,4. Here we present a comprehensive human proteomic landscape, generated by profiling more than 13,000 proteins across 2,856 samples using data-independent acquisition mass spectrometry. The dataset spans 58 major tissue types, 251 specific tissue subtypes and 25 distinct carcinomas. This resource enables the depiction of spatially resolved proteome trajectories across tissue types and physiological states, including fetal, tumour, adjacent non-tumour and healthy adult tissue, thereby providing insight into both developmental processes and oncogenic progression. Furthermore, quantitative proteomics comparisons across diverse tissue types and states facilitate the indication of organ-specific toxicity, the identification of repurposable anticancer drug candidates and the prioritization of therapeutic targets for cancers. This study establishes a quantitative resource for navigating the proteome in the human body and in common cancers.

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Main

Grounded in a genetic blueprint, diverse human tissues and organs with distinct functions arise during development. These functions can become dysregulated in pathological conditions, such as tumours. Characterizing proteomic variation across tissue types in both developmental and pathological contexts is essential for enhancing our understanding of human biology and advancing therapeutic development. Although transcriptomic repositories, such as ArrayExpress[5], RNA-Seq Atlas[6] and the BioGPS portal[7], have provided initial annotations for tissue expression, and the Adult GTEx project has further expanded this using genomic and transcriptomic data from tissue sites that are not affected by disease8,9,10, mRNA abundance correlates only moderately with the expression of proteins, which are the main functional and druggable molecules.

The Human Protein Atlas (HPA), which was launched in 2005, began by incorporating immunohistochemistry-based proteomic data from healthy and cancerous tissues, and has since expanded continuously[11]. By 2015, the HPA had integrated transcriptomic data from 32 healthy tissue types and proteomic data, based on 20,456 antibodies, from 44 healthy tissue types[3]. Although antibody-based protein measurement has an advantage in that it provides localized protein information, its semi-quantitative nature limits the reliable quantification of thousands of proteins, particularly those for which effective antibodies are lacking. By contrast, mass spectrometry (MS) offers a comprehensive, multiplexed and unbiased alternative for quantitative proteomic measurement12,13.

In 2014, two MS-based human proteome drafts reported the identification of approximately 85% of proteins encoded by human genes across around 30 tissue types and cell lines1,2. A few years later, another study[14] characterized 15,210 protein groups across 29 tissues using label-free data-dependent acquisition MS, and, more recently[4], researchers quantified 12,027 proteins across 32 tissue types using tandem mass tag-based MS. Although these studies advanced human tissue proteomics, they focused on a limited set of approximately 30 major tissues, leaving many uncharted, and lacked comprehensive comparisons between healthy and cancerous tissue.

Consortia such as The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have generated extensive multi-omics datasets for specific tumours15,16; however, challenges in cross-tumour type comparisons limit the insights into differences between cancers that can be obtained from these resources[17]. Comprehensive proteome profiling across diverse human tissues and states requires broad tissue coverage and in-depth, high-throughput proteomics, and this need is addressed effectively by data-independent acquisition mass spectrometry (DIA-MS)18,19,20. Here, using DIA-MS, we present a rich data resource detailing the spatial distribution of 13,609 proteins. This coverage includes 58 healthy adult tissues, paired tumour and non-tumour samples from 25 cancer types, and 22 fetal tissue types, encompassing nearly all solid human tissues, body fluids and major cancer types (Fig. 1 and Supplementary Information). This resource provides a foundation for navigating the human body proteome[21], and will facilitate cancer drug discovery.

Fig. 1: Overview of the draft human proteome.

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Summary of the sample types in our data. Each sample type is marked with a corresponding number. The specific sampling positions of the eye, blood, human embryo, ear, nose and brain are listed on the left. Twenty-five types of cancer are shown at the bottom. All samples are labelled with the full and abbreviated names of the organ, tissue or cancer, and the numbers in parentheses indicate the number of proteins retained in each submatrix that were quantified in more than 50% of the samples in the specific tissue types. In this figure, ‘female genitalia’ is used as a category label for the fallopian tubes, uterus and vagina, and ‘male genitalia’ is used as a category label for the epididymis, seminal vesicle, seminiferous duct and Cowper’s gland. Components of this figure were created in BioRender; Guomics Lab https://BioRender.com/hwyxuac and https://BioRender.com/96s0zl0 (2026); other components were created using Magnific.

Source data

Samples and proteomic profiling

We collected 2,856 samples from 9 post-mortem adult donors, 8 healthy participants, 9 post-mortem fetal donors and 1,015 patients with cancer (Supplementary Table 1). We first constructed a comprehensive human spectral library from these samples, containing 15,332 protein groups (Extended Data Fig. 1a and Supplementary Information). To ensure the quality of large-scale proteomic data, we searched the DIA-MS raw data against a combined spectral library that integrated the human spectra with entrapment spectra from non-human species (Methods and Supplementary Information). A total of 13,609 proteins were quantified across 3,005 MS files, with a global false discovery rate (FDR) of 0.1% at protein level (Fig. 1, Extended Data Fig. 1b and Supplementary Information). The proteomes showed substantial heterogeneity across tissues and sample types (Fig. 2a, Extended Data Figs. 1c, 2b and 3b–e and Supplementary Information), high reproducibility among replicates and minimal batch effects, indicating high overall data quality (Extended Data Figs. 1d–f and 2c,d and Supplementary Information).

Fig. 2: Proteome transformation in tissue development and oncogenesis.

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a,b, t-SNE plots of tumour (T), paired non-tumour (NT), normal (healthy) adult (N) and fetal (F) samples with sample type (a) and tissue type (b) classification highlighted. Shaded areas represent 80% (a) and 90% (b) confidence ellipses (multivariate t-distribution) for each group. c, Heat map highlighting trajectory-associated proteins across biological states. The protein matrix was pre-filtered to exclude contaminant proteins, and this was followed by differential expression analysis using a one-way ANOVA across sample types. Significant proteins (B-H adjusted P < 0.05) were clustered into eight co-expression modules (M1–M8) on the basis of their expression in F, T, NT and N, with n indicating the number of proteins in each module. The z-scores reflect relative protein abundance across the columns (F, T, NT and N). On the right, each module is annotated with its top five enriched Gene Ontology: Biological Process (GOBP) terms, ranked by −log_10 (B-H adjusted P) for pathway enrichment. ncRNA, noncoding RNA; tRNA, transfer RNA. Representative proteins belonging to the top-ranked GOBP term of each module are labelled on the left.

Source data

A t-distributed stochastic neighbour embedding (t-SNE) analysis of all the samples showed an orderly arrangement of fetal (F), tumour (T), paired non-tumour (NT) and normal (healthy) adult (N) samples along the opposite direction of the t-SNE 1 axis (F–T–NT–N; Fig. 2a and Supplementary Table 3), mirroring the degree of tissue differentiation. Notably, brain and liver tumours and their paired non-tumour tissues deviated from this F–T–NT–N pattern, clustering together rather than with their respective sample types (Fig. 2b). Next, we applied trajectory analysis to quantify these observations by assigning a pseudotime value to each sample on the basis of its relative position along the developmental trajectory from fetal samples, thereby highlighting tissue-specific F–T–NT–N transitions. Brain tissues exhibited exceptional proteomic stability during malignant transformation and development, characterized by low pseudotime values and minimal variance across all four states (Extended Data Fig. 3a)—consistent with functionally constrained gene expression during brain development[22]. Conversely, liver tumour and non-tumour samples clustered at high pseudotime values, distant from fetal liver, which could be due to the adaptive plasticity of the liver in response to variable environmental factors[22].

To elucidate the specific proteins and biological processes that underpin this trend, we conducted unsupervised clustering analysis on all of the samples, and identified eight distinct protein modules characterized by coherent expression patterns (Fig. 2c and Supplementary Table 3). Notably, module 3 showed a descending F–T–NT–N trend and was highly enriched for RNA splicing, reflecting its crucial role in both organ development and oncogenesis[23]. By contrast, module 8 exhibited a progressive upregulation along the F–T–NT–N axis and was significantly enriched for the humoral immune response (Fig. 2c), possibly reflecting suppressed or incomplete humoral immunity in prenatal and tumour samples24,25. Unsupervised clustering analysis of liver and brain samples separately showed that although decreased RNA splicing and increased immune activation were also observed in the F–T–NT–N trend, tissue-specific functions—such as synaptic transmission pathways in brain samples and metabolic activities in liver samples—were also enriched (Supplementary Information).

Tissue-specific protein expression

To assess whether subtypes were appropriately grouped into major tissue categories in the normal samples, we compared Euclidean distances and correlation coefficients within and across tissue categories. Tissues exhibiting high within-group heterogeneity, such as eye and cartilage, were further divided into specific subtypes, resulting in 74 refined tissue types (Supplementary Information). All of the refined tissue types showed significantly smaller distances and higher correlations within their respective categories than across categories (Extended Data Fig. 3b–e and Supplementary Information). Global t-SNE embedding showed pronounced inter-tissue differences, with discrete clusters formed by special tissue types such as body fluids, testis, cochlea and semicircular canal (Extended Data Fig. 3f). Physiologically related tissues, such as peripheral nerves, brain and spinal cord, clustered closely, distinct from other tissue types (Extended Data Fig. 3f).

Next we classified proteins into six groups according to the HPA criteria[3]: not detected, tissue-enriched, group-enriched, expressed in all tissues, tissue-enhanced and mixed (Fig. 3a, Supplementary Table 4 and Supplementary Information). The brain contained the most tissue-enriched proteins, and the crystalline lens showed the highest summed abundance ratio of tissue-enriched proteins to all identified proteins (Fig. 3a). Hierarchical clustering based on tissue- and group-enriched proteins grouped samples that were physiologically related, such as the brain and spinal cord (Fig. 3a). There were some notable exceptions; for example, the clustering of the mammary gland with connective tissue-rich tissues, such as bone, tendon and cartilage; this might reflect age-related involution, which is consistent with the paucity of mammary alveoli observed in haematoxylin and eosin (H&E) staining (Supplementary Information). Similarly, the trachea co-clustered with the salivary gland, probably owing to the prominent presence of glandular epithelial cells in the sampled region of the trachea.

Fig. 3: Tissue specificity of protein and drug targets.

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a, Bar plot showing the protein count and summed abundance ratio of each specificity category relative to all quantified proteins in each tissue type. The bottom dendrogram was generated by hierarchical clustering of tissues based on the median abundance of all characterized tissue-enriched proteins in each tissue type. b, Heat map showing tissue-specific drug targets. Only the top five most enriched proteins are shown for each kind of anatomical classification. Tissue specificities of the targets are compared with previous drafts of the human proteome: Wang et al.[14], TSomics[4], HPA immunohistochemistry data (HPA_IHC)[3] and HPA RNA-sequencing data (HPA_RNA)[3]. For each drug target, the corresponding drug number and information are shown in coloured bars. prt_enriched_not_spec, tissue-enriched (but not tissue-specific) proteins; prt_hk, housekeeping proteins; prt_specific, tissue-specific proteins.

Source data

Of the 1,717 tissue-enriched proteins identified, 749 were previously reported as enriched in the corresponding tissue types in published human proteome4,14 or transcriptome datasets[3] (Supplementary Table 5). Of these, 666 were supported at the protein level and 426 showed concordant enrichment in HPA RNA-sequencing data. Across 36 tissues overlapping with the HPA, we identified 832 proteins uniquely enriched in our dataset and 122 exclusively enriched in the HPA RNA data. Notably, 480 tissue-enriched proteins were identified in 24 tissue types that were underrepresented in previous studies, underscoring the expanded tissue coverage. Among these, we identified PANX3, which is at present documented as ‘not detected’ in the HPA dataset[3], as the top cochlea-enriched protein (Supplementary Table 5). We further synthesized two unique peptides (LVQHMLK and YFEFPLLER) from PANX3 to confirm its presence and its cochlear-specific expression (Extended Data Fig. 4a and Supplementary Table 2). Functional enrichment of tissue-enriched proteins aligned with specialized tissue functions. Proteins associated with metabolism, synaptic function, meiotic cell cycle, cardiac chamber morphogenesis and lens development were uniquely enriched in the liver, brain, testis, heart and crystalline lens, respectively. Hormone metabolic processes were co-enriched in proteins from exocrine organs, including the thyroid and adrenal glands (Extended Data Fig. 4b and Supplementary Table 5).

Tissue distribution of drug targets

Because tissue-specific drug target expression might contribute to off-target toxicity[26], we mapped tissue-enriched proteins to DrugBank[27] targets, identifying 402 proteins corresponding to 2,598 drugs across 34 tissue types (Supplementary Table 5). We found that the liver contained the most tissue-enriched drug targets (Supplementary Table 5), potentially explaining the high incidence of drug-induced liver injury. The liver’s unique exposure through portal circulation, in which absorbed drugs reach the liver directly before systemic distribution, further increases its susceptibility to drug toxicity. Cytochrome P450 2C8 (CYP2C8), which was highly enriched in the liver both in this study and in published MS-based proteome drafts3,4, is targeted by 302 drugs, including antivirals, antidiabetic agents and anticancer agents (Fig. 3b). Most of these drugs act as inhibitors and substrates—including gemfibrozil (Supplementary Table 5), which functions as an irreversible inhibitor of CYP2C8 (ref. [28]). Consequently, co-administration of gemfibrozil with CYP2C8-metabolized drugs can induce severe toxicity by increasing plasma concentrations of the drugs by eight- to tenfold28,29,30. Clinically, this manifests as rhabdomyolysis and acute kidney injury when gemfibrozil is combined with statins[29], or severe hypoglycaemia when it is combined with antidiabetic agents[30]. Furthermore, the sustained metabolic burden on hepatocytes aggravates the risk of drug-induced liver injury, creating a detrimental cycle of impaired metabolism and increasing toxicity[31].

We investigated potential off-target organ effects by comparing tissue-enriched drug targets with adverse effects reported in epidemiological studies and clinical cases, focusing on tissues other than the primary target or liver. We identified two epidemiological studies associating thyroid dysfunction with exposure to triclosan, a broad-spectrum antimicrobial that is applied topically. One study showed that exposure to triclosan affected thyroid autoimmunity and homeostasis[32], and the other study reported inverse associations between maternal triclosan exposure and maternal levels of free thyroxine and neonatal levels of triiodothyronine in a birth cohort[33]. Triclosan targets thyroid peroxidase, an enzyme found to be enriched in the thyroid, both in this study and in the two published MS-based proteome drafts3,4 (Fig. 3b). Thyroid peroxidase catalyses thyroid hormone synthesis and is crucial for thyroid homeostasis, whereas its perturbation can affect thyroid autoimmunity and hormone levels. Collectively, these findings underscore the utility of mapping the tissue-specific distribution of drug targets to elucidate the molecular aetiology of off-target adverse effects.

Pan-cancer protein changes

Paired tumour and non-tumour samples were used to generate a pan-cancer proteomic landscape (Extended Data Fig. 2a), providing a pan-cancer proteomic dataset with a unified pipeline and platform, and with a batch design that minimizes multi-batch effects[34]. Quality control demonstrated high reproducibility (Extended Data Fig. 2c,d) and minimal batch effects (Extended Data Fig. 1f). Consensus clustering of the pan-cancer proteome identified the grouping of various squamous carcinomas in cluster 1 and the unique metabolic profile of cluster 3 shared by hepatocellular carcinoma (HCC) and gastrointestinal stromal tumours (GIST; Extended Data Fig. 6a).

Comparative analysis of paired tumour and non-tumour proteomic profiles using a linear mixed model identified 8,940 differentially expressed proteins (DEPs) across 25 tumour types. Colon carcinoma, rectum carcinoma and testis carcinoma had the highest numbers of DEPs, whereas glioblastoma had the fewest (Extended Data Fig. 2e and Supplementary Table 6), consistent with the clustering of T and NT brain samples in the t-SNE map based on the global proteome (Fig. 2b). Most upregulated DEPs were cancer-specific or shared between two cancers (Extended Data Fig. 5a,b). A total of 33 DEPs were upregulated in more than 20 cancer types (Extended Data Fig. 5b,c), including reported drivers of tumorigenesis, such as MCM4 (ref. [35]) and NUDT1 (also known as MTH1)[36], indicating that conserved pan-cancer proteomic remodelling can underlie tumorigenesis.

Out of the DEPs across 25 tumour types, 2,878 were tumour-specific, meaning that they were significantly up- or downregulated in only one specific tumour type (Fig. 4a and Supplementary Table 8). HCC had the most tumour-specific DEPs, followed by diffuse large B-cell lymphoma (DLBCL) and GIST, whereas oesophageal carcinoma had the fewest. Most of the HCC-specific DEPs were downregulated and enriched in metabolic pathways (Supplementary Table 8). Of note, synaptic signalling (GO: 0099536, q = 1.10 × 10−4) was enriched among the GIST-specific upregulated DEPs, consistent with the origin of GIST from the interstitial cells of Cajal (ICCs)—gut pacemaker cells that have neuronal properties. Furthermore, KIT and ANO1, established markers for ICCs37,38, were identified as GIST-specific upregulated DEPs (Supplementary Table 8), reflecting the specific cell of origin. Targeted MS-based quantification validated the cancer-specific upregulation of CPT1C and FXYD6 in GIST; both were expressed predominantly in neurons (Fig. 4c and Extended Data Fig. 6b), confirming the enhanced neuronal properties seen in GIST. Among these tumour-specific DEPs, 131 were tissue-enriched in the corresponding normal tissue, representing locally enriched DEPs (LEDEPs). Downregulated tumour-specific LEDEPs reflected the loss of specialized tissue functions, consistent with the dedifferentiation in T inferred from the F–T–NT–N trend (Fig. 2a). For example, RBP2 and PLS1 were suppressed only in small-intestine tumours (GIST); LIPF and GKN1 were reduced specifically in gastric carcinoma; and exocrine markers (for example, CELA2A, CPA1 and PNLIP) were downregulated uniquely in pancreatic carcinoma (Fig. 4g). Conversely, upregulated LEDEPs were observed mostly in testis carcinoma (Fig. 4a), and included TSPY2 (ref. [39]), suggesting the exploitation of intrinsic germ cell proliferative programs.

Fig. 4: Tumour-specific protein signatures.

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a, Number of tumour-specific DEPs, LEDEPs and tumour-enriched proteins across cancer types. b,c, Parallel reaction monitoring (PRM) validation of cancer-specific protein expression (PAX5 (b) and CPT1C (c)). Two-tailed paired t-tests; B-H adjusted P values for multiple comparisons are shown only if less than 0.05. Paired carcinoma-adjacent tissue samples are connected by lines (red, upregulation; green, downregulation). d,e, Expression profiles of the membrane-localized tumour-enriched proteins TYROBP (d) and KIT (e), identified from DIA data. f, Concordant expression of the tumour-enriched protein PAX5, validated by both DIA and PRM methodologies. Statistical significance was determined by linear mixed-effects model (DIA) and paired t-test (PRM), with FDR correction. In b–f, box plots show protein abundance distributions: centre line, median; centre point, mean; box limits, 25th and 75th percentiles; whiskers, 1.5 × interquartile range; individual data points overlaid. n denotes the number of patients. For the tumour-enriched statistical analysis, tumour and normal samples were analysed without merging technical replicates, as detailed in Supplementary Tables 3 and 6. g, Hypothetical systems view of the cancer-specific LEDEPs in several organs. Up and down arrows represent significant upregulation or downregulation in tumour compared with paired non-tumour samples. Blue and red blocks represent activated or suppressed biological activities, respectively, inferred by cancer-specific LEDEPs. Colours of circles represent different module networks derived from cancer-specific LEDEPs in the same tissue type. AT1 cells, alveolar type 1 cells; AT2 cells, alveolar type 2 cells. The protein–protein interaction network was constructed using Metascape-based enrichment analysis of cancer-specific LEDEPs, with functional annotations of modules labelled in blocks adjacent to the corresponding network clusters. The schematic representation of the human body was manually drafted and integrated into BioRender. All other graphical elements and the final layout of this panel were created in BioRender; Guomics Lab https://BioRender.com/hkqg5rr (2026).

Source data

We cross-validated the DEPs with TCGA transcriptomic and CPTAC proteomic datasets (Extended Data Fig. 7a,b). Despite differences in platforms and cohorts, we identified 4,263 upregulated and 1,716 downregulated DEPs that were consistent across our data, CPTAC and TCGA. We further integrated our DEP list with prognostic data from the Human Pathology Atlas[40], and identified 7,336 proteins associated with clinical outcomes across 16 cancer types (Extended Data Fig. 7c and Supplementary Table 7). Notably, PLOD2 was upregulated in 13 of 25 tumours (Supplementary Table 7). A previous pan-cancer study also found that PLOD2 was often upregulated in T compared with adjacent NT tissue, and associated this upregulation with poor prognosis in renal carcinoma, lung adenocarcinoma and pancreatic ductal adenocarcinoma[17], suggesting that PLOD2 represents a high-confidence candidate for biomarker development and therapeutic targeting.

Drug repurposing and target discovery

Next, we sought to demonstrate the application of this data resource in drug target prioritization. First, we examined the therapeutic relevance of upregulated DEPs shared among tumours. We screened 77 such DEPs targeted by 36 biomolecular drugs, mostly receptor tyrosine kinase (RTK) inhibitors, which were evaluated in 2,084 clinical trials (Extended Data Fig. 8a and Supplementary Table 7). Notably, many fewer drugs are approved or under investigation for endometrial carcinoma (ENCA) than for breast carcinoma (BRCA) (Extended Data Fig. 8a), highlighting an unmet therapeutic need. Two examples illustrate the repurposing potential. First, Trodelvy (sacituzumab govitecan), an antibody–drug conjugate that combines a humanized anti-TROP2 antibody with the TOP1 inhibitor SN-38, is approved by the US Food and Drug Administration for triple-negative breast cancer. Notably, TROP2 and TOP1 are co-upregulated in both ENCA and BRCA (Extended Data Fig. 8b), suggesting that Trodelvy could be efficacious in ENCA. This hypothesis is supported by a phase II clinical trial (NCT04251416)[41] and is being further evaluated in a phase III clinical study (NCT06486441). Similarly, olaparib, a PARP1 and PARP2 inhibitor approved for ovarian cancer and BRCA, might have therapeutic potential in ENCA, given the upregulation of PARP1 across multiple gynaecological cancers, including BRCA, ovarian carcinoma, ENCA and cervical carcinoma (Extended Data Fig. 8c). This inference is further supported by the promising responses seen in phase I and II clinical trials42,43.

Second, we integrated our dataset with drug sensitivity data and CRISPR gene essentiality data to prioritize candidate protein targets and drugs. Specifically, we mapped upregulated DEPs to the ProCan-DepMapSanger proteomic dataset[44], which links protein abundance with both half-maximal inhibitory concentration (IC_50) values (drug sensitivity) and CRISPR–Cas9 gene essentiality in pan-cancer cell lines. This integration identified a total of 35 drugs targeting 9 DEPs, predominantly RTKs (Fig. 5b and Supplementary Table 9). Given the frequent occurrence of RTK alterations at both the gene and the protein level (Fig. 5a and Extended Data Fig. 8d), we narrowed our focus to RTKs. We prioritized those RTKs that were simultaneously (1) significantly upregulated in tumours (Hedges’ g ≥ 0.5, Benjamini–Hochberg (B-H) adjusted P < 0.05) and (2) predictive of a higher drug potency at a higher protein intensity (β < −0.1, FDR < 0.01; Fig. 5b). We found that high expression of MET and BCL2L1 correlated with increased potency of savolitinib, tepotinib and merestinib (MET inhibitors) and navitoclax (a BCL2 family inhibitor) in colorectal carcinoma cell lines. In rectum carcinoma, the therapeutic viability of targeting MET was further supported by the correlation between MET protein levels and CRISPR gene essentiality, with higher abundance correlating with greater dependency (β < −0.1, FDR < 0.01; Fig. 5c).

Fig. 5: Potential drug targets and drug repurposing.

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a, Number of dysregulated proteins in TCGA-curated pathways of each kind of cancer. b,c, Druggable carcinoma-dysregulated targets mapped to ProCan-DepMapSanger drug–protein (b) and CRISPR–protein (c) association data. The colour of a point represents its gene name. The drug–protein association results for BCL2L1 and MET are shown at the bottom. In b,c, the x axis shows Hedges’ g, estimated from a fitted linear mixed-effects model as the effect of sample type (T versus NT), standardized by the residual s.d. and corrected for small-sample bias based on the model degrees of freedom. The y axis shows β, calculated as the ordinary least-squares regression coefficient for the protein abundance term in the linear model relating protein abundance to drug response or CRISPR–Cas9 gene essentiality.

Source data

Third, we aimed to minimize adverse effects by prioritizing tumour-enriched proteins that were significantly upregulated in tumours relative to both paired non-tumour tissues and all of the normal tissues. We identified 41 unique tumour-enriched proteins located at the plasma membrane (Supplementary Table 8). Several are already targeted by clinically approved antibody–drug conjugates (for example, CD79B)[45], supporting this prioritization strategy. The remaining candidates, particularly uninvestigated transmembrane proteins, represent novel therapeutic targets. TYROBP, a transmembrane signalling adaptor identified as tumour-enriched in ten tumour types (for example, colorectal, gastric, renal and pancreatic carcinoma, Fig. 4d and Supplementary Table 8), is a potential shared antibody–drug conjugate target, although its myeloid expression requires toxicity evaluation. KIT, the established pacemaker of ICCs, was identified as both GIST-specific and GIST-enriched (Fig. 4e and Supplementary Table 8), indicating the therapeutic potential of targeting aberrant ICC-like signalling in GIST. Targeted proteomic analysis confirmed PAX5 as tumour-specific and tumour-enriched in DLBCL (Fig. 4b,f). PAX5 maintains B cell identity, prevents terminal differentiation and contributes to lymphomagenesis through sustained transcriptional activation[46]. Evidence also shows that PAX5 inhibition enhances the efficacy of BTK blockade in DLBCL preclinical models[47], highlighting its potential as a therapeutic target in this malignancy.

Conclusions

Here we present an anatomically resolved human proteome atlas spanning four pathophysiological states including fetal, healthy adult, tumour and paired non-tumour tissues, providing a unified resource to delineate tissue-specific proteomic distribution and prioritize therapeutic targets. We identified 1,717 tissue-enriched proteins across 74 refined tissue types, including 480 from previously uncharacterized tissues. The spatial distribution of tissue-enriched drug targets provides a molecular reference for interpreting potential organ-specific toxicities. This data resource allowed us to identify possible therapeutic targets, including 41 tumour-enriched proteins with potentially minimal off-target toxicity. Our comprehensive proteome landscape of the human body and prevalent cancers represents a key first step in the creation of a digital navigator for the human body.

We acknowledge several limitations of our study. The normal samples are predominantly from older donors, which might affect the presentation of some tissues, such as the mammary gland, owing to involution. In addition, specialized protocols were used for certain tissue types, such as hair and body fluids, because of their unique characteristics[48]. These protocol variations might introduce artefacts in cross-tissue comparisons, and we therefore avoided prioritizing tissue-enriched proteins from these tissues. Our focus on comprehensive tissue coverage limited the number of patients per tumour type, constraining the characterization of inter-patient heterogeneity. Nevertheless, our uniform platform and standardized pipeline ensure data quality and comparability, and our findings will improve researchers’ understanding of tumorigenesis in multiple organs, as well as aiding in cancer drug target discovery.

Methods

Collection of tissue samples

The autopsy samples involved in this study were collected from the body donation centre of Dalian Medical University and Shanghai Jiao Tong University. The post-mortem interval was within 10 h. The project was approved by the Ethics Committee of Dalian Medical University (institutional review board (IRB): Dalian Medical 2019-05) and Shanghai Jiao Tong University (IRB: 2022-A-01, 2021-03). Written informed consent was obtained from the legally authorized representatives for all donors. To ensure anatomical accuracy, we used (1) macrodissection by two experienced anatomists (included in the author list) following a standardized protocol; and (2) histopathological validation through H&E-stained formalin-fixed paraffin-embedded (FFPE) sections reviewed independently by two pathologists (included in the author list) for tissue types that might not be clearly partitioned at the anatomical level.

Paired tumours and non-tumour samples from surgery were collected from Harbin Medical University Cancer Hospital (from December 2019 to December 2022) and Huashan Hospital (from January 2018 to December 2018). The project was approved by the Ethics Committee of Harbin Medical University Cancer Hospital (IRB: KY2019-08, KY2023-03 and KY2019-16) and Huashan Hospital (KY2021-064). Written informed consent for each patient was collected. The samples mentioned above were stored in FFPE format. We systematically compared the cancer types, number and types of samples of our pan-cancer cohort with those in TCGA[15] and CPTAC[49]. According to the NCI Genomic Data Commons[50], our study includes two rare malignancies not covered by either TCGA or CPTAC: GIST and fallopian tube carcinoma. Conversely, TCGA and CPTAC include several cancer types absent in our current cohort, including adrenal cortical carcinoma, bladder cancer, leukaemia, mesothelioma, skin cancers and melanoma. As regards sample size, our study profiles approximately 40 patients per cancer type (Supplementary Table 1), whereas TCGA typically includes around 200 cases per type and CPTAC includes about 160. With respect to normal control tissues, TCGA mainly uses blood samples for germline genomic analysis, whereas CPTAC often includes matched adjacent non-tumour tissues (either fully or partially paired). In our pan-cancer cohort, we incorporated matched adjacent non-tumour tissues for most patients, allowing tumour–non-tumour comparisons at the proteome level.

Autopsy specimens of fetuses, from abortions, were collected from Shenzhen Baoan District Maternal and Child Health Hospital (during 16 June 2020 to 16 September 2020). The post-mortem interval was within 10 h. The project was approved by the Ethics Committee of Shenzhen Baoan District Maternal and Child Health Hospital (IRB: LLSCHY 2019-10-36). Written informed consent was obtained from all donors of tissues. The fetal samples were stored in formalin at 4 °C before use.

Sample preparation for LC–MS

The sample preparation process was similar to that described previously51,52,53. In brief, we punched each FFPE sample and weighed about 1.0 mg. Heptane was used for dewaxing, gradient (100%, 90%, 75%) ethanol for hydration and 0.1% formic acid (FA) for acid hydrolysis. The obtained product was then placed in pressure cycling technology (PCT) microtubes, and Tris-HCl (pH 10, freshly prepared) was added at 95 °C for 30 min for alkaline hydrolysis. After rapid cooling, lysis buffer (6 M urea, 2 M thiourea), Tris(2-carboxyethyl)phosphine (TCEP) and iodoacetamide (IAA) were added for reductive alkylation. PCT-assisted lysis was performed in the Barocycler NEP2320-Enhanced (Pressure BioSciences) for 90 cycles at 30 °C, with each cycle consisting of 30 s at high pressure (45,000 psi/~310 MPa) followed by 10 s at ambient pressure (AP). LysC (enzymatic substrate concentration ratio 1:80, Hualishi Tech) and trypsin (enzymatic substrate concentration ratio 1:20, sequencing grade, Hualishi Tech) were then added for digestion. The process was conducted with PCT for 120 cycles at 30 °C, with each cycle consisting of 50 s at high pressure (20,000 psi/~138 MPa) followed by 10 s at AP. After the enzymatic hydrolysis process was terminated by 10% trifluoroacetic acid (TFA), we used C18 spin columns (The Nest Group) for desalination.

The dried samples were resuspended with buffer A (high-performance liquid chromatography (HPLC)-grade water, pH 10). The fractionation was performed using an UltiMate 3000 RSLCnano system (Thermo Fisher Scientific) with an HPLC C18 column (diameter 4.6 mm, length 25 cm, particle size 300 Å). The flow rate was 0.5 μl per min. The gradient was 5% to 95% buffer B (98% acetonitrile (ACN), pH 10) in 60 min. A total of 60 fractions were collected. Then, 60 fractionations were combined into 10. The combined samples were evaporated using vacuum centrifugation (CentriVap, Labconco) at 45 °C.

For bone tissues, we cut a piece approximately one-fifth of the volume of a 2 ml centrifuge tube. The teeth were shattered with a clean hammer and placed into the 2 ml tube. After the dewaxing and alkaline hydrolysis described above, we added lysis buffer and grinding beads (about one-third of the volume of the 2 ml tube). The sample was frozen in liquid nitrogen, ground in a tissue grinder for 1 min and refrozen in liquid nitrogen. This procedure was repeated three to five times until the sample turned into a bone slurry (except for the teeth). We then added 1 ml of 10% FA solution and incubated the sample at 4 °C overnight for digestion. Afterwards, approximately 4 mg of the insoluble wet weight was transferred to a PCT tube, and 150 µl Tris-HCl (pH 10) was added to neutralize any excess acid, followed by removal of the supernatant. Then, 20 µl Tris-HCl (pH 10) and 30 µl 6 M urea–2 M thiourea lysis buffer were added, and subsequent PCT-assisted lysis and enzymatic digestion were performed as described above.

Hair samples (4 cm) or nail samples (1 mg) were immersed in 50% methanol or ethanol, chopped, and vortexed, then transferred to a PCT tube. To the tube, 30 µl lysis buffer (30% trifluoroethanol, Tris-HCl buffer, pH 8) and 2.5 µl of 200 mM TCEP solution were added, and the tube was sealed with a PCT pestle and placed in a PCT device. The samples underwent 180 cycles in the Barocycler (50 s at 45,000 psi (~310 MPa), 10 s at atmospheric pressure, 70 °C). After cooling to room temperature, 2.5 µl of 800 mM IAA solution was added, and the samples were incubated in the dark at room temperature for 30 min with gentle mixing (800 rpm). After dilution with 150 µl Tris-HCl buffer (pH 8), 1.25 µg of LysC and 5 µg of trypsin were added, and the tube was sealed and subjected to 120 cycles in the PCT (20,000 psi (~138 MPa) for 50 s, atmospheric pressure for 10 s, 30 °C). After the cycles, 15 µl of 10% TFA solution was added to stop the digestion, and the samples were desalted using C18 tips.

Whole blood was collected by venipuncture into commercial EDTA-containing sampling containers. It was separated into PRP1, erythrocyte, platelet-rich plasma (PRP), platelet-free plasma (PPP) and the pure platelet fraction, according to a previously published article[54]. PRP1 was the supernatant in the first centrifuge at 200g for 10 min. PRP was the supernatant in the centrifuge of PRP1 at 200g for 10 min. Then, 1 ml whole blood was transferred from the anticoagulation tube into a 50 ml centrifuge tube, 10 ml 1× red blood cell lysate (Invitrogen, 00-4333) was added for 10 min at room temperature (strictly controlled time) and 25 ml phosphate-buffered saline (PBS; total volume 36 ml) was added to stop the reaction. It was then divided into 15 ml centrifuge tubes (9 ml in each tube) and centrifuged at 500g for 15 min at 4 °C, followed by aspirating and discarding the supernatant. At this time, 200 μl was allowed to remain in each 15 ml centrifuge tube. The remaining liquid and cell pellet from each tube were pipetted into a 1.5 ml centrifuge tube, which was centrifuged at 500g for 5 min at 4 °C, followed by aspirating the supernatant as much as possible, to leave the cell pellet. The cell pellet was washed with 1× PBS three times and centrifuged (500g for 5 min, 4 °C) to get a clean white blood cell pellet.

The first stage of the process was the depletion of the top 14 proteins in the blood, PPP, PRP, and PRP1: samples taken out from storage at −80 °C were immediately put on ice. The depletion column was equilibrated to room temperature, following the manufacturer’s instructions (Thermo Fisher Scientific, High-Select Top14 Abundant Protein Depletion Resin, A36371). The second stage was protein concentration, following the manufacturer’s instructions (Thermo Fisher Scientific, Pierce Protein Concentrators PES, 3K MWCO, 0.5 ml, 88512). Then, 500 μl of 8 M urea–100 mM ammonium bicarbonate was added to the concentrator, which was spun at 12,000g for 40 min. Finally, the remaining volume was adjusted to 50 µl. The third stage of the process was protein digestion, which was done as described in a previous study[55]; however, the TCEP and IAA processes were both 40 min. Then, the protein extracts were digested with LysC and trypsin to the sample at 1:50. Digested peptides were cleaned using C18 (Thermo Fisher Scientific, 60209-001). The fourth stage was peptide fractionation, as described previously[56]. In brief, peptides were separated into 122 fractions, which were consolidated into 10 fractions. The samples were separated at a flow rate of 1 ml per min using a gradient from 5% ACN in 10 mM ammonia (pH 10.0) to 8% in 7.5 min; the percentage was increased to 18% in 30 min and to 32% in 55 min, then to 95% in 61 min; this 95% composition was held for 6 min; and finally it was dropped to 5% in 68 min. The fractions were combined according to the following strategy: (1) combine the 1st to 6th, 77th to 82nd and 97th, 98th, 107th and 108th fractions; (2) combine the 7th to 10th, 73rd to 76th, 93rd, 94th, 111st and 112nd fractions; (3) combine the 11th to 14th, 69th to 72nd, 89th, 90th, 115th and 116th fractions; (4) combine the 15th to 18th, 65th to 68th, 83rd, 84th, 121st and 122nd fractions; (5) combine the 19th to 22nd, 61st to 64th, 99th, 100th, 105th and 106th fractions; (6) combine the 23rd to 26th, 57th to 60th, 95th, 96th, 109th and 110th fractions; (7) combine the 27th to 30th, 53rd to 56th, 91st, 92nd, 113rd and 114th fractions; (8) combine the 31st to 34th, 49th to 52nd, 85th, 86th, 119th and 120th fractions; (9) combine the 35th to 38th, 45th to 48th, 101st, 102nd, 103rd and 104th fractions; (10) combine the 39th to 44th, 87th, 88th, 117th and 118th fractions. The combined fractions were subsequently dried and redissolved in 2% ACN/0.1% FA, and then analysed by liquid chromatography–mass spectrometry (LC–MS) (Bruker, timsTOF Pro).

For the collection of saliva, we strictly controlled the conditions to minimize contamination from food debris and oral bleeding, according to the instructions from Salimetrics (https://salimetrics.com/saliva-collection-handbook/). In brief, we collected about 1 ml of saliva per person from four healthy participants before eating meals and at least 45 min after brushing teeth, keeping records of the start and end times. Participants had to avoid the following before saliva collection: strenuous exercise, oral issues, intake of a high-sugar diet, intake of a highly sour diet, coffee, alcohol, nicotine and drugs. We collected 1 ml of the middle part of the morning urine. The saliva and urine samples were stored in at −80 °C before use. The reduction, alkylation, digestion, and desalting steps were performed as described for plasma samples in a previous study[55].

LC–MS/MS analysis

The peptides redissolved in 98% water:2% ACN:0.1% FA (v/v/v) were separated and analysed by a coupled ultra-high-performance liquid chromatography (UHPLC)–trapped ion mobility spectrometry–tandem mass spectrometry (MS/MS) system. For each acquisition, the peptides were loaded onto a Thermo Fisher Scientific Trap Cartridge (100 Å 5.0 μm, 0.3 mm × 5 mm) and separated by an in house-made analytical column (120 Å 1.9 μm, 150 mm × 0.075 mm) with a Bruker Daltonics nanoElute UHPLC system using a 95 min liquid chromatography gradient at a flow rate of 0.3 μl per min. The composition of the gradient was as follows: linearly increased from 2% B to 22% B in the first 80 min, then from 22% B to 35% B in 10 min, and then from 35% B to 80% B for 2 min and maintained at 80% B for the last 3 min. The mobile phase A was 99.9% water:0.1% FA (v/v), whereas mobile phase B was 99.9% acetonitrile:0.1% FA (v/v). All reagents were MS grade.

A Bruker Daltonics trapped ion mobility quadrupole time-of-flight mass spectrometer (timsTOF Pro) was used to perform data acquisition. In both data-dependent acquisition (DDA) and DIA mode, the MS full scans were acquired over an m/z range of 100 to 1,700 and an ion mobility range of 0.60 to 1.60 V per s per cm^2. The ion mobility over m/z heat maps were filtered using an inclusion–exclusion polygon region with vertices (150, 0.6), (1,300, 1.6), (1,700, 0.6), and (1,700, 1.6). In DDA parallel accumulation–serial fragmentation (ddaPASEF) mode, the method consisted of an MS full scan followed by 10 PASEF MS/MS scans, which consumed 1.17 s of total cycle time. The accumulation time and ramp time were both 100 ms. The target intensity was 20,000, and the intensity threshold was 2,500. In DIA parallel accumulation–serial fragmentation (diaPASEF) mode, the PASEF MS/MS scans were collected from 64 windows in an m/z range of 400 to 1,200 and corresponding ion mobility range of 0.57 to 1.47 V per s per cm^2, adapted from the standard 16 diaPASEF scans scheme[57].

Database searching and library generation

For generating consensus PASEF spectral libraries, we used the FragPipe58,59,60,61 computational platform (v.18) with MSFragger60,61 (v.3.5), Philosopher[59] (v.4.2.2) components and the EasyPQP (https://github.com/grosenberger/easypqp/) (v.0.1.29) Python package. Our workflow was based on the FragPipe DIA_SpecLib_Quant workflow[58], modified accordingly.

The database searching of raw files was performed by the MSFragger search engine, referring to the UniProtKB/Swiss-Prot Homo sapiens proteome ID UP000005640 (containing 20,377 canonical protein sequences; downloaded on 7 May 2020), appended with reverse protein sequences as decoys generated by the built-in Philosopher decoys generation tool. Then, Philosopher was used for further evaluation, protein inference and two-dimensional (2D) FDR filtering with a threshold of 0.01; the picked FDR algorithm was used to scale FDRs for large datasets. The built-in library generation script gen_con_spec_lib.py coupled with EasyPQP was used to build consensus PASEF spectral libraries with 0.01 FDR control on both the peptide level and the protein level. Most of the parameters were maintained as reported previously[62], except for the application of the semi-enzymatic tryptic digestion rule, a 0.05 Da fragment mass tolerance and retention time (RT) alignment based on spiked indexed RT (iRT) peptides. Thus, each peptide was identified either as a unique peptide to a specific protein (or protein group) or as a razor peptide to the protein (or protein group) with the most peptide evidence. Correspondingly, each protein (or protein group) was identified by either a unique peptide or a razor peptide detected for it. In downstream DIA analysis, protein groups containing indistinguishable proteins were excluded from the quantification reports.

Spectral library evaluation

The information on precursors, fragment ions and modifications in the spectral library was analysed to evaluate its quality according to an algorithm modified from the published DIALib-QC tool[63]. The Perl code for visualization was re-implemented in R. The ratios of sequence length, ion charge, peptide modification, fragment ion type and missed cleavage were counted, respectively, and presented in bar charts. The distributions of precursor m/z and peptide sequence coverage were displayed as histograms, and the protein sequence database read-in was performed by the seqinR[64] package. Pearson’s correlation of iRT values between +2 and +3 ion charge states of the same peptide sequence was measured (R^2), and the 2D kernel density estimation was performed using the function stat_density2d in the ggplot2 package.

We further compared the proteins and peptides in our spectral library with PeptideAtlas[65], which consisted of 16,702 proteins and 2,701,150 peptides (as of 6 July 2021), and ProteomicsDB[66], which covered 13,553 proteins and 913,326 peptides (as of 6 July 2021). The comparison results were presented as a Venn diagram generated by an R script.

To rigorously assess and control false discoveries in the DIA analysis, we applied a combined entrapment strategy[67]. We constructed a spectral library that merged empirical human spectra from the project-specific DDA library with entrapment spectra generated from non-human species[68]. This design produced the desired ratio of entrapment-to-original target entries at both levels: 16.714 for protein groups (256,259 entrapments versus 15,332 targets) and 1.072 for precursors (739,318 entrapments versus 689,568 targets).

Analysis of DIA data by DIA-NN

DIA searching was performed by DIA-NN (v.2.2.0 Academia) with the FragPipe-generated spectral library, with its existing protein inference results[62]. Both the MS1 and the MS2 mass accuracy were set as 15 ppm, and a robust liquid chromatography (high-precision) quantification strategy and RT-dependent cross-run normalization were chosen for quantitative results. Moreover, the different runs were treated as unrelated with match-between-runs disabled. For a shorter waiting time, the 2,856 sample runs, including replicates, and 149 pooled sample runs were randomly grouped and searched on two computers in parallel. The 3,005 runs were reanalysed with the generated .quant files in a single run to generate the final report.

By default, DIA-NN iteratively selects the best elution peak per precursor using target decoy-trained classifiers, quantifies each precursor by summing the top six fragment intensities and computes protein group intensities from both proteotypic and shared precursors passing the specified q value thresholds, with PG.Q.Values estimated using all precursors assigned to each Protein.Group and Protein.Q.Values computed using only proteotypic precursors assigned to the corresponding genes. To increase stringency, we retained only proteins supported by unique peptides (that is, protein groups without ‘indistinguishable’ members in the FragPipe-annotated DDA library), and only proteotypic peptides (proteotypic = 1 in the DIA-NN report).

For downstream quantification and statistical analyses, we filtered the DIA-NN main report using enforced stringent FDR thresholds based on a series of false discovery proportion (FDP) estimates. The FDP values among the targets and entrapment discoveries (T ⋃ ET) were calculated using the equation below:

$${\hat{{\rm{F}}{\rm{D}}{\rm{P}}}}_{T\cup {E}_{{\rm{T}}}}=\frac{{N}_{{\rm{E}}}(1+1/r)}{{N}_{{\rm{T}}}+{N}_{{\rm{E}}}},$$

where NT is the number of target discoveries, NE is the number of entrapment discoveries and r is the entrapment-to-target ratio in the spectral library.

To select suitable q value thresholds, we evaluated precursor and global protein-group q value cut-offs across the range 1 × 10−4 to 0.01, while keeping the run-specific protein-group q value fixed at 0.01. To balance sensitivity and confidence, we finally adopted the following criteria: proteotypic = 1, Q.Value < 0.001, PG.Q.Value < 0.01, Global.Q.Value < 0.001 and Global.PG.Q.Value < 0.001. These settings ensured tight global control of the FDP across all reporting levels in our multi-batch DIA dataset.

Quality control and preprocessing

We used both biological replicates (independent processing of aliquots from the same tissue sample) and technical replicates (repeated injections of identical peptide samples), with random assignment to minimize bias. Samples were randomized by reshuffling each collection batch, and clinical metadata were blinded during processing. Quality control used a dual approach: a project-specific pool prepared by mixing equal amounts of peptides from early-collected samples, and a universal standard (mouse liver digest) monitored by specialized MS administrators. Both quality control samples were injected before each batch to monitor instrument stability. All samples, including project-specific pool samples, were analysed by DIA-NN and preprocessed together (3,005 raw files in total).

Before quality control analysis, proteins with 50% or greater missing values across all the tissue types were removed from the protein matrix. The remaining protein-level intensities were quantile-normalized across samples using the normalize.quantiles function in the preprocessCore[69] package (v.1.58.0), followed by log_2 transformation. Missing values were then imputed using 0.5× the minimal value in the whole matrix. Batch effects were estimated using principal variance component analysis and corrected using the removeBatchEffect function provided by the limma[70] package (v.3.52.4) in two steps. Unless stated otherwise, this corrected matrix is used in all subsequent analyses.

Consistent processes were implemented for the corresponding data from pooled peptide samples to calculate the coefficients of variation (CVs) of protein abundances and Pearson correlation coefficients. For both technical and biological replicates, CVs were calculated by the same method that was used for pooled peptide samples, and Pearson’s r was calculated in all pairs (combined number). Similar quality control analysis was subsequently performed with the corrected matrix.

Protein identification and quantification

The number of proteins in tissue-specific libraries and DIA-quantified peptides and proteins were visualized in circular bar charts using the canvasXpress R package (v.1.29.6) and retouched using Adobe Illustrator.

t-SNE visualization

The whole matrix, excluding pooled samples, was standardized and dimensionality-reduced using t-SNE with the Rtsne[71] package (v.0.16) in R. The perplexity parameter was set to 10. The 2D result was visualized in a scatter plot. To visualize group-level spread, we overlaid data ellipses based on a multivariate t-distribution for each sample type using stat_ellipse() from ggplot2 (v.4.0.1) in R.

Trajectory analysis of sample types

Trajectory analysis was performed using Monocle3 (v.1.4.26)[72] to reconstruct the pseudotemporal ordering of samples on the basis of their proteomic profiles. After removing hair, nail and body fluid samples, the data were first processed with principal component analysis and then embedded with uniform manifold approximation and projection (UMAP) for dimensionality reduction. The trajectory graph was generated without partitions and without loop closing, with fetal samples set as the starting point of the trajectory, and pseudotime values were obtained for all samples.

To identify proteins whose expression changed along the progression of sample types, we applied a one-way ANOVA with FDR adjustment (Padj < 0.05) and removed background contaminants. For these significant proteins, we computed their median intensities across the four sample types (F, T, NT and N) and performed k-means clustering (k = 4–8) using the ClusterGVis[73] package (v.0.1.4). Each cluster was subsequently annotated through Gene Ontology: Biological Process (GOBP) enrichment analysis, and the top five enriched terms for each cluster were retained for display purposes.

Tissue heterogeneity analysis

The proteomic heterogeneity of normal samples was assessed using a preprocessed and quality-controlled protein expression matrix. Samples were hierarchically classified by anatomical classification (major category) and specific tissue name (subcategory). We calculated pairwise Pearson correlations (r) and Euclidean distances for all samples using the ‘stats’ package (v.4.4.1). We summarized the distribution of these metrics across combinations of two major categories and two subcategories (Supplementary Table 4). Tissue heterogeneity was further investigated by ranking median distances of major tissue types and subcategory pairs within the same major tissue type. Statistical differences in median distances and correlations across these hierarchical groups were evaluated using robust non-parametric comparisons in the ggstatsplot package (v.0.13.6). Subcategories with fewer than two samples were excluded to ensure statistical rigour. Global heterogeneity trends were visualized using multi-panel violin plots, and point-range distributions were generated with ggplot2 (v.4.0.1).

H&E staining

Sections of 7 μm thickness were cut using a HistoCore AUTOCUT microtome (Leica Biosystems) and mounted on positively charged glass slides. Slides were stained with H&E using the HistoCore SPECTRA Automated Slide Staining Workstation (Leica Biosystems) according to the manufacturer’s standard protocol. In brief, sections were deparaffinized, rehydrated through graded alcohols, stained with haematoxylin to visualize nuclei, differentiated, blued and counterstained with eosin to visualize cytoplasm and extracellular matrix. After staining, sections were dehydrated through ascending ethanol concentrations, cleared in xylene and coverslipped automatically. Stained sections were examined and photographed using a Zeiss Axio Observer 3. Images were captured at 40× magnification and processed by ZEN Blue (v.3.4).

Comparison with previous human transcriptome and proteome draft

For comparison with previous human proteome drafts, we used Ensembl[74] stable gene IDs (v.GRCh37.p13) as our primary mapping strategy, ensuring consistency across data versions and minimizing gene-symbol ambiguity. We exclusively used canonical reviewed proteins from UniProt[75], mapped to stable gene IDs by BioMart[76], thereby eliminating isoform mismatch issues. For transcriptomic integration, we retrieved the HPA-normalized transcripts per million (TPM) matrix and associated tissue specificity annotations.

To address quantification differences, we implemented two complementary strategies: (1) z-score normalization of overlapped proteins before Pearson correlation analysis for cross-study expression comparisons; and (2) comparison of tissue specificity labels derived from independent multi-tissue proteome analyses within each study, following established methodology.

To assess the consistency of tissue specificity annotations across independent datasets, we manually aligned the anatomical classification labels used in our data to the tissue nomenclatures used in previous studies3,4,14. We specifically cross-referenced tissue-enriched and group-enriched protein categories for corresponding tissues between datasets. For correlation analyses, the HPA[3] and Wang et al.[14] expression matrices were z-score-normalized. Because the GTEx[4] dataset uses a tandem mass tag-based ratio matrix, we performed median normalization before applying z-score normalization. From these standardized matrices, we calculated pairwise Pearson correlation coefficients to quantify inter-study proteomic and transcriptomic consistency.

Tissue specificity analysis

The tissue specificity  analysis was only performed on healthy adult autopsy specimens, excluding pan-cancer specimens, body fluids, hair, nail, umbilical cord and aborted fetus, using the modified HPA classification method[3]. The protein abundances of each tissue type were aggregated by the median of all samples after imputing missing values to 0.5× the minimal value in the whole matrix. We changed the categories of tissue-enriched, group-enriched and tissue-enhanced proteins from fivefold higher abundance to threefold for our more varied tissue types.

Tissue-enriched proteins were extracted for each tissue type and used to compare functional profiles based on GOBP annotations. Enrichment analysis was performed using the clusterProfiler[77] package (v.4.12.6) in R. The gene sets with genes between 10 and 500 were selected, with a q value threshold of 0.05 and a minimum protein count of 3. In the bubble plot, the top five most significant tissue types were highlighted for their uniquely enriched pathways. Network relationships among enriched pathways were generated using the enrichmentNetwork function in the aPEAR[78] package (v.1.0), applying a Jaccard similarity matrix and hierarchical clustering to identify pathway clusters, and selecting the term with the minimal q value as the cluster name.

Differential expression analysis

Differential expression was analysed separately for each cancer type using only patients with paired tumour and adjacent non-tumour samples. Given the limited sample size per cancer type, inherent tumour heterogeneity and potential confounding from technical batch effects, we applied a linear mixed model for comparative analysis between paired T and NT samples for each cancer type. Batch effects and clinical factors (sex, age and subtype) were explicitly included as covariates in differential expression analyses to ensure robustness, with standardized effects calculated after Hedges’ small-sample correction[79]. This dual strategy enhances the reliability of biological signal detection while minimizing false positives attributable to technical variation80,81,82. We removed proteins with missing values in more than 50% of samples within each cancer type, and imputed the remaining missing values under a left-censoring (limit-of-detection) assumption. Missing values were replaced using a global intensity floor multiplied by a random noise term drawn from a narrow normal distribution. Specifically, the imputed value Iimp was calculated as:

$${I}_{{\rm{imp}}}={I}_{{\rm{global}},\min }\times 0.5\times {\epsilon },$$

where Iglobal,min is the minimum intensity observed across the cancer dataset (the global floor), and ϵ represents a random noise term drawn from a narrow normal distribution with a mean of 1 and a s.d. of 0.001.

After log_2 transformation, we performed differential expression analysis for each protein by fitting a linear mixed model using the lmerTest[83] package (v.3.1.3), in which the log-scaled intensity was the outcome. The model included sample type (tumour versus non-tumour) as the primary fixed effect and a patient-specific random intercept to account for the paired sample structure. Additional fixed covariates (cancer subtype, sex, age and dataset) were included only if supported by the available data. The tumour–non-tumour contrast was obtained from the coefficient of the sample-type term (\(\hat{{\beta }_{1}}\)). To quantify the bias-corrected standardized effect size, the Hedges’ g statistic[84] was computed as:

$$g=\frac{\hat{{\beta }_{1}}}{\hat{\sigma }}\times J(\nu ),$$

where \(\hat{\sigma }\) is the fitted model’s residual s.d., ν denotes the degrees of freedom and J(ν) is the small-sample bias correction factor defined by the Gamma function (Γ):

$$J(\nu )=\frac{\Gamma (\nu /2)}{\sqrt{\nu /2}\,\Gamma ((\nu -1)/2)}.$$

The P values were adjusted for multiple testing within each cancer type using the Benjamini–Hochberg procedure, and proteins were considered significantly DEPs if they satisfied Padj < 0.05 and |Hedges’ g| ≥ 0.5. Proteins exhibiting statistically significant up- or downregulation in a single tumour type were classified as tumour-specific DEPs. A single protein could be categorized as a tumour-specific DEP in more than one cancer type, exhibiting different regulation patterns (up- or downregulation) across these distinct tumour types. The DEPs corresponding to RTKs were standardized and shown in a heat map with hierarchical clustering. The DEPs were also mapped to TCGA-curated pathways[85], and the degree of pathway dysregulation was assessed by the protein count for each pathway in each cancer type. Here, we present the pseudocode for this analysis:

Input: Proteomics matrix X, sample metadata M Output: Per-cancer results R = (c, j, q, gadj) FUNCTION EstimableCovariates (Mc)

V ← ∅

FOR EACH covariate v ∈ {cancer_subtype, sex, age_centered, dataset}: IF |unique values of v in Mc | ≥ 2 THEN V ← V ∪ {v} ENDIF RETURN V ENDFUNCTION FOR EACH cancer type c: 1. Subset Xc, Mc to samples where cancer_type = c 2. Retain only complete tumour–normal pairs (indexed by patient_ID); let n = number of pairs 3. Center age: agec ← age − mean (age) 4. V ← EstimableCovariates (Mc) FOR EACH protein j ∈ {1, …, P}: 5. Fit linear mixed-effects model via REML: yij = β_0 + β × sample_typei + γTVi + uj + εij where uj ~ N(0, τ^2) (patient random intercept), εij ~ N(0, σ^2), sample_type coded as ordered factor {normal < tumour} 6. If model fails to converge, skip protein j 7. Extract from the tumour coefficient:

β^, SE, t, df, p

8. Compute bias-corrected effect size:

\({g}_{\mathrm{adj}}={\rm{J}}(\mathrm{df})\times (\hat{\beta }/\hat{\sigma })\) ▷ Hedges’ g

ENDFOR 9. Apply Benjamini–Hochberg FDR correction to {pj} within c → {qj} 10. Append {c, j, gadj, qj} to R ENDFOR

Consensus clustering analysis

The pan-cancer data were extracted from the corrected protein matrix, and subsequently preprocessed by selecting proteins whose CV values were larger than the median across all samples. Consensus clustering was then performed using the ConsensusClusterPlus[86] package (v.1.67.0) with the k-means algorithm (k = 2–20) based on Euclidean distance. Silhouette analysis was applied to remove samples with silhouette widths less than 0. The remaining ‘core’ samples were subsequently used for differential protein expression analysis for each cluster versus all others using the Wilcoxon rank-sum test, which identified significantly upregulated and downregulated proteins (|log_2 fold change| ≥ 1.5, Padj < 0.05). Cluster-enriched proteins were also computed using the tissue specificity classification algorithm. The final protein signature set was derived from either significant proteins or cluster-enriched proteins, and was used for single-sample gene set enrichment analysis (ssGSEA[87]) against the MSigDB Hallmark gene sets[88] using the GSVA[89] package (v.1.51.1). The resulting pathway enrichment scores were statistically compared between the patient clusters using the limma framework.

Tumour-enriched proteins

Tumour-enriched proteins were defined for each tumour type as proteins significantly upregulated in the tumour (T) compared with both paired non-tumour (NT) and a comprehensive panel of normal (healthy) tissues (N) from diverse organs. The T–NT comparisons were taken from the ‘Differential expression analysis’ section in the Methods. The T–N differences were computed using a linear mixed model similar to the T–NT analysis, but including only the sample collection batch as an additional fixed covariate, because the corrected matrix was used here. To ensure stringent tumour enrichment, the maximum intensity across all of the N samples was required to be lower than the 25th percentile intensity in the T samples, and the highest median intensity across the N samples was required to be less than half of the median intensity in the T samples.

Locally enriched DEPs

The list of tumour-specific DEPs was mapped to tissue-enriched proteins from normal samples. Locally enriched DEPs were defined as the intersection of them.

External database cross-referencing

The list of cancer dysregulated proteins was mapped to the DrugBank online database[90] to find out potential targeted drugs, and the cancer–target–drug combinations were used to query clinical trials information from the ClinicalTrials.gov online database (https://clinicaltrials.gov). We downloaded 8,179 DrugBank records (6,653 targets; 7 February 2023), filtering for 528 biotech drugs targeting 573 proteins. Among these targets, 301 were quantified in our paired tumour dysregulation analysis, 260 of which were DEPs. By matching these drugs to ClinicalTrials.gov records for cancer types in which their targets were upregulated, we identified 36 drugs linked to 2,084 trials involving 77 upregulated DEPs across 10 cancer types (Supplementary Information). All matched clinical trials were visualized in a circular scatter plot.

We used the pan-cancer proteomic map (ProCan-DepMapSanger) drug response and CRISPR–Cas9 gene-essentiality screens dataset[44] as a validation. In the ProCan-DepMapSanger dataset, linear regression analysis was performed to test the associations between protein abundances and either drug sensitivity or CRISPR–Cas9 gene dependency. Calculated nc_fdr and nc_beta denote the FDR and the corresponding regression coefficient from models, respectively. We filtered the protein–drug or protein–knocked gene associations of nc_fdr <0.01 and nc_beta < −0.1. Meanwhile, we selected the association pairs where the protein functions as the target of the drug or corresponds to the CRISPR perturbed gene (PPI = TRUE). The gene names were transformed into Swiss-Prot protein IDs and mapped to our list of cancer dysregulated proteins. The beta without-transcriptomics covariate, Hedges’ g and cancer of each validated protein–drug relationship were visualized in a scatter plot.

The favourable prognostic, unfavourable prognostic and cancer-enriched genes, from 14 types of human cancer (head and neck cancer, thyroid cancer, lung cancer, liver cancer, testis cancer, prostate cancer, stomach cancer, colorectal cancer, breast cancer, endometrial cancer, ovarian cancer, cervical cancer, pancreatic cancer and renal cancer), were obtained from the Human Pathology Atlas[40] (https://www.proteinatlas.org/humanproteome/cancer). Our list of dysregulated proteins in cancer was mapped to the above public datasets for the same cancer types and visualized using a Sankey diagram.

The list of cancer dysregulated proteins was also mapped to TCGA and CPTAC datasets by proteins and corresponding cancer types, with the results displayed as scatter plots.

Parallel reaction monitoring validation

A total of 137 cancer-specific dysregulated proteins were selected from pan-cancer data acquired before the year 2022 for parallel reaction monitoring (PRM) validation. Each selected precursor has no modification and no missed cleavage, with the peptide length ranging from 8 to 20, identified using Skyline (v.21.1). In total, 56 peptide precursors from 47 proteins and 9 iRT peptide precursors were analysed. Among the 47 cancer-specific proteins, 10 were still classified as cancer-specific. Skyline[91] was used to analyse and quantify the targeted PRM data before generating the peptide matrix. The peptide matrix was transformed into the protein matrix using the online ProteomeExpert Peptide2Protein tool[92] by means of the top three precursor intensities and with log_2 transformation and quantile normalization. Differential expression analysis was performed after filling missing values with 0.5 times the minimum value in the whole matrix. DEPs were identified as proteins with a B-H adjusted P < 0.05 (paired Wilcoxon rank-sum test) and an absolute log_2 fold change of means ≥ 1. The cancer-enriched proteins and cancer-specific dysregulated proteins were selected using the methods described above. Validated proteins were visualized in box plots.

Validation of protein detection

Peptides were synthesized for PANX3 (SLAHTAAEYMLSDALLPDR, LVQHMLK and YFEFPLLER) by Jietai Biotechnology (http://www.synpeptide.cn/). These peptides were redissolved in 98% water:2% ACN:0.1% FA (v/v/v), mixed and analysed by the same LC–MS method that was used in the library construction process. Tandem MS spectra of three PANX3 peptides were manually reviewed in DDA files of cochlea in FragPipe.

The data analysis mentioned above was performed in R (v.4.4.0).

Development of a data resource website

The website was constructed using a standard three-tier web architecture comprising an HTML-based presentation layer, Java- and Python-based business logic layer and MySQL-based data persistence layer. The front-end interface communicates with back-end services via the HTTP protocol, with all structured data stored in optimized MySQL relational tables. User queries are processed through database matching operations followed by Python-based analytical processing before being returned to the presentation layer. For enhanced data visualization, DEP results are dynamically rendered using the ECharts JavaScript library (https://echarts.apache.org), enabling interactive graphical representations.

Reporting summary

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

Data availability

All of the qualitative and quantitative data that were used for analysis in this paper are openly accessible and fully available. Owing to the limits on publication length, the proteome data presented here cover some selected tissues and carcinomas only. To better visualize and present the entire database, we have built a website (https://db.prottalks.com/) that supports both protein-centric and tissue-centric queries. Specifically, pairs of tissues of interest can be subjected to differential expression analysis online, and the DEPs are shown in an instant heat map. Raw data as well as corresponding metadata annotations following the Sample and Data Relationship Format (SDRF-Proteomics) standard have been deposited at the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) through the iProX93,94 resource (PXD077178, IPX0003578000) and the PRIDE95,96 repository (PXD063370). Source data are provided with this paper.

Code availability

The code used for bioinformatic analyses in this study is publicly available on GitHub at https://github.com/guomics-lab/TPHP.

Change history

- 19 June 2026

In the HTML version of this article initially published, the Fig. 1 image was a duplicate of Fig. 4; Fig. 1 is now updated in the HTML version of the article.

References

1. Kim, M. S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).

2. Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).

3. Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

4. Jiang, L. et al. A quantitative proteome map of the human body. Cell 183, 269–283 (2020).

5. Brazma, A. et al. ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).

6. Krupp, M. et al. RNA-Seq Atlas—a reference database for gene expression profiling in normal tissue by next-generation sequencing. Bioinformatics 28, 1184–1185 (2012).

7. Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl Acad. Sci. USA 101, 6062–6067 (2004).

8. The GTEx Consortium et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

9. Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

10. The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

11. Uhlén, M. et al. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol. Cell. Proteom. 4, 1920–1932 (2005).

12. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

13. Guo, T., Steen, J. A. & Mann, M. Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature 638, 901–911 (2025).

14. Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).

15. The Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

16. Aaltonen, L. A. et al. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

17. Li, Y. et al. Proteogenomic data and resources for pan-cancer analysis. Cancer Cell 41, 1397–1406 (2023).

18. Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteom. 11, O111.016717 (2012).

19. Ludwig, C. et al. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol. Syst. Biol. 14, e8126 (2018).

20. Xiao, Q. et al. High-throughput proteomics and AI for cancer biomarker discovery. Adv. Drug Deliv. Rev. 176, 113844 (2021).

21. He, F. et al. π-HuB: the proteomic navigator of the human body. Nature 636, 322–331 (2024).

22. Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).

23. Singh, A. et al. Broad misappropriation of developmental splicing profile by cancer in multiple organs. Nat. Commun. 13, 7664 (2022).

24. van Weverwijk, A. & de Visser, K. E. Mechanisms driving the immunoregulatory function of cancer cells. Nat. Rev. Cancer 23, 193–215 (2023).

25. Gabrilovich, D. I., Ostrand-Rosenberg, S. & Bronte, V. Coordinated regulation of myeloid cells by tumours. Nat. Rev. Immunol. 12, 253–268 (2012).

26. Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).

27. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

28. Ogilvie, B. W. et al. Glucuronidation converts gemfibrozil to a potent, metabolism-dependent inhibitor of CYP2C8: implications for drug–drug interactions. Drug Metab. Dispos. 34, 191–197 (2006).

29. Backman, J. T., Kyrklund, C., Neuvonen, M. & Neuvonen, P. J. Gemfibrozil greatly increases plasma concentrations of cerivastatin. Clin. Pharmacol. Ther. 72, 685–691 (2002).

30. Niemi, M., Backman, J. T., Neuvonen, M. & Neuvonen, P. J. Effects of gemfibrozil, itraconazole, and their combination on the pharmacokinetics and pharmacodynamics of repaglinide: potentially hazardous interaction between gemfibrozil and repaglinide. Diabetologia 46, 347–351 (2003).

31. Tujios, S. & Fontana, R. J. Mechanisms of drug-induced liver injury: from bedside to bench. Nat. Rev. Gastroenterol. Hepatol. 8, 202–211 (2011).

32. Skarha, J. et al. Cross-sectional associations between urinary triclosan and serum thyroid function biomarker concentrations in women. Environ. Int. 122, 256–262 (2019).

33. Wang, X. et al. Maternal urinary triclosan concentration in relation to maternal and neonatal thyroid hormone levels: a prospective study. Environ. Health Perspect. 125, 067017 (2017).

34. Rasnic, R., Brandes, N., Zuk, O. & Linial, M. Substantial batch effects in TCGA exome sequences undermine pan-cancer analysis of germline variants. BMC Cancer 19, 783 (2019).

35. Shima, N. et al. A viable allele of Mcm4 causes chromosome instability and mammary adenocarcinomas in mice. Nat. Genet. 39, 93–98 (2007).

36. Gad, H. et al. MTH1 inhibition eradicates cancer by preventing sanitation of the dNTP pool. Nature 508, 215–221 (2014).

37. Hulzinga, J. D. et al. W/kit gene required for interstitial cells of Cajal and for intestinal pacemaker activity. Nature 373, 347–349 (1995).

38. Hannigan, K. I. et al. A novel intramuscular interstitial cell of Cajal is a candidate for generating pacemaker activity in the mouse internal anal sphincter. Sci. Rep. 10, 10378 (2020).

39. Halder, A., Kumar, P., Jain, M. & Iyer, V. K. Copy number variations in testicular maturation arrest. Andrology 5, 460–472 (2017).

40. Uhlen, M. et al. A pathology atlas of the human cancer transcriptome. Science 357, eaan2507 (2017).

41. Santin, A. D. et al. Efficacy and safety of sacituzumab govitecan in patients with advanced solid tumors (TROPiCS-03): analysis in patients with advanced endometrial cancer. J. Clin. Oncol. 42, 3421–3429 (2024).

42. Post, C. C. B. et al. Efficacy and safety of durvalumab with olaparib in metastatic or recurrent endometrial cancer (phase II DOMEC trial). Gynecol. Oncol. 165, 223–229 (2022).

43. Westin, S. N. et al. Phase Ib dose expansion and translational analyses of olaparib in combination with capivasertib in recurrent endometrial, triple-negative breast, and ovarian cancer. Clin. Cancer Res. 27, 6354–6365 (2021).

44. Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849 (2022).

45. Davis, J. A., Shockley, A., Herbst, A. & Hendrickson, L. Polatuzumab vedotin for the front-line treatment of diffuse large B-cell lymphoma: a new standard of care? J. Adv. Pract. Oncol. 14, 67–72 (2023).

46. Cobaleda, C., Schebesta, A., Delogu, A. & Busslinger, M. Pax5: the guardian of B cell identity and function. Nat. Immunol. 8, 463–470 (2007).

47. Oien, D. B. et al. BET inhibition targets ABC-DLBCL constitutive B-cell receptor signaling through PAX5. Blood Adv. 7, 5108–5121 (2023).

48. Zhu, Y., Aebersold, R., Mann, M. & Guo, T. SnapShot: clinical proteomics. Cell 184, 4840–4840 (2021).

49. Ellis, M. J. et al. Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. Cancer Discov. 3, 1108–1112 (2013).

50. Heath, A. P. et al. The NCI Genomic Data Commons. Nat. Genet. 53, 257–262 (2021).

51. Cai, X. et al. High-throughput proteomic sample preparation using pressure cycling technology. Nat. Protoc. 17, 2307–2325 (2022).

52. Gao, H. et al. Accelerated lysis and proteolytic digestion of biopsy-level fresh-frozen and FFPE tissue samples using pressure cycling technology. J. Proteome Res. 19, 1982–1990 (2020).

53. Zhu, Y. & Guo, T. High-throughput proteomic analysis of fresh-frozen biopsy tissue samples using pressure cycling technology coupled with SWATH mass spectrometry. Methods Mol. Biol. 1788, 279–287 (2018).

54. Geyer, P. E. et al. Plasma proteome profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol. Med. 11, e10427 (2019).

55. Gou, W. et al. Gut microbiota, inflammation, and molecular signatures of host response to infection. J. Genet. Genom. 48, 792–802 (2021).

56. Nie, X. et al. Multi-organ proteomic landscape of COVID-19 autopsies. Cell 184, 775–791 (2021).

57. Meier, F. et al. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nat. Methods 17, 1229–1236 (2020).

58. Yu, F. et al. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nat. Commun. 14, 4154 (2023).

59. da Veiga Leprevost, F. et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat. Methods 17, 869–870 (2020).

60. Yu, F. et al. Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. Mol. Cell. Proteom. 19, 1575–1585 (2020).

61. Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).

62. Demichev, V. et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat. Commun. 13, 3944 (2022).

63. Midha, M. K. et al. DIALib-QC an assessment tool for spectral libraries in data-independent acquisition proteomics. Nat. Commun. 11, 5251 (2020).

64. Charif, D. & Lobry, J. R. in Structural Approaches to Sequence Evolution: Molecules, Networks, Populations (eds Bastolla, U. et al.) 207–232 (Springer, 2007).

65. Desiere, F. et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655–D658 (2006).

66. Schmidt, T. et al. ProteomicsDB. Nucleic Acids Res. 46, D1271–D1281 (2018).

67. Wen, B. et al. Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment. Nat. Methods 22, 1454–1463 (2025).

68. Liang, S. et al. Large-scale metaproteomics of human gut microbiota reveals microbial functions in metabolic diseases and aging. Cell Metab. 38, 995–1011.e6 (2026).

69. Bolstad, B. preprocessCore: a collection of pre-processing functions. R version 1.65.0 https://doi.org/10.18129/B9.bioc.preprocessCore (2013).

70. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

71. Krijthe, J. Rtsne: t-distributed stochastic neighbor embedding using a Barnes-Hut implementation. R version 0.16 https://cran.r-project.org/package=Rtsne (2014).

72. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

73. Zhang, J. ClusterGVis: one-step to cluster and visualize gene expression data. R version 0.1.4 https://doi.org/10.18129/B9.bioc.ClusterGVis (2022).

74. Dyer, S. C. et al. Ensembl 2025. Nucleic Acids Res. 53, D948–D957 (2025).

75. The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023).

76. Kinsella, R. J. et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database 2011, bar030 (2011).

77. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

78. Kerseviciute, I. & Gordevicius, J. aPEAR: an R package for autonomous visualization of pathway enrichment networks. Bioinformatics 39, btad672 (2023).

79. Hedges, L. V. Distribution theory for Glass’s estimator of effect size and related estimators. J. Educ. Stat. 6, 107–128 (1981).

80. Nygaard, V., Rødland, E. A. & Hovig, E. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics 17, 29–39 (2016).

81. Čuklina, J. et al. Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial. Mol. Syst. Biol. 17, e10240 (2021).

82. Chen, W. et al. A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing. Comput. Struct. Biotechnol. J. 18, 861–873 (2020).

83. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

84. Hedges, L. V. & Olkin, I. Statistical Methods for Meta-Analysis (Academic Press, 1985).

85. Sanchez-Vega, F. et al. Oncogenic signaling pathways in The Cancer Genome Atlas. Cell 173, 321–337 (2018).

86. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).

87. Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

88. Liberzon, A. et al. The Molecular Signatures Database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

89. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14, 7 (2013).

90. Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).

91. Sherrod, S. D. et al. Label-free quantitation of protein modifications by pseudo selected reaction monitoring with internal reference peptides. J. Proteome Res. 11, 3467–3479 (2012).

92. Zhu, T. et al. ProteomeExpert: a Docker image-based web server for exploring, modeling, visualizing and mining quantitative proteomic datasets. Bioinformatics 37, 273–275 (2021).

93. Ma, J. et al. iProX: an integrated proteome resource. Nucleic Acids Res. 47, D1211–D1217 (2019).

94. Chen, T. et al. iProX in 2021: connecting proteomics data sharing with big data. Nucleic Acids Res. 50, D1522–D1527 (2022).

95. Jones, P. et al. PRIDE: a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res. 34, D659–D663 (2006).

96. Perez-Riverol, Y. et al. The PRIDE database at 20 years: 2025 update. Nucleic Acids Res. 53, D543–D553 (2025).

Acknowledgements

We are grateful to the donors and patients who participated in this project for donating their body or body tissue for scientific research. We thank the Westlake University High-Performance Computing Center for providing advanced computing services. This study was partly supported by the π-HuB project.

Funding

T.G. discloses support from the National Natural Science Foundation of China (grant numbers U24A20476 and 92259201) and the ‘Pioneer’ and ‘Leading Goose’ R&D Program of Zhejiang (grant number 2023C03056). Y.L. discloses support from the STI 2030 Major Project from the Ministry of Science and Technology (grant number 2021ZD0201104) and the Shanghai Municipal Health Commission (grant number 2022JC008). D.L. discloses support from the National Natural Science Foundation of China (grant numbers 82522005 and 32270574) and the Guangdong Basic and Applied Basic Research Foundation (grant number 2025B1515020052).

Author information

Author notes

1. These authors contributed equally: Liang Yue, Wenhao Jiang, Sainan Li, Meng Luo

Authors and Affiliations

1. Affiliated Hangzhou First People’s Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, Hangzhou, China

Liang Yue, Wenhao Jiang, Sainan Li, Rui Sun, Honghan Cheng, Zhangzhi Xue, Tian Lu, Zongxiang Nie, Mengge Lyu, Jun A, Yingrui Wang, Nan Xiang, Lu Li, Qi Xiao, Yi Zhu & Tiannan Guo

2. Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China

Liang Yue, Wenhao Jiang, Sainan Li, Rui Sun, Honghan Cheng, Zhangzhi Xue, Tian Lu, Zongxiang Nie, Mengge Lyu, Jun A, Yingrui Wang, Nan Xiang, Lu Li, Qi Xiao, Yi Zhu & Tiannan Guo

3. Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China

Liang Yue, Wenhao Jiang, Sainan Li, Rui Sun, Honghan Cheng, Zhangzhi Xue, Tian Lu, Zongxiang Nie, Mengge Lyu, Jun A, Yingrui Wang, Nan Xiang, Lu Li, Qi Xiao, Yi Zhu & Tiannan Guo

4. Department of Anatomy, College of Basic Medical Sciences, Dalian Medical University, Dalian, China

Meng Luo & Ning Fan

5. Harbin Medical University Cancer Hospital, Harbin, China

Xiaolu Zhan, Tong Liu, Qianhe Zhou & Kexin Chen

6. Department of Gynecology, Southern Medical University Shenzhen Hospital, Shenzhen, China

Fang Guo

7. Department of Gynecology, Shenzhen Qianhai Taikang Hospital, Shenzhen, China

Fang Guo

8. Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medical Sciences, Hainan Academy of Medical Sciences, Hainan Medical University, Haikou, China

Dongwei Li

9. Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

Dongwei Li

10. Westlake Omics (Hangzhou) Biotechnology, Hangzhou, China

Weigang Ge, Yingdan Chen & Zhenhai Fu

11. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

Fengchao Yu, Guo Ci Teo & Alexey I. Nesvizhskii

12. Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

Alexey I. Nesvizhskii & Meng Wang

13. Stanley and Judith Frankel Institute for Heart and Brain Health, University of Michigan, Ann Arbor, MI, USA

Meng Wang

14. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

Michael P. Snyder

15. School of Biological Sciences, Queen’s University Belfast, Belfast, UK

Ben C. Collins

16. Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

Ruedi Aebersold

17. Faculty of Science, University of Zurich, Zurich, Switzerland

Ruedi Aebersold

18. Department of Anatomy, College of Medical Sciences, South China University of Technology, Guangdong, China

Fei Xu

19. Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China

Hui Yang

20. State Key Laboratory of Frigid Zone Cardiovascular Diseases, Laboratory of Cardiovascular Disease and Molecular Intervention, Harbin Medical University, Harbin, China

Sijia Zhang & Yong Ji

21. Department of Critical Care Medicine, Second Affiliated Hospital, Harbin Medical University, Harbin, China

Yi Han

22. Department of Emergency and Critical Disease, Songjiang Research Institute, Shanghai Key Laboratory of Emotion and Affective Disorders, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

Yan Li

23. Department of Anatomy and Physiology of College of Basic Medical Science, Shanghai Jiao Tong University, Shanghai, China

Yan Li

Contributions

T.G. and Y.Z. conceived and designed the project. L.Y. and W.J. conducted experiments, analysed data and wrote the manuscript under the guidance of T.G. and Y.Z. S.L., M. Luo, T. Lu, R.S., Q.X. and N.X. assisted in sample preparation and MS data acquisition. Y.L., F.X., F.G., D.L. and N.F. conducted the autopsy and collected and labelled the autopsy samples. Y.J., T. Liu, H.Y., Q.Z., S.Z., Y.H. and X.Z. procured the clinical samples and information. K.C., Q.Z. and T. Liu verified the pathological information of clinical samples. Y.L. and T. Liu checked the H&E staining and labelled the abnormal tissue. M. Lyu and J.A. assisted in the analysis of the public database. H.C. and Z.X. assisted in the bioinformatic analysis. W.G., Y.C., Z.F. and Z.N. assisted in building the data portal. F.Y., G.C.T. and A.I.N. assisted in generating the spectral library. B.C.C., M.P.S., M.W. and R.A. assisted in optimizing the experimental and data analysis workflow. R.S., Y.W., Y.Z. and R.A. helped with manuscript writing and editing. M. Luo and L.L. proposed a list of tissue types for sample collection. T.G., Y.Z., Y.L. and Y.J. supported and supervised the project. All of the authors read and approved the manuscript.

Corresponding authors

Correspondence to Yi Zhu, Yong Ji, Yan Li or Tiannan Guo.

Ethics declarations

Competing interests

T.G. and Y.Z. are shareholders of Westlake Omics. A.I.N. is the founder of Fragmatics and serves on the scientific advisory boards of Protai Bio and Infinitopes. F.Y. is a paid consultant for Fragmatics. A.I.N. and F.Y. have financial conflicts of interest owing to the licensing of MSFragger and IonQuant to commercial entities. The data acquisition was partly supported by Bruker. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Thomas Oellerich, Mathias Uhlen and Juan Antonio Vizcaíno for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Analysis workflow and data quality.

a, Workflow of construction of DDA-based spectral library. b, Workflow of DIA quantitative data analysis. c, Number of identified proteins in ddaPASEF data and quantified proteins in diaPASEF data. Each radius in the figure represents one sample. The radar plot in the centre shows the number of proteins identified in the relative tissue-specific library for each sample. From the centre of the circle, the 1st circle shows the sample type, whether it is a tumour (T), tumour paired non-tumour sample (NT), normal (N), or fetal sample (F). The 2nd circle represents the number of proteins identified in the sample, and the 3rd circle represents the number of peptides identified in the sample. The 4th circle presents the sample-type abbreviations matching those used in Fig. 1. d,e, CV (d) and Pearson’s correlation (e) of pooled QC samples and replicates. Box plots display median (centre line), interquartile range (box), and 1.5× IQR (whiskers). In d, the centre point indicates the median. f, PVCA results generated from the corrected proteomic matrix. The donut chart displays the proportion of variance attributed to major experimental, biological, and interaction effects included in the model. Illustrations in a,b created in BioRender; Guomics Lab https://BioRender.com/zirfv3r (a) and https://BioRender.com/g3kl2ru (b) (2026).

Source data

Extended Data Fig. 2 Overview of the pan-cancer quantitative proteomic data.

a, Workflow of pan-cancer analysis. b, The numbers of identified proteins in the tumour and non-tumour samples in 25 cancers. Box plots display median (centre line), interquartile range (box), and 1.5× IQR (whiskers). c,d, CV (c) and Pearson’s correlation (d) of pool and replicates. e, Numbers of significantly up- and downregulated proteins between paired tumour and non-tumour for each tumour type. Statistical significance determined by linear mixed-effects model with FDR correction with detailed P values listed in Supplementary Table 6. Workflow diagram in a created in BioRender; Guomics Lab https://BioRender.com/42fsadi (2026).

Source data

Extended Data Fig. 3 Global proteome-based inter- and intra-tissue heterogeneity.

a, Ridge plots showing pseudotime distributions across 23 tissue types with multiple tissue states, stratified by sample type (colour-coded). Pseudotime values were calculated through trajectory analysis conducted by Monocle3 based on their proteomic profiles. b,c, Box plots showing intra- and inter-tissue median Euclidean distances (b) and median Pearson correlation coefficients (c) calculated for detailed tissue subtypes. d,e, Box plots showing intra- versus inter-tissue type median Euclidean distances (d) and median Pearson correlations (e) for major tissue subtypes. Box plots display median (centre line), interquartile range (box), and 1.5× IQR (whiskers). The red circles denote the arithmetic mean for each group. Individual data points representing each tissue pair comparison are overlaid with n denoting the number of tissue pairs. Statistical significance was determined using a two-tailed Welch’s t-test. f, t-SNE visualization of all normal tissue samples coloured and shaped by major tissue type based on global proteome data.

Source data

Extended Data Fig. 4 Tissue-enriched proteins and pathways.

a, Annotated fragment ion spectra of proteotypic PANX3 peptides detected in human tissue samples (upper) and the pooled synthetic peptide sample (bottom). b, Functional enrichment of tissue-enriched proteins for each tissue. The x axis stands for median -log_10(B-H adjusted P) of each enriched term. The y axis stands for the number of tissues enriched in this term. Network analysis of unique pathways enriched only in liver, brain, testis, heart or crystalline lens.

Source data

Extended Data Fig. 5 Most common upregulated proteins among tumours.

a, Upset plot showing the intersections of upregulated proteins shared across different tumor types. Sets are arranged in descending order according to the number of proteins they contain. Sets with the same number are only marked once. Only sets with at least four proteins are shown. b, Number of upregulated proteins in each group, with groups defined by the number of organs showing protein upregulation. c, Common differential expression illustrated by analysing proteins upregulated in over 20 organs. Statistical significance determined by linear mixed-effects model with FDR correction with detailed P values listed in Supplementary Table 6.

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Extended Data Fig. 6 ssGSEA enrichment and PRM validation.

a, Heat map displaying ssGSEA enrichment scores for hallmark gene sets across five identified clusters. Pie charts indicate the proportion of tumours per cluster. Stacked bar plots show the distribution of each cancer type across clusters. Colour scale represents enrichment scores (−3 to 3). b, PRM validation of cancer-specific protein expression. Box plots display protein abundance distributions: centre line, median; centre point, mean; box limits, 25th and 75th percentiles; whiskers, 1.5× interquartile range; individual data points overlaid. Paired carcinoma-adjacent tissue samples are connected by lines (red, upregulation; blue, downregulation). Two-tailed paired t-tests were used, with B-H adjustment for multiple comparisons. The notation n denotes the number of patients.

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Extended Data Fig. 7 Dysregulated proteins in our data, TCGA and CPTAC.

a,b, Cross-validation in TCGA (a) and CPTAC (b) datasets. c, Overlap with HPA pathology dataset, with Sankey diagram colours indicating different cancer types and black used for text when the corresponding colour lacks sufficient contrast.

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Extended Data Fig. 8 Pan-cancer analysis of dysregulated proteins between paired tumour and non-tumour samples.

a, DEPs from all cancer types mapped to targets and clinical trials. The clockwise direction represents the start time of clinical trials, circular layers indicate drug types, track colours correspond to cancer types, and symbols denote trial phases. b,c, Scatter plots showing the expressions of the target of sacituzumab govitecan (b) and olaparib (c). Points in red and green represent carcinoma and adjacent samples, respectively. Box plots show protein expression; centre lines indicate the medians and centre points indicate the means; box limits represent the first and third quartiles; whiskers extend 1.5 times the interquartile range from the quartiles to outliers; translucent scatters show all sample points. Coloured lines connect the carcinoma-adjacent pairs, while red lines represent up-regulations and blue lines represent down-regulations. The notation n indicates the number of patients. d, RTK dysregulations. The colour of a square represents Hedges’ g and non-detected cases are coloured in grey. The B-H adjusted P values are indicated by stars, * P < 0.05, ** P < 0.01, *** P < 0.001. Exact P values are provided in Supplementary Table 6. The coefficient for sample type in each linear mixed-effects model was tested using a two-sided t-test with Satterthwaite-approximated degrees of freedom, and the resulting P values were B-H adjusted.

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Proteome transformation in development and oncogenesis.

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Differential expression analysis.

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Yue, L., Jiang, W., Li, S. et al. Spatial distribution of the proteome in the human body and in cancers. Nature 656, 227–236 (2026). https://doi.org/10.1038/s41586-026-10660-y

- Received: 23 November 2024

- Accepted: 14 May 2026

- Published: 17 June 2026

- Version of record: 17 June 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10660-y

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Zero-shot design of drug-binding proteins via neural iterative selection−expansion

Benjamin Fry,

Kaia Slaw &

Nicholas F. Polizzi

Nature volume 656, pages 237–249 (2026)

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Abstract

The design of proteins that bind to small molecules has been challenging because it requires simultaneous optimization of the protein sequence, protein structure and ligand conformation1,2,3,4,5,6,7. Current deep-learning algorithms have struggled to navigate this landscape, precluding the zero-shot design of binders. Here we show that by combining two neural networks in an iterative design algorithm, small-molecule binding proteins can be created from scratch with high accuracy. We trained a graph neural network—ligand-aware sequence engineering message-passing neural network (LASErMPNN)—to design compatible protein sequences for an input protein backbone and docked ligand. We paired  LASErMPNN with a structure predictor that models a three-dimensional protein–ligand complex for an input protein sequence and ligand identity. The closed-loop iteration of these reciprocal networks optimized sequence–structure–ligand compatibility, and outperformed a comparable design loop using a physics-based energy function. We used our strategy, termed neural iterative selection–expansion (NISE), to design proteins that, using different folds, specifically bind to two chemically distinct small-molecule drugs, exatecan and apixaban, with success rates of 100% and 83%, respectively. The tightest NISE binders had nanomolar-to-picomolar affinities, surpassing those of the next-leading method by 70-fold for exatecan and nearly 10,000-fold for apixaban. LASErMPNN then suggested two amino-acid substitutions that improved the affinity of the tightest exatecan binder by 100-fold without any experimental input. The optimized binder protected the labile lactone ring of exatecan from hydrolysis for days. Our work describes a general recipe for using neural networks to automate the design of small-molecule binding proteins for applications in drug delivery, sensing and catalysis.

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- Protein design

- Computational biophysics

Main

The de novo design of small-molecule binding proteins remains a considerable challenge1,2,5,6,8,9,10,11,12,13,14, despite rapid progress elsewhere in the field15,16,17,18,19,20,21,22,23,24,25,26. Notable successes1,2,3,4,6,13,27,28,29 have relied mainly on high-throughput experimental selection. The few cases3,4 with high computational hit rates (33%) approximated the functional groups of ligands as parts of amino acids. To generalize binder design, neural networks can in principle learn directly from training data to predict protein sequences and protein–ligand co-structures.

Self-consistency describes the agreement between the intended structure of a designed sequence and its predicted structure. Maximizing self-consistency has been a guiding principle for the design of new topologies[30] and of binders to proteins and peptides15,16,23,31. However, this principle has not been extended to small-molecule binder design because of the complexities of encoding non-amino-acid chemistry. Models such as RoseTTAFold-All Atom (RFAA)[8], Boltz-1/2 (refs. 32,33) and AlphaFold3 (AF3) (ref. [34]) can predict a protein–ligand co-structure from a protein sequence and a ligand simplified molecular-input line-entry system (SMILES) string. Using such models, a self-consistent design has not only a predicted structure that closely resembles the intended backbone, but also a ligand that is predicted to bind in the intended site (Fig. 1a). This additional dimension should enable a more nuanced assessment of design quality. We reasoned that maximizing sequence–structure–ligand self-consistency would lead to the design of high-affinity small-molecule binding proteins with good success rates.

Fig. 1: A self-consistency optimization algorithm for the design of small-molecule binding proteins.

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a, The principle of self-consistency shown for a four-helix bundle and a docked small molecule (shown as a stick model in blue). Top, two self-consistent designs with respect to backbone, computed with, for example, AF2. Bottom, one of these self-consistent backbone designs is self-consistent with respect to the ligand (predicted docked ligand in magenta), computed using, for instance, RFAA. The schematic heat map shows a typical outcome for a NISE trajectory with the goal to populate the bottom left corner. Marginal distributions are shown using coloured bars for protein sequences 1–3 (s1–s3). b, Schematic of the NISE protocol. NISE starts with an initial protein structure (backbone coordinates only) and a docked ligand location (magenta). The NISE process iteratively applies neural-network-based sequence design (expansion) and co-structure prediction. lig, ligand; P, probability; seq, sequence; struct, structure. High-confidence (high ligand pLDDT) self-consistent designs are used as new inputs (selection) for sequence design. The depicted proteins and ligands are coloured by model confidence (low to high, red– yellow–green–cyan–blue). c, Comparison of neural and energy-based iterative selection–expansion (ISE) protocols for generating modified protein–ligand coordinates. In both cases, LASErMPNN was used to design the sequences. For energy-based ISE, the co-structure predictor was replaced with Rosetta energy minimization and designs were selected on the basis of low ligand energy. After 35 rounds of ISE, the structures of all designed sequences were predicted using RFAA. Ligand pLDDT (red, third quartile) and sequence negative log-likelihood (NLL; blue, first quartile) were plotted against design iteration. NISE (but not energy-based ISE) simultaneously optimized both ligand confidence (higher pLDDT) and protein sequence quality (lower NLL). Data are from a NISE trajectory using the input structure that produced EPIC (Fig. 3) and with exatecan used as the ligand. Quartiles were produced from n = 1,500 designs per iteration (n = 500 for the first round). d, Simultaneous optimization of designs along two reciprocal conditional-probability distributions, P(seq|struct, lig) and P(struct, lig|seq), in c suggests that NISE is optimizing within the joint probability distribution, P(seq, struct, lig). conf., conformation.

To design binders, we implemented a self-consistency optimization algorithm, NISE, which explicitly considers small-molecule ligands. We applied NISE to two small-molecule drugs, exatecan and apixaban, and achieved highest affinity binders with dissociation constants (Kd) of 120 nM and 80 pM, respectively. These Kd values surpass other methods4,6 considerably.

NISE sampling algorithm

The de novo design of small-molecule binding proteins typically begins by docking a ligand into a precomputed protein scaffold, and then designing a sequence for the resulting pose1,4. Because the initial pose is rarely optimal, the designed sequence is also unlikely to be optimal. Therefore, a means of jointly refining the sequence, backbone and ligand conformation in a coupled manner is needed. A few methods, such as COMBS3,4, RIFdock2,6 and even brute-force rigid-body docking (Supplementary Information), can position ligands into a blank backbone for subsequent sequence design, with optional constraints on ligand orientation and burial. NISE then performs the joint refinement.

To maximize tripartite self-consistency for a given input structure (backbone and docked ligand), we iteratively designed sequences and predicted co-structures (Fig. 1b). For each sequence, a protein–ligand co-structure was computed and compared to the input structure from the previous round. Only designs with high self-consistency (low root mean square deviation (r.m.s.d.)) with respect to both backbone and ligand coordinates were kept. We then selected a few co-structures with the most confidently predicted ligands as new inputs (both backbone and ligand atoms) for the next round of sequence design. Throughout the NISE process, we avoid using energy functions, relying instead on neural networks and model confidence to guide optimization. To avoid getting trapped in local minima, we encouraged exploration by sampling many sequences for a given backbone–ligand pair in each round, drawing from the probability distribution of the LASErMPNN neural network (see below) at a high softmax temperature. The structures, sequences and ligand conformers become progressively more compatible after each round (Fig. 1c, left).

This approach is similar to iterative coordinate ascent[35], in which alternating argmax sampling from two conditional distributions, P(a|b) and P(b|a), locally climbs to a high probability mode in the joint distribution, P(a, b). NISE samples broadly from P(sequence|structure, ligand conformation) and takes a confidence-based argmax over P(structure, ligand conformation|sequence). Together, this means that NISE climbs towards a high probability mode in the joint distribution representative of the underlying training data, protein–ligand co-structures in the Protein Data Bank (PDB; Fig. 1d).

LASErMPNN neural network

We set out to train a neural network to learn the probability distribution of protein sequences conditioned on the three-dimensional structures of protein–ligand complexes. LASErMPNN is a heterograph neural network trained on protein–ligand co-crystal structures from the PDB. It predicts protein sequences and side-chain dihedral angles from protein backbone coordinates and ligand atomic coordinates (Fig. 2a and Supplementary Fig. 1). During inference, LASErMPNN autoregressively decodes each residue from an input protein–ligand complex using a randomly chosen decoding order. We trained the model until the accuracies of the training and validation sets overlapped. We then tuned the hyperparameters of the model to maximize foldability of the designed sequences without markedly losing sequence recovery (Supplementary Fig. 2). The model accurately recovered native protein sequences in a held-out test set, outperformed a similarly trained ligand-free version of itself, and was comparable to (even slightly outperformed) a retrained version of a similar model, LigandMPNN[36] (Fig. 2b).

Fig. 2: LASErMPNN designs protein sequences conditioned on protein–ligand co-structures.

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a, LASErMPNN is an encoder–decoder network. It uses protein–ligand co-structures as input (protein backbone atoms and ligand atoms) and is trained to autoregressively predict (box labelled autoregressive sampling) the amino-acid identity and side-chain dihedral angles of each protein residue. LASErMPNN forms a heterograph from the protein–ligand co-structures, encoding the protein and ligand nodes separately. The ligand node embeddings (e_1, e_2, etc) are taken from a pretrained ligand encoder (box labelled pretrained ligand encoder) and locked during training. The ligand encoder is trained to predict the atomic properties of ligands (such as the partial atomic charge) conditioned on atomic coordinates and elements. Autoregressive sampling (AS) is performed in turn for each L residue of the protein. Each new residue prediction is conditioned on previously decoded residue identities and their associated dihedral angles (green). LASErMPNN outputs a three-dimensional structure of the sampled side-chain coordinates built from predicted residue identities (Ai) and side-chain dihedral angles (X_1, X_2, X_3 and X_4). b, LASErMPNN performance on a held-out test set of proteins compared with a similarly trained protein-only model (left, all ligand information removed) and a retrained LigandMPNN model (right). These plots show the prediction accuracy across binding-site residues when the model is tasked with predicting the entire protein sequence of each test protein (argmax sampling). c, Left, LASErMPNN scores the wild-type (WT) sequence of monomeric streptavidin favourably, with high-probability amino acids (a.a.; shown in magenta) located in the biotin binding site and protein core. Right, the LASErMPNN design with highest binding-site sequence recovery (out of 10,000 sequences). Accurately predicted residues are depicted in green and two designed mutations in red. This design was in the top 25 when ranked by buried, non-hydrogen-bonded polar atoms. d, LASErMPNN designs using the PiB structure (PDB: 8TN6_A) as the input. RFAA-predicted co-structures for 1,000 designs were filtered and ranked by the number of buried, polar non-hydrogen-bonded atoms. The original high-affinity PiB sequence was ranked fourth.

LASErMPNN differs from LigandMPNN in several key ways. LASErMPNN contains a distinct, pretrainable ligand encoder; performs simultaneous decoding of side-chain dihedral angles and amino-acid identity; and includes ligand nodes in each round of encoding and decoding. To enable accurate decoding of side-chain dihedrals in the presence of backbone noise, we first idealized and then noised entire backbone ‘frames’. This was necessary to reduce the memorization of crystal-structure artefacts, and contrasts with the approach of LigandMPNN, which applies noise to backbone atoms independently (Supplementary Information). Ablation studies showed that the performance of LASErMPNN depends on the simultaneous prediction of side-chain dihedral angles during sequence decoding and on pretraining a ligand encoder module using a large set of synthetic ligands37,38. The training task of the ligand encoder was to predict atom-level properties derived from quantum chemical computations, such as partial charge (Extended Data Table 1). A useful by-product of ligand-encoder pretraining is that predicted partial charges provide a diagnostic read-out of the model’s understanding of new ligands, a capability that LigandMPNN does not have. In a head-to-head comparison between LASErMPNN and LigandMPNN, we observed a general tendency for LigandMPNN to produce designs that were more overpacked, as measured by an increased density of protein heavy atoms (within 5 Å of the ligand) and a higher van der Waals repulsion energy of both the ligand and overall structure (Kolmogorov–Smirnov test, P < 1 × 10−16; Extended Data Fig. 1).

As a strict in silico test case, we rigorously held out the streptavidin fold (on the basis of sequence, structure and evolutionary similarity) from the training set. The streptavidin–biotin complex is one of the strongest known non-covalent complexes in nature[39], and recovery of this sequence would be a convincing indication that LASErMPNN is performing as intended. LASErMPNN scored the native sequence of monomeric streptavidin favourably[40], displaying high probabilities for native core and binding-site residues (Fig. 2c). Designed sequences for monomeric streptavidin with bound biotin showed high sequence recovery of binding-site residues (53% and 38% on average, with and without biotin, respectively, Extended Data Fig. 2). Although RFAA struggled to predict self-consistent structures for this β-barrel topology, many of the designed sequences were predicted by Boltz-1 to fold without using multiple sequence alignments (65% success rate for Boltz-1 versus 0.4% for RFAA; protein Cα and ligand heavy-atom r.m.s.d. < 2.5 Å, Supplementary Fig. 3). These results contrast with a retrained model of LigandMPNN, which had an average binding-site sequence recovery of 36% and 34% with and without biotin, respectively, and a 13% success rate for self-consistent structures by Boltz-1 (Extended Data Fig. 2 and Supplementary Fig. 4). We ranked LASErMPNN-designed self-consistent structures using a formula that penalizes buried, non-hydrogen-bonded, polar atoms. This showed an inverse correlation with binding-site sequence recovery (Supplementary Fig. 5). The top-25 streptavidin designs showed up to 94% binding-site sequence recovery (out of 10,000 total designs; Fig. 2c). The few residues that were in disagreement were biochemically plausible and represented in a multiple sequence alignment (Supplementary Information). Thus, LASErMPNN can rapidly sample binding-site sequences that have taken millions of years to evolve.

We tested the same filtering and ranking approach on a previously de novo designed drug-binding protein[3], PiB, a four-helix bundle that tightly binds to the drug rucaparib (Fig. 2d and Supplementary Fig. 6). The structure of PiB was held out from the training sets of both LASErMPNN and RFAA, and therefore represents a benchmark closely aligned with our design objective. We used LASErMPNN to design 1,000 sequences for the PiB backbone bound to rucaparib. Most designed sequences had self-consistent co-structures predicted by RFAA. We filtered RFAA-folded designs by self-consistency (protein Cα and ligand r.m.s.d.) and ligand confidence (predicted local distance difference test, pLDDT), then ranked the remaining designs using the same formula that penalizes buried, non-hydrogen-bonded, polar atoms. The top-ranked design had a binding-site sequence recovery of 74% (differing mainly at surface residues) and a recovery of 80% for all core residues. This provides confidence that the design method and ranking scheme can produce and prioritise high-quality de novo binders. When assessed using the same metrics, the PiB sequence was ranked near the top of the designed sequences (fourth out of 1,001).

Exatecan design objective

Exatecan is a member of the camptothecin class of clinically approved small-molecule anticancer therapeutics. It binds to and inhibits[41] the DNA–protein complex of topoisomerase I (Fig. 3a). It is one of the most soluble and potent members of the camptothecin class (cellular half-maximum inhibitory concentration (IC_50) of around 1 nM) and is therefore a popular payload in antibody–drug conjugates42,43. Like other camptothecins, exatecan has a characteristic lactone ring that is prone to hydrolysis at physiological pH. In plasma, the free drug is hydrolysed[41] with a half-life of around 2 h (Figs. 3a and 5a). The hydrolysed, ring-open, carboxylate form of the drug is considerably less bioactive, owing in part to its lower cell permeability and because this form is bound predominantly by human serum albumin41,43 (HSA). We sought to design a small protein that encapsulates the labile lactone ring of exatecan, thus protecting it from hydrolysis (Figs. 3b,c and 5c). A designed binder could be used as a delivery vehicle for the bioactive form of the drug or as a sponge for free drug molecules that were liberated from antibody–drug conjugates prematurely. There are no structures of exatecan in the PDB or the Cambridge Structural Database, and few structures of related compounds (Extended Data Fig. 3a), making this a challenging target for binder design with neural networks.

Fig. 3: Design of an exatecan-binding protein using NISE.

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a, Chemical structure of exatecan. b, Traditional design algorithm using COMBS and Rosetta to design an exatecan-binding protein. Starting from an ensemble of computationally generated four-helix bundles (coloured by AF2 Cα pLDDT; blue > 90), COMBS uses van der Mers (yellow) to dock exatecan (blue) into the bundle. A flexible-backbone design protocol using Rosetta then designs the remainder of the sequence. c, The NISE design algorithm uses backbone and ligand coordinates as the input. The sequence of the traditional design in b was stripped and its structure was used as input for NISE. Four designs were chosen from the NISE output. The design model for EPIC, the highest-affinity protein, is shown in grey. d, Structural refinement of the design model using NISE (grey); the input structure (green) is shown for comparison. A 3_10 helix (green) near the ligand (red box in c) is converted to a canonical helix (grey) and a wide helix–helix interface (green) is narrowed. Exatecan (blue) is drawn deeper into the bundle. e, Density plots of protein and ligand r.m.s.d. values for two iterations of the NISE protocol using the input structure in c. Note the increase in protein–ligand self-consistency in iteration 35 compared with iteration 0 (blue arrow). Marginal distributions are shown (n = 4,000). f, Fluorescence anisotropy of exatecan as a function of titrating different proteins (converted to the fraction of bound exatecan (bound fraction)): the highest-affinity NISE design (EPIC, Kd = 0.12 ± 0.03 µM), the highest-affinity traditional design (Kd = 8 ± 0.7 µM) and HSA (Kd = 43 ± 3 µM). [Exatecan] = 50 nM; fits to a 1:1 binding model are shown as solid lines. g, Fluorescence anisotropy of several fluorescent ligands binding to EPIC. [Ligand] = 50 nM; fits to a 1:1 binding model are shown as solid lines. Irinotecan showed negligible binding at the highest protein concentrations, precluding an accurate fit. Dexamethasone (dex) and apixaban (apx) were conjugated to fluorescein isothiocyanate (FITC) and showed no appreciable binding. f,g, Data are mean ± s.d. of three technical replicates. h, Structure–activity relationship for the series of camptothecin-class molecules shown in g. The change in binding free energy (ΔΔG) was computed relative to the affinity of the exatecan–EPIC complex. FL118, ΔΔG = 2.3 kcal mol−1; belotecan, ΔΔG = 3.0 kcal mol−1; and camptothecin, ΔΔG = 3.9 kcal mol−1. Kd values represent the mean of 1,000 fits performed by bootstrapping of optimal-fit residuals; bounds are 95% confidence intervals derived from the bootstrapped fits.

Design of exatecan binders using NISE

We performed family-wide hallucination of single-chain four-helix bundles to create initial scaffolds for docking exatecan (Supplementary Information). Helical bundles contain a central cavity that can accommodate a variety of small molecules, and their small size, high designability[44] and thermostability make them attractive scaffolds for de novo binder design3,4,28,45. In brief, we created four-helix coiled-coil domains using parametric equations4,44, generated loops using RFdiffusion[15] (with loop lengths determined by structural bioinformatics[46]), designed sequences for each bundle using ProteinMPNN[47], and predicted bundle structures with AlphaFold2 (AF2)[48]. The resulting focused library consisted of 40 AF2-predicted four-helix bundles with very high confidence (Cα pLDDT). Each structure had at least eight different sequences that passed confidence thresholds, making each sufficiently designable as a scaffold for small-molecule binders.

We generated two exatecan conformers using an X-ray crystal structure of a camptothecin derivative from the Cambridge Structural Database (CSD) as a starting point. Newly built coordinates were optimized with a molecular mechanics forcefield (Supplementary Fig. 7 and Supplementary Information). These conformers were docked into each of our 40 backbone templates using a previously described method, COMBS3,4. Docked ligands were filtered by burial in the protein.

The docked ligands underwent a traditional design process, first using COMBS and van der Mers3,4 to design the binding site, and then using Rosetta[49] to compute the remainder of the sequence with a flexible backbone algorithm (Fig. 3b). Final designs passed metrics for ordering, such as self-consistency by co-structure prediction with RFAA (low r.m.s.d. for overall backbone, binding site and ligand). We ordered synthetic DNA for 16 designs, representing 8 distinct ligand positions and backbones (that is, poses), for a direct comparison to NISE.

For the initial input to NISE, we chose a model from the 16 COMBS–Rosetta designs. The designs were ranked by self-consistency using a linear combination of ligand heavy-atom r.m.s.d., binding-site residue Cα r.m.s.d. and overall backbone Cα r.m.s.d. values (Supplementary Information methods). The sequence of the top-ranked design was discarded and only the backbone and ligand coordinates were used as input to NISE. No experimental characterization of the designs was performed before this choice.

We implemented the NISE protocol with RFAA, optimizing designs by selecting the top three self-consistent structures by ligand confidence (pLDDT) from each round. These were then expanded in the next round by sampling 1,000 sequences for each structure (designing the entire sequence with LASErMPNN each time). Using NISE, we noticed a striking asymmetry: most designed sequences were predicted by RFAA to be self-consistent with respect to Cα coordinates, but far fewer were predicted to bind to the ligand in the correct orientation, although most ligands had an accurately predicted centre of mass (Supplementary Fig. 8). More ligands were predicted to bind in the correct orientation in later design cycles, but these remained the minority compared with alternative binding modes that tended to expose most of the polar atoms of exatecan to solvent (Fig. 3e). These results strongly indicate that self-consistency with respect to the ligand is a much more stringent computational screen for binding than self-consistency with respect to the backbone alone, although backbone-only self-consistency has been used with some success recently[13].

Although we only explicitly selected for self-consistency and ligand confidence, we observed that NISE also optimized designed sequences according to LASErMPNN confidence (that is, the negative log-likelihood (NLL) decreases; Fig. 1c, left). Thus, by iteratively optimizing reciprocal conditionals, NISE climbs the nearest mode in the joint distribution of P(sequence, structure, ligand conformation) (Fig. 1d). This contrasts with an alternative iterative optimization scheme using Rosetta energy minimization to sample new backbone and ligand coordinates, in which the top structures were selected by ligand energy (Fig. 1c, right, and Supplementary Figs. 9 and 10). Iteratively selecting Rosetta-minimized structures by ligand energy and expanding them using sequence design with LASErMPNN did not reduce the NLL of designed sequences nor increase the pLDDT of ligands in predicted co-structures. This implies that these structures are not sufficiently altered to more strongly encode optimal sequences. These results show that both neural networks in NISE are important for optimization.

After pooling and filtering the designs by self-consistency (Supplementary Figs. 11 and 12), we selected four NISE designs (40% mean pairwise sequence identity) with the least number of buried, non-hydrogen-bonded, polar atoms. The NISE algorithm altered exatecan binders from the original input in several ways (Fig. 3d). First, as expected, the sequences were overwhelmingly changed relative to the input design from COMBS and Rosetta, including most binding-site residues (Supplementary Fig. 13 and Supplementary Table 1). Second, a 3_10 helix segment near the binding site was remodelled into a more ideal coiled coil domain. Third, the gap in a wide helix–helix interface was narrowed to enable better interhelical side-chain packing and increased supercoiling of the bundle. This global structural rearrangement, which reduced the superhelical radius from 7.5 Å to around 7.2 Å, enabled the inclusion of more contacts with the ligand. Fourth, the ligand was translated and rotated by several Å into the bundle interior, increasing its buried hydrophobic surface area. The intended binding mode was maintained, with the labile lactone ring buried in the core of the protein, although some ligand torsional angles became distorted by RFAA. The four designs maintained the same binding mode, yet sampled various degrees of ligand burial, binding-pocket polarity and protein supercoiling, differing from each other and from the input backbone by an average Cα r.m.s.d. of 1.2 Å and 1.3 Å, respectively. The most marked changes in the sequence and structure occurred at early iterations of NISE; subtle refinements in later rounds were important for minimizing the number of buried, non-hydrogen-bonded polar atoms (Supplementary Table 2).

Characterization of exatecan binders

We ordered synthetic DNA sequences of each of the NISE-designed proteins and expressed the proteins in Escherichia coli. Each design was expressed at good yield and was monomeric as assessed by size-exclusion chromatography (SEC; Supplementary Fig. 14). Exatecan is intrinsically fluorescent, so its binding can be tracked in fluorescence polarization experiments using an unlabeled ligand. Fluorescence polarization experiments showed that all four designs bound to exatecan (Fig. 3f and Supplementary Fig. 15), with Kd values ranging from 0.12 µM to 17 µM (three designs had a Kd < 10 µM). In comparison, HSA, a non-specific binder of exatecan, had a Kd of 43 µM. The highest-affinity design, which we named exatecan–protein interaction construct (EPIC), bound to exatecan approximately 360-fold more tightly than HSA. Structurally, the binding site of EPIC contained the fewest polar residues of the four NISE designs, although they all shared a buried Gln51 residue (near the lactone ring) and Asp132, which engaged with the amine group of exatecan. EPIC also buried more of the ligand apolar surface area than did the weakest NISE binder.

We also expressed 16 COMBS-designed proteins. Only three were found to bind to exatecan, with Kd values of 8 µM, 12 µM and 44 µM (Fig. 3f, Supplementary Fig. 15 and Supplementary Table 1). The design used as the input pose for NISE caused an increase in fluorescence polarization when mixed with exatecan. However, we could not fit the data to a single-site model and protein aggregation was detected by SEC. The three monomeric binders were the top-ranked designs by both self-consistency (ligand r.m.s.d. and backbone Cα r.m.s.d.) and ligand pLDDT (Supplementary Fig. 16). Two additional COMBS–Rosetta designs for the same input pose also bound to exatecan but with weaker affinities (Kd values of 15 µM and 43 µM; Supplementary Fig. 15), suggesting that the improvements by NISE stem from joint sequence–structure–ligand optimization rather than from pose selection alone.

The substantially higher affinities of NISE designs compared with those of COMBS cannot be attributed to the use of RFAA as a filter for quality, since designs from both methods incorporated self-consistent co-structures using RFAA. Instead, we attribute the tighter binding achieved by NISE to its ability to resculpt the backbone and binding site to make the overall sequence more compatible with both folding and binding (Fig. 3d). Indeed, the binding site of EPIC was almost completely changed compared with the input COMBS model, which contained several residues that formed hydrogenbonds with polar atoms of the ligand (Supplementary Fig. 13). The binding site of EPIC takes a more subtle balance between shape-complementary apolar packing and engagement with ligand polar groups, and only one of the original hydrogen-bonding residues (Asp132) was preserved.

EPIC is highly thermostable (Supplementary Fig. 17) and specific to the camptothecin class of drugs; its affinity is proportional to the steric and chemical similarity of exatecan (Fig. 3g,h and Supplementary Fig. 15). EPIC bound to exatecan at least 50-fold more tightly than did any other ligand tested. Fluorescence polarization experiments showed progressively weaker binding to FL118, belotecan and camptothecin (Kd values of 6 µM, 19 µM and 90 µM, respectively), with a clear structure–activity relationship for the fluorophenyl ring substituents and amine group of exatecan. Whereas elimination of either functional group was detrimental to binding, desolvation and packing of the fluoro and methyl groups (change in binding free energy, ΔΔG = 3.0 kcal mol−1) seemed to drive affinity more than did the interaction with the exposed amine (ΔΔG = 2.3 kcal mol−1). EPIC showed no appreciable binding to irinotecan, which contains a bulky prodrug substituent that is sterically incompatible with the designed binding pocket. As expected for a well-sculpted pocket, EPIC does not bind to drugs of other classes, such as anticoagulants (apixaban) and steroids (dexamethasone). Although we did not explicitly bias against off-target ligands during design (that is, negative design), assessment of the EPIC lineage in the NISE trajectory showed a consistently low ligand pLDDT when co-folded with the off-target ligand, camptothecin. By contrast, the ligand pLDDT of exatecan progressively increased in agreement with the experimental results, which showed that EPIC binds to exatecan most tightly (Supplementary Table 2).

Neural proofreading of the EPIC sequence

We wanted to determine whether the affinity of EPIC for exatecan could be improved through computation alone. Some strategies use an additional round of backbone diversification starting from the computational structure, followed by ligand docking and several rounds of sequence design to create and test thousands of new sequences1,6. Instead, we reasoned that LASErMPNN could refine the EPIC sequence in a more focused approach, noting that even a single amino-acid change could contribute considerably to the binding affinity of the designed protein. We therefore used LASErMPNN to ‘proofread’ the EPIC sequence by suggesting single amino-acid substitutions in the binding site that decrease its NLL (Fig. 4a–c).

Fig. 4: Neural proofreading by LASErMPNN increases the affinity of EPIC for exatecan.

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a, The computational model of EPIC (grey, RFAA prediction) was superimposed onto the same model after coordinate relaxation using the Amber molecular mechanics energy function (yellow). After energy minimization, exatecan moved further into the binding pocket and the backbone around the ligand moved slightly. These new backbone and ligand coordinates were input into LASErMPNN for neural proofreading. b, Neural proofreading of EPIC using LASErMPNN to compute the probability of an amino acid (Ai) given the entire context of the remainder of the designed sequence (A−i). Examples are shown for neural proofreading of residues 51 (top) and 97 (bottom). c, The single amino-acid substitutions have binding affinities that are more than ten-fold higher than that of EPIC, as measured by fluorescence anisotropy of exatecan (converted to the fraction of bound exatecan). Fits are shown as solid lines and [exatecan] = 50 nM. EPIC(Q51N), Kd = 8.0 ± 1.6 nM; EPIC(M97L), Kd = 7.4 ± 0.7 nM; and EPIC(Q51N/M97L), Kd  = 1.2 ± 0.2 nM. Data are mean and s.d. of three technical replicates. Kd values are mean and s.d. of optimal fits for three biological replicates. The quadratic form of a 1:1 binding model was used for fitting. Global fits are provided in the Supplementary Information. d–f, The X-ray crystal structure of EPIC is in close agreement with the design model. d, Overlay of the EPIC design model (grey) with the X-ray crystal structure of the exatecan–EPIC complex (EPIC in orange and exatecan in blue). The side chains of the core residues are shown. The enlarged binding site shows Asp132, Met97 and Gln51. e, The composite omit map for the structure of EPIC with a resolution of 2.0 Å (orange, left; contoured at 1σ) shows clear density (grey mesh) for the ligand (blue) and surrounding side chains. Similarly, the composite omit map for the structure of EPIC(Q51N) with a resolution of 2.2 Å (cyan, right; contoured at 1σ) shows clear density (grey mesh) for the ligand (green) and side chains, including Asn51. f, Side view of EPIC (orange) and EPIC(Q51N) (cyan) highlighting hydrogen-bonding interactions (dashed lines) between exatecan and Gln51 or Asn51. Exatecan moves slightly deeper into the pocket of EPIC(Q51N) to interact with Asn51.

There are a few reasons to expect that the same model that designed the EPIC sequence can also offer meaningful suggestions for alterations. During NISE, LASErMPNN designs sequences autoregressively at a high temperature, which means that only the last residue is chosen in the full sequence context. Revisiting each binding-site residue at a lower temperature, with all the other residues fixed, can shift the conditional distribution. We also used RFAA-predicted (and Amber-relaxed)[50] coordinates of the EPIC sequence as input, providing a subtly different structure that can influence model probabilities (Fig. 5a and Supplementary Fig. 18).

Using an Amber-relaxed, RFAA-predicted model, neural proofreading by LASErMPNN suggested several possible alterations to the EPIC binding site (Fig. 4a,b and Supplementary Fig. 18). We individually characterized the single amino-acid substitutions, EPIC(Q51N) and EPIC(M97L), each of which improved the NLL of EPIC. These alterations were prioritized because the residues were in direct contact with the ligand and represented both hydrophobic packing and polar hydrogen-bonded interactions. Fluorescence polarization experiments showed that each alteration increased the affinity of EPIC for exatecan by more than ten-fold (Kd values of 8.0 nM and 7.4  nM; ΔΔG ≅ −1.6 kcal mol−1; Fig. 4c and Supplementary Fig. 19). We combined these sterically compatible alterations in a double-mutant protein, EPIC(Q51N/M97L), and found that the substitutions are energetically additive, resulting in a 100-fold higher affinity than EPIC for exatecan (Kd = 1.2 nM; ΔΔG = −2.7 kcal mol−1). Another mutant, EPIC(G104M), was predicted to decrease affinity (increase NLL for the ligand-bound structure) while stabilizing the unbound state (decrease NLL for the ligand-free structure). As predicted, this mutant showed markedly weakened binding to exatecan (Kd = 57 µM; Supplementary Fig. 20). SEC experiments showed that each mutant protein remained monomeric (Supplementary Fig. 21). These results demonstrate the predictive power of proofreading for in silico affinity maturation without the use of an experimentally determined structure.

Interestingly, using models such as Boltz-1/2 and AF3, we observed a correlation between measured binding affinity and ligand pLDDT for EPIC and its proofread variants, further motivating the optimization of ligand pLDDT by NISE (Extended Data Figs. 6 and 7).

Crystal structures of EPIC and EPIC(Q51N)

The structure of EPIC was determined at a resolution of 2.0 Å and showed strong density for bound exatecan (Fig. 4d,e, Extended Data Table 2 and Supplementary Fig. 22). The binding mode of the ligand was as predicted, with expected deviation near the δ-lactone owing to RFAA having struggled to accurately model the correct bond angles for the ligand in this region (Fig. 4d and Extended Data Fig. 4; the aromatic-ring superposition of the originally modelled conformer onto that from RFAA shows a 0.9 Å heavy-atom r.m.s.d. at the δ-lactone). Boltz-1 and AF3 modelled the observed ligand conformer more accurately (Extended Data Fig. 5 and Supplementary Information). Asp132 accepts the intended hydrogen bond from the ligand amine, whereas Gln51 receives a hydrogen bond from the hydroxyl group of exatecan. However, the large side chain of Gln51 is too sterically constrained to donate a hydrogen bond to the carbonyl group of the lactone ring. The backbone agreed closely with the input backbone to LASErMPNN that was used directly upstream in the NISE design cycle (Cα r.m.s.d. 0.8 Å). Rotamers of both core and binding-site residues were accurately predicted by LASErMPNN (Fig. 4d). After superposition on binding-site residues (Cα within 8 Å of ligand heavy atoms, r.m.s.d. 0.4 Å), the ligand coordinates differed from those in the input structure by an average atomic displacement of less than 1 Å and a rotation of 14° around a central axis (Fig. 4d). Thus, the designed structure of EPIC was largely achieved. The structure of EPIC shares no similarity with that of topoisomerase I, including at the drug-binding site, showing that the NISE design algorithm can generate highly original de novo binders (Extended Data Fig. 3).

The structure of exatecan-bound EPIC(Q51N) (2.2 Å resolution) provided insight into the higher affinity of this complex compared with that of EPIC (Fig. 4e,f). The shorter side chain of Asn51 in EPIC(Q51N) draws exatecan slightly further into the pocket (by approximately 0.5 Å), and its carboxamide group engages in a bidentate hydrogen-bonding interaction with the hydroxyl group and lactone carbonyl of exatecan (Fig. 4f). As a result, the ligand is buried more deeply and further desolvated .

EPIC protects exatecan from hydrolysis

The lactone ring of exatecan hydrolyses rapidly under physiological conditions (Fig. 5a,b), decreasing to a 15% steady-state level in human plasma[41] within 10 h. The high concentration of HSA in plasma drives HSA binding to exatecan but does not prevent hydrolysis (Fig. 5c–e), consistent with a camptothecin-bound structure (PDB: 4L9K) in which the ring-open form is stabilized by nearby Arg residues (Supplementary Fig. 23). EPIC behaves oppositely: by sequestering the labile lactone ring from the solvent, EPIC and its mutants markedly repartition the equilibrium towards the intact bioactive form of the drug (Fig. 5d,e). Time-resolved absorption experiments using EPIC(Q51N/M97L) showed that more than 99% of exatecan stayed in the ring-closed form in PBS (pH 7.4) at room temperature for at least 50 h; there was no major hydrolysis product detected during this time. EPIC(Q51N/M97L) also protected exatecan in the presence of physiological concentrations (500 µM) of HSA (Supplementary Figs. 24 and 25). Thus, EPIC and its variants maintain a reservoir of the bioactive form of the drug, which has potential applications in drug delivery and targeting.

Fig. 5: EPIC protects exatecan from hydrolysis.

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a, Equilibrium populations of closed (C) lactone (blue) and open (O) carboxylate (red) forms of exatecan at room temperature (RT) in PBS at pH 7.4. b, C versus O states can be monitored spectroscopically by absorbance. The O state (red) has a characteristic red-shifted absorbance and a narrowing of the two prominent peaks. The absorbance difference (ΔOD) for the O–C spectrum is shown as a grey dashed line. The conversion from C to O forms is nearly complete within 6 h (3 half-lives) at room temperature and pH 7.4. c, Thermodynamic schemes for EPIC (top) and HSA (bottom). After binding, the lactone ring of exatecan becomes buried in EPIC, protecting it from hydrolysis. By contrast, exatecan binding to HSA does not protect the lactone ring from hydrolysis. P, protein. The dominant equilibrium species is underlined. d, Time-resolved absorbance difference spectra for exatecan alone (20 µM) and exatecan with HSA (500 µM), EPIC (20 µM) or EPIC(Q51N/M97L) (20 µM). The concentration of exatecan was 20 µM in all experiments. Experiments were performed at room temperature (295 K) in PBS (pH 7.4) and were initiated using 100% ring-closed exatecan from a DMSO stock. The key for each graph denotes the time delay (t; in h). Difference spectra were computed by subtracting the absorbance spectrum at t = 0 h (t = 1 h for HSA owing to baseline shifts at early time points) from absorbance spectra recorded at the stated time delays. Spectra are labeled using the predominant species at the longest time delay. Purple and green arrows are positioned at the corresponding wavelengths shown in e, illustrating the direction of spectral evolution. e, Kinetic traces at 358 nm and 390 nm for the absorbance difference plots in d. For clarity, more data points are shown in the kinetic traces than for the spectra. No significant spectral evolution was observed for EPIC(Q51N/M97L) up to 50 h. The equilibrium yield for O state (YO) was computed from a kinetic fit of a two-state mechanism. For HSA, the O state was considered as O + PO (HSA bound to ring-open exatecan). Representative single datasets in b, d and e are shown; results are robust across at least two biological replicates.

Design of apixaban binders using NISE

To test the robustness of the NISE algorithm, we applied it to a different protein fold and small-molecule target. As scaffolds, we chose the mixed α/β NTF2 fold. Like helical bundles, NTF2 backbones have previously been used in small-molecule binder design6,27,51 and contain a variable-sized pocket. As the target, we picked the factor Xa inhibitor, apixaban, a clinically approved anticoagulant52,53 (Fig. 6a). There remains a clinical need for an inexpensive antidote to apixaban that avoids the procoagulant effects observed with the recently withdrawn antidote, andexanet alfa[53], a catalytically inactive variant of factor Xa.

Although apixaban was a previous target for the COMBS design algorithm, which used four-helix bundles[4] (with a lowest Kd of 600 nM, two binders out of six tested designs), the fold and target were chosen, in part, because NTF2 scaffolds have been used more recently with LigandMPNN and Rosetta to design a binder to apixaban[6]. In that study, three cycles of LigandMPNN-based design and Rosetta minimization on thousands of apixaban-docked NTF2 backbones yielded four binders out of 9,024 tested designs (best Kd = 680 nM). The computational algorithm differs notably from NISE in that designs were not iteratively optimized to improve neural-network-based confidence metrics: no selection–expansion of the best candidates was performed during the design trajectory.

We set out to design an apixaban binder using NISE and a small, Boltz-2-predicted subset of 50 of the same NTF2 backbones6,51 used previously for this target (Fig. 6b). Using the same backbones and small-molecule target enabled a head-to-head comparison of the methods. Our design process used LASErMPNN for sequence design and replaced RFAA with Boltz-2 for co-structure prediction. We found that using Boltz reduced the number of sequences needed per round of NISE to pass self-consistency thresholds while maximizing design metrics such as ligand pLDDT (Supplementary Figs. 26 and 27).

Instead of using COMBS, we docked two crystallographic conformers of apixaban (from PDB: 6W70; Supplementary Fig. 28) into the NTF2 backbones using random rotations and translations (Supplementary Information). We filtered candidate ligand positions by depth of burial in the protein and steric clashing with the protein backbone. We clustered the remaining ligands by their docked orientations, then used LASErMPNN to design three sequences each and Boltz-2 to predict co-structures. We filtered these designs by self-consistency (protein Cα and ligand heavy-atom r.m.s.d. < 2.0 Å).

For the remaining co-structures, we performed an additional round of sequence design (five sequences each) and co-structure prediction, then ranked the remaining poses using a linear combination of ligand pLDDT and binary affinity probability (P(bind)) in Boltz-2. Unlike ligand pLDDT, P(bind) was explicitly trained to differentiate between binders and non-binders. Because this closely aligns with our design task, and because P(bind) is not trivially correlated with ligand pLDDT (Extended Data Fig. 8), we linearly combined P(bind) with ligand pLDDT to create a composite score for ranking designs. We found that additional selection by P(bind) further biased designs with a high ligand pLDDT towards a higher shape complementarity (Supplementary Fig. 29).

For a few top-ranked poses, we ran NISE trajectories to optimize the score (Fig. 6c), resulting in changes to the protein structure, sequence and ligand conformation (Supplementary Fig. 30). After pooling, filtering and ranking designs from the NISE trajectories (Supplementary Figs. 31 and 32 and Supplementary Information), we ultimately chose six protein sequences for experimental analyses (four distinct poses, 29% mean pairwise sequence identity).

We expressed the chosen proteins in E. coli and purified them. All proteins were monomeric as assessed by SEC (Supplementary Fig. 33), and five of the six designs bound tightly to apixaban in competitive binding experiments (Kd < 50 nM, Fig. 6e,f and Extended Data Fig. 9). The highest affinity binder, apixaban-binding protein exemplar (APEX; Fig. 6c,d), bound the drug extremely tightly (Kd = 80 pM; 95% confidence interval of 54–122 pM; Fig. 6e,f and Supplementary Fig. 34), rivaling the native target of apixaban[52], factor Xa (inhibition constant (Ki) = 80–700 pM), at one-third the size (13 kDa compared with 43 kDa). Binding of apixaban was specific: APEX showed no detectable signs of binding exatecan (Fig. 6e).

Fig. 6: Design of high-affinity apixaban-binding proteins using NISE and NTF2 folds.

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a, Chemical structure of apixaban and a FITC-appended derivative. b, An ensemble of 50 computationally generated NTF2 folds was used (coloured by Boltz-2 Cα pLDDT; blue > 95). c, Apixaban (blue) was placed in these NTF2 folds using rigid-body docking (Supplementary Information). NISE trajectories (14–28 iterations each) were then run for a few docked poses using Boltz-2 instead of RFAA. Six diverse NISE designs were chosen for experimental testing. The area indicated by the green box is shown in d. The design model is shown (in grey) for the highest-affinity protein, APEX. d, Close-up of the Boltz-2 predicted structure of APEX. APEX forms several hydrogen bonds with apixaban using both main-chain and side-chain interactions. A Cα hydrogen bond is also formed through a Gly residue. e, Fluorescence anisotropy experiments show tight, specific binding of APEX to apixaban–FITC (converted to the fraction of bound apixaban–FITC). f, Fluorescence anisotropy competition experiments using a constant amount of apixaban–FITC and APEX protein, incubated with varying amounts of apixaban. Data for three concentrations were fitted globally to determine the Kd of APEX with apixaban–FITC (e; Kd = 990 pM, 95% confidence interval 720–1,280 pM) and apixaban (f; Kd = 80 pM, 95% confidence interval 54–122 pM). Fits are shown as solid lines. In e, APEX shows no appreciable binding to exatecan (red; [exatecan] = 50 nM; the connecting line is not a fit and anisotropy values were converted to the bound fraction on the basis of maximum and minimum values from apixaban–FITC). Data in e and f are mean ± s.d. of three technical replicates. Kd values are the mean of 1,000 fits performed by bootstrapping of the optimal-fit residuals, and bounds are 95% confidence intervals derived from bootstrapped fits.

Analysis of the five verified binders showed a weak correlation between the ligand pLDDT from Boltz-2 and the measured binding affinity, whereas P(bind) discerned between on-target and off-target ligands but did not correlate with relative affinity (Extended Data Fig. 7c,d). We therefore asked whether these metrics capture binding specificity across the APEX design lineage. An assessment of the NISE trajectory leading to APEX showed a consistently low ligand pLDDT and P(bind) when co-folded with the off-target ligand, exatecan. It also showed an increasingly high ligand pLDDT and P(bind) when co-folded with the intended target, apixaban, in agreement with experimental results (Supplementary Table 3). The binding pocket of APEX is predicted to form a mixture of both main-chain and side-chain hydrogen-bonded interactions with apixaban (Fig. 6d). This is distinct from both factor Xa and a previously designed apixaban-binding helical bundle[4] (Supplementary Fig. 35), highlighting the novelty of the binding solution found using our approach.

Discussion

Tight coupling between two generative neural networks unlocks the zero-shot design of small-molecule binders, with one network used to sample broadly in sequence space and the other to model coupled changes in protein and ligand coordinates. As a result, NISE can accomplish design tasks that other methods struggled with. First, we used NISE to design helical bundles that bind to exatecan. All four experimentally tested designs bound the drug, contrasting with the state-of-the-art method, COMBS; the tightest COMBS binder was nearly 70-fold weaker. The tightest binder designed by NISE, EPIC, was further improved through neural proofreading and shown to protect the labile drug from hydrolysis for several days. To test NISE on an equal footing with an alternative algorithm using LigandMPNN and Rosetta1,6, we next tackled an identical design challenge: creating binders to apixaban using NTF2 backbones. Here, NISE was implemented with LASErMPNN and Boltz-2, and achieved a success rate (83%) that was more than three orders of magnitude higher than the alternative approach. This result is striking because we used the same previously published backbones as starting points for design. Moreover, tight binding was achieved by NISE using vastly different sequences and binding poses (Extended Data Fig. 9 and Supplementary Table 4). Notably, specialized docking methods were not needed: brute-force rigid-body docking was sufficient to seed NISE. The success rate alone does not fully capture the scale of the advance, as the highest affinity binder from NISE, APEX (Kd = 80 pM), bound to apixaban nearly 10,000-fold more tightly than did the best binder from LigandMPNN[6] or COMBS[4], rivalling even the native drug target[52], factor Xa.

NISE differs from previous protocols in a few important ways. First, it allocates most of its compute to extensively sample a few designs. We first perform a broad search to nominate initial self-consistent designs, then NISE executes a deep search to optimize them. Second, NISE uses only neural networks for closed-loop optimization. We investigated the effects of replacing the co-structure predictor with a traditional approach to modelling structure using Rosetta (Fig. 1c, right). In this case, the designs failed to optimize, suggesting that current empirical energy functions cannot fully capture the nuances of productive protein–ligand interactions. Geometrically, the gradient of the Rosetta energy lies partly orthogonal to that of P(structure, sequence, ligand conformation), so following it does not efficiently climb the joint distribution. Further iterations of any Rosetta-based loop would likely not overcome this limitation (Fig. 1c and Supplementary Figs. 9 and 10). By contrast, NISE samples directly from the learned distributions of two reciprocal neural networks, iteratively climbing to a higher probability mode in the joint probability manifold, making the designs look more similar to the training data (structures in the PDB).

NISE is agnostic to the specific networks used; as these models improve, so will NISE. Indeed, a RFAA-based assessment would have led us to discard our apixaban binders (Supplementary Fig. 36); the use of Boltz-2 was important to select binders for experimental testing. LigandMPNN could be substituted for LASErMPNN to produce similar aggregate metrics (Supplementary Figs. 37 and 38), although we observed that LigandMPNN tends to overpack designs (Extended Data Fig. 1 and Supplementary Figs. 39 and 40). Differences in packing might stem from differences in the sampling algorithm: whereas LigandMPNN predicts rotamers only after the entire sequence is designed, LASErMPNN jointly determines the sequence and rotamers.

We focused on optimizing ligand pLDDT during NISE trajectories. This parameter is highly correlated with other metrics, such as protein Cα pLDDT and interfacial predicted aligned error (iPAE), which are commonly used for protein-binder design15,23 (Extended Data Fig. 8 and Supplementary Fig. 41). Adding P(bind) from Boltz-2 increased shape complementarity moderately. Maximizing model confidence and agreement was necessary but perhaps not sufficient by themselves. We found that performing NISE iterations beyond the plateau in ligand pLDDT or score was important for downstream filtering by additional biophysical metrics. Indeed, both EPIC and APEX came from later iterations. Using LASErMPNN and Boltz-2, a typical NISE trajectory of 14 iterations only takes around 5 h on four A6000 graphics processing units (with the majority of compute dedicated to co-structure prediction).

We found that positive design—focusing on moulding the binding pocket only to the target ligand—was sufficient to install a high degree of binding specificity. We showed that both EPIC and APEX were specific: they had the highest affinity for their target ligands and did not bind to dissimilar off-target ligands (Figs. 3g and 6e). For similar on- and off-target molecules, an even wider gap in affinities could be produced explicitly using NISE by selecting representatives from each cycle that maximize both on-target ligand pLDDT (positive design) and the gap in ligand pLDDT (or predicted affinity) of on- and off-target ligands (negative design).

In this work, we initialised designs from two precomputed sets of designable backbone scaffolds, helical bundles and NTF2 folds. These scaffolds have pockets that can fit a variety of ligands for future design targets. As generative backbone models improve (such as RFdiffusion-All Atom[8], BoltzDesign1 (ref. [54]) and BoltzGen[55]), we could also use bespoke backbones conditioned on target ligands as inputs to NISE. Currently, we find that using a small, precomputed set of highly designable scaffolds is a computationally efficient approach to achieving high-quality poses. Using structures that can be encoded by many sequences—such as helical bundles and NTF2 folds—enables the efficient sampling of sequences that support both folding and binding. Success with four-helix bundles and mixed α/β NTF2 folds shows that NISE is a general algorithm that does not rely on any specific protein fold, and we expect NISE to work with any fold that can be modelled accurately by co-structure predictors.

The design of small-molecule binding proteins is now approaching the hit rates of the design of PCR primers, yet the rules of protein–ligand molecular recognition are vastly more complex than Watson–Crick base pairing. We have shown that modern deep neural networks, coupled with appropriate training data, can capture and distil this complexity, which can ultimately be extracted using design algorithms such as NISE. We anticipate that these capabilities will catalyse the use of bespoke proteins as rapidly generated reagents to manipulate biology at the single-molecule level.

Methods

See Supplementary Information.

Reporting summary

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

Data availability

Coordinates and data files for the X-ray crystal structures of exatecan-bound EPIC and exatecan-bound EPIC(Q51N) have been deposited in the PDB with accession codes 9NZE (EPIC) and 9NZG (EPIC(Q51N)). Other referenced PDB IDs include 6W70, 4JNJ, 4L9K and 8TN6. The X-ray crystal structure of the camptothecin derivative used to build exatecan conformers was retrieved from Cambridge Structure Database (CSD) deposition number 1504071. All data used to train LASErMPNN are publicly available from the PDB or the SPICE v.2.0.1 dataset[56]. The processed datasets used to train the LASErMPNN model are available at Zenodo[57].

Code availability

All model inference code, training code, processed datasets and dataset split information can be found at GitHub (https://github.com/polizzilab/LASErMPNN). A Google Colab notebook implementing LASErMPNN sequence design and proofreading can be found at https://colab.research.google.com/github/polizzilab/LASErMPNN/blob/main/run_lasermpnn.ipynb. An implementation of the NISE protocol using Boltz-1/2 can be found at GitHub (https://github.com/polizzilab/NISE). A minimal implementation of NISE using LASErMPNN and Boltz-2 can be found at https://colab.research.google.com/github/polizzilab/NISE/blob/main/NISE_LASErMPNN.ipynb. An implementation of the rigid ligand–protein docking protocol used to generate NISE inputs for apixaban binders can be found at GitHub (https://github.com/benf549/CARPdock). Analysis code and PDB files of designs can be found at Zenodo[58].

References

1. An, L. et al. Binding and sensing diverse small molecules using shape-complementary pseudocycles. Science 385, 276–282 (2024).

2. Dou, J. et al. De novo design of a fluorescence-activating β-barrel. Nature 561, 485–491 (2018).

3. Lu, L. et al. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 384, 106–112 (2024).

4. Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule-binding proteins. Science 369, 1227–1233 (2020).

5. Kortemme, T. De novo protein design—From new structures to programmable functions. Cell 187, 526–544 (2024).

6. Lee, G. R. et al. Small-molecule binding and sensing with a designed protein family. Nat. Commun. 17, 4533 (2026).

7. Chen, Y. et al. Emergence of specific binding and catalysis from a designed generalist binding protein. Nat. Chem. https://doi.org/10.1038/s41557-026-02125-6 (2026).

8. Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).

9. Marchand, A. et al. Targeting protein–ligand neosurfaces with a generalizable deep learning tool. Nature 639, 522–531 (2025).

10. Noske, J., Kynast, J. P., Lemm, D., Schmidt, S. & Höcker, B. PocketOptimizer 2.0: a modular framework for computer-aided ligand-binding design. Protein Sci. 32, e4516 (2023).

11. Glasgow, A. A. et al. Computational design of a modular protein sense-response system. Science 366, 1024–1028 (2019).

12. Leonard, A. C. et al. Rationalizing diverse binding mechanisms to the same protein fold: insights for ligand recognition and biosensor design. ACS Chem. Biol. 19, 1757–1772 (2024).

13. Zhu, J. et al. De novo design of transmembrane fluorescence-activating proteins. Nature 640, 249–257 (2025).

14. Zhang, Z., Shen, W. X., Liu, Q. & Zitnik, M. Efficient generation of protein pockets with PocketGen. Nat. Mach. Intell. 6, 1382–1395 (2024).

15. Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).

16. Torres, S. V. et al. De novo design of high-affinity binders of bioactive helical peptides. Nature 626, 435–442 (2024).

17. Vázquez Torres, S. et al. De novo designed proteins neutralize lethal snake venom toxins. Nature 639, 225–231 (2025).

18. Eguchi, R. R., Choe, C. A. & Huang, P.-S. Ig-VAE: generative modeling of protein structure by direct 3D coordinate generation. PLoS Comput. Biol. 18, e1010271 (2022).

19. Bhat, S. et al. De novo design of peptide binders to conformationally diverse targets with contrastive language modeling. Sci. Adv. 11, eadr8638 (2025).

20. Shuai, R. W., Ruffolo, J. A. & Gray, J. J. IgLM: infilling language modeling for antibody sequence design. Cell Syst. 14, 979–989 (2023).

21. Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070–1078 (2023).

22. Bennett, N. R. et al. Atomically accurate de novo design of antibodies with RFdiffusion. Nature 649, 183–193 (2026).

23. Pacesa, M. et al. One-shot design of functional protein binders with BindCraft. Nature 646, 483–492 (2025).

24. Zambaldi, V. et al. De novo design of high-affinity protein binders with AlphaProteo. Preprint at https://doi.org/10.48550/arXiv.2409.08022 (2024).

25. Bryant, P. & Elofsson, A. Peptide binder design with inverse folding and protein structure prediction. Commun. Chem. 6, 229 (2023).

26. Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850–858 (2025).

27. Tinberg, C. E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).

28. Polizzi, N. F. et al. De novo design of a hyperstable non-natural protein-ligand complex with sub-Å accuracy. Nat. Chem. 9, 1157–1164 (2017).

29. McCann, J. J. et al. Computational design of a sensitive, selective phase-changing sensor protein for the VX nerve agent. Sci. Adv. 8, eabh3421 (2022).

30. Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003).

31. Goudy, O. J., Nallathambi, A., Kinjo, T., Randolph, N. Z. & Kuhlman, B. In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models. Proc. Natl Acad. Sci. USA 120, e2307371120 (2023).

32. Wohlwend, J. et al. Boltz-1 democratizing biomolecular interaction modeling. Preprint at bioRxiv https://doi.org/10.1101/2024.11.19.624167 (2024).

33. Passaro, S. et al. Boltz-2: towards accurate and efficient binding affinity prediction. Preprint at bioRxiv https://doi.org/10.1101/2025.06.14.659707 (2025).

34. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

35. Besag, J. On the statistical analysis of dirty pictures. J. R. Stat. Soc. B 48, 259–302 (1986).

36. Dauparas, J. et al. Atomic context-conditioned protein sequence design using LigandMPNN. Nat. Methods 22, 717–723 (2025).

37. Eastman, P., Pritchard, B. P., Chodera, J. D. & Markland, T. E. Nutmeg and SPICE: models and data for biomolecular machine learning. J. Chem. Theory Comput. 20, 8583–8593 (2024).

38. Eastman, P. et al. SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials. Sci. Data 10, 11 (2023).

39. McConnell, D. B. Biotin’s lessons in drug design. J. Med. Chem. 64, 16319–16327 (2021).

40. Demonte, D., Drake, E. J., Lim, K. H., Gulick, A. M. & Park, S. Structure-based engineering of streptavidin monomer with a reduced biotin dissociation rate. Proteins 81, 1621–1633 (2013).

41. Minami, H. et al. Phase I and pharmacological study of a new camptothecin derivative, exatecan mesylate (DX-8951f), infused over 30 minutes every three weeks. Clin. Cancer Res. 7, 3056–3064 (2001).

42. Fu, Z., Li, S., Han, S., Shi, C. & Zhang, Y. Antibody drug conjugate: the “biological missile” for targeted cancer therapy. Signal Transduct. Target. Ther. 7, 93 (2022).

43. Li, W. et al. Synthesis and evaluation of camptothecin antibody-drug conjugates. ACS Med. Chem. Lett. 10, 1386–1392 (2019).

44. Grigoryan, G. & DeGrado, W. F. Probing designability via a generalized model of helical bundle geometry. J. Mol. Biol. 405, 1079–1100 (2011).

45. Petrenas, R. et al. Rapid assessment of size, shape, and chemical complementarity of ligands for computational protein design. Preprint at bioRxiv https://doi.org/10.1101/2025.06.30.662286 (2025).

46. Zhou, J. & Grigoryan, G. Rapid search for tertiary fragments reveals protein sequence-structure relationships. Protein Sci. 24, 508–524 (2015).

47. Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).

48. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

49. Leman, J. K. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods 17, 665–680 (2020).

50. Eastman, P. et al. OpenMM 8: molecular dynamics simulation with machine learning potentials. J. Phys. Chem. B 128, 109–116 (2024).

51. Yeh, A. H.-W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).

52. Luettgen, J. M., Knabb, R. M., He, K., Pinto, D. J. P. & Rendina, A. R. Apixaban inhibition of factor Xa: microscopic rate constants and inhibition mechanism in purified protein systems and in human plasma. J. Enzyme Inhib. Med. Chem. 26, 514–526 (2011).

53. Jankowski, W. et al. Engineering and evaluation of FXa bypassing agents that restore hemostasis following apixaban associated bleeding. Nat. Commun. 15, 3912 (2024).

54. Cho, Y., Pacesa, M., Zhang, Z., Correia, B. & Ovchinnikov, S. BoltzDesign1: inverting all-atom structure prediction model for generalized biomolecular binder design. Preprint at bioRxiv https://doi.org/10.1101/2025.04.06.647261 (2025).

55. Stark, H. et al. BoltzGen: toward universal binder design. Preprint at bioRxiv https://doi.org/10.1101/2025.11.20.689494 (2025).

56. Eastman, P. et al. SPICE 2.0.1 (2.0.1) [Data set]. Zenodo https://doi.org/10.5281/ZENODO.10975225 (2024).

57. Fry, B., & Polizzi, N. LASErMPNN Training Dataset (REDUCE Protonated PDB) [PDB File Format], Chunk 1/2 [Data set]. Zenodo https://doi.org/10.5281/ZENODO.17990180 (2025).

58. Polizzi, N. Designs and analysis code for neural iterative selection–expansion paper. Zenodo https://doi.org/10.5281/ZENODO.18308430 (2026).

Acknowledgements

N.F.P. and B.F. thank SBGrid for support of on-site computing infrastructure and crystallography software. N.F.P. and K.S. thank J. Alberta for help with equipment and resources for experiments and AI Proteins for sharing sumo-tag and sumo-protease expression plasmids for protein expression and purification, respectively. K.S. thanks R. Azad for help with setting up crystal trays for X-ray crystallography. N.F.P. thanks E. S. Fischer for helpful comments on the manuscript. This research used resource 17-ID-1 of the National Synchrotron Light Source II, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under contract no. DE-SC0012704.

Funding

N.F.P. and K.S. disclose support for this work from the National Institutes of Health (R00GM135519), Dana-Farber Cancer Institute (Innovation Research Fund), and the Chleck Family Foundation. B.F. discloses support from the National Science Foundation Graduate Research Fellowship Program.

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Author notes

1. These authors contributed equally: Benjamin Fry, Kaia Slaw

Authors and Affiliations

1. Harvard Graduate Program in Biophysics, Harvard University, Boston, MA, USA

Benjamin Fry

2. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

Benjamin Fry, Kaia Slaw & Nicholas F. Polizzi

3. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

Benjamin Fry, Kaia Slaw & Nicholas F. Polizzi

Contributions

B.F. performed the computations and trained LASErMPNN. K.S. performed the experiments. B.F., K.S. and N.F.P. performed the analyses. B.F., K.S. and N.F.P. wrote the paper.

Corresponding author

Correspondence to Nicholas F. Polizzi.

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Competing interests

B.F., K.S. and N.F.P. are inventors on a provisional patent application submitted by the Dana-Farber Cancer Institute for the design, composition and function of the proteins described in this study.

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Nature thanks Timothy Jenkins 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 LigandMPNN generates binding-site sequences that are more overpacked than those from LASErMPNN.

We assessed direct outputs and Rosetta-relaxed (sidechains only) outputs from both LASErMPNN and LigandMPNN on a diverse set of apixaban–NTF2 docked poses generated by CARPdock (30,000 generated sequences total across 10,000 poses). a, LigandMPNN packer places more protein heavy atoms within 5.0 Å of the ligand than LASErMPNN. b, After Rosetta fixed backbone, fixed ligand Relax protocol, LigandMPNN places more atoms within 5.0 Å of ligand heavy atoms. c,d, LASErMPNN sequences have lower (more favorable) total ligand energies than LigandMPNN sequences before and after a sidechain-only Rosetta Relax protocol (fixed ligand, fixed backbone). While LigandMPNN sequences have lower ligand attractive van der Waals energies (fa_atr) than LASErMPNN sequences before and after a sidechain-only Rosetta Relax protocol (e,f), LigandMPNN sequences have substantially higher ligand repulsive van der Waals energies (fa_rep) than LASErMPNN sequences both before and after a sidechain-only Rosetta Relax protocol (g,h). This makes the packed ligands from LigandMPNN higher energy on average (i.e. more energetically unfavorable) than those from LASErMPNN (c,d). For these computations, hydrogens were stripped from all protein and ligand structures. Both LASErMPNN and LigandMPNN models output packed sidechain coordinates directly. Direct MPNN outputs and Rosetta-Relaxed outputs were scored by Rosetta score_jd2 and Relax protocols, respectively. Differences in both means and empirical distributions are statistically significant for all panels (Student’s t-test, Mann-Whitney U-test, and Kolmogorov-Smirnov test; p < 1e-16; all statistical tests two-sided).

Extended Data Fig. 2 A LASErMPNN model trained on the streptavidin-holdout dataset outperforms a reimplementation of the LigandMPNN model trained on the same dataset.

We used LASErMPNN and a retrained LigandMPNN to design sequences for monomeric streptavidin (PDB 4jnj), with and without the bound biotin. The mean of overall sequence recovery and binding-site sequence recovery are plotted as red dashed lines. Each plot shows 10,000 sequences sampled by each model at overall temperature 0.1 and binding-site temperature 1.0 with (left) and without (right) the biotin ligand. Specifically, we designed 2,500 sequences for each of 4 biological assemblies (monomers) in the PDB 4jnj.

Extended Data Fig. 3 Binding sites of camptothecin in Topoisomerase I and exatecan in EPIC are dissimilar.

The crystal structures of Topoisomerase I (left, a) and EPIC (right, b) were aligned by ring atoms in camptothecin and exatecan, respectively. Each row shows the same view of the ligands (magenta) in the two proteins. Residues with heavy atoms within 5 Å of ligand heavy atoms are shown as stick representation. Residues that participate in H-bonding interactions with the ligand are labeled. The binding site of camptothecin in Topoisomerase I is highly dominated by DNA, in which the ligand is intercalated via π-stacking interactions. In contrast, the binding site of exatecan in EPIC is exclusively comprised of protein interactions, where aliphatic sidechains and Gly Cα atoms pack against exatecan.

Extended Data Fig. 4 Differences in the predicted and crystallographic conformers of exatecan.

Exatecan from the crystal structure (blue) of EPIC was superimposed onto exatecan from the RFAA-predicted structure (magenta) of EPIC via the rigidly linked core of the ligand (A, B, C, and D rings). After superposition, the heavy atoms of the lactone ring had an RMSD of 0.9 Å, with the largest displacement of 1.7 Å occurring between the carbonyl oxygens (yellow dash). The amine of exatecan also showed some discrepancy between the RFAA prediction and the crystal structure.

Extended Data Fig. 5 Performance of co-structure predictors on EPIC structure.

Comparison of X-ray structure of EPIC (orange protein, blue exatecan) with predicted co-structures (magenta) from RFAA, Boltz-1, and AF3. While all models accurately predict the protein backbone and sidechain conformations, AF3 predicts the co-structure most accurately of the three.

Extended Data Fig. 6 Correlation between predicted ligand confidence and measured binding affinity.

Protein–ligand co-structure prediction for various camptothecin-class compounds with EPIC and its mutants, using a, RFAA, b, Boltz-1, and c, AF3. RFAA was run with 100 recycles. Points labeled with a ligand name and without a protein name are co-folded with EPIC protein sequence. For RFAA, points in red were predicted to bind in a different binding mode than exatecan in the crystal structures of EPIC and EPICQ51N. For Boltz-1 and AF3, all ligands were predicted to bind in the same orientation as exatecan (consistent with crystal structures of exatecan-bound EPIC and EPICQ51N). If only including the three points predicted to bind in the observed crystallographic binding mode (blue points), the correlation between affinity and ligand pLDDT is strong (R^2 = 1) but statistically underpowered. The correlation drops dramatically if including the red points (R^2 = 0.46). Boltz-1 shows a stronger correlation and AF3 shows the strongest correlation with affinity. Data for non-binders (not shown): EPIC and dexamethasone, ligand pLDDT (70.4, 35.9, 72.7 for RFAA, Boltz-1, and AF3, respectively); EPIC and apixaban, ligand pLDDT (83.4, 52.5, 74.8 for RFAA, Boltz-1, and AF3, respectively).

Extended Data Fig. 7 Correlation between predicted metrics from Boltz-2 and binding affinity of NISE-designed proteins.

a, Protein–ligand co-structure prediction by Boltz-2 for various camptothecin-class compounds with EPIC and its mutants. Points labeled with a ligand name and without a protein name are co-folded with EPIC protein sequence. pAffinity is Boltz-2’s affinity_pred_value, which is the predicted log_10[IC_50(µM)], and P(bind) is Boltz-2’s affinity_probability_binary. Ligand pLDDT shows a weak positive correlation with experimentally determined binding affinity, pAffnity shows an even weaker correlation, and P(bind) shows an inverse correlation between binding affinity. ρ is the spearman rank correlation, R^2 is correlation from least squares fitting (black line). b, Same data as in a but with additional results from co-structure predictions with apixaban and dexamethasone (red). Note that apixaban-peg-FITC and dexamethasone-peg-FITC did not show any experimentally detectable binding, so marked affinities of 1000 µM here are only for illustrative purposes. c, d, Same analysis as in a, b but for the 6 NISE-designed apixaban binders. These results show that P(bind) is a suitable classifier for predicting binding vs non-binding (its training domain) but less so for predicting relative affinity between similar ligands.

Extended Data Fig. 8 Ligand pLDDT is correlated with other confidence metrics but not Boltz-2’s P(bind).

Ligand pLDDT is highly correlated with ipTM, ipAE, and protein Cα pLDDT (protein_plddt). It is weakly correlated with affinity_probability_binary, which we call P(bind), the predicted probability of binding generated by the ligand affinity prediction module. 3,000 datapoints are shown in each plot from a random subsample of 30,000 LASErMPNN sequences generated from 10,000 CARPdock poses of apixaban docked into our NTF2 scaffold library. We re-predicted co-structures with Boltz-2’s affinity module to extract P(bind).

Extended Data Fig. 9 NISE designs bind apixaban with high affinity.

Left, Protein–ligand co-structures predicted by Boltz-2 are shown for each NISE design. Middle, Binding curves monitoring fluorescence polarization of apixaban-FITC (50 nM) as a function of protein concentration. Right, Competition binding experiments monitoring fluorescence polarization of apixaban-FITC (50 nM) for constant protein concentration and varying apixaban concentration. Data (blue circles) is fit globally (red lines) for each row (i.e. each design) separately. The dissociation constants for apixaban-FITC and apixaban are fit simultaneously to a 1-to-1 binding model, sharing maximum anisotropy values. Minimum anisotropy was allowed to float locally but values did not differ significantly. Confidence intervals (95%) of the fit dissociation constants are shown in parentheses, derived from bootstrapped distributions of the fits for 500 trials. Blue points are the mean of three replicates with error bars representing standard deviation.

Extended Data Table 1 LASErMPNN Ablation Studies*

Extended Data Table 2 Data collection and refinement statistics (molecular replacement)

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Fry, B., Slaw, K. & Polizzi, N.F. Zero-shot design of drug-binding proteins via neural iterative selection−expansion. Nature 656, 237–249 (2026). https://doi.org/10.1038/s41586-026-10670-w

- Received: 22 April 2025

- Accepted: 15 May 2026

- Published: 24 June 2026

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Improving the generalizability of protein-ligand binding predictions with AI-Bind

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Atomic context-conditioned protein sequence design using LigandMPNN

Article Open access 28 March 2025

Improving de novo protein binder design with deep learning

Article Open access 06 May 2023

Designed to bind

- Francesco Zamberlan

Nature Chemical Biology Research Highlight 20 Jul 2026

Amendments & Corrections

- Author Correction: Authigenic mineral phases as a driver of the upper-ocean iron cycle

- Author Correction: OR7A10 GPCR engineering boosts CAR-NK therapy against solid tumours

- Retraction Note: Sub-second periodicity in a fast radio burst

Author Correction Published: 10 July 2026

Author Correction: Authigenic mineral phases as a driver of the upper-ocean iron cycle

Alessandro Tagliabue,

Kristen N. Buck,

Laura E. Sofen,

Benjamin S. Twining,

Olivier Aumont,

Philip W. Boyd,

Salvatore Caprara,

William B. Homoky,

Rod Johnson,

Daniela König,

Daniel C. Ohnemus,

Bettina Sohst &

…

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The Original Article was published on 02 August 2023

Correction to: Nature https://doi.org/10.1038/s41586-023-06210-5 Published online 2 August 2023

In the version of this article initially published, ref. 59 was incorrect; it should have appeared as follows: “Liu, X. & Millero, F. J. The solubility of iron hydroxide in sodium chloride solutions. Geochim. Cosmochim. Acta 63, 3487–3497 (1999)”. The reference has been updated in the HTML and PDF versions of the article.

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Present address: Department of Oceanography, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, Honolulu, HI, USA

Authors and Affiliations

1. School of Environmental Sciences, University of Liverpool, Liverpool, UK

Alessandro Tagliabue & Daniela König

2. College of Marine Science, University of South Florida, St. Petersburg, FL, USA

Kristen N. Buck & Salvatore Caprara

3. College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA

Kristen N. Buck

4. Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA

Laura E. Sofen & Benjamin S. Twining

5. LOCEAN, IRD-CNRS-Sorbonne Université-MNHN, IPSL, Paris, France

Olivier Aumont

6. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia

Philip W. Boyd

7. School of Earth and Environment, University of Leeds, Leeds, UK

William B. Homoky

8. Bermuda Institute of Ocean Sciences, St. George’s, Bermuda

Rod Johnson

9. Skidaway Institute of Oceanography, University of Georgia, Department of Marine Sciences, Savannah, GA, USA

Daniel C. Ohnemus

10. Department of Ocean and Earth Sciences, Old Dominion University, Norfolk, VA, USA

Bettina Sohst & Peter Sedwick

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Tagliabue, A., Buck, K.N., Sofen, L.E. et al. Author Correction: Authigenic mineral phases as a driver of the upper-ocean iron cycle. Nature 656, E1 (2026). https://doi.org/10.1038/s41586-026-10853-5

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Author Correction Published: 13 July 2026

Author Correction: OR7A10 GPCR engineering boosts CAR-NK therapy against solid tumours

Luojia Yang,

Paul A. Renauer,

Kaiyuan Tang,

Josh Saskin,

Liqun Zhou,

Charles Zou,

Seok-Hoon Lee,

Madison Fox,

Samuel Johnson-Noya,

Benedict Weiss,

Stephanie Deng,

Paris Fang,

Binfan Chen,

Giacomo Sferruzza,

Saba Fooladi,

Kai Zhao,

Daniel Park,

Feifei Zhang,

Jiayi Tu,

Jing Chen,

Jennifer Moliterno,

Murat Gunel,

Lei Peng &

…

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Correction to: Nature https://doi.org/10.1038/s41586-026-10149-8 Published online 25 February 2026

In the version of this article initially published, in the Extended Data Fig. 7k CAR-NK;OR7A10(STOP) group, mouse IVIS images of D21 and D27 were inadvertently duplicated. The figure is now updated in the HTML and PDF versions of the article.

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1. These authors contributed equally: Luojia Yang, Paul A. Renauer

2. These authors jointly supervised this work: Lei Peng, Sidi Chen

Authors and Affiliations

1. Department of Genetics, Yale University School of Medicine, New Haven, CT, USA

Luojia Yang, Paul A. Renauer, Kaiyuan Tang, Josh Saskin, Liqun Zhou, Charles Zou, Seok-Hoon Lee, Madison Fox, Samuel Johnson-Noya, Benedict Weiss, Stephanie Deng, Paris Fang, Binfan Chen, Giacomo Sferruzza, Saba Fooladi, Kai Zhao, Daniel Park, Feifei Zhang, Lei Peng & Sidi Chen

2. System Biology Institute, Yale University, West Haven, CT, USA

Luojia Yang, Paul A. Renauer, Kaiyuan Tang, Josh Saskin, Liqun Zhou, Charles Zou, Seok-Hoon Lee, Madison Fox, Samuel Johnson-Noya, Benedict Weiss, Stephanie Deng, Paris Fang, Binfan Chen, Giacomo Sferruzza, Saba Fooladi, Kai Zhao, Daniel Park, Feifei Zhang, Lei Peng & Sidi Chen

3. Center for Cancer Systems Biology, Yale University, West Haven, CT, USA

Luojia Yang, Paul A. Renauer, Kaiyuan Tang, Josh Saskin, Liqun Zhou, Charles Zou, Seok-Hoon Lee, Madison Fox, Stephanie Deng, Binfan Chen, Giacomo Sferruzza, Saba Fooladi, Kai Zhao, Feifei Zhang, Lei Peng & Sidi Chen

4. Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT, USA

Luojia Yang, Kaiyuan Tang, Josh Saskin, Liqun Zhou, Charles Zou, Madison Fox & Sidi Chen

5. Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA

Luojia Yang, Kaiyuan Tang, Josh Saskin, Charles Zou & Sidi Chen

6. Yale MD–PhD Program, Yale University School of Medicine, New Haven, CT, USA

Josh Saskin, Stephanie Deng & Sidi Chen

7. Immunobiology Program, Yale University, New Haven, CT, USA

Liqun Zhou, Madison Fox & Sidi Chen

8. Department of Medicine, The University of Chicago, Chicago, IL, USA

Jiayi Tu & Jing Chen

9. Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA

Jennifer Moliterno, Murat Gunel, Lei Peng & Sidi Chen

10. Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA

Sidi Chen

11. Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT, USA

Sidi Chen

12. Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA

Sidi Chen

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Yang, L., Renauer, P.A., Tang, K. et al. Author Correction: OR7A10 GPCR engineering boosts CAR-NK therapy against solid tumours. Nature 656, E2 (2026). https://doi.org/10.1038/s41586-026-10855-3

- Published: 13 July 2026

- Version of record: 13 July 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10855-3

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Retraction Note Published: 23 June 2026

Retraction Note: Sub-second periodicity in a fast radio burst

The CHIME/FRB Collaboration,

Bridget C. Andersen,

Kevin Bandura,

Mohit Bhardwaj,

P. J. Boyle,

Charanjot Brar,

Daniela Breitman,

Tomas Cassanelli,

Shami Chatterjee,

Pragya Chawla,

Jean-François Cliche,

Davor Cubranic,

Alice P. Curtin,

Meiling Deng,

Matt Dobbs,

Fengqiu Adam Dong,

Emmanuel Fonseca,

B. M. Gaensler,

Utkarsh Giri,

Deborah C. Good,

Alex S. Hill,

Alexander Josephy,

J. F. Kaczmarek,

Zarif Kader,

Joseph Kania,

Victoria M. Kaspi,

Calvin Leung,

D. Z. Li,

Hsiu-Hsien Lin,

Kiyoshi W. Masui,

Ryan Mckinven,

Juan Mena-Parra,

Marcus Merryfield,

B. W. Meyers,

D. Michilli,

Arun Naidu,

Laura Newburgh,

C. Ng,

Anna Ordog,

Chitrang Patel,

Aaron B. Pearlman,

Ue-Li Pen,

Emily Petroff,

Ziggy Pleunis,

Masoud Rafiei-Ravandi,

Mubdi Rahman,

Scott Ransom,

Andre Renard,

Pranav Sanghavi,

Paul Scholz,

J. Richard Shaw,

Kaitlyn Shin,

Seth R. Siegel,

Saurabh Singh,

Kendrick Smith,

Ingrid Stairs,

Chia Min Tan,

Shriharsh P. Tendulkar,

Keith Vanderlinde,

D. V. Wiebe,

Dallas Wulf &

…

Andrew Zwaniga

Nature volume 656, page 260 (2026)

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Retraction to: Nature https://doi.org/10.1038/s41586-022-04841-8 Published online 13 July 2022

The authors have retracted this article after identifying a crucial error: due to an unusual calibration problem, CHIME’s digital beams were incorrectly pointed when FRB 20191221A was detected. The sub-second periodicity that they associated with the putative fast radio burst FRB 20191221A was, in reality, a periodic signal from the known Galactic pulsar PSR J0248 + 6021. This directly impacts the conclusions drawn about the periodicity of FRB 20191221A and its possible origins. The authors note that the paper also reports the tentative detections of periodicity in two other sources, FRBs 20210206A and 20210213A, which are unaffected by the aforementioned short-lived digital mispointing issue. No other FRB detections published by CHIME/FRB were affected by this very unusual problem. The technical details for it have now been published by the author as a preprint [1].

All authors agree to this retraction.

References

1. FRB Collaboration. A series of unfortunate events: CHIME/FRB misclassification of a Galactic pulsar as a periodic fast radio burst https://doi.org/10.48550/arXiv.2606.07087.

Author information

Authors and Affiliations

1. Department of Physics, McGill University, Montréal, Québec, Canada

Bridget C. Andersen, Mohit Bhardwaj, P. J. Boyle, Charanjot Brar, Pragya Chawla, Jean-François Cliche, Alice P. Curtin, Matt Dobbs, Emmanuel Fonseca, Alexander Josephy, Zarif Kader, Victoria M. Kaspi, Marcus Merryfield, D. Michilli, Arun Naidu, Chitrang Patel, Aaron B. Pearlman, Emily Petroff, Ziggy Pleunis, Seth R. Siegel, Saurabh Singh, Chia Min Tan, Dallas Wulf & Andrew Zwaniga

2. McGill Space Institute, McGill University, Montréal, Québec, Canada

Bridget C. Andersen, Mohit Bhardwaj, P. J. Boyle, Charanjot Brar, Pragya Chawla, Jean-François Cliche, Alice P. Curtin, Matt Dobbs, Emmanuel Fonseca, Alexander Josephy, Zarif Kader, Victoria M. Kaspi, Marcus Merryfield, D. Michilli, Arun Naidu, Aaron B. Pearlman, Emily Petroff, Ziggy Pleunis, Seth R. Siegel, Saurabh Singh, Chia Min Tan, Dallas Wulf & Andrew Zwaniga

3. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

Kevin Bandura & Pranav Sanghavi

4. Center for Gravitational Waves and Cosmology, West Virginia University, Morgantown, WV, USA

Kevin Bandura, Joseph Kania & Pranav Sanghavi

5. Department of Physics, University of Toronto, Toronto, Ontario, Canada

Daniela Breitman

6. Dunlap Institute for Astronomy & Astrophysics, University of Toronto, Toronto, Ontario, Canada

Daniela Breitman, Tomas Cassanelli, B. M. Gaensler, Ryan Mckinven, C. Ng, Chitrang Patel, Ue-Li Pen, Ziggy Pleunis, Andre Renard, Paul Scholz & Keith Vanderlinde

7. David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto, Toronto, Ontario, Canada

Daniela Breitman, Tomas Cassanelli, B. M. Gaensler, Ryan Mckinven & Keith Vanderlinde

8. Cornell Center for Astrophysics and Planetary Science, Cornell University, Ithaca, NY, USA

Shami Chatterjee

9. Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada

Davor Cubranic, Fengqiu Adam Dong, Deborah C. Good, B. W. Meyers, J. Richard Shaw, Ingrid Stairs & D. V. Wiebe

10. Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada

Meiling Deng, Utkarsh Giri, Ue-Li Pen, Masoud Rafiei-Ravandi & Kendrick Smith

11. Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, Penticton, British Columbia, Canada

Meiling Deng, Alex S. Hill, J. F. Kaczmarek & Anna Ordog

12. Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada

Utkarsh Giri & Masoud Rafiei-Ravandi

13. Department of Computer Science, Math, Physics, & Statistics, University of British Columbia, Kelowna, British Columbia, Canada

Alex S. Hill & Anna Ordog

14. Department of Physics and Astronomy, West Virginia University, Morgantown, WV, USA

Joseph Kania

15. MIT Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Calvin Leung, Kiyoshi W. Masui, Juan Mena-Parra, D. Michilli & Kaitlyn Shin

16. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

Calvin Leung, Kiyoshi W. Masui, D. Michilli & Kaitlyn Shin

17. Cahill Center for Astronomy and Astrophysics, California Institute of Technology, Pasadena, CA, USA

D. Z. Li

18. Institute of Astronomy and Astrophysics, Academia Sinica, Taipei, Taiwan

Hsiu-Hsien Lin & Ue-Li Pen

19. Canadian Institute for Theoretical Astrophysics, Toronto, Ontario, Canada

Hsiu-Hsien Lin & Ue-Li Pen

20. Department of Physics, Yale University, New Haven, CT, USA

Laura Newburgh

21. Canadian Institute for Advanced Research, Toronto, Ontario, Canada

Ue-Li Pen

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

Emily Petroff

23. Sidrat Research, Toronto, Ontario, Canada

Mubdi Rahman

24. National Radio, Astronomy Observatory, Charlottesville, VA, USA

Scott Ransom

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

Shriharsh P. Tendulkar

26. National Centre for Radio Astrophysics, Pune, India

Shriharsh P. Tendulkar

Consortia

The CHIME/FRB Collaboration

Corresponding author

Correspondence to D. Michilli.

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Cite this article

The CHIME/FRB Collaboration., Andersen, B.C., Bandura, K. et al. Retraction Note: Sub-second periodicity in a fast radio burst. Nature 656, 260 (2026). https://doi.org/10.1038/s41586-026-10799-8

- Published: 23 June 2026

- Version of record: 23 June 2026

- Issue date: 06 August 2026

- DOI: https://doi.org/10.1038/s41586-026-10799-8

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Amendments & Corrections

Author Correction: Authigenic mineral phases as a driver of the upper-ocean iron cycle

Author Correction: OR7A10 GPCR engineering boosts CAR-NK therapy against solid tumours

Retraction Note: Sub-second periodicity in a fast radio burst