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This Week

- Don’t sleepwalk from computer-vision research into surveillance

- In the face of anti-science politics, silence is not without cost

- Missile defence won’t prevent the health crises that rock global security

- We need to predict the people disasters will hit, not just the places

EDITORIAL 25 June 2025

Don’t sleepwalk from computer-vision research into surveillance

The output of computer-vision research is overwhelmingly aimed towards monitoring humans. The potential ethical implications need more scrutiny.

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London’s police force is expanding its use of facial-recognition technology in public places.Credit: Leon Neal/Getty

The nature of scientific progress is that it sometimes provides powerful tools that can be wielded for good or for ill: splitting the atom and nuclear weapons being a case in point. In such cases, it’s necessary that researchers involved in developing such technologies participate actively in the ethical and political discussions about the appropriate boundaries for their use.

Computer vision is one area in which more voices need to be heard. This week in Nature, Pratyusha Ria Kalluri, a computer scientist at Stanford University in California, and her colleagues analysed 19,000 papers published between 1990 and 2020 by the Conference on Computer Vision and Pattern Recognition (CVPR) — a prestigious event in the field of artificial intelligence (AI) concerned with extracting meaning from images and video. Studying a random sample, the researchers found that 90% of the papers and 86% of the patents that cited those articles involved imaging humans and their spaces (P. R. Kalluri et al. Nature https://doi.org/10.1038/s41586-025-08972-6; 2025). Just 1% of papers and 1% of patents involved only non-human data. This study backs up with clear evidence what many have long suspected: that computer-vision research is being used mainly in surveillance-enabling applications. The researchers also found that papers involving analysis of humans often refer to them as ‘objects’, which obfuscates how such research might ultimately be applied, the authors say.

This should give researchers in the field cause to interrogate how, where and by whom their research will be applied. Regulators and policymakers bear ultimate responsibility for controlling the use of surveillance technology. But researchers have power too, and should not hesitate to use it. The time for these discussions is now.

Computer vision has applications ranging from spotting cancer cells in medical images to mapping land use in satellite photos. There are now other possibilities that few researchers could once have dreamed of — such as real-time analysis of activities that combines visual, audio and text data. These can be applied with good intentions: in driverless cars, facial recognition for mobile-phone security, flagging suspicious behaviour at airports or identifying banned fans at sports events.

This summer, however, will see the expanding use of live facial-recognition cameras in London; these match faces to a police watch list in real time. Hong Kong is also installing similar cameras in its public spaces. London’s police force say that its officers will physically be present where cameras are in operation, and will answer questions from the public. Biometric data from anyone who does not have a match on the list will be wiped immediately.

But AI-powered systems can be prone to error, including biases introduced by human users, meaning that the people already most disempowered in society are also the most likely to be disadvantaged by surveillance. And, ethical concerns have been raised. In 2019, there was backlash when AI training data concerning real people were found to have been created without their consent. There was one such case at Duke University in Durham, North Carolina, involving two million video frames of footage of students on campus. Others raised the alarm that computer-vision algorithms were being used to target vulnerable communities. Since then, computer-science conferences, including CVPR and NeurIPS, which focuses on machine learning, have added guidelines that submissions must adhere to ethical standards, including taking special precautions around human-derived data. But the impact of this is unclear.

According to a study from Kevin McKee at Google DeepMind, based in London, less than one-quarter of papers involving the collection of original human data, submitted to two prominent AI conferences, outlined that they had applied appropriate standards, such as independent ethical review or informed consent (K. R. McKee IEEE-TTS 5, 279–288; 2024). Duke’s now-retracted data set still racks up hundreds of mentions in research papers each year.

Science that can help to catch criminals, secure national borders and prevent theft can also be used to monitor political groups, suppress protest and identify battlefield targets. Laws governing surveillance technologies must respect fundamental rights to privacy and freedom, core to the Universal Declaration of Human Rights.

In many countries, legislation around surveillance technologies is patchy, and the conditions surrounding consent often lie in a legal grey area. As political and industry leaders continue to promote surveillance technologies, there will be less space for criticism, or even critical analysis, of their planned laws and policies. For example, in 2019, San Francisco, California, became the first US city to ban police use of facial recognition. But last year, its residents voted to boost the ability of its police force to use surveillance technologies. Earlier this year, the UK government renamed its AI Safety Institute; it became the AI Security Institute. Some researchers say that a war in Europe and a US president who supports ‘big tech’ have shifted the cultural winds.

For Yves Moreau, a computational biologist at the Catholic University of Leuven in Belgium who studies the ethics of human data, the rise of surveillance is an example of ‘technological somnambulism’, in which humans sleepwalk into use of a technology without much reflection.

Publishers should enforce their own ethical standards. Computer-vision researchers should refer to humans as humans, making clear to all the possible scope of their research’s use. Scientists should consider their own personal ethics and use them to guide what they work on. Science organizations should speak out on what is or is not acceptable. “In my own work around misuse of forensic genetics, I have found that entering the social debate and saying ‘this is not what we meant this technology for’, this has an impact,” says Moreau. Reflection must happen now.

Nature 642, 839-840 (2025)

doi: https://doi.org/10.1038/d41586-025-01965-5

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EDITORIAL 24 June 2025

In the face of anti-science politics, silence is not without cost

More scientific leaders need to speak out about anti-science agendas and threats to academic freedom across the world.

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The Pontifical Academy of Sciences has expressed concern about the impact of political interference on science around the world.Credit: Piotr Bednarczyk/Shutterstock

The administration of US President Donald Trump is pursuing a destructive agenda against science. The White House seems to be intent on telling funding agencies what they can fund, universities who they can hire, and researchers what they can study. Research grants are being slashed, with a particular emphasis on science that goes against the administration’s ideological line, be it on topics such as climate change or on the inclusion and support of under-represented groups in society. Failure to comply with these edicts has been met with threats to hike taxes on university endowments and to restrict the pipeline of international students, who have long been instrumental to the success of US science and innovation.

The administration might not get its way on everything, if legal challenges against its directives succeed or if Congress rediscovers its role as a check on the executive. But if Congress doesn’t step up, the long-term damage to science will be profound. In the next fiscal year, the National Science Foundation and the Environmental Protection Agency are in line to lose more than half of their budgets, and the National Institutes of Health 40% of its funding.

The scale of these actions is unprecedented. Earlier this year, we urged scientific leaders to take a stand, to support at-risk colleagues and to do more to make citizens aware of the consequences of such recklessness. These attacks on science will harm lives and livelihoods, the economy and public health, and the environment on which we all depend. Globally influential scientific organizations have a particular responsibility to speak out because, as we wrote at the time, “an assault on science anywhere is an assault on science everywhere”.

Unfortunately, many responses have been vague, at best. Some organizations, such as the InterAcademy Partnership, a network of the world’s science academies, and the World Academy of Sciences, have told us they have no plans to make any statements. The Paris-based International Science Council said last week that international scientific collaboration is vulnerable. London’s Royal Society said in February that it would “use its voice and the expertise of our Fellows to resist the various challenges to science”. A more vocal “statement of concern” was issued last week by the Pontifical Academy of Sciences in Vatican City and summarized in correspondence to Nature by academy president Joachim von Braun.

The academy’s members, who are scientists from all over the world, acknowledge that “scientific institutions are being undermined through political pressure, budgetary and workforce cuts, and censorship. Evidence-based findings are ignored or openly mis-represented”. The document adds that: “In extreme cases, scientists are harassed, marginalized, or personally threatened for their work.”

The statement does not single out the United States, pointing out that “attacks are not confined to a particular region or political ideology; they are surfacing in democracies and authoritarian systems alike, in the global North and South”. Among other things, it calls on scientists to uphold rigour and transparency, politicians and policymakers to protect the independence of institutions, and religious and moral leaders to play their part in restoring public trust in science as a force for good.

This statement will come as a surprise to some, and perhaps be greeted with scepticism, given the uneasy historical relationship between the Catholic Church and science. Such reactions are out of date. Although the pontifical academy’s members have the backing of the papacy, the academy has had the freedom to study science and its relationship with the environment and society, without interference, for at least 85 years.

We recognize that not all scientific leaders are in a position to be able to speak out, particularly those in countries where doing so could incur a penalty — or even punishment. That is why our call is to international scientific organizations. Academies in countries where the freedom to dissent is protected should also make their concerns known. All need to be aware that silence is also not without cost.

The United States is not the first country to try to dictate the kind of science that can be done within its borders, and it won’t be the last. But in terms of its national and global impact, the United States stands apart. The country has been a global powerhouse of scientific research since the Second World War, and has long been a model of scientific freedom that has attracted the best and brightest to its shores.

What happens in the United States is watched closely by people in positions of power around the world. The playbook being written in Washington DC offers a template to those who wish to follow the same path in their own countries. That in itself is a reason why everyone in a position of responsibility in world science needs to gather evidence to rebut authoritarian, anti-science narratives, and speak out.

The pontifical academy is to be congratulated for using its voice at this crucial time, and for inviting its international counterparts to join it in a global coalition of stakeholders to work together “across nations, sectors, and beliefs — to defend the right to seek and speak scientific truth”. Having the freedom to defend science is a privilege. Those who have it owe it to everyone the world over to make their voices heard.

Nature 642, 840 (2025)

doi: https://doi.org/10.1038/d41586-025-01966-4

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WORLD VIEW 25 June 2025

Missile defence won’t prevent the health crises that rock global security

As was realized after the Second World War, peace and prosperity stem from partnership and sustained investment in human development.

By

Nelson Evaborhene

As geopolitical tensions rise, high-income countries (HICs) are directing more resources towards defence and fewer towards international development. But addressing many of the most destabilizing challenges of our time requires building resilient societies, not weapons.

Under President Donald Trump, the United States has suspended multiple foreign-aid programmes and taken steps to withdraw from the World Health Organization. Trump is proposing discretionary-budget increases of 13% for the defence department and 65% for homeland security.

Other countries have likewise signalled a retreat from multilateral development. The United Kingdom plans to reduce its official development assistance from 0.7% of its gross national income to just 0.3% from 2027; France has cut its aid budget by 35%; and Germany’s incoming coalition plans to reduce humanitarian aid by 50%.

At the 2024 United Nations Climate Change Conference (COP29), countries pledged to mobilize US$1.3 trillion annually by 2035 to fund efforts to address climate change. Yet, without robust development support, many low- and middle-income countries (LMICs) will lack the institutional infrastructure to access, deploy or govern these funds effectively.

In global health, these cuts are coming as the consequences of previous reductions in aid are already being felt. A 70% decline in official development assistance to Africa since 2021 has contributed to a $220-million funding deficit for countries’ response to the mpox outbreak of the past few years.

After the Second World War, the United States approved funding — which would eventually rise to more than $12 billion — for the rebuilding of western Europe. Known as the Marshall Plan, it was not merely an act of generosity. It was a calculated effort to rebuild economies, stabilize democracies and contain the spread of extremism.

Building resilient societies abroad will similarly make people safer in their own nations today. No missile-defence system could have stopped the COVID-19 pandemic, for instance, which destabilized economies and overwhelmed health systems, including in militarily powerful nations. Similarly, climate-related disasters, environmental degradation and lack of investment in education are driving armed conflict and contributing to a global rise in nationalist political movements.

Development must be reclaimed as a crucial means for sustaining long-term global and national security.

This means world leaders and societies recognizing that investments in public-health infrastructure, education and climate adaptation are not acts of charity or means to exert soft power, but essential strategic investments. It means treating development as a cornerstone of global security in national budgets and shielding development finance from short-term political pressures. This could be done, for instance, by codifying aid targets into law.

Some countries, such as Sweden and Norway, have consistently upheld high levels of official development assistance through long-standing political consensus, even without legal mandates. But in many HICs, development assistance is often subject to cuts when domestic priorities shift or fiscal pressures mount.

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Nature 642, 841 (2025)

doi: https://doi.org/10.1038/d41586-025-01967-3

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Competing Interests

The author declares no competing interests.

WORLD VIEW 24 June 2025

We need to predict the people disasters will hit, not just the places

Local authorities can deal with natural hazards more effectively if they know ahead of time who will be affected.

By

Jonathan Colmer

Whether for hurricanes, heatwaves, wildfires or floods, preparing for disasters and issuing warnings and evacuation orders are essential to save lives. However, not everyone is able to respond to the same degree. Illness, disability, lack of transport and financial constraints can all exacerbate the risk for people in vulnerable groups. This can lead to disproportionate consequences. For example, between 1930 and 2015, people aged over 65 accounted for 46% of excess deaths associated with hurricanes in the United States (R. Young and S. Hsiang Nature 635, 121–128; 2024).

To help the people most at risk, local authorities and first responders need to know in advance who lives in the areas most affected, so they can deploy targeted resources. But they cannot easily access this information.

To demonstrate what could be possible, my colleagues and I at the Environmental Inequality Lab have developed a framework for rapid-response analysis. Using the best available data, this method can deliver timely insights to local authorities and first responders before, during and after disasters.

Our approach combines demographic and socio-economic data provided by the US Census Bureau with forecasts released by the US National Hurricane Center and Weather Prediction Center. The population data are privacy-protected and consist of aggregated counts by age, race, sex and income decile, mapped in grid cells of approximately one square kilometre. This provides localized insights while ensuring that no individual or household can be identified.

Bringing these data sets together tells us in real time where and when a disaster is expected to strike, as well as who is likely to be most exposed to risks such as high winds, storm surges and flooding. We are developing similar tools to evaluate the risks of wildfires and heatwaves.

We tested our work flow during Hurricane Milton, which made landfall on Florida’s west coast late on 9 October 2024. On 8 and 9 October, we released a report (see go.nature.com/4jwkuro) showing who was forecast to be exposed to the greatest hazards. The next week, we released a follow-up report (see go.nature.com/3hicifv) comparing the forecasts with data on the storm’s actual extent. In this case, the forecasts turned out to be a reasonable guide to what happened.

Our analysis highlighted how different populations faced different levels of risk. The people who experienced hurricane-force winds and excessive rainfall during Milton were disproportionately older, disproportionately Hispanic and disproportionately relatively poor (falling in the bottom 20% of the national income distribution).

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Nature 642, 842 (2025)

doi: https://doi.org/10.1038/d41586-025-01968-2

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Competing Interests

The author declares no competing interests.

News in Focus

- Judge rules against NIH grant cuts — and calls them discriminatory

- Who is on RFK Jr’s new vaccine panel — and what will they do?

- First ever skull from ‘Denisovan’ reveals what ancient people looked like

- Mysterious link between Earth’s magnetism and oxygen levels baffles scientists

- How you breathe is like a fingerprint that can identify you

- Tiny human hearts grown in pig embryos for the first time

- Eureka! The brain science behind lightbulb moments

- How to make America healthy: the real problems — and best fixes

NEWS 16 June 2025

Judge rules against NIH grant cuts — and calls them discriminatory

The decision means that the US biomedical agency has to restore funding to hundreds of research projects, but the government is likely to appeal.

By

Max Kozlov

On 16 June at the US District Court for the District of Massachusetts in Boston, a judge ruled that the Trump administration’s slashing of some research grants is illegal.Credit: Sean Pavone/Alamy

A US judge has ordered hundreds of terminated research projects at the US National Institutes of Health (NIH) to be reinstated, calling the processes that led to their cancellation “bereft of reasoning — virtually, in their entirety”.

The ruling came about two hours after a tense hearing in which lawyers representing US researchers and a coalition of 16 states presented arguments, for the first time, that the NIH’s massive cuts to research are illegal. Since Republican President Donald Trump took office earlier this year, the agency has cancelled funding for a long list of projects, including research on diversity, equity and inclusion (DEI), people from sexual and gender minorities (LGBTQ+) and COVID-19.

In a blistering 15-minute rebuke after issuing his ruling, judge William Young of the US District Court for the District of Massachusetts in Boston excoriated the Trump administration for targeting research on the health of LGBTQ+ individuals and people from minority ethnic groups.

“This represents racial discrimination and discrimination against America’s LGBTQ community. I would be blind not to call it out,” said Young, who was appointed by former US president Ronald Reagan, a Republican. “I’ve been on the bench for 40 years — I’ve never seen government racial discrimination like this.”

This ruling, which the Trump administration is likely to appeal, means that the NIH will begin disbursing billions of dollars of cancelled funding. “I’m thrilled and overjoyed,” Katie Edwards, an interdisciplinary public-health specialist at the University of Michigan in Ann Arbor, told Nature. Edwards had six NIH grants axed and was one of the researchers who sued the agency. “What the judge said today was spot on — and it highlights the crisis we’re facing right now, where racial and ethnic minorities are being erased,” she said.

The order will restore funding only to the scientists named in the lawsuits and in the 16 states that sued the government — or about 800 of the more than 2,400 NIH projects that have been cut. These lawsuits also did not touch on wholesale grant cuts at institutions, such as Harvard University in Cambridge, Massachusetts, that the Trump administration has alleged did not protect their students from discrimination, including antisemitism, on campus.

Andrew Nixon, a spokesperson for the NIH’s parent agency, the Department of Health and Human Services (HHS), says the agency “stands by its decision to end funding for research that prioritized ideological agendas over scientific rigour and meaningful outcomes for the American people” and that it is “exploring all legal options, including filing an appeal”. Neither the HHS nor the NIH, which is the world’s largest public funder of biomedical research, responded to Nature’s query about whether and when they would follow the court’s order.

A question of procedure

In an unprecedented move, the NIH began to terminate research grants in late February because they no longer met “agency priorities”. (It typically cancels only a handful of projects each year, in response to serious concerns about misconduct or fraud — and does so only as a last resort, after taking other actions, such as suspension.)

Lawsuits challenging these cuts were filed in early April, arguing that they were unlawful because the NIH did not follow proper procedures and did not give adequate reasoning for their cancellation.

Representatives of the HHS and billionaire Elon Musk’s US Department of Government Efficiency issued directives to NIH staff members to slash grants on disfavoured topics, and they provided boilerplate language for the employees to use in termination letters to scientists, Nature previously reported. Today, the judge ruled that these directives were “arbitrary and capricious” and, thus, illegal.

Termination letters sent to some scientists called their projects “often unscientific” or deemed them to support “unlawful discrimination”, without backing up the claims. At today’s hearing, Young asked the government’s lawyers whether they could provide any examples of grants that supported these claims. “There’s nothing I can point the court to,” a lawyer responded.

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Nature 642, 845-846 (2025)

doi: https://doi.org/10.1038/d41586-025-01914-2

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NEWS 12 June 2025 Update 12 June 2025 Correction 17 June 2025

Who is on RFK Jr’s new vaccine panel — and what will they do?

Critics fear that the anti-vaccine health-department leader’s picks for a crucial US committee will be a ‘disaster for public health’.

By

Heidi Ledford &

Rachel Fieldhouse

Eight new members have been appointed to the powerful Advisory Committee on Immunization Practices, which plays a large part in deciding which vaccines are administered to US children and adults. Credit: Joseph Prezioso/AFP/Getty

An emergency physician, critics of COVID-19 vaccines and an obstetrician who once advised a supplement company are among the eight advisers hand-picked by vaccine sceptic Robert F. Kennedy Jr, the head of the US Department of Health and Human Services (HHS), to provide advice on vaccines to the federal government.

Kennedy announced his roster for the influential Advisory Committee on Immunization Practices (ACIP) on 11 June — just two days after he fired all 17 existing members of the committee, which he accused of “malevolent malpractice”. The ACIP advises US public-health officials as to who should receive approved vaccines, and when. Those recommendations are often used to guide whether public and private health-insurance programmes will pay for the vaccines.

Kennedy has pledged that the ACIP will re-evaluate the US vaccine schedule for children — the list of which vaccines children should get and when they should get them. The shake-up of the committee is “a major step towards restoring public trust in vaccines”, Kennedy said in a post on the social-media platform X.

Several of the new ACIP members have expressed public support for vaccines. But a number have also expressed scepticism: one serves on the board of an anti-vaccination organization, and a second has been a prominent sceptic of COVID-19 vaccines on social media. As first reported by the biomedical news outlet STAT, Kennedy included four of the new committee members in the dedication to his 2021 book The Real Anthony Fauci: Bill Gates, Big Pharma, and the Global War on Democracy and Public Health.

“This is a disaster for public health,” says Adam Ratner, a paediatric infectious-diseases physician in New York City. “It has the potential to set us back decades.” The HHS did not respond to a request for comment before this article was published.

Far-reaching implications

Infectious-disease specialists worry about the implications if the ACIP were to vote to recommend fewer vaccines or fewer doses than are currently advised. Paul Offit — an infectious-diseases paediatrician at the Children’s Hospital of Philadelphia in Pennsylvania who co-invented a rotavirus vaccine and served on the ACIP from 1998 to 2003 — says insurers don’t have to cover vaccines that aren’t recommended by the committee. And “doctors or pharmacists who give vaccines may feel that they would be liable for giving that vaccine”, he adds.

Even people who can pay for vaccines out of pocket might find them harder to get without an ACIP recommendation, says Arthur Reingold, an epidemiologist at the University of California, Berkeley, because overall demand would decrease, potentially leading some pharmacies to stop stocking them.

Offit notes that at the ACIP’s next meeting, which starts on 25 June, the panel is scheduled to discuss whether to recommend vaccines for COVID-19, human papillomavirus, meningococcal disease and respiratory syncytial virus. “These are established vaccines, and we’re voting on them?” Offit says. “The entire childhood and adult immunization schedule is on the table.”

Kennedy has criticized the previous ACIP line-up for what he calls rampant conflicts of interest. On 12 June, the biopharma news outlet Endpoints News reported that two members of Kennedy’s ACIP roster, Robert Malone and Martin Kulldorff, have received payments for serving as expert witnesses in lawsuits against US pharmaceutical company Merck concerning two of its vaccines. Malone said on X that this work ended six years ago.

Kennedy has also said that past committee members did not demand safety trials that he considers to be adequate, with control groups that received a placebo, before recommending vaccines.

But many vaccine studies do include placebo controls, Ratner says, unless doing so is unethical. And Reingold, who has previously served on the ACIP, says that the committee has strict policies regarding conflicts of interest and that members must recuse themselves from any vote that might pose a conflict. “The issue of potential conflicts of interest has been radically overblown and unfairly called into question the objectivity of this panel,” he says.

Offit says several independent groups have reviewed previous ACIP members and found no conflicts of interest. “Now, the conflict of interest is real, because these folks are indebted to RFK Jr, who just gave them this position,” he says.

Vetting process

Researchers are also concerned about a loss of expertise on the panel. With a dearth of vaccine specialists, the committee’s new line-up is “disturbing”, says Nancy Bennett, a public-health specialist at the University of Rochester Medical Center in New York.

In the past, members were nominated and then vetted by staff at the US Centers for Disease Control and Prevention (CDC) before their names were sent to the head of the CDC and, finally, the head of the HHS for approval. Bennett says the vetting process sometimes took years. “The ACIP was meant to be composed of people with deep expertise in the area,” Ratner says. “That’s what we have lost.”

Here, Nature takes a look at all eight new members of the panel.

Joseph Hibbeln

Joseph Hibbeln is a psychiatrist and neuroscientist who once worked as a researcher at the US National Institutes of Health. In the past few years, his papers[1] have focused on the connection between nutrition and various disorders, including mental-health conditions, and his LinkedIn profile states that twenty-first-century diets provide “inadequate brain nutrients and are likely contributing to the high burden of mental illnesses worldwide”. A search of PubMed, a database of biomedical papers, did not bring up any papers he has authored about vaccines or infectious disease. He did not respond to a request for comment.

Martin Kulldorff

Martin Kulldorff is a Swedish epidemiologist and senior scholar at the Brownstone Institute, a think tank based in West Hartford, Connecticut, that formed “to provide an independent voice for personal liberty” and to oppose lockdown policies instituted by public officials during the COVID-19 pandemic. Along with Jayanta Bhattacharya, the current head of the US National Institutes of Health, Kulldorff was among the writers of the 2020 Great Barrington Declaration, which advocated against COVID-19 lockdowns except for members of vulnerable populations and drew much pushback from the medical community.

Last year, Kulldorff wrote in the policy magazine City Journal that he had been fired from Harvard University in Cambridge, Massachusetts, for refusing a COVID-19 vaccine, even though he already had immunity as a result of being infected. He also wrote that “vaccines are a vital medical invention, allowing people to obtain immunity without the risk that comes from getting sick”, but said that trials of COVID-19 vaccines early in the pandemic were not properly designed. Kulldorff did not respond to Nature’s request for comment.

Retsef Levi

Retsef Levi is a researcher focusing on operations management at the Massachusetts Institute of Technology in Cambridge. He has published several papers on COVID-19, including one[2] expressing concerns about side effects of COVID-19 vaccines. In a post on X in 2023, Levi said: “The evidence is mounting and indisputable that MRNA vaccines cause serious harm including death, especially among young people. We have to stop giving them immediately!” He did not respond to a request for comment.

Robert Malone

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Nature 642, 846-848 (2025)

doi: https://doi.org/10.1038/d41586-025-01852-z

With additional reporting by Humberto Basilio, Traci Watson and Lauren Wolf.

Updates & Corrections

- Update 12 June 2025: This story has been updated with information about two vaccine panelists’ work on lawsuits against a pharmaceutical company.

- Correction 17 June 2025: An earlier version of this story stated incorrectly that the National Vaccine Advisory Program is a part of the US Centers for Disease Control and Prevention.

References

1. Kris-Etherton, P. M. et al. Nutr. Rev. 79, 247–260 (2021).

2. Sun, C. L. F., Jaffe, E. & Levi, R. Sci. Rep. 12, 6978 (2022).

3. Reis, G. et al. N. Engl. J. Med. 386, 1721–1731 (2022).

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NEWS 18 June 2025

First ever skull from ‘Denisovan’ reveals what ancient people looked like

Ancient proteins and calcified dental plaque identify heavy-browed fossil from China as a Denisovan.

By

Dyani Lewis

A reconstruction of the hominin source of the ‘Dragon Man’ cranium in his habitat. The fossil has now been identified as coming from a Denisovan.Credit: Chuang Zhao

A prominent brow ridge with a brain as large as modern humans and Neanderthals — that’s what the archaic human group, the Denisovans, looked like, according to work published this week in Cell[1] and Science[2].

Palaeontologists used ancient molecules to identify a cranium found near Harbin in northeastern China as belonging to the group. It’s the first time a near-complete skull has been definitively linked to the extinct people.

The fossil, which is at least 146,000 years old, ends a decade and a half of speculation about the Denisovans’ appearance. This had remained a mystery since scientists identified them from unique DNA taken from a finger bone found in a Siberian cave in 2010.

A virtual reconstruction of the fossil cranium found near Harbin, China.Credit: Xijun Ni

“It’s really exciting to finally have Denisovan DNA from a nearly complete cranium,” says Janet Kelso, a computational biologist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. “We finally have some insights into the cranial morphology of the Denisovans,” she says.

Dragon Man

The “massive” cranium — the upper portion of the skull, lacking the lower jawbone — is one of the best preserved of all archaic human fossils, according to researchers who first described it in 2021[3].

Qiang Ji, a palaeontologist at Hebei GEO University in Shijiazhuang, China, obtained the specimen from an unnamed man in 2018. The man — who Ji suspects discovered the artefact himself but failed to report it to authorities — claimed that his grandfather unearthed the fossil in 1933 during bridge-construction work over Long Jiang (which means dragon river), and buried it in an abandoned well, where it remained until a deathbed confession.

In 2021, Ji and his colleagues determined that the ‘Dragon Man’ fossil represented a new archaic human species, which they crowned Homo longi[4].

Molecular sleuthing

When Ji published those findings, Qiaomei Fu, a geneticist at the Institute of Vertebrate Paleontology and Paleoanthropology in Beijing, got in touch. Fu worked on the very first Denisovan DNA from the Siberian finger bone and wanted to see whether the Dragon Man fossil contained any ancient molecules.

She and her team first attempted to extract ancient DNA from a part of the skull called the petrous bone — often a good source — and from an attached tooth. They didn’t recover any genetic material but did extract and sequence fragments from 95 ancient proteins from the petrous samples.

Fu compared these with Neanderthal, modern human and Denisovan sequences. One protein sequence from the Harbin fossil was identical to that of a protein from the Siberian finger bone, as well as from Denisovans uncovered in Tibet and Taiwan, but differed from proteins in modern humans and Neanderthals. That suggested the Dragon Man individual was a Denisovan. Fu’s team identified two further, less conclusive, protein matches.

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Nature 642, 848-849 (2025)

doi: https://doi.org/10.1038/d41586-025-01899-y

References

1. Fu, Q. et al. Cell https://doi.org/10.1016/j.cell.2025.05.040 (2025).

2. Fu, Q. et al. Science https://doi.org/10.1126/science.adu9677 (2025).

3. Ni, X. et al. Innovation 2, 100130 (2021).

4. Ji, Q., Wu., W., Ji, Y., Li, Q. & Ni, X. Innovation 2, 100132 (2021).

5. Tsutaya, T. et al. Science 388, 176–180 (2025).

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NEWS 13 June 2025 Correction 19 June 2025

Mysterious link between Earth’s magnetism and oxygen levels baffles scientists

As Earth’s magnetic field has risen, so have the conditions that helped complex life to thrive — a discovery that could aid the search for life on distant planets.

By

Davide Castelvecchi

A forest in the Permian period (artist’s impression), which saw atmospheric oxygen levels peak at more than 35%.Credit: Richard Jones/Science Photo Library

The strength of Earth’s magnetic field seems to rise and fall in line with the abundance of oxygen in the planet’s atmosphere, a study of geological records spanning the past half a billion years has found.

Explaining what underlies the link could help to reveal fundamental trends in the evolution of life on Earth — and give astronomers clues to the most promising places to look for signs of complex life on other planets. But, so far, it is unclear whether Earth’s magnetism plays a direct part in keeping oxygen levels high — and sustaining animal life — or whether both are influenced by other, as yet unidentified mechanisms.

Source: Ref. 1

“We don’t really have a good explanation for it,” says Benjamin Mills, a biogeochemist at the University of Leeds, UK, and a co-author of the study, which was published in Science Advances on 13 June[1]. However, the study does suggest “some potential causes that are exciting and potentially testable”, says Aubrey Zerkle, a biogeochemist at the University of St Andrews, UK.

Knowing how Earth’s deep interior influences the evolution of the atmosphere is “critical to understanding what makes our planet habitable”, says Richard Bono, a geophysicist at Florida State University in Tallahassee who has helped to compile long-term records of geomagnetism.

Geological clues

Oxygen is the main component of Earth’s crust and mantle. But molecular oxygen only began to accumulate in the atmosphere around 2.5 billion years ago, after organisms that produce oxygen through photosynthesis started to evolve. And it has only been during the current eon, covering the past 540 million years or so, that oxygen has reached concentrations that are sufficient to sustain most animals.

There is no direct way to measure the composition of the atmosphere in the deep past, but geochemists can use indirect clues[2] to reconstruct oxygen levels as far back as the Cambrian period, which began around 540 million years ago. For example, oxygen concentration “has a strong relationship with how easy it is to start and maintain wildfires”, says Mills, and the frequency of large wildfires can be worked out by looking at ancient charcoal deposits, among other factors.

Geophysicists have been able to reconstruct how the strength and direction of Earth’s geomagnetic field has varied over even longer stretches of the planet’s history, by studying rocks produced by ancient volcanic eruptions[3]. This is because magnetic crystals that form in the solidifying lava align themselves with the field, acting like tiny compasses frozen in time.

To put these two long records side by side and compare them, Mills teamed up with geophysicists Weijia Kuang and Ravi Kopparapu, both at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and exobiologist Joshua Krissansen-Totton at the University of Washington in Seattle. The authors found a strong correlation — both oxygen levels and geomagnetic intensity have increased over the past half a billion years, and some of the major spikes and drops in both measurements occurred in the same geological periods (see ‘Long-term trends’).

Potential explanations

The paper discusses some possible reasons for the correlation. Earth’s magnetic field is known to have a protective effect on the upper atmosphere because it deflects solar wind, a stream of charged particles from the Sun that would otherwise cause oxygen and other gases to slowly escape into space. But the team calculated that the loss of oxygen caused by a drastic weakening of the field would still be small compared with the amounts generated by photosynthesis — and relative to the amounts consumed by other organisms and by geological cycles in which elements are exchanged between the atmosphere, the crust and the mantle.

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Nature 642, 850-851 (2025)

doi: https://doi.org/10.1038/d41586-025-01862-x

Updates & Corrections

- Correction 19 June 2025: An earlier version of this story gave the wrong unit for the virtual geomagnetic axial dipole moment. This has now been corrected.

References

1. Kuang, W., Kopparapu, R., Krissansen-Totton, J. & Mills, B. J. W. Sci. Adv. 11, eadu8826 (2025).

2. Mills, B. J. W., Krause, A. J., Jarvis, I. & Cramer, B. D. Annu. Rev. Earth Planet. Sci. 51, 253–276 (2023).

3. Bono, R. K., Paterson, G. A. & Biggin, A. J. Geophys. Res. Lett. 49, e2022GL100898 (2022).

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NEWS 12 June 2025

How you breathe is like a fingerprint that can identify you

Your inhalation and exhalation pattern is not only unique to you, it can be a marker of your physical and mental state, study suggests.

By

Humberto Basilio

Every breath you take ... could add to a breathing pattern that is unique to you, a study finds.Credit: Anusak Laowilas/NurPhoto via Getty

Like the swirls in fingerprints, a person’s breathing pattern might be unique to them — offering a way not only to identify individuals, but also to identify some of their physical and mental traits.

A team of researchers measured the breathing of 97 healthy people for 24 hours, and found that they could identify participants with relatively high accuracy from their breathing pattern alone. What’s more, they found that these patterns can be correlated with body-mass index (BMI) and signs of depression and anxiety.

“In a way, we’re reading the mind through the nose,” says co-author Noam Sobel, a neurobiologist at the Weizmann Institute of Science in Rehovot, Israel. “This could be a very powerful diagnostic tool.” The team published its study today in Current Biology[1].

Taking a breath

Breathing is deeply connected to the brain. Every inhalation and exhalation is coordinated to supply the oxygen needed for the brain to manage the body’s systems. Sobel and his team wondered: if every brain functions differently, shouldn’t every person’s breathing be unique, too?

To test this, the researchers developed a custom, wearable device that records airflow through each of a person’s nostrils. Mounted on the back of the neck, the device, which has tubes fitted under the nose, tracks people’s breathing during their everyday routines, both while they are awake and while they are asleep.

Researchers measured study participants’ breathing patterns over 24 hours, using a custom device that sits on the back of the neck.Credit: Soroka et al., Current Biology

To characterize a person’s breath pattern, the team extracted 24 parameters from the airflow data, including duration of inhalation and exhalation and airflow asymmetry between nostrils. They separated the periods when participants were awake and asleep, and trained a machine-learning algorithm with the data.

When 42 of the participants came back to the laboratory weeks, months and even two years later, to take part in another 24-hour measurement, the trained algorithm could identify them from their breath patterns. Data from periods when the participants were awake gave more accurate results than did those from sleeping periods, but when the researchers used a 100-parameter characterization of a full data set instead of one using 24 parameters, they could pick individuals out with 96.8% accuracy.

Given this success, Sobel and his colleagues began wondering whether they could learn more from the breath patterns.

Healthy breathing

The researchers collected data on the participants’ BMIs, and from questionnaires that assess levels of depression and anxiety. An analysis found correlations between this information and the breathing patterns, even though most participants had low-level scores on the questionnaires.

For instance, the breathing profiles during sleep of people with higher BMIs were different from those of people with lower BMIs. And those who scored higher on the questionnaires for anxiety or depression had distinct patterns in how they inhaled and exhaled.

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Nature 642, 851-852 (2025)

doi: https://doi.org/10.1038/d41586-025-01835-0

References

1. Soroka, T. et al. Curr. Biol. https://doi.org/10.1016/j.cub.2025.05.008 (2025).

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NEWS 13 June 2025

Tiny human hearts grown in pig embryos for the first time

The hearts started to beat in the pig–human hybrids, which survived for 21 days.

By

Smriti Mallapaty

Pig embryo at 33 days. Pig embryos with human heart cells have survived for 21 days.Credit: Daniel Sambraus/Science Photo Library

Researchers have reported growing hearts containing human cells in pig embryos for the first time. The embryos survived for 21 days, and in that time their tiny hearts started beating. The findings were presented this week at the annual meeting of the International Society for Stem Cell Research in Hong Kong.

Scientists developing human–animal chimaeras grow human cells in animal embryos, with the aim of one day generating animals with human organs that can be transplanted into people. This could provide a way to address the global shortage of organs for transplantation.

One approach to developing chimaeras involves creating animal embryos that lack some of the genes needed to produce a specific organ, such as the heart. Human stem cells are then injected into the embryos, with the hope that the human cells — rather than those of the animal — will form that organ. Several groups have used this method to grow human muscle and blood-vessel cells in pig embryos.

Pigs are a suitable donor species because the size and anatomy of their organs are comparable with those of humans, says Lai Liangxue, a developmental biologist at the Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, who led the latest work. Lai’s team has previously grown early-stage human kidneys in pig embryos that survived for up to a month in pregnant sows. He wanted to see whether similar results were possible for the heart.

Beating heart

In their study, which has not been peer reviewed, Lai and his team reprogrammed human stem cells to bolster their ability to survive in a pig, by introducing genes that prevent cell death and enhance cell growth. They then generated pig embryos in which two specific genes that have key roles in heart development were knocked out. A handful of human stem cells were introduced into the pig embryos at the morula stage, soon after fertilization — a point at which the embryo consists of a ball of about a dozen cells that are rapidly dividing. The embryos were then transferred to surrogate pigs.

The team found that the embryos grew for up to 21 days, after which they did not survive. Lai says it’s possible the human cells disrupted the function of the pig hearts.

When the researchers took a closer look at the embryonic hearts, they found that they had grown to a size equivalent to that of a human heart at the same stage of development — the size of a fingertip — and were beating, says Lai. The human cells could be identified because they had been tagged with a luminescent biomarker and were glowing, he adds.

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Nature 642, 852 (2025)

doi: https://doi.org/10.1038/d41586-025-01854-x

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NEWS FEATURE 25 June 2025

Eureka! The brain science behind lightbulb moments

Experiences of insight come with a burst of brain activity — and a memory boost.

By

Humberto Basilio

Illustration: DAQ

Mindia Wichert has taken part in plenty of brain experiments as a cognitive-neuroscience graduate student at the Humboldt University of Berlin, but none was as challenging as one he faced in 2023. Inside a stark white room, he stared at a flickering screen that flashed a different image every 10 seconds. His task was to determine what familiar object appeared in each image. But, at least at first, the images looked like nothing more than a jumble of black and white patches.

“I’m very competitive with myself,” says Wichert. “I felt really frustrated.”

Cognitive neuroscientist Maxi Becker, now at Duke University in Durham, North Carolina, chose the images in an attempt to spark a fleeting mental phenomenon that people often experience but can’t control or fully explain. Study participants puzzling out what is depicted in the images — known as Mooney images, after a researcher who published a set of them in the 1950s[1] — can’t rely on analytical thinking. Instead, the answer must arrive all at once, like a flash of lightning in the dark (take Nature’s Mooney-images quiz below).

Becker asked some of the participants to view the images while lying inside a functional magnetic resonance imaging (fMRI) scanner, so she could track tiny shifts in blood flow corresponding to brain activity. She hoped to determine which regions produce ‘aha!’ moments.

Over the past two decades, scientists studying such moments of insight — also known as eureka moments — have used the tools of neuroscience to reveal which regions of the brain are active and how they interact when discovery strikes. They’ve refined the puzzles they use to trigger insight and the measurements they take, in an attempt to turn a self-reported, subjective experience into something that can be documented and rigorously studied. This foundational work has led to new questions, including why some people are more insightful than others, what mental states could encourage insight and how insight might boost memory.

Becker’s study aimed to find out how the rapid reorganization and integration of knowledge that she and others think is a defining feature of insight happens in the brain and whether it’s linked to memory[2]. Through such work, researchers could better explore memory and learning more generally, and perhaps find ways to enhance both.

“We are at this extremely exciting verge, where we can get closer to insight than we have ever come before,” says Becker.

Capturing the flash

Whereas analytical thinking involves using logic and reasoning to arrive at a solution in a step-by-step way, insight is a sudden realization that seems to pop into conscious awareness. These mental leaps can lead to a grand discovery or solution, or something more mundane — the answer to a daily word puzzle, for example.

Throughout the twentieth century, cognitive psychologists wrestled with how to distinguish insight from analytical problem solving. Although consensus was growing that insight was distinct, not everyone agreed. Cognitive psychologist Robert Weisberg at Temple University in Philadelphia, Pennsylvania, has argued, for example, that insight might not be as different from analytical thinking as it seems. He has suggested that insight, too, comes from the brain gradually building on what it already knows — incorporating new information with each failed attempt. For him, the main feature of insight is the emotion that someone feels after finding an answer or creating something that seems new.

“It’s true that we get aha! experiences,” says Weisberg. “But that doesn’t mean the underlying process is different. It just means the outcome knocks your socks off.”

Cognitive neuroscientist John Kounios, who began studying insight in the 1990s at Tufts University in Medford, Massachusetts, has a different view. For him, insight isn’t about adding up knowledge to arrive at an answer. Instead, it’s when a person spontaneously forms new knowledge. Sometimes, says Kounios, now at Drexel University in Philadelphia, “it’s the solution to a problem they didn’t even know they had”.

Most early insight research was based on self-reports alone. Kounios decided to bring a different type of data into the field. In the early 2000s, he began using technologies including fMRI and electroencephalogram (EEG) — which captures electrical activity — to look for a distinct signature of insight in the brain. “We were prepared to be proven wrong,” he says.

In the laboratory, he and cognitive neuroscientist Mark Beeman at Northwestern University in Evanston, Illinois, used what are known as remote associate problems to trigger aha! moments. Participants were tasked with finding a word that connects three seemingly unconnected ones, such as ‘home’, ‘sea’ and ‘bed’. (The answer is ‘sick’.) After each attempt, they reported whether the solution came with an aha! feeling. If so, they rated the strength of the feeling. Kounios and Beeman used fMRI scans and EEGs to monitor participants’ brains as they solved the puzzles.

In their early experiments[3], Kounios, Beeman and their colleagues found that insight was accompanied by a burst of activity and blood-flow changes in the right side of the brain, in a region called the right superior temporal gyrus, which is associated with learning, memory and language processing. This activity occurred just 300 milliseconds before participants pressed a button to report being consciously aware of the answer. Kounios and Beeman had detected an aha! signal in the brain.

The pair also found that neural activation linked to insight is more sudden and localized than that for analytical problem-solving, supporting the notion that insight is an abrupt realization of knowledge rather than a gradual accumulation.

Further studies have shown that insight consistently includes a burst of high-frequency gamma waves that can involve different areas of the brain. Another common region of activity is the anterior cingulate cortex, which is involved in attention, emotion and decision-making.

Kounios, Beeman and others have done “really rigorous research” to demonstrate how insight is grounded in brain activity, says cognitive psychologist Daniel Schacter at Harvard University in Cambridge, Massachusetts, adding that such work will improve our understanding of other forms of creative cognition.

In 2020, cognitive neuroscientist Carola Salvi at John Cabot University in Rome reported another line of evidence supporting the idea that insight and analytical problem-solving are distinct processes. In an experiment with 38 participants, Salvi discovered that people’s pupils rapidly dilated about 500 milliseconds before they reported having an insight — signalling a shift in awareness[4]. When participants solved problems analytically, their eyes instead made tiny, rapid movements known as microsaccades.

Early cognitive psychologists who described insight as a distinct process were onto something, says Salvi. “A hundred years later, we were finally able to say they were right,” she says.

Memory follows insight

Salvi thinks that pupil dilation reflects a shift in cognitive processing linked to activity in a brain network involved in regulating attention and arousal, which might also influence memory formation.

A link to memory would make sense, Salvi says. Psychologists have observed that people tend to better remember moments of their lives marked by strong emotions. “That’s why you can remember a lot of details of events like your first date or your wedding,” says Salvi.

For the past decade, cognitive psychologist Amory Danek at the Technical University of Munich, Germany, has been studying whether such a memory boost also comes with the emotional experience of insight.

She decided to move away from the three-word puzzles that other researchers had been using. She suggests that these stimuli lack an element present in real-world aha! moments: an initial false representation that forced people to restructure the problem to solve it. “They were quite boring,” says Danek. “I was not satisfied with that.”

Instead, Danek decided to collaborate with a professional magician for her experiments. After showing study participants videos of a magician performing tricks, she asks the participants to attempt to work out how the tricks were done. Participants come up with a solution and report whether they arrived at it through insight. “Magicians put the observers in the wrong mental set before they do a trick,” says Danek. “Observers have to break free from this initial wrong problem representation in order to understand how it’s done.”

Danek also thought magic tricks would elicit more intense emotions, which people easily recognize and can thus reliably report. She asks study participants reporting a solution to rate on a scale from 0 to 100 their feelings of suddenness, certainty and pleasure, for example.

In one experiment, participants tried to remember the solutions two weeks after watching the tricks[5]. Danek found that people who reported discovering how a magic trick was achieved through insight were better able to remember the solution than were those who didn’t experience insight. She calls this memory boost the “insight memory advantage”[6].

Cognitive neuroscientist Roberto Cabeza at Duke University says that insight often comes with mental processes related to memory, such as semantic learning — when people find that solutions align well with what they already know — and emotional memory, which strengthens recall through emotional engagement.

Other research hints that people are better at remembering unrelated, random information that they encounter around the time of aha! moments, as well as ‘d’oh!’ moments, when a solution is revealed and suddenly feels obvious[7].

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Nature 642, 854-856 (2025)

doi: https://doi.org/10.1038/d41586-025-01963-7

References

1. Mooney, C. M. Can. J. Psychol. 11, 219–226 (1957).

2. Becker, M., Sommer, T. & Cabeza, R. Nature Commun. 16, 4341 (2025).

3. Jung-Beeman, M. et al. PLoS Biol. 2, E97 (2004).

4. Salvi, C., Simoncini, C., Grafman, J. & Beeman, M. NeuroImage 217, 116933 (2020).

5. Danek, A. H., Fraps, T., von Müller, A., Grothe, B. & Öllinger, M. Front. Psychol. 5, 1408 (2014).

6. Danek, A. H. & Wiley, J. Cognition 205, 104411 (2020).

7. Salvi, C., Keller, N., Cooper, S. E., Leiker, E. & Dunsmoor, J. Cogn. Affect. Behav. Neurosci. 24, 505–516 (2024).

8. Kizilirmak, J. M., Thuerich, H., Folta-Schoofs, K., Schott, B. H. & Richardson-Klavehn, A. Front. Psych. 7, 1693 (2016).

9. Kizilirmak, J. M. et al. Front. Aging Neurosci. 13, 661346 (2021).

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NEWS FEATURE 24 June 2025

How to make America healthy: the real problems — and best fixes

The United States has lower life expectancy than most similarly wealthy nations. Chronic disease is part of the cause, but so are guns, drugs and cars.

By

Helen Pearson

Collage: Wes Fernandes/Nature; Getty

Since taking over as the top US health official in February, Robert F. Kennedy Jr has overseen radical changes that have alarmed many public-health experts. The agency he leads announced that it would cut its workforce by 20,000, and cancelled billions of dollars in federal funding for research and public health. Earlier this month, Kennedy replaced all the members of an influential vaccine advisory committee with hand-picked ones, including some who have expressed scepticism about vaccines. His mission, he says, is to ‘Make America Healthy Again’. “We are the sickest nation in the world,” he said in March, “and we have the highest rate of chronic disease.”

His diagnosis holds some truth, say public-health specialists and analysts. Relative to other similarly wealthy nations, the United States has the shortest life expectancy despite spending the most on health care. It “has the highest rates of preventable and treatable deaths”, says Reginald Williams, a health-policy specialist at the Commonwealth Fund, a think tank in New York City that publishes regular comparisons of health-care systems around the world. And researchers agree that high rates of chronic disease, including heart disease and obesity, are key contributors to Americans’ higher death rates, as Kennedy emphasizes.

But researchers say that Kennedy — widely known as RFK Jr — has mostly ignored other leading causes of death and ill health, including car accidents, drug overdoses and gun violence. In these areas, the United States “is a really clear outlier”, says Colin Angus, who studies health policy at the University of Sheffield, UK. “But, you know, I don’t see RFK talking about those things.” Kennedy has also promoted medical misinformation and conspiracy theories — particularly with regards to the safety of vaccines.

With Kennedy’s plans for US health beginning to come into focus, Nature has dug into the data to explore how unhealthy America is, how it got there and why many researchers say that some of Kennedy’s proposed solutions are misguided.

Shorter lives

To gauge US health, life expectancy — the average number of years a person is expected to live — is a good place to start. Many analyses show that the United States has lower life expectancy than most similar nations. A comparison by KFF, a non-profit health-policy research organization based in San Francisco, California, shows that US life expectancy at birth in 2023 was 78.4 years. This is 4.1 years shorter than the average of 11 comparably large wealthy countries, including Australia, Germany, Japan and the United Kingdom (see ‘Low expectations’). “The US is just like nothing else. It’s shocking,” says Angus.

Source: Peterson-KFF Health System Tracker

People in the United States also spend fewer of their years in good shape. Healthy life expectancy — the average number of years lived in good health — was 64.4 years in 2021. This ranks below that of almost all other high-income countries, according to data from the Global Burden of Disease study[1], a massive epidemiological project to measure health loss.

The gap wasn’t always so wide. Life expectancy in the United States was closer to the average for its peers around 1980 and gradually improved, according to KFF’s analyses. The gains were driven partly by a drop in smoking and increased use of cholesterol-lowering drugs known as statins, which cut deaths from cardiovascular and other chronic diseases, says Thomas Bollyky, who directs the global-health programme at the Council on Foreign Relations, a think tank headquartered in New York City.

Although that trend continued in most peer nations, improvements in life expectancy gradually slowed in the United States, plateauing around 2010. The COVID-19 pandemic temporarily widened the gap, because of higher excess mortality in the United States compared with its peers. So what’s going on?

To find out, KFF researchers looked at death data across age groups. The biggest difference in death rates has been in people aged 15–49 (see ‘Early deaths’). Among these younger people, the death rate has been falling much more slowly in the United States than in peer countries — and it spiked drastically owing to COVID-19. “More people die younger,” says Lynne Cotter, a senior health-policy researcher at KFF. And because young deaths erase more years of life than do older ones, they drag down overall life expectancy.

Source: Human Mortality Database

How much does chronic disease contribute to the high US death rate? A lot. Overall, chronic conditions — heart disease, cancer, stroke and respiratory disease — take up four out of five spots on the country’s list of biggest killers. Cotter attributes about one-third of excess premature deaths in people under the age of 70 to cardiovascular disease, respiratory disease and kidney disease. “Chronic conditions certainly make up a large portion,” she says. KFF estimates that Americans are about twice as likely to die from cardiovascular disease before reaching 70 as are people in similar countries.

One of the biggest drivers of those deadly conditions is obesity, say researchers. As of 2022, about 42% of adults were considered obese in the United States, compared with 27% in the United Kingdom and 5.5% in Japan. Obesity increases the risks of developing diabetes, heart disease, cancer and many other conditions. “The US has, particularly around diet, obesity and overweight, adopted unhealthier lifestyles at a higher rate than our country peers,” Bollyky says.

In a May report[2], the Make America Healthy Again (MAHA) Commission, which Kennedy leads, blamed childhood chronic disease mainly on ultra-processed foods, excessive screen time, lack of physical activity, exposure to chemicals and overprescription of medications. A spokesperson for the US Department of Health and Human Services (HHS), which Kennedy leads, did not directly address the criticisms and questions raised by sources in this article. They said that the MAHA report focuses on “a disturbing reality unfolding across the nation — the scale of the chronic disease epidemic and the institutional failures that allowed it to grow unchecked for decades”, and that the agency will next publish a report outlining policy recommendations. Americans “elected President Trump and put Kennedy in office — to Make America Healthy Again — and that’s exactly what we’re doing”, they said.

Unhealthy diets are one factor driving obesity-related chronic conditions in the United States. Credit: Derek Davis/Portland Press Herald/Getty

Research supports Kennedy’s argument that ultra-processed foods might be partly to blame for poor health. Their consumption has been linked to increased risks of obesity and some other chronic diseases, and is relatively high in the United States. Such foods comprise an estimated 58% of US daily energy intake — similar to that in the United Kingdom, but greater than the 48% in Canada and 31% in France[3].

Williams says that the problems caused by chronic disease are compounded by poor health care. Compared with a group of similar high-income countries, the United States is the only one that lacks universal health-insurance coverage. “We have still 26 million people that remain uninsured,” Williams says. Lack of health insurance, high costs and other barriers prevent people from getting diagnoses and treatment early on. “And then when they do seek care, they’re at a place where their disease or needs are much higher.”

By contrast, death rates from cancer in the under 70s have remained similar in the United States and other countries, probably because of widespread cancer screening and proactive cancer care, say public-health specialists. “Cancer has moved from basically a death sentence to a manageable chronic disease,” says Williams. “So we need to have that same level of engagement around obesity, diabetes, cardiovascular care.”

The other big contributors to lower life expectancy in the United States — and what really sets the country apart, researchers say — are high death rates from substance misuse, car accidents, suicide and homicide (see ‘Varied causes’). These tend to kill people of working age.

Source: Peterson-KFF Health System Tracker

Deaths from substance misuse are explained mainly by overdoses of synthetic opioids such as fentanyl — part of the US opioid crisis. Many Americans are killed in traffic accidents, partly because they tend to spend proportionately more time driving, and in bigger cars, than people in many other nations. “We’re a very car-centric society,” says Mary Pat Campbell, a life-insurance specialist who works at the investment-management firm Conning in Hartford, Connecticut, and blogs about mortality data. “We don’t have a lot of good mass-transit alternatives.”

Gun deaths account for about 80% of US murders and 55% of suicides. This led physician Vivek Murthy to call gun violence a public-health crisis in 2024, when he was US surgeon-general, and to highlight that it was the leading cause of death in children and adolescents. (The HHS removed this advisory from its website in March, according to news reports.) More gun deaths are suicides than homicides, says Campbell. “It gets very bleak thinking about some of these things,” she says.

It gets bleaker. All told, the death rates in working-age people mean that one 5-year-old out of every 20 — or roughly one in every school class — will die before the age of 45, according to Angus’s calculations. The comparable figure is one in 50 in the United Kingdom and one in 100 in Switzerland.

Regional differences

Although the life expectancy for the United States suggests that the country is relatively unhealthy, the average masks a more complex picture. Closer examination reveals that the figure varies drastically across the country.

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Nature 642, 857-860 (2025)

doi: https://doi.org/10.1038/d41586-025-01969-1

References

1. GBD 2021 US Burden of Disease Collaborators. Lancet 404, 2314–2340 (2024).

2. The MAHA Commission. Make Our Children Healthy Again: Assessment (US Government, 2025).

3. Srour, B. et al. Lancet Gastroenterol. Hepatol. 7, 1128–1140 (2022).

4. Holford, T. R., McKay, L., Tam, J., Jeon, J. & Meza, R. JAMA Netw. Open 8, e257695 (2025).

5. Dwyer-Lindgren, L. et al. Lancet 404, 2299–2313 (2024).

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Books & Arts

- ‘Natural history museums can save the world’: anti-colonialism, conservation and climate change

- Stories that are out of this world: Books in brief

BOOK REVIEW 23 June 2025

‘Natural history museums can save the world’: anti-colonialism, conservation and climate change

Zoologist Jack Ashby explains why it’s vital to invest in protecting specimens stored in scientific collections.

By

Davide Castelvecchi

Jack Ashby is assistant director of the University Museum of Zoology in Cambridge, UK. Credit: Jacqueline Garget

Nature’s Memory: Behind the Scenes at the World’s Natural History Museums Jack Ashby Allen Lane (2025)

Natural history museums are crucial for conservation — and for communicating its importance to the public. But step through the ‘staff only’ doors, and museums look very different. In vaults and laboratories, curators store, catalogue and preserve millions of specimens collected from the natural world — sometimes in controversial ways.

In Nature’s Memory, zoologist Jack Ashby explains how all of this works, and the human choices that it entails. Ashby, who studies marsupials and monotremes, is assistant director of the University Museum of Zoology in Cambridge, UK. He is also president of the Society for the History of Natural History in London.

In his office, plastered with posters of some of the world’s best nature dioramas, Ashby told Nature about his quest to communicate the importance of natural history museums.

Do you have a favourite?

One is the Biological Museum in Stockholm. The building looks like a wooden Norwegian church, and it’s effectively one giant diorama that goes up three storeys. It covers all of the Nordic biomes. And it shows that we do have some exciting wildlife in Europe.

Another is the National Natural History Museum in Paris. You walk into the comparative-anatomy section and it’s one giant wall with thousands of skeletons all facing towards you, so tightly packed that you cannot walk between them.

These probably shouldn’t be my favourites, because they are so old fashioned, but they are stunning.

Do museums teach science in a neutral way? In one book chapter, you highlight male biases in specimen collection and display.

Natural history museums are amazing, but of course they are built by people, and people have interests and biases. One study, by biologist Natalie Cooper at the Natural History Museum in London and her collaborators, looked at more than 2 million specimens at 5 museums, and found that only 40% of the birds were female (N. Cooper et al. Proc. R. Soc. B 286, 20192025; 2019). For mammals, the figure was 48%, but in some of the mammalian groups, particularly artiodactyls — such as deer and antelope — only 40% were female. In another study, curator Rebecca Machin found that almost three-quarters of the natural-history specimens on display at the Manchester Museum, UK, were male (R. Machin Museum Soc. 6, 54–67; 2008).

The numbers are huge, but it’s also about how they are displayed, presented and interpreted. For example, descriptions of male specimens are much more likely to give general facts: this is where the animal lives, how it’s adapted to its environment, and so on. Whereas, for a female specimen, you tend to have more of a story of ‘this is how the species reproduces’.

How did the idea of collecting natural-history specimens arise?

In a sense, all museums — but certainly natural history museums — have their philosophical origins in the Wunderkammern, the private cabinets of curiosity maintained by aristocrats and natural philosophers in the sixteenth to eighteenth centuries. Some museums today are direct descendants of those collections.

During the Enlightenment period, when scientific proof became important, collecting at an institutional level grew. And it went hand in hand with the ‘age of discovery’. Some of those voyages explicitly focused on finding out what resources were out there in the world that could be traded or acquired. And museums were both a place to study those resources — be they animal, vegetable or mineral — and a tool to promote the mission: ‘Look what we’ve got in our newly found colony’.

The British colony in southeastern Australia was intended to be founded in what’s now Botany Bay, near Sydney. And the colonists called it Botany Bay — it already had a name, Kamay, in the Indigenous language Dharawal — because, in 1770, on the voyage of Captain James Cook, naturalist Joseph Banks spent weeks collecting plants there. He came back and later said to Parliament, on the basis of what he had found, ‘this is where you should set up a colony’. Those plants are now in the Natural History Museum in London.

Specimens at the National Museum of Ireland — Natural History in Dublin.Credit: Lucas Vallecillos/VWPics/Redux/eyevine

Banks also wanted to collect the heads of Aboriginal people for his anatomical studies. Isn’t that problematic?

It was. Scientists at the time had theorized a racial hierarchy of people across the world, and then sought people’s remains without consent to try to back up this theory. It fed into the eugenics movement starting in the late nineteenth century, which has repercussions today. The desire to categorize people is inherently linked to extraordinary forms of violence and has been used in a pseudoscientific way to justify horrific social policy.

How are museums dealing with this legacy of colonialism?

There isn’t one museum that I would say is doing colonial history particularly well. But a lot of the research being done in natural history museums is, partly, understanding the true origins of the collections and who really collected them.

I bring up nineteenth-century naturalist Alfred Russel Wallace in that context, because he was relatively good at giving credit to people that he was working with. Two Malay teenagers called Ali and Baderoon, in particular, helped him during his eight-year voyage to the Malay Archipelago. Out of the 125,000 specimens that we lazily say were ‘collected by Wallace’, we know he attributed many to other people. But the institutions of science decided to ignore that and give the credit to Wallace.

Some museums have begun to repatriate human remains and artefacts. Should every collection be repatriated?

The important question is, where does an object have the most meaning? It’s always going to be on a story-by-story basis. Take the thylacine, for example, the extinct Tasmanian tiger (Thylacinus cynocephalus). There are nearly 800 specimens in museums around the world, and there are more in Australia, where they were native, than anywhere else. It is good that all of the thylacines aren’t in Australia. Because we talk about human-driven extinction in museums all around the world, that’s an important story — objects have power by being spread around.

But there are no specimens of gorillas in museums in any of the gorilla’s home states. If anyone who studies gorillas in Central Africa wants to use museum collections, they have to go to another part of the world, which is not right. If a museum has many gorilla specimens, to repatriate some of them would be a good thing.

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Nature 642, 861-863 (2025)

doi: https://doi.org/10.1038/d41586-025-01970-8

This interview has been edited for length and clarity.

Competing Interests

The author declares no competing interests.

BOOK REVIEW 20 June 2025

Stories that are out of this world: Books in brief

Andrew Robinson reviews five of the best science picks.

By

Andrew Robinson

The Infrastructure Book

Sybil Derrible Prometheus (2025)

In 1995, a massive heatwave in Chicago, Illinois, took at least 739 lives. The city authorities assumed that a lack of air conditioning was responsible for most deaths, but an investigation attributed them mainly to social isolation. As Chicago-based engineer Sybil Derrible notes in his penetrating analysis of urban infrastructure: “Technology comes and goes, but infrastructure stays because infrastructure is all about people.” Surveying 16 large cities globally, he investigates water, transport, energy and telecommunications networks.

Free Creations of the Human Mind

Diana Kormos Buchwald & Michael D. Gordin Oxford Univ. Press (2025)

Of the physics Nobel prizes awarded since 2000, “no fewer than seven ... stem directly from Einstein’s work in 1905 and 1915”, point out historians of science Diana Buchwald and Michael Gordin. Their brief, appealing book discusses the general theory of relativity and quantum theory, but is preoccupied mainly with Albert Einstein’s life, personality and philosophy, especially his complex relationship with war — including the design of the atomic bomb — and pacifism.

Amazing Worlds of Science Fiction and Science Fact

Keith Cooper Reaktion (2025)

Astronomers observed the first confirmed exoplanet in 1992. Some 5,900 are now known, in about 4,500 planetary systems, with around 1,000 containing several planets, according to NASA. No life has been detected yet, showing just “how rare our planet Earth still is” and how “the imagination imbued within science fiction can only carry us so far”, notes science journalist Keith Cooper. His engaging book, based on interviews with writers and researchers, examines what science fiction has got right and wrong, and what science can learn from it.

Yearning for Immortality

Rune Nyord Univ. Chicago Press (2025)

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Nature 642, 863 (2025)

doi: https://doi.org/10.1038/d41586-025-01950-y

Opinion

- Medical AI can transform medicine — but only if we carefully track the data it touches

COMMENT 23 June 2025

Medical AI can transform medicine — but only if we carefully track the data it touches

The uncontrolled deployment of machine learning in medicine can distort patient information and sacrifice long-term data reliability for short-term benefits.

By

Akhil Vaid

Clinical trials for breast-cancer screening are under way using imaging devices assisted by artificial intelligence. Credit: Klaudia Radecka/NurPhoto/Getty

The practice of modern medicine is built on pattern recognition — whether in a patient’s history, physical examination, laboratory results or response to treatment. A skilled physician can identify crucial patterns early and distinguish them from others that appear deceptively similar.

But some patterns are too chaotic, too subtle or too fleeting to raise red flags. No doctor can reliably catch early-stage pancreatic cancer from routine blood tests, for example. Answers to many questions of profound importance that demand knowledge of the future[1], such as whether a tumour will spread or how long a person might live, are thus subjective — often coming down to a physician’s cumulative experience or ‘gut feeling’.

One approach to reducing subjectivity in medicine is through supervised machine learning — a technique based on creating computer models that can detect patterns by learning from labelled data. For instance, by examining many mammogram images that either include or lack tumours, models can learn how to recognize the statistical features that tend to go with one label or the other, even when those features aren’t obvious to the human eye.

Unsurprisingly, interest in predictive modelling has exploded. In cases involving tumour spread, organ failure or narrow treatment windows, accurate knowledge of how someone’s condition might unfold can conserve resources, reduce suffering and save lives. In 2024 alone, the citation database PubMed indexed more than 26,000 studies mentioning artificial intelligence (AI), machine learning or deep learning in patient health care and clinical medicine. The global market for AI in health care is projected to exceed US$46 billion by the end of this year, and $200 billion by 2030.

Yet any model, no matter how sophisticated, is still a source of uncertainty. If it underestimates risk, it contributes to clinicians overlooking serious concerns. And if it overestimates risk, it could lead to unnecessary tests and interventions, and wasted resources.

A model’s usefulness is typically judged by how well it generalizes to previously unseen data, which is treated as a proxy for real-world performance. But there’s a catch: in learning to predict outcomes, models also absorb the clinical decisions, relationships and biases that are baked into the data used to train them. Supervised learning relies on the assumption that these conditions, including the biases, will remain stable during model use. Without this foundation, things fall apart.

For example, ‘Is this patient at risk of dying tomorrow?’ is a different question in a rural outpatient clinic than in a cardiac intensive-care unit, and a model trained in one setting is likely to perform poorly in the other.

Current best practices[2] emphasize transparency in data sources and encourage testing models in the environments where they will be used. Still, given that many medical data sets are small, biased or tied to narrow populations, the odds that models will underperform or stop working altogether remain uncomfortably high.

However, the greatest threat to the widespread adoption of predictive modelling in health care could come not from the instances in which the model fails outright, but rather those in which it succeeds in delivering results.

Data contamination

Wherever machine learning is used in a health-care setting, it is typically built on the foundation of the electronic health record (EHR) for patients. Although EHR adoption varies globally, it is deeply embedded in many high-income countries, where it serves as both the source of training data for predictive models and the system through which those predictions are returned to clinicians. At its core, the EHR is a dynamic database that continuously logs almost all aspects of patient care — including lab results, medication, clinical notes and key events such as infections or deaths.

By expanding the amount of patient data available, the EHR enables a standardized workflow: data are pulled from the EHR to train models, and once the models are deployed, they analyse fresh patient data to predict potential health risks. These predictions can guide clinical decisions — for example, prompting a physician to order a chest X-ray or to begin administering antibiotics if a model flags a high risk of pneumonia, even before classic symptoms fully develop.

But the EHR is also the destination of the predictions of models — and the consequences of those predictions. Take, for example, a model designed to detect early signs of the onset of sepsis. Ideally, the physician is alerted and takes timely action in administering antibiotics or fluids to prevent the condition from progressing. This is exactly the kind of impact we want from AI in health care. Sepsis is notoriously hard to catch early and has a mortality rate of 30–40%, so swift intervention can save lives.

But therein lies the rub: because the physician intervened, the patient doesn’t develop sepsis. As a result, the pattern the model flagged — originally linked to sepsis — is now recorded in the EHR as being associated with a non-septic outcome. This creates a ‘contaminated association’[3] in the data, in which warning signs of sepsis seem to lead to good outcomes, simply because of successful intervention. As these associations accumulate, they begin to erode the reliability of existing and even future models.

Over time, even well-performing AI models can degrade. Shifts in patient demographics, evolving standards of care, new medications or changes in clinical practice can all cause a model’s predictions to become less accurate — a phenomenon known as model drift.

A physician in a French hospital studies an X-ray in which an artificial-intelligence model has flagged possible fractures. Credit: Damien Meyer/AFP/Getty

Retraining models on newer, more representative data is widely considered the best way to recover performance[4]. But as the EHR database gets corrupted with false associations, retraining becomes effectively impossible. The data set used to train the model now contains a pattern that implies sepsis, but also ‘not-sepsis’. This is the equivalent of teaching addition to a child by telling them that two plus two is four. Sometimes. At other times it’s three, but only when it’s not five[3].

Serious conditions such as pneumonia, acute kidney injury (AKI) and sepsis often occur together during a single illness or hospital stay. A model that successfully prevents one of these conditions might indirectly prevent the others as well. This introduces misleading associations into the EHR — not just for current models, but for those yet to be built[3].

Things get even more complicated when multiple AI models are used in the same clinical setting. For example, one model might predict the risk of AKI, while another might forecast blood clots. These are different conditions, but both rely on the same lab values, such as measurements of the waste product creatinine, blood platelets or inflammatory markers. If a physician responds to the AKI alert by adjusting fluids or medications, that could render the predictions of the blood-clot model obsolete, or unreliable. In this way, an intervention triggered by one model can quietly disrupt another, even if they are focused on entirely different outcomes[3].

Higher-order effects

Current approaches to predictive modelling in health care don’t account for how models interact with each other or with clinical decision-making. This raises serious questions about some of the field’s core practices, starting with how researchers monitor model performance after deployment.

If a model helps to prevent an adverse event, its predictions do not occur — for example, patients do not die of sepsis — and its real-world performance might appear to decline[5]. That said, a drop in performance could also mean that the model isn’t working well in practice and is making poor predictions. It is often difficult to tell the difference between these two situations.

One way to improve understanding of what’s happening is to regularly compare outcomes between periods when the model is active and when it is not. This kind of side-by-side comparison can help to determine whether the model is truly effective or if it’s falling short. In this scenario, an expected range of performance change should be established as part of the evaluation process. If performance drops beyond this range, it might indicate model degradation. If the drop is smaller than expected, it could point to limited model use or ineffective integration into clinical practice. Estimating this range in advance can be difficult, because factors such as model drift or clinical variability might interfere. A more reliable approach might be to determine the range experimentally, under controlled conditions.

Unfortunately, real-world patient care, especially in environments with multiple models and providers, is far removed from controlled conditions. Although randomized controlled trials[6] (RCTs) remain the gold standard for evaluating clinical treatments and models, applying that level of control in day-to-day clinical settings is rarely possible. In practice, clinicians might need to choose between several overlapping or even conflicting models. As the number of deployed models grows, the results of isolated, tightly controlled studies become less reliable as indicators of real-world effectiveness. Unless a model is going to be used in exactly the same controlled environment in which it was tested — free from competing models, system changes or drift — its performance in isolation should be interpreted with caution.

Even if we accept RCTs at face value as being able to provide usable proof of a predictive model’s effectiveness, they come with substantial financial and time costs. A more practical way to assess a model is to test it on entirely new data — such as from another hospital or site. This process, often called external validation, helps to show whether the model can detect real biological patterns rather than just those specific to the data it was trained on. But akin to the challenges related to retraining models, this kind of testing becomes much harder[7] when previous models have already shaped or influenced the data being used for testing.

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Nature 642, 864-866 (2025)

doi: https://doi.org/10.1038/d41586-025-01946-8

References

1. Topol, E. J. Science 384, eadp7977 (2024).

2. Collins, G. S. et al. BMJ Open 11, e048008 (2021).

3. Vaid, A. et al. Ann. Int. Med. 176, 1358–1369 (2023).

4. Finlayson, S. G. et al. N. Engl. J. Med. 385, 283–286 (2021).

5. Carter, R. E., Anand, V., Harmon Jr, D. M. & Pellikka, P. A. Intell. Based Med. 6, 100058 (2022).

6. Stanley, K. Circulation 115, 1164–1169 (2007).

7. Chekroud, A. M. et al. Science 383, 164–167 (2024).

8. Johnson, A. E. W. et al. Sci. Data 10, 1 (2023).

9. The All of Us Research Program Investigators. N. Engl. J. Med. 381, 668–676 (2019).

10. Greene, S. M., Reid, R. J. & Larson, E. B. Ann. Int. Med. 157, 207–210 (2012).

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Competing Interests

A.V. works in an advisory capacity for Verily Inc. and serves on the Scientific Advisory Board of HeartSciences Inc. Both organizations are involved in health-care-related AI.

Work

- How do I choose a principal investigator for my next postdoc?

- Tardigrade-obsessed: meet the researchers trying to turn water bears into neuroscience models

- Ice society: looking for life at unlikely altitudes

CAREER FEATURE 02 June 2025

How do I choose a principal investigator for my next postdoc?

A computer scientist is struggling to trust their postdoctoral research adviser after negative experiences with the previous two. How do you find one that’s right for you?

By

Nikki Forrester

Find a new job

Illustration: David Parkins

The problem

Dear Nature,

I am a postdoc working at an academic institute. I admire my principal investigator (PI) and am working on a project that fits my skill set and interests. However, before this position, I had two difficult experiences in consecutive postdoc jobs that led me to leave both labs. During my interviews, the PIs and I discussed working on projects that I was passionate about, but when I joined each lab, I was assigned to other projects that didn’t align with my interests. The PIs were also unclear about where my funding came from. For instance, after working in my first postdoc position for several months, I found out that my salary was being paid by another PI in the department. At the time, I felt misled. These positions weren’t what I signed up for.

I tried to discuss my concerns with both PIs, but ultimately felt that my only option was to look for new positions. I’m now in my third postdoc role in three years, which doesn’t look great for job applications. My new PI has been transparent about funding, and the projects I’m working on are what I expected, but I’m still having a hard time trusting him. How does someone choose the right PI? — An uncertain computer scientist

The advice

Nature’s careers team reached out to two academics and two career coaches for advice.

“You did exactly the right thing by moving on,” says Emma Williams, founder of the Nerd Coach, an academic coaching business in Cambridge, UK. The relationship between a PI and postdoc “is so key to everything in your career going forwards”.

In terms of choosing the right PI for a postdoc, all four said the first step is self-reflection. “Start by identifying what you want to get out of your postdoc,” says LaShan Hendrix, a biomedical engineer at the University of Cincinnati in Ohio. “Do you want to get more papers? Do you want to take on a new role? Do you want teaching or grant-writing experience?”

Then, Hendrix suggests, explore what mentoring style you like. Do you prefer a mentor who is hands-on, or someone who is less involved? If you had issues with your PhD adviser, Hendrix recommends looking for a PI for your postdoc who can provide you with a different personality and mentoring style.

Next, consider what laboratory environment you need to thrive. For instance, in a large lab group, you might have the opportunity to take on a leadership role and collaborate with graduate students. In a smaller lab, it might just be you and the PI working closely on a project. “It can feel quite claustrophobic, or the two of you could be a brilliant team,” Williams says.

Navigating interviews

Understanding what you want as a researcher isn’t enough on its own, however. The next step is interviewing potential PIs. Keep in mind that this process isn’t just about the PI questioning you — you’re also interviewing them to see whether they will be the right fit. All of the specialists Nature spoke to said that it’s important to discuss ongoing research projects as well as where the funding comes from and what the PI expects of you. Williams suggests asking for written documentation about the project you’ll be working on, and for a copy of the grant that will support your work. If the PI is unwilling to provide those documents, that could be a warning sign, she says.

Williams also says that the way you phrase your questions can affect the type of information you receive. For instance, instead of asking a hypothetical question about how the PI will support your career, ask how they have supported their postdocs’ careers in the past, which encourages them to provide you with evidence of their previous efforts. Looking into how long researchers have stayed in the lab for can also be helpful. “If people are only staying six months, that would be a big red flag for me,” says Williams.

Hendrix says it’s crucial to clarify which intellectual ideas from your postdoc research you will be able to take with you after you move on, and which aspects will stay with the lab. For example, if your PI works on the 3D microenvironment in breast cancer, maybe you can go on to study the 3D microenvironment in lung cancer instead. Another example could be examining different pathways in the same disease.

“A red flag would be if they make it clear that they don’t want you to take anything,” she says. “Another thing to look out for is whether your future research interests are exactly what that PI is working on, because then they might see you as competition. You want to have some separation in your research interests.”

If you want to do independent research, it’s important to ask whether you can work on it during the postdoc. “I usually encourage young fellows or postdocs to look for their own small grants, which can help them to establish themselves,” says Adesola Ogunniyi, a retired neurologist who worked at the University of Ibadan in Nigeria.

Talk to other postdocs

Along with speaking to the PI, all four experts recommend talking to current and previous postdocs. “It’s important to be professional and respectful, but also to get the information you need,” says Claartje van Sijl, who owns the academic coaching practice Van Sijl Counseling & Training in Utrecht, the Netherlands. As part of your due diligence while exploring the role’s suitability, she recommends framing questions using language such as: ‘I’ve learnt from experience how crucial the working relationship is, so I’d love to hear more about how things unfolded over time in this group. What made it work and what would you advise for someone new?”

The other specialists recommend asking what day-to-day life looks like in the lab, and how current and previous postdocs would define the lab’s culture. This can help you to determine whether the lab environment will suit you.

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Nature 642, 1107-1108 (2025)

doi: https://doi.org/10.1038/d41586-025-01493-2

This is part of a series in Nature in which we share advice on career issues faced by readers. Have a problem? E-mail us at naturecareerseditor@nature.com

TECHNOLOGY FEATURE 23 June 2025 Correction 25 June 2025

Tardigrade-obsessed: meet the researchers trying to turn water bears into neuroscience models

For water-bear enthusiasts, it’s the creature’s legs that make it stand out.

By

Benjamin Plackett

Coloured image of the tardigrade Hypsibius exemplaris — the organisms are also known as ‘water bears’. Credit: Ana Lyons

There’s no two ways about it: tardigrades are badass.

These eight-legged, microscopic water-dwellers have survived the vacuum of space, extreme dehydration, and temperatures ranging from near absolute zero to above boiling point. In 2021, researchers fired frozen tardigrades from a gun onto a target several metres away, to simulate a meteorite impact, and showed that they lived to tell the tale[1].

Tardigrades are also transparent, skin-shedding and occasionally cannibalistic — traits seemingly at odds with their cuddly appearance and ‘water bear’ nickname. And they have great PR. They have a dedicated subreddit on which people showcase their tardigrade tattoos and soft toys. There are tardigrade fan clubs and memes, as well as online merchandise. Water bears might not care, but the Internet certainly does. People can’t seem to get enough of them.

But for a burgeoning community of researchers getting ready to converge this month on Tsuruoka, Japan, for the 16th International Symposium on Tardigrada, it’s all about the legs.

Tardigrades “are one of the smallest animals on Earth that we know of that have limbs,” explains Ana Lyons, a neuroscientist at Keio University’s Institute for Advanced Biosciences in Tsuruoka City. “At the same time, they also have simple eye spots, a central brain and a peripheral nervous system.”

Tardigrades, therefore, exist in a sweet spot: simple enough to study properly and intricate enough for it to be worth doing so. That’s one of the reasons that neuroscientists such as Lyons are working to make tardigrades a model organism for studying how neural circuits work — a field of research called systems neuroscience.

Not every organism has what it takes to be ‘model’, however — there are basic requirements. Ideally, they are easy to maintain and breed in the lab and have a short life cycle. They should also be genetically tractable and, for systems neuroscientists, have well-understood neurocircuitry. It is these latter two issues that scientists need to work on if tardigrades are ever to transition from meme to model organism.

Fortunately, tardigrade enthusiasts are a confident bunch.

“There’s work to be done, but I have faith in the tardigrade community,” says Jasmine Nirody, an organismal biologist at the University of Chicago in Illinois. “We’re determined.”

‘A little space alien’

Lyons has been into tardigrades since the age of 15. In rural Michigan, where she grew up, there were no advanced science courses available at her school, so she took a course at a specialized ‘magnet’ school. In search of a topic for a project, she wandered around the local library. An encyclopaedia of North American invertebrates caught her eye.

“It had a black and white [scanning electron microscope] photograph of a heterotardigrade” on the cover, she recalls. “Those are the really cool tardigrades that have all the cilia on their backs and really prominent eye spots and their claws on their legs are very distinct. It just looked like a little space alien to me.”

Ana Lyons wants to help build a ‘neural atlas’ of tardigrades.Credit: Ana Lyons

Fascinated, she reached out to the author of the tardigrade chapter and struck up a conversation. That author, Diane Nelson, invited Lyons to visit her laboratory at East Tennessee State University in Johnson City. “I spent my 16th birthday there, learning how to handle and identify tardigrades,” Lyons says. They ended up co-authoring a paper when Lyons was an undergraduate at the Massachusetts Institute of Technology (MIT) in Cambridge — although, because there were no tardigrade labs at MIT at the time, she mostly studied fruit flies and yeast.

Lyons returned to water bears during a fellowship year in Germany and continued her work as a graduate student at the University of California, Berkeley. For her postdoc, she joined the lab of neuroscientist Saul Kato. A worm biologist, Kato was looking to branch into a new experimental system when he started his lab at the University of California, San Francisco — he describes tardigrades as “Caenorhabditis elegans with feet”. They met at a tardigrade conference in 2018, and she joined his lab in 2022.

A Goldilocks-shaped niche

As Lyons and Kato outline in a preprint review posted earlier this year, tardigrades could fill a Goldilocks-shaped niche in the systems-neuroscience space[2]. Mice and fruit flies (Drosophila), tardigrade-enthusiasts argue, are too complicated for the field. It’s too difficult to isolate and connect behaviours with neural networks in these species. “You have to understand a simple system before you can understand a complex system,” says Kato. “We are so far from even having the language or frameworks to truly understand how these large, complex neuro systems work in mice, and arguably it’s the same with Drosophila.”

At the other extreme, nematodes such as the roundworm C. elegans are too simple. “You can’t get at the question of ‘what is the role of the central nervous systems versus the peripheral nervous system when it comes to limb movement’ [in worms], because worms don’t have limbs,” says Lyons.

Tardigrades do; they “have a very trimmed-down neural system but still have a brain and ganglia throughout their bodies and have limbs and can walk”, says Lyons. Most organisms that walk need thousands of neurons to do so, she explains, but not water bears. “We don’t yet know how many neurons tardigrades have, but they seem to be in the order of 300 to 700,” she says.

That simplicity makes it relatively easy — in theory — to tinker with tardigrade neurobiology and observe the consequences. It has also led some researchers to wonder whether tardigrades might be similar to, if not the ancestors of, arthropods: the group that includes insects, spiders and crustaceans. “It is one of the most ancestral states,” says Georg Mayer, a zoologist at the University of Kassel in Germany, of tardigrades; studying them “almost enables you to look back in time at a living fossil”.

Another string to the bow of tardigrades as a model organism, is their translucency. Pretty much any of their cells can be observed while the animal is alive and in motion. That’s especially important when you’re trying to study the role of neurons in delivering various behaviours. “You can actually see them,” says Lyons. “We’re trying to genetically encode florescent reporter proteins so that, in theory, we could see all their neurons.”

Tardigrades also behave in interesting and unusual ways for such small creatures. “How do they both crawl in a very organized, coordinated fashion, and have their legs move independently and grasp things?” asks Kato. “There seems to be both local control of the limb and top-down control.” Kato wants to know more about how the animal’s nervous system accomplishes both at the same time.

“There are so many other versions of this type of question in neuroscience and we think this is the perfect animal to study and find out,” Kato adds.

And then there’s the question of aesthetics — water bears are basically charismatic microfauna. “Tardigrades are definitely cuter than worms,” Kato says, “Nobody calls a worm cute.” That cuteness, he admits, has probably contributed to the water bears’ appeal as a research subject. “They’re sort of the darlings of the Internet.”

Basic barriers

Given all these potential upsides, why do researchers know more about worms and Drosophila than they do about the seemingly ideal tardigrade? Why have water bears been “leap-frogged”, as Kato puts it, by people who want to study systems neuroscience?

For one thing, tardigrades were late to the party. Researchers have conventionally gravitated towards either worms or fruit flies, because that’s where most of the early grunt work had been done and key genetic tools created. “That’s not the case with tardigrades,” says Nirody. “We still have to pin down our basic understanding of some of their genetics and neurology.”

Saul Kato thinks tardigrades could be models for systems neuroscience.Credit: Saul Kato

Researchers aren’t even sure which specific type of tardigrade to focus on. There are around 1,300 tardigrade species, and it’s not yet clear which would be the most suitable as a model organism. But as a group, they have many traits in common.

For example, says Mayer, tardigrade genomes tend to include lots of duplicated genetic material. “They have multiple copies of some gene families, which is crazy, since they’ve kept their genome so small,” says Mayer. “They have a small generation time and so it’s a disadvantage to them to have to duplicate a lot of DNA.” Why those duplications exist is a mystery that would be helpful to solve.

Tardigrade researchers are also struggling to genetically manipulate the animals — especially when it comes to inserting genes into their genome, a technique known as knock-in transgenics. At a conference in March on genetic tools for new model organisms, Lyons “was reassured to learn” that tardigrade researchers aren’t alone in facing this difficulty. “It’s all emerging model organisms, such as cuttlefish,” she says. “It seems to be much more straightforward to knock out genes.”

A 2024 paper from a Japanese group showed limited success with one approach[3] that used the editing tool CRISPR to insert genetic material into a tardigrade genome. But not at scale. Researchers need to be able to routinely knock-in hundreds of base pairs, and that isn’t yet possible.

“We don’t yet know the best way to do knock-in for tardigrades,” Lyons concludes. “Is it CRISPR? Transposons? Viruses? Or something else? Do we target tardigrades when they’re early-stage embryos? Or do we try to get in through the ovaries of the mother tardigrade?”

Once scientists answer those questions, they’ll need to work out how to do it without popping the egg or killing the embryo. “That took a long time to figure out in Drosophila, for example, but once we realized you slightly dehydrate the embryos and then replace the liquid, boom. That was it. So, there are lots of little things like that which we need to know,” says Kato.

One not-so-little unknown for systems neuroscientists is basic neuroanatomy. Tardigrade enthusiasts don’t know how many cells the typical organism has, let alone how many neurons. Nor do they know what types of neuron the animals have, nor what they do.

Lyons is applying a battery of fluorescence-microscopy techniques to find out. “I’m finding all kinds of weird and cool cell morphologies,” she says, “and some of the suspected neurons look very different from what we see in worms or fruit flies. To be sure they’re all neurons, we really need to confirm the presence of synapses using electron microscopy.”

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Nature 642, 1109-1111 (2025)

doi: https://doi.org/10.1038/d41586-025-01971-7

Updates & Corrections

- Correction 25 June 2025: An earlier version of this feature mistakenly described Ana Lyons as being based in Tokyo and over-estimated the number of attendees expected at the 2025 tardigrade conference.

References

1. Traspas, A. & Burchell, M. J. Astrobiology 21, 845–852 (2021).

2. Lyons, A. M. & Kato, S. Preprint at arXiv https://doi.org/10.48550/arXiv.2501.06606 (2025).

3. Kondo, K., Tanaka, A. & Kunieda, T. PLoS Genet. 20, e1011298 (2024).

4. Tanaka, S., Aoki, K. & Arakawa, K. Proc. Natl Acad. Sci. USA 120, e2216739120 (2023).

WHERE I WORK 23 June 2025

Ice society: looking for life at unlikely altitudes

James Bradley studies the microbes thriving in extremely cold environments.

By

James Mitchell Crow

James Bradley is a geomicrobiologist at the Mediterranean Institute of Oceanography in Marseille, France. Credit: Jacopo Pasotti for Nature

“This photograph, taken in February, captured the first return of the light to the world’s most northerly permanent settlement, Ny-Ålesund, on the Arctic archipelago of Svalbard, Norway.

I have been working in Svalbard for more than a decade, studying the microbial life in this extreme place. The surfaces of its glaciers, for example, are colonized by a huge diversity of microorganisms. My colleagues and I at the Mediterranean Institute of Oceanography in Marseille, France, have been learning how, by coming in and out of dormancy as the temperature fluctuates, these microbes can thrive despite nutrient shortages, low temperatures, high ultraviolet stress and a lack of liquid water.

With everything that we’ve discovered about ice microbes, we began to wonder whether an active microbial community could flourish in a similarly cold and nutrient-scarce environment: the atmosphere. A new direction in our work is to try to measure and understand the microbial inhabitants of the air. A key question is whether the atmosphere is just a dispersal mechanism for dormant cells, or whether it harbours active microbes that can grow and divide.

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Nature 642, 1114 (2025)

doi: https://doi.org/10.1038/d41586-025-01972-6

This interview has been edited for length and clarity.

Research

- Evidence for a sub-Jovian planet in the young TWA 7 disk

- A neutral-atom Hubbard quantum simulator in the cryogenic regime

- Evidence of Coulomb liquid phase in few-electron droplets

- Herring spawned poleward following fishery-induced collective memory loss

- Genomic and genetic insights into Mendel’s pea genes

- Deep origin of eukaryotes outside Heimdallarchaeia within Asgardarchaeota

- The pivot penalty in research

- Goal-specific hippocampal inhibition gates learning

- A distributed coding logic for thermosensation and inflammatory pain

- Two distinct host-specialized fungal species cause white-nose disease in bats

- Concurrent loss of the Y chromosome in cancer and T cells impacts outcome

- Oncogene aberrations drive medulloblastoma progression, not initiation

- Divergent DNA methylation dynamics in marsupial and eutherian embryos

- Chromosome end protection by RAP1-mediated inhibition of DNA-PK

- Chromatin loops are an ancestral hallmark of the animal regulatory genome

Article Open access Published: 25 June 2025

Evidence for a sub-Jovian planet in the young TWA 7 disk

A.-M. Lagrange,

C. Wilkinson,

M. Mâlin,

A. Boccaletti,

C. Perrot,

L. Matrà,

F. Combes,

H. Beust,

D. Rouan,

A. Chomez,

J. Milli,

B. Charnay,

S. Mazevet,

O. Flasseur,

J. Olofsson,

A. Bayo,

Q. Kral,

A. Carter,

K. A. Crotts,

P. Delorme,

G. Chauvin,

P. Thebault,

P. Rubini,

F. Kiefer,

A. Radcliffe,

J. Mazoyer,

T. Bodrito,

S. Stasevic &

…

M. Langlois

Nature (2025)

Abstract

Planets are thought to form from dust and gas in protoplanetary disks, with debris disks being the remnants of planet formation. Aged a few million up to a few billion years, debris disks have lost their primordial gas, and their dust is produced by steady-state collisions between larger, rocky bodies1,2. Tens of debris disks, with sizes of tens, sometimes hundreds, of astronomical units have been resolved with high-spatial-resolution, high-contrast imagers at optical and near-infrared or (sub)millimetre interferometers3,4. They commonly show cavities, ring-like structures and gaps, which are often regarded as indirect signatures of the presence of planets that gravitationally interact with unseen planetesimals2,5. However, no planet responsible for these features has been detected yet, probably because of the limited sensitivity (typically 2–10 MJ) of high-contrast imaging instruments (see, for example, refs. 6,7,8,9) before the James Webb Space Telescope. Here we have used the unprecedented sensitivity of the James Webb Space Telescope’s Mid-Infrared Instrument10,11 in the thermal infrared to search for such planets in the disk of the approximately 6.4-Myr-old star TWA 7. With its pole-on orientation, this three-ring debris disk is indeed ideally suited for such a detection. We unambiguously detected a source 1.5 arcsec from the star, which is best interpreted as a cold, sub-Jupiter-mass planet. Its estimated mass (about 0.3 MJ) and position (about 52 au, de-projected) can thoroughly account for the main disk structures.

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Main

The disk around TWA 7 is one of the youngest (6.4 ± 1 Myr old; ref. [12]) debris disks known to date. TWA 7 is a close (about 34 pc; ref. [13]), low-mass (0.46 M☉; ref. [14]) member of the young TW Hydra association, sometimes classified as a weak-line, non-accreting T-Tauri star[15]. The disk, resolved by the Near Infrared Camera and Multi-Object Spectrometer (NICMOS) on the Hubble Space Telescope (HST)[16], is one of the rare ones resolved around M stars. It is seen almost pole-on17,18, a most favourable configuration to precisely estimate its radial distribution and to look for planets. The most recent modelling of the disk surface density deduced from polarimetric data from the Spectro-Polarimetic High Contrast Imager for Exoplanets Research (SPHERE) on the Very Large Telescope (VLT)[18] includes a ring peaking at 28 au and extending out to more than 100 au (R1), a narrow (less than 7 au full-width at half-maximum) ring at 52 au (R2) and a broader (more than 40 au full-width at half-maximum) structure (93 au; R3; Figs. 1 and 4 from ref. [18]). No planet has been detected so far, with detection limits roughly estimated to 0.5–1 MJ beyond 50 au (Extended Data Fig. 6 and Supplementary Information).

The coronagraphic images of TWA 7 taken with the F1140C filter (central wavelength = 11.3 μm, bandwidth = 0.8 μm) of the Mid-Infrared Instrument (MIRI) on the James Webb Space Telescope (JWST)[19] were obtained on 21 June 2024 during cycle 2 (ID 3662; principal investigator, A.-M.L.; Extended Data Table 1). The details on the data reduction procedure are described in the Methods. The main critical step is the subtraction of the residual diffracted light leaking from the MIRI coronagraph using a reference star observed with the same set-up. This process is necessary to bring the contrast ratio with respect to the star to a level of 10−5–10−4 beyond angular separations of about 0.5 arcsec. The final image, presented in Fig. 1, reveals three sources within 10 arcsec from TWA 7, the properties of which are listed in Extended Data Table 2. One source at about 4.7 arcsec from TWA 7 (position angle 107°) was classified as a stellar background source, already detected in ancillary optical data from the Space Telescope Imaging Spectrograph (STIS) on the HST as well as near-infrared data from the NICMOS on the HST and the SPHERE on the VLT. The second one, located about 6.7 arcsec east of TWA 7, and spatially resolved, has no counterpart in the data from the SPHERE on the VLT, or in the data from the NICMOS or the STIS on the HST (taking into account TWA 7 proper motion (−118.751 ± 0.023 mas yr−1, −19.648 ± 0.026 mas yr−1; ref. [20])). Its location in the MIRI image is consistent with that of a bright source in Atacama Large Millimeter Array (ALMA) band 7 (346 GHz) data from 2016 (ref. [21]), given the proper motion of the TWA 7 system between the ALMA and MIRI observations. This object therefore has the characteristics of a highly reddened background source. It is reminiscent of the JWST observations of the HR 8799 multi-planetary system, for which a z ≈ 1 galaxy was detected in the MIRI data taken at both 10 and 15 μm, and in ALMA band 7 data as well, but never identified in the near-infrared[22]. The third source, located about 1.5 arcsec northwest of TWA 7 (about 51 au, projected separation) is unique to these MIRI observations. Hence, this source (hereafter, CC#1) is extremely red, and is not compatible with any background or foreground star.

Fig. 1: JWST MIRI image of TWA 7 in the F1140C filter.

North is up, and east is left. The status of three identified sources is indicated. Note that the faint signal north of the background galaxy is an artefact. bgd, background.

No data are available to test whether this third source shares a common proper motion with the star. In this context, in the following, we discuss the possible nature of this object. The first origin that one can consider is a Solar System object. Yet, most Solar System objects have proper motion between 5 and 40 arcsec h−1 (ref. [23]). Even very remote, low-proper-motion small Solar System objects such as the dwarf planets Eris (semi-major axis of about 68 au) and Sedna (semi-major axis of about 510 au) showed proper motion of 1.4 arcsec h−1 (ref. [24]) and 1.7  arcsec h−1 (ref. [25]), respectively, at the time of their discovery. No trail was observed during the 2-h-long exposures, and no apparent motion was observed between the two images recorded during the sequences taken 2 h apart, indicating that CC#1 has a proper motion of less of than about 0.05  arcsec h−1. A Solar System object with such a low proper motion would be located at more than 200 au. For such a cold object, reflected light would dominate in the MIRI F1140C filter and would require a Neptune-like size to fit the flux measured for CC#1 (for a geometric albedo of 0.1–0.3). It was checked whether such a scenario could be compatible with the hypothetical Planet Nine[26]. On the basis of the constraints from the planetary ephemeris[27] and the predictions of the orbit of Planet Nine[28], a Solar System origin for CC#1 can definitely be excluded.

The second possible origin is a background galaxy. Like the background galaxy seen east of TWA 7, CC#1 has no reported counterpart at optical and near-infrared wavelengths. However, in contrast to this galaxy, it has no detected counterpart in the ALMA band 7 data (see details in Supplementary Information). The detection of this unresolved source at 11.3 μm, its non-detection in ALMA band 7 and the measured upper limits at 1.6 μm in the data from the SPHERE on VLT (Supplementary Information) could still be compatible with intermediate-redshift star-forming galaxies. Using such galaxy templates at various redshifts and published galaxy counts in JWST fields of view, we estimated that the probability of finding one such galaxy in a region of 1.5 arcsec radius centred on TWA 7 is about 0.34% (Methods). This probability is low, albeit non-zero. The location of the source with respect to the disk structure, right in a circumstellar ring gap (see below), makes the galaxy hypothesis even more unlikely.

The third and last possible origin is a planet. A forward modelling approach is used to constrain the properties of this planet. Using the HADES model[29], it is possible to find fits for the JWST photometric data point while accounting for the 5σ upper limits provided by the high-contrast images at 1.59 μm (H2) and 1.67 μm (H3) from the SPHERE on the VLT (Fig. 2). HADES considers the thermal evolution of the planet; it assumes that the planet and the stars are coeval. Atmospheric fits incorporating water clouds indicate a narrow range, regardless of other parameters, for the effective temperature between 305 and 335 K (Extended Data Fig. 3 and Extended Data Table 3), and a mass of about 0.3 MJ. A metallicity range above solar is required. Additional data will be necessary to further constrain this parameter.

Fig. 2: Fitting of the candidate companion’s available data using the HADES model.

Modelled photometry is shown with crosses. The 1–15-μm spectra correspond to representative solutions that fit the observed flux of TWA 7b in the JWST F1140C filter (blue), with respect to the 5σ upper limits from the VLT SPHERE data in the H2 and H3 filters (red), and are consistent with an age of 6.4 ± 1 Myr. The bandwidths of H2 and H3 are 0.052 μm and 0.054 μm, respectively. A zoomed-in view around H2 and H3 highlights their bandwidths and the integrated model spectrum points below the upper limit. met, metallicity; Teff, effective temperature; fsed, sedimentation rate.

As a comparison and extra mass estimation, evolutionary models of cold, low-mass planets[30] combined with our estimation of effective temperature are used; they lead to a comparable mass of close to 0.3 MJ (Extended Data Fig. 4) for metallicities less than 2.5, as available in their framework. These two consistent results indicate that the planet mass is substantially below 1 MJ. The current best estimate is around 0.3 MJ, and is only weakly dependent on the underlying details of the two models used. It, however, depends on the age of the planet, assumed here to be coeval with its parent star. A younger planet would lead to a smaller mass.

The observed source is located right on the R2 narrow ring, and, moreover, within a region identified by ref. [18] as underdense compared to the rest of the ring (Fig. 3a). This is very reminiscent of simulations of resonant rings predicted by early works31,32 for closer and less massive planets, which led to the proposal of such a possible situation for the TWA 7 system[18].

Fig. 3: Image of the TWA disk and candidate companion and simulations.

a, Polarimetric image (in log scale) of the disk composed of the sum of three epochs (26 April 2016 presented in ref. [17], 20 March 2017 presented in ref. [18], and a new epoch, 8 February 2022, reduced as in ref. [18]) from the SPHERE Infrared Dual-Band Imager and Spectrograph (IRDIS), with the MIRI image (resampled to the SPHERE pixel size) as an overlay with contours. The log of these data is provided in Supplementary Table 2. The peak densities of the rings are also indicated. The central hatched disk is a numerical mask to hide the stellar residuals. b, Disk simulations. Top view of a disk of massless planetesimals perturbed by a 0.34-MJ planet at 52 au, on a circular orbit, after 6 Myr (see details in text). The orbit of the perturbing planet is sketched in green and the location of the planet on its orbit is shown in red.

Dedicated N-body simulations were conducted for a planet with a mass of 0.34 MJ, located at 52 au around the 0.46 M☉ central star. This value is consistent with the measured projected separation, assuming that the planet and the ≈13°-inclined disk are coplanar. The simulation also included a disk of 200,000 planetesimals, distributed between 20 and 130 au. These parameters were selected to roughly match the boundaries of the observed disk. The planetesimal disk was assumed to be coplanar with the planet’s orbit, with an initial surface density proportional to 1/r. The eccentricities of the planetesimals were chosen to range between 0 and 0.01, and the planet was assumed to evolve on a circular orbit. The simulations, which spanned 10 Myr, were performed using the symplectic N-body code swift_rmv3 (ref. [33]), which provides a first-order treatment of close encounters.

A top view of the resulting distribution of planetesimals after 6 Myr is shown in Fig. 3b. Planetary perturbations efficiently carve the disk over about 30 au, but leave a narrow ring at 52 au, as well as a relative void (underdensity) around the planet. The latter structure is characteristic of a ring of co-orbital planetesimals with the planets, trapped in a 1:1 resonance with it. The similarity between the TWA 7 disk image and the simulation (Fig. 3a,b) is remarkable. In this context, the observed R2 ring would correspond to the ring of co-orbiters with the planet. We nevertheless expect some differences between both distributions owing to the radiation pressure acting on the grains. Additional information on the grain size distribution is needed to compute the effects of radiation pressure and refine the dynamical modelling.

The low likelihood of a background galaxy, the successful fit of the MIRI flux and SPHERE upper limits by a 0.3-MJ planet spectrum and the fact that an approximately 0.3-MJ planet at the observed position would naturally explain the structure of the R2 ring, its underdensity at the planet’s position and the gaps provide compelling evidence supporting a planetary origin for the observed source. Like the planet β Pictoris b, which is responsible for an inner warp in a well-resolved—from optical to millimetre wavelengths—debris disk[34], TWA 7b is very well suited for further detailed dynamical modelling of disk–planet interactions. To do so, deep disk images at short and millimetre wavelengths are needed to constrain the disk properties (grain sizes and so on). Refining the planet mass determination can be carried out with additional JWST photometry and possibly spectroscopy. Measuring the orbital parameters (eccentricity, in particular) is more challenging given the long orbital period (about 550 yr) of the planet. Yet, one notes that a planet on an eccentric orbit would rapidly destroy R2.

As it is angularly well resolved from the star, TWA 7b is suited for direct spectroscopic investigations, providing the opportunity to study the interior and the atmosphere of a non-irradiated sub-Jupiter-mass, cold (about 320 K) exoplanet, and start comparative studies with our much older and cooler Solar System giants, as well as with the recently imaged cold (about 275 K) but more massive (6 MJ) planet eps Ind Ab[35]. Improved estimations of its metallicity and temperature will further constrain its mass.

The present results show that the JWST MIRI has opened up a new window in the study of sub-Jupiter-mass planets using direct imaging. Indeed, TWA 7b (about 100 M⊕) is at least ten times lighter than the exoplanets directly imaged so far, and planets as light as 25–30 M⊕ could have been detected if present at 1.5 arcsec from the star or beyond.

Methods

JWST observations and data reduction and analysis

Data

Coronagraphic observations were performed with the 4QPM_1140 coronagraph paired with the F1140C filter. The details of the observations are given in Extended Data Table 1. We obtained two roll angles (difference of 7.835°) to mitigate the attenuation of the coronagraph in the field of view, in case an object falls close to one phase transition of the 4QPM. Each coronagraphic observation was 2 h long, hence a total of 4 h on the science target. Background observations were observed immediately after the science exposures in a two-point dithering mode, for a total of 4 h.

A reference star, CD-23-9765, was observed back-to-back with the target in the same configuration with the aim to subtract the starlight diffraction after the coronagraph. The reference shares similar brightness and spectral type with the target, and is angularly close. It was observed with nine-point small-grid dithering (SGD) to apply post-processing algorithms such as principal component analysis (PCA)[36]. In total, the reference star was observed for about 1 h and comes with dedicated background observations.

Using comparison with simulated coronagraphic images, as in ref. [37], we were able to estimate a pointing accuracy on the 4QPM of about 2 mas per axis, significantly lower than the 10-mas step of the SGD. We also confirmed the detector coordinates of the 4QPM_1140 mask (119.758, 112.158 as provided in the JWST Calibration Reference Data System).

Data analysis

The data reduction follows the steps described in refs. 21,38. Level 1 data are retrieved from the Mikulski Archive for Space Telescopes (MAST), processed with v1.14.0 of the pipeline together with Calibration Reference Data System file 1241. Images are registered to the coronagraph centre. Calibrated files (‘cal’ files) are produced in-house with the JWST pipeline for each roll, by subtracting the background and converting the photometric units (data numbers per second to mega-janskys per steradian). The background is built from the minimum per pixel of the two dithers. We skipped the flat-field correction, which is not appropriate for the MIRI coronagraph[22].

We took advantage of the diversity brought by the SGD mode to build a reference frame to subtract the stellar diffraction. We tested various algorithms and retained a linear combination (which uses the downhill simplex minimization) of the nine SGD reference star images, as well as the PCA, as the two algorithms providing the best detection of CC#1. To mitigate the over-subtraction effect, a numerical masking is implemented to ignore some parts of the image. We obtained the best compromise by selecting the annular region between 0.5 arcsec and 3 arcsec, and the three sources were masked with a 1 λ/D patch, with λ representing the observing wavelength and D the telescope diameter (we checked that the CC#1 flux measurements are similar with a different region: 2–3 arcsec). We proceeded similarly for the PCA, using eight components to build the final image that is subtracted to the data, in an annular region from 0.5 arcsec to 5 arcsec (point sources not masked). Despite the bad-pixel correction applied on the raw coronagraphic images, the subtracted images with the reference star are still affected by a few bad pixels, both with the linear combination and PCA. We further apply a σ-clipping function to correct for these remaining bad pixels. Finally, the images are rotated to align north up, considering the aperture position angles: 121.45° for roll 1 and 129.27° for roll 2, as well as the V3 axis orientation on the detector (4.835°).

Next, extracting the flux and position of the CC#1 requires modelling its point spread function (PSF), which can vary spatially and nonlinearly owing to the attenuation of the coronagraph for which the phase transitions extend across the whole field of view. We used both the diffraction code developed in refs. 39,40 and WebbPSF[41] to simulate the MIRI PSF, taking into account the coronagraph, considering the configuration of mask, stop and filter (‘FQPM1140’, ‘MASKFQPM’ and ‘F1140C’, according to the WebbPSF terminology). The position of CC#1 is approximated with a Gaussian fit, passing the sky coordinates to the former diffraction code and detector coordinates to WebbPSF to calculate the PSF of CC#1 accounting for the coronagraph attenuation. We measured the coronagraph transmission with both PSF estimates. The flux of the PSF model at the position of CC#1 is integrated in a 1.5 λ/D aperture (to match the aperture used for photometric measurements), and compared to that at 10 arcsec (far away from the coronagraph influence). The two approaches give similar transmissions: 0.66 and 0.62 in roll 1 and 0.31 and 0.28 in roll 2. Therefore, CC#1 is significantly closer to one 4QPM transition in roll 2 than in roll 1, so both its astrometry and photometry can be affected (Extended Data Fig. 1). We measured a signal-to-noise ratio of 30 and 18, respectively, for roll 1 and roll 2, using the linear combination method. In the roll 2 image, the PSF is more asymmetrical as the planet is closer to the quadrant edge in comparison to roll 1, so we decided to consider only roll 1 data for the photometric analysis.

On the basis of these CC#1 PSF models, we extracted the flux and the photometry of the object by minimizing the residuals between the reduced JWST data and a PSF model in a 1.5 λ/D area with three free parameters (positions and flux) and using either a downhill simplex algorithm or the Nelder–Mead algorithm[42]. For comparison, we also used aperture photometry, but this required implementation of an aperture correction based on simulated PSF (ratio of the total flux in the PSF to the flux integrated in the 1.5 λ/D aperture).

The flux extraction is applied both on the photometrically calibrated files (.cal), which directly provides CC#1 flux in mega-janskys per steradian, and on the uncalibrated files (.rate), which requires to measure a contrast with respect to the non-coronagraphic image of the star. As detailed previously[22], the contrast measurement relies on commissioning data either on target acquisition images that come with the telescope pointing procedure but are obtained with a neutral density filter, or from images obtained on and off the coronagraph on another star. The method using target acquisition shows some net discrepancies, probably because the target acquisition filter is very broad (about 8–18 μm) and the targets have different spectral types (M3 for TWA 7 and K0 or K5 for the commissioning targets). Besides, the emission of the TWA 7 disk is expected to become significant beyond 15 μm. As a result, we did not use the target acquisition method in the following. The final photometric values are based on the linear combination and PCA technique to suppress the starlight. We averaged the values of the different methods (calibrated files and contrast, aperture and PSF model for the photometric extraction) and the error bar is built from the extreme values ((max − min)/2) for being conservative. We measured a flux density of 5.60 ± 0.97 × 10−19 W m−2 μm−1 for CC#1. The fluxes of the other sources are given in Extended Data Table 2. Note that we also tested the typical ‘injection–recovery’ method directly in the raw data, but did not notice any significant differences for the extracted flux of the planet.

Estimation of the probability for an intermediate-redshift star-forming galaxy

To estimate the probability that the source labelled CC#1 is a galaxy, we have taken into account the three constraints on the fluxes or upper limits at 1.6, 11 and 870 μm. The source has a flux of 22 μJy at 11 μm, and is not detected with ALMA at 870 μm, but with a tapered resolution of 2 arcsec. The measured 3σ upper limit for an unresolved source at the position of the MIRI source is 96 μJy (ref. [21]). Combining all ALMA observations (see Extended Data Fig. 5), the 3σ upper limit is 76 μJy, in a beam of 0.29 arcsec × 0.24 arcsec (see a detailed analysis and estimation in the Supplementary Information). The third constraint is from the non-detection at 1.6 μm with VLT SPHERE, with an upper limit of approximately 0.6 μJy for a point source (about 60 mas in size). However, we have to take into account the fact that a low-z galaxy could be extended in the calculation of its maximum flux at 2 μm. Indeed, the size of a galaxy is expected to vary from one wavelength to the other: at 1.6 μm, the disk of old stars dominates, so the source is more extended, of the order of 10 kpc, whereas at 11 μm and 870 μm, the nuclear star-forming region dominates, and will appear more concentrated, of the order of 100–500 pc. We therefore considered the flux limit of an extended (0.6 arcsec) source in the SPHERE data (that is, 60 μJy).

Two types of galaxy might comply with these three constraints: star-forming galaxies or active nucleus (AGN) or a combination of both, with a redshift between z = 0.1 and 1. For such galaxies, at lower redshifts, where 1 arcsec is smaller than 2 kpc, the source would probably look extended (up to 5 arcsec) for wavelengths between 1 and 4 μm (ref. [43]). At higher redshifts, the peak of the emission usually located at 100 μm will enter the ALMA domain at about 1 mm, and it should have been detected by ALMA[44].

As the density of the cosmic star formation rate increases considerably with redshift between z = 0 and 1, starbursts at z = 0 are good templates for star-forming galaxies at z = 0.1 to 1. The starburst is in general nuclear. It can be highly peaked in the centre, or distributed in a ring of about 100-pc radius (like in M82), but the emitting size at 100-μm rest frame will be no more than typically 1 kpc. This corresponds to an angular size of about 0.55 arcsec at z = 0.1, about 0.16 arcsec at z = 0.5, and about 0.12 arcsec at z = 1. We also considered AGN templates. The nucleus is then a point source at long wavelengths, while the galaxy host is extended (about 10 kpc) at the shortest wavelengths.

The probability to find a z = 0.1 to 1 star-forming galaxy with a flux at 11 μm of 22 ± 3.8 μJy (hence, within a range of 7.6 μJy) in a field of view of 10 × 10 arcsec can be estimated readily through the source counts, from refs. 45,46,47,48. A probability of 50% is found in the redshift range z = 0.1 to 1.

To take into account the other constraints at 1.6 and 870 µm, we used the Spitzer Wide-Area Infrared Extragalactic Survey (SWIRE) templates, from ref. [49], http://www.iasf-milano.inaf.it/~polletta/templates/swire_templates.html which include 14 templates of starbursts and AGN, representative of star-forming and active intermediate-redshift galaxies. These templates were computed for a half-dozen redshifts, and calibrated to have a flux of 22 μJy at 11 μm. An illustrative plot of their spectral energy distribution (SED) is shown in Extended Data Fig. 2, in which the flux constraints at 1.6 and 870 µm are also indicated by black symbols (square and triangle).

We used the rest-frame 8-μm luminosity function of galaxies at redshifts between z = 0.1 and z = 1 in the Great Observatories Origins Deep Survey (GOODS) fields[50] to estimate the abundance of these star-forming galaxies. The 8-μm luminosity was deduced from the templates. This led to a probability of 12% to find such a galaxy in the right redshift range (z = 0.1 to 1) in the 10 × 10 arcsec field of view. We estimated that there are about 80% star-forming galaxies and 20% active nuclei, including low-luminosity ones such as Seyfert galaxies51,52. As can be seen in Extended Data Fig. 2, all templates comply with the three constraints below z = 0.5, and some starbursts are found brighter at 870 μm above this redshift. We therefore reduced the probability accordingly for z > 0.5, attributing each star-forming template equal probability, as shown by the observations[53]. The final probability to find such a galaxy in the 10 × 10 arcsec field of view is about 5%, with significant uncertainties: +3/−2%. Hence, in a radius of 1.5 arcsec around TWA 7, the probability to have such a galaxy is about 0.34% (+0.22/−0.14%). We note that the distribution of galaxies on the sky might be clustered in some rare places, and our error bars should be increased, but no more than 30% given the typical errors on luminosity function of galaxies45,47,50. Altogether, the probability of having a galaxy satisfying the various constraints within 1.5 arcsec would be 0.34% (+0.29/−0.18%). Note that the current calculation applies to these specific observational constraints. The probability for CC#1 to be a rare type of background galaxy compatible with the JWST, ALMA and SPHERE observational constraints is therefore low. Such a low-probability event can admittedly arise would a large number of independent datasets be analysed. However, this is not the case for this study. Indeed, only two datasets among about 30 JWST MIRI datasets dedicated to exoplanet searches are analysed. Moreover, most of the other datasets are not as deep as the present ones, and there are no corresponding 1.6- and 870-μm data available. Hence, the likelihood that any research group would have found such a peculiar background galaxy contaminant within the whole corpus of relevant JWST data is much lower than the simple 10.2% (30 × 0.34%) one could derive.

Data availability

All observational data in this work are available through public data archives. The JWST data were collected through General Observer programme number 3662 (principal investigator A.-M.L.) and will be available through the MAST database (https://mast.stsci.edu) from 21 June 2025. The data described here are available via the MAST archive at https://doi.org/10.17909/4qvz-sw62. The present study made use of ALMA data collected during the 2015.1.01015.S programme (principal investigator A. Bayo). The present study made use of detection limits derived from SPHERE data collected during the programme 198.C-0209, and of SPHERE data collected during the programmes 097.C-0319(A), 105.209E.001 and 198.C-0209(F). The data are available at https://archive.eso.org.

Code availability

JWST MIRI data reduction and analysis codes are available via GitHub at https://github.com/mathildemalin/ExoCAT. Data reduction was carried out using the publicly available JWST pipeline https://jwst-pipeline.readthedocs.io/en/latest/, and the stpf package https://stpsf.readthedocs.io/en/latest/ to simulate PSFs for JWST. We used various functions of the following software packages to perform the analysis and create the figures: numpy, astropy, scipy, matplotlib and photutils.

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Acknowledgements

This work is based on observations made with the JWST of NASA (National Aeronautics and Space Administration), the European Space Agency and the Canadian Space Agency. The data were obtained from the Mikulski Archive for Space Telescopes at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST. These observations are associated with programme number 3662. This paper makes use of ALMA data. ALMA is a partnership of the European Organisation for Astronomical Research in the Southern Hemisphere (representing its member states), the National Science Foundation (USA) and the National Institutes of Natural Sciences (Japan), together with the National Research Council of Canada (Canada), the National Science Council and the Academia Sinica Institute of Astronomy and Astrophysics (Taiwan) and the Korea Astronomy and Space Science Institute (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by the European Organisation for Astronomical Research in the Southern Hemisphere, the National Radio Astronomy Observatory operated by Associated Universities, Inc., and the National Astronomical Observatory of Japan. This paper makes use of observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere. This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (COBREX; grant agreement number 885593). L.M. acknowledges funding by the European Union through the E-BEANS ERC project (grant number 100117693), and by the Irish Research Council under grant number IRCLA-2022-3788. Views and opinions expressed are those of the authors alone and do not necessarily reflect those of the European Union or the ERC Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Author information

Authors and Affiliations

1. LIRA, Observatoire de Paris, Université PSL, Sorbonne Université, Université Paris Cité, CY Cergy Paris Université, CNRS, Meudon, France

A.-M. Lagrange, C. Wilkinson, A. Boccaletti, C. Perrot, D. Rouan, A. Chomez, B. Charnay, Q. Kral, P. Thebault, F. Kiefer, A. Radcliffe, J. Mazoyer & S. Stasevic

2. Université Grenoble Alpes, CNRS, IPAG, Grenoble, France

A.-M. Lagrange, H. Beust, A. Chomez, J. Milli & P. Delorme

3. Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD, USA

M. Mâlin

4. Space Telescope Science Institute, Baltimore, MD, USA

M. Mâlin, A. Carter & K. A. Crotts

5. School of Physics, Trinity College Dublin, University of Dublin, Dublin, Ireland

L. Matrà

6. Observatoire de Paris, LUX, PSL University, Collège de France, Sorbonne University, CNRS, Paris, France

F. Combes

7. Observatoire de la Côte d’Azur, Université Côte d’Azur, Nice, France

S. Mazevet & G. Chauvin

8. Centre de Recherche Astrophysique de Lyon, Université Claude Bernard Lyon 1, CNRS, ENS, Saint-Genis-Laval, France

O. Flasseur

9. European Southern Observatory, Garching bei Muenchen, Germany

J. Olofsson, A. Bayo & M. Langlois

10. Pixyl, La Tronche, France

P. Rubini

11. Département d’Informatique de l’École Normale Supérieure (ENS-PSL, CNRS, Inria), Paris, France

T. Bodrito

Contributions

A.-M.L. led the proposal, the interpretation of the various data and the writing of the paper. A. Boccaletti, M.M. and C.P. designed the JWST MIRI observing sequence, and reduced the data and participated in their interpretation. C.W. conducted the SED fitting. B.C. participated in the vetting of the Solar System origin of the source. A.R., B.C. and S.M. participated in discussions on the SED fitting. A. Chomez provided the SPHERE detection limit maps used in this paper. A. Chomez, O.F., M.L., P.D. and G.C. participated in their interpretation. S.S. and T.B. tried to improve the detection limits using alternative algorithms. J. Milli participated in the building of the proposal, produced the SPHERE polarimetric images and participated the interpretation of the JWST MIRI data. A. Bayo and J.O. provided their analysis of the ALMA data, and participated in their interpretation. L.M. reanalysed the ALMA data and led the interpretation of these data. D.R. and F.C. conducted the estimation of the galaxy background probability. H.B. performed the N-body dynamical simulations. Q.K. and P.T. participated in the writing of the proposal and in the discussion of the disk–planet interactions. J. Mazoyer participated in the discussion of the disk properties. F.K. participated in the analysis of Gaia data, and general discussion. A. Carter and K.C. participated in the discussion on the astrometry of the source. P.R. participated in the proposal writing and the analysis of the data. All authors commented on the manuscript.

Corresponding author

Correspondence to A.-M. Lagrange.

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Extended data figures and tables

Extended Data Fig. 1 JWST individual data sets.

Orientation of the 4QPM and its phase transitions in the sky plane for the two telescope rolls. North is up, East is left.

Extended Data Fig. 2 SED of a set of 14 representative starburst galaxies and AGN at various redshifts.

The curves are distinguished by their colors, and the corresponding labels, valid for all panels. All curves are calibrated to have a flux of 22 mJy at 11 μm. The two additional constraints are marked by black symbols (square and triangle): the flux should be lower than 60 mJy at 1.6 μm, and 96 mJy at 870 μm. The SED which are not consistent with the limits are plotted in dashed lines.

Extended Data Fig. 3 Planet characterization.

Corner plot of cloudy forward modeling using SPHERE upper limits and age constraints of 6.4+/− 1 Myr. The associated priors are listed in Extended Data Table 3. The mass found is 0.34+/− 0.06 MJ (considering errors from the MCMC only). Core given in Earth mass, Tint corresponds to the intrinsic temperature and fsed the sedimentation rate of the considered clouds.

Extended Data Fig. 4 Cooling models.

Thermal evolution curves from ref. [30], showing the effective temperature evolution of cloudy planets with an [Fe/H] of 0.4 dex. The estimated age and effective temperature of TWA 7b supposing the planet and star are coeval is represented by the blue point. The effective temperature used is 316+19/−23K (derived from the forward modeling) and the age 6.4+/− 1 Myr.

Extended Data Fig. 5 ALMA image of TWA7.

Combined ALMA 0.88 mm image of the TWA 7 system obtained with Briggs 0.5 weighting, centered at the phase center of the April compact configuration observations. The background galaxy is clearly detected East of the expected stellar position, at a position consistent with the East source detected by MIRI (black and white circle). The stellar location at each of the 2016 ALMA epochs is shown by the green star (positions largely overlapping), whereas the stellar location at the 2024 MIRI observation is shown by the orange star. The position of the CC#1 source at the epoch of the 2024 MIRI observation is shown by the cyan circle. The image (not primary beam-corrected) has a resolution of 0.19” x 0.18” (shown as the circle in the bottom left of the image) and an RMS noise level of 23 μJy/beam.

Extended Data Fig. 6 SPHERE detection limits.

Contrast 5-sigma confidence level curves of the SPHERE observation used to compute the upper limits on the candidate companion flux.

Extended Data Table 1 Log of observations

Extended Data Table 2 Astrometry and photometry of sources detected within 10” from TWA 7

Extended Data Table 3 Uniform priors used in forward modeling approach

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Tables 1 and 2 and References.

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Lagrange, AM., Wilkinson, C., Mâlin, M. et al. Evidence for a sub-Jovian planet in the young TWA 7 disk . Nature (2025). https://doi.org/10.1038/s41586-025-09150-4

- Received: 25 July 2024

- Accepted: 13 May 2025

- Published: 25 June 2025

- DOI: https://doi.org/10.1038/s41586-025-09150-4

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A neutral-atom Hubbard quantum simulator in the cryogenic regime

Muqing Xu,

Lev Haldar Kendrick,

Anant Kale,

Youqi Gang,

Chunhan Feng,

Shiwei Zhang,

Aaron W. Young,

Martin Lebrat &

…

Markus Greiner

Nature (2025)

Abstract

Ultracold fermionic atoms in optical lattices offer pristine realizations of Hubbard models[1], which are fundamental to modern condensed-matter physics2,3. Despite notable advancements4,5,6, the accessible temperatures in these optical lattice material analogues are still too high to address many open problems7,8,9,10. Here we demonstrate a several-fold reduction in temperature6,11,12,13, bringing large-scale quantum simulations of the Hubbard model into an entirely new regime. This is accomplished by transforming a low-entropy product state into strongly correlated states of interest via dynamic control of the model parameters14,15, which is extremely challenging to simulate classically[10]. At half-filling, the long-range antiferromagnetic order is close to saturation, leading to a temperature of \(T/t=0.0{5}_{-0.05}^{+0.06}\) based on comparisons with numerically exact simulations. Doped away from half-filling, it is exceedingly challenging to realize systematically accurate and predictive numerical simulations[9]. Importantly, we are able to use quantum simulation to identify a new pathway for achieving similarly low temperatures with doping. This is confirmed by comparing short-range spin correlations to state-of-the-art, but approximate, constrained-path auxiliary-field quantum Monte Carlo simulations16,17,18. Compared with the cuprates2,19,20, the reported temperatures correspond to a reduction from far above to below room temperature, at which physics such as the pseudogap and stripe phases may be expected3,19,21,22,23,24. Our work opens the door to quantum simulations that solve open questions in material science, develop synergies with numerical methods and theoretical studies, and lead to discoveries of new physics8,10.

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Main

The Hubbard model is a paradigmatic description of strongly correlated electrons that is central to our understanding of quantum materials[3]. After decades of concentrated study, it is strongly believed that this model hosts a variety of exotic quantum states25,26, including the intriguing phases observed in the cuprate high-temperature superconductors2,3,22. Despite its apparent simplicity, solving the two-dimensional (2D), square lattice Hubbard model has been an outstanding challenge. In recent years, building on decades of algorithmic development, combined use of the most advanced numerical methods has established certain aspects of the properties of the model21,23,24,27,28,29,30. Nevertheless, a full description of the low-temperature phase diagram remains beyond the reach of theoretical tools and computational methods[9].

Its broad applicability and numerical intractability make the Hubbard model an ideal candidate for quantum simulation8,10. In particular, ultracold fermionic atoms in optical lattices can natively implement the requisite fermionic degrees of freedom and serve as a pristine model system for strongly correlated electrons moving and interacting in a crystalline lattice[1]. The Hubbard model is described by the geometry of the lattice, a tunnelling amplitude t between neighbouring sites and a contact interaction energy U, which can all be widely programmed in cold-atom systems. However, the enlarged length and time scales in cold-atom systems lead to very small energy scales, making it challenging to reach regimes corresponding to real materials at low temperatures[7]. The lowest reported experimental temperatures of T = 0.25(2)t (refs. 6,11,12,13) (Fig. 1a) in 2D Hubbard quantum simulators with intermediate interaction strengths of U/t ≃ 8 correspond to an effective temperature of approximately 700 K in the cuprates by normalizing temperatures with superexchange energy J = 4t^2/U (refs. 2,19,20) (Methods). One needs to reach well below room temperature to investigate the properties relevant to cuprates, including pseudogap behaviour, stripe order and unconventional superconductivity3,19,21,22,23,24. New cooling schemes are, therefore, needed for cold-atom-based platforms to offer competitive simulations of quantum materials.

Fig. 1: Cooling ultracold atoms by transforming a low-entropy product state to strongly correlated states.

a, Schematic of the 2D Hubbard phase diagram. The lowest temperatures reported in refs. 6,11,12,13 (grey dashed) and the estimated temperatures in this work (red band) are marked. b, To reach lower entropy, we prepare a gapped BI in contact with a gapless metallic reservoir. Entropy flows from the BI to the reservoir[31]. After isolating the two parts, the BI is transformed into a strongly correlated state of interest by dynamically changing the lattice geometry, Hubbard parameters and density, while preserving the very low entropy of the original BI. c, The BI has a filling of two atoms per site, which appear as empty sites in a parity-projected fluorescence image (left), in contrast to the dilute reservoir. We then halve the lattice filling by doubling the number of lattice sites, thereby making the BI visible (centre). The strongly correlated state at the end of the preparation spans about 340 sites, in which empty sites correspond to coherent doublon–hole pairs indicating low entropy (right). The central state of interest and reservoir are shaped by optical potentials programmed with DMDs (not shown). d, To double the number of sites, we continuously decrease the long-spacing lattice depth VL and increase the short-spacing lattice depth VS. This effectively splits doubly occupied sites into dimers, which are then connected to form a square lattice.

In this work, we demonstrate a substantial reduction in the temperatures achievable in cold-atom-based quantum simulations of the Hubbard model on a 2D square lattice. At half-filling, we achieve temperatures of \(T=0.0{5}_{-0.05}^{+0.06}\,t\) with U/t ≃ 8 in a large system consisting of about 340 lattice sites, which corresponds to a several-fold improvement of the state of the art6,11,12,13. We compare the measured spin correlations, which extend out to long range, to exact numerical results, and find excellent agreement.

The behaviour of the Hubbard model is not fully understood at finite doping, which has hindered the development of cooling techniques in this regime, even at the level of theoretical proposals. Here, we experimentally propose and demonstrate a novel pathway to realize low temperatures with finite values of doping δ between 2% and 21%. Detailed comparisons with state-of-the-art approximate numerical computations16,17,18 indicate that our temperatures are T ≲ 0.1t, comparable to those achieved at half-filling. These temperatures correspond to a reduction to markedly below room temperature in cuprates.

Our scheme hinges on the efficient preparation of product states with extremely low entropies[31]. Seminal proposals suggested that these states can be adiabatically connected to strongly correlated states of interest to reach low temperatures14,15,32,33,34. Despite experimental progress with bosonic systems35,36,37, practical implementations of these schemes for fermions have been challenging beyond proof-of-principle realizations at half-filling31,38,39,40. This holds especially true in large Hubbard systems, in which finite-time, out-of-equilibrium dynamics are out of reach of numerical simulations41,42. Our work, therefore, showcases how quantum simulators can experimentally address these optimization problems.

Experimental scheme

The spirit of our protocol is similar to most cooling cycles. In a first generalized compression step, entropy is extracted from the system of interest to a reservoir in thermal contact by reducing the density of states of the system14,31,43. After the system is isolated from the reservoir, increasing the density of states by generalized expansion decreases the temperature. In practice, our scheme involves initializing cold atoms in a low-entropy band insulator (BI), and transforming the BI into a strongly correlated state of interest31,39 (Fig. 1b).

The BI can be prepared with extremely high fidelity because the chemical potential lies within the band gap featuring low density of states, which allows for efficient entropy redistribution to a metallic reservoir11,14,31,36,43. We load a spin-balanced mixture of fermionic 6Li atoms in the lowest two hyperfine states into an optical lattice (long-spacing lattice). We set the magnetic bias field at 550 G close to a broad Feshbach resonance to minimize interaction strengths for BI formation. Programmable optical potentials created by two separate digital micromirror devices (DMDs) first confine about 340 atoms into a BI covering 170 sites, then isolate the BI from the reservoir. We estimate a low initial entropy per particle of s = 0.025(4)kB (ref. [31]), where kB is the Boltzmann constant, based on a measured singly occupied site density of ns = 0.7(2)% in a central disk of around 110 sites, although an accurate estimation is complicated by gradients at the edge of the confining potential due to finite optical resolution (Methods).

Keeping the subsequent transformation slow relative to the relevant many-body timescales is crucial to realizing low final temperatures. This is particularly challenging as this protocol involves a substantial change in lattice filling from two in the BI to around one atom per site in strongly correlated Hubbard systems. This massive change in the state typically involves very slow many-body timescales, which requires excellent local control of density31,36,38,39,40. To address this challenge, we take advantage of an optical lattice whose geometry is dynamically adjustable12,44,45. In particular, we continuously split single lattice sites into double wells and subsequently connect these double wells together into a square lattice, realizing a scheme similar to the one proposed in ref. [15]. This doubles the number of sites at fixed total atom number (Fig. 1c,d and Extended Data Fig. 1) and naturally converts the BI into half-filled Fermi systems. However, this doubling is not exact in the experiment because of variations of the optical potentials. To precisely reach the target filling, our protocol further relies on tunable interactions and DMD potentials to ensure controlled preparation, isolation and expansion of the system of interest (Methods).

Splitting an insulator

Dynamically changing the lattice geometry leads to rich physics even at very large interactions of U/t = 18.6(8), in which the half-filled state resulting from splitting the BI is a Mott insulator with a large charge gap. Here, the Hubbard model simplifies to a spin model with antiferromagnetic Heisenberg coupling J = 4t^2/U. In thermodynamic limit, this Heisenberg model is known to host a quantum phase transition between a disordered phase in the dimerized lattice, described as a product of isolated singlet states within double wells, and a Néel ordered phase in the square lattice with long-range antiferromagnetic correlations46,47 (Fig. 2a). In our experiment, which is a finite-sized system, the many-body spin gap is expected to decrease monotonically during splitting, with a minimal value of ΔH ∝ J/L^2 corresponding to the Anderson tower of states with broken symmetry[48]. These two lattice geometries are continuously connected by tuning the coupling between double wells, which preserves total spin Stot = 0 and enables efficient low-entropy state preparation[15]. We perform parametrization of the transformation of lattice geometry by a single parameter α describing the ratios of tunnelling amplitudes within and between double wells (Fig. 2a and Methods).

Fig. 2: Splitting a BI into a Heisenberg antiferromagnet.

a, Similar to the long-spacing lattice limit, the ground state at half-filling on a disconnected dimerized lattice is a product state of singlets. This state is then adiabatically connected to a long-range antiferromagnet on a square lattice by coupling nearest dimers. b, Spin correlations \({C}_{{\bf{d}}}^{zz}({\bf{r}})\) between nearest neighbours are localized on dimers when they are weakly coupled (α = 0.3, left) and become uniform across all nearest-neighbour bonds in the square lattice limit (α = 1.0, right). c, Spin correlations as a function of bond displacement \({C}_{{\bf{d}}}^{zz}\), averaged over an r = 6 region at half-filling at U/t = 18.6(8). The range of the antiferromagnetic correlations starts to grow around α = 0.7. d, Energy levels of the Heisenberg model on dimerized lattices as coupling strengths are tuned, in units of the spin exchange coupling J = 4t^2/U (Methods). e, The measured staggered magnetization increases at couplings α > 0.7 similar to the critical point of the quantum phase transition in the ground-state Heisenberg model[47]. Solid lines indicate simulation of dimerized Heisenberg model at temperature T/J = 0.5.

Source Data

To track how spin order changes with lattice geometry, we experimentally measure the spin correlation function in the z-direction between sites at positions r and r + d (ref. [12]):

$${C}_{{\bf{d}}}^{zz}({\bf{r}})=\frac{1}{{S}^{2}}(\langle {S}_{{\bf{r}}}^{z}{S}_{{\bf{r+d}}}^{z}\rangle -\langle {S}_{{\bf{r}}}^{z}\rangle \langle {S}_{{\bf{r+d}}}^{z}\rangle ).$$

(1)

In the α → 0 limit of disconnected dimers, which corresponds to the BI, we detect only saturated spin correlations between nearest-neighbour sites, |d| = 1 within the dimers and consistent with 0 everywhere else (Fig. 2b). As these dimers are connected, spin correlations start to grow between the dimers at α ≃ 0.7 and become uniform and isotropic in the square lattice α = 1.0.

To probe spin correlations at longer bond distances |d| > 1, we obtain \({C}_{{\bf{d}}}^{zz}\) by averaging \({C}_{{\bf{d}}}^{zz}({\bf{r}})\) over a Mott insulating region within a central disc of radius r = 6, and account for spatial inversion symmetry (Methods). We confirm that spin correlations are antiferromagnetic and localized on the intra-dimer bonds in the dimerized lattice α = 0.3 and that they extend to long range in the square lattice with α = 1.0 (Fig. 2c).

The nature of magnetic order during the lattice transformation is captured by the staggered magnetization mz (ref. [11]). We compute (mz)2 by averaging the sign-corrected spin correlations \({(-)}^{| {d}_{x}+{d}_{y}| }{C}_{{\bf{d}}}^{zz}\) up to a cutoff distance dmax (Methods and equation (3)). We observe an increase in (mz)2 around α ≃ 0.7 (Fig. 2e), consistent with the range of critical values reported in previous numerical studies of dimerized Heisenberg models46,47. The experimental data are also consistent with quantum Monte Carlo simulations performed at a fixed temperature of T = 0.5J = 0.12t.

We experimentally check ramp adiabaticity and confirm that the spin correlations have saturated by varying the ramp duration, which could be limited by heating. Surprisingly, we find that the strengths of the DMD potentials confining the Mott insulator, which may lead to atom transport into and out of the central region, can strongly affect the final spin correlations, and therefore temperatures (Methods and Extended Data Fig. 3). These measurements suggest that charge transport may be crucial to temperature optimization49,50 apart from spin dynamics of the idealized model.

Ultralow temperatures in the strongly correlated regime

At intermediate interaction strengths of U/t ≃ 8 (Fig. 3a), which are most relevant to cuprate physics, the Hubbard model remains an antiferromagnet at half-filling and can thus be reached by the same adiabatic ramp as in the Heisenberg regime. However, owing to the reduced interaction strength, the system has increased compressibility, which can lead to enhanced charge transport. As a result, we find that the harmonic confinement produced by the lattice laser intensity profile results in density variation across the system after splitting. Thus, if the central density is to remain n = 1, the edge of the system must be at n < 1. As the entirety of the system began as a BI, this means atoms at the edge of the trap must be spilled out as the insulating gap is reduced during splitting. We achieve this by dynamically adjusting the strength of the DMD potentials (Fig. 3b). During this step, we empirically find that it is helpful to allow for transport to occur at low interaction strengths, which could be related to the fact that a Fermi liquid is more compressible than a Mott insulator41,42,50 (Fig. 4a). The timing and shape of the above ramps are extensively optimized based on the final temperature.

Fig. 3: Cold Hubbard antiferromagnets in the strongly correlated regime.

a, Intermediate interaction strengths, here U/t = 8.3(2), lead to density fluctuations apart from spin fluctuations at half-filling. b, To achieve the target filling (here n = 1), we adjust the confining potential created by DMDs to spill excess atoms. A repulsive wall isolates the cold region from the reservoir, whereas a volcano-shaped potential enables a spilling of excess atoms from the centre. c, Spin correlations \({C}_{{\bf{d}}}^{zz}\) as a function of bond displacement d, and averaged over a radius r = 3 region, show nearly saturated long-range antiferromagnetic correlations. The error bars denote 1 s.e.m. d, Experimental data are compatible with numerically exact simulations (lines, DQMC and AFQMC) at temperatures T/t < 0.1 and interactions U/t = 8. e, Low temperatures yield a sharp peak of the spin structure factor S(q) at quasimomentum q = (π, π). f, We estimate temperature by computing the staggered magnetization with cutoff d = 6 on bond distance, for both DQMC–AFQMC data (interpolated to U/t = 8.3(2), line) and experimental data (1σ confidence interval shown as shaded area). Their comparison yields a temperature \(T/t=0.0{5}_{-0.05}^{+0.06}\) (Methods).

Source Data

Fig. 4: Hole-doping cold Fermi Hubbard systems.

a,b, To coherently introduce hole dopants into the half-filled state after splitting the BI, we increase the kinetic energy and decrease the interaction strength by reducing the lattice depths out of the tight-binding regime. This prepares cold weakly interacting Fermi liquid that inherits the low initial entropy, which is adiabatically expanded to reach a given target density. Reloading the lattices and ramping up interactions create cold, hole-doped Hubbard systems at U/t = 8.0(3) (III). Schemes of splitting the BI into a half-filled antiferromagnetic Mott insulator at strong interaction of U/t = 18.6(8) (I) and intermediate interaction of U/t = 8.3(2) (II) are also marked. c, Doping scheme in quasimomentum space. Splitting the BI, which completely fills the Brillouin zone, into the square lattice doubles the number of sites and the size of the Brillouin zone. The population of quasimomentum states remain nearly unchanged. Expanding in real space then decreases the size of the Fermi surface and coherently introduces doping. d, Spin correlations as a function of bond displacements \({C}_{{\bf{d}}}^{zz}\) measured in an ROI of r = 3. The range and magnitude of the antiferromagnetic correlation decreases with increased doping. e, Azimuthal average of the sign-corrected spin correlations shown in d. At small dopings of δ ≤ 10%, the antiferromagnetism still remains long-ranged over the ROI. As we increase the number of hole dopants, the strengths of spin correlations at all distances are reduced by similar amounts. f, Short-range spin correlations \({C}_{d}^{zz}\) at different distances \(d=1,\sqrt{2},2,\sqrt{5}\) as we dope the system. The nearest-neighbour correlations at d = 1 show quantitative agreement between experimental data and CP-AFQMC simulations at U/t = 8 and T/t ∈ [0, 0.1] (solid line). At longer bond distances d > 1, experimental data show stronger correlations than CP-AFQMC simulations. The error bars denote 1 s.e.m.

Source Data

With the optimized experimental sequence, we observe antiferromagnetic spin correlations \({C}_{{\bf{d}}}^{zz}\) extending across the entire half-filling region with a radius r = 5 for an interaction strength of U/t = 8.3(2) (Fig. 3c and Methods). We compare these experimentally measured correlations after sign-correction and azimuthal average \({(-)}^{| {d}_{x}+{d}_{y}| }{C}_{d}^{zz}\) with the numerical results from simulations using finite-temperature determinant quantum Monte Carlo (DQMC) and ground-state auxiliary-field quantum Monte Carlo (AFQMC) methods, under open boundary conditions. At half-filling, the numerical results are in principle exact, although care must be taken to obtain accurate and unbiased spin correlations, especially at larger bond distances d (Methods). We find the measured \({(-)}^{| {d}_{x}+{d}_{y}| }{C}_{d}^{zz}\) are close to saturation and consistent with numerical data at T/t ∈ [0, 0.1] (Fig. 3d). These strong, long-ranged correlations translate to a narrow peak in the spin structure factor S(q) at quasimomentum q = (π, π) (Fig. 3e), which is obtained as the Fourier transformation of \({C}_{{\bf{d}}}^{zz}\) (Methods). Comparing numerical DQMC data with the experimental value of the squared staggered magnetization (mz)2 computed up to a cutoff dmax = 6 allows us to extract a temperature of \(T/t=0.0{5}_{-0.05}^{+0.06}\) (Fig. 3f and Extended Data Fig. 7).

Doping cold antiferromagnets

The most intriguing and poorly understood physics of the Hubbard model resides in the doped regime. At low temperatures of T/t < 0.2, and with intermediate interactions of U/t ≃ 8, the doped Hubbard model is extremely challenging. Unlike for half-filling, no numerically exact results for spin correlations \({C}_{d}^{zz}\) are available in this regime for sufficiently large system sizes, which have systematically explored parameters such as temperature and interaction. Extending our cooling scheme to the above regime is, therefore, very valuable to advance our understanding, as quantum simulation can offer direct measurements, potentially adding a powerful new element in conjunction with approximate numerical simulations and analytic theory.

An outstanding challenge is that dopants must be coherently introduced and delocalized into the nearly half-filled system after splitting to avoid detrimental heating31,49,50. To address this challenge, we must adapt our cooling scheme to reach a final state with a central density n < 1. We begin with a BI and then double the density of lattice sites, resulting in a central half-filled region. Additional expansion of atoms out of this region is then required to achieve n < 1. In previous work, however, this expansion incurred substantial heating[31], resulting in final temperatures of T/t > 0.4. In this work, we circumvent these challenges by performing the expansion in a shallow lattice, which increases the tunnelling amplitudes and reduces interaction strengths, facilitating the efficient transport of particles. At the same time, this decreases dissipative effects from the lattice light, reducing the background heating rate.

Our cooling scheme for doped systems is thus as follows (Fig. 4a,b): after forming the BI, we reduce the lattice depth by 80% and perform splitting. The large tunnellings and weak interactions transform the BI into a Fermi liquid. We then expand the atoms by reducing the strength of the confining DMD potential, while ramping the magnetic bias field to its final value of 590 G. The depth of the square lattice is then ramped up to its final value, reaching the strongly correlated regime of U/t = 8.0(3) (Methods). At the same time, the strengths of the DMD potentials are adjusted to minimize transport during lattice loading49,50 (Methods).

In Fig. 4d, we report the spin correlations \({C}_{{\bf{d}}}^{zz}\) averaged over a region of interest (ROI) of radius r = 3. We observe antiferromagnetic spin correlations extending over this entire region for hole dopings of up to δ = 10%. To understand how spin correlations evolve with increasing doping, we plot the azimuthal average of the sign-corrected spin correlations \({(-)}^{| {d}_{x}+{d}_{y}| }{C}_{d}^{zz}\). Larger doping induces stronger relative suppression of spin correlations at longer bond distances, which reduces the range of antiferromagnetism. The absolute magnitude of correlation reduction seems insensitive to bond distances, which suggests that doping decreases a long-range offset instead of affecting the short-range structure (Fig. 4e).

We compare the experimentally measured spin correlations systematically with the numerical results obtained with the constrained-path auxiliary-field quantum Monte Carlo (CP-AFQMC) method16,17,18, which is a state-of-the-art approximate numerical technique. The spin correlations predicted by CP-AFQMC grow monotonically as temperature decreases. For a hole-doping range of δ ∈ [2%, 21%], we find good agreement between the experimentally measured nearest-neighbour spin correlations \({C}_{1}^{zz}\) and CP-AFQMC simulations at temperatures of T/t ∈ [0, 0.1]. Simulations at T/t = 0.15 exhibit significantly weaker correlations (Fig. 4f).

For bond distances of d > 1, although the agreement is good on the scale of the variation between modestly low temperatures (T/t = 0.25) and the ground state, the experiment shows stronger correlations than predicted by CP-AFQMC. It is not quite at the level of the agreement found at half-filling between experimental data and numerically exact results for all bond distances. We verify that our observations do not depend on the weak spatial variation of doping due to harmonic confinement by changing the radius r of the experimental ROI and further confirm that the system is at thermal equilibrium (Extended Data Fig. 3). Uncertainty in the calibration of experimental parameters (Methods) is insufficient to explain the deviation, but other details in Hamiltonian difference (for example, higher-order corrections) could potentially contribute, especially at lower temperatures. The discrepancy may also hint at an underestimation of long-range correlations by CP-AFQMC. This would be consistent with data at elevated temperatures of T/t = 0.25, 0.33, at which numerically exact DQMC results seem to be more consistent with experimentally measured spin correlations, and CP-AFQMC results show slightly lower correlations (Methods and Extended Data Fig. 5). These systematic comparisons are an example of the synergies that now exist between quantum simulations and numerics in the regime in which obtaining exact numerical results are very challenging.

Previous attempts[31] at adiabatic state engineering in the Hubbard model suffered from excessive heating during the transformation from BI to Hubbard systems. Our scheme departs from these attempts in two ways: the use of a geometry tunable optical lattice enables us to halve the filling of the system without macroscopic density redistribution and working at weak interactions in a shallow optical lattice minimizes heating effects during the remaining expansion. To probe the the importance of the latter choice, we prepare a BI in a square lattice and expand it to a half-filled system in the same lattice by shaping the DMD potentials and measure the temperature of the final state as a function of the expansion duration τ and the normalized lattice depth η during expansion (η = 1 indicates full depth).

To characterize the adiabaticity of the expansion, we scan the expansion duration at fixed depth η = 0.2 (Fig. 5a). We observe a rapid increase in the temperature at short expansion durations, indicating τ = 20 ms ≃ 35ħ/t as a critical timescale below which diabatic heating limits the temperature. As the lattice depth is increased, the tunnelling energy decreases, requiring longer expansion durations to remain adiabatic. To probe the effects of lattice depth, we, therefore, measure the final temperature as a function of lattice depth at a fixed normalized expansion duration τ ≃ 60ħ/t, in which t is computed from η using a bandstructure calculation (Methods). As shown in Fig. 5b, the final temperature rapidly rises when the lattice depth is increased beyond a critical value η ≃ 0.6, which cannot be because of purely single-particle adiabaticity effects. Rather, we attribute it to a combination of interaction-induced heating during particle transport, which worsens as U/t is increased, and dissipative effects induced by the lattice light and DMD light, which worsen because of the increase in η and τ.

Fig. 5: Adiabaticity of expansion.

a, Adiabaticity of expansion from a BI to a to half-filled Hubbard system in the same lattice as a function of expansion duration τ at normalized lattice depth η = 0.2. We find the ramp adiabaticity and, therefore, temperature improves quickly as the τ is increased from 1.76ħt (1 ms), which starts to saturate at τ ≃ 35ħ/t (20 ms). b, Spin correlations and estimated temperatures after expansion of fermions for a fixed normalized duration measured by tunnelling times τ ≃ 60ħ/t (taking ħ = 1) at different lattice depths η. Expansion with η ≥ 0.8 show strong heating that cannot be explained exclusively by adiabaticity. At critical lattice depth η ≃ 0.6, the interaction strength U/t ≃ 1.5 is similar to the interaction at which a rapid increase in transport timescales was observed in ref. [52]. This indicates the heating may be largely dependent on a combination of interaction-induced effects and lattice heating. The error bars denote 1 s.e.m.

Source Data

The interaction-induced heating may be related to theoretical and experimental studies that indicate that transport in strongly correlated systems can experience an exponential slowdown42,51,52 and induce heating from density redistribution49,50. We note that the interaction strength U/t ≃ 1.5 at the critical lattice depth indicated in Fig. 5b is similar to the interaction at which a marked increase in transport timescales was observed in ref. [52]. These measurements highlight the importance of performing the expansion step in a shallow lattice, apart from the use of a superlattice to double the density of sites.

Outlook

In this work, we have demonstrated a substantial reduction in the temperatures achievable in large-scale quantum simulations of the Hubbard model, both with and without doping. Through careful dynamical tuning of parameters in a programmable optical lattice, we are able to transform a trivial low-entropy state into a cold, strongly correlated quantum many-body state in equilibrium. Moreover, the dynamics involved in this transformation are exceedingly difficult to tackle by analytical techniques, or to simulate classically, and so must be optimized empirically using the quantum simulator itself. The above approach is broadly applicable to situations in which one can prepare low-entropy product states with high fidelity, for example, with optical tweezers[53]. These techniques can also be extended to other lattice geometries, including triangular and kagome lattices, which may host quantum spin liquids[26], and square lattices with diagonal tunnelling t′, which may host unconventional superconductivity[54].

The temperatures and system sizes achieved in this work probably allow one to enter phases of the Hubbard model that have not yet been explored in cold-atom quantum simulators, including phases involving charge order3,6,21,22,23. Recent advancements involving comparisons of different approximate numerical simulations in equilibrium27,28 have shed some light on these behaviours, including the interplay between the stripe and pseudogap phases and d-wave superconductivity24,54. However, a complete microscopic understanding has remained unknown. Quantum simulations in the appropriate regimes offer a unique opportunity to study the features that are challenging to simulate classically, including spectral properties and real-time dynamics. Furthermore, in equilibrium, quantum simulations provide a valuable extra data point that complements approximate numerical simulations by taking different assumptions and approximations.

Although advanced numerical simulations have long informed the design and operation of cold-atom-based quantum simulators, this work opens the possibility of a fruitful exchange, in which the results obtained by quantum simulation can also be used to develop and benchmark more efficient and accurate numerical techniques. The benefits of combining classical and quantum tools go beyond simply serving as mutual benchmarks. The success of the preparation scheme in this work suggests that, in the future, hybrid classical-quantum algorithms that use the results obtained from a quantum simulator to optimize state preparation55,56 could provide substantial advantages, leading to lower temperatures, and possibly a definitive understanding of the Hubbard model in and out of equilibrium.

Methods

Lattice potential

In this work, the lattice potential is formed by three retro-reflected laser beams. Two beams are overlapped and mode-matched, propagating in the x-direction, and are referred to as the X and \(\bar{X}\) beams, respectively. The third beam is propagating along the y-direction, orthogonal to the other two, and is referred to as the Y beam. As described in previous works12,57, the X and Y beams are phase stabilized to form an interfering lattice. The frequency of the \(\bar{X}\) beam is detuned by about 1.6 GHz from X and Y (Extended Data Fig. 1d,e). The frequency offset is converted to a lattice phase shift of π upon reflection from the retro-reflecting mirror, shifting the \(\bar{X}\) lattice by half a site relative to the X lattice. This allows the \(\bar{X}+Y\) lattice to split each unit cell in the X + Y lattice symmetrically into two sites. All three beams are reflected from a super-polished substrate in the z-direction at an angle of θ = 69.2(1)° before being retro-reflected[12], which forms a three-dimensional lattice potential. The lattice potential in the z-direction provides confinement with a trapping frequency much larger than all relevant energy scales in the xy plane lattices, and the tunnelling along the z-direction is negligible during the experimental sequences. This allows for a near-ideal realization of a 2D system in the xy plane after the atoms are selectively loaded into a single layer of the z lattice. The potential of the 2D lattice can be written as

$$\begin{array}{l}V(x,y)\,=\,-\frac{{V}_{x}{\bar{r}}^{4}(1+\cos (2\theta ))}{4}\cos (2{k}_{x}\,x)+\frac{{V}_{\bar{x}}{\bar{r}}^{4}(1+\cos (2\theta ))}{4}\cos (2{k}_{x}\,x)\\ \,\,\,\,\,-\frac{{V}_{y}{\bar{r}}^{4}(1+\cos (2\theta ))}{4}\cos (2{k}_{y}\,y)\\ \,\,\,\,\,-{\bar{r}}^{2}{\cos }^{2}\theta \frac{\sqrt{{V}_{x}{V}_{y}}}{4}(\cos ({k}_{x}\,x-{k}_{y}\,y+\phi )+{\bar{r}}^{2}\cos ({k}_{x}\,x+{k}_{y}\,y+\phi )\\ \,\,\,\,\,+\,{\bar{r}}^{2}\cos ({k}_{x}\,x+{k}_{y}\,y-\phi )+{\bar{r}}^{4}\cos ({k}_{x}\,x-{k}_{y}\,y-\phi ))\\ \,\,\,\,-\frac{{V}_{x}{\bar{r}}^{2}(1+{\bar{r}}^{4})(1+\cos (2\theta ))}{8}-\frac{{V}_{\bar{x}}{\bar{r}}^{2}(1+{\bar{r}}^{4})(1+\cos (2\theta ))}{8}\\ \,\,\,\,\,-\frac{{V}_{y}{\bar{r}}^{2}(1+{\bar{r}}^{4})(1+\cos (2\theta ))}{8}\end{array}$$

(2)

Here, \(\bar{r}=8.27(1) \% \) denotes the Fresnel loss present at the surface of the glass cell, kx = ky = 2π sin θ/λ are the lattice vectors in the xy plane, and λ = 1,064 nm. Owing to loss, the four interference terms cannot be combined except when the interference time phase ϕ between X and Y is 0 or π. Note that the lattice depths \({V}_{x,\bar{x},y}\) do not directly correspond to the lattice depths in an ideal retro-reflected square lattice, because of the finite incident angle in the z-direction and losses due to the presence of the glass cell.

Experimental methods

As in previous work[12], we prepare an ultracold, spin-balanced Fermi gas of 6Li atoms in the lowest two hyperfine states by evaporative cooling in a crossed optical dipole trap. Spin balance is achieved through a microwave mixing process[58] with a duration of 300 ms, ensuring the SU(2) symmetry of the Fermi gas. The ultracold 6Li atoms are then loaded from the optical dipole trap into the interfering (long-spacing) lattice. The interfering lattice is a square lattice with √2 larger spacing than the non-interfering (short-spacing) lattice and is formed by the actively phase-stabilized X and Y beams[12] using equal intensity. This initial lattice loading is performed in 100 ms, and the final lattice depths after loading are VX = VY = 2.88(1)ER. Here, ER = h^2/(8ma^2) = 25.49(4) kHz denotes the recoil energy and a denotes the lattice spacing. We set the magnetic field to 550 G, resulting in an s-wave scattering length of as ≃ 84a_0, and ensure that the applied field settles before lattice loading. At the start of lattice loading, we turn on a blue-detuned confining potential formed by one of two DMDs, which we refer to as DMD0 and DMD1. DMD0 is turned on for 60 ms (see section ‘Characterization of BI’) and, together with the harmonic confinement provided by the lattice, controls the density profile of the atoms. This redistributes the entropy in the system, creating a BI that is in thermal contact with a dilute metallic reservoir (see section ‘Characterization of BI’). Once the loading is complete, we turn on a second blue-detuned DMD potential projected using DMD1 in 5 ms (ref. [31]), which acts as a wall to separate the BI from the reservoir in subsequent steps in the experiment.

Experimental sequence for the half-filling data

As described in ref. [15], the phase transition from a BI to a half-filled Mott insulator with long-range antiferromagnetic correlations can be tuned by adjusting the ratio between inter- and intra-dimer tunnelling amplitudes. In the Hubbard model, the intra-dimer tunnelling td is proportional to the band gap Δg in the BI limit. The large band gap of Δg ≃ 100 kHz allows for a fast ramp of the \(\bar{X}\) and Y beams in 2 ms, while remaining adiabatic relative to motional energy scales. The final Y and \(\bar{X}\) lattice depths are 9.29(3)ER and 6.28(2)ER, respectively. These values satisfy the relation \({V}_{X}+{V}_{\bar{X}}\approx {V}_{Y}\), ensuring that the harmonic confinement induced by the Gaussian intensity profile of the lattice beams is approximately rotationally symmetric. Next, we ramp the depth of X to 0.19(1)ER in 15 ms, and simultaneously increase the depth of \(\bar{X}\) to 9.06(3)ER. This compensates for the decrease of VX, and maintains \({V}_{X}+{V}_{\bar{X}}\approx {V}_{Y}\). During this ramp, each site in the long-spacing lattice is split into a dimer, as shown in Extended Data Fig. 1d. In quasimomentum space, this corresponds to reducing the band gap Δg between the ground band and the first excited band, while keeping the gaps from the ground band to higher bands nearly unchanged. The ramp needs to be slower than the band gap Δg between the ground and first excited bands to be adiabatic. This gap decreases with decreasing X lattice depth. We separate the ramp into two segments with a duration of 3 ms before and 10 ms after depths of \({V}_{X}=0.52(1){E}_{{\rm{R}}},{V}_{\bar{X}}=8.74(3){E}_{{\rm{R}}}\). The slower ramp at lower X depths helps to accommodate the requirement of adiabaticity.

The set point of VX is further reduced to 0.032ER, and \({V}_{\bar{X}}\) increased to its final value of 9.22(3)ER. Note that there is a systematic uncertainty in VX below 0.1ER because of residual leakage from the retro-reflected \(\bar{X}\) lattice onto the X lattice intensity regulation photodiode. This causes an offset as large as 0.01ER and decreases the actual X lattice depth. During the above ramp, the phase transition from spin singlets to antiferromagnetic order is expected to occur. The ramp speed is 50 ms, which is a balance between adiabaticity and heating from the optical lattice. Despite the presence of long-range antiferromagnetic correlations, the resulting state still exhibits dimerized spin correlations due to the imbalanced inter- and intra-dimer tunnelling along the x-direction (see section ‘Calibration of tunnelling and lattice parameters’). We further decrease the set point of the X lattice over 30 ms to 0.016ER, which is the lowest depth that allows us to maintain phase stabilization between the X and Y lattices. The depth of VX = 0.016ER is not sufficient to remove the dimerization of the tunnelling, and so we further ramp the setpoint of the phase stabilization from ϕ = 0 to π/2. Note that the contribution from phase noise to intensity noise is negligible at these low values of VX. Although this step is supposed to eliminate the interference lattice[59], loss in the retro-reflected lattice beam (see section ‘Lattice potential’) results in imperfect suppression of the interference term[60], and thus a potential offset between the A and B sublattices of the square lattice. We use numerical simulations to confirm that this sublattice offset does not alter the relevant physics at half-filling (see section ‘Lattice potential calibration’).

To carry out the experiments shown in Fig. 4 (see section ‘Experimental sequence for doped data’) on the doped Hubbard model, we upgraded the apparatus with a non-reciprocal attenuator (see section ‘Non-reciprocal attenuator’). This allows us to decrease VX to a depth of 3.2(3) × 10−5ER, corresponding to negligible tunnelling dimerization. With this upgrade, we can avoid ramping the phase stabilization setpoint ϕ and simply trigger the attenuator at the beginning of the dimerized lattice ramp. As a verification, we repeat the experiments at half-filling with the attenuator while keeping ϕ = 0. Under these conditions, we obtain a temperature of T/t ≃ 0.1, as shown in Extended Data Fig. 2. These measurements were performed without careful optimization and obtained over a 12-h period without realignment. The achieved temperature is consistent with those obtained without the attenuator for similar data taking durations without realignment. We therefore attribute the slight increase in temperature compared to the data shown in Fig. 3 to lattice drifts (see section ‘Effects of alignment’).

In the short-spacing square lattice, the harmonic confinement imposed by the Gaussian intensity envelope is not compensated. As a result, the final density profile is not flat except in a radius r = 5 region near the lattice centre at half-filling. If all of the atoms in the initial BI are kept inside the DMD0 confining potential, the harmonic confinement will increase the chemical potential μ. According to DQMC simulations at the Hubbard interaction studied in most parts of this work of U/t ≃ 8, |μ| > t will lead to non-negligible doping δ > 1%. Therefore, harmonic confinement may introduce particle dopants into the system. If the DMD0 potential had arbitrarily fine spatial resolution, allowing for an infinitely sharp wall, then as long as the DMD0 potential VD remains smaller than the band gap and VD ≫ U, the system would have a well-defined open boundary. However, the resolution of the DMD0 potential is limited by the numerical aperture (NA = 0.7) of the microscope objective, and the wavelength of 650 nm used to create the DMD potentials. The excess atoms residing on the finite slope of the DMD0 potential will further increase the chemical potential μ of the central system as a function of the strength of the DMD0 potential, causing additional particle doping. This doping leads to charge transport through the system at strong Hubbard interaction, which we suspect leads to substantial heating. To minimize charge transport and heating, as described in the main text, the potential strength of DMD0 is scanned and optimized for each ramp. We find the lowest temperatures are realized when the DMD0 potential is set to maintain μ ≃ 0 in the lattice centre, such that no transport occurs at this location throughout the entire ramp. Close to the boundary of the DMD0 potential, the decreased local chemical potential causes excess atoms to flow out of the central region and form a secondary reservoir. This could be beneficial for reducing the final temperature by realizing a secondary entropy redistribution step. We start to ramp the magnetic field to its final value of 620 G after the dimerized lattice has mostly been formed, and where the density distribution matches that of the short-spacing square lattice. We find this is important to reach low temperatures, which we ascribe to interaction-induced heating in transport at large U/t and to interaction-driven changes in the density profile.

The tunnelling amplitudes for all datasets are summarized in Extended Data Table 1. Once the lattice and DMD0 ramp finishes, we immediately quench the lattice potential to \({V}_{\bar{X},Y} > 60{E}_{{\rm{R}}}\) within 50 μs to freeze the dynamics before imaging. We confirmed that this quench is faster than doublon–hole dynamics and slower than band excitations. The probed singlon density starts to increase once the quench time is slower than 100 μs because of the combination of virtual doublon–hole excitations and starts to decrease once the quench time is faster than 20 μs because of excitation to higher bands.

Experimental sequence for doped data

The transport described in section ‘Experimental sequence for the half-filling data’ is necessary only because of imperfections of the potential, including finite optical resolution and harmonic confinement. Splitting a BI in the long-spacing lattice naturally gives a half-filled state in the short-spacing lattice with μ ≃ 0. To reach a final state with finite doping, coherent redistribution of atom density across the entire system is required, which poses more challenges to suppress heating during atom transport.

After isolating the BI, we first reduce the lattice depth to VX = VY = 0.50(1)ER in 30 ms, which increases the kinetic energy scale and further reduces the interaction strength before splitting into a Fermi liquid. This depth corresponds to around 20% of the original lattice depth and is chosen to maximize the tunnelling energy in the lowest band to enhance adiabaticity and reduce lattice-induced heating, while still maintaining a bandwidth smaller than the height of the DMD0 potential. The latter constraint is important because otherwise the atoms would have enough kinetic energy to move out of the confining potential, leading to uncontrolled transport. Another heuristic intuition is to maintain a finite band gap such that the Fermi surface before and after splitting are approximately matched. In the non-interacting limit, the quasimomenta of the atoms are conserved in a homogeneous lattice, and deformation of the occupation in quasimomentum space leads to diabatic transfer of atoms to excited states. In an ideal tight-binding model with only nearest-neighbour tunnelling, the full first Brillouin zone of the long-spacing lattice matches the Fermi surface of the half-filled first Brillouin zone of the short-spacing lattice (Fig. 4c). The approximate match between the occupied states before and after splitting may minimize the deformation of the Fermi surface, and therefore the heating process above. A finite band gap also suppresses diabatic excitation into higher bands. The reduced lattice depths before splitting weaken the confinement in the z-direction, potentially leading to tunnelling in the z-direction. To avoid this, we turn on a vertical lattice to allow the system to always be treated as 2D.

Unlike at half-filling, where the Mott insulating state is robust against potential variations, doped Hubbard systems are compressible and sensitive to potential offsets. We, therefore, upgraded the lattices with a non-reciprocal attenuator that allows us to almost completely turn off the X lattice, while maintaining phase stabilization between the X and Y lattices at ϕ = 0 (see section ‘Non-reciprocal attenuator’). An interference phase setpoint of ϕ = 0 ensures no potential offset, and therefore no density offset between the A and B sublattices. We ramp on \(\bar{X}\) to 0.50(1)ER, and ramp X to 3.2(3) × 10−5ER in 30 ms. Owing to its low value, the X depth is directly calibrated using a power meter (PM100D and S121C from Thorlabs). We trigger the attenuator at the beginning of this ramp to maintain phase stabilization while ramping X. At the end of this ramp, the lattice has negligible tunnelling dimerization. At this stage, the splitting sequence is complete, and the first Brillouin zone doubled.

Expansion of the atom cloud is achieved by lowering the DMD0 potential in 50 ms, accompanied by a ramp of the magnetic field from 550 G to 590 G. This expansion time is chosen to allow the magnetic field to settle. Decreasing the DMD0 potential allows atoms to flow out of the confining potential, forming a dilute secondary reservoir confined by the DMD1 wall. The DMD0 potential is chosen to adjust the central density to the desired value in the final state, which will minimize transport during lattice reloading. As expansion corresponds to a reduction of the Fermi momentum, a change in quasimomenta is necessary. As a result, it is possible that increased scattering lengths due to the higher magnetic field may facilitate thermalization during expansion.

The prepared state is a weakly interacting Fermi gas in equilibrium, which is similar to the state used to load the lattice in previous works[11], but at a much lower temperature. We then load the non-interfering lattice by ramping the \(\bar{X}\) and Y lattice depths up to \({V}_{\bar{X}}=11.0(1){E}_{R},\)\({V}_{Y}=11.0(1){E}_{R}\). Here, the lattice depths are deeper than in the half-filling protocol to allow us to reach an interaction strength of U/t ≃ 8 at a lower magnetic field of 590 G (see section ‘Choice of lattice depths and magnetic field’) to maintain the atom density profile, and minimize transport in the regime in which U/t is large, we lower the DMD0 potential and the DMD1 wall to accommodate reduced tunnelling energies at higher lattice depths during the above ramp. After lattice reloading, we freeze the dynamics within 50 μs before imaging.

Potentials of DMD0

We use an incoherent light source at 650 nm to illuminate the DMDs, which is blue detuned of the D1 and D2 transitions in Li, and forms a repulsive potential. For experiments at half-filling, the volcano-shaped potential created by DMD0 is composed of a paraboloid, which is chosen to compensate the harmonic confinement imposed by the lattice beams11,31, and a circular central cutout region that we will refer to as the crater (Extended Data Fig. 1). The transition from the paraboloid to the crater is sharp (within 1 pixel) on DMD0, which induces diffraction fringes on the potential due to the finite resolution of the imaging system.

To allow expansion for doped systems, we create the flattened volcano-shaped potential (see section ‘Adiabaticity of expansion after splitting’) by applying a cutoff on the maximum amplitude of the volcano potential (Extended Data Fig. 1g). This forms a flat ring-shaped region surrounding the crater. As described in section ‘Characterization of BI’, the flattened volcano-shaped potential may result in worse BI fidelity than without flattening. We choose to work with the largest flattened region for which we do not detect a reduction in BI fidelity.

Non-reciprocal attenuator

As described in section ‘Experimental sequence for doped data’, even when ϕ = 0, the intensity of the X lattice needs to be reduced by more than that in the half-filled case to avoid sublattice offsets. This is challenging because of the reciprocal nature of most optical attenuators, including acousto-optical modulators or ND filters, which act on the beam both in the forward direction (towards the atoms) and in the reverse direction (on retro-reflection, returning to the detection photodiode). A 50-dB attenuation of the X lattice beam, therefore, leads to a 100-dB attenuation of the laser intensity on the phase stabilization photodiode, resulting in greatly decreased gain and signal-to-noise ratio, making it impossible to perform effective stabilization. We solve this problem by introducing a non-reciprocal attenuator[60], which applies variable and differing levels of attenuation to the forward and reverse beams. This allows us to reduce the X lattice depth by five orders of magnitude, while keeping the interference phase actively stabilized. Under these conditions, we detect no tunnelling dimerization or potential offset (see section ‘Experimental verification’).

Effects of alignment

We find that the main source of instability in the experiment is the drift of the lattice positions (especially of the \(\bar{X}\) lattice relative to the X lattice). This results in drifts in the lattice harmonic confinement and corresponding shifts in the position of the peak atom density in the cloud. The lattice position is affected by temperature and humidity fluctuations in the lab and remains stable for about 0.5 h, which is the typical duration of a contiguous scan. We observe drifts in lattice position on longer timescales, which may lead to heating due to excess transport during the lattice ramps. Empirically, we find the strongest effects of heating due to lattice drifts at half-filling.

For the doped data presented in Fig. 4, we average over ROIs at the centre of the trap with different radii, in which the effects of harmonic confinement are minimized. However, the lattice drifts shift the atom distribution relative to the DMD0 potential, which introduces a potential gradient within the ROI. Therefore, for data presented in Figs. 3 and 4, we re-centre the lattice beam positions about every 1 h by maximizing the light back-coupled into the lattice optical fibres.

The drifts of the DMD potentials along the x- and y-directions are strongly suppressed by the magnification of the high NA objective used to project them. We find that focus drifts along the z-direction occur over the course of weeks, which is convenient to correct.

Choice of lattice depths and magnetic field

The Hamiltonian of ultracold fermionic atoms moving and interacting in optical lattices naturally realizes the Hubbard model, with corrections in the form of beyond-nearest-neighbour tunnelling, density-assisted tunnelling, off-site interactions and higher bands effects[61]. Shallow lattices are preferred from an experimental perspective because, at a fixed target value of U/t, larger tunnelling energies make all energy scales higher. Harmonic confinement is reduced in shallower lattices too, which, combined with the increased tunnelling energies, results in a more homogeneous density distribution. However, once the lattices are too shallow, longer-range tunnelling grows appreciably, and the band gap decreases. These effects lead to deviations from the Hubbard Hamiltonian and a breakdown of the tight-binding and single-band approximation. As lattice depth increases the band gap increases, the Wannier function are more localized, a smaller scattering length as is needed to achieve the same value of U/t, and the above corrections are exponentially suppressed compared with nearest-neighbour tunnelling t and on-site interaction U. Deep lattices are, therefore, preferred from a theoretical perspective, and a tradeoff between experimental performance and asymptotic realization of the Hubbard model in an exact manner needs to be made. In the non-interfering (short-spacing) lattice, in the half-filled case with \({V}_{\bar{X}}=9.22(3){E}_{{\rm{R}}},{V}_{Y}=9.29(3){E}_{{\rm{R}}}\), which yields a radial band gap of Δxy,g ≃ 83 kHz and a vertical band gap Δz,g ≃ 42 kHz, we find the next-nearest-neighbour d = (2, 0), (0, 2) tunnelling to be t″ ≃ 0.042t. In the doped case with \({V}_{\bar{X}}={V}_{Y}=11.0(1){E}_{{\rm{R}}}\), which yields a radial band gap of Δxy,g ≃ 93 kHz and a vertical band gap Δz,g ≃ 47 kHz, we find the next-nearest-neighbour tunnelling to be t″ ≃ 0.029t. Note that the radial lattice is nearly separable in the x- and y-directions, resulting in vanishing tunnelling along directions other than x or y. Therefore, for the experimental parameters in this work, the single-band, 2D and tight-binding approximations are well satisfied. Note that the lattice depths provided here take Fresnel loss and the angle of polarization in the apparatus into account. The quoted depths are, therefore, higher than those of an idealized retro-reflected square lattice with the same band properties.

In practice, we find that the maximum s-wave scattering length as available to achieve the targeted Hubbard parameter U/t sets the limit on the magnitude of tunnelling amplitudes. This is because shallower lattices yield larger tunnellings and smaller integrals of the Wannier functions, which requires increased as to achieve a targeted U/t. In the vicinity of the Feshbach resonance, the universal scaling of the fermion three-body recombination rate is \(\kappa \propto {a}_{{\rm{s}}}^{6}\) (ref. [62]). Increasing the s-wave scattering length will eventually lead to excessive three-body loss and thus heating. We find that if the atoms are kept in a lattice with a depth of about 10ER, as applies to the half-filling data, as = 512a_0 at 620 G is the highest scattering length that does not lead to noticeable excess heating. To achieve U/t = 8, we set the lattice depths to 9.2ER. To obtain the doped data, the lattices are ramped down to 0.5ER for expansion. Here, the lack of protection against three-body recombination from the lattice leads to significant heating at 620 G magnetic field. We, therefore, choose to work at as = 294.5a_0 at 590 G and set the final lattice depth after reloading to 11ER. Details of the lattice parameters are listed in Extended Data Table 1.

Imaging procedure and fidelities

We perform site-resolved fluorescence imaging in the short-spacing square lattice as described in ref. [63]. The fidelity of correctly determining the occupation of a lattice site is Fi = 99.4(6)%.

Imaging in the long-spacing lattice differs from imaging in the short-spacing lattice and is described in ref. [12]. To image the BI with parity projection, we set the frequency detuning between \(\bar{X}\) and X to 850 MHz using a radiofrequency synthesizer, which ensures good overlap with the X–Y and imaging lattices. This allows for deterministic atom transfer between the physics and imaging lattices, despite the fact that the imaging lattice contains twice as many sites as the physics lattice. As described in ref. [60], doublons are converted into molecules by ramping through a narrow Feshbach resonance at 543 G and are subsequently lost because of light-assisted collisions. We report a combined imaging fidelity, including detection fidelity and physics-imaging transfer fidelity, of 99% (ref. [57]).

Imaging with full charge resolution in the long-spacing and dimer lattices is described in ref. [57]. The dimer lattice is adiabatically connected to the long-spacing lattice before the gap between the ground band and the first excited band closes. We set the frequency detuning of \(\bar{X}\) relative to X to 1,552 MHz, which sets the position of the potential minimum of the \(\bar{X}+Y\) lattice to be symmetric with respect to each unit cell in the long-spacing lattice formed by X + Y, and in the dimer lattice formed by \(X+\bar{X}+Y\). Each site in the long-spacing and dimer lattice is symmetrically split into two, with a negligible potential offset between the two minima of \(\bar{X}+Y\). Moreover, we ramp the magnetic field to 610 G to generate strong on-site interactions between the atoms on doubly occupied sites. This facilitates adiabatic transfer of doubly occupied sites in the long-spacing and dimer lattice to singly occupied sites in the \(\bar{X}+Y\) lattice. We find the doublon detection fidelity to be 98% after image reconstruction.

Data analysis

In Figs. 2 (excluding Fig. 2b), 3 and 4, two-point spin correlation functions are spatially averaged over all pairs of sites within a circular ROI centred on the atomic cloud. Atomic density decreases away from the centre as a result of the confining potential imposed by the lattice beams and the DMDs. A large potential gradient would enhance the effective superexchange interaction J for a site-to-site potential offset of ΔV < U and suppress magnetic interactions with a kinetic origin[57]. We, therefore, chose an ROI radius of r = 6 sites in Fig. 2, r = 5 sites in Fig. 3, and r = 3, 4 and 5 sites in Fig. 4, to limit the potential variation. This limits the variation of the radially averaged singlon density to about 2% within the ROI. The correlation maps for r = 4 and 5 of the doped data are shown in Extended Data Fig. 3a,b. For large dopings, for which only short-range antiferromagnetic correlations are present, the sign-corrected, radially averaged spin correlations \({(-)}^{| {d}_{x}+{d}_{y}| }{C}_{d}^{zz}\) may become negative at certain long bond distances. This could be induced by harmonic confinement or residual disorder of the lattice potentials and will be explored in future works.

In bipartite dimerized lattices (Fig. 2), we separately average the spin correlation function \({\langle {S}_{{\bf{r}}}^{z}{S}_{{\bf{r}}+{\bf{d}}}^{z}\rangle }_{{\mathcal{S}}}\) over reference sites \({\bf{r}}\in {\mathcal{S}}\) belonging to one of the two sublattices \({\mathcal{S}}={\mathcal{A}},{\mathcal{B}}\) and for all bond vectors d. Assuming inversion symmetry, we obtain and plot the sublattice-averaged spin correlation function \(\langle {S}_{{\bf{r}}}^{z}{S}^{z}{\bf{r}}+{\bf{d}}\rangle =({\langle {S}_{{\bf{r}}}^{z}{S}_{{\bf{r}}+{\bf{d}}}^{z}\rangle }_{{\mathcal{A}}}+{\langle {S}_{{\bf{r}}}^{z}{S}_{{\bf{r}}-{\bf{d}}}^{z}\rangle }_{{\mathcal{B}}})/2\). All error bars indicate the 1σ confidence interval obtained using bootstrap sampling across all experimental snapshots of a given dataset with 500 randomly selected samples. The number of experimental realizations for each dataset is reported in Extended Data Table 1.

To convert the experimentally measured singlon density \({n}_{{\rm{s}},det}\) to doping δ in Fig. 4, we first correct for imaging fidelity to extract \({n}_{{\rm{s}},{\rm{corrected}}}={n}_{{\rm{s}},\det }/{F}_{i}\). We then use the doublon density nd as a function of density n obtained from CP-AFQMC simulations at T/t = 0 to reconstruct the doublon density as a function of singlon density nd(ns) = nd(n − 2nd). Finally, we compute the density \({n}_{\exp }={n}_{{\rm{s}},{\rm{corrected}}}\,+\) \(2{n}_{{\rm{d}}}({n}_{{\rm{s}},{\rm{corrected}}})\) and doping \({\delta }_{\exp }=1-{n}_{\exp }\) of the experimental data.

In Extended Data Fig. 4b, we plot the temperature dependence of the doublon density up to n = 0.85. Similar to half-filling, the temperature dependence is negligible compared with the statistical errors in our detected densities for T/t < 0.15. Although in these CP-AFQMC calculations, the nd results are not as accurate at higher densities of n ∈ (0.85, 1), interpolating between n = 0.85 and half-filling suggests that the temperature dependence of the doublon density is still negligible for T/t < 0.15. This is supported by the fact that the energy scale associated with doublons is the interaction strength of U ≫ t ≫ T. Therefore, using the ground-state doublon density leads to negligible systematic errors in the reported quantities.

Discrepancy between experimentally measured spin correlations and CP-AFQMC results

To better understand the discrepancy between the experimental measurements of beyond-nearest-neighbour spin correlations in the presence of doping and CP-AFQMC simulations in Fig. 4, we explore a few possible sources of similar deviations.

As described in section ‘Calibration of Hubbard interaction U’, we calibrate U/t using the singlon density at half-filling, where ns reaches its maximum. We also compare experimentally measured spin correlations as a function of the measured singlon density ns to T = 0 data from CP-AFQMC. The singlon density is directly measured in the experiment and does not involve converting ns to doping using numerical data. We confirmed that a miscalibration of U/t seems insufficient to explain the observed discrepancy. However, higher-order corrections to the experimental Hamiltonian, including density-dependent terms, may lead to systematic deviations at finite doping from the calibration performed at half-filling. A complete comparison between experimental and numerical data requires a comprehensive characterization of the experimental Hamiltonian and the development of calibration techniques that directly probe the doped regimes.

The constrained-path approximation used in the numerical simulations may also introduce systematic errors. To explore this possibility, we compare experimental and CP-AFQMC data at elevated temperatures with numerically exact DQMC simulations. When simulating the doped Hubbard model, the sign problem in DQMC leads to an exponential overhead with decreasing temperature. Given accessible computational resources, we first perform DQMC at T/t = 0.25 and U/t = 8 in an 8 × 8 system and compare the results to those obtained with CP-AFQMC with the same parameters but in a 12 × 12 system (Fig. 4). Both the DQMC and CP-AFQMC simulations use periodic boundary condition (PBC). For these DQMC simulations, we used the QUEST package[64] with a time step of δτ = 0.02, 5,000 thermalization sweeps and 200,000 measurement sweeps, which are repeated with random seeds for up to 4,000 runs for values of the density with severe sign problems. The experimental data are obtained with U/t = 8.2(2) and using the standard loading sequence (without engineering the DMD potential) in refs. 12,63. The harmonic confinement due to the lattice intensity profile leads to a slow variation of the filling from the centre to the edge of the trap. We tune the atom number such that it reaches half-filling at the centre of a region of radius r = 6. This allows us to perform accurate thermometry of the entire atom cloud by comparing with numerically exact simulation results at half-filling without the need for numerical results in the doped regime. At larger distances from the trap centre of r > 6, the filling slowly decreases. Radially binning the data allows us to probe spin correlations as a function of varying singlon density and, therefore, doping at the calibrated temperatures.

We focus on the short-range spin correlations \({C}_{d}^{zz}\) up to \(d=\sqrt{5}\) in Extended Data Fig. 5a,b. Given the short correlation lengths at this elevated temperature, and at finite doping, finite-size effects are expected to be negligible. At a density of n = 0.995 and chemical potential of μ/t = −1.0, in which the correlation lengths are expected to be longer than at larger doping, we find that the DQMC and CP-AFQMC simulations are in good agreement for all short-range correlations. Moreover, at T/t = 0.33, we confirm that there is no difference between DQMC simulations performed in 8 × 8 and 12 × 12 lattices.

We find that good agreement between experimental data, DQMC and CP-AFQMC on the nearest-neighbour correlation \({C}_{1}^{zz}\) holds for all doping values at which we performed measurements or simulations. However, for a doping range of δ ∈ [5%, 10%] and bond distances \(d=\sqrt{2},2,\sqrt{5}\), the results obtained from DQMC suggest stronger correlations, with which experimental data show good agreement (Extended Data Fig. 5a). This trend is consistent with the behaviour shown in Fig. 4. The large statistical error bars in the DQMC data make it difficult to draw a definitive conclusion, especially because the estimated errors are themselves unreliable because of the vanishing average signs.

At even higher temperatures of T/t = 0.33, the sign problem is less severe, and we can compare DQMC and CP-AFQMC in a 12 × 12 system. We find smaller, but qualitatively similar, discrepancies in which spin correlations \({C}_{\sqrt{2}}^{zz},{C}_{2}^{zz}\) computed from DQMC are stronger than CP-AFQMC for doping below 15% (Extended Data Fig. 5b). Owing to the reduced magnitude of the spin correlations, experimental data performed at this temperature seem to be statistically consistent with both numerical simulations. Future work should be able to address this by using a better trial density matrix or an improved form of the self-consistent constraint in CP-AFQMC and more systematic studies.

Lattice potential calibration

To measure the lattice trapping potential, we measure the density profile of a non-interacting spin-polarized Fermi gas loaded into the lattice, and, by taking the local density approximation, invert the non-interacting equation of state to obtain the local chemical potential. The spin-polarized Fermi gas is prepared by performing evaporative cooling with states 1 and 2 at 321 G, followed by a magnetic gradient-assisted spill-out of state 1 at 27 G, where state 2 is magnetically insensitive and therefore remains trapped. Pauli exclusion in a spin-polarized Fermi gas prevents double occupancy (and associated parity projection during imaging), meaning that the measured density can be mapped unambiguously to a particular value of the chemical potential. The uncertainty in the above calibration procedure is dominated by statistical errors and is not strongly dependent on the assumed temperature of the Fermi gas. We take a conservative estimate of T/t = 0.5 based on independent calibrations of the spin-polarized Fermi gas, but the results do not change significantly when assuming a temperature in the range T/t ∈ [0, 1].

Using the above procedure, the harmonic confinement of doped data (Fig. 4) is measured as VH = 0.0152(6)t/(site)2r^2, where r denotes the radial distance measured in sites from the lattice centre.

The half-filled data at U/t = 8.3(2) (Fig. 3) was taken before the addition of the non-reciprocal attenuator; so an offset between the A and B sublattices is present. Using the procedure described above, we find that the difference in the mean local potential in the A and B sublattices is Δμ = 0.75(3)t. This is in good agreement with the expected offset of 0.8(4)t given the geometry of the lattice. The uncertainty in the expected offset is primarily because of uncertainty in the applied X lattice depth at very low values.

To investigate the effects of sublattice offsets on spin correlations, we perform DQMC simulations at T/t = 0.15, 0.3 on an 8 × 8 system and CP-AFQMC simulations at T/t = 0 on a 12 × 12 system. DQMC simulation at T/t ≥ 0.15 show that an offset as large as Δμ = 2t (defined as a symmetric sublattice offset about mean μ = 0) has little effect on spin correlations (Extended Data Fig. 4a). At T/t = 0, CP-AFQMC shows no effects on the spin correlations for Δμ = t and a small decrease in spin correlations for Δμ = 2t. This decrease is smaller than the statistical error in the experimental data; so we conclude that sublattice offsets are not a concern for the above dataset.

Characterization of BI

BI fidelity

We characterize the fidelity of the BI using the singlon density FBI = 1 − ns, assuming that a perfect BI has a doublon on every site. We can image the BI state with parity projection in the long-spacing lattice, in which doublons are lost because of light-assisted collisions[63] (Fig. 1c, left). Alternatively, we can directly image the doublon population by splitting each long-spacing site into two after freezing the dynamics to reconstruct the population with full density resolution[57] (Fig. 1c, middle).

Using density-resolved imaging, we measure the doublon density to be nd = 98.2(5)%, the singlon density to be ns = 1.8(5)%, and the hole density nh to be consistent with zero within a central ROI that does not include the boundary of the crater. The ROI covers a circular region with a radius of r = 6 sites in the long-spacing lattice (about r = 9 in the short-spacing lattice), which is one site (two sites) smaller than the radius of the crater. Note that given the fidelity of doublon detection in the experiment, the above numbers are consistent with a doublon fraction of unity. The total number of atoms detected in the crater is Nr=10 = 342(1).

The above measurements confirm that the empty sites appearing in the parity-projected images are doublons instead of holes. Given this observation, parity-projected imaging offers better sensitivity to the doublon population because it is immune to atom loss during fluorescence imaging. The remaining errors in parity-projected imaging of doublons include the fidelity of removing doublons through light-assisted collisions and atoms hopping to a different 2D layer during imaging. At present, we do not have an independent calibration of these errors; so the measurements of the BI fidelity below constitute a lower bound.

Using parity-projected imaging, we find that most of the singly occupied sites occur at the edge of the crater. We note that the crater is not aligned to the optical lattice in a site-resolved manner. When combined with the finite resolution of the imaging system, this results in imperfectly controlled local potentials on the sites falling along the edge of the crater. The equation of state on these sites may not favour doublons. Moreover, the local density approximation may not hold in the presence of this abrupt potential variation. We minimize the population of singlons on the edge by increasing the lattice depths and DMD0 potential strengths, such that the tunnelling energy t is small compared with the site-to-site potential difference at the crater edge. We also set the magnetic field at 550 G to lower the Hubbard interaction U, which prevents the formation of a wide Mott plateau. The detected singlon density is ns,r=4 = 0.5(3)% in a central disk with r = 4, and ns,r=6 = 0.7(2)% in a disk with r = 6. Including the edge, the singlon population in the crater is Nr=10 = 11.9(7). However, the presence of singlons on the edge obscures the estimation of entropy in the BI because these singlons are not necessarily excitations. By contrast, because the density of states along the crater edge can be finite, these singlons can host a significant amount of entropy. Taking this into account, we choose to define the BI fidelity FBI = 1 − ns,r=6 in the central region with r = 6 as a figure of merit to optimize the entropy redistribution procedure.

To optimize the initial loading of the BI, the DMD0 potential ramp is split into a linear ramp and a holding phase. Once the lattice depths are high and the tunnelling energy correspondingly small, the atoms may not redistribute for continued changes in the DMD0 potential (that is, the ramp is not adiabatic). We also limit the initial ramp rate to avoid creating band excitations in shallow lattices.

The volcano-shaped DMD0 potential allows the formation of a BI that is in thermal contact with a reservoir when the lattice is shallow, and isolated when the lattice is deep. When using the flattened volcano-shaped potential, we find that the quality of the separation between system and reservoir deteriorates if the plateau surrounding the crater becomes too large, because some atoms may remain in the region with no potential gradient.

Heating in the BI

Heating processes in a BI are strongly suppressed because of the vanishing density of states, and the only active processes must excite atoms into higher bands. Relevant heating mechanisms include incoherent light scattering from the optical lattice, intensity and position noise of the optical lattice, and background gas collision. To quantify the loss, we hold the BI for a variable duration of up to 1,600 ms and measure the total density n using both parity-projected imaging and full charge-resolved imaging as a probe of BI fidelity. In the parity-projected imaging, we measure the defect singlon density in the long-spacing lattice. In the full charge-resolved imaging, we measure the density after splitting the doublons into singlons in the short-spacing lattice. We find that the loss rate of density in the lattice centre is dnr=6/dt = 5.1(3)% s−1 using full charge-resolved imaging, and dnr=6/dt = 4.8(4)% s−1 using parity-projected imaging, assuming that the detection of a singlon corresponds to the loss of a single particle (Extended Data Fig. 3c,d). The zero-time intercept extracted from charge-resolved imaging is n = 1.983(3). The intercept from parity-projected imaging is n = 0.008(4), which is consistent with the directly measured singlon density. The difference between the parity-projected measurement and the charge-resolved measurement is consistent with the limitations imposed by imaging fidelity. Given the loading time of 100 ms, the above loss rate is consistent with the measured BI fidelity of FBI,r=6 = 99.3(2)%.

Calibration of Hubbard interaction U

Owing to light-assisted collisions, in the short-spacing lattice, we can only perform parity-projected density imaging, and therefore detect the density of singly occupied sites \({n}_{{\rm{s}},det}\) (ref. [63]). We correct the detected singlon density by the imaging fidelity to estimate the actual singlon density ns. At half-filling (Figs. 2 and 3), the singlon density is a function of interaction and temperature: ns(U, T). We rely on numerical simulations using DQMC to obtain the doublon density nd(U, T) and compute the singlon density as ns(U, T) = 1 − 2nd(U, T). We find that nd(U, T) is sensitive to interaction strengths but has only a weak dependence on temperature for 0.15 < T/t ≤ 0.25 and saturates for T/t ≤ 0.15 (Extended Data Fig. 4c). Using both the singlon density and spin correlations \({C}_{{\bf{d}}}^{zz}\), we estimate the possible ranges for interaction and temperature as 8 < U/t < 9 and T/t ≤ 0.15. The fact that nd is insensitive to temperature, and therefore effectively becomes a function of only interaction, allows us to invert this equation of state to calibrate U/t using only the measured singlon density ns. With U/t calibrated, we can then estimate the temperature using spin correlations (see section ‘Calibration of temperature T at half-filling’).

To obtain the doped data shown in Fig. 4, the lattice depths and magnetic fields are slightly different from the ones used to obtain the half-filling data shown in Fig. 3 (see section ‘Experimental methods’). Therefore, interaction strength U/t needs to be recalibrated for the doped systems. We adjust the amount of expansion to prepare a sample with half-filling n = 1 in the centre, which can be identified as the maximum of singlon density ns as filling n is increased. We then apply the same calibration protocol described above to this half-filled state to obtain the Hubbard interaction U/t given by the lattice and scattering length parameters used for the doped systems.

There is no available numerical simulation for the large U/t Hubbard model at low temperatures, so a different approach is required to calibrate the interaction strength U/t for the data taken in the Heisenberg limit. We first perform measurements at 580 G in the same lattice configuration as the final data, and with T/t ≤ 0.15 such that the temperature dependence of the singlon density is negligible. The resulting measured value of the singlon density is ns(580 G) = 0.898(7), corresponding to U/t = 8.3(4). Next, using as(620 G)/as(380 G) = 312/233 ≈ 2.18, we obtain U/t = 18.6(8). If we ignore the temperature dependence of the singlon density and compare with numerical linked cluster expansion data at T/t = 0.2 (ref. [65]), our measured singlon density of ns(620 G) = 0.976(3) gives 19.4(1.7), which is consistent with the above measurement.

Estimation of doping

To convert singlon density ns to density n, we rely on numerical simulations using CP-AFQMC to obtain the doublon density nd(n, U, T) as a function of density, interaction strength and temperature. From this, we can compute the singlon density ns(n, U, T) = n − 2nd(n, U, T). Similar to half-filling, the variation of doublon density reduces with temperature T and becomes negligible for T/t ≤ 0.15 (Extended Data Fig. 4b).

We plot the experimentally measured spin correlations as a function of singlon density ns together with numerically simulated spin correlations as a function of computed singlon density ns(n, U, T) in Extended Data Fig. 3f. CP-AFQMC simulation data of nd is not shown in the density range 0.85 < n < 1 because of poor convergence. Based on comparisons of the nearest-neighbour spin correlations, the data are consistent with temperatures of T/t ≤ 0.15 for all values of the doping for which CP-AFQMC is performed.

Taken in combination, the above observations allow us to invert the equation of state and estimate the doping δ = 1 − n(ns, U, 0) using numerical simulations performed at T/t = 0, which have been studied systematically in previous works[17].

Calibration of temperature T at half-filling

For the half-filled data taken at U/t = 8.3(2), good agreement between experimental and numerical data (Fig. 3) allows us to measure temperature using a physically motivated observable that aggregates information from spin correlations beyond nearest neighbours. Specifically, we consider the staggered magnetization mz, which is defined as a sign-corrected average of the spin correlation function up to a cutoff d in bond distance:

$${({m}^{z})}^{2}=\frac{1}{{{\mathcal{N}}}_{{\Omega }_{d}}}\sum _{(i,j),{i}^{2}+{j}^{2}\le {d}^{2}}{(-1)}^{i+j}{C}_{(i,j)}^{zz}\,.$$

(3)

We estimate the value of \({m}_{\text{exp}}^{z}\), as well as the 1σ error \(\delta {m}_{\text{exp}}^{z}\) in this estimate, from bootstrapping, and compute the expected value of \({m}_{{\rm{num}}}^{z}\) as a function of temperature using the DQMC.

We check the effect of boundary conditions by obtaining DQMC numerical data on 12 × 12 and 16 × 16 square systems with open boundary conditions and PBCs (Extended Data Fig. 7a). PBCs tend to overestimate spin correlations \({C}_{d}^{zz}\) at long range, whereas the difference between 12 × 12 and 16 × 16 data is not substantial for d ≤ 6. This motivates our choice of performing subsequent calculations using open boundary conditions and a 12 × 12 system.

Spin correlations are computed at U/t = 8 and U/t = 9 (Extended Data Fig. 7b), converted into magnetization \({m}_{{\rm{num}}}^{z}\) and interpolated to produce a continuous function of temperature and interaction strength \({m}_{{\rm{num}}}^{z}={f}_{U}(T)\) (using cubic splines along T and linear functions along U). At a fixed value of U based on experimental calibrations, we convert our knowledge of the true experimental magnetization, which we model as a Gaussian random variable \(\widetilde{{m}^{z}} \sim {\mathcal{N}}({m}_{\text{exp}}^{z},{(\delta {m}_{\text{exp}}^{z})}^{2})\), into temperature by computing the probability distribution function of \(\widetilde{T}=g(\widetilde{{m}^{z}})\), where g is the inverse of the interpolated numerical data, \(g({m}^{z})={f}_{U}^{-1}({m}^{z})\) if mz ≤ fU(0), and g(mz) = 0 if mz > fU(0). We report the mean value and the ±1σ confidence interval of the temperature distribution.

Experimental and numerical magnetizations and their associated temperature estimates are shown in Extended Data Fig. 7c,d. At the experimentally calibrated value of U/t = 8.3, increasing the bond cutoffs from d = 3 to d = 6 (Extended Data Fig. 7a, vertical dashed lines) does not change the inferred mean temperature of T = 0.05. This indicates a good agreement between numerical and experimental data at all ranges and confirms the calibrated interaction strength (Extended Data Fig. 7c). With a cutoff d = 3 on bond distance, below which numerical data only weakly depends on boundary conditions (Extended Data Fig. 7a), the inferred mean value of T/t varies by about 0.01 within the reported uncertainty on interaction strength U/t = 8.3 ± 0.2, with again a weak variation of the confidence interval. We report in the main text a temperature and asymmetric errors of \(T/t=0.0{5}_{-0.05}^{+0.06}\) obtained with a cutoff d = 6, and a single notable digit capturing the overall magnitude of the systematic errors related to interaction calibration and finite-size simulation effects.

Calibration of tunnelling and lattice parameters

As described in section ‘Experimental methods’, the key step to ramp from a dimerized lattice to the short-spacing lattice is to ramp down the long-spacing lattice by reducing the X lattice depth and to ramp up the short-spacing lattice by increasing the \(\bar{X}\) lattice depth. Ramping the interference time phase ϕ from 0 to π/2 eliminates tunnelling dimerization but introduces a potential offset between the A and B sublattices. The non-reciprocal attenuator allows for ϕ to be stabilized over a much larger range of X lattice depths, allowing us to suppress tunnelling dimerization without introducing potential offsets. We use the calibrated lattice depths and interference parameters12,57 to numerically compute the band structure of the 2D lattices. The absolute amplitudes of tunnellings (in Hz) can then be computed by constructing maximally localized Wannier orbitals[66] or by fitting the band structure with a tight-binding model. We find that the two methods give consistent results for our lattice geometry (Extended Data Fig. 6a) and report the resulting tunnelling amplitudes in Extended Data Table 1.

The tunnelling dimerization is given by the interference term in the lattice potential, which scales as \(\sqrt{{V}_{{x}}}\). It is important to quantitatively determine the dependence of intra- and inter-dimer tunnelling energies td, ti and the perpendicular tunnelling energy tp on VX. With lattice depths VY and \({V}_{X}+{V}_{\bar{X}}\) fixed, the variation of the different tunnelling energies with VX is shown in Extended Data Fig. 6a. At the end of the ramp VX = 3.2(3) × 10−5ER, and the tunnelling energies are all balanced within systematic uncertainties. To obtain the doped data, the X is ramped down to VX = 3.2(3) × 10−5ER in the shallow lattice condition and kept the same for the rest of the experimental sequence.

Experimental verification

Adiabaticity of splitting at strong interaction

In the strong interaction limit U/t = 18.6(8), the adiabaticity is decided relative to the many-body spin gap for an ideal finite-size system with sharp open boundaries and homogeneous potentials. The gap separating the ground state and the Anderson tower of states scales as 1/L^2, whereas the gap for spin wave excitations scales as 1/L, where L is the linear system size15,48. In the absence of heating, slower ramp speed results in more adiabatic evolution.

Experimentally, as we increase the ramp duration from 20 ms to 80 ms, crossing the critical point predicted by previous numerical works[47], we find the spin correlations in the final state reach saturation and do not increase (Extended Data Fig. 6g). We suspect that for longer ramp durations heating competes with the improvement of adiabaticity. Holding during or after the ramp results in strong heating, which manifests as a reduction of spin correlations. We also find that heating is related to the magnetic bias field. When holding in the middle of the ramp, the heating is much stronger at 620 G, where we operate to achieve U/t = 18.6(8), than at 550 G, where the BI is prepared. This hints at heating due to three-body processes, which are strongly enhanced as the scattering length as is increased.

The saturation of spin correlations with increasing ramp time is measured at the same DMD potential strengths as when the BI is formed. We find that with weaker DMD potentials, 20 ms ramps are no longer adiabatic and result in reduced singlon density and spin correlations (Extended Data Fig. 6g). This suggests that atoms are leaving the crater region. However, we do not have a complete understanding of this dynamical process because the time dependence of the magnetic field ramp also plays an important part. Higher magnetic bias fields lead to stronger interaction strengths, which compete against the confining potential and may alter the physics of the transport process.

Adiabaticity of splitting compared with tunnelling dimerization ramp

In a homogeneous Hubbard system at half-filling, or in a Heisenberg spin system, the adiabaticity criterion is determined by the speed of the ramp compared with the energy gap of spin excitations[15]. In our case the relevant ramp is of the tunnelling dimerization, which is controlled by the interference phase. Experimentally, at U/t = 18.6(8), we find that a ramp time longer than 5 ms is sufficiently adiabatic and allows intra- and inter-dimer spin correlations to equalize. Ramps with a duration of less than 1 ms are diabatic and result in clear deviations. This ramp has no detectable effects on longer-range correlations, which is probably decided by the rate of the previous ramp across the quantum critical point. For longer ramp times, we find significant heating when the DMD0 potential is not optimized, leading to atom transport. The coldest reported temperatures are, therefore, obtained by dynamically balancing the confinement of the DMD0 potential, the tunnelling energy and interaction strengths. At U/t = 8.3(2), we find that heating is much less problematic and choose a ramp time of 25 ms to ensure adiabaticity. A systematic study of the phase transition and the resulting requirements on adiabaticity would require proper flattening of the potential and site-resolved preparation of the initial BI.

Characterization of tunnelling dimerization in doped systems

The lattice phase stabilization is kept active between consecutive experimental sequences, and the interference time phase is maintained. The optical lattice position is, therefore, tracked at the single-site level in fluorescence atom images. This allows us to probe spin correlations in a bond-resolved manner (see section ‘Data analysis’). Different y, intra-dimer and inter-dimer tunnelling energies ty, td, ti lead to different superexchange interactions \({J}_{y},{J}_{{\rm{d}}},{J}_{{\rm{i}}}=4{t}_{y}^{2}/U,4{t}_{{\rm{d}}}^{2}/U,4{t}_{{\rm{i}}}^{2}/U\) on the respective bonds, and thus different spin correlations. In Fig. 2, we show how ramping the interference time phase ϕ = π/2 eliminates the tunnelling dimerization and correlation imbalance in the Heisenberg limit. To obtain the doped data, at the end of the ramp to VX = 3.2(3) × 10−5ER (using the non-reciprocal attenuator), we observe that the y, intra-dimer and inter-dimer spin correlations agree to within statistical errors (Extended Data Fig. 6d). The density profile also shows no offset between the A and B sublattices, confirming the absence of potential offsets even with ϕ = 0 (Extended Data Fig. 6b,c).

Tunnelling dimerization has striking effects on the doped Hubbard systems. In contrast to the half-filled case, in which long-range correlations are negligibly affected by dimerization under the conditions VX = 0.016ER, ϕ = 0, the doped systems show only correlations within the dimers. This suggests that dimerization will lead to different physics in the doped Hubbard systems, so it is crucial to remove this dimerization to observe the physics of the isotropic doped Hubbard model.

Adiabaticity of expansion after splitting

Splitting a band insulating state naturally gives rise to a half-filled state. To obtain doped Hubbard systems, we decrease the DMD0 potential such that the atoms can expand out of the crater. The boundary of the expansion is set by the wall formed by the DMD1 potential. Expansion in this ring-shaped region can be strongly affected by the shape of the DMD0 potential. We find that a volcano-shaped DMD0 potential (Extended Data Fig. 1f) with a sharp, outward slope (in which the potential decreases as distance from the centre increases) optimizes the separation between the BI and the reservoir. However, it is more difficult to expand the atoms adiabatically with this potential. The sharp slope acts as a potential barrier separating the crater and the region closer to the DMD1 wall potential, which forms two potential minima in the radial direction. This is similar to the broken symmetry state in a double-well system. The initial BI state is elevated from the true ground state with a small gap given by the tunnelling amplitude between the double well, which is exponentially suppressed with increasing strength of the potential barrier created by DMD0. An alternative, classical picture is that as the atoms flow out of the crater, they will accelerate downhill on the sharp slope, which is not a reversible process and therefore not adiabatic. A bowl-shaped potential that increases until reaching the DMD1 wall as the radial distance from the centre increases alleviates the above issues but will trap additional atoms during the BI preparation leading to increased entropy of the initial state. To balance these considerations, we choose the flattened volcano-shaped potential as a compromise (Extended Data Fig. 1g). We confirmed that the energy gap during expansion is similar to the case in which the potential has an inward slope using exact diagonalization in one dimension.

Larger tunnelling and smaller U/t leads to more adiabatic expansion for fixed expansion duration, suggesting that shallow lattices are favourable. However, we find this does not hold true in the limit for which the lattices are almost fully extinguished. We perform round-trip measurements of the BI fidelity by first decreasing the lattice depths to the values used in expansion and then increasing the lattice depths back to those used to obtain the BI state. Excitations created during the ramp will manifest as singlon defects in the BI. We find a significant increase in the number of these defects when lattice depths are decreased to below 0.5ER during expansion. As a result, we opt to use a lattice depth of 0.3ER during expansion, which corresponds to about 20% of the lattice depth used to load the BI. We believe this behaviour is because of band gaps becoming smaller than the tunnelling energy, allowing diabatic excitations to higher bands. After ramping to these conditions, we start the splitting procedure, which merges the two lowest bands in the long-spacing lattice into a single band in the short-spacing lattice.

We experimentally investigate the dynamics during expansion by varying the duration of the expansion step. We measure the final spin correlations after reloading for the expansion times of τexp = 1 ms, 15.5 ms and 30 ms. No statistically significant variation is observed in these measurements (Extended Data Fig. 6e), suggesting that 1 ms is sufficient for the expansion to be adiabatic. This is consistent with the large bandwidth and Fermi energy of around 10 kHz during expansion. To further facilitate thermalization during expansion, and to allow the field to settle before reloading happens, we ramp the magnetic field from 550 G to 590 G during the expansion step, which increases the scattering length as from around 86a_0 to 295a_0, and set the expansion time to a much longer duration of 50 ms.

Adiabaticity of expansion with fixed duration

In Fig. 5, we show the adiabaticity of expansion in the long-spacing lattice with fixed duration in units of the tunnelling time ħ/t. The temperatures are obtained by comparing the d = 1 and d =  \(\sqrt{2}\) spin correlations \({C}_{d}^{zz}\) averaged in the central half-filled region to DQMC simulations. The goal is to probe adiabaticity as a function of interaction strengths, independent of the absolute value of tunnelling amplitudes. Although expansion in deeper lattices may lead to more lattice heating because of the increased laser intensity and smaller tunnelling amplitudes, little heating was observed in previous work[57] involving measurements performed at similar lattice depths and ramp times as for the deepest lattice explored in Fig. 5 (η = 1). The increased kinetic energy scales at shallower lattices may also reduce the effects of potential disorder, whose contribution to expansion adiabaticity requires future studies. We also measured the adiabaticity of expansion with a fixed duration of τ = 120 ms as shown in Extended Data Fig. 3e, which corresponds to 18.5ħ/t for η = 1. We find similar heating as in Fig. 5a despite the holding time being about four times shorter. This suggests that diabatic heating and dissipative heating are occurring on a similar scale. According to Fig. 5b, however, 120 ms should be slow enough to incur only about 0.2t increase in temperature, even at η = 1 (it would correspond to roughly 4 ms in Fig. 5). This indicates that interactions probably play a part in inhibiting transport at τ = 120 ms by placing more stringent requirements on adiabaticity.

Thermalization of splitting at half-filling

To check whether the half-filled state in Fig. 3 has equilibrated, we added a hold time of τh = 0 ms, 10 ms, and 20 ms after ramping into the final antiferromagnetic state. During this measurement, the lattice alignment relative to the DMD potentials was not optimized, which may explain the higher resulting temperatures. We find no change in the singlon density as a function of radial distance r from the trapping potential centre, or in the overall profile of the spin correlations as a function of bond distance d (Extended Data Fig. 6h). However, we observe a slight decrease of the nearest-neighbour correlation, which may be attributed to heating during the hold time. This suggests that the ramp to split a BI into a half-filled system realizes a state in thermal equilibrium.

Thermalization during reloading

After unloading the BI, splitting into a half-filled Fermi liquid, and expanding into a Fermi liquid with filling n < 1, the sample is in a similar state to previous works11,63, but at lower entropy. We reload the lattice using a linear ramp-up of the lattice depth in 100 ms, which is chosen to balance adiabaticity and lattice heating. Moreover, we experimentally confirmed the final quantum state is in equilibrium by holding for τh = 0 ms, 20 ms and 40 ms, corresponding to 0ħ/t, 20ħ/t and 40ħ/t. No statistically significant variation is observed for these hold times (Extended Data Fig. 6f).

Furthermore, in the doped system, we can cross-check the thermalization throughout the sample by comparing temperature estimates obtained at different locations in the cloud. As described in section ‘Data analysis’, the temperatures shown in the main text are obtained in an ROI with radii r = 3, 4 and 5 in the centre of the cloud by including only pairs of sites within the ROI (which we refer to as strict binning). We can also obtain temperature estimates from the radially averaged spin correlations, although these estimates suffer from averaging over larger potential variations and different bin shapes. Nevertheless, in Extended Data Fig. 8, we reanalyse the data from Fig. 4f to plot \({C}_{{\rm{bin}}}^{zz}(d)\) against the measured singlon density ns,bin. \({C}_{{\rm{bin}}}^{zz}(d)\) denotes the measured zz spin correlation at bond distance d for the corresponding bin. Note that the above analysis does not apply strict binning, so only one of the two sites used to compute a given correlation function must be contained within the bin. This leads to systematic deviations from the results obtained with strict binning. For example, the correlations with longer bond distances will be underestimated for the bin with the shortest distances, as sites at lower densities are involved. Nevertheless, this analysis offers a qualitative view that the atom cloud has thermalized across different densities.

Exact diagonalization of BI splitting

We use the QuSpin package[67] to compute the evolution of energy levels during splitting with exact diagonalization. For the Heisenberg model (Fig. 2), we choose a system size of 6 × 5, with a PBC in the long direction and twisted boundary condition in the short direction. The twisted boundary condition is defined as connecting (i, 5) → ((i + 1)%6, 1). The spin exchange coupling along the y-direction is fixed to J = 1, and \(\alpha =\sqrt{{J}_{{\rm{d}}}/J}=\sqrt{J/{J}_{{\rm{i}}}}\) are varied from 10 to 1.

Numerical simulations at half-filling

Our simulations at half-filling were performed with finite-temperature DQMC and ground-state AFQMC methods. AFQMC and DQMC are numerically exact formulations for studying quantum many-body systems. The approach begins with the Trotter decomposition, which breaks the original imaginary-time propagator into smaller pieces. To handle the on-site interaction, an auxiliary field is introduced using the Hubbard–Stratonovich transformation, transforming the interaction operator into a sum of one-body operators. These steps allow the partition function to be expressed as a sum over the products of two Slater determinants—one for spin-up electrons and one for spin-down electrons—whose matrix elements can be computed analytically. However, the product may take both positive and negative values, leading to a sign problem (except in specific cases, such as the half-filled 2D repulsive Hubbard model, in which particle–hole symmetry eliminates this issue).

In our simulations (Fig. 3), finite-temperature results are obtained using the DQMC method with 7,000 thermalization sweeps, 200,000 measurement sweeps and a time step of Δτ = 0.05. Ground-state data are generated with the AFQMC method, using the Metropolis algorithm with force bias updates[68], thousands of sweeps and a time step of Δτ = 0.02. We verify that the Trotter error at these values of Δτ is smaller than the statistical error, ensuring the robustness of our results. To mitigate ergodicity issues and fluctuations in spin correlations caused by the SU(2) symmetry breaking in the spin-z Hubbard–Stratonovich transformation, we adopt the charge Hubbard–Stratonovich transformation for both the finite-temperature and ground-state calculations. Additionally, the infinite variance problem[69] in spin correlation measurements is significantly reduced when using the charge decomposition instead of the spin decomposition.

CP-AFQMC simulation

For doped systems, we use both finite-temperature16,70 and ground-state[71] CP-AFQMC methods to compute the properties of the system at zero and finite temperatures, respectively. The finite-temperature CP-AFQMC method shares several of the building blocks from the standard DQMC method, including the use of Trotter decomposition and the Hubbard–Stratonovich transformation. However, it introduces two key differences. First, in systems suffering from the sign problem, an exact condition is derived for the paths in auxiliary-field space that cause the sign problem[70]. It is shown that only a small subset of paths in the auxiliary-field space contribute to the partition function. The remaining paths are symmetrically distributed and cancel each other, which adds noise to the computation of observables. To address this, the CP-AFQMC method imposes a constraint, acting as a gauge condition, to isolate and sample the relevant paths. This constraint is exact when the many-body Hamiltonian is used in the constraint. Second, to implement the constraint and sample configurations efficiently, the algorithm uses a branching random walk with importance sampling to construct complete paths. In practical applications, an effective Hartree–Fock Hamiltonian is used as a trial Hamiltonian to define the constraint[16]. The parameters Ueff and βeff are tuned so that the spin correlations obtained by the effective Hamiltonian closely match those of the many-body system[18].

Ground-state CP-AFQMC[71] is a projection-based quantum Monte Carlo method that leverages the principle that the ground state can be accessed by applying an imaginary-time evolution operator to an initial wave function, provided the trial wave function has a nonzero overlap with the ground state. Similar to the finite-temperature algorithm, ground-state CP-AFQMC also combines the Trotter decomposition with the Hubbard–Stratonovich transformation to reformulate the imaginary-time evolution as a stochastic process—a random walk through the space of Slater determinants. Ground-state properties are calculated as statistical averages over the configurations sampled during this random walk. To address the fermion sign problem, an unrestricted Hartree–Fock solution is used as a trial wave function, introducing a constraint that ensures each Slater determinant in the random walk maintains a positive overlap with the trial wave function. Extensive benchmarks in the literature21,23,27,28 demonstrate that CP-AFQMC delivers high accuracy for studying ground-state and finite-temperature properties of quantum many-body systems. Our ground-state AFQMC calculations followed similar procedures to ref. [17] (although at half-filling we applied the charge decomposition as discussed earlier). In our simulations (Fig. 4), the Trotter time step is typically 0.05 and 0.01 in finite temperature and ground-state calculations, and we verify that the Trotter errors are limited. The population of random walkers is around 5,000 in these calculations.

Quantum Monte Carlo for Heisenberg model

Finite-temperature numerical simulations of the Heisenberg model in Fig. 2 and Extended Data Fig. 9 are performed with the SpinMonteCarlo.jl package (www.juliapackages.com/p/spinmontecarlo), using a loop quantum Monte Carlo algorithm with 1,024 warmup updates and 8,192 measurement updates, over 16 × 16 dimerized lattice unit cells (512 sites) with PBCs. Individual spin correlators \(\langle {S}_{z}({\bf{r}}){S}_{z}({{\bf{r}}}^{{\prime} })\rangle \) are grouped by pairs of sites \(({\bf{r}},{{\bf{r}}}^{{\prime} })\) with the same bond vector \({\bf{r}}-{{\bf{r}}}^{{\prime} }\), distinguishing pairs that belong to the same sublattice from those that do not. These correlators are averaged at each measurement update, resulting in a relative statistical error of the order of 10−3. The resulting correlation maps are shown in Extended Data Fig. 9a for representative values of the coupling parameter α at temperature T/J = 0.5. We estimate that the systematic errors due to finite-size effects are at most 5 × 10−3 in the square lattice at this temperature, based on a comparison to a 32 × 32 system.

Staggered magnetization mz is obtained by averaging the sign-corrected spin correlations over bond distance (Extended Data Fig. 9b), in this case truncated to a square 13 × 13 region (Extended Data Fig. 9a), and matching the ROI used in the analysis of experimental data. We observe a distinct increase and temperature dependence of mz at couplings above α = 0.65, which we attribute to the existence of a quantum phase transition to an antiferromagnetic phase at zero temperature. This critical value is comparable to ground-state Heisenberg simulations performed in similar dimerized lattice geometries[47]—note that the parameter α in our work is defined as a ratio of tunnel couplings and is related to ratios of antiferromagnetic spin couplings in the equivalent Heisenberg model by a square root.

After normalizing experimental data by the square spin moment \({n}_{{\rm{s}}}^{2}\), where ns = 0.95 is the averaged singlon density in this dataset, we observe good agreement between numerical Heisenberg data compared with bond distance and experimental spin correlations \(| {\bf{r}}-{{\bf{r}}}^{{\prime} }| \) in the square lattice at U/t = 18.6(8) (Extended Data Fig. 9c). A least-squares fit of correlations up to \(| {\bf{r}}-{{\bf{r}}}^{{\prime} }| \le 8\) results in an estimated temperature of T/J = 0.458(3).

Effective temperature in Hubbard systems compared with cuprates

The single-band Hubbard model can be related to the high-Tc superconducting cuprates through a few steps of reduction and approximation. A more realistic approximate model for the cuprates is the three-band Hubbard model, in which tunnelling t and interaction strength U can be correspondingly related to energy scales in cuprates[2]. Therefore, it is not straightforward to relate the temperatures in the one-band Hubbard model with doping to an effective temperature in cuprates.

However, at half-filling, the physics is well understood in the Hubbard model[3] and cuprates2,19,20 and can be described by an antiferromagnetic Heisenberg model. In this regime, the characteristic energy scale is the exchange energy J, which can be readily probed in cuprates and is J = 4t^2/U in the one-band Hubbard model. This correspondence allows us to convert the temperature in the half-filled Hubbard model using T/J to an effective temperature in cuprates. Taking J = 125(5) meV in yttrium barium copper oxide19,20 and U/t = 8, a temperature of T/t = 0.25 corresponds to about 725 K, and a temperature of T/t = 0.05 corresponds to around 145 K.

Data availability

The datasets generated and analysed during this study are available from the corresponding author on request. Source data are provided with this paper.

Code availability

The codes used for the analysis are available from the corresponding author on request.

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Acknowledgements

We thank D. Greif, G. Ji and C. Chiu for early experimental contributions; A. Bohrdt, E. Demler, T. Esslinger, A. Georges, F. Grusdt, E. Khatami and M. Zwierlein for their discussions; and Y.-Y. He, Y. Yang and Z.-Q. Wan for help with our calculations. We acknowledge support from the Gordon and Betty Moore Foundation, grant no. GBMF-11521; the National Science Foundation (NSF), grants nos. PHY-1734011, OAC-1934598 and OAC-2118310; ONR, grant no. N00014-18-1-2863; the Department of Energy, QSA Lawrence Berkeley Lab award no. DE-AC02-05CH11231; QuEra, grant no. A44440; ARO/AFOSR/ONR DURIP, grant nos. W911NF-20-1-0104 and W911NF-20-1-0163; the ARO ELQ, award no. W911NF2320219; the Flatiron Institute, a division of the Simons Foundation (C.F. and S.Z.); the NSF Graduate Research Fellowship Program (L.H.K. and A.K.); the AWS Generation Q Fund at the Harvard Quantum Initiative (Y.G.); the Swiss National Science Foundation (M.L.); and the Intelligence Community Postdoctoral Research Fellowship Program at Harvard administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy and the Office of the Director of National Intelligence (ODNI) (A.W.Y.).

Author information

Authors and Affiliations

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

Muqing Xu, Lev Haldar Kendrick, Anant Kale, Youqi Gang, Aaron W. Young, Martin Lebrat & Markus Greiner

2. Center for Computational Quantum Physics, Flatiron Institute, New York, NY, USA

Chunhan Feng & Shiwei Zhang

Contributions

M.X., L.H.K., A.K., Y.G., A.W.Y. and M.L. performed the experiment and collected and analysed the data; C.F. and S.Z. performed the numerical DQMC, AFQMC and CP-AFQMC simulations; M.X. performed part of the numerical DQMC simulation in the Methods; M.L. performed the Heisenberg model QMC simulations; and M.G. supervised the study. All authors contributed to the interpretation of the results and production of the paper.

Corresponding authors

Correspondence to Muqing Xu or Markus Greiner.

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Competing interests

M.G. is co-founder and shareholder of QuEra Computing.

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Extended data figures and tables

Extended Data Fig. 1 Schematic of the experimental details used in the cooling protocol.

(a). The experimental sequence of the ramps used for the optical lattice and dipole potentials to initialize a BI with ultra-low entropy. (b). The experimental sequence of the ramps used for the optical lattice and dipole potentials to split the BI into a cold antiferromagnet at half-filling in the U/t = 8.3(2) Hubbard model. (c). The experimental sequence of the ramps used for the optical lattice and dipole potentials to split the BI and expand into a cold doped Hubbard system at U/t = 8.3(2). (d). Schematic of the lattice beams. Two orthogonal laser beams X and Y, whose phase are interferometrically stabilized, are retroreflected to create an interference lattice. A frequency-offseted laser beam \(\bar{X}\), co-propagating with X, are also retro-reflected to create a second lattice along the x direction shifted by half a site. (e). As we decrease the intensity of the X beam and increase the intensity of the \(\bar{X}\) beam, the interfering long-spacing lattice is ramped down and the non-interfering short-spacing lattice is ramped up. The lattice geometry changes from a long-spacing square lattice through a dimerized lattice to a short-spacing square lattice. (f). The volcano-shaped potential used in Fig. 3. A sharp, outward sloped potential facilitates preparation of the BI but prevents adiabatic expansion of the atom cloud. (g). The flattened volcano-shaped potential used in Fig. 4. Removing the potential barrier to form a flattened region allows adiabatic expansion. Each pixel of the DMD can be turned on or off. A continuous potential function is binarized using error diffusion algorithm[11].

Extended Data Fig. 2 Comparison between experimental methods with and without non-reciprocal attenuator at half-filling.

Left to right: averages of two-dimensional singlon density, radially averaged singlon density on A/B sublattices, nearest neighbor spin correlation Czz(1) for intra-dimer, inter-dimer and perpendicular bonds, spin correlations averaged over an ROI of r = 5. The sharp edge in the spatial singlon density profile is due to the edge of the camera sensor. (a)-(d). Without the non-reciprocal attenuator, we ramp the interference time phase to ϕ = π/2 to remove the tunneling dimerization. At half-filling, the resulting sublattice potential offset would not induce a density offset in the Mott insulator region but would cause offsets away from half-filling. The density offset is clearly visible in the outer reservoir region. (e)-(h). With the non-reciprocal attenuator, we could keep the interference phase at ϕ = 0. This suppresses tunneling dimerization below statistical fluctuations without inducing sublattice potential offsets. We find no difference between the densities on the two sublattices. No density modulation is detected in the reservoir.

Source Data

Extended Data Fig. 3 Supplementary experimental data.

(a). Correlation map and the azimuthal average computed in an ROI of r = 4. (b). Correlation map and the azimuthal average computed in an ROI of r = 5. (c). Using parity-projected imaging, the defect singlon density measured in the long-spacing lattice as a function of holding time. (d). Using full charge-resolved imaging, the density measured in the short-spacing lattice as a function of holding time. There is a factor of 2 difference in the measured slope due to there are twice the number of sites. (e). Here we allow the atom cloud to expand for a constant time τh = 120 ms instead of constant time measured by tunneling times ħ/t. The heating remain nearly the same despite the ramp time and therefore lattice heating is reduced by ≃ 4 times for the deepest lattice depth. The expansion time τh ≃ 15ħ/t in the deepest lattice depth, which according to Fig. 5 should be sufficiently slow to cause negligible heating. (f). Spin correlations shown in Fig. 4 as a function of singlon density ns. For experimental data we correct for imaging fidelity to obtain ns,corrected as ns.

Source Data

Extended Data Fig. 4 Supplementary data from numerical simulations.

(a). The effects of symmetric A/B sublattice potential offset by ± Δμ on spin correlations at T/t = 0.15, 0.3 using DQMC and T/t = 0 using CP-AFQMC, with Δμ = 0.0t, 1.0t, 2.0t that is symmetric ± Δμ/2 from 0 on A/B sublattices. We set the chemical potential μ = 0 at half-filling therefore there is no effect on the average density n = 1. The densities deviation on the A/B sublattices from the average density are ± 0, ± 2%, ± 4% correspondingly. We plot the spin correlations (top) and the difference from the case with no offset (bottom). No statistically significant effect on the spin correlations is detected for T/t≥0.15 or T/t = 0, Δμ≤1.0t. At T/t = 0, a potential offset of Δμ = 2.0t seems to decrease the strength of spin correlations. Note here we use PBC and the long-range AFM order enhanced by finite size effects can be seen as saturation of spin correlation as a function of distance. (b). (Top) The doublon density nd as a function of density n computed using CP-AFQMC at different temperatures. Below T/t = 0.15 the variation of nd as a function of temperature is small compared to the statistical errors of the detected densities for all dopings where CP-AFQMC simulation is performed. (Bottom) The difference of finite temperature and ground state doublon densities nd(T) − nd(0). (c). (Top) The doublon density nd as a function of temperature T/t at half-filling with interaction U/t = 8 computed using AFQMC. Below T/t = 0.2 the variation of nd as a function of temperature is small compared to the statistical errors of the detected singlon densities. Below T/t = 0.15 the nd is consistent with staying constant as it ground state value. (Bottom) The doublon density nd is sensitive to interaction strengths U/t computed using DQMC for T/t = 0.1, 0.13, 0.2. The saturation of nd as a function of temperature below T/t = 0.13 holds for a wide range of U/t.

Source Data

Extended Data Fig. 5 Discrepancy between CP-AFQMC results and experimental data or DQMC results.

(a). Spin correlations computed using DQMC in an 8 × 8 system and using CP-AFQMC in a 12 × 12 system at T/t = 0.23 and obtained from experimental data with a calibrated U/t = 8.2(2), T/t = 0.264(4). (b). Spin correlations computed using DQMC and using CP-AFQMC in a 12 × 12 system at T/t = 0.33 and obtained from experimental data with a calibrated U/t = 8.2(2), T/t = 0.332(7). The experimental data are taken using standard loading with a harmonic trap similar to ref. [63]. We tune the atom number to reach half-filling in the center of the trap where the atoms form a large Mott insulator that can be used for thermometry. Outside the Mott insulating region, the density decrease as radial distance increases. We obtain spin correlations in the doped region by performing radial binning of the data. The singlon density ns is converted to density n using DQMC data at T/t = 0.23.

Source Data

Extended Data Fig. 6 Supplemental experimental data to investigate potential homogeneity and verify the state to be thermalized and equilibrated.

(a). Tunnelings td, ti, tp as a function of X lattice depth computed by fitting the band structure or constructing wannier orbitals. To balance the tunnelings within 3%, we need to decrease VX below 10−4ER. DS7 is used as an example to show the density and correlation profile. (b). The spatial profile of the detected singlon density of the final two dimensional doped Hubbard system in the combined potential defined by the optical lattice and DMDs. (c). Nearest neighbor spin correlation Czz(1) for intra-dimer, inter-dimer and perpendicular bonds. The spin correlations are statistically consistent, confirming the systematical error caused by tunneling dimerization is small compared to statistical error. (d). Azimuthal average of the singlon density profile shown in (b) on A/B sublattices. We find no difference between the densities on the two sublattices which confirms the absence of sublattice potential offset with ϕ = 0. (e). Density and spin correlations as a function of holding time before the final lattice ramp after expansion. We find no change both in the detected singlon density profile and spin correlation Czz(∣d∣) for bond distances d = (1, 0), (1, 1), (2, 0), (2, 1). This confirms 1 ms is sufficiently adiabatic for expansion. (f). Density and spin correlations as a function of holding time after the final lattice ramp. This confirms the system is in thermal equilibrium. (g). (Top) At full DMD potential strengths as used to prepare the BI, no statistically significant changes in density or spin correlations are measured as ramp duration is increased from 20 ms to 30 ms. (Bottom) At reduced DMD potential strengths, fast ramps of 20 ms becomes no adiabatic, resulting in significant reduction in density and spin correlations. (h). Density and spin correlations as a function of holding time after the splitting ramp performed at half-filling as in Fig. 3. The shape of density as a function of radial distances r and spin correlations as a function of bond distance d remain the same after holding suggests the system has thermalized and equilibrated. The reduction of the nearest-neighbor correlation may be due to lattice heating.

Source Data

Extended Data Fig. 7 Systematic errors on temperature at half-filling.

a, Effect of boundary conditions on DQMC simulations of the sign-corrected spin correlation function \({(-1)}^{d}{C}_{{\bf{d}}}^{zz}\). b, Effect of interaction strength U/t on simulated \({(-1)}^{d}{C}_{{\bf{d}}}^{zz}\). c, Effect of cutoff bond distance on staggered magnetization square \({({m}^{z})}^{2}\) and estimated temperatures. Shaded rectangles along the y- and x-axis indicate 1σ confidence intervals on measured \({({m}^{z})}^{2}\) and estimated T/t, respectively. d, Effect of uncertainty on calibrated interaction strength on estimated temperatures.

Source Data

Extended Data Fig. 8 Radially averaged spin correlations vs radially averaged densities.

(a)-(f). Non-strict binned radially averaged spin correlations plotted against the densities of the corresponding bins, for central singlon density ns = 0.744(7), 0.766(7), 0.800(9), 0.812(9), 0.833(7), 0.864(8). Each bin contains 60 sites, which is the same as an ROI of r = 4.

Source Data

Extended Data Fig. 9 Numerical Quantum Monte Carlo simulation of the Heisenberg model in a dimerized square lattice.

a, Bond maps of spin correlation for coupling α = 0.3, 0.7, 1.0 at temperature T/J = 0.5. b, Staggered magnetization as a function of coupling and temperature. c, Sign-corrected spin correlations as a function of bond distance for experimental data at U/t = 18.6(8) (markers) and fit of square Heisenberg data, yielding a temperature T/J = 0.458(3).

Source Data

Extended Data Table 1 Summary of experimental datasets

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Source Data Extended Data Fig. 9

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Xu, M., Kendrick, L.H., Kale, A. et al. A neutral-atom Hubbard quantum simulator in the cryogenic regime. Nature (2025). https://doi.org/10.1038/s41586-025-09112-w

- Received: 28 January 2025

- Accepted: 07 May 2025

- Published: 11 June 2025

- DOI: https://doi.org/10.1038/s41586-025-09112-w

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Article Open access Published: 25 June 2025

Evidence of Coulomb liquid phase in few-electron droplets

Jashwanth Shaju,

Elina Pavlovska,

Ralfs Suba,

Junliang Wang,

Seddik Ouacel,

Thomas Vasselon,

Matteo Aluffi,

Lucas Mazzella,

Clément Geffroy,

Arne Ludwig,

Andreas D. Wieck,

Matias Urdampilleta,

Christopher Bäuerle,

Vyacheslavs Kashcheyevs &

…

Hermann Sellier

Nature (2025)

Abstract

Emergence of universal collective behaviour from interactions within a sufficiently large group of elementary constituents is a fundamental scientific concept[1]. In physics, correlations in fluctuating microscopic observables can provide key information about collective states of matter, such as deconfined quark–gluon plasma in heavy-ion collisions[2] or expanding quantum degenerate gases3,4. Mesoscopic colliders, through shot-noise measurements, have provided smoking-gun evidence on the nature of exotic electronic excitations such as fractional charges5,6, levitons[7] and anyon statistics[8]. Yet, bridging the gap between two-particle collisions and the emergence of collectivity[9] as the number of interacting particles increases[10] remains a challenging task at the microscopic level. Here we demonstrate all-body correlations in the partitioning of electron droplets containing up to N = 5 electrons, driven by a moving potential well through a Y-junction in a semiconductor device. Analysing the partitioning data using high-order multivariate cumulants and finite-size scaling towards the thermodynamic limit reveals distinctive fingerprints of a strongly correlated Coulomb liquid. These fingerprints agree well with a universal limit at which the partitioning of a droplet is predicted by a single collective variable. Our electron-droplet scattering experiments illustrate how coordinated behaviour emerges through interactions of only a few elementary constituents. Studying similar signatures in other physical platforms such as cold-atom simulators4,11 or collections of anyonic excitations8,12 may help identify emergence of exotic phases and, more broadly, advance understanding of matter engineering.

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Main

Breaking up matter into pieces and studying the statistics of fragments is one of the basic epistemic strategies in physics. Arguably the most notable pursuit of this strategy is the success of high-energy particle colliders in discovering and quantifying the fundamental types of matter within the Standard Model of elementary particles. In the studies of strong interaction, relativistic ion collisions are used to induce the deconfinement of the nuclear matter (composed of correlated hadrons) into a hot plasma of more fundamental particles (quarks and gluons)[13]. The statistical fluctuations of collision products carry rich information about the collective dynamics[9]. In particular, measurements of high-order cumulants have been used2,14 to pinpoint the critical point in the phase diagram of quantum chromodynamics (QCD)15,16. In solid-state nanoelectronic circuits, charged quasiparticles can be launched with on-demand single-electron sources and guided to a small interaction area such as a quantum point contact (QPC)7,17 or an energy barrier18,19,20, creating a collider analogue for electronic matter. Second-order correlations in steady-state and on-demand collisions have provided an essential tool to decode partitioning noise of composite particles5,6, fermionic7,17 and anyonic[8] exchange statistics and two-particle Coulomb interactions18,19,20. In previous research, temporal electronic correlations in nanostructures have been extensively studied21,22,23,24,25,26,27. These experiments primarily investigated the Coulomb interaction between two neighbouring electrons as they traverse a QPC, quantum dot (QD) or tunnel junction.

Higher-order (k > 2) correlations in current fluctuations28,29 and electron-counting statistics25,30,31 have been recognized as important signatures of Coulomb interactions. Yet, evidence for the corresponding collective behaviour in on-chip transport has been difficult to interpret[32] owing to limited control over the number N of interacting particles and the dominating randomness of tunnelling times. Investigating the gap between few-particle correlations and the thermodynamic limit[10] for 2D electron systems is motivated by their rich phase diagram as a function of electron density, temperature and magnetic field, including strongly correlated Coulomb liquid, Wigner crystals and quantum Hall phases[33].

Here, by drawing an analogy with relativistic ion collisions, we investigate partitioning of a small electron-plasma droplet containing a precise number N of electrons. Analysing the partitioning data of our synthesized electron droplet using multivariate cumulants enables us to identify its corresponding strongly correlated state of electronic matter.

On-chip multi-electron splitter

We have implemented the partitioning of a charge droplet of interacting electrons using a Y-junction in a GaAs semiconductor heterostructure, as illustrated in Fig. 1. Two single-electron sources and two single-shot detectors are made of gate-defined QDs paired with nearby QPCs used as charge sensors. By recording the QPC current IQPC of each QD before and after the experiment, the precise number of released electrons (source QD) and captured electrons (detector QD) is measured. Several pairs of parallel electrodes define depleted quasi-1D transport rails, guiding the electrons from the sources to the detectors. A 40-μm-long central channel is used to control the electron droplet properties before partitioning. This channel includes a narrow 30-nm-wide barrier gate that enables precise tuning of the confining potential in the direction perpendicular to the rail. At the end of the channel, a Y-junction splits the electron droplet into two parts and directs the ‘reaction products’ towards the detectors. The counting statistics is accumulated into the probabilities P(N−n,n), in which n and N − n are the numbers of electrons measured, after each single-shot partitioning, in detectors D1 and D2, respectively.

Fig. 1: Partitioning of an electron droplet.

a, Schematic of the experiment. An electron source (S1) delivers a few-electron droplet, which is split in-flight at a Y-junction. The output of the partitioning is analysed by two single-shot detectors (D1 and D2). b, Schematic of the electron droplet transport inside the selected potential minimum of a SAW. Electrostatic gates (yellow) are used to guide the electron droplet and create a Y-junction. Scale bar, 200 nm. c, Scanning electron microscope image of the device showing the metallic surface gates (light grey). The electron source (S1) consists of a QD (shown in the top-left inset) coupled to a QPC for charge sensing. The plunger gate (yellow) is used to inject a precise number of electrons into a single SAW minimum. A second electron source (S2) is connected to the central channel to inject more electrons. The Y-junction at the end of the central channel (see bottom inset) enables partitioning of the electron droplet. Scale bars, 2 μm (main); 200 nm (insets).

In our experiment, the electron droplet is transported within a single piezoelectric potential minimum of a surface acoustic wave (SAW)34,35,36. An interdigital transducer (IDT), positioned 1.5 mm away, generates a 180-μm-long SAW train. By applying a voltage pulse VP on the plunger gate of the source QD, with a duration much shorter than the SAW period, a well-defined number of electrons (ranging from 1 to 5) can be loaded into a single minimum of the SAW potential (see Supplementary Note 1). When the SAW propagates across the device, these electrons remain confined in the moving QD[37], which shuttles them along the rails.

For droplets with more than three electrons, the injection from a single source becomes technically challenging and we instead prepare these droplets using two sources, synchronized to the same SAW minimum. The two parts then merge in the central channel, in which the voltage VB on the barrier gate is tuned to ensure that the electrons lose their history and become statistically indistinguishable (see Supplementary Note 3).

Partitioning of an electron droplet

We illustrate our ability to control the partitioning in Fig. 2, in which the counting statistics P(N−n,n) is shown for N = 4 as a function of the voltage difference Δ = VU − VL between the two electrodes defining the central channel. This parameter acts as a tunable impact parameter for the collision with the Y-junction.

Fig. 2: Partitioning of an electron droplet containing N = 4 electrons.

a–c, Detection probabilities P(N−n,n) versus side-gate detuning voltage Δ. Each data point is extracted from 3,000 single-shot measurements. Error bars on probabilities are smaller than the symbol size (see Methods). The labels (N − n, n) correspond to the events in which n electrons are measured at detector D1 and N − n electrons at detector D2. In a, the four electrons are distributed across different SAW minima, as illustrated in the top inset. In b, the four electrons are loaded into adjacent minima, with two electrons in each. In c, all four electrons are confined into a single minimum. The solid lines in a are predictions based on independently measured single-electron partitioning probabilities (see Supplementary Note 4). d–f, Multivariate cumulants κ_1…κN calculated from the measured probabilities shown in a–c. The inset in d shows the evolution of κ_1 across the entire range and the solid line is the partitioning probability P(0,1) of a single electron. In e, two non-equivalent cumulants contribute to κ_2 (see Methods and Supplementary Note 6). Solid lines in e and f are fits using the Ising model of equation (1).

Source data

The simplest case is when all of the electrons are placed in different SAW minima (Fig. 2a) such that they are prevented from forming a droplet and cannot interact. We find that the counting statistics P(N−n,n) can be reconstructed from single-electron partitioning data (solid lines) and thus follows a binomial distribution, with electrons scattering at the Y-junction independently of each other. Such statistics corresponds to fixed-N samples of a non-interacting electron gas.

To induce correlations, we group the electrons in two pairs, placed in adjacent SAW minima (Fig. 2b). An increase of the probability P(2,2) can be observed compared with the non-interacting case, indicating antibunching of the two electrons contained in each pair[18]. To obtain a strongly correlated state, we place all four electrons in the same SAW minimum (Fig. 2c) and note a similar increase in P(2,2) but the maxima of P(1,3) and P(3,1) now exceed P(2,2). Although the probabilities in Fig. 2b,c are qualitatively different, the multi-electron interdependencies are difficult to interpret directly from the counting statistics.

Multivariate cumulants

To interrogate the nature of the many-electron state in the droplet, we aim to characterize its internal correlations and decompose them into irreducible components, known as cumulants[38]. Cumulants are convenient as they capture not only pairwise but also higher-order correlations. This is crucial for understanding complex many-body systems in which strong enough pairwise interactions can lead to correlations of all orders, heralding the emergence of a new collective state. One possibility is to consider the high-order cumulants ⟪nk⟫, or their combinations such as skewness and kurtosis, of the collective variable n, as it is measured directly2,31. Yet, in this representation, contributions of individual particles are not resolved. To explain few-electron correlation effects, it is crucial to separate these correlations by order, corresponding to the number of particles involved. We achieve this by recognizing that n = T_1 + T_2 +…+ TN is a sum of several variables Tj, corresponding to the partitioning outcome of each electron (Tj = 1 or 0 if the jth electron is detected at D1 or D2, respectively). Instead of ⟪nk⟫, we consider the irreducible correlation functions ⟪TiTj…Tk⟫, known as multivariate cumulants in statistics[39] or connected diagrams in field theory[40], to quantify the effect of interactions. Notably, if the presence of the ith electron does not influence the jth electron, all multivariate cumulants involving both Ti and Tj will be zero. Here we focus on the symmetrized multivariate cumulants κk defined by averaging the cumulants over all possible combinations of exactly k distinct variables Tj out of N. If electrons are statistically indistinguishable (all placed in the same SAW minimum or all in different SAW minima), all terms in the averaging are equal and κk = ⟪T_1T_2…Tk⟫. In this case, the multivariate cumulants κk are entirely determined by the counting probability distribution P(N−n,n) and can be computed from both measurements and models (see Methods).

We now illustrate the meaning of multivariate cumulants using the experimental data from Fig. 2a–c (in which N = 4) and show the corresponding cumulants (κ_1…κ_4) in Fig. 2d–f. The first-order average κ_1 = ⟨T_1⟩ = ⟨n⟩/N is simply the marginal probability for one electron to be transmitted into D1; it changes monotonously from 0 to 1 with detuning parameter Δ. For a binomial distribution of independent trials, all high-order cumulants κk>1 are zero, and this is indeed the case in Fig. 2d in which all four electrons are distributed into separate SAW minima. To gain intuition about the second-order correlations, we consider the second central moment ⟨n^2⟩ − ⟨n⟩2 of the counting statistics, which is always equal to Nκ_1(1 − κ_1) + N(N − 1)κ_2. It consists of two terms: the ideal gas contribution proportional to N and representing independent shot-noise accumulation, and the interaction-driven term proportional to κ_2. Coulomb repulsion leads to negative two-body correlations and the corresponding suppression of fluctuations in n (antibunching). κ_2 = ⟪T_1T_2⟫ < 0 means the choice, that one electron makes, tends to be opposite to what the other electrons do. Indeed, we observe κ_2 < 0 in both Fig. 2e for two electron pairs and Fig. 2f for a quadruplet. The difference between the two cases is revealed by considering higher orders of correlation: although κ_3 and κ_4 are close to zero when electrons are restricted to interact in pairs only (Fig. 2e), all cumulants κk up to k = N are generally non-zero when all N electrons are placed in the same SAW minimum (Fig. 2f for N = 4 and Extended Data Fig. 2 for N = 3–5). Higher-order cumulants oscillate with detuning and exhibit k − 1 extrema separated by k − 2 zeros. Even cumulants are symmetrical whereas odd cumulants are antisymmetrical, reflecting the symmetry of the Y-junction already evident from the partitioning probabilities in Fig. 2a–c.

In the following, we show that the observed pattern of high-order correlations aligns with the universal signature of a strongly correlated liquid and locate the droplet state within the phase diagram associated with the gas–liquid crossover.

Universal signatures of a Coulomb liquid

The relevant state of matter for our electron droplets is a one-component Coulomb plasma[41], which can undergo a temperature-driven crossover from a Coulomb gas at T > Tc to a Coulomb liquid at T < Tc. The crossover temperature Tc is determined by the competition between entropy and Coulomb repulsion energy and is characterized by the dimensionless plasma parameter Γ(pl) ≈ Tc/T. In more conventional transport experiments, in which statically confined electrons are connected to external reservoirs, transition to a Coulomb liquid can manifest itself as an energy gap on the order kBTc/N for particle addition (Coulomb blockade in QDs) or as temperature saturation of compressibility of a two-dimensional electron gas (2DEG)[42].

Here we rely solely on the finite-N counting statistics to estimate the state of our Coulomb plasma, building an analogy with relativistic ion collisions used to study the phase diagram of QCD. In particular, at low baryonic densities[43], the transition from quark–gluon plasma at temperatures \(T > {T}_{{\rm{c}}}^{{\rm{QCD}}}\) to hadronic fluid at \(T < {T}_{{\rm{c}}}^{{\rm{QCD}}}\) is not a sharp phase transition but rather a smooth crossover44,45. Freeze-out of fluctuations (owing to quench of equilibrium during expansion[16]) determines the cumulants in the number of produced hadrons, which have been used to estimate \({k}_{{\rm{B}}}{T}_{{\rm{c}}}^{{\rm{QCD}}}\approx 170\,{\rm{MeV}}\) (ref. [2]). Unlike in QCD, in which particles are created from the vacuum, the equilibrium ensemble for our Coulomb droplets is canonical, as the number of electrons N is conserved in collisions with the Y-junction.

In the ideal gas limit of a Coulomb plasma, Γ(pl) ≪ 1, the multivariate cumulants κk take universal (albeit trivial) values, κk>1 → 0. In the opposite limit of an interaction-dominated liquid, Γ(pl) ≫ 1, we can derive a universal form of the large-N scaling of the cumulants κk from the condition that the distribution of n is governed by Coulomb repulsion rather than by the statistical fluctuations. For interactions to dominate the variance of the observable n, the interaction term must asymptotically cancel the ideal gas term, hence κ_2 → −κ_1(1 − κ_1)N−1. Extending the argument by induction to higher k, we find that κk ∝ N−k+1, with the proportionality coefficient given by a specific parameter-free polynomial of order k in κ_1, as shown in Fig. 3a (see Methods). In statistical physics, the liquid phase is characterized by low compressibility, as it resists changes in particle number. Thus the strongly correlated limit, derived here from the condition of vanishing fluctuations in the thermodynamic limit, ⟪n^2⟫/N → 0, corresponds to an incompressible fluid.

Fig. 3: Scaling of correlation functions with the number of particles.

a, Leading-term universal asymptotics of the repulsion-dominated cumulants in the large-N limit, as a function of κ_1 according to equation (11). b–e, Measured cumulants κk of order k = 2–5 for droplets with N = k…5. Lines show corresponding simulations of sudden partitioning of an equilibrium Coulomb plasma confined in a quartic parabolic potential with realistic microscopic parameters (see Methods).

As our experiment allows the tuning of κ_1, we plot in Fig. 3b–e the rescaled cumulants κkN−k+1 as functions of κ_1 for k up to 5, using our experimental data for N = k…5. We observe that the scaling with N expected in the interaction-dominated limit is obeyed as soon as N ≥ 3. The pattern and magnitude of oscillations in Fig. 3b–e are also in good qualitative agreement with the universal prediction in Fig. 3a, confirming that our droplets are large enough to exhibit the emergent behaviour of a strongly correlated liquid.

Effective Ising model

For quantitative analysis of our finite-N data in terms of interaction strength and thermodynamic phase diagram, we use the archetype of classical lattice gas models[46] and describe the gas–liquid transition with the Ising model on a complete graph (all-to-all interactions). This model is defined by the following Hamiltonian, expressed in terms of directly measurable partitioning variables,

$${\mathcal{H}}=U\mathop{\mathop{\sum }\limits_{i,j=1}^{N}}\limits_{i\ne j}\left({T}_{i}-\frac{1}{2}\right)\left({T}_{j}-\frac{1}{2}\right)+\mu \mathop{\sum }\limits_{i=1}^{N}{T}_{i}$$

(1)

in which U is the interaction strength and μ controls the upper–lower charge balance within the central channel, with μ = 0 corresponding to symmetric partitioning statistics P(N−n,n) = P(n,N−n). We find that a sudden quench of equilibrium fluctuations governed by the Ising Hamiltonian (equation (1)) at temperature T accurately reproduces the measured cumulants as a function of μ = −α(Δ − Δ_0), as shown by solid lines in Fig. 2e,f for N = 4 and in Extended Data Figs. 1 and 2 for N = 2–5. In this model, U/kBT and Δ_0 are fitted independently for each N, whereas α/kBT is fixed globally (α is the voltage-to-energy conversion factor). Fitted values are listed in Extended Data Table 1.

The Ising model establishes a useful analogy between the phases of magnetically interacting spins si = 2Ti − 1 = ±1 and our partitioning statistics (Fig. 4a). U is the energy cost for two spins to be parallel (for two electrons to exit on the same side of the Y-junction), with a positive U making configurations with opposite spins preferable (antiferromagnetic coupling). As the Coulomb plasma is characterized by repulsive interactions (U > 0), the gas-to-liquid transition corresponds to the paramagnetic-to-antiferromagnetic transition of Ising spins. Unlike the usual sharp phase transition in the ferromagnetic case (U < 0), here it is a crossover in temperature because an all-to-all antiferromagnetic coupling (U > 0) imposes global correlations but not a particular spin pattern. In the large-N limit and in the absence of polarizing field (μ = 0), the characteristic scale of the antiferromagnetic crossover is the Néel temperature kBTN = UN/2 (see Methods), which we identify with the crossover temperature Tc of the strongly correlated Coulomb liquid.

Fig. 4: Effective Ising model and Monte Carlo simulation of a Coulomb liquid droplet.

a, Interpretation of partitioning in terms of magnetic spin interactions. Uncorrelated partitioning (U = 0, binomial distribution in the middle), bunching (U < 0) and antibunching (U > 0) correspond, respectively, to paramagnetic, ferromagnetic and antiferromagnetic phases of the Ising model on a complete graph, for which counting statistics gives the distribution of the total magnetization. b, Phase diagram of the antiferromagnetic crossover in the thermodynamic limit of the Ising model, with appropriately scaled negative pair correlations κ_2N as the order parameter. The axes are given by the temperature T and the magnetic field μ, scaled by the Néel temperature TN. The measured correlations for N = 3, 4 and 5 at μ = 0 are shown by colours in small squares. The horizontal position T/TN of the squares is obtained from the fits of the Ising model to the partitioning curves (Extended Data Table 1). The slight deviations in colour between the phase diagram and the measured values (36%, 20% and 11%, respectively) are dominated by the finite-N effect, not by discrepancy with the model. c, Four configurations of the 2D confining potential in the central channel (level lines), together with snapshots of the spatial positions of N = 5 electrons (red dots) from Monte Carlo simulations of a classical Coulomb plasma (see Methods). The colour scale shows the calculated average electron density. The four panels correspond to (from left to right) Δ − Δ_0 = 101, 60, 21 and 0 mV.

The cumulants κk serve as the irreducible correlation functions of the Ising spins and can be used as order parameters to quantitatively trace the crossover in a phase diagram. This is illustrated for κ_2N in Fig. 4b, in which the scale TN represents the characteristic energy required to destroy the correlated state, by either thermal fluctuations (T axis) or by an external field favouring ferromagnetic order (μ axis). Converting the fitted parameters U/(kBT) to T/TN for each N (see Extended Data Table 1) allows us to compare the measured correlations with their values in the thermodynamic limit. In terms of plasma state, we find the results to be closer to liquid than gas (T < TN) for N = 3–5, consistent with the observation of cumulant scaling (see Fig. 3) characteristic of the liquid state.

Finally, we complement the coarse-grained effective Ising model with an explicit microscopic simulation of the Coulomb plasma[47] under the specific conditions of our experiment. Figure 4c illustrates the case of N = 5 electrons, interacting through an unscreened Coulomb potential and placed in a quartic-parabolic confining potential for different values of detuning voltage Δ. The colour represents the canonical probability density drawn from classical Monte Carlo simulations and the red dots represent a particular snapshot of the corresponding electron positions. The simulated partitioning cumulants are shown in Fig. 3b–e with continuous lines. As the shape of the confinement potential is computed from an electrostatic modelling (see Methods), the only adjustable parameter is the effective temperature of the droplet, T = 25 K (consistent with other estimates; see Supplementary Note 2). We observe that these microscopic simulations are consistent with the observed deviations from the ideal scaling and account for finite-size and temperature effects. We have verified that the qualitative agreement with the scaling limit (Fig. 3a) is robust to the choice of the confinement potential and to the competition of Coulomb and exchange correlations, as long as the plasma parameter is large enough.

Conclusion and outlook

Inspired by relativistic ion colliders used to study quark–gluon plasma, we have successfully created a plasma droplet of strongly correlated electrons on a microchip. Confining electrons with a SAW enables precise manipulation of the number of interacting particles within the electron-plasma droplet and a gate-tunable Y-junction provides deterministic control of the effective impact parameter.

A multivariate cumulant analysis of the partitioning data reveals the formation of a strongly correlated Coulomb liquid, emerging with as few as three electrons in the droplet. This mirrors the incompressible liquid of hadrons bound by nuclear forces, but—unlike high-energy ion collisions—our approach allows us to trace universal signatures of collectivity at very low particle numbers.

Drawing a powerful analogy with condensed-matter physics, our results align well with an Ising model on a complete graph. The observed electron antibunching, governed by Coulomb interactions, can be compellingly interpreted as antiferromagnetic ordering below the Néel temperature.

In future, an exciting direction would be to extend this methodology to lower effective temperatures and strong magnetic fields, for which quantum Hall states emerge in 2D electron systems[33] and have already been simulated for small particle numbers[48]. Notably, evidence of electron bunching in a pair-partitioning experiment[49] under high magnetic fields suggests the potential formation of a Laughlin state droplet[50], opening new avenues for engineering exotic correlated states in electron systems.

Methods

Device description

The device is fabricated in a Si-doped GaAs/AlGaAs heterostructure grown by molecular-beam epitaxy. The 2DEG resides 110 nm below the surface, with electron density 2.8 × 10^11 cm−2 and mobility 9 × 10^5 cm^2 V−1 s−1. Metallic gates (Ti, 3 nm; Au, 14 nm) are deposited on the surface of the semiconductor using electron-beam lithography. All measurements are performed at a temperature of about 20 mK in a 3He/4He dilution refrigerator. The sample and measurement scheme are the same as in ref. [18]. A set of negative gate voltages is applied to the surface gates to deplete the 2DEG underneath and create the nanostructures, including four QDs, four QPCs and two guiding rails, which are fully depleted. These rails connect the source QDs to the detector QDs and merge in the centre to form a single 40-μm-long channel, equipped with a narrow barrier gate in the middle to tune the shape of the confining potential from a single well to a double well.

SAW generation

The SAW is generated using a double-finger IDT deposited on the surface and placed at a distance of 1.5 mm from the device. The metallic fingers are fabricated using electron-beam lithography and thin-film evaporation (Ti, 3 nm; Al, 27 nm) on the heterostructure. The IDT consists of 111 cells with a periodicity of 1 μm and a resonance frequency of 2.86 GHz at low temperature. The aperture of the transducer is 50 μm. To perform electron transport by SAW, a radiofrequency signal is applied on the IDT at its resonance frequency for a duration of 60 ns. To have a strong SAW confinement potential, the signal is amplified to 28 dB using a high-power amplifier before being injected into a coaxial line of the cryostat through a series of attenuators. The velocity of the SAW is 2,860 m s−1.

Electron transfer

Each single-shot experiment corresponds to the transfer of one or a few electrons from the source QDs to the detector QDs using the SAW as the transport carrier. To prepare a given number N of electrons in a source QD, we use a sequence of fast voltage pulses to the channel gate and reservoir gate controlling the tunnel barriers of the QD. This sequence consists of three steps: initializing the QD, loading the electrons into the QD and preparing the QD for electron transfer. To initialize the source QD, electrons previously present in the QD are removed. Then, a given number of electrons are loaded into the QD by accessing a particular loading position in the charge-stability diagram of the QD. Finally, these electrons are trapped within the QD by switching to a holding configuration, from which they will be taken away by the SAW. At the same time, the two detector QDs are set in a configuration for which the electrons transported by the SAW will be captured with high fidelity. For more details, see Supplementary Note 1. By sensing the QPC currents of both the source and detector QDs with single-electron resolution and comparing their values before and after the electrons are transferred by the SAW, the precise number of electrons transferred to each detector can be determined. When calculating the partitioning probability, the very few events that do not conserve the total number of electrons are excluded by a post-selection routine.

In our experiment, the electrons are deterministically loaded into specific locations within the SAW train. The plunger gate of the QD is used to trigger the sending of the electrons into a precise minimum of the periodic SAW potential with a 30 ps resolution. This precise control allows for the formation of an electron droplet containing up to five electrons, using the two source QDs of the device. To synchronize the two trigger pulses with the radiofrequency signal generating the SAW, we use two arbitrary waveform generators combined with a synchronization module. The outputs of the arbitrary waveform generators are connected to the plunger gates by means of high-bandwidth bias tees for voltage pulsing and dc biasing.

Electron partitioning

The electron droplet is partitioned at the Y-junction located at the end of the central channel, after a flight time of 14 ns. By applying a voltage detuning Δ = VU − VL, in which VU and VL are the voltages applied to the side gates of the central channel, we can control the partitioning ratio between the two detectors D1 and D2. For all partitioning experiments reported here, the barrier gate voltage is set to VB = −1.25 V to have a single central channel with a weak double-well potential profile. Careful analysis of the double-well potential and the electron number equilibration in the central channel is described in Supplementary Notes 2 and 3.

Statistical uncertainty

The error in estimating the probability pn from the counting statistics is dictated by the distribution of independent Bernoulli trials. The corresponding likelihood function of measuring exactly Nn outcomes of n particles in detector D1 out of Nrep repetitions is \(\left(\begin{array}{c}{N}_{{\rm{rep}}}\\ {N}_{n}\end{array}\right){p}_{n}^{{N}_{n}}\)\({(1-{p}_{n})}^{{N}_{{\rm{rep}}}-{N}_{n}}\). We use the mean Nn/Nrep as the statistical estimate for pn. The boundaries of the corresponding confidence interval (CI) at confidence level 1 − c = 95% are determined from the likelihood function by inverting a one-tailed binomial test at significance level c/2, for the lower bound and the upper bound separately. At the extreme count Nn = 0 (Nn = Nrep), the lower (upper) boundary is set to 0 (1) and the other boundary is computed at significance level c. These CIs of probability measurements are used in the Ising model parameter estimation. For 0.06 < pn < 0.94 in our measurements with Nrep = 3,000, the CIs are almost symmetric and approximately given by \(\pm 1.96\sqrt{{p}_{n}(1-{p}_{n})/{N}_{{\rm{rep}}}}\). Because the maximum width of the CI is about 0.036 (at pn = 0.5), a value smaller than the size of the data points, we did not represent the error bars on the graphs of probabilities.

Symmetrized multivariate cumulants

In our experiment, the observable is the number n of electrons measured at detector D1, which can be expressed as a sum of binary variables Tj. From \(n={\sum }_{j=1}^{N}{T}_{j}\) and \({T}_{j}^{2}={T}_{j}\), we derive the general relation

$${m}_{k}={\left(\begin{array}{c}N\\ k\end{array}\right)}^{-1}\mathop{\sum }\limits_{n=k}^{N}\left(\begin{array}{c}n\\ k\end{array}\right){p}_{n}$$

(2)

between the probabilities pn = P(N−n,n) of the full counting statistics (FCS)[21] and the kth-order symmetrized multivariate moments mk defined as averages of all permutations of k distinct variables,

$${m}_{k}={\left(\begin{array}{c}N\\ k\end{array}\right)}^{-1}\sum _{1\le {j}_{1} < {j}_{2} < \ldots < {j}_{k}\le N}\langle {T}_{{j}_{1}}{T}_{{j}_{2}}\ldots {T}_{{j}_{k}}\rangle ,$$

(3)

in which \(\left(\begin{array}{c}N\\ k\end{array}\right)=N!/[k!(N-k)!]\) is the binomial coefficient. The corresponding symmetrized multivariate cumulants

$${\kappa }_{k}={\left(\begin{array}{c}N\\ k\end{array}\right)}^{-1}\sum _{1\le {j}_{1} < {j}_{2} < \ldots < {j}_{k}\le N}\langle \langle {T}_{{j}_{1}}{T}_{{j}_{2}}\ldots {T}_{{j}_{k}}\rangle \rangle $$

(4)

are, in general, not uniquely determined by FCS probabilities and their calculation requires further information (such as symmetry constraints or a microscopic model).

For statistically equivalent particles (that is, full permutational symmetry of the multivariate probability distribution), all terms in equations (3) and (4) are equal, and the moments mk can be related to cumulants κk through standard univariate relations[51], \(\text{ln}(1+{\sum }_{k=1}^{\infty }{m}_{k}{z}^{k}/k!)={\sum }_{k=1}^{\infty }{\kappa }_{k}{z}^{k}/k!\). Using an explicit formula in terms of Bell polynomials[52], we can write

$${\kappa }_{k}=\mathop{\sum }\limits_{j=1}^{k}(j-1)!{(-1)}^{j-1}{B}_{kj}({m}_{1},{m}_{2},\ldots ,{m}_{k-j+1}).$$

(5)

See Supplementary Note 5 for the derivation of equation (2) and explicit formulas for κk for k = 1–5. An example of correlated partitioning, in which equation (5) is not valid and the general combinatorial expressions for multivariate cumulants39,53 need to be used, is shown in Fig. 2e and described in detail in Supplementary Note 6.

There is an important distinction between our method for extracting interaction signatures and the approach of so-called factorial cumulants considered in the context of electron transport[32] and particle physics[54]. The multivariate moments defined by equation (3) can be written mk = ⟨(n)k⟩/(N)k, in which (x)k = x(x − 1) ×…× (x − k + 1) is the falling factorial and ⟨(n)k⟩ is known as the factorial moment of FCS[32]. The k-dependent denominator (N)k in this expression for mk makes the κk distinct from the factorial cumulants; see further discussion in Supplementary Note 5.

Ising model on a complete graph

The Ising model on a complete graph is exactly solvable[55] and, hence, equilibrium fluctuations at any freeze-out quench temperature T can be computed for any N. The Ising Hamiltonian of equation (1) can be expressed as a quadratic form of the observable \(n={\sum }_{j=1}^{N}{T}_{j}\),

$${\mathcal{H}}=U{n}^{2}+(\mu -NU)n+UN(N-1)/4.$$

(6)

The corresponding exact counting statistics in a canonical ensemble is pn = cn/Z with the partition function \(Z={\sum }_{n=0}^{N}{c}_{n}\) and the statistical weights

$${c}_{n}=\left(\begin{array}{c}N\\ n\end{array}\right){e}^{-\beta Un(n-N)-\beta \mu n},$$

(7)

in which β = 1/kBT. Together with equations (2) and (5), this gives a way to calculate the exact multivariate cumulants κk of all orders k ≤ N at any N.

To make the connection with the thermodynamic phase diagram in terms of μ and T in the large-N limit, explicit analytic expressions are obtained following ref. [55]. We apply the lowest-order Stirling’s formula \(m!\approx {m}^{m}{e}^{-m}\sqrt{2\pi m}\) to the factorials in the binomial coefficient \(\left(\begin{array}{c}N\\ n\end{array}\right)\) in equation (7) and perform expansion of ln(cn) near its maximum n ≈ ⟨n⟩ up to quadratic order. This results in a Gaussian approximation to pn of the form

$${p}_{n}\propto {e}^{-(\beta +{\beta }^{{\prime} })U{(n-{\kappa }_{1}N)}^{2}},$$

(8)

in which β′ = [4κ_1(1 − κ_1)kBTN]−1 and kBTN = UN/2 is the zero-field Néel temperature for the antiferromagnetic crossover. The relation between the effective magnetic field μ and the effective magnetization κ_1 = ⟨n⟩/N in the large-N limit is given by the transcendental equation[56]

$$2{\kappa }_{1}-1=\tanh \left[-\frac{{T}_{{\rm{N}}}}{T}\left(2{\kappa }_{1}-1-\frac{1}{2}\frac{\mu }{{k}_{{\rm{B}}}{T}_{{\rm{N}}}}\right)\right],$$

(9)

which has only one solution for the antiferromagnetic sign of the coupling (U > 0).

To quantify the antiferromagnetic correlations in the thermodynamic limit, we choose the pair correlation function ⟪T_1T_2⟫ = κ_2 as the order parameter. It is obtained from the identity ⟨n^2⟩ − ⟨n⟩2 = Nκ_1(1 − κ_1) + N(N − 1)κ_2 in the large-N limit. Treating n as a continuous variable and using the Gaussian approximation of equation (8), this gives the leading-order behaviour of κ_2 at a fixed T/TN and N → ∞,

$${\kappa }_{2}N=-\frac{4{\kappa }_{1}^{2}{(1-{\kappa }_{1})}^{2}}{4{\kappa }_{1}(1-{\kappa }_{1})+T/{T}_{{\rm{N}}}}.$$

(10)

A numerical solution to equation (9) together with equation (10) is used for the phase diagram in Fig. 4b.

As there is no lattice on a full graph favouring a particular pattern of staggered magnetization, the antiferromagnetic transition here is not a second-order phase transition but a crossover. The corresponding change in free energy has a weaker divergence (logN) in the thermodynamic limit than at the ferromagnetic transition. The corresponding singular part[46] of the free energy change between T = ∞ and T → 0+ is βΔFU>0 = (1/2)ln(1 + TN/T).

On the ferromagnetic side (U < 0), we note that κ_2N diverges when the temperature T approaches the Curie temperature TC = −TN > 0 as κ_2N ∝ (T − TC)−1 for \(T\to {T}_{{\rm{C}}}^{+}\). At T ≤ TC, the Gaussian approximation of equation (8) breaks down and strong ferromagnetic order sets in. This corresponds to the droplet scattering at the Y-junction as a whole (without partitioning), with probability κ_1 to go to detector D1 and with κ_2 = κ_1(1 − κ_1) > 0 in the large-N limit. For a large but finite droplet, there is no symmetry breaking, hence κk>1 = O(1), unlike O(N−k+1) in the antiferromagnetic case.

Universal scaling of partitioning cumulants

The interaction-dominated partitioning of a large droplet at U > 0 is described by the antiferromagnetic phase of the effective Ising model with T/TN → 0 and N → ∞. The Boltzmann factor in equation (7) suppresses the fluctuations of n around ⟨n⟩ = κ_1N and caps the large-N asymptotics of univariate cumulants from ⟪nk⟫ = O(N) (Gaussian limit of binomial distribution) to ⟪nk⟫ = O(1). From the latter condition (which is independent of the specifics of the Ising model), we derive the asymptotics κk = Gk(κ_1)N−k+1 + O(N−k) for k ≥ 2, in which the prefactor

$${G}_{k}({\kappa }_{1})=-\frac{(k-1)!}{2}{C}_{k}^{(-1/2)}\,(2{\kappa }_{1}-1)$$

(11)

is universal and given by the ultraspherical (Gegenbauer) polynomials \({C}_{k}^{(a)}\) of degree k and parameter a = −1/2. The first polynomials up to k = 5 are plotted in Fig. 3a, to show the universal strong-correlation asymptotics of the scaled cumulants κkNk−1. Note that G_2 = −κ_1(1 − κ_1) is also the zero-temperature limit of equation (10).

The polynomials Gk(κ_1) have exactly k − 2 zeros for 0 < κ_1 < 1, which explains the observed oscillation pattern and provides an exact specific example of oscillations in high-order cumulants[31]. We note that a similar generic N−k+1 scaling has been discussed for cumulants of initial density perturbations in heavy-ion collisions[57], in which it arises for different reasons (dominance of autocorrelations in the independent point-sources model).

In contrast to antiferromagnetic correlations decaying with N as κk ∝ N−k+1, the fluctuations in the ferromagnetic case are between n = 0 and n = N only, hence κk = O(1), and the limiting form for the unpartitioned scattering (T/TC → 0 in the Ising model) is the polynomial \({\kappa }_{k}=-{{\rm{Li}}}_{1-k}\left(\frac{{\kappa }_{1}}{{\kappa }_{1}-1}\right)\), in which Li is the polylogarithm.

Coulomb liquid simulations

We model a finite droplet of Coulomb plasma in 2D using a confining single-electron potential V1e and an unscreened Coulomb potential[47], which results in the total potential

$$U({{\bf{r}}}_{1},\ldots ,{{\bf{r}}}_{N})=\mathop{\sum }\limits_{i=1}^{N}{V}_{1e}({{\bf{r}}}_{i})+\sum _{i < j}\frac{{e}^{2}}{4\pi {{\epsilon }}_{0}{{\epsilon }}_{r}| {{\bf{r}}}_{i}-{{\bf{r}}}_{j}| },$$

(12)

in which ri = (xi, yi) is the in-plane coordinate of the ith electron and ϵr = 12.1 is the relative dielectric permittivity in GaAs. The equilibrium distribution of electron coordinates is determined by a classical canonical ensemble at an effective temperature T. We sample electron positions {ri} using a random walk Metropolis Monte Carlo algorithm designed to sample the canonical distribution. The convergence of the corresponding Markov chain is controlled by the Kolmogorov–Smirnov test[58]. For each set of parameters, a statistics of positions is collected with the estimated effective sample size ranging from 10^3 to 10^5 depending on parameters. The statistics of positions is translated to partitioning statistics of a sudden quench using binary variables Ti = Θ(yi), in which Θ is the Heaviside step function. This corresponds to an observable n = T_1 +…+ TN counting the number of particles in the y > 0 half-plane.

The confining electrostatic potential of our experiment can be approximated as a double-well quartic-parabolic 2D potential

$${V}_{1e}({\bf{r}})={V}_{{\rm{b}}}+{\mu }_{{\rm{q}}}\frac{y}{{y}_{0}}-8{V}_{{\rm{b}}}\frac{{y}^{2}}{{y}_{0}^{2}}+16{V}_{{\rm{b}}}\frac{{y}^{4}}{{y}_{0}^{4}}+\frac{m{\omega }_{x}^{2}{x}^{2}}{2},$$

(13)

in which Vb is the height of the central barrier, y_0 is the distance between the two minima, μq is the transverse energy detuning proportional to the side-gates voltage difference Δ − Δ_0 (which controls the partitioning of the droplet) and ωx is the oscillation frequency in the longitudinal direction, resulting from the confinement potential of the SAW.

The barrier height Vb = 27.5 meV and the distance between minima y_0 = 220 nm are estimated from an electrostatic simulation of the gate-controlled potential as explained in Supplementary Note 2. The transverse oscillation frequency in the two potential minima is then calculated as \({\omega }_{y}=\sqrt{32{V}_{{\rm{b}}}/(m{y}_{0}^{2})}=7.0\,{\rm{THz}}\) using the effective mass m = 0.067me for electrons in GaAs. The longitudinal oscillation frequency in the SAW potential is estimated from the peak-to-peak amplitude ASAW = 42 meV and wavelength λSAW = 1 μm, using the relation ωx = (π/λSAW)(2ASAW/m)1/2 = 1.5 THz (see Supplementary Note 4 in ref. [18]). The aspect ratio of the 2D confinement is thus ωx/ωy = 0.21.

The potential being entirely determined, the effective electron temperature T is the only free parameter to be chosen for good agreement with the experimental data, as shown by the solid lines in Fig. 3b–e using T = 25 K. This value is also consistent with the one extracted from the barrier-height dependence of the thermally activated hopping rate between the two wells of the quartic potential, as estimated in Supplementary Note 2.

Data availability

All data from this study are included in the paper and its Supplementary Information. Source data are provided with this paper.

Code availability

All codes used in this study are available from the corresponding authors on request.

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Acknowledgements

We acknowledge fruitful discussions with U. A. Wiedemann, D. Zavickis and B. Sacépé during the preparation of this manuscript. This project has received financing from the European Union Horizon 2020 research and innovation programme under grant agreement no. 862683, ‘UltraFastNano’. C.B., H.S. and J.S. acknowledge financing from Agence Nationale de la Recherche under the France 2030 programme, reference ANR-22-PETQ-0012. C.B. acknowledges financial support from the Agence Nationale de la Recherche project QUABS ANR-21-CE47-0013-01. J.W. acknowledges the European Union Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 754303. M.A. acknowledges the Marie Skłodowska-Curie Actions cofund QuanG grant no. 101081458, financed by the European Union and the programme QuanTEdu-France no. ANR-22-CMAS-0001 France 2030. L.M. acknowledges the programme QuanTEdu-France no. ANR-22-CMAS-0001 France 2030. T.V. acknowledges financing from the French Laboratory of Excellence project ‘LANEF’ (ANR-10-LABX-0051). A.D.W. and A.L. thank the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through ML4Q EXC 2004/1 - 390534769, the BMBF-QR.X project 16KISQ009 and the DFH/UFA project CDFA-05-06. E.P., R.S. and V.K. have been supported by the Latvian Quantum Initiative within European Union Recovery and Resilience Facility project no. 2.3.1.1.i.0/1/22/I/CFLA/001 and grant no. lzp2021/1-0232 from the Latvian Council of Science. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the granting authority. Neither the European Union nor the granting authority can be held responsible for them.

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Author notes

1. These authors contributed equally: Jashwanth Shaju, Elina Pavlovska

Authors and Affiliations

1. Université Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, Grenoble, France

Jashwanth Shaju, Junliang Wang, Seddik Ouacel, Thomas Vasselon, Matteo Aluffi, Lucas Mazzella, Clément Geffroy, Matias Urdampilleta, Christopher Bäuerle & Hermann Sellier

2. Department of Physics, University of Latvia, Riga, Latvia

Elina Pavlovska, Ralfs Suba & Vyacheslavs Kashcheyevs

3. Lehrstuhl für Angewandte Festkörperphysik, Ruhr-Universität Bochum, Bochum, Germany

Arne Ludwig & Andreas D. Wieck

Contributions

J.S. performed the experiment, with expert help from J.W. and technical support from S.O., T.V., M.A., L.M., C.G. and M.U. J.W. fabricated the sample. E.P. performed the cumulant calculations and R.S. the Monte Carlo simulations, with guidance from V.K. A.L. and A.D.W. provided the high-quality GaAs/AlGaAs heterostructure. J.S., E.P., C.B., V.K. and H.S. wrote the manuscript, with feedback from all authors. H.S. and C.B. supervised the experimental work. V.K. conceptualized the theoretical interpretation. C.B. initiated the project.

Corresponding authors

Correspondence to Vyacheslavs Kashcheyevs or Hermann Sellier.

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Extended data figures and tables

Extended Data Fig. 1 Experimental data for partitioning of N = 2 electrons.

a, Partitioning probabilities when the two electrons are distributed in two different minima and are uncorrelated. b, Partitioning probabilities when both electrons are in the same SAW minimum and are interacting. c,d, The multivariate cumulants corresponding to a and b, respectively. Lines in a are reconstructions using single-electron partitioning data. Lines in b and d are fitting curves from the Ising model using the parameters given in Extended Data Table 1.

Source data

Extended Data Fig. 2 Experimental data for partitioning of N = 3, 4 and 5 electrons.

a–f, The partitioning probabilities and the corresponding multivariate cumulants for electrons distributed across different SAW minima (uncorrelated electrons). g–l, The same quantities when all electrons are placed in the same SAW minimum (interacting electrons). Lines in a–c are reconstructions using single-electron partitioning data. Lines in g–l are fitting curves from the Ising model using the parameters given in Extended Data Table 1.

Source data

Extended Data Table 1 Fitting parameters of the Ising model for partitioning statistics

Supplementary information

Supplementary Information

This file contains Supplementary Notes, Supplementary Figs. 1–8, Supplementary Tables 1 and 2 and Supplementary References

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Source data

Source Data Fig. 2 and Extended Data Figs. 1 and 2

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Shaju, J., Pavlovska, E., Suba, R. et al. Evidence of Coulomb liquid phase in few-electron droplets. Nature (2025). https://doi.org/10.1038/s41586-025-09139-z

- Received: 10 December 2024

- Accepted: 12 May 2025

- Published: 25 June 2025

- DOI: https://doi.org/10.1038/s41586-025-09139-z

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Aril Slotte,

Are Salthaug,

Sindre Vatnehol,

Espen Johnsen,

Erik Askov Mousing,

Åge Høines,

Cecilie Thorsen Broms,

Sigurvin Bjarnason,

Eydna í Homrum,

Øystein Skagseth &

…

Erling Kåre Stenevik

Nature (2025)

Abstract

Entrainment is a process in schooling migratory fish whereby routes to suitable habitats are transferred from repeat spawners to recruits over generations through social learning[1]. Selective fisheries targeting older fish may therefore result in collective memory loss and disrupted migration culture[2]. The world’s largest herring (Clupea harengus) population has traditionally migrated up to 1,300 km southward from wintering areas in northern Norwegian waters to spawn at the west coast. This conservative strategy is proposed to be a trade-off between high energetic swimming costs and enhanced larval survival under improved growth conditions[3]. Here an analysis of extensive data from fisheries, scientific surveys and tagging experiments demonstrates an abrupt approximately 800-km poleward shift in main spawning. The new migration was established by a large cohort recruiting when the abundance of older fish was critically low due to age-selective fisheries. The threshold of memory required for cultural transfer was probably not met—a situation that was further exacerbated by reduced spatiotemporal overlap between older fish and recruits driven by migration constraints and climate change. Finally, a minority of survivors from older generations adopted the migration culture from the recruits instead of the historically opposite. This may have profound consequences for production and coastal ecology, challenging the management of migratory schooling fish.

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Main

Collective behaviour is recognized as an important factor driving critical transitions in migrations of animals navigating dynamic environments4,5. By acting collectively, they may obtain and process information better (collective learning) and established knowledge can be transferred to new uninformed group members through social interaction (social learning); both processes have a pivotal role in decision-making[6]. The accumulation of knowledge during group navigation may lead to a migratory culture at the population level7,8, whereas an absence of informed leaders arising from dynamics in landscape structure, mortality and recruitment can lead to disrupted migrations[9]. When cultural behaviour substantially influences fitness, it may drive selection for traits that enhance cultural learning, leading to a reciprocal coevolution of genes and culture[10].

In marine fish, the entrainment hypothesis proposes that migrations between suitable habitats develop over generations through a process of social learning between naive recruits and experienced repeat spawners[1]. Migration shifts typically occur when numerically dominant cohorts overflow the population11,12. The underlying mechanism is probably a knife-edged response in group behaviour linked to the minimum proportion of informed and determined individuals required to lead a school13,14. Moreover, the quality of information that they hold[14], individual traits like boldness15,16 and the overall heterogeneity of the group[17] may have a substantial role for effective leadership. Size can be a limiting factor for social transfer of knowledge as optimal swimming speed is proportional to body length[18]. Theoretical modelling further proposes that we may fish out the collective memory of migratory schools when targeting older fish, with abrupt changes in migration culture as the potential outcome[2].

Norwegian spring spawning (NSS) herring, the world’s largest herring population[19], exhibits size-dependent migrations between feeding, wintering and spawning grounds3,20,21 (Fig. 1), probably driven by a coevolution of genetics and culture[10] in response to environmental dynamics and spatiotemporal availability of its primary prey, Calanus finmarchicus21,22,23,24,25. Despite fluctuating population levels and changes in feeding and wintering distributions, the population has mainly spawned on the Norwegian west coast since at least 1950[20]. This conservative strategy, involving high energetic swimming costs[26], is expected to enhance larval survival under improved growth conditions3,24,27,28 during transport to Barents Sea nurseries[29]. Here they reside until ages 3–5[30], before mixing with adults feeding in the Norwegian Sea.

Fig. 1: Migration strategies of herring in a dynamic environment.

a, The study area, highlighting the poleward shift in main spawning from Møre to Lofoten. b, The main currents of the area. Data are from a previous study[51]. c, Seasonal migrations (mig.) between feeding, wintering and spawning grounds during 1988–2020. Note that NSS herring spawn at hard substrates down to 250 m depth. d, The migration pattern during 2021–2024. These dynamics in migration patterns are also visualized empirically in e. e, The spatiotemporal dynamics in the herring fishery. The aggregated Norwegian, Icelandic and Faroese catch (above 80% of total catch) within each 0.5° latitude, 1° longitude rectangle by periods seasons is shown. Rectangles are coloured according to the scale at the bottom right. Note that a change in landings during feeding season from 2011 onwards reflects fishing strategies in a period with quota reductions and not availabilities.

Our study details an abrupt and unprecedented approximately 800-km poleward shift in main spawning of NSS herring from Møre to Lofoten (Fig. 1a), primarily elucidated through fishery-induced collective memory loss[2]. We examine whether reduced mixing between a depleted population of older fish and a bursting cohort prevented effective cultural transfer and prompted the naive recruits to establish independent migrations later adopted by a minority of older survivors. Moreover, we examine whether migration constraints3,26 or climate change[31] triggered the abandonment of Møre or contributed to disrupted migration culture by affecting mixing between older fish and recruits. Finally, we discuss cascading effects of this event on production, coastal ecosystems and management of migratory schooling fish.

Disrupted migration linked to fishing

We have detailed the spatiotemporal dynamics in the Norwegian, Icelandic and Faroese catch distribution over the period 1995–2024 to demonstrate migrations between feeding, wintering and spawning areas (Fig. 1e). During 2021–2024, the spawning migration of NSS herring substantially changed, as reflected by a poleward shift in fishing pattern during the spawning season (Figs. 1e and 2a), mapping of the density distribution during annual scientific acoustic-trawl surveys in February (Fig. 3b) and tag-recapture experiments (Fig. 4).

Fig. 2: Linking distributional change to herring population dynamics.

a, The centre of gravity (COG) in Norwegian landings at a latitudinal scale during 15–29 February 2000–2023. b, The proportion of recruits aged 4 years in the population. Numerical dominance (above 0.5) is marked with a dotted line. c, Development in spawner biomass during 1988–2023. The precautionary reference point is marked with a dotted line. Trends for fish older than the 2002 and 2016 cohort are highlighted. The perception from the 2023 assessment is compared with retrospective patterns from the 1999 and 2009 assessments. d, Total catch, highlighting the recent overfishing relative to quota advice. e, Fishing mortality (F) of adult herring (aged 5–12+ years, weighted with abundance and unweighted average). The precautionary reference point for F is marked with a dotted line. f, Exploitation pattern (F at age), where the 2016 cohort at ages 2–7 years is depicted with black dots. Note that age groups 11 and 12+ are coupled to the same F with overlaying trends. g, Dynamics in the proportion of the 2016 cohort in the total international catch and three acoustic surveys: February (spawning), spring (IESNS) and summer (IESSNS). Numerical dominance (above 0.5) is marked with a dotted line. h, Somatic growth in terms of body length of at age 7 (n = 17,609). i, Energetic status of in terms of Fulton’s condition factor (CF) for pre-spawning fish (n = 36,501). Confidence bands for spawner biomass (c) and F (e) denote that estimated levels with 95% probability lie within the limits; for growth (h) and condition (i), we show the 95% confidence intervals of the means of all individual fish.

Fig. 3: The abrupt poleward shift in herring spawning linked to the recruitment of the 2016 cohort.

a, Proportion of the 2016 cohort (N_2016/N≤2016) in Norwegian, Icelandic and Faroese samples from individual fishing operations in scientific surveys and commercial fisheries assumed to represent herring age structures at the school level. Trends are traced over the years 2017–2024 and seasonal migration cycles from feeding (Q2–3) and wintering (Q3–4) 1 year until spawning (Q1) the next. b, Quantification of the poleward shift in spawning through estimates of abundance within different spatial strata from acoustic trawl research surveys (see transects in grey) covering the full distribution during an approximately 10 day period in late February 2018–2024. Each estimate of abundance is shown as pie chart midpoints within strata, where the size of the pie is scaled to highest estimate over the study period and split into cohorts (Co) of older than 2016, the 2016 cohort and younger fish.

Fig. 4: Migration dynamics of herring tagged in the coastal wintering area.

a, The total numbers of herring tagged during 2016–2023 with RFID technology, PIT tags, and the estimated cohort compositions. b,c, Corresponding total numbers and spatiotemporal dynamics in catch (in tonnes) scanned for tags (b) and recaptures (c) over the years 2018–2024. Data from 2024 are only from quarter one. The tagging location is marked (red box) in the map for year 2018 recaptures.

We propose that this resulted from altered demography following a bursting recruitment in 2016 (Fig. 2b) combined with a depleted population of older spawners (Fig. 2c) linked to age-selective fisheries (Fig. 2d–f). The fishing pressure on adults aged 5–12+ years is within precautionary limits, although point estimates suggest that there have been periods with unsustainable fishing (Fig. 2e) partly linked to an overestimated population in the past (Fig. 2c) and with quotas being set higher than what is considered precautionary today. However, the increasing fishing pressure with age (Fig. 2f) is probably the main human impact on the altered demography.

After the birth of the 2016 cohort international quota, negotiations among the fishing nations failed to reach agreement on the sharing of the total allowable catch, resulting in sustained overfishing of advised quotas by 40% during 2017–2022 (Fig. 2d). Despite this, the recruitment of the 2016 cohort managed to maintain total spawner biomass just above defined precautionary levels, while the biomass of the older generations plummeted by 68%, dropping from approximately 4.0 to 1.3 million tonnes from 2019 to 2023 (Fig. 2c). By 2021 onwards, the proportion of the 2016 cohort surpassed the older fish in landings and acoustic surveys (Fig. 2g) during the feeding (Extended Data Figs. 1 and 2) and spawning (Fig. 3b) seasons.

Why memory loss could explain the shift

We argue that, under the absence of the minimum proportion of informed elders required for effective leadership13,14, the recruits found their own way within the population in accordance with historical observations11,12. However, the actual mechanism preventing effective learning processes and transfer of migration culture evolves at the level of group formation4,5,6. To elucidate fishery-induced collective memory loss as a potential explanation for the abrupt poleward shift in spawning of NSS herring, we explored the spatiotemporal development in numerical dominance of the 2016 cohort at the school level. All Norwegian, Icelandic and Faroese data on age composition derived from commercial fishing (trawl and purse seine) and research surveys (trawl) were combined to address this essential process. We defined the structure within a single sampled catch as representative of the cohort composition at the school level and presented the proportion of the 2016 cohort as quarterly distributions over the period 2017–2024 (Fig. 3a).

The observed dynamics at the school level revealed a crucial process in which the older fish fed and wintered to the southwest of the recruiting 2016 cohort, resulting in reduced mixing (Fig. 3a). The older fish mainly entered the coast from offshore wintering areas south of Lofoten and migrated along the shelf to spawn off Møre, while the 2016 cohort migrated from the north-easternmost wintering grounds to Lofoten as a first-time spawner in 2020. This pattern persisted for the 2016 cohort over 2021–2024, ultimately defining an established culture. However, it gradually extended the feeding migration westward as it grew older, predominating in schools over larger areas within the Norwegian Sea. Concurrently, the spawning distribution of the older age classes also shifted rapidly poleward to Lofoten. The distinct process where the elders adopted the spawning area of the 2016 cohort was further quantified by acoustic trawl surveys covering the distribution over 10 days in late February 2017–2024 (Fig. 3b). This snapshot of the prespawning situation also revealed that cohorts born after 2016 were incorporated into the new migration culture (Fig. 3b).

Support for the development of the new migration culture was strengthened at the individual-fish level through tagging experiments. Over the years 2016–2023, we tagged 202,155 individual herring with passive integrated transponder (PIT) tags (Fig. 4a) in the north-easternmost wintering area inside the fjords (Figs. 1 and 4c). Here the 2016 cohort relative to older fish predominated from the winter situation (November–December) in 2020 onwards. From 2018–2024, we scanned 2.2 million tonnes of herring for tags in landings at Norwegian and Icelandic factories with radio frequency identification (RFID) antenna systems, covering the feeding, wintering and spawning distributions (Fig. 4b). The corresponding 10,716 recaptures demonstrated that tagged herring adopted a western feeding distribution, offshore wintering and southward spawning distribution towards Møre by 2020 (Fig. 4c). However, over the next years 2021–2024, when the 2016 cohort within the tagged and non-tagged population became predominant, the tagged fish stopped migrating south, aligning with the observations at the school level (Fig. 3a) and quantified acoustic abundance (Fig. 3b).

The tag data also documented straying to other populations. Between 2021 and 2024, some recaptures were linked to catches south of 62° N in target fisheries for North Sea autumn-spawning herring. Similarly, recaptures southwest of Iceland in 2021 and 2023 originated from catches of Icelandic summer spawners. Such straying may occur when a minority of NSS herring mixes with schools from other populations during the feeding season and is subsequently guided to their home grounds.

Memory threshold preserving culture

Theoretical models suggest that even small proportions of informed individuals can significantly influence collective behaviour, with thresholds as low as 0.1 for effective leadership13,14. In NSS herring, using a similar modelling framework, the probability of changing wintering areas was 50% when the proportion of informed repeat spawners fell to 0.18, and dropped below 10% if it exceeded 0.3 (ref. [12]). However, our study offers insights into these models, indicating that disrupted migration culture may occur even at higher proportions of repeat spawners. Future models must account for the fact that cohort bursts recruit into the Norwegian Sea over a period of 2–3 years[30], and that successful knowledge transfer depends heavily on the spatiotemporal overlap between recruits and elders, which could be hindered by hetereogenety[17] in terms of size-dependent migration potential3,21.

Relatedly, as 2016 was the first large cohort in 12 years (Fig. 2b), there was a high degree of heterogeneity within the adult population. Implicit in this scenario was the necessity for the 2016 cohort to enhance its migration potential through body growth before effectively mingling with the remaining older generations, which were exploring the borderlines of their distribution[21]. By the time of full mixture in schools all over the Norwegian Sea feeding area, the 2016 cohort had already established its own migration culture (Fig. 3a,b). The quality of accumulated knowledge it held at that moment[14] probably also influenced its boldness as leaders15,16. Thus, being both determined and numerically dominant[2] relative to the older cohorts, it acted as a demonstrator of migration routes rather than a follower. This effect was further amplified as subsequent cohorts adopted the new migration culture (Fig. 3b).

The development of the 2002 cohort may exemplify how traditional migration culture can be preserved under normal conditions (Extended Data Figs. 3 and 4). It was also numerically dominant compared with older generations (Fig. 2b), which were rapidly depleted by age-selective fisheries (Fig. 2c,f). However, while recruiting (2005–2007; Extended Data Fig. 4a), the 2002 cohort interacted with older fish from the start during feeding, wintering and spawning seasons (Extended Data Fig. 3). The feeding distribution at that time and the smaller age and size gap between the 2002 recruits and the dominant 1998 and 1999 cohorts (Fig. 2b and Extended Data Fig. 4a) probably facilitated greater mixing between generations. The fastest-growing individuals from the 2002 cohort were able to follow the numerically dominant elders towards the Møre spawning grounds and, as the cohort grew and became fully recruited, larger fractions migrated farther south, eventually dominating throughout the spawning range (Extended Data Fig. 4b–d).

In such a scenario, in which learning occurs gradually over a three-year recruitment period, the proportion of informed repeat spawners necessary for leadership13,14 could be maintained, ensuring the successful transfer of migration culture even in large cohort influxes. Moreover, this suggests that slow-growing members of a cohort can learn not only from elders but also from the faster-growing repeat spawners within their own cohort.

Influence of migration constraints

Diverging size between the recruits and elders may hinder learning processes during the spawning migration. We propose that social transfer must be from elders or repeat spawners from same cohort with comparable migration potential. This would, under the state-dependent migration hypothesis, be a gradual process in which recruits may start to spawn in the north and follow elder fish southwards as the migration potential increases, also constrained by capacities linked to energy stored over the feeding season[3].

Originating from the north easternmost current wintering areas at 70 °N, the direct migration route to the southern tip of Møre at 62 °N spans approximately 1,300 km along the shelf edge (Fig. 1c). We investigated whether migration constraints could account for the observed reduction in spawning migration distance of around 800 km. Time-series data on body growth in terms of total length at age 7 (Fig. 2h) and initial energy stores in terms of Fulton’s condition factor (W L−3) (Fig. 2i) from 1988–2023 were analysed. The data indicated that the average fish from the 2016 cohort in 2023 was shorter than 7-year-old fish of some past cohorts due to density-dependent growth[30]. However, the growth was comparable to the 2002 cohort, which wintered at the same latitudes and still spawned off Møre from age 4 onwards (Fig. 1e and Extended Data Figs. 3 and 4). Moreover, there were no indications in the condition factor suggesting constraints on initial energy compared with previous periods.

During the southward spawning migration, the herring faces the same coastal currents that later transport their offspring to the north27,28. To examine the aggregated effects of internal constraints and external forces for selection of spawning locations, a simple bioenergetic model was developed. The model considered both the observed body growth, condition and spatiotemporal variability in counter current velocities along the migration route. We simulated the migration potential of an average fish from the 2016 cohort from the first fraction spawning in 2020 until full recruitment in 2023 (Fig. 5). In accordance with observations (Fig. 3), the model predicted northern spawning close to Lofoten in 2020 due to high northward velocities in combination with a small size as first-time spawner. However, subsequent simulations indicated that the cohort should have been capable of following older fish farther south in the succeeding years as it grew larger and with weaker coastal currents. Ultimately the model predicted spawning closer to Møre in 2023. These simulated dynamics offer insights into the potential natural process if the transfer of migration culture could progress under normal conditions.

Fig. 5: The migration potential of the 2016 cohort predicted from bioenergetic simulations.

a, The predicted migration distance in 2020–2023 over a defined route of 972 km along 13 transects. b, The velocity of surface currents during migration in January–February exemplified by conditions in 2020. c–e, Loss in total weight (c), total energy (d) and condition factor (CF) after spawning (e) when migrating southwards along the 13 transects. The migration costs include the effects of interannual and spatial variability in the velocity of coastal currents. Note here that the absolute minimum level of CF at 0.6 after spawning is shown with a dotted line (e) to illustrate the migration constraints related to initial size and condition (c,e). When reaching this limit, they must stop and spawn (a) to survive for sequential spawning events. f, Interannual variability in upstream velocity is illustrated as the actual swimming speed required to move the set distance of 1 (body lengths per s) along each transect.

Influence of climate change

In general, marine ectotherms are more vulnerable to warming than terrestrial ones[32]. In response to global warming, marine fish tend to seek colder waters and gradually shift distributions towards the poles[31]. We explored the climate change hypothesis by fitting generalized additive models (GAMs) to a 30-year time series (1995–2024) of zooplankton data from three cross-sections and temperature data from three monitoring stations overlapping with spawning migration and larval drift of NSS herring (Extended Data Figs. 5–8). The GAM model predictions (Extended Data Fig. 5) did not show signals supporting environmental change as trigger for the abrupt poleward shift in spawning after 2020. Rather, the results aligned with previous studies proposing that the energetic costs of migrating to Møre[26] may be traded off against enhanced larval survival[3] in warmer waters[28] with higher prey availabilities24,27.

In fact, NSS herring persistently spawned off Møre for two decades (Figs. 1e and 2a), producing large cohorts (Fig. 2b) despite the warmer waters after 2000 (Extended Data Fig. 5f,i). Relatedly, there is a lack of a discernible trend in the timing of the spring phytoplankton bloom in the Northeast Atlantic during the period of climate change33,34. The primary factors driving spring bloom timing, such as the sun’s zenith angle and day length, vary with latitude but not interannualy[33]. As herring spawning is also regulated by photoperiod[35], this suggests a long-standing adaptation to the latitudinal dynamics of spring blooming23,34 along the Norwegian shelf and the associated production[23] and cross shelf transport[24] of C. finmarchicus.

We propose that ocean warming indirectly contributed to the abrupt poleward shift in spawning by affecting mixing between recruits and elders. The summer–autumn blooms in the Northeast Atlantic, which are largely unaffected by photoperiod, have been significantly delayed under the period with climate change34,36. After 2005, NSS herring extended their feeding range far southwest into late autumn[21] (Fig. 1), coinciding with unexpectedly high densities of second-generation C. finmarchicus[25]. Warmer waters probably allowed for the emergence of two generations, which NSS herring capitalized on[21]. A consequence was delayed return migration, with wintering occurring closer to the feeding range. This shift contributed to the observed split distribution between older generations and the recruiting 2016 cohort in the Norwegian Sea (Figs. 1 and 3a and Extended Data Figs. 1 and 2), which may have hindered the successful transmission of traditional migration culture.

Although northern spawning may reduce production over the long term, it can still yield large cohorts. Recruitment trends of fish populations across the Northeast Atlantic show common patterns linked to environmental and ecological dynamics[37], and the occurrence of large NSS herring cohorts appears tied to specific conditions in the coastal current[38]. Our data on cyclic trends in zooplankton and the ambient temperatures experienced by adult spawners and their offspring (Extended Data Fig. 5c,f,i) also suggest synchronized peaks along the coast during the birth years of large cohorts, such as in 2002, 2004 and 2016, with a recent promising upward trend.

Consequences for coastal ecology

The annual spawning of NSS herring has a crucial role in transporting large amounts of energy from spring–summer feeding in the Norwegian Sea to the Norwegian coast[39]. At Møre, hundreds of thousands of tonnes of NSS herring have spawned over small areas in just a few days[40]. Such spawning waves have proven also to be vital for predatory fish[41]. Moreover, when spawning south, hatched larvae will spread over large areas during their northward drift with coastal currents towards Barents Sea Nurseries27,28. Some even end up in coastal nurseries[29], both acting as a buffer for recruitment and enriching local fjord ecology. The NSS herring spawning holds particular importance for endangered puffin colonies, where nesting sites are closely tied to historical availabilities of fish larvae42,43. Over time, we may therefore witness cascade effects along the coast for species that rely on herring spawning events in the south.

Consequences for management

Recent reviews highlight the importance of longevity conservation in animals in general[44] and in social fish specifically[45]. In fisheries, age-based indicators and reference points are suggested as key tools to mitigate negative effects of truncated age structure[46], but they do not consider the threshold of memory required to preserve migration culture12,13,14. NSS herring has spawned off southern Norway for centuries interspersed with periods of absence under climate change[47], which could be explained by failed knowledge transfer due to reduced mixing between elders and recruits when long periods of recruitment failure end with a bursting cohort. Age-selective fisheries may further accelerate such natural processes by directly removing migration memory from the population.

In Pacific herring (Clupea pallasi), for which population growth, climate change and fishing pressure have led to the erosion of population portfolio, finer-spatial-scale management strategies are suggested tools[48]. Their complex metapopulation structure is proposed to emerge from two alternative strategies; either recruits home to natal habitats in fixed proportions, or they adopt migration patterns based on the abundance of experienced spawners at each spawning site[49]. If managed spatially with optimal fishing pressure under a model assuming natal homing while recruits instead just follow the older fish, model simulations predict a potential loss of 35% of all spawning sites[50].

Our study provides empirical evidence in support of such cultural erosion linked to age-selective fisheries. It complements existing theoretical frameworks, highlighting the need for further research, especially regarding the memory threshold required to preserve migration culture. Over time, through increased migration potential or population growth, likely combined with an inherent genetic predisposition for favourable environments in the south, NSS herring will probably re-establish spawning at Møre. Still, to avoid such disrupted culture and periodical loss of spawning sites in schooling fish, it is critically important to integrate knowledge of social learning into future management strategies.

Methods

Catch data

The catch data used in this study, while not comprehensive in representing the entire distribution of NSS herring, serve as a crucial source for understanding spatiotemporal dynamics. Our analysis integrates catch data from Norway, Iceland and the Faroe Islands, accounting for approximately 80% of total landings spanning the years 1995 to 2023. These data are reported annually to the International Council for the Exploration of the Seas (ICES) quarterly and organized within ICES rectangles (0.5° latitude and 1° longitude)[52]. Our analyses on the changes in distribution and the defined seasonal migration culture within the population were based on monthly aggregated data per ICES rectangle prepared by each nation for the study.

Furthermore, to demonstrate the long-term stability in spawning at the west coast of Norway followed by an abrupt poleward shift, we calculated the COG in the Norwegian fishery over 14–29 February, representing fish that have arrived at their designated spawning location[53]. These data were restricted to the period 2000 onwards, when the Norwegian catch data were available at the level of individual landings from the Norwegian Directorate of Fisheries (Fangstdata (seddel) koblet med fartøydata (åpne data), Fiskeridirektoratet; https://www.fiskeridir.no/statistikk-tall-og-analyse/data-og-statistikk-om-yrkesfiske/apne-data-fangstdata-seddel-koblet-med-fartoydata). The COG was calculated as the arithmetic mean of midpoint positions in statistical rectangles (system of the Norwegian directorate of fisheries, mostly 0.5° latitude and 1° longitude) weighted by the corresponding total catch within the rectangles and the specified date interval. Note that we describe the temporal dynamics in COG only on the latitudinal scale.

Population dynamics data

Numerical dominance of recruiting cohorts in the population as well as longer term trends in spawner biomass, catch, fishing mortalities and exploitation patterns, were described based on data available from the assessment reported by ICES[52], covering the period 1988–2023.

Note that the assessment of NSS herring has been considerably revised over this period following changes in input data and model framework. Thus, retrospective patterns in the perception of trends in spawner biomass were demonstrated by adding data from the 1999 and 2009 ICES NSS herring assessments54,55.

We defined numerical dominance of a cohort as when the proportion of fish aged 4 years among fish aged 4 years and older exceeds 0.5. Here the 2016 cohort stands out as the most numerically dominant one over the period 1988–2023. Our main hypothesis was that this numerical dominance led the cohort to take its own decisions, and that the older fish, following high fishing pressure and plummeting, abundance adopted the newly established migration culture. To illustrate how the 2016 cohort left its Barents Sea nurseries and recruited to the spawning population over the period 2017–2023, we presented the proportion in abundance estimates from specific acoustic trawl surveys covering the full feeding distributions in the Norwegian Sea in spring (IESNS survey) and summer (IESSNS survey) as well as the spawning season (Spawning survey) based on data from the recent assessment report[52].

Moreover, given the expected importance of mixing processes between recruits and elders during feeding in the Norwegian Sea, spatial variation in abundance was detailed as 1 nmi nautical area back-scattering coefficient (NASC)[56] values along the defined transects during the IESNS and IESSNS surveys. Further details from these surveys and the methodology used is available in full survey reports attached in the annual ICES herring assessments in 2017–202352,57,58,59,60,61,62.

One parameter linked to population dynamics of specific relevance to migration potential is body growth20,26. Fluctuations in growth during 1988–2023 were analysed using IMRs biological data on total body lengths (L) in cm from fully recruited 7-year-old individuals sampled during quarter 1 (n = 17,609) collected in the commercial fishery and research vessel trawl hauls.

Another parameter having significant effect on spawning migration specifically is the amount of energy reserves available in this non-feeding period3,26. Thus, the corresponding trends in body condition were analysed with a combination of L and total weight (W) in g of individual fish, using Fulton’s condition factor (CF =W/L^3 × 100)[63], to characterize the energetic status. Here we included all data from maturing fish (L ≥ 27 cm[64]) in January (n = 36,501), which should represent the initial condition of the population at the onset of spawning migration.

Spatiotemporal cohort data

An important assumption of our study is that the effects of numerical domination for learning processes and transfer of migration culture occur at the school level. To explore the spatiotemporal development in numerical dominance of the 2016 cohort in schools linked to the abrupt poleward shift in spawning, we analysed a vast material of biological samples (n = 3,226) from single-trawl hauls and purse seine sets over the period 2017–2024. These were from Norwegian, Icelandic and Faroese fisheries as well as relevant surveys, including the international ecosystems surveys in the Norwegian Sea in spring (IESNS) and summer (IESSNS) and the spawning survey in February. We filtered the samples containing randomly aged individuals from the 2016 cohort and older fish (total n = 91,878) assumed to represent the cohort structure within a school. The spatiotemporal dynamics of the 2016 cohort at the school level were then demonstrated by mapping the proportion of this cohort in all samples quarterly over annual migration cycles, starting during the feeding season in quarter 2 and ending during the spawning season quarter 1.

A similar analysis was conducted for the available samples (n = 1,748) and aged fish (n = 61,616) of the 2002 cohort and older fish during the period that the 2002 cohort recruited to the spawning population (2003–2010). This comparative analysis was relevant as the 2002 cohort wintered at same latitudes as the 2016 cohort but still migrated all the way to spawn off the Norwegian west coast. To illustrate how body growth influenced the progress of southward spawning of the 2002 cohort, we first described how it gradually became numerically dominant in acoustic trawl surveys conducted during late autumn in the northern wintering areas over the years 2004–2006 according to data from the 2007 ICES assessment[65]. Second, we showed how these dynamics were related to growth based on IMR biological data on development in body lengths of this cohort during the wintering situation in quarter 4 over the years 2004–2006 (n = 1,781). Third, we demonstrated how the proportion of the cohort progressed with the distance of the spawning migrations on a latitudinal range and over the years 2005–2007 based on aged individuals from quarter 1 (n = 8,135). Finally, we addressed how these dynamics corresponded with dynamics in body lengths of the 2002 cohort (n = 2,338).

While both data on fisheries and fraction of the 2016 cohort in schools served as evidence for an abrupt poleward shift in spawning of NSS herring, the main quantification of this process was derived from the Spawning surveys during 2018–2024. All details from these surveys and the methodology used is available in full survey reports attached in the annual ICES NSS herring assessments in 2018–202352,58,59,60,61,62, and in the 2024 IMR survey report[66]. Note that, in these years, the execution of the acoustic trawl surveys was directly comparable, running northwards against the migration direction covering the full distribution within the confined spawning areas over around 10 days and the same dates (14–25 February) using either three (2018–2020) or two vessels (2021–2024). The survey transects were specifically designed to maintain a high degree of coverage, with trawling regularly on the acoustic registrations for biological sampling and ageing of ~50 specimens per haul. The saware (StoX[67]) and statistical approach was used to estimate the cohort abundance within prespecified strata for all surveys. The poleward shift in distribution linked to the spatiotemporal development in numerical dominance of the 2016 cohort was explored by comparing the acoustic abundance relative to the aggregated abundance of older and younger fish as pie charts at strata midpoints in maps. Here the size of pies was weighted to the highest acoustic abundance estimate at any strata within each survey year.

Tag-recapture data

In the present study, we also demonstrate the migration behaviour of individually tagged herring to support the observed dynamics at the population and school level. The tagging program on NSS herring using RFID technology was initiated by IMR in 2016 for assessment purposes and migration studies. All data relevant to the tagging program are open to the public through APIs[68].

During 2016–2023, herring was tagged on annual basis over a period of 3 weeks in the wintering areas in fjords of northern Norway during November–January. PIT tags, type ISO FDX-B 134.3 kHz, 3.85 × 23 mm biocompatible glass tags are used in the experiments. Herring were tagged at random ages 2–20 years, 20–24 cm body lengths and approximately 50% of each sex. The total number tagged on an annual basis (total sample size in experiment) was estimated to be within a range that gives adequate uncertainty levels when data are used as input to age-based assessments of population size.

IMR rents a commercial purse sein vessel for the tagging surveys. Here herring are captured on daily basis with purse seine early in the morning and pumped gently onboard to the refrigerated sea water storage tanks though pipes with sea water. These tanks are normally used to store the catch cold until landed at a factory, but here they are specially equipped with small keeping nets for the purpose of holding the live herring until tagging. From these tanks, the individual fish is dip-netted and tags are injected into the abdomen. All tagged fish are transferred to a smaller storage tank and released into the sea in schools of 200 individuals. Regarding animal welfare, the tagging experiments are approved by the Norwegian Food Safety Authority (FOTS), which handles all applications to use animals in scientific experiments. During the tagging process, a PC-reader system with RFID antenna continuously records the unique tag IDs together with the body length and other details relevant for the experiment, which frequently are synchronized with an IMR database over internet. Moreover, biological samples with age and length measurements forms the basis for forward age length keys describing age probabilities on the basis of size[69], which is used to estimate numbers released by each cohort.

The PIT-tagged herring are later recaptured at Norwegian and Icelandic factories producing landings from the commercial fishery for human consumption. Here several factories are equipped with monitoring systems including RFID antennas specially designed for pipes (round antennas) or conveyor-belt systems (flat antennas) detecting tagged fish during the production process. These antennas are connected to PC-reader systems that communicate directly with the IMR database providing updated information of recaptures in real time.

Moreover, all relevant data from the catches scanned for PIT-tags, including vessel info, catch quantum, catch position (ICES rectangle), catch date and production time, are uploaded to the database at a later stage. Finally, allocations between recaptures and catches are based on the combined info from time of recapture and the specific catch produced at that same time. In this study, we present information on the distribution and biomass of all the catches scanned for tags, as well as the distribution of recaptures from all the experiments 2016–2023.

Bioenergetic migration model

NSS herring do not feed during spawning migration, they rely on stored reserves[26]. The energy used during the spawning migration was modelled using a simple migration model coupled with a respiration model. In the migration model, the fish follow a predetermined route defined from the observed distribution of 1 nmi NASCs during the acoustic trawl survey in 2018[58]. For each latitude increment of 0.5°, the COG was calculated, resulting in 14 locations, connected by 13 transects and with a total swimming route length of 927 km.

The southward spawning migration of NSS herring is constrained by the external forces of the coastal currents. In the migration model, the interannual variations in coastal currents were included in terms mean velocities (u and v components) along each of the 13 transects of the migration route in 2020–2023. The velocities were obtained from the ORAS5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) provided at 0.25° × 0.25° grids averaged over January–February[70]. The ocean currents on the Norwegian shelf and continental slope are largely wind driven[71]. The Norwegian Atlantic Slope Current has little vertical shear over the upper 300 m (ref. [72]), where the herring migrate[58]. We therefore assume that the surface currents are representative of the velocities that herring were facing during upstream spawning migration.

Simulations were performed for observed L and W for the 2016 cohort of herring in 2020 (27.5 cm, 168 g), 2021 (29 cm, 210 g), 2022 (30 cm, 242 g) and 2023 (31.5 cm, 284 g) measured at onset of spawning migration in January. The condition was described using Fulton’s condition factor. The dry weight (DW) was then calculated on the basis of a water content of 68%, and the DW was partitioned into 15% gonads and 85% somatic tissues with equal parts of fat and solids, typical for this time of year26,73. The total initial energy was then calculated based on energy of gonads (25 kJ g−1 DW) derived from the 68% water content and known wet weight energy (8 kJ g−1 WW[73]), and the energy of somatic tissues using the known energy of fat (39.75 kJ g−1 DW)[74] and solids (20.92 kJ g−1 DW)[74].

During migration, the fish was set to swim with a net swimming speed of 1 body length per second, consistent with the observed migration speed of NSS herring between wintering and spawning areas[53] as well as cruising speed in tank experiments[75]. Moreover, at each transect, the swimming speed was increased proportionally to the counter current speed. Specifically, this was done by first calculating the swimming time (t_0) of a transect assuming no currents. The fish was then advected for this amount of time, and the actual distance under the influence of currents calculated. Lastly, the actual swimming speed (t_1) was increased to account for the extra swimming distance imposed by advection (t_1 = t_0).

Respiration loss (\({R}_{{{\rm{O}}}_{2}}\), grams O_2 per g per day) was calculated using a classical Hewett and Johnson model[76]:

$${R}_{{{\rm{O}}}_{2}}=f(W)\times f(T)\times f({\rm{SS}},L)$$

where f(W) is the weight dependence on respiration, f(T) is the impact of temperature on resting metabolism, and f(SS,L) is the impact of active metabolism (that is, swimming activity). The weight dependence was represented by an allometric scaling function as:

$$f(W)=\alpha \times {W}^{\beta }$$

where W is the fish weight and α and β are the intercept (0.0033 g O_2 per g per day) and slope (−0.227), respectively. The temperature dependence was calculated as an exponential function as:

$$f(T)={e}^{\theta \times T}$$

where T is the water temperature, set to 8 °C according to results of the present study, and θ is a constant (0.0548 °C−1) describing the impact of temperature on the resting metabolism. The impact of swimming activity was calculated as an exponential function as:

$$f({\rm{S}}{\rm{S}},L)={{\rm{e}}}^{{T}_{{\rm{o}}}\times {\rm{S}}{\rm{S}}\times L}$$

where SS is the relative swimming speed, Lis the length of the fish and To is a constant (0.03 °C−1) describing the impact of swimming on the active metabolism. Finally, the specific respiration loss was converted to specific energy loss using an oxy-calorific coefficient of 15.062 J g−1.

During the simulations, energy consumption for swimming was extracted from the somatic tissue fat pool. If the fat pool was depleted, the fish would shift to using solids. Moreover, energy was transformed from somatic tissues into gonads at a rate of 0.01 d−1 until they constituted 20% of total DW, a typical level for pre-spawners in February[26].

The costs in terms of loss in W and total energy were predicted along the full migration route of 13 transects, although this evidently would not be feasible to survive for sequential spawning events. To predict the actual migration potential the condition factor after spawning (CF*) was also calculated between each transect by subtracting the gonad weight from the total weight. CF* was then used to assess how far the 2016 cohort would be able to swim in the years 2020–2023 before having to spawn assuming a lower threshold of CF* = 0.60. This threshold was set according to the observed 10th percentile of CF* among spent herring ≥27 cm analysed by IMR over the period 1935–2023 (n = 26,452). We assume that surpassing this threshold would increase the risk of mortality.

Zooplankton biomass and temperature data

To examine whether the recent abrupt poleward shift in NSS herring spawning could be linked to changes in the biotic and abiotic environment, we analysed IMRs 30-year long time series (1995–2024) of zooplankton data from WP2 net hauls at three cross-sections (Svinøy, Gimsøy and Fugløy) and temperature data from conductivity, temperature and depth (CTD) casts at three monitoring stations (Bud, Eggum, Ingøy) overlapping with the period from onset of spawning migration 15 January[53] until the time when most offspring have reached metamorphosis 30 June[3].

IMR temperature data from CTD casts at monitoring stations are publicly available (https://www.imr.no/forskning/forskningsdata/stasjoner/view/initdownload), with records at standard depths. We used data from depths of 1, 5, 10, 20, 30, 50, 75, 100, 125, 150 and 200 m in our analyses.

The zooplankton data were extracted from IMRs local data. They were collected with WP2 nets using mesh sizes 180 μm, according to the standard procedure for the surveys. The net was hauled vertically from 200 m to the surface or from the bottom whenever bottom depth was less than 200 m. The exception was stations at the Fugløy cross section, where standard hauls start at 100 m depth. All of the samples were sieved into the size fractions 180–1,000 µm, 1,000–2,000 µm and >2,000 µm, dried and weighed and data presented as g DW per m^2. In our analyses, we included only the data from the smallest size fraction of zooplankton (180–1,000 µm) as indices for the prey availability for larvae.

For each time series, we fitted separate GAM models to the data. Zooplankton biomass were log-transformed before analysing the effects of sampling year and day of year:

$$\log ({\rm{D}}{\rm{W}}1000{\rm{\_}}180) \sim s({\rm{Y}}{\rm{e}}{\rm{a}}{\rm{r}},k=17)+s({\rm{D}}{\rm{a}}{\rm{y}}\,{\rm{o}}{\rm{f}}\,{\rm{y}}{\rm{e}}{\rm{a}}{\rm{r}},k=5)$$

For temperature, we added the effect of sampling depth to the model:

$${\rm{T}}{\rm{e}}{\rm{m}}{\rm{p}} \sim s({\rm{Y}}{\rm{e}}{\rm{a}}{\rm{r}},k=17)+s({\rm{D}}{\rm{a}}{\rm{y}}\,{\rm{o}}{\rm{f}}\,{\rm{y}}{\rm{e}}{\rm{a}}{\rm{r}},k=5)+s({\rm{D}}{\rm{e}}{\rm{p}}{\rm{t}}{\rm{h}},k=5)$$

We allowed the number of knots k for the sampling year to be as high as possible without overfitting interannual fluctuations and, at the same time, we restricted k for smoothers of day of year and depth where data were expected to follow a clear trend over season.

As we wanted to look at trends in ambient temperatures, we ran two different models for temperature split into depth intervals and periods overlapping with adult spawning migration and incubated eggs on one side (depth = 50–200 m, day of year = 15–90)40,53,58 and herring larvae from first feeding through metamorphosis on the other (depth = 1–50 m, day of year = 90–180)3,77,78.

We inspected the GAM-model diagnostics and QQ-plots showed some minor tail issues, especially for ambient temperature of larvae in cases in which there were some high outliers. All models were still considered to be acceptable given that R^2 values were high (deviations explained were at same level) and all smoothers including the intercepts were significant.

Finally, based on the accepted GAM models, the zooplankton and temperature (T, °C) responses of sampling year, day of year and depth (only for T) were predicted at set values of covariates: year was set to 2021 to represent the poleward shift of spawning, 1 March and 150 m was used as typical time and depth of spawning[40], 1 April was considered to be the first feeding date of larvae[77], whereas 1 May and 25 m represented the time and depth of early growth larvae[78].

Oceanographic currents in study area

In our introduction, we give a schematic overview over main oceanographic currents in our study area (adopted from a previous study[51]), to demonstrate the dynamic environment herring is experiencing during it season migrations between wintering, spawning and feeding habitats.

Ethics oversight

We have included data from PIT-tagging experiments on herring. These kinds of experiment fall within the same category as laboratory experiments with animals. All our experiments have been approved by the Norwegian Food Safety Authority (https://www.mattilsynet.no/en). The survey methodologies used for the present study, including the sampling of herring and zooplankton, follow recommendations in protocols from the International Council for the Exploration of the Sea[79].

Reporting summary

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

Data availability

Data are publicly available online (https://git.imr.no/pelagic/public/paper_numeric_dominance_herring). Some data were extracted from databases of which the data are publicly available on request; all national biological herring data and monthly catch data aggregated by ICES rectangles (0.5° latitude and 1° longitude) were delivered by IMR, MFRI and FAMRI for this study. Likewise, the zooplankton data and estimates of herring abundance at age by strata in StoX projects from spawning surveys were extracted from local IMR databases, while, for IESNS and IESSNS 1 nmi NASC values along acoustic transects were extracted from Stox projects in ICES Working Group on International Pelagic Surveys (WGIPS). Other data used are publicly available for download; temperature data from IMRs coastal monitoring stations (https://www.imr.no/forskning/forskningsdata/stasjoner/view/initdownload), current velocities along the Norwegian coast in January–February from Copernicus Marine Service Information (CMEMS) (https://doi.org/10.48670/moi-00024), individual catch data in February from The Norwegian Directorate of Fisheries (Fangstdata (seddel) koblet med fartøydata (åpne data), Fiskeridirektoratet; https://www.fiskeridir.no/statistikk-tall-og-analyse/data-og-statistikk-om-yrkesfiske/apne-data-fangstdata-seddel-koblet-med-fartoydata) and the PIT-tag data for IMRs database (https://doi.org/10.21335/NMDC-2114050995). Finally, all population-level data are publicly available in ICES reports52,54,55.

Code availability

R v.4.4.2 (31 October 2024) was used for analyses and plotting included in the Article, and all codes are made available online (https://git.imr.no/pelagic/public/paper_numeric_dominance_herring). StoX v.3.6.3 was used for abundance estimation in the spawning survey, and Qgis v.3.22.7 was used for mapping of these surveys and acoustic abundance estimates by strata. Moreover, v.SAS 9.4 was used to calculate the COG based on single catch data from the Norwegian Directorate of Fisheries.

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Acknowledgements

We thank P. Mariani for comments and contributions to improving the manuscript; all our colleagues for their contributions and for the extensive human and economic resources put into the collection of the highly valuable empirical data used in our study. Without such time series, it would have been impossible to explore the mechanisms underlying changes in the migration of herring.

Author information

Authors and Affiliations

1. Institute of Marine Research (IMR), Bergen, Norway

Aril Slotte, Are Salthaug, Sindre Vatnehol, Espen Johnsen, Erik Askov Mousing, Åge Høines, Cecilie Thorsen Broms, Øystein Skagseth & Erling Kåre Stenevik

2. Norwegian Meteorological Institute (NMI), Oslo, Norway

Erik Askov Mousing

3. Marine and Freshwater Institute (MFRI), Hafnarfjörður, Iceland

Sigurvin Bjarnason

4. Faroe Marine Research Institute (FAMRI), Tórshavn, Faroe Islands

Eydna í Homrum

Contributions

A. Slotte, A. Salthaug, E.J. and E.K.S. conceived the study. A. Slotte wrote the manuscript and all of the authors contributed to revisions and general discussions. A. Slotte, A. Salthaug, S.V. and Å.H. carried out the analyses. E.A.M. developed the bioenergetic migration model together with A. Slotte. C.T.B. contributed as a zooplankton expert with data. Ø.S. contributed as oceanographic expert with data on currents. S.B. contributed with Icelandic PIT-tag data, catch data and biological data. E.í.H. contributed with Faroese catch data and biological data.

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Correspondence to Aril Slotte.

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Extended data figures and tables

Extended Data Fig. 1 Distribution and acoustic densities of NSS herring during the early feeding season.

Shown are 1 nmi NASC (Nautical Area Scattering Coefficient) values along acoustic transects from the international survey IESNS running from late April to early June in the Norwegian Sea 2017–2023. NASC values are scaled linearly to the highest value over the time series; 19516 in 2023.

Extended Data Fig. 2 Distribution and acoustic densities of NSS herring during the late feeding season.

Shown are 1 nmi NASC (Nautical Area Scattering Coefficient) values along acoustic transects from the international survey IESSNS running from start of July to early August in the Norwegian Sea 2017–2023. NASC values are scaled linearly to the highest value over the time series, 42825 in 2020.

Extended Data Fig. 3 Spatiotemporal dynamics in proportion of the 2002-cohort in schools.

Shown is the proportion of 2002-cohort (P 2002/P ≤ 2002) in Norwegian, Icelandic and Faroese samples from individual fishing operations in scientific surveys and commercial fisheries. Trends are traced over the years 2003–2010 and seasonal migration cycles from feeding (Q2-3) and wintering (Q3-4) one year until spawning (Q1) next year.

Extended Data Fig. 4 The recruitment of the 2002-cohort to the spawning population.

a, Cohort abundance estimated during IMRs acoustic trawl surveys in the northeastern wintering areas in November 2004–2006. b, Length distribution of the 2002 cohort during quarter 4 in 2004–2006. c, The proportion of the 2002-cohort relative to older fish on a latitudinal range during quarter 1 2005–2007. d, Latitudinal differences in mean body length with 95% CIs of the 2002-cohort during the quarter 1 in 2005–2007 (n = 2338). Note that b, c and d is based on all biological data collected from acoustic trawl surveys and commercial fishery by IMR.

Extended Data Fig. 5 Spatiotemporal dynamics in zooplankton and temperatures at the Norwegian shelf.

a, zooplankton biomass time series (1000-180 µm) were from cross sections Svinøy (blue), Gimsøy (green) and Ingøy (red) whereas temperature time series were from Bud (blue), Eggum (green) and Ingøy (red) monitoring stations. b, c, d, e, f, g, h, i, Predictions from GAM models with 95% CIs fitted to 30 years of data (see Extended Data Figs. 6–8). b, c, Development in zooplankton biomass: b, over day of year in 2021 and c, over years at first larval feeding 1st April. d, e, f, Trends in temperature during spawning migration: d, with depth when spawning 1st March in 2021, e, over day of year when spawning at 150 m depth in 2021, and f, over years when spawning at 150 m depth on 1st March. g, h, i, Trends in temperature during the larval period: g, with depth 1st May in 2021, h, over day of year at 25 m depth in 2021 and i, over year at 1st May and 25 m depth.

Extended Data Fig. 6 GAM-model diagnostics of zooplankton biomass at the Norwegian shelf.

The log dry weigh (DW) (g m−2) of the smallest size group (1000-180 µm) relevant for herring larvae was recorded using WP2 nets at three cross sections south to north (Svinøy, Gimsøy and Fugløy) (Extended Data Fig. 5a), and stations included in the analyses were those located on shelf (depth <500 m) overlapping with herring larval dispersal. We analysed data from a 30-year period (1995−2024), with focus on the season 15 January through June to representing both the spawning migration of adult fish along the coast, and the following northward larval drift period through metamorphosis. A GAM model was fitted separately to data from each of the three cross sections: DW1000_180 ~ s(Year, k = 17) + s(DayOfYear, k = 5). All three models, Svinøy (n = 457, R^2 = 0.717, p < 0.001), Gimsøy (n = 382, R^2 = 0.831, p < 0.001) and Fugløy (n = 546, R^2 = 0.678, p < 0.001) as well as their intercepts and smoothers (DayOfYear, Year, p < 0.001) were highly significant. Output: a, Distribution of raw data over day of year for the three models. b, c, Partial effects of smoother terms at their covariate means (values inside each panel, DayOfYear = Doy) with 95% CIs. d, e, Model diagnostic plots showing d, fitted values versus residuals and e, QQ plots.

Extended Data Fig. 7 GAM-model diagnostics of ambient temperatures during spawning migration.

Temperature data were analysed from three monitoring stations (Bud, Eggum and Ingøy) located at different latitudes along the Norwegian shelf (Extended Data Fig. 5a). Data were collected over a 30-year period (1995–2024) from 15 January through March at depths 50–200 m, representing water masses experienced by adult herring on spawning migration and their incubated eggs until peak hatching. A GAM model was fitted separately to data from each of the three monitoring stations: Temp ~ s(Year, k = 17) + s(DayOfYear, k = 5) + s(Depth, k = 5). All three models, Bud (n = 642, R^2 = 0.743, p < 0.001), Eggum (n = 1162, R^2 = 0.720, p < 0.001) and Ingøy (N = 1032, R^2 = 0.589, p < 0.001) as well as their intercepts and smoothers (DayOfYear, Depth, Year, p < 0.001) were highly significant. Output: a, Distribution of raw data over day of year for the three models. b, c, d Partial effects of smoother terms at their covariate means (values inside each panel, DayOfYear = Doy) with 95% CIs. e, f, Model diagnostic plots showing e, fitted values versus residuals and f, QQ plots.

Extended Data Fig. 8 GAM-model diagnostics of ambient temperatures during larval stage.

Temperature data were analysed from three monitoring stations (Bud, Eggum and Ingøy) located at different latitudes along the Norwegian shelf (Extended Data Fig. 5a). Data were collected over a 30-year period (1995-2024) from 1.April through June at depths 1-50 m, representing water masses experienced by herring larvae from peak hatching through metamorphosis. A GAM model was fitted separately to data from each of the three monitoring stations: Temp ~ s(Year, k = 17) + s(DayOfYear, k = 5) + s(Depth, k = 5). All three models, Bud (n = 869, R^2 = 0.773, p < 0.001), Eggum (n = 1583, R^2 = 0.847, p < 0.001) and Ingøy (n = 1260, R^2 = 0.813, p < 0.001) as well as their intercepts and smoothers (DayOfYear, Depth, Year, p < 0.001) were highly significant, with one exception of p = 0.001 for smoother Depth at station Fugløy. Output: a, Distribution of raw data over day of year for the three models. b, c, d Partial effects of smoother terms at their covariate means (values inside each panel, DayOfYear = Doy) with 95% CIs. e, f, Model diagnostic plots showing e, fitted values versus residuals and f, QQ plots.

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Slotte, A., Salthaug, A., Vatnehol, S. et al. Herring spawned poleward following fishery-induced collective memory loss. Nature (2025). https://doi.org/10.1038/s41586-025-08983-3

- Received: 12 April 2024

- Accepted: 04 April 2025

- Published: 07 May 2025

- DOI: https://doi.org/10.1038/s41586-025-08983-3

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Genomic and genetic insights into Mendel’s pea genes

Cong Feng,

Baizhi Chen,

Julie Hofer,

Yan Shi,

Mei Jiang,

Bo Song,

Hong Cheng,

Lu Lu,

Luyao Wang,

Alex Howard,

Abdel Bendahmane,

Anissa Fouchal,

Carol Moreau,

Chie Sawada,

Christine LeSignor,

Cuijun Zhang,

Eleni Vikeli,

Georgios Tsanakas,

Hang Zhao,

Jitender Cheema,

J. Elaine Barclay,

Junliang Hou,

Liz Sayers,

Luzie Wingen,

Marielle Vigouroux,

Martin Vickers,

Mike Ambrose,

Marion Dalmais,

Paola Higuera-Poveda,

Pengfeng Li,

Quan Yuan,

Rebecca Spanner,

Richard Horler,

Roland Wouters,

Smitha Chundakkad,

Tian Wu,

Xiaoxiao Zhao,

Xiuli Li,

Yuchen Sun,

Zejian Huang,

Zhen Wu,

Xing Wang Deng,

Burkhard Steuernagel,

Claire Domoney,

Noel Ellis,

Noam Chayut &

…

Shifeng Cheng

Nature (2025)

Abstract

Mendel[1] studied in detail seven pairs of contrasting traits in pea (Pisum sativum), establishing the foundational principles of genetic inheritance. Here we investigate the genetic architecture that underlies these traits and uncover previously undescribed alleles for the four characterized Mendelian genes2,3,4,5,6,7, including a rare revertant of Mendel’s white-flowered a allele. Primarily, we focus on the three remaining uncharacterized traits and find that (1) an approximately 100-kb genomic deletion upstream of the Chlorophyll synthase (ChlG) gene disrupts chlorophyll biosynthesis through the generation of intergenic transcriptional fusion products, conferring the yellow pod phenotype of gp mutants; (2) a MYB gene with an upstream Ogre element insertion and a CLE peptide-encoding gene with an in-frame premature stop codon explain the v and p alleles, which disrupt secondary cell wall thickening and lignification, resulting in the parchmentless, edible-pod phenotype; and (3) a 5-bp exonic deletion in a CIK-like co-receptor kinase gene, in combination with a genetic modifier locus, is associated with the fasciated stem (fa) phenotype. Furthermore, we characterize genes and alleles associated with diverse agronomic traits, such as axil ring anthocyanin pigmentation, seed size and the ‘semi-leafless’ form. This study establishes a foundation for fundamental research, education in biology and genetics, and pea breeding practices.

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Main

Pea is an Old World crop that was first cultivated about 10,000 years ago in the Fertile Crescent[8]. About three quarters of the cultivated area is for dry seed, and the remaining quarter is for use as a vegetable, with a total export value of about US $3 billion in export value in 2022 (https://www.fao.org/faostat/en/#data/). Pea also has a minor use as a fodder crop and is often grown in home gardens. The nutritional and environmental advantages of this pulse crop, including the rising popularity of pea protein and its nitrogen-fixing root nodule symbiosis, have been discussed elsewhere9,10.

Pea is predominantly inbreeding, with large flowers and many easily distinguishable characteristics, making it an ideal model for Mendel’s studies of inheritance1,11. The seven variants that Mendel studied in detail were clearly distinguished in the seed catalogues of the time[12], representing different agronomic forms, end uses or market types, as they still do today. Mendel’s work on peas was described by Allan Franklin as “The best experiments ever done”[13]. In addition to its important historical contribution to the development of genetics, pea continues to serve as an excellent plant model system, with approximately 100 pea genes characterized at the molecular level[14]. However, much remains unknown about the molecular nature of the seven pairs of contrasting traits that Mendel studied, even though the genetic loci were named more than a century ago[15]. The four cloned genes R (seed shape), Le (plant height), I (seed colour) and A (flower colour) have been characterized for some time2,3,4,5,6,7,16, but the extent and distribution of their natural allelic variation remains poorly understood in a genomic context14,17,18. The gene identities of Mendel’s three other traits, P and V (pod form), Gp (pod colour) and Fa (fasciation), are uncharacterized. Candidates for Gp and P have been proposed on the basis of specific genome-wide association studies (GWASs) and bi-parental mapping studies19,20,21; however, further work was needed to confirm or reject these proposals.

Here we present a sequence-based population genomic map and a trait-based phenotypic variation map, coupled with haplotype–phenotype association analyses across a wide range of traits in one of the world’s major Pisum germplasm collections[14]. We focus on elucidating the gene identities and genomic context of alleles underlying Mendel’s seven well-known traits. We further demonstrate how this approach can be expanded to uncover the molecular basis of a wide range of agronomic and horticultural characters with breeding potential.

Variation map of a Pisum collection

It is widely accepted that Pisum has two wild species Pisum fulvum and Pisum elatius and two independently domesticated taxa Pisum abyssinicum and P. sativum (Supplementary Table 1). We selected a core diversity panel from the widely used John Innes Pisum Germplasm Collection[14]. This panel includes 500 accessions, selected using Corehunter 3 on the basis of prior genotyping data22,23. The selection was further supplemented with 130 lines previously chosen for other diversity studies (https://pcgin.org/), parents of mutant and mapping populations, and 67 lines comprising selected P. abyssinicum accessions and all accessions designated P. elatius, P. fulvum or Pisum humile (Fig. 1a). We conducted next-generation short-read whole-genome resequencing for these 697 Pisum accessions, generating approximately 80 Gb of clean reads with an average coverage of around 20× for each accession (Supplementary Table 2). We then built a genomic variation map encompassing 154.8 million high-quality single-nucleotide polymorphisms (SNPs) with respect to the ZW6 assembly[18], as well as to the inbred pea cultivar Caméor version 1a[17] (Supplementary Tables 3 and 4 and Supplementary Fig. 1). Population genomic diversity analysis reveals the pattern of accession relationships and population structure at high resolution[23] (Supplementary Tables 5 and 6). We identify eight major Pisum groups (G1–G8) (Fig. 1b,d), with P. fulvum and P. abyssinicum forming distinct branches within G8. The remaining G8 accessions consist mainly of P. elatius, and G7 includes P.sativum together with other sub-types. P. sativum accessions are widely distributed across the groups G1 to G7. These accessions do not have a simple tree-like relationship, but instead form a reticulated network, reflecting the complex history of crossing and introgression between subgroups (Fig. 1e).

Fig. 1: Genotypic and phenotypic variation with respect to population and genome structure within Pisum.

a, Taxa types and other classifications indicated by colour on the right, including wild taxa (P. fulvum, P. elatius and other wild taxa listed in Supplementary Tables 1 and 5) and domesticated taxa (P. abyssinicum and P. sativum), further divided into cultivars, landraces and other, which mostly comprises genetic stocks. The number in brackets denotes the number of accessions for each classification. b, Admixture analyses at K = 3 (average of 5 runs), K = 5 (average of 3 runs) and K = 8 (one run that splits K = 5 groups). Accessions strongly assigned to admixture groups are colour-coded, with grey indicating admixture (see Supplementary Table 5). c, Distribution of phenotypes for Mendel’s seven pea traits, with initials labelled as follows: R (round, pale) versus W (wrinkled, black), seed shape; Y (yellow) versus G (green), cotyledon colour; P (pigmented, purple) versus W (white, pale), flower colour; I (inflated, pale) versus C (constricted, black), pod shape; G (green) versus Y (yellow), pod colour; A (axial, pale) versus T (terminal, black), flower position; and T (tall) versus D (dwarf), internode length. The bar length is proportional to internode length. d, Principal component analysis (PCA) of PLINK distance matrix for all accessions, with accessions having Q value > 0.75 colour-coded. e, Splits Tree[61] analysis of accessions with Q value > 0.75, with colours matching PCA groups. f, Pisum genomic variation map across all seven chromosomes, including SNPs, insertions and deletions (<50 bp), large-scale structural variations (SVs) and the linkage disequilibrium (LD)-based haplotype map.

Source data

Within the diversity panel, we recorded phenotypic variation for each of Mendel’s seven pairs of contrasting traits (Fig. 1c and Supplementary Table 7). The association of this phenotypic diversity with genomic data (Fig. 1f and Supplementary Tables 8–12), coupled with linkage analysis from bi-parental mapping populations and gene functional investigations, elucidated the genetic architecture and allelic variants associated with each of these traits (Fig. 2 and Extended Data Fig. 1). We show that, for each trait, a small number of genetic loci contribute to the trait variation.

Fig. 2: Genetic architecture and genomic diversity of the genes underlying the seven pairs of contrasting traits that Mendel studied in detail.

a, Images of the contrasting phenotypes of the seven traits. b, Manhattan plots from the GWAS showing the genomic regions with strong peaks associated with phenotypic differences of each trait as scored in this study and plotted against the ZW6 assembly. c, Gene models for R, I, A, Le, P and Fa and associated genomic regions (Gp and V), depicting the wild-type and natural mutant alleles underlying each of the seven traits. Text and illustrations marked in red indicate those identified in this study. Details are provided in the text and Supplementary Information. aa, amino acids.

Novel alleles in four characterized genes

We confirm the previous interpretations for the four characterized Mendelian genes (R, I, Le and A)2,3,4,5,6,7 and identify novel alleles (Fig. 2 and Supplementary Tables 13–16). For the R gene, explaining the round versus wrinkled seed phenotype, we detect a single strong, broad signal at the expected genomic position of PsSBEI[2] and present the full-length sequence of the Ips-r element inserted in the last exon (Supplementary Fig. 2). R versus r is also the primary determinant of seed protein content variation (Supplementary Fig. 3), suggesting that altered seed starch quality and quantity indirectly affect the ratio of storage protein classes and overall protein content[24]. For I versus i4,5,6, which corresponds to yellow versus green cotyledons, we characterize two key alleles: i-1 (the insertion of a 5,696-nt TAR element, a Ty1-Copia long terminal repeat (LTR) retrotransposon) and i-2 (a novel 408-bp deletion in the promoter) of the Mg-dechelatase gene (Supplementary Figs. 4 and 5). The phenotypic variability associated with these alleles, driven by transcriptional differences and confounding genetic and environmental factors is described in Supplementary Figs. 6 and 7. For the A gene, which encodes a bHLH transcription factor that is responsible for the presence or absence of anthocyanin pigmentation (A versus a)[7], we present several novel alleles (Supplementary Fig. 8), including a rare intragenic suppressor mutation that restores the most abundant a allele to a functional wild-type A allele (Supplementary Fig. 9). Finally, for the Le gene, which encodes GA 3-oxidase1 (also known as GA 3β-hydroxylase), we observe five haplotypes (Supplementary Fig. 10), but the reduced-height le variants were exclusively found in haplotype 1, which carries the known G > A substitution at chromosome (chr.) 5:639,901,919.

Three previously uncharacterized traits

We investigated the gene identities and allelic variants underlying three of Mendel’s seven traits that remained poorly characterized[16]: ‘the difference in the colour of the unripe pod’ (Gp); ‘the difference in the shape of the ripe pod’ (conditioned by either of two loci, P or V); and ‘the difference in the position of the flowers’ (thought to be conditioned by either of two loci, Fa or Fas).

Pod colour

Although Gp is usually discussed in relation to pod colour, Mendel noted that this is just one feature of the gp mutant[1]. In mature flowering and fruiting plants, yellow tissues are seen in the petiole, rachis, tendrils and leaflet midribs of young leaves, as well as in the pedicel, peduncle and sepals (Fig. 3a and Supplementary Fig. 11). There are also substantial differences in chloroplast development[25], as well as in the physiological and biochemical properties of pod and leaf tissues between green (Gp/Gp) and yellow-podded (gp/gp) varieties (Fig. 3b,c). Here we found that even the green leaves of gp lines show disturbed development of thylakoid membranes (Supplementary Fig. 12), which correlates with a productivity difference between Gp and gp isolines (Supplementary Fig. 13).

Fig. 3: Characterization of the gp mutant.

a, General view of near-isogenic plants (BC6 S1 generation from the cross JI0015 gp/gp x Caméor Gp/Gp) developed in this study. Pot diameter is 9 cm. b, Transmission electron microscopic (TEM) sections of pod mesocarp cells. Scale bars, 1 μm. c, TEM sections of leaflet spongy mesophyll cells. Note the poorly developed thylakoid membranes (arrows) in gp compared to Gp. Scale bars, 0.5 μm. d, An approximately 100-kb genomic deletion adjacent to ChlG is illustrated for gp compared to the reference genome ZW6 (Gp). The deletion event in gp lines is illustrated on the Gp reference genome by the dashed box. The approximately 100-kb deletion event was called according to genome assembly comparisons between JI0015 and JI2822 (ref. [62]). Far right, expression of transcript types T0 and T1–T5 (Gp/Gp wild type; top right) and t0 and t1–t6 (gp/gp mutant; bottom right); numbers at ends of (or in) bars indicate the expression (in transcripts per million (TPM)). More details are provided in Supplementary Figs. 15–19 and Supplementary Tables 23–27. e, Crossing scheme for a complementation test between Caméor M4 TILLING line 411.1 carrying one lethal allele of ChlG and gp (JI0015), with the two types of expected F_1 genotype. ChlGWT and ChlGW121* represent the wild-type and TILLING alleles of ChlG. WT represents the presence of the wild-type (Caméor) sequence between ChlG and the TIR-NBS-LRR gene, and Δgp represents the approximately 100-kb deletion, which co-segregates with gp. The question being addressed is whether ChlGW121*-WT complements gp (ChlGWT-Δgp). f, F_1 pods segregating for green versus yellow. The number after the underscore is the plant number; the parental lines (TILL_6 het and JI0015) and wild-type Caméor are also shown. g, Codominant PCR marker test confirming that all plants presumed to be F_1 are Gp/gp heterozygotes (top) and a dCAPS marker PCR test confirming that only the yellow-podded F_1 plants inherited the ChlGW121* TILLING allele (bottom). M, DNA size marker (0.5 – 3 kb; 100 bp ladder lane from 1 kb to 0.5 kb and below).

All yellow-podded lines in the John Innes Pisum germplasm collection were found to be allelic to gp, confirming that there is only one known yellow pod locus and here we show that there is only one gp allele. Genetic mapping and association genomics analysis found that all yellow-podded lines carried a deletion of around 100 kb within the GWAS interval that co-segregated with gp (Supplementary Figs. 14 and 15 and Supplementary Tables 17–22). With respect to the ZW6 assembly, this large genomic deletion removes one Ogre LTR retrotransposon and an open reading frame encoding a partial NBS-LRR gene, as well as part of exon 5 and the whole of exons 6 and 7 from a gene encoding a TIR-NBS-LRR protein (NLR (also known as Psat03G0414100)) (Fig. 3d).

This deletion is adjacent to the chlorophyll synthase gene (ChlG (also known as Psat03G0413700)), but the ChlG gene is structurally intact in all gp lines, with its encoded amino acid sequence being identical to the wild type. Mapping RNA-sequencing (RNA-seq) reads of the gp lines JI0015 and JI2366 to their matched genome assemblies predicted novel transcripts and isoform variants, including alternative splicing within the NLR–ChlG region (Fig. 3d and Supplementary Fig. 16). Transcriptional fusions, generated by intergenic splicing between the truncated NLR and ChlG, breakpoint read-through of the truncated NLR transcript, and intron read-through of ChlG transcripts were confirmed by PCR with reverse transcription and cDNA sequencing (Supplementary Fig. 17 and Supplementary Table 23–25). It can be predicted that the encoded fusion or truncated ChlG proteins would not participate in chlorophyll biosynthesis, nor would they insert into the thylakoid membrane owing to either internalization or the lack of the transit peptide (Supplementary Fig. 18). RNA-seq and quantitative PCR (qPCR) data showed that in gp pods, functional ChlG transcript abundance was reduced to about 6% of that in the wild type (Fig. 3d). In gp mutants, the intact t0 transcript was more abundant in leaves compared with pods but the fused NLR–ChlG t1 and t2 transcript levels were similar in pods and leaves (Supplementary Fig. 19 and Supplementary Tables 26 and 27). We propose that transcriptional interference and aberrant transcripts disrupt ChlG function and chlorophyll synthesis, causing the yellowness of otherwise green tissues in the gp mutant.

To test whether Gp corresponds to ChlG, we obtained a TILLING (targeting-induced local lesions in genomes) mutant[26] with a premature stop codon (W121*) in ChlG (Fig. 3e). This mutant could not be recovered as a homozygote, although the mutant allele was transmitted through both pollen and egg cells. We conclude that the homozygous mutation is embryo lethal, but not lethal in either gametophyte. We reasoned that the phenotype of a ∆gp/Gp, ChlGwt/ChlGW121* double heterozygote would be informative; if Gp did not correspond to ChlG, then the plant should be viable and green-podded, refuting our hypothesis. Conversely, if Gp did correspond to a functional ChlG, then it should be yellow-podded. Of the 16 F_1 progeny that we derived from the cross between gp/gp and the TILLING mutant heterozygotes, 10 had yellow pods, and all yellow-podded F_1 plants carried the ChlGW121* null allele (Fig. 3f,g). This result supported our hypothesis and confirmed that the gp mutant does not provide a fully functional ChlG.

The evidence presented above demonstrates that a ChlG deficiency mediates the mutant phenotype and establishes that ChlG is allelic to Gp. The wild-type alleles of other genes in the vicinity cannot rescue the gp mutant phenotype. While the detailed molecular and regulatory mechanism underlying this defect in chlorophyll synthesis remains to be established, our current understanding predicts that ablation of the NLR gene in a gp mutant, thereby removing the fused NLR–ChlG transcripts, would restore the wild-type green pod colour.

Pod shape

In 1537, Ruel described the difference in the shape of the ripe pod as “Valvulae etia recetes eorum quae nullo pedameto fulciuntur, ante que durescat, edendo sunt”[27]; roughly translated, this means “Those where the valves provide little support are to be eaten before they harden”, indicating that as today, these were vegetable peas. The lack of a sclerenchyma layer (pod parchment) is conditioned by the recessive allele at either (or both) of the genes: P and V[16]. It is uncertain which of these genes Mendel was discussing; he could have worked with either, or perhaps both. Our GWAS analysis identified regions that are statistically correlated with this phenotype, two of which correspond significantly to the expected positions of P and V (Fig. 2b), suggesting that both p and v alleles are relatively common. Additional signals detected may correspond to genes affecting pod wall thickness (N) or structure (Sin)[28] (Extended Data Fig. 2a).

Within our 8.3 Mb GWAS peak on chromosome 1, the gene Psat01G0420500 had the greatest significance, which is consistent with a 0.92 Mb interval defined in the JI2822 × JI0816 F_2 mapping population (Extended Data Fig. 2b–d). Psat01G0420500 encodes a dodeca-CLE peptide that is identical to the tracheary element differentiation inhibitory factor (TDIF) of Arabidopsis CLE4121,29 (designated here as PsCLE41; Supplementary Fig. 20). One allele within this single-exon gene, which carries an in-frame premature stop codon (R79*) upstream of the TDIF motif (Extended Data Fig. 2e–g), fully explains the p phenotype. CLE41 or CLE44 peptides are known to repress the formation of xylem[30] in Arabidopsis thaliana and specify positional information that determines the rate and orientation of cell divisions in vascular tissue in conjunction with the receptor kinase PXY[31]. TDIF is proposed to function as a non-cell autonomous signalling peptide controlling cell fate[32] and lignification[33]. This suggests a model for P whereby this TDIF peptide interacts with a PXY-like protein to specify pea pod sclerenchyma development via the established TDIF–PXY–WOX signalling pathway34,35.

The genomic interval corresponding to V, as identified by GWAS, spans a broad region (chromosome 6, 610–650 Mb) within which the most significant 2-Mb segment (chromosome 6, 629–631 Mb) overlaps with a 1.25-Mb interval (chromosome 6, 629.23–630.48 Mb) defined by an F_2 mapping population[36], narrowing the V candidate region (Extended Data Fig. 3a–e). A detailed haplotype–phenotype association study within this interval excluded several previously proposed gene candidates (Supplementary Tables 28 and 29 and Supplementary Fig. 21). We found that accessions with parchmentless pods, with or without the R79* mutation (ppvv or PPvv), are clustered into a single haplotype (Extended Data Fig. 3g) for Psat05G0804500, a homologue of the Arabidopsis MYB26 gene (referred to here as PsMYB26, which has also been designated as PsMYB73 (ref. [37]; Supplementary Fig. 22). AtMYB26, which encodes a master transcription factor that directly activates its downstream NAC-domain transcription factors in A. thaliana[38], has been reported to promote secondary wall thickening and lignification of the endothelium, and MYB26 is required for sclerenchyma formation in legume pods[39]. PsMYB26 exhibits tissue-specific expression in the pod endocarp and is the most highly differentially expressed gene between wild-type (P/P V/V) pods and mutant (P/P v/v) pods (Extended Data Fig. 3f,h and Supplementary Tables 30 and 31). RNA-seq and qPCR with reverse transcription (RT–qPCR) analyses of both P/P v/v and p/p v/v mutant lines both show reduced expression of PsMYB26 and the PsNAC (Supplementary Information) compared with wild-type P/P V/V lines (Extended Data Fig. 4a–f and Supplementary Fig. 23). Furthermore, a virus-induced gene-silencing (VIGS) experiment followed by RT–qPCR analyses demonstrated the downregulation of PsMYB26 and PsNAC genes in the silenced lines (Extended Data Fig. 4g). PsMYB26 is downregulated in p/p V/V mutant lines, suggesting that PsMYB26 and PsNAC have a role in controlling the parchmentless phenotype and supporting an epistatic relationship between PsCLE41 and PsMYB26 (Extended Data Fig. 4f–h).

A 23 kb Ogre-type LTR retrotransposon insertion, located upstream of PsMYB26 and with the same polarity, is present in all P/P v/v genotypes in our diversity panel (Supplementary Fig. 24 and Supplementary Tables 32 and 33). Although PsMYB26 and the associated Ogre insertion are strong candidates for V, further work is needed to fully elucidate the detailed molecular mechanisms underlying the v allele.

Fasciation

Mendel used the name Pisum umbellatum when describing “the position of the flowers” on the stem of pea, a term previously used by Gerard[40] to describe the fasciated form with an umbellate inflorescence. Fasciation in pea can vary in its severity, from stem bifurcation to an extreme clustering of flowers at the apex. There are several pea genes that, when mutant, confer a fasciated phenotype; of these, Fa (chromosome 4, linkage group IV) is considered to be the gene Mendel studied41,42.

GWAS analysis identified a broad signal (chromosome 4, 0–40 Mb) (Fig. 2b) that underwent further refinement through investigation of F_2 populations. Bulked segregant analysis (BSA) narrowed this region down to a 15-Mb interval (Supplementary Fig. 25) and fine mapping led to the delineation of a 1.33 Mb candidate interval (chromosome 4, 18.18–19.51 Mb, ZW6) (Extended Data Fig 5a–e and Supplementary Tables 18–20, 34 and 35). Within this 1.33-Mb interval, we found that all fasciated accessions were clustered together within haplotype 5 (Extended Data Fig. 5f); however, accession JI1713, also in haplotype 5, usually is not fasciated (see below). Analysis of each gene within this interval showed that only one gene, Psat04G0031700, co-segregated with fasciation. All fasciated accessions are clustered into Hap 3 of this gene, which is characterized by a 5-bp deletion in exon 2, creating a frameshift and premature stop codon, rendering the protein non-functional and explaining fasciation in fa lines (Extended Data Fig. 5g,h and Supplementary Table 36). This gene encodes a cell membrane-localized senescence-associated receptor-like kinase, a class of CLAVATA3 INSENSITIVE RECEPTOR KINASES (CIK) signalling (co-)receptor kinases, known for their role in maintaining the structure of the shoot apical meristem[43] (designated as PsCIK2/3; Supplementary Fig. 26). Field phenotyping and microscopic observations of fasciated versus wild-type plants showed that the bunched apical flowers of the mutant are borne on a wider stem with additional vascular strands, derived from a broadened apical meristem (Extended Data Fig. 6a–e). Comparative transcriptome analysis in Caméor revealed high expression of PsCIK2/3, CLV3 and PsCLV2 in the stem (Extended Data Fig. 6f and Supplementary Tables 30 and 31). PsCLV1, also expressed in stems, showed its highest expression in root, whereas PsWUS is uniquely expressed in apical bud. RT–qPCR analysis showed that PsCIK2/3 expression was significantly downregulated in both the apical bud and stem of the mutant line (JI0814, fa/fa) compared with a wild-type line (Caméor), whereas PsCLV3 was significantly ‘upregulated’ in the apical bud of the mutant line (Extended Data Fig. 6f–h). The ‘upregulation’ likely reflects the enlarged size of the fasciated apical meristem, although further investigation in pea is needed to clarify this point. Interactions between PsCIK2/3 and both PsCLV1 and PsCLV2 (Extended Data Fig. 6i,j) were revealed by yeast two-hybrid assays. We hypothesize that PsCIK2/3 functions within the conserved meristem homeostasis CLV3-WUS genetic regulatory pathway (Supplementary Fig. 27), regulating shoot apex development and meristem structure maintenance[44].

A second minor GWAS signal was detected on chromosome 6, a region that was not previously associated with fasciation (Fig. 2). This genomic region was also identified in our bi-parental mapping populations (Extended Data Fig. 7a). In the JI2822 (Fa) × JI0816 (fa) F_2 population, we observed that out of 397 individuals scored, 32 exhibited a wild-type phenotype but carried the recessive allele at fa (Supplementary Tables 18–20), which is consistent with the GWAS and BSA studies. This suggests a model whereby the recessive allele of a gene in this region of chr6LGII masks the fasciated phenotype. Accordingly, we designated this enigmatic second locus as modifier of fa (mfa). In this model, individuals that are recessive for both loci—the fa/fa mfa/mfa genotype—have a wild-type appearance (Extended Data Fig. 7b,c). This may also explain why some accessions, such as JI1713, that carry the 5 bp deletion in Psat04G0031700 (PsCIK2/3) usually are not fasciated, incidentally accounting for the secondary GWAS peak. Previous studies have highlighted complexity in the segregation of fasciation, with reports of both reversals of dominance and 2-factor segregation ratios (15:1), rather than the expected 1-factor segregation ratio (3:1), in F_2 populations for some crosses[45]. These unusual features may, in part, be explained by the previously unrecognized gene Mfa. The nature of Mfa remains to be determined, but it resides within the interval ZW6 chr. 6:244,689,457-253,701,016 identified in this study.

In the F_3 generation, the Fa–Mfa genetic model successfully explains the observed phenotypes on the basis of the genetic marker data (Extended Data Fig. 7). The fa/fa mfa/mfa genotypes can appear wild type, on the basis of the position of lateral inflorescence, but may also exhibit some features of fasciation, demonstrating an incomplete penetrance or variable expressivity of mfa, which requires further investigation. We propose that mfa/mfa homozygotes either delay or prevent the formation of structures that would cause a fa/fa plant to be scored as a fasciated phenotype (Supplementary Notes).

Complex and quantitative traits

It has been argued that Mendel’s motivation in studying inheritance was related to an applied plant breeding programme[46]. To connect further the traits and genes discovered in pea with their agronomic interests and breeding values, we analysed 72 additional agriculturally relevant traits, including seed, pod, flower, leaf, root and plant architecture characteristics measured within our Pisum diversity panel (Extended Data Fig. 8a–c and Supplementary Table 37). A comprehensive GWAS established hundreds of significant marker–trait associations (Supplementary Table 38) including 14 previously cloned genes (Fig. 4a and Supplementary Tables 39 and 40) and determined the physical locations of 22 loci that were previously known from classical genetics to within an average genomic interval of 12 Mb. In addition to the four newly characterized genes associated with three of Mendel’s pea traits, our study uncovered dozens of potentially important new loci, many with breeding implications. For example, the SDN locus (seed number per pod) and SDY locus (seed total weight per plant) related to yield components (Extended Data Fig. 8g,i and Supplementary Table 39). The organ size locus (Os1), which controls pod width and seed weight, is validated below. These results demonstrate the high quality of our dataset, and the robustness of the association genomics analyses, laying a solid foundation for future functional elucidation of pea traits and their application in breeding programs.

Fig. 4: A Genome–phenome association map for the identification of genetic loci that confer agronomic traits.

a, Summary of the most significant trait–marker associations underlying a variety of agronomic traits presented as a combined Manhattan plot. Gene symbols shown in a circle correspond to Mendel’s loci. b, Manhattan plot of GWAS data for seed protein content, showing a peak overlapped with the R gene locus. c, Manhattan plot of GWAS data for the presence or absence of axial ring pigmentation, on a subset of phenotypic data excluding accessions carrying white flowers (a/a). These data were collected at Harbin (northern China, 2022). A peak at the expected genomic position of D is significantly associated with the accumulation of axillary anthocyanin, and the peak at chromosome 6 is the location of A. d, Genomic interval of D locus on chromosome 2 defined by recombinant inbred lines (RIL) mapping and GWAS analyses, further defined by bioinformatic analysis of FN mutants as a MYB gene cluster7,47,48, with the genes PsMYB104 and PsMYB106 both deleted in the d mutant line FN1218/6. The region outlined in red line indicates the approximate position of the deletion detected in FN1218/6 from mapping of sequence reads. e, Manhattan plot of GWAS data for Af/af (semi-leafless phenotype). Scale bar, 5 cm. f, Manhattan plot of GWAS data for hundred seed weight (HSW) and pod width (PW). The HSW and pod width genomic intervals span the same 8 Mb genomic region, named Organ Size 1 (PsOs1). Scale bar, 2 cm. g, Narrowed genomic interval of PsOs1 on chromosome 2 defined by two F_2 mapping populations and BSA analysis (Methods) as a 1.01-Mb region encompassing 11 protein-coding genes, of which Psat02G0011300 (marked in yellow) is the most highly expressed gene. Photographs in c,e,f show the corresponding contrasting phenotypes.

Source data

Axil ring pigmentation

In his 1866 paper, Mendel noted the pleiotropic effects of the seed coat and flower colour trait (A versus a) and specifically referred to the presence or absence of axil ring pigmentation as one of these effects. The A gene regulates the presence or absence of anthocyanin pigmentation throughout the plant and a is epistatic to D, which regulates the pattern of axil ring pigmentation[45]. Axil pigmentation patterns in pea (Supplementary Fig. 29) are reminiscent of leaf marking in Trifolium[47] and Medicago[48], which are controlled by similar MYB transcription factors.

GWAS analysis revealed two strong signals associated with axil ring pigmentation (in coloured flower lines) (Fig. 4c). One of these corresponds to A (chromosome 6), whereas the other is at the expected position of D (chromosome 2), where there is a cluster of MYB genes[49] (Supplementary Fig. 30 and Supplementary Table 41). The potential role of one of these MYB genes was investigated further by VIGS, which showed that the MYB-encoding gene Psat02G0138300 (also known as PsMYB16) affects the accumulation of the axil ring anthocyanin pigmentation (Supplementary Fig. 31). Furthermore, deletion of another two MYB genes at the same locus, PsMYB104 and PsMYB106 (ref. [37], in the induced Fast Neutron mutant line FN1218/6 resulted in the complete absence of axil ring pigmentation (Supplementary Figs. 32–34). We show that the FN1218/6 deletion is allelic to the d allele in JI0073 and JI2202 (P. abyssinicum, a taxon that lacks axil ring pigmentation) (Fig. 4d), implicating these genes as corresponding to D.

The results presented here reveal the genetic complexity of axil ring pigmentation regulated by D. There are multiple alleles of D within the MYB gene cluster, and many spontaneous conversions from one allelic form to another have been reported[45], suggesting that it is the combination of alleles at several of these MYB genes that determines the presence, absence or pattern of this pigmentation. Both a and a2 are epistatic to D, and we can postulate that the MYB genes involved in the D and d phenotypes are part of a MYB (D)–bHLH (A)–WD40 (A2) complex7,50.

Organ size

Mendel examined the segregation of traits that have clear alternative states but noted that seed size (among other traits) differed between his parental lines, although he considered that this quantitative difference was not suitable for his analyses. Seed size in pea defines some market classes, such as the ‘marrowfat’ types, which have large, irregular-shaped seeds and a high protein content. Seed size has been the subject of quantitative trait locus (QTL) analyses[51], and we have investigated this further within our diversity panel.

We found a significant locus on chromosome 2 that influences both pod width and hundred seed weight (HSW) (Fig. 4f and Supplementary Fig. 35), which is in a similar location to a previously described seed size QTL in Medicago and pea[52]. We designated this locus as PsOs1. Combining fine mapping and differential gene expression analysis, we identified Psat02G0011300, which encodes a SIAMESE-related protein (SIM or SMR), a cyclin-dependent protein kinase inhibitor (CKI), that influences cell division and enlargement during the cell cycle and consequently alters plant cell size[53], as a gene candidate for PsOs1 (Fig. 4g, Supplementary Figs. 36–38 and Supplementary Tables 42 and 43). VIGS-based functional validation, coupled with a transgenic overexpression line in Arabidopsis (Supplementary Figs. 39–42) demonstrate the key role of PsOs1 in regulating seed weight and pod width.

Architectural innovation in pea breeding

The adoption of afila types represents the most important innovation in modern pea breeding[54]. These types include the ‘leafless’ (af/af st/st) and ‘semi-leafless’ (af/af St/St) varieties[54]. Figure 4b shows a strong GWAS signal for this character at the expected position of Af (afila) at the end of chromosome 2. In addition to the five haplotypes (Hap1–5) corresponding to the wild-type phenotypes (Af), our analysis identified three deletion haplotypes (Hap6, Hap7 and Hap8), which correspond to hap_4, hap_2 and hap_3, respectively[54] (Supplementary Figs. 43 and 44), all of which remove the genes PALM1a and PALM1b, associated with the recessive allele (af). These deletion haplotypes are the most abundant and appear in similar relative proportions here and in the previous analysis[54].

The presence of multiple af haplotypes confirmed independent origins of the afila alleles. Here, we refined the deletion end points at a nucleotide level resolution for the 30 afila lines showing that these haplotypes are not further differentiated by their deletion end points; the gene content of the deleted segments is thus more precisely defined (Supplementary Tables 44 and 45). This is important because afila alleles delete different combinations of flanking genes, notably PsNaOD1, PsNaOD2 and PsNaOD3, which affect seed yield and seed weight[55].

Discussion

Despite the clarity of his 1866 paper, there is some dispute about what Mendel did in his detailed research. It has been argued that Mendel was not primarily interested in inheritance56,57, or that he had a pre-formed theory of inheritance that he sought to demonstrate, even to the extent of fabricating data to conform with his theory[58]. These views are mutually exclusive, and we reject them both46,59.

We have shown a remarkable diversity of mutational mechanisms in the genes associated with the seven pairs of contrasting traits that Mendel studied. There are several point mutations in a, one affecting the pattern of splicing and two different single nucleotide insertions affecting the reading frame, whereas le corresponds to an amino acid substitution caused by a missense mutation. There is a single nucleotide substitution that generates a premature stop codon in the CLE41 gene at the P locus, and insertion events of class I (i and v) and class II (r) transposons have been observed2,5. We have also uncovered additional novel types of variation, corresponding to DNA deletions that lead to loss of function, such as the remarkable case of gp, with a large DNA deletion upstream of ChlG, a promoter deletion in the i-2 allele, the fa allele, with a small deletion within an exon, and new alleles of a, with one or more deleted exons. An unexpected finding in this study was the existence of an intragenic suppressor allele of A that implies that the a allele was in existence long enough for this unlikely second site mutation to have occurred. This intragenic suppressor mutation corresponds to a shift in the position of an intron, which is rarely identified, even in inter-specific comparisons of many genes[60].

The biological processes that these genes represent range from variation in the activity of enzymes in primary metabolism (r, i and gp), hormone interconversion (le), transcription factors for regulation of secondary metabolism (a) and cell wall thickening (v), the regulation of cell fate by a small signalling peptide (p), and a cell membrane (co-)receptor kinase (fa). The two green-versus-yellow phenotypic differences (cotyledon or pod colour) correspond to disruption in either the final step of chlorophyll synthesis (gp) or the first step of chlorophyll degradation (i). This difference between synthesis versus degradation explains which phenotype, green or yellow, corresponds to the dominant versus recessive allele. Although the elucidation of the biochemical and regulatory mechanisms underlying these genes is beyond the scope of this study, the genomic and genetic discoveries presented here are essential for advancing our understanding of Mendel’s pea traits. For example, on the basis of the discovery of the fused aberrant transcripts arising from the NLR–CHLG genomic region, we propose that transcript stability is altered by transcriptional interference during chlorophyll synthesis or through a nonsense-mediated decay pathway, leading to an increased degradation rate of ChlG transcripts.

A longstanding question in relation to Mendel’s pea work was whether the phenotypic variation he described corresponded to rare variants of genes that explain only a minor proportion of the genetic variation for that trait. Our GWAS analyses emphatically show that this is not the case. Indeed, in one case in which genetic heterogeneity was expected (fasciation), the variation that we detected corresponded to a single genetic locus (Fa), albeit with a previously unsuspected modifier locus (Mfa). There are three caveats to this claim. The first is that the parchmentless pod trait is (as has long been known) determined by either p or v, or the combination of these two distinct and independent genetic loci. A second caveat is that for the green versus yellow cotyledon phenotype, there are clearly multiple GWAS peaks, albeit with lower significance than that of I. This probably reflects the influence of the seed maturation process on the penetrance of this phenotype, as was noted by Mendel in his 1866 paper. Finally, we observed an unusual feature of the GWAS peak corresponding to Gp, where there is a broad shoulder corresponding to most of the short arm of this chromosome. The reason for this is unknown.

This raises two general questions regarding GWAS analyses in defining genetic variation underlying traits: whether broad GWAS peaks provide sufficient resolution to identify a manageable number of candidate genes; and how the positions of significant GWAS signals correspond to previously described genetic variants. We have seen that for the seven Mendelian traits (and D), the GWAS peaks are significant, and all correspond well to the expected genetic loci. Furthermore, in our broad survey of many other agronomic traits for genotype–phenotype associations, nearly all the GWAS peaks correspond to the location of previously described genetic loci. This demonstrates that pea is an excellent model system for association genomics studies and GWAS is a suitable first step for trait–gene discovery and functional elucidation. The reliability of GWAS in pea is partly due to the fact that an unusually high proportion of pea genes are single copy[17], even though the pea genome is large and gene density is low throughout the chromosomes, maintaining a strong extended linkage disequilibrium. Presumably this is in part because of the strict inbreeding habit of pea.

We have shown how complementary approaches can narrow down these intervals to identify candidate genes. For the genes characterized in this study, GWAS intervals alone were insufficient to delineate small sets of candidate genes. Additional resources and experimental approaches are necessary, such as induced mutants and specific bi-parental mapping populations, comparative transcriptomes, and genetic functional validation. Future work will require innovative approaches and new technologies, such as long-read DNA and RNA sequencing, which could address the limitations of short-read sequencing technologies and are crucial for investigating how large-scale structural variations and transposons contribute to phenotypic changes. Furthermore, a mature genetic transformation system and targeted gene editing in pea are urgently needed. These tools would enable a detailed examination of the biochemical genetics underlying complex mutations, such as the aberrant transcripts produced at the gp locus, the MYB gene clusters at the D locus, and the LTR retrotransposon insertion upstream of the gene encoding the transcription factor MYB26 (V locus).

We leveraged the rich reservoir of genetic diversity from a global Pisum diversity panel and established a high-quality genomic and phenotypic variation map. The large numbers of genotype–phenotype associations that we have found represent the beginning of a new phase of systematic trait dissection at the molecular level in pea. This genotype-to-phenotype strategy can be expanded, with the identified genes and alleles projected into other crops, particularly legumes. This study is essential for pea basic research, education in biology and genetics, and breeding practices.

Methods

Plant materials and methods

Germplasm panel

A total of 697 accessions, maximizing genetic diversity, were selected from the JI Pisum Germplasm Collection for this study (Supplementary Table 1 and https://www.seedstor.ac.uk/). These germplasm accessions were introduced in 2019 to the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, China, where they are grown and investigated annually.

DNA extraction for whole-genome resequencing

Genomic DNA was extracted from approximately 50 mg leaf tissue of 3-week-old seedlings. Extraction used the oKtopure system (LGC Biosearch Technology) following tissue desiccation with silica for 48 h. A bespoke protocol was used with the following volumes per sample: 250 µl lysis buffer, 170 µl binding buffer, 20 µl sbeadexTM suspension, 300 µl PN1 wash buffer, 300 µl PN2 wash buffer, 300 µl PN2 wash buffer (×3 wash cycles) and using 75 µl final elution buffer. For each accession, a minimum of 6 μg of genomic DNA was used to construct a 150-bp paired-end sequencing library with an insert size of 500 bp, following the manufacturer’s protocols (employing PCR-free methods), which was subsequently sequenced on the DNBSEQ Platform at BGI-Shenzhen resulting in ~80 Gb clean reads with a coverage of ~20× for each accession.

Phenotyping

DNA was extracted from a single plant whose seed was bulked up for progeny phenotyping. The diversity panel was planted in three different sites, Norwich, UK (52.62° N, 1.28° E), Shenzhen (Southern China, 22.61° N, 114.51° E) and Harbin (northern China, 45.86° N, 126.83° E). In China, four rounds of phenotyping were conducted. Specific subsets of accessions and some F_2 populations were grown indoors in the greenhouse of Shenzhen Agricultural Field Farm, with 16 h of light/8 h of darkness. Phenotypes collected at the three stations (2020–2023) and a historical JIC phenotype dataset were curated in Seedstor (https://www.seedstor.ac.uk/). In Shenzhen, peas were planted in winter (October) and harvested in March the following year, whereas in Norwich and Harbin they were planted in spring (March to April) and harvested in August to October of the same year.

For the phenotyping of pod colour (green versus yellow-podded lines), a field trial of three 1 m^2 microplots of 100 seeds each was sown in spring 2023, in which a 1:1 ratio of BC6 S3 Gp/Gp and gp/gp seeds (selfed seed of S2 homozygotes) were mixed and sown at random in each plot. At the pod filling stage, the Gp plants were tagged and at plot harvest seed was collected from individual plants to determine the yield of Gp and gp homozygotes. Seeds were weighed and counted on a Data Count R25+ machine (https://data-technologies.com/). Pod length and width were measured on 25 randomly selected pods. For the phenotyping of organ size, pod width (PW) and HSW were measured in mature pods of the F_2 and F2:3 populations after harvest. In the F_2 populations, PW was assessed using 15 representative pods, divided into 3 groups of 5, with the total width of each group measured sequentially. For the F2:3 populations, PW was determined using 5 representative pods, with their total width measured in a similar manner. HSW was calculated by randomly weighing 100 seeds from each accession and repeating the process 3 times to obtain an average weight for each accession. Other more specific phenotypes were collected as described in Supplementary Table 37 and in line with published descriptors (https://www.seedstor.ac.uk/search-phenotypes.php).

Construction of the pea genomic variation map

Read mapping, SNP calling and SNP annotation

The trimmed clean reads of each accession were aligned against the reference genome of pea (P. sativum) cultivar, ZW6 (ref. [18]) and Caméor v.1.0 (ref. [17]), using BWA-MEM (v.0.7.17) with default parameters[63]. Unmapped, non-unique and duplicated reads were filtered out using SAMtools64,65 (v.1.9) and Picard (v.2.20.3-SNAPSHOT) before variants were called by a standard pipeline of Genome Analysis Toolkit (GATK[65] v.4.1.2) and Sentieon[66] (v.202112.01). SNPs were further filtered to remove low-quality variants defined as (1) SNPs with more than two alleles; (2) SNPs with QD < 2.0, FS > 60.0, MQ < 40.0, SOR > 3.0, MQRankSum < −12.5, ReadPosRankSum < −8.0; (3) SNPs with observed heterozygosity (Hobs) exceeding the maximum calculated value (Hobs_max) based on the inbreeding coefficient (F), where F was calculated as 1 − (Hobs/Hexp), with Hexp defined as 2p(1 − p) using the frequency of the non-reference allele, and Hobs_max was determined as 10 × (1 − Fmedian) × Hexp for variants with F > 0 and minor allele frequency (MAF) > 0.05; (4) SNPs with missing rate >20% and MAF < 0.01. SnpEff[67] (v.4.3t) was used to annotate the SNPs, and functional significance was then categorized on the basis of their positions with respect to genes (intergenic regions, exons, introns, splicing sites, untranslated regions, upstream and downstream regions) and mutation consequences (missense, start codon gain or loss, stop codon gain or loss and splicing mutations).

Identification of indels, gene PAV and gene CNVs, and SV

Small insertion–deletion mutants (indels; ≤50 bp) were called using GATK (v.4.1.2) and filtered following the criteria: QD < 2.0 || low_QD || FS > 200.0 || high_FS || ReadPosRankSum < −20.0 || low_ReadPosRankSum before they were annotated using SnpEff (v.4.3t). Read depth variation from read mapping analysis was used to identify gene presence and absence variation (PAV) and gene copy number variation (CNV) through normalization and correction in statistical analyses, following five steps: (1), mapped read depth at each gene was counted for each accession; (2), a correction for read depth variation (RDV) was applied, accounting for highly similar genes through all-versus-all coding sequence (CDS) alignment using BLASTN. Recently duplicated genes were collapsed into representative genes to minimize depth bias, which were further normalized by dividing the corrected read depth of the gene by the average sequencing depth of the accession; (3) the distribution of read depth versus GC content was used to correct read depth bias for each gene resulting from differential GC contents; (4), read depth variation was corrected for genomic regions with insertions or deletions in the genome reference; (5), subspecies-unique and shared CNVs were characterized by calculating the number of accessions with different copy numbers for each gene within each subspecies.

Different categories of structural variants (SVs: duplication, inversion, translocation and large-scale deletion or insertion) were detected on the basis of read mapping (read depth and read pair relationships) on PCR-duplicate-marked bam files using Delly (v 0.8.7) with default parameters; a summary of SVs identified is given in Supplementary Table 11.

Linkage disequilibrium analysis and pea haplotype map

A two-step LD pruning process was implemented to generate a high-quality core SNP dataset for the construction of a haplotype map[68]. Initially, SNPs were pruned on the basis of linkage disequilibrium (LD) using PLINK[69], with a window size of 10 kb, a window step of one SNP, and an r^2 threshold of 0.8. A second round of LD pruning was conducted with a window size of 50 kb, a window step of one SNP, and the same r^2 threshold of 0.8. For population LD-based haplotype analysis, the filtered SNPs were phased using Beagle (v.21Apr21.304)[70]. Subsequently, haplotype blocks were delineated utilizing PLINK with specific parameters (--blocks no-pheno-req --blocks-max-kb 1000 --geno 0.1 --blocks-min-maf 0.05). To merge adjacent blocks maintaining significant LD, D’ statistic values were calculated between all SNPs of consecutive blocks. If the lower quartile (Q1) exceeded 0.98, the adjacent blocks were merged. After filtering for the inbreeding coefficient, HAPPE[71] was employed to identify haplotype clusters (haplogroups) for each block.

Construction of mapping and validation populations

JI2822 × JI0816 F_2 population

Lines JI0816 and JI2822 (Supplementary Table 18), both of short stature, are maintained in the John Innes Pisum germplasm collection (https://www.seedstor.ac.uk/). JI0816, also known as WBH 1185, has pink flowers, a fasciated stem and yellow pods lacking pod parchment, corresponding to the mutant alleles b, fa, gp and p, respectively. JI2822, a recombinant inbred line derived from the cross JI0015 × JI0399, is wild type at these four loci. JI0015 and JI0816 share the gp allele, indicating that these two lines had a common parent; therefore segments of the genetic map are devoid of segregating alleles. 1,000 F_2 seeds from 9 F_1 plants (JI2822 × JI0816) were sown at the JIC field station in spring 2022. DNA preps from 942 plants were prepared from individual leaflets using the Qiagen DNeasy protocol (https://www.qiagen.com). Of these, 405 were genotyped using an Axiom SNP array as described[49]. The phenotypic and genotypic data are available in Supplementary Tables 18–20, and the sequences corresponding to the Axiom markers are available in supplementary table 3 of Ellis et al.[49].

JI0015 × JI0399 and JI2822 × JI2833

Three populations have been used for mapping Gp. The first to be used was the previously described recombinant inbred population JI0015 × JI0399 (Supplementary Table 21), later genotyped by Neogen, using an Infinium array as described previously[51]. The second was an F_2 population derived from a cross between two of these RILs (JI2822 Gp/Gp and JI2833 gp/gp), which was screened using PCR for markers already mapped in JI0015 × JI0399 in order to identify informative individuals (Supplementary Table 22). These, together with selected RILs with informative recombination events were genotyped with Axiom markers as described elsewhere49,65. Gp also segregates in the JI2822 × JI0816 F_2 population as described above. The marker data are available in the supplementary file: Gp mapping in JI0015 × JI0399 (Supplementary Tables 21 and 22).

Other F_2 mapping populations and BSA

We selected parental lines with contrasting pairs of traits to map genetic loci of interest in F_2 populations using mapping by sequencing[72] of bulked segregants. For genetic loci controlling uncharacterized Mendel traits: flower position (axial versus terminal), pod colour (yellow versus green), and pod shape (inflated versus constricted), crosses were made between Caméor (axial) × JI0814 (fasciated) and JI1995 (green pod) × JI2366 (yellow pod). F_2 populations for the P/V loci (pod shape) were derived from the cross between JI0074 (P/P v/v) as the male parent and JI1995 (P/P V/V) as the female parent, and the cross between JI2822 × JI0816. F_2 populations for the D locus (one (Dco) or two (Dw) axial rings of anthocyanin pigmentation) were derived from three crosses, with JI0191 (Dw), JI0794 (Dw) and JI1669 (Dw) as male parents and JI0328 (Dco) as the female parent. F_2 populations for the Fn/Fna loci (flower number per node, fpn) were derived from four crosses, with JI0441 (1fpn), JI2410 (3fpn), JI0745 (2fpn) and JI0746 (3fpn) as male parents and JI1995 (2fpn) as the female parent. The marker and BSA analysis of the F_2 population is as described[36].

Approximately 300 plants from the F_2 population of each of these crosses were planted in Shenzhen, China. Wild-type and mutant bulked DNA samples were prepared by mixing equal amounts of DNA from 30 accessions with the dominant and recessive phenotypes, respectively. DNA was isolated from fresh leaves using the CTAB method[73]. 50× depth genome sequences for each of the parents and the bulked samples were generated. Short reads were aligned against the ZW6 reference genome using BWA-MEM (v.0.7.17) and SNPs were identified using Samtools (v.1.9). The variation dataset was analysed using the G’s value method of the QTLseqr package (v.0.7.5.2).

Genetic mapping of Gp

Green versus yellow pod colour segregates in the recombinant inbred (RIL) population derived from the cross between JI0015 (gp/gp) and JI0399 (Gp/Gp). The JI0015 × JI0399 RIL population comprises 90 recombinant inbred lines, which, together with their parents, were genotyped using an Infinium array (Neogen) that detected 13,204 biallelic SNPs. This enabled us to position 5,209 PsCam markers on a genetic map (JI0015 × JI0399) and place Gp between the markers PsCam005046 and PsCam056084 (and their co-segregating markers). Additional mapping was undertaken, using an Axiom SNP array with 84,691 features[49] of selected F_2 progeny of a cross between JI2822 (Gp) and JI2833 (gp) together with RILs from JI0015 × JI0399 known to have recombination events at informative locations. JI2822 and JI2833 are both RILs from the JI0015 × JI0399 population. With respect to the ZW6 assembly[18], this placed Gp between the Axiom markers AX-183865165 (chr. 2:320968993) and AX-183571028 (chr. 3:325580858) (JI0015 × JI0399). Analysis of an F_2 population derived from crosses between JI2822 (Gp) and JI0816 (gp) placed Gp between the Axiom markers AX-183571050 (chr. 3:321020350) and AX-183879077, (chr. 3:324762848; Supplementary Table 18).

We performed different association genomics analysis for pod colours, including the SNP-based GWAS, LD-based haplotype GWAS, kmer-derived IBS-based haplotype GWAS, and the SV-based GWAS (Supplementary Fig. 14), all resulting in consistent and significant single GWAS peaks for pod colour located in the expected position of Gp, as seen in Manhattan plots (Supplementary Fig. 14).

Allelism tests for gp

Crosses were made between pairs of yellow-podded lines in the JIC germplasm collection (Supplementary Table 18). Seed and vegetative phenotypes were used to identify F_1 progeny plants, and those accessions allelic, or non-allelic, to gp were identified by their yellow or green pod colour, respectively.

Near-isogenic lines for Gp versus gp

The JI0015 gp allele was introgressed into the Caméor background by sequential back-crossing and F_1 progeny testing using a codominant PCR marker assay with one forward (25994_F) and two reverse (25994_15R and 25994_399R) primers (Supplementary Table 18). Gp (596 bp) and gp (688 bp) alleles were distinguished in a 35 cycle, 10s–30s–60s Touchdown PCR reaction that reduces the initial 62 °C annealing temperature to 50 °C in the first 10 cycles.

Marker development and QTL mapping for PsOs1

The organ size-related quantitative trait locus (PsOs1) was fine-mapped using 21 Kompetitive Allele Specific PCR (KASP) markers for SNPs distinguishing accessions JI0074 and JI1995 after whole-genome resequencing in the candidate region. Each KASP marker was designed with two allele-specific forward primers (Supplementary Table 47) and one common reverse primer, on the basis of 200-bp sequences upstream and downstream of target genic SNPs, following the standards of LGC Biosearch Technologies. The genetic linkage map was constructed using JoinMap v.4.0 software. Windows QTL Cartographer v.2.5 software facilitated inclusive composite interval mapping (ICIM) for identifying and analysing QTLs. A logarithm of odds (LOD) score of ≥3.0 was deemed indicative of a QTL.

Genome-wide association study

The multi-location and multi-season phenotypic dataset was used to perform GWASs with the SNP matrix using GEMMA (v.0.98.1)[74], and employing the following parameters (gemma-0.98.1-linux-static -miss 0.9 –gk -o kinship.txt and gemma-0.98.1-linux-static -miss 0.9 -lmm -k kinship.txt). The structural variation matrix was used to test for association with phenotypic variation for each of the selected traits using the same parameters as above. The haplotype map was used to test for association with phenotypic variation for each of the selected traits using RTM-GWAS[75] with the following parameters (rtm-gwas-gsc –vcf in.vcf –out out.matrix and rtm-gwas-assoc –vcf in.vcf --covar out.matrix.evec --no-gxe). We used GEMMA’s Wald tests and directly visualized the resulting P values as –log10-transformed values in Manhattan plots. In-house R scripts were employed for data plotting. To identify SNPs of interest, we applied a Bonferroni correction to the significance threshold (α = 0.1) based on the total number of tested variants (9,214,461 SNPs). This yielded a threshold of –log10(0.1/9,214,461), and any SNP surpassing this cutoff was considered noteworthy and highlighted in the Manhattan plots.

Orthologues and gene family analysis

Phylogenetic analyses were conducted on key gene families in this study, such as MYB, CLE and CIK/SERK, following a consistent workflow. Relevant A. thaliana orthologous genes containing the required domains were retrieved from TAIR (https://www.arabidopsis.org), and profile hidden Markov models (HMMs) were constructed using HMMER (v.3.1b1) on the basis of multiple sequence alignments generated by MAFFT (v.7.475). These HMMs were then employed to identify putative homologues in the pea (P. sativum) ZW6 genome. Multiple sequence alignments for each family were trimmed with trimAl (v.1.5.rev.0), and the best-fit amino acid substitution models were selected using ModelTest-NG (v.0.1.7). Phylogenetic trees were constructed by IQ-TREE (v.2.1.2) with 1,000 ultrafast bootstrap replicates. For synteny analysis for each gene family, we used OrthoFinder (v.2.5.4) to identify orthologous clusters among pea and related legumes (for example, Vicia sativa, Medicago truncatula, Cicer arietinum, Lotus japonicus, Vigna radiata, Phaseolus vulgaris and Glycine max), and visualized collinearity blocks with JCVI (v.1.2.7).

Gene functional validation experiments

Fast neutron mutants

Several Fast Neutron mutants from a population described by Domoney et al.[76], were included in this project. These were: FN1453/1sil-like; FN1091/4 lacking axil ring pigmentation, allelic to d; FN1218/6 lacking axil ring pigmentation, allelic to d; FN2026/7coch2 candidate; FN2073/5 lacking axil ring pigmentation, not allelic to d; and FN2076/5VicA FN deletion line.

Crosses were made between pairs of lines lacking axil ring pigmentation (Supplementary Fig. 32) to test for complementation. Where possible, vegetative phenotypes were used to identify F_1 progeny plants, and those accessions allelic, or non-allelic, to d were identified by the absence, or presence of pigmented axil rings, respectively.

Complementation test

A reverse genetics screen for the ChlG gene in pea was carried out in an ethane methane sulfonate-mutagenised targeting induced local lesions in genomes (TILLING) population in background Caméor[26]. Line 411.1, with a G>A mutation 1,900 bp after the ATG, resulting in a W121* nonsense mutation, was identified. Eight M4 seeds were sown and seedlings were genotyped with a cleaved amplified polymorphic sequence (CAPS) marker (Supplementary Table 47). No seedlings were homozygous mutants (signified by a single undigested 1,125-bp band), 6 were heterozygous (signified by 3 bands of sizes 1,125 bp, 699 bp and 426 bp) and 2 were homozygous wild type (signified by 2 bands of sizes 699 bp and 426 bp). A complementation test was carried out by crossing heterozygous seedlings with a homozygous JI0015 gp mutant (13 crosses with male JI0015 and F_1 identified by long internodes, and 3 crosses with female JI0015 and F_1 identified by yellow cotyledons). Nine out of 16 F1 progeny plants had yellow pods, indicating non-complementation.

Virus-induced gene silencing

VIGS in peas was performed using a published methodology as described previously[77]. To target genes of interest, a 200–500 bp fragment from the CDS region of each gene were amplified. The primers for VIGS constructs, including VIGS-PsChlG, VIGS-PsOs1, VIGS-PsMYB26 and PsMYB16, are provided in Supplementary Table 47. SpeI/XbaI and EcoRI were used to linearize the pCAPE2 vector, which was kindly provided by Li et al.[78], and corresponding fragments of gene targets were ligated into the vector to construct the vectors for VIGS assays. For VIGS-PsChlG, the negative control vector, pCAPE2-Con, was constructed in the same way by replacing the PsChlG fragment in pCAPE2-PsChlG with a 529-bp insert derived from a cDNA fragment of Bean Yellow Mosaic Virus (GenBank accession AJ622899). The positive control vector, pCAPE2-PDS, targeting the phytoene desaturase gene, was also provided by Li et al.[78]. These vectors were transferred into Agrobacterium tumefaciens (GV3101) (Shanghai Weidi Biotechnology) and VIGS assays carried out following the protocol described by Constantin et al.[79]. In brief, Agrobacterium strains carrying these vectors were shaken separately until OD_600 = 1.2, followed by the collection and resuspension of the bacteria in injection buffer (NaCl: 10 mM, CaCl_2: 10 mM, acetosyringone: 0.1 mM) to a concentration of OD_600 = 1.2. After resting for 2–3 h, the solution of pCAPE2-target gene, pCAPE2-PDS (positive control), and pCAPE2-Con (negative control) was mixed with pCAPE1, separately, in equal proportions, and injected into 10-day-old compound leaves of the acceptant lines (Yunnan2070 or JI1995). Specifically, pCAPE1 and pCAPE2 are plasmid vectors used to induce gene silencing in plants such as M. truncatula and P. sativum. After 24 h of darkness, they were transferred to long day conditions. New leaves of positive control plants bleached in about 10 days, indicating successful silencing of PDS. VIGS was employed with the same procedure for PsMYB16 gene within the D locus and VIGS-MYB26 for the V candidate gene. For PsOs1, which is described in detail below, all the gene-specific primers used for VIGS constructs are listed in Supplementary Table 47.

Transformation, gene overexpression and silencing of PsOs1

The PsOs1 coding sequence of JI0074 was amplified (primers listed in Supplementary Table 47) and integrated into the pCAMBIA1305 vector, resulting in the pCAMBIA1305-PsOs1JI0074 construct. The plasmid was then introduced into A. tumefaciens GV3101, which was subsequently employed to transform A. thaliana (Col-0) via the floral dip technique. T_3 generation homozygous transgenic Arabidopsis lines were selected for measurement of thousand-seed weight and the dimensions of elongated siliques.

GUS staining, GFP fluorescence observations and flow cytometry

The pCAMBIA1305-PsOs1JI1995 vector was constructed using the same methodology, with primers detailed in Supplementary Table 47. Both vectors, pCAMBIA1305-PsOs1JI1995 and pCAMBIA1305-PsOs1JI0074, were introduced into the A. tumefaciens strain GV3101. In these experiments, H2B-mCherry served as a nucleus marker. The agrobacteria were resuspended and infiltrated into Nicotiana benthamiana leaf epidermal cells using an infiltration buffer consisting of 10 mM MES (pH 5.6), 10 mM MgCl_2, and 150 μM acetosyringone, at an OD_600 of 0.8. Fluorescence was observed 48 h after infiltration using a confocal laser-scanning microscope.

To compare the promoter activities of JI0074 and JI1995 alleles of PsOs1, we cloned sequences 3,000-bp upstream of the coding region and inserted them into pCAMBIA1300-GUS, resulting in the constructs ProJI0074-GUS and ProJI1995-GUS. These were expressed in Nicotiana tabacum leaves and subsequently stained using a GUS Staining Kit (Coolaber Biotech). GUS activity was quantified using the GUS Gene Quantitative Detection Kit (Coolaber Biotech). For a detailed examination of PsOs1 expression patterns in Arabidopsis, various Arabidopsis tissues were sampled from ProJI0074-GUS transgenic plants. After ethanol decolourization, observations and photographs were taken under a microscope. Details of the primers used are provided in Supplementary Table 47.

Intact nuclei from pea pods were isolated using LB01 lysis buffer (Coolaber Biotech), followed by RNA removal and subsequent propidium iodide staining. The nuclei were then quantified using a CytoFLEX flow cytometer. A minimum of 20,000 nuclei were counted for each sample, and each experiment was replicated at least three times. Data analysis was conducted using FLOWJO software, and representative images were presented. The endoreduplication index (EI) was calculated using the formula: EI = [(0 × percentage of 2C nuclei) + (1 × percentage of 4C nuclei) + (2 × percentage of 8C muclei) + (3 × percentage of 16C nuclei) + (4 × percentage of 32C nuclei)]/100.

Yeast two-hybrid experiment

Yeast two-hybrid assays were conducted according to the protocols outlined in the Yeast Protocols Handbook (Clontech). The CDS of PsCIK2/3 was cloned into the bait plasmid pBT3-SUC, while the CDS of PsCLV1 or PsCLV2 was cloned into the prey plasmid pPR3-N. The primer sequences used for cloning are provided in Supplementary Table 47. These plasmids were co-transformed into the yeast strain NMY51 in different combinations. Transformants were initially screened on SD/-Trp/-Leu medium to confirm successful co-transformation. Interaction assays were then performed on SD/-Trp/-Leu/-His/-Ade medium containing the chromogenic substrate X-α-Gal at 30 °C to detect protein-protein interactions.

Anatomical studies and TEM

Confocal images were collected with Leica TCS SP8 confocal laser-scanning microscope (Leica). After sampling, the shoot apices of Caméor and fa mutant line JI0814, the young leaves (gp/gp, JI2366; Gp/Gp JI0817), and the pod walls of JI0074 and JI1995 were immediately preserved in formaldehyde/alcohol/acetic acid fixative. Paraffin sectioning was performed following established methodologies. Staining was conducted using safranin and fast green (JI0074 and JI1995) and toluidine blue (Caméor and JI0814). Prepared slides were scanned using a NanoZoomer, and cell quantification was carried out using NDP.view2 software. For pods, the resin block was sliced at 60–80 nm on an ultrathin slicer, and the slices were picked up on a 150-mesh copper mesh. The copper mesh was stained with a 2% uranyl acetate saturated alcohol solution in the dark for 8 min; washed 3 times with 70% alcohol; washed 3 times with ultrapure water; stained with a 2.6% lead citrate solution in the dark for 8 min; washed 3 times with ultrapure water, and slightly dried with filter paper. The copper mesh sections were placed in a copper mesh box and dried overnight at room temperature. The observation was under a transmission electron microscope and images collected for analysis.

For TEM studies, pea leaflets and pods (18 days after flowering) were removed from BC3 S2 gp/gp and Gp/Gp plants, after 9 h of daylight. Tissue (1 mm^2) pieces were placed in a solution of 2.5% (v/v) glutaraldehyde in 0.05 M sodium cacodylate, pH 7.3 for fixation. Samples were left overnight at room temperature and then processed for embedding (Leica EM TP embedding machine) by washing out the fixative with three successive 15-min washes in 0.05 M sodium cacodylate, followed by fixation in 1% (w/v) OsO_4 in 0.05 M sodium cacodylate for 2 h at room temperature. After three 15 min washes in distilled water, samples were dehydrated in an ethanol series (30%, 50%, 70%, 95% and two changes of 100% ethanol), then infiltrated with LR White resin (London Resin Company) by successive changes of resin:ethanol mixes at room temperature (1:1 for 1 h, 2:1 for 1 h, 3:1 for 1 h, 100% resin for 1 h, then 100% resin for 16 h, and 100% resin for a further 8 h). Samples were polymerized in LR White resin at 60 °C for 16 h, then sectioned with a diamond knife (Leica UC7 ultramicrotome). Ultrathin sections (approximately 90 nm) were placed on 200 mesh formvar and carbon-coated copper grids (Agar Scientific). Sections were stained with 2% (w/v) uranyl acetate for 1 h and 1% (w/v) lead citrate for 1 min, washed in distilled water and air dried. Grids were viewed in a FEI Talos 200 C transmission electron microscope (FEI) at 200 kV and imaged using a Gatan OneView 4K × 4K digital camera (Gatan) to record DM4 files.

In situ hybridization

Tissues were rinsed with PBS and immediately placed in the in situ hybridization fixative solution for more than 12 h. Paraffin section preparation and in situ hybridization of the probes were performed according to standard protocols[80]. The sequences of the digoxigenin (DIG)-labelled antisense riboprobes used for in situ hybridization are provided in Supplementary Table 47. The hybridization signal was detected with an alkaline phosphatase-conjugated anti-DIG antibody (200-052-156, Jackson ImmunoResearch). Finally, images were obtained and analysed using a Pannoramic MIDI digital slide scanner (3DHISTECH).

RNA-seq, iso-seq and gene expression

RNA extraction and pea transcriptome

We built a transcriptome atlas from the reference line Caméor (Supplementary Table 30), and selected various accessions that display the contrasting pairs of traits studied here (Supplementary Table 31). In China, plant tissues (seed, root, nodule, leaflet, stem, flower, pod, stipule, tendril and apical bud) at different development stages (seedling, flowering and podding) were collected and fixed in Trizol before RNA extraction. Tissues were ground in liquid nitrogen and the FastPure Universal Plant Total RNA Isolation Kit (Vazyme Biotech) was used to extract total RNA, the quality of which was assessed by gel electrophoresis. For each sample, we performed short-read RNA-sequencing using the DNBSEQ Platform at BGI Group to generate 6–8 Gb raw RNA reads for each accession.

At JIC, RNA was prepared from young developing pods (flat pod stage, ~60–70 mm in length) of each of the parental and RI lines derived from the cross between JI0015 (gp/gp) and JI0399 (Gp/Gp). Developing seeds were removed from the pods, which were then rapidly frozen in liquid nitrogen. High-quality RNA lacking genomic DNA was extracted from 97 individual pod samples, using a Spectrum Plant Total RNA Kit (Sigma-Aldrich), and used for PCR with reverse transcription and RNA-seq experiments focussed on the identification and characterisation of gene candidates for gp. For the latter analysis, green-podded and yellow-podded RILs (95 in total) were assigned to three groups for each phenotype, ensuring that lines with contrasting plant phenotypes (e.g. plant height) were randomly distributed among the replicate groups (G1, G2 and G3 for green-podded RILs; Y1, Y2 and Y3 for yellow-podded RILs, with 15–17 RILs per pool). Equal amounts of RNA from every line within a group were pooled. RNA-seq (Illumina HiSeq4000) and initial bioinformatic analyses were carried out by the Earlham Institute.

We performed Iso-seq sequencing for a subset of accessions for the target organ at specific developmental stages. We used the Iso-Seq (v.4.0.0) pipeline to process PacBio SMRT Cell subreads and generate high-quality, full-length transcripts. First, subreads from each SMRT Cell were processed with ccs (v.3.4.1) to produce one circular consensus sequence per zero-mode waveguide, applying a minimum read quality of 0.9. Next, primer removal and demultiplexing were performed with lima (v.2.9.0) in Iso-Seq mode, removing unwanted primer combinations and orienting reads from 5′ to 3′. Full-length reads were then refined by trimming poly(A) tails and removing concatemers. When multiple SMRT cells were sequenced, the resulting full-length non-concatemer (FLNC) BAM files were merged before clustering. Iso-Seq cluster (v.4.0.0) was applied to produce polished consensus transcripts, partitioned into high-quality (HQ) and low-quality (LQ) sets on the basis of predicted accuracy. The final consensus transcripts were aligned to the ZW6 and JI0074 reference genomes using pbmm2 (v.1.14.99). Last, iso-seq collapse was used to collapse redundant isoforms and generate GFF files, which were converted to GFF3 with gffread (0.12.7) for downstream analyses.

RT–qPCR

RT–qPCR was conducted to analyse gene expression. Total RNA was extracted using the FastPure Plant Total RNA Isolation Kit (Vazyme Biotech) following the manufacturer’s instructions, including an on-column DNase I digestion step to remove genomic DNA. Subsequently, 1 μg of RNA was used for cDNA synthesis with the All-in-One First-Strand cDNA Synthesis SuperMix for qPCR kit (TransGen Biotech). Green qPCR SuperMix kit (TransGen Biotech) was used for amplification on a CFX384TM Real-Time System (Bio-Rad). The method of collecting plant material for the detection of P/PsCLE41 and V/PsMYB26 was as follows: (1) pods were collected 10 days after flowering. After removing the seeds, the pods were cut into pieces and frozen in liquid nitrogen; (2) apical buds were collected 14 days after emergence and, after removing the extra young leaves under a microscope, the buds were cut into pieces and frozen in liquid nitrogen; (3) stems were collected 14 days after emergence at the third node from the top of the stem, cut into pieces and frozen in liquid nitrogen; (4) seed cotyledons were collected 12 days after flowering; after removing the testa, the cotyledons were cut into pieces and frozen in liquid nitrogen.

To validate the Gp transcriptional fusion and aberrant transcripts, the total RNA was reverse transcribed to cDNA using HiScript III First Strand cDNA Synthesis Kit (+gDNA wiper, Vazyme Biotech). RT–qPCR analysis was conducted using Taq Pro Universal SYBR qPCR Master Mix (Vazyme Biotech), employing specific primers, with PsACTIN serving as the internal standard. Expression levels of genes were quantified relative to the control based using the 2−ΔΔCT method. All RT–qPCR results represent the mean ± s.d. from three separate biological experiments. The primers used for RT–qPCR primers used are provided in Supplementary Table 47.

DNA methylation sequencing

Bisulfite treatment and libraries were prepared accordingly to the standard protocol of methylation library construction. PE150 sequencing was performed using Illumina NovaSeq X Plus sequencing platform 25B chip. Quality control and adapter trimming of the raw whole-genome bisulfite sequencing (WGBS) data was then performed using Trimmomatic (v.0.39). The resulting WGBS reads were mapped to the JI0074 reference genome using Bismark (v.0.23.0) and PCR duplicates were removed from the aligned reads.

Statistical methods

General statistical analysis

Statistical analyses were conducted in R software suite (v.4.2; https://www.r-project.org/) unless otherwise stated. The correlation between different traits was tested by calculating the coefficients of Pearson correlation, as well as the P values, using the cor.test package, with the method set to ‘Pearson’ for the correlation analyses between quantitative traits. Traits collected at different locations and in different years were analysed by calculating their rank correlations by setting the option ‘method’ to ‘Spearman’. The correlation between qualitative traits was assessed using the chi-square test using the ‘chisq.test’ package in R. Gene expression levels in different lines or organs under different treatments or at different developmental stages (at least three biological replicates for each sample) were analysed using DESeq2 (ref. [81]), in which the genes with a false discovery rate (Bonferroni) lower than 0.01 were defined as significantly regulated genes, unless there is an alternative explanation in a specific legend. The min–max scaling (normalization) approach was used to calculate the expression level for comparison of each gene across stages and organs by the formula: Xscaled = (X − Xmin)/(Xmax − Xmin).

PCA (main text Fig. 1) was performed on the PLINK distance matrix using an Excel add-in downloaded from RIKEN, now available at https://systemsomicslab.github.io/compms/others/main.html#Statistics.

Statistics and reproducibility

All experiments were designed with explicit consideration of statistical power and reproducibility. Each experiment was independently repeated at least three times (biological replicates) with consistent results across repetitions, including microscopy analyses used at least three samples or technical replicates per experimental condition (such as Fig. 3b,c,g and Extended Data Figs. 4a–c,e and 6b,h,i); phenotypic observations were validated across ≥3 independent growth cycles (Norwich, Shenzhen, Harbin, multiple sites and multiple years). Micrographs shown are representative of at least three independent experiments. Complete protocols and raw data supporting the conclusions are available in the Data availability and Supplementary Information. Data are presented as mean ± s.e.m., and statistical significance was determined using two-tailed Students’ t-tests (‘t.test’ package in R software v.4.2) in the analyses of the phenotypes, such as seed weight and pod width, between different accessions.

Population structure analysis

The core high-quality SNP dataset was used for population structural analyses. PCA and t-distributed stochastic neighbour embedding analyses were first performed using beta Python modules sklearn.decomposition and sklearn.manifold. ADMIXTURE[82] (version 1.3.0) was employed to analyse the population structure, with K increasing from 2 to 16. The Q value represents the estimated proportion of an individual’s genome that originates from each inferred ancestral population cluster.

Genetic differentiation (Fst) and nucleotide diversity (π) were calculated with VCFtools (version 0.1.13). Fst scores were calculated within nonoverlapping 100-kb windows and π was calculated for each individual site and averaged across the genome for each group. LD was calculated on SNP pairs within a 500-kb window using PopLDdecay[83] (version 3.31; https://github.com/BGI-shenzhen/PopLDdecay) and the decay was measured by the distance at which the Pearson’s correlation efficient (r^2) dropped to half of the maximum. Splits tree analysis of the PLINK distance matrix was performed using SplitsTree4 (ref. [61]).

Germplasm availability

All the germplasm described and used in this work is available to order from the John Innes Centre Germplasm Resources Unit (https://www.seedstor.ac.uk/). The 697 sequenced single seed descent lines derived from John Innes Pisum Germplasm accessions, were imported in 2019 to the Agricultural Genomics Institute at Shenzhen (AGIS), Chinese Academy of Agricultural Sciences (CAAS), where they have been grown and phenotyped annually since then.

Reporting summary

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

Data availability

All whole-genome sequence data, transcriptome data, and population variant map data have been deposited at the National Genomics Data Center (NGDC) Genome Sequence Archive (GSA) under BioProject accession PRJCA023166. The datasets are available under GSA accession CRA014669, CRA023374 and CRA023375. Long-term phenotype curation information is available on SeedStor (https://www.seedstor.ac.uk). Data and analyses are also available from our established portal at https://mendelpea.com/dataAvailable and https://mendelpea.com/toolkits. Source data are provided with this paper.

Code availability

Code associated with this project is available at Github: https://github.com/ShifengCHENG-Laboratory/MendelPeaG2P.

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Acknowledgements

The authors thank J. J. Doyle, M. Bennett, S. Huang, B. Lucas, D. Sanders and G. Moore for their invaluable support and comments for this project; colleagues for assistance in pea field trial and phenotyping work from experimental stations across northern and southern China; Y. Zhong and S. Liu; and L. Zhang for providing ZW1 genome assembly and annotation for comparison in this work; E. Jones and E. Crawford for plant phenotype data; M. Trick and S. Griffiths for valuable discussions at the John Innes Centre (JIC); the JIC NBI Computing Infrastructure for Science and JIC Bioinformatics groups for support in data handling and analysis; the JIC Field Trials and Horticultural Services teams for support with field and glasshouse experiments; the Molecular Genetics, Genotyping and DNA Extraction Platforms for support in experimental biology; and Bioimaging and Scientific Photography Platforms for phenotype visualization. This work was supported by the Program for Guangdong ‘ZhuJiang’ Introducing Innovative and Entrepreneurial Teams (2019ZT08N628), the National Natural Science Foundation of China (32022006), the Agricultural Science and Technology Innovation Program (CAAS-ASTIP-2021-AGIS-ZDRW202101), the Shenzhen Science and Technology Program (AGIS-ZDKY202002), the National Key Research and Development Program of China (2023YFF1000100), and the National Key R&D Program of China (grant number 2023YFA0914600) to S. Cheng, and the National Natural Science Foundation of China (32401853) to Y. Shi. The work in the UK was possible due to the long-term investment of the UK Research Infrastructure Biotechnology and Biological Sciences Research Council (UKRI-BBSRC) through Institute Strategic Programme (ISP) grants, Institute Development Grant funds, and the Germplasm Resources National Capability Programme (BBS/E/J/000PR8000) and the National Bioscience Research Infrastructure grant (BBS/E/JI/23NB0001). We also acknowledge support from UKRI-BBSRC grants BB/J004561/1, BB/W510695/1 and BBS/E/J/000PR9799, the UK Department for Environment, Food, and Rural Affairs (Defra) through the Pulse Crop Genetic Improvement Network (grants CH0103 and CH0111) and the Provision and Maintenance of the Pea Genebank to Facilitate R&D Need grant (C5515), and JIC through its Institute Strategic Fund. We acknowledge the source of the TILLING mutant used in the analysis of gp as UMR1403-INRAE-IPS2, UMR1347-Agroecologie, France and the European Union FP6 Integrated Project Grain Legumes, FOOD-CT-2004-506223.

Author information

Author notes

1. These authors contributed equally: Cong Feng, Baizhi Chen, Julie Hofer, Yan Shi, Mei Jiang

Authors and Affiliations

1. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

Cong Feng, Baizhi Chen, Yan Shi, Mei Jiang, Bo Song, Hong Cheng, Lu Lu, Luyao Wang, Cuijun Zhang, Hang Zhao, Junliang Hou, Pengfeng Li, Quan Yuan, Tian Wu, Xiaoxiao Zhao, Xiuli Li, Yuchen Sun, Zejian Huang, Zhen Wu & Shifeng Cheng

2. John Innes Centre, Norwich Research Park, Norwich, UK

Julie Hofer, Alex Howard, Carol Moreau, Chie Sawada, Eleni Vikeli, Georgios Tsanakas, Jitender Cheema, J. Elaine Barclay, Liz Sayers, Luzie Wingen, Marielle Vigouroux, Martin Vickers, Mike Ambrose, Paola Higuera-Poveda, Rebecca Spanner, Richard Horler, Roland Wouters, Smitha Chundakkad, Burkhard Steuernagel, Claire Domoney, Noel Ellis & Noam Chayut

3. INRAE UMR 1403, Institute of Plant Sciences Paris-Saclay, Gif-sur-Yvette, France

Abdel Bendahmane, Anissa Fouchal & Marion Dalmais

4. Paleogenomics Laboratory, INRAE Clermont-Auvergne-Rhône-Alpes, CS 60032, Clermont-Ferrand, France

Carol Moreau

5. INRAE UMR1347-Agroecologie pôle GEAPSI, DIJON Cedex, France

Christine LeSignor

6. EMBL-EBI, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, UK

Jitender Cheema

7. Department of Plant Pathology, University of Minnesota, St Paul, MN, USA

Rebecca Spanner

8. State Key Laboratory of Wheat Improvement, School of Advanced Agricultural Sciences and School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

Xing Wang Deng

Contributions

S. Cheng and N.C. coordinated and managed the international consortium. S. Cheng conceived the project and designed the manuscript. S. Cheng, C.F., Y. Shi and M.J. led the genomics, association genetics analysis, field trial and phenotyping, mapping genetics and functional validation work. C.F., Y. Shi and M.J. led genomics and bioinformatics pipeline development, haplotype–phenotype and association genetics analyses under supervision of S. Cheng. S. Cheng, C.F., B.C. and M.J. led the gene identity and variation discovery for Gp, P, V, Fa, D locus and Os1, as well as for the four cloned genes R, I, A and Le. B.C., Y. Shi, L.L., L. Wang and Y. Sun led the field trial and phenotyping, gene cloning, RNA-seq and VIGS gene-silencing experiments. B. Song, H.Z., H.C., X.Z., X.L., J. Hou, T.W., Q.Y., P.L., C.Z., Z.W. and Z.H. participated in the whole-genome resequencing and bioinformatics analysis. X.W.D. participated in bioinformatics analysis and computation support from Peking University. J. Hofer and N.E. undertook genetic and genomic analyses of A, D, Gp and Fa, including the allelism tests and TILLING validation of the nature of gp. M.D., A.F. and C.L. identified and provided ChlG TILLING mutant seeds. B. Steuernagel coordinated and managed bioinformatic and genomic data analysis at JIC. N.E. commented on experimental results and assisted in data analysis of all the project results. N.C., C.D., N.E. and L. Wingen selected the germplasm panel. M. Vickers and R.W. assembled the JI0015 genome reference under supervision of B. Steuernagel and, together with J.C. C.L., A.B., M.D., C.S., C.M., R.S., M. Vigouroux and G.T., supported the Gp genetic and genomic explorations under the coordination of C.D. E.V., M.A., A.H., N.E., J. Hofer, N.C., P.H.-P., S. Chundakkad and J.E.B. contributed to germplasm phenotyping the sequenced panel. M.A., L.S. and N.C. contributed through germplasm curation and R.H. contributed by digitizing germplasm data. S. Cheng, N.C. and C.D. secured the project funding. S. Cheng, N.E., C.F., J. Hofer, M.J. and N.C. prepared the main figures, extended data figures, supplementary figures, supplementary tables and Methods. S. Cheng and N.E. drafted the manuscript and S. Cheng finalized the manuscript, with additional help from C.F., B.C., J. Hofer, N.C., C.D. and B. Song. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Noel Ellis, Noam Chayut or Shifeng Cheng.

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Competing interests

The authors declare no competing interests.

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Nature thanks Aureliano Bombarely, Nataliya Yelina and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Schematic illustration of the genetic loci for each of Mendel’s seven traits plotted along the seven chromosomes (linkage groups).

The four previously cloned genes (R, I, A, Le) are annotated in black text, while the remaining genes with their gene identities and variations, or proposed candidates and variants, elucidated in this study (P, V, Gp, Fa) are highlighted in red text. The Mfa genetic locus, where the gene identity remains unknown, is also marked. Difference in the form of the ripe pods on chromosome 1 (LGVI, PP/pp) and 5 (LGIII, VV/vv); yellow versus green cotyledons (II/ii) on chromosome 2 (LGI); round seed versus wrinkled seed (RR/rr) and the colour of unripe pods (GpGp/gpgp) on chromosome 3 (LGV); difference in the position of the flower (FaFa/fafa) on chromosome 4 (LGIV); tall versus dwarf plants (LeLe/lele) on chromosome 5 (LGIII); seed coat (and flower) colour (AA/aa) on chromosome 6 (LGII).

Extended Data Fig. 2 Gene identity and functional variation underlying parchmentless pods (P vs. p).

a, Manhattan plot from GWAS analysis for the parchmentless pod trait, based on the ZW6 genome reference. b, Close-up view of the Manhattan plot in the most significant region identified in panel a. c, F_2 genetic mapping interval derived from the cross JI2822 x JI0816, showing the mapped locus between markers AX-183563747 and AX-183563750 (chr1: 380,049,894-380,967,975) (Supplementary Tables 18–20). d, Map of gene positions within the P interval with Psat01G0420500 encoding a tracheary element differentiation inhibition factor CLE41 indicated in yellow. e, Allelic and haplotype variations for Psat01G0420500. Note that Hap1 carries a silent A-to-C transversion at chr1_380699321, close to chr1_380699320 of Hap3, where the T-to-A transversion is responsible for the Arg79* nonsense mutation. f, Haplotypes of Psat01G0420500 corresponding to accessions with ‘parchmentless’ phenotypes. g, Predicted amino acid sequence of Psat01G0420500 indicating the position of the Arg79* mutation in relation to the TDIF[84] motif. h, Gene expression patterns for the 10 candidate genes from the genomic interval, in various organs and developmental stages of Caméor, showing that Psat01G0420500 (PsCLE41) is expressed exclusively in pod. Three biological replicates were used for each sample. The Min-Max scaling (normalization) approach was used to calculate the expression level for each gene across stages and organs by the formula: X_scaled = (X - X_min)/(X_max - X_min), where X is the original gene expression value, X_scaled is the scaled value, X_min is the minimum value of X, and X_max is the maximum value of X.

Source data

Extended Data Fig. 3 V and parchmentless pods.

a, Manhattan plot of GWAS analysis based on the ZW6 genome reference for a subset of accessions carrying only the R79* allele (haplotype 3 in Extended Data Fig. 2e) of gene Psat01G0420500 and wild type accessions (i.e. no v/v mutants), showing the P GWAS signal but not the V GWAS signal. b, Manhattan plot of GWAS analysis from a subset of accessions excluding those with haplotype 3 (Extended Data Fig. 2e) of gene Psat01G0420500 (i.e. no p/p but only the v/v mutants). This analysis shows only the V GWAS signal but not the P GWAS signal. c, Close-up view of the local details of the chromosome 5 GWAS peak corresponding to V. d, Genetic mapping of V vs v from previous studies[36]. e, Candidate genes (19) within the V genetic interval, with Psat05G0804500 (PsMYB26) highlighted in orange. f, Gene expression level in the pod tissues (8 days post flowering, three biological replicates for each sample) compared between the wild-type line (P/P V/V, JI1995) and the mutant line (P/P v/v, JI0074), across the 19 candidate genes under the interval. g, Allelic and haplotype variation in Psat05G0804500 (PsMYB26) across the diversity panel. All parchmentless accessions, except those carrying the R79* allele of P, are associated with a 23 kb Ogre retrotransposon element insertion. h, Gene expression patterns for the 19 candidate genes in various organs and developmental stages (three biological replicates for each sample) of Caméor, showing that Psat05G0804500 (PsMYB26) is expressed exclusively in pod and endocarp (red box). The normalization and statistical approach is the same as described in Extended Data Fig. 2h.

Source data

Extended Data Fig. 4 Anatomical and molecular characterization of pod endocarp development and associated pathways.

a-c, Microscopic imaging showing the anatomical patterns of the pod endocarp in wildtype (P/P V/V, JI2776) at different developmental stages: 3 days, 8 days, and 12 days post flowering. d, Gene expression levels, measured by RNA-seq approach, of key genes involved in the conserved well-established TDIF-PXY-WOX signalling pathway (PsCLE41, PsSERK, PsPXY, PsWOX4, PsWOX14) and two key component genes (PsMYB26, PsNAC) which have been reported to be involved in the secondary cell wall thickening and lignification in Arabidopsis. Expression data were obtained from various organs at different developmental stages (three biological replicates for each sample) in Caméor. The normalization and statistical approach was the same as described in Extended Data Fig. 2h. e, Comparative microscopic imaging of pod endocarp anatomical patterns at 12 days post flowering in four genotypes: P/P V/V (JI0190), p/p V/V (JI0466), P/P v/v (JI0074), and p/p v/v (JI0134). f, qRT_PCR analysis of PsCLE41, PsPXY, PsMYB26, and PsNAC in lines with different genotypes (P/P V/V, p/p V/V, P/P v/v, p/p v/v). The pod samples were obtained 8 days post flowering. Two different lines were selected for each genotype, with five biological replicates (n = 5) for each sample. Data are presented as mean ± SEM, and statistical significance was determined using a two-sided t-test (which applies to g). g, qRT_PCR results of PsMYB26 and PsNAC compared between the control and the VIGS-silenced lines. Three to five biological replicates were used for each sample both in the control and silenced lines. h, Proposed model illustrating the functional roles of PsCLE41 and PsMYB26 in pod endocarp development and lignin biosynthesis.

Source data

Extended Data Fig. 5 Identification of PsCIK2/3 as a candidate gene for Fa associated with fasciation.

a, Manhattan plot of GWAS based on the ZW6 genome reference, revealing a significant peak for fasciation between 0 and 40 Mb on chromosome 4; b, Close up of Manhattan plot of GWAS in the region of the peak in panel a; c, Bulked segregant mapping analyses (BSA) from sequencing fasciated and wild-type bulks of the F_2 populations derived from the cross: Caméor (Fa/Fa) x JI0814 (fa/fa), and JI2822 (Fa/Fa) x JI0816 (fa/fa), further refining the genetic interval for Fa; d, Fine mapping of the Fa locus using two populations. In Caméor x JI0814 (Mapping 1), the region was narrowed down to chr4: 18,144,306-19,945,776 using 8 pairs of KASP markers (Supplementary Table 34); in the JI2822 x JI0816 population (Mapping 2), the interval was further confined chr4:18,180,969-19,506,907 (marker interval AX-183636277-AX183633456, Supplementary Table 18). e, Local detail of the fine-mapped genomic interval from panel d, showing 20 protein-coding genes, with Psat04G0031700 (encoding a Senescence-Associated Receptor-Like Kinase, PsCIK2/3) highlighted in orange; f, Population-based haplotype clustering analysis across the diversity panel for the 1.33Mb Fa region, identifying a cluster of fasciated accessions in Hap5; g, Haplotype clustering analysis of Psat04G0031700 (PsCIK2/3) reveals a 5 bp deletion associated with the fasciated phenotype, clustering all fasciated accessions into Hap3. h, Amino acid sequence alignment of PsCIK2/3 proteins from the wild-type line (JI2822, Fa, Psat04G0031700), the mutant line (JI0816, fa, Psat04G0031700-5bp), and the ortholog from Arabidopsis (AT2G23950.1, AtCIK2).

Source data

Extended Data Fig. 6 Functional characterization of PsCIK2/3.

a, Flowering stage phenotype of Caméor (Fa/Fa, left) and JI0814 (fa/fa, right). b, Longitudinal section of 40-day-old stems from Caméor and JI0814, stained with safranin and fast green; the red crosses mark the region of the shoot apical meristem (SAM) in both genotypes. c, Transverse section of the apical meristem of 14-day-old paraffin-embedded stems, from Caméor (left) and JI0814 (right), stained with toluidine blue. d, Quantification of the number of vascular bundles in the longitudinal section of 40-day-old stems of Caméor and JI0814. e, Cross-sectional area of the apical meristem in 14-day-old stems of Caméor and JI0814. Three biological duplicates were used. ** represents a significant level at P < 0.01 using a Student’s t-test in (d) and (e). f, Gene expression level from RNA-seq data for key genes involved in the CLV3-WUS signalling pathway measured across different organs and developmental stages (three biological replicates for each sample) in Caméor. The normalization and statistical approach is the same as described in Extended Data Fig. 2h. g, qRT_PCR analysis of gene expression for key genes involved in the CLV3-WUS signalling pathway, comparing the apical bud and stem between the wild-type line (Caméor) and the fasciated line (JI0814). Three biological replicates (n = 3) were used for each sample. The samples were obtained 14 days post budding. Data are presented as mean ± SEM, and statistical significance was determined using a two-sided t-test. The calculation used H3 as the reference gene, with P_value <= 0.001 marked as “***”, P_value <= 0.01 marked as “**”, P_value <= 0.05 marked as “*”, P_value >0.05 marked as “ns”. h, In situ hybridization of PsCIK2/3 in the apical bud compared between the wild-type line (JI2716) and the fasciated line (JI0814). i, Subcellular localization of PsCIK2/3 in Nicotiana benthamiana, showing co-localization with the cell membrane. j, Yeast two-hybrid assay showing interaction of PsCIK2/3 with PsCLV1 and PsCLV2.

Source data

Extended Data Fig. 7 Segregation analysis of Fa and Mfa.

a, Genotype data from the JI2822 x JI0816 F_2 population, presented in an Excel spreadsheet format. The F_2 individuals are sorted left to right according to their phenotype and their genotypic scores at Fa and Mfa. In the central upper part of the figure, homozygous JI0816 genotypes (fa/fa) are represented in yellow, homozygous JI2822 genotypes (Fa/Fa) are represented in green, and heterozygotes (Fa/fa) are represented in blue. In the central lower part of the figure, homozygous JI0816 genotypes (Mfa/Mfa) are represented in yellow, homozygous JI2822 genotypes (mfa/mfa) are represented in green, and heterozygotes (Mfa/mfa) are represented in blue. The limits of recombination intervals are marked by horizontal black lines. Wild-type (dark green) and fasciated (orange) phenotype scores are shown above the genotyping data. Homozygous and heterozygous genotypes at a proposed modifier locus, mfa, are shown below the genotyping data. F_2 individuals informative for the positioning of Fa are marked with a red box; b, Tables explaining a one gene model of the summarised numerical data from panel a, where genotype fa/fa is fasciated; c, Tables explaining a two gene model of the summarised numerical data from panel a, showing the postulated Fa Mfa interaction, where the dominant allele Mfa is required for fasciation to occur. In this model fa/fa mfa/mfa is wild type but fa/fa Mfa/_ is fasciated. In both tables the numbers in red are F_2 individuals with unexpected genotype/phenotype combinations, which were further tested and confirmed in the F_3 population (Supplementary Notes and Supplementary Fig. 28).

Extended Data Fig. 8 Identification of genomic loci associated with major agronomic traits.

a, Multi-site phenotyping experiments were conducted to measure 79 traits in total from distinct climate zones at three different locations: Southern China (22°N, Shenzhen), Northern China (45°N, Harbin), and the UK (52°N, Norwich). Map created using the maps package (3.4.0) in R (version 4.2). b, Illustrative photographs and drawings of phenotypic data collected for different trait categories scored in this study. The points in the hexagon represent the total number of sub-traits collected for each category, with the red line indicating the total number of phenotypes assessed (Supplementary Table 37). c, Significant marker-trait associations (MTAs) and their genetic effects for component traits from seeds, pods, leaves, flowers, roots and plant architecture. The number of sub-traits for each category is shown in parentheses. Specific examples for some of the selected Manhattan plots are shown to explain: d, The acute vs. blunt pod tip phenotypes, corresponding to the Bt locus (a locus known from classical genetics alone)[85]. e, The pod neoplasm phenotype, the development of pustular-like growths, known as ‘neoplasms’. The locus known from classical genetics is Np[86]. f, Green pod vs purple pod phenotypes corresponding to the known genetic loci: Pur and Pu[45]. g, A new locus at the end of chromosome 3, underlying the seed number (SDN) per pod. h, Variations in flower number per axillary inflorescence corresponding to the known genetic loci: Fn and Fna[87]. i, A new locus on chromosome 3, underlying the total seed weight per plant (SDY), a yield component trait. j, Variation in flowering time corresponding to the Hr locus[88]. k, A new locus underlying flower size (FLS), on chromosome 1. l, Brown vs black hilum colour phenotypes, corresponding to the Pl locus[89]. m, A historical locus (Ser1) at chromosome 5, explaining the phenotypic variation in leaflet margin serration.

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Feng, C., Chen, B., Hofer, J. et al. Genomic and genetic insights into Mendel’s pea genes. Nature (2025). https://doi.org/10.1038/s41586-025-08891-6

- Received: 31 May 2024

- Accepted: 12 March 2025

- Published: 23 April 2025

- DOI: https://doi.org/10.1038/s41586-025-08891-6

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Deep origin of eukaryotes outside Heimdallarchaeia within Asgardarchaeota

Jiawei Zhang,

Xiaoyuan Feng,

Meng Li,

Yang Liu,

Min Liu,

Li-Jun Hou &

…

Hong-Po Dong

Nature (2025)

Abstract

Research on the morphology, physiology and genomics of Asgard archaea has provided valuable insights into the evolutionary history of eukaryotes1,2,3. A previous study suggested that eukaryotes are nested within Heimdallarchaeia[4], but their exact phylogenetic placement within Asgard archaea remains controversial4,5. This debate complicates understanding of the metabolic features and timescales of early eukaryotic ancestors. Here we generated 223 metagenome-assembled nearly complete genomes of Asgard archaea that have not previously been documented. We identify 16 new lineages at the genus level or higher, which substantially expands the known phylogenetic diversity of Asgard archaea. Through sophisticated phylogenomic analysis of this expanded genomic dataset involving several marker sets we infer that eukaryotes evolved before the diversification of all sampled Heimdallarchaeia, rather than branching with Hodarchaeales within the Heimdallarchaeia. This difference in the placement of eukaryotes is probably caused by the previously underappreciated chimeric nature of Njordarchaeales genomes, which we find are composed of sequences of both Asgard and TACK archaea (Asgard’s sister phylum). Using ancestral reconstruction and molecular dating, we infer that the last Asgard archaea and eukaryote common ancestor emerged before the Great Oxidation Event and was probably an anaerobic H_2-dependent acetogen. Our findings support the hydrogen hypothesis of eukaryogenesis, which posits that eukaryotes arose from the fusion of a H_2-consuming archaeal host and a H_2-producing protomitochondrion.

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Main

The origin of eukaryotic cells has long remained a mystery[6]. Asgard archaea contain more genes encoding eukaryotic signature proteins (ESPs) than other archaea and are deemed to be the closest relatives of eukaryotes4,5,7,8,9. Isolation and cultivation of the first Asgard archaeon (‘Candidatus Prometheoarchaeum (Ca. P.) syntrophicum’ MK-D1), belonging to Lokiarchaeia, revealed that the archaeon has long and branching protrusions and is able to degrade amino acids syntrophically[1]. These morphological and metabolic features provide new insights into the episodes of eukaryogenesis. For example, it is assumed that cell protrusion may mediate cell–cell interactions, leading to engulfment of its partner and gradual formation of mitochondrion. Subsequently, the second Asgard archaeon (‘Candidatus Lokiarchaeum (Ca. L.) ossiferum’) was recently enriched, and cryo-electron tomography and immunostaining revealed the presence of the actin-like cytoskeletal filament and Lokiactin expression[2]. It is inferred that the Lokiactin may have served as a scaffold for the Asgard archaeal cell and played a role in maintenance of cell shape, cell division and molecular trafficking, similar to actin filaments in eukaryotic cells10,11. The results and hypotheses implicate that the eukaryotic nuclear lineages evolved from within the archaea.

Although some eukaryotic-like features have been identified in pure culture or enrichment of Asgard archaea, with such a limited number of strains, it is challenging to infer the exact phylogenetic position, timescales and metabolic characteristics of early eukaryotic ancestors. In contrast, phylogenomic analyses based on a wealth of archaeal genomic data can compensate for these shortcomings[12]. By using supertree and coalescent methods using more than 3,000 gene families in archaea and eukaryotes, the Heimdallarchaeota have been proposed as the closest relatives of eukaryotes[13]. However, the study used only six Asgard archaeal genomes and failed to represent the currently expanded diversity of Asgard archaea. Recently, two studies obtained inconsistent results revolving around the relationship between eukaryotes and Asgard archaea based on an expanded genomic sampling of Asgard archaea. One suggested that eukaryotes are more likely to have branched from within Asgard archaea as a sister group to the Heimdallarchaeota–Wukongarchaeota branch or are a deeper branch within archaea[5], whereas another proposed that eukaryotes branch as a sister lineage to Hodarchaeales within Heimdallarchaeia[4]. The former used only 29 concatenated markers to infer phylogenies, whereas the latter used non-ribosomal proteins as markers in combination with extensive exclusion of the sites from the alignments. The exact relationship between eukaryotes and archaea remains to be further clarified.

Here we present 223 new Asgard archaeal genomes, including the identification of 16 additional order-, family- or genus-level lineages, recovered from metagenomic samples generated from 14 sites across coastal wetlands of China, which significantly expand the diversity of Asgard archaea. By analysing the expanded genomic sampling of Asgard archaea leveraging sophisticated phylogenomic approaches, including recoding of alignments, use of complex site-heterogeneous evolution models in maximum likelihood (ML) and Bayesian inferences and reduction of rate heterogeneity, we robustly place eukaryotes within the Asgard archaea as a sister clade to Heimdallarchaeia. By applying ancestral reconstruction together with molecular dating, the timescale and metabolic traits of the last Asgard archaea and eukaryote common ancestor (LAECA) were delineated and found to differ significantly from previous studies1,4.

Expanded diversity of Asgard archaea

The increase in genomic diversity of Asgard contributes to resolving the evolutionary relationship between eukaryotes and Asgard archaea4,5,7. To this end, we collected 40 sediment samples from salt marsh and mangrove wetlands across China and performed metagenomic sequencing (Supplementary Table 1 and Supplementary Fig. 1a). After de novo assembly and binning of scaffolds, we reconstructed 11,878 metagenome-assembled genomes (MAGs) (Supplementary Fig. 1b). Of these MAGs, 223 belonged to the Asgard archaea, with greater than 70% completeness and less than 10% contamination (Supplementary Table 2 and Supplementary Fig. 1c). These MAGs were combined with 395 publicly available genomes with high quality. By using dRep[14] to select representatives at species level (ANI at least 95%), we finally obtained a set of 411 Asgard genomes, of which 136 are derived from this study (Supplementary Table 2). Among these, two are from cultured Asgard archaea (Ca. P. syntrophicum and Ca. L. ossiferum) while the remaining genomes are MAGs recovered from environmental samples (Fig. 1). These genomes have a mean completeness of 85.3% and a mean contamination of 3.6%.

Fig. 1: Phylogenomic analysis of 53 concatenated archaeal markers (GTDB.ar53) in GTDB r207.

ML tree inferred using IQ-TREE under the LG + C60 + F + G + PMSF model, based on a set of 579 archaeal taxa (411 Asgard archaea, 51 DPANN archaea, 47 Euryarchaea and 70 representatives of TACK archaea). Only Asgard archaeal lineages are shown in the tree. Two cultured Asgard archaeal strains are highlighted with a red star. Newly identified genomes in this study are displayed by coloured bars in the outermost ring. Bootstrap support values of at least 95% are represented by black dots. The scale bar denotes the average expected number of substitutions per site.

To resolve the phylogenetic position of these clades, we inferred an ML tree of a concatenated set of 53 archaeal-specific marker proteins in the Genome Taxonomy Database (GTDB)[15] (GTDB.ar53) for a phylogenetically diverse set of archaeal genomes (411 Asgard archaea, 51 DPANN (Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota and Nanohaloarchaeota) archaea, 47 Euryarchaea and 70 TACK (Thaumarchaeota, Aigarchaeota, Crenarchaeota, Korarchaeota) archaea representatives, Supplementary Table 3). The tree confirmed the existence of ten class-level Asgard archaeal lineages (Fig. 1). Based on the relative evolutionary distance[16], we identified two putative order-level lineages in Odinarchaeia and Heimdallarchaeia (relative evolutionary distance less than 0.53), which were provisionally named Yangjianarchaeales and Wenzhongarchaeales. In addition, four new family-level clades (represented by six MAGs) and ten new genus-level clades (represented by 18 MAGs) were identified within Sifarchaeia, Hodarchaeales, Hermodarchaeia and Lokiarchaeia. In six out of the ten Asgard archaea classes, we presented 108 species-level new genomes (Fig. 1). Currently, the placement of the recently proposed Njordarchaeales in archaeal phylogeny is intensely debated4,17,18. Recently, it was placed within Heimdallarchaeia in the tree inferred using new alternative markers (NM57), forming a clade with Gerdarchaeales, Kariarchaeaceae and Heimdallarchaeaceae[4]. We selected 12 sets of frequently used marker proteins from previous studies (Supplementary Table 4)4,5,7,8,13,19,20,21,22,23 that are conserved in most of archaeal clades (Methods). ML phylogenomic analyses of these marker sets, based on a set of 579 archaeal representatives including the enlarged genomic sampling of Asgard archaea, were performed under a sophisticated evolutionary model (LG + C60 + F + G + PMSF). These results showed that, except for in the NM57 tree, Njordarchaeales was placed within the TACK superphylum and branched as a sister lineage to Korarchaeota with high support in the other 11 trees (Supplementary Fig. 2). Given that Njordarchaeales often branched with eukaryotes in previous phylogenomic analyses4,18, it is essential to identify the factors underlying the conflicting topologies among different trees for position of Njordarchaeales within archaea.

Revisit of Njordarchaeales placement

Eme et al.[4] argued that the monophyly of Njordarchaeales and Korarchaeota in the RP56 tree may be caused by high compositional similarity in RP56 ribosomal marker protein sequences, which is connected with thermophilic lifestyle of the two lineages. Based on this hypothesis, they proposed a new marker dataset consisting of 57 proteins of archaeal origin that excluded ribosomal proteins to infer the phylogeny of archaea. The NM57 markers are selected from a set of 200 archaeal conserved marker proteins (NM200) identified by Petitjean et al.[21] and most of them are functional proteins involved in metabolic and cellular processes. We observed that, despite the absence of ribosomal proteins, the ML tree of the NM200 dataset positioned Njordarchaeales as a sister clade to Korarchaeota within the TACK superphylum (Supplementary Fig. 2k). This suggests that the clustering of Njordarchaeales and Korarchaeota is not necessarily related to ribosomal proteins. The inconsistent topologies may be attributed to the differences in the number and nature of amino acid positions in the alignments of the two datasets. In contrast to the NM57 topology, the NM200 tree, with more amino acid sites, is more likely to represent the true evolutionary relationship between Njordarchaeales and other archaea.

In phylogenomic analysis, the even distribution of marker proteins in the different archaeal phyla or clades is crucial for clarifying the phylogenetic relationships between archaeal clades. To this end, we integrated the 12 sets of frequently used marker proteins from previous studies (Supplementary Table 4) and selected 67 markers (S67) that are conserved across all sampled archaeal genomes (identified in at least 60% of representatives of each of the archaeal clades; Methods). The S67 marker set was comprised of 39 ribosomal proteins and 28 functional proteins involved in diverse cellular activities (Supplementary Table 4). ML phylogenomic analyses of the S67 dataset under sophisticated evolutionary models confirmed Njordarchaeales as a sister to Korarchaeota and resolved all nodes of the Asgard archaea with high support (Bootstrap values at least 95) (Extended Data Fig. 1).

Given that concatenation of large amount of data may strengthen non-phylogenetic signal and lead to artifactual trees with high support, we next evaluated the effect of data exclusion on phylogenetic inference by removing mutationally saturated or rate-biased sites24,25,26,27. We tracked the evolution of bootstrap support for the monophyly of either (1) Njordarchaeales and Korarchaeota or (2) Njordarchaeales and Heimdallarchaeia in trees inferred from the S67 alignments using complex site-heterogeneous evolution model, as fast-evolving sites are progressively removed. The grouping of Njordarchaeales and Korarchaeota consistently received strong support until 80% or more of the fastest-evolving sites were removed, whereas the monophyly of Njordarchaeales and Heimdallarchaeia was never supported (Extended Data Fig. 2a). The significant loss of phylogenetic signal probably accounted for the decline in support observed after the removal of 80% or more of the fastest-evolving sites.

Nevertheless, it is observed that the incremental exclusion of the fastest-evolving sites from the NM57 alignments also strongly supported the grouping of Njordarchaeales and Heimdallarchaeia (Extended Data Fig. 2b). To identify the cause of this effect, we determined the taxonomic profiles of contigs of the ten Njordarchaeales representatives using CAT28,29 and MMseqs2 (refs. 30,31) taxonomy tools. Although the two tools use different algorithms, they generated comparable results. In each genome, approximately 24–60% of the contigs (Fig. 2a,b) were assigned to TACK archaea, which collectively accounted for 22–51% of the genome size (Supplementary Fig. 3). In contrast, 3–35% of the contigs were assigned to Asgard archaea, whose combined lengths comprised 1–34% of the genome size (Supplementary Fig. 3). The results indicate that these Njordarchaeales MAGs may contain high levels of contamination stemming from misbinning, or alternatively, that extensive horizontal gene transfers (HGT) have occurred between TACK and Asgard archaea, specifically into the Njordarchaeales. Subsequently, we examined the distribution patterns of contigs of these MAGs across several metagenomes from the same sampling location4,32,33 using read recruitment. We found that contigs from four out of the ten MAGs were mapped by reads from at least two samples. Based on their sequence composition and differential coverage patterns across different metagenomes, the contigs of each of the four MAGs were partitioned into two to four distinct clusters (Fig. 2c,d). For the B7_G17_GCA_029856635, contigs in its two larger clusters were used separately to infer ML phylogeny based on the NM57 markers. The contigs in one cluster were placed as a sister to Korarchaeota, while those in another cluster branched with Asgardarchaeia within Asgard archaea (Extended Data Fig. 3). The results indicate that these Njordarchaeales MAGs may represent chimeric assemblies derived from two or three distinct populations. Compared with other datasets, The NM57 dataset may harbour more robust phylogenetic signals that support the Asgard part of these MAGs, possibly because the NM57 markers were selected based on their distribution across Asgard archaea[4]. The taxonomic profiles of Njordarchaeales and its phylogenetic trees inferred using ribosomal proteins-containing marker sets (Fig. 2, Supplementary Figs. 2 and 4 and Supplementary Discussion) revealed that the bulk of these MAGs appeared to be affiliated with the TACK archaea. The inclusion of such chimeras in tree reconstruction can be expected to impact the evolutionary position of eukaryotes within archaea.

Fig. 2: Taxonomic profiles and clustering of contigs/scaffolds in Njordarchaeales genomes.

a,b, Percentage of contigs/scaffolds assigned to Thermoproteota (formerly known as the TACK superphylum) or Asgardarchaeota based on count for each of the ten Njordarchaeales representatives; classification of contigs/scaffolds was determined using CAT (a) or MMseqs2 (b) tools. c,d, Hierarchical clustering of contigs/scaffolds in Njordarchaeales B7_G17 and B20_G9 (c) and B62_G16 and S143_49 (d) genomes based on their sequence composition and differential mean coverage across different metagenomes.

Eukarya emerged outside Heimdallarchaeia

Our expanded Asgard archaeal genomes help to resolve the placement of eukaryotes among archaea. To this end, we added 14 commonly used eukaryotic taxa4,5,7 (Supplementary Table 3) into the S67 dataset to generate a supermatrix: ES67. The ML tree of the ES67 dataset placed eukaryotes within the TACK superphylum as a sister clade to Njordarchaeales and Korarchaeota (Supplementary Fig. 5a), which had never been observed previously in previous studies4,17. This may be caused by the high compositional similarity between the Asgard archaeal sequences in the Njordarchaeales genomes and eukaryotic sequences. To mitigate the effect of distant outgroups on the placement of eukaryotes, we removed DPANN, Euryarchaea, Korarchaeota and Njordarchaeales sequences from the ES67 dataset while retaining 50 representatives of TACK archaea as the outgroup; alternatively, all outgroup sequences were omitted. The resulting supermatrices were named tES67 and AsES67, respectively. ML analysis of the tES67 dataset showed that eukaryotes branched with Heimdallarchaeia with high support (Heimdallarchaeia-sister) (Fig. 3a and Supplementary Fig. 5b). The unrooted ML phylogeny of Asgard archaea and eukaryotes on the basis of the AsES67 dataset also strongly supported the Heimdallarchaeia-sister topology (Supplementary Fig. 5c).

Fig. 3: Phylogenomic analyses of several sets of concatenated marker proteins, showing the placement of eukaryotes relative to genomically sampled Asgard archaea.

a, ML phylogenetic analysis of 67 concatenated marker proteins based on 461 archaeal taxa and 14 eukaryotes (inferred using IQ- IQ-TREE under LG + C60 + F + G + PMSF model), using 50 TACK archaea as the outgroup (tES67 alignment; 13,348 sites). The number below Lokiarchaeales represents 150 MAGs and the genomes of two cultured Asgard strains. Meanwhile, the alignment was SR4-recoded and its Bayesian inference was performed using the CAT + GTR model (Supplementary Fig. 6a; two chains; 50,000 generations). PP support for the node of eukaryotes and the closest Asgard relatives is shown (0.69). b, ML phylogenetic analysis of 97 concatenated marker proteins based on 411 Asgard archaeal taxa and 14 eukaryotes (inferred using IQ- IQ-TREE under the LG + C60 + F + G + PMSF model) (S97 alignment; 20,067 sites). This tree is unrooted. In addition, Bayesian inferences of the SR4-recoded S97, S150 and NM57 supermatrices were performed using the CAT + GTR model (Supplementary Figs. 7 and 8b). PP support for the node of eukaryotes and the closest Asgard relatives is shown (PP = 1 for S97, PP = 0.8 for S150 and PP = 1 for NM57). c, Evolution of bootstrap support for the grouping of eukaryotes with Heimdallarchaeia (Heimdallarchaeia-sister, EHeim) or Hodarchaeales (Hodarchaeales-sister, EHod) in phylogenetic trees inferred from S97 and NM57 datasets, as the fastest-evolving sites were progressively removed.

Based on the tES67 and AsES67 datasets, we examined the effect of rate heterogeneity on the position of eukaryotes by progressively removing fast-evolving sites of the two datasets. Along the decreasing heterogeneity gradient, ML phylogenies were computed to assess the support for Heimdallarchaeia-sister and the monophyly of eukaryotes and Hodarchaeales (Hodarchaeales-sister). Support for Hodarchaeales-sister was never observed, whereas support for Heimdallarchaeia-sister fluctuated as rate heterogeneity decreased (Extended Data Fig. 4). We inferred that the fluctuation in support for Heimdallarchaeia-sister may be ascribed to the dynamic relationship between the loss of phylogenetic signals and the level of rate heterogeneity. Although Bayesian inferences of the SR4-recoded[34] tES67 and AsES67 datasets displayed the Heimdallarchaeia-sister topology, support was weak (posterior probability (PP) support values of 0.69 and 0.5, respectively) (Fig. 3a and Supplementary Fig. 6). The results suggest that the S67 marker set does not contain enough phylogenetic signal to resolve the node between eukaryotes and Asgard clades.

To address the issue, we relaxed our marker selection criterion. Based on conservation of marker proteins in the expanded Asgard genomes, we selected two additional marker sets from previously reported marker proteins that are conserved in archaea4,5,7,13,19,21. The first marker set consisted of 97 markers (S97) that were found in at least 60% of representatives of each of the Asgard clades, whereas the second marker set included 150 markers (S150) that were identified in at least 80% of Asgard archaeal genomes (Supplementary Table 4). ML trees inferred from the untreated S97 dataset and its fastest-evolving site-excluded subsets based on Asgard archaea and eukaryotic taxa provided significant support for the Heimdallarchaeia-sister topology (bootstrap support greater than 95) (Fig. 3b,c). In contrast, an ML tree inferred from the untreated S150 dataset strongly supported the clustering of eukaryotes and Hodarchaeales (Extended Data Fig. 5a). Given that the larger amount of missing data contained in the S150 supermatrix could bias the estimation of the site evolutionary rates, a site-by-site desaturation strategy was also applied to the S150 supermatrix. We observed that support for the Hodarchaeales-sister was replaced by support for the Heimdallarchaeia-sister after the 40% fastest-evolving sites were removed (Extended Data Fig. 5b). The grouping of eukaryotes with Heimdallarchaeia was also confirmed by the Bayesian inferences of the SR4-recoded S97 and S150 datasets (PP values of 1.0 and 0.8, respectively; Fig. 3b, Supplementary Fig. 7).

In the study by Eme et al.[4], based on the NM57 marker set, eukaryotes were positioned within Heimdallarchaeia, as a sister lineage to Hodarchaeales. We redid phylogenomic analyses of the NM57 dataset using the expanded Asgard archaea and eukaryotes. Although the ML tree of the untreated NM57 supported the Hodarchaeales-sister topology (Supplementary Fig. 8a), the support for Hodarchaeales-sister decreased dramatically, whereas support for Heimdallarchaeia-sister rose markedly as fastest-evolving sites were progressively reduced (Fig. 3c). The Bayesian inferences of the SR4-recoded[34] NM57 dataset also confidently placed eukaryotes outside Heimdallarchaeia (PP, 1.0) (Supplementary Fig. 8b). The result indicated that the relationship between eukaryotes and Asgard archaea reported[4] was probably artifactual. Additional tests using vertically evolving markers that support the Heimdallarchaeia-sister topology are delineated in Supplementary Information (Supplementary Table 4 and Supplementary Figs. 9–12). In addition, we examined the distribution of ESPs in the expanded sampling of Asgard archaea and found that the previously delineated ESPs specific to Hodarchaeales were also present in other Asgard clades (Supplementary Results and Discussion and Supplementary Figs. 13–16).

Taken together, our phylogenetic analyses indicate that eukaryotes may have evolved before the diversification of all sampled Heimdallarchaeia. The results were consistent with one of scenarios inferred using the 29-marker tree reported previously by Liu et al.[5], but challenged the recently proposed Hodarchaeales-sister hypothesis[4]. Compared with the study of Liu et al.[5], our results were more robust as they were generated using an expanded genomic sampling of Asgard archaea, in combination with exclusion of outgroups, removal of fast-evolving sites and application of complex site-heterogeneous evolution models and several marker sets. Our finding helps to infer the nature of the eukaryotic ancestors.

Timescale and traits of the LAECA

We used MCMCTree[35] to estimate the divergence times of key nodes within the Asgard phylum using a relaxed molecular clock model. An archaeal root age range of 4.29–3.8 billion years ago (Ga) and uniform age priors for the other fossil calibrations were applied in the molecular dating analysis (Supplementary Table 5). The last Asgard archaea common ancestor (LAsCA) emerged at around 3.38 Ga (confidence interval (CI), 3.72–3.06 Ga) in the Archean (Supplementary Fig. 17). The estimate for the age of the Heimdallarchaeia and Lokiarchaeia common ancestor was about 3.16 Ga (CI, 3.54–2.82 Ga) in the Mesoarchean. As the sister of eukaryotes, crown Heimdallarchaeia diverged 3.12–2.26 Ga, predating the Great Oxidation Event[36]. The result indicates that eukaryotic stem lineages evolved before the Great Oxidation Event, which is basically consistent with the timescale of stem eukaryotes reported by Betts et al.[3]. Recently, based on the evolutionary history of ATP synthases, it was suggested that eukaryotes diverged from Hodarchaeales within Heimdallarchaeia 2.67–2.19 Ga[37]. This partially overlaps in time with our estimates for divergence of Heimdallarchaeia and eukaryotes.

We used a probabilistic gene–tree species-tree reconciliation method[38] (amalgamated likelihood estimation (ALE)) to infer ancestral events and gene content on key nodes of the Asgard archaeal species tree. To refine the metabolic reconstruction, a subset of the aforementioned archaeal genomes with more than 80% completeness and less than 5% contamination was selected for ALE analyses. This subset, which included 235 Asgard archaea, 47 Euryarchaea and 60 TACK archaea, had a mean completeness of 88.5% and a mean contamination of 2.7% (Supplementary Tables 2 and 3). In the species tree, a gradual increase in ancestral gene content was observed during the evolution from LAsCA to Heimdallarchaeia ancestor (Supplementary Fig. 18). Along the evolutionary route, rates of gene loss also increased gradually, implying that a large amount of gene gain occurred before Heimdallarchaeia radiation.

Next, we reconstructed metabolic traits of key ancestors of Asgard archaea based on presence probability of genes in nodes reported by ALE (Supplementary Table 6) and gene frequencies across Asgard archaeal genomes (Supplementary Table 7). Recent studies on two cultured Asgard archaea, Ca. P. syntrophicum and Ca. L. ossiferum, have shown their ability to degrade amino acids anaerobically by syntrophy1,2. Based on these physiological features, it was inferred that the LAsCA was probably an amino-acid-degrading anaerobe that produced H_2 and fatty acids as byproducts[39]. However, our analysis revealed that the two cultivated Asgard strains and their three closely related MAGs lacked most of the key genes for the H_4MPT methyl branch of the archaeal Wood–Ljungdahl pathway[40] (WLP), but encoded three key enzymes in the H_4F methyl branch of bacterial WLP: formyltetrahydrofolate synthetase (fhs), methylenetetrahydrofolate dehydrogenase (folD) and methylenetetrahydrofolate reductase (metF) (Supplementary Fig. 19). This partial H_4F branch can convert glycine, serine and histidine to formate that mediates interspecies electron transfer[1] (Supplementary Fig. 20). In contrast, the LAsCA was inferred to harbour a complete archaeal WLP but lacked Fhs in the bacterial WLP (Fig. 4a, Supplementary Tables 6 and 7 and Supplementary Figs. 19 and 20). It also encoded various NiFe hydrogenases (Supplementary Table 6 and Supplementary Fig. 21). These results suggest that the LAsCA was a H_2-dependent chemolithoautotroph. Abundant CO_2 in the Archean ocean[41] and marine H_2 from serpentinization[42] would have provided ample substrates for these organisms.

Fig. 4: Metabolic reconstruction of key Asgard archaeal ancestors and distribution of WLP in lineages of Heimdallarchaeia.

a, Transition from the Asgard common ancestor to Lokiarchaeia and Heimdallarchaeia ancestors. Based on ALE results, it is inferred that the LAsCA was a H_2-dependent chemolithoautotroph. The archaeal WLP was inferred to be present in all four ancestors; thus, it could also have been present in the LAECA. Four genes related to acetogenesis (pta, ack, acs and acd) were predicted to be present in the Heimdallarchaeia ancestor, suggesting that the LAECA may have been an anaerobic H_2-dependent acetogen. b, Distribution of key enzymes of the WLP in Heimdallarchaeia. As a basal branch within Heimdallarchaeia, Hodarchaeales possessed a complete WLP, supporting presence of the pathway in the Heimdallarchaeia ancestor. During the transition from Hodarchaeales to Heimdallarchaeaceae and Kariarchaeaceae, these key enzymes of the WLP appeared to be progressively lost. Fully filled circles indicate that the gene was detected in at least half of the genomes of the clade. Half-filled circles indicate that the gene was detected in fewer than half of the genomes of the clade. EMP, Embden–Meyerhof–Parnas; NOPPP, nonoxidative pentose phosphate pathway; RuMP, ribulose monophosphate pathway; sp/np-ED, semi/non-phosphorylative Entner–Doudoroff pathway; TCA cycle, citrate cycle; AA, amino acid metabolism; β, β-oxidation; H_4F, tetrahydrofolate methyl branch; H_4MPT, H_4MPT methyl branch tetrahydromethanopterin; Fdh, formate dehydrogenase; Mvh/Hdr, F_420-non-reducing hydrogenase and heterodisulfide reductase complex; Pyr, pyruvate; Ech, energy-converting hydrogenase; F_420, coenzyme F_420-reducing hydrogenase; Mcr, methyl-CoM reductase; Nucl, nucleotide; H, Heimdallarchaeaceae; K, Kariarchaeaceae; G, Gerdarchaeales; Hod, Hodarchaeales; W, Wukongarchaeia; fwd, formylmethanofuran dehydrogenase; ftr, formylmethanofuran-tetrahydromethanopterin N-formyltransferase; mch, methenyltetrahydromethanopterin cyclohydrolase; mtd, methylenetetrahydromethanopterin dehydrogenase; mer, 5,10-methylenetetrahydromethanopterin reductase; cdh, acetyl-CoA decarbonylase/synthase, CODH/ACS complex.

Lokiarchaeia included both Helarchaeales and Lokiarchaeales. All members of Helarchaeales (13 MAGs) are found to encode a methyl-CoM reductase-like enzyme (Supplementary Table 7) that is similar to that found in butane-oxidizing archaea43,44. ALE analysis inferred the presence of the methyl-CoM reductase-like enzyme in Lokiarchaeia ancestor (Supplementary Table 6), suggesting that this ancestor had the potential to degrade hydrocarbon anaerobically by coupling the β-oxidation of fatty acids with reverse WLP (Fig. 4a and Supplementary Fig. 20).

Contrary to the result by Eme et al.[4], our inference revealed that the Heimdallarchaeia and Hodarchaeales ancestors also possessed a complete archaeal WLP pathway (Fig. 4a, Supplementary Fig. 20 and Supplementary Table 6). We found that, in the MAGs of Heimdallarchaeia (including three Hodarchaeales genomes) used by Eme et al.[4], genes encoding several key enzymes in the archaeal WLP are missing. However, we presented 22 new genomes of Hodarchaeales, representing two new families and seven new genera, in which several genomes or lineages contained a complete archaeal WLP (Fig. 4b). As a basal-branching group in Heimdallarchaeia, the presence of archaeal WLP in Hodarchaeales supported an autotrophic lifestyle of crown Heimdallarchaeia. Except for the WLP, the crown Heimdallarchaeia was inferred to encode a phosphotransacetylase (pta) and an acetate kinase (ack) that are required for the conversion from acetyl-CoA to acetate, as well as an energy-converting hydrogenase (ech) that can potentially serve as an ion-translocating chemiosmotic coupling site, and an ATP synthase (Supplementary Tables 6 and 7 and Supplementary Fig. 20). Gene frequency analysis revealed that ack genes were found exclusively in members of Heimdallarchaeia (Supplementary Fig. 21a). These data inspired the inference that Heimdallarchaeia ancestor was an anaerobic H_2-dependent acetogen, as shown in a previous study[45].

As a parent of crown Heimdallarchaeia, the LAECA may also be a H_2-dependent acetogenic archaeon. Our metabolic evidence supported the hydrogen hypothesis, which posits that eukaryogenesis was mediated initially by a syntrophic partnership between a H_2-consuming archaeal host and a H_2-producing protomitochondrion living under anoxic conditions, with the protomitochondrion being a facultative aerobe46,47. We identified an incomplete cytochrome bd-I ubiquinol oxidase and an incomplete cytochrome o ubiquinol oxidase in 38 members of Hodarchaeales and 16 members of Kariarchaeaceae (Supplementary Fig. 21b and Supplementary Table 7). However, only two subunits of the cytochrome o ubiquinol oxidase were inferred to be present in the Heimdallarchaeia ancestor (Supplementary Fig. 20 and Supplementary Table 6), which may have been acquired through HGT from a bacterial partner. These incomplete complexes probably lacked the capacity to transport electrons or reduce molecular oxygen to water. The inferred presence of extremely O_2 sensitive enzymes, such as acetyl-CoA decarbonylase/synthase (cdh), 2-oxoacid: ferredoxin oxidoreductases (kor, vor and por) and NiFe hydrogenases in the Heimdallarchaeia ancestor (Supplementary Fig. 20 and Supplementary Table 6 and 7), together with the anoxic deep ocean environment at that time[46], suggests that this ancestor must have grown anaerobically.

In summary, we used sophisticated phylogenomic methods in combination with a significantly expanded sampling of Asgard archaea to re-evaluate relationships among Asgard archaea, Njordarchaeales and eukaryotes. We revealed that the Njordarchaeales genomes probably represent chimeric assemblies comprising mainly TACK and Asgard archaeal sequences. These weird assembled genomes would significantly affect phylogenetic position of eukaryotes in archaea. After excluding outgroups, using several marker sets, eukaryotes were placed confidently within Asgard archaea as a sister to Heimdallarchaeia instead of being nested within Heimdallarchaeia branching with Hodarchaeales. Ancestral reconstructions inferred that the host lineage at eukaryotic origin was an anaerobic, H_2-dependent chemolithoautotroph. Our findings rectified the existing knowledge and filled some gaps in episodes of the early evolution of eukaryotes. The further expansion of Heimdallarchaeia diversity and their isolation will help to refine evolutionary reconstructions[48] of eukaryogenesis.

Methods

Sample acquisition and DNA sequencing

We collected 40 sediment samples from six different locations in coastal wetlands across China: Techeng Island (mangrove swamp); Qingmei Port (mangrove swamp); Tongming Port (mangrove swamp); Dongzai Port (mangrove swamp); Changjiang Estuary (salt marsh) and Luchao Port (salt marsh) (Supplementary Table 1 and Supplementary Fig. 1). A peat sampler was employed to collect sediment cores. After collection, sediment samples were sealed in sterile plastic bags, placed immediately in a pre-chilled icebox and transported to the laboratory as quickly as possible, where they were stored at −80 °C until DNA extraction. Detailed information of the sampling is presented in the following sections.

Samples from Techeng Island

Two 1-m-deep sediment cores were collected from a mangrove swamp on Techeng Island (Zhanjiang) on November 25, 2018. Three sections with depths of 15–25, 40–45 and 95–100 cm each core were taken in an anoxic glove box. Genomic DNA was extracted from 5–10 g sediment using a PowerSoil DNA Isolation Kit (MoBio Laboratories) according to the manufacturer’s instructions. Sequencing libraries were prepared using the NEBNext Ultra DNA Library Prep Kit (New England Biolabs) and sequenced on Illumina HiSeq 2500 instruments at Guangdong MagiGene Technology Corporation, generating approximately 60 Gbp of raw sequencing data (2 × 150 bp) for each sample.

Samples from Qingmei Port

Two 0.5-m-deep sediment cores were collected from the Qingmeigang Mangrove Reserve (Sanya) in September 2020. Four fractions taken from depths of 0–5, 10–15, 25–30 and 45–50 cm, respectively, for each core were used for DNA extraction. Metagenomic sequencing was carried out on Illumina HiSeq 2500 platform at Suzhou Genewiz Biotechnology Company. Each sample yielded 80–120 Gbp of sequencing data in the form of 2 × 150-bp paired-end reads.

Samples from Tongming Port

Three 1-m-deep sediment cores were acquired from a mangrove swamp near Tongming Harbour (Zhanjiang) in June 2021. Samples for DNA extraction were taken from the layers of 15–20 and 45–50 cm of each core. Metagenomic sequencing was performed on Illumina HiSeq 2500 platform at Novogene Biotechnology Company (Tianjin, China), yielding 80–120 Gbp of sequencing data for each sample.

Samples from Dongzai Port

Three sediment cores were obtained from the Dongzhai Port Mangrove Nature Reserve (Haikou) in September 2021. Six samples of each of these cores were taken from depths of 15–20 and 45–50 cm. One additional sample was collected from the 95–100 cm layer of one of the cores. Metagenomic sequencing were conducted as described for samples of Tongming Harbour.

Samples from Changjiang Estuary

Two sediment cores were obtained in November 2018 from the DongTan salt marsh wetland (Shanghai), covered with reeds, located at the estuary of the Yangtze River. Six samples of the two cores were taken from depths of 15–20, 40–55 and 95–100 cm for DNA extraction. Metagenomic sequencing was generated using Illumina HiSeq 2500 by Novogene, yielding 80–120 Gbp of sequencing data for each sample.

Samples from Luchao Port

In November 2021, two sediment cores were obtained from a salt marsh wetland covered with Spartina alterniflora near the Luchao Port of Shanghai. Samples for DNA extraction were collected from the layers of 20–25, 40–45 and 95–100 cm of each core. Nucleic acids were sequenced using Illumina HiSeq 2500 by Novogene. Each sample produced 100 Gbp of sequencing data.

Metagenomic assembly and genome reconstruction

A total of 3.54 Tbp of raw reads was obtained from metagenomes generated from the 40 sediment samples. These reads were trimmed using Trimmomatic[49] (v.0.39) to remove the adaptors and low-quality regions. The resulting clean reads were single-sample de novo assembled using SPAdes[50] (v.3.15.3) with the parameters: ‘-k 21,33,55,77,99 -meta’. The assembled scaffolds were used to recruit reads from their own metagenomes and other metagenomes from the same geographical location using Bowtie2 (ref. [51]) (v.2.4.4). The SAM files generated were converted to BAM files using Samtools[52] (v.1.9). The coverage for each of BAM files was then calculated with the jgi_summarize_bam_contig_depth script in MetaBAT2 (ref. [53]) (v.2.15). These coverage files were applied as input files for Metabat2 (ref. [53]) binning analysis. For each sample, binning was performed using both single-coverage and multi-coverage methods[54], combined with eight different combinations of specificity and sensitivity parameters: --maxP 95 or 60, --minS 95 or 60, and --maxEdges 200 or 500. As for a single assembly, an optimized, non-redundant set of bins was chosen by using DAS-Tool[55] (v.1.1.6). Scaffolds in each bin with divergent GC content or tetranucleotide signatures or coverage profiles were removed with mmgenome[56] and RefineM[57]. In the end, 11,878 genome bins were obtained from these samples (Supplementary Fig. 1b) and their quality including the completeness, contamination and strain heterogeneity were assessed using CheckM[58] (v.1.1.3).

Asgard archaeal genome set

A total of 223 high-quality Asgard MAGs (completeness at least 70%, contamination less than or equal to 10%) were reconstructed in this study. An additional 395 high-quality Asgard MAGs plus 15 Njordarchaeales MAGs were downloaded from publicly available databases (NCBI and GTDB[15]) as of September 23, 2023. Redundant genomes at species level (ANI at least 95%) were then removed using dRep[14] (v.3.4.3) with parameters ‘-comp 70 -con 10 --S_ani 0.95’, resulting in 411 Asgard genome representatives. Of these, 136 were derived from this study, constituting approximately 32% of the total (Supplementary Table 2). Protein coding genes, ribosomal RNA genes and transfer RNA genes in these genomes were identified using Prodigal[59] (v.2.6.3), Barrnap (v.0.9) (https://github.com/tseemann/barrnap) and tRNAscan-SE[60] (v.2.0.9), respectively.

Phylogenomic analysis

Selection of other archaeal genomes outside Asgardarchaeota

Taxa in the GTDB[61] archaeal reference tree (gtdb_r207_ar53_decorated_fullids_unrooted.tree) were clustered using TreeCluster[62] with a distance threshold of length 1 (parameter: --threshold 1). After excluding these singleton clusters, the highest quality genome in a cluster was retained. To balance the taxon sampling, 10–15 genomes were selected randomly from each order. For orders with fewer than ten genomes, all available genomes were used. Finally, we obtained 51 DPANN archaea, 47 Euryarchaea and 70 TACK archaea representatives as the outgroup of the following Asgard phylogenies (Supplementary Table 3).

Phylogenomic analyses of previously used archaeal marker sets

A phylogeny of 53 archaeal marker proteins from the GTDB Toolkit[20] (GTDB-Tk) was inferred to examine phylogenetic diversity of Asgard archaea. In addition, to resolve the phylogenetic position of Njordarchaeales in archaea, an additional 12 sets of pre-existing markers that are conserved across archaea were used for inference of trees. Information on these markers is as follows: (1) the RP55 set comprising 55 ribosomal protein sequences7,8, (2) the Dacunha_35uni set consisting of 21 ribosomal proteins and 14 functional proteins[19], (3) the Zare_48uni set consisting of 31 ribosomal proteins and 17 functional proteins[7], (4) the Spang_udin56ar set comprising 25 ribosomal proteins and 31 functional proteins[23], (5) the Spang_udin28ar set consisting of ten ribosomal proteins and 18 functional proteins[23], (6) the TomNEE_21 set consisting of nine ribosomal proteins and 12 functional proteins[13], (7) the Liu_209 set consisting of 45 ribosomal proteins and 164 functional proteins[5], (8) the RP15 set consisting of 15 ribosomal proteins[25], (9) the PV4 set consisting of 30 ribosomal proteins and seven functional proteins[22], (10) the Liu_29 set consisting of 23 ribosomal proteins and six functional proteins[5], (11) the NM200 set consisting of 200 non-ribosomal proteins[21] and (12) the NM57 set consisting of 57 non-ribosomal proteins[4] (Supplementary Table 4). These markers were identified in genomes of 411 Asgard archaea and the outgroup by searching against a self-built database composed of all arCOG[63] sequences as well as partial sequences from COG[64], AsCOG[5], Pfam[65] and TIGRFAMs[65], using either BLASTP[66] or HMMER[67]. For the RP15 set, these ribosomal proteins were adopted if their genes were located on a scaffold harbouring at least five out of 15 ribosomal protein genes. The marker sequences were aligned with MAFFT[68] (v.7.487, -linsi) and pruned using BMGE[69] (v.1.12) (-m BLOSUM30). IQ-TREE[70] (v.2.2.2.6) was used to infer phylogenetic trees under the LG + C60 + F + G + PMSF model. Branch support was assessed with 1,000 ultrafast bootstrap approximations[71] (-B 1,000). Phylogenetic trees were visualized using iTol[72] (https://itol.embl.de). Phylogenomic analyses of all marker sets placed Njordarchaeales within TACK superphylum as a sister clade to Korarchaeota, except for the NM57 dataset.

Construction and phylogenomic analyses of new marker sets

The expanded diversity of Asgard genomes necessitates new sets of markers that are distributed evenly across Asgard lineages or other archaeal phyla. To further resolve phylogenetic position of Njordarchaeales and eukaryotes within archaea, we integrated these previously reported marker sets used for confidently inferring the archaeal phylogeny4,5,13,21 and selected 67 markers (S67) that are conserved across all sampled archaeal genomes. The 67 markers were selected because they were identified in at least 60% of representatives of each of the archaeal clades as well as in 60% of eukaryotic taxa. The S67 marker set was comprised of 39 ribosomal proteins and 28 functional proteins involved in diverse cellular activities (Supplementary Table 4). We used the 67 markers to build four supermatrices: S67, ES67, tES67 and AsES67. The S67 supermatrix was constructed by including all sampled archaeal genomes (411 Asgard archaea, 51 DPANN archaea, 47 Euryarchaea, and 70 TACK archaea representatives), whereas the ES67 supermatrix was built by adding 14 representative eukaryotic genomes into the S67 dataset. These eukaryotic taxa were chosen according to previous studies4,7. The tES67 supermatrix was created by removing DPANN, Euryarchaea, Korarchaeota and Njordarchaeales sequences from the ES67 supermatrix, whereas the AsES67 supermatrix was built by including only 411 Asgard archaea and 14 eukaryotic taxa.

To increase the phylogenetic signal of supermatrices when outgroups were excluded, we relaxed our marker selection criterion and generated two additional marker sets: S150 and S97 (Supplementary Table 4). Both of these were extracted from the aforementioned 13 set of marker proteins4,5,7,8,13,15,19,21,22. The S150 set comprises 150 proteins, including 52 ribosomal proteins and 98 functional proteins, which were selected based on presence of these markers in at least 80% of Asgard archaeal genomes. The S97 set consists of 97 proteins, including 41 ribosomal proteins and 56 functional proteins, which were selected based on their presence of at least 60% of representatives of each of the Asgard clades. The two marker sets were used to build two supermatrices: S150 and S97, by including 411 Asgard archaea and 14 eukaryotic taxa.

Before inferring phylogenies of these marker sets, preliminary trees of each individual marker, inferred using FastTree2 (ref. [73]) under the LG + CAT model, were checked manually to identify the correct orthologues for each taxon and to detect any instances of paralogues. Any identified paralogous sequences were removed manually from the remaining trees. Single-gene trees with paralogous sequences removed have been uploaded to Figshare (https://figshare.com/s/6e523322b0b647b91dda)[74]. In addition, by excluding markers that failed to recover archaea and bacteria as reciprocally monophyletic domains, we obtained 47, 60 and 99 markers (S47, S60 and S99) from the S67, S97 and S150 marker sets, respectively (Supplementary Table 4). Detailed analyses of these marker sets are provided in the Supplementary Information. ML trees of these amino acid supermatrices were inferred using IQ-TREE[75] with the LG + C60 + F + G + PMSF model. The model was selected because it can capture variations in the substitution or replacement process across sites. The robustness of the resulting trees was assessed with 1,000 ultrafast bootstrap approximations[71] (-B 1,000). Bayesian inferences were performed using PhyloBayes-MPI[76] (v.1.9) with CAT + GTR after the alignments were recoded into four categories using the SR4 scheme[34]. Two independent Markov chains were run until a sufficient effective sample size (greater than 300) was achieved. For consensus tree reconstruction, the first 5,000 cycles (for the run with 20,000 iteration times) or the first 10,000 cycles (for the run with 50,000 iteration times) were discarded as burn-in. In all Bayesian inferences, the two chains did not converge, probably due to the large size of the supermatrices and computational limitations.

To examine effect of mutationally saturated or rate-biased sites on tree reconstruction24,26,77, we tracked the bootstrap support values of branches of interest. The evolutionary rate of sites (evolving-rate score) was estimated using IQ-TREE[70] with the empirical Bayesian algorithm[78] (--rate). The sites were classified into ten categories from the fastest to the slowest evolving. A series of alignments were generated by removing 10% to 90% of the data in a stepwise fashion. The resulting alignments were used to compute 1,000 ultrafast bootstrap[71] support values in IQ-TREE[75], using the LG + C60 + F + G + PMSF model. In the end, the bootstrap support for each bipartition of interest was calculated and tracked as the fastest-evolving sites were progressively removed.

Assessment of Njordarchaeales representative MAGs

The taxonomic profiles of contigs of Njordarchaeales MAGs were determined using two state-of-the-art tools: CAT28,29 and MMseqs2 (refs. 30,31), by comparison with TACK and Asgard archaeal sequences from the GTDB database[79] (r220) excluding Njordarchaeales-related sequences. CAT is a tool for taxonomic annotation of contigs based on protein homologies to a reference database, while MMseqs2 determines the taxonomic identity of contigs through a weighted voting mechanism. The two tools both rely on a well-defined reference database. Given that the position of Njordarchaeales moved between TACK superphylum and Asgardarchaeota in species trees, we retrieved representative genomes of TACK and Asgard archaea only from the GTDB[15] database (r220) to construct the reference database. This strategy aims to minimize the impact of other archaeal sequences on taxonomic classification. As shown in Fig. 2a,b, 20–73% of the contigs in each MAG were not assigned to a specific classification, probably indicating that these contigs contain sequences that are too divergent from those in the database.

Metagenomic samples from which Njordarchaeales MAGs were reconstructed were downloaded from publicly available databases4,32,33. These metagenomes were generated from deep-sea hydrothermal vent sediments. Metagenomic reads were mapped to these Njordarchaeales MAGs using Bowtie2 (ref. [80]) (v.2.3.5). The resulting SAM files were converted to BAM files with Samtools[52] (v.1.9), which were used to create an input index for Anvi’o[80]. Coverage and GC content of contigs were recorded. Contigs of these MAGs were clustered according to their sequence composition and coverage of contigs. Contigs originating from the same organism typically exhibit similar sequence coverage. Therefore, those with divergent coverage profiles are often considered as potential contaminants81,82. These contaminating contigs are particularly prone to displaying differential coverage patterns across different metagenomic samples[81]. In this study, four Njordarchaeales MAGs were completely matched by reads from several metagenomes generated from similar habitats. The average coverages of contigs in the clusters differed across metagenomes.

Phylogenetic divergence time estimation

Molecular dating analysis was carried out with the MCMCtree program in PAML[83] using the WAG model. The analysis was run iteratively until convergence was achieved. Seven nodes in the phylogenetic tree were calibrated using four types of constraint[84]: (1) the Archaeal root constraint was set at 4.29–3.80 Ga, (2) the chitin age had a minimum constraint of 1.58 Ga, (3) the oxygen age had a maximum constraint of 2.32 Ga and (4) the HGT from Viridiplantae to Thaumarchaeota was constrained to 1.49–0.75 Ga. Further details are provided in Supplementary Table 5.

Ancestral metabolic reconstruction

For analyses of amalgamated likelihood estimation38,85, a set of archaeal genomes with more than 80% completeness and less than 5% contamination (mean completeness of 88.5% and mean contamination of 2.7%) was selected, comprising 235 Asgard archaea, 47 Euryarchaea and 60 TACK archaea representatives (Supplementary Table 2 and 3). These genomes had a mean quality score of 75%, with the lowest quality value[57] being 51%. All predicted protein sequences from these genomes were annotated by searching against arCOGs[63] and nr database using BLASTP[86] (E-value less than 1 × 10−5) or Pfam[65] and TIGRFAMs[87] databases using HMMER[88] with a GA bitscore cutoff. The SwissProt database[89] was further used to validate protein functions. Protein sequences with identical functional descriptions were clustered to infer individual gene trees. Sequences were aligned and pruned using MAFFT[68] (-linsi) and BMGE[69] (-m BLOSUM30). Gene tree profiles were then inferred using FastTree2 (ref. [73]) with the LG + CAT model. Subsequently, the species tree of the S47 dataset, along with the gene tree profiles, were applied to perform gene–tree species tree reconciliations using ALEobserve (v.1.0) and ALEml_undated. Events of loss, transfer, origination or duplication as well as presence/absence of gene (copies) were considered only if they had a raw reconciliation frequency of at least 0.1. The copy number (proteome size) and the events are summarized in Supplementary Table 6 and 8, and visualized on the branches of the species tree (Supplementary Fig. 18). The resulting data were then used for metabolic reconstruction. To support the inferences of ALE, gene frequencies for key metabolic pathways across Asgard archaea are provided in Supplementary Table 7.

Reporting summary

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

Data availability

All sequence data are archived in the NCBI database under BioProject ID PRJNA1162170. All raw data underlying phylogenomic analyses (raw and processed alignments and corresponding phylogenetic trees) are available at Figshare (https://figshare.com/s/6e523322b0b647b91dda)[74].

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Acknowledgements

H.-P.D. and L.-J.H. were supported by the Chinese National Key Programmes for Fundamental Research and Development (2024YFF0808800), the National Natural Science Foundation of China (42476110, 42030411, 41971125) and the Ocean Negative Carbon Emissions (ONCE) Program. M. Li was supported by the National Natural Science Foundation of China (32393971, 32225003, 32393970, 92251306), Guangdong Major Project of Basic and Applied Basic Research (2023B0303000017) and Shenzhen University 2035 Program for Excellent Research (2022B002). X.F. was supported by Guangdong Basic and Applied Basic Research (2025A1515012817).

Author information

Authors and Affiliations

1. State Key Laboratory of Estuarine and Coastal Research, Yangtze Delta Estuarine Wetland Ecosystem Observation and Research Station, Ministry of Education, East China Normal University, Shanghai, China

Jiawei Zhang, Li-Jun Hou & Hong-Po Dong

2. Archaeal Biology Center, Synthetic Biology Research Center, Shenzhen Key Laboratory of Marine Microbiome Engineering, Key Laboratory of Marine Microbiome Engineering of Guangdong Higher Education Institutes, Institute for Advanced Study, Shenzhen University, Shenzhen, China

Xiaoyuan Feng, Meng Li & Yang Liu

3. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China

Min Liu

Contributions

This study was conceived by H.-P.D. with input from J.Z. and L.-J.H. Metagenomic assembly, binning and phylogenetic analyses were conducted primarily by J.Z. X.F. provided bioinformatics support, with input from M. Li and Y.L. H.-P.D., L.-J.H. and M. Liu contributed data. H.-P.D. wrote the manuscript and Supplementary Information, with input from all authors.

Corresponding authors

Correspondence to Li-Jun Hou or Hong-Po Dong.

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The authors declare no competing interests.

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Nature thanks William Martin 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 The maximum likelihood phylogenomic analysis of the S67 dataset.

The tree was inferred using IQ-TREE under the LG + C60 + F + G + PMSF model, based on 579 archaeal taxa (11,860 sites, 67 concatenated proteins, 579 taxa). This tree was rooted to DPANN and Euryarchaeota. Bootstrap support values ≥ 95% are represented by black dots. The scale bar denotes the average expected number of substitutions per site.

Extended Data Fig. 2 Evolution of ultrafast bootstrap support for either the monophyly of Njordarchaeales and Heimdallarchaeia (NHeim, red line) or the monophyly of Njordarchaeales and Korarchaeia (NKor, blue line).

The bootstrap values were obtained from phylogenomic trees inferred from the S67 (a) and NM57 (b) datasets, based on 579 archaeal taxa, with the fastest-evolving sites progressively removed. The trees were inferred using IQ-TREE under the LG + C60 + F + G + PMSF model.

Extended Data Fig. 3 The maximum likelihood phylogenomic analysis of NM57 marker set, showing the position of contigs in two larger clusters of Njordarchaeales B7_G17_GCA_029856635.

Contigs of Njordarchaeales B7_G17_GCA_029856635 were grouped into three clusters (Fig. 2c). a, Phylogenetic position of contigs in cluster 1. b, Phylogenetic position of contigs in cluster 2. The trees were inferred using IQ-TREE under the LG + C60 + F + G + PMSF model and rooted to DPANN and Euryarchaeota. Bootstrap support values ≥ 95% are represented by black dots. The scale bar denotes the average expected number of substitutions per site.

Extended Data Fig. 4 Evolution of ultrafast bootstrap support for the monophyly of either eukaryotes and Heimdallarchaeia (EHeim, red line) or the monophyly of eukaryotes and Hodarchaeales (EHod, blue line).

The bootstrap values were obtained from the phylogenies inferred from the tES67 dataset (a) and AsES67 dataset (b), as the fastest-evolving sites were progressively removed. The trees were inferred using IQ-TREE under the LG + C60 + F + G + PMSF model.

Extended Data Fig. 5 Phylogenetic analyses and site-exclusion treatment of the S150 dataset for 411 Asgard archaeal taxa and 14 eukaryotic taxa.

a, The maximum likelihood phylogenomic analysis based on the S150 dataset (32,277 sites, 150 concatenated proteins, 425 taxa). The trees were inferred using IQ-TREE under the LG + C60 + F + G + PMSF model. Bootstrap support values ≥ 95% are represented by black dots. The scale bar denotes the average expected number of substitutions per site. b, Evolution of ultrafast bootstrap support for the monophyly of either eukaryotes and Heimdallarchaeia (EHeim, red line) or the monophyly of eukaryotes and Hodarchaeales (EHod, blue line), in the phylogenies inferred from the S150 dataset. The trees were inferred using IQ-TREE under the LG + C60 + F + G + PMSF model.

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Results and Discussion 1–4, References, Figs. 1–21 and legends for Tables 1–9.

Reporting Summary

Supplementary Table 1

Sampling information.

Supplementary Table 2

Information of Asgard archaeal MAGs, proposed taxonomy.

Supplementary Table 3

Information on reference genomes used for phylogenetic trees in this study.

Supplementary Table 4

Marker sets used in this study.

Supplementary Table 5

Genomes and calibration nodes used for phylogenetic divergence time estimation.

Supplementary Table 6

Reconciliation frequency of genes in key Asgard ancestors inferred by ALE.

Supplementary Table 7

Main metabolic pathways in Asgard archaea and their corresponding gene frequencies.

Supplementary Table 8

Results of ALE analyses.

Supplementary Table 9

Distribution of ESPs in Asgard lineages.

Peer Review File

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Zhang, J., Feng, X., Li, M. et al. Deep origin of eukaryotes outside Heimdallarchaeia within Asgardarchaeota. Nature (2025). https://doi.org/10.1038/s41586-025-08955-7

- Received: 18 September 2024

- Accepted: 28 March 2025

- Published: 07 May 2025

- DOI: https://doi.org/10.1038/s41586-025-08955-7

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Article Open access Published: 28 May 2025

The pivot penalty in research

Ryan Hill,

Yian Yin,

Carolyn Stein,

Xizhao Wang,

Dashun Wang &

…

Benjamin F. Jones

Nature (2025)

Abstract

Scientists and inventors set the direction of their work amid evolving questions, opportunities and challenges, yet the understanding of pivots between research areas and their outcomes remains limited1,2,3,4,5. Theories of creative search highlight the potential benefits of exploration but also emphasize difficulties in moving beyond one’s expertise6,7,8,9,10,11,12,13,14. Here we introduce a measurement framework to quantify how far researchers move from their existing work, and apply it to millions of papers and patents. We find a pervasive ‘pivot penalty’, in which the impact of new research steeply declines the further a researcher moves from their previous work. The pivot penalty applies nearly universally across science and patenting, and has been growing in magnitude over the past five decades. Larger pivots further exhibit weak engagement with established mixtures of prior knowledge, lower publication success rates and less market impact. Unexpected shocks to the research landscape, which may push researchers away from existing areas or pull them into new ones, further demonstrate substantial pivot penalties, including in the context of the COVID-19 pandemic. The pivot penalty generalizes across fields, career stage, productivity, collaboration and funding contexts, highlighting both the breadth and depth of the adaptive challenge. Overall, the findings point to large and increasing challenges in effectively adapting to new opportunities and threats, with implications for individual researchers, research organizations, science policy and the capacity of science and society as a whole to confront emergent demands.

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Main

Science has been described as an endless frontier1,3,15,16. New opportunities and challenges continuously emerge, from synthetic biology or climate change to the COVID-19 pandemic, and researchers and research organizations must consider adapting their research portfolios to address these emergent demands4,5,17,18,19. Adaptability is thus crucial for scientific and technological progress1,3,15, and adaptive success or failure can underpin the relative progress or collapse of organizations, economic regions and societies1,3,15,16,20,21.

The adaptability of research streams hinges on researchers, who must regularly consider the direction of their work and their potential to engage with new areas. Researchers face consequential choices across large or small changes in their research directions, but the degree to which research directions are adaptable depends on fundamental trade-offs and unknowns. On one hand, shifts in research may be difficult[14], because the specialization of expertise12,13,22, the design of funding systems23,24 and the nature of research incentives, culture and communities7,25,26,27 may all limit the capacity of a given individual to respond effectively to changing opportunities and demands28,29,30,31,32. On the other hand, the value of novelty8,33,34 and exploration6,9,35 in creative search suggests that moving further from one’s usual research area might be particularly fruitful10,11,14,36, and new entrants or ‘outsiders’ to a given area are sometimes thought to be especially capable of transformative ideas7,37. Indeed, a researcher who continues to exploit an existing direction may face diminishing returns and miss opportunities afforded in other areas6,38. Exploring new areas might be risky, but it may also be more likely to produce high-impact insights.

Here we study the adaptability of scientists and inventors, and examine the outcomes when researchers work in areas nearer or further from their existing research portfolio. We introduce a measurement framework for research pivots and then study adaptability in both general and specific settings. We first apply the measurement framework at high scale across scientific and technological domains, studying millions of scientific articles indexed by Dimensions from 1970 to 2020 and US patents granted from 1985 to 2020 (Supplementary sections 1.1–2). The core finding is that there is a substantial pivot penalty, meaning that the further a researcher moves from their previous work, the worse the research performs in terms of citation impact, publication success and a host of other outcomes. The negative effects of pivoting occur for individual researchers, across wide-ranging fields of inquiry, and have been increasing over time. We then evaluate the pivot penalty in terms of canonical conceptual frameworks, and investigate potential mechanisms, drawing on ideas of reputation and audience32,39,40,41, as well as creativity frameworks in the production of new ideas6,8,12. Finally, we turn to case studies of substantial interest to science and in which exogenous events can elicit research pivots. We study ‘push’ events, in which existing knowledge is revealed to be incorrect or unreliable, pushing researchers away from their previous research streams. We also study a ‘pull’ event—the COVID-19 pandemic—that drew researchers into an important new research area. We find that despite the wide-ranging nature of these events, researchers pivot to an unusually large degree after these events, and the pivot penalty persists in each case. The pandemic also allows us to examine a consequential, society-scale event and the capacity of science as a whole to address the new research demands. We conclude with a discussion about the implications of these findings for researchers, research organizations and science policy.

Measurement framework

To quantify pivots for researchers, we calculated a cosine-similarity metric that measures the extent to which a given new work departs from a researcher’s previous body of work (Fig. 1a and Pivot size in the Methods). For papers, we considered the referenced journals, comparing the focal work with the previous body of work for that author. The pivot measure, Φ, varies on the [0,1] interval. It takes the value 0 (zero pivot) if the focal paper draws on exactly the same distribution of journals as the author’s previous work, and takes the value 1 (full pivot) if the focal paper draws on an entirely different set of journals. In the patent context, for which journal information is not available, we use technological field codes to measure pivots (see Pivot size and Supplementary section 2.2 for details and alternative constructions of the pivot measure).

Fig. 1: Quantifying research pivots.

a, The pivot measure compares a focal work against previous works by the same researcher. An increasing value on the [0,1] interval indicates a larger pivot from the researcher’s previous work. In the sciences, journals are used to define research areas (pictured); in patenting, technology classes are used. b, The distribution of author pivots in 2020 (n = 8.32 million author-by-paper observations) is dispersed across the [0, 1] interval. c, The distribution of inventor pivots in 2020 (n = 166,000 inventor-by-patent observations) is dispersed across the [0, 1] interval and is bimodal. COVID-19 papers (b) showed higher median pivots than other papers in 2020. Fig. 1a, icons adapted from the Noun Project (https://thenounproject.com).

Source data

Figure 1 shows the distribution of pivoting behaviour, focusing on the year 2020. Pivoting values ranged across the [0, 1] interval in both the science and patenting contexts, indicating that pivoting is prevalent for both scientists and inventors (Fig. 1b,c). We also observed a sharp increase in pivot size for research related to COVID-19, in that scientists who engaged with COVID-19 exhibited unusually large pivots; whereas papers not related to COVID-19 in 2020 have a median of \(\bar{\Phi }=0.60\), COVID-19 papers present a substantially larger median pivot size of \(\bar{\Phi }=0.82\) (P < 0.001, chi-squared test for median differences). The variable nature of pivot size is particularly prominent in patenting, for which we observed a bimodal distribution (Fig. 1c), showing a tendency for both small and large jumps. Supplementary section S2.2 provides further analysis of these patterns, demonstrates their robustness across alternative pivot measures and offers specific examples of pivoting.

The pivot penalty

When scientists and inventors shift away from their earlier research, a central question is how impactful their new work becomes. We first considered 25.8 million papers published from 1970 to 2015 across 154 fields. To quantify impact, we calculated a binary, paper-level indicator for whether a given work was in the upper 5% of citations received in its field and publication year[42]. In Fig. 2a, the data are presented as binned scatterplots, with papers grouped by pivot size into 20 equally sized groups and showing the mean rate of high-impact papers for each group (see Binned scatterplots in the Methods). Figure 2a reveals a striking fact: across the whole of science, papers with larger average pivots have a systematically lower propensity for high impact. Indeed, we observed a large, monotonic decrease in the average hit rate as the pivot size rises. The lowest-pivot work had high impact 7.4% of the time, which is 48% higher than the baseline rate (P < 0.001 in one sample t-test), whereas the highest-pivot work had high impact only 2.2% of the time, which is a 56% reduction from the baseline (P < 0.001). Figure 2b normalizes impact for individual researchers using regressions with individual fixed effects (see Regressions with individual fixed effects in the Methods), showing an impact penalty that is both substantial and less steep than in the raw data. Within a given researcher’s portfolio, the lowest-pivot work was 2.1% more likely (P < 0.001 in regression t-test) to have high impact than that researcher’s other work, and their highest-pivot work was 1.8% less likely (P < 0.001) to have high impact, again showing large deviations from the 5% baseline.

Fig. 2: The pivot penalty.

a, In a study of 25.8 million papers published from 1970 to 2015, papers with higher pivot size have substantially lower probabilities of being high impact. b, For a particular author, relative impact for their papers declines steeply with pivot size. c, In a study of 1.72 million US patents granted from 1980 to 2015, patents with higher pivot size have substantially lower probabilities of being high impact. d, For a particular inventor, relative impact for their patents declines with pivot size. e,f, Over time, the relationship between pivot size and high-impact works has become increasingly negative in science publishing (e) and patenting (f).

Source data

We next considered 1.72 million patents granted from 1980 to 2015 across 127 technology classes, and we similarly calculated the patent-level hit rate based on being in the upper 5% of citations received in the patent’s technology classification and application year. We again found a monotonic decrease in impact as pivot size increased (Fig. 2c). The lowest-pivot patents had high impact 8.0% of the time, which is 60% higher than the baseline rate (P < 0.001 in one-sample t-test), but the highest-pivot patents had high impact only 3.8% of the time, a 24% reduction from the baseline (P < 0.001). This decline in impact with larger pivots was robust to measuring inventor pivots at any technology-classification level, from the broadest to the narrowest (Supplementary Fig. 1). Figure 2d further normalizes impact for individual inventors and continues to show the pivot penalty.

The relationship between pivot size and impact in science has become increasingly negative over the past five decades, both in the raw data (Fig. 2e) and when looking at individual researchers (Extended Data Fig. 1a,b). Furthermore, these findings generalize widely across scientific fields. Studying each of the 154 subfields separately, the negative relationship between impact and pivot size held for 93% of fields, and the increasing severity of the pivot penalty over time occurred in 88% of all scientific fields (Supplementary Table 1). Turning to patenting, we again observed an increasingly steep pivot penalty with time (Fig. 2f). Studying 127 level-2 technology classes separately, the negative relationship between impact and pivot size held in 91% of classes, with the severity of the pivot penalty growing over time in 76% of patent classes (Supplementary Table 2). This steepening pivot penalty among inventors was also seen when using broader or narrower technological classifications (Supplementary Fig. 2). Earlier years for patenting showed flatter, less-monotonic relationships in the raw data (Fig. 2f) and within inventors’ portfolios (Extended Data Fig. 1c,d).

The findings of a substantial impact penalty are robust to many alternative measures and sample restrictions (see Supplementary sections 2.2–2.3 and 3.1–3.2 for analysis and further examination of high-pivot cases and outlier fields). Robustness tests considered: alternative time windows to determine citation impact (Extended Data Fig. 2); alternative measures of citation impact (Extended Data Fig. 3); sample restrictions to papers with larger reference counts (Supplementary Table 3); alternative pivot-size computation based on referenced papers’ field coding, as opposed to their journals (Supplementary Fig. 3); alternative field encodings for patents (Extended Data Fig. 4 and Supplementary Figs. 1, 2 and 4); and hand checks on high-pivot researchers (Supplementary section 3.1).

When examining outcomes, one can also look beyond the citation impact. For papers, we further measured whether a published paper was referenced in a future patent3,43, indicating the use of the idea beyond science. There was a large decline in patent references to high-pivot articles, with the probability of being cited in a patented invention declining by 43% (P < 0.001 in a two-sample t-test) when comparing the highest-pivot with the lowest-pivot vigintiles of papers (Extended Data Fig. 5b). We also examined the propensity for preprints to be published and found that higher-pivot preprints were published at substantially lower rates, with publication rates for the highest-pivot papers declining by 35% (P < 0.001) compared with the lowest-pivot papers, indicating another form of the pivot penalty (Extended Data Fig. 6). For patents, we considered the invention’s market value based on how a company’s stock price moved in response to the patent being issued[44]. The market value of a patented invention decreased steeply with pivot size, declining by 29% (P < 0.001) when comparing the highest-pivot with the lowest-pivot patents (Extended Data Fig. 7). These findings indicate that the pivot penalty also appears when considering publication success, practical use and market value, pointing to a constellation of outcomes that go beyond the citation behaviour within a community of researchers.

Altogether, we observed striking empirical regularities that generalize across science and technology. Despite the distinct nature of scientific articles and patents, the institutional contexts in which they are produced, the wide range of research fields and their alternative outcome measures, they have remarkable commonalities: for both scientists and inventors, greater pivots bring large penalties, and this increases over time.

Conceptual frameworks and mechanisms

Our findings indicate that researchers face substantial challenges when entering new subject areas, heightening concerns in innovation communities that research with wide reach or new orientations is difficult12,13,14,18,25. Entering new areas may be challenging as a matter of reception, whereby a scholar has difficulty penetrating new audiences, and it may be challenging as a matter of idea generation, as scholars can face problems generating valuable ideas outside their key areas of competency. To further inform the nature of the pivot penalty, we next examined the pivot penalty in view of both reputational perspectives and idea generation.

An established reputation in a local research community may provide impact advantages within that community but be a relative disadvantage outside it[39]. For example, the ‘Matthew effect’39,40 suggests advantages of established eminence within a community, but ‘typecasting’32,41,45 may undermine the reception when entering new areas. These and other reputational considerations indicate that the pivot penalty may emerge because researchers move beyond their usual audience. To test these considerations, we first examined pivots holding the researcher’s field or local audience fixed. Specifically, we examined what happens when a given researcher publishes multiple papers with different pivot sizes but in the same time frame and field, even in the same journal (Supplementary Table 4). We found that the pivot-penalty regression coefficient was approximately 26% less steep (Supplementary Table 4) when an individual published in the same journal, an attenuation that is consistent with a weakening of reputational forces when looking within a common audience, but most of the relationship remained. The pivot penalty thus persisted when the researcher published in a consistent field or before a consistent, local readership. A related approach considered impact within a given, distant audience. Recalling the findings for patented applications (Extended Data Fig. 5) and market value (Extended Data Fig. 7), the pivot penalty also appeared when examining how inventors draw on science or how investors value inventions. These evaluations are made by individuals who are far away from the focal researcher. In sum, the pivot penalty appears not simply as a matter of movement across fields, or from a local audience to a distant audience. Rather, it appears for a researcher within a given field or journal, and it appears within distant communities focused on practical use and market returns.

Reputational considerations may be further informed by considering career stage. Specifically, younger researchers, with less-formed reputations, may see less advantage (the Matthew effect) from staying in a given area or less penalty (typecasting) from venturing outside it41,46. Studying career stage, the pivot penalty was slightly stronger (1.6% steeper per year, P < 0.001, regression coefficient t-test; Supplementary Table 5) with advancing career age, consistent with these reputational frameworks. Yet the pivot penalty appears regardless of career stage, including very early in the career (Supplementary Table 5). The findings continued to indicate adaptive challenges, beyond the force of established reputations, when entering new research terrain.

Turning to frameworks of idea generation, a canonical perspective emphasizes an ‘explore versus exploit’ trade-off in creative search. Here, exploitation involves lower-risk but potentially lower-return search around the edges of one’s current focus, whereas exploration involves higher-risk but potentially higher-return forays into more-distant areas6,37,38. Related views indicate an advantage of outsiders in bringing new perspectives and driving breakthroughs10,11,47. Our analyses looked at upper-tail outcomes, but it is possible that the value of large pivots lies in even rarer, more extreme, positive outcomes. Surprisingly, however, we found that high-pivot research is increasingly under-represented at higher impact levels, whereas low-pivot research has advantages (Supplementary Fig. 5a,b). For example, studying the upper 1% and 0.1% of scientific works by citation impact, papers in the lowest decile of pivot size were over-represented by 65% and 91%, respectively (P < 0.001 in one-sample t-tests). By contrast, papers in the highest pivot size decile were under-represented by 69% and 73%, respectively (P < 0.001), among the upper 1% and 0.1% of citation impact. Rather than indicating a trade-off between risk and reward in exploratory search, or outsider advantages, these findings continue to indicate a fundamental difficulty of venturing into new areas.

Alternative idea-generation frameworks emphasize the value of specialized expertise. These frameworks link creative advantages less to outsider ideas and more to the accumulated facts, theories and methods built in an area by previous scholars47,48. The emphasis on expertise and the value of prior knowledge is consistent with Newton’s famous statement that “if I have seen further, it is by standing on the shoulders of giants”[49]. Furthermore, the steepening of the pivot penalty with time is consistent with increasingly narrow expertise as science progresses and knowledge deepens12,50,51. The publication findings (Extended Data Fig. 6) showing a monotonic decline in publication success rates as pivot size increased, and where the highest-pivot preprints were 35% less likely to be published in any journal, suggest the presence of substantive issues with these works, consistent with challenges in moving beyond one’s established areas of expertise. Related creativity frameworks emphasize that new works can be seen as new combinations of existing material52,53,54. Previous studies have shown that high-impact research is characterized primarily by highly conventional mixtures of prior knowledge but also tending to inject, simultaneously, a small dose of atypical combinations that are unusual in previous research8,55. Following this literature, we further measured the novelty and conventionality of combinations in a given paper and related these measures to pivot size and impact (Extended Data Fig. 8 and Supplementary Table 9). We found that high-pivot work was associated with a higher propensity for atypical combinations (Extended Data Fig. 8a), a feature also reflected in work linking inventors who switch fields to new technology combinations[14]. For example, 31% of the lowest-pivot vigintile of papers were characterized by high tail novelty, and 49–58% of papers in high-pivot vigintiles had high tail novelty. In other words, when pivoting, a researcher not only does something new personally, but also tends to introduce previously unseen combinations of knowledge to the broader research domain. However, at the same time, high-pivot papers showed distinctly low conventionality (Extended Data Fig. 8b), locating a key characteristic that such exploratory work tends to miss: a prevalence towards well-established mixtures of knowledge. For example, 79% of the lowest-pivot papers exhibited mixtures with high median conventionality, whereas only 27–30% of papers in high-pivot vigintiles had this characteristic. These findings indicate that researchers, as they shift to new areas personally, are equipped for novelty but limited in their relevant or conventional expertise, underscoring the difficulty researchers may face in venturing beyond their specialized knowledge.

Pivoting in response to external events

The pivot penalty indicates that larger pivots are strongly associated with lower impact. However, the research landscape is constantly shifting, and researchers must weigh opportunities nearer to and further from their current research streams. To further probe pivoting behaviour and the pivot penalty, we considered external events that may provoke researchers to pivot. External events can provide quasi-experimental settings and help to establish causal interpretations of the pivot penalty, and may further inform the tensions regarding how researchers navigate a shifting research landscape.

We first considered events that may push researchers away from an existing research stream. Specifically, previous research is sometimes revealed as incorrect or unreliable, which may encourage researchers who had been building on that work to move in new directions. Here we focus on paper retractions, which are of growing interest to the science community56,57,58. Using Retraction Watch and the Dimensions database, we identified 13,455 retractions over the 1975–2020 period. As a treatment group, we considered researchers whose work referenced a retracted paper before it was retracted (but who were not authors of the retracted study). As a control group, we considered researchers who referenced other papers appearing in the same journal and year as the retracted paper. We further used coarsened exact matching[59] to match treated and control authors by their publication rates before the retraction year. We then compared pivots and hit rates between the treatment and control groups, over the four years before and the four years after retraction events, in a difference-in-differences design (Fig. 3a and Difference-in-differences).

Fig. 3: Pivots and retraction events.

a, This difference-in-differences analysis compares treated scientists who directly cite a paper before its retraction with control scientists who cited other papers in the same journal and year as the retracted paper, but not the retracted paper. There are 164,988 treated authors who cited a retracted paper at least once (18,505 treated authors who cited it at least twice) before its retraction but are not themselves authors of the retracted papers. Pivot size and impact of papers from these treated scientists is compared with papers from equal numbers of matched control scientists before and after the year of retraction. b, Pivot size significantly increases for treated scientists relative to control scientists after the retraction (0.025 ± 0.001 s.e. pivot-size increase, P < 0.0001, regression, n = 5.82 million author-by-paper observations). The effect is larger when focusing on scientists who cited the retracted paper at least twice (0.037 ± 0.001 s.e. pivot-size increase, P < 0.0001, regression, n = 2.96 million author-by-paper observations). c, Hit rates fall for treated scientists after retraction (−0.004 ± 0.001, P < 0.0001, n = 5.82 million), and again the effect is stronger for those citing the retracted paper at least twice (−0.007 ± 0.001, P < 0.0001, n = 2.96 million). d,e, Year-by-year analysis comparing treated and control authors further shows that the increase in pivot size is statistically significant (P < 0.001) starting immediately in the retraction year (d) and the decrease in hit rate becomes statistically significant (P < 0.05) starting the year after the retraction (e). In b–e, bars and markers represent the difference-in-differences regression coefficients, and the whiskers show the 95% confidence interval derived from the regression standard errors (see Difference-in-differences). Fig. 3a, icons adapted from Apple.

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We found that pivot sizes increased markedly after a retraction event (Fig. 3b). Consider first the 164,988 treated researchers who referenced a retracted paper at least once before its retraction. The mean pivot size for these researchers’ works after the retraction increased by 2.5% (P < 0.001) compared with control researchers’ works. We also studied a smaller treatment group of 18,505 researchers who referenced a retracted paper multiple times, indicating more intensive use. For this group, pivoting was larger, with mean pivot sizes increasing by 3.7% (P < 0.001) after the retraction, compared with the control authors (Fig. 3b). Turning to paper impact, treated authors experienced a 0.4% decline (P < 0.001) in hit rate after the shock, compared with control authors (Fig. 3c). Among treated authors who drew on the retracted study multiple times, we saw not only larger pivots (Fig. 3b), but also a larger 0.7% decline (P < 0.001) in hit rates after the retraction event (Fig. 3c).

Difference-in-differences analyses on a year-by-year basis reinforced these findings. Figure 3d shows an increase in pivoting starting in the retraction year. Similarly, Fig. 3e shows a sustained decline in hit rates after the retraction. Two-stage least-squares regressions, with the retraction event as an instrument, further show that these ‘push’ pivots predict substantial declines in impact (Supplementary Table 6). Robustness tests using hit rates and citation counts over alternative periods, or using alternative definitions of the treated group, showed confirmatory results (Supplementary section 2.7.1, Supplementary Table 6 and Supplementary Figs. 6 and 7). We further considered a smaller case study of replication failures, rather than retractions, drawing on a landmark 2015 study of reproducibility in psychology[60], for which 100 papers were quasi-randomly chosen for evaluation and 64 contained non-reproducible results. Deploying the same treatment and control method as for paper retractions, this smaller study provided confirmatory results for pivoting and impact (Supplementary Section 2.7.2 and Supplementary Table 7). Altogether, we saw pivoting increases and hit-rate declines in response to these external shocks. These analyses further confirm the findings of the pivot penalty, now in response to external events that push authors into new areas.

Beyond push-type events, researchers may also be pulled into new subject areas when new and important research questions emerge. This leads to our second case study, analysing how researchers shifted to engage with the COVID-19 pandemic. The advent of the pandemic enabled large-scale investigation of individual researcher pivots and further showed how science as a whole responds to a new and consequential demands on the research community. Indeed, confronted by COVID-19, the world looked to science to understand, manage and construct solutions, all in a rapid fashion. Given that few researchers were studying coronaviruses or pandemics before 2020, and none were studying COVID-19 specifically, the emergence of COVID-19 called on researchers across the frontiers of knowledge to consider shifting their work to address new, high-demand research questions61,62,63.

Figure 4 shows that pivoting to address COVID-19 was widespread. Although the earliest papers on COVID-19 did not appear until January 2020 (refs. 64,65), by May 2020, 4.5% of all new scientific papers were related to COVID-19 (Fig. 4a). Furthermore, although fields differed in the share of their papers that engaged COVID-19, all fields produced at least some COVID-19-related research (Fig. 4b and Extended Data Fig. 9). Health sciences exhibited the greatest COVID-19 orientation, but the social-science fields of economics, education and law also addressed COVID-19 intensely, speaking to the pandemic’s socio-economic challenges66,67. Furthermore, studying each field that had at least 20 COVID-19 papers, mean pivot sizes were larger for COVID-19 papers than for other papers in that field (Fig. 4d; mean difference positive for 100% of fields, t-test significant at P < 0.05 for 97% of fields). Figure 4c also tracks a cohort of scientists across the body of their work, comparing authors who wrote a COVID-19 paper in 2020 with a control set of authors who did not (Supplementary section S2.8). We found that pivot size presented a clear jump for COVID-19-related work, for which COVID-19 authors pivoted to an unusual degree compared with their own publication history (P < 0.001 in t-tests of means), their non-COVID 2020 papers (P < 0.001) and the control authors (P < 0.001). In sum, unusually large individual pivots were a widespread phenomenon as scientists sought to address COVID-19.

Fig. 4: Pivots and the COVID-19 pandemic.

a, Science rapidly shifted to COVID-19 (COVID) research in 2020, when COVID-19 publications rose to 4.5% of all science publications in May 2020 and maintained high rates thereafter. b, Health sciences and social sciences featured the strongest responses, but all scientific fields engaged in COVID-19 research. c, Scientists who wrote COVID-19 papers pivoted to a greater extent than they did in their previous work, in their other 2020 work, or than matched control scientists did. d, Comparing COVID-19 and non-COVID-19 papers in each field in 2020, unusually large pivots have been a universal feature of COVID-19 research. e, COVID-19 papers experienced an impact premium, but the pivot penalty appeared in both COVID-19 and non-COVID-19 work. Comparing at the median pivot sizes (dashed lines), the COVID-19 impact premium was substantially offset by the pivot penalty, given its larger median pivot size. f,g,h, Engaging new collaborators was particularly common for COVID-19 researchers, who worked with new collaborators to an unusual degree compared with their own previous work, their other 2020 publications and with control scientists (f). Nonetheless, the pivot penalty persisted for big and small teams (g) and when engaging new or existing co-authors (h). i,j, Higher-pivot work was substantially less likely to acknowledge funding support in the sciences as a whole (blue) and among COVID-19 papers (red). COVID-19 papers were particularly unlikely to acknowledge grant support (i), yet the pivot penalty appeared even among both funded work and non-funded work (j). k, Although individual, collaborative and funding features sharply conditioned the adaptive response of science, in regression analysis they did not individually or collectively overcome the fundamental pivot penalty. Coronavirus icon adapted from the Noun Project (https://thenounproject.com).

Source data

We next turned to impact. Given that 2020 papers have had less chance to receive citations[68], we examined journal placement, for which each journal was assigned the historical hit rate of its publications within its field and year (Supplementary section 2.3). Figure 4e considers papers published in 2020, separating them into 82,900 COVID-19 papers and 2.63 million non-COVID papers. We found a premium associated with COVID-19 papers, as reflected by the upward shift in journal placement, consistent with the extreme interest in the pandemic. Yet the negative relationship between pivoting and impact persisted: comparing the highest-pivot and lowest-pivot bins, COVID-19 and non-COVID-19 papers had declines in hit rate of 61% and 59%, respectively (P < 0.001 in t-tests of means). Thus, scientists who ventured further from their previous work to write COVID-19 papers were not immune to the pivot penalty; rather, they produced research with less impact, on average, relative to low-pivot COVID-19 papers. The pivot penalty in COVID-19 papers also appeared net of individual fixed effects (Supplementary Fig. 8). Importantly, the pivot penalty was sufficiently steep that the COVID-19 impact premium was mostly offset by the unusually large pivots associated with COVID-19 research. For example, the upper 45% of COVID-19 papers by pivot size had lower average journal placement than did non-COVID papers with median or smaller pivot size.

In sum, the ‘pull’ nature of COVID-19 presented two extremely strong yet contrasting relationships regarding impact. On one hand, this work experienced an impact premium, consistent with the value of researching high-demand areas. On the other hand, greater pivot size markedly predicted less-impactful work. These findings underscore a central tension for individual researchers and the adaptability of science in response to external opportunities: working in a high-demand area has value, but pivoting leads to penalties that offset it.

Building on the science of science literature, we further considered numerous potential moderating factors and forms of heterogeneity that may facilitate pivots. These include researcher career stage, productivity, project-level team size, the use of new co-authors, and funding8,35,42,69 (see Binned scatterplots and Supplementary section 2.9). For example, early-career researchers may have greater creative flexibility7,26,47, and larger team size or new co-authors may extend reach33,70. When examining impact, however, we found that the pivot penalty persisted, regardless of these features (Fig. 4f–j and Supplementary Table 5). We further used regression methods to incorporate detailed controls for all these potential moderating factors together and found that the pivot penalty appears net of all these features (Fig. 4k), highlighting the depth and breadth of the adaptive challenge.

Discussion

Science must regularly adapt to new opportunities and challenges. The findings in this study, however, highlight difficulties in adapting research streams, with implications for individual researchers, research organizations, and science and society as a whole. At an individual level, a researcher must consider whether to continue exploiting a familiar research stream or explore opportunities that lie further away. Research on creativity reveals the value of exploration, novelty and outsider advantages6,7,8,9,10,11,33,34,35,37, indicating a risk-versus-reward trade-off when researchers venture further from their existing expertise. However, other viewpoints emphasize the value of deep expertise, especially in drawing on the frameworks, facts and tools built by previous scholars12,47. As Einstein observed: “Knowledge has become vastly more profound in every department of science. But the assimilative power of the human intellect is and remains strictly limited. Hence it was inevitable that the activity of the individual investigator should be confined to a smaller and smaller section”[50]. Consistent with both Einstein’s observation and previous studies indicating increasing specialization and disadvantages when inventors switch fields12,14, we found that researchers face systematic challenges to pivoting their research, and these increase with time. This pivot penalty applies to knowledge production in both science and technology, generalizes across research subfields and extends beyond impact and publication measures to the practical use and market value of ideas, external to the research domain. Our analyses deploy numerous proxies for quality, such as citation impact, home-run rates, publication success, novelty, conventionality and applied value, but the intrinsic quality of a paper or patent is a multidimensional and open concept.

The pivot penalty also appears in response to external events that may push a researcher away from a given area or pull them into a new one. The enormous demand for COVID-19-related research attracted numerous researchers and provided an impact premium, yet the pivot penalty continued to appear strongly among scholars who reached further to engage with COVID-19 research. All told, the pivot penalty applies to a range of outcomes that are of central interest to researchers and research institutions, and it applies in high-stakes contexts for society as a whole.

The pivot penalty, and its steepening with time, raises key questions for research organizations and research policy. For example, businesses and other organizations are often displaced by new entrants52,71, despite R&D efforts by the incumbents, which often fail to understand or embrace new technological opportunities6,38,72. The pivot penalty underscores this challenge and points towards tactics such as ‘acquihires’, in which a research organization seeks to hire relevant experts, rather than expecting success by pivoting their existing personnel73,74.

More broadly, the pre-positioning of researchers seems to be a fundamental constraint on adaptability. In Pasteur’s words, “chance favours only the prepared mind”, and without the pre-positioning of relevant human capital, the COVID-19 pandemic would probably have been even more costly. Portfolio theory suggests diversified investments as a key tool to manage risk[75], but the pivot penalty indicates that adjustments to the research portfolio are governed by substantial inertia[76]. From this perspective, investing explicitly in a diverse set of scientists is crucial from a risk-management standpoint. A diverse portfolio of investments can then have essential roles in advancing human progress in ordinary times7,77 and in expanding the capacity to confront emerging challenges.

Science and technology present evolving demands from many areas, from artificial intelligence and genetic engineering to climate change, creating complex issues, risks and urgency. This study shows that pivoting research is difficult, with researchers’ pivots facing a growing impact penalty. The pivot penalty not only appears generally across scientific fields and patenting domains, but also arises around important events in science, including when previous research areas become devalued, for example after a paper has been retracted, and when high-demand areas emerge, such as the COVID-19 pandemic. Nevertheless, studying adaptability in different settings and timescales, including longer-run research shifts, are key areas for future work. For example, researchers should consider whether to give up in the likely event of a failed pivot or instead further develop their expertise in the new area and stick to the new path. Exploring such sequential dynamics may help us to better understand how to create conditions to enable adaptive success. Finally, pivoting to address emerging challenges is not unique to science and technology, but may underpin the dynamics of success and survival for individuals, companies, regions and governments across human society5,72,78,79,80,81, indicating that the pivot penalty may be a generic property of many social and economic systems, with potential applicability in broader domains.

Methods

Pivot size

We quantified researcher pivots using a cosine-similarity metric (Fig. 1a). Specifically, in the sciences, for an author i and a focal paper j, we calculated a vector, \({{\bf{R}}}_{{\rm{i}}}^{{\rm{j}}}\), representing the distribution of journals referenced by j. Similarly, we counted the frequency in which different journals were referenced in i’s previous work, defining a vector Ri. An individual’s work includes any paper for which the individual is a listed author. The pivot measure, \({\varPhi }_{{\rm{i}}}^{{\rm{j}}}\), is then defined as 1 minus the cosine of these two vectors:

$${\varPhi }_{{\rm{i}}}^{{\rm{j}}}=1-\frac{{{\bf{R}}}_{{\rm{i}}}^{{\rm{j}}}\cdot {{\bf{R}}}_{{\rm{i}}}}{\parallel {{\bf{R}}}_{{\rm{i}}}^{{\rm{j}}}\parallel \parallel {{\bf{R}}}_{{\rm{i}}}\parallel }.$$

(1)

The measure \({\varPhi }_{{\rm{i}}}^{{\rm{j}}}\) therefore took the value 0 if the focal paper drew on exactly the same distribution of journals as the author’s previous work, and took the value 1 if the focal paper drew entirely on new journals for that author. The measure featured in the main text calculates pivoting in the focal paper compared with the past three years of the author’s work. We also calculated our measure by using all previous work of a given author, arriving at similar conclusions (Supplementary section 2.2.1 and Extended Data Fig. 10). Finally, we considered the pivot measure based on the fields of the cited references, rather than their journals, and again found confirmatory results for our main analyses using this alternative measure (Supplementary section 2.2.1 and Supplementary Fig. 3).

For patents, given that journal information was not available, we used technological field codes to define the reference vectors. Specifically, we used the distribution of Cooperative Patent Classification technology field codes among a patent’s cited references to build the reference vectors and cosine similarity metric in equation (1). These technology codes are hierarchical, providing alternative levels of granularity in defining technology areas. Our main analyses used the detailed level-4 technological classification (comprising 9,987 distinct technology areas). We further examined all possible classification levels in the Supplementary Information, considering pivoting from the broadest level-1 classification level (9 sections) to the most detailed level-5 classification (210,347 subgroups). Intuitively, the pivot distribution for inventors shifted left when using broader technology categories (Extended Data Fig. 4), so that inventors pivoted less from their broadest technology areas (the section or section-class level). Regardless of the technological classification used, the pivot penalty was robust (Supplementary Fig. 1).

Outcome measures

We used citation-based and non-citation-based outcome measures. Our citation-based measures normalized outcomes for each work by its field and year. For papers, the primary citation measure was an indicator for being in the upper fifth percentile among all articles published in the same year and the same field. The field designation was the L1 field of research designation, for which there are 154 fields in the Dimensions database. For patents, we similarly used an indicator for being in the upper fifth percentile of citations received among all patents from the same year and technology area, using the Cooperative Patent Classification class-level designation, for which there are 128 technology areas.

As presented in the Supplementary Information, we considered numerous alternative citation-based measures. These include smoother (non-binary) outcomes, in which a paper’s citation count is normalized by the mean citation counts to articles in the same field and publication year. We further considered the outcome as the percentile rank of the paper’s citations among all articles published in the same field and year. To examine time frames, we further considered citation counts over two, five and ten-year forward citation windows. Finally, we considered alternative binary indicators to further emphasize the locus of the very highest-income work, defining a ‘hit’ paper as being alternatively in the upper 10, 5, 1, 0.5 or 0.1% percent of all publications in a given field and year. Supplementary section 2.3.2 provides further details and associated robustness tests for all these alternatives.

Among the non-citation-based measures, we considered numerous other outcomes. These included measures of publication success, for which we considered preprints from 2015 to 2018 and examined whether they were successfully published over an ensuing five-year window. Drawing on the Reliance on Science database[43], we examined whether a paper appeared as a prior art reference in a future patent, providing an indicator for the usefulness of the idea beyond science[3]. We considered journal placement for recent work. For patents, we also used stock-market event study data[44], providing a market-value measure for patents in publicly traded firms. Supplementary section 2.3.3 provides further details and results for all these outcomes.

Binned scatterplots

To reveal potentially nonlinear relationships between pivot size and outcome variables, we use binned scatterplots[82]. In Fig. 2a, papers are ordered by average pivot size along the x axis and binned into 20 evenly sized groups. Each marker is placed at the mean (x,y) value within each group. Binned scatterplots of raw data are further presented in Figs. 2c,e,f and 4e,g–j, and Extended Data Figs. 2, 3, 5–8 and 10b. Student’s t-tests are used to test mean differences from baseline rates (one sample t-tests) or when comparing outcomes between high and low pivot-size vigintiles (two sample t-tests) in raw data. For simplicity, we report P < 0.001, but note that with observation counts in the millions, these mean tests tend to reject common means with extremely high t-statistics and extremely low P values.

Figure 4e uses the binned-scatterplot approach for papers in the year 2020, splitting them into articles related or unrelated to COVID-19. Similarly, Fig. 4g–j presents binned scatterplots, further splitting the 2020 papers according to the noted criteria (team size, use of new collaborators and funding). In Fig. 4k, we account for multivariate controls. We consider regression of the form

$${{\rm{Impact}}}_{{\rm{i}}}=\alpha +f({{\rm{Pivot}}\_{\rm{size}}}_{{\rm{i}}})+{\boldsymbol{\theta }}{{\bf{X}}}_{{\rm{i}}}+{\varepsilon }_{{\rm{i}}},$$

where Xi is a vector of control variables with associated vector of coefficients Θ; f(Pivot_sizei) allows for a nonlinear relationship between the outcome and pivot size; and εi is the error term. Control variables include fixed effects for average previous impact, author age, team size, number of new collaborators and an indicator variable for funding. In practice, we ran two regressions to residualize pivot size and impact, net of the controls, following the Frisch–Waugh–Lovell theorem. Figure 4k presents the binned scatterplot for the residualized measures.

Regressions with individual fixed effects

The panel regression with individual fixed effects in general takes the form:

$${{\rm{Impact}}}_{{\rm{ipt}}}={\mu }_{{\rm{i}}}+{\gamma }_{{\rm{t}}}+f({\rm{Pivot}}\_{{\rm{size}}}_{{\rm{ipt}}})+{\boldsymbol{\theta }}{{\bf{X}}}_{{\rm{ipt}}}+{\varepsilon }_{{\rm{ipt}}},$$

where i indicates a given researcher, p indicates a given work (paper or patent) and t indexes the year (publication year for a paper and application year for a patent); μi are individual-fixed effects, γt are time-fixed effects and Xipt is a vector of other potential control variables. Observations are at the paper-by-researcher level. As before, we allowed for potentially nonlinear relationships between pivot size and impact, and hence took a non-parametric approach. Specifically for Extended Data Fig. 1, we generated bins of pivot size and included indicator dummies for a work appearing in the relevant bin. Given the very large number of individual fixed effects, we ran these models in Stata using the reghdfe command suite[83]. Standard errors are clustered at the researcher level. The statistical significance of different pivot-size bin coefficients was calculated using t-tests. For simplicity, we report P < 0.001, but note that with observation counts in the millions, these tests present extremely high t-statistics and extremely low P values.

Difference-in-differences

When studying external shocks, we continued to use the researcher panel data model with individual-fixed effects. We implemented standard difference-in-differences methods, comparing treated researchers with control researchers, before and after the external event. The regressions take the form:

$${{\rm{Pivot}}\_{\rm{size}}}_{{\rm{ipt}}}={\mu }_{{\rm{i}}}+{\gamma }_{{\rm{t}}}+\beta {{\rm{Treat}}\_{\rm{Post}}}_{{\rm{ipt}}}+\gamma {{\rm{Post}}}_{{\rm{ipt}}}+{\varepsilon }_{{\rm{ipt}}}$$

$${{\rm{Impact}}}_{{\rm{ipt}}}={\mu }_{{\rm{i}}}+{\gamma }_{{\rm{t}}}+\beta {{\rm{Treat}}\_{\rm{Post}}}_{{\rm{ipt}}}+\gamma {{\rm{Post}}}_{{\rm{ipt}}}+{\varepsilon }_{{\rm{ipt}}},$$

where Postipt is an indicator for the period after the shock. The indicator for being in the treatment group is absorbed with an individual’s fixed effect and so does not appear separately in the regression. Treat_Postipt is an indicator for being in the treatment group after the shock and provides the reported difference-in-differences estimate in Fig. 3. The implications of the external event for pivot size and the reduced form results for impact are shown in Fig. 3b,c. We also present ‘event study’-style difference-in-differences plots in Fig. 3d,e, showing how the treatment effect evolved before and after the retraction date. Here we replace the Treat_Postipt variable and Postipt variable with a series of relative year indicators and their interactions with treatment status.

Reporting summary

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

Data availability

The de-identified data necessary to reproduce the main plots and statistical analyses (including individual-level pivot size and other key variables) are freely available. Patent data are publicly available at https://patentsview.org/download/data-download-tables. Paper-retractions data are publicly available at https://www.crossref.org/categories/retractions/. NSF grant data are publicly available at https://www.nsf.gov/awardsearch/. NIH grant data are publicly available at https://reporter.nih.gov/. Reliance on Science data are publicly available at https://doi.org/10.5281/zenodo.5803985 (ref. [84]). KPSS patent-value data are publicly available at https://github.com/KPSS2017/ Technological-Innovation-Resource- Allocation-and-Growth-Extended-Data. Those interested in raw Dimensions data should contact Digital Science directly. Data are available through the main project folder at https://doi.org/10.6084/m9.figshare.28074941 (ref. [85]). All other data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The code necessary to reproduce the main plots and statistical analyses is available at https://doi.org/10.6084/m9.figshare.28074941 (ref. [85]).

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Acknowledgements

We thank seminar participants at University College London, the University of California, Berkeley, Brigham Young University, Georgetown University, the University of Michigan, Northwestern University, Yale University and conference participants at the American Economic Association annual meetings for comments. We acknowledge grant support from the Air Force Office of Scientific Research (award number FA9550-19-1-0354), the National Science Foundation (award number 2404035), the Alfred P. Sloan Foundation (award number G-2019-12485) and the Peter G. Peterson Foundation (award number 21048).

Author information

Author notes

1. These authors contributed equally: Ryan Hill, Yian Yin

Authors and Affiliations

1. Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA

Ryan Hill, Yian Yin, Dashun Wang & Benjamin F. Jones

2. Kellogg School of Management, Northwestern University, Evanston, IL, USA

Ryan Hill, Xizhao Wang, Dashun Wang & Benjamin F. Jones

3. Ryan Institute on Complexity, Northwestern University, Evanston, IL, USA

Yian Yin, Dashun Wang & Benjamin F. Jones

4. McCormick School of Engineering, Northwestern University, Evanston, IL, USA

Yian Yin & Dashun Wang

5. Department of Information Science, Cornell University, Ithaca, NY, USA

Yian Yin

6. Haas School of Business, University of California, Berkeley, Berkeley, CA, USA

Carolyn Stein

7. Department of Economics, University of California, Berkeley, Berkeley, CA, USA

Carolyn Stein

8. Northwestern Innovation Institute, Northwestern University, Evanston, IL, USA

Dashun Wang

9. National Bureau of Economic Research, Cambridge, MA, USA

Benjamin F. Jones

Contributions

R.H., Y.Y., C.S., D.W. and B.F.J. conceptualized and designed the research and reviewed all the results. R.H. and Y.Y. did the primary statistical analysis, and C.S. led in several analyses. X.W. led in studying retraction events. B.F.J. and D.W led in drafting the manuscript, and all authors contributed to revisions.

Corresponding authors

Correspondence to Dashun Wang or Benjamin F. Jones.

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Extended data figures and tables

Extended Data Fig. 1 The pivot penalty in science and invention over time, within individual researchers.

Using a 5% subsample of paper authors and all patent inventors, we divide the data into two periods, 1986–2000 (n = 5.7 million author-paper pairs, n = 568 thousand inventor-patent pairs) and 2001–2015 (n = 23.2 million author-paper pairs, n = 2.0 million inventor-patent pairs). In each period, we run regressions with individual fixed effects. (a-d) The relationship between hit rates and pivot size is estimated non-parametrically, with fixed effects for different ranges of pivot size. The figures present the coefficient for each pivot size group, with indicated 95% confidence intervals. The slope of the pivot penalty is increasing over time when looking within individual researchers. For papers, the recent period (b) shows a monotonic decrease in hit rate with pivot size, within the body of work of individual researchers (confidence intervals are too small to be seen). The earlier period (a) similarly shows a monotonic decrease in hit rate with pivot size, but the slope of the relationship is shallower. For patents, the recent period (d) shows a monotonic decrease in hit rate with pivot size, within the body of work of individual researchers. The earlier period (c) has noisier estimates, with a flatter relationship to pivot size and potential non-monotonicity, but where high pivots face large impact penalties. Overall, we see an increasingly steep pivot penalty with time.

Source data

Extended Data Fig. 2 The pivot penalty over alternative time horizons.

The baseline pivot penalty (Fig. 2a) uses the hit rate measure, normalizing impact by field and publication year, providing one means for addressing different time horizons for citations from different publication years. Alternatively, for the same data (n = 25.8 million papers published from 1970–2015), one can count and normalize citations received over a fixed window of time after the publication year. (b-d) Hit rates are computed using citations received by each paper over, alternatively, (b) 2 year, (c) 5 year, and (d) 10 year forward windows. The pivot penalty is robust using all of these alternatives.

Source data

Extended Data Fig. 3 The pivot penalty with smoother citation measures.

In addition to binary measures of impact, one can consider more continuous measures using the same data (n = 25.8 million published from 1970–2015). In (a) we normalize each paper’s citation count as a ratio to the mean citations for papers in that field and publication year. Citations are approximately 30% above the field mean for low pivot papers on average and 55% below the field mean for the highest pivot papers on average. In (b) we normalize each paper’s citations by its percentile in the citation distribution for all papers published in the same field and year. The pivot penalty is also robust to this measure of impact.

Source data

Extended Data Fig. 4 Quantifying pivot size using various levels of patent technology classification.

For patents granted from 1975–2015, the pivot size distribution is bimodal, with more weight on pivots of size zero and one (n = 3.3 million inventor-by-patent observations). The average pivot size increases as the definition of technology class used to calculate pivoting narrows. The available levels of technology class are: (a) 9 sections (e.g., “B”), (b) 128 classes (e.g., “B29”), (c) 662 subclasses (e.g., “B29C”), (d) 9,987 groups (e.g., “B29C45”), and (e) 210,347 subgroups (e.g., “B29C45/64”). The main analysis in Figs. 1 and 2 use level-4 groups to define pivot size.

Source data

Extended Data Fig. 5 Patent references to papers.

The probability that an academic paper is referenced by at least one patent declines at larger pivot sizes. The data considers 37 million papers published from 1970–2019. Panel (a) considers raw data, with no controls, and indicates non-monotonicity at lower pivot sizes. Panel (b) considers the relationship net of level-1 field fixed effects, which accounts for the fact that some fields (e.g., astronomy) are far less likely to be referenced in patents than others (e.g., nanotechnology). As seen in the figure, controlling for field largely eliminates the non-monotonicity. Comparing the highest and lowest pivot size bins in (b), the probability of being cited in a patented invention declines by 43% (p < .001 in two-sample t-test of means).

Source data

Extended Data Fig. 6 Successful publication.

This figure analyzes all 1.07 million preprints released from 2015–2018 on preprint databases such as arXiv and SSRN. For each preprint, we examine whether it has been published within a five-year window from its preprint date. Virtually all low pivot size papers are published. But publication success declines smoothly with pivot size. Comparing the highest and lower pivot size bins, the publication success rate declines by 35% (p < .001 in two-sample t-test of means). The monotonic decline in publication success provides a further dimension of the pivot penalty. See Section S2.3.3 for further discussion.

Source data

Extended Data Fig. 7 Patent market value.

The estimated market value of patents is decreasing in average pivot size. Market value is estimated using changes in stock prices around the announcement of patent grants for public companies. The sample is 802,599 patents published between 1980 and 2015 that were granted to public corporations. Market valuations are as calculated in[44]. Comparing the highest and lowest pivot size bins, market value declines by 29% (p < .001 in two-sample t-test of means).

Source data

Extended Data Fig. 8 Novelty, conventionality and pivot size.

The probability that a paper is characterized by (a) high tail novelty or (b) high median conventionality in relation to pivot size. Measures are calculated using combinations of references in new academic papers, examining 20.8 million papers over the 1970–2015 period[8]. Overall, novelty is increasing with pivot size while conventionality decreases. A researcher who is pivoting not only does something new personally but also tends to combine prior knowledge in a way that is unusual in science. At the same time, high pivots are associated with distinctly low conventionality, consistent with a weaker grounding in conventional domain knowledge.

Source data

Extended Data Fig. 9 COVID share by subfield.

This figure reports COVID-19 papers as a fraction of all 2020 publications in specific level-1 fields. Presented here are the 20 medical and 20 non-medical level-1 fields that have the highest fraction of COVID papers.

Source data

Extended Data Fig. 10 Quantifying pivot size using an author’s full publication record.

In the main text, we measure pivot size comparing the author’s focal paper with that author’s prior three years of work. Here we examine pivot size using the entire history of that author’s work (n = 8.43 million author-paper pairs). (a) The large shift in pivot size for COVID papers is evident when pivot size is measured by comparing 2020 papers to all past work. This shift is comparable to Fig. 1b, where pivot size is measured using only papers published in the prior three years. (b) The negative relationship between pivot size and impact is similar in slope when using the full career pivot metric here or the 3-year metric as shown in Fig. 4e.

Source data

Supplementary information

Supplementary Information

Supplementary Sections 1–5, including Data descriptions, Supplementary Methods, Additional Analyses, Supplementary Figs. 1–13, Supplementary Tables 1–10 and Supplementary References.

Reporting Summary

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Source data

Source Data Figs. 1–3 and 4 and Source Data Extended Data Figs. 1–9 and 10.

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Hill, R., Yin, Y., Stein, C. et al. The pivot penalty in research. Nature (2025). https://doi.org/10.1038/s41586-025-09048-1

- Received: 25 May 2021

- Accepted: 18 April 2025

- Published: 28 May 2025

- DOI: https://doi.org/10.1038/s41586-025-09048-1

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Abstract

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An animal’s survival depends on accurately identifying and navigating to new food locations. Rapid spatial learning depends on the CA3 hippocampal region in which recurrently connected pyramidal cells form rapid associations, including associations between locations and other information, such as outcomes1,2,3,4. In new environments, these pyramidal cells quickly form location-specific firing fields that tile the space to represent many positions with more pyramidal cells firing at goal locations5,6,7,8,9. To associate location with crucial outcomes such as food, pyramidal firing patterns representing paths to and from food locations are reactivated when an animal pauses to eat9,10,11,12. This reactivation, which is essential for spatial learning and memory, drives cells to fire together on short timescales and promotes plasticity between recurrently connected pyramidal cells13,14. Thus, pyramidal cells over-represent and reactivate crucial locations, yet the mechanisms by which these changes occur selectively at important locations are unclear. Signals indicating that a location is crucial to the animal could act directly on excitatory cells or act through inhibitory activity that powerfully affects excitatory cells (Fig. 1a). We hypothesized that hippocampal interneurons have a key role in identifying and remembering goal locations through selective decreases in inhibitory activity around food locations. Decreases in inhibition after a reward would be well suited for enhancing representations and reactivation of reward locations, and decreases in inhibition that predict a reward would facilitate learning about locations that lead to reward.

Fig. 1: Interneuron firing decreases as mice approach learned goals.

a, Potential mechanisms of goal learning. b, The task with electrophysiology recording. c, The timeline (track C is not shown). d, Example receiver operating characteristic (ROC) curves indicating a decrease in mouse movement speed in the zone prior to the RZ of familiar (fam.; black) and novel (nov.; green) tracks. e, Performance based on the raw AUC (left, day 1 versus 2, P = 0.001; day 1 versus 3, P = 0.001) and the percentage change in AUC (right, day 1 versus 2, P < 0.0001; day 1 versus 3, P < 0.0001; linear mixed-effects model (LMM) with Tukey correction). n = 14 (familiar) and 13 (novel) sessions from 7 mice. f, Raw (left) and residual (right) NS interneuron normalized firing rates according to the position in the familiar track with RZs (pink). g, Normalized firing rates as in f according to the distance to the RZ for each cell type. h, The raw firing rates averaged across NS interneurons. i, Normalized residual firing over distance (left) or time (right) to the RZ for NS interneurons (NS Int., blue, n = 196 cells) and pyramidal cells (Pyr., red, n = 1,157 cells). Significant effect of position (NS interneurons, P < 2.2 × 10−16; pyramidal cells, P < 2.2 × 10−16) and time (NS interneurons, P < 2.2 × 10−16;  pyramidal cells, P < 2.2 × 10−16; LMM). j, The proportion of units with significantly decreased (NS interneurons, left, n = 12, 14 and 14 sessions, days 1–3) or increased (pyramidal cells, right, n = 13, 14 and 14 sessions, days 1–3) firing (novel day 1 versus 2, P = 0.003; day 1 versus 3, P = 0.02; LMM with Tukey correction). k, Example spikes of an opto-tagged unit. str. pyr., stratum pyramidale. l, The residual firing (left) and the cell-averaged change in firing (right) for PV cells (n = 41 cells). Significant effect of position (P < 2.2 × 10−16, LMM). For e,h–j,l, data are mean ± s.e.m. For i,l, the coloured bars indicate significantly different bins (one-sided t-tests with Bonferroni correction). The mouse illustration was adapted from scidraw.io. *P < 0.05, ***P < 0.005, ****P < 0.001. The brain (F. Caudi) in the mouse head (E. Tyler and L. Kravitz) diagrams in b,k were adapted under a CC BY 4.0 licence; see the Methods for further details.

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Hippocampal inhibitory interneurons both respond to and control coordinated excitatory cell firing15,16,17. Previous studies have shown that inhibitory cells have location-selective decreases in firing that tile the environment like reverse place fields, and these decreases can regulate place-field formation and refinement18,19,20,21,22,23. We therefore hypothesized that preceding and at reward locations, coordinated decreases in inhibitory activity across multiple cells enable the identification and learning of goal locations. Previous research has shown changes in interneuron firing in the CA1 after animals received a reward, but these changes included a mix of both increases and decreases in interneuron firing21,24. It is not known whether coordinated spatially specific decreases in inhibitory activity occur preferentially at important locations, such as food sources, nor whether decreases in inhibitory activity precede reward locations. Reward-selective coordinated decreases in interneuron firing preceding reward locations could facilitate learning not only locations in which reward was found but also locations that lead to reward, a crucial process to finding reward. The CA3 is an essential nexus of rapid spatial learning in a new environment and is required for the reactivation of previous patterns of activity2,3,4,12,25,26. Accordingly, we determined whether CA3 interneurons decrease firing preferentially at reward locations during learning and whether such decreases enable learning and learning-associated plasticity events, like reactivation of goal locations after reward.

Rapid learning of new goal locations

To investigate hippocampal activity during new spatial learning, we developed a virtual reality (VR) behavioural paradigm in which we could quantify progressive learning of goal locations in a controlled environment. In an annular track with 36 equally sized zones with unique wall patterns, mice had to lick in three reward zones (RZs) to receive a reward (Fig. 1b and Extended Data Fig. 1a). We used timeouts to discourage mice from licking indiscriminately and placed three RZs unevenly around the track to prevent mice from using a distance-based strategy. After 7–14 days of training in a familiar track (track A), mice reduced their movement speed by an average of 29% and increased their lick rate by an average of 21% when they approached the zone immediately preceding the RZ, the anticipatory zone (AZ), indicating that they had learned the RZ locations (Extended Data Fig. 1a). Successful task performance required visual cues; mice did not show a significant change in movement speed or licking around well-learned goal areas if visual cues were removed (Extended Data Fig. 1b).

On the first day of recording, mice alternated between sessions navigating on the familiar track and a novel track (track B or C) where they learned three new RZs through trial and error over 3 days (Fig. 1c and Extended Data Fig. 1a). Consistent with knowing the reward locations, the mouse movement speed in the familiar AZs was lower than in non-reward zones (NRZs)—control zones 30° after each RZ within the same environment (Fig. 1d and Extended Data Fig. 1c). On the first day of novel-track exposure, mice showed no significant difference in movement speed between the novel AZ and NRZ, as is expected when the mouse does not know the reward locations (Fig. 1d and Extended Data Fig. 1d). By day 2 or 3, mice slowed before arriving at the RZs, indicating that they had learned to differentiate reward areas (Fig. 1d and Extended Data Fig. 1d). The performance increased by approximately 20% on day 2 and 30% on day 3 compared with day 1 in the novel environment on average (Fig. 1e and Extended Data Fig. 1e).

Inhibitory decreases at goal locations

We hypothesized that CA3 interneurons have a role in selectively gating excitatory activity around goals. In such a case, reward information would lead to coordinated decreases in inhibitory activity across many interneurons as animals approach and enter important locations. Decreases in inhibitory firing would precede increases in excitatory activity over seconds and promote representations and reactivation of important locations over days (Fig. 1a). To test this hypothesis, we first examined whether CA3 interneurons show a spatially selective firing pattern around learned goal locations. We recorded 3,489 classified single units in the CA3 during active spatial navigation in head-fixed mice and identified putative pyramidal cells and a fast-spiking group of interneurons with narrow spike widths (NS interneurons; Supplementary Fig. 1 and Supplementary Tables 1 and 2). Using opto-tagging, we confirmed PV interneurons overlap with the classification of NS interneurons recorded (Supplementary Fig. 2).

We found a marked goal-specific decrease in the firing activity of the majority of NS interneurons in the CA3 around three different RZs along the familiar track (Fig. 1f–h). Raw firing rates of NS interneurons decreased by 14.01 Hz on average (Fig. 1h and Extended Data Fig. 2a,e). To account for changes due to speed, we regressed out the estimated contributions of movement speed and lick rate to the raw firing rates (Methods and Extended Data Fig. 2). Regressing out the effects of speed, we observed an approximately 15–20% decrease in firing on average during the approach to familiar RZs, starting several seconds before the RZ (Fig. 1i). The observed patterns in the raw firing data were preserved in residuals, indicating that these results are not explained by position-dependent changes in movement speed or lick rate (Fig. 1f,h,i). At the population level, the decrease in inhibition preceded both RZ entry and increases in excitatory activity (Fig. 1i). We found a significant firing reduction in 40–70% of all recorded NS interneurons in the familiar environment (Fig. 1j). As expected, individual pyramidal cells tended to fire at one or a few locations that collectively tiled environment, with approximately 15–25% of units with increased activity around the RZ (Fig. 1j). Thus, a majority of NS interneurons showed goal-specific reductions in firing. The reduction in NS interneuron activity around goals developed rapidly over days in a novel environment as animals learned where to find new goal locations (Fig. 1j).

Multiple control analyses and experiments indicate that NS interneurons reduce firing rates around goals beyond what is expected by behavioural changes. First, the amount of variance explained by movement speed and lick rate was less than 25% for more than 99% of units (Extended Data Fig. 2b–d,f,g). Thus, speed modulation of neuronal firing is present, but our analysis examines changes in firing that are not explained by speed. Second, we found significant firing rate differences in individual interneurons between the AZ and RZ and baseline no-VR periods even when animals moved at similar speeds in 7 out of 8 comparisons made in both environments (Extended Data Fig. 3a). Third, we found no significant difference in the firing rates of the same neurons at the familiar AZ when animals moved at different speeds (Extended Data Fig. 3b). Thus, the firing rate of NS interneurons depends on the task context and on the animal’s proximity to the RZ (Extended Data Fig. 3a,b). Finally, we introduced a new trial type in the familiar environment in a subset of animals (n = 4). To dissociate position-related visual cues in VR from the animal’s movement on the treadmill, on RZ entry, the visual cues on the screen froze for 3–10 s. This screen freeze occurred even if the animal was moving on the treadmill, when the visual cues would normally progress forward through the track (Extended Data Fig. 3c). After RZ entry, when the movement speed and lick rates were similar but the visual cues dissimilar, the firing rates of NS interneurons were lower on average when reward-associated visual cues stayed fixed during screen-freeze trials than during normal trials. These results show that inhibitory firing is reduced for goal-related cues and not purely for speed changes (Extended Data Fig. 3c). Furthermore, these goal-selective inhibitory decreases were not solely due to local sharp wave ripple (SWR) activity, as significant decreases in NS interneuron activity around the RZ occurred during trials with and without SWRs (Extended Data Fig. 4c,d).

We found significant decreases in activity around goals in multiple putative subtypes of NS interneurons, including basket cells, axo-axonic cells (AAC), cholecystokinin (CCK) cells and bistratified cells, identified on the basis of these subtypes’ different patterns of firing relative to theta phase and SWRs27,28,29,30 (Extended Data Fig. 4). As all of these subtypes express PV, including a subset of CCK interneurons[31], we further investigated PV interneurons optogenetically (Supplementary Fig. 2). Opto-tagged PV interneurons had goal-selective decreases in firing on approach to and in RZs, with firing decreasing by 30% on average (Fig. 1k,l). Previous studies have found direct inhibitory connections from PV interneurons to pyramidal cells in the CA3, but PV cells also synapse onto other interneurons[32]. We therefore used optogenetic stimulation of PV cells to determine the causal effect of PV firing on pyramidal cell activity. We found that optogenetically stimulating PV cells resulted in decreased pyramidal firing, showing that CA3 PV cells inhibit the activity of CA3 pyramidal cells (Extended Data Fig. 5).

Our data are consistent with goal-selective inhibitory gating in which inhibitory activity is selectively decreased around reward locations and occurs before increases in firing of pyramidal cells during the approach to and traversal of the RZ. Furthermore, the magnitude of NS firing reduction approaching and just after entry to the RZ was higher on correct trials than on incorrect trials (Extended Data Fig. 6a,b). These results show that a goal-specific reduction in inhibitory activity precedes and coincides with successful identification of a goal location.

New goal learning requires PV decreases

We identified that the goal-associated reduction in inhibitory activity is necessary for learning new goal locations. We used optogenetic stimulation to disrupt the normal inhibitory reduction around goals during active spatial navigation. We focused our stimulation on PV interneurons because we observed a robust pre-RZ activity reduction in the majority of NS interneurons and specifically in opto-tagged PV interneurons (Fig. 1i,k,l). Furthermore, PV interneurons are strong inhibitors of pyramidal cells at the soma33,34. For PV-specific targeting in vivo, we crossed the PV-Cre mouse line with Ai32 mice to achieve Cre-dependent expression of the blue-light-sensitive opsin channelrhodopsin-2 (ChR2) in PV interneurons. Our light delivery approach stimulated PV cells in a small volume of CA3 (ref. [35]) (Methods and Supplementary Fig. 2a).

We compared goal stimulation, delivered in the AZ and RZ, to sham stimulation, delivered in a control NRZ of the same size, with each delivered in separate novel environments (Fig. 2a). As different genotypes of mice behave differently, and to control for non-specific effects of expressing opsins and stimulating PV cells, we compared PV stimulation around goals to sham stimulation away from goals within PVxAi32 mice in two different environments. In one novel environment, animals underwent goal stimulation in which PV activity was stimulated specifically in the AZ (one zone before the RZ) and RZ. In the other novel environment, the same animals underwent sham stimulation in which PV activity was stimulated in two consecutive control zones, away from the RZ. This sham stimulation covered the same length of track as the goal stimulation but was away from the reward locations. This design was used to determine whether the selective decreases in NS interneuron activity around RZs are important for learning and to control for the potential off-target effects of this artificial optogenetic PV stimulation.

Fig. 2: Goal-associated inhibitory reduction is required for new goal learning.

a, Optogenetic PV stimulation protocol with stimulation locations and intensities for goal and sham stimulation (stim.). The mouse illustration was adapted from scidraw.io. b, Disrupting normal goal-associated inhibitory firing reduction (blue) impaired the learning of new goal locations. Performance based on speed differentiation over 3 days of learning for all trial types (goal stimulation, blue, n = 5 mice; sham stimulation, orange, n = 5 mice; day 1 versus day 2, P = 0.0045; day 1 versus day 3, P = 0.0001; LMM followed by Tukey correction). c, The RZ identification speed as in b, but for trials with low (left), high (middle) or no (right) stimulation. There was a significant performance increase from day 1 with sham, but not goal, stimulation at all intensities (low stimulation, day 1 versus day 2, P = 0.006; day 1 versus day 3, P < 0.0001; high stimulation, day 1 versus day 3, P = 0.03; no stimulation, day 1 versus day 2, P = 0.01, day 1 versus day 3, P = 0.002; LMM with Tukey correction). d, The normalized change in pyramidal cell firing from the baseline over time with goal (blue) or sham (orange) stimulation across stimulation intensities (top). The dashed line indicates the stimulation onset. Bottom row, there were no significant differences in the mean firing rates in the 2-s period after the start of goal (n = 307 cells) or sham (n = 270 cells) stimulation across intensities. e, The change in firing rate as in d, but for PV interneurons. n = 19 (goal stimulation) and 16 (sham stimulation) cells. For b–e, data are mean ± s.e.m. NS, not significant. **P < 0.01. The brain (F. Claudi) in the mouse head (E. Tyler and L. Kravitz) diagrams in a were adapted under a CC BY 4.0 licence.

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Notably, disrupting the reduction in goal-associated firing in a small subset of CA3 PV cells by goal stimulation substantially impaired the learning of new goal locations. Mice that underwent PV goal stimulation did not show a significant speed difference between the AZ and NRZ over 3 days but did have intact learning and improvement over days when they received sham stimulation in a different novel environment (Fig. 2b,c, Extended Data Fig. 1f and Supplementary Figs. 3 and 4). We found significant effects of learning day, stimulation condition and the interaction between day and stimulation condition, confirming performance deficits with the goal-related PV stimulation.

Importantly, goal and sham stimulation conditions did not differ in how they affected firing of PV and pyramidal cells. The average reduction in normalized firing from the baseline did not differ significantly between the goal and sham stimulation conditions with low or high stimulation for pyramidal cells nor for PV cells (Fig. 2d,e). At light offset, there were no significant differences between goal and sham stimulation (PV cells, P = 0.83; pyramidal cells, P = 0.22; two-sided Wilcoxon rank-sum test). Goal stimulation increased PV firing relative to the baseline, therefore preventing decreases in PV activity at the goal location; yet it did not result in the complete shut-down of excitatory activity like a temporary lesion (Extended Data Figs. 5f and 6c,d). We used relatively low light intensities of a small subset of PV cells (Methods). Indeed, high stimulation resulted in small decreases in pyramidal firing, from 10 to 15%, with even smaller decreases in response to low stimulation (Extended Data Fig. 6d). Furthermore, SWRs still occurred during PV stimulation.

Impaired learning could not be explained by PV stimulation directly affecting speed or licking behaviour. First, we took advantage of the fact that there were three RZs in each environment and varied our stimulation intensity across RZs. Notably, we found that goal stimulation at other RZs disrupted inhibitory decreases in the AZ and RZ, where no stimulation was applied (Extended Data Fig. 6c,d). Furthermore, mice did not learn the RZ that was not stimulated (Fig. 2c (no-stimulation trials only)). Second, we found no significant differences in the licking and movement speed between no stimulation and low stimulation in both the familiar and novel environments (Supplementary Fig. 5a,b). Thus, the direct effects of stimulation on speed or licking behaviour cannot explain the deficits in new goal learning even at a low stimulation intensity. Third, there was no significant difference in the overall rate of licking outside the goal or stimulation locations between goal and sham conditions, ruling out the possibility that PV stimulation altered licking behaviour (goal stimulation, 1.3 ± 0.14 licks per s versus sham stimulation, 1.5 ± 0.09 licks per s; P = 0.47, two-sided Wilcoxon rank-sum test). Thus, we conclude that disrupting interneuron firing reduction specifically at goal locations impairs new goal learning that is not explained by differences in licking, running speed or task engagement.

At the end of the session, when mice no longer received stimulation at any goal location, mice continued to perform poorly, suggesting that there was no latent learning that emerged even when PV firing reduction was no longer disrupted. Goal-selective NS interneuron responses did not immediately appear after the stimulation ended, although we did observe a trend of a decrease (Supplementary Fig. 6). These findings suggest that stimulation prevents the formation of goal-selective NS interneuron responses and that changes in firing of these neurons over trials are required to form goal-selective responses.

Disrupting goal-associated inhibitory reduction with PV stimulation at well-learned goals in the familiar environment did not compromise behavioural performance at any stimulation intensity (Supplementary Fig. 5c). This finding is consistent with the observations that our goal stimulation did not completely silence pyramidal cell activity or impair the animal’s running or licking ability. Taken together, these results show that goal-associated reduction in CA3 PV inhibition is required for learning new goal information but not for retrieving previously stored goal information.

Reduction develops during learning

As decreased inhibitory activity at goals is essential for learning, we hypothesized that goal-selective decreases in inhibition develop early during learning when place cell activity around goals undergoes refinement and stabilization. To capture RZ-related changes in firing, we selected units with a significant increase or decrease in activity around goals and determined when the population showed significant coordinated changes in activity around the RZ. Consistent with animals not having learned the new RZs yet, NS interneurons with a significant change around the RZ did not show a significant coordinated activity decrease around the RZ on day 1 (Fig. 3a,b and Extended Data Fig. 7a). By day 2, these NS interneurons exhibited a clear and persistent reduction in activity that appeared even before arriving at the RZ, and this pattern continued on day 3 (Fig. 3a,b and Extended Data Fig. 7a).

Fig. 3: Goal-associated decrease in inhibitory activity develops over learning coinciding with refinement and stabilization of excitatory goal representations.

a, The normalized residual firing rates of NS interneurons according to the distance to the novel RZ (pink) per day. The mouse illustration was adapted from scidraw.io. b, The percentage change in firing as a function of the distance (left) or time (right) to the novel RZ (pink), averaged across cells with a significant increase or decrease at goals (Methods; n = 9, 20 and 20 cells for days 1–3). The triangles indicate the start of the persistent firing decrease. c,d, The field stability (c) and spatial information (d) of goal cells in familiar (shades of black, n = 185, 159 and 209 cells for days 1–3) and novel (shades of green, n = 146, 191 and 204 cells for days 1–3) environments. There is a significant interaction of novel day and environment (field stability, P = 5.4 × 10−6; spatial information, P = 0.009; LMM). e, The proportion of pyramidal cells that are goal cells over days (familiar, black, n = 13, 15 and 14 sessions for days 1–3; novel, green, n = 13, 14 and 14 sessions for days 1–3; LMM). f, The normalized residual firing rates averaged over putative PV cells in the first (left) and last (right) blocks of 25 trials during the initial novel exposure (n = 10 and 3 cells for the first and last blocks). g, The change in residual firing from the baseline, trial-averaged for the first (light turquoise) and last (dark turquoise) trial blocks in f. There is a significant effect of position (first block, P < 2.2 × 10−16; last block, P < 2.2 × 10−16; LMM). The bars above indicate bins that significantly differ from the baseline (one-sided t-tests with Bonferroni correction; n = 25 trials). h, Speed-based ROC curves for the first and last trial blocks (n = 25 trials per block, 7 mice). For b,e,g, data are mean ± s.e.m. The brain (F. Claudi) in the mouse head (E. Tyler and L. Kravitz) diagrams in a were adapted under a CC BY 4.0 licence.

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We found that the development of goal-associated inhibitory reduction coincides with the development of goal-representing cells, or pyramidal cells that have at least one receptive field with significant spatial modulation around goals. Previous studies have shown that such goal representation predicts successful goal-directed behaviour and improves with learning5,6,7,24,36,37. During learning, field stability, measured through rate-map correlations across trials, and spatial information of goal-representing cells increased over days as animals performed better in the task (Fig. 3c,d). Field stability and spatial information of goal-representing cells were significantly higher in the familiar environment than the novel environment (Fig. 3c,d). The proportion of goal-representing cells did not change over days (Fig. 3e). As these responses develop over days, inhibitory activity decreases before excitatory increases on the timescale of seconds on days 2 and 3 (Extended Data Fig. 7).

As we found that putative PV cells have a causal role in goal learning, we examined how goal responses develop over trials in putative PV cells (Methods). On the first day in the first novel environment, putative PV interneuron responses developed over trials from initially no decrease around the reward locations in the first 25 trials to a significant decrease on approach to and in the reward location in the last 25 trials (Fig. 3f,g and Extended Data Fig. 7b–d). This development of inhibitory decreases coincided with improvements in behaviour (Fig. 3h). These findings reveal that decreases in interneuron activity develop from no decrease initially to anticipating the reward locations as animals learn on the first day of exposure to a new environment.

Together, these results show that goal-selective decreases in inhibitory activity develop rapidly during learning, when excitatory cells are undergoing refinement and stabilization of goal representations. Over seconds, NS interneuron activity decreases before pyramidal activity increases. Over days, pyramidal place fields and NS interneuron responses around reward locations develop together.

Stable place codes require PV decreases

We determined that inhibitory decreases around goals are required for stabilization and refinement of excitatory goal representations (Extended Data Fig. 8a–c). Although the stabilization of new goal fields, or field stability, increased over days with sham stimulation, it did not with goal stimulation (Extended Data Fig. 8d). Furthermore, spatial information of goal-representing cells increased over days with sham stimulation but not with goal stimulation (Extended Data Fig. 8e). The proportion of goal-representing cells over days did not change significantly (Extended Data Fig. 8f). Furthermore, goal-representing place cells had lower peak firing rates with goal stimulation than with the sham stimulation, and a higher vector strength as a function of theta, potentially from narrower preferred theta phases (Supplementary Fig. 7). Theta was altered in power but still clearly present during goal stimulation (Supplementary Fig. 7). Thus, disrupting inhibitory decreases around goals impaired the stability, refinement and amplitude of pyramidal goal representations.

As pyramidal cells within the CA3 form sequences, goal representations could influence spatial coding away from goals. We found that goal stimulation disrupted the stabilization of place cells in positions away from goals, outside the stimulated portion of the track (Extended Data Fig. 8g). The amount of spatial information of place fields away from goals also did not increase over days with goal stimulation but did with sham stimulation (Extended Data Fig. 8h). The proportion of non-goal-representing cells did not change significantly (Extended Data Fig. 8i). These results support a goal-selective gating role of inhibition that recruits excitatory neurons to sharpen and stabilize new spatial representations during learning.

Goal reactivation requires PV decreases

When animals pause to consume a reward, pyramidal firing patterns representing paths to and from the reward location are reactivated and this reactivation enhances plasticity and binds spatial locations to key outcomes such as food9,10,12. Reactivation occurs during SWRs, which are required for rapid learning[13], and these bursts of population activity affect synapses related to new spatial learning[38]. NS interneurons decreased firing around the AZ and RZ over seconds, then rapidly and transiently increased firing during SWRs on average, consistent with previous work39,40 (Extended Data Fig. 9a,b and Supplementary Fig. 8). We hypothesized that this decrease in inhibition is required for intact SWR activity at goal locations in the novel environment. To control for animal position and position-related variability in behaviour, we included only SWRs that occurred while the animal was in the AZ or RZ. Disrupting goal-associated decreases in PV firing resulted in a lower rate of SWRs occurring around goal locations than in the sham stimulation group (Fig. 4a,b and Extended Data Fig. 9e). The coactivation probability among simultaneously recorded pairs of goal-representing pyramidal cells during SWRs was lower in the goal stimulation group than in the sham stimulation group, as was SWR power and duration (Fig. 4c–e and Extended Data Fig. 9f–h). Neither SWR power nor duration in the familiar environment differed significantly with or without goal stimulation in the same PVxAi32 mice (Extended Data Fig. 9c,d). These results show that inhibitory reduction specifically at new goal locations is required for intact SWR activity during learning in novel environments.

Fig. 4: Inhibitory reduction is required for goal-related sharp-wave ripple reactivation.

a, Raw (top) and band-pass-filtered (bottom) local field potential (LFP) traces with SWRs (stars). b, The SWR rate in the goal (blue) and sham (orange) stimulation groups in a novel environment (goal, n = 26 sessions; sham, n = 20 sessions; P = 0.04, LMM). c, The coactivation probability of goal cell pairs during SWRs with coactivity greater than 0.05 (left) (goal, n = 27 pairs; sham, n = 46 pairs; P = 8.1 × 10−12, LMM). Right, the fraction of cell pairs with a coactivation probability equal to or less than 0.05 (P = 0.007, χ^2 test; cell pairs were subsampled to be equal across stimulation conditions). d, SWR power (goal, n = 71 SWRs from 16 sessions; sham, n = 78 SWRs from 15 sessions; P = 0.002, LMM). Right, sessions with high SWR power (top 50% of all sessions) were selected for visualization purposes only. e, As in d, but for SWR duration (P = 2.5 × 10−8, LMM). f, The spatial probability of decoded ripple content for the goal (n = 94 SWRs) and sham (n = 198 SWRs) stimulation groups. g, The proportions of SWRs carrying near-goal versus far-goal content (goal, n = 11 sessions, P = 0.27; sham, n = 12 sessions, P = 0.003; two-sided Wilcoxon signed-ranked test). h, The proportions of SWRs as in g, but for WT mice (familiar, n = 36 sessions; P = 1.06 × 10−6; novel, n = 26 sessions, P = 0.0004; two-sided Wilcoxon signed-ranked tests). For b–e, the distribution, median (white circle), quartiles (thicker lines) and 1.5× the interquartile range (whiskers) are shown. The mouse illustration was adapted from scidraw.io. For g,h, data are mean ± s.e.m. The brain (F. Claudi) in the mouse head (E. Tyler and L. Kravitz) diagrams in f,h were adapted under a CC BY 4.0 licence.

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Notably, the goal-related inhibitory reduction is essential for preferential reactivation of goal locations during SWRs. We used sequenceless decoding of SWR content to identify the most likely location represented during each SWR. We found significant differences in the information bias of SWRs between goal and sham stimulation. SWRs in the sham stimulation sessions were much more likely to represent locations around goals than locations far from goals, similar to what we observed with no stimulation (Fig. 4f,g). On the other hand, when PV firing reduction was disrupted in the same animals, there was no significant difference in the proportion of SWRs representing locations near or far from goals, indicating that goal-related locations were not over-represented during SWRs (Fig. 4f,g). These findings show that goal-associated inhibitory reduction is required for preferential reactivation of goal-related information. Consistent with this idea, we observed that SWRs in wild-type (WT) mice were much more likely to code for locations near goals than far from goals in both familiar and novel environments (Fig. 4h). Overall, our results show that goal-associated reduction in PV interneuron firing gates SWRs and goal-informative SWR reactivation that represents locations most pertinent to task performance.

Inhibitory decreases not due to salience

Goal-selective decreases in interneuron activity also occurred in a decision-making task in a familiar environment in the CA1 hippocampal subregion. Furthermore, we identified that NS interneuron activity decreases do not occur in response to other non-rewarding but salient features of the environment, like cues that instruct navigation behaviour. Mice were trained to navigate a Y-maze using visual cues displayed on the wall in the central arm of the track[41] (Fig. 5a,b). On most trials, a cue presented at the start of the central arm indicated which arm of the track (left or right) was the rewarded location. On a subset of trials, a second visual cue appeared when mice reached a specific location after a short delay period. During the second cue, called the update cue, the visual patterns appeared on the opposite wall from the original cue indicating that the reward location switched from the initial arm, and the animals must change their initial planned goal arm maintained in memory to the opposite choice. We found significant decreases in CA1 NS interneuron activity leading to, and in, the reward locations in this task (Fig. 5c and Supplementary Table 3). In this task, the update cue is highly salient to the animal as it instructs them to change their planned trajectory. NS interneuron activity did not decrease significantly around the update cue; indeed, there was a trend of an increase (Fig. 5d). These findings establish that goal-selective decreases in interneuron activity occur in a decision-making task in a familiar environment in the CA1 hippocampal subregion. Importantly, these results show that decreases in NS interneuron activity in the CA1 are selective for goal locations and not for other salient features of the environment.

Fig. 5: Goal-associated decreases in interneuron activity occur in the CA1 during a choice task.

a, WT mice (n = 7) chose between left and right arms based on visual cues displayed on the centre walls of a virtual Y-maze. On most trials (top, delay only), the initial cue (the black dashed line marks start of initial cue presentation) indicated the rewarded arm (checkmark). On a subset of trials, an additional (update) cue was presented (teal dashed line), which indicated whether the rewarded arm stayed the same as the initial cue (stay, middle), or switched to the opposite arm (switch, bottom). The mice received a reward after entering the correct RZ at the end of the rewarded arm (pink dashed line). b, Electrophysiology analysis during Y-maze navigation. The mouse illustration was adapted from scidraw.io. c, Top, schematic indicating the rewarded areas. Left, the normalized residual firing rate of NS interneurons. Right, the cell-averaged percentage change in normalized residual firing as a function of the time to the RZ (pink) for NS interneurons. n = 485 cells. There was a significant effect of time to RZ (P < 2.2 × 10−16, LMM). The blue bar indicates bins that significantly differ from zero or the baseline (two-sided t-test followed by Bonferroni correction). d, The normalized firing rate and the percentage change in firing rate as in c, but for the time around the update cue (teal, n = 485 cells). There was a significant effect of time to the update cue (P = 5.19 × 10−13, LMM); however, no individual time bins were significantly different. For c,d, data are mean ± s.e.m. The update task and trial type[41] (a,c,d), VR schematic[41] (b), mouse head (E. Tyler and L. Kravitz; b) and the brain in mouse head (F. Claudi; b) diagrams were adapted under a CC BY 4.0 licence.

Source data

Discussion

Together, our data show that reward-predictive coordinated decreases in CA3 inhibition gate new learning of crucial information in service of goal-directed navigation (Extended Data Fig. 10). By selectively promoting plasticity, this goal-associated disinhibition facilitates learning of reward locations and paths to reward locations. Our findings provide a direct inhibitory link to previously reported hippocampal signatures of learning about goal locations: enhanced goal representation by pyramidal cells and SWR reactivation. Goal-associated inhibitory decreases were required for new goal learning and for stable representations and reactivation of goal locations, which predict stable memory formation6,37,42. Previous studies show that inhibition regulates place-field formation or refinement and that changes in interneuron firing precede SWRs22,23,24,43,44,45. However, these studies do not demonstrate coordinated inhibitory decreases preceding or within reward locations. Disrupting goal-associated decreases in inhibition around goals impaired SWR reactivation of goals, information that must be learned for successful task performance. Furthermore, we found that NS interneuron activity did not decrease around a salient wall cue that directs the animal to change its previously planned path. These results indicate that decreases in NS interneuron activity are specific to goal locations rather than generally salient features. Previous research has shown mixed increases and decreases in interneuron firing after reward. A study[24] found that most CA1 vasoactive intestinal peptide cells, which are disinhibitory to CA1, decreased activity after reward delivery, which would inhibit pyramidal cells. In analyses that controlled for the effects of speed, vasoactive intestinal peptide cells showed a wide range of pre-reward responses with some increasing and some decreasing activity. In another study examining interneuron firing around reward locations[21], it was found that bistratified cells and somatostatin-positive cells ramped up their activity during approach to a familiar reward location, a different pattern from our observations. In analyses that controlled for animal speed, they found interneuron activity decreased after animals entered the RZ, but not before. Ultimately, our findings are different because we show coordinated decreases in interneuron activity that precedes the RZ and we see these patterns in the CA3, upstream of the CA1. As the decreases in interneuron activity in our study predict rather than respond to reward, they are well positioned to enhance representations of reward-predictive locations.

On the timescale of seconds, decreases in NS interneuron activity clearly occur before pyramidal activity increases. On the timescale of days, the timescale over which these responses are refined, the pyramidal and NS interneuron responses develop concurrently. These different timescales are linked by previous work showing that artificial place fields can be induced by depolarizing a cell in a particular location46,47. Thus, decreases in NS interneuron firing and resulting disinhibition of pyramidal cells preceding and in the RZ may enhance place-field development and stabilization in that location. We found interneuron firing activity occurred over long, behaviourally relevant timescales (seconds), similar to recently discovered behavioural time-scale synaptic plasticity (BTSP)47,48,49,50. While BTSP appears to be present without goal enrichment, reduced PV activity may have a role in BTSP. We show that decreases in interneuron activity shift to anticipating the reward location as animals learn reward locations in the new environment. In this way, goal-selective disinhibition facilitates learning and representations of both reward locations and locations that lead to reward, which is crucial for navigating to goals.

Methods

Animals

All procedures involving animals were performed in accordance with the guidelines provided by the Institutional Animal Care and Use Committee at the Georgia Institute of Technology. Our study used C57BL/6J adult male WT mice (n = 9 mice) and PVxAi32 mice (n = 11 mice) at 10–12 weeks of age (3.5–5 months at the time of the recordings). For optogenetics experiments, we crossed male PV-Cre knockin (The Jackson Laboratory, 017320) mice with female homozygous Ai32 mice (The Jackson Laboratory, 024109) to generate PVxAi32 mice (n = 11 mice) that express ChR2 specifically in PV-positive interneurons. The mice were housed in a reverse dark–light-cycle room (07:00 light off, 19:00 light on) with ad libitum access to food and water. Animal housing rooms are equipped with a ventilation system that provides 12 air changes per hour, temperature range of 64–79 °F and 30–70% relative humidity. We performed all behavioural training during the dark cycle. Sample sizes were determined based on sample sizes used in previous studies23,24,42. We aimed for sample sizes of 5–7 mice per group with a large number of single units per animal. No power analysis was performed. Owing to the nature of experiment monitoring during electrophysiology recording, the experimenter could not be blinded to the condition. Experimenters were blinded to stimulation and novelty conditions for analyses. We used a within-subject design, in which individual animals were exposed to both conditions (novel and familiar or goal and sham stimulation), thus randomization to groups was not relevant.

Graphical illustrations of a mouse in VR were created by B. Mariner for the Singer laboratory. The drawing of mouse head profile (E. Tyler and L. Kravitz, mouse drinking, Zenodo, https://doi.org/10.5281/zenodo.3925985) and brain (F. Claudi, mouse brain sagittal, Zenodo, https://doi.org/10.5281/zenodo.3925911) were adapted from scidraw.io.

Surgery

Mice were handled for at least 3 days before stereotaxic surgery. For head-plate implantations, mice were deeply anaesthetized with isoflurane, and head-plates were affixed to the skull with dental cement (Parkell C&B Metabond). For acute extracellular recordings, craniotomies of 600–900 µm in diameter were made on either hemisphere of well-trained mice 1 day before the first recording. We used the following coordinates to target CA3 stratum pyramidale (from bregma): −1.8 mm anteroposterior (AP), 2.4 mm mediolateral (ML) and about 2.5 mm dorsoventral (DV).

VR spatial-learning task

Mice began habituation to head fixation on a treadmill at least 7 days after head-plate implantation. We trained mice to run on either a linear treadmill (n = 4 WT mice; PhenoSys SpeedBelt) or a spherical Styrofoam treadmill floating on air (n = 5 WT mice, 11 PVxAi32 mice) to determine whether changes in interneuron activity were consistent across systems. To increase motivation for running, animals were food-deprived gradually to 85–90% of their original body weight. Mice were head-fixed and trained daily to run unidirectionally on the virtual track for progressively longer periods. Mice received drops of sweetened condensed milk as a reward, delivered through a plastic needle. Licks were detected using either a piezoelectric sensor attached to the reward needle or a custom-built photointerruptor-based system. All virtual tracks were designed and interfaced with animals using the open-source software Virtual Reality MATLAB Engine (ViRMEn) open-source software (in MATLAB v.2015b) as previously described[51]. Proximal and distal cues were projected onto a cylindrical screen, creating a rich and immersive environment for virtual navigation. Voluntary movement of a mouse on the linear or spherical treadmill automatically advanced movement in the virtual track environment.

We used three annular tracks (tracks A, B and C; Fig. 1c) of the same size but with distinct visual cues, reward locations and distances between the reward locations. All of the mice were initially trained on track A as the familiar environment and later during recordings were introduced to tracks B and C (not shown) as the novel condition. Speed gain was set such that 1° advancement through the virtual environment was equivalent to the movement of about 1.5 cm on the belt for a total distance of approximately 540 cm around each track. Animals completed a full lap (360°) in 66 s on average (range of 12–346 s). Gain on the spherical treadmill was adjusted to ensure similar virtual experience as with the linear treadmill. Each of the tracks had 36 non-overlapping, equally sized wall cues (10° per cue), and three of them were associated with reward (RZs, each 10°). The RZs were irregularly placed to prevent animals from solely using the inter-reward distances to perform the task across multiple environments.

For the first 3–5 days of training in the virtual environment, mice received rewards automatically when they arrived at the three RZs on the track. After this first phase of training, the mice showed anticipatory licking immediately before the reward delivery. In the subsequent training and recording sessions, the mice were required to lick in the RZs to trigger a reward delivery. To prevent generalized licking irrespective of position on training days, licking more than 25–50 times cumulatively outside the anticipatory and RZs triggered a 4-s time-out period. During a time-out period, the animal was teleported to a grey box and received no reward. After 4 s, the mouse resumed running from the location it had left off before the time-out. We introduced mice to novel tracks on days of recording only after a mouse was deemed to have learned and reached behavioural criteria indicative of good performance in the familiar environment. Mice were required to have 85% correct or higher performance in the familiar environment for at least two consecutive sessions before recording commenced. Behavioural performance criteria were set based on an animal’s propensity to slow down and lick more in the AZs.

Behavioural data analysis

Raw behavioural data were divided based on an animal’s position (in degrees) into either 360° laps around the whole environment or RZ-centred trials that spanned the area 60° before and after each 10° RZ for analyses. Mean speed (in ° s−1) was calculated by dividing the total distance travelled in each 2° position bin by the total time spent in that bin. The lick probability was calculated by taking the ratio of the number of licks per position bin over the total number of licks within the lap or trial. The lick rate (in licks per s) was calculated by dividing the number of total licks per position by the total time spent in that bin over all position bins. Speed and lick behaviour for each lap or trial was smoothed with a Gaussian-weighted moving-average filter (s.d. = 2 bins). Once learned, mice tended to show stereotypical behaviour in all three RZs. Thus, speed and lick behavioural data around all three RZs were concatenated and averaged for each session. We used ROC curves to quantify behavioural performance based on how well mice differentiated between the pre-RZ (AZ) and the non-reward control zone that appeared 30° after the end of each RZ. For lick-latency-based performance, we quantified the time between when an animal entered the RZ and the first lick compared with the control lick latency at the NRZ. We quantified learning over days using area under the receiver-operating characteristic curves in which larger AUC values indicate better behavioural performance. The performance metric using movement speed data tended to be better at position differentiation than licking-based metrics, probably because mice were not required to lick in the AZ to receive a reward and well-trained animals licked very sparsely (Extended Data Fig. 1e).

Electrophysiology

All extracellular electrophysiology recordings were performed using a poly 5 two-shank 64-channel silicone probe or a 64-channel optoelectrode of the same channel geometry (NeuroNexus). Neural data were acquired using either two 32-channel Upright Headstages with Trodes software (v.2-2-3, SpikeGadgets) or two RHD 32-Channel Recording Headstages and Intan RHD2000 Evaluation system (version 1.5.4 with MATLAB file read_Intan_RHD2000_file v.2.0, Intan) at a sampling rate of 30 kHz with a ground pellet used as reference. The location was determined by stereotaxic coordinates, depth and electrophysiological signatures. Electrophysiological features indicative of the CA3 were closely monitored: high-amplitude theta activity during running, prominent sharp-wave ripples during stillness and high-amplitude (100 + µV) action potentials appearing on many channels.

Each recording day consisted of at least two sessions of 15 to 30 min in the familiar (track A) and novel (track B or C) environments (always starting with the familiar environment). Behavioural sessions in the VR environment were separated by a shorter (5–10 min) baseline recording period in the dark. The movement direction was consistent (clockwise) across environments. We performed recordings from the same craniotomy (typically from the right hemisphere first) for three consecutive days, using the same novel track during this period. After the first set of novel learning days, a second craniotomy was made on the contralateral hemisphere on the fourth or fifth day, and mice learned a brand new track for three consecutive days starting on the following day (details on the number of cells recorded per mouse are provided in Supplementary Tables 1 and 2).

Optogenetic stimulation during behaviour

We recorded and stimulated neural activity simultaneously using a customized two-shank 64-channel optoelectrode with two optical fibres terminating 100–200 µm above the top recording site on each shank. Ferrule patch cables (Ø105 µm core, 0.22 NA SMA905 to Ø1.25 mm; Thorlabs, M63L01) were connected to optical fibres on one end through ceramic split mating sleeves (Thorlabs, ADAL1-5) and 470-nm fibre-coupled LEDs (Thorlabs, M470F3) with T-Cube LED driver (Thorlabs, LEDD1B) with a 15 V power supply unit (Thorlabs, KPS101) on the other end. This approach delivered truncated cones of light in the dorsal CA3 that were approximately 2 mm^3. On the basis of light spread and attenuation measured in brain tissue in previous work, we estimate a volume of 0.005–0.02 mm^3 received light at intensities needed to drive channelrhodopsin (greater than 1 mW mm−2)52,53. Previous studies estimate between 500 and 2,500 PV cells per mm^3 in the CA3 (refs. 54,55). We confirmed the optical targeting of 2–3 PV units on average in a single recording. Consistent with light-induced activation, we observed an increase in PV firing activity within around 3 ms of blue LED light (470 nm) onset (Supplementary Fig. 2c). We controlled the onset, offset and intensity of blue light with a National Instruments data acquisition system and custom MATLAB code. Both fibres were illuminated at the same time and intensity. Position-specific stimulation was triggered in real time by animal position in the virtual environment. In the goal location-specific condition (goal stimulation), light turned on when an animal entered the AZ, the zone immediately before the RZ and stayed on for up to 10 s or until the animal left the RZ, whichever came first. In the sham stimulation condition, the light turned on when the animal entered the NRZ and stayed on for up to 10 s or until the animal left the zone immediately after the NRZ. Sham stimulation controlled for non-specific effects of optogenetic stimulation and for the effects of PV activity on novelty in general as both types of stimulation were in novel environments. A subset of animals was stimulated at the anticipatory or RZ only, or for a fixed duration of 3 s regardless of position. For each of the three stimulation zones, we stimulated at the light intensity of either 0, 5 or 13 mW mm−2, referred to as no-stim, low-stim or high-stim, respectively, from the fibre tip. The stimulation intensity remained the same for each goal location in the same novel environment over 3 days. We randomized the order of stimulation intensities across three stimulated areas in each environment across animals. Each stimulation session consisted of 20 min of stimulation trials followed by 5 min of stimulation-free trials to assess the potentially lasting effects of stimulation at the end of a session. To examine the effects of goal stimulation in the familiar environment, all PVxAi32 mice received a shorter (about 10 min) session with goal location-specific stimulation trials in the familiar environment (track A) as their final session of the last day of recording.

Behaviour and recording in Y-maze with update cue

To test flexible decision-making in rodents in response to new information, we designed a VR update task that requires animals to perform a memory-guided decision-making task. On most trials, the task presents animals with an initial visual cue that indicates the correct goal location in a Y-maze environment, and the animals must then run down the track to the correct arm to receive a reward. On a subset of trials, a second visual cue appears that indicates that the reward location has either changed (switch trials) or stayed the same (stay trials). On these trials, the mice must choose to keep their original goal destination or switch to the other goal destination. The mice were rewarded at the end of the track if they selected the correct arm, followed by a VR screen freeze for 3 s and then an intertrial interval period of 6 s with a grey screen. On incorrect trials, no reward was delivered and the intertrial interval was 12 s, a longer interval as a form of punishment. Over the course of a session, animals successfully performed the behaviour across all trial types.

Behaviour training and recording methods are described in detail previously[41]. Additional eight-week-old C57Bl/6 WT mice (n = 7) were implanted and recovered as described above. Over a period of about 8 weeks (55.43 ± 7.38 days of training, mean ± s.e.m.), food-restricted mice underwent several phases of training for about 1 h per day 5–7 days per week to ultimately learn the task. In brief, animals first learned to run on a linear track, then trained in a short Y-maze, followed by a long Y-maze. In the Y-maze, the visual cues on the walls indicated which goal arm was rewarded. At first, the cues were visible for the entire track. A delay was then introduced between the cue turning off and the choice point when animals had to select a goal arm. This delay got progressively longer. After animals performed well with a long delay, the update cue was introduced. After animals demonstrated mastery of the task with the update cue, recordings were made in CA1 using a 64-channel, dual-shank NeuroNexus probe (targeting −1.8 to –2.0 mm AP, 1.5–1.8 mm ML and about 1.4 mm DV). Recordings were made over 6–12 session per animal (details on the number of cells recorded and trial types performed per animal are provided in Supplementary Table 3). Data were analysed as described above. The RZ onset was defined as when the animal received reward, and the update cue onset was defined as when the update cue was presented.

Histological verification of the probe location

On the last day of recording, the neural probe was dipped in fluorescent dye diI (0.9 mg ml−1) before recording. After recording, the mice were deeply anaesthetized with isoflurane and perfused with 4% paraformaldehyde. The brains were extracted and drop-fixed in 4% paraformaldehyde for 24 h then rinsed in 1× phosphate-buffered saline (PBS) and either sectioned by a vibratome or cryoprotected in 20% sucrose solution overnight before being frozen for sectioning on a cryostat. Fixed brain tissues were cut coronally on a vibratome or a cryostat. Tissue sections were stained for nuclei with DAPI, mounted (Vectashield Antifade Mounting Media) and confocal imaged at ×10 using the LSM 700 laser-scanning confocal microscope (Zeiss).

VR screen freeze manipulation at RZs

A small group of animals (n = 4) was exposed to a behavioural manipulation in VR on the last recording day to control for position-dependent changes in speed and licking activity. In this session, we introduced an automatic screen freeze at the three RZs in the familiar environment for a fixed duration of 3 to 10 s regardless of the animal’s movement on the ball. The goal of this manipulation was to dissociate movement speed from position-specific visual cues. After VR manipulation, we examined the effects of distinct visual cues on firing rates of the same neurons within the same animals at similar running speeds and licking activity.

Preprocessing of LFPs and detection of sharp-wave ripples and theta periods

To obtain LFPs, recorded signals were downsampled to 2 kHz, band-pass filtered between 1 and 300 Hz and interpolated over outliers (noise) defined as 15 s.d. above the mean of the prefiltered signal. In all of our LFP analyses, we used the channel with the highest envelope amplitude for the ripple band signal as the proxy site of the stratum pyramidale[56]. For analyses of theta, gamma and SWR periods, the LFP was band-pass-filtered based on frequency bands (4–12 Hz for theta, 1–4 Hz for delta, 12–30 for beta and 125–250 Hz for SWR) using a finite impulse response equiripple filter. SWR events were detected when the envelope amplitude of the filtered SWR trace (125–250 Hz) was greater than 3 s.d. above the mean for at least 20 ms (refs. 12,57,58). We excluded any events with a power ratio (power from 100 to 250 Hz/power from 250 to 400 Hz) less than four based on the typically observed frequency range of SWRs[59]. We only included in our ripple analyses the periods with at least one multi-unit spike and excluded periods during which the movement speed was above 5° s−1 during the 2-s time window, 1 s before and after the mid-point of each SWR event. Multi-unit spikes were extracted by band-pass filtering the raw recorded signal between 300 and 6,000 Hz and thresholding the filtered signal above 5 s.d. from the mean. Extracted periods of interest were then visually inspected to ensure accurate detection. The duration of SWRs was defined as the length of time that the SWR envelope was greater than the threshold of 3 s.d. above the mean. SWR power was quantified as s.d. above the mean ripple power for the entire recording session. The SWR rate was quantified by dividing the total number of SWR events by the total duration of stopped periods (movement speed fell below 2° s−1 for at least 2 s) for individual recording sessions with at least ten ripples detected. Coactivation probability during SWRs was defined as the number of SWRs during which both neurons in a pair had spikes, divided by the total number of SWRs[58]. To show the spread of data with individual datapoints, we used violin plots. Violin plots show the kernel density estimate of the distribution (lighter shaded area), individual data points (coloured circles) and box plot indicating the median (white circle), first and third quartiles (thicker lines) and 1.5× the interquartile range (whiskers).

Single-unit isolation

We identified and sorted putative single units using the automatic clustering software Kilosort2[60], followed by visualization and manual curation using Phy 2.0. Only well-isolated units with the signal-to-noise ratio of greater than 1 and <0.01% refractory period violations (interspike interval < 1 ms) were included in the study. We visually verified the firing rate stability of single units by ensuring the firing rates during periods in between VR sessions did not fall below the threshold set at 10% of the peak firing rate of the entire duration of the recording.

Cell-type classification

We classified single units as pyramidal cells and interneurons with narrow and wide waveforms based on the spike waveform’s trough-to-peak latency, inter-spike interval statistics such as burst index[15] and the autocorrelogram (ACG) fitted with a triple-exponential equation using CellExplorer (v.1.2) software[61]:

$${{\rm{ACG}}}_{{\rm{fit}}}=\max \left(c\left(\exp \left(\frac{-(x-{t}_{{\rm{refrac}}})}{{\tau }_{{\rm{rise}}}}\right)-d\times \exp \left(\frac{-(x-{t}_{{\rm{refrac}}})}{{\tau }_{{\rm{rise}}}}\right)\right)+h\times \exp \left(\frac{-(x-{t}_{{\rm{refrac}}})}{{\tau }_{{\rm{rise}}}}\right)+{{\rm{rate}}}_{{\rm{asymptote}}},0\right)$$

where c is the ACG τ decay amplitude, d is the ACG τ rise amplitude, h is the burst amplitude and trefrac is the ACG refractory period (ms). The CellExplorer software automatically identifies putative NS interneurons with trough-to-peak latency ≤ 0.425 ms, wide spike-width (WS) interneurons with trough-to-peak latency > 0.425 ms and ACG τ rise amplitude > 6 ms and the remaining units assigned as pyramidal cells. We visualized all pre-labelled units, and manually excluded bad units or relabelled a cell type based on the ACG and firing characteristics. This process led to some overlap in the distributions of identified cell types, consistent with examples in CellExplorer. We recorded a total of 3,489 well-isolated single units recorded in this study (Supplementary Fig. 1). Among these, we identified a total of 437 NS interneurons, 254 WS interneurons and 2,798 pyramidal cells. We confirmed that PV interneurons verified by opto-tagging had similar waveform properties and firing rates to NS interneurons that we recorded. To identify optically tagged PV-positive interneurons, we used the stimulus-associated spike latency test as previously described[62] and performed manual verification by visualizing light-evoked firing activity within 1–3 ms of light onset. Putative PV cells in WT animals were identified as NS interneurons with mean firing rate greater than lowest quarter of opto-tagged PV cells in familiar environment (about 20.1 Hz) to capture the population of fast-spiking interneurons contributing to behaviourally relevant network oscillations like theta (intrinsic firing rate, 21 ± 5 Hz)[63], gamma (intrinsic firing rate, 32.70 ± 0.793 Hz)[64] and SWRs (intrinsic firing rate, 122 ± 32 Hz)[63].

Previous work has shown that different subtypes of NS interneurons in hippocampus have different firing patterns in relation to theta oscillations and SWRs27,28,29,30. Thus, to investigate how reward-related firing patterns are related to these network-state related patterns and NS interneuron subtypes, we assessed their preferred spiking phase during theta oscillations and their firing rate around SWRs27,28,29,30. We assigned cells to putative NS interneuron subtypes using a multi-step process first assessing preferred theta firing phase, then firing patterns around SWR. To assess firing as a function of theta phase, spikes during theta periods (see the ‘Preprocessing of LFPs and detection of sharp-wave ripples and theta periods’ section) for each cell were sorted into 18° theta phase bins, including all theta cycles. For each potential NS interneuron subtype, we performed a one-tailed t-test to compare the raw spike counts between that subtype’s preferred phase (203–339° for PV-expressing basket cells (PVBCs), 130–240° for axo-axonic cells (AACs), 74–236° for cholecystokinin-expressing (CCK) cells and 347–171° for bistratified cells) and the non-preferred phases. Cells with a false-discovery rate (FDR)-corrected P < 0.05, indicating strong preferred theta phase within one or more of the tested subtypes, were sorted by their t-scores for each subtype. For each cell, if the top t-score was more than 0.5 above the next highest t-score, the cell was assigned the single subtype associated with the top t-score. If any of the top three t-scores were within 0.5 of each other, then a cell was assigned multiple subtypes associated with the highest t-scores. The theta-phase assignment was then curated by firing rate around SWRs. For each cell, we assessed the firing patterns as a function of binned time periods during and around SWRs, comparing the spike counts around the SWRs to shuffled non-theta, non-SWR baseline periods, as described previously[28]. As the SWRs are asymmetrical, we normalized the time around the SWR as follows: the period between the start and middle of the ripple was divided into four bins, and the same bin size was used for the pre-ripple period. Similarly, the period between the middle and end of the ripple was divided into four bins, and the same bin size was used for the post-ripple period. For comparison, we randomly selected 100 non-theta, non-SWR periods baseline periods of the same length as the SWRs. A cell was classified as a PVBC or a bistratified cell if spiking was elevated during SWRs, specifically the spike counts in six bins around the middle of the ripple were higher than the mean + 2 s.d. of the baseline. If a cell exhibited a biphasic pattern, meaning that its spike counts in the four bins around the ripple start was higher than the mean + 1 s.d. of the baseline, and the spike counts in ten following bins was lower than the mean − 2 s.d. of the baseline, then this cell was classified as AAC. As a previous study showed that CCK cells have little change in firing rate during the SWRs[30], we assigned CCK to the cell if the spike counts in four bins around the ripple start, and four bins around the ripple end, were both within the range of mean ± 2 s.d. of the baseline. For cells with multiple subtype assignments based on theta phase, if their theta-assigned subtypes overlapped with the SWR-assigned subtypes, then the overlapping subtype was the final subtype assignment. On the basis of these criteria, we identified 137 out of 203 NS interneurons with a single subtype assignment, among which there were 41 AACs, 21 PVBCs, 27 bistratified cells and 48 CCK cells.

Place-field analyses

The occupancy-normalized firing rate map of putative pyramidal cells was quantified from spike counts and time spent in 5° position bins each smoothed with a Gaussian kernel (s.d. = 2 bins), excluding periods of stillness (movement speed <2° s−1). The rate map was constructed by dividing the smoothed spike count by the smoothed occupancy for each bin. The following criteria were used to identify place cells among putative pyramidal cells: (1) mean firing rate of less than 10 Hz to exclude potential interneurons; (2) peak firing rate of at least 1 Hz; and (3) spatial information content greater than the 95th percentile of the information content generated from shuffled (repeated 1,000 times) data. Spatial information, expressed in bits per spike, was computed using the formula[65]:

$$I=\mathop{\sum }\limits_{i=1}^{N}{p}_{i}\frac{{\lambda }_{i}}{\lambda }{\log }_{2}\frac{{\lambda }_{i}}{\lambda }$$

where pi is the probability of the animal occupying the position bin i, λi is the mean firing rate of the cell in bin i and λ is the mean firing rate across all position bins. Goal-representing cells (goal cells) were pyramidal cells that had a place field peak in the AZ or RZ. Non-goal representing cells (non-goal cells) were defined as pyramidal cells without a place field peak within 20° (2 zones) of the start of the RZ. The field stability of goal or non-goal cells was measured using the Pearson correlation coefficient of trial-by-trial rate map correlations.

Multiple linear regression

To control for the well-documented relationship between movement speed and interneuron activity, we used multiple linear regression. We also accounted for potential effects of position-specific lick rates on firing activity. For each unit, we fit a multiple linear regression model with position-binned trial-by-trial firing rates as the response and position-binned speed, lick rate, and the interaction between speed and lick rate as predictors. We then subtracted the expected effects of speed and lick behaviour from the observed firing rates. The remaining data (residuals) were used in further analyses as estimated neural activity that could not be attributed to position-related changes in speed or lick rate across trials. For visualizing population activity, we first normalized residual firing rates over the binned position by dividing each bin by the peak firing rate per unit and averaged rewarded trials per unit. Thus, the normalized firing was scaled from 0 to 1 with 1 being the peak residual firing of each cell. To illustrate the change in residual firing activity from the baseline, we subtracted the baseline firing rate defined as the mean normalized firing rate of the first two bins (in the position range of [−60, −50]) degrees where zero refers to the start of the RZ. The resulting change in normalized firing is a fractional change (or percentage change) and was shown as the mean and s.e.m. averaged over cells or shown per cell. In heat maps, we then sorted units based on the binned position with the peak or trough activity, for pyramidal cells or interneurons, respectively. We also tested other nonlinear regression approaches and found that the linear regression performed similarly to or better than other methods (Supplementary Table 4).

Quantifying firing activity around goal locations

To quantify the proportion of units with significantly increased or decreased activity in the familiar environment, we first created an occupancy normalized firing rate map (as described above) using a 2° bin size for each RZ-centred trial (from −60° to 70° with zero being the start of the RZ) per unit. We then used a generalized linear model to find the best linear fit for the rate map over the pre-RZ position bins (−60° to 0°) and obtain a P value and estimated coefficient (slope) per unit. Only units with significant adjusted P values after correcting for the FDR of 0.01 were identified as units with significant activity change. This approach was applied to both pyramidal cells and interneurons to find significant changes in firing around goals. On the basis of our observation of decreased activity on average in interneurons and increased activity on average in pyramidal cells around goals, we aimed to identify interneurons with significant decreases in activity and pyramidal cells with significantly increased activity around goals. We included interneurons with significant negative coefficients as interneurons with significantly reduced activity and we included pyramidal cells with significant positive coefficients as pyramidal cells with significantly increased activity.

The firing rate changes around the new RZs in the novel environment tended to be more variable than in the familiar environment. We therefore chose a different approach to identify units with a significant increase or decrease in firing in the novel environment to average over in Fig. 3b. We randomly shuffled both the position-binned trial-by-trial maps of firing rates, movement speed and lick rates used for multiple linear regression. This process was repeated 1,000 times for each unit to generate a shuffled distribution. We compared these shuffled distributions in each position bin against the observed session-averaged residual firing rate in the same position bin. We identified units with a significant decrease or increase at each bin if the observed residual was either less than the 10th percentile (for interneurons) or greater than 90th percentile (for pyramidal cells), respectively, of the shuffled distribution at each position bin. The population averages (Fig. 3b) are the average of units that had at least one bin determined to be significant 10° before and after the RZ. We indicated the first bin within −40° of the RZ with a persistent decrease in firing, meaning that firing decreased below the baseline and stayed below the baseline until the RZ (Fig. 3b (triangles)). The persistent decrease in firing on day 1 started at −45°.

We assessed how goal-selective inhibitory decreases develop within the first day of exposure to the first novel environment. On this first day, animals have already learned the parameters of the VR environment and task from the familiar environment, and now must learn where to lick for reward in a new environment for the first time. We analysed interneuron firing over blocks of 25 trials as animals learned the novel track. ROC curves were generated for the same trial blocks using pooled speeds in the AZ and NRZ across 7 WT animals. The trial-level speeds were min–max normalized per day and aggregated across animals for group-level visualization of the first and last trial blocks.

Ripple content decoding

To control for animal position and position-related variability in behaviour, we included only SWRs that occurred while the animal was in the AZ or RZ in this analysis. To determine the content of individual ripple events, we performed sequenceless decoding to decode each ripple as a single time window[66]. Before the decoding position during ripples, we confirmed decoding of current position during running in the familiar environment as this neural activity would be expected to represent current position. For each ripple, we calculated the spatial probability distribution using a simple Bayesian decoder as previously described57,66. In brief, the probability of particular positions given the spiking activity at each timepoint (expressed in nPositionBins × nTime) was calculated using the formula:

$$P(X| {{\bf{N}}}_{1}^{C})=\frac{\left(\mathop{\prod }\limits_{i=1}^{C}P({N}_{i}| X)\times P(X)\right)}{P({{\bf{N}}}_{1}^{C})}$$

where P(X|\({{\bf{N}}}_{1}^{C}\)) is the probability of position X (distances relative to the RZ using 5° bins) given \({{\bf{N}}}_{1}^{C}\), a vector of spike counts for all C cells recorded simultaneously within the specified time window. For all ripple events, we used a single 250-ms time window centred at the midpoint of each event regardless of its duration, and the decoder was applied to spiking within this window. P(Ni|X), the probability that cell i fires Ni spikes at position X, was computed using the population firing rate map averaged across trials (a nUnits × nBins matrix using 5° position bins as a function of distance to RZ, from −60 to 70°) multiplied by the decoding time window. P(X|\({{\bf{N}}}_{1}^{C}\)) was then normalized across position bins to sum to 1. From the single probability estimate, we identified the decoded position bin with the highest spatial probability as the most likely spatial information (expressed as the relative distance to RZ) carried by the population activity during each event.

Theta modulation of firing

Theta modulation was analysed during running periods when the movement speed was greater than 2° s−1 in the AZ. For each cell, the theta phase of spikes occurring in the defined running periods was extracted. The preferred theta phase was calculated as the circular mean of the theta phases for the spikes. The vector strength was calculated for each cell by implementing the ‘circ_r’ function from the ‘Circular Statistics Toolbox (Directional Statistics)’ in MATLAB (v.R2023a).

Immunohistochemistry

We used an additional six PVxAi32 mice (3 females) to verify the expression of ChR2 localized in PV-expressing interneurons. Thirty-micrometre coronal sections were blocked in 5% goat serum in 1× PBS for 30 min followed by 30 min in 5% goat serum with 0.3% Triton X-100. After blocking, the sections were incubated in primary antibody rabbit anti-parvalbumin (Swant, PV27, 1:5,000) at 4 °C overnight, washed in 1× PBS three times for 5 min each, incubated with secondary antibody goat anti-rabbit IgG Alexa Fluor Plus 647 (Invitrogen, A55055, 1:2,000) at room temperature for 1 h and washed three times in 1× PBS. The sections were mounted and imaged at ×10 or ×20 with an LSM 700 laser-scanning confocal microscope (Zeiss). Images were analysed using Zen Blue (v.3.3) microscopy software.

Statistical analysis

For data with repeated samples from the same animal and day, we used LMM analysis in R (v.4.2.2) and lme4 package[67] (v.1.1.35.1) to evaluate significant differences while controlling for repeated measures from sessions or animals. This approach was used for most analyses including data from multiple cells recorded from the same animal on the same day. Statistically significant differences were first estimated with an ANOVA with Kenward–Roger’s methods using the lmerTest package (v.3.1.3) with F-statistics reported[68]. The emmeans package (v.1.8.9; https://cran.r-project.org/web/packages/emmeans/index.html) was used to adjust P values for multiple comparisons. For pairwise comparisons, we assessed significant differences using estimated marginal means and reported Tukey-adjusted P values and T-ratios (indicating β/s.e.(β), where β is the regression coefficient). To determine statistically significant learning over days in WT mice, we included the areas under the behavioural ROC curves as the dependent variable; day, environment and day by environment interaction terms as fixed effects; and animal IDs as a random effect. Thus, the model specification for WT mice was as follows: AUC ~ day + environment + day × environment + (1|AnimalID). For PVxAi32 mice, our main comparison was between goal stimulation and sham stimulation conditions that occurred in the novel environment only; we therefore included AUC as the dependent variable; day, stimulation condition (not environment) and the interaction between the two as fixed effects; and animal ID as a random effect. Thus, for PVxAi32 mice, we used the following model specification: AUC ~ day + stimulation condition + day × stimulation condition + (1|AnimalID). Comparison of spatial firing (including spatial information, rate-map correlation, effects of position and temporal bins, theta modulation, peak firing rate) between groups were compared using the following model specification: for WT mice, spatial information ~ day × environment + (1|AnimalID/CellID); for PVxAi32 mice with goal and sham stimulation comparisons, spatial information ~ day × stimulation_condition + (1|AnimalID/CellID). To show significant interaction effects, asterisks are indicated between novel days 1–3 and familiar days 1–3 or sham stimulus days 1–3 and goal stimulus days 1–3 in the figures.

For data that were sampled per session, we tested for statistically significant differences using a nonparametric two-sided Wilcoxon rank-sum test for non-uniformly distributed data. This approach applied to things like the proportion of ripples with near versus far goal content, which was computed per day and normalized within animal. Note that P < 2.2 × 10−16 indicates that the computed P value is below the minimum possible value reported by R. Similarly P < 0.001 indicates that the P value is the minimum possible value for tests that control for multiple comparisons.

For nonparametric paired comparisons, we used Wilcoxon signed-rank tests with the Bonferroni method to correct for multiple corrections. To identify position or time bins with firing rate changes significantly different from zero, we used one-sample permutation t-tests (5,000 times) and indicated these with colour coded horizontal bars above plots of cell-averaged firing as a function of position or time.

Data with error bars were reported as mean ± s.e.m. Details on the statistical analyses for each figure panel are reported in Supplementary Tables 5–22.

Reporting summary

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

Data availability

Processed data used in this study are available at FigShare[69] (https://figshare.com/s/421cf9870016f963fcf5). Data from this study are available for research purposes. Source data are provided with this paper.

Code availability

Custom code created for this paper is available at GitHub (https://github.com/singerlabgt/Inhibitory_Gating_Project.git).

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Acknowledgements

We thank B. Mariner and the members of scidraw.io for graphical illustrations; and L. Walker, J. E. Markowitz, M. J. M. Rowan, L. Frank, B. Mensh and the members of the Singer laboratory for their comments on the manuscript. A.C.S. was supported by the Packard Foundation, the National Institutes of Health (NIH)-National Institute of Neurological Disorders and Stroke Grants R01 NS109226 and 2RF1 NS109226, the NIH National Institute of Aging Grant RF1 AG078736-01, McCamish Foundation, Friends and Alumni of Georgia Tech, and the Lane Family; N.J. by the Michael Kuhar Neuroscience Research Fellowship; A.L.P. by NIH National Research Service Award Grant 5F31AG066410, NIH Grant T32 NS007480-18, the Fulton County Elder Health Science Fellowship, the Wright Family and the J. Norman and Rosalyn Wells Fellowship; and S.M.P. by NSF GRFP grant DGE-1444932, NIH grant T32NS096050-22 and the Emory University Women’s Club Graduate Research Fellowship.

Author information

Author notes

1. These authors contributed equally: Nuri Jeong, Xiao Zheng

Authors and Affiliations

1. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA

Nuri Jeong, Xiao Zheng, Abigail L. Paulson, Stephanie M. Prince, Victor P. Nguyen, Sherina R. Thomas, Caroline E. Gilpin, Matthew C. Goodson & Annabelle C. Singer

2. Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA

Nuri Jeong & Stephanie M. Prince

3. Bioinformatics Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA

Xiao Zheng

Contributions

N.J. and A.C.S. conceived the study and designed the experiments. N.J. performed all the behaviour, recording and stimulation experiments in the CA3 in the annular maze with help from X.Z. S.M.P. performed all of the behaviour and recording experiments in the CA1 for the decision-making task and developed the task. N.J. developed recording and behaviour methods with help from X.Z. and A.L.P.; N.J., X.Z. and A.L.P. wrote code and analysed the data. V.P.N., S.R.T., C.E.G. and X.Z. trained animals and analysed behaviour during training. S.R.T., M.C.G. and N.J. performed histology and histology analysis. A.C.S. provided funding, training and supervision. N.J., X.Z., A.L.P. and A.C.S. wrote the manuscript with input and approval from all of the authors.

Corresponding author

Correspondence to Annabelle C. Singer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

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Nature thanks James Carmichael 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 Mice rapidly learn new goal locations in a novel virtual reality spatial learning task.

a, Experimental timeline. Box, typical behaviour of a well-trained animal. Mean ± SEM change in lick rate (purple) and movement speed (yellow) of an example animal, averaged over trials (n = 80) in a single session in the familiar environment. b-d, Example animal’s speed distributions in AZ (black in familiar or green in novel) or NRZ (tan) and respective ROC curves over three days during no-VR baseline periods (b), familiar navigation (c), and novel navigation (d). e, Raw (top row) or percent change (bottom row) in AUC from Day 1, based on speed (left), lick latency (middle), or lick rate (right). LMM followed by Tukey correction (n = 14 familiar sessions and 13 novel sessions from 7 mice). f, Same as e for PVxAi32 mice with sub-selected trials with low- or high-intensity stimulation zones only (left, “L + H stim zones only”), or trials where stimulation duration did not exceed 5 s (right, “Short stim only”). LMM followed by Tukey correction (n = 15 goal and 15 sham stimulation sessions from 5 mice each). Data in e-f represent mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.005; ****P < 0.001.

Source data

Extended Data Fig. 2 Residual firing after estimating and removing contributions of speed and licking activity on neuronal firing rates.

a, Raw firing rates for each NS interneuron (row) as a function of distance to RZ (pink rectangles) across movement speed quartiles. b, Model construction for performing multiple linear regression with an example unit’s model (illustrated in a grid) plotted against the trial data (blue circles) with its r-squared value on top right. c, Normalized raw (left) and residual (right) firing rates for all recorded NS interneurons (top, blue) and pyramidal cells (bottom, red). Each row is a single unit. Some pyramidal cells that fired around one RZ, also fired at other RZs. d, Percent variance explained by the multiple linear regression model fit for each unit for NS interneurons (top, blue) and pyramidal cells (bottom, red). For >99% of neurons, less than 25% of the variance in the neural firing rate data is explained by movement speed and lick rate. e, Raster plots of three example NS interneurons as a function of time to the RZ. Each dot is a spike. f, Linear correlation between normalized NS interneuron firing rate and normalized speed in the familiar (black) and novel (green) environments. Each dot represents a spatial bin, with the y-value indicating the average firing rate of all NS interneurons recorded within a day across trials and x-value indicating the average speed across trials within a day. Familiar Days 1, 2, and 3 and Novel Days 1, 2, and 3 were pooled from WT animals (n = 7 mice). Speed predicted 15–31% of the variability in NS interneuron firing consistent with our hypothesis that other variables like proximity to reward also affect NS interneuron firing. g, Same as f for correlation with lick rate. The brain (F. Caudi) in mouse head (E. Tyler and L. Kravitz) diagrams in e were adapted under a CC BY 4.0 licence; see the Methods for further details.

Source data

Extended Data Fig. 3 Inhibitory firing reduction cannot be explained by position-related changes in movement speed or lick rate.

a, Firing rates of NS interneurons in speed-matched trials during no-VR versus navigation in the familiar (a_1) and novel (a_2) environments across speed quartiles (two-sided Wilcoxon sign-rank test with Bonferroni correction). Faint lines are individual neurons. Dark lines indicate mean ± SEM. a_3, Absolute speed values corresponding to the speed quartiles in a_1 and a_2. Faint lines indicate each individual day. b, As in a for firing rates at AZ for NS interneurons (b_1, blue) and pyramidal cells (b_1, red), with absolute speed quartiles (b_2). LMM with Tukey correction. c, Normalized change from baseline, for trial-averaged lick rate (top), movement speed (middle), or NS inhibitory firing rate (bottom) as a function of time to RZ or freeze onset between normal (grey) and screen-freeze trials (teal) across four animals (c1-4). NS interneuron firing was not corrected for speed or licking behaviour. Dashed boxes indicate quantification periods used in c_5 when speed was not significantly different. Only NS interneurons with stable firing rates and significant decreases at AZ and RZ were selected and averaged across trials in each animal (n = 7, 2,7, and 1 NS interneurons in 224, 130, 30, and 38 normal and screen freeze trials for animals 1-4 respectively. Trials were subsampled to be equal across trial types in each animal). Significant difference in NS interneuron firing rates (freeze vs. normal, P = 0.0018, one-sided paired t-test with Bonferroni correction). Bar plots with error bars indicate mean ± SEM; two-sided paired t-test for velocity and lick rate; one-sided paired t-test for firing rate; the p-values were corrected for multiple testing using the Bonferroni method. n.s. (not significant); **P < 0.01; ***P < 0.005, ****P < 0.001.

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Extended Data Fig. 4 Goal-associated inhibitory decreases generalize across subtypes of NS interneurons and are not due to SWR occurrence.

a, Flow diagram for assigning putative NS interneuron subtypes (see Methods). b, NS interneurons of WT animals (n = 7 mice) were putatively classified as PV+ basket cells (top row), axo-axonic cells (second row), CCK+ cells (middle row), bistratified cells (fourth row), or ungrouped (last row). We found decreases in firing in the AZ/RZ in all putative subtypes (far left and centre left) with some cells of each subtype showing significant decreases (centre right) and some showing no significant change (far right). Even among cells with no significant change in firing at the AZ/RZ there were often similar trends of a decrease around the AZ/RZ on average (far right). Familiar Days 1, 2, and 3 were pooled. c-d, Population average percent change in normalized residual firing during trials without (c) or with (d) detected SWRs as a function of distance to RZ (pink dashed lines) for NS interneurons (n = 203 cells). Significant effect of position (with SWRs, P < 2.2e-16; without SWRs, P < 2.2e-16; LMM). Data in b-d represent mean ± SEM with colour-coded lines above plots indicate bins that are significantly (P < 0.05) different from zero or baseline using data pooled from all days (two-sided t-test followed by Bonferroni correction).

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Extended Data Fig. 5 PV interneuron firing following optogenetic stimulation inhibits pyramidal cell activity.

a, Raster plot (left) and histogram (right) showing example PV interneuron firing as a function of time to light onset (light blue bar). In all panels, light remained on for over 1 s. b, Normalized raw firing rates of all optically tagged PV interneurons (n = 35 cells) as a function of time to light onset for all stimulation conditions. c, Firing rates of PV interneurons (blue dots) from b, before (“pre-stim”) and during stimulation (“during-stim”). d, Population average percent change in normalized raw firing as a function of time to light onset (dashed line) for PV interneurons. Significant effect of time to light onset (P < 2.2e-16, LMM). e, Same as a for an example pyramidal cell. f-h, As in b-d for all recorded pyramidal cells (n = 594 pyramidal cells). Significant effect of time to light onset in h (P < 2.2e-16, LMM). In a, d, e, and h colour-coded lines above the raster plot and histogram indicate bins that are significantly (P < 0.05) different from zero or baseline using data pooled from all stimulation conditions (one-sided t-test followed by Bonferroni’s correction). Data in d and h represent mean ± SEM.

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Extended Data Fig. 6 Goal-associated inhibitory firing reductions on correct and incorrect trials and in different stimulation conditions.

a, Top row, Normalized change in residual firing rates of NS interneurons (“NS Int.” left) and pyramidal cells (“Pyr.” right) across distance to the familiar RZ during correct (black) or incorrect (burgundy) trials from 7 WT mice. Bottom row, Trough (for NS interneurons) or peak (for pyramidal cells) residual firing rate change around the RZ (from −10 to 5 degrees) during correct and incorrect trials. Each dot represents a single unit. Significant difference between correct and incorrect trials (NS interneurons n = 196 and 75 cells, P = 0.013; pyramidal cells n = 1168 and 375 cells, P = 6.7e-5; LMM). b, As in a for novel environments with no significant difference (NS interneurons n = 193 and 146 cells, pyramidal cells n = 1144 and 898 cells for correct and incorrect trials, respectively). c-d, Normalized change in firing rates of opto-tagged PV cells (c) or pyramidal cells (d) around novel AZ entry (goal stimulation onset) per stimulation intensity (blue). Cells from unstimulated WT animals (green) are shown as reference. Colour-coded lines above indicate bins significantly different from zero. Significant effect of time around RZ entry with goal stimulation for both cell types (n = 29 PV interneurons, P < 2.2e-16, P < 2.2e-16, P = 4.6e-11 for low, high, no stimulation; n = 330 pyramidal cells, P < 2.2e-16, P < 2.2e-16, P = 3.7e-06 for low, high, no stimulation; LMM) and in WT animals (n = 110 putative PV interneurons, P < 2.2e-16; n = 1178 pyramidal cells, P < 2.2e-16; LMM). Data in a-d were pooled from all days per environment and represent mean ± SEM. n.s. (not significant); *P < 0.05; ****P < 0.001.

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Extended Data Fig. 7 Goal-associated inhibitory decreases emerge during the first day of navigation of a novel environment.

a, Percent change of normalized residual firing rate from baseline for NS interneurons (blue) and pyramidal cells (red) that had a significant change in firing around the RZ as a function of time to RZ over three days of navigation of the first novel environment. (NS interneurons; Day 1, n = 9 cells, no significant effect of time; Day 2, n = 20 cells, P < 2.2e-16, main effect of time; Day 3, n = 20 cells, P = 3.6e-08, main effect of time; LMM. Pyramidal cells; Day 1, n = 184 cells, P = 0.0005, main effect of time; Day 2, n = 175 cells, P = 2.5e-10, main effect of time; Day 3, n = 199 cells, P < 2.2e-16, main effect of time; LMM). b, Raw firing rates of putative PV interneurons as a function of distance to the novel RZ during the first (light turquoise) and last block of 25 trials (dark turquoise) on the first day of navigation in the first novel environment. c, Cell-averaged bin counts with a significant PV firing reduction (n = 10, 6, 3, 3 cells for trial blocks 1-4). d, Field stability (left) and spatial information (right) for all pyramidal cells (n = 138 cells) on the first day of navigation in the first novel environment were not significantly different from early (lightest red) to late (darkest red) blocks of 25 trials. Data in a-c represent mean ± SEM.

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Extended Data Fig. 8 Disrupting goal-associated inhibitory decreases impairs stabilization and refinement of new spatial representations by pyramidal cells.

a, Schematic of possible effects of goal stimulation on stabilization and refinement of place cells. b, Example firing rates of goal-representing cells (“Goal cell”, top) or non-goal cells (bottom) during sessions with goal stimulation (“Goal stim,” blue) in a novel environment. The firing rate over the full track is shown inset for non-goal cells, to visualize the location of the place field away from the RZ (pink). c, As in b for sham stimulation (“Sham stim”, orange). d-e, field stability (d) and spatial information (e) of goal-representing pyramidal cells (“goal cells”) across days with goal stimulation (“Goal stim D1-3”, shades of blue, n = 55, 45, 44 cells for Days 1-3) and sham stimulation (“Sham stim D1-3”, shades of orange, n = 53, 60, 55 cells for Days 1-3). Significant interaction of day and stimulation condition for field stability (P = 3.9e-06) and spatial information (P = 4.0e-06; LMM with Tukey correction). f, percent of “goal cells” across days with goal stimulation and sham stimulation (n = 5 mice per day for each stimulation condition). g-i, As in d-e for pyramidal cells with significant spatial modulation outside goals (“non-goal cells,” n = 41, 36, 46 cells for Day 1-3 with goal stimulation; n = 16, 23, 38 cells for Day 1-3 with sham stimulation). i, As in f for number of “non-goal cells” (n = 5 mice per day for each stimulation condition). Significant interaction of day and stimulation condition for field stability (P = 0.0002) and spatial information (P = 0.01; LMM with Tukey correction). Data in b-c,f, i represent mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.005; ****P < 0.001.

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Extended Data Fig. 9 Sharp-wave ripples during goal and sham stimulation in familiar and novel environments.

a, Change in normalized firing rate of NS interneurons (“NS Int.” dark blue) or pyramidal cells (“Pyr.” red) around the SWR midpoint, averaged across SWR events in sessions with goal (“Novel Goal Stim,” left) or sham stimulation (“Novel Sham Stim,” right) in the novel environment. Inset, zoomed-in to show 1 sec. b, Normalized firing rate change between goal stimulation (“Goal stim,” blue) and sham stimulation (“Sham stim,” orange) for NS interneurons (left) and pyramidal cells (right) in the familiar environment. c-d, No significant different in SWR power (c) or duration (d) between goal stimulation (blue, n = 10 SWRs) and no stimulation (black, n = 390 SWRs) in the familiar environment (LMM). Note that part of the y-axis is removed to show full distributions; no data points were removed. e, SWR rate during sessions with goal (blue, n = 26 sessions) and sham (orange, n = 20 sessions) stimulation in a novel environment. Main effect of stimulation condition (P = 0.04, LMM). f, Coactivation probability of goal cell pairs during SWRs in goal (n = 111 pairs) and sham (n = 516 pairs) stimulation session (P = 8.1e-12, LMM). g-h, As in e for SWR power (g) and duration (h) for goal (n = 71 SWRs) and sham (n = 78 SWRs) stimulation (power: P = 0.002, LMM, not shown: 10.87 in sham stimulation; duration: P = 2.5e-09, LMM, not shown: 0.34, 0.50, 0.66, 1.00 in sham stimulation). Data in a-b represent mean ± SEM. c-h show distribution, median (white circle), quartiles (thicker lines), and whiskers (thin lines). In e-h, all goal stimulation days and all sham stimulation days pooled. n.s. (not significant); *P < 0.05; ***P < 0.005; ****P < 0.001. The brain (F. Caudi) in mouse head (E. Tyler and L. Kravitz) diagrams in b were adapted under a CC BY 4.0 licence; see the Methods for further details.

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Extended Data Fig. 10 Model of Goal-Selective Disinhibition to Promote Learning of Reward Locations.

This simplified schematic shows how PV interneurons (blue) and pyramidal (red) cells’ firing changes around reward locations as animals learn reward locations (pink). Our data is consistent with a model in which coordinated decrease in inhibition around reward locations develop rapidly to promote excitatory representations and reactivation of reward locations. Top, When a naïve animal approaches and traverse a new reward location (track schematic above shows animals location in the track), NS interneurons inhibit pyramidal cells with no clear reliable response to the reward location (“No experience” row). After an animal has received a few rewards, NS interneurons exhibit coordinated decreases in firing around the reward locations (“Received some rewards” row). These goal-selective decreases co-occur with and are required for the refinement and stabilization of excitatory place fields around goal locations. Over seconds NS interneuron activity decreases before increases in pyramidal activity. Over trials and days, NS interneurons and pyramidal cells response around reward locations develop together. Bottom, once animals have learned the reward locations, NS interneuron firing not only decreases in reward locations but also before reward locations. Such reward-predictive decreases in inhibition are well-suited to facilitate learning about locations that lead to reward.

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Jeong, N., Zheng, X., Paulson, A.L. et al. Goal-specific hippocampal inhibition gates learning. Nature (2025). https://doi.org/10.1038/s41586-025-08868-5

- Received: 29 January 2024

- Accepted: 25 February 2025

- Published: 09 April 2025

- DOI: https://doi.org/10.1038/s41586-025-08868-5

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A distributed coding logic for thermosensation and inflammatory pain

Nima Ghitani,

Lars J. von Buchholtz,

Donald Iain MacDonald,

Melanie Falgairolle,

Minh Q. Nguyen,

Julia A. Licholai,

Nicholas J. P. Ryba &

…

Alexander T. Chesler

Nature (2025)

Abstract

Somatosensory neurons encode detailed information about touch and temperature and are the peripheral drivers of pain1,2. Here by combining functional imaging with multiplexed in situ hybridization[3], we determined how heat and mechanical stimuli are encoded across neuronal classes and how inflammation transforms this representation to induce heat hypersensitivity, mechanical allodynia and continuing pain. Our data revealed that trigeminal neurons innervating the cheek exhibited complete segregation of responses to gentle touch and heat. By contrast, heat and noxious mechanical stimuli broadly activated nociceptor classes, including cell types proposed to trigger select percepts and behaviours4,5,6. Injection of the inflammatory mediator prostaglandin E2 caused long-lasting activity and thermal sensitization in select classes of nociceptors, providing a cellular basis for continuing inflammatory pain and heat hypersensitivity. We showed that the capsaicin receptor TRPV1 (ref. [7]) has a central role in heat sensitization but not in spontaneous nociceptor activity. Unexpectedly, the responses to mechanical stimuli were minimally affected by inflammation, suggesting that tactile allodynia results from the continuing firing of nociceptors coincident with touch. Indeed, we have demonstrated that nociceptor activity is both necessary and sufficient for inflammatory tactile allodynia. Together, these findings refine models of sensory coding and discrimination at the cellular and molecular levels, demonstrate that touch and temperature are broadly but differentially encoded across transcriptomically distinct populations of sensory cells and provide insight into how cellular-level responses are reshaped by inflammation to trigger diverse aspects of pain.

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Main

Sensory neurons innervating the skin provide animals with valuable input about their immediate surroundings, including the ability to sense touch and temperature1,2. In keeping with their varied roles, the receptor neurons with cell bodies in dorsal root and trigeminal ganglia have diverse peripheral morphology, central projections and biophysical profiles1,2,8. They differentially express a wide range of marker genes, including receptors, ion channels and neuropeptides2,8,9, and can be divided into about a dozen distinctive transcriptomic classes on the basis of single-cell RNA sequencing10,11,12,13. One key unanswered question is how distinct natural stimuli encoded across these classes allow us to instantaneously localize and discriminate a world of perceptions, for example, distinguish a drop of rain from an insect crawling on the skin, and elicit emotional responses ranging from pleasure to disgust or pain. Equally important would be to understand how this input is altered by injury or inflammation to trigger localized hypersensitivity, allodynia and continuing pain.

Over the past 30 years, identification of thermosensitive7,14,15,16 and mechanosensory17,18 ion channels has established a mechanistic framework for how physical stimuli are transduced to evoke neuronal firing in sensory neurons. These proteins also help explain some aspects of sensory coding but often exhibit complex expression across multiple classes10,11,12,13 and functional redundancy[16]. Therefore, we applied a platform that we developed for matching in vivo function and transcriptomic class[3] to decode how heat and naturalistic mechanical stimuli are differentially represented in the trigeminal system, the role of the capsaicin and noxious heat receptor (TRPV1) and how sensory detection is transformed during inflammation to drive pain.

Peripheral coding of heat

To evaluate how the somatosensory system detects and encodes thermal and mechanical stimuli, we used calcium imaging to record single-cell responses from the trigeminal ganglion while stimulating the cheek3,19. Across 15 mice, brush, pinch and defined heat pulses activated hundreds of neurons (Extended Data Fig. 1a), which could be divided into thermally or mechanically selective and polymodal cells (Extended Data Fig. 1b). We next used post hoc multigene in situ hybridization (ISH)[3] to determine how transcriptomic class governs the function of trigeminal neurons (Extended Data Fig. 2) and matched sensory tuning to ten distinct molecular classes. Data from 1,588 neurons (Fig. 1a) revealed clear segregation of gentle touch and heat responses at both the cellular and transcriptomic-class levels. By contrast, nociceptors were often polymodal[20] (Fig. 1a,b); nonetheless, their tuning varied according to class (Fig. 1a–d) with very few Aδ-nociceptors (Aδ-NOC) responsive to heat, which instead primarily activated unmyelinated C-fibres.

Fig. 1: Detection of heat and differential tuning of somatosensory neuron classes.

a, Heat maps showing in vivo GCaMP responses from 1,588 transcriptomically classified neurons from 15 mice tested with mechanical stimulation and heat. Heat maps are grouped by neuronal class with changes in GCaMP fluorescence (% ΔF/F) colour-coded as indicated by the scale bar. b, Proportions of heat (red), polymodal (orange) and mechanically tuned neurons (grey) for each class; the number of neurons and mice used for assignment are indicated. c, Mean responses of heat-responsive nociceptors by class (solid trace; numbers of heat-responsive cells indicated); the shaded area represents 95% confidence interval. d, Fractional recruitment of cheek-responsive neurons by class and temperature. e, Fractional representation of heat responses by class across the temperature range. Colour coding is as in d with LTMRs in grey; numbers of mice and cells are detailed in Supplementary Table 1. Scale bars, 10 s.

Many studies have identified peptidergic (PEP) nociceptors that express high levels of the heat- and capsaicin-activated ion channel Trpv1 and the nerve growth factor receptor Ntrk1 as canonical detectors of noxious heat2,8,21,22,23. Our data confirmed that this neuronal class is particularly sensitive and selective for heat over pinch, with about half of the cells in this class responding only to temperature (Fig. 1b–e). However, far from being selectively activated by noxious heat (45–50 °C), PEP neurons typically detected the full temperature range (Fig. 1c–e), exhibiting graded responses that increased in magnitude and duration across both the innocuous (37–42 °C) and noxious ranges (Fig. 1c,d). PEP constituted only a small subset of the heat-responsive cells. For example, just as many itch-related10,24 cells expressing Nppb and Sst (NP3) exhibited responses across the full temperature range (Fig. 1c–e). Moreover, half the noxious heat-responsive cells were in three other non-peptidergic classes (Mrgprd-, Mrgpra3- and Mrgprb4-expressing cells (NP1, NP2A and NP2B, respectively)), which are not generally considered to be thermonociceptors4,5,21,25 despite reports suggesting thermosensitivity22,26 (Fig. 1c–e). In fact, in the noxious temperature range, heat responses were dominated by classes other than the PEP nociceptors (Fig. 1e).

Non-canonical thermosensors

NP1 is a transcriptomically homogeneous class of mouse somatosensory neurons that has been suggested to function primarily in mechanonociception[21] and to have a minor role in thermonociception22,27. NP2B neurons are transcriptomically similar to NP2A itch-related cells[11] but have been reported to detect brush but not pinch[4] and to have a role in affective touch and sexual behaviour[5]. By contrast, our data (Fig. 1) indicate that NP1 and NP2B neurons innervating the cheek have closely related activity profiles, preferentially responding to high-threshold mechanical stimuli and noxious heat. A recent study also suggested that NP1- and NP2B-lineage neurons have similar receptive tuning[28]. To better understand these differences and to benchmark our ISH-based approach, we crossed the two Cre lines4,29 that were used in previous studies4,5,28 into the Ai95 (RCL-GCaMP6f) background[30].

The well-characterized Mrgprd-CreER line faithfully captures NP1 neurons in adult mice28,29. Functional recordings from these cells (Fig. 2a,b) were consistent with the results of the ISH-based approach (Fig. 1), confirming that NP1 cells respond to noxious thermal and mechanical stimulation. The Mrdprb4-tdT-2a-Cre line that was used to target NP2B cells4,5,28 mediates Cre recombination more broadly (Extended Data Fig. 4). However, although tdTomato (tdT)-positive neurons were a small subset of GCaMP-expressing cells, 99% of tdT cells (396 of 400 neurons from three mice) expressed Mlc1, a marker of NP2B cells (Extended Data Fig. 3a,b). Functional imaging of tdT-positive neurons supported the results from the comprehensive analysis of neuronal function (Fig. 1) by showing that NP2B neurons were not selectively activated by gentle stimulation of the cheek but instead responded most strongly to higher-intensity mechanical stimuli and heating (Fig. 2a). Quantification of responses (Fig. 2c) confirmed close correspondence between ISH and Cre recombination-based datasets. Thus, NP1 and NP2B neurons detect noxious stimuli applied to the hairy skin of the cheek.

Fig. 2: In vivo functional imaging of Cre lines confirmed that NP1 and NP2B neurons detect noxious mechanical and thermal stimuli.

a, Heat maps showing calcium transients from Ai95 carrying knock-in alleles to label NP1 (upper panel; 561 neurons; four mice) and NP2B (lower panel; 72 neurons; three mice) neurons. Changes in GCaMP fluorescence in response to stimuli applied to the cheek are colour-coded by % ΔF/F (inset scale). b,c, Percentage of neurons (mean ± s.e.m.) responding to each stimulus for cells identified by ISH after functional imaging (pale grey) or Cre-mediated labelling (dark grey). b, NP1 cells: ISH, n = 13; CreER, n = 4. c, NP2B cells: ISH, n = 12; tdT, n = 3. See Supplementary Table 2 for details and statistics. Scale bar, 10 s.

Previous studies of NP2B neurons4,5,28 investigated peripheral targets of dorsal root ganglia (DRG) rather than the trigeminal ganglion, raising the possibility that DRG and trigeminal NP2B neurons are differentially responsive to mechanical stimuli. Therefore, we recorded lumbar DRG responses of Mrdprb4-tdT-2a-Cre labelled neurons to mechanical stimulation of hairy skin (Extended Data Fig. 3d). Responses from the full lineage were consistent with recently published results[28] showing a small fraction (approximately 5%) to be brush-sensitive. However, when we analysed NP2B neurons (tdT-positive cells), they exhibited pinch responses but were unresponsive to brushing (Extended Data Fig. 3d), similar to their trigeminal counterparts, further demonstrating that these neurons are not selective gentle touch receptors[4].

NP1 neurons are divided into polymodal and pinch-selective functional types, despite their homogeneous transcriptomic profile10,11,12 (Figs. 1b and 2a). We used a series of pinches to provide a naturalistic mechanical stimulus to a broad area of the cheek and applied the thermal probe to a similar area of the skin. However, differences in stimulus location, rather than biological differences, may contribute to the impression that some cells are narrowly tuned. We observed individual pinches that almost exclusively activated polymodal cells and other pinches (at different locations) that selectively recruited heat-insensitive nociceptors (Extended Data Fig. 4). This suggests that differential tuning of NP1 neurons can largely be explained by their small receptive field3,29 coupled with our naturalistic stimulation approach (Methods). Other classes of C-nociceptors also included both selective and polymodal cells but with quite different proportions tuned to heat or pinch (Fig. 1b). We suggest that the long-standing debate about the nature of polymodal and narrowly responsive nociceptors31,32 simply reflects the mode of stimulus application coupled with the relative sensitivity of different classes of nociceptor to thermal versus mechanical stimuli and resultant differences in their effective receptive fields for these two modalities.

Taken together, detailed analysis of how the different types of C-nociceptors respond to heat and noxious mechanical stimulation (Figs. 1 and 2) challenges the view that various transcriptomic classes have distinct and independent roles in thermo- and mechanosensory detection and discrimination4,5,22,24,25,33. Instead, our data suggest that all classes of C-nociceptors are broadly tuned with overlapping but distinct response profiles much like the cones in the human visual system[34]. By analogy, such tuning should allow efficient and reliable encoding of multiple stimulus features, including modality, position, extent and intensity, which are important for triggering appropriate behavioural responses. Moreover, noxious mechanical stimuli, but not thermal stimuli, recruit an array of low-threshold mechanoreceptors (LTMRs), augmenting the discriminatory power of the system.

Nociceptor activity during inflammation

A crucial role of the somatosensory system is to evoke pain as a protective mechanism for preventing tissue damage and promoting healing2,35. For example, injury and inflammation result in continuing localized pain, alter the evaluation of sensory input and evoke a range of attending behaviours[35]. It is well known that sensory afferents dramatically change their firing properties under these conditions2,32,36. To better characterize these changes, we carried out longitudinal imaging to track and classify which neurons are affected by injection of the fast-acting inflammatory mediator prostaglandin E2 (PGE2). This paradigm induces swelling, spontaneous pain behaviour, hyperalgesia and allodynia within 10 min of injection with recovery after about 2 h (ref. [37]).

Most sensory neurons that respond to stimulation of the cheek were quiescent when held at 30 °C under baseline conditions. By contrast, after PGE2 injection, many cheek-innervating nociceptors became active in the absence of applied stimuli (Fig. 3a,b and Supplementary Video 1). Spontaneous activity developed over the first 5 min and lasted for more than an hour. To quantify the magnitude of spontaneous activity, we assessed both the frequency and amplitude of responses (Fig. 3b). This analysis (Fig. 3c) revealed that spontaneous activity was almost entirely restricted to nociceptors (primarily Aδ-NOC, PEP, NP2A and NP3) but not LTMRs, providing a compelling explanation for localized continuing pain associated with inflammation. PGE2 receptor expression11,12,13 does not match the classes of neurons displaying spontaneous activity, suggesting that indirect inflammatory effectors are probably involved.

Fig. 3: PGE2-mediated inflammation differentially affects select classes of nociceptors.

a, Heat maps showing the effect of PGE2-induced inflammation on the thermal sensitivity of 582 C-nociceptors (eight mice) that responded to heat and/or pinch of the cheek (cheek-innervating neurons) grouped by class; for display purposes, baseline (left) and continuing inflammation (right) were independently sorted on the basis of the amplitude of heat response. b, Example of GCaMP traces for individual neurons at baseline (left) and after PGE2 injection (right) for classes with spontaneous activity during inflammation; red points and bars in the lowest example trace identify transients and their amplitudes (see Methods for details). c, Quantification of unstimulated activity (mean sum of transient amplitudes ± s.e.m.) in neuronal classes in wild-type mice before (grey) and after (red) PGE2-induced inflammation. d,e, Heat response for all cheek-innervating neurons of a given class over the full temperature range (mean area under the curve (AUC) ± s.e.m.). Wild-type mice (d) and Trpv1−/− mice (e) before and after PGE2 injection of the cheek. Additional information about heat sensisitization is shown in Extended Data Figs. 5 and 6. f, Unstimulated activity (mean sum of transient amplitudes ± s.e.m.) in neuronal class activity during inflammation in wild-type mice (red) and Trpv1−/− mice (blue). Note that the red bars in c and f show the same dataset for comparative purposes; *P < 0.05; **P < 0.01; ***P < 0.001; for details of statistical tests and number of mice and cells, see Supplementary Tables 1 and 2. KO, knockout. Scale bars, 10 s.

PGE2 injection potentiated heat detection by C-nociceptors (Fig. 3a,d), which is consistent with previous results[31]. This can be clearly observed in the time-locked temperature responses and is superimposed on the generally lower-magnitude spontaneous nociceptor activity induced by PGE2 (Fig. 3a and Extended Data Fig. 5a). All other classes of neurons remained insensitive to heat (Fig. 3d). A more detailed analysis (Extended Data Fig. 5b) revealed that responses to temperatures in the normally innocuous, warm range were increased in PEP, NP2A and NP3, suggesting a cellular logic for inflammatory thermal allodynia. PGE2 injection also causes heat hyperalgesia at noxious temperatures (45–50 °C) characterized by a variety of exaggerated responses and coping behaviours[2]. Over this temperature range, our results indicate that NP1 and NP2B also contribute to elevated pain responses (Extended Data Fig. 5b).

PEP, NP2A and NP3 neurons express the ion channel TRPV1, which has widely been reported to have only a minor role in baseline thermal behaviour22,38,39 but is essential for heightened responses to heat after inflammation38,39. Therefore, we next tested how the peripheral representation of heat and inflammatory sensitization was affected by the absence of this ion channel. Trigeminal neuron responses to heat were dramatically reduced in Trpv1−/− mice (Fig. 3e), with smaller proportions of PEP, NP3 and NP2A responsive to temperature and changes in the relative tuning of these neuron classes to heat and pinch (Extended Data Fig. 6a–c). These large-scale changes were surprising given the modest effects of Trpv1 knockout on thermal behaviour22,38,39 and suggest that the small amount of residual nociceptor activity (Fig. 3e and Extended Data Fig. 6), perhaps combined with the inhibition of cooling-responsive neurons[40], provides sufficient heat discrimination for many behavioural paradigms. Notably, PGE2-induced inflammation had only modest effects on the temperature sensitivity of nociceptors in Trpv1−/− animals (Fig. 3e), which explains the crucial role of this ion channel in inflammation-related thermal allodynia and hyperalgesia38,39. This was also true for the sensitization of NP1 and NP2B neurons (Fig. 3e), which did not prominently express Trpv1. We suggest that low levels of this ion channel in these cell types or perhaps non-cell autonomous effects, for example, neuroinflammation[41], contribute to heat hyperalgesia. Finally, unlike for heat sensitization, where TRPV1 has a major role, the stimulation of continuing nociceptor activity after PGE2 injection was largely unaltered in Trpv1−/− animals (Fig. 3f and Extended Data Fig. 6e,f). Thus, PGE2 must affect more than one signalling pathway in nociceptors, including one that generates spontaneous activity and another that potentiates heat responses through TRPV1.

Logic for inflammatory tactile allodynia

Tactile allodynia, a major consequence of many types of inflammation[2], is dependent on signalling through the mechanosensory ion channel PIEZO2 (refs. 42,43). Therefore, we evaluated whether the detection of gentle brushing, a stimulus that elicits pain-related behavioural responses during inflammation[2], was affected by PGE2 injection. Qualitatively, there were minimal changes in LTMR calcium transients (Fig. 4a), although quantification revealed a slight decrease in C-LTMR responses but no significant changes in Aδ- and Aβ-LTMR activity (Supplementary Table 2). Thus, although it is possible that small differences in the firing of Aβ-neurons[44] contribute to inflammatory mechanical allodynia, such changes are no larger than natural variation in brush responses of LTMRs (Fig. 4a).

Fig. 4: Stable representation of gentle touch at the periphery during inflammation.

a, Heat maps showing the effect of PGE2-induced inflammation on the brush sensitivity of 339 LTMRs (eight mice) that responded to pinch and/or brush of the cheek grouped by class. b, Percentage of neurons responding to at least 50% of individual brushes before (grey) and after (red) PGE2; mean ± s.e.m.; n = 8. c, Brush response magnitude (mean AUC ± s.e.m.) for all cheek-innervating neurons of a given class before (grey) and after (red) PGE2 injection; background and PGE2-induced activity (mean AUC ± s.e.m.) during an equivalent period without stimulation (pale grey and pink) showed no increase in LTMR responses after PGE2 injection and that nociceptor firing during inflammation is largely brush-independent (Extended Data Fig. 7). Supplementary Tables 1 and 2 include full details of statistical tests and numbers. Scale bar, 10 s.

We next analysed whether nociceptors are sensitized to brush after PGE2. Here, inflammation-related continuing activity in nociceptors complicates the analysis. However, unlike LTMRs, very few nociceptors reliably detected brushing, that is, responded during 50% or more brushes, either at baseline or after PGE2 (Fig. 4b). Moreover, comparison of response profiles (Extended Data Fig. 7) and quantification of calcium transients (Fig. 4c) demonstrated that brushing after PGE2 injection did not augment the continuing activity or mechanical sensitivity of any nociceptor class (see Supplementary Table 2 for statistical analysis). Because gentle touch remains largely unchanged during inflammation, the question remains how PGE2 injection changes the behavioural response of mice to this type of stimulation[37]. Notably, our data (Fig. 4c and Extended Data Fig. 7) showed that inflammation-induced continuing activity alters the population of peripheral neurons that are active during brushing. Therefore, we reasoned that this touch-independent nociceptor activity may transform the central (for example, spinal cord processing) of normal LTMR input to serve as a driver of localized pain. This new model is completely consistent with the crucial role of PIEZO2 (refs. 42,43) and the importance of LTMRs[44] in inflammatory tactile allodynia.

The hypothesis that coincident firing of LTMRs with nociceptors is the basis for tactile allodynia during inflammation makes two testable predictions. First, if spontaneous nociceptor firing drives tactile allodynia, silencing these neurons should block sensitization. Therefore, we developed an intersectional approach to express a fragment of the tetanus toxin (TeNT) that potently blocks synaptic transmission in nociceptors (Extended Data Fig. 8a). These mice have no gross motor deficits and exhibit normal baseline von Frey responses (Fig. 5a). Just as predicted, PGE2 injection to the hind paw failed to trigger tactile allodynia in these TeNT-expressing mice (Fig. 5a), whereas littermate controls exhibited strong sensitization. Second, we reasoned that any continuing activity in nociceptors should be sufficient to trigger touch-evoked pain. Here we took advantage of the unique molecular expression profile of NP3 neurons, one of the cell classes most strongly activated by PGE2 injection (Fig. 3e). It was recently reported that LY344864, a synthetic agonist for the serotonin receptor HTR1F, which is specifically expressed in NP3 cells (Fig. 5b), triggers itch through activation of this cell class[45]. We demonstrated that local subcutaneous injection of LY344864 in the cheek potently activated trigeminal NP3 neurons with high selectivity (Fig. 5c). Using an Sst-Cre driver to target these cells in the DRG, we showed that both LY344864 and PGE2 induced NP3 firing with comparable intensity and duration (Fig. 5d and Extended Data Fig. 8b–d). Notably, LY344864 also induced potent tactile allodynia when injected into the hind paw (Fig. 5e and Extended Data Fig. 9a,b). Moreover, blocking synaptic transmission in these cells (Fig. 5e) or acute inhibition of these cells in adult mice (Fig. 5f) abolished the LY344864-mediated sensitization, confirming the importance of NP3 nociceptors in mediating inflammatory allodynia in this model. Taken together, these results show how touch-independent nociceptor activity can make a normally innocuous mechanical stimulus painful, without changing the way this stimulus is detected at the periphery.

Fig. 5: Continuing nociceptor activity drives tactile allodynia.

a, Time course of von Frey 50% withdrawal threshold (mean ± s.e.m.) before (time = 0) and after injection of PGE2 into the hind paw of mice with silenced Trpv1-lineage nociceptors (red; n = 6) and littermate controls (black; n = 7). PGE2 injection induced mechanical sensitization in controls but not in mice with silenced nociceptors (P < 0.05) over 60 min. b, Violin-plot analysis of expression level (log-normalized single-cell RNA sequencing data)11,28 showing selective expression of Htr1f in NP3 nociceptors. c, Percentage and level of activation (ΔAUC) of neurons after injection of the selective HTR1F agonist LY344864 into the cheek. d, Heat maps of DRG imaging for mice expressing GCaMP under the control of Sst-Cre. Responses from 200 neurons of six mice showed that Sst-Cre labelled a population of brush cells and a separate population of LY344864-responsive neurons. Below, quantification of LY344864-stimulated activity (mean sum of transient amplitudes ± s.e.m.) for brush and non-brush cells. e, von Frey 50% withdrawal threshold (mean ± s.e.m.) before (time = 0) and after injection of LY344864 into the hind paw of wild-type mice (magenta; n = 7) and littermates with silenced Trpv1-lineage nociceptors (grey; n = 6). LY344864 injection induced mechanical sensitization in wild-type mice for at least 60 min but was ineffective in mice with silenced nociceptors. f, von Frey 60% withdrawal thresholds (mean ± s.e.m.; n = 7) at baseline (grey) and after paw injection of LY344864 in mice expressing KORD[53] in Trpv1-lineage nociceptors. Mice received intraperitoneal dimethylsulfoxide (vehicle; magenta) or the selective KORD ligand Salvinorin B (SalB; blue) immediately before paw injection of LY344864. See Supplementary Tables 1 and 2 for full details of statistical tests and numbers; *P < 0.05; **P < 0.01. Scale bar, 10 s.

Finally, we explored whether spontaneous nociceptor firing drives inflammatory pain in the absence of externally applied stimulation. As predicted, hind paw injection of PGE2 evoked typical pain behaviours, including copious paw licking in wild-type mice but not in TeNT littermates in which nociceptors were silenced (Extended Data Fig. 9c,d). When injected in the cheek, PGE2 did not immediately evoke the wiping response typically associated with painful agents[46]. Instead, within 5 min of injection, face-directed behaviours (wiping, grooming and scratching) were almost completely suppressed (Extended Data Fig. 9e,f), with mice often standing motionless and hunched for extended periods (Supplementary Videos 2–4). Taken together, these results support a role for spontaneous nociceptor firing as a driver for continuing inflammatory pain.

Discussion

One of the most remarkable features of the somatosensory system is its ability to encode and discriminate diverse physical stimuli while also providing information about the position, extent, pleasant versus aversive nature and intensity of the stimulus1,2,8,35. Here we combined functional imaging with transcriptomic classification to dissect the cellular logic by which touch and heat are encoded at the periphery and how this representation is altered during inflammation.

Simultaneously interrogating the response properties of all the major types of cutaneous somatosensory neurons to complex stimuli revealed key features underlying the coding of heat and noxious mechanical stimuli. First, our data show the complete segregation of cell classes that respond to gentle mechanical stimulation from those that detect heat. Second, although Trpv1-expressing neurons are particularly sensitive to temperature and reliably detect warming, several other types of C-nociceptors are prominently activated by noxious heat. Interestingly, Mrgprb4-expressing NP2B cells that have previously been linked to gentle stroking, affective touch and sexual response4,5 have the receptive tuning of nociceptors rather than LTMRs throughout the body. Third, matching receptive tuning to cell class allows differential effects of functionally important molecules in distinct classes of cell to be determined[3]. Using knockout mice, we showed that the capsaicin receptor TRPV1 is largely responsible for the particular thermosensitivity of PEP, NP2A and NP3 neurons. In keeping with its function in the oral cavity[47], TRPV1 mediates almost all responses to innocuous warming but, in contrast to predictions from its in vitro threshold[7], has a lesser role in the detection of noxious heat. Together, these data showed that, rather than classes of nociceptor being selectively tuned to a single modality4,5,24,25, there was an inverse relationship between mechanical and thermal sensitivity, such that all types of C-nociceptors responded to heat and noxious mechanical stimulation, albeit with differential graded tuning. The distinct but overlapping response spectra of cell classes imply that the somatosensory system uses combinatorial coding to achieve its remarkable discriminatory power.

Changes in the firing patterns of somatosensory neurons have long been linked to peripheral inflammation and are assumed to drive associated pain2,8,32,36. Our analysis demonstrated that PGE2-mediated inflammation induces stimulus-independent activity in several types of nociceptors and sensitizes these cells to heat but not gentle mechanical stimulation. Inflammatory heat sensitization was greatly reduced in Trpv1−/− mice (Fig. 3d,e), matching behavioural measures of heat allodynia and hyperalgesia during inflammation38,39,48. By contrast, spontaneous activity, which we considered as important for continuing inflammatory pain, was well preserved in the knockout mice (Fig. 3c,f). Thus, TRPV1 antagonists might be expected to alleviate heat sensitization but be less effective for other aspects of inflammatory pain. Our results showed that spontaneous nociceptor firing drives inflammatory tactile allodynia, and eliminating nociceptor input blunts this sensory transformation (Figs. 4 and 5). These findings are consistent with the crucial role of PIEZO2 in this type of pain[42], despite its relatively minor role in mechanonociception3,17,43. It also helps to explain how excitotoxic-mediated ablation of nociceptors can be effective in treating several forms of pain without substantially affecting gentle touch or mechanonociception[49].

There are limitations to our study. For example, we only studied the functional coding of neurons innervating hairy skin using functional imaging. Calcium transients provide a reliable proxy for neuronal firing3,19 but may miss subtle effects that could contribute to pain[44]. However, in day-to-day life, subtle variations in LTMR firing provide discriminatory information without normally causing pain, further supporting our hypothesis. We also note that although our dissection of cell classes was as comprehensive as that in a recent study using Cre drivers[28], further subdividing the Aβ-LTMRs, Aδ-NOC and PEP populations may reveal more nuances. In the future, extending these findings to glabrous skin and internal targets of somatosensory neurons (bone, muscle and viscera) will also be important. Similarly, we examined a short-term inflammatory model of pain, but there are many longer-term disease states that cause pain in humans, including diabetes, cancer and blood disorders (for example, sickle cell disease). Dissecting the extent to which these pathologies induce similar changes in sensory coding across cell classes should be informative for developing appropriate pain therapies. It will also be interesting to determine how inputs from the various heat-responsive nociceptors converge in the dorsal horn and spinal trigeminal nucleus to support combinatorial coding and how inflammation-dependent spontaneous firing might alter this representation. Finally, all these studies were performed in mice; human somatosensory neurons exhibit differences at the molecular[50] and class-based51,52 levels, which may affect the precise details. However, the underlying principles we report, including distributed coding of heat across nociceptor classes and selective effects of inflammation on nociceptor activity and thermal sensitivity, will likely be relevant for human sensation and pain.

Methods

Experimental animals

Experiments using animals were performed in accordance with the guidelines set forth by the National Institutes of Health and approved by the National Institute of Neurological Disorders and Stroke or the National Institute of Dental and Craniofacial Research Animal Care and Use Committees. Mouse lines Ai95(RCL-GCaMP6f)-D (no. 024105)[30], Mrgprd-CreERT2 (no. 031286)[29], Mrdprb4-tdT-2a-Cre (no. 021077)[4], Trpv1-Cre (no. 017769)[54], Sst-Cre (no. 013044)[55] and Trpv1−/− (no. 003770)[38] were purchased from The Jackson Laboratory. Tac1-tagRFP-2a-TVA mouse line was described previously[56], and the Avil-Flp line[57] was a gift from D. Ginty. We also generated a new Rosa-Cag-LSL-soma-jGCaMP8s line used in crosses with Sst-Cre, an Avil-LSL-2A-TeNT line and a Rosa-CAG-FSF-LSL-KORD line using CRISPR–Cas9-mediated recombination[56]. Male and female mice were used in all experiments and given ad libitum access to standard laboratory chow and water. The mice were housed in a controlled environment (23 °C and 50% humidity with a 12-h light–dark cycle); no statistical methods were used to predetermine sample size.

Injection of adeno-associated virus in mouse pups and CreERT2 induction

Left intracerebroventricular injection of 1 μl of AAV9-Cag-Cre virus (2 × 10^12 to 2 × 10^13 virions ml−1; catalogue no. CV17187-AAV9; Vigene) or AAV9-CMV-Cre virus (more than 1 × 10^13 virions ml−1; catalogue no. 105537-AAV9; Addgene) in 1- to 3-day-old mouse pups containing Ai95 was used to achieve stochastic expression of GCaMP in neurons of the trigeminal ganglion as described previously[42]. To induce CreERT2 recombination, tamoxifen (Sigma-Aldrich) was dissolved in corn oil at a concentration of 20 mg ml−1 at 37 °C overnight. Mrgprd-CreERT2 mice were injected intraperitoneally at a dosage of 75 mg tamoxifen per kg of body weight using an insulin syringe at least a week before calcium imaging.

In vivo calcium imaging

For fluorescent calcium imaging of the trigeminal neurons, adult (over 8 weeks old) animals were subjected to one of three experimental regimes. Experimental regime A assessed both mechanical and temperature responses (Figs. 1 and 2 and Extended Data Figs. 1–5). The animals were anaesthetized with isoflurane and surgically prepared for optical access to the trigeminal ganglion as described previously[19]. The hairy skin of the cheek was mechanically stimulated using a series of manually delivered brushes with a cotton-tipped applicator and pinches with surgical forceps as described previously[3]. For improved temperature transfer, the mouse cheek was treated for less than 300 s with a depilatory cream (Veet) using a cotton-tip applicator. An eye ointment was applied to prevent damage to the cornea and conjunctiva, fur was removed, the cheek was washed at least three times with saline and dried with Kimwipes and a custom-built 5 mm^2 Peltier probe (TCS2; QST.Lab) was applied directly to the skin for thermal stimulation. Peltier application did not induce long-term activity of trigeminal neurons, and LTMRs did not respond to heating (Fig. 1), ruling out major confounding effects related to mechanical stimulation by the probe. The skin was held at 30 °C for baseline non-stimulated activity measurements; temperature stimulation at 37, 39, 42, 45 and 50 °C was for 4 s.

Experimental regime B assessed the mechanical sensitization after inflammation (Fig. 4 and Extended Data Fig. 7). Baseline mechanical stimulation was as in experimental regime A. Inflammation of the cheek was then induced using subdermal injection of 20–30 μl, 0.5 mM PGE2 (Sigma-Aldrich) to three sites. After 10 min of incubation, inflammation was confirmed and mechanical stimulation was repeated.

Experimental regime C assessed the thermal sensitization after inflammation (Figs. 3 and 5 and Extended Data Figs. 5 and 6). Mouse cheek depilation was carried out the day before functional imaging. Skin was subjected to a series of pinches and thermal stimulation as in experimental regime A. PGE2 or LY344864 (20–30 μl; 5 mg ml−1; MilliporeSigma) was injected as in experimental regime B, and stimulation was repeated. To evaluate consistency, responses to common stimuli were compared between experimental regimes A and C (Extended Data Fig. 10).

The functional activity of DRG neurons in the L5 and L6 ganglia was determined using fluorescent calcium imaging. PGE2 and LY344864 were injected as a single injection (volumes and concentrations as above) into the plantar hind paw. Mechanical stimulation was also applied to the plantar surface, which was held at 30 °C for the unstimulated recordings. For Mrgprb4-tdT-2a-Cre mice, the hairy skin of the leg was stimulated as NP2B neurons selectively innervated hairy skin[4].

Calcium imaging was performed as described previously3,58 using a custom-built epifluorescence Cerna microscope (Thorlabs) and a pco.panda 4.2 bi CMOS camera; 40-s recording episodes were acquired at 5 Hz. For each experiment requiring post hoc ISH, either red fluorescent tagRFP images were collected or the trigeminal ganglion was briefly superfused with 500 μl of 1 M KCl to activate and visualize all GCaMP-expressing neurons after the experiment to provide alignment guide-posts. In vivo images were aligned and processed as described previously[3].

Spatial activity maps and analysis of fluorescence dynamics

Spatial activity maps and regions of interest (ROI) were generated as described previously[3]. In brief, activity induced by repetitive mechanical stimulation was visualized as standard deviation over time for each pixel. Heat-induced activity was visualized by subtracting the mean fluorescence before stimulation from the mean fluorescence during stimulation. ROI were manually extracted using the ‘Cell Magic Wand’ plugin in ImageJ. Overlapping cell ROI that were contaminated by each other’s responses were excluded from the analysis while blind to transcriptomic information. Relative change in GCaMP fluorescence was calculated as ΔF/F (%) for each cell, and potential contaminant signal from the underlying out-of-focus tissue and neighbouring cells was removed by subtracting the fluorescence of a doughnut-shaped area surrounding each cell using a custom MATLAB script[42]. Cell category-specific activity maps were generated by overlaying a category-specific mask over the activity map of the cognate stimuli (heat for heat-specific and polymodal cells; brush and pinch for mechano-specific cells).

Whole-mount ISH of trigeminal ganglia

Whole-mount ISH of trigeminal ganglia after in vivo imaging and of tissue sections was performed as described previously[3] using combinations of the following hybridization chain reaction probes (Molecular Instruments): Trpm8 (GenBank NM_134252; full length), S100b (NM_009115; full length), Fxyd2 (NM_007503; full length), Scn10a (NM_001205321; coding sequence), Calca (NM_007587; full length), Trpv1 (NM_001001445; full length), Tmem233 (NM_001101546; full length), Mrgprd (NM_203490; full length), Nppb (NM_008726; full length), Sst (NM_009215; full length), Mlc1(NM_133241; full length), tagRFP-TVA, tdT and EGFP (which detects GCaMP expression). Two-dimensional dorsal views of the surface of whole-mount ganglia were collapsed by maximum intensity projection from confocal Z stacks with 10-μm intervals to capture the convex surface of the ganglion.

Aligning whole-mount ISH images to in vivo recordings

Alignment of whole-mount ISH images to in vivo fluorescent images was performed as described previously[3] using either tagRFP-positive guide-post cells in Tac1-tagRFP/TVA animals or GCaMP-expressing cells stimulated with high K+ directly applied to the trigeminal ganglion. In brief, multi-channel two-dimensional ISH images were crudely aligned to in vivo fluorescence by scaled rotation using the TurboReg plugin and a custom macro in ImageJ/Fiji. Guide-post cells were then manually matched to their in vivo fluorescent counterparts using a custom ImageJ macro that identified coordinate pairs for each guide-post. The ISH image was morphed to match its in vivo counterpart using these coordinates with a custom Python script that builds on the OpenCV library[3]. Several rounds of ISH were aligned to each other using probes labelling partially overlapping sets of cells in both rounds to provide guide-posts for morphing. As shown previously[3], this type of image alignment does not produce a pixel-to-pixel match but accurately identifies ISH-positive cells that respond functionally (Extended Data Fig. 2).

Analysis of gene expression and transcriptomic classification

Cell ROI (responding cells) were manually analysed for expression (negative, weak or strong) of every gene with diagnostic ISH data[3] (Extended Data Fig. 2c). Binary expression patterns were decoded into transcriptomic cell classes using the rules outlined in Extended Data Fig. 2b and Supplementary Table 3, which also explains how transcriptomic class nomenclature[10] is related to other classification schemes.

Single-cell sequencing data from DRG[28] were obtained from GEO Series GSE254789 and analysed with Seurat v.5 in RStudio. Cells with less than 800 expressed genes or more than 5% of mitochondrial transcripts were excluded, and datasets were combined using canonical correlation analysis integration after principal component analysis reduction to 30 components. Neuronal and non-neuronal cell clusters were identified in UMAP by analysing the expression of Snap25, Mbp, Apoe, Qk, Pecam1, Slc17a7 and Slc17a6. Doublets were identified using DoubletFinder v.3, and doublets and non-neuronal cells were removed from the dataset. After neurons were renormalized, reduced to 40 principal components and reintegrated, neuronal clusters were calculated using the Louvain algorithm with a resolution of 0.2 and identified/combined on the basis of the genes shown in Extended Data Fig. 2.

Behavioural assessment of allodynia

For behavioural experiments, groups of adult C57Bl/6, Trpv1-Cre::Avil-LSL-TeNT and control littermates were tested. RNAscope ISH of fresh frozen sections of DRG[12] (Advanced Cell Diagnostics) was used to examine the extent and selectivity of TeNT recombination. The cheek or plantar surface of the hind paw was injected with PGE2, LY344864 or phosphate-buffered saline (PBS) as described above. Mice (male and female; more than 8 weeks old) were habituated to the testing chambers for at least two sessions in the days preceding the behavioural tests. When mice were used for more than one experiment, they were allowed at least 7 days to recover between tests. The experimenter was blinded to the genotype of the animals.

For the data shown in Fig. 5a,e, mechanical thresholds (50% withdrawal threshold) were determined using von Frey stimulation by the simplified up–down method[59] at multiple time points up to 2 h after injection of PGE2 or LY344864. The experimenter was blinded to the genotypes of the mice. For Fig. 5f, mechanical threshold (60% withdrawal threshold; three responses in five trials) was determined using von Frey stimulation by a standard up–down method at baseline and a single time point 15–30 min after intraperitoneal injection of vehicle (dimethylsulfoxide) or Salvinorin B (Hello Bio; 10 mg ml−1; 10 mg kg−1) and paw injection of LY344864 (as described above). The experimenter was blinded to the injected compound. The mice were tested twice (opposite paws; 6 days apart), with four receiving Salvinorin B in the first test and the other three in the second round. The assignment of mice to the two groups was pseudorandom. Brush allodynia was determined in the same behavioural apparatus by stimulating C57Bl/6 mice. As shown in Extended Data Fig. 9a, 20 brushes were delivered using a paint brush (7950-5 Round; KINGART) once every minute before and 10–30 min after paw injection. A response was counted as any withdrawal from the stimulation. As shown in Extended Data Fig. 9b, a single brush with a fluffed cotton swab was delivered at baseline and at each time point after paw injection of PBS or LY344864. Pain-like behaviours (repetitive or extended lifting and guarding) and brief response to brushing were scored. The experimenter was blinded to the injected compound; thus, mice were randomly assigned to groups.

Spontaneous pain behaviours following the injection of 500 µM PGE2 (in 20 µl of PBS) or PBS alone into the hind paw or cheek were recorded and scored offline using BORIS[60] by an observer blinded to the genotype and/or the compounds used. In mice injected in the hind paw, licking of the injected paw was scored for 15 min and quantified for a 10-min period, starting 5 min after injection to match the development of inflammation[37]. For cheek injection blinding, the animals were randomly assigned to groups. Following injections, the mice were placed in cylindrical plexiglass chambers surrounded by mirrors. All face-directed behaviours and periods of inactivity greater than 1 s were scored for the first 15 min after injection and quantified for the same 10-min period used for the paw. Although inactivity may represent freezing-like behaviour (Supplementary Video 3), it may also represent sitting or sleeping. The single mouse injected with PBS that displayed considerable inactivity did not appear to enter a freeze-like state, whereas the majority of the PGE2-injected mice did. To avoid judgement errors in scoring at the resolution of the videos, inactivity was analysed without trying to assess whether the mouse was in distress. The right cheek was partially depilated 2 days before behavioural recording to aid injection.

Quantification and statistical analysis

The numbers of animals and responding cells that were tested for each transcriptomic class are listed in Supplementary Table 1. All quantification and statistical analyses were performed using Python v.3.8, Pandas v.1.1.3, Numpy v.1.19.2 and Scipy v.1.5.2.

Spontaneous activity was detected as peaks in ΔF/F traces with a minimum prominence of 4% ΔF/F, a minimum absolute peak of 4% ΔF/F and a minimum interpeak interval of 0.6 s using the Scipy find_peaks function. The amplitude of an event was calculated as the difference between peak height and its preceding minimum. Spontaneous activity was quantified over multiple time windows when the cheek or paw was held at 30 °C (105 s for trigeminal neurons and 40 s for DRG) by summing event amplitudes.

Temperature-induced responses were identified as peaks with a minimum prominence of 5% ΔF/F, a minimum interpeak interval of 0.6 s and an onset during the temperature stimulation window. The end of a response was defined as the time point when signal dropped below 10% peak height. The area under the ΔF/F curve from onset to end of the response was used to quantify response to temperature. A cell was considered responsive to a temperature stimulus if AUC exceeded a defined minimum (corresponding to a mean of 3.5% ΔF/F over the 4-s stimulation window). A cell was mechanosensitive if its peak amplitude exceeded 15% ΔF/F during the stimulus application window. Cells were polymodal if the ratio between the mechanical and temperature stimuli was smaller than 5:1 and larger than 1:5.

Quantification of responses for a given stimulus varies according to the choice of cells included in the analysis. For example, cells may have spontaneous activity but not respond to mechanical or thermal stimulation of the cheek. For consistency and to allow comparison between figures, we report the response magnitudes (and numbers) of cheek-innervating neurons, that is, cells that responded to any stimulus applied to the cheek.

To distinguish brush responses from spontaneous activity after chemical induction, individual time-locked brush responses within a 1-s window of stimulus application were identified as peaks with a minimum prominence of 5% ΔF/F and a minimum absolute peak height of 20% ΔF/F. Bona fide brush cells were identified as cells that responded to at least 50% of brushes, which cover largely but not completely overlapping fields of the cheek.

Percentages of transcriptomic cell classes contributing to a functional cell category were calculated as described previously[3] by dividing the number of responding cells positive for a given class by the number of responding cells that were tested with ISH probes for that class. Because not all classes were tested in all individual animals, the summed percentages do not necessarily add up to 100%. To display proportions in stacked bar graphs, the percentages were further normalized to 100% in these graphs.

The effects of PGE2 injection were analysed using two-tailed paired Student’s t-test. Attenuation of heat responses by Trpv1 knockout was analysed using one-tailed Welch’s t-test (allowing for unequal variances between different conditions). The effects of Trpv1 knockout on spontaneous activity were analysed using two-tailed Welch’s t-test.

Holm–Šidák correction was applied to all statistical tests to adjust for multiple comparisons when investigating several transcriptomic classes. One-tailed Wilcoxon signed-rank tests were used for von Frey thresholds and brush-induced behaviour when comparing time points after allodynia induction to paired baseline values. In Extended Data Fig. 9b, data were pooled across time points after injection and compared between experimental groups with a chi-squared test. All other comparisons between behavioural groups used the Mann–Whitney U-test. Detailed statistical information is provided in Supplementary Table 2.

Reporting summary

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

Data availability

Data needed to interpret, verify and extend the research (calcium traces, ISH annotations and behavioural data) are available at Zenodo (https://doi.org/10.5281/zenodo.14907827)[61]. Data from GEO Series GSE254789 were also analysed.

Code availability

Custom code for analysis is available at Zenodo (https://doi.org/10.5281/zenodo.14907827)[61].

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Acknowledgements

This study was supported in part by the Intramural Program of the National Institutes of Health, National Institute of Dental and Craniofacial Research (N.J.P.R.; ZIA DE000561), National Center for Complementary and Integrative Health and National Institute of Neurological Disorders and Stroke (A.T.C.; ZIA AT000028). D.I.M. was supported by the Branco Weiss Fellowship – Society in Science. We thank Y. Liu, M. Hoon and members of our groups for input. The National Institute of Neurological Disorders and Stroke Light Imaging Facility was used for microscopy. We thank J. Pickel, R. Szabo, A. Cho, A. Limaye and the NIMH and National Institute of Dental and Craniofacial Research (ZIC DE000744) Gene Targeting Cores for help in generating knock-in lines.

Author information

Author notes

1. These authors contributed equally: Nima Ghitani, Lars J. von Buchholtz

Authors and Affiliations

1. National Center for Complementary and Integrative Health, Bethesda, MD, USA

Nima Ghitani, Donald Iain MacDonald, Melanie Falgairolle & Alexander T. Chesler

2. National Institute of Dental and Craniofacial Research, Bethesda, MD, USA

Lars J. von Buchholtz, Minh Q. Nguyen, Julia A. Licholai & Nicholas J. P. Ryba

3. National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA

Alexander T. Chesler

Contributions

Conceptualization: N.G., L.J.v.B., N.J.P.R. and A.T.C. Software: L.J.v.B. Methodology: N.G., D.I.M., M.F., M.Q.N. and L.J.v.B. Investigation: N.G., L.J.v.B., D.I.M., M.F., M.Q.N., J.A.L. and N.J.P.R. Formal analysis: L.J.v.B. Data curation: N.G. and L.J.v.B. Writing—original draft: N.G., L.J.v.B., N.J.P.R. and A.T.C. Writing—review and editing: N.G., L.J.v.B., D.I.M., M.F., M.Q.N., J.A.L., N.J.P.R. and A.T.C. Supervision: N.J.P.R. and A.T.C.

Corresponding authors

Correspondence to Nicholas J. P. Ryba or Alexander T. Chesler.

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Competing interests

The authors declare no competing interests.

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Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Response properties of trigeminal neurons during mechanical and thermal stimulation of the mouse cheek.

a) Heatmap showing the in vivo GCaMP responses from 2085 neurons responding to mechanical and thermal stimulation of the cheek in 15 mice; changes in GCaMP fluorescence (% ΔF/F) are color-coded as indicated in the scale bar. (b, c) Spatial activity map showing the full functional imaging field from an example mouse; cellular response magnitude is indicated by brightness and response to heat (50 °C, green) and (b) pinch or (c) brush responses (magenta). Note significant overlap of heat and noxious mechanical (b) but not gentle touch (c); scale bar: 50 µm.

Extended Data Fig. 2 Basis for ISH-based mapping of functional responses to transcriptomic class.

a) Violin-plot analysis of expression level (log normalized single cell RNA sequencing data)11,28 showing expression patterns of several marker genes used in this study across 10 major transcriptomic classes[10] in the order shown in (b). At the single cell level, some of these classes can be further divided into subclasses11,12 but neither the use of Cre-lines[28] nor our approach recapitulate this resolution due to lack of markers with appropriate specificity and expression level. (b) Simplified representation of expression of a subset of these markers (red, positive; pink, weak expression; check, not diagnostic; white, negative) defines trigeminal neural classes. (c) Example images of a region of trigeminal ganglion subjected to functional imaging that was aligned to post-hoc multigene ISH. Upper left panel shows ISH for GCaMP with positive cells outlined in green. These cell outlines have been transferred to the ISH images for eight genes used in classification to demonstrate the diagnostic power of our approach. The positive and negative expression of these markers allowed unambiguous class assignment for all GCaMP expressing cells (lower right panel). Note, the viral approach for GCaMP expression (see Methods) results in stochastic GCaMP expression in a subset of sensory neurons (approx. 40% in the image shown) accounting for ISH-positive cells that are not GCaMP positive and therefore not outlined in green. (d) Upper panels show ISH for GCaMP (green) superimposed on the aligned in vivo functional imaging data for pinch, brush and heat (maximum projection images, magenta). Previously[3], we quantified the fidelity of alignment and showed that although there was not a pixel-to-pixel match between the different imaging modes, <5% of cells were displaced by more than 30 % of their diameter. Lower panels show the responding cells color-coded for transcriptomic class. As expected, each stimulus only activates a small subset of the GCaMP-positive trigeminal neurons since we only target stimulation to a small region of the cheek. In this example, the cLTMRs (green) responded to brush and pinch but not heat. Over the complete dataset for this study, in experiments where probes for all classes were assayed, more than 96% of functionally responding cells could be classified unambiguously and less than 2% had an ambiguous pattern that could not be resolved using the classification logic. (e) The red outlined GCaMP expressing neurons (dotted white outlines in the other ISH images) were classified and their GCaMP-transients are shown to the right. For the Ca-traces, ΔF/F and time are indicated by scale bars and arrows above the heat traces indicate the start and duration of heating pulses.

Extended Data Fig. 3 NP2B neurons, specificity of Mrgprb4-tdT-Cre recombination and DRG responses.

(a) Whole mount ISH images comparing methods used to distinguish NP2A and NP2B cells. NP2 cells (orange outlines) can be assigned by expression of Tmem233 and Fxyd2 but not Mrgprd or Nppb/Sst. NP2B cells (green outlines) also express Mlc1 and a lower level of Calca than NP2A cells (magenta outlines); scale bar = 50 µm. In animals where both methods were applied, there was >90 % agreement. (b) Example triple label ISH of a section from an Ai95 mouse carrying an Mrdprb4-tdT-Cre allele showing GCaMP (blue), Mlc1 (green) and tdT (red). Note perfect overlap of Mlc1 and tdT showing that tdT marks NP2B cells; GCaMP is much more broadly expressed. (c) Example image showing alignment of in vivo tdT fluorescence (red) and a spatial map of stimulus evoked activity (green, all stimuli combined) showing that most responding neurons in these mice are not NP2B cells; scale bars (b, b) = 50 µm for merged views. (d) Heatmap showing Ca-transients from lumbar DRG neurons from Ai95 mice (n = 5) carrying the Mrdprb4-tdT-Cre allele divided into 747 GCaMP-only cells and 109 NP2B neurons expressing tdT; changes in GCaMP fluorescence (% ΔF/F) are color-coded as indicated in the scale bar.

Extended Data Fig. 4 Tuning of NP1 neurons reflects stimulus application rather than heterogeneity in this class of sensory neurons.

Heatmaps showing Ca-transients from trigeminal neurons of three Ai95 mice carrying the Mrgprd-CreER knockin allele after tamoxifen induction; changes in GCaMP fluorescence in response to stimuli applied to the cheek are color-coded by % ΔF/F. Responding neurons were sorted based on their response to heating. Note that for each mouse, there were individual pinches that primarily activated neurons that were not temperature responsive (an example boxed, green) and other pinches that generally activated temperature responsive neurons (an example boxed, red). This strongly suggests that all NP1 neurons innervating the cheek are polymodal and that differences in position of stimulus activation or size of thermal versus mechanical receptive field account for apparent variation in tuning selectivity.

Extended Data Fig. 5 Inflammatory heat sensitization and sensory stimulus ongoing activity of nociceptors in wild type mice.

a) Response magnitude (mean AUC ± s.e.m) at the holding temperature of 30 °C (No stimulus) and at 45 °C before (light or dark grey) and after PGE2 injection (pink or red) expose the relative contributions of ongoing activity and thermal sensitization that occur in this model of inflammatory pain. There was a significant increase in the magnitude of the temperature response (response at 45 °C minus response without stimulation) in PEP and NP2A nociceptors after PGE2 induced inflammation. (b) Heat response magnitude (mean AUC ± s.e.m) for wild type mice before (grey) and after (red) PGE2 injection to the cheek. p < 0.05, *; p < 0.01, **; p < 0.001, ***; for details of statistical tests and numbers of mice and cells see Supplementary Information, Tables 1 and 2.

Extended Data Fig. 6 Inflammatory heat sensitization and sensory stimulus ongoing activity of nociceptors in Trpv1-/- mice.

a) Heatmaps showing the effect of PGE2-induced inflammation on the thermal sensitivity of 371 C-nociceptors in 6 Trpv1-/- mice that responded to heat and/or pinch of the cheek grouped by class; for display purposes baseline (left) and ongoing inflammation (right) were independently sorted based on magnitude of heat response. (b) Proportions of heat (red), polymodal (orange) and mechanically tuned neurons (grey) for each class in wild type mice (upper panel) or Trpv1-/- mice (lower panel); the number of neurons and mice used for assignment are indicated. Note that the relative number of PEP and NP3 neurons as well as the proportion responding to temperature is reduced in the knockout animals. (c, d) Heat response magnitudes (mean AUC ± s.e.m) displayed to compare wild type and Trpv1-/- mice (c) before and (d) after PGE2 injection to the cheek; note that the wild type data are a repeat of the data displayed in (Extended Data Fig. 6b). (e) Quantitation of unstimulated activity in Trpv1-/- mice (mean sum of transient amplitudes ± s.e.m.) before (pale blue) and after PGE2 induced inflammation (dark blue). (f) Change in unstimulated activity induced by PGE injection of the cheek (mean sum of transient amplitudes ± s.e.m.) for wild type (grey) and Trpv1-/- (blue) mice; p < 0.05, *; p < 0.01, **; p < 0.001, ***; for details of statistical tests and numbers of mice and cells see Supplementary Information, Tables 1 and 2.

Extended Data Fig. 7 Nociceptor responses to brushing are not increased during PGE2 induced inflammation.

Heatmaps showing the effect of PGE2-induced inflammation on the brush sensitivity and stimulus independent activity (right column) of 580 nociceptors (from 8 mice) that responded to mechanical stimulation of the cheek grouped by class. Note that spontaneous activity in Aδ-NOC, PEP, NP3, NP2A and NP2B neurons accounts for apparent activity during brushing i.e., brush responses were not increased during inflammation. Weak brush activity in NP1 neurons was not changed after PGE2 injection (see Fig. 4c).

Extended Data Fig. 8 Silencing nociceptors and targeting NP3 cells as controls for strategies used to functionally determine nociceptor contribution to allodynia.

(a) Triple label ISH validating our approach for silencing a large subset of nociceptors (Trpv1 expressing PEP, NP3 and NP2A neurons). In DRGs, >96% of Trpv1 expressing neurons were TeNT positive (624/643 cells in sections from 3 mice) and >97% of TeNT positive cells were nociceptors i.e., co-expressed Trpv1 and/or Scn10a. (b) Sst-Cre was used to target NP3 cells; Cre-recombination (green) occurred in 63% of NP3 neurons expressing Sst/Nppb (red, 246/389) with variable (50-80%) recombination across 3 animals. Moreover, NP3 cells accounted for only 29% of the recombined neurons (246/847). Recombination also labeled S100b-positive neurons (blue), which do not express Sst, these cells were large-diameter and accounted for the other 71% of recombined cells. (c) Functional responses (magenta) were aligned to gene expression (green) showing that small diameter Sst/Nppb-NP3 cells never respond to brush but are heat sensitive consistent with data in Fig. 1. Brush but not heat activates the large diameter S100b-expressing neurons; scale bars, (a, c) 50 µm. (d) Heatmaps of DRG imaging for mice expressing GCaMP under the control of Sst-Cre. Responses from 174 neurons from 7 mice show that Sst-Cre labeled neurons are spontaneously active after injection of PGE2 into the paw, but brush cells were not activated by inflammation.

Extended Data Fig. 9 Dynamic tactile allodynia induced by PGE2 or LY344864 and unstimulated behavioral responses to inflammation.

a) The right hind paws of C57B/6 mice were brushed 20 times before and after injection of PBS (vehicle, n = 12), PGE2 (n = 7) or LY344864 (n = 5). Percentage response of each mouse (black lines) and the mean response of the group before (circles) and after injection (squares) are shown. (b) Time course showing that hind paw injection of LY344864 but not PBS induces pain-like behaviors to gentle brush (repetitive or extended lifting and guarding) resembling the time course for von Frey sensitization (Fig. 5e). (c-f) Changes in behavior were observed following (c,d) hind paw and (e, f) facial injection of PGE2 in wild type mice. (c) Ethograms showing time spent licking the injected hind paw for wild type mice (grey bars, n = 8) and littermates expressing TeNT in nociceptors (red bars, n = 8) for 15 min after injection of PGE2. Note that licking of the injected hind paw develops slowly minutes after PGE injection corresponding to induction of inflammation[37] and is rarely seen in TeNT mice where nociceptor signaling is blocked. (d) Quantification of time spent licking the affected paw (mean ± s.e.m.) for the 10-minute period at the start of inflammation highlighted in yellow in panel (c). (e) Ethograms showing scored face directed behaviors, grooming of the face with both front paws (yellow), wiping with the right (green) or left (red) front paw and hind paw scratching of the right (purple) or left cheek (grey) for 15 min after facial injection of PBS (n = 5) or PGE2 (n = 6). Also shown are periods of inactivity greater than 1 s (black). Note that mice injected with PGE2 became inactive, often standing in a hunched posture within 5 min of PGE2 injection (Supplementary Information, Videos 2, 3), mirroring the induction of inflammation[37] and nociceptor activity (Fig. 3a). Notably, PGE2 induced inflammation did not elicit the wiping or scratching observed after injection of strong agonists of select nociceptor classes[46] but instead significantly reduced face directed behaviors. Anecdotally, PGE2 injected animals were sometimes observed to raise their paw towards the injected cheek but refrain from touching the skin (Supplementary Information, Video 4). (f) Quantitation of time spent in face directed behavior (left) and inactivity (right) after PBS (grey) or PGE2 (red) for the 10-minute period highlighted in yellow in panel (e). p < 0.05, *; p < 0.01, **; for details of statistical tests and numbers of mice see Supplementary Information, Tables 1 and 2.

Extended Data Fig. 10 Similar response profiles in mice with different depilation timing.

We used three different populations of mice during this study because we needed the fur intact to assess brushing and depilated skin for temperature series. Data in Figs. 1 and 2 were obtained from mice where the fur was removed acutely (during the functional imaging after mechanical stimulation had been performed). Data in other figures either used no fur removal (brush studies) or depilation was the previous day (approx. 24 h before recording) to minimize potential irritation from the chemical fur removal needed for temperature series. Note studies of inflammation were all carried out without acute fur removal. Response magnitudes (AUC mean ± s.e.m.) for same day (pale grey) and previous day (dark grey) depilation regimes for the different cell classes indicate only minimal differences in sensitivity; p < 0.05, *; p < 0.01, **; p < 0.001, ***. For details of statistical tests and numbers of mice and cells see Supplementary Information, Tables 1 and 2.

Supplementary information

Supplementary Table 1

Experimental regimes and numbers of animals and cells. Sample size information.

Reporting Summary

Supplementary Table 2

Statistical reporting. Statistical tests and P values.

Supplementary Table 3

Logic for ISH-based classification. Details of cell classification strategies.

Supplementary Video 1

Spontaneous activity in trigeminal neurons before and after PGE2-induced inflammation. Videos (2.4 times recording speed) of ganglion imaging (ΔF/F) for the same cheek-responsive field of the trigeminal ganglion at baseline (left) and during PGE2-induced continuing inflammation (right). The cheek was held at 30 °C for the duration of the recordings.

Supplementary Video 2

Behavioural response of a mouse minutes after cheek injection of PBS. Mouse injected in the right cheek with PBS exhibiting a typical range of behaviours.

Supplementary Video 3

Behavioural response of a mouse minutes after cheek injection of PGE2. Mouse injected in the right cheek with PGE2; note that the mouse remained immobile for extended periods (a behaviour rarely seen after PBS injection), adopted a hunched posture and exhibited noticeable tremors.

Supplementary Video 4

A mouse exhibiting incomplete face-directed behavioural responses after cheek injection of PGE2. Mouse injected in the right cheek with PGE2 showing unusual behavioural responses approximately 4 min after injection. Note that the mouse repeatedly raised a paw towards the injected cheek but failed to touch the skin.

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Ghitani, N., von Buchholtz, L.J., MacDonald, D.I. et al. A distributed coding logic for thermosensation and inflammatory pain. Nature (2025). https://doi.org/10.1038/s41586-025-08875-6

- Received: 04 March 2024

- Accepted: 06 March 2025

- Published: 23 April 2025

- DOI: https://doi.org/10.1038/s41586-025-08875-6

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Nature (2025)

Abstract

The emergence of infectious diseases, particularly those caused by fungal pathogens, poses serious threats to public health, wildlife and ecosystem stability[1]. Host–fungus interactions and environmental factors have been extensively examined2,3,4. However, the role of genetic variability in pathogens is often less well-studied, even for diseases such as white-nose in bats, which has caused one of the highest disease-driven death tolls documented in nonhuman mammals[5]. Previous research on white-nose disease has primarily focused on variations in disease outcomes attributed to host traits or environmental conditions6,7,8, but has neglected pathogen variability. Here we leverage an extensive reference collection of 5,479 fungal isolates from 27 countries to reveal that the widespread causative agent is not a single species but two sympatric cryptic species, each exhibiting host specialization. Our findings provide evidence of recombination in each species, but significant genetic differentiation across their genomes, including differences in genome organization. Both species contain geographically differentiated populations, which enabled us to identify the species introduced to North America and trace its source population to a region in Ukraine. In light of our discovery of the existence of two cryptic species of the causative agent of white-nose disease, our research underscores the need to integrate the study of pathogen variability into comprehensive disease surveillance, management and prevention strategies. This holistic approach is crucial for enhancing our understanding of diseases and implementing effective measures to prevent their spread.

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Main

The emergence of infectious diseases, especially those caused by fungal pathogens, is occurring at an increasing rate[9]. This trend presents substantial and far-reaching threats to public and wildlife health, global food security and the stability of ecosystems worldwide[1]. Among the notorious fungal pathogens is the ascomycete Pseudogymnoascus destructans, a bat-specific pathogen responsible for white-nose disease—also commonly referred to as white-nose syndrome10,11—which affects hibernating bats[10]. P. destructans is native to Eurasia, where it is widely distributed, and has been recently introduced to North America12,13. Although the disease occurs across both Eurasia and North America14,15,16, mass mortality in bats has been observed solely in North America15,17. Previous research has revealed important insights into the interaction between the fungus and environmental conditions and between the fungus and its hosts, with different species of bat showing various levels of susceptibility[18]. However, this body of literature has developed entirely under the postulate that P. destructans is a single clonally expanding isolate in North America and a single fungal species with a broad distribution across the Palaearctic region. Although earlier investigations have documented a genetically diverse population of P. destructans in Europe13,19 and identified three genetically distant isolates from Asia[12], the limited geographical sampling and lack of ecologically relevant data have hindered interpretations of these findings, which have remained overlooked since.

On the basis of an extensive reference collection of 5,479 isolates originating from 264 sites in 27 countries (5,446 from Eurasia and 33 isolates from North America), we use molecular data to demonstrate the presence of two cryptic fungal species that cause the disease and use fine-scale population structure across Europe to identify the probable geographical source population of the North American introduction. We further integrate molecular data with field-based ecological data to investigate differences in ecological niche, including abiotic factors (temperature and humidity) and biotic factors (host specialization), between the two pathogen species.

Two sympatric clades cause the disease

The 5,479 isolates obtained from bats and/or hibernacula environments were genotyped at 18 polymorphic microsatellite loci[20] (Fig. 1a, Supplementary Tables 1–3 and Supplementary Fig. 6). Phylogenetic reconstruction of genotype relationships (Fig. 1b) revealed a clear separation into two clades, a division further corroborated by principal component analysis (Fig. 1c). These clades are hereafter called Pd-1 and Pd-2, where Pd-1 corresponds to P. destructans sensu stricto (Supplementary Figs. 1 and 2). Both Pd-1 and Pd-2 cause white-nose disease, as both were isolated from bats that exhibited lesions diagnostic of the disease14,17,21,22. At the broad scale, these two clades are geographically sympatric and were found present together at 23 of the sites we studied, which makes it unlikely that geographical factors currently play a major part in their differentiation (Fig. 1a). Occasionally, isolates from both clades were syntopic (that is, isolated from the same swab, n = 18). The North American isolates, which are known to have a single clonal origin12,23, clustered with Eurasian isolates of Pd-1 (Fig. 1b,c and Supplementary Fig. 2).

Fig. 1: Multilocus microsatellite typing reveals two clades of P. destructans.

a, Sampling locations in Eurasia (North American sites not shown; n = 255). b, Phylogenetic tree, based on the DA distance, representing the relationship between all 1,866 unique multilocus genotypes (North American isolates are indicated with an asterisk) originating from the 5,479 isolates from 264 sites based on 18 microsatellite loci (for the DA distance, see the section ‘Analyses of multilocus genotypes’ in the Methods; the monophyly of Pd-1 and Pd-2 was consistently recovered when jackknifing loci). For better visualization of both clades, the section containing Pd-2 was magnified. c, Principal component analysis (bottom) of isolates (Pd-1 was subsampled to ensure even sampling between clades and to maximize geographical coverage, which resulted in 234 isolates for Pd-1 and 92 isolates of Pd-2; Supplementary Table 1). Density (top) of principal component 1 (PC1) coefficients after random subsampling of Pd-1 to obtain the same number of sampling sites as for Pd-2 (51 sites, replicated 100,000 times). Dotted lines represent the absolute edges of the distribution (see the section ‘Analyses of multilocus genotypes’ in the Methods for details).

Apart from geography, host specialization can act as a driver of population differentiation[24]. Therefore, we investigated the relationship between Pd-1 and Pd-2 isolates and the species of bat from which they were isolated. Out of the 1,463 swabs collected from a total of 16 bat species sampled at 241 sites across Europe, 87% of those containing Pd-1 isolates originated from Myotis myotis and Myotis blythii (M. myotis/blythii) bats, whereas none were found on Myotis daubentonii. By contrast, the majority of swabs containing Pd-2 isolates originated from M. daubentonii (39%), whereas only 34% originated from M. myotis/blythii (Extended Data Fig. 1). These contrasting data reveal a significant association between the fungal clades (Pd-1 and Pd-2) and the bat host species when tested in a Bayesian hierarchical model (Supplementary Table 7). The estimated 95% credible intervals for the probability of different bat species with Pd-2 were as follows: 1.07 × 10–42–4.98 × 10–5 for Myotis dasycneme; 8.07 × 10–26–1.65 × 10–5 for M. myotis/blythii; and 2.38 × 10–22–0.017 for the Myotis nattereri species complex (M. nattereri, M. escalerai and M. crypticus). Myotis mystacinus was equally likely to be infected by both P. destructans clades, whereas M. daubentonii had a near-certain probability (almost 1.00) of harbouring Pd-2 (Supplementary Table 7), which indicated that it had exclusive infection with Pd-2. Such host specialization cannot be attributed to species distributions, as M. daubentonii occurs throughout the area sampled (Supplementary Fig. 3). Furthermore, on the basis of the bat monitoring data available for the 172 sites studied, we had direct evidence that at least 63% of the sites containing Pd-1 isolates are also used as hibernacula by M. daubentonii. The latter species of bat is therefore expected to come into contact with Pd-1 isolates, yet does not seem to be susceptible to infection by them.

These findings of host specialization highlight the need to discriminate fungal species when elucidating the strategies, such as resistance or tolerance, used by host species when faced with a pathogen. This approach is particularly relevant in the context of diseases such as white-nose, for which disentangling the interactions between hosts and pathogens is crucial for comprehending disease dynamics[10]. Although we do not yet have data linking these characteristics to host specialization, the colouration of agar medium in laboratory cultures was significantly different (based on 45 isolates for Pd-1 and 34 isolates for Pd-2; two-sided t-test t = –11.58, d.f. = 60.06, P < 0.001) between P. destructans clades, with only limited overlap (Extended Data Fig. 2 and Supplementary Table 8). However, Pd-1 and Pd-2 were encountered in similar abiotic conditions—cold (about 7–8 °C) with high absolute humidity (around 6–8 g m–3; Extended Data Fig. 3 and Supplementary Table 3)—and had similar colony expansion rates in culture (based on 45 and 34 isolates for Pd-1 and Pd-2, respectively; Extended Data Fig. 4 and Supplementary Table 9) with no significant difference in final culture size after 7 weeks (two-sided t-test, t = –0.044, d.f. = 68.54, P = 0.96). These similarities are consistent with the observation that Pd-1 and Pd-2 share the same geographical range and are often found in the same caves. Therefore, according to existing evidence, host specialization seems to be an important factor that distinguishes their ecological niches. The precise mechanisms that underlie P. destructans host specialization may involve a combination of host behavioural differences, immune competence, microbiome composition, skin properties and thermal and hibernation physiology. However, such factors are inherently difficult to disentangle because of their interconnected nature.

Genomic divergence between clades

To further characterize the extent of genomic divergence between clades, we selected ten Eurasian P. destructans isolates (five from each Pd-1 and Pd-2 clade), one North American P.  destructans isolate (Pd-1) and one Pseudogymnoascus sp. outgroup for full-genome long-read sequencing (MinION nanopore; Supplementary Table 10). High quality (>98% complete BUSCO genes) and contiguous genomes (median of 39 contigs; range of 18–132) were assembled using Flye[25] and polished using 150 bp paired-end Illumina reads (Supplementary Table 11). To infer the relationship between isolates, we extracted 664 single-copy BUSCO genes (1,648,545 bp alignment) common to 19 isolates: 12 sequenced in this study (including the outgroup) and 7 previously published[12] (5 from Europe, 1 from Mongolia and 1 from China). The high bootstrap (100%), site concordance factors (92% for Pd-1 and 76% for Pd-2) and congruence between gene trees (Fig. 2b and Supplementary Fig. 4) confirmed the monophyly of the two clades identified using microsatellites (Fig. 1b,c). This result was further corroborated by a phylogenenetic tree that was reconstructed from partitioning the genome into 10 kb windows (Supplementary Fig. 5). The population differentiation, as measured using the fixation index (FST), was consistently high between clades (median = 0.79; Extended Data Fig. 5). Both clades included samples from Europe and Asia, a result that further confirmed that the separation is not driven by geography. For example, isolate Gd4986 (clade Pd-2) from the Ural Mountains in Russia was more similar to isolates from Spain, Mongolia and China than to another isolate from the same cave but from clade Pd-1 (Gd4985). Across 664 conserved BUSCO genes, inter-clade sequence divergence (median of 0.14%) was >5 times greater than intra-clade divergence (0.022–0.026%; Fig. 2c), whereas the inter-clade divergence across the full genomes averaged 1.6% (Extended Data Fig. 6). To gain further insights into the genomic differentiation of the clades across the full genome of a larger number of isolates, we sequenced pools of DNA from 69 and 63 isolates from Pd-1 and Pd-2, respectively (Supplementary Table 15). Short reads of each pool were mapped to the reference genome of Pd-1 and Pd-2 to estimate the FST between pools (Supplementary Fig. 6). Irrespective of the reference genome used, the differentiation between clades was strong, with a minimum FST of 0.25 and a median FST of 0.88 (mean = 0.85; window size of 200 single-nucleotide polymorphisms (SNPs); Fig. 2d and Extended Data Fig. 7). Less than 2.7% of SNPs were shared between the clades. Approximate Bayesian computation methods were used to model the long-term demographic history of both clades, and a strict-isolation model (that is, no gene flow) was identified as the most likely scenario (Supplementary Table 16). The divergence between the clades was estimated at 750,000 generations (95% credible interval 114,000 to 1.5 million; Supplementary Table 16). Moreover, the widespread recombination detected in the clades (Extended Data Fig. 8 and Supplementary Fig. 1) demonstrated that P. destructans displays a degree of non-clonality, which provides strong evidence of genetic exchange among isolates in the clades for which both mating types are common (57% for MAT1-1 and 43% for MAT1-2 in each clade; Supplementary Fig. 1). This genetic exchange in the clades, combined with the strict isolation between sympatric clades, provides compelling evidence that isolation mechanisms other than clonality or geography prevent detectable genetic exchange between Pd-1 and Pd-2 clades.

Fig. 2: Genomic differentiation between clades.

a, Sampling locations of the 18 isolates used for phylogenetic and sequence divergence analyses. b, Phylogenetic tree of 664 BUSCO genes with 1,000 bootstraps and site concordance factors for nodes of interest (as percentages). The branch to the outgroup Gd267 has been shortened for visualization purposes. c, Boxplot of the pairwise genetic distances between isolates (each isolate is compared with the 17 other isolates) for the 664 BUSCO genes, partitioned between intra-clade and inter-clade distance. The darker line indicates the median, and the lower and upper hinges represent the first and third quartiles, respectively. The whiskers extend to 1.5 times the interquartile range, with any data points beyond this range marked as outliers. d, Genomic differentiation between pools of 69 and 63 individuals from clades Pd-1 and Pd-2, respectively, estimated using FST across a window size of 200 SNPs, using Gd293 (Pd-1 clade) as the reference genome, with its 18 contigs successively coloured. e, Heatmap of phylogenomic profiling depicting clade-specific synteny network clusters, with isolates in rows and clusters in columns (n = 1,365; profile for run 13, Supplementary Table 17). Grey denotes the absence of a gene, whereas other colours indicate the number of gene copies (see the key).

In addition to sequence divergence, genome organization can reveal key differences between clades. We performed network-based microsynteny analyses on the annotated genomes to reconstruct the phylogenetic relationship between isolates based on microsynteny and to detect and quantify clade-specific syntenic clusters. A typical synteny cluster is a group of syntenic genes shared by individuals, which reflects the phylogenetic relatedness of their genomic architecture. Analyses, replicated with 25 different input settings, recovered synteny for more than 92% of genes, which belonged to 9,136 clusters on average (Supplementary Table 17). A mean of 1,316 of these clusters (14.4%), spread across contigs, were not shared between clades, of which 1,250 contained single-copy genes and 66 multicopy genes (Fig. 2e). The phylogeny reconstructed from binary coding of the presence or absence of clusters consistently recovered both P. destructans clades as monophyletic with maximal support (Supplementary Table 17). These analyses reveal substantial genomic structural differences between the clades, differences that have been associated with reproductive isolation in other fungal species26,27.

This level of genomic differentiation strongly supports the presence of two cryptic Pseudogymnoascus sp. according to the established recommendations for fungal species delimitation (for example, refs. 28,29,30). We further substantiated our conclusion by demonstrating reciprocal monophyly, recombination within clades and the absence of gene flow between the divergent yet sympatric and syntopic clades. These three features are primary indicators of completed speciation. Combining the molecular, ecological (host specialization) and distribution data, we conclude that there are two pathogenic cryptic species in the Pseudogymnoascus genus, native to Eurasia, which are both infecting hibernating bats and causing white-nose. Whether speciation occurred in allopatry or in sympatry, possibly through host specialization as the barrier to gene flow[24], cannot be confirmed with the current dataset. Nevertheless, our data clearly identified P. destructans and its cryptic relative as a previously unknown system in which to investigate speciation through host specialization in a mammalian fungal pathogen. In terms of implications for conservation, the presence of another pathogenic fungus with different host specialization that is able to cause white-nose poses a risk to bat conservation, particularly outside its current range. On the basis of a risk assessment conducted in Australia, which concluded that in the next 10 years, the introduction of P. destructans is very likely to almost certain, we can confidently infer that Pd-1 and Pd-2 are highly likely to be introduced to regions where they are currently absent[31]. Microsatellite data provided clear evidence that Pd-1 is largely dominant in Europe, accounting for 95% of samples. Despite the limited number of isolates genetically characterized from East Asia (n = 4; Supplementary Tables 10 and 14), the exclusive identification of Pd-2 in this region implies its potential dominance, with Pd-1 yet to be confirmed. Although Pd-2 is currently absent from North America, its introduction could pose a serious risk to bat species previously unaffected by Pd-1. Furthermore, species recovering from Pd-1 exposure may face new challenges if Pd-2 is introduced. The emergence of chytrid fungi provides a cautionary example of the consequences of delayed action. For example, Batrachochytrium dendrobatidis, a generalist pathogen, was linked to amphibian declines as early as the 1970s. However, at the time, there was no awareness that a second, more specialized species could emerge[32]. When Batrachochytrium salamandrivorans was finally identified in 2013, it was already too late to prevent its introduction to Europe[32]. By contrast, with Pd-2, we have the advantage of foresight—an opportunity to strengthen global biosecurity and prevent its spread beyond its native range.

Population structure and origin

In the dataset from Eurasia (5,446 isolates in total), we detected 1,766 distinct multilocus genotypes (called ‘genotypes’ hereafter) belonging to Pd-1 (5,165 isolates from 227 sites) and 92 genotypes in Pd-2 (281 isolates from 51 sites). To test whether an isolate could be genetically assigned to its site of origin among all sampled sites in Europe, we performed discriminant analysis of principal components (DAPC)[33] for each clade separately. As many as 64% of isolates from Pd-1 and 69% from Pd-2 from Eurasia were successfully reassigned to their exact site of origin. In terms of distances, isolates were reassigned at an average distance of only 52.6 km (Pd-1) and 42.9 km (Pd-2) from their site of origin, which is very near given that samples originated from sites up to several thousands of kilometres apart. These high reassignment rates cannot be attributed to chance as they sharply contrasted with a ‘null-DAPC’, whereby information on sites was randomized before running the DAPC, which resulted in only 0.55% and 2.15% of isolates correctly assigned to sites for Pd-1 and Pd-2, respectively (Fig. 3b and Extended Data Fig. 9). The strong observed population structure, which resulted from the limited effective movement of the fungi across the landscape, could be attributed to either restricted movement of P. destructans through bats or the reduced fitness of emigrant P. destructans isolates attempting to establish in sites already occupied by other P. destructans isolates[34]. To obtain a spatially informed overview of the genetic discontinuities of each clade, we conducted an estimation of effective migration surfaces[35]. The analysis revealed three genetic discontinuities for Pd-1: one between the Balkans and the rest of Europe, one dividing Europe in two (from Poland through to Slovenia) and the last one dividing France and Iberia from the rest of Europe (Fig. 3a; see Extended Data Fig. 9 for clade Pd-2). Notably, none of these discontinuities was associated with a discontinuity in the main host species M. myotis (for examples, see refs. 36,37), which provides evidence that the main host population structure is probably not a key driver of the population structure of the pathogens (see also ref. [34]). Moreover, previous studies of Myotis sp. hosting P. destructans have indicated near panmictic populations on a large scale[38], thereby further supporting the notion that despite them moving across the landscape, bats are not effectively transporting P. destructans, even over limited distances of a few hundreds of kilometres[34]. Although both fungal species are native to Eurasia, bats may be at risk from exposure to more virulent strains originating from long-distance inter-specific or intra-specific genetic exchanges. Moreover, such exchange may be exacerbated by human-mediated movements of the fungus, as already observed between eastern and western North America[39].

Fig. 3: Strong population differentiation in Pd-1.

a, Estimation of effective migration (m) surfaces based on 2,261 isolates from Pd-1 in Europe (all sites excluding Russia and the United States after clone correction, n = 225 sites). For visualization, results from eight independent runs (each with 8 million iterations and between 100 and 450 demes) were combined. Different shades of colour represent variable levels of high (blue) or low (brown) effective migration rates. Sampling locations are represented by red dots. b, Distribution of the distance between the true and assigned site of each Eurasian isolate of Pd-1 (2,191 isolates; dataset limited to 20 isolates per site) for the observed and randomized datasets of DAPCs (bin width, 100 km).

Comprehensive sequencing of more than 60 isolates from North America has shown that they all originated from a single clonal source12,23. Genotypes obtained from North America for this study, clonal descendants of the introduced isolate, unequivocally belonged to Pd-1 (Figs. 1b,c and 2b). Furthermore, the strong population structure observed in the native range of P. destructans enabled us to trace back the most likely source population of the North American introduction to the region of Podillia in Western Ukraine. This conclusion was strongly supported by results from two independent methods: multivariate DAPC and spatial Bayesian inference (SPASIBA analysis)[40]. Results from multivariate DAPC confidently assigned the origin of all 33 North American isolates to a single site in Podillia (posterior probability of 1; Supplementary Table 5). SPASIBA analysis also identified Podillia as the most probable source of the North American introduction (Fig. 4 and Supplementary Table 18). Both methods identified the source of the North American introduction within a 9 km radius in Podillia (Fig. 4b), despite the potential assignment zone spanning more than 5 million square kilometres, thereby strengthening confidence in this result. In agreement with these findings, all phylogenetic analyses—whether based on microsatellites (Fig. 1b and Supplementary Fig. 2), BUSCO genes (Fig. 2b) or whole genomes[12] (Supplementary Fig. 5)—consistently identified isolates from Podillia as the closest relatives to those from North America. Podillia is home to some of the longest caves in the world, such as the giant maze caves Optymistychna (longest in Europe; 267 km) and Ozerna (143 km), which are of major international speleological interest. Moreover, since the dissolution of the Soviet Union in 1991, the region has attracted cavers from around the world, including the United States, with notable exchanges involving caving communities from upstate New York41,42, the area where Pd-1 was introduced to North America[16]. Although absolute evidence may be unattainable, the observed pattern of caving exchanges between the source region and the site of introduction provides strong support for the proposed mechanism behind the initial introduction of Pd-1 to North America (Supplementary Note 1). This finding highlights the substantial risk posed by international caving activities in the spread of biological agents and underscores the need to improve knowledge of pathogen pollution while raising awareness of essential biosecurity measures[43], such as limiting the movement of caving equipment, particularly when not properly decontaminated[44].

Fig. 4: Assignment of the source of the North American introduction of P. destructans using Bayesian inference.

a, Bayesian inference (SPASIBA analysis; trained on 5,162 Pd-1 isolates) was performed independently for each of the 33 North American isolates across the continuous landscape (>5 million square kilometres). We then calculated the log-likelihood of the assignment across all 33 isolates (Supplementary Table 5), depicted on the map by a colour scale from blue (lowest log-likelihood) to red (highest log-likelihood). Green dots represent sites from which Pd-1 samples were collected, and the two yellow dots, in Podillia, Ukraine, indicate the inferred origin of the introduced ancestor of the 33 North American isolates (Supplementary Table 16). The zoomed-in area shown in b is represented here by the black rectangle. b, Details of the region with the most probable assignment. This area has a 6 × 10^9 greater likelihood of assignment than any pixel occurring outside this region (Supplementary Table 5). The central black-bordered pixel has the highest likelihood of assignment and contains the site to which the DAPC assigned all 33 North American isolates. The remaining black-bordered pixels have a likelihood within an order of magnitude of the central black-bordered pixel. Scale bar, 50 km.

The identification of the region of origin and the discovery of a second causative agent were achievable thanks to the rapid and extensive sampling effort across a substantial part of the native range of the species. Such intensive and synchronous sampling (typically conducted around February–March each year) at a continental scale was only possible through the combined effort of hundreds of volunteers (Supplementary Note 2). Our study demonstrates the potential of tapping into the synergism of citizen engagement for future surveillance of emerging pathogens.

Methods

Sample and field data collection

Swab samples of P. destructans were collected from bat hibernacula. Sampling from hibernating bats was conducted without capture or handling by collecting samples while the bats remained freely hanging. The samples were collected by lightly swabbing the infected areas with a sterile dry swab (Polyester swab 164KS01, Copan). This method is considered minimally invasive or even noninvasive45,46. The timing of sample collection was usually between January and April, when the highest numbers of bats with visible infection have been reported[15]. When no bats were present or sampling them was not possible, wall swabs were collected by touching the swab to hibernacula walls (ideally close to where bats usually hang to hibernate; see ref. [34] for more details). Four isolates were also obtained from sediment samples collected from inside hibernacula and 22 were collected from caving gear (that is, caving suits and harnesses), which most probably originated from contact with hibernacula environments[44].

When a sample was taken from a bat or in close proximity (within about 10 cm), the bat species was also recorded. Temperature and relative humidity were measured in the hibernacula. Absolute humidity was then calculated from measures of relative humidity and temperature by applying a previously described formula[47].

Our work adhered to the ethical wildlife research guidelines of the American Society of Mammalogists for the use of wild mammals in research and education[48]. Furthermore, this work was conducted under permission from the following authorities: Italy, Regional Speleological Federation of Emilia-Romagna (FSRER) and the Management Bodies of the Parks of Emilia-Romagna; Poland, Genarny Dyrektor Ochrony Środowiska (General Director for Environmental Protection) and Regional Directorate for Environmental Protection in Gorzów Wielkopolski (Regionalna Dyrekcja Ochrony Środowiska w Gorzowie Wielkopolskim); Switzerland, Kantonaler Fledermausschutz Aargau; Germany, Umweltamt, Veterinäramt, Untere Landschaftsbehörde Siegen-Wittgenstein, Untere Naturschutzbehörde Umweltamt Landkreis Harz and Referat Verbraucherschutz, Veterinärangelegenheiten Landesverwaltungsamt Sachsen-Anhalt, Untere Naturschutzbehörde des Landkreises Vorpommern-Greifswald, Regierung von Unterfranken, Regierung von Mittelfranken, Struktur- und Genehmigungs Direktion Nord/Süd, NLWKN Niedersächsischer Landesbetrieb für Wasserwirtschft, Küsten- und Naturschutz and Region Hannover–Fachbereich Umwelt; Austria, Department of Nature Conservation for Carinthia, Lower Austria, Upper Austria, Salzburg, Styria and Vorarlberg; Hungary, Pest Megyei Kormányhivatal, Országos Környezetvédelmi, Természetvédelmi és Hulladékgazdálkiodási Főosztály (Pest County Government Office, National Department of Environment Protection, Nature Conservation and Waste Management) and the Ministry of Environment and Water; Bulgaria, Bulgarian Ministry of Environment and Water; France, DDTM-Morbihan and DREAL; Republic of Latvia, Nature Conservation Agency; Belgium, Gouvernement Wallon; Denmark, The Nature Agency and Daugbjerg Kalkgruber; Romania, Speleological Heritage Commission; Estonia, Estonian Environmental Board; England, Natural England; Finland, Southwest Finland Centre for Economic Development, Transport and the Environment; Sweden, Uppsala djurförsöksetiska nämnd, Swedish board of Agriculture and the Swedish Environmental Protection Agency; Norway, Miljødirektoratet; Luxemburg, Ministère du Développement durable et des Infrastructures du Luxembourg; Croatia, Croatian Ministry of Environment and Nature; Russian Federation, Game Management Directorate of the Republic of Karelia, Institute of Plant and Animal Ecology and the Ural Division of the Russian Academy of Sciences; Slovak Republic, Ministry of the Environment of the Slovak Republic and the Department of State Administration for Nature and Landscape Protection; the Netherlands, Dutch Ministry of Economic affairs; Republic of Moldova, Government of Republic of Moldova–Ministry of Environment.

Laboratory materials and methods

Cultures and genotyping

Previously published DNA extraction and genotyping protocols were used20,34, and are briefly outlined here. P. destructans was collected using sterile swabs from hibernating bats and the walls of sites where bats hibernate. The collected fungal material was cultured on dextrose peptone yeast agar[49] using classical mycological procedures and sterilization of tools between each use. After observing germination, typically 3–5 days after plating, plates were screened with a microscope to identify germinated single spores (identified as colonies expanding from a single germinating spore). Depending on availability, 1–3 (mean = 3.0, median = 3) and 1–5 (mean = 3.6, median = 2) single spores were typically isolated from bat and wall swabs, respectively. Isolation was performed by excising a plug with a sterile 3-mm biopsy punch and transferring it to a fresh 6-cm Petri dish. The plates were then visually monitored for 1 week to confirm that no additional spores germinated on the plug. Plates were grown at 10−15 °C until there was sufficient material to extract DNA (usually after several weeks to months). Each of these colonies is then referred to as an isolate or a culture. DNA extraction was done using a KingFisher Flex extraction robot (Thermo Scientific) with a MagMAX Plant DNA Isolation kit (Thermo Scientific). After DNA extraction, isolates were genotyped using 18 microsatellite markers and two mating-type markers in four multiplexes. The two mating-type markers were two independent primer sets used to amplify segments of the two mating types (MAT1-1 and MAT1-2) that are different in length and therefore diagnosable through fragment-length analysis[20]. Genotyping was carried out on an ABI 3130 Genetic Analyser (Applied Biosystems), and GeneMapper software (v.5; Applied Biosystems) was used for fragment analysis.

DNA extraction for MinION and Illumina reads

Material was collected from P. destructans cultures using sterilized tweezers. We used a sorbitol wash buffer (100 mM Tris-HCl pH 8.0, 0.35 M sorbitol, 5 mM EDTA pH 8.0 and 1% (w/v) polyvinylpyrrolidone (PVP-40)) to clean the fungal material and to remove most of the culture medium from the hyphae (the wash was repeated twice with 5 min of incubation at room temperature each time). After removing the sorbitol wash buffer the second time (through centrifugation and removal of the liquid supernatant), 500 μl CTAB lysis buffer (preheated to 65 °C; 0.01 M Tris HCl pH 7.5, 25 mM EDTA pH 8.0, 1.5 M NaCl and 2% CTAB powder (w/v)), 30 μl proteinase K and 5 μl 1 M DTT were added for digestion and incubated overnight at 56 °C, mixing material after the first hour. After letting samples cool for 5 min at room temperature, 4 μl RNase A was added and left to incubate at room temperature for 10 min. One volume chloroform–isoamyl alcohol (24:1 v/v) was added, after which tubes were inverted 30 times and centrifuged for 5 min at maximum speed, keeping the supernatant. We then added a second step of proteinase K (30 μl) and RNase A (4 μl) treatment with an incubation for 30 min at 56 °C, as it was found to reduce the presence of RNA and result in better quality DNA. To remove these enzymes, we performed a second chloroform–isoamyl alcohol (24:1 v/v) extraction step by adding 1 volume, inverting 30 times and centrifuging at maximum speed for 5 min, after which the supernatant was kept. Precipitation of DNA was achieved with the use of 1/10 volume sodium acetate, 2 volumes ethanol (>99% purity) and centrifugation for 20 min at maximum speed. After gentle removal of the sodium acetate–ethanol mixture, the resulting pellet (containing the DNA) was washed twice with 70% ethanol. DNA was then eluted in ddH_2O and stored in the fridge. The DNA content was determined using Qubit (ThermoFisher).

Sample preparation for MinION nanopore sequencing

We performed long-read Oxford Nanopore Technology (ONT) sequencing of 12 isolates (5 Eurasian per clade, 1 North American and 1 outgroup; Supplementary Table 10) using MinION flowcells (FLO-MIN-106) using libraries prepared with an ONT Ligation Sequencing kit SQK-LSK109, following the manufacturer’s instructions. Statistics of the long-read sequencing and the associated assembled genomes are presented in Supplementary Tables 8 and 9.

Sample preparation for Illumina sequencing (individual isolates)

Illumina sequencing was performed for all the isolates for which we performed MinION long-read sequencing except for Gd1111, for which Illumina sequences were already available (Gd1111 = 20631-21, subculture of the type isolate). For all samples except Gd45 and Gd293, Illumina-indexed libraries were prepared for each isolate according to a previously described protocol[50] with modifications as proposed in a previous study[51]. Libraries were then sequenced (150 bp, paired-end) by Novogene on an Illumina NovaSeq 6000. For Gd45 and Gd293, libraries were prepared using TruSeq DNA PCR Free (350) and TrueSeq Nano DNA (350) kits, respectively, before being sequenced (150 bp, paired-end) by Macrogen on an Illumina HiSeq X.

Sample preparation for Illumina sequencing (Pool-seq)

We pooled DNA from multiple isolates (details on their origin presented in Supplementary Table 1) in equal concentrations into sample pools for sequencing. A total of four pools were prepared per clade with each isolate appearing in one pool only. For Pd-1, a total of 69 isolates were used (pool sizes: 17, 17, 17 and 18 isolates), whereas for Pd-2, a total of 63 isolates was used (pool sizes: 15, 16, 16 and 16 isolates). Within clades, isolates were assigned to a pool on the basis of the DNA concentration of their extracts (that is, the 17 and 15 isolates with highest DNA concentration for Pd-1 and Pd-2, respectively, were pooled together). The strategy of pooling samples into four pools per clade was used to validate the consistency of the results generated by each pool individually and the combined dataset (see the section ‘Genotyping’). Isolates were chosen to maximize both the geographical distance among sites and the genotypic richness within each clade. After DNA extraction (and quantification) of each isolate, DNA was combined to result in equal concentrations of isolates with a total of 500 ng DNA in a volume of 60 μl per pool. Illumina-indexed libraries were prepared for each pool (that is, isolates were not individually indexed for Pool-seq) according to a published protocol[50] with modifications as previously proposed[51]. Libraries were then sequenced (150 bp, paired-end) by Novogene on an Illumina NovaSeq 6000.

Analyses of multilocus genotypes

The analyses of multilocus genotypes (MLGs) were run in R (v.4.1.1)[52], except for estimated effective migration surfaces (EEMS), using packages for specific analyses. Specifically, the package poppr (v.2.9.3)[53] was extensively used as it provides the tools needed for population genetic analyses of haploid species with clonal reproduction (such as P. destructans).

MLGs were defined by their unique combination of alleles across the 18 polymorphic microsatellite loci. This set of markers is sufficient to reliably identify the identity of MLGs both among and even within sites, for which MLGs are usually less differentiated[34]. Across all isolates, the allelic richness was high, ranging from 10 to 93 alleles per locus (mean = 37); however, we found that some alleles were fixed in the Pd-2 clade (Supplementary Table 6). Only isolates with a maximum of 4 missing alleles (that is, successfully genotyped at 14 microsatellites or more) were used for analyses, which resulted in a dataset comprising 5,479 isolates.

Principal component analysis (PCA) was used to visualize the differentiation among isolates (package adegenet (v.2.1.5)[54]). As the outcome of PCAs depends on sampling intensity[55], it was important to select roughly equal sample sizes among clades to capture their differentiation in Eurasia. To achieve this, we chose 51 sites (the same number of sites in which Pd-2 was found) from Pd-1 in a way that maximized geographical distance among them (that is, thinning sites) and used up to 20 isolates per site (again, to ensure that sampling among sites was not markedly uneven). This resulted in a dataset containing 234 and 92 isolates for Pd-1 and Pd-2, respectively, over 51 sites each (using unique MLGs only). PCA was then performed using this Pd-1 dataset subset and the full dataset for Pd-2. The results revealed two clusters, which were completely differentiated. The position of the North American isolates on the PCA plot was simply determined a posteriori by projecting or predicting their coordinates using the function ‘suprow’. Considering that maximizing distance among sites may also have an influence, we confirmed these findings on the Eurasian dataset by randomly subsampling 51 sites of Pd-1 repeatedly (100,000 times, but with the number of isolates per site still capped at 20) without considering the geographical distance between chosen sites. After running these 100,000 subsampled PCAs (with Pd-2 unchanged, the geographically thinned PCA run was added, which resulted in a total of 100,001 PCAs), the density of values observed for PC1 was consistent with the values obtained for the thinned dataset, which indicated that the signal of differentiation between Pd-1 and Pd-2 was strong and independent of geography or identity of the chosen sites (Fig. 1b).

We also used the microsatellite dataset to investigate the presence of population differentiation in each of the discovered clades. For this purpose, we investigated only Eurasian isolates (that is, excluding the 33 isolates from the United States) and treated Pd-1 and Pd-2 separately.

First, we used DAPC[33] (using the package adegenet) to assign each isolate to one site among all European sites sampled. If populations from different sites are genetically differentiated, one would expect the DAPC to assign isolates to their true site of origin (that is, where they were sampled) more often than expected by chance. Here each isolate was probabilistically assigned to sites based on the observed allele frequencies (no assumptions were made, for example, about the independence of loci). Each isolate was run in an independent DAPC, which resulted in a set of 2,191 and 279 DAPCs for Pd-1 and Pd-2, respectively (excluding all isolates from North America and with a limited number of 20 isolates per site, see also Supplementary Table 1; 120 PCA axes and 100 discriminant analysis (DA) axes retained in all runs). As some isolates will always be correctly assigned by chance, it was important to quantify the percentage of correct assignments by chance compared with correct assignments based on observed allele frequencies. Hence, we ran the same sets of DAPCs after randomizing the site names (independently for each run) to ascertain the frequency of correct assignments occurring by chance and the expected distances between the isolates’ site of origin and their assigned sites if assignment was no better than random (randomized DAPC). The distances between assigned sites and sites of origin are presented in the main article for Pd-1 (Fig. 3b) and in Extended Data Fig. 9 for Pd-2.

To identify the European sites of origin of the North American introduction, we performed two separate analyses with two different methods: multivariate analyses (DAPC[33]) and spatial Bayesian inferences (SPASIBA[40]). DAPC analyses assign samples to sites already contained in the dataset, whereas the SPASIBA method can perform continuous assignment to any location (that is, coordinates) in the range defined by the user. First, we built a DAPC with all 5,162 Pd-1 isolates from 226 sites in Europe (the site in the Ural Mountains was excluded). The aim of this DAPC was to differentiate sites; hence, sites were used as pre-defined groups. We then used the ‘predict.dapc’ function from the adegenet R package to predict site memberships of the 33 isolates from North America. Running the DAPC while limiting the number of isolates per site to 20 provided identical assignment results. Second, we used the SPASIBA model, a spatial Bayesian inference (SPASIBA) method for geospatial assignment that models the spatial frequency of alleles by a set of spatially autocorrelated random variables with Gaussian distribution. Implementation was carried out in R using the SPASIBA (v.24.6.27) and INLA (v.0.0.4) packages based on the same dataset as described above for the DAPC analysis. That is 5,162 Pd-1 isolates from 226 sites in Europe for the reference data (‘geno.ref’) and 33 isolates from 9 sites in North America as individuals of unknown geographical origin to be assigned (‘geno.unknown’). For SPASIBA, we used the function ‘SPASIBA.inf’ with a ploidy level of 1 and a flat domain (sphere = false). To achieve a uniform spatial resolution, we configured the grid to consist of 198 pixels in longitude and 120 pixels in latitude. This setup ensured a consistent resolution across both dimensions, with each pixel representing an approximate resolution of 0.169°. To identify the most probable source of introduction inferred across the 33 North American isolates, we calculated the average assignment likelihood for each pixel across the 33 isolates (Supplementary Table 5), from which we computed the log-likelihood for representation (Fig. 4).

The visualization of EEMS (https://github.com/dipetkov/eems) was used to evaluate geographical barriers linked to patterns of gene flow[35]. This method differs from PCA in that genetic differentiation is visualized as a function of migration rates rather than through genetic or genotypic distance. This method uses a population genetic model to compare expected pairwise genetic dissimilarities in relation to their geographical distances (that is, under a model of isolation-by-distance) with observed patterns across the sampled area. Specifically, a triangular grid with specific density (number of demes) is built over the area containing geo-referenced samples. For the edge of each grid, the migration parameter is estimated by Bayesian inference and Markov chain Monte Carlo sampling, which means that migration is estimated in an approximated stepping-stone model between neighbouring grid cells. As sampling locations will fall into the same or neighbouring cells depending on the grid cell size, the number of demes (defining the overall density of grid cells and hence their size) influences the outcome of estimated migration rates (particularly at small geographical scales). For this reason, we calculated the EEMS for a range of deme sizes (n = 8, 100–450 demes) in independent runs of 8 million iterations each (after a burn-in of 1 million iterations), as previously recommended[35]. Runs were combined in a single figure for visualization of robust migration rates using the R package reemsplot2 (v.0.1.0)[35]. It should be noted that estimated migration rates are most accurate closer to sampling locations and less accurate in sparsely sampled geographical areas. For this reason, in addition to clone-correction based on site identity (each genotype appears only once per site, additional occurrences are removed), the Russian isolates were excluded before calculating EEMS for both clades. This resulted in a total of 2,261 isolates used for Pd-1 and 107 isolates for Pd-2 (Supplementary Table 1). Markers Pd10 and Pd14 were uninformative for Pd-2 and hence removed for the EEMS (removed for the Pd-2 dataset only), which left a dataset with 16 markers.

Phylogenetic relationships between the 5,479 isolates (or 1,866 MLGs) were reconstructed using Nei’s ‘Da’ genetic distance and Cavalli–Sforza and Edwards Chord distance ‘Dch’, as both distance measures were found to be the best performing ones to retrieve the relationships between individuals[56]. Genetic distances were then clustered using the UPGMA algorithm as implemented in Phangorn (v.2.11.1)[57] in R. Given that the topology for the nodes of interest was identical for both methods (data not shown), only the results from the Da distance are presented. Analyses were performed in R using functions implemented in hierfstat (v.0.5.11)[58]. For visualization purposes of Fig. 1b, the ‘fish eye’ function of FigTree (v.1.4.4.)[59] was used to zoom into the section of the tree containing the Pd-2 clade. We jackknifed loci one at a time to test for the support of both Pd-1 and Pd-2 clades monophyly using the ‘is.monophyletic’ function in ape (v.5.7.1)[60].

Analyses of genomes de novo assembled from long-reads

Base calling

Base calling of fast5 files (from MinION sequencing) was performed on a GPU computer (hosted by the Montpellier Bioinformatics Biodiversity platform) by running the software Guppy (v.5.0.7), a state-of-the-art neural network base caller[61].

Genome assembly

To assemble high-quality haploid genomes of P.  destructans (P. destructans is haploid), we carried out adaptor trimming with Porechop on all base-called reads (https://github.com/rrwick/Porechop)[62]. These were then parsed into Flye (v.2.9)[25] with the --nano-hq flag. Other arguments were left as default (including automatic minimum overlaps). Genomes were polished once using pre-trimmed Illumina reads with HyPo (v.1.0.3) after initial mapping using paired-end mapper Burrows–Wheeler aligner—Maximal Exact Match, bwa-mem (v.0.7.17-r1188)63,64. HyPo arguments included approximate genome length of 35 megabases (-s 35m; based on a previous study[65]) and the average read depth of each genome (-c) (calculated with Samtools depth[66]).

To remove contigs that potentially resulted from contamination, contigs with exceptionally low GC content (identified using infoseq EMBOSS (v.6.6.0.0)[67]) were individually compared with the nucleotide BLAST database68,69. On the basis of these results, we removed contigs not originating from P. destructans from four isolates (which contained clear contamination by Cellulosimicrobium cellulans, Penicillium solitum, Pyrenophora teres f. teres and Shiraia bambusicola).

Mitochondrial contigs, with a characteristic lower GC content and known length (about 32 kb), were also identified through BLAST and removed from all further analyses. To remove noise in our genomes resulting from spurious assembly, all contigs below 10,000 bp were removed using SeqKit (v.0.16.1)[70] (-m 10000). Statistics of the assembled genomes are presented in Supplementary Table 11.

Repeat annotation

We annotated repeat content of each genome with RepeatModeler and RepeatMasker tools. Build Database was followed by RepeatModeler (v.2.0.1), which we ran individually on all 11 genomes of P. destructans isolates to identify repeat regions with default parameters. All novel consensus repeat sequences were combined using CD-HIT-est (v.4.8.1) to remove redundancy in the clustered library[71] (-aS 1 -c 1 -r 1 -g 1 -p 0). All final repeat sequences not annotated were then removed from the repeat library. Furthermore, BLASTN (v.2.9.0+) searches of the repeat library were carried out against the 9,405 annotated protein-coding genes of the P. destructans reference genome assembly downloaded from the NCBI (GCF_001641265.1_ASM164126v1). Any repeats with a sequence identity over 80% for over 80% of the query length (-outfmt 6, -perc_identity 80, manual removal of qcovs > 80) were also removed[72]. Finally, we removed duplicated fasta entries with Samtools (faidx). Masked assemblies as used downstream were produced using RepeatMasker (v.4.1.2)[73] (-xsmall) with the curated repeat library (-pa 5 -a -s -gff -no _is). The same pipeline was then used for the outgroup (Gd267), which was treated independently from P. destructans isolates.

Genome annotation

For gene annotation of all isolates, we chose to use the Funannotate pipeline (v.1.8.15) dedicated specifically for fungal genome annotation[74]. We used the pipeline through the Galaxy Europe cluster (https://usegalaxy.eu). We started by soft masking all the genome assemblies using RepeatMasker tools (v.4.1.2; https://www.repeatmasker.org/RepeatMasker/)[73] with the transposable element (TE) library generated in this study (see the section ‘Repeat annotation’). We then used Illumina paired-end RNA sequencing data from European (Sequence Read Archive accessions SRP041673 and SRR1270711) and North American isolates[75] (SRP041668, SRR1270148, SRR1270408 and SRR1270412) in addition to the data from P. destructans-infected bats[76] (SRP055976) used for annotation of the published reference sequence[74]. All RNA sequence paired-end reads were then mapped on the soft-masked genome assemblies using the RNA STAR mapping tool (v.2.7.10b)[77] (--genomeSAindexNbases 11bp). Funannotate-predict returns gene models based on read mapping using Augustus (v.3.4.0). It also uses curated databases (UniProtKb/SwissProt databank) for proteins to help predict probable gene structures. For the initial training of predictors, Funannotate-predict also uses BUSCO (v.5.2.2)[78] for initial Augustus species training. For this step, we used the phylogenetically closest species available on the Galaxy server: Fusarium (orthoDB v.10). We did not use the ab initio predictor dedicated to fungal genomes as the option created fragmented gene models. The results of the Funannotate-predict were combined to generate functional genome annotations using Funannotate-functional. Protein evidence generated was compared with the Funannotate database (v.2022-01-17-193541).

BUSCO genes

Genome assembly for each isolate was benchmarked with BUSCO (v.5.2.2) (hmmsearch v.3.1 and metaeuk v.5.34c21f2) using the option -m genome flag for the Kingdom fungi odb10 database from orthoDB (v.10). The fungi database contains 758 orthologous gene sequences79,80. Basic statistics of reads were obtained with NanoStat and NanoPlot (v.1.42.0)[81].

Sequence divergence

Sequence divergence for BUSCO genes was calculated from the MAFFT alignment (described below) in R, with the function ‘dist.dna’ from the ape package[60]. For each of the assemblies of full genomes, subcontigs were obtained by deleting the repeated and low-complexity sequences detected using RepeatModeler and RepeatMasker pipelines (as described above). A local alignment of the subcontigs was carried out with NUCmer4 (v.4.0.0)[82] for all the isolates against each other and interpreted using the show-coords program by applying a minimum percentage of sequence identity of 80%. The aligned regions were used to calculate the weighted average identity.

Synteny

To gain better insight into the similarities and differences in genomic structure and organization between clades, we performed network-based microsynteny analysis. This enabled us to investigate gene-copy number, to identify synteny conservation, to detect and quantify clade-specific syntenic clusters and to reconstruct phylogenetic relationships between isolates based on microsynteny. The detection of syntenic blocks performs best when using high-contiguity genomes assembled de novo (that is, without a reference). According to a previously published systematic evaluation[83], the characteristics of our genomes (mean N50 of 1.8 Mb; gene density estimated around 270 per Mb: 10,000 genes and 36.9 Mb per genome on average; Supplementary Table 11) should allow for a robust synteny analysis.

Using annotated genes from the 11 P. destructans genomes (detailed in the section ‘Genome assembly’), all inter-pairwise and intra-pairwise all-vs-all protein similarity searches were conducted using DIAMOND (v.2.1.7), for which the top 5 hits were kept in searches[84]. The output generated by DIAMOND was used as input for the MCScanX algorithm[85] to perform pairwise synteny blocks detection. To verify that parameters used for MCScanX had no impact on our results, we performed microsynteny block detection (and all downstream analyses) under 25 different parameter settings (Supplementary Table 17). These parameter settings involved the two key parameters of MCScanX, namely, the minimum required number of genes (anchors) to call a syntenic block (-s, MACH_SIZE: 10, 15, 20, 25, 30) and the number of upstream and downstream genes to search for anchors (-m, MAX_GAPS: 15, 20, 25, 30, 35). All the syntenic genes identified in the syntenic blocks were used to build a microsynteny network using the Infomap algorithm (implemented in the infer_syntenet function in the R package Syntenet (v.1.8.1)[86], which has been demonstrated to be the best clustering method available for synteny networks[87]. In the synteny network, genes represent nodes, and syntenic relationships between genes are represented by edges connecting the nodes. A median average clustering coefficient of 0.95 was obtained across parameter settings (min–max: 0.94–0.95%; Supplementary Table 17), which revealed that genes tend to form a complete subgraph or cluster with their syntenic neighbours across multiple genomes. Phylogenomic profiling was then performed using the inferred syntenic clusters, which resulted in a matrix mij of the phylogenomic profiles, which represented the number of genes from cluster j that can be found in genome i. To reveal synteny clusters that were conserved and clade-specific clusters, clusters were visualized as a heatmap with clusters clustered using Ward’s clustering on a matrix of Euclidean distance (Fig. 2e). The phylogenetic signal present in the synteny network was used to infer a microsynteny-based phylogeny of the 11 genomes. The binarized matrix of phylogenomic profiles was used in IQTREE2 (v.2.0.6) applying the MK + FO + R model with node support evaluated by two methods: 1,000 bootstrap replicates and 1,000 replicates for the SH-like approximate likelihood ratio test. The mid-point method was applied to root the tree using the ‘midpoint’ function in the ‘phangorn’ package[57] and the monophyly for the clades Pd-1 and Pd-2 was evaluated using the ‘AssessMonophyly’ function in ‘MonoPhy’ (v.1.3.2)[88].

Analyses of Illumina reads (individually tagged isolates)

Data checking and mapping

Illumina sequences were available for 18 isolates (listed in Supplementary Table 10). Fastp (v.0.23.4) was used to remove bases with a phred quality value lower than 30 (‘-q 30’). Subsequently, bwa-mem2 (v.2.2.1) was used to align the Illumina reads to each of the two reference genomes (Gd293 for Pd-1 and Gd45 for Pd-2). Samtools view (v.1.16.1) was used to only keep reads with mapped (-F 0×4), properly paired (‘-f 0×2’), and high-confidence mapping quality (MAPQ values of 60: ‘-q 60’). Samtools sort and Samtools index were used to respectively sort and index the obtained BAM files. Reads were assigned using Picard (v.2.27.1) and the AddOrReplaceReadGroups tool. The SortSam tool (from Picard) was used to sort the BAM file by queryname (QNAME) before removing duplicate reads using the MarkDuplicates tool (from Picard). SortSam was subsequently used to sort the BAM file by coordinate before indexing using Samtools index.

Base calling, genotyping and filtering

We then performed base calling with ‘gatk HaplotypeCaller’ (gatk v.4.2.6.1) using the ‘-ERC BP_RESOLUTION’ mode and a ploidy of 1. Genotyping was done with ‘gatk GenotypeGVCFs’. ‘gatk SelectVariants’ was then used to extract the non-variable positions in one file and the variable positions (SNP only) in another file (insertions and deletions were discarded). Hard filtering of variable positions that did not meet the criteria ‘QUAL < 30.0’, ‘QUAL/DP < 2.0’, ‘SOR > 3.0’, ‘FS > 60.0’, ‘DP < 20.0’ and ‘MQ < 40.0’ was carried out using ‘gatk VariantFiltration’ and ‘gatk SelectVariants’. Isolate genotypes with a read depth lower than 20× at a given position were set to no-call. ‘gatk SortVcf’ was then used to merge the filtered variable and non-variable positions into a single VCF file. ‘bedtools subtract’ was subsequently used to remove masked positions from the reference genome and positions called as heterozygous when considering the isolates as diploid (see the section ‘Filtering repetitive DNA’).

For each contig of each of the two reference genomes (Gd293 and Gd45), we then checked for the mean read depth, when mapping the 18 isolates to each reference genome, to detect unusually high read depth that would indicate the presence of repetitive sequences in the regions that nevertheless passed our filters described above. For each contig, we also checked for the average number of isolates with missing data per position to identify contigs with high levels of missing data. For Gd45, out of eight contigs that were smaller than 50 kb, two had no reads mapped, and five out of the six remaining had unusual mean read depth (lower or higher than other contigs) and/or a high average number of isolates with missing data (Supplementary Table 12). Therefore, all eight contigs below 50 kb, representing in total only 0.46% of the Gd45 genome were excluded from the analyses. For Gd293, all contigs were larger than 50 kb and a single contig (contig_44) deviated from the values observed for other contigs by having a read depth about 100× higher than other contigs and having on average 13 isolates with missing data per position (Supplementary Table 12). This contig, most likely an accessory chromosome with a high proportion of repetitive sequences, was therefore excluded from further analyses. After all filtering steps were performed (Supplementary Table 13), the average read depths were 237 and 240 when mapping on Gd293 and Gd45, respectively. The correlation between the read depths of the 18 genomes when mapped on Gd293 and Gd45 was 0.996 (Spearman’s rank correlation ρ; P < 0.001), which highlighted that the mapping worked equally well on either reference genome.

On average, each isolate had a genotype for over 95% of positions (Supplementary Table 13). In the end, we obtained data on 21,609,224 positions when using Gd293 as the reference (including 92,593 biallelic SNPs) and 22,041,192 positions when using Gd45 as the reference (including 95,638 biallelic SNPs). These data were stored in a single VCF file (with 18 samples) per reference genome. Furthermore, to evaluate the quality of our full procedure, we checked the number of differences obtained by mapping the Illumina reads of Gd293 to the de novo-assembled MinION genome (reference genome Gd293, see the section ‘Genome assembly’ for assembly details). We did a similar analysis for Gd45 mapped to the reference genome Gd45. For Gd293, the number of differences with the reference genome (itself) was 19 variants out of 23,660,591 positions in the final VCF file, which indicated a combined mapping, base calling and genotyping error rate of 8.030231 × 10–7; that is 1 error every 1,245,294 bp, which corresponded to a quality score of Q60.95. For Gd45 (mapped on the reference genome Gd45), the error rate was also extremely low: 2.444712 × 10–6 (54 variants out of 22,088,490 bp), or 1 error every 409,046 bp (Q56.11). These data provide evidence that our pipeline, which incorporated several stringent filters, recovered highly reliable SNPs while preserving a large amount of data (>22 Mb accounting for around 60% of the reference genomes).

Diversity and differentiation calculation

Genetic diversity π was calculated per clade (11 for Pd-1 and 7 for Pd-2) across 50 kb windows using pixy (v.1.2.7.beta1), which takes into account missing data in calculations and hence provides unbiased estimates[89]. The index of genetic differentiation (FST; Weir and Cockerham’s method) between the two clades was calculated using a modified version of vcftools (https://github.com/jydu/vcftools) to allow the computation of statistics with haploid data.

Phylogenetic relationships

The relationships between the 18 P. destructans isolates (Supplementary Table 10) and the Pseudogymnoascus sp. outgroup (isolate 267) were reconstructed using maximum likelihood in IQ-TREE2 (v.2.0.6)[90]. The relationships were reconstructed when considering two methods to partition the genome: (1) BUSCO genes alone, and (2) 10 kb windows across the whole genome. Each of these partition methods were used in combination with each of the two reference genomes (Gd293 and Gd45; see the section ‘Data checking and mapping’), which resulted in a total of four datasets. All four datasets converged towards the same topology, recovering Pd-1 and Pd-2 as two reciprocally monophyletic clades (Fig. 2b and Supplementary Fig. 5).

BUSCO phylogeny

To produce the phylogenetic tree using newly sequenced and publicly available BUSCO genes of the isolates, we used all single copy, complete orthologues as identified from the fungi odb10 database, that were common to all 18 isolates (Supplementary Table 11) and the Pseudogymnoascus sp. outgroup (isolate Gd267). This resulted in 664 and 662 BUSCO genes when mapping on Gd293 and Gd45, respectively. We extracted the genes from each assembly with BEDTools (v.2.30.0-8)[91] using the command getfasta before individually aligning with MAFFT (v.7.453)[92] (--auto, --adjustdirection). In IQ-TREE2, a concatenation-based species tree with edge-linked proportional partition model with 1,000 ultrafast bootstrap (-B 1000 -T AUTO) was produced[93]. To produce the species concordance factor, which measures how consistent the genealogical relationships are across different loci, the orthologue and species trees were used (--scf 100--prefix concord -T 10) by IQ-TREE2. The final tree was manually rooted at the outgroup, Gd267, in FigTree (v.1.4.4; http://tree.bio.ed.ac.uk/software/figtree/). To represent conflicting phylogenetic signals between gene trees (for example, owing to recombination and/or incomplete lineage sorting), we used the function consensusNet from the phangorn R package[94] and computed the consensus network from the splits occurring in the different gene trees. Only splits occurring in at least 10% of trees were represented in the network.

Whole-genome phylogeny

The relationship between the 18 P. destructans isolates (Supplementary Table 10) and the Pseudogymnoascus sp. outgroup (isolate 267) was reconstructed using maximum likelihood in IQ-TREE2 (v.2.0.4)[90]. We used vcftools with the ‘missing-site’ function to extract missingness on a per-site basis from the VCF file containing the genotypes of the 19 (18 + 1) individuals (see the section ‘Base calling, genotyping and filtering’). Then, we calculated the number of positions with no missing data over 10 kb non-overlapping windows and only kept windows with at least 5,000 positions with no missing data across the 19 isolates. For each of the 19 isolates, we then used BEDTools getfasta to extract sequences (FASTA format) from these windows. In each window, only positions without missing data (that is, minimum 5 kb) were kept. We obtained a total of 1,275 and 1,288 genomic partitions when using the reference genomes from each clade, Gd293 and Gd45, respectively. A concatenation-based species tree with edge-linked proportional partition model with 1,000 ultrafast bootstrap (-B 1000 -T AUTO) was then produced. This procedure was applied in parallel to the data mapped on reference genomes from each clade, Gd293 and Gd45 (VCF files).

Analyses of recombination

To test for the presence of recombination in each of the two clades, we used two tests, the pairwise homoplasy index (PHI or Φw) test and the four-gamete test (FGT). The Φw test calculates a pairwise similarity index between closely linked sites (situated in w bases) and compares observed values to values obtained after permutation of the sites. Under the null hypothesis of no recombination, the genealogical correlation between adjacent sites remains unchanged even if the order of the sites is shuffled. This is because all sites share the same evolutionary history when recombination is absent. However, when recombination occurs, the order of the sites becomes important as distant sites tend to have weaker genealogical correlations compared with adjacent sites[95]. We used the Φw test as implemented in Splitstree CE (v.6.3.27)[96] to test the null hypothesis of clonality. The FGT test involves counting the number of allelic combinations between any pair of SNPs on the same contig. Assuming an infinite site model, the observation of four combinations is incompatible with the absence of recombination. For example, if two SNPs have the alleles C/T and A/G, then the observation of the haplotypes CA, CG, TA and TG must be the result of a recombination event. We calculated the number of haplotypes using the script FGT.pl (https://github.com/dbsloan/fgt). In each clade, we performed FGT on windows of 100 kb, with at least 20 kb of unmasked nucleotide positions and no missing data. We performed these analyses on the genomic data (Illumina data from individually tagged isolates) from the 18 isolates (11 from Pd-1 and 7 from Pd-2; described in section ‘Analyses of Illumina reads (individually tagged isolates)’) mapped on the reference genomes (Gd293 and Gd45) of Pd-1 and Pd-2 clades, respectively.

Next, we estimated the population recombination rate (r = 2Ner, where r is the recombination rate per bp per generation and Ne is the effective population size) using LDhelmet software[97] (v.1.10; https://github.com/popgenmethods/LDhelmet). We used the parameters -t 0.0005 and -t 0.001 for the population mutation rate (q = 2Nem, where m is the mutation rate per bp per generation) for Pd-1 and Pd-2, respectively, -w 200, and we estimated the mutation transition matrix using the IQTREE2 substitution model GTR[98]. Default values were used for other parameters following the LDhelmet manual.

Apart from formal recombination tests carried out as explained above, we used the reconstructed phylogenetic tree obtained with microsatellite data (described in the section ‘Analyses of multilocus genotypes’) to map the two mating types. This analysis demonstrated the presence of both mating types in Pd-1 and Pd-2 clades, and the presence of both mating types throughout the phylogenetic tree depicting relationships between MLGs in each clade. These data provide further evidence for recombination in Pd-1 and Pd-2 clades (Supplementary Fig. 1).

Modelling demographic history

To model the evolutionary history of both clades, we implemented Approximate Bayesian Computation comparing two demographic models with and without contemporaneous migration. Specifically, we evaluated the isolation-migration model (IM) and the strict-isolation model (SI). These two models imply a population split at a time Tsplit in the past, but this event is followed by constant migration in the IM model or no migration in the SI model. The ancestral population and the derived population have an independent population size (Na, N_1 and N_2). Population size is also free to vary within population 1 and 2 independently once at time Tdem1 and Tdem2 in the past for population 1 and 2, respectively. Coalescence simulations were generated using msnsam (October 2007 version)[99], which is a modified version of ms[100]. The priors for population size, Tsplit and migration rate were generated using uniform distributions. Priors and summary statistics were computed using scripts taken from DILS[101] (https://github.com/popgenomics/DILS_web). One million simulations were performed for both models using a mutation rate of 1.5 × 10–9 mutation per site per generation, a recombination rate half of the mutation rate, a Tsplit between 10 and 10 million generations and an N between 10 and 10 million individuals per population. As performing the analyses on the full genome would be computationally too intensive (for the simulations part), we instead used a representative sample across the genome. To accomplish this, we selected 1 kb sequence windows, spaced 10 kb apart. This resulted in a dataset of 4,832 and 2,881 windows for the datasets when using Pd-1 and Pd-2 reference genomes, respectively.

Model selection was performed using the random forest method implemented in the R package abcrf (v.1.9)[102] and using the postPr function of the R package abc (v.2.2.1)[103]. Parameter estimations were obtained using the neural network method implemented in the abc package using 5,000 simulations. Finally, we computed a goodness-of-fit statistic for each of the summary statistics as the proportion of simulations of the posterior distributions with a statistic superior or inferior to the observed statistic with this proportion P always smaller than 0.5.

The model selection procedure led to the selection of the SI model, with posterior probabilities of 0.77 and 0.75 with the random forest method for references Gd293 and Gd45, respectively. The rejection method led to the selection of the SI model 79% and 76% of the times for reference Gd293 and Gd45, respectively. Therefore, both model-selection methods support the model without contemporaneous migration. The full set of parameters estimated from the posterior distributions of the SI model using the neural network method is presented in Supplementary Table 16. Based on the SI model, the divergence time between Pd-1 and Pd-2 clades was estimated to have occurred between 114,000 and 1.5 generations ago (Supplementary Table 16).

Analyses of Illumina reads (Pool-seq data)

Data checking and mapping

We checked and mapped the Illumina data (each of the four pools per clade) as detailed in the section ‘Data checking and mapping’ for Illumina reads above.

Data pooling

For each of Pd-1 and Pd-2, we had four pools (hereafter called ‘subpools’) of isolates (with 17, 18, 17 and 17 isolates for Pd-1 and 16, 16, 16 and 15 isolates for Pd-2). Before combining the four subpools per clade, we calculated the number of reads per subpool and subsampled them with Samtools view to have the same number of reads per isolate (1,704,260 reads) in each subpool. We then used Samtools merge to merge BAM files for the four subpools per clade, which resulted in one final pool (pool of four subpools) of 69 isolates for Pd-1 (117,589,122 reads) and one final pool (pool of four subpools) of 63 isolates for Pd-2 (107,355,848 reads).

Genotyping

Genotyping was carried out separately for each pool (one for Pd-1 and one for Pd-2), mapped on the reference genome from clade 1 and 2, which resulted in four separate analyses. Samtools mpileup was then used to generate pileup outputs from BAM files. For SNP calling, we applied a Bayesian approach specifically designed for pools (SNAPE-pooled104,105) that calculates the posterior probability that a site is polymorphic. This approach has been validated using both simulations and empirical approaches and is among the best performing method currently available[104]. SNAPE-pooled prior parameters were sets as follows: θ  =  0.0005, D  =  0.0025 or D = 0.0005 when mapping the pool on the reference from the different versus same clade respectively, prior = ‘informative’, fold = ‘folded’, and nchr = number of (haploid) isolates in the pool. We converted the SNAPE-pooled output file to a VCF and as recommended by the program developers, we only considered positions with a posterior probability ≥0.9 as being polymorphic. In all other cases, positions were marked as monomorphic. To avoid including sequencing errors as rare alleles, we adopted two complementary strategies. First, when converting the SNAPE-pooled output to a VCF, we only included alleles that were supported by at least five reads in the pool (that is, the four subpools together). Second, we performed SNAPE-pooled analyses on each subpool (with parameters as described above) to identify alleles that were not supported by at least two reads in two subpools. These alleles were then removed from the VCF file of the pool.

Data filtering

To limit false-positive SNPs, we applied further filters to the VCF file to remove the following: (1) regions where repetitive DNA elements were either confirmed or suspected; (2) regions with low or high read depth (based on the Pool-seq data); and (3) regions that were identified as problematic based on read depth of individually tagged isolates (see the section ‘Base calling, genotyping and filtering’ of Illumina reads).

Filtering repetitive DNA

The removal of regions with confirmed or suspected repetitive DNA (including paralogues) that could not be confidently mapped with short-read data was carried out using three complementary approaches.

First, we removed regions that were masked from the reference genome (see the section ‘Repeat annotation’).

Second, we removed regions suspected to contain hidden repetitive DNA. Although we removed regions that were masked from the reference genome (see above), a single or a set of genomes (18 in our case, see section ‘Repeat annotation’) is unlikely to harbour the full extent of the repertoire existing in repetitive DNA in a larger number of isolates such as in the Pool-seq dataset. Hidden repetitive DNA can generate spurious heterozygous genotypes that can confound the estimation of genetic differentiation between populations or species (for example, ref. [106]). Hence, when using reads from an isolate that was not used to build the repeat library or the filters (for example, Pool-seq data), the unique mapping of its reads to a reference genome does not per se confirm that these reads originated from non-duplicated regions. We used levels of genetic diversity (π) as a proxy to detect hidden repetitive DNA. Repetitive DNA elements sharing a common ancestor accumulate mutations; therefore, when such loci (two or more copies) are erroneously merged together as a single locus, one expects higher levels of genetic diversity provided that everything else remains equal. We therefore removed regions with levels of π greater than 1%. This threshold was obtained by calculating the level of genetic diversity observed across 18 genomes rather than pools of individuals (see the section ‘Analyses of Illumina reads (individually tagged isolates)’; Supplementary Table 10) for which only 0.43% and 1.43% of sites for Pd-1 and Pd-2, respectively, showed π values greater than 1%. π was calculated per site using pixy (v.1.2.7.beta1), which takes into account missing data in calculations and hence provides unbiased estimates[89]. Site-based calculations from pixy were then averaged in R (function ‘runner’) over 100 bp sliding windows by summing the raw counts and recomputing the differences/comparisons ratios (https://pixy.readthedocs.io/en/latest/output.html). For the Pool-seq data, genetic diversity (named ‘Q1’; see refs. 107,108) was calculated using the function ‘computeFST’ in the poolfstat (v.2.0.0) package in R. Sites included in sliding windows with genetic diversity greater than 1% were stored in a BED file. Bedops (v.2.4.41) was used to flatten all disjoint, overlapping and adjoining element regions into contiguous, disjoint regions. BEDTools (v.2.30.0-8) merge was used to merge features that were separated by 500 bp maximum.

Third, using the same rationale as the second filter detailed above but with a different approach, we took advantage of the nature of the data (P. destructans isolates are haploid) to identify and exclude regions of the genome where mapping would lead to the calling of a heterozygote when performing the analyses of a haploid isolate in diploid mode[109]. Each of the 18 isolates (as listed in Supplementary Table 10 (except the outgroup)) were mapped against the reference genome (as per the section ‘Data checking and mapping’ for Illumina reads) and analysed in diploid mode (that is, considering that the isolate is diploid). For this, we used the same pipeline as for base calling isolates (with gatk; see the section ‘Analyses of Illumina reads (individually tagged isolates)’) but with ploidy of 2. Sites scored as heterozygote in at least one isolate were recorded in a BED file. A total of 183,756 heterozygote sites (spread across contigs) were identified in those 18 genomes when using Gd293 as the reference and 177,785 when using Gd45 as the reference. All those heterozygote sites were removed from all 18 genomes, irrespective the isolates they originated from. Although these data alone might indicate that P. destructans is diploid, results from the microsatellite analysis firmly refuted this hypothesis. Indeed, we genotyped 5,479 isolates originating from single spore (that is, monosporic isolation) cultures for 18 microsatellite loci (see the section ‘Cultures and genotyping’), and out of the 98,622 genotypes (5,479 × 18), we never encountered 2 alleles per locus for any single spore isolate. Two alleles for some loci and some isolates would be expected if P. destructans was diploid. Furthermore, such levels of heterozygosity have already been reported when base calling haploids in diploid mode in other fungal species (for example, ref. [109]).

The BED files created for the three complementary approaches detailed above were then processed in Bedops to flatten all disjoint, overlapping and adjoining element regions into contiguous, disjoint regions and BEDTools merge was used to merge features that were separated by 500 bp maximum. These regions contained in the final BED file were then excluded from the VCF file in BEDTools substract.

Filtering based on read depth

Many species of fungi have accessory chromosomes, and the results from the section ‘Base calling, genotyping and filtering’ provide strong evidence that this is also the case for both of the P. destructans clades. As a result, stringent filtering based on read depth alone was not possible. We therefore only performed light filtering by setting sites with a read depth below 20× or above 600× per pool as missing data. Based on read depth and levels of missing data from individually tagged isolates (section ‘Data checking and mapping’ for Illumina reads; Supplementary Table 12), we identified a few problematic contigs that were also filtered out from the Pool-seq data.

Filtering outcomes

The filtering steps described in the previous two sections resulted in a narrowing of the 95% highest density interval (hdi95) of the read depth for both clades when mapped to the reference genome of both clades (Supplementary Fig. 6). When using reference genome Gd45, Pd-1 hdi95 narrowed down from 5–529 to 234–560 whereas Pd-2 hdi95 narrowed down from 124–470 to 217–470. When using reference genome Gd293, Pd-1 hdi95 narrowed down from 92–512 to 236–523 whereas Pd-2 hdi95 narrowed down from 5–511 to 227–542. Positions filtered out were mostly, although not exclusively, positions with read depth less than 200 (Supplementary Fig. 6), which meant that accessory chromosomes constitute a substantial part of the data filtered out. This result was expected, as accessory chromosomes are known to be repeat-rich[110], hence they are expected to be more heavily filtered out than core chromosomes. After filtering, the dataset consisted of 17,110,071 and 16,733,801 positions for Pd-1 and Pd-2 pools, respectively, mapped to Gd293, and 16,675,338 and 17,286,690 positions for Pd-1 and Pd-2 pools, respectively, mapped to Gd45. The median read depths were 446 and 382 for Pd-1 and Pd-2, respectively, mapped on Gd45. The median read depths were 425 and 437 for Pd-1 and Pd-2, respectively, mapped on Gd293. A similar number of SNPs (55,919 and 57,330) was identified when mapping the pools on Gd293 and Gd45, respectively.

Calculation of the index of differentiation F ST

The VCF file was then imported into R using poolfstat (v.2.0.0)[108] and the vcf2pooldata function (designating the pool size as 69 and 63 for clade Pd-1 and Pd-2, respectively, max.cov.per.pool = 600, min.cov.per.pool = 20). The multilocus FST was then calculated across 200 SNPs with the compute.FST function.

Variations in growth rates and growth-medium colouration

To evaluate variations in culture-related properties, a subset of isolates was photographed in a custom-built photobox using the same camera, lens and settings (Canon EOS 600D with 60 mm Canon Macro lens EF-S, shutter speed = 1/6,000, aperture = 3.2, ISO = 400, evaluative metering; see ref. [111]), biweekly for 8 weeks. For this purpose, 45 and 34 isolates of Pd-1 and Pd-2, respectively, were re-cultured on the same day on the same batch of culture medium. Six days later, for each isolate, a single germinating spore was physically moved to a new plate with growth medium (detailed in the section ‘Cultures and genotyping’) and stored upside down at 10 °C. The identity of isolates is provided in Supplementary Table 1.

Analysis of growth

The analysis of the pictures was carried out in R (v.4.1.1)[52] using the packages EBImage (v.4.3)[112], ks (v.1.14.1)[113], adehabitatHR (v.0.4.21)114,115, sp (v.1.4-6)[116] and adimpro (v.0.9.6)[117]. Images were imported into R using the readImage function that extracts the intensity of each pixel of the red, green and blue channels. Pixels with red intensity above 0.7 were characteristic of cultures, whereas the background (culture medium) was below 0.7. Coordinates (that is, their position in the image) of pixels with red intensity above 0.7 were stored and used to calculate the minimum convex polygon (MCP) using the mcp function of the adehabitatHR package. The MCP was used to outline the edge of the fungal growth and to calculate the number of pixels it covered. To overcome potential issues with dark non-fungal material present on the plate (for example, droplets of water or dirt) that would be included in 100% MCPs and hence artificially increase culture size, we calculated 11 MCPs per isolate, excluding 5−15% of outliers in steps of 1 (11 values; that is, MCP 85 until 95%). We then extrapolated the number of pixels covered by the fungus (for example, for an isolate covering 180 pixels calculated with an MCP 90%: 180/90 × 100 = 200 pixels). For each MCP, pixels were finally transformed to square centimetres with the use of cross multiplication in relation to an object of known size (5.207681 cm^2). For each isolate, the average across the 11 MCPs was used, and the quality of the estimates was evaluated using the standard deviation of the estimate made for the 11 MCPs and visual inspection of the edge of the MCPs depicted on top of the original picture. Resulting culture sizes are visualized in Extended Data Fig. 4 and Supplementary Table 9.

Analysis of culture darkness

The colouration of culture medium as a result of culture growth was measured from the same pictures taken for the analysis of growth (see the section ‘Analysis of growth’; 45 isolates for Pd-1 and 34 isolates for Pd-2). Analyses were carried out in R using EBImage (v.4.3)[112]. Colour images were transformed into black and white images, and the pixel density (a value from 0 to 1; 1 being white and 0 being black) was recorded in 3 rectangles distributed on the area showing the culture medium (that is, not touching the edge of the culture and also avoiding the centre with fungal growth). The median pixel density among the three rectangles was used as a proxy for culture darkness. For each isolate, the difference in pixel density between the picture taken after 1 week and that taken after 8 weeks was calculated (8 weeks – 1 week, resulting in a positive value if darkness increased, a negative value when darkness decreased). Results are presented in Extended Data Fig. 2 and Supplementary Table 8.

Maps and plotting

Unless otherwise stated, figures were produced in R using functions from base R[52] and ggplot2 (v.3.5.0)[118]. Maps (Figs. 1a and 2a) were downloaded as tiles from Stadia Maps (https://stadiamaps.com/) with data by OpenStreetMap (map tiles by Stamen Design under ODbL, under CC BY 4.0) and plotted using the ggmap package (v.3.0.2)[119]. They represent maps of type ‘stamen_terrain_background’, with colours representing natural vegetation colours and elevation (through shading). Maps for Figs. 3a and 4a,b were obtained from the R packages rworldmap (v.1.3.8)[120] and rworldxtra (v.1.0.1)[121]. Bat species distribution were recovered from the International Union for Conservation of Nature (IUCN) website[122] as shape files (https://www.iucnredlist.org/) and plotted in R. Inkscape[123] (v1.1.1; https://inkscape.org) was used to optimize visualizations.

Statistical analyses

We tested the relationship between clade identity (Pd-1 or Pd-2; binary response) and environmental factors, including bat species (nominal variable), latitude and longitude (both numerical variables were scaled). For this analysis, closely related bat species that are challenging to identify during hibernation were pooled together. Furthermore, to improve model convergence and to ensure identifiability of the model, only the most commonly infected species, that is, species from which we isolated Pd at ten or more sites were included in the model. This resulted in a dataset comprising 4,295 isolates from 231 sites in Europe, broken down as follows: M. mystacinus (28 isolates, 10 sites); M. dasycneme (107 isolates, 11 sites); M. daubentonii (111 isolates, 17 sites); Myotis species complex (322 isolates, 38 sites); and M. myotis/blythii (3,727 isolates, 187 sites). We fit a Bayesian hierarchical model using clade identity as our response variable and a logit link function. Bat species, latitude and longitude were included as population-level effects (analogous to a fixed effect in a frequentist approach), whereas samples (nested within sites) and sites were included as group effect (analogous to a random effect in a frequentist approach) with random intercept. The group-level effects were used to account for variations between sites and between samples within site. Given that both P. destructans clades were found in roughly equal numbers in M. mystacinus (13 and 15 isolates for Pd-1 and Pd-2, respectively), this bat species was used as the baseline category. To estimate the model parameters and to perform Bayesian inference, the model was fitted using 8 chains with 10,000 iterations each. To improve convergence, the ‘adapt_delta’ parameter, controlling the acceptance rate of the algorithm, was set to 0.99. The first 1,000 iterations of each chain were discarded as warm-up (burn-in) to ensure convergence. Chains were sampled using the NUTS (No-U-Turn Sampler) algorithm in Stan (https://mc-stan.org/) with the brms (v. 2.20.3)[124] package in R. Effective sample size measures (Bulk_ESS and Tail_ESS) were calculated to assess the quality of the draws, and the potential scale reduction factor (Rhat) was used to evaluate the convergence of the chains as previously proposed[125]. Collinearity among the explanatory variables was assessed using the generalized variance inflation factor, computed through the ‘check_collinearity’ function available in the ‘performance’ (v.0.12.4)[126] package in R.

Analyses of barcoding genes

We investigated two universal barcoding genes that are single copy genes, the translation elongation factor 1α (TEF1) and the DNA-directed RNA polymerase II subunit B (RPB2)[127] (RPB2). We retrieved sequences of these two genes from the full genomes of 18 isolates (based on Illumina read mapped on reference genome Gd293; see the section ‘Analyses of Illumina reads (individually tagged isolates)’) and from the filtered Pool-seq datasets of each P. destructans clade, Pd-1 and Pd-2 containing 69 and 63 isolates, respectively (also mapped on Gd293; see the section ‘Analyses of Illumina reads (Pool-seq data)’). This was done to search for fixed positions that could discriminate the P. destructans clades.

Based on sequences from 150 isolates (69 and 63 from Pool-seq data; 18 from full genomes), we identified six substitutions in TEF1 (at positions 3155612, 3155783, 3156573, 3157304, 3157318 and 3157602 on contig 34 of Gd293) and three in RPB2 (at positions 523497, 525708 and 526300 on contig 34 of Gd293) that fully discriminated Pd-1 and Pd-2 clades. These nine substitutions were fixed in clades. Based on the sequences from the 18 isolates mentioned above and the outgroup Gd267, both clades formed monophyletic groups when building a phylogenetic tree for each gene separately (data not shown).

Based on these discriminating sites in TEF1 and RPB2, we searched in published nucleotide sequences in NCBI for P. destructans to classify isolates to either clade Pd-1 or Pd-2. Using this approach, a set of isolates from the Czech Republic (n = 3), Portugal (n = 13) and South Korea (n = 2) could be identified as belonging to clade Pd-2 (Supplementary Table 14), for example. These data combined with the data presented in the main text confirmed that Pd-1 and Pd-2 co-occur in Europe, but thus far, only Pd-2 has been found in East Asia (one isolate from China, one from Mongolia, two from South Korea). This result suggests that Pd-1 is rarer in East Asia or perhaps even absent.

Reporting summary

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

Data availability

The genomic sequences and assembled genomes have been deposited into the Sequence Read Archive under accession numbers SRR30476767–SRR30476787 and SRR30476795–SRR30476796 (Supplementary Table 10), and the Pool-seq data are available under accession numbers SRR30476788–SRR30476794 and SRR30476766 (Supplementary Table 15). These data can be accessed through BioProject PRJNA862744 at the National Center for Biotechnology Information. Metadata along with microsatellite genotypes data are provided in Supplementary Table 1. Temperature, absolute humidity and M. daubentonii presence is provided in Supplementary Table 3. Colouration of agar medium is provided in Supplementary Table 8, and colony expansion rates in Supplementary Table 9. All other data are available in the article or the Supplementary information.

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Acknowledgements

We thank the 366 volunteers, whose names are presented in Supplementary Note 2, who spent a considerable amount of time to survey and sample for this project; F. Delsuc and R. Allio for their initial help with MinION sequencing and base calling, I. Römer, S. Fregin and A. Magdeleine for help in the laboratory; G. Kerth for providing access to laboratory facilities; M. Bekaert for initial analyses with Illumina sequencing; E. Douzery for discussion about phylogenetic reconstruction; G. Wibbelt, D. Lindner, A. Kubátová and A. Barlow for providing us with 29, 18, 12 and 2 cultures, respectively; N. Galtier, P. Gladieux and J. Taylor for comments on the manuscript; C. Smadja and P.-A. Gagnaire for discussions on speciation and recombination respectively; and B. Ridush, T. Ermakova and V. Rogozhnikov for information about caving in Podillia. This research was funded, in whole or in part, by Bat Conservation International, Institut Universitaire de France (IUF), Agence Nationale de la Recherche (FunAdapt), Deutsche Forschungsgemeinschaft (PU 527/2-1), National Geographic Society (WW201ER-17), Bulgarian Ministry of Education and Science under the National Research Programme “Young scientists and postdoctoral students” (DCM 577/17.08.2018), Academy of Finland (331515) and the Kone Foundation (201710231).

Funding

Open access funding provided by Universität Greifswald.

Author information

Authors and Affiliations

1. Zoological Institute and Museum, University of Greifswald, Greifswald, Germany

Nicola M. Fischer, Ruth-Marie Stecker, Serena E. Dool, Marcus Fritze & Sebastien J. Puechmaille

2. Institut des Sciences de l’Évolution Montpellier (ISEM), University of Montpellier, CNRS, EPHE, IRD, Montpellier, France

Nicola M. Fischer, Imogen Dumville, Benoit Nabholz, Marie-Ka Tilak, Clothilde Chenal, Anna-Sophie Fiston-Lavier & Sebastien J. Puechmaille

3. Institut Universitaire de France, Paris, France

Benoit Nabholz, Anna-Sophie Fiston-Lavier & Sebastien J. Puechmaille

4. National Museum of Natural History, Bulgarian Academy of Sciences, Sofia, Bulgaria

Violeta Zhelyazkova

5. Department of Biology, University of Turku, Turku, Finland

Anna S. Blomberg

6. Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland

Anna S. Blomberg

7. PHIM Plant Health Institute, University of Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France

Serena E. Dool

8. CIRAD, UMR PHIM, Montpellier, France

Serena E. Dool

9. German Bat Observatory, Berlin, Germany

Marcus Fritze

10. Competence Center for Bat Conservation in Saxony Anhalt, South Harz Karst Landscape Biosphere Reserve, Südharz, Germany

Marcus Fritze

11. Institute of Ecology of the Carpathians, National Academy of Sciences of Ukraine, Lviv, Ukraine

Andriy-Taras Bashta

12. DIADE, University of Montpellier, CIRAD, IRD, Montpellier, France

Clothilde Chenal

13. MIVEGEC, University of Montpellier, CIRAD, IRD, Montpellier, France

Clothilde Chenal

Contributions

Collected samples or data in the field: A.S.B., S.E.D., M.F., V.Z., A.T.B. and S.J.P. Cultured samples: N.M.F., A.S.B., S.E.D., M.F., V.Z., R.-M.S. and S.J.P. Expansion rate and darkness experiment: N.M.F. and S.J.P. Performed molecular work and sequencing: N.M.F., A.S.B., M.-K.T., R.-M.S. and S.J.P. Genome assembly and annotation: I.D., A.-S.F.-L. and S.J.P. Genomic analyses: I.D., B.N., C.C., A.-S.F.-L. and S.J.P. Analyses of microsatellites: N.M.F. and S.J.P. Statistical analyses: N.M.F. and S.J.P. Methodology: N.M.F., A.-S.F.-L. and S.J.P. Validation: N.M.F. and S.J.P. Resources: A.-S.F.-L. and S.J.P. Data curation: N.M.F. and S.J.P. Writing original draft: N.M.F. and S.J.P. Writing, review and editing: N.M.F., I.D., B.N., C.C., A.S.B., S.E.D., M.F., V.Z., R.-M.S., M.-K.T., A.-T.B., A.-S.F.-L. and S.J.P. Visualization: N.M.F. and S.J.P. Supervision: A.-S.F.-L. and S.J.P. Project administration: S.J.P. Funding acquisition: S.J.P. Conceptualization: S.J.P.

Corresponding author

Correspondence to Sebastien J. Puechmaille.

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Extended data figures and tables

Extended Data Fig. 1 Percentages of swab samples collected from the 6 most frequently sampled bat species or combination of bat species.

(Myotis myotis/M. blythii, Myotis nattereri/M. crypticus/M. escalerai, M. mystacinus, Myotis daubentonii, Myotis dasycneme, M. brandtii) and all other species or combination of species combined (“Other”) per clade (Eurasian sites only). Morphologically cryptic/highly similar species were treated together due to the difficulty of reliable species identification during winter hibernation when bats are not handled to minimise disturbance. Samples from substrates other than bats (n = 267) or without bat species information (n = 1) were not included in this figure, resulting in data from 1,388 and 92 swabs for Pd-1 and Pd-2, respectively. Note that 17 swabs out of 1,463 harboured isolates from both clades and are thus used to calculate percentages in both graphs. See the section ‘Statistical analyses’ in the Methods for statistical analyses formally testing the relationship between clade identity (Pd-1 or Pd-2) and environmental factors, including bat species.

Extended Data Fig. 2 Colouration of culture medium by growth of Pd-1 and Pd-2.

Left: Difference in colour (darkness) of culture medium after 7 weeks using pixel density as a proxy. Photos were taken of 45 and 34 isolates of Pd-1 and Pd-2 respectively and analysed in R (see section ‘Analysis of culture darkness’ in the Methods and Table S8). To calculate the difference, the median pixel density at 8 weeks was subtracted from the pixel density at 1 week whereby a positive value indicates an increase in darkness. The black line indicates the median, while the lower and upper hinges represent the first and third quartiles, respectively. The whiskers extend to 1.5 times the interquartile range. Right: Examples of analysed photos (original) for Pd-1 and Pd-2. Clades Pd-1 and Pd-2 differ significantly in terms of colouration of the agar medium (Week 1 – Week 8; two-sided t-test: t = −11.58, df = 60.06, p < 0.001).

Extended Data Fig. 3 Temperature and absolute humidity recorded in Eurasian hibernacula.

Results are shown as raw data (dots) and as violin plots (coloured shading) showing the probability density estimate of the variables per clade. Temperature data were obtained from 152 and 26 sites in which clades Pd-1 and Pd-2 were sampled while absolute humidity was recorded in 91 and 22 sites per clade. There was no significant difference between the clades, either for temperature (two-sided t-test, t = 1.65, df = 35.52, p = 0.11) or absolute humidity (two-sided t-test, t = −0.56, df = 37.96, p = 0.58).

Extended Data Fig. 4 Bi-weekly size of cultures belonging to clades Pd-1 and Pd-2.

Measurement of culture sizes for 45 and 34 isolates of Pd-1 and Pd-2 respectively recorded for a growth period of 7 weeks (after which point growth ceases) at 15 °C. The size was measured from photos using R software (for more information see section ‘Analysis of growth’ in the Methods). The black line indicates the median, while the lower and upper hinges represent the first and third quartiles, respectively. The whiskers extend to 1.5 times the interquartile range. There was no significant difference in culture size of isolates belonging to Pd-1 compared to Pd-2 (two-sided t-test, p ranging from 0.07 to 0.97 for each week).

Extended Data Fig. 5 Density of multi-locus F[S][T] (a), π for Pd-1 (b) and π for Pd-2 (c) when mapping the 18 individually tagged isolates (11 Pd-1 and 7 Pd-2) on Gd293 reference genome (blue) or on Gd45 reference genome (orange).

To better visualize π values, values greater than 0.01 were omitted (representing less than 0.25% of values). 95% highest density intervals for multi-locus FST are 0.50—0.95 and 0.51—0.95 when using ref Gd293 and ref Gd45 respectively. For π, the means for Pd-1 are 4.2 × 10−4 and 7.1 × 10−4, and the 95% highest density intervals for Pd-1 are 6.5 × 10−5 — 8.3 × 10−4 and 0 — 0.9 × 10−4 when using ref Gd293 and ref Gd45 respectively. For Pd-2, the means are 4.6 × 10−4 and 7.3 × 10−4, and the 95% highest density intervals for π are 2.1 × 10−4 — 1.5 × 10−3 and 1.9 × 10−4 — 1.5 × 10−3 when using reference genome Gd293 and reference genome Gd45 respectively. See section ‘Diversity and differentiation calculation’ in the Methods, and Table S10 for further information on methodology and isolates, respectively.

Extended Data Fig. 6 Boxplot of the pairwise distance between isolates for 11 full genomes, partitioned between intra- and inter-clade distance.

Intra-clade Pd-1 & Pd-2 divergence are coloured in blue (Pd-1) and orange (Pd-2) while inter-clade divergence is coloured in green (as per Fig. 2c). The black line represents the median while the lower and upper hinges correspond to the first and third quartiles and the whiskers extend to 1.5 times the interquartile range with data points beyond this range shown as outlier points.

Extended Data Fig. 7 Density of multi-locus FST across the genome when mapping the Pool-seq data on Gd293 reference genome (blue) or on Gd45 reference genome (orange).

Note that the distributions are extremely similar independently of the reference genome. Indeed, the median multi-locus FST values are 0.88 (95% highest density interval [hdi], 0.50—0.99) and 0.88 (95% hdi, 0.49—0.97) when using reference genome Gd293 and reference genome Gd45 respectively.

Extended Data Fig. 8 Genomic location of within-clade recombination breakpoints.

Based on the four-gamete test using SNPs from clade Pd-1 (a, c; n = 11 isolates) and Pd-2 (b, d, n = 7 isolates) when using Gd293 as reference genome (a, b), or Gd45 (c, d). When using Gd293 reference genome: 264 out of 331 and 130 out of 215 windows with at least two SNPs show evidence of recombination in Pd-1 and Pd-2 respectively. When using Gd45 reference genome: 261 out of 309 and 117 out of 210 windows with at least two SNPs show evidence of recombination in Pd-1 and Pd-2 respectively. The Φw test of recombination significantly rejected clonality (p = 0.0) in all four instances (within each of the two Pd clades, whether considering Gd45 or Gd293 as reference genome). Contiguous regions alternate between blue and red at break points estimated by the four-gamete test. The population recombination rate (r = 2 Ne r; see section ‘Analyses of recombination’ in the Methods) was estimated at 1.0 × 10−4 and 4.6 × 10−5 for Pd-1 and Pd-2, respectively. The recombination rate was lower in Pd-2 than in Pd-1, confirming the result obtained with the FGT test.

Extended Data Fig. 9 Population differentiation in Pd-2.

a, Estimation of effective migration surfaces based on 107 isolates from Pd-2 in Europe (all sites excluding Russia after clone correction). For visualization, results from eight independent runs (each with 8 million iterations and between 100 and 450 demes), were combined. Different shades of a colour represent variable levels of high (blue) or low (brown) effective migration rates. Sampling locations are represented by black dots. b, Distribution of distance between true and assigned site of each isolate (n = 279) for the observed and randomized datasets (binwidth = 100 km). The mean distance of assignment was 42.88 km with a median of 0 km. In the Null-DAPC with randomization of sites before assignment, the mean and median were 913.68 km and 774.05 km, respectively.

Supplementary information

Supplementary Information

This file includes Supplementary Notes 1 and 2, Supplementary Figs. 1 and 3–6 and Supplementary Tables 6–18 that support the main article with raw data and summaries. Supplementary references are also included.

Reporting Summary

Supplementary Fig. 2

This file presents a phylogenetic tree constructed from 18 microsatellites for all isolates, including clade information, bat species of origin and sampled substrates.

Peer Review File

Supplementary Tables

This file includes five sheets with raw data for genotypic analyses and associated metadata (Table S1), along with sample sizes, clades, and bat species by country (Table S2) and site (Table S3). Additionally, results from DAPC (Table S4) and SPASIBA (Table S5) analyses are included.

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Fischer, N.M., Dumville, I., Nabholz, B. et al. Two distinct host-specialized fungal species cause white-nose disease in bats. Nature (2025). https://doi.org/10.1038/s41586-025-09060-5

- Received: 15 November 2024

- Accepted: 23 April 2025

- Published: 28 May 2025

- DOI: https://doi.org/10.1038/s41586-025-09060-5

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Ekaterina K. Koltsova,

Vinicius F. Calsavara,

Simon R. V. Knott &

…

Dan Theodorescu

Nature (2025)

Abstract

Loss of the Y chromosome (LOY) in peripheral blood mononuclear cells (PBMCs) is the most common somatic alteration in men and is associated with higher mortality from epithelial cancers1,2,3. In tumours, epithelial LOY is also associated with poor survival4,5,6,7. This raises several fundamental questions, such as why LOY in PBMCs drives cancer mortality and whether there is a relationship between LOY in PBMCs, PBMC-derived immune cells and cancer cells (and, if so, what its consequences are). We sought to answer these questions through a comprehensive pan-cancer analysis of bulk and single-cell RNA sequencing data from 29 human tumour types, along with autochthonous and syngeneic mouse models. In human and mouse tumours, malignant epithelial cells had the highest LOY prevalence, yet LOY was also present in tumour stromal and immune cells, with LOY in malignant epithelial cells predicting LOY in benign cells. LOY also correlated between paired tumour and PBMC samples from patients. Among benign cells, LOY induced the strongest shift in CD4+ and CD8+ T cells, with both showing transcriptomic signatures of immunosuppression. Furthermore, the magnitude of LOY in epithelial cells, CD4+ T cells and CD8+ T cells independently predicts survival, with tumours exhibiting concurrent epithelial and T cell LOY having the worst outcomes. Here we establish a model that links LOY in immune cells to LOY in malignant cells, which may explain in part why LOY in PBMCs is associated with increased cancer mortality.

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Main

LOY in tumour cells is associated with poor prognosis4,5,6. This was found to be partially due to immune evasion from T cell exhaustion in some tumour types[7]. LOY in PBMCs is the most common somatic alteration in healthy men and has been associated with increased risk8,9 and mortality from cancer1,2,3. As PBMCs consist of immune cells including lymphocytes (T cells, B cells and natural killer (NK) cells), monocytes and dendritic cells[10], these data raise the question of whether LOY in PBMCs, PBMC-derived tumour immune cells and malignant cells are related, and whether this relationship drives increased cancer mortality.

Here we address this question through a comprehensive pan-cancer analysis of the genomic, histopathological, biological and clinical consequences of LOY in benign and malignant cells, using bulk and single-cell RNA sequencing (scRNA-seq) from human tumour types. Further, to gain additional insights that are difficult to obtain from human data and to generalize our findings, we also examine numerous data types from autochthonous, syngeneic and human xenograft models of cancer in mice.

Our data show that a substantial proportion of non-malignant cells, including both immune and non-immune cell subtypes in the tumour microenvironment (TME), have LOY. The presence of LOY in non-malignant cells is associated strongly with malignant epithelial cell LOY in human data and in mouse models. Furthermore, the proportion of LOY in PBMCs is correlated with that in immune cells and malignant cells in the TME. When present in benign cells, LOY induces the strongest phenotypic shift in CD4+ T and CD8+ T cells, with both cell types showing signs of immunosuppression. Clinically, the prognosis of patients with LOY epithelial tumours deteriorated further when LOY CD4+ T and CD8+ T cells were present in the TME, with tumour cells and T cells independently affecting prognosis.

Together, these findings document a relationship between LOY in PBMCs and in non-malignant and malignant cells in the TME. We also show that LOY in T cells in the TME can affect cancer mortality in patients. Given the prevalence of LOY in healthy older men, these findings may also have implications for tumour-infiltrating T cell (TIL) and chimeric antigen receptor (CAR) T cell therapies. More broadly, they lay the conceptual foundation for an experimentally tractable model that may explain in part why men with LOY in PBMCs are at higher risk for cancer death.

Tumour LOY and cohort characteristics

To investigate the impact of LOY at a pan-cancer level, we developed a ‘Y chromosome transcriptional signature’ (YchrS) that predicts DNA-based LOY classification (LOY/Y− (LOYDNA) versus wild type/Y+ (WTYDNA))[5]. Differential analysis of Y chromosome genes between LOYDNA and WTYDNA tumours from the Cancer Genome Atlas (TCGA) with available exome capture data identified 26 genes with lower expression in LOYDNA tumours, which we then filtered further using the Human Protein Atlas to focus on nine genes (DDX3Y, UTY, KDM5D, USP9Y, ZFY, RPS4Y1, TMSB4Y, EIF1AY and NLGN4Y) that are expressed stably at both the RNA and protein levels across several male tissues[11] (Fig. 1a and Extended Data Fig. 1a).

Fig. 1: Tumour LOY is associated with aetiologic, demographic and clinical characteristics.

a, Expression levels of 284 Y chromosome genes and clinical parameters in 4,127 tumours across 29 types, grouped by LOY status confirmed by DNA analysis[5]. Genes used to develop YchrS are indicated. b, YchrS score distribution across 29 cancer types (top) and corresponding proportions of LOYDNA and WTYDNA tumours (bottom). Box limits, quartiles; whiskers, data range within 1.5× interquartile range (dots, outliers); centre lines, median. Sample sizes: KIRP (kidney renal papillary cell carcinoma, n = 203), ESCA (oesophageal carcinoma, 115), KICH (37), LUSC (lung squamous cell carcinoma, 284), STAD (212), ACC (adrenocortical carcinoma, 28), PAAD (pancreatic adenocarcinoma, 92), HNSC (272), UVM (uveal melanoma, 44), CHOL (16), KIRC (kidney renal clear cell carcinoma, 316), DLBC (lymphoid neoplasm diffuse large B-cell lymphoma, 20), LUAD (lung adenocarcinoma, 183), READ (rectum adenocarcinoma, 63), COAD (colon adenocarcinoma, 155), BLCA (221), SKCM (skin cutaneous melanoma, 249), MESO (mesothelioma, 61), SARC (sarcoma, 85), LIHC (214), TGCT (testicular germ cell tumours, 120), LGG (brain lower grade glioma, 262), LAML (acute myeloid leukemia, 63), THCA (thyroid carcinoma, 127), PRAD (prostate adenocarcinoma, 457), GBM (glioblastoma multiforme, 89), THYM (thymoma, 58), PCPG (pheochromocytoma and paraganglioma, 73), BRCA (breast invasive carcinoma, 8). c, Area under the receiver operating characteristic curves comparison of YchrS and YwholeS in predicting LOYDNA and WTYDNA (AUC differences tested by two-sided roc.test in the pROC R package). d, YchrS score distribution by patient age group (years) (20–39, n = 423; 40–59, n = 1,475; 60–79, n = 1,994; older than 79, n = 194). Data are mean ± s.e.m.; two-sided Wilcoxon rank sum test. e, YchrS scores by ethnicity (white, n = 2,973; Asian, n = 289; Black or African American (Bl/AA), n = 236). Dots, individual patients; box presentation as in b. Two-sided Wilcoxon rank sum test. f, YchrS scores in HPV-negative (n = 168) versus HPV-positive (n = 27) patients with HNSC. Data presentation as in e. Two-sided Wilcoxon rank sum test. g, PCC of YchrS with CTA expression across 26 cancers. Bars and colours represent PCC in each cancer type; sizes represent −log_10 adjusted P value, with darker edges denoting significance (P < 0.05). Pearson correlation test; Benjamini–Hochberg correction. h, Kaplan–Meier survival curves for OS and DSS, stratified by YchrS (LOYBR versus WTYBR). Statistics calculated using a univariate Cox proportional hazard model. CI, confidence interval. HPV, human papilloma virus; LOYBR/WTYBR, LOY/WTY sample by bulk RNA-seq; PCC, Pearson correlation coefficient.

Mean YchrS scores are anticorrelated with LOYDNA frequency across cancer types, and, compared with a signature including all Y chromosome genes (YwholeS), YchrS was more predictive of LOYDNA (area under the curve (AUC)) 0.98 versus 0.90; P < 0.01; Fig. 1b,c and Extended Data Fig. 1b). We further validated the YchrS signature using data from 778 male cell lines in the Cancer Cell Line Encyclopedia (CCLE), integrating YchrS with copy number alteration (CNA) data from whole-exome sequencing (WES). Cell lines were classified into LOYBR (LOY identified using bulk RNA-seq evaluation) and WTYBR (WTY chromosome identified using bulk RNA-seq evaluation) groups based on the mean YchrS value. Examining CNAs, we found LOYBR cell lines have extremely low average Y chromosome-specific CNAs compared with WTYBR lines, indicating there was prominent LOY in the former group (Extended Data Fig. 1c–e).

Next, we used TCGA data to examine whether YchrS was associated with any specific clinical characteristics (Supplementary Table 1). Patient age is correlated inversely with YchrS (Fig. 1d), and we also found significant racial differences in the degree of tumour LOY, with Black/African American patients having the lowest YchrS (Fig. 1e). There is also an inverse association between YchrS and smoking: in head and neck squamous cell carcinoma (HNSC) and kidney chromophobe renal cell carcinoma (KICH), patients who have never smoked exhibit significantly higher YchrS scores than current or former smokers (Extended Data Fig. 2a). Human papilloma virus (HPV)-positive patients with HNSC have higher YchrS scores than HPV-negative patients (Fig. 1f), but no association is observed between YchrS and hepatitis B virus (HBV) or hepatitis C virus (HCV) in liver hepatocellular carcinoma (LIHC), or between YchrS and Epstein–Barr virus (EBV) in stomach adenocarcinoma (STAD; Extended Data Fig. 2b). We found a strong negative correlation between the YchrS and cancer-testis antigen (CTA) signatures (Fig. 1g)—proteins that are normally restricted to germ cells but often overexpressed in cancers and linked to worse survival[12].

We noted that the YchrS distribution appeared to be bimodal and that the mean served as a better separator of the two peaks while also performing better than the median in a receiver operating characteristic (ROC) analysis, with the mean falling closer to the top-left corner (Extended Data Fig. 2c,d). Given this, we next investigated whether mean YchrS could stratify patient outcomes. Indeed, patients with LOYBR have worse outcomes, including poorer overall survival (OS) and disease-specific survival (DSS; Fig. 1h and Supplementary Table 2). When analysing TCGA data, we found that the YchrS signature effectively stratifies pan-cancer survival outcomes in white and Asian patients (Extended Data Fig. 2e). However, this stratification was not significant in Black patients (Extended Data Fig. 2e). Similarly, at the genetic ancestry13,14 level, the YchrS signature stratifies pan-cancer survival in European, African-admixed and East Asian patients, but not in African patients (Extended Data Fig. 2f). Moreover, a multivariate Cox proportional hazards analysis found that the WTYBR/LOYBR classification is independent from genetic ancestry, self-reported race and tumour type in predicting OS, suggesting that it has a foundational role in cancer outcomes (Extended Data Table 1). Finally, we also found that higher expression of each of the nine YchrS genes predicted better OS and DSS in TCGA data (Extended Data Fig. 2g,h).

Cancer cell LOY drives tumour aggression

To investigate why men with tumour LOY have worse survival1,4,5,7, we examined genomic and transcriptional signatures in TCGA tumours based on their LOYBR classification (Supplementary Table 3). Tumour mutation burden (TMB) and tumour neoantigen burden (TNB) are associated with worse survival without immunotherapy but better immune checkpoint blockade (ICB) response15,16,17,18. We found that LOYBR tumours have elevated TMB and TNB at both the small insertion/deletion mutation (Indel) and single nucleotide variant (SNV) levels, as well as higher mutation rates (both non-silent and silent mutations; Extended Data Fig. 3a). YchrS scores are also correlated inversely with aneuploidy and ‘fraction altered’ across several cancers (Extended Data Fig. 3b). Moreover, homologous recombination defects (HRD, including in HRD score[19], loss of heterozygosity (LOH)[20], large-scale state transitions[21] and telomeric allelic imbalance (TAI)[22]) are higher in LOYBR tumours (Extended Data Fig. 3c). LOYBR tumours also have higher signature scores for homologous recombination, mismatch repair, DNA damage response and DNA repair.

In terms of phenotype, LOYBR tumours have higher stemness signature scores, encompassing DNA methylation-based stemness, epigenetically regulated DNA methylation-based stemness, differentially methylated probe-based stemness and enhancer element methylation-based stemness[23] (Extended Data Fig. 3d). LOYBR tumours also have higher signature scores for proliferation and activation of proliferation-related pathways, including DNA replication, cell cycle regulation, mitotic spindle, KRAS signalling, G2M checkpoint, E2F targets and apoptosis. They also have higher scores for oncogenesis-related pathways such as MYC, MTORC1 and epithelial-mesenchymal transition (EMT; Extended Data Fig. 3d). However, on the basis of gene expression, they demonstrate decreased activity of the Hedgehog signalling pathway.

Beyond the pathways listed above, LOYBR tumours have elevated angiogenesis, glycolysis and hypoxia pathway scores, suggesting a distinct TME (Extended Data Fig. 3e,f). Furthermore, they display diminished androgen response and elevated oestrogen response scores (Extended Data Fig. 3g). Finally, LOYBR tumours have increased expression of immune checkpoints and co-inhibitory genes such as CTLA4, PDCD1, TIGIT, LAG3, CD27, IL2RA, HAVCR2, CD40, CD274 and PDCD1LG2 (Extended Data Fig. 3h), indicating they have an immunosuppressive TME.

To study the implications of LOY at the cell-type level, we consolidated scRNA-seq data from 17 epithelial cancer types (Fig. 2a, Extended Data Fig. 4a–d and Supplementary Table 4). Because we found that YchrS often misclassified low-RNA-count cells as having LOY, we developed a machine-learning model to predict single-cell-level LOY or WT status in scRNA-seq data (LOYSCR and WTYSCR), by training a Random Forest model to predict male versus female cells from adjacent normal scRNA-seq samples using expression levels of the nine YchrS genes (Fig. 2b and Extended Data Fig. 5a–c). When the model was used to classify male tumour cells, those predicted to have LOY had minimal expression of Y chromosome genes, but had normal overall sequencing counts, and had normal expression of genes not known to be affected by LOY such as GAPDH, Y gene paralogues on the X chromosome and genes located on other chromosomes (Fig. 2c and Extended Data Fig. 5d–f). To further validate model accuracy, we analysed a colorectal cancer (CRC) dataset[24] comprised of 23 tumours with paired WES, bulk RNA-seq and scRNA-seq data. Here we found a strong positive correlation between average Y chromosome CNA (WES) and YchrS scores (bulk RNA), and negative correlations between LOYSCR cell proportions and the former two measures (Extended Data Fig. 5g).

Fig. 2: LOY in malignant epithelial cells promotes anaerobic metabolic reprogramming and reduces immunogenicity.

a, scRNA-seq database constituted by 497,055 cells from 105 male tumour samples, coloured by six principal cell types (left) and 12 cancer types (right). b, LOYSCR predictions for cells in a. c, Mean expression (colour) and percentage of expressing cells (size) for the nine Y chromosome genes used in the Random Forest model, their X chromosome paralogues and GAPDH across different cell types and LOYSCR status. Paralogue gene pairs share colours. d, Database of 157,029 male epithelial cells, coloured by cancer type. e, Normalized enrichment scores (NES) for the top five upregulated and 15 downregulated pathways in LOYSCR versus WTYSCR epithelial cells, categorized into four functional groups. Bar colour denotes −log_10-adjusted P value, calculated by gseGO in the clusterProfiler package. f, Glycolysis (left) and hypoxia (right) signatures in LOYSCR (n = 73,576) versus WTYSCR (n = 83,453) epithelial cells. Data are mean ± s.e.m.; two-sided Wilcoxon rank sum tests. g, MHC class I gene expression in LOYSCR versus WTYSCR epithelial cells. Dot plot (bottom) presentation as in c. Bars (top) show log_2FC, with colours indicating −log_10-adjusted P value, calculated with sc.tl.rank_genes_group in the scanpy package using two‐sided Wilcoxon rank‐sum test with Benjamini–Hochberg correction. h,i, Scores for proliferation-related (h) and genomic instability-related (i) pathways in LOYSCR versus WTYSCR epithelial cells. Dot colour represents mean scaled pathway score; dot size represents −log_10 P value (two-sided Wilcoxon rank sum tests, Benjamini–Hochberg correction). j, GSEA pathway enrichment analysis of CRISPR Y-KO and CRISPR Y-Scr MB49 cells. NES > 0 indicates pathway enrichment in Y-KO cells. Statistics calculated as in e. k, Expression of genes related to genomic instability, cell cycle and antigen presentation in CRISPR Y-Scr (n = 3) and CRISPR Y-KO (n = 3) cells. Dots, replicates (mean ± s.e.m.); Student’s t-tests. BER, base excision repair; HDR, homology-directed repair; NER, nucleotide excision repair; NHEJ, non-homologous DNA end joining.

Gene set enrichment analysis (GSEA) revealed that epithelial cells (Fig. 2d) with LOYSCR downregulate pathways associated with immune recognition (major histocompatibility complex (MHC)), aerobic energy metabolism (regulation of aerobic respiration, aerobic electron transport chain, mitochondrial ATP synthesis coupled electron transport, ATP synthesis coupled electron transport) and pathways related to protein synthesis (ribosomal large subunit biogenesis, cytoplasmic translation; Fig. 2e and Supplementary Table 5). LOYSCR epithelial cells also upregulate pathways associated with glycolysis (glycolytic process through fructose-6-phosphate and glucose catabolic process) and hypoxia, altogether indicating a metabolic shift consistent with tumorigenesis and progression25,26 (Fig. 2f). Finally, LOYSCR epithelial cells also have lowered expression of specific MHC class I and MHC class II genes (Fig. 2g and Extended Data Fig. 6a), indicating that these cells may have immune evasion capabilities27,28.

In terms of oncogenic pathway signature expression, LOYSCR epithelial cells have elevated KRAS, MYC, RAS, TGFβ and oestrogen response activity (Extended Data Fig. 6b). They also display upregulation of proliferation-related pathways, such as the cell cycle, DNA replication, mitotic spindle formation and the G2M checkpoint (Fig. 2h). As for genomic instability, LOYSCR epithelial cells express high levels of signatures corresponding to repair of double-strand breaks (DSB) through homologous recombination, non-homologous end joining and the p53 pathway (Fig. 2i). A signature of repair of single-strand breaks (SSB) via mismatch repair was also increased, while base excision repair and nucleotide excision repair pathways show less pronounced changes. Inferred copy number variation (inferCNV) analysis also indicated elevated CNAs in LOYSCR epithelial cells, particularly those from bladder cancers (BLCA) and cholangiocarcinoma (CHOL) (Extended Data Fig. 6c,d).

To develop a mouse model to study LOY mechanistically, we performed RNA-seq on fluorescence-activated cell sorting (FACS)-isolated CD45− cells from tumours generated by subcutaneous injection of CRISPR induced Y-KO (confirmed by WES to have lost the Y chromosome) (Extended Data Fig. 7a) or control cells that were infected with a scrambled CRISPR guide (Y-Scr). Excluding Y-linked genes to prevent bias, we identified LOY-associated changes through differential expression analysis (Supplementary Table 6). A LOY phenotype score, calculated by comparing scaled signature scores of genes that were upregulated or downregulated between Y-KO and Y-Scr CD45− tumour fractions, was higher in LOYBR BLCA samples than in WTYBR BLCA samples in the TCGA (Extended Data Fig. 7b), confirming the relevance of our mouse model findings. The LOY signature was also higher in LOYBR tumours from other cancer types (Extended Data Fig. 7c).

To study the genes altered in LOY cancer cells, we used in vitro[7] RNA-seq data on MB49 cells with CRISPR–Cas9-mediated selective elimination of the Y chromosome (Y-KO). GSEA pathway enrichment analysis of the Y-KO and Y-Scr in vitro RNA-seq data indicated that the former cells have increased genomic instability (DSB repair) and cell cycle regulation and reduced antigen presentation (Fig. 2j and Extended Data Fig. 7d,e) compared with Y-Scr cells. Specifically, key DNA repair genes are overexpressed in Y-KO cells (Fig. 2k), including Brca1 (homologous recombination for DSB repair), Atm (DSB repair), Msh2 (mismatch repair) and Tert (DNA repair and telomere maintenance). Genes that regulate chromosome segregation, such as Bub1b, Aurkb, Ccnb1 and Plk1, are also upregulated in Y-KO cells. Furthermore, cell cycle regulation-related genes (Cdk2, Ccna2, Ccnb1, Ccnd1, Mki67) show upregulation, whereas antigen-presentation genes (H2-D1, B2m) show downregulation (Fig. 2k). Finally, WES revealed higher numbers of large-scale chromosomal alterations in Y-KO cells. These include unique single-nucleotide polymorphisms (SNPs) and indels, which appear preferentially enriched in intronic and intergenic regions, suggesting that LOY may predispose cells to widespread genomic alterations rather than targeting specific genomic loci (Extended Data Fig. 7f–h).

LOY in cancer and benign cells correlate

In studying LOYSCR cell percentages in human scRNA-seq data, we noticed that, whereas LOYSCR frequency is the highest in epithelial cells, other cell types also show significant LOYSCR (Fig. 3a; top). This finding was supported when we examined a signature (YchrSmus) representing Y mouse chromosome genes (Ddx3y, Uty, Kdm5d and Usp9y) in scRNA-seq data from autochthonous mouse bladder tumours generated with the N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN) carcinogen protocol29,30 (Fig.3a (bottom) and Extended Data Fig. 8a–d). To determine whether there was a relationship between epithelial LOY and the prevalence of LOY in non-epithelial cells in the same tumour, we measured correlations of LOYSCR cell proportions between different cell types. Including all tumour types in our human scRNA-seq dataset, we found that the rates of LOYSCR in any one cell type are highly predictive of LOYSCR rates in other cells (Fig. 3b).

Fig. 3: LOY in malignant and benign cells shows concurrent enrichment in tumours.

a, LOYSCR and WTYSCR cell proportions across principal cell populations in human tumours (top) and mouse bladder tumours induced by BBN (bottom). b, Correlation of cell group proportions across human tumours, defined by cell type and LOYSCR status. Cell type proportions across samples are shown in yellow (x axis, left; y axis, top), while LOYSCR cell proportions are in blue (x axis, right; y axis, bottom). Within groups, cell types are ordered alphabetically. c, Proportion of Y chromosome signal-positive nuclei per tissue core (FISH experiment), categorized by CD45 status. Data are presented as mean ± s.e.m., normalized to the normal male Y chromosome signal. Values <0 or >1 arise from normalization and do not reflect absolute signal levels. d, Y chromosome gene (Kdm5d, Uty, Eif2s3y, Ddx3y) DNA levels in each cell line before injection, confirming LOY status. Data are mean ± s.e.m.; dots, biological and technical replicates. e, DNA qPCR analysis of Y chromosome genes in immune cells (CD45+) across MB49 WTY (Y+), MB49 LOY (Y−) and MB49 clone 5 (C.5) groups. Y+, n = 8; Y−, n = 15 for Kdm5d and n = 12 for other genes; MB49 C.5, n = 10. Data are mean ± s.e.m. Two-sided Wilcoxon rank sum tests. n = total number of all the technical duplicates obtained from all the samples used for the assay. f, DNA qPCR analysis of Y chromosome genes in epithelial (CD45−, TdTomato+, left) and immune cells (CD45+, right) from CRISPR Y-Scr and CRISPR Y-KO groups. For CD45− Y‐Scr, n = 3; Y‐KO, n = 6. For CD45+ Y‐Scr, n = 7; Y‐KO, n = 27 (Kdm5d) and n = 24 (other genes). Data are mean ± s.e.m. Two-sided Wilcoxon rank sum tests. n = total number of all the technical triplicates obtained from all the samples used for the assay. g, Genomic DNA qPCR was performed to assess Y chromosome genes (Kdm5d, Uty, Ssty1, Ssty2) in CD3+ T cells from PBMCs of mice bearing CRISPR Y-Scr or CRISPR Y-KO tumours. Fold change (FC) was calculated relative to the average in vivo values and normalized to both endogenous controls separately. Purple dots, normalization to Gapdh; yellow dots, normalization to Actb. Each dot denotes pooled samples from six independent tumours. Data are mean ± s.e.m. Two-sided Wilcoxon rank sum tests.

Source Data

We also used the inferCNV algorithm on the human scRNA-seq data to assess whether non-malignant cells in the TME, particularly macrophages, display tumour-like copy number variation (CNV) signatures, which could indicate the presence of ambient or phagocytosed nucleic acids from malignant cells. Epithelial cells displayed elevated CNV scores, confirming their distinct genomic profiles, whereas all examined immune cell types, including macrophages, CD8+ T cells and CD4+ T cells, showed minimal CNV signals (Extended Data Fig. 8e,f). These findings suggest that the LOY signals of benign cells are unlikely to be the result of debris from malignant cells. This also indicates that the YchrS scores used in the survival curves above (Fig. 1h) and DNA-based LOY evaluations[5] used to assess malignant cell LOY may reflect the overall LOY levels from a variety of cell types within the TME and not just cancer cells.

Next, we analysed orthogonal datasets to test for evidence of LOY in benign cells. scRNA-seq data on mouse BLCA (MB49) LOY tumours and normal bladder samples from male and female C57BL/6N mice as controls[31] revealed that benign cell types within LOY tumours display higher LOY levels compared with normal bladders (Extended Data Fig. 9a). Furthermore, scRNA-seq or single-nucleus RNA sequencing of CD45+ and CD45− sorted mouse hepatocellular carcinoma (HCC) tumour populations indicate that LOY is present in both malignant cells and immune cells whose sequencing libraries were prepared separately from the malignant cell fraction (Extended Data Fig. 9b,c). We also analysed available human scRNA-seq data from CD45-based FACS-sorted HNSC[32], CHOL[33] and BLCA[34] samples (Supplementary Table 7), which revealed that the CD45+ (immune) populations exhibit LOY (Extended Data Fig. 9d). Notably, we also observed a positive correlation between LOY proportions in CD45− and CD45+ cells from the same patients in these data (Extended Data Fig. 9e). scRNA-seq data from a CRC xenograft model[35], in which human LOY CRC cells were subcutaneously injected into immunocompromised mice, also displayed higher LOY levels in benign mouse cells in the TME compared with corresponding cells from normal male mouse tissues (Extended Data Fig. 9f). Further, on intact tissue sections, we combined a clinical grade fluorescence in situ hybridization (FISH) assay used for prenatal sex determination with CD45 immunohistochemistry (IHC) to examine LOY in 33 BLCA specimens and found that both CD45− and CD45+ cells in cancer samples exhibit LOY (Fig. 3c and Extended Data Fig. 9g).

As a final validation that stromal and immune cells in the TME display LOY, and to better investigate whether malignant cell LOY promotes LOY in benign populations, we injected lineage-related Y+ and LOY MB49 mouse BLCA cells subcutaneously into C57Bl/6N mice. We used three models for this experiment: (1) naturally occurring Y+ and LOY cells[7]; (2) a LOY clonal line; and (3) CRISPR Y-KO and CRISPR Y-Scr cells[7]. For each model, the DNA levels of four Y chromosome genes (Kdm5d, Uty, Eif2s3y and Ddx3y) were assessed, before subcutaneous injection of the cells into immune-competent mice (Fig. 3d). After formation, tumours were then disrupted mechanically, and cells separated on the basis of surface CD45 expression using double selection by magnetic beads and FACS (Supplementary Fig. 1). As the genetically engineered Y-KO and Y-Scr lines co-express a TdTomato fluorescent marker and the Cas9 gene, we also sorted these cells by TdTomato expression to minimize epithelial cell contamination in the immune cell fractions. We then used DNA qPCR to detect the Cas9 gene in each sorted population, confirming the purity of the different compartments (Extended Data Fig. 10a,b). As expected, only the TdTomato+ epithelial cells showed a high copy number of Cas9, whereas the immune compartments exhibited negligible Cas9 levels, indicating successful separation (Extended Data Fig. 10a,b). Next, the four Y chromosome genes used to validate LOY above were analysed via DNA qPCR to assess their presence in CD45+ immune and CD45− non-immune cell populations. There were lower DNA levels of Y chromosome genes in the CD45+ cells from tumours generated by LOY MB49 cells compared with those generated with Y+ lines (Fig. 3e,f). Furthermore, when this experiment was repeated with a sorting strategy that allowed T cell LOY to be examined specifically (CD45+CD11b−B220−CD3+TCRb+), we found that infiltrating CD3+TCRb+ T cells in Y-KO tumours had increased LOY compared with those from Y-Scr tumours (Extended Data Fig. 10c). However, no evidence of LOY was found in PBMCs from mice bearing Y+ or LOY tumours (Fig. 3g).

Cancer cell LOY correlates with PBMC LOY

Given the incidence of LOY in PBMCs and its relationship to increased cancer mortality[1], other key questions remain, such as how LOY levels in human PBMCs compare with those in tumour-infiltrating immune cells. If the latter exhibit higher LOY levels than PBMCs, that would suggest either preferential accumulation of PBMCs with LOY into tumours or tumour-induced LOY in Y+ PBMCs that have infiltrated the tumour. To begin answering this question, we examined a dataset from patients with renal cell carcinoma (RCC)[36] that included tumour and PBMC samples from the same patients. Our analysis indicated that T cells and myeloid cells in tumours exhibit significantly higher LOY proportions than their matched PBMC counterparts (Fig. 4a). In both the above and one additional HNSC dataset37,38 on CD45-sorted tumour cells and matched PBMCs, we confirmed that LOY proportions were correlated in the tumour and blood (Fig. 4b–d). We also analysed specific immune cell subtypes and found that tumour immune cells, especially T and B cells, had significantly higher LOY proportions than PBMCs (Fig. 4e). To put the mouse data in context, PBMCs from mice treated with the BBN protocol showed no evidence of LOY (Extended Data Fig. 10d).

Fig. 4: Malignant cell LOY levels correlate with those in human PBMCs.

a, LOYSCR cell proportions across different immune cell types in paired tumour and blood samples from seven patients with RCC. Each dot represents one patient. Paired Wilcoxon rank sum test was used for comparing the LOY proportions of specific cell types from tumour and blood samples, with sample pairs shown at the bottom right of each plot. b, Database of 79,858 cells from 19 patients with HNSC, coloured by five principal cell types. No cluster of epithelial cells was observed. c, LOYSCR and WTYSCR cell proportions of paired TIL and PBMC samples from patients in b. d, LOYSCR cell proportions in matched TIL and PBMC samples from patients in b; each dot represents an individual patient. e, LOYSCR cell proportions in different immune cell types in paired TIL and PBMC samples from 19 patients in b; each dot represents one patient. Paired Wilcoxon rank sum test was used to compare the LOY proportions of specific cell types from TIL and PBMC samples, with sample pairs shown at the bottom right of each plot. B, blood sample; T, tumour sample.

Cancer cell LOY affects benign TME cells

To examine the molecular profiles associated with LOY in non-epithelial cell populations in the TME, we conducted a cell-type-specific differential expression and pathway analysis (Fig. 5a–e and Supplementary Table 8). GSEA using the Gene Ontology database (https://www.geneontology.org/) found that LOY has the greatest impact on CD8+ and CD4+ T cells (Fig. 5a). LOYSCR CD4+ T cells have elevated expression of several immune checkpoint and co-stimulatory molecules (for example, IL2RA, HAVCR2, ICOS, TNFRSF9, TNFRSF4, CTLA4 and TIGIT (Fig. 5b)) as well as increased expression of signatures of exhaustion and the T regulatory (Treg) cell phenotype (Fig. 5c), while displaying lower expression of CD4+ T cell specific signatures of naive and T cell receptor (TCR) signalling. Conversely, LOYSCR CD8+ T cells show reduced expression of HAVCR2, BTLA, TNFRSF9, LAG3, CTLA4, PDCD1, TIGIT, ITGB2 and TOX, as well as signatures of cytotoxicity, TCR signalling and exhaustion (Fig. 5d,e). LOYSCR CD8+ T cells also increase expression of a naive CD8+ T cell signature, indicating these cells are not contributing to an active anti-tumour immune reaction.

Fig. 5: Impact of LOY in benign cells and its synergistic contribution with LOY malignant epithelium to survival of patients with cancer.

a, Summary of GSEA analysis between LOYSCR and WTYSCR cells across cell types. Colours represent NES of differentially expressed pathways (adjusted P < 0.05 in any one cell type), calculated by gseGO in the clusterProfiler package. b, Immune checkpoint gene expression in LOYSCR and WTYSCR CD4+ T cells. Dot colour represents mean scaled gene expression; dot size indicates the fraction of expressing cells. Bars above plots show log_2FC, coloured by −log_10 P value, calculated with sc.tl.rank_genes_group in the scanpy package using two‐sided Wilcoxon rank‐sum test with Benjamini–Hochberg correction. c, Scaled signatures scores of functional pathways in LOYSCR (n = 12,802) and WTYSCR (n = 49,206) CD4+ T cells. Data are mean ± s.e.m.; significance determined via two-sided Wilcoxon rank sum tests. d, Immune checkpoint gene expression in LOYSCR and WTYSCR CD8+ T cells. Data presentation as in b. e, Scaled signatures scores of functional pathways in LOYSCR (n = 19,159) and WTYSCR (n = 69,767) CD8+ T cells. Data presentation as in c. f, Kaplan–Meier survival curves for 4,127 TCGA male samples, stratified by LOYSCR and WTYSCR signature scores from CD4+ T cells, CD8+ T cells and epithelial cells. Statistics (P values, hazard ratio, CI) were calculated using a univariate Cox proportional hazards model. g, Kaplan–Meier survival curves for TCGA male samples based on combinatorial scores of LOYSCR epithelial and CD4+ or CD8+ T cell signatures. P values for pairwise comparisons through log-rank tests are shown at the bottom. h, Nomogram predicting less than 2-year, less than 5-year and less than 8-year OS probabilities based on LOYSCR CD8+ T cell, CD4+ T cell, epithelial signature score levels and age. Bottom, cumulative scores and survival estimates. Example: TCGA-02-2483 (43-year-old, high LOYSCR epithelial/CD8+ score, low CD4+ score) with a total score of 223, predicting 15% (less than 2 years), 28.6% (less than 5 years) and 39.1% (less than 8 years) mortality risk.

To further explore the relationship between T cell LOY and T cell exhaustion, we conducted in vitro experiments in which mouse primary T cells were subjected to prolonged activation with anti-CD3 and anti-CD28 coated plates over a 3-week period. Although LOY was not observed in T cells after extended culture and stimulation, markers of exhaustion such as Pdcd1, Tim3 and Ctla4 were induced, whereas the naive T cell marker Tcf7 was reduced as expected (Extended Data Fig. 10e–g). This indicates that chronic activation in vitro alone does not drive LOY in T cells. In addition, we also analysed publicly available transcriptomic datasets from Giles et al.[39], in which gp33-specific CD8+ T cells from TCR-transgenic mice were subjected to acute (LCMV Armstrong) and chronic (LCMV Clone 13) LCMV infections. Over the course of the infection, T cells in the chronic model exhibited upregulation of canonical exhaustion markers such as Tox, Pdcd1 and Ctla4 and downregulation of Tcf7 (Extended Data Fig. 10h). As in the previous experiment, expression levels of Y-linked genes (Uty, Kdm5d, Ddx3y, Usp9y) remained unchanged in exhausted T cells compared with their non-exhausted counterparts, indicating that chronic activation alone does not drive LOY in T cells (Extended Data Fig. 10i).

LOY in tumour and T cells worsens survival

Given that CD4+ T cells and CD8+ T cells are the benign TME cells most affected by LOY (Fig. 5a), we further examined their role in patient prognosis and whether combined loss of the Y chromosome in epithelial cells and in these immune cell types may have clinical implications. To this end, we developed cell-type-specific LOYSCR and WTYSCR gene signatures and conducted Kaplan–Meier survival analysis using TCGA data (Supplementary Table 9). This analysis suggests that LOYSCR CD4+ T cells, CD8+ T cells and epithelial cells are associated with patient survival outcomes, including OS and DSS (Fig. 5f and Extended Data Fig. 11a). Specifically, LOYSCR epithelial cell, CD4+ T cell and CD8+ T cell signature expression levels emerged as risk factors, whereas WTYSCR epithelial cell, CD4+ T cell and CD8+ T cell signature expression levels were identified as protective. Moreover, patients with high predicted levels of LOY epithelial cells in conjunction with elevated predicted levels of LOY CD4+ T or CD8+ T cells have the poorest survival outcomes, whereas those with low predicted levels of LOY epithelial and T cells show the most favourable survival (Fig. 5g).

To validate the findings above, we analysed a publicly available liver cancer scRNA-seq dataset that includes 94 male patients with matched survival data[40]. By calculating the proportions of LOYSCR epithelial, CD4+ and CD8+ T cells in each sample and correlating these proportions with long-term outcomes, we find patients with co-existent high proportions of LOYSCR in epithelial cells and in CD8+ or CD4+ T cells have the worst survival outcomes compared with patients with co-existent low proportions of LOYSCR in all cell types (Extended Data Fig. 11b,c). Patients with co-existent high epithelial and CD8+ T cell LOYSCR show worse outcomes than patients with high epithelial cell LOYSCR alone. This pattern was consistent with our bulk pan-cancer RNA-seq findings, reinforcing the link between LOYSCR epithelial and T cells and poor patient prognosis in cancer (Extended Data Fig. 11b,c).

We next conducted a multivariate Cox analysis to determine whether the LOYSCR epithelial, LOYSCR CD8+ T cell, and LOYSCR CD4+ T cell signatures independently contribute to patient outcomes. Owing to the unavailability of tumour stage, smoking and race-related data for certain cancer types, and recognizing the previously published correlation between age and LOY, only age was included in our analysis. As the LOYSCR epithelial signature shows non-linearity, we categorized patients into low or high LOYSCR epithelial, CD8+ T cell or CD4+ T cell signature groups, based on corresponding cut-offs determined from the Kaplan–Meier plot analysis above (Fig. 5f), where we observed that all variables are independent risk factors for poor OS (Extended Data Table 2).

Finally, we constructed a nomogram that incorporates age, as well as LOYSCR signature scores for epithelial, CD4+ and CD8+ T cells to provide ‘proof of principle’ for a potentially clinically beneficial tool that could forecast survival rates among patients with cancer (Fig. 5h). Each patient is assigned a score corresponding to each prognostic parameter, wherein a higher total score indicated a poorer prognosis for the patient. The nomogram offers three distinct probability scales for predicting mortality, in less than 2 years, 5 years or 8 years. Additionally, calibration plots demonstrated that the performance of the nomogram closely resembles that of an ideal model (Extended Data Fig. 11d).

Discussion

LOY in a portion of PBMCs is associated with increased risk of death from many cancer types1,2,3. This is particularly interesting because PBMCs comprise key immune cells such as T cells and B cells, monocytes/macrophages, NK cells and others that form part of the TME[10]. Several possible models could explain how this happens. For example, LOY in PBMCs may be a marker of genome damage due to environment (smoking, carcinogens and so on) or aging[41], with such damage driving both LOY and tumour progression in the malignant cells. This latter notion is supported by the finding that LOY in tumours is associated with poor prognosis4,5,6,7. It is also possible that LOY in PBMCs, and thus in key immune cells, may cause these cells to be less competent in restricting tumour growth. There is a paucity of evidence to support any of these models. Our study extends previous findings that LOY contributes to immune evasion, tumour growth and poorer survival in BLCA1,5,7 to a pan-cancer context. Our WES findings revealed that LOY cells harbour a higher burden of large-scale chromosomal alterations compared with WTY cells. This observation supports a model in which LOY could impair genomic maintenance systems, leading to a ‘vicious cycle’ of escalating chromosomal aberrations.

Our work found significant LOY in benign cells in the TME and that the prevalence of LOY in malignant epithelial cells is predictive of LOY in other cell types within the same tumour. Speculatively, if LOY tumour cells express specific chemokines, cytokines or adhesion molecules that preferentially attract LOY immune cells, this would explain how a relatively small percentage of PBMCs with LOY can have an impact on cancer death1,2,3. We also show evidence for another possible mechanism to explain this correlation, namely the ability of LOY epithelial cells to induce LOY in benign cells, including T cells. One mechanism underlying such a phenomenon could involve tumour-derived extracellular vehicles[42] containing pro-inflammatory or genome-altering cargo transferred from LOY cancer cells that stimulate molecular changes leading to LOY in neighbouring benign cells. Notably, the ‘preferential attraction’ and ‘induction’ models are not mutually exclusive and could be operating in the same tumour.

One consequence of LOY in T cells is a prominent increase in the Treg signature in CD4+ T cells. Treg cells are characterized by their immunosuppressive properties[43]. Our analysis also revealed that LOY CD8+ T cells exhibit decreased activation, TCR-signalling and cytotoxicity signatures, indicating reduced contribution to an immune response. These findings allow us to posit a speculative model in whichk tumours with high LOY are more aggressive owing to increased levels of dysfunctional LOY T cells and enrichment of LOY Treg cells. Finding LOY in T cells has also potentially important implications for TIL and CAR-T cell therapy in patients with cancer44,45. Given the features of LOY CD8+ T cells compared with those without this change, enrichment or selection of cells for such engineering without knowing their Y chromosome status may lead to variable clinical efficacy and confound clinical trial outcomes. This caveat would be particularly relevant in tumours with high levels of epithelial LOY, which have a proportionately high T cell LOY. This possible dual role of LOY, driving both genomic instability in tumour cells and functional exhaustion in immune cells, provides a plausible explanation for its association with shortened survival.

Our study has several limitations. There is an absence of relevant scRNA-seq data for certain tumour types, primarily because of incomplete gender information or quality control issues. Related to this, we were able to identify only one scRNA-seq dataset in which there were enough male patients with long-term follow up to allow analysis of the consequences of combinatorial LOY in epithelial and T cell compartments. Thus, we had to use bulk RNA-seq deconvolution based on cell-type-specific signatures of LOY to perform this analysis in a pan-cancer manner. Also, the nomogram that was developed here needs thorough validation on additional datasets and in specific tumour types. Although our RNA-seq analyses reveal significant changes in gene expression and pathway enrichment, it is important to recognize that these findings are correlative. Functional assays are essential to establish causative relationships between LOY and the observed biological processes. Laboratory experiments are also needed to substantiate the observed associations, particularly concerning the mechanisms by which interactions between epithelial cancer cells with LOY and benign cells in the TME lead to LOY in the latter.

In summary, our research lays a new conceptual framework that will serve to accelerate the nascent field of Y chromosome-focused cancer research. These results provide new insights such as showing the profound impact of epithelial LOY on reshaping the TME, the prevalence of LOY in various benign cell types in a tumour, the ability of LOY epithelial cells to promote LOY in benign cells (including T cells) and the consequences of LOY in benign cells. Finally, our study provides evidence that LOY in both epithelial and non-epithelial cells are independent prognostic markers of outcomes, demonstrating the impact of multicellular LOY in cancer.

Methods

TCGA data acquisition and processing

In this study, we used bulk RNA-seq, WES somatic mutation data, and clinical data (Supplementary Table 1) sourced from TCGA project (https://portal.gdc.cancer.gov/). The data matrices obtained from UCSC Xena (https://xena.ucsc.edu/) have been standardized, normalized and corrected for batch effects and platform differences. Additionally, mutation data generated by the PanCancer Atlas consortium (https://gdc.cancer.gov/about-data/publications/pancanatlas) were incorporated. A total of 29 tumour types and 4,127 male participants were included in our analysis. Survival outcome metrics, including OS, OS time, DSS and DSS time were calculated as in Liu et al.[46] (Supplementary Table 2).

Classification of LOY based on transcriptome data

We used the DESeq2 R package (v.1.42.1) to uncover Y chromosome gene expression differences between LOYDNA and WTYDNA[47]. Differentially expressed genes were called on the basis of a log_2FC cut-off of −1 and a −log_10-adjusted P value cut-off of 200. This gene set resulted in the LOY prediction signature. Subsequently, we conducted a gene set intersection analysis with a gene set in the male-specific region of the Y chromosome seen expressed across 24 human tissues. This analysis required that genes exceeded 0.1 reads per kilobase of transcript per million reads mapped per tissue or that they had presence in IHC data from the Human Protein Atlas RNA-seq[11]. This approach identified nine signature genes making up our Y chromosome signature (YchrS): DDX3Y, UTY, KDM5D, USP9Y, ZFY, RPS4Y1, TMSB4Y, EIF1AY and NLGN4Y. Based on single-sample GSEA (ssGSEA)[48] conducted with the GSVA[49] R package (v.1.44.5), we observed that patients with low levels of YchrS exhibited characteristics similar to those of people with LOYDNA, whereas those with high YchrS levels resembled people with an intact Y chromosome (WTYDNA). We partitioned all patients into low YchrS group (LOYBR) and high YchrS group (WTYBR) with a mean value cut-off. Additionally, a similar approach was applied to analyse the Ywhole signature (YwholeS), using a signature comprising all Y chromosome genes.

YchrS validation by using CCLE data

To validate the nine-gene Ychr Signature (YchrS), we downloaded the batch-corrected transcriptomic data and corresponding CNA data for 778 male cancer cell lines from the CCLE project (https://depmap.org/portal/ccle/). YchrS was calculated by the same method as for TCGA data. Cell lines with a YchrS lower than the mean value were categorized as LOYBR, otherwise they were called WTYBR. Average CNA for chromosome i in each cell line was calculated using the following formula to evaluate its integrity:

$${\rm{Average}}\;{{\rm{CNA}}}_{i}=\,\frac{{\sum }_{j=1}^{n}({\rm{estimated}}\;{\rm{segment}}\;{{\rm{CNA}}}_{j}\times {\rm{segment}}\;{{\rm{length}}}_{j})}{{\sum }_{j=1}^{n}{\rm{segment}}\;{{\rm{length}}}_{j}}$$

n is the total number of segments in chromosome i.

Genetic ancestry

Consensus ancestry for TCGA cases was obtained from ref. [14], determined by combining ancestry inference from five independent classification methods using SNP array and/or WES data. Only ancestries with more than 50 patients were included in the survival analysis, which spanned 3,893 patients: European (n = 3,319), East Asian (n = 286), African (n = 190) and African-admixed (n = 98).

Genomic instability and stemness features

Aneuploidy scores for TCGA cases were obtained from ref. [50]. Arm-level statistics were calculated using the GISTIC (v.2.0)[51] copy number significance software. These scores were derived by tallying the total number of amplified or deleted arms, collectively termed ‘altered’. Samples were initially categorized by tumour type, alteration type (amplification or deletion) and chromosome arm. Subsequently, samples were clustered on the basis of specific arm attributes, and arms were classified as altered if part of a cluster had a mean fraction altered of at least 80%. Intratumour heterogeneity used to generate DNA damage scores was determined by ABSOLUTE[52]. ABSOLUTE analysed segmentation data from Affymetrix SNP6.0 arrays and variant calls from the MC3 variant file.

We used two measures to assess HRD. The first was derived by Knijnenburg et al.[53] and quantifies HRD by aggregating separate metrics of genomic scarring: large (more than 15 Mb) non-arm-level regions with LOH[20], large-scale state transitions (breaks between adjacent segments of greater than 10 Mb, LST[21] and subtelomeric regions with allelic imbalance. The second measure, introduced by Telli et al.[19], incorporates LOH and TAI[22]. The HRD score for samples analysed via custom hybridization sequencing assay were computed using reads covering SNP positions[54]. The HRD score was determined as the unweighted sum of LOH, TAI and LST, represented as: HRD score = LOH + TAI + LST.

We screened samples using mRNA and DNA methylation profiles to compute four stemness scores: DNA methylation-based stemness score, epigenetically regulated DNA methylation-based stemness score, differentially methylated probe-based stemness score and enhancer elements/DNA methylation-based stemness score as outlined in previous studies[23].

Quantification of TNB and mutation data

Two methodologies were used to identify potential neoantigens arising from SNVs and Indels. For SNVs, somatic nonsynonymous coding SNVs were identified and minimal peptides encompassing mutation sites were extracted, followed by prediction of binding to autologous MHC using NetMHCpan v.3.0 (ref. [55]). On the other hand, Indel variants meeting specific criteria were extracted, and downstream protein sequences obtained to generate nine-mer peptides. These peptides were then evaluated for their ability to bind MHC molecules using the pVAC-Seq v.4.0.8 pipeline[56], also using NetMHCpan v.3.0. The mutation data, specifically encompassing missense mutations and nonsense mutations, were obtained from the PanCancer Atlas consortium and used in this analysis. The dataset (https://gdc.cancer.gov/about-data/publications/pancanatlas) underwent filtering, requiring mutation calls to be generated by two or more mutation callers (NCALLERS > 1).

Signature calculation for bulk-seq data

To study the consequences of LOY, a literature review was performed, and a variety of tumour-associated signatures gathered (Supplementary Table 3)57,58,59,60,61,62. Each signature was assessed using ssGSEA implemented through the GSVA R package[49] (v.1.44.5). Scoring methods are in the figure legends. Where information is not provided in the figure legends, the methodologies were documented in the respective citations.

To validate the CRISPR–Cas9-mediated Y-KO MB49 (Y-KO)-derived gene expression signature in human BLCA data, we first performed a differential expression analysis between the Y-KO and Y-Scr groups, which identified a robust set of upregulated and downregulated genes. We then calculated a Y phenotype score by dividing the signature scores of upregulated genes by those of downregulated genes, with all values scaled to the [0,1] interval.

Pan-cancer scRNA-seq data collection

For pan-cancer scRNA-seq data, transcriptome data of 346 samples from 251 people across 20 scRNA-seq datasets were obtained from public studies63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82, from which tumour samples were selected for later analysis. Accession numbers for each scRNA-seq dataset and detailed clinical information for patients and samples are summarized in Supplementary Table 4. To avoid issues related to platform heterogeneity, only datasets generated from 10x Genomics droplet based scRNA-seq datasets were included.

Quality control and preprocessing of pan-cancer scRNA-seq data

We performed quality control filtering and integration using the Scanpy package (v.1.9.5). Filtering was performed based on (1) confirmation that information was available for all nine Y signature genes, (2) cells had greater than 200 detected genes and (3) the mitochondrial gene counts were below 20%. Additional quality filters were applied to the data to remove barcodes that fell into any of the following categories: possible debris with too few genes expressed (less than 400) and too few unique molecular identifiers (UMIs) (less than 500), possibility of duplicate cells based on genes expressed (more than 5,500) or UMIs (more than 30,000). Count matrices and AnnData objects were then combined using a concatenate function, normalized to log transcripts per million units using the ‘sc.pp.normalize_total’ function, and log-transformed using the ‘sc.pp.log1p’ function. The normalized HNSC dataset from GSE150430 was then combined. Subsequently, non-tumour samples were removed, and we retained 1,030,968 high-quality cells and 14,689 genes for downstream analysis.

Combining and batch effect correction of pan-cancer scRNA-seq data

We used the scVI Python package (scvi-tools; v.1.0.4)[83] to integrate and batch correct scRNA-seq data. The scVI model was trained on the scRNA-seq data, considering samples as covariates. Following batch correction, the corrected data were integrated if multiple batches were present. The effectiveness of batch correction was evaluated by assessing the reduction in batch-specific variation while ensuring preservation of signal. Downstream analyses such as clustering, differential expression analysis or pathway enrichment analysis were performed on the batch-corrected data. Visualization of the results was achieved through two-dimensional UMAP plots, illustrating cell types, batches, datasets, gender, organs and cancer types.

Cell-type annotation of pan-cancer scRNA-seq data

To annotate cells, we used the scANVI algorithm from the scVI Python packages (scvi-tools; v.1.0.4) and the Luo et al.[71] dataset, where cells were pre-labelled as epithelium, endothelium, fibroblast, lymphocyte, myeloid or plasma cell. Subsequently, we performed unsupervised clustering of the scANVI latent space, and then used Leiden clustering, followed by cluster assignment to specific cell types. The scANVI model was configured with max_epochs = 20 and cluster labels were transferred and predicted, guided by a sample size of n_samples_per_label = 100. The integrated latent embedding provided by scANVI served as the basis for downstream analysis, with the dataset segregated by cell type for further investigation. To delineate cell subtypes within myeloid cell, lymphocyte and plasma cell populations, we merged corresponding AnnData and mitigated batch effects and other sources of variation using scVI. Subsequently, we predicted subtypes using Celltypist[84] (v.1.6.2), using ‘majority voting’ with default parameters and the pre-trained ‘Immune_All_Low.pkl’ model.

Annotating LOY cells in pan-cancer scRNA-seq data via Random Forest

We collected scRNA-seq pan-cancer data from paired tumour and adjacent normal samples and, following preprocessing, categorized cells from adjacent normal samples as: male cells as having wild-type Y (WTY) chromosomes and female cells as LOY cells. Employing the train_test_split function from sklearn.model_selection, we divided data from the normal samples into training and test sets, with a split ratio of 70% for training and 30% for testing. Next, we trained a Random Forest classifier model (RandomForestClassifier from the sklearn.ensemble Python package (v.1.3.2)) to differentiate LOY and WTY cells based on the expression levels of the nine Y chromosome genes used for the bulk RNA-seq classification of LOY samples. The performance of the model was assessed using the test set, achieving an accuracy score of 0.83.

To further validate LOY prediction by a Random Forest model, we obtained 23 samples from Liu et al.[24], including sequencing data for single-cell RNA (GSE245552), bulk RNA (GSE255163) and WES (GSE255165). YchrS and average CNA of Y chromosome were calculated using the same method as CCLE data. LOY at the single-cell level were predicted by the same Random Forest model used for the pan-cancer single-cell datasets. Owing to absence of RPS4Y1 expression, it was set as 0 for all the cells when applying the Random Forest model.

Genomic DNA isolation and WES

Genomic DNA was isolated from CRISPR Y-KO cells and CRISPR–Cas9-mediated Scr MB49 Y+ control (CRISPR Y-Scr) cell lines using the Invitrogen kit (catalogue no. K1820) following the manufacturer’s instructions. DNA samples for WES were submitted to Novogene, where library preparation, sequencing and bioinformatics analysis were conducted. The genomic DNA was fragmented randomly into short pieces, end-repaired, A-tailed and ligated with Illumina adaptors. Following PCR amplification, size selection and purification, hybridization capture of libraries was performed. Captured libraries were further enriched by PCR amplification and assessed for quality using Qubit and bioanalyzer systems. The libraries were pooled and sequenced using Illumina platforms with the PE150 strategy. Sequencing data were processed using the GATK best practices workflow. Paired-end clean reads were aligned to the mouse reference genome (GRCm39/mm39) using the Burrows–Wheeler aligner. The resulting alignments were sorted with Sambamba and duplicate reads were marked using Picard. The coverage and sequencing depth were computed, and SNP and INDEL variants were identified.

scRNA-seq of mouse tumour tissues

A total of 1 × 10^5 LOY MB49 cells (a LOY clonal line, MB49 clone 5 (C5)) were injected subcutaneously into C57Bl/6N mice (n = 4). Once the tumours reached 500 mm^3, they were removed and processed for scRNA-seq. The tumours were cut and transferred immediately to MACS C-tubes along with chilled DMEM and tumour dissociation enzymes for mouse (Miltenyi Biotech, catalogue no. 130-096-730). The dissociated tumours were then processed using ACK Lysis buffer (Gibco, catalogue no. 2537772), dead cell removal kit (Stem Cell Technologies, catalogue no. 17899) and EasySep Mouse CD45 Positive Selection Kit (Stem Cell Technologies, catalogue no. 18945). The CD45-enriched cells were next stained with Hashtag antibodies (TotalSeq-B0301 anti-mouse Hashtag 1 Antibody; TotalSeq-B0302 anti-mouse Hashtag 2 Antibody) and stained sequentially for CD45, CD3, CD4, CD8, CD11b, F4/80 and B220 along with 4′,6-diamidino-2-phenylindole (DAPI) and sorted for CD4+, CD8+ and CD11b+ and mixed into equal ratios. This mixture of highly enriched CD45+ cells was combined in a 1:1 ratio with live CD45− cells to make a final mixture that was sent for scRNA-seq.

The Cedars-Sinai Applied Genomics, Computation and Translational Core used 10x genomics 3′ scRNA-seq to sequence all samples to around 60% saturation. Samples were processed using Cell Ranger (10x genomics) based on a pre-mRNA GRCh38 reference. Since the samples were not hashed, potential doublet cells were identified using Scrublet applied to the filtered feature barcode matrices from Cell Ranger. Scrublet analysed the 10% most variable genes, identified by Scanpy package (v.1.9.5, scanpy.pp.highly_variable_genes function), predicting a 10% doublet rate, and then discarded doublet cells. Finally, nuclei with over 10% of their UMIs linked to mitochondrial genes or those in the top and bottom 5% based on the number of unique genes and UMI count were also removed.

One female and one male normal healthy C57BL/6N bladder sample from our previous study[31] were also analysed. Filtering was performed based on: (1) cells had more than 200 detected genes and (2) the mitochondrial gene counts were below 20%. Additional quality filters were applied to the data to remove barcodes that fell into any of the following categories: possible debris with too few genes expressed (less than 400) and too few UMIs (less than 500), possibility of duplicate cells based on genes expressed (more than 30,000) or UMIs (more than 5,500). We normalized the data to 1 × 10^4 counts per cell and calculated the base-10 logarithm. We used sc.pp.combat to remove the batch effect and applied subsequent downstream analyses on the batch-corrected data. To annotate cells, we used the scVI and scANVI algorithm from the scVI Python packages (scvi-tools).

Analysis for xenograft scRNA-seq datasets

To further investigate the ability of LOY malignant cells to induce LOY in benign cells in the TME, we downloaded public scRNA-seq datasets of human xenografts in immunocompromised mice from GSE254890 (ref. [35]) (SW480 cells from a male patient with CRC injected into male mice, 14 samples were incorporated as SW480 group), GSE110501 (ref. [85]; only eight samples from normal tissues were incorporated as male control) and GSE144236 (ref. [86]; A431, SCC and CAL27 injected into female mice, three samples incorporated as female control). Based on scRNA-seq and bulk RNA-seq data provided, SW480 cells used were LOY cells, which matched with the Y chromosome information obtained by our CCLE analysis on RNA-seq data. Mouse cells were selected either by tumour cell depletion using FITC conjugated antibodies, or by expression level of mouse genes compared with human genes. Potential debris (cells with fewer than 200 expressed genes or 400 UMIs) and possible doublets (cells with more than 8,500 expressed genes or 30,000 UMIs) were filtered out. After normalization, batch correction and cell type annotation were performed by scVI and scANVI. YchrS was calculated ‘scanpy.tl.score_genes’ using all Ychr gene expression.

Mouse HCC studies

Mice

MUP-uPA+ on C57BL/6 (ref. [87]) background 1 were bred and housed under specific pathogen-free conditions in an American Association for Accreditation of Laboratory Animal Care-approved barrier facility at Cedars-Sinai Medical Center. MUP-uPA+ mice were fed a Western diet (Teklad, catalogue no. TD.88137) for 8 months beginning at 8 weeks after birth. HCC development was analysed at 10 months of age.

Tissue preparation for mouse scRNA-seq and single-nuclei RNA-seq

Mice were killed by CO_2 inhalation and livers were perfused with PBS containing 2% of heparin (20 USP units ml−1) to remove traces of blood. For single-nucleus preparation, livers were isolated, tumour tissues were dissected and cut into 50 mg tumour tissue pieces for single-nucleus isolation and sequencing. Tissue was frozen in dry ice (solid CO_2) and kept in liquid nitrogen for long-term storage. For single-cell preparation, livers were isolated, and tumour tissues were dissected and digested using a cocktail of digestion enzymes containing collagenase I (450 U ml−1) (Sigma-Aldrich, catalogue no. C0130) and DNase I (120 U m−1) (Sigma-Aldrich, catalogue no. D4263) in PBS (with Ca2+/Mg2+) for 30 min at 37°C with gentle shaking at 150 rpm for liver immune cell isolation. After incubation, cell suspensions were filtered through a 70 µm cell strainer. Immune cells were enriched by density-gradient centrifugation over Percoll (GE Healthcare, catalogue no. 17-0891-01) at 1,000g for 25 min without brake (40% Percoll in RPMI-1640 and 80% Percoll in 5% FBS/PBS). Leukocyte rings on a border of gradient were collected, washed and stained. Immune cell suspensions were stained with Zombie Aqua (BioLegend, catalogue no. 423101) on ice for 15 min to exclude dead cells, incubated with Fc Block TruStain FcX (Clone 93, BioLegend, catalogue no. 101320, RRID: AB_1574975) for 20 min in 2% FBS-PBS and then stained with fluorochrome labelled antibody for 30 min on ice (CD45-PerCP/Cyanine5.5 (QA17A26, BioLegend, catalogue no. 157612; RRID, catalogue no. AB_2832558, 1:100)). All the flow cytometry antibodies were validated by the manufacturer (BioLegend) and were validated in the laboratory in single channel controls. Live, CD45+ cells were sorted by BD sorter Aria III using a 100-µm nozzle.

Mouse scRNA-seq

The single-cell droplets were generated with a Chromium X controller using Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (Dual Index) (10X Genomics, catalogue no. 1000268). Approximately 8,000 to 10,000 cells were collected to make cDNA at the single-cell level. cDNA amplification and library construction were performed according to the manufacturer’s instructions. All cDNA and libraries were quantified via Agilent Technologies 2100 Bioanalyzer. Gene expression libraries were sequenced at a targeted depth of 50,000 reads per cell on the Illumina Novaseq X plus (Illumina) at Novogene. Fastq files were obtained and then processed with Cell Ranger v.8.0.1 aligning to the mouse (mm10) 2020-A reference genome on 10x Genomics Cloud Analysis.

Mouse single-nucleus RNA sequencing

Single nuclei were isolated from frozen tumour tissues using the Chromium Nuclei Isolation Kit (10x Genomics, catalogue no. 1000493) according to the manufacturer’s instructions. cDNA amplification, library construction, sequencing and genome mapping were performed in the same way as for mouse scRNA-seq.

Validation of LOY effect using independent scRNA-seq data

Processed scRNA-seq data and corresponding cell type information for 116 liver cancer samples from 94 male patients[40] were analysed using our Random Forest model to predict LOY at the single-cell level. Only primary tumour or metastasis samples were included in the survival analysis.

InferCNV analysis

For the results presented in Extended Data Fig. 8e,f, CNVs in the scRNA-seq data were predicted using the InferCNV tool (https://github.com/broadinstitute/inferCNV; v.1.13.0), so that differences in the frequencies between the LOYSCR and WTYSCR epithelial cells of gains or deletions of entire chromosomes or large chromosomal segments could be identified. The algorithm was run with default parameters, using all WTYSCR stromal cells and immune cells as reference cells. For the results presented in Extended Data Fig. 6c,d, the analysis and figure were generated using Infercnvpy (https://github.com/icbi-lab/infercnvpy; v.0.4.5).

Functional signature calculation for scRNA-seq data

We used the ‘scanpy.tl.score_genes’ function from the Scanpy Python package (v.1.9.5) to compute gene set scores across cells. This function calculates gene scores for each gene listed62,88,89 in ‘gene_list’ across all cells stored within the dataset.

Sorting immune and epithelial cells from tumours and PBMCs

We used three distinct models to examine LOY in vivo: (1) naturally occurring Y+ and LOY (Y−) cells[7], (2) an established LOY clonal line (C5) and (3) CRISPR-engineered Y-KO and Y-Scr cells[7]. For the tumour challenge, 1 × 10^5 cells from each line—MB49 clone 5 (C5), Y− (LOY), Y+ (WTY), CRISPR Y-KO and CRISPR Y-Scr—were injected subcutaneously into the flanks of C57Bl/6N mice (n = 7 per group) obtained from Taconic Biosciences[7].The DNA levels of Y chromosome genes in each engineered cell type were checked for abundance before injection. Subcutaneous tumours from each group were disrupted mechanically, filtered through a 70 µm cell strainer (Corning, catalogue no. 352350) with RPMI-1640 cell culture grade media (Gibco, catalogue no. 11875093). ACK lysis buffer (Gibco, catalogue no. A1049201) was used to disrupt the infiltrating red blood cells. The single cells were then centrifuged and resuspended in 1× HBSS buffer (Gibco, catalogue no. 14025092) and counted in a cell counter machine. Tumours with viability of greater than 60% were used for subsequent procedures. EasySep Dead Cell Removal (Annexin V) Kit (Stem Cell Technologies, catalogue no. 17899) was used to increase the viability of each tumour and remove the dead cells. Viable cells were next processed with EasySep Mouse CD45 Positive Selection Kit (Stem Cell Technologies, catalogue no. 18945). The CD45 cells with the positive magnetic beads were purified and resuspended in EasySep Buffer (Stem Cell Technologies, catalogue no. 20144). These cells were next stained with CD45-Alexa Fluor 488 (BioLegend, catalogue no. 103122, 1:40) and Viability ghost dye Red 710 (Cytek, catalogue no. SKU 13-0871-T100) and sorted for only CD45+ cells in BD AriaIII machine. These highly purified CD45+ immune cells were next used for isolating high-quality genomic DNA with Monarch Nucleic Acid Purification Kits (NEB) and quantified. The flow through obtained after the CD45 positive selection, containing stromal, endothelial or other blood cells, was next processed with the EasySep Mouse Epithelial Cell Enrichment Kit II (Stem Cell Technologies, catalogue no. 19868), to isolate only the epithelial cells. The purity of pre- and post-isolation populations was assessed by antibody staining of random samples from each group, using the CD45 AF488 (BioLegend, catalogue no. 103122, 1:40) antibody and the BD Symphony A5, with results being analysed using Flow Jo software v.10.9.0.

For isolating T cells from these tumours (n = 6 each group) and their respective PBMCs, the following panel was used: CD45 (BioLegend, catalogue no. 103116; 1:40); CD3 (BD, catalogue no. 749276, 1:40); TCR β chain (TCRb, BioLegend, catalogue no. 109205, 1:40); CD11b (BD, catalogue no. 563553, 1:40); CD45R (BD, catalogue no. 612950, 1:40); Ghost dye Red 710 viability dye (Tonbo Biosciences, catalogue no. 13-0871-T100). Three compartments were sorted with BD FACSymphony S6 machine: epithelial (Ghost Red 710 dye−CD45− or CD45−TdTomato+); non-T immune cells (Ghost Red 710 dye−CD45+CD11b+B220+) and T cells (Ghost Red 710 dye−CD45+CD11b−B220−CD3+TCRb+). The sorting gates and FACS data are shown in Supplementary Fig. 1. Subsequently, the sorted cell compartments from tumours from the both groups were used for high-quality DNA extraction and subsequent qPCR. FC was calculated by comparing individual dCT values to respective average WTY(Y+) dCt or average Y SCR dCt values.

To get substantial amount of viable T cells for DNA isolation from subcutaneous tumours, the tumours of one group were mashed and pooled together and then proceeded for sorting. However, after sorting, the T cells were aliquoted randomly to four tubes and then processed for DNA isolation, to increase the efficacy of the result and decrease the chances of human error. Therefore, even if each group had six tumours pooled together, the graph in Extended Data Fig. 10c (right panel) shows four dots of one colour.

RNA-seq of CD45− cells from CRISPR Y-KO and Y-Scr tumours

Tumours derived from subcutaneous injection of CRISPR Y_KO and CRISPR Y-Scr cells into the flanks of C57BL/6N mice were removed and processed. Tumour-derived single-cell suspensions were subjected to FACS to isolate CD45− cells as described above. Total RNA was extracted from the isolated cells using the RNeasy Plus Mini Kit with gDNA Eliminator columns (Qiagen), following the manufacturer’s protocol. RNA sequencing library preparation and sequencing were performed by Novogene. Quality assessment of RNA-seq data, including sequence, alignment and quantification metrics, was conducted using FastQC v.0.12.1 and summarized with MultiQC v.1.13. Illumina Truseq adaptor, polyA and polyT sequences were trimmed using Trimmomatic v.0.39. The trimmed reads were aligned to the mouse genome (GRCm39/mm39) using STAR aligner v.2.5.2b, with parameters aligned to the ENCODE long RNA-seq pipeline recommendations (https://github.com/ENCODE-DCC/long-rna-seq-pipeline). Gene-level expression was quantified using featureCounts v.1.5.3, using Ensembl gene annotations (release v.113) for both alignment and quantification.

Genes with low expression were filtered out by applying a threshold of sum of estimated counts (from featureCounts) of at least ten. Differential gene expression analysis was performed on filtered estimated read counts using the R Bioconductor package DESeq2 v.1.42.1, using a generalized linear model with a negative binomial distribution. Differentially expressed genes were identified based on a Benjamini–Hochberg adjusted P value < 0.05 and FC cut-off (≥2 or ≤−2). To validate the Y-KO-derived gene phenotype signature in human cancer data, we first excluded all Y-linked genes to prevent bias in the differential expression results. We then calculated a LOY gene phenotype score by dividing the signature scores of up-regulated genes by those of down-regulated genes, with scores scaled to a [0,1] interval.

BBN treatment and PBMC isolation

For the BBN experiment, 8-week-old mice (n = 3–4 mice per timepoint) were administered with 0.5% BBN water for 12 weeks. After 12 weeks, BBN was replaced with standard tap water. Mice were killed at 2, 4, 12, 20 and 25 weeks using isoflurane. Subsequently, blood was drawn directly from the heart using an ethylenediaminetetraacetic acid (EDTA)-prewet insulin syringe and collected in an EDTA Microvette. From each mouse, a range of 700 to 1,000 µl of blood was collected. To enhance yield and DNA quality, mice were pooled in each group. Pooled samples were diluted 1:1 (v:v) with PBS-EDTA 0.1 M and then stratified on Histopaque-1077 Ficoll (Sigma, catalogue no. GE17-5446-02) at a ratio of 3:1 (v:v). Samples were centrifuged for 30 min at 400 rcf, with acceleration and break ramps set to 0 to allow gradual phase separation. The resulting PBMC ring was collected and washed once with PBS (1X). A subsequent centrifugation of 5 min at 400 rcf (with acceleration and break ramps set to maximum speed) resulted in a pellet that was processed for DNA extraction.

Tissue microarray

A human BLCA tissue microarray (TMA) with 33 unique cases comprised of triplicate cores from each patient tumour with an individual core size of 1 mm was used. The TMA was comprised of both male (n = 18) and female (n = 15) patients. Cores from female patients were used as controls for FISH.

XY FISH staining

The unstained TMA formalin-fixed paraffin-embedded sections (4 μm) were baked at 55–60 °C overnight before subjecting the slide to the following steps on the Abbott VP2000 FISH Instrument: (1) deparaffination of the slide using xylene, (2) pre-treatment of the slide using 0.2 N HCl and 1 M NaSCN, (3) protease treatment with pepsin, (4) fixation in 10% buffered formalin and, finally, (5) dehydration in series of increasing concentration (70%, 85% and 100%) ethanol. The slide was then subjected to a co-denaturation step using ThermoBrite (melting temperature 73 °C, 5 min; hybridization temperature 37 °C overnight). Post hybridization the slide was washed twice with SSC/0.3% NP-4 72 ± 1 °C for 2 min and twice with 2× SSC/0.3% NP-4 15–30 °C for 1 min. Finally, the slide was counterstained with nuclear DAPI before sealing with coverslip for visualization. The fluorescence tags were as follows: CEPX Xp11.1-q11.1—Spectrum Green (excitation, 497 nm; emission, 524 nm) CEPY Yp11.1-q11.3—Spectrum Orange (excitation, 559 nm; emission, 588 nm) and 18S RNA probe—Spectrum Aqua (excitation, 433 nm; emission, 480 nm).

IHC staining

Formalin-fixed paraffin-embedded samples were sectioned at 4-μm thickness onto Superfrost Plus slides (Fisher Scientific, catalogue no. 12-550-15). IHC staining was performed on the Ventana Discovery Ultra Instrument (Roche) as described[90]. After applying antigen retrieval buffer (CC1 (Tris, pH 8.0) (Roche Ventana, catalogue no. 950-124), CD45 primary antibody (Cell Signaling, catalogue no. 13917S, rabbit monoclonal) was applied. Primary antibody was diluted antibody dilution buffer (Roche Ventana, catalogue no. ADB250) for 1 h at room temperature: anti-CD45 (1:500). DISC anti-Rabbit HQ (Roche Ventana, catalogue no. 760-4815) was then applied for chromagen staining. After DAPI nuclear counterstain, the tissue area was covered coverslipped and mounted with ProLong antifade medium (Invitrogen, catalogue no. P36984).

Whole-slide imaging

XY FISH immunofluorescence slides were scanned using the ZEISS Axio Scan.Z1 whole slide scanner at ×20 magnification (Plan-Apochromat lens (numerical aperture, 0.8; M27)). TMA tissue cores were outlined with permanent marker pen on the coverslip for tissue detection. Region of scan was generated by a polygon tool and the raw focus map was generated using the ‘every-2-tiles’ strategy (z-range 150 µm, 21.04-µm step size) under ×5 lens (Fluor ×5/0.25 M27), while a fine focus map was generated using onion skin (z-range 100 µm, 2.06-µm step size, 0.1 density, 24 maximum number of points). Both focus maps were generated in the DAPI channel at 2% LED intensity, 50-ms exposure time. Spectrum Green (X probe) was excited at 495-nm wavelength (5% LED intensity, 150-ms exposure time) and detected at 500–550-nm bandwidth. Spectrum Orange (Y probe) was excited at 548-nm wavelength (25% LED intensity, 150 ms exposure time), detected at 570–640-nm bandwidth. Nuclear DAPI fluorescence was excited at 420-nm wavelength (2.5% LED intensity, 50-ms exposure time), detected at 430–470-nm bandwidth. Spectrum Aqua (18S RNA probe) was excited at 434-nm wavelength (5% LED intensity, 150-ms exposure time) and detected at 460–500-nm bandwidth. All signals were detected by Hamamatsu Orca Flash camera and 16-bit depth format image setting was applied. Standard IHC slide was imaged using the Leica Aperio AT2 whole slide scanner at ×20 magnification.

Image quantitation and analysis: HALO AI module

The whole slide image obtained from Zeiss Axio scan system was imported into HALO AI Module (Indica Labs), v.4.0.5107.318, for analysis. Upon import, the TMA image underwent segmentation to identify individual tissue cores. Missing cores were identified and removed from the analysis. The remaining cores were processed using the Nuclei Segmentation AI module, following the manufacturer’s guidelines. For AI training, seven distinct regions of interest were selected, comprising a total of 43 nuclei, to refine the Nuclei Segmentation plugin. Following segmentation, FISH analysis was conducted using the HALO FISH module, v.3.2.3, to detect nuclear signals. The resulting data were exported as.csv files, containing object-level (cell) data for subsequent analysis.

Validation of LOY correlation via FACS-sorted scRNA-seq data

To further validate the results and minimize the impact of mis-annotating LOY epithelial cells as LOY immune cells, we analysed 21 CD45-based FACS-sorted samples from three independent public scRNA-seq datasets (HNSC, GSE182227 (ref. [32]); CHOL, GSE171899 (ref. [33]) and BLCA, GSE211388 (ref. [34])). This collection included 12 matched CD45+ and CD45− samples from six tumours. Detailed dataset and sample information are provided in Supplementary Table 7. The same quality control, normalization and batch correction procedures described for the pan-cancer human scRNA-seq datasets were applied. CD45 expression was validated to ensure the purity of the FACS-selected samples. LOY cells were predicted using a Random Forest model.

To further validate the accumulation of LOY immune cells in tumours, we analysed two additional datasets: RCC dataset[36] (accession number, EGAD0001008030), comprising 14 matched tumour (also included in the pan-cancer datasets) and blood samples. HNSC dataset (accession number, GSE139324 (ref. [37])), containing 38 matched TIL and PBMC samples. Detailed dataset and sample information are presented in Supplementary Tables 4 and 7. The same quality control, normalization, batch correction and LOY cell prediction methods were applied. Additionally, cell types were identified using the scANVI algorithm and marker gene expression, with annotated pan-cancer scRNA-seq datasets serving as references.

Long-term in vitro T cell stimulation assay

Mouse CD8+ T cells were isolated from spleens of C57BL/6N mice using the Mouse CD8+ T Cell Isolation kit (Miltenyi Biotec, catalogue no. 130-104-075). CD8+ T cells were then activated by seeding onto six-well plates coated with 1 µg ml−1 anti-CD3 (clone 2C11, BioLegend, catalogue no. 100302) and anti-CD8 (clone 37.51, BioLegend, catalogue no. 102102). T cells were cultured in RPMI supplemented with 10% fetal calf serum, 1% penicillin–streptomycin, 50 µM 2-mercaptoethanol, 1% insulin transferrin sodium selenite as well as 0.1 µg ml−1 IL-7 (Peprotech, catalogue no. 217-17) and IL-2 (Peprotech, catalogue no. 212-12). T cells were kept at no higher than 1 × 10^6 ml−1 and transferred to new coated plates every 2–3 days to maintain activation. DNA and RNA was isolated at the timepoints indicated using the Monarch Genomic DNA Purification Kit (New England Biolabs, catalogue no. T3010L) or RNeasy Plus Mini Kit with gDNA Eliminator (Qiagen, catalogue no. 74134), respectively. CDNA was generated with Maxima H Minus cDNA Synthesis Master Mix (Thermo Fisher, catalogue no. M1662) followed by qPCR. Data were normalized to day 1.

Quantitative PCR

Genomic DNA (10 ng per reaction) was used to detect and quantify Y‐chromosome-specific genes associated with LOY signatures—Kdm5d, Uty, Eif2s3y, Ddx3y, Ssty1, Ssty2 and Zfy1/2—as well as to detect the presence of Cas9 in immune and epithelial compartments. Housekeeping genes (B2m, Gapdh and Actb) served as endogenous controls. All qPCR reactions were carried out using SYBR Green Universal Master Mix (Applied Biosystems, catalogue no. 4309155) on a Quant Studio 6 Flex Real-Time PCR system (Applied Biosystems). To assess Y chromosome copy number in various TIL immune cell populations (T cells and non-T cells), we compared cycle threshold (Ct) values from each sorted population against Ct values from wild-type male tumour cells, using the ΔΔCt method to calculate FC. For Cas9 detection, Ct values in sorted immune populations were compared with DNA from wild-type C57Bl/6N mice, which have no Cas9 integration, using Gapdh and Actb. Absence (or near-absence) of Cas9 amplification in immune compartments confirmed that these cells were not contaminated by, or had phagocytosed, genome-edited epithelial cells (Supplementary Fig. 1).

Details of primer sequences used in this manuscript for DNA qPCR are as follows:

Uty forward, TCACCCTCTTCAGCCATTTC; reverse, GTTCTCATGCCCTTCTCCATTA

Kdm5d forward, CTGCAAGATGGCTGCATTTC; reverse, TCGCTCCTCCTGTACCATAA

Ddx3y forward, AGCAGATTCAGTGGAGGATTT; reverse, CCACTACTTCGGCTGCTATT

Eif2s3y forward, CGTTATGCCGAGCAGATAGAA; reverse, CCGTCTCAGTAGGAAGTAGGA

Sssty1 forward, TGAAGAAGAGGAGGAGGAAGT; reverse, TTGGGTGACAGGCTCATTAC

Ssty2 forward, GGTGCCATTCTTACAGGACTAT; reverse, GTGGAGGTTACCTTCCTTGTAG

Zfy1/2 forward, CACCAAGAAAGCAGAACACATC; reverse, GCCTTTGTGTGAACGGAAATTA

Gapdh forward, AACAGCAACTCCCACTCTTC; reverse, CCTGTTGCTGTAGCCGTATT

Actb forward, ACCCAGGCATTGCTGACAGG; reverse, GGACAGTGAGGCCAGGATGG

B2m forward, ACAGTTCCACCCGCCTCACATT; reverse, TAGAAAGACCAGTCCTTGCTGAAG

Cas9 forward, CCCAAGAGGAACAGCGATAAG; reverse, CCACCACCAGCACAGAATAG

RNA primers:

Pdcd1 forward, CGGTTTCAAGGCATGGTCATTGG; reverse, TCAGAGTGTCGTCCTTGCTTCC

Havcr2 (Tim3) forward, GTATCCTGCAGCAGTAGGTC; reverse, CCCTGCAGTTACACTCTACC

Ctla4 forward, GTACCTCTGCAAGGTGGAACTC; reverse, CCAAAGGAGGAAGTCAGAATCCG

Tcf7 forward, CCTGCGGATATAGACAGCACTTC; reverse, TGTCCAGGTACACCAGATCCCA

Gapdh forward, ATGCCTCCTGCACCACCAACT; reverse, ATGGCATGGACTGTGGTCATGAGT

Actb forward, ACCCAGGCATTGCTGACAGG; reverse, GGACAGTGAGGCCAGGATGG.

FC values were calculated using the ΔΔCt method relative to the appropriate wild-type controls. Data were used to quantify the relative copy number of Y chromosome genes in tumour‐derived immune subsets or to confirm the absence of Cas9 in cells not genetically engineered.

Validation of T cell exhaustion impact on LOY via scRNA-seq

The publicly available processed scRNA-seq dataset from Giles et al.[39] was analysed to investigate the impact of chronic stimulation on the stability of the Y chromosome in T cells. This dataset included gp33-specific CD8+ T cells from TCR-transgenic mice subjected to acute (LCMV Armstrong) and chronic (LCMV Clone 13) LCMV infections. Chronic stimulation of T cells was validated through the upregulation of canonical exhaustion markers, including Tox, Pdcd1 and Ctla4, and the downregulation of Tcf7. To evaluate LOY, the expression levels of Y-linked genes (Uty, Kdm5d, Ddx3y, Usp9y) were analysed.

Cell-type-specific gene signatures for deconvolution analysis

To generate cell-type-specific gene signatures for LOYSCR/WTYSCR epithelial cells, CD4+ T cells, and CD8+ T cells from scRNA-seq data, we conducted differential analysis using the ‘sc.tl.rank_genes_groups’ function of the Scanpy (v.1.9.5) package. This analysis used Wilcoxon rank sum (Mann–Whitney U) tests to identify significant differences across each LOYSCR and WTYSCR cell type. We first identified genes significantly up-regulated (log_2FC > 1, adjusted P value < 0.05) in the LOYSCR versus WTYSCR epithelial cells, CD4+ T cells and CD8+ T cells separately. To establish unique signatures for each cell type, we then excluded genes expressed in more than 15% of any other LOYSCR or WTYSCR cell type. We then performed deconvolution on normalized bulk expression data from TCGA cancer types using the ssGSEA algorithm, evaluating the relationship of these signatures with patient outcome.

Survival analysis

Time-to-event outcomes were presented by using Kaplan–Meier curves and compared by using log-rank test or univariate Cox proportional hazards model (survival R package; v.3.5.8) as noted in each figure. Two multivariable Cox proportional hazards models were fitted, each as a function of (1) YchrS with ancestry and race as known risk factors and confounders; (2) LOY signatures scRNA-seq signatures, including LOYSCR CD4+ T cell, LOYSCR CD8+ T cell, and LOYSCR epithelial cell signatures, with age as known risk factors and confounders. Hazard ratio along with 95% CI based on multivariable Cox proportional hazards models were reported. The function surv_cutpoint from the survminer R package (v.0.4.9) was used to determine the optimal cut-off value for the LOYSCR signatures in relation to the time-to-event outcome. This method uses maximally selected rank statistics from the maxstat[91] R package (v.0.7-25) to classify two groups (low- versus high-risk) based on the optimal cut-point. Moreover, continuous variables included as covariates in the Cox proportional hazards model were evaluated[92]. Linearity was assessed to ensure model adequacy.

Development and validation of prognostic nomogram

According to clinical risk factors and risk scores of multivariate Cox regression coefficients for Extended Data Table 2, a prognostic nomogram was established using the ‘rms’ R software package (v.6.8-0), and the prediction accuracy of the nomogram was assessed using the calibration curve to evaluate the match between expected and observed events at 2, 5 and 8 years.

Ethics statement

Human samples

All human specimens and associated data were collected following protocols approved by the Institutional Review Board (IRB protocol 43021) at Cedars-Sinai Medical Center, adhering strictly to the Declaration of Helsinki guidelines. Written informed consent was obtained from each participant or their legal guardian. Detailed information regarding patient recruitment, sample collection (including TMA preparation), and data management can be found within IRB protocol 43021.

Animal studies

All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC protocol 8253) at Cedars-Sinai Medical Center. Experiments were conducted in strict accordance with the guidelines specified in the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. Protocol 8253 comprehensively describes animal housing conditions, care standards and experimental methodologies. All animal experiments were performed in accordance with institutional IACUC protocols. Mouse were housed under standard conditions with a 12-h light/12-h dark cycle, temperatures maintained between 68 °F and 79 °F (20–26 °C), and relative humidity between 30% and 70%.

Statistical analysis

All analyses were conducted using R (v.4.3.1) and Python (v.3.10.9). Before commencing tests, data were assessed for normality using the Kolmogorov–Smirnov test, followed by Bartlett or Levene tests to evaluate homogeneity of variances. For normally distributed variables, the unpaired Student’s t-test (Stats R package; v.4.3.1) was applied, whereas non-normally distributed variables were analysed using Wilcoxon rank sum tests. The correlation between paired variables was assessed using Spearman’s correlation coefficients. Data presentation and multiple comparison corrections are as stated in figure legends. Statistical significance was considered when P values were less than 0.05, including adjusted P values. Discovery analyses involving more than 20 comparisons underwent multiple testing correction using the p.adjust function in R or multipletests function in Python, applying the Benjamini–Hochberg method to control the false discovery rate at 0.05. To compare ROC curves, we used the roc.test function from the pROC R package (v.1.18.5). This allowed us to assess differences between AUC of YchrS and the AUC of YwholeS or LOYDNA. Python packages such as Scanpy (v.1.9.5), Pandas (v.2.0.0), Statsmodels (v.0.14.0), NumPy (v.1.24.2), Scipy (v.1.10.1), Matplotlib (v.3.8.0), Seaborn (v.0.11.2) and Sklearn (v.1.3.2), were used for data analysis. The R package ComplexHeatmap (v.2.11.1) was used to generate heat maps, and visualization was facilitated using ggplot2 (v.3.3.5), ggpubr (v.0.6.0), ggrepel (v.0.9.2), Statannot (v.0.6.0), Circlize (v.0.4.16), GseaVis (v.0.0.5), Enrichplot (v.1.22.0), GridExtra (v.2.3.0), Pheatmap(v.1.0.12) and DEGreport (v.1.38.5) R packages. For data manipulation, Readr (v.2.1.5), Readxl (v.1.4.3), Dplyr (v.1.1.4), Plyr (v.8.9), Apeglm (v.1.24.0), Tidyr (v.1.3.1), Tidyverse (v.2.0.0), Tibble (v. 3.2.1), Iranges (v.2.36.0), Biobase (v.2.62.0), BiocGenerics (v.0.48.1), Lubridate (v.1.9.3), Stringr (v.1.5.1) and AnnotationDbi (v.1.64.1) were used for analysis.

Reporting summary

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

Data availability

The data supporting the findings of this study are available within the Article and Supplementary Information. All bulk RNA-seq, single-cell and nuclear RNA-seq data and WES data generated for this study are available in the Gene Expression Omnibus (accession numbers GSE290112, GSE290113, GSE290114, GSE290115, GSE290587 and GSE290588). Source data are provided with this paper.

Code availability

Software packages, notebooks and scripts used for analysis are available at https://github.com/KnottLab/NATURE_LOY_Pancancer.

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Acknowledgements

We thank G. Martins and J. Dos Santos for their expert advice and assistance with FACS, H. Ye for interpreting the histological slides used in FISH immunofluorescence experiments and J. Xiao of the Theodorescu laboratory for generating MB49 subcutaneous xenografts and the corresponding scRNA-seq data. This work was supported in part by NIH grants CA278732 (to D.T.) and CA273925 (to E.K.K.), as well as by the NIH National Center for Advancing Translational Sciences through UCLA CTSI grant UL1 TR001881 (to V.F.C.).

Author information

Author notes

1. Xingyu Chen

Present address: Johns Hopkins University, Baltimore, MD, USA

2. Dan Theodorescu

Present address: University of Arizona Comprehensive Cancer Center, Tucson, AZ, USA

3. These authors contributed equally: Yiling Shen, Suhyeon Choi

Authors and Affiliations

1. Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Xingyu Chen, Yiling Shen, Hany A. Abdel-Hafiz, Mukta Basu, Lena Hoelzen, Martina Tufano, Saravana Kumar Kailasam Mani, Maryam Ranjpour & Dan Theodorescu

2. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Xingyu Chen, Yiling Shen, Suhyeon Choi, Jiani Zhu, Ekaterina K. Koltsova & Simon R. V. Knott

3. School of Medicine, Tsinghua University, Beijing, China

Yiling Shen

4. Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Jiani Zhu & Ekaterina K. Koltsova

5. Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

V. Krishnan Ramanujan

6. Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Vinicius F. Calsavara

7. Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Simon R. V. Knott

Contributions

D.T. had the original idea and conceptualized and initiated the study. X.C., S.R.V.K. and D.T. conceived the study design and analyses. X.C., Y.S., S.C., S.R.V.K. and D.T. developed the methodology. X.C., Y.S., S.C., M.B., S.K.K.M., J.Z., E.K.K., V.K.R. and V.F.C. acquired data, performed bioinformatics and biostatistical analyses and generated graphs. M.B., L.H., M.T., H.A.A.-H, V.K.R., M.R., E.K.K. and J.Z. carried out the wet-lab experiments, generated the data and performed the related statistical analyses. S.K.K.M. analysed the mouse HCC sequencing data and generated associated graphs. V.F.C. provided guidance on multivariate Cox analysis and nomogram development. X.C., S.R.V.K. and D.T. wrote the manuscript with input and specific sections from all authors. D.T. and S.R.V.K. supervised the study. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Simon R. V. Knott or Dan Theodorescu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Joshua Rubin 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 Development and Validation of the Y Chromosome Transcriptional Signature (YchrS).

a. Differential gene expression analysis for Y Chromosome genes comparing LOYDNA and WTYDNA male TCGA samples. Dot color represents -log_10 adjusted P-values for each gene. Vertical lines indicate log_2FC of -1 and 1 and the horizontal line indicates -log_10 adjusted P-values of 200. The 9 genes used for the RNA-based YchrS are annotated. b. YchrS scores in LOYDNA and WTYDNA TCGA tumor samples, where samples are separated by cancer types. Boxes denote the median (center line) and the first and third quartiles (lower and upper edges of the box). Whiskers extend to either the minimum or maximum values within 1.5 × the interquartile range (IQR). Any data points outside these whiskers are considered outliers. c. Distribution of chromosome segments and corresponding Copy Number Alteration (CNA). Each dot represents one chromosome segment in a particular cell line, with midpoint location shown in X axis and log_2 mean CNA shown in Y axis. The color of the dots represents the YchrS level of the samples. d. Average CNA at the chromosome level for the 625 CCLE male cell lines with available Y chromosome CNA, with the lines separated into WTYBR (n = 442) and LOYBR (n = 183) groups. Data presentation as in (a). Two‐sided Wilcoxon rank‐sum test. e. YchrS and corresponding average CNA of each chromosome for samples in (d). Each dot represents a cell line, with YchrS level shown by color.

Extended Data Fig. 2 Integration of Clinical Features with LOY Status.

a. YchrS scores for male TCGA tumors separated by smoking status (Current/Ever, n = 1,019; Never, n = 307) across nine cancer types. Boxes denote the median (center line) and the first and third quartiles (lower and upper edges of the box). Whiskers extend to either the minimum or maximum values within 1.5 × the interquartile range (IQR). Any data points outside these whiskers are considered outliers. Two‐sided Wilcoxon rank-sum tests. b. YchrS scores for Liver Hepatocellular Carcinoma (LIHC) samples, separated by Hepatitis B virus (HBV) or Hepatitis C virus (HCV) infection status (left), and YchrS scores for Stomach Adenocarcinoma (STAD) samples separated by Epstein-Barr Virus (EBV) infection status (right). Boxplot presentation as in (a). Two‐sided Wilcoxon rank-sum tests. c. The distribution of YchrS values (Black line) is shown with two proposed thresholds indicated by vertical dashed lines: mean (Red) and median (Light blue). d. AUROC analysis comparing specificity (X-axis, 0–1) against sensitivity (Y-axis, 0–1) for mean and median YchrS cut-offs used to detect LOY. Arrows indicate sensitivity and specificity for each threshold. e. Kaplan-Meier survival curves for Overall Survival (OS) in WTYBR and LOYBR tumors across three ethnicities from the TCGA pan-cancer dataset. Statistics were calculated using the univariate Cox-Proportional-Hazard (coxPH) model. CI, Confidence Interval. f. Kaplan-Meier survival curves for OS in WTYBR and LOYBR tumors across four genetic ancestries from the TCGA pan-cancer dataset. Statistics were calculated using the univariate coxPH model. g-h. Kaplan-Meier survival curves illustrating OS (g) and Disease-Specific Survival (DSS, h) for TCGA patients, grouped by expression levels of nine Y chromosome genes. Statistics were calculated using the univariate coxPH model.

Extended Data Fig. 3 Tumors with LOY have more genomic instability, enhanced proliferation and stemness, and an immunosuppressive microenvironment.

a. tumor mutational burden (TMB, left), tumor neoantigen burden (TNB, Indel and SNV, middle) and Mutation rates (silent and non-silent, right) in WTYBR (n = 2,685) and LOYBR (n = 1,442) tumors. Data are presented as mean ± s.e.m. Two‐sided Wilcoxon rank sum tests. Indel, Insertion/Deletion. SNV, Single Nucleotide Variant). b. Pearson correlation scores and corresponding P-values comparing YchrS scores and aneuploidy scores (left) and altered fractions (right) in 29 different cancer types. c. Comparison of different genomic instability pathway scores between WTYBR and LOYBR tumors. Dot colors represent mean scaled scores for each pathway in each group. The size of dots represent -log_10 P-values adjusted by Benjamini-Hochberg correction as calculated based on two‐sided Wilcoxon rank sum tests. HRD, Homologous Recombination Deficiency; LOH, Loss of Heterozygosity; LST, Large-scale State Transitions; TAI, Telomeric Allelic Imbalance. d. Comparison of different functional pathways scores between WTYBR and LOYBR tumors. The size of squares represents -log_10 P-values adjusted by Benjamini-Hochberg correction as assessed by two‐sided Wilcoxon rank sum tests. ENHss, Enhancer element Methylation-based Stemness; DMPss, Differentially Methylated Probe-based Stemness; EREG-METHss, Epigenetically Regulated DNA Methylation-based Stemness; DNAss, DNA methylation-based Stemness; EMT, Epithelial-Mesenchymal Transition. e. Comparison of angiogenesis signature scores between WTYBR (n = 2,685) and LOYBR (n = 1,442) tumors. Data are presented as mean ± s.e.m. and significance was assessed based on two‐sided Wilcoxon rank sum tests. f. Comparison of glycolysis (left) and hypoxia (right) signature scores between WTYBR (n = 2,685) and LOYBR (n = 1,442) tumors. Data are presented as mean ± s.e.m. and significance was assessed based on two‐sided Wilcoxon rank sum tests. g. Comparison of hormone-related pathway signature scores between WTYBR (n = 2,685) and LOYBR (n = 1,442) tumors. Data are presented as mean ± s.e.m. and significance was assessed based on two‐sided Wilcoxon rank sum tests. h. Comparison of expression levels for receptor and co-inhibitory genes between WTYBR and LOYBR tumors. Square color indicates mean scaled gene expression in each group and square size represents -log_10 P-values adjusted by Benjamini-Hochberg correction, as assessed by two‐sided Wilcoxon rank sum tests.

Extended Data Fig. 4 QC Framework and Cell Type Annotation for the Pan-Cancer scRNA-seq Dataset.

a. Schematic depicting tumor sources of the human scRNA-seq pan-cancer datasets developed and analyzed in this study. b. Quality control metrics for the pan-cancer scRNA-seq datasets with vertical lines indicating selection criteria: 400 <Gene counts <5500 (top) and 500 <Total counts <30,000 (center and bottom) for further analysis. c. Cells from the pan-cancer scRNA-seq data colored by original dataset (top left), organ (top right), sex information (bottom left) and cancer type (bottom center) and major cell type (bottom right). d. Expression levels of marker genes across the major cell types identified in the pan-cancer scRNA-seq datasets. Dot size indicates the proportion of expressing cells and color indicates mean expression levels.

Extended Data Fig. 5 Development and Validation of the Prediction Model for Determining LOYSCR Status in a Pan-Cancer scRNA-seq Dataset.

a. Schematic of the development of the Random Forest model utilized to predict LOYSCR status in individual cells. b. Expression levels of 9 YchrS signature genes in male and female samples. Dot size indicates the proportion of expressing cells and color indicates mean expression levels. c. Proportion of predicted LOYSCR in samples in 6 major cell types in normal female and male samples from the scRNA-seq datasets. Error bars represent the 95% Confidence Interval (CI) of the mean value. In female samples, the number of analyzed cells per cell type was as follows: B/Plasma cells (n = 23,797), Endothelial cells (n = 24,755), Epithelial cells (n = 174,909), Fibroblasts (n = 69,688), Myeloid cells (n = 69,306), and T/NK cells (n = 171,458). In male samples, the corresponding numbers were B/Plasma cells (n = 25,999), Endothelial cells (n = 22,908), Epithelial cells (n = 157,029), Fibroblasts (n = 31,122), Myeloid cells (n = 74,698), and T/NK cells (n = 185,299). d. Expression levels of the 9 YchrS signature genes in LOYSCR and WTYSCR cells from male tumor samples. Dot size indicates the proportion of expressing cells and color indicates mean expression levels. e. Total counts per cell type, where cells are separated by their cell type and LOYSCR and WTYSCR status. Violin plots show the full distribution of total read counts per cell type. The box plots overlaid within each violin denote the median (center line) and the first and third quartiles (lower and upper edges of the box). Whiskers extend to either the minimum or maximum values within 1.5 × the interquartile range (IQR). Any data points outside these whiskers (if shown) are considered outliers. The number of cells analyzed per category was as follows: in LOYSCR samples, B/Plasma cells (n = 5,935), Endothelial cells (n = 7,975), Epithelial cells (n = 73,576), Fibroblasts (n = 9,950), Myeloid cells (n = 23,292), and T/NK cells (n = 40,675); in WTYSCR samples, B/Plasma cells (n = 20,064), Endothelial cells (n = 14,933), Epithelial cells (n = 83,453), Fibroblasts (n = 21,172), Myeloid cells (n = 51,406), and T/NK cells (n = 144,624). f. Scores for chromosome-specific signatures (with each signature comprising all genes from the corresponding chromosome) for male cells, where cells are separated by their LOYSCR and WTYSCR status. Data are presented as mean values ± 95% Confidence Interval (CI). The number of cells analyzed per category was as follows: in LOYSCR samples, B/Plasma cells (n = 5,935), Endothelial cells (n = 7,975), Epithelial cells (n = 73,576), Fibroblasts (n = 9,950), Myeloid cells (n = 23,292), and T/NK cells (n = 40,675); in WTYSCR samples, B/Plasma cells (n = 20,064), Endothelial cells (n = 14,933), Epithelial cells (n = 83,453), Fibroblasts (n = 21,172), Myeloid cells (n = 51,406), and T/NK cells (n = 144,624). g. Correlations between YchrS from bulk RNA-seq data and average Y chromosome Copy Number Alteration (CNA) from whole-exome sequencing (WES) data (left), the correlation between YchrS and the proportion of LOYSCR cells identified through single-cell RNA sequencing (middle), and the correlation between the proportion of LOYSCR cells and average Ychr CNA (right). Male and female samples are represented by blue and red dots, respectively, with each dot representing a sample. Lines show the linear regression results for male and female samples, with shaded regions showing the 95% confidence interval. R, Pearson correlation coefficient; P-value is calculated by Pearson correlation test.

Extended Data Fig. 6 Characteristics of LOYSCR Epithelial cells.

a. Expression levels of Major Histocompatibility Complex (MHC) class II genes in LOYSCR and WTYSCR epithelial cells. Dot sizes (bottom) indicate the proportion of expressing cells, colored by mean standardized expression levels. Bars (top) indicate the log_2Fold Change (FC) between in LOYSCR and WTYSCR epithelial cells, where color indicates the corresponding -log_10 adjusted P-value, calculated via sc.tl.rank_genes_group in scanpy package using two‐sided Wilcoxon rank‐sum test with Benjamini-Hochberg correction. b. Scores for pathways related to hormone and oncogenesis in LOYSCR and WTYSCR epithelial cells. Dot color indicates the mean pathway score, while dot size indicates the -log_10 adjusted P-value of the difference between cell populations, as calculated by two‐sided Wilcoxon rank sum tests with Benjamini/Hochberg correction. c. Mean Copy Number Variation (CNV) scores of LOYSCR and WTYSCR epithelial cells, as estimated by the inferCNVpy algorithm and where the significance of the difference was calculated based on the two‐sided Wilcoxon rank-sum test. d. Distribution of CNV scores as obtained by the inferCNVpy algorithm for LOYSCR and WTYSCR epithelial cells for 12 cancer types.

Extended Data Fig. 7 Malignant epithelial cells harboring LOY exhibited higher genomic instability.

a. Sequencing depth of Whole Exome Sequencing (WES) data from CRISPR Y-KO and Y-Scr MB49 cells. b. LOY gene phenotype scores in LOYBR (n = 74) versus WTYBR (n = 147) TCGA Bladder Cancer (BLCA) samples. Data are presented as mean ± s.e.m. and significance was assessed based on two‐sided Wilcoxon rank sum tests. c. LOY gene phenotype scores across multiple cancer types in the TCGA dataset. Significant LOY gene phenotype trends were observed in Lung Adenocarcinoma (LUAD), Pancreatic Adenocarcinoma (PAAD), and Stomach Adenocarcinoma (STAD), with variable significance across other cancers. ACC (LOYBR, n = 14; WTYBR, n = 14), BLCA (LOYBR, n = 74; WTYBR, n = 147), BRCA (LOYBR, n = 0; WTYBR, n = 8), CHOL (LOYBR, n = 6; WTYBR, n = 10), COAD (LOYBR, n = 70; WTYBR, n = 85), DLBC (LOYBR, n = 7; WTYBR, n = 13), ESCA (LOYBR, n = 82; WTYBR, n = 33), GBM (LOYBR, n = 4; WTYBR, n = 85), HNSC (LOYBR, n = 143; WTYBR, n = 129), KICH (LOYBR, n = 20; WTYBR, n = 17), KIRC (LOYBR, n = 132; WTYBR, n = 184), KIRP (LOYBR, n = 168; WTYBR, n = 35), LAML (LOYBR, n = 4; WTYBR, n = 59), LGG (LOYBR, n = 16; WTYBR, n = 246), LIHC (LOYBR, n = 64; WTYBR, n = 150), LUAD (LOYBR, n = 77; WTYBR, n = 106), LUSC (LOYBR, n = 151; WTYBR, n = 133), MESO (LOYBR, n = 17; WTYBR, n = 44), PAAD (LOYBR, n = 44; WTYBR, n = 48), PCPG (LOYBR, n = 1; WTYBR, n = 72), PRAD (LOYBR, n = 9; WTYBR, n = 448), READ (LOYBR, n = 33; WTYBR, n = 30), SARC (LOYBR, n = 22; WTYBR, n = 63), SKCM (LOYBR, n = 93; WTYBR, n = 156), STAD (LOYBR, n = 137; WTYBR, n = 75), TGCT (LOYBR, n = 26; WTYBR, n = 94), THCA (LOYBR, n = 4; WTYBR, n = 123), THYM (LOYBR, n = 2; WTYBR, n = 56), UVM (LOYBR, n = 22; WTYBR, n = 22), d-e. Pathway enrichment of the running enrichment scores (top), and positions of pathway genes ordered by log fold change (bottom) in Gene Set Enrichment Analysis (GSEA) comparing CRISPR Y-KO MB49 cells with CRISPR Y-Scr MB49 cells. NES, normalized enrichment score. NES > 0 indicate the pathway is enriched in Y-KO cells, otherwise in Y-Scr cells. f-g. Stacked bar plots showing counts of Single Nucleotide Polymorphisms (SNPs, f) and Insertion and Deletions (Indels, g) in different genomic regions. CDS, coding DNA sequence. h. Ratio of mutation counts (SNPs and Indels) between CRISPR Y-KO cells and Y-Scr MB49 cells across different chromosomes.

Extended Data Fig. 8 Validation of the BBN-induced bladder cancer mouse model through scRNA-seq analysis, aligned with human scRNA-seq results.

a. Single cell RNA sequencing data of 85,759 cells from male and female tumors induced by N-butyl-N-(4-hydroxybutyl)-nitrosamine, where cells are colored by sample (FB prefix indicates female and MB indicates male), cell type and male/female status. b. Expression levels of the 4 genes used as the mouse chrY signature (YchrSmus) genes in male and female cells from the tumors shown in (a). Dot size indicates the proportion of cells expressing the gene and color indicates the mean expression level. c. Expression levels of 4 YchrSmus genes in male and female cells from the tumors shown in (a), where cells are also separated by cell type. Dot size indicates the proportion of cells expressing the gene and color indicates the mean expression level. d. Distribution of YchrSmus across all cells (left), male cells (middle), and female cells (right). e-f. Inferred Copy Number Variation (CNV) profiles across chromosomes (columns) in single cells (rows), grouped by major cell types (colored bars on the left). (e) Cells classified as LOYSCR. (f) Cells as WTYSCR. Each column spans chromosome 1 through 22, arranged along the horizontal axis. Red indicates relative copy number gains, and blue indicates losses. The color scale (−0.2 to 0.2) reflects the smoothed expression deviations estimated by inferCNV. Differences in chromosomal signal patterns highlight potential aneuploidies and region‐specific alterations linked to LOY status.

Extended Data Fig. 9 The impact of LOY on the benign cells in the TME.

a. Mean YchrSmus score across various cell types. Displayed are scRNA-seq data for normal bladder tissues from female (left) and male (middle) C57BL/6 N mice; tumors from C57BL/6 N mice following subcutaneous inoculation with LOY MB49 mouse bladder cancer cell lines (right). Error bars represent the 95% confidence interval (CI) of the mean value, estimated by bootstrap resampling. Sample sizes (n) for each cell type were as follows: Normal Bladder (Female): Epithelium (n = 443), Fibroblast (n = 6,361), Endothelium (n = 427), CD8+ T cell (n = 51), Myeloid cell (n = 350), Other immune cell (n = 47), CD4+ T cell (n = 52); Normal Bladder (Male): Epithelium (n = 976), Fibroblast (n = 10,338), Endothelium (n = 612), CD8+ T cell (n = 57), Myeloid cell (n = 640), Other immune cell (n = 89), CD4+ T cell (n = 68); LOY MB49 tumor: Epithelium (n = 178), Fibroblast (n = 97), Endothelium (n = 23), CD8+ T cell (n = 1,600), Myeloid cell (n = 2,447), Other immune cell (n = 2,242), CD4+ T cell (n = 1,335). b-c. Analysis of LOY percentage in cell populations from 6 mouse hepatocellular carcinoma (HCC) tumor samples. (b) LOY percentages of sorted CD45+ immune cell populations evaluated by scRNAseq from 3 independent samples. (c) LOY percentage in 3 independent whole tumor cell populations evaluated by snRNAseq. d. Proportion of LOYSCR and WTYSCR in individual CD45+ samples from the HNSC[32] (left), CHOL[33] (middle), and BLCA[34] datasets (right). HNSC, Head and Neck Squamous Cell Carcinoma. CHOL, Cholangiocarcinoma. BLCA, Bladder cancer. e. LOYSCR cell proportion in matched CD45+ and CD45− samples in HNSC and BLCA datasets. Each dot represents one patient. f. Mean scaled YchrSmus values across 3 different cell types from a xenograft scRNA-seq dataset—endothelium, fibroblast, and myeloid cells—for 3 groups: Female Control (orange), Group SW480 (male CRC cells, red), Male Control (blue). Error bars represent the 95% confidence interval (CI) of the mean value, estimated by bootstrap resampling. Sample sizes (n) for each group and cell type were as follows: Female Control: Endothelium (n = 4), Fibroblast (n = 95), Myeloid cell (n = 1,068); Group SW480: Endothelium (n = 654), Fibroblast (n = 3,871), Myeloid cell (n = 6,204); Male Control: Endothelium (n = 2,946), Fibroblast (n = 4,490), Myeloid cell (n = 432). g. Representative FISH images selected from the human patient TMA cores. Smaller regions (80 micron x 80 micron) of images from the normal male and LOY cancer male patient tissue microarray cores (original diameter ~1 mm) are displayed corresponding to CD45-high and CD45-low regions as shown. CD45 signals were probed by immunohistochemistry staining and bright field imaging. Pseudocolor immunofluorescence images of nuclear DAPI, X chromosome FISH probe, Y chromosome FISH probe and the control 18sRNA FISH probe are shown as indicated in the figure. Scale bar = 20um. These data are representative of male (n = 18) staining and imaging experiments that yielded similar results.

Extended Data Fig. 10 Validation of Y chromosome loss in MB49 tumor models and immune cells.

a-b. Genomic DNA (10 ng per reaction) was isolated from sorted epithelial cells, non–T cells, and T cells derived from CRISPR‐engineered (Y-Scr or Y-KO) MB49 tumors. Wild‐type (WT) C57Bl/6 N genomic DNA (lacking Cas9) served as the reference in the ΔΔCt analysis. (a) Ct values were normalized to Gapdh and plotted as fold change relative to WT. (b) Ct values were normalized to Actb and similarly plotted as fold change over WT. Error bars represent mean ± s.e.m. from 4 biological replicates in each group. c. DNA qPCR analysis of Y chromosome genes in TdTomato+ CD45- epithelial cells (left) and CD3+ T cells (right) from CRISPR Y-Scr and CRISPR Y-KO groups. Fold changes normalized to Gapdh (purple dots) and Actb (yellow dots). P-values from two-way ANOVA with Fisher’s LSD test and Geisser-Greenhouse correction. Error bars represent mean ± s.e.m.; Each dot denotes pooled samples from six independent tumors. Six independent tumors were mashed and pooled together for substantial number of T cells isolation and then randomly aliquoted to 4 tubes to increase the efficacy of the result and reduce human error. Each dot of one color represent data from one such tube. d. DNA qPCR analysis of Y chromosome genes (Kdm5d, Uty, Eif2s3y, and Ddx3y) in PBMCs extracted from BBN-treated mice. Each dot represents the pooled DNA sample from 3-4 mice. On X axis: the time points of samples collection, along with a timeline of BBN administration and bladder tumor onset; on the Y axis the fold change expressed as relative gene expression level compared to untreated ctrl at initiation of BBN. e. Changes in DNA levels of Y chromosome genes (Ddx3y, Kdm5d, Zfy1/2, Ssty1, Ssty2) upon long term activation of primary mouse T cells in vitro. qPCR data normalized to housekeeping genes Actin (left) or B2m (right) relative to day 1 after mouse T cell isolation. Mean ± SD, n = 4. SD, Standard Deviation. f-g. Expression changes of T cell exhaustion markers (Ctla4, Pdcd1, Tcf7, Tim3) during long term activation of primary mouse T cells in vitro. Expression normalized to housekeeping genes Actin (f) or Gapdh (g) are shown relative to day 1 post T cell isolation. Mean ± SD, n = 4. h-i. Mean expression of exhaustion (h) and Y chromosome genes (i) in Naïve P14 CD8+ T cells at baseline (Day 0) and compared to at Days 8, 15, and 30 post-infection under acute (LCMV Armstrong; solid lines) or chronic (LCMV Clone 13; dashed lines) conditions. Data were extracted from publicly available transcriptomic datasets[39].

Source Data

Extended Data Fig. 11 Patient Survival and Calibration of Nomogram.

a. Kaplan-Meier Disease-Specific Survival (DSS) curves for TCGA samples with high or low signature scores for LOYSCR and WTYSCR CD4+ T cells (two leftmost plots), for LOYSCR and WTYSCR CD8+ T cells (two middle plots), and for LOYSCR and WTYSCR epithelial cells (two rightmost plots). Significance was assessed based on univariate Cox Proportional Hazard (coxPH) model. b. Kaplan-Meier curves of overall survival (OS) for liver cancer scRNA-seq dataset samples stratified by the proportion of LOYSCR CD4+ T cells in immune cells(left), LOYSCR CD8+ T cells in immune cells (middle), and LOYSCR cells in tumor cells (right). P-values calculated using univariate coxPH model. c. Kaplan-Meier survival curves of OS for samples in (b) stratified by the combinatorial proportions of LOYSCR tumor cells and LOYSCR CD4+ T cells, and LOYSCR tumor cells and LOYSCR CD8+ T cells. P-values for pairwise comparisons between each combinatorial group are also displayed below Kaplan-Meier curves, calculated using log-rank tests. d. Calibration plots used to assess the accuracy of the OS probabilities predicted by the nomogram (Fig. 5h) at 2,5, and 8 years compared to the observed OS rates (%). Each colored dot represents a group of subjects randomly selected from the whole cohort. Each plot includes the total number of subjects (n = 4,010), the number of events (1,203), and 500 subjects per plotted group.

Extended Data Table 1 Multivariate CoxPH Model for YchrS Level, Ancestry, Race and tumor Histology

Extended Data Table 2 Multivariate CoxPH Model for Age and scRNAseq Signatures

Supplementary information

Supplementary Fig. 1

Spectral flow gating strategies. a, Representative FACS plots illustrating the sequential gating strategy for live, single-cell tumour suspensions derived from CRISPR Y-KO and CRISPR Y-Scr tumours. tumours were dissociated mechanically, stained with Ghost Red 710 viability dye (Tonbo Biosciences) and antibodies against CD45, CD3, TCRβ, CD11b and CD45R/B220, followed by sorting of the CD3+TCRb+ T cells using the BD S6 flow cytometry machine. Debris and doublets were excluded through sequential gating (All Events → P1 → P2). Within the CD45− compartment (purple in the Alexa Fluor 700 versus APC‐Cy7 panel), TdTomato+ tumour epithelial cells were identified (bottom left). Immune cells displayed minimal presence of TdTomato (very less percentage population), confirming their purity in both Y-KO and Y-Scr groups (top panels). T cells (CD45+CD11b−B220−CD3+TCRβ+) were gated from the CD45+ population (green or orange in the FITC versus BUV805 panel). The final sorted cell populations included: (1) Ghost Red 710−CD45− (containing TdTomato+ tumour cells), (2) Ghost Red 710−CD45+CD11b+B220+ myeloid and B cells (non-T population) and (3) Ghost Red 710−CD45+CD11b−B220−CD3+TCRβ+ T cells. All sorting was performed using a BD FACSymphony S6. The red events represent both CD45− and CD45+ cells, while black events indicate CD45+ immune cells (top panels), which showed minimal tdTomato expression. This confirms the successful separation of tumour epithelial cells from immune cell fractions in both Y-KO and Y-Scr groups.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–9.

Source data

Source Data Fig. 3

Source Data Extended Data Fig. 10

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Chen, X., Shen, Y., Choi, S. et al. Concurrent loss of the Y chromosome in cancer and T cells impacts outcome. Nature (2025). https://doi.org/10.1038/s41586-025-09071-2

- Received: 20 May 2024

- Accepted: 24 April 2025

- Published: 04 June 2025

- DOI: https://doi.org/10.1038/s41586-025-09071-2

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Oncogene aberrations drive medulloblastoma progression, not initiation

Konstantin Okonechnikov,

Piyush Joshi,

Verena Körber,

Anne Rademacher,

Michele Bortolomeazzi,

Jan-Philipp Mallm,

Jan Vaillant,

Patricia Benites Goncalves da Silva,

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Mari Sepp,

Ioannis Sarropoulos,

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Andrea Wittmann,

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Petra Fiesel,

Barbara Jones,

Natalie Jäger,

Till Milde,

Kristian W. Pajtler,

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Olaf Witt,

Konrad Bochennek,

Katharina Johanna Weber,

Lisa Nonnenmacher,

Christian Reimann,

David R. Ghasemi,

Ulrich Schüller,

Martin Mynarek,

Stefan Rutkowski,

David T. W. Jones,

Andrey Korshunov,

Karsten Rippe,

Frank Westermann,

Supat Thongjuea,

Thomas Höfer,

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…

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Abstract

Despite recent advances in understanding disease biology, treatment of group 3/4 medulloblastoma remains a therapeutic challenge in paediatric neuro-oncology[1]. Bulk-omics approaches have identified considerable intertumoural heterogeneity in group 3/4 medulloblastoma, including the presence of clear single-gene oncogenic drivers in only a subset of cases, whereas in most cases, large-scale copy number aberrations prevail2,3. However, intratumoural heterogeneity, the role of oncogene aberrations, and broad copy number variation in tumour evolution and treatment resistance remain poorly understood. To dissect this interplay, we used single-cell technologies (single-nucleus RNA sequencing (snRNA-seq), single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC-seq) and spatial transcriptomics) on a cohort of group 3/4 medulloblastoma with known alterations in the oncogenes MYC, MYCN and PRDM6. We show that large-scale chromosomal aberrations are early tumour-initiating events, whereas the single-gene oncogenic events arise late and are typically subclonal, but MYC can become clonal upon disease progression to drive further tumour development and therapy resistance. Spatial transcriptomics shows that the subclones are mostly interspersed across tumour tissue, but clear segregation is also present. Using a population genetics model, we estimate medulloblastoma initiation in the cerebellar unipolar brush cell lineage starting from the first gestational trimester. Our findings demonstrate how single-cell technologies can be applied for early detection and diagnosis of this fatal disease.

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Main

Intratumoural heterogeneity, a hallmark of cancer, refers to the presence of diverse molecular and functional cell populations within a single tumour[4]. Intratumoural heterogeneity is driven by genetic mutations, transcriptomic or epigenomic plasticity and reprogramming of the microenvironment[5]. The malignant childhood tumour medulloblastoma is heterogeneous[2], especially in groups 3 and 4. This heterogeneity makes effective treatment of these tumours difficult and contributes to overall low survival rates[6]. Advanced DNA methylation profiling has classified group 3/4 tumours into eight distinct molecular subgroups[7]. In addition, single-cell transcriptomic profiling has unveiled the intricate regulatory activity of transcription factors and signalling pathways that orchestrate cellular diversity8,9. Despite these advances, the role of oncogenes in shaping intratumour heterogeneity remains unknown.

A minority of group 3/4 medulloblastoma tumours harbour single-gene oncogenic drivers, including MYC[10] and MYCN[11] amplifications as well as PRDM6 overexpression owing to enhancer hijacking by means of a tandem duplication of the adjacent SNCAIP gene[2]. By contrast, most group 3/4 tumours display recurrent, large-scale copy number changes2,12,13, including loss of chromosomes 8 and 11 and gain of chromosome 7 and isochromosome 17q. A fundamental question of which genetic events initiate and drive these tumours remains unanswered. Using single-cell multi-omics and spatial transcriptomic approaches, we determined the interplay between large-scale copy number variants (CNVs) and single-gene somatic events in driving medulloblastoma heterogeneity and evolution.

Driver oncogenic events are subclonal

To understand the clonal genetic events in tumour initiation, evolution and progression, we molecularly profiled a specific tumour cohort with a known amplification of MYCN or MYC or overexpression of PRDM6 (n = 16 primary, n = 4 relapses; Fig. 1a and Supplementary Table 1). In larger datasets, amplification or activation of these oncogenes is present in approximately 30% of group 3/4 medulloblastoma cases[2], whereas approximately 70% of cases lack a single-gene somatic event. The presence of focal MYC/MYCN amplifications or the SNCAIP tandem duplication was verified using bulk molecular profiles or fluorescence in situ hybridization (FISH) for each sample in this cohort (Supplementary Table 1). We analysed single-nucleus profiles of our target cohort, examining both snRNA-seq (n = 20) and snATAC-seq (n = 16) in the same nuclei.

Fig. 1: Single-nucleus transcriptional profiling of 16 oncogene-associated group 3/4 medulloblastoma primary tumour samples.

a, Overview of target cohort with annotation. Two primary-relapse pairs (MB272/R, INF_P/R_637) are from the same patients. b, UMAP of snRNA-seq merged dataset, with medulloblastoma subgroups annotated. c–e, Feature plots showing MYC (c), MYCN (d) and PRDM6 (e) expression within the UMAP of the merged snRNA-seq dataset. G3, group 3; G4, group 4; G34, group 3/4 medulloblastoma; F, female; M, male.

Uniform manifold approximation and projection (UMAP) visualization of the snRNA-seq data from primary tumours showed group 3/4 subgroup-specific clusters without batch effect adjustments (Fig. 1b); mixed normal cell types arising from different samples clustered together as expected (Extended Data Fig. 1a). Expression of the oncogenes MYC, MYCN and PRDM6 demonstrated clear sample specificity (Fig. 1c–e and Extended Data Fig. 1b). In addition, expression of known marker genes delineated non-tumour cell types, including PTPRC (microglia), IGFBP7 (meningeal) and AQP4 (astroglia) (Extended Data Fig. 1c–e). Non-tumour cell clusters were also verified from CNV profiling of the full combined snRNA-seq dataset (Extended Data Fig. 1f). This separation of structure was recapitulated with snATAC-seq, as visualized by means of UMAP (Extended Data Fig. 1g,h). For samples with multi-omics data, non-tumour cells in snATAC-seq were labelled on the basis of their associated non-tumour clusters from the snRNA-seq data (Extended Data Fig. 1g).

To investigate the clonal heterogeneity of MYCN-amplified tumour samples (subgroups V/VII), we adapted the inferCNV[14] approach (Methods) to infer CNV profiles of cell clusters per sample, using both snRNA-seq and snATAC-seq data. To verify the single-cell CNV calls, we calculated the correlation between pseudobulk from single-cell and bulk DNA methylation CNV profiles (Supplementary Table 1) across all cases, as shown in Extended Data Fig. 2a–c. A full cohort cross-comparison demonstrated that most of the snRNA-seq pseudobulk CNV profiles matched well, with the exception of n = 3 false positive cases (Extended Data Fig. 2d). CNV profiles from snATAC-seq data showed the highest correlation to the correct control bulk profile for all samples (Extended Data Fig. 2e), demonstrating the benefit of using this data type for CNV calling.

From inspection of the CNV results per sample, in most cases, we observed clusters with discordant CNVs, which we labelled as subclones. For example, in all MYCN-amplified tumours (n = 4), we identified two distinct subclones: with (C1) and without (C2) MYCN amplification, respectively (Fig. 2a and Extended Data Fig. 3a). Notably, in all cases, reconstruction of the putative phylogenetic trees showed that MYCN amplification was not the initiating event for the tumour. Instead, large-scale CNVs, such as loss of chromosomes 8 and 10 or gain of chromosome 7 or 17q, were already present in the presumptive founder clone (C0) (Fig. 2b). Moreover, further unique CNVs were found only within MYCN- and non-MYCN-amplified subclones. Detailed visualizations of single-cell CNV profiles per sample are available through the interactive online web application (Methods).

Fig. 2: Clonal proliferation and differentiation gradients are independent of oncogene expression.

a, Copy numbers derived from snATAC-seq data in MYCN-amplified sample MB183. Red, chromosome loss. Green, chromosome gain. Right side, inset of MYCN chromosome region. b, Somatic phylogeny trees for MYCN samples. Blue, proportion of MYCN-expressing cells. c, snRNA-seq UMAP of single MYCN sample MB183. Grey boxes, proliferating cell clusters with strong proliferation enrichment. Blue, C1 clone. Orange, C2 clone. d, MYCN expression in C1 and C2 clones. e, Per cell gene set variance analysis (GSVA) enrichments of proliferation, progenitor-like activity and differentiation in single sample shown in c. f, Somatic phylogeny trees for MYC samples. Red, proportion of MYC-expressing cells. Red square, cases MB292 and MB248 with somatic mutations in C0. g, snRNA-seq UMAP of single MYC sample MB89. Grey boxes, proliferating cell clusters with strong proliferation enrichment. Red, MYC-expressing C2 clone. Orange, C1 clone. Aquamarine, C3 clone. h, MYC expression in C1, C2 and C3 clones. i, Per cell GSVA enrichments of proliferation, progenitor-like activity and differentiation in single sample shown in g. j, Somatic phylogeny trees for PRDM6 samples. Purple, proportion of PRDM6-expressing cells. Red square, case MB249 with somatic mutations outside CNV regions. k, snRNA-seq UMAP of single PRDM6 sample MB249. Grey box, proliferating cell cluster with strong proliferation enrichment. Purple, PRDM6-expressing C2 clone. Orange, C1 clone. Aquamarine, differentiation signal enrichment in C3 clone. l, PRDM6 expression in C1, C2 and C3 clones. m, Per cell GSVA enrichments of proliferation, progenitor-like activity and differentiation in single sample shown in k.

Next, we examined the differentiation, proliferation and aggressive progenitor-like activity states of individual cells within each subclone using snRNA-seq expression of established reference gene lists for these defined medulloblastoma cell states[8]. We identified that both MYCN-amplified and non-amplified subclones maintained separate proliferating and differentiated compartments (Fig. 2c,d and Extended Data Fig. 3b,c). The MYCN subclone was also uniquely enriched with a progenitor-like gene expression signature (Fig. 2e). As cells differentiated, the oncogene itself showed lower expression within the MYCN-amplified subclone (Pearson correlation = −0.23, P < 2.2 × 10−16; Extended Data Fig. 3d), demonstrating that MYCN is connected to the undifferentiated state[15]. Similar differentiation levels among subclones and a slight bias towards progenitor activity in MYCN-amplified subclones were observed in all four MYCN-amplified tumours (Extended Data Fig. 3e).

By inspecting differentially expressed genes specific for each subclone (Supplementary Table 2), we also identified a unique property of non-MYCN subclones with a stronger enrichment of genes expressed in unipolar brush cell progenitors (P < 1.14 × 10−6), the cell of origin of group 3/4 medulloblastoma16,17, whereas MYCN subclone-associated genes were not enriched in these genes (P > 0.05). This observation was also confirmed using subclone-specific genes associated with cis-regulatory elements derived from the integration of snATAC-seq data (Supplementary Table 3).

Performing single-cell CNV analyses on MYC-amplified tumour samples (subgroups II/V), we identified a subclonal MYC amplification in six of seven samples (Fig. 2f and Extended Data Fig. 3f). Similar to MYCN-amplified tumours, the common and likely initiating events in the founder clone (C0) were large-scale chromosome 10 loss and/or chromosome 17q gain, with subclonal MYC amplification occurring later during tumour evolution. Remarkably, the clonal structure of MYC-amplified tumours was more complex (n = 3 of 7 cases), with the formation of three or more unique subclones (Fig. 2f, bottom). Typically, MYC-amplified subclones had their own proliferating and differentiating compartments (Fig. 2g,h and Extended Data Fig. 3g,h). Similar to the MYCN-amplified clones, only MYC-amplified clones demonstrated strong enrichment of progenitor-like activity compared with non-MYC-amplified compartments (Fig. 2i). Differentially expressed genes specific to MYC-amplified subclones were enriched in known MYC target genes[18] (P < 1.11 × 10−16; Supplementary Table 2) and MYC expression decreased as cells differentiated (Pearson correlation = −0.18, P < 2.2 × 10−16; Extended Data Fig. 3i), whereas expressed genes in non-MYC subclones demonstrated enrichment in unipolar brush cell-related genes (P < 1.54 × 10−11; Supplementary Table 2). The MYC subclone-specific genes were not found to be enriched in any corresponding subclone-specific CNVs. Across six samples with subclonal MYC amplifications, the differentiation level was similar among subclones; however, progenitor-like activity was significantly enriched in MYC-amplified subclones (Extended Data Fig. 3j).

Last, we examined tumours with enhanced PRDM6 expression (subgroups VII/VIII), in which SNCAIP gene duplication leads to aberrant activation of PRDM6 by means of enhancer hijacking[2]. In our cohort, we identified three of five samples in which PRDM6 overexpression was subclonal (Fig. 2j and Extended Data Fig. 4a). Chromosome 17q gain was the most frequent CNV within the founder clone (C0). In contrast to MYC and MYCN clones, we could not identify a distinct proliferating compartment in PRDM6-specific clones (Fig. 2k,l). Instead, we found only an overall small proportion of cells (less than 5%) with the proliferation gene signature in PRDM6 subclones (Extended Data Fig. 4b). We also did not identify enriched progenitor-like activity in the PRDM6 subclones (Fig. 2m and Extended Data Fig. 4c), except in one specific case in which we detected an MYCN-amplified subclone that additionally harboured an SNCAIP duplication with associated PRDM6 overexpression (Fig. 2b, bottom right).

To further verify the presence of subclones, we performed single-cell whole-genome DNA and RNA sequencing from the same cells, in a subset of MYCN- and MYC-amplified cases, n = 6 (Supplementary Table 1). In all tested samples, the presence of the corresponding specific subclones and their CNV profile matched between the snMultiomic data (combined snRNA-seq and snATAC-seq data from the same cell), on the basis of single-cell RNA projection (Extended Data Fig. 5a) and whole-genome sequencing (WGS) CNV analysis (Extended Data Fig. 5b–h).

Despite the low mutational burden in medulloblastoma[2], we also inspected the somatic single-nucleotide variants (SNVs) to confirm the tumour phylogeny composition predicted by snRNA-seq and snATAC-seq data. Using WGS data, we examined mutations in CNV regions specific to the founder clone (C0) or not lying within CNVs. In 9 of 12 samples, we did not identify the presence of drivers or co-mutations (exceptions: two MYC-amplified cases, Fig. 2f, and one PRDM6 case, Fig. 2j). We further investigated the mutational landscape at the single-cell level by increasing the sequencing coverage of the snATAC-seq data in three MYC-amplified cases. This approach allowed us to recover up to 40% (range, 20% to 60%) of somatic mutations per sample (Supplementary Table 4). The positive correlation (maximum P value: 7.4 × 10−6) of their variant allele frequency (VAF) and the corresponding bulk WGS profiles verified the accuracy of the approach (Extended Data Fig. 4d–f). Importantly, in all three cases, it was possible to identify unique somatic mutations specific for the respective subclone, further supporting a common origin of these clones and secondary subclonal evolution (Extended Data Fig. 4g–i). Moreover, by comparing the extended somatic SNVs identified in the snATAC data with germline SNVs (see the Methods for details), the subclone-specific SNVs were observed to have approximately four times lower mean VAF in comparison with those SNVs that are common among subclones (Extended Data Fig. 4j–l).

Collectively, these findings nominate large-scale CNVs as likely tumour-initiating events in group 3/4 medulloblastoma, with focal oncogene aberrations occurring only during tumour evolution.

Tumour onset from first trimester onwards

To investigate clonal dynamics during the initiation of group 3/4 medulloblastomas, we analysed WGS data from the medulloblastoma International Cancer Genome Consortium (ICGC) cohort[2] (Supplementary Table 5). Somatic tissues accumulate SNVs continuously over time19,20,21, and hence SNV density in the tumour cell of origin (in population genetics, ‘most recent common ancestor’ (MRCA)) can be interpreted as a measure for the patient’s age at tumour initiation22,23. To time the developmental origin of medulloblastoma with this approach, we quantified clonal SNV densities from the allele frequency distribution of somatic variants in 181 primary medulloblastomas of all subgroups (Extended Data Fig. 6a,b; comprising 108 group 3/4 medulloblastomas, 21 infant Sonic Hedgehog (SHH)-medulloblastomas, 35 childhood/adulthood SHH-medulloblastomas and 17 WNT-medulloblastomas). Overall, the clonal SNV densities across subgroups recapitulated the age-incidence distribution of the disease, with infant SHH-medulloblastoma having the lowest densities (0.02 ± 0.01 SNVs per megabase (Mb)), followed by group 3/4 medulloblastoma (0.1 ± 0.08 SNVs per Mb), WNT-medulloblastoma (0.28 ± 0.47 SNVs per Mb) and adult SHH-medulloblastoma (0.41 ± 0.46 SNVs per Mb; Extended Data Fig. 6c). Clonal SNV densities were also correlated with age at diagnosis (Spearman’s ρ = 0.73, P < 2.2 × 10−16; Extended Data Fig. 6d), collectively supporting our approach to infer the evolutionary dynamics at medulloblastoma onset from somatic SNVs.

To estimate age of tumour initiation in group 3/4 medulloblastomas, we analysed 109 tumour samples of this subgroup in more detail (Fig. 3a). As with the entire cohort, clonal SNV densities were likewise correlated with the age at diagnosis among group 3/4 medulloblastoma (Spearman’s ρ = 0.51, P = 3.959 × 10−8; Extended Data Fig. 6e). However, contrary to the clear temporal order in tumour initiation of the major medulloblastoma groups, clonal SNV densities were statistically indistinguishable between group 3/4 medulloblastoma subgroups I–VIII (Wilcoxon rank sum test, all adjusted P values > 0.05), indicating that growth of the final tumour mass begins around the same developmental time window in all group 3/4 medulloblastoma subgroups (Fig. 3b).

Fig. 3: Somatic mutation profiles and association with cell of origin.

a, Group 3/4 medulloblastoma subgroups analysed by bulk WGS. b, SNV densities at MRCA per group 3/4 medulloblastoma subgroup (I, n = 3; II, n = 15; III, n = 10; IV, n = 6; V, n = 12; VI, n = 12; VII, n = 19; VIII, n = 31). Shown are mean and 95% CI (estimated by bootstrapping the genomic segments 1,000 times). c, Early medulloblastoma evolution. Driver mutation in an ECA spawns a pre-malignant lesion. Malignant transformation occurs upon further drivers in the tumour’s MRCA. d, SNV densities at ECA and MRCA for group 3/4 medulloblastoma (n = 108). Mean and 95% CI, estimated by bootstrapping the genomic segments 1,000 times. e, Model fit to SNV densities at ECA. Line, mean and standard deviation (estimated by bootstrapping the genomic segments 1,000 times) of the measured SNV densities; green and grey areas, 95% credible interval of the model fit, and of key time points. f, As in e, but for SNV densities at MRCA. g, 95% credible intervals of modelled tissue of origin (blue) and pre-malignant clone (green). Grey areas as in f. h, Mutation spectrum with timing information (‘ECA’, CNV uniquely timed to ECA; ‘MRCA’, CNV uniquely timed to MRCA; ‘ECA or MRCA’, CNV in agreement with both ECA and MRCA; ‘clonal’, CNV/small mutation was clonal, no further mapping to ECA/MRCA possible; ‘subclonal’, CNV/small mutation was subclonal; ND, no data). Subclonality information for amplification of MYC/MYCN and duplication of SNCAIP from single-cell data. i, SNV density at ECA in group 3/4 medulloblastoma with and without driver in MYC/MYCN/PRDM6. P value, unpaired Wilcoxon rank sum test (n = 80 without, n = 28 with driver). j, As in i, but for SNV density at MRCA. 95% CI, 95% confidence interval; scRNA-seq, single-cell RNA-seq; SSNV, somatic single-nucleotide variants.

To refine our analysis, we timed the acquisition of clonal CNVs (copy number gains or loss of heterozygosity (LOH)) relative to the tumour’s MRCA. To this end, we compared densities of clonal SNVs acquired before a chromosomal gain, and hence present on multiple copies of a chromosomal region, with the density of clonal SNVs overall (Methods). Similar to neuroblastoma[23] and other tumour entities[22], 34 of 109 group 3/4 medulloblastomas showed evidence of having acquired at least one copy number gain in an early common ancestor (ECA), antecedent to the tumour’s MRCA (Fig. 3c,d). The number of such early CNVs varied between 1 and 16 per tumour (mean, 5.3), with no significant difference in SNV density between early CNVs within a tumour (Extended Data Fig. 6f). Hence, in tumours with early CNVs, all early CNVs probably arose in an ECA during a confined time window before the onset of tumour growth. In the remaining cases, where we identified no early CNVs, clonal chromosomal gains probably occurred concomitantly with, or shortly before, the onset of tumour growth. To corroborate these observations, we contrasted our approach[23] with an alternative computational tool, MutationTimeR[22], which yielded similar results (Extended Data Fig. 6g). Hence, our data suggest that at least some CNVs in group 3/4 medulloblastoma arise before the onset of tumour growth, in line with multiple rounds of mutation and selection at tumour initiation.

To date these events in actual time, we calibrated a population genetics model of mutation and selection during tumour initiation[23] with the measured SNV densities at ECA and MRCA, along with the patient age at diagnosis (see the Methods for details). Briefly, the model assumes that medulloblastoma initiation is driven by clonal selection for two consecutive drivers in the transient cell population of (differentiating) unipolar brush progenitor cells from the rhombic lip16,17,24 (Extended Data Fig. 6h). Acquisition of the first driver defines a pre-malignant state, arising before a tumour’s MRCA. Although in principle the first driver can be any type of mutation, small driver mutations are overall rare in group 3/4 medulloblastoma[2], and thus are probably not the major driving force of early tumour evolution. Hence, we focused on cases in which early CNVs defined an ECA, and associated the time point at which the first driver mutation was acquired with the mutation density in the ECA. We assumed that the second driver emerges subsequently in the pre-malignant clone, spawned by the ECA. Hence, we associated the mutation density in the MRCA with the acquisition of the second driver and the onset of tumour growth. Finally, upon malignant transformation of the tumour’s MRCA, we assumed exponential growth to a tumour size of 10^9 cells (corresponding to a few cubic centimetres) at the age of diagnosis[25] (see Methods for details).

Using the bespoke model, we estimated driver mutation rates and associated selective advantages from the clonal SNV densities at ECA and MRCA measured across group 3/4 medulloblastomas. Simultaneously, we estimated per-tumour doubling times using age and the subclonal VAFs of 35 tumours with sufficient data quality and information on age at diagnosis (Supplementary Table 5). Consistent with a higher activity of S-phase genes and MYC target genes and poorer overall survival[26] (Extended Data Fig. 6i–k), we estimated shorter tumour doubling times in medulloblastomas at the group 3 pole as compared with tumours at the group 4 pole (Extended Data Fig. 6j), confirming our modelling approach. We then used the per-tumour doubling times to translate SNV densities at ECA and MRCA into real-time, finding that the first oncogenic event (that is, the ECA) occurs within the first gestational trimester in 24% of cases, during late gestation in around 35% of cases and within the first year of life in 26% of cases (Fig. 3e and Extended Data Fig. 6l). The onset of tumour growth from its MRCA is placed considerably later, within the first decade of life (Fig. 3f), suggesting a long latency phase between pre-malignancy and the detection of a symptomatic tumour. Overall, the inferred dynamics of tumour initiation are consistent with a tumour origin in (differentiating) unipolar brush progenitor cells24,27, sustaining a pre-malignant clone that outlives the cell state of origin for several years (Fig. 3g).

Early acquisition of large-scale CNVs

To gain mechanistic insight into group 3/4 medulloblastoma initiation, we asked whether particular mutations occur predominantly early or late. To address this question, we first focused on CNVs that were found more frequently than expected by chance, and hence are likely drivers of malignancy. Combining the enrichment results obtained in our cohort (Extended Data Fig. 7a and Methods) with published data[28], we classified gains of chromosomes 1q, 4, 7, 12, 17/17q and 18, as well as LOH on chromosomes 5q, 8, 10/10q, 11 and 17p, as putative drivers of group 3/4 medulloblastoma initiation. Except for four cases, in which no ECA was identifiable, all group 3/4 medulloblastomas harboured at least one of these CNVs clonally (Fig. 3h). To search for putative drivers located on these regions, we analysed the expression of genes lying in commonly gained or lost regions. For this purpose, we contrasted gene expression between tumours with and without particular CNVs in a bulk RNA-seq cohort of group 3/4 medulloblastoma[3] (Supplementary Table 6a) and inspected differentially expressed genes specific for group 3/4 medulloblastoma in a global central nervous system tumour cohort[24] (Supplementary Table 6b). Among these identified genes, up to 10% have been described as known somatic drivers, and thus associated with medulloblastoma evolution[29].

Overall, gains of chromosome 17 or 17q were the most frequent aberrations in group 3/4 medulloblastoma, followed by gain of whole chromosome 7 and LOH of whole chromosome 8. The SNV densities at chromosomal gains were often smaller than the SNV densities at the tumour’s MRCA, in particular for gains of chromosomes 4, 7, 12 and 17 and LOH of chromosomes 8 and 11 (Extended Data Fig. 7b). Although we cannot rule out that small mutations or chromosomal losses preceded these gains, their consistent early timing suggests that chromosomal gains or losses might be among the earliest events during group 3/4 medulloblastoma initiation. In contrast to the high abundance of CNVs, focal events in known driver genes (SNVs, indels, focal amplifications/deletions or structural rearrangement) were overall rare (Fig. 3h). Among these, amplification of MYC or MYCN and duplication of SNCAIP leading to PRDM6 overexpression were the most frequent alterations (Fig. 3h). However, the single-cell analysis (compare with Fig. 2b,f,j) showed that these mutations were mostly subclonal. Interestingly, group 3/4 medulloblastomas with amplification of MYC or MYCN or duplicated SNCAIP had significantly higher SNV densities at both ECA (Fig. 3i) and MRCA (Fig. 3j) than the remaining tumours, suggesting that later onset of (pre-)malignancy may predispose to the subsequent acquisition of these drivers. In general, the mutational landscape in group 3/4 medulloblastoma suggests a fundamental role of CNVs during tumour initiation, although further mutations acquired during disease progression seem to drive subclonal evolution in a subset of tumours only.

Subclonal spatial heterogeneity

To better understand the spatial relationship of the tumour subclones and disclose further insight into their evolution, we performed spatial transcriptomics on samples with available material (n = 13 primary, n = 4 relapse). We used technology that applies multiplexed in situ hybridization of a selected gene set to achieve single-cell spatial resolution of a tumour sample (Supplementary Tables 7 and 8). UMAP visualization of merged spatial data from primary tumour samples reflected combined snMultiomic profiling, allowing us to distinguish subgroup-specific properties and identify non-tumour cell types (Extended Data Fig. 8a–e). The spatial locations of tumour cells were determined by the expression of MYC, MYCN and PRDM6 (Fig. 4a), along with other genes associated with proliferation (for example, MKI67; Extended Data Fig. 8f). The tumour microenvironment, including glial, immune and meningeal cells, was characterized using cell type-specific markers (Extended Data Fig. 8f).

Fig. 4: Spatial heterogeneity across oncogene-associated group 3/4 medulloblastoma samples.

a, Spatial gene expression of MYC, MYCN and PRDM6. Last row, projection of clones derived from snRNA-seq. b, Spatial data UMAP of representative MYCN sample. c, Spatial visualization of clones of sample in b. Enlarged view of a fragment in the bottom right. d, Proximity of each compartment to each other of sample in b. e, MKI67 spatial expression of sample in b. f, Spatial data UMAP of representative MYC sample. g, Spatial visualization of clones of sample in f. Magnification of specific region in bottom right. h, Proximity of each compartment to each other of sample in f. i, MKI67 spatial expression of sample in f. j, Spatial data UMAP of representative PRDM6 sample. k, Spatial visualization of clones of sample in j. Magnification of specific region in top right. l, Proximity of each compartment to each other of sample in j. m,n, PRDM6 (m) and MKI67 (n) spatial expression of sample in j. diff, differentiated; prolif, proliferating.

To determine the spatial distribution of the identified subclones, we projected the snRNA-seq data onto the spatial data (Fig. 4a, last row). Overall, we distinguished two major spatial localization patterns: interspersed, in which independent subclones mixed throughout the tumour sample, and segregated, in which a clear boundary between independent subclones could be delineated. In most cases, the subclones were interspersed, as observed from the spatial distribution of the corresponding marker gene expression.

In MYCN-amplified tumours, the observed clonal architecture derived from snRNA-seq data was also present in the spatial data (Fig. 4b). The subclones exhibited an interspersed spatial pattern (Fig. 4c), with pockets of MYCN and non-MYCN clones highlighted through neighbourhood enrichment within the tumour sample (Fig. 4c, inset). The proliferating compartments within these subclones were also interspersed across the tumour tissue, observed by MKI67 expression (Fig. 4e and Extended Data Fig. 8f). On a smaller scale, however, neighbourhood enrichment analysis showed that proliferating cells of subclones clustered together, away from the differentiating cells (Fig. 4d). The normal cells were mostly isolated from the tumour subcompartments.

A similarly interspersed spatial pattern of MYC and non-MYC subclones was present in MYC-amplified tumours (Fig. 4f,g), yet islands of segregated non-MYC subclones were also observed (Fig. 4g, inset). Proliferating cells (MKI67+) were interspersed throughout the tumour tissue (Fig. 4i and Extended Data Fig. 8f). The differentiated tumour cell compartment within the MYC subclone (C1-diff) was in closer proximity to the proliferating cell compartment within the same subclone (C1-prolif; Fig. 4h). Normal cells were largely isolated from the MYC-amplified subclone compartments.

We observed a segregated spatial separation of subclones in the PRDM6 sample (Fig. 4j,k,m). This spatial segregation of clones was confirmed in another region of the same tumour specimen (Extended Data Fig. 8g,h). Although the subclones were segregated, the proliferating cell compartments within the subclones were interspersed within the spatial block (Fig. 4n and Extended Data Fig. 8i). As expected, the neighbourhood enrichment analysis in this sample showed the separation of the PRDM6 clone from other compartments (Fig. 4i). In another sample we also confirmed the dual-oncogene, the PRDM6-MYCN subclone (Fig. 2b, bottom right), in which cells with activity in both genes reside in the same spatial regions (Extended Data Fig. 8j,k).

Together, the observed spatial patterns suggest that clonal evolution does not lead to spatial compartmentalization within tumours. Instead, cellular migration may dictate communication among the clones, which can then drive competing or collaborative interactions among the co-existing tumour populations.

MYC subclones take over at relapse

We identified a primary tumour sample with two distinct subclones (MB272), harbouring MYC (C3) and MYCN (C2) amplifications simultaneously (Figs. 2f and 5a–c). This subgroup II sample was originally characterized as MYC-amplified only on the basis of bulk methylation profiling (Extended Data Fig. 9a), whereas the MYCN-amplified subclones were observed in the single-cell multiome and spatial transcriptomics analysis. This discrepancy most probably results from examining different fragments of the tumour tissue for each analysis. The MYC and MYCN subclones in this tumour sample had proliferating and differentiating compartments (Fig. 5b,c). The MYC subclone was strongly enriched with progenitor-like activity (Extended Data Fig. 9b). Remarkably, a clear spatial separation was observed between MYC- and MYCN-expressing cells (Fig. 5d and Extended Data Fig. 9c,d). This spatial segregation (Fig. 5e,f) reflected the phylogenetic tree of tumour evolution projected from the snRNA-seq CNV annotation (Fig. 2f). Further sets of subclone-specific genes also showed explicit spatial specificity (Extended Data Fig. 9e,f). As expected, low contact proximity was identified between MYC and MYCN subclones (Extended Data Fig. 9g).

Fig. 5: Independent oncogene subclones may co-occur in one tumour, but subclones are lost at relapse.

a, Copy number profiles of snATAC-seq data from MYC-MYCN sample MB272. Red, chromosome loss. Green, chromosome gain. b, snRNA-seq UMAP of sample shown in a. Grey boxes, proliferating cell clusters with strong proliferation enrichment. Blue, MYCN-expressing C2 clone. Red, MYC-expressing C3 clone. Orange, C1 clone. c, MYC and MYCN expression in C1, C2 and C3 clones. d, Spatial gene expression of MYC (red) and MYCN (blue) from original signals. e, Spatial data UMAP of sample shown in d. f, Spatial visualization of clones of sample in d. g, Somatic phylogeny trees for MYC relapse samples. h, Copy number profiles of snATAC-seq data of relapse sample arising from primary sample shown in a–f. i, Spatial gene expression of MYC (red) and MYCN (blue) in spatial transcriptomic relapse sample of case shown in a–f. Scale bars, 400 μm (d,f), 300 μm (i).

According to current knowledge, MYC and MYCN amplifications are considered mutually exclusive events[30] in medulloblastoma (Extended Data Fig. 9h) and other tumour types[31]. Because of the unexpected occurrence of both amplifications in this case, we conducted a systematic analysis across a larger medulloblastoma cohort to identify further cases in which these oncogenes may co-occur. We identified six putative cases on the basis of DNA methylation CNV profiles (Extended Data Fig. 9i). Using immunohistochemistry, we identified another case in which both MYC and MYCN staining were seen in the same tumour sample (Extended Data Fig. 6j). During the preparation of this manuscript, a case study reported a primary tumour sample in which both MYC- and MYCN-amplified cells were present[32]. Together, these independent cases suggest the possibility that MYC and MYCN amplifications co-occur within the same tumour more frequently than originally thought. Nevertheless, it is noteworthy that individual cells within the tumour express only one of these oncogenes and are spatially segregated.

The presence of MYC and MYCN subclones cannot be distinguished using bulk profile techniques owing to the potential low presence of cells of a particular subclone in the obtained data. Therefore, we generated unique signatures of MYC and MYCN subclones derived from single-cell data to identify further samples harbouring two oncogene amplifications. We performed a deconvolution analysis of bulk transcriptome profiles, using the MYC/MYCN case as the reference control. Using this method, we detected further samples in which MYC and MYCN subclones may co-occur in the same sample (Extended Data Fig. 9k). We validated this finding using FISH on an identified sample with available material (Extended Data Fig. 6l).

We next checked whether this information could be exploited for diagnostic purposes. Therefore, we investigated whether the relative presence of MYC or MYCN subclones derived from the deconvolution analysis predicted patient outcomes. In subgroup V, 4 of 41 cases harboured a known MYC amplification, on the basis of CNV profiles, and correlated with a low probability of survival (Extended Data Fig. 9m). We identified 14 potential cases with an occurrence of an MYC-amplified subclone on the basis of deconvolution. These patients had a lower overall survival (Extended Data Fig. 9n). Therefore, the poor outcomes of subgroup V patients may be explained by an undiagnosed MYC subclone that potentially outcompetes other subclones to drive relapse.

To further test this possibility, we performed single-nucleus molecular profiling on four relapse MYC-amplified cases. In all relapse cases, new subclones arose, but all tumour cells harboured the MYC amplification (Fig. 5g). For example, in the matched relapse MYC/MYCN case, the MYCN subclone was lost at relapse (Fig. 5h). This loss of MYCN expression was confirmed using spatial transcriptomics (Fig. 5i and Extended Data Fig. 9o,p). These results suggest that the MYC subclone outcompetes other subclone(s) during tumour progression and hence the presence of subclonal MYC amplification at diagnosis may predict the probability of relapse.

Discussion

Despite advances in understanding the cellular origin of group 3/4 medulloblastoma, the tumour-initiating and driving mechanisms remain elusive. In our study, we use single-cell multi-omics data analysis combined with spatial profiling to identify the somatic subclone properties within these tumours and demonstrate that the genetic aberrations that lead to overexpression of oncogenes are not the likely initiating events in group 3/4 medulloblastoma. Therefore, MYC and MYCN are probably not the primary ‘drivers’, but instead are acquired after malignant transformation and probably accelerate tumour growth. Instead, our results suggest that the initiating or ‘driving’ events in group 3/4 medulloblastoma are large-scale CNVs. This finding is in line with the hypothesis that tetraploidization is a frequent early event in medulloblastoma[12] and is associated with intermediate survival rates and high risk of relapse[33]. Using mutational clocks and mathematical modelling, we find that medulloblastoma initiation is probably a multi-step process. Specifically, our model suggests that early CNVs, acquired as early as in fetal development, drive a pre-malignant clone, in which malignant transformation occurs within the first decade of life. Although this process is similar to observations in mouse models, in which an initial hyperplastic state precedes medulloblastoma growth34,35, our modelling approach has some limitations. We here assume that unipolar brush cell (UBC) progenitors, the likely cell of origin of group 3/4 medulloblastomas16,17, divide at a fairly constant rate and that tumour growth can be approximated with exponential growth. Both assumptions may oversimplify the true biology, which may influence our timing estimates. Reassuringly, however, a disease origin that dates back to fetal development, or the first year of life, agrees with the detection of UBC progenitors in human brain samples from this time span[24]. Intriguingly, another paediatric tumour, neuroblastoma, has a similar order of genetic events: CNVs are the initiating event and occur early in the first trimester of pregnancy[23]. How exactly large-scale CNVs drive early tumourigenesis in different cell types and whether this knowledge can potentially be exploited for early cancer detection remain to be explored.

Group 3/4 medulloblastoma with MYC, MYCN or PRDM6 alterations have complex subclonal structures, with each subclone having unique properties. Therefore, our results strongly argue against the dogmatic ‘cancer stem cell’ hierarchy for group 3/4 medulloblastoma, as these tumours maintain distinct subclones with separate proliferating and differentiating compartments, irrespective of the presence of recognized oncogenes. Although MYC or MYCN amplifications could arise from the formation of circular extrachromosomal DNA[36], our data show that all subclones with MYC/MYCN amplification demonstrated other large, specific CNV gains and losses within the oncogene-amplified subclone, suggesting these clones are not the founder clone.

Single-cell spatial data from group 3/4 tumours allowed us to inspect the composition of the subclones across tumour tissue fragments; however, owing to the fixed number of genes (n = 100) and limited image size (maximum 2 mm), some other spatial properties, such as formation of blood vessels, were not covered in our study. The application of new spatial techniques that overcome such limitations will be an important research direction in the future.

In addition, undetected MYC/MYCN subclones challenge the bulk analysis approach in the diagnostic space. These findings, along with support from others[37], suggest that single-cell analyses may be an important diagnostic tool in the future, especially for group 4 and subgroup V tumours. In addition, our data challenge the cutoffs used for a tumour to be called MYC/MYCN-amplified by FISH, as even the smallest MYC subclones, which initially have low abundance, have the potential to expand into the dominant clone during relapse. MYC amplification may drive disease progression and contribute to therapy resistance and relapse. Such a pattern of MYC dominance in subclonal evolution has been observed in other tumours including gliomas[38], suggesting that our results may be relevant also to other tumour entities associated with MYC oncogenesis.

Methods

Target cohort selection and verification

Target tumour tissue samples were collected from global medulloblastoma published materials (ICGC[2], fresh-frozen paraffin-embedded (FFPE)[13] and Individualized Therapy For Relapsed Malignancies in Childhood (INFORM)[39] cohorts). For each selected case, the copy number/structural variant profiles from methylation and/or WGS data were used to identify MYC/MYCN amplification and SNCAIP structural variant presence. Bulk gene expression RNA-seq profiles from these samples were used to inspect MYC/MYCN/SNCAIP/PRDM6 expression as well. For some cases with sufficiently available FFPE material, further FISH experiments were performed to verify the selection (details in Supplementary Table 1). No statistical methods were used to predetermine sample size.

Single-nucleus multi-omics sequencing

Flash-frozen tumour samples were processed to extract nuclei as described earlier[27]. Extracted nuclei were processed using Chromium Single Cell Multiome ATAC Gene expression kit and Chromium Controller instrument (10x Genomics) as per manufacturer’s recommendations. One sample, MB248, was processed with Chromium Next GEM Single Cell 3′ reagent kit as per the manufacturer’s recommendation. In total, 15,000–20,000 nuclei were loaded per channel along with the multiome gel bead. Libraries were quantified using Qubit Flurometer (Thermo Fisher Scientific) and profiled using Fragment Analyzer. snRNA-seq and snATAC-seq libraries were sequenced using a NextSeq2000 to the recommended lengths. If the snATAC-seq library was not of good quality, we still used the obtained snRNA-seq library if that was found to be appropriate on the basis of quality control parameters. snRNA-seq and snATAC-seq datasets were further analysed separately.

snRNA-seq data analysis

De-multiplexed reads were aligned to human genome assembly GRCh38 (v. p13, release 37, gencodegenes.org). Comprehensive gene annotation (PRI) was customized by filtering to transcripts with the following biotype: protein coding, lncRNA, IG and TR gene and pseudogene as recommended for cellranger mkgtf wrapper. Reads were aligned using STARsolo with parameters: --soloType CB_UMI_Simple --soloFeatures Gene GeneFull --soloUMIfiltering MultiGeneUMI --soloCBmatchWLtype 1MM_multi_pseudocounts --soloCellFilter None --outSAMmultNmax 1 --limitSjdbInsertNsj 1500000. For overlapping genes for which intronic alignment recovered low counts, exonic alignment counts were used. Cells were separated from debris using the diem pipeline[40]. Cells with mitochondria fraction above 2%, number of detected genes above 6,600 and an intronic fraction (number of reads aligned to intron/total number of reads aligned to exon + intron) less than 25% were also filtered out. Filtered cells were corrected for background signature using the SoupX pipeline[41]. Finally, the scrublet[42] tool was used to remove putative doublets. Further, the gene expression matrices from all samples were merged together in the full matrix and processed by means of the Seurat package[43] to normalize, compute top principal complements (n = 30), find most highly variable genes (n = 2,500) and visualize by means of UMAP. After distinguishing non-tumour cells on the basis of corresponding markers and combined UMAPs, per sample processing was performed using the Seurat toolkit using the same settings combined with cell clustering. The enrichment of proliferation, differentiation and progenitor-like activity of medulloblastoma-specific markers per cell was performed using the single sample function from the GSVA R package[44] using two independent reference datasets8,9. Cell clusters enriched with proliferation signals were selected and marked on the basis of maximum GSVA signal enrichment from manual inspection per sample.

snATAC-seq data analysis

ATAC-seq reads were aligned to GRCh38 using the Cellranger arc wrapper. The selected cells were processed using the Signac R package[45] to filter the doublets/outliers on the basis of signal per cell distribution analysis and to inspect the cell compartments by means of UMAP visualization after normalization and identification of the most highly variable regions. snRNA-seq data information was used to annotate the cells from corresponding processed data.

To identify differentially enriched cis-regulatory elements per sample per annotation, peaks were first called on the merged snATAC-seq data using the ArchR R package[46] by first creating pseudobulk replicates using addGroupCoverages (minCells=2000, maxCells=5000, minReplicates=2, maxReplicates=5, groupBy=“Sample”, maxFragments=100 * 10^6) and calling reproducible peaks using addReproduciblePeakSet(groupBy=“Sample”, maxPeaks=150000). Obtained peaks were further filtered on the basis of presence in at least 5% of cells in any sample. Marker peaks were then obtained using getMarkerFeatures() on the basis of subclonal annotation and filtering on the basis of area under the curve > 0.52 and false discovery rate (FDR) < 0.01.

Genes correlated to the peaks were identified using addPeak2GeneLinks() using the subset of cells with dual snRNA-seq and snATAC-seq profiles in the merged data. Correlated genes (more than 0.1) were intersected with peaks associated with genes identified on the basis of GREAT[47] annotation to identify the robust pairs of peaks to gene links.

Single-cell CNV phylogeny reconstruction

Initially, CNV analysis was performed on a subset of samples (n = 2 for each oncogene) using the InferCNV tool[14] on the raw gene expression matrix with droplet protocol adjusted parameters (average read counts cutoff 0.5, smooth method runmeans, denoise active) and hierarchical clustering by means of the ward.D2 method to derive the clonal phylogeny. The main subclones obtained from this phylogeny demonstrated a close match to the initial Seurat clustering results (mean purity evaluation metrics across samples: 0.921). To further improve the visualization and increase computational efficiency, the CNV analysis was performed on the full cohort by transferring single cells into meta-cells, on the basis of the established method[48]. For this purpose, we computed the sum of gene expression counts across n = 5 cells combined within the clusters derived from Seurat processing. The meta-cell InferCNV calling was performed for each sample separately with read counts cutoff 0.5, and the phylogeny clustering results were visualized in UMAPs with k as number of clusters, varying from 2 to 5. Differentially expressed genes for identified subclones per sample were computed by means of a Wilcoxon rank sum test. Significant MYC/MYCN/PRDM6 differential expression and progenitor-like activity enrichment values were computed per cell to finalize the derived phylogeny cut limit for each case after manual inspection. The selected cut limit for the number of subclones in the phylogeny was verified by using random tree subcluster partition in the phylogeny reconstruction with a minimum P  < 0.05.

All tumour samples (n = 16) were also merged together and CNV profiling was performed to verify the status of non-tumour cells. The annotation of verified normal cells was further used as reference control for each sample.

For snATAC-seq data, a matrix with all genomic regions as raw and read counts per column per sample was used to adjust for InferCNV input format. Further meta-cell formation and the same CNV calling procedure as for snRNA-seq were performed on the derived matrices. The subclone annotation derived from snRNA-seq data was used to assign corresponding cluster phylogeny per sample.

Interactive CNV visualization for the results from snRNA-seq and snATAC-seq data per sample is available through ShinyApp: http://kokonech.shinyapps.io/mbOncoAberrations.

Mutation calling from snATAC-seq data

For n = 3 MYC cases the number of reads in snATAC-seq data was increased up to 3 × 10^9 per sample to have sufficient coverage for mutation calling. The alignment of novel reads was performed using the same strategy as described above. Initial subclone annotation was used to classify the cells. Afterwards, the mutation calling was performed using the SComatic method[49] on the BAM files extracted for each subclone. Initial mutation filtering was performed on the basis of inspection of somatic mutations derived from WGS data[2] as the main control. Results were also confirmed using the bcftools method[50] on the full merged pseudobulk snATAC-seq data with standard settings. The VAF from the full merged pseudobulk mutation calling was used to compare with the VAF from bulk control results. The visualization of somatic mutation presence across subclones per sample was performed by means of the R package ComplexHeatmap. For more relaxed filtering control, blood bulk WGS control was applied to exclude non-somatic mutations, while mutation filtering limits were strengthened: min coverage 20× and minimum support 5×. Annotation of identified mutations was performed using Annovar toolkit[51] using gencode v.38 materials.

Single-cell DNA and RNA sequencing procedure

Flash-frozen tumour samples were processed to extract nuclei as described previously[52]. Single nuclei were sorted into the wells of DNA LoBind 96-well plates using fluorescence activated cell sorting. Each plate was used for downstream genome and transcriptome isolation, reverse transcription of the transcriptomes and primary template-directed amplification using the ResolveOME Multiomic kit from Bioskryb, according to the manufacturer’s protocol. Selected wells were used for quality control before library preparation to assess the quantity and quality of the extracted genome or transcriptome DNA, using the 4200 TapeStation System. Sequencing libraries were prepared using Illumina-compatible unique dual index adaptors, according to the manufacturer’s protocol as provided in the ResolveOME kit. After amplification, barcoded libraries were pooled and purified using Ampure Beads to select 250–1,000-base pair (bp) fragments with an average peak size of 400–500 bp. Multiplex libraries were sequenced using one lane of the NovaSeq 6000 System each, with 100-bp paired-end sequencing for the genome libraries and 100-bp single-read sequencing for the transcriptome libraries.

Single-cell DNA and RNA sequencing data analysis

DNA sequencing reads per cell were initially processed (quality control, alignment) by means of the BJ-DNA-QC Nextflow-based pipeline from BioSkryb using hg38 as the main reference. CNV profiling was performed with the Ginkgo tool[53] on the basis of resolution 100 kilobases (kb). Outlier signal adjustment was performed on the basis of cut for mean plus 2 s.d. Heatmap visualization was performed by means of the ComplexHeatmap R package using the clustering method ward.D2 with Euclidean distance.

RNA-seq reads per cell were aligned to the hg38 reference by means of the STAR tool[54]. Gene expression counts were computed by means of the FeatureCounts option from the Subread toolkit[55] using gencode v.38 reference. Combined gene expression matrix analysis was performed by means of the Seurat toolkit[43]. Non-tumour cells as well as subclone specificity were identified on the basis of the inspection of known gene markers and projection into snRNA-seq profiles using the transfer Seurat R toolkit function.

Molecular cartography

The specific gene set (n = 100) covering group 3/4 known driver genes alongside marker genes of the developing cerebellum non-malignant cell types was selected for the protocol (Supplementary Table 7). The gene selection was on the basis of specific properties. First, the main selection was split into two blocks: tumour-specific genes (60%) and normal cell markers (40%). The tumour-specific genes had several blocks of selection: (1) known target markers of group 3/4 tumours including MYC, MYCN, SNCAIP and PRDM6; (2) proliferation, differentiation and cell-cycle activity markers; (3) cells-of-origin-associated markers. The normal cell type markers were selected on the basis of knowledge about normal cell types of the cerebellum that could also be present in the tumour. For each cell type, at least two markers were selected. All selected genes were also verified on available snRNA-seq data as well as bulk profiles.

Optimal cutting temperature compound (OCT)-embedded samples were cryo-sectioned into 10-µm sections onto a molecular cartography slide. Fixation, permeabilization, hybridization and automated fluorescence microscopy imaging were performed according to the manufacturer’s protocol (Molecular preparation of human brain (beta), Molecular colouring, workflow setup) as described previously[52].

Spatial data analysis

The detection of cell boundaries was performed with CellPose[56]. Afterwards, gene expression counts were computed per cell and extracted using further custom Python scripts. Initial cell filtering was performed by assigning the minimum number of counts/genes per cell and size of the cells. Afterwards, the analysis of the formed gene expression matrix, including clustering and UMAP visualization, was executed using the Seurat toolkit[43]. Annotation of cell states and types was achieved through direct projection with the snRNA-seq data by means of transfer function and verified by visual inspection of marker genes. Spatial-specific analysis, the detection of closest cell connections, was conducted using the Giotto toolkit[57].

Deconvolution analysis of MYC/MYCN cases

The assigned MYC/MYCN single-cell dataset (MB272) with annotation of compartments was used as reference control for the CIBERSORT method[58] to perform deconvolution on a set of bulk FFPE medulloblastoma RNA-seq profiles from MYC/MYCN samples[13]. For each case, MYC/MYCN status was derived from methylation copy number profile. The deconvolution values obtained from CIBERSORT results were visualized by using the ComplexHeatmap R package in order to demonstrate compartment enrichments per sample.

Survival analyses on the basis of expression of MYC as well as the computed deconvolution of MYC compartment proportion of multiple genes were performed using the Kaplan–Meyer algorithm with applied Bonferroni correction for multiple testing. The resulting plots were generated by means of the R2 Genomics Analysis and Visualization Platform (http://r2.amc.nl).

Bulk RNA-seq data analysis

Bulk RNA-seq data from the ICGC cohort overlapping with WGS data were available for 85 group 3/4 medulloblastomas of our cohort[2]. We performed GSVA on reads per kilobase of transcript per million mapped read scores using the R package GSEABase. We used the following gene sets: MYC target genes: ‘HALLMARK_MYC_TARGETS_V2’, ‘MYC_UP.V1_UP’, ‘DANG_MYC_TARGETS_UP’[26]; S-phase genes: ‘REACTOME_S_PHASE’, ‘SA_G1_AND_S_PHASES’.

Extended gene expression and associated CNV profile metadata for 405 group 3/4 medulloblastoma samples were integrated for inspection of common CNV gains/losses[3]. The chromosomal arm gains or losses as provided in this corresponding study were binarized. DESeq2 was used to compute differentially expressed genes using lfcShrink(type=“apeglm”) and filtered using adjusted P < 0.001 and log fold change (lfc) > 0.5 criteria to obtain a list of statistically significant altered gene expression. We then used msigdbr and clusterProfiler R packages to identify chromosomal loci of the differentially expressed genes.

Additionally evident differentially expressed genes (adjusted P < 0.05) among the central nervous system tumours provided in the corresponding study[24] were inspected to identify those that are specific for common group 3/4 gain (overexpressed, lfc > 0.5) and loss (low expressed, lfc < 0.5) regions.

WGS data

Mutation calls (SNVs, indels, CNVs and structural variants (SVs)) of previously published WGS data from medulloblastomas of all subtypes were taken from the ICGC dataset[2]. Only samples from primary tumours with clear subtype annotation and clear ploidy status were included; see Supplementary Table 5 for an overview on these samples and associated clinical data.

Driver mutations (SNVs and CNVs)

Non-synonymous SNVs, small indels and small structural rearrangements (amplifications, defined as copy number gains ≥ 10, homozygous deletions with less than 0.9 copy numbers and translocations with a minimal event score of 5) were classified as driver mutations if they targeted a splice-site or an exonic region of PRDM6, MYC or a gene listed as a putative driver of medulloblastoma in the cancer driver database intogen[59] (release date 31 May 2023). Moreover, we included TERT promoter mutations at hg19 positions 1295228 and 1295250 as drivers. High-level amplifications affecting MYC or MYCN (identified from methylation/WGS copy number profiles) and duplications of SNCAIP, leading to overexpression of PRDM6 (identified from WGS SV calling), were additionally integrated from a previous global data analysis[2].

Large-scale CNVs were defined as CNVs spanning at least 1 Mb and with a coverage ratio less than 0.9 or a coverage ratio greater than 1.1, according to the output by ACEseq. Retained CNVs with a size of at least 25% the size of the p arm of a respective chromosome were further classified as affecting both arms if the CNV spanned the centromere, or else as affecting the p arm or the q arm. Among these CNVs, we tested for positive enrichment of particular chromosomes in the cohort using a binomial test with success probability 1/24 (that is, assuming that each chromosome has equal probability to be affected by the CNV). Chromosomes with an adjusted P < 0.05 (Holm’s correction) were classified as likely drivers of medulloblastoma. This analysis was separately performed for gained and lost chromosomes. Among group 3/4 medulloblastomas, we identified gains of chromosomes 4, 7/7q, 12 and 17/17q and losses of chromosomes 8, 10/10q, 11 and 17p as significant. We augmented this list by gains of chromosomes 18 and 1q and loss of 5q, as was reported previously[60].

Timing of CNVs, ECA and MRCA

Quantification of mutation densities at copy number gains was performed using the R package NBevolution v.0.0.0.9000, which is described in detail in the corresponding study[23]. In brief, we counted clonal mutations separately on each autosome, stratified by copy number state using the function count.clonal.mutations() with max.CN=4, excluding chromosomal segments with length less than 10^7 bp. count.clonal.mutations() fits a binomial mixture model with success probabilities according to the expected mean values of the clonal VAF peaks, which, for an impure sample with tumour cell content \(\rho \), are given by

$${\rm{VAFs}}\in \left\{\frac{\rho }{\zeta },\frac{({\rm{CN}}-b)\rho }{\zeta },\frac{b\rho }{\zeta }\right\},$$

(1)

where CN denotes the copy number of a given segment, b denotes the copy number of the minor allele (that is, the allele with the smallest number of copies) on this segment and

$$\zeta =\rho {\rm{CN}}+2(1-\rho )$$

(2)

is the average copy number of a given locus in the sample. Mutation densities (SSNVs per bp) at MRCA and ECA, denoted by \({\widetilde{m}}_{\text{MRCA}}\) and \({\widetilde{m}}_{\text{ECA}}\), respectively, were computed using the function MRCA.ECA.quantification(). In brief, MRCA.ECA.quantification() first estimates \({\widetilde{m}}_{\text{MRCA}}\) from the number of all clonal mutations and the total size of the analysed genome, \(g={\sum }_{l}{g}_{l}\), where the index \(l\) labels individual segments contributing to the analysis, yielding

$${\mathop{m}\limits^{ \sim }}_{{\rm{M}}{\rm{R}}{\rm{C}}{\rm{A}}}=\sum _{l}\frac{{n}_{1,l}+{n}_{{\rm{C}}{\rm{N}}-b,l}({\text{CN}}_{l}-{b}_{l})+{n}_{b,l}{b}_{l}}{g\,{\text{CN}}_{l}},$$

(3)

where \({n}_{k,l}\) denotes the number of clonal mutations present on \(k\) copies of the \(l\)-th segment. Note that \({\widetilde{m}}_{\text{MRCA}}\) normalizes mutation densities per copy and hence can be interpreted as molecular time. The function MRCA.ECA.quantification() also computes lower and upper 95% confidence bounds for \({\widetilde{m}}_{\text{MRCA}}\) by bootstrapping the genomic segments 1,000 times. In the next step, MRCA.ECA.quantification() asks for evidence for an earlier common ancestor in the data. If a chromosomal gain occurs concomitantly with the onset of tumour growth, the density of amplified clonal mutations (that is, present on multiple copies of a gained allele) will, on average, be equal to the density of clonal mutations on non-amplified chromosomes or chromosomal regions. By contrast, if a chromosomal gain occurs earlier, the density of amplified clonal mutations on the gained allele will be smaller than the density of clonal mutations on non-amplified chromosomes or chromosomal regions. To distinguish these two cases, MRCA.ECA.quantification() tests for each gained segment whether or not the density of amplified clonal mutations agrees with the mutation density at MRCA, on the basis of a negative binomial distribution. If the mutation density of amplified clonal mutations is significantly smaller than the mutation density at MRCA, the segment is assigned to an earlier time point or else to the MRCA. MRCA.ECA.quantification() then asks whether segments assigned to earlier time points emerged in the same time window or during different time windows. We define as the null hypothesis that all CNVs emerged in an ECA. To test the null hypothesis, MRCA.ECA.quantification() computes the mutation densities at the ECA from the number of clonal mutations on the amplified minor allele, \({n}_{b,l}\) (if b > 1), and from the number of mutations on the amplified major allele, \({n}_{\text{CN}-b,l}\), as

$${\widetilde{m}}_{{\rm{ECA}}}=\frac{{\sum }_{l,{P}_{{\rm{adj}},l}\le 0.01}{n}_{b,l}+{n}_{{\rm{CN}}-b,l}}{{\sum }_{l,{P}_{{\rm{adj}},l}\le 0.01}{g}_{l,b}+{g}_{l,{\rm{CN}}-b}},$$

(4)

where \({P}_{{\rm{adj}},l}\) is the adjusted P value of segment l belonging to the MRCA, and \({g}_{l,b}\) and \({g}_{l,\text{CN}-b}\) are the length of segment l, contributed by the amplified minor and major alleles, respectively. In analogy to \({\widetilde{m}}_{\text{MRCA}}\), MRCA.ECA.quantification() also estimates lower and upper 95% confidence bounds by bootstrapping. On the basis of a negative binomial distribution (and an FDR of 0.01), MRCA.ECA.quantification() then tests for each early segment whether or not its mutation density indeed conforms to a joined ECA, as defined by \({\widetilde{m}}_{\text{ECA}}\). Only segments with mutation densities conforming to \({\widetilde{m}}_{\text{ECA}}\) are assigned to the ECA, whereas all other segments are reported as conforming neither to the ECA nor to the MRCA.

Upon timing MRCA and ECA for each sample, we translated mutation densities into weeks post conception (p.c.) by inferring SSNV rates per diploid genome and embryonic day (\(\mu \lambda \)), using the measured VAF distributions and age at diagnosis as outlined below in section ‘Real-time estimate of cell division rate’. As mutation calling was performed by comparing tumours against a matched blood control, mutation densities correlate with the time after gastrulation (at approximately 2 weeks after conception). Thus, the mutation density per haploid genome (3.3 × 10^9 bp), \(\widetilde{m}\), relates to the time p.c. according to \(\widetilde{m}(t)=\frac{\mu \lambda }{{\rm{day}}}\frac{1}{3.3\times {10}^{9}}(t-14\,{\rm{d}})\). The estimated time of birth was taken as 38 weeks after gastrulation (40 weeks p.c.).

Timing of SNVs and small indels

We classified SNVs and small indels as subclonal or clonal on the basis of the number of variant reads, \({n}_{\mathrm{var}}\), the number of reference reads, \({n}_{\text{ref}}\), tumour purity \(\rho \) and copy number \(k\). Specifically, mutations were classified as subclonal if the probability to sample at most \({n}_{\mathrm{var}}\) variant reads out of \({n}_{\mathrm{var}}+{n}_{\text{ref}}\) total reads according to a binomial distribution with success probability \(\frac{\rho }{\rho k+2(1-\rho )}\) was smaller than 5%. If a mutation was classified as clonal and fell on a region with \(k=3\), we moreover classified the mutation as early clonal (that is, acquired before the chromosomal gain on the gained chromosome and hence present on at least two copies) if the probability to sample at most \({n}_{\mathrm{var}}\) variant reads out of \({n}_{\mathrm{var}}+{n}_{\text{ref}}\) total reads was at least 5% according to a binomial distribution with success probability \(\frac{2\rho }{\rho k+2(1-\rho )}\), or else as late clonal.

Modelling medulloblastoma initiation

We modelled medulloblastoma initiation and growth with a population genetics model originally developed for neuroblastoma, as described previously[23]. In brief, the model assumes that disease initiation is driven by two consecutive drivers in a transiently expanding tissue of origin, which for group 3/4 medulloblastoma is probably the population of unipolar brush cell progenitors (UBCPs16,17,24). The two driver events are associated with the ECA and the MRCA of the tumour, and spawn, respectively, a pre-malignant and the malignant tumour clone. We assumed that both drivers occur with small probabilities \({\mu }_{1}\) and \({\mu }_{2}\) during cell divisions, and confer a selective advantage (\(r\) and \(s\), respectively) that acts by reducing cell differentiation. Moreover, we assumed that UBCPs acquire on average \(\mu \) neutral somatic variants per cell division, which we modelled with a Poisson process. The population of UBCPs has been experimentally described from week 9 p.c. until the time of birth24,27. To capture this trend, we modelled an initial phase of exponential growth at rate \({\lambda }_{1}-{\delta }_{1},\,{\lambda }_{1} > {\delta }_{1}\) until time \(T\), where \({\lambda }_{1}\) and \({\delta }_{1}\) denote the division and differentiation rate, respectively, and a subsequent phase of exponential decline at rate \({\lambda }_{2}-{\delta }_{2},\,{\lambda }_{2} < {\delta }_{2}\).

Following refs. 23,61, we calculated the probability of the MRCA to occur at time t according to

$${P}_{{\rm{MRCA}}}=\left\{\begin{array}{c}{P}_{{\rm{MRCA}},{\rm{I}}},\,t < T\\ 1-(1-{P}_{{\rm{MRCA}},{\rm{I}}})(1-{P}_{{\rm{MRCA}},{\rm{II}}})(1-{P}_{{\rm{MRCA}},{\rm{III}}}),\,t\ge T\end{array},\right.$$

(5)

with[40]

$${P}_{{\rm{MRCA}},{\rm{I}}}(t)\,=\,\mathop{\sum }\limits_{x=1}^{N(t)-1}{e}^{-{\mu }_{1}(x-1)}(1-{e}^{-{\mu }_{1}})\left(1-\exp \left\{-\frac{{\mu }_{1}{\mu }_{2}{\lambda }_{1}{TN}(t)F}{1-\frac{{\delta }_{1}}{{\lambda }_{1}}}\right\}\right),$$

$${P}_{{\rm{MRCA}},{\rm{II}}}(t)=1-\exp \left(-\frac{{\mu }_{1}{\mu }_{2}{\lambda }_{1}{\lambda }_{2}{\nu }_{2,{\rm{D}}}T}{{\lambda }_{2}-{\delta }_{2}/r}N(T)\{{e}^{({\lambda }_{2}-{\delta }_{2}/r)(t-T)}-1\}\right),$$

$${P}_{{\rm{MRCA}},{\rm{III}}}(t)=1-\exp \left(-\frac{{\mu }_{1}{\mu }_{2}{\lambda }_{2}^{2}{\nu }_{2,{\rm{D}}}N(T)}{{\delta }_{2}\left(\frac{1}{r}-1\right)}\left\{\frac{{e}^{({\lambda }_{2}-{\delta }_{2})(t-T)}-1}{{\lambda }_{2}-{\delta }_{2}}-\frac{{e}^{\left({\lambda }_{2}-\frac{{\delta }_{2}}{r}\right)(t-T)}-1}{{\lambda }_{2}-\frac{{\delta }_{2}}{r}}\right\}\right),$$

where \({\nu }_{2,{\rm{D}}}=1-\frac{{\delta }_{2}}{{s\lambda }_{2}}\) is the survival probability of a cell undergoing the second oncogenic event while the population decays, \(F={\int }_{0}^{1}{\nu }_{2,E}/({\nu }_{2,E}{z}^{\alpha }){dz},\,{\nu }_{2,E}=1-\frac{{\delta }_{1}}{{s\lambda }_{1}}\) and \(\alpha =\frac{{\delta }_{1}-{s\lambda }_{1}}{{\delta }_{1}-{\lambda }_{1}}\). Moreover, we calculated the probability of the ECA to occur at \({t}_{1}\), conditioned on the MRCA occurring at \({t}_{2}\), as described previously[23]

$$P({t}_{1}|{t}_{2})=\frac{{t}_{1}}{{t}_{2}};{t}_{1} < {t}_{2}\le T,$$

and

$$\begin{array}{l}P({t}_{1}|{t}_{2})\,=\\ \frac{\Theta (T-{t}_{1}){\lambda }_{1}{\delta }_{2}\left(1-\frac{1}{r}\right){t}_{1}+\Theta ({t}_{1}-T)[{\lambda }_{2}+{\lambda }_{1}T{\delta }_{2}(1-1/r)-{\lambda }_{2}{e}^{{\delta }_{2}(1-1/r)(T-{t}_{1})}]}{{\lambda }_{2}+{\lambda }_{1}T{\delta }_{2}(1-1/r)-{\lambda }_{2}{e}^{{\delta }_{2}(1-1/r)(T-{t}_{2})}};\\ {t}_{2} > T,\end{array}$$

(6)

where \(\Theta (\bullet )\) is the Heavyside step function and \({0\le t}_{1} < {t}_{2}\).

To estimate the model parameters from the WGS data, we contrasted the probability of acquiring the first and second drivers with the measured distribution of SNV densities at ECA and MRCA in group 3/4 medulloblastomas using approximate Bayesian computation with sequential Monte-Carlo sampling (ABC-SMC), as implemented in pyABC[62]. We used a population size of 1,000 parameter sets and 25 SMC generations or \(\varepsilon \le 0.05\) as termination criteria. The model fit was performed in analogy to Körber et al.[23] (code and pseudo-code are available at https://github.com/kokonech/mbOncoAberrations). 95% posterior-probability bounds for the model fits were estimated by simulating the model with each sampled parameter set and cutting off 2.5% at each end of the simulated distribution.

Modelling medulloblastoma growth

We modelled medulloblastoma growth from the MRCA as exponential growth with rate \({\lambda }_{T}-{\delta }_{T}\), where \({\lambda }_{T}\) denotes the division rate and \({\delta }_{T}\) the loss rate (owing to differentiation or death) in the tumour. Denoting the number of tumour cells with \({N}_{T}(t)\) and assuming that neutral mutations are on average acquired at a rate \(\mu {\lambda }_{T}{N}_{T}(t)\) per haploid genome during tumour growth, the site frequency spectrum of neutral variants at \({t}_{\text{end}}\) is, on average[63],

$${S}_{k}(i,\mu )={\int }_{0}^{{t}_{\text{end}}}{P}_{1,i}({\lambda }_{T},{\delta }_{T},{t}_{\text{end}}-t)\mu k{\lambda }_{T}{N}_{T}(t){dt},$$

(7)

where \(k\) is the chromosomal copy number, and \({P}_{1,i}({\lambda }_{T},{\delta }_{T},{t}_{\text{end}}-t)\) is the probability to grow from a single cell to a clone of size i within a time span \({t}_{\text{end}}-t\), according to a supercritical linear birth–death process (for example, see ref. [64]).

To estimate \(\mu \) and \({\delta }_{T}/{\lambda }_{T}\) from the WGS data, we followed the strategy described previously[23] to compare \({M}_{k}(a,\mu )\), the cumulative allele frequency histogram of variants present in at least \(a\) cells in regions with copy number \(k\), given a mutation rate \(\mu \), between model and data. Here,

$${M}_{k}(a,\mu )=\mathop{\sum }\limits_{i=a}^{b}{S}_{k}(i,\mu )\approx {\int }_{0}^{{t}_{\text{end}}}\mu k{\lambda }_{T}{N}_{T}(t)\frac{{P}_{1,b}({\lambda }_{T},\,{\delta }_{T},{t}_{\text{end}}-t)-{P}_{1,a}({\lambda }_{T},\,{\delta }_{T},{t}_{\text{end}}-t)}{\log \beta ({t}_{\text{end}}-t)}{dt},$$

(8)

$$\beta (t)=\frac{{\lambda }_{T}({e}^{({\lambda }_{T}-{\delta }_{T})t}-1)}{{\lambda }_{T}{e}^{({\lambda }_{T}-{\delta }_{T})t}-{\delta }_{T}}.$$

To this end, we used ABC-SMC[62] with a population size of 1,000 parameter sets and 25 generations or \(\varepsilon \le 0.05\) as termination criteria. To learn the dynamics of tumour growth with confidence, we included tumours with well-defined subclonal tails and no evidence for subclonal selection. Tumours were selected (Supplementary Table 5) on the basis of visual inspection of the VAF histograms, to remove cases with poor subclonal resolution. In addition, we removed cases without age information and those with evidence for subclonal selection as suggested by the evolutionary model implemented in Mobster[65] (setting autosetup = ‘FAST’), which we ran on autosomes and upon computing pseudo-heterozygous VAFs, \(\widetilde{\text{VAF}}\), defined as 50% of the mutant sample fraction, SF (hence, \(\widetilde{\text{VAF}}=\frac{\zeta }{2k}{\rm{VAF}}\), where \(k\) is the number of alleles carrying the mutation and VAF is the actual VAF). For the 35 retained group 3/4 medulloblastomas, we followed the strategy outlined by Körber et al.[23] to estimate the model parameters from the measured VAF distribution (code and pseudo-code are available at https://github.com/kokonech/mbOncoAberrations).

Real-time estimate of cell division rate

From the model fits of medulloblastoma initiation and growth to WGS data, we estimated differentiation/loss rates and mutation rates relative to the rate of cell divisions. To convert these estimates to real-time, we used the age distribution at diagnosis for calibration. In a first step, we estimated the cell division rate of UBCPs, \(\lambda \), from the number of generations between gastrulation and MRCA plus the number of generations between MRCA and diagnosis (tD), which can be inferred from the mutational burden in the tumour[23]. Recall the selective advantage of the growing tumour, \(s\). With λT = sλ, this yields

$$\lambda =\frac{1}{{t}_{{\rm{D}}}}\left(\frac{{\mathop{m}\limits^{ \sim }}_{\text{MRCA}}}{\mu }+\frac{\log {N}_{T}({t}_{{\rm{D}}})}{1-\frac{{\delta }_{T}}{s\lambda }}\right)=\frac{1}{{t}_{{\rm{D}}}}\left(\frac{{\mathop{m}\limits^{ \sim }}_{\text{MRCA}}}{\mu }+\frac{\log {N}_{T}({t}_{{\rm{D}}})}{\mu }{\mu }_{\text{eff}}\right),$$

(9)

where we used the estimate for \(\mu \) from the parameter inference for medulloblastoma initiation and the estimate for the effective mutation rate, \({\mu }_{\text{eff}}=\frac{\mu }{1-\frac{{\delta }_{T}}{s\lambda }}\), from the parameter inference for medulloblastoma growth. Assuming a tumour mass in the order of a few cubic centimetres and hence \({N}_{{\rm{T}}}({t}_{\text{D}})\,=\,\) 10^9 cells, and defining \({t}_{\text{D}}\) as the age at diagnosis, A, plus, on average, 250 d of embryogenesis after gastrulation, we obtained for each tumour (labelled with index i) an estimate for the division rate with mean, \(\langle {\lambda }_{i}\rangle =\frac{1}{2\langle \mu \rangle ({A}_{i}+250\,{\rm{d}})}(\langle 2{\mathop{m}\limits^{ \sim }}_{{\rm{M}}{\rm{R}}{\rm{C}}{\rm{A}},i}\rangle +\log {10}^{9}\langle {\mu }_{{\rm{e}}{\rm{f}}{\rm{f}},i}\rangle )\), and standard deviation, \({\sigma }({\lambda }_{i})=\frac{1}{2\langle \mu \rangle ({A}_{i}+250\,{\rm{d}})}\left(\frac{2\langle {\mathop{m}\limits^{ \sim }}_{{\rm{M}}{\rm{R}}{\rm{C}}{\rm{A}},i}\rangle +\log {10}^{9}\langle {\mu }_{{\rm{e}}{\rm{f}}{\rm{f}},i}\rangle }{2\langle \mu \rangle }{\sigma }\langle 2\mu \rangle +{\sigma }(2{\mathop{m}\limits^{ \sim }}_{{\rm{M}}{\rm{R}}{\rm{C}}{\rm{A}},i})+{\sigma }({\mu }_{{\rm{e}}{\rm{f}}{\rm{f}},i})\right)\), in actual time (where the factor 2 accounts for the fact that \(\langle \mu \rangle \) and \({\mathop{m}\limits^{ \sim }}_{\text{MRCA},i}\) measure the mutation rate and the mutation density, respectively, per haploid genome).

Finally, we computed the mutation rate per day during tumour initiation, by computing \(\mu {\lambda }_{T,i}\), with associated uncertainty \(\mu {\Delta \lambda }_{T,i}+{\lambda }_{T,i}{\sigma }(\mu )\), which relates the molecular clock to real-time. For this purpose, we averaged across the inferences from all tumours that went into the analysis.

Reporting summary

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

Data availability

The DNA whole-genome sequencing mutation results were integrated from the corresponding medulloblastoma molecular landscape study[2] deposited at the European Genome-Phenome Archive under accession number EGAS00001001953. Single-nucleus RNA and ATAC data are available at the GEO database under accession numbers GSE253557 and GSE253573. All raw images and processed data after cell segmentation from spatial transcriptomics experiments are available at the GEO database and can be accessed under accession number GSE252090. Variant calls from WGS data are available from Mendeley Data (https://doi.org/10.17632/g4r22w9pp8.1). scRNA/ATAC and WGS data analysis results can be visually inspected on the interactive web application http://kokonech.shinyapps.io/mbOncoAberrations.

Code availability

All custom Python and R scripts as well as details about the external software environment applied during the data analysis are available at GitHub (https://github.com/kokonech/mbOncoAberrations) and at Zenodo (https://doi.org/10.5281/zenodo.15083518)[66].

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Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 819894) and Seventh Framework Programme (FP7-2007-2013) (grant agreement no. 615253). The INFORM programme is financially supported by the German Cancer Research Center (DKFZ), several German health insurance companies, the German Cancer Consortium (DKTK), the German Federal Ministry of Education and Research (BMBF), the German Federal Ministry of Health (BMG), the Ministry of Science, Research and the Arts of the State of Baden-Württemberg (MWK BW), the German Cancer Aid (DKH), the German Childhood Cancer Foundation (DKS), RTL television and the aid organization BILD hilft e.V. (Ein Herz für Kinder), and a generous private donation of the Scheu family. The study was in parts funded by the European Horizon 2020 Programme—‘iPC - individualizedPaediatricCure’ (grant no. 826121) to S.M.P. and by The Medulloblastoma Initiative to L.M.K. and S.M.P. V.K. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 526169089). K.J.W. was funded by the Mildred Scheel Career Center Frankfurt (Deutsche Krebshilfe). We also thank C. Maus, E. Wang, L. Weiser and G. Warsow for their highly dedicated support in data management and processing, and R. Autry, G. Balasubramanian, C. Previti and R. Kabbe for their sincere and dedicated contribution to the bioinformatics analyses.

Funding

Open access funding provided by Deutsches Krebsforschungszentrum (DKFZ).

Author information

Author notes

1. These authors contributed equally: Konstantin Okonechnikov, Piyush Joshi, Verena Körber

2. These authors jointly supervised this work: Lena M. Kutscher, Stefan M. Pfister

Authors and Affiliations

1. Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany

Konstantin Okonechnikov, Piyush Joshi, Jan Vaillant, Patricia Benites Goncalves da Silva, Britta Statz, Andrea Wittmann, Kathrin Schramm, Mirjam Blattner-Johnson, Petra Fiesel, Barbara Jones, Natalie Jäger, Till Milde, Kristian W. Pajtler, Cornelis M. van Tilburg, Olaf Witt, David R. Ghasemi, David T. W. Jones, Andrey Korshunov, Frank Westermann, Supat Thongjuea, Lena M. Kutscher & Stefan M. Pfister

2. National Center for Tumor Diseases (NCT) Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany

Konstantin Okonechnikov, Piyush Joshi, Jan Vaillant, Patricia Benites Goncalves da Silva, Britta Statz, Andrea Wittmann, Kathrin Schramm, Mirjam Blattner-Johnson, Petra Fiesel, Barbara Jones, Natalie Jäger, Till Milde, Kristian W. Pajtler, Cornelis M. van Tilburg, Olaf Witt, David R. Ghasemi, David T. W. Jones, Andrey Korshunov, Frank Westermann, Supat Thongjuea, Lena M. Kutscher & Stefan M. Pfister

3. Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany

Konstantin Okonechnikov, Piyush Joshi, Britta Statz, Natalie Jäger, Kristian W. Pajtler, David R. Ghasemi, Supat Thongjuea & Stefan M. Pfister

4. Developmental Origins of Pediatric Cancer Junior Research Group, German Cancer Research Center (DKFZ), Heidelberg, Germany

Piyush Joshi, Jan Vaillant, Patricia Benites Goncalves da Silva & Lena M. Kutscher

5. Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany

Verena Körber & Thomas Höfer

6. MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK

Verena Körber

7. Division of Chromatin Networks, German Cancer Research Center (DKFZ) and Bioquant, Heidelberg, Germany

Anne Rademacher & Karsten Rippe

8. Single-cell Open Lab, German Cancer Research Center (DKFZ), Heidelberg, Germany

Michele Bortolomeazzi & Jan-Philipp Mallm

9. Division of Pediatric Glioma Research, German Cancer Research Center (DKFZ), Heidelberg, Germany

Jan Vaillant, Andrea Wittmann, Kathrin Schramm, Mirjam Blattner-Johnson, Barbara Jones, David T. W. Jones & Henrik Kaessmann

10. Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

Jan Vaillant

11. Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany

Mari Sepp, Ioannis Sarropoulos & Tetsuya Yamada

12. CCU Neuropathology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany

Petra Fiesel & Andrey Korshunov

13. Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany

Barbara Jones, Till Milde, Kristian W. Pajtler, Cornelis M. van Tilburg, Olaf Witt & Stefan M. Pfister

14. CCU Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany

Till Milde, Cornelis M. van Tilburg & Olaf Witt

15. Frankfurt University Hospital, Goethe University, Frankfurt, Germany

Konrad Bochennek

16. Goethe University Frankfurt, University Hospital, Neurological Institute (Edinger Institute), Frankfurt am Main, Germany

Katharina Johanna Weber

17. German Cancer Consortium (DKTK), Heidelberg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany

Katharina Johanna Weber

18. Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany

Katharina Johanna Weber

19. Goethe University Frankfurt, University Hospital, University Cancer Center (UCT) Frankfurt, Frankfurt am Main, Germany

Katharina Johanna Weber

20. University Children’s Hospital, Ulm, Germany

Lisa Nonnenmacher & Christian Reimann

21. Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

David R. Ghasemi, Ulrich Schüller, Martin Mynarek & Stefan Rutkowski

22. Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany

David R. Ghasemi & Ulrich Schüller

23. Department of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

Ulrich Schüller

24. Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

Martin Mynarek

25. Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany

Andrey Korshunov

26. Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany

Frank Westermann

Contributions

K.O., P.J. and V.K. performed the main data processing and multi-level bioinformatics analysis. A.R., M.B. and J.-P.M. contributed with spatial data generation and analysis. P.B.G.S., B.S., J.V., D.R.G., A.W. and L.M.K. prepared tumour materials. M.S., I.S., T.Y. and H.K. contributed to the project from an evolutionary biology perspective. K.S., M.B.-J., P.F., B.J., N.J., T.M., K.W.P., C.M.v.T., O.W., K.B., K.J.W., L.N., C.R., U.S., M.M., S.R. and D.T.W.J. provided tumour tissue samples and feedback for the manuscript. A.K., K.R., F.W., S.T. and T.H. contributed to the design of the study. K.O., P.J., V.K., L.M.K. and S.M.P. prepared the figures and wrote the manuscript on the basis of feedback from all authors. L.M.K. and S.M.P. co-led the study.

Corresponding authors

Correspondence to Lena M. Kutscher or Stefan M. Pfister.

Ethics declarations

Competing interests

C.M.v.T. participates on advisory boards for Alexion, Bayer and Novartis. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Richard Gilbertson 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 Group 3/4 medulloblastoma single-nuclei RNA and ATAC data properties.

a) UMAP of snRNA-seq merged dataset with medulloblastoma groups annotated. Non-tumor cells marked by dotted box. Feature plots showing b) SNCAIP, c) PTPRC, d) IGFBP7 and e) AQP4 expression within UMAP of merged snRNA-seq dataset. f) Global CNV profiles derived from snRNA-seq data. Top fragment: non-tumor cells. G) UMAP of snATAC-seq merged dataset with medulloblastoma groups annotated. Green box, normal cells. h) UMAP of snATAC-seq merged dataset with medulloblastoma subgroups annotated.

Extended Data Fig. 2 Validation of copy number profiling using single-nuclei RNA and ATAC data.

a) Merged pseudo-bulk CNV profile of snRNA-seq data from sample MB292 b) Methylation data-derived CNV profiles from sample MB292. c) Correlation plot of CNV values across 500 Kbp bins between snRNA-seq pseudo-bulk and methylation bulk profiles from sample MB292. d) Cross-comparison of snRNA-seq CNV profiles against bulk profiles. Red boxes, 3 cases where the highest correlation does not correspond to the same sample. e) Cross-comparison of snATAC-seq CNV profiles against bulk profiles.

Extended Data Fig. 3 Copy number profiling of single-nuclei profiles from Group 3/4 MYC- and MYCN-amplified samples.

a) Copy number profile of snRNA-seq data from MYCN samples MB183. b,c) Per cell GSVA enrichment of proliferation (b) and differentiation (c) markers within UMAP of MB183 MYCN-amplified sample. d) Differentiation signal compared to ranked MYCN expression within MYCN-amplified subclone in sample MB183. MYCN normalized expression cutoffs: low = zero, intermediate > 0 and <2, high > 2. e) Boxplots showing difference in mean signal of progenitor-like activity (left) and differentiation (right) between MYCN-amplified and non-MYCN-amplified subclones in n = 4 tumor cases. f) Copy number profiles of snATAC-seq data from MYC sample MB89. Right side: zoom-in on MYC region. g,h) Per cell GSVA enrichment of proliferation (g) and differentiation (h) markers within UMAP of MB89 MYC-amplified sample. i) Differentiation signal compared to ranked MYC expression within MYC-specific sublclone in sample MB89. MYC normalized expression cutoffs: low = zero, intermediate > 0 and <2, high > 2. j) Boxplots showing difference in mean signal of progenitor-like activity (left side, t-test p-val: 0.003) and differentiation (right side) between MYC-amplified and non-MYC-amplified subclones in n = 6 tumor cases.

Extended Data Fig. 4 Copy number profiling of single nuclei profiles from Group 3/4 PRDM6 samples.

a) Copy number profiles of snATAC-seq from SCNAIP-PRDM6 sample MB249. b) Per cell GSVA enrichment of proliferation markers within UMAP of MB249 PRDM6 sample. c) Boxplots showing the difference in the mean signal of progenitor-like activity (left) and differentiation (right) between PRDM6- and non-PRDM6 subclones in n = 3 tumor cases. d-e) Correlation of VAF between mutations called from snATAC and bulk WGS data from n = 3 MYC cases: MB272 (d), MB89 (e), MB248 (f). g-i) Mutation heatmaps obtained via snATAC-seq data across subclones f from n = 3 MYC cases: MB272 (g), MB89(h), MB248 (i). j-l) Boxplots of comparison for number of subclone-specific vs. common mutations across snATAC profiles from n = 3 MYC cases: MB272 (j), MB89(k), MB248 (l) SNVs were filtered using bulk germline control and additional filtering parameters (see Methods for details).

Extended Data Fig. 5 Single-nuclei DNA and RNA data from Group 3/4 samples confirm CNV subclones.

a) Projection of RNA data into annotation of subclones. b-c) Cross-comparison of snRNA-seq (b) and snATAC-seq (c) subclonal CNV pseudo-bulk profiles against scDNA subclonal CNV profiles. d-i) Single-cell DNA copy number profiles from MYCN samples MB165 (d), MB183 (e), and MYC samples MB248 (f), MB89 (g), MB272 primary (h) and MB272 relapse (i). Zoom-in for target region (MYC or MYCN) is provided on the right side.

Extended Data Fig. 6 Early evolution of Group 3/4 medulloblastoma.

a) Medulloblastoma samples analyzed by bulk WGS. b) SNV variant allele frequencies on disomic chromosomes for sample MB104 (G34_VIII). Green line, fitted clonal SNV density; dashed line, true clonal VAF estimated with ACEseq. c), SNV densities at MRCA (39 MB G3, 69 MB G4, 21 MB SHH INF, 35 MB SHH CHL/AD, and 17 MB WNT; 4 MB SHH CHL/AD and 2 MB WNT had clonal densities between 0.7 and 2.9 SNVs/Mb and are not shown). Mean and 95% CI (estimated by bootstrapping genomic segments 1,000 times). d) Mean SNV densities at MRCA versus age at diagnosis (n = 173 cases with age information). e) As in d but for G3/4 subgroups (n = 105 cases with age information). f) Left panel, number of early CNVs per tumor. Right panel, percentage of early CNVs with SNV densities agreeing with a single ECA. Data are from G3/4 medulloblastomas with evidence for an ECA. g), Comparison between mutation density estimates obtained in this paper and with MutationTimeR[22]. Estimates at gains/LOH were computed relative to the MRCA using both methods. Shown is the percentage of CNVs per tumor with overlapping 95% CIs. Data are from 38 MB G3, 66 MB G4, 15 MB SHH INF, 28 SHH CHL/AD, and 9 MB WNT with clonal gains/LOH at copy number ≤4 and at least 10^7 bp length. h) Population genetics model of tumors initiation in two steps. i) GSVA scores for MYC target genes and S-phase genes (86 G3/4 medulloblastomas with RNAseq data). j) Overall survival of 23 Group 3 and 36 Group 4 medulloblastomas with available data. k) Doubling times estimated from 35 G3/4 medulloblastomas using the population-genetics model outlined in h). l) Posterior probabilities for the model fit to all G3/4 MBs (n = 108). <µ1, µ_1>, geometric mean of the driver mutation rate.

Extended Data Fig. 7 Clonal copy number changes in Group 3/4 medulloblastoma.

a) Percentage of tumors with copy number gains and losses ≥1 Mb along the genome. Red, regions where CNVs were significantly more frequent than expected, according to a Binomial test with padj < 0.01; Holm correction for multiple sampling. Shown are data from 109 Group 3/4 medulloblastomas. b, SNV densities at clonal chromosomal gains and at MRCA. Shown are mean and 95% confidence intervals (confidence intervals for SNV densities at chromosomal gains/LOH were estimated according to a Poisson distribution; confidence intervals for SNV densities at MRCA were estimated by bootstrapping genomic segments 1,000 times.

Extended Data Fig. 8 Spatial resolution of sub-clonal tumor populations.

a) UMAP of spatial merged dataset with medulloblastoma groups annotation. Normal cells marked. b) UMAP of spatial merged dataset with medulloblastoma subgroups annotation. c-e) Feature plots showing c) AQP4, d) IGFBP7 and e) PTPRC expression within UMAP of merged spatial dataset. f) Spatial gene expression of MKI67, EOMES, AQP4, IGFBP7 and PTPRC across samples. g) Spatial visualization of clones of PRDM6 sample in 2nd image fragment. h) PRDM6 and i) MKI67 spatial expression of sample in g. j) PRDM6 and k) MYCN spatial gene expression in image fragment of sample MB292.

Extended Data Fig. 9 Independent oncogene clones may co-occur in one tumor.

a) CNV profile of MB272 cases bulk methylation data. b) Per cell GSVA enrichments of proliferating, progenitor-like and differentiation in single sample MB272. Spatial expression of c) MYC, d) MYCN, e) CA10 and f) GABRA5 in sample MB272. g) Proximity of each compartment to each other in sample MB272 spatial data. h) Negative correlation (R = −0.287, P = 1.08e-09) between MYC and MYCN expression within medulloblastoma FFPE bulk RNA-seq cohort (n = 435). i) CNV profile of bulk methylation data from a Group 3/4 tumor with amplifications of MYC and MYCN. j) Identification of MYC (left) and MYCN (right) signals in the same sample using immunohistochemistry (IHC). k) CIBERSORT deconvolution results across subset of MYC/MYCN cases from medulloblastoma bulk FFPE RNA-seq cohort. MB272 single cell data with subclones annotation used as a reference, the data from control case is marked with c, target sample marked with asterisk. l) Identification of MYC (red) and MYCN (green) signals in the highlighted target Group 3/4 sample described in panel (k) using FISH. m) Kaplan–Meyer overall survival probability curves for medulloblastoma Subgroup V tumors with (red) and without (blue) MYC amplification as identified from bulk data CNV profiling. n) Kaplan–Meyer overall survival probability curves for medulloblastoma Subgroup V tumors with high (red) and low (blue) MYC subclone level enrichment. o) MYC and p) MYCN expression in spatial transcriptomic relapse sample.

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Okonechnikov, K., Joshi, P., Körber, V. et al. Oncogene aberrations drive medulloblastoma progression, not initiation. Nature (2025). https://doi.org/10.1038/s41586-025-08973-5

- Received: 03 January 2024

- Accepted: 03 April 2025

- Published: 07 May 2025

- DOI: https://doi.org/10.1038/s41586-025-08973-5

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Divergent DNA methylation dynamics in marsupial and eutherian embryos

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Abstract

Based on seminal work in placental species (eutherians)1,2,3,4,5,6,7,8,9,10, a paradigm of mammalian development has emerged wherein the genome-wide erasure of parental DNA methylation is required for embryogenesis. Whether such DNA methylation reprogramming is, in fact, conserved in other mammals is unknown. Here, to resolve this point, we generated base-resolution DNA methylation maps in gametes, embryos and adult tissues of a marsupial, the opossum Monodelphis domestica, revealing variations from the eutherian-derived model. The difference in DNA methylation level between oocytes and sperm is less pronounced than that in eutherians. Furthermore, unlike the genome of eutherians, that of the opossum remains hypermethylated during the cleavage stages. In the blastocyst, DNA demethylation is transient and modest in the epiblast. However, it is sustained in the trophectoderm, suggesting an evolutionarily conserved function for DNA hypomethylation in the mammalian placenta. Furthermore, unlike that in eutherians, the inactive X chromosome becomes globally DNA hypomethylated during embryogenesis. We identify gamete differentially methylated regions that exhibit distinct fates in the embryo, with some transient, and others retained and that represent candidate imprinted loci. We also reveal a possible mechanism for imprinted X inactivation, through maternal DNA methylation of the Xist-like noncoding RNA RSX[11]. We conclude that the evolutionarily divergent eutherians and marsupials use DNA demethylation differently during embryogenesis.

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Main

Marsupials diverged from eutherians 160 million years ago, and provide unique insight into mammalian embryology[12]. Relative to that of eutherians, formation of the placenta occurs late, and implantation is transient. By contrast, pups are born early and complete development outside the uterus. Like eutherians, marsupials undergo X chromosome inactivation (XCI). However, they lack the eutherian XCI-initiating noncoding RNA Xist. Instead, the alternative noncoding RNA, RSX, is implicated in marsupial XCI[11]. Furthermore, in eutherians, XCI in the soma is random, but in marsupials, it is imprinted, with the paternal X always chosen for silencing[13].

Epigenomic profiling has not been performed in preimplantation marsupial embryos. DNA methylation, which in eutherians is globally reprogrammed and linked to key events in embryo development, is of particular interest[14]. In eutherians, the paternal genome undergoes active demethylation after fertilization. Subsequently, the paternal and maternal genomes undergo passive demethylation, resulting in global hypomethylation in the early blastocyst1,2,3,4,5,6. Global erasure of DNA methylation is not observed in non-mammalian vertebrates15,16, and is thought to serve a mammal-specific function (for example, to permit early embryonic genome activation (EGA), erasure of paternal methylation and epimutations, formation of the trophectoderm, establishment of pluripotency or expression of transposons regulating embryo development)7,8,9,10. These embryonic milestones occur over a more protracted period in the marsupial, making it a useful alternative model to investigate the role of DNA demethylation in mammalian embryogenesis. Marsupials possess the core vertebrate DNA methylation machinery, including de novo methyltransferase genes (DNMT3A and DNMT3B), DNMT3L, TET1–3, UHRF1 and two maintenance methyltransferase genes (DNMT1A and DNMT1B)[17]. However, the status of DNA methylation in gametes and during preimplantation development has not been explored.

Cleavage embryos remain hypermethylated

To examine global methylation during marsupial embryogenesis, we generated low-input bisulfite sequencing (BS-seq) libraries from opossum sperm, oocytes and male and female preimplantation embryos, collected daily from embryonic day (E) 1.5 to E7.5, as well as triplicate adult somatic tissues representing the three germ layers (Fig. 1a and Supplementary Table 1). Our embryo series included the time points of EGA (E3.5), XCI (E3.5), blastocyst formation (E5.5) and lineage divergence to form the trophectoderm and epiblast (EPI; E6.5 and E7.5; Supplementary Table 1). Control BS-seq on mice recapitulated published results[3], with sperm and brain exhibiting hypermethylation and a bimodal methylation pattern, and blastocysts showed global hypomethylation (mean methylation 76% in sperm, 71% in brain and 16% in blastocysts; Extended Data Fig. 1a).

Fig. 1: DNA methylation dynamics in opossum embryos.

a, Light microscopy images of gametes, E1.5–E7.5 opossum embryos and brain. ED, embryonic disc; TE, trophectoderm. Scale bars, 10 μm (sperm), 100 μm (embryos) and 0.5 cm (brain). The black arrowhead marks the zona pellucida; the asterisk marks the mucoid coat; the white arrowheads mark blastomeres. Sample numbers are provided in Supplementary Table 1. b, Histograms of DNA methylation distribution at CpG sites captured at ≥5× coverage (note different scales on y axes). c, Mean DNA methylation level across developmental time. d, Micrographs: pronuclear-stage embryos (29.5 h post coitum) immunostained for histone H3 (n = 7), 5mC (n = 8) or 5hmC (n = 3) and co-immunostained for H3K9me3. The two pronuclei (Pat, paternal; Mat, maternal) are of equivalent size (unlike in mice), but the paternal pronucleus is often marked by proximity to the sperm tail (arrowhead in first panel). Scale bars, 20 μm. Plots: quantification of the data. The centre line indicates the median, the bounds indicate the first and third quartiles, and the whiskers indicate 1.5× interquartile range (IQR). The more granular appearance of H3K9me3 in the middle and bottom panel is due to acid treatment of these samples, which is required for 5mC and 5hmC visualization.

Source data

In our opossum experiments, we obtained between 47 million and 337 million reads per library, with mapping rates between 15.7% and 71% (Supplementary Table 1). Principal component analysis separated sperm from other samples, with further separation by time point along principal component 2 (Extended Data Fig. 1b). After in silico pooling per time point, we captured between 49% and 91% of CpGs at 1× coverage, and 4.2% and 83% of CpG sites at 5× coverage (Extended Data Fig. 1c and Supplementary Table 1). As genomic coverage did not reach saturation in lower-input time points, we examined the proportion of genomic regions captured in each sample, and found that they were broadly similar between time points (Extended Data Fig. 1d). We therefore interpreted the global methylation distributions of each time point as representative of the overall genome.

Examination of the opossum methylation data revealed similarities to and differences from eutherians (Fig. 1b,c and Extended Data Fig. 2a). In opossums, like eutherians3,4,5,6, the sperm genome was hypermethylated relative to the oocyte. However, in opossums, the magnitude of the difference was smaller (mean methylation 77% in sperm and 65% in oocyte). As a result, the methylation level in oocytes was similar to that in somatic tissues (mean methylation 72%, 69% and 55% in brain, liver and spleen, respectively). Furthermore, following fertilization in the opossum, the genome remained relatively hypermethylated (mean methylation 62.5%). The E1.5 methylome was more similar to that of the oocyte, suggesting that some sites in the sperm are demethylated and reprogrammed to an oocyte-like state (Fig. 1b,c and Extended Data Fig. 1b). Nevertheless, the high levels of methylation in the early embryo demonstrated that the global loss of DNA methylation typical of eutherians did not occur in the opossum. To support this conclusion, we immunostained for 5-methylcytosine (5mC) and its derivative 5-hydroxymethylcytosine (5hmC) in opossum zygotes (Fig. 1d). In eutherians, demethylation of the paternal genome after fertilization results in lower 5mC and higher 5hmC levels in the paternal than in the maternal pronucleus1,2,18,19. We reproduced these findings in mice (Extended Data Fig. 2b). In the opossum, the paternal and maternal pronucleus contained equal levels of histone H3 but could be distinguished by histone 3 Lys9 tri-methylation (H3K9me3) staining, the level of which was lower in the paternal pronucleus (see Fig. 1d caption for details). In contrast to those of eutherians, 5mC and 5hmC levels in the opossum were similar between the paternal and maternal pronucleus. DNA methylation dynamics in the paternal pronucleus therefore differ between opossums and eutherians.

We next tracked DNA methylation levels during cleavage and blastocyst formation. The hypermethylated state persisted up to and including E4.5, when the mean methylation was 64% (Fig. 1b,c). Subsequently, in the blastocyst, methylation initially decreased to 54% at E5.5 and 45% at E6.5, before rising to 49% at E7.5. Differential methylation testing of CpG sites between paired samples revealed that more than 90% of captured sites did not undergo significant changes in methylation level (Extended Data Fig. 2c). The stability in methylation was observed at all specific genomic features, including genes, intergenic regions and transposons (Extended Data Figs. 2d and 3a). In the last case, we nevertheless observed expression of multiple transposable element families at EGA (Extended Data Fig. 3b). This may be because expression derives from individual subfamily loci for which we did not capture sufficient BS-seq coverage to reveal locus-specific patterns. Alternatively, expression may be triggered independently of DNA demethylation. As observed in eutherians[20], promoters and CpG islands (CGIs) were largely hypomethylated across developmental time (Extended Data Fig. 2d). We conclude that, in contrast to the case in eutherians, in opposums, EGA and the cleavage divisions take place in the context of a hypermethylated genome.

Divergent DNA methylation in EPI and trophectoderm

In the opossum, lineage divergence initiates at E6.5. To establish whether the decrease in methylation levels revealed by bulk analysis affected all cells or specific lineages, we performed BS-seq on isolated E7.5 embryonic disc (comprising EPI and primitive endoderm) and trophectoderm. The level of methylation was higher in the embryonic disc (63%) than in the trophectoderm (47%; Fig. 1b,c). This observation was consistent with the results of previous methylation-sensitive enzyme assays[21]. One explanation for this finding is that DNA methylation is retained in the EPI but reduced in the trophectoderm. An alternative is that methylation is reduced in both cell types and is then rapidly re-established in the EPI. To distinguish these possibilities, we performed single-cell multi-omics on embryos collected before lineage divergence (E5.5) and after lineage divergence (E6.5 and E7.5; Fig. 2a). As embryos transitioned from E5.5 to E6.5, a decrease in methylation was observed in both the EPI and the trophectoderm. Subsequently, at E7.5, the level of methylation increased in the EPI but continued to decrease in the trophectoderm. The level of methylation in the trophectoderm was similar to that described for eutherian post-implantation extraembryonic tissues[22]. Partially methylated domains, a characteristic of the eutherian trophectoderm, were also present in the opossum (Fig. 2b). Thus, relative to that in eutherians, DNA demethylation in opossums occurs late, is transient and modest in the EPI, and is sustained in the trophectoderm. Furthermore, a lower level of methylation and formation of partially methylated domains in the extraembryonic tissue is a conserved feature of therian mammals.

Fig. 2: Differential methylation in opossum trophectoderm and EPI.

a, Mean methylation level in blastocyst single cells before and after lineage segregation. nE5.5 = 58, nE6.5 EPI = 19, nE6.5 TE = 19, nE7.5 EPI = 24, nE7.5 TE = 14. The centre line indicates the median, the bounds indicate the first and third quartiles, and the whiskers indicate 1.5× IQR. b, Segmentation of the genome into contiguous regions with similar methylation levels (segments 1–3) according to embryonic disc (ED) and trophectoderm (TE) methylation patterns. Presence of long segments with an intermediate level of methylation in the trophectoderm is indicative of partially methylated domains in this tissue. c, Mean expression of DNA methylation machinery derived from previous RNA-seq data[23]. Error bars represent 1.96 × s.e.m. noocyte = 6, nE1.5 = 7, nE2.5 = 21, nE3.5 = 62, nE4.5 = 55, nE5.5 = 140, nE6.5 = 201, nE7.5 EPI = 108, nE7.5 TE = 55.

Source data

To investigate the mechanisms behind DNA methylation changes during blastocyst development, we examined the mRNA expression of DNA methylation enzymes in our published opossum embryo single-cell RNA sequencing (RNA-seq)[23] data (Fig. 2c) and new multi-omics dataset (Extended Data Fig. 4a), which showed good coherence. Expression of the maintenance methyltransferases DNMT1A, DNMT1B and the DNMT1 binding partner UHRF1 was high during cleavage and then declined before blastocyst formation. Notably, between E6.5 and E7.5, the expression of these factors, and of the establishment methyltransferases DNMT3A and DNMT3B, increased in the EPI concomitant with increased DNA methylation. Conversely, mRNA expression of these enzymes fell in the trophectoderm, concomitant with reduced DNA methylation. The DNMT cofactor DNMT3L was not expressed. Expression of the ten-eleven translocation methylcytosine dioxygenase TET1 was observed; notably, however, levels of expression of TET2 and TET3, the latter of which influences methylation in eutherian embryos18,24,25,26,27,28, were very low. Our findings suggest that differences in DNMT enzyme expression could drive the distinct DNA methylation profiles in the EPI and trophectoderm.

DNA methylation landscape of gametes

We next identified differentially methylated regions (DMRs) in opossum oocytes and sperm. We found 20,800 sperm-specific and 22,921 oocyte-specific DMRs. As in eutherians, oocyte DMRs were relatively enriched in intragenic regions and CGIs, whereas sperm DMRs were enriched in intergenic regions (Fig. 3a). To identify transient and long-term autosomal imprints, we searched for DMRs that exhibited intermediate levels of methylation (40–60%) at E3.5, and tracked their fate in E7.5 embryonic disc and trophectoderm, and adult brain, liver and spleen. We found 12,096 intermediately methylated regions at E3.5 (Fig. 3b). Around half of these were retained at E7.5, some in a trophectoderm- or embryonic disc-specific manner, and 85 persisted in all 3 adult tissues. As in eutherians, most retained DMRs were of maternal origin (Supplementary Table 2). In adult tissues, we identified 78 DMR-proximal genes, 3 of which are known to be imprinted[29] (NKRFL2, ZFP68 and RWDD2A; example locus in Fig. 3c), and the remainder of which are imprinting candidates (Supplementary Table 2).

Fig. 3: DNA methylation in sperm and oocytes.

a, Genomic distribution of gamete DMRs. b, Fate of gamete DMRs during embryogenesis. c, DNA methylation levels at individual CpG sites at a representative region of the imprinted NKRFL2 locus. d, DNA methylation profiles in eutherian and opossum oocytes at genes transcribed in oocytes (active), genes not transcribed in oocytes (inactive) and intergenic regions. The centre line indicates the median, the grey circle indicates the mean, the bounds indicate the first and third quartiles, and the whiskers indicate 1.5× IQR.

Source data

Eutherian oocytes exhibit divergent DNA methylation patterns, with methylation restricted to the bodies of expressed genes in rodents and extending into non-transcribed regions in other species3,4,5,30. To understand the relationship between oocyte transcription and methylation in opossums, we integrated our methylation datasets with previously published RNA-seq data, and compared our findings with those in other eutherians23,31,32,33,34,35,36 (Fig. 3d and Supplementary Table 3). Transcribed genes were heavily methylated across the gene body in eutherians and opossum. Non-transcribed regions (inactive genes and intergenic regions) were methylated at low levels in mice and rats, moderately methylated in humans, and more highly methylated in cows, consistent with previous findings. Opossums exhibited an extreme scenario, with non-transcribed regions exhibiting a very high level of methylation. DNMT3L was not expressed in oocytes, indicating that, as in humans[37], de novo DNA methylation in opossum oocytes is DNMT3L independent. We also found that non-CpG methylation is relatively enriched in opossum oocytes, as in eutherian oocytes, indicating that this feature is conserved in therians (Extended Data Fig. 4b).

Inactive X hypomethylation

The active X and inactive X (Xi) exhibit distinct DNA methylation patterns in eutherians. At a chromosome-wide level, the Xi is slightly less methylated than its active counterpart, but the Xi exhibits hypermethylation at CGI promoters38,39,40,41,42. We confirmed these findings in adult mouse somatic tissues using a model exhibiting skewed XCI (Extended Data Fig. 5a,b). By contrast, in marsupials, the Xi is hypomethylated at CGIs and transcription start sites43,44,45,46,47,48. However, patterns of DNA methylation on the X chromosome have not been examined in preimplantation embryos.

We found that in adult opossum tissues, the level of X-chromosome DNA methylation in female animals was approximately half of that in male animals, in agreement with findings in another marsupial, the koala[48] (Fig. 4a and Extended Data Fig. 5c). Allele-specific analysis shows that this was due to hypomethylation of the silenced paternal X chromosome (Extended Data Fig. 5d). Hypomethylation was observed at all genomic features on the Xi, except at XCI escapees[45], where methylation levels were equivalent to those on the active allele (Extended Data Fig. 5e,f). To examine when Xi hypomethylation is established, we assessed X-chromosome DNA methylation levels in embryos (Fig. 4b), which we sexed by quantification of X- and Y-mapping reads (Extended Data Fig. 5g). In the sperm and oocyte, X-chromosome DNA methylation levels were equivalent, demonstrating that the Xi is not inherited from the sperm in a hypomethylated state. Loss of methylation on the Xi occurred gradually during the cleavage stages, with hypomethylation clear from the blastocyst stage (Fig. 4b). Methylation levels therefore differ between chromosomes, being retained on the active X and the autosomes, but lost on the Xi during embryo development.

Fig. 4: DNA methylation status of the opossum X chromosome and RSX locus.

a, Methylation distribution of the autosomes and X chromosome in adult male and female brain represented as density plots showing the distribution of the data and the probability of a variable being a certain value. b, Methylation distribution of the autosomes and X chromosome in male and female gametes and embryos. c, Methylation at the RSX locus in gametes, embryos and adult tissues. d, Quantitative PCR analysis in DNMT1-knockout (KO) immortalized male fibroblasts. ncontrol = 3, nDNMT1 KO = 3. Unpaired t-test. Each point represents the mean of the replicates. Error bars represent s.e.m. e, RSX RNA fluorescence in situ hybridization in wild-type and DMNT1-KO day-8 immortalized male fibroblasts. DAPI, 4′,6-diamidino-2-phenylindole. Scale bars, 20 μm. f, Quantification of RSX clouds in RNA fluorescence in situ hybridization at 4 and 8 days after DNMT1 deletion. g, X/A gene expression ratios at 4 and 8 days after DNMT1 deletion. ncontrol = 3, nDNMT1 KO = 3. Unpaired t-test. Error bars represent s.d. Each point represents the median X/A ratio per replicate. h, Schematic of DNA methylation status in eutherian (left) and opossum (right) embryos.

Source data

A candidate mechanism for imprinted XCI

In most eutherians, XCI in the early embryo is random49,50. The mouse is an important exception, with random XCI preceded by silencing of the paternal X imprinted in the preimplantation embryo51,52. Imprinted XCI in mice is independent of DNA methylation[53], relying instead on H3K27me3-mediated silencing of the maternal Xist allele[54]. The contribution of DNA methylation to XCI imprinting in marsupials is unclear. Methylation of the maternal RSX promoter has been observed in fetal opossum tissue[45], but whether this epigenetic mark is inherited from the gamete is unknown. We identified a DMR encompassing the RSX promoter that was highly methylated in the oocyte and hypomethylated in sperm (Fig. 4c). Intermediate methylation of this region in female embryos and adult tissues suggests that this pattern is retained, consistent with an instructive role in imprinted XCI.

To assess its role in RSX regulation, we ablated DNA methylation in bulk immortalized male opossum fibroblasts using CRISPR-mediated deletion of DNMT1A and DNMT1B (hereafter DNMT1). Loss of DNMT1 reduced methylation genome wide, including at the RSX promoter (Extended Data Fig. 6a,b). It also caused ectopic expression of the normally silent RSX allele in male cells, as assayed by quantitative PCR (Fig. 4d). This ectopic expression was accompanied by formation of RSX clouds in male cells (Fig. 4e,f).

Ectopic expression of RSX in male fibroblasts was associated with suppression of X-gene activity, with X genes over-represented among all downregulated genes (Extended Data Fig. 6c) and the X-to-autosome (X/A) ratio mildly but significantly decreased (Fig. 4g). Loss of DNMT1 also led to a decrease in the level of DNA methylation and an increased level of expression of multiple transposable element families (Extended Data Fig. 6d). We also examined expression of the autosomal H19 locus, which is imprinted in marsupials[55] (H19 is not present in the opossum assembly and was therefore not recovered in our earlier imprinted screen). The level of H19 expression increased following DNMT1 deletion (Extended Data Fig. 6e), suggesting that DNA methylation may be a general mechanism regulating imprinting in opossums.

Discussion

Here we define the DNA methylation landscape of early development in a marsupial. We demonstrate that opossums do not undergo a eutherian-like global demethylation phase during cleavage (Fig. 4f). DNA demethylation is mild and transient in the EPI but sustained in the trophectoderm. On the basis of our findings, we suggest that in therian mammals, DNA demethylation may play an especially important role in trophectoderm formation. Differences in DNA methylation between the EPI and trophectoderm are presumably achieved through cell-autonomous mechanisms, because the marsupial blastocyst is unilaminar. We propose that this is regulated by differential expression of the DNA methylation machinery. DNA demethylation is also proposed to erase germline-acquired epimutations[7]. This model would predict that epimutations are more stably maintained in marsupials than in eutherians.

We also identify new candidate marsupial imprinted genes that will be useful for investigating conservation and evolution of imprints in therians. In eutherians, imprints are maintained in embryos through the Krüppel-associated box zinc-finger proteins ZFP57 and ZFP445. Of the two, only ZFP445 is conserved in marsupials[56] and may contribute to imprint maintenance in these mammals. We offer a mechanism for marsupial imprinted XCI, mediated by differential methylation of RSX. Once methods to epigenetically edit the marsupial genome become available, it would be possible to further test this mechanism by targeted ablation of DNA methylation at the RSX promoter. This DNA methylation-mediated mechanism of imprinted XCI differs to that in mice, which achieve imprinted XCI using H3K27me3. We previously identified XSR, an RSX antisense RNA expressed in oocytes and from the maternal X chromosome in embryos[23]. Although its contribution to imprinted XCI is not established, XSR may help establish RSX promoter methylation through a mechanism reminiscent of Tsix-mediated Xist promoter methylation[23].

Our work highlights the strength of the marsupial model to understand evolutionary epigenetics. Given that post-fertilization global DNA methylation erasure is absent in the opossum, and zebrafish15,16, we propose that this process evolved after the marsupial–eutherian split. In this context, it is noteworthy that Dppa3 (also known as Stella or Pgc7) evolved after the marsupial–eutherian divergence[57]. In eutherians, Dppa3 is important for preimplantation development58,59,60, and has been implicated in both passive demethylation and protection against demethylation57,61,62,63,64,65,66,67,68. Our current findings are consistent with the hypothesis that evolutionary acquisition of Dppa3 accompanied the development of oocyte and embryonic DNA demethylation9,57.

Methods

Animals

Opossums and mice (C57BL/6J and Mus spretus) were maintained in the Francis Crick Institute Biological Research Facility in accordance with UK Animal Scientific Procedures Act 1986 regulations (project licence P8ECF28D9) and subject to Francis Crick Institute’s internal ethical review. Additional opossums were housed at the University of Texas Rio Grande Valley under Institutional Animal Care and Use Committee protocol AUP-19-31. No randomization or blinding was performed and no statistical methods were used to predetermine sample size. Mice were housed in individually ventilated cages (GM500, Tecniplast) with a 12:12-h light/dark cycle, a temperature of 20–24 °C and humidity of 55% ± 10%. Adults (8 weeks–6 months) were housed in groups of 3–4 animals, with the different sexes housed separately. Mice had free access to water and food and were provided enrichment activities including rodent balls and nesting boxes. Matings for timed collection of embryos were conducted by placing female mice into the cage of male mice at approximately 17:00. Observation of a vaginal plug the following morning was taken to indicate that mating had occurred.

Opossums were individually housed in Double Decker cages (GR1800, Tecniplast), with male and female animals housed in separate rooms except during mating periods. The temperature of the housing was maintained between 24 °C and 28 °C, and humidity was maintained between 55% and 75%, with a 14 h/10 h light/dark cycle. Opossums had free access to dried food and water, supplemented every second day with live mealworms, and weekly by fresh fruit. To induce oestrous before mating, adult male and female (≥6 months) mice were placed in single-storey rat cages immediately adjacent to each other for 2 days, and then swapped into each other’s cages for an additional 2 days. Subsequently, pairs were placed into the same cage and kept together for 10 days, during which period animals were monitored by infrared CCTV camera for mating behaviour[69].

Collection of gamete, embryo and tissue samples

Mouse E0.5 (11 h post coitum (hpc)) and E3.5 (82–84 hpc) embryos were recovered by flushing the uteri with PBS (Gibco number 14190-094) from a blunt-ended needle under a Leica MC80 dissecting microscope, aspirated using a Stripper pipette with a 275-μm tip (MXL3-STR and MXL3-275, Cooper Surgical), and washed three times through drops of clean PBS. For immunofluorescence, zygotes were fixed in 4% PFA in PBS with 0.2% Triton X-100 for 60 min at room temperature and washed three times in 0.2% Triton X-100 in PBS with 0.01% polyvinyl alcohol (PVA; number P8136, Sigma). For sequencing of blastocysts, the zona pellucida was removed by incubation in acid Tyrode’s solution (T1788, Sigma-Aldrich), and then the embryo was washed three times in PBS, snap-frozen on dry ice and stored at −80 °C until processing. Picking-buffer-only negative controls were collected in parallel, and processed through the library preparation procedure to verify the absence of contamination.

Opossum embryos were recovered from the uteri in PBS under a Leica MC80 dissecting microscope at E0.5 (29 hpc), E1.5 (36 hpc), E2.5 (60 hpc), E3.5 (84 hpc), E4.5 (108 hpc), E5.5 (132 hpc), E6.5 (156 hpc) and E7.5 (180 hpc). In the case of oocyte collection, the female animal was mated to a vasectomized male animal, and oocytes were recovered from the uterus at 36 hpc. Embryos were aspirated using a Stripper pipette with a 600-µm tip (MXL3-600, Cooper Surgical), or a micropipette fitted with a 200-µl tip for large E7.5 embryos, and washed three times in PBS. Samples were imaged using a Leica DMIL LED microscope with a 20× objective and then further processed for sequencing or immunofluorescence. The shell coat was perforated with 1-μm dissecting needles (10130-20, FST) and incubated in 5 mg ml−1 protease in PBS (P8811-100MG, Sigma) at 32 °C for between 2 and 7 min. The sample was transferred into fresh PBS, and any remaining mucoid coat was removed by disaggregation with dissecting needles.

For sequencing, the oocytes and E0.5–7.5 embryos were washed through three drops of fresh PBS, dispensed to 0.2 µl tubes, and snap-frozen on dry ice and stored at −80 °C until further processing. We collected multiple single embryos for each time point, and in addition collected pooled litters of oocyte, E2.5 and E3.5 samples (Supplementary Table 1). For E7.5 embryonic disc and trophectoderm samples, after removal of the shell coat, the embryonic disc was isolated from the trophectodermT by dissection with 1-μm needles, washed through three drops of PBS, and snap-frozen as above. For single-cell sequencing, embryos were washed through three drops of fresh PBS with BSA, and incubated in TrypLE (12604013, ThermoFisher) diluted to 0.5× in PBS for 2–8 min at 35 °C. Following TrypLE incubation, embryos were moved to PBS with BSA and disaggregated to single cells by repeated pipetting through a narrow-bore pipette, before nucleosome, methylome and transcriptome (NMT)-sequencing processing. Picking-buffer controls were included as described above. For immunofluorescence, E0.5 zygotes were fixed and washed as above for mouse embryos.

For collection of sperm from mouse and opossum adult male animals, epididymides were dissected from the testes and rinsed in PBS. Cauda epididymides were transferred to 2 ml Bigger–Whitten–Whittingham buffer (opossums) or TYH buffer (mice). Several small incisions were made, followed by incubation at 37 °C for 30 min to facilitate sperm swim out. The swim-out was diluted approximately 1:10 in PBS, and visualized on a dissecting microscope using a dark field. Individual sperm were aspirated using a Stripper pipette with a 100-µl tip (MXL3-100, Cooper Surgical) and washed three times in PBS before collection in 5 µl RLT Plus (1053393, Qiagen) containing 2% β-mercaptoethanol. For collection of pools of sperm, multiple sperm cells were picked in one pipette and processed together through washing, collection and freezing as for single sperm cells. Picking-buffer controls were included as described above.

For collection of genomic DNA from adult mice, and genomic DNA and RNA samples from adult opossums, brain, liver and spleen tissues were dissected into ≈10-mm pieces, snap-frozen in liquid nitrogen, and stored at −80 °C. To facilitate allele-specific analyses, a cross was set up with parent animals from the LL2 opossum stock, and tissue samples were collected from the parents for whole-genome sequencing (WGS) and genomic variant identification, and from three male and three female littermates for BS-seq and RNA-seq. Mouse tissue samples were collected from a model exhibiting skewed XCI through a targeted Xist deletion[70] (that is, C57BL/6J Xisttm1Jae × Mus spretus F_1 hybrid cross). We collected three heterozygous female offspring in which XCI is completely skewed, and three male littermates with Xist deleted. Frozen tissue was pulverized using a pestle and mortar pre-cooled on dry ice, and used for genomic DNA extraction with the PureLink Genomic DNA Mini kit (K18290-02, Invitrogen) or RNA extraction using the Ambion RNAqueous-Micro Kit (AM1931, ThermoFisher). Samples were then processed for WGS, BS-seq or RNA-seq library preparation.

BS-seq

Library preparation was performed with oligonucleotides compatible with the NEBNext library preparation kit (E7535S) following an established method[71] and described briefly here. For gamete and embryo samples, samples were lysed in 2.5 µl RLT Plus and processed following the low-input library method[71]. For brain, liver, spleen and fibroblast samples, 6 ng of genomic DNA was used to prepare libraries following the bulk method[71]. Samples were spiked with 6 fg of Lambda DNA (D152A, Promega) and bisulfite-converted using the EZ Methylation Kit (D5020, Zymo). Bisulfite-converted DNA was purified using the PureLink PCR Purification Column Kit (K310050, Invitrogen) with an additional treatment with M-desulfonation buffer (EZ Methylation Kit, Zymo). Samples were eluted into a mixture containing 0.4 µM Preamp primer (5′-CTACACGACGCTCTTCCGATCTNNNNNN-3′) and submitted to one (brain, liver and spleen samples) or five (gamete and embryo samples) rounds of pre-amplification with Klenow exo− (M0212M, NEB). Unused oligonucleotides were degraded by incubation with exonuclease I (M0293L, NEB), and samples were purified with Ampure XP beads (A63881, Beckman Coulter). Samples were resuspended in a mixture containing 0.4 µM Adaptor 2 Oligo for NEB indices (5′-CAGACGTGTGCTCTTCCGATCTNNNNNN-3′) and adaptor-tagged by incubation with Klenow exo−. Samples were purified using Ampure XP beads and resuspended in a mixture containing 0.2 μM NEBNext Universal Adaptor and 0.2 μM NEBNext Index Adaptor (E7535S, NEB), and library amplification was performed using KAPI Hifi HotStart polymerase (KK2502, KAPA Biosystems). Varying numbers of PCR cycles were performed depending on input amount (opossum oocytes and E1.5–E5.5 embryos: 19 cycles; mouse E3.5 embryos and opossum E6.5 and E7.5 embryos: 10–14 cycles; sperm: 10–18 cycles; brain, liver and spleen: 10 cycles). Library sequencing was carried out by the Francis Crick Institute Advanced Sequencing Facility (ASF). Gamete and embryo libraries were sequenced (100-base-pair (bp) paired end) on an Illumina HiSeq 4000, yielding between 47 million and 337 million reads per library. Brain, liver and spleen libraries were sequenced (150-bp paired end) on an Illumina HiSeq 4000, yielding between 198 million and 363 million reads per library.

RNA-seq

Purified RNA was submitted to the Francis Crick Institute ASF for preparation of cDNA using the SMART-Seq v4 Ultra Low Input RNA Kit (634894, Takara), followed by library preparation using the Nextera XT DNA Library Preparation Kit (FC-131-1096, Illumina). Sequencing (100-bp paired end) was performed on an Illumina HiSeq 4000, generating between 54 million and 156 million reads per library.

NMT-seq

Disaggregated single cells were deposited into individual wells of a 96-well plate, and NMT-seq libraries were prepared as previously described[71]. In brief, cells were incubated with M.CviPI for 15 min, followed by separation of mRNA and genomic DNA using Oligo-dT beads, and subsequent preparation of RNA-seq and BS-seq libraries. Sequencing (100-bp paired end) was performed on an Illumina HiSeq 4000.

DNMT1-knockout opossum fibroblasts

Primary opossum fibroblasts were derived from a newborn male animal and immortalized using SV40-tag virus infection. Single-cell clonal selection was performed to identify an euploid cell line. Opossum fibroblasts were maintained in DMEM (Gibco) supplemented with 20% fetal bovine serum, 1% GlutaMax (Gibco), 1% sodium pyruvate (Gibco) and 1% penicillin–streptomycin (Gibco, 10,000 U ml−1) and were routinely tested and found to be negative for mycoplasma. Single-guide RNAs (sgRNAs) targeting all opossum DNMT1 paralogues (DNMT1A, DNMT1B and DNMT1Ψ) were designed using the online tool CRISPRdirect (https://crispr.dbcls.jp/): DNMT1 gRNA 1: 5′-TCTGAAGGCTTTCATCAAGC-3′; DNMT1 gRNA 2: 5′-CATTGTGGGCCATTGAAATG-3′. sgRNAs were annealed and ligated into the targeting plasmid px333-puro. The plasmid px333-puro was obtained after cloning a puromycin-resistance cassette isolated from px459v2 (gift from F. Zhang, Addgene number 62988) into the px333 vector (gift from A. Ventura, Addgene number 64073)[72].

Immortalized opossum fibroblasts were seeded onto gelatin-coated wells of 6-well plates. The following day, fibroblasts were transfected using PEI MAX (49553-93-7). A 2 µg quantity of plasmid (with sgRNAs or empty plasmid for negative control) was added to 200 µl of Opti-MEM (Gibco) and 8 µl of PEI MAX (1 mg ml−1). One day after transfection, puromycin (2.5 µg ml−1) was added for 48 h to select successfully transfected cells. Control and DNMT1-knockout cells were fixed for RNA fluorescence in situ hybridization (RNA FISH) or frozen down for quantitative PCR with reverse transcription (qRT–PCR) at specific time points.

RNA FISH

Cells were washed in cold PBS and treated with ice-cold permeabilizing solution (0.5% Triton X-100, 2 mM vanadyl ribonucleoside complex in PBS) for 10 min. After fixation using ice-cold 4% PFA in PBS for 10 min, cells were rinsed in ice-cold PBS twice, dehydrated through ice-cold 70%, 80%, 95% and 100% ethanol for 3 min each, and air-dried. BAC VM-18-303M7 (CHORI) was used for RSX RNA FISH. BAC DNA was labelled using Nick Translation Kit (Abbott) with fluorescent nucleotides (spectrum orange-dUTP; 02N33-050, Abott), and cells were hybridized with a denatured mix of probes along with 1 µg salmon sperm DNA in hybridization buffer (50% formamide, 10% dextran sulfate, 1 mg ml−1 PVP, 0.05% Triton X-100, 0.5 mg ml−1 BSA, 1 mM vanadyl ribonucleoside complex in 2× SSC) at 37 °C overnight in a humid chamber. Stringency washes were performed on a hot plate, three times for 5 min in 50% formamide in 1× SSC (pH 7.2–7.4) preheated to 45 °C, and three times for 5 min in 2× SSC (pH 7–7.2) preheated to 45 °C. Cells were mounted in antifade containing DAPI (Vector) with a coverslip and stored at −20 °C.

RNA purification and qRT–PCR

RNA from control and DNMT1-knockout male opossum fibroblasts across three different time points and three replicates was purified using RNAqueous-Micro Total RNA Isolation kit (Invitrogen, AM1931). Purified RNA was retrotranscribed using the Maxima First Strand cDNA synthesis kit (Thermo Scientific, K1641). The following primers were used to assess gene expression in RT–PCR assays using PowerUp SYBR Green (Applied Biosystems, A25780): RSX (5′-AGAAGGGACCCCAAGACAC-3′, 5′-TGGGTCACTTCCACTTCCTC-3′); DNMT1 (5′-GACGCAGTAACACTGGAGCA-3′, 5′-ATCCCATTCCAACCTTCCAT-3′); H19 (5′-TCCAGCAGCAGTCAGTGAAC-3′, 5′-TCATCCATCCATGAGCAGAG-3′); ABCD4 (5′-ATCGATAATCCGGACCAGCG-3′, 5′-ATGATCAGCTTGCTGGCCAT-3′), GAPDH (5′-TAAATGGGGAGATGCTGGAG-3′, 5′-ATGCCGAAGTTGTCGTGAA-3′).

Fibroblast bulk RNA-seq

Libraries from control and DNMT1-knockout RNA samples from day 4 and 8 were prepared with the NEBNext Ultra II Directional PolyA mRNA kit according to the manufacturer's instructions. Libraries were sequenced on the Illumina NovaSeq 6000 system (paired end, 100-bp read length). Raw RNA-seq reads were processed using the RNA-seq nf-core pipeline (v3.2); star_rsem was used to generate raw reads counts. The read counts were processed in R using the DESeq2 package (v1.36). Genes expressed at very low levels were filtered out by applying a rowSums filter of ≥5 to the raw counts table. Raw counts were normalized using the DESeq() function, specifying ~genotype_day in the design formula. log_2[fold change] and adjusted P values between DNMT1 knockout and control were calculated using the lfcShrink() function in DESeq2, specifying type = ‘ashr’, analysing day 4 and day 8 separately.

X/A ratios were calculated using the median expression of X and autosomal genes in each sample after filtering out genes expressed at low levels (transcripts per million (TPM) of <1). X/A ratios between control and DNMT1-knockout samples were compared by Student’s t-test. Repetitive element expression was analysed as above for opossum embryos.

WGS

Samples of 3 μg of genomic DNA prepared from ear snips from one male and one female opossum from the LL2 stock were submitted to the Francis Crick Institute ASF for library construction (KAPA HyperPlus). Libraries were sequenced (150-bp paired end) on a HiSeq 4000, producing 256,834,288 reads (99.23% mapped) for the female animal and 122,371,579 reads (99.27% mapped) for the male animal. Data were used for identification of genomic variants, described below.

Preparation of a modified MonDom5 reference genome and annotations

We modified the MonDom5 reference genome to include a gap-filling long-read sequence of the RSX locus[73]. We prepared modified gene, repeat and CGI annotation files with corrected coordinates on the gap-filled X chromosome. We also included annotation for both opossum DNMT1 paralogues[17] in our modified gene annotation file.

Expression analysis of opossum DNA methylation factors and repetitive elements

Opossum embryo RNA-seq data[23] and DNMT1-knockout RNA-seq data were mapped to the modified MonDom5 reference genome and ASM229v1 reference genome, respectively, using HISAT2 (ref. [74]) with the command hisat2 -3 0 -5 9 --fr --no-mixed --no-discordant, and read summarization at genes was performed using the Rsubread package[75], excluding multi-mapping reads. Fragments per kilobase of transcript per million mapped read (FPKM) values were calculated using the scater package[76]. Read summarization at repetitive elements and calculation of counts per million were performed using Telescope[77]. Line plots showing mean and standard error of gene and repeat expression were generated using ggplot2.

Analysis of methylation at repetitive elements

To generate a bigwig file per time point, the filtered methylKit csv files were converted from csv to bedGraph using awk and then converted to bigwig using the UCSC bedGraphToBigWig utility. Genomic coordinates of the transposable elements, specifically L1, MIR and ERV1, were obtained from the mondom5 RepeatMasker GTF file, selecting for those in the ‘forward’ orientation. Using deepTools[78], the computeMatrix function in scale-regions mode was used to calculate the methylation score, using the parameters --binSize 50 --averageTypeBins mean -a 1000 -b 1000. The plotProfile function was used to generate the profile plot for each transposable element, using the parameters --perGroup --yMin 0 --yMax 1.

Methylation segmentation of E7.5 embryonic disc and trophectoderm

The methSeg function from the R package MethylKit[79] was used to divide the genome into contiguous stretches of similar methylation level in E7.5 embryonic disc and trophectoderm samples. Parameters used were minSeg = 5, G = 1:3, join.neighbours = TRUE. Individual CpG sites with a minimum coverage of five reads in both samples were used for the analysis. The length and average methylation level of each segment were plotted using ggplot2.

Single-cell methylation analysis

Single-cell RNA-seq data were aligned to the opossum ASM229v1 genome, using the nf-core rnaseq pipeline (3.2), using the parameters –aligner star_rsem –bam-csi-index. Raw counts were loaded into Seurat (4.3.0)[80] for analysis in R (4.2.2). In total, there were 74 E5.5 samples, 142 E6.5 samples and 42 E7.5 samples. Each dataset was normalized using NormalizeData, and then datasets were integrated together using the functions SelectIntegrationFeatures, FindIntegrationAnchors and then IntegrateData. The integrated dataset was scaled used ScaleData. Principal component analysis was applied using RunPCA. Uniform manifold approximation and projection was estimated for the integrated dataset using RunUMAP. Module scores for EPI and trophectoderm were calculated using the AddModuleScore function, with the following genes used as markers for each cell lineage. EPI: NANOG, PRDM14, POU5F1 and POU5F3; trophectoderm: GATA2, GATA3, TEAD4, AQP3 and KLF4.

Single-cell BS-seq data were trimmed using the TrimGalore![81] command trim_galore --clip_R1 6 --three_prime_clip_r1 6, mapped using Bismark[82] with the command bismark --non_directional --un --ambiguous --multicore 2 and deduplicated using the command deduplicate_bismark. Methylation information was extracted with the command bismark_methylation_extractor --comprehensive --multicore 2 --bedGraph --CX --cytosine_report --nome-seq. Average CpG methylation was calculated per cell and plotted according to the cell lineage determined for the corresponding RNA-seq library (module score, above).

Analysis of methylation in eutherian oocytes

Raw RNA-seq and BS-seq data were processed using the nf-core rnaseq (3.12) and methylseq (2.5.0) pipelines, respectively. Gene bodies and intergenic regions for each organism were identified using the GenomicFeatures[83] R package. Following a methodology similar to that in ref. [34], genes were classified as active if their TPM was >5 and inactive if their TPM was ≤1, and Bismark CpG methylation calls were imported into R using the methylKit[79] R package, then destranded, pooled by sample condition and filtered for CpG sites with a minimum coverage of three reads. The regionCounts function was used to calculate the methylation level across active genes, inactive genes and intergenic regions. These were visualized as violin plots used ggplot2 (ref. [84]).

Identification of genomic variants

WGS data from LL2 parent opossums were used to identify genomic variants. WGS reads were trimmed using TrimGalore![81] with the command trim_galore --cores 4 -- paired --fastqc --gzip --retain_unpaired --clip_R1 10 --clip_R2 10 -- three_prime_clip_R1 5 --three_prime_clip_R2 5. Libraries were mapped to the MonDom5 reference genome using BWA-MEM[85] with the command bwa mem -t 32 -M -R. Paired and unpaired mapped reads were then merged, sorted and indexed using SAMTools[86]. Variants were called using the GATK best practices pipeline[87]. For the base recalibration step, known variants were not available for opossums. Therefore, variants were initially called independently with three pipelines: BCFtools[88], Varscan[89] and GATK[87]. Variants identified by all three pipelines were considered high-confidence variants and were used for GATK base recalibration. Subsequently, variants were called for each opossum, and then combined, and genotypes were annotated to produce a variant call file. BEDtools maskfasta was used to create an N-masked version of the MonDom5 reference genome from the complete set of 25 million variants. Using a custom R script, variants were filtered to include only hemizygous and heterozygous single nucleotide polymorphisms (SNPs). The resultant 2 million SNPs were used in the SNPsplit pipeline[90].

For mice, genomic variant data (41,668,158 SNPs) were derived from the C57BL/6J and M. spretus genomes (Mouse Genomes Project[91]), and the program SNPsplit[90] was used to generate an mm10 reference genome in which parental genomic variants were N-masked.

Methylation analysis

BS-seq reads were trimmed using the TrimGalore![81] command trim_galore --clip_R1 6 --three_prime_clip_r1 6. Reads were mapped to the mm10 (mouse) or MonDom5 (opossum) reference genome using Bismark[82] on single-end mode with the command bismark --non_directional --un – ambiguous. For adult libraries, reads were mapped to the N-masked genomes, and bam files were generated for all mapped reads as well as for allele-specific reads using SNPsplit[90] with the command SNPsplit --bisulfite –conflicting. Library statistics (Supplementary Table 1) were extracted from TrimGalore! and Bismark output reports. Bam files were deduplicated and methylation calls were extracted using Bismark. CpG methylation calls were imported into R[92] using the package methylKit[79], and principal component analysis was performed using all CpG methylation calls for each individual sample. Data were destranded and pooled by sample condition, and the number of CpG sites captured at different coverage thresholds was calculated. For subsequent analyses, data were filtered for CpG sites with a minimum coverage of five reads. Methylation distribution histograms, mean methylation plot and genomic coverage plots were generated using ggplot2 (ref. [84]). This workflow was independently reproduced to ensure that the results were accurate and robust.

Analysis of gamete DMRs

DMRs between gametes were identified as follows: 100-bp non-overlapping tiles covered by a minimum of three reads in both oocyte and sperm samples and containing a minimum of one CpG in a tile were identified. The methylation level of each tile was compared between oocyte and sperm using the diffMeth function from the R package methylKit[79], with the parameters difference = 80 and qvalue = 0.01. Gamete DMRs with putative transient or life-long retention of differential methylation were defined as tiles with intermediate methylation levels (40−60% methylated), in either E3.5 and E7.5 embryonic disc or trophectoderm (transient embryonic or trophectoderm DMRs), or in E3.5, E7.5 embryonic disc, and all three of brain, liver and spleen (life-long DMRs). The gene nearest to each life-long DMR was identified using the nearest function from the R package GenomicRanges[83], and this list was manually checked for previously reported marsupial imprinted genes29,93. To retain maximum read coverage in low-input embryo samples, all libraries per time point were in silico-pooled for this analysis. The dataset therefore included male and female embryos, precluding analysis of sex differences or the X chromosome.

Sex-specific methylation analysis

CpG methylation calls were destranded, pooled by sample condition (sex and tissue) and filtered for minimum coverage of five reads in all conditions. For embryo samples, sex was inferred from the ratio of reads mapping to chromosome X and pseudo-Y (coding sequence of 19 known opossum Y-chromosome genes)94,95. Autosomes and X-chromosome allelic methylation distributions were represented as ridgeline plots using the R package ggridges[96].

Allele-specific methylation analysis

Allele-specific CpG methylation calls were destranded, pooled by sample condition (sex, tissue and parental genome) and filtered for minimum coverage of five reads in all conditions. To avoid loss of X-linked sites in female samples due to low coverage of the X chromosome in male files, data import and filtering for X chromosomes was performed separately with the coverage parameters as for autosomes, but excluding paternal genome files from male samples. Ridgeline plots were generated using ggridges[96] as above.

Escape-gene methylation analysis

To generate a list of genes expressed in each tissue, RNA-seq reads were trimmed using TrimGalore![81] with the command trim_galore --paired -- clip_R1 10 --clip_R2 10. Trimmed reads were aligned to the N-masked MonDom5 reference genome using HISAT2 (ref. [8]) with the command hisat2 --no-softclip --no-mixed --no-discordant. Mapped files were converted to bam files and merged by sample using SAMtools[86]. Reads overlapping annotated genes were quantified using the command featureCounts from the R package Rsubread[75], excluding multi-mapping reads. Files were merged by biological replicate, and FPKM values were calculated using the R package DESeq2 (ref. [97]) using the robust median ratio method. Gene models annotated as pseudogenes were excluded, and a threshold of FPKM > 1 was imposed. Expressed genes were categorized as escaping or subject to XCI on the basis of published work[45]. Methylation level at genes was calculated using the methylKit functions regionCounts and percMethylation, and represented as violin plots for each category using ggplot2 (ref. [84]).

Immunofluorescent staining of mouse and opossum embryos

Embryos were permeabilized in 0.5% Triton X-100 in PBS at 4 °C overnight, followed by three washes in 0.2% Triton X-100 in PBS with 0.1% PVA (0.2% TX–PBS–PVA). After 3.5 M HCl treatment for 30 min at room temperature, embryos were washed three times in 0.2% TX–PBS–PVA, blocked for 1–4 h at room temperature in 3% BSA in 0.2% TX–PBS–PVA (0.22-µM-filtered, blocking solution) and incubated overnight at 4 °C in primary antibody in blocking solution. Antibodies used were 5mC (BI-MECY-0100, Eurogentec) at 1:100, 5hmC (75-268, NeuroMab) at 1:1,000, H3K9me3 (07-442, MerckMillipore) at 1:200, H3 (ab1791, Abcam) at 1:100. Embryos were washed three times in 0.2% TX–PBS–PVA and incubated in Alexa Fluor-conjugated secondary antibodies at 1:250 in blocking solution for 2 h at room temperature. Three further 0.2% TX–PBS–PVA washes were performed, and DNA was counterstained with 10 µg ml−1 propidium iodide (P4170, Sigma) for 1–2 h at room temperature. Samples were washed three times with PBS with 0.1% PVA and mounted in Vectashield (H-1000-10, Vector Laboratories). Samples were imaged using an LSM710 confocal microscope with 1-µm z-sections. Images were processed and fluorescence intensity was measured using Fiji[98].

Reporting summary

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

Data availability

BS-seq and RNA-seq data have been deposited at the Gene Expression Omnibus (GEO) under the accession number GSE206499. WGS data have been deposited at the Sequence Read Archive under the accession number PRJNA819000. Publicly available datasets analysed in this study are accessible via the Gene Expression Omnibus under the accession numbers GSE163620, GSE71434, GSE101571, GSE163620 and GSE71985; DDBJ under the accession numbers DRA006642 and DRA000570; and ArrayExpress under the accession number E-MTAB-7515. Source data are provided with this paper.

Code availability

Code used in preparation and analysis of data and the generation of figures is available via GitHub at https://github.com/bleeke/opossum-methylation.

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Acknowledgements

We thank the Francis Crick Institute Biological Research Facility, Advanced Sequencing Facility, Bioinformatics and Biostatistics Facility and High-Performance Computing team, M. Sangrithi, N. Fogarty, S. Clark, W. Reik, S. Smallwood, G. Kelsey, P. Skoglund and K. Niakan for advice on the project, and D. Odom and members of the laboratory of J.M.A.T. for comments and discussion on the manuscript. Work in the laboratory of J.M.A.T. is supported by the European Research Council (CoG 647971) and the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001193), the UK Medical Research Council (FC001193) and the Wellcome Trust (FC001193). Some of the work at the University of Texas Rio Grande Valley was conducted in facilities constructed with support from the NIH grant C06 RR020547. Research in the laboratory of R.J.O. is supported by grants from the Bernice Bibby Research Trust and The Paradifference Foundation, the Wellcome Trust through the research equipment grant (212917/Z/18/Z), the NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, the MRC (MR/V038664/1) and the King’s College London Innovation Fund. A.T. is supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 101098236 ERC-2022-AdG). 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

Open Access funding provided by The Francis Crick Institute.

Author information

Author notes

1. These authors contributed equally: Bryony J. Leeke, Wazeer Varsally

Authors and Affiliations

1. Sex Chromosome Biology Laboratory, The Francis Crick Institute, London, UK

Bryony J. Leeke, Wazeer Varsally, Sugako Ogushi, Jasmin Zohren, Sergio Menchero, Aurélien Courtois, Daniel M. Snell, Obah Ojarikre, Shantha K. Mahadevaiah & James M. A. Turner

2. MRC Laboratory of Medical Sciences, London, UK

Bryony J. Leeke

3. Institute of Clinical Sciences, Imperial College London, London, UK

Bryony J. Leeke

4. Advanced Sequencing Facility, The Francis Crick Institute, London, UK

Daniel M. Snell

5. INSERM U934, CNRS UMR3215, Institut Curie, PSL Research University, Paris, France

Aurélie Teissandier

6. Comparative Medicine, Novartis, Basel, Switzerland

Fanny Decarpentrie

7. Department of Medical and Molecular Genetics, King’s College London, London, UK

Rebecca J. Oakey

8. Division of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA

John L. VandeBerg

Contributions

B.J.L. and W.V. are equal first authors, and S.O. and J.Z. are equal second authors. B.J.L. and J.M.A.T. conceived and designed the project and wrote the manuscript. B.J.L. performed gamete, embryo, single-cell and adult tissue collections, BS-seq, RNA-seq, WGS and computational analyses. W.V. performed computational analyses. J.Z. performed adult tissue collections, WGS and computational analyses. S.O. performed embryo immunostaining and fibroblast RNA FISH. S.O. generated DNMT1-knockout fibroblasts. S.M. performed single-cell collections, DNMT1-knockout experiments and analyses. A.C. imaged and quantified RNA FISH experiments. S.K.M. performed gamete collections. D.M.S. processed NMT-seq libraries. F.D. made initial findings of the RSX DMR. J.L.V. provided additional opossum material. O.O. managed the animal colonies. A.T. performed computational analyses. J.M.A.T., J.L.V. and R.J.O. acquired funding.

Corresponding authors

Correspondence to Bryony J. Leeke or James M. A. Turner.

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The authors declare no competing interests.

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Nature thanks Déborah Bourc’his, Takashi Sado 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 Description of mouse and opossum BS-seq data.

a. Histograms of methylation distribution at CpG sites captured at ≥ 5x coverage in mouse sperm (n = 2 libraries of ~100 sperm, in silico pooled), E3.5 embryos (n = 3, in silico pooled), and adult brain (n = 2, in silico pooled) (please note variable scales on y-axes). b. Principal component analysis of opossum methylation in all samples. c. Percentage of CpG sites captured at different coverage thresholds. d. Percentage of captured CpG sites for different chromosomes and genomic features.

Source data

Extended Data Fig. 2 Opossum BS-seq data and mouse immunoanalysis.

a. Histograms of methylation distribution at CpG sites captured at ≥ 5x coverage in opossum adult liver and spleen. b. Pronuclear stage mouse embryos immunostained for 5-methylcytosine (5mC; n = 51), or 5-hydroxymethylcytosine (5hmC; n = 6), each co-immunostained for H3K9me3. mat = maternal, pat = paternal, pb = polar body. Scale bar = 10μm. c. Pairwise comparisons of percentage significantly differentially methylated sites (Fisher’s exact test, q-value <= 0.01, methylation difference 0.25) across entire developmental time course. For each comparison, “higher” and “lower” refers to the status of the second sample listed on the x-axis relative to the first sample d. Histograms of DNA methylation distribution at CpG sites captured at ≥ 5x coverage for each genomic feature (please note variable scale on y-axes).

Source data

Extended Data Fig. 3 DNA methylation and expression of repetitive elements in opossum embryos.

a. Metaplots of methylation across L1, MIR, and ERV1 loci in opossum embryos. b. Expression of LINE, SINE and LTR repetitive elements in opossum embryos. Repetitive element subfamilies are labelled according to Repeatmasker nomenclature, including ‘?’ notation for presumptive subfamily. Noocyte = 12, NE1.5 = 14, NE2.5 = 42, NE3.5 = 124, NE4.5 = 110, NE5.5 = 280, NE6.5 = 402, NE7.5 EPI = 216, NE7.5 TE = 110. Error bars = 1.96*SE. Each point represents the mean of the replicates.

Source data

Extended Data Fig. 4 Expression of methylation enzymes and non-CpG methylation levels.

A. Expression of methylation enzymes in EPI and TE using the multi-omics dataset. NE5.5 = 58, NE6.5 EPI = 19, NE6.5 TE = 19, NE7.5 EPI = 24, NE7.5 TE = 14. Error bars = 1.96*SE. Each point represents the mean of the replicates. b. Levels of non-CpG methylation (CHH and CHG sites) in opossum gametes and embryos.

Source data

Extended Data Fig. 5 DNA methylation status of the opossum X chromosome cont.

a Allele-specific methylation distribution of the autosomes and X chromosome in adult mouse male and female brain and liver, represented as density plots showing the distribution of the data and the probability of a variable being a certain value. b. CGI methylation on the inactive and active X for data in a. c. Methylation distribution of the autosomes and X chromosome in adult opossum male and female liver. d. Allele-specific methylation analysis of the paternal and maternal alleles for data in c. e. Methylation at specific genomic features on the inactive and active X in adult opossum brain, liver and spleen. f. Gene body methylation on the inactive and active X for genes subject to or escaping XCI for female samples in e. g. Sexing of gamete and embryo samples via read mapping to the X and Y chromosome.

Source data

Extended Data Fig. 6 Deletion of DNMT1 in opossum immortalised male fibroblasts.

a. Metaplot of 100,000 randomly selected 1000 nucleotide regions demonstrating global decrease in DNA methylation in DNMT1-deleted fibroblasts at day 4. b. Mosaic loss of DNA methylation at the RSX promoter post-DNMT1 deletion at day 4. c. Proportion of upregulated and downregulated genes by chromosome in DNMT1-deleted fibroblasts at day 8. d. Methylation and expression of repetitive elements following DNMT1-deletion. Above, metaplots showing decreased DNA methylation at L1, MIR and ERV1 repetitive elements 4 days after DNMT1-deletion. Below, line graphs showing transcriptional de-repression of L1, MIR, ERV1 and ERVK elements by day 8 after DNMT1-deletion. Ncontrol = 3, NDNMT1 KO = 3. Error bars = 1.96*SE. Each point represents the mean of the replicates. e. qPCR analysis of H19 in DNMT1 deletant fibroblasts. Ncontrol = 3, NDNMT1 KO = 3. Unpaired t-test. Error bars = SEM. Each point represents the mean of the replicate.

Source data

Supplementary information

Supplementary Table 1

BS-seq library information including sample descriptions, numbers of samples per time point, and sequencing, mapping and coverage statistics. Tab 1 contains information for each individual sequencing library. Tab 2 contains information for in silico-pooled data (per sample and time point).

Reporting Summary

Supplementary Table 2

Information about gamete DMRs identified as potential imprinted regions. Tab 1 contains information including genomic position, methylation level at different time points, and nearest annotated gene. Tab 2 contains information about the number of gamete DMRs retaining intermediate methylation levels in different time points, broken down by gamete of origin.

Supplementary Table 3

Sources of publicly available sequencing data used in this study. Tab 1 contains information for BS-seq datasets. Tab 2 contains information for RNA-seq datasets

Peer Review File

Source data

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Leeke, B.J., Varsally, W., Ogushi, S. et al. Divergent DNA methylation dynamics in marsupial and eutherian embryos. Nature (2025). https://doi.org/10.1038/s41586-025-08992-2

- Received: 21 June 2022

- Accepted: 08 April 2025

- Published: 14 May 2025

- DOI: https://doi.org/10.1038/s41586-025-08992-2

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Chromosome end protection by RAP1-mediated inhibition of DNA-PK

Patrik Eickhoff,

Ceylan Sonmez,

Charlotte E. L. Fisher,

Oviya Inian,

Theodoros I. Roumeliotis,

Angela dello Stritto,

Jörg Mansfeld,

Jyoti S. Choudhary,

Sebastian Guettler,

Francisca Lottersberger &

…

Max E. Douglas

Nature (2025)

An Author Correction to this article was published on 23 May 2025

This article has been updated

Abstract

During classical non-homologous end joining (cNHEJ), DNA-dependent protein kinase (DNA-PK) encapsulates free DNA ends, forming a recruitment platform for downstream end-joining factors including ligase 4 (LIG4)[1]. DNA-PK can also bind telomeres and regulate their resection2,3,4, but does not initiate cNHEJ at this position. How the end-joining process is regulated in this context-specific manner is currently unclear. Here we show that the shelterin components TRF2 and RAP1 form a complex with DNA-PK that directly represses its end-joining function at telomeres. Biochemical experiments and cryo-electron microscopy reveal that when bound to TRF2, RAP1 establishes a network of interactions with KU and DNA that prevents DNA-PK from recruiting LIG4. In mouse and human cells, RAP1 is redundant with the Apollo nuclease in repressing cNHEJ at chromosome ends, demonstrating that the inhibition of DNA-PK prevents telomere fusions in parallel with overhang-dependent mechanisms. Our experiments show that the end-joining function of DNA-PK is directly and specifically repressed at telomeres, establishing a molecular mechanism for how individual linear chromosomes are maintained in mammalian cells.

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Main

Mammalian telomeres are protected from cNHEJ by the shelterin subunit TRF2, which is proposed to hide the chromosome end from DNA repair factors by forming a lariat structure referred to as a t-loop2,5,6. T-loop assembly requires a terminal 3′ overhang[7], which at blunt telomeres resulting from leading strand DNA replication is formed via a 5′ resection step mediated by Apollo exonuclease8,9,10,11,12. DNA-PK is required for this resection step, but is unable to activate cNHEJ even after Apollo deletion[13], suggesting that a mechanism is in place to directly block the end-joining process at telomeres. We hypothesized that this mechanism may involve the conserved shelterin subunit RAP1. In budding yeast, Rap1 binds directly to telomeres and protects them from cNHEJ[14]. However, in mammalian cells, RAP1 is recruited via TRF2 and its role in end protection remains elusive15,16,17,18,19,20.

To examine this idea, we used telomere fluorescence in situ hybridization (FISH) to measure chromosome fusions in mouse embryonic fibroblasts (MEFs) deleted for Apollo (also known as Dclre1b) and/or Rap1 (also known as Terf2ip) (Extended Data Fig. 1a). In agreement with previous studies, Apollo-deleted MEFs showed some chromatid fusions that were LIG4-independent and thus were not caused by cNHEJ[13] (Fig. 1a–c). No fusions were observed upon CRISPR-mediated deletion of Rap1 in Apollo-proficient MEFs, also consistent with previous work19,20,21 (Fig. 1a–c). However, when Rap1 and Apollo were deleted together, approximately 15% of telomeres per metaphase were engaged in LIG4-dependent chromosome-type fusions (Fig. 1a,c). A similar effect was induced by mutating the binding sites for Apollo and RAP1 on TRF2, and the resulting fusions were prevented by deleting the inhibitor of DNA damage response (iDDR) motif (Extended Data Fig. 1b–d), which restores telomeric overhangs in the absence of Apollo[13]. These data strongly suggest that 3′ overhangs and RAP1 can each protect mouse telomeres from cNHEJ. To test whether this is also the case for human telomeres, we repeated the analysis in non-transformed human cells. Unlike in cancer cell lines22,23 telomere fusions were not observed in TP53−/− RPE-1 cells lacking APOLLO (also known as DCLRE1B) (Fig. 1d,e and Extended Data Fig. 1g). However, consistent with the experiments above, 20–30% of telomeres fused in a DNA-PK-dependent manner when RAP1 was also removed (Fig. 1d,e, and Extended Data Fig. 1e–h). A proportion of these fusions involved only one telomere per chromosome end, and in line with the established role of Apollo10,11, these telomeres had exclusively been replicated as the leading strand (Extended Data Fig. 1g,i). We conclude that in mouse and human cells, telomeres are protected from cNHEJ by two equally effective and parallel pathways: 3′ overhangs and the presence of RAP1.

Fig. 1: RAP1 and Apollo redundantly prevent cNHEJ at telomeres in mouse and human cells.

a, Representative FISH of metaphase spreads of Apollofl/fl Lig4+/+ MEFs 108 h after transduction with single guide RNA (sgRNA) targeting Rap1 (sgRap1) and/or Hit&Run Cre. Telomeres were detected with Cy3-(TTAGGG)3 (green) and DNA was stained with DAPI (magenta). White and green arrows highlight chromatid-type and chromosome-type fusions, respectively. See also Extended Data Fig. 1a. Scale bars 10 µm. b,c, Percentage of telomeres involved in chromatid-type (b) or chromosome-type (c) fusions per metaphase after removal of Apollo and/or RAP1 as indicated, in the presence or absence of LIG4. Data from 3 independent experiments, 10 metaphases per experiment (n = 30 total), with median. d, Representative FISH of metaphase spreads from TP53−/− RAP1+/+ (RAP1 is also known as TERF2IP) or TP53−/− RAP1−/− RPE-1 cells 120 h after transduction with Cas9 and control sgRNA (sgControl) or sgAPOLLO as indicated. Scale bars, 10 µm. e, Quantification of the percentage of telomeres fused per metaphase after removal of Apollo as described in d. Data from 3 independent experiments, 10 metaphases per experiment (n = 30 total), with median. See also Extended Data Fig. 1e–g. Ordinary one-way analysis of variance (ANOVA). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001; NS, not significant.

We focused our attention on the protective function of RAP1. As shelterin co-precipitates with KU and the DNA-PK catalytic subunit (DNA-PKcs) in cell extracts3,4,24,25,26,27, we considered whether RAP1 might prevent cNHEJ by binding directly to DNA-PK. To test this idea, we used DNase I footprinting to examine the position of purified shelterin and DNA-PK on a blunt-ended telomeric template (Fig. 2a and Extended Data Fig. 2a). DNA-PK protected 32 bp from the telomere end, consistent with the assembly of a terminally positioned complex[28] (Fig. 2b). Addition of shelterin reduced the overall efficiency of DNase I cleavage. However, a footprint of precisely 10 bp (the ‘shelterin’ region) that was not observed with shelterin alone was also visible directly next to DNA-PK. Combined addition of RAP1 and TRF2 (ref. [29]), but not each component alone, reproduced this effect (Fig. 2c–e), which was specific to telomeric DNA (Fig. 2f) and was observed over a range of protein concentrations (Extended Data Fig. 2b). Extended Data Fig. 2c shows the same footprint adjacent to DNA-PK was also observed in the presence of a 3′ overhang, but only in the absence of POT1–TPP1, which otherwise prevented DNA-PK assembly.

Fig. 2: TRF2, RAP1 and DNA-PK form a terminal complex at telomeric DNA ends.

a, Outline of DNase I footprinting experiment. The 32P-labelled 5′ end is highlighted with a red asterisk. Radiolabelled template is incubated with KU and DNA-PKcs prior to the addition of shelterin, comprising TRF1, TRF2, RAP1, TIN2, POT1 and TPP1. DNase I-digested products are then analysed by denaturing urea polyacrylamide gel electrophoresis (urea-PAGE). b–f, DNase I footprinting of telomere end-binding complexes formed in the presence of DNA-PK and shelterin (b), TRF2 and RAP1 (c), TRF2 alone (d), RAP1 alone (e) TRF2 and RAP1 with telomeric or non-telomeric DNA (f). Nucleotides from the 5′ telomeric end indicated. For gel source data see Supplementary Fig. 1.

The data above suggest that DNA-PK bound to telomeric ends can form a sequence-specific complex with TRF2 and RAP1. To examine the architecture of this complex, we determined which domains were required for the DNA-PK-proximal footprint (Fig. 3a and Extended Data Fig. 2d,e). Binding of RAP1 to TRF2 via the RCT (RAP1 C-terminal domain) on RAP1 and the RBM (RAP1-binding motif) on TRF2 (ref. [30]), and binding of TRF2 to DNA via the Myb- but not the basic domain was necessary for the extended footprint, suggesting a model in which RAP1 recruitment to DNA by TRF2 is required for the complex to form (Fig. 3b,c and Extended Data Fig. 2f). We tested whether this is the primary function of TRF2 in the assay by fusing RAP1 to the DNA-binding domain of fission yeast Teb1, which recognizes TTAGGG repeats[15]. In the presence of DNA-PK, the Teb1–RAP1 fusion protein generated a 10-bp footprint that was indistinguishable from that of RAP1 in complex with TRF2 and was prevented by cleavage of the Teb1–RAP1 linker (Fig. 3d and Extended Data Fig. 2g,h). Thus, the role of TRF2 in forming the complex is to recruit RAP1 to DNA, and the extended footprint is caused by RAP1, and not by TRF2.

Fig. 3: Three distinct interfaces are required for the complex with DNA-PK.

a, Domain organization of RAP1 and TRF2. TRFH, TRF homology domain. b–d, DNase I footprinting of telomere end-binding complexes, testing the requirement for RAP1 RCT or TRF2 RBM (b) TRF2 Myb domain, basic domain or both Myb and basic domains (ΔMΔB) (c), or testing the requirement for TRF2 in the presence of Teb1, RAP1 or Teb1–RAP1 (d). Nucleotides from the 5′ telomeric end indicated. See Extended Data Fig. 2 for details. WT, wild type. e, Protein crosslinking analysis of RAP1 and KU in the presence of DNA. Proteins were mixed with crosslinker and reaction products were separated on a denaturing tris-acetate polyacrylamide gel and analysed by silver staining or immunoblotting as indicated. Arrowheads mark the position of crosslinked species containing only KU, or KU and RAP1 as indicated. Bottom and top bands observed with KU alone are presumed to represent KU dimers and tetramers, respectively. f,g, DNase I footprinting of telomere end-binding complexes, testing the requirement for BRCT or Myb domains of RAP1 (f) and rescue of RAP1(ΔBRCT) by fusion to the LIG4 BRCT domain (g). Nucleotides from the 5′ telomeric end indicated. For gel source data see Supplementary Fig. 1.

As RAP1 has been proposed to bind KU in cell extracts25,26, we examined whether purified RAP1 and KU could bind each other in pulldown assays, but did not detect an interaction. However, when RAP1 and KU were mixed in the presence of an amine-specific crosslinker and DNA, we observed a crosslinked species on silver-stained polyacrylamide gels that contained RAP1 and KU as demonstrated by immunoblotting, suggesting that RAP1 mediates a weak interaction with KU (Fig. 3e). Crosslinking efficiency was unaffected by a fragment of TRF2 that contained the RBM and Myb domains, suggesting it is not limited by the proximity of RAP1 to DNA (Extended Data Fig. 2i). Deleting either the BRCT or Myb domain of RAP1 disrupted binding to KU (Fig. 3e) and prevented the extended signal in a footprinting assay (Fig. 3f). To test whether the BRCT domain could be substituted for a different DNA-PK-binding peptide, we fused RAP1(∆BRCT) to the first BRCT domain of LIG4, which recognizes KU70 (refs. 31,32). Figure 3g shows that adding the LIG4 BRCT domain to RAP1(∆BRCT) rescued the 10-bp footprint next to DNA-PK, confirming that the necessary function of the RAP1 BRCT domain in forming the complex is recognition of KU.

Although it is possible that one or other of these regions directly protects the 10 bp next to DNA-PK (see below), these data reveal that three molecular interfaces are required for TRF2 and RAP1 to form a complex with DNA-PK: binding of TRF2 to DNA, binding of TRF2 to RAP1, and binding of RAP1 to KU. The requirement for TRF2 to bind DNA explains why the extended footprint is not formed at non-telomeric ends (Fig. 2f), which do not contain a TRF2-binding site.

To determine how this complex might inhibit cNHEJ at telomeres (Fig. 1), we used cryo-electron microscopy (cryo-EM) to examine the structure of TRF2, RAP1 and DNA-PK on telomeric DNA. Consecutive rounds of classification and focused refinement yielded an overall structure at 3.58 Å resolution (Fig. 4a, Extended Data Fig. 3 and Supplementary Video 1). Our map did not contain TRF2, consistent with some inherent flexibility[33] and the notion that its primary role in the complex is to recruit RAP1 to DNA rather than stably bind DNA-PK. The conformation of DNA-PKcs was consistent with previous structures of the unphosphorylated enzyme[34] (Extended Data Fig. 4).

Fig. 4: Cryo-EM structure of the RAP1–DNA-PK complex.

a, Bottom, composite electron density map with protein domains coloured as indicated. Top, schematic of proteins used for structure determination. Uncoloured domains were not visualized. CTD, C-terminal domain; FAT, FRAP–ATM–TRRAP domain; M-HEAT, middle Huntington–EF3–PP2A–TOR1 repeat; N-HEAT, N-terminal Huntington–EF3–PP2A–TOR1 repeat; vWA, von Willebrand A domain. b, Subsection of the structure in a, showing KU70 SAP and RAP1 Myb domains bound to DNA. c,d, The BRCT domain of RAP1 bound to the KU70 vWA domain (c), highlighting RAP1 and KU residues that mediate the interaction (d). e–g, DNase I footprinting analysis of telomere end-binding complexes with RAP1 variants R133E (e) and ΔBRCT and KR/DE (f) and KU variant DE/KR (g). Nucleotides from the 5′ telomeric end indicated. For gel source data see Supplementary Fig. 1. h, Cryo-EM model showing binding of RAP1 BRCT to KU70 and KU80 with (bottom) and without (top) the LIG4 BRCT domain from Protein Data Bank (PDB) structure 7LT3 overlaid.

Three additional regions of density were observed beyond the core DNA-PK complex: the first sits on the DNA as it enters KU and was modelled as the KU70 SAP domain (Fig. 4a,b and Extended Data Fig. 5a). The SAP domain is positioned on the minor groove with K575, K595 and K596, which have been proposed to bind DNA[35], coordinating the phosphate backbone (Extended Data Fig. 5a). The second region engages the neighbouring 10 bp that are protected by RAP1 in our footprinting assays and was modelled as the RAP1 Myb domain (Fig. 4a,b and Extended Data Fig. 5b). The Myb domain adopts a canonical homeodomain arrangement with the recognition helix inserted into the major groove and a conserved N-terminal arm[36] formed by R133 reaching into the minor groove bound by the KU70 SAP (Fig. 4b and Extended Data Fig. 5b–d). Chemical crosslinking mass spectrometry confirms the proximity of the Myb and SAP regions (Extended Data Fig. 5e,f). This density is surprising because human RAP1 does not appreciably bind DNA in isolation owing to a lack of surface-exposed positive charge29,37,38. Our structure suggests that DNA-PK complements this deficit by anchoring the Myb domain close to DNA through a neighbouring loop bound to the side of KU80 (Extended Data Fig. 5g). The KU70 SAP domain is proposed to bind other homeodomain proteins[39] and additional contacts with RAP1 may also be in place but are not resolved in our structure. A single point mutation in the N-terminal arm (R133E) blocked the footprint next to DNA-PK (Fig. 4e), consistent with the Myb–DNA interaction protecting this region.

The third region sits on the KU70 vWA domain and was modelled as the BRCT domain of RAP1 (Fig. 4a,c), positioning a conserved basic patch on RAP1 next to an acidic patch on KU (Fig. 4d and Extended Data Fig. 6a). Consistent with our structure, charge reversal mutations in these regions of RAP1 (K39D/R40E/R55E, abbreviated as RAP1(KR/DE)) or KU (KU70 (D496K/E499R) plus KU80 (D327K), abbreviated as KU(DE/KR)) prevented the extended signal in a footprinting assay (Fig. 4f,g and Extended Data Fig. 6b) and crosslinking of RAP1 to KU (Extended Data Fig. 6c). Remarkably, overlaying the RAP1 BRCT domain with the LIG4 BRCT domain bound to KU[32] shows that RAP1 at this position directly occludes the binding site for LIG4 (Fig. 4h). These data suggest that forming a complex with DNA-PK, TRF2 and RAP1 may prevent cNHEJ by simply blocking LIG4 recruitment.

To test this idea, we developed a pulldown assay in which purified KU was immunoprecipitated after incubation with purified DNA-PKcs, purified XRCC4–LIG4 and a short DNA template (Fig. 5a). DNA-PKcs associated with KU in a DNA-dependent manner, consistent with the assembly of DNA-PK at a DNA end (Extended Data Fig. 7a). XRCC4–LIG4 recruitment was sensitive to mutations in the acidic patch on KU that is required to bind RAP1 and known to bind LIG4 (ref. [32]) (Extended Data Fig. 7a). Whereas TRF2 or RAP1 had a negligible effect on the assay individually, recruitment of XRCC4–LIG4 was blocked when they were added together, even when XRCC4–LIG4 was preincubated with DNA-PK and present in large excess (Fig. 5b and Extended Data Fig. 7b,c). To test whether the ability of TRF2–RAP1 to block LIG4 recruitment required binding to DNA-PK, we repeated the experiment with RAP1(∆BRCT) or RAP1(KR/DE), which are unable to bind KU (Fig. 4). Inhibition of LIG4 recruitment was not observed under these conditions, but could be restored by fusing the N-terminal BRCT domain of LIG4 to the N-terminus of RAP1(∆BRCT) (Fig. 5c and Extended Data Fig. 7d). Figure 5d shows that RAP1(R133E) was also defective in preventing recruitment of LIG4, suggesting that multiple contacts between RAP1, KU and DNA are required for this effect.

Fig. 5: TRF2 and RAP1 prevent cNHEJ by directly blocking recruitment of XRCC4–LIG4 to DNA-PK.

a, Outline of the KU pulldown assay. Details in Methods. b–d, KU-bound proteins from reactions containing KU70–KU80 (KU70/80), DNA-PKcs, XRCC4–LIG4, TRF2, RAP1 and template DNA together with wild-type RAP1 (b), RAP1(ΔBRCT) or RAP1(KR/DE) (c), or RAP1(ΔMyb) or RAP1(R133E) (d) were separated by SDS–PAGE and immunoblotted as indicated. TRF2, RAP1 and LIG4 were detected with anti-strep tag antibody, KU70 was detected with anti-Flag antibody. Association of TRF2 with KU is mediated by template DNA. For gel source data see Supplementary Fig. 1. e, Percentage of telomeres per metaphase involved in chromosome fusions upon over-expression of mouse RAP1, RAP1(KR/DE) and RAP1(R130E) (equivalent to human RAP1(R133E)) after CRISPR- and Cre-mediated deletion of Rap1 and Apollo, respectively in Apollofl/fl Lig4+/+ MEFs. Data from 3 independent experiments, 10 metaphases per experiment (n = 30 total), with median. Ordinary one-way ANOVA. See Extended Data Fig. 8 for further details. f, Percentage of telomeres fused per metaphase upon CRISPR-mediated deletion of APOLLO in TP53−/− RPE-1 cells with wild-type RAP1 or RAP1(KR/DE). Data from 3 independent experiments, 10 metaphases per experiment (n = 30 total), with median. Statistics as in e. See Extended Data Fig. 8 for further details. g, Model for the direct inhibition of DNA-PK by TRF2 and RAP1 at mammalian telomeres. When assembled on telomeric DNA, DNA-PK and its associated DNA is bound by the Myb and BRCT domains of RAP1. The BRCT domain acts as a circuit breaker, preventing DNA-PK from engaging LIG4.

To examine whether inhibition of LIG4 recruitment is required for RAP1 to prevent cNHEJ at telomeres, Rap1−/−Apollo−/− MEFs were complemented with the RAP1 mutants examined above. Figure 5e and Extended Data Fig. 8a–c show that RAP1 that is unable to bind DNA-PK and block LIG4 recruitment could not protect telomeres from cNHEJ. Figure 5f and Extended Data Fig. 8d–g demonstrate that this is also the case in human RPE-1 cells.

RAP1 is the most conserved component of eukaryotic chromosome ends[29], yet its role at mammalian telomeres has remained elusive. Our results reveal that it is the defining component of an inhibitory pathway, coordinated by TRF2, in which RAP1 directly and specifically supresses the end-joining function of DNA-PK by preventing the recruitment of LIG4 (Fig. 5g). Binding of RAP1 to DNA may also restrict cNHEJ by preventing KU from translocating inwards, as proposed for budding yeast Rap1 (ref. [40]). These findings provide a molecular basis for previous studies that implicate human RAP1 in chromosome end protection15,16,17,18, offer a mechanism for how cNHEJ can be blocked at telomeres with diverse end structures or lacking t-loops13,41, and resolve the long-standing paradox that DNA-PK can bind to telomeres and regulate their resection without activating cNHEJ3,4,23. By demonstrating that the assembly of DNA-PK can be functionally uncoupled from its end-joining activity through LIG4 recruitment, our study also expands the mechanisms available to regulate pathway choice during double strand break repair.

Apollo and RAP1 are recruited to telomeres via TRF2, which therefore acts as a master regulator of two equally effective pathways to block cNHEJ. Why is RAP1 used when Apollo is apparently sufficient? Processing of telomeres by Apollo depends on DNA-PK[23] and a TRF2-binding motif that is specific to vertebrate homologues[42]. As the RAP1–KU interaction is more broadly conserved across metazoa (Extended Data Fig. 9), we propose that the pathway described here predates the telomeric function of Apollo and enabled DNA-PK at leading strand telomeres to be coopted into a resection role by preventing it from activating cNHEJ. In vertebrate cells, RAP1 will ensure that DNA-PK on blunt leading strand ends cannot engage cNHEJ either before resection occurs or in instances where resection fails. There may also be scenarios in which RAP1 is the primary protective factor. For example, RAP1 deletion alone increases cNHEJ at telomeres in senescent cells16,43. Given that only a small number of telomeric repeats are required for TRF2 and RAP1 to prevent LIG4 recruitment in vitro (Fig. 5b), the mechanism described here may be adept at protecting critically short telomeres in these cells from cNHEJ.

Methods

DNA templates

For DNase I footprinting experiments, a 2.8 kb plasmid containing 360 bp of telomeric DNA was amplified in SURE2 Escherichia coli cells grown at 30 °C and extracted using a QIAGEN Plasmid Maxi kit. The plasmid was linearized by BsmFI digestion for 1 h at 37 °C leaving one end with telomeric TTAGGG repeats. The DNA was dephosphorylated with Quick-CIP for 30 min at 37 °C and cleaned up by phenol chloroform extraction and ethanol precipitation. The DNA was subsequently phosphorylated using PNK and [γ-32P]ATP for 1 h at 37°C, passed over a G-50 desalting column and phenol chloroform extracted. Labelled DNA was digested with SacI for 1 h at 37 °C to isolate a 390-bp DNA fragment ending in 60 TTAGGG repeats from the rest of the plasmid. Digested DNA was run on a 10% TBE-PAGE gel (Invitrogen) after which the telomeric fragment was excised and gel extracted by shaking in 10 mM Tris pH 8.0, 300 mM NaCl, 1 mM EDTA overnight at 21 °C. The final DNA fragment (PE1; see Supplementary Table 1 for sequence) was precipitated with isopropanol and resuspended in 1× TE buffer. A non-telomeric DNA fragment (PE2; see Supplementary Table 1 for sequence) containing 360 bp of random DNA sequence in place of TTAGGG repeats was prepared through the same procedure using a non-telomeric plasmid. Telomeric DNA templates for experiments in Extended Data Fig. 2c were prepared as above but with initial digestion by Esp3I instead of BsmFI, thereby yielding a different DNA end compatible with ssDNA overhang ligation. To prepare a 15-nt overhang template, dephosphorylated DNA was additionally mixed with oligonucleotide PE5 at a 1:75 DNA:oligonucleotide molar ratio and incubated overnight at 16 °C with T4 DNA ligase (NEB). The ligated template was subsequently cleaned up from excess oligonucleotide using two rounds of HighPrep PCR cleanup beads (MAGBIO), resuspended in TE buffer and γ-32P-labelled as above.

Telomeric DNA substrates for cryo-EM, crosslinking and DNA-PK pulldown experiments were prepared by mixing oligonucleotides PE3 and PE4 at a 1:1 molar ratio, heating to 95 °C and cooling to room temperature over 2 h.

Protein expression

Open reading frames for human KU70/80, RAP1 or TRF2 were codon-optimized for Spodoptera frugiperda and cloned into pACEBAC1 vector. The mutants indicated were generated using PCR-based mutagenesis (see Supplementary Table 2 for mutant details). The shelterin genes (TERF1, TERF2, RAP1, TINF2, ACD (also known as TPP1) and POT1), codon-optimized for E. coli, were synthesized by GenScript and cloned into pACEBAC1, using a nicking cloning system[44]. Vectors were transposed into DH10 MultiBac Competent E. coli cells and grown in LB medium overnight at 37 °C shaking at 200 rpm. Bacmid DNA was extracted and used to transfect Sf9 insect cells which were subsequently grown at 27 °C, shaking at 130 rpm over several cell passages. At passage three (P3), 200 ml of High Five insect cells at a 1.5 × 10^6 density were infected with baculoviruses for protein expression, incubating at 27 °C with 130 rpm. After 3 days, the cell count was checked, and cells were harvested by centrifugation at 760g for 20 min at 4 °C. Cell pellets were then resuspended in PBS, transferred into 50-ml Falcon tubes and pelleted again by centrifugation at 470g for 25 min. Supernatants were discarded, the pellets flash frozen in liquid nitrogen and placed at -80 °C until required.

Nuclear extract preparation

HeLa cell pellets were resuspended in buffer A (10 mM HEPES pH 8.0, 10 mM KCl, 1.5 mM MgCl_2, 0.5 mM DTT, 0.5 mM AEBSF) and incubated for 10 min at 4 °C. Following centrifugation at 1,033g for 10 min at 4 °C, the pellet was resuspended in two pellet volumes of buffer A and lysed by 20 strokes in a Dounce homogenizer (pestle type B). Lysate was centrifuged at 25,000g for 20 min at 4 °C and the pellet (containing nuclei) was resuspended in 1.3× pellet volumes of buffer C (20 mM HEPES pH 8.0, 420 mM NaCl, 1.5 mM MgCl_2, 0.2 mM EDTA, 25% glycerol, 0.5 mM DTT, 0.5 mM AEBSF). After 20 strokes in a Dounce homogenizer (pestle type B), nuclei were incubated for 30 min at 4 °C and centrifuged at 25,000g for 30 min at 4 °C. The supernatant was collected and flash frozen in liquid nitrogen.

Protein purification

DNA-PKcs

HeLa cell nuclear extract was diluted into DPKQ buffer (20 mM HEPES pH 7.6, 100 mM NaCl, 2 mM MgCl_2, 0.5 mM EDTA, 10% glycerol, 0.5 mM DTT, 0.5 mM AEBSF) and ultracentrifuged at 50,000g for 1 h at 4 °C. The supernatant was collected and filtered through a 0.45-µm syringe filter before injection onto a Q-sepharose column equilibrated in DPKQ buffer. The column was washed in DPKQ buffer and proteins eluted over a 100 mM–1 M NaCl gradient. Fractions were spotted onto a nitrocellulose membrane and Western blotted to identify DNA-PKcs-containing fractions, which were pooled, diluted into DPKQ buffer and loaded onto a heparin column equilibrated in the same buffer. The column was washed with DPKQ buffer and proteins eluted over a 100 mM–1 M NaCl gradient. DNA-PKcs-containing fractions were pooled, dialysed into DPKC buffer (50 mM Tris pH 7.3, 0.5 mM EDTA, 5% glycerol, 2.5 mM DTT, 0.5 mM AEBSF) with 50 mM KCl, and loaded onto a dsDNA-conjugated CNBr-activated Sepharose column equilibrated in the same buffer. The column was washed in DPKC buffer with 50 mM KCl and bound proteins were eluted in DPKC buffer with 411 mM KCl. DNA-PKcs-containing fractions were pooled, diluted into DPKC buffer with 100 mM KCl and 0.02% Tween-20 and loaded onto a MonoQ column equilibrated in the same buffer. The column was washed in DPKC buffer with 100 mM KCl and 0.02% Tween-20 and proteins were eluted over a 100 mM–1 M KCl gradient. DNA-PKcs fractions were pooled, diluted into DPKC buffer with 100 mM KCl and 0.02% Tween-20 and loaded onto a MonoS column equilibrated in the same buffer. The column was washed in DPKC buffer with 100 mM KCl and 0.02% Tween-20 and proteins were eluted over a 100 mM–1 M KCl gradient. Final DNA-PKcs fractions were pooled, concentrated using a 100 kDa Amicon Ultra centrifugal filter, flash frozen in liquid nitrogen and stored at −80 °C.

KU70/80

Cell pellets were resuspended in 50 mM Tris pH 8.0, 2 mM β-mercaptoethanol with protease inhibitor tablets and incubated stirring for 20 min at 4 °C. Cells were lysed by the addition of 16.7% glycerol and 300 mM NaCl, stirring for 30 min at 4 °C. Lysed cells were ultracentrifuged at 125,000g for 30 min at 4°C and the supernatant was incubated with anti-Flag resin for 2 h at 4 °C. Beads were successively washed in Ku buffer (50 mM Tris pH 8.0, 5% glycerol, 2 mM β-mercaptoethanol) with 300 mM NaCl followed by Ku buffer with 150 mM NaCl. Proteins were eluted using the latter buffer supplemented with 0.5 mg ml−1 3× Flag peptide. Proteins were subsequently separated on a Superdex 200 gel filtration column, equilibrated and run using Ku buffer with 150 mM NaCl. Final Ku70/80 fractions were pooled, concentrated using a 30 kDa Amicon Ultra centrifugal filter, flash frozen in liquid nitrogen and stored at −80 °C.

TRF2 and RAP1

For TRF2 and RAP1, cell pellets were resuspended in 50 mM HEPES pH 7.6, 1 mM DTT with EDTA-free protease inhibitor tablets (one per 50 ml, Roche) and incubated with stirring for 20 min at 4 °C. Protein was extracted by the addition of 16.7% glycerol and 300 mM NaCl, stirring for 30 min at 4°C. Extract was cleared by ultracentrifugation at 125,000g for 30 min at 4 °C. The supernatant was applied to Strep-Tactin XT 4Flow resin equilibrated in shelterin buffer (50 mM HEPES pH 7.6, 500 mM NaCl, 10% glycerol, 1 mM DTT). Beads were washed in shelterin buffer and proteins were eluted with the same buffer supplemented with 10 mM biotin. Proteins were subsequently separated on a Superdex 200 10/300 column equilibrated and run in shelterin buffer. Final TRF2 or RAP1 fractions were pooled, concentrated using a 30 kDa Amicon Ultra centrifugal filter, flash frozen in liquid nitrogen and stored at −80 °C. To cleave the TEB1–RAP1 fusion protein, 0.24 µM PreScission protease was incubated with 1.2 µM TEB1–RAP1 for 2 h at 4 °C prior to experiments.

Shelterin

For the shelterin complex with and without POT1–TPP1, cell pellets containing all 6 shelterin subunits or all subunits except POT1 and TPP1 overexpressed were resuspended in lysis buffer (50 mM Hepes pH 8.0, 300 mM NaCl, 10% glycerol, 1 mM MgCl_2, 10 mM beta-mercaptoethanol, 0.1 µl ml−1 Base muncher nuclease, 8 µg ml−1 Avidin, 1 mM AEBSF and EDTA-free protease inhibitor tablets (one per 50 ml, Roche)) and cells were lysed by sonication. Lysate was cleared by centrifugation at 48,380g for 1 h, 4 °C. Cleared lysate was passed through a 0.45-µm filter and applied to a StrepTrap column equilibrated with StrepTrap wash buffer (50 mM Hepes 8.0, 300 mM NaCl, 10 glycerol and 1 mM tris-2-carboxyethyl phosphine (TCEP)), which was washed with 20 column volumes StrepTrap wash buffer prior to elution with 5 column volumes StrepTrap wash buffer supplemented with 10 mM d-desthiobiotin. Eluate (the 0.5 ml eluate containing the highest concentration of shelterin) was applied to a Superose 6 10/300 column preequilibrated in 50 mM Hepes pH 8.0, 300 mM NaCl, 10% glycerol and 1 mM TCEP, and run in the same buffer. The desired fractions were aliquoted and flash frozen for storage. Further characterization of the full shelterin complex will be reported elsewhere.

XRCC4–LIG4

Cell pellets were resuspended in 50 mM HEPES pH 7.6, 1 mM DTT, 1 mM EDTA with EDTA-free protease inhibitor tablets (Roche, 1 per 50 ml buffer) and incubated with stirring for 20 min at 4 °C. Proteins were extracted by the addition of glycerol to 16.7% and NaCl to 300 mM with stirring for 30 min at 4 °C. Extract was centrifuged at 41,656g for 30 min at 4 °C and the supernatant was applied to Strep-Tactin XT 4Flow resin equilibrated in X4 buffer (50 mM HEPES pH 7.6, 300 mM NaCl, 10% glycerol, 1 mM DTT, 1 mM EDTA). Beads were washed in X4 buffer and proteins were eluted with the same buffer supplemented with 30 mM biotin. Protein fractions were pooled, the NaCl concentration diluted to 100 mM then applied to a 1 ml heparin column and subjected to a linear gradient from 0.1 M to 1 M NaCl over 20 column volumes. Proteins were subsequently separated on a Superdex 200 gel filtration column equilibrated and run in 100 mM NaCl buffer (50 mM HEPES pH 7.6, 100 mM NaCl, 10% glycerol, 1 mM DTT, 1 mM EDTA). Final XRCC4–LIG4 fractions were pooled, concentrated using a 30 kDa Amicon Ultra centrifugal filter, flash frozen in liquid nitrogen and stored at −80 °C.

Nano differential scanning fluorimetry

Purified proteins as indicated were analysed by nano differential scanning fluorometry to assess thermal protein stability using a Tycho NT.6 (Nanotemper) with 10 µl capillaries, monitoring fluorescence at 330 and 350 nm over a 35–95 °C temperature ramp (30 °C min−1).

DNase I footprinting

[γ-32P]-labelled PE1 (telomeric) or PE2 (non-telomeric) template (2 nM) was mixed with 30 nM DNA-PKcs and 50 nM KU70/80 in 25 mM HEPES pH 7.6, 80 mM KCl, 1.5 mM CaCl_2, 1.5 mM MgCl_2, 5% glycerol, 50 µg ml−1 BSA, 2 mM DTT and incubated on ice for 5 min. 5 nM shelterin or TRF2–RAP1 (dimer concentration of TRF2) was added to DNA-bound DNA-PK and incubated at 37 °C for 10 min. Nuclease cleavage was initiated by addition of DNase I to 0.5 U ml−1 and the reactions were incubated for a further 2 min at 37 °C before quenching with 25 mM EDTA, 0.2% SDS, 0.2 mg ml−1 Proteinase K. Following incubation at 37 °C for 10 min, samples were extracted with phenol chloroform, ethanol precipitated and resuspended in 2 µl 99% formamide, 5 mM EDTA, bromophenol blue. Samples were boiled for 2 min and run on an 8% urea-PAGE sequencing gel in 1× TBE. Gels were subsequently dried and exposed to a BAS-MS imaging plate before phosphor imaging using a Typhoon Biomolecular Imager (Amersham). Images were analysed in ImageJ2 (v.2.14.0) and Adobe Photoshop (v.25.0.0) and figures were prepared using Adobe Illustrator (v.25.0.0). Footprinting experiments in Extended Data Fig. 2b,c were prepared as above but with higher protein and/or DNA concentrations as indicated in figure legend. Extended Data Fig. 2c was also performed with coincident addition of shelterin, KU and DNA-PKcs.

Electrophoretic mobility shift assay

[γ-32P]-labelled PE1 (telomeric) template (1 nM) was mixed with 2, 15 or 40 nM TRF2 in 25 mM HEPES pH 7.6, 80 mM KCl, 1.5 mM CaCl_2, 1.5 mM MgCl_2, 5% glycerol, 50 µg ml−1 BSA, 2 mM DTT. 10 µl reactions were incubated at 37 °C for 15 min. Samples were supplemented with 1% sucrose, Orange G and run on a 1.5% agarose gel in 0.5× TBE. Gels were dried and analysed by phosphor imaging as with DNase I footprinting experiments.

Crosslinking

KU70/80 (200 nM) was mixed with 200 nM RAP1 in 20 mM HEPES pH 7.6, 200 nM NaCl, 2 mM MgCl_2, 0.5 mM EDTA, 10% glycerol, 0.5 mM DTT, 0.5 mM AEBSF and incubated for 5 min at 4 °C. Samples were supplemented by 100 nM annealed PE3/PE4 DNA substrate and incubated for 10 min at 4 °C. Proteins were crosslinked by addition of 2 mM DSSO and incubated for 60 min at room temperature. Reactions were stopped with 20 mM Tris pH 7.6. Samples were run on a Criterion XT 3-8% Tris-Acetate PAGE gel in 1× XT Tricine and analysed by silver staining (SilverQuest, Invitrogen) or immunoblotting, probing for KU70 (Flag) or RAP1 (strep) (see ‘Antibodies’ and ‘Immunoblotting’). The crosslinking experiment in Extended Data Fig. 2i was performed as above with 200 nM TRF2(ΔBΔTRFH) (with cleaved off strep tag) added together with RAP1.

DNA-PK pulldown

Annealed PE3/PE4 DNA substrate (5 nM) preincubated with a twofold excess of streptavidin (IBA) was incubated with 10 nM KU70/80 and 15 nM DNA-PKcs for 3 min at 30 °C in 20 mM HEPES pH 7.6, 80 mM KCl, 5% glycerol, 0.01% NP-40 and 1 mM DTT in a final volume of 25 µl in protein low bind tubes (Alpha Labs). 5 nM TRF2–RAP1 complex (dimer concentration of TRF2) was added, and after a further 3 min, 30 nM XRCC4–LIG4 complex was added. After a further 3 min, the complete reaction was added to 1 µl equivalent of anti-Flag M2 magnetic beads (Sigma), and the mixture incubated at 4 °C with shaking for 30 min. Beads were pelleted on a magnetic rack, washed 3× with 50 µl 25 mM HEPES pH 7.6, 80 mM KCl, 10% glycerol, 0.02% NP-40 and 1 mM DTT with a brief vortex included for each wash. Beads were resuspended in wash buffer supplemented with 0.25 mg ml−1 3× Flag peptide and incubated for 20 min at 18 °C with shaking. Eluted proteins were supplemented with SDS loading buffer, run on a 4–12% TGX precast gel (Bio-Rad), transferred onto nitrocellulose membrane at 80 V for 90 min and detected by immunoblotting with the antibodies indicated (see ‘Antibodies’ and ‘Immunoblotting’). Pulldown experiments in Extended Data Fig. 7b,c were prepared as above but with 30–120 nM XRCC4–LIG4 as indicated.

Cryo-EM sample preparation

Annealed PE3/PE4 DNA substrate (see ‘DNA templates’) was mixed 1:1 with streptavidin in TE buffer and incubated for 30 min at room temperature. Streptavidin-bound DNA was diluted to 250 nM in 40 mM HEPES pH 7.6, 100 mM NaCl, 3 mM MgCl2, 1 mM DTT, 2.5% glycerol and incubated with 250 nM KU70/80 and 250 nM DNA-PKcs for 10 min at 4 °C. 250 nM TRF2–RAP1 (dimer concentration of TRF2) was added and incubated for another 5 min at 4 °C. Samples were supplemented with 0.05% CHAPS prior to cryo-EM grid preparation.

Cryo-EM data acquisition and image processing

Copper R1.2/1.3 grids (300-mesh, Quantifoil) were coated with a thin layer or carbon and glow-discharged at 15 mA for 30 s (PELCO easiGlow). Three microlitres of sample was applied to glow-discharged grids and incubated for 5 s followed by blotting for 3 s using a Vitrobot Mark IV (Thermo Scientific) operated at 4 °C and 100% humidity. Grids were subsequently plunge-frozen in liquid ethane. Cryo-EM data were acquired at 200 kV on a Glacios Cryo-TEM (Thermo Scientific) equipped with a Falcon 4i Direct Electron Detector (Thermo Scientific). In total, 30,604 movies with 30 frames were collected at 150,000× magnification (0.94 Å pixel size) with a total electron dose of 50 e− Å−2 and a defocus range of −1.0 to −2.5 μm (see Extended Data Table 1). Subsequent image processing was performed in cryoSPARC (v4.3.1)[45]. Movies were motion corrected using patch alignment with all frames followed by patch contrast transfer function estimation. Particles were picked through automated template-based picking (template EMD-6803) and extracted with 4× binning using a box size of 96 pixels. Following 2D classification 1,464,362 DNA-PK particles were selected to reconstruct an ab initio 3D model, subsequently used as a starting model for heterogeneous refinement using 5 classes. The most prominent DNA-PK class was selected and subjected to homogeneous refinement followed by local refinement using a focus mask encompassing the KU70/80–DNA core. A 3D classification without alignment using the same mask and ten classes was performed to identify particles containing additional RAP1 and KU densities. Classes lacking either the RAP1 BRCT the RAP1 Myb domain or the KU70 SAP domain were excluded. A total of 526,885 selected particles was re-extracted unbinned with a 384-pixel box size and a 3D map was reconstructed through homogeneous refinement. Following global contrast transfer function refinement a structure of the full DNA end-binding complex was resolved to 3.58 Å using homogeneous refinement. The KU–RAP1–DNA core was locally refined to 3.32 Å using a focus mask excluding DNA-PKcs. The DNA-PKcs–DNA density was similarly refined using a mask excluding the KU–RAP1–DNA core and subjected to 3D classification without alignment using the same mask. Some flexibility in DNA-PKcs conformation was observed and classes of the most prominent conformation containing 370,172 particles were selected. A DNA-PKcs–DNA structure from these particles was resolved to 3.40 Å by homogeneous refinement followed by local refinement using a DNA-PKcs focus mask. Maps for the full end-binding complex and the locally refined densities were sharpened in cryoSPARC and combined using Phenix (v1.20.1) combine_focused_maps[46]. The composite map (EMD-19065) was subsequently used for model building in Coot[47] and figure generation in ChimeraX[48].

Model building and validation

Molecular models for human DNA-PK (PBD 7K1K), RAP1 Myb (PDB 1FEX) and KU70 SAP (PDB 1JJR) were docked into the cryo-EM map using the Fit in Map command in ChimeraX[48]. The RAP1 BRCT domain from a KU–RAP1 AlphaFold model (see ‘AlphaFold modelling’) was similarly docked into the cryo-EM map. Models were refined against the map using Namdinator[49] followed by manual inspection in Coot[47]. Unoccupied protein densities and nucleic acids were modelled de novo. The resulting model was iteratively refined using Phenix (v1.20.1) real_space_refinement[50] with geometry and secondary structure restraints followed by manual adjustment in Coot. The quality of the final atomic model (PDB 8RD4) was evaluated by MolProbity[51] in Phenix (see Extended Data Table 1).

AlphaFold modelling

Full-length human RAP1, KU70 and KU80 were analysed using AlphaFold 3 on the online AlphaFold server, with the top ranked prediction shown. For Extended Data Fig. 9, the additional sequences analysed were as follows. Salmo salar: NP_001133439.1, XP_014030197.1 and XP_045561280.1; Strongylocentrotus purpuratus: XP_030845408.1, XP_030843748.1, XP_001198957.2; Nematostella vectensis: XP_001641354.1, EDO36674.1, EDO44451.1; Trichoplax adhaerens: XP_002117640.1, XP_002117043.1, XP_002112721.1.

Protein alignments

Pre-computed protein alignments were analysed using the ProViz online tool[52].

Antibodies

Human DNA-PKcs was detected with antibody 18-2 (Invitrogen MA5-13238) at 1:100 dilution, human RAP1 with antibody A300-306A (Bethyl Laboratories) at 1:4,000 dilution, human TRF2 with antibody D1Y5D (Cell Signaling 13136) at 1:1,000 dilution, human Apollo with antibody HPA064934 (Atlas Antibodies) at 1:100 dilution and human α-tubulin with antibody T9026 (Sigma) at 1:1,000 dilution. Recombinant human KU70 was detected via an N-terminal Flag tag with antibody M2 (Sigma F1804) at 1:1,000 dilution. Recombinant human LIG4, TRF2 and RAP1 were detected via a dual strep tag using antibody ab76949 (Abcam) at 1:1,000 dilution. Recombinant human XRCC4 was detected with antibody C-4 (Santa Cruz sc-271087) at 1:500 dilution. Primary antibodies for human proteins were detected with goat anti-rabbit IgG–horseradish peroxidase (HRP) (Cell Signalling 7074) or horse anti-mouse IgG–HRP (Cell Signalling 7076) secondary antibody. Mouse RAP1 was detected with antibody D9H4 (Cell Signalling 5433) at 1:1,000 dilution, mouse TRF2 with antibody D1Y5D (Cell Signaling 13136) at 1:1,000 dilution and mouse β-actin with antibody 8H10D10 (Cell Signaling 3700) at 1:4,000 dilution followed by donkey anti-rabbit IgG–HRP (NA934V, Cytiva), goat anti-rabbit IgG–HRP (31460, Invitrogen) or goat anti-mouse IgG–HRP peroxidase (31430, Invitroen) secondary antibody.

Cell lines and viral gene delivery

SV40-LT Apollofl/fl Lig4+/+, Apollofl/fl Lig4−/− and Trf2fl/flRosa26cre-ERT1 MEFs have been previously described10,53. hTERT immortalized RPE-1 cells have been previously described[54]. All MEFs were immortalized with pBabeSV40LargeT and cultured in Dulbecco’s Modified Eagle Medium (DMEM, Cytiva) supplemented with 15% fetal bovine serum (FBS, Gibco), non-essential amino acids (Cytiva), l-glutamine (Cytiva), penicillin-streptomycin (Cytiva), 50 µM β-mercaptoethanol (Sigma). 293 T and Phoenix eco cells (ATCC) were cultured in DMEM (Cytiva) supplemented with 10% HyClone Bovine Calf Serum (Cytiva), non-essential amino acids (Cytiva), l-glutamine (Cytiva), and penicillin-streptomycin (Cytiva). RPE-1 cells were cultured in DMEM/F12 medium supplemented with 10% (v/v) FBS, 1% (v/v) penicillin-streptomycin, 1% Glutamax, 0.5 µg ml−1 Amphotericin B and 0.26% sodium bicarbonate. To generate TP53−/− RPE-1 clones by CRISPR–Cas9 mediated gene editing, cells were electroporated with Cas9–sgRNA ribonucleoparticles targeting the sequences 5′-AAATTTGCGTGTGGAGTATT-3′ and 5′-TCCACTCGGATAAGATGCTG-3′[55] using the Neon Transfection system as described[56]. After 4 days, single cells were sorted into 96-well plates containing medium supplemented with 10 µM nutlin-3a. After 14 days, surviving clones were expanded, and p53 status was analysed by immunoblotting and sequencing of the TP53 locus as described[55]. CRISPR–Cas9 mediated editing of human RAP1 in TP53−/− RPE-1 cells was carried out using phosphorothioated single-stranded DNA repair templates (ssODN) and selection for positive integrands by ouabain as described57,58. In brief, RAP1 guide RNA 5′-GGCCCAGCCCGGCCAAGCGT-3′ was cloned into the BspI site of Addgene vector 86613. Repair templates for ATPA1 and RAP1 were synthesized by Integrated DNA Technologies (IDT) with the following sequences: ATP1A1: C*A*ATGTTACTGTGGATTGGAGCGATTCTTTGTTTCTTGGCTTATAGCATCAGAGCTGCTACAGAAGAGGAACCTCAAAACGATGACGTGAGTTCTGTAATTCAGCATATCGATTTGTAGTACACATCAGATATC*T*T; RAP1: C*A*TTCCTCGACTCTGTTCGTGAGGGACGACGGCAGCTCCATGTCCTTCTACGTGCGGCCCAGCCCGGCCGACGAGCGCCTCTCGACGCTCATCCTGCACGGCGGCGGCACGGTGTGCGAGGTGCAGGAGCCCGGGGCCGTGCTGCTGGCCCAGCCCGGGGAGGCGCTGGCCGAGGCCTCGGGTGATTTCATCTCCACG*C*A, where * denotes a phosphorthiolated base. To generate RAP1(KR/DE) clones, 300,000 TP53−/− RPE-1 cells were electroporated with the Neon Transfection System using a 10 µl tip and two pulses at 1,350 V and 20 ms with 500 ng RAP1 guide RNA/Addgene plasmid #86613, 2 pmol of ATP1A1 ssODN and 6 pmol RAP1 ssODN. To generate RAP1−/− clones, the procedure was repeated omitting RAP1 ssODN. After 3–4 days, cells were expanded into 15-cm dishes and treated with 0.25 µM ouabain, followed by isolation of single clones 7–12 days after drug selection. Genomic DNA was prepared using EZNA Tissue DNA kit according to the manufacturer’s instructions. RAP1 was amplified by PCR using oligonucleotide sequences AGTGCTGCGCTTCGCGGC and CGCCTTCCGCTTGAGCTTCTG. Editing was analysed by restriction digestion with SalI and Sanger sequencing, and positive clones were single cell sorted and expanded prior to freezing. For retro or lentiviral transduction, a total of 20 µg of plasmid DNA was transfected into Phoenix eco or 293 T cells, respectively, using CaPO_4 precipitation. The viral supernatant was filtered through a 0.45-μm filter, supplemented with 4 μg ml−1 polybrene, and used for the transduction of target cells. Lentiviral particles containing the sgRNA against mouse Rap1 (target: 5′-GCAGTCTAGGATGTACTGCG-3′) in lentiCRISPR v2 (Addgene plasmid #52961, a gift from F. Zhang) were introduced into target MEF cells with three infections per day (6–12 h intervals) over 2 days, followed by 2 days in 2–4 µM Puromycin or 340 µM Hygromycin. The same approach was used to target human Apollo in TP53−/− RPE-1 cells with lentiviral particles containing lentiCRISPR v2 with the guide sequence 5′-CTGGTTCCAACGCAGCATGT-3′[23], or non-targeting control sequence 5′-CGCCAAACGTGCCCTGACGG-3′. For MEF experiments, Cre was induced with three infections per day (6–12 h intervals) over two days with pMMP Hit&Run Cre retrovirus produced in Phoenix eco cells. Time-point 0 was set 12 h after the first Hit&Run Cre infection. For the Rap1-complementation assay, Apollofl/fl MEFs were transduced with retroviral particle containing Rap1, Rap1KR/DE or Rap1R130E cloned in Plpc vector for a total of 4 infections at 6–12-h intervals, selected for 2–3 days in 2–4 µM Puromycin, transduced with the sgRNA against mouse Rap1 cloned in LcV2-Hygro (Addgene plasmid #91977, a gift from J. Mendell), selected for 2 days in 340 µM Hygromycin and then transduced with pMMP Hit&Run Cre as previously described. For the Trf2-complementation assays, Trf2fl/flRosa26cre-ERT1 MEFs were transduced with retroviral particle containing the Trf2 mutants, selected for 2 days in 2–4 µM Puromycin and then treated with 1 μM 4-OHT for 24 h. Time-point 0 was set at the time of 4-OHT addition. All cell lines in this study routinely tested negative for mycoplasma contamination. RPE-1 cells were validated by whole-genome sequencing, while MEFs were automatically genotyped after isolation by TransnetYX for the presence of Apollo or Trf2 flox alleles and/or ligase 4 deletion or RsCre.

Generation of Trf2-mutant alleles

PCR was used to delete the RAP1-binding motif (RBM) or insert the S367A mutation into MYC-tagged Trf2, Trf2F120A, Trf2ΔiDDR and Trf2F120A ΔiDDR alleles cloned in pLPC retroviral vector[13] using the following primers: Trf2ΔRBM-F: 5′-AATCTGGCATCCCCATCATCAC-3′; Trf2ΔRBM-R: 5′-TCTGCTTGGAGGCTCTCTAAG-3′; Trf2S367A-F: 5′-GCGCCAGCCCACAAACACAAGAGACC-3′; Trf2S367A-R: 5′-TGATGGGGATGCCAGATTAGCAAG-3′.

Fluorescence in situ hybridization

Telomere FISH on mouse and human cells was performed as previously described[59]. In brief, cells were treated with 0.2 µg ml−1 Colcemid (Biowest/Roche) for 2 h before collection by trypsinization. Collected cells were swollen in a hypotonic solution of 75 mM KCl at 37 °C for 10–20 min before fixation in methanol:acetic acid (3:1) overnight at 4 °C. Cells were dropped onto glass slides and allowed to age overnight. The slides were then dehydrated through an ethanol series of 70%, 95% and 100% and allowed to air dry. Telomere ends were hybridized with Cy3-OO-(TTAGGG)3 in hybridization solution (70% formamide, 1 mg ml−1 blocking reagent (1109617601, Roche), and 10 mM Tris-HCl pH 7.2) for 2 h following an initial 5–10 min denaturation step at 80 °C, washed twice with 70% formamide; 0.1% BSA; 10 mM Tris-HCl, pH 7.2 for 15 min each, and thrice in 0.08% Tween-20; 0.15 M NaCl; 0.1 M Tris-HCl, pH 7.2 or PBS for 5 min each. For MEFs, chromosomal DNA was counterstained with the addition of DAPI (D1306, Invitrogen) to the second wash. Slides were left to air dry and mounted in antifade reagent (Prolong Gold Antifade P36934, Fisher). For RPE-1 cells, air-dried slides were mounted in DAPI-supplemented Vectashield mounting medium (Vector laboratories) Micrographs for mouse cell experiments were collected on a DeltaVision RT microscope, Micrographs for RPE-1 cell experiments were collected on a Zeiss Axio Observer Z1 Marianas TM microscope (operated with 3i SlideBook) equipped with a CSU-X1 spinning disk (Yokogawa). Metaphases were analysed in FIJI and scored fusions were plotted using GraphPad Prism. Figures were prepared in Adobe Illustrator (v25.0.0). CO-FISH analysis of RPE-1 cells was performed as previously described[23] with the following modifications: RPE-1 cells were treated with BrdU:BrdC for 14 h, slides were treated with UV for 10 min using a Blak ray model UV-21 365 nm handheld lamp at a distance of 8 cm and telomeres were detected with Cy3-OO-(TTAGGG)3 and FITC-OO-(CCCTAA)3.

Immunoblotting

Cells were lysed in 2× Laemmli buffer at 5 × 10^3 or 1 × 10^4 cells per μl and the lysate was denatured for 10 min at 95 °C before shearing with an insulin needle or sonication. Lysate equivalent to 1–2 × 10^5 cells was resolved using SDS–PAGE and transferred to a nitrocellulose membrane. For mouse cell experiments, Western blot was performed with 5% milk in PBS containing 0.1% (v/v) Tween-20 (PBS-T). For RPE-1 cell experiments, Western blotting was performed in TBS buffer supplemented with 0.1% Tween-20. Additional reagents are described in the ‘antibodies’ section. Immunoblots were developed using chemiluminescence western blotting detection reagents (Cytiva or Cell Signalling or Millipore) and imaged on a ChemiDoc (Bio-Rad) imaging system or using Amersham Hyperfilm MP (Cytiva) and a CURIX 60 processor (AGFA). Images were analysed in Adobe Photoshop (v25.0.0) and figures were prepared using Adobe Illustrator (v25.0.0).

Chemical crosslinking mass spectrometry analysis

Complex assembly and crosslinking with DSSO was performed as described in ‘Crosslinking’. After the crosslinking reaction, triethylammonium bicarbonate buffer (TEAB) was added to the sample at a final concentration of 100 mM. Proteins were reduced and alkylated with 5 mM TCEP and 10 mM iodoacetamide simultaneously and digested overnight with trypsin at final concentration 50 ng μl−1 (Pierce). Sample was dried and peptides were fractionated with high-pH Reversed-Phase (RP) chromatography using the XBridge C18 column (1.0 × 100 mm, 3.5 μm, Waters) on an UltiMate 3000 HPLC system. Mobile phase A was 0.1% v/v ammonium hydroxide and mobile phase B was acetonitrile, 0.1% v/v ammonium hydroxide. The peptides were fractionated at 70 μl min−1 with the following gradient: 5 min at 5% B, up to 15% B in 3 min, for 32 min gradient to 40% B, gradient to 90% B in 5 min, isocratic for 5 min and re-equilibration to 5% B. Fractions were collected every 100 s, SpeedVac dried and pooled into 12 samples for mass spectrometry analysis. Liquid chromatography–mass spectrometry analysis was performed on an UltiMate 3000 UHPLC system coupled with the Orbitrap Ascend Mass Spectrometer (Thermo). Each peptide fraction was reconstituted in 30 μl 0.1% formic acid and 15 μl were loaded to the Acclaim PepMap 100, 100 μm × 2 cm C18, 5 μm trapping column at 10 μl min−1 flow rate of 0.1% formic acid loading buffer. Peptides were then subjected to a gradient elution on the Acclaim PepMap (75 μm × 50 cm, 2 μm, 100 Å) C18 capillary column connected to the EASY-Spray source at 45 °C with an EASY-Spray emitter (Thermo, ES991). Mobile phase A was 0.1% formic acid and mobile phase B was 80% acetonitrile, 0.1% formic acid. The gradient separation method at flow rate 300 nl min−1 was as follows: for 80 min gradient from 5–35% B, for 5 min up to 95% B, for 5 min isocratic at 95% B, re-equilibration to 5% B in 5 min, for 5 min isocratic at 5% B. Precursors between 380 and 1,400 m/z and charge states 3–8 were selected at 120,000 resolution in the top speed mode in 3 s and were isolated for stepped HCD fragmentation (collision energies (%) = 21, 27, 34) with quadrupole isolation width 1.6 Th, Orbitrap detection with 30,000 resolution and 70 ms maximum injection time. Targeted mass spectrometry precursors were dynamically excluded from further isolation and activation for 45 s with 10 ppm mass tolerance. Identification of crosslinked peptides was performed in Proteome Discoverer 2.4 (Thermo) with the Xlinkx search engine in the MS2 mode for DSSO/+158.004 Da (K). Precursor and fragment mass tolerances were 10 ppm and 0.02 Da respectively with maximum 2 trypsin missed cleavages allowed. Carbamidomethyl at C was selected as static modification. Spectra were searched against a UniProt FASTA file containing Homo sapiens reviewed entries. Crosslinked peptides were filtered at FDR < 0.01 using the percolator node and target–decoy database search.

Statistics and reproducibility

Three biological replicates were performed for metaphase spreads, except for those in Extended Data Fig. 1h,i, which were repeated twice. All other experiments were independently replicated a minimum of three times except for Fig. 3c and Extended Data Figs. 2f,g and 7c which were repeated twice. All attempts at replication were successful.

Reporting summary

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

Data availability

The cryo-EM composite map of the RAP1–DNA-PK complex was deposited to the Electron Microscopy Data Bank under accession code EMD-19065. Corresponding atomic coordinates were deposited to the Protein Data Bank under PDB ID 8RD4. Constituent cryo-EM maps for locally refined KU–RAP1 and DNA-PKcs regions as well as a consensus map of the full complex were deposited under accession codes EMD-19249, EMD-19252 and EMD-19245 respectively. Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE[60] partner repository with the dataset identifier PXD047643.

Change history

- 23 May 2025

A Correction to this paper has been published: https://doi.org/10.1038/s41586-025-09121-9

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Acknowledgements

The authors thank P. Martin, R. Broderick, A. Radzisheuskaya and H. Mischo for reagents and advice and G. Coster and T. de Lange for comments on the manuscript. M.E.D. is funded by a Cancer Research UK Career Development Award (C68409/A28129). F.L. is supported by grants from Cancerfonden (21 1732 Pj), Vetenskapsrådet (2021-02788) and Knut and Alice Wallenberg Foundation. J.M. is funded by a Cancer Research UK Senior Cancer Research Fellowship (RCCSCF-Nov22/100001). S.G. is supported by a Wellcome Trust Investigator Award (214311/Z/18/Z) and a Lister Institute Research Prize Fellowship.

Author information

Authors and Affiliations

1. Telomere Biology Laboratory, The Institute of Cancer Research, London, UK

Patrik Eickhoff, Charlotte E. L. Fisher & Max E. Douglas

2. Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden

Ceylan Sonmez, Angela dello Stritto & Francisca Lottersberger

3. Structural Biology of Cell Signalling, The Institute of Cancer Research, London, UK

Oviya Inian & Sebastian Guettler

4. Functional Proteomics, The Institute of Cancer Research, London, UK

Theodoros I. Roumeliotis & Jyoti S. Choudhary

5. Post-translational Modifications and Cell Proliferation, The Institute of Cancer Research, London, UK

Jörg Mansfeld

Contributions

M.E.D. conceived the study, with genetic experiments devised in collaboration with F.L. MEF experiments were performed by C.S. with help from A.d.S. RPE-1 cell experiments were performed by M.E.D. and P.E. with advice from J.M. Experiments in Figs. 2, 3 and 5 were performed by P.E. with help from C.E.L.F. For Fig. 2b, S.G. designed the expression construct for shelterin, which was purified by O.I. Samples for Fig. 4 were prepared by P.E. and C.E.L.F. O.I prepared and screened grids and P.E and C.E.L.F. performed data processing. T.I.R. performed crosslinking mass spectrometry for Extended Data Fig. 5, on a sample prepared by P.E. Supervision was provided by J.S.C., S.G., F.L. and M.E.D. The manuscript was written by M.E.D. with input from all authors.

Corresponding authors

Correspondence to Francisca Lottersberger or Max E. Douglas.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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Extended data figures and tables

Extended Data Fig. 1 RAP1 and APOLLO redundantly prevent cNHEJ at telomeres in mouse and human cells.

a. Immunoblot showing effective removal of Rap1 and persistence of TRF2 as indicated after Crispr-mediated Rap1 deletion and/or Cre-mediated deletion of Apollo in ApolloF/F Lig4+/+ or ApolloF/F Lig4−/− MEFs. Beta-actin as loading control. b. Immunoblot showing expression of TRF2 in SV40LT-immortalized TRF2F/F RsCre-ERT1 MEFs transduced with an empty vector control (-), mouse TRF2 WT or TRF2 alleles habouring a F120A mutation and/or deleted of the RAP1 binding motif (∆RBM) and/or deleted of the DNA-damage response motif (∆iDDR) 120 h after deletion of endogenous TRF2 with 4-OHT. Beta-actin as loading control. c. Representative FISH of metaphase spreads from cells as described in b. Telomeres detected with Cy3-(TTAGGG)3 (green). DNA stained with DAPI (magenta). White and green arrows highlight chromatid- and chromosome type fusions respectively. Scale bar = 10 µm d. Quantification of percentage telomeres involved in chromatid and chromosome fusions per metaphase after deletion of endogenous TRF2 in MEFs expressing the indicated TRF2 mutants, as described in b. Data from 3 independent experiments, 10 metaphases per experiment (n = 30 total), with median. Statistics by ordinary One-way ANOVA. ‘ns’ not significant, ****P ≤ 0.0001. e. Sequencing of the RAP1 locus in p53−/− RAP1+/+ and p53−/− RAP1−/− RPE-1 cells. 19 bp deletion in knockout clones generates an altered reading frame terminating in a stop codon after a total of 135 amino acids. f. Immunoblot showing Apollo protein levels 120 h after transduction of p53−/− RAP1+/+ and p53−/− RAP1−/− RPE−1 cells with Cas9 and sgApollo or sgControl. Alpha-tubulin as loading control. g. Chromatid- and chromosome-type telomere fusions quantified after Apollo deletion as in (f). From three independent experiments, 10 metaphases from each, with median. Statistics as in (d). h. Upper panel as in (g) but cells treated with 2 µM DNA-PK inhibitor NU7741 for 48 h prior to cell collection. From two independent experiments, 10 metaphases each, with median. Statistics as in (d). Lower panel as in (f). i. Representative coFISH images of RAP1 KO clone 2 120 h after transduction with Cas9 and sgApollo. Telomeres detected with Cy3-(TTAGGG)3 (red = leading) and FITC-(CCCTTA)3 (green = lagging). Arrows highlight leading:leading telomere fusions. For gel source data see Supplementary Fig. 1.

Extended Data Fig. 2 Deletion constructs, purified proteins and data relating to Figs. 2 and 3.

a. Purified proteins as indicated, separated on a denaturing tris-glycine polyacrylamide gel and stained with Instant Blue. b. DNase I footprinting performed as in Fig. 2a with shelterin or TRF2/RAP1 at increasing concentrations (left panel) or with KU70/80, DNA-PKcs and TRF2/RAP1 each at 250 nM (right panel). Template concentation increased to 20 nM for the experiment on the right. Nucleotides from the 5′ telomeric end indicated. c. DNase I footprinting performed as in Fig. 2a, except shelterin was added coincident with KU70/80 and DNA-PKcs. Lanes 9-22 contained 8 nM template, 100 nM KU/DNA-PKcs and 40 nM shelterin or TRF2:RAP1 as indicated. Cleavage within the 10 bp footprint derives from a change in template sequence register compared with the standard template in Figs. 2–4. Nucleotides from the 5′ telomeric end indicated. d. Purified proteins as indicated, separated on a denaturing tris-glycine polyacrylamide gel and stained with Instant Blue. e. Domain organisation of RAP1 and TRF2 mutants. TRF2 L330R (equivalent to L288R in the shorter TRF2 isoform) prevents binding to RAP1 (Chen et al, 2011). PreScission cleavage site highlighted by 3 C. Also see Supplementary Information Table 2. f. Electrophoretic mobility shift assay of telomeric DNA bound by TRF2. g. DNaseI footprinting performed as in Fig. 3d with proteins omitted as indicated. Nucleotides from the 5′ telomeric end indicated. h. Cleavage of Teb1 DBD from RAP1 using PreScission protease. Proteins were separated on a denaturing tris-glycine polyacrylamide gel and stained with Instant Blue. i. Protein cross-linking analysis of KU, RAP1 and TRF2 containing only RBM and myb domains, in the presence of DNA. Arrowheads mark the position of cross-linked species containing only KU, or KU, RAP and TRF2. For gel source data see Supplementary Fig. 1.

Extended Data Fig. 3 Cryo-EM data processing pipeline for the RAP1:DNA-PK complex on DNA.

Schematic showing the cryoSPARC classification and refinement steps used to obtain the RAP1:DNA-PK structure. Also see Extended Data Table 1.

Extended Data Fig. 4 DNA-PKcs conformation in the RAP1:DNA-PK complex.

DNA-PKcs models from the structures indicated, showing the M-HEAT and N-HEAT domains adopting an ‘inactive’ conformation in the telomere end binding complex.

Extended Data Fig. 5 Binding of the KU70 SAP and RAP1 myb domains to DNA.

a. Density map and model of the KU70 SAP:DNA interaction extracted from the complete structure, showing K575, K595 and K596 coordinating the phosphate backbone b. Density map and model of the RAP1 myb:DNA interaction extracted from the complete structure, showing the recognition helix sitting in the major groove, and R133 acting as an N-terminal arm inserted into the neighbouring minor groove. Region protected from DNase I indicated c. Sequence alignment of the human RAP1 myb domain. Alignment adopts Clustal X colouring d. Comparison of the DNA-bound human RAP1 myb domain (blue) with homeotic protein antennapedia (purple - 1ahd) and HOX-B1 (pink - 1b72). Structures were aligned via the DNA. e. Table of intermolecular chemical crosslinks detected by XLMS. Data were thresholded with an XlinkX score ≥90 and crosslinks between KU and DNA-PKcs were excluded. f. Intermolecular chemical crosslinks detected by XLMS. Data were thresholded with an XlinkX score ≥90. g. Density map and model of the RAP1 loop C-terminal to the myb domain anchored to KU80.

Extended Data Fig. 6 Binding of the RAP1 BRCT domain to KU70 vWA.

a. Sequence alignment of the RAP1 (left), KU70 (middle) and KU80 (right) regions that engage in the BRCT:KU interaction. Asterisks mark K39, R40 and R55 in RAP1, which are changed to aspartate or glutamate in the KR/DE mutant. Also, D496 and E499 in KU70 and D326 in KU80, which are changed to lysine or arginine in the KU DE/KR mutant. Alignment adopts Clustal X colouring b. Nano-scale differential scanning fluorimetry analysis of the RAP1 BRCT or KU mutants as indicated, showing no significant effect of the point mutations on protein folding. c. Protein cross-linking analysis of RAP1 and KU in the presence of DNA. Proteins were mixed with crosslinker and reaction products were separated on a denaturing tris-acetate polyacrylamide gel and analysed by silver staining or immunoblotting as indicated. ΔB and ΔM mark Rap1 BRCT and myb domain deletion mutants respectively. RAP1 KR/DE (KR) contains K39D, R40E and R55E mutations. KU DE/KR (DE) contains KU70 D496K, E499R and KU80 D327K mutations. For gel source data see Supplementary Fig. 1.

Extended Data Fig. 7 Supplementary DNA-PK binding assays related to Fig. 5.

a. KU pulldown experiment without TRF2 or RAP1, executed as described in Fig. 5a. KU DE/KR contains the mutations KU70 D496K, E499R and KU80 D327K b. As in (a), but with reactions containing 5 nM TRF2/RAP1 complex and increasing concentrations of XRCC4/LIG4 as indicated. c. As in (b). ‘X/L pre-bind’ indicates reactions where XRCC4/LIG4 was incubated with KU70/80, DNA-PKcs and DNA template for 5 min prior to the addition of TRF2/RAP1. d. As in (b), but with 30 nM XRCC4/LIG4, and the RAP1 proteins indicated. For gel source data see Supplementary Fig. 1.

Extended Data Fig. 8 Mutation analysis of RAP1 in mouse and human cells.

a. Immunoblot showing over-expression of mouse RAP1, RAP1 KR/DE and RAP1 R130E after Crispr- and Cre-mediated deletion of Rap1 and Apollo respectively in ApolloF/F Lig4+/+ MEFs. Beta-actin as loading control. b. Representative FISH of metaphase spreads of ApolloF/F Lig4+/+ MEFs expressing the indicated RAP1 mutants or a control empty vector (EV) 96-120 h after deletion of endogenous Rap1 with sgRNA and 120 h after deletion of APOLLO with Hit & Run Cre. Telomeres detected with Cy3-(TTAGGG)3 (green). DNA stained with DAPI (magenta). White and green arrows highlight chromatid- and chromosome type fusions respectively. Scale bar = 10 µm c. Quantification of percentage telomeres involved in chromatid fusions per metaphase after expression of Rap1 or the empty vector control (-) and removal of endogenous Rap1 and Apollo as described in b. Data from 3 independent experiments, 10 metaphases per experiment (n = 30 total), with median. Statistics by ordinary One-way ANOVA. ‘ns’ not significant. d. Sequence analysis of the human RAP1 locus in RAP1 WT and RAP1 KR/DE p53−/− RPE-1 cells. Targeted mutations in KR/DE are marked with an asterisk, with altered amino acids highlighted in red e. Immunoblot showing Apollo, RAP1 and TRF2 levels after Crispr-mediated deletion in p53−/− RAP1 WT and p53−/− RAP1 KR/DE RPE-1 cells. Asterisk marks a non-specific band detected by the anti-Apollo antibody. Alpha-tubulin as loading control.f. Representative FISH of metaphase spreads of RAP1 WT and RAP1 KR/DE p53−/− RPE-1 cells 120 h after transduction with Cas9 and sgApollo or sgControl as indicated. Telomeres detected with Cy3-(TTAGGG)3 (green). DNA stained with DAPI (magenta). White and green arrows highlight chromatid- and chromosome type fusions respectively. g. Quantification of the percentage of telomeres involved in chromatid and chromosome fusions per metaphase after removal of Apollo in RAP1 WT and RAP1 KR/DE p53−/− RPE-1 cells as indicated. Data from 3 independent experiments, 10 metaphases per experiment (n = 30 total), with median. Statistics by ordinary One-way ANOVA. ‘ns’ not significant, ****P ≤ 0.0001. For gel source data see Supplementary Fig. 1.

Extended Data Fig. 9 Conservation analysis of the RAP1 BRCT:KU interaction.

Amino acid sequences of RAP1 from the metazoan species indicated were aligned using the Muscle algorithm. The basic patch composed of K39, R40, R41 and R55 in the BRCT domain is shown with basic residues displayed in red. Corresponding structure predictions for the RAP1:KU complex made using AlphaFold 3 are shown for the species indicated, revealing that the BRCT:KU interaction is likely to occur widely across metazoa.

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics

Supplementary information

Supplementary information

This file contains Supplementary Tables 1 and 2 and Supplementary Fig. 1. Supplementary Table 1: Oligonucleotides used. Supplementary Table 2. Constructs used. Supplementary Fig. 1: Raw data and uncropped gels.

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Supplementary Video 1

Video showing the cryo-EM composite map of the RAP1–DNA-PK complex with annotation.

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Eickhoff, P., Sonmez, C., Fisher, C.E.L. et al. Chromosome end protection by RAP1-mediated inhibition of DNA-PK. Nature (2025). https://doi.org/10.1038/s41586-025-08896-1

- Received: 12 December 2023

- Accepted: 13 March 2025

- Published: 16 April 2025

- DOI: https://doi.org/10.1038/s41586-025-08896-1

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Chromatin loops are an ancestral hallmark of the animal regulatory genome

Iana V. Kim,

Cristina Navarrete,

Xavier Grau-Bové,

Marta Iglesias,

Anamaria Elek,

Grygoriy Zolotarov,

Nikolai S. Bykov,

Sean A. Montgomery,

Ewa Ksiezopolska,

Didac Cañas-Armenteros,

Joan J. Soto-Angel,

Sally P. Leys,

Pawel Burkhardt,

Hiroshi Suga,

Alex de Mendoza,

Marc A. Marti-Renom &

…

Arnau Sebé-Pedrós

Nature (2025)

Abstract

In bilaterian animals, gene regulation is shaped by a combination of linear and spatial regulatory information. Regulatory elements along the genome are integrated into gene regulatory landscapes through chromatin compartmentalization1,2, insulation of neighbouring genomic regions3,4 and chromatin looping that brings together distal cis-regulatory sequences[5]. However, the evolution of these regulatory features is unknown because the three-dimensional genome architecture of most animal lineages remains unexplored6,7. To trace the evolutionary origins of animal genome regulation, here we characterized the physical organization of the genome in non-bilaterian animals (sponges, ctenophores, placozoans and cnidarians)8,9 and their closest unicellular relatives (ichthyosporeans, filastereans and choanoflagellates)[10] by combining high-resolution chromosome conformation capture11,12 with epigenomic marks and gene expression data. Our comparative analysis showed that chromatin looping is a conserved feature of genome architecture in ctenophores, placozoans and cnidarians. These sequence-determined distal contacts involve both promoter–enhancer and promoter–promoter interactions. By contrast, chromatin loops are absent in the unicellular relatives of animals. Our findings indicate that spatial genome regulation emerged early in animal evolution. This evolutionary innovation introduced regulatory complexity, ultimately facilitating the diversification of animal developmental programmes and cell type repertoires.

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Main

A fundamental characteristic of animal multicellularity is the existence of specialized cell types. These cell types result from differential access to genomic information in each cell. Thus, evolutionary changes in genome regulation are proposed to be a major innovation linked to the emergence of complex multicellularity with stable cell differentiation6,10. This idea is supported by comparative genomic analyses showing that gene innovation at the origin of animals was less extensive than previously thought10,13, thus suggesting that an important animal innovation was the ability to coregulate existing genes in different combinations.

In bilaterian animals, genome spatial compartmentalization mediates the organization of gene neighbourhoods that can be independently regulated3,4,6 and that are specific to different cell types[14]. Another mechanism contributing to elaborate gene regulation in bilaterians is the combinatorial interaction of distal cis-regulatory elements and gene promoters by means of chromatin loops that bring distant regions into spatial proximity through genome folding, contrasting with the predominant regulation by proximal promoter elements in unicellular eukaryotes[10]. Comparative analyses of histone posttranslational modifications have shown that candidate distal enhancer elements, as defined by chromatin features, predate the origin of bilaterian animals8,15,16, whereas such enhancers are absent in the closest unicellular relatives of animals[17]. However, it is still unclear whether distal regulation in early-branching metazoans is mediated by physical interactions with gene promoters or linked to the existence of insulated gene regulatory landscapes.

To investigate the origins of animal gene regulation, here we comparatively studied chromatin architecture at subkilobase resolution in non-bilaterian animal lineages and their closest unicellular relatives of animals (Fig. 1). This includes the two phyla proposed as the sister group to all other animals9,18: ctenophores, which are mostly pelagic, marine predators that swim using ciliated comb cells and have complex nerve nets19,20; and sponges, which are sessile, benthic organisms that filter-feed using collared choanocyte cells8,21. We also examined placozoans, which are millimetre-sized, flat animals that feed on microbial mats by gliding using ciliary movement and mucus secretion, controlled by peptidergic secretory cells[22], and cnidarians, the sister group to bilaterians that includes jellyfishes, corals and anemones[23]. Finally, we studied three unicellular relatives of animals, known as unicellular holozoans: ichthyosporeans, which are osmotrophic unicellular eukaryotes that reproduce through multinucleated coenocytes[24]; filastereans, which are heterotrophic protists with complex life cycles, including aggregative multicellular stages17,25 and choanoflagellates, which are heterotrophic flagellates that show both single-cell and colonial forms and are the closest living relatives to animals[26]. The comparative analysis of chromatin maps across these lineages allows us to reconstruct the evolutionary history of genome regulation in animals.

Fig. 1: Chromatin architecture in early animal evolution.

a, Comparison of genomic features across metazoans and unicellular holozoans. For H. sapiens, we used previously published mCG methylation percentage data from H1 ESCs cells. Of note, although distal cis-regulatory elements (dCRE) were identified in Amphimedon queenslandica[15], their presence in E. muelleri had not been reported previously. mCG, CG methylation; TEs, transposable elements. b, Top left, phylogenetic tree showing the taxon sampling in this study, along with the number of profiled species per clade. Top right and below, Micro-C interaction maps of specific genomic regions (S. arctica, chr. 2: 3400000–3700000, bin 1 kb; C. owczarzaki, chr. 01: 3660000–3800000, bin 400 bp; S. rosetta, chr. 21: 800000–1100000, bin 800 bp; M. leidyi, chr. 8: 15500000–15700000, bin 400 bp; E. muelleri, Emue22: 2200000–2400000, bin 800 bp; T. adhaerens, TadhH1_4: 3880000–4180000, bin 800 bp; N. vectensis, NC_064040.1: 11650000–12000000, bin 1 kb; D. melanogaster, chr. 3L: 20480000–20820000, bin 800 bp; and H. sapiens, chr. 12: 69000000–71000000, bin 5 kb), showing examples of insulation boundaries or chromatin loops. All interaction maps were balanced using ICE normalization.

Large-scale genome organization

We used Micro-C11,12 to map genome-wide chromatin contacts at single-nucleosome resolution in representatives of non-bilaterian animal lineages (Fig. 1, Extended Data Fig. 1, Supplementary Table 1 and Supplementary Text 1): the ctenophore Mnemiopsis leidyi19,20, the sponge Ephydatia muelleri[21], the placozoan Trichoplax adhaerens[22] and the cnidarian Nematostella vectensis[23]. As outgroup species, we studied chromatin architecture in three unicellular holozoans: the ichthyosporean Sphaeroforma arctica[24], unicellular filasterean amoeba Capsaspora owczarzaki17,25 and the choanoflagellate Salpingoeca rosetta[26]. We also compared our chromatin maps with existing datasets from two bilaterians: Drosophila melanogaster[27] and Homo sapiens[12]. To analyse our chromatin contact experiments, we first resequenced de novo and assembled to chromosome-scale the genomes of M. leidyi, E. muelleri and C. owczarzaki using a combination of Nanopore (Oxford Nanopore Technology) long-read sequencing and Micro-C data (Extended Data Fig. 2). For S. arctica, S. rosetta and T. adhaerens, we rescaffolded existing genomes22,24,26 to chromosome level using Micro-C data. In addition, to interpret the observed contact features, we generated genome-wide maps of chromatin accessibility (assay for transposase-accessible chromatin with high-throughput sequencing or ATAC-seq), chromatin modifications (chromatin immunoprecipitation with sequencing (ChIP–seq) for H3K4me3, H3K4me2, H3K4me1) and gene expression (RNA sequencing or RNA-seq), or used published datasets when available (Supplementary Table 2). We integrated three-dimensional (3D) chromatin data with linear chromatin marks to systematically compare genome architectural features at different resolutions3,4,7 (compartmentalization, insulation and chromatin looping) and across phylogenetically distant species with diverse genome sizes, gene densities and transposable element content (Fig. 1a).

We first analysed global chromosomal compartmentalization, which results from the spatial segregation of distinct chromatin states genome-wide (active, A; inactive, B) and is influenced by histone marks, DNA methylation and gene transcription, among other phenomena28,29. As such, compartmentalization is often considered an intrinsic biophysical property of the chromatin driven by phase separation30,31. To compare the degree of self-affinity and segregation between major chromatin compartments, we defined A/B compartment limits in each species. We then calculated the intensity of compartmentalization in genomic bins with compartment A and B interaction frequency in the top 20th percentile (Fig. 2a). Compartmentalization strength in each species was quantified as the ratio of homotypic (AA, BB) to heterotypic (AB) interactions (Fig. 2b). The relative resolutions were obtained by partitioning genomes into equal number of bins across species (Extended Data Fig. 3a,b), but the differences between species remained consistent regardless of the number of bins used (Fig. 2b). Furthermore, we assigned an intermediate compartment (I) to regions with weak spatial separation (Extended Data Fig. 3c,d).

Fig. 2: Chromatin compartments and insulation boundaries across species.

a, Saddle plots showing contact interactions between A and B compartments in each species, organized by eigenvector ranking. To obtain the distance-normalized matrix, the ratio of observed-over expected interactions is calculated, followed by eigenvector decomposition. The eigenvectors are oriented and sorted from the lowest (B compartment) to the highest (A compartment) values. The bins of the interaction matrix then reordered according to the rank of the eigenvector. The observed (O) and expected (E) values are averaged to create a saddle plot. The top 20% of the interaction values were used to calculate the compartment strength values shown on the saddle plots. Cowc, C. owczarzaki; Dmel, D. melanogaster; Emue, E. muelleri; Hsap, H. sapiens; Mlei, M. leidyi; Nvec, N. vectensis; Sarc, S. arctica; Sros, S. rosetta; Tadh, T. adhaerens. b, Compartment strength quantification at different relative resolutions. The barplot below shows the contribution of homotypical chromatin interactions within active (AA) and inactive (BB) chromatin states. c, Aggregate plots showing contact enrichment within a rescaled region between two insulation boundaries. The boundaries are identified using the sliding diamond window to detect the changes in contact frequencies in each genomic bin. To plot pile-ups, regions between insulation boundaries are rescaled and their normalized observed and expected contact frequencies are averaged. d, Insulation score distributions illustrating the degree of isolation between linear genomic neighbourhoods. Number of annotated strong boundaries is indicated in blue, with a vertical line representing the median value of each distribution. e, Classification of insulation boundaries using hierarchical assignment of structural and genomic features. f, Size distribution of annotated chromatin loops in each species. The boxplots show the median (centre line), 25th and 75th percentiles (box limits) and the whiskers show the range of variability, excluding outliers, which are shown as individual points. g, Annotation of chromatin loop anchors with promoter (P) and enhancer (E) signatures based on normalized H3K4me3 and H3K4me2 or H3K4me1 ChIP–seq coverage. Chromatin loop anchors with undefined (U) epigenetic signature are shown in grey.

Our analysis revealed that, with exception of M. leidyi, animal genomes were globally segregated into transcriptionally active, gene-dense compartments and transcriptionally inactive, transposable element-rich compartments, similar to what is observed in bilaterian animals (Extended Data Fig. 3d). In these species, we detected a strong separation of A and B compartments in saddle plots (Fig. 2a) and the compartment strength values above 1.8 (Fig. 2b). Moreover, these compartments encompass relatively large contiguous regions across the genome (Extended Data Fig. 3b). By contrast, unicellular holozoans and M. leidyi did not show strong separation of large A and B compartments (Fig. 2a,b), similar to what is observed in yeast[32] and other protists[33]. The absence of large-scale chromatin compartments in M. leidyi is unusual among animals, although it has been previously reported in certain species[34]. This lack of compartmentalization may be due to the absence of constitutively silenced regions across different cell types. Overall, our results indicate that A/B chromosomal compartmentalization is a phylogenetically conserved feature across animal genomes.

Insulation and micro-scale contacts

We next characterized small-scale chromosomal features across species by defining spatial insulation boundaries between neighbouring loci. The boundary elements that partition genome into domains can arise from active transcription, silenced repetitive regions or binding of sequence-specific architectural proteins at insulator or tethering elements5,27,35,36. Thus, our first goal was to identify the occurrence of insulation boundaries in each species (Fig. 2c,d and Extended Data Fig. 4), and then classify these points into different regulatory or structural features (domain boundaries, gene bodies, regulatory loops and so on) (Fig. 2e). To this end, we calculated insulation scores for each species, representing the difference in contact frequencies between each genomic bin and its neighbouring bins. We used different resolutions and sliding window sizes (Extended Data Fig. 4a,b) and, for each species, we selected the resolution and two window sizes that yielded the maximal insulation signal, indicating the strongest partitioning of the genome into isolated structural and functional domains. The median distance between successive identified boundary elements varied between 6.4 kilobases (kb) in S. rosetta and 190 kb in H. sapiens, yet the median number of genes per interval was consistently similar across species, with two to four genes (Extended Data Fig. 4c).

The presence of self-interacting domains, contiguous regions of the genome with enriched interactions, was assessed by examining the average pile-up plots between insulation boundaries (Fig. 2c). We observed weak contact enrichment between pairs of insulated boundaries in unicellular holozoans and E. muelleri. In M. leidyi, boundary elements were tethered through strong focal contacts and without intradomain interactions, contrary to what would be expected within topologically associating domains (TADs)[3]. By contrast, D. melanogaster showed intradomain enrichment without focal contacts, in agreement with previously reported domains[37]. T. adhaerens and N. vectensis showed a certain degree of self-affinity within insulated neighbourhoods, as well as focal point enrichment (Fig. 2c). The degree of insulation of genomic regions could be quantified from the distribution of genome-wide insulation scores (Fig. 2d and Extended Data Fig. 4c). M. leidyi, T. adhaerens, N. vectensis, H. sapiens and S. arctica genomes contained strong boundary elements in comparison with E. muelleri and, especially, the weakly insulated genomes of C. owczarzaki and S. rosetta (Fig. 2d and Extended Data Fig. 4c).

After identifying insulation points, we investigated the genomic features associated with these boundaries (Fig. 2e and Extended Data Fig. 4d). We first assigned insulation boundaries to annotated chromatin loops, followed by the transcription start sites (TSSs) of genes not involved in chromatin looping and then accessible chromatin regions that may represent other regulatory elements. Remaining boundaries were assigned to A/B compartment limits. This analysis revealed that most insulation boundaries in unicellular holozoans and E. muelleri were associated with active TSSs (Fig. 2e), suggesting that active transcription is the main factor defining insulation in these species[37]. By contrast, many insulation boundaries could be assigned to chromatin loop anchors in M. leidyi (77%; compared to 78% in H. sapiens human embryonic stem cells) and in T. adhaerens (38%), whereas in N. vectensis, we identified 166 chromatin loops that represented only 1.6% of insulation boundaries. The number of chromatin loops in M. leidyi (4,261) and T. adhaerens (3,065) was much higher than those found in N. vectensis (166) and D. melanogaster (313)[27], despite their similar genome sizes and gene densities (Fig. 1a). Loop sizes were comparable in these four species (median 21–28 kb), but much smaller than in H. sapiens (median 140 kb) with a genome 15–30 times larger (Fig. 2f). To further characterize these distal contacts, we examined genome-wide H3K4me3, H3K4me2 and H3K4me1 to classify many of the identified loop anchor sites as promoter-like elements (Fig. 2g). In M. leidyi and N. vectensis, chromatin loops predominantly occurred between promoters and enhancers (77 and 69%, respectively), similar to H. sapiens (63%). By contrast, 79% of loops in T. adhaerens connected promoters to other promoters, similarly to what is observed in D. melanogaster (49%)[38]. Our results show that enhancer–promoter and promoter–promoter long-range chromatin loops are shared between bilaterians and early-branching animal lineages, and possibly date back to the origin of animal multicellularity.

Protists, sponges and cnidarians

In unicellular holozoans, we did not observe any spatial contact patterns indicative of chromatin loops. However, manual inspection revealed a few regions enriched in distal contacts. For example, in S. arctica, we could identify 296 self-interacting insulated domains that also contact each other (Extended Data Fig. 5a,b). These regions were depleted of active histone marks and were enriched in transposable elements, probably representing repressed chromatin domains that cosegregate (Extended Data Fig. 5c). In S. rosetta, there were 183 distally interacting regions that contained lowly expressed genes (Extended Data Fig. 5d,e) and were enriched in H3K4me1 and H3K27me3 or lacked profiled marks (Extended Data Fig. 5f). These may also represent repressed regions[39], albeit they do not form well-defined domains like in S. arctica. In C. owczarzaki, we observed a plaid pattern indicative of chromatin microcompartments (Extended Data Fig. 5g), reflecting the spatial cosegregation of active promoters of highly transcribed genes with a strong H3K4me3 signal (Extended Data Fig. 5h,i). These microcompartment contacts form a regional small-scale checkerboard pattern with alternating loci of high and low interactions. Furthermore, we also detected high-frequency contact domains over gene bodies of highly expressed genes (Extended Data Fig. 5j).

In the sponge E. muelleri, we identified local interactions perpendicular to the main diagonal, and visually reminiscent to fountains observed in mouse, zebrafish and C. elegans[40] (Extended Data Fig. 6a,b). Manual inspection further revealed 84 focal contacts between distal genomic loci (Extended Data Fig. 6c), including gene promoters interacting with other regions showing promoter or enhancer-like chromatin signatures (Extended Data Fig. 6d,e). These weak distal interactions occurred between extended genomic regions, in contrast to the point-to-point contacts typical of chromatin loops (Extended Data Fig. 6c). Although chromatin loops were absent in E. muelleri, we identified 243 distal cis-regulatory elements, consistent with findings in other sponge species[15]. These elements were characterized by chromatin accessibility, with surrounding regions showing high H3K4me1 and low H3K4me3 signals, and were mostly intergenic but close to annotated TSS (median 3.8 kb) (Extended Data Fig. 6f). This distance-to-TSS distribution was similar to that of annotated enhancer elements in M. leidyi, T. adhaerens, N. vectensis and D. melanogaster that do not form loops (median 5.6 kb, compared to 31 kb in loop-forming enhancers) (Extended Data Fig. 6f), suggesting that sponges’ enhancer elements might function by proximity without the need for stable looping[41].

Genome folding in the cnidarian N. vectensis was characterized by the presence of chromatin loops, as well as weakly insulated self-interacting domains (Extended Data Fig. 7). We identified 166 chromatin loops forming both promoter–promoter and promoter–enhancer contacts, and with some loops spanning nearly 1 megabase (Mb) (Extended Data Fig. 7a–c). Chromatin loops have also been reported in the hydrozoan Hydra vulgaris[42], suggesting they are a conserved feature in cnidarians. Notably, some of the identified chromatin loops in N. vectensis showed a one-sided stripe pattern similar to those observed in other species, which are generated by cohesin extrusion[43]. Moreover, we identified an enriched GTGT motif (FC = 327, P = 1 × 10−40) present in 32% of loop anchors (Extended Data Fig. 7d). This motif resembles sequences with G-quadruplex-forming potential[44], which have been shown to stabilize enhancer–promoter interactions in other species[45]. Beyond chromatin loops, we also observed self-interacting domains in N. vectensis (Extended Data Fig. 7e). The insulation boundaries of these domains were enriched for the YY1 motif (FC = 9,016, P = 1 × 10−87) (Extended Data Fig. 7f), which is known to mediate chromatin interactions35,45. These regions represent high-frequency contacts within the same gene regulatory landscape, but are not stabilized by chromatin loops as in vertebrate TADs[3], nor are they as strongly insulated as the domains defined by insulator elements in D. melanogaster[27].

3D promoter hubs in placozoans

Our high-resolution chromatin contract maps revealed a complex 3D genome organization in the placozoan T. adhaerens, characterized by many loop contacts forming 3D interaction hubs (Fig. 3a). To confirm this observation, we profiled chromatin contacts in a distantly related placozoan species, Cladtertia collaboinventa, which showed a very similar pattern (Fig. 3a). Most of these interactions are promoter–promoter hubs (n = 2,413 for T. adhaerens and n = 3,239 for C. collaboinventa) (Extended Data Fig. 8a). Notably, 7–10% of chromatin contacts (n = 241 for T. adhaerens, n = 394 for C. collaboinventa) connected promoters with intronic or intergenic enhancer regions (Extended Data Fig. 8a,b), revealing the presence of distal cis-regulatory elements in placozoans.

Fig. 3: Promoter hubs in placozoans.

a, Example of syntenic genomic regions in placozoans T. adhaerens (TadhH1_4: 3860000–4060000, bin 800 bp) and C. collaboinventa (chr. 4: 8983000–9183000, bin 800 bp). b, Gaudí plots projecting ATAC-seq, H3K4me3 and exon annotation signals onto a two-dimensional Kamada–Kawai graph layout (top left) represented by the top 20% of contact pairs with solid colours highlighting statistically significant regions (P < 0.05) identified using a one-sided permutation test. The high–high (HH) signal marks genomic bins enriched in signal and that are in spatial proximity with other bins enriched in signal; low–low (LL) bins are depleted in signal as well as neighbourhoods are depleted in signal; high–low (HL) and low–high (LH) are bins that are enriched in signal, but not their neighbourhood, and in reverse. c, Classification of T. adhaerens genes into three categories (GP1, GP2 and GP3) on the basis of structural and epigenetic features. Top, example regions containing genes classified into GP1, GP2 and GP3 groups. The resolution of Micro-C maps is 800 bp, maximum intensity value of ICE normalized Micro-C maps is as in a. Bottom, average loop strength between promoter regions of the genes from each groups is measured with APA. The colour bar of pile-up plots shows enrichment of observed over expected values. d, Sequence motif found in loop regions, which are also overlapping GP1 promoter regions (left panel), is present in promoter regions of orthologous GP1 genes in other placozoan species (right panel). The total number of shared orthologues is indicated. TrH2, Trichoplax sp. H2; Hhon, Hoilungia hongkongensis; HoiH23, C. collaboinventa. e, Heatmaps showing CPM normalized ATAC-seq and ChIP–seq coverage, motif scores and Mutator transposable element density ±5 kb around the TSS of GP1, GP2 and GP3 genes. Each heatmap scale starts at zero.

We identified 321 promoter hub regions in the T. adhaerens genome and 331 in C. collaboinventa, involving 1,695 and 2,191 genes, respectively, with a median of four promoters in each hub in T. adhaerens and five in C. collaboinventa. To further reconstruct the 3D organization of these hubs, we used METALoci to calculate spatial correlation between genome folding and epigenetic (ATAC, H3K4me3 ChIP) or genomic (exon annotation) features (Fig. 3b and Extended Data Fig. 8c). This analysis revealed a nested structure where accessible promoter regions were central to the 3D interactions tightly clustered in space (ATAC-seq in Fig. 3b), whereas gene bodies and the first nucleosome (H3K4me3) occupied more peripheral locations (Fig. 3b). Furthermore, genes within spatial promoter hubs were linearly grouped along the genome, resembling the arrangement of housekeeping genes observed in mouse embryonic stem cells[46]. Alternatively, these structures could be associated with active transcription and the formation of micro-compartmentalized RNA polymerase II-driven transcription hotspots[47].

Notably, not all collinear genes formed promoter hubs. Following this observation, we categorized genes into three groups based on their spatial and epigenetic organization (Fig. 3c and Extended Data Fig. 8d,e). This includes group 1 genes (GP1, n = 2,978 in T. adhaerens, n = 3,973 in C. collaboinventa) that had both ATAC and H3K4me3 peaks and formed chromatin loops, with an average interaction strength in aggregate peak analysis (APA) of 1.32, indicating the enrichment of Micro-C signal at loop anchors. Group 2 genes (GP2: n = 3,681 in T. adhaerens, n = 3,119 in C. collaboinventa) also showed ATAC and H3K4me3 peaks, but lacked strong distal contacts (APA = 1.12). Last, group 3 genes (GP3: n = 3,851 in T. adhaerens, n = 4,238 in C. collaboinventa) had neither chromatin loops (APA = 0.968) nor active chromatin marks (Fig. 3c). On average, GP1 genes showed a stronger H3K4me3 ChIP–seq signal and higher expression levels compared to genes in GP2 and GP3 (Extended Data Fig. 8d) and were associated with housekeeping functions, including intracellular trafficking, translation and messenger RNA processing (Extended Data Fig. 8f). By contrast, GP3 genes were enriched in cell type-specific functions related to peptidergic cells (Extended Data Fig. 8g,h), potentially explaining the lack of chromatin features in our bulk epigenomic experiments.

To understand what distinguishes placozoan GP1 genes, we analysed loop anchor sequences in both species using genomic sequences as background. We identified an enriched motif at chromatin loop anchor regions in both placozoan species (Fig. 3d and Extended Data Fig. 8i,j) and found that GP1 promoters frequently contained insertions of Mutator DNA transposable elements (Fig. 3e and Extended Data Fig. 8e), with the terminal inverted repeat (TIR) sequence of this transposon containing the identified sequence motif. To further explore this association, we constructed a phylogenetic tree including all intact Mutator TIR sequences in four placozoan species (Extended Data Fig. 8k and Supplementary Data 1). This analysis revealed a Mutator family shared across species and with consensus TIR sequences resembling the motif found in chromatin loops anchors (Extended Data Fig. 8k). The connection between chromatin loops and the Mutator transposable element suggests a potential evolutionary and functional relationship. One possibility is that an architectural protein in placozoans evolved to recognize the sequence motif within the Mutator TIRs, leading to ‘domestication’ of these sites as regulatory elements. Alternatively, the presence of the motif and Mutator TIR sequences may indicate targeted integration of Mutator transposons into promoter regions of highly expressed genes. Overall, our analyses showed that roughly one-third of T. adhaerens and C. collaboinventa genes are part of promoter hubs mediated by chromatin loops and that these contacts are associated with the presence of conserved Mutator DNA transposons harbouring a specific sequence motif.

Enhancer–promoter loops in ctenophores

The physical architecture of M. leidyi genome is dominated by thousands of chromatin loops (n = 4,261) (Fig. 4a), primarily connecting promoter and enhancer elements (61%), as well as enhancer to enhancer regions (16%) (Fig. 2g and Extended Data Fig. 9a,b). In total, we identified 916 gene promoters participating in chromatin loops, with each promoter contacting between one (50%) and up to 15 enhancers (Fig. 4b). These enhancers are mainly located in intronic (69%) and intergenic (24%) regions at one to eight genes from the contacted promoters. We also observed the accumulation of cohesin at loop anchor sites using ChIP–seq against SMC1 cohesin subunit (Extended Data Fig. 9c). To assess whether these features are conserved across ctenophores, we profiled chromatin contacts, albeit at lower resolution, in the cydippid ctenophore Hormiphora californensis (Extended Data Fig. 9d), which diverged from the lobate ctenophore M. leidyi roughly 180 million years ago[9]. At the sampled resolution, we detected 239 strong chromatin loops in H. californensis. In both ctenophores, genes involved in chromatin loop formation showed higher expression (Extended Data Fig. 9e).

Fig. 4: Chromatin loops in the ctenophore M. leidyi.

a, Example genomic region showing chromatin loops between promoters and enhancers at 400 bp resolution. b, Left, histogram of enhancer contacts per promoter. Right, genomic location of enhancers. c, Sequence motif enriched in loop anchors. d, DNA methylation profiles centred around motifs located at promoter and enhancer loop regions, or outside loops. e, Chromatin-bound proteome of M. leidyi, showing identified proteins sorted by abundance with architectural proteins CTEP1 and CTEP2 as the most abundant zf-C2H2s. f, DAP-seq signals around GC-motif sites with high (left) versus low (right) methylation levels, and sites located within (top) or outside (bottom) of loop anchors. CTEP1 showed higher affinity for unmethylated GC-rich motifs in DAP-seq assays with native or PCR amplified gDNA (lacking methylation). g, Boxplots showing PhastCons conservation scores across three ctenophore species (B. microptera, P. bachei and H. californensis). The boxplot limits indicate the interquartile range (IQR), with the median as the middle line and whiskers extending to 1.5× IQR. Two-sided Wilcoxon rank sum test showed significant conservation differences between intergenic enhancers (n = 969) and promoters in loops (n = 778) (***P = 1.3 × 10−15) and between promoters in loops and promoters outside loops (n = 14,996) (***P < 2.22 × 10−16), whereas intergenic enhancers and promoters outside loops showed no significant difference (not significant (NS), P = 0.88). h, Syntenic conservation within M. leidyi chromatin loops compared to H. californensis. Left plot, barplot showing the fraction of conserved orthologues (OGs) in all alignable genomic regions across ctenophore species (***P = 5.5 × 10−4, chi-squared test for given probabilities). Right plot, boxplot of shared orthologues between individual genomic regions within chromatin loops (n = 115) versus in random genomic regions (n = 259) of similar size (***P = 2.4 × 10−5, Wilcoxon rank sum test with continuity correction). Boxplot limits as in g. Silhouette of H. californensis in h reproduced from PhyloPic (https://www.phylopic.org/), created by S. Haddock and K. Wothe under a CC0 1.0 Universal Public Domain licence.

To investigate whether the chromatin loops in ctenophores are formed in a sequence-specific manner, we searched for the enriched motif in loop anchors of both species, using GC-normalized genomic random sequences as a background. We identified GC-rich motif (FC = 8,522; P = 1 × 10−497) that was present in over 75% of loop anchors (Fig. 4c and Extended Data Fig. 9f) and at both promoter (79%) and enhancer sites (74%) involved in chromatin loops (Extended Data Fig. 9g). In addition, this motif was found in an extra 3,348 gene promoters (21% of all genes) with no chromatin loops detected (Extended Data Fig. 9g,h).

As the identified GC-rich motif contains two CpG dinucleotides, we examined DNA methylation using long-read Nanopore sequencing data. The overall methylation level in M. leidyi was low (6.8%), in agreement with previous reports using whole-genome bisulfite sequencing[48]. However, at loop anchor sites motifs showed low cytosine methylation, whereas motif occurrences outside loop anchor points showed high methylation (Fig. 4d and Extended Data Fig. 9i). Thus, we propose that DNA methylation of this GC-rich motif serves as a regulatory mechanism of loop formation in M. leidyi, potentially controlling the binding of an unknown, methylation-sensitive architectural DNA-binding factor, similar to mechanisms described for CCCTC-binding factor (CTCF) and other transcription factors[49].

The presence of DNA-binding proteins was further supported by the ATAC-seq footprint profile at motif regions in loop anchors (Extended Data Fig. 9j). To identify these potential architectural proteins, we profiled the chromatin-bound proteome of M. leidyi (Fig. 4e). We then selected the most abundant zf-C2H2 domain-containing proteins and analysed their DNA-binding specificity using DAP-seq, as zf-C2H2 factors are often associated with chromatin looping in other species2,5,35,50. This analysis identified two proteins, named here CTEP1 (Ctenophore-specific Tethering Protein 1) and CTEP2, which overlapped with 80% of detected loop anchor regions and showed strong affinity for the same GC-rich motif we had previously identified (Extended Data Fig. 9k–m). Moreover, DAP-seq confirmed that the binding of both proteins was inhibited at sites with high DNA methylation (Fig. 4f and Extended Data Fig. 9k,m). Thus, we conclude that CTEP1 and CTEP2 bind unmethylated GC-rich motif sites at chromatin loops. Notably, these proteins are conserved across ctenophore species (Extended Data Fig. 9n and Supplementary Table 3), but are absent from genomes of other metazoans.

Finally, we analysed evolutionary conservation of the sequences at the loop anchor points. To this end, we calculated genome-wide conservation scores from alignments of M. leidyi genome with three other ctenophore species (Bolinopsis microptera[9], Pleurobrachia bachei[20] and H. californensis[51]). Chromatin loop anchors, both at intronic and intergenic regions, showed higher sequence conservation compared to other introns or random genomic regions, respectively (Fig. 4g). The promoters of genes involved in distal contacts showed lower conservation score compared to other promoters (Fig. 4g), with conservation levels similar to those of random intergenic regions. Moreover, these promoters had a high frequency of transposable element integrations and elevated DNA methylation (Fig. 4d and Extended Data Fig. 9o). Furthermore, we found that genes located within enhancer–promoter loop regions in M. leidyi have higher syntenic conservation across ctenophore species compared to other genomic regions of similar size (Fig. 4h and Extended Data Fig. 9p). Overall, the conservation of loop anchor regions across ctenophore species and the increased syntenic linkage of genes suggest that gene positioning is constrained by genome architecture. These findings indicate that the distal chromatin contacts identified in M. leidyi represent an evolutionary conserved mechanism of genome regulation present in both lobate and cydippid ctenophores.

Discussion

Genome architecture is the result of both physicochemical and regulatory processes3,4,31. In unicellular organisms, chromatin contact patterns are shaped by the polymer nature of the chromatin fibre[32] and by gene transcriptional states[52]. For example, gene body contact domains are observed in highly transcribed genes in S. cerevisiae and S. pombe[52], and in Arabidopsis thaliana[53]. Also, insulation boundaries resulting from highly transcribed genes in divergent orientations are described in dinoflagellate genomes[33]. In unicellular holozoans, we observed similar insulation patterns around TSSs, but without evidence of further regulatory features or sequence-specific determinants associated with insulation boundaries. We also found cosegregating inactive chromatin regions in the large genome of S. arctica, and to a lesser extent in S. rosetta[39]. By contrast, these structures are absent in unicellular organisms such as C. owczarzaki or S. cerevisiae, which both have gene-dense genomes without heterochromatic regions.

In bilaterian species, extra chromatin structures involved in gene regulation have been observed, often mediated by architectural proteins binding to specific sequences2,5,35,50. These include discrete chromatin loops between cis-regulatory elements and promoters, mediated by tethering elements[27], as well as insulated gene regulatory landscapes, such as loop TADs bounded by convergent CTCF sites in vertebrates[3]. Notably, TAD-like domain structures can also result from the passive cosegregation of active versus inactive chromatin states37,54, rather than being determined by sequence-specific insulation elements. Examples of these are Polycomb bodies[55] and other heterochromatic compartment domains28,29. In early-branching animals we did not identify loop-bound TADs or any evidence of sequence-defined insulated TADs. However, we did detect chromatin loops spanning tens of kilobases and linking distal cis-regulatory elements and promoters in cnidarians, ctenophores and placozoans. In the case of ctenophores, thousands of chromatin loops link enhancers and promoters, showing that distal loops can be extremely frequent even in small genomes (roughly 200 Mb). Another example is the thousands of chromatin loops in placozoans, with even smaller genomes (roughly 100 Mb). Both placozoans and ctenophores complex looping architectures are associated with transposable elements. Although the causal relationship between transposable elements and chromatin loops is unclear, this observation suggests that complex 3D genome architectures might be influenced by lineage-specific transposable element invasion histories[56].

The mechanisms and factors responsible for loop formation in non-bilaterians and most invertebrates remain unknown[7]. The zf-C2H2 protein CTCF is the main architectural protein in vertebrates and is conserved across bilaterians. In annelids it has been associated to open chromatin regions[57] and in cephalopods it defines TAD boundaries[58]. Given that CTCF is absent in non-bilaterians[36], other factors, possibly from the zf-C2H2 family (Extended Data Fig. 9q), might be involved in the formation of these loops. In fact, a variety of architectural proteins other than CTCF have been described in Drosophila, many of which are zf-C2H2 proteins with restricted phylogenetic distributions such as the insect-specific CP190 factor2,50,59. Similarly, we identified two ctenophore-specific zf-C2H2 proteins (CTEP1 and CTEP2) associated with loop anchor regions in M. leidyi. It is possible that other, yet unidentified, lineage-specific zf-C2H2 proteins contribute to chromatin architecture in different animal lineages.

Globally, our findings suggest an evolutionary scenario (Fig. 5) in which chromatin compartment domains defined by transcriptional states[28] (but lacking sequence-specific insulation or tethering elements) were present in the unicellular ancestor of animals, as seen in extant unicellular holozoans. At the origin of animals, distal cis-regulatory elements evolved, requiring sequence-determined, stable chromatin looping mechanisms to link these enhancers with gene promoters (at least at certain distances[41]). This added an extra layer of regulatory complexity to cell type-specific gene regulation. The origin of this distal gene regulation would also explain the existence of regulatory-linked genomic regions showing conserved synteny[60], as observed in ctenophore regions between loop anchor points. Moreover, domains insulated by sequence elements probably originated at the root of bilaterian animals, as they are observed in vertebrates, insects and probably spiralians57,58. In the specific case of vertebrates these domains are formed by a mechanism of CTCF-dependent loop extrusion so far not observed in any other lineage[7], which further exemplifies the potential diversity of mechanisms involved in chromatin architecture across metazoans. Future extended taxon sampling will further refine this evolutionary scenario and help solve open questions such as whether there are conserved or lineage-specific factors involved in the establishment of chromatin loops across animals, how dynamic these structures are in development and across cell types or when did sequence-determined, insulated TADs first emerged in animal evolution.

Fig. 5: The evolution of animal regulatory genome architecture.

a, Phylogenetic tree illustrating the taxonomic distribution of 3D-chromatin features. b, Schematic depicting major innovations in animal genome regulation at different ancestral nodes. LCA, last common ancestor.

Methods

Cell and animal cultures, sample preparation and crosslinking

S. arctica coenocytic culture was grown in marine broth (Difco, 3704 g l−1) at 12 °C in 25 cm^2 flasks. Cells were passaged every 7 days using a 1:100 dilution. To synchronize cells in the G1/early S phase, an 8-day old culture was treated with 200 mM hydroxyurea (Sigma-Aldrich, catalogue no. H8627) for 18 h in the presence of 0.3% dimethylsulfoxide (DMSO). Synchronized cells were pelleted at 2,000g for 5 min at 12 °C, washed twice with Ca2+/Mg2+-free artificial sea water (CMFSW) (10 mM HEPES (pH 7.4), 450 mM NaCl, 9 mM KCl, 33 mM Na_2SO_4, 2.5 mM NaHCO_3) and flash-frozen in liquid nitrogen. Frozen cells were then reconstituted in CMFSW and crosslinked with 1% formaldehyde (Thermo Scientific, catalogue no. 28906) for 10 min under vacuum. The crosslinking reaction was quenched with 128 mM glycine for 5 min in the vacuum desiccator, followed by a 15 min incubation on ice. Cells were pelleted at 4 °C for 10 min at 2,000g, washed once with CMFSW, reconstituted in CMFSW to the concentration of 2 M ml−1 and crosslinked with 3 mM DSG (Thermo Scientific, catalogue no. A35392) for 40 min at room temperature on a rotating wheel. The reaction was quenched with 400 mM glycine for 5 min. Double-crosslinked cells were pelleted at 4 °C for 15 min at 2,000g and flash-frozen in liquid nitrogen.

C. owczarzaki strain ATCC30864 was maintained in axenic culture at 23 °C in the ATCC (American Type Culture Collection) medium 1034 (modified PYNFH medium) in 25 cm^2 flasks. For subculture, filopodial cells were passaged every 2–3 days using a dilution of 1:100. Before collection, filopodial cells were synchronized in G1 or early S phase by treating a filopodial culture of 70–80% confluency with 100 mM hydroxyurea (Sigma-Aldrich, catalogue no. H8627) for 18 h (ref. [61]). Synchronized cells were scraped off the surface and pelleted at 2,200g for 5 min at room temperature. Collected cells were crosslinked as described in ref. [62]. Briefly, cells were crosslinked with 1% formaldehyde (Thermo Scientific, catalogue no. 28906) in PBS for 10 min on a rotating wheel at room temperature. The crosslinking reaction was quenched with 128 mM glycine for 5 min at room temperature followed by extra incubation on ice for 15 min. The crosslinked cells were pelleted at 4 °C for 10 min at 2,000g and washed once with ice-cold PBS. Cells were diluted in PBS to the concentration of 2 M ml−1 and also crosslinked with 3 mM DSG (Thermo Scientific, catalogue no. A35392) for 40 min at room temperature on a rotating wheel. The crosslinking was quenched with 400 mM glycine for 5 min. Cells were pelleted at 4 °C for 15 min at 2,000g and flash-frozen in aliquots of 2 million cells.

S. rosetta was cocultured with Echinicola pacifica bacteria in artificial sea water supplemented with 20% cereal grass media (CGM3) at 23 °C. To synchronize the cell culture in the G1 or early S phase, cells from a 3-day-old culture were pelleted at 2,000g for 10 min and diluted in 4% CGM3 in artificial sea water to the concentration of 300,000 cells per ml. Cells were treated with 0.05 mM aphidicolin (Sigma-Aldrich, catalogue no. 178273) in the presence of 0.3% DMSO. After 18 h of incubation, cells, including chain colonies, fast and slow swimmers, were pelleted at 2,000g for 15 min. To remove bacteria from the choanoflagellate culture, collected cells reconstituted in 1 ml of culture media were passed through a Ficoll layer (1.6% Ficoll (Sigma-Aldrich, catalogue no. F5415), 0.5 M sorbitol, 50 mM Tris-HCl (pH 8.8), 15 mM MgCl_2, 1% artificial sea water) by centrifugation at 1,000g for 10 min at 4 °C. Pelleted choanoflagellate cells were then double-crosslinked with 1% formaldehyde in CMFSW and 3 mM DSG in CMFSW as described above for C. owczarzaki. The crosslinked cells were pelleted at 4 °C for 15 min at 2,000g and flash-frozen in liquid nitrogen.

E. muelleri sponges gemmules were hatched and grown for 1 week in Strekal’s media[63] in 150 × 25 mm culture dishes (Corning, catalogue no. 353025). To isolate phagocytic choanocyte cell population, specimens were fed for 10 min with 0.5 µm fluorescent carboxylate-modified FluoSpheres (Invitrogen, catalogue no. F8813) added to Strekal’s media to final 0.02% concentration (1:100 dilution of stock 2% FluoSpheres slurry)[64]. Sponges were washed once with Strekal’s media, and 1% formaldehyde solution in Strekal’s media was added to crosslink specimens for 10 min at room temperature with occasional mixing. To quench formaldehyde, 128 mM glycine was added and incubated for 5 min at room temperature and 15 min on ice. Crosslinked sponge specimens were washed twice with ice-cold Strekal’s media. Roughly 80 specimens were transferred in 5 ml of the Strekal’s media and dissociated by trituration until all tissue was removed from the gemmule husks (roughly ten trituration passages). The dissociated cell suspension was filtered through a 40-µm cell strainer, and cells were diluted to 2 M ml−1 concentration. The second crosslinking was performed with 3 mM DSG (Thermo Scientific, catalogue no. 20593) in Strekal’s media for 40 min at room temperature on a rotating wheel. The reaction was quenched with 400 mM glycine for 5 min at room temperature. Crosslinked cells were pelleted at 4 °C for 15 min at 2,000g, and then resuspended in 2 ml of ice-cold Strekal’s media with 2 µg ml−1 Hoechst 33342 (Thermo Scientific, catalogue no. 62249). Choanocytes were isolated using a BD FACS Aria II sorter with BD FACSDiva v.6.1.3 (BD Biosciences) as cells showing both FluoSphere fluorescence and Hoechst nuclei staining. Fluorescence-activated cell sorting (FACS) profiles were analysed with FlowJo v.10.7 (Extended Data Fig. 1b).

M. leidyi specimens were kept in 300-ml glass beakers with 5–10 individuals at 21 °C in artificial sea water (Red Sea, catalogue no. R11055) with a salinity of 27 ppt. Ctenophores were fed daily with a mixture of living rotifers (Brachionus sp.) and brine shrimps (Artemia salina). The water was exchanged once a week. For all experiments, adult lobate animals were starved for 2 days before collection. To dissociate animal tissue, roughly five adult animals (10 mm long) were transferred into CMFSW and washed twice to exchange the buffer. Animal tissue was dissociated into single cells in 5 ml of fresh CMFSW by triturating every 2 min for a total of 10 min. The efficiency of tissue dissociation was monitored under the microscope. Dissociated cells were filtered through a 40-µm cell strainer and diluted to 2 M ml−1 for the subsequent formaldehyde crosslinking. Cells were crosslinked in 1% formaldehyde in CMFSW for 10 min at room temperature. The reaction was stopped with 128 mM glycine for 5 min at room temperature and 15 min on ice. Crosslinked cells were pelleted at 4 °C for 10 min at 2,000g, washed once with CMFSW and resuspended to 2 M ml−1 for a second crosslinking with 3 mM DSG in CMFSW. The crosslinking reaction was stopped after 40 min of incubation at room temperature on a rotating wheel with 400 mM glycine for 5 min. The crosslinked cells were pelleted at 4 °C for 15 min at 2,000g.

H. californensis specimens from the first generation (F1) of a laboratory-reared culture at the Monterey Bay Aquarium (USA) were flash-frozen and pulverized in liquid nitrogen. Extracted cells and nuclei were filtered through a 40-µm cell strainer and pelleted by centrifugation at 4 °C for 10 min at 2,000g. Cells were double-crosslinked with 1% formaldehyde in CMFSW and 3 mM DSG in CMFSW as described for M. leidyi.

T. adhaerens and C. collaboinventa colonies were grown in 200 × 30 mm glass Petri dishes at 21 °C in artificial sea water (Red Sea, catalogue no. R11055) with a salinity of 33 ppt. Placozoans were fed once a week with unicellular algae (Pyrenomonas sp.), the water was exchanged every second week. To prepare single-cell suspension, roughly 500 animals were collected, washed twice with CMFSW and resuspended in 1 ml of CMFSW supplemented with 2 mM EDTA. Animal tissue was triturated every 2 min for a total of 10 min at room temperature. The efficiency of dissociation was monitored under the microscope. Dissociated cells were filtered through a 40-µm cell strainer, diluted to 2 M ml−1 and crosslinked with 1% formaldehyde in CMFSW for 10 min at room temperature on a rotating wheel. The reaction was quenched with 128 mM glycine for 5 min at room temperature and 15 min on ice. Cells were pelleted at 4 °C for 10 min at 2,000g, washed once with CMFSW and resuspended in 3 mM DSG in CMFSW for a second crosslinking. After 40 min of incubation at room temperature on a rotating wheel, 400 mM glycine was added to stop the reaction and cells were pelleted at 4 °C for 15 min at 2,000g.

N. vectensis NvElav1::mOrange transgenic line[65] was maintained in one-third artificial sea water (Red Sea, catalogue no. R11055) with salinity of 14 ppt. To isolate NvElav1::mOrange positive cells, 1.5–2-month-old animals starved for 1 day before the experiment were crosslinked with 1% formaldehyde in Ca2+/Mg2+-free one-third sea water (one-third CMF: 17 mM HEPES (pH 7.4), 167 mM NaCl, 9 mM NaHCO_3, 3.3 mM KCl) for 10 min under vacuum. The crosslinking reaction was stopped by adding 128 mM glycine and incubating the tissue under vacuum for 5 min, followed by a 15 min incubation on ice. The crosslinked tissue was dissociated into single cells by incubating the tissue with 10 mg ml−1 of Protease XIV (Sigma-Aldrich, catalogue no. P5147) in one-third CMF and 1 mM CaCl_2 for 5 min at 24 °C triturating the tissue every 1 min. The digested tissue was pelleted at 800g for 5 min, reconstituted in one-third CMF supplemented with 2 mM EDTA and 2 µg ml−1 Hoechst 33342 (Thermo Scientific, catalogue no. 62249), and the trituration continued for another 5–10 min. Dissociated cells were filtered through a 40-µm cell strainer, and neurons were isolated using a BD FACS Aria II as cells showing both the mOrange signal and Hoechst nuclei staining (Extended Data Fig. 1b). Isolated NvElav1::mOrange positive cells were also crosslinked with 3 mM DSG for 40 min at room temperature.

Micro-C library preparation

Micro-C libraries were prepared as previously described11,12 with the following modification. Double-crosslinked cells (2 million cells per sample) with 1% formaldehyde and 3 mM DSG were permeabilized with 500 µl of MB1 buffer (10 mM Tris-HCl (pH 7.4), 50 mM NaCl, 5 mM MgCl_2, 1 mM CaCl_2, 0.2% NP-40, protease inhibitor cocktail) for 20 min on ice with occasional trituration. Cells were pelleted at 4,500g for 5 min at 4 °C and washed once with MB1 buffer. To digest chromatin to a 80% monomers to 20% dimer and oligomers nucleosome ratio, an appropriate amount of MNase (Takara Bio, catalogue no. 2910a) was added (Extended Data Fig. 1a), and samples were incubated for 10 min at 37 °C with mixing at 850 rpm. The digestion reaction was stopped with 4 mM EGTA (pH 8.0) followed by incubation at 65 °C for 10 min without agitation. Cells were washed twice with ice-cold MB2 buffer (10 mM Tris-HCl (pH 7.4), 50 mM NaCl, 10 mM MgCl_2, 0.1% BSA) and pelleted at 4,500g for 5 min at 4 °C. Next, to repair the fragment ends after MNase digestion, pelleted cells were resuspended in the repair reaction mix (5 µl of 10× NEBuffer 2.1, 34 µl of nuclease-free water, 1 µl of 100 mM ATP, 2.5 µl of 100 mM DTT) supplemented with 2.5 µl of 10 U µl−1 T4 PNK (NEB, catalogue no. M0201). After 15 min of incubation at 37 °C with 850 rpm agitation, 5 µl of 5 U µl−1 Klenow Fragment (NEB, catalogue no. M0210) was added to generate 3′–5′ overhangs in the absence of dNTPs for a subsequent incorporation of biotin-labelled dNTPs. The reaction mixture was incubated for another 15 min at 37 °C at 850 rpm. To biotinylate DNA fragment ends, the mixture of dNTPs was added to the reaction mix (2.5 µl of 10× T4 DNA Ligase buffer, 11.875 µl of nuclease-free water, 5 µl of 1 mM Biotin-dATP (Jena Bioscience, catalogue no. NU-835-BIO14), 5 µl of 1 mM Biotin-dCTP (Jena Bioscience, catalogue no. NU-809-BIOX), 0.5 µl of a mixture of 10 mM dTTP and dGTP, 0.125 µl of 20 mg ml−1 BSA). After 45 min of incubation at room temperature with interval mixing at 850 rpm, the reaction was stopped with 30 mM EDTA (pH 8.0) followed by incubation at 65 °C for 20 min without agitation. The chromatin from lysed cells and nuclei was pelleted at 10,000g for 10 min at 4 °C and washed twice with MB3 buffer (50 mM Tris-HCl (pH 7.5), 10 mM MgCl_2). Finally, the chromatin was resuspended in 1,200 µl of proximity ligation mix (920 µl of nuclease-free water, 120 µl of 10× T4 DNA Ligase buffer, 100 µl of 10% Triton X-100, 12 µl of 20 mg ml−1 BSA, 36 µl of 50% PEG 4000, 12 µl of 5 U µl−1 T4 DNA ligase (Thermo Scientific, catalogue no. EL0012)) and incubated at room temperature for at least 2.5 h. To remove biotin from unligated ends, pelleted chromatin was treated with 2 µl of 100 U µl−1 Exonuclease III (NEB, catalogue no. M0206) for 5 min at 37 °C and agitation 850 rpm. Then, chromatin was decrosslinked and deproteinased overnight at 65 °C at 850 rpm in the presence of 350 mM NaCl, 1% SDS and 1 mg ml−1 proteinase K (Roche, catalogue no. 3115879001). The DNA was purified using DNA Clean & Concentrator-5 kit (Zymo Research, catalogue no. D4014) and eluted in 50 µl of 10 mM Tris-HCl (pH 8.0) (Extended Data Fig. 1c). Next, biotinylated proximity ligated DNA fragments were captured with Dynabeads MyOne Streptavidin (Life Technologies, catalogue no. 65602). DNA ends were prepared for adapter ligation and dA-tailed using NEBNext End repair/dA-tailing mix (NEB, catalogue no. E7546). The Y-shaped Illumina adapters were ligated with NEBNext Ultra II Ligation Module (NEB, catalogue no. E7595S), and the final library was amplified using NEBNext High-Fidelity 2× PCR Master Mix (NEB, catalogue no. M0541). The final libraries were double-size selected with Ampure XP (Beckman Coulter, catalogue no. A63881) resulting in libraries ranging from 350 to 750 bp in length. The detailed Micro-C stepwise protocol is reported in Supplementary Text 1.

High molecular weight gDNA extraction for genome sequencing

Genomic DNA (gDNA) from C. owczarzaki (Cowc) strain ATCC30864 was extracted with Blood & Cell Culture DNA Mini Kit (Qiagen, catalogue no. 13323). The library was constructed by the use of Ligation Sequencing Kit (Oxford Nanopore, catalogue no. SQK-LSK109) and NEBNext Companion Module (NEB, catalogue no. E7180), and sequenced with the R9.4.1 Flow Cell set on a MinION device (Oxford Nanopore). We obtained 4.3 M reads with an estimated Oxford Nanopore N50 of 5.4 kb.

E. muelleri gDNA was isolated using the Nanobind Tissue (Circulomics, catalogue no. NB-900-701-01) from 177 mg of frozen tissue of clonal juvenile sponges hatched from overwintering cysts (gemmules). Gemmules were obtained from the head tank of the Kapoor Tunnel (Sooke Reservoir), part of the drinking water system of the city of Victoria, British Columbia, Canada[21]. Short DNA fragments of less than 10 kb were removed with Short Read Eliminator Kit (Circulomics, catalogue no. SS-100-101-01). gDNA was quantified with a Qubit fluorometer and sequenced on an Oxford Nanopore using a PromethION flow cell (R9.4), producing 5.31 million reads with an estimated Oxford Nanopore N50 of 18.97 kb.

To reduce the level of heterozygosity during the assembly of M. leidyi genome (below), an animal culture was established from a single individual through self-fertilization. High molecular weight DNA was isolated from 5–8 animals (3–5 cm) starved for 24 h before flash-freezing. Frozen tissues were powdered with mortar and pestle, dissolved in 10 ml of urea extraction buffer (50 mM Tris-HCl (pH 8.0), 7 M Urea, 312.5 mM NaCl, 20 mM EDTA (pH 8.0), 1% w/v N-lauroylsarcosine sodium salt) as described in ref. [66] and incubated for 10 min at room temperature on a rocking platform 20 rpm. gDNA was then purified twice with a phenol-chloroform-isoamyl alcohol mixture pH 7.7–8.3 (Sigma-Aldrich, catalogue no. 77617), precipitated with 0.7 volume of 100% isopropanol and subsequently washed twice with 70% ethanol. Finally, the isolated DNA was subjected to another round of purification with Nanobind Tissue kit (Circulomics, catalogue no. NB-900-701-01), followed by short-read elimination with the Short Read Eliminator Kit (Circulomics, catalogue no. SS-100-101-01). Sequencing was performed on Oxford Nanopore using PromethION flow cell (R9.4). We obtained 4.54 million reads with an estimated Oxford Nanopore N50 of 36.84 kb.

ATAC-seq library preparation

ATAC-seq libraries from M. leidyi and from sorted choanocytes of E. muelleri were prepared using Omni-ATAC protocol as described previously[67]. Briefly, two M. leidyi adult specimens were dissociated using CMFSW with 0.25% α-Chymotrypsin (Sigma-Aldrich, catalogue no. C8946). To isolate nuclei, dissociated cells were transferred into cold hypotonic ATAC lysis buffer adjusted for marine animals (10 mM Tris-HCl (pH 7.5), 35 mM NaCl, 3 mM MgCl2, 0.1% Tween-20, 0.01% NP-40, 0.01% digitonin, 70 µM Pitstop (Abcam, AB1206875MG)). Cell lysis was stopped after 2 min by adding marine ATAC wash buffer (10 mM Tris-HCl (pH 7.5), 35 mM NaCl, 3 mM MgCl_2, 0.1% Tween-20, 1% BSA). Nuclei were then pelleted and resuspended in cold PBS buffer with 0.8 M Sorbitol. We used 50,000 nuclei per each tagmentation reaction.

To sort choanocytes of E. muelleri, 7 days posthatching sponges were fed with 0.5 µm fluorescent carboxylate-modified FluoSpheres (Invitrogen, catalogue no. F8813). After 10 min of incubation, sponges were washed twice with Strekal’s media, collected and dissociated for 15 min at 28 °C using Protease XIV (Sigma-Aldrich, catalogue no. P5147) in Strekal’s media and 1 mM CaCl_2. Cells were pelleted at 800g for 5 min at room temperature and resuspended in Strekal’s media with 2 mM EDTA (pH 8.0). Further dissociation and trituration of sponge tissue continued for another 15 min at room temperature. Cells were filtered through a 40-µm cell strainer, stained with 2 µg ml−1 Hoechst 33342 and sorted using FACS. Sorted cells were lysed for 3 min in ATAC lysis buffer (10 mM Tris-HCl (pH 7.5), 10 mM NaCl, 3 mM MgCl_2, 0.1% NP-40, 0.1% Tween-40, 0.01% digitonin). For each tagmentation reaction we used 100,000 nuclei. ATAC-seq libraries were prepared as described previously[67] and sequenced on Illumina NextSeq 500 using High-Output 75 cycles.

iChIP–seq library preparation

For S. arctica, C. owczarzaki, S. rosetta, M. leidyi, E. muellleri, T. adhaerens, C. collaboinventa and N. vectensis, double-crosslinked cells, as above, were washed with PBS, resuspended in 500 µl of cell lysis buffer (20 mM HEPES (pH 7.5), 10 mM NaCl, 0.2% IGEPAL CA-630, 5 mM EDTA, protease inhibitors cocktail) and incubated on ice for 10 min. Samples were centrifuged at 16,000g for 10 min at 4 °C. The resulting pellets were resuspended in bead beating buffer (20 mM HEPES (pH 7.5), 10 mM NaCl, 5 mM EDTA, protease inhibitors cocktail), and then transferred to 0.2 ml tubes containing acid-washed glass beads (Sigma-Aldrich, G8772). Cells were lysed by vortexing five times for 30 s. The supernatant was transferred to a 1.5-ml sonication tube, SDS was added to 0.6% and samples were sonicated 3–5 cycles of 30 s on, 30 s off in a Bioruptor Pico (Diagenode) to generate 200–300 bp fragments. Chromatin was diluted with 5 volumes of dilution buffer (20 mM HEPES (pH 7.5), 140 mM NaCl), centrifuged at 16,000g for 10 min at 4 °C and stored at −80 °C before use.

Chromatin immunoprecipitation was performed as previously described[68] with the following modifications. Briefly, for each species 100 ng of chromatin was used for immunoprecipitation. The pool of chromatin was incubated for 14–16 h at 4 °C with 5 µl (1:50 dilution) of anti-H3K4me1 (Cell Signaling, catalogue no. 5326), 6 µl (3.4 µg) of anti-H3K4me2 (Abcam, catalogue no. ab32356), 2.5 µl (1:100 dilution) of anti-H3K4me3 (Millipore, catalogue no. 07-473), 5 µl (5 µg) of anti-SMC1 (Thermo Fisher, A300-055A) or 2 µl (2 µg) of anti-H3 (Abcam, catalogue no. ab1791) and recovered using a 1:1 mix of Protein A (Sigma-Aldrich, catalogue no. 16-661) and Protein G (Sigma-Aldrich, catalogue no. 16-662) magnetic beads. Immunoprecipitated complexes were washed, reverse crosslinked for 3 h at 68 °C, deproteinased and then purified using Ampure XP beads (Beckman Coulter, catalogue no. A63881). Final libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit (New England BioLabs) according to the manufacturer’s protocol. ChIP–seq libraries were sequenced on Illumina NextSeq 500 sequencer using High-Output 75 cycles.

MARS-seq library preparation

Single-cell libraries were prepared from freshly dissociated and sorted choanocytes of E. muelleri as previously described[8]. To collect cells for MARS-seq libraries, 7 days posthatching, sponges were fed with 0.5 µm fluorescent carboxylate-modified FluoSpheres (Invitrogen, catalogue no. F8813). Animal tissues were dissociated and prepared for sorting as described above for ATAC-seq. Dissociated cells were sorted through FACS into four 384-well MARS-seq plates. In total, 1,536 single-cell libraries were prepared and sequenced on an Illumina NextSeq 500 using High-Output 75 cycles.

Chromatin proteomics

Chromatin proteomics samples were prepared as previously descibed[69] with minor modifications. Briefly, double-crosslinked cells of M. leidyi (1 million per replicate) were solubilized in 1 ml of lysis buffer (4 M guanidine thiocyanate, 100 mM Tris-HCl (pH 8.0), 10 mM EDTA, 2% N-lauroylsarcosine sodium salt) and incubated for 10 min. Next, before adding DNA-binding beads (Invitrogen, catalogue no. 37002D), cell lysate was mixed with 1 ml of 2-propanol. The beads were separated on a magnet and the supernatant was saved as the unbound control. The beads were washed using 1 ml of wash buffer (1:1 lysis buffer to 2-propanol ratio), transferred to a 1.5-ml sonication tube and washed again with 1 ml of 80% ethanol. The chromatin was then eluted in 200 µl of 10 mM Tris-HCl (pH 8.0) containing proteinase inhibitors (Roche, catalogue no. 04693132001) and sonicated using a Bioruptor Pico at 4 °C for 3 cycles (30 s ON, 30 s OFF). To remove RNA-binding proteins, RNase A (Roche, catalogue no. 10109142001) was added to the sonicated samples, which were then incubated at 37 °C with agitation in a thermomixer. Afterwards, chromatin was re-bound to the beads by adding 250 µl of lysis buffer, vortexing and then sequentially adding 300 µl of 2-propanol. The beads were washed twice with 1 ml of 80% ethanol, and proteins were digested on the beads using trypsin (Promega, catalogue no. V5111) and LysC (NEB, catalogue no. P8109S).

Chromatographic and mass spectrometric analysis

Samples were analysed using an Orbitrap Eclipse mass spectrometer (Thermo Fisher Scientific) coupled to an EASY-nLC 1200 (Thermo Fisher Scientific (Proxeon)). Peptides were loaded directly onto the analytical column and were separated by reversed-phase chromatography using a 50-cm column with an inner diameter of 75 μm, packed with 2-μm C18 particles (Thermo Fisher Scientific, catalogue no. ES903).

Chromatographic gradients started at 95% buffer A (0.1% formic acid in water) and 5% buffer B (0.1% formic acid in 80% acetonitrile) with a flow rate of 300 nl min−1 and gradually increased to 25% buffer B and 75% A in 52 min and then to 40% buffer B and 60% A in 8 min. After each analysis, the column was washed for 10 min with 100% buffer B.

The mass spectrometer was operated in positive ionization mode with nanospray voltage set at 2.4 kV and source temperature at 305 °C. The acquisition was performed in data-dependent acquisition mode and full mass spectrometry scans with one micro-scan at resolution of 120,000 were used over a mass range of m/z 350–1,400 with detection in the Orbitrap mass analyser. Automatic gain control was set to ‘standard’ and injection time to ‘auto’. In each cycle of data-dependent acquisition analysis, following each survey scan, the most intense ions above a threshold ion count of 10,000 were selected for fragmentation. The number of selected precursor ions for fragmentation was determined by the ‘Top Speed’ acquisition algorithm and a dynamic exclusion of 60 s. Fragment ion spectra were produced by means of high-energy collision dissociation at normalized collision energy of 28% and they were acquired in the ion trap mass analyser. Automatic gain control and injection time were set to ‘Standard’ and ‘Dynamic’, respectively, and an isolation window of 1.4 m/z was used. Digested bovine serum albumin (NEB, catalogue no. P8108S) was analysed between each sample to avoid sample carryover and to assure stability of the instrument, and Qcloud[70] was used to control instrument longitudinal performance during the project.

Acquired spectra were analysed using the Proteome Discoverer software suite (v.2.5, Thermo Fisher Scientific) and the Mascot search engine (v.2.6, Matrix Science[71]). The data were searches against M. leidyi database and a list of common contaminants (16,042 entries)[72] as well as all the corresponding decoy entries. For the peptide identification a precursor ion mass tolerance of 7 ppm was used for the MS1 level, trypsin was chosen as enzyme and up to three missed cleavages were allowed. The fragment ion mass tolerance was set to 0.5 Da for MS2 spectra. Oxidation of methionine and N-terminal protein acetylation were used as variable modifications whereas carbamidomethylation on cysteines was set as a fixed modification. False discovery rate in peptide identification was set to a maximum of 1%.

Peptide quantification data were retrieved from the ‘Precursor ions quantifier’ node from Proteome Discoverer (v.2.5) using 2-ppm mass tolerance for the peptide extracted ion current. The obtained values were used to calculate protein fold-changes and their corresponding P value and adjusted P values.

DAP-seq (DNA affinity purification sequencing) library preparation

The DNA-binding domains of candidate zf-C2H2 proteins were cloned from complementary DNA (cDNA) library of M. leidyi into the pIX-HALO vector using NEBuilder HiFi DNA Assembly Master Mix (NEB, catalogue no. E2621). The obtained HALO-fusion constructs were translated using the TnT SP6High-Yield Wheat Germ Protein Expression System (Promega, catalogue no. L3260). Next, an adapter-ligated DNA library was prepared from native gDNA of M. leidyi using NEBNext Ultra II FS DNA library prep kit (NEB, catalogue no. E7805) or PCR amplified gDNA. The binding to HALO-zf-C2H2 fusion proteins and recovery of adapter-ligated gDNA libraries was performed as described in ref. [73]. The generated DAP-seq libraries were sequenced in paired-end mode on an Illumina NextSeq 500 using High-Output 75 cycles.

De novo genome assembly and scaffolding

We made preliminary genome assemblies of C. owczarzaki from Oxford Nanopore reads basecalled by Guppy v.6.0.1 using NextDenovo v.2.5.0 (ref. [74]), Flye v.2.9.0 (ref. [75]) and NECAT v.0.0.1 (ref. [76]), which produced 20, 141 and 56 contigs including the mitochondrial genome, respectively. For the Flye assembly, we only used 5,000 bp or longer reads. We then integrated the three assemblies by manually comparing them to each other, with a help of reciprocal large-scale alignments generated with minimap2 (ref. [77]). The integrated assembly was polished with the Nanopore reads using Flye ten times, and with Illumina reads[78] using HyPo[79] twice. A chromosome-scale duplication, which was in the end included in chromosome 15 after the 3D assembly (Extended Data Fig. 2d), was temporarily removed before annotating the genome. Finally, we manually inspected the whole assembly sequence together with the mapped Illumina data, Nanopore data and the previous Sanger sequence data[25], and navigated them on the Integrative Genomic Viewer (IGV)[80] to find and fix errors occurred during the consensus calling. We also manually phased chimeric haplotypes for some genes using the long reads. In total, 7,937 nucleotides were manually inserted or deleted at 430 sites and 1,193 nucleotides were substituted at 1,081 sites.

We produced two new genome assemblies for E. muelleri and M. leidyi. In both cases, we used Oxford Nanopore reads after base call correction using Guppy v.5.0.17 (using the dna_r9.4.1_450bps_sup_prom.cfg configuration the super-accurate base calling model, and a filtering reads with min_qscore=10). Then, we used two different long-read assemblers (Flye v.2.9-b1768, ref. [75] and Shasta v.0.8.0, ref. [81]) and various assembly strategies (filtering by read length at 0, 10 and 50 kb), and selected the best resulting draft assemblies for each species. To that end, we evaluated the contiguity (measured using the contig N50), completeness and occurrence of uncollapsed haplotypes for each draft (Extended Data Fig. 2a,b). Contiguity was evaluated using total assembly length and contig N50. Completeness was measured with the fraction of conserved orthologues recovered by BUSCO v.5.1.2 (ref. [82]) (using the genome mode and the metazoa_odb10) and the fraction of mappable genes from the original assemblies (mapped using Liftoff v.1.6.1, ref. [83]). The presence of uncollapsed haplotypes was assessed with the distribution of per-base sequencing depths, calculated using the pbcstat utility in purge_dups v.1.2.5 (ref. [84]) (for which we remapped the input reads to the assembly with minimap2 2.18-r1015 (ref. [77]), using the -x map-ont preset for long-read mapping) (Extended Data Fig. 2e,f).

The best drafts for each species were produced using the following parameter combinations: (1) for E. muelleri, we used the Shasta assembler with the Nanopore configuration (--config Nanopore-Oct2021 flag), without filtering by read length (estimated sequencing depth roughly 100×) and (2) for M. leidyi, we used Flye with reads filtered at 50 kb (estimated sequencing depth roughly 150×), the raw Nanopore read configuration (--nano-raw flag) and an estimated total assembly size of 200 Mb.

Then, we used purge_dups to collapse putative uncollapsed haplotypes in each assembly, in the following manner: (1) we split the assembly into contigs with the split_fa utility; (2) we aligned the genome to itself with minimap2 and the -x asm5 preset; (3) we used the read alignments to the unsplit assembly (produced with minimap2 -x map-ont) to obtain the sequencing depth histogram and calculate coverage cutoffs with pbcstat and calcuts, respectively; (4) we used these cutoffs and the mapped reads to remove haplotigs and overlaps for the draft, with purge_dups proper and using two rounds of alignment chaining (-2 flag) and finally (5) we reevaluated the assembly quality using per-base sequencing depth distributions (above) and reductions in the fraction of duplicated BUSCO orthologues.

Chromosome-level assembly

To obtain chromosome-level genome assemblies, generated Micro-C libraries were mapped to de novo draft genome assemblies (C. owczarzaki, E. muelleri and M. leidyi) or current genome assemblies (T. adhaerens ASM15027v1, ref. [22], S. arctica[24], S. rosetta GCA_000188695.1, ref. [26], C. collaboinventa[85]) using Juicer v.1.6 (ref. [86]) with an option -p assembly. Proximity ligation alignments were used by 3D de novo assembly pipeline[87] to order and orient available contigs into chromosomes with the following parameters: S. arctica -r 3 --editor-repeat-coverage 10, C. owczarzaki -r 0 --editor-repeat-coverage 4, S. rosetta -r 3 --editor-repeat-coverage 2, E. muelleri -r 2 --editor-repeat-coverage 10, M. leidyi -r 2 -i 1000 --editor-repeat-coverage 2, T. adhaerens -r 3 --editor-repeat-coverage 2 and C. collaboinventa -r 3 --editor-repeat-coverage 2. The resulting assemblies were manually reviewed and corrected with Juicebox Assembly Tools[88] (Extended Data Fig. 2c–f). Finally, chromosome-level genome assemblies were polished with Medaka (v.1.5.0) to correct possible sequence errors such as indels and mismatches, as follows: (1) first, we mapped the Nanopore reads to the chromosome-level assembly using the minimap2-based mini_align utility; (2) we then used Medaka consensus to obtain consensus sequences, specifying a batch size of 200 (--batch 200 flag) and the r941_prom_sup_g507 configuration (--model flag) and (3) we merged the consensus and variant calls for all chromosomes into a polished assembly using Medaka stitch.

Genome annotation

To annotate the C. owczarzaki genome, we did not mask the repeats because the intergenic regions are very small[25] and, thus, masking only increased annotation failure on duplicated genes. We used BRAKER2 (ref. [89]) with OrthoDB[90] protein sequence collections as hint data, as well as with RNA-seq data from a previous study[61]. The three preliminary annotations, evidenced by metazoan proteins, protozoan proteins and RNA-seq data, were combined with TSEBRA[91], giving rise to 9,069 annotated transcripts. Finally, we manually searched and fixed wrong annotations by navigating the assembly on IGV[80], comparing the combined annotation with the three preliminary annotations together with the mapped RNA-seq data. By this careful inspection, we modified or newly annotated 1,871 transcripts including alternatively spliced ones. Compared to the previously published proteome[25] (v.2), only 4,076 out of 8,792 proteins (including alternatively spliced ones) had completely matched sequences to the those predicted in this study, allowing simple amino acid mismatches probably accounting for polymorphisms.

To annotate M. leidyi genome we first downloaded developmental Illumina RNA-seq samples (GSE93977), trimmed them with fastp and built a de novo Trinity assembly, which was mapped to the genome using gmap[92]. The RNA-seq was also directly mapped to genome using HISAT2 (ref. [93]) with the –dta parameter, and genome-based transcriptomes were built for each sample using StringTie[94]. Merged mapped RNA-seq samples were then used to find high-quality intron junctions using Portcullis. The combination of Trinity, StringTie and Portcullis intron junctions were then fed to Mikado for transcript selection. The best resulting gene models based on mapping to UniProt were then used to train an Augustus model for M. leidyi. Augustus was used for an ab initio gene prediction, using exonic hints from Mikado, intron hints from Portcullis and coding sequence hints from a MetaEuk[95] run with query fasta files combining proteins from H. californensis and UniProt. Mikado transcripts and Augustus gene models were then merged using EVidenceModeler (scores of 10 for Mikado transcripts and 2 for Augustus gene models). The resulting gene models were updated with PASA[96] to incorporate the untranslated regions from the Mikado transcripts.

To annotate S. arctica, S. rosetta, E. muelleri, T. adhaerens and C. collaboinventa genome assemblies, gene models from previous assemblies were mapped onto new coordinates using Liftoff (v.1.6.1)[83] with -overlap 1 -flank 1 options.

Repeat annotation

Repetitive sequences and transposable elements were annotated using EDTA (v.2.1.0)[97] with the following parameters: --sensitive 1 --anno 1 (Extended Data Fig. 2g). For H. sapiens, we used RepeatMasker (v.open-4-0-3) annotation of GRCh38 genome released by UCSC.

DNA methylation calling from Nanopore long-read sequencing data

The fast5 files obtained from the PromethION were used as input for Megalodon (v.2.5), with the Remora model dna_r9.4.1_e8 sup for 5hmc_5mc modification only on CG dinucleotides. We then built bigwig files using the bedGraphToBigWig tool from UCSC. The Megalodon CG methylation calls were compared to previously published Whole-Genome Bisulfite Sequencing remapped to the new reference genomes using Bismark (SRR8346013 and SRR10356110)21,48. Both data sources were congruent, yet Nanopore had deeper and broader coverage, we used Megalodon methylation data for subsequent analysis.

Micro-C data processing

Micro-C data were processed using the 4D Nucleome processing pipeline[98]. Briefly, raw reads were mapped to the reference genome using bwa mem (v.0.7.17-r1188) with the -SP5M option. The mapped reads were sorted and filtered with pairtools (v.0.3.0)[99]. Pairs that mapped within a 2-bp distance from each other were considered duplicates. We also discarded reads mapping within the distance of 200 bp, which eliminates self-ligated pairs and reads mapping to adjacent nucleosomes. Only uniquely mapping pairs and 5′ most unique alignments of multiple ligations pairs were aggregated into 200-bp bin contact matrices and multiresolution .cool or .hic files (Extended Data Fig. 1d). Contact matrices were normalized with cooler (v.0.8.11)[100] using the iterative correction and eigenvector (ICE) balancing method[101] for .cool files or with Juicer tools[86] using Knight–Ruiz balancing[102] for .hic files. All contact heatmaps were visualized with either Cooltools (v.0.5.1)[103] or Coolbox (v.0.3.8)[104] and genome assembly heatmaps were visualized using HiGlass[105].

Reproducibility between replicates was estimated using the stratum-adjusted correlation coefficient (SCC) implemented in HiCRep[106] at resolutions of 1, 2, 5, 10, 25 and 50 kb (Extended Data Fig. 1f). The SCC scores were averaged across chromosomes. Biological replicates with SCC score estimated above 0.7 at resolutions equivalent to roughly 20,000 bins per species genome (resolution of 10 kb for S. arctica, 1 kb for C. owczarzaki, 2 kb for S. rosetta, 10 kb for E. muelleri, 10 kb for M. leidyi, 5 kb for H. californensis, 5 kb for T. adhaerens, 5 kb for C. collaboinventa and 10 kb for N. vectensis) were pooled to obtain final chromatin interaction matrices. Technical replicates were first merged, deduplicated and only then combined into the final contact maps.

The decay of the average contact frequency over genomic distance from 1 kb to 100 Mb was calculated using Cooltools (v.0.5.1)[103]. The decay curves were calculated for each chromosome separately, and then averaged across chromosomes (Extended Data Fig. 1g).

Compartment analysis

Compartment analysis was performed on observed-over-expected contact maps at resolutions equivalent to 5,000, 10,000, 20,000 and 50,000 bins per species genome (Extended Data Fig. 3a) using Cooltools eigs-cis[103]. We visually examined calculated eigenvectors, and, for each organism, the E1 vector corresponded to the compartmentalization pattern of contact maps. Active (A) and inactive (B) compartment types were assigned by GC content (for all species except C. owczarzaki) or H3K4me3 chromatin signal for C. owczarzaki, such that higher GC regions or positively correlated with H3K4me3 signal regions correspond to A compartment. Saddle plots were generated using the Cooltools saddle module. Specifically, the eigenvectors were sorted from lowest to highest value and combined into 40 groups according to their eigenvector value. The first (bottom 2.5% E1 values) and last (top 2.5% E1 values) groups were ignored to exclude potential outliers. The observed-over-expected value of the remaining 38 groups was averaged across all bins and chromosomes and visualized as saddle plots.

Compartment strength was calculated as the ratio of homotypic (AA + BB) over heterotypic (AB + BA) compartment contacts. We choose the top 20% of observed-over-expected values for both homotypic and heterotypic interactions. To assess the error in estimating compartment strength, we compared the compartments strength across different resolutions as well as performed visual inspection of the contact maps (Extended Data Fig. 3b). The latter showed varying degrees of accuracy in identifying compartment types between species, with the algorithm performing particularly poorly on M. leidyi due to the lack of well-defined chromatin compartmentalization in this species in our sample. Therefore, we assigned an extra intermediate compartment I to the intermediate eigenvalues close to zero. To that end, we modelled the genome-wide eigenvalues distribution as a Gaussian mixture with three components using the normalmixEM function from the mixtools R package (v.2.0.0) as described[107]. The B–I and I–A thresholds were defined as intersection points between components (Extended Data Fig. 3c).

To characterize the distribution of genomic features in the A, I and B compartments, we calculated cumulative H3K4me3 chromatin signals and RNA-seq expression values for each compartment region. Furthermore, we estimated the percentage of bases annotated as transposable elements or coding gene regions within these compartments. All the values are presented as −log_2(1 − the value’s quantile). Thus, a normalized value of six means that the coverage is in the upper 1–2−6 quantile, that is, in the upper 1/64th of the distribution (Extended Data Fig. 3d).

Insulation profiles and boundaries classification

To compute the insulation profiles, we first determined the optimal resolution and window sizes for a target genome. To that end, we calculated insulation scores using Cooltools insulation module[103] at resolutions roughly equivalent to 50,000, 100,000, 200,000 and 400,000 genomic bins per species genome with a sliding window for each resolution that is ×5, ×10 and ×25 the applied resolution (Extended Data Fig. 4a). The resolution and two window sizes with maximum average insulation scores were considered optimal because they reflected the strongest partitioning of genomes into isolated domains. Insulation boundaries located within two bins of unmappable genomic region were removed.

Identified insulation boundaries were categorized into strong and weak using the peak prominence of their boundary strength distributions (Li threshold) as implemented in the Cooltools insulation score module. Strong boundaries were further annotated with overlapping genomic features that fall within one bin of the annotated feature from the insulation boundary. For example, if compartment boundaries were called at the resolution 5 kb, then the maximum distance to the closest insulation boundary is ±10 kb.

To estimate internal interactions within contact domains, rescaled pile-ups were generated using coolpup.py[108]. Contact domains were defined as valleys between two strongly insulated regions, which were not further from each other than 100 kb.

Loop calling and annotation

Chromatin loops were identified using SIP v.1.6.1 (ref. [109]) on KR-normalized contact matrices. In M. leidyi, the SIP peak caller was applied with the following parameters: -norm KR -g 3.0 -min 2.0 -max 2.0 -mat 5000 -d 10 -res 400 -sat 0.01 -t 2000 -nbZero 6 -factor 4 -fdr 0.05 -isDroso false. For T. adhaerens, chromatin loops were called with the following parameters: -norm KR -g 5.0 -min 4.0 -max 4.0 -mat 5000 -d 20 -res 100 -sat 0.01 -t 2000 -nbZero 6 -factor 4 -fdr 0.05 -isDroso false; for C. collaboinventa: -norm VC_SQRT -g 1.5 -min 3.0 -max 3.0 -mat 5000 -d 20 -res 500 -sat 0.01 -t 2000 -nbZero 6 -factor 2 -fdr 0.05 -isDroso false; for H. californensis: -norm KR -g 2.5 -min 3.0 -max 3.0 -mat 5000 -d 10 -res 1000 -sat 0.01 -t 2000 -nbZero 6 -factor 4 -fdr 0.05 -isDroso false. Identified loops were then filtered based on APSscore, removing high-intensity signals outside the normal distribution of APSscore values. This threshold ensured accurate removal of false positive regions that corresponded to structural genomic rearrangements, such as inversions or assembly artefacts. For H. californensis, we kept only annotated loops with values greater than ten. Chromatin loops in N. vectensis and focal chromatin contacts in E. muelleri were annotated manually.

Each loop anchor was assigned a promoter or enhancer identity based on their epigenetic signature. We calculated quantile normalized counts per million (CPM) coverage of H3K4me3, H3K4me2 and H3K4me1 ChIP signals in 1-kb (T. adhaerens, C. collaboinventa, N. vectensis), 2-kb (M. leidyi, E. muelleri, D. melanogaster) or 10-kb (H. sapiens) windows from a centre of a loop anchor.

METALoci autocorrelation analysis

METALoci[110] (v.0.3.0) analysis was applied to explore the spatial distribution and autocorrelation of epigenetic signal in T. adhaerens contact maps. For each region of interest at 800 bp resolution, the top 20% pairs of contacts were used to create a two-dimensional graph layout by means of the Kamada–Kawai algorithm[111] (Fig. 3b, top left panel) using the ‘metaloci layout’ with default parameters. Next, the signal of interest (H3K4me3 ChIP, ATAC, genic exon annotation) measured in the 800-bp genomic bin was mapped onto the built graph layout using ‘metaloci lm’ with default parameters. Spatial and epigenetic signals were embedded into Voronoi diagrams for enhanced visualization as a Gaudí plot (Extended Data Fig. 8c), and the local Moran’s index (LMI) analysis112,113 was applied for each bin of the Gaudí plot.

According to LMI analysis, each bin is assigned to one of the four distinct groups, called LMI quadrants, based on the signal value in a bin and average signal value in its neighbourhood. If a bin and its neighbourhood have similar amounts of signal (low or high), then this bin is assigned to a low–low (blue) or high–high (red) quadrant. If a bin and its neighbourhood have different amounts of signal, then the bin is assigned to a low–high (cyan) or high–low (orange) quadrants, respectively. Significantly colocalized bins according to LMI, in which a P value is obtained using a permutation test, are highlighted by colour in the LMI scatterplots (Extended Data Fig. 8c,j, left panels). An analogous colouring scheme is applied to the Voronoi diagrams of the Gaudí plots. Hence, the highlighted blue and red bins on a Gaudí plot represent bins in which the signal is significantly colocalized in the space. Thus, ATAC-seq, H3K4me3 and motif score signals are significantly enriched inside the nested focal contacts (Fig. 3b and Extended Data Fig. 8c), whereas exons are significantly enriched outside loop contacts (Fig. 3b). METALoci code is available at the GitHub repository (https://github.com/3DGenomes/METALoci).

Motif analysis

Loop anchor regions of M. leidyi (n = 8,523) and H. californensis (n = 478) were scanned for enriched motifs with HOMER[114] in de novo motif discovery mode. As background sequences, we used random genomic regions of equivalent size and GC content (n = 38,810 in M. leidyi and n = 49,097 in H. californensis). For motif enrichment analysis in T. adhaerens (n = 3,557) and C. collaboinventa (n = 4,037), we scanned loop anchor regions using random genomic sequences of equivalent size (n = 32,004 in T. adhaerens and n = 36,178 in C. collaboinventa) as background. Loop anchor regions of N. vectensis (n = 327) were scanned for enriched motif using random genomic sequences (n = 45,268) of equivalent size and GC content as background. In addition, we used ATAC-seq accessible neuronal promoter regions (n = 22,961) as background to scan for enriched motifs in genomic regions that overlap ATAC-seq peaks located at the non-loop insulation boundaries (n = 9,016). To annotate genomic regions with identified motifs, we used the monaLisa package[115], selecting percentile threshold of motif scores by comparing the motif score distributions in the target regions with genome-wide motif score distributions (Extended Data Figs. 7d,f, 8i and 9f).

Whole-genome alignment and sequence conservation analyses

We evaluated the degree of sequence conservation of the M. leidyi genome by comparing it to other ctenophores (B. microptera, P. bachei and Hormiphora californiensis). To that end, we first aligned all genomes to each other using Cactus v.2.6.4 (ref. [116]), following a progressive approach guided by the species trees of ctenophores, namely: ((M. leidyi, B. microptera), (H. californiensis, P. bachei)). Second, we used the hal2maf utility from the HAL toolkit v.2.2 (ref. [117]) to create MAF (multiple alignment format) alignments of each chromosome, using M. leidyi as reference. To identify conserved regions in these genomes, we used the rphast v.1.6.1 implementation of the Phast toolkit[118], as follows: (1) we used phyloFit[119] to create an initial null model of neutral change based on the fourfold degenerate codon positions of each genome’s coding regions, using a general reversible nucleotide transition matrix and the predefined species tree; (2) we used phastCons to optimize this model using the expectation–maximization procedure, re-estimating transition probabilities and tree parameters at each step (the optimization step was performed using only the longest chromosome in each genome).

Loop synteny analysis in M. leidyi

We evaluated the degree of syntenic conservation of the loop regions in M. leidyi compared to the other ctenophore genomes, and compared it to that of length-matched regions not involved in loops. To that end, we first identified orthologous genes across the four ctenophore species (M. leidyi, B. microptera, P. bachei and H. californensis) using Broccoli v.1.2 (ref. [120]) to obtain orthologous gene pairs (step 4), using predicted peptide sequences as input (longest isoform per gene only). Within Broccoli, we used the maximum-likelihood gene tree inference algorithm (based on IQ-TREE[121]) and set a k-mer length of 10,000 to avoid the removal of paralogous sequences from the analysis. Second, we mapped pairs of loop anchor regions from M. leidyi (2,353 pairs of promoter–enhancer and 99 promoter–promoter loops, n = 2,452 in total) to their closest overlapping genes, and used these genes and their orthologs as anchors to map these regions to the other ctenophore genomes. In parallel, we randomly selected length-matched, non-loop overlapping regions from the M. leidyi genome to compare their synteny conservation with that of loop regions (using the randomizeRegions function in the regioneR R package[122] to select 3× background regions, n = 7,356). Then, for each pair of species, we evaluated the synteny conservation of the foreground (loop) and background regions (random non-loops) from the point of view of the flanking synteny-anchoring genes, using two different metrics: (1) the fraction of shared orthologous genes between the flanking genes across all regions in the foreground and background sets (testing the significance of the difference using a χ^2 test for given probabilities) and (2) the distributions of per-region shared orthologs (tested using the one-sided Wilcoxon rank sum test with continuity correction).

ATAC-seq analysis

We used previously published datasets of ATAC-seq for C. owczarzaki[17], S. rosetta[39], T. adhaerens[123], C. collaboinventa[123], D. melanogaster[124] and H. sapiens[125] as well as newly generated datasets for N. vectensis, M. leidyi and E. muelleri. Sequenced reads were demultiplexed and converted to fastq files using bcl2fastq v.2.20 Illumina. Raw reads were filtered and trimmed with Trimmomatic (v.0.39)[126] before mapping to the reference genome with bwa mem (v.0.7.17-r1188) and duplicates were marked with bamsormadup from biobambam2 (https://github.com/gt1/biobambam2). Using deeptools alignmentSieve aligned reads were filtered and shifted with -ATACshift, which corresponds to mate reads being shifted +4 and −5 bp for positive and negative strands, respectively. To generate nucleosome-position data tracks, nucleosome-free and nucleosome-bound regions were defined using the following length thresholds 0–120 and 150–240 bp, respectively. ATAC peaks were called with MACS2 (ref. [127]) on shifted nucleosome-free regions. Footrpint ATAC score was calculated using TOBIAS v.0.13.3 (ref. [128]).

ChIP–seq analysis

We analysed publicly available dataset for D. melanogaster[129] and H. sapiens[130] and 34 newly generated ChIP–seq datasets as described below. Raw reads after removal of 3′-adapters and quality filtering with Trimmomatic (v.0.39)[126] were aligned to the reference genome with bwa mem (v.0.7.17-r1188). Duplicated reads were marked with bamsormadup (https://github.com/gt1/biobambam2), and peaks were called using MACS2 (v.2.2.6)[127]. Aggregated density plots were visualized with deeptools (v.3.1.3)[131].

DAP-seq analysis

Raw reads from amplified and native gDNA fragments bound by HALO-zf-C2H2 protein fusions were analysed as described for ChIP–seq. Motif enrichment analysis was performed using HOMER[114] in de novo motif discovery mode for MACS2 identified narrow peaks resized to 300 bp (for CTEP1 n = 14,638; for CTEP2 n = 10,615). GC- and size-normalized random genomic regions were used as background (for CTEP1 n = 25,964; for CTEP2 n = 30,744).

RNA-seq analysis

We used previously published datasets of bulk poly-A enriched RNA-seq for S. arctica[132], C. owczarzaki[61], S. rosetta[39], D. melanogaster[124] and H. sapiens[133] (Supplementary Table 2). To process data, raw reads were aligned to the reference genome using STAR (v.020201)[134] in --quantMode to estimate the number of read counts per gene. In downstream analysis, gene counts were reported as −log_2(1 − gene counts quantile).

MARS-seq analysis and single-cell expression atlases

Single-cell MARS-seq libraries generated previously8,135 were aligned to new reference genomes of E. muelleri (GCA_049114765.1), M. leidyi (GCA_048537945.1) and N. vectensis (GCA_932526225.1) using Liftoff or de novo annotated gene models. To improve single-cell RNA-seq quantification, gene annotations for E. muelleri and M. leidyi have been extended using GeneExt[136]. Briefly, MARS-seq alignment files have been subsampled to 100 M reads and MACS2 (ref. [127]) was used to call peaks using default parameters. Intergenic peaks were filtered based on the 20th percentile of the genic peak coverage and each gene was extended to the most distant peak within 5,000 nucleotides. Metacell and clustering analyses were performed as previously described[8]. The single-cell expression atlas for T. adhaerens was obtained from a previously published dataset[123].

Public datasets used in this study

All public datasets used in this study are listed in Supplementary Table 2. ATAC-seq, ChIP–seq and RNA-seq datasets were analysed as described above.

Reporting summary

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

Data availability

Raw and processed high-throughput sequencing data are available in a Genome Expression Omnibus (GEO) repository under accession number GEO GSE260572. Raw proteomics data have been deposited to the PRIDE[101] repository with the dataset identifier PXD056500. The de novo sequenced genome of C. owczarzaki is deposited under BioProject PRJDB19057; for M. leidyi genome, BioProject PRJNA1174117 (genome accession number GCA_048537945.1) and for the E. muelleri genome, BioProject PRJNA1175447 (genome accession number GCA_049114765.1). Furthermore, sequenced and assembled genome sequences, genome annotations and genomic intervals used in this study, such as chromatin loop anchors, insulation boundaries and compartmentalization domains are also available on GitHub (https://github.com/sebepedroslab/early-metazoa-3D-chromatin). In addition, datasets can be explored in interactive genome browsers[137] for each species at A.S.-P.’s laboratory site (https://sebelab.crg.eu/3d-genomes-arc-jb2).

Code availability

Scripts to reproduce the data processing and downstream analysis are available at GitHub (https://github.com/sebepedroslab/early-metazoa-3D-chromatin). Unless otherwise specified, scripts are based on R v.4.3.2 and Python v.3.7.7, and the language-specific libraries specified in the Methods section.

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Acknowledgements

We thank I. Maeso and M. Irimia for critical comments on the manuscript. We thank A. Hino and S. Denbo for greatly helping us to re-sequence and annotate the Capsaspora genome de novo. We are very grateful to S. B. Johnson, S. Haddock, MBARI and the David and Lucile Packard Foundation for sending us H. californensis samples. Research in A.S-P.’s group was supported by the European Research Council (grant no. ERC-StG 851647) and the Spanish Ministry of Science and Innovation (grant no. PID2021-124757NB-I00). I.V.K. was supported by a Juan de la Cierva postdoctoral fellowship (grant no. FJC2020-043131-I) from the Spanish Ministry of Science and Innovation. X.G.-B. is supported by the European Union’s H2020 research and innovation programme under Marie Sklodowska-Curie grant agreement no. 101031767. A.E. and C.N. are supported by FPI PhD fellowships from the Spanish Ministry of Science and Innovation. S.A.M. is supported by the EMBO postdoctoral fellowship grant no. ALTF 066-2022. H.S. acknowledges the financing support from MEXT/JSPS KAKENHI grant no. JP22K06343 and PUH Research Grant Programmes in 2015 and 2016. S.P.L. was supported by NSERC Discovery grant (no. 2016-15446). J.J.S.-A. and P.B. were supported by the Michael Sars Centre core budget from the University of Bergen. A.d.M. is supported by the European Research Council (grant no. ERC-StG 950230). M.A.M.-R. acknowledges support by the Spanish Ministerio de Ciencia e Innovación (grant no. PID2020-115696RB-I00).

Author information

Author notes

1. These authors contributed equally: Cristina Navarrete, Xavier Grau-Bové

Authors and Affiliations

1. Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

Iana V. Kim, Cristina Navarrete, Xavier Grau-Bové, Marta Iglesias, Anamaria Elek, Grygoriy Zolotarov, Sean A. Montgomery, Ewa Ksiezopolska, Didac Cañas-Armenteros, Marc A. Marti-Renom & Arnau Sebé-Pedrós

2. Centre Nacional d’Anàlisis Genòmic (CNAG), Barcelona, Spain

Iana V. Kim, Nikolai S. Bykov & Marc A. Marti-Renom

3. Universitat Pompeu Fabra (UPF), Barcelona, Spain

Cristina Navarrete, Marc A. Marti-Renom & Arnau Sebé-Pedrós

4. Michael Sars Centre, University of Bergen, Bergen, Norway

Joan J. Soto-Angel & Pawel Burkhardt

5. Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

Sally P. Leys

6. Department of Life and Environmental Sciences, Faculty of Bioresource Sciences, Prefectural University of Hiroshima, Shobara, Japan

Hiroshi Suga

7. School of Biological and Behavioral Sciences, Queen Mary University of London, London, UK

Alex de Mendoza

8. ICREA, Barcelona, Spain

Marc A. Marti-Renom & Arnau Sebé-Pedrós

9. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK

Arnau Sebé-Pedrós

Contributions

I.V.K. and A.S.-P. conceived the project. I.V.K., X.G.-B. and H.S. sequenced and assembled genomes to chromosome level. A.d.M., H.S., X.G.-B., G.Z. and A.S.-P. annotated genomes. I.V.K. collected biological material, performed Micro-C experiments and conducted bioinformatic analysis. C.N. performed ChIP–seq and chromatin proteomics. M.I. performed ATAC-seq. A.d.M. performed DNA methylation analysis. X.G.-B. performed phylogenetic and sequence conservation analyses. E.K. and S.A.M. performed DAP-seq. A.E. configured JBrowser servers. N.S.B. and M.A.M.-R. performed METALoci and microcompartment analysis. A.S.-P. performed MARS-seq analysis. S.P.L., J.J.S.-A. and P.B. provided biological samples. D.C.-A. maintained animal cultures. M.A.M.-R. and A.S.-P. provided supervision. I.V.K. and A.S.-P. wrote the manuscript with contributions from all authors.

Corresponding authors

Correspondence to Iana V. Kim or Arnau Sebé-Pedrós.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Giacomo Cavalli, Ferdinand Marlétaz 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 Micro-C experimental design and quality metrics.

a, Overview of the input material for Micro-C experiments, library preparation strategy, and sequencing statistics in each species. The D. melanogaster dataset[27] was subsampled to match the coverage of generated Micro-C maps in this study. b, Top, fluorescence-activated cell sorting (FACS) profile of crosslinked phagocytic choanocytes from E. muelleri labelled by feeding sponges with fluorescent microspheres. Only cells positive for nuclei Hoechst 33342 staining together with fluorescent beads were sorted. The sorted cell population (P3) was selected using sequential gating strategy through P1 and P2. Right, fluorescent microscopy image of sorted choanocytes, where PH stands for phase contrast, nuclei are in blue, FluoSpheres beads are in green. Scale bar (top right corner) is 50 µm. Below, the sequential FACS gating strategy (P1 - P3) to sort mOrange::NvElav+ neuronal cells (P4) from the N. vectensis transgenic line[65]. Wild type animals lacking the mOrange fluorescent protein were used to verify the gating strategy. The FACS sorting experiment data for E. muelleri and N. vectensis are representative of at least 6 independent experiments. c, The quality of chromatin digestion with MNase and followed proximity ligation was assessed with High sensitivity D5000 ScreenTapes using the Agilent 2200 TapeStation systems. The optimal chromatin fragmentation with Mnase results in up to 80% mononucleosomes profile. d, Barplots showing the percentage of reads mapped to the genome of each species. e, Barplots illustrating the distibution of intrachromosomal (cis) and interchromosomal (trans) interactions in each replicate experiment. The percentage of trans-contacts observed is species-specific but can be influenced by several factors: (i) the type of nuclear organization, such as Rabl-like configuration or the presence of chromosome territories, (ii) a high chromosome count, as seen in S. arctica and S. rosetta, (iii) and the reduction in nuclear diameter during the growth of coenocytes in S. arctica. f, Heatmap showing pairwise similarity scores between biological and technical replicates calculated as the stratum adjusted correlation coefficient (SCC). Below, SCC scores were estimated for a range of resolutions of 1 Kb, 2 Kb, 5 Kb, 10 Kb, 25 Kb, and 50 Kb. Differences in pairwise comparisons between experimental replicates are shown as mean ± s.d. The number of replicates per species as in (d-e). g, Top, cis-decay plots showing the rate of decay of contact frequency over genomic distance. The contact probability is averaged over all chromosomes. For C. owczarzaki, samples obtained from mitotic (blue) and synchronised G1/S stage (in orange) show different contact frequency behavior at short (below 10 Kb) and long (over 1 Mb) genomic distances. Bottom, log-derivative of cis-decay plots that predicts the folding of DNA into genomic structures and their size, most commonly chromatin loops, which tend to be the dominant micro-scale contacts. The first pronounced peak and dip at log-derivative cis-decay plots (highlighted in grey) in M. leidyi is observed at the scale from 10 Kb to 100 Kb. In H. sapiens, the peak size (highlighted in grey) ranges from 100 Kb to 1 Mb, which in both cases correspond to the average loop sizes in each species.

Extended Data Fig. 2 Genome sequencing, assembly and annotation.

a, Genome assembly strategy. b, Genome assembly statistics. BUSCO completeness score was calculated using genome mode or protein mode against metazoan BUSCO dataset for all species except unicellular holozoans, where eukaryotic dataset was used. c, Chromosome-level re-assembly of S. arctica, S. rosetta, T. adhaerens and C. collaboinventa genomes using Micro-C data resulting in total of 27, 36, 6 and 6 chromosomes, respectively. Both S. arctica and S. rosetta posses genome-wide telomere clustering, whereas placozoans display strong interchromosomal compartmentalization signal. d, Left, genome-wide Micro-C contact map showing the chromosome-level assembly of C. owczarzaki. C. owczarzaki exhibit increased interactions between telomeres and between centromeres, suggesting Rabl-like chromosome configuration[138]. Right, chromosomal rearrangements in C. owczarzaki. Visual inspection of chromatin interaction maps revealed heterozygous deletions on C. owczarzaki chromosome 2, which is also confirmed by the uneven distribution of anti-H3 ChIP-seq coverage. In addition, one arm of the chromosome 13 exhibits genome-wide increase in the interaction frequency with other chromosomes, as well as two-fold coverage of H3 ChIP-seq, suggesting the gain of a chromosome arm pair. Finally, chromosome 15 v shares one arm with chromosome 15 and appears to be whole-arm translocation. e, Same as (d) for M. leidyi. The presence of uncollapsed haplotypes was estimated by distribution of per-base sequencing depth (left). Chromosomes in M. leidyi exhibit telomere clustering as well as increased intrachromosomal interactions similar to chromosome territories. f, Same as (d) for E. muelleri genome assembly with chromosome organisation similar to chromosome territories. g, Repeat content for each assembled genome, annotated using EDTA[97].

Extended Data Fig. 3 Genome compartmentalisation analysis.

a, Table translating relative resolutions of contact maps that were used to calculate compartmentalisation signal into base-pair resolutions. b, Example genomic regions showing eigenvector coefficients E1 and compartment annotation into A (active, red), B (inactive, blue) and I (intermediate, yellow). c, Density plots showing genome-wide distribution of E1 eigenvalues and the relative abundance of each defined compartments (stacked barplot on top). Compartments were defined using fitting of Gaussian mixture with three components (k = 3). A Bayesian Information Criterion (BIC) was computed for the specified mixture (bottom plot) (see Method section). d, Association between chromatin compartments and different genomic features calculated per genomic bin at a relative resolution of 20,000 bins per genome, as in (a). The proportion of features in each compartment category follows the classification as in (c). The boxplots indicate the relative signal (measured as genome-wide quantiles) of the different features in the genomic bins belonging to each compartment category (active, intermediate, inactive). The mean value of distributions is shown as the center line on the boxplots, with interquartile range (IQR) as the box limits and whiskers extending to 1.5x IQR.

Extended Data Fig. 4 Genomic insulation and chromatin loop analysis.

a, Insulation score profiles aligned at insulation boundary regions. Insulation score profiles were calculated for multiple resolutions with window sizes corresponding to 5x, 10x and 25x the chosen resolution. For example, for 400 bp resolution, we used window sizes of 2,000 bp (5*bin), 4,000 bp (10*bin), and 10,000 bp (25*bin). Parameters showing two strongest average insulation scores were considered optimal. For each of our studied species, an example contact map region with calculated insulation profile is shown. b, Left, overlap between regions annotated as strong boundaries using strategy described in this paper (see Method section) and previously published datasets12,27. c, Left, distribution of boundary strength values per species. Insulation boundaries (marked in blue) were selected using Li threshold as implemented in cooltools[103]. Middle, distribution of linear distances (Kb) between successive boundaries, with the number of examined region between boundaries indicated. Right, boxplots showing the number of genes located between insulation boundaries (same number of examined regions as in the previous plot). Boxplots center line shows the median value, with box limits indicating the IQR and whiskers as 1.5x IQR. d, Epigenetic, structural and gene features associated to insulation boundaries in each species. Note that a boundary can be annotated with multiple features (e.g. TSS, ATAC and H3K4me3 peaks).

Extended Data Fig. 5 Genome architecture in unicellular holozoans.

a, Example genomic regions in S. arctica illustrating the co-segregation of inactive chromatin regions. The interacting regions, highlighted in grey, fold into chromatin domains that exhibit local compartmentalised interactions. b, The manually annotated 296 compartment domains have a median size of 18 Kb. Middle, the observed long-distance interactions within the domains display a local checkerboard pattern, where contacts are enriched within certain set of loci, while contacts between them are depleted. To quantify the contact distribution, we calculated the sum of ICE (iterative correction and eigenvector decomposition)-normalized contacts within the segregated regions and their flanking regions (30 Kb) at a resolution of 2,800 bp, across the size range of 50 Kb to 5 Mb. The contact interaction pattern observed over the silenced regions showed a reduced interaction frequency across the region body compared to flanking loci. This interaction pattern is typical for checkerboard compartmentalisation, in contrast to loop interactions, which manifest as local peaks in interaction frequencies. Right, genes located within the compartment domains are lowly expressed or silenced (*** p-value < 2.2e−16, Wilcoxon rank sum test). Boxplots center line shows the median value, with box limits indicating the IQR and whiskers as 1.5x IQR. c, Distribution of epigenetic signals across compartment domains. The regions within the annotated domains were located within the inactive B compartment and were enriched in transposable elements, predominantly Gypsy LTRs, which accounted for 63% of the total TEs in these regions. d, Size distribution of manually annotated 183 contact regions in S. rosetta that harbour lowly expressed genes (*** p-value = 4.8e−6, Wilcoxon rank sum test), boxplots as in (b). e, Example genomic regions in S. rosetta forming distal interactions. f, Same as (c) for S. rosetta. The interacting regions show weak enrichment in H3K4me1 and H3K27me3 signals compared to random genomic regions. g, Same as (e) for C. owczarzaki. h, Distal contacts in C. owczarzaki connect promoter regions of highly expressed genes (*** p-value = 5.3e−12, Wilcoxon rank sum test), boxplots as in (b). i, Distal contacts in C. owczarzaki are indicative of micro-compartmentalisation signal because of the characteristic alternating contact pattern and the decreased cumulative interactions in the promoter regions of the target genes compared to the concentration typically seen in chromatin loops annotated in T. adhaerens, M. leidyi, N. vectensis, D. melanogaster and H. sapiens. To quantify the distribution of contact interactions around TSS-TES sites, we calculated and compared the sum of ICE (iterative correction and eigenvector decomposition)-normalized contacts at species-specific resolutions (400 bp for C. owczarzaki, T. adhaerens and D. melanogaster, 500 bp for N. vectensis, 800 bp for M. leidyi and 5 Kb for H. sapiens). To eliminate confounding signals from distal compartmentalisation pattern or other long-distance interaction patterns, the sum of considered interactions was restricted to contacts that fall within the range size of annotated loops or interacting regions (4–100 Kb for C. owczarzaki, T. adhaerens, 5–250 Kb for D. melanogaster, 10–360 Kb for N. vectensis, 5–150 Kb for M. leidyi and 50–1,060 Kb for H. sapiens). To calculate the average distribution of interaction contacts around the TSS-TES sites we used the function stackup form the pybbi package version 0.4.0 (https://github.com/nvictus/pybbi). The TSS-TES regions were rescaled into 50 bins with flanking regions of 10 Kb for each species except H. sapiens with 100 Kb flanking regions. Additionally, we flipped the TSS-TES regions and their corresponding flanking regions for negative-stranded genes. Notice that in C. owczarzaki, the sum of interactions around the TSS was lower than average interactions within the gene body. This is due to a small-scale local checkerboard pattern, where regions between interaction loci showed low contact frequency. As a result, cumulative interactions at promoters were even lower than average background signal and signal over gene bodies. In contrast, in other examined species, including T. adhaerens, M. leidyi, N. vectensis, D. melanogaster and H. sapiens, where chromatin loops connected examined promoter regions to cis-regulatory elements, the contact frequency at loop anchor regions was enriched and higher than the average across gene bodies. These differences highlight distinct modes of chromatin organization of C. owczarzaki with other species. j, In C. owczarzaki, a subset of highly expressed genes (274) exhibit increased interaction frequencies between TSS and TES forming gene body interaction domains.

Extended Data Fig. 6 Genome architecture in the sponge Ephydatia muelleri.

a, Example E. muelleri genomic regions showing contact patterns perpendicular to the diagonal of the Micro-C matrix and visually resembling flares[139], jets[140], or fountains40,141. b, Aggregated contact strength around the midpoints of flare regions. Random genomic regions anchored at the TSS of expression-matched genes were used as a control. Boxplots center line shows the median value, with box limits indicating the IQR and whiskers as 1.5x IQR. c, Example E. muelleri genomic regions showing distal interactions connecting promoter and enhancer-like anchor regions. Unlike typical chromatin loops, the preferential contact interactions in E. muelleri are diffuse and do not form a conspicuous dot contact pattern. d, A total of 84 manually annotated focal contacts connecting distal regulatory elements were classified as enhancers or promoters based on their H3K4me3 to H3K4me1 ratio. e, Aggregate plots demonstrating contact enrichment within rescaled contact regions, compared to random genomic regions anchored at TSS of expression-matched genes on one side and distance-matched random points on the other side. Boxplot limits are as in (b). f, Non-promoter cis-regulatory elements were identified based on chromatin state, defined by low H3K4me3 and high H3K4me1 enrichment around regions of accessible chromatin. The plots illustrate the distribution of these elements and their proximity to the nearest transcription start site (TSS) or other contact anchors within loop-forming enhancers. Notice the distance-to-TSS distribution of E. muelleri enhancer-like elements is similar to that of enhancers that do not form stable loops in other species.

Extended Data Fig. 7 Genome architecture in the cnidarian Nematostella vectensis.

a, Example genomic region in N. vectensis showing chromatin loop contacts with loop anchors highlighted in grey. b, Loop anchor regions were classified as promoter-side if characterized by high H3K4me3 ChIP-seq signal levels and low H3K4me2 or low H3K4me1 signal. Enhancer-side loop anchors were defined as regions with low H3K4me3 and high H3K4me2 or H3K4me1. c, Most loop anchors retained their original classification, regardless of whether the H3K4me3/H3K4me2 or H3K4me3/H3K4me1 ratio was used. For N. vectensis, the ratio of H3K4me3/H3K4me1 outperformed H3K4me3/H3K4me2 in classifying loop anchors, as most of the disputed loops anchors annotated as promoters with H3K4me3/H3K4me2 were predominantly located in intronic and intergenic regions (pie chart). d, Left, aggregated contact strength of chromatin loop interactions, showing the overall intensity and frequency of chromatin contacts across loop anchor points. Right, loop anchors in N. vectensis show GTGT-motif enrichment (FC = 327, p-value = 1e−40) compared to GC-normalised background genomic regions. e, Genomic regions in N. vectensis displaying non-loop self-interacting domains. f, Same as (d), but for regions between insulation boundaries that also harbour self-interacting domains. Right, motif enrichment analysis was focused on accessible chromatin regions at the insulation boundaries. Accessible promoter regions in neuronal Elav+ cells were used as the background for comparison.

Extended Data Fig. 8 Placozoan genome architecture additional analyses.

a, Annotation of chromatin loop anchors with promoter and enhancer chromatin signatures for T. adhaerens and C. collaboinventa. Loop anchors annotated as enhancers were mostly located within promoter regions of other genes. To resolve this ambiguity, such loop anchors were classified as promoters based on their genomic context. b, Example contact map regions depicting promoter-enhancer distal interactions highlighted in grey in syntenic regions of placozoans. c, Local Moran’s Index scatterplot visualises assignment of genomic bins to four distinct groups: High-High (HH), where examined signal (ATAC or H3K4me3) spatially co-localises in a neighbourhood of other bins with high signal; Low-Low (LL) bin has low examined signal and located in a neighbourhood of bins with low signal; when bin and its neighbourhood have different levels of signal, then the bin is assigned to Low-High (LH) or High-Low (HL) quadrants. Statistically significant values are in solid colors. Right panel illustrates intensity of examined signal layered over the two-dimensional Kamada-Kawai representation of top 20% contact interactions. p-values and r-values (Pearson correlation coefficients) were determined using a one-sided permutation test. A linear least-squares regression was then performed between z-scores of ATAC or H3K4me3 values and the signal’s spatial lag. The 95% confidence interval of the regression is shown as a grey shadow. d, Boxplots showing relative gene expression (RNA-seq) and peak intensity (H3K4me3) at promoter regions of genes from GP1, GP2, and GP3 groups. For each pairwise comparison for both T. adhaerens (GP1: n = 2,978; GP2: n = 3,681; GP3; n = 3,851) and C. collaboinventa (GP1: n = 3,973; GP2: n = 3,119; GP3: n = 4,238), *** indicates p-values below 2.22e−16, two-sided Wilcoxon rank sum test. Boxplots center line shows the median value, with box limits indicating the IQR and whiskers as 1.5x IQR. e, Left, heatmaps showing CPM normalised ATAC-seq and ChIP-seq coverage, motif scores and Mutator transposable element density within 5 Kb of the TSSs of GP1, GP2, and GP3 genes in C. collaboinventa. Each heatmap scale starts at zero. Middle, aggregate peak analysis displaying the contact strength between gene promoters within each annotated group. Right: Genes in C. collaboinventa from various gene groups, classified based on the presence of chromatin loops and their epigenetic states, demonstrate overlap with orthologous genes from GP1, GP2, and GP3 in T. adhaerens. f, GO-term enrichment analysis of GP1 genes with p-values determined using Fisher’s exact test. g, Barplots showing the cell type (from previously published dataset[123]) in which genes belonging to each group are maximally expressed. Only variable genes (with a fold-change higher than 1.8) are included. h, Scatterplot showing total gene expression (x-axis) versus gene expression variability (y-axis) across cell types. i, Distribution of motif scores in loop anchor regions compared to the genome-wide background. C. collaboinventa harbour similar motif to T. adhaerens (similarity score = 0.93) in 60% of annotated loop anchor regions. j, Local Moran’s Index scatterplot and Gaudí plots demonstrate spatial co-localisation of sequence motif identified in promoters of GP1 genes of T. adhaerens (motif score above 80th percentile). Statistically significant values are calculated as in (c). k, Schematic phylogenetic tree of TIR sequences of Mutator DNA transposons from four placozoan species (Trichoplax adhaerens, Trichoplax sp. H2, Hoilungia hongkongensis, Cladtertia collaboinventa). Placozoan Mutator DNA TIRs can be classified into 5 clades with consensus sequences. The similarity score between the TIR consensus sequence and the sequence motif in GP1 promoters is indicated. Pie charts shows the proportion of Mutator transposons harbouring the consensus TIR sequences.

Extended Data Fig. 9 Ctenophore genome architecture additional analyses.

a, Scatter plot showing the normalised H3K4me3 and H3K4me2 ChIP-seq coverage in 2 Kb region around loop anchor. b, Comparison of loop anchor annotation using either H3K4me3/H3K4me2 or H3K4me3/H3K4me1 ratios. For M. leidyi, H3K4me3/H3K4me2 ratio were more effective in annotating loop anchors, as many loop anchors classified as promoters using H3K4me3/H3K4me1 were found within intergenic or intronic regions (pie chart). The discrepancy is attributed to the high background noise observed in the H3K4me1 ChIP-seq signal. c, Normalised coverage for different chromatin features around loop anchors classified as promoters and enhancers. d, Genomic regions in H. californensis showing chromatin loops. In total, we annotated 239 chromatin loops, with 51% of loop anchors located within intronic or intergenic regions. High-resolution chromatin maps are expected to significantly increase the number of reported loops in H. californensis. e, Boxplots showing the total expression in scRNA-seq data[8] for M. leidyi or RNA-seq data[51] for C. californensis of genes with a loop anchor at their promoter regions, in their introns (enhancer sites), and genes not involved in distal chromatin interactions (outside loops). *** stands for p-value < 2.22e−16 of two-sided Wilcoxon rank sum test. Boxplots center line shows the median value, with box limits indicating the IQR and whiskers as 1.5x IQR. f, Motif score distributions at loop anchors (max score in 2,000 bp window around the center of a loop anchor) compared to genomic background. In H. californensis, we detected similar to M. leidyi GC-rich motif (similarity score = 0.96) enriched in 38% of loop anchors. g, Fraction of loop anchor sites containing the identified GC-rich motif at promoter sites (in orange), at enhancer sites (green) or at the promoters of genes not involved in chromatin loops (cyan). h, Scatterplot showing total gene expression (x-axis) versus gene expression variability (y-axis) across cell types, highlighting genes with their promoter involved in chromatin loops (orange) and also genes containing the GC-rich motif in their promoters but not involved in loops (cyan). These motif-containing genes without detected loops showed lower and more variable expression across cell types than genes with detected loops, suggesting the former could be forming loops in low-abundance cell types that we are unable to detect in bulk Micro-C experiments. i, DNA methylation levels at GC-motif sites located at chromatin loops (left) compared to methylation levels in motif occurrences outside detected chromatin loops (right). j, Bias-corrected ATAC footprint profiles centered around motifs located at loop anchors. k, Distribution of CTEP1 and CTEP2 bound DAP-seq peaks across genomic regions with varying DNA methylation levels and within annotated loop anchors. Below, the number of DAP-seq peaks containing the identified GC-rich motif. l, Number of loop anchor regions that contain CTEP1 and CTEP2 DAP-seq peaks. m, DAP-seq quantile normalized CPM coverage around GC-rich motif from CTEP2 binding assay using native genomic DNA fragments or unmethylated PCR amplified genomic DNA. CTEP2 as well as CTEP1 (Fig. 4f) exhibited higher affinity for the unmethylated GC-rich motif. n, Multiple sequence alignments of CTEP1 and CTEP2 genes were performed against the dataset of 358 metazoan genomes (Supplementary Table 3). The significant hits against CTEP proteins, exhibiting sequence identity above 50%, were found exclusively within ctenophores. o, Left, boxplots showing the number of transposable element insertions per promoter region of genes involved in chromatin loops compare to genes that are outside loops (*** indicates p-value < 2.22e−16, two-sided Wilcoxon rank sum test). Right, barplots showing the fraction of promoters in loops containing TE insertions compared to promoters not involved in loops and random genomic regions. Over 90% of promoter regions involved in distal interactions harbour insertion of DNA transposon. Additionally, promoters in loops have higher frequency of insertions of LTR and Unknown type transposons. Boxplot limits as in (e). p, Syntenic conservation within M. leidyi chromatin loops compared to Pleurobrachia bachei or Bolinopsis microptera. Left, barplot showing the fraction of conserved orthologs in all alignable genomic regions across ctenophore species (chi-squared test for given probabilities). Right, boxplot showing the fraction of shared orthologs between individual genomic regions within chromatin loops (P. bachei: n = 105; B. microptera: n = 332) versus in random genomic regions of similar size (P. bachei: n = 198; B. microptera: n = 945). p-value significance was calculated using two-sided Wilcoxon rank sum test. Boxplot limits as in (e). q, Number of predicted genes with zf-C2H2 protein domain in the different species studied included in this study.

Supplementary information

Supplementary Text 1

Detailed Micro-C protocol used in this study.

Reporting Summary

Supplementary Table 1

Quality control metrics for each Micro-C replicate.

Supplementary Table 2

List of publicly available datasets and their corresponding sequence read archive accession numbers used in this study.

Supplementary Table 3

This file contains a list of orthologous genes for CTEP1 and CTEP2 architectural proteins in other ctenophore species, as well as a list of 358 publicly available metazoan genome datasets used for multiple sequence alignments of CTEP1 and CTEP2.

Supplementary Data 1

Phylogenetic tree of TIR sequences of placozoan Mutator DNA transposable element.

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Kim, I.V., Navarrete, C., Grau-Bové, X. et al. Chromatin loops are an ancestral hallmark of the animal regulatory genome. Nature (2025). https://doi.org/10.1038/s41586-025-08960-w

- Received: 07 February 2024

- Accepted: 31 March 2025

- Published: 07 May 2025

- DOI: https://doi.org/10.1038/s41586-025-08960-w

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Amendments & Corrections

- Author Correction: DNA-guided transcription factor interactions extend human gene regulatory code

- Author Correction: GluD1 is a signal transduction device disguised as an ionotropic receptor

Author Correction Open access Published: 03 June 2025

Author Correction: DNA-guided transcription factor interactions extend human gene regulatory code

Zhiyuan Xie,

Ilya Sokolov,

Maria Osmala,

Xue Yue,

Grace Bower,

J. Patrick Pett,

Yinan Chen,

Kai Wang,

Ayse Derya Cavga,

Alexander Popov,

Sarah A. Teichmann,

Ekaterina Morgunova,

Evgeny Z. Kvon,

Yimeng Yin &

…

Jussi Taipale

Nature (2025)

The Original Article was published on 09 April 2025

Correction to: Nature https://doi.org/10.1038/s41586-025-08844-z Published online 9 April 2025

In the version of the article initially published, in Extended Data Fig. 7a, the image for “#3 (Tandem)” was a duplicate of “#2 (Tandem)”. Extended Data Fig. 7a has now been amended with the correct “#3 (Tandem)” image in the HTML and PDF version of the article.

Author information

Author notes

1. These authors contributed equally: Zhiyuan Xie, Ilya Sokolov

Authors and Affiliations

1. State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China

Zhiyuan Xie, Xue Yue, Kai Wang & Yimeng Yin

2. Department of Biochemistry, University of Cambridge, Cambridge, UK

Ilya Sokolov, Yinan Chen, Ayse Derya Cavga & Jussi Taipale

3. Generative and Synthetic Genomics Programme, Wellcome Sanger Institute, Hinxton, UK

Ilya Sokolov, Yinan Chen & Jussi Taipale

4. Applied Tumor Genomics Program, Biomedicum, University of Helsinki, Helsinki, Finland

Maria Osmala & Jussi Taipale

5. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA

Grace Bower & Evgeny Z. Kvon

6. Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, UK

J. Patrick Pett

7. European Synchrotron Radiation Facility (ESRF), Grenoble, France

Alexander Popov

8. Department of Medicine and Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

Sarah A. Teichmann

9. Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

Ekaterina Morgunova & Jussi Taipale

10. Clinical Center for Brain and Spinal Cord Research, Tongji University, Shanghai, China

Yimeng Yin

Corresponding authors

Correspondence to Yimeng Yin or Jussi Taipale.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Xie, Z., Sokolov, I., Osmala, M. et al. Author Correction: DNA-guided transcription factor interactions extend human gene regulatory code. Nature (2025). https://doi.org/10.1038/s41586-025-09129-1

- Published: 03 June 2025

- DOI: https://doi.org/10.1038/s41586-025-09129-1

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Author Correction Published: 06 June 2025

Author Correction: GluD1 is a signal transduction device disguised as an ionotropic receptor

Jinye Dai,

Christopher Patzke,

Kif Liakath-Ali,

Erica Seigneur &

…

Thomas C. Südhof

Nature (2025)

The Original Article was published on 16 June 2021

Correction to: Nature https://doi.org/10.1038/s41586-021-03661-6 Published online 16 June 2021

In the version of the article initially published, the Source data files and Extended Data Figures of this paper contained various errors. None of the errors affect the conclusions of the paper or have a major impact on its figures.

1. 1.
In various source data files, we mislabeled the PPR measurements for Extended Data Fig. 1 as “ED Figure 1f” instead of “ED Figure 1k”, the slope measurements for Fig. 4 as “Figure 4g” instead of “Figure 4h”, the data in the source file for Fig. 3f–h as “Ctrl/KO+Nrxn3bN3-SS4+” instead of “Ctrl/KO+Nrxn3bN1-SS4+”, and the data in the source file for Fig. 5g–i and Extended Data Fig. 9 as “GluD1 KO+ Motif3S945A” instead of “GluD1 KO+ Motif3S944A”.

2. 2.
In the source data file for Fig. 2f, we erroneously copy-pasted the AMPA amplitude of the third cell in the “control” condition from another cell (correct value = 26.6683 pA; corresponding AMPA/NMDA ratio = 1.9721).

3. 3.
In the source data file for Extended Data Fig. 1, we accidentally copy-pasted the +40 mV EPSC decay time constants for the sixth “CRISPR” cell of panel i from the –70 mV EPSC decay time constants (correct fast-tau and slow-tau time constants = 0.0811 and 0.2522) and the coefficient of variation of the last “control” cell of panel j from the previous cell (correct value = 0.4279). The correction produces an imperceptible adjustment in the graph. Corrected and original figures can be found in the Supplementary information accompanying this notice.

4. 4.
In the representative immunoblots of Extended Data Fig. 3a, we erroneously cropped the immunoblots from images of two independent but identical experiments instead of from images of the same experiments. The now corrected Extended Data Fig. 3a was cropped from the red boxes in the blot shown in the original Supplementary Fig. 1b. In the Supplementary information, we now also include an additional blot used for the summary graph of Extended Data Fig. 3b.

5. 5.
In the source data for Extended Data Fig. 7k, we accidentally copy-pasted the “GluD1 KO + GluD1-CD4Mut3 and Nrxn1bSS4+” condition data of cell 14 from cell 13 (correct values = 1.64577, 1.59422, 0.729221) and duplicated the “GluD1 KO + GluD1-CD4Mut4 and Nrxn3bSS4+” condition data of cell 10. The correction produces an imperceptible adjustment in the graph.

6. 6.
In the representative immunoblots shown in Supplementary Fig. 1c, we misassembled and mislabeled the immunoblots, leading to miscalculations of protein levels in Extended Data Fig. 4b. In the corrected Supplementary Fig. 1c, red boxes indicate the signals analyzed for the corrected Extended Data Fig. 4b (‘other construct’ lanes were loaded with not-analyzed constructs). The Source Data file was also corrected accordingly. Note that of the five immunoblotting experiments performed, immunoblot A was used in the Extended Data Fig. 4a (which is correct). Individual blots may look slightly different due to adjustments of image brightness/contrast/rotation.

7. 7.
We acknowledge that some parties have deemed the statistical analyses in our original article as insufficient. To address this concern, we re-analyzed our data. Non-parametric Kruskal-Wallis tests confirmed that the data in all figure panels of our paper are statistically significant, arguing against Type I errors (false positives). Following this, we conducted planned Mann-Whitney U-tests for predefined comparisons, justified by the pre-specified statistical design which was employed in order to control for Type II errors (false negatives). The results of these analyses also confirm our conclusions. We acknowledge that some statisticians prioritize minimizing false positives over false negatives. while others argue that pre-specified statistical analyses are generally inappropriate. To accommodate these particular perspectives, we additionally performed multiple comparisons using the Mann-Whitney U-test with the Holm-Šídák correction, which also confirmed the overall findings. Even with this more conservative approach key comparisons remain statistically significant, albeit with lower apparent P values (see additional statistical analyses included in the Supplementary information).

These corrections have been made to the HTML and PDF versions of the article.

Author information

Author notes

1. Christopher Patzke

Present address: Boler-Parseghian Center for Rare and Neglected Diseases, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

Authors and Affiliations

1. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Jinye Dai & Thomas C. Südhof

2. Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA

Jinye Dai, Christopher Patzke, Kif Liakath-Ali, Erica Seigneur & Thomas C. Südhof

Corresponding authors

Correspondence to Jinye Dai or Thomas C. Südhof.

Supplementary information

Original and corrected figures

Statistical analysis

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Dai, J., Patzke, C., Liakath-Ali, K. et al. Author Correction: GluD1 is a signal transduction device disguised as an ionotropic receptor. Nature (2025). https://doi.org/10.1038/s41586-025-09146-0

- Published: 06 June 2025

- DOI: https://doi.org/10.1038/s41586-025-09146-0

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