Nature Neuroscience, Published online: 24 April 2026; doi:10.1038/s41593-026-02284-2
Addendum: Neural anticipation of virtual infection triggers an immune response
<![CDATA[FDA accelerates psychedelic drug development to advance agents for TRD, PTSD, and AUD. ]]>
Three reasons why DeepSeek’s new model matters
On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that helps it handle large amounts of text more efficiently. Like DeepSeek’s previous models, V4 is open source, meaning it is available for anyone to download, use, and modify.
V4 marks DeepSeek’s most significant release since R1, the reasoning model it launched in January 2025. R1, which was trained on limited computing resources, stunned the global AI industry with its strong performance and efficiency, turning DeepSeek from a little-known research team into China’s best-known AI company almost overnight. It also helped set off a wave of open-weight model releases from other Chinese AI firms.
DeepSeek has kept a relatively low profile since then—but earlier this month, it effectively teased V4’s release when it added “expert” and “flash” modes to the online version of its model, prompting speculation that the updates were tied to a bigger upcoming release.
While the company has become a powerful symbol of China’s AI ambitions, its big return to cutting-edge frontier models comes after months of scrutiny—including major personnel departures, delays to previous model launches, and growing scrutiny from both the US and Chinese governments.
So, will V4 shake the AI field the way R1 did? Almost certainly not, but here are three big reasons why this release matters.
1. It breaks new ground for an open-source model.
As with R1 before it, DeepSeek claims that V4’s performance rivals the best models available at a fraction of the price. This is great news for developers and for companies using the tech, because it means they can access frontier AI capabilities on their own terms, and without worrying about skyrocketing costs.
The new model comes in two versions, both of which are available on DeepSeek’s website and in its app, with API access also open to developers. V4-Pro is a larger model built for coding and complex agent tasks, and V4-Flash is a smaller version designed to be faster and cheaper to run. Both versions offer reasoning modes, in which the model can carefully parse a user’s prompt and show each step as it works through the problem.
For V4-Pro, DeepSeek charges $1.74 per million input tokens and $3.48 per million output tokens, a fraction of the cost of comparable models from OpenAI and Anthropic. V4-Flash is even cheaper, at about $0.14 per million input tokens and about $0.28 per million output tokens, making it one of the cheapest top-tier models available. This would make it a very appealing model to build applications on.
In terms of performance, V4 is, perhaps unsurprisingly, a huge jump from R1—and it seems to be a strong alternative to just about all the latest big AI models. On the major benchmarks, according to results shared by the company, DeepSeek V4-Pro competes with leading closed-source models, matching the performance of Anthropic’s Claude-Opus-4.6, OpenAI’s GPT-5.4, and Google’s Gemini-3.1. And compared to other open-source models, such as Alibaba’s Qwen-3.5 or Z.ai’s GLM-5.1, DeepSeek V4 exceeds them all on coding, math, and STEM problems, making it one of the strongest open-source models ever released.
DeepSeek also says that V4-Pro now ranks among the strongest open-source models on benchmarks for agentic coding tasks and performs well on other tests that measure ability to carry out multistep problems. Its writing ability and world knowledge also leads the field, according to benchmarking results shared by the company.
In a technical report released alongside the model, DeepSeek shared results from an internal survey of 85 experienced developers: More than 90% included V4-Pro among their top model choices for coding tasks.
DeepSeek says it has specifically optimized V4 for popular agent frameworks such as Claude Code, OpenClaw, and CodeBuddy.
2. It delivers on a new approach to memory efficiency.
One of the key innovations of V4 is its long context window—the amount of text the model can process at once. Both versions can handle 1 million tokens, which is large enough to fit all three volumes of The Lord of the Rings and The Hobbit combined. The company says this context window size is now the default across all DeepSeek services and it matches what is offered by cutting-edge versions of models like Gemini and Claude.
But it’s important to know not just that DeepSeek has made this leap, but how it did so. V4 makes significant architectural changes to the company’s former models—especially in the attention mechanism, which is the feature of AI models that helps them understand each part of a prompt in relation to the rest. As the prompt text gets longer, these comparisons become much more costly, making attention one of the main bottlenecks for long-context models.
DeepSeek’s innovation was to make the model more selective about what it pays attention to. Instead of treating all earlier text as equally important, V4 compresses older information and focuses on the parts most likely to matter in the present moment, while still keeping nearby text in full so it does not miss important details.
DeepSeek says this sharply reduces the cost of using long context. In a 1-million-token context, V4-Pro uses only 27% of the computing power required by its previous model, V3.2, while cutting memory use to 10%. The reduction in V4-Flash is even larger, using just 10% of the computing power and 7% of the memory. In practice, this could make it cheaper to build tools that need to work across huge amounts of material, such as an AI coding assistant that can read an entire codebase or a research agent that can analyze a long archive of documents without constantly forgetting what came before.
DeepSeek’s interest in long context windows didn’t start with V4. Over the past year and a half, the company has quietly published a series of papers on how AI models “remember” information, experimenting with compression and mathematical techniques to extend what AI models could realistically handle.
3. It marks the first steps on the hard road away from Nvidia.
V4 is DeepSeek’s first model optimized for domestic Chinese chips, such as Huawei’s Ascend—a move that has turned the launch into something of a test of whether China’s homegrown AI industry can begin to loosen its dependence on US chip giant Nvidia.
This was largely expected, since The Information reported earlier this month that DeepSeek did not give American chipmakers like Nvidia and AMD early access to V4, though prerelease access is common to allow chipmakers to optimize support of the new model ahead of a launch. Instead, the company reportedly gave early access only to Chinese chipmakers.
On Friday, Huawei said its Ascend supernode products, based on the Ascend 950 series, would support DeepSeek V4. This means that companies and individuals who want to run their own modified version of Deepseek V4 will be able to use Huawei chips easily.
Reuters previously reported that Chinese government officials recommended that DeepSeek integrate Huawei chips in its training process. And this pressure fits a broader pattern in China’s industrial policy: Strategic sectors are often pushed, and sometimes effectively required, to align with national self-reliance goals. But there’s a particular urgency when it comes to AI. Since 2022, US export controls have cut Chinese firms off from Nvidia’s most powerful chips, and they later also restricted access to downgraded China-market versions. Beijing’s response has been to accelerate the push for a domestic AI stack, from chips to software frameworks to data centers.
Chinese authorities have reportedly been pushing data centers and public computing projects to use more domestic chips, including through reported bans on foreign-made chips, sourcing quotas, and requirements to pair Nvidia chips with Chinese alternatives from companies such as Huawei and Cambricon.
Still, replacing Nvidia is not as simple as swapping one chip for another. Nvidia’s advantage lies not only in its chips, but in the software ecosystem developers have spent years building around them. Moving to Huawei’s Ascend chips means adapting model code, rebuilding tools, and proving that systems built around those chips are stable enough for serious use.
To be clear, DeepSeek does not appear to have fully moved beyond Nvidia. The company’s technical report reveals that it is using Chinese chips to run the model for inference, or when someone asks the model to complete a task. But Liu Zhiyuan, a computer science professor at Tsinghua University, told MIT Technology Review that DeepSeek appears to have adapted only part of V4’s training process for Chinese chips. The report does not say whether some key long-context features were adapted to domestic chips, so Liu says V4 may still have been trained mainly on Nvidia chips. Multiple sources who spoke on the condition of anonymity, due to political sensitivity around these issues, told MIT Technology Review that Chinese chips still don’t perform as well as Nvidia chips but are better suited for inference than training.
DeepSeek is also tying the future costs of V4 to this hardware shift. The company says V4-Pro prices could fall significantly after Huawei’s Ascend 950 supernodes begin shipping at scale in the second half of this year.
If that works, V4 could be an early sign that China is successfully building a parallel AI infrastructure.
CYBHI: Building the Future of Mental Health, One Student at a Time
High school students participating in the Youth Mental Health Academy are able to engage with peers, learn about mental health and discover professional passion.
The post CYBHI: Building the Future of Mental Health, One Student at a Time appeared first on Child Mind Institute.
One Biosciences Chooses Albany, NY, as Its U.S. Location
Paris-based One Biosciences, an Institut Curie-backed startup, plans to set up, staff, and equip a high-complexity lab and computational analytics operation in Albany, NY, as its first U.S. location.
Empire State Development is supporting this expansion with up to $525,000 in performance-based Excelsior Jobs Program tax credits in exchange for the company’s job commitments, which anticipate 42 life science jobs and $18 million in investments over the next five years.
Officials at One Biosciences say the company will bring its proprietary technology to the first-of-its-kind hub in Albany to address the unmet clinical and scientific needs to characterize the tumor ecosystem by means of a single-cell profiling approach.
“We are excited to accelerate support of our pharma, biotech, and academic collaborators through our AI-driven single-cell technologies, which will ultimately benefit physicians and their patients,” added Vincent Miller, MD, executive chairman, One Biosciences. “The local Albany life sciences ecosystem gives us access to a community of like-minded researchers and physicians committed to leveraging technology to improve health and is an ideal location from where to serve the U.S. globally.”
“Life science research and development is vital to creating the treatments that help people heal, survive and live longer,” said New York governor Kathy Hochul. “Through our targeted efforts, we are working to ensure that cutting edge companies like One Biosciences not only grow here, but that the next generation of medical breakthroughs happen in New York State.”
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New Low-Toxicity Transplant Method Reverses Type 1 Diabetes in Mice
Researchers at Stanford Medicine say they have developed a combination treatment method that cured or prevented type 1 diabetes in mouse models by pairing blood stem cell transplantation with pancreatic islet cell transplantation under a substantially reduced preconditioning regimen. The approach creates a mixed immune system from both donor and recipient cells, which stopped autoimmune destruction of insulin-producing cells while also producing long-term tolerance to the transplanted tissue. The findings, published in the Journal of Clinical Investigation Insight, show that reversing type 1 diabetes can be accomplished without chronic immunosuppression or the toxic conditioning via radiation or chemotherapy currently used for hematopoietic stem cell (HCT) transplantation.
“The possibility of translating these findings into humans is very exciting,” said senior author Seung K. Kim, MD, PhD, a professor of developmental biology at Stanford. “The key steps in our study—which result in animals with a hybrid immune system containing cells from both the donor and the recipient—are already being used in the clinic for other conditions. We believe this approach will be transformative for people with type 1 diabetes or other autoimmune diseases, as well as for those who need solid organ transplants.”
Type 1 diabetes is an autoimmune disease that attacks pancreatic islet cells. While islet transplantation can restore insulin production, it typically requires immunosuppressive drugs that carry risks including infection, malignancy, and organ damage. The Stanford team’s approach reduced these negative effects by inducing immune tolerance through mixed hematopoietic chimerism, a state in which donor and recipient immune cells coexist.
“Mixed hematopoietic chimerism after hematopoietic cell transplantation (HCT) can modulate the immune system and induce tolerance to allogeneic tissues,” the researchers wrote. “However, bone marrow conditioning-related toxicities preclude wider adoption of HCT for transplant allotolerance.”
The current findings by the Stanford team builds on a series of research initiatives beginning with work published in 2022, in which the researchers showed they could cure toxin-induced diabetes in mouse models using antibody-based immune conditioning combined with moderate radiation (200–300 cGy), followed by transplantation of donor-matched blood stem cells and islets. This study served as a proof of concept but used radiation at levels that are potentially toxic.
A November study published in JCI, along with the new research addressed two significant challenges for developing an effective transplantation protocol: autoimmune diabetes, in which the immune system targets islet cells, and the need to reduce conditioning toxicity. In the November study, the researchers added an immune-modulating drug used in autoimmune disease to their regimen. This change enabled the formation of a hybrid immune system that both accepted donor islets and prevented autoimmune attack. All treated mice were protected from developing diabetes, and those with already possessing the disease were cured.
To further reduce toxicity, the April study added additional agents, baricitinib, venetoclax, and an αCD47 antibody, to go with αCD117 antibody and transient T cell depletion. These agents were selected because they target distinct biological pathways involved in immune regulation and bone marrow niche clearance. Baricitinib, a JAK1/2 inhibitor, reduces inflammatory signaling and supports donor cell engraftment. Venetoclax promotes apoptosis of specific immune cells, and αCD47 disrupts a signaling pathway that normally protects cells from clearance, which helped in the removal of host stem cells to make space for donor cells.
“We systematically tested baricitinib (JAK1/2 inhibitor), venetoclax (Bcl2 inhibitor), and αCD47 antibody, agents in current clinical use, and quantified hematopoietic chimerism after HCT,” the researchers wrote. “Combined with αCD117 antibody, transient T cell depletion, and just 10 centigray (cGy) total body irradiation (TBI), these agents enabled durable mixed chimerism and matching allo-islet tolerance, to cure diabetes without evidence of [graft-versus-host disease].”
This new combination allowed researchers to reduce radiation exposure to 10 cGy, a fraction of the levels used in conventional bone marrow transplantation. Mice treated using this regimen showed stable engraftment of donor cells, maintained fertility, and experienced no graft-versus-host disease. They also remained insulin-independent for the duration of the study.
The findings provide a potential pathway toward clinical adoption, which could be speedier than usual since many of the agents used for this approach are already approved or under evaluation in humans.
Work at Stanford will continue in this area and will focus on testing the reduced-intensity regimen in autoimmune diabetes models, refining conditioning strategies, and exploring alternative sources of islet cells, including those derived from stem cells.
If successfully, translated to the clinic, this treatment regimen could reduce or eliminate the need for lifelong insulin therapy, while expanding the use of transplantation-based therapies across a wider set of patients.
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Effect of a WeChat Intervention Based on the Common-Sense Model on Breast Cancer–Related Lymphedema Preventive Behaviors: Quasi-Experimental Study
Background: Breast cancer–related lymphedema is the most prevalent postoperative complication among breast cancer survivors. Although mobile health tools are increasingly used for patient education, evidence supporting their efficacy in lymphedema prevention remains limited. Objective: This study aimed to evaluate the effectiveness of a WeChat-based intervention grounded in the common-sense model (CSM) in improving preventive behaviors, modifying illness perceptions, and reducing lymphedema incidence among breast cancer survivors and to validate the targets of the intervention. Methods: This study used a quasi-experimental design. Participants (N=192) were recruited from the breast cancer department of a cancer hospital in Guangzhou, China. The control group (n=98) received routine care. The intervention group (n=94) participated in a 3-month CSM-guided WeChat mini-program (“Nantian e-Care”) delivering tailored educational articles, exercise tutorials, arm circumference monitoring, and real-time nurse consultations. Outcomes, including preventive behaviors, illness perceptions, and lymphedema incidence, were assessed 1, 3, and 6 months post surgery. Generalized estimating equations were used for the analysis. Results: The intervention group exhibited significant improvements in lifestyle adjustments (Wald =6.9, =.03) and physical exercise adherence (Wald =6.9, =.03) compared with the control group. Illness perception, including identity (Wald =8.1, =.04), timeline cyclical (Wald =8.5, =.04), personal control (Wald =9.3, =.03), illness coherence (Wald =29.8, <.001), and behavioral (Wald =19.5, <.001) and physical factors (Wald =24.1, <.001) were markedly enhanced. Mechanistically, skin care improvements were driven by intervention effects, personal control, illness coherence, and behavioral attribution. Lifestyle changes were correlated with intervention and illness coherence. Adherence to physical exercise was not statistically significantly affected by the intervention, although a trend was observed. Critically, the intervention group demonstrated a lower incidence of lymphedema at 6 months (7.50% vs 16.48%, =3.9, =.048). Conclusions: The CSM-guided WeChat intervention effectively promoted preventive behaviors, optimized illness perceptions, and reduced lymphedema risk. These findings underscore the value of integrating theory-driven mobile health tools into postoperative care and highlight scalable strategies for chronic disease management in resource-limited settings. Trial Registration: Chinese Clinical Trial Registry ChiCTR2100048798; https://www.chictr.org.cn/showprojEN.html?proj=130038 International Registered Report Identifier (IRRID): RR2-10.1007/s00520-024-09078-x
Optimizing Navigation and Text Messaging Interventions to Promote Participation in a Food Is Medicine Program Among People Participating in Cardiac Rehabilitation: Human-Centered Design Study
Background: Food Is Medicine (FIM) programs integrate interventions such as medically tailored meals or produce prescriptions into clinical care. However, there is limited evidence on how to design these programs to be responsive to the lived experiences of participants to optimize initiation, engagement, and long-term retention. Objective: The objective of the study was to develop interventions to promote initiation, engagement, and retention in FIM programs that are responsive to the lived experiences of participants. Methods: We used a human-centered design approach to engage current and former cardiac rehabilitation participants in the development of interventions to promote participation and engagement in a FIM program. We recruited participants through invitations sent via electronic health record messages. We interviewed participants about their experiences, preferences, and unmet needs related to healthy eating and program design. Additionally, we elicited participant feedback on draft versions of patient navigator scripts and text messages promoting healthy eating habits. Results: A total of six participants identified themes across Theory of Planned Behavior constructs and emergent themes, including the cost of healthy food, cultural appropriateness, clear and timely communication, transportation, local food access, scheduling flexibility, the ability to provide feedback to the program, and personalized support for navigating food resources. Participants described financial strain as a key barrier to healthy eating and noted that social influence often shaped eating behaviors. Feedback on navigator scripts led to revisions clarifying program logistics, addressing barriers such as language and cultural dietary restrictions, and tailoring positive endorsements to individual health goals. Based on participant feedback, text messages were made more concise, reframed positively (eg, humor and gratitude), and encouraged to be warmer, with respectful language that is easy to understand, while avoiding stigmatizing or overly clinical phrasing. Participants also suggested that messages should reflect empathy and offer actionable information to increase trust and engagement with the program. Trust in the health care system and a sense of dignity in receiving food support emerged as critical themes influencing overall satisfaction and retention. Participants emphasized that endorsement from their health care team and cardiologist was important for building trust in the program. Communication between health care navigators and FIM navigators could help reduce the burden placed on patients to navigate food resources. Conclusions: Using a human-centered design approach, we gained insights about participant-identified needs for navigation scripts and text messages that are culturally sensitive and personalized to promote optimal participation in a FIM program.
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AI Learns to Predict Breast Cancer Risk from How Single Cells Respond to Pressure
A study headed by researchers at City of Hope and the University of California, Berkeley has found that physical and mechanical properties of normal human mammary epithelial cells can offer a “functional readout” of biological age and breast cancer susceptibility.
The team created a novel, high-throughput microfluidic platform that can assess women’s breast cancer risk at the cellular level. The mechano-node-pore sensing (mechano-NPS) platform, which the researchers claim is the first of its kind, squeezes individual breast epithelial cells, creating a taxing environment to measure how they deform, recover, and behave under stress.
Using the platform the researchers uncovered an unexpected insight, which is that breast cells appear to have a “mechanical age” separate from a person’s chronological age, demonstrated by how the cells physically respond to stress. For their study the team developed a machine learning classifier, MechanoAge, to estimate chronological age based on the mechanical phenotypes, and a biological age-based risk index, Mechano-RISQ.
“We learned that the older the mechanical age, as determined by how cells respond to being squeezed through our microfluidic device, the higher the risk for breast cancer,” explained Lydia Sohn, PhD, the Almy C. Maynard and Agnes Offield Maynard Chair in Mechanical Engineering at UC Berkeley. The researchers suggest that, as more than 90% of women lack a known genetic predisposition to or a family history of breast cancer, their innovative approach could fill a critical gap in risk assessment and save countless lives.
Sohn is co-senior author of the team’s published paper in eBioMedicine, titled “MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells,” in which they concluded, “Age-related biomechanical changes may represent a fundamental hallmark of cellular function, with distinct mechanical phenotypes underlying critical processes in aging, cancer, and potentially other diseases. Recognizing and utilizing these biomechanical markers could greatly enhance early detection, refine risk stratification, and improve targeted intervention strategies.”
Breast cancer is one of the most frequently diagnosed cancers worldwide and a leading cause of cancer-related mortality among women, the authors noted, and “… has long been the subject of efforts to improve risk stratification and early-detection strategies.”
About 6% of women who develop breast cancer carry known genetic mutations. But for women outside this group, risk is estimated indirectly based on population models or measurements like breast density. These approaches can both overestimate and underestimate women’s individual breast cancer risk, leading to over-screening, under-screening, unnecessary worry or missed warning signs. And despite significant progress in screening technologies and therapeutic interventions, accurately determining which individuals—particularly among those considered average risk—are most likely to develop breast cancer remains what the team calls “one of the most persistent challenges in oncology and public health.”
For these “ostensibly average-risk individuals,” the team added, “it remains difficult to identify those with latent risk that stems from cellular, molecular, and biophysical alterations that current models are not designed to capture.”
![Researchers Mark LaBarge of City of Hope (right) and Lydia Sohn (left) UC Berkeley [City of Hope and UC Berkeley]](https://www.genengnews.com/wp-content/uploads/2026/04/Low-Res_Sohn-LaBarge1-300x169.jpg)
Currently, there is no non-genetic test available that can identify women at higher risk for breast cancer. A downside to screening mammograms is that they can catch cancer only once it has begun to grow. Co-senior author, Mark LaBarge, PhD, a professor in the Department of Population Sciences at City of Hope, said “For women with a known genetic risk factor for breast cancer, there are things you can do like follow a higher-risk screening protocol. For everybody else, you’re left wondering, ‘Am I at high risk?’”
Emerging evidence links cellular aging and biophysical alterations with cancer susceptibility. For their reported study the researchers used the mechano-NPS platform to profile primary human mammary epithelial cells (HMECs) from women of different ages and risk backgrounds. They also developed a machine learning algorithm that identifies and measures cells that show signs of accelerated aging, quantifying an individual breast cancer risk score.
In this type of mechano-node-pore sensing, an electrical current is measured across a liquid-filled channel, much like how current is measured across a wire. As cells pass through, they disrupt the current, generating measurements about the cells’ size and shape. By making parts of the channel very narrow, researchers squeeze cells, then measure how long it takes each cell to recover its normal shape.
Machine-learning algorithms developed by the researchers were then used to detect differences in cells from older and younger women. The researchers found that the physical properties of breast cells changed with age; cells from older women were stiffer and took longer to bounce back after being squeezed.
Then came a surprising finding: a subset of younger women had cells that behaved like they came from older women. These cells came from women with genetic mutations that put them at high risk of breast cancer. Researchers then refined the algorithm to assign a risk score based on all the mechanical and physical properties measured in the cells. This algorithm successfully identified women with known genetic risks. Next the team used it to compare cells from healthy women, women who had family history of breast cancer and cells taken from the healthy breast of women with breast cancer in the other breast. “Normal epithelial cells from women with germline mutations, strong family history of cancer, or contralateral breast cancer exhibit mechanically aged phenotypes despite normal histology,” the investigators stated. “Together with prior molecular and epigenetic studies, these findings support a model in which accelerated biological aging of mammary epithelia may underpin breast cancer susceptibility across genetic and non-genetic risk groups.”
Using the MechanoAge platform, researchers shifted the scientific lens to the cellular level, calculating risk by looking for physical changes in individual cells. “Mechanical phenotyping captures an integrative cellular state that reflects underlying molecular networks rather than single biomarkers,” the team noted. “Mechano-RISQ offers a proof of principle approach for identifying individuals at elevated risk of breast cancer, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of mammary epithelial cell aging.”
“With accuracy, we were able to figure out which women were at high risk of breast cancer and which women didn’t seem to be,” LaBarge said. “By translating physical changes in cells into quantifiable data, this tool gives women something tangible to discuss with their doctors—not just risk estimates, but evidence drawn directly from their own cells.” In their paper the scientists further stated, “This approach could enable earlier, individualized risk stratification, particularly for women who lack identifiable high-risk mutations yet harbor susceptible tissue states.”
Importantly, the AI platform uses simple electronics that would be easy and affordable to replicate on a large scale. “Our team isn’t the first to measure the mechanical properties of cells; however, other approaches require advanced imaging technology that’s expensive, cumbersome and has limited availability,” said Sohn. “In contrast, MechanoAge uses computer chips that are simpler than an Apple Watch and ‘Radio Shack parts’ that are cheap and easy to assemble, potentially making the device highly scalable.”
While engineers study the aging of materials such as metals, concrete and polymers, this is the first time that mechanical age has been quantified in biological cells. The finding that cells have a “mechanical age” separate from the individual’s chronological age would not have been possible without MechanoAge.
This work grew out of more than 12 years of collaboration between the two labs, combining engineering innovation with cancer and aging biology. The long-term partnership enabled discoveries that neither group could have reached alone. “It’s a true collaboration. We’ve learned a lot from each other,” Sohn said. “In my view, this is what happens when you have a real collaboration that develops over a long time,” LaBarge added. “This result is not what we imagined at the beginning.”
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Single-Cell Atlas of the Prenatal Brain Reveals How Down Syndrome Reshapes Development
A cellular-resolution molecular map details how Down syndrome alters human brain development before birth. The study analyzed more than 100,000 nuclei from human prenatal neocortex samples collected across 26 pre-genotyped donors during gestational weeks 13 to 23—the only window during which all the cortical neurons a person will carry for their entire life are generated. The findings suggest that Down syndrome disrupts the developmental sequence of that process, creating shifts that may help explain later differences in cognition, learning, and sensory processing.
This work is published in Science in the paper, “A single-cell multiomic analysis identifies molecular and gene-regulatory mechanisms dysregulated in developing Down syndrome neocortex.“
“There’s a new level of detail here that had never existed before,” said Luis de la Torre-Ubieta, PhD, an assistant professor of psychiatry and biobehavioral sciences at UCLA and a member of the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research. “For the first time, we can really try to understand systematically what’s going on in the developing brain of individuals with Down syndrome.”
“No one had looked at the developing human brain in Down syndrome directly using single-cell genomics,” he continued.
The Down syndrome research field has historically focused on two areas: the adult brain and the disorder’s connection to neurodegeneration. What remained largely unexamined, despite clear indicators that Down syndrome is a developmental condition, was how the condition shapes the developing brain itself.
The development of the prenatal neocortex typically follows a tightly orchestrated sequence. Progenitor cells must first divide repeatedly to expand their own pool, building up a sufficient base for all future neurons. Only then do they begin differentiating into neurons, starting with deep-layer cell types and progressing toward upper-layer cells in a carefully timed order.
The study found that progenitor cells appear to rush prematurely into neuron production, depleting their own pool and skewing the balance of neuron types generated. Specifically, the researchers observed a relative increase in upper-layer intratelencephalic neurons and a reduction in deep-layer corticothalamic neurons.
Those two cell populations play fundamentally different roles: CT neurons project outward from the cortex—connecting to brain structures and to the spinal cord to govern sensation and movement; IT neurons wire within the cortex, connecting the two hemispheres and contributing to information processing. This finding offers a new hypothesis for how early developmental changes might contribute to the cognitive profile of the condition.
The finding also offers a new answer to a longstanding question in the field: Why do people with Down syndrome tend to have smaller brains? Earlier theories centered on elevated rates of cell death. The current study found less evidence of widespread neuronal death and instead points to the depletion of the progenitor pool.
The study employed paired single-nucleus multiomics to reconstruct not just a snapshot of which cells are present, but the regulatory programs that guide cell fate—and how those programs are disrupted in Down syndrome. Systems-level approaches also led them to uncover alterations in cell metabolism and changes in how the vasculature interacts with the developing nervous system, both of which could speed up neuron production.
The study’s significance extends beyond Down syndrome. The researchers specifically tested for overlap between the molecular disruptions they identified and the genetic risk signatures associated with other neurodevelopmental and neuropsychiatric conditions, including autism, epilepsy, and developmental delay. They found substantial convergence, particularly in the gene-regulatory networks governing the specification of IT versus CT neurons.
“Down syndrome could be a model to understand intellectual disability and neuropsychiatric disorders more broadly,” de la Torre-Ubieta said. “Also to uncover the shared biology underlying these conditions—because the mechanisms are often still unknown.”
The publication coincides with a companion paper from researchers at the University of Wisconsin-Madison, appearing in the same issue of Science. While the UCLA study focuses on the prenatal period, the Wisconsin team examined the postnatal brain, studying Down syndrome between approximately one and five years of age.
Together, the two papers provide a continuous molecular view of Down syndrome brain development from mid-gestation through infancy—a resource that did not previously exist and that the researchers expect will serve as a reference for their field for years to come.
While the researchers are careful to emphasize that the findings do not point to a near-term clinical application, the study provides the clearest picture yet of the cellular and molecular events that distinguish the Down syndrome brain during development, and a framework for identifying future therapeutic targets.
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