CellPub Date : 2026-09-17DOI: 10.1016/j.cell.2026.08.035
Jesse Zhang,Airol A. Ubas,Valentine Svensson,Nicole Thomas,Vishvak Subramanyam,Christopher Carpenter,Aidan Winters,Richard de Borja,Noam Teyssier,Neha Thakar,Umair Khan,Pooja V. Akella,Harper J. Green,John D. Thompson,Vuong Tran,Joseph Pangallo,Efthymia Papalexi,Ajay Sapre,Hoai Nguyen,Oliver Sanderson,Maria Nigos,Olivia Kaplan,Sarah Schroeder,Bryan Hariadi,Simone Marrujo,Crina Curca,Alec Salvino,Guillermo Gallareta Olivares,Ryan Koehler,Alexander Rosenberg,Charles Roco,Chiara Ricci-Tam,Matthew G. Jones,Alexander Dobin,Brian S. Plosky,Danielle E. Lyons,Daniele Merico,Nima Alidoust,Hani Goodarzi,Johnny Yu
{"title":"Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution","authors":"Jesse Zhang,Airol A. Ubas,Valentine Svensson,Nicole Thomas,Vishvak Subramanyam,Christopher Carpenter,Aidan Winters,Richard de Borja,Noam Teyssier,Neha Thakar,Umair Khan,Pooja V. Akella,Harper J. Green,John D. Thompson,Vuong Tran,Joseph Pangallo,Efthymia Papalexi,Ajay Sapre,Hoai Nguyen,Oliver Sanderson,Maria Nigos,Olivia Kaplan,Sarah Schroeder,Bryan Hariadi,Simone Marrujo,Crina Curca,Alec Salvino,Guillermo Gallareta Olivares,Ryan Koehler,Alexander Rosenberg,Charles Roco,Chiara Ricci-Tam,Matthew G. Jones,Alexander Dobin,Brian S. Plosky,Danielle E. Lyons,Daniele Merico,Nima Alidoust,Hani Goodarzi,Johnny Yu","doi":"10.1016/j.cell.2026.08.035","DOIUrl":"https://doi.org/10.1016/j.cell.2026.08.035","url":null,"abstract":"We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced “cell villages.” Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.","PeriodicalId":9656,"journal":{"name":"Cell","volume":"10 1","pages":""},"PeriodicalIF":64.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17Epub Date: 2026-07-27DOI: 10.1016/j.cell.2026.07.009
Fei Liu, Kai Wang, Hui Xu, Cheng Tang, Xian Shen, Meihao Wang, Lei Yang, Li Yang, Li Liu, Changxi Hu, Gen Li, Wei Wu, Zixing Zou, Bingzhou Li, Sian Liu, Jin Kang, Jungho Kong, Ting Li, Io Nam Wong, Xiaoying Huang, Gang Chen, Wenyang Lu, Ian Ziyar, Charlotte L Zhang, Yiwen Sun, Weihong Lin, Caiwen Ou, Manson Fok, Taiwa Hou, Winston Wang, Kanmin Xue, Yun Yin, Hao Zhu, Jonathan Gootenberg, Omar O Abudayyeh, Michael Karin, Alexandre Loupy, John E J Rasko, Trey Ideker, Huiyan Luo, Eric Oermann, Kang Zhang
{"title":"Advancing cancer detection and treatment using longitudinal routine clinical data.","authors":"Fei Liu, Kai Wang, Hui Xu, Cheng Tang, Xian Shen, Meihao Wang, Lei Yang, Li Yang, Li Liu, Changxi Hu, Gen Li, Wei Wu, Zixing Zou, Bingzhou Li, Sian Liu, Jin Kang, Jungho Kong, Ting Li, Io Nam Wong, Xiaoying Huang, Gang Chen, Wenyang Lu, Ian Ziyar, Charlotte L Zhang, Yiwen Sun, Weihong Lin, Caiwen Ou, Manson Fok, Taiwa Hou, Winston Wang, Kanmin Xue, Yun Yin, Hao Zhu, Jonathan Gootenberg, Omar O Abudayyeh, Michael Karin, Alexandre Loupy, John E J Rasko, Trey Ideker, Huiyan Luo, Eric Oermann, Kang Zhang","doi":"10.1016/j.cell.2026.07.009","DOIUrl":"10.1016/j.cell.2026.07.009","url":null,"abstract":"<p><p>Cancer management remains fragmented across its continuum, from late-stage diagnosis and salvage therapies to non-personalized surveillance. Here, we present Oncoformer, a unified multimodal transformer model trained on the China Oncology Multimodal Prediction and Surveillance Study (COMPASS) cohort (3.67 million individuals, 17.7 million clinical visits) and validated on independent external cohorts, including the UK Biobank. Oncoformer integrates longitudinal electronic health records with chest X-ray imaging to address multiple clinical tasks: pan-cancer diagnosis (area under the receiver operating characteristic curve [AUROC] = 0.956), future cancer prediction up to 1 year before diagnosis (AUROC = 0.869), tumor stage inference (mean AUROC > 0.90), patient-specific treatment-response forecasting, and recurrence-free survival stratification across ten cancer types (all p < 0.01). Staging predictions were independently validated against postoperative pathological endpoints and shown to converge on core cancer genomic pathways. By translating routine clinical data into a dynamic view of cancer evolution, Oncoformer provides a framework for risk-informed cancer prediction and treatment stratification using routine clinical data.</p>","PeriodicalId":9656,"journal":{"name":"Cell","volume":" ","pages":"5995-6011.e8"},"PeriodicalIF":45.1,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17DOI: 10.1016/j.cell.2026.08.026
Alex Zhavoronkov,Vladimir Naumov,Denis Sidorenko,Alex Aliper,Vladimir Aladinskiy,Ramin Hasani,Alexander Amini,Katerina Nasto,Mathieu Reymond,Rim Shayakhmetov,Zulfat Miftakhutdinov,Vadim N. Gladyshev,Fedor Galkin
{"title":"An open benchmark and language models for AI in aging biology","authors":"Alex Zhavoronkov,Vladimir Naumov,Denis Sidorenko,Alex Aliper,Vladimir Aladinskiy,Ramin Hasani,Alexander Amini,Katerina Nasto,Mathieu Reymond,Rim Shayakhmetov,Zulfat Miftakhutdinov,Vadim N. Gladyshev,Fedor Galkin","doi":"10.1016/j.cell.2026.08.026","DOIUrl":"https://doi.org/10.1016/j.cell.2026.08.026","url":null,"abstract":"Over the past two decades, human aging has been characterized across DNA methylation, transcriptomic, proteomic, and clinical modalities, yet no benchmark evaluates whether AI systems can interpret these heterogeneous data types in the context of aging biology. We introduce LongevityBench, an open suite of 17 tasks spanning five biodata domains, and use it to assess 18 frontier AI systems from six developer teams. Despite recent advances in AI, no single model dominates all tasks, with omics-based age prediction being the hardest task regardless of scale. To test whether these gaps can be closed without frontier-scale resources, we fine-tuned a family of five multitask Longevity-LLMs on domain-specific aging data. The compact (0.6B–9B parameters) Longevity-LLMs matched or exceeded far larger frontier systems on LongevityBench, showing that general-purpose language models can be adapted to structured-omics tasks. We publicly release the benchmark, models, and Longevity Claw, an agentic research interface for aging researchers.","PeriodicalId":9656,"journal":{"name":"Cell","volume":"10 1","pages":""},"PeriodicalIF":64.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17Epub Date: 2026-08-11DOI: 10.1016/j.cell.2026.07.024
Alessandro Mineo, Laura Blackie, Alexandra Milona, Emily Strachan, Pedro Gaspar, Nuno Miguel Luis, Alvaro Castano-Medina, Tomotsune Ameku, Bryon Silva, Anadika Prasad, Clara L Essmann, Jake Jacobson, Todd Fallesen, Vanessa Dao, Sanjay Khadayate, George Young, Filipe Cabreiro, Frank Schnorrer, Irene Miguel-Aliaga
{"title":"The sex and reproductive plasticity of intestinal muscles instruct gut size.","authors":"Alessandro Mineo, Laura Blackie, Alexandra Milona, Emily Strachan, Pedro Gaspar, Nuno Miguel Luis, Alvaro Castano-Medina, Tomotsune Ameku, Bryon Silva, Anadika Prasad, Clara L Essmann, Jake Jacobson, Todd Fallesen, Vanessa Dao, Sanjay Khadayate, George Young, Filipe Cabreiro, Frank Schnorrer, Irene Miguel-Aliaga","doi":"10.1016/j.cell.2026.07.024","DOIUrl":"10.1016/j.cell.2026.07.024","url":null,"abstract":"<p><p>Adult organs enlarge or regress in response to functional demands: changes commonly attributed to the dynamics of their resident stem cells. A striking example is the intestine, whose length varies with diet and reproductive status. We find that the size of the adult gut is extrinsically controlled by its surrounding muscles. In Drosophila, intestinal muscle differs in size and structure between sexes and grow in females during reproduction. By altering the sex or reproductive status of gut muscle cells, we establish that muscle-intrinsic sex determinants determine baseline sex differences in muscle myofibril width and organ size. Female muscles integrate hormonal and nutritional inputs during reproduction to elongate their sarcomeres; this reduces intestinal transit and further increases organ size. Such remodeling is adaptive and also occurs in mice. Our findings redefine the intestinal muscle as a responder to specific signals that adjust adult organ-level features and sustain reproductive demands.</p>","PeriodicalId":9656,"journal":{"name":"Cell","volume":" ","pages":"6078-6096.e9"},"PeriodicalIF":45.1,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148711609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17DOI: 10.1016/j.cell.2026.08.034
Alexander Maman,Yuko Nobe,Kristin A. Fluke,Katrin Weidenbach,Alexandra N. Harte,Brett W. Burkhart,Jakub Nowak,Priyadarshini Mukherjee,Deepak Kumar Choudhary,Anatoly Kustanovich,Ronit Nir,Katharina Vogl,Kiall Suazo,Danijela Radovanović,Donna Matzov,Walter Rossmanith,Jordan L. Meier,Moran Shalev-Benami,Dina Grohmann,Kylie D. Allen,Eric Westhof,Sebastian Glatt,Ruth A. Schmitz,Thomas J. Santangelo,Masato Taoka,Schraga Schwartz
{"title":"Phosphate backbone epitranscriptomics: Discovery of natural RNA phosphorothioates and their writer machinery","authors":"Alexander Maman,Yuko Nobe,Kristin A. Fluke,Katrin Weidenbach,Alexandra N. Harte,Brett W. Burkhart,Jakub Nowak,Priyadarshini Mukherjee,Deepak Kumar Choudhary,Anatoly Kustanovich,Ronit Nir,Katharina Vogl,Kiall Suazo,Danijela Radovanović,Donna Matzov,Walter Rossmanith,Jordan L. Meier,Moran Shalev-Benami,Dina Grohmann,Kylie D. Allen,Eric Westhof,Sebastian Glatt,Ruth A. Schmitz,Thomas J. Santangelo,Masato Taoka,Schraga Schwartz","doi":"10.1016/j.cell.2026.08.034","DOIUrl":"https://doi.org/10.1016/j.cell.2026.08.034","url":null,"abstract":"Over 150 modifications expand the RNA alphabet, yet all known natural modifications occur on nucleobases or ribose sugars, with none identified on the phosphate backbone. In contrast, phosphorothioates (PSs), in which a non-bridging phosphate oxygen is replaced with sulfur, are central to RNA therapeutics but have never been reliably detected in natural RNAs. Here, we develop sequencing- and mass spectrometry-based approaches to quantitatively map RNA PSs at single-nucleotide resolution. Across diverse archaeal species, we identify stereospecific PS modifications at rRNA and tRNA hotspots, which are dynamically regulated by sulfur availability and temperature. We uncover a diverse enzyme family that selectively modifies tRNA/rRNA substrates and whose evolutionary presence/absence matches the distribution of PSs. Enzyme loss causes inviability or temperature sensitivity, and functional analyses reveal that tRNA PSs enhance tRNA stability. These findings establish the first natural RNA phosphate-backbone modification and its enzymatic machinery, providing a foundation for mechanistic and functional exploration.","PeriodicalId":9656,"journal":{"name":"Cell","volume":"1 1","pages":""},"PeriodicalIF":64.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17DOI: 10.1016/j.cell.2026.08.040
John W. Pham
{"title":"Evolving Cell with evolving science","authors":"John W. Pham","doi":"10.1016/j.cell.2026.08.040","DOIUrl":"https://doi.org/10.1016/j.cell.2026.08.040","url":null,"abstract":"","PeriodicalId":9656,"journal":{"name":"Cell","volume":"13 1","pages":""},"PeriodicalIF":64.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"World models for biomedicine","authors":"Ayush Noori,Nic Fishman,Ada Fang,Lukas Fesser,Marinka Zitnik","doi":"10.1016/j.cell.2026.08.032","DOIUrl":"https://doi.org/10.1016/j.cell.2026.08.032","url":null,"abstract":"Biological systems respond dynamically to changes in their environment, and predicting these responses before complex interventions are applied could change how computational models are used in biology and medicine. In artificial intelligence, world models represent the state of a system and simulate how it evolves under alternative actions. We envision extending this paradigm to biomedicine, with world models that span molecular, cellular, tissue, and clinical scales and simulate biological trajectories under alternative interventions. Building such models will require methods that combine observational data with interventional and longitudinal measurements across modalities. Biomedical world models could open new modes of inquiry by predicting how biological systems respond to novel perturbations, searching for interventions that drive them toward desired states, and planning experimental campaigns over extended time horizons.","PeriodicalId":9656,"journal":{"name":"Cell","volume":"29 1","pages":""},"PeriodicalIF":64.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17Epub Date: 2026-07-28DOI: 10.1016/j.cell.2026.07.003
Yonatan Stelzer, Amos Tanay
{"title":"Why machines don't speak biology: Toward native biological language models.","authors":"Yonatan Stelzer, Amos Tanay","doi":"10.1016/j.cell.2026.07.003","DOIUrl":"10.1016/j.cell.2026.07.003","url":null,"abstract":"<p><p>Recent advances in AI inspire visions of universal models of biology. Yet living systems are evolved, emergent processes whose behaviors cannot be inferred from their parts alone. We propose grounding AI in canonical biological processes, constructing data-driven world models with explicit mechanistic links across molecules, cells, and their dynamics in space and time.</p>","PeriodicalId":9656,"journal":{"name":"Cell","volume":" ","pages":"5905-5908"},"PeriodicalIF":45.1,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17Epub Date: 2026-07-31DOI: 10.1016/j.cell.2026.07.007
Jerry C Wang, Damian Sanchez, Santhosh Arul, Belda Gülsuyu, Adnan Gopinadhan, Tanzila Mukhtar, Joseph Kim, John P Andrews, Marcus Alonso Cee Williams, Yewon Jung, Mana L Hashimoto, Chang N Kim, Shubhang Bhalla, Nathan A Ewing-Crystal, John M Bernabei, Vijay Letchuman, Alexander F Haddad, Kafi Hemphill, Shantel M Weinsheimer, Helen Kim, Kazim H Narsinh, Daniel L Cooke, Cathryn R Cadwell, Thomas Wälchli, Fanny M Elahi, Andrew C Yang, Peng He, Edward F Chang, Ari B Molofsky, Ethan A Winkler
{"title":"Spatial atlas of the human brain vasculature reveals specialized cell ensembles.","authors":"Jerry C Wang, Damian Sanchez, Santhosh Arul, Belda Gülsuyu, Adnan Gopinadhan, Tanzila Mukhtar, Joseph Kim, John P Andrews, Marcus Alonso Cee Williams, Yewon Jung, Mana L Hashimoto, Chang N Kim, Shubhang Bhalla, Nathan A Ewing-Crystal, John M Bernabei, Vijay Letchuman, Alexander F Haddad, Kafi Hemphill, Shantel M Weinsheimer, Helen Kim, Kazim H Narsinh, Daniel L Cooke, Cathryn R Cadwell, Thomas Wälchli, Fanny M Elahi, Andrew C Yang, Peng He, Edward F Chang, Ari B Molofsky, Ethan A Winkler","doi":"10.1016/j.cell.2026.07.007","DOIUrl":"10.1016/j.cell.2026.07.007","url":null,"abstract":"<p><p>The brain vasculature comprises diverse specialized cells that are essential for brain function, yet their spatial organization remains poorly understood. Here, we construct a comprehensive cerebrovascular cell atlas encompassing 314,535 transcriptomes that captures the arteriovenous axis and defines consensus cell states. We then perform spatial transcriptomics to map 1,529,740 cells across the human temporal cortex and hippocampus, uncovering stereotyped micro-communities termed vascular cell ensembles. These ensembles comprise specialized subsets of endothelial cells, mural cells, fibroblasts, and perivascular macrophages that align with the arteriovenous architecture to coordinate segment-specific functions, such as neurovascular coupling, blood-brain barrier transport, and immune surveillance. By overlaying genetic risk and pharmacologic reactivity, we identify ensemble-specific susceptibilities and candidate therapeutic targets across neurological diseases, including small vessel disease and stroke. This study provides a resource to dissect the spatial and functional logic underlying human cerebrovascular biology and establishes a blueprint for decoding neurological disease susceptibility and therapeutic response.</p>","PeriodicalId":9656,"journal":{"name":"Cell","volume":" ","pages":"6125-6152.e8"},"PeriodicalIF":45.1,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13577656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CellPub Date : 2026-09-17Epub Date: 2026-08-31DOI: 10.1016/j.cell.2026.07.052
Abhinav K Adduri, Dhruv Gautam, Beatrice Bevilacqua, Mohsen Naghipourfar, Alishba Imran, Rohan Shah, Noam Teyssier, Rishi Verma, Christopher Carpenter, Basak Eraslan, Francis Chalissery, Rajesh Ilango, Vishvak Subramanyam, Chiara Ricci-Tam, Sanjay Nagaraj, Aidan Winters, Mingze Dong, Stefanie Fellinger, Adam Krejci, Tilmann Burckstummer, Sravya Tirukkovular, Jeremy Sullivan, Brian S Plosky, Nicholas D Youngblut, Jure Leskovec, Luke A Gilbert, Silvana Konermann, Patrick D Hsu, Alexander Dobin, Dave P Burke, Hani Goodarzi, Yusuf H Roohani
{"title":"Predicting cellular responses to perturbation across diverse contexts with State.","authors":"Abhinav K Adduri, Dhruv Gautam, Beatrice Bevilacqua, Mohsen Naghipourfar, Alishba Imran, Rohan Shah, Noam Teyssier, Rishi Verma, Christopher Carpenter, Basak Eraslan, Francis Chalissery, Rajesh Ilango, Vishvak Subramanyam, Chiara Ricci-Tam, Sanjay Nagaraj, Aidan Winters, Mingze Dong, Stefanie Fellinger, Adam Krejci, Tilmann Burckstummer, Sravya Tirukkovular, Jeremy Sullivan, Brian S Plosky, Nicholas D Youngblut, Jure Leskovec, Luke A Gilbert, Silvana Konermann, Patrick D Hsu, Alexander Dobin, Dave P Burke, Hani Goodarzi, Yusuf H Roohani","doi":"10.1016/j.cell.2026.07.052","DOIUrl":"10.1016/j.cell.2026.07.052","url":null,"abstract":"<p><p>While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. State is trained using single-cell gene expression data to predict perturbation effects across sets of cells. State improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling, and chemical perturbations with significantly improved accuracy compared with baselines. Its cell embeddings trained on observational data from 167 million cells enable the identification of strong perturbations in cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that can be used to evaluate future models. Overall, the performance and flexibility of State set the stage for scaling the development of AI models of cell state.</p>","PeriodicalId":9656,"journal":{"name":"Cell","volume":" ","pages":"5914-5931.e20"},"PeriodicalIF":45.1,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148863409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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