Nature MethodsPub Date : 2025-10-10DOI: 10.1038/s41592-025-02851-8
{"title":"Deciphering single-cell epigenomic language with a foundation model.","authors":"","doi":"10.1038/s41592-025-02851-8","DOIUrl":"https://doi.org/10.1038/s41592-025-02851-8","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145275289","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}
Nature MethodsPub Date : 2025-10-09DOI: 10.1038/s41592-025-02852-7
Claudia Fichtel, Daniel Huber
{"title":"Mouse lemurs","authors":"Claudia Fichtel, Daniel Huber","doi":"10.1038/s41592-025-02852-7","DOIUrl":"10.1038/s41592-025-02852-7","url":null,"abstract":"Studying the gray mouse lemur (Microcebus murinus), one of the world’s smallest primates, in its natural habitat and in the laboratory provides unique perspectives on primate brain evolution, cognition, aging and neurodegenerative diseases, on an accelerated timescale and at a substantially lower cost as compared with larger primate models.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"1999-2001"},"PeriodicalIF":32.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248865","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}
Nature MethodsPub Date : 2025-10-09DOI: 10.1038/s41592-025-02874-1
Arunima Singh
{"title":"BioEmu is a biomolecular emulator for sampling protein structure ensembles","authors":"Arunima Singh","doi":"10.1038/s41592-025-02874-1","DOIUrl":"10.1038/s41592-025-02874-1","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2008-2008"},"PeriodicalIF":32.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248866","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}
Nature MethodsPub Date : 2025-10-09DOI: 10.1038/s41592-025-02872-3
Lei Tang
{"title":"Enhancing prime editing via AI-guided binder design","authors":"Lei Tang","doi":"10.1038/s41592-025-02872-3","DOIUrl":"10.1038/s41592-025-02872-3","url":null,"abstract":"AI-designed binders can enhance prime editing performance by inhibiting the DNA mismatch repair pathway.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2007-2007"},"PeriodicalIF":32.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248864","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}
Nature MethodsPub Date : 2025-10-09DOI: 10.1038/s41592-025-02883-0
{"title":"What’s new at Nature Methods","authors":"","doi":"10.1038/s41592-025-02883-0","DOIUrl":"10.1038/s41592-025-02883-0","url":null,"abstract":"Team news, editorial projects and initiatives, plus a preview of what’s to come in 2026.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"1997-1997"},"PeriodicalIF":32.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02883-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248862","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}
Nature MethodsPub Date : 2025-10-08DOI: 10.1038/s41592-025-02845-6
Max A Betjes, Rutger N U Kok, Sander J Tans, Jeroen S van Zon
{"title":"Cell tracking with accurate error prediction.","authors":"Max A Betjes, Rutger N U Kok, Sander J Tans, Jeroen S van Zon","doi":"10.1038/s41592-025-02845-6","DOIUrl":"https://doi.org/10.1038/s41592-025-02845-6","url":null,"abstract":"<p><p>Cell tracking is an indispensable tool for studying development by time-lapse imaging. However, existing cell trackers cannot assign confidence to predicted tracks, which prohibits fully automated analysis without manual curation. We present a fundamental advance: an algorithm that combines neural networks with statistical physics to determine cell tracks with error probabilities for each step in the track. From these, we can obtain error probabilities for any tracking feature, from cell cycles to lineage trees, that function like P values in data interpretation. Our method, OrganoidTracker 2.0, greatly speeds up tracking analysis by limiting manual curation to rare low-confidence tracking steps. Importantly, it also enables fully automated analysis by retaining only high-confidence track segments, which we demonstrate by analyzing cell cycles and differentiation events at scale for thousands of cells in multiple intestinal organoids. Our approach brings cell dynamics-based organoid screening within reach and enables transparent reporting of cell-tracking results and associated scientific claims.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251997","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}
Nature MethodsPub Date : 2025-10-08DOI: 10.1038/s41592-025-02826-9
Josef Lorenz Rumberger, Noah F. Greenwald, Jolene S. Ranek, Potchara Boonrat, Cameron Walker, Jannik Franzen, Sricharan Reddy Varra, Alex Kong, Cameron Sowers, Candace C. Liu, Inna Averbukh, Hadeesha Piyadasa, Rami Vanguri, Iris Nederlof, Xuefei Julie Wang, David Van Valen, Marleen Kok, Sean C. Bendall, Travis J. Hollmann, Dagmar Kainmueller, Michael Angelo
{"title":"Automated classification of cellular expression in multiplexed imaging data with Nimbus","authors":"Josef Lorenz Rumberger, Noah F. Greenwald, Jolene S. Ranek, Potchara Boonrat, Cameron Walker, Jannik Franzen, Sricharan Reddy Varra, Alex Kong, Cameron Sowers, Candace C. Liu, Inna Averbukh, Hadeesha Piyadasa, Rami Vanguri, Iris Nederlof, Xuefei Julie Wang, David Van Valen, Marleen Kok, Sean C. Bendall, Travis J. Hollmann, Dagmar Kainmueller, Michael Angelo","doi":"10.1038/s41592-025-02826-9","DOIUrl":"10.1038/s41592-025-02826-9","url":null,"abstract":"Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pretrained model that uses the underlying images to classify marker expression of individual cells as positive or negative across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M, and that Nimbus matches or exceeds the accuracy of previous approaches that must be retrained on each dataset. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference . Nimbus, a deep learning model, uses a large multiplexed imaging dataset to predict the likelihood of marker positivity in single cells.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2161-2170"},"PeriodicalIF":32.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248863","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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