Pub Date : 2026-08-12DOI: 10.1038/s41587-026-03259-1
Anna Schwager, Eve Moutaux, Adeline Durand, Alexandra Van Keymeulen, Amélie Viaene, Mélanie Miranda, Louisa Hadj Abed, Simon Besson-Girard, Marion Lambault, Délia Dupré, Grégoire Jouault, Melissa Saichi, Juliette Bertorello, Simon Dumas, Mathias Schwartz, Marthe Laisné, Justine Marsolier, Manuel Guthmann, Lorraine Bonneville, Urvashi Chitnavis, Déborah Bourc'his, Elisabetta Marangoni, Nicolas Servant, Cédric Blanpain, Leïla Perié, Céline Vallot
Simultaneous mapping of chromatin states and transcriptomes in rare cell populations is challenging, as most methods require thousands of cells and are limited in their ability to capture multiple molecular layers accurately within the same cell. Here we introduce OneCell CUT&Tag, a method that provides matched high-resolution epigenome, full-transcriptome and surface marker quantification from every cell, with input as low as one cell, without relying on computational aggregation into metacells. Using this approach, we uncover epigenomic priming of basal cells in the mammary gland and capture the dynamics of basal-to-luminal transdifferentiation, suggesting that epigenomic and transcriptional remodeling do not occur in complete synchrony during cell-fate conversion. Adaptable to diverse samples and tissues, this method also reveals the role of H3K27me3 in shaping zygotic expression programs. By matching multiple layers of molecular information within individual cells, OneCell CUT&Tag reveals how complementary regulatory layers shape cellular identity and state, enabling the study of rare biological samples in development and disease.
{"title":"Simultaneous single-cell profiling of chromatin, transcriptome and surface markers with OneCell CUT&Tag captures epigenomic reprogramming.","authors":"Anna Schwager, Eve Moutaux, Adeline Durand, Alexandra Van Keymeulen, Amélie Viaene, Mélanie Miranda, Louisa Hadj Abed, Simon Besson-Girard, Marion Lambault, Délia Dupré, Grégoire Jouault, Melissa Saichi, Juliette Bertorello, Simon Dumas, Mathias Schwartz, Marthe Laisné, Justine Marsolier, Manuel Guthmann, Lorraine Bonneville, Urvashi Chitnavis, Déborah Bourc'his, Elisabetta Marangoni, Nicolas Servant, Cédric Blanpain, Leïla Perié, Céline Vallot","doi":"10.1038/s41587-026-03259-1","DOIUrl":"10.1038/s41587-026-03259-1","url":null,"abstract":"<p><p>Simultaneous mapping of chromatin states and transcriptomes in rare cell populations is challenging, as most methods require thousands of cells and are limited in their ability to capture multiple molecular layers accurately within the same cell. Here we introduce OneCell CUT&Tag, a method that provides matched high-resolution epigenome, full-transcriptome and surface marker quantification from every cell, with input as low as one cell, without relying on computational aggregation into metacells. Using this approach, we uncover epigenomic priming of basal cells in the mammary gland and capture the dynamics of basal-to-luminal transdifferentiation, suggesting that epigenomic and transcriptional remodeling do not occur in complete synchrony during cell-fate conversion. Adaptable to diverse samples and tissues, this method also reveals the role of H3K27me3 in shaping zygotic expression programs. By matching multiple layers of molecular information within individual cells, OneCell CUT&Tag reveals how complementary regulatory layers shape cellular identity and state, enabling the study of rare biological samples in development and disease.</p>","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":" ","pages":""},"PeriodicalIF":44.5,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148727452","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}
Pub Date : 2026-08-12DOI: 10.1038/s41587-026-03144-x
Zhenhao Fang, Joshua Saskin, Seok-Hoon Lee, Charles Zou, Shan Xin, Xiaoyu Huang, Xingxin Pan, Chuanpeng Dong, Ardavan Abiri, Yanzhi Feng, Nidhi Sahni, S Stephen Yi, Lei Peng, Sidi Chen
Cell surface display (CSD) elements are a major class of bioengineering modules, but systematic rules linking CSD sequence to functional potency are lacking. Here we develop DeepSCan, a suite of deep learning and artificial intelligence models to systematically map CSD sequence-function relationships to reliably capture potent elements' conserved features and iteratively design and develop potent de novo CSD modules for mRNA antigen display. We experimentally quantify surface expression across >570 chimeric antigens, derive cell surface translocation strength labels for ~310 CSD elements and compile ~45 independent training datasets. We train three generations of DeepSCan models and evaluate their performance. Guided by these models, we computationally design 3,700 and experimentally validate approximately 120 generative CSDs, identifying 7 generative CSDs that match or exceed the cell surface translocation strength of the most potent naturally occurring CSDs. Enhanced surface displays are validated across multiple cell types and are functional in an antigen-specific CAR-T cytotoxicity assay.
{"title":"Discovery and design of potent cell surface display elements.","authors":"Zhenhao Fang, Joshua Saskin, Seok-Hoon Lee, Charles Zou, Shan Xin, Xiaoyu Huang, Xingxin Pan, Chuanpeng Dong, Ardavan Abiri, Yanzhi Feng, Nidhi Sahni, S Stephen Yi, Lei Peng, Sidi Chen","doi":"10.1038/s41587-026-03144-x","DOIUrl":"10.1038/s41587-026-03144-x","url":null,"abstract":"<p><p>Cell surface display (CSD) elements are a major class of bioengineering modules, but systematic rules linking CSD sequence to functional potency are lacking. Here we develop DeepSCan, a suite of deep learning and artificial intelligence models to systematically map CSD sequence-function relationships to reliably capture potent elements' conserved features and iteratively design and develop potent de novo CSD modules for mRNA antigen display. We experimentally quantify surface expression across >570 chimeric antigens, derive cell surface translocation strength labels for ~310 CSD elements and compile ~45 independent training datasets. We train three generations of DeepSCan models and evaluate their performance. Guided by these models, we computationally design 3,700 and experimentally validate approximately 120 generative CSDs, identifying 7 generative CSDs that match or exceed the cell surface translocation strength of the most potent naturally occurring CSDs. Enhanced surface displays are validated across multiple cell types and are functional in an antigen-specific CAR-T cytotoxicity assay.</p>","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":" ","pages":""},"PeriodicalIF":44.5,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148727023","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}
Pub Date : 2026-08-12DOI: 10.1038/s41587-026-03261-7
Alvin Hsu, Peter J Chen, Angus H Li, Colin F Hemez, Xin D Gao, Markus Terrey, Charlie Nelson, Vijay Selvam, Ana Cristian, Amber N McElroy, Benjamin J Steinbeck, Gandhar K Mahadeshwar, Smriti Pandey, Zachary Barsdale, Paul Z Chen, Alexander A Sousa, Holt A Sakai, Rachel A Silverstein, Ilias Morad, Ryan K Krueger, Max W Shen, Benjamin P Kleinstiver, Cathleen M Lutz, Jakub Tolar, Bruce R Blazar, Mark J Osborn, David R Liu
Prime editing (PE) can make specific local changes to genomic DNA in living systems but its efficient application currently requires extensive optimization of PE guide RNA (pegRNA) sequences. Here we present OptiPrime, a machine learning model of PE efficiency based on current understanding of PE mechanisms. OptiPrime achieves state-of-the-art accuracy on PE efficiency prediction and enables prediction of nicking guide RNA (PE3) and dual pegRNA (twinPE) outcomes. We validate that OptiPrime has learned the determinants of mammalian mismatch repair (MMR) and is well suited for nominating MMR-evasive silent edits that improve PE efficiency. We demonstrate the use of OptiPrime in a variety of prospective therapeutic contexts in primary human and mouse cells. Lastly, we show that OptiPrime can be used to achieve streamlined and efficient in vivo correction of a pathogenic mutation in the brain of a mouse model of KIF1A-associated neurological disorder. We provide a webserver for OptiPrime ( https://optipri.me/ ) as a community resource.
{"title":"Mechanistic machine learning for prediction of prime editing outcomes.","authors":"Alvin Hsu, Peter J Chen, Angus H Li, Colin F Hemez, Xin D Gao, Markus Terrey, Charlie Nelson, Vijay Selvam, Ana Cristian, Amber N McElroy, Benjamin J Steinbeck, Gandhar K Mahadeshwar, Smriti Pandey, Zachary Barsdale, Paul Z Chen, Alexander A Sousa, Holt A Sakai, Rachel A Silverstein, Ilias Morad, Ryan K Krueger, Max W Shen, Benjamin P Kleinstiver, Cathleen M Lutz, Jakub Tolar, Bruce R Blazar, Mark J Osborn, David R Liu","doi":"10.1038/s41587-026-03261-7","DOIUrl":"10.1038/s41587-026-03261-7","url":null,"abstract":"<p><p>Prime editing (PE) can make specific local changes to genomic DNA in living systems but its efficient application currently requires extensive optimization of PE guide RNA (pegRNA) sequences. Here we present OptiPrime, a machine learning model of PE efficiency based on current understanding of PE mechanisms. OptiPrime achieves state-of-the-art accuracy on PE efficiency prediction and enables prediction of nicking guide RNA (PE3) and dual pegRNA (twinPE) outcomes. We validate that OptiPrime has learned the determinants of mammalian mismatch repair (MMR) and is well suited for nominating MMR-evasive silent edits that improve PE efficiency. We demonstrate the use of OptiPrime in a variety of prospective therapeutic contexts in primary human and mouse cells. Lastly, we show that OptiPrime can be used to achieve streamlined and efficient in vivo correction of a pathogenic mutation in the brain of a mouse model of KIF1A-associated neurological disorder. We provide a webserver for OptiPrime ( https://optipri.me/ ) as a community resource.</p>","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":" ","pages":""},"PeriodicalIF":44.5,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148725415","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}
Pub Date : 2026-08-11DOI: 10.1038/s41587-026-03265-3
Carrie Arnold
From circulating microRNAs to menstrual-fluid transcriptomics, biotech companies are betting on radically different biomarkers to detect a disease that still lacks a clear molecular definition.
{"title":"Menstrualome aids difficult endometriosis diagnosis","authors":"Carrie Arnold","doi":"10.1038/s41587-026-03265-3","DOIUrl":"10.1038/s41587-026-03265-3","url":null,"abstract":"From circulating microRNAs to menstrual-fluid transcriptomics, biotech companies are betting on radically different biomarkers to detect a disease that still lacks a clear molecular definition.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"44 8","pages":"1247-1249"},"PeriodicalIF":44.5,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148704540","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}
Pub Date : 2026-08-11DOI: 10.1038/s41587-026-03275-1
Dario Ummarino
{"title":"Killing resistant tumors with a click","authors":"Dario Ummarino","doi":"10.1038/s41587-026-03275-1","DOIUrl":"10.1038/s41587-026-03275-1","url":null,"abstract":"","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"44 8","pages":"1281-1281"},"PeriodicalIF":44.5,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148704572","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}
Pub Date : 2026-08-11DOI: 10.1038/s41587-026-03257-3
Recent moves of note in and around the biotech and pharma industries.
生物技术和制药行业的最新动向。
{"title":"People","authors":"","doi":"10.1038/s41587-026-03257-3","DOIUrl":"10.1038/s41587-026-03257-3","url":null,"abstract":"Recent moves of note in and around the biotech and pharma industries.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"44 8","pages":"1412-1412"},"PeriodicalIF":44.5,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148704571","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}
Pub Date : 2026-08-11DOI: 10.1038/s41587-026-03242-w
Garth W. Strohbehn, S. Sean Tu, Xiaodong Wang, Mark J. Ratain
Obvious chemical substitutions could be used to generate powerful new composition-of-matter patents, highlighting the need for patent and antitrust policy reforms that better separate strategic pursuit of exclusivity from true therapeutic innovation.
{"title":"From cabozantinib to zanzalintinib: obvious chemical modifications as a new frontier in product hopping","authors":"Garth W. Strohbehn, S. Sean Tu, Xiaodong Wang, Mark J. Ratain","doi":"10.1038/s41587-026-03242-w","DOIUrl":"10.1038/s41587-026-03242-w","url":null,"abstract":"Obvious chemical substitutions could be used to generate powerful new composition-of-matter patents, highlighting the need for patent and antitrust policy reforms that better separate strategic pursuit of exclusivity from true therapeutic innovation.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"44 8","pages":"1274-1279"},"PeriodicalIF":44.5,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148704537","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}
Pub Date : 2026-08-11DOI: 10.1038/s41587-026-03271-5
{"title":"Tech giants plunge into life sciences","authors":"","doi":"10.1038/s41587-026-03271-5","DOIUrl":"10.1038/s41587-026-03271-5","url":null,"abstract":"","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"44 8","pages":"1248-1248"},"PeriodicalIF":44.5,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148704568","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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