Pub Date : 2025-12-08DOI: 10.1038/s41592-025-02971-1
R. Clay Reid
Functional connectomics is a new approach, involving calcium imaging of neuronal activity followed by correlated electron microscopy connectomics of the same neurons, that relates connections made by neurons to their in vivo function. I believe that this combined approach for studying structure and function will continue with ever-larger networks, including entire nervous systems.
{"title":"The past and future of functional connectomics","authors":"R. Clay Reid","doi":"10.1038/s41592-025-02971-1","DOIUrl":"10.1038/s41592-025-02971-1","url":null,"abstract":"Functional connectomics is a new approach, involving calcium imaging of neuronal activity followed by correlated electron microscopy connectomics of the same neurons, that relates connections made by neurons to their in vivo function. I believe that this combined approach for studying structure and function will continue with ever-larger networks, including entire nervous systems.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2479-2480"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708771","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 : 2025-12-08DOI: 10.1038/s41592-025-02903-z
Palash Sashittal, Richard Y Zhang, Benjamin K Law, Henri Schmidt, Alexander Strzalkowski, Adriano Bolondi, Michelle M Chan, Benjamin J Raphael
During development, cells differentiate through a hierarchy of increasingly restricted cell types, a process that is summarized by a cell differentiation map. Recent technologies profile lineages and cell types at scale, but existing methods to infer cell differentiation maps from these data rely on heuristic models with restrictive assumptions about the developmental process. Here we introduce a quantitative framework to evaluate cell differentiation maps and develop an algorithm, called Carta, that infers an optimal differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the tradeoff between the complexity of the map and the number of unobserved cell type transitions on the lineage tree. We show that, in models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods, including convergent differentiation of cell types, progenitor differentiation dynamics and new intermediate progenitors.
{"title":"Inferring cell differentiation maps from lineage tracing data.","authors":"Palash Sashittal, Richard Y Zhang, Benjamin K Law, Henri Schmidt, Alexander Strzalkowski, Adriano Bolondi, Michelle M Chan, Benjamin J Raphael","doi":"10.1038/s41592-025-02903-z","DOIUrl":"10.1038/s41592-025-02903-z","url":null,"abstract":"<p><p>During development, cells differentiate through a hierarchy of increasingly restricted cell types, a process that is summarized by a cell differentiation map. Recent technologies profile lineages and cell types at scale, but existing methods to infer cell differentiation maps from these data rely on heuristic models with restrictive assumptions about the developmental process. Here we introduce a quantitative framework to evaluate cell differentiation maps and develop an algorithm, called Carta, that infers an optimal differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the tradeoff between the complexity of the map and the number of unobserved cell type transitions on the lineage tree. We show that, in models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods, including convergent differentiation of cell types, progenitor differentiation dynamics and new intermediate progenitors.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708637","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 : 2025-12-08DOI: 10.1038/s41592-025-02936-4
Lionel Christiaen
The ascidian tunicate Ciona, one of the closest relatives of the vertebrates, inhabits shallow temperate waters in the worldwide ocean. A unique combination of simple stereotyped embryogenesis, regulative post-embryonic stages and ecologically relevant diversity makes Ciona a premier model for marine systems life sciences, from cells and molecules to populations and ecosystems.
{"title":"The tunicate Ciona","authors":"Lionel Christiaen","doi":"10.1038/s41592-025-02936-4","DOIUrl":"10.1038/s41592-025-02936-4","url":null,"abstract":"The ascidian tunicate Ciona, one of the closest relatives of the vertebrates, inhabits shallow temperate waters in the worldwide ocean. A unique combination of simple stereotyped embryogenesis, regulative post-embryonic stages and ecologically relevant diversity makes Ciona a premier model for marine systems life sciences, from cells and molecules to populations and ecosystems.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2467-2469"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708738","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 : 2025-12-08DOI: 10.1038/s41592-025-02879-w
Markus Marks, Uriah Israel, Rohit Dilip, Qilin Li, Changhua Yu, Emily Laubscher, Ahamed Iqbal, Elora Pradhan, Ada Ates, Martin Abt, Caitlin Brown, Edward Pao, Shenyi Li, Alexander Pearson-Goulart, Pietro Perona, Georgia Gkioxari, Ross Barnowski, Yisong Yue, David Van Valen
Cells are a fundamental unit of biological organization, and identifying them in imaging data—cell segmentation—is a critical task for various cellular imaging experiments. Although deep learning methods have led to substantial progress on this problem, most models are specialist models that work well for specific domains but cannot be applied across domains or scale well with large amounts of data. Here we present CellSAM, a universal model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells, yeast and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. Additionally, we demonstrate how CellSAM can be applied across diverse bioimage analysis workflows. A deployed version of CellSAM is available at https://cellsam.deepcell.org/ . CellSAM uses an object detector, CellFinder, to detect cells and prompt the Segment Anything Model (SAM) to generate segmentations. This universal model achieves human-level performance across a range of bioimaging data encompassing mammalian cells, yeast and bacteria.
{"title":"CellSAM: a foundation model for cell segmentation","authors":"Markus Marks, Uriah Israel, Rohit Dilip, Qilin Li, Changhua Yu, Emily Laubscher, Ahamed Iqbal, Elora Pradhan, Ada Ates, Martin Abt, Caitlin Brown, Edward Pao, Shenyi Li, Alexander Pearson-Goulart, Pietro Perona, Georgia Gkioxari, Ross Barnowski, Yisong Yue, David Van Valen","doi":"10.1038/s41592-025-02879-w","DOIUrl":"10.1038/s41592-025-02879-w","url":null,"abstract":"Cells are a fundamental unit of biological organization, and identifying them in imaging data—cell segmentation—is a critical task for various cellular imaging experiments. Although deep learning methods have led to substantial progress on this problem, most models are specialist models that work well for specific domains but cannot be applied across domains or scale well with large amounts of data. Here we present CellSAM, a universal model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells, yeast and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. Additionally, we demonstrate how CellSAM can be applied across diverse bioimage analysis workflows. A deployed version of CellSAM is available at https://cellsam.deepcell.org/ . CellSAM uses an object detector, CellFinder, to detect cells and prompt the Segment Anything Model (SAM) to generate segmentations. This universal model achieves human-level performance across a range of bioimaging data encompassing mammalian cells, yeast and bacteria.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2585-2593"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02879-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708578","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}
Pub Date : 2025-12-08DOI: 10.1038/s41592-025-02984-w
Mary Melissa Roland, Alok V. Joglekar
TIRTL-seq is an innovative method that allows efficient and cost-effective identification of αβ TCR clones from millions of T cells with the aid of a pairing algorithm called T-SHELL, which provides high accuracy and throughput in sequencing paired TCR clones.
{"title":"TIRTL-seq: heroes with T-SHELL","authors":"Mary Melissa Roland, Alok V. Joglekar","doi":"10.1038/s41592-025-02984-w","DOIUrl":"10.1038/s41592-025-02984-w","url":null,"abstract":"TIRTL-seq is an innovative method that allows efficient and cost-effective identification of αβ TCR clones from millions of T cells with the aid of a pairing algorithm called T-SHELL, which provides high accuracy and throughput in sequencing paired TCR clones.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"16-17"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708717","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 : 2025-12-08DOI: 10.1038/s41592-025-02943-5
Ramin Khajeh, Wei-Chung Allen Lee
Connectomics, the comprehensive mapping of neural circuits at nanoscale resolution, has historically relied on electron microscopy (EM), both transmission (TEM) and scanning (SEM). However, as connectomics scales towards larger brain volumes and whole mammalian brains, substantial technical challenges emerge. Here, we highlight key challenges and advancing approaches that hold promise, particularly those that integrate three-dimensional, multi-resolution and time-resolved imaging to capture both long-range and local wiring, down to supramolecular resolution.
{"title":"Connectomics beyond electron microscopy","authors":"Ramin Khajeh, Wei-Chung Allen Lee","doi":"10.1038/s41592-025-02943-5","DOIUrl":"10.1038/s41592-025-02943-5","url":null,"abstract":"Connectomics, the comprehensive mapping of neural circuits at nanoscale resolution, has historically relied on electron microscopy (EM), both transmission (TEM) and scanning (SEM). However, as connectomics scales towards larger brain volumes and whole mammalian brains, substantial technical challenges emerge. Here, we highlight key challenges and advancing approaches that hold promise, particularly those that integrate three-dimensional, multi-resolution and time-resolved imaging to capture both long-range and local wiring, down to supramolecular resolution.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2487-2489"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708615","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 : 2025-12-08DOI: 10.1038/s41592-025-02957-z
Lei Tang
Protein editors complement genome editing tools by enabling direct modifications to protein molecules.
蛋白质编辑器通过直接修改蛋白质分子来补充基因组编辑工具。
{"title":"Protein editing","authors":"Lei Tang","doi":"10.1038/s41592-025-02957-z","DOIUrl":"10.1038/s41592-025-02957-z","url":null,"abstract":"Protein editors complement genome editing tools by enabling direct modifications to protein molecules.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2496-2496"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708694","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 : 2025-12-08DOI: 10.1038/s41592-025-02951-5
Lin Tang
Virtual cells based on artificial intelligence models are on the horizon
基于人工智能模型的虚拟细胞即将出现
{"title":"The virtual cell","authors":"Lin Tang","doi":"10.1038/s41592-025-02951-5","DOIUrl":"10.1038/s41592-025-02951-5","url":null,"abstract":"Virtual cells based on artificial intelligence models are on the horizon","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2493-2493"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708763","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：