Pub Date : 2026-01-05DOI: 10.1038/s41592-025-02991-x
Vivien Marx
Since the chance discovery of nanobodies in the late 1980s, their uses and applications have kept growing. Researchers are now exploring new ways to harness nanobody versatility.
{"title":"What nanobodies can do for you","authors":"Vivien Marx","doi":"10.1038/s41592-025-02991-x","DOIUrl":"10.1038/s41592-025-02991-x","url":null,"abstract":"Since the chance discovery of nanobodies in the late 1980s, their uses and applications have kept growing. Researchers are now exploring new ways to harness nanobody versatility.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"11-15"},"PeriodicalIF":32.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906264","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":"Conference daycare","authors":"Vivien Marx","doi":"10.1038/s41592-025-02990-y","DOIUrl":"10.1038/s41592-025-02990-y","url":null,"abstract":"Not all conferences offer childcare, but when they do, these scientists, who are also mothers, rejoice. The toys are pretty good, too.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"2-2"},"PeriodicalIF":32.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906203","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 : 2025-12-31DOI: 10.1038/s41592-025-02995-7
Henry Pinkard, Nils Norlin
{"title":"The missing data for intelligent scientific instruments.","authors":"Henry Pinkard, Nils Norlin","doi":"10.1038/s41592-025-02995-7","DOIUrl":"https://doi.org/10.1038/s41592-025-02995-7","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878879","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 : 2025-12-31DOI: 10.1038/s41592-025-02909-7
Lukas Heumos, Yuge Ji, Lilly May, Tessa D. Green, Stefan Peidli, Xinyue Zhang, Xichen Wu, Johannes Ostner, Antonia Schumacher, Karin Hrovatin, Michaela Müller, Faye Chong, Gregor Sturm, Alejandro Tejada, Emma Dann, Mingze Dong, Gonçalo Pinto, Mojtaba Bahrami, Ilan Gold, Sergei Rybakov, Altana Namsaraeva, Amir Ali Moinfar, Zihe Zheng, Eljas Roellin, Isra Mekki, Chris Sander, Mohammad Lotfollahi, Herbert B. Schiller, Fabian J. Theis
Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size and complexity of such studies require a scalable analysis framework that takes existing biological context into account. Here we present pertpy, a Python-based modular framework for the analysis of large-scale single-cell perturbation experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods, such as automatic metadata annotation or perturbation distances, to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing single-cell analysis libraries and is designed to be easily extended. Pertpy serves as a one-stop and performant framework for single-cell perturbation data analysis.
{"title":"Pertpy: an end-to-end framework for perturbation analysis","authors":"Lukas Heumos, Yuge Ji, Lilly May, Tessa D. Green, Stefan Peidli, Xinyue Zhang, Xichen Wu, Johannes Ostner, Antonia Schumacher, Karin Hrovatin, Michaela Müller, Faye Chong, Gregor Sturm, Alejandro Tejada, Emma Dann, Mingze Dong, Gonçalo Pinto, Mojtaba Bahrami, Ilan Gold, Sergei Rybakov, Altana Namsaraeva, Amir Ali Moinfar, Zihe Zheng, Eljas Roellin, Isra Mekki, Chris Sander, Mohammad Lotfollahi, Herbert B. Schiller, Fabian J. Theis","doi":"10.1038/s41592-025-02909-7","DOIUrl":"10.1038/s41592-025-02909-7","url":null,"abstract":"Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size and complexity of such studies require a scalable analysis framework that takes existing biological context into account. Here we present pertpy, a Python-based modular framework for the analysis of large-scale single-cell perturbation experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods, such as automatic metadata annotation or perturbation distances, to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing single-cell analysis libraries and is designed to be easily extended. Pertpy serves as a one-stop and performant framework for single-cell perturbation data analysis.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"350-359"},"PeriodicalIF":32.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02909-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878839","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}
Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation demonstrates that SpatialZ accurately preserves cell identities, gene expression patterns and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells. This dense atlas enables new capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, 3D mapping of query tissue sections, and discovery of 3D spatial molecular architectures through new synthesized views. To demonstrate its extensibility beyond transcriptomics, we applied SpatialZ to imaging mass cytometry data from human breast cancer, successfully deciphering 3D spatial gradients within the tumor microenvironment. Our approach generates cell atlases that provide previously unattainable 3D resolution of spatial molecular landscapes. SpatialZ generates virtual single-cell spatial transcriptomics slices between experimentally measured sections, enabling accurate and efficient building of 3D cell atlases of different tissues.
{"title":"Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases","authors":"Senlin Lin, Zhikang Wang, Yan Cui, Qi Zou, Chuangyi Han, Rui Yan, Zhidong Yang, Wei Zhang, Rui Gao, Jiangning Song, Michael Q. Zhang, Hanchuan Peng, Jintai Yu, Jianfeng Feng, Yi Zhao, Zhiyuan Yuan","doi":"10.1038/s41592-025-02969-9","DOIUrl":"10.1038/s41592-025-02969-9","url":null,"abstract":"Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation demonstrates that SpatialZ accurately preserves cell identities, gene expression patterns and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells. This dense atlas enables new capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, 3D mapping of query tissue sections, and discovery of 3D spatial molecular architectures through new synthesized views. To demonstrate its extensibility beyond transcriptomics, we applied SpatialZ to imaging mass cytometry data from human breast cancer, successfully deciphering 3D spatial gradients within the tumor microenvironment. Our approach generates cell atlases that provide previously unattainable 3D resolution of spatial molecular landscapes. SpatialZ generates virtual single-cell spatial transcriptomics slices between experimentally measured sections, enabling accurate and efficient building of 3D cell atlases of different tissues.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"360-372"},"PeriodicalIF":32.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878825","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 : 2025-12-31DOI: 10.1038/s41592-025-02871-4
Sen Li, Xiangjie Meng, Bo Zhou, Wenfeng Tian, Liangyi Chen, Yang Zhang
Super-resolution microscopy (SRM) has revolutionized nanoscale cellular imaging, providing detailed insights into cellular architecture, organelle organization, molecular interactions and subcellular dynamics. Artificial intelligence (AI) has shown its transformative potential for improving SRM to advance our understanding of complex cellular structures and dynamics. This Review begins by offering a comprehensive overview of AI techniques in computer vision, focusing on their application to SRM. Additionally, this Review provides a thorough summary of publicly available code and datasets that can support the development and evaluation of AI-empowered SRM. Notably, many AI techniques in the domain of computer vision remain underexplored in SRM. The ongoing evolution of AI promises to unlock new potential in SRM, and the integration of cutting-edge AI technologies is poised to pioneer breakthroughs in nanoscale cellular imaging.
{"title":"AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.","authors":"Sen Li, Xiangjie Meng, Bo Zhou, Wenfeng Tian, Liangyi Chen, Yang Zhang","doi":"10.1038/s41592-025-02871-4","DOIUrl":"10.1038/s41592-025-02871-4","url":null,"abstract":"<p><p>Super-resolution microscopy (SRM) has revolutionized nanoscale cellular imaging, providing detailed insights into cellular architecture, organelle organization, molecular interactions and subcellular dynamics. Artificial intelligence (AI) has shown its transformative potential for improving SRM to advance our understanding of complex cellular structures and dynamics. This Review begins by offering a comprehensive overview of AI techniques in computer vision, focusing on their application to SRM. Additionally, this Review provides a thorough summary of publicly available code and datasets that can support the development and evaluation of AI-empowered SRM. Notably, many AI techniques in the domain of computer vision remain underexplored in SRM. The ongoing evolution of AI promises to unlock new potential in SRM, and the integration of cutting-edge AI technologies is poised to pioneer breakthroughs in nanoscale cellular imaging.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878847","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 : 2025-12-31DOI: 10.1038/s41592-025-02963-1
We introduce TransBrain, a computational framework for bidirectional translation of whole-brain phenotypes between humans and mice. TransBrain enables quantitative cross-species comparison in a unified latent space and facilitates functional modeling of the human brain in mouse models.
{"title":"Whole-brain phenotype mapping between humans and mice","authors":"","doi":"10.1038/s41592-025-02963-1","DOIUrl":"10.1038/s41592-025-02963-1","url":null,"abstract":"We introduce TransBrain, a computational framework for bidirectional translation of whole-brain phenotypes between humans and mice. TransBrain enables quantitative cross-species comparison in a unified latent space and facilitates functional modeling of the human brain in mouse models.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"295-296"},"PeriodicalIF":32.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878906","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 : 2025-12-31DOI: 10.1038/s41592-025-02919-5
Eric Van Buren, Yi Zhang, Xihao Li, Margaret Sunitha Selvaraj, Zilin Li, Hufeng Zhou, Nicholette D. Palmer, Donna K. Arnett, John Blangero, Eric Boerwinkle, Brian E. Cade, Jenna C. Carlson, April P. Carson, Yii-Der Ida Chen, Joanne Curran, Ravindranath Duggirala, Myriam Fornage, Nora Franceschini, Misa Graff, Charles Gu, Xiuqing Guo, Jiang He, Nancy Heard-Cosa, Lifang Hou, Yi-Jen Hung, Rita R. Kalyani, Sharon L. R. Kardia, Eimear Kenny, Charles Kooperberg, Brian G. Kral, Leslie Lange, Dan Levy, Changwei Li, Simin Liu, Donald Lloyd-Jones, Ruth J. F. Loos, Ani W. Manichaikul, Lisa Warsinger Martin, Rasika Mathias, Ryan L. Minster, Braxton D. Mitchell, Josyf C. Mychaleckyj, Take Naseri, Kari North, Jeff O’Connell, James A. Perry, Patricia A. Peyser, Bruce M. Psaty, Laura M. Raffield, Ramachandran S. Vasan, Susan Redline, Alex P. Reiner, Stephen S. Rich, Jennifer A. Smith, Brian Spitzer, Hua Tang, Kent D. Taylor, Russell Tracy, Satupa’itea Viali, Lisa Yanek, Wei Zhao, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Jerome I. Rotter, Gina M. Peloso, Pradeep Natarajan, Xihong Lin
Understanding how rare genetic variants influence complex traits remains a major challenge, particularly when these variants lie in noncoding regions of the genome. The effects of variants within candidate cis-regulatory elements (cCREs) often depend on the cell type, making interpretation difficult. Here we introduce cellSTAAR, which integrates whole-genome sequencing data with single-cell assay for transposase-accessible chromatin using sequencing data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and regulatory elements. To reflect the uncertainty in cCRE–gene linking, cellSTAAR uses a comprehensive strategy to link cCREs to their target genes. We applied cellSTAAR to data from the Trans-Omics for Precision Medicine consortium (n ≈ 60,000) and replicated our findings using the UK Biobank (n ≈ 190,000). Across four lipid traits, cellSTAAR improved the detection of biologically meaningful associations and enhanced biological interpretability. These results demonstrate the potential of cell-type-aware approaches to boost discovery in rare variant whole-genome sequencing association studies. cellSTAAR advances noncoding rare variant association analysis by integrating single-cell genomics data.
了解罕见的遗传变异如何影响复杂性状仍然是一个主要的挑战，特别是当这些变异位于基因组的非编码区域时。候选顺式调控元件（cCREs）变异的影响通常取决于细胞类型，这使得解释变得困难。在这里，我们介绍cellSTAAR，它将全基因组测序数据与转座酶可及染色质的单细胞测定相结合，利用测序数据通过构建细胞类型特异性功能注释和调控元件来捕获不同细胞类型间染色质可及性的可变性。为了反映cCREs基因连接的不确定性，cellSTAAR使用一种综合策略将cCREs与其靶基因连接起来。我们将cellSTAAR应用于来自Trans-Omics for Precision Medicine联盟的数据（n≈60,000），并使用UK Biobank （n≈190,000）复制了我们的发现。在四种脂质性状中，cellSTAAR改进了对生物学意义关联的检测，增强了生物学可解释性。这些结果证明了细胞类型感知方法促进罕见变异全基因组测序关联研究发现的潜力。
{"title":"cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions","authors":"Eric Van Buren, Yi Zhang, Xihao Li, Margaret Sunitha Selvaraj, Zilin Li, Hufeng Zhou, Nicholette D. Palmer, Donna K. Arnett, John Blangero, Eric Boerwinkle, Brian E. Cade, Jenna C. Carlson, April P. Carson, Yii-Der Ida Chen, Joanne Curran, Ravindranath Duggirala, Myriam Fornage, Nora Franceschini, Misa Graff, Charles Gu, Xiuqing Guo, Jiang He, Nancy Heard-Cosa, Lifang Hou, Yi-Jen Hung, Rita R. Kalyani, Sharon L. R. Kardia, Eimear Kenny, Charles Kooperberg, Brian G. Kral, Leslie Lange, Dan Levy, Changwei Li, Simin Liu, Donald Lloyd-Jones, Ruth J. F. Loos, Ani W. Manichaikul, Lisa Warsinger Martin, Rasika Mathias, Ryan L. Minster, Braxton D. Mitchell, Josyf C. Mychaleckyj, Take Naseri, Kari North, Jeff O’Connell, James A. Perry, Patricia A. Peyser, Bruce M. Psaty, Laura M. Raffield, Ramachandran S. Vasan, Susan Redline, Alex P. Reiner, Stephen S. Rich, Jennifer A. Smith, Brian Spitzer, Hua Tang, Kent D. Taylor, Russell Tracy, Satupa’itea Viali, Lisa Yanek, Wei Zhao, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Jerome I. Rotter, Gina M. Peloso, Pradeep Natarajan, Xihong Lin","doi":"10.1038/s41592-025-02919-5","DOIUrl":"10.1038/s41592-025-02919-5","url":null,"abstract":"Understanding how rare genetic variants influence complex traits remains a major challenge, particularly when these variants lie in noncoding regions of the genome. The effects of variants within candidate cis-regulatory elements (cCREs) often depend on the cell type, making interpretation difficult. Here we introduce cellSTAAR, which integrates whole-genome sequencing data with single-cell assay for transposase-accessible chromatin using sequencing data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and regulatory elements. To reflect the uncertainty in cCRE–gene linking, cellSTAAR uses a comprehensive strategy to link cCREs to their target genes. We applied cellSTAAR to data from the Trans-Omics for Precision Medicine consortium (n ≈ 60,000) and replicated our findings using the UK Biobank (n ≈ 190,000). Across four lipid traits, cellSTAAR improved the detection of biologically meaningful associations and enhanced biological interpretability. These results demonstrate the potential of cell-type-aware approaches to boost discovery in rare variant whole-genome sequencing association studies. cellSTAAR advances noncoding rare variant association analysis by integrating single-cell genomics data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"338-349"},"PeriodicalIF":32.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878874","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 : 2025-12-30DOI: 10.1038/s41592-025-02993-9
Julian O. Kimura, Brandon Weissbourd
The jellyfish Clytia hemisphaerica has long been established as a model for studying embryogenesis and gametogenesis because of its transparency, simple tissue architecture and evolutionary position. With the recent development of efficient transgenesis, this jellyfish is now poised for tackling systems-level questions in comparative neuroscience and regeneration.
{"title":"The jellyfish Clytia hemisphaerica","authors":"Julian O. Kimura, Brandon Weissbourd","doi":"10.1038/s41592-025-02993-9","DOIUrl":"10.1038/s41592-025-02993-9","url":null,"abstract":"The jellyfish Clytia hemisphaerica has long been established as a model for studying embryogenesis and gametogenesis because of its transparency, simple tissue architecture and evolutionary position. With the recent development of efficient transgenesis, this jellyfish is now poised for tackling systems-level questions in comparative neuroscience and regeneration.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"3-5"},"PeriodicalIF":32.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145863824","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 : 2025-12-30DOI: 10.1038/s41592-025-02930-w
We developed SmartEM, a method that integrates machine learning directly into the image acquisition process of an electron microscope. By allocating imaging time in a specific manner — scanning quickly at first, then rescanning only critical areas more slowly — we are able to accelerate the mapping of neural circuits up to sevenfold without sacrificing accuracy.
{"title":"AI-guided electron microscopy accelerates brain mapping","authors":"","doi":"10.1038/s41592-025-02930-w","DOIUrl":"10.1038/s41592-025-02930-w","url":null,"abstract":"We developed SmartEM, a method that integrates machine learning directly into the image acquisition process of an electron microscope. By allocating imaging time in a specific manner — scanning quickly at first, then rescanning only critical areas more slowly — we are able to accelerate the mapping of neural circuits up to sevenfold without sacrificing accuracy.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"28-29"},"PeriodicalIF":32.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145863833","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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