Nature Methods最新文献

{"title":"Reproducible annotation of T cell subsets and activation states with gene expression programs","authors":"","doi":"10.1038/s41592-025-02792-2","DOIUrl":"10.1038/s41592-025-02792-2","url":null,"abstract":"We created T-CellAnnoTator (TCAT), a computational method that helps to identify T cell subsets, activation states and functions. It does this using reproducible gene expression programs found across many disease contexts and tissues. TCAT outperforms conventional approaches for T cell subset prediction, is easy to use programmatically or through a website, and can be adapted for other cell types.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1774-1775"},"PeriodicalIF":32.1,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992917","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":"Reproducible single-cell annotation of programs underlying T cell subsets, activation states and functions","authors":"Dylan Kotliar, Michelle Curtis, Ryan Agnew, Kathryn Weinand, Aparna Nathan, Yuriy Baglaenko, Kamil Slowikowski, Yu Zhao, Pardis C. Sabeti, Deepak A. Rao, Soumya Raychaudhuri","doi":"10.1038/s41592-025-02793-1","DOIUrl":"10.1038/s41592-025-02793-1","url":null,"abstract":"T cells recognize antigens and induce specialized gene expression programs (GEPs), enabling functions like proliferation, cytotoxicity and cytokine production. Traditionally, different T cell classes are thought to exhibit mutually exclusive responses, including TH1, TH2 and TH17 programs. However, single-cell RNA sequencing has revealed a continuum of T cell states without clearly distinct subsets, necessitating new analytical frameworks. Here, we introduce T-CellAnnoTator (TCAT), a pipeline that improves T cell characterization by simultaneously quantifying predefined GEPs capturing activation states and cellular subsets. Analyzing 1,700,000 T cells from 700 individuals spanning 38 tissues and five disease contexts, we identify 46 reproducible GEPs reflecting core T cell functions including proliferation, cytotoxicity, exhaustion and effector states. We experimentally demonstrate new activation programs and apply TCAT to characterize activation GEPs that predict immune checkpoint inhibitor response across multiple tumor types. Our software package starCAT generalizes this framework, enabling reproducible annotation in other cell types and tissues. TCAT is a pipeline that can simultaneously capture gene expression programs related to T cell subsets and activation states for accurate T cell characterization.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1964-1980"},"PeriodicalIF":32.1,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992963","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}

{"title":"Unified mass imaging maps the lipidome of vertebrate development","authors":"Halima Hannah Schede, Leila Haj Abdullah Alieh, Laurel Ann Rohde, Antonio Herrera, Anjalie Schlaeppi, Guillaume Valentin, Alireza Gargoori Motlagh, Albert Dominguez Mantes, Chloe Jollivet, Jonathan Paz-Montoya, Laura Capolupo, Irina Khven, Andrew C. Oates, Giovanni D’Angelo, Gioele La Manno","doi":"10.1038/s41592-025-02771-7","DOIUrl":"10.1038/s41592-025-02771-7","url":null,"abstract":"Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here we established uMAIA, an analytical framework for the joint analysis of large MSI datasets, which enables the construction of multidimensional metabolomic atlases. Employing this framework, we mapped the four-dimensional (4D) distribution of over a hundred lipids at micrometric resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits. uMAIA is an analytical framework designed to enable the construction of metabolic atlases at high resolution using mass spectrometry imaging data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1981-1994"},"PeriodicalIF":32.1,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992923","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}

{"title":"Translon: a single term for translated regions","authors":"Michał I. Świrski, Jack A. S. Tierney, M. Mar Albà, Dmitry E. Andreev, Julie L. Aspden, John F. Atkins, Michal Bassani-Sternberg, Marla J. Berry, Stefano Biffo, Kathleen Boris-Lawrie, Mark Borodovsky, Ian Brierley, Matthew Brook, Marie A. Brunet, Janusz M. Bujnicki, Neva Caliskan, Lorenzo Calviello, Anne-Ruxandra Carvunis, Jamie H. D. Cate, Can Cenik, Kung Yao Chang, Yiwen Chen, Sonia Chothani, Jyoti S. Choudhary, Patricia L. Clark, Jim Clauwaert, Lynn Cooley, Erik Dassi, Kellie Dean, Jean-Jacques Diaz, Christoph Dieterich, Rivka Dikstein, Jonathan D. Dinman, Sergey E. Dmitriev, Olga A. Dontsova, Christine M. Dunham, Sandeep M. Eswarappa, Philip J. Farabaugh, Pouya Faridi, Ivo Fierro-Monti, Andrew E. Firth, David Gatfield, Fátima Gebauer, Mikhail S. Gelfand, Nicola K. Gray, Rachel Green, Chris H. Hill, Ya-Ming Hou, Norbert Hübner, Zoya Ignatova, Pavel Ivanov, Shintaro Iwasaki, Rory Johnson, Ahmad Jomaa, Marko Jovanovic, Irwin Jungreis, Manolis Kellis, Jeffrey S. Kieft, Alex V. Kochetov, Eugene V. Koonin, Andrei A. Korostelev, Joanna Kufel, Ivan V. Kulakovskiy, Leo Kurian, Denis L. J. Lafontaine, Ola Larsson, Gary Loughran, Julius Lukeš, Marco Mariotti, Elena S. Martens-Uzunova, Thomas F. Martinez, Akinobu Matsumoto, Joel McManus, Jan Medenbach, Sergey V. Melnikov, Gerben Menschaert, Catharina Merchante, Martin Mikl, W. Allen Miller, Oliver Mühlemann, Olivier Namy, Danny D. Nedialkova, Jozef Nosek, Sandra Orchard, Petar Ozretić, Mihaela Pertea, Dmitri D. Pervouchine, Luísa Romão, David Ron, Xavier Roucou, Maria P. Rubtsova, Jorge Ruiz-Orera, Alan Saghatelian, Steven L. Salzberg, Lucia A. Seale, Cathal Seoighe, Petr V. Sergiev, Premal Shah, Nikolay Shirokikh, Sarah A. Slavoff, Nahum Sonenberg, Timothy J. Stasevich, Roman J. Szczesny, Tiina Tamm, Marek Tchórzewski, Ivan Topisirovic, Michel L. Tremblay, Tamir Tuller, Igor Ulitsky, Leoš Shivaya Valášek, Petra Van Damme, Gabriella Viero, Juan Antonio Vizcaino, Christine Vogel, Edward W. J. Wallace, Jonathan S. Weissman, Eric Westhof, Nicola Whiffin, Daniel N. Wilson, Zhi Xie, Jonathan W. Yewdell, Martina M. Yordanova, Chien-Hung Yu, Vyacheslav Yurchenko, Bojan Zagrovic, TRANSLACORE, Eivind Valen, Pavel V. Baranov","doi":"10.1038/s41592-025-02810-3","DOIUrl":"10.1038/s41592-025-02810-3","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2002-2006"},"PeriodicalIF":32.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962058","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":"Functional phenotyping of genomic variants using joint multiomic single-cell DNA–RNA sequencing","authors":"Dominik Lindenhofer, Julia R. Bauman, John A. Hawkins, Donnacha Fitzgerald, Umut Yildiz, Haeyeon Jung, Anastasiia Korosteleva, Mikael Marttinen, Moritz Kueblbeck, Judith B. Zaugg, Kyung-Min Noh, Sascha Dietrich, Wolfgang Huber, Oliver Stegle, Lars M. Steinmetz","doi":"10.1038/s41592-025-02805-0","DOIUrl":"10.1038/s41592-025-02805-0","url":null,"abstract":"Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution. We developed single-cell DNA–RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. Furthermore, we demonstrate that in primary B cell lymphoma samples, cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease. This study introduces SDR-seq, a droplet-based single-cell DNA–RNA sequencing platform, enabling the study of gene expression profiles linked to both noncoding and coding variants.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2032-2041"},"PeriodicalIF":32.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02805-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962013","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}

{"title":"Laser flash melting cryo-EM samples to overcome preferred orientation","authors":"Monique S. Straub, Oliver F. Harder, Nathan J. Mowry, Sarah V. Barrass, Jakub Hruby, Marcel Drabbels, Ulrich J. Lorenz","doi":"10.1038/s41592-025-02796-y","DOIUrl":"10.1038/s41592-025-02796-y","url":null,"abstract":"Sample preparation remains a bottleneck for protein structure determination by cryo-electron microscopy. A frequently encountered issue is that proteins adsorb to the air–water interface of the sample in a limited number of orientations. This makes it challenging to obtain high-resolution reconstructions, or may even cause projects to fail altogether. We have previously observed that laser flash melting and revitrification of cryo-EM samples reduces preferred orientation for large, symmetric particles. Here we demonstrate that our method can in fact be used to scramble the orientation of proteins of a range of sizes and symmetries. The effect can be enhanced for some proteins by increasing the heating rate during flash melting or by depositing amorphous ice onto the sample prior to revitrification. This also allows us to shed light onto the underlying mechanism. Our experiments establish a set of tools for overcoming preferred orientation that can be easily integrated into existing workflows. Individual proteins tend to adopt preferred orientations when subjected to vitrification for cryo-electron microscopy analysis. A laser flash melting procedure followed by rapid revitrification provides a simple approach to mitigate this issue, reducing the number of micrographs required for successful structure determination at high-resolution.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1880-1886"},"PeriodicalIF":32.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962039","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}

{"title":"Reconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data.","authors":"Feng Xiong, Lijuan Liu, Hanchuan Peng","doi":"10.1038/s41592-025-02784-2","DOIUrl":"https://doi.org/10.1038/s41592-025-02784-2","url":null,"abstract":"<p><p>Brain networks, or connectomes, have inspired research at macro-, meso- and micro-scales. However, the rise of single-cell technologies necessitates inferring connectomes consisting of individual neurons projecting throughout the brain. Her, we present a scalable approach to map single-neuron connectivity at the whole-brain scale using two complementary methods. We first generated an arbor-net by probabilistically pairing dendritic and axonal arbors of 20,247 neurons registered to the Allen Brain Atlas. We also produced a bouton-net based on 2.57 million putative axonal boutons from 1,877 fully reconstructed neurons and probabilistic pairing of these full-morphology datasets. Cross-validation of both networks showed statistical consistency in spatially and anatomically modular distributions of neuronal connections, corresponding to functional modules in the mouse brain. We found that single-neuron connections correlated more strongly with gene coexpression than the full-brain mesoscale connectome. Our network analysis, comparing the connectomes with alternative brain architectures, identified nonrandom subnetwork patterns. Overall, our data indicate rich granularity and strong modular diversity in mouse brain networks.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962028","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":"A realistic phantom dataset for benchmarking cryo-ET data annotation","authors":"Ariana Peck,&nbsp;Yue Yu,&nbsp;Jonathan Schwartz,&nbsp;Anchi Cheng,&nbsp;Utz Heinrich Ermel,&nbsp;Joshua Hutchings,&nbsp;Saugat Kandel,&nbsp;Dari Kimanius,&nbsp;Elizabeth A. Montabana,&nbsp;Daniel Serwas,&nbsp;Hannah Siems,&nbsp;Feng Wang,&nbsp;Zhuowen Zhao,&nbsp;Shawn Zheng,&nbsp;Matthias Haury,&nbsp;David A. Agard,&nbsp;Clinton S. Potter,&nbsp;Bridget Carragher,&nbsp;Kyle Harrington,&nbsp;Mohammadreza Paraan","doi":"10.1038/s41592-025-02800-5","DOIUrl":"10.1038/s41592-025-02800-5","url":null,"abstract":"Cryo-electron tomography (cryo-ET) is a powerful technique for imaging molecular complexes in their native cellular environments. However, identifying the vast majority of molecular species in cellular tomograms remains prohibitively difficult. Machine learning (ML) methods provide an opportunity to automate the annotation process, but algorithm development has been hindered by the lack of large, standardized datasets. Here we present an experimental phantom dataset with comprehensive ground-truth annotations for six molecular species to spur new algorithm development and benchmark existing tools. This annotated dataset is available on the CryoET Data Portal with infrastructure to streamline access for methods developers across fields. A standardized, realistic phantom dataset consisting of ground-truth annotations for six diverse molecular species is provided as a community resource for cryo-electron-tomography algorithm benchmarking.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1819-1823"},"PeriodicalIF":32.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144961869","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}

{"title":"DeepMVP: deep learning models trained on high-quality data accurately predict PTM sites and variant-induced alterations","authors":"Bo Wen,&nbsp;Chenwei Wang,&nbsp;Kai Li,&nbsp;Ping Han,&nbsp;Matthew V. Holt,&nbsp;Sara R. Savage,&nbsp;Jonathan T. Lei,&nbsp;Yongchao Dou,&nbsp;Zhiao Shi,&nbsp;Yi Li,&nbsp;Bing Zhang","doi":"10.1038/s41592-025-02797-x","DOIUrl":"10.1038/s41592-025-02797-x","url":null,"abstract":"Post-translational modifications (PTMs) are critical regulators of protein function, and their disruption is a key mechanism by which missense variants contribute to disease. Accurate PTM site prediction using deep learning can help identify PTM-altering variants, but progress has been limited by the lack of large, high-quality training datasets. Here, we introduce PTMAtlas, a curated compendium of 397,524 PTM sites generated through systematic reprocessing of 241 public mass-spectrometry datasets, and DeepMVP, a deep learning framework trained on PTMAtlas to predict PTM sites for phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation. DeepMVP substantially outperforms existing tools across all six PTM types. Its application to predicting PTM-altering missense variants shows strong concordance with experimental results, validated using literature-curated variants and cancer proteogenomic datasets. Together, PTMAtlas and DeepMVP provide a robust platform for PTM research and a scalable framework for assessing the functional consequences of coding variants through the lens of PTMs. DeepMVP is a deep learning framework for predicting PTM sites and variant-induced alterations across six modification types, including phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1857-1867"},"PeriodicalIF":32.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962084","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}

{"title":"Ultrack: pushing the limits of cell tracking across biological scales.","authors":"Jordão Bragantini, Ilan Theodoro, Xiang Zhao, Teun A P M Huijben, Eduardo Hirata-Miyasaki, Shruthi VijayKumar, Akilandeswari Balasubramanian, Tiger Lao, Richa Agrawal, Sheng Xiao, Jan Lammerding, Shalin Mehta, Alexandre X Falcão, Adrian Jacobo, Merlin Lange, Loïc A Royer","doi":"10.1038/s41592-025-02778-0","DOIUrl":"10.1038/s41592-025-02778-0","url":null,"abstract":"<p><p>Tracking live cells across two-dimensional, three-dimensional (3D) and multichannel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, accurately tracking cells remains challenging, particularly in complex and crowded tissues where cell segmentation is often ambiguous. We present Ultrack, a versatile and scalable cell tracking method that tackles this challenge by considering candidate segmentations derived from multiple algorithms and parameter sets. Ultrack leverages temporal consistency to select optimal segments, ensuring robust performance even under segmentation uncertainty. We validate our method on diverse datasets, including terabyte-scale developmental time-lapse recordings of zebrafish, fruit fly and nematode embryos, as well as multicolor and label-free cellular imaging. We demonstrate that Ultrack achieves superior or comparable performance in the cell tracking challenge, particularly when tracking densely packed 3D embryonic cells over extended periods. Moreover, we propose an approach to tracking validation via dual-channel sparse labeling that enables high-fidelity ground-truth generation, pushing the boundaries of long-term cell tracking assessment. Our method is freely available as a Python package with Fiji and Napari plugins and can be deployed in a high-performance computing environment, facilitating widespread adoption by the research community.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962036","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}