Nature Methods最新文献

Benchmarking segmentation methods for spatial transcriptomics. 空间转录组学的基准分割方法。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-23 DOI: 10.1038/s41592-026-03272-x

Xabier Martínez-de-Morentin, Sumeer A Khan, Alberto Maillo, Robert Lehmann, Narsis A Kiani, Vincenzo Lagani, David Gomez-Cabrero, Jesper Tegner

{"title":"Benchmarking segmentation methods for spatial transcriptomics.","authors":"Xabier Martínez-de-Morentin, Sumeer A Khan, Alberto Maillo, Robert Lehmann, Narsis A Kiani, Vincenzo Lagani, David Gomez-Cabrero, Jesper Tegner","doi":"10.1038/s41592-026-03272-x","DOIUrl":"https://doi.org/10.1038/s41592-026-03272-x","url":null,"abstract":"Cell segmentation is a critical yet under-standardized step in spatial transcriptomics, directly affecting cell identity assignment, spatial organization and downstream biological interpretation. Despite rapid advances in spatial transcriptomics technologies, systematic evaluations of segmentation methods across platforms and biological contexts remain limited. Here we benchmark 11 image-based, transcriptomics-based and multimodal segmentation methods across 15 datasets generated by 5 spatial transcriptomics technologies, spanning 12 human and mouse tissue types. We introduce biologically informed evaluation metrics that quantify segmentation quality, transcript recovery, cell composition, morphology, expression purity and spatial organization. Benchmarking reveals substantial variation in performance across datasets, technologies and evaluation criteria, highlighting trade-offs among segmentation completeness, transcriptomic fidelity and biological interpretability. Cross-technology analyses further demonstrate that segmentation choice critically shapes downstream analyses, including cell-type specificity, transcript contamination and ligand-receptor inference. Together, our unified Nextflow pipeline provides a scalable foundation for standardized evaluation of segmentation and method selection in spatial transcriptomics.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149245608","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}

引用次数: 0

CauTrigger: deciphering biological system state transitions by hierarchical causal decomposition. CauTrigger：通过层次因果分解破译生物系统状态转换。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-22 DOI: 10.1038/s41592-026-03274-9

Chengming Zhang, Deyu Cai, Kazuyuki Aihara, Luonan Chen

{"title":"CauTrigger: deciphering biological system state transitions by hierarchical causal decomposition.","authors":"Chengming Zhang, Deyu Cai, Kazuyuki Aihara, Luonan Chen","doi":"10.1038/s41592-026-03274-9","DOIUrl":"https://doi.org/10.1038/s41592-026-03274-9","url":null,"abstract":"Biological systems exhibit complex hierarchical regulation, with interactions across multiple layers governing transitions between states. Identifying the causal architecture underlying these state transitions remains challenging because confounding can produce spurious associations in observational data. Here we present CauTrigger, a hierarchical deep latent variable model that integrates do-calculus to identify potential causal triggers and predict perturbation outcomes across multiple regulatory layers. By disentangling causal and spurious latent factors, CauTrigger recursively reconstructs the underlying regulatory hierarchy and enables interpretable reasoning from observational data. Across synthetic and real-world datasets with multilayer regulatory structures, CauTrigger outperforms existing methods in identifying causal triggers and forecasting system-level responses. Applied to single-cell multi-omics and gut microbiome datasets, CauTrigger prioritizes candidate causal triggers and uses in silico perturbations to generate testable hypotheses about biological state transitions and inform potential strategies for state navigation. Together, these results establish CauTrigger as a general-purpose framework for causal discovery in complex biological systems.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149244720","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}

引用次数: 0

OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics. OpenDVP：社区授权的深度视觉蛋白质组学的实验和计算框架。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-22 DOI: 10.1038/s41592-026-03262-z

Jose Nimo, Sonja Fritzsche, Daniela S Valdes, Minh Trinh, Tancredi Pentimalli, Melissa Klingeberg, Simon Schallenberg, Frederick Klauschen, Florian Herse, Stefan Florian, Nikolaus Rajewsky, Fabian Coscia

{"title":"OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics.","authors":"Jose Nimo, Sonja Fritzsche, Daniela S Valdes, Minh Trinh, Tancredi Pentimalli, Melissa Klingeberg, Simon Schallenberg, Frederick Klauschen, Florian Herse, Stefan Florian, Nikolaus Rajewsky, Fabian Coscia","doi":"10.1038/s41592-026-03262-z","DOIUrl":"https://doi.org/10.1038/s41592-026-03262-z","url":null,"abstract":"Deep visual proteomics (DVP) is an emerging approach for cell-type-specific and spatially resolved proteomics. However, its broad adoption has been constrained by the lack of an open-source end-to-end workflow within a community-driven ecosystem. Here we introduce openDVP, an experimental and computational framework for accessible and scalable DVP. OpenDVP combines open-source image analysis tools (MCMICRO, QuPath and napari) with scverse formats (AnnData and SpatialData) for multiomics integration. It provides two workflows: a fast-track pipeline requiring no image analysis expertise, and an artificial intelligence-powered pipeline incorporating state-of-the-art methods for image preprocessing, segmentation and spatial analysis. We apply openDVP across three archival tissue studies (human placenta, early stage lung cancer and locally relapsed breast cancer), integrating spatial single-cell phenotypes with exploratory proteomic measurements. Finally, we introduce deep proteomic profiling of cellular neighborhoods as a scalable approach for spatial discovery proteomics.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149244693","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}

引用次数: 0

Tracing causal cascades underlying biological state changes. 追踪生物状态变化背后的因果级联。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-22 DOI: 10.1038/s41592-026-03273-w

引用次数: 0

Multi-immersion oblique plane microscope (miOPM): a reconfigurable platform for high-resolution light-sheet fluorescence microscopy. 多浸入斜平面显微镜（miOPM）：一个可重构的高分辨率光片荧光显微镜平台。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-19 DOI: 10.1038/s41592-026-03245-0

Bingying Chen, Alfred Millett-Sikking, Bo-Jui Chang, Seweryn Gałecki, Stephan Daetwyler, Jenny Jiou, Jim Monistrol, Qionghua Shen, Felix Zhou, Hsin-Yu Lin, Edward Jenkins, Miriam C Stein, Madeleine Marlar-Pavey, Gabriel Sturm, Alexander L Li, Qiaosi Tang, Brian Y Feng, Ulises Diaz, Yannan Chen, Aryaman Shalizi, Astrid Gillich, Jonathan R Friedman, Raju Tomer, Wallace F Marshall, Sarah Shahmoradian, Kevin M Dean, Reto Fiolka

{"title":"Multi-immersion oblique plane microscope (miOPM): a reconfigurable platform for high-resolution light-sheet fluorescence microscopy.","authors":"Bingying Chen, Alfred Millett-Sikking, Bo-Jui Chang, Seweryn Gałecki, Stephan Daetwyler, Jenny Jiou, Jim Monistrol, Qionghua Shen, Felix Zhou, Hsin-Yu Lin, Edward Jenkins, Miriam C Stein, Madeleine Marlar-Pavey, Gabriel Sturm, Alexander L Li, Qiaosi Tang, Brian Y Feng, Ulises Diaz, Yannan Chen, Aryaman Shalizi, Astrid Gillich, Jonathan R Friedman, Raju Tomer, Wallace F Marshall, Sarah Shahmoradian, Kevin M Dean, Reto Fiolka","doi":"10.1038/s41592-026-03245-0","DOIUrl":"https://doi.org/10.1038/s41592-026-03245-0","url":null,"abstract":"Light-sheet fluorescence microscopy (LSFM) enables gentle, rapid and efficient volumetric imaging of biological specimens. Despite its potential, LSFM has fragmented into multiple variants, each narrowly optimized for specific samples or imaging regimes, limiting its broad applicability. Here we introduce the multi-immersion oblique plane microscope (miOPM), an adaptable LSFM platform for high-resolution imaging ranging from subcellular dynamics to whole organisms and cleared tissues. miOPM supports seamless interchangeability of oil, water and air objectives and maintains diffraction-limited performance across a refractive index range of 1.33 to 1.51. It is compatible with standard sample mounting, current clearing protocols and high-throughput 3D imaging. We leverage miOPM to image diverse biological specimens at multiple spatial scales. By expanding the adaptability and ease of use of LSFM, miOPM is poised to democratize advanced 3D imaging.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224511","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}

引用次数: 0

AmpliconSuite: an end-to-end workflow for analyzing focal amplifications in cancer genomes. AmpliconSuite：端到端的工作流程，用于分析癌症基因组中的病灶扩增。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-16 DOI: 10.1038/s41592-026-03264-x

Jens Luebeck, Ted Liefeld, Ivy Tsz-Lo Wong, Edwin Huang, Forrest Kim, Bhargavi Dameracharla, Michael Chan, Rohil Ahuja, Kyra Fetter, Dhruv Khatri, Kaiyuan Zhu, Thorin Tabor, Daniel Schreyer, Gino Prasad, Aditi Gnanasekar, Siavash Raeisi Dehkordi, Simone Spandau, Michał Adamaszek, Rishaan Kenkre, Tushar Agashe, Devika Torvi, Mădălina Giurgiu-Kraljič, Soyeon Kim, Hoon Kim, Peter Bailey, Roel G W Verhaak, Viraj Deshpande, Michael Reich, Paul S Mischel, Jill P Mesirov, Vineet Bafna

{"title":"AmpliconSuite: an end-to-end workflow for analyzing focal amplifications in cancer genomes.","authors":"Jens Luebeck, Ted Liefeld, Ivy Tsz-Lo Wong, Edwin Huang, Forrest Kim, Bhargavi Dameracharla, Michael Chan, Rohil Ahuja, Kyra Fetter, Dhruv Khatri, Kaiyuan Zhu, Thorin Tabor, Daniel Schreyer, Gino Prasad, Aditi Gnanasekar, Siavash Raeisi Dehkordi, Simone Spandau, Michał Adamaszek, Rishaan Kenkre, Tushar Agashe, Devika Torvi, Mădălina Giurgiu-Kraljič, Soyeon Kim, Hoon Kim, Peter Bailey, Roel G W Verhaak, Viraj Deshpande, Michael Reich, Paul S Mischel, Jill P Mesirov, Vineet Bafna","doi":"10.1038/s41592-026-03264-x","DOIUrl":"https://doi.org/10.1038/s41592-026-03264-x","url":null,"abstract":"Here we present AmpliconSuite, a toolkit for focal amplification analysis from cancer whole-genome sequencing that identifies extrachromosomal DNA (ecDNA), breakage-fusion-bridges and other amplification modes. Its ecContext module categorizes ecDNA structures to understand formation mechanisms. Validation across 175 metaphase fluorescence in situ hybridization-probed genes in 72 models demonstrated accurate predictions. Analysis of 6,506 patient pan-cancer samples revealed ecDNA-mediated oncogene co-amplification preferences. To democratize data sharing, we created AmpliconRepository, a community-contributable platform, enabling investigation of results across >15,000 publicly hosted samples.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207282","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}

引用次数: 0

Recent developments of fluorescent proteins and biosensors driven by cross-disciplinary insights. 跨学科见解驱动的荧光蛋白和生物传感器的最新发展。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-16 DOI: 10.1038/s41592-026-03268-7

Anneliese M M Gest, Sohum Mehta, Jin Zhang

引用次数: 0

MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics. MassNet：十亿规模的人工智能质谱语料库，支持蛋白质组学的可扩展深度学习。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-14 DOI: 10.1038/s41592-026-03247-y

Jun A, Xiang Zhang, Xiaofan Zhang, Jiaqi Wei, Te Zhang, Yamin Deng, Pu Liu, Zongxiang Nie, Yiwen Yu, Yi Chen, Nanqing Dong, Zhiqiang Gao, Siqi Sun, Tiannan Guo

{"title":"MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics.","authors":"Jun A, Xiang Zhang, Xiaofan Zhang, Jiaqi Wei, Te Zhang, Yamin Deng, Pu Liu, Zongxiang Nie, Yiwen Yu, Yi Chen, Nanqing Dong, Zhiqiang Gao, Siqi Sun, Tiannan Guo","doi":"10.1038/s41592-026-03247-y","DOIUrl":"10.1038/s41592-026-03247-y","url":null,"abstract":"Here we present MassNet, inspired by large-scale data corpora of text and images; MassNet is an artificial intelligence (AI)-ready data corpus for mass spectrometry (MS)-based proteomics. The corpus contains 1.5 billion tandem MS spectra from high-quality data-dependent acquisition (DDA) MS data across 35 species. Approximately 1.7 million peptide precursors are from human samples. We further present an AI-ready DDA data format called Mass Spectrometry DDA Tensor (MSDT) for compact and standardized storage, curation and reuse of data from multiple sources, which can be optionally converted to a Lightning Memory-Mapped Database for accelerated input/output in deep learning. On the basis of MassNet, we develop XuanjiNovo, a nonautoregressive transformer model that leverages a curriculum learning strategy for de novo peptide sequencing. XuanjiNovo achieves 38.35-143.57% improvement over state-of-the-art methods on human Astral data. MassNet also supports additional downstream tasks, including retention time prediction and PSM rescoring. MassNet represents a foundational MS-based proteomics data resource, marking a critical milestone for AI proteomics research.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149183147","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}

引用次数: 0

LoL-align: sensitive and fast probabilistic protein structure alignment. LoL-align：灵敏、快速的概率蛋白质结构比对。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-14 DOI: 10.1038/s41592-026-03249-w

Lasse Reifenrath, Michel van Kempen, Gyuri Kim, Soo Hyun Kim, Mohammadreza Radnezhad, Cameron L M Gilchrist, Milot Mirdita, Martin Steinegger, Johannes Söding

引用次数: 0

Integrating sex as a biological variable into one's science. 把性作为一个生物学变量纳入自己的科学研究。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-10-14 DOI: 10.1038/s41592-026-03266-9

Vivien Marx

引用次数: 0