Nature Methods最新文献

{"title":"Accurate and well-powered case-control analysis of spatial molecular data.","authors":"Yakir A Reshef, Lakshay Sood, Michelle Curtis, Laurie Rumker, Daniel J Stein, Mukta G Palshikar, Saba Nayar, Andrew Filer, Anna Helena Jonsson, Ilya Korsunsky, Soumya Raychaudhuri","doi":"10.1038/s41592-026-03236-1","DOIUrl":"10.1038/s41592-026-03236-1","url":null,"abstract":"<p><p>As spatial molecular data grow in scope, there is a pressing need to identify disease-associated spatial structures. Current approaches typically make restrictive assumptions such as representing tissue regions by abundances of discrete cell types and samples by abundances of discrete niches; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method combining deep learning with principled statistics to discover disease-associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to summarize the contents of every small tissue patch in a dataset via numeric 'fingerprints'. It uses these to define many data-dependent, overlapping 'microniches' and meta-analyzes them to identify microniches whose abundance correlates significantly with case-control status. We confirm VIMA's calibration, power and spatial accuracy in simulations. We then apply VIMA to spatial datasets spanning three diseases and spatial modalities, recapitulating known biology and identifying new spatial features of disease.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149112347","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":"Flexible discovery of disease-associated tissue structures.","authors":"","doi":"10.1038/s41592-026-03242-3","DOIUrl":"https://doi.org/10.1038/s41592-026-03242-3","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149112349","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":"Resolving cell–cell interaction networks and their molecular logic in complex tissues","authors":"Lei Tang,&nbsp;Kang Tian,&nbsp;Xiaolei Fu,&nbsp;Yingjia Xu,&nbsp;Jincheng Wu,&nbsp;Jinsong Zhang,&nbsp;Xi-Wen Wang,&nbsp;Chunxiang Ye,&nbsp;Qiong Wu,&nbsp;Wei Wu,&nbsp;Changjiang Feng,&nbsp;Qiangfeng Cliff Zhang","doi":"10.1038/s41592-026-03237-0","DOIUrl":"10.1038/s41592-026-03237-0","url":null,"abstract":"Cells in complex organisms function through extensive interactions, yet mapping these interaction networks at scale remains challenging. Here we present CCI-seq, a high-throughput method to unbiasedly capture cell–cell interactions across a proximity continuum by combining cell clump combinatorial indexing with single-cell sequencing. CCI-seq identified known interactions and fine-grained cellular organization in mouse kidney and intestine, and uncovered aberrant interactions and disrupted spatial organization in adenomatous polyposis coli knockout (Apc-KO) intestines. Applied to human colorectal cancer, it revealed subtype-specific interactions linked to clinical classifications. By leveraging a single-cell RNA sequencing backbone, CCI-seq achieves deep transcriptome coverage, enabling analysis of molecular states arising from interactions. This functional resolution revealed that interacting cells associate with distinct transcriptional programs—driving ion transport in kidney, antigen presentation in Apc-KO villi and NF-κB inflammatory activation in colorectal cancer. Thus, CCI-seq provides a scalable platform to unveil large-scale cell–cell interaction networks and dissect their molecular and functional impacts, enhancing our understanding of complex multicellular systems. CCI-seq leverages combinatorial cell clump indexing and single-cell sequencing to capture large-scale cell–cell interaction networks.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"2042-2053"},"PeriodicalIF":28.3,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-026-03237-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100802","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":"Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.","authors":"Fleming Kretschmer, Eva-Maria Harrieder, Michael Witting, Sebastian Böcker","doi":"10.1038/s41592-026-03243-2","DOIUrl":"https://doi.org/10.1038/s41592-026-03243-2","url":null,"abstract":"<p><p>Liquid chromatography is a predominant technology for the separation of small molecules. Hundreds of retention time prediction models have been published over the past decades, yet truly transferable prediction (requiring no training data from the target chromatographic system) remains an open challenge. Unfortunately, retention times may change massively, even for nominally identical chromatographic conditions. Retention order is considerably more conserved; but even retention order may change if chromatographic conditions vary. Here we present 2-step, a two-step method for the prediction of retention times in reversed-phase chromatography. In the first step, a machine-learning model predicts a retention order index, taking into account chromatographic conditions. In the second step, we map predicted indices to absolute retention times. Disentangling these two tasks finally enables transferable retention time prediction across chromatographic conditions and compound classes, without requiring any target-system training data. Our 2-step method outperforms existing methods that were trained on the target dataset. Finally, we systematically study what chromatographic conditions result in notable changes of retention order.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149112361","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":"CytoVI: deep generative modeling of antibody-based single cell data","authors":"Florian Ingelfinger,&nbsp;Nathan Levy,&nbsp;Can Ergen,&nbsp;Artemy Bakulin,&nbsp;Alexander Becker,&nbsp;Pierre Boyeau,&nbsp;Martin Kim,&nbsp;Diana Ditz,&nbsp;Jan Dirks,&nbsp;Jonas Maaskola,&nbsp;Tobias Wertheimer,&nbsp;Robert Zeiser,&nbsp;Corinne C. Widmer,&nbsp;Ido Amit,&nbsp;Nir Yosef","doi":"10.1038/s41592-026-03224-5","DOIUrl":"10.1038/s41592-026-03224-5","url":null,"abstract":"Antibody-based single-cell technologies, such as flow cytometry, mass cytometry and CITE-seq, have become widely used in clinical diagnostics and basic research; however, their analysis is complicated by technical noise, batch effects, platform differences and restricted antibody panels. Here we present CytoVI, a probabilistic generative model for statistically rigorous unified analysis of antibody-based single-cell data. CytoVI generates informative cell embeddings, imputes missing measurements, performs differential protein expression testing and automates annotation of cells in a single probabilistic model. We applied CytoVI to build an integrated B cell maturation atlas spanning 350 proteins and identified proteins associated with immunoglobulin class-switching. In a cohort of patients with B cell non-Hodgkin lymphoma profiled by flow cytometry and CITE-seq, CytoVI uncovered disease-associated T cell states. CytoVI is available as open-source software at scvi-tools.org . CytoVI is a deep generative model for statistically rigorous analysis of antibody-based single-cell data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"2054-2065"},"PeriodicalIF":28.3,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-026-03224-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100803","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":"Spatially resolved chromatin architectures in mammalian brain tissues","authors":"Zhenping Chen \u0000 (,&nbsp;),&nbsp;Maoni Guo \u0000 (,&nbsp;),&nbsp;Lin Zhang \u0000 (,&nbsp;),&nbsp;Hao Yu \u0000 (,&nbsp;),&nbsp;Xinxin Wang \u0000 (,&nbsp;),&nbsp;Xinrui Yu,&nbsp;Mingyue Chen \u0000 (,&nbsp;),&nbsp;Junjie Lv \u0000 (,&nbsp;),&nbsp;Zhisong Chen \u0000 (,&nbsp;),&nbsp;Changjun Peng \u0000 (,&nbsp;),&nbsp;Qian Gong \u0000 (,&nbsp;),&nbsp;Qiao Zhang \u0000 (,&nbsp;),&nbsp;Ru Guo \u0000 (,&nbsp;),&nbsp;Yiping Huang \u0000 (,&nbsp;),&nbsp;Lei Gao \u0000 (,&nbsp;),&nbsp;Shan Jiang \u0000 (,&nbsp;),&nbsp;Junmei Wang \u0000 (,&nbsp;),&nbsp;Zhihua Zhang \u0000 (,&nbsp;),&nbsp;Jianliang Qian,&nbsp;Jianrong Wang,&nbsp;Jiang Liu \u0000 (,&nbsp;),&nbsp;Xuepeng Chen \u0000 (,&nbsp;)","doi":"10.1038/s41592-026-03218-3","DOIUrl":"10.1038/s41592-026-03218-3","url":null,"abstract":"Unraveling 3D chromatin architectures is crucial for decoding regulatory programs of development and disease. However, current sequencing-based methods typically profile 3D genome organizations while sacrificing the spatial context of tissues. Here we present spatial high‑throughput chromosome conformation capture (Hi-C), a technology that resolves genome-wide chromatin structures directly within tissue sections. Spatial Hi-C could decipher 3D chromatin structures in mouse embryos and brains with high fidelity and reproducibility. In the cerebellum, granular layer subclusters defined by distinct 3D genomes, aligned with anatomical lobules, link chromatin topology to spatial microdomain functions. Furthermore, spatial Hi-C of the adult cortex could reconstruct single-spot 3D chromatin structures at 10-μm resolution, similar to single-cell data. Finally, we found an increasing radial gradient of the short-versus-long chromatin interaction ratio on coronal sections in developing brains, which could track neuronal maturity. In summary, spatial Hi-C establishes a paradigm to investigate spatially resolved chromatin structures and their regulatory functions within complex tissue microenvironments. Spatial Hi-C allows the study of chromatin organization in a spatially resolved manner. This approach shows regional and cell-type-specific differences in the adult and developing mouse brain.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"2029-2041"},"PeriodicalIF":28.3,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049733","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":"Solid-state nanopore sensing: the next workhorse of biophysical characterization","authors":"Casey M. Platnich,&nbsp;Raluca-Elena Alexii,&nbsp;Ulrich F. Keyser","doi":"10.1038/s41592-026-03222-7","DOIUrl":"10.1038/s41592-026-03222-7","url":null,"abstract":"Nanopore sequencing has entered the mainstream, with DNA and RNA sequencing now possible on portable, user-friendly devices. In the wake of these successes, the attention of the nanopore community has turned toward the challenge of de novo protein sequencing. Biological nanopores are primed to take on this task and have thus taken center stage. Here we draw focus back toward solid-state nanopores. We argue that these size-tunable and easily functionalized pores are key to studying the macromolecular superstructures inherent to natural and synthetic biochemical systems. While sequencing is undoubtedly a worthy goal, we advocate for the use of nanopores in new applications, toward understanding the supramolecular chemistry at the heart of biology and beyond. With ongoing improvements in hardware, nanopore functionalization and experimental design, new avenues for nanopore sensing are continually arising. Looking forward, we anticipate opportunities for solid-state nanopores in drug screening, protein engineering and synthetic self-assembled systems. This Perspective discusses opportunities and challenges of using solid-state nanopores for broader biophysical characterization.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"1950-1961"},"PeriodicalIF":28.3,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149040037","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":"ResolVI: addressing noise and bias in spatial transcriptomics","authors":"Can Ergen,&nbsp;Nir Yosef","doi":"10.1038/s41592-026-03212-9","DOIUrl":"10.1038/s41592-026-03212-9","url":null,"abstract":"Technologies for estimating RNA expression at high throughput, in intact tissue slices and with high spatial resolution (spatial transcriptomics) shed new light on how cells communicate and tissues function. A fundamental step in analyzing data generated by subcellular resolution spatial transcriptomics technologies is quantification, namely, segmenting the plane into regions, each approximating a cell, and then collating the molecules inside each region to estimate the cellular expression profile. Despite many advances in this area, a persistent problem is that of the incorrect assignment of molecules to cells, which limits many current applications to the level of a priori-defined cell subsets and complicates the discovery of novel cell states. Here we develop resolVI, a model that operates downstream of any segmentation algorithm to generate a probabilistic representation, correcting for the misassignment of molecules, as well as for batch effects and other nuisance factors. We demonstrate that resolVI improves our ability to distinguish between cell states, identify subtle expression changes in space and perform integrated analysis across datasets. ResolVI is available as open source software within scvi-tools. Segmentation and quantification of spatial transcriptomics data is plagued by specific noise and bias. ResolVI tackles this challenge by generating error- and batch-corrected probabilistic representations of spatial transcriptomics data, contributing to improved performance in multiple analysis tasks.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"2003-2014"},"PeriodicalIF":28.3,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-026-03212-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149039971","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":"NanoTS: a deep learning tool for accurate SNP calling in nanopore long-read transcriptome data","authors":"Zelin Liu,&nbsp;Feng Wang,&nbsp;Robert Wang,&nbsp;David W. Wu,&nbsp;Nicole DeBruyne,&nbsp;Kelsey Keith,&nbsp;Elizabeth M. McCormick,&nbsp;Joseph Jee-Hwan Park,&nbsp;Matthew T. Sullenberger,&nbsp;Andrew C. Edmondson,&nbsp;Marni J. Falk,&nbsp;Lan Lin,&nbsp;Yi Xing","doi":"10.1038/s41592-026-03225-4","DOIUrl":"10.1038/s41592-026-03225-4","url":null,"abstract":"Accurate variant detection using nanopore long-read transcriptome data remains challenging. Here we present NanoTS—a deep learning-based tool for single nucleotide polymorphism detection from diverse types of nanopore transcriptome sequencing data. NanoTS outperforms existing methods, achieving F1 scores above 0.980 and 0.966 on nanopore direct RNA and cDNA sequencing data, respectively, for single nucleotide polymorphisms with at least five supporting reads. Notably, NanoTS shows strong improvements over existing methods for allelically imbalanced variants. We also demonstrate that NanoTS enables accurate detection and genotype calling of pathogenic variants underlying Mendelian disorders, highlighting its potential clinical utility. NanoTS facilitates nanopore long-read transcriptome sequencing based SNP detection and genotype calling using deep learning.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"1968-1972"},"PeriodicalIF":28.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-026-03225-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149022260","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":"SVPG: a pangenome-based structural variant detection approach and rapid augmentation of pangenome graphs with new samples","authors":"Tao Jiang,&nbsp;Heng Hu,&nbsp;Runtian Gao,&nbsp;Shuqi Cao,&nbsp;Zhongjun Jiang,&nbsp;Murong Zhou,&nbsp;Wentao Gao,&nbsp;Shengming Zhou,&nbsp;Guohua Wang","doi":"10.1038/s41592-026-03219-2","DOIUrl":"10.1038/s41592-026-03219-2","url":null,"abstract":"Breakthrough advances in long-read sequencing have opened unprecedented opportunities to study genetic variations through pangenome analysis, yet tools that effectively leverage such frameworks for structural variant (SV) detection remain limited. In addition, efficient construction of pangenome graphs becomes increasingly challenging with the acquisition of larger numbers of samples. Here we present SVPG, an approach that leverages haplotype-resolved pangenome reference for accurate SV detection and rapid pangenome graph augmentation from long-read sequencing data. Compared with state-of-the-art SV callers, SVPG maintained superior overall performance across different sequencing technologies and coverages. SVPG also achieved notable improvements in calling individual-specific SVs, including rare and somatic SVs. Furthermore, in a benchmark involving 20 samples, SVPG accelerated pangenome graph augmentation by nearly tenfold compared with traditional augmentation strategies. These results indicate that SVPG has the potential to improve SV detection and serve as an effective tool, offering new possibilities for advancing pangenomic research. SVPG performs accurate pangenome-facilitated structural variant detection and accelerates pangenome graph augmentation using long-read sequencing data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"1984-1993"},"PeriodicalIF":28.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-026-03219-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017585","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}