Nature Methods最新文献_第5页

The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes 新的微生物组：利用人类肿瘤测序数据研究微生物的挑战和机遇。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02807-y

Yingjie Li, Anjun Ma, Evan Johnson, Charis Eng, Subhajyoti De, Sizun Jiang, Zihai Li, Daniel Spakowicz, Qin Ma

{"title":"The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes","authors":"Yingjie Li, Anjun Ma, Evan Johnson, Charis Eng, Subhajyoti De, Sizun Jiang, Zihai Li, Daniel Spakowicz, Qin Ma","doi":"10.1038/s41592-025-02807-y","DOIUrl":"10.1038/s41592-025-02807-y","url":null,"abstract":"Microbes within tumors have been recognized and experimentally related to oncogenesis, tumor growth, metastasis and therapeutic responsiveness. Studying the tumor microbiome presents difficulties, as early indications suggest that microbe populations are low in abundance, sparse and highly heterogeneous. Disparate results from computational profiling of the tumor microbiome have cast doubt on the premise of microbes in tumors. Yet decades of experimental evidence support the presence of tumor microbes, at least in a limited number of tumor types. In this Perspective, we discuss the importance of iteratively improving microbe-targeted sequencing techniques, established analytical pipelines, robust computational tools and solid validations to address current challenges and fill existing knowledge gaps. The vast amount of human tumor sequencing data available could greatly enhance systematic investigations of microbiome–tumor interactions with methods to quantify the composition of the tumor microbiome accurately. This Perspective explores the challenges and future directions in the world of human tumor microbiome research.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1788-1799"},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069909","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

De novo discovery of conserved gene clusters in microbial genomes with Spacedust 用Spacedust重新发现微生物基因组中的保守基因簇。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02816-x

Ruoshi Zhang, Milot Mirdita, Johannes Söding

{"title":"De novo discovery of conserved gene clusters in microbial genomes with Spacedust","authors":"Ruoshi Zhang, Milot Mirdita, Johannes Söding","doi":"10.1038/s41592-025-02816-x","DOIUrl":"10.1038/s41592-025-02816-x","url":null,"abstract":"Metagenomics has revolutionized environmental and human-associated microbiome studies. However, the limited fraction of proteins with known biological processes and molecular functions presents a major bottleneck. In prokaryotes and viruses, evolution favors keeping genes participating in the same biological processes colocalized as conserved gene clusters. Conversely, conservation of gene neighborhood indicates functional association. Here we present Spacedust, a tool for systematic, de novo discovery of conserved gene clusters. To find homologous protein matches, Spacedust uses fast and sensitive structure comparison with Foldseek. Partially conserved clusters are detected using novel clustering and order conservation P values. We demonstrate Spacedust’s sensitivity with an all-versus-all analysis of 1,308 bacterial genomes, identifying 72,843 conserved gene clusters containing 58% of the 4.2 million genes. It recovered 95% of antiviral defense system clusters annotated by the specialized tool PADLOC. Spacedust’s high sensitivity and speed will facilitate the annotation of large numbers of sequenced bacterial, archaeal and viral genomes. This work presents Spacedust, a tool for de novo identification of conserved gene clusters from metagenomic data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2065-2073"},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02816-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069916","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}

引用次数: 0

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE 扩大大尺寸组织的空间转录组学：用iSCALE揭示超出传统平台的细胞水平组织结构。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02770-8

Amelia Schroeder, Melanie L. Loth, Chunyu Luo, Sicong Yao, Hanying Yan, Daiwei Zhang, Sarbottam Piya, Edward Plowey, Wenxing Hu, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Su Jing Chan, Taylor L. Reynolds, Thomas Carlile, Patrick Cullen, Ji-Youn Sung, Hui-Hsin Tsai, Jeong Hwan Park, Tae Hyun Hwang, Baohong Zhang, Mingyao Li

{"title":"Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE","authors":"Amelia Schroeder, Melanie L. Loth, Chunyu Luo, Sicong Yao, Hanying Yan, Daiwei Zhang, Sarbottam Piya, Edward Plowey, Wenxing Hu, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Su Jing Chan, Taylor L. Reynolds, Thomas Carlile, Patrick Cullen, Ji-Youn Sung, Hui-Hsin Tsai, Jeong Hwan Park, Tae Hyun Hwang, Baohong Zhang, Mingyao Li","doi":"10.1038/s41592-025-02770-8","DOIUrl":"10.1038/s41592-025-02770-8","url":null,"abstract":"Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while preserving crucial spatial context within tissues. However, existing ST platforms are constrained by high costs, long turnaround times, low resolution, limited gene coverage and inherently small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that reconstructs large-scale, super-resolution gene expression landscapes and automatically annotates cellular-level tissue architecture in samples exceeding capture areas of current ST platforms. The performance of iSCALE was assessed by comprehensive evaluations involving benchmarking experiments, immunohistochemistry staining and manual annotations by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion-associated cellular characteristics undetectable by conventional ST experiments. Our results demonstrate the utility of iSCALE in analyzing large tissues by enabling unbiased annotation, resolving cell type composition, mapping cellular microenvironments and revealing spatial features beyond the reach of standard ST analysis or routine histopathological assessment. iSCALE leverages histology and spatial transcriptomics to infer gene expression at super resolution in large tissues.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1911-1922"},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069954","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}

引用次数: 0

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST 利用深度学习和GHIST在组织学上的单细胞分辨率的空间基因表达。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02795-z

Xiaohang Fu, Yue Cao, Beilei Bian, Chuhan Wang, Dinny Graham, Nirmala Pathmanathan, Ellis Patrick, Jinman Kim, Jean Yee Hwa Yang

{"title":"Spatial gene expression at single-cell resolution from histology using deep learning with GHIST","authors":"Xiaohang Fu, Yue Cao, Beilei Bian, Chuhan Wang, Dinny Graham, Nirmala Pathmanathan, Ellis Patrick, Jinman Kim, Jean Yee Hwa Yang","doi":"10.1038/s41592-025-02795-z","DOIUrl":"10.1038/s41592-025-02795-z","url":null,"abstract":"The increased use of spatially resolved transcriptomics provides new biological insights into disease mechanisms. However, the high cost and complexity of these methods are barriers to broader application. Consequently, methods have been created to predict spot-based gene expression from routinely collected histology images. Recent benchmarking showed that current methodologies have limited accuracy and spatial resolution, constraining translational capacity. Here, we introduce GHIST, a deep learning-based framework that predicts spatial gene expression at single-cell resolution by leveraging subcellular spatial transcriptomics and synergistic relationships between multiple layers of biological information. We validated GHIST using public datasets and The Cancer Genome Atlas data, demonstrating its flexibility across different spatial resolutions and superior performance. Our results underscore the utility of in silico generation of single-cell spatial gene expression measurements and the capacity to enrich existing datasets with a spatially resolved omics modality, paving the way for scalable multi-omics analysis and biomarker identification. GHIST is a deep learning-based method that can predict spatial gene expression at high resolution using histology data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1900-1910"},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069956","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}

引用次数: 0

A tale of tumors 一个关于肿瘤的故事。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02838-5

引用次数: 0

Bridging histology and spatial gene expression across scales 跨尺度连接组织学和空间基因表达。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02806-z

Ying Ma

引用次数: 0

Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data 基于单细胞DNA拷贝数和空间组学测序数据的癌症亚克隆检测。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-15 DOI: 10.1038/s41592-025-02773-5

Chi-Yun Wu, Jiazhen Rong, Anuja Sathe, Paul R. Hess, Billy T. Lau, Susan M. Grimes, Sijia Huang, Hanlee P. Ji, Nancy R. Zhang

{"title":"Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data","authors":"Chi-Yun Wu, Jiazhen Rong, Anuja Sathe, Paul R. Hess, Billy T. Lau, Susan M. Grimes, Sijia Huang, Hanlee P. Ji, Nancy R. Zhang","doi":"10.1038/s41592-025-02773-5","DOIUrl":"10.1038/s41592-025-02773-5","url":null,"abstract":"Somatic mutations such as copy number alterations accumulate during cancer progression, driving intratumor heterogeneity that impacts therapy effectiveness. Understanding the characteristics and spatial distribution of genetically distinct subclones is essential for unraveling tumor evolution and improving cancer treatment. Here we present Clonalscope, a subclone detection method using copy number profiles, applicable to spatial transcriptomics and single-cell sequencing data. Clonalscope implements a nested Chinese Restaurant Process to identify de novo tumor subclones, which can incorporate prior information from matched bulk DNA sequencing data for improved subclone detection and malignant cell labeling. On single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing data from gastrointestinal tumors, Clonalscope successfully labeled malignant cells and identified genetically different subclones with thorough validations. On spatial transcriptomics data from various primary and metastasized tumors, Clonalscope labeled malignant spots, traced subclones and identified spatially segregated subclones with distinct differentiation levels and expression of genes associated with drug resistance and survival. Clonalscope is a method for cancer subclone detection leveraging copy number profiles estimated using spatial and single-cell sequencing data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1846-1856"},"PeriodicalIF":32.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069903","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

Joint profiling of chromatin accessibility and CRISPR edits via double-stranded DNA deaminases 通过双链DNA脱氨酶对染色质可及性和CRISPR编辑进行联合分析。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-11 DOI: 10.1038/s41592-025-02812-1

引用次数: 0

Biophysics-based protein language models for protein engineering 蛋白质工程中基于生物物理学的蛋白质语言模型。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-11 DOI: 10.1038/s41592-025-02776-2

Sam Gelman, Bryce Johnson, Chase R. Freschlin, Arnav Sharma, Sameer D’Costa, John Peters, Anthony Gitter, Philip A. Romero

{"title":"Biophysics-based protein language models for protein engineering","authors":"Sam Gelman, Bryce Johnson, Chase R. Freschlin, Arnav Sharma, Sameer D’Costa, John Peters, Anthony Gitter, Philip A. Romero","doi":"10.1038/s41592-025-02776-2","DOIUrl":"10.1038/s41592-025-02776-2","url":null,"abstract":"Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose mutational effect transfer learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics. We fine-tune METL on experimental sequence–function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL’s ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering. Mutational effect transfer learning (METL) is a protein language model framework that unites machine learning and biophysical modeling. Transformer-based neural networks are pretrained on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1868-1879"},"PeriodicalIF":32.1,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040974","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}

引用次数: 0

Coupling CRISPR scanning with targeted chromatin accessibility profiling using a double-stranded DNA deaminase 使用双链DNA脱氨酶耦合CRISPR扫描与靶向染色质可及性分析。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-11 DOI: 10.1038/s41592-025-02811-2

Heejin Roh, Simon P. Shen, Yan Hu, Hui Si Kwok, Allison P. Siegenfeld, Ceejay Lee, Marcanthony U. Zepeda, Chun-Jie Guo, Shelby A. Roseman, Caroline Comenho, Vijay G. Sankaran, Jason D. Buenrostro, Brian B. Liau

{"title":"Coupling CRISPR scanning with targeted chromatin accessibility profiling using a double-stranded DNA deaminase","authors":"Heejin Roh, Simon P. Shen, Yan Hu, Hui Si Kwok, Allison P. Siegenfeld, Ceejay Lee, Marcanthony U. Zepeda, Chun-Jie Guo, Shelby A. Roseman, Caroline Comenho, Vijay G. Sankaran, Jason D. Buenrostro, Brian B. Liau","doi":"10.1038/s41592-025-02811-2","DOIUrl":"10.1038/s41592-025-02811-2","url":null,"abstract":"Genome editing enables sequence-function profiling of endogenous cis-regulatory elements, driving understanding of their mechanisms. However, these approaches lack direct, scalable readouts of chromatin accessibility across long single-molecule chromatin fibers. Here we leverage double-stranded DNA cytidine deaminases to profile chromatin accessibility at endogenous loci of interest through targeted PCR and long-read sequencing, a method we term targeted deaminase-accessible chromatin sequencing (TDAC-seq). With high sequence coverage at targeted loci, TDAC-seq can be integrated with CRISPR perturbations to link genetic edits and their effects on chromatin accessibility on the same single chromatin fiber at single-nucleotide resolution. We employed TDAC-seq to parse CRISPR edits that activate fetal hemoglobin in human CD34+ hematopoietic stem and progenitor cells (HSPCs) during erythroid differentiation as well as in pooled CRISPR and base-editing screens tiling an enhancer controlling the globin locus. We further scaled the method to interrogate 947 variants in a GFI1B-linked enhancer associated with myeloproliferative neoplasm risk in a single pooled CRISPR experiment in CD34+ HSPCs. Together, TDAC-seq enables high-resolution sequence-function mapping of single-molecule chromatin fibers by genome editing. This paper presents TDAC-seq, a targeted chromatin-accessibility-profiling method using cytidine deaminases and long-read sequencing, to resolve the effects of CRISPR edits on single chromatin fibers.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 10","pages":"2083-2093"},"PeriodicalIF":32.1,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040976","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