Nature Methods最新文献_第10页

Decoding mechanoregulation in immunological synapses using biomimetic artificial cells 利用仿生人工细胞解码免疫突触的机械调节。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-11 DOI: 10.1038/s41592-026-03199-3

Xiaolei Yu, Vincent Mukwaya, Minxing Yue, Shuo Yang, Qian Li, Weili Zhao, Chunhai Fan, Lin Wang, Yingxi Zhao, Hongkai Yang, Jiacan Su, Li Wang, Hongjing Dou

{"title":"Decoding mechanoregulation in immunological synapses using biomimetic artificial cells","authors":"Xiaolei Yu, Vincent Mukwaya, Minxing Yue, Shuo Yang, Qian Li, Weili Zhao, Chunhai Fan, Lin Wang, Yingxi Zhao, Hongkai Yang, Jiacan Su, Li Wang, Hongjing Dou","doi":"10.1038/s41592-026-03199-3","DOIUrl":"10.1038/s41592-026-03199-3","url":null,"abstract":"Mechanical force-driven signaling has emerged as a key regulator of cell–cell interactions (CCIs), which can enhance immune cell function. However, current biochemical approaches for studying CCIs offer minimal direct control over cellular bulk phenotypes, while synthetic biomaterial systems fail to mimic the dynamic complexity of cells. Here we introduce kpiCells, a biomaterial-based platform that uses a biomimetic membrane–endoplasmic architecture to enable finely tuned phenocopying of cellular states via modular mechanical, chemical and topographical inputs. We demonstrate that kpiCells can engage in physiological CCIs and reproduce critical subcellular features. In T cell systems, kpiCells enable integrated interrogation of afferent mechanosensing pathways and efferent force-exertion pathways, and support measurement of piconewton-scale forces at individual T cell antigen receptors as well as single cell–cell force fingerprints that define activation thresholds. This work establishes kpiCells as a bionic model that enables synthetic material design with the level of functional complexity approaching living cell systems. kpiCells are a biomimetic artificial cell system to investigate mechanoregulation in cell–cell interactions.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1882-1894"},"PeriodicalIF":28.3,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713303","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

Inference of tumor spatial habitats 肿瘤空间栖息地推断。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-06 DOI: 10.1038/s41592-026-03192-w

Lin Tang

引用次数: 0

Code to plot 代码绘图。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-06 DOI: 10.1038/s41592-026-03173-z

Alexander Lex, Helena Klara Jambor

引用次数: 0

Neuronal recordings with DNA origami 用DNA折纸记录神经元。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-06 DOI: 10.1038/s41592-026-03189-5

Nina Vogt

引用次数: 0

Early-career researcher co-review, one year in 早期研究人员共同审查，一年。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-06 DOI: 10.1038/s41592-026-03197-5

引用次数: 0

A human blood–retina barrier-on-a-chip 芯片上的人类血液视网膜屏障。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-06 DOI: 10.1038/s41592-026-03191-x

Rosie Bunton-Stasyshyn

引用次数: 0

AI-designed antibodies with Germinal ai设计的含有Germinal的抗体。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-06 DOI: 10.1038/s41592-026-03190-y

Arunima Singh

引用次数: 0

GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors GHT-SELEX显示了出乎意料的高内在序列特异性和许多人类转录因子的复杂DNA结合。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-08-05 DOI: 10.1038/s41592-026-03177-9

Arttu Jolma, Aldo Hernandez-Corchado, Ally W. H. Yang, Ali Fathi, Kaitlin U. Laverty, Alexander Brechalov, Rozita Razavi, Mihai Albu, Hong Zheng, The Codebook Consortium, Ivan V. Kulakovskiy, Hamed S. Najafabadi, Timothy R. Hughes

{"title":"GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors","authors":"Arttu Jolma, Aldo Hernandez-Corchado, Ally W. H. Yang, Ali Fathi, Kaitlin U. Laverty, Alexander Brechalov, Rozita Razavi, Mihai Albu, Hong Zheng, The Codebook Consortium, Ivan V. Kulakovskiy, Hamed S. Najafabadi, Timothy R. Hughes","doi":"10.1038/s41592-026-03177-9","DOIUrl":"10.1038/s41592-026-03177-9","url":null,"abstract":"There is ongoing debate regarding the degree to which transcription factors (TFs) independently specify genomic binding: TF binding motifs are typically short and degenerate, yielding many more binding site predictions than observed in cells. Here we present genomic high-throughput SELEX (GHT-SELEX)—a scalable method that surveys intrinsic binding of purified TFs to the fragmented, naked and unmodified genome. GHT-SELEX peaks for 179 diverse human TFs display surprisingly high overlap with chromatin immunoprecipitation sequencing peaks for the same TF. Comparable overlap can be obtained from motifs using appropriate analytical approaches. For C2H2 zinc finger (zf) proteins—the largest class of human TFs—GHT-SELEX shows that modular, alternative engagement of C2H2-zf domains is the norm, enabling several types of distinct target sites, and frequently involving internal duplication and divergence within the C2H2-zf array. Altogether, it is common for TFs to delineate a large fraction of in vivo genomic binding sites independently of other cellular factors. GHT-SELEX—high-throughput SELEX with fragmented genomic DNA—shows that transcription factors can be rather particular about which genomic loci they bind, and that C2H2 zinc finger proteins often engage different fingers at different sites.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1775-1785"},"PeriodicalIF":28.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679411","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

SpaMTP: integrative statistical analysis and visualization of spatial metabolomics and transcriptomics data 空间代谢组学和转录组学数据的综合统计分析和可视化。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-07-31 DOI: 10.1038/s41592-026-03140-8

Andrew Causer, Tianyao Lu, Jurgen Kriel, Joel J. D. Moffet, Christopher C. J. Fitzgerald, Andrew Newman, Hani Vu, Xiao Tan, Tuan Vo, Cedric Cui, Vinod K. Narayana, James R. Whittle, Sarah A. Best, Saskia Freytag, Quan Nguyen

引用次数: 0

CellTune: an integrative software for accurate cell classification in spatial proteomics CellTune：空间蛋白质组学中精确细胞分类的集成软件。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-07-31 DOI: 10.1038/s41592-026-03162-2

Yuval Bussi, Dana Shainshein, Eli Ovits, Sarah Posner, Nofar Azulay, Noa Maimon, Tal Keidar Haran, Raz Ben-Uri, Caitlin Brown, Noam Schuldiner, Eylon Yaniv, David Van Valen, Idan Milo, Ofer Elhanani, Robert Schiemann, Leeat Keren

{"title":"CellTune: an integrative software for accurate cell classification in spatial proteomics","authors":"Yuval Bussi, Dana Shainshein, Eli Ovits, Sarah Posner, Nofar Azulay, Noa Maimon, Tal Keidar Haran, Raz Ben-Uri, Caitlin Brown, Noam Schuldiner, Eylon Yaniv, David Van Valen, Idan Milo, Ofer Elhanani, Robert Schiemann, Leeat Keren","doi":"10.1038/s41592-026-03162-2","DOIUrl":"10.1038/s41592-026-03162-2","url":null,"abstract":"Spatial proteomics measures multiple proteins in situ, capturing tissue complexity. However, cell classification in densely packed tissues remains challenging because of the lack of efficient classification algorithms, annotation tools and high-quality labeled datasets to benchmark computational methods. We introduce CellTune, an integrated software for analysis of large spatial proteomics datasets, which streamlines precise cell classification through an optimized human-in-the-loop active learning workflow. It advances core capabilities for analysis of large datasets with an intuitive and code-free interface. To evaluate CellTune, we created CellTuneDepot, a resource of 40,000 manually annotated cells and 3.5 million high-quality labeled cells across 60 cell types. CellTune outperforms alternative methods, achieving accuracy comparable to human performance while enabling increased classification resolution and discovery of novel cell types. Together, CellTune and CellTuneDepot provide researchers with a tool for state-of-the-art classification accuracy and resolution at scale to drive biological insights. CellTune enables high precision analysis of spatial proteomics datasets through a human-in-the-loop active learning workflow.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 8","pages":"1574-1587"},"PeriodicalIF":28.3,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648638","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