Nature Methods最新文献_第7页

The next generation of in situ multi-omics 下一代原位多组学。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-27 DOI: 10.1038/s41592-024-02571-5

Maren Salla, Klara Penkert, Leif S. Ludwig

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Author Correction: A comprehensive human embryo reference tool using single-cell RNA-sequencing data 作者更正：使用单细胞rna测序数据的综合人类胚胎参考工具。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-24 DOI: 10.1038/s41592-025-02601-w

Cheng Zhao, Alvaro Plaza Reyes, John Paul Schell, Jere Weltner, Nicolás M. Ortega, Yi Zheng, Åsa K. Björklund, Laura Baqué-Vidal, Joonas Sokka, Ras Trokovic, Brian Cox, Janet Rossant, Jianping Fu, Sophie Petropoulos, Fredrik Lanner

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Author Correction: Resolving tissue complexity by multimodal spatial omics modeling with MISO 作者更正：通过MISO的多模态空间组学建模来解决组织复杂性。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-24 DOI: 10.1038/s41592-025-02600-x

Kyle Coleman, Amelia Schroeder, Melanie Loth, Daiwei Zhang, Jeong Hwan Park, Ji-Youn Sung, Niklas Blank, Alexis J. Cowan, Xuyu Qian, Jianfeng Chen, Jiahui Jiang, Hanying Yan, Laith Z. Samarah, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Joshua D. Rabinowitz, Yanxiang Deng, Edward B. Lee, Alexander Lazar, Jianjun Gao, Emma E. Furth, Tae Hyun Hwang, Linghua Wang, Christoph A. Thaiss, Jian Hu, Mingyao Li

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Mapping the topography of spatial gene expression with interpretable deep learning 利用可解释的深度学习绘制空间基因表达图谱。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-23 DOI: 10.1038/s41592-024-02503-3

Uthsav Chitra, Brian J. Arnold, Hirak Sarkar, Kohei Sanno, Cong Ma, Sereno Lopez-Darwin, Benjamin J. Raphael

{"title":"Mapping the topography of spatial gene expression with interpretable deep learning","authors":"Uthsav Chitra, Brian J. Arnold, Hirak Sarkar, Kohei Sanno, Cong Ma, Sereno Lopez-Darwin, Benjamin J. Raphael","doi":"10.1038/s41592-024-02503-3","DOIUrl":"10.1038/s41592-024-02503-3","url":null,"abstract":"Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice—analogous to a map of elevation in a landscape—using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment. Gene expression topography analysis by GASTON portrays domain organization and spatial gradients of gene expression and cell type composition using spatially resolved transcriptomics data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"298-309"},"PeriodicalIF":36.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029207","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

Precision control of cellular functions with a temperature-sensitive protein 用温度敏感蛋白精确控制细胞功能。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-23 DOI: 10.1038/s41592-024-02573-3

引用次数: 0

A temperature-inducible protein module for control of mammalian cell fate 用于控制哺乳动物细胞命运的温度诱导蛋白模块。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-23 DOI: 10.1038/s41592-024-02572-4

William Benman, Zikang Huang, Pavan Iyengar, Delaney Wilde, Thomas R. Mumford, Lukasz J. Bugaj

{"title":"A temperature-inducible protein module for control of mammalian cell fate","authors":"William Benman, Zikang Huang, Pavan Iyengar, Delaney Wilde, Thomas R. Mumford, Lukasz J. Bugaj","doi":"10.1038/s41592-024-02572-4","DOIUrl":"10.1038/s41592-024-02572-4","url":null,"abstract":"Inducible protein switches are currently limited for use in tissues and organisms because common inducers cannot be controlled with precision in space and time in optically dense settings. Here, we introduce a protein that can be reversibly toggled with a small change in temperature, a stimulus that is both penetrant and dynamic. This protein, called Melt (Membrane localization using temperature) oligomerizes and translocates to the plasma membrane when temperature is lowered. We generated a library of Melt variants with switching temperatures ranging from 30 °C to 40 °C, including two that operate at and above 37 °C. Melt was a highly modular actuator of cell function, permitting thermal control over diverse processes including signaling, proteolysis, nuclear shuttling, cytoskeletal rearrangements and cell death. Finally, Melt permitted thermal control of cell death in a mouse model of human cancer. Melt represents a versatile thermogenetic module for straightforward, non-invasive and spatiotemporally defined control of mammalian cells with broad potential for biotechnology and biomedicine. The Melt (Membrane localization using temperature) protein translocates to the plasma membrane upon temperature shift. Melt variants with a range of switching temperatures enable straightforward thermogenetic control of diverse cellular processes.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"539-549"},"PeriodicalIF":36.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029206","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

Author Correction: Arkitekt: streaming analysis and real-time workflows for microscopy 作者更正：Arkitekt：流式分析和实时工作流程的显微镜。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-21 DOI: 10.1038/s41592-025-02597-3

Johannes Roos, Stéphane Bancelin, Tom Delaire, Alexander Wilhelmi, Florian Levet, Maren Engelhardt, Virgile Viasnoff, Rémi Galland, U. Valentin Nägerl, Jean-Baptiste Sibarita

引用次数: 0

UDA-seq: universal droplet microfluidics-based combinatorial indexing for massive-scale multimodal single-cell sequencing. UDA-seq：用于大规模多模态单细胞测序的通用微流控组合索引。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-20 DOI: 10.1038/s41592-024-02586-y

Yun Li, Zheng Huang, Lubin Xu, Yanling Fan, Jun Ping, Guochao Li, Yanjie Chen, Chengwei Yu, Qifei Wang, Turun Song, Tao Lin, Mengmeng Liu, Yangqing Xu, Na Ai, Xini Meng, Qin Qiao, Hongbin Ji, Zhen Qin, Shuo Jin, Nan Jiang, Minxian Wang, Shaokun Shu, Feng Zhang, Weiqi Zhang, Guang-Hui Liu, Limeng Chen, Lan Jiang

{"title":"UDA-seq: universal droplet microfluidics-based combinatorial indexing for massive-scale multimodal single-cell sequencing.","authors":"Yun Li, Zheng Huang, Lubin Xu, Yanling Fan, Jun Ping, Guochao Li, Yanjie Chen, Chengwei Yu, Qifei Wang, Turun Song, Tao Lin, Mengmeng Liu, Yangqing Xu, Na Ai, Xini Meng, Qin Qiao, Hongbin Ji, Zhen Qin, Shuo Jin, Nan Jiang, Minxian Wang, Shaokun Shu, Feng Zhang, Weiqi Zhang, Guang-Hui Liu, Limeng Chen, Lan Jiang","doi":"10.1038/s41592-024-02586-y","DOIUrl":"10.1038/s41592-024-02586-y","url":null,"abstract":"<p><p>The use of single-cell combinatorial indexing sequencing via droplet microfluidics presents an attractive approach for balancing cost, scalability, robustness and accessibility. However, existing methods often require tailored protocols for individual modalities, limiting their automation potential and clinical applicability. To address this, we introduce UDA-seq, a universal workflow that integrates a post-indexing step to enhance throughput and systematically adapt existing droplet-based single-cell multimodal methods. UDA-seq was benchmarked across various tissue and cell types, enabling several common multimodal analyses, including single-cell co-assay of RNA and VDJ, RNA and chromatin, and RNA and CRISPR perturbation. Notably, UDA-seq facilitated the efficient generation of over 100,000 high-quality single-cell datasets from three dozen frozen clinical biopsy specimens within a single-channel droplet microfluidics experiment. Downstream analysis demonstrated the robustness of this approach in identifying rare cell subpopulations associated with clinical phenotypes and exploring the vulnerability of cancer cells.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008736","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

Capture of membrane proteins in their native membrane milieu 膜蛋白在其原生膜环境中的捕获。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-20 DOI: 10.1038/s41592-024-02518-w

引用次数: 0

Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision 作者更正：circRNA检测工具的大规模基准测试显示灵敏度差异很大，但精度差异不大。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-01-17 DOI: 10.1038/s41592-024-02569-z

Marieke Vromman, Jasper Anckaert, Stefania Bortoluzzi, Alessia Buratin, Chia-Ying Chen, Qinjie Chu, Trees-Juen Chuang, Roozbeh Dehghannasiri, Christoph Dieterich, Xin Dong, Paul Flicek, Enrico Gaffo, Wanjun Gu, Chunjiang He, Steve Hoffmann, Osagie Izuogu, Michael S. Jackson, Tobias Jakobi, Eric C. Lai, Justine Nuytens, Julia Salzman, Mauro Santibanez-Koref, Peter Stadler, Olivier Thas, Eveline Vanden Eynde, Kimberly Verniers, Guoxia Wen, Jakub Westholm, Li Yang, Chu-Yu Ye, Nurten Yigit, Guo-Hua Yuan, Jinyang Zhang, Fangqing Zhao, Jo Vandesompele, Pieter-Jan Volders

引用次数: 0