Nature Methods最新文献_第10页

BioNumPy: array programming for biology BioNumPy：生物学数组编程。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-17 DOI: 10.1038/s41592-024-02483-4

Knut Dagestad Rand, Ivar Grytten, Milena Pavlović, Chakravarthi Kanduri, Geir Kjetil Sandve

引用次数: 0

Image processing tools for petabyte-scale light sheet microscopy data 百万亿字节级光片显微镜数据的图像处理工具。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-17 DOI: 10.1038/s41592-024-02475-4

Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E. Milkie, Joshua L. Lillvis, Alexander Kuhn, Johnny Gan Chong, Jason Li Hong, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N. Killilea, Eric Betzig, Srigokul Upadhyayula

{"title":"Image processing tools for petabyte-scale light sheet microscopy data","authors":"Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E. Milkie, Joshua L. Lillvis, Alexander Kuhn, Johnny Gan Chong, Jason Li Hong, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N. Killilea, Eric Betzig, Srigokul Upadhyayula","doi":"10.1038/s41592-024-02475-4","DOIUrl":"10.1038/s41592-024-02475-4","url":null,"abstract":"Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson–Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments. PetaKit5D offers versatile processing workflows for light sheet microscopy data including performant image input/output, geometric transformations, deconvolution and stitching. The software is efficient and scalable to petabyte-size datasets.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2342-2352"},"PeriodicalIF":36.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02475-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470440","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

Scientists who marry scientists 科学家与科学家结婚。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-15 DOI: 10.1038/s41592-024-02490-5

Vivien Marx

引用次数: 0

Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP 利用 STAMP 对空间转录组学进行可解释的空间感知降维。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-15 DOI: 10.1038/s41592-024-02463-8

Chengwei Zhong, Kok Siong Ang, Jinmiao Chen

{"title":"Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP","authors":"Chengwei Zhong, Kok Siong Ang, Jinmiao Chen","doi":"10.1038/s41592-024-02463-8","DOIUrl":"10.1038/s41592-024-02463-8","url":null,"abstract":"Spatial transcriptomics produces high-dimensional gene expression measurements with spatial context. Obtaining a biologically meaningful low-dimensional representation of such data is crucial for effective interpretation and downstream analysis. Here, we present Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP), an interpretable spatially aware dimension reduction method built on a deep generative model that returns biologically relevant, low-dimensional spatial topics and associated gene modules. STAMP can analyze data ranging from a single section to multiple sections and from different technologies to time-series data, returning topics matching known biological domains and associated gene modules containing established markers highly ranked within. In a lung cancer sample, STAMP delineated cell states with supporting markers at a higher resolution than the original annotation and uncovered cancer-associated fibroblasts concentrated on the tumor edge’s exterior. In time-series data of mouse embryonic development, STAMP disentangled the erythro-myeloid hematopoiesis and hepatocytes developmental trajectories within the liver. STAMP is highly scalable and can handle more than 500,000 cells. Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP) is a versatile and scalable method for dimension reduction in spatially resolved transcriptomics that enables discovery of biologically relevant tissue domains.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 11","pages":"2072-2083"},"PeriodicalIF":36.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02463-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470442","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

Restoring protein glycosylation with GlycoShape 用 GlycoShape 恢复蛋白质糖基化。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-14 DOI: 10.1038/s41592-024-02464-7

Callum M. Ives, Ojas Singh, Silvia D’Andrea, Carl A. Fogarty, Aoife M. Harbison, Akash Satheesan, Beatrice Tropea, Elisa Fadda

{"title":"Restoring protein glycosylation with GlycoShape","authors":"Callum M. Ives, Ojas Singh, Silvia D’Andrea, Carl A. Fogarty, Aoife M. Harbison, Akash Satheesan, Beatrice Tropea, Elisa Fadda","doi":"10.1038/s41592-024-02464-7","DOIUrl":"10.1038/s41592-024-02464-7","url":null,"abstract":"Despite ground-breaking innovations in experimental structural biology and protein structure prediction techniques, capturing the structure of the glycans that functionalize proteins remains a challenge. Here we introduce GlycoShape ( https://glycoshape.org ), an open-access glycan structure database and toolbox designed to restore glycoproteins to their native and functional form in seconds. The GlycoShape database counts over 500 unique glycans so far, covering the human glycome and augmented by elements from a wide range of organisms, obtained from 1 ms of cumulative sampling from molecular dynamics simulations. These structures can be linked to proteins with a robust algorithm named Re-Glyco, directly compatible with structural data in open-access repositories, such as the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and AlphaFold Protein Structure Database, or own. The quality, performance and broad applicability of GlycoShape is demonstrated by its ability to predict N-glycosylation occupancy, scoring a 93% agreement with experiment, based on screening all proteins in the PDB with a corresponding glycoproteomics profile, for a total of 4,259 N-glycosylation sequons. GlycoShape is an open-access web-based platform designed to supplement three-dimensional glycoprotein structures with missing structural information on glycans. To link them, the Re-Glyco algorithm evaluates the steric complementarity of glycans using their conformational ensemble with the protein surface.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 11","pages":"2117-2127"},"PeriodicalIF":36.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02464-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142470445","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

Single-shot 20-fold expansion microscopy 单次 20 倍扩展显微镜。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-11 DOI: 10.1038/s41592-024-02454-9

Shiwei Wang, Tay Won Shin, Harley B. Yoder II, Ryan B. McMillan, Hanquan Su, Yixi Liu, Chi Zhang, Kylie S. Leung, Peng Yin, Laura L. Kiessling, Edward S. Boyden

{"title":"Single-shot 20-fold expansion microscopy","authors":"Shiwei Wang, Tay Won Shin, Harley B. Yoder II, Ryan B. McMillan, Hanquan Su, Yixi Liu, Chi Zhang, Kylie S. Leung, Peng Yin, Laura L. Kiessling, Edward S. Boyden","doi":"10.1038/s41592-024-02454-9","DOIUrl":"10.1038/s41592-024-02454-9","url":null,"abstract":"Expansion microscopy (ExM) is in increasingly widespread use throughout biology because its isotropic physical magnification enables nanoimaging on conventional microscopes. To date, ExM methods either expand specimens to a limited range (~4–10× linearly) or achieve larger expansion factors through iterating the expansion process a second time (~15–20× linearly). Here, we present an ExM protocol that achieves ~20× expansion (yielding <20-nm resolution on a conventional microscope) in a single expansion step, achieving the performance of iterative expansion with the simplicity of a single-shot protocol. This protocol, which we call 20ExM, supports postexpansion staining for brain tissue, which can facilitate biomolecular labeling. 20ExM may find utility in many areas of biological investigation requiring high-resolution imaging. 20ExM achieves isotropic ~20× expansion of cells and tissues in a single shot for super-resolution imaging with <20-nm resolution on a conventional microscope.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 11","pages":"2128-2134"},"PeriodicalIF":36.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02454-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406626","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 decade of neuroscience 神经科学的十年

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-09 DOI: 10.1038/s41592-024-02448-7

Nina Vogt

引用次数: 0

Listening to an RNA orchestra and seeing CRISPR in action 聆听 RNA 管弦乐，观看 CRISPR 实际操作。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-09 DOI: 10.1038/s41592-024-02444-x

Nicole Rusk

引用次数: 0

Mapping the brain: an editor’s journey 绘制大脑地图：编辑之旅。