Nature Methods最新文献

{"title":"Ultrafast and accurate sequence alignment and clustering of viral genomes.","authors":"Andrzej Zielezinski, Adam Gudyś, Jakub Barylski, Krzysztof Siminski, Piotr Rozwalak, Bas E Dutilh, Sebastian Deorowicz","doi":"10.1038/s41592-025-02701-7","DOIUrl":"https://doi.org/10.1038/s41592-025-02701-7","url":null,"abstract":"<p><p>Viromics produces millions of viral genomes and fragments annually, overwhelming traditional sequence comparison methods. Here we introduce Vclust, an approach that determines average nucleotide identity by Lempel-Ziv parsing and clusters viral genomes with thresholds endorsed by authoritative viral genomics and taxonomy consortia. Vclust demonstrates superior accuracy and efficiency compared to existing tools, clustering millions of genomes in a few hours on a mid-range workstation.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078224","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":"Dark sectioning boosts segmentation accuracy and image quality in fluorescence microscopy.","authors":"","doi":"10.1038/s41592-025-02668-5","DOIUrl":"https://doi.org/10.1038/s41592-025-02668-5","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036412","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":"Prediction of protein subcellular localization in single cells.","authors":"Xinyi Zhang, Yitong Tseo, Yunhao Bai, Fei Chen, Caroline Uhler","doi":"10.1038/s41592-025-02696-1","DOIUrl":"https://doi.org/10.1038/s41592-025-02696-1","url":null,"abstract":"<p><p>The subcellular localization of a protein is important for its function, and its mislocalization is linked to numerous diseases. Existing datasets capture limited pairs of proteins and cell lines, and existing protein localization prediction models either miss cell-type specificity or cannot generalize to unseen proteins. Here we present a method for Prediction of Unseen Proteins' Subcellular localization (PUPS). PUPS combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images. We demonstrate that the protein sequence input enables generalization to unseen proteins, and the cellular image input captures single-cell variability, enabling cell-type-specific predictions. Experimental validation shows that PUPS can predict protein localization in newly performed experiments outside the Human Protein Atlas used for training. Collectively, PUPS provides a framework for predicting differential protein localization across cell lines and single cells within a cell line, including changes in protein localization driven by mutations.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029035","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":"Dark-based optical sectioning assists background removal in fluorescence microscopy.","authors":"Ruijie Cao, Yaning Li, Yao Zhou, Meiqi Li, Fangrui Lin, Wenyi Wang, Guoxun Zhang, Gang Wang, Boya Jin, Wei Ren, Yu Sun, Zhifeng Zhao, Wei Zhang, Jing Sun, Yiwei Hou, Xinzhu Xu, Jiakui Hu, Wei Shi, Shuang Fu, Qianxi Liang, Yanye Lu, Changhui Li, Yuxuan Zhao, Yiming Li, Dong Kuang, Jiamin Wu, Peng Fei, Junle Qu, Peng Xi","doi":"10.1038/s41592-025-02667-6","DOIUrl":"https://doi.org/10.1038/s41592-025-02667-6","url":null,"abstract":"<p><p>In fluorescence microscopy, a persistent challenge is the defocused background that obscures cellular details and introduces artifacts. Here, we introduce Dark sectioning, a method inspired by natural image dehazing for removing backgrounds that leverages dark channel prior and dual frequency separation to provide single-frame optical sectioning. Unlike denoising or deconvolution, Dark sectioning specifically targets and removes out-of-focus backgrounds, stably improving the signal-to-background ratio by nearly 10 dB and structural similarity index measure of images by approximately tenfold. Dark sectioning was validated using wide-field, confocal, two/three-dimensional structured illumination and one/two-photon microscopy with high-fidelity reconstruction. We further demonstrate its potential to improve the segmentation accuracy in deep tissues, resulting in better recognition of neurons in the mouse brain and accurate assessment of nuclei in prostate lesions or mouse brain sections. Dark sectioning is compatible with many other microscopy modalities, including light-sheet and light-field microscopy, as well as processing algorithms, including deconvolution and super-resolution optical fluctuation imaging.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144064294","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":"FISHnet: detecting chromatin domains in single-cell sequential Oligopaints imaging data.","authors":"Rohan Patel, Kenneth Pham, Harshini Chandrashekar, Jennifer E Phillips-Cremins","doi":"10.1038/s41592-025-02688-1","DOIUrl":"https://doi.org/10.1038/s41592-025-02688-1","url":null,"abstract":"<p><p>Sequential Oligopaints DNA FISH is an imaging technique that measures higher-order genome folding at single-allele resolution via multiplexed, probe-based tracing. Currently there is a paucity of algorithms to identify 3D genome features in sequential Oligopaints data. Here, we present FISHnet, a graph theory method based on optimization of network modularity to detect chromatin domains in pairwise distance matrices. FISHnet sensitively and specifically identifies domains and boundaries in both simulated and real single-allele imaging data and provides statistical tests for the identification of cell-type-specific domains-like folding patterns. Application of FISHnet across multiple published Oligopaints datasets confirms that nested domains consistent with TADs and subTADs are not an emergent property of ensemble Hi-C data but also observable on single alleles. We make FISHnet code freely available to the scientific community, thus enabling future studies aiming to elucidate the role of single-allele folding variation on genome function.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019789","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":"Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy.","authors":"Zhi Lu, Manchang Jin, Shuai Chen, Xiaoge Wang, Feihao Sun, Qi Zhang, Zhifeng Zhao, Jiamin Wu, Jingyu Yang, Qionghai Dai","doi":"10.1038/s41592-025-02698-z","DOIUrl":"10.1038/s41592-025-02698-z","url":null,"abstract":"<p><p>Light-field microscopy (LFM) and its variants have significantly advanced intravital high-speed 3D imaging. However, their practical applications remain limited due to trade-offs among processing speed, fidelity, and generalization in existing reconstruction methods. Here we propose a physics-driven self-supervised reconstruction network (SeReNet) for unscanned LFM and scanning LFM (sLFM) to achieve near-diffraction-limited resolution at millisecond-level processing speed. SeReNet leverages 4D information priors to not only achieve better generalization than existing deep-learning methods, especially under challenging conditions such as strong noise, optical aberration, and sample motion, but also improve processing speed by 700 times over iterative tomography. Axial performance can be further enhanced via fine-tuning as an optional add-on with compromised generalization. We demonstrate these advantages by imaging living cells, zebrafish embryos and larvae, Caenorhabditis elegans, and mice. Equipped with SeReNet, sLFM now enables continuous day-long high-speed 3D subcellular imaging with over 300,000 volumes of large-scale intercellular dynamics, such as immune responses and neural activities, leading to widespread practical biological applications.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144034320","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":"A universal language for finding mass spectrometry data patterns.","authors":"Tito Damiani, Alan K Jarmusch, Allegra T Aron, Daniel Petras, Vanessa V Phelan, Haoqi Nina Zhao, Wout Bittremieux, Deepa D Acharya, Mohammed M A Ahmed, Anelize Bauermeister, Matthew J Bertin, Paul D Boudreau, Ricardo M Borges, Benjamin P Bowen, Christopher J Brown, Fernanda O Chagas, Kenneth D Clevenger, Mario S P Correia, William J Crandall, Max Crüsemann, Eoin Fahy, Oliver Fiehn, Neha Garg, William H Gerwick, Jeffrey R Gilbert, Daniel Globisch, Paulo Wender P Gomes, Steffen Heuckeroth, C Andrew James, Scott A Jarmusch, Sarvar A Kakhkhorov, Kyo Bin Kang, Nikolas Kessler, Roland D Kersten, Hyunwoo Kim, Riley D Kirk, Oliver Kohlbacher, Eftychia E Kontou, Ken Liu, Itzel Lizama-Chamu, Gordon T Luu, Tal Luzzatto Knaan, Helena Mannochio-Russo, Michael T Marty, Yuki Matsuzawa, Andrew C McAvoy, Laura-Isobel McCall, Osama G Mohamed, Omri Nahor, Heiko Neuweger, Timo H J Niedermeyer, Kozo Nishida, Trent R Northen, Kirsten E Overdahl, Johannes Rainer, Raphael Reher, Elys Rodriguez, Timo T Sachsenberg, Laura M Sanchez, Robin Schmid, Cole Stevens, Shankar Subramaniam, Zhenyu Tian, Ashootosh Tripathi, Hiroshi Tsugawa, Justin J J van der Hooft, Andrea Vicini, Axel Walter, Tilmann Weber, Quanbo Xiong, Tao Xu, Tomáš Pluskal, Pieter C Dorrestein, Mingxun Wang","doi":"10.1038/s41592-025-02660-z","DOIUrl":"https://doi.org/10.1038/s41592-025-02660-z","url":null,"abstract":"<p><p>Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006366","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":"Empowering chemists to mine high-throughput mass spectrometry datasets.","authors":"","doi":"10.1038/s41592-025-02661-y","DOIUrl":"https://doi.org/10.1038/s41592-025-02661-y","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144011086","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":"Transcriptome-wide and single-base m⁶A quantification with ultra-low RNA input.","authors":"","doi":"10.1038/s41592-025-02681-8","DOIUrl":"https://doi.org/10.1038/s41592-025-02681-8","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036413","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":"Extended culture of 2D gastruloids to model human mesoderm development.","authors":"Bohan Chen, Hina Khan, Zhiyuan Yu, LiAng Yao, Emily Freeburne, Kyoung Jo, Craig Johnson, Idse Heemskerk","doi":"10.1038/s41592-025-02669-4","DOIUrl":"https://doi.org/10.1038/s41592-025-02669-4","url":null,"abstract":"<p><p>Micropatterned human pluripotent stem cells treated with BMP4 (two-dimensional (2D) gastruloids) are among the most widely used stem cell models for human gastrulation. Due to its simplicity and reproducibility, this system is ideal for high-throughput quantitative studies of tissue patterning and has led to many insights into the mechanisms of mammalian gastrulation. However, 2D gastruloids have been studied only up to about 2 days owing to a loss of organization beyond this time with earlier protocols. Here we report an extended 2D gastruloid model to up to 10 days. We discovered a phase of highly reproducible morphogenesis between 2 and 4 days during which directed migration from the primitive streak-like region gives rise to a mesodermal layer beneath an epiblast-like layer. Multiple types of mesoderm arise with striking spatial organization including lateral plate mesoderm-like cells on the colony border and paraxial mesoderm-like cells further inside the colony. Single-cell transcriptomics showed strong similarity of these cells to mesoderm in human and nonhuman primate embryos. Our results illustrate that extended culture of 2D gastruloids provides a powerful model for human mesoderm differentiation and morphogenesis.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037048","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}