Nature Methods最新文献

{"title":"'FILMing' the metabolic landscape of individual cell organelles.","authors":"","doi":"10.1038/s41592-026-03091-0","DOIUrl":"https://doi.org/10.1038/s41592-026-03091-0","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840398","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":"FILM: mapping organellar metabolism by mid-infrared photothermal-modulated fluorescence.","authors":"Jianpeng Ao, Jiaze Yin, Haonan Lin, Guangrui Ding, Youchen Guan, Marzia Savini, Bethany Weinberg, Dashan Dong, Qing Xia, Zhongyue Guo, Bowen Liu, Biwen Gao, Ji-Xin Cheng, Meng C Wang","doi":"10.1038/s41592-026-03090-1","DOIUrl":"https://doi.org/10.1038/s41592-026-03090-1","url":null,"abstract":"<p><p>Metabolism unfolds within specific organelles in eukaryotic cells. Lysosomes are highly metabolically active organelles, and their metabolic states dynamically influence signal transduction, cellular homeostasis and organismal physiopathology. Despite the importance of lysosomal metabolism, a method for its in vivo measurement is currently lacking. Here we report a fluorescence-detected mid-infrared photothermal microscope (FILM) implemented with optical boxcar demodulation, artificial intelligence-assisted data denoising and spectral deconvolution, to map metabolic activity and composition of individual lysosomes in living cells and organisms. Using this method, we uncovered lipolysis and proteolysis heterogeneity across lysosomes within the same cell, as well as early-onset lysosomal dysfunction during organismal aging. In addition, we discovered organelle-level metabolic changes associated with diverse lysosomal storage diseases. This method holds the broad potential to profile metabolic fingerprints of individual organelles within their native context and quantitatively assess their dynamic changes under different physiological and pathological conditions, providing a high-resolution chemical cellular atlas.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840415","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":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\"><ns0:math><ns0:mrow><ns0:mi>Micro</ns0:mi> <ns0:mi>S</ns0:mi> <ns0:mi>plit</ns0:mi></ns0:mrow> </ns0:math> : semantic unmixing of fluorescent microscopy data.","authors":"Ashesh Ashesh, Federico Carrara, Igor Zubarev, Vera Galinova, Melisande Croft, Melissa Pezzotti, Daozheng Gong, Francesca Casagrande, Elisa Colombo, Stefania Giussani, Elena Restelli, Eugenia Cammarota, Juan Manuel Battagliotti, Nikolai Klena, Moises Di Sante, Raghabendra Adhikari, Daniel Feliciano, Gaia Pigino, Elena Taverna, Oliver Harschnitz, Nicola Maghelli, Norbert Scherer, Damian Edward Dalle Nogare, Joran Deschamps, Francesco Pasqualini, Florian Jug","doi":"10.1038/s41592-026-03082-1","DOIUrl":"https://doi.org/10.1038/s41592-026-03082-1","url":null,"abstract":"<p><p>Fluorescence microscopy is constrained by optical limits, fluorophore chemistry and finite photon budgets, imposing trade-offs between imaging speed, resolution and phototoxicity. Here we introduce <math><mrow><mi>Micro</mi> <mi>S</mi> <mi>plit</mi></mrow> </math> , a deep learning-based computational multiplexing method that enables multiple cellular structures to be imaged simultaneously in a single fluorescent channel and then computationally unmixed. We show that <math><mrow><mspace></mspace> <mrow><mrow><mi>M</mi> <mi>i</mi> <mi>c</mi> <mi>r</mi> <mi>o</mi></mrow> </mrow> <mrow><mrow><mi>S</mi></mrow> </mrow> <mrow><mrow><mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi></mrow> </mrow> </mrow> </math> separates up to four superimposed noisy structures into distinct, denoised image channels, enabling faster and more photon-efficient imaging. Built on Variational Splitting Encoder-Decoder networks, <math> <mrow><mrow><mi>M</mi> <mi>i</mi> <mi>c</mi> <mi>r</mi> <mi>o</mi></mrow> </mrow> <mrow><mrow><mi>S</mi></mrow> </mrow> <mrow><mrow><mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi></mrow> </mrow> </math> models a posterior distribution over solutions, allowing uncertainty-aware predictions and the estimation of spatially resolved prediction errors from posterior variability. We demonstrate robust performance across diverse datasets, noise levels and imaging conditions, and show that <math> <mrow><mrow><mi>M</mi> <mi>i</mi> <mi>c</mi> <mi>r</mi> <mi>o</mi></mrow> </mrow> <mrow><mrow><mi>S</mi></mrow> </mrow> <mrow><mrow><mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi></mrow> </mrow> </math> improves downstream analysis while reducing photon exposure. All methods, data and trained models are released as open resources, enabling immediate adoption of computational multiplexing in biological imaging.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840418","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":"Unsupervised transfer learning enables multi-animal tracking without training annotation.","authors":"Yixin Li, Qi Zhang, Yuanlong Zhang, Jiaqi Fan, Zhi Lu, Xinhong Xu, Xinyang Li, Ziwei Li, Jiamin Wu, Qionghai Dai","doi":"10.1038/s41592-026-03051-8","DOIUrl":"https://doi.org/10.1038/s41592-026-03051-8","url":null,"abstract":"<p><p>Quantitative ethology necessitates accurate tracking of animal locomotion, especially for population-level analyses involving multiple individuals. However, current methods mostly rely on laborious annotations for supervised training and have restricted performance under challenging conditions. Here we present an unsupervised deep transfer learning method for multi-animal tracking (UDMT) that achieves state-of-the-art performance without requiring training annotations. By synergizing a bidirectional closed-loop tracking strategy, a spatiotemporal transformer network and three dedicated modules for localization refining, bidirectional identity correction and automatic parameter tuning, UDMT can track multiple animals accurately under various challenging conditions, such as crowding, occlusion, rapid motion, low image contrast and cross-species experiments. We demonstrate the versatility of UDMT on five different kinds of model animals, including mice, rats, Drosophila, Caenorhabditis elegans and Betta splendens. Combined with a head-mounted miniaturized microscope, we illustrate the power of UDMT for neuroethological interrogations to decipher the correlations between animal locomotion and neural activity.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840406","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":"Double robustness.","authors":"Christoph F Kurz, Martin Krzywinski, Naomi Altman","doi":"10.1038/s41592-026-03099-6","DOIUrl":"https://doi.org/10.1038/s41592-026-03099-6","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840354","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":"Call your AI agent.","authors":"Vivien Marx","doi":"10.1038/s41592-026-03088-9","DOIUrl":"https://doi.org/10.1038/s41592-026-03088-9","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147817779","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":"Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.","authors":"Mariia Bilous, Daria Buszta, Jonathan Bac, Senbai Kang, Yixing Dong, Stephanie Tissot, Sylvie Andre, Marina Alexandre Gaveta, Christel Voize, Solange Peters, Krisztian Homicsko, Raphael Gottardo","doi":"10.1038/s41592-026-03089-8","DOIUrl":"https://doi.org/10.1038/s41592-026-03089-8","url":null,"abstract":"<p><p>Spatial transcriptomics enables high-resolution gene expression mapping in intact tissues. Xenium is widely adopted for its reliability, accessibility and data quality, yet the properties and limitations of Xenium-derived data remain poorly characterized. Here we present one of the most comprehensive Xenium datasets so far, encompassing over 40 breast and lung tumor sections profiled using diverse gene panels. Leveraging this resource, we systematically dissect technical noise-including transcript spillover-along with assay specificity, panel performance and segmentation strategies. We demonstrate that single-nucleus RNA sequencing enables precise quantification of transcript contamination. Building on these insights, we introduce SPLIT (Spatial Purification of Layered Intracellular Transcripts), a method that improves signal purity by resolving mixed transcriptomic signals. SPLIT enhances background correction and cell-type resolution and enables the revelation of T-cell exhaustion signatures associated with malignant cell colocalization-signals that would otherwise remain obscured. Together, our findings provide a critical benchmark for Xenium performance and introduce a scalable strategy for signal refinement.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147817802","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":"eSIG-Net: an interaction language model that decodes the protein code of single mutations.","authors":"Xingxin Pan, Aditya Shrawat, Sidharth Raghavan, Chuanpeng Dong, Yuntao Yang, Zhao Li, W Jim Zheng, S Gail Eckhardt, Erxi Wu, Juan I Fuxman Bass, Daniel F Jarosz, Sidi Chen, Daniel J McGrail, Gloria M Sheynkman, Jason H Huang, Nidhi Sahni, S Stephen Yi","doi":"10.1038/s41592-026-03086-x","DOIUrl":"https://doi.org/10.1038/s41592-026-03086-x","url":null,"abstract":"<p><p>Most proteins act through interactions with other molecules, yet predicting how single mutations perturb these interactions-defined as 'protein codes'-remains a central challenge in computational biology. Here we introduce eSIG-Net, the edgetic mutation sequence-based interaction grammar network, a language model that integrates protein sequence embeddings with syntax-aware and evolution-aware mutation encoding and contrastive learning to predict mutation-driven interaction changes. eSIG-Net outperforms state-of-the-art sequence-based and structure-based methods, nominates causal variants and provides mechanistic insights. Together, eSIG-Net is a mutation-centric interaction language model that accurately predicts interaction-specific network rewiring from sequence information alone and generalizes across biological contexts.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147777036","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":"Single-molecule localization and diffusivity microscopy reveals dynamic biomolecular organization in living cells.","authors":"Zuhui Wang, Yiwen Liu, Bo Wang, Xiangyu Liu, Wulan Deng","doi":"10.1038/s41592-026-03078-x","DOIUrl":"https://doi.org/10.1038/s41592-026-03078-x","url":null,"abstract":"<p><p>Single-molecule tracking in living cells measures protein diffusivity but requires sparse imaging, limiting high-density mapping. Here we introduce single-molecule localization and diffusivity microscopy (SMLDM), a deep learning-based approach that accurately estimates single-molecule movement tracks and diffusion coefficients directly from single-frame snapshots, eliminating the need for trajectory linking. Implemented as mobility photoactivated localization microscopy (MPALM) with bright photoactivatable fluorophores and U-Net-based single-molecule segmentation, this method achieves a 50- to 300-fold increase in data density compared to conventional tracking-based approaches, generating high-density, spatially super-resolved maps of molecular diffusivity and organization in living human cells. We applied MPALM to diverse dynamic cellular processes, uncovering nucleosome clustering into low-mobility chromatin domains, pathway-biased μ-opioid receptor dynamic clustering, focal adhesion movement and nonuniform molecular diffusivity and microcondensate organization during early droplet coalescence. SMLDM provides a powerful tool for resolving biomolecular organization and dynamics at single-molecule resolution in live cells.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147777002","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":"Self-localized ultrafast pencil beam for volumetric multiphoton imaging.","authors":"Honghao Cao, Sarah Spitz, Li-Yu Yu, Kunzan Liu, Zhengyu Zhang, Federico Presutti, Francesca Michela Pramotton, Subhash Kulkarni, Roger D Kamm, Sixian You","doi":"10.1038/s41592-026-03067-0","DOIUrl":"https://doi.org/10.1038/s41592-026-03067-0","url":null,"abstract":"<p><p>The formation of organized optical states in multidimensional systems is crucial for understanding light-matter interaction and advancing light-shaping technologies. Here we report the observation of a self-localized, ultrafast pencil beam near the critical power in a standard multimode fiber. We demonstrate that self-focusing, traditionally considered detrimental, facilitates a nonlinear spatiotemporal localized state with a sidelobe-suppressed Bessel-like profile and markedly improved stability. Generated simply by an on-axis Gaussian launch, this beam is readily integrated into standard multiphoton microscopes. We applied this self-localized beam to two-photon imaging of mouse enteric nervous systems, where it outperformed conventional Bessel beams through reduced sidelobes and enhanced aberration resilience. Lastly, we monitored transferrin uptake dynamics in a live human blood-brain barrier model using minute-resolved three-dimensional scans, revealing spatiotemporal heterogeneity across different cell types. Our findings offer a robust approach for generating ultrafast pencil beams, enabling high-throughput three-dimensional biosystem imaging to elucidate biological transport pathways.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776955","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}