Pub Date : 2025-12-08DOI: 10.1038/s41592-025-02945-3
Jan Funke
As connectomics datasets grow in size and quantity, future reconstruction methods will have to work with minimal or no human supervision. For that, we will need methods that can quantify data and model uncertainty in order to assess the level of trust we can put in the downstream analysis of connectomes.
{"title":"Uncertainty quantification for connectomics","authors":"Jan Funke","doi":"10.1038/s41592-025-02945-3","DOIUrl":"10.1038/s41592-025-02945-3","url":null,"abstract":"As connectomics datasets grow in size and quantity, future reconstruction methods will have to work with minimal or no human supervision. For that, we will need methods that can quantify data and model uncertainty in order to assess the level of trust we can put in the downstream analysis of connectomes.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2484-2486"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708704","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}
Pub Date : 2025-12-08DOI: 10.1038/s41592-025-02946-2
Marta Costa
Advances in connectomics are enabling the mapping of connectomes across individuals, sexes or species. Multiple comparisons enable the categorization of differences in these wiring diagrams as either technical or biological variability, or those that might impact circuit function. Testing these predictions experimentally will help us understand how evolution operates in neural circuits.
{"title":"Using comparative connectomics to understand variability and evolution in neural circuits","authors":"Marta Costa","doi":"10.1038/s41592-025-02946-2","DOIUrl":"10.1038/s41592-025-02946-2","url":null,"abstract":"Advances in connectomics are enabling the mapping of connectomes across individuals, sexes or species. Multiple comparisons enable the categorization of differences in these wiring diagrams as either technical or biological variability, or those that might impact circuit function. Testing these predictions experimentally will help us understand how evolution operates in neural circuits.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2481-2483"},"PeriodicalIF":32.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708761","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}
Pub Date : 2025-12-04DOI: 10.1038/s41592-025-02892-z
Nondestructively mapping biological tissues in 3D with nanoscale detail is essential to scale up the study of how cells interact in their environment, such as in neuronal circuits. We resolved such ultrastructure in brain tissue using coherent X-ray phase-contrast imaging techniques, which extends the volume imaging toolbox with nondestructive approaches.
{"title":"Unlocking the potential of X-rays to scale up tissue ultrastructure mapping","authors":"","doi":"10.1038/s41592-025-02892-z","DOIUrl":"10.1038/s41592-025-02892-z","url":null,"abstract":"Nondestructively mapping biological tissues in 3D with nanoscale detail is essential to scale up the study of how cells interact in their environment, such as in neuronal circuits. We resolved such ultrastructure in brain tissue using coherent X-ray phase-contrast imaging techniques, which extends the volume imaging toolbox with nondestructive approaches.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2512-2513"},"PeriodicalIF":32.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678346","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}
Pub Date : 2025-12-04DOI: 10.1038/s41592-025-02893-y
France Rose, Monika Michaluk, Timon Blindauer, Bogna M. Ignatowska-Jankowska, Liam O’Shaughnessy, Greg J. Stephens, Talmo D. Pereira, Marylka Y. Uusisaari, Katarzyna Bozek
Pose estimation methods and motion capture systems have opened doors to quantitative measurements of animal kinematics. While animal behavior experiments are expensive and complex, tracking errors sometimes make large portions of the experimental data unusable. Here our deep learning method, Deep Imputation for Skeleton data (DISK), uncovers dependencies between keypoints and their dynamics to impute missing tracking data without the help of any manual annotations. We demonstrate the utility and performance of DISK on seven animal skeletons including multi-animal setups. The imputed recordings allow us to detect more episodes of motion, such as steps, and obtain more statistically robust results when comparing these episodes between experimental conditions. In addition, by learning to impute the missing content, DISK learns meaningful representations of the data capturing, for example, underlying actions. This stand-alone imputation package, available at https://github.com/bozeklab/DISK.git/ , is applicable to outputs of tracking methods (marker-based or markerless) and allows for varied types of downstream analysis. Analysis of behavioral data often involves tracking animal keypoints in video and motion capture recordings. DISK imputes missing keypoints, thereby improving downstream analyses.
姿态估计方法和动作捕捉系统为动物运动学的定量测量打开了大门。虽然动物行为实验既昂贵又复杂，但跟踪错误有时会使大部分实验数据无法使用。在这里，我们的深度学习方法，深度Imputation for Skeleton data (DISK)，揭示关键点及其动态之间的依赖关系，在没有任何手动注释的帮助下，输入缺失的跟踪数据。我们演示了DISK在七种动物骨骼（包括多动物设置）上的效用和性能。输入的记录使我们能够检测到更多的运动事件，例如步骤，并在比较实验条件下的这些事件时获得更具统计稳健性的结果。此外，通过学习输入缺失的内容，DISK学习数据捕获的有意义的表示，例如，底层操作。这个独立的输入包，可在https://github.com/bozeklab/DISK.git/上获得，适用于跟踪方法的输出（基于标记或无标记），并允许各种类型的下游分析。
{"title":"Deep Imputation for Skeleton data (DISK) for behavioral science","authors":"France Rose, Monika Michaluk, Timon Blindauer, Bogna M. Ignatowska-Jankowska, Liam O’Shaughnessy, Greg J. Stephens, Talmo D. Pereira, Marylka Y. Uusisaari, Katarzyna Bozek","doi":"10.1038/s41592-025-02893-y","DOIUrl":"10.1038/s41592-025-02893-y","url":null,"abstract":"Pose estimation methods and motion capture systems have opened doors to quantitative measurements of animal kinematics. While animal behavior experiments are expensive and complex, tracking errors sometimes make large portions of the experimental data unusable. Here our deep learning method, Deep Imputation for Skeleton data (DISK), uncovers dependencies between keypoints and their dynamics to impute missing tracking data without the help of any manual annotations. We demonstrate the utility and performance of DISK on seven animal skeletons including multi-animal setups. The imputed recordings allow us to detect more episodes of motion, such as steps, and obtain more statistically robust results when comparing these episodes between experimental conditions. In addition, by learning to impute the missing content, DISK learns meaningful representations of the data capturing, for example, underlying actions. This stand-alone imputation package, available at https://github.com/bozeklab/DISK.git/ , is applicable to outputs of tracking methods (marker-based or markerless) and allows for varied types of downstream analysis. Analysis of behavioral data often involves tracking animal keypoints in video and motion capture recordings. DISK imputes missing keypoints, thereby improving downstream analyses.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"236-247"},"PeriodicalIF":32.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02893-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678223","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}
Pub Date : 2025-12-04DOI: 10.1038/s41592-025-02880-3
Daniel T. Haas, Daniel Weindl, Pamela Kakimoto, Eva-Maria Trautmann, Julia P. Schessner, Xia Mao, Mathias J. Gerl, Maximilian Gerwien, Timo D. Müller, Christian Klose, Xiping Cheng, Jan Hasenauer, Natalie Krahmer
Systematic proteomic organelle profiling methods including protein correlation profiling and LOPIT have advanced our understanding of cellular compartmentalization. To manage the complexity of organelle profiling data, we introduce C-COMPASS, a user-friendly open-source software that employs a neural network-based regression model to predict the spatial cellular distribution of proteins. C-COMPASS handles complex multilocalization patterns and integrates protein abundance to model organelle composition changes across conditions. We apply C-COMPASS to mice with humanized livers to elucidate organelle remodeling during metabolic perturbations. Additionally, by training neural networks with co-generated marker protein profiles, C-COMPASS extends spatial profiling to lipids, overcoming the lack of organelle-specific lipid markers, allowing for determination of localization and tracking of lipid species across different compartments. This provides integrated snapshots of organelle lipid and protein compositions. Overall, C-COMPASS offers an accessible tool for multiomic studies of organelle dynamics without needing advanced computational skills, empowering researchers to explore new questions in lipidomics, proteomics and organelle biology. C-COMPASS is an open-source software designed to predict the spatial cellular distribution of proteins and lipids from cellular organelle profiling using a neural network-based regression model.
{"title":"C-COMPASS: a user-friendly neural network tool profiles cell compartments at protein and lipid levels","authors":"Daniel T. Haas, Daniel Weindl, Pamela Kakimoto, Eva-Maria Trautmann, Julia P. Schessner, Xia Mao, Mathias J. Gerl, Maximilian Gerwien, Timo D. Müller, Christian Klose, Xiping Cheng, Jan Hasenauer, Natalie Krahmer","doi":"10.1038/s41592-025-02880-3","DOIUrl":"10.1038/s41592-025-02880-3","url":null,"abstract":"Systematic proteomic organelle profiling methods including protein correlation profiling and LOPIT have advanced our understanding of cellular compartmentalization. To manage the complexity of organelle profiling data, we introduce C-COMPASS, a user-friendly open-source software that employs a neural network-based regression model to predict the spatial cellular distribution of proteins. C-COMPASS handles complex multilocalization patterns and integrates protein abundance to model organelle composition changes across conditions. We apply C-COMPASS to mice with humanized livers to elucidate organelle remodeling during metabolic perturbations. Additionally, by training neural networks with co-generated marker protein profiles, C-COMPASS extends spatial profiling to lipids, overcoming the lack of organelle-specific lipid markers, allowing for determination of localization and tracking of lipid species across different compartments. This provides integrated snapshots of organelle lipid and protein compositions. Overall, C-COMPASS offers an accessible tool for multiomic studies of organelle dynamics without needing advanced computational skills, empowering researchers to explore new questions in lipidomics, proteomics and organelle biology. C-COMPASS is an open-source software designed to predict the spatial cellular distribution of proteins and lipids from cellular organelle profiling using a neural network-based regression model.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"118-130"},"PeriodicalIF":32.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02880-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678161","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}
We propose a latent space-based statistical network analysis (LatentSNA) method that implements network science in a generative Bayesian framework, preserves neurologically meaningful brain topology and improves statistical power for imaging biomarker detection. LatentSNA (1) addresses the lack of power and inflated type II errors in current analytic approaches when detecting imaging biomarkers, (2) allows unbiased estimation of the influence of biomarkers on behavioral variants, (3) quantifies uncertainty and evaluates the likelihood of estimated biomarker effects against chance and (4) improves brain–behavior prediction in new samples as well as the clinical utility of neuroimaging findings. LatentSNA is broadly applicable across multiple imaging modalities and outcome measures in developing, aging and transdiagnostic cohorts, totaling 8,003 to 11,861 participants. LatentSNA achieves substantial accuracy gains (averaging 110–150%) and replicability improvements (averaging 153%) over existing approaches in moderate to large datasets. As a result, LatentSNA elucidates how network topology is implicated in brain–behavior relationships. LatentSNA is a method for network analysis in human neuroimaging. It facilitates linking neural activity with behavior and improves biomarker prediction by reducing type II errors.
{"title":"Latent space-based network analysis for brain–behavior linking in neuroimaging","authors":"Selena Wang, Xinzhi Zhang, Yunhe Liu, Wanwan Xu, Xinyuan Tian, Yize Zhao","doi":"10.1038/s41592-025-02896-9","DOIUrl":"10.1038/s41592-025-02896-9","url":null,"abstract":"We propose a latent space-based statistical network analysis (LatentSNA) method that implements network science in a generative Bayesian framework, preserves neurologically meaningful brain topology and improves statistical power for imaging biomarker detection. LatentSNA (1) addresses the lack of power and inflated type II errors in current analytic approaches when detecting imaging biomarkers, (2) allows unbiased estimation of the influence of biomarkers on behavioral variants, (3) quantifies uncertainty and evaluates the likelihood of estimated biomarker effects against chance and (4) improves brain–behavior prediction in new samples as well as the clinical utility of neuroimaging findings. LatentSNA is broadly applicable across multiple imaging modalities and outcome measures in developing, aging and transdiagnostic cohorts, totaling 8,003 to 11,861 participants. LatentSNA achieves substantial accuracy gains (averaging 110–150%) and replicability improvements (averaging 153%) over existing approaches in moderate to large datasets. As a result, LatentSNA elucidates how network topology is implicated in brain–behavior relationships. LatentSNA is a method for network analysis in human neuroimaging. It facilitates linking neural activity with behavior and improves biomarker prediction by reducing type II errors.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"225-235"},"PeriodicalIF":32.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678252","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}
Pub Date : 2025-12-03DOI: 10.1038/s41592-025-02975-x
Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Raut Altae-Tran, Magnus S. Bauer, Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, David Baker
Designing new enzymes typically begins with idealized arrangements of catalytic functional groups around a reaction transition state, then attempts to generate protein structures that precisely position these groups. Current AI-based methods can create active enzymes but require predefined residue positions and rely on reverse-building residue backbones from side-chain placements, which limits design flexibility. Here we show that a new deep generative model, RoseTTAFold diffusion 2 (RFdiffusion2), overcomes these constraints by designing enzymes directly from functional group geometries without specifying residue order or performing inverse rotamer generation. RFdiffusion2 successfully generates scaffolds for all 41 active sites in a diverse benchmark, compared to 16 using previous methods. We further design enzymes for three distinct catalytic mechanisms and identify active candidates after experimentally testing fewer than 96 sequences in each case. These results highlight the potential of atomic-level generative modeling to create de novo enzymes directly from reaction mechanisms. RFdiffusion2, an extension of the RFdiffusion framework, builds de novo enzyme active sites using atom-level functional group constraints.
{"title":"Atom-level enzyme active site scaffolding using RFdiffusion2","authors":"Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Raut Altae-Tran, Magnus S. Bauer, Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, David Baker","doi":"10.1038/s41592-025-02975-x","DOIUrl":"10.1038/s41592-025-02975-x","url":null,"abstract":"Designing new enzymes typically begins with idealized arrangements of catalytic functional groups around a reaction transition state, then attempts to generate protein structures that precisely position these groups. Current AI-based methods can create active enzymes but require predefined residue positions and rely on reverse-building residue backbones from side-chain placements, which limits design flexibility. Here we show that a new deep generative model, RoseTTAFold diffusion 2 (RFdiffusion2), overcomes these constraints by designing enzymes directly from functional group geometries without specifying residue order or performing inverse rotamer generation. RFdiffusion2 successfully generates scaffolds for all 41 active sites in a diverse benchmark, compared to 16 using previous methods. We further design enzymes for three distinct catalytic mechanisms and identify active candidates after experimentally testing fewer than 96 sequences in each case. These results highlight the potential of atomic-level generative modeling to create de novo enzymes directly from reaction mechanisms. RFdiffusion2, an extension of the RFdiffusion framework, builds de novo enzyme active sites using atom-level functional group constraints.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"96-105"},"PeriodicalIF":32.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02975-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145669068","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}
Pub Date : 2025-12-03DOI: 10.1038/s41592-025-02938-2
Vivien Marx
Leading a lab is both a venture and an adventure. It’s double that for these researchers.
领导一个实验室既是一种冒险，也是一种冒险。对这些研究人员来说是双倍的。
{"title":"Two as one: when scientists run a lab together","authors":"Vivien Marx","doi":"10.1038/s41592-025-02938-2","DOIUrl":"10.1038/s41592-025-02938-2","url":null,"abstract":"Leading a lab is both a venture and an adventure. It’s double that for these researchers.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 12","pages":"2465-2466"},"PeriodicalIF":32.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145669021","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}
Pub Date : 2025-12-02DOI: 10.1038/s41592-025-02911-z
Shihang Luo (, ), Xian’ao Zhao (, ), Yuanyuan Li (, ), Chunyan Fan (, ), Ruina Liu (, ), Ran Gong (, ), Weixing Li (, ), Nana Ma (, ), Zhenghong Yang (, ), Tao Xu (, ), Wei Ji (, ), Lusheng Gu (, )
Three-dimensional (3D) nanoscale imaging reveals the detailed morphology of subcellular structures; however, conventional single-molecule localization microscopy is constrained by limited axial resolution. Here we introduce ROSE-3D, an interferometric localization approach that enables isotropic 3D super-resolution imaging with uniform performance across the entire depth of field. Compared with conventional astigmatism-based methods, ROSE-3D improves lateral localization precision by 2–6 times and axial precision by 3.5–8 times over a depth of field of approximately 1.2 μm. Leveraging its multicolor and whole-cell imaging capabilities, ROSE-3D resolves, in situ, the nanoscale organization of nuclear lamins and the assemblies of mitochondrial fission-related protein DRP1. These results establish ROSE-3D as a powerful tool for interrogating nanoscale cellular architecture. ROSE-3D is a single-molecule localization microscopy approach that achieves high isotropic resolution via interferometric localization. The approach is capable of whole-cell and multicolor imaging.
{"title":"Molecular-scale isotropic 3D super-resolution microscopy via interference localization","authors":"Shihang Luo \u0000 (, ), Xian’ao Zhao \u0000 (, ), Yuanyuan Li \u0000 (, ), Chunyan Fan \u0000 (, ), Ruina Liu \u0000 (, ), Ran Gong \u0000 (, ), Weixing Li \u0000 (, ), Nana Ma \u0000 (, ), Zhenghong Yang \u0000 (, ), Tao Xu \u0000 (, ), Wei Ji \u0000 (, ), Lusheng Gu \u0000 (, )","doi":"10.1038/s41592-025-02911-z","DOIUrl":"10.1038/s41592-025-02911-z","url":null,"abstract":"Three-dimensional (3D) nanoscale imaging reveals the detailed morphology of subcellular structures; however, conventional single-molecule localization microscopy is constrained by limited axial resolution. Here we introduce ROSE-3D, an interferometric localization approach that enables isotropic 3D super-resolution imaging with uniform performance across the entire depth of field. Compared with conventional astigmatism-based methods, ROSE-3D improves lateral localization precision by 2–6 times and axial precision by 3.5–8 times over a depth of field of approximately 1.2 μm. Leveraging its multicolor and whole-cell imaging capabilities, ROSE-3D resolves, in situ, the nanoscale organization of nuclear lamins and the assemblies of mitochondrial fission-related protein DRP1. These results establish ROSE-3D as a powerful tool for interrogating nanoscale cellular architecture. ROSE-3D is a single-molecule localization microscopy approach that achieves high isotropic resolution via interferometric localization. The approach is capable of whole-cell and multicolor imaging.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"183-192"},"PeriodicalIF":32.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145661543","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：