Nature Methods最新文献_第5页

A wireless modular platform for neuro-behavioral recording and closed-loop manipulation in small animals 一种用于小动物神经行为记录和闭环操作的无线模块化平台。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-10 DOI: 10.1038/s41592-026-03220-9

Zifang Zhao, Hongyu Chang, Praveen Paudel, Jaehyo Park, Can Liu, Maria Q. Aurelio, Azahara Oliva, Antonio Fernandez-Ruiz

{"title":"A wireless modular platform for neuro-behavioral recording and closed-loop manipulation in small animals","authors":"Zifang Zhao, Hongyu Chang, Praveen Paudel, Jaehyo Park, Can Liu, Maria Q. Aurelio, Azahara Oliva, Antonio Fernandez-Ruiz","doi":"10.1038/s41592-026-03220-9","DOIUrl":"10.1038/s41592-026-03220-9","url":null,"abstract":"Investigating the neural mechanisms of social group interactions and other naturalistic behaviors in small animals remains limited by current technology. Tethered neural recording systems are incompatible with many of these behaviors, while existing wireless devices for small animals are constrained by weight, bandwidth, recording duration and the lack of closed-loop modulation capabilities. Here we overcome these limitations by developing a Wireless, Interactive, Lightweight Datalogger (WILD) with integrated flexible neural probes, optogenetics, an inertial measurement unit, an ultrasonic microphone and a head-mounted camera. This platform enables simultaneous, long-term recording of neural activity, locomotor variables, vocalizations and eye movements from groups of freely moving mice in both laboratory and outdoor settings. Model-based real-time signal processing detects specific neural events and behavioral motifs to trigger closed-loop neural interventions. By combining multimodal recordings with advanced onboard signal-processing capabilities in a compact device, WILD enables the investigation of neural mechanisms underlying a broad range of natural behaviors in small animals. WILD is a wireless device that can record neuronal activity, perform optogenetic stimulations, record animal movements, record the environment or eye movements via an integrated camera, analyze the data and trigger optogenetic stimulation. This device facilitates studies of brain activity in freely moving animals such as mice, both in the laboratory and under naturalistic conditions.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"2099-2109"},"PeriodicalIF":28.3,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148930604","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

Wireless neural recordings from groups of mice inside and outside the laboratory 实验室内外老鼠的无线神经记录。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-10 DOI: 10.1038/s41592-026-03221-8

引用次数: 0

Intend to stay, intent on the dream 打算停留，专心于梦想。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-08 DOI: 10.1038/s41592-026-03240-5

Vivien Marx

引用次数: 0

MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography. MemBrain v2：一个端到端的工具，用于分析低温电子断层扫描膜。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-08 DOI: 10.1038/s41592-026-03178-8

Lorenz Lamm, Simon Zufferey, Hanyi Zhang, Ricardo D Righetto, Florent Waltz, Wojciech Wietrzynski, Kevin A Yamauchi, Alister Burt, Ye Liu, Antonio Martinez-Sanchez, Sebastian Ziegler, Fabian Isensee, Julia A Schnabel, Benjamin D Engel, Tingying Peng

{"title":"MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.","authors":"Lorenz Lamm, Simon Zufferey, Hanyi Zhang, Ricardo D Righetto, Florent Waltz, Wojciech Wietrzynski, Kevin A Yamauchi, Alister Burt, Ye Liu, Antonio Martinez-Sanchez, Sebastian Ziegler, Fabian Isensee, Julia A Schnabel, Benjamin D Engel, Tingying Peng","doi":"10.1038/s41592-026-03178-8","DOIUrl":"https://doi.org/10.1038/s41592-026-03178-8","url":null,"abstract":"Cryo-electron tomography provides unique insights into macromolecular complexes in their native environments, yet membrane analysis remains a major bottleneck due to low signal-to-noise ratios, missing wedge artifacts and the complexity of membrane-associated particles. Existing tools often require extensive manual annotation, struggle with generalization across datasets and lack integrated solutions for segmentation, particle localization and quantitative analysis. We introduce MemBrain v2, a deep-learning-enabled framework that unifies these tasks into a streamlined pipeline. MemBrain-seg leverages a diverse, collaboratively generated training dataset and specialized model training strategies to achieve generalizable membrane segmentation across variable tomographic conditions. MemBrain-pick enables data-efficient localization of membrane-bound particles by integrating geometric constraints with deep learning, reducing the need for extensive manual annotation. MemBrain-stats provides quantitative insights into particle distributions, computing spatial metrics to analyze intramembrane particle organization. MemBrain v2 integrates seamlessly into cryo-electron tomography workflows, providing an accessible and structured approach to membrane analysis.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912321","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

Reconstructing signaling histories of single cells via perturbation screens and transfer learning 通过扰动筛选和迁移学习重建单细胞的信号历史。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-08 DOI: 10.1038/s41592-026-03213-8

Nicholas T. Hutchins, Miram Meziane, Claire Lu, Maisam Mitalipova, David S. Fischer, Pulin Li

{"title":"Reconstructing signaling histories of single cells via perturbation screens and transfer learning","authors":"Nicholas T. Hutchins, Miram Meziane, Claire Lu, Maisam Mitalipova, David S. Fischer, Pulin Li","doi":"10.1038/s41592-026-03213-8","DOIUrl":"10.1038/s41592-026-03213-8","url":null,"abstract":"Manipulating the signaling environment is an effective approach to alter cellular states for broad-ranging applications. Such manipulation requires knowing the signaling states and histories experienced by cells in vivo, for which high-throughput discovery methods are lacking. Here we present an integrated experimental–computational framework that learns transferable signaling response signatures from a high-throughput in vitro perturbation atlas and infers signaling activities and histories in in vivo cell types with high accuracy and temporal resolution. We generated a signaling perturbation atlas on human pluripotent stem cells and used it to train IRIS, a neural-network model. Applying IRIS to mouse embryo single-cell atlases, we uncovered global features of combinatorial signaling code usage, identified biologically meaningful heterogeneity and reconstructed signaling histories along diverse developmental lineages. This framework reveals that diverse cell types share conserved signaling response signatures, and provides a scalable solution for mapping complex signaling interactions in vivo to guide targeted interventions and enable cell fate engineering. IRIS learns conserved signaling representations from in vitro perturbation data to reconstruct signaling states and histories across diverse in vivo cellular contexts.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 10","pages":"1973-1983"},"PeriodicalIF":28.3,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-026-03213-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912472","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

Rapid robust high-fidelity 3D neuronal extraction from multiview calcium imaging datasets. 快速鲁棒高保真三维神经元提取从多视图钙成像数据集。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-07 DOI: 10.1038/s41592-026-03215-6

Yujia Chen, Guoxun Zhang, Mingrui Wang, Yuanlong Zhang, Jingyu Xie, Zhifeng Zhao, Ruqi Huang, Jiamin Wu, Qionghai Dai

{"title":"Rapid robust high-fidelity 3D neuronal extraction from multiview calcium imaging datasets.","authors":"Yujia Chen, Guoxun Zhang, Mingrui Wang, Yuanlong Zhang, Jingyu Xie, Zhifeng Zhao, Ruqi Huang, Jiamin Wu, Qionghai Dai","doi":"10.1038/s41592-026-03215-6","DOIUrl":"https://doi.org/10.1038/s41592-026-03215-6","url":null,"abstract":"Recent developments in imaging facilitate large-scale three-dimensional (3D) neuronal recording. While the resulting large datasets shed light on population-level neural coding, extracting neuronal calcium dynamics from 3D volumes remains more challenging than from two-dimensional images due to noise and scattering. Here we present DeepWonder3D, a general end-to-end pipeline for rapid and robust 3D neuronal extraction with high fidelity. Instead of processing voxel by voxel, DeepWonder3D works on the multiview projections of 3D imaging data obtained either digitally or optically through specific point spread functions and is therefore applicable to diverse techniques, including point-scanning microscopy, light-field microscopy and two-photon synthetic aperture microscopy. Integrating denoising, resolution registration, background removal, neuronal extraction and multiview fusion into a unified pipeline tailored for large-scale high-resolution datasets contaminated by noise and scattering, DeepWonder3D outperforms state-of-the-art methods in 3D localization accuracy with a tenfold reduction in computational costs, validated by numerical simulations and a hybrid two-photon/light-field imaging system. With the RUSH3D mesoscope, DeepWonder3D achieves high-fidelity 3D calcium extraction of tens of thousands of neurons across the mouse cortex within hours.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":28.3,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906524","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

Benchmarking biomedical foundation models 对标生物医学基础模型。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-04 DOI: 10.1038/s41592-026-03182-y

Julio Saez-Rodriguez, Philipp Sven Lars Schäfer, Nikolas Kalavros, Gustavo Stolovitzky

{"title":"Benchmarking biomedical foundation models","authors":"Julio Saez-Rodriguez, Philipp Sven Lars Schäfer, Nikolas Kalavros, Gustavo Stolovitzky","doi":"10.1038/s41592-026-03182-y","DOIUrl":"10.1038/s41592-026-03182-y","url":null,"abstract":"A transparent evaluation and proof of reproducibility, generalization and replicability of algorithms are the bedrock of method development in computational biology. Many benchmarking efforts have been developed for problems ranging from structural biology to translational biomedicine. Rigor is relatively controllable for tasks such as the prediction of patient outcomes or the outcomes of biological assays, but the problem is exacerbated when the aim is to benchmark foundation models. The parameters constituting them are supposed to capture the patterns underlying the data; therefore, the models are parameterized embodiments of the phenomena that gave rise to the data. How can we test the limitations of these models? Here, we discuss the epistemological value of foundation models; whether they can be refuted, verified or evaluated primarily on the basis of utility; what principles should guide their benchmarking; and what role the scientific community should play in that benchmarking process. Foundation models are becoming increasingly popular across various biological domains, yet rigorous methods to comprehensively evaluate their full potential remain underdeveloped. This Perspective discusses current limitations and challenges in benchmarking these models and proposes guidelines for more robust and meaningful evaluation.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 9","pages":"1724-1733"},"PeriodicalIF":28.3,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891966","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

A viral ORFeome 病毒式传播。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-04 DOI: 10.1038/s41592-026-03231-6

Lin Tang

引用次数: 0

Consequences of missing data 丢失数据的后果。

IF 28.3 1区生物学

Nature Methods Pub Date : 2026-09-04 DOI: 10.1038/s41592-026-03203-w

Tanujit Dey, Stuart R. Lipsitz, Garrett Fitzmaurice, Martin Krzywinski, Naomi Altman

引用次数: 0

The graphical abstract 图形摘要。