Nature Methods最新文献_第2页

Benchmarking methods for mapping functional connectivity in the brain. 绘制大脑功能连接的基准方法。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 Epub Date: 2025-06-06 DOI: 10.1038/s41592-025-02704-4

Zhen-Qi Liu, Andrea I Luppi, Justine Y Hansen, Ye Ella Tian, Andrew Zalesky, B T Thomas Yeo, Ben D Fulcher, Bratislav Misic

{"title":"Benchmarking methods for mapping functional connectivity in the brain.","authors":"Zhen-Qi Liu, Andrea I Luppi, Justine Y Hansen, Ye Ella Tian, Andrew Zalesky, B T Thomas Yeo, Ben D Fulcher, Bratislav Misic","doi":"10.1038/s41592-025-02704-4","DOIUrl":"10.1038/s41592-025-02704-4","url":null,"abstract":"The networked architecture of the brain promotes synchrony among neuronal populations. These communication patterns can be mapped using functional imaging, yielding functional connectivity (FC) networks. While most studies use Pearson's correlations by default, numerous pairwise interaction statistics exist in the scientific literature. How does the organization of the FC matrix vary with the choice of pairwise statistic? Here we use a library of 239 pairwise statistics to benchmark canonical features of FC networks, including hub mapping, weight-distance trade-offs, structure-function coupling, correspondence with other neurophysiological networks, individual fingerprinting and brain-behavior prediction. We find substantial quantitative and qualitative variation across FC methods. Measures such as covariance, precision and distance display multiple desirable properties, including correspondence with structural connectivity and the capacity to differentiate individuals and predict individual differences in behavior. Our report highlights how FC mapping can be optimized by tailoring pairwise statistics to specific neurophysiological mechanisms and research questions.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1593-1602"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248779","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

Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression. Spotiflow：荧光显微镜精确、高效的斑点检测，具有深度立体流回归。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 Epub Date: 2025-06-06 DOI: 10.1038/s41592-025-02662-x

Albert Dominguez Mantes, Antonio Herrera, Irina Khven, Anjalie Schlaeppi, Eftychia Kyriacou, Georgios Tsissios, Evangelia Skoufa, Luca Santangeli, Elena Buglakova, Emine Berna Durmus, Suliana Manley, Anna Kreshuk, Detlev Arendt, Can Aztekin, Joachim Lingner, Gioele La Manno, Martin Weigert

{"title":"Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression.","authors":"Albert Dominguez Mantes, Antonio Herrera, Irina Khven, Anjalie Schlaeppi, Eftychia Kyriacou, Georgios Tsissios, Evangelia Skoufa, Luca Santangeli, Elena Buglakova, Emine Berna Durmus, Suliana Manley, Anna Kreshuk, Detlev Arendt, Can Aztekin, Joachim Lingner, Gioele La Manno, Martin Weigert","doi":"10.1038/s41592-025-02662-x","DOIUrl":"10.1038/s41592-025-02662-x","url":null,"abstract":"Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow .","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1495-1504"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248781","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

High-throughput method for decoding the protein sequence determinants of condensates. 解码冷凝物蛋白质序列决定因素的高通量方法。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 DOI: 10.1038/s41592-025-02727-x

引用次数: 0

Physics-driven self-supervised learning for fast high-resolution robust 3D reconstruction of light-field microscopy. 物理驱动的自监督学习，用于光场显微镜的快速高分辨率鲁棒3D重建。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 Epub Date: 2025-05-12 DOI: 10.1038/s41592-025-02698-z

Zhi Lu, Manchang Jin, Shuai Chen, Xiaoge Wang, Feihao Sun, Qi Zhang, Zhifeng Zhao, Jiamin Wu, Jingyu Yang, Qionghai Dai

{"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":"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.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1545-1555"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240845/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A visual-omics foundation model to bridge histopathology with spatial transcriptomics. 将组织病理学与空间转录组学连接起来的视觉组学基础模型。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 Epub Date: 2025-05-29 DOI: 10.1038/s41592-025-02707-1

Weiqing Chen, Pengzhi Zhang, Tu N Tran, Yiwei Xiao, Shengyu Li, Vrutant V Shah, Hao Cheng, Kristopher W Brannan, Keith Youker, Li Lai, Longhou Fang, Yu Yang, Nhat-Tu Le, Jun-Ichi Abe, Shu-Hsia Chen, Qin Ma, Ken Chen, Qianqian Song, John P Cooke, Guangyu Wang

{"title":"A visual-omics foundation model to bridge histopathology with spatial transcriptomics.","authors":"Weiqing Chen, Pengzhi Zhang, Tu N Tran, Yiwei Xiao, Shengyu Li, Vrutant V Shah, Hao Cheng, Kristopher W Brannan, Keith Youker, Li Lai, Longhou Fang, Yu Yang, Nhat-Tu Le, Jun-Ichi Abe, Shu-Hsia Chen, Qin Ma, Ken Chen, Qianqian Song, John P Cooke, Guangyu Wang","doi":"10.1038/s41592-025-02707-1","DOIUrl":"10.1038/s41592-025-02707-1","url":null,"abstract":"Artificial intelligence has revolutionized computational biology. Recent developments in omics technologies, including single-cell RNA sequencing and spatial transcriptomics, provide detailed genomic data alongside tissue histology. However, current computational models focus on either omics or image analysis, lacking their integration. To address this, we developed OmiCLIP, a visual-omics foundation model linking hematoxylin and eosin images and transcriptomics using tissue patches from Visium data. We transformed transcriptomic data into 'sentences' by concatenating top-expressed gene symbols from each patch. We curated a dataset of 2.2 million paired tissue images and transcriptomic data across 32 organs to train OmiCLIP integrating histology and transcriptomics. Building on OmiCLIP, our Loki platform offers five key functions: tissue alignment, annotation via bulk RNA sequencing or marker genes, cell-type decomposition, image-transcriptomics retrieval and spatial transcriptomics gene expression prediction from hematoxylin and eosin-stained images. Compared with 22 state-of-the-art models on 5 simulations, and 19 public and 4 in-house experimental datasets, Loki demonstrated consistent accuracy and robustness.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1568-1582"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182930","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

Interpretable representation learning for 3D multi-piece intracellular structures using point clouds. 基于点云的三维多片胞内结构的可解释表示学习。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 Epub Date: 2025-07-03 DOI: 10.1038/s41592-025-02729-9

Ritvik Vasan, Alexandra J Ferrante, Antoine Borensztejn, Christopher L Frick, Philip Garrison, Nathalie Gaudreault, Saurabh S Mogre, Fatwir S Mohammed, Benjamin Morris, Guilherme G Pires, Daniel Saelid, Susanne M Rafelski, Julie A Theriot, Matheus P Viana

{"title":"Interpretable representation learning for 3D multi-piece intracellular structures using point clouds.","authors":"Ritvik Vasan, Alexandra J Ferrante, Antoine Borensztejn, Christopher L Frick, Philip Garrison, Nathalie Gaudreault, Saurabh S Mogre, Fatwir S Mohammed, Benjamin Morris, Guilherme G Pires, Daniel Saelid, Susanne M Rafelski, Julie A Theriot, Matheus P Viana","doi":"10.1038/s41592-025-02729-9","DOIUrl":"10.1038/s41592-025-02729-9","url":null,"abstract":"A key challenge in understanding subcellular organization is quantifying interpretable measurements of intracellular structures with complex multi-piece morphologies in an objective, robust and generalizable manner. Here we introduce a morphology-appropriate representation learning framework that uses three-dimensional rotation-invariant autoencoders and point clouds. This framework is used to learn representations of complex shapes that are independent of orientation, compact and interpretable. We apply our framework to intracellular structures with punctate morphologies (for example, DNA replication foci) and polymorphic morphologies (for example, nucleoli). We explore the trade-offs in the performance of this framework compared to image-based autoencoders by performing multi-metric benchmarking across efficiency, generative capability and representation expressivity metrics. We find that the proposed framework, which embraces the underlying morphology of multi-piece structures, can facilitate the unsupervised discovery of subclusters for each structure. We show how this approach can also be applied to phenotypic profiling using a dataset of nucleolar images following drug perturbations.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1531-1544"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560562","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

Sequencing DNA in the air. 在空气中测序DNA。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 DOI: 10.1038/s41592-025-02758-4

Lin Tang

引用次数: 0

CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets. CryoDRGN-AI：具有挑战性的低温em和低温et数据集的神经从头算重建。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 Epub Date: 2025-06-26 DOI: 10.1038/s41592-025-02720-4

Axel Levy, Rishwanth Raghu, J Ryan Feathers, Michal Grzadkowski, Frédéric Poitevin, Jake D Johnston, Francesca Vallese, Oliver Biggs Clarke, Gordon Wetzstein, Ellen D Zhong

{"title":"CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets.","authors":"Axel Levy, Rishwanth Raghu, J Ryan Feathers, Michal Grzadkowski, Frédéric Poitevin, Jake D Johnston, Francesca Vallese, Oliver Biggs Clarke, Gordon Wetzstein, Ellen D Zhong","doi":"10.1038/s41592-025-02720-4","DOIUrl":"10.1038/s41592-025-02720-4","url":null,"abstract":"Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind and perform chemistry. Cryo-electron microscopy and cryo-electron tomography can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, three-dimensional reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-electron microscopy and cryo-electron tomography as a high-throughput tool for structural biology and discovery.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1486-1494"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144506869","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

Decoding 3D cell morphology with interpretable point cloud models. 解码三维细胞形态与可解释的点云模型。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 DOI: 10.1038/s41592-025-02752-w

Siewert Hugelier

引用次数: 0

Krakencoder: a unified brain connectome translation and fusion tool. Krakencoder：一个统一的脑连接体翻译和融合工具。

IF 36.1 1区生物学

Nature Methods Pub Date : 2025-07-01 Epub Date: 2025-06-05 DOI: 10.1038/s41592-025-02706-2

Keith W Jamison, Zijin Gu, Qinxin Wang, Ceren Tozlu, Mert R Sabuncu, Amy Kuceyeski

{"title":"Krakencoder: a unified brain connectome translation and fusion tool.","authors":"Keith W Jamison, Zijin Gu, Qinxin Wang, Ceren Tozlu, Mert R Sabuncu, Amy Kuceyeski","doi":"10.1038/s41592-025-02706-2","DOIUrl":"10.1038/s41592-025-02706-2","url":null,"abstract":"Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here, we present the Krakencoder, a joint connectome mapping tool that simultaneously bidirectionally translates between structural and functional connectivity, and between different atlases and processing choices via a common latent representation. These mappings demonstrate exceptional accuracy and individual-level identifiability; the mapping between structural and functional connectivity has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This fusion representation better reflects familial relatedness, preserves age- and sex-relevant information, and enhances cognition-relevant information. The Krakencoder can be applied, without retraining, to new out-of-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a notable leap forward in capturing the relationship between multimodal brain connectomes in an individualized, behaviorally and demographically relevant way.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":"1583-1592"},"PeriodicalIF":36.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234588","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