Nature Methods最新文献_第6页

CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy CLEM-Reg：一种基于自动点云的体相关光学和电子显微镜配准算法。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02794-0

Daniel Krentzel, Matouš Elphick, Marie-Charlotte Domart, Christopher J. Peddie, Romain F. Laine, Cameron Shand, Ricardo Henriques, Lucy M. Collinson, Martin L. Jones

{"title":"CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy","authors":"Daniel Krentzel, Matouš Elphick, Marie-Charlotte Domart, Christopher J. Peddie, Romain F. Laine, Cameron Shand, Ricardo Henriques, Lucy M. Collinson, Martin L. Jones","doi":"10.1038/s41592-025-02794-0","DOIUrl":"10.1038/s41592-025-02794-0","url":null,"abstract":"Volume correlative light and electron microscopy (vCLEM) is a powerful imaging technique that enables the visualization of fluorescently labeled proteins within their ultrastructural context. Currently, vCLEM alignment relies on time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the three-dimensional alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques. Point clouds are derived from segmentations of common structures in each modality, created by state-of-the-art open-source methods. CLEM-Reg drastically reduces the registration time of vCLEM datasets to a few minutes and achieves correlation of fluorescent signal to submicron target structures in electron microscopy on three newly acquired vCLEM benchmark datasets. CLEM-Reg was then used to automatically obtain vCLEM overlays to unambiguously identify TGN46-positive transport carriers involved in protein trafficking between the trans-Golgi network and plasma membrane. Datasets are available on EMPIAR and BioStudies, and a napari plugin is provided to aid end-user adoption. CLEM-Reg automates the three-dimensional alignment of volume correlative light and electron microscopy datasets by leveraging probabilistic point cloud registration techniques for fast and accurate results across diverse datasets.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1923-1934"},"PeriodicalIF":32.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145033720","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

Benchmarking genomic language models 对标基因组语言模型。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02829-6

Lin Tang

引用次数: 0

Deciphering regulatory sequence grammar 破译规则序列语法。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02827-8

Lei Tang

引用次数: 0

Taming cancer evolution surprises 驯服癌症进化令人惊讶。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02802-3

Vivien Marx

引用次数: 0

Optical pooled screens shed light on Ebola infection 光学池屏揭示了埃博拉感染。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02831-y

Ivanka Kamenova

引用次数: 0

SNAP-tag2 improves live-cell imaging SNAP-tag2改善活细胞成像。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02830-z

Arunima Singh

引用次数: 0

ViSOR puts the periphery in focus 遮阳板使周边的焦点。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02828-7

Joseph Willson

引用次数: 0

Cichlid fishes 丽鱼科的鱼。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-10 DOI: 10.1038/s41592-025-02809-w

Scott A. Juntti

引用次数: 0

WISDEM: a hybrid wireless integrated sensing detector for simultaneous EEG and MRI WISDEM：一种用于同时进行EEG和MRI的混合无线集成传感检测器。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-08 DOI: 10.1038/s41592-025-02798-w

Yi Chen, Wei Qian, Daniel Razansky, Xin Yu, Chunqi Qian

{"title":"WISDEM: a hybrid wireless integrated sensing detector for simultaneous EEG and MRI","authors":"Yi Chen, Wei Qian, Daniel Razansky, Xin Yu, Chunqi Qian","doi":"10.1038/s41592-025-02798-w","DOIUrl":"10.1038/s41592-025-02798-w","url":null,"abstract":"Concurrent recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) signals reveals cross-scale neurovascular dynamics crucial for explaining fundamental linkages between function and behaviors. However, MRI scanners generate artifacts for EEG detection. Despite existing denoising methods, cabled connections to EEG receivers are susceptible to environmental fluctuations inside MRI scanners, creating baseline drifts that complicate EEG signal retrieval from the noisy background. Here we show that a wireless integrated sensing detector for simultaneous EEG and MRI can encode fMRI and EEG signals on distinct sidebands of the detector’s oscillation wave for detection by a standard MRI console over the entire duration of the fMRI sequence. Local field potential and fMRI maps are retrieved through low-pass and high-pass filtering of frequency-demodulated signals. From optogenetically stimulated somatosensory cortex in ChR2-transfected Sprague Dawley rats, positive correlation between evoked local field potential and fMRI signals validates strong neurovascular coupling, enabling cross-scale brain mapping with this two-in-one transducer. WISDEM is a hybrid detector for simultaneous EEG and fMRI recordings without artifacts or crosstalk, which allows access to neural activity at high temporal and spatial resolution, respectively, as demonstrated in rats.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1944-1953"},"PeriodicalIF":32.1,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023914","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

Scvi-hub: an actionable repository for model-driven single-cell analysis Scvi-hub：用于模型驱动的单细胞分析的可操作存储库。

IF 32.1 1区生物学

Nature Methods Pub Date : 2025-09-08 DOI: 10.1038/s41592-025-02799-9

Can Ergen, Valeh Valiollah Pour Amiri, Martin Kim, Ori Kronfeld, Aaron Streets, Adam Gayoso, Nir Yosef

{"title":"Scvi-hub: an actionable repository for model-driven single-cell analysis","authors":"Can Ergen, Valeh Valiollah Pour Amiri, Martin Kim, Ori Kronfeld, Aaron Streets, Adam Gayoso, Nir Yosef","doi":"10.1038/s41592-025-02799-9","DOIUrl":"10.1038/s41592-025-02799-9","url":null,"abstract":"The growing availability of single-cell omics datasets presents new opportunities for reuse, while challenges in data transfer, normalization and integration remain a barrier. Here we present scvi-hub: a platform for efficiently sharing and accessing single-cell omics datasets using pretrained probabilistic models. It enables immediate execution of fundamental tasks like visualization, imputation, annotation and deconvolution on new query datasets using state-of-the-art methods, with massively reduced storage and compute requirements. We show that pretrained models support efficient analysis of large references, including the CZI CELLxGENE Discover Census. Scvi-hub is built within the scvi-tools open-source environment and integrated into scverse. Scvi-hub offers a scalable and user-friendly framework for accessing and contributing to a growing ecosystem of ready-to-use models and datasets, thus putting the power of atlas-level analysis at the fingertips of a broad community of users. Scvi-hub is a versatile and efficient platform for model-based analysis of single-cell sequencing studies with access to a diverse array of datasets and downstream analysis.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 9","pages":"1836-1845"},"PeriodicalIF":32.1,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023900","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