Nature Methods最新文献

Pushing the limits of MRI brain imaging 突破磁共振成像脑成像的极限

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-07 DOI: 10.1038/s41592-024-02489-y

Michael Eisenstein

引用次数: 0

The bearded dragon Pogona vitticeps 胡须龙 Pogona vitticeps

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-07 DOI: 10.1038/s41592-024-02485-2

Lorenz A. Fenk, Felix Baier, Gilles Laurent

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Multi-pass nanopore for single-molecule protein sequencing 用于单分子蛋白质测序的多通道纳米孔

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-07 DOI: 10.1038/s41592-024-02509-x

Arunima Singh

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Microscopic art 显微艺术

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-07 DOI: 10.1038/s41592-024-02531-z

引用次数: 0

Fallopian tube assembloids 输卵管积水

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-07 DOI: 10.1038/s41592-024-02510-4

Madhura Mukhopadhyay

引用次数: 0

A leap for mesoscale imaging 中尺度成像的飞跃

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-11-07 DOI: 10.1038/s41592-024-02508-y

Rita Strack

引用次数: 0

Uncovering cell cycle speed modulations with statistical inference. 用统计推理揭示细胞周期速度调制。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-31 DOI: 10.1038/s41592-024-02484-3

Pengzhi Zhang, Guangyu Wang

引用次数: 0

Proteome-scale recombinant standards and a robust high-speed search engine to advance cross-linking MS-based interactomics. 蛋白质组规模的重组标准和强大的高速搜索引擎，推进基于交联质谱的相互作用组学。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-31 DOI: 10.1038/s41592-024-02478-1

Milan Avila Clasen, Max Ruwolt, Cong Wang, Julia Ruta, Boris Bogdanow, Louise U Kurt, Zehong Zhang, Shuai Wang, Fabio C Gozzo, Tao Chen, Paulo C Carvalho, Diogo Borges Lima, Fan Liu

引用次数: 0

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations. 利用流形约束的 RNA 速度模型进行统计推断，揭示细胞周期的速度调节。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-31 DOI: 10.1038/s41592-024-02471-8

Alex R Lederer, Maxine Leonardi, Lorenzo Talamanca, Daniil M Bobrovskiy, Antonio Herrera, Colas Droin, Irina Khven, Hugo J F Carvalho, Alessandro Valente, Albert Dominguez Mantes, Pau Mulet Arabí, Luca Pinello, Felix Naef, Gioele La Manno

{"title":"Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.","authors":"Alex R Lederer, Maxine Leonardi, Lorenzo Talamanca, Daniil M Bobrovskiy, Antonio Herrera, Colas Droin, Irina Khven, Hugo J F Carvalho, Alessandro Valente, Albert Dominguez Mantes, Pau Mulet Arabí, Luca Pinello, Felix Naef, Gioele La Manno","doi":"10.1038/s41592-024-02471-8","DOIUrl":"10.1038/s41592-024-02471-8","url":null,"abstract":"Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142558289","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

Quality control for single-cell analysis of high-plex tissue profiles using CyLinter. 使用 CyLinter 对高复合组织图谱进行单细胞分析的质量控制。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-30 DOI: 10.1038/s41592-024-02328-0

Gregory J Baker, Edward Novikov, Ziyuan Zhao, Tuulia Vallius, Janae A Davis, Jia-Ren Lin, Jeremy L Muhlich, Elizabeth A Mittendorf, Sandro Santagata, Jennifer L Guerriero, Peter K Sorger

{"title":"Quality control for single-cell analysis of high-plex tissue profiles using CyLinter.","authors":"Gregory J Baker, Edward Novikov, Ziyuan Zhao, Tuulia Vallius, Janae A Davis, Jia-Ren Lin, Jeremy L Muhlich, Elizabeth A Mittendorf, Sandro Santagata, Jennifer L Guerriero, Peter K Sorger","doi":"10.1038/s41592-024-02328-0","DOIUrl":"10.1038/s41592-024-02328-0","url":null,"abstract":"Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artifacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly and feature extraction. Here we show that these artifacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artifacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years before data collection, such as those from clinical trials.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546485","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