Nature Methods最新文献_第10页

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

引用次数: 0

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

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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

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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

{"title":"Proteome-scale recombinant standards and a robust high-speed search engine to advance cross-linking MS-based interactomics","authors":"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","doi":"10.1038/s41592-024-02478-1","DOIUrl":"10.1038/s41592-024-02478-1","url":null,"abstract":"Advancing data analysis tools for proteome-wide cross-linking mass spectrometry (XL-MS) requires ground-truth standards that mimic biological complexity. Here we develop well-controlled XL-MS standards comprising hundreds of recombinant proteins that are systematically mixed for cross-linking. We use one standard dataset to guide the development of Scout, a search engine for XL-MS with MS-cleavable cross-linkers. Using other, independent standard datasets and published datasets, we benchmark the performance of Scout and existing XL-MS software. We find that Scout offers an excellent combination of speed, sensitivity and false discovery rate control. The results illustrate how our large recombinant standard can support the development of XL-MS analysis tools and evaluation of XL-MS results. This Article reports cross-linking mass spectrometry (XL-MS) standard datasets with fully controlled protein interactions that are an order of magnitude more complex than existing ones. These datasets are used to benchmark XL-MS software and establish a fast, error-controlled search tool for XL-MS with cleavable reagents.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2327-2335"},"PeriodicalIF":36.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02478-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142558288","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

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. VeloCycle is a manifold-constrained generative framework to estimate RNA velocity during the cell cycle.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2271-2286"},"PeriodicalIF":36.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02471-8.pdf","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":"OA","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. Microscopy artifacts and tissue imperfections interfere with single-cell analysis. CyLinter software offers quality control for high-plex tissue profiling by removing artifactual cells, thereby facilitating accuracy of biological interpretation.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2248-2259"},"PeriodicalIF":36.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02328-0.pdf","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics 从空间解析转录组学推断等位基因特异性拷贝数畸变和肿瘤系统地理学

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-30 DOI: 10.1038/s41592-024-02438-9

Cong Ma, Metin Balaban, Jingxian Liu, Siqi Chen, Michael J. Wilson, Christopher H. Sun, Li Ding, Benjamin J. Raphael

{"title":"Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics","authors":"Cong Ma, Metin Balaban, Jingxian Liu, Siqi Chen, Michael J. Wilson, Christopher H. Sun, Li Ding, Benjamin J. Raphael","doi":"10.1038/s41592-024-02438-9","DOIUrl":"10.1038/s41592-024-02438-9","url":null,"abstract":"Analyzing somatic evolution within a tumor over time and across space is a key challenge in cancer research. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genomic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and reconstruct spatial tumor evolution, or phylogeography, from SRT data. CalicoST identifies important classes of CNAs—including copy-neutral loss of heterozygosity and mirrored subclonal CNAs—that are invisible to total copy number analysis. Using nine patients’ data from the Human Tumor Atlas Network, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. CalicoST reconstructs a tumor phylogeography in three-dimensional space for two patients with multiple adjacent slices. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals mirrored subclonal CNAs on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space. This work presents CalicoST for inferring allele-specific copy numbers and reconstructing spatial tumor evolution by using spatial transcriptomics data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2239-2247"},"PeriodicalIF":36.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02438-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546484","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

CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction CryoSTAR：利用结构先验和约束进行低温电子显微镜异质重建。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-10-29 DOI: 10.1038/s41592-024-02486-1

Yilai Li, Yi Zhou, Jing Yuan, Fei Ye, Quanquan Gu

{"title":"CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction","authors":"Yilai Li, Yi Zhou, Jing Yuan, Fei Ye, Quanquan Gu","doi":"10.1038/s41592-024-02486-1","DOIUrl":"10.1038/s41592-024-02486-1","url":null,"abstract":"Resolving conformational heterogeneity in cryogenic electron microscopy datasets remains an important challenge in structural biology. Previous methods have often been restricted to working exclusively on volumetric densities, neglecting the potential of incorporating any preexisting structural knowledge as prior or constraints. Here we present cryoSTAR, which harnesses atomic model information as structural regularization to elucidate such heterogeneity. Our method uniquely outputs both coarse-grained models and density maps, showcasing the molecular conformational changes at different levels. Validated against four diverse experimental datasets, spanning large complexes, a membrane protein and a small single-chain protein, our results consistently demonstrate an efficient and effective solution to conformational heterogeneity with minimal human bias. By integrating atomic model insights with cryogenic electron microscopy data, cryoSTAR represents a meaningful step forward, paving the way for a deeper understanding of dynamic biological processes. CryoSTAR is a deep neural network model that resolves continuous conformational heterogeneity from cryo-EM datasets using an initial atomic model as the reference to generate both density maps and reasonable coarse-grained models for different conformations.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2318-2326"},"PeriodicalIF":36.1,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546483","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