Nature Methods最新文献_第10页

Studying tension with DNA origami 用 DNA 折纸研究张力

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-10 DOI: 10.1038/s41592-024-02419-y

Nina Vogt

引用次数: 0

Wobble base improves precision in RNA editing 摇摆碱基提高了 RNA 编辑的精确度

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-10 DOI: 10.1038/s41592-024-02420-5

Lei Tang

引用次数: 0

Bioimaging for all 全民生物成像

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-10 DOI: 10.1038/s41592-024-02397-1

Caroline Seydel

引用次数: 0

MiLoPYP: self-supervised molecular pattern mining and particle localization in situ MiLoPYP：自监督分子模式挖掘和粒子原位定位

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-09 DOI: 10.1038/s41592-024-02403-6

Qinwen Huang, Ye Zhou, Alberto Bartesaghi

{"title":"MiLoPYP: self-supervised molecular pattern mining and particle localization in situ","authors":"Qinwen Huang, Ye Zhou, Alberto Bartesaghi","doi":"10.1038/s41592-024-02403-6","DOIUrl":"10.1038/s41592-024-02403-6","url":null,"abstract":"Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP’s ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination. MiLoPYP is a two-step, dataset-specific contrastive learning-based method for fast and accurate detection and localization of a diverse range of target structures in cryo-electron tomography data, enabling improved in situ structural biology.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02403-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218902","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

Simultaneous multicolor fluorescence imaging using PSF splitting 利用 PSF 分光技术同时进行多色荧光成像。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-06 DOI: 10.1038/s41592-024-02383-7

Robin Van den Eynde, Fabian Hertel, Sergey Abakumov, Bartosz Krajnik, Siewert Hugelier, Alexander Auer, Joschka Hellmeier, Thomas Schlichthaerle, Rachel M. Grattan, Diane S. Lidke, Ralf Jungmann, Marcel Leutenegger, Wim Vandenberg, Peter Dedecker

引用次数: 0

Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains 小鼠和人类大脑中三维神经元形态的协作增强重建。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-04 DOI: 10.1038/s41592-024-02401-8

Lingli Zhang, Lei Huang, Zexin Yuan, Yuning Hang, Ying Zeng, Kaixiang Li, Lijun Wang, Haoyu Zeng, Xin Chen, Hairuo Zhang, Jiaqi Xi, Danni Chen, Ziqin Gao, Longxin Le, Jie Chen, Wen Ye, Lijuan Liu, Yimin Wang, Hanchuan Peng

{"title":"Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains","authors":"Lingli Zhang, Lei Huang, Zexin Yuan, Yuning Hang, Ying Zeng, Kaixiang Li, Lijun Wang, Haoyu Zeng, Xin Chen, Hairuo Zhang, Jiaqi Xi, Danni Chen, Ziqin Gao, Longxin Le, Jie Chen, Wen Ye, Lijuan Liu, Yimin Wang, Hanchuan Peng","doi":"10.1038/s41592-024-02401-8","DOIUrl":"10.1038/s41592-024-02401-8","url":null,"abstract":"Digital reconstruction of the intricate 3D morphology of individual neurons from microscopic images is a crucial challenge in both individual laboratories and large-scale projects focusing on cell types and brain anatomy. This task often fails in both conventional manual reconstruction and state-of-the-art artificial intelligence (AI)-based automatic reconstruction algorithms. It is also challenging to organize multiple neuroanatomists to generate and cross-validate biologically relevant and mutually agreed upon reconstructions in large-scale data production. Based on collaborative group intelligence augmented by AI, we developed a collaborative augmented reconstruction (CAR) platform for neuron reconstruction at scale. This platform allows for immersive interaction and efficient collaborative editing of neuron anatomy using a variety of devices, such as desktop workstations, virtual reality headsets and mobile phones, enabling users to contribute anytime and anywhere and to take advantage of several AI-based automation tools. We tested CAR’s applicability for challenging mouse and human neurons toward scaled and faithful data production. Collaborative augmented reconstruction (CAR) is a platform for large-scale reconstruction of neurons and other cells from multi-dimensional imaging datasets. It can be accessed from a variety of devices simultaneously for efficient and accurate reconstruction.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02401-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133274","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

Mapping cellular interactions from spatially resolved transcriptomics data 从空间解析的转录组学数据中绘制细胞相互作用图。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-03 DOI: 10.1038/s41592-024-02408-1

James Zhu, Yunguan Wang, Woo Yong Chang, Alicia Malewska, Fabiana Napolitano, Jeffrey C. Gahan, Nisha Unni, Min Zhao, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Guo, Zhuo Zhao, Danny Z. Chen, Raquibul Hannan, Siyuan Zhang, Guanghua Xiao, Ping Mu, Ariella B. Hanker, Douglas Strand, Carlos L. Arteaga, Neil Desai, Xinlei Wang, Yang Xie, Tao Wang

{"title":"Mapping cellular interactions from spatially resolved transcriptomics data","authors":"James Zhu, Yunguan Wang, Woo Yong Chang, Alicia Malewska, Fabiana Napolitano, Jeffrey C. Gahan, Nisha Unni, Min Zhao, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Guo, Zhuo Zhao, Danny Z. Chen, Raquibul Hannan, Siyuan Zhang, Guanghua Xiao, Ping Mu, Ariella B. Hanker, Douglas Strand, Carlos L. Arteaga, Neil Desai, Xinlei Wang, Yang Xie, Tao Wang","doi":"10.1038/s41592-024-02408-1","DOIUrl":"10.1038/s41592-024-02408-1","url":null,"abstract":"Cell–cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand–receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications. Spacia is a multiple-instance learning model for cell–cell communication (CCC) interference in single-cell resolution spatially resolved transcriptomics data. Spacia can map complex CCCs by modeling cell proximity and CCC-driven gene perturbation.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126215","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

The SplitsTree App: interactive analysis and visualization using phylogenetic trees and networks SplitsTree 应用程序：使用系统发生树和网络进行交互式分析和可视化。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-02 DOI: 10.1038/s41592-024-02406-3

Daniel H. Huson, David Bryant

引用次数: 0

Cell Painting Gallery: an open resource for image-based profiling 细胞绘画画廊：基于图像的剖析开放资源。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-09-02 DOI: 10.1038/s41592-024-02399-z

Erin Weisbart, Ankur Kumar, John Arevalo, Anne E. Carpenter, Beth A. Cimini, Shantanu Singh

引用次数: 0

Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling 整合质谱和质谱成像技术，进行空间分辨单细胞代谢谱分析。

IF 36.1 1区生物学

Nature Methods Pub Date : 2024-08-29 DOI: 10.1038/s41592-024-02392-6

Joana B. Nunes, Marieke E. Ijsselsteijn, Tamim Abdelaal, Rick Ursem, Manon van der Ploeg, Martin Giera, Bart Everts, Ahmed Mahfouz, Bram Heijs, Noel F. C. C. de Miranda

引用次数: 0