Pub Date : 2024-09-10DOI: 10.1038/s41592-024-02419-y
Nina Vogt
DNA origami tension sensors can provide insights into mechanotransduction in a physiological environment.
DNA 折纸张力传感器可以让人们深入了解生理环境中的机械传导。
{"title":"Studying tension with DNA origami","authors":"Nina Vogt","doi":"10.1038/s41592-024-02419-y","DOIUrl":"10.1038/s41592-024-02419-y","url":null,"abstract":"DNA origami tension sensors can provide insights into mechanotransduction in a physiological environment.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218899","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}
Pub Date : 2024-09-10DOI: 10.1038/s41592-024-02420-5
Lei Tang
{"title":"Wobble base improves precision in RNA editing","authors":"Lei Tang","doi":"10.1038/s41592-024-02420-5","DOIUrl":"10.1038/s41592-024-02420-5","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218896","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}
Pub Date : 2024-09-10DOI: 10.1038/s41592-024-02397-1
Caroline Seydel
The global imaging community is pursuing innovative approaches to achieve more equitable access to instruments and expertise.
全球成像界正在寻求创新方法，以实现更公平地获取仪器和专业知识。
{"title":"Bioimaging for all","authors":"Caroline Seydel","doi":"10.1038/s41592-024-02397-1","DOIUrl":"10.1038/s41592-024-02397-1","url":null,"abstract":"The global imaging community is pursuing innovative approaches to achieve more equitable access to instruments and expertise.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02397-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218900","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}
Pub Date : 2024-09-09DOI: 10.1038/s41592-024-02403-6
Qinwen Huang, Ye Zhou, Alberto Bartesaghi
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.
{"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}
Pub Date : 2024-09-06DOI: 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
We present a way to encode more information in fluorescence imaging by splitting the original point spread function (PSF), which offers broadband operation and compatibility with other PSF engineering modalities and existing analysis tools. We demonstrate the approach using the ‘Circulator’, an add-on that encodes the fluorophore emission band into the PSF, enabling simultaneous multicolor super-resolution and single-molecule microscopy using essentially the full field of view. Point spread function (PSF) splitting with the ‘Circulator’, which encodes the fluorophore emission band into the PSF, improves the information content of fluorescence microscopy and enables improved super-resolution imaging and single-particle tracking.
{"title":"Simultaneous multicolor fluorescence imaging using PSF splitting","authors":"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","doi":"10.1038/s41592-024-02383-7","DOIUrl":"10.1038/s41592-024-02383-7","url":null,"abstract":"We present a way to encode more information in fluorescence imaging by splitting the original point spread function (PSF), which offers broadband operation and compatibility with other PSF engineering modalities and existing analysis tools. We demonstrate the approach using the ‘Circulator’, an add-on that encodes the fluorophore emission band into the PSF, enabling simultaneous multicolor super-resolution and single-molecule microscopy using essentially the full field of view. Point spread function (PSF) splitting with the ‘Circulator’, which encodes the fluorophore emission band into the PSF, improves the information content of fluorescence microscopy and enables improved super-resolution imaging and single-particle tracking.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145994","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}
Pub Date : 2024-09-04DOI: 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
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.
从显微图像中对单个神经元复杂的三维形态进行数字重建，是个人实验室和以细胞类型和大脑解剖为重点的大型项目所面临的重要挑战。无论是传统的手动重建还是基于人工智能（AI）的最新自动重建算法，这项任务都经常失败。在大规模数据生产过程中，组织多名神经解剖学家生成并交叉验证与生物相关且相互认可的重建结果也是一项挑战。基于人工智能增强的协作群体智能，我们开发了一个用于大规模神经元重建的协作增强重建（CAR）平台。该平台允许使用台式工作站、虚拟现实头盔和手机等多种设备对神经元解剖结构进行沉浸式交互和高效协作编辑，使用户能够随时随地作出贡献，并利用多种基于人工智能的自动化工具。我们测试了 CAR 在挑战小鼠和人类神经元方面的适用性，以实现规模化和忠实的数据生产。
{"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}
Pub Date : 2024-09-03DOI: 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
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.
{"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}
Pub Date : 2024-09-02DOI: 10.1038/s41592-024-02406-3
Daniel H. Huson, David Bryant
{"title":"The SplitsTree App: interactive analysis and visualization using phylogenetic trees and networks","authors":"Daniel H. Huson, David Bryant","doi":"10.1038/s41592-024-02406-3","DOIUrl":"10.1038/s41592-024-02406-3","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120265","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}
Pub Date : 2024-09-02DOI: 10.1038/s41592-024-02399-z
Erin Weisbart, Ankur Kumar, John Arevalo, Anne E. Carpenter, Beth A. Cimini, Shantanu Singh
{"title":"Cell Painting Gallery: an open resource for image-based profiling","authors":"Erin Weisbart, Ankur Kumar, John Arevalo, Anne E. Carpenter, Beth A. Cimini, Shantanu Singh","doi":"10.1038/s41592-024-02399-z","DOIUrl":"10.1038/s41592-024-02399-z","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120264","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}
Pub Date : 2024-08-29DOI: 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
The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components. The authors present a workflow integrating imaging mass cytometry and imaging mass spectrometry to deconvolute metabolic heterogeneity at the single-cell level.
{"title":"Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling","authors":"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","doi":"10.1038/s41592-024-02392-6","DOIUrl":"10.1038/s41592-024-02392-6","url":null,"abstract":"The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components. The authors present a workflow integrating imaging mass cytometry and imaging mass spectrometry to deconvolute metabolic heterogeneity at the single-cell level.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02392-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142109674","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}
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