Pub Date : 2024-09-13DOI: 10.1038/s41592-024-02416-1
FICTURE software addresses a critical challenge in spatial omics analysis: making high-resolution inference with only a few molecules per square micron. This tool fully realizes the potential of contemporary spatial platforms by learning latent spatial factors from the whole transcriptome while preserving the resolution of each technology at scale.
{"title":"Analyzing submicron spatial transcriptomics data at their original resolution","authors":"","doi":"10.1038/s41592-024-02416-1","DOIUrl":"10.1038/s41592-024-02416-1","url":null,"abstract":"FICTURE software addresses a critical challenge in spatial omics analysis: making high-resolution inference with only a few molecules per square micron. This tool fully realizes the potential of contemporary spatial platforms by learning latent spatial factors from the whole transcriptome while preserving the resolution of each technology at scale.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218890","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 : 2024-09-13DOI: 10.1038/s41592-024-02425-0
All life sciences research is potentially subject to ethical considerations. Institutions should support collaborations with professional ethicists and philosophers to help life scientists navigate ethical crossroads.
{"title":"Research ethics matter","authors":"","doi":"10.1038/s41592-024-02425-0","DOIUrl":"10.1038/s41592-024-02425-0","url":null,"abstract":"All life sciences research is potentially subject to ethical considerations. Institutions should support collaborations with professional ethicists and philosophers to help life scientists navigate ethical crossroads.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02425-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231162","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 : 2024-09-12DOI: 10.1038/s41592-024-02415-2
Yichen Si, ChangHee Lee, Yongha Hwang, Jeong H. Yun, Weiqiu Cheng, Chun-Seok Cho, Miguel Quiros, Asma Nusrat, Weizhou Zhang, Goo Jun, Sebastian Zöllner, Jun Hee Lee, Hyun Min Kang
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE’s cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST. FICTURE is a segmentation-free approach for identifying tissue architecture in spatial transcriptomics data. FICTURE is compatible with both imaging-based and sequencing-based methods and is uniquely suited for handling the largest available datasets.
空间转录组学（ST）技术已发展到能以亚微米分辨率对大面积区域进行全转录组基因表达分析。然而，高分辨率 ST 的分析往往受到复杂组织结构的挑战，现有的细胞分割方法因细胞大小和形状不规则而难以实现，而且缺乏可扩展到全转录组分析的无分割方法。在这里，我们提出了 FICTURE（超高分辨制图转录组因式推断），这是一种免分割空间因式分解方法，可处理标有数十亿亚微米分辨率空间坐标的全转录组数据，并与基于测序和成像的 ST 数据兼容。FICTURE 使用多层 Dirichlet 模型对像素级空间因子进行随机变量推断，其效率比现有方法高出几个数量级。FICTURE 可以揭示具有挑战性的组织的微观 ST 结构，如真实数据中的血管、纤维化、肌肉和脂质沉积区域，而以往的方法都无法做到这一点。FICTURE 的跨平台通用性、可扩展性和精确性使其成为探索高分辨率 ST 的强大工具。
{"title":"FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics","authors":"Yichen Si, ChangHee Lee, Yongha Hwang, Jeong H. Yun, Weiqiu Cheng, Chun-Seok Cho, Miguel Quiros, Asma Nusrat, Weizhou Zhang, Goo Jun, Sebastian Zöllner, Jun Hee Lee, Hyun Min Kang","doi":"10.1038/s41592-024-02415-2","DOIUrl":"10.1038/s41592-024-02415-2","url":null,"abstract":"Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE’s cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST. FICTURE is a segmentation-free approach for identifying tissue architecture in spatial transcriptomics data. FICTURE is compatible with both imaging-based and sequencing-based methods and is uniquely suited for handling the largest available datasets.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218891","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 : 2024-09-11DOI: 10.1038/s41592-024-02413-4
Zach Marin, Xiaoding Wang, Dax W. Collison, Conor McFadden, Jinlong Lin, Hazel M. Borges, Bingying Chen, Dushyant Mehra, Qionghua Shen, Seweryn Gałecki, Stephan Daetwyler, Steven J. Sheppard, Phu Thien, Baylee A. Porter, Suzanne D. Conzen, Douglas P. Shepherd, Reto Fiolka, Kevin M. Dean
{"title":"Navigate: an open-source platform for smart light-sheet microscopy","authors":"Zach Marin, Xiaoding Wang, Dax W. Collison, Conor McFadden, Jinlong Lin, Hazel M. Borges, Bingying Chen, Dushyant Mehra, Qionghua Shen, Seweryn Gałecki, Stephan Daetwyler, Steven J. Sheppard, Phu Thien, Baylee A. Porter, Suzanne D. Conzen, Douglas P. Shepherd, Reto Fiolka, Kevin M. Dean","doi":"10.1038/s41592-024-02413-4","DOIUrl":"10.1038/s41592-024-02413-4","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218892","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}
Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances. SN2N, a Self-inspired Noise2Noise module, offers a versatile solution for volumetric time-lapse super-resolution imaging of live cells. SN2N uses self-supervised data generation and self-constrained learning for training with a single noisy frame.
在活细胞超分辨率（SR）显微镜中，每一个收集到的光子都弥足珍贵。在此，我们介绍一种数据高效、基于深度学习的去噪解决方案，以改进各种 SR 成像模式。该方法名为 SN2N，是一种自启发 Noise2Noise 模块，具有自监督数据生成和自约束学习过程。SN2N 与监督学习方法相比完全具有竞争力，而且无需大量训练集和干净的地面实况，只需单个噪声帧进行训练。我们的研究表明，SN2N 可将光子效率提高一到两个数量级，并可与多种成像模式兼容，用于体积、多色、延时 SR 显微镜。我们进一步将 SN2N 集成到不同的 SR 重建算法中，以有效减少图像伪影。我们预计，SN2N 将改善实时 SR 成像，并推动进一步的发展。
{"title":"Self-inspired learning for denoising live-cell super-resolution microscopy","authors":"Liying Qu, Shiqun Zhao, Yuanyuan Huang, Xianxin Ye, Kunhao Wang, Yuzhen Liu, Xianming Liu, Heng Mao, Guangwei Hu, Wei Chen, Changliang Guo, Jiaye He, Jiubin Tan, Haoyu Li, Liangyi Chen, Weisong Zhao","doi":"10.1038/s41592-024-02400-9","DOIUrl":"10.1038/s41592-024-02400-9","url":null,"abstract":"Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances. SN2N, a Self-inspired Noise2Noise module, offers a versatile solution for volumetric time-lapse super-resolution imaging of live cells. SN2N uses self-supervised data generation and self-constrained learning for training with a single noisy frame.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218893","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 : 2024-09-11DOI: 10.1038/s41592-024-02407-2
Jouni Sirén, Parsa Eskandar, Matteo Tommaso Ungaro, Glenn Hickey, Jordan M. Eizenga, Adam M. Novak, Xian Chang, Pi-Chuan Chang, Mikhail Kolmogorov, Andrew Carroll, Jean Monlong, Benedict Paten
Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k-mer counts in the reads. We implement the approach in the vg toolkit ( https://github.com/vgteam/vg ) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods. This work introduces a k-mer-based approach to customizing a pangenome reference, making it more relevant to a new sample of interest. This method enhances the accuracy of genotyping small variants and large structural variants.
{"title":"Personalized pangenome references","authors":"Jouni Sirén, Parsa Eskandar, Matteo Tommaso Ungaro, Glenn Hickey, Jordan M. Eizenga, Adam M. Novak, Xian Chang, Pi-Chuan Chang, Mikhail Kolmogorov, Andrew Carroll, Jean Monlong, Benedict Paten","doi":"10.1038/s41592-024-02407-2","DOIUrl":"10.1038/s41592-024-02407-2","url":null,"abstract":"Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k-mer counts in the reads. We implement the approach in the vg toolkit ( https://github.com/vgteam/vg ) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods. This work introduces a k-mer-based approach to customizing a pangenome reference, making it more relevant to a new sample of interest. This method enhances the accuracy of genotyping small variants and large structural variants.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218894","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/s41592-024-02422-3
Jean Nakhle
{"title":"Non-invasive metabolic imaging of brown adipose tissue","authors":"Jean Nakhle","doi":"10.1038/s41592-024-02422-3","DOIUrl":"10.1038/s41592-024-02422-3","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":"142218897","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/s41592-024-02414-3
Siewert Hugelier, Qing Tang, Hannah Hyun-Sook Kim, Melina Theoni Gyparaki, Charles Bond, Adriana Naomi Santiago-Ruiz, Sílvia Porta, Melike Lakadamyali
Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts. Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE) is a robust feature extraction and classification pipeline for diverse and heterogeneous structures in both 2D and 3D single-molecule localization microscopy data.
{"title":"ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data","authors":"Siewert Hugelier, Qing Tang, Hannah Hyun-Sook Kim, Melina Theoni Gyparaki, Charles Bond, Adriana Naomi Santiago-Ruiz, Sílvia Porta, Melike Lakadamyali","doi":"10.1038/s41592-024-02414-3","DOIUrl":"10.1038/s41592-024-02414-3","url":null,"abstract":"Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts. Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE) is a robust feature extraction and classification pipeline for diverse and heterogeneous structures in both 2D and 3D single-molecule localization microscopy data.","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/s41592-024-02414-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218901","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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