{"title":"Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding.","authors":"Yuzhe Li, Jinsong Zhang, Xin Gao, Qiangfeng Cliff Zhang","doi":"10.1016/j.cels.2024.05.001","DOIUrl":null,"url":null,"abstract":"<p><p>Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered \"cell communities\"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"578-592.e7"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cels.2024.05.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/31 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered "cell communities"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.
单细胞分辨率空间转录组学(ST)数据分析需要计算方法来揭示单个细胞如何发挥组织特异性功能的空间组织原理。在这里,我们介绍了通过交互感知细胞嵌入(SPACE)进行的空间转录组学数据分析,这是一种深度学习方法,可从单细胞分辨率的空间转录组学数据中识别细胞类型和发现组织模块。SPACE 通过特殊的空间分布模式和不同的近邻相互作用细胞类型,识别出了有空间信息的细胞亚型。SPACE 还自动发现了 "细胞群落"--具有明显边界和统一空间分布的组成细胞类型的组织模块。对于每个细胞群落,SPACE 都会输出一个与生理过程相关的近端细胞-细胞相互作用网络,该网络可用于完善基于配体-受体的细胞间信号分析。我们设想 SPACE 可用于大规模 ST 项目,以了解近端细胞-细胞相互作用如何促进细胞群落内新出现的生物功能。这篇论文的同行评审过程非常透明,相关记录见补充信息。