{"title":"Precise detection of cell-type-specific domains in spatial transcriptomics.","authors":"Zhihan Ruan, Weijun Zhou, Hong Liu, Jinmao Wei, Yichen Pan, Chaoyang Yan, Xiaoyi Wei, Wenting Xiang, Chengwei Yan, Shengquan Chen, Jian Liu","doi":"10.1016/j.crmeth.2024.100841","DOIUrl":null,"url":null,"abstract":"<p><p>Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11384096/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.crmeth.2024.100841","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/9 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.
细胞类型特异域是空间解析转录组(SRT)组织中特定细胞类型巧合富集的解剖域。使用现有的计算方法检测细胞类型比例较低的特异性结构域具有挑战性,因为这些结构域部分与其他细胞类型特异性结构域重叠,甚至位于其他细胞类型特异性结构域内部。在这里,我们提出了 De-spot,它将分割和去卷积合成为一个集合,生成细胞类型模式,检测低比例细胞类型特异性结构域,并直观地显示这些结构域。实验评估表明,De-spot 使我们能够发现癌症相关成纤维细胞和免疫相关细胞之间的共定位,这些共定位显示了特定切片中潜在的肿瘤微环境(TME)域,而以前的计算方法却掩盖了这些域。我们进一步阐明了已确定的区域,发现Srgn可能是SRT切片中关键的TME标记物。通过解密乳腺癌组织中的 T 细胞特异性结构域,De-spot 还发现浸润癌与导管癌中衰竭 T 细胞的比例显著增加。