Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis

Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we decipher for the first time the full compositions of ESCC tumor based on analyzing 208,659 single-cell transcriptomes in ESCC derived from 60 individuals. We identify 8 essential expression programs from malignant epithelial cells and discovered 43 cell types including 26 immune cell and 17 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and also explicate the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we have linked the cancer cell transcriptomes to the somatic mutations and identified several markers significantly associated with survival time in patients, which may be relevant to precision cares of ESCC patients. These results renew our understanding of ESCC and provide a valuable example and resource for investigating other types of solid tumor. Overall design: We performed scRNA-seq on 60 ESCC tumor and 4 adjacent normal tissue samples obtained from 60 individuals using 10X Genomics platform. Raw data is available at https://bigd.big.ac.cn/gsa-human/ under the accession ID of HRA000195. For the details or customized demand of Raw and Processed data, please feel free to contact Prof Zhang ([e-mail]). We are glad to help.

食管鳞状细胞癌（Esophageal squamous-cell carcinoma, ESCC）是最常见且致死率极高的恶性肿瘤之一，其肿瘤生态系统复杂且机制未明。本研究首次基于对60名ESCC患者的208659个单细胞转录组进行分析，解析了该肿瘤的完整细胞构成。研究团队从恶性上皮细胞中鉴定出8种核心表达程序，并在肿瘤微环境（tumor microenvironment, TME）中发现了43种细胞亚型，其中包括26种免疫细胞亚型与17种非免疫基质细胞亚型；同时阐明了肿瘤细胞与其他细胞间的相互作用，以及肿瘤微环境内不同细胞类型之间的交互关系。此外，本研究将肿瘤细胞的转录组特征与体细胞突变进行关联分析，鉴定出多个与患者生存时间显著相关的标志物，这些标志物或可为ESCC患者的精准诊疗提供参考。本研究成果更新了学界对ESCC的认知，为其他实体瘤的研究提供了极具价值的范例与数据资源。整体实验设计：本研究依托10X Genomics平台，对60名患者的60份ESCC肿瘤组织样本及4份配对癌旁正常组织样本开展了单细胞RNA测序（single-cell RNA-seq, scRNA-seq）。原始测序数据可于https://bigd.big.ac.cn/gsa-human/获取，登录编号为HRA000195。如需获取原始数据与处理后数据的详细信息或定制化服务，请联系张教授（邮箱：[e-mail]），我们将竭诚为您提供帮助。