Computational and Systems Biology, Genome Institute of Singapore, Singapore.
Developmental Cellomics Laboratory, Genome Institute of Singapore, Singapore.
Nat Genet. 2017 May;49(5):708-718. doi: 10.1038/ng.3818. Epub 2017 Mar 20.
Intratumoral heterogeneity is a major obstacle to cancer treatment and a significant confounding factor in bulk-tumor profiling. We performed an unbiased analysis of transcriptional heterogeneity in colorectal tumors and their microenvironments using single-cell RNA-seq from 11 primary colorectal tumors and matched normal mucosa. To robustly cluster single-cell transcriptomes, we developed reference component analysis (RCA), an algorithm that substantially improves clustering accuracy. Using RCA, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs). Additionally, epithelial-mesenchymal transition (EMT)-related genes were found to be upregulated only in the CAF subpopulation of tumor samples. Notably, colorectal tumors previously assigned to a single subtype on the basis of bulk transcriptomics could be divided into subgroups with divergent survival probability by using single-cell signatures, thus underscoring the prognostic value of our approach. Overall, our results demonstrate that unbiased single-cell RNA-seq profiling of tumor and matched normal samples provides a unique opportunity to characterize aberrant cell states within a tumor.
肿瘤内异质性是癌症治疗的主要障碍，也是肿瘤整体分析的一个重要混杂因素。我们使用来自 11 个原发性结直肠肿瘤及其匹配的正常黏膜的单细胞 RNA-seq，对结直肠肿瘤及其微环境中的转录异质性进行了无偏倚分析。为了稳健地聚类单细胞转录组，我们开发了参考成分分析（RCA），这是一种算法，可以显著提高聚类准确性。使用 RCA，我们鉴定出两种不同的癌症相关成纤维细胞（CAF）亚型。此外，上皮-间充质转化（EMT）相关基因仅在肿瘤样本的 CAF 亚群中上调。值得注意的是，先前基于批量转录组学被归为单一亚型的结直肠肿瘤，现在可以通过单细胞特征划分为具有不同生存概率的亚组，从而突出了我们方法的预后价值。总的来说，我们的研究结果表明，对肿瘤和匹配的正常样本进行无偏倚的单细胞 RNA-seq 分析为描述肿瘤内异常细胞状态提供了独特的机会。
