Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

Shyam Prabhakar, Paul Robson, Iain Beehuat Tan and colleagues characterize the cellular heterogeneity of colorectal tumors and their microenvironment on the basis of single-cell RNA–seq data analyzed with their newly developed clustering algorithm, reference component analysis (RCA). Their analyses identify two subtypes of cancer-associated fibroblasts and further divide tumors into subgroups with divergent survival probabilities. 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.