Abstract 2443: Massively parallel single-cell RNA-Seq identifies diverse subpopulations displaying EMT and stem-like features

Abstract

Abstract Single-cell transcriptome analysis enables a new paradigm for studying complex systems in cancer. As opposed to bulk sequencing, which averages genomic signals across thousands or millions of cells and obscures the presence of rare subtypes, single cell sequencing enables the interrogation of individual cells. In cancer, intratumoral heterogeneity is observed at both genomic and epigenomic levels, and its analysis enables the discovery of new actionable targets and treatment modalities tailored to individual subpopulations. As an example, many cancer cell lines and those derived from patients contain subpopulations marked by distinct patterns of surface markers such as CD44, and are linked to drug resistant and tumor initiating phenotypes. A complete characterization of such cellular populations ideally requires marker-free sampling, followed by clustering into distinct subgroups. In this study, we demonstrate the significant advantages of such an approach; we utilize a high-throughput single-cell RNA-Seq method to characterize the transcriptomic profiles of cellular populations. We performed single-cell RNA-Seq on thousands of cells in the matched SW480 (primary) and SW620 (metastatic) colorectal cell lines using a microfluidic droplet barcoding technology that enables the tracking of single cells during library preparation. By focusing on genes with high inter-cell variability, we discovered a small subpopulation of cells that displayed a distinct gene expression signature from the major subpopulation. Differential gene expression analysis of this subpopulation yielded genes virtually all enriched in the epithelial-to-mesenchymal transition (EMT) pathway. These cells showed significant increases in canonical mesenchymal marker genes such as VIM, CD44, and SOX9. Gene expression profiles of these subpopulations also correlated with established EMT signatures. Remarkably, this subpopulation did not display mutual exclusivity in gene expression with the epithelial marker EPCAM, which possibly indicates an intermediate mesenchymal phenotype. We also observed in the major population cluster a small subset of cells totaling less than 1% of the population that were significantly enriched for LGR5 expression, a common stem-like marker in colorectal cancer. Overall, we demonstrate the use of single-cell RNA-Seq to discover and characterize a diversity of cellular states that would otherwise be impossible from bulk analysis. Citation Format: Billy Lau, Jiamin Chen, Hanlee P. Ji. Massively parallel single-cell RNA-Seq identifies diverse subpopulations displaying EMT and stem-like features [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2443. doi:10.1158/1538-7445.AM2017-2443