Abstract 3578: Unmasking T-cell heterogeneity via single cell transcriptomic profiling

Single-cell gene expression analysis has been shown to delineate cellular heterogeneity. Such analyses result in the discovery of genes that identify subtypes of cells, or that mark intermediate states during a biological process. Cellular heterogeneity, plasticity, and diversity of T cells facilitate a wide range of functional flexibility that give rise to a remarkable breadth of potential responses against many different pathogens and in various disease settings. Understanding how T cells leverage these cellular characteristics particularly during dynamic processes such as development, differentiation, and antigenic response is important and well suited for single-cell analysis. Unbiased approaches to evaluate cellular states and subpopulations of single T cells can identify important aspects that may be concealed by targeted approaches such as FACS sorting on cell-surface antigens, or bulk expression analysis. Resolving these complexities in T cells is of particular interest as classical phenotypic criteria has been deemed insufficient for distinguishing different T cell subtypes and transitional states, particularly in the context of dysfunctional T cell states in autoimmunity and tumor-related exhaustion. We activated purified 2.5 million CD4+ T cells from Cynomolgus monkey (M. fascicularis) using equal amounts of biotinylated antibodies against CD2 (human), CD3 (primate) and CD283 (human). To determine the single cell transcriptomic profile of these activated T cells, we performed drop seq sequencing at 0 hours and 24 hours post activation. Cells at both time points were split into four technical replicates to determine variability in the sequencing process. In addition, RNA was also isolated from these cells and whole transcriptome analysis was performed using Illumina9s Next Generation Sequencing technology. Our analysis show how cellular subpopulations can be identified from transcriptional data, and derive characteristic gene expression signatures that distinguish these states. We ordered single cells in pseudotime, placing them along a trajectory corresponding to individual cell9s asynchronous progression during activation. Monocle was used for this purpose as it orders cells by learning an explicit principal graph from the single cell genomics data with Reversed Graph Embedding, an advanced machine learning technique, which robustly and accurately resolves complicated biological processes. This information could not have been obtained from bulk RNA-Seq analysis. Our results show that single-cell RNA-Seq is a powerful technique to study the cellular heterogeneity in T cells, a paradigm that will be of great value in the development of immunotherapeutic strategies. Citation Format: Mukta Dutta, Tuuli Saloranta, Inah Golez, Kerry Deutsch, Cara Lord, Vickie Satele, Steve Anderson, Anup Madan. Unmasking T-cell heterogeneity via single cell transcriptomic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3578.