Single-Cell Transcriptome Analysis of T Cells

Abstract

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology.

Keywords: Alignment; Clustering; Differential gene expression; Gene expression matrix; Single-cell RNA sequencing; T cell receptor reconstruction; T cells; scRNA-seq.