Single-Cell Transcriptome Analysis of T Cells. Academic Article uri icon

Overview

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.

publication date

  • January 1, 2019

Research

keywords

  • Computational Biology
  • RNA-Seq
  • Single-Cell Analysis
  • T-Lymphocytes

Identity

Scopus Document Identifier

  • 85071280035

Digital Object Identifier (DOI)

  • 10.1007/978-1-4939-9728-2_16

PubMed ID

  • 31396939

Additional Document Info

volume

  • 2048