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.
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Van Der Byl, W., Rizzetto, S., Samir, J., Cai, C., Eltahla, A.A., Luciani, F. (2019). Single-Cell Transcriptome Analysis of T Cells. In: Kaneko, S. (eds) In Vitro Differentiation of T-Cells. Methods in Molecular Biology, vol 2048. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9728-2_16
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