Chapter title |
Single-Cell Transcriptome Analysis of T Cells.
|
---|---|
Chapter number | 16 |
Book title |
In Vitro Differentiation of T-Cells
|
Published in |
Methods in molecular biology, January 2019
|
DOI | 10.1007/978-1-4939-9728-2_16 |
Pubmed ID | |
Book ISBNs |
978-1-4939-9727-5, 978-1-4939-9728-2
|
Authors |
Van Der Byl, Willem, Rizzetto, Simone, Samir, Jerome, Cai, Curtis, Eltahla, Auda A, Luciani, Fabio, Eltahla, Auda A. |
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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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 5 | 25% |
Student > Master | 4 | 20% |
Student > Ph. D. Student | 3 | 15% |
Student > Bachelor | 1 | 5% |
Other | 1 | 5% |
Other | 3 | 15% |
Unknown | 3 | 15% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 5 | 25% |
Immunology and Microbiology | 5 | 25% |
Chemical Engineering | 1 | 5% |
Environmental Science | 1 | 5% |
Agricultural and Biological Sciences | 1 | 5% |
Other | 3 | 15% |
Unknown | 4 | 20% |