High-Throughput Single-Cell RNA Sequencing and Data Analaysis

Sagar, Sagar; Hermann, Josip Stefan; Pospisilik, John Andrew; Grün, Dominic

doi:10.1007/978-1-4939-7768-0_15

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High-Throughput Single-Cell RNA Sequencing and Data Analaysis

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Sagar, Sagar
Max Planck Institute of Immunobiology and Epigenetics, Max Planck Society;

Hermann, Josip Stefan
Max Planck Institute of Immunobiology and Epigenetics, Max Planck Society;

Pospisilik, John Andrew
Max Planck Institute of Immunobiology and Epigenetics, Max Planck Society;

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引用

Sagar, S., Hermann, J. S., Pospisilik, J. A., & Grün, D. (2018). High-Throughput Single-Cell RNA Sequencing and Data Analaysis. Methods in Molecular Biology, 1766, 257-283. doi:10.1007/978-1-4939-7768-0_15.

引用: https://hdl.handle.net/21.11116/0000-0002-64C0-4

要旨

Understanding biological systems at a single cell resolution may reveal several novel insights which remain masked by the conventional population-based techniques providing an average readout of the behavior of cells. Single-cell transcriptome sequencing holds the potential to identify novel cell types and characterize the cellular composition of any organ or tissue in health and disease. Here, we describe a customized high-throughput protocol for single-cell RNA-sequencing (scRNA-seq) combining flow cytometry and a nanoliter-scale robotic system. Since scRNA-seq requires amplification of a low amount of endogenous cellular RNA, leading to substantial technical noise in the dataset, downstream data filtering and analysis require special care. Therefore, we also briefly describe in-house state-of-the-art data analysis algorithms developed to identify cellular subpopulations including rare cell types as well as to derive lineage trees by ordering the identified subpopulations of cells along the inferred differentiation trajectories.