High-throughput single-cell RNA sequencing and data analysis
Sagar , Herman JS, Pospisilik JA, Gruen D (2018)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2018
Journal
Publisher: Humana Press Inc.
Series: Methods in Molecular Biology
Book Volume: 1766
Pages Range: 257-283
DOI: 10.1007/978-1-4939-7768-0_15
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Sagar, ., Herman, J.S., Pospisilik, J.A., & Gruen, D. (2018). High-throughput single-cell RNA sequencing and data analysis. In (pp. 257-283). Humana Press Inc..
MLA:
Sagar, , et al. "High-throughput single-cell RNA sequencing and data analysis." Humana Press Inc., 2018. 257-283.
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