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CpG Islands pp 257–283Cite as

High-Throughput Single-Cell RNA Sequencing and Data Analysis

Part of the Methods in Molecular Biology book series (MIMB,volume 1766)

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.

Key words

  • Single cell RNA sequencing
  • High-throughput
  • Single cell data analysis
  • CEL-Seq2
  • Next-generation sequencing

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Acknowledgments

The authors would like to thank Thomas Boehm, Sebastian Hobitz, and Ulrike Bönisch for their help in developing the protocol.

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Correspondence to Dominic Grün .

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Sagar, Herman, J.S., Pospisilik, J.A., Grün, D. (2018). High-Throughput Single-Cell RNA Sequencing and Data Analysis. In: Vavouri, T., Peinado, M. (eds) CpG Islands. Methods in Molecular Biology, vol 1766. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7768-0_15

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  • DOI: https://doi.org/10.1007/978-1-4939-7768-0_15

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