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Mol Cell. 2017 Feb 16;65(4):631-643.e4. doi: 10.1016/j.molcel.2017.01.023.

Comparative Analysis of Single-Cell RNA Sequencing Methods.

Author information

1
Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany.
2
Ludwig Institute for Cancer Research, Box 240, 171 77 Stockholm, Sweden; Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden.
3
CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain; Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain.
4
Department of Biology II and Center for Integrated Protein Science Munich (CIPSM), Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany.
5
Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany. Electronic address: enard@bio.lmu.de.

Abstract

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

KEYWORDS:

cost-effectiveness; method comparison; power analysis; simulation; single-cell RNA-seq; transcriptomics

PMID:
28212749
DOI:
10.1016/j.molcel.2017.01.023
[Indexed for MEDLINE]
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