Comparative Analysis of Single-Cell RNA Sequencing Methods

被引：988

作者：

Ziegenhain, Christoph ^{[1
]}

Vieth, Beate ^{[1
]}

Parekh, Swati ^{[1
]}

Reinius, Bjorn ^{[2
,3
]}

Guillaumet-Adkins, Amy ^{[4
,5
]}

Smets, Martha ^{[6
,7
]}

Leonhardt, Heinrich ^{[6
,7
]}

Heyn, Holger ^{[4
,5
]}

Hellmann, Ines ^{[1
]}

Enard, Wolfgang ^{[1
]}

机构：

[1] Ludwig Maximilians Univ Munchen, Anthropol & Human Genom, Dept Biol 2, Grosshaderner Str 2, D-82152 Martinsried, Germany

[2] Ludwig Inst Canc Res, Box 240, S-17177 Stockholm, Sweden

[3] Karolinska Inst, Dept Cell & Mol Biol, S-17177 Stockholm, Sweden

[4] BIST, Ctr Genom Regulat CRG, CNAG CRG, Barcelona 08028, Spain

[5] UPF, Barcelona 08002, Spain

[6] Ludwig Maximilians Univ Munchen, Dept Biol 2, Grosshaderner Str 2, D-82152 Martinsried, Germany

[7] Ludwig Maximilians Univ Munchen, CIPSM, Grosshaderner Str 2, D-82152 Martinsried, Germany

来源：

MOLECULAR CELL | 2017年 / 65卷 / 04期

关键词：

DIFFERENTIAL EXPRESSION ANALYSES; GENE-EXPRESSION; LIBRARY PREPARATION; SEQ; NOISE;

D O I：

10.1016/j.molcel.2017.01.023

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：631 / +

页数：17

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