Comparative Analysis of Single-Cell RNA Sequencing Methods
Comparative Analysis of Single-Cell RNA Sequencing Methods
复制标题
单细胞RNA测序方法的比较分析
DOI：
10.1016/j.molcel.2017.01.023
复制
发表时间：
2017-02-16
期刊：
影响因子：
16
通讯作者：
Enard, Wolfgang
中科院分区：
文献类型：
--
作者：
Ziegenhain, Christoph;Vieth, Beate;Enard, Wolfgang
单细胞RNA测序（scRNA-seq）为解决生物学和医学问题提供了新的可能性。然而，缺乏对不同scRNA-seq方案性能的系统比较。我们从583个小鼠胚胎干细胞中生成数据，以评估六种主要的scRNA-seq方法：CEL-seq 2，Drop-seq，MARS-seq，SCRB-seq，Smart-seq和Smart-seq 2。虽然Smart-seq 2检测到每个细胞和跨细胞的大多数基因，但CEL-seq 2，Drop-seq，MARS-seq和SCRB-seq由于使用独特的分子标识符（UMI）而以较少的扩增噪音定量mRNA水平。不同测序深度的功率模拟表明，Drop-seq对于大量细胞的转录组量化更具成本效益，而MARS-seq、SCRB-seq和Smart-seq 2在分析较少细胞时更有效。我们的定量比较为在六种主要的scRNA-seq方法中进行明智的选择提供了基础，并为scRNA-seq方案的进一步改进提供了基准框架。
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.