Comparative Analysis of Single-Cell RNA Sequencing Methods

• Published in: Molecular cell Vol. 65; no. 4; pp. 631 - 643.e4
• Main Authors: Ziegenhain, Christoph, Vieth, Beate, Parekh, Swati, Reinius, Björn, Guillaumet-Adkins, Amy, Smets, Martha, Leonhardt, Heinrich, Heyn, Holger, Hellmann, Ines, Enard, Wolfgang
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 16.02.2017; Elsevier
• Subjects: Animals; Base Sequence; Cell Line; Computer Simulation; Cost-Benefit Analysis; Cost-effectiveness; embryonic stem cells; Embryonic Stem Cells - chemistry; genes; High-Throughput Nucleotide Sequencing - economics; messenger RNA; Method comparison; Mice; Models, Economic; Power analysis; RNA - genetics; RNA - isolation & purification; sequence analysis; Sequence Analysis, RNA - economics; Sequence Analysis, RNA - methods; simulation; Single-Cell Analysis - economics; Single-Cell Analysis - methods; Single-cell rna-seq; transcriptome; Transcriptomics; transcriptomics; cost-effectiveness; single-cell RNA-seq; method comparison; simulation; power analysis
• ISSN: 1097-2765; 1097-4164
• DOI: 10.1016/j.molcel.2017.01.023

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. [Display omitted] •The study represents the most comprehensive comparison of scRNA-seq protocols•Power simulations quantify the effect of sensitivity and precision on cost efficiency•The study offers an informed choice among six prominent scRNA-seq methods•The study provides a framework for benchmarking future protocol improvements Ziegenhain et al. generated data from mouse ESCs to systematically evaluate six prominent scRNA-seq methods. They used power simulations to compare cost efficiencies, allowing for informed choice among existing protocols and providing a framework for future comparisons.