Ziegenhain, ChristophVieth, Beate I.H.Parekh, SwatiReinius, BjörnGuillaumet-Adkins, AmySmets, MarthaLeonhardt, HeinrichHeyn, HolgerHellmann, InesEnard, Wolfgang2018-06-192018-06-192017Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M et al. Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell. 2017 Feb 16;65(4):631-43.e4. DOI: 10.1016/j.molcel.2017.01.0231097-2765http://hdl.handle.net/10230/34928Single-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.application/pdfeng© Elsevier This is the published version of an article http://dx.doi.org/10.1016/j.molcel.2017.01.023 that appeared in the journal Molecular Cell. It is published in an Open Archive under an Elsevier user license. Details of this licence are available here: https://www.elsevier.com/about/our-business/policies/open-access-licenses/elsevier-user-licenseinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.molcel.2017.01.023Cost-effectivenessMethod comparisonPower analysisSingle-cell rna-seqTranscriptomicsinfo:eu-repo/semantics/openAccess