Comprative Analysis of Single-Cell RNA Sequencing Methods

Identification: Ziegenhain, Christoph


Comprative Analysis of Single-Cell RNA Sequencing Methods

Christoph Ziegenhain1, Beate Vieth1, Swati Parekh1, Björn Reinius2, Amy Guillaumet-Adkins3, Martha Smets4, Heinrich Leonhardt4, Holger Heyn3, Ines Hellmann1, Wolfgang Enard1*

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

*Corresponding author

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.


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