Comparative Analysis of Single-Cell RNA Sequencing Methods

By C. Ziegenhain, B. Vieth, Swati Parekh, Björn Reinius, Martha Smets, H. Leonhardt, Ines Hellmann, Wolfgang Enard. bioRxiv, 2016

943 citations recorded by the source corpus.

Background Single-cell RNA sequencing (scRNA-seq) offers exciting possibilities to address biological and medical questions, but a systematic comparison of recently developed protocols is still lacking. Results We generated data from 447 mouse embryonic stem cells using Drop-seq, SCRB-seq, Smart-seq (on Fluidigm C1) and Smart-seq2 and analyzed existing data from 35 mouse embryonic stem cells prepared with CEL-seq. We find that Smart-seq2 is the most sensitive method as it detects the most genes per cell and across cells. However, it shows more amplification noise than CEL-seq, Drop-seq and SCRB-seq as it cannot use unique molecular identifiers (UMIs). We use simulations to model how the observed combinations of sensitivity and amplification noise affects detection of differentially expressed genes and find that SCRB-seq reaches 80% power with the fewest number of cells. When considering cost-efficiency at different sequencing depths at 80% power, we find that Drop-seq is preferable when quantifying transcriptomes of a large numbers of cells with low sequencing depth, SCRB-seq is preferable when quantifying transcriptomes of fewer cells and Smart-seq2 is preferable when annotating and/or quantifying transcriptomes of fewer cells as long one can use in-house produced transposase. Conclusions Our analyses allows an informed choice among five prominent scRNA-seq protocols and provides a solid framework for benchmarking future improvements in scRNA-seq methodologies.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dcbd9e859711c4012ba4dcf857bd0c40ef9f5a50

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Citations in the corpus

Comparison and evaluation of statistical error models for scRNA-seq (2021, methodology): “The Poisson distribution constrains the variance of a random variable to be equal to its mean, and has been utilized for modeling UMI counts in multiple previous studies [15, 16].”
Massively-parallel single nucleus RNA-seq with DroNc-seq (2017, positive): “Conversely, massively parallel scRNA-seq methods, such as Drop-seq 7 and related methods 8–10 can be readily applied at scale 11 in a cost-effective manner 12 , but require intact single cell suspension as input.”
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, positive): “This enables the computational removal of amplication noise and thus increases the power to detect expression di erences between cells [8, 9].”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, methodology): “Our Cj7mESmicrowell-seq data were comparedwith a series of mES single cell data obtained using different technologies (Ziegenhain et al., 2017).”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, background): “termining the relative abundance of mRNA transcripts within a pool [32, 33].”
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2017, neutral): “15).”
CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data (2022, background): “Single-cell RNA sequencing (scRNA-seq) enables wholetranscriptome profiling of single cell, revealing exciting biological and medical insights and providing new possibilities for solving biological and medical questions (26).”