Quantitative assessment of single-cell RNA-sequencing methods
Open accessAbstract
Interest in single-cell whole-transcriptome analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. We compared commercially available single-cell RNA amplification methods with both microliter and nanoliter volumes, using sequence from bulk total RNA and multiplexed quantitative PCR as benchmarks to systematically evaluate the sensitivity and accuracy of various single-cell RNA-seq approaches. We show that single-cell RNA-seq can be used to perform accurate quantitative transcriptome measurement in individual cells with a relatively small number of sequencing reads and that sequencing large numbers of single cells can recapitulate bulk transcriptome complexity.
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The C1 platform has advantages over droplet-based platforms: smaller samples can be processed and capture efficiency of genes and RNA molecules is markedly higher.
“The automated C1 microfluidic platform (Fluidigm), while more expensive per sample, has been shown to confer several advantages over platforms that make use of droplets to capture single cells321. In particular, smaller samples can be processed using the C1 (when cell numbers are limiting), and the C1 capture efficiency of genes (and RNA molecules) is markedly higher.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [21]
In scRNA-seq experiments, individual cells are typically not sequenced to saturation, leading to sparse count matrices, where only highly expressed genes are detected with counts greater than zero.
“In scRNA-seq experiments, individual cells are typically not sequenced to saturation, leading to sparse count matrices, where only highly expressed genes are detected with counts greater than zero.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [34]
The optimal read depths were ~10,000 in most evaluations, relatively low compared to previous recommendations.
“Specifically, we found the optimal read depths to be ~10,000 in most evaluations, which is relatively low compared to previous recommendations34–36,80,81.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [34]
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- Authors
- Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., & Quake, S. R.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Quake, Stephen R
- ORCID
- Venue
- Nature Methods
- Published
- 2013-10-20
- Volume
- 11
- Issue
- 1
- Pages
- 41-46
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4022966
- OpenAlex
- W2171501288
- Cited by
- 696
- References
- 36
- Citations by year
- 2026: 10 · 2025: 30 · 2024: 27 · 2023: 39 · 2022: 36 · 2021: 56 · 2020: 58 · 2019: 64 · 2018: 77 · 2017: 109 · 2016: 76 · 2015: 65 · 2014: 44 · 2013: 4
- FWCI
- 28.24
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (U01HL099995, U01HL099999)
- National Cancer Institute (P01CA139490, U01CA154209)
- National Institute of Neurological Disorders and Stroke (RC4NS073015)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- single-cell RNA sequencing, method comparison, sensitivity, accuracy, reproducibility
- MeSH terms
- Electronic Data Processing, Data Interpretation, Statistical, Gene Expression Regulation, Humans, Reproducibility of Results, Polymerase Chain Reaction, Sequence Analysis, DNA, Sequence Analysis, RNA, Gene Expression Profiling, Nucleic Acid Amplification Techniques, Microfluidics, HCT116 Cells, Single-Cell Analysis, Transcriptome
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