Quantitative assessment of single-cell RNA-sequencing methods

Angela R. Wu; Norma F. Neff; Tomer Kalisky; Piero Dalerba; Barbara Treutlein; Michael E. Rothenberg; Francis M. Mburu; Gary L. Mantalas; Sopheak Sim; Michael F. Clarke; Stephen R. Quake

doi:10.1038/nmeth.2694

Quantitative assessment of single-cell RNA-sequencing methods

Angela R. Wu, Norma F. Neff, Tomer Kalisky, Piero Dalerba, Barbara Treutlein, Michael E. Rothenberg, Francis M. Mburu, Gary L. Mantalas, Sopheak Sim, Michael F. Clarke, Stephen R. Quake

Bar-Ilan University - The Alexander Kofkin Faculty of Engineering

Research output: Contribution to journal › Article › peer-review

559 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	41-46
Number of pages	6
Journal	Nature Methods
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/nmeth.2694
State	Published - 2014

Bibliographical note

Funding Information:
The authors would like to acknowledge W. Pan for helping with part of the sample preparation, W. Koh and B. Passarelli for help and discussions regarding bioinformatics pipelines and statistical analysis, and I. de Vlaminck for critical reading of the manuscript. A.R.W. was supported by US National Institutes of Health (NIH) U01HL099999-01 and RC4NS073015. N.F.N. was supported by NIH U01HL099999-01 and U01CA154209. T.K. was supported by P01CA139490. B.T. was supported by U01HL099995-01. F.M.M. was sponsored by the Stanford Institute of Medicine Summer Research Program. P.D. was supported by a scholarship from the Thomas and Stacey Siebel Foundation and by a BD Biosciences Stem Cell Research Grant (Summer 2011).

Funding

The authors would like to acknowledge W. Pan for helping with part of the sample preparation, W. Koh and B. Passarelli for help and discussions regarding bioinformatics pipelines and statistical analysis, and I. de Vlaminck for critical reading of the manuscript. A.R.W. was supported by US National Institutes of Health (NIH) U01HL099999-01 and RC4NS073015. N.F.N. was supported by NIH U01HL099999-01 and U01CA154209. T.K. was supported by P01CA139490. B.T. was supported by U01HL099995-01. F.M.M. was sponsored by the Stanford Institute of Medicine Summer Research Program. P.D. was supported by a scholarship from the Thomas and Stacey Siebel Foundation and by a BD Biosciences Stem Cell Research Grant (Summer 2011).

Funders	Funder number
BD Biosciences Stem Cell Research	Summer 2011
NIH U01HL099999-01	P01CA139490, U01CA154209, U01HL099995-01
Stanford Institute of Medicine
National Institutes of Health	RC4NS073015, U01HL099999-01
Foundation for the National Institutes of Health
National Heart, Lung, and Blood Institute	U01HL099995
Thomas and Stacey Siebel Foundation

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10.1038/nmeth.2694

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abstract = "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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AU - Quake, Stephen R.

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Quantitative assessment of single-cell RNA-sequencing methods

Abstract

Bibliographical note

Funding

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