title: Quantitative assessment of single-cell RNA-sequencing methods.
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.
authors.affiliation: Department of Bioengineering, Stanford University, Stanford, California, USA.
authors.forename: Angela R
authors.initials: AR
authors.lastname: Wu
authors.affiliation: Department of Bioengineering, Stanford University, Stanford, California, USA.
authors.forename: Norma F
authors.initials: NF
authors.lastname: Neff
authors.affiliation: 1] Department of Bioengineering, Stanford University, Stanford, California, USA. [2].
authors.forename: Tomer
authors.initials: T
authors.lastname: Kalisky
authors.affiliation: 1] Department of Medicine, Division of Oncology, Stanford University Medical Center, Stanford, California, USA. [2] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University Medical Center, Stanford, California, USA. [3] The Ludwig Cancer Center, Stanford University Medical Center, Stanford, California, USA.
authors.forename: Piero
authors.initials: P
authors.lastname: Dalerba
authors.affiliation: Department of Bioengineering, Stanford University, Stanford, California, USA.
authors.forename: Barbara
authors.initials: B
authors.lastname: Treutlein
authors.affiliation: Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University Medical Center, Stanford, California, USA.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Rothenberg
authors.affiliation: 1] Department of Bioengineering, Stanford University, Stanford, California, USA. [2] Howard Hughes Medical Institute, Stanford, California, USA.
authors.forename: Francis M
authors.initials: FM
authors.lastname: Mburu
authors.affiliation: Department of Bioengineering, Stanford University, Stanford, California, USA.
authors.forename: Gary L
authors.initials: GL
authors.lastname: Mantalas
authors.affiliation: Institute for Stem Cell Biology and Regenerative Medicine, Stanford University Medical Center, Stanford, California, USA.
authors.forename: Sopheak
authors.initials: S
authors.lastname: Sim
authors.affiliation: 1] Department of Medicine, Division of Oncology, Stanford University Medical Center, Stanford, California, USA. [2] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University Medical Center, Stanford, California, USA. [3] The Ludwig Cancer Center, Stanford University Medical Center, Stanford, California, USA.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Clarke
authors.affiliation: 1] Department of Bioengineering, Stanford University, Stanford, California, USA. [2] Howard Hughes Medical Institute, Stanford, California, USA. [3] Department of Applied Physics, Stanford University, Stanford, California, USA.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Quake
mesh_terms: D003627:Data Interpretation, Statistical; D001330:Electronic Data Processing; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D045325:HCT116 Cells; D006801:Humans; D044085:Microfluidics; D021141:Nucleic Acid Amplification Techniques; D016133:Polymerase Chain Reaction / Q000379:methods; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA; D017423:Sequence Analysis, RNA / Q000379:methods*; D059010:Single-Cell Analysis / Q000379:methods*; D059467:Transcriptome