TitleQuantitative assessment of single-cell RNA-sequencing methods
Data descriptionSingle-cell RNA-seq of 102 HCT116 cells from 2 human colon cancer cell line
Doi10.1038/nmeth.2694
Web-link of the paperhttp://www.nature.com/articles/nmeth.2694
Data typescRNA-seq
DatabaseGene Expression Omnibus (GEO)
Accession numberGSE51254
URL of the datahttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51254
SpeciesHuman
TissueColon cancer cell
Cell typeHCT116 cell
Number of cells102
Cell capture platformFluidigm C1
FACS
Library preparation protocolSMARTer
Unique molecular identifier (Y/N)N
Spike-in (Y/N)Y
Full-length (Y/N)N
Brief summary of the scientific questionCompared 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. 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.
Brief summary of the bioinformatics processingSchematic of the experimental strategy. Reproducibility, as evaluated by the percentage of genes detected in pairs of replicate samples out of the mean total number of genes detected in pair of samples. Sensitivity, as evaluated by overlap between genes detected by single-cell and bulk RNA-seq measurement. Correlation between single-cell RNA-seq and single-cell multiplexed qPCR for each sample preparation method. Comparison of gene expression distributions between samples prepared in microliter and nanoliter volumes: Frequency distribution of expression values from single-cell qPCR and from single-cell RNA-seq. Created a synthetic ensemble data set by computationally pooling raw reads from all the single-cell RNA-seq data to mimic a bulk RNA-seq experiment. Constructed saturation curves for each preparation method by subsampling the raw reads from each library and determining the number of genes detected.
CitationWu, A., Neff, N., Kalisky, T., Dalerba, P., Treutlein, B., & Rothenberg, M. et al. (2013). Quantitative assessment of single-cell RNA-sequencing methods. Nature Methods, 11(1), 41-46.
Web-link of the paperhttp://www.nature.com/articles/nmeth.2694