Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.
At a glance
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- Supplementary Text and Figures (1M)
Supplementary Figs. 1–11
- Supplementary Table 1 (45K)
List of Smart-Seq and standard mRNA-Seq data generated
- Supplementary Table 2 (16K)
List of studies reporting total RNA amount per cell for different mammalian cell types
- Supplementary Table 3 (45K)
List of exons with significantly different inclusion levels in cancer cell line cells
- Supplementary Table 4 (5M)
Differentially expressed genes between circulating tumor cells, primary melanocytes and melanoma cell lines
- Supplementary Table 5 (16K)
Functional categories enriched among differentially expressed genes