Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

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Abstract

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.

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Figure 1: Smart-Seq read coverage across transcripts.
Figure 2: Sensitivity and variability in Smart-Seq from few or single cells.
Figure 3: Transcriptional and post-transcriptional analyses of cancer cell line cells using Smart-Seq.
Figure 4: Single-cell transcriptomes of circulating tumor cells.

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Acknowledgements

We thank C. Burge and G. Winberg for critical reading of the manuscript, T. Juarez and J. Cotton at the University of California San Diego for their help in Internal Review Board protocol preparation and aquisition of clinical samples, A.A. Talasaz and G. Cann for assistance with the Magsweeper, members of the Science for Life laboratory (Stockholm) for assistance with MiSeq sequencer. Y.-C.W. was supported by a fellowship from the Marie Mayer Foundation. L.C.L. was supported by US National Institutes of Health (NIH) K12HD001259. J.F.L. was supported by NIH R33MH87925 and California Institute for Regenerative Medicine (CL1-00502, RT1-01108, TR1-01250, and RN2-00931). R.S. was supported by European Research Council (starting grant 243066), Swedish Research Council (2008-4562), Foundation for Strategic Research (FFL4) and Åke Wiberg Foundation (756194131).

Author information

D.R. designed and performed the computational analyses of sequencing reads, prepared figures, tables and methods, and contributed manuscript text. S.L. and R.L. developed protocols and created libraries. I.K. and S.L. did primary data analysis. Y.-C.W., G.A.D. and J.F.L. prepared melanoma circulating tumor cells, melanocytes and melanoma cell line cells. O.R.F. and Q.D. contributed additional sequencing libraries. L.C.L. and G.P.S. contributed to study design and manuscript text. R.S. designed the study and prepared the manuscript, with input from other authors.

Correspondence to Rickard Sandberg.

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Competing interests

S.L., R.L., I.K. and G.P.S. are employees and shareholders of Illumina.

Supplementary information

Supplementary Text and Figures

Supplementary Figs. 1–11 (PDF 1379 kb)

Supplementary Table 1

List of Smart-Seq and standard mRNA-Seq data generated (XLS 42 kb)

Supplementary Table 2

List of studies reporting total RNA amount per cell for different mammalian cell types (XLS 13 kb)

Supplementary Table 3

List of exons with significantly different inclusion levels in cancer cell line cells (XLS 42 kb)

Supplementary Table 4

Differentially expressed genes between circulating tumor cells, primary melanocytes and melanoma cell lines (XLS 5249 kb)

Supplementary Table 5

Functional categories enriched among differentially expressed genes (XLS 15 kb)

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Ramsköld, D., Luo, S., Wang, Y. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30, 777–782 (2012) doi:10.1038/nbt.2282

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