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Quantitative assessment of single-cell RNA-sequencing methods


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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Figure 1: Initial validation of single-cell RNA-seq methods.
Figure 2: Correlation between single-cell RNA-seq and single-cell multiplexed qPCR for each sample preparation method.
Figure 3: Comparison of gene expression distributions for 40 genes between samples prepared in microliter and nanoliter volumes.
Figure 4: Merging many single-cell transcriptomes quantitatively recreates the bulk measurement.
Figure 5: Saturation curves for the different sample preparation methods.

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  1. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

    Article  CAS  Google Scholar 

  2. Kalisky, T., Blainey, P. & Quake, S.R. Genomic analysis at the single-cell level. Annu. Rev. Genet. 45, 431–445 (2011).

    Article  CAS  Google Scholar 

  3. Bengtsson, M., Ståhlberg, A., Rorsman, P. & Kubista, M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388–1392 (2005).

    Article  CAS  Google Scholar 

  4. Raj, A., Peskin, C.S., Tranchina, D., Vargas, D.Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006).

    Article  Google Scholar 

  5. Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat. Protoc. 7, 813–828 (2012).

    Article  CAS  Google Scholar 

  6. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    Article  CAS  Google Scholar 

  7. Tang, F. et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6, 468–478 (2010).

    Article  CAS  Google Scholar 

  8. Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5, 516–535 (2010).

    Article  CAS  Google Scholar 

  9. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  Google Scholar 

  10. Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013).

    Article  Google Scholar 

  11. Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    Article  CAS  Google Scholar 

  12. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  Google Scholar 

  13. Liu, C.L., Bernstein, B.E. & Schreiber, S.L. Whole genome amplification by T7-based linear amplification of DNA (TLAD): II. second-strand synthesis and in vitro transcription. CSH Protoc. 2008, pdb.prot5003 (2008).

  14. Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  Google Scholar 

  15. Tariq, M.A., Kim, H.J., Jejelowo, O. & Pourmand, N. Whole-transcriptome RNAseq analysis from minute amount of total RNA. Nucleic Acids Res. 39, e120 (2011).

    Article  CAS  Google Scholar 

  16. Bullard, J.H., Purdom, E., Hansen, K.D. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11, 94 (2010).

    Article  Google Scholar 

  17. VanGuilder, H.D., Vrana, K.E. & Freeman, W.M. Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 44, 619–626 (2008).

    Article  CAS  Google Scholar 

  18. Matz, M. et al. Amplification of cDNA ends based on template-switching effect and step-out PCR. Nucleic Acids Res. 27, 1558–1560 (1999).

    Article  CAS  Google Scholar 

  19. Gonzalez-Roca, E. et al. Accurate expression profiling of very small cell populations. PLoS ONE 5, e14418 (2010).

    Article  CAS  Google Scholar 

  20. Kurn, N. et al. Novel isothermal, linear nucleic acid amplification systems for highly multiplexed applications. Clin. Chem. 51, 1973–1981 (2005).

    Article  CAS  Google Scholar 

  21. Au, K.F., Jiang, H., Lin, L., Xing, Y. & Wong, W.H. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res. 38, 4570–4578 (2010).

    Article  CAS  Google Scholar 

  22. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  Google Scholar 

  23. Griffith, M. et al. Alternative expression analysis by RNA sequencing. Nat. Methods 7, 843–847 (2010).

    Article  CAS  Google Scholar 

  24. Marcy, Y. et al. Nanoliter reactors improve multiple displacement amplification of genomes from single cells. PLoS Genet. 3, 1702–1708 (2007).

    Article  CAS  Google Scholar 

  25. Jaiswal, S. et al. CD47 is upregulated on circulating hematopoietic stem cells and leukemia cells to avoid phagocytosis. Cell 138, 271–285 (2009).

    Article  CAS  Google Scholar 

  26. Oldenborg, P.-A., Gresham, H.D. & Lindberg, F.P. Cd47-signal regulatory protein α (Sirpα) regulates Fcγ and complement receptor–mediated phagocytosis. J. Exp. Med. 193, 855–862 (2001).

    Article  CAS  Google Scholar 

  27. Willingham, S.B. et al. The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Proc. Natl. Acad. Sci. USA 109, 6662–6667 (2012).

    Article  CAS  Google Scholar 

  28. The External RNA Controls Consortium. et al. The External RNA Controls Consortium: a progress report. Nat. Methods 2, 731–734 (2005).

  29. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

    Article  CAS  Google Scholar 

  30. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal Vol. 17 (2011).

  31. Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

    Article  CAS  Google Scholar 

  32. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  33. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

  34. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  Google Scholar 

  35. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  36. Hoaglin, D.C., Mosteller, F. & Tukey, J.W. Understanding Robust and Exploratory Data Analysis (Wiley, New York, 1983).

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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).

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Authors and Affiliations



A.R.W., N.F.N., T.K., P.D., M.E.R., M.F.C. and S.R.Q. conceived of the study and designed the experiments. A.R.W., N.F.N., T.K., P.D., M.E.R., B.T., F.M.M., G.L.M. and S.S. performed the experiments. A.R.W., N.F.N., T.K., P.D., M.E.R., B.T., M.F.C. and S.R.Q. analyzed the data and/or provided intellectual guidance in their interpretation. P.D., M.E.R. and M.F.C. provided samples and reagents. A.R.W., N.F.N, B.T. and S.R.Q. wrote the paper.

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Correspondence to Stephen R Quake.

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S.R.Q. is a founder and consultant for Fluidigm Corporation.

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Supplementary Figures 1–7, Supplementary Tables 1 and 2 and Supplementary Note 1 (PDF 3157 kb)

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Wu, A., Neff, N., Kalisky, T. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods 11, 41–46 (2014).

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