Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

Abstract

Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of genetic variation. Here we measure the expression of 92 genes affected by Wnt signaling in 1,440 single cells from 15 individuals to associate single-nucleotide polymorphisms (SNPs) with gene-expression phenotypes, while accounting for stochastic and cell-cycle differences between cells. We provide evidence that many heritable variations in gene function—such as burst size, burst frequency, cell cycle–specific expression and expression correlation/noise between cells—are masked when expression is averaged over many cells. Our results demonstrate how single-cell analyses provide insights into the mechanistic and network effects of genetic variability, with improved statistical power to model these effects on gene expression.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Single-cell gene expression distributions.
Figure 2: Properties of gene expression noise.
Figure 3: The heritability of single-cell expression.

Similar content being viewed by others

References

  1. Nica, A.C. et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet. 7, e1002003 (2011).

    Article  CAS  Google Scholar 

  2. Li, H. & Deng, H. Systems genetics, bioinformatics and eQTL mapping. Genetica 138, 915–924 (2010).

    Article  Google Scholar 

  3. Bak, P. et al. Self-organized criticality: an explanation of the 1/f noise. Phys. Rev. Lett. 59, 381–384 (1987).

    Article  CAS  Google Scholar 

  4. 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 

  5. Nicolae, D.L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  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. Ramskold, 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 

  8. Livak, K.J. et al. Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells. Methods 59, 71–79 (2013).

    Article  CAS  Google Scholar 

  9. International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

  10. Coghlan, M.P. et al. Selective small molecule inhibitors of glycogen synthase kinase-3 modulate glycogen metabolism and gene transcription. Chem. Biol. 7, 793–803 (2000).

    Article  CAS  Google Scholar 

  11. Dar, R.D. et al. Transcriptional burst frequency and burst size are equally modulated across the human genome. Proc. Natl. Acad. Sci. USA 109, 17454–17459 (2012).

    Article  CAS  Google Scholar 

  12. Bengtsson, M., Stahlberg, 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 

  13. Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule Sensitivity in single cells. Science 329, 533–538 (2010).

    Article  CAS  Google Scholar 

  14. Bublik, D.R.R., Scolz, M., Triolo, G., Monte, M. & Schneider, C. Human GTSE-1 regulates p21(CIP1/WAF1) stability conferring resistance to paclitaxel treatment. J. Biol. Chem. 285, 5274–5281 (2010).

    Article  CAS  Google Scholar 

  15. Choy, E. et al. Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines. PLoS Genet. 4, e1000287 (2008).

    Article  Google Scholar 

  16. Im, H.K.K. et al. Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer. PLoS Genet. 8, e1002525 (2012).

    Article  CAS  Google Scholar 

  17. Cuddapah, S. et al. Global analysis of the insulator binding protein CTCF in chromatin barrier regions reveals demarcation of active and repressive domains. Genome Res. 19, 24–32 (2009).

    Article  CAS  Google Scholar 

  18. Hardy, R.R. & Hayakawa, K. B cell development pathways. Annu. Rev. Immunol. 19, 595–621 (2001).

    Article  CAS  Google Scholar 

  19. Wu, B., Piatkevich, K.D., Lionnet, T., Singer, R.H. & Verkhusha, V.V. Modern fluorescent proteins and imaging technologies to study gene expression, nuclear localization, and dynamics. Curr. Opin. Cell Biol. 23, 310–317 (2011).

    Article  CAS  Google Scholar 

  20. Siegel, A.F. Robust regression using repeated medians. Biometrika 69, 242–244 (1982).

    Article  Google Scholar 

  21. Johnstone, I.M. & Velleman, P.F. The resistant line and related regression methods. J. Am. Stat. Assoc. 80, 1041–1054 (1985).

    Article  Google Scholar 

  22. Sen, P.K. Estimates of the regression coefficient based on Kendall's Tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).

    Article  Google Scholar 

Download references

Acknowledgements

Many thanks to L. Toji at the Coriell Institute for her valuable input on the cell line growth and transformation characteristics. Also, thanks to the following people at Fluidigm: B. Jones for his overall support, G. Harris and D. Wang for their help with primer design, and the meticulous technical assistance of K. Datta and R. Mittal. C.H. and T.E. are funded by the Medical Research Council of the UK. T.E. is also funded by Leukaemia Lymphoma Research and EuroSyStem.

Author information

Authors and Affiliations

Authors

Contributions

Q.F.W. and C.H. conceived and designed the study. A.J.T. and T.E. ran the initial flow cytometry characterization and cell culture optimization. A.J.G. and D.W.S. ran the main study's cell culture and flow cytometry, further optimizing the sample characterization. K.J.L. designed and optimized the single-cell RNA assays, and generated the gene expression chip data. Q.F.W. analyzed the data and wrote the manuscript.

Corresponding author

Correspondence to Quin F Wills.

Ethics declarations

Competing interests

K.L. is an employee of the Fluidigm Corporation.

Supplementary information

Supplementary Text and Figures

Supplementary Notes, Supplementary Figures 1–11 and Supplementary Tables 1–2 (PDF 2231 kb)

Supplementary Data

Supplementary Data (XLSX 944 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wills, Q., Livak, K., Tipping, A. et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol 31, 748–752 (2013). https://doi.org/10.1038/nbt.2642

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.2642

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing