Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments (original) (raw)

Nature Biotechnology volume 31, pages 748–752 (2013)Cite this article

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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.

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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.

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Authors and Affiliations

  1. Department of Statistics, University of Oxford, Oxford, UK
    Quin F Wills & Chris Holmes
  2. Fluidigm Corporation, South San Francisco, California, USA
    Kenneth J Livak
  3. Stem Cell Laboratory, UCL Cancer Institute, University College London, London, UK
    Alex J Tipping & Tariq Enver
  4. UEA Flow Cytometry Services, BioMedical Research Centre, School of Biological Sciences, University of East Anglia, Norwich, UK
    Andrew J Goldson
  5. BioMedical Research Centre, Norwich Medical School, University of East Anglia, Norwich, UK
    Darren W Sexton
  6. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
    Chris Holmes
  7. Nuffield Department of Medicine, University of Oxford, Oxford, UK
    Chris Holmes
  8. Medical Research Council Harwell, Harwell Science and Innovation Campus, UK
    Chris Holmes

Authors

  1. Quin F Wills
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  2. Kenneth J Livak
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  3. Alex J Tipping
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  4. Tariq Enver
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  5. Andrew J Goldson
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  6. Darren W Sexton
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  7. Chris Holmes
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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.

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Correspondence toQuin F Wills.

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Competing interests

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

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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

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