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Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation

Nature Microbiology volume 4, pages 480–491 (2019)Cite this article

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Abstract

Phenotypic cell-to-cell variability is a fundamental determinant of microbial fitness that contributes to stress adaptation and drug resistance. Gene expression heterogeneity underpins this variability but is challenging to study genome-wide. Here we examine the transcriptomes of >2,000 single fission yeast cells exposed to various environmental conditions by combining imaging, single-cell RNA sequencing and Bayesian true count recovery. We identify sets of highly variable genes during rapid proliferation in constant culture conditions. By integrating single-cell RNA sequencing and cell-size data, we provide insights into genes that are regulated during cell growth and division, including genes whose expression does not scale with cell size. We further analyse the heterogeneity of gene expression during adaptive and acute responses to changing environments. Entry into the stationary phase is preceded by a gradual, synchronized adaptation in gene regulation that is followed by highly variable gene expression when growth decreases. Conversely, sudden and acute heat shock leads to a stronger, coordinated response and adaptation across cells. This analysis reveals that the magnitude of global gene expression heterogeneity is regulated in response to different physiological conditions within populations of a unicellular eukaryote.

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Fig. 1: Imaging and transcriptome analysis of single fission yeast cells.
Fig. 2: Cell-to-cell variability of the fission yeast transcriptome.
Fig. 3: Cell-size dependence of the fission yeast transcriptome.
Fig. 4: Gene-expression heterogeneity of fission yeast populations in response to environmental changes.

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Data availability

All raw scRNA-seq datasets are available in ArrayExpress accession number E-MTAB-6825. Cell size measurement and all smFISH data are available as Supplementary Material. The bayNorm package is available from Bioconductor: https://bioconductor.org/packages/release/bioc/html/bayNorm.html. All figures except Fig. 1a and Supplementary Fig. 2a contain original data.

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Acknowledgements

We thank T. Livermore for his help during the initial part of this project and C. Stefanelli for her contribution to the development of bayNorm. We are grateful to S. Parrinello, A. Martinez-Segura and M. Priestman for their input on the manuscript. This research was supported by the UK Medical Research Council, a Leverhulme Research Project Grant (grant no. RPG-2014-408) and a Wellcome Trust Senior Investigator Award to J.B. (grant no. 095598/Z/11/Z). We used the computing resources of the UK Medical Bioinformatics partnership (UK MED-BIO), which is supported by the UK Medical Research Council (grant no. MR/L01632X/1) and the Imperial College High Performance Computing Service.

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Author notes

  1. François Bertaux

    Present address: Institut Pasteur, Paris, France

  2. Anna Köferle

    Present address: Munich Center for Neurosciences, Ludwig-Maximilian-Universität, Planegg, Germany

  3. These authors contributed equally: François Bertaux, Wenhao Tang.

Authors and Affiliations

  1. MRC London Institute of Medical Sciences, London, UK

    Malika Saint, François Bertaux, Xi-Ming Sun, Laurence Game & Samuel Marguerat

  2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK

    Malika Saint, François Bertaux, Xi-Ming Sun, Laurence Game & Samuel Marguerat

  3. Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK

    François Bertaux, Wenhao Tang & Vahid Shahrezaei

  4. Research Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, UK

    Anna Köferle & Jürg Bähler

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Contributions

M.S. set up the scRNA-seq protocol in yeast cells, performed all sequencing experiments and part of the computational analysis. W.T. and F.B. developed bayNorm and performed most computational analyses together with S.M. and V.S. X.M.S. performed all smFISH and growth experiments. A.K. developed the first generation of the single-cell isolation and PCR amplification protocol under the supervision of S.M. and J.B. L.G. assisted with the developement of the scRNA-seq protocol. S.M., V.S. and J.B. supervised this study. S.M., V.S., J.B., W.T. and M.S. wrote the paper.

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Correspondence to Vahid Shahrezaei or Samuel Marguerat.

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Supplementary Information

Supplementary Figures 1–6, Supplementary References.

Reporting Summary

Supplementary Tables 1–10.

Samples description and statistics, description of experimental conditions, description of cells and ctRNA, raw counts, filtered normalized counts, gene statistics and features, smFISH data, smFISH probes sequences and fluorochromes, primers used for single-cell RNA sequencing, sequence analysis of HGV promoters.

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Saint, M., Bertaux, F., Tang, W. et al. Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation. Nat Microbiol 4, 480–491 (2019). https://doi.org/10.1038/s41564-018-0330-4

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