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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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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  1. 1.

    Kaern, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

  2. 2.

    Shahrezaei, V. & Swain, P. S. The stochastic nature of biochemical networks. Curr. Opin. Biotechnol. 19, 369–374 (2008).

  3. 3.

    Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

  4. 4.

    Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

  5. 5.

    Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nat. Rev. Genet. 10, 443–456 (2009).

  6. 6.

    Battich, N., Stoeger, T. & Pelkmans, L. Control of transcript variability in single mammalian cells. Cell 163, 1596–1610 (2015).

  7. 7.

    Shahrezaei, V. & Marguerat, S. Connecting growth with gene expression: of noise and numbers. Curr. Opin. Microbiol. 25, 127–135 (2015).

  8. 8.

    Mellor, J. The molecular basis of metabolic cycles and their relationship to circadian rhythms. Nat. Struct. Mol. Biol. 23, 1035–1044 (2016).

  9. 9.

    Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).

  10. 10.

    Stubbington, M. J. T., Rozenblatt-Rosen, O., Regev, A. & Teichmann, S. A. Single-cell transcriptomics to explore the immune system in health and disease. Science 358, 58–63 (2017).

  11. 11.

    Baslan, T. & Hicks, J. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat. Rev. Cancer 17, 557–569 (2017).

  12. 12.

    Griffiths, J. A., Scialdone, A. & Marioni, J. C. Using single-cell genomics to understand developmental processes and cell fate decisions. Mol. Syst. Biol. 14, e8046 (2018).

  13. 13.

    Saliba, A.-E., C Santos, S. & Vogel, J. New RNA-seq approaches for the study of bacterial pathogens. Curr. Opin. Microbiol. 35, 78–87 (2017).

  14. 14.

    Gasch, A. P. et al. Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLoS Biol. 15, e2004050 (2017).

  15. 15.

    Soumillon, M., Cacchiarelli, D., Semrau, S., van Oudenaarden, A. & Mikkelsen, T. S. Characterization of directed differentiation by high-throughput single-cell RNA-Seq. Preprint at bioRxiv https://doi.org/10.1101/003236(2014)

  16. 16.

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

  17. 17.

    Tang, W. et al. bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data. Preprint at bioRxiv https://doi.org/10.1101/384586(2018).

  18. 18.

    Marguerat, S. et al. Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. Cell 151, 671–683 (2012).

  19. 19.

    Newman, J. R. S. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).

  20. 20.

    Bar-Even, A. et al. Noise in protein expression scales with natural protein abundance. Nat. Genet. 38, 636–643 (2006).

  21. 21.

    Rustici, G. et al. Periodic gene expression program of the fission yeast cell cycle. Nat. Genet. 36, 809–817 (2004).

  22. 22.

    Peng, X. et al. Identification of cell cycle-regulated genes in fission yeast. Mol. Biol. Cell 16, 1026–1042 (2005).

  23. 23.

    Oliva, A. et al. The cell cycle-regulated genes of Schizosaccharomyces pombe. PLoS Biol. 3, e225 (2005).

  24. 24.

    Marguerat, S. et al. The more the merrier: comparative analysis of microarray studies on cell cycle-regulated genes in fission yeast. Yeast 23, 261–277 (2006).

  25. 25.

    Cooper, S. On a heuristic point of view concerning the expression of numerous genes during the cell cycle. IUBMB Life 64, 10–17 (2012).

  26. 26.

    Duncan, C. D. S., Rodríguez-López, M., Ruis, P., Bähler, J. & Mata, J. General amino acid control in fission yeast is regulated by a nonconserved transcription factor, with functions analogous to Gcn4/Atf4. Proc. Natl Acad. Sci. USA 115, E1829–E1838 (2018).

  27. 27.

    Ravarani, C. N. J., Chalancon, G., Breker, M., de Groot, N. S. & Babu, M. M. Affinity and competition for TBP are molecular determinants of gene expression noise. Nat. Commun. 7, 10417 (2016).

  28. 28.

    Tirosh, I., Weinberger, A., Carmi, M. & Barkai, N. A genetic signature of interspecies variations in gene expression. Nat. Genet. 38, 830–834 (2006).

  29. 29.

    Landry, C. R., Lemos, B., Rifkin, S. A., Dickinson, W. J. & Hartl, D. L. Genetic properties influencing the evolvability of gene expression. Science 317, 118–121 (2007).

  30. 30.

    Lehner, B. Selection to minimise noise in living systems and its implications for the evolution of gene expression. Mol. Syst. Biol. 4, 170 (2008).

  31. 31.

    Blake, W. J., KÆrn, M., Cantor, C. R. & Collins, J. J. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003).

  32. 32.

    Weinberger, L. et al. Expression noise and acetylation profiles distinguish HDAC functions. Mol. Cell 47, 193–202 (2012).

  33. 33.

    Chen, D. et al. Global transcriptional responses of fission yeast to environmental stress. Mol. Biol. Cell 14, 214–229 (2003).

  34. 34.

    Pancaldi, V., Schubert, F. & Bähler, J. Meta-analysis of genome regulation and expression variability across hundreds of environmental and genetic perturbations in fission yeast. Mol. Biosyst. 6, 543–552 (2010).

  35. 35.

    Koch, E. N. et al. Conserved rules govern genetic interaction degree across species. Genome Biol. 13, R57 (2012).

  36. 36.

    López-Maury, L., Marguerat, S. & Bähler, J. Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nat. Rev. Genet. 9, 583–593 (2008).

  37. 37.

    Rhind, N. et al. Comparative functional genomics of the fission yeasts. Science 332, 930–936 (2011).

  38. 38.

    Lane, K. et al. Measuring signaling and RNA-Seq in the same cell links gene expression to dynamic patterns of NF-κB activation. Cell Syst. 4, 458–469.e5 (2017).

  39. 39.

    Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34, 199–203 (2016).

  40. 40.

    Nichterwitz, S. et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat. Commun. 7, 12139 (2016).

  41. 41.

    Turner, J. J., Ewald, J. C. & Skotheim, J. M. Cell size control in yeast. Curr. Biol. 22, R350–R359 (2012).

  42. 42.

    Kuang, Z. et al. High-temporal-resolution view of transcription and chromatin states across distinct metabolic states in budding yeast. Nat. Struct. Mol. Biol. 21, 854–863 (2014).

  43. 43.

    Silverman, S. J. et al. Metabolic cycling in single yeast cells from unsynchronized steady-state populations limited on glucose or phosphate. Proc. Natl Acad. Sci. USA 107, 6946–6951 (2010).

  44. 44.

    Slavov, N., Airoldi, E. M., van Oudenaarden, A. & Botstein, D. A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes. Mol. Biol. Cell 23, 1986–1997 (2012).

  45. 45.

    Marguerat, S. & Bähler, J. Coordinating genome expression with cell size. Trends Genet. 28, 560–565 (2012).

  46. 46.

    Schmoller, K. M. & Skotheim, J. M. The biosynthetic basis of cell size control. Trends Cell Biol. 25, 793–802 (2015).

  47. 47.

    Mata, J., Lyne, R., Burns, G. & Bähler, J. The transcriptional program of meiosis and sporulation in fission yeast. Nat. Genet. 32, 143–147 (2002).

  48. 48.

    Metzl-Raz, E. et al. Principles of cellular resource allocation revealed by condition-dependent proteome profiling. eLife 6, e28034 (2017).

  49. 49.

    Malecki, M. et al. Functional and regulatory profiling of energy metabolism in fission yeast. Genome Biol. 17, 240 (2016).

  50. 50.

    Moreno, S., Klar, A. & Nurse, P. Molecular genetic analysis of fission yeast Schizosaccharomyces pombe. Methods Enzymol. 194, 795–823 (1991).

  51. 51.

    Takahashi, C. N., Miller, A. W., Ekness, F., Dunham, M. J. & Klavins, E. A low cost, customizable turbidostat for use in synthetic circuit characterization. ACS Synth. Biol. 4, 32–38 (2015).

  52. 52.

    Semrau, S. et al. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat. Commun. 8, 1096 (2017).

  53. 53.

    Keifenheim, D. et al. Size-dependent expression of the mitotic activator Cdc25 suggests a mechanism of size control in fission yeast. Curr. Biol. 27, 1491–1497 (2017).

  54. 54.

    Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

  55. 55.

    Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

  56. 56.

    Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

  57. 57.

    Lun, A. T. L., Calero-Nieto, F. J., Haim-Vilmovsky, L., Göttgens, B. & Marioni, J. C. Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data. Genome Res. 27, 1795–1806 (2017).

  58. 58.

    Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).

  59. 59.

    Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S. & Marioni, J. C. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14, 565–571 (2017).

  60. 60.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  61. 61.

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

  62. 62.

    Bacher, R. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14, 584–586 (2017).

  63. 63.

    Lun, L., A., T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

  64. 64.

    Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).

  65. 65.

    Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I. & Enard, W. Quantitative single-cell transcriptomics. Brief. Funct. Genomics 17, 220–232 (2018).

  66. 66.

    Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

  67. 67.

    Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

  68. 68.

    Chen, H.-I. H., Jin, Y., Huang, Y. & Chen, Y. Detection of high variability in gene expression from single-cell RNA-seq profiling. BMC Genomics 17, 508 (2016).

  69. 69.

    Jaakkola, M. K., Seyednasrollah, F., Mehmood, A. & Elo, L. L. Comparison of methods to detect differentially expressed genes between single-cell populations. Brief. Bioinform. 18, 735–743 (2017).

  70. 70.

    Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

  71. 71.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

  72. 72.

    Bailey, T. L. & Machanick, P. Inferring direct DNA binding from ChIP-seq. Nucleic Acids Res. 40, e128 (2012).

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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.

Author information

Author notes

    • François Bertaux

    Present address: Institut Pasteur, Paris, France

    • Anna Köferle

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

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


  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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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.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Vahid Shahrezaei or Samuel Marguerat.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–6, Supplementary References.

  2. Reporting Summary

  3. 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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