Single-cell RNA sequencing has revealed extensive cellular heterogeneity within many organisms, but few methods have been developed for microbial clonal populations. The yeast genome displays unusually dense transcript spacing, with interleaved and overlapping transcription from both strands, resulting in a minuscule but complex pool of RNA that is protected by a resilient cell wall. Here, we have developed a sensitive, scalable and inexpensive yeast single-cell RNA-seq (yscRNA-seq) method that digitally counts transcript start sites in a strand- and isoform-specific manner. YscRNA-seq detects the expression of low-abundance, noncoding RNAs and at least half of the protein-coding genome in each cell. In clonal cells, we observed a negative correlation for the expression of sense–antisense pairs, whereas paralogs and divergent transcripts co-expressed. By combining yscRNA-seq with index sorting, we uncovered a linear relationship between cell size and RNA content. Although we detected an average of ~3.5 molecules per gene, the number of expressed isoforms is restricted at the single-cell level. Remarkably, the expression of metabolic genes is highly variable, whereas their stochastic expression primes cells for increased fitness towards the corresponding environmental challenge. These findings suggest that functional transcript diversity acts as a mechanism that provides a selective advantage to individual cells within otherwise transcriptionally heterogeneous populations.

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Code availability

Custom code generated in this study can be downloaded from http://steinmetzlab.embl.de/yscRNASeq/.

Data availability

All data generated in this study has been uploaded to Gene Expression Omnibus under accession number GSE122392.

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  1. 1.

    Picelli, S. Single-cell RNA-sequencing: the future of genome biology is now. RNA Biol. 14, 637–650 (2017).

  2. 2.

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

  3. 3.

    Miura, F. et al. Absolute quantification of the budding yeast transcriptome by means of competitive PCR between genomic and complementary DNAs. BMC Genomics 9, 574 (2008).

  4. 4.

    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).

  5. 5.

    Saint, M., Bertaux, F., Tang, W., Sun, X.-M. & Game, L. Single-cell phenotyping and RNA sequencing reveal novel patterns of gene expression heterogeneity and regulation during growth and stress adaptation in a unicellular eukaryote. Preprint at bioRxiv https://doi.org/10.1101/306795 (2018).

  6. 6.

    Pelechano, V., Wei, W. & Steinmetz, L. M. Extensive transcriptional heterogeneity revealed by isoform profiling. Nature 497, 127–131 (2013).

  7. 7.

    Pelechano, V., Wei, W. & Steinmetz, L. M. Widespread co-translational RNA decay reveals ribosome dynamics. Cell 161, 1400–1412 (2015).

  8. 8.

    Hennig, B. P. et al. Large-scale low-cost NGS library preparation using a robust Tn5 purification and tagmentation protocol. G3 8, 79–89 (2018).

  9. 9.

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

  10. 10.

    Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018).

  11. 11.

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

  12. 12.

    Xu, Z. et al. Bidirectional promoters generate pervasive transcription in yeast. Nature 457, 1033–1037 (2009).

  13. 13.

    Wei, W. et al. Genome sequencing and comparative analysis of Saccharomyces cerevisiae strain YJM789. Proc. Natl Acad. Sci. USA 104, 12825–12830 (2007).

  14. 14.

    David, L. et al. A high-resolution map of transcription in the yeast genome. Proc. Natl Acad. Sci. USA 103, 5320–5325 (2006).

  15. 15.

    Pelechano, V., Wei, W., Jakob, P. & Steinmetz, L. M. Genome-wide identification of transcript start and end sites by transcript isoform sequencing. Nat. Protoc. 9, 1740–1759 (2014).

  16. 16.

    Granovskaia, M. V. et al. High-resolution transcription atlas of the mitotic cell cycle in budding yeast. Genome Biol. 11, R24 (2010).

  17. 17.

    Kellis, M., Birren, B. W. & Lander, E. S. Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae. Nature 428, 617–624 (2004).

  18. 18.

    Xu, Z. et al. Antisense expression increases gene expression variability and locus interdependency. Mol. Syst. Biol. 7, 468 (2011).

  19. 19.

    Lenstra, T. L., Coulon, A., Chow, C. C. & Larson, D. R. Single-molecule imaging reveals a switch between spurious and functional ncRNA transcription. Mol. Cell 60, 597–610 (2015).

  20. 20.

    Murray, S. C. et al. Sense and antisense transcription are associated with distinct chromatin architectures across genes. Nucleic Acids Res. 43, 7823–7837 (2015).

  21. 21.

    Aldea, M., Jenkins, K. & Csikász-Nagy, A. Growth rate as a direct regulator of the start network to set cell size. Front. Cell Dev. Biol. 5, 57 (2017).

  22. 22.

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

  23. 23.

    Schmoller, K. M., Turner, J. J., Kõivomägi, M. & Skotheim, J. M. Dilution of the cell cycle inhibitor Whi5 controls budding-yeast cell size. Nature 526, 268–272 (2015).

  24. 24.

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

  25. 25.

    Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

  26. 26.

    Zajac, P., Islam, S., Hochgerner, H., Lönnerberg, P. & Linnarsson, S. Base preferences in non-templated nucleotide incorporation by MMLV-derived reverse transcriptases. PLoS ONE 8, e85270 (2013).

  27. 27.

    Spellman, P. T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998).

  28. 28.

    Teste, M.-A., Duquenne, M., François, J. M. & Parrou, J.-L. Validation of reference genes for quantitative expression analysis by real-time RT-PCR in Saccharomyces cerevisiae. BMC Mol. Biol. 10, 99 (2009).

  29. 29.

    Houser, J. R. et al. An improved short-lived fluorescent protein transcriptional reporter for Saccharomyces cerevisiae. Yeast 29, 519–530 (2012).

  30. 30.

    Huber, F. et al. Protein abundance control by non-coding antisense transcription. Cell Rep. 15, 2625–2636 (2016).

  31. 31.

    Venturelli, O. S., Zuleta, I., Murray, R. M. & El-Samad, H. Population diversification in a yeast metabolic program promotes anticipation of environmental shifts. PLoS Biol. 13, e1002042 (2015).

  32. 32.

    Wang, J. et al. Natural variation in preparation for nutrient depletion reveals a cost–benefit tradeoff. PLoS Biol. 13, e1002041 (2015).

  33. 33.

    Janke, C. et al. A versatile toolbox for PCR-based tagging of yeast genes: new fluorescent proteins, more markers and promoter substitution cassettes. Yeast 21, 947–962 (2004).

  34. 34.

    Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2013).

  35. 35.

    Pelechano, V., Wei, W. & Steinmetz, L. M. Genome-wide quantification of 5ʹ-phosphorylated mRNA degradation intermediates for analysis of ribosome dynamics. Nat. Protoc. 11, 359–376 (2016).

  36. 36.

    McDavid, A. et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29, 461–467 (2013).

  37. 37.

    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).

  38. 38.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

  39. 39.

    Ri Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, 193–200 (2007).

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The authors would like to thank S. Linnarsson for providing reagents during the initial tests with yscRNA-seq. We thank the Protein Expression and Purification Core Facility at EMBL, B. Hennig and L. Velten for providing in-house purified Tn5. We thank R. Böttcher for fruitful discussions. We thank D. Caetano-Anollés for editing and refining the manuscript. M.N.-R. was a recipient of an EMBO long-term fellowship (Stanford University) and later of a Maria de Maeztu Postdoctoral Fellowship (Doctores Banco de Santander-María de Maeztu at Universitat Pompeu Fabra). P.L. is a recipient of a FI Predoctoral Fellowship (Generalitat de Catalunya). This work was supported by the National Institutes of Health and a European Research Council Advanced Investigator Grant (grant no. AdG-294542 to L.M.S.) and the National Key Research and Development Program of China (grant no. 2017YFC0908405 to W.W.). The study was also supported by grants from the Spanish Ministry of Economy and Competitiveness (grant nos BFU2015-64437-P, FEDER, BFU2014-52125-REDT and BFU2014-51672-REDC to F.P.; BFU2017-85152-P and FEDER to E.d.N.), the Catalan Government (grant no. 2017 SGR 799), the Fundación Botín, the Banco Santander through its Santander Universities Global Division to F.P. and the Unidad de Excelencia Maria de Maeztu, grant no. MDM-2014-0370. F.P. is a recipient of an ICREA Acadèmia (Generalitat de Catalunya).

Author information

Author notes

  1. These authors contributed equally: Mariona Nadal-Ribelles, Saiful Islam, Wu Wei, Pablo Latorre.


  1. Department of Genetics, Stanford University, School of Medicine, Stanford, CA, USA

    • Mariona Nadal-Ribelles
    • , Saiful Islam
    • , Wu Wei
    • , Michelle Nguyen
    •  & Lars M. Steinmetz
  2. Stanford Genome Technology Center, Stanford University, Stanford, CA, USA

    • Mariona Nadal-Ribelles
    • , Saiful Islam
    • , Wu Wei
    • , Michelle Nguyen
    •  & Lars M. Steinmetz
  3. Cell Signaling Research Group. Departament de Ciències Experimentals i de la Salut., Universitat Pompeu Fabra , Barcelona, Spain

    • Mariona Nadal-Ribelles
    • , Pablo Latorre
    • , Eulàlia de Nadal
    •  & Francesc Posas
  4. Cell Signaling. Institute for Research in Biomedicine. Barcelona Institute of Science and Technology, Barcelona, Spain

    • Mariona Nadal-Ribelles
    • , Pablo Latorre
    • , Eulàlia de Nadal
    •  & Francesc Posas
  5. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    • Wu Wei
  6. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    • Lars M. Steinmetz


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M.N.-R., S.I., W.W. and L.M.S. conceived the project. M.N.-R., S.I., W.W., P.L. and M.N. developed the protocol and performed the analyses. M.N.-R., S.I. and M.N. performed the experiments. W.W. and P.L. performed the computational analyses. M.N.-R., S.I., P.L., W.W., E.d.N., F.P. and L.M.S. participated in experimental design, data analysis and writing the manuscript. E.d.N., F.P. and L.M.S. supervised the work. All authors read and edited the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Lars M. Steinmetz.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–4, and Supplementary Tables 1 and 2.

  2. Reporting Summary

  3. Supplementary Table 3

    Raw expression BY4741 yscRNA-seq libraries after applying the quality filter criteria (total of 127 cells). Table contains raw number of molecules for each gene (rows) for each cell (rows).

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