OpenLB
Open Library of Bioscience
Advanced Search
eLife
05 18, 2020;9

A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast.

Abbas Jariani, Lieselotte Vermeersch, Bram Cerulus, Gemma Perez-Samper, Karin Voordeckers, Thomas Van Brussel, Bernard Thienpont, Diether Lambrechts, Kevin J Verstrepen
Author Information
  1. Abbas Jariani: Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, Belgium. ORCID
  2. Lieselotte Vermeersch: Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, Belgium. ORCID
  3. Bram Cerulus: Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, Belgium.
  4. Gemma Perez-Samper: Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, Belgium.
  5. Karin Voordeckers: Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, Belgium.
  6. Thomas Van Brussel: Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium.
  7. Bernard Thienpont: Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium. ORCID
  8. Diether Lambrechts: Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium.
  9. Kevin J Verstrepen: Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, Belgium. ORCID
PMID: 32420869 DOI: 10.7554/eLife.55320

Abstract

Current methods for single-cell RNA sequencing (scRNA-seq) of yeast cells do not match the throughput and relative simplicity of the state-of-the-art techniques that are available for mammalian cells. In this study, we report how 10x Genomics' droplet-based single-cell RNA sequencing technology can be modified to allow analysis of yeast cells. The protocol, which is based on in-droplet spheroplasting of the cells, yields an order-of-magnitude higher throughput in comparison to existing methods. After extensive validation of the method, we demonstrate its use by studying the dynamics of the response of isogenic yeast populations to a shift in carbon source, revealing the heterogeneity and underlying molecular processes during this shift. The method we describe opens new avenues for studies focusing on yeast cells, as well as other cells with a degradable cell wall.

Keywords

S. cerevisiae chromosomes gene expression heterogeneity infectious disease lag phase metabolic shift microbiology single-cell RNA-seq tools & methods

Associated Data

GEO | GSE144820; GSE116246

References

  1. Nat Microbiol. 2019 Apr;4(4):683-692 [PMID: 30718850]
  2. Nature. 2003 Oct 16;425(6959):686-91 [PMID: 14562095]
  3. Mol Microbiol. 2003 May;48(3):713-24 [PMID: 12694616]
  4. Curr Biol. 2013 Dec 2;23(23):2336-45 [PMID: 24210615]
  5. Elife. 2018 Mar 27;7: [PMID: 29580379]
  6. Bioinformatics. 2016 Sep 15;32(18):2847-9 [PMID: 27207943]
  7. Curr Biol. 2014 Sep 22;24(18):2189-2194 [PMID: 25220054]
  8. PLoS Biol. 2014 Jan;12(1):e1001764 [PMID: 24453942]
  9. Yeast. 1998 Jan 30;14(2):115-32 [PMID: 9483801]
  10. BMC Syst Biol. 2008 Oct 16;2:87 [PMID: 18925958]
  11. Nat Commun. 2017 Jan 16;8:14049 [PMID: 28091601]
  12. Nat Methods. 2014 Feb;11(2):163-6 [PMID: 24363023]
  13. Mol Biol Cell. 2000 Mar;11(3):833-48 [PMID: 10712503]
  14. Nat Struct Mol Biol. 2008 Dec;15(12):1263-71 [PMID: 19011635]
  15. Elife. 2020 May 18;9: [PMID: 32420869]
  16. BMC Bioinformatics. 2009 Feb 03;10:48 [PMID: 19192299]
  17. Mol Cell. 2017 Feb 16;65(4):631-643.e4 [PMID: 28212749]
  18. Proc Natl Acad Sci U S A. 1992 Apr 1;89(7):3010-4 [PMID: 1557406]
  19. Mol Biol Cell. 2013 Jun;24(12):2045-57 [PMID: 23615444]
  20. Cell. 2016 Dec 15;167(7):1853-1866.e17 [PMID: 27984732]
  21. Mol Cell. 2017 Jan 19;65(2):285-295 [PMID: 27989441]
  22. Elife. 2018 Oct 09;7: [PMID: 30299256]
  23. Nucleic Acids Res. 2014 Jan;42(Database issue):D717-25 [PMID: 24265222]
  24. Elife. 2020 Jan 27;9: [PMID: 31985403]
  25. Integr Biol (Camb). 2015 Nov;7(11):1466-76 [PMID: 26331465]
  26. Nat Protoc. 2012 Feb 02;7(2):408-19 [PMID: 22301778]
  27. Mol Cell. 2015 May 21;58(4):610-20 [PMID: 26000846]
  28. Nat Commun. 2014 Sep 02;5:4798 [PMID: 25178355]
  29. Nat Commun. 2016 Oct 14;7:13182 [PMID: 27739429]
  30. Nat Microbiol. 2019 Mar;4(3):480-491 [PMID: 30718845]
  31. Curr Biol. 2010 May 25;20(10):895-903 [PMID: 20471265]
  32. Genome Res. 2014 Mar;24(3):496-510 [PMID: 24299736]
  33. Science. 2012 Apr 13;336(6078):183-7 [PMID: 22499939]
  34. Nature. 2011 Dec 07;480(7376):250-3 [PMID: 22158248]
  35. Science. 2004 Jun 18;304(5678):1811-4 [PMID: 15166317]
  36. Genetics. 2009 Sep;183(1):365-83 [PMID: 19581448]
  37. PLoS Biol. 2017 Dec 14;15(12):e2004050 [PMID: 29240790]
  38. Curr Biol. 2016 May 9;26(9):1138-47 [PMID: 27068419]
  39. Nat Biotechnol. 2018 Jun;36(5):411-420 [PMID: 29608179]
  40. Nature. 2017 May 11;545(7653):238-242 [PMID: 28467820]
  41. PLoS Biol. 2012;10(5):e1001325 [PMID: 22589700]
  42. Cell Mol Immunol. 2019 Mar;16(3):242-249 [PMID: 30796351]
  43. Nat Struct Mol Biol. 2011 Dec 18;19(1):31-9 [PMID: 22179789]
  44. Nature. 2014 Oct 16;514(7522):376-9 [PMID: 25186725]
  45. Biotechnol J. 2016 Sep;11(9):1169-78 [PMID: 27312776]
  46. PLoS One. 2010 Nov 16;5(11):e15442 [PMID: 21103382]
  47. J Vis Exp. 2013 Jul 18;(77):e50456 [PMID: 23892428]
  48. Genome Biol. 2011 Aug 04;12(8):R71 [PMID: 21816052]
  49. Curr Biol. 2020 Dec 7;30(23):4563-4578.e4 [PMID: 32976801]
  50. Cell. 2018 Jan 11;172(1-2):176-190.e19 [PMID: 29328912]
  51. Cell. 2016 Dec 15;167(7):1867-1882.e21 [PMID: 27984733]
  52. J Clin Microbiol. 2004 Jan;42(1):453-7 [PMID: 14715804]
  53. Curr Opin Microbiol. 2013 Apr;16(2):184-91 [PMID: 23485258]
  54. Genome Res. 2011 Jun;21(6):925-35 [PMID: 21536723]
  55. Cell. 2019 Jun 13;177(7):1888-1902.e21 [PMID: 31178118]
  56. Science. 2012 Jan 6;335(6064):82-5 [PMID: 22174126]
  57. mBio. 2018 Oct 30;9(5): [PMID: 30377274]
  58. Exp Mol Med. 2018 Aug 7;50(8):1-14 [PMID: 30089861]
  59. Proc Natl Acad Sci U S A. 2009 Sep 15;106(37):15651-6 [PMID: 19720993]
  60. Nat Rev Nephrol. 2018 Aug;14(8):479-492 [PMID: 29789704]
  61. Curr Opin Microbiol. 2018 Oct;45:30-38 [PMID: 29477028]

Grants

  1. 246 RGP0050/2013/Human Frontier Science Program
  2. COG682009/European Research Council

MeSH Term

Carbon
Energy Metabolism
Gene Expression
Gene Expression Regulation, Fungal
Glucose
Maltose
RNA
RNA-Seq
Saccharomyces cerevisiae
Single-Cell Analysis
Spheroplasts
Transcription, Genetic
Transcriptome

Chemicals

RNA
Maltose
Carbon
Glucose
Journal Article Research Support, Non-U.S. Gov't Validation Study

Links to CNCB-NGDC Resources

GEN: GEND000326 (Adapting the 10x Genomics Platform for single-cell RNA-seq in yeast reveals the importance of stochastic gene expression during the lag phase)

Word Cloud

Similar Articles

Cited By