Single-Cell Expression Variability Implies Cell Function.

Daniel Osorio, Xue Yu, Yan Zhong, Guanxun Li, Peng Yu, Erchin Serpedin, Jianhua Z Huang, James J Cai
Author Information
  1. Daniel Osorio: Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
  2. Xue Yu: Department of Veterinary Pathobiology, Texas A&M University, College Station, TX 77843, USA.
  3. Yan Zhong: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  4. Guanxun Li: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  5. Peng Yu: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
  6. Erchin Serpedin: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
  7. Jianhua Z Huang: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  8. James J Cai: Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA. ORCID

Abstract

As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type's function, from which the scRNA-seq data used to identify HVGs were generated-e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the "variation is function" hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

Keywords

References

  1. Nature. 2010 Sep 9;467(7312):167-73 [PMID: 20829787]
  2. Bioinformatics. 2019 Nov 07;: [PMID: 31697351]
  3. Cell. 2008 Oct 17;135(2):216-26 [PMID: 18957198]
  4. Nat Protoc. 2018 Apr;13(4):599-604 [PMID: 29494575]
  5. Nat Methods. 2011 Nov 20;9(1):72-4 [PMID: 22101854]
  6. Paediatr Respir Rev. 2004;5 Suppl A:S35-40 [PMID: 14980241]
  7. Bioessays. 2018 Feb;40(2): [PMID: 29251357]
  8. Comput Biol Chem. 2016 Aug;63:52-61 [PMID: 26951854]
  9. Lab Invest. 2006 Nov;86(11):1193-200 [PMID: 17053803]
  10. Nature. 1983 Apr 14;302(5909):575-81 [PMID: 6300689]
  11. Bioinformatics. 2008 Jul 1;24(13):i390-8 [PMID: 18586739]
  12. Nat Rev Genet. 2005 Jun;6(6):451-64 [PMID: 15883588]
  13. BMC Bioinformatics. 2013 Apr 15;14:128 [PMID: 23586463]
  14. Proc Natl Acad Sci U S A. 2016 Jan 5;113(1):122-7 [PMID: 26699463]
  15. BMC Bioinformatics. 2009 Feb 03;10:48 [PMID: 19192299]
  16. Science. 1997 Oct 10;278(5336):298-301 [PMID: 9323210]
  17. Cell Syst. 2017 Nov 22;5(5):471-484.e4 [PMID: 29102610]
  18. PLoS Comput Biol. 2010 Aug 26;6(8): [PMID: 20865155]
  19. Nat Biotechnol. 2012 Jul 10;30(7):639-47 [PMID: 22781693]
  20. Cell Stem Cell. 2018 Oct 4;23(4):586-598.e8 [PMID: 30290179]
  21. Cell Stem Cell. 2016 Apr 7;18(4):541-53 [PMID: 26971820]
  22. Nature. 2006 Mar 9;440(7081):174-80 [PMID: 16525464]
  23. Cell. 2018 Aug 23;174(5):1293-1308.e36 [PMID: 29961579]
  24. Nat Rev Immunol. 2019 Apr;19(4):205-217 [PMID: 30770905]
  25. Nat Biotechnol. 2015 Feb;33(2):155-60 [PMID: 25599176]
  26. Nucleic Acids Res. 2018 Jan 4;46(D1):D1284 [PMID: 29161433]
  27. Immunity. 2018 Jun 19;48(6):1258-1270.e6 [PMID: 29884461]
  28. J Clin Invest. 2003 Oct;112(7):1108-15 [PMID: 14523047]
  29. JCI Insight. 2018 Aug 23;3(16): [PMID: 30135312]
  30. Adv Wound Care (New Rochelle). 2016 Mar 1;5(3):119-136 [PMID: 26989578]
  31. Genome Res. 2015 Oct;25(10):1491-8 [PMID: 26430159]
  32. Nat Rev Immunol. 2015 Mar;15(3):160-71 [PMID: 25698678]
  33. Bioessays. 2016 Feb;38(2):172-80 [PMID: 26625861]
  34. Nat Methods. 2013 Nov;10(11):1093-5 [PMID: 24056876]
  35. Nat Biotechnol. 2019 Dec;37(12):1482-1492 [PMID: 31796933]
  36. Curr Opin Biotechnol. 2013 Aug;24(4):752-9 [PMID: 23566377]
  37. Sci Data. 2019 Jul 4;6(1):112 [PMID: 31273215]
  38. Trends Genet. 2012 May;28(5):221-32 [PMID: 22365642]
  39. Mol Cell. 2019 Jan 3;73(1):130-142.e5 [PMID: 30472192]
  40. Cell. 2018 Jul 26;174(3):716-729.e27 [PMID: 29961576]
  41. Genome Res. 2014 Mar;24(3):496-510 [PMID: 24299736]
  42. Bioessays. 2000 Apr;22(4):381-7 [PMID: 10723035]
  43. Eur Respir J. 2015 Apr;45(4):1150-62 [PMID: 25700381]
  44. Development. 2017 Jan 1;144(1):17-32 [PMID: 28049689]
  45. PLoS Biol. 2012 Jan;10(1):e1001249 [PMID: 22291574]
  46. Sci Rep. 2018 Jan 12;8(1):685 [PMID: 29330484]
  47. Hum Genet. 2016 Jul;135(7):797-811 [PMID: 27131873]
  48. PLoS Comput Biol. 2014 Jul 17;10(7):e1003696 [PMID: 25032992]
  49. Nature. 2008 May 22;453(7194):544-7 [PMID: 18497826]
  50. J Invest Dermatol. 2018 Apr;138(4):802-810 [PMID: 29080679]
  51. Nat Biotechnol. 2018 Jun;36(5):411-420 [PMID: 29608179]
  52. Science. 2017 Mar 31;355(6332):1433-1436 [PMID: 28360329]
  53. Nat Biotechnol. 2018 Dec 03;: [PMID: 30531897]
  54. BMC Genomics. 2009 Jan 14;10:22 [PMID: 19144180]
  55. Biophys Rev. 2019 Feb;11(1):89-94 [PMID: 30617454]
  56. Nature. 2013 Sep 26;501(7468):506-11 [PMID: 24037378]
  57. Genome Med. 2015 Jan 28;7(1):8 [PMID: 25632304]
  58. Science. 2012 Apr 27;336(6080):425-6 [PMID: 22539709]
  59. PLoS Genet. 2011 Aug;7(8):e1002207 [PMID: 21852951]
  60. Aging Cell. 2017 Oct;16(5):1043-1050 [PMID: 28699239]
  61. Nat Biotechnol. 2010 May;28(5):495-501 [PMID: 20436461]
  62. Mol Syst Biol. 2011 May 24;7:495 [PMID: 21613984]
  63. Hum Mutat. 2008 Jun;29(6):861-8 [PMID: 18412279]
  64. Genome Biol. 2015 Jun 09;16:122 [PMID: 26056000]
  65. Proc Natl Acad Sci U S A. 2018 Mar 20;115(12):E2888-E2897 [PMID: 29514960]
  66. Nucleic Acids Res. 2016 Jul 8;44(W1):W90-7 [PMID: 27141961]
  67. Nature. 2010 Feb 18;463(7283):913-8 [PMID: 20164922]
  68. Genome Res. 2018 Jul;28(7):1053-1066 [PMID: 29752298]
  69. Nucleic Acids Res. 2018 Jan 4;46(D1):D649-D655 [PMID: 29145629]
  70. Science. 2014 Jun 20;344(6190):1392-6 [PMID: 24903562]
  71. Hum Genet. 1986 Aug;73(4):320-6 [PMID: 3017841]
  72. Nature. 2010 Jul 8;466(7303):267-71 [PMID: 20581820]
  73. BMC Genomics. 2016 Aug 22;17 Suppl 7:508 [PMID: 27556924]
  74. Cell Metab. 2016 Oct 11;24(4):593-607 [PMID: 27667667]
  75. Bioessays. 1992 May;14(5):341-6 [PMID: 1637366]
  76. Immunity. 2019 Mar 19;50(3):616-628.e6 [PMID: 30850343]
  77. Evol Bioinform Online. 2013 Sep 01;9:355-62 [PMID: 24027418]
  78. PLoS Comput Biol. 2019 Oct 25;15(10):e1007432 [PMID: 31652259]
  79. Nature. 2009 Sep 24;461(7263):520-3 [PMID: 19710653]
  80. Science. 2005 Sep 23;309(5743):2010-3 [PMID: 16179466]
  81. Nature. 2018 Nov;563(7730):197-202 [PMID: 30356220]

Grants

  1. R21AI126219/NIH HHS

MeSH Term

Algorithms
Cell Line
Gene Expression Profiling
Gene Expression Regulation
Gene Regulatory Networks
Humans
Organ Specificity
Sequence Analysis, RNA
Single-Cell Analysis