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Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq


Cellular heterogeneity can emerge from the expression of only one parental allele. However, it has remained controversial whether, or to what degree, random monoallelic expression of autosomal genes (aRME) is mitotically inherited (clonal) or stochastic (dynamic) in somatic cells, particularly in vivo. Here we used allele-sensitive single-cell RNA–seq on clonal primary mouse fibroblasts and freshly isolated human CD8+ T cells to dissect clonal and dynamic monoallelic expression patterns. Dynamic aRME affected a considerable portion of the cells' transcriptomes, with levels dependent on the cells' transcriptional activity. Notably, clonal aRME was detected, but it was surprisingly scarce (<1% of genes) and mainly affected the most weakly expressed genes. Consequently, the overwhelming majority of aRME occurs transiently within individual cells, and patterns of aRME are thus primarily scattered throughout somatic cell populations rather than, as previously hypothesized, confined to patches of clonally related cells.

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Figure 1: The vast majority of aRME in primary mouse fibroblasts is dynamic.
Figure 2: Scarce clonal aRME in weakly expressed genes.
Figure 3: Dynamic and clonal aRME in human T cells.
Figure 4: Cellular size and cell cycle phase affect the degree of dynamic aRME.

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Gene Expression Omnibus

Sequence Read Archive


  1. 1

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

    CAS  Google Scholar 

  2. 2

    Suter, D.M. et al. Mammalian genes are transcribed with widely different bursting kinetics. Science 332, 472–474 (2011).

    CAS  PubMed  Google Scholar 

  3. 3

    Cook, D.L., Gerber, A.N. & Tapscott, S.J. Modeling stochastic gene expression: implications for haploinsufficiency. Proc. Natl. Acad. Sci. USA 95, 15641–15646 (1998).

    CAS  PubMed  Google Scholar 

  4. 4

    McAdams, H.H. & Arkin, A. Stochastic mechanisms in gene expression. Proc. Natl. Acad. Sci. USA 94, 814–819 (1997).

    CAS  PubMed  Google Scholar 

  5. 5

    Raj, A., Rifkin, S.A., Andersen, E. & van Oudenaarden, A. Variability in gene expression underlies incomplete penetrance. Nature 463, 913–918 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    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).

    CAS  PubMed  Google Scholar 

  7. 7

    Eckersley-Maslin, M.A. & Spector, D.L. Random monoallelic expression: regulating gene expression one allele at a time. Trends Genet. 30, 237–244 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Chess, A. Mechanisms and consequences of widespread random monoallelic expression. Nat. Rev. Genet. 13, 421–428 (2012).

    CAS  PubMed  Google Scholar 

  9. 9

    Reinius, B. & Sandberg, R. Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation. Nat. Rev. Genet. 16, 653–664 (2015).

    CAS  PubMed  Google Scholar 

  10. 10

    Gimelbrant, A., Hutchinson, J.N., Thompson, B.R. & Chess, A. Widespread monoallelic expression on human autosomes. Science 318, 1136–1140 (2007).

    CAS  PubMed  Google Scholar 

  11. 11

    Zwemer, L.M. et al. Autosomal monoallelic expression in the mouse. Genome Biol. 13, R10 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Nag, A. et al. Chromatin signature of widespread monoallelic expression. eLife 2, e01256 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. 13

    Savova, V. et al. Genes with monoallelic expression contribute disproportionately to genetic diversity in humans. Nat. Genet. 48, 231–237 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Eckersley-Maslin, M.A. et al. Random monoallelic gene expression increases upon embryonic stem cell differentiation. Dev. Cell 28, 351–365 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Gendrel, A.-V. et al. Developmental dynamics and disease potential of random monoallelic gene expression. Dev. Cell 28, 366–380 (2014).

    CAS  PubMed  Google Scholar 

  16. 16

    Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA–seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

    CAS  Google Scholar 

  17. 17

    Marinov, G.K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Borel, C. et al. Biased allelic expression in human primary fibroblast single cells. Am. J. Hum. Genet. 96, 70–80 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  Google Scholar 

  20. 20

    Baker, D.E.C. et al. Adaptation to culture of human embryonic stem cells and oncogenesis in vivo. Nat. Biotechnol. 25, 207–215 (2007).

    CAS  PubMed  Google Scholar 

  21. 21

    Pinter, S.F. et al. Allelic imbalance is a prevalent and tissue-specific feature of the mouse transcriptome. Genetics 200, 537–549 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Blom, K. et al. Temporal dynamics of the primary human T cell response to yellow fever virus 17D as it matures from an effector- to a memory-type response. J. Immunol. 190, 2150–2158 (2013).

    CAS  PubMed  Google Scholar 

  23. 23

    Paul, W.E. Fundamental Immunology (Lippincott Williams & Wilkins, 2012).

  24. 24

    Padovan-Merhar, O. et al. Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. Mol. Cell 58, 339–352 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Sandberg, R. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11, 22–24 (2014).

    CAS  PubMed  Google Scholar 

  26. 26

    Miller, J.D. et al. Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines. Immunity 28, 710–722 (2008).

    CAS  PubMed  Google Scholar 

  27. 27

    Ohlsson, R. et al. Random monoallelic expression of the imprinted IGF2 and H19 genes in the absence of discriminative parental marks. Dev. Genes Evol. 209, 113–119 (1999).

    CAS  PubMed  Google Scholar 

  28. 28

    Miyanari, Y. Torres-Padilla, M.-E. Control of ground-state pluripotency by allelic regulation of Nanog. Nature 483, 470–473 (2012).

    CAS  PubMed  Google Scholar 

  29. 29

    Faddah, D.A. et al. Single-cell analysis reveals that expression of Nanog is biallelic and equally variable as that of other pluripotency factors in mouse ESCs. Cell Stem Cell 13, 23–29 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Filipczyk, A. et al. Biallelic expression of Nanog protein in mouse embryonic stem cells. Cell Stem Cell 13, 12–13 (2013).

    CAS  PubMed  Google Scholar 

  31. 31

    Bix, M. & Locksley, R.M. Independent and epigenetic regulation of the interleukin-4 alleles in CD4+ T cells. Science 281, 1352–1354 (1998).

    CAS  PubMed  Google Scholar 

  32. 32

    Nutt, S.L. et al. Independent regulation of the two Pax5 alleles during B-cell development. Nat. Genet. 21, 390–395 (1999).

    CAS  PubMed  Google Scholar 

  33. 33

    Holländer, G.A. et al. Monoallelic expression of the interleukin-2 locus. Science 279, 2118–2121 (1998).

    PubMed  Google Scholar 

  34. 34

    Savova, V., Patsenker, J., Vigneau, S. & Gimelbrant, A.A. dbMAE: the database of autosomal monoallelic expression. Nucleic Acids Res. 44, D753–D756 (2016).

    CAS  PubMed  Google Scholar 

  35. 35

    Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively-scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Dobin, A. et al. STAR: ultrafast universal RNA–seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Jeffries, A.R. et al. Stochastic choice of allelic expression in human neural stem cells. Stem Cells 30, 1938–1947 (2012).

    PubMed  Google Scholar 

  38. 38

    Li, S.M. et al. Transcriptome-wide survey of mouse CNS-derived cells reveals monoallelic expression within novel gene families. PLoS One 7, e31751 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Ramsköld, D., Wang, E.T., Burge, C.B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

    PubMed  PubMed Central  Google Scholar 

  40. 40

    Storvall, H., Ramsköld, D. & Sandberg, R. Efficient and comprehensive representation of uniqueness for next-generation sequencing by minimum unique length analyses. PLoS One 8, e53822 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Bolotin, D.A. et al. MiTCR: software for T-cell receptor sequencing data analysis. Nat. Methods 10, 813–814 (2013).

    CAS  PubMed  Google Scholar 

  42. 42

    Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Lefranc, M.-P. et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 37, D1006–D1012 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Huang, W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Google Scholar 

  45. 45

    Huang, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Google Scholar 

  46. 46

    Whitfield, M.L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

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

    CAS  Google Scholar 

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We thank D. Edsgärd for assistance in handling sequence data and members of the Sandberg laboratory for their input. J.M. was supported by a Human Frontiers Science Program Long-Term Fellowship (LT-000231/2011-l), and the work was supported by grants from the Swedish Research Council, the European Research Council (648842), the Swedish Foundation for Strategic Research, the Swedish Cancer Society, the Karolinska Institute, Tobias Stiftelsen, the Strategic Research Programme in Stem Cells and Regenerative Medicine at Karolinska Institutet (StratRegen), Knut och Alice Wallenbergs Stiftelse, and Torsten Söderbergs Stiftelse.

Author information




B.R. designed mouse experiments, derived and sequenced the transcriptomes of mouse cells, performed computational experiments, prepared figures and tables, and wrote the manuscript. J.E.M. designed human experiments, performed FACS, sequenced the transcriptomes of human T cells, and analyzed TCR sequences. D.R. performed computational experiments and prepared figures. Q.D. designed mouse experiments and derived mouse cells. P.J. performed cell cycle classification. J.M. designed human experiments and performed FACS. J.F. designed human experiments. R.S. designed mouse experiments, supervised the work, and wrote the manuscript.

Corresponding author

Correspondence to Rickard Sandberg.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–22 and Supplementary Note. (PDF 10057 kb)

Supplementary Table 1

List of RNA–seq libraries. (XLSX 114 kb)

Supplementary Table 2

Genes escaping X inactivation in primary mouse fibroblasts. (XLSX 104 kb)

Supplementary Table 3

Imprinted genes detected by single-cell RNA-seq and bulk RNA-seq. (XLSX 42 kb)

Supplementary Table 4

Genes with clonal aRME in primary mouse fibroblasts. (XLSX 66 kb)

Supplementary Table 5

GO analysis of clonal aRME genes identified in mouse primary fibroblasts. (XLSX 59 kb)

Supplementary Table 6

Genes with clonal aRME in ex vivo–expanded human T cells. (XLSX 64 kb)

Supplementary Table 7

GO analysis of genes differentially expressed in fibroblasts with large and small diameters. (XLSX 98 kb)

Supplementary Table 8

GO analysis of genes positively correlated with the total amount of mRNA per fibroblast cell. (XLSX 219 kb)

Supplementary Table 9

GO analysis of genes negatively correlated with the total amount of mRNA per fibroblast cell. (XLSX 232 kb)

Supplementary Table 10

Genes differentially expressed in large and small fibroblasts. (XLSX 63 kb)

Supplementary Table 11

Gene expression level correlated with the total number of mRNA molecules per fibroblast cell. (XLSX 1527 kb)

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Reinius, B., Mold, J., Ramsköld, D. et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq. Nat Genet 48, 1430–1435 (2016).

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