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

Nature Genetics

Volumn 48, Issue 11, 2016, Pages 1430-1435

  Reinius, Björn ^a,b   Mold, Jeff E ^a   Ramsköld, Daniel ^a,b   Deng, Qiaolin ^a   Johnsson, Per ^b   Michaëlsson, Jakob ^c   Frisén, Jonas ^a   Sandberg, Rickard ^a,b  

^a KAROLINSKA INSTITUTE   (Sweden)

^b LUDWIG INSTITUTE FOR CANCER RESEARCH   (Sweden)

^c KAROLINSKA UNIVERSITY HOSPITAL   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

RNA; TRANSCRIPTOME;

ADULT; ALLELE; ANIMAL CELL; ARTICLE; CD8+ T LYMPHOCYTE; CELL HETEROGENEITY; CONTROLLED STUDY; FEMALE; FIBROBLAST; GENE AND NUCLEIC ACID PARAMETERS; GENE EXPRESSION PROFILING; GENETIC TRANSCRIPTION; HUMAN; HUMAN CELL; MEMORY T LYMPHOCYTE; MOLECULAR DYNAMICS; MOLECULAR GENETICS; MOUSE; NONHUMAN; NUCLEOTIDE SEQUENCE; PRIMARY CELL; PRIORITY JOURNAL; RANDOM MONOALLELIC EXPRESSION OF AUTOSOMAL GENE; RNA SEQUENCE; SINGLE CELL ANALYSIS; SOMACLONAL VARIATION; SOMATIC CELL; TRANSCRIPTOMICS; ANIMAL; C57BL MOUSE; CELL CLONE; CELL CULTURE; INBRED MOUSE STRAIN; MALE; METABOLISM; PROCEDURES; SEQUENCE ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM;

ALLELES; ANIMALS; CD8-POSITIVE T-LYMPHOCYTES; CELLS, CULTURED; CLONE CELLS; FEMALE; FIBROBLASTS; GENE EXPRESSION PROFILING; HUMANS; MALE; MICE; MICE, INBRED C57BL; MICE, INBRED STRAINS; POLYMORPHISM, SINGLE NUCLEOTIDE; SEQUENCE ANALYSIS, RNA;

EID: 84988692357     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/ng.3678     Document Type: Article

References (47)

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).
2. Suter, D.M. et al. Mammalian genes are transcribed with widely different bursting kinetics. Science 332, 472-474 (2011).
3. Cook, D.L., Gerber, A.N. & Tapscott, S.J. Modeling stochastic gene expression: implications for haploinsuffciency. Proc. Natl. Acad. Sci. USA 95, 15641-15646 (1998).
4. McAdams, H.H. & Arkin, A. Stochastic mechanisms in gene expression. Proc. Natl. Acad. Sci. USA 94, 814-819 (1997).
5. Raj, A., Rifkin, S.A., Andersen, E. & van Oudenaarden, A. Variability in gene expression underlies incomplete penetrance. Nature 463, 913-918 (2010).
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).
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).
8. Chess, A. Mechanisms and consequences of widespread random monoallelic expression. Nat. Rev. Genet. 13, 421-428 (2012).
9. Reinius, B. & Sandberg, R. Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation. Nat. Rev. Genet. 16, 653-664 (2015).
10. Gimelbrant, A., Hutchinson, J.N., Thompson, B.R. & Chess, A. Widespread monoallelic expression on human autosomes. Science 318, 1136-1140 (2007).
11. Zwemer, L.M. et al. Autosomal monoallelic expression in the mouse. Genome Biol. 13, R10 (2012).
12. Nag, A. et al. Chromatin signature of widespread monoallelic expression. eLife 2, e01256 (2013).
13. Savova, V. et al. Genes with monoallelic expression contribute disproportionately to genetic diversity in humans. Nat. Genet. 48, 231-237 (2016).
14. Eckersley-Maslin, M.A. et al. Random monoallelic gene expression increases upon embryonic stem cell differentiation. Dev. Cell 28, 351-365 (2014).
15. Gendrel, A.-V. et al. Developmental dynamics and disease potential of random monoallelic gene expression. Dev. Cell 28, 366-380 (2014).
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).
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).
18. Borel, C. et al. Biased allelic expression in human primary fbroblast single cells. Am. J. Hum. Genet. 96, 70-80 (2015).
19. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profling in single cells. Nat. Methods 10, 1096-1098 (2013).
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).
21. Pinter, S.F. et al. Allelic imbalance is a prevalent and tissue-specifc feature of the mouse transcriptome. Genetics 200, 537-549 (2015).
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).
23. Paul, W.E. Fundamental Immunology (Lippincott Williams & Wilkins, 2012).
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).
25. Sandberg, R. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11, 22-24 (2014).
26. + T cell responses to smallpox and yellow fever vaccines. Immunity 28, 710-722 (2008).
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).
28. Miyanari, Y. Torres-Padilla, M.-E. Control of ground-state pluripotency by allelic regulation of Nanog. Nature 483, 470-473 (2012).
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).
30. Filipczyk, A. et al. Biallelic expression of Nanog protein in mouse embryonic stem cells. Cell Stem Cell 13, 12-13 (2013).
31. + T cells. Science 281, 1352-1354 (1998).
32. Nutt, S.L. et al. Independent regulation of the two Pax5 alleles during B-cell development. Nat. Genet. 21, 390-395 (1999).
33. Holländer, G.A. et al. Monoallelic expression of the interleukin-2 locus. Science 279, 2118-2121 (1998).
34. Savova, V., Patsenker, J., Vigneau, S. & Gimelbrant, A.A. dbMAE: the database of autosomal monoallelic expression. Nucleic Acids Res. 44, D753-D756 (2016).
35. Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively-scaled sequencing projects. Genome Res. 24, 2033-2040 (2014).
36. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013).
37. Jeffries, A.R. et al. Stochastic choice of allelic expression in human neural stem cells. Stem Cells 30, 1938-1947 (2012).
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).
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).
40. Storvall, H., Ramsköld, D. & Sandberg, R. Effcient and comprehensive representation of uniqueness for next-generation sequencing by minimum unique length analyses. PLoS One 8, e53822 (2013).
41. Bolotin, D.A. et al. MiTCR: software for T-cell receptor sequencing data analysis. Nat. Methods 10, 813-814 (2013).
42. Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821-829 (2008).
43. Lefranc, M.-P. et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 37, D1006-D1012 (2009).
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).
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).
46. Whitfeld, M.L. et al. Identifcation of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977-2000 (2002).
47. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093-1095 (2013).