Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Nature Biotechnology

Volumn 33, Issue 2, 2015, Pages 155-160

  Buettner, Florian ^a,b   Natarajan, Kedar N ^b   Casale, F Paolo ^b   Proserpio, Valentina ^b   Scialdone, Antonio ^b   Theis, Fabian J ^a,c   Teichmann, Sarah A ^b   Marioni, John C ^b   Stegle, Oliver ^b  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CASE BASED REASONING; CELLS; COMPUTATIONAL METHODS; GENE EXPRESSION; GENES; MOBILE SECURITY;

COMPUTATIONAL ANALYSIS; COMPUTATIONAL APPROACH; DIFFERENT STAGES; LATENT VARIABLE MODELING; LATENT VARIABLE MODELS; RNA SEQUENCING; TECHNICAL DEVELOPMENT; TRANSCRIPTOMES;

CYTOLOGY;

RNA; TRANSCRIPTOME;

ANIMAL CELL; ARTICLE; CD4+ T LYMPHOCYTE; CELL CYCLE; CELL DIVISION; CELL FUNCTION; CELL HETEROGENEITY; CELL SUBPOPULATION; COMPUTATIONAL FLUID DYNAMICS; CONTROLLED STUDY; GENE EXPRESSION; GENETIC VARIABILITY; LYMPHOCYTE DIFFERENTIATION; NONHUMAN; PRIORITY JOURNAL; RNA SEQUENCE; SINGLE CELL ANALYSIS; SINGLE CELL LATENT VARIABLE MODEL; STRUCTURAL EQUATION MODELING; TH2 CELL; ANIMAL; BIOLOGY; CELL DIFFERENTIATION; CELL LINEAGE; CYTOLOGY; GENE EXPRESSION REGULATION; GENETIC HETEROGENEITY; GENETICS; MOUSE; MOUSE EMBRYONIC STEM CELL; SEQUENCE ANALYSIS; THEORETICAL MODEL;

ANIMALS; CELL DIFFERENTIATION; CELL LINEAGE; COMPUTATIONAL BIOLOGY; GENE EXPRESSION REGULATION, DEVELOPMENTAL; GENETIC HETEROGENEITY; MICE; MODELS, THEORETICAL; MOUSE EMBRYONIC STEM CELLS; RNA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TH2 CELLS; TRANSCRIPTOME;

EID: 84923292191     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.3102     Document Type: Article

References (46)

1. Levsky, J.M., Shenoy, S.M., Pezo, R.C. & Singer, R.H. Single-cell gene expression profiling. Science 297, 836-840 (2002).
2. Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533-538 (2010).
3. Raj, A., van den Bogaard, P., Rifkin, S.A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877-879 (2008).
4. Liu, J., Hansen, C. & Quake, S.R. Solving the "world-to-chip" interface problem with a microfluidic matrix. Anal. Chem. 75, 4718-4723 (2003).
5. Citri, A., Pang, Z.P., Sudhof, T.C., Wernig, M. & Malenka, R.C. Comprehensive qPCR profiling of gene expression in single neuronal cells. Nat. Protoc. 7, 118-127 (2012).
6. Wheeler, A.R. et al. Microfluidic device for single-cell analysis. Anal. Chem. 75, 3581-3586 (2003).
7. Marcus, J.S., Anderson, W.F. & Quake, S.R. Microfluidic single-cell mRNA isolation and analysis. Anal. Chem. 78, 3084-3089 (2006).
8. Guo, G. et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18, 675-685 (2010).
9. Burton, A. et al. Single-cell profiling of epigenetic modifiers identifies PRDM14 as an inducer of cell fate in the mammalian embryo. Cell Reports 5, 687-701 (2013).
10. Luo, L. et al. Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat. Med. 5, 117-122 (1999).
11. Chiang, M.K. & Melton, D.A. Single-cell transcript analysis of pancreas development. Dev. Cell 4, 383-393 (2003).
12. Tang, F. et al. RNA-seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5, 516-535 (2010).
13. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160-1167 (2011).
14. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163-166 (2014).
15. Yan, L. et al. Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1131-1139 (2013).
16. Tang, F. et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-seq analysis. Cell Stem Cell 6, 468-478 (2010).
17. Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236-240 (2013).
18. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381-386 (2014).
19. Pollen, A.A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053-1058 (2014).
20. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093-1095 (2013).
21. Leek, J.T. & Storey, J.D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161 (2007).
22. Stegle, O., Parts, L., Durbin, R. & Winn, J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput. Biol. 6, e1000770 (2010).
23. Li, S. et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32, 888-895 (2014).
24. Xue, Z. et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500, 593-597 (2013).
25. Mahata, B. et al. Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Reports 7, 1130-1142 (2014).
26. Newman, J.R. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840-846 (2006).
27. Gold, D., Mallick, B. & Coombes, K. Real-time gene expression: statistical challenges in design and inference. J. Comput. Biol. 15, 611-623 (2008).
28. Singh, A.M. et al. Cell-cycle control of developmentally regulated transcription factors accounts for heterogeneity in human pluripotent cells. Stem Cell Reports 1, 532-544 (2013).
29. Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833-835 (2011).
30. Fusi, N., Stegle, O. & Lawrence, N.D. Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies. PLoS Comput. Biol. 8, e1002330 (2012).
31. Lawrence, N.D. Gaussian process latent variable models for visualisation of high dimensional data. Adv. Neural Inf. Process. Syst. 16, 329-336 (2004).
32. Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013).
33. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637-640 (2014).
34. Fox, C.J., Hammerman, P.S. & Thompson, C.B. Fuel feeds function: energy metabolism and the T-cell response. Nat. Rev. Immunol. 5, 844-852 (2005).
35. Nelms, K., Keegan, A.D., Zamorano, J., Ryan, J.J. & Paul, W.E. The IL-4 receptor: signaling mechanisms and biologic functions. Annu. Rev. Immunol. 17, 701-738 (1999).
36. H2 cells and inhibition of Th1 cell-specific factors. Cell Res. 16, 3-10 (2006).
37. Stritesky, G.L. et al. The transcription factor STAT3 is required for T helper 2 cell development. Immunity 34, 39-49 (2011).
38. Zhou, M. et al. Kruppel-like transcription factor 13 regulates T lymphocyte survival in vivo. J. Immunol. 178, 5496-5504 (2007).
39. Betz, B.C. et al. Batf coordinates multiple aspects of B and T cell function required for normal antibody responses. J. Exp. Med. 207, 933-942 (2010).
40. H2 cell-specific IL--24 gene expression. J. Immunol. 186, 4098-4109 (2011).
41. Jensen, L.J. et al. STRING 8 - a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412-D416 (2009).
42. Chang, C.H. et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239-1251 (2013).
43. Garcia-Sanz, J.A., Mikulits, W., Livingstone, A., Lefkovits, I. & Mullner, E.W. Translational control: a general mechanism for gene regulation during T cell activation. FASEB J. 12, 299-306 (1998).
44. Bird, J.J. et al. Helper T cell differentiation is controlled by the cell cycle. Immunity 9, 229-237 (1998).
45. Wilson, C.B., Makar, K.W. & Perez-Melgosa, M. Epigenetic regulation of T cell fate and function. J. Infect. Dis. 185 (suppl. 1), S37-S45 (2002).
46. Stegle, O., Teichmann, S.A. & Marioni, J.C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. (in the press).