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Transcriptome-wide noise controls lineage choice in mammalian progenitor cells

Abstract

Phenotypic cell-to-cell variability within clonal populations may be a manifestation of ‘gene expression noise’1,2,3,4,5,6, or it may reflect stable phenotypic variants7. Such ‘non-genetic cell individuality’7 can arise from the slow fluctuations of protein levels8 in mammalian cells. These fluctuations produce persistent cell individuality, thereby rendering a clonal population heterogeneous. However, it remains unknown whether this heterogeneity may account for the stochasticity of cell fate decisions in stem cells. Here we show that in clonal populations of mouse haematopoietic progenitor cells, spontaneous ‘outlier’ cells with either extremely high or low expression levels of the stem cell marker Sca-1 (also known as Ly6a; ref. 9) reconstitute the parental distribution of Sca-1 but do so only after more than one week. This slow relaxation is described by a gaussian mixture model that incorporates noise-driven transitions between discrete subpopulations, suggesting hidden multi-stability within one cell type. Despite clonality, the Sca-1 outliers had distinct transcriptomes. Although their unique gene expression profiles eventually reverted to that of the median cells, revealing an attractor state, they lasted long enough to confer a greatly different proclivity for choosing either the erythroid or the myeloid lineage. Preference in lineage choice was associated with increased expression of lineage-specific transcription factors, such as a >200-fold increase in Gata1 (ref. 10) among the erythroid-prone cells, or a >15-fold increased PU.1 (Sfpi1) (ref. 11) expression among myeloid-prone cells. Thus, clonal heterogeneity of gene expression level is not due to independent noise in the expression of individual genes, but reflects metastable states of a slowly fluctuating transcriptome that is distinct in individual cells and may govern the reversible, stochastic priming of multipotent progenitor cells in cell fate decision.

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Figure 1: Robust clonal heterogeneity.
Figure 2: Restoration of heterogeneity from sorted cell fractions.
Figure 3: Clonal heterogeneity governs differentiation potential.
Figure 4: Clonal heterogeneity of Sca-1 expression reflects transcriptome-wide noise.

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Gene Expression Omnibus

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The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) under the GEO Series accession number GSE10772.

References

  1. Blake, W. J. et al. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003)

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Elowitz, M. B. et al. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Pedraza, J. M. & van Oudenaarden, A. Noise propagation in gene networks. Science 307, 1965–1969 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Raser, J. M. & O’Shea, E. K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rosenfeld, N. et al. Gene regulation at the single-cell level. Science 307, 1962–1965 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Kaern, M. et al. Stochasticity in gene expression: from theories to phenotypes. Nature Rev. Genet. 6, 451–464 (2005)

    Article  CAS  PubMed  Google Scholar 

  7. Spudich, J. L. & Koshland, D. E. Non-genetic individuality: chance in the single cell. Nature 262, 467–471 (1976)

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Sigal, A. et al. Variability and memory of protein levels in human cells. Nature 444, 643–646 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  9. van de Rijn, M. et al. Mouse hematopoietic stem-cell antigen Sca-1 is a member of the Ly-6 antigen family. Proc. Natl Acad. Sci. USA 86, 4634–4638 (1989)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cantor, A. B., Katz, S. G. & Orkin, S. H. Distinct domains of the GATA-1 cofactor FOG-1 differentially influence erythroid versus megakaryocytic maturation. Mol. Cell. Biol. 22, 4268–4279 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Koschmieder, S. et al. Role of transcription factors C/EBPα and PU.1 in normal hematopoiesis and leukemia. Int. J. Hematol. 81, 368–377 (2005)

    Article  CAS  PubMed  Google Scholar 

  12. Tsai, S. et al. Lymphohematopoietic progenitors immortalized by a retroviral vector harboring a dominant-negative retinoic acid receptor can recapitulate lymphoid, myeloid, and erythroid development. Genes Dev. 8, 2831–2841 (1994)

    Article  CAS  PubMed  Google Scholar 

  13. Holmes, C. & Stanford, W. L. Concise review: stem cell antigen-1: expression, function, and enigma. Stem Cells 25, 1339–1347 (2007)

    Article  CAS  PubMed  Google Scholar 

  14. Guido, N. J. et al. A bottom-up approach to gene regulation. Nature 439, 856–860 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Uhlenbeck, G. E. & Ornstein, L. S. On the theory of Brownian Motion. Phys. Rev. 36, 823–841 (1930)

    Article  ADS  CAS  Google Scholar 

  16. Kurchan, J. & Laloux, L. Phase space geometry and slow dynamics. J. Phys. Math. Gen. 29, 1929–1948 (1996)

    Article  ADS  MathSciNet  Google Scholar 

  17. Chang, H. H. et al. Multistable and multistep dynamics in neutrophil differentiation. BMC Cell Biol. 7, 11 (2006)

    Article  PubMed  PubMed Central  Google Scholar 

  18. Huang, S. et al. Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev. Biol. 305, 695–713 (2007)

    Article  CAS  PubMed  Google Scholar 

  19. Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huang, S. et al. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94, 128701 (2005)

    Article  ADS  PubMed  Google Scholar 

  21. Enver, T., Heyworth, C. M. & Dexter, T. M. Do stem cells play dice? Blood 92 348–351 discussion 352 (1998)

    CAS  PubMed  Google Scholar 

  22. Orkin, S. H. & Zon, L. I. Hematopoiesis and stem cells: plasticity versus developmental heterogeneity. Nature Immunol. 3, 323–328 (2002)

    Article  CAS  Google Scholar 

  23. Eichler, G. S., Huang, S. & Ingber, D. E. Gene expression dynamics inspector (GEDI): for integrative analysis of expression profiles. Bioinformatics 19, 2321–2322 (2003)

    Article  CAS  PubMed  Google Scholar 

  24. Zenger, V. E. et al. Quantitative flow cytometry: inter-laboratory variation. Cytometry 33, 138–145 (1998)

    Article  CAS  PubMed  Google Scholar 

  25. Wang, R., Clark, R. & Bautch, V. L. Embryonic stem cell-derived cystic embryoid bodies form vascular channels: an in vitro model of blood vessel development. Development 114, 303–316 (1992)

    CAS  PubMed  Google Scholar 

  26. Tsai, S. et al. Lymphohematopoietic progenitors immortalized by a retroviral vector harboring a dominant-negative retinoic acid receptor can recapitulate lymphoid, myeloid, and erythroid development. Genes Dev. 8, 2831–2841 (1994)

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was funded by grants to S.H. from the Air Force Office of Scientific Research and, in part, from the National Institutes of Health. H.H.C. is partially supported by the Presidential Scholarship and the Ashford Fellowship of Harvard University. M.H. and M.B. are supported by the Life Sciences Interface and Mathematics panels of the Engineering and Physical Sciences Research Council of the UK. D.E.I. is supported by the National Health Institutes and the Army Research Office. We thank K. Orford, P. Zhang, A. Mammoto, J. Daley, J. Pendse and M. Shakya for experimental assistance, and W. Press and K. Farh for discussions.

Author Contributions H.H.C. designed the study, performed the experiments, analysed the data, participated in the theoretical analysis and drafted the manuscript. M.H. constructed the theoretical model and performed the theoretical analysis. M.B. constructed the model, supervised the work and revised the manuscript. D.E.I. supervised the work and revised the manuscript. S.H. conceived of the study, designed experiments, supervised the work, participated in the experimental and theoretical analysis and drafted the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sui Huang.

Supplementary information

This Supplementary Information file contains the following sections:

S1. Supplementary Methods: This section contains additional experimental methods not included in the "Methods" section at the end of the main text. S2. Supplementary Discussion: This section contains additional discussions regarding two questions: (1) What other factors could contribute to the observed level of heterogeneity in Sca-1 within one clonal population (Fig. 1 in the main text)? (2) What biological process may drive the (re)generation of the parental Sca-1 distribution from the three sorted, more homogeneous population fractions? These discussions were originally part of the main text but have been restructured for the Supplementary Information due to considerations for text length. S3. Supplementary Figures and Legends: This section contains experimental supplementary figures along with their legends (Supplementary Figures 1-12). S4. Supplementary Table: This section contains one experimental supplementary table along with its legend (Supplementary Table 1). S5. Theoretical Methods: This is an extended section outlying the theoretical methods employed in the paper, including relevant theoretical supplementary figures (Supplementary Figures 13-18) and tables (Supplementary Table 2-4). S6. Supplementary Notes: This section contains the references for the entire Supplementary Information. The numbering of references here is independent of that for the main text. (PDF 1338 kb)

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Chang, H., Hemberg, M., Barahona, M. et al. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544–547 (2008). https://doi.org/10.1038/nature06965

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