Transcriptome-wide noise controls lineage choice in mammalian progenitor cells (original) (raw)

Nature volume 453, pages 544–547 (2008)Cite this article

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.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 51 print issues and online access

$199.00 per year

only $3.90 per issue

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Additional access options:

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

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

Download references

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.

Author information

Author notes

  1. Martin Hemberg & Sui Huang
    Present address: Present addresses: Department of Ophthalmology, Children’s Hospital Boston, Boston, Massachusetts 02215, USA (M.H.); Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta T2N 1N4, Canada (S.H.).,

Authors and Affiliations

  1. Department of Pathology and Surgery, Vascular Biology Programme, Children’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA,
    Hannah H. Chang, Donald E. Ingber & Sui Huang
  2. Programme in Biophysics,,
    Hannah H. Chang
  3. MD-PhD Programme, Harvard Medical School, Boston, Massachusetts 02115, USA ,
    Hannah H. Chang
  4. Department of Bioengineering and Institute for Mathematical Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
    Martin Hemberg & Mauricio Barahona
  5. Harvard Institute for Biologically Inspired Engineering, Cambridge, Massachusetts 02139, USA ,
    Donald E. Ingber

Authors

  1. Hannah H. Chang
    You can also search for this author inPubMed Google Scholar
  2. Martin Hemberg
    You can also search for this author inPubMed Google Scholar
  3. Mauricio Barahona
    You can also search for this author inPubMed Google Scholar
  4. Donald E. Ingber
    You can also search for this author inPubMed Google Scholar
  5. Sui Huang
    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toSui 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)

Rights and permissions

About this article

Cite this article

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

Download citation

Editorial Summary

Pluripotency: Cell-to-cell variations

Even in clonal populations of cells, there is significant phenotypic variation from cell to cell. This could reflect the 'noise' inherent in gene expression: or the various cell states could represent stable phenotypic variants. Chang et al. analysed the behaviour of an 'outlier' in clonal populations of mouse haematoipoietic stem cells that had very high expressions of the stem cell marker Sca-1 and found that outliers possessed distinct transcriptomes. Though the transcriptomes eventually reverted back to that of the median cells, while they differed they could drive the cells to express characteristics of distinct cell fates. Thus clonal heterogeneity of gene expression may not be due to noise in the expression of individual genes, but rather is a manifestation of metastable states of a slowly fluctuating transcriptome. These fluctuations may govern the reversible, stochastic priming of multipotent progenitor cells in cell fate decision.