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Transcriptome-wide noise controls lineage choice in mammalian progenitor cells

Author

Listed:
  • Hannah H. Chang

    (Vascular Biology Programme, Children’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
    Programme in Biophysics,
    MD-PhD Programme, Harvard Medical School, Boston, Massachusetts 02115, USA)

  • Martin Hemberg

    (Imperial College London, South Kensington Campus
    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.).)

  • Mauricio Barahona

    (Imperial College London, South Kensington Campus)

  • Donald E. Ingber

    (Vascular Biology Programme, Children’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
    Harvard Institute for Biologically Inspired Engineering, Cambridge, Massachusetts 02139, USA)

  • Sui Huang

    (Vascular Biology Programme, Children’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
    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.).)

Abstract

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.

Suggested Citation

  • Hannah H. Chang & Martin Hemberg & Mauricio Barahona & Donald E. Ingber & Sui Huang, 2008. "Transcriptome-wide noise controls lineage choice in mammalian progenitor cells," Nature, Nature, vol. 453(7194), pages 544-547, May.
    • Handle: RePEc:nat:nature:v:453:y:2008:i:7194:d:10.1038_nature06965
    DOI: 10.1038/nature06965
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    1. Rabajante, Jomar Fajardo & Talaue, Cherryl Ortega, 2015. "Equilibrium switching and mathematical properties of nonlinear interaction networks with concurrent antagonism and self-stimulation," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 166-182.
    2. Angélique Richard & Loïs Boullu & Ulysse Herbach & Arnaud Bonnafoux & Valérie Morin & Elodie Vallin & Anissa Guillemin & Nan Papili Gao & Rudiyanto Gunawan & Jérémie Cosette & Ophélie Arnaud & Jean-Ja, 2016. "Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process," PLOS Biology, Public Library of Science, vol. 14(12), pages 1-35, December.
    3. Julián Candia & Ryan Maunu & Meghan Driscoll & Angélique Biancotto & Pradeep Dagur & J Philip McCoy Jr & H Nida Sen & Lai Wei & Amos Maritan & Kan Cao & Robert B Nussenblatt & Jayanth R Banavar & Wolf, 2013. "From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-10, September.
    4. Johnston Iain G., 2014. "Efficient parametric inference for stochastic biological systems with measured variability," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 379-390, June.
    5. Kazunari Mouri & Yasushi Sako, 2013. "Optimality Conditions for Cell-Fate Heterogeneity That Maximize the Effects of Growth Factors in PC12 Cells," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-15, November.
    6. Margaret J Tse & Brian K Chu & Cameron P Gallivan & Elizabeth L Read, 2018. "Rare-event sampling of epigenetic landscapes and phenotype transitions," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-28, August.
    7. Linghua Zhou & Yong Shen & Libo Jiang & Danni Yin & Jingxin Guo & Hui Zheng & Hao Sun & Rongling Wu & Yunqian Guo, 2015. "Systems Mapping for Hematopoietic Progenitor Cell Heterogeneity," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    8. Masa Tsuchiya & Vincent Piras & Sangdun Choi & Shizuo Akira & Masaru Tomita & Alessandro Giuliani & Kumar Selvarajoo, 2009. "Emergent Genome-Wide Control in Wildtype and Genetically Mutated Lipopolysaccarides-Stimulated Macrophages," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-13, March.
    9. Yelyzaveta Shlyakhtina & Bianca Bloechl & Maximiliano M. Portal, 2023. "BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    10. Tian Hong & Jianhua Xing & Liwu Li & John J Tyson, 2011. "A Mathematical Model for the Reciprocal Differentiation of T Helper 17 Cells and Induced Regulatory T Cells," PLOS Computational Biology, Public Library of Science, vol. 7(7), pages 1-13, July.
    11. Tsuchiya, Masa & Selvarajoo, Kumar & Piras, Vincent & Tomita, Masaru & Giuliani, Alessandro, 2009. "Local and global responses in complex gene regulation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1738-1746.
    12. Miles Miller & Marc Hafner & Eduardo Sontag & Noah Davidsohn & Sairam Subramanian & Priscilla E M Purnick & Douglas Lauffenburger & Ron Weiss, 2012. "Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-18, July.
    13. Suzanne Gaudet & Sabrina L Spencer & William W Chen & Peter K Sorger, 2012. "Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-15, April.
    14. Peter D Tonge & Victor Olariu & Daniel Coca & Visakan Kadirkamanathan & Kelly E Burrell & Stephen A Billings & Peter W Andrews, 2010. "Prepatterning in the Stem Cell Compartment," PLOS ONE, Public Library of Science, vol. 5(5), pages 1-10, May.
    15. Gautam Dey & Gagan D Gupta & Balaji Ramalingam & Mugdha Sathe & Satyajit Mayor & Mukund Thattai, 2014. "Exploiting Cell-To-Cell Variability To Detect Cellular Perturbations," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.

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