IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/2000640.html
   My bibliography  Save this article

Cell Fate Decision as High-Dimensional Critical State Transition

Author

Listed:
  • Mitra Mojtahedi
  • Alexander Skupin
  • Joseph Zhou
  • Ivan G Castaño
  • Rebecca Y Y Leong-Quong
  • Hannah Chang
  • Kalliopi Trachana
  • Alessandro Giuliani
  • Sui Huang

Abstract

Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. But how progenitor cells overcome the stability of their gene expression configuration (attractor) to exit the attractor in one direction remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or myeloid lineage is preceded by the destabilization of their high-dimensional attractor state, such that differentiating cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space based on decrease of correlation between cells and concomitant increase of correlation between genes as cells approach a tipping point. The detection of “rebellious cells” that enter the fate opposite to the one intended corroborates the model of preceding destabilization of a progenitor attractor. Thus, early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition, does not require knowledge of specific pathways, and could be used to predict impending major shifts in development and disease.Author Summary: A certain type of multipotent progenitor cell of the blood can commit to either the white (myeloid) or the red (erythroid) blood cell lineage, thus making a discrete binary cell fate decision. To test a theory on fundamental principles of cell fate dynamics (as opposed to the usually studied molecular mechanisms), we monitored such a fate decision in vitro using single-cell resolution gene expression analysis. We found that blood progenitor cells undergoing a fate decision to commit to either lineage after treatment with fate-determining cytokines, according to theory, first destabilized their original state. Cell states hereby diversified, manifesting the predicted flattening of an attractor’s potential well, which allows the increasingly vacillating progenitor cells to “spill” into adjacent potential wells corresponding to either lineage—myeloid or erythroid. This destabilization of an old stable state until suddenly opening access to new stable states is consistent with a critical transition (tipping point). We propose and demonstrate a new type of early warning signal that precedes critical transitions: an index IC based on a change in the high-dimensional cell population structure obtained from single-cell resolution measurements. This index may be used to predict imminent tipping point–like transitions in multicell systems, e.g., before pathological changes in tissues.

Suggested Citation

  • Mitra Mojtahedi & Alexander Skupin & Joseph Zhou & Ivan G Castaño & Rebecca Y Y Leong-Quong & Hannah Chang & Kalliopi Trachana & Alessandro Giuliani & Sui Huang, 2016. "Cell Fate Decision as High-Dimensional Critical State Transition," PLOS Biology, Public Library of Science, vol. 14(12), pages 1-28, December.
  • Handle: RePEc:plo:pbio00:2000640
    DOI: 10.1371/journal.pbio.2000640
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2000640
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.2000640&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.2000640?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sui Huang, 2010. "Cell Lineage Determination in State Space: A Systems View Brings Flexibility to Dogmatic Canonical Rules," PLOS Biology, Public Library of Science, vol. 8(5), pages 1-4, May.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Sui Huang, 2010. "Cell Lineage Determination in State Space: A Systems View Brings Flexibility to Dogmatic Canonical Rules," PLOS Biology, Public Library of Science, vol. 8(5), pages 1-4, May.
    3. 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.
    4. 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.
    5. Francesco Luigi Massimo Vallania & Marc Sherman & Zane Goodwin & Ilaria Mogno & Barak Alon Cohen & Robi David Mitra, 2014. "Origin and Consequences of the Relationship between Protein Mean and Variance," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.
    6. 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.
    7. 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.
    8. Ruben Perez-Carrasco & Pilar Guerrero & James Briscoe & Karen M Page, 2016. "Intrinsic Noise Profoundly Alters the Dynamics and Steady State of Morphogen-Controlled Bistable Genetic Switches," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-23, October.
    9. 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.
    10. 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.
    11. Warren Pilbrough & Trent P Munro & Peter Gray, 2009. "Intraclonal Protein Expression Heterogeneity in Recombinant CHO Cells," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-11, December.
    12. 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.
    13. Narito Suzuki & Chikara Furusawa & Kunihiko Kaneko, 2011. "Oscillatory Protein Expression Dynamics Endows Stem Cells with Robust Differentiation Potential," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-15, November.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pbio00:2000640. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.