IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003320.html
   My bibliography  Save this article

Optimality Conditions for Cell-Fate Heterogeneity That Maximize the Effects of Growth Factors in PC12 Cells

Author

Listed:
  • Kazunari Mouri
  • Yasushi Sako

Abstract

Recently, the heterogeneity that arises from stochastic fate decisions has been reported for several types of cancer-derived cell lines and several types of clonal cells grown under constant environmental conditions. However, the relation between this stochasticity and the responsiveness to extracellular stimuli remains largely unknown. Here we focused on the fate decisions of the PC12 cell line, which was derived from rat pheochromocytoma, and is a model system to study differentiation into sympathetic neurons. Whereas epidermal growth factor (EGF) stimulates the proliferation of populations of PC12 cells, nerve growth factor (NGF) promotes the differentiation of neurites to neuron-like cells. We found that phenotypic heterogeneity increased with time at several surrounding serum concentrations, suggesting stochastic cell-fate decisions in single cells. We made a simple mathematical model assuming Markovian transitions of the cell fates, and estimated the transition rates based on Bayes' theorem. The model suggests that depending on the serum concentration, EGF (NGF) even directs differentiation (proliferation) at the single-cell level. The maximum effects of the growth factors were ensured when the transition rates were appropriately controlled by the serum concentration to produce a nonextremal, moderate amount of cell-fate heterogeneity. Our model was validated by the experimental finding that the means and variances of the local cell densities obey a power-law relationship. These results suggest that even when efficient responses to growth factors are observed at the population level, the growth factors stochastically direct the cell-fate decisions in different directions at the single-cell level.Author Summary: Elucidation of the mechanisms that regulate cell fate has become one of the primary goals of research in cell biology and regenerative medicine. Growth factors are often used to regulate cell fate. However, stochastic cellular responses to growth regulators have prevented precise control of cell fate. We report our investigation of the relationship between heterogeneity and responsiveness in cell fate decisions by both single cells and populations of cells. Our study involved PC12, a cultured cell line for which cell-fates are affected by exposure to growth factors and culture conditions. Computational methods using a mathematical model enabled us to determine the cell-fate decisions rate in single PC12 cells and analyze the population responses to growth factors from experimental data. Our findings reveal that growth factors control cell-fate decisions rate in single PC12 cells, and suggest distinct differences in the mechanisms of actions of growth factors under different culture conditions. In addition, we observed maximum effects of growth factors when a nonextremal, moderate amount of cell-fate heterogeneity exists. Our results give several insights into stochastic cell responses, including the effects of anticancer agents on cancer cells and the optimization of methods to induce the differentiation of stem cells.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1003320
    DOI: 10.1371/journal.pcbi.1003320
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003320
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003320&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003320?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. Gürol M. Süel & Jordi Garcia-Ojalvo & Louisa M. Liberman & Michael B. Elowitz, 2006. "An excitable gene regulatory circuit induces transient cellular differentiation," Nature, Nature, vol. 440(7083), pages 545-550, March.
    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. 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.
    2. Matthieu Wyart & David Botstein & Ned S Wingreen, 2010. "Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-14, November.
    3. Sandra H Dandach & Mustafa Khammash, 2010. "Analysis of Stochastic Strategies in Bacterial Competence: A Master Equation Approach," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-11, November.
    4. 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.
    5. Karin Münch & Richard Münch & Rebekka Biedendieck & Dieter Jahn & Johannes Müller, 2019. "Evolutionary model for the unequal segregation of high copy plasmids," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-17, March.
    6. 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.
    7. Payne, Joshua L., 2016. "No tradeoff between versatility and robustness in gene circuit motifs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 192-199.
    8. Ruoyu Luo & Lin Ye & Chenyang Tao & Kankan Wang, 2013. "Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    9. 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.
    10. 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.
    11. 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.
    12. Margaritis Voliotis & Philipp Thomas & Ramon Grima & Clive G Bowsher, 2016. "Stochastic Simulation of Biomolecular Networks in Dynamic Environments," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-18, June.
    13. Yu, Haiyan & Liu, Quansheng & Bi, Yuanhong, 2023. "Lévy noise-induced phase transition in p53 gene regulatory network near bifurcation points," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    14. 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.
    15. 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.
    16. 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 1-12, June.
    17. Greg J Stephens & Bethany Johnson-Kerner & William Bialek & William S Ryu, 2008. "Dimensionality and Dynamics in the Behavior of C. elegans," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-10, April.
    18. 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.
    19. 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.
    20. Cabrera Fernández, Juan Luis & Herrera-Almarza, Gioconda C. & Gutiérrez M., Esther D., 2018. "Chromosome progression and mitotic times behavior are mimicked by an stochastic unstable dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1121-1127.

    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:pcbi00:1003320. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.