IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v459y2009i7245d10.1038_nature08012.html
   My bibliography  Save this article

Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis

Author

Listed:
  • Sabrina L. Spencer

    (Center for Cell Decision Processes, Harvard Medical School, Boston, Massachusetts 02115, USA
    Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA)

  • Suzanne Gaudet

    (Center for Cell Decision Processes, Harvard Medical School, Boston, Massachusetts 02115, USA
    Present address: Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.)

  • John G. Albeck

    (Center for Cell Decision Processes, Harvard Medical School, Boston, Massachusetts 02115, USA)

  • John M. Burke

    (Center for Cell Decision Processes, Harvard Medical School, Boston, Massachusetts 02115, USA)

  • Peter K. Sorger

    (Center for Cell Decision Processes, Harvard Medical School, Boston, Massachusetts 02115, USA)

Abstract

Apoptosis: an explanation for 'fractional killing'? Noisy gene expression or unequal partition of molecules during cell division are increasingly recognized as key sources of non-genetic cell-to-cell heterogeneity but the consequences for disease progression and drug efficiency are little understood. Through single-cell imaging, Spencer et al. now show that pre-existing cell-to-cell differences in the levels of signalling proteins determine whether the addition of an external death signal will kill a cell by apoptosis or not — and how quickly it happens. The mechanism may explain the phenomenon of 'fractional killing', in which repeated rounds of chemotherapy kill some but not all cells in a tumour. From an evolutionary perspective, such systems-level phenotypic variation — not based on genetic or epigenetic modifications — offers wider adaptive potential to populations of living organisms.

Suggested Citation

  • Sabrina L. Spencer & Suzanne Gaudet & John G. Albeck & John M. Burke & Peter K. Sorger, 2009. "Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis," Nature, Nature, vol. 459(7245), pages 428-432, May.
    • Handle: RePEc:nat:nature:v:459:y:2009:i:7245:d:10.1038_nature08012
    DOI: 10.1038/nature08012
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature08012
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature08012?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lucy Ham & Megan A. Coomer & Kaan Öcal & Ramon Grima & Michael P. H. Stumpf, 2024. "A stochastic vs deterministic perspective on the timing of cellular events," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Subhadip Raychaudhuri, 2010. "A Minimal Model of Signaling Network Elucidates Cell-to-Cell Stochastic Variability in Apoptosis," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-7, August.
    3. Dirke Imig & Nadine Pollak & Frank Allgöwer & Markus Rehm, 2020. "Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-17, June.
    4. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    5. Jan Hasenauer & Christine Hasenauer & Tim Hucho & Fabian J Theis, 2014. "ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-17, July.
    6. Chad Liu & Chuan-Yuan Li & Fan Yuan, 2014. "Mathematical Modeling of the Phoenix Rising Pathway," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-10, February.
    7. Andreas Doncic & Umut Eser & Oguzhan Atay & Jan M Skotheim, 2013. "An Algorithm to Automate Yeast Segmentation and Tracking," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-11, March.
    8. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
    9. Christopher C Govern & Arup K Chakraborty, 2013. "Stochastic Responses May Allow Genetically Diverse Cell Populations to Optimize Performance with Simpler Signaling Networks," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    10. 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.
    11. Leighton T Izu & Tamás Bányász & Ye Chen-Izu, 2015. "Optimizing Population Variability to Maximize Benefit," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-17, December.
    12. 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.
    13. Szymon Stoma & Alexandre Donzé & François Bertaux & Oded Maler & Gregory Batt, 2013. "STL-based Analysis of TRAIL-induced Apoptosis Challenges the Notion of Type I/Type II Cell Line Classification," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-14, May.
    14. Andreas Raue & Marcel Schilling & Julie Bachmann & Andrew Matteson & Max Schelke & Daniel Kaschek & Sabine Hug & Clemens Kreutz & Brian D Harms & Fabian J Theis & Ursula Klingmüller & Jens Timmer, 2013. "Lessons Learned from Quantitative Dynamical Modeling in Systems Biology," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-17, September.

    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:nat:nature:v:459:y:2009:i:7245:d:10.1038_nature08012. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.