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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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. 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.
    12. 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.
    13. 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.

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