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Live-cell imaging and mathematical analysis of the “community effect” in apoptosis

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Abstract

As a cellular intrinsic mechanism leading to cellular demise, apoptosis was thoroughly characterized from a mechanistic perspective. Nowadays there is an increasing interest in describing the non-cell autonomous or community effects of apoptosis, especially in the context of resistance to cancer treatments. Transitioning from cell-centered to cell population-relevant mechanisms adds a layer of complexity for imaging and analyzing an enormous number of apoptotic events. In addition, the community effect between apoptotic and living cells is difficult to be taken into account for complex analysis. We describe here a robust and easy to implement method to analyze the interactions between cancer cells, while under apoptotic pressure. Using this approach we showed as proof-of-concept that apoptosis is insensitive to cellular density, while the proximity to apoptotic cells increases the probability of a given cell to undergo apoptosis.

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Data availability

The datasets generated and analyses in this study are available from the corresponding author on reasonable request.

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Acknowledgements

Funding from Institute Convergence PLAsCAN (ANR-17-CONV-0002), LabEx DEVweCAN (University of Lyon), Agence Nationale de la Recherche (ANR) Young Researchers Project (ANR-18-CE13-0005-01), La Ligue Nationale Contre le Cancer and Fondation de France supported this work. We thank Brigitte Manship for reviewing the manuscript.

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Author notes

  1. Diane Coursier and David Coulette have contributed equally to this work

Authors and Affiliations

  1. Cancer Research Center of Lyon (CRCL) INSERM 1052, CNRS 5286, Lyon, France

    Diane Coursier & Gabriel Ichim

  2. Cancer Cell Death Laboratory, Part of LabEx DEVweCAN, Université de Lyon, Lyon, France

    Diane Coursier & Gabriel Ichim

  3. ENS-Lyon, UMR CNRS 5669 ‘UMPA’ and INRIA Lyon, Project NUMED, Lyon, 69364, France

    David Coulette, Hélène Leman & Emmanuel Grenier

Authors
  1. Diane Coursier
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  2. David Coulette
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  3. Hélène Leman
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  4. Emmanuel Grenier
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  5. Gabriel Ichim
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Contributions

Conceptualization: GI, DC, HL and EG; Methodology: GI, DC, HL and EG; Formal analysis: GI, DC and HL; Investigation: GI, DC, DC and HL; Resources: GI, DC, HL and EG; Writing—Original Draft and Editing: GI; All authors reviewed and edited the manuscript; Supervision: GI, DC, HL and EG; Project administration and funding acquisition: GI.

Corresponding author

Correspondence to Gabriel Ichim.

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10495_2022_1783_MOESM1_ESM.tif

(Related to Figure 3).A, B. Probability estimates (upper panels) and the corresponding p-value (lower panels) for the occurrence of apoptosis in ABT-737/UMI-77-treated WM115 H2B-mCherry cells surrounded by 2 or 3 neighboring cells. C, D. Similar analysis as in (A, B), albeit apoptosis was triggered by doxycycline induction of pro-apoptotic BAX protein.Supplementary file1 (TIF 36041 kb)

10495_2022_1783_MOESM2_ESM.tif

(Related to Figure 4). A, B. Probability estimates (upper panels) and the corresponding p-value (lower panels) for the occurrence of apoptosis in ABT-737/UMI-77-treated WM115 H2B-mCherry cells surrounded by 2 or 3 apoptotic neighboring cells. C, D. Similar analysis as in (A, B), albeit apoptosis was triggered by doxycycline induction of pro-apoptotic BAX protein.Supplementary file2 (TIF 36031 kb)

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Coursier, D., Coulette, D., Leman, H. et al. Live-cell imaging and mathematical analysis of the “community effect” in apoptosis. Apoptosis 28, 326–334 (2023). https://doi.org/10.1007/s10495-022-01783-4

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