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Two-level modeling approach to identify the regulatory dynamics capturing drug response heterogeneity in single-cells

Authors
  • Chaves, Madalena1
  • Gomes-Pereira, Luis C.1, 2
  • Roux, Jérémie2
  • 1 Biocore Team, Sophia Antipolis, France , Sophia Antipolis (France)
  • 2 Institut de Recherche sur le Cancer et le Vieillissement de Nice, Centre Antoine Lacassagne, Nice, 06107, France , Nice (France)
Type
Published Article
Journal
Scientific Reports
Publisher
Springer Nature
Publication Date
Oct 21, 2021
Volume
11
Issue
1
Identifiers
DOI: 10.1038/s41598-021-99943-0
Source
Springer Nature
Disciplines
  • article
License
Green

Abstract

Single-cell multimodal technologies reveal the scales of cellular heterogeneity impairing cancer treatment, yet cell response dynamics remain largely underused to decipher the mechanisms of drug resistance they take part in. As the phenotypic heterogeneity of a clonal cell population informs on the capacity of each single-cell to recapitulate the whole range of observed behaviors, we developed a modeling approach utilizing single-cell response data to identify regulatory reactions driving population heterogeneity in drug response. Dynamic data of hundreds of HeLa cells treated with TNF-related apoptosis-inducing ligand (TRAIL) were used to characterize the fate-determining kinetic parameters of an apoptosis receptor reaction model. Selected reactions sets were augmented to incorporate a mechanism that leads to the separation of the opposing response phenotypes. Using a positive feedback loop motif to identify the reaction set, we show that caspase-8 is able to encapsulate high levels of heterogeneity by introducing a response delay and amplifying the initial differences arising from natural protein expression variability. Our approach enables the identification of fate-determining reactions that drive the population response heterogeneity, providing regulatory targets to curb the cell dynamics of drug resistance.

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