Bayesian inference of cell-to-cell interactions in low-nutrient neuroblastoma cell cultures

Abstract

Neuroblastoma is characterised by significant intratumoural heterogeneity which complicates treatments. Phenotypic plasticity, i.e., the ability of cells to alter their phenotype without genetic mutations, is a key factor contributing to this heterogeneity. In this study, we quantify cell actions and cell-to-cell interactions that lead to phenotypic adaptation in vitro under stress, specifically low-nutrient conditions. We initially record dynamic cell counts under various nutrient conditions for two neuroblastoma cell lines with known phenotype differences. Subsequently, we compile a list of plausible biological processes that could transpire within the aforementioned systems. We then construct candidate models employing mass-action kinetics. To quantitatively infer processes that lead to phenotypic adaptation, we perform a model selection based on computational Bayesian inference methods.Our results suggest that cell-to-cell interactions promote phenotypic adaptation and that the rate of this adaptation increases with decreasing nutrient concentrations. We perform flow cytometry-based experiments to corroborate changes in phenotype composition in response to both nutrient-deprived and treatmentinduced stress conditions.