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

Neuroblastoma is characterised by significant intratumoural heterogeneity which complicates treatments. Phenotypic adaptation, 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. We first record dynamic cell counts in low-nutrient conditions for two cell lines: IMR-32 and SK-N-BE(2). We next formulate a list of permissible biological processes that might occur in the regarded systems and, from the list, construct candidate models via mass action kinetics. To quantitatively infer processes that lead to phenotypic adaptation, we perform a model selection based on computational Bayesian inference methods using the probabilistic programming language Stan. Our results suggest that cell-to-cell interactions promote phenotypic adaptation and that the rate of phenotypic adaptation increases with decreasing nutrient concentrations. We then perform flowcytometry assays to confirm the change in phenotype in the two models and show that the switch occurs as a function of initial condition.