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

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.

Profiling the Non-genetic Origins of Cancer Drug Resistance with a Single-Cell Functional Genomics Approach Using Predictive Cell Dynamics.

Non-genetic heterogeneity observed in clonal cell populations is an immediate cause of drug resistance that remains challenging to profile because of its transient nature. Here, we coupled three single-cell technologies to link the predicted drug response of a cell to its own genome-wide transcriptomic profile. As a proof of principle, we analyzed the response to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL) in HeLa cells to demonstrate that cell dynamics can discriminate the transient transcriptional states at the origin of cell decisions such as sensitivity and resistance.