Identifying cell-to-cell variability in internalisation using flow cytometry

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalisation. Given that internalisation is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalisation is of high biological interest. Yet, it is common for studies of internalisation to neglect cell-to-cell variability. We develop a simple mathematical model of internalisation that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalisation of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. Our model reproduces experimental observations, identifies cell-to-cell variability in the internalisation and recycling rates, and, importantly, provides information relating to inferential uncertainty. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalisation assays and similar experiments that probe cellular dynamical processes.