CloVarS: a simulation of single-cell clonal variability

Abstract

High-throughput time-lapse microscopy has allowed researchers to monitor individual cells as they grow into colonies and react to treatments, but a deeper understanding of the data obtained after image analysis is still lacking. This is partly due to the biological and computational challenges related to long-running experiments and single-cell tracking. In this work, we present the Clonal Variability Simulator (CloVarS), a Python tool for generating synthetic data of single-cell lineage trees to model time-lapse microscopy experiments. After colony initialization, each individual cell is simulated for a given number of simulation frames. Over the course of the simulation, cells are able to migrate, enter mitosis (divide), and enter apoptosis (die). These events are determined by distributions of cell division and death times, which can be inferred from and fit to experimental data. Colonies have an adjustable fitness memory, meaning cells are able to inherit their parent cells fitness (i.e. their parents time to division/death) to a higher or lesser degree. Arbitrary treatments are able to be delivered to colonies at any time point, modifying their division and death distributions, and fitness memory. CloVarS is an important asset for quickly exploring colony fitness dynamics, testing biological hypotheses, bench-marking cell tracking algorithms, and ultimately improving our understanding of single-cell lineage data.