Impact of Variability in Cell Cycle Periodicity on Cell Population Dynamics

Cell cycle synchronization has been pivotal in the development of our understanding of cell population dynamics. Intriguingly, when cells are released from a synchronized state, they do not maintain synchronized cell division and rapidly become asynchronous. Here, using a combination of experiments and model simulations, we investigate this process of "cell cycle desynchronization" in cervical cancer cells (HeLa) that are arrested at the G1/S boundary. We tracked DNA content overtime at regular intervals to monitor cell cycle progression and developed a custom auto-similarity function to quantify the convergence to asynchronicity. In parallel, using experimental data, we developed a single-cell phenomenological model that returns DNA concentration across the cell cycle stages from a desynchronizing cell population. Our simulations revealed that desynchronization is primarily sensitive to cell cycle variability. We tested this prediction by introducing lipopolysaccharide to increase cellular noise, which resulted in greater cell cycle variability with an enhanced rate of desynchronization. Our results show that the desynchronization rate of cell populations can be used a proxy of the degree of variance in cell cycle periodicity.

CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data

Summary Recent advancements in high-dimensional single-cell technologies, such as mass cytometry, enable longitudinal experiments to track dynamics of cell populations and identify change points where the proportions vary significantly. However, current research is limited by the lack of tools specialized for analyzing longitudinal mass cytometry data. In order to infer cell population dynamics from such data, we developed a statistical framework named CYBERTRACK2.0. The framework’s analytic performance was validated against synthetic and real data, showing that its results are consistent with previous research. Availability and implementation CYBERTRACK2.0 is available at https://github.com/kodaim1115/CYBERTRACK2. Supplementary information Supplementary data are available at Bioinformatics online.