Transcriptional fluctuations govern the serum dependent cell cycle duration heterogeneities in Mammalian cells

Mammalian cells exhibit a high degree of intercellular variability in cell cycle period and phase durations. However, the factors orchestrating the cell cycle duration heterogeneities remain unclear. Herein, by combining cell cycle network-based mathematical models with live single-cell imaging studies under varied serum conditions, we demonstrate that fluctuating transcription rates of cell cycle regulatory genes across cell lineages and during cell cycle progression in mammalian cells majorly govern the robust correlation patterns of cell cycle period and phase durations among sister, cousin, and mother-daughter lineage pairs. However, for the overall cellular population, alteration in serum level modulates the fluctuation and correlation patterns of cell cycle period and phase durations in a correlated manner. These heterogeneities at the population level can be finetuned under limited serum conditions by perturbing the cell cycle network using a p38-signaling inhibitor without affecting the robust lineage level correlations. Overall, our approach identifies transcriptional fluctuations as the key controlling factor for the cell cycle duration heterogeneities, and predicts ways to reduce cell-to-cell variabilities by perturbing the cell cycle network regulations. Significance statementIn malignant tumors, cells display a diverse pattern in cell division time. This cell-to-cell variability in cell cycle duration had been observed even under culture conditions for various mammalian cells. Here we used live-cell imaging studies to monitor FUCCI-HeLa cells and quantified the cell cycle period and time spent in different phases under varied serum conditions. We proposed a set of stochastic cell cycle network-based mathematical models to investigate the live-cell imaging data and unraveled that the transcription rate variation across cell lineages and during cell cycle phases explains every aspect of the cell cycle duration variabilities. Our models identified how different deterministic effects and stochastic fluctuations control these variabilities and predicted ways to alter these cell cycle duration variabilities.