Mathematical models incorporating a multi-stage cell cycle replicate normally-hidden inherent synchronization in cell proliferation

Abstract

<jats:p>We present a suite of experimental data showing that cell proliferation assays, prepared using standard methods thought to produce asynchronous cell populations, persistently exhibit inherent synchronization. Our experiments use fluorescent cell cycle indicators to reveal the normally hidden cell synchronization, by highlighting oscillatory subpopulations within the total cell population. These oscillatory subpopulations would never be observed without these cell cycle indicators. On the other hand, our experimental data show that the total cell population appears to grow exponentially, as in an asynchronous population. We reconcile these seemingly inconsistent observations by employing a multi-stage mathematical model of cell proliferation that can replicate the oscillatory subpopulations. Our study has important implications for understanding and improving experimental reproducibility. In particular, inherent synchronization may affect the experimental reproducibility of studies aiming to investigate cell cycle-dependent mechanisms, including changes in migration and drug response.</jats:p>

Synchronised oscillations in growing cell populations are explained by demographic noise

“…In this work we highlight the importance of demographic noise to the early dynamics of growing populations with non-exponentially distributed generation times. We demonstrate that finitesample effects can recapitulate the synchronisation in the cell-cycle phase that we previously observed [Vittadello et al, 2019]. To provide insight in these observations we adopt a multistage approach to model both the total cell-cycle time distribution and the distribution of the G1 duration and we derive both a deterministic and a stochastic representation for the time evolution of the ratio Q.…”

“…Due to recent technological advances, we are now able to access accurate data revealing the structure of dynamic cell populations [Chao et al, 2018 using, amongst other tools, proliferation assays: an in vitro experimental protocol in which the growth of cell populations is monitored over time [Riss et al, 2016]. In particular, in recent work [Vittadello et al, 2019], we assayed the proliferation of melanoma cells labelled with FUCCI (Fluorescent Ubiquitous Cell Cycle Indicator [Sakaue-Sawano et al, 2008, Haass et al, 2014] -see Figure 1 and Section 4.2 of Materials and Methods) which allowed us to track the number of cells in particular phases of the cell cycle over a timespan of 48 hours (h). Strikingly, the proportion of cells in the first phase of the cell-cycle, gap 1 (G1), demonstrates clear and unexpected fluctuations during the entire duration of the experiment.…”

“…Inspired by our experimental findings, we build a multi-stage mathematical model for cell proliferation which represents the cell-cycle as a series of discrete stages [Vittadello et al, 2019].…”

“…Inspired by our experimental findings, we build a multi-stage mathematical model for cell proliferation which represents the cell-cycle as a series of discrete stages [16]. The waiting time distribution between consecutive stages is exponential, meaning that the cell cycle time (CCT) follows a more general class of distributions, known as hypoexponential Yates et al .…”

Examining go-or-grow using fluorescent cell-cycle indicators and cell cycle-inhibiting drugs

“…Our data consist of time-series images, acquired every 15 min for 48 h, from 2-D proliferation assays using the melanoma cell lines C8161, WM983C and 1205Lu [6,20,28,29], which have respective mean cell-cycle durations of approximately 21, 23 and 37 h [6]. The cell lines have very different ratios of durations in G1 to S/G2/M (Supplementary Material).…”