Mathematical modelling reveals unexpected inheritance and variability patterns of cell cycle parameters in mammalian cells

Combining lineage correlations and a small molecule inhibitor to detect circadian control of the cell cycle

Chronotherapy offers an exciting possibility for improving cancer treatments by leveraging the influence of the circadian clock on the cell cycle. While several molecular interactions coupling the two oscillators have been identified, whether they lead to emergent control of cellular proliferation remains unclear. Using stochastic simulations, we demonstrate that the established gene networks underlying the two oscillators are sufficient to generate lineage correlations in cell cycle times, as observed in single-cell microscopy data. The interactions also create a 'therapeutic window' between cancer and normal cell proliferation peaks that can be leveraged for chronotherapy. Surprisingly, our model predicts that KL001, a clock inhibitor, minimally affects population growth but significantly alters lineage correlations. Our results suggest that clock control of the cell cycle may not be detectable by measuring changes in population dynamics, but combining measurements of lineage correlations with KL001 treatment may provide a more sensitive approach to detecting the coupling.

Interdependency of estradiol-mediated ERα activation and subsequent PR and GREB1 induction to control cell cycle progression

Various groups of chemicals that we encounter in every-day life are known to disrupt the endocrine system, such as estrogen mimics that can disturb normal cellular development and homeostasis. To understand the effect of estrogen on intracellular protein dynamics and how this relates to cell proliferation, we aimed to develop a quantitative description of transcription factor complexes and their regulation of cell cycle progression in response to estrogenic stimulation. We designed a mathematical model that describes the dynamics of three proteins, GREB1, PR and TFF1, that are transcriptionally activated upon binding of 17β-estradiol (E2) to estrogen receptor alpha (ERα). Calibration of this model to imaging data monitoring the expression dynamics of these proteins in MCF7 cells suggests that transcriptional activation of GREB1 and PR depends on the association of the E2-ERα complex with both GREB1 and PR. We subsequently combined this ER signaling model with a previously published cell cycle model and compared this to quantification of cell cycle durations in MCF7 cells following nuclei tracking based on images segmented with deep neural networks. The resulting model predicts the effect of GREB1 and PR knockdown on cell cycle progression, thus providing mechanistic insight in the molecular interactions between ERα-regulated proteins and their relation to cell cycle progression. Our findings form a valuable basis to further investigate the pharmacodynamics of endocrine disrupting chemicals and their influence on cellular behavior.

Modeling the Circadian Control of the Cell Cycle and Its Consequences for Cancer Chronotherapy

The mammalian cell cycle is governed by a network of cyclin/Cdk complexes which signal the progression into the successive phases of the cell division cycle. Once coupled to the circadian clock, this network produces oscillations with a 24 h period such that the progression into each phase of the cell cycle is synchronized to the day-night cycle. Here, we use a computational model for the circadian clock control of the cell cycle to investigate the entrainment in a population of cells characterized by some variability in the kinetic parameters. Our numerical simulations showed that successful entrainment and synchronization are only possible with a sufficient circadian amplitude and an autonomous period close to 24 h. Cellular heterogeneity, however, introduces some variability in the entrainment phase of the cells. Many cancer cells have a disrupted clock or compromised clock control. In these conditions, the cell cycle runs independently of the circadian clock, leading to a lack of synchronization of cancer cells. When the coupling is weak, entrainment is largely impacted, but cells maintain a tendency to divide at specific times of day. These differential entrainment features between healthy and cancer cells can be exploited to optimize the timing of anti-cancer drug administration in order to minimize their toxicity and to maximize their efficacy. We then used our model to simulate such chronotherapeutic treatments and to predict the optimal timing for anti-cancer drugs targeting specific phases of the cell cycle. Although qualitative, the model highlights the need to better characterize cellular heterogeneity and synchronization in cell populations as well as their consequences for circadian entrainment in order to design successful chronopharmacological protocols.

Characterization of cell-to-cell variation in nuclear transport rates and identification of its sources

Nuclear transport is an essential part of eukaryotic cell function. Here, we present scFRAP, a model-assisted fluorescent recovery after photobleaching (FRAP)- based method to determine nuclear import and export rates independently in individual live cells. To overcome the inherent noise of single-cell measurements, we performed sequential FRAPs on the same cell. We found large cell-to-cell variation in transport rates within isogenic yeast populations. For passive transport, the variability in NPC number might explain most of the variability. Using this approach, we studied mother-daughter cell asymmetry in the active nuclear shuttling of the transcription factor Ace2, which is specifically concentrated in daughter cell nuclei in early G1. Rather than reduced export in the daughter cell, as previously hypothesized, we found that this asymmetry is mainly due to an increased import in daughters. These results shed light on cell-to-cell variation in cellular dynamics and its sources.

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 the 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 fine-tuned 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.

Patterns of interdivision time correlations reveal hidden cell cycle factors

The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator, and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.

Single-cell imaging of the cell cycle reveals CDC25B-induced heterogeneity of G1 phase length in neural progenitor cells

Although lengthening of the cell cycle and G1 phase is a generic feature of tissue maturation during development, the underlying mechanism remains poorly understood. Here, we develop a time-lapse imaging strategy to measure the four cell cycle phases in single chick neural progenitor cells in their endogenous environment. We show that neural progenitors are widely heterogeneous with respect to cell cycle length. This variability in duration is distributed over all phases of the cell cycle, with the G1 phase contributing the most. Within one cell cycle, each phase duration appears stochastic and independent except for a correlation between S and M phase duration. Lineage analysis indicates that the majority of daughter cells may have a longer G1 phase than mother cells, suggesting that, at each cell cycle, a mechanism lengthens the G1 phase. We identify that the CDC25B phosphatase known to regulate the G2/M transition indirectly increases the duration of the G1 phase, partly through delaying passage through the restriction point. We propose that CDC25B increases the heterogeneity of G1 phase length, revealing a previously undescribed mechanism of G1 lengthening that is associated with tissue development.

Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, arising from the interplay of deterministic and stochastic processes. However, it remains challenging to quantify single-cell behaviour from time-lapse microscopy data, owing to the difficulty of extracting reliable cell trajectories and lineage information over long time-scales and across several generations. Therefore, we developed a hybrid deep learning and Bayesian cell tracking approach to reconstruct lineage trees from live-cell microscopy data. We implemented a residual U-Net model coupled with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, we developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data. Using our approach, we extracted 20,000 + fully annotated single-cell trajectories from over 3,500 h of video footage, organised into multi-generational lineage trees spanning up to eight generations and fourth cousin distances. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results with our data, with minimal requirement for manual curation. To demonstrate the robustness of our minimally supervised cell tracking methodology, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships in 5,000 + fully annotated cell lineages. We observe vanishing cycle duration correlations across ancestral relatives, yet reveal correlated cyclings between cells sharing the same generation in extended lineages. These findings expand the depth and breadth of investigated cell lineage relationships in approximately two orders of magnitude more data than in previous studies of cell cycle heritability, which were reliant on semi-manual lineage data analysis.

Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns

Regulation of quiescence and cell cycle entry is pivotal for the maintenance of stem cell populations. Regulatory mechanisms, however, are poorly understood. In particular, it is unclear how the activity of single stem cells is coordinated within the population or if cells divide in a purely random fashion. We addressed this issue by analyzing division events in an adult neural stem cell (NSC) population of the zebrafish telencephalon. Spatial statistics and mathematical modeling of over 80,000 NSCs in 36 brain hemispheres revealed weakly aggregated, nonrandom division patterns in space and time. Analyzing divisions at 2 time points allowed us to infer cell cycle and S-phase lengths computationally. Interestingly, we observed rapid cell cycle reentries in roughly 15% of newly born NSCs. In agent-based simulations of NSC populations, this redividing activity sufficed to induce aggregated spatiotemporal division patterns that matched the ones observed experimentally. In contrast, omitting redivisions leads to a random spatiotemporal distribution of dividing cells. Spatiotemporal aggregation of dividing stem cells can thus emerge solely from the cells’ history.

An interdisciplinary study of the rules governing cell divisions in a population of neural stem cells in the zebrafish brain reveals the existence of aggregated spatio-temporal division patterns of rapid cell cycles in stem cells, and shows that these patterns can be explained by a simple agent-based model relying solely on the cells‘ division history.

Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli

Regulation of future RNA and protein numbers is a key process by which cells continuously best fit the environment. In bacteria, RNA and proteins exist in small numbers and their regulatory processes are stochastic. Consequently, there is cell-to-cell variability in these numbers, even between sister cells. Traditionally, the two most studied sources of this variability are gene expression and RNA and protein degradation, with evidence suggesting that the latter is subject to little regulation, when compared to the former. However, time-lapse microscopy and single molecule fluorescent tagging have produced evidence that cell division can also be a significant source of variability due to asymmetries in the partitioning of RNA and proteins. Relevantly, the impact of this noise differs from noise in production and degradation since, unlike these, it is not continuous. Rather, it occurs at specific time points, at which moment it can introduce major fluctuations. Several models have now been proposed that integrate noise from cell division, in addition to noise in gene expression, to mimic the dynamics of RNA and protein numbers of cell lineages. This is expected to be particularly relevant in genetic circuits, where significant fluctuations in one component protein, at specific time moments, are expected to perturb near-equilibrium states of the circuits, which can have long-lasting consequences. Here we review stochastic models coupling these processes in Escherichia coli, from single genes to small circuits.

Stochastic cell renewal process and lengthening of cell cycle

Evolution of the stem cell population responsible for homeostatic cell renewal processes is analyzed. We assume that this regime is the product of a delicate balance between symmetric divisions that, after each cell cycle, originates a new stem cell or its disappearance (through cell differentiation). This dynamics leads to a monoclonal population, that is for an initial homogeneous set of stem cells, fixation of each clone is equiprobable. In this work we show that if there is an altered stem cell with a longer cell cycle than the rest, the fixation of this altered clone is more likely. We also study the consequeces of the appearance of successive alterations with these characteristics and their fixations. This effect is purely due to inherent characteristics of the cell renewal dynamics and as time goes by it leads to a quiescence state for stem cells owing to the recurrent fixation of such altered cells. Therefore it would contribute to the aging process.