Hidden heterogeneity and circadian-controlled cell fate inferred from single cell lineages

Apoptotic signaling: Beyond cell death

Apoptosis is the best described form of regulated cell death, and was, until relatively recently, considered irreversible once particular biochemical points-of-no-return were activated. In this manuscript, we examine the mechanisms cells use to escape from a self-amplifying death signaling module. We discuss the role of feedback, dynamics, propagation, and noise in apoptotic signaling. We conclude with a revised model for the role of apoptosis in animal development, homeostasis, and disease.

p53 and p21 dynamics encode single-cell DNA damage levels, fine-tuning proliferation and shaping population heterogeneity

Cells must accurately and quickly detect DNA damage through a set of checkpoint mechanisms that enable repair and control proliferation. Heterogeneous levels of cellular stress and noisy signaling processes can lead to phenotypic variability but little is known about their role in underlying proliferation heterogeneity. Here we study two previously published single cell datasets and find that cells encode heterogeneous levels of endogenous and exogenous DNA damage to shape proliferation heterogeneity at the population level. Using a comprehensive time series analysis of short- and long-term signaling dynamics of p53 and p21, we show that DNA damage levels are quantitatively translated into p53 and p21 signal parameters in a gradual manner. Analyzing instantaneous proliferation and signaling differences among equally-radiated cells, we identify time-localized changes in the period of p53 pulses that drive cells out of a low proliferative state. Our findings suggest a novel role of the p53-p21 network in quantitatively encoding DNA damage strength and fine-tuning proliferation trajectories.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

Daughter cell fate choice instructed preemptively by mother cells facing nutrient limitation

Nutrients are vital to cellular activities, yet it is largely unknown how individual cells respond to nutrient deprivation. Live imaging results show that unlike the removal of amino acids or glutamine that immediately halts cell cycle progression, glucose withdrawal does not prevent cells from completing their current cycle. Although cells that begin to experience glucose withdrawal in S phase give rise to daughter cells with an equal choice of proliferation or quiescence, those enduring such experience in G1 phase give rise to daughter cells that predominantly enter quiescence. This fate choice difference stems from p21 protein accumulated during G2/M of the latter cells. Induced degradation of p21 permits daughter cells to enter S phase but with a consequent accumulation of DNA damage. These results suggest that mother cells that begin to experience glucose limitation in G1 phase take preemptive steps toward preventing daughter cells from making a harmful choice.

Toward a systems-level probing of tumor clonality

Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.

Association of circadian timing of initial infusions of immune checkpoint inhibitors with survival in advanced melanoma

Aims: Chronomodulation of immune checkpoint inhibitors (ICIs) is not well understood. The authors evaluated the circadian timing of initial ICI infusions. Patients & methods: A retrospective cohort study of patients with advanced melanoma (n = 121) was conducted. Results: Exclusive afternoon timing of the first four infusions was associated with worse overall survival (5.5 vs 24.9 months; p < 0.001) and progression-free survival (3.3 vs 7.6 months; p = 0.009) on Kaplan-Meier curves. In multivariable Cox analysis, the rate of overall survival was lower in patients who received all first four ICI infusions in the afternoon versus patients who received ≥1 of the first four infusions in the morning (hazard ratio: 2.4; p = 0.004). Conclusion: Deliberate morning scheduling for the first several ICI treatments may improve patient-centered outcomes.

FOXO nuclear shuttling dynamics are stimulus-dependent and correspond with cell fate

FOXO transcription factors are regulators of cellular homeostasis linked to increased lifespan and tumor suppression. FOXOs are activated by diverse cell stresses including serum starvation and oxidative stress. FOXO activity is regulated through posttranslational modifications that control shuttling of FOXO proteins to the nucleus. In the nucleus, FOXOs up-regulate genes in multiple, often conflicting pathways, including cell-cycle arrest and apoptosis. How cells control FOXO activity to ensure the proper response for a given stress is an open question. Using quantitative immunofluorescence and live-cell imaging, we found that the dynamics of FOXO nuclear shuttling is stimulus-dependent and corresponds with cell fate. H2O2 treatment leads to an all-or-none response where some cells show no nuclear FOXO accumulation, while other cells show a strong nuclear FOXO signal. The time that FOXO remains in the nucleus increases with the dose and is linked with cell death. In contrast, serum starvation causes low-amplitude pulses of nuclear FOXO and predominantly results in cell-cycle arrest. The accumulation of FOXO in the nucleus is linked with low AKT activity for both H2O2 and serum starvation. Our findings suggest the dynamics of FOXO nuclear shuttling is one way in which the FOXO pathway dictates different cellular outcomes.

The role of mitosis in generating fitness heterogeneity

Cancer cells have heterogeneous fitness, and this heterogeneity stems from genetic and epigenetic sources. Here, we sought to assess the contribution of asymmetric mitosis (AM) and time on the variability of fitness in sister cells. Around one quarter of sisters had differences in fitness, assessed as the intermitotic time (IMT), from 330 to 510 min. Phenotypes related to fitness, such as ERK activity (herein referring to ERK1 and ERK2, also known as MAPK3 and MAPK1, respectively), DNA damage and nuclear morphological phenotypes were also asymmetric at mitosis or turned asymmetric over the course of the cell cycle. The ERK activity of mother cell was found to influence the ERK activity and the IMT of the daughter cells, and cells with ERK asymmetry at mitosis produced more offspring with AMs, suggesting heritability of the AM phenotype for ERK activity. Our findings demonstrate how variabilities in sister cells can be generated, contributing to the phenotype heterogeneities in tumor cells.

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.

Toxicity of Orthodontic Brackets Examined by Single Cell Tracking

Subtle toxic effects may be masked in traditional assays that average or summate the response of thousands of cells. We overcome this by using the recent method of single cell tracking in time-lapse recordings. This follows the fate and behavior of individual cells and their progeny and provides unambiguous results for multiple simultaneous biological responses. Further, single cell tracking permits correlation between progeny relationships and cell behavior that is not otherwise possible, including disruption by toxins and toxicants of similarity between paired sister cells. Notably, single cell tracking seems not to have been previously used to study biomaterials toxicity. The culture medium was pre-conditioned by 79 days incubation with orthodontic brackets from seven separate commercial sources. Metal levels were determined by Inductively Coupled Plasma Mass Spectrometry. Metal levels varied amongst conditioned media, with elevated Cr, Mn, Ni, and Cu and often Mo, Pb, Zn, Pd, and Ag were occasionally found. The effect on human dermal fibroblasts was determined by single cell tracking. All bracket-conditioned media reduced cell division (p < 0.05), while some reduced cell migration (p < 0.05). Most bracket-conditioned media increased the rate of asynchronous sister cell division (p < 0.05), a seemingly novel measure for toxicity. No clear effect on cell morphology was seen. We conclude that orthodontic brackets have cytotoxic effects, and that single cell tracking is effective for the study of subtle biomaterials cytotoxicity.

Cell-Projection Pumping of Fibroblast Contents into Osteosarcoma SAOS-2 Cells Correlates with Increased SAOS-2 Proliferation and Migration, as well as Altered Morphology

We earlier reported that cell-projection pumping transfers fibroblast contents to cancer cells and this alters the cancer cell phenotype. Here, we report on single-cell tracking of time lapse recordings from co-cultured fluorescent fibroblasts and SAOS-2 osteosarcoma cells, tracking 5201 cells across 7 experiments. The fluorescent lipophilic marker DiD was used to label fibroblast organelles and to trace the transfer of fibroblast cytoplasm into SAOS-2 cells. We related SAOS-2 phenotypic change to levels of fluorescence transfer from fibroblasts to SAOS-2 cells, as well as what we term 'compensated fluorescence', that numerically projects mother cell fluorescence post-mitosis into daughter cells. The comparison of absolute with compensated fluorescence allowed us to deduct if the phenotypic effects in mother SAOS-2 cells were inherited by their daughters. SAOS-2 receipt of fibroblast fluorescence correlated by Kendall's tau with cell-profile area and without evidence of persistence in daughter cells (median tau = 0.51, p < 0.016); negatively and weakly with cell circularity and with evidence of persistence (median tau = -0.19, p < 0.05); and very weakly with cell migration velocity and without evidence of persistence (median tau = 0.01, p < 0.016). In addition, mitotic SAOS-2 cells had higher rates of prior fluorescence uptake (median = 64.9 units/day) than non-dividing cells (median = 35.6 units/day, p < 0.016) and there was no evidence of persistence post-mitosis. We conclude that there was an appreciable impact of cell-projection pumping on cancer cell phenotype relevant to cancer histopathological diagnosis, clinical spread and growth, with most effects being 'reset' by cancer cell mitosis.

Multi-omics integration in the age of million single-cell data

An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

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.

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.

The role of the circadian clock in cancer hallmark acquisition and immune-based cancer therapeutics

The circadian system temporally regulates physiology to maintain homeostasis. Co-opting and disrupting circadian signals appear to be distinct attributes that are functionally important for the development of a tumor and can enable or give rise to the hallmarks that tumors use to facilitate their initiation, growth and progression. Because circadian signals are also strong regulators of immune cell proliferation, trafficking and exhaustion states, they play a role in how tumors respond to immune-based cancer therapeutics. While immuno-oncology has heralded a paradigm shift in cancer therapeutics, greater accuracy is needed to increase our capability of predicting who will respond favorably to, or who is likely to experience the troubling adverse effects of, immunotherapy. Insights into circadian signals may further refine our understanding of biological determinants of response and help answer the fundamental question of whether certain perturbations in circadian signals interfere with the activity of immune checkpoint inhibitors. Here we review the body of literature highlighting circadian disruption as a cancer promoter and synthesize the burgeoning evidence suggesting circadian signals play a role in how tumors respond to immune-based anti-cancer therapeutics. The goal is to develop a framework to advance our understanding of the relationships between circadian markers, cancer biology, and immunotherapeutics. Bolstered by this new understanding, these relationships may then be pursued in future clinical studies to improve our ability to predict which patients will respond favorably to, and avoid the adverse effects of, traditional and immune-based cancer therapeutics.

Cell cycle inertia underlies a bifurcation in cell fates after DNA damage

The G₁-S checkpoint is thought to prevent cells with damaged DNA from entering S phase and replicating their DNA and efficiently arrests cells at the G₁-S transition. Here, using time-lapse imaging and single-cell tracking, we instead find that DNA damage leads to highly variable and divergent fate outcomes. Contrary to the textbook model that cells arrest at the G₁-S transition, cells triggering the DNA damage checkpoint in G₁ phase route back to quiescence, and this cellular rerouting can be initiated at any point in G₁ phase. Furthermore, we find that most of the cells receiving damage in G₁ phase actually fail to arrest and proceed through the G₁-S transition due to persistent cyclin-dependent kinase (CDK) activity in the interval between DNA damage and induction of the CDK inhibitor p21. These observations necessitate a revised model of DNA damage response in G₁ phase and indicate that cells have a G₁ checkpoint.

Cell Cycle Heritability and Localization Phase Transition in Growing Populations

The cell cycle duration is a variable cellular phenotype that underlies long-term population growth and age structures. By analyzing the stationary solutions of a branching process with heritable cell division times, we demonstrate the existence of a phase transition, which can be continuous or first order, by which a nonzero fraction of the population becomes localized at a minimal division time. Just below the transition, we demonstrate the coexistence of localized and delocalized age-structure phases and the power law decay of correlation functions. Above it, we observe the self-synchronization of cell cycles, collective divisions, and the slow "aging" of population growth rates.

The Making and Breaking of RNAs: Dynamics of Rhythmic RNA Expression in Mammals

The identification and characterization of rhythmically expressed mRNAs have been an active area of research over the past 20 years, as these mRNAs are believed to produce the daily rhythms in a wide range of biological processes. Circadian transcriptome studies have used mature mRNA as a primary readout and focused largely on rhythmic RNA synthesis as a regulatory mechanism underlying rhythmic mRNA expression. However, RNA synthesis, RNA degradation, or a combination of both must be rhythmic to drive rhythmic RNA profiles, and it is still unclear to what extent rhythmic synthesis leads to rhythmic RNA profiles. In addition, circadian RNA expression is also often tissue specific. Although a handful of genes cycle in all or most tissues, others are rhythmic only in certain tissues, even though the same core clock mechanism is believed to control the rhythmic RNA profiles in all tissues. This review focuses on the dynamics of rhythmic RNA synthesis and degradation and discusses how these steps collectively determine the rhythmicity, phase, and amplitude of RNA accumulation. In particular, we highlight a possible role of RNA degradation in driving tissue-specific RNA rhythms. By unifying findings from experimental and theoretical studies, we will provide a comprehensive overview of how rhythmic gene expression can be achieved and how each regulatory step contributes to tissue-specific circadian transcriptome output in mammals.

Circadian clock effects on cellular proliferation: Insights from theory and experiments

Oscillations of the cellular circadian clock have emerged as an important regulator of many physiological processes, both in health and in disease. One such process, cellular proliferation, is being increasingly recognized to be affected by the circadian clock. Here, we review how a combination of experimental and theoretical work has furthered our understanding of the way circadian clocks couple to the cell cycle and play a role in tissue homeostasis and cancer. Finally, we discuss recently introduced methods for modeling coupling of clocks based on techniques from survival analysis and machine learning and highlight their potential importance for future studies.

The Autonomic Nervous System Pulls the Strings to Coordinate Circadian HSC Functions

As for many other adult stem cells, the behavior of hematopoietic stem and progenitor cells (HSPCs) is subjected to circadian regulatory patterns. Multiple HSPC functions, such as proliferation, differentiation or trafficking exhibit time-dependent patterns that require a tight coordination to ensure daily blood cell production. The autonomic nervous system, together with circulating hormones, relay circadian signals from the central clock-the suprachiasmatic nucleus in the brain-to synchronize HSC niche physiology according to light/darkness cycles. Research over the last 20 years has revealed how specific neural signals modulate certain aspects of circadian HSC biology. However, only recently some studies have started to decipher the cellular and molecular mechanisms that orchestrate this complex regulation in a time-dependent fashion. Here we firstly review some of the recent key findings illustrating how different neural signals (catecholaminergic or cholinergic) regulate circadian HSC egress, homing, maintenance, proliferation, and differentiation. In particular, we highlight the critical role of different neurotransmitter receptors in the bone marrow microenvironment to channel these neural signals and regulate antagonistic processes according to circadian cues and organismal demands. Then, we discuss the potential biological meaning of HSC circadian regulation and its possible utility for clinical purposes. Finally, we offer our perspective on emerging concepts in HSC chronobiology.

Replicational Dilution of H3K27me3 in Mammalian Cells and the Role of Poised Promoters

Polycomb repressive complex 2 (PRC2) places H3K27me3 at developmental genes and is causally implicated in keeping bivalent genes silent. It is unclear if that silence requires minimum H3K27me3 levels and how the mark transmits faithfully across mammalian somatic cell generations. Mouse intestinal cells lacking EZH2 methyltransferase reduce H3K27me3 proportionately at all PRC2 target sites, but ∼40% uniform residual levels keep target genes inactive. These genes, derepressed in PRC2-null villus cells, remain silent in intestinal stem cells (ISCs). Quantitative chromatin immunoprecipitation and computational modeling indicate that because unmodified histones dilute H3K27me3 by 50% each time DNA replicates, PRC2-deficient ISCs initially retain sufficient H3K27me3 to avoid gene derepression. EZH2 mutant human lymphoma cells also require multiple divisions before H3K27me3 dilution relieves gene silencing. In both cell types, promoters with high basal H3K4me2/3 activate in spite of some residual H3K27me3, compared to less-poised promoters. These findings have implications for PRC2 inhibition in cancer therapy.

The effects of proliferation status and cell cycle phase on the responses of single cells to chemotherapy

DNA-damaging chemotherapeutics are widely used in cancer treatments, but for solid tumors they often leave a residual tumor-cell population. Here we investigated how cellular states might affect the response of individual cells in a clonal population to cisplatin, a DNA-damaging chemotherapeutic agent. Using a live-cell reporter of cell cycle phase and long-term imaging, we monitored single-cell proliferation before, at the time of, and after treatment. We found that in response to cisplatin, cells either arrested or died, and the ratio of these outcomes depended on the dose. While we found that the cell cycle phase at the time of cisplatin addition was not predictive of outcome, the proliferative history of the cell was: highly proliferative cells were more likely to arrest than to die, whereas slowly proliferating cells showed a higher probability of death. Information theory analysis revealed that the dose of cisplatin had the greatest influence on the cells’ decisions to arrest or die, and that the proliferation status interacted with the cisplatin dose to further guide this decision. These results show an unexpected effect of proliferation status in regulating responses to cisplatin and suggest that slowly proliferating cells within tumors may be acutely vulnerable to chemotherapy.

Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing

Fractional killing, which is a significant impediment to successful chemotherapy, is observed even in a population of genetically identical cancer cells exposed to apoptosis-inducing agents. This phenomenon arises not from genetic mutation but from cell-to-cell variation in the activation timing and level of the proteins that regulates apoptosis. To understand the mechanism behind the phenomenon, we formulate complex fractional killing processes as a first-passage time (FPT) problem with a stochastically fluctuating boundary. Analytical calculations are performed for the FPT distribution in a toy model of stochastic p53 gene expression, where the cancer cell is killed only when the p53 expression level crosses an active apoptotic threshold. Counterintuitively, we find that threshold fluctuations can effectively enhance cellular killing by significantly decreasing the mean time that the p53 protein reaches the threshold level for the first time. Moreover, faster fluctuations lead to the killing of more cells. These qualitative results imply that fluctuations in threshold are a non-negligible stochastic source, and can be taken as a strategy for combating fractional killing of cancer cells.

Hidden long-range memories of growth and cycle speed correlate cell cycles in lineage trees

Cell heterogeneity may be caused by stochastic or deterministic effects. The inheritance of regulators through cell division is a key deterministic force, but identifying inheritance effects in a systematic manner has been challenging. Here, we measure and analyze cell cycles in deep lineage trees of human cancer cells and mouse embryonic stem cells and develop a statistical framework to infer underlying rules of inheritance. The observed long-range intra-generational correlations in cell-cycle duration, up to second cousins, seem paradoxical because ancestral correlations decay rapidly. However, this correlation pattern is naturally explained by the inheritance of both cell size and cell-cycle speed over several generations, provided that cell growth and division are coupled through a minimum-size checkpoint. This model correctly predicts the effects of inhibiting cell growth or cycle progression. In sum, we show how fluctuations of cell cycles across lineage trees help in understanding the coordination of cell growth and division.

Anticancer Properties of Curcumin and Interactions With the Circadian Timing System

The phytochemical curcumin is a major component of turmeric. It has recognized activity against cancer cells and affects several intracellular signaling pathways. Many molecules targeted by curcumin also regulate the circadian timing system that has effects on carcinogenesis, tumor growth, and metastasis. Although the circadian clock within cells may be suppressed in tumors, cancer cells are subjected to daily hormonal and neural activity that should be considered when timing optimal curcumin treatments. Rapid curcumin degradation in blood and tissues provides a challenge to maintaining sustained levels suitable for inducing cancer cell death, increasing the need to identify when during the circadian cycle rhythmically expressed molecular targets are present. Curcumin is well tolerated by individuals ingesting it for possible cancer prevention or in combination with conventional cancer therapies, and it shows low toxicity toward noncancerous cells at low dosages. In contrast, curcumin is particularly effective against cancer stem cells, which are treatment-resistant, aggressive, and tumor-initiating. Although curcumin has poor bioavailability, more stable curcumin analogs retain the anti-inflammatory, antioxidant, antimitotic, and pro-apoptotic benefits of curcumin. Anticancer properties are also present in congeners of curcumin in turmeric and after curcumin reduction by intestinal microbes. Various commercial curcuminoid products are highly popular dietary supplements, but caution is warranted. Although antioxidant properties of curcumin may prevent carcinogenesis, studies suggest curcumin interferes with certain chemotherapeutic agents. This review delves into the complex network of curcuminoid effects to identify potential anticancer strategies that may work in concert with daily physiological cycles controlled by the circadian timing system.

Designing combination therapies with modeling chaperoned machine learning

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.