Single-cell approaches to cell competition: High-throughput imaging, machine learning and simulations.

Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either by biochemical signaling or by mechanical forces within the tissue. In both cases, competition has generally been characterized based on the elimination of loser cells at the population level, but significantly less attention has been focused on determining how single-cell dynamics and interactions regulate population-wide changes. In this review, we describe quantitative strategies and outline the outstanding challenges in understanding the single cell rules governing tissue-scale competition dynamics. We propose quantitative metrics to characterize single cell behaviors in competition and use them to distinguish the types and outcomes of competition. We describe how such metrics can be measured experimentally using a novel combination of high-throughput imaging and machine learning algorithms. We outline the experimental challenges to quantify cell fate dynamics with high-statistical precision, and describe the utility of computational modeling in testing hypotheses not easily accessible in experiments. In particular, cell-based modeling approaches that combine mechanical interaction of cells with decision-making rules for cell fate choices provide a powerful framework to understand and reverse-engineer the diverse rules of cell competition.

Cell-scale biophysical determinants of cell competition in epithelia.

How cells with different genetic makeups compete in tissues is an outstanding question in developmental biology and cancer research. Studies in recent years have revealed that cell competition can either be driven by short-range biochemical signalling or by long-range mechanical stresses in the tissue. To date, cell competition has generally been characterised at the population scale, leaving the single-cell-level mechanisms of competition elusive. Here, we use high time-resolution experimental data to construct a multi-scale agent-based model for epithelial cell competition and use it to gain a conceptual understanding of the cellular factors that governs competition in cell populations within tissues. We find that a key determinant of mechanical competition is the difference in homeostatic density between winners and losers, while differences in growth rates and tissue organisation do not affect competition end result. In contrast, the outcome and kinetics of biochemical competition is strongly influenced by local tissue organisation. Indeed, when loser cells are homogenously mixed with winners at the onset of competition, they are eradicated; however, when they are spatially separated, winner and loser cells coexist for long times. These findings suggest distinct biophysical origins for mechanical and biochemical modes of cell competition.

Polarization of Myosin II Refines Tissue Material Properties to Buffer Mechanical Stress.

As tissues develop, they are subjected to a variety of mechanical forces. Some of these forces are instrumental in the development of tissues, while others can result in tissue damage. Despite our extensive understanding of force-guided morphogenesis, we have only a limited understanding of how tissues prevent further morphogenesis once the shape is determined after development. Here, through the development of a tissue-stretching device, we uncover a mechanosensitive pathway that regulates tissue responses to mechanical stress through the polarization of actomyosin across the tissue. We show that stretch induces the formation of linear multicellular actomyosin cables, which depend on Diaphanous for their nucleation. These stiffen the epithelium, limiting further changes in shape, and prevent fractures from propagating across the tissue. Overall, this mechanism of force-induced changes in tissue mechanical properties provides a general model of force buffering that serves to preserve the shape of tissues under conditions of mechanical stress.

Local cellular neighborhood controls proliferation in cell competition.

Cell competition is a quality-control mechanism through which tissues eliminate unfit cells. Cell competition can result from short-range biochemical inductions or long-range mechanical cues. However, little is known about how cell-scale interactions give rise to population shifts in tissues, due to the lack of experimental and computational tools to efficiently characterize interactions at the single-cell level. Here, we address these challenges by combining long-term automated microscopy with deep-learning image analysis to decipher how single-cell behavior determines tissue makeup during competition. Using our high-throughput analysis pipeline, we show that competitive interactions between MDCK wild-type cells and cells depleted of the polarity protein scribble are governed by differential sensitivity to local density and the cell type of each cell's neighbors. We find that local density has a dramatic effect on the rate of division and apoptosis under competitive conditions. Strikingly, our analysis reveals that proliferation of the winner cells is up-regulated in neighborhoods mostly populated by loser cells. These data suggest that tissue-scale population shifts are strongly affected by cellular-scale tissue organization. We present a quantitative mathematical model that demonstrates the effect of neighbor cell-type dependence of apoptosis and division in determining the fitness of competing cell lines.

ROS Production and Scavenging under Anoxia and Re-Oxygenation in Arabidopsis Cells: A Balance between Redox Signaling and Impairment.

Plants can frequently experience low oxygen concentrations due to environmental factors such as flooding or waterlogging. It has been reported that both anoxia and the transition from anoxia to re-oxygenation determine a strong imbalance in the cellular redox state involving the production of reactive oxygen species (ROS) and nitric oxide (NO). Plant cell cultures can be a suitable system to study the response to oxygen deprivation stress since a close control of physicochemical parameters is available when using bioreactors. For this purpose, Arabidopsis cell suspension cultures grown in a stirred bioreactor were subjected to a severe anoxic stress and analyzed during anoxia and re-oxygenation for alteration in ROS and NO as well as in antioxidant enzymes and metabolites. The results obtained by confocal microscopy showed the dramatic increase of ROS, H2O2, and NO during the anoxic shock. All the ascorbate-glutathione related parameters were altered during anoxia but restored during re-oxygenation. Anoxia also induced a slight but significant increase of α-tocopherol levels measured at the end of the treatment. Overall, the evaluation of cell defenses during anoxia and re-oxygenation in Arabidopsis cell cultures revealed that the immediate response involving the overproduction of reactive species activated the antioxidant machinery including ascorbate-glutathione system, α-tocopherol and the ROS-scavenging enzymes ascorbate peroxidase, catalase, and peroxidase making cells able to counteract the stress toward cell survival.

Towards pancreatic cancer diagnosis using EIS biochips.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal cancers in Europe and the United States. It has a very low 5 years-survival rate and its diagnosis is often late and imprecise due to the lack of specificity of currently used markers for PDAC. As previously demonstrated PDAC patients' sera may contain autoantibodies towards phosphorylated α-enolase (ENOA), which in combination with other standard markers can increase specificity in diagnosis of PDAC. In this context we realized a microfluidic platform with integrated EIS biosensors. We achieved a specific antibodies detection by immobilizing onto electrodes peptides corresponding to a portion of ENOA. Phosphorylation of peptides was found to influence the recognition of antibodies in PDAC patients' sera detected by the developed biochip thus validating the EIS technique as a strong tool for quick, cost-saving and label-free analysis of serum samples. Biochip results are in agreement with those from traditional techniques, such as ELISA and western blot, but measurements are much more sensitive and specific, increasing the possibility of PDAC diagnosis. In addition this approach is faster and more reproducible compared to traditional techniques making the developed biochips ideal for a quick, cost-saving and label-free analysis of serum samples.