Cell competition is a quality-control mechanism through which tissues eliminate unfit cells. Automated microscopy with deep-learning image analysis was used to measure single-cell behavior during competition. Strikingly, the single-cell analysis reveals that tissue-scale population shifts are strongly affected by cellular-scale tissue organization.

SummaryAs 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.

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.

As tissues develop, they are subjected to a variety of mechanical forces. Some of these forces, such as those required for morphogenetic movements, are instrumental to the development and sculpting of tissues. However, mechanical forces can also lead to accumulation of substantial tensile stress, which if maintained, can result in tissue damage and impair tissue function.Despite our extensive understanding of force-guided morphogenesis, we have Results MyoII is essential for setting tissue stiffness and elasticity.Cell shape is defined by the balance of forces exerted on cells through the external environment (such as cell-cell and cell-ECM adhesion) and the forces exerted by intracellular cell components such as the actomyosin cortex (Mao and Baum, 2015). Therefore, the pathways controlling these processes are likely to be critical in responses to mechanical stress. We focused on the nonmuscle Myosin II (MyoII) contractility pathway, as MyoII had been shown to be recruited to the cell cortex in force-driven morphogenetic processes such as mesoderm invagination in gastrulation as well as by deformation applied through micropipette aspiration (Fernandez-Gonzalez et al., 2009;Pouille et We are grateful to Nic Tapon, in whose lab the development of the stretching device was initiated, for his support. We thank GREM (Griffon Gravure) for building the first prototype of stretching/compression device. We thank Duncan Farquharson, Simon Townsend, Piotr Sienkiewicz from Mechanical and Electronic Workshops at University College London for design and execution of final version of stretching/compression device. We thank Davide Heller for help with junctional myosin intensity measurements. We thank John Zhang for his help drawing the photo masks, and JDPhoto-Tools for printing.

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.

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.

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.

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 two fundamental mechanisms of cell competition, driven by short-range biochemical signalling or by long-range mechanical stresses within the tissue. In both scenarios, the outcome of cell competition has generally been characterised using population-scale metrics. However, the underlying strategies for competitive interactions at the single-cell level remain elusive.Here, we develop a cell-based computational model for competition assays informed by highthroughput timelapse imaging experiments. By integrating physical cell interactions with cellular automata rules for proliferation and apoptosis, we find that the emergent modes of cell competition are determined by a trade-off between entropic and energetic properties of the mixed tissue. While biochemical competition is strongly sensitive to local tissue organisation, mechanical competition is largely driven by the difference in homeostatic pressures of the two competing cell types. These findings suggest that competitive cell interactions arise when the local tissue free energy is high, and proceed until free energy is minimised. RESULTS Cell-based model for competition.Our cell-based model for competition consists of two distinct computational layers that simulate:(i) mechanical interactions between cells and the underlying substrate, and (ii) a cellular automaton that makes decisions for cell growth, mitosis and apoptosis ( Figure 1A). Physical interactions at the cell-cell and cell-substrate interfaces are simulated using the Cellular Potts Model [21] (Methods).This implementation was preferred to the less computationally costly vertex model [22], because we calibrate our model to our in vitro competition experiments [15] that start from a sub-confluent state. In the Potts model, each cell type is assigned a value of adhesion energy with other cells and the substrate, as well as a preferred (target) area, A T , and a compressibility modulus, λ. While the balance of forces between adhesion and elasticity determines equilibrium cell shapes, changes in cell size during growth, division and apoptosis are controlled by a second computational layer comprising cell automata rules. It is in this layer that cellular decision-making is implemented at