Deep learning reveals a damage signalling hierarchy that coordinates different cell behaviours driving wound re-epithelialisation.

One of the key tissue movements driving closure of a wound is re-epithelialisation. Earlier wound healing studies describe the dynamic cell behaviours that contribute to wound re-epithelialisation, including cell division, cell shape changes and cell migration, as well as the signals that might regulate these cell behaviours. Here, we have used a series of deep learning tools to quantify the contributions of each of these cell behaviours from movies of repairing wounds in the Drosophila pupal wing epithelium. We test how each is altered after knockdown of the conserved wound repair signals Ca2+ and JNK, as well as after ablation of macrophages that supply growth factor signals believed to orchestrate aspects of the repair process. Our genetic perturbation experiments provide quantifiable insights regarding how these wound signals impact cell behaviours. We find that Ca2+ signalling is a master regulator required for all contributing cell behaviours; JNK signalling primarily drives cell shape changes and divisions, whereas signals from macrophages largely regulate cell migration and proliferation. Our studies show deep learning to be a valuable tool for unravelling complex signalling hierarchies underlying tissue repair.

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using deep learning. Hence, we develop a pipeline of deep learning models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge.

Telomere dynamics and oxidative stress in Arabidopsis grown in lunar regolith simulant.

NASA envisions a future where humans establish a thriving colony on the Moon by 2050. Plants will be essential for this endeavor, but little is known about their adaptation to extraterrestrial bodies. The capacity to grow plants in lunar regolith would represent a major step towards this goal by minimizing the reliance on resources transported from Earth. Recent studies reveal that Arabidopsis thaliana can germinate and grow on genuine lunar regolith as well as on lunar regolith simulant. However, plants arrest in vegetative development and activate a variety of stress response pathways, most notably the oxidative stress response. Telomeres are hotspots for oxidative damage in the genome and a marker of fitness in many organisms. Here we examine A. thaliana growth on a lunar regolith simulant and the impact of this resource on plant physiology and on telomere dynamics, telomerase enzyme activity and genome oxidation. We report that plants successfully set seed and generate a viable second plant generation if the lunar regolith simulant is pre-washed with an antioxidant cocktail. However, plants sustain a higher degree of genome oxidation and decreased biomass relative to conventional Earth soil cultivation. Moreover, telomerase activity substantially declines and telomeres shorten in plants grown in lunar regolith simulant, implying that genome integrity may not be sustainable over the long-term. Overcoming these challenges will be an important goal in ensuring success on the lunar frontier.

What good is maths in studies of wound healing?

Wound healing is an aspect of normal physiology that we all take for granted until it goes wrong, such as, for example, the scarring that results from a severe burn, or those patients who suffer from debilitating chronic wounds that fail to heal. Ever since wound repair research began as a discipline, clinicians and basic scientists have collaborated to try and understand the cell and molecular mechanisms that underpin healthy repair in the hope that this will reveal clues for the therapeutic treatment of pathological healing. In recent decades mathematicians and physicists have begun to join in with this important challenge. Here we describe examples of how mathematical modeling married to biological experimentation has provided insights that biology alone could not fathom. To date, these studies have largely focused on wound re-epithelialization and inflammation, but we also discuss other components of wound healing that might be ripe for similar interdisciplinary approaches.