Biology subjects

Weavers, H.

Publications and source records attributed to Weavers, H..

3 recordsLinked to original sources

bioRxiv2026

Orphan GPR84 facilitates uropod de-adhesion to terminate leukocyte diapedesis during inflammation

Leukocyte migration through venular walls is an essential component of effective immunity. While the key molecular players driving the initial steps of this response are known, the terminating signals remain unclear. Here, we have identified a conserved role for GPR84 family GPCRs in successful completion of the final stages of leukocyte extravasation through acutely inflamed vessels. The integration of high resolution intravital imaging with cell-specific genetics revealed that genetic deficiency of GPR84 orthologues in mice and Drosophila, results in defective detachment of transmigrating immune cells from vessel walls. Mechanistically, transcriptomics revealed GPR84-deficient neutrophils exhibit defective actin cytoskeletal regulation and cellular adhesion/de-adhesion. Consistent with this, our fly-murine pipeline shows that GPR84 supports localized and dynamic Rho activation to enable detachment of the immune cell uropod from vessel exit sites. Moreover, pharmacological blockade of GPR84 signaling dampened immune cell migration in multiple murine acute inflammatory settings. Collectively, our findings present GPR84 as a novel physiological regulator of immune cell extravasation that is amenable to therapeutic targeting for modulating leukocyte infiltration into inflamed tissues. SummaryHere we identify the GPR84 family of GPCRs as key regulators of effective immune cell extravasation in vivo. Mechanistically, through integrating genetically tractable Drosophila and murine in vivo models, we show how leukocyte GPR84 supports dynamic Rho-dependent detachment of stretched uropods as these cells exit vessels.

immunology↗

bioRxiv2024

AI 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 have described 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 use 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 following knockdown of the conserved wound repair signals, Ca2+ and JNK, as well as ablation of macrophages which 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 Ca signalling is a master regulator required for all contributing cell behaviours; JNK signalling primarily drives cell shape changes and divisions, whereas signals from macrophages regulate largely cell migration and proliferation. Our studies show AI to be a valuable tool for unravelling complex signalling hierarchies underlying tissue repair.

developmental biology↗

bioRxiv2023

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. Highlights{square} Accurate and efficient detection of epithelial cell divisions can be automated by deep learning of dynamic time-lapse imaging data {square}Optimal division detection is achieved using multiple timepoints and dual channels for visualisation of nuclei and cell boundaries {square}Epithelial cell divisions are orientated according to lines of global tissue tension after post-division shuffling {square}Spatio-temporal cell division analyses following wounding reveal spatial synchronicity that scales with wound size {square}Additional deep learning tools enable rapid analysis of cell division orientation

bioinformatics↗