Cells in a pancreatic islet are not all the same, and this project will watch how individual signalling proteins behave on their surfaces to understand why. Current methods for classifying cells rely on static snapshots of gene activity, which miss how proteins actually move and organise in real time. This matters because the same type of cell can behave differently depending on its environment, and that variation is lost when cells are studied outside the tissue. The researchers will use genome editing, super-resolution imaging, and spatial transcriptomics to map the organisation of G protein-coupled receptors (GPCRs) on individual cells within intact pancreatic islets. They will then link those protein patterns to the cells’ underlying gene activity and functional behaviour. If successful, this work will provide the first high-resolution view of how signalling protein organisation relates to cell state and activity across a whole tissue. The immediate impact is fundamental: it will change how scientists think about cell heterogeneity and tissue function. In the longer term, a deeper understanding of how GPCR organisation shifts during metabolic stress could inform new approaches to treating diabetes, where islet cell dysfunction is central.
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The current view of tissue complexity is mainly based upon single-cell screening technologies, which classify cells according to shared traits, such as maturity, proliferative capacity and mutational potential. Often, inferences about cell function are based upon measurements made outside of the tissue context, as well as the characteristics of the state to which the cell belongs. Thus, immature cells tend to be considered as proliferative, but poorly functional, whereas more mature cells are long-lived and highly functional. While single-cell screening approaches are high-dimensional, they do not have the spatiotemporal resolution inherent to light microscopy. The present proposal will leverage recent advances in genome editing, protein labelling, super-resolution imaging and spatial transcriptomics to provide a higher-order in situ organization of cell heterogeneity at the tissue level, with repercussions for our understanding of tissue (dys)function. Using pancreatic islets as an exemplar micro-organ, and GPCRs as candidate cell surface signalling proteins, we will: 1) map GPCR organization/dynamics at the cell population level and integrate this information with underlying transcriptomic features, before re-classifying cell states; 2) understand how higher-order GPCR organization/dynamics change during cell stimulation, metabolic stress and other states of tissue perturbation; 3) functionally interrogate cell states defined by GPCR organization/dynamics using novel activity integrators and cell-specific transcription factor re-expression; and 4) examine higher-order cell heterogeneity across species. The proposed work will show for the first time how the organization and dynamics of individual signalling proteins relate to cell state and cell activity across the cell population. More broadly, these studies will establish a high-resolution view of cell heterogeneity, leading to a step-change in our understanding of the functional organisation of complex tissues.
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