Deep multi-omic integration to understand inter-organ relationships that regulate systemic immunometabolism

Aims:
1) Build from existing multimodal integration work of spatially resolved imaging techniques to define multicellular molecular and morphological signatures of tissue associated with metabolic dysregulation in the liver, kidney, and heart and the associated resident and infiltrating immune microenvironment phenotypes.
2) Interpretation of transcriptomic data in models of metabolic syndrome have been confounded by inability to distinguish transcriptomic shift from cellular remodelling. Use multicellular signatures from IMC for better understanding of the cell types contributing to the transcriptomic changes associated with early changes in metabolism detected in each organ. Deep learning integration will then be used to distinguish data resulting from differential cell infiltration and regulated gene expression.
3) Matched clinical data for better understanding of systemic immunometabolism and model translation with the potential for novel target identification.
4) Build a spatiotemporal model of multicellular signalling, inflammation and metabolomic interdependencies to produce a dynamic model of the aetiology and contribution of each organ during normal systemic function and upon perturbation such as in early and established metabolic dysfunction.