Quantitative Normalization of Spatial Metabolomics for Molecular Signatures of Tissue Heterogeneity

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PROJECT SUMMARY The molecular and biochemical basis of tissue heterogeneity in normal and disease states can be addressed with spatial omic analysis. Advances in spatial metabolomic analysis of tissue biopsies using MALDI-MSI could enable the identification of numerous biochemical pathways as signatures of specific histopathology features. However, to extract the most robust disease- relevant biomarkers and pathways from spatial omics platforms will require quantitation of spatial molecules for automation and statistical analysis. A key obstacle to adoption of spatial metabolomics is the lack of rigorous normalization of biomolecules across tissue sections. SygnaMap proposes to build MSI-DeepPath as a computational platform to quantify spatially distinct metabolite abundancies with histopathologic features. Data from MALDI-MSI and bulk metabolomics from serial sections of human kidney and liver tissue will be used as the data source for our platform development. SygnaMap is developing MSI-DeepPath, a drug discovery and development computational platform that incorporates ground-breaking improvements in MALDI- MSI with computational pathology. Quantitation of spatial metabolomics for automation and statistical analysis as presented in this SBIR proposal will enable the extraction of the most robust disease-relevant pathways from metabolites. The rigorous deconvolution of structure-relevant pathways will enable a highly impactful therapeutic target discovery and drug development platform for use by biotech and pharmaceutical companies for development of therapeutics alongside biomarkers.