70NANB24H198

Advances in spatial metabolomic analysis of tissue biopsies using MALDI-MSI have enabled the identification of numerous biochemical pathways as signatures of specific histopathological features. However, reproducibility issues across experimental studies, due to the lack of a common internal standard, limit the application of MALDI-MSI in drug development. Addressing the critical question of quantifying data outputs despite spatial variations in tissue structure and molecular composition is essential for robust analysis. In Phase I, SygnaMap identified common reference metabolite standards and employed the MSI-DeepPath computational platform for quantitative normalization. This platform developed a normalization algorithm that reduced batch effects and enhanced reproducibility across different samples from the same study group, optimizing data interpretation. The quantitative normalization algorithm is crucial for analyzing the rich data from spatial metabolomics, facilitating therapeutic target and biomarker discovery for drug development. Extracting the most robust disease-relevant biomarkers and pathways from spatial omics platforms necessitates a robust quantitative algorithm developed from calibration standards. In this Phase II proposal, SygnaMap aims to tackle this challenge by further developing a computational quantitative normalization approach suitable for robust statistical analysis across tissue sections from normal, diseased, and drug-treated samples, thereby enhancing the reliability and applicability of spatial metabolomics in drug development.