ABSTRACT Kinetic mass spectrometry imaging (kMSI) integrates imaging-MS with stable isotope labelling to elucidate metabolic fluxes in a spatiotemporal manner. kMSI studies are hampered by high volumes of complex data and a lack of computational workflows for data analysis that additionally address replicated experiments. To meet these challenges, we developed KineticMSI, an open-source R-based tool for processing and analyzing kMSI datasets. KineticMSI includes statistical tools to quantify tracer incorporation across replicated treatment groups spatially in tissues. It allows users to make data-driven decisions by elucidating affected pathways associated with changes in metabolic turnover. We demonstrate a validation of our method by identifying metabolic changes in the hippocampus of a transgenic Huntington’s disease (HD) mouse model as compared to wild-type mice. We discovered significant changes in metabolism of neuronal cell body lipids (phosphatidylinositol and cardiolipins) in HD mice, previously masked by conventional statistical approaches that compare mean tracer incorporation across brain regions.

KineticMSI, an R-based framework for relative quantification of spatial isotopic incorporation in mass spectrometry imaging experiments