Mesenchymal stem cells (MSCs) are widely used as therapeutics targets for numerous autoimmune diseases. However, MSC therapies have had limited success so far in clinical trials, mainly being heterogenous population it is difficult to determine MSCs efficiencies. It is critical to understand internal signaling of individual MSCs population that directly affect the cell phenotype. Lipid signaling is closely associated with cell shape so, a holistic approach to understand how changes in lipid metabolites trickles all the way to single cell phenotype could reveal deeper understanding of MSCs functional regulation. So, we aim to evaluate lipid metabolic profiles of single cell MSCs with known variability in immune regulation and explore the phenotypic changes that occur because of differences in lipid signaling. We use longitudinal label free phase imaging strategies to obtain cell phenotypic features which are directly correlated with single cell lipid metabolome obtained using advanced MALDI-MSI technique. Correlation maps indicate associations between lipid signaling and phenotypic changes in MSCs. Moreover, a novel machine learning clustering approach detects the heterogeneity in the MSCs subpopulation then methodically see how each heterogenous population is being impacted by the changes in lipid profiles which could be linked to the functional behaviors of the cell.