It is necessary to characterize and classify neural stem cells (NSCs) and differentiated cells (DCs) for potential use of NSC to treat neurodegenerative diseases. We therefore performed an analysis of NSCs and DCs using gas chromatography mass spectrometry (GC-MS) and direct infusion mass spectrometry (DI-MS) with elaborate multivariate statistical analysis for the characterization and classification of rat NSCs and DCs. GC-MS and DI-MS detected a total of 92 metabolites and lipids in NSCs and DCs, and the levels of 72 of them differed significantly between NSCs and DCs. The optimal model for partial least squares (PLS) discriminant analysis was constructed by applying 3 and 2 PLS components with a unit-variance scaling method for classifying NSCs and DCs based on the data obtained in the GC-MS and DI-MS analyses, respectively. The obtained results from PCA and PLS-DA suggest that creatinine, lactic acid, lysine, glutamine, glycine, pyroglutamic acid, PG 18:1/20:2, PS 18:0/20:2, PI 18:0/20:3, PC 16:0/20:4, PI 16:0/20:4, and PI 18:1/20:4 were the main contributors that provided distinct characteristics of NSCs and DCs. The results of this study suggest objective and complementary criteria for the characterization and classification of NSCs and DCs for potential clinical applications. Graphical abstract.
Keywords: DI-MS; GC-MS; Lipidomic profiling; Metabolic profiling; Neural stem cells.