Metabolic profiling of dividing cells in live rodent brain by proton magnetic resonance spectroscopy (1HMRS) and LCModel analysis.

Dividing cells can be detected in the live brain by positron emission tomography or optical imaging. Here we apply proton magnetic resonance spectroscopy (1HMRS) and a widely used spectral fitting algorithm to characterize the effect of increased neurogenesis after electroconvulsive shock in the live rodent brain via spectral signatures representing mobile lipids resonating at ∼1.30 ppm. In addition, we also apply the same 1HMRS methodology to metabolically profile glioblastomas with actively dividing cells growing in RCAS-PDGF mice. 1HMRS metabolic profiles were acquired on a 9.4T MRI instrument in combination with LCModel spectral analysis of: 1) rat brains before and after ECS or sham treatments and 2) RCAS-PDGF mice with glioblastomas and wild-type controls. Quantified 1HMRS data were compared to post-mortem histology. Dividing cells in the rat hippocampus increased ∼3-fold after ECS compared to sham treatment. Quantification of hippocampal metabolites revealed significant decreases in N-acetyl-aspartate but no evidence of an elevated signal at ∼1.3 ppm (Lip13a+Lip13b) in the ECS compared to the sham group. In RCAS-PDGF mice a high density (22%) of dividing cells characterized glioblastomas. Nile Red staining revealed a small fraction (3%) of dying cells with intracellular lipid droplets in the tumors of RCAS-PDGF mice. Concentrations of NAA were lower, whereas lactate and Lip13a+Lip13b were found to be significantly higher in glioblastomas of RCAS-PDGF mice, when compared to normal brain tissue in the control mice. Metabolic profiling using 1HMRS in combination with LCModel analysis did not reveal correlation between Lip13a+Lip13b spectral signatures and an increase in neurogenesis in adult rat hippocampus after ECS. However, increases in Lip13a+Lip13b were evident in glioblastomas suggesting that a higher density of actively dividing cells and/or the presence of lipid droplets is necessary for LCModel to reveal mobile lipids.