The three glioma rat models C6, F98 and RG2 exhibit different metabolic profiles: in vivo 1H MRS and ex vivo 1H HRMAS combined with multivariate statistics

Glioblastomas are the most malignant subtypes of glioma and many efforts are currently made to improve their characterization though molecular, microvascular, immunogenic and metabolomic approaches. The variability within pre-clinical tumor models may mimic glioma heterogeneity and force the development of innovative analytical methodologies. In this study, we investigate the metabolic variability within three rat models of glioma: C6, RG2 and F98, using in vivo magnetic resonance spectroscopy (1H MRS) and ex vivo high resolution magic angle spinning (1H HRMAS MRS). We used a multivariate statistic approach with orthogonal projection to latent structure-discriminant analysis (OPLS-DA) that was compared with univariate statistic. OPLS-DA reveals a clear separation between C6, RG2 and F98 tumors and, with the help of shared and unique structure plot (SUS-Plot), promotes a comprehensive view of their metabolic differences. Both in vivo and ex vivo analyses are similar but ex vivo 1H HRMAS MRS provides more robust results. In conclusion, MRS-based OPLS-DA appears sensitive enough to correctly predict the classification of tumors and to investigate the relationship between the host brain metabolism and the grafted tumor.