My PhD project is situated within the domain of metabolomics, which is the large-scale study of small molecules, known as metabolites, in a biological sample. Metabolites are affected by both endogenous and exogenous factors and therefore contain a lot of information. Because of this, the metabolome is thought to be the most predictive of the phenotype, making metabolites interesting targets for biomarker research.
Metabolomic analysis usually combines reversed-phase and hydrophilic interaction liquid chromatography (LC) with high-resolution mass spectrometry (MS) detection. Supercritical fluid chromatography (SFC) uses sub/supercritical carbon dioxide containing an organic modifier as mobile phase. Coupled to MS, it allows the analysis of compounds with a wide variety in polarity. The main aim of this research proposal is to investigate whether SFC-MS enables the high-throughput analysis of a broader range of brain metabolites in one run, with the same sensitivity compared to LC-MS. Therefore, a high-throughput SFC-MS method will be developed. In a later stage, the method will be used to address the role of astrocyte signaling in temporal lobe epilepsy by studying the metabolomic fingerprint in brain samples. Due to the complex nature of these fingerprints, multivariate data analysis will be required for the selection of candidate biomarkers. Afterwards quantitative supercritical fluid chromatography triple quadrupole mass spectrometric assays will be developed and validated for these selected compounds to confirm their true potential as biomarker.