Fatty acid profiling on gas chromatography–mass spectrometry (GC–MS) platforms is typically performed offline by manually derivatizing and analyzing small batches of samples. A GC–MS system with a fully integrated robotic autosampler can significantly improve sample handling, standardize data collection, and reduce the total hands-on time required for sample analysis. In this study, we report an optimized high-throughput GC–MS-based methodology that utilizes trimethyl sulfonium hydroxide (TMSH) as a derivatization reagent to convert fatty acids into fatty acid methyl esters. An automated online derivatization method was developed, in which the robotic autosampler derivatizes each sample individually and injects it into the GC–MS system in a high-throughput manner. This study investigated the robustness of automated TMSH derivatization by comparing fatty acid standards and lipid extracts, derivatized manually in batches and online automatically from four biological matrices. Automated derivatization improved reproducibility in 19 of 33 fatty acid standards, with nearly half of the 33 confirmed fatty acids in biological samples demonstrating improved reproducibility when compared to manually derivatized samples. In summary, we show that the online TMSH-based derivatization methodology is ideal for high-throughput fatty acid analysis, allowing rapid and efficient fatty acid profiling, with reduced sample handling, faster data acquisition, and, ultimately, improved data reproducibility.

Glioblastoma (GBM), the most prevalent primary malignant brain tumor, remains challenging to treat due to extensive inter- and intra-tumor heterogeneity. This variability demands combination treatments to improve therapeutic outcomes. A significant obstacle in treating GBM is the expression of O⁶-methylguanine-DNA methyltransferase, a DNA repair enzyme that reduces the efficacy of the standard alkylating agent, temozolomide, in about 50% of patients. This underscores the need for novel, more targeted therapies. Our study investigates the metabolic–epigenetic impact of combining SN-38, a novel topoisomerase inhibitor inducing DNA double-strand breaks, with rabusertib, a checkpoint kinase 1 inhibitor. We identified this synergistic combination through high-throughput drug screening across a panel of GBM cell lines using a cancer drug library combined with SN-38. A secondary metabolic screening with the PEDS algorithm demonstrated a synergistic modulation of purine, one-carbon, and redox metabolism. Furthermore, the combined treatment led to the significant depletion of epigenetically relevant metabolites such as 5-methyl-cytosine, acetyl-lysine, and trimethyl-lysine. Reduced intermediates of the glutathione cycle indicated increased cellular stress following combinatorial treatment. Overall, the combination of SN-38 and rabusertib synergistically disrupts metabolites associated with epigenetic adaptations, leading to cytotoxicity independent of O⁶-methylguanine-DNA methyltransferase status, thereby underpinning this combination as a promising candidate for combinatorial therapy in GBM.