GC-MS Method Development for Targeted and Untargeted Metabolomics: Challenges and Solutions

Abstract

Gas Chromatography-Mass Spectrometry (GC-MS) has emerged as a powerful analytical tool for metabolomics, enabling the identification and quantification of metabolites in complex biological samples. Method development for GC-MS in metabolomics, whether targeted or untargeted, presents unique challenges due to the diversity of metabolites, their varying chemical properties, and the complexity of sample matrices. This review explores the critical steps in GC-MS method development, including sample preparation, derivatization, chromatographic separation, and mass spectrometric detection. We discuss the challenges associated with achieving high sensitivity, selectivity, and reproducibility, particularly in untargeted metabolomics, where the goal is to capture a broad spectrum of metabolites. Additionally, we highlight innovative solutions and recent advancements, such as the use of machine learning for data analysis, improved derivatization techniques, and hybrid instrumentation.