Optimization of a GC-MS metabolic fingerprint method and its application in characterizing engineered bacterial metabolic shift

Metabolomics influences many aspects of life sciences including microbiology. Here, we describe the systematic optimization of metabolic quenching and a sample derivatization method for GC-MS metabolic fingerprint analysis. Methanol, ethanol, acetone, and acetonitrile were selected to evaluate their metabolic quenching ability, and acetonitrile was regarded as the most efficient agent. The optimized derivatization conditions were determined by full factorial design considering temperature, solvent, and time as parameters. The best conditions were attained with N,O-bis(trimethylsiyl) trifluoroacetamide as derivatization agent and pyridine as solvent at 75°C for 45 min. Method validation ascertained the optimized method to be robust. The above method was applied to metabolomic analysis of six different strains and it is proved that the metabolic trait of an engineered strain can be easily deduced by clustering analysis of metabolic fingerprints.