Optimization of a GC-MS Injection-Port Derivatization Methodology to Enhance Metabolomics Analysis Throughput in Biological Samples.
Elisabet Foguet-RomeroIris SamarraMaria GuirroMarc RiuJorge JovenJavier A MenendezNuria CanelaAntoni DelPino-RiusSalvador Fernández-ArroyoPol HerreroPublished in: Journal of proteome research (2022)
Advances in metabolomics analysis and data treatment increase the knowledge of complex biological systems. One of the most used methodologies is gas chromatography-mass spectrometry (GC-MS) due to its robustness, high separation efficiency, and reliable peak identification through curated databases. However, methodologies are not standardized, and the derivatization steps in GC-MS can introduce experimental errors and take considerable time, exposing the samples to degradation. Here, we propose the injection-port derivatization (IPD) methodology to increase the throughput in plasma metabolomics analysis by GC-MS. The IPD method was evaluated and optimized for different families of metabolites (organic acids, amino acids, fatty acids, sugars, sugar phosphates, etc.) in terms of residence time, injection-port temperature, and sample/derivatization reagent ratio. Finally, the method's usefulness was validated in a study consisting of a cohort of obese patients with or without nonalcoholic steatohepatitis. Our results show a fast, reproducible, precise, and reliable method for the analysis of biological samples by GC-MS. Raw data are publicly available at MetaboLights with Study Identifier MTBLS5151.
Keyphrases
- gas chromatography mass spectrometry
- ms ms
- mass spectrometry
- liquid chromatography
- liquid chromatography tandem mass spectrometry
- high performance liquid chromatography
- gas chromatography
- simultaneous determination
- healthcare
- big data
- adipose tissue
- type diabetes
- artificial intelligence
- high resolution
- weight loss
- bariatric surgery
- data analysis
- deep learning
- water soluble