GC-MS
Definition
Gas Chromatography-Mass Spectrometry (GC-MS) is an analytical technique that combines gas chromatography's separation capabilities with mass spectrometry's detection power to identify and quantify metabolites in biological samples. The method first vaporizes and separates compounds based on their volatility and interaction with a stationary phase, then fragments and identifies them by their mass-to-charge ratios. GC-MS excels at detecting volatile and semi-volatile metabolites including amino acids, fatty acids, organic acids, and sugars after chemical derivatization. This technique is fundamental in metabolomics for generating comprehensive metabolic profiles, enabling researchers to understand cellular metabolism, identify biomarkers, and map metabolic pathways in health and disease states.
Visualize GC-MS in Nodes Bio
Researchers can visualize GC-MS metabolomics data as networks where nodes represent detected metabolites and edges show biochemical relationships, correlations, or pathway connections. Nodes Bio enables integration of GC-MS datasets with genomic or proteomic data to create multi-omics networks, revealing how genetic variations or protein expression changes affect metabolic flux. Users can map metabolite abundance changes across experimental conditions and identify metabolic pathway perturbations through network topology analysis.
Visualization Ideas:
- Metabolite-metabolite correlation networks showing co-regulated compounds across samples
- Metabolic pathway networks with GC-MS detected metabolites mapped to KEGG or Reactome pathways
- Multi-omics integration networks connecting GC-MS metabolites to genes, proteins, and phenotypes
Example Use Case
A pharmaceutical team investigating diabetes drug mechanisms uses GC-MS to profile metabolites in liver tissue from treated and control mice. They detect significant changes in 87 metabolites involved in glucose metabolism, lipid biosynthesis, and amino acid catabolism. By visualizing these metabolites as a network in Nodes Bio and overlaying fold-change data, they identify a previously unknown connection between the drug's primary target and branched-chain amino acid metabolism. This network view reveals that the drug's beneficial effects partially operate through an unexpected metabolic shunt, informing next-generation drug design.