LC-MS
Definition
Liquid Chromatography-Mass Spectrometry (LC-MS) is an analytical technique that combines the physical separation capabilities of liquid chromatography with the mass analysis capabilities of mass spectrometry. In metabolomics, LC-MS separates complex biological mixtures into individual metabolites based on their chemical properties, then identifies and quantifies them by measuring their mass-to-charge ratios. This powerful method enables comprehensive profiling of small molecules (metabolites) in biological samples, detecting thousands of compounds simultaneously. LC-MS is essential for untargeted metabolomics studies, biomarker discovery, and understanding metabolic pathways, offering high sensitivity, specificity, and dynamic range for analyzing diverse metabolite classes including lipids, amino acids, nucleotides, and secondary metabolites.
Visualize LC-MS in Nodes Bio
Researchers can visualize LC-MS metabolomics data as metabolite-metabolite correlation networks or integrate metabolite profiles with metabolic pathway maps. Nodes Bio enables mapping of significantly altered metabolites onto biochemical pathways, revealing network-level perturbations in disease states or drug responses. Users can connect metabolite nodes to genes, proteins, and phenotypes, creating multi-omics networks that uncover regulatory relationships and identify key metabolic hubs driving biological processes.
Visualization Ideas:
- Metabolite correlation networks showing co-regulated metabolic features
- Integrated metabolic pathway maps with differentially abundant metabolites highlighted
- Multi-omics networks connecting LC-MS metabolites to transcriptomics and proteomics data
Example Use Case
A pharmaceutical team investigating drug-induced liver toxicity performs LC-MS metabolomics on hepatocyte samples treated with candidate compounds. They detect 2,500 metabolites and identify 180 significantly altered features. By visualizing these metabolites as a network in Nodes Bio, they discover coordinated disruptions in bile acid metabolism, oxidative stress pathways, and mitochondrial energy production. The network reveals that multiple toxic compounds converge on glutathione depletion, suggesting a common mechanism. This insight guides structural modifications to improve drug safety profiles before clinical trials.