How do you design and interpret metabolomics studies?

Metabolomics experimental design requires: sample collection with rapid quenching to stop metabolism, appropriate extraction for target metabolite classes (polar, lipids), internal standards for quantification, and quality control samples throughout. Analytical platforms include LC-MS/MS for polar metabolites, GC-MS for volatile compounds after derivatization, and NMR for non-destructive, quantitative analysis. Data processing involves peak detection, alignment, normalization, and metabolite identification using databases (HMDB, METLIN, MassBank). Statistical analysis includes univariate tests, multivariate analysis (PCA, PLS-DA), and pathway enrichment. Interpretation integrates with transcriptomics/proteomics for systems understanding. Challenges include metabolite coverage, isomer distinction, and biological variation versus technical variation.