Metabolomics has emerged as a powerful discipline for characterizing the small molecules that define cellular physiology, environmental responses, and disease states. As technologies advance, researchers face an expanding landscape of analytical platforms, data-processing strategies, and integrative approaches that require clear guidance for effective application. This chapter was written to provide a comprehensive and accessible resource for students, clinicians, and researchers entering or advancing in the field. We outline the fundamentals of metabolomics, describe major analytical methodologies-including MS, NMR, chromatography, and imaging-and summarize key considerations for experimental design, data preprocessing, statistical analysis, and functional interpretation. We also address current challenges related to metabolite identification, reproducibility, and multi-omic integration, and highlight emerging innovations such as stable-isotope tracing, spatial metabolomics, and AI-driven analytics. Together, these elements offer a detailed roadmap for conducting robust, reproducible, and insightful metabolomic studies.
Praveen et al. (2026) studied this question.