Beyond Isolation: Modern Analytical and Computational Approaches to Unraveling Plant Metabolomes

Abstract

Plant metabolomes consist of a complex and dynamic mixture of small molecules, which are chemically diverse and spatially organized, and that link the genotype, environment, development, and phenotype. While conventional phytochemical isolation is important for structural confirmation, its compound-by-compound workflow doesn't comprehensively capture this complexity. This review discusses the analytical and computational shift from targeted isolation to integrated plant metabolomics. The robust profiling of primary metabolites by gas chromatography–mass spectrometry is complemented by the ability of liquid chromatography–high-resolution mass spectrometry to profile lipids and specialized metabolites. Nuclear magnetic resonance spectroscopy provides quantitative reproducibility and structural information, and capillary electrophoresis, ion mobility, and mass-spectrometry imaging provide chemical variation information for ions, isomers, and spatial information. Reliable discovery requires good experimental design, rapid quenching, representative extraction, pooled quality-control samples, internal standards, and transparent metadata. Computational workflows allow raw signals to be transformed into aligned features, correct analytical drift, prioritize molecular formulae, match spectral library, simulate fragmentation, construct molecular network, and integrate statistical learning and pathway and multi-omics evidence.
There are many challenges that persist, such as limited metabolome coverage, ion suppression, batch effects, unrecognized features, ambiguous feature annotation, poor cross-study comparability, and lack of reference standards. Curated training data, reporting of uncertainties, chemical validation, and the FAIR deposition of data are needed for emerging spatial and single-cell methods, field-deployable instrumentation, machine learning, foundation models and automated laboratories to facilitate discovery. Complementary platforms, coupled with evidence-aware computation and biological-specific confirmation, can enable the translation of multidimensional signals to reproducible metabolite identities, biomarkers, pathways and causal hypotheses. This integrated approach enables crop improvement, stress biology, food quality, chemotaxonomy, nutrition and natural product discovery.