Maize Genomics and Genetics 2025, Vol.16, No.6, 284-293 http://cropscipublisher.com/index.php/mgg 286 3 Metabolomics Approaches in Salt Stress Research 3.1 Advanced analytical platforms enable comprehensive profiling of salt-responsive metabolites High-resolution and high-throughput analytical platforms function as essential equipment for scientists to study all biochemical reactions that occur in maize plants when they experience salt stress. The three methods for metabolite analysis consist of GC-MS and LC-MS and UPLC-QTOF-MS. The LC-MS method stands as the primary choice because it offers superior sensitivity and identifies numerous metabolites. The UPLC-QTOF-MS system delivers the highest resolution and precise mass detection capabilities for identifying both primary and secondary metabolites. GC-MS maintains its position as the top method for detecting volatile compounds and derivatized polar metabolites including organic acids and amino acids and sugars. The low sensitivity of NMR spectroscopy does not reduce its value because it provides absolute metabolite quantification with high reproducibility and quantitative accuracy. The field of metabolomics has achieved better resolution through three new methods: direct-infusion MS (DIMS) performs rapid metabolite detection and ion mobility spectrometry–MS (IMS-MS) separates identical metabolites and imaging MS produces tissue maps that help researchers study salt stress effects on root and leaf and reproductive organ heterogeneity. The platforms function as a unified system which provides superior metabolome analysis through broader coverage and improved analytical accuracy (Xia and Wishart, 2016; Pang et al., 2024). 3.2 Multivariate statistics and pathway enrichment reveal stress-responsive patterns The analysis of raw metabolomic data requires powerful statistical and bioinformatics methods because these datasets contain numerous thousands of features that change between different genotypes and treatment conditions and developmental phases. The unsupervised analysis of global metabolic changes becomes possible through principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) allows supervised discrimination between tolerant and sensitive genotypes based on metabolic signatures. The most affected metabolites become evident using univariate methods in combination with volcano plots. The KEGG and Plant Metabolic Network (PMN) pathway enrichment analysis shows that amino acid metabolism and glycolysis and flavonoid biosynthesis pathways receive the most substantial changes from these modifications. Network-based approaches such as weighted correlation network analysis (WGCNA) further reveal co-regulated metabolite clusters tightly associated with salt tolerance traits (Chong et al., 2018; Pang et al., 2024). The end-to-end workflow of MetaboAnalyst and metaX software platforms includes preprocessing and normalization and multivariate statistics and pathway analysis functions while PhenoMeNal operates as a cloud-based system for large-scale metabolomics studies (Wen et al., 2017; Sun and Xia, 2023). 3.3 Multi-omics integration enhances mechanistic understanding The interpretation of biochemical end-products from cellular regulation becomes more straightforward when metabolomics data is integrated with information from other omics layers. Scientists use metabolite and transcript relationship analysis to understand transcriptional control of metabolic pathways by studying cases such as salt-stressed tolerant maize which produces more proline through elevated P5CS (Δ¹-pyrroline-5-carboxylate synthase) gene expression. The combination of proteomics with enzyme abundance analysis allows scientists to investigate metabolic flux and ionomics delivers extra data about ionic equilibrium (Chong et al., 2018). The research on maize salt stress now uses two systems biology frameworks which include genome-scale metabolic models (GEMs) and metabolic flux analysis (MFA). The models use multi-omics data integration to forecast how metabolic fluxes change under stress conditions which enables researchers to connect genetic information with metabolic responses and adaptive results. The integration of multiple omics approaches enables researchers to move beyond descriptive biomarker analysis of metabolomics data which results in mechanistic knowledge about salt tolerance in maize.
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