Advances in dual-column chromatography for metabolomics: Opportunities, challenges, and applications

Metabolomics, a dynamic and rapidly evolving omics discipline, provides critical insights into the biochemical state of biological systems and holds transformative potential in fields such as biomarker discovery, disease mechanisms, and precision medicine. As the demand for high-throughput, unbiased, and reproducible metabolite profiling grows, particularly in clinical and translational settings, there is an urgent need for analytical platforms that can simultaneously address chemical diversity, sensitivity, and standardization. Traditional single-column chromatographic systems often fall short in capturing the full spectrum of metabolites due to limited polarity range and separation capacity, leading to analytical blind spots and suboptimal data integration. In response to these limitations, dual-column systems have emerged as a promising solution by integrating orthogonal separation chemistries (e.g., reversed-phase; RP and hydrophilic interaction chromatography; HILIC) within a single analytical workflow. These systems offer superior performance by enabling the concurrent analysis of both polar and nonpolar metabolites, thereby reducing analysis time, improving sensitivity, and enhancing coverage, particularly in hybrid designs that unify targeted and untargeted metabolomics approaches. However, despite their growing adoption, a comprehensive assessment of dual-column platforms, including design, performance, and advantages for metabolomics applications, remains limited. This review addresses this gap by systematically covering dual-column LC-MS applications in metabolomics reported between 1984 and 2025, comparing targeted and untargeted approaches and evaluating platform configurations, analytical capabilities, and contributions to workflow standardization. Furthermore, by discussing key methodological aspects, such as sample preparation, column setup, detection strategies, and data analysis, we highlight how these systems impact data quality and facilitate robust biological interpretation.