AROMA-GC: Green Automated Plasma Metabolome Profiling

ABSTRACT:

Metabolomics enables the comprehensive profiling of low–molecular weight metabolites that reflect the interplay between environmental exposures and endogenous biological processes. As the downstream output of genomic, transcriptomic, proteomic, and epigenomic networks, the metabolome provides the closest biochemical representation of phenotype, making metabolomic signatures highly sensitive indicators of physiological states, pathological alterations, and therapeutic responses. Despite its promise for biomarker discovery and precision diagnostics, routine clinical implementation remains limited by labor-intensive workflows, high operational costs, and the reliance on technically demanding analytical platforms. Human plasma is an attractive matrix for translational metabolomics due to its molecular diversity, clinical accessibility, and ability to capture systemic metabolic changes; however, conventional plasma metabolomics methods typically require solvent-based extraction, derivatization, and large injection volumes that hinder scalability and reproducibility.

Gas chromatography–mass spectrometry (GC–MS) offers robust, high-resolution analysis of volatile and semi-volatile compounds, yet its application in plasma metabolomics has traditionally been constrained by complex sample preparation and environmental concerns associated with solvent use. Principles of green analytical chemistry highlight the need for simplified, solvent-minimized approaches. Headspace solid-phase microextraction (HS-SPME), particularly with the advanced PAL SPME Arrow system, provides an attractive alternative by increasing extraction capacity, improving robustness, and enabling full automation. Although widely applied in environmental and food analysis, its use in clinical metabolomics remains underexplored.

Here, we present an expedited, solvent-minimized, and automatable GC–MS workflow employing headspace PAL SPME Arrow for metabolic profiling of human plasma. The method requires minimal sample volume, eliminates derivatization, reduces chemical waste, and is compatible with routine laboratory automation. Systematic optimization of sample dilution, extraction temperature, incubation time, and desorption parameters demonstrated that HS-SPME Arrow coupled with GC–MS enables the reliable detection of a broad array of clinically relevant metabolites. By integrating principles of green analytical chemistry with high analytical performance, this workflow offers a practical, scalable platform to support translational metabolomics and future diagnostic and prognostic applications.

INSTRUMENT(S): Gas Chromatography MS -

PROVIDER: MTBLS13319 | MetaboLights | 2026-08-24

REPOSITORIES: MetaboLights