Metabolomics is the extensive study of small molecules in biological systems. This discipline has become a key strategy for understanding biochemical pathways and identifying new biomarkers. Due to its accessibility and its ability to reflect metabolic conditions, blood serum is the widely used matrix for metabolomics studies. As a complex fluid that integrates signals from multiple pathways, serum provides a “snapshot” of physiological and pathological states and is therefore highly informative for clinical and biomedical research [1,2]. However, the extraordinary chemical diversity of this matrix, ranging from highly polar to strongly apolar species, makes it difficult to achieve complete coverage with a single extraction method. To address this challenge, non-target analysis (NTA) has emerged as a valuable strategy for capturing the widest possible chemical space. By combining comprehensive two-dimensional gas chromatography with mass spectrometry (GC×GC–MS), NTA workflows allow the simultaneous detection of hundreds of molecular features and enable powerful chemometric analysis for data elucidation [3].
In this study, we evaluated the effect of different solvent systems on the extraction of serum metabolites for untargeted GC×GC–MS analysis. Four mixtures were tested: methanol, methanol/chloroform (3:1), isopropanol/acetonitrile/water (3:3:2), and methanol/acetonitrile/acetone (1:1:1). The results showed distinct solvent-dependent profiles, with methanol/acetonitrile/acetone (1:1:1) ensuring efficient extraction of sterols and triphasic isopropanol/acetonitrile/water (3:3:2) of amino acids and sugars. Importantly, no single solvent system was able to capture the full serum metabolome. Instead, each mixture selectively enriched different metabolite classes, underlining the need for complementary extraction strategies in non-targeted workflows. Overall, our findings demonstrate that non-targeted GC×GC–MS is essential for capturing the diversity of serum volatile metabolome. By linking solvent choice to metabolome coverage, this study supports the value of non-target analysis in expanding the accessible chemical space and enabling the discovery of new biomarkers relevant to biomedical research.
Kiseleva O, Kurbatov I, et al., Metabolites, 12 (2022), DOI: https://doi.org/10.3390/metabo12010015
Özge C Z, Cemil C E, et al., Journal of Pharmaceutical and Biomedical Analysis, 190 (2020), DOI: https://doi.org/10.1016/j.jpba.2020.113509
Hollender J, Schymanski E.L., et al., Environ. Sci. Eur., 35 (2023), DOI: https://doi.org/10.1186/s12302-023-00779-4