Investigating Variability in Metabolomics: A Comparative Study of Analytical Platforms and Blood Matrices Using HPLC-HRMS.

Untargeted metabolomics faces significant challenges in standardization due to variability introduced by sample preparation and analytical workflows. We systematically evaluated the impact of biological matrices, extraction protocols, and chromatographic configurations to establish a mechanism-informed framework aimed at improving reproducibility in large-scale clinical and epidemiological studies. Three extraction protocols were compared using an in-house pooled heparin plasma: monophasic protein precipitation with isopropanol (IPA), methanol:acetonitrile (MeOH:ACN), and a modified Matyash biphasic method. The most reproducible protocol was then applied to four blood matrices. Samples were analysed using untargeted metabolomics on hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) HPLC columns, with mass spectrometry data processed using Compound Discoverer. Both IPA and MeOH:ACN extractions achieved over 80% of features with coefficient of variation (CV%) ≤ 30% for both RP and HILIC, whereas the Matyash method showed higher variability, with a larger proportion of metabolites exhibiting CV% > 30%. Across matrices, RP chromatography detected over 80% of metabolites with CV% < 30%, while HILIC showed higher variability, with at least 20% of metabolites above this threshold. Among matrices, serum and heparin plasma outperformed EDTA and citrate in reproducibility. We propose a standardized workflow in which monophasic extractions combined with RP chromatography maximize reproducibility and metabolite coverage, minimizing methodological artefacts and providing a reliable framework for robust biological discovery in large-scale untargeted metabolomics studies.