Formic Acid Pretreatment Enhances Untargeted Serum and Plasma Metabolomics

Untargeted metabolic profiling of plasma and serum by liquid chromatography-mass spectrometry (LC-MS) is becoming increasingly important in clinical and translational research; however, sample preparation protocols can have a significant impact on study outcomes, and there is currently a lack of standardized approaches. In this study we demonstrate that pretreatment of serum and plasma samples with 1% formic acid (FA, v/v) prior to acetonitrile (MeCN)-induced protein precipitation significantly enhances analytical performance in untargeted metabolomics using reversed-phase liquid chromatography (RPLC)-MS. We show an increase in sample preparation reproducibility and signal intensity across both positive and negative ionization modes. In two independent serum cohorts (OPTIMA and VITACOG), FA-based extraction improved multivariate modeling (orthogonal partial least-squares discriminant analysis, OPLS-DA), with consistently higher classification accuracy, sensitivity, and specificity, alongside reduced variability and increased fold-changes in discriminatory compound-features. We investigated factors potentially involved in the enhanced performance and observed outcomes consistent with the disruption of noncovalent protein-metabolite interactions and the stabilization of labile species. We found no correlation with either protein depletion or differential adduct formation. The results were also not attributable to lowering pH after metabolite extraction. In summary, we demonstrate that FA pretreatment of plasma and serum, prior to protein precipitation, significantly improves sample reproducibility and detection sensitivity in untargeted RPLC-MS metabolomics. This optimized sample preparation strategy offers clear advantages for clinical and translational metabolomics, with the potential to enhance biomarker discovery and metabolic phenotyping.