In recent years, metabolomics has surfaced as an innovative research strategy in human metabolism, whereby selection of the biological matrix and its inherent metabolome is of crucial importance. However, focusing on a single matrix may imply that relevant molecules of complementary physiological pathways, covered by other matrices, are missed. To address this problem, this study presents a unique multi-matrix platform for polar metabolic fingerprinting of feces, plasma and urine, applying ultra-high performance liquid-chromatography coupled to hybrid quadrupole-Orbitrap high-resolution mass spectrometry, that is able to achieve a significantly higher coverage of the system's metabolome and reveal more significant results and interesting correlations in comparison with single-matrix analyses. All three fingerprinting approaches were proven 'fit-for-purpose' through extensive validation in which a number of endogenous metabolites were measured in representative quality control samples. For targeted and untargeted validation of all three matrices, excellent linearity (coefficients of determination R2 ≥ 0.99 or 0.90 respectively), recovery and precision (coefficients of variance ≤ 15% or 30% respectively) were observed. The potential of the platform was demonstrated by subjecting fecal, urine and plasma samples (collected within one day) from ten healthy volunteers to metabolic fingerprinting, yielding respectively 9 672, 9 647, and 6122 components. Orthogonal partial least-squares discriminant analysis provided similar results for feces and plasma to discriminate according to gender (p-value, R2(X), R2(Y) and Q2(Y)), suggesting feces as an excellent alternative biofluid to plasma. Moreover, combining the different matrices improved the model's predictivity, indicating the superiority of multi-matrix platforms for research purposes in biomarker detection or pathway elucidation and in the selection of the most optimal matrix for future clinical purposes.
Keywords: Blood plasma; Feces; Metabolic fingerprinting; Polar metabolomics; UHPLC-Q-exactive(TM) orbitrap HRMS; Urine.
Copyright © 2018. Published by Elsevier B.V.