New sample preparation approach for mass spectrometry-based profiling of plasma results in improved coverage of metabolome

• Efficiency of sample preparation methods was compared for metabolite profiling.
• Compared number of molecules, reproducibility, recovery of spiked standards.
• Methanol–ethanol extraction increased recovery of hydrophobic molecules versus methanol.
• MTBE increased extraction of hydrophobic lipids such as ceramides and triglycerols.
• MTBE + SPE recovered more molecules than MTBE alone.

Sample preparation remains a challenge in untargeted metabolomics studies and no method currently results in complete extraction of all metabolite classes in human plasma. Because a large variety of molecules, with vast differences in dynamic range, could be involved in human disease, there is an urgent need to develop analytical techniques that result in comprehensive coverage of metabolites. Furthermore, analysis of more focused molecular classes could be necessary to more fully interrogate markers of human disease. However, such techniques, which generally involve multiple steps, often result in high variability. We have optimized a combined liquid–liquid and solid phase extraction method for plasma and have compared that to traditional methanol precipitation using spiked internal standards as controls. This method, based largely on previously published methods, results in 5 separate fractions enriched for aqueous species, phospholipids, fatty acids, neutral lipids, and hydrophobic lipids. Using liquid chromatography mass spectrometry as the analytical method, we detect over 3806 metabolites using the new method versus 1851 metabolites using methanol alone. Qualitative analysis and quantitative analysis of both internal standards (ISTDs) and endogenous metabolites demonstrate excellent reproducibility with CV's below 15% for the combined method compared to 30% using the methanol method. While both methods have applications for clinical metabolomics, fractionation resulted in greater overall coverage and can be used for initial classification of molecular species.