Validated Ultra-High-Performance Liquid Chromatography Hybrid High-Resolution Mass Spectrometry and Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry for Salivary Metabolomics.

Whereas urine and blood are typically targeted in clinical research, saliva represents an interesting alternative because its intrinsic metabolome is chemically diverse and reflective for various biological processes. Moreover, saliva collection is easy and noninvasive, which is especially valuable for cohorts in which sample collection is challenging, for example, infants and children. With this rationale, we established a validated ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) method for salivary metabolic profiling and fingerprinting. Hereby, 450 μL of saliva was centrifuged and passed over a 0.45-μm polyamide membrane filter, after which the extract was subjected to chromatographic analysis (HSS T3 column) and Q-Exactive Orbitrap-MS. For the majority of the profiled metabolites, good linearity (R2 ≥ 0.99) and precision (coefficient of variance ≤ 15%) was achieved. The fingerprinting performance was evaluated based on the complete metabolome (11 385 components), whereby 76.8% was found compliant with the criteria for precision (coefficient of variance ≤ 30%) and 82.7% with linearity (R2 ≥ 0.99). In addition, the method was proven fit-for-purpose for a cohort of 140 adolescents (6-16 years, stratified according to weight), yielding relevant profiles (45 obesity-related metabolites) and discriminative fingerprints (Q2 of 0.784 for supervised discriminant analysis). Alternatively, laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) was established for rapid fingerprinting of saliva, thereby using a Nd:YAG laser and Xevo G2-XS QToF-MS. With an acquisition time of 0.5 min per sample, LA-REIMS offers unique opportunities for point-of-care applications. In conclusion, this work presents a platform of UHPLC-HRMS and LA-REIMS, complementing each other to perform salivary metabolomics.