Database-Assisted Globally Optimized Targeted Mass Spectrometry (dGOT-MS): Broad and Reliable Metabolomics Analysis with Enhanced Identification

Targeted mass spectrometry (MS) is an important measurement approach in metabolomics with strong analytical performance, given its specificity, sensitivity, and quantitative capacity. However, traditional targeted-MS relies heavily on chemical standards for the development of various detection panels; thus, its metabolite coverage is often limited to those well-known and commercially available compounds. To address this fundamental gap, we previously developed a novel approach [ H. Gu et al. Anal. Chem. 2015, 87, 12355-12362 ], globally optimized targeted (GOT)-MS, which enables reliable metabolic analysis with broad coverage using a single triple quadrupole instrument. In the present study, we further developed and optimized an innovative targeted MS approach, database-assisted globally optimized targeted (dGOT)-MS, which utilizes the HMDB and METLIN databases to significantly improve both identification and metabolite coverage. As it is well-known, these metabolomics databases have a comprehensive collection of metabolites and their tandem MS spectra; therefore, in this study, multiple reaction monitoring transitions (MRMs) were directly obtained from the databases and, after optimizing MS parameters for those MRMs, 927 metabolites were measured from a plasma aqueous extract sample with high reliability by dGOT-MS. Of these, 310 were confirmed using pure chemical standards while the rest were annotated by identification level using database entries. Furthermore, using breast cancer diagnosis as a proof-of-principle metabolomics application, we showed dGOT-MS to significantly outperform a traditional large-scale targeted MS assay for potential biomarker discovery. In principle, dGOT-MS is able to cover all metabolites (including lipids) that have been characterized in these comprehensive metabolomics databases from various types of biological samples. Therefore, dGOT-MS opens a novel avenue for MS measurements and may play an important role in many future metabolomics studies.