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

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.