Lipid coverage is crucial in comprehensive lipidomics studies challenged by high diversity in lipid structures and wide dynamic range in lipid levels. Current state-of-the-art lipidomics technologies are mostly based on mass spectrometry (MS), including direct-infusion MS, chromatography-MS, and matrix-assisted laser desorption ionization (MALDI) imaging MS, each with its pros and cons. Due to the need or favorability for measurement of isomers and isobars, chromatography-MS is preferable for lipid profiling. The ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based nontargeted lipidomics approach and UHPLC-tandem MS (UHPLC-MS/MS)-based targeted approach are two representative methodological platforms for chromatography-MS. In the present study, we developed a high coverage pseudotargeted lipidomics method combining the advantages of nontargeted and targeted lipidomics approaches. The high coverage of lipids was achieved by integration of the detected lipids derived from nontargeted UHPLC-HRMS lipidomics analysis of multiple matrices (e.g., plasma, cell, and tissue) and the predicted lipids speculated on the basis of the structure and chromatographic retention behavior of the known lipids. A total of 3377 targeted lipid ion pairs with over 7000 lipid molecular structures were defined. The pseudotargeted lipidomics method was well validated with satisfactory analytical characteristics in terms of linearity, precision, reproducibility, and recovery for lipidomics profiling. Importantly, it showed better repeatability and higher coverage of lipids than the nontargeted lipidomics method. The applicability of the developed pseudotargeted lipidomics method was testified in defining differential lipids related to diabetes. We believe that comprehensive lipidomics studies will benefit from the developed high coverage pseudotargeted lipidomics approach.

Carnitines play important roles in fatty acid oxidation and branched chain amino acid metabolism. The disturbance of acylcarnitines is associated with occurrence and development of many diseases. Comprehensive acylcarnitine identification can greatly benefit their targeted detection, following disease differential diagnosis and possible mechanism study. In this study, we developed a novel strategy to identify as many acylcarnitines as possible based on liquid chromatography-high-resolution mass spectrometry (LC-HRMS). The layer-layer progressive strategy first integrated the initial full scan MS/data-dependent MS/MS monitoring (ddMS) acquisition and the following parallel reaction monitoring (PRM) to analyze a pooled biological sample. Also 733 possible acylcarnitines were identified containing characteristic high-resolution MS/MS features. Further, accurate mass, retention rules, and HRMS/MS information were used to define subclasses and predict undetected acylcarnitine homologues in each subclass, leading to more acylcarnitines to our newly constructed database. As a result, 758 acylcarnitines were contained in the database, having exact mass, retention time, and MS/MS information, which is the most comprehensive list of acylcarnitines reported to date. Applying this database, 241, 515, and 222 acylcarnitines were rapidly and reliably annotated in human plasma, human urine, and rat liver tissue. This novel strategy enables large-scale identification of acylcarnitines, and a similar method can also be used for identification of other metabolites.