Enhanced MS/MS coverage for metabolite identification in LC-MS-based untargeted metabolomics by target-directed data dependent acquisition with time-staggered precursor ion list

Abstract

Metabolite identification is one of the major bottlenecks in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics owing to the difficulty of acquiring MS/MS information of most metabolites detected. Data dependent acquisition (DDA) has been currently used to acquire MS/MS data in untargeted metabolomics. When dealing with the complex biological samples, top-n-based DDA method selects only a small fraction of the ions for fragmentation, leading to low MS/MS coverage of metabolites in untargeted metabolomics. In this study, we proposed a novel DDA method to improve the performance of MS/MS acquisition in LC-MS-based untargeted metabolomics using target-directed DDA (t-DDA) with time-staggered precursor ion lists (ts-DDA). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment, ion filtration, and ion fusion, the target precursor ion list was generated for subsequent t-DDA and ts-DDA. Compared to the conventional DDA, the ts-DDA exhibits the better MS/MS coverage of metabolomes in a plasma sample, especially for the low abundant metabolites. Even in high co-elution zones, the ts-DDA also showed the superiority in acquiring MS/MS information of co-eluting ions, as evidenced by better MS/MS coverage and MS/MS efficiency, which was mainly attributed to the pre-selection of precursor ion and the reduced number of concurrent ions. The newly developed method might provide more informative MS/MS data of metabolites, which will be helpful to increase the confidence of metabolite identification in untargeted metabolomics.

Keywords: Data dependent acquisition; Metabolite identification; Metabolomics; Target-directed DDA; Time-staggered precursor ion list.