论文部分内容阅读
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.