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.

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.

Wang Y ，Feng R ，Wang R ，Yang F ，Li P ，Wan JB ... -  《-》

An integrated strategy to improve data acquisition and metabolite identification by time-staggered ion lists in UHPLC/Q-TOF MS-based metabolomics.

The narrow linear range and the limited scan time of the given ion make the quantification of the features challenging in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics with the full-scan mode. And metabolite identification is another bottleneck of untargeted analysis owing to the difficulty of acquiring MS/MS information of most metabolites detected. In this study, an integrated workflow was proposed using the newly established multiple ion monitoring mode with time-staggered ion lists (tsMIM) and target-directed data-dependent acquisition with time-staggered ion lists (tsDDA) to improve data acquisition and metabolite identification in UHPLC/Q-TOF MS-based untargeted metabolomics. Compared to the conventional untargeted metabolomics, the proprosed workflow exhibited the better repeatability before and after data normalization. After selecting features with the significant change by statistical analysis, MS/MS information of all these features can be obtained by tsDDA analysis to facilitate metabolite identification. Using time-staggered ion lists, the workflow is more sensitive in data acquisition, especially for the low-abundant features. Moreover, the metabolites with low abundance tend to be wrongly integrated and triggered by full scan-based untargeted analysis with MSE acquisition mode, which can be greatly improved by the proposed workflow. The integrated workflow was also successfully applied to discover serum biosignatures for the genetic modification of fat-1 in mice, which indicated its practicability and great potential in future metabolomics studies.

Wang Y ，Feng R ，He C ，Su H ，Ma H ，Wan JB ... -  《-》

Improved data-dependent acquisition for untargeted metabolomics using gas-phase fractionation with staggered mass range.

Yan Z ，Yan R 《-》

Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions.

Zhang Y ，Liao J ，Le W ，Wu G ，Zhang W ... -  《-》

DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data.

Guo J ，Shen S ，Xing S ，Huan T ... -  《-》