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

作者：Zhang, Yuhao; Liao, Jingyu; Le, Wanqi; Wu, Gaosong^*; Zhang, Weidong^*

来源：Analytical Chemistry, 2023.

DOI：10.1021/acs.analchem.3c02888

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Metabolomicsbased on high-resolution mass spectrometry has becomea powerful technique in biomedical research. The development of variousanalytical tools and online libraries has promoted the identificationof biomarkers. However, how to make mass spectrometry collect moredata information is an important but underestimated research topic.Herein, we combined full-scan and data-dependent acquisition (DDA)modes to develop a new targeted DDA based on the inclusion list ofdifferential and preidentified ions (dpDDA). In this workflow, theMS(1) datasets for statistical analysis and metabolite preidentificationwere first obtained using full-scan, and then, the MS/MS datasetsfor metabolite identification were obtained using targeted DDA ofquality control samples based on the inclusion list. Compared withthe current methods (DDA, data-independent acquisition, targeted DDAwith time-staggered precursor ion list, and iterative exclusion DDA),dpDDA showed better stability, higher characteristic ion coverage,higher differential metabolites' MS/MS coverage, and higherquality MS/MS spectra. Moreover, the same trend was verified in theanalysis of large-scale clinical samples. More surprisingly, dpDDAcan distinguish patients with different severities of coronary heartdisease (CHD) based on the Canadian Cardiovascular Society anginaclassification, which we cannot distinguish through conventional metabolomicsdata collection. Finally, dpDDA was employed to differentiate CHDfrom healthy control, and targeted metabolomics confirmed that dpDDAcould identify a more complete metabolic pathway network. At the sametime, four unreported potential CHD biomarkers were identified, andthe area under the receiver operating characteristic curve was greaterthan 0.85. These results showed that dpDDA would expand the discoveryof biomarkers based on metabolomics, more comprehensively explorethe key metabolites and their association with diseases, and promotethe development of precision medicine.

• 单位
  中国医学科学院北京协和医院; 河北医科大学; 广东药学院