Comparing Targeted vs. Untargeted MS² Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSⁿ spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSⁿ spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS² DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC-MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC-MS features selected as the precursor ion for MS², the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.