BoxCar data-dependent acquisition improves the MS/MS coverage in liquid chromatography-mass spectrometry-based metabolomics analysis

Tandem mass spectrometry (MS2) information is always applied for compound identification or annotation in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics study. Due to the complex chemical composition of the biological sample, acquiring MS2 spectra that cover all components is still challenging. Data-dependent acquisition (DDA) is commonly applied to obtain the MS2 data, but intensity-based trigger criteria restrict low abundance ions for fragmentation. BoxCar DDA was introduced on an LC- quadrupole time of flight (Q-TOF) MS in this study to improve the MS2 coverage of conventional DDA. BoxCar DDA with sub-mass ranges of 50, 25, and 10 was applied to perform sample analysis, respectively. The performance of multi-parallel collision-induced dissociation MS (MSe), DDA, and BoxCar DDA was compared. The results showed BoxCar DDA significantly increased the MS2 coverage and improved the signal quality. The MS2 coverage increased with the reduction of the sub-mass range. BoxCar (10) DDA exhibited the highest coverage of MS2 information. The signal quality acquired from DDA is better than that obtained under MSe, and the BoxCar (10) also showed superior for ions with low intensity. The plant material of gross saponin of Tribulus Terrestris L. fruit (GSTTF) was detected using the established BoxCar DDA, and the informative MS2 data was used to perform compound identification. The newly developed BoxCar DDA method could provide comprehensive MS2 data of analyte, which will not only be helpful to increase the confidence of compound identification in untargeted metabolomics but also facilitate ion pair detection in pseudotargeted analysis.