Simultaneously targeted and untargeted multicomponent characterization of Erzhi Pill by offline two-dimensional liquid chromatography/quadrupole-Orbitrap mass spectrometry

Large-scale targeted and untargeted metabolites characterization can be achieved by feat of different liquid chromatography/mass spectrometry (LC-MS) platforms by multiple MS experiments or using data-independent acquisition followed by precursor-product ions matching based on certain algorithms. The resulting insufficiency in efficiency and availability greatly restricts the applicability of these strategies in large-scale profiling and identification of various metabolites. A strategy simultaneously enabling both the targeted and untargeted metabolites characterization is established on a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer, by integrating precursor ions list-triggered data-dependent MS2 acquisition (PIL/dd-MS2) of the targeted components and using the If idle-pick others (IIPO) function to induce untargeted metabolites fragmentation. A compounds-specific mass defect filter (MDF) algorithm is proposed as a method to generate the PIL. As a proof of concept, this strategy coupled with offline two-dimensional liquid chromatography (2D-LC) was applied to identify the multicomponents of a traditional Chinese medicine formula Erzhi Pill (EZP). A rigid MDF vehicle was elaborated by orthogonal screening of the integer mass and integer mass-dependent dynamic mass defects considering a variation of 20 ppm. The Full MS/dd-MS2 method enabling PIL and IIPO exhibited better performance than Full MS/dd-MS2 and Targeted SIM/dd-MS2 (selected ion monitoring) in respect of the sensitivity in identifying the targeted components and the ability to characterize more untargeted ones. As a consequence, 270 components were separated from EZP, and 146 thereof were selectively characterized. In conclusion, it is a practical, multifaced strategy facilitating the in-depth metabolites profiling and characterization of complex herbal and biological samples. (C) 2018 Elsevier B.V. All rights reserved.