An enhanced strategy integrating offline two-dimensional separation and step-wise precursor ion list-based raster-mass defect filter: Characterization of indole alkaloids in five botanical origins of Uncariae Ramulus Cum Unicis as an exemplary application

Comprehensive chemical profiling is of great significance for understanding the therapeutic material basis and quality control of herbal medicines, which is challenging due to its inherent chemical diversity and complexity, as well as wide concentration range. In this study, we introduced an enhanced strategy integrating offline two-dimensional (2D) separation and the step-wise precursor ion list-based raster-mass defect filter (step-wise PIL-based raster-MDF) scan by tandem LTQ-Orbitrap mass spectrometer. A comprehensive analysis of indole alkaloids in five botanical origins of Uncariae Ramulus Cum Unicis (Gou-Teng) was used as an exemplary application. A positively charged reversed phase (PR) x conventional RP LC system in different pH conditions was constructed with the orthogonality of 74%. A theoretical stepwise PIL among 310-950 Da with the step-size of 2 Da was developed to selectively trigger fragmentations and extend the coverage of potential indole alkaloids. Simultaneously, by defining parent mass width (PMW) of the step-wise PIL to +/- 55 mDa, a raster-MDF screening was achieved in the acquisition process. Additionally, subtype classification and structural elucidation were facilitated by a four-step interpretation strategy. As a result, a total of 1227 indole alkaloids were efficiently exposed and characterized from five botanical origins of Gou-Teng, which showed high chemical diversity. A systematic comparison among five species was first performed and only 66 indole alkaloids were common. For method validation, three new alkaloid N-oxides were isolated and unambiguously identified by NMR. The present study provides a novel data-dependent acquisition method with improved target coverage and high selectivity. The integrated strategy is practical to efficiently expose and comprehensively characterize complex components in herbal medicines. (C) 2018 Elsevier B.V. All rights reserved.