A four-dimensional separation approach by offline 2D-LC/IM-TOF-MS in combination with database-driven computational peak annotation facilitating the in-depth characterization of the multicomponents from Atractylodis Macrocephalae Rhizoma (Atractylodes macrocephala)

Abstract

Plant metabolites represent complex chemical system, which renders it difficult to clarify the chemical composition by conventional liquid chromatography/mass spectrometry (LC/MS) due to the limited selectivity and peak capacity. The rhizomes of Atractylodes macrocephala have been utilized as a traditional Chinese medicine Atractylodis Macrocephalae Rhizoma (Bai-Zhu), and have been reported containing multiple categories of plant metabolites. Targeting the multicomponents from A. macrocephala, an integral approach by offline two-dimensional liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS) was established and validated. By configuring an XBridge Amide column of Hydrophilic Interaction Chromatography and an Atlantis Premier BEH C18AX column of mixed ion exchange and reversed-phase modes, the established 2D-LC/IM-QTOF-MS system showed high orthogonality up to 0.91. Dimension-enhanced, data-independent high-definition MSE (HDMSE) in the positive ESI mode was conducted on a Vion IM-QTOF mass spectrometer, and its hyphenation to offline 2D-LC could enable the four-dimensional separation (each dimension in 2D-LC, IM, and MS). Particularly, HDMSE facilitated the acquisition of high-definition MS1 and MS2 spectra. In-house library-driven computational peak annotation by the bioinformatics platform UNIFI could efficiently process and annotate the HDMSE data for the structural elucidation. By integrating reference compounds comparison, we could identify or tentatively characterize 251 components from A. macrocephala (including 115 sesquiterpenoids, 90 polyacetylenes, 11 flavonoids, 9 benzoquinones, 12 coumarins, and 14 others), which indicated large improvement in identifying those minor plant components, compared with the conventional LC/MS approach. Conclusively, offline 2D-LC/IM-QTOF-HDMSE in combination with computational data interpretation proves to be powerful facilitating the in-depth multicomponent characterization of herbal medicine.