Integration of feature-based molecular networking and high-definition data-dependent acquisition for the...

Systematically identifying the chemical constituents in complex matrices is a challenge due to the inherent characteristics of compounds. The combination of liquid chromatography-tandem mass spectrometry (LC-MS) and classical molecular networking (CLMN) is a powerful technology for annotating small molecules. However, the low coverage from inappropriate acquisition modes and the inseparability of isomeric compound nodes still hinders the comprehensive metabolite characterization. A novel strategy that integrated high-definition data-dependent acquisition (HDDDA) from traveling-wave ion mobility mass spectrometry (TWIMS) and feature-based molecular networking (FBMN) was developed to improve chemical component characterization and enhance isomeric component discernment. The data-dependent acquisition (DDA) and HDDDA, were effectively and visually evaluated by CLMN and FBMN via the number of nodes, clustered nodes and clusters. Moreover, the efficiency of the three strategies was validated. The results strongly demonstrated that the HDDDA-FBMN strategy improves MS coverage and offers significant advantages for isomer identification. With the assistance of the UNIFI platform, the developed strategy was successfully applied to systematically investigate the chemical profile of Honghua Xiaoyao Tablet (HHXYT), a traditional folk empirical prescription for treating various gynecological diseases. 184 compounds were unambiguously identified or tentatively characterized, including 12 pairs of isomers, and two unreported compounds. In conclusion, this hybrid approach achieves dimensionally enhanced MS data acquisition and visual recognition of isomeric compounds, accelerating the structural characterization in complex systems. We anticipate that HDDDA-FBMN strategies will be a flexible and versatile tool for the chemical components in a complex system of TCMs.