Enhancing coverage of annotated compounds in traditional Chinese medicine formulas: Integrating MSE and Fast-DDA molecular network with AntDAS—Case study of Xiao Jian Zhong Tang

Abstract

The chemical characterisation of traditional Chinese medicine formulas (TCMFs) using mass spectrometry poses notable challenges owing to their complex and diverse chemical compositions. While acquisition modes such as data-dependent acquisition (DDA) and data-independent acquisition (DIA) offer new insights, DDA's tendency to overlook low-abundance ions and DIA's complicated data processing, particularly in matching MS¹ and MS² information, limit the effective annotation of valuable compounds in TCMFs. Herein, we present a new integrated strategy to enhance the coverage of annotated compounds in TCMFs, using Xiao Jian Zhong Tang (XJZ) as a case study. First, we characterised the components of XJZ through UNIFI software in Fast-DDA and DIA modes. We then summarised the diagnostic ions and substituent information of the identified compounds based on the Fast-DDA data, integrating molecular networks and AntDAS to predict unknown components and uncover potential components. Ultimately, we characterised a total of 785 components in XJZ, including 43 that were unique to XJZ when compared to the individual herbs involved. The presence of these new components may result from the recombination of substituents during compatibility. In conclusion, this new integrated strategy facilitates more in-depth characterisation of components in TCMFs, providing a new direction for exploring the compatibility principles among TCMFs.