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Researchers have developed an innovative strategy for compound annotation in offline 2D-LC–MS analysis. The approach combines hand-in-hand alignment with targeted molecular networking (TMN) and was successfully applied to analyze the chemical profile of Yupingfeng (YPF), a traditional Chinese medicine prescription, resulting in the identification of 497 compounds and demonstrating its efficiency and scalability in complex sample analysis.
Component overlapping and long analysis times have posed challenges for data processing in offline two-dimensional liquid chromatography–mass spectrometry (offline 2D-LC–MS) systems. To address these issues, researchers have developed a novel strategy combining hand-in-hand alignment with targeted molecular networking (TMN) to annotate compounds in offline 2D-LC–MS data. In a recent study, published in the Journal of Chromatography A, the researchers successfully applied this approach to analyze the chemical profile of Yupingfeng (YPF), a classical traditional Chinese medicine (TCM) prescription, demonstrating its potential for efficient and rapid compound annotation in complex samples (1).
The study began by constructing an offline 2D-LC–MS system for the separation and data acquisition of YPF extract. The data obtained from 12 fractions of YPF were processed using a hand-in-hand alignment method, which resulted in a significant reduction in component overlapping by 49.2% and improved the quality of MS2 spectra. Subsequently, a self-building Python script computed the MS2-similarity adjacency matrix of focused parent ions, enabling the construction of an innovative TMN. This TMN successfully distinguished and visualized co-elution, in-source fragmentations, and multi-type adduct ions in a clustering network.
The MS2-similarity adjacency matrix of focused parent ions refers to a mathematical representation of the similarities between the fragmentation patterns (MS2 spectra) of precursor ions in a mass spectrometry analysis. It quantitatively assesses the degree of similarity between the spectra, indicating how closely related the compounds are in terms of their fragmentation patterns. By constructing this adjacency matrix, an innovative targeted molecular networking (TMN) can be created, which visually organizes and clusters compounds based on their similarity, facilitating the identification and visualization of co-elution, in-source fragmentations, and multi-type adduct ions. This approach allows researchers to gain insight into the relationships and structural similarities between compounds in a complex sample, aiding in compound annotation and analysis.
TMN analyses guided by product ions filtering (PIF) and neutral loss filtering (NLF) are strategies used to enhance the identification and annotation of compounds in a targeted molecular networking (TMN) approach. PIF involves filtering the MS2 spectra based on the presence of specific product ions that are characteristic of certain compounds or structural features, allowing for the selective inclusion of relevant ions in the analysis. On the other hand, NLF focuses on filtering based on the presence of specific neutral losses, which are indicative of specific functional groups or modifications. By applying these filtering techniques, the TMN analysis becomes more targeted and specific, allowing for the identification of compounds with desired characteristics and facilitating the interpretation of the clustering network. These strategies aid in the efficient discovery and annotation of compounds in complex samples, leading to a more comprehensive understanding of the chemical profile and potential biological activities.
Using this integrated strategy, the researchers identified a total of 497 compounds in YPF by performing seven TMN analyses guided by product ions filtering (PIF) and neutral loss filtering (NLF). This approach not only improved the efficiency of targeted compound discovery in offline 2D-LC–MS data but also demonstrated its scalability and applicability for accurate compound annotation in complex samples. Overall, the study provides valuable insights and tools for efficient compound annotation in traditional Chinese medicine prescriptions and other complex samples, highlighting the potential of the developed strategy in advancing research and analysis in the field of natural products and metabolomics.
(1) Zhu, H.; He, L.; Wu, W.; Duan, H.; Chen, J.; Xiao, Q.; Lin, P.; Qin, Z.; Dai, Y.; Wu, W.; Hu, L.; Yao, Z. A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study. J. Chromatogr. A 2023, 1702, 464045.DOI: https://doi.org/10.1016/j.chroma.2023.464045