Enhanced Structure-guided Molecular Networking Annotation Method for Untargeted Metabolomics Data from Orbitrap Astral Mass Spectrometer

作者：Xin Wang, Yao Chen, Zaifang Li, Ziquan Fan, Rui Zhong, Tian Liu, Xiangjun Li, Xin Lu, Guowang Xu · 发表于：Analytical Chemistry · 年份：2025 · DOI：10.1021/acs.analchem.5c00314 · 被引用次数：5 · 研究领域：Metabolomics and Mass Spectrometry Studies、Computational Drug Discovery Methods、Bioinformatics and Genomic Networks

The rapid, efficient, and accurate annotation of compounds in complex samples remains a significant challenge in metabolomics. The recently developed Orbitrap Astral mass spectrometer (MS) integrates a traditional quadrupole Orbitrap with a novel Astral mass analyzer, providing fast MS/MS scanning speed and high sensitivity. However, existing metabolomics annotation methods have not fully exploited the advanced capabilities of Astral MS. In this study, an enhanced structure-guided molecular networking (E-SGMN) method was developed, which is specifically tailored for the Orbitrap Astral mass spectrometer (MS). Unlike previous network annotation methods, E-SGMN extracted both previously detected metabolites and those potentially detected by Astral from the metabolome database, enabling more efficient and accurate network construction through structural similarity. E-SGMN expands annotation coverage by accurately improving network size, while minimizing the inclusion of irrelevant compounds, achieving a balance between annotation scale and accuracy. Validation results revealed that Astral-E-SGMN achieved an annotation coverage and accuracy of 76.84% and 78.08%, respectively, for a spiked plasma, significantly outperforming E-SGMN-Q Exactive HF (E-SGMN-QE HF). Notably, 5440 metabolite features from NIST SRM 1950 human plasma were annotated by Astral-E-SGMN, a 3.6-fold increase over QE HF-SGMN. Comparative analyses for six types of typical biological samples demonstrate that E-SGM...