Related Experiment Video
Updated: Aug 6, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
Published on: March 14, 2013
Enhanced Structure-guided Molecular Networking Annotation Method for Untargeted Metabolomics Data from Orbitrap
Xinxin Wang1,2,3, Yao Chen1,2,3, Zaifang Li1,3
1State Key Laboratory of Medical Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
Abstract:
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-SGMN-Astral enhanced metabolite annotations by 3.7-44.2 times compared to conventional annotation methods, highlighting E-SGMN's wider metabolite annotation coverage. This method not only enhances annotation coverage, but also provides a transformative tool for understanding complex biological systems, holding significant potential for life science and clinical medicine.
Related Concept Videos
Tandem Mass Spectrometry
Mass Spectrometry: Overview
Mass Spectrometry: Complex Analysis
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

