Enhanced Structure-guided Molecular Networking Annotation Method for Untargeted Metabolomics Data from Orbitrap...

Area of Science:

• Metabolomics
• Analytical Chemistry
• Bioinformatics

Background:

• Accurate compound annotation in complex samples is a major metabolomics challenge.
• Existing methods do not fully utilize advanced Orbitrap Astral mass spectrometry (MS) capabilities.
• The Orbitrap Astral MS offers high sensitivity and fast MS/MS scanning speeds.

Purpose of the Study:

• To develop an enhanced structure-guided molecular networking (E-SGMN) method tailored for Orbitrap Astral MS.
• To improve the efficiency, accuracy, and coverage of metabolite annotation.
• To exploit the full potential of the Astral MS for metabolomics research.

Main Methods:

• Developed an enhanced structure-guided molecular networking (E-SGMN) method.
• Integrated metabolome databases with structural similarity for network construction.
• Applied E-SGMN specifically to data generated by Orbitrap Astral MS.

Main Results:

• Astral-E-SGMN achieved 76.84% annotation coverage and 78.08% accuracy on spiked plasma.
• Significantly outperformed previous methods (e.g., E-SGMN-QE HF).
• Annotated 5440 metabolite features in human plasma, a 3.6-fold increase over QE HF-SGMN.

Conclusions:

• E-SGMN-Astral significantly enhances metabolite annotation coverage and accuracy compared to conventional methods.
• The method demonstrates a 3.7-44.2 fold increase in metabolite annotations across various biological samples.
• This approach offers a transformative tool for complex biological system analysis in life sciences and clinical medicine.