Isotopologue pattern based data mining for selenium species from HILIC-ESI-Orbitrap-MS-derived spectra

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biochemistry

Background:

• Identifying selenium-containing molecules is challenging for mass spectrometry software.
• Existing methods often require complex setups or suffer from poor metabolite retention.

Purpose of the Study:

• To develop and evaluate a comprehensive pattern matching approach for automated selenium metabolite detection.
• To assess the reproducibility and accuracy of selenium isotope selection in complex biological samples.
• To improve the efficiency and validation of selenometabolite screening.

Main Methods:

• Utilized hydrophilic interaction liquid chromatography (HILIC) for metabolite separation.
• Applied a pattern matching approach based on intra-isotopologue distance and isotopologue ratio data.
• Incorporated absolute mass defect (MD) data and multivariate statistical analysis for screening and validation.
• Used inductively coupled plasma-MS for chromatographic verification.

Main Results:

• Achieved a >88% recovery rate and <10% false positive rate for selenium-containing molecules.
• Identified 75 selenium species, proposing elemental compositions for 72 using accurate mass and deamination processes.
• Demonstrated the effectiveness of 78Se-80Se and 80Se-82Se isotope pairs for detection.
• Absolute MD data successfully differentiated false positive entities.

Conclusions:

• The developed pattern matching approach significantly enhances the automated detection of selenium metabolites.
• The method offers high efficiency and accuracy, reducing artefacts in selenometabolite analysis.
• This technique advances the study of selenium biochemistry in biological systems.