Comprehensive Strategy to Construct In-House Database for Accurate and Batch Identification of Small Molecular...

Area of Science:

• Analytical Chemistry
• Biochemistry
• Metabolomics

Background:

• Metabolite identification is essential for interpreting physiological and pathological processes.
• Current liquid chromatography-tandem mass spectrometry (LC-MSn) based metabolomics studies face challenges due to complexity, chemical diversity, and limited standard databases.
• Accurate and high-throughput metabolite identification remains a bottleneck in untargeted metabolomics.

Purpose of the Study:

• To develop a comprehensive strategy for accurate and batch metabolite identification in untargeted metabolomics.
• To improve the reliability and efficiency of metabolite identification using LC-MSn data.
• To provide a robust method for analyzing complex biological samples.

Main Methods:

• A standardized procedure was established to generate reliable LC-MS2 data, including retention time (tR), MS1, and MS2 information.
• An in-house database of approximately 2000 metabolites was constructed.
• Metabolite identification involved retention time calibration, precursor ion alignment and fusion, automated MS2 data extraction, and database searching with scoring.

Main Results:

• The developed strategy successfully identified metabolites in untargeted metabolic profiling.
• Analysis of a pooled serum sample identified 202 metabolites in positive ion mode.
• The method demonstrated accuracy and batch processing capabilities.

Conclusions:

• The proposed comprehensive strategy is effective for accurate and batch metabolite identification in LC-MSn-based untargeted metabolomics.
• This approach enhances the ability to interpret complex metabolic profiles.
• The strategy offers a valuable tool for advancing metabolomics research.