An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biochemistry

Background:

• Untargeted metabolomics aims for comprehensive metabolite profiling and biomarker discovery.
• Current methods require optimization in sample preparation, separation, and data processing for improved metabolome coverage.
• Accurate metabolite identification is crucial for downstream biological analysis.

Purpose of the Study:

• To develop an improved detection and identification strategy for untargeted metabolomics using Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS).
• To enhance metabolite identification confidence by integrating multiple data sources and analytical techniques.
• To validate the strategy's efficacy using biological samples.

Main Methods:

• Optimized sample preparation considering metabolite chemical properties.
• Employed dual-column chromatography and positive/negative ion mode MS detection.
• Developed an integrated identification strategy using fragment simulation, MS/MS library searching (HMDB, METLIN), decision tree analysis, and a lab-developed database.
• Validated the approach with liver samples from obese mice and controls.

Main Results:

• The developed strategy significantly improved metabolite detection and identification accuracy.
• Accurate detection of 238 metabolites was achieved in the validation study.
• The integrated approach enhanced confidence in metabolite identification by considering fragmentation, biological source, and function.

Conclusions:

• The novel UPLC-MS based strategy markedly improves metabolite identification accuracy in untargeted metabolomics.
• This enhanced identification facilitates subsequent biomarker discovery and pathway analysis.
• The developed method provides a robust platform for comprehensive metabolomic investigations.