MS² and LC libraries for untargeted metabolomics: Enhancing method development and identification confidence

Area of Science:

• Metabolomics
• Life Sciences
• Analytical Chemistry

Background:

• Untargeted metabolomics faces challenges in metabolite identification and analytical bias.
• Accurate compound annotation is crucial for biological data interpretation.
• Current methods require improvement for comprehensive metabolomic analysis.

Purpose of the Study:

• To develop a rapid and adaptable method for generating in-house MS2 libraries.
• To enhance metabolite identification confidence in untargeted metabolomics.
• To optimize analytical conditions for broad metabolome coverage.

Main Methods:

• Developed a method for generating in-house MS2 spectral libraries.
• Created a library of over 4,000 fragmentation spectra for 506 compounds across 6 normalized collision energies (NCEs).
• Established a liquid chromatography (LC) library by evaluating 57 LC-MS conditions for 294 compounds.

Main Results:

• Successfully generated extensive MS2 and LC libraries.
• Developed and validated an untargeted metabolomics screening workflow using the generated libraries.
• Demonstrated the workflow's effectiveness in a study of 360 human serum samples.

Conclusions:

• The developed workflow significantly enhances metabolite identification confidence in LC-MS/MS-based metabolomics.
• The method allows for the selection of optimal analytical conditions tailored to specific research needs.
• This approach provides a robust strategy for advancing metabolomic data analysis and biological discovery.