Objective:

To introduce untargeted multiple reaction monitoring (uMRM) as a data-driven framework for converting untargeted MS/MS datasets into optimized MRM transitions, enhancing scalability and reproducibility in metabolomics and lipidomics.

Key Findings:

• uMRM shows strong agreement with experimentally optimized methods across seven instruments.
• The method allows for the conversion of large discovery datasets into lighter quantitative outputs.
• AI enhances the adaptability and transferability of the quantitation process across laboratories.

Interpretation:

uMRM provides a systematic and reproducible approach to quantitative mass spectrometry, addressing challenges in standardization and scalability in metabolomics and lipidomics.

Limitations:

• Dependence on the availability of empirical datasets for effective AI modeling.
• Potential variability in results due to differences in laboratory practices.

Conclusion:

uMRM represents a significant advancement in bridging untargeted and quantitative mass spectrometry, facilitating better data integration and consistency across research settings.