Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification

Area of Science:

• Analytical Chemistry
• Metabolomics
• Spectroscopy

Background:

• Tandem mass spectrometry (MS/MS) is crucial for identifying small molecules and metabolites.
• MS/MS spectrum variability poses challenges for building standardized reference libraries.
• Existing spectral libraries often lack comprehensive data across various acquisition parameters.

Purpose of the Study:

• To develop a method for augmenting existing MS/MS spectral libraries.
• To improve the identification accuracy of small molecules and metabolites by addressing data gaps.
• To enhance the utility of MS/MS reference libraries through statistical interpolation.

Main Methods:

• Statistically interpolating spectra at unreported collision energies.
• Augmenting experimental spectra data sets with interpolated data.
• Validating interpolated spectra against experimental data from the same instrument.

Main Results:

• Highly accurate spectral approximations can be interpolated from as few as three experimental spectra.
• Interpolated spectra are consistent with true spectra acquired on the same instrument.
• Supplementing spectral databases with interpolated spectra consistently improves identification accuracy across various instruments and precursor types.

Conclusions:

• The interpolation method significantly improves spectral matching, identifying ~10% more spectra correctly in large datasets.
• This approach enhances spectral matching across different instrument types and collision energies.
• The method provides a quick and adept tool for improving spectral matching when reference libraries are insufficient.