Decoding the Oxylipin Chemical Space Using Ion Identity Molecular Networking

Area of Science:

• Lipidomics
• Bioactive Lipid Mediators
• Mass Spectrometry

Background:

• Oxylipins are crucial bioactive lipid mediators involved in various biological and pathological processes.
• The chemical diversity of oxylipins presents significant challenges for identification using untargeted LC-MS/MS analyses.
• Current methods for comprehensive oxylipin characterization are limited.

Purpose of the Study:

• To implement and evaluate the Ion Identity Molecular Networking (IIMN) strategy for mapping the chemical space of oxylipins.
• To systematically assess factors affecting accurate annotation of MS/MS datasets for oxylipins.
• To develop a robust bioinformatic tool for decoding oxylipin diversity and discovering novel mediators.

Main Methods:

• Implementation of a fully local IIMN analysis strategy using mzmine software.
• Creation of a high-quality MS/MS spectral library from 67 oxylipin standards.
• Integration of ion species characterization into IIMN and application of a modified cosine algorithm.
• Analysis of mouse spleen extracts using IIMN with combined spectral libraries.

Main Results:

• The IIMN strategy effectively mapped the oxylipin chemical space, reducing network complexity by integrating ion species characterization.
• The modified cosine algorithm successfully separated oxylipin forms and clustered structurally related compounds.
• Application to biological samples facilitated oxylipin organization into molecular families, aiding structural characterization and novel species discovery.
• A publicly available reference spectral library was generated.

Conclusions:

• Ion Identity Molecular Networking (IIMN) is established as a robust bioinformatic tool for oxylipin analysis.
• The developed workflow significantly improves the interpretability and understanding of oxylipin MS/MS data.
• This approach provides a successful strategy for characterizing oxylipins and discovering novel mediators in biological matrices, advancing redox lipidomics research.