Computational mass spectrometry accelerates C = C position-resolved untargeted lipidomics using oxygen attachment...

Area of Science:

• Lipidomics
• Mass Spectrometry
• Biochemistry

Background:

• Lipidomics provides a molecular-level view of organismal lipidomes.
• Accurate characterization of double bond positions in lipids is crucial but challenging.
• Existing methods lack comprehensive double bond position resolution.

Purpose of the Study:

• To develop and validate a novel approach for double bond position-resolved untargeted lipidomics.
• To enhance the annotation rate of lipid structures, particularly polyunsaturated fatty acids (PUFAs).
• To characterize tissue-specific lipid profiles with high structural resolution.

Main Methods:

• Utilized oxygen attachment dissociation (OAD) combined with computational mass spectrometry.
• Validated the platform using 85 authentic lipid standards and 52 biogenic PUFA-containing molecules.
• Analyzed human and mouse-derived samples, including cultured cells and tissues.

Main Results:

• Successfully characterized 648 unique lipids at the double bond position-resolved level across 24 LIPIDMAPS subclasses.
• Demonstrated high accuracy through validation with authentic and biogenic standards.
• Identified unique profiles of tissue-specific very long-chain PUFAs (≥C28, ≥4 double bonds) in mouse eye, testis, and brain.

Conclusions:

• The developed platform significantly improves the annotation rate and structural resolution in untargeted lipidomics.
• This method provides unprecedented insights into the complex lipidome, especially concerning PUFA structures.
• Revealed novel insights into tissue-specific lipid compositions relevant to biological functions.