Optimized 13 C-TrEnDi Enhances the Sensitivity of Plasmenyl Ether Glycerophospholipids and Demonstrates Compatibility with Other Derivatization Strategies.

The identification and quantitation of plasmalogen glycerophospholipids is challenging due to their isobaric overlap with plasmanyl ether-linked glycerophospholipids, susceptibility to acid degradation, and their typically low abundance in biological samples. Trimethylation enhancement using diazomethane (TrEnDi) can be used to significantly enhance the signal of glycerophospholipids through the creation of quaternary ammonium groups producing fixed positive charges using 13 C-diazomethane in complex lipid extracts. Although TrEnDi requires a strong acid for complete methylation, we report an optimized protocol using 10 mM HBF 4 with the subsequent addition of a buffer solution that prevents acidic hydrolysis of plasmalogen species and enables the benefits of TrEnDi to be realized for this class of lipids. These optimized conditions were applied to aliquots of bovine liver extract (BLE) to achieve permethylation of plasmalogen lipids within a complex mixture. Treating aliquots of unmodified and TrEnDi-derivatized BLE samples with 80% formic acid and comparing their liquid chromatography mass spectrometry (LCMS) results to analogous samples not treated with formic acid, enabled the identification of 29 plasmalogen species. On average, methylated plasmalogen species from BLE demonstrated 2.81-fold and 28.1-fold sensitivity gains over unmodified counterparts for phosphatidylcholine and phosphatidylethanolamine plasmalogen species, respectively. Furthermore, the compatibility of employing 13 C-TrEnDi and a previously reported iodoacetalization strategy was demonstrated to effectively identify plasmenyl-ether lipids in complex biological extracts at greater levels of sensitivity. Overall, we detail an optimized 13 C-TrEnDi derivatization strategy that enables the analysis of plasmalogen glycerophospholipids with no undesired cleavage of radyl groups, boosting their sensitivity in LCMS and LCMS/MS analyses.