Differentiation of Alkyl- and Plasmenyl-phosphatidylcholine by Endogenous Sphingomyelin RT-XLOGP3 Regression for Coronary Artery Disease Plasma Lipidomics Analysis

Accurate identification between alkyl- and plasmenyl-phosphatidylcholine (PC-(O-) and PC-(P-)) isomers is a major analytical challenge in lipidomics studies due to a lack of structure-specific ions in conventional tandem mass spectrometry (MS/MS) methods and the absence of universal retention time (RT) references. Given the importance of PC-(O-) and PC-(P-), an easy-to-apply method for current research is urgently needed. In this study, we present a quadratic RT-XLOGP3SM regression model that uses endogenous sphingomyelin (SM) species in blood samples as retention time (RT) indicators to predict the RTs of PC-(O-) and PC-(P-) species by coupling their calculated partition coefficients based on XLOGP3. The prediction results were obtained with a root-mean-square error (RMSE) of 0.12 min (1.3%) for the RRHD (rapid resolution high definition) nonlinear LC condition. A lipidomic analysis with RT-XLOGP3SM regression was used to study lipid regulation in coronary artery disease (CAD) outpatient plasma samples, and we found that the types of exhibited regulation were highly dependent on the lipid subclasses in comparison to the healthy control group. In conclusion, given that the quadratic RT-XLOGP3SM regression model predicts the RTs of PC species based on the relative value of XLOGP3 and the RTs of endogenous SM species, it can be expected that most of the C18-based lipidomics analyses could apply this method to increase the identification ability of the PC-(O-) and PC-(P-) subclasses and to improve the understanding of their physiological functions.