Effective Carbon Number and Inter-Class Retention Time Conversion Enhances Lipid Identifications in Untargeted Clinical Lipidomics

Chromatography is often used as a method for reducing sample complexity prior to analysis by mass spectrometry, the use of retention time (RT) is becoming increasingly popular to add valuable supporting information in lipid identification. The RT of lipids with the same headgroup in reverse-phase separation can be predicted using the effective carbon number (ECN) model. This model describes the effect of acyl chain length and degree of saturation on lipid RT, which increases predictably with acyl chain length and degree of saturation. Furthermore, we have found a robust correlation in the chromatographic separation of lipids with different headgroups that share the same fatty acid motive. By measuring a small number of lipids from each subclass it is possible to build a model that allows for the prediction of the RT of one lipid subclass based on another. Here, we utilise ECN modelling and inter-class retention time conversion (IC-RTC) to build a glycerophospholipid RT library with 481 entries based on 136 MS/MS characterised lipid RTs from NIST SRM-1950 plasma and lipid standards. The library was tested on a patient cohort undergoing coronary artery bypass grafting surgery (n=37). A total of 129 unique circulating glycerophospholipids were identified, of which, 57 (4 PC, 24 PE, 4 PG, 15 PI, 10 PS) were detected with IC-RTC, thereby demonstrating the utility of this technique for the identification of lipid species not found in commercial standards.