Mathematical Model-Assisted UHPLC-MS/MS Method for Global Profiling and Quantification of Cholesteryl Esters in Hyperlipidemic Golden Hamsters.

Cholesteryl esters (CEs) are formed by the 3-hydroxyl group of cholesterol and a fatty acyl chain through an ester bond and function as a biologically inert storage form of cholesterol. Abnormal CE levels are often related to various diseases, particularly hyperlipidemia and atherosclerosis. Herein, we developed a mathematical model-assisted ultrahigh performance liquid chromatography-mass spectrometry (UHPLC-MS) method for the untargeted identification to targeted quantification of CEs in plasma, different density lipoprotein samples from humans, rats, and golden hamsters. Using UHPLC-quadrupole-time-of-flight mass spectrometry (UHPLC-QTOF-MS), 81 CE candidates were detected in the above samples, of which 24 CEs were reported in the Human Metabolome Database and 57 CEs were newly identified based on an in-house database of theoretically possible CEs, including the computationally generated precursor ion m/ z mass of CE, carbon number and double bond numbers of the fatty acyl chain. Then three mathematical models based on the characteristic chromatographic retention behavior related to structural features were established and validated using commercial and synthetic CE standards. The mathematical model-assisted UHPLC-MS/MS strategy was proposed to provide a global profiling and identification of CEs, especially unknown CEs. With the efficient strategy, 74 CEs in the plasma of golden hamsters were identified and then quantified in normal and hyperlipidemic golden hamsters by dynamic multiple reaction monitoring (dMRM). A total of 21 CEs among 35 shared potential biomarkers were newly found for hyperlipidemia. Our work will contribute to the in-depth study of the functions of CEs and the discovery of disease biomarkers.