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.