Derivatization Strategy for the Comprehensive Characterization of Endogenous Fatty Aldehydes Using HPLC-Multiple Reaction Monitoring

Fatty aldehydes are crucial substances that mediate a wide range of vital physiological functions, particularly lipid peroxidation. Fatty aldehydes such as acrolein and 4-hydroxynonenal (4-HNE) are considered potential biomarkers of myocardial ischemia and dementia, but analytical techniques for fatty aldehydes are lacking. In the present study, a comprehensive characterization strategy with high sensitivity and facility for fatty aldehydes based on derivatization and high-performance liquid chromatography-multiple reaction monitoring (HPLC-MRM) was developed. The fatty aldehydes of a biosample were derivatized using 2,4-bis­(diethylamino)-6-hydrazino-1,3,5-triazine under mild and efficient reaction conditions at 37 °C for 15 min. The limit of detection (LOD) of the fatty aldehydes varied from 0.1 to 1 pg/mL, depending on the structures of these molecules. General MRM parameters were forged for the analysis of endogenous fatty aldehydes. “Heavy” derivatization reagents with 20 deuterium atoms were synthesized for both the discovery and comprehensive characterization of fatty aldehydes. More than 80 fatty aldehydes were detected in the biosamples. The new strategy was successfully implemented in global fatty aldehyde profiling of plasma and brain tissue of the bilateral common carotid artery (2VO) dementia rat model. Dozens of fatty aldehydes were significantly changed between the control and model groups. These findings further highlight the importance of endogenous fatty aldehydes

Automatic Identification Approach for High-Performance Liquid Chromatography-Multiple Reaction Monitoring Fatty Acid Global Profiling

Fatty acids (FAs) are a group of lipid molecules that are essential to organisms. As potential biomarkers for different diseases, FAs have attracted increasing attention from both biological researchers and the pharmaceutical industry. A sensitive and accurate method for globally profiling and identifying FAs is required for biomarker discovery. The high selectivity and sensitivity of high-performance liquid chromatography-multiple reaction monitoring (HPLC-MRM) gives it great potential to fulfill the need to identify FAs from complicated matrices. This paper developed a new approach for global FA profiling and identification for HPLC-MRM FA data mining. Mathematical models for identifying FAs were simulated using the isotope-induced retention time (RT) shift (IRS) and peak area ratios between parallel isotope peaks for a series of FA standards. The FA structures were predicated using another model based on the RT and molecular weight. Fully automated FA identification software was coded using the Qt platform based on these mathematical models. Different samples were used to verify the software. A high identification efficiency (greater than 75%) was observed when 96 FA species were identified in plasma. This FAs identification strategy promises to accelerate FA research and applications