Lipid Mass Tags via Aziridination for Probing Unsaturated Lipid Isomers and Accurate Relative Quantification. (24th August 2022)

• Record Type: Journal Article
• Authors: Yang, Tingyuan; Tang, Shuli; Kuo, Syuan‐Ting; Freitas, Dallas; Edwards, Madison; Wang, Hongying; Sun, Yuxiang; Yan, Xin
• Is Part Of: Angewandte Chemie. Volume 134:Number 39(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-24
• Subjects: Aziridination -- Double Bond Positional Isomer -- Isobaric Tag -- Lipid Isomer -- Lipid Quantification; Chemistry -- Periodicals; 540
• DOI: 10.1002/ange.202207098
• Languages: English
• ISSNs: 0044-8249

Abstract

Abstract: Knowing concentrations of lipids is essential for understanding their physiological functions and discovering new disease biomarkers. However, it is highly challenging to accurately quantify lipids due to structural diversity and multiple isomeric forms of lipids. To address these critical gaps, we have developed a novel aziridine‐based isobaric tag labelling strategy that allows (i) determination of lipid double‐bond positional isomers, (ii) accurate relative quantification of unsaturated lipids, and (iii) improvement of ionization efficiencies of nonpolar lipids. The power of this method is demonstrated in characterization and quantification of various categories of lipids such as fatty acids, phosphoglycerol lipids, cholesteryl esters (CE), and glycerides. 17 CE lipid isomers were identified and quantified simultaneously from Alzheimer's disease (AD) mouse serum without using lipid standards. Among them, 6 CE isomers showed significant changes in concentrations in AD serum. Abstract : An aziridine‐based isobaric labeling strategy is developed for lipid accurate relative quantification, isomer identification, and ionization efficiency improvement. This strategy allows multiplex analysis without using lipid internal standards and characterization of lipid double‐bond positional isomers. We envision it will accelerate the discovery of lipid biomarkers for early disease diagnosis, progression monitoring, and therapeutic response prediction.