Development and Application of Feature-Based Molecular Networking for Phospholipidomics Analysis
Xiaoying Chen, Xiaoyu Peng, Xian Sun, Lina Pan, Jiachen Shi, Yu Gao, Yuanluo Lei, Fan Jiang, Ruizhi Li, Yuanfa Liu, Yong‐Jiang Xu
Abstract
Phospholipids are small but critical lipids in milk. Conventional lipidomics is a powerful method for the analysis of lipids in milk. Although the number of lipidomics software has drastically increased over the past five years, reducing false positives and obtaining structurally accurate annotations of phospholipids remain a significant challenge. In this study, we developed a rapid and accurate method for measuring a wide spectrum of phospholipids in milk. The developed approach that employed information-dependent acquisition (IDA) mode and feature-based molecular networking has exhibited better performance on data processing and lipid annotation when compared with sequential window acquisition of all theoretical mass spectra (SWATH) and MS-DIAL. This validated method was further evaluated using three kinds of sheep milk. A total of 150 phospholipids were identified, including rarely reported phospholipids containing ethers or vinyl ethers. The result indicated that phospholipids could be used as potential markers to distinguish sheep milk from different varieties and origins. The experimental and computational methods provide a rapid and reliable method for the investigation of phospholipids in milk.