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基于特征的分子网络在磷脂组学分析中的发展与应用。

Development and Application of Feature-Based Molecular Networking for Phospholipidomics Analysis.

机构信息

State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Reacher Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, No. 1800, Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.

Ausnutria Dairy (China) Co., Ltd., Changsha 410200, Hunan, People's Republic of China.

出版信息

J Agric Food Chem. 2022 Jun 29;70(25):7815-7825. doi: 10.1021/acs.jafc.2c01770. Epub 2022 Jun 16.

DOI:10.1021/acs.jafc.2c01770
PMID:35709392
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.

摘要

磷脂是牛奶中较小但至关重要的脂质。传统的脂质组学是分析牛奶中脂质的强大方法。尽管过去五年中脂质组学软件的数量急剧增加,但降低假阳性和获得磷脂结构准确注释仍然是一个重大挑战。在这项研究中,我们开发了一种快速准确的方法来测量牛奶中广泛的磷脂。与顺序窗口采集所有理论质谱(SWATH)和 MS-DIAL 相比,所开发的采用信息依赖性采集(IDA)模式和基于特征的分子网络的方法在数据处理和脂质注释方面表现出更好的性能。该验证方法还使用三种绵羊奶进行了进一步评估。总共鉴定出 150 种磷脂,包括含有醚或乙烯醚的罕见报道的磷脂。结果表明,磷脂可用作潜在标志物,以区分来自不同品种和来源的绵羊奶。该实验和计算方法为研究牛奶中的磷脂提供了一种快速可靠的方法。

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