脂质在代谢、癌症、新冠疫情以及其他疾病的相关研究中所显示出的重要性，将脂质组学领域推向了临床研究的前沿。为了理解脂质物种之间的生物学相互作用，需要进行定量和综合分析。然而，由于生物系统中的脂质具有不同的组成结构、多样的理化性质和广泛的浓度动态范围，脂质组学分析常常具有挑战性。为了研究全面的脂质组，我们开发了基于亲水相互作用液相色谱串联质谱（HILIC-MS/MS）的筛查方法，可以检测19个（亚）类中的1200个脂质特征，包括非极性和极性脂质。我们选择了HILIC-MS/MS方法，基于其类别分离性能和脂肪酰链水平信息。我们建立了类别色谱保留行为的三维模型，并进行了跨类别和同类别干扰的评估，以避免对这些特征进行过度报道。该定向HILIC-MS/MS方法经过了完全验证，具有可接受的线性、精密度、重复性和恢复率分析参数。使用每个类别的多内标和事后校正，实现了对SRM 1950 NIST血浆中608个脂质物种的准确定量，通过提供脂肪酰链级别上解析的脂质浓度，扩展了当前数据库。从文献中获得的浓度值与测得浓度之间的总相关系数（R2）范围为0.64至0.84。通过发现与COVID-19严重程度相关的520个差异脂质特征，证明了开发的定向脂质组学方法的适用性。这种高覆盖率和定向性方法将有助于未来在各种疾病背景下对脂质组的研究。版权所有 © 2023 Elsevier B.V. 发布。

The importance of lipids seen in studies of metabolism, cancer, the recent COVID-19 pandemic and other diseases has brought the field of lipidomics to the forefront of clinical research. Quantitative and comprehensive analysis is required to understand biological interactions among lipid species. However, lipidomic analysis is often challenging due to the various compositional structures, diverse physicochemical properties, and wide dynamic range of concentrations of lipids in biological systems. To study the comprehensive lipidome, a hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS)-based screening method with 1200 lipid features across 19 (sub)classes, including both nonpolar and polar lipids, has been developed. HILIC-MS/MS was selected due to its class separation property and fatty acyl chain level information. 3D models of class chromatographic retention behavior were established and evaluations of cross-class and within-class interferences were performed to avoid over-reporting these features. This targeted HILIC-MS/MS method was fully validated, with acceptable analytical parameters in terms of linearity, precision, reproducibility, and recovery. The accurate quantitation of 608 lipid species in the SRM 1950 NIST plasma was achieved using multi-internal standards per class and post-hoc correction, extending current databases by providing lipid concentrations resolved at fatty acyl chain level. The overall correlation coefficients (R2) of measured concentrations with values from literature range from 0.64 to 0.84. The applicability of the developed targeted lipidomics method was demonstrated by discovering 520 differential lipid features related to COVID-19 severity. This high coverage and targeted approach will aid in future investigations of the lipidome in various disease contexts.Copyright © 2023. Published by Elsevier B.V.