High Coverage Quantitative Lipidomic Analysis for Multiple Biological Matrices Using Ultrahigh-Performance Liquid-Chromatography and Tandem Mass Spectrometry

The lipid composition (lipidome) in biological samples is extremely complex, having diverse biofunctions. Quantifying lipidomes with high coverage is vital to understand such functions but challenging due to their levels spanning several orders of magnitude, limited available standards, and poor chromatographic performances for many acidic lipids such as sphingosine-1-phosphate, phosphatidylserines, and phosphatidic acids. Here, we report a reliable method for high-coverage quantitative lipidomics using ultrahigh-performance liquid chromatography and tandem mass spectrometry (UHPLC-MS/MS). By using both pH and ammonium gradients in elution, all lipids, especially acidic ones, had obviously improved LC separation. By using 267 lipid standards in 49 subclasses, we also established quantitative structure–retention relationship models to predict the retention time (tR) with good accuracy (ΔtR E. coli, Arabidopsis leaves, mouse liver tissue, and feces. This offers a high-coverage quantitative method for understanding molecular phenotypes associated with lipid functions in physiology and pathophysiology.生物样本中的脂质组（lipidome）组成极为复杂，且具备多样的生物学功能。实现高覆盖度的脂质组定量分析对解析此类功能至关重要，但却颇具挑战：这是因为脂质的浓度跨度可达数个数量级、可用标准品有限，且多数酸性脂质（如鞘氨醇-1-磷酸（sphingosine-1-phosphate）、磷脂酰丝氨酸（phosphatidylserines）与磷脂酸（phosphatidic acids））的色谱分离表现欠佳。本研究报道了一种基于超高效液相色谱-串联质谱（UHPLC-MS/MS）的高覆盖度定量脂质组学可靠分析方法。通过在洗脱过程中同时采用pH梯度与铵盐梯度，所有脂质（尤其是酸性脂质）的液相色谱分离效果均得到显著提升。本研究针对49个亚类的267种脂质标准品，构建了定量结构-保留关系模型以预测脂质保留时间（tR），且预测精度良好，并将该方法应用于大肠杆菌（E. coli）、拟南芥叶片、小鼠肝组织与粪便四类典型生物基质中。该方法可为解析生理与病理生理过程中与脂质功能相关的分子表型提供一种高覆盖度的定量分析手段。