Department of Chemistry , University of Rome "La Sapienza" , Piazzale Aldo Moro 5 , Rome , Italy.
Department of Chemistry, Biology and Biotechnology , University of Perugia , Via Elce di Sotto 8 , 06123 Perugia , Italy.
Anal Chem. 2018 Oct 16;90(20):12230-12238. doi: 10.1021/acs.analchem.8b03482. Epub 2018 Sep 24.
The work describes the chromatographic separation optimization of polar lipids on Kinetex-EVO, particularly focusing on sulfolipids in spirulina microalgae ( Arthrospira platensis). Gradient shape and mobile-phase modifiers (pH and buffer) were tested on lipid standards. Different conditions were evaluated, and resolution, peak capacity, and peak shape were calculated both in negative mode, for sulfolipids and phospholipids, and in positive mode, for glycolipids. A high-confidence lipid identification strategy was also applied. In collaboration with software creators and developers, Lipostar was implemented to improve the identification of phosphoglycerolipids and to allow the identification of glycosylmonoradyl- and glycosyldiradyl-glycerols classes, the last being the main focus of this work. By this approach, an untargeted screening also for searching lipids not yet reported in the literature could be accomplished. The optimized chromatographic conditions and database search were tested for lipid identification first on the standard mixture, then on the polar lipid extract of spirulina microalgae, for which 205 lipids were identified.
该工作描述了在 Kinetex-EVO 上对极性脂质的色谱分离优化,特别是针对蓝藻( Arthrospira platensis)中的硫酸脂。在脂质标准品上测试了梯度形状和流动相改性剂(pH 值和缓冲液)。评估了不同条件,并在负离子模式下计算了分辨率、峰容量和峰形,用于硫酸脂和磷脂,以及在正离子模式下,用于糖脂。还应用了高可信度的脂质鉴定策略。与软件创建者和开发者合作,实现了 Lipostar,以提高磷甘油酯的鉴定能力,并允许鉴定糖基单脂基和糖基二脂基甘油酯类,后者是本工作的主要重点。通过这种方法,可以完成非靶向筛选,以寻找文献中尚未报道的脂质。优化的色谱条件和数据库搜索首先在标准混合物上进行了脂质鉴定测试,然后在蓝藻微藻的极性脂质提取物上进行了测试,共鉴定出 205 种脂质。