Sousa Bebiana C, Klein Zulema Gonzalez, Taylor Diane, West Greg, Huipeng Aveline Neo, Wakelam Michael J O, Lopez-Clavijo Andrea F
Lipidomics Facility, Babraham Institute, Babraham Research Campus, Cambridge, UK.
Centro de Biotecnología y Genómica de Plantas (CBGP), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Madrid, Spain.
Rapid Commun Mass Spectrom. 2025 May;39 Suppl 1(Suppl 1):e9472. doi: 10.1002/rcm.9472. Epub 2023 Feb 9.
The present work shows comprehensive chromatographic methods and MS conditions that have been developed based on the chemical properties of each lipid subclass to detect low-abundance molecular species. This study shows that the developed methods can detect low- and/or very-low-abundant lipids like phosphatidic acid (PA) in the glycerophospholipid (GP) method; dihydroceramide (dhCer) and dihydrosphingosine/sphinganine (dhSPB) in the sphingolipid (SP) method; and lysophosphatidic acid (LPA), LPI, LPG and sphingosine-1-phosphate (SPBP) in the lysolipid method.
An optimised method for the extraction of lysolipids in plasma is used in addition to Folch extraction. Then, four chromatographic methods coupled with mass spectrometry using targeted and untargeted approaches are described here. Three of the methods use a tertiary pumping system to enable the inclusion of a gradient for analyte separation (pumps A and B) and an isocratic wash (pump C). This wash solution elutes interfering compounds that could cause background signal in the subsequent injections, reducing column lifetime.
Semi-quantitative values for 37 lipid subclasses are reported for a plasma sample (NIST SRM 1950). Furthermore, the methods presented here enabled the identification of 338 different lipid molecular species for GPs (mono- and diacyl-phospholipds), SPs, sterols and glycerolipids. The methods have been validated, and the reproducibility is presented here.
The comprehensive analysis of the lipidome addressed here of glycerolipids, GPs, sterols and SPs is in good agreement with previously reported results, in the NIST SRM 1950 sample, by other laboratories. Ten lipid subclasses LPS, LPI, alkyl-lysophosphatidic acid/alkenyl-lysophosphatidic acid, alkyl-lysophosphatidylethanolamine/alkenyl-lysophosphatidylethanolamine, dhCer (d18:0), SPB (d18:1), dhSPB (d18:0) and SPBP (d18:2) have been detected using this comprehensive method and are uniquely reported here.
本研究展示了基于各脂质亚类化学性质开发的综合色谱方法和质谱条件，用于检测低丰度分子物种。本研究表明，所开发的方法能够检测低丰度和/或极低丰度的脂质，如甘油磷脂（GP）方法中的磷脂酸（PA）；鞘脂（SP）方法中的二氢神经酰胺（dhCer）和二氢鞘氨醇/鞘氨醇（dhSPB）；以及溶血脂质方法中的溶血磷脂酸（LPA）、溶血磷脂酰肌醇（LPI）、溶血磷脂酰甘油（LPG）和鞘氨醇-1-磷酸（SPBP）。
除了Folch提取法外，还使用了一种优化的血浆溶血脂质提取方法。然后，本文描述了四种采用靶向和非靶向方法的色谱-质谱联用方法。其中三种方法使用三级泵系统，以便能够纳入用于分析物分离的梯度（泵A和泵B）和等度洗脱（泵C）。这种洗脱液能洗脱可能在后续进样中导致背景信号并缩短色谱柱寿命的干扰化合物。
报告了血浆样本（NIST SRM 1950）中37种脂质亚类的半定量值。此外，本文所介绍的方法能够鉴定出甘油磷脂（单酰基和二酰基磷脂）、鞘脂、固醇和甘油酯的338种不同脂质分子物种。这些方法已经过验证，并展示了其重现性。
本文对甘油酯、甘油磷脂、固醇和鞘脂脂质组的综合分析结果与其他实验室先前在NIST SRM 1950样本中报告的结果高度一致。使用这种综合方法检测到了十种脂质亚类，即脂多糖（LPS）、溶血磷脂酰肌醇（LPI）、烷基溶血磷脂酸/烯基溶血磷脂酸、烷基溶血磷脂酰乙醇胺/烯基溶血磷脂酰乙醇胺、二氢神经酰胺（d18:0）、鞘氨醇（d18:1）、二氢鞘氨醇（d18:0）和鞘氨醇-1-磷酸（d18:2），本文对此进行了独特报道。
