Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.
CNR NANOTEC, Campus Ecotekne, University of Salento, Via Monteroni, 73100 Lecce, Italy.
Anal Chem. 2021 Nov 16;93(45):15042-15048. doi: 10.1021/acs.analchem.1c02944. Epub 2021 Nov 2.
High-resolution mass spectrometry is the foremost technique for qualitative and quantitative lipidomics analyses. Glycerophospholipids and sphingolipids, collectively termed polar lipids, are commonly investigated by hyphenated liquid chromatography-mass spectrometry (LC-MS) techniques that reduce aggregation effects and provide a greater dynamic range of detection sensitivity compared to shotgun lipidomics. However, automatic polar lipid identification is hindered by several isobaric and isomer mass overlaps, which cause software programs to often fail to correctly annotate the lipid species. In the present paper, a buffer modification workflow based on the use of labeled and unlabeled acetate ions in the chromatographic buffers was optimized by Box-Behnken design of the experiments and applied to the characterization of phosphocholine-containing lipids in human plasma samples. The contemporary generation of [M + CHCOO], [M + CDCOO], and [M - CH] coupled with a dedicated data processing workflow, which was specifically set up on Compound Discoverer software, allowed us to correctly determine adduct composition, molecular formulas, and grouping, as well as granting a lower false-positive rate and streamlining the manual validation step compared to commonly employed lipidomics platforms. The proposed workflow represents a robust yet easier alternative to the existing approaches for improving lipid annotation, as it does not require extensive sample pretreatment or prior isotopic enrichment or derivatization.
高分辨率质谱是定性和定量脂质组学分析的首要技术。甘油磷脂和鞘脂,统称为极性脂质,通常通过连接的液相色谱-质谱(LC-MS)技术进行研究,与 shotgun 脂质组学相比,该技术可减少聚集效应并提供更大的检测灵敏度动态范围。然而,由于存在几种等质量和异构体质量重叠,自动极性脂质识别受到阻碍,这导致软件程序经常无法正确注释脂质种类。在本文中,通过实验的 Box-Behnken 设计优化了基于在色谱缓冲液中使用标记和未标记的乙酸盐离子的缓冲液修饰工作流程,并将其应用于人血浆样品中含磷胆碱脂质的表征。当代的 [M + CHCOO]、[M + CDCOO] 和 [M - CH] 与专门设置在 Compound Discoverer 软件上的数据处理工作流程相结合,使我们能够正确确定加合物组成、分子式和分组,与常用的脂质组学平台相比,假阳性率更低,简化了手动验证步骤。与现有的改进脂质注释的方法相比,所提出的工作流程代表了一种更强大且更简单的替代方法,因为它不需要广泛的样品预处理或先前的同位素富集或衍生化。