Large-scale lipid analysis with C=C location and sn-position isomer resolving power
Large-scale lipid analysis with C=C location and sn-position isomer resolving power
复制标题
具有 C=C 位置和 Sn 位异构体分辨率的大规模脂质分析
DOI：
10.1038/s41467-019-14180-4
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发表时间：
2020-01-17
影响因子：
16.6
通讯作者：
Ma, Xiaoxiao
中科院分区：
文献类型：
--
作者：
Cao, Wenbo;Cheng, Simin;Ma, Xiaoxiao
脂质在生物过程中起着关键作用，通过质谱法（MS）进行脂质分析显著推进了脂质组学研究。虽然脂质分析的结构特异性对于研究脂质的生物学功能至关重要，但目前用于大规模脂质分析的主流方法只能识别脂质类别和脂肪酰基链，而C=C位置和sn-位置无法识别。在这项研究中，结合光化学和串联MS，我们开发了一种简单但有效的工作流程，使大规模和接近完整的脂质结构表征具有同时识别C=C位置和sn位置的强大能力。实现了在多个特异性水平上的脂质结构异构体的定量，并且成功地区分了人乳腺癌细胞的不同亚型。值得注意的是，人肺癌组织只能使用脂质C=C位置和sn-位置异构体两者的定量结果来与相邻的正常组织区分开。
Lipids play a pivotal role in biological processes and lipid analysis by mass spectrometry (MS) has significantly advanced lipidomic studies. While the structure specificity of lipid analysis proves to be critical for studying the biological functions of lipids, current mainstream methods for large-scale lipid analysis can only identify the lipid classes and fatty acyl chains, leaving the C=C location and sn-position unidentified. In this study, combining photochemistry and tandem MS we develop a simple but effective workflow to enable large-scale and near-complete lipid structure characterization with a powerful capability of identifying C=C location(s) and sn-position(s) simultaneously. Quantitation of lipid structure isomers at multiple levels of specificity is achieved and different subtypes of human breast cancer cells are successfully discriminated. Remarkably, human lung cancer tissues can only be distinguished from adjacent normal tissues using quantitative results of both lipid C=C location and sn-position isomers.