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Enhanced coverage of lipid analysis and imaging by matrix-assisted laser desorption/ionization mass spectrometry via a strategy with an optimized mixture of matrices

机译:通过具有具有优化基质混合物的策略,通过基质辅助激光解吸/电离质谱法提高脂质分析和成像的覆盖率

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摘要

In matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) analysis and imaging of lipids, comprehensive ionization of lipids simultaneously by a universal matrix is a very challenging problem. Ion suppression of readily ionizable lipids to others is common. To overcome this obstacle and enhance the coverage of MALDI MS analysis and imaging of lipids, we developed a novel strategy employing a mixture of matrices, each of which is capable of selective ionization of different lipid classes. Given that MALDI MS with either 9-aminoacridine (9-AA) or N-(1-naphthyl) ethylenediamine dihydrochloride (NEDC) yields weak in-source decay which is critical for analysis of complex biological samples and possesses orthogonal selectivity for ionization of lipid classes, we tested the mixtures of NEDC and 9-AA with different ratios for analysis of standard lipids and mouse brain lipid extracts. We determined 1.35 of NEDC/9-AA as an optimized molar ratio. It was demonstrated that an enhanced coverage with the optimized mixture was obtained, which enabled us to analyze and map all the major classes of phospholipids and sulfatide from either lipid extracts or tissue slides, respectively. We believe that this powerful novel strategy can enhance lipidomics analysis and MALDI MS imaging of lipids in a high-throughput and semi-quantitative fashion. (c) 2017 Elsevier B.V. All rights reserved.
机译:在基质辅助激光解吸/电离质谱(MALDI MS)分析和脂质成像中,通过通用基质同时脂质的综合电离是一个非常具有挑战性的问题。离子抑制其他可电离的脂质对他人是常见的。为了克服这种障碍并增强MALDI MS分析和脂质成像的覆盖,我们开发了一种采用基质混合物的新策略,其每个策略能够选择性离子化不同的脂质类别。鉴于具有9-氨基丙酮(9-AA)或N-(1-萘基)乙二胺二盐酸盐(NEDC)的MALDI MS产生弱的源极衰减,这对于分析复杂的生物样品是关键的,并且具有对脂质的电离的正交选择性。类别,我们用不同比例测试了NEDC和9-AA的混合物,用于分析标准脂质和小鼠脑脂肪提取物。我们确定了1.35的NEDC / 9-AA作为优化的摩尔比。结果证明,获得了具有优化混合物的增强覆盖,使我们能够分别从脂质提取物或组织载玻片中分析和映射所有主要类磷脂和硫酸硫。我们认为,这种强大的新策略可以以高通量和半定量方式增强脂质体分析和脂质的MALDI MS成像。 (c)2017 Elsevier B.v.保留所有权利。

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