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用于硫酸脑苷脂检测的苯基肉桂酸衍生物作为基质辅助激光解吸电离质谱(MALDI-MS)基质的结构-性能关系

Structure-performance relationships of phenyl cinnamic acid derivatives as MALDI-MS matrices for sulfatide detection.

作者信息

Tambe Suparna, Blott Henning, Fülöp Annabelle, Spang Nils, Flottmann Dirk, Bräse Stefan, Hopf Carsten, Junker Hans-Dieter

机构信息

Aalen University of Applied Science, Beethovenstr. 1, 73430, Aalen, Germany.

Applied Research Center in Biomedical Mass Spectrometry (ABIMAS), Paul-Wittsack-Straße 10, 68163, Mannheim, Germany.

出版信息

Anal Bioanal Chem. 2017 Feb;409(6):1569-1580. doi: 10.1007/s00216-016-0096-6. Epub 2016 Dec 1.

Abstract

A key aspect for the further development of matrix-assisted laser desorption ionization (MALDI)-mass spectrometry (MS) is a better understanding of the working principles of MALDI matrices. To address this issue, a chemical compound library of 59 structurally related cinnamic acid derivatives was synthesized. Potential MALDI matrices were evaluated with sulfatides, a class of anionic lipids which are abundant in complex brain lipid mixtures. For each matrix relative mean S/N ratios of sulfatides were determined against 9-aminoacridine as a reference matrix using negative ion mass spectrometry with 355 and 337 nm laser systems. The comparison of matrix features with their corresponding relative mean S/N ratios for sulfatide detection identified correlations between matrix substitution patterns, their chemical functionality, and their MALDI-MS performance. Crystal structures of six selected matrices provided structural insight in hydrogen bond interactions in the solid state. Principal component analysis allowed the additional identification of correlation trends between structural and physical matrix properties like number of exchangeable protons at the head group, MW, logP, UV-Vis, and sulfatide detection sensitivity. Graphical abstract Design, synthesis and mass spectrometric evaluation of MALDI-MS matrix compound libraries allows the identification of matrix structure - MALDI-MS performance relationships using multivariate statistics as a tool.

摘要

基质辅助激光解吸电离(MALDI)-质谱(MS)进一步发展的一个关键方面是更好地理解MALDI基质的工作原理。为了解决这个问题,合成了一个包含59种结构相关肉桂酸衍生物的化合物库。使用硫酸脑苷脂(一类在复杂脑脂质混合物中含量丰富的阴离子脂质)对潜在的MALDI基质进行了评估。对于每种基质,使用355和337 nm激光系统的负离子质谱,以9-氨基吖啶作为参考基质,测定硫酸脑苷脂的相对平均信噪比。将基质特征与其相应的硫酸脑苷脂检测相对平均信噪比进行比较,确定了基质取代模式、化学官能团与其MALDI-MS性能之间的相关性。六种选定基质的晶体结构提供了固态氢键相互作用的结构见解。主成分分析还可以确定结构和物理基质性质(如头部基团可交换质子的数量、分子量、logP、紫外可见光谱和硫酸脑苷脂检测灵敏度)之间的相关趋势。图形摘要 MALDI-MS基质化合物库的设计、合成和质谱评估允许使用多变量统计作为工具来确定基质结构-MALDI-MS性能关系。

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