甲基化结合高分辨质谱的磷脂酸高通量分析方法的建立和应用
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摘要
基于甲基化反应,建立了同时测定磷脂酸和溶血磷脂酸的超高液相色谱-静电轨道阱高分辨质谱(UPLC-Q Exactive Orbitrap MS)高通量定性与定量分析方法,并用于小鼠衰老模型脑组织样品的测定。样品经氯仿-甲醇溶剂进行全脂提取,使用三甲基硅烷化重氮甲烷衍生化试剂改善磷脂酸和溶血磷脂酸的色谱峰形,并采用高分辨质谱的数据依赖型扫描模式(Full MS/dd-MS~2),利用Tracefinder软件自建本地二级碎片数据库,结合正离子和负离子两种扫描模式下的碎片规律进行高通量的鉴定和定量分析。在本实验条件下,标准品PA(14∶0/14∶0)的线性相关指数大于0.99,定量限为0.125μg/mL,日内和日间精密度小于8.5%。将本方法用于C57BL/6小鼠衰老模型脑组织中,成功鉴定并定量检测了14个溶血磷脂酸和磷脂酸分子。脂肪酸链的数据分析结果表明,含有多不饱和脂肪酸链的磷脂酸在衰老过程中呈现下降趋势。本方法具有较高灵敏度及准确度,适用于生物体内磷脂酸和溶血磷脂酸的定性与定量分析。
A methylation-based approach for analysis of phosphatidic acids (PAs) using ultra-high performance liquid chromatography-Orbitrap mass spectrometry with incorporation of in-house database was established. This method allowed high throughput analysis with confident molecular identification and quantitation. Phosphatidic acids were extracted using the mixture of chloroform and methanol with subsequent derivatization using( trimethylsilyl) diazomethane. Derivatized PAs (Me-PA) were analyzed in data dependent acquisition (Full MS/dd-MS~2) mode of high resolution mass spectrometer. In-house database compatible with Tracefinder was derived allowing lipid identification based on MS/MS spectra. In this work,to achieve confident molecular identification and quantitation,combinatorial analysis of positive and negative ion modes was performed. The method was evaluated by the linearity,lower limit of detection (LLOQ),inter-and intra-day precision using standard molecule PA(14∶ 0/14∶ 0),and R~2>0.99,LLOD of 0.125 μg/m L,interand intra-day precision < 8. 47% were achieved. PAs in aged mice brains were analyzed using this high throughput approach. A total of 14 phosphatidic acid molecules were identified and quantified in C57BL/6 mice brain. Moreover,decreased level of phosphatidic acid containing polyunsaturated fatty acid chain was observed in aged brains. Conclusively,this orbitrap-based platform with methylation provides a sensitive and accurate approach to analyze phosphatidic acid quantitatively.
A methylation-based approach for analysis of phosphatidic acids (PAs) using ultra-high performance liquid chromatography-Orbitrap mass spectrometry with incorporation of in-house database was established. This method allowed high throughput analysis with confident molecular identification and quantitation. Phosphatidic acids were extracted using the mixture of chloroform and methanol with subsequent derivatization using( trimethylsilyl) diazomethane. Derivatized PAs (Me-PA) were analyzed in data dependent acquisition (Full MS/dd-MS~2) mode of high resolution mass spectrometer. In-house database compatible with Tracefinder was derived allowing lipid identification based on MS/MS spectra. In this work,to achieve confident molecular identification and quantitation,combinatorial analysis of positive and negative ion modes was performed. The method was evaluated by the linearity,lower limit of detection (LLOQ),inter-and intra-day precision using standard molecule PA(14∶ 0/14∶ 0),and R~2>0.99,LLOD of 0.125 μg/m L,interand intra-day precision < 8. 47% were achieved. PAs in aged mice brains were analyzed using this high throughput approach. A total of 14 phosphatidic acid molecules were identified and quantified in C57BL/6 mice brain. Moreover,decreased level of phosphatidic acid containing polyunsaturated fatty acid chain was observed in aged brains. Conclusively,this orbitrap-based platform with methylation provides a sensitive and accurate approach to analyze phosphatidic acid quantitatively.
引文
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10 Chen Y L,Xu Y.J.Chromatogr.B,2001,753(2):355-363
11 Sugiura T,Nakane S,Kishimoto S,Waku K,Yoshioka Y,Tokumura A.J.Lipid Res.,2002,43(12):2049-2055
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13 Jesionowska A,Cecerska E,Dolegowska B.Anal.Biochem.,2014,453:38-43
14 Meleh M,Pozlep B,Mlakar A,Meden-Vrtovec H,Zupancic-Kralj L.J.Chromatogr.B,2007,858(1-2):287-291
15 Lisa M,Cifkova E,Holcapek M.J.Chromatogr.A,2011,1218(31):5146-5156
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22 Bligh E G,Dyer W J.Can.J.Biochem.Physiol.,1959,37(8):911-917
23 Folch J,Lees M,Stanley G H S.J.Biol.Chem.,1957,226(1):497-509
24 Tang H,Wang X,Xu L,Ran X,Li X,Chen L,Zhao X,Deng H,Liu X.Talanta,2016,156-157:163-171