Development of Isotope Labeling Liquid Chromatography Mass Spectrometry for Mouse Urine Metabolomics: Quantitative Metabolomic Study of Transgenic Mice Related to Alzheimer鈥檚 Disease

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• 作者：Jun Peng ; Kevin Guo ; Jianguo Xia ; Jianjun Zhou ; Jing Yang ; David Westaway ; David S. Wishart ; Liang Li
• 刊名：Journal of Proteome Research
• 出版年：2014
• 出版时间：October 3, 2014
• 年：2014
• 卷：13
• 期：10
• 页码：4457-4469
• 全文大小：568K
• ISSN：1535-3907

文摘

Because of a limited volume of urine that can be collected from a mouse, it is very difficult to apply the common strategy of using multiple analytical techniques to analyze the metabolites to increase the metabolome coverage for mouse urine metabolomics. We report an enabling method based on differential isotope labeling liquid chromatography mass spectrometry (LC鈥揗S) for relative quantification of over 950 putative metabolites using 20 渭L of urine as the starting material. The workflow involves aliquoting 10 渭L of an individual urine sample for ¹²C-dansylation labeling that target amines and phenols. Another 10 渭L of aliquot was taken from each sample to generate a pooled sample that was subjected to ¹³C-dansylation labeling. The ¹²C-labeled individual sample was mixed with an equal volume of the ¹³C-labeled pooled sample. The mixture was then analyzed by LC鈥揗S to generate information on metabolite concentration differences among different individual samples. The interday repeatability for the LC鈥揗S runs was assessed, and the median relative standard deviation over 4 days was 5.0%. This workflow was then applied to a metabolomic biomarker discovery study using urine samples obtained from the TgCRND8 mouse model of early onset familial Alzheimer鈥檚 disease (FAD) throughout the course of their pathological deposition of beta amyloid (A尾). It was showed that there was a distinct metabolomic separation between the AD prone mice and the wild type (control) group. As early as 15鈥?7 weeks of age (presymptomatic), metabolomic differences were observed between the two groups, and after the age of 25 weeks the metabolomic alterations became more pronounced. The metabolomic changes at different ages corroborated well with the phenotype changes in this transgenic mice model. Several useful candidate biomarkers including methionine, desaminotyrosine, taurine, N1-acetylspermidine, and 5-hydroxyindoleacetic acid were identified. Some of them were found in previous metabolomics studies in human cerebrospinal fluid or blood samples. This work illustrates the utility of this isotope labeling LC鈥揗S method for biomarker discovery using mouse urine metabolomics.

Keywords:

Alzheimer鈥檚 disease; mouse model; mouse urine; isotope labeling; LC鈭扢S; metabolomics