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

代谢组学 代谢组 尿 代谢物 色谱法 化学 样品制备 液相色谱-质谱法 重复性 生物标志物发现 质谱法 生物标志物 蛋白质组学 生物化学 基因
作者
Jun Peng,Kevin Guo,Jianguo Xia,Jianjun Zhou,Jing Yang,David Westaway,David S. Wishart,Liang Li
出处
期刊:Journal of Proteome Research [American Chemical Society]
卷期号:13 (10): 4457-4469 被引量:50
标识
DOI:10.1021/pr500828v
摘要

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–MS) for relative quantification of over 950 putative metabolites using 20 μL of urine as the starting material. The workflow involves aliquoting 10 μL of an individual urine sample for 12C-dansylation labeling that target amines and phenols. Another 10 μL 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–MS to generate information on metabolite concentration differences among different individual samples. The interday repeatability for the LC–MS 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's disease (FAD) throughout the course of their pathological deposition of beta amyloid (Aβ). It was showed that there was a distinct metabolomic separation between the AD prone mice and the wild type (control) group. As early as 15–17 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–MS method for biomarker discovery using mouse urine metabolomics.
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