Department of Chemistry, University of Alberta , Edmonton, Alberta, Canada.
Anal Chem. 2014 Jul 1;86(13):6540-7. doi: 10.1021/ac5011684. Epub 2014 Jun 10.
Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and discovering potential metabolite biomarkers for diagnosis of bladder cancer.
大规模代谢组学研究需要一种定量方法，以便在长时间内以高技术重现性生成代谢组数据。我们报告了一种通用代谢物标准（UMS）方法，结合化学同位素标记液相色谱-质谱（LC-MS），以提供长期分析重现性，并促进不同数据集之间的代谢组比较。在这种方法中，预先制备由同位素试剂标记的特定类型样品的 UMS。在代谢组学研究中，将 UMS 掺入由另一种同位素试剂标记的任何单个样品中。将所得混合物通过 LC-MS 进行分析，以提供单个样品代谢组相对于 UMS 的相对定量。UMS 独立于研究工作以及分析时间，并且可用于在多个研究中对同类型的样品进行分析。在这项工作中，开发并应用 UMS 方法进行膀胱癌尿液代谢组学研究。通过（13）C2-丹磺酰化 20 个健康个体的混合样本制备人尿 UMS。该方法首先用于对发现样本进行分析以生成可能对膀胱癌检测有用的潜在生物标志物列表，然后在大约一年后用于分析验证样本。在发现样本集中，使用质控样本检查了三个月的技术重现性，发现所有定量代谢物的平均 CV 为 13.9%，中位数 CV 为 9.4%。对尿液代谢组数据的统计分析显示，膀胱癌组和对照组在发现样本中明显分离，这在验证样本中得到了证实。接收者操作特性（ROC）测试表明，在发现数据集和验证数据集中 AUC 分别为 0.956 和 0.935。这些结果证明了 UMS 方法在长期代谢组学和发现潜在代谢物生物标志物用于膀胱癌诊断中的实用性。
