Determining and enhancing metabolite fitness for metabolomics measurements

项目总结/摘要 通过高通量代谢物谱技术定量测量代谢物正在成熟 作为临床和实验室技术。然而，这种代谢物分析的可靠结果取决于 确保被分析的样品“适合”分析。金属化合物是一组不同的化学物质， 分子，并受到化学，酶和微生物的不同程度的敏感性 降解和/或生产。由于外部因素（如样品）导致的代谢物浓度变化 制备、存储、处理等引入了不需要的噪声，这限制了这些多路复用研究的能力。 例如，降解代谢物和癌症中报告的代谢物存在一些重叠 生物标志物研究，如谷氨酸。 虽然几项研究表明，降解确实会改变人体内观察到的代谢物浓度， 血浆和尿液中，目前没有定量工具可用于评估 样本适合度此外，关于降解过程的全面信息有限， 捕获肿瘤代谢组学研究中常用的样本类型范围。因此， 拟议的研究是：1）将样品中发生的变化作为降解的函数进行编目， 各种生物标本类型; 2）创建一个软件工具（SAMPLES），供科学家使用 社区评估样本变化的程度独立于生物变异，和3）检查 可能的微生物污染的程度和类型，并检查样品的替代方法 保存。 我们建议检查与肿瘤学研究中的生物标志物研究特别相关的生物流体，即： 人血清、人尿液和人血浆（EDTA和肝素抗凝剂）。的结果 定量建模将是一个样本的适应度评分，以评估样本的完整性。这些工具将 在R统计计算语言框架的开源包中实现。我们进一步 我建议研究几种替代杀生物剂对传统的添加钠的方法的适用性 叠氮化物用于样品保存，作为同时测试R包的方法， 以提供增强的样品适合未来的样品收集。最后，我们建议使这个工具 适用于使用常见分析平台（包括核磁共振光谱）的研究 和质谱分析。

Project Summary / Abstract Quantitative measurement of metabolites through high-throughput metabolite profiling technologies is maturing as a clinical and laboratory technique. Reliable results from such metabolite analysis however depend on ensuring that the samples being analyzed are `fit' for analysis. Metabolites are a diverse set of chemical molecules, and are subject to various degrees of susceptibility to chemical, enzymatic and microbial degradation and/or production. Changes in metabolite concentration due to extrinsic factors such as sample preparation, storage, handling etc introduce unwanted noise that limits the power of these multiplexed studies. For example, there is some overlap in metabolites subject to degradation and those reported in cancer biomarker studies, such as glutamate. While several studies have shown that degradation does alter observed metabolite concentrations in human plasma and urine, there are currently no quantitative tools publically available which allow for an assessment of sample fitness. Furthermore, comprehensive information on degradation processes are limited, and do not capture the range of sample types commonly used in oncology metabolomics research. As a result, the foci of the proposed research are to 1) catalogue changes which occur in samples as a function of degradation in various biospecimen types; 2) create a software tool (SAMPLES) which will be made available to the scientific community to assess the extent of sample change independent of biological variation, and 3) examine the extent and type of possible microbial contamination and examine alternative approaches for sample preservation. We propose to examine biofluids particularly relevant to biomarkers studies in oncology research, namely: human serum, human urine, and human plasma (EDTA and heparin anti-coagulants). The outcome of quantitative modeling will be a sample fitness score to assess the integrity of the samples. These tools will be implemented in an open-sourced package for the R statistical computing language framework. We further propose to examine the suitability of several alternate biocides to the conventional approach of adding sodium azide for sample preservation as a means of simultaneously testing the R package and potentially being able to provide enhanced sample fitness to future sample collection. Finally, we propose to make this tool applicable to studies using common analytical platforms including nuclear magnetic resonance spectroscopy and mass spectrometry.