Determining and enhancing metabolite fitness for metabolomics measurements

项目摘要/摘要 通过高通量代谢物图谱技术对代谢物进行定量测量正在走向成熟 作为一种临床和实验室技术。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”进行分析。代谢物是一组不同的化学物质 分子，并对化学、酶和微生物有不同程度的敏感性 降解和/或生产。样品等外在因素引起的代谢物浓度变化 准备、储存、处理等引入了不想要的噪声，限制了这些多路传输研究的能力。 例如，易降解的代谢物与癌症中报道的代谢物有一些重叠。 生物标志物研究，如谷氨酸。 虽然一些研究表明，降解确实会改变人体内观察到的代谢物浓度 血浆和尿液，目前还没有公开可用的定量工具来评估 体能样本。此外，关于退化过程的综合信息有限，而且不 获取肿瘤学代谢组学研究中常用的样本类型范围。因此，重点关注的是 建议的研究是：1)将样品中发生的变化作为退化的函数进行分类 各种类型的生物样品；2)创建一个软件工具(样本)，供科学工作者使用 社区评估样本变化的程度，独立于生物变异，以及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 保护。 我们建议在肿瘤学研究中检查与生物标记物研究特别相关的生物液，即： 人血清、人尿和人血浆(EDTA和肝素抗凝剂)。其结果是 定量建模将以样本适合度得分来评估样本的完整性。这些工具将是 在R统计计算语言框架的开源程序包中实现。我们进一步 建议研究几种替代杀菌剂对添加钠的传统方法的适用性 用于样品保存的叠氮化物，作为同时测试R包的一种手段，并可能能够 为日后的样本收集提供更佳的样本适合度。最后，我们建议将该工具 适用于使用包括核磁共振波谱在内的常用分析平台的研究 和质谱学。

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.