Determining and enhancing metabolite fitness for metabolomics measurements

确定和增强代谢组学测量的代谢物适应性

• 批准号: 9241674
• 负责人: Aalim M Weljie
• 金额: $ 19.76万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9241674
• 关键词: Address; Adoption; Anticoagulants; Biocide; Biological; Biological Markers; Biological Preservation; Catalogs; Chemicals; Chemistry; Clinic; Clinical; Clinical Chemistry; Clinical Laboratory Techniques; Collection; Communities; Data; Data Analytics; Development; Drug Industry; Edetic Acid; Ensure; Environment; Evaluation; Future; Genetic Screening; Glutamates; Goals; Heparin; Hour; Human; Individual; Laboratories; Language; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Measures; Metabolic Diseases; Methods; Modality; Modeling; Modernization; Morphologic artifacts; NMR Spectroscopy; Names; Nature; Noise; Outcome; Pathology; Plasma; Predisposition; Preparation; Process; Production; Protocols documentation; Publishing; Quality Indicator; Reporting; Reproducibility; Research; Research Personnel; Sampling; Science; Screening procedure; Serum; Sodium Azide; Software Tools; Source; Standardization; Statistical Computing; Statistical Models; Technology; Temperature; Testing; Time; Training; Urine; Variant; Work; base; biobank; biomarker discovery; cancer biomarkers; clinical application; clinically relevant; design; fitness; innovation; innovative technologies; liquid chromatography mass spectrometry; metabolomics; microbial; oncology; open source; response; sample collection; stability testing; tool

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.

项目摘要 /摘要 通过高通量代谢物分析技术对代谢产物进行定量测量正在成熟 作为临床和实验室技术。但是，这种代谢物分析的可靠结果 确保被分析的样品被“适合”进行分析。代谢物是一组化学物质 分子，并受到化学，酶和微生物的不同程度的敏感性 退化和/或生产。由于外部因素（例如样品），代谢产物浓度的变化 制备，存储，处理等引入了不良的噪声，从而限制了这些多重研究的力量。 例如，代谢物在降解中存在一些重叠，而在癌症中报道了。 生物标志物研究，例如谷氨酸。 虽然几项研究表明，降解确实改变了人类观察到的代谢产物浓度 血浆和尿液，目前尚无广泛可用的定量工具，可以评估 样品健身。此外，有关退化过程的全面信息是有限的，并且不 捕获肿瘤学代谢组学研究中常用的样本类型的范围。结果， 拟议的研究是1）样品中发生的目录变化是降解的函数 各种生物循环类型； 2）创建一个软件工具（样品），该工具将提供给科学 社区评估独立于生物学变异的样本变化程度，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。 我们建议研究与肿瘤学研究中生物标志物研究特别相关的生物流体，即： 人血清，人类尿液和人血浆（EDTA和肝素抗凝集剂）。结果 定量建模将是评估样品完整性的样本适应性评分。这些工具将是 在开源软件包中用于R统计计算语言框架。我们进一步 建议检查几种替代杀菌剂对添加钠的常规方法的适用性 用于样品保存的叠氮化物作为同时测试R包的一种手段并有可能能够 为未来的样本收集提供增强的样本健身。最后，我们建议制作此工具 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。