A Comprehensive Platform for High-Throughput Profiling of the Human Reference Metabolome

用于人类参考代谢组高通量分析的综合平台

• 批准号: 10237905
• 负责人: Gary J Patti
• 金额: $ 44.87万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10237905
• 关键词: Address; Aging; Animal Model; Animals; Automation; Benchmarking; Biological; Cell Lineage; Cells; Clinical; Communities; Complex; Complication; Computer software; Credentialing; Data; Data Analyses; Data Set; Databases; Detection; Escherichia coli; Experimental Designs; Fishes; Future; Genetic; Hepatocyte; Human; Informatics; Isotopes; Manuals; Mass Spectrum Analysis; Metabolism; Methods; Morphologic artifacts; Names; Neurosciences; Oligodendroglia; Organism; Problem Solving; Process; Protocols documentation; Research; Research Personnel; Resources; Sampling; Signal Transduction; Technology; Testing; Translating; Validation; Work; Zebrafish; adduct; automated analysis; base; cell type; complex data; cost; data tools; experience; experimental study; graphical user interface; informatics tool; interest; liquid chromatography mass spectrometry; melanocyte; metabolic abnormality assessment; metabolome; metabolomics; prevent; professor; programs; stable isotope; tool; trend; tumor immunology

Project Summary The last decade has seen two complementary trends: (i) technology to perform untargeted metabolomics with liquid chromatography/mass spectrometry (LC/MS) has become readily available to most investigators, and (ii) interest in metabolism has continued to heighten in many disparate research fields ranging from cancer and immunology to neuroscience and aging. Accordingly, the number of investigators who are acquiring untargeted metabolomic data with LC/MS is dramatically increasing. Yet, informatic tools to analyze the acquired data have lagged far behind and interpretation of the results remains a serious challenge, even for experienced users. Thus, there is a substantial number of investigators performing untargeted metabolomics with LC/MS who either cannot interpret the data generated or, even worse, are interpreting it incorrectly. When untargeted metabolomics is performed on a typical biological sample, it is common to detect thousands to tens of thousands of signals (aka features). Translating these signals into metabolite names is the biggest informatic barrier limiting biomedical applications of the technology. The process is arduous, particularly for inexperienced investigators, because the majority of signals detected do not correspond to non- redundant metabolites originating from the biological sample. Rather, most signals (up to 95% in some of our experiments) are due to complicating factors such as contaminants, artifacts, fragments, etc. Because many of these complicating signals are not currently in metabolomic databases such as METLIN, they can be challenging to annotate for inexperienced users. While there are software programs available to annotate the signals within the data, these tools are beyond the reach of most clinical and biological investigators because (i) they are not automated with a graphical user interface, and (ii) they rely on a costly experimental design involving isotopes to find contaminants and artifacts. We propose to develop an automated solution to name and quantify most of the metabolites detected in untargeted metabolomic LC/MS experiments. Our strategy is to assume the computational burden of completely annotating all detected metabolites in untargeted metabolomic data, which only has to be performed once for a given sample type, so that less-experienced investigators do not have to in their future experiments. We will completely annotate untargeted metabolomic data sets from different biological samples using the mz.unity software and credentialing technology developed by the Patti lab. Based on experiments that we have already performed, we expect to find ~5,000 unique bonafide metabolites per sample. We will then use these endogenous signals to develop targeted LC/MS methods that enable automated analysis of all detectable metabolites (i.e., the “reference metabolome”). This will allow investigators with minimal expertise in metabolomics to profile the unique and bonafide metabolites in their samples at an untargeted scale, but without informatic barriers that have historically limited progress in the field.

项目摘要 过去十年出现了两个互补的趋势：（i）进行非靶向代谢组学的技术 随着液相色谱/质谱（LC/MS）已变得容易为大多数研究者所用， 以及（ii）在许多不同的研究领域中，对代谢的兴趣持续增加， 免疫学到神经科学和衰老。因此，正在获取的调查人员人数 LC/MS的非靶向代谢组学数据显著增加。然而，信息化工具来分析 获得的数据远远落后，对结果的解释仍然是一个严重的挑战，即使是 经验丰富的用户。因此，有相当数量的研究人员进行非靶向代谢组学 与LC/MS谁要么不能解释产生的数据，甚至更糟的是，不正确地解释它。 当对典型的生物样品进行非靶向代谢组学时， 数千到数万个信号（也称为特征）。将这些信号转化为代谢物名称是 这是限制该技术在生物医学领域应用的最大信息障碍。这个过程是艰苦的， 特别是对于没有经验的调查人员，因为大多数检测到的信号不对应于非- 源自生物样品的多余代谢物。相反，大多数信号（在我们的一些国家中高达95%） 实验）是由于复杂的因素，如污染物，文物，碎片等，因为许多 这些复杂的信号目前还没有在代谢组学数据库如METLIN中，它们可以是 这对于没有经验的用户来说是很有挑战性的。虽然有软件程序可用于注释 这些工具超出了大多数临床和生物学研究者的能力范围， (i)它们不是用图形用户界面自动化的，并且（ii）它们依赖于昂贵的实验设计 用同位素来寻找污染物和文物 我们建议开发一种自动化的解决方案，以命名和量化在代谢物中检测到的大多数代谢物。 非靶向代谢组学LC/MS实验。我们的策略是假设计算负担 在非靶向代谢组学数据中完全注释所有检测到的代谢物， 对给定的样本类型进行一次，以便经验不足的研究者在未来不必进行 实验我们将完全注释来自不同生物样本的非靶向代谢组学数据集 使用MZ.Unity软件和Patti实验室开发的认证技术。以实验为基础 我们已经进行了，我们预计每个样品会发现~ 5，000种独特的bonafide代谢物。我们将 然后使用这些内源性信号开发靶向LC/MS方法， 可检测的代谢物（即，“参考代谢组”）。这将使具有最低专业知识的调查人员能够 代谢组学以非目标规模分析其样品中独特和真正的代谢物，但 没有历史上限制该领域进展的信息障碍。