Experimental Core

实验核心

• 批准号: 9589598
• 负责人: CHARLES ROBERT EVANS
• 金额: $ 47.38万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9589598
• 关键词: Address; Biological; Catalogs; Chemicals; Collaborations; Data; Data Quality; Databases; Development; Effectiveness; Electrospray Ionization; Fractionation; Funding; Future; Individual; Investigation; Ions; Laboratories; Libraries; Liquid Chromatography; Mass Spectrum Analysis; Methodology; Methods; Michigan; Morphologic artifacts; Parents; Recurrence; Research; Resolution; Sampling; Site; Stable Isotope Labeling; Systems Biology; Techniques; adduct; analytical method; base; cost; data warehouse; improved; in vivo; indexing; instrumentation; interest; ion mobility; ionization technique; metabolomics; novel; prevent; screening; tool; ultra high pressure

Experimental Core - Project Summary Advances in analytical instrumentation are a key factor that enabled the development of metabolomics as a systems biology research tool. Continued advances in instrumentation have increased the number of detectable features in metabolomics data, but the proportion of unidentified features in a typical untargeted metabolomics study has remained high; this challenge continues to limit interpretation of potentially biologically informative data. Secondly, most attempts to identify features in metabolomics data are performed in an ad- hoc manner, and are usually undertaken only when biological data suggests differential abundance of an unknown feature between sample groups, and even then, identification is only pursued on a few of the highest- priority targets. While this approach superficially reduces analyst burden by limiting identification to only those features of biological interest, its actual effect is to increase the overall burden of compound identification, since these efforts are not carried out in a systematic manner, and their results are rarely indexed in major databases. Finally, MS/MS spectra are not routinely acquired for all target metabolites, and there exists no universal, cross-laboratory workflow for routine compound identification, so each laboratory is effectively required to set up its own extensive library of authentic standards to aid in compound identification. This is a costly proposition, which results in variable identification of even well-known metabolites. The Experimental Core of the Michigan Compound Identification Development Core (MCIDC) will help address these challenges by carrying out the following Specific Aims. First, we will attempt to identify recurrent unknown features in untargeted metabolomics data using a systematic, prioritized workflow. Secondly, we will develop and implement novel and cutting-edge analytical techniques and apply them to identification of unknown features in metabolomics data from biomedically-relevant samples. These techniques will include sample pre-fractionation and off-line multidimensional liquid chromatography, ultra-high-pressure separations, chemical derivatization and in-vivo stable isotope labeling, ion mobility mass spectrometry, and high-resolution NMR analysis. Finally, we will generate a high-quality metabolite library from the unknown features we identify in addition to previously characterized knowns, which will include a range of empirical details to aid in future compound identification efforts.

试验性核心-项目总结 分析仪器的进步是使代谢组学发展成为一种 系统生物学研究工具。仪器设备的持续进步增加了 代谢组学数据中可检测的特征，但未识别特征的比例在典型的非靶向 代谢组学研究一直居高不下；这一挑战继续限制了对潜在生物学的解释 信息量大的数据。其次，识别代谢组学数据中的特征的大多数尝试都是在广告中执行的- 通常只在生物数据显示出不同的丰度时才进行 样本组之间的未知特征，即使这样，识别也只在少数最高的- 优先目标。虽然这种方法通过将身份识别仅限于以下内容，表面上减轻了分析师的负担 生物兴趣的特征，其实际效果是增加了化合物鉴定的整体负担， 由于这些努力没有以系统的方式进行，其成果很少被编入主要索引 数据库。最后，并非所有目标代谢物的MS/MS谱都是常规获取的，并且不存在 通用、跨实验室的常规化合物鉴定工作流程，因此每个实验室都能有效地 需要建立自己的大量正品标准库，以帮助进行化合物鉴定。这是一个 昂贵的提议，这导致了对甚至是众所周知的代谢物的可变识别。 密歇根化合物鉴定开发核心(MCIDC)的实验核心将帮助解决 这些挑战通过实现以下具体目标来实现。首先，我们将尝试确定复发 使用系统化、优先化的工作流程，非靶向代谢组学数据中的未知特征。其次，我们将 开发和实施新的尖端分析技术，并将其应用于识别 来自生物医学相关样本的代谢组学数据中的未知特征。这些技术将包括 样品预分离和离线多维液相色谱、超高压分离、 化学衍生化和体内稳定同位素标记、离子迁移质谱学和高分辨率 核磁共振分析。最后，我们将从我们识别的未知特征中生成一个高质量的代谢物库 除了先前描述的已知知识外，还将包括一系列经验细节，以在未来提供帮助 复合身份识别的努力。