Universal Metabolite Tagging

通用代谢物标签

• 批准号: 10240660
• 负责人: Christopher K Arnatt
• 金额: $ 44.04万
• 依托单位: SAINT LOUIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10240660
• 关键词: Address; Affinity; Binding; Bioinformatics; Carbon; Cardiac; Cations; Charge; Chemicals; Clutterings; Computer Models; Credentialing; Data; Data Analyses; Data Set; Deuterium; Diabetes Mellitus; Digestive System Disorders; Disease; Evaluation; Exclusion; Geography; Heart Diseases; Human; Human body; Hydrophobicity; Informatics; Injections; Investigation; Islets of Langerhans; Isotope Labeling; Isotopes; Laboratories; Malignant Neoplasms; Mass Spectrum Analysis; Metabolism; Methods; Modeling; Noise; Nutrient; Outcome; Pathway interactions; Performance; Process; Protocols documentation; Protons; Punch Biopsy; Research Personnel; Resolution; Salts; Sampling; Scheme; Series; Signal Transduction; Sodium Chloride; Solvents; Structure; System; Technology; Validation; Vertebral column; adduct; base; bioinformatics tool; cost; data complexity; data quality; functional group; improved; innovative technologies; large datasets; liquid chromatography mass spectrometry; metabolome; metabolomics; novel; piperidine; response; stem; targeted biomarker; technology research and development; therapeutic target; virtual

Project Summary/Abstract A major impediment to mass spectrometry based metabolomics unleashing its full potential is the complexity of the data which is cluttered with solvent and salt adducts. This is called degeneracy and gives multiple peaks from one analytes which diminish analyte signal and need to be discarded using bioinformatic tools. In response to PAR-17-045 which calls for “focused technology research and development,” a multi-PI team will develop a series of three distinct chemical tagging platforms based on our recent universal proton affinity tags. These tags react with virtually all metabolites and eliminate degeneracy, increase signal, allow for multi-charging, and analysis of ultra-small samples. Aim 1 will develop a universal proton affinity tagging scheme with multi-dimensional liquid chromatography mass spectrometry platform which allows for pre- concentrating all metabolites and minimal degeneracy. Aim 2 will synthesize and develop two sets of isotope labeled tags for ~$2/sample. The first set are isobaric tags for targeted analyses using low resolution mass spectrometry. The second set are neucode based tags for high resolution mass spectrometry capable of analyzing up to 60 samples simultaneously. Aim 3 uses a novel tag which fragments across the carbon-carbon backbone to allow identification of new metabolites using fragmentation modeling. In the final aim of the proposal we will leverage the increase in sensitivity and multiplexing of the previous aims to analyze small samples. The methods developed here will be evaluated for robustness and transferability by comparing performance across multiple independent laboratories. The outcomes for this proposal are three distinct technologies which solve multiple critical barriers in metabolomics.

项目概要/摘要 基于质谱的代谢组学充分发挥其潜力的一个主要障碍是 数据的复杂性，其中充满了溶剂和盐加合物，这称为简并性。 并给出来自一种分析物的多个峰，这些峰会减弱分析物信号并且需要 使用生物信息学工具丢弃 响应 PAR-17-045 号召“重点关注” 技术研发”，一个多 PI 团队将开发一系列三种不同的技术 基于我们最近的通用质子亲和标签的化学标签平台这些标签会发生反应。 几乎所有代谢物并消除简并性，增加信号，允许多次充电， 目标 1 将开发一种通用的质子亲和标记方案。 具有多维液相色谱质谱平台，可进行预 目标 2 将合成和开发两种代谢物并使其简并性最小化。 同位素标记标签组，价格约为 2 美元/样本 第一组是用于目标分析的同位素标签。 第二组是基于 neucode 的高分辨率标签。 Aim 3 能够同时分析多达 60 个样品。 一种新颖的标签，它可以在碳-碳主链上断裂，以便识别新的 在该提案的最终目标中，我们将利用碎片模型。 以前的灵敏度和多重检测的增加旨在分析小样本。 这里开发的方法将通过比较来评估稳健性和可转移性 该提案的结果是三个。 解决代谢组学中多个关键障碍的独特技术。