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.

项目总结/摘要 基于质谱的代谢组学释放其全部潜力的主要障碍是 数据的复杂性与溶剂和盐加合物混杂。这叫做简并 并从一种分析物给出多个峰，这减少了分析物信号， 使用生物信息学工具丢弃。根据PAR-17-045的要求， 技术研发，“一个多PI团队将开发一系列三个不同的 基于我们最近的通用质子亲和标签的化学标签平台。这些标签反应 与几乎所有的代谢物和消除简并，增加信号，允许多充电， 和超小样品的分析。AIM 1将开发一个通用的质子亲和标记方案 与多维液相色谱质谱平台，允许预- 浓缩所有代谢物和最小的简并性。目标2将综合并开发两个 同位素标记的标签套，约2美元/样品。第一组是用于目标分析的等压标签 使用低分辨率质谱法。第二组是基于neucode的标签，用于高 分辨率质谱仪能够同时分析多达60个样品。Aim 3用途 一种新的标签，其在碳-碳主链上断裂，以允许识别新的 代谢物使用碎片建模。在提案的最终目标中，我们将利用 增加灵敏度和多路复用的目的是分析小样品。的 本文所开发的方法将通过比较来评估其稳健性和可移植性 多个独立实验室的性能。该提案的结果有三个 这些独特的技术解决了代谢组学中的多个关键障碍。