Universal Metabolite Tagging

项目摘要/摘要 基于质谱学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，其中混杂着溶剂和盐加合物。这被称为退化 并从一个分析物给出多个峰，其减弱分析物信号并且需要 使用生物信息学工具丢弃。为了响应PAR-17-045的要求， 技术研发“多PI团队将开发出三个截然不同的系列 基于我们最新的通用质子亲和标签的化学标签平台。这些标签会对 与几乎所有的代谢物和消除退化，增加信号，允许多次充电， 以及对超小样本的分析。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，允许预 集中所有代谢物，最大限度地减少退化。目标2将合成和开发两个 每个样本约2美元的同位素标记标签集。第一组是用于靶向分析的等压标记物 使用低分辨率质谱仪。第二组是基于Nucode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，它跨越碳-碳骨架，以允许识别新的 代谢物使用碎片化建模。在提案的最终目标中，我们将利用 提高了灵敏度和多重化，以前的目的是分析小样本。这个 这里开发的方法将通过比较以下方法来评估健壮性和可移植性 跨多个独立实验室的性能。这项提案的结果有三个 解决代谢组学中多个关键障碍的独特技术。

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.