Untargeted Analysis Resource

摘要（非目标分析资源） 作为儿童健康暴露分析资源中心，我们建立了全面的 用于检测、注释和鉴定来源于内源性肿瘤的信号的非靶向方法 化合物、与环境相关的化学品、药物和药物以及食物摄入。我们使用 用于非目标分析的几种系统，包括液相色谱（LC）和高分辨率 轨道阱和飞行时间（TOF）质谱（MS）系统，以及气相色谱（GC）耦合 根据机构承诺，还安装了GC-Q-Exactive高分辨率MS系统。 我们有信心，我们可以捕捉数以万计的信号，并确定一个多样化的内源性 化合物（例如，氨基酸、胺、羧酸、糖、酰基肉毒碱、核苷、脂肪酸和 溶血磷脂），环境相关化合物（例如，来自烷基磷酸酯农药的代谢物， 邻苯二甲酸酯、多环芳烃、挥发性有机化合物、全氟化合物、代谢物 烟草制品、环境酚类和对羟基苯甲酸酯）、食物摄入产生的代谢物 (e.g.,多酚及其代谢物）、药物（例如对乙酰氨基酚、磺胺胍、二甲双胍），以及 滥用药物（例如，海洛因、吗啡、阿片类药物及其代谢物）。我们还使用Lipidomics，UPLC-Ion 迁移率-MS、LC-电化学检测（ECD）、NMR以及GC和LC多反应监测方法 以捕获使用非靶向平台难以检测和识别的分析物的信号。使用我们 方法，我们在NIH共同基金代谢组学项目环试验中表现出色， 在CHEMICAL交叉实验室比较中。我们将继续扩大对信号的识别， 通过使用a）大数据分析来注释信号，然后进行确认， B）确保信号关于环境的代谢命运被注释， 化合物，以及c）与开发核心（DC）和HHEAR计划合作，进一步开发 用于非靶向方法检测不到的分析物的广谱方法和样本组。我们使用Ontology 我们实验室开发的系统，为所有信号注释和代谢物提供证据基础 识别，确保我们的结果与客户，数据分析中心和数据的最佳通信 储存库。我们的核心使用统计分析和建模方法来确定代谢物， 区分研究表型，并揭示环境相关化学品之间的关联， 内源性代谢型和健康表型。

ABSTRACT (UNTARGETED ANALYSIS RESOURCE) As a Children's Health Exposure Analysis Resource (CHEAR) Hub, we have established comprehensive untargeted methods for detection, annotation, and identification of signals derived from endogenous compounds, environmentally relevant chemicals, drugs and medications, and ingestion of foods. We use several systems for Untargeted Analysis, including Liquid Chromatography (LC) coupled to High-Resolution Orbitrap and Time of Flight (TOF) Mass Spectrometry (MS) systems, and Gas Chromatography (GC) coupled to TOF MS. Under an Institutional commitment are also installing a GC-Q-Exactive High-Resolution MS system. We are confident that we can capture tens of thousands of signals, and identify a diverse range of endogenous compounds (e.g., amino acids, amines, carboxylic acids, sugars, acylcarnitines, nucleosides, fatty acids, and lysophospholipids), environmentally relevant compounds (e.g., metabolites from alkyl phosphate pesticides, phthalates, polycyclic aromatic hydrocarbons, volatile organic compounds, perfluoro compounds, metabolites of tobacco products, environmental phenols, and parabens), metabolites produced by the ingestion of food (e.g., polyphenols and their metabolites), medications (e.g. acetaminophen, sulfaguanidine, metformin), and drugs of abuse (e.g., heroin, morphine, opioids, and their metabolites). We also use Lipidomics, UPLC-Ion Mobility-MS, LC-electrochemical detection (ECD), NMR, and GC- and LC- multiple reaction monitoring methods to capture signals for analytes that are difficult to detect and identify using untargeted platforms. Using our methods, we have had outstanding performance in the NIH Common Fund Metabolomics Program Ring Trial, and in the CHEAR Cross Laboratory Comparison. We will continue to expand the identifications of signals on the untargeted platforms through using a) Big Data analytics for annotation of signals, followed by confirmation with standards, b) ensuring that signals are annotated in respect to the metabolic fate of the environmental compounds, and c) collaborating with the Development Core (DC), and HHEAR program to further develop broad spectrum methods and panels for analytes not detected using untargeted methods. We use an Ontology System developed by our laboratory that provides the evidence basis for all signal annotations and metabolite identifications, ensuring optimal communications of our results to the client, the data analysis center, and data repositories. Our core uses statistical analysis and modelling approaches to determine metabolites that distinguish study phenotypes, and to reveal the associations between environmentally relevant chemicals, endogenous metabotypes, and health phenotypes.