Untargeted Analysis Resource

摘要(非目标分析资源) 作为儿童健康暴露分析资源(CHEAR)中心，我们建立了全面的 用于检测、注释和识别源自内源的信号的非目标方法 化合物、与环境有关的化学品、药物和药物，以及食物摄取。我们用 几种非靶向分析系统，包括与高分辨率联用的液相色谱仪(LC) 轨道和飞行时间(TOF)质谱仪(MS)和气相色谱(GC)联用 根据机构承诺，TOF MS还将安装GC-Q-Exactive高分辨率MS系统。 我们有信心能够捕获数以万计的信号，并识别不同范围的内源性 化合物(例如，氨基酸、胺、羧酸、糖、酰肉碱、核苷、脂肪酸和 溶血磷脂)、与环境有关的化合物(例如来自烷基磷酸盐农药的代谢物， 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 烟草产品、环境酚和对羟基苯甲酸酯)，即摄取食物所产生的代谢物 (如多酚及其代谢物)、药物(如对乙酰氨基酚、磺胺基鸟苷、二甲双胍)，以及 滥用药物(如海洛因、吗啡、类阿片及其代谢物)。我们也使用脂质组学，UPLC-Ion 流动-MS、LC-电化学检测(ECD)、核磁共振、GC-和LC-多反应监测方法 为使用非靶标平台难以检测和识别的分析物捕获信号。使用我们的 方法，我们在NIH共同基金代谢组学计划环试验中表现突出， 在CHEAR的跨实验室比对中。我们会继续扩大讯号的识别范围。 通过使用a)大数据分析对信号进行注释，然后进行确认 用标准，b)确保信号是关于环境的新陈代谢命运的注解 化合物，以及c)与开发核心(DC)和HHEAR计划合作，以进一步开发 未使用非目标方法检测的分析物的广谱方法和面板。我们使用本体论 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，确保将我们的结果最佳地传达给客户、数据分析中心和数据 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，并揭示与环境相关的化学物质之间的联系， 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.