Untargeted Analysis Resource

无针对性的分析资源

• 批准号: 10200814
• 负责人: SUSAN J SUMNER
• 金额: $ 264.42万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-05 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10200814
• 关键词: Acetaminophen; Amines; Amino Acids; Aromatic Polycyclic Hydrocarbons; Big Data Methods; Budgets; Carboxylic Acids; Chemicals; Child Health; Client; Code; Collaborations; Communication; Consult; Consultations; Coupled; Data; Data Analyses; Data Analytics; Databases; Detection; Development; Ensure; Fatty Acids; Food; Funding; Gas Chromatography; Health; Heroin; Human; Ingestion; Laboratories; Laboratory Research; Libraries; Liquid Chromatography; Lysophospholipids; Mass Spectrum Analysis; Metabolic; Metformin; Methods; Morphine; North Carolina; Nuclear Magnetic Resonance; Nucleosides; Ontology; Opioid; Parabens; Performance; Pesticides; Pharmaceutical Preparations; Phenols; Phenotype; Preparation; Procedures; Process; Protocols documentation; Quality Control; Reporting; Reproducibility; Research; Resolution; Resources; Sampling; Science; Services; Signal Transduction; Speed; Statistical Data Interpretation; Statistical Models; System; TimeLine; United States National Institutes of Health; Update; Work; acylcarnitine; cost effectiveness; data acquisition; data repository; data standards; data submission; detection method; drug of abuse; inorganic phosphate; instrument; ion mobility; lipidomics; member; metabolic phenotype; metabolomics; method development; multiple reaction monitoring; phthalates; polyphenol; programs; quality assurance; sugar; time of flight mass spectrometry; tobacco products; volatile organic compound; web site

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.

摘要（非目标分析资源） 作为儿童健康暴露分析资源 (CHEEAR) 中心，我们建立了全面的 用于检测、注释和识别内源性信号的非靶向方法 化合物、与环境相关的化学品、药物和药物以及食物的摄入。我们使用 多种用于非目标分析的系统，包括与高分辨率耦合的液相色谱 (LC) Orbitrap 和飞行时间 (TOF) 质谱 (MS) 系统以及气相色谱 (GC) 耦合 至 TOF 质谱仪。根据机构承诺，我们还安装了 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，并识别各种内源性信号 化合物（例如氨基酸、胺、羧酸、糖、酰基肉碱、核苷、脂肪酸和 溶血磷脂）、环境相关化合物（例如烷基磷酸酯农药的代谢物、 邻苯二甲酸盐、多环芳烃、挥发性有机化合物、全氟化合物、代谢物 烟草制品、环境酚和对羟基苯甲酸酯）、摄入食物产生的代谢物 （例如多酚及其代谢物）、药物（例如对乙酰氨基酚、磺胺胍、二甲双胍）和 滥用药物（例如海洛因、吗啡、阿片类药物及其代谢物）。我们还使用脂质组学、UPLC-Ion 淌度-MS、LC-电化学检测 (ECD)、NMR 以及 GC- 和 LC- 多反应监测方法 捕获使用非目标平台难以检测和识别的分析物信号。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出色， 以及 CEAR 跨实验室比较。我们将继续扩大信号识别范围 通过使用 a) 大数据分析来注释非目标平台，然后进行确认 符合标准，b）确保信号根据环境的代谢命运进行注释 化合物，以及 c) 与开发核心 (DC) 和 HHEAR 计划合作，进一步开发 使用非靶向方法未检测到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，确保我们的结果以最佳方式传达给客户、数据分析中心和数据 存储库。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物质之间的关联， 内源代谢型和健康表型。