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; 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（Chear）中心，我们已经建立了全面的 未定位的方法用于检测，注释和识别来自内源性的信号 化合物，与环境相关的化学物质，药物和药物以及食物的摄入。我们使用 几种用于非靶向分析的系统，包括液相色谱法（LC）耦合到高分辨率 Orbitrap和飞行时间（TOF）质谱（MS）系统和气相色谱法（GC）耦合 到MS。在机构承诺下，还安装了GC-Q激进的高分辨率MS系统。 我们有信心我们可以捕获成千上万的信号，并确定各种各样的内源性 化合物（例如氨基酸，胺，羧酸，糖，酰基肉碱，核苷，脂肪酸和脂肪酸和 溶物磷脂），与环境相关的化合物（例如，来自磷酸烷基烷基农药的代谢产物， 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 烟草产品，环境酚和对羟基苯甲酸酯），由食物摄入而产生的代谢产物 （例如，多酚及其代谢产物），药物（例如，对乙酰氨基酚，磺胺类，二甲苯胺）和 滥用药物（例如海洛因，吗啡，阿片类药物及其代谢产物）。我们还使用脂质组学，UPLC-ION Mobility-MS，LC-电气化学检测（ECD），NMR和GC-和LC-多重反应监测方法 捕获难以检测和使用非靶向平台识别的分析物的信号。使用我们的 方法，我们在NIH普通基金代谢组学计划环试验中表现出色， 在Chear跨实验室比较中。我们将继续扩展信号的标识 通过a）使用a）大数据分析来注释信号的平台，然后进行确认 使用标准，b）确保对环境的代谢命运的信号注释 化合物，以及c）与开发核心（DC）和HHEAR计划合作以进一步开发 用于未使用非靶向方法检测到的分析物的宽光谱方法和面板。我们使用本体论 我们的实验室开发的系统为所有信号注释和代谢物提供了证据基础 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 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，并揭示与环境相关的化学物质之间的关联 内源性代谢和健康表型。