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（非针对性分析资源）