Project 1

项目1

• 批准号: 10349751
• 负责人: Erin S Baker
• 金额: $ 20.72万
• 依托单位: TEXAS A&M UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10349751
• 关键词: Address; Algorithms; Big Data; Characteristics; Chemical Exposure; Chemicals; Classification; Collaborations; Commerce; Complex; Computing Methodologies; Coupled; Data; Data Analyses; Databases; Defect; Development; Disasters; Emergency Situation; Environmental Engineering technology; Environmental Impact; Environmental Monitoring; Environmental Risk Factor; Environmental Science; Evaluation; Event; Exposure to; Funding; Grouping; Hazardous Chemicals; Hazardous Substances; Health; Human; In Vitro; Investigation; Libraries; Liquid Chromatography; Location; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Modeling; Molecular; Parents; Phase; Process; Property; Research; Resolution; Risk; Sampling; Sampling Studies; Science; Solid; Specific qualifier value; Spectrometry; Statistical Data Interpretation; Structure; Superfund; Testing; Time; Toxicology; Training; Translations; Water; Work; Xenobiotics; analytical method; base; chemical spill; cheminformatics; cloud based; community engagement; computerized data processing; data dissemination; data management; detection method; environmental chemical; experience; hands-on learning; in vivo; instrument; instrumentation; interest; ion mobility; machine learning algorithm; novel; organ on a chip; public database; response; screening; soil sampling; stem; tool; trend; volatile organic compound

Project 1 Abstract The comprehensive assessment of hazardous substances in complex environmental samples is essential in understanding the “environmental exposome” and identifying potential human health and environmental risks. Although targeted analyses are commonly used to measure between 10 and 100 specific substances per study, their precise parameters and limited coverage are not suitable for evaluating other potentially hazardous substances that may be present in the samples. This limitation has showcased the importance of untargeted measurements as hundreds of new chemicals are being introduced annually that need to be assessed. Since untargeted analyses can focus on all detected features, they are able to evaluate those with statistical significance between sample type and location, in addition to features with extremely high abundance. The information from the untargeted studies therefore provides the evaluation of novel and legacy hazardous substances in addition to their metabolites, intermediates and degradants which can be more hazardous than the parent compounds. However, untargeted measurements are greatly challenged by how to optimize instruments for broad characterization and then how to analyze all of the “big” data that are generated by the new analytical methods. Thus, both analytical and computational developments are necessary. By combining ion mobility spectrometry (IMS)-derived structural information, mass spectrometry (MS)-derived high-resolution m/z measurements and new data processing algorithms, we aim to create a uniform workflow for evaluation of complex environmental mixtures in the untargeted studies of samples obtained before, during and after environmental emergencies. To enable comprehensive analytical characterization, we will couple the multidimensional IMS-MS analyses with steps including sample concentration, extraction and liquid chromatography (LC) separations to allow an in-depth characterization of the mixtures. The information obtained from the untargeted IMS-MS and LC-IMS-MS studies will include molecular properties such as m/z, Kendrick Mass Defect (KMD), retention time (RT) and collision cross section (CCS). As these values have shown utility in targeted studies for molecular classification, they will be combined with our targeted library of >3,000 environmental chemicals from the past funding period and processed with cheminformatics and machine learning algorithms to annotate and classify the unknown features from the untargeted studies. We will also utilize both the targeted and untargeted studies to enable better disaster-related evaluation of potential chemical exposures by creating a list containing thousands of hazardous substances for rapid characterization with automated solid phase sample cleanup and IMS-MS. This automated SPE-IMS-MS platform will provide 10 s sample-to-sample throughput and when coupled with cloud-based data assessment, it will enable the rapid chemical analyses of complex environmental samples from disaster situations that may involve chemical spills.

项目1