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 摘要 复杂环境样品中有害物质的综合评估至关重要 了解“环境暴露组”并识别潜在的人类健康和环境风险。 尽管目标分析通常用于每项研究测量 10 到 100 种特定物质， 它们的精确参数和有限的覆盖范围不适用于其他潜在危险 样品中可能存在的物质这一限制显示了非目标的重要性。 自此以来，每年都有数百种新化学品被引入，需要进行评估。 无针对性的分析可以集中于所有检测到的特征，他们能够评估那些具有统计特征的特征 除了具有极高丰度的特征之外，样本类型和位置之间的重要性。 因此，来自非针对性研究的信息可以对新的和遗留的危险物质进行评估。 除了其代谢物、中间体和降解物之外，这些物质可能比其他物质更危险 然而，非目标测量面临着如何优化的巨大挑战。 进行广泛表征的仪器，然后如何分析由仪器生成的所有“大”数据 因此，分析和计算的发展是必要的。 离子迁移谱 (IMS) 衍生的结构信息、质谱 (MS) 衍生的高分辨率 m/z 测量和新的数据处理算法，我们的目标是创建一个统一的工作流程来评估 对之前、期间和之后获得的样本进行非针对性研究中的复杂环境混合物 为了实现全面的分析表征，我们将耦合 多维 IMS-MS 分析，步骤包括样品浓缩、萃取和液体 色谱 (LC) 分离可对所获得的信息进行深入表征。 来自非目标 IMS-MS 和 LC-IMS-MS 研究的结果将包括分子特性，例如 m/z、Kendrick 质量缺陷 (KMD)、保留时间 (RT) 和碰撞截面 (CCS) 这些值已显示出实用性。 分子分类的针对性研究，它们将与我们超过 3,000 个的目标库相结合 过去资助期间的环境化学品，并经过化学信息学和机器处理 我们还将使用学习算法对非针对性研究中的未知特征进行注释和分类。 利用有针对性和无针对性的研究，更好地评估潜在化学品的灾害相关情况 通过创建包含数千种有害物质的列表来快速表征暴露 自动化固相样品净化和 IMS-MS 该自动化 SPE-IMS-MS 平台将提供 10 秒的时间。 样本到样本的吞吐量，与基于云的数据评估相结合，它将能够实现快速 对可能涉及化学品泄漏的灾难情况下的复杂环境样本进行化学分析。