Project 1

项目1

• 批准号: 10707434
• 负责人: Erin S Baker
• 金额: $ 20.73万
• 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/10707434
• 关键词: Address; Algorithms; Big Data; Characteristics; Chemical Exposure; Chemicals; Classification; Collaborations; Commerce; Complex; Computing Methodologies; Coupled; Data; Data Analyses; Databases; Defect; Development; Dimensions; 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; chemical spill; cheminformatics; cloud based; community engagement; computerized data processing; data dissemination; data management; detection method; dimensional analysis; environmental chemical; experience; feature detection; hands-on learning; in vivo; instrument; instrumentation; interest; ion mobility; liquid chromatography mass spectrometry; machine learning algorithm; novel; organ on a chip; public database; remediation; 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 对复杂环境样本中有害物质的全面评估在 了解“环境宣布”，并确定潜在的人类健康和环境风险。 尽管有针对性的分析通常用于测量每项研究10至100个特定物质，但 它们的精确参数和有限的覆盖范围不适合评估其他潜在危险 样品中可能存在的物质。这种限制表明了不靶向的重要性 每年引入数百种新化学品，需要评估数百种新化学物质。自从 非目标分析可以专注于所有检测到的功能，他们能够评估那些具有统计的人 除了非常高的特征外，样本类型和位置之间的重要性。这 因此，来自未靶向研究的信息提供了对新颖和遗产危险的评估 除了其代谢产物，中间体和降解剂外，它们可能更危险 父级化合物。但是，如何优化挑战不靶向的测量。 广泛表征的仪器，然后如何分析由该数据产生的所有“大”数据 新的分析方法。这是必要的分析和计算发展。通过组合 离子迁移率光谱法（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，我们旨在创建一个统一的工作流程以评估 在对在之前，之中和之后获得的样品的不定目标研究中的复杂环境混合物 环境紧急情况。为了启用全面的分析表征，我们将融入 多维IMS-MS分析，包括样品浓度，提取和液体包括样本 色谱（LC）分离以允许对混合物的深入表征。获得的信息 来自未靶向的IMS-MS和LC-IMS-MS研究将包括M/Z等分子特性 质量缺陷（KMD），保留时间（RT）和碰撞横截面（CCS）。由于这些值已显示在 针对分子分类的有针对性研究，它们将与我们的目标库结合使用> 3,000 过去资金期的环境化学品，并使用化学信息和机器进行处理 学习算法以注释和分类未靶向研究的未知特征。我们也会 利用目标和非目标研究来实现与灾难相关的潜在化学评估 通过创建一个包含数千种危险物质的清单来进行暴露，以快速表征 自动化的固相样品清理和IMS-MS。这个自动化的SPE-IMS-MS平台将提供10 s 样本到样本吞吐量以及与基于云的数据评估相结合时，它将能够快速 从灾难情况下对可能涉及化学溢出的复杂环境样品进行了化学分析。