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 对复杂环境样品中的有害物质进行综合评价， 了解"环境问题"并确定潜在的人类健康和环境风险。 虽然每次研究通常使用目标分析来测量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合于评估其他潜在的危险 可能存在于样品中的物质。这种限制表明了无针对性的重要性 每年都有数百种新的化学品被引入，需要进行评估。以来 非目标分析可以集中在所有检测到的特征上，它们能够评估那些具有统计学意义的特征。 除了丰度极高的特征外，还需要确定样本类型和位置之间的重要性。的 因此，来自非靶向研究的信息提供了新的和遗留的危险评价， 除了其代谢物、中间体和降解物之外，其他物质的危险性可能比 母体化合物。然而，非目标测量受到如何优化的极大挑战 工具进行广泛的表征，然后如何分析所有的"大"数据产生的 新的分析方法。因此，分析和计算的发展是必要的。通过组合 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，我们的目标是建立一个统一的工作流程， 在对之前、期间和之后获得的样品进行非目标研究时， 环境紧急情况。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、提取和液体 色谱（LC）分离，以允许深入表征的混合物。获得的信息 来自非靶向IMS-MS和LC-IMS-MS研究的数据将包括分子性质，例如m/z、Kendrick 质量缺陷（KMD）、保留时间（RT）和碰撞截面（CCS）。由于这些值已显示出在 针对分子分类的目标研究，它们将与我们的目标库（> 3，000）相结合 过去供资期间的环境化学品，并使用化学信息学和机器进行处理 学习算法来注释和分类来自非目标研究的未知特征。我们还将 利用有针对性的和无针对性的研究，以便更好地对潜在的化学品进行灾害评估。 通过创建包含数千种危险物质的列表， 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 当与基于云的数据评估相结合时，它将实现快速的 对来自可能涉及化学品泄漏的灾害情况的复杂环境样本进行化学分析。