Koelmel Jeremy P, Paige Matthew K, Aristizabal-Henao Juan J, Robey Nicole M, Nason Sara L, Stelben Paul J, Li Yang, Kroeger Nicholas M, Napolitano Michael P, Savvaides Tina, Vasiliou Vasilis, Rostkowski Pawel, Garrett Timothy J, Lin Elizabeth, Deigl Chris, Jobst Karl, Townsend Timothy G, Godri Pollitt Krystal J, Bowden John A
School of Public Health, Yale University, New Haven, Connecticut 06520, United States.
Center for Environmental and Human Toxicology & Department of Physiological Sciences, University of Florida, Gainesville, Florida 32611, United States.
Anal Chem. 2020 Aug 18;92(16):11186-11194. doi: 10.1021/acs.analchem.0c01591. Epub 2020 Aug 6.
Thousands of per- and polyfluoroalkyl substances (PFAS) exist in the environment and pose a potential health hazard. Suspect and nontarget screening with liquid chromatography (LC)-high-resolution tandem mass spectrometry (HRMS/MS) can be used for comprehensive characterization of PFAS. To date, no automated open source PFAS data analysis software exists to mine these extensive data sets. We introduce FluoroMatch, which automates file conversion, chromatographic peak picking, blank feature filtering, PFAS annotation based on precursor and fragment masses, and annotation ranking. The software library currently contains ∼7 000 PFAS fragmentation patterns based on rules derived from standards and literature, and the software automates a process for users to add additional compounds. The use of intelligent data-acquisition methods (iterative exclusion) nearly doubled the number of annotations. The software application is demonstrated by characterizing PFAS in landfill leachate as well as in leachate foam generated to concentrate the compounds for remediation purposes. FluoroMatch had wide coverage, returning 27 PFAS annotations for landfill leachate samples, explaining 71% of the all-ion fragmentation (CF) related fragments. By improving the throughput and coverage of PFAS annotation, FluoroMatch will accelerate the discovery of PFAS posing significant human risk.
环境中存在数千种全氟和多氟烷基物质（PFAS），它们对健康构成潜在危害。利用液相色谱（LC）-高分辨率串联质谱（HRMS/MS）进行可疑和非目标筛查可用于全面表征PFAS。迄今为止，尚无自动化的开源PFAS数据分析软件来挖掘这些庞大的数据集。我们推出了FluoroMatch，它可自动进行文件转换、色谱峰识别、空白特征过滤、基于前体和碎片质量的PFAS注释以及注释排序。该软件库目前包含约7000种基于标准和文献得出的规则的PFAS碎片模式，并且该软件为用户提供了添加其他化合物的自动化流程。使用智能数据采集方法（迭代排除）使注释数量几乎增加了一倍。通过对垃圾渗滤液以及为修复目的而生成的用于浓缩化合物的渗滤液泡沫中的PFAS进行表征，展示了该软件的应用。FluoroMatch具有广泛的覆盖范围，在垃圾渗滤液样本中返回了27种PFAS注释，解释了71%的全离子碎裂（CF）相关碎片。通过提高PFAS注释的通量和覆盖范围，FluoroMatch将加速发现对人类构成重大风险的PFAS。
