SWATH-F: A Novel Nontarget Strategy Based on the SWATH-MS Deconvolution Method Assisting in Annotating PFAS Homologues in Multisample Studies.

In order to identify emerging per- and polyfluoroalkyl substances (PFASs) and their alternatives in the environment or population, we need to perform extensive profiling of PFASs to determine their distribution in samples. The sequential window acquisition of all theoretical fragment-ion spectra (SWATH mode) is capable of obtaining a wide range of MS2 spectra but is difficult for direct identification of PFASs due to its complex MS2 spectra, and the nontarget screening method is difficult to identify due to its lack of a priori information. In this study, we demonstrated the great potential of SWATH-F, a nontarget fragment-based homologue screening method in combination with the SWATH-MS deconvolution, for detecting PFASs. We evaluated the application of SWATH-F to gradient spiked samples and real population serum samples, compared it with nontarget homologue screening in the information-dependent acquisition mode (IDA mode), and obtained better results for SWATH-F with 276% improvement (IDA:17 PFASs, SWATH-F: 64 PFASs) in identification. In addition, we automated the screening and identification process of SWATH-F to facilitate its use by researchers. SWATH-F is freely available on GitHub (https://github.com/njuIrene/SWATH-F) under an MIT license.

Comprehensive Exposure Studies of Per- and Polyfluoroalkyl Substances in the General Population: Target, Nontarget Screening, and Toxicity Prediction.

Novel per- and polyfluoroalkyl substances (PFASs) in the environment and populations have received extensive attention; however, their distribution and potential toxic effects in the general population remain unclear. Here, a comprehensive study on PFAS screening was carried out in serum samples of 202 individuals from the general population in four cities in China. A total of 165 suspected PFASs were identified using target and nontarget analysis, including seven identified PFAS homolog series, of which 16 PFASs were validated against standards, and seven PFASs [4:2 chlorinated polyfluorinated ether sulfonate (4:2 Cl-PFESA), 7:2 chlorinated polyfluorinated ether sulfonate (7:2 Cl-PFESA), hydrosubstituted perfluoroheptanoate (H-PFHpA), chlorine-substituted perfluorooctanoate (Cl-PFOA), chlorine-substituted perfluorononanate (Cl-PFNA), chlorine-substituted perfluorodecanoate (Cl-PFDA), and perfluorodecanedioic acid (PFLDCA n = 8)] were reported for the first time in human serum. The Tox21-GCN model (a graph convolutional neural network model based on the Tox21 database) was established to predict the toxicity of the discovered PFASs, revealing that PFASs containing sulfonic acid groups exhibited multiple potential toxic effects, such as estrogenic effects and stress responses. Our study indicated that the general population was exposed to various PFASs, and the toxicity prediction results of individual PFASs suggested potential health risks that could not be ignored.

Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS.

Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an automatic PFAS identification platform (APP-ID) that screens for PFAS in environmental samples using an enhanced molecular network and identifies unknown PFAS structures using machine learning. Our networking algorithm, which enhances characteristic fragment matches, has lower false-positive rate (0.7%) than current algorithms (2.4 to 46%). Our support vector machine model identified unknown PFAS in test set with 58.3% accuracy, surpassing current software. Further, APP-ID detected 733 PFASs in real fluorochemical wastewater, 39 of which are previously unreported in environmental media. Retrospective screening of 126 PFASs against public data repository from 20 countries show PFAS substitutes are prevalent worldwide.