Exploring PFAS Exposures In Utero: Streamlining Non-Targeted Analysis of Per- and Polyfluoroalkyl Substances (PFAS) in Neonatal Dried Blood Spots using FluoroMatch Suite

Non-targeted analysis (NTA) of environmental contaminants in biological matrices can provide a comprehensive assessment of environmental influences on health. PFAS are an emerging class of persistent contaminants of public health concern, comprising of 10000+ compounds that require NTA approaches. Comprehensive detection of PFAS requires time-intensive data processing, and NTA on PFAS mass spectrometry data is prone to error. While automated approaches can compile all MS evidence and provide ranking or scoring metrics for annotations, confident assignment of structure still often requires extensive manual review of the data. To address these challenges in PFAS NTA, we developed FluoroMatch Suite, a free, open-source software which provides a fully automated approach compiling all MS evidence to discern PFAS homologous series and structures from non-targeted datasets. FluoroMatch Suite consists of FluoroMatch Modular, FluoroMatch Flow, FluoroMatch Generator, and FluoroMatch Visualizer to cover the entire NTA workflow, including MS file conversion, peak picking, blank filtering, identification, annotation and data visualization. FluoroMatch Visualizer is interactive and shows normalized mass defect plots, retention time plots, MS/MS spectra, formula prediction results based on isotopic pattern, and tables of annotations and metadata. We applied FluoroMatch Suite for PFAS NTA on dried blood spots (DBS) through the California Biobank Program from newborns in Southern California. Analysis revealed 18 PFAS-like compounds in neonatal DBS; 11 of these compounds did not overlap with the targeted panel of 30 PFAS reported by the CDC. These annotations included unsaturated PFCAs, H-substituted PFCAs, perfluorosulfonic acids, and ether containing PFCAs and PFSAs. These findings demonstrate the utility of FluoroMatch in PFAS NTA and the use of this discovery approach has the potential to identify novel associations between previously unknown PFAS and disease outcomes.