PFΔScreen – An open-source tool for automated PFAS feature prioritization in non-target HRMS data

Abstract

Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C–F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization by several PFAS specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2) and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub (https://github.com/JonZwe/PFAScreen).

Can Qualitative Nontarget Data Be Indicative of PFAS Contamination? First Evidence by Correlation with EOF in Environmental Samples

“…An explanation is, that several samples are known to be dominated by many other PFAS (e.g., precursors) rather than PFAAs [e.g., diPAPs, fluorotelomer sulfonic acids (FTSAs), N-ethyl perfluorooctane sulfonamide ethanol−based phosphate diester (diSAmPAP), fluorotelomer alkyl phosphate esters (FTMAPs), several substituted PFSAs (e.g., Cl-PFOS)] that contribute substantially to the determined EOF. 27 For instance, the maximum explainable EOF from semiquantified PFAAs reached only ∼31%. This highlights that prioritization and semiquantification via the MD/C−m/C dimension are associated with a good indication of the overall PFAS burden.…”

“…27 Extracts were measured undiluted and in a 1:10 dilution in negative and positive electrospray ionization (ESI) mode in both full-scan and data-dependent acquisition (ddMS 2 ) (scan range 100−1700 m/z, 3 spectra/s, details on the ddMS 2 method in Zweigle et al 2024). 27 As quality control, labeled internal standards of several PFAAs (Table S1) were spiked in each sample via the autosampler (20 pg of each labeled PFAA internal standard). At least every 10th sample a MeOH blank and a PFAA standard mix (Table S1) were measured to monitor the instrument response.…”

“…Agilent MS data files were converted to mzML using the MSConvert software. 28 To detect features in the HRMS raw data, several modified Python scripts from the PFΔScreen software tool 27 were used (feature detection details in the Supporting Information). 29,30 To remove background signals from solvent and LC system, a blank correction was performed (details in the Supporting Information).…”

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry

“…Specific tools have been developed to detect differences in mass defect, 109 with the ability to discern PFAS by evaluating the relationship between mass defect (MD) and mass (m) relative to the number of carbons (C) (MD/C − m/ C). 110 These analyses has been applied to the discovery of novel PFAS in historical pine-needle samples by the combination of the negative mass defect and characteristically low CCS values. 111 Quantitative Structure−Activity Relationship Evaluation.…”

“…The negative mass defect of fluorine and identification of homologous series can be leveraged to prioritize legacy and emerging PFAS. Specific tools have been developed to detect differences in mass defect, with the ability to discern PFAS by evaluating the relationship between mass defect (MD) and mass (m) relative to the number of carbons (C) (MD/C – m/C) . These analyses has been applied to the discovery of novel PFAS in historical pine-needle samples by the combination of the negative mass defect and characteristically low CCS values …”