Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Latest trends in the environmental analysis of PFAS including nontarget analysis and EOF-, AOF-, and TOP-based methodologies

Ubiquitous environmental occurrence of per- and polyfluoroalkyl substances (PFAS) underscores the critical need to broaden investigative efforts in effective screening, risk assessment, and remediation. Owing to the broad spectrum of PFAS, various analytical techniques have been extensively utilized to attain inclusivity, with notable attention given to methods such as extractable organic fluorine (EOF), adsorbable organic fluorine (AOF), and the total oxidizable precursor (TOP) assay. These techniques expand the scope of PFAS analysis by estimating perfluoroalkyl acid precursors or the total organochlorine fraction. This review offers a comprehensive comparative overview of up-to-date methodologies, alongside acknowledging the inherent limitations associated with their applications. When coupled with target analysis via low-resolution tandem mass spectrometry, these techniques offer a potential estimation of total PFAS concentrations. Yet, analytical challenges such as the limited availability of reference analytical standards, partial PFAS adsorption, and the entrapment of fluorinated inorganic anions on adsorbent materials often restrict the comprehensiveness of PFAS analysis. So, integrating nontarget analysis using high-resolution mass spectrometry (HRMS) tools fortifies these PFAS mass balance approaches, enabling the development of a more holistic approach for an environmental analysis framework. This review provides additional insights into the comparative advantages of PFAS analytical approaches and explores various data prioritization strategies in nontarget screening methods. It advocates for the necessary optimization of PFAS extraction methods, asserting that integrating the nontarget approach would foster the establishment of a comprehensive monitoring framework across diverse environmental matrices. Such integration holds promise for enhancing scientific comprehension of PFAS contamination across diverse environmental matrices.

Investigating the chemical space coverage of multiple chromatographic and ionization methods using non-targeted analysis on surface and drinking water collected using passive sampling

Multiple non-targeted analysis tools were used to look for a broad range of possible chemical contaminants present in surface and drinking water using liquid chromatography separation and high-resolution mass spectrometry detection, including both quadrupole time of flight (Q-ToF) and Orbitrap instruments. Two chromatographic techniques were evaluated on an LC-Q-ToF with electrospray ionization in both positive and negative modes: (1) the traditionally used reverse phase C18 and (2) the hydrophilic interaction liquid chromatography (HILIC) aimed to capture more polar contaminants that may be present in water. Multiple ionization modes were evaluated with an LC-Orbitrap, including electrospray (ESI) and atmospheric pressure chemical ionization (APCI), also in both positive and negative modes. A suspect screening library of over 1300 possible environmental contaminants, including pesticides, pharmaceuticals, personal care products, illicit drugs/drugs of abuse, and various anthropogenic markers was made with experimentally collected data with the LC-Q-ToF with both column types, with 227 chemicals being retained by the HILIC column. The non-targeted methods using multiple chromatographic and ionization modes were applied to environmental water samples collected with polar organic chemical integrative samplers (POCIS), including surface water upstream and downstream from wastewater effluent discharge, and the downstream drinking water intake and treated drinking water for three distinct sampling events. For the LC-Q-ToF, 442 chemical features were detected on the C18 column and 91 with the HILIC column in the POCIS extracts, while 556 features were found on the Orbitrap workflow by ESI and 131 features detected by APCI. Over 100 chemicals were tentatively identified by suspect screening and database searching. The comprehensive and systematic evaluation of these methods serve as a step in characterizing the chemical space covered when utilizing different chromatography and ionization methods, or different instrument workflows on complex environmental mixtures.

Non-targeted analysis and suspect screening of organic contaminants in temperate snowfall using liquid chromatography high-resolution mass spectrometry

Contaminants released into the atmosphere that undergo regional and long-range transport can deposit back to Earth through snowfall. When snow melts, these contaminants re-enter the environment, sometimes far from their original emission sources. Here we present the first comprehensive characterization of organic contaminants in snow from North America. Fresh snowfall samples were collected in the central United States over a three-year period and measured by liquid chromatography high-resolution mass spectrometry for suspect screening and non-targeted analysis. The resulting data set was screened against experimental MS/MS libraries and underwent supplemental in silico MS/MS analysis. In total, 91 possible compounds were tentatively identified in snow, and 17 were successfully confirmed and semi-quantified with reference standards. These contaminants were mostly anthropogenic in origin and included six herbicides, three insect repellants, one insecticide metabolite, and one fungicide. The most prominent compounds present in all samples were N-cyclohexylformamide (known contaminant in tire leachate), DEET (insect repellent), and dimethyl phthalate (plasticizer), with median deposition fluxes of 4032, 284, and 262 ng m-2, respectively. Three additional compounds were detected in 100% of samples: coumarin (phytochemical and fragrance additive), 5-methylbenzotriazole (antifreeze component), and quinoline (heterocyclic aromatic). The Peto-Peto test revealed statistically significant differences in deposition fluxes for these six contaminants (p < 0.05), with weak but statistically significant positive associations between coumarin and DEET and between coumarin and quinoline according to a Kendall's tau correlation analysis. These findings demonstrate the utility of in silico analysis to complement MS/MS matching with experimental databases. Even so, thousands of unidentified features remained in the data set, highlighting the limitations of current strategies in non-targeted analysis of environmental samples.

Consolidating two-dimensional liquid chromatography-high-resolution tandem mass spectrometry (LC×LC-HRMS/MS) technique for the non-targeted analysis of poly- and perfluorinated substances: A trial on aqueous film-forming foams

To date, poly- and perfluoroalkyl substances (PFAS) represent a real threat for their environmental persistence, wide physicochemical variability, and their potential toxicity. Thus far a large portion of these chemicals remain structurally unknown. These chemicals, therefore, require the implementation of complex non-targeted analysis workflows using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) for their comprehensive detection and monitoring. This approach, even though comprehensive, does not always provide the much-needed analytical resolution for the analysis of complex PFAS mixtures such as fire-fighting aqueous film-forming foams (AFFFs). This study consolidates the advantages of the LC×LC technique hyphenated with high-resolution tandem mass spectrometry (HRMS/MS) for the identification of PFAS in AFFF mixtures. A total of 57 PFAS homolog series (HS) were identified in 3M and Orchidee AFFF mixtures thanks to the (i) high chromatographic peak capacity (n'2D,c ~ 300) and the (i) increased mass domain resolution provided by the "remainder of Kendrick Mass" (RKM) analysis on the HRMS data. Then, we attempted to annotate the PFAS of each HS by exploiting the available reference standards and the FluoroMatch workflow in combination with the RKM defect by different fluorine repeating units, such as CF2, CF2O, and C2F4O. This approach resulted in 12 identified PFAS HS, including compounds belonging to the HS of perfluoroalkyl carboxylic acids (PFACAs), perfluoroalkyl sulfonic acids (PFASAs), (N-pentafluoro(5)sulfide)-perfluoroalkane sulfonates (SF5-PFASAs), N-sulfopropyldimethylammoniopropyl perfluoroalkane sulfonamides (N-SPAmP-FASA), and N-carboxymethyldimethylammoniopropyl perfluoroalkane sulfonamide (N-CMAmP-FASA). The annotated categories of perfluoroalkyl aldehydes and chlorinated PFASAs represent the first record of PFAS HS in the investigated AFFF samples.

Supramolecular solvents: a gateway to all-in-one extractions in chemical exposomics

The characterization of the human chemical exposome through daily estimated intakes or biomonitoring has become paramount to understand the causal pathways leading to common diseases. The paradigm shift that has taken place in looking at health has moved research from the classical biomedical model based on "one exposure, one disease" to a more comprehensive approach based on multiple chemicals and low dose effects. For this purpose, untargeted and/or suspect analysis of chemicals based on liquid chromatography and high-resolution mass spectrometry (LC-HRMS) has been proposed as the most relevant strategy for sequencing the exposome. A key aspect in this respect is the development of unbiased sample preparation methods that efficiently concentrate the wide range of untargeted/suspected chemicals while minimizing interference from sample matrices. Here, we aim to critically discuss the potential of tailored supramolecular solvents (SUPRAS) for achieving all-in-one extractions in chemical exposomics, as an alternative to overcome the limitations of the current sample treatment strategies, on the basis of their intrinsic properties and the applications reported so far.

Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries

Nontargeted and suspect screening with liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become an indispensable tool for quality assessment in the aquatic environment - complementary to targeted analysis of organic (micro)contaminants. An LC-HRMS method is presented, suitable for the analysis of a wide variety of water related matrices: surface water, groundwater, wastewater, sediment and sludge, including extracts from passive samplers and on-site solid phase enrichment, while focusing on the data processing aspect of the method. A field study is included to demonstrate the practical application and versatility of the whole process. HRMS/MS data were recorded following LC separation in both (ESI) positive and negative ionization modes using data dependent as well as data independent acquisition. Two vendor (Agilent's Personal Compound Database and Library and from National Institute of Standards and Technology) and one open (MassBank/EU) tandem mass spectral libraries were utilized for the identification of compounds via mass spectral match. The development of a novel software tool for parsing, grouping and reduction of MS/MS features in data files converted to mascot generic format (MGF) helped to substantially decrease the amount of time and effort needed for MS library search. While applying the method, in the course of the entire field study, 18771 detections (from 870 individual compounds) in total were recorded in 275 samples, resulting in 68.3 identified compounds per sample, on average. Among the top ten most frequently detected contaminants across all samples and sample types were pharmaceutical compounds carbamazepine, 4-acetamidoantipyrine, 4-formylaminoantipyrine, tramadol, lamotrigine and phenazone and industrial contaminants toluene-2-sulfonamide, tolytriazole, tris(2-butoxyethyl) phosphate and benzotriazole. Exploratory data analysis methods and tools enabled us to discover organic pollutant occurrence patterns within the comprehensive sets of qualitative data collected from various projects between the years 2018-2023. The results may be used as valuable inputs for future water quality monitoring programs.

Exploring the chemical subspace of RPLC: A data driven approach

BACKGROUND

The chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC.

RESULTS

We present a generic framework that uses a data driven approach to predict whether molecules fall 'inside', 'maybe' inside, or 'outside' of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall 'outside' of the RPLC subspace.

SIGNIFICANCE AND NOVELTY

The RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.

"PAW" a smart analytical process assessing lipophilicity of solutes in mixtures

BACKGROUND

The analysis of mixtures of contaminants remains a challenging task in many fields, including water quality and waste management. For example, the degradation of industrial waste such as plastics, leads to complex mixtures with hundreds of organic contaminants and often non-referenced analytes. In such cases, non-targeted or effects-based analyses provide complementary information to classical targeted-analyses, regarding contaminants nature or properties (molecular mass, lability, toxicity). In this study, a novel analytical method is proposed to characterise mixtures of unknown organic contaminants, with a focus on the lipophilicity of solutes.

RESULTS

The proposed process, named "PAW" (Partition of Aqueous Waste), aims at the quantification of octanol-water partition coefficients (POW) of mixed organic analytes. The process is based on sequential liquid-liquid partition equilibria. The output result is a lipophilicity histogram of the solutes, screened according to the chosen detection method. The process quantifies the distribution of analytes as a function of their octanol-water partition coefficients, without requiring any identification or prior knowledge. The PAW process is applicable with various detectors (UV-Visible, total carbon, liquid scintillation, etc.) allowing to focus on specific families of contaminants (e.g. organic solutes, colloids, 14C-bearing, etc.). Experimental proofs of concept are proposed, illustrating process implementation and possible fields of application. The first example deals with purity analysis of synthetic radiolabeled compounds. The second example aims the monitoring of cellulose degradation and quantification of the lipophilicity of degradation products.

SIGNIFICANCE

The PAW analytical process seems especially useful for characterisation of mixtures containing both hydrophilic and lipophilic compounds, e.g. neutral and ionizable organic contaminants, hardly characterisable simultaneously by chromatographic methods. It could be complementary to more detailed targeted or screening analysis of samples and effluents. For example it may help assessing the composition and environmental fate of mixtures of unknown analytes, thus facilitating waste management or mitigation strategies.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

Estimating LoD-s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS

Nontargeted LC/ESI/HRMS aims to detect and identify organic compounds present in the environment without prior knowledge; however, in practice no LC/ESI/HRMS method is capable of detecting all chemicals, and the scope depends on the instrumental conditions. Different experimental conditions, instruments, and methods used for sample preparation and nontargeted LC/ESI/HRMS as well as different workflows for data processing may lead to challenges in communicating the results and sharing data between laboratories as well as reduced reproducibility. One of the reasons is that only a fraction of method performance characteristics can be determined for a nontargeted analysis method due to the lack of prior information and analytical standards of the chemicals present in the sample. The limit of detection (LoD) is one of the most important performance characteristics in target analysis and directly describes the detectability of a chemical. Recently, the identification and quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization efficiency, risk scores, and retention times) have significantly improved due to employing machine learning. In this work, we hypothesize that the predicted ionization efficiency could be used to estimate LoD and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted method for the detection of suspected chemicals even if analytical standards are lacking. For this, 221 representative compounds were selected from the NORMAN SusDat list (S0), and LoD values were determined by using 4 complementary approaches. The LoD values were correlated to ionization efficiency values predicted with previously trained random forest regression. A robust regression was then used to estimate LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD values were used for prioritization of the unknown features. Furthermore, we present LoD values for the NORMAN SusDat list with a reversed-phase C18 LC method.

Emerging contaminants: A One Health perspective

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Non-targeted screening and photolysis transformation of tire-related compounds in roadway runoff

Roadway runoff serves as a crucial pathway for transporting contaminants of emerging concern (CECs) from urban environments to receiving water bodies. Tire-related compounds originating from tire wear particles (TWPs) have been frequently detected, posing a potential ecological threat. Yet, the photolysis of tire-related compounds within roadway runoff remains inadequately acknowledged. Addressing this deficit, our study utilized high-resolution mass spectrometry (HRMS) to characterize the chemical profile of roadway runoff across eight strategically selected sites in Guangzhou, China. 219 chemicals were identified or detected within different confidence levels. Among them, 29 tire-related contaminants were validated with reference standards, including hexa(methoxymethyl)melamine (HMMM), 1,3-diphenylguanidine (DPG), dicyclohexylurea (DCU), and N-cyclohexyl-2-benzothiazol-amine (DCMA). HMMM exhibited with the abundance ranging from 2.30 × 104-3.10 × 106, followed by DPG, 1.69 × 104-8.34 × 106. Runoff sample were exposed to irradiation of 500 W mercury lamp for photodegradation experiment. Photolysis results indicated that tire-related compounds with a low photolysis rate, notably DCU, DCMA, and DPG, are more likely to persist within the runoff. The photolytic rates were significantly correlated with the spatial distribution patterns of these contaminants. Our findings underscore TWPs as a significant source of pollution in water bodies, emphasizing the need for enhanced environmental monitoring and assessment strategies.

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors

The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.

Quantification of chemicals in non-targeted analysis without analytical standards - Understanding the mechanism of electrospray ionization and making predictions

The constant creation and release of new chemicals to the environment is forming an ever-widening gap between available analytical standards and known chemicals. Developing non-targeted analysis (NTA) methods that have the ability to detect a broad spectrum of compounds is critical for research and analysis of emerging contaminants. There is a need to develop methods that make it possible to identify compound structures from their MS and MS/MS information and quantify them without analytical standards. Method refinements that utilize machine learning algorithms and chemical descriptors to estimate the instrument response of particular compounds have made progress in recent years. This narrative review seeks to summarize the current state of the field of non-targeted analysis (NTA) toward quantification of unknowns without the use of analytical standards. Despite the limited accumulation of validation studies on real samples, the ongoing enhancement in data processing and refinement of machine learning tools could lead to more comprehensive chemical coverage of NTA and validated quantitative NTA methods, thus boosting confidence in their usage and enhancing the utility of quantitative NTA.

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

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 using 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 ).