FluoroMatch workflow

FluoroMatch and FluoroMatch Visualizer were developed to be comprehensive, modifiable, user-friendly, and open source. The software is flexible, but a few sample types are recommended. During sample preparation, the user should incorporate method blanks which are carried throughout all field collection, extraction, storage, and sample handling procedures. These blanks can be analyzed throughout the acquisition queue to also account for carryover, instrument contamination, and other acquisition-related background and artifacts. This also enables the software to remove any peaks which are not related to the samples. Furthermore, MS/MS is required for annotation, and we recommend acquiring MS/MS on pooled samples or representative samples in an iterative MS/MS or intelligent acquisition mode to increase coverage.^42,48 FluoroMatch can use data-dependent and targeted MS/MS files as well, but currently does not support data-independent approaches, as these approaches are challenging when most precursors share the same fragments (e.g., [C₂F₅]⁻). Finally, full-scan data is required for all samples for peak integration and statistics across samples; MS/MS files can be used for full-scan data if at least 10–15 scans are acquired across the chromatographic peak.

The data processing workflow starts with FluoroMatch Flow (or Modular), which can be used to directly process vendor files (Figure 1); the software is freely available at innovativeomics.com/software. FluoroMatch Modular uses the same annotation algorithms as FluoroMatch Flow but can be used to annotate feature tables generated using the users’ own peak picking and filtering algorithms/software (vendor or open source including Profinder, Compound Discoverer, MZMine, and XCMS). FluoroMatch Flow, which covers the entire PFAS nontargeted data processing workflow (Figure 1), uses MZMine,⁴⁹ MSConvert,⁵⁰ and in-house code using R and C# in the background to process the files. FluoroMatch Flow works for most vendor files including Waters, Agilent, Thermo, and Bruker. Together FluoroMatch Flow, FluoroMatch Modular, FluoroMatch Generator, and FluoroMatch Visualizer are termed FluoroMatch Suite, and this suite of software offers several workflows (Figure 1). After following the proper naming conventions outlined for this software program, users simply drag files onto the interface, choose blank filtering parameters and their preferred output directory, and click run. The software performs file conversion, peak processing (including peak picking, alignment, and gap-filling), blank filtering, compilation of annotation evidence, annotation, and assignment of confidence (Figure 1). Blank filtering is performed by removing features that have sample quartiles that are not above a certain fold-change of the average of the blanks plus 3 times the standard deviation of the blanks (threshold parameters, such as which quartile to use of the samples is experiment dependent and user-modifiable). Confidence assignments are based on an in-house scoring framework^42,43 (Figure 2) and the Schymanski scheme.⁵¹ Recently, a specific schema has been developed by the community for assigning confidence to PFAS annotations,⁵² and this schema may be integrated into future releases of FluoroMatch. Comparison of FluoroMatch Flow with other software, including Compound Discoverer and EnviMass, shows unique advantages and disadvantages of various software.^44,45 In previous studies, FluoroMatch Flow has been shown to have similar or lower false positive rates among high-confidence annotations and has similar or more comprehensive annotation when the same peak list is used,^44,45 but vendor or open-source peak picking algorithms designed specifically for the methods/instruments deployed often have better peak picking coverage then built-in algorithms in FluoroMatch Flow. To increase coverage, users can use their own peak picking workflow optimized for their instrument and gradient, using FluoroMatch Modular for annotation, compiling evidence, and scoring; this workflow is currently recommended for expert users. FluoroMatch Modular works with any peak picking/processing software (both vendor and open source), which outputs a txt file with m/z and retention time information.

Figure 1.

The FluoroMatch Suite covers the entire PFAS non-targeted data processing and manual validation workflow. User acquisition and data processing workflow using FluoroMatch Modular, FluoroMatch Flow, and FluoroMatch Visualizer are shown. DDA, data-dependent analysis; MS/MS, fragmentation spectra.

Figure 2.

FluoroMatch Suite includes a systematic scoring framework to communicate confidence for every single feature, alongside also reporting confidence levels via the Schymanski et al. schema. All scores are shown alongside visual and descriptive definitions. Circles refer to features and are depicted within or not within mass defect series. The type of fills of the circles represents evidence used for PFAS assignment. ^*B– are features with fluorine-containing fragments using a list of 777 fluorine-containing fragments for screening derived from standards, but the fragments observed are not common fragments with only one possible predicted formula.

FluoroMatch Visualizer requires the desktop version of Power BI which can be freely downloaded at https://powerbi.microsoft.com/en-us/downloads/. An online version is being developed for future use. Using the FluoroMatch Visualizer Power BI file, users upload their data processing output file from FluoroMatch Modular or FluoroMatch Flow.

FluoroMatch visualizer description and possible workflow

In this section, different methods for validating and discovering likely PFAS series are covered. The software automatically determines series by grouping features which have the same CF₂ normalized mass defect (e.g., within 5 ppm, or user-selected precursor mass accuracy), and are spaced out by 50 Da (CF₂ nominal mass). Other series (e.g., CF₂CF₂O), as well as multiple series simultaneously, can be searched for as well (see tutorial videos). By default, retention time order is also used to include members of a series, and if features do not increase in retention time with increasing nominal mass, they are flagged as potential false positives. This series detection does not include retention time trends other than order, nor MS/MS spectra (other than in scoring members of a series), nor isotopic pattern, and therefore manual review using FluoroMatch Visualizer and other tools is helpful to validate series. Because exact mass alone cannot distinguish isomers or even provide an exact formula in most cases, these other lines of evidence must be reviewed before confidently assigning a series.

FluoroMatch Visualizer includes three interactive graphs: m/z vs retention time (Figure 3B), normalized mass defects (Figure 3C), and MS/MS spectra (Figure 3E). These graphs can be filtered by score and chemical series (Figure 3A). Data is further summarized in two tables: annotated and scored features (Figure 3F) and annotated fragments for fragment screening (Figure 3D). For all tables and graphs, users can drag over entries to visualize PFAS chemical structures, or substructures related to fragmentation. For this, we developed a novel tool for interpreting SMILES within Power BI.

Figure 3.

The FluoroMatch interface is designed so that all relevant information can be observed simultaneously. The user view of the FluoroMatch Visualizer interface is shown. The interface consists of three filters (by MS/MS file, score, and chemical series; A), three visuals (m/z vs retention time, normalized mass defect plot, and MS/MS spectra; (B, C, and E, respectively), and two tables (table of fragments, and table of annotated features; D and F, respectively).

All graphs and tables can be filtered by user-selected feature(s). For example, if a row in the feature table is selected then only that feature is displayed in the charts and vice versa. Cross-filtering is important so that all evidence for a feature, PFAS series, or other group of features, can be analyzed simultaneously. FluoroMatch Visualizer additionally includes tool tips, which provide further information when the user hovers over a feature or fragment in the MS/MS spectra. When hovering over a feature in graphs showing m/z vs retention time (Figure 3B) or normalized mass defects (Figure 3C), the chemical series, formula, SMILES structure, name/class, score, and x/y-axis values are shown. In the graph of MS/MS spectra (Figure 3E), any fragment annotation, potential SMILES substructure of fragment, fragment m/z, fragment intensity, and ppm error of any fragment annotation are shown. In addition, features aligned with presented fragments are presented, which is important when MS/MS spectra from multiple features are overlaid.

User workflows employing FluoroMatch Visualizer can be diverse, especially considering that using Power BI Desktop new graphs, variables, and tables can be designed and added by users familiar with the platform. For example, new columns can be added to tables containing information of interest, new plots, for example, mass defect versus retention time can be added, and new splicers and filters can be developed. Here we describe a simple workflow for determining false positives and true positives, in “Application of FluoroMatch visualizer to identify PFAS in a legacy AFFF mixture” section. This workflow will be exemplified using an AFFF mixture.

Application of FluoroMatch Visualizer to identify PFAS in a legacy AFFF mixture

Nontargeted data provide a wealth of information and in most datasets the majority of chemical features are not PFAS related. By looking at all features simultaneously, it can be difficult to determine patterns and discover potential PFAS series. Often thousands of features are observed (3686 for the AFFF mixture after blank filtering), and no user can look at all the data and make sense of it simultaneously; for example, when all features are displayed the resulting plots show few clear patterns (Figures 4 and 5).

Figure 4.

Several PFAS series can be discovered by looking at certain regions of mass defect plots normalized to CF₂. Without filtering data by series or score the visual is overly complex and challenging to make sense of. Mass defect plot (normalized to CF2) showing a PFAS-specific region and all features. Horizontal evenly spaced (intervals of 50 Da) points of the same color represent potential PFAS homologous series.

Figure 5.

Retention time vs m/z plots are overly complex without filtering; certain PFAS-rich regions, blank signal, and other non-PFAS series can readily be discovered using these plots. Retention time vs m/z for all features (A) and for features that belong to high-confidence series with one or more PFAS with a score of A (B). A specific region in the plot is where most PFAS can be found, and certain outliers of high confident series can easily be found (B). Red highlighted areas show background noise, yellow highlighted area shows PFAS region of plot, and gray highlighting shows another potential non-PFAS series.

Because of the complexity and richness of nontargeted data, users need to look at a subsection of PFAS features and multiple filters are provided in FluoroMatch Visualizer to prioritize which group of features to investigate. Ideally, the user would want to know which series have confident annotations and which series have no annotations, in order to evaluate the most confident annotations first. Filtering by score (Figure 6) allows users to determine which features to focus on based on the evidence provided, by selecting the series which contains the highest scoring features. A description of the scores can be found in Figure 2 and is described previously.⁴³ Briefly, features assigned as A (A, A−) contain class-based MS/MS fragments required for confident assignments, features assigned B (B+, B, and B−) have an in-silico or fragment screening match, features assigned C (C+, C, and C−) are in homologous series with B’s or A’s, and D’s form homologous series or have accurate mass evidence consistent with PFAS but no MS/MS information; E’s are the remaining features which are likely not PFAS and therefore can be generally ignored, drastically reducing the number of features needing manual validation.⁴³

Figure 6.

By filtering to only include those series with the most confident annotations, true positives and false negatives can manually be determined using clear trends. Shown above are Kendrick mass defect plots normalized to CF2 (KMD [CF2]) for annotations with high confidence (A) and series containing one or more high confident annotations (C). As an example, for high-confidence annotation, the series for perfluoroalkyl sulfonic acids (PFSA) is selected (A) and the retention time vs m/z plot is shown for this series (B). Retention time vs m/z plots is also shown for all high-confidence series’ (D). Note other series beyond CF₂ can also be automatically classified by the software, and these series repeating units can be generated by the user.

By filtering to retain only features with high-confidence scores (e.g., scores of A; Figure 6A), known series (about a 5% false positive rate)^42,43 can be readily viewed. In the case of the AFFF mixture, 18 PFAS series were determined, covering 62 individual species with a high-confidence score (Figure 6A). These series can then be expanded to all features which fall within the series (including lower confidence annotations or no annotations), in which case the coverage of species with likely annotations expanded to 257 individual species for the AFFF mixture (Figure 6C). After establishing these series containing high-confidence annotations it is important to distinguish which members of the series are false positives via manual review.

Organizing features by series is essential for determining false positives, as clear patterns are observed for each series. If a member of a series falls outside of this pattern, it may be considered a false positive. Therefore, using the drop-down menu (or by selecting series in the legend), users can select one or more series to start determining true and false positives, as well as false negatives. Each member of a series should follow a clear diagonal pattern in m/z vs retention time plots as shown for only high-confidence annotations of perfluorosulfonic acids (PFSAs) (Figure 6B) and any outliers can be considered false positives (Figure 6D). Furthermore, members of the series should also follow a clear trend in the normalized mass defect plots. Because the tool automatically assigns series by mass defect and nominal mass, the PFAS series will follow trends in these plots automatically and no manual removal of false positives is needed (Figure 6A and C).

Cross-filtering is especially important when looking at MS/MS spectra, as multiple MS/MS spectra can be overlaid. If certain members of a series MS/MS spectra are overlaid, for example by sorting and selecting them in the plots (e.g., as selected in Figure 6A for the PFSA series) or feature table, the trends for neutral losses and fragment ions become clear (Figure 7). Any MS/MS spectra which do not have fragments consistently observed in other features of the same series can then be labeled as potential false positives. Furthermore, assignments with low confidence may attain higher confidence by the user after surveying the MS/MS spectra overlaid with other members of the series.

Figure 7.

Averaging spectra across series can be readily done in the visualizer platform and used to determine common fragmentation patterns and false positives. Combined mass spectra after highlighting the perfluoroalkyl sulfonic acids (PFSA) series (Figure 5A), zoomed out (A) and zoomed in (B) with annotations (annotations are provided by the software upon dragging over a fragment peak).

Fragment screening can also be achieved by annotated fragments from MS/MS spectra, which can aid in finding unknowns and identifying chemical series/classes. Common fragments which were annotated can be selected and all MS/MS spectra and features across all charts will be shown, which contain these annotated fragments. This can aid in grouping compounds that contain certain moieties. For example, [SO₃F]⁻ and [SO₂F]⁻ may be selected to find PFAS with sulfonic acid groups, and [CF₃]⁻ and [C₂F₅]⁻ may be selected to identify carbon–fluorine chains. PFAS-containing sulfonic acid groups (Figure 8) and PFAS-containing pentafluorosulfide (Figure 9) were screened in this manner. One strategy to determine which fragments to use for fragment screening is to sort the annotated fragments by fragment abundance and then select those representing a moiety of interest. Fragments related to sulfonic acid and the PFAS carbon–fluorine chain were the most abundant fragments when all fragments across all features were summed (automatically provided by FluoroMatch Visualizer) (Figure 8A). Many potential false positives were determined using retention time vs m/z plots (Figure 8B), showing the importance of this visualization.

Figure 8.

Fragment screening can be deployed to determine PFAS series with specific moieties and by sorting annotated fragments by abundance, the most common PFAS fragments can be determined out of a database containing over 777 potential PFAS fragments. Topmost abundant annotated fragments for an entire dataset containing fluorine atoms (A), sulfonic acid-related fragments are selected (A) to identify all PFAS series (C) with sulfonic acids. Numerous outliers are shown in retention time vs m/z plots which may not belong to PFAS series, as well as likely true positives (B).

Figure 9.

Certain fragments are very specific to chemical moieties and hence can be advantageous to use in fragment screening, for example, the [SF₅]⁻ fragment is specific to pentafluosulfide-containing species found in AFFFs. Features (A) and their scores, homologous series, retention time, mass to charge ratio (m/z), and level A annotations, filtered by performing fragment screening on the [SF₅]⁻ fragment is shown. [SF₅]⁻ fragment intensities from different fragment scans/features are shown within a 5-ppm window (B), and these fragments were used to screen for pentafluorosulfide-containing series (Figure 9C). Most species form clear trends in retention time vs m/z plots, with some obvious outliers (Figure 9D).

Fragment screening using the [SF₅]⁻ fragment for pentafluorosulfide-containing PFAS showed that while the fragment was of low abundance across all features (Figure 9B) 11 features were determined (Figure 9A). When looking at all members of those series containing at least one feature with a [SF₅]⁻ fragment, 83 features were determined (Figure 9C and D). For one series, many false positives (teal series containing m/z 257.0472, 307.0419, and 357.0414) were observed (Figure 9D) and the entire series (blue) was also likely a false positive (no retention time order, data not shown).

Beyond fragment filtering and overlapping series MS/MS spectra, MS/MS spectra and features can be filtered by MS/MS file, in case certain files represent very different sample types, which may have different PFAS isomers. Furthermore, techniques using different MS/MS methods or parameters can be cross-compared readily using filter by MS/MS file. Here we compared different acquisitions of iterative exclusion (IE), where repeated injections of a sample using a rolling exclusion list of ions previously selected are deployed (ensuring that each new injection has MS/MS acquired on new ions not previously selected). As shown in mass defect plots in the PFAS region filtered by each MS/MS file, new series and new members are discovered for each additional round of IE (Figure 10A).^42,48 Note these plots are cumulative, showing all past acquisitions and new acquisitions. Furthermore, by summing and plotting the number of features and filtering by score, it’s shown that the advantage dissipates for high-confidence annotations after about the fourth injection, meaning fewer injections are needed if high-confidence annotations are the goal (Figure 10B–D).

Figure 10.

By filtering by MS/MS file, the benefit of different MS/MS acquisition techniques can readily be explored. Features with MS/MS after four iterations of IE, showing increased coverage in the PFAS region of Kendrick mass defect plots with additional injections (A). The increase in MS/MS coverage is more representative of features with low abundance after iterative injections; whereas the number of new features with MS/MS increases linearly throughout all injections (B), after the third and fourth injections new ions selected are of too low abundance for MS/MS to have high enough spectral quality for most confident assignments (C and D). All scans represent any score A through E (B), scans with PFAS MS/MS evidence represent scores of A through B (except B–) (C), and confident annotations refer to those with scores of A and A− (D).