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GNPS - Supplemental for the manuscript

ABSTRACT: This dataset contains the .raw files (PFAS analyzed in extracts from MSW leachate and foam from agitated leachate). It also contains the first release of the FluoroMatch software. This dataset is associated with the manuscript: "Towards comprehensive PFAS annotation using FluoroMatch Software and Intelligent LC-HRMS/MS Acquisition Methods "

The latest version of the FluoroMatch software can be found at:

innovativeomics.com

Thousands of per- and polyfluoroalkyl substances (PFAS) exist in the environment which pose a potential health hazard. Suspect and non-target screening with liquid chromatography (LC) high-resolution tandem mass spectrometry (HRMS/MS) can be used for comprehensive characterization of PFAS. However, to date, no automated PFAS data analysis software exists to mine these extensive datasets. Therefore, we introduce FluoroMatch, which automates file conversion (vendor neutral), peak picking, blank feature filtering, PFAS annotation based on precursor and fragment masses, and annotation ranking. The software library currently contains ~7,000 PFAS fragmentation patterns based on rules derived from standards and literature and the software automates a process for users to add additional compounds. The use of intelligent data-acquisition methods (specifically iterative exclusion) nearly doubled the number of annotations. Software application is demonstrated by examining partitioning of PFAS into the foam phase of agitated landfill leachate, which may serve as mechanism for removal of PFAS from waste streams. FluoroMatch had wide coverage, returning 27 PFAS annotations for landfill leachate samples, explaining 75% of the all-ion fragmentation CF2 related fragments.

***The license included with this dataset only applies to the .raw files, not to the software.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Na

SUBMITTER: Krystal G. Pollitt John A. Bowden

PROVIDER: MSV000085244 | GNPS | Tue Apr 07 09:19:00 BST 2020

REPOSITORIES: GNPS

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Project description:This is the GC Orbitrap data accompanying the FluoroMatch GC Library manuscript. References and software are available here: https://innovativeomics.com/software/fluoromatch-gc-modular-pfas-annotation/ Despite thousands of documented PFAS, approaches to characterize volatile and semi-volatile PFAS are limited. To address this gap, we developed a non-targeted gas chromatography high-resolution mass spectrometry (GC-HRMS) workflow to extend coverage of volatile and semi-volatile PFAS. This workflow includes new FlouroMatch GC mass spectral libraries (>1900 EI and PCI predicted and experimental spectra) and characterization of thousands of PFAS specific EI fragments for fragment screening. Liquid chromatography (LC) and GC-HRMS approaches were highly complementary, with GC-HRMS revealing diverse PFAS profiles in 8 different environmental and biological matrices distinct from PFAS detected in LC approaches. For example, we detected a previously unreported PFAS, 2-(perfluorohexyl)ethanethiol in multiple firefighting foam (AFFF) formulations and a novel feature tentatively annotated as N-methyl-N-(2-hydroxyethyl) perfluorooctanesulfonamide (MeFOSE alcohol) that was consistently detected in various matrices related to point and non-point PFAS exposures including dried blood spots, leachate, industrial effluent, and settled dust collected from homes, which were not detected in corresponding LC approaches. Personal exposure to volatile and semi-volatile PFAS was found to vary significantly across individuals. In a cohort of 48 children in Connecticut wearing wristband passive samplers, GC-HRMS fragment screening revealed that 46% of 40 unique airborne PFAS detected only occurred in a single child, and 58% of 47 unique PFAS detected in settled dust collected from 11 households were only detected in a single home. Findings highlight the importance of personal monitoring using a combination of non-targeted GC and LC-HRMS approaches to comprehensively characterize PFAS exposures. The novel methods that we have established will allow the health burden PFAS to be rigorously evaluated, ultimately informing regulatory policies and public health interventions.

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Project description:This Dataset goes alongside the 2023 manuscript: Novel perfluoroalkyl substances (PFAS) discovered in whole blood using automated non-targeted analysis of dried blood spots by Jeremy P. Koelmel, Elizabeth Z. Lin, Emily Parry, Paul Stelben, Emma E.Rennie, Krystal J.Godri Pollitt A small subset of per- and polyfluoroalkyl substances (PFAS) are routinely screened in human blood. These compounds generally explain <50 percent of the total PFAS in human blood. The percentage of known PFAS in human blood has been decreasing as replacement PFAS and more complex PFAS chemistries are introduced to the market. Most of these novel PFAS have not been previous identified. Non-targeted methods are required to characterize this dark matter PFAS. Our objective was to apply non-targeted PFAS analysis to human blood to gain an understanding about the sources, concentrations, and toxicity of these compounds. A high-resolution tandem mass spectrometry (HRMS) and software workflow for PFAS characterization in dried blood spots is reported. Dried blood spots are a less invasive collection technique compared to venous blood draws, allowing collection from vulnerable populations. Biorepositories of archived dried blood spots are available internationally from newborns and present opportunities to study prenatal exposure to PFAS. In this study, dried blood spot cards were analyzed using iterative MS/MS by liquid chromatography HRMS. Data processing was conducted using FluoroMatch Suite including a visualizer tool that presents homologous series, retention time vs m/z plots, MS/MS spectra, feature tables, annotations, and fragments for fragment screening. The researcher performing data-processing and annotation was blinded to the fact that standards were spiked in, and was able to annotated 95 percent of standards spiked on dried blood spot samples, signifying a low false negative rate using FluoroMatch Suite. A total of 28 PFAS (20 standards and 4 exogenous compounds) were detected across five homologous series with Schymanski Level 2 confidence. Of these four, 3 were perfluoroalkyl ether carboxylic acids (PFECA), a chemical class of PFAS which is increasingly being detected in environmental and biological matrices but is not currently screened in most targeted analyses. A further 86 potential PFAS were detected using fragment screening. PFAS are extremely persistent and widespread yet remain largely unregulated. Our findings will contribute to an improved an understanding of exposures. Application of these methods in environmental epidemiology studies have the potential to inform policy with regards to PFAS monitoring, regulation, and individual-level mitigation strategies.

Project description:Background: Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes. Results: We annotate granulocyte lncRNAs and mRNAs in RNA-seq data from ten healthy individuals, identifying multiple lncRNAs absent from reference annotations, and use this to investigate three known features (higher tissue-specificity, lower expression, and reduced splicing efficiency) of lncRNAs relative to mRNAs. Expression variability was examined in seven individuals sampled three times at one or more than one month intervals. We show that lncRNAs display significantly more inter-individual expression variability compared to mRNAs. We confirm this finding in 2 independent human datasets by analyzing multiple tissues from the GTEx project and lymphoblastoid cell lines from the GEUVADIS project. Using the latter dataset we also show that including more human donors into the transcriptome annotation pipeline allows identification of an increasing number of lncRNAs, but minimally affects mRNA gene number. Conclusions: A comprehensive annotation of lncRNAs is known to require an approach that is sensitive to low and tight tissue-specific expression. Here we show that increased inter-individual expression variability is an additional general lncRNA feature to consider when creating a comprehensive annotation of human lncRNAs or proposing their use as prognostic or disease markers. We used PolyA+ RNA-seq data from human primary granulocytes of 10 healthy individuals to de novo annotate lncRNAs and mRNAs in this cell type and ribosomal depleted (total) RNA-seq data from seven of these individuals sampled three times to analyze lncRNA amd mRNA expression variability

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