Non-Target Screening of Surface Water Samples to Identify Exposome-Related Pollutants: A Case Study From Luxembourg

peer reviewedTalk given by Dagny Aurich at the ICNTS in Erdin

Non-target screening of surface water samples to identify exposome-related pollutants: a case study from Luxembourg

peer reviewedBackground Non-target screening of surface water samples collected over an extended period can reveal interesting temporal patterns in exposome-related pollutants. Additionally, geographical data on pollution sources close to the sampling sites, chemical classification data and the consideration of flow paths can provide valuable information on the origins and potential threat of tentatively identified chemical compounds. In this study, 271 surface water samples from 20 sampling sites across Luxembourg were analysed using high-resolution mass spectrometry, complementing routine target monitoring efforts in 2019–2022. Data analysis was performed using the open source R-package patRoon, which offers a customizable non-target workflow. By employing open source workflows featuring scoring terms, like spectral match and applying identification levels, tentative identifications can be prioritized, e.g. based on spectral similarity. Furthermore, by utilizing supplementary database information such as PubChemLite annotation categories and classification software such as classyFire, an overall assessment of the potential threats posed by the tentatively identified chemicals was conducted, enabling the prioritization of chemicals for future confirmation through targeted approaches. Results The study tentatively identified 378 compounds associated with the exposome including benzenoids, organoheterocyclic compounds, and organic phosphoric acids and derivatives (11 classyFire superclasses, 50 subclasses). The classification analysis not only revealed temporal variations in agrochemicals, with the majority of identifications occurring in May to July, but also highlighted the prevalence of pharmaceuticals such as venlafaxine in surface waters. Furthermore, potential sources of pollutants, like metallurgic industry or household products were explored by considering common uses and geographical information, as commercial uses of almost 100% of the identified chemicals are known. 41 chemicals were suggested for potential inclusion to governmental monitoring lists for further investigation. Conclusions The findings of this study complement existing knowledge on the pollution status of surface water in Luxembourg and highlight the usefulness of non-target screening for identifying temporal and spatial trends in pollutant levels. This approach, performed in a complementary manner to routine monitoring, can help to tentatively identify chemicals of concern for potential inclusion in target monitoring methods following additional confirmation and quantification efforts.R-AGR-3703 - IAS - LuxTIME (01/06/2020 - 15/01/2025) - FICKERS Andrea

High Resolution Mass Spectrometry of Polyfluorinated Polyether-Based Formulation

High resolution mass spectrometry (HRMS) was successfully applied to elucidate the structure of a polyfluorinated polyether (PFPE)-based formulation. The mass spectrum generated from direct injection into the MS was examined by identifying the different repeating units manually and with the aid of an instrument data processor. Highly accurate mass spectral data enabled the calculation of higher-order mass defects. The different plots of MW and the nth-order mass defects (up to n = 3) could aid in assessing the structure of the different repeating units and estimating their absolute and relative number per molecule. The three major repeating units were -C2H4O-, -C2F4O-, and -CF2O-. Tandem MS was used to identify the end groups that appeared to be phosphates, as well as the possible distribution of the repeating units. Reversed-phase HPLC separated of the polymer molecules on the basis of number of nonpolar repeating units. The elucidated structure resembles the structure in the published manufacturer technical data. This analytical approach to the characterization of a PFPE-based formulation can serve as a guide in analyzing not just other PFPE-based formulations but also other fluorinated and non-fluorinated polymers. The information from MS is essential in studying the physico-chemical properties of PFPEs and can help in assessing the risks they pose to the environment and to human health

Non-target screening reveals time trends of polar micropollutants in a riverbank filtration system

The historic emissions of polar micropollutants in a natural drinking water source were investigated by nontarget screening with high-resolution mass spectrometry and open cheminformatics tools. The study area consisted of a riverbank filtration transect fed by the river Lek, a branch of the lower Rhine, and exhibiting up to 60-year travel time. More than 18,000 profiles were detected. Hierarchical clustering revealed that 43% of the 15 most populated clusters were characterized by intensity trends with maxima in the 1990s, reflecting intensified human activities, wastewater treatment plant upgrades and regulation in the Rhine riparian countries. Tentative structure annotation was performed using automated in silico fragmentation. Candidate structures retrieved from ChemSpider were scored based on the fit of the in silico fragments to the experimental tandem mass spectra, similarity to openly accessible accurate mass spectra, associated metadata, and presence in a suspect list. Sixty-seven unique structures (72 over both ionization modes) were tentatively identified, 25 of which were confirmed and included contaminants so far unknown to occur in bank filtrate or in natural waters at all, such as tetramethylsulfamide. This study demonstrates that many classes of hydrophilic organics enter riverbank filtration systems, persisting and migrating for decades if biogeochemical conditions are stable

Open and FAIR transformation product data for improved suspect/non-target screening: REFTPs in the NORMAN-SLE, PubChem and patRoon

peer reviewedPresentation given at ICCE, Venice, 11 - 15 June 2023

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Dioxin2023 Plenary: Exploring Millions of PFAS with FAIR and Open Science

editorial reviewedPlenary presentation for Dioxin 2023 in Maastricht - Tuesday 12 September Exploring Millions of PFAS with FAIR and Open Science This presentation features a sound track created by Jamie Perera (slide 27) on "Our Chemical Past, Present and Future", which can be downloaded on Vimeo (video) or Soundcloud (sound only). Please leave feedback there if you enjoy it

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Virtual Podium Keynote: Compound Identification and Exposomics: DIY Databases?

In light of recent events, many of us have been impacted by the cancellation of conferences and meetings. We are not only losing the opportunity to present our research, but a chance to connect with our community. Virtual Podium is a platform and opportunity to present and learn about compelling scientific research. Our third session will be focused on Compound Identification. Our keynote speaker this week will be Emma Schymanski who is the PI of Environmental Cheminformatics at the University of Luxembourg. Session 3: Compound Identification Friday, April 10, 2020 at 12:00-1:00PM PDT (3:00-4:00PM EDT) Session 3 - Compound Identification: https://www.eventbrite.com/e/10142661373