1
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Aggerbeck MR, Frøkjær EE, Johansen A, Ellegaard-Jensen L, Hansen LH, Hansen M. Non-target Analysis of Danish Wastewater Treatment Plant Effluent: Statistical Analysis of Chemical Fingerprinting as a Step Toward a Future Monitoring Tool. ENVIRONMENTAL RESEARCH 2024:119242. [PMID: 38821457 DOI: 10.1016/j.envres.2024.119242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/25/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024]
Abstract
In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.
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Affiliation(s)
- Marie Rønne Aggerbeck
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600 Silkeborg.
| | - Emil Egede Frøkjær
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
| | - Anders Johansen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
| | - Lea Ellegaard-Jensen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600 Silkeborg
| | - Lars Hestbjerg Hansen
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg, Denmark
| | - Martin Hansen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600 Silkeborg
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2
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Tkalec Ž, Antignac JP, Bandow N, Béen FM, Belova L, Bessems J, Le Bizec B, Brack W, Cano-Sancho G, Chaker J, Covaci A, Creusot N, David A, Debrauwer L, Dervilly G, Duca RC, Fessard V, Grimalt JO, Guerin T, Habchi B, Hecht H, Hollender J, Jamin EL, Klánová J, Kosjek T, Krauss M, Lamoree M, Lavison-Bompard G, Meijer J, Moeller R, Mol H, Mompelat S, Van Nieuwenhuyse A, Oberacher H, Parinet J, Van Poucke C, Roškar R, Togola A, Trontelj J, Price EJ. Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment. ENVIRONMENT INTERNATIONAL 2024; 186:108585. [PMID: 38521044 DOI: 10.1016/j.envint.2024.108585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 03/25/2024]
Abstract
The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.
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Affiliation(s)
- Žiga Tkalec
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Jožef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia.
| | | | - Nicole Bandow
- German Environment Agency, Laboratory for Water Analysis, Colditzstraße 34, 12099 Berlin, Germany.
| | - Frederic M Béen
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; KWR Water Research Institute, Nieuwegein, The Netherlands.
| | - Lidia Belova
- Toxicological Center, University of Antwerp, 2610 Wilrijk, Belgium.
| | - Jos Bessems
- Flemish Institute for Technological Research (VITO), Mol, Belgium.
| | | | - Werner Brack
- Helmholtz Centre for Environmental Research GmbH - UFZ, Department of Effect-Directed Analysis, Permoserstraße 15, 04318 Leipzig, Germany; Goethe University Frankfurt, Department of Evolutionary Ecology and Environmental Toxicology, Max-von-Laue-Strasse 13, 60438 Frankfurt, Germany.
| | | | - Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
| | - Adrian Covaci
- Toxicological Center, University of Antwerp, 2610 Wilrijk, Belgium.
| | - Nicolas Creusot
- INRAE, French National Research Institute For Agriculture, Food & Environment, UR1454 EABX, Bordeaux Metabolome, MetaboHub, Gazinet Cestas, France.
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
| | - Laurent Debrauwer
- Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France.
| | | | - Radu Corneliu Duca
- Unit Environmental Hygiene and Human Biological Monitoring, Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg; Environment and Health, Department of Public Health and Primary Care, Katholieke Universiteit of Leuven (KU Leuven), 3000 Leuven, Belgium.
| | - Valérie Fessard
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory of Fougères, Toxicology of Contaminants Unit, 35306 Fougères, France.
| | - Joan O Grimalt
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Catalonia, Spain.
| | - Thierry Guerin
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Strategy and Programs Department, F-94701 Maisons-Alfort, France.
| | - Baninia Habchi
- INRS, Département Toxicologie et Biométrologie Laboratoire Biométrologie 1, rue du Morvan - CS 60027 - 54519, Vandoeuvre Cedex, France.
| | - Helge Hecht
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
| | - Juliane Hollender
- Swiss Federal Institute of Aquatic Science and Technology - Eawag, 8600 Dübendorf, Switzerland; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland.
| | - Emilien L Jamin
- Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France.
| | - Jana Klánová
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
| | - Tina Kosjek
- Jožef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia.
| | - Martin Krauss
- Helmholtz Centre for Environmental Research GmbH - UFZ, Department of Effect-Directed Analysis, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Marja Lamoree
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
| | - Gwenaelle Lavison-Bompard
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory for Food Safety, Pesticides and Marine Biotoxins Unit, F-94701 Maisons-Alfort, France.
| | - Jeroen Meijer
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
| | - Ruth Moeller
- Unit Medical Expertise and Data Intelligence, Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg.
| | - Hans Mol
- Wageningen Food Safety Research - Part of Wageningen University and Research, Akkermaalsbos 2, 6708 WB, Wageningen, The Netherlands.
| | - Sophie Mompelat
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory of Fougères, Toxicology of Contaminants Unit, 35306 Fougères, France.
| | - An Van Nieuwenhuyse
- Environment and Health, Department of Public Health and Primary Care, Katholieke Universiteit of Leuven (KU Leuven), 3000 Leuven, Belgium; Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg.
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Insbruck, 6020 Innsbruck, Austria.
| | - Julien Parinet
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory for Food Safety, Pesticides and Marine Biotoxins Unit, F-94701 Maisons-Alfort, France.
| | - Christof Van Poucke
- Flanders Research Institute for Agriculture, Fisheries And Food (ILVO), Brusselsesteenweg 370, 9090 Melle, Belgium.
| | - Robert Roškar
- University of Ljubljana, Faculty of Pharmacy, Slovenia.
| | - Anne Togola
- BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France.
| | | | - Elliott J Price
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
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3
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Szabo D, Falconer TM, Fisher CM, Heise T, Phillips AL, Vas G, Williams AJ, Kruve A. Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry. Anal Chem 2024; 96:3707-3716. [PMID: 38380899 PMCID: PMC10918621 DOI: 10.1021/acs.analchem.3c05705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.
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Affiliation(s)
- Drew Szabo
- Department
of Materials and Environmental Chemistry, Stockholm University, Stockholm 106 91, Sweden
| | - Travis M. Falconer
- Forensic
Chemistry Center, Office of Regulatory Science, Office of Regulatory
Affairs, US Food and Drug Administration, Cincinnati, Ohio 45237, United States
| | - Christine M. Fisher
- Center
for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland 20740, United States
| | - Ted Heise
- MED
Institute Inc, West Lafayette, Indiana 47906, United States
| | - Allison L. Phillips
- Center
for Public Health and Environmental Assessment, US Environmental Protection Agency, Corvallis, Oregon 97333, United States
| | - Gyorgy Vas
- VasAnalytical, Flemington, New Jersey 08822, United States
- Intertek
Pharmaceutical Services, Whitehouse, New Jersey 08888, United States
| | - Antony J. Williams
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, Durham, North Carolina 27711, United States
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Stockholm 106 91, Sweden
- Department
of Environmental Science, Stockholm University, Stockholm 106 91, Sweden
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4
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Brueck CL, Xin X, Lupolt SN, Kim BF, Santo RE, Lyu Q, Williams AJ, Nachman KE, Prasse C. (Non)targeted Chemical Analysis and Risk Assessment of Organic Contaminants in Darkibor Kale Grown at Rural and Urban Farms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:3690-3701. [PMID: 38350027 DOI: 10.1021/acs.est.3c09106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
This study investigated the presence and human hazards associated with pesticides and other anthropogenic chemicals identified in kale grown in urban and rural environments. Pesticides and related compounds (i.e., surfactants and metabolites) in kale samples were evaluated using a nontargeted data acquisition for targeted analysis method which utilized a pesticide mixture containing >1,000 compounds for suspect screening and quantification. We modeled population-level exposures and assessed noncancer hazards to DEET, piperonyl butoxide, prometon, secbumeton, terbumeton, and spinosyn A using nationally representative estimates of kale consumption across life stages in the US. Our findings indicate even sensitive populations (e.g., pregnant women and children) are not likely to experience hazards from these select compounds were they to consume kale from this study. However, a strictly nontargeted chemical analytical approach identified a total of 1,822 features across all samples, and principal component analysis revealed that the kale chemical composition may have been impacted by agricultural growing practices and environmental factors. Confidence level 2 compounds that were ≥5 times more abundant in the urban samples than in rural samples (p < 0.05) included chemicals categorized as "flavoring and nutrients" and "surfactants" in the EPA's Chemicals and Products Database. Using the US-EPA's Cheminformatics Hazard Module, we identified that many of the nontarget compounds have predicted toxicity scores of "very high" for several end points related to human health. These aspects would have been overlooked using traditional targeted analysis methods, although more information is needed to ascertain whether the compounds identified through nontargeted analysis are of environmental or human health concern. As such, our approach enabled the identification of potentially hazardous compounds that, based on their hazard assessment score, merit follow-up investigations.
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Affiliation(s)
- Christopher L Brueck
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Xiaoyue Xin
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Sara N Lupolt
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Risk Sciences and Public Policy Institute, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Brent F Kim
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Raychel E Santo
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Qinfan Lyu
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Washington, North Carolina 27711, United States
| | - Keeve E Nachman
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Risk Sciences and Public Policy Institute, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Carsten Prasse
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Risk Sciences and Public Policy Institute, Johns Hopkins University, Baltimore, Maryland 21205, United States
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5
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Li Y, Lu Z, Zhang X, Wang J, Zhao S, Dai Y. Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates. ENVIRONMENTAL RESEARCH 2024; 243:117806. [PMID: 38043899 DOI: 10.1016/j.envres.2023.117806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/05/2023]
Abstract
Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 μg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.
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Affiliation(s)
- Yubo Li
- Shanghai Municipal Engineering Design Institute (Group) Co. LTD., Shanghai, 200092, PR China
| | - Zhibo Lu
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, Shanghai, 200092, PR China.
| | - Xin Zhang
- Shanghai Municipal Engineering Design Institute (Group) Co. LTD., Shanghai, 200092, PR China
| | - Juan Wang
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, Shanghai, 200092, PR China
| | - Shuiqian Zhao
- Shanghai Municipal Engineering Design Institute (Group) Co. LTD., Shanghai, 200092, PR China
| | - Yuxuan Dai
- Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology, Hong Kong, 999077, PR China
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6
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Pu S, McCord JP, Bangma J, Sobus JR. Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances. Anal Bioanal Chem 2024; 416:1249-1267. [PMID: 38289355 PMCID: PMC10850229 DOI: 10.1007/s00216-023-05117-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 02/09/2024]
Abstract
Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration "surrogates," which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from "global" chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using "global" surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using "expert-selected" surrogates (n = 3) instead of "global" surrogates (n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases.
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Affiliation(s)
- Shirley Pu
- US Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
| | - James P McCord
- US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
| | - Jacqueline Bangma
- US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA
| | - Jon R Sobus
- US Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
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7
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Johnson TA, Abrahamsson DP. Quantification of chemicals in non-targeted analysis without analytical standards - Understanding the mechanism of electrospray ionization and making predictions. CURRENT OPINION IN ENVIRONMENTAL SCIENCE & HEALTH 2024; 37:100529. [PMID: 38312491 PMCID: PMC10836048 DOI: 10.1016/j.coesh.2023.100529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
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.
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Affiliation(s)
- Trevor A Johnson
- Division of Environmental Pediatrics, Department of Pediatrics, Grossman School of Medicine, New York University
| | - Dimitri P Abrahamsson
- Division of Environmental Pediatrics, Department of Pediatrics, Grossman School of Medicine, New York University
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8
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Phillips KA, Chao A, Church RL, Favela K, Garantziotis S, Isaacs KK, Meyer B, Rice A, Sayre R, Wetmore BA, Yau A, Wambaugh JF. Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1802-1812. [PMID: 38217501 DOI: 10.1021/acs.est.3c05092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.
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Affiliation(s)
- Katherine A Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Alex Chao
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Rebecca L Church
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Kristin Favela
- Southwest Research Institute, San Antonio, Texas 78238, United States
| | - Stavros Garantziotis
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Brian Meyer
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Annette Rice
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Risa Sayre
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Barbara A Wetmore
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Alice Yau
- Southwest Research Institute, San Antonio, Texas 78238, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
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9
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Tisler S, Kilpinen K, Pattison DI, Tomasi G, Christensen JH. Quantitative Nontarget Analysis of CECs in Environmental Samples Can Be Improved by Considering All Mass Adducts. Anal Chem 2024; 96:229-237. [PMID: 38128072 PMCID: PMC10782417 DOI: 10.1021/acs.analchem.3c03791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023]
Abstract
Quantitative nontarget analysis (qNTA) for liquid chromatography coupled to high-resolution mass spectrometry enables a more comprehensive assessment of environmental samples. Previous studies have shown that correlations between a compound's ionization efficiency and a range of molecular descriptors can predict the compound's concentration within a factor of 5. In this study, the qNTA approach was further improved by considering all mass adducts instead of only the protonated ion. The model was based on a quantitative structure-property relationship (QSPR), including 216 contaminants of emerging concern (CECs), of which 80 exhibited adduct formation that accounted for >10% of the total peak intensity. When all mass adducts were included, the test set coefficient of determination improved to Q2 = 0.855 compared to Q2 = 0.670 when only the protonated ions were considered (test set median RF error factor 1.6). The inclusion of all adducts was also important to transfer the RF QSPR model reliably. It was assumed that RF variations are sequence-dependent; therefore, a second QSPR model for the prediction of the transferability factor was built for each sequence. For validation, samples were analyzed up to two years apart. The median prediction fold change was 1.74 for analytical standards (63 compounds) and 2.4 for enriched wastewater effluent samples (41 compounds), with 80% of the compounds predicted within a fold change of 2.4 and 3.3, respectively. The model was also validated on a second instrument, where 80% of the 26 compounds in wastewater effluent were predicted within a factor of 3.8.
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Affiliation(s)
- Selina Tisler
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Kristoffer Kilpinen
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
- Eurofins
Miljø Denmark A/S, Ladelundvej 85, 6600 Vejen, Denmark
| | - David I. Pattison
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Giorgio Tomasi
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Jan H. Christensen
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
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10
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Siegel HG, Nason SL, Warren JL, Prunas O, Deziel NC, Saiers JE. Investigation of Sources of Fluorinated Compounds in Private Water Supplies in an Oil and Gas-Producing Region of Northern West Virginia. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17452-17464. [PMID: 37923386 PMCID: PMC10653085 DOI: 10.1021/acs.est.3c05192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/20/2023] [Accepted: 10/25/2023] [Indexed: 11/07/2023]
Abstract
Per- and polyfluoroalkyl substances (PFASs) are a class of toxic organic compounds that have been widely used in consumer applications and industrial activities, including oil and gas production. We measured PFAS concentrations in 45 private wells and 8 surface water sources in the oil and gas-producing Doddridge, Marshall, Ritchie, Tyler, and Wetzel Counties of northern West Virginia and investigated relationships between potential PFAS sources and drinking water receptors. All surface water samples and 60% of the water wells sampled contained quantifiable levels of at least one targeted PFAS compound, and four wells (8%) had concentrations above the proposed maximum contaminant level (MCL) for perfluorooctanoic acid (PFOA). Individual concentrations of PFOA and perfluorobutanesulfonic acid exceeded those measured in finished public water supplies. Total targeted PFAS concentrations ranged from nondetect to 36.8 ng/L, with surface water concentrations averaging 4-fold greater than groundwater. Semiquantitative, nontargeted analysis showed concentrations of emergent PFAS that were potentially higher than targeted PFAS. Results from a multivariate latent variable hierarchical Bayesian model were combined with insights from analyses of groundwater chemistry, topographic characteristics, and proximity to potential PFAS point sources to elucidate predictors of PFAS concentrations in private wells. Model results reveal (i) an increased vulnerability to contamination in upland recharge zones, (ii) geochemical controls on PFAS transport likely driven by adsorption, and (iii) possible influence from nearby point sources.
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Affiliation(s)
- Helen G. Siegel
- School
of the Environment, Yale University, 195 Prospect Street, New Haven, Connecticut 06511, United States
| | - Sara L. Nason
- Connecticut
Agricultural Experiment Station, 123 Huntington Street, New
Haven, Connecticut 06504, United States
| | - Joshua L. Warren
- School
of Public Health, Yale University, 60 College Street, New Haven, Connecticut 06510, United States
| | - Ottavia Prunas
- Swiss
Tropical and Public Health Institute, 2 Kreuzstrasse, Allschwill, Basel 4123, Switzerland
| | - Nicole C. Deziel
- School
of Public Health, Yale University, 60 College Street, New Haven, Connecticut 06510, United States
| | - James E. Saiers
- School
of the Environment, Yale University, 195 Prospect Street, New Haven, Connecticut 06511, United States
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11
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Sepman H, Malm L, Peets P, MacLeod M, Martin J, Breitholtz M, Kruve A. Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS 2 Data. Anal Chem 2023; 95:12329-12338. [PMID: 37548594 PMCID: PMC10448440 DOI: 10.1021/acs.analchem.3c01744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 07/23/2023] [Indexed: 08/08/2023]
Abstract
Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.
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Affiliation(s)
- Helen Sepman
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Louise Malm
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
| | - Pilleriin Peets
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
| | - Matthew MacLeod
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Jonathan Martin
- Science
for Life Laboratory, Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Magnus Breitholtz
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
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12
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Buckley TJ, Egeghy PP, Isaacs K, Richard AM, Ring C, Sayre RR, Sobus JR, Thomas RS, Ulrich EM, Wambaugh JF, Williams AJ. Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency. ENVIRONMENT INTERNATIONAL 2023; 178:108097. [PMID: 37478680 PMCID: PMC10588682 DOI: 10.1016/j.envint.2023.108097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/05/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.
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Affiliation(s)
- Timothy J Buckley
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States.
| | - Peter P Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Kristin Isaacs
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Caroline Ring
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Risa R Sayre
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Russell S Thomas
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
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13
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Fecke A, Saw NMMT, Kale D, Kasarla SS, Sickmann A, Phapale P. Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics. Metabolites 2023; 13:844. [PMID: 37512551 PMCID: PMC10383057 DOI: 10.3390/metabo13070844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.
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Affiliation(s)
- Antonia Fecke
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
- Department Hamm 2, Hochschule Hamm-Lippstadt, Marker-Allee 76-78, 59063 Hamm, Germany
| | - Nay Min Min Thaw Saw
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Dipali Kale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Siva Swapna Kasarla
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Prasad Phapale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
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14
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Manz KE, Feerick A, Braun JM, Feng YL, Hall A, Koelmel J, Manzano C, Newton SR, Pennell KD, Place BJ, Godri Pollitt KJ, Prasse C, Young JA. Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:524-536. [PMID: 37380877 PMCID: PMC10403360 DOI: 10.1038/s41370-023-00574-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/30/2023]
Abstract
Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).
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Affiliation(s)
- Katherine E Manz
- School of Engineering, Brown University, Providence, RI, 02912, USA.
| | - Anna Feerick
- Agricultural & Environmental Chemistry Graduate Group, University of California, Davis, Davis, CA, 95616, USA
| | - Joseph M Braun
- Department of Epidemiology, Brown University, Providence, RI, 02912, USA
| | - Yong-Lai Feng
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Amber Hall
- Department of Epidemiology, Brown University, Providence, RI, 02912, USA
| | - Jeremy Koelmel
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Carlos Manzano
- Department of Chemistry, Faculty of Science, University of Chile, Santiago, RM, Chile
- School of Public Health, San Diego State University, San Diego, CA, USA
| | - Seth R Newton
- Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA
| | - Kurt D Pennell
- School of Engineering, Brown University, Providence, RI, 02912, USA
| | - Benjamin J Place
- National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD, 20899, USA
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Carsten Prasse
- Department of Environmental Health & Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Risk Sciences and Public Policy Institute, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Joshua A Young
- Division of Biology, Chemistry and Materials Science, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
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15
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Strynar M, McCord J, Newton S, Washington J, Barzen-Hanson K, Trier X, Liu Y, Dimzon IK, Bugsel B, Zwiener C, Munoz G. Practical application guide for the discovery of novel PFAS in environmental samples using high resolution mass spectrometry. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:575-588. [PMID: 37516787 PMCID: PMC10561087 DOI: 10.1038/s41370-023-00578-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/31/2023]
Abstract
BACKGROUND The intersection of the topics of high-resolution mass spectrometry (HRMS) and per- and polyfluoroalkyl substances (PFAS) bring together two disparate and complex subjects. Recently non-targeted analysis (NTA) for the discovery of novel PFAS in environmental and biological media has been shown to be valuable in multiple applications. Classical targeted analysis for PFAS using LC-MS/MS, though growing in compound coverage, is still unable to inform a holistic understanding of the PFAS burden in most samples. NTA fills at least a portion of this data gap. OBJECTIVES Entrance into the study of novel PFAS discovery requires identification techniques such as HRMS (e.g., QTOF and Orbitrap) instrumentation. This requires practical knowledge of best approaches depending on the purpose of the analyses. The utility of HRMS applications for PFAS discovery is unquestioned and will likely play a significant role in many future environmental and human exposure studies. METHODS/RESULTS PFAS have some characteristics that make them standout from most other chemicals present in samples. Through a series of tell-tale PFAS characteristics (e.g., characteristic mass defect range, homologous series and characteristic fragmentation patterns), and case studies different approaches and remaining challenges are demonstrated. IMPACT STATEMENT The identification of novel PFAS via non-targeted analysis using high resolution mass spectrometry is an important and difficult endeavor. This synopsis document will hopefully make current and future efforts on this topic easier to perform for novice and experienced alike. The typical time devoted to NTA PFAS investigations (weeks to months or more) may benefit from these practical steps employed.
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Affiliation(s)
- Mark Strynar
- USEPA Office of Research and Development Center for Environmental Measurement and Modeling, Durham, NC and Athens, GA, USA.
| | - James McCord
- USEPA Office of Research and Development Center for Environmental Measurement and Modeling, Durham, NC and Athens, GA, USA
| | - Seth Newton
- USEPA Office of Research and Development Center for Environmental Measurement and Modeling, Durham, NC and Athens, GA, USA
| | - John Washington
- USEPA Office of Research and Development Center for Environmental Measurement and Modeling, Durham, NC and Athens, GA, USA
| | | | - Xenia Trier
- Section of Environmental Chemistry and Physics, Department of Plant and Environmental Sciences (PLEN), University of Copenhagen, Thorvaldsensvej 40, DK-1871, Frederiksberg, Denmark
| | - Yanna Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 100085, Beijing, China
| | - Ian Ken Dimzon
- Ateneo de Manila University, Loyola Heights, Quezon City, Philippines
| | - Boris Bugsel
- Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstr. 94-96, 72076, Tübingen, Germany
| | - Christian Zwiener
- Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstr. 94-96, 72076, Tübingen, Germany
| | - Gabriel Munoz
- Université de Montréal, Montreal, QC, H3C 3J7, Canada
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16
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Cao D, Schwichtenberg T, Duan C, Xue L, Muensterman D, Field J. Practical Semiquantification Strategy for Estimating Suspect Per- and Polyfluoroalkyl Substance (PFAS) Concentrations. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:939-947. [PMID: 37018384 DOI: 10.1021/jasms.3c00019] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Semiquantitation of suspect per- and polyfluoroalkyl substances (PFAS) in complex mixtures is challenging due to the increasing number of suspect PFAS. Traditional 1:1 matching strategies require selecting calibrants (target-surrogate standard pairs) based on head group, fluorinated chain length, and retention time, which is time-consuming and requires expert knowledge. Lack of uniformity in calibrant selection for estimating suspect concentrations among different laboratories makes comparing reported suspect concentrations difficult. In this study, a practical approach whereby the area counts for 50 anionic and 5 zwitterionic/cationic target PFAS were ratioed to the average area of their respective stable-isotope labeled surrogates to create "average PFAS calibration curves" for suspects detected in negative- and positive-ionization mode liquid chromatography quadrupole time-of-flight mass spectrometry. The calibration curves were fitted with log-log and weighted linear regression models. The two models were evaluated for their accuracy and prediction interval in predicting the target PFAS concentrations. The average PFAS calibration curves were then used to estimate the suspect PFAS concentration in a well-characterized aqueous film-forming foam. Weighted linear regression resulted in more target PFAS that fell within 70-130% of their known standard value and narrower prediction intervals than the log-log transformation approach. The summed suspect PFAS concentrations calculated by weighted linear regression and log-log transformation were within 8 and 16% of those estimated by a 1:1 matching strategy. The average PFAS calibration curve can be easily expanded and can be applied to any suspect PFAS even if the confidence in the suspect structure is low or unknown.
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Affiliation(s)
- Dunping Cao
- Department of Chemistry, Oregon State University, Corvallis, Oregon 97331, United States
| | - Trever Schwichtenberg
- Department of Chemistry, Oregon State University, Corvallis, Oregon 97331, United States
| | - Chenyang Duan
- Department of Statistics, Oregon State University, Corvallis, Oregon 97331, United States
| | - Lan Xue
- Department of Statistics, Oregon State University, Corvallis, Oregon 97331, United States
| | - Derek Muensterman
- Department of Chemistry, Oregon State University, Corvallis, Oregon 97331, United States
| | - Jennifer Field
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon 97331, United States
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17
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Sloop JT, Chao A, Gundersen J, Phillips AL, Sobus JR, Ulrich EM, Williams AJ, Newton SR. Demonstrating the Use of Non-targeted Analysis for Identification of Unknown Chemicals in Rapid Response Scenarios. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:3075-3084. [PMID: 36796018 PMCID: PMC10198433 DOI: 10.1021/acs.est.2c06804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Several thousand intentional and unintentional chemical releases occur annually in the U.S., with the contents of almost 30% being of unknown composition. When targeted methods are unable to identify the chemicals present, alternative approaches, including non-targeted analysis (NTA) methods, can be used to identify unknown analytes. With new and efficient data processing workflows, it is becoming possible to achieve confident chemical identifications via NTA in a timescale useful for rapid response (typically 24-72 h after sample receipt). To demonstrate the potential usefulness of NTA in rapid response situations, we have designed three mock scenarios that mimic real-world events, including a chemical warfare agent attack, the contamination of a home with illicit drugs, and an accidental industrial spill. Using a novel, focused NTA method that utilizes both existing and new data processing/analysis methods, we have identified the most important chemicals of interest in each of these designed mock scenarios in a rapid manner, correctly assigning structures to more than half of the 17 total features investigated. We have also identified four metrics (speed, confidence, hazard information, and transferability) that successful rapid response analytical methods should address and have discussed our performance for each metric. The results reveal the usefulness of NTA in rapid response scenarios, especially when unknown stressors need timely and confident identification.
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Affiliation(s)
- John T Sloop
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27709, United States
- Oak Ridge Institute for Science and Education (ORISE) Participant, Research Triangle Park, North Carolina 27709, United States
| | - Alex Chao
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27709, United States
| | - Jennifer Gundersen
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island 02882, United States
| | - Allison L Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, North Carolina 27709, United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27709, United States
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27709, United States
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27709, United States
| | - Seth R Newton
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27709, United States
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18
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Nanusha MY, Frøkjær EE, Liigand J, Christensen MR, Hansen HR, Hansen M. Unravelling the occurrence of trace contaminants in surface waters using semi-quantitative suspected non-target screening analyses. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 315:120346. [PMID: 36202272 DOI: 10.1016/j.envpol.2022.120346] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/26/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Several classes of anthropogenic chemicals such as pesticides and pharmaceuticals are frequently used in human-related life activities and are discharged into the aquatic environment. These compounds can exert an unknown effect on aquatic life and humans if the water is used for human consumption. Thus, unravelling their occurrence in the aquatic system is crucial for the well-being of life and monitoring purposes. To this end, we used nanoflow-liquid and ion-exchange chromatography hyphenated with orbitrap high-resolution tandem mass spectrometry to detect several thousands of features (chemical entities) in surface water. Later, the features were narrowed down to a few focused lists using a stepwise filtering strategy, for which the structural elucidation was made. Accordingly, the chemical structure was confirmed for 83 compounds from different application areas, mainly being pharmaceuticals, pesticides, and other multiple application industrial compounds and xenobiotic degradation products. The compounds with the highest concentration were lamotrigine (27.6 μg/L), valsartan (14.4 μg/L), and ibuprofen (12.7 μg/L). Some compounds such as prosulfocarb, fluopyram, and tris(3-chloropropyl) phosphate were found to be the most abundant and widespread contaminants. Of the 32 sampling sites, nearly half of the sites (47%) contained more than 30 different compounds. Two sampling sites were far more contaminated than other sites based on the estimated concentration and the number of identified contaminants they contained. Our triplicate analysis revealed a low relative standard deviation between replicates, advocating for the added value in analysing more sampling sites instead of sample repetition. Overall, our study elucidated the occurrence of organic contaminants from a variety of sources in the aquatic environment. Furthermore, our findings highlighted the role of suspected non-target screening in exposing a snapshot of the chemical composition of surface water and the localized possible contamination sources.
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Affiliation(s)
- Mulatu Yohannes Nanusha
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark
| | - Emil Egede Frøkjær
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark
| | - Jaanus Liigand
- Quantem Analytics OÜ, Narva mnt 149-8, Tartu, 51008, Estonia
| | | | - Helle Rüsz Hansen
- Danish Environmental Protection Agency, Tolderlundsvej 5, 5000, Odense C, Denmark
| | - Martin Hansen
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.
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19
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Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods. Anal Bioanal Chem 2022; 414:6455-6471. [PMID: 35796784 PMCID: PMC9411239 DOI: 10.1007/s00216-022-04203-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/17/2022] [Accepted: 06/24/2022] [Indexed: 11/06/2022]
Abstract
Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.
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