Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

. 2021 Aug 24 ; 8 (1) : 223. [epub] 20210824

Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu dataset, časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid34429429

Grantová podpora
AUFF- T-2017-FLS-7-4 Aarhus Universitets Forskningsfond (Aarhus University Research Foundation)

Odkazy

PubMed 34429429
PubMed Central PMC8384892
DOI 10.1038/s41597-021-01002-w
PII: 10.1038/s41597-021-01002-w
Knihovny.cz E-zdroje

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Aarhus University Department of Environmental Science Environmental Metabolomics Lab Frederiksborgvej 399 4000 Roskilde Denmark

BRGM F 45060 Orleans France

Colorado State University Soil and Crop Sciences Department Plant Sciences C117 Fort Collins CO 80523 United States

Consiglio Nazionale delle Ricerche Istituto di Ricerca Sulle Acque Via De Blasio 5 70132 Bari Italy

Department of Aquatic Sciences and Assessment Swedish University of Agricultural Sciences SE 75007 Uppsala Sweden

Department of Chemistry University of Natural Resources and Life Sciences BOKU Vienna Muthgasse 18 1190 Vienna Austria

Department of Environment and Health Faculty of Science Amsterdam Institute of Molecular and Life Sciences Vrije Universiteit Amsterdam De Boelelaan 1085 1081 Amsterdam HV The Netherlands

Eawag Swiss Federal Institute of Aquatic Science and Technology 8600 Duebendorf Switzerland

Environmental and Public Health Analytical Chemistry Research Institute for Pesticides and Water University Jaume 1 Avda Vincent Sos Baynat s n 12071 Castelló de la Plana Castellón Spain

Eurolab Srl Via Monsignore Rodolfi 22 IT 36022 Cassola 6 Italy

Flemish Institute for Technological Research Unit Separation and Conversion Technology Boeretang 200 2400 Mol Belgium

INRAE UR RiverLy F 69625 Villeurbanne France

Institut Català de Recerca de l'Aigua Catalan Institute for Water Research Edifici H2O Parc Científic i Tecnològic Universitat de Girona Carrer Emili Grahit 101 E 17003 Girona Spain

Instituto di Ricerca Sulle Acque Consiglio Nazionale delle Ricerche Via Mulino 19 IT 20861 Brugherio MB Italy

Man Technology Environment Research Centre School of Science and Technology Örebro University Fakultetsgatan 1 701 82 Örebro Sweden

Masaryk University Faculty of Science RECETOX Kamenice 753 5 625 00 Brno Czech Republic

Ministry of Infrastructure and Water Management Rijkswaterstaat Zuiderwagenplein 2 8224 Lelystad AD Netherlands

National and Kapodistrian University of Athens Athens Greece

Norwegian Institute for Water Research Gaustadalléen 21 0349 Oslo Norway

OSU EFLUVE Univ Paris Est Creteil CNRS F 94010 Creteil France

Plentzia Marine Station Department of Analytical Chemistry University of the Basque Country Areatza Pasealekua 48620 Plentzia Basque Country Spain

Queensland Alliance for Environmental Health Sciences The University of Queensland 202 Cornwall Street QLD 4102 Woolloongabba Australia

Univ Lyon CNRS Université Claude Bernard Lyon 1 Institut des Sciences Analytiques UMR 5280 5 rue de la Doua F 69100 Villeurbanne France

Univ Paris Est Creteil Ecole des Ponts LEESU F 94010 Creteil France

University of South Bohemia in České Budějovice Faculty of Fisheries and Protection of Waters South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses Zátiší 728 2 CZ 389 25 Vodňany Czech Republic

Van 't Hoff Institute for Molecular Sciences University of Amsterdam Science Park 904 1098 XH Amsterdam The Netherlands

Vitens N 5 Oude Veerweg 1 Zwolle 8001 BE The Netherlands

Zobrazit více v PubMed

Schulze, B. et al. An assessment of Quality Assurance/Quality Control Efforts in High Resolution Mass Spectrometry Non-Target Workflows for Analysis of Environmental Samples. Trends Anal. Chem. 133, 116063 (2020).

Bletsou AA, Jeon J, Hollender J, Archontaki E, Thomaidis NS. Targeted and non-targeted liquid chromatography-mass spectrometric workflows for identification of transformation products of emerging pollutants in the aquatic environment. Trends Anal. Chem. 2015;66:32–44. doi: 10.1016/j.trac.2014.11.009. DOI

Martínez-Bueno MJ, Gómez Ramos MJ, Bauer A, Fernández-Alba AR. An overview of non-targeted screening strategies based on high resolution accurate mass spectrometry for the identification of migrants coming from plastic food packaging materials. Trends Anal. Chem. 2019;110:191–203. doi: 10.1016/j.trac.2018.10.035. DOI

Milman BL, Zhurkovich IK. The chemical space for non-target analysis. Trends Anal. Chem. 2017;97:179–187. doi: 10.1016/j.trac.2017.09.013. DOI

Oberacher H, Arnhard K. Current status of non-targeted liquid chromatography-tandem mass spectrometry in forensic toxicology. Trends Anal. Chem. 2016;84, Part B:94–105. doi: 10.1016/j.trac.2015.12.019. DOI

Albergamo V, et al. Nontarget Screening Reveals Time Trends of Polar Micropollutants in a Riverbank Filtration System. Environ. Sci. Technol. 2019;53:7584–7594. doi: 10.1021/acs.est.9b01750. PubMed DOI PMC

Samanipour S, Martin JW, Lamoree MH, Reid MJ, Thomas KV. Letter to the Editor: Optimism for Nontarget Analysis in Environmental Chemistry. Environ. Sci. Technol. 2019;53:5529–5530. doi: 10.1021/acs.est.9b01476. PubMed DOI

Hohrenk LL, et al. Comparison of Software Tools for Liquid Chromatography–High-Resolution Mass Spectrometry Data Processing in Nontarget Screening of Environmental Samples. Anal. Chem. 2020;92:1898–1907. doi: 10.1021/acs.analchem.9b04095. PubMed DOI

Schymanski EL, et al. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environ. Sci. Technol. 2014;48:2097–2098. doi: 10.1021/es5002105. PubMed DOI

Schymanski EL, et al. Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis. Anal. Bioanal. Chem. 2015;407:6237–55. doi: 10.1007/s00216-015-8681-7. PubMed DOI

Tian Z, et al. A ubiquitous tire rubber–derived chemical induces acute mortality in coho salmon. Science. 2021;37:185–189. doi: 10.1126/science.abd6951. PubMed DOI

Ulrich EM, et al. EPA’s non-targeted analysis collaborative trial (ENTACT): genesis, design, and initial findings. Anal. Bioanal. Chem. 2019;411:853–866. doi: 10.1007/s00216-018-1435-6. PubMed DOI PMC

Rostkowski P, et al. The strength in numbers: comprehensive characterization of house dust using complementary mass spectrometric techniques. Anal. Bioanal. Chem. 2019;411:1957–1977. doi: 10.1007/s00216-019-01615-6. PubMed DOI PMC

Hollender J, Schymanski EL, Singer HP, Ferguson PL. Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go? Environ. Sci. Technol. 2017;51:11505–11512. doi: 10.1021/acs.est.7b02184. PubMed DOI

Hites RA, Jobst KJ. Response to “Letter to the Editor: Optimism for Nontarget Analysis in Environmental Chemistry”. Environ. Sci. Technol. 2019;53:5531–5533. doi: 10.1021/acs.est.9b02473. PubMed DOI

Samanipour, S., Reid, M. J. & Thomas, K. V. Statistical Variable Selection: An Alternative Prioritization Strategy during the Nontarget Analysis of LC-HR-MS Data. Anal. Chem. 89, 10, 5585–5591 (2017). PubMed

Samanipour S, Reid MJ, Bæk K, Thomas KV. Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography−High-Resolution Mass Spectrometry Results. Environ. Sci. Technol. 2018;52:4694–4701. doi: 10.1021/acs.est.8b00259. PubMed DOI

Samanipour S, et al. Machine learning combined with non-targeted LC-HRMS analysis for a risk warning system of chemical hazards in drinking water: A proof of concept. Talanta. 2019;195:426–432. doi: 10.1016/j.talanta.2018.11.039. PubMed DOI

Escher BI, Stapleton HM, Schymanski EL. Tracking complex mixtures of chemicals in our changing environment. Science. 2020;367:388–392. doi: 10.1126/science.aay6636. PubMed DOI PMC

Gosetti F, Mazzucco E, Gennaro MC, Marengo E. Contaminants in water: non-target UHPLC/MS analysis. Environ. Chem. Lett. 2016;14:51–65. doi: 10.1007/s10311-015-0527-1. DOI

Alygizakis NA, et al. NORMAN digital sample freezing platform: A European virtual platform to exchange liquid chromatography high resolution-mass spectrometry data and screen suspects in “digitally frozen” environmental samples. Trends Anal. Chem. 2019;115:129–137. doi: 10.1016/j.trac.2019.04.008. DOI

Blaženović I, Kind T, Ji J, Fiehn O. Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics. Metabolites. 2018;8:31. doi: 10.3390/metabo8020031. PubMed DOI PMC

Chow CWK, et al. Development of smart data analytics tools to support wastewater treatment plant operation. Chemom. Intell. Lab. Syst. 2018;177:140–150. doi: 10.1016/j.chemolab.2018.03.006. DOI

Brodsky L, Moussaieff A, Shahaf N, Aharoni A, Rogachev I. Evaluation of Peak Picking Quality in LC−MS Metabolomics Data. Anal. Chem. 2010;82:9177–9187. doi: 10.1021/ac101216e. PubMed DOI

Samanipour S, O’Brien JW, Reid MJ, Thomas KV. Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data. Anal. Chem. 2019;91:10800–10807. doi: 10.1021/acs.analchem.9b02422. PubMed DOI

Wilkinson MD, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data. 2016;3:160018. doi: 10.1038/sdata.2016.18. PubMed DOI PMC

FAIR Principles. GO FAIRhttps://www.go-fair.org/fair-principles/.

Alygizakis NA, et al. Exploring the Potential of a Global Emerging Contaminant Early Warning Network through the Use of Retrospective Suspect Screening with High-Resolution Mass Spectrometry. Environ. Sci. Technol. 2018;52:5135–5144. doi: 10.1021/acs.est.8b00365. PubMed DOI

2020. MassBank consortium and its contributors. MassBank/MassBank-data: Release version 2020.06. Zenodo. DOI

Vrana B, et al. Passive sampling techniques for monitoring pollutants in water. Trends Anal. Chem. 2005;24:845–868. doi: 10.1016/j.trac.2005.06.006. DOI

Miège, C. et al. Position paper on passive sampling techniques for the monitoring of contaminants in the aquatic environment – Achievements to date and perspectives. Trends Environ. Anal. Chem. 8, 20–26 (2015).

Aalizadeh R, Nika M-C, Thomaidis NS. Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants. J. Hazard. Mater. 2019;363:277–285. doi: 10.1016/j.jhazmat.2018.09.047. PubMed DOI

Samanipour S, et al. Assessing sample extraction efficiencies for the analysis of complex unresolved mixtures of organic pollutants: A comprehensive non-target approach. Anal. Chim. Acta. 2018;1025:92–98. doi: 10.1016/j.aca.2018.04.020. PubMed DOI

Samanipour S, et al. The effect of extraction methodology on the recovery and distribution of naphthenic acids of oilfield produced water. Sci. Total Environ. 2019;652:1416–1423. doi: 10.1016/j.scitotenv.2018.10.264. PubMed DOI

Vrana B, et al. Mobile dynamic passive sampling of trace organic compounds: Evaluation of sampler performance in the Danube River. Sci. Total Environ. 2018;636:1597–1607. doi: 10.1016/j.scitotenv.2018.03.242. PubMed DOI

US EPA, O. EPA Method 3570 (SW-846): Microscale Solvent Extraction (MSE). US EPAhttps://www.epa.gov/esam/epa-method-3570-sw-846-microscale-solvent-extraction-mse (2019).

Samanipour S. 2021. NORMAN Collaborative Trial on Passive Sampling and Non-target Screening (NTS) Instruction file. University of Amsterdam. DOI

Samanipour, S. et al. Two stage algorithm vs commonly used approaches for the suspect screening of complex environmental samples analyzed via liquid chromatography high resolution time of flight mass spectroscopy: A test study. J. Chromatogr. A1501, 68–78 (2017). PubMed

Deutsch EW. File Formats Commonly Used in Mass Spectrometry. Proteomics. Mol. Cell. Proteomics MCP. 2012;11:1612–1621. doi: 10.1074/mcp.R112.019695. PubMed DOI PMC

Chambers MC, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012;30:918–920. doi: 10.1038/nbt.2377. PubMed DOI PMC

Samanipour S. 2021. NORMAN Collaborative Trial on Passive Sampling and Non-target Screening (NTS) - Pre-defined method (individual files) figshare. DOI

Samanipour S. 2021. Inter-laboratory dataset from a collaborative trial for future use in the development of non-target analysis. MassIVE. DOI

Samanipour S. 2021. NORMAN Collaborative Trial on Passive Sampling and Non-target Screening (NTS) - Own method (individual files) figshare. DOI

Samanipour S. 2021. NORMAN Collaborative Trial on Passive Sampling and Non-target Screening (NTS) - Metadata. figshare. DOI

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