1
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Lai Y, Koelmel JP, Walker DI, Price EJ, Papazian S, Manz KE, Castilla-Fernández D, Bowden JA, Nikiforov V, David A, Bessonneau V, Amer B, Seethapathy S, Hu X, Lin EZ, Jbebli A, McNeil BR, Barupal D, Cerasa M, Xie H, Kalia V, Nandakumar R, Singh R, Tian Z, Gao P, Zhao Y, Froment J, Rostkowski P, Dubey S, Coufalíková K, Seličová H, Hecht H, Liu S, Udhani HH, Restituito S, Tchou-Wong KM, Lu K, Martin JW, Warth B, Godri Pollitt KJ, Klánová J, Fiehn O, Metz TO, Pennell KD, Jones DP, Miller GW. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12784-12822. [PMID: 38984754 PMCID: PMC11271014 DOI: 10.1021/acs.est.4c01156] [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: 02/01/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024]
Abstract
In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.
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Affiliation(s)
- Yunjia Lai
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Jeremy P. Koelmel
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Douglas I. Walker
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elliott J. Price
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Stefano Papazian
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National
Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Katherine E. Manz
- Department
of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Delia Castilla-Fernández
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - John A. Bowden
- Center for
Environmental and Human Toxicology, Department of Physiological Sciences,
College of Veterinary Medicine, University
of Florida, Gainesville, Florida 32611, United States
| | | | - Arthur David
- Univ Rennes,
Inserm, EHESP, Irset (Institut de recherche en santé, environnement
et travail) − UMR_S, 1085 Rennes, France
| | - Vincent Bessonneau
- Univ Rennes,
Inserm, EHESP, Irset (Institut de recherche en santé, environnement
et travail) − UMR_S, 1085 Rennes, France
| | - Bashar Amer
- Thermo
Fisher Scientific, San Jose, California 95134, United States
| | | | - Xin Hu
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elizabeth Z. Lin
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Akrem Jbebli
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Brooklynn R. McNeil
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Dinesh Barupal
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Marina Cerasa
- Institute
of Atmospheric Pollution Research, Italian National Research Council, 00015 Monterotondo, Rome, Italy
| | - Hongyu Xie
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Vrinda Kalia
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Renu Nandakumar
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Randolph Singh
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Zhenyu Tian
- Department
of Chemistry and Chemical Biology, Northeastern
University, Boston, Massachusetts 02115, United States
| | - Peng Gao
- Department
of Environmental and Occupational Health, and Department of Civil
and Environmental Engineering, University
of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- UPMC Hillman
Cancer Center, Pittsburgh, Pennsylvania 15232, United States
| | - Yujia Zhao
- Institute
for Risk Assessment Sciences, Utrecht University, Utrecht 3584CM, The Netherlands
| | | | | | - Saurabh Dubey
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Kateřina Coufalíková
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Hana Seličová
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Helge Hecht
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Sheng Liu
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Hanisha H. Udhani
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Sophie Restituito
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kam-Meng Tchou-Wong
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kun Lu
- Department
of Environmental Sciences and Engineering, Gillings School of Global
Public Health, The University of North Carolina
at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jonathan W. Martin
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National
Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Benedikt Warth
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - Krystal J. Godri Pollitt
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Jana Klánová
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Oliver Fiehn
- West Coast
Metabolomics Center, University of California−Davis, Davis, California 95616, United States
| | - Thomas O. Metz
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Kurt D. Pennell
- School
of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Dean P. Jones
- Department
of Medicine, School of Medicine, Emory University, Atlanta, Georgia 30322, United States
| | - Gary W. Miller
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
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2
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Yin XL, Peng ZX, Pan Y, Lv Y, Long W, Gu HW, Fu H, She Y. UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties. Food Res Int 2024; 178:113923. [PMID: 38309902 DOI: 10.1016/j.foodres.2023.113923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/17/2023] [Accepted: 12/21/2023] [Indexed: 02/05/2024]
Abstract
Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.
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Affiliation(s)
- Xiao-Li Yin
- College of Life Sciences, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434025, China
| | - Zhi-Xin Peng
- College of Life Sciences, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434025, China
| | - Yuan Pan
- College of Life Sciences, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434025, China
| | - Yi Lv
- Key Laboratory of Quality and Safety of Wolfberry and Wine for State Administration for Market Regulation, Ningxia Food Testing and Research Institute, Yinchuan 750004, China
| | - Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Hui-Wen Gu
- College of Life Sciences, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434025, China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China.
| | - Yuanbin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
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3
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Chaker J, Gilles E, Monfort C, Chevrier C, Lennon S, David A. Scannotation: A Suspect Screening Tool for the Rapid Pre-Annotation of the Human LC-HRMS-Based Chemical Exposome. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19253-19262. [PMID: 37968235 DOI: 10.1021/acs.est.3c04764] [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: 11/17/2023]
Abstract
In an increasingly chemically polluted environment, rapidly characterizing the human chemical exposome (i.e., chemical mixtures accumulating in humans) at the population scale is critical to understand its impact on health. High-resolution mass spectrometry (HRMS) profiling of complex biological matrices can theoretically provide a comprehensive picture of chemical exposures. However, annotating the detected chemical features, particularly low-abundant ones, remains a significant obstacle to implementing such approaches at a large scale. We present Scannotation (https://github.com/scannotation/Scannotation_software), an automated and user-friendly suspect screening tool for the rapid pre-annotation of HRMS preprocessed data sets. This software tool combines several MS1 chemical predictors, i.e., m/z, experimental and predicted retention times, isotopic patterns, and neutral loss patterns, to score the proximity between features and suspects, thus efficiently prioritizing tentative annotations to verify. Scannotation and MS-DIAL4 were used to annotate blood serum samples of 75 Breton adolescents. Scannotation's combination of MS1-based chemical predictors allowed us to annotate 89 chemically diverse environmental compounds with high confidence (confirmed by MS2 when available). These compounds included 62% of emerging molecules, for which no toxicological or human biomonitoring data are reported in the literature. The complementarity observed with MS-DIAL4 results demonstrates the relevance of Scannotation for the efficient pre-annotation of large-scale exposomics data sets.
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Affiliation(s)
- Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Erwann Gilles
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Christine Monfort
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Cécile Chevrier
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Sarah Lennon
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
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4
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Wang XC, Ma XL, Liu JN, Zhang Y, Zhang JN, Ma MH, Ma FL, Yu YJ, She Y. A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics. Anal Chim Acta 2023; 1254:341127. [PMID: 37005031 DOI: 10.1016/j.aca.2023.341127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 03/29/2023]
Abstract
Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.
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Affiliation(s)
- Xing-Cai Wang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Xing-Ling Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Jia-Nan Liu
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Yang Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Jia-Ni Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Meng-Han Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Feng-Lian Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China.
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310032, China.
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5
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Liao J, Zhang Y, Zhang W, Zeng Y, Zhao J, Zhang J, Yao T, Li H, Shen X, Wu G, Zhang W. Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data. J Chromatogr A 2023; 1687:463700. [PMID: 36508769 DOI: 10.1016/j.chroma.2022.463700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022]
Abstract
In untargeted liquid chromatography‒mass spectrometry (LC‒MS) metabolomics studies, data preprocessing and metabolic pathway recognition are crucial for screening important pathways that are disturbed by diseases or restored by drugs. Here, we collected high-resolution mass spectrometry data of serum samples from 221 coronary heart disease (CHD) patients under two different chromatographic columns (BEH amide and C18 column) and evaluated the three commonly used software programs (XCMS, Progenesis QI, MarkerView) from four aspects (including signal drift, peak number, metabolite annotation and metabolic pathway enrichment). The results showed that the data preprocessed by the three software programs have different degrees of signal drift, but the StatTarget could improve the data quality to meet the data analysis requirement after correction. In addition, XCMS surpassed other software in detection of real chromatographic peaks and Progenesis QI was the best performer in terms of the number of metabolite annotation. XCMS and Progenesis QI showed different performance in pathway enrichment. However, metabolic pathways based on the combination of XCMS and Progenesis QI had a high coincidence with Progenesis QI. In addition, we also reported that C18 and amide columns were highly complementary and have great potential for cooperation in the context of metabolic pathways. In this study, the effects of different chromatographic columns and software pretreatments on metabolomics data were evaluated based on clinical large cohort samples, which will provide a reference for the metabolomics of clinical samples and guide subsequent mechanistic research.
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Affiliation(s)
- Jingyu Liao
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; School of Pharmacy, Guangdong Pharmaceutical University, Guangdong 510006, China
| | - Yuhao Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Wendan Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Yuanyuan Zeng
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing 100700, China
| | - Jing Zhao
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing 100700, China
| | - Jingfang Zhang
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing 100700, China
| | - Tingting Yao
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing 100700, China
| | - Houkai Li
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xiaoxu Shen
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing 100700, China.
| | - Gaosong Wu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Weidong Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; School of Pharmacy, Second Military Medical University, Shanghai 200433, China
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6
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El Abiead Y, Milford M, Schoeny H, Rusz M, Salek RM, Koellensperger G. Power of mzRAPP-Based Performance Assessments in MS1-Based Nontargeted Feature Detection. Anal Chem 2022; 94:8588-8595. [PMID: 35671103 PMCID: PMC9218958 DOI: 10.1021/acs.analchem.1c05270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/24/2022] [Indexed: 11/29/2022]
Abstract
When performing chromatography-mass spectrometry-based nontargeted metabolomics, or exposomics, one of the key steps in the analysis is to obtain MS1-based feature tables. Inapt parameter settings in feature detection will result in missing or wrong quantitative values and might ultimately lead to downstream incorrect biological interpretations. However, until recently, no strategies to assess the completeness and abundance accuracy of feature tables were available. Here, we show that mzRAPP enables the generation of benchmark peak lists by using an internal set of known molecules in the analyzed data set. Using the benchmark, the completeness and abundance accuracy of feature tables can be assessed in an automated pipeline. We demonstrate that our approach adds to other commonly applied quality assurance methods such as manual or automatized parameter optimization techniques or removal of false-positive signals. Moreover, we show that as few as 10 benchmark molecules can already allow for representative performance metrics to further improve quantitative biological understanding.
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Affiliation(s)
- Yasin El Abiead
- Department
of Analytical Chemistry, University of Vienna, Vienna 1090, Austria
| | - Maximilian Milford
- Department
of Analytical Chemistry, University of Vienna, Vienna 1090, Austria
| | - Harald Schoeny
- Department
of Analytical Chemistry, University of Vienna, Vienna 1090, Austria
| | - Mate Rusz
- Department
of Analytical Chemistry, University of Vienna, Vienna 1090, Austria
- Department
of Inorganic Chemistry, University of Vienna, Vienna 1090, Austria
| | - Reza M. Salek
- International
Agency for Research on Cancer, Section of Nutrition and Metabolism, Lyon 96008, France
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7
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Analytical strategies to profile the internal chemical exposome and the metabolome of human placenta. Anal Chim Acta 2022; 1219:339983. [DOI: 10.1016/j.aca.2022.339983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/02/2022] [Accepted: 05/22/2022] [Indexed: 11/20/2022]
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8
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Dereumeaux C, Mercier F, Soulard P, Hulin M, Oleko A, Pecheux M, Fillol C, Denys S, Quenel P. Identification of pesticides exposure biomarkers for residents living close to vineyards in France. ENVIRONMENT INTERNATIONAL 2022; 159:107013. [PMID: 34890902 DOI: 10.1016/j.envint.2021.107013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 11/29/2021] [Accepted: 11/29/2021] [Indexed: 05/12/2023]
Abstract
Biomonitoring can be relevant for assessing pesticides exposure of residents living close to vineyards (LCTV). However, because xenobiotics are generally present at low levels in human biological matrices and the sources of pesticide exposure are multiple, several challenges need to be overcome to reliably assess exposure in residents LCTV. This includes particularly identifying the most appropriate exposure biomarkers, the biological matrices in which they should be measured, and analytical methods that are sufficiently sensitive and specific to quantify them. The aim of the present study was to develop a tiered approach to identify relevant biomarkers and matrices for assessing pesticide exposure in residents LCTV. We used samples from a biobank for 121 adults and children included in a national prevalence study conducted between 2014 and 2016 who lived near or far from vineyards. We analyzed five priority pesticides (folpet, mancozeb, tebuconazole, glyphosate, and copper) and their metabolites in urine and hair samples. We identified relevant biomarkers according to three criteria related to: i) the detection frequency of those pesticides and metabolites in urine and hair, ii) the difference in concentrations depending on residence proximity to vineyards and, iii) the influence of other environmental and occupational exposure sources on pesticide levels. This tiered approach helped us to identify three relevant metabolites (two metabolites of folpet and one of tebuconazole) that were quantified in urine, tended to be higher in residents LCTV than in controls, and were not significantly influenced by occupational, dietary, or household sources of pesticide exposure. Our approach also helped us to identify the most appropriate measurement strategies (biological matrices, analytical methods) to assess pesticide exposure in residents LCTV. The approach developed here was a prerequisite step for guiding a large-scale epidemiological study aimed at comprehensively measuring pesticides exposures in French residents LCTV with a view to developing appropriate prevention strategies.
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Affiliation(s)
- Clémentine Dereumeaux
- Direction of Environmental and Occupational Health, Santé Publique France, Saint Maurice Cedex, France.
| | - Fabien Mercier
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France
| | - Pauline Soulard
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France
| | - Marion Hulin
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES), 14 rue Pierre et Marie Curie, 94701 Maisons-Alfort, France
| | - Amivi Oleko
- Direction of Environmental and Occupational Health, Santé Publique France, Saint Maurice Cedex, France
| | - Marie Pecheux
- Direction of Environmental and Occupational Health, Santé Publique France, Saint Maurice Cedex, France
| | - Clémence Fillol
- Direction of Environmental and Occupational Health, Santé Publique France, Saint Maurice Cedex, France
| | - Sébastien Denys
- Direction of Environmental and Occupational Health, Santé Publique France, Saint Maurice Cedex, France
| | - Philippe Quenel
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France
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9
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Chaker J, Kristensen DM, Halldorsson TI, Olsen SF, Monfort C, Chevrier C, Jégou B, David A. Comprehensive Evaluation of Blood Plasma and Serum Sample Preparations for HRMS-Based Chemical Exposomics: Overlaps and Specificities. Anal Chem 2022; 94:866-874. [DOI: 10.1021/acs.analchem.1c03638] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - David Møbjerg Kristensen
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
- Department of Neurology, Danish Headache Center, Rigshospitalet, University of Copenhagen, Copenhagen 1165, Denmark
| | - Thorhallur Ingi Halldorsson
- Center for Fetal Programming, Department of Epidemiology Research, Statens Serum Institut, Copenhagen 2300, Denmark
- The Unit for Nutrition Research, Faculty of Food Science and Nutrition, School of Health Sciences, University of Iceland, Reykjavik 101, Iceland
| | - Sjurdur Frodi Olsen
- Center for Fetal Programming, Department of Epidemiology Research, Statens Serum Institut, Copenhagen 2300, Denmark
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, United States
| | - Christine Monfort
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Cécile Chevrier
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Bernard Jégou
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
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10
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David A, Chaker J, Price EJ, Bessonneau V, Chetwynd AJ, Vitale CM, Klánová J, Walker DI, Antignac JP, Barouki R, Miller GW. Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives. ENVIRONMENT INTERNATIONAL 2021; 156:106630. [PMID: 34004450 DOI: 10.1016/j.envint.2021.106630] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/15/2021] [Accepted: 05/03/2021] [Indexed: 05/18/2023]
Abstract
The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.
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Affiliation(s)
- Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France.
| | - Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Elliott J Price
- Faculty of Sports Studies, Masaryk University, Brno, Czech Republic; RECETOX Centre, Masaryk University, Brno, Czech Republic
| | - Vincent Bessonneau
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France
| | - Andrew J Chetwynd
- School of Geography Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | | | - Jana Klánová
- RECETOX Centre, Masaryk University, Brno, Czech Republic
| | - Douglas I Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Robert Barouki
- Unité UMR-S 1124 Inserm-Université Paris Descartes "Toxicologie Pharmacologie et Signalisation Cellulaire", Paris, France
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
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11
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David A, Chaker J, Multigner L, Bessonneau V. [Chemical exposome and non-targeted approaches]. Med Sci (Paris) 2021; 37:895-901. [PMID: 34647878 DOI: 10.1051/medsci/2021088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The technological advances in high-resolution mass spectrometry (HRMS), associated with the development of bioinformatics tools, allows the simultaneous detection of tens of thousands of chemical signals in biological matrices, including exogenous (i.e. xenobiotics) and endogenous molecules. These novel approaches based on HRMS, called "non-targeted" approaches, provide a unique opportunity to capture exposures to a wide range of chemicals (i.e. the internal chemical exposome) in populations, and to better understand the links between chemical exposures and the occurrence of chronic diseases.
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Affiliation(s)
- Arthur David
- Univ Rennes, Inserm, École des hautes études en santé publique (EHESP), Institut de recherche en santé, environnement et travail (Irset) - UMR_S 1085, 15 avenue du Professeur Léon Bernard, 35043 Rennes, France
| | - Jade Chaker
- Univ Rennes, Inserm, École des hautes études en santé publique (EHESP), Institut de recherche en santé, environnement et travail (Irset) - UMR_S 1085, 15 avenue du Professeur Léon Bernard, 35043 Rennes, France
| | - Luc Multigner
- Univ Rennes, Inserm, École des hautes études en santé publique (EHESP), Institut de recherche en santé, environnement et travail (Irset) - UMR_S 1085, 15 avenue du Professeur Léon Bernard, 35043 Rennes, France
| | - Vincent Bessonneau
- Univ Rennes, Inserm, École des hautes études en santé publique (EHESP), Institut de recherche en santé, environnement et travail (Irset) - UMR_S 1085, 15 avenue du Professeur Léon Bernard, 35043 Rennes, France
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12
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Lassen J, Nielsen KL, Johannsen M, Villesen P. Assessment of XCMS Optimization Methods with Machine-Learning Performance. Anal Chem 2021; 93:13459-13466. [PMID: 34585906 DOI: 10.1021/acs.analchem.1c02000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The metabolomics field is under rapid development. In particular, biomarker identification and pathway analysis are growing, as untargeted metabolomics is usable for discovery research. Frequently, new processing and statistical strategies are proposed to accommodate the increasing demand for robust and standardized data. One such algorithm is XCMS, which processes raw data into integrated peaks. Multiple studies have tried to assess the effect of optimizing XCMS parameters, but it is challenging to quantify the quality of the XCMS output. In this study, we investigate the effect of two automated optimization tools (Autotuner and isotopologue parameter optimization (IPO)) using the prediction power of machine learning as a proxy for the quality of the data set. We show that optimized parameters outperform default XCMS settings and that manually chosen parameters by liquid chromatography-mass spectrometry (LC-MS) experts remain the best. Finally, the machine-learning approach of quality assessment is proposed for future evaluations of newly developed optimization methods because its performance directly measures the retained signal upon preprocessing.
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Affiliation(s)
- Johan Lassen
- Bioinformatics Research Center, Aarhus University, CF Moellers Alle 8, DK-8000 Aarhus, Denmark
| | - Kirstine Lykke Nielsen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus, Denmark
| | - Mogens Johannsen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus, Denmark
| | - Palle Villesen
- Bioinformatics Research Center, Aarhus University, CF Moellers Alle 8, DK-8000 Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark
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13
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Zhang P, Carlsten C, Chaleckis R, Hanhineva K, Huang M, Isobe T, Koistinen VM, Meister I, Papazian S, Sdougkou K, Xie H, Martin JW, Rappaport SM, Tsugawa H, Walker DI, Woodruff TJ, Wright RO, Wheelock CE. Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective. ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2021; 8:839-852. [PMID: 34660833 PMCID: PMC8515788 DOI: 10.1021/acs.estlett.1c00648] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 08/31/2021] [Accepted: 08/31/2021] [Indexed: 05/02/2023]
Abstract
The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.
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Affiliation(s)
- Pei Zhang
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Key
Laboratory of Drug Quality Control and Pharmacovigilance (Ministry
of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, P. R. China
| | - Christopher Carlsten
- Air
Pollution Exposure Laboratory, Division of Respiratory Medicine, Department
of Medicine, University of British Columbia, Vancouver, British Columbia V5Z 1M9, Canada
| | - Romanas Chaleckis
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Kati Hanhineva
- Department
of Life Technologies, Food Chemistry and Food Development Unit, University of Turku, Turku 20014, Finland
- Department
of Biology and Biological Engineering, Chalmers
University of Technology, Gothenburg SE-412 96, Sweden
- Department
of Clinical Nutrition and Public Health, University of Eastern Finland, Kuopio 70210, Finland
| | - Mengna Huang
- Channing
Division of Network Medicine, Brigham and
Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Tomohiko Isobe
- The
Japan Environment and Children’s Study Programme Office, National Institute for Environmental Sciences, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Ville M. Koistinen
- Department
of Life Technologies, Food Chemistry and Food Development Unit, University of Turku, Turku 20014, Finland
- Department
of Clinical Nutrition and Public Health, University of Eastern Finland, Kuopio 70210, Finland
| | - Isabel Meister
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Stefano Papazian
- Science
for Life Laboratory, Department of Environmental Science, Stockholm University, Stockholm SE-114 18, Sweden
| | - Kalliroi Sdougkou
- Science
for Life Laboratory, Department of Environmental Science, Stockholm University, Stockholm SE-114 18, Sweden
| | - Hongyu Xie
- Science
for Life Laboratory, Department of Environmental Science, Stockholm University, Stockholm SE-114 18, Sweden
| | - Jonathan W. Martin
- Science
for Life Laboratory, Department of Environmental Science, Stockholm University, Stockholm SE-114 18, Sweden
| | - Stephen M. Rappaport
- Division
of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California 94720-7360, United States
| | - Hiroshi Tsugawa
- RIKEN Center
for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- RIKEN Center
for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Department
of Biotechnology and Life Science, Tokyo
University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo 184-8588 Japan
- Graduate
School of Medical life Science, Yokohama
City University, 1-7-22
Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Douglas I. Walker
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York10029-5674, United States
| | - Tracey J. Woodruff
- Program
on Reproductive Health and the Environment, University of California San Francisco, San Francisco, California 94143, United States
| | - Robert O. Wright
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York10029-5674, United States
| | - Craig E. Wheelock
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Department
of Respiratory Medicine and Allergy, Karolinska
University Hospital, Stockholm SE-141-86, Sweden
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14
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Aalizadeh R, Alygizakis NA, Schymanski EL, Krauss M, Schulze T, Ibáñez M, McEachran AD, Chao A, Williams AJ, Gago-Ferrero P, Covaci A, Moschet C, Young TM, Hollender J, Slobodnik J, Thomaidis NS. Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening. Anal Chem 2021; 93:11601-11611. [PMID: 34382770 DOI: 10.1021/acs.analchem.1c02348] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
There is an increasing need for comparable and harmonized retention times (tR) in liquid chromatography (LC) among different laboratories, to provide supplementary evidence for the identity of compounds in high-resolution mass spectrometry (HRMS)-based suspect and nontarget screening investigations. In this study, a rigorously tested, flexible, and less system-dependent unified retention time index (RTI) approach for LC is presented, based on the calibration of the elution pattern. Two sets of 18 calibrants were selected for each of ESI+ and ESI-based on the maximum overlap with the retention times and chemical similarity indices from a total set of 2123 compounds. The resulting calibration set, with RTI set to range between 1 and 1000, was proposed as the most appropriate RTI system after rigorous evaluation, coordinated by the NORMAN network. The validation of the proposed RTI system was done externally on different instrumentation and LC conditions. The RTI can also be used to check the reproducibility and quality of LC conditions. Two quantitative structure-retention relationship (QSRR)-based models were built based on the developed RTI systems, which assist in the removal of false-positive annotations. The applicability domains of the QSRR models allowed completing the identification process with higher confidence for substances within the domain, while indicating those substances for which results should be treated with caution. The proposed RTI system was used to improve confidence in suspect and nontarget screening and increase the comparability between laboratories as demonstrated for two examples. All RTI-related calculations can be performed online at http://rti.chem.uoa.gr/.
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Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece
| | - Nikiforos A Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece.,Environmental Institute, Okružná 784/42, 97241 Koš, Slovak Republic
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg.,Eawag: Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Martin Krauss
- Department Effect-Directed Analysis, Helmholtz-Centre for Environmental Research-UFZ, Leipzig, Germany
| | - Tobias Schulze
- Department Effect-Directed Analysis, Helmholtz-Centre for Environmental Research-UFZ, Leipzig, Germany
| | - María Ibáñez
- Research Institute for Pesticides and Water, University Jaume I, Castellón 12071, Spain
| | - Andrew D McEachran
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop, D143-02, 109 T.W. Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Alex Chao
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop, D143-02, 109 T.W. Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Mail Drop, D143-02, 109 T.W. Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research (IDAEA) Severo Ochoa Excellence Center, Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, E-08034 Barcelona, Spain.,Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), P. O. Box 7050, SE-750 07 Uppsala, Sweden
| | - Adrian Covaci
- Toxicological Center, University of Antwerp, 2610 Wilrijk, Belgium
| | - Christoph Moschet
- Department of Civil and Environmental Engineering, University of California, Davis, California 95616, United States
| | - Thomas M Young
- Department of Civil and Environmental Engineering, University of California, Davis, California 95616, United States
| | - Juliane Hollender
- Eawag: Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland.,Institute of Biogeochemistry and Pollutant Dynamics, IBP, ETH Zurich, 8092 Zurich, Switzerland
| | | | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece
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15
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Barupal DK, Baygi SF, Wright RO, Arora M. Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics. Front Public Health 2021; 9:653599. [PMID: 34178917 PMCID: PMC8222544 DOI: 10.3389/fpubh.2021.653599] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 01/27/2023] Open
Abstract
Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.
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16
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Improving Exposure Assessment Using Non-Targeted and Suspect Screening: The ISO/IEC 17025: 2017 Quality Standard as a Guideline. J Xenobiot 2021; 11:1-15. [PMID: 33530331 PMCID: PMC7838891 DOI: 10.3390/jox11010001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/13/2021] [Accepted: 01/21/2021] [Indexed: 01/27/2023] Open
Abstract
The recent advances of novel methodologies such as non-targeted and suspect screening based on high-resolution mass spectrometry (HRMS) have paved the way to a new paradigm for exposure assessment. These methodologies allow to profile simultaneously thousands of small unknown molecules present in environmental and biological samples, and therefore hold great promises in order to identify more efficiently hazardous contaminants potentially associated with increased risks of developing adverse health outcomes. In order to further explore the potential of these methodologies and push the transition from research applications towards regulatory purposes, robust harmonized quality standards have to be implemented. Here, we discuss the feasibility of using ISO/IEC 17025: 2017 as a guideline to implement non-targeted and suspect screening methodologies in laboratories, whether it is for accreditation purposes or not. More specifically, we identified and then discussed how specificities of non-targeted HRMS methodology can be accounted for in order to comply with the specific items of ISO/IEC 17025: 2017. We also discussed other specificities of HRMS methodologies (e.g., need for digital storage capacity) that are so far not included in the ISO/IEC 17025 requirements but should be considered. This works aims to fuel and expand the discussion in order to subsidize new opportunities of harmonization for non-targeted and suspect screening.
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