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Habchi B, Alves S, Streel S, Guillaume M, Donneau AF, Appenzeller BMR, Rutledge DN, Paris A, Rathahao-Paris E. Chemical Exposure Highlighted without Any A Priori Information in an Epidemiological Study by Metabolomic FT-ICR-MS Fingerprinting at High Throughput and High Resolution. Chem Res Toxicol 2023. [PMID: 37729183 DOI: 10.1021/acs.chemrestox.3c00158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Epidemiological studies aim to assess associations between diseases and risk factors. Such investigations involve a large sample size and require powerful analytical methods to measure the effects of risk factors, resulting in a long analysis time. In this study, chemical exposure markers were detected as the main variables strongly affecting two components coming from a principal component analysis (PCA) exploration of the metabolomic data generated from urinary samples collected on a cohort of about 500 individuals using direct introduction coupled with a Fourier-transform ion cyclotron resonance instrument. The assignment of their chemical identity was first achieved based on their isotopic fine structures detected at very high resolution (Rp > 900,000). Their identification as dimethylbiguanide and sotalol was obtained at level 1, thanks to the available authentic chemical standards, tandem mass spectrometry (MS/MS) experiments, and collision cross section measurements. Epidemiological data confirmed that the subjects discriminated by PCA had declared to be prescribed these drugs for either type II diabetes or cardiac arrhythmia. Concentrations of these drugs in urine samples of interest were also estimated by rapid quantification using an external standard calibration method, direct introduction, and MS/MS experiments. Regression analyses showed a good correlation between the estimated drug concentrations and the scores of individuals distributed on these specific PCs. The detection of these chemical exposure markers proved the potential of the proposed high-throughput approach without any prior drug exposure knowledge as a powerful emerging tool for rapid and large-scale phenotyping of subjects enrolled in epidemiological studies to rapidly characterize the chemical exposome and adherence to medical prescriptions.
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Affiliation(s)
- Baninia Habchi
- Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), 75005 Paris, France
| | - Sandra Alves
- Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), 75005 Paris, France
| | - Sylvie Streel
- Department of Public Health, University of Liège, 4000 Liège, Belgium
| | - Michèle Guillaume
- Department of Public Health, University of Liège, 4000 Liège, Belgium
| | | | - Brice M R Appenzeller
- Human Biomonitoring Research Unit, Luxembourg Institute of Health (LIH), 4354 Esch-sur-Alzette, Luxembourg
| | - Douglas N Rutledge
- Muséum National d'Histoire Naturelle, MCAM, UMR7245 CNRS─MNHN, 75005 Paris, France
- Faculté de Pharmacie, Université Paris-Saclay, 91400 Orsay, France
| | - Alain Paris
- Muséum National d'Histoire Naturelle, MCAM, UMR7245 CNRS─MNHN, 75005 Paris, France
| | - Estelle Rathahao-Paris
- Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), 75005 Paris, France
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, 91191 Gif-sur-Yvette, France
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Delvaux A, Rathahao-Paris E, Alves S. Different ion mobility-mass spectrometry coupling techniques to promote metabolomics. MASS SPECTROMETRY REVIEWS 2022; 41:695-721. [PMID: 33492707 DOI: 10.1002/mas.21685] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.
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Affiliation(s)
- Aurélie Delvaux
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
| | - Estelle Rathahao-Paris
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
- Département Médicaments et Technologies pour la Santé (DMTS), SPI, Université Paris-Saclay, CEA, INRAE, Gif-sur-Yvette, 91191, France
| | - Sandra Alves
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
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Yang Q, Wang Y, Zhang Y, Li F, Xia W, Zhou Y, Qiu Y, Li H, Zhu F. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res 2020; 48:W436-W448. [PMID: 32324219 PMCID: PMC7319444 DOI: 10.1093/nar/gkaa258] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 03/21/2020] [Accepted: 04/04/2020] [Indexed: 12/23/2022] Open
Abstract
Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.
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Affiliation(s)
- Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Weiqi Xia
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation & The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation & The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Honglin Li
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Zang X, Monge ME, Fernández FM. Mass Spectrometry-Based Non-targeted Metabolic Profiling for Disease Detection: Recent Developments. Trends Analyt Chem 2019; 118:158-169. [PMID: 32831436 PMCID: PMC7430701 DOI: 10.1016/j.trac.2019.05.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Mass spectrometry (MS) plays an important role in seeking biomarkers for disease detection. High-quality quantitative data is needed for accurate analysis of metabolic perturbations in patients. This article describes recent developments in MS-based non-targeted metabolomics research with applications to the detection of several major common human diseases, focusing on study cohorts, MS platforms utilized, statistical analyses and discriminant metabolite identification. Potential disease biomarkers recently discovered for type 2 diabetes, cardiovascular disease, hepatocellular carcinoma, breast cancer and prostate cancer through metabolomics are summarized, and limitations are discussed.
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Affiliation(s)
- Xiaoling Zang
- School of Chemistry and Biochemistry, Georgia Institute of Technology and Petit Institute for Biochemistry and Bioscience, Atlanta, Georgia 30332, United States
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology and Petit Institute for Biochemistry and Bioscience, Atlanta, Georgia 30332, United States
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Rathahao-Paris E, Alves S, Boussaid N, Picard-Hagen N, Gayrard V, Toutain PL, Tabet JC, Rutledge DN, Paris A. Evaluation and validation of an analytical approach for high-throughput metabolomic fingerprinting using direct introduction-high-resolution mass spectrometry: Applicability to classification of urine of scrapie-infected ewes. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2019; 25:251-258. [PMID: 30335517 DOI: 10.1177/1469066718806450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Direct injection-mass spectrometry can be used to perform high-throughput metabolomic fingerprinting. This work aims to evaluate a global analytical workflow in terms of sample preparation (urine sample dilution), high-resolution detection (quality of generated data based on criteria such as mass measurement accuracy and detection sensitivity) and data analysis using dedicated bioinformatics tools. Investigation was performed on a large number of biological samples collected from sheep infected or not with scrapie. Direct injection-mass spectrometry approach is usually affected by matrix effects, eventually hampering detection of some relevant biomarkers. Reference compounds were spiked in biological samples to help evaluate the quality of direct injection-mass spectrometry data produced by Fourier Transform mass spectrometry. Despite the potential of high-resolution detection, some drawbacks still remain. The most critical is the presence of matrix effects, which could be minimized by optimizing the sample dilution factor. The data quality in terms of mass measurement accuracy and reproducible intensity was evaluated. Good repeatability was obtained for the chosen dilution factor (i.e., 2000). More than 150 analyses were performed in less than 16 hours using the optimized direct injection-mass spectrometry approach. Discrimination of different status of sheeps in relation to scrapie infection (i.e., scrapie-affected, preclinical scrapie or healthy) was obtained from the application of Shrinkage Discriminant Analysis to the direct injection-mass spectrometry data. The most relevant variables related to this discrimination were selected and annotated. This study demonstrated that the choice of appropriated dilution faction is indispensable for producing quality and informative direct injection-mass spectrometry data. Successful application of direct injection-mass spectrometry approach for high throughput analysis of a large number of biological samples constitutes the proof of the concept.
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Affiliation(s)
- Estelle Rathahao-Paris
- 1 UMR Ingénierie Procédés Aliments, AgroParisTech, Inra, Université Paris-Saclay, Massy, France
- 2 Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Paris, France
| | - Sandra Alves
- 2 Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Paris, France
| | - Nawel Boussaid
- 1 UMR Ingénierie Procédés Aliments, AgroParisTech, Inra, Université Paris-Saclay, Massy, France
| | - Nicole Picard-Hagen
- 3 Toxalim, Université de Toulouse, INRA (Institut National de la Recherche Agronomique), INP (Institut National Polytechnique de Toulouse)-ENVT (Ecole Nationale Vétérinaire de Toulouse), Toulouse, France
| | - Véronique Gayrard
- 3 Toxalim, Université de Toulouse, INRA (Institut National de la Recherche Agronomique), INP (Institut National Polytechnique de Toulouse)-ENVT (Ecole Nationale Vétérinaire de Toulouse), Toulouse, France
| | | | - Jean-Claude Tabet
- 2 Sorbonne Université, Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Paris, France
- 5 CEA-INRA, Service de Pharmacologie et d'Immunoanalyse, Laboratoire d'Etude du Métabolisme des Médicaments, MetaboHUB, Université Paris-Saclay, Gif-sur-Yvette cedex, France
| | - Douglas N Rutledge
- 1 UMR Ingénierie Procédés Aliments, AgroParisTech, Inra, Université Paris-Saclay, Massy, France
| | - Alain Paris
- 6 Unité Molécules de Communication et Adaptation des Microorganismes (MCAM), Muséum National d'Histoire Naturelle, CNRS, CP54, Paris, France
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