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Comparison of chemometric strategies for potential exposure marker discovery and false-positive reduction in untargeted metabolomics: application to the serum analysis by LC-HRMS after intake of Vaccinium fruit supplements. Anal Bioanal Chem 2022; 414:1841-1855. [PMID: 35028688 DOI: 10.1007/s00216-021-03815-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/18/2021] [Accepted: 11/30/2021] [Indexed: 11/01/2022]
Abstract
Untargeted liquid chromatographic-high-resolution mass spectrometric (LC-HRMS) metabolomics for potential exposure marker (PEM) discovery in nutrikinetic studies generates complex outputs. The correct selection of statistically significant PEMs is a crucial analytical step for understanding nutrition-health interactions. Hence, in this paper, different chemometric selection workflows for PEM discovery, using multivariate or univariate parametric or non-parametric data analyses, were comparatively tested and evaluated. The PEM selection protocols were applied to a small-sample-size untargeted LC-HRMS study of a longitudinal set of serum samples from 20 volunteers after a single intake of (poly)phenolic-rich Vaccinium myrtillus and Vaccinium corymbosum supplements. The non-parametric Games-Howell test identified a restricted group of significant features, thus minimizing the risk of false-positive retention. Among the forty-seven PEMs exhibiting a statistically significant postprandial kinetics, twelve were successfully annotated as purine pathway metabolites, benzoic and benzodiol metabolites, indole alkaloids, and organic and fatty acids, and five (i.e. octahydro-methyl-β-carboline-dicarboxylic acid, tetrahydro-methyl-β-carboline-dicarboxylic acid, citric acid, caprylic acid, and azelaic acid) were associated to Vaccinium berry consumption for the first time. The analysis of the area under the curve of the longitudinal dataset highlighted thirteen statistically significant PEMs discriminating the two interventions, including four intra-intervention relevant metabolites (i.e. abscisic acid glucuronide, catechol sulphate, methyl-catechol sulphate, and α-hydroxy-hippuric acid). Principal component analysis and sample classification through linear discriminant analysis performed on PEM maximum intensity confirmed the discriminating role of these PEMs.
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2
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Menyhart O, Weltz B, Győrffy B. MultipleTesting.com: A tool for life science researchers for multiple hypothesis testing correction. PLoS One 2021; 16:e0245824. [PMID: 34106935 PMCID: PMC8189492 DOI: 10.1371/journal.pone.0245824] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/14/2021] [Indexed: 11/18/2022] Open
Abstract
Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of associations will be false positives, clouding real discoveries. Drawing valid conclusions require taking into account the number of performed statistical tests and adjusting the statistical confidence measures. Several strategies exist to overcome the problem of multiple hypothesis testing. We aim to summarize critical statistical concepts and widely used correction approaches while also draw attention to frequently misinterpreted notions of statistical inference. We provide a step-by-step description of each multiple-testing correction method with clear examples and present an easy-to-follow guide for selecting the most suitable correction technique. To facilitate multiple-testing corrections, we developed a fully automated solution not requiring programming skills or the use of a command line. Our registration free online tool is available at www.multipletesting.com and compiles the five most frequently used adjustment tools, including the Bonferroni, the Holm (step-down), the Hochberg (step-up) corrections, allows to calculate False Discovery Rates (FDR) and q-values. The current summary provides a much needed practical synthesis of basic statistical concepts regarding multiple hypothesis testing in a comprehensible language with well-illustrated examples. The web tool will fill the gap for life science researchers by providing a user-friendly substitute for command-line alternatives.
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Affiliation(s)
- Otília Menyhart
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary
- Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary
| | - Boglárka Weltz
- Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary
- A5 Genetics Ltd, Und, Hungary
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary
- Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary
- 2 Department of Pediatrics, Semmelweis University, Budapest, Hungary
- * E-mail:
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3
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Pasias IN, Theodorou K, Raptopoulou KG, Evaggelaras C, Floros G, Ladavos A, Asimakopoulos AG, Calokerinos AC, Proestos C. Rapid, Low-Cost Spectrophotometric Characterization of Olive Oil Quality to Meet Newly Implemented Compliance Requirements. ANAL LETT 2021. [DOI: 10.1080/00032719.2021.1925679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- I. N. Pasias
- General Chemical Lab of Research and Analysis, Lamia, Greece
| | - K. Theodorou
- Laboratory of Food Technology, Department of Business Administration of Food and Agricultural Enterprises, University of Patras, Patras, Greece
| | | | - Ch. Evaggelaras
- Lamos, Extra Virgin Olive Oil Enterprise, Raches, Fthiotida, Greece
| | - G. Floros
- Lamos, Extra Virgin Olive Oil Enterprise, Raches, Fthiotida, Greece
| | - A. Ladavos
- Laboratory of Food Technology, Department of Business Administration of Food and Agricultural Enterprises, University of Patras, Patras, Greece
| | - A. G. Asimakopoulos
- Department of Chemistry, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - A. C. Calokerinos
- Department of Chemistry, Food Chemistry Laboratory, National and Kapodistrian University of Athens, Athens, Greece
| | - Ch. Proestos
- Department of Chemistry, Food Chemistry Laboratory, National and Kapodistrian University of Athens, Athens, Greece
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Prasad M, Postma G, Franceschi P, Morosi L, Giordano S, Falcetta F, Giavazzi R, Davoli E, Buydens LMC, Jansen J. A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data. Gigascience 2020; 9:giaa131. [PMID: 33241286 PMCID: PMC7688471 DOI: 10.1093/gigascience/giaa131] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/28/2020] [Accepted: 11/01/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection. RESULTS The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method. CONCLUSIONS In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.
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Affiliation(s)
- Mridula Prasad
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
- Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all’ Adige, Italy
| | - Geert Postma
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, 38010 San Michele all’ Adige, Italy
| | - Lavinia Morosi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Silvia Giordano
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Francesca Falcetta
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Raffaella Giavazzi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Enrico Davoli
- Mass Spectrometry Laboratory, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19-20156 Milan, Italy
| | - Lutgarde M C Buydens
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
| | - Jeroen Jansen
- IMM/ Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ Nijmegen, Netherlands
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Fox A, Dutt TS, Karger B, Rojas M, Obregón-Henao A, Anderson GB, Henao-Tamayo M. Cyto-Feature Engineering: A Pipeline for Flow Cytometry Analysis to Uncover Immune Populations and Associations with Disease. Sci Rep 2020; 10:7651. [PMID: 32377001 PMCID: PMC7203241 DOI: 10.1038/s41598-020-64516-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 04/13/2020] [Indexed: 12/04/2022] Open
Abstract
Flow cytometers can now analyze up to 50 parameters per cell and millions of cells per sample; however, conventional methods to analyze data are subjective and time-consuming. To address these issues, we have developed a novel flow cytometry analysis pipeline to identify a plethora of cell populations efficiently. Coupled with feature engineering and immunological context, researchers can immediately extrapolate novel discoveries through easy-to-understand plots. The R-based pipeline uses Fluorescence Minus One (FMO) controls or distinct population differences to develop thresholds for positive/negative marker expression. The continuous data is transformed into binary data, capturing a positive/negative biological dichotomy often of interest in characterizing cells. Next, a filtering step refines the data from all identified cell phenotypes to populations of interest. The data can be partitioned by immune lineages and statistically correlated to other experimental measurements. The pipeline’s modularity allows customization of statistical testing, adoption of alternative initial gating steps, and incorporation of other datasets. Validation of this pipeline through manual gating of two datasets (murine splenocytes and human whole blood) confirmed its accuracy in identifying even rare subsets. Lastly, this pipeline can be applied in all disciplines utilizing flow cytometry regardless of cytometer or panel design. The code is available at https://github.com/aef1004/cyto-feature_engineering.
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Affiliation(s)
- Amy Fox
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Taru S Dutt
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Burton Karger
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Mauricio Rojas
- Grupo de Inmunología Celular e Inmunogenética, Facultad de Medicina, Unidad de Citometría de Flujo, Sede de Investigación Universitaria, Universidad de Antioquia, Medellin, Colombia
| | - Andrés Obregón-Henao
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - G Brooke Anderson
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Marcela Henao-Tamayo
- Mycobacteria Research Laboratories, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, 80523, USA.
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Wu HY, Ma MC, Pan YY, Shih CL, Zgoda V, Li CS, Lin LC, Liao PC. Assessing the Similarity between Random Copolymer Drug Glatiramer Acetate by Using LC-MS Data Coupling with Hypothesis Testing. Anal Chem 2019; 91:14281-14289. [PMID: 31590482 DOI: 10.1021/acs.analchem.9b02488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The full characterization of nonbiological complex drugs (NBCDs) is not possible, but analytical approaches are of urgent need to evaluate the similarity between different lots and compare with their follow-up versions. Here, we propose a hypothesis testing-based approach to assess the similarity/difference between random amino acid copolymer drugs using liquid chromatography mass spectrometry (LC-MS) analysis. Two glatiramer acetate (GA) drugs, commercially available Copaxone and in-house synthesized SPT, and a negative control were digested by Lys-C and followed by HILIC-MS analysis. After retention time alignment and feature identification, 1627 features matched to m/z values in an elemental composition database were considered as derived from active drug ingredients. A hypothesis testing approach, the sum of squared deviations test, was developed to process high-dimensional data derived from LC-MS spectra. The feasibility of this approach was first demonstrated by testing 5 versus 5 lots of Copaxone and Copaxone versus SPT, which suggested a significant similarity by obtaining the estimated 95th percentile of the distribution of the estimator (ρ̂(95%)) at 0.0056 (p-value = 0.0026) and 0.0026 (p-value < 0.0001), respectively. In contrast, the ρ̂ was 0.036 (p-value = 1.00), while comparing Copaxone and the negative control, implying a lack of similarity. We further synthesized nine stable isotope-labeled peptides to validate the proposed amino acid sequences in the database, demonstrating the correctness in sequence identification. The quantitation variations in our analytical procedures were determined to be 6.8-7.7%. This approach was found to have a great potential for evaluating the similarity between generic NBCDs and listed reference drugs, as well as to monitor the lot-to-lot variation.
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Affiliation(s)
- Hsin-Yi Wu
- Instrumentation Center , National Taiwan University , Taipei 106 , Taiwan
| | - Mi-Chia Ma
- Department of Statistics , National Cheng Kung University , Tainan 701 , Taiwan
| | - Yu-Yi Pan
- Department of Statistics , National Cheng Kung University , Tainan 701 , Taiwan
| | - Chia-Lung Shih
- Department of Environmental and Occupational Health, College of Medicine , National Cheng Kung University , Tainan 701 , Taiwan
| | - Victor Zgoda
- Orekhovich Institute of Biomedical Chemistry , Moscow 119121 , Russia
| | - Chin-Shang Li
- School of Nursing , The State University of New York, University at Buffalo , Buffalo , New York 14214 , United States
| | | | - Pao-Chi Liao
- Department of Environmental and Occupational Health, College of Medicine , National Cheng Kung University , Tainan 701 , Taiwan
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7
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Hamid Z, Basit A, Pontis S, Piras F, Assogna F, Bossù P, Pontieri FE, Stefani A, Spalletta G, Franceschi P, Reggiani A, Armirotti A. Gender specific decrease of a set of circulating N-acylphosphatidyl ethanolamines (NAPEs) in the plasma of Parkinson's disease patients. Metabolomics 2019; 15:74. [PMID: 31053995 PMCID: PMC6499742 DOI: 10.1007/s11306-019-1536-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 04/25/2019] [Indexed: 11/11/2022]
Abstract
INTRODUCTION Current markers of Parkinson's disease (PD) fail to detect the early progression of disease state. Conversely, current omics techniques allow the investigation of hundreds of molecules potentially altered by disease conditions. Based on evidence previously collected by our group in a mouse model of PD, we speculated that a particular set of circulating lipids might be significantly altered by the pathology. OBJECTIVES The aim of current study was to evaluate the potential of a particular set of N-acyl-phosphatidylethanolamines (NAPEs) as potential non-invasive plasma markers of ongoing neurodegeneration from Parkinson's disease in human subjects. METHODS A panel of seven NAPEs were quantified by LC-MS/MS in the plasma of 587 individuals (healthy controls, n = 319; Parkinson's disease, n = 268); Random Forest classification and statistical modeling was applied to compare Parkinson's disease versus controls. All p-values obtained in different tests were corrected for multiplicity by controlling the false discovery rate (FDR). RESULTS The results indicate that this panel of NAPEs is able to distinguish female PD patients from the corresponding healthy controls. Further to this, the observed downregulation of these NAPEs is in line with the results in plasma of a mouse model of Parkinson's (6-OHDA). CONCLUSIONS In the current study we have shown the downregulation of NAPEs in plasma of PD patients and we thus speculate that these lipids might serve as candidate biomarkers for PD. We also suggest a molecular mechanism, explaining our findings, which involves gut microbiota.
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Affiliation(s)
- Zeeshan Hamid
- D3Validation, Fondazione Istituto Italiano di Tecnologia, via Morego 30, 16163, Genoa, Italy
- Scuola Superiore Sant'Anna, via Piazza Martiri della Libertà, 33, 56127, Pisa, Italy
| | - Abdul Basit
- D3Validation, Fondazione Istituto Italiano di Tecnologia, via Morego 30, 16163, Genoa, Italy
| | - Silvia Pontis
- D3Validation, Fondazione Istituto Italiano di Tecnologia, via Morego 30, 16163, Genoa, Italy
| | - Fabrizio Piras
- Laboratorio di Neuropsichiatria, IRCCS Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
| | - Francesca Assogna
- Laboratorio di Neuropsichiatria, IRCCS Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
| | - Paola Bossù
- Laboratorio di Neuropsichiatria, IRCCS Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
| | - Francesco Ernesto Pontieri
- Laboratorio di Neuropsichiatria, IRCCS Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University, Via di Grottarossa 1035, 00189, Rome, Italy
| | - Alessandro Stefani
- Department of Medicine of Systems, Tor Vergata University, Viale Oxford 81, 00133, Rome, Italy
| | - Gianfranco Spalletta
- Laboratorio di Neuropsichiatria, IRCCS Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
| | - Pietro Franceschi
- Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, San Michele all'Adige, TN, Italy
| | - Angelo Reggiani
- D3Validation, Fondazione Istituto Italiano di Tecnologia, via Morego 30, 16163, Genoa, Italy
| | - Andrea Armirotti
- Analytical Chemistry Lab, Fondazione Istituto Italiano di Tecnologia, via Morego 30, 16163, Genoa, Italy.
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8
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Albanese D, Riccadonna S, Donati C, Franceschi P. A practical tool for maximal information coefficient analysis. Gigascience 2018; 7:1-8. [PMID: 29617783 PMCID: PMC5893960 DOI: 10.1093/gigascience/giy032] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 03/22/2018] [Indexed: 01/03/2023] Open
Abstract
Background The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual information-based measures of association are particularly promising, in particular after the recent introduction of the TICe and MICe estimators, which combine computational efficiency with superior bias/variance properties. An open-source software implementation of these two measures providing a complete procedure to test their significance would be extremely useful. Findings Here, we present MICtools, a comprehensive and effective pipeline that combines TICe and MICe into a multistep procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated. Conclusions We show that MICtools, combining TICe and MICe, is able to highlight associations that would not be captured by conventional strategies.
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Affiliation(s)
- Davide Albanese
- Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
| | - Samantha Riccadonna
- Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
| | - Claudio Donati
- Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
| | - Pietro Franceschi
- Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
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9
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Ulaszewska MM, Weinert CH, Trimigno A, Portmann R, Andres Lacueva C, Badertscher R, Brennan L, Brunius C, Bub A, Capozzi F, Cialiè Rosso M, Cordero CE, Daniel H, Durand S, Egert B, Ferrario PG, Feskens EJM, Franceschi P, Garcia-Aloy M, Giacomoni F, Giesbertz P, González-Domínguez R, Hanhineva K, Hemeryck LY, Kopka J, Kulling SE, Llorach R, Manach C, Mattivi F, Migné C, Münger LH, Ott B, Picone G, Pimentel G, Pujos-Guillot E, Riccadonna S, Rist MJ, Rombouts C, Rubert J, Skurk T, Sri Harsha PSC, Van Meulebroek L, Vanhaecke L, Vázquez-Fresno R, Wishart D, Vergères G. Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies. Mol Nutr Food Res 2018; 63:e1800384. [PMID: 30176196 DOI: 10.1002/mnfr.201800384] [Citation(s) in RCA: 154] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 07/10/2018] [Indexed: 12/13/2022]
Abstract
The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.
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Affiliation(s)
- Marynka M Ulaszewska
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Christoph H Weinert
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Alessia Trimigno
- Department of Agricultural and Food Science, University of Bologna, Italy
| | - Reto Portmann
- Method Development and Analytics Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Cristina Andres Lacueva
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - René Badertscher
- Method Development and Analytics Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Lorraine Brennan
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
| | - Carl Brunius
- Department of Biology and Biological Engineering, Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Achim Bub
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Francesco Capozzi
- Department of Agricultural and Food Science, University of Bologna, Italy
| | - Marta Cialiè Rosso
- Dipartimento di Scienza e Tecnologia del Farmaco Università degli Studi di Torino, Turin, Italy
| | - Chiara E Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco Università degli Studi di Torino, Turin, Italy
| | - Hannelore Daniel
- Nutritional Physiology, Technische Universität München, Freising, Germany
| | - Stéphanie Durand
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Bjoern Egert
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Paola G Ferrario
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Edith J M Feskens
- Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands
| | - Pietro Franceschi
- Computational Biology Unit, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Mar Garcia-Aloy
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Franck Giacomoni
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Pieter Giesbertz
- Molecular Nutrition Unit, Technische Universität München, Freising, Germany
| | - Raúl González-Domínguez
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Kati Hanhineva
- Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Lieselot Y Hemeryck
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Joachim Kopka
- Department of Molecular Physiology, Applied Metabolome Analysis, Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Sabine E Kulling
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Rafael Llorach
- Biomarkers & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Barcelona, Spain. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Claudine Manach
- INRA, UMR 1019, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy.,Center Agriculture Food Environment, University of Trento, San Michele all'Adige, Italy
| | - Carole Migné
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Linda H Münger
- Food Microbial Systems Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Beate Ott
- Else Kröner Fresenius Center for Nutritional Medicine, Technical University of Munich, Munich, Germany.,ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany
| | - Gianfranco Picone
- Department of Agricultural and Food Science, University of Bologna, Italy
| | - Grégory Pimentel
- Food Microbial Systems Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
| | - Estelle Pujos-Guillot
- Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, INRA, Human Nutrition Unit, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Samantha Riccadonna
- Computational Biology Unit, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Manuela J Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Caroline Rombouts
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Josep Rubert
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Thomas Skurk
- Else Kröner Fresenius Center for Nutritional Medicine, Technical University of Munich, Munich, Germany.,ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany
| | - Pedapati S C Sri Harsha
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
| | - Lieven Van Meulebroek
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Lynn Vanhaecke
- Laboratory of Chemical Analysis, Department of Veterinary Public Health and Food Safety, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Rosa Vázquez-Fresno
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Canada
| | - David Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Canada
| | - Guy Vergères
- Food Microbial Systems Research Division, Agroscope, Federal Office for Agriculture, Berne, Switzerland
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10
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11
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Hits and misses in research trends to monitor contaminants in foods. Anal Bioanal Chem 2018; 410:5331-5351. [DOI: 10.1007/s00216-018-1195-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 05/31/2018] [Accepted: 06/12/2018] [Indexed: 01/26/2023]
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12
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Abstract
Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.
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Affiliation(s)
- Carsten Jaeger
- Molecular Cancer Research Center (MKFZ), Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Jan Lisec
- Molecular Cancer Research Center (MKFZ), Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Division 1.7 Analytical Chemistry, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany.
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13
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Saw NMMT, Moser C, Martens S, Franceschi P. Applying generalized additive models to unravel dynamic changes in anthocyanin biosynthesis in methyl jasmonate elicited grapevine ( Vitis vinifera cv. Gamay) cell cultures. HORTICULTURE RESEARCH 2017; 4:17038. [PMID: 28758015 PMCID: PMC5527128 DOI: 10.1038/hortres.2017.38] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 06/07/2017] [Accepted: 06/30/2017] [Indexed: 05/26/2023]
Abstract
Plant cell cultures represent important model systems to understand metabolism and its modulation by regulatory factors. Even in controlled conditions, cell metabolism is highly dynamic and can be fully characterized only by time course experiments. Here, we show that statistical analysis of this type of data gains power if it moves to approaches able to compare the 'trends' of the different metabolites. In particular, we show how generalized additive models can be used to model the time-dependent profile of anthocyanin synthesis in grapevine cell suspension cultures (Vitis vinifera cv. Gamay), following treatment with 100 μm methyl jasmonate. The sampling was performed daily for 20 days of culturing following elicitation at day 5. All samples were analyzed by UPLC-MS/MS for the identification and quantification of fifteen anthocyanin compounds. The models confirmed the separation in the anthocyanin biosynthetic pathway between delphinidin-based and cyanidin-based compounds, showing that methyl jasmonate modulates the anthocyanin concentration profiles. Our results clearly indicate that the combination of high-throughput metabolomics and state of the art statistical modeling is a powerful approach to study plant metabolism. This approach is expected to gain popularity due to the growing availability of low-cost high-throughput 'omic' assays.
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Affiliation(s)
- Nay Min Min Thaw Saw
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, San Michele all'Adige 38010, Italy
| | - Claudio Moser
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, San Michele all'Adige 38010, Italy
| | - Stefan Martens
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, San Michele all'Adige 38010, Italy
| | - Pietro Franceschi
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, San Michele all'Adige 38010, Italy
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14
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Kim YJ, Huh I, Kim JY, Park S, Ryu SH, Kim KB, Kim S, Park T, Kwon O. Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation. Nutrients 2017; 9:nu9030233. [PMID: 28273855 PMCID: PMC5372896 DOI: 10.3390/nu9030233] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 02/19/2017] [Accepted: 02/28/2017] [Indexed: 01/01/2023] Open
Abstract
Various statistical approaches can be applied to integrate traditional and omics biomarkers, allowing the discovery of prognostic markers to classify subjects into poor and good prognosis groups in terms of responses to nutritional interventions. Here, we performed a prototype study to identify metabolites that predict responses to an intervention against oxidative stress and inflammation, using a data set from a randomized controlled trial evaluating Korean black raspberry (KBR) in sedentary overweight/obese subjects. First, a linear mixed-effects model analysis with multiple testing correction showed that four-week consumption of KBR significantly changed oxidized glutathione (GSSG, q = 0.027) level, the ratio of reduced glutathione (GSH) to GSSG (q = 0.039) in erythrocytes, malondialdehyde (MDA, q = 0.006) and interleukin-6 (q = 0.006) levels in plasma, and seventeen NMR metabolites in urine compared with those in the placebo group. A subsequent generalized linear mixed model analysis showed linear correlations between baseline urinary glycine and N-phenylacetylglycine (PAG) and changes in the GSH:GSSG ratio (p = 0.008 and 0.004) as well as between baseline urinary adenine and changes in MDA (p = 0.018). Then, receiver operating characteristic analysis revealed that a two-metabolite set (glycine and PAG) had the strongest prognostic relevance for future interventions against oxidative stress (the area under the curve (AUC) = 0.778). Leave-one-out cross-validation confirmed the accuracy of prediction (AUC = 0.683). The current findings suggest that a higher level of this two-metabolite set at baseline is useful for predicting responders to dietary interventions in subjects with oxidative stress and inflammation, contributing to the emergence of personalized nutrition.
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Affiliation(s)
- You Jin Kim
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul 03760, Korea.
| | - Iksoo Huh
- Department of Statistics, Seoul National University, Seoul 08826, Korea.
| | - Ji Yeon Kim
- Department of Food Science and Technology, Seoul National University of Science and Technology, Seoul 01811, Korea.
| | - Saejong Park
- Department of Sport Science, Korea Institute of Sport Science, Seoul 01794, Korea.
| | - Sung Ha Ryu
- College of Pharmacy, Dankook University, Chungnam 31116, Korea.
| | - Kyu-Bong Kim
- College of Pharmacy, Dankook University, Chungnam 31116, Korea.
| | - Suhkmann Kim
- Department of Chemistry and Chemistry Institute for Functional Materials, Pusan National University, Busan 46241, Korea.
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea.
| | - Oran Kwon
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul 03760, Korea.
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15
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Carlin S, Vrhovsek U, Franceschi P, Lotti C, Bontempo L, Camin F, Toubiana D, Zottele F, Toller G, Fait A, Mattivi F. Regional features of northern Italian sparkling wines, identified using solid-phase micro extraction and comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry. Food Chem 2016; 208:68-80. [PMID: 27132825 DOI: 10.1016/j.foodchem.2016.03.112] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 03/18/2016] [Accepted: 03/29/2016] [Indexed: 11/28/2022]
Abstract
We carried out comprehensive mapping of volatile compounds in 70 wines, from 48 wineries and 6 vintages, representative of the two main production areas for Italian sparkling wines, by HS-SPME-GCxGC-TOF-MS and multivariate analysis. The final scope was to describe the metabolomics space of these wines, and to verify whether the grape cultivar signature, the pedoclimatic influence of the production area, and the complex technology were measurable in the final product. The wine chromatograms provided a wealth of information, with 1695 compounds being found. A large number of putative markers influenced by the cultivation area was observed. A subset of 196 biomarkers fully discriminated between the two types of sparkling wines investigated. Among the new compounds, safranal and α-isophorone were observed. We showed how correlation-based network analysis could be used as a tool to detect the differences in compound behaviour based on external/environmental influences.
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Affiliation(s)
- Silvia Carlin
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy
| | - Urska Vrhovsek
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy
| | - Pietro Franceschi
- Biostatistics and Data Management, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all'Adige, TN, Italy
| | - Cesare Lotti
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy
| | - Luana Bontempo
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy
| | - Federica Camin
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy
| | - David Toubiana
- French Associates Institute for Agriculture and Biotechnology of Drylands (FAAB), The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
| | - Fabio Zottele
- Department of Experimentation and Technology Services, Technology Transfer Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy
| | - Giambattista Toller
- Department of Experimentation and Technology Services, Technology Transfer Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy
| | - Aaron Fait
- French Associates Institute for Agriculture and Biotechnology of Drylands (FAAB), The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010-San Michele all'Adige, Italy.
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16
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Yu Y, Skočaj M, Kreft ME, Resnik N, Veranič P, Franceschi P, Sepčić K, Guella G. Comparative lipidomic study of urothelial cancer models: association with urothelial cancer cell invasiveness. MOLECULAR BIOSYSTEMS 2016; 12:3266-3279. [DOI: 10.1039/c6mb00477f] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
A joint NMR/LC-MS approach allows to establish significant differences in the lipidoma of invasive urothelial carcinoma cells (T24) with respect to noninvasive urothelial cells (RT4).
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Affiliation(s)
- Yang Yu
- Bioorganic Chemistry Laboratory
- Department of Physics
- University of Trento
- Trento
- Italy
| | - Matej Skočaj
- Institute of Cell Biology
- Faculty of Medicine
- University of Ljubljana
- Ljubljana
- Slovenia
| | - Mateja Erdani Kreft
- Institute of Cell Biology
- Faculty of Medicine
- University of Ljubljana
- Ljubljana
- Slovenia
| | - Nataša Resnik
- Institute of Cell Biology
- Faculty of Medicine
- University of Ljubljana
- Ljubljana
- Slovenia
| | - Peter Veranič
- Institute of Cell Biology
- Faculty of Medicine
- University of Ljubljana
- Ljubljana
- Slovenia
| | - Pietro Franceschi
- Biostatistics and Data Management
- Research and Innovation Centre-Fondazione Edmund Mach
- S. Michele all'Adige
- Italy
| | - Kristina Sepčić
- Department of Biology
- Biotechnical Faculty
- University of Ljubljana
- Ljubljana
- Slovenia
| | - Graziano Guella
- Bioorganic Chemistry Laboratory
- Department of Physics
- University of Trento
- Trento
- Italy
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17
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Franceschi P, Mylonas R, Shahaf N, Scholz M, Arapitsas P, Masuero D, Weingart G, Carlin S, Vrhovsek U, Mattivi F, Wehrens R. MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments. Front Bioeng Biotechnol 2014; 2:72. [PMID: 25566535 PMCID: PMC4267269 DOI: 10.3389/fbioe.2014.00072] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 11/30/2014] [Indexed: 12/15/2022] Open
Abstract
Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework.
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Affiliation(s)
- Pietro Franceschi
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Roman Mylonas
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Nir Shahaf
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy ; Institute of Plant Sciences, Faculty of Agriculture, The Hebrew University of Jerusalem , Rehovot , Israel
| | - Matthias Scholz
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Panagiotis Arapitsas
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Domenico Masuero
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Georg Weingart
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Silvia Carlin
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Urska Vrhovsek
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Fulvio Mattivi
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
| | - Ron Wehrens
- Research and Innovation Centre, Fondazione E. Mach , San Michele all'Adige, Trento , Italy
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