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High precision mass measurements for wine metabolomics.

Roullier-Gall C, Witting M, Gougeon RD, Schmitt-Kopplin P - Front Chem (2014)

Bottom Line: An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification.UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2).In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

View Article: PubMed Central - PubMed

Affiliation: UMR PAM Université de Bourgogne/AgroSup Dijon, Institut Universitaire de la Vigne et du Vin Jules Guyot, Dijon, France ; Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München Neuherberg, Germany.

ABSTRACT
An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2). In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

No MeSH data available.


Related in: MedlinePlus

Van krevelen diagram (A) and Kendrick Mass Defects plot (B) representations of (−) FTICR-MS data corresponding to the complete data set (152 samples) after filtration; annotation of distinctive areas and lines of regions of predominant appearances of metabolite classes and formal chemical alterations: (A) methylation/demethylation (CH2); (B) hydrogenation/dehydrogenation (H2); (C) hydratation/condensation (H2O) and (D) oxidation/reduction (O). Colors correspond to elemental formulas composition: (CHO) blue; (CHOS) green; (CHON) red; and (CHONS) orange.
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Figure 1: Van krevelen diagram (A) and Kendrick Mass Defects plot (B) representations of (−) FTICR-MS data corresponding to the complete data set (152 samples) after filtration; annotation of distinctive areas and lines of regions of predominant appearances of metabolite classes and formal chemical alterations: (A) methylation/demethylation (CH2); (B) hydrogenation/dehydrogenation (H2); (C) hydratation/condensation (H2O) and (D) oxidation/reduction (O). Colors correspond to elemental formulas composition: (CHO) blue; (CHOS) green; (CHON) red; and (CHONS) orange.

Mentions: Non-targeted FTICR-MS analysis generates a tremendous amount of data and requires pre-treatment prior to the application of statistical tools. Raw data were first aligned in order to discover occurring patterns, to identify outliers, to reduce the dimensionality of the data, and also to compress large datasets into smaller and more discernable ones (Lucio, 2009). A common approach for the identification of unknown molecules is the calculation of possible elemental formulas. Molecular formulae were calculated using an in-house software tool with the following chemical constraints: N rule; O/C ratio ≤ 1; H/C ratio ≤ 2n + 2; element counts: C ≤ 100, H ≤ 200, O ≤ 80, N ≤ 3, S ≤ 3, and P ≤ 1 (Gougeon et al., 2009; Schmitt-Kopplin et al., 2010; Roullier-Gall et al., 2014a,b). On the total of 21419 masses composing the matrix after filtration, 8455 unambiguous elemental formulas were found. Due to the expected high complexity of the metabolome, visualization strategies dealing with very complex data matrices have been adapted (Hertkorn et al., 2007). The Van Krevelen diagram displays the hydrogen/carbon (H/C) vs. oxygen/carbon (O/C) ratios of these elemental formulas and provide a commonly used qualitative description of the molecular complexity of wine data (Gougeon et al., 2011; Roullier-Gall et al., 2014a,b). This plot enables the localization of areas covering metabolite classes, which are specified by different elemental compositions, enabling a representation of a sample's composition (Figure 1A). The richness of the observed mass gives evidence of the compositional diversity of molecules as carbohydrates, polyphenols or amino acids and chemical alteration as hydrogenation / dehydrogenation (Figure 1A ligne B) for example. A second plot for visualization and interpretation of ultrahigh resolution mass spectrometry data is the CH2 Kendrick plot (Hertkorn et al., 2007), which is based on distinct mass defects calculated from each elemental composition (Figure 1B). Molecules of different elemental composition differ in their mass defect and it became possible distinguish homologous series of compounds from each other.


High precision mass measurements for wine metabolomics.

Roullier-Gall C, Witting M, Gougeon RD, Schmitt-Kopplin P - Front Chem (2014)

Van krevelen diagram (A) and Kendrick Mass Defects plot (B) representations of (−) FTICR-MS data corresponding to the complete data set (152 samples) after filtration; annotation of distinctive areas and lines of regions of predominant appearances of metabolite classes and formal chemical alterations: (A) methylation/demethylation (CH2); (B) hydrogenation/dehydrogenation (H2); (C) hydratation/condensation (H2O) and (D) oxidation/reduction (O). Colors correspond to elemental formulas composition: (CHO) blue; (CHOS) green; (CHON) red; and (CHONS) orange.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230200&req=5

Figure 1: Van krevelen diagram (A) and Kendrick Mass Defects plot (B) representations of (−) FTICR-MS data corresponding to the complete data set (152 samples) after filtration; annotation of distinctive areas and lines of regions of predominant appearances of metabolite classes and formal chemical alterations: (A) methylation/demethylation (CH2); (B) hydrogenation/dehydrogenation (H2); (C) hydratation/condensation (H2O) and (D) oxidation/reduction (O). Colors correspond to elemental formulas composition: (CHO) blue; (CHOS) green; (CHON) red; and (CHONS) orange.
Mentions: Non-targeted FTICR-MS analysis generates a tremendous amount of data and requires pre-treatment prior to the application of statistical tools. Raw data were first aligned in order to discover occurring patterns, to identify outliers, to reduce the dimensionality of the data, and also to compress large datasets into smaller and more discernable ones (Lucio, 2009). A common approach for the identification of unknown molecules is the calculation of possible elemental formulas. Molecular formulae were calculated using an in-house software tool with the following chemical constraints: N rule; O/C ratio ≤ 1; H/C ratio ≤ 2n + 2; element counts: C ≤ 100, H ≤ 200, O ≤ 80, N ≤ 3, S ≤ 3, and P ≤ 1 (Gougeon et al., 2009; Schmitt-Kopplin et al., 2010; Roullier-Gall et al., 2014a,b). On the total of 21419 masses composing the matrix after filtration, 8455 unambiguous elemental formulas were found. Due to the expected high complexity of the metabolome, visualization strategies dealing with very complex data matrices have been adapted (Hertkorn et al., 2007). The Van Krevelen diagram displays the hydrogen/carbon (H/C) vs. oxygen/carbon (O/C) ratios of these elemental formulas and provide a commonly used qualitative description of the molecular complexity of wine data (Gougeon et al., 2011; Roullier-Gall et al., 2014a,b). This plot enables the localization of areas covering metabolite classes, which are specified by different elemental compositions, enabling a representation of a sample's composition (Figure 1A). The richness of the observed mass gives evidence of the compositional diversity of molecules as carbohydrates, polyphenols or amino acids and chemical alteration as hydrogenation / dehydrogenation (Figure 1A ligne B) for example. A second plot for visualization and interpretation of ultrahigh resolution mass spectrometry data is the CH2 Kendrick plot (Hertkorn et al., 2007), which is based on distinct mass defects calculated from each elemental composition (Figure 1B). Molecules of different elemental composition differ in their mass defect and it became possible distinguish homologous series of compounds from each other.

Bottom Line: An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification.UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2).In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

View Article: PubMed Central - PubMed

Affiliation: UMR PAM Université de Bourgogne/AgroSup Dijon, Institut Universitaire de la Vigne et du Vin Jules Guyot, Dijon, France ; Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München Neuherberg, Germany.

ABSTRACT
An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2). In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

No MeSH data available.


Related in: MedlinePlus