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Chemometric evaluation of Saccharomyces cerevisiae metabolic profiles using LC-MS.

Farrés M, Piña B, Tauler R - Metabolomics (2014)

Bottom Line: The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks.Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA-Variable Importance in Projection scores.A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies.

View Article: PubMed Central - PubMed

Affiliation: Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain.

ABSTRACT

A new liquid chromatography mass spectrometry (LC-MS) metabolomics strategy coupled to chemometric evaluation, including variable and biomarker selection, has been assessed as a tool to discriminate between control and stressed Saccharomyces cerevisiae yeast samples. Metabolic changes occurring during yeast culture at different temperatures (30 and 42 °C) were analysed and the complex data generated in profiling experiments were evaluated by different chemometric multivariate approaches. Multivariate curve resolution alternating least squares (MCR-ALS) was applied to full spectral scan LC-MS preprocessed data multisets arranged in augmented column-wise data matrices. The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks. The investigation of possible relationships between MCR-ALS resolved chromatographic peak areas and culture temperature was then investigated by partial least squares discriminant analysis (PLS-DA). Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA-Variable Importance in Projection scores. A metabolite identification workflow was developed utilizing MCR-ALS resolved pure MS spectra and high-resolution accurate mass measurements to confirm assigned structures based on entries in metabolite databases. A total of 65 metabolites were identified. A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies.

No MeSH data available.


a PCA scores plot for the eight yeast samples (MS TIC chromatograms). Convex hulls are drown around each yeast culture temperature group with the same color as the corresponding symbols. b PLS-DA scores plot for the eight yeast samples at the two temperatures using their MS TIC chromatograms. c Variables importance in projection (VIP) plot resulting from PLS-DA analysis of full scan MS TIC yeast chromatograms. Horizontal red line shows the threshold value used to select the variables with the most important VIP scores. d PLS-DA scores of autoscaled chromatographic peak areas obtained by MCR-ALS analysis of full scan MS chromatographic data of the analysed yeast samples. In a, b and dblue solid circles are control samples cultured at 30 °C and brown triangles are yeast samples cultured at 42 °C (Color figure online)
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Fig2: a PCA scores plot for the eight yeast samples (MS TIC chromatograms). Convex hulls are drown around each yeast culture temperature group with the same color as the corresponding symbols. b PLS-DA scores plot for the eight yeast samples at the two temperatures using their MS TIC chromatograms. c Variables importance in projection (VIP) plot resulting from PLS-DA analysis of full scan MS TIC yeast chromatograms. Horizontal red line shows the threshold value used to select the variables with the most important VIP scores. d PLS-DA scores of autoscaled chromatographic peak areas obtained by MCR-ALS analysis of full scan MS chromatographic data of the analysed yeast samples. In a, b and dblue solid circles are control samples cultured at 30 °C and brown triangles are yeast samples cultured at 42 °C (Color figure online)

Mentions: When principal component analysis (PCA) was applied to the mean-centered MS TIC data matrix (with 8 samples and 2020 measured chromatographic retention times), three principal components already explained 88.83 % of data variance. In Fig. 2a, scores of the first two components are given. PC1 explains 47.76 % of the data variance and separates the samples in relation to the yeast culture temperatures. Samples grouped in the negative side of PC1 axis were grown at 42 ºC and samples grouped on the positive side of PC1 axis are the control samples grown at 30 ºC. Variances explained by PC2 and PC3 are related to other unknown variability sources not dependent of temperature.Fig. 2


Chemometric evaluation of Saccharomyces cerevisiae metabolic profiles using LC-MS.

Farrés M, Piña B, Tauler R - Metabolomics (2014)

a PCA scores plot for the eight yeast samples (MS TIC chromatograms). Convex hulls are drown around each yeast culture temperature group with the same color as the corresponding symbols. b PLS-DA scores plot for the eight yeast samples at the two temperatures using their MS TIC chromatograms. c Variables importance in projection (VIP) plot resulting from PLS-DA analysis of full scan MS TIC yeast chromatograms. Horizontal red line shows the threshold value used to select the variables with the most important VIP scores. d PLS-DA scores of autoscaled chromatographic peak areas obtained by MCR-ALS analysis of full scan MS chromatographic data of the analysed yeast samples. In a, b and dblue solid circles are control samples cultured at 30 °C and brown triangles are yeast samples cultured at 42 °C (Color figure online)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig2: a PCA scores plot for the eight yeast samples (MS TIC chromatograms). Convex hulls are drown around each yeast culture temperature group with the same color as the corresponding symbols. b PLS-DA scores plot for the eight yeast samples at the two temperatures using their MS TIC chromatograms. c Variables importance in projection (VIP) plot resulting from PLS-DA analysis of full scan MS TIC yeast chromatograms. Horizontal red line shows the threshold value used to select the variables with the most important VIP scores. d PLS-DA scores of autoscaled chromatographic peak areas obtained by MCR-ALS analysis of full scan MS chromatographic data of the analysed yeast samples. In a, b and dblue solid circles are control samples cultured at 30 °C and brown triangles are yeast samples cultured at 42 °C (Color figure online)
Mentions: When principal component analysis (PCA) was applied to the mean-centered MS TIC data matrix (with 8 samples and 2020 measured chromatographic retention times), three principal components already explained 88.83 % of data variance. In Fig. 2a, scores of the first two components are given. PC1 explains 47.76 % of the data variance and separates the samples in relation to the yeast culture temperatures. Samples grouped in the negative side of PC1 axis were grown at 42 ºC and samples grouped on the positive side of PC1 axis are the control samples grown at 30 ºC. Variances explained by PC2 and PC3 are related to other unknown variability sources not dependent of temperature.Fig. 2

Bottom Line: The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks.Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA-Variable Importance in Projection scores.A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies.

View Article: PubMed Central - PubMed

Affiliation: Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain.

ABSTRACT

A new liquid chromatography mass spectrometry (LC-MS) metabolomics strategy coupled to chemometric evaluation, including variable and biomarker selection, has been assessed as a tool to discriminate between control and stressed Saccharomyces cerevisiae yeast samples. Metabolic changes occurring during yeast culture at different temperatures (30 and 42 °C) were analysed and the complex data generated in profiling experiments were evaluated by different chemometric multivariate approaches. Multivariate curve resolution alternating least squares (MCR-ALS) was applied to full spectral scan LC-MS preprocessed data multisets arranged in augmented column-wise data matrices. The results showed that sectioning the MS-chromatograms in different windows and analysing them by MCR-ALS enabled the proper resolution of very complex coeluted chromatographic peaks. The investigation of possible relationships between MCR-ALS resolved chromatographic peak areas and culture temperature was then investigated by partial least squares discriminant analysis (PLS-DA). Selection of most relevant resolved chromatographic peaks associated to yeast culture temperature changes was achieved according to PLS-DA-Variable Importance in Projection scores. A metabolite identification workflow was developed utilizing MCR-ALS resolved pure MS spectra and high-resolution accurate mass measurements to confirm assigned structures based on entries in metabolite databases. A total of 65 metabolites were identified. A preliminary interpretation of these results indicates that the strategy described in this study can be proposed as a general tool to facilitate biomarker identification and modelling in similar untargeted metabolomic studies.

No MeSH data available.