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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.


Example of MCR-ALS resolution (Multivariate curve resolution-alternating least squares) simultaneously applied to the column-wise augmented data matrix () corresponding to time window III including the control and the stressed yeast samples.  is the matrix of MCR-ALS resolved elution profiles.  is the matrix of MCR-ALS resolved MS pure spectra. Compound labels identification in this example are: h hypoxanthine, p palmitic acid, r arbitol/ribitol, d deoxyguanosine
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Fig3: Example of MCR-ALS resolution (Multivariate curve resolution-alternating least squares) simultaneously applied to the column-wise augmented data matrix () corresponding to time window III including the control and the stressed yeast samples. is the matrix of MCR-ALS resolved elution profiles. is the matrix of MCR-ALS resolved MS pure spectra. Compound labels identification in this example are: h hypoxanthine, p palmitic acid, r arbitol/ribitol, d deoxyguanosine

Mentions: Figure 3 is an example of MCR-ALS applied to the column-wise augmented data matrix () corresponding to time window III (elution times from 1.82 to 2.69). In this example, the two standard mixture samples were omitted from the figure because there was no standard compound eluting in this time window. As it can be seen in Fig. 3, the four coeluted components (h, p, r, d) were successfully separated by MCR-ALS analysis. Elution profiles () and pure mass spectra () are shown. The four contributions (h, p, r, d) were identified by their mass spectra as explained in Sects. 4 and 5.4 for their further metabolite identification). There were other components (not shown) which were sections of chromatographic peaks corresponding to windows II and IV. There were also some minor noise interferences without chromatographic peak shape and very imprecise spectra, which were finally not shown in the figure for clarity.Fig. 3


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

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

Example of MCR-ALS resolution (Multivariate curve resolution-alternating least squares) simultaneously applied to the column-wise augmented data matrix () corresponding to time window III including the control and the stressed yeast samples.  is the matrix of MCR-ALS resolved elution profiles.  is the matrix of MCR-ALS resolved MS pure spectra. Compound labels identification in this example are: h hypoxanthine, p palmitic acid, r arbitol/ribitol, d deoxyguanosine
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4289532&req=5

Fig3: Example of MCR-ALS resolution (Multivariate curve resolution-alternating least squares) simultaneously applied to the column-wise augmented data matrix () corresponding to time window III including the control and the stressed yeast samples. is the matrix of MCR-ALS resolved elution profiles. is the matrix of MCR-ALS resolved MS pure spectra. Compound labels identification in this example are: h hypoxanthine, p palmitic acid, r arbitol/ribitol, d deoxyguanosine
Mentions: Figure 3 is an example of MCR-ALS applied to the column-wise augmented data matrix () corresponding to time window III (elution times from 1.82 to 2.69). In this example, the two standard mixture samples were omitted from the figure because there was no standard compound eluting in this time window. As it can be seen in Fig. 3, the four coeluted components (h, p, r, d) were successfully separated by MCR-ALS analysis. Elution profiles () and pure mass spectra () are shown. The four contributions (h, p, r, d) were identified by their mass spectra as explained in Sects. 4 and 5.4 for their further metabolite identification). There were other components (not shown) which were sections of chromatographic peaks corresponding to windows II and IV. There were also some minor noise interferences without chromatographic peak shape and very imprecise spectra, which were finally not shown in the figure for clarity.Fig. 3

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.