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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.


Schematic representation of the workflow following untargeted (LC–MS) data generation. The workflow involved experimental analysis, data pre-processing and data analysis in order to identify possible biomarkers (yeast metabolites)
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Fig1: Schematic representation of the workflow following untargeted (LC–MS) data generation. The workflow involved experimental analysis, data pre-processing and data analysis in order to identify possible biomarkers (yeast metabolites)

Mentions: No further arrangement was required for the single TIC data matrix. In contrast, every full scan individual preprocessed MS chromatogram data matrix was divided in ten separate submatrices corresponding to different time windows. This MS chromatogram window subdivision was done manually according to peak shape and peak density, and more specifically, not to miss those chromatographic peaks that could change with temperature. The first time window was discarded for MCR-ALS analysis since it only contained signal background and noise, i.e. all chromatograms from blanks, standards and yeast samples had the same shape profile at that initial time window. Therefore a final number of nine windows (j = I, II,…, IX) were selected from every full scan MS chromatogram data matrix of the ten analyzed samples (4 control yeast samples, k = 1,2,3,4; 4 temperature stressed yeast samples, k = 5,6,7,8 and two standard mixture samples, k = 9,10) (see Fig. 1). Therefore, for each of the ten analyzed samples, k = 1,…, 10, nine time windows were obtained, j = 1, …, 9 giving the individual data submatrices . As can be seen in Fig. 1, individual data submatrices () corresponding to the same chromatographic window for the different analyzed samples were arranged in nine column-wise augmented data matrices. The dimensions of these nine column-wise augmented data matrices depended on the dimensions of the selected time windows (selected retention times). Thus, from window I to window IX the augmented matrices dimensions were: 1010 × 546, 1110 × 546, 2210 × 546, 2910 × 546, 2080 × 546, 2760 × 546, 3410 × 546, 3210 × 546 and 4010 × 546. In every case the first dimension refers to the sum of the retention times of the ten included samples (4 control, 4 stressed samples and 2 standards), and the second dimension is equal to the number of mz values included in the analysis, which were in all cases 546 mz values (from 55 to 600 Da).Fig. 1


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

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

Schematic representation of the workflow following untargeted (LC–MS) data generation. The workflow involved experimental analysis, data pre-processing and data analysis in order to identify possible biomarkers (yeast metabolites)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Schematic representation of the workflow following untargeted (LC–MS) data generation. The workflow involved experimental analysis, data pre-processing and data analysis in order to identify possible biomarkers (yeast metabolites)
Mentions: No further arrangement was required for the single TIC data matrix. In contrast, every full scan individual preprocessed MS chromatogram data matrix was divided in ten separate submatrices corresponding to different time windows. This MS chromatogram window subdivision was done manually according to peak shape and peak density, and more specifically, not to miss those chromatographic peaks that could change with temperature. The first time window was discarded for MCR-ALS analysis since it only contained signal background and noise, i.e. all chromatograms from blanks, standards and yeast samples had the same shape profile at that initial time window. Therefore a final number of nine windows (j = I, II,…, IX) were selected from every full scan MS chromatogram data matrix of the ten analyzed samples (4 control yeast samples, k = 1,2,3,4; 4 temperature stressed yeast samples, k = 5,6,7,8 and two standard mixture samples, k = 9,10) (see Fig. 1). Therefore, for each of the ten analyzed samples, k = 1,…, 10, nine time windows were obtained, j = 1, …, 9 giving the individual data submatrices . As can be seen in Fig. 1, individual data submatrices () corresponding to the same chromatographic window for the different analyzed samples were arranged in nine column-wise augmented data matrices. The dimensions of these nine column-wise augmented data matrices depended on the dimensions of the selected time windows (selected retention times). Thus, from window I to window IX the augmented matrices dimensions were: 1010 × 546, 1110 × 546, 2210 × 546, 2910 × 546, 2080 × 546, 2760 × 546, 3410 × 546, 3210 × 546 and 4010 × 546. In every case the first dimension refers to the sum of the retention times of the ten included samples (4 control, 4 stressed samples and 2 standards), and the second dimension is equal to the number of mz values included in the analysis, which were in all cases 546 mz values (from 55 to 600 Da).Fig. 1

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.