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Combination of CE‐MS and advanced chemometric methods for high‐throughput metabolic profiling
Author(s) -
OrtizVillanueva Elena,
Jaumot Joaquim,
Benavente Fernando,
Piña Benjamín,
SanzNebot Victoria,
Tauler Romà
Publication year - 2015
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.201500027
Subject(s) - metabolomics , partial least squares regression , mass spectrometry , chemistry , capillary electrophoresis , yeast , multivariate statistics , chromatography , chemometrics , principal component analysis , computational biology , analytical chemistry (journal) , biochemistry , biology , artificial intelligence , computer science , mathematics , statistics
In this work, an untargeted approach based on capillary electrophoresis‐mass spectrometry (CE‐MS) in combination with multivariate data analyses is proposed as a high‐throughput general methodology for metabolomic studies. First, total ion electropherograms (TIEs) were considered for exploratory and classification purposes by means of principal component analysis (PCA) and partial least squares discriminant analysis (PLS‐DA). Then, multivariate curve resolution alternating least squares (MCR‐ALS) was applied to the multiple full scan MS data sets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. The proposed approach allowed solving additional electrophoretic issues, such as background noise contributions, low signal‐to‐noise ratios, asymmetric peaks and migration time shifts. The usefulness of the proposed methodology is demonstrated in a comparative study of the metabolic profiles from baker's yeast ( Saccharomyces cerevisiae ) samples cultured at two temperatures, 30°C and 37°C. A total number of 80 metabolites were relevant to yeast samples differentiation at the two temperatures and almost 50 of them were tentatively identified based on their accurate experimental molecular mass. The results show that changes in amino acid, nucleotide and lipid metabolic pathways participated in the acclimatization of yeast cells to grow at 37°C.