Premium Combination of CE‐MS and advanced chemometric methods for high‐throughput metabolic profilingPremium
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.
Subject(s)analytical chemistry (journal) , artificial intelligence , biochemistry , biology , capillary electrophoresis , chemistry , chemometrics , chromatography , computational biology , computer science , mass spectrometry , mathematics , metabolomics , multivariate statistics , partial least squares regression , principal component analysis , statistics , yeast
SCImago Journal Rank0.666
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