A Strategy for Identifying Differences in Large Series of Metabolomic Samples Analyzed by GC/MS

Abstract

In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.

An optimized procedure for metabonomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry

“…CDF files were extracted using custom scripts (revised Matlab toolbox HDA, developed by Jonsson et al [26,27]) in the MATLAB 7.0 (The MathWorks, Inc., USA) for data pretreatment procedures such as baseline correction, de-noising, smoothing and alignment, time-window splitting, and peak feature extraction (based on multivariate curve resolution algorithm) [27]. The resulting output data organized as arbitrary peak index (retention time-m/z pairs), sample names (observations), and peak intensity information (variables) were introduced into Simca-P 11.5 software (Umetrics, Umeå, Sweden) for principal components analysis (PCA), partial least squares (PLS) regression [28] and orthogonal partial least squares project to latent structures-discriminant analysis (OPLS-DA) [29].…”

Suppression of metabolic defects of yeast isocitrate dehydrogenase and aconitase mutants by loss of citrate synthase

“…Other extraction procedures, e.g. with chloroform/methanol/water (1:3:1) [21,22], were also tested. As previously reported [20], recoveries of standard metabolites of interest were not substantially different from those obtained by extraction in water.…”

Tissue metabolomic fingerprinting reveals metabolic disorders associated with human gastric cancer morbidity

“…recent technological advances in NMr spectroscopy and mass spectrometry have further improved the sensitivity and spectral resolution of cancer metabolomic study (21). Among the various techniques conventionally used for cancer metabolic profiling, gas chromatography/mass spectrometry (GC/Ms) has been proven to be a robust metabolomic tool and is widely applied in metabolite identification and quantification based on its high sensitivity, peak resolution and reproducibility (22)(23)(24). since cancers are known to possess highly unique metabolic phenotypes, identification of specific biomarkers using metabolomics may be useful for early cancer detection and prognosis.…”