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Sample classification of GC-ToF-MS metabolomics data without the requirement for chromatographic deconvolution

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Abstract

Metabolic footprinting has been applied as a non-invasive approach to study the behaviour and responses of cultured cells to a range of genetic and environmental perturbations. Gas chromatography interfaced with time-of-flight mass spectrometry (GC-ToF-MS) has become a powerful tool for the analysis of metabolome-derived samples. Generally, two data analysis strategies are used to interrogate and understand the biological patterns within the multi-dimensional data. The first strategy, a commoner one, uses multivariate analysis after chromatographic and mass spectral deconvolution, and the second strategy directly employs multivariate analysis of non-deconvoluted data. Here, two strategies have been assessed for the separation and classification of metabolic footprints (exometabolomes) of two strains of Candida albicans grown on three different carbon sources (glycerol, glucose and galactose). We describe a semi-automated approach that simultaneously processes all samples using the chromatographic dimension data with principal components analysis (PCA), which can include data pre-processing before PCA analysis. The preprocessed and non-deconvoluted total ion chromatogram (TIC) data showed good separation of classes defined by growth on different carbon sources and when comparing the two strains grown on the same carbon source separation was achieved for strains grown on glucose and glycerol after preprocessing. The discrimination observed is greater for preprocessed and non-deconvoluted TIC data than for that of preprocessed and non-deconvoluted single ion chromatogram data. The results from the proposed approach with those produced by MZmine were compared. The results from MZmine data depicted separations in PCA space according to carbon source, but no separation was seen when studying strains grown on the same carbon source. Our research showed that the non-deconvoluted strategy is suitable for fast comparison of large sets of GC-MS data although it will not directly provide biological information. The non-deconvoluted strategy can avoid problems of analyzing complex samples using deconvolution software.

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Acknowledgments

The MZmine program is downloaded free; details may be obtained from the website http://mzmine.sourceforge.net/. The experimental work was carried out in the Bioanalytical Sciences Group, Department of Chemistry, and Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester. We are grateful to Warwick B. Dunn for helpful comments on the manuscript and Douglas B. Kell for his excellent scientific support. We thank Marie C. Brown and David Broadhurst for their helpful discussion. We also thank Siobhan Mulhern and Geraldine Butler of University College, Dublin for supplying the samples. This work was supported by grant China Partnering Award from BBSRC (grant PA 1479). H.L. also thanks National Natural Science Foundation of China for support of the projects (No. 20975115 and No. 20745005), China Hunan Provincial science and technology department for support of the project (No. 2009GK3095), Central South University for special support of the basic scientific research project (No. 2010QZZD007), China Postdoctoral Science Foundation for support of the project (No. 20100471230) and the Postdoctoral Science Foundation of Central South University.

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  1. Cancer Research Institution, Central South University, 410078, Changsha, People’s Republic of China

    Hongmei Lu

  2. College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, People’s Republic of China

    Hongmei Lu, Dandan Gan, Zhimin Zhang & Yizeng Liang

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Lu, H., Gan, D., Zhang, Z. et al. Sample classification of GC-ToF-MS metabolomics data without the requirement for chromatographic deconvolution. Metabolomics 7, 191–205 (2011). https://doi.org/10.1007/s11306-010-0247-2

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