alexa Development and validation of a gas chromatography mass spectrometry method for the metabolic profiling of human colon tissue.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Proteomics & Bioinformatics

Author(s): Mal M, Koh PK, Cheah PY, Chan EC

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Abstract In this study, a gas chromatography/mass spectrometry (GC/MS) method was developed and validated for the metabolic profiling of human colon tissue. Each colon tissue sample (20 mg) was ultra-sonicated with 1 mL of a mixture of chloroform/methanol/water in the ratio of 20:50:20 (v/v/v), followed by centrifugation, collection of supernatant, drying, removal of moisture using anhydrous toluene and finally derivatization using N-methyl-N-trifluoroacetamide (MSTFA) with 1\% trimethylchlorosilane (TMCS). A volume of 1 microL of the derivatized mixture was injected into the GC/MS system. A total of 53 endogenous metabolites were separated and identified in the GC/MS chromatogram, all of which were selected to evaluate the sample stability and precision of the method. Of the identified endogenous metabolites 19 belonging to diverse chemical classes and covering a wide range of the GC retention times (Rt) were selected to investigate the quantitative linearity of the method. The developed GC/MS method demonstrated good reproducibility with intra- and inter-day precision within relative standard deviation (RSD) of +/-15\%. The metabolic profiles of the intact tissue were determined to be stable (100 +/- 15\%) for up to 90 days at -80 degrees C. Satisfactory results were also obtained in the case of other stability-indicating studies such as freeze/thaw cycle stability, bench-top stability and autosampler stability. The developed method showed a good linear response for each of the 19 analytes tested (r(2) > 0.99). Our GC/MS metabolic profiling method was successfully applied to discriminate biopsied colorectal cancer (CRC) tissue from their matched normal tissue obtained from six CRC patients using orthogonal partial least-squares discriminant analysis [two latent variables, R(2)Y = 0.977 and Q(2) (cumulative) = 0.877]. Copyright 2009 John Wiley & Sons, Ltd. This article was published in Rapid Commun Mass Spectrom and referenced in Journal of Proteomics & Bioinformatics

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