Multivariate Modeling Strategy for Intercompartmental Analysis of Tissue and Plasma 1H NMR Spectrotypes

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• 作者：Ivan Montoliu ; Franois-Pierre J. Martin ; Sebastiano Collino ; Serge Rezzi ; Sunil Kochhar
• 刊名：Journal of Proteome Research
• 出版年：2009
• 出版时间：May 1, 2009
• 年：2009
• 卷：8
• 期：5
• 页码：2397-2406
• 全文大小：493K
• 年卷期：v.8,no.5(May 1, 2009)
• ISSN：1535-3907

文摘

Multicompartmental metabolic profiling combined with multivariate data analysis offers a unique opportunity to explore the multidimensional metabolic relationships between various biological matrices. Here, we applied unsupervised chemometric methods for integrating ¹H NMR metabolic profiles from mouse plasma, liver, pancreas, adrenal gland and kidney cortex matrices in order to infer intercompartments functional links. Principal Component Analysis (PCA) revealed metabolic differences between matrices but contained limited information on intercompartment metabolic relationships. Multiway PCA enabled the assessment of interindividual metabolic variability across multiple compartments in a single model and, therefore, metabolic correlations between different organs and circulating biofluids. However, this approach does not provide information on the relative contribution of one compartment to another. Integration of metabolic profiles using Multivariate Curve Resolution (MCR) and Parallel Factor Analysis (PARAFAC) methods provided an overview of functional relationships across matrices and enabled the characterization of compartment-specific metabolite signatures, the spectrotypes. In particular, the spectrotypes describe biochemical profiles specific or common to different biological compartments. Consequently, MCR-ALS and PARAFAC appeared to be better adapted for stepwise variable and compartment selection for further correlation analysis. Such a combination of chemometric techniques could provide new research avenues to assess the efficacy of drug or nutritional interventions on targeted organs.