A Statistical Framework for Biomarker Analysis and HR-MAS 2D Metabolite Identification

Abstract

Metabolomics is an exponentially growing field of “omics” research concerned with the high throughout comparison, identification and quantification of large numbers of metabolites in biological system. This emergent science of metabolomics brings increasing promise to identify biomarker diseases that integrate biochemical changes in disease and predict human reaction to treatment. In this context, the 2D High Resolution Magic Angle Spinning (HR-MAS) Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as an ideal platform for studying metabolites of biopsies. In this study, we particularly focus on the 2D Heteronuclear Single Quantum Coherence (HSQC) NMR spectrum analysis. The metabolomic analysis requires comparison of metabolite profiles obtained from multiple replicates of samples exposed to different experimental conditions. What adds difficulty to automating this analysis process is that each peak of a given metabolite (a set of peaks with specified locations) can be shifted slightly from one sample to the next. In this study, we propose a new framework to detect and align simultaneously peaks representing different metabolites within a biopsy for metabonomic analysis. The method was validated on synthetic and real HSQC spectra.