Advances in NMR-based biofluid analysis and metabolite profiling

Abstract

Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.

A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer

“…Versatile analytical techniques mainly based on Nuclear Magnetic Resonance spectroscopy (NMR) and Mass Spectrometry (MS) allow the analysis of various samples from bio-fluids such as serum or urine, and exhaled breath condensate, to intact cells or tissues [12][13][14][15]. Meanwhile, metabolomic approaches have already found promising applications in different fields from toxicology [16], functional genomics [11,17] , to oncology [18,19].…”

NMR metabolomic of frontal cortex extracts: First study comparing two neurodegenerative diseases, Alzheimer disease and amyotrophic lateral sclerosis

“…However, only few NMR studies of this type are reported [9,10]. The main reason is probably the large number of patients required for this kind of study considering the high individual and inter-individual variability of metabolic profiles found in biological fluids.…”

Recent Advances and Applications of Metabolomics to Investigate Neurodegenerative Diseases

“…Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are the two analytical platforms most predominantly used in metabolomics. NMR permits the analysis of complex mixtures of metabolites with little or no sample preparation in a rapid and non-destructive way (Zhang, Nagana-Gowda, Ye, & Raftery, 2010) and has been largely applied in metabolomics (Zhang & Powers, 2012). Although NMR spectra are very informative, low sensitivity and high data complexity limit quantitative profiling to less than100 compounds in most biological samples (Bain et al, 2009).…”