[Show abstract][Hide abstract] ABSTRACT: Because of its highly reproducible and quantitative nature and minimal requirements for sample preparation or separation, (1)H nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling small-molecule metabolites in biofluids. However (1)H NMR spectra contain many overlapped peaks. In particular, blood serum/plasma and diabetic urine samples contain high concentrations of glucose, which produce strong peaks between 3.2 ppm and 4.0 ppm. Signals from most metabolites in this region are overwhelmed by the glucose background signals and become invisible. We propose a simple "Add to Subtract" background subtraction method and show that it can reduce the glucose signals by 98% to allow retrieval of the hidden information. This procedure includes adding a small drop of concentrated glucose solution to the sample in the NMR tube, mixing, waiting for an equilibration time, and acquisition of a second spectrum. The glucose-free spectra are then generated by spectral subtraction using Bruker Topspin software. Subsequent multivariate statistical analysis can then be used to identify biomarker candidate signals for distinguishing different types of biological samples. The principle of this approach is generally applicable for all quantitative spectral data and should find utility in a variety of NMR-based mixture analyses as well as in metabolite profiling.
[Show abstract][Hide abstract] ABSTRACT: This study aims to investigate the metabolic difference between male and female healthy adults using a combination of GC–MS
and NMR metabolomics techniques. While metabolomics has shown wide applications in characterizing the status and progression
of many diseases, physiological factors such as gender often contribute high levels of variability that can hinder the detection
of biomarkers of interest, such as in disease detection. We carried out a detailed exploration of gender related metabolic
profiling of human urine using a Headspace-SPME/GC–MS approach and detected over two hundred peaks. Fifty-nine metabolites
were identified using the NIST library. 1H NMR spectroscopy was also utilized, and resulted in the identification of eighteen metabolites. We find that both GC–MS
and NMR are able to capture human gender metabolic differences, and their combination allows a significantly better understanding
of this difference. Subtle differences between genders are found to be related to the metabolism of fats, amino acids, and
TCA cycle intermediates.
[Show abstract][Hide abstract] ABSTRACT: Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.
[Show abstract][Hide abstract] ABSTRACT: NMR spectroscopy is a powerful analytical tool for both qualitative and quantitative analysis. However, accurate quantitative analysis in complex fluids such as human blood plasma is challenging, and analysis using one-dimensional NMR is limited by signal overlap. It is impractical to use heteronuclear experiments involving natural abundance (13)C on a routine basis due to low sensitivity, despite their improved resolution. Focusing on circumventing such bottlenecks, this study demonstrates the utility of a combination of isotope enhanced NMR experiments to analyze metabolites in human blood plasma. (1)H-(15)N HSQC and (1)H-(13)C HSQC experiments on the isotope tagged samples combined with the conventional (1)H one-dimensional and (1)H-(1)H TOCSY experiments provide quantitative information on a large number of metabolites in plasma. The methods were first tested on a mixture of 28 synthetic analogues of metabolites commonly present in human blood; 27 metabolites in a standard NIST (National Institute of Standards and Technology) human blood plasma were then identified and quantified with an average coefficient of variation of 2.4% for 17 metabolites and 5.6% when all the metabolites were considered. Carboxylic acids and amines represent a majority of the metabolites in body fluids, and their analysis by isotope tagging enables a significant enhancement of the metabolic pool for biomarker discovery applications. Improved sensitivity and resolution of NMR experiments imparted by (15)N and (13)C isotope tagging are attractive for both the enhancement of the detectable metabolic pool and accurate analysis of plasma metabolites. The approach can be easily extended to many additional metabolites in almost any biological mixture.
[Show abstract][Hide abstract] 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.
The Analyst 04/2010; 135(7):1490-8. · 4.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: An increased interest in metabolite profiling is driving the need for improved analytical techniques with greater performance for a variety of important applications. Despite their limited sensitivity, nuclear magnetic resonance (NMR) methods are attractive because of their simplicity, reproducibility, quantitative nature, and wide applicability. The use of chemoselective isotopic tags has the potential to advance the application of NMR for analyzing metabolites in complex biofluids by allowing detection of metabolites down to the low micromoalr level with high resolution and specificity. Here, we report a new (13)C-tagging method using (13)C-formic acid that delivers high sensitivity, good quantitation, and excellent resolution for (1)H-(13)C 2D NMR profiling of amino metabolites. High reproducibility (coefficient of variation (CV) = 2%) was observed for metabolites in urine with concentrations down to 10 microM. As amino compounds comprise an important class of metabolites and small molecules of biological roles, this new method therefore should be amenable to a variety of applications.
[Show abstract][Hide abstract] ABSTRACT: Metabolic profiling has received increasing recognition as an indispensable complement to genomics and proteomics for probing biological systems and for clinical applications. (1)H nuclear magnetic resonance (NMR) is widely used in the field but is challenged by spectral complexity and overlap. Improved and simple methods that quantitatively profile a large number of metabolites are sought to make further progress. Here, we demonstrate a simple isotope tagging strategy, in which metabolites with carboxyl groups are chemically tagged with (15)N-ethanolamine and detected using a 2D heteronuclear correlation NMR experiment. This method is capable of detecting over 100 metabolites at concentrations as low as a few micromolar in biological samples, both quantitatively and reproducibly. Carboxyl-containing compounds are found in almost all metabolic pathways, and thus this new approach should find a variety of applications.
[Show abstract][Hide abstract] ABSTRACT: The 1H NMR spectrum of urine exhibits a large number of detectable and quantifiable metabolites and hence urine metabolite profiling is potentially useful for the study of systems biology and the discovery of biomarkers for drug development or clinical applications. While a number of metabolites (50-100) are readily detectable in urine by NMR, a much larger number is potentially available if lower concentration species can be detected unambiguously. Lower concentration metabolites are thought to be more specific to certain disease states and thus it is important to detect these metabolites with certainty. We report the identification of 4-deoxythreonic acid, a relatively low concentration endogenous metabolite that has not been previously identified in the 1H NMR spectrum of human urine. The use of HPLC and NMR spectroscopy facilitated the unequivocal and non-invasive identification of the molecule in urine which is complicated by extensive peak overlap and multiple, similar resonances from other metabolites such as 3-hydroxybutanoic acid. High-resolution detection and good sensitivity were achieved by the combination of multiple chromatographic fraction collection, sample pre-concentration, and the use of a cryogenically cooled NMR probe.
Journal of pharmaceutical and biomedical analysis 07/2009; 50(5):878-85. · 2.45 Impact Factor