Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for ea...

We report on the development of a monitoring test for recurrent breast cancer, using metabolite-profiling methods. Using a combination of nuclear magnetic resonance (NMR) and two-dimensional gas chromatography–mass spectrometry (GC×GC-MS) methods, we analyzed the metabolite profiles of 257 retrospective serial serum samples from 56 previously diagnosed and surgically treated breast cancer patients. One hundred sixteen of the serial samples were from 20 patients with recurrent breast cancer, and 141 samples were from 36 patients with no clinical evidence of the disease during ∼6 years of sample collection. NMR and GC×GC-MS data were analyzed by multivariate statistical methods to compare identified metabolite signals between the recurrence samples and those with no evidence of disease. Eleven metabolite markers (seven from NMR and four from GC×GC-MS) were shortlisted from an analysis of all patient samples by using logistic regression and 5-fold cross-validation. A partial least squares discriminant analysis model built using these markers with leave-one-out cross-validation provided a sensitivity of 86% and a specificity of 84% (area under the receiver operating characteristic curve = 0.88). Strikingly, 55% of the patients could be correctly predicted to have recurrence 13 months (on average) before the recurrence was clinically diagnosed, representing a large improvement over the current breast cancer–monitoring assay CA 27.29. To the best of our knowledge, this is the first study to develop and prevalidate a prediction model for early detection of recurrent breast cancer based on metabolic profiles. In particular, the combination of two advanced analytical methods, NMR and MS, provides a powerful approach for the early detection of recurrent breast cancer. Cancer Res; 70(21); 8309–18. ©2010 AACR.

Quantitative NMR-based metabolite profiling is challenged by the deleterious effects of abundant proteins in the intact blood plasma/serum, which underscores the need for alternative approaches. Protein removal by ultrafiltration using low molecular weight cutoff filters thus represents an important step. However, protein precipitation, an alternative and simple approach for protein removal, lacks detailed quantitative assessment for use in NMR based metabolomics. In this study, we have comprehensively evaluated the performance of protein precipitation using methanol, acetonitrile, perchloric acid, and trichloroacetic acid and ultrafiltration approaches using 1D and 2D NMR, based on the identification and absolute quantitation of 44 human blood metabolites, including a few identified for the first time in the NMR spectra of human serum. We also investigated the use of a “smart isotope tag,” ¹⁵N-cholamine for further resolution enhancement, which resulted in the detection of a number of additional metabolites. ¹H NMR of both protein precipitated and ultrafiltered serum detected all 44 metabolites with comparable reproducibility (average CV, 3.7% for precipitation; 3.6% for filtration). However, nearly half of the quantified metabolites in ultrafiltered serum exhibited 10–74% lower concentrations; specifically, tryptophan, benzoate, and 2-oxoisocaproate showed much lower concentrations compared to protein precipitated serum. These results indicate that protein precipitation using methanol offers a reliable approach for routine NMR-based metabolomics of human blood serum/plasma and should be considered as an alternative to ultrafiltration. Importantly, protein precipitation, which is commonly used by mass spectrometry (MS), promises avenues for direct comparison and correlation of metabolite data obtained from the two analytical platforms to exploit their combined strength in the metabolomics of blood.