Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

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Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra


Abstract Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or â??quantitativeâ?? metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

• Content Type Journal Article
• Pages 1-17
• DOI 10.1007/s10858-011-9480-x
• Authors
  • Pascal Mercier, Chenomx Inc, Edmonton, AB T5K 2J1, Canada
  • Michael J. Lewis, Chenomx Inc, Edmonton, AB T5K 2J1, Canada
  • David Chang, Chenomx Inc, Edmonton, AB T5K 2J1, Canada
  • David Baker, Pfizer Inc, Groton, CT USA
  • David S. Wishart, Department of Computing Science and Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada

• • Journal Journal of Biomolecular NMR
  • Online ISSN 1573-5001
  • Print ISSN 0925-2738

Source: Journal of Biomolecular NMR