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Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN

Nature Protocols volume 9, pages 1416–1427 (2014)Cite this article

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Abstract

Data processing for 1D NMR spectra is a key bottleneck for metabolomic and other complex-mixture studies, particularly where quantitative data on individual metabolites are required. We present a protocol for automated metabolite deconvolution and quantification from complex NMR spectra by using the Bayesian automated metabolite analyzer for NMR (BATMAN) R package. BATMAN models resonances on the basis of a user-controllable set of templates, each of which specifies the chemical shifts, J-couplings and relative peak intensities for a single metabolite. Peaks are allowed to shift position slightly between spectra, and peak widths are allowed to vary by user-specified amounts. NMR signals not captured by the templates are modeled non-parametrically by using wavelets. The protocol covers setting up user template libraries, optimizing algorithmic input parameters, improving prior information on peak positions, quality control and evaluation of outputs. The outputs include relative concentration estimates for named metabolites together with associated Bayesian uncertainty estimates, as well as the fit of the remainder of the spectrum using wavelets. Graphical diagnostics allow the user to examine the quality of the fit for multiple spectra simultaneously. This approach offers a workflow to analyze large numbers of spectra and is expected to be useful in a wide range of metabolomics studies.

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Figure 1: Deconvolution and peak quantification in metabolomic NMR spectra is complicated by peak overlap and shift.
Figure 2: Workflow of a single BATMAN run.
Figure 3: Typical fit results for two spectra of bacterial supernatants.
Figure 4: Chemical-shift sorting and spline fitting.
Figure 5: Fitting complex multiplets with empirical and raster templates.
Figure 6: Diagnostic scatter plots for threonine in the bacterial supernatant data.
Figure 7: Diagnostic mirrored stack plots.
Figure 8: Chemical shift distribution plots.

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Acknowledgements

T.M.D.E., M.D.I., W.A. and J.H. acknowledge support from the Biotechnology and Biological Sciences Research Council (BBSRC), UK, grant BB/E20372/1. J.G.B., T.M.D.E., J.H. and M.L. acknowledge support from the Natural Environment Research Council (NERC), UK, grant NE/H009973/1. T.M.D.E. and J.H. acknowledge support from the European Commission COSMOS project, contract EC312941. We thank the eMICE consortium for use of the synthetic mixture spectra.

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Author notes

  1. Manuel Liebeke

    Present address: Present address: Max Planck Institute for Marine Microbiology, Bremen, Germany.,

Authors and Affiliations

  1. Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, London, UK

    Jie Hao, Manuel Liebeke, Jacob G Bundy & Timothy M D Ebbels

  2. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada

    William Astle

  3. Department of Statistical Science, University College London, London, UK

    Maria De Iorio

Authors
  1. Jie Hao
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  2. Manuel Liebeke
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  3. William Astle
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  4. Maria De Iorio
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  6. Timothy M D Ebbels
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Contributions

T.M.D.E. and M.D.I. conceived the project and supervised the development of the Bayesian model. W.A. developed the Bayesian model and J.H. implemented the corresponding R package. J.G.B. and M.L. provided theoretical and practical input on NMR spectroscopy, including software testing and ideas for new features. T.M.D.E. and J.H. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Timothy M D Ebbels.

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The authors declare no competing financial interests.

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Supplementary Data

Comparison of BATMAN and Chenomx NMR Suite results. (PDF 805 kb)

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Hao, J., Liebeke, M., Astle, W. et al. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat Protoc 9, 1416–1427 (2014). https://doi.org/10.1038/nprot.2014.090

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