TY - JOUR
T1 - Combining DI-ESI–MS and NMR datasets for metabolic profiling
AU - Marshall, Darrell D.
AU - Lei, Shulei
AU - Worley, Bradley
AU - Huang, Yuting
AU - Garcia-Garcia, Aracely
AU - Franco, Rodrigo
AU - Dodds, Eric D.
AU - Powers, Robert
N1 - Publisher Copyright:
© 2014, Springer Science+Business Media New York.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - Abstract: Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI–MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI–MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI–MS datasets yields a substantial improvement in the analysis of metabolome alterations induced by neurotoxin treatment. Graphical abstract: [Figure not available: see fulltext.]
AB - Abstract: Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI–MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI–MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI–MS datasets yields a substantial improvement in the analysis of metabolome alterations induced by neurotoxin treatment. Graphical abstract: [Figure not available: see fulltext.]
KW - DI-ESI–MS
KW - Metabolomics
KW - Multiblock PCA
KW - Multiblock PLS
KW - Multivariate statistics
KW - NMR
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U2 - 10.1007/s11306-014-0704-4
DO - 10.1007/s11306-014-0704-4
M3 - Article
C2 - 25774104
AN - SCOPUS:84942810813
SN - 1573-3882
VL - 11
SP - 391
EP - 402
JO - Metabolomics
JF - Metabolomics
IS - 2
ER -