[en] Abstract
Introduction The use of 2D NMR data sources (COSY in this paper) allows to reach general metabolomics results which
are at least as good as the results obtained with 1D NMR data, and this with a less advanced and less complex level of preprocessing.
But a major issue still exists and can largely slow down a generalized use of 2D data sources in metabolomics:
the experiment duration.
Objective The goal of this paper is to overcome the experiment duration issue in our recently published MIC strategy by
considering faster 2D COSY acquisition techniques: a conventional COSY with a reduced number of transients and the use of
the Non-Uniform Sampling (NUS) method. These faster alternatives are all submitted to novel 2D pre-processing workflows
and to Metabolomic Informative Content analyses. Eventually, results are compared to those obtained with conventional
COSY spectra.
Methods To pre-process the 2D data sources, the Global Peak List (GPL) workflow and the Vectorization workflow are used.
To compare this data sources and to detect the more informative one(s), MIC (Metabolomic Informative Content) indexes
are used, based on clustering and inertia measures of quality.
Results Results are discussed according to a multi-factor experimental design (which is unsupervised and based on human
urine samples). Descriptive PCA results and MIC indexes are shown, leading to the direct and objective comparison of the
different data sets.
Conclusion In conclusion, it is demonstrated that conventional COSY spectra recorded with only one transient per increment
and COSY spectra recorded with 50% of non-uniform sampling provide very similar MIC results as the initial COSY
recorded with four transients, but in a much shorter time. Consequently, using techniques like the reduction of the number
of transients or NUS can really open the door to a potential high-throughput use of 2D COSY spectra in metabolomics.
Research Center/Unit :
CIRM - Centre Interdisciplinaire de Recherche sur le Médicament - ULiège
Disciplines :
Pharmacy, pharmacology & toxicology
Author, co-author :
Féraud, Baptiste; Université Catholique de Louvain - UCL
Martineau, Estelle; Université de Nantes
Leenders, Justine; Université de Liège - ULiège
Govaerts, Bernadette; Université Catholique de Louvain - UCL
De Tullio, Pascal ; Université de Liège - ULiège > Département de pharmacie > Chimie pharmaceutique
Giraudeau, Patrick; Université de Nantes
Language :
English
Title :
Combining rapid 2D NMR experiments with novel pre-processing workflows and MIC quality measures for metabolomics
Barna, J. C., & Laue, E. D. (1987). Conventional and exponential sampling for 2D NMR experiments with application to a 2D NMR spectrum of a protein. Journal of Magnetic Resonance (1969), 75(2), 384–389.
Bax, A. D., & Freeman, R. (1981). Investigation of complex networks of spin-spin coupling by two-dimensional NMR. Journal of Magnetic Resonance (1969), 44(3), 542–561.
Dalvit, C., & Bovermann, G. (1995). Pulsed field gradient one-dimensional NMR selective ROE and TOCSY experiments. Magnetic Resonance in Chemistry, 33(2), 156–159.
Delikatny, E. J., Hull, W. E., & Mountford, C. E. (1991). The effect of altering time domains and window functions in two-dimensional proton COSY spectra of biological specimens. Journal of Magnetic Resonance (1969), 94(3), 563–573.
Dumez, J. N., Milani, J., Vuichoud, B., Bornet, A., Lalande-Martin, J., Tea, I., et al. (2015). Hyperpolarized NMR of plant and cancer cell extracts at natural abundance. Analyst, 140(17), 5860–5863.
Feraud, B., Govaerts, B., Verleysen, M., & De Tullio, P. (2015). Statistical treatment of 2D NMR COSY spectra in metabolomics: Data preparation, clustering-based evaluation of the Metabolomic Informative Content and comparison with 1H-NMR. Metabolomics, 11(6), 1756–1768.
Feraud, B., Leenders, J., Martineau, E., Giraudeau, P., Govaerts, B., & De Tullio, P. (2019). Two data pre-processing workflows to facilitate the discovery of biomarkers by 2D NMR metabolomics. Metabolomics, 15, 63. 10.1007/s11306-019-1524-3.
Frydman, L., Scherf, T., & Lupulescu, A. (2002). The acquisition of multidimensional NMR spectra within a single scan. Proceedings of the National Academy of Sciences of the United States of America, 99(25), 15858–15862.
Hoch, J. C., Maciejewski, M. W., Mobli, M., Schuyler, A. D., & Stern, A. S. (2014). Nonuniform sampling and maximum entropy reconstruction in multidimensional NMR. Accounts of Chemical Research, 47(2), 708–717.
Hyberts, S. G., Takeuchi, K., & Wagner, G. (2010). Poisson-gap sampling and forward maximum entropy reconstruction for enhancing the resolution and sensitivity of protein NMR data. Journal of the American Chemical Society, 132(7), 2145–2147.
Jezequel, T., Deborde, C., Maucourt, M., Zhendre, V., Moing, A., & Giraudeau, P. (2015). Absolute quantification of metabolites in tomato fruit extracts by fast 2D NMR. Metabolomics, 11(5), 1231–1242.
Kazimierczuk, K., & Orekhov, V. Y. (2011). Accelerated NMR spectroscopy by using compressed sensing. Angewandte Chemie, 123(24), 5670–5673.
Le Guennec, A., Dumez, J. N., Giraudeau, P., & Caldarelli, S. (2015). Resolution-enhanced 2D NMR of complex mixtures by non-uniform sampling. Magnetic Resonance in Chemistry, 53(11), 913–920.
Le Guennec, A., Tea, I., Antheaume, I., Martineau, E., Charrier, B., Pathan, M., et al. (2012). Fast determination of absolute metabolite concentrations by spatially encoded 2D NMR: Application to breast cancer cell extracts. Analytical Chemistry, 84(24), 10831–10837.
Liland, K. H. (2011). Multivariate methods in metabolomics, from pre-processing to dimension reduction and statistical analysis. TrAC Trends in Analytical Chemistry, 30(6), 827–841.
Maciejewski, M. W., Mobli, M., Schuyler, A. D., Stern, A. S., & Hoch, J. C. (2011). Data sampling in multidimensional NMR: Fundamentals and strategies. In M. Billeter & V. Orekhov (Eds.), Novel sampling approaches in higher dimensional NMR (pp. 49–77). Berlin: Springer.
Marchand, J., Martineau, E., Guitton, Y., Dervilly-Pinel, G., & Giraudeau, P. (2017). Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics. Current Opinion in Biotechnology, 43, 49–55.
Marchand, J., Martineau, E., Guitton, Y., Le Bizec, B., Dervilly-Pinel, G., & Giraudeau, P. (2018). A multidimensional 1H-NMR lipidomics workflow to address chemical food safety issues. Metabolomics, 14(5), 60.
Martin M., Legat B., Leenders J., Vanwinsberghe J., Rousseau R., et. al.,(2017) PepsNMR for the 1H-NMR metabolomic data pre-processing. ISBA Discussion Paper, 2017/22, http://hdl.handle.net/2078.1/187159.
Robson, S., Arthanari, H., Hyberts, S. G., & Wagner, G. (2019). Nonuniform sampling for NMR spectroscopy. Methods in Enzymology, 614, 263–291.
Rouger, L., Gouilleux, B., & Giraudeau, P. (2017). Fast n-dimensional data acquisition methods. In G. E. Tranter & D. W. Koppenaal (Eds.), Encyclopedia of spectroscopy and spectrometry (3rd ed., pp. 588–596). Oxford: Academic Press.
Xi, Y., de Ropp, J. S., Viant, M. R., Woodruff, D. L., & Yu, P. (2006). Automated screening for metabolites in complex mixtures using 2D COSY NMR spectroscopy. Metabolomics, 2(4), 221–233.
