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NMR Biomed. 2012 Aug;25(8):985-92. doi: 10.1002/nbm.1816. Epub 2012 Feb 14.

Absolute quantification of metabolites in breast cancer cell extracts by quantitative 2D (1) H INADEQUATE NMR.

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Université de Nantes, CNRS, CEISAM UMR 6230, B.P. 92208, 2 rue de la Houssinière, F-44322, Nantes Cedex 03, France.


Metabolomic studies by NMR spectroscopy are increasingly employed for a variety of biomedical applications. A very standardized 1D proton NMR protocol is generally employed for data acquisition, associated with multivariate statistical tests. Even if targeted approaches have been proposed to quantify metabolites from such experiments, quantification is often made difficult by the high degree of overlap characterizing (1) H NMR spectra of biological samples. Two-dimensional spectroscopy presents a high potential for accurately measuring concentrations in complex samples, as it offers a much higher discrimination between metabolite resonances. We have recently proposed an original approach relying on the (1) H 2D INADEQUATE pulse sequence, optimized for fast quantitative analysis of complex metabolic mixtures. Here, the first application of the quantitative (1) H 2D INADEQUATE experiment to a real metabonomic study is presented. Absolute metabolite concentrations are determined for different breast cancer cell line extracts, by a standard addition procedure. The protocol is characterized by high analytical performances (accuracy better than 1%, excellent linearity), even if it is affected by relatively long acquisition durations (15 min to 1 h per spectrum). It is applied to three different cell lines, expressing different hormonal and tyrosine kinase receptors. The absolute concentrations of 15 metabolites are determined, revealing significant differences between cell lines. The metabolite concentrations measured are in good agreement with previous studies regarding metabolic profile changes of breast cancer. While providing a high degree of discrimination, this methodology offers a powerful tool for the determination of relevant biomarkers.

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