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Eliminating the dication-induced intersample chemical-shift variations for NMR-based biofluid metabonomic analysis
Authors:Limiao Jiang  Jing Huang  Yulan Wang  Huiru Tang
Institution:State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Center for Biospectroscopy and Metabonomics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, 430071, P.R. China. Huiru.tang@wipm.ac.cn.
Abstract:NMR-based urinary metabonomic analysis is an essential aspect of systems biology for understanding mammalian physiology and pathophysiology though intersample chemical-shift variations can cause serious problems. Here, we report two optimized and validated methods to eliminate such variations resulting from intersample differences in pH and dication concentration. We found that the Ca(2+) concentration was 7.41 ± 3.48, 1.03 ± 0.34 and 0.87 ± 0.52 mM whereas the Mg(2+) concentration was 3.02 ± 1.41, 2.65 ± 1.20 and 0.80 ± 0.59 mM in rat, mouse and human urine samples, respectively; urinary Ca-EDTA, Mg-EDTA and free EDTA had spin-lattice relaxation time values (600.13 MHz) of 0.38, 0.41 and 0.55 s, respectively. We also found that the combined treatments with potassium fluoride, phosphate buffer and a small amount of K(3)EDTA eliminated intersample chemical-shift variations for all metabolites. EDTA treatment followed with phosphate buffer also achieved similar results although resonances from EDTA and its complexes obscured some metabolite signals. We systematically optimized the amount of additives for rat, mouse and human urine samples taking into consideration the pH control, signal-to-noise ratio and intersample uniformity for metabolite chemical-shifts. Based on thorough validation, we established some optimized procedures for rat, mouse and human urine, respectively. By eliminating both pH and dication effects, these methods enable the reduction of intersample chemical-shift variations to 1.5 Hz for all metabolites. The methods will offer ensured data quality for high-throughput, especially robotic urinary metabonomics studies with no need for peak alignments or corrections.
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