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Multicenter Study
. 2013 Jun 18;85(12):5801-9.
doi: 10.1021/ac4004776. Epub 2013 May 29.

High-resolution Quantitative Metabolome Analysis of Urine by Automated Flow Injection NMR

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Free PMC article
Multicenter Study

High-resolution Quantitative Metabolome Analysis of Urine by Automated Flow Injection NMR

Laeticia Da Silva et al. Anal Chem. .
Free PMC article

Abstract

Metabolism is essential to understand human health. To characterize human metabolism, a high-resolution read-out of the metabolic status under various physiological conditions, either in health or disease, is needed. Metabolomics offers an unprecedented approach for generating system-specific biochemical definitions of a human phenotype through the capture of a variety of metabolites in a single measurement. The emergence of large cohorts in clinical studies increases the demand of technologies able to analyze a large number of measurements, in an automated fashion, in the most robust way. NMR is an established metabolomics tool for obtaining metabolic phenotypes. Here, we describe the analysis of NMR-based urinary profiles for metabolic studies, challenged to a large human study (3007 samples). This method includes the acquisition of nuclear Overhauser effect spectroscopy one-dimensional and J-resolved two-dimensional (J-Res-2D) (1)H NMR spectra obtained on a 600 MHz spectrometer, equipped with a 120 μL flow probe, coupled to a flow-injection analysis system, in full automation under the control of a sampler manager. Samples were acquired at a throughput of ~20 (or 40 when J-Res-2D is included) min/sample. The associated technical analysis error over the full series of analysis is 12%, which demonstrates the robustness of the method. With the aim to describe an overall metabolomics workflow, the quantification of 36 metabolites, mainly related to central carbon metabolism and gut microbial host cometabolism, was obtained, as well as multivariate data analysis of the full spectral profiles. The metabolic read-outs generated using our analytical workflow can therefore be considered for further pathway modeling and/or biological interpretation.

Figures

Figure 1
Figure 1
(A) Pipeline of the quantitative metabolomics analysis of urine by FIA-NMR, including an approximate timing for each step (method implementation time is not included). (B) Step-by-step description of the automated FIA-NMR setup.
Figure 2
Figure 2
1H NMR metabolomics profiles of urine, acquired as a routine analysis by FIA-NMR: (A) flow cell profile; (B) 1H NMR NOESY-1D; (C) 1H J-Res-2D; citric acid signal in 1H NMR NOESY-1D and 1H J-Res-2D (E) spectra of (gray) urine and (black) standard compound.
Figure 3
Figure 3
(A) Anomeric proton of sucrose, appearing as a doublet at δH = 5.416 ppm, indicating the valley (v)-to-peak (p) ratio (%) as a measurement to quantify the shimming quality of a 1H NMR spectrum. (B–D) Stability of the FIA-NMR during 4 months of analyses. The quality controls acquired in-between analyses are depicted, after data extraction using a bin width of 0.0005 (n = 371). (B) Signal intensity variation, expressed as variation of total area of 1H NMR NOESY-1D to average ratio (%). (C) Shimming quality, expressed as % v/p of anomeric proton of sucrose. (D) Chemical shift stability of anomeric proton of sucrose, expressed as a difference to average (ppm).
Figure 4
Figure 4
Pareto-scaled scores plot (principal component 1 vs principal component 2) derived from a principal component analysis of 2879 urine spectra (number of rectangular bins used: 287, taken from 12–0.05 ppm), scaled to intensity of the TSP signal, with a confidence level of 99.00%. The five seemingly outliers correspond to samples with a high concentration of glucose, as confirmed by spectral inspection.

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