Simultaneous Quantification and Identification of Individual Chemicals in Metabolite Mixtures by Two-Dimensional Extrapolated Time-Zero ¹H−¹³C HSQC (HSQC₀)

Abstract

Quantitative one-dimensional (1D) 1H NMR spectroscopy is a useful tool for determining metabolite concentrations because of the direct proportionality of signal intensity to the quantity of analyte. However, severe signal overlap in 1D 1H NMR spectra of complex metabolite mixtures hinders accurate quantification. Extension of 1D 1H to 2D 1H−13C HSQC leads to the dispersion of peaks along the 13C dimension and greatly alleviates peak overlapping. Although peaks are better resolved in 2D 1H−13C HSQC than in 1D 1H NMR spectra, the simple proportionality of cross peaks to the quantity of individual metabolites is lost by resonance-specific signal attenuation during the coherence transfer periods. As a result, peaks for individual metabolites usually are quantified by reference to calibration data collected from samples of known concentration. We show here that data from a series of HSQC spectra acquired with incremented repetition times (the time between the end of the first 1H excitation pulse to the beginning of data acquisition) can be extrapolated back to zero time to yield a time-zero 2D 1H−13C HSQC spectrum (HSQC0) in which signal intensities are proportional to concentrations of individual metabolites. Relative concentrations determined from cross peak intensities can be converted to absolute concentrations by reference to an internal standard of known concentration. Clustering of the HSQC0 cross peaks by their normalized intensities identifies those corresponding to metabolites present at a given concentration, and this information can assist in assigning these peaks to specific compounds. The concentration measurement for an individual metabolite can be improved by averaging the intensities of multiple, nonoverlapping cross peaks assigned to that metabolite.

Mechanism of E-cadherin dimerization probed by NMR relaxation dispersion

“…d6-methanol was used to calibrate sample temperature (32 The relative quantity of monomers and dimers for a given sample was determined from the ratio of peak volumes in 1 H-15 N HSQC spectra acquired with 6-s recycle delay. The ratio was corrected for the differential loss of magnetization for monomers and dimers during magnetization transfer periods in the sequence according to a previously reported method (34 Fluorescence Experiments. The experiments were performed at 299 K in buffers containing 10 mM CaCl 2 , 120 mM NaCl, 10 mM Tris·HCl, pH 7.9, identical to those for NMR experiments.…”

Influence of Iron and Aeration on Staphylococcus aureus Growth, Metabolism, and Transcription

“…Data collection. 1D data collection was performed as described previously (30), while 2D time zero extrapolated 1 H- 13 C HSQC (HSQC 0 ) NMR spectra were processed as described previously (31). For each sample, a total of 1,024 data points with a spectrum width of 10.00 ppm in the 1 H dimension and a total of 64 data points with a spectrum width of 140.00 ppm in the 13 C dimension were collected.…”

Paving the Way for Lignin Valorisation: Recent Advances in Bioengineering, Biorefining and Catalysis

“…Indeed, progress has been achieved for quantitative HSQC NMR, using so-called QQ-HSQC and HSQC 0 ,w ith pulse sequences allowing better quantification of the identified functionalities. [169][170][171] However,t hese methods still fail for rapidly and differentially relaxing samples.F or example,i nl ignins,c orrelation peaks from the more mobile endgroups,including the p-coumarates and p-hydroxybenzoates that adorn some lignin sidechains, relax much more slowly than those from units in the polymer backbone and are consequently overestimated by often large factors.Methods for overcoming such issues are still actively sought. Regardless,r egular HSQC NMR experiments still offer highly valuable semi-quantitative,r elative information on the linkage abundance,a llowing for comparison of lignin structures and whole plant cell compositions.…”