Robust Deconvolution of Complex Mixtures by Covariance TOCSY Spectroscopy

Abstract

A fundamental problem in many areas of chemistry is the identification of components in chemical mixtures, such as different solutes in a solution. The recent advent of metabolomics has generated a critical demand for powerful analysis methods for fluid mixtures in the food and life sciences. [1][2][3] While important progress is being made in potentially laborious and costly hyphenated methods, [4] spectroscopic methods have the power to circumvent or reduce the need for hyphenation prior to analysis. [5] Most compounds contain multiple NMR-active spins that are J-coupled and allow the identification of spin-spin coupling networks for discrimination between components, as well as their subsequent identification by screening against a database. Particularly useful in this regard is the 2D NMR 1 H-1 H TOCSY experiment, [6] which monitors multiple relay transfers of spin magnetization within a spin system to provide a wealth of information on scalar spin-spin coupling connectivity with high sensitivity. Because experimental efficiency is a prerequisite for high-throughput applications, TOCSY is combined here with covariance NMR, [7][8][9] which produces high-resolution spectra largely independent of the number of increments along the indirect time domain t 1 .A recently proposed unsupervised deconvolution method [10] uses principal-component analysis (PCA) of the covariance TOCSY spectrum of a mixture. In the absence of significant spectral overlap, the dominant PCA eigenmodes approximate well the 1D spectra of the individual components. For increasing degrees of spectral overlap between components, however, "mixed modes" emerge whose assignment to known compounds can pose a significant challenge. [10] The method presented here, which is termed DemixC (C stands for clustering), overcomes this limitation by identifying for each component characteristic traces that are essentially free of overlap and can be identified and assigned with high confidence.The DemixC method is demonstrated for three samples of differing complexity. Sample I consists of three amino acids (Glu, Lys, Val) dissolved in D 2 O. Sample II contains four amino acids (Glu, Leu, Lys, Val) in D 2 O. The amino acid concentration of samples I and II is 7 mm. Sample III contains the cyclic decapeptide antamanide [11] [-Val-Pro-Pro-Ala-PhePhe-Pro-Pro-Phe-Phe-] dissolved in deuterated chloroform at a concentration of 1 mm. While the dissolved peptide of sample III is not an actual mixture, in terms of its proton NMR properties it behaves like a mixture of 10 amino acids at 1 mm concentration each. The low variability of the amino acid composition (four phenylalanine and four proline residues) leads to significant resonance overlap [12] providing a rigorous test case for the performance of the proposed method.Two-dimensional TOCSY experiments for samples I and II were performed at 600 MHz with mixing times t m of 97 and 62 ms with 2048 complex points in t 2 and 1024 points in t 1 in TPPI mode. The TOCSY spectra of sample III were recorded at 800 MHz with m...

Bacterial Attraction and Quorum Sensing Inhibition in Caenorhabditis elegans Exudates

“…We also used COLMAR DemixC, a recently developed semi-automated method to analyze NMR spectra of complex mixtures (Zhang and Brüschweiler, 2007; Zhang et al, 2007). About 20% of the compounds could be identified using COLMAR, including sugars (glucose and trehalose), lactic acid, and amino acids (Val, Lys, Leu, Ile, Glu) (Supplementary Fig.…”

“…The 2D TOCSY data were processed using a covariance algorithm (Brüschweiler and Zhang, 2004; Trbovic et al, 2004) that yields spectra with equally high resolution along both frequency dimensions. The covariance TOCSY spectra were then deconvoluted by COLMAR DemixC (Zhang and Brüschweiler, 2007; Zhang et al, 2007), which extracts 1D spectral traces that represent individual compounds by identifying spin systems with minimal likelihood of overlaps between different compounds in the covariance TOCSY spectra of the intact mixtures. In the final step, chemical shifts from individual spin systems derived from the DemixC traces were screened against the BMRB metabolomics spectral database (Seavey et al, 1991) using our COLMAR query webserver (Robinette et al, 2008; Snyder et al, 2008), which outputs a ranked list of the highest scoring compounds.…”

Analytic Properties of Statistical Total Correlation Spectroscopy Based Information Recovery in 1H NMR Metabolic Data Sets

“…There are now numerous applications of STOCSY and closely related covariance techniques for enhanced information recovery from low dimensional NMR data sets on multiple and single samples [3][4][5][6][7][8][9][10][11][12][13] . The STOCSY approach has been applied to the structural assignment problem in a variety of NMR metabolic profiling contexts, such as deconvolution of overlapped chromatographic peaks in LC-NMR 7 , delineation of drug metabolism in molecular epidemiology studies 5 and separation of different molecular signatures in diffusion edited spectroscopy 6 .…”

Use of optimized 1D TOCSY NMR for improved quantitation and metabolomic analysis of biofluids

“…Statistical TOCSY, or STOCSY, developed by the Nicholson and coworkers, uses statistical covariation over a population of biofluid samples to resolve the proton NMR spectra for certain individual metabolites (Cloarec et al 2005). The covariation TOCSY approach developed by Bruschweiler and Zhang uses the covariation inherent in an individual molecule’s chemical shift frequencies and J couplings, as they evolve over a sampled time domain, to separate the spectra of the individual chemical species in a mixture using a reduced number of increments in the 2D spectra (Zhang and Bruschweiler 2004, 2007). Recent work by the Emsley group has shown the utility of intraspectral correlation to better define peak integral limits for improved data analysis (Holmes et al 2007).…”