Deconvolution of Chemical Mixtures with High Complexity by NMR Consensus Trace Clustering

Abstract

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural ¹³C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance ¹H–¹H TOCSY and ¹³C–¹H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of ¹³C–¹H HSQC and ¹³C–¹H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.