Abstract
The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) (1)H-(13)C heteronuclear single quantum coherence (HSQC) and 2D (1)H-(1)H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D (1)H-(13)C HSQC-TOCSY and 2D (1)H-(13)C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete (1)H-(1)H and (1)H-(13)C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a (13)C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D (1)H-(13)C HMBC spectra was collected for 94 common metabolites. (13)C-(13)C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the (1)H-(13)C HSQC-TOCSY spectra. The resulting (13)C-(13)C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.