13C NMR has many advantages for a metabolomics study,
including a large spectral dispersion, narrow singlets at natural
abundance, and a direct measure of the backbone structures of metabolites.
However, it has not had widespread use because of its relatively low
sensitivity compounded by low natural abundance. Here we demonstrate
the utility of high-quality 13C NMR spectra obtained using
a custom 13C-optimized probe on metabolomic mixtures. A
workflow was developed to use statistical correlations between replicate
1D 13C and 1H spectra, leading to composite
spin systems that can be used to search publicly available databases
for compound identification. This was developed using synthetic mixtures
and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able
to obtain useful 13C–13C statistical
correlations from metabolites with as little as 60 nmol of material.
The lower limit of 13C NMR detection under our experimental
conditions is approximately 40 nmol, slightly lower than the requirement
for statistical analysis. The 13C and 1H data
together led to 15 matches in the database compared to just 7 using 1H alone, and the 13C correlated peak lists had
far fewer false positives than the 1H generated lists.
In addition, the 13C 1D data provided improved metabolite
identification and separation of biologically distinct groups using
multivariate statistical analysis in the D. melanogaster extracts and mouse serum.