Application of Two-Dimensional Nuclear Magnetic Resonance for Signal Enhancement by Spectral Integration Using a Large Data Set of Metabolic Mixtures

Abstract

Nuclear magnetic resonance (NMR) spectroscopy has tremendous advantages of minimal sample preparation and interconvertibility of data among different institutions; thus, large data sets are frequently acquired in metabolomics studies. Previously, we used a novel analytical strategy, named signal enhancement by spectral integration (SENSI), to overcome the low signal-to-noise ratio (S/N ratio) problem in 13C NMR by integration of hundreds of spectra without additional measurements. In this letter, the development of a SENSI 2D method and application to >1000 2D JRES NMR spectra are described. Remarkably, the obtained SENSI 2D spectrum had an approximate 14-fold increase in the S/N ratio and 80-250 additional peaks without any additional measurements. These results suggest that SENSI 2D is a useful method for assigning weak signals and that the use of coefficient of variation values can support the assignment information and extraction of features from the population characteristics among large data sets.