An overview of methods using 13C for improved compound identification in metabolomics and natural products - Clendinen, Chaevien S.; Stupp, Gregory S.; Ajredini, Ramadan; Lee-McMullen, Brittany; Beecher, Chris; Edison, Arthur S. (Frontiers in Plant Science, 2015)
Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography-mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward. We have designed and implemented several NMR and LC-MS approaches that utilize 13C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) 13C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5% and 95% channels. For NMR applications, we describe two 13C-based approaches. For samples at natural abundance, we have developed a workflow to obtain 13C-13C and 13C-1H statistical correlations using 1D 13C and 1H NMR spectra. For samples that can be isotopically labeled, we describe another NMR approach to obtain direct 13C-13C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which 13C NMR can be used to identify unknown compounds from IROA experiments. By combining technologies with the same samples, we can identify important biomarkers and corresponding metabolites of interest.