Many diseases lead to characteristic changes in the chemical composition profiles of biological tissues and fluids and these can be used to identify a disease and follow its progression. A major obstacle to metabolic profiling is that variations in diet, genetic makeup and general lifestyle can also significantly change the profile, with less abundant metabolites often being masked by those that are present in higher concentrations. Researchers from the National Cancer Institute at Frederick, Maryland and the Thomas Jefferson University in Philadelphia have published results in an early access article in the Journal of Proteome Research, which shows how conducting 2D NMR experiments simplifies data analysis and increases the chances of finding meaningful differences between control and affected groups. NMR and mass spectrometry (MS) are the most common methods of conducting metabolic profiling, with one of the first examples having been conducted by Nobel Laureate, Professor Linus Pauling using a gas chromatograph (GC). According to the authors the majority of published metabolic NMR profiling studies use 1D proton spectra followed by statistical pattern recognition methods. However, less abundant metabolites are often not observed because their spectroscopic signatures lie hidden beneath NMR peaks that originate from more concentrated metabolites. To overcome this 'crowding' problem in 1D NMR spectra, the researcher utilised a two-dimensional 1H-1H TOCSY (total correlation spectroscopy) experiment to compare the metabolic profiles of urine obtained from wild-type and Abcc6-knockout mice. These mice have previously been validated as a model for pseudoxanthoma elasticum (PXE) which is a heritable recessive connective tissue disorder that causes the elastic fibres found in the skin, retina and blood vessels to become calcified and lose their elasticity. PXE affects around 1 in 75,000 people and while there are many ways to minimise its effects there is currently no cure. The researchers studied the urine taken from both wild-type and Abcc6-knockout mice using a Varian INNOVA 500 Hz spectrometer and compared the results gained from both 1D and 2D NMR experiments. "Although acquiring 2D data is time consuming, the data provides a more comprehensive, global metabolic profile and increases the chances of finding meaningful differences between control and affected groups than 1D NMR data," write the authors. "Additionally, we observed that metabolite identifications were much easier to achieve using 2D NMR data and ambiguities in peak identification were minimised due to better signal dispersion."