Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardised methods for elucidating the structures of candidate disease biomarkers.
In this paper, published in Nature, the authors describe ‘a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data mode[l]ling’.
They provide a multi-platform system with eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This system involves the use of a variety of statistical spectroscopic tools to identify other signals in the NMR spectra relating to the same molecule, as well as two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and the extraction of data from existing databases. ‘
Deploying the entire system with all eight workflows would take up to a month, the authors contend – however, easier identification cases, using fewer steps, would take just two or three days.
‘This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies,’ they state.
‘It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.’