Identifying unknown metabolites using NMR-based metabolic profiling techniques.
Isabel Garcia-PerezJoram Matthias PosmaJose Ivan Serrano ContrerasClaire L BoulangéQueenie ChanGary S FrostJeremiah StamlerPaul ElliottJohn C LindonElaine HolmesJeremy K NicholsonPublished in: Nature protocols (2020)
Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we 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 modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 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. 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.
Keyphrases
- magnetic resonance
- high resolution
- solid state
- liquid chromatography
- solid phase extraction
- mass spectrometry
- tandem mass spectrometry
- high resolution mass spectrometry
- high throughput
- simultaneous determination
- molecular docking
- single molecule
- molecularly imprinted
- electronic health record
- ultra high performance liquid chromatography
- high performance liquid chromatography
- big data
- contrast enhanced
- liquid chromatography tandem mass spectrometry
- ms ms
- machine learning
- healthcare
- magnetic resonance imaging
- deep learning
- computed tomography
- density functional theory
- data analysis