Metabolic profiling of biological samples provides a molecular window on multiple disease and physiological processes and is increasingly used in the characterization of multiple pathologies and therapeutic interventions including nutritional management. A major bottleneck in defining the metabolic consequences of biological processes is the lack of automated annotation and standardised methods for structure elucidation of candidate biomarkers. Current published methods and protocols typically describe the application of single or simple experimental strategy for the identification of unknown metabolites. However, here we draw together multiple methods and describe a robust integrated protocol for identification of molecular species derived from Nuclear Magnetic Resonance (NMR) spectroscopy based metabolic phenotyping studies. Examples of assignments made using this approach will be discussed including N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide (biomarker of onion intake) and ascorbic acid. This strategy utilises a composite cross-disciplinary pipeline of NMR spectroscopic methods, which can then be integrated with statistical methods and hyphenated analytical platforms to address the challenge of assignment of molecular species. The statistical pipeline outlined in this webinar is efficient, 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.
The evaluation of spectroscopic data obtained from body fluids often points to signals of compounds which cannot be identified readily, e.g. by comparison to spectra in libraries or data bases. If the signal belongs to a potential biomarker, it is of fundamental importance to solve the identity of this compound. If NMR and mass spectroscopic data are available for such a study, statistical hetero correlation spectroscopy (SHY) can be employed to correlate molecular mass information to signals in the NMR spectrum of the sample of interest. For this, a large number of samples must be measured. In the case the NMR/MS correlation belongs to the same molecule in the mixture, valuable information can be obtained to identify the marker already. In many cases, however, it is not possible to identify the compound of interest readily. Here, the compound must be chromatographically isolated and the structure elucidated after acquiring the NMR data required. For the isolation step it is important to distinguish between polar and unpolar components:
If no further cleaning is required, NMR data can be obtained directly in order to solve the structure.
In this Select Science webinar, Professor Elaine Holmes, Head of the Division of Computational and Systems Medicine at Imperial College London and Professor of Computational Medicine at Murdoch University in Perth and Dr. Markus Godejohann, senior application scientist at Bruker BioSpin will discuss:
This webinar will be of interest to scientists involved in the isolation, identification and structure elucidation of unknowns in body fluids. It is also relevant for academics and scientists working in the field of fast natural product de-replication and structure elucidation of unknowns.
Important note: The methods and solutions discussed during the webinar are for research use only and not for use in clinical diagnostic procedures
Professor Elaine Holmes
Professor of Chemical Biology and the Head of the Division of Computational and Systems Medicine at Imperial College London - United Kingdom
Dr. Markus Godejohann
Application scientist, AIC hyphenation group - Bruker BioSpin GmbH