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New approach for the study of metabolic diseases

Forcisi S, Moritz F, Lucio M, Lehmann R, Stefan N, Schmitt-Kopplin P. Solutions for Low and High Accuracy Mass Spectrometric Data Matching: A Data-Driven Annotation Strategy in Nontargeted Metabolomics. doi: 10.1021/acs.analchem.5b02049. Analytical Chemistry. September 1, 2015

 

While very high mass accuracy is the first prerequisite for proper compound annotation, it is not compatible to state of the art high resolution liquid chromatography (the most frequently used technique in metabolomics). This incompatibility furthermore impairs the capacity of common metabolomics workflows to integrate data generated on both, high resolution/accuracy mass spectrometry (e.g. Fourier transform ion cyclotron resonance mass spectrometry: FT-ICR/MS) and ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) platforms. DZD scientists from the Research Unit Analytical BioGeoChemistry (BGC) at the Helmholtz Zentrum München have devised a data-driven retention time (RT) based strategy, which allows for high confidence annotations on UHPLC-MS data. Interestingly, the workflow is independent on column chemistry, and therefore it is resistant to sample matrix based alterations of the chemistry a given UHPLC separation is based on.

The strategy is demonstrated on the basis of samples from the Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tübingen. Within the so called TULIP study, lifestyle intervention study of a cohort in Tübingen, they investigated plasma metabolome trends that mirror Insulin sensitivity in non-alcoholic fatty liver diseased (NAFLD) specimens. The strategy showed that UHPLC-MS and FT-ICR/MS address metabolome snapshots of largely differing composition. Still, the same compound classes were found to be relevant for the NAFLD phenotype. This comparison was only possible due to the retention-time-mass-difference network based annotation strategy, as UHPLC-MS signals are normally too inaccurate to perform robust and consistent sum formula assignments.

Original publication:
Forcisi S, Moritz F, Lucio M, Lehmann R, Stefan N, Schmitt-Kopplin P. Solutions for Low and High Accuracy Mass Spectrometric Data Matching: A Data-Driven Annotation Strategy in Nontargeted Metabolomics. doi: 10.1021/acs.analchem.5b02049. Analytical Chemistry. September 1, 2015

Link to the publication:
http://pubs.acs.org/doi/10.1021/acs.analchem.5b02049