Journal
MOLECULES
Volume 27, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/molecules27010283
Keywords
vicinal diols; epoxide hydrolase; double precursor ion scan; online post-column derivatization
Funding
- National Institute of Environmental Health Sciences Superfund Research Program [P42 ES004699, R35 ES030443]
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Vicinal diols, important signaling metabolites in inflammatory diseases, can be profiled using the LC-PCD-DPIS-MS method. This method offers improved selectivity and sensitivity compared to traditional derivatization techniques and full scan MS methods. It retains the separation capacity of liquid chromatography and allows for easy identification of vicinal-diol metabolites.
Vicinal diols are important signaling metabolites of various inflammatory diseases, and some of them are potential biomarkers for some diseases. Utilizing the rapid reaction between diol and 6-bromo-3-pyridinylboronic acid (BPBA), a selective and sensitive approach was established to profile these vicinal diols using liquid chromatography-post column derivatization coupled with double precursor ion scan-mass spectrometry (LC-PCD-DPIS-MS). After derivatization, all BPBA-vicinal-diol esters gave a pair of characteristic isotope ions resulting from Br-79 and Br-81. The unique isotope pattern generated two characteristic fragment ions of m/z 200 and 202. Compared to a traditional offline derivatization technique, the new LC-PCD-DPIS-MS method retained the capacity of LC separation. In addition, it is more sensitive and selective than a full scan MS method. As an application, an in vitro study of the metabolism of epoxy fatty acids by human soluble epoxide hydrolase was tested. These vicinal-diol metabolites of individual regioisomers from different types of polyunsaturated fatty acids were easily identified. The limit of detection (LOD) reached as low as 25 nM. The newly developed LC-PCD-DPIS-MS method shows significant advantages in improving the selectivity and therefore can be employed as a powerful tool for profiling vicinal-diol compounds from biological matrices.
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