Issue 15, 2018

A novel, fast and sensitive supercritical fluid chromatography-tandem mass spectrometry (SFC-MS/MS) method for analysis of arachidonic acid metabolites

Abstract

The development of a rapid, sensitive and reliable method for the quantification of bioactive arachidonic acid metabolites (AA-metabolites) in biological samples is quite challenging due to the minute concentration, short half-life and their structural complexity arising from different isomers. In this study, a simple, fast and environmentally friendly supercritical fluid chromatography-tandem mass spectrometry (SFC-MS/MS) method was developed and validated for simultaneous measurement of five (PGD2, PGE2, PGF, 6KetoPGF and LTB4) AA-metabolites in biological samples. These analytes were extracted by protein precipitation followed by separation and quantification. The analysis was completed within 3 minutes. The matrix matched linear calibration ranged from 0.5–100 ng mL−1 (r2 ≥ 0.995), whilst, the limit of quantification of PGD2, PGE2, PGF, and LTB4 was 0.5 ng mL−1 and was 2.5 ng mL−1 for 6KetoPGF. The interday and intraday precisions of the method were less than 15% while the accuracy of most of the analytes varied between 83 and 109%. Finally, as a proof of concept, the method was successfully applied for the determination of eicosanoids in human samples, which expands the possibility to explore physiological states, disease phenotypes, and novel biomarkers.

Graphical abstract: A novel, fast and sensitive supercritical fluid chromatography-tandem mass spectrometry (SFC-MS/MS) method for analysis of arachidonic acid metabolites

Article information

Article type
Paper
Submitted
27 ဧပြီ 2018
Accepted
09 ဇွန် 2018
First published
20 ဇွန် 2018
This article is Open Access
Creative Commons BY-NC license

Analyst, 2018,143, 3661-3669

A novel, fast and sensitive supercritical fluid chromatography-tandem mass spectrometry (SFC-MS/MS) method for analysis of arachidonic acid metabolites

S. J. Kumari A. Ubhayasekera, S. R. Acharya and J. Bergquist, Analyst, 2018, 143, 3661 DOI: 10.1039/C8AN00788H

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