Determination of corticosterone from rat hair samples by HPLC-MS method

Abstract

Corticosterone is an adrenocortical steroid hormone with glucocorticoid and mineralocorticoid effects. Based on previous studies, the plasma level of corticosterone correlates with the stress exposure of rodents. Because the half-life of corticosterone in blood is short, its plasma concentration can be used as an acute stress marker. But hair is accumulating the systemic and locally produced corticosterone, therefore it can be used to study chronic stress. However, the accurate quantification of corticosterone is an analytical challenge owing to the very low amount of hormone found in a complicated biological matrix. The high performance liquid chromatography coupled with mass spectrometry (HPLC-MS) can provide the required selectivity and sensitivity for this purpose. Currently published methods for corticosterone quantification involve complicated sample preparation and long run time. Accordingly, the aims of the study were to simplify the extraction method of the corticosterone from rat hair samples and to develop an optimized HPLC-MS method for the accurate quantification. The rat hair samples were washed with methanol, dried and cut, then extracted with methanol at room temperature for 24 hours. The lipids were precipitated with formic acid aqueous solution and eliminated by centrifugation. The corticosterone was separated from other compounds with reverse phase chromatography using acetonitrile and 0,1% aqueous solution of formic acid as mobile phase. The detection was performed in positive SIM mode measuring the 347 m/z molecular ion. A six point calibration was performed in the range of 0,5-20 ng/ml, the accuracy was tested with quality control samples at two different concentration level. The total run time is only 4,2 minutes and the lower limit of quantification (LLOQ) is 0,5 ng/ml, with 10 pg absolute sensitivity. By determining the quantity of the hormone for a well-defined hair region, based on the speed of hair growth, we can characterize the retrospective stress exposure of the animals in different conditions.

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