Sensitive Quantification Of Key Oxysterols In Biological Matrix Using High Resolution Accurate Mass Spectrometry

      Arndt, D.; Della Gatta, S.; Bentley, M.
      Conference date
      Jun 9, 2013
      Conference name
      ASMS - The 61st American Society for Mass Spectrometry Conference

      Introduction: Oxysterols are oxygenated derivatives of cholesterol. Most of the known oxysterols are formed from cholesterol by enzymatic or free radical oxidation involving reactive oxygen and nitrogen species. Oxysterols play an important role in many biological processes as in cholesterol turnover, apoptosis, necrosis, inflammation, and immunosuppression. In addition, abnormal levels of certain oxysterols have been found in blood of patients with cardiovascular diseases. Therefore it might be important to monitor these oxysterols as early indicator for disease onset. However, oxysterols are found in blood and tissues only in very small amounts as oxygenated derivatives of cholesterol. Determining them accurately requires advanced instrumentation and methods. We describe a sensitive, robust and specific method for the quantification of key oxysterols using accurate mass. Methods: Liquid/Liquid Extraction (LLE) according to an adapted procedure described by Bligh/Dyer was performed on mouse plasma samples spiked with labeled internal standards using dichloromethane and methanol. After protein precipitation the samples were treated with 10m potassium hydroxide to hydrolyze the acyl lipids and the sterol esters to water soluble species. Samples were evaporated and reconstituted without additional derivatization prior to injection on high resolution accurate mass spectrometer (Thermo Fisher Gexactive). Target analytes (9 different oxysterol species) were extracted based on their accurate mass from full scan data with a mass window of 5ppm. The method is applicable for human plasma as well as mouse plasma. Preliminary Data: In order to monitor and quantify the key oxysterols in mouse as well as in human plasma, namely 7-α-hydroxycholesterol, 7-ß hydroxycholesterol, 7-ketocholesterol, 5α,6 α-epoxycholestanol, 5 ß,6ß-epoxycholestanol 24 hydroxycholesterol, 25-hydroxycholesterol, 27-hydroxycholesterol and 6α-hydroxy-5α-cholestane a fast screening method using Atmospheric Pressure Chemical Ionization (APCI) in positive mode on accurate mass spectrometry (Orbitrap platform-based Thermo Fisher Gexactive) was developed. For each of the analytes the corresponding labeled standard was used and an internal standard solution of these labeled analytes was added to all samples prior to LLE. With optimized chromatographic conditions a good separation of the oxysterols in biological matrices could be achieved. With a minimum of sample preparation (no need of additional derivatization) reduced matrix effects could be observed compared to the traditional MRM Approach using triple quadrupole techniques. The method shows robust and sensitive results and is comparable with traditional MRM Approaches with the taken advantages of full scan data for retrospective data evaluation and without the need of additional derivatization. As a result, good linearity with regression coefficients of > 0.9900 was achieved for seven of the nine analytes, within a calibration range from 5ng/ml to 5000ng/ml. The concentrations of analytes in calibration standards and Quality Control (QC) samples were determined using the response ratio of analyte to the corresponding labeled internal standard. Standard compound spiking-in recovery test showed analytical accuracy of ± 10% for all of the analytes. In summary, this work provided a robust and sensitive tool for absolute oxysterol quantification in biological matrices using full scan data derived from high resolution accurate Mass Spectrometry. Data sets could be re analyzed due to the use of full scan (scan range 150da-800da) data for retrospective monitoring of disease indicators which were not under consideration at the moment of analyses. Novel Aspect: Compared to GS/MS or LC-MS/MS Approaches this method shows robustness and sensitivity without derivatization and opportunity for retrospective data evaluation.