A simultaneous identification and quantification strategy for determination of sulfhydryl-containing metabolites in normal- and high-fat diet hamsters using stable isotope labeling combined with LC-MS.

Sulfur-containing metabolites are related to several physiologic disorders and metabolic diseases. In this study, a simultaneous identification and quantification strategy in one batch for determination of sulfhydryl-containing metabolites was developed using stable isotope labeling combined with liquid chromatography-tandem mass spectrometry (SIL-LC-MS). In the proposed method, a pair of isotope labeling reagents, D/D-N-ethylmaleimide (D/D-NEM), was used to derivatize sulfhydryl-containing metabolites in blood and plasma of normal- and high-fat-diet (NFD and HFD) hamsters for reduced (-SH) and total (-SH, -S-S-, S-glutathionylated proteins) analysis. Quality control (QC) samples and test samples were prepared for LC-MS analysis. First, both QC samples and stable isotope labeled internal standards were used to monitor the status of the instrument and ensure the reliability of the analysis. Subsequently, an inhouse database containing 45 sulfhydryl-containing metabolites was established by MS based on QC samples. Then, qualitatively differential sulfhydryl-containing metabolites were found by MS between the NFD and HFD hamsters of the test samples, including 3 in reduced and 8 in total analysis of blood samples, and 2 in reduced and 2 in total analysis of plasma samples. Next, in quantitative analysis, satisfied linearities for 6 sulfhydryl-containing metabolites were obtained with the correlation coefficient (R) > 0.99 and absolute quantification was carried out. The results showed that glutathione and cysteine have different concentrations in blood and plasma of hamsters. Finally, the correlation of sulfhydryl-containing metabolites with blood lipid and oxidative stress levels was determined, which provided insight into the hyperlipidemia-related oxidative stress. Taken together, the developed method of simultaneous identification with the inhouse database and MS and quantification with standards in one batch provides a promising strategy for the analysis of sulfhydryl-containing metabolites in biological samples, which may promote the in-depth investigation on sulfhydryl-containing metabolites and related diseases.