Effects of Freeze–Thaw Cycles of Blood Samples on High-Coverage Quantitative Metabolomics

Blood metabolomics has been widely used for discovering potential metabolite biomarkers of various diseases. In this study, we report our investigation of the effects of freeze–thaw cycles (FTCs) of human serum samples on quantitative metabolomics using a differential chemical isotope labeling (CIL) LC-MS method. A total of 99 serum samples collected from healthy individuals (47 females and 52 males) were subjected to five FTCs, followed by 12C-/13C-dansylation labeling LC-MS analysis. A total of 2790 peak pairs or metabolites were relatively quantified among the 495 comparative samples, including 150 positively identified metabolites, 235 high-confident putatively identified metabolites and 1949 mass-matched metabolites from database searches. Multivariate analysis of the metabolome data showed a clustering of the third to fifth FTC samples in contrast to the separation of the first and second FTC samples, indicating that the extent of FTC-induced metabolome changes became smaller after the third cycle. The changing patterns among the FTC-effected metabolites were found to be complex. Using sex as a biological factor for grouping, we observed a clear separation of males and females when the samples were subjected to the same number of FTCs. However, when the male- and female-samples with different numbers of FTCs were compared, the number of significant metabolites found in male–female comparison increased dramatically, indicating that FTC effects could lead to a large number of false positives in biomarker discovery. Finally, we proposed a method of detecting the FTC effects by reanalyzing the original samples after subjecting them to an additional FTC.