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.