An Analytical Strategy for Reliable Metabolome Analysis of Clinical Leftover Sera Using Timed Aliquoting

Accurate metabolome analysis depends not only on advanced
analytical
techniques but also on strict control of preanalytical variables.
This study presents an analytical strategy for reliable untargeted
metabolomics using clinical leftover sera, focusing on the impact
of timed aliquoting during short-term storage. Leftover serum samples
from routine clinical testing, typically stored at 4 °C for up
to 7 days, offer a valuable and accessible resource for biomarker
discovery. However, variable delays in sample aliquoting and storage
can compromise metabolite stability. We used high-coverage 12C-/13C-dansylation LC–MS to profile the amine/phenol
submetabolome in serum samples collected from healthy individuals
at multiple time points postdraw. The study included 630 LC–MS
runs (105 subjects × 6 time points) in the discovery set and
280 runs (70 subjects × 4 time points) in the validation set,
quantifying 1382 and 1352 metabolites, respectively. Although time-dependent
changes in metabolite abundances were observed, these shifts were
relatively small between adjacent time points. Notably, clear sex-based
metabolic separation was observed when using samples aliquoted at
the same time or within 24-h intervals, whereas discriminatory power
diminished when samples with longer storage time differences were
combined. These findings demonstrate that, with carefully timed aliquoting,
clinical leftover sera can be reliably used for metabolomics. Our
study establishes a practical and scalable workflow to control preanalytical
variationspecifically by minimizing storage time differencesthereby
enabling broader use of clinical samples in biomarker discovery and
population-scale metabolomics.