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

Deying Chen1, Shuang Zhao2, Guanghua Ma1

  • 1State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China.

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|November 15, 2025
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Summary

Clinical leftover sera can be reliably used for untargeted metabolomics. Timed aliquoting minimizes preanalytical variation, enabling robust biomarker discovery from stored samples.

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Area of Science:

  • Metabolomics
  • Clinical Chemistry
  • Biomarker Discovery

Background:

  • Accurate metabolome analysis requires strict control of preanalytical variables.
  • Clinical leftover sera are a valuable resource but susceptible to metabolite degradation due to variable storage and aliquoting times.

Purpose of the Study:

  • To develop and validate an analytical strategy for reliable untargeted metabolomics using clinical leftover sera.
  • To assess the impact of timed aliquoting and short-term storage on metabolite stability and metabolic profiles.

Main Methods:

  • Utilized high-coverage 12C-/13C-dansylation LC-MS for submetabolome profiling.
  • Analyzed serum samples from healthy individuals at multiple time points postdraw (discovery and validation sets).
  • Quantified a large number of metabolites (1382 and 1352) across numerous LC-MS runs.

Main Results:

  • Observed relatively small, time-dependent changes in metabolite abundances between adjacent time points.
  • Demonstrated clear sex-based metabolic separation when samples were aliquoted within 24-hour intervals.
  • Found diminished discriminatory power when samples with longer storage time differences were combined.

Conclusions:

  • Clinical leftover sera can be reliably used for metabolomics with carefully timed aliquoting.
  • Minimizing storage time differences is crucial for controlling preanalytical variation.
  • Established a practical workflow for broader use of clinical samples in population-scale metabolomics and biomarker discovery.