, Volume 1, Issue 2, pp 159-168

Relative contribution of specific sources of systematic errors and analytical imprecision to metabolite analysis by HPLC–ECD

  • Yevgeniya I. ShuruborAffiliated withDementia Research Service, Burke Medical Research Institute
  • , Wayne R. MatsonAffiliated withESA Inc.
  • , Rolf J. MartinAffiliated withDementia Research Service, Burke Medical Research InstituteLane College
  • , Bruce S. KristalAffiliated withDementia Research Service, Burke Medical Research InstituteDepartment of Neuroscience, Cornell University Medical College Email author 

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Objective interpretation of metabolomics data requires understanding both analytical and biological measurement errors. Here we address analytical measurement errors, the sources of these errors, and how this variability can impact metabolomic profiles. Sources considered include room temperature exposure (which could affect sample stability), spiking with authentic standards, the number of study replicates, the overall temporal design of the experimental series, and the complexity of the biological matrix of the samples (individual or pooled sera). The study focused on the analysis of ∼80 rat sera metabolites by HPLC coupled with coulometric array detectors. Time delay and room temperature exposure had minimal effects on the total relative metabolite concentrations and variability (mean: ∼94–98% of control, CVmedian: ±5–7%), but the concentrations of some specific metabolites were significantly altered. Changes observed in the concentrations of specific metabolites ranged as high as ±7-fold, with changes in variability ranging from 0.3% to 68%. Spiked samples demonstrated more complex behavior when allowed to decay over time than did control samples. The spiking of sera and standards with 43 known metabolites increased variability of the apparent concentrations of metabolites up to ∼24% as opposed to ∼3% in pure sera. Increased variability was metabolite-specific. In both pure and spiked sera, ∼80–95% of metabolites had CVs equivalent to standard analytical CVs for these metabolites. Experimental design, number of replicates, and complexity of the biological matrix had comparable effects. These results suggest that, under carefully controlled conditions, these analytical issues are not significant sources of variability relative to biological variation for most metabolites.