Characterization of differences between blood sample matrices in untargeted metabolomics

Academic Article
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publication date

  • February 2011

abstract

  • Large-scale proteomic and metabolomic technologies are increasingly gaining attention for their use in the diagnosis of human disease. In order to ensure the statistical power of relevant markers, such analyses must incorporate a large number of representative samples. While in a best-case scenario these samples are collected through a study design that is specifically tailored for the desired analysis, often studies must rely upon the analysis of large numbers of previously banked samples that may or may not have complete and accurate documentation of their associated collection and storage methods. In this study, several human blood matrices were analyzed and compared for the quality of metabolomic output. The sample types that were tested include plasma prepared with a variety of anticoagulants and serum collected by venipuncture and capillary blood collection protocols. Analysis with liquid chromatography-mass spectrometry (LC-MS) revealed only subtle differences between the various plasma preparation methods. Differences between the serum and plasma samples appear to be largely peptide/protein-based and are consistent with the biological distinction of the two matrices. Interestingly, the small molecule lysophosphatidylinositol was found to be in higher abundance in plasma, as a possible consequence of the effect of the intrinsic clotting cascade on adjacent metabolic pathways. Comparison of the small-molecule profiles of the capillary- and venipuncture-collected samples revealed 23 statistically significant compound differences between these sample types. Most of these features can be attributed to surfactants and detergents used to pretreat the skin in order to maintain the sterility of sample collection. However, several have identical mass and molecular formulas as endogenous human metabolites and could be erroneously attributed to actual metabolic perturbations. Understanding the extent of these matrix effects is important for control of systematic bias and ensuring the quality of metabolomic analysis.