Characterization of Differences between Blood Sample Matrices in Untargeted Metabolomics

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.

Chronic caloric restriction partially protects against age-related alteration in serum metabolome

“…This analysis resulted in the extraction of 7,343 features from the LC-MS data of mouse sera samples. The number of features detected in our analysis are comparable to other plasma and serum metabolomics studies using this methodology (Denery et al 2011, Dunn et al 2011. Unsupervised PCA score plots that show the first (PC1) and second (PC2) components for three pair-wise comparisons (Y-AL vs. A-AL, A-AL vs. A-CR, and Y-AL vs. A-CR) are presented in Fig.…”

Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood

“…But is serum also appropriate for metabolomics studies? A targeted and a nontargeted metabolomics study reported an acceptable correlation of the metabolic pattern between serum and plasma (16,17 ). However, in serum the concentrations of 104 of 121 metabolites were higher (around 10%), and 9 metabolites associated with the clotting process during serum generation showed obvious differences (20%-50%) (16 ).…”

“…Selected preanalytical aspects of targeted metabolomics investigations of human blood and urine by GC-MS and LC-MS (11)(12)(13)(14)(15)(16)(17), as well as the effects of delayed storage on human cerebrospinal fluid metabolomes (18 ), have been reported. Recently, protocols for metabolic profiling of serum and plasma have been published (3,7,19 ).…”

Current trends and challenges in sample preparation for global metabolomics using liquid chromatography–mass spectrometry

“…For instance, lysophosphatidylinositol has been implicated in cancer, but recent reports show that levels of this compound are very sensitive to the activation or inactivation of the blood-clotting cascade, thus necessitating stringent sample-preparation procedures (time to processing, temperature of processing) to avoid artifacts [48]. This study suggests that tentative role of lysophosphatidylinositol as a potential biomarker of cancer must be examined to ensure the analytical approach was sufficient to detect true changes in this metabolite and does not reflect differences in preanalytical handling of the collected blood samples.…”