Quality Markers Addressing Preanalytical Variations of Blood and Plasma Processing Identified by Broad and Targeted Metabolite Profiling

Abstract

BACKGROUND:Metabolomics is a valuable tool with applications in almost all life science areas. There is an increasing awareness of the essential need for high-quality biospecimens in studies applying omics technologies and biomarker research. Tools to detect effects of both blood and plasma processing are a key for assuring reproducible and credible results. We report on the response of the human plasma metabolome to common preanalytical variations in a comprehensive metabolomics analysis to reveal such high-quality markers.

Quality Control of Serum and Plasma by Quantification of (4E,14Z)-Sphingadienine-C18-1-Phosphate Uncovers Common Preanalytical Errors During Handling of Whole Blood

“…To the best of our knowledge, there have been few published studies on tools to detect changes in the quality of whole blood samples that have undergone delayed processing (4,29,30 ). In these studies, either the use of complex, difficult-to-analyze biomarker patterns has been suggested (4 ) or less robust parameters (such as lactate) have been used (29,30 ).…”

“…However, even highly standardized pro-cesses are not infallible, and random and systematic errors (e.g., inaccuracies during blood collection or handling, delays during blood transportation, interruptions in the cold chain) may occur. These errors greatly affect the quality of the samples (1)(2)(3)(4). Currently, substantial efforts are being undertaken worldwide to set up high-quality standardized biobanks that will store billions of sample aliquots, including those from national cohort studies (5)(6)(7)(8)(9)(10)(11).…”

Influence of preanalytical sampling conditions on the 1H NMR metabolic profile of human blood plasma and introduction of the Standard PREanalytical Code used in biobanking

“…Additionally, the high number of samples needed for discovery and validation metabolomics is often gathered from multiple research centers, clinics or biobanks, increasing the likelihood of discrepancies between sample handling (Teahan et al 2006). It has been described for liquid chromatography (LC)-MS-based metabolomics in particular that preanalytical changes can have a major impact on the quality of samples, impeding interpretation of analytical results and decreasing the credibility of research outcomes (Dunn et al 2008;Wood et al 2008;Yang et al 2013;Yin et al 2013;Kamlage et al 2014). As this can potentially affect clinical implementation of metabolomics, it is clear that the impact of clinical sources of preanalytical bias on the plasma 1 H NMR metabolome needs to be elucidated.…”

Plasma metabolomics profiling for the prediction of cytomegalovirus DNAemia and analysis of virus–host interaction in allogeneic stem cell transplant recipients

“…Plasma were then separated and an aliquot conserved (less than 1 h after separation) at 280 uC until metabolomic analysis. Plasma employed for metabolomic analysis had not been thawed previously (Kamlage et al, 2014).…”