Evaluating Metabolite Stability of Blood Samples for Clinical Applications

Metabolon characterized metabolite stability of blood using a variety of sample collection, handling and processing procedures.

It is estimated that 60 to 70% of all decisions regarding patient’s diagnosis, treatment, hospital admission, and discharge are based on clinical laboratory test results. Clinical metabolomics plays an increasingly important role in clinical decision making for patients with the with rare diseases as well as other diseases with metabolic signatures. As such, the quality of data generated by the clinical laboratory at Metabolon requires intense scrutiny.  Unfortunately, great part of the analytical variability in clinical testing occurs during the pre-analytical stages, including sample collection and handling, transportation, preparation for analysis and storage. In fact, pre-analytical errors account for up to 70% of all mistakes made in laboratory diagnosis, often outside of the laboratory and well before sample analysis.¹

At Metabolon, we have now extensively characterized how different pre-analytical aspects dealing with the collection, handling and processing of blood samples may affect the metabolome. Our data show the following:

• Delayed blood processing significantly affects the plasma metabolome and precise methods should be utilized in order to acquire consistent results. ²
• The choice and use of plasma or serum type using consistent anticoagulant additives is critical as one cannot mix and match sample types in order to obtain meaningful comparisons using metabolomics. ³
• Repeated freeze thawing and extended bench top processing of plasma samples show significantly fewer changes compared to other aspects of improper sample handling.⁴

Pre-analytical error is a particular concern for clinical metabolomics where EDTA plasma must be separated from red cells within one hour of sample collection and the plasma immediately frozen until testing can be performed. ² Fortunately, since clinical metabolomics measures large numbers of analytes, some of which are artificially altered due to incorrect sample processing, we have designed quality control (QC) checks on clinical metabolomics data to ensure pre-analytical sample processing compliance. The most obvious QC check is to evaluate each sample for EDTA signal to ensure that the correct sample collection tube is submitted for analysis. Additionally, we showed that the metabolic profiles of serum, heparin plasma, and citrate plasma are significantly different from EDTA plasma such that the different sample types should not be compared using clinical metabolomics.  These differences included several biomarkers associated with rare diseases and biomarkers of liver function and kidney function³. Another approach has been to assess biomarkers that are artificially changed due to delays in plasma separation. For instance, when whole blood remains at room temperature for long periods of time, white blood cells and erythrocytes in whole blood consume glucose and other nutrients for energy production, profoundly affecting the biochemical profile of a sample. ² These changes in metabolite levels in plasma can be used to identify mishandled samples.

In a recent publication in Metabolomics⁴ scientists at Metabolon evaluated and documented pre-analytical errors associated with freeze thaw cycling and extended bench top processing of EDTA plasma samples. Prior to this publication, it was generally believed that multiple freeze thaw cycles of plasma samples had a significant effect on metabolite levels. However, Goodman et.al.  reported that less than 3% of the metabolome are affected by multiple freeze-thaw cycles or extended thawing at 4^oC. ³

Metabolon’s extensive work establishes criteria for evaluating metabolite stability in clinical metabolomic applications and provides guidelines for disqualifying unstable metabolites deriving from sample handling and processing in clinical metabolomic studies to ensure pre-analytical compliance.

Contact us at Metabolon to discuss your experimental design and how to best collect, handle and process your specific sample type.

References

1. Lippi, J.J. Chance, S. Church, P. Dazzi, R. Fontana, D. Giavarina, K. Grankvist, W. Huisman, T. Kouri, V. Palicka, M. Plebani, V. Puro, G.L. Salvagno, S. Sandberg, K. Sikaris, I. Watson, A.K. Stankovic, A.M. Simundic, Pre-analytical quality improvement: from dream to reality. Clin Chem Lab Med. 2011 Jul; 49(7):1113-26
2. Jain, A.D. Kennedy, S.H. Elsea, M.J. Miller, Analytes related to erythrocyte metabolism are reliable biomarkers for pre-analytical error due to delayed plasma processing in metabolomics studies. Clin. Chim. Acta. 466 (2017) 105-111.
3. Kennedy, A. D., Ford, L., Wittmann, B., Conner, J., Wulff, J., Mitchell, M., Evans, A. M., & Toal, D. R. (2021). Global biochemical analysis of plasma, serum and whole blood collected using various anticoagulant additives. PloS one, 16(4), e0249797.
4. K. Goodman, M. Mitchell, A.M. Evans, L.A.D. Miller, L. Ford, B. Wittmann, A.D., Kennedy, D. Toal, Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma using untargeted metabolomics. Metabolomics (2021) 17:31.