Variable blood processing procedures contribute to plasma proteomic variability

Plasma is a potentially rich source of protein biomarkers for disease progression and drug response. Large multi-center studies are often carried out to increase the number of samples analyzed in a given study. This may increase the chances of variation in blood processing and handling, leading to altered proteomic

Variable blood processing procedures contribute significantly to plasma proteomic variation and may give rise to increased intracellular proteins in plasma. Accounting for these effects can be important both at study design and data analysis stages. This understanding will be valuable to incorporate in the planning of protein-based biomarker discovery efforts in the future.

Initially two batches of patient plasma samples (120 and 204 samples, respectively) were analyzed using LC-MS/MS shotgun proteomics. Follow-up experiments were designed and carried out on healthy donor blood in order to examine the effects of different centrifugation conditions, length of delay until first centrifugation, storage temperature and anticoagulant type on

Variable levels of intracellular proteins were observed in subsets of patient plasma samples from the initial batches analyzed. This observation correlated strongly with the site of collection, implicating variability in blood processing procedures. Results from the healthy donor blood analysis did not demonstrate a significant impact of centrifugation conditions to plasma proteome variation. The time delay until first centrifugation had a major impact on variability, while storage temperature and anticoagulant showed less pronounced but still significant effects. The intracellular proteins associated with study site effect in patient plasma samples were significantly altered by delayed processing also. Conclusions: Variable blood processing procedures contribute significantly to plasma proteomic variation and may give rise to increased intracellular proteins in plasma. Accounting for these effects can be important both at study design and data analysis stages. This understanding will be valuable to incorporate in the planning of protein-based biomarker discovery efforts in the future.