The state of RBC metabolism may be a better indicator of cellular quality than traditional hematologic variables.

• Eight extracellular biomarkers define the metabolic age of stored RBCs.• Metabolomics defines a universal signature of RBC storage lesion.Metabolomic investigations of packed red blood cells (RBCs) stored under refrigerated conditions in saline adenine glucose mannitol (SAGM) additives have revealed the presence of 3 distinct metabolic phases, occurring on days 0-10, 10-18, and after day 18 of storage. Here we used receiving operating characteristics curve analysis to identify biomarkers that can differentiate between the 3 metabolic states. We first recruited 24 donors and analyzed 308 samples coming from RBC concentrates stored in SAGM and additive solution 3. We found that 8 extracellular compounds (lactic acid, nicotinamide, 5-oxoproline, xanthine, hypoxanthine, glucose, malic acid, and adenine) form the basis for an accurate classification/regression model and are able to differentiate among the metabolic phases. This model was then validated by analyzing an additional 49 samples obtained by preparing 7 new RBC concentrates in SAGM. Despite the technical variability associated with RBC processing strategies, verification of these markers was independently confirmed in 2 separate laboratories with different analytical setups and different sample sets. The 8 compounds proposed here highly correlate with the metabolic age of packed RBCs, and can be prospectively validated as biomarkers of the RBC metabolic lesion. (Blood. 2016;128(13):e43-e50)

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology ( basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model () to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.