Hitchhiker's guide to the red cell storage galaxy: Omics technologies and the quality issue

The Role of α1-Microglobulin (A1M) in Erythropoiesis and Erythrocyte Homeostasis-Therapeutic Opportunities in Hemolytic Conditions

α₁-microglobulin (A1M) is a small protein present in vertebrates including humans. It has several physiologically relevant properties, including binding of heme and radicals as well as enzymatic reduction, that are used in the protection of cells and tissue. Research has revealed that A1M can ameliorate heme and ROS-induced injuries in cell cultures, organs, explants and animal models. Recently, it was shown that A1M could reduce hemolysis in vitro, observed with several different types of insults and sources of RBCs. In addition, in a recently published study, it was observed that mice lacking A1M (A1M-KO) developed a macrocytic anemia phenotype. Altogether, this suggests that A1M may have a role in RBC development, stability and turnover. This opens up the possibility of utilizing A1M for therapeutic purposes in pathological conditions involving erythropoietic and hemolytic abnormalities. Here, we provide an overview of A1M and its potential therapeutic effect in the context of the following erythropoietic and hemolytic conditions: Diamond-Blackfan anemia (DBA), 5q-minus myelodysplastic syndrome (5q-MDS), blood transfusions (including storage), intraventricular hemorrhage (IVH), preeclampsia (PE) and atherosclerosis.

Early Career Scientists' Guide to the Red Blood Cell - Don't Panic!

Why should we take interest in studying red blood cells? This mini review attempts to answer this question and highlights the problems that authors find most appealing in this dynamic research area. It addresses the early career scientists who are just starting their independent journey and facing tough times. Despite unlimited access to information, the exponential development of computational and intellectual powers, and the seemingly endless possibilities open to talented and ambitious early career researchers, they soon realize that the pressure of imminent competition for financial support is hard. They have to hit deadlines, produce data, publish, report, teach, manage, lead groups, and remain loving family members at the same time. Are these countless hardships worth it? We think they are. Despite centuries of research, red blood cells remain a mysterious and fascinating study objects. These cells bring together experts within the family of the European Red Cell Society and beyond. We all share our joy for the unknown and excitement in understanding how red cells function and what they tell us about the microenvironments and macroenvironments they live in. This review is an invitation to our colleagues to join us on our quest.

Real Age: Red Blood Cell Aging During Storage

BACKGROUND

During cold storage, some red blood cell (RBC) units age more rapidly than others. Yet, the Food and Drug Administration has set a uniform storage limit of 42 days. Objectives of this review are to present evidence for an RBC storage lesion and suggest that functional measures of stored RBC quality-which we call real age-may be more appropriate than calendar age.

METHODS

During RBC storage, biochemical substances and byproducts accumulate and RBC shape alters. Factors that influence the rate of degradation include donor characteristics, bio-preservation conditions, and vesiculation. Better understanding of markers of RBC quality may lead to standardized, quantifiable, and operationally practical measures to improve donor selection, assess quality of an RBC unit, improve storage conditions, and test efficacy of the transfused product.

RESULTS

The conundrum is that clinical trials of younger versus older RBC units have not aligned with in vitro aging data; that is, the units transfused were not old enough. In vitro changes are considerable beyond 28 to 35 days, and average storage age for older transfused units was 14 to 21 days.

CONCLUSIONS

RBC product real age varies by donor characteristics, storage conditions, and biological changes during storage. Metrics to measure temporal changes in quality of the stored RBC product may be more appropriate than the 42-day expiration date. Randomized trials and observational studies are focused on average effect, but, in the evolving age of precision medicine, we must acknowledge that vulnerable populations and individuals may be harmed by aging blood.

Systems biology as an emerging paradigm in transfusion medicine

Blood transfusions are an important part of modern medicine, delivering approximately 85 million blood units to patients annually. Recently, the field of transfusion medicine has started to benefit from the “omic” data revolution and corresponding systems biology analytics. The red blood cell is the simplest human cell, making it an accessible starting point for the application of systems biology approaches.

In this review, we discuss how the use of systems biology has led to significant contributions in transfusion medicine, including the identification of three distinct metabolic states that define the baseline decay process of red blood cells during storage. We then describe how a series of perturbations to the standard storage conditions characterized the underlying metabolic phenotypes. Finally, we show how the analysis of high-dimensional data led to the identification of predictive biomarkers.

The transfusion medicine community is in the early stages of a paradigm shift, moving away from the measurement of a handful of chosen variables to embracing systems biology and a cell-scale point of view.

Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. 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 (Q₁₀) 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 Q₁₀ 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 Q₁₀ 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 Q₁₀ 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.

Transfusion-related immunomodulation and cancer

Blood and blood-component therapy triggers immunological reactions in recipients. Transfusion-related immunomodulation [TRIM] is an important complex biological immune reaction to transfusion culminating in immunosuppression. The mechanisms underlying TRIM include the presence of residual leukocytes and apoptotic cells, the transfusion of immunosuppressive cytokines either present in donor components or generated during blood processing, the transfer of metabolically active growth factor-loaded microparticles and extracellular vesicles and the presence of free hemoglobin or extracellular vesicle-bound hemoglobin. TRIM variables include donor-specific factors as well as processing variables. TRIM may explain, at least in part, the controversial negative clinical outcomes observed in cancer patients receiving transfusion in the context of curative-intent surgeries. The use of novel technologies including metabolomics and proteomics on stored blood may pave the way for a deeper understanding of TRIM in general and its impact on cancer progression.