Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine

Abstract

Transfusion of red blood cells (RBCs) is one of the most valuable and widespread treatments in modern medicine. Lifesaving RBC transfusions are facilitated by the cold storage of RBC units in blood banks worldwide. Currently, RBC storage and subsequent transfusion practices are performed using simplistic workflows. More specifically, most blood banks follow the "first-in-first-out" principle to avoid wastage, whereas most healthcare providers prefer the "last-in-first-out" approach simply favoring chronologically younger RBCs. Neither approach addresses recent advances through -omics showing that stored RBC quality is highly variable depending on donor-, time-, and processing-specific factors. Thus, it is time to rethink our workflows in transfusion medicine taking advantage of novel technologies to perform RBC quality assessment. We imagine a future where lab-on-a-chip technologies utilize novel predictive markers of RBC quality identified by -omics and machine learning to usher in a new era of safer and precise transfusion medicine.

Keywords: preservation; red blood cells; transfusion.

Fig. 1.

The current and proposed future workflow for RBC storage and transfusion medicine. Donors vary in sex, age, ethnicity, genetic disposition, body mass index, metabolism, and habits. These variations can lead to different initial quality levels for donated RBCs and to different quality changes during storage. Currently, blood banks store donated RBCs at 4 °C for 42 d. This storage can induce injuries collectively termed the “storage lesions” in a donor-, time-, and processing-dependent manner. Thus, the injury to each stored RBC unit can be unique. The current quality assessments performed on individual RBC units before transfusion are infectious disease marker testing and donor screening, blood typing, and visual inspection for bacterial contamination. These assessments are agreed upon and mandated by health agencies worldwide—including the FDA and CDC. RBC units are generally allocated following the “first-in-first-out” protocol. However, this workflow may result in posttransfusion complications and significantly reduced efficacy. The advances in LOC technologies armed with information from -omics and ML studies offer a better alternative. We envision a future where each RBC unit is assessed continuously during storage using multiple quality metrics based on objective unbiased measurements via LOC technologies. Patients with different medical conditions will then be administered the right RBC unit via data-driven allocation. Such an approach will maximize the efficacy of transfusion therapies and reduce complications.

Fig. 2.

Donor-, time-, and processing-dependent RBC storage lesions. RBCs undergo various injuries during storage in their biochemical content, oxidative state, and morphology. Major biochemical changes include the depletion of ATP and 2,3-DPG. This is accompanied by an increase in reactive oxygen species damaging proteins and lipids (through increased malondialdehyde—MDA), among other injuries affecting the cellular integrity. These injuries are often observed as irreversible RBC morphological changes resulting in loss of integrity (hemolysis), deformability, and discoid morphology. While these alterations have a general time-dependent trend, they also depend on the donor and the initial blood processing, which varies among blood collection centers. These donor-, time-, and processing-dependent changes are not monitored in today’s practice of blood banking.

Fig. 3.

LOC platforms for RBC quality assessment. Figure (A) shows different aspects, components, and materials of LOC platforms from design and fabrication to operation (flow) and sensing. Such platforms enable detection of various biomarkers ranging from cells and proteins to ions. Figure (B) shows a schematic representation of one of the most commonly used RBC deformability measurements using LOC platforms. Such platforms use microfluidic constrictions to quantify the degree of deformability.

Fig. 4.

Convergent future of stored RBC assessment toward precision transfusion. Novel -omics and ML technologies have recently been used to assess RBC storage lesions. We envision that the integration of these technologies will identify key quality metrics (quality indices) for stored RBCs in the near future. These quality indices can then guide LOC platforms armed with novel biosensors to continuously monitor stored RBC quality and then match the properties of the RBC unit to the needs of the patient.