Red Blood Cell Omics and Machine Learning in Transfusion Medicine: Singularity Is Near

Background: Blood transfusion is a life-saving intervention for millions of recipients worldwide. Over the last 15 years, the advent of high-throughput, affordable omics technologies - including genomics, proteomics, lipidomics, and metabolomics - has allowed transfusion medicine to revisit the biology of blood donors, stored blood products, and transfusion recipients. Summary: Omics approaches have shed light on the genetic and non-genetic factors (environmental or other exposures) impacting the quality of stored blood products and efficacy of transfusion events, based on the current Food and Drug Administration guidelines (e.g., hemolysis and post-transfusion recovery for stored red blood cells). As a treasure trove of data accumulates, the implementation of machine learning approaches promises to revolutionize the field of transfusion medicine, not only by advancing basic science. Indeed, computational strategies have already been used to perform high-content screenings of red blood cell morphology in microfluidic devices, generate in silico models of erythrocyte membrane to predict deformability and bending rigidity, or design systems biology maps of the red blood cell metabolome to drive the development of novel storage additives. Key Message: In the near future, high-throughput testing of donor genomes via precision transfusion medicine arrays and metabolomics of all donated products will be able to inform the development and implementation of machine learning strategies that match, from vein to vein, donors, optimal processing strategies (additives, shelf life), and recipients, realizing the promise of personalized transfusion medicine.

Automated summary

With the advancement of high-throughput omics technologies, transfusion medicine has been able to explore the biology of blood donors, stored blood products, and transfusion recipients. These approaches have revealed the impact of genetic and non-genetic factors on the quality of stored blood products and efficacy of transfusion events. In the near future, precision transfusion medicine arrays and metabolomics will enable the development and implementation of machine learning strategies for personalized transfusion medicine.