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

• Published in: Transfusion medicine and hemotherapy Vol. 50; no. 3; pp. 174 - 183
• Main Author: D’Alessandro, Angelo
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.06.2023
• Subjects: Backup software; Biological products; Blood donors; Blood transfusion; Genomics; Learning strategies; Machine learning; Metabolites; Review; Review Article; Red blood cells; Storage; Transfusion; Machine learning; Omics
• ISSN: 1660-3796; 1660-3818
• DOI: 10.1159/000529744

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.