Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.
Journal:
Scientific reports
PMID:
40044706
Abstract
Assessment of the morphology of red blood cells (RBCs) can improve clinical benefits following blood transfusion. Deep machine learning surpasses traditional microscopy-based classification methods, offering more accurate and consistent results while reducing time and labor intensity. RBCs from teenage males, teenage females, senior males, and senior females were biologically age-profiled or density-separated into dense/old RBCs (O-RBCs) and less-dense/young (Y-RBCs) throughout hypothermic storage and assessed using image flow cytometry with deep machine learning analysis. Regardless of biological age, morphology index decreased with hypothermic storage. Significant differences in RBC morphology index were not seen when comparing unseparated RBCs (U-RBCs), O-RBCs, and Y-RBCs, although the proportions of morphology subclasses revealed differences between RBCs groups from different donor groups and in samples with different biological age. Cold storage remains the most significant influence on morphology, although teenage male donors demonstrated slightly more susceptibility to storage lesions compared with senior males and females. Our work highlights that hypothermic storage most significantly impacts RBC morphology over biological age and donor characteristics, emphasizing the importance of storage effects on transfusion quality and safety.