Abstract

The Rhesus D (RhD) protein, expressed on red blood cells, is an important determinant in human blood transfusion medicine. Due to its polymorphism and immunogenicity1 correct classification is of utmost importance. Harmful anti-D immunization caused by transfusion of seemingly D- negative blood units is still a common cause of haemolytic anemia2.The abundance of low level RhD expression is analyzed by the adsorption-elution test. However, this method lacks standardized protocols and has been reported to fail in detection of very weakly expressed D variants (called DEL, D-elute), offering the possibility of anti-D immunization3. Thus, we developed a complementary approach consisting of high-resolution fluorescence microscopy, automated image processing combined with machine learning. This workflow enables highly sensitive detection of even low levels of RhD expression as seen in DEL phenotypes. Four different erythrocyte populations were analyzed: D+ (high expression), weak D (low expression), DEL (very low expression) and D- (no expression). All phenotypes were incubated with fluorescently labelled monoclonal antibodies binding different epitopes of the D antigen. Images of red blood cells obtained by fluorescence microscopy show single peaks of different signal intensities correlating with the expression level. Automated image parametrization and feature extraction form the basis for subsequent application of machine learning algorithms enabling accurate Rhesus D blood group classification. For the first time, highly sensitive fluorescent verification of weakly expressed D variants is achieved.1. Avent, N. D. & Reid, M. E. The Rh blood group system: a review. Blood 95, 375-387 (2000).2. Zalpuri, S. et al. Red-blood-cell alloimmunization and number of red-blood-cell transfusions. Vox Sang. 102, 144-149 (2012).3. Krog, G. R. et al. Is current serologic RhD typing of blood donors sufficient for avoiding immunization of recipients? (CME). Transfusion 51, 2278-2285 (2011).

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