Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification
D. M. Borgmann, S. Mayr, H. Polin, S. Schaller, V. Dorfer, L. Obritzberger, T. Endmayr, C. Gabriel, S. M. Winkler, J. Jacak - Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification - Scientific Reports, Vol. 6, No. 32317, 2016
In transfusion medicine, the identification of the Rhesus D type is important to prevent anti-D immunisation in Rhesus D negative recipients. In particular, the detection of the very low expressed DEL phenotype is crucial and hence constitutes the bottleneck of standard immunohaematology. The current method of choice, adsorption-elution, does not provide unambiguous results. We have developed a complementary method of high sensitivity that allows reliable identification of D antigen expression. Here, we present a workflow composed of high-resolution fluorescence microscopy, image processing, and machine learning that - for the first time - enables the identification of even small amounts of D antigen on the cellular level. The high sensitivity of our technique captures the full range of D antigen expression (including D+, weak D, DEL, D−), allows automated population analyses, and results in classification test accuracies of up to 96%, even for very low expressed phenotypes.