Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification

Daniela Martina Borgmann, Sandra Mayr, Helene Polin, Susanne Schaller, Viktoria Dorfer, Lisa Obritzberger, Tanja Endmayr, Christian Gabriel, Stephan Winkler, Jaroslaw Jacak

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number32317
JournalScientific Reports
Volume6
Issue number32317
DOIs
Publication statusPublished - 1 Sep 2016

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