37 Car, a Novel Mediator of Erythroid Differentiation and Migration, Is Specifically Downregulated in Erythropoietic Progenitor Cells in MDS

Bauer, Karin; Machherndl‐Spandl, Sigrid; Suessner, Susanne; Danzer, Martin; Proell, Johannes; Lauf, Jeroen; Eisenwort, Gregor; Sperr, Wolfgang R.; Klein, Helmut W.; Hoermann, Gregor; Haefner, N.; Béné, Marie‐Christine; Platzbecker, Uwe; Weltermann, Ansgar; Gabriel, Christian; Lion, Thomas; Germing, Ulrich; Bettelheim, Peter; Valent, Peter

doi:10.1016/s0145-2126(15)30038-2

Leukemia Research

2015

DOI: 10.1016/s0145-2126(15)30038-2

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37 Car, a Novel Mediator of Erythroid Differentiation and Migration, Is Specifically Downregulated in Erythropoietic Progenitor Cells in MDS

Karin Bauer¹,

Sigrid Machherndl‐Spandl²,

Susanne Suessner³

et al.

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“…Information about protein abundance of known alleles is given by the RhesusBase. In case of novel alleles the D antigen expression level has to be quantified, commonly by flow cytometry 23 : Using flow cytometry, the D antigen is detected by fluorescently labelled standard antibodies 24 25 26 . This technique, however, exhibits huge inter-laboratory variability 27 , lacks standardised reagents 28 , and frequently fails to detect D variants with low expression due to an inherent lack of sensitivity (limit of detection: 22 antigens/cell) 13 14 19 .…”

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

Borgmann

Mayr

Polin

et al. 2016

Sci Rep

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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.

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mentioning

confidence: 99%

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

Borgmann

Mayr

Polin

et al. 2016

Sci Rep

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37 Car, a Novel Mediator of Erythroid Differentiation and Migration, Is Specifically Downregulated in Erythropoietic Progenitor Cells in MDS

Cited by 1 publication

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

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

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