AccScience Publishing / IJB / Volume 8 / Issue 2 / DOI: 10.18063/ijb.v8i2.528
SHORT COMMUNICATION

Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids

Kevin Tröndle1* Guilherme Miotto2 Ludovica Rizzo3 Roman Pichler4 Fritz Koch1 Peter Koltay1 Roland Zengerle1,2 Soeren S. Lienkamp3 Sabrina Kartmann1,2 Stefan Zimmermann1
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1 University of Freiburg, IMTEK - Department of Microsystems Engineering, Freiburg, 79110, Germany
2 Hahn-Schickard, Freiburg, 79110, Germany
3 Institute of Anatomy, University of Zurich, Zurich, Switzerland
4 Renal Division, Department of Medicine, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
Submitted: 2 November 2021 | Accepted: 19 January 2022 | Published: 19 January 2022
© 2022 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

We used arrays of bioprinted renal epithelial cell spheroids for toxicity testing with cisplatin. The concentrationdependent cell death rate was determined using a lactate dehydrogenase assay. Bioprinted spheroids showed enhanced sensitivity to the treatment in comparison to monolayers of the same cell type. The measured dose-response curves revealed an inhibitory concentration of the spheroids of IC50 = 9 ± 3 µM in contrast to the monolayers with IC50 = 17 ± 2 µM. Fluorescent labeling of a nephrotoxicity biomarker, kidney injury molecule 1 indicated an accumulation of the molecule in the central lumen of the spheroids. Finally, we tested an approach for an automatic readout of toxicity based on microscopic images with deep learning. Therefore, we created a dataset comprising images of single spheroids, with corresponding labels of the determined cell death rates for training. The algorithm was able to distinguish between three classes of no, mild, and severe treatment effects with a balanced accuracy of 78.7%.

Keywords
Bioprinting
Spheroids
Kidney
Nephrotoxicity
Deep learning
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing