SBI – Department of Systems Biology and Bioinformatics
Faculty of Computer Science and Electrical Engineering
University of Rostock
Ulmenstrasse 69 | 18057 Rostock
Germany
+49 381 498-7571
olaf.wolkenhauer@uni-rostock.de
iRhythmics: Programming pacemaker cells for in vitro drug testing
The project addresses the generation and establishment of programmed pacemaker cells for an in vitro drug testing possibility to perform predictive tests. This may lead to an improved treatment of cardiac arrhythmias or an accurate identification of potential drug molecules at an early stage of development. Important benefits will arise in verifying the safety of a wide variety of medicines while reducing animal testing.
The data is managed in FairdomHub - https://fairdomhub.org/projects/28
This project is funded by the European Social Fund (ESF) program of the European Union (ESF/14-BM-A55-0027).
Visit our official Project-Website:
Related publications
Monitoring the maturation of the sarcomere network: a super-resolution microscopy-based approach
Skorska A, Johann L, Chabanovska O, Vasudevan P, Kussauer S, Hillemanns M, Wolfien M, Jonitz-Heincke A, Wolkenhauer O, Bader R, Lang H, David R, Lemcke H
CLMS (2022)
Personalized cell therapy for patients with peripheral arterial diseases in the context of genetic alterations: Artificial intelligence-based responder and non-responder prediction
Salybekov AA, Wolfien M, Kobayashi S, Steinhoff G, Asahara T
Cells 2021
Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling
Bej S, Galow AM, David R, Wolfien M, Wolkenhauer O
BMC Bioinformatics 2021
Quality control in scRNA-Seq can discriminate pacemaker cells – the mtDNA bias
Galow AM, Kussauer S, Wolfien M, Brunner R, Goldammer T, David R, Hoeflich A
AMES: Automated evaluation of sarcomere structures in cardiomyocytes
Hillemanns M, Lemcke H, David R, Martinetz T, Wolfien M, Wolkenhauer O
bioRxiv 2021
Cardiomyocyte Transplantation after Myocardial Infarction Alters the Immune Response in the Heart
Vasudevan P, Wolfien M, Lemcke H, Lang CI, Skorska A, Gaebel R, Koczan D, Lindner T, Engelmann R, Vollmar B, Krause BJ, Wolkenhauer O, Lang H, Steinhoff G, David R
Cells 2020, 9(8), 1825
Integrative cluster analysis of whole hearts reveals proliferative cardiomyocytes in adult mice
Galow AM, Wolfien M, Müller P, Bartsch M, Brunner RM, Hoeflich A, Wolkenhauer O, David R, Goldammer T
Cells 2020, 9 (5), 1144
A benchmark of hemoglobin blocking during library preparation for mRNA-Sequencing of human blood samples
Uellendahl-Werth F, Wolfien M, Franke A, Wolkenhauer O, Ellinghaus D
Scientific Reports 2020, 10, 5630
Single Nuclei Sequencing of entire Mammalian Hearts: Strain-dependent Cell Type Composition and Velocity
Wolfien M, Galow AM, Müller P, Bartsch M, Brunner RM, Goldammer T, Wolkenhauer O, Hoeflich A, David R
RNA-Based Strategies for Cardiac Reprogramming of Human Mesenchymal Stromal Cells
Mueller P, Wolfien M, Ekat K, Lang CI, Koczan D, Wolkenhauer O, Hahn O, Peters K, Lang H, David R, Lemcke H
Cells 2020, 9 (2), 504
Single Nuclei Sequencing of an entire Mammalian Heart: Cell Type Composition and Velocity
Wolfien M, Galow AM, Müller P, Bartsch M, Brunner RM, Goldammer T, Wolkenhauer O, Hoeflich A, David R
Cells 2020, 9, 318
Single-Cell RNA Sequencing Procedures and Data Analysis
Wolfien M, David R, Galow AM
Exon Publications, Brisbane, Australia (2021), chapter in Bioinformatics.
ISBN 13 (online): 978-0-6450017-1-6
DOI: https://doi.org/10.36255/exonpublications.bioinformatics.2021.ch2
URL: https://exonpublications.com/index.php/exon/article/view/265
Tools for Understanding miRNA–mRNA Interactions for Reproducible RNA Analysis
Bagnacani A, Wolfien M, Wolkenhauer O
Springer (2019), chapter in Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912.
Workflow Development for the Functional Characterization of ncRNAs
Wolfien M, Brauer DL, Bagnacani A, Wolkenhauer O
Springer (2019), chapter in Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912.
Comparison of Deep Learning Approaches for Cardiomyocyte Evaluation
Maximilian Hillemanns
2020
Arrhythmias are severe cardiac diseases and lethal if untreated. To serve as an in vitro drug testing option for anti-arrhythmic agents, cardiomyocytes and especially pacemaker cells are being generated in vitro from induced pluripotent stem cells (iPSCs). These generated cardiomyocytes resemble fetal cardiac tissue rather than adult cardiomyocytes. An automated tool for evaluations of cardiomyocytes would help the establishment of new generation protocols. In this work, a novel approach for this task is presented and evaluated.
Different convolutional neural networks (CNNs) including transfer models and native 2D and 3D models were trained on fluorescence images of cardiomyocytes, which were rated based on their sarcomerisation and the orientation of sarcomeres (directionality) beforehand. The CNNs were trained to perform classifications on sarcomerisation and directionality ratings and cell source, as cardiomyocytes from five different sources were used in this work. In this thesis, it could be shown that cellular fluorescence images can be analysed with CNNs. This classifier can be used to make trustworthy predictions on the quality of a cardiomyocyte which will hopefully benefit the generation of cardiomyocytes from iPSCs. This classifier is currently being