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Automated image analysis system for studying cardiotoxicity in human pluripotent stem cell-Derived cardiomyocytes

Authors
  • Cao, Lu1
  • der Meer, Andries D. van2
  • Verbeek, Fons J.1
  • Passier, Robert2, 3
  • 1 Imaging and Bioinformatics group, Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1, Leiden, 2333 CA, The Netherlands , Leiden (Netherlands)
  • 2 Dept of Applied Stem Cell Technologies, MIRA Institute, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The Netherlands , Enschede (Netherlands)
  • 3 Dept of Anatomy and Embryology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands , Leiden (Netherlands)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 14, 2020
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s12859-020-3466-1
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundCardiotoxicity, characterized by severe cardiac dysfunction, is a major problem in patients treated with different classes of anticancer drugs. Development of predictable human-based models and assays for drug screening are crucial for preventing potential drug-induced adverse effects. Current animal in vivo models and cell lines are not always adequate to represent human biology. Alternatively, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) show great potential for disease modelling and drug-induced toxicity screenings. Fully automated high-throughput screening of drug toxicity on hiPSC-CMs by fluorescence image analysis is, however, very challenging, due to clustered cell growth patterns and strong intracellular and intercellular variation in the expression of fluorescent markers.ResultsIn this paper, we report on the development of a fully automated image analysis system for quantification of cardiotoxic phenotypes from hiPSC-CMs that are treated with various concentrations of anticancer drugs doxorubicin or crizotinib. This high-throughput system relies on single-cell segmentation by nuclear signal extraction, fuzzy C-mean clustering of cardiac α-actinin signal, and finally nuclear signal propagation. When compared to manual segmentation, it generates precision and recall scores of 0.81 and 0.93, respectively.ConclusionsOur results show that our fully automated image analysis system can reliably segment cardiomyocytes even with heterogeneous α-actinin signals.

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