ArticleBMC bioinformatics2020
Automated image analysis system for studying cardiotoxicity in human pluripotent stem cell-Derived cardiomyocytes.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Review
- Multiparametric dynamic optical coherence tomography for evaluation of human cardiac organoids subjected to external stress.Biomedical optics express · 2026Article
- Metabolic cardiomyopathies: untangling clinical heterogeneity with human stem-cell derived models.EMBO molecular medicine · 2025Review
- Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph.PLoS computational biology · 2025Article
- Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis.Computational intelligence and neuroscience · 2022Article
- Modeling Precision Cardio-Oncology: Using Human-Induced Pluripotent Stem Cells for Risk Stratification and Prevention.Current oncology reports · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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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Registered trials
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