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Automated Contraction Analysis of Human Engineered Heart Tissue for Cardiac Drug Safety Screening
Published on: April 15, 2017
Development of a drug screening platform based on engineered heart tissue
Arne Hansen1, Alexandra Eder, Marlene Bönstrup
1Department of Experimental and Clinical Pharmacology and Toxicology, Cardiovascular Research Center, University Medical Center Hamburg-Eppendorf, Germany.
Circulation Research
|May 8, 2010
Summary
We developed a simple, automated method for creating engineered heart tissues (EHTs) for high-throughput drug screening and disease modeling. This technique offers reproducible results and stability for weeks, advancing in vitro research capabilities.
Area of Science:
- Biotechnology
- Tissue Engineering
- Cardiovascular Research
Background:
- Current tissue engineering methods for in vitro drug and disease modeling lack high-throughput capabilities.
- Induced pluripotent stem cell technology offers potential but requires scalable platforms.
Purpose of the Study:
- To develop a miniaturized and automated method for generating engineered heart tissues (EHTs).
- To enable high-throughput drug screening and disease modeling using EHTs.
Main Methods:
- Fibrin-based mini-EHTs (FBMEs) were created using neonatal rat heart cells, fibrinogen/Matrigel, and thrombin in casting molds.
- Contractile activity was monitored video-optically and analyzed by custom software.
- FBMEs were cultured for 8-10 days, allowing them to mature into contractile muscle strips.
Main Results:
- FBMEs exhibited rhythmic post deflection, enabling calculation of rate, force (0.1-0.3 mN), and kinetics.
- Constructs showed a well-developed cardiac muscle network and interspersed vascular structures.
- High yield, reproducibility, and stability for weeks were demonstrated, with concentration-dependent responses to drugs like doxorubicin.
Conclusions:
- A simple, automated technique for constructing and evaluating large series of EHTs was developed.
- This method is suitable for high-throughput drug screening.
- The technique is valuable for disease modeling applications.
