Automated, Non-Invasive Characterization of Stem Cell-Derived Cardiomyocytes from Phase-Contrast Microscopy

  • Mahnaz Maddah
  • Kevin Loewke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Stem cell-derived cardiomyocytes hold tremendous potential for drug development and safety testing related to cardiovascular health. The characterization of cardiomyocytes is most commonly performed using electrophysiological systems, which are expensive, laborious to use, and may induce undesirable cellular response. Here, we present a new method for non-invasive characterization of cardiomyocytes using video microscopy and image analysis. We describe an automated pipeline that consists of segmentation of beating regions, robust beating signal calculation, signal quantification and modeling, and hierarchical clustering. Unlike previous imaging-based methods, our approach enables clinical applications by capturing beating patterns and arrhythmias across healthy and diseased cells with varied densities. We demonstrate the strengths of our algorithm by characterizing the effects of two commercial drugs known to modulate beating frequency and irregularity. Our results provide, to our knowledge, the first clinically-relevant demonstration of a fully-automated and non-invasive imaging-based beating assay for characterization of stem cell-derived cardiomyocytes.


Beat Signal Beat Rate Beat Pattern Beating Frequency Beating Signal 
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  1. 1.
    Grskovic, M., Javaherian, A., Strulovici, B., Daley, G.: Induced Pluripotent Stem Cells - Opportunities for Disease Modeling and Drug Discovery. Nature Reviews Drug Discovery (2011)Google Scholar
  2. 2.
    Sun, N., Yazawa, M., Liu, J., Han, L., Sanchez-Freire, V., Abilez, O.J., Navarrete, E.G., Hu, S., Wang, L., Lee, A., Chen, R., Hajjar, R.J., Snyder, M.P., Dometsch, R.E., Butte, M.J., Ashley, E.A., Longaker, M.T., Robbins, R.C., Wu, J.C.: Patient-Specific Induced Pluripotent Stem Cells as a Model for Familial Dilated Cardiomyopathy. Science Translational Medicine 4(130) (2012)Google Scholar
  3. 3.
    Navarrete, E.G., Liang, P., Lan, F., Sanchez-Freire, V., Simmons, C., Gong, T., Sharma, A., Burridge, P.W., Patlolla, B., Lee, A.S., Wu, H., Beygui, R.E., Wu, S.M., Robbins, R.C., Bers, D.M., Wu, J.C.: Screening Drug-Induced Arrhythmia Events Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes and Low-Impedance Microelectrode Arrays. Circulation (2013)Google Scholar
  4. 4.
    Harris, K., Aylott, M., Cui, Y., Louttit, J., McMahon, N.C., Sridhar, A.: Comparison of Electrophysiological Data from Human-induced Pluripotent Stem cell-derived Cardiomyocytes to Functional Preclinical Safety Assays. Toxicol. Sci. 134(2), 412–426 (2013)CrossRefGoogle Scholar
  5. 5.
    Hayakawa, T., Kunihiro, T., Dowaki, S., Uno, H., Matsui, E., Uchida, M., Kobayashi, S., Yasuda, A., Shimizu, T., Okano, T.: Noninvasive Evaluation of Contractile Behavior of Cardiomyocyte Monolayers Based on Motion Vector Analysis. Tissue Engineering 18(1) (2012)Google Scholar
  6. 6.
    Liu, X., Iyengar, S.G., Rittscher, J.: Monitoring Cardiomyocyte Motion in Real Time Through Image Registration and Time Series Analysis. In: ISBI, pp. 1308–1311 (2012)Google Scholar
  7. 7.
    Liu, X., Padfield, D.: Motion-Based Segmentation for Cardiomyocyte Characterization. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds.) STIA 2012. LNCS, vol. 7570, pp. 137–146. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mahnaz Maddah
    • 1
  • Kevin Loewke
    • 1
  1. 1.Cellogy Inc.Menlo ParkUSA

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