Automated quantification study of human cardiomyocyte synchronization using holographic imaging

Area of Science:

• Cardiology
• Biophysics
• Stem Cell Biology

Background:

• Human induced pluripotent stem cells (iPS) are crucial for disease modeling and drug discovery.
• Cardiomyocytes derived from iPS cells offer a scalable model for studying cardiac function.
• Assessing cardiomyocyte synchronization is vital for understanding cardiac electrophysiology and disease.

Purpose of the Study:

• To investigate the rhythm strip and synchronization parameters of iPS-derived cardiomyocytes.
• To develop and validate a method for automated evaluation of cardiomyocyte synchronization.
• To explore the application of this method in disease studies and drug safety testing.

Main Methods:

• Utilized time-lapse digital holographic imaging to capture quantitative phase images of beating cardiomyocytes.
• Employed quantitative monitoring of dry mass redistribution to derive physical contraction-relaxation signals.
• Applied k-means clustering and watershed segmentation for cell extraction and analysis of cell-to-cell synchronicity.

Main Results:

• Demonstrated that time-lapse microscopic holographic imaging can automatically evaluate mature cardiomyocyte synchronization.
• Successfully extracted individual cardiac muscle cells from multi-cellular phase images.
• Established a quantitative method to assess synchronization parameters from holographic data.

Conclusions:

• Time-lapse digital holographic imaging provides a robust method for evaluating cardiomyocyte synchronization.
• The developed technique is suitable for automated analysis in research settings.
• This approach has significant potential for advancing studies on cardiac disorders and drug safety evaluations.