Document resource
Background The objective assessment of age-related cardiomyocyte dysfunction is crucial for understanding the progression of heart failure. The conventional analysis, based on subjective visual interpretation, lacks the quantitative studies. This study introduces a novel physics-guided mathematical framework to objectively quantify the stochasticity of contractile dynamics from live-cell microscopy time-series data.Methodology The framework analyses 64,000 cells per well using high-resolution microscopy videos of iPSC-derived cardiomyocytes. Dense optical flow fields are computed using the Farneback algorithm to generate pixel-wise motion vectors. The Euclidean magnitude of these vectors is then calculated and discretised into a 32-bin histogram, forming a probability mass function that represents the motion within the frame. The Shannon Entropy of this distribution is then computed that quantifies the dynamical disorder of the contractile event.Result The framework was validated on a time-series dataset comprising 678 frame-pair analyses. The calculated motion entropy across the dataset had a mean of 0.7584 with a standard deviation of 0.4046. The values spanned a wide, interpretable range, from a minimum of 0.0628 (indicating highly periodic and predictable motion) to a maximum of 3.0838 (indicating highly chaotic and unpredictable motion). The distribution of these values was found to be right-skewed, with the majority of frames exhibiting low to moderate entropy. This quantitative spectrum directly supports the hypothesis that motion entropy can serve as a robust biomarker for distinguishing between ordered (healthy) and disordered (aged or damaged) cellular dynamics.Conclusion and Future Work This provides a foundational, physics-guided biomarker for objectively studying the progression of cellular ageing. The future includes fully automated, real-time phenotyping and further advancement in the field.