Quantitative, noninvasive assessment of cardiomyocyte contractility is essential for in vitro cardiac models, yet optics offer limited metrics and many quantitative approaches rely on staining or mechanical contact, restricting longitudinal and intratissue analyses. Here, we present a label-free, contactless, and in situ contractility quantification framework using an off-axis digital holographic microscopy (DHM) microplatform with real-time single-shot optical path difference (OPD) reconstruction. From dynamic holograms, we extract three complementary spatiotemporal waveforms: an OPD waveform for axial contractile dynamics, an optical-flow-derived waveform for in-plane directional contractility, and an energy-based intensity waveform for direction-agnostic dynamics. Across six grooved gelatin patterns, we validated structural alignment measured by immunofluorescence with holography-derived contraction directionality quantified by lock-in optical-flow metrics. We further demonstrate within-tissue studies by continuously monitoring the same region of interest (ROI) during sequential pharmacological modulation with isoproterenol followed by propranolol antagonism, and during in situ electrical pacing over a voltage-frequency grid. The results reveal substrate-dependent enhancement of directional contractility, with G20-10 yielding consistently stronger contraction directionality and a higher work proxy. Moreover, electrical pacing produced the maximum work proxy at 7 V and 1.5 Hz under the tested conditions. This proxy was defined as a frequency-amplitude-based optical activity index, thereby validating the capability of the proposed system for label-free, in situ monitoring and comparative analysis under controlled perturbations. This work establishes a practical hologram-derived waveform analysis paradigm for high-precision, longitudinal evaluation of cardiac tissues, and is expected to enable broadly applicable, label-free functional phenotyping for in tissue engineering and cardiac organoid development, as well as scalable drug screening and stimulation-optimization workflows for disease modeling and regenerative medicine.
Dong et al. (Thu,) studied this question.