Speckle‐Tracking Strain Analysis for Mapping Spatiotemporal Contractility of Induced Pluripotent Stem Cell (iPSC)‐Derived Cardiomyocytes

Abstract

AbstractHuman induced pluripotent stem cell (hiPSC)‐derived cardiomyocytes (hiPSC‐CMs) hold tremendous potential for cardiovascular disease modeling, drug screening, personalized medicine, and pathophysiology studies. The availability of a robust protocol and functional assay for studying phenotypic behavior of hiPSC‐CMs is essential for establishing an in vitro disease model. Many heart diseases manifest due to changes in the mechanical strain of cardiac tissue. Therefore, non‐invasive evaluation of the contractility properties of hiPSC‐CMs remains crucial to gain an insight into the pathogenesis of cardiac diseases. Speckle tracking–based strain analysis is an efficient non‐invasive method that uses video microscopy and image analysis of beating hiPSC‐CMs for quantitative evaluation of mechanical contractility properties. This article presents step‐by‐step protocols for extracting quantitative contractility properties of an hiPSC‐CM system obtained from five members of a family, of whom three were affected by DiGeorge syndrome, using speckle tracking–based strain analysis. The hiPSCs from the family members were differentiated and purified into hiPSC‐CMs using metabolic selection. Time‐lapse images of hiPSC‐CMs were acquired using high‐spatial‐resolution and high‐time‐resolution phase‐contrast video microscopy. Speckled images were characterized by evaluating the cross‐correlation coefficient, speckle size, speckle contrast, and speckle quality of the images. The optimum parameters of the speckle tracking algorithm were determined by performing sensitivity analysis concerning computation time, effective mapping area, average contraction velocity, and strain. Furthermore, the hiPSC‐CM response to adrenaline was evaluated to validate the sensitivity of the strain analysis algorithm. Then, we applied speckle tracking–based strain analysis to characterize the dynamic behavior of patient‐specific hiPSC‐CMs from the family members affected/unaffected by DiGeorge syndrome. Here, we report an efficient and manipulation‐free method to analyze the contraction displacement vector and velocity field, contraction‐relaxation strain rate, and contractile cycles. Implementation of this method allows for quantitative analysis of the contractile phenotype characteristics of hiPSC‐CMs to distinguish possible cardiac manifestation of DiGeorge syndrome. © 2023 Wiley Periodicals LLC.Basic Protocol 1: Differentiation of iPSCs into iPSC‐derived cardiomyocytes (iPSC‐CMs) and metabolic selection of differentiated iPSC‐CMsSupport Protocol 1: Culture, maintenance, and expansion of human iPSCsSupport Protocol 2: Immunohistochemistry of iPSC‐CMsBasic Protocol 2: Time‐lapse speckle imaging of iPSC‐CMs and speckle quality characterizationSupport Protocol 3: Enhancement of local contrast of videos by applying contrast limited adaptive histogram equalization (CLAHE) to all framesSupport Protocol 4: Evaluation of average speckle sizeSupport Protocol 5: Evaluation of average speckle contrastSupport Protocol 6: Determination of relative peak height, Pc(x), of consecutive images acquired from video microscopy of iPSC‐CMsBasic Protocol 3: Speckle tracking–based analysis of beating iPSC‐CMsSupport Protocol 7: Validation of sensitivity of the speckle tracking analysis for mapping the contractility of iPSC‐CMsBasic Protocol 4: Data extraction, visualization, and mapping of contractile cycles of iPSC‐CMs