Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.
Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary.
Curr Protoc. 2023 Sep;3(9):e889. doi: 10.1002/cpz1.889.
Human 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-CMs Support Protocol 1: Culture, maintenance, and expansion of human iPSCs Support Protocol 2: Immunohistochemistry of iPSC-CMs Basic Protocol 2: Time-lapse speckle imaging of iPSC-CMs and speckle quality characterization Support Protocol 3: Enhancement of local contrast of videos by applying contrast limited adaptive histogram equalization (CLAHE) to all frames Support Protocol 4: Evaluation of average speckle size Support Protocol 5: Evaluation of average speckle contrast Support Protocol 6: Determination of relative peak height, Pc(x), of consecutive images acquired from video microscopy of iPSC-CMs Basic Protocol 3: Speckle tracking-based analysis of beating iPSC-CMs Support Protocol 7: Validation of sensitivity of the speckle tracking analysis for mapping the contractility of iPSC-CMs Basic Protocol 4: Data extraction, visualization, and mapping of contractile cycles of iPSC-CMs.
人类诱导多能干细胞(hiPSC)衍生的心肌细胞(hiPSC-CMs)在心血管疾病建模、药物筛选、个性化医疗和病理生理学研究方面具有巨大潜力。建立体外疾病模型,需要有一个强大的协议和功能测定方法来研究 hiPSC-CMs 的表型行为。许多心脏病是由于心脏组织的机械应变变化引起的。因此,非侵入性评估 hiPSC-CMs 的收缩性能对于深入了解心脏病的发病机制仍然至关重要。斑点追踪应变分析是一种有效的非侵入性方法,它使用视频显微镜和跳动的 hiPSC-CMs 的图像分析来定量评估机械收缩性能。本文提供了使用斑点追踪应变分析从患有 DiGeorge 综合征的五名家庭成员之一中提取 hiPSC-CM 系统的定量收缩性能的逐步方案。使用代谢选择将来自家庭成员的 hiPSC 分化并纯化为 hiPSC-CMs。使用高空间分辨率和高时间分辨率相差显微镜获取 hiPSC-CMs 的延时图像。通过评估图像的互相关系数、斑点大小、斑点对比度和斑点质量来描述斑点图像。通过针对计算时间、有效映射区域、平均收缩速度和应变进行灵敏度分析来确定斑点跟踪算法的最佳参数。此外,还评估了 hiPSC-CM 对肾上腺素的反应,以验证应变分析算法的灵敏度。然后,我们应用斑点追踪应变分析来描述受 DiGeorge 综合征影响/未受影响的家族成员的患者特异性 hiPSC-CMs 的动态行为。在这里,我们报告了一种高效且无需操作的方法来分析收缩位移矢量和速度场、收缩-松弛应变率以及收缩周期。该方法的实施允许对 hiPSC-CMs 的收缩表型特征进行定量分析,以区分 DiGeorge 综合征可能的心脏表现。© 2023 年 Wiley 期刊 LLC。基础方案 1:将 iPSCs 分化为 iPSC 衍生的心肌细胞(iPSC-CMs)和分化的 iPSC-CMs 的代谢选择支持方案 1:人 iPSCs 的培养、维持和扩增支持方案 2:iPSC-CMs 的免疫组织化学基础方案 2:iPSC-CMs 的延时斑点成像和斑点质量特征化支持方案 3:通过对所有帧应用对比度限制自适应直方图均衡化 (CLAHE) 来增强视频的局部对比度支持方案 4:评估平均斑点大小支持方案 5:评估平均斑点对比度支持方案 6:确定视频显微镜获取的连续图像的相对峰值高度,Pc(x)支持方案 7:确定平均斑点对比度支持方案 3:基于斑点追踪的跳动 iPSC-CMs 分析支持方案 8:验证斑点跟踪分析对映射 iPSC-CMs 收缩性的敏感性基础方案 4:iPSC-CMs 收缩周期的数据提取、可视化和映射。