Department of Mechanical Engineering, Boston University, Boston, MA, United States of America.
Center for Multiscale and Translational Mechanobiology, Boston University, Boston, MA, United States of America.
PLoS One. 2024 Mar 26;19(3):e0298863. doi: 10.1371/journal.pone.0298863. eCollection 2024.
Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.
推动人类诱导多能干细胞衍生心肌细胞(hiPSC-CM)技术的发展将带来重大进展,涵盖疾病建模、药物发现到再生组织工程。然而,伴随着这些潜在机会的是一个关键挑战:获得成熟的 hiPSC-CM 组织。目前,有多种技术可促进 hiPSC-CMs 的成熟,包括物理平台和细胞培养方案。然而,在对功能行为进行定量比较时,可靠且可重复地计算适合直接跨系统比较的功能指标的选择有限。此外,目前从心脏微球收缩的延时图像中获得的(即后力、平均组织应力)功能标准指标并未充分利用这些数据中存在的可用信息(即全场组织位移和应变)。因此,我们提出了“MicroBundleCompute”,这是一种用于从心脏微球电影自动量化基于形态的机械指标的计算框架。简而言之,这个计算框架提供了用于自动组织分割、跟踪和分析搏动心脏微球的明场和相差电影的工具。它易于实现,无需用户干预,所需的输入参数设置选择很少,并且计算成本低廉。在本文中,我们描述了这个计算框架的方法,展示了我们广泛验证研究的结果,并演示了探索异质组织变形和应变作为功能指标的效用。通过本文,我们将“MicroBundleCompute”作为一个开源计算工具进行传播,旨在使搏动心脏微球的自动定量分析更容易为社区所接受。