Quantification of Cardiomyocyte Contraction In Vitro and Drug Screening by MyocytoBeats

Cheng, Zhiyang; Yang, Yuxin; Jiang, Kai; Nie, Hongyi; Yang, Xingbo; Tu, Zizhuo; Liang, Jiayi; Xiang, Yaozu

doi:10.1007/s12265-023-10357-x

Cited by 1 publication

(1 citation statement)

References 41 publications

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“…Fundamentally, this challenge is driven by multiple factors, ranging from the high volume of data collected with these testbeds [ 29 ], to challenges associated with reproducing results when software and data are not shared under open-source licenses, or when extracting quantities of interest from data requires significant manual processing. To date, there have been multiple non-destructive image-based methods for quantifying the contractile action of cardiac microbundles [ 21 , 30 – 39 ], often inspired by related approaches to assessing the contractile behavior of cardiomyocytes [ 30 , 40 – 50 ]. Broadly speaking, most of these tools can be grouped into four main categories: (1) edge detection systems [ 31 , 32 ], (2) pillar tracking-based methods [ 33 – 37 , 39 ], (3) inter-frame pixel disparity methods [ 21 , 38 ], and (4) optical flow-based tracking [ 30 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles

Kobeissi,

Jilberto,

Karakan

et al. 2024

PLoS ONE

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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.

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