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,
requires little to no parameter tuning, and runs quickly on a personal
computer. 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.Comment: 16 main pages, 7 main figures, Supplementary Information included as
appendice