Structural Phenotyping of Stem Cell-Derived Cardiomyocytes

Abstract

SummaryStructural phenotyping based on classical image feature detection has been adopted to elucidate the molecular mechanisms behind genetically or pharmacologically induced changes in cell morphology. Here, we developed a set of 11 metrics to capture the increasing sarcomere organization that occurs intracellularly during striated muscle cell development. To test our metrics, we analyzed the localization of the contractile protein α-actinin in a variety of primary and stem-cell derived cardiomyocytes. Further, we combined these metrics with data mining algorithms to unbiasedly score the phenotypic maturity of human-induced pluripotent stem cell-derived cardiomyocytes.

Multiscale Characterization of Engineered Cardiac Tissue Architecture

“…More recently, it has been utilized to describe organization for biological applications [29][30][31][32][33]. The parameter can be derived with a variety of methods including calculating coefficients of the Fourier series and by taking the eigenvalues of the structure tensor [17,41]. The orientational order parameter is commonly labeled as OOP [33,41], f 2D [31], or S [28].…”

“…The parameter can be derived with a variety of methods including calculating coefficients of the Fourier series and by taking the eigenvalues of the structure tensor [17,41]. The orientational order parameter is commonly labeled as OOP [33,41], f 2D [31], or S [28]. By convention, the order parameter ranges from 0 to 1, which in the structure tensor method can be accomplished by taking the deviatoric portion.…”

Fibrous scaffolds for building hearts and heart parts

“…1A). Comprehensive, quantitative comparison of engineered tissues versus healthy, mature tissues using machine learning approaches [44, 45] and statistical metrics, such as strictly standardized mean difference, could provide robust, standardized quality assurance rubrics for determining the fitness of engineered tissues for regenerative therapy applications [46]. Traditional tissue engineering approaches involve scaffold to tissue fabrication: scaffold production, in vitro cell seeding, in vitro cell-scaffold conditioning to form tissue, and finally implantation.…”

“…Further, it is necessary to develop quantitative metrics that allow robust measurement and comparison of the parameters that define heart function. Computational algorithms allow quantitative assessment of traditionally qualitative measurements of biological form and function, such as calculation of global sarcomere alignment [45] or nuclear eccentricity [220] from fluorescence micrographs. Machine learning and statistical approaches for integrating the values from a variety of biochemical, structural, and functional experimental measurements into a single quality assessment score will allow comprehensive and reliable determination of engineered tissue quality [44, 46, 108].…”

“…Statistical quality control inspection practices have been refined into a quality control paradigm commonly known as —Six Sigma that has been successfully utilized to minimize the occurrence of product defects by holding process variation to one-sixth the difference between process mean and the upper/lower design specification limits [256]. Multi-parameter statistical quality assessment strategies for stem cell manufacturing [45, 46] have already been implemented as well. We suggest the same can be done for fibrous tissue engineered products.…”