Structural Phenotyping of Stem Cell-Derived Cardiomyocytes

• Published in: Stem cell reports Vol. 4; no. 3; pp. 340 - 347
• Main Authors: Pasqualini, Francesco Silvio, Sheehy, Sean Paul, Agarwal, Ashutosh, Aratyn-Schaus, Yvonne, Parker, Kevin Kit
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 10.03.2015; Elsevier
• Subjects: Algorithms; Animals; Biometry - methods; Cell Differentiation; Cytoskeleton - metabolism; Humans; Machine Learning; Mice; Myocytes, Cardiac - cytology; Myocytes, Cardiac - metabolism; Phenotype; Rats; Stem Cells - cytology
• ISSN: 2213-6711; 2213-6711
• DOI: 10.1016/j.stemcr.2015.01.020

Structural 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. •Image processing metrics for characterizing the contractile cytoskeleton•Unbiased data mining strategies to assess cardiomyocyte maturation•Structural phenotyping of stem cell-derived cardiomyocytes In this article, Parker and colleagues develop metrics to quantitatively characterize myofibrillogenesis and sarcomerogenesis, the processes by which striated muscle cells assemble their contractile cytoskeleton. Further, they coupled these metrics with machine-learning algorithms to unbiasedly score the phenotypic maturity of primary and stem cell-derived cardiomyocytes.