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Area of Science:

  • Cardiovascular Biology
  • Regenerative Medicine
  • Biomedical Imaging

Background:

  • Direct reprogramming offers a potential strategy for cardiac regeneration by converting fibroblasts into cardiomyocytes.
  • Current methods for assessing reprogramming efficiency often rely on transcriptional signatures, which may not fully capture cellular maturity or functionality.
  • Optimizing reprogramming protocols necessitates objective, scalable methods to evaluate cardiomyocyte phenotype.

Purpose of the Study:

  • To develop and validate automated image analysis techniques for quantifying cardiomyocyte morphology and sarcomere structure.
  • To assess the phenotypic variability of induced cardiac-like myocytes (iCLMs) generated through direct reprogramming.
  • To compare the characteristics of iCLMs with neonatal mouse cardiomyocytes.

Main Methods:

  • Automated segmentation of reprogrammed cardiomyocytes from immunofluorescence images.
  • Analysis of cell morphology, including size and shape.
  • Quantification of sarcomere structure using Haralick texture features via SarcOmere Texture Analysis (SOTA).

Main Results:

  • Induced cardiac-like myocytes (iCLMs) exhibited significant variability in cardiomyocyte marker expression and morphology.
  • iCLMs displayed less organized sarcomere structure and reduced sarcomere length compared to neonatal mouse cardiomyocytes.
  • Automated image analysis revealed phenotypic heterogeneity not typically observed in vivo.

Conclusions:

  • Traditional assessments of cardiomyocyte reprogramming based solely on marker protein induction may be inadequate for predicting functional outcomes.
  • Automated image analysis, including SOTA, provides objective metrics for evaluating cellular phenotype and improving reprogramming efficiency.
  • These advanced imaging techniques offer a more systematic approach to advancing cardiac regeneration strategies beyond transcriptome profiling.