| Autor: |
Sutcliffe MD; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA., Tan PM; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA., Fernandez-Perez A; Department of Internal Medicine, Division of Cardiology, UT Southwestern Medical Center, Dallas, TX, 75390, USA., Nam YJ; Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Munshi NV; Department of Internal Medicine, Division of Cardiology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.; Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.; McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, 75390, USA.; Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, 75390, USA., Saucerman JJ; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA. jsaucerman@virginia.edu. |
| Abstrakt: |
Direct reprogramming of fibroblasts into cardiomyocytes is a promising approach for cardiac regeneration but still faces challenges in efficiently generating mature cardiomyocytes. Systematic optimization of reprogramming protocols requires scalable, objective methods to assess cellular phenotype beyond what is captured by transcriptional signatures alone. To address this question, we automatically segmented reprogrammed cardiomyocytes from immunofluorescence images and analyzed cell morphology. We also introduce a method to quantify sarcomere structure using Haralick texture features, called SarcOmere Texture Analysis (SOTA). We show that induced cardiac-like myocytes (iCLMs) are highly variable in expression of cardiomyocyte markers, producing subtypes that are not typically seen in vivo. Compared to neonatal mouse cardiomyocytes, iCLMs have more variable cell size and shape, have less organized sarcomere structure, and demonstrate reduced sarcomere length. Taken together, these results indicate that traditional methods of assessing cardiomyocyte reprogramming by quantifying induction of cardiomyocyte marker proteins may not be sufficient to predict functionality. The automated image analysis methods described in this study may enable more systematic approaches for improving reprogramming techniques above and beyond existing algorithms that rely heavily on transcriptome profiling. |
