High content analysis identifies unique morphological features of reprogrammed cardiomyocytes

Abstract 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 phe...

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Autores principales:	Matthew D. Sutcliffe, Philip M. Tan, Antonio Fernandez-Perez, Young-Jae Nam, Nikhil V. Munshi, Jeffrey J. Saucerman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ed4cfada737b43d2ae2fd966138a9dc7
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spelling	oai:doaj.org-article:ed4cfada737b43d2ae2fd966138a9dc72021-12-02T15:08:55ZHigh content analysis identifies unique morphological features of reprogrammed cardiomyocytes10.1038/s41598-018-19539-z2045-2322https://doaj.org/article/ed4cfada737b43d2ae2fd966138a9dc72018-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-19539-zhttps://doaj.org/toc/2045-2322Abstract 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.Matthew D. SutcliffePhilip M. TanAntonio Fernandez-PerezYoung-Jae NamNikhil V. MunshiJeffrey J. SaucermanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-11 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthew D. Sutcliffe Philip M. Tan Antonio Fernandez-Perez Young-Jae Nam Nikhil V. Munshi Jeffrey J. Saucerman High content analysis identifies unique morphological features of reprogrammed cardiomyocytes
description	Abstract 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.
format	article
author	Matthew D. Sutcliffe Philip M. Tan Antonio Fernandez-Perez Young-Jae Nam Nikhil V. Munshi Jeffrey J. Saucerman
author_facet	Matthew D. Sutcliffe Philip M. Tan Antonio Fernandez-Perez Young-Jae Nam Nikhil V. Munshi Jeffrey J. Saucerman
author_sort	Matthew D. Sutcliffe
title	High content analysis identifies unique morphological features of reprogrammed cardiomyocytes
title_short	High content analysis identifies unique morphological features of reprogrammed cardiomyocytes
title_full	High content analysis identifies unique morphological features of reprogrammed cardiomyocytes
title_fullStr	High content analysis identifies unique morphological features of reprogrammed cardiomyocytes
title_full_unstemmed	High content analysis identifies unique morphological features of reprogrammed cardiomyocytes
title_sort	high content analysis identifies unique morphological features of reprogrammed cardiomyocytes
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/ed4cfada737b43d2ae2fd966138a9dc7
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_version_	1718387972969070592

High content analysis identifies unique morphological features of reprogrammed cardiomyocytes

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