A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells

Abstract Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibro...

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Autores principales:	Alexander Hillsley, Javier E. Santos, Adrianne M. Rosales
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b44ba0f6c560436a94cdb7e5c91f61d1
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spelling	oai:doaj.org-article:b44ba0f6c560436a94cdb7e5c91f61d12021-11-14T12:20:44ZA deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells10.1038/s41598-021-01304-42045-2322https://doaj.org/article/b44ba0f6c560436a94cdb7e5c91f61d12021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01304-4https://doaj.org/toc/2045-2322Abstract Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha-smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha-smooth muscle actin stress fibers into 2 classes (α-SMA SF+ and α-SMA SF-), with a high degree of accuracy (cell accuracy: 77%, F1 score 0.79). The model combines standard image processing methods with deep learning techniques to achieve semantic segmentation of the different cell phenotypes. We apply this model to cardiac fibroblasts cultured on hyaluronic acid-based hydrogels of various moduli to induce alpha-smooth muscle actin stress fiber formation. The model successfully predicts the same trends in stress fiber identification as obtained with a manual analysis. Taken together, this work demonstrates a process to automate stress fiber identification in in vitro fibrotic models, thereby increasing reproducibility in fibroblast phenotypic characterization.Alexander HillsleyJavier E. SantosAdrianne M. RosalesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexander Hillsley Javier E. Santos Adrianne M. Rosales A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells
description	Abstract Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha-smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha-smooth muscle actin stress fibers into 2 classes (α-SMA SF+ and α-SMA SF-), with a high degree of accuracy (cell accuracy: 77%, F1 score 0.79). The model combines standard image processing methods with deep learning techniques to achieve semantic segmentation of the different cell phenotypes. We apply this model to cardiac fibroblasts cultured on hyaluronic acid-based hydrogels of various moduli to induce alpha-smooth muscle actin stress fiber formation. The model successfully predicts the same trends in stress fiber identification as obtained with a manual analysis. Taken together, this work demonstrates a process to automate stress fiber identification in in vitro fibrotic models, thereby increasing reproducibility in fibroblast phenotypic characterization.
format	article
author	Alexander Hillsley Javier E. Santos Adrianne M. Rosales
author_facet	Alexander Hillsley Javier E. Santos Adrianne M. Rosales
author_sort	Alexander Hillsley
title	A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells
title_short	A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells
title_full	A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells
title_fullStr	A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells
title_full_unstemmed	A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells
title_sort	deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b44ba0f6c560436a94cdb7e5c91f61d1
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A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells

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