Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

Abstract Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed dee...

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Autores principales:	Ariel Waisman, Alessandra Marie Norris, Martín Elías Costa, Daniel Kopinke
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4fb0940eedd8452893246d8bd5b0135b
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spelling	oai:doaj.org-article:4fb0940eedd8452893246d8bd5b0135b2021-12-02T15:56:57ZAutomatic and unbiased segmentation and quantification of myofibers in skeletal muscle10.1038/s41598-021-91191-62045-2322https://doaj.org/article/4fb0940eedd8452893246d8bd5b0135b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91191-6https://doaj.org/toc/2045-2322Abstract Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.Ariel WaismanAlessandra Marie NorrisMartín Elías CostaDaniel KopinkeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ariel Waisman Alessandra Marie Norris Martín Elías Costa Daniel Kopinke Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle
description	Abstract Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.
format	article
author	Ariel Waisman Alessandra Marie Norris Martín Elías Costa Daniel Kopinke
author_facet	Ariel Waisman Alessandra Marie Norris Martín Elías Costa Daniel Kopinke
author_sort	Ariel Waisman
title	Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle
title_short	Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle
title_full	Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle
title_fullStr	Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle
title_full_unstemmed	Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle
title_sort	automatic and unbiased segmentation and quantification of myofibers in skeletal muscle
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4fb0940eedd8452893246d8bd5b0135b
work_keys_str_mv	AT arielwaisman automaticandunbiasedsegmentationandquantificationofmyofibersinskeletalmuscle AT alessandramarienorris automaticandunbiasedsegmentationandquantificationofmyofibersinskeletalmuscle AT martineliascosta automaticandunbiasedsegmentationandquantificationofmyofibersinskeletalmuscle AT danielkopinke automaticandunbiasedsegmentationandquantificationofmyofibersinskeletalmuscle
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Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

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