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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-91191-6 |
| _version_ | 1818851802153484288 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T07:10:49Z |
| format | Article |
| id | doaj.art-4fb0940eedd8452893246d8bd5b0135b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-19T07:10:49Z |
| publishDate | 2021-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4fb0940eedd8452893246d8bd5b0135b2022-12-21T20:31:12ZengNature PortfolioScientific Reports2045-23222021-06-0111111410.1038/s41598-021-91191-6Automatic and unbiased segmentation and quantification of myofibers in skeletal muscleAriel Waisman0Alessandra Marie Norris1Martín Elías Costa2Daniel Kopinke3CONICET - Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI), Laboratorio de Investigación Aplicada a Neurociencias (LIAN)Department of Pharmacology and Therapeutics, University of Florida College of MedicineUniversidad de Buenos AiresDepartment of Pharmacology and Therapeutics, University of Florida College of MedicineAbstract 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.https://doi.org/10.1038/s41598-021-91191-6 |
| spellingShingle | Ariel Waisman Alessandra Marie Norris Martín Elías Costa Daniel Kopinke Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle Scientific Reports |
| title | 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_short | Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle |
| title_sort | automatic and unbiased segmentation and quantification of myofibers in skeletal muscle |
| url | https://doi.org/10.1038/s41598-021-91191-6 |
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