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Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

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dc.contributor.author Waisman, Ariel
dc.contributor.author Norris, Alessandra
dc.contributor.author Costa, Martín Elías
dc.contributor.author Kopinke, Daniel
dc.date.accessioned 2021-02-09T12:06:22Z
dc.date.available 2021-02-09T12:06:22Z
dc.date.issued 2021-01-22
dc.identifier.citation Waisman A, Norris AM, Elías Costa M, Kopinke D. Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle. Sci Rep. 2021 Jun 3;11(1):11793. doi: 10.1038/s41598-021-91191-6 en_US
dc.identifier.uri https://doi.org/10.1038/s41598-021-91191-6
dc.identifier.uri https://repositorio.fleni.org.ar/handle/123456789/360
dc.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 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, identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber size 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. en_US
dc.language.iso eng en_US
dc.publisher Nature Publishing Group en_US
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Myofibers en_US
dc.subject Miofibrilla en_US
dc.subject Muscle, Skeletal en_US
dc.subject Músculo Esquelético en_US
dc.title Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle en_US
dc.type info:eu-repo/semantics/publishedVersion
dc.type info:eu-repo/semantics/article en_US
dc.description.fil Fil: Waisman, Ariel. Fleni. Laboratorio de Investigación Aplicada a Neurociencias; Argentina.
dc.description.fil Fil: Norris, Alessandra. University of Florida College of Medicine. Department of Pharmacology and Therapeutics; Estados Unidos.
dc.description.fil Fil: Costa, Martín Elías. Universidad de Buenos Aires; Argentina.
dc.description.fil Fil: Kopinke, Daniel. University of Florida College of Medicine. Department of Pharmacology and Therapeutics; Estados Unidos.
dc.relation.ispartofVOLUME 11
dc.relation.ispartofNUMBER 1
dc.relation.ispartofPAGINATION 11793
dc.relation.ispartofCOUNTRY Reino Unido
dc.relation.ispartofCITY Londres
dc.relation.ispartofTITLE Scientific reports
dc.relation.ispartofISSN 2045-2322
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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