Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

• Autores: Waisman, Ariel; Norris, Alessandra; Elias Costa, Martin; Kopinke, Daniel
• Año de publicación: 2021
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: SKELETAL MUSCLE; IMAGE ANALYSIS; CELL SEGMENTATION
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Waisman, Ariel. Fundacion P/la Lucha C/enferm.neurologicas Infancia. Instituto de Neurociencias. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Instituto de Neurociencias.; Argentina
Fil: Norris, Alessandra. University of Florida; Estados Unidos
Fil: Elias Costa, Martin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Kopinke, Daniel. University of Florida; Estados Unidos