ArticleSkeletal muscle2020
MyoSight-semi-automated image analysis of skeletal muscle cross sections.
Article in Skeletal muscle, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- A semi-automated pipeline for quantitation of Pax7+, myonuclei, and cross-sectional area by fiber type.Physiological reports · 2026Article
- Type-specific and hibernation-associated skeletal muscle remodeling of the back muscle in Syrian hamsters.The journal of physiological sciences : JPS · 2026Article
- Nociceptor neurons suppress antitumor immunity in breast cancer.Research square · 2026Article
- Cilostazol use and calf muscle pathophysiology in people with peripheral artery disease.Vascular medicine (London, England) · 2026Article
- PGC-1α pathway dysregulation disrupts myofiber specification in a mouse model of SBMA.JCI insight · 2026Article
- A novel machine-learning classification model detects oxidative fiber type transitions in a rabbit model of cerebral palsy.bioRxiv : the preprint server for biology · 2026Article
- A skeletal muscle atlas shows neuromuscular junction adaptations to growth and atrophy.Developmental cell · 2026Article
- Titin's P-zone domains A164-167 are essential for thick filament structural arrangement.The Journal of general physiology · 2026Article
- HUWE1 in Skeletal Muscle Prevents Muscle Fatigue via Maintaining Iron and Calcium Homeostasis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Automated computer vision and dose-response modeling improve throughput and accuracy of an ex vivo functional precision medicine platform.Scientific reports · 2025Article
- Semi-automated image acquisition and analyses for broad users utilizing macro keyboards.Microscopy (Oxford, England) · 2025Article
- A role for the transcriptional coregulator RIP140 in the control of muscle endurance fitness.JCI insight · 2025Article
- Muscle-specific increased expression ofProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Automated Computer Vision and Dose-Response Modeling Improve Throughput and Accuracy of anResearch square · 2025Article
- MyoAnalyst: an ImageJ plugin for accurate and automatic myofiber segmentation and analysis in skeletal muscle cross sections.Histochemistry and cell biology · 2025Article
- Understanding the role of NOTCH2 mutation in centronuclear myopathy.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- Bioactive lipid mediator class switching regulates myogenic cell progression and muscle regeneration.Nature communications · 2025Article
- A semi-automated observation approach to quantify mouse skeletal muscle differentiation using immunohistochemistry.Physiological reports · 2025Article
- Multiomic Analysis of Calf Muscle in Peripheral Artery Disease and Chronic Kidney Disease.Circulation research · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundManual analysis of cross-sectional area, fiber-type distribution, and total and centralized nuclei in skeletal muscle cross sections is tedious and time consuming, necessitating an accurate, automated method of analysis. While several excellent programs are available, our analyses of skeletal muscle disease models suggest the need for additional features and flexibility to adequately describe disease pathology. We introduce a new semi-automated analysis program, MyoSight, which is designed to facilitate image analysis of skeletal muscle cross sections and provide additional flexibility in the analyses.
resultsWe describe staining and imaging methods that generate high-quality images of immunofluorescent-labelled cross sections from mouse skeletal muscle. Using these methods, we can analyze up to 5 different fluorophores in a single image, allowing simultaneous analyses of perinuclei, central nuclei, fiber size, and fiber-type distribution. MyoSight displays high reproducibility among users, and the data generated are in close agreement with data obtained from manual analyses of cross-sectional area (CSA), fiber number, fiber-type distribution, and number and localization of myonuclei. Furthermore, MyoSight clearly delineates changes in these parameters in muscle sections from a mouse model of Duchenne muscular dystrophy (mdx).
conclusionsMyoSight is a new program based on an algorithm that can be optimized by the user to obtain highly accurate fiber size, fiber-type identification, and perinuclei and central nuclei per fiber measurements. MyoSight combines features available separately in other programs, is user friendly, and provides visual outputs that allow the user to confirm the accuracy of the analyses and correct any inaccuracies. We present MyoSight as a new program to facilitate the analyses of fiber type and CSA changes arising from injury, disease, exercise, and therapeutic interventions.
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