MuscleJ: a high-content analysis method to study skeletal muscle with a new Fiji tool

Alicia Mayeuf-Louchart (1) , David Hardy (2, 3) , Quentin Thorel (1) , Pascal Roux (4) , Lorna Gueniot (2, 3) , David Briand (2, 3) , Aurélien Mazeraud (2, 3) , Adrien Bouglé (2) , Spencer Shorte (4) , Bart Staels (1) , Fabrice Chretien (2, 3) , Hélène Duez (1) , Anne Danckaert (4, 2, 3)

1 RNMCD - Récepteurs nucléaires, maladies cardiovasculaires et diabète - U 1011
2 Neuropathologie expérimentale - Experimental neuropathology
3 IP - Institut Pasteur [Paris]
4 UTechS PBI - BioImagerie Photonique – Photonic BioImaging

Skeletal muscle has the capacity to adapt to environmental changes and regenerate upon injury. To study these processes, most experimental methods use quantification of parameters obtained from images of immunostained skeletal muscle. Muscle cross-sectional area, fiber typing, localization of nuclei within the muscle fiber, the number of vessels, and fiber-associated stem cells are used to assess muscle physiology. Manual quantification of these parameters is time consuming and only poorly reproducible. While current state-of-the-art software tools are unable to analyze all these parameters simultaneously, we have developed MuscleJ, a new bioinformatics tool to do so.

Mots clés: Fiber typing; Histology; Image automated quantification; In situ cartography; Stem cells; Skeletal muscle fiber; Vessels; Satellite cells

Domaines: Anatomie, Histologie, Anatomopathologie [q-bio.TO]

Alicia Mayeuf-Louchart, David Hardy, Quentin Thorel, Pascal Roux, Lorna Gueniot, et al.. MuscleJ: a high-content analysis method to study skeletal muscle with a new Fiji tool. Skeletal Muscle, 2018, 8 (1), pp.25. ⟨10.1186/s13395-018-0171-0⟩. ⟨pasteur-01926223⟩