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

Abstract

BackgroundSkeletal 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.MethodsRunning on the popular open source Fiji software platform, MuscleJ simultaneously analyzes parameters from immunofluorescent staining, imaged by different acquisition systems in a completely automated manner.ResultsAfter segmentation of muscle fibers, up to three other channels can be analyzed simultaneously. Dialog boxes make MuscleJ easy-to-use for biologists. In addition, we have implemented color in situ cartographies of results, allowing the user to directly visualize results on reconstituted muscle sections.ConclusionWe report here that MuscleJ results were comparable to manual observations made by five experts. MuscleJ markedly enhances statistical analysis by allowing reliable comparison of skeletal muscle physiology-pathology results obtained from different laboratories using different acquisition systems. Providing fast robust multi-parameter analyses of skeletal muscle physiology-pathology, MuscleJ is available as a free tool for the skeletal muscle community.Electronic supplementary materialThe online version of this article (10.1186/s13395-018-0171-0) contains supplementary material, which is available to authorized users.

Myosoft: an automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software

“…Histological analysis of muscle sections has been a staple of muscle physiology and neuromuscular disease research for decades; indeed, a number of neuromuscular diseases were originally named for their distinct histopathological features [25][26][27][28]. Myovision, SMASH, and MuscleJ [16,18,19]. Of these, MuscleJ has by far the most complete collection of features, with a number of capabilities that are absent even in Myosoft.…”

“…However, despite the value of such analysis, it is often performed manually, which is both labor-intensive and time-consuming. To offset this obstacle, several groups have developed software that automates analysis of muscle histology [16][17][18][19].…”

Asymmetrical myofiber architecture along the murine tibialis anterior suggests distinct functional regions

“…Myofiber segmentation was carried out with the laminin staining using the MuscleJ macro in ImageJ (Mayeuf-Louchart et al , 2018), after following modifications: the image pre-processing step (prior to segmentation) was changed to match our image resolution, implementation of an automatic loading of the whole image set, and an automatic saving of the segmented mask. Poorly segmented images were removed from further analysis.…”

Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software

“…However, despite the value of such analysis, it is often performed manually, which is both labor-intensive and time-consuming. To offset this obstacle, several groups have developed software that automates analysis of muscle histology [16][17][18][19]. SMASH, reported in 2014 [16], and an unnamed Image-J plug-in for muscle analysis, published in 2016 [17,20], introduced automatic analysis of muscle histology to the field using a watershed algorithm for segmentation.…”

“…Additionally, these programs lack the ability to discern all fiber types and mixed-fiber type combinations. MyoVision [18] and Muscle J [19], published in 2018, were the first programs to offer fully automated analysis of muscle histology. Myovision deploys a k-means binarization followed by detection of incompletely and completely segmented 'seeds' which the program processes independently.…”