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Updated: Jun 10, 2026

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition
Published on: August 31, 2017
Development of image analysis tool for the classification of muscle fibre type using immunohistochemical staining
Bruno Meunier1, Brigitte Picard, Thierry Astruc
1INRA, UR1213 Herbivores, 63122, Saint Genès Champanelle, France. bruno.meunier@clermont.inra.fr
Abstract:
An accurate characterisation of muscle fibres is essential for studying muscle plasticity. During some transient events such as ageing, myogenesis, physical activity or conversion of muscle to meat, the morphological parameters and/or the fibre type distribution may change. Nowadays, this information is generally obtained using immunohistology techniques, but these analyses are acknowledged to be laborious and time-consuming. In fact, each myofibre, from thousands, must be measured individually and its expression profile in response to different anti-myosin antibodies must be established step by step. In this paper, we describe a new histological approach using double-labelling (laminin, myosin) serial sections, fluorescence microscopy visualisation and, finally, semi-automatic image analysis. The goal of the study was to propose a tool allowing faster fibre type characterisation, including the identification of hybrid fibres from pure ones. The steps in the image processing prone to subjectivity have been fully automated. On the other hand, the expert retained control of all image analysis procedures requiring visual diagnosis. The tool that we developed with the Visilog software allowed a rapid and objective fibre typing and morphometric characterisation of two different bovine muscles. The results were in agreement with our previous histological and densitometric assays. The method and the tool proved to be potentially more efficient than other techniques used in our institute or described in the literature. A more global evaluation will be considered in other laboratories as well as on other animal species.

