Automated fiber-type-specific cross-sectional area assessment and myonuclei counting in skeletal muscle

Abstract

Skeletal muscle is an exceptionally adaptive tissue that compromises 40% of mammalian body mass. Skeletal muscle functions in locomotion, but also plays important roles in thermogenesis and metabolic homeostasis. Thus characterizing the structural and functional properties of skeletal muscle is important in many facets of biomedical research, ranging from myopathies to rehabilitation sciences to exercise interventions aimed at improving quality of life in the face of chronic disease and aging. In this paper, we focus on automated quantification of three important morphological features of muscle: 1) muscle fiber-type composition; 2) muscle fiber-type-specific cross-sectional area, and 3) myonuclear content and location. We experimentally prove that the proposed automated image analysis approaches for fiber-type-specific assessments and automated myonuclei counting are fast, accurate, and reliable.

Keywords: cross-sectional area; image segmentation; muscle; myonuclei counting.

Fig. 1.

An illustration of linear separable support vector machine (SVM) classifier. A SVM classifier is trained to find the decision boundary that maximizes the margins between the supporting vectors. See text for definition of terms.

Fig. 2.

The illustration of the rigorous and quantitative criteria to define interstitial cell nuclei, myonuclei, and central myonuclei. The green contour denotes the muscle fiber boundary. A, nonmyofiber nucleus (the overlapped region is denoted as green, and it is <50% of the area of the nucleus). B, myonucleus (the overlapped region is >100% of the area of the nucleus). C, central myonucleus (the nucleus is completely inside the muscle fiber).

Fig. 3.

Examples of automated fiber typing results. The segmented muscle fiber boundaries, shown in green, are superimposed on the original images. The predicted fiber types are labeled on the images using white fonts.

Fig. 4.

A pie graph showing the distribution of different fiber types in the digitized images.

Fig. 5.

The distribution of cross-sectional areas (CSAs) with respect to different fiber types. The curves are generated using kernel density estimation. The x-axis represents the CSA, and the y-axis represents the percentage of the muscle fibers with a specific CSA value. Different colors are utilized to present different types of muscle fiber CSA distribution curves.

Fig. 6.

The illustration of our proposed automated myonuclei counting method. A: the original digitized image. B: automated segmentation of each individual muscle fiber; delineated boundaries of each muscle fiber are labeled with green contours. C: automated segmentation results of each individual nucleus. Myonuclei are represented with solid yellow, while other nuclei are labeled with solid white. D: some high magnification images that correspond to the dotted rectangle regions in B and C for better illustration purposes. Myonuclei are labeled with yellow contours, other nuclei are labeled with white contours, and muscle fiber boundaries are labeled with green contours.

Fig. 7.

The illustration of our proposed automated central nuclei detection method using regenerating mouse plantaris images. A: original digitized image. B: automated segmentation of each individual muscle fibers; boundaries of each muscle fiber are labeled with green contours. C: automated segmentation results of each individual nucleus. Central nuclei are represented with solid yellow, while other nuclei are labeled with solid white. D: some high-magnification images that correspond to the dotted rectangle regions in B and C for better illustration purposes. Central nuclei are labeled with yellow contours, other nuclei are labeled with white contours, and muscle fiber boundaries are labeled with green contours.