For example, Hepple ( 11) introduced the quantification of capillary contacts (CC no. Nowadays, fiber type-specific capillarization is quantified in more detail than the traditional measurements of capillary per fiber and capillary density. The capillary network surrounding the muscle fibers plays an important role in the oxygen supply and nutrient delivery to the muscle ( 11, 12, 21, 22), and different semiautomatic methods have been described to estimate the size of this region ( 2, 23, 35). Despite these advances, most of the techniques still require either substantial programming skills or significant manual correction of errors, and we are unaware of any method capable of computing all parameters of interest in one single analysis with little manual effort.
The available methods to date can compute fiber, capillary, myonuclei and satellite cell counts, fiber type, and centrally located myonuclei, as well as fiber morphometric parameters such as cross-sectional area (CSA) and Feret diameter or perimeter ( 2, 6, 15, 16, 18, 23, 24, 27, 30, 35, 41). Once the settings are customized for the specific samples of interest and high-quality outputs are confirmed, automated methods can perform the analyses in an unbiased manner, which arguably represent an improvement over the manual method. To overcome these issues, a variety of semiautomatic and fully automatic approaches to analyze muscle immunohistochemical images have been developed. This manual work may suffer from low reproducibility and risk of bias due to the subjective nature of the analysis procedure ( 3, 5). Unfortunately, immunohistochemical image analysis is a time-consuming process that requires extensive manual effort. Immunohistochemistry applies antibodies and immunofluorescent labeling on muscle cross-section, which, with the use of immunofluorescence microscopy, allows for visualization and subsequent quantification of the targeted morphological parameters. Although several methodological approaches have been used to study these adaptive responses at the muscle fiber level, one of the most commonly used techniques is immunohistochemistry. Skeletal muscle is a plastic tissue that rapidly adjusts to external demands and environmental stimuli such as exercise, inactivity, and disease ( 9, 13). Thus, the Muscle2View is a viable tool for researchers aiming to quantify immunohistochemical variables from skeletal muscle biopsies. In addition to fiber typing, myonuclei counting, and the quantification of fiber type-specific morphological measurements, the Muscle2View pipeline can identify the complex capillary-to-fiber network from a batch of images within minutes. NEW & NOTEWORTHY Here, we developed a freely available CellProfiler-based pipeline termed Muscle2View, which provides unbiased, high-content analysis of muscle cross-sectional immunohistochemistry images. This robust analysis is done in one single run within a user-friendly and flexible environment based on the free and widely used image software CellProfiler. In addition to the classical morphological measurements, the Muscle2View can identify the complex capillary-to-fiber network and myonuclear density in a fiber type-specific manner.
Collectively, we demonstrate that the Muscle2View pipeline can provide unbiased and high-content analysis of muscle cross-sectional immunohistochemistry images. For several variables, however, there were differences (5–15%) between values computed by manual counting and Muscle2View, suggesting that the methods should not necessarily be used interchangeably. When comparing the Muscle2View pipeline to manual or semiautomatic analysis, overall the results revealed strong correlations. The novel identification of the capillary-to-fiber interface allowed for the calculation of microvascular factors such as capillary contacts (CC), individual capillary-to-fiber ratio (C/Fi), and capillary-to-fiber perimeter exchange (CFPE) index. Provided that the images are of sufficient quality and the settings are configured for the specific study, the pipeline allows for automatic and unsupervised analysis of fiber borders, myonuclei, capillaries, and morphometric parameters in a fiber type-specific manner from large batches of images in <10 min/tissue sample. Here, we developed Muscle2View, a free CellProfiler-based pipeline that integrates all key fiber-morphological variables, including the novel quantification of the capillary-to-fiber interface, in one single tool.
Because manual immunohistochemical analysis of features such as skeletal muscle fiber typing, capillaries, myonuclei, and fiber size-related parameters is time consuming and prone to user subjectivity, automatic computational methods could allow for faster and more objective evaluation.
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