Automated high-content morphological analysis of muscle fiber histology

Muscle fibre morphometric analysis (MusMA) correlates with muscle function and cardiovascular risk prognosis

Morphometry of striated muscle fibres is critical for monitoring muscle health and function. Here, we evaluated functional parameters of skeletal and cardiac striated muscle in two experimental models using the Morphometric Analysis of Muscle Fibre tool (MusMA). The collagen-induced arthritis model was used to evaluate the function of skeletal striated muscle and the non-alcoholic fatty liver disease model was used for cardiac striated muscle analysis. After euthanasia, we used haeamatoxylin and eosin stained sections of skeletal and cardiac muscle to perform muscle fibre segmentation and morphometric analysis. Morphometric analysis classified muscle fibres into six subpopulations: normal, regular hypertrophic, irregular hypertrophic, irregular, irregular atrophic and regular atrophic. The percentage of atrophic fibres was associated with lower walking speed (p = 0.009) and lower body weight (p = 0.026), respectively. Fibres categorized as normal were associated with maximum grip strength (p < 0.001) and higher march speed (p < 0.001). In the evaluation of cardiac striated muscle fibres, the percentage of normal cardiomyocytes negatively correlated with cardiovascular risk markers such as the presence of abdominal adipose tissue (p = .003), miR-33a expression (p = .001) and the expression of miR-126 (p = .042) Furthermore, the percentage of atrophic cardiomyocytes correlated significantly with the Castelli risk index II (p = .014). MusMA is a simple and objective tool that allows the screening of striated muscle fibre morphometry, which can complement the diagnosis of muscle diseases while providing functional and prognostic information in basic and clinical research.

MyoV: a deep learning-based tool for the automated quantification of muscle fibers

Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatically labeling over 660 000 muscle fibers to create a large dataset. Subsequently, an automated image segmentation and quantification tool named MyoV is designed using mask regions with convolutional neural networks and a residual network and feature pyramid network as the backbone network. This design enables the tool to allow muscle fiber processing with different sizes and ages. MyoV, which achieves impressive detection rates of 0.93-0.96 and precision levels of 0.91-0.97, exhibits a superior performance in quantification, surpassing both manual methods and commonly employed algorithms and software, particularly for whole slide images (WSIs). Moreover, MyoV is proven as a powerful and suitable tool for various species with different muscle development, including mice, which are a crucial model for muscle disease diagnosis, and agricultural animals, which are a significant meat source for humans. Finally, we integrate this tool into visualization software with functions, such as segmentation, area determination and automatic labeling, allowing seamless processing for over 400 000 muscle fibers within a WSI, eliminating the model adjustment and providing researchers with an easy-to-use visual interface to browse functional options and realize muscle fiber quantification from WSIs.

Histological Methods to Assess Skeletal Muscle Degeneration and Regeneration in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a progressive disease caused by the loss of function of the protein dystrophin. This protein contributes to the stabilisation of striated cells during contraction, as it anchors the cytoskeleton with components of the extracellular matrix through the dystrophin-associated protein complex (DAPC). Moreover, absence of the functional protein affects the expression and function of proteins within the DAPC, leading to molecular events responsible for myofibre damage, muscle weakening, disability and, eventually, premature death. Presently, there is no cure for DMD, but different treatments help manage some of the symptoms. Advances in genetic and exon-skipping therapies are the most promising intervention, the safety and efficiency of which are tested in animal models. In addition to in vivo functional tests, ex vivo molecular evaluation aids assess to what extent the therapy has contributed to the regenerative process. In this regard, the later advances in microscopy and image acquisition systems and the current expansion of antibodies for immunohistological evaluation together with the development of different spectrum fluorescent dyes have made histology a crucial tool. Nevertheless, the complexity of the molecular events that take place in dystrophic muscles, together with the rise of a multitude of markers for each of the phases of the process, makes the histological assessment a challenging task. Therefore, here, we summarise and explain the rationale behind different histological techniques used in the literature to assess degeneration and regeneration in the field of dystrophinopathies, focusing especially on those related to DMD.

Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.

A User-Friendly Approach for Routine Histopathological and Morphometric Analysis of Skeletal Muscle Using CellProfiler Software

Adult skeletal muscle is capable of active and efficient differentiation in the event of injury in both physiological and pathological conditions, such as in Duchenne muscular dystrophy (DMD). DMD is characterized by different features, such as continuous cycles of degeneration/regeneration, fiber heterogeneity, chronic inflammation and fibrosis. A well-defined and standardized approach for histological and morphometric analysis of muscle samples is necessary in order to measure and quantify specific regenerative parameters in myopathies. Indeed, non-automatic methods are time-consuming and prone to error. Here, we describe a simple automatized computational approach to quantify muscle parameters with specific pipelines to be run by CellProfiler software in an open-source and well-defined fashion. Our pipelines consist of running image-processing modules in CellProfiler with the aim of quantifying different histopathological muscle hallmarks in mdx mice compared to their wild-type littermates. Specifically, we quantified the minimum Feret diameter, centrally nucleated fibers and the number of macrophages, starting from multiple images. Finally, for extracellular matrix quantification, we used Sirius red staining. Collectively, we developed reliable and easy-to-use pipelines that automatically measure parameters of muscle histology, useful for research in myobiology. These findings should simplify and shorten the time needed for the quantification of muscle histological properties, avoiding challenging manual procedures.

The emerging role of the sympathetic nervous system in skeletal muscle motor innervation and sarcopenia

Examining neural etiologic factors'role in the decline of neuromuscular function with aging is essential to our understanding of the mechanisms underlying sarcopenia, the age-dependent decline in muscle mass, force and power. Innervation of the skeletal muscle by both motor and sympathetic axons has been established, igniting interest in determining how the sympathetic nervous system (SNS) affect skeletal muscle composition and function throughout the lifetime. Selective expression of the heart and neural crest derivative 2 gene in peripheral SNs increases muscle mass and force regulating skeletal muscle sympathetic and motor innervation; improving acetylcholine receptor stability and NMJ transmission; preventing inflammation and myofibrillar protein degradation; increasing autophagy; and probably enhancing protein synthesis. Elucidating the role of central SNs will help to define the coordinated response of the visceral and neuromuscular system to physiological and pathological challenges across ages. This review discusses the following questions: (1) Does the SNS regulate skeletal muscle motor innervation? (2) Does the SNS regulate presynaptic and postsynaptic neuromuscular junction (NMJ) structure and function? (3) Does sympathetic neuron (SN) regulation of NMJ transmission decline with aging? (4) Does maintenance of SNs attenuate aging sarcopenia? and (5) Do central SN group relays influence sympathetic and motor muscle innervation?

Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle

Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.

MyoSight-semi-automated image analysis of skeletal muscle cross sections

BACKGROUND

Manual analysis of cross-sectional area, fiber-type distribution, and total and centralized nuclei in skeletal muscle cross sections is tedious and time consuming, necessitating an accurate, automated method of analysis. While several excellent programs are available, our analyses of skeletal muscle disease models suggest the need for additional features and flexibility to adequately describe disease pathology. We introduce a new semi-automated analysis program, MyoSight, which is designed to facilitate image analysis of skeletal muscle cross sections and provide additional flexibility in the analyses.

RESULTS

We describe staining and imaging methods that generate high-quality images of immunofluorescent-labelled cross sections from mouse skeletal muscle. Using these methods, we can analyze up to 5 different fluorophores in a single image, allowing simultaneous analyses of perinuclei, central nuclei, fiber size, and fiber-type distribution. MyoSight displays high reproducibility among users, and the data generated are in close agreement with data obtained from manual analyses of cross-sectional area (CSA), fiber number, fiber-type distribution, and number and localization of myonuclei. Furthermore, MyoSight clearly delineates changes in these parameters in muscle sections from a mouse model of Duchenne muscular dystrophy (mdx).

CONCLUSIONS

MyoSight is a new program based on an algorithm that can be optimized by the user to obtain highly accurate fiber size, fiber-type identification, and perinuclei and central nuclei per fiber measurements. MyoSight combines features available separately in other programs, is user friendly, and provides visual outputs that allow the user to confirm the accuracy of the analyses and correct any inaccuracies. We present MyoSight as a new program to facilitate the analyses of fiber type and CSA changes arising from injury, disease, exercise, and therapeutic interventions.

Semiautomatic morphometric analysis of skeletal muscle obtained by needle biopsy in older adults

Analysis of skeletal muscle mass and composition is essential for studying the biology of age-related sarcopenia, loss of muscle mass, and function. Muscle immunohistochemistry (IHC) allows for simultaneous visualization of morphological characteristics and determination of fiber type composition. The information gleaned from myosin heavy chain (MHC) isoform, and morphological measurements offer a more complete assessment of muscle health and properties than classical techniques such as SDS-PAGE and ATPase immunostaining; however, IHC quantification is a time-consuming and tedious method. We developed a semiautomatic method to account for issues frequently encountered in aging tissue. We analyzed needle-biopsied vastus lateralis (VL) of the quadriceps from a cohort of 14 volunteers aged 74.9 ± 2.2 years. We found a high correlation between manual quantification and semiautomatic analyses for the total number of fibers detected (r² = 0.989) and total fiber cross-sectional area (r² = 0.836). The analysis of the VL fiber subtype composition and the cross-sectional area also did not show statistically significant differences. The semiautomatic approach was completed in 10-15% of the time required for manual quantification. The results from these analyses highlight some of the specific issues which commonly occur in aged muscle. Our methods which address these issues underscore the importance of developing efficient, accurate, and reliable methods for quantitatively analyzing the skeletal muscle and the standardization of collection protocols to maximize the likelihood of preserving tissue quality in older adults. Utilizing IHC as a means of exploring the progression of disease, aging, and injury in the skeletal muscle allows for the practical study of muscle tissue down to the fiber level. By adding editing modules to our semiautomatic approach, we accurately quantified the aging muscle and addressed common technical issues.

Muscle2View, a CellProfiler pipeline for detection of the capillary-to-muscle fiber interface and high-content quantification of fiber type-specific histology

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. 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. 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. 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. When comparing the Muscle2View pipeline to manual or semiautomatic analysis, overall the results revealed strong correlations. 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. Collectively, we demonstrate that the Muscle2View pipeline can provide unbiased and high-content analysis of muscle cross-sectional immunohistochemistry images. 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. 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.

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. 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. Thus, the Muscle2View is a viable tool for researchers aiming to quantify immunohistochemical variables from skeletal muscle biopsies.

SLCV-a supervised learning-computer vision combined strategy for automated muscle fibre detection in cross-sectional images

Muscle fibre cross-sectional area (CSA) is an important biomedical measure used to determine the structural composition of skeletal muscle, and it is relevant for tackling research questions in many different fields of research. To date, time consuming and tedious manual delineation of muscle fibres is often used to determine the CSA. Few methods are able to automatically detect muscle fibres in muscle fibre cross-sections to quantify CSA due to challenges posed by variation of brightness and noise in the staining images. In this paper, we introduce the supervised learning-computer vision combined pipeline (SLCV), a robust semi-automatic pipeline for muscle fibre detection, which combines supervised learning (SL) with computer vision (CV). SLCV is adaptable to different staining methods and is quickly and intuitively tunable by the user. We are the first to perform an error analysis with respect to cell count and area, based on which we compare SLCV to the best purely CV-based pipeline in order to identify the contribution of SL and CV steps to muscle fibre detection. Our results obtained on 27 fluorescence-stained cross-sectional images of varying staining quality suggest that combining SL and CV performs significantly better than both SL-based and CV-based methods with regards to both the cell separation- and the area reconstruction error. Furthermore, applying SLCV to our test set images yielded fibre detection results of very high quality, with average sensitivity values of 0.93 or higher on different cluster sizes and an average Dice similarity coefficient of 0.9778.

Integrated method for quantitative morphometry and oxygen transport modeling in striated muscle

Identifying structural limitations in O2 transport is primarily restricted by current methods employed to characterize the nature of physiological remodeling. Inadequate resolution or breadth of available data has impaired development of routine diagnostic protocols and effective therapeutic strategies. Understanding O2 transport within striated muscle faces major challenges, most notably in quantifying how well individual fibers are supplied by the microcirculation, which has necessitated exploring tissue O2 supply using theoretical modeling of diffusive exchange. With capillary domains identified as a suitable model for the description of local O2 supply and requiring less computation than numerically calculating the trapping regions that are supplied by each capillary via biophysical transport models, we sought to design a high-throughput method for histological analysis. We present an integrated package that identifies optimal protocols for identification of important input elements, processing of digitized images with semiautomated routines, and incorporation of these data into a mathematical modeling framework with computed output visualized as the tissue partial pressure of O2 (Po2) distribution across a biopsy sample. Worked examples are provided using muscle samples from experiments involving rats and humans.

NEW & NOTEWORTHY Progress in quantitative morphometry and analytical modeling has tended to develop independently. Real diagnostic power lies in harnessing both disciplines within one user-friendly package. We present a semiautomated, high-throughput tool for determining muscle phenotype from biopsy material, which also provides anatomically relevant input to quantify tissue oxygenation, in a coherent package not previously available to nonspecialist investigators.

Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle

Adult skeletal muscle is capable of complete regeneration after an acute injury. The main parameter studied to assess muscle regeneration efficacy is the cross-sectional area (CSA) of the myofibers as myofiber size correlates with muscle force. CSA analysis can be time-consuming and may trigger variability in the results when performed manually. This is why programs were developed to completely automate the analysis of the CSA, such as SMASH, MyoVision, or MuscleJ softwares. Although these softwares are efficient to measure CSA on normal or hypertrophic/atrophic muscle, they fail to efficiently measure CSA on regenerating muscles. We developed Open-CSAM, an ImageJ macro, to perform a high throughput semi-automated analysis of CSA on skeletal muscle from various experimental conditions. The macro allows the experimenter to adjust the analysis and correct the mistakes done by the automation, which is not possible with fully automated programs. We showed that Open-CSAM was more accurate to measure CSA in regenerating and dystrophic muscles as compared with SMASH, MyoVision, and MuscleJ softwares and that the inter-experimenter variability was negligible. We also showed that, to obtain a representative CSA measurement, it was necessary to analyze the whole muscle section and not randomly selected pictures, a process that was easily and accurately be performed using Open-CSAM. To conclude, we show here an easy and experimenter-controlled tool to measure CSA in muscles from any experimental condition, including regenerating muscle.

A neural network approach to analyze cross-sections of muscle fibers in pathological images

Morphological characteristics of muscle fibers, such as their cross-sections, are important indicators of the health and function of the musculoskeletal system. However, manual analysis of muscle fiber morphology is a labor-intensive and time-consuming process that is prone to errors. Overall, the procedure involves high inter- and intra-observer variability. Therefore, it is desirable for biologists to have a tool that can produce objective and reproducible analysis for muscle fiber images. In this work, we propose a deep convolutional neural network (DCNN) followed by post-processing for detecting and measuring the cross-sections of muscle fibers. We evaluate three segmentation networks for muscle boundary segmentation: (1) U-net, (2) FusionNet, and (3) a customized FusionNet. The customized FusionNet, which had the highest Dice coefficient on the test set, was used for subsequent morphological analysis of the muscle fibers. The proposed method was tested on microscopic images of the tibialis anterior muscles of a pre-clinical model of muscular dystrophy. The dataset contained four mosaic images, totalling more than 3400 fibers. Because of the severity of muscle injury in this pre-clinical model, its muscle fiber images present a challenge for quantitative analysis for several reasons. First, the muscle fibers had inhomogeneous spatial distribution and very different sizes. Second, the membranes of the muscle fibers had uneven signal intensity due to the loss of a membrane protein. Third, the shapes of intact muscle fibers were very different. All these factors contributed to the difficulty of acquiring good training data in the first place. Despite these difficulties, we achieved an average muscle fiber overlay precision of 0.65 and an average recall of 0.49. In this context, overlaid fibers are defined as fibers that have one or more pixels overlaying in the manual and DCNN cross-section segmentation. For the overlaid fibers, the proposed method achieved excellent segmentation accuracy of 94% ± 10.26%, as measured by the Dice-Sorensen coefficient.

MyoVision: software for automated high-content analysis of skeletal muscle immunohistochemistry

Analysis of skeletal muscle cross sections is an important experimental technique in muscle biology. Many aspects of immunohistochemistry and fluorescence microscopy can now be automated, but most image quantification techniques still require extensive human input, slowing progress and introducing the possibility of user bias. MyoVision is a new software package that was developed to overcome these limitations. The software improves upon previously reported automatic techniques and analyzes images without requiring significant human input and correction. When compared with data derived by manual quantification, MyoVision achieves an accuracy of ≥94% for basic measurements such as fiber number, fiber type distribution, fiber cross-sectional area, and myonuclear number. Scientists can download the software free from www.MyoVision.org and use it to automate the analysis of their own experimental data. This will improve the efficiency and consistency of the analysis of muscle cross sections and help to reduce the burden of routine image quantification in muscle biology. NEW & NOTEWORTHY Scientists currently analyze images of immunofluorescently labeled skeletal muscle using time-consuming techniques that require sustained human supervision. As well as being inefficient, these techniques can increase variability in studies that quantify morphological adaptations of skeletal muscle at the cellular level. MyoVision is new software that overcomes these limitations by performing high-content analysis of muscle cross sections with minimal manual input. It is open source and freely available.