Automated methods for the analysis of skeletal muscle fiber size and metabolic type

Inter-software reliability and agreement for follicular and luteal morphometric and echotextural ultrasonographic parameters in beef cattle

This study aimed to compare the inter-software and inter-observer reliability and agreement for the assessment of follicular and luteal morphometry and echotexture parameters in beef crossbreed females (3/8 Bos taurus indicus and 5/8 Bos taurus taurus). B-mode and color Doppler ultrasonographic ovarian images were obtained at specific time points of estradiol-progesterone-based protocols for timed artificial insemination (TAI). Sonograms were analyzed by two observers using a licensed (IASP1) and an open access (IASP2) software package. A total of 292 snap-shot sonograms were analyzed for morphometric parameters and 504 for echotexture parameters. inter-software reliability was judged moderate to excellent (ICC or CCC=0.73-0.98), whereas inter-observer reliability for morphometric parameters was deemed good to excellent (ICC or CCC=0.82-0.98). A small percentage (up to 10.95 %) of measured parameters fell outside the limits of inter-software and inter-observer agreement. For echotexture parameters, inter-software reliability varied widely (ICC or CCC=0.16-0.95) based on the size of regions of interest (ROI), while inter-observer reliability ranged from moderate to excellent (ICC or CCC= 0.71-0.97). The highest inter-software reliability for pixel value and heterogeneity value was observed for the corpus luteum (ICCs=0.81-0.95; P>0.05), followed by the peripheral follicular antrum (ICCs=0.75-0.78; P<0.05). However, lower reliability was determined for the follicular wall (ICCs=0.08-0.33; P<0.0001) and perifollicular stroma (ICCs=0.16-0.46; P<0.05). In conclusion, both software packages showed high reproducibility for morphometric measurements, while echotexture measurements were more challenging to replicate based on ROI sizes. Caution is advised when selecting ROI sizes for echotexture measurements in bovine ovaries.

A deep learning-based automated image analysis for histological evaluation of broiler pectoral muscle

Global market demand for chicken breast muscle with high yield and quality, together with the high incidence rate of breast muscle abnormalities in recent years highlights the need for tools that can provide a rapid and precise evaluation of breast muscle development and morphology. In this study, we used a novel deep learning-based automated image analysis workflow combining Fiji (ImageJ) with Cellpose and MorphoLibJ plugins to generate an automated diameter and cross-sectional area quantification for broiler breast muscle. We compared data of myofiber diameter from 14-day-old broiler chicks, generated either by manual analysis or by automated analysis. Comparison between manual and automated analysis methods exhibited a striking accuracy rate of up to 99.91%. Moreover, the automated analysis method was much faster. When the automated analysis method was implemented on 84 breast muscle cross-section images it characterized 59,128 myofibers within 4.2 h, while manual analysis of 27 breast muscle cross-section images enabled analysis of 17,333 myofibers in 54 h. The automated image analysis method was also more productive, producing data sets of both diameter and cross-sectional area at an 80-fold higher rate than the manual analysis (26,279 vs. 321 data sets per hour, respectively). In order to demonstrate the ability of this automated image analysis tool to detect differences in breast muscle histomorphology, we applied it on cross sections from chicks of control and in ovo feeding group, injected with a methionine source [2-hydroxy-4-(methylthio) butanoic calcium salt (HMTBa)], known to effect skeletal muscle histomorphology. Analysis was performed on 19,807 myofibers from the control group and 21,755 myofibers from the HMTBa group and was completed in less than 1 h. The clear advantages of this automated image analysis workflow characterized by high precision, high speed, and high productiveness demonstrate its potential to be implemented as a reproducible and readily adaptable research or diagnostic tool for chicken breast muscle development and morphology.

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.

Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology.

Automated image-analysis method for the quantification of fiber morphometry and fiber type population in human skeletal muscle

Background

The quantitative analysis of muscle histomorphometry has been growing in importance in both research and clinical settings. Accurate and stringent assessment of myofibers’ changes in size and number, and alterations in the proportion of oxidative (type I) and glycolytic (type II) fibers is essential for the appropriate study of aging and pathological muscle, as well as for diagnosis and follow-up of muscle diseases. Manual and semi-automated methods to assess muscle morphometry in sections are time-consuming, limited to a small field of analysis, and susceptible to bias, while most automated methods have been only tested in rodent muscle.

Methods

We developed a new macro script for Fiji-ImageJ to automatically assess human fiber morphometry in digital images of the entire muscle. We tested the functionality of our method in deltoid muscle biopsies from a heterogeneous population of subjects with histologically normal muscle (male, female, old, young, lean, obese) and patients with dermatomyositis, necrotizing autoimmune myopathy, and anti-synthetase syndrome myopathy.

Results

Our macro is fully automated, requires no user intervention, and demonstrated improved fiber segmentation by running a series of image pre-processing steps before the analysis. Likewise, our tool showed high accuracy, as compared with manual methods, for identifying the total number of fibers (r = 0.97, p < 0.001), fiber I and fiber II proportion (r = 0.92, p < 0.001), and minor diameter (r = 0.86, p < 0.001) while conducting analysis in ~ 5 min/sample. The performance of the macro analysis was maintained in pectoral and deltoid samples from subjects of different age, gender, body weight, and muscle status. The output of the analyses includes excel files with the quantification of fibers’ morphometry and color-coded maps based on the fiber’s size, which proved to be an advantageous feature for the fast and easy visual identification of location-specific atrophy and a potential tool for medical diagnosis.

Conclusion

Our macro is reliable and suitable for the study of human skeletal muscle for research and for diagnosis in clinical settings providing reproducible and consistent analysis when the time is of the utmost importance.

Electronic supplementary material

The online version of this article (10.1186/s13395-019-0200-7) contains supplementary material, which is available to authorized users.

Isobaric tags for relative and absolute quantitation‑based proteomics reveals potential novel biomarkers for the early diagnosis of acute myocardial infarction within 3 h

Acute myocardial infarction (AMI) is one of the most common and life-threatening cardiovascular diseases. However, the ability to diagnose AMI within 3 h is currently lacking. The present study aimed to identify the differentially expressed proteins of AMI within 3 h and to investigate novel biomarkers using isobaric tags for relative and absolute quantitation (ITRAQ) technology. A total of 30 beagle dogs were used for establishing the MI models successfully by injecting thrombin powder and a polyethylene microsphere suspension. Serum samples were collected prior to (0 h) and following MI (1, 2 and 3 h). ITRAQ-coupled liquid chromatography-mass spectrometry (LC-MS) technology was used to identify the differentially expressed proteins. The bioinformatics analysis selected several key proteins in the initiation of MI. Further analysis was performed using STRING software. Finally, western blot analysis was used to evaluate the results obtained from ITRAQ. In total, 28 proteins were upregulated and 23 were downregulated in the 1 h/0 h group, 28 proteins were upregulated and 26 were downregulated in the 2 h/0 h group, and 24 proteins were upregulated and 19 were downregulated in the 3 h/0 h group. The Gene Ontology (GO) annotation and functional enrichment analysis identified 19 key proteins. Protein-protein interactions (PPIs) were investigated using the STRING database. GO enrichment analysis revealed that a number of key proteins, including ATP synthase F1 subunit β (ATP5B), cytochrome c oxidase subunit 2 and cytochrome c, were components of the electron transport chain and were involved in energy metabolism. The western blot analysis demonstrated that the expression of ATP5B decreased significantly at all three time points (P<0.01), which was consistent with the ITRAQ results, whereas the expression of fibrinogen γ chain increased at 2 and 3 h (P<0.01) and the expression of integrator complex subunit 4 increased at all three time points (P<0.01), which differed from the ITRAQ results. According to the proteomics of the beagle dog MI model, ATP5B may serve as the potential biomarkers of AMI. Mitochondrial dysfunction and disruption of the electron transport chain may be critical indicators of early MI within 3 h. These finding may provide a novel direction for the diagnosis of AMI.

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.

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

Background

Skeletal 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.

Methods

Running 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.

Results

After 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.

Conclusion

We 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 material

The online version of this article (10.1186/s13395-018-0171-0) contains supplementary material, which is available to authorized users.

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.

A Rapid Automated Protocol for Muscle Fiber Population Analysis in Rat Muscle Cross Sections Using Myosin Heavy Chain Immunohistochemistry

Quantification of muscle fiber populations provides a deeper insight into the effects of disease, trauma, and various other influences on skeletal muscle composition. Various time-consuming methods have traditionally been used to study fiber populations in many fields of research. However, recently developed immunohistochemical methods based on myosin heavy chain protein expression provide a quick alternative to identify multiple fiber types in a single section. Here, we present a rapid, reliable and reproducible protocol for improved staining quality, allowing automatic acquisition of whole cross sections and automatic quantification of fiber populations with ImageJ. For this purpose, embedded skeletal muscles are cut in cross sections, stained using myosin heavy chains antibodies with secondary fluorescent antibodies and DAPI for cell nuclei staining. Whole cross sections are then scanned automatically using a slide scanner to obtain high-resolution composite pictures of the entire specimen. Fiber population analyses are subsequently performed to quantify slow, intermediate and fast fibers using an automated macro for ImageJ. We have previously shown that this method can identify fiber populations reliably to a degree of ±4%. In addition, this method reduces inter-user variability and time per analyses significantly using the open source platform ImageJ.

Automated muscle fiber type population analysis with ImageJ of whole rat muscles using rapid myosin heavy chain immunohistochemistry

INTRODUCTION

Skeletal muscle consists of different fiber types which adapt to exercise, aging, disease, or trauma. Here we present a protocol for fast staining, automatic acquisition, and quantification of fiber populations with ImageJ.

METHODS

Biceps and lumbrical muscles were harvested from Sprague-Dawley rats. Quadruple immunohistochemical staining was performed on single sections using antibodies against myosin heavy chains and secondary fluorescent antibodies. Slides were scanned automatically with a slide scanner. Manual and automatic analyses were performed and compared statistically.

RESULTS

The protocol provided rapid and reliable staining for automated image acquisition. Analyses between manual and automatic data indicated Pearson correlation coefficients for biceps of 0.645-0.841 and 0.564-0.673 for lumbrical muscles. Relative fiber populations were accurate to a degree of ± 4%.

CONCLUSIONS

This protocol provides a reliable tool for quantification of muscle fiber populations. Using freely available software, it decreases the required time to analyze whole muscle sections. Muscle Nerve 54: 292-299, 2016.

Fiber Composition of the Grasscutter (Thryonomys swinderianus, Temminck 1827) Thigh Muscle: An Enzyme-histochemical Study

The aim of this study was to describe de fiber composition in the thigh muscles of grass cutter (Thryonomys swinderianus, Temminck 1827). Ten 4 to 6-month-old (3 to 4 kg) male grasscutter were used in this study. Eleven skeletal muscles of the thigh [M. biceps femoris (BF), M. rectus femoris (RF), M. vastus lateralis (VL), M. vastus medialis (VM), M. tensor fasciae latae (TFL), M. semitendinosus (ST), M. semimembranosus (SM), M. semimembranosus accessorius (SMA), M. Sartorius (SRT), M. pectineus (PCT), M. adductor magnus (AM)] were collected after animals euthanasia and examined by light microscopy. Three muscle fiber types (I, IIB and IIA) were found in these muscles using enzyme histochemical techniques [myosine adenosine triphosphatase (ATPase) and nicotinamide adenine dinucleotide tetrazolium reductase (NADH-TR)]. Ten of these eleven muscles are composed by 89% to 100% of fast contracting fibers (types IIA and IIB), while the SMA was almost exclusively formed by slow contracting fibers.