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

Overview of attention for article published in Skeletal Muscle, August 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 292)
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

29 tweeters


22 Dimensions

Readers on

65 Mendeley
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MuscleJ: a high-content analysis method to study skeletal muscle with a new Fiji tool
Published in
Skeletal Muscle, August 2018
DOI 10.1186/s13395-018-0171-0
Pubmed ID

Alicia Mayeuf-Louchart, David Hardy, Quentin Thorel, Pascal Roux, Lorna Gueniot, David Briand, Aurélien Mazeraud, Adrien Bouglé, Spencer L. Shorte, Bart Staels, Fabrice Chrétien, Hélène Duez, Anne Danckaert


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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 29 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 26%
Researcher 13 20%
Student > Master 8 12%
Student > Bachelor 7 11%
Other 2 3%
Other 3 5%
Unknown 15 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 26%
Agricultural and Biological Sciences 10 15%
Sports and Recreations 7 11%
Engineering 5 8%
Immunology and Microbiology 2 3%
Other 6 9%
Unknown 18 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2018.
All research outputs
of 15,922,891 outputs
Outputs from Skeletal Muscle
of 292 outputs
Outputs of similar age
of 279,488 outputs
Outputs of similar age from Skeletal Muscle
of 1 outputs
Altmetric has tracked 15,922,891 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 292 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 279,488 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them