Evidence map›Paper›PMID 30081940›Full record

ArticleSkeletal muscle2018

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

Alicia Mayeuf-Louchart, David Hardy, Quentin Thorel, Pascal Roux, Lorna Gueniot, David Briand, Aurélien Mazeraud, Adrien Bouglé, Spencer L Shorte, Bart Staels and 3 more

Open access · goldAbstract read
In one paragraph

Article in Skeletal muscle, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed
5.8field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

103 citing papers in PubMed, 166 citations in OpenAlex.

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43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 1 country.

Alicia Mayeuf-LouchartInserm, CHU Lille, Institut Pasteur de Lille, University of Lille, U1011 - EGID, 1 rue du Pr. Calmette, F-59000, Lille, France. alicia.mayeuf-louchart@pasteur-lille.fr.ORCID 0000-0002-3787-7450
David HardyExperimental Neuropathology Unit, Infection and Epidemiology Department, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
Quentin ThorelInserm, CHU Lille, Institut Pasteur de Lille, University of Lille, U1011 - EGID, 1 rue du Pr. Calmette, F-59000, Lille, France.
Pascal RouxUTechS PBI (Imagopole)-Citech, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
Lorna GueniotExperimental Neuropathology Unit, Infection and Epidemiology Department, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
David BriandExperimental Neuropathology Unit, Infection and Epidemiology Department, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
Aurélien MazeraudExperimental Neuropathology Unit, Infection and Epidemiology Department, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
Adrien BougléExperimental Neuropathology Unit, Infection and Epidemiology Department, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
Spencer L ShorteUTechS PBI (Imagopole)-Citech, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
Bart StaelsInserm, CHU Lille, Institut Pasteur de Lille, University of Lille, U1011 - EGID, 1 rue du Pr. Calmette, F-59000, Lille, France.
Fabrice ChrétienExperimental Neuropathology Unit, Infection and Epidemiology Department, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France.
Hélène DuezInserm, CHU Lille, Institut Pasteur de Lille, University of Lille, U1011 - EGID, 1 rue du Pr. Calmette, F-59000, Lille, France.
Anne DanckaertExperimental Neuropathology Unit, Infection and Epidemiology Department, Institut Pasteur, 25, rue du Docteur Roux, 75015, Paris, France. anne.danckaert@pasteur.fr.
Institut Pasteur · FRInserm · FR

Funding

Agence Nationale de la Recherche ANR-10-INSB-04-01Agence Nationale de la Recherche ANR-10-LABX-46H2020 European Research Council 694717
6 · The paper itself

Abstract

backgroundSkeletal 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.

methodsRunning 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.

resultsAfter 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.

conclusionWe 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.

Indexed as

SoftwareAnimalsImage Processing, Computer-AssistedMiceMuscle Fibers, SkeletalOptical ImagingFiber typingHistologyImage automated quantificationIn situ cartographySatellite cellsSkeletal muscle fiberStem cellsVessels

Identifiers

PMID30081940
PMCPMC6091189
OpenAlexW2885080211

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.