Automated image-analysis method for the quantification of fiber morphometry and fiber type population in human skeletal muscle.
Adolescent
Adult
Aged
Aged, 80 and over
Aging
/ pathology
Autoimmune Diseases
/ diagnostic imaging
Dermatomyositis
/ diagnostic imaging
Female
Fluorescent Antibody Technique
Healthy Volunteers
Humans
Image Interpretation, Computer-Assisted
/ methods
Male
Middle Aged
Muscle Fibers, Skeletal
/ classification
Muscle, Skeletal
/ diagnostic imaging
Muscular Diseases
/ diagnostic imaging
Myositis
/ diagnostic imaging
Obesity
/ diagnostic imaging
Software
Young Adult
Automated image analysis
Biomedical imaging
Clinical diagnosis
Fiber type populations
Human muscle cross-sections
Muscle fiber quantification
Journal
Skeletal muscle
ISSN: 2044-5040
Titre abrégé: Skelet Muscle
Pays: England
ID NLM: 101561193
Informations de publication
Date de publication:
27 05 2019
27 05 2019
Historique:
received:
11
09
2018
accepted:
02
05
2019
entrez:
29
5
2019
pubmed:
28
5
2019
medline:
6
5
2020
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 31133066
doi: 10.1186/s13395-019-0200-7
pii: 10.1186/s13395-019-0200-7
pmc: PMC6537183
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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