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New tools for the investigation of muscle fiber-type spatial distributions across histological sections
BACKGROUND: The functional and metabolic properties of skeletal muscles are partly a function of the spatial arrangement of fibers across the muscle belly. Many muscles feature a non-uniform spatial pattern of fiber types, and alterations to the arrangement can reflect age or disease and correlate w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122286/ https://www.ncbi.nlm.nih.gov/pubmed/37087439 http://dx.doi.org/10.1186/s13395-023-00316-0 |
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author | Redmond, Anna K. Davies, Tilman M. Schofield, Matthew R. Sheard, Philip W. |
author_facet | Redmond, Anna K. Davies, Tilman M. Schofield, Matthew R. Sheard, Philip W. |
author_sort | Redmond, Anna K. |
collection | PubMed |
description | BACKGROUND: The functional and metabolic properties of skeletal muscles are partly a function of the spatial arrangement of fibers across the muscle belly. Many muscles feature a non-uniform spatial pattern of fiber types, and alterations to the arrangement can reflect age or disease and correlate with changes in muscle mass and strength. Despite the significance of this event, descriptions of spatial fiber-type distributions across a muscle section are mainly provided qualitatively, by eye. Whilst several quantitative methods have been proposed, difficulties in implementation have meant that robust statistical analysis of fiber type distributions has not yielded new insight into the biological processes that drive the age- or disease-related changes in fiber type distributions. METHODS: We review currently available approaches for analysis of data reporting fast/slow fiber type distributions on muscle sections before proposing a new method based on a generalized additive model. We compare current approaches with our new method by analysis of sections of three mouse soleus muscles that exhibit visibly different spatial fiber patterns, and we also apply our model to a dataset representing the fiber type proportions and distributions of the mouse tibialis anterior. RESULTS: We highlight how current methods can lead to differing interpretations when applied to the same dataset and demonstrate how our new method is the first to permit location-based estimation of fiber-type probabilities, in turn enabling useful graphical representation. CONCLUSIONS: We present an open-access online application that implements current methods as well as our new method and which aids the interpretation of a variety of statistical tools for the spatial analysis of muscle fiber distributions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13395-023-00316-0. |
format | Online Article Text |
id | pubmed-10122286 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101222862023-04-23 New tools for the investigation of muscle fiber-type spatial distributions across histological sections Redmond, Anna K. Davies, Tilman M. Schofield, Matthew R. Sheard, Philip W. Skelet Muscle Methodology BACKGROUND: The functional and metabolic properties of skeletal muscles are partly a function of the spatial arrangement of fibers across the muscle belly. Many muscles feature a non-uniform spatial pattern of fiber types, and alterations to the arrangement can reflect age or disease and correlate with changes in muscle mass and strength. Despite the significance of this event, descriptions of spatial fiber-type distributions across a muscle section are mainly provided qualitatively, by eye. Whilst several quantitative methods have been proposed, difficulties in implementation have meant that robust statistical analysis of fiber type distributions has not yielded new insight into the biological processes that drive the age- or disease-related changes in fiber type distributions. METHODS: We review currently available approaches for analysis of data reporting fast/slow fiber type distributions on muscle sections before proposing a new method based on a generalized additive model. We compare current approaches with our new method by analysis of sections of three mouse soleus muscles that exhibit visibly different spatial fiber patterns, and we also apply our model to a dataset representing the fiber type proportions and distributions of the mouse tibialis anterior. RESULTS: We highlight how current methods can lead to differing interpretations when applied to the same dataset and demonstrate how our new method is the first to permit location-based estimation of fiber-type probabilities, in turn enabling useful graphical representation. CONCLUSIONS: We present an open-access online application that implements current methods as well as our new method and which aids the interpretation of a variety of statistical tools for the spatial analysis of muscle fiber distributions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13395-023-00316-0. BioMed Central 2023-04-22 /pmc/articles/PMC10122286/ /pubmed/37087439 http://dx.doi.org/10.1186/s13395-023-00316-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Redmond, Anna K. Davies, Tilman M. Schofield, Matthew R. Sheard, Philip W. New tools for the investigation of muscle fiber-type spatial distributions across histological sections |
title | New tools for the investigation of muscle fiber-type spatial distributions across histological sections |
title_full | New tools for the investigation of muscle fiber-type spatial distributions across histological sections |
title_fullStr | New tools for the investigation of muscle fiber-type spatial distributions across histological sections |
title_full_unstemmed | New tools for the investigation of muscle fiber-type spatial distributions across histological sections |
title_short | New tools for the investigation of muscle fiber-type spatial distributions across histological sections |
title_sort | new tools for the investigation of muscle fiber-type spatial distributions across histological sections |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122286/ https://www.ncbi.nlm.nih.gov/pubmed/37087439 http://dx.doi.org/10.1186/s13395-023-00316-0 |
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