Analysis of Skeletal Fibers in Three Dimensional Images: Methodological considerations
2007 (English)In: XXXVIth European Muscle Conference of the European Society for Muscle Research: European Muscle Conference 2007, 2007, 130- p.Conference paper (Other academic)
Knowledge of the detailed three dimensional organization of nuclei in skeletal muscle fibers is of fundamental importance for the understanding of the basic mechanisms involved in muscle wasting associated with for example neuromuscular disorders and aging. An ongoing interdisciplinary collaboration between the Centre for Image Analysis (CBA), and the Muscle Research Group (MRG), both at Uppsala University, addresses the issue of spatial distribution of myonuclei using confocal microscopic techniques together with advanced methods for computerized image analysis.
Performing quantitative analysis on true three dimensional volume images captured by confocal microscopy gives us the option to perform in-depth statistical analysis of the relationship between neighboring myonuclei. The three dimensional representation enables extraction of a number of features for individual myonuclei, e.g., size and shape of a nucleus, and the myonuclear domain (in which each myonucleus control the gene products). This project investigates data sets from single muscle fibers sampled from mouse, rat, pig, human, horse and rhino to determine the myonuclei arrangement between species with a 100,000 fold difference in body weight.
The appropriate image analysis tools needed for gaining the understanding of organization in three dimensional volume images are developed within the project to facilitate the analysis of similarities between species, and unique features within a species. The accumulated understanding of the spatial organization of myonuclei, and the effect of individual myonuclei size, will lead to an increased knowledge of basic mechanisms underlying muscle wasting in various neuromuscular disorders. This knowledge will hopefully lead to new therapeutic strategies that can be evaluated in experimental animal models prior to clinical testing trials in patients.
Place, publisher, year, edition, pages
2007. 130- p.
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:uu:diva-12567OAI: oai:DiVA.org:uu-12567DiVA: diva2:40336
Abstract at p. 101.2008-01-072008-01-072010-05-07Bibliographically approved