Software for muscle fibre type classification and analysis

Abstract

Fibre type determination requires a large series of differently stained muscle sections. The manual identification of individual fibres through the series is tedious and time consuming. This paper presents a software that enables (i) adjusting the position of individual fibres through a series of differently stained sections (image registration) and identification of individual fibres through the series as well as (ii) muscle fibre classification and (iii) quantitative analysis. The data output of the system is the following: numerical and areal proportions of fibre types, fibre type size and optical density (grey level) of the final reaction product in every fibre. The muscle fibre type can be determined stepwise, based on one set of stained sections while further, newly stained sections can be added to the already defined muscle fibre profile. Several advantages of the presented software application in skeletal muscle research are presented. The system is semiquantitative, flexible, and user friendly.

Muscle Fibre Phenotyping from a Single Section: Is It as Informative as from Serial Sections?

“…Obviously metabolic profile can be identified in next two consecutive muscle sections where tracing of muscle fibres is easier than in more distant muscle sections. Adjustments of orientations of muscle profiles in distant sections are sometimes necessary with specialised computer programs for image analysis as (21). Theoretically total of six emission/detection channels can be used (13), if primary antibodies are directly coupled with fluorophores which have very narrow emission spectrum.…”

Quantifiable diagnosis of neuromuscular diseases through network analysis

“…To the best of our knowledge, this is the largest and most comprehensive dataset of images analyzed in a neuromuscular study. Previous reports have been published of attempts to facilitate the automated extraction of geometric characteristics from muscle biopsies [11][12][13][14][15][16]. These studies rely on the development of segmentation methods using a very small number of samples to only extract morphometric information.…”

“…The selection of relevant features for each comparison required a training process based on known biological data. Performing the training by using 18 images from control and 20 images from MD samples (Additional file 2: Table S1), NDICIA selected one geometrical (15) and two network characteristics (18 and 19) for the identification of dystrophies. PCA graphs for the control and MD datasets were generated using the selected features.…”

“…First, we performed a new feature selection step using more control images to cover a wider range of age and muscle types (87 images: 50 controls, 20 MD, 17 NA; Additional file 2: Table S1). When the training procedure was performed on the 82 features, nine characteristics were subsequently selected (15,16,25,29,30,32,34,62,69). It is of interest that only two of these are geometric characteristics, pointing again to the high discrimination power of network features.…”

“…A large panel of different histochemical and histoenzymatic techniques are necessary to identify pathologic changes in the routine diagnostic process [9,10] (Additional file 1: Figure S1). Previous attempts to automate the extraction of geometrical characteristics from normal muscle biopsies have been published [11][12][13][14][15][16], but those methods fail to provide an automated analysis or adequate scrutiny of the information derived from the analysis. Our analysis begins at this point, taking into account a large number of samples to study both geometrical and network data to include morphometric and organizational information.…”