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小鼠骨骼肌纤维的简化免疫组织化学分类

A simplified immunohistochemical classification of skeletal muscle fibres in mouse.

作者信息

Kammoun M, Cassar-Malek I, Meunier B, Picard B

机构信息

UMR1213 Herbivores (UMRH) Clermont University, VetAgro Sup.

出版信息

Eur J Histochem. 2014 Jun 24;58(2):2254. doi: 10.4081/ejh.2014.2254.

Abstract

The classification of muscle fibres is of particular interest for the study of the skeletal muscle properties in a wide range of scientific fields, especially animal phenotyping. It is therefore important to define a reliable method for classifying fibre types. The aim of this study was to establish a simplified method for the immunohistochemical classification of fibres in mouse. To carry it out, we first tested a combination of several anti myosin heavy chain (MyHC) antibodies in order to choose a minimum number of antibodies to implement a semi-automatic classification. Then, we compared the classification of fibres to the MyHC electrophoretic pattern on the same samples. Only two anti MyHC antibodies on serial sections with the fluorescent labeling of the Laminin were necessary to classify properly fibre types in Tibialis Anterior and Soleus mouse muscles in normal physiological conditions. This classification was virtually identical to the classification realized by the electrophoretic separation of MyHC. This immunohistochemical classification can be applied to the total area of Tibialis Anterior and Soleus mouse muscles. Thus, we provide here a useful, simple and time-efficient method for immunohistochemical classification of fibres, applicable for research in mouse.

摘要

在广泛的科学领域,尤其是动物表型分析中,肌纤维分类对于研究骨骼肌特性具有特殊意义。因此,定义一种可靠的纤维类型分类方法很重要。本研究的目的是建立一种简化的小鼠纤维免疫组织化学分类方法。为实现这一目标,我们首先测试了几种抗肌球蛋白重链(MyHC)抗体的组合,以便选择最少数量的抗体来进行半自动分类。然后,我们将纤维分类与相同样本上的MyHC电泳图谱进行比较。在正常生理条件下,仅需在连续切片上使用两种抗MyHC抗体并结合层粘连蛋白的荧光标记,就能对小鼠胫骨前肌和比目鱼肌的纤维类型进行正确分类。这种分类与通过MyHC电泳分离实现的分类几乎相同。这种免疫组织化学分类可应用于小鼠胫骨前肌和比目鱼肌的整个区域。因此,我们在此提供了一种有用、简单且省时的纤维免疫组织化学分类方法,适用于小鼠研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc03/4083319/6860cd0096f3/ejh-2014-2-2254-g001.jpg

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