Manual analysis of cross-sectional area, fiber-type distribution, and total and centralized nuclei in skeletal muscle cross sections is tedious and time consuming, necessitating an accurate, automated method of analysis. While several excellent programs are available, our analyses of skeletal muscle disease models suggest the need for additional features and flexibility to adequately describe disease pathology. We introduce a new semi-automated analysis program, MyoSight, which is designed to facilitate image analysis of skeletal muscle cross sections and provide additional flexibility in the analyses. We describe staining and imaging methods that generate high-quality images of immunofluorescent-labelled cross sections from mouse skeletal muscle. Using these methods, we can analyze up to 5 different fluorophores in a single image, allowing simultaneous analyses of perinuclei, central nuclei, fiber size, and fiber-type distribution. MyoSight displays high reproducibility among users, and the data generated are in close agreement with data obtained from manual analyses of cross-sectional area (CSA), fiber number, fiber-type distribution, and number and localization of myonuclei. Furthermore, MyoSight clearly delineates changes in these parameters in muscle sections from a mouse model of Duchenne muscular dystrophy (mdx). MyoSight is a new program based on an algorithm that can be optimized by the user to obtain highly accurate fiber size, fiber-type identification, and perinuclei and central nuclei per fiber measurements. MyoSight combines features available separately in other programs, is user friendly, and provides visual outputs that allow the user to confirm the accuracy of the analyses and correct any inaccuracies. We present MyoSight as a new program to facilitate the analyses of fiber type and CSA changes arising from injury, disease, exercise, and therapeutic interventions.

中文翻译：

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MyoSight — 骨骼肌横截面的半自动图像分析


对骨骼肌横截面中的横截面积、纤维类型分布以及总核和集中核进行手动分析既繁琐又耗时，因此需要一种准确的自动化分析方法。虽然有几个优秀的程序可用，但我们对骨骼肌疾病模型的分析表明需要额外的特征和灵活性来充分描述疾病病理学。我们引入了一种新的半自动分析程序 MyoSight，该程序旨在促进骨骼肌横截面的图像分析，并在分析中提供额外的灵活性。我们描述了生成小鼠骨骼肌免疫荧光标记横截面的高质量图像的染色和成像方法。使用这些方法，我们可以在单个图像中分析多达 5 种不同的荧光团，从而可以同时分析核周、中心核、纤维尺寸和纤维类型分布。 MyoSight 在用户中表现出高度的可重复性，生成的数据与手动分析横截面积 (CSA)、纤维数量、纤维类型分布以及肌核的数量和定位获得的数据非常一致。此外，MyoSight 清楚地描绘了杜氏肌营养不良 (mdx) 小鼠模型肌肉切片中这些参数的变化。 MyoSight 是一款基于算法的新程序，用户可以对其进行优化，以获得高度准确的纤维尺寸、纤维类型识别以及每根纤维的核周和中心核测量结果。 MyoSight 结合了其他程序中单独提供的功能，用户友好，并提供可视化输出，允许用户确认分析的准确性并纠正任何不准确之处。 我们将 MyoSight 作为一个新程序来促进分析由损伤、疾病、运动和治疗干预引起的纤维类型和 CSA 变化。