Skeletal muscle function is inferred from the spatial arrangement of muscle fiber architecture, which corresponds to myofiber molecular and metabolic features. Myofiber features are often determined using immunofluorescence on a local sampling, typically obtained from a median region. This median region is assumed to represent the entire muscle. However, it remains largely unknown to what extent this local sampling represents the entire muscle. We present a pipeline to study the architecture of muscle fiber features over the entire muscle, including sectioning, staining, imaging to image quantification and data-driven analysis with Myofiber type were identified by the expression of myosin heavy chain (MyHC) isoforms, representing contraction properties. We reconstructed muscle architecture from consecutive cross-sections stained for laminin and MyHC isoforms. Examining the entire muscle using consecutive cross-sections is extremely laborious, we provide consideration to reduce the dataset without loosing spatial information. Data-driven analysis with over 150,000 myofibers showed spatial variations in myofiber geometric features, myofiber type, and the distribution of neuromuscular junctions over the entire muscle. We present a workflow to study histological changes over the entire muscle using high-throughput imaging, image quantification, and data-driven analysis. Our results suggest that asymmetric spatial distribution of these features over the entire muscle could impact muscle function. Therefore, instead of a single sampling from a median region, representative regions covering the entire muscle should be investigated in future studies.

中文翻译：

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使用数据驱动分析发现整个肌肉的纤维类型结构

骨骼肌功能是从肌纤维结构的空间排列推断出来的，它对应于肌纤维的分子和代谢特征。肌纤维特征通常使用局部采样的免疫荧光来确定，通常从中间区域获得。假定该中间区域代表整个肌肉。然而，在很大程度上仍不清楚这种局部采样在多大程度上代表了整个肌肉。我们提出了一个研究整个肌肉的肌纤维特征结构的管道，包括切片、染色、成像到图像量化和数据驱动分析，肌纤维类型通过肌球蛋白重链 (MyHC) 亚型的表达来识别，代表收缩特性。我们从染色层粘连蛋白和 MyHC 亚型的连续横截面重建肌肉结构。使用连续横截面检查整个肌肉非常费力，我们考虑在不丢失空间信息的情况下减少数据集。对超过 150,000 根肌纤维的数据驱动分析显示了肌纤维几何特征、肌纤维类型和神经肌肉接头在整个肌肉上的分布的空间变化。我们提出了一个使用高通量成像、图像量化和数据驱动分析来研究整个肌肉组织学变化的工作流程。我们的研究结果表明，这些特征在整个肌肉上的不对称空间分布可能会影响肌肉功能。因此，不是从中间区域进行单次抽样，