Data for FiNuTyper: design and validation of an automated deep learning-based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle

Abstract

Aim:
While manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. To address this challenge, we assembled an automated image analysis pipeline, FiNuTyper (Fiber and Nucleus Typer).

Methods:
We integrated recently developed deep learning-based image segmentation methods, optimized for unbiased evaluation of fresh and postmortem human skeletal muscle, and utilized SERCA1 and SERCA2 as type-specific myonucleus and myofiber markers after validating them against the traditional use of MyHC isoforms.

Results:
Parameters including cross-sectional area, myonuclei per fiber, myonuclear domain, central myonuclei per fiber, and grouped myofiber ratio were determined in a fiber type-specific manner, revealing that a large degree of sex- and muscle-related heterogeneity could be detected using the pipeline. Our platform was also tested on pathological muscle tissue (ALS, IBM) and adapted for the detection of other resident cell types (leukocytes, satellite cells, capillary endothelium).

Conclusion:
In summary, we present an automated image analysis tool for the simultaneous quantification of myofiber and myonuclear types, to characterize the composition and structure of healthy and diseased human skeletal muscle.

Keywords
Skeletal muscle, myonuclei, myofibers, SERCA, automated image analysis

摘要

目标：尽管手动定量分析在骨骼肌组织学分析中仍被视为金标准，但其过程耗时且易受研究者主观偏见的影响。为应对这一挑战，本研究构建了一套自动化图像分析流程，命名为FiNuTyper（纤维与核型分类器）。

方法：本研究整合了近期开发的基于深度学习的图像分割方法，这些方法针对新鲜及尸检的人类骨骼肌进行了优化，并使用SERCA1和SERCA2作为特异性肌核和肌纤维的标记物，在验证其与传统肌球蛋白同型使用方法的一致性后进行应用。

结果：本研究以纤维类型特异性的方式确定了包括横截面积、每纤维肌核数、肌核域、每纤维中心肌核数和分组肌纤维比率等参数，揭示了使用该流程可以检测到大量与性别和肌肉相关的异质性。此外，本研究平台还针对病理肌肉组织（如肌萎缩侧索硬化症、多发性硬化症）进行了测试，并进行了调整以检测其他驻留细胞类型（如白细胞、卫星细胞、毛细血管内皮细胞）。

结论：总之，本研究提出了一种自动化图像分析工具，用于同时对肌纤维和肌核类型进行定量分析，以表征健康和疾病状态下的人类骨骼肌的组成和结构。

关键词：骨骼肌，肌核，肌纤维，SERCA，自动化图像分析