Highlander Morgan, Ward Shelby, LeHoty Bradley, Garrett Teresa, Elbasiouny Sherif
Department of Biomedical, Industrial, and Human Factors Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH 45435, USA.
Boonshoft School of Medicine, Wright State University, Dayton, OH 45435, USA.
Bioengineering (Basel). 2025 Jul 14;12(7):761. doi: 10.3390/bioengineering12070761.
Structural analysis of motoneuron somas and their associated proteins via immunohistochemistry (IHC) remains tedious and subjective, requiring costly software or adapted 2D manual methods that lack reproducibility and analytical rigor. Yet, neurodegenerative disease and aging research demands precise structural comparisons to elucidate mechanisms driving neuronal degeneration. To address this need, we developed a novel algorithm that automates repetitive and subjective IHC analysis tasks, enabling thorough, objective, blinded, order-agnostic, and reproducible 3D batch analysis. With no manual tracing, the algorithm produces 3D Cartesian reconstructions of motoneuron somas from 60× IHC images of mouse lumbar spinal tissue. From these reconstructions, it measures 3D soma volume and efficiently quantitates net somatic protein expression and macro-cluster size. In this validation study, we applied the algorithm to assess soma size and C-bouton expression in various healthy control mice, comparing its measurements against manual measurements and across multiple algorithm users to confirm its accuracy and reproducibility. This novel, customizable tool enables efficient and high-fidelity 3D motoneuron analysis, replacing tedious, qualitative, cell-by-cell manual tuning with automatic threshold adaptation and quantified batch settings. For the first time, we attain reproducible results with quantifiable accuracy, exhaustive sampling, and a high degree of objectivity.
通过免疫组织化学（IHC）对运动神经元胞体及其相关蛋白进行结构分析仍然繁琐且主观，需要昂贵的软件或经过改进的二维手动方法，而这些方法缺乏可重复性和分析严谨性。然而，神经退行性疾病和衰老研究需要精确的结构比较来阐明驱动神经元退化的机制。为满足这一需求，我们开发了一种新颖的算法，该算法可自动执行重复性和主观性的IHC分析任务，实现全面、客观、盲法、与顺序无关且可重复的三维批量分析。无需手动追踪，该算法可从小鼠腰段脊髓组织的60倍IHC图像生成运动神经元胞体的三维笛卡尔重建图。基于这些重建图，它可测量三维胞体体积，并有效定量胞体蛋白净表达量和大簇大小。在这项验证研究中，我们应用该算法评估各种健康对照小鼠的胞体大小和C型终扣表达，将其测量结果与手动测量结果进行比较，并在多个算法用户之间进行比较，以确认其准确性和可重复性。这种新颖的、可定制的工具能够实现高效且高保真的三维运动神经元分析，用自动阈值调整和定量批量设置取代繁琐、定性的逐个细胞手动调整。我们首次获得了具有可量化准确性、详尽采样和高度客观性的可重复结果。
