Histopathological analysis of cellular changes in the stroked brain provides critical information pertaining to inflammation, cell death, glial scarring, and other dynamic injury and recovery responses. However, commonly used manual approaches are hindered by limitations in speed, accuracy, bias, and the breadth of morphological information that can be obtained. Here, a semi-automated high-content imaging (HCI) and CellProfiler histological analysis method was developed and used in a Yucatan miniature pig permanent middle cerebral artery occlusion (pMCAO) model of ischemic stroke to overcome these limitations. Evaluation of 19 morphological parameters in IBA1⁺ microglia/macrophages, GFAP⁺ astrocytes, NeuN⁺ neuronal, FactorVIII⁺ vascular endothelial, and DCX⁺ neuroblast cell areas was conducted on porcine brain tissue 4 weeks post pMCAO. Out of 19 morphological parameters assessed in the stroke perilesional and ipsilateral hemisphere regions (38 parameters), a significant change in 3838 measured IBA1⁺ parameters, 3438 GFAP⁺ parameters, 3238 NeuN⁺ parameters, 3138 FactorVIII⁺ parameters, and 2838 DCX⁺ parameters were observed in stroked vs. non-stroked animals. Principal component analysis (PCA) and correlation analyses demonstrated that stroke-induced significant and predictable morphological changes that demonstrated strong relationships between IBA1⁺, GFAP⁺, and NeuN⁺ areas. Ultimately, this unbiased, semi-automated HCI and CellProfiler histopathological analysis approach revealed regional and cell specific morphological signatures of immune and neural cells after stroke in a highly translational porcine model. These identified features can provide information of disease pathogenesis and evolution with high resolution, as well as be used in therapeutic screening applications.

对中风后脑部细胞变化的组织病理学分析提供了有关炎症，细胞死亡，神经胶质瘢痕形成以及其他动态损伤和恢复反应的重要信息。但是，由于速度，准确性，偏差以及可获取的形态学信息的广度有限，阻碍了常用的手动方法。在这里，开发了一种半自动化的高内涵成像（HCI）和CellProfiler组织学分析方法，并将其用于缺血性卒中的尤卡坦微型猪永久性大脑中动脉闭塞（pMCAO）模型中，以克服这些局限性。评估IBA1 ⁺小胶质细胞/巨噬细胞，GFAP ⁺星形胶质细胞，NeuN ⁺神经元，因子VIII ^+中的19种形态学参数在pMCAO后4周，在猪脑组织上进行血管内皮和DCX ⁺神经母细胞区域的检测。在脑卒中病灶周围和同侧半球区域评估的19个形态学参数中（38个参数），3838测量的IBA1 ⁺参数，3438 GFAP ⁺参数，3238NeuN ⁺参数，3138FactorVIII ⁺参数，以及2838在中风与非中风动物中观察到DCX ⁺参数。主成分分析（PCA）和相关性分析表明，中风引起的显着和可预测的形态变化表明IBA1 ⁺，GFAP ⁺和NeuN ⁺区域之间存在密切关系。最终，这种无偏见的半自动化HCI和CellProfiler组织病理学分析方法揭示了在高度翻译性猪模型中风后免疫细胞和神经细胞的区域和细胞特定形态特征。这些确定的特征可以提供高分辨率的疾病发病机理和进化信息，并可以用于治疗性筛查应用中。