Semi-Automated Cell and Tissue Analyses Reveal Regionally Specific Morphological Alterations of Immune and Neural Cells in a Porcine Middle Cerebral Artery Occlusion Model of Stroke - Spellicy, Samantha E.; Scheulin, Kelly M.; Baker, Emily W.; Jurgielewicz, Brian J.; Kinder, Holly A.; Waters, Elizabeth S.; Grimes, Janet A.; Stice, Steven L.; West, Franklin D. (Frontiers in Cellular Neuroscience, 2021)
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.