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
Regenerative Bioscience Center, University of Georgia, Athens, GA, United States.
Medical College of Georgia, University System of Georgia MD/Ph.D. Program, Augusta, GA, United States.
Front Cell Neurosci. 2021 Jan 22;14:600441. doi: 10.3389/fncel.2020.600441. eCollection 2020.
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 measured IBA1 parameters, GFAP parameters, NeuN parameters, FactorVIII parameters, and 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.
对中风后大脑细胞变化进行组织病理学分析,可提供有关炎症、细胞死亡、胶质瘢痕形成以及其他动态损伤和恢复反应的关键信息。然而,常用的手动方法在速度、准确性、偏差以及可获取的形态学信息广度方面存在局限性。在此,开发了一种半自动高内涵成像(HCI)和CellProfiler组织学分析方法,并将其应用于尤卡坦小型猪永久性大脑中动脉闭塞(pMCAO)缺血性中风模型,以克服这些局限性。在pMCAO术后4周,对猪脑组织中IBA1小胶质细胞/巨噬细胞、GFAP星形胶质细胞、NeuN神经元、FactorVIII血管内皮细胞和DCX神经母细胞区域的19个形态学参数进行评估。在中风病灶周围和同侧半球区域评估的19个形态学参数(共38个参数)中,在中风动物与未中风动物之间观察到测量的IBA1参数、GFAP参数、NeuN参数、FactorVIII参数和DCX参数有显著变化。主成分分析(PCA)和相关性分析表明,中风诱导了显著且可预测的形态学变化,这些变化表明IBA1、GFAP和NeuN区域之间存在密切关系。最终,这种无偏差的半自动HCI和CellProfiler组织病理学分析方法揭示了在高度可转化的猪模型中中风后免疫和神经细胞的区域及细胞特异性形态特征。这些识别出的特征可以高分辨率提供疾病发病机制和演变的信息,还可用于治疗筛选应用。