Reactive astrogliosis is a condition where astrocytes, a type of brain cell, undergo morphological – shape - changes upon exposure to brain injury. Morphological changes of astrocytes are a key indicator of activation and can be beneficial in stopping initial brain injury effects, but chronic activation can drive glial scarring, which is detrimental for full recovery of normal brain function. Glial scarring has been linked to several diseases, including traumatic brain injury, Alzheimer’s Disease, and dementia. The purpose of this work is to quantitatively analyze the relationship between frequency and extent of reactive astrogliosis with relation to distance from the primary site of brain injury. My approach is to build a Python-based image analysis pipeline to quantify astrocyte cell features. The Nance Lab’s prior work using Python packages was effective in developing a pipeline to identify and quantify microglial - a different type of brain cell - shape properties. We are now building a pipeline to study astrocytes in brain slices from the injured preterm ferret brain, which were stained with an antibody for glial fibrillary acidic protein (GFAP). Images of cells at 20x magnification are provided by Dr. Tommy Wood. Since response to injury can be brain region and animal sex dependent, I analyze astrocyte cell features in each region of the brain from both sexes of ferrets. I used SciKit-Image along with other packages to segment, label, and quantify features of our cells, including perimeter, area, and circularity, among others. Expected results include a significant reduction in area and an increase in perimeter (larger surface-to-volume ratio) of cells that are closer to the injury. This image analysis pipeline will give us quantitative information about the cells morphology which are associated with biological markers that can be targets for future therapeutic treatment. Clear biological markers help researchers develop better treatment.

The study of cell morphology is important and prevalent in understanding normal and pathological conditions in the brain. Brain cells are common targets for treatment for brain diseases. Specifically, microglia – the brain’s resident immune cells – undergo a range of morphological changes in response to injury and are targets of many mitigating treatments. Image processing has been a valuable tool to assess microglial cell morphology via the analysis of microglial shape features and there continue to be additional opportunities for further investigation. Prior research has indicated a connection between features such as solidity and extent, two shape features that measure the ratio of cell areas. In this study, we examine three shape features of fluorescently labeled microglia: Euler number, extent, and solidity, in the context of ischemic injury. Ischemic injury was modeled using oxygen-glucose deprivation (OGD) in cultured whole hemisphere brain slices. Using python, images were thresholded with the Otsu threshold. Shape features were extracted from the binarized images. These shape features were analyzed based on brain region (cortex, hippocampus, thalamus), generalized treatment type (non-treated, injured, injured with treatment), and specific treatment type (OGD 0.5 hour, 1.5 hours, 3 hours, 1.5 hours with azithromycin treatment, 3 hours with superoxide dismutase treatment) and visualized using seaborn. The results verified trends in effects of injury and recovery after treatment on extent and solidity. Both findings support the expected shift from a circular shape of microglia in the injured state to more branched in the healthier state. The Otsu thresholding is limited in its accuracy, and, hence, these results provide an opportunity to optimize cell segmentation protocol for higher quality thresholded images. The results of this work have the potential to be applied to various forms of injury and cell types.