An Engineering Framework to Quantify Microglial Morphology in Models of Neurodevelopmental Disease

Abstract

Microglia, the brain’s immune cells, play major roles in responding to injury and disease, supporting neurodevelopment, and maintaining tissue homeostasis. In response to changes in the brain environment, microglia rapidly alter their shape in association with transcriptomic, proteomic, metabolomic, and epigenetic changes. By quantifying microglial shapes in neural images, we gain insight into the state of the brain. One of the most common ways to visualize microglia morphology is immunofluorescent staining and imaging. While fluorescent imaging is a highly prevalent method to observe microglial morphology, the wealth of information captured from the images is unexplored mainly due to the lack of robust image processing methods to capture morphological variation. When we completed a systematic review of immunofluorescent imaging for microglia, less than 10% of publications that included microglial images included image processing methods for morphological feature quantification. To quantify a variety of morphological cell features, I developed a non-destructive data science pipeline that captures microglial morphology from fluorescent images at both the level of individual microglia and microglial populations across various regions and entire brain slices. We then applied the pipeline to images from ex vivo and in vivo models of neurodevelopmental disease from multiple species, including the mouse, ferret, rat, and pig, to determine which morphologies are associated with injury and restoration. In ex vivo slice models, we observed time, sex, regional, and therapeutic-dependent microglial responses. Microglial exhibit different extents of shape changes across regions, with white matter regions in the ferret exhibiting opposite trends. In a rat model, we quantified the time-based dependence of microglial response to treatment with restorative morphologies appearing at 48 hours. In non-treated in vivo models, microglia from different species show different branching complexities. With databases, statistics, and clinical collaborators, we combined my microglial morphology pipeline with other biological characterization methods and treatments to predict neuroprotection based on microglial morphology. Image processing of immunofluorescent images is a powerful tool for understanding the brain environment across scales, models, and species.