In cases of brain injury, degeneration and repair, defining microglia and astrocytic activation using cellular markers alone remains a challenging task. We developed MORPHIOUS, an unsupervised machine learning workflow that utilizes a one-class support vector machine to segment clusters of activated glia by only referencing examples of non-activated glia. Here, glial activation was triggered using focused ultrasound to permeabilize the hippocampal blood-brain barrier. Analyzing the hippocampal sections seven days later, MORPHIOUS identified two classes of microglia which showed characteristic activation features, including increases in ionized calcium-binding adapter molecule 1 expression, soma size, and de-ramification. MORPHIOUS was further used to identify clusters of activated astrocytes, which showed increased expression of glial fibrillary acidic protein and branching. Thus, by only referencing untreated glia morphologies, MORPHIOUS can identify diverse and novel manifestations of glial activation. This provides significant improvements for characterizing glial activation in cases of injury, neurodegeneration, and regeneration.
bioRxiv Subject Collection: Neuroscience