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Updated: Sep 19, 2025

Cell-based Assay to Study Antibody-mediated Tau Clearance by Microglia
Published on: November 9, 2018
Role of astroglia and microglia in Alzheimer's disease and multiple therapeutic interventions
Lei Li1, Ying Wang1, Qilin Feng2
1Rehabilitation Department, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Abstract:
Alzheimer's disease (AD) is characterized by deposition of amyloid-β (Aβ) and neurofibrillary tangles (NFTs) formed by aggregates of hyperphosphorylated tau proteins. It presents a formidable global health challenge, prompting the exploration of innovative therapeutic strategies. This review aims to provide a thorough discussion of astrocytes and microglia to examine whether they are overall beneficial or detrimental for AD on the global level. Based on this, this review describes the treatment solutions that are likely to entail the manipulation of glial cells to reduce inflammation, opting to boost clearance of toxic proteins, thus stabilizing the effects of AD. These glial entities, inherent to the central nervous system, extend their functions beyond structural support, actively engaging in various physiological and pathological processes associated with AD. Both astroglia and microglia contribute significantly to the neuroinflammatory response observed in AD. Reactive astrocytes release inflammatory mediators, while activated microglia release cytokines, chemokines, and reactive oxygen species, collectively assisting a chronic state of neuroinflammation. Additionally, astrocytes partake in the clearance of Aβ, while microglia play a pivotal role in phagocytosing Aβ plaques. In AD, ongoing inflammation may cause a buildup of Aβ, which causes problems with the functions of astroglia and microglia and also worsens these issues with communication between neurons, a key factor in cognitive decline. In addition, there are tremendous opportunities to identify new biomarkers specific to glial disorders, genomic and epigenomic approaches for the selection of patients, using multimodal imaging techniques, and the application of machine learning algorithms in the future for personalized glial-targeted therapies.
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