bioRxivNeurosciencePreprint
Reactive microglia fail to respond to environmental damage signals in a viral-induced mouse model of temporal lobe epilepsy
Glenna J. WallisiD, Laura A. BelliD, John N. Wagner, Lauren Buxton, Lakshmini BalachandariD, Karen S. WilcoxiD
Abstract
Microglia are highly adaptable innate immune cells that rapidly respond to damage signals in the brain through adoption of a reactive phenotype and production of defensive inflammatory cytokines. Microglia express a distinct transcriptome, encoding receptors that allow them to dynamically respond to pathogens, damage signals, and cellular debris. Expression of one such receptor, the microglia-specific purinergic receptor P2ry12, is known to be downregulated in reactive microglia. Here, we explore the microglial response to purinergic damage signals in reactive microglia in the TMEV mouse model of viral brain infection and temporal lobe epilepsy. Using two-photon calcium imaging in acute hippocampal brain slices, we found that the ability of microglia to detect damage signals, engage calcium signaling pathways, and chemoattract towards laser-induced tissue damage was dramatically reduced during the peak period of seizures, cytokine production, and infection. Using combined RNAscope in situ hybridization and immunohistochemistry, we found that during this same stage of heightened infection and seizures, microglial P2ry12 expression was reduced, while the pro-inflammatory cytokine TNF-a expression was upregulated in microglia, suggesting that the depressed ability of microglia to respond to new damage signals via P2ry12 occurs during the time when local elevated cytokine production contributes to seizure generation following infection. Therefore, changes in microglial purinergic receptors during infection likely limit the ability of reactive microglia to respond to new threats in the CNS and locally contain the scale of the innate immune response in the brain.
- organism: mouse
- assay: spatial transcriptomics
- research focus: cell type annotation
- approach: statistical model
- Citations
- 2
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