Investigating acute microglia response to seizure activity in vivo: Combining 2-Photon imaging and EEG recording

Epilepsy affects about 50 to 65 million people globally. It summarizes a spectrum of neurological disorders that have in common a hyperactivity of the neuronal network resulting in seizures. A common assumption is that an imbalance between neuronal excitation and inhibition is a key mechanism in seizure generation and epileptogeneisis. In at least one-third of the patients, current therapies have proven unsuccessful in treating seizure progression. One potential reason could be that the therapies only focus on neurons. Recent studies suggest that neuronal hyperactivity causes a microglial response, which reinstates brain homeostasis. Additionally, interactions between microglia and neurons have been shown to inhibit neuronal firing and dampen seizure activity. However, the exact relationship between microglia and seizure progression in epilepsy is yet to be elucidated. A main bottleneck is that several studies investigate microglia dynamics in ex vivo slice models, which can severely affect the microglia dynamics due to their rapid response to environmental changes. On the other hand, in vivo studies focus mostly on behavior characterization of the epileptic seizure phenotype and their long-term consequences on microglia activity leaving out the direct consequences of acute seizure activity on microglia dynamics. Here, we perform a pilot study to combine electroencephalography (EEG) and in vivo live imaging to directly monitor and correlate the onset of seizure activity with microglia response. To induce seizures, we take advantage of the kainic acid (KA) model, which represents similar neuropathological and electroencephalographic features seen in human patients with temporal lobe epilepsy (TLE). After confirmation of induction of the seizure and microglia activity in the hippocampus as a focal point, we investigated whether these changes also reached the primary visual cortex (V1) as a secondary generalized seizure activity. Indeed, we found that microglia changed their morphology at high doses of KA in the V1. Next, we optimized each of the two methodological components: for the EEG recording, our initial attempts under the microscope suffered from extensive electrical noise, which overlaid the actual signal. Thus, we built a customized Faraday-cage and confirmed that the signal-to-noise ratio was sufficiently reduced to be able to record brain oscillatory activity. For the in vivo live imaging of microglia, we had to optimize the imaging parameters, so that we would be able to detect microglial processes in a sufficient resolution to track their process changes. Finally, we combined both methodologies with the KA model. We confirmed that KA induced seizure activity and found first indication that those correlate with microglia volume changes. Overall, we have developed a first methodological approach, which allows the analysis of the acute effects of seizure onset on microglia. Future studies will have to continue to optimize the drift during imaging recording and the post-image analysis.