SPATIAL TRANSCRIPTOMICS AT THE BRAIN-ELECTRODE INTERFACE IN RAT MOTOR CORTEX AND THE RELATIONSHIP TO RECORDING QUALITY
Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state. To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality. For these experiments, rats were chronically implanted with Michigan-style microelectrode arrays, from which electrophysiological recordings were taken. Brain tissue sections surrounding each electrode were then mounted on 10x Genomics, Visium microscope slides for spatial transcriptomics processing. The tissue was immunolabeled for neurons and astrocytes which provided both a spatial reference for spatial transcriptomics and a quantitative measure of glial fibrillary acidic protein (GFAP) and neuronal nuclei (NeuN) immunolabeling surrounding each implant. Results from rat motor cortex within 300um of the implanted electrodes at 24 hours, 1 week, and 6 weeks post-implantation showed up to 553 differentially expressed genes between implanted and non-implanted tissue sections. Further computational analysis of the spatial transcriptomics data against measures of electrical activity (Multiunit activity, Local field potential) and physiological changes (Neuronal Density, GFAP) let us understand and visualize the relationships between these datasets, resulting in narrowing down the dataset of 23139 genes to 5-6 genes most affected by the electrode implant and most strongly associated with each of the datasets (Multiunit activity, Local field potential, Neuronal Density, GFAP).