Spatiotemporal expression of RNA-seq identified proteins at the electrode interface.

Implantation of electrodes in the brain can be used to record from or stimulate neural tissues to treat neurological disease and injury. However, the tissue response to implanted devices can limit their functional longevity. Recent RNA-seq datasets identify hundreds of genes associated with gliosis, neuronal function, myelination, and cellular metabolism that are spatiotemporally expressed in neural tissues following the insertion of microelectrodes. To validate mRNA as a predictor of protein expression, this study evaluates a sub-set of RNA-seq identified proteins (RSIP) at 24-hours, 1-week, and 6-weeks post-implantation using quantitative immunofluorescence methods. This study found that expression of RSIPs associated with glial activation (Glial fibrillary acidic protein (GFAP), Polypyrimidine tract binding protein-1 (Ptbp1)), neuronal structure (Neurofilament heavy chain (Nefh), Proteolipid protein-1 (Plp1), Myelin Basic Protein (MBP)), and iron metabolism (Transferrin (TF), Ferritin heavy chain-1 (Fth1)) reinforce transcriptional data. This study also provides additional context to the cellular distribution of RSIPs using a MATLAB-based approach to quantify immunofluorescence intensity within specific cell types. Ptbp1, TF, and Fth1 were found to be spatiotemporally distributed within neurons, astrocytes, microglia, and oligodendrocytes at the device interface relative to distal and contralateral tissues. The altered distribution of RSIPs relative to distal tissue is largely localized within 100µm of the device injury, which approaches the functional recording range of implanted electrodes. This study provides evidence that RNA-sequencing can be used to predict protein-level changes in cortical tissues and that RSIPs can be further investigated to identify new biomarkers of the tissue response that influence signal quality. STATEMENT OF SIGNIFICANCE: Microelectrode arrays implanted into the brain are useful tools that can be used to study neuroscience and to treat pathological conditions in a clinical setting. The tissue response to these devices, however, can severely limit their functional longevity. Transcriptomics has deepened the understandings of the tissue response by revealing numerous genes which are differentially expressed following device insertion. This manuscript provides validation for the use of transcriptomics to characterize the tissue response by evaluating a subset of known differentially expressed genes at the protein level around implanted electrodes over time. In additional to validating mRNA-to-protein relationships at the device interface, this study has identified emerging trends in the spatiotemporal distribution of proteins involved with glial activation, neuronal remodeling, and essential iron binding proteins around implanted silicon devices. This study additionally provides a new MATLAB based methodology to quantify protein distribution within discrete cell types at the device interface which may be used as biomarkers for further study or therapeutic intervention in the future.