bioRxivNeurosciencePreprint
A systematic analysis of brain tissue response to microelectrode material and size with single-cell spatial transcriptomics
Cort H. ThompsoniD, Anirban ChakrabortyiD, Lauren C. Wade-Kleyn, Mark A. ReimersiD, Erin K. PurcelliD
Abstract
Implantable microelectrode arrays can interface with the central nervous system to record from and/or stimulate neural tissues to treat neurological disease and injury. The chronic tissue response to implanted electrodes is believed to be a driving factor behind microelectrode failure. Next-generation electrodes have been developed to attenuate the tissue response by reducing electrode size and/or incorporating softer materials. In this study, we used single-cell-resolution spatial transcriptomics to quantify the tissue response to implanted electrodes within custom-classified cell types in the rat brain. To test the effects of implant material and size, we assessed polyimide and silicon electrodes of 10 {micro}m and 100 {micro}m cross-sectional dimensions over 6-weeks post-implantation. Our data indicate that implants are associated with upregulation of inflammatory genes in glia that are coupled to damage-initiated losses in synaptic transmission and subsequent engagement of compensatory protective mechanisms (e.g., re-myelination, antioxidant production) to preserve local neurons. While bulk tissue analysis reinforced previously reported observations of glial scar consolidation over time, single cell analysis revealed an unexpected, progressive heightening of the expression of inflammatory genes in individual device-reactive astrocytes. With respect to design features, the impact of device dimensions more heavily influenced responses than material type, particularly by the 6-week time point. Our results add single-cell resolution observations to the growing use of transcriptomics to understand the biocompatibility of devices implanted in the brain.
- organism: rat
- assay: spatial transcriptomics
- research focus: cell type annotation
- approach: statistical model
- Citations
- 2
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