Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and...

The foreign body reaction to implanted electrodes in the brain has long been recognized as a major challenge impacting the performance and reliability of indwelling neurotechnologies. Spatially resolved transcriptomic approaches have enabled high-resolution mapping of cellular and molecular dynamics at the device-tissue interface, yielding novel insight into both acute and chronic tissue responses. Recent whole-transcriptome profiling methods generate exceptionally dense gene expression datasets from individual samples, offering unprecedented resolution and analytical power. Yet, limited studies have explored aggregated results from larger datasets and sample-to-sample variation within an implanted cohort using such techniques due to high costs and complicated downstream analyses. In this work, we provide a comprehensive report of spatial transcriptomics data collected from an expanded cohort of rats (n = 14 rats) implanted with silicon microelectrode arrays in the motor cortices for 1 week (acute) and 6 weeks (chronic). This larger dataset enabled us to explore the variation in results across samples, assess outliers, and examine potential batch effects. We employed differential expression analysis to identify top differentially expressed genes (DEGs) in spatially defined regions at the device-tissue interface to reveal novel biomarkers in the aggregated dataset. We assessed sample-to-sample variabilities, and applied a factorization strategy to identify prominent cell-type contributors of the top DEGs. Using network-based co-expression analysis, we identified gene modules, hub genes, and central regulatory processes governing the device-tissue interface. Our results show: (a) greater variation of top DEGs across samples at the 1-week time point with notable microglial and astroglial cell-type contributors, (b) lower variation of top DEGs across samples and a shift to prominent astroglial cell-type contributors at the 6-week time point, and (c) novel biomarkers that suggest major macrophage- and microglial mediated processes and homeostasis events at the 1-week time point, and greater tissue remodeling, apoptotic and synaptic changes at the 6-week time point. These findings support previous ideas on the evolving tissue response to implanted devices, and present novel details on biomarkers, biological processes and sample variation. Additionally, this study provides a framework for assessing larger datasets employing high-dimensional spatial transcriptomics and highlight key considerations related to across-sample variability and batch effects.