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Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording
Quentin Whitsitt1, Akash Saxena2, Bella Patel1
1Department of Biomedical Engineering and Institute of Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, United States of America.
Researchers used spatial transcriptomics to study the brain
Area of Science:
- Neuroscience and Bioengineering
- Molecular Biology
- Biomaterials Science
Background:
- Implanted electrodes in the brain trigger a foreign body response, altering the local tissue environment.
- This response involves microglial activation, astrogliosis, and neuronal cell death, potentially affecting device performance.
- Understanding these changes is crucial for developing advanced neuroprostheses and improving electrophysiological recordings.
Purpose of the Study:
- To investigate physiological changes around implanted brain electrodes using spatial transcriptomics (ST).
- To identify gene expression alterations caused by electrode implantation.
- To correlate differential gene expression with glial reactivity, neuronal loss, and recording quality.
Main Methods:
- Chronic implantation of Michigan-style microelectrode arrays in rats.
- Acquisition of electrophysiological recordings (multi-unit activity, local field potential) over six weeks.
- Spatial transcriptomics on brain tissue cryosections, alongside immunolabeling for neurons and astrocytes.
Main Results:
- Identified up to 553 significantly differentially expressed genes within 300 µm of electrodes at various time points.
- Regression analysis revealed 6-7 key genes strongly correlated with histological and electrophysiological metrics.
- These genes represent potential biomarkers for recording quality and tissue response to implants.
Conclusions:
- Spatial transcriptomics provides novel insights into the mechanisms of tissue response to implanted electrodes.
- Identified candidate biomarkers that may predict or reflect signal quality.
- Developed a new transcriptomics-based approach to analyze brain tissue response to implants and relate it to functional outcomes.
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