Differential Co-Expression Analysis of RNA-Seq Data Reveals Novel Potential Biomarkers of Device-Tissue Interaction.
Differential Co-Expression Analysis of RNA-Seq Data Reveals Novel Potential Biomarkers of Device-Tissue Interaction.
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RNA-Seq数据的差异共表达分析揭示了器械-组织相互作用的新的潜在生物标志物。
DOI：
10.1109/embc48229.2022.9871437
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发表时间：
2022-07
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对植入大脑中的电极的生物反应一直是实现稳定的组织装置界面的长期障碍。了解这种反应的机制可以解释一些现象，包括记录不稳定性和丢失、刺激阈值变化、神经调节的脱靶效应以及刺激诱导的神经兴奋性抑制。我们之前的工作检测到了植入装置后数百个基因的差异表达。在这里，我们通过使用差异共表达分析提供新的分析来扩展这项工作，该分析确定了与对照组织相比在界面处检测到的基因组之间的相关结构的变化。我们使用了“特征基因”的方法来确定与每个模块相关的枢纽基因。我们的工作增加了越来越多的文献，这些文献将分子生物学和计算分析中的新技术应用于围绕电极与大脑整合的长期问题。
The biological response to electrodes implanted in the brain has been a long-standing barrier to achieving a stable tissue device-interface. Understanding the mechanisms underlying this response could explain phenomena including recording instability and loss, shifting stimulation thresholds, off-target effects of neuromodulation, and stimulation-induced depression of neural excitability. Our prior work detected differential expression in hundreds of genes following device implantation. Here, we extend upon that work by providing new analyses using differential co-expression analysis, which identifies changes in the correlation structure between groups of genes detected at the interface in comparison to control tissues. We used an “eigengene” approach to identify hub genes associated with each module. Our work adds to a growing body of literature which applies new techniques in molecular biology and computational analysis to long-standing issues surrounding electrode integration with the brain.