Spatiotemporal expression of RNA-seq identified proteins at the electrode interface
Spatiotemporal expression of RNA-seq identified proteins at the electrode interface
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DOI：
10.1016/j.actbio.2023.04.028
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发表时间：
2023-05-26
影响因子：
9.7
通讯作者：
Purcell，Erin K.
中科院分区：
文献类型：
--
作者：
Thompson，Cort H.;Evans，Blake M.;Purcell，Erin K.
在大脑中植入电极可用于记录或刺激神经组织以治疗神经疾病和损伤。然而，对植入器械的组织反应可能会限制其功能寿命。最近的RNA-seq数据集识别了数百个与神经胶质增生、神经元功能、髓鞘形成和细胞代谢相关的基因，这些基因在插入微电极后在神经组织中时空表达。为了验证mRNA作为蛋白质表达的预测因子，本研究使用定量免疫荧光方法评估了植入后24小时，1周和6周的RNA-seq鉴定蛋白质（RSIP）子集。这项研究发现，与胶质细胞活化（胶质细胞酸性蛋白（GFAP），聚嘧啶束结合蛋白-1（Ptbp 1）），神经元结构（神经丝重链（Nefh），蛋白脂质蛋白-1（Plp 1），髓鞘碱性蛋白（MBP））和铁代谢（转铁蛋白（TF），铁蛋白重链-1（Fth 1））相关的RSIP的表达增强了转录数据。这项研究还提供了额外的上下文RSIP的细胞分布使用MATLAB为基础的方法来量化特定细胞类型内的免疫荧光强度。发现Ptbp 1、TF和Fth 1时空分布在相对于远端和对侧组织的器械界面处的神经元、星形胶质细胞、小胶质细胞和少突胶质细胞内。相对于远端组织的RSIP分布改变主要局限于器械损伤的100µm范围内，这接近植入电极的功能记录范围。这项研究提供的证据表明，RNA测序可用于预测皮质组织中蛋白质水平的变化，RSIPs可以进一步研究，以确定新的生物标志物的组织反应，影响信号quality.Statement的重要性植入大脑的微电极阵列是有用的工具，可用于研究神经科学和治疗病理条件的临床设置。然而，组织对这些器械的反应会严重限制其功能寿命。转录组学通过揭示器械插入后差异表达的许多基因，加深了对组织反应的理解。这份手稿提供了使用转录组学来表征组织反应的验证，通过评估一个子集的已知差异表达基因在蛋白质水平上植入电极周围随着时间的推移。除了验证设备界面处的mRNA与蛋白质的关系外，本研究还确定了植入硅设备周围涉及神经胶质激活、神经元重塑和必需铁结合蛋白的蛋白质时空分布的新趋势。该研究还提供了一种新的基于MATLAB的方法，用于量化设备界面处离散细胞类型内的蛋白质分布，可用作未来进一步研究或治疗干预的生物标志物。
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 SignificanceMicroelectrode 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.