Computational analysis of single-nucleus sequencing data for studying the cell type-specific basis of opioid use disorders

项目总结 阿片类药物流行是一种公共卫生危机，影响着美国近200万人 每年花费数十亿美元。长期使用阿片类药物可导致耐受、依赖，并在 最严重的情况是上瘾。上瘾的特征是强迫寻求毒品的行为，尽管 不良后果，以及即使在长期禁欲之后也有复发的倾向。这 这表明强迫性药物使用会导致大脑关键区域的持续变化，这些变化持续下去 停止使用毒品，导致与上瘾有关的行为。 越来越多的证据表明，基因表达的持续变化可能是一个关键 滥用药物导致与成瘾行为相关的神经回路改变的机制。 接触使人上瘾的药物会导致各种脑细胞类型的广泛转录变化。 然而，药物滥用对不同脑细胞类型的基因和调控途径的影响 推动这些变化的原因在很大程度上仍不清楚。此外，大多数与成瘾有关的基因变异都是 发现于非编码基因组区域，通常位于细胞类型特异的增强子和启动子中。 这些观察表明，基因表达的持续变化与阿片成瘾和 驱动这些变化的转录调控途径可能是特定于细胞类型的。然而，现有的 这一领域的知识在很大程度上是基于对关键大脑中的不同样本进行批量测序 区域，无法捕获特定细胞类型的信号。单细胞测序数据是唯一能够 检测不同细胞类型的分子差异，但对阿片成瘾的单细胞研究一直是 仅限于血细胞或急性药物治疗。这阻碍了更高分辨率的理解 机制涉及长期药物诱导的神经生物学变化和对成瘾的易感性。 该方案将对新的单核RNA-seq(SnRNA-seq)和单核RNA-seq进行计算分析. 从扩展访问羟考酮的有效大鼠模型生成的核ATAC-SEQ数据 在单细胞分辨率下研究阿片使用障碍(ODS)的分子基础的自我给药。细胞 易感大鼠基因表达和染色质可及性的类型特异性比较 将进行成瘾与抵抗行为的测量，以揭示 强迫性阿片类药物在特定脑细胞类型中的使用，并确定假定的调节关系。统计 还将使用模型和深度学习来开发一个框架，以确定 非编码遗传变异，并提高对OUD遗传风险的理解。这项工作是临床上的 具有重要意义，并将有助于更好地了解OOD并确定以下监管机制 改善治疗方法的治疗目标。

PROJECT SUMMARY The opioid epidemic is a public health crisis that affects almost two million people in the United States and costs billions of dollars annually. The chronic use of opioids can lead to tolerance, dependence, and in the most severe cases, addiction. Addiction is characterized by compulsive drug-seeking behavior despite negative consequences, as well as a propensity for relapse even after extended periods of abstinence. This suggests that compulsive drug use induces persistent changes in key brain regions which persist following cessation of drug use that give rise to addiction-related behaviors. Increasing evidence indicates that persistent changes in gene expression might be a critical mechanism by which drugs of abuse lead to changes in neural circuits associated to addictive behaviors. Exposure to addictive drugs causes widespread transcriptional changes across various brain cell types. However, the genes affected by drugs of abuse in distinct brain cell types and the regulatory pathways that drive these changes remain mostly unclear. Additionally, most genetic variants associated with addiction are found in noncoding genomic regions and frequently located in cell type-specific enhancers and promoters. These observations indicate that persistent changes in gene expression associated with opioid addiction and the transcriptional regulatory pathways that drive these changes are likely cell type-specific. However, existing knowledge in this area has largely been based on bulk sequencing heterogeneous samples from key brain regions, which cannot capture cell type-specific signals. Single-cell sequencing data is uniquely capable of detecting molecular differences across different cell types, but single-cell studies of opioid addiction have been limited to blood cells or acute drug treatment. This has impeded a higher resolution understanding of the mechanisms involved in long-term drug-induced neurobiological changes and susceptibility to addiction. This proposal will computationally analyze novel single-nucleus RNA-seq (snRNA-seq) and single- nucleus ATAC-seq (snATAC-seq) data generated from a validated rat model of extended access oxycodone self-administration to study the molecular basis of opioid use disorders (OUDs) at single cell resolution. Cell type-specific comparisons of gene expression and chromatin accessibility between rats selected as vulnerable versus resistant to behavioral measures of addiction will be conducted to reveal the long-term effects of compulsive opioid use in specific brain cell types and identify putative regulatory relationships. Statistical models and deep learning will also be used to develop a framework for identifying the functional effects of noncoding genetic variants and improve understanding of genetic risk in OUDs. This work is clinically significant and will contribute to a better understanding of OUDs and identify regulatory mechanisms as therapeutic targets to improve OUD treatment approaches.