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

项目摘要 阿片类药物流行病是一场影响美国近200万人的公共卫生危机 每年花费数十亿美元。长期使用阿片类药物可导致耐受性、依赖性， 最严重的情况是上瘾成瘾的特点是强迫性的药物寻求行为， 消极后果，以及即使在长期禁欲后复发的倾向。这 表明，强迫性药物使用会引起关键大脑区域的持续变化， 停止导致成瘾相关行为的药物使用。 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因素， 滥用药物导致与成瘾行为相关的神经回路变化的机制。 暴露于成瘾药物会导致各种脑细胞类型的广泛转录变化。 然而，在不同的脑细胞类型中受药物滥用影响的基因以及 这些变化的驱动力大多尚不清楚。此外，大多数与成瘾相关的遗传变异是 发现于非编码基因组区域，并且经常位于细胞类型特异性增强子和启动子中。 这些观察结果表明，与阿片类药物成瘾相关的基因表达的持续变化， 驱动这些变化的转录调节途径可能是细胞类型特异性的。但现有 这一领域的知识主要是基于对来自关键脑的异质样品进行批量测序， 区域，其不能捕获细胞类型特异性信号。单细胞测序数据是唯一能够 检测不同细胞类型之间的分子差异，但阿片类药物成瘾的单细胞研究一直是一个难题。 仅限于血细胞或急性药物治疗。这阻碍了对 涉及长期药物诱导的神经生物学变化和成瘾易感性的机制。 该提案将计算分析新的单核RNA-seq（snRNA-seq）和单核RNA-seq。 从经验证的羟考酮扩展使用大鼠模型生成的核ATAC-seq（snATAC-seq）数据 自我给药，以研究单细胞分辨率的阿片类药物使用障碍（OUD）的分子基础。细胞 选择为易感大鼠之间基因表达和染色质可及性的类型特异性比较 与抵抗成瘾行为的措施将进行，以揭示长期的影响， 强迫性阿片类药物使用在特定的脑细胞类型，并确定推定的调节关系。统计 模型和深度学习也将用于开发一个框架，用于识别 非编码遗传变异，提高对OUD遗传风险的理解。这项工作在临床上 这将有助于更好地理解OUD，并确定监管机制， 治疗目标，以改善OUD治疗方法。

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.