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

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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.