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

> **NIH NIH F31** · SALK INSTITUTE FOR BIOLOGICAL STUDIES · 2023 · $26,397

## Abstract

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 approa...

## Key facts

- **NIH application ID:** 10663815
- **Project number:** 5F31DA056226-02
- **Recipient organization:** SALK INSTITUTE FOR BIOLOGICAL STUDIES
- **Principal Investigator:** Jessica Lu Zhou
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $26,397
- **Award type:** 5
- **Project period:** 2022-04-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10663815

## Citation

> US National Institutes of Health, RePORTER application 10663815, Computational analysis of single-nucleus sequencing data for studying the cell type-specific basis of opioid use disorders (5F31DA056226-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10663815. Licensed CC0.

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