IDENTIFYING EPIGENOMICS AND CONNECTOMICS OF PROTRACTED OPIOID WITHDRAWAL AND RELAPSE USING CELLULAR BARCODING

SUMMARY The staggering incidence of opioid addiction continues at epidemic levels, disrupting and destroying the lives of millions of Americans with a yearly financial toll nearing $80 billion. A major hurdle in treating opioid addiction is a chronic cycle of withdrawal and relapse. Rodent studies have identified long-term changes in gene expression, epigenetics, and circuit connectivity after opioid exposure and withdrawal, but few studies have investigated the interaction of these changes across modalities. We recently developed a new class of brain mapping tools based on cellular barcoding that can relate the three modalities in single cells and experiments. These tools, which include MAPseq and BARseq, use RNA barcodes to uniquely label thousands of neurons per experiment and map their inter-regional connections. In each labeled neuron, barcodes are trafficked into the axons, where we can detect them by high-throughput sequencing. Matching up barcode sequences across potential target regions then produces the single-cell projection matrix for all barcoded neurons. As barcodes are mRNAs, they are in the same modality as the endogenous transcriptome, allowing us to natively bridge single-cell connectomic measurements with single-cell measures of gene expression and genome accessibility in the same cells. Finally, combination of these technologies with spatial transcriptomics methods, including STARmap and BARseq2, lets us map the observed changes with high resolution in 3D brain space. Here we apply our multi-omic tools to measure the changes induced in the medial orbitofrontal cortex (mOFC) in two rodent models of opioid withdrawal and relapse. Our collaborators in the Shaham lab recently identified opposing functional connectivity changes in rat mOFC after electric barrier-induced voluntary abstinence. In the same rat model using our tools, we will define which cell types are responsible for these connectional changes, how single-cell connection patterns change, and how gene expression, epigenetics, and connectivity changes interact at the level of single cells. Critically, we define cell types holistically across transcriptomic, epigenomic, and connectomic modalities. We then compare these changes to those observed in mOFC in a second rodent model, a classical opioid withdrawal conditioned place aversion (CPA) model in mice. We hope to identify a set of changes robust to the two behaviors and conserved across species, highlighting them as core features of opioid withdrawal and relapse. We will causally manipulate epigenetic factors associated with these core changes by cell-type-specific knockouts and overexpression, and assess their effects on behavior. The public health impact of this work lies in the fundamental understanding of the long-term circuit changes induced by opioid withdrawal and relapse at unprecedented multi-omic resolution and the potential identification of a set of conserved core changes as candidates for treatment.