Cell-type-specific transcriptional signatures of cocaine relapse in the rat medial prefrontal cortex

Authors

• KE Savell
• R Madangopal
• DJ Thompson
• OR Drake
• DQ Pham
• KD Woods
• P Saravanan
• MB Brenner
• RG Palaganas
• BT Hope

Abstract

Relapse is an ongoing clinical problem, and there are currently no effective treatments to reduce the risk of relapse to psychostimulants like cocaine. Environmental stimuli previously associated with drug-taking can precipitate relapse long after cessation of drug use. These maladaptive cue-drug associations are hypothesized to be encoded within specific patterns of neurons (neuronal ensembles) that are selectively activated by drug-related cues. Our lab and others have shown causal roles for neuronal ensembles in reward-seeking behaviors and identified unique molecular and functional alterations within them. However, due to methodological limitations, previous studies could not characterize cell-type diversity of ensembles or identify molecular alterations within specific ensemble cell-types.
To address this gap, we developed MultipleXed Population Selection and Enrichment single nuclei RNA-sequencing (XPoSE-seq) to determine cell-type composition of rare ensemble populations and define their transcriptional profiles following cocaine relapse. We trained rats to self-administer cocaine during twice daily 3 h sessions. Following 21 days of abstinence, we tested rats for cocaine-seeking or no test control and collected brains 3 h after the test (peak ensemble marker expression). We isolated ensemble and non-ensemble neuronal nuclei as input for XPoSE-seq. Our analysis revealed distinct clusters corresponding to known cell types in the mPFC (excitatory and inhibitory neurons) that further subcluster into expected layer and interneuron sub-types within mPFC. Using this unbiased approach, ongoing analysis aims to characterize cell-type and ensemble-specific transcriptional signatures that contribute to drug seeking behaviors. Future work will manipulate key candidate genes and identify relevant circuits.

Scientific Focus Area: Neuroscience

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