Wojtasiewicz, C. | bioRxiv Science

bioRxiv2024

Pyramidal cell types and 5-HT2A receptors are essential for psilocybin's lasting drug action

Psilocybin is a serotonergic psychedelic with therapeutic potential for treating mental illnesses1-4. At the cellular level, psychedelics induce structural neural plasticity5,6, exemplified by the drug-evoked growth and remodeling of dendritic spines in cortical pyramidal cells7-9. A key question is how these cellular modifications map onto cell type-specific circuits to produce psychedelics behavioral actions10. Here, we use in vivo optical imaging, chemogenetic perturbation, and cell type-specific electrophysiology to investigate the impact of psilocybin on the two main types of pyramidal cells in the mouse medial frontal cortex. We find that a single dose of psilocybin increased the density of dendritic spines in both the subcortical-projecting, pyramidal tract (PT) and intratelencephalic (IT) cell types. Behaviorally, silencing the PT neurons eliminates psilocybins ability to ameliorate stress-related phenotypes, whereas silencing IT neurons has no detectable effect. In PT neurons only, psilocybin boosts synaptic calcium transients and elevates firing rates acutely after administration. Targeted knockout of 5-HT2A receptors abolishes psilocybins effects on stress-related behavior and structural plasticity. Collectively these results identify a pyramidal cell type and the 5-HT2A receptor in the medial frontal cortex as playing essential roles for psilocybins long-term drug action.

neuroscience↗

bioRxiv2024

Classification of psychedelic drugs based on brain-wide imaging of cellular c-Fos expression

Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for characterizing and validating psychoactive drugs with psychedelic properties.

neuroscience↗