Abstract
Chronic pain conditions are often driven by hyperexcitability of nociceptors, the peripheral sensory neurons that detect noxious stimuli. Recent single-cell transcriptomic studies have begun to clarify the molecular identity of distinct peripheral sensory neuron subtypes, but tools that restrict transgene expression to nociceptors while sparing other dorsal root ganglion (DRG) subtypes remain limited. Here, we combined single-nucleus multi-omic profiling of human and mouse DRG with in vivo AAV enhancer screening to identify cis -regulatory elements that drive biased AAV expression in mouse DRG nociceptors and human iPSC-derived nociceptors. We then validated that an enhancer AAV designed to express K ir 2.1 preferentially in nociceptors reduces DRG neuronal excitability. Leveraging these multi-omic datasets, we trained a sequence-based model to decode the cis -regulatory logic of nociceptors, enabling both the prioritization of native candidate elements and the design of synthetic enhancers with a range of nociceptor targeting properties. Together, these cross-species multi-omic resources define conserved DRG regulatory programs and provide a viral toolkit for pain research with potential translational applications for patients with refractory pain.
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Abstract
Chronic pain conditions are often driven by hyperexcitability of nociceptors, the peripheral sensory neurons that detect noxious stimuli. Recent single-cell transcriptomic studies have begun to clarify the molecular identity of distinct peripheral sensory neuron subtypes, but tools that restrict transgene expression to nociceptors while sparing other dorsal root ganglion (DRG) subtypes remain limited. Here, we combined single-nucleus multi-omic profiling of human and mouse DRG with in vivo AAV enhancer screening to identify cis-regulatory elements that drive biased AAV expression in mouse DRG nociceptors and human iPSC-derived nociceptors. We then validated that an enhancer AAV designed to express Kir2.1 preferentially in nociceptors reduces DRG neuronal excitability. Leveraging these multi-omic datasets, we trained a sequence-based model to decode the cis-regulatory logic of nociceptors, enabling both the prioritization of native candidate elements and the design of synthetic enhancers with a range of nociceptor targeting properties. Together, these cross-species multi-omic resources define conserved DRG regulatory programs and provide a viral toolkit for pain research with potential translational applications for patients with refractory pain.
Competing Interest Statement
Competing interests: W.R. has received research funding from Pfizer and Eli Lilly. The authors declare that they have no other competing interests.
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Figure 2 dimensions adjusted to fit with BioRxiv requirements; Consortium author added
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