CellNeurosciencePeer-reviewed journal article
Molecular architecture of human dermal sleeping nociceptors
Jannis KörneriD, Derek HowardiD, Hans Jürgen SolinskiiD, Marisol Mancilla MorenoiD, Natja HaagiD, Andrea Fiebig, Anna MaxioniD, Shamsuddin A. BhuiyaniD, Idil ToklucuiD, Raya Bott, Ishwarya SankaranarayananiD, Diana Tavares‐FerreiraiD, Stephanie I. ShiersiD, Nikhil Nageshwar InturiiD, Esther EberhardtiD, Lisa ErnstiD, Lorenzo BonaguroiD, Jonas Schulte-SchreppingiD, Marc D. BeyeriD, Thomas StiehliD, William R. RenthaliD, Ingo KurthiD, Jenny TigerholmiD, Serra Jordi, Theodore John PriceiD, , Angelika LampertiD
Abstract
Human dermal sleeping nociceptors display ongoing activity in neuropathic pain, affecting 10% of the population. Despite advances in rodents, a molecular marker for these mechano-insensitive C-fibers (CMis) in human skin remains elusive, preventing targeted therapy. In this translational Patch-seq study, we combine single-cell transcriptomics following electrophysiological characterization with single-nucleus and spatial transcriptomics from pigs and humans. We functionally identified CMis in pig sensory neurons with patch-clamp using adapted protocols from human microneurography. We identified oncostatin-M-receptor (OSMR) and somatostatin (SST) as marker genes for CMis. Following dermal injection in healthy human volunteers, oncostatin-M, the ligand of OSMR, exclusively modulates CMis. We identified the entire molecular architecture of human dermal sleeping nociceptors, providing new therapeutic targets and the basis for a mechanistic understanding of neuropathic pain. One Sentence SummaryWe identify the molecular architecture and specifically OSMR and SST as molecular markers for human dermal sleeping nociceptors, key players in the generation of neuropathic pain. Short versionIn this Patch-seq study, we identify OSMR and SST as molecular markers for human dermal sleeping nociceptors, key players in the generation of neuropathic pain.
- organism: human
- organism: plant (other)
- assay: scRNA-seq
- assay: spatial transcriptomics
- assay: electronic health records
- research focus: cell type annotation
- approach: experimental assay
- Citations
- 26
- FWCI
- 99.25
- Field percentile
- top 1%
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