Abstract
Prurigo nodularis (PN) is a chronic, inflammatory skin condition that disproportionately affects African Americans and features intensely pruritic, hyperkeratotic nodules on the extremities and trunk. PN is understudied compared to other inflammatory skin diseases, with the spatial organization of the cutaneous infiltrate in PN yet to be characterized. In this work, we employ spatial imaging mass cytometry to visualize prurigo nodularis lesional skin inflammation and architecture with single cell resolution through an unbiased machine learning approach. PN lesional skin has increased expression of caspase 3, NFkB, and pSTAT3 as compared to healthy skin. Keratinocytes in lesional skin are subdivided into CD14+CD33+, CD11c+, CD63+, and caspase 3+ innate subpopulations. CD14+ macrophage populations expressing pERK1/2 correlate positively with patient-reported itch (p=0.006). Hierarchical clustering reveals a cluster of prurigo nodularis patients with greater atopy, increased NFkB+pSTAT3+pERK1/2+ MoDCs, and increased vimentin expression (p<0.05). Neighborhood analysis finds interactions between CD14+ macrophages, CD3+ T cells, MoDCs, and keratinocytes expressing innate immune markers. These findings highlight pERK+CD14+ macrophages as contributors to itch and suggest an epithelial-immune axis in prurigo nodularis pathogenesis.
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