Netherton syndrome (NS) is a severe genetic skin disease with constant atopic manifestations that is caused by mutations in the serine protease inhibitor Kazal-type 5 (SPINK5) gene, which encodes the protease inhibitor lymphoepithelial Kazal-type–related inhibitor (LEKTI). Lack of LEKTI causes stratum corneum detachment secondary to epidermal proteases hyperactivity. This skin barrier defect favors allergen absorption and is generally regarded as the underlying cause for atopy in NS. We show for the first time that the pro-Th2 cytokine thymic stromal lymphopoietin (TSLP), the thymus and activation-regulated chemokine, and the macrophage-derived chemokine are overexpressed in LEKTI-deficient epidermis. This is part of an original biological cascade in which unregulated kallikrein (KLK) 5 directly activates proteinase-activated receptor 2 and induces nuclear factor κB–mediated overexpression of TSLP, intercellular adhesion molecule 1, tumor necrosis factor α, and IL8. This proinflammatory and proallergic pathway is independent of the primary epithelial failure and is activated under basal conditions in NS keratinocytes. This cell-autonomous process is already established in the epidermis of Spink5−/− embryos, and the resulting proinflammatory microenvironment leads to eosinophilic and mast cell infiltration in a skin graft model in nude mice. Collectively, these data establish that uncontrolled KLK5 activity in NS epidermis can trigger atopic dermatitis (AD)–like lesions, independently of the environment and the adaptive immune system. They illustrate the crucial role of protease signaling in skin inflammation and point to new therapeutic targets for NS as well as candidate genes for AD and atopy.

Single-cell transcriptomics offers unprecedented opportunities to infer the ligand–receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.