bioRxivCell BiologyPreprint
Pathogenic Keratinocyte States and Fibroblast Niches Define the Tissue Microenvironment in Severe Hidradenitis Suppurativa
Xinyi Du-HarpuriD, Clarisse GânieriD, Pavel MaziniD, Chris CheshireiD, Jeyrroy GabrieliD, Ellie RashidghamatiD, Nicholas M. LuscombeiD, Magnus LynchiD, Fiona M. WattiD
Abstract
Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease characterized by recurrent painful abscesses and tunnels in flexural sites. The mechanisms driving HS pathogenesis, particularly the roles of keratinocytes and fibroblasts in the HS inflammatory microenvironment, remain poorly understood. To characterise the cellular and molecular landscape of HS, we analyzed lesional skin from severe HS patients using single-cell RNA-sequencing and spatial transcriptomics to identify keratinocyte states and patterns of cellular interactions, with a focus on fibroblast-keratinocyte crosstalk. We identified a migratory S100+ pathogenic keratinocyte state enriched in HS lesions and observed spatially distinct fibroblast populations associated with different tissue compartments. COL6A5+ fibroblasts were preferentially co-localised with undifferentiated keratinocyte states, whereas APOD+ fibroblasts with enriched in immune cell-rich regions containing activated B and plasma cells consistent with tertiary lymphoid organ (TLO)-like structures. Ligand-receptor inference and spatial co-localisation analyses predicted extensive fibroblast-keratinocyte and fibroblast-immune interactions within HS lesions. Together, these data define the cellular organisation of the HS tissue microenvironment and identify spatially distinct stromal and epithelial niches associated with severe disease. These findings provide a framework for understanding how epithelial, stromal and immune cell populations coexist within HS lesions and may help to explain the limited efficacy of therapies targeting individual inflammatory pathways in the treatment of HS.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: cell-cell communication
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 1
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