bioRxivCell BiologyPreprint
A regenerative fibroblast competence field supports multiple cell fates in adult skin regeneration
Chih-Han HuangiD, Chih-Kuan CheniD, Chen Yuan KamiD, Ben Van Handel, Arijita SarkariD, Yen‐Chun HoiD, Denis A. EvseenkoiD, Cheng‐Ming ChuongiD, Hans I‐Chen HarniD
Abstract
Adult mammalian wounds typically restore tissue integrity through fibrotic repair rather than regeneration of the original architecture. Here, we compared small non-regenerative and large regenerative skin wounds to investigate how fibroblasts acquire regenerative competence and enter alternative cell-fate programs. Integrated single-cell RNA sequencing identified a transient signaling and extracellular-matrix-remodeling niche enriched in large wounds, together with NCAM1-associated competent fibroblast states. Time-resolved analysis positioned these states within a root-like fibroblast manifold connected to hair dermal-condensate, endothelial, pericyte and glial-associated fate-entry programs. Inferred cell-cell communication networks revealed shared matrix and adhesion signals, superimposed with branch-associated inputs, including VEGF signaling toward NCAM1-positive endothelial-transition cells. Lineage tracing showed that PDGFRA- and NCAM1-expressing lineages contributed to vascular- and nerve/glial-associated regenerative compartments. Functionally, partial NCAM1 perturbation reduced vascular organization while increasing hair follicle neogenesis, whereas stronger inhibition impaired both outcomes. These findings identify NCAM1 as a regulator of regenerative competence and fate specification within a regenerative competence field rather than a marker of a single terminal lineage. We propose that regenerative wounds activate an NCAM1-associated fibroblast competence field whose quantitative modulation biases competent fibroblasts toward alternative outcomes.
- organism: mouse
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: cell-cell communication
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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