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
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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
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