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Single-cell and spatial transcriptomic atlas of pathological scars uncovers neuro-fibroblast crosstalk mechanisms across scar types

GSE307504 Homo sapiens Expression profiling by high throughput sequencing; Other 6 samples Submitted 2025/09/14 Platform GPL24676
Summary
Pathological scars, including hypertrophic scars and keloids, arise from excessive production and deposition of connective tissue such as collagen during wound healing, posing significant clinical challenges. Understanding the cellular and molecular dynamics underlying these scars is critical for developing effective therapies. Here, we integrate single-cell and spatial transcriptomics to characterize the cellular heterogeneity and molecular signatures of key scar-associated cell types, including pericytes, fibroblasts, and endothelial cells. Notably, we identify ECM-activated fibroblasts as a prominent subtype associated with pathological scarring. Cell-cell communication analysis reveals significantly enhanced interactions between fibroblasts and neural cells in pathological scars, especially in hypertrophic scars—a finding that aligns with clinical features such as fibrosis and pruritus. Moreover, we identify subtype-specific ligand-receptor pairs, including LAMB2–ITGA6 in hypertrophic scar and NLGN2–NRXN1 in keloid, mediating these fibroblast–neural communications. To support future research, we developed CellCellMarker 3.0, an updated and curated database integrating experimentally validated and transcriptomics-derived scar marker genes, along with ScarGPT, an intelligent Q&A system optimized using the DeepSeek R1 large language model, integrated with CellCellMarker data and a large corpus of scar-related publications.
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Direct links to NCBI, no account and no request form: the whole study as GSE307504_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 6 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1321889. Searching any of these in the dataset finder brings you back here.

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