Delineating Tissue-Specific Cell Identity of Oral Mucosa in Humans and Mice From a Single-Cell Perspective

从单细胞角度阐明人和小鼠口腔黏膜的组织特异性细胞身份

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Abstract

The oral mucosa exhibits superior healing and minimal scarring. Although mouse models are widely used to study wound healing and various diseases, their translational relevance remains unclear. Here, we performed a comparative single-cell transcriptomic analysis of human and mouse oral mucosa to identify both shared and species-specific mechanisms. A total of 34,969 cells from human and mouse datasets were integrated using Harmony for batch effect correction, allowing us to establish a unified oral mucosa transcriptome atlas. Fibroblasts emerged as the prominent cell population in both species, displaying conserved gene expression profiles and cell communication networks, underscoring their central role in tissue homeostasis. Key pathways involved in extracellular matrix remodelling and wound healing were highly conserved, supporting the utility of mouse models for studying fibroblast-mediated tissue regeneration. These findings suggest that mouse models can effectively replicate human fibroblast biology, offering valuable insights for developing translational therapies that target fibroblast activity and regulatory gene networks to enhance wound healing and tissue regeneration. Additionally, we identified species-specific cell populations, including human-specific capillary endothelial cells and melanocytes, as well as mouse-specific salivary gland epithelial cells. Their distinct cellular composition and functional differences suggest that these subpopulations may not be directly translatable from mouse models to human contexts. Overall, our study highlights the evolutionary conservation of fibroblasts while identifying species-specific differences that warrant consideration in translational research. These findings provide a valuable resource for researchers using mouse models to study oral mucosa-related diseases, facilitating the translation of preclinical discoveries into clinical applications.

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