Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing

Abstract

Wound healing is a highly orchestrated, multiphase process that involves various cell types and molecular pathways. Recent advances in single-cell transcriptomics and machine learning have provided unprecedented insights into the complexity of this process, enabling the identification of novel cellular subpopulations and molecular mechanisms underlying tissue repair. In particular, single-cell RNA sequencing (scRNA-seq) has revealed significant cellular heterogeneity, especially within fibroblast populations, and has provided valuable information on immune cell dynamics during healing. Machine learning algorithms have enhanced data analysis by improving cell clustering, dimensionality reduction, and trajectory inference, leading to a better understanding of wound healing at the single-cell level. This review synthesizes the latest findings on the application of scRNA-seq and machine learning in wound healing research, with a focus on fibroblast diversity, immune responses, and spatial organization of cells. The integration of these technologies has the potential to revolutionize therapeutic strategies for chronic wounds, fibrosis, and tissue regeneration, offering new opportunities for precision medicine. By combining computational approaches with biological insights, this review highlights the transformative impact of scRNA-seq and machine learning on wound healing research.

Keywords

Deep learning / Single-cell / Machine learning / Fibroblast diversity / Trajectory inference / Cellular plasticity / Immune cell dynamics

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