Abstract Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have revolutionized dermatological research by providing unprecedented resolution of cellular heterogeneity and spatial tissue architecture. These technologies surpass bulk RNA sequencing by enabling the identification of rare cell populations, mapping cellular interactions, and uncovering dynamic cell states in complex skin tissues. The application of scRNA-seq and spatial omics has elucidated disease mechanisms and identified novel biomarkers across diverse skin conditions, including melanoma, inflammatory diseases, and inherited disorders. Integration with cross-species datasets advances the translational relevance. Advances in specimen preparation, computational workflows, and machine-free protocols have improved accessibility and data quality. Furthermore, artificial intelligence enhances data interpretation and clinical translation. Despite challenges from cost, sample variability, and computational complexity, these tools pave the way for precision dermatology by enabling spatially resolved molecular profiling that informs diagnosis, prognosis, and the development of targeted therapies. Future efforts focusing on integrated workflows and clinical adaptable pipelines will be critical to bridge research output with clinical practice.
Cheng et al. (Thu,) studied this question.