BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by itching and rashes, influenced by genetic, environmental, and immune factors. Despite significant research, the molecular mechanisms underlying AD are not fully understood. This study aims to integrate single-cell RNA sequencing (scRNA-seq) with Mendelian Randomization (MR) to uncover genetic and metabolic pathways contributing to AD. MATERIALS AND METHODS: Data from scRNA-seq and bulk RNA sequencing datasets were analyzed to identify differentially expressed genes. The edgeR package was used for differential expression analysis, and candidate genes were explored using MR, employing eQTL data to determine causal relationships with AD. The inverse variance weighted method facilitated MR analysis, while gene set enrichment analysis (GSEA) was used to identify pathways associated with AD. Single-cell analysis was performed with the Seurat package to explore cellular heterogeneity, and pseudotime and cellular communication analyses were conducted to understand cell differentiation and interactions in AD. RESULTS: The study identified key genes-PCLAF, MICB, CHAD, and CA4-linked to AD, with PCLAF notably acting as a risk factor. These genes are involved in cell cycle regulation, immune evasion, cell adhesion, and metabolic processes. The MR analysis highlighted lipid, amino acid, and energy metabolism as critical pathways in AD. Single-cell analysis revealed increased cellular communication in AD, especially in Langerhans cells, keratinocytes, and T cells, signifying dysregulated immune responses and inflammatory pathways. Pseudotime analysis indicated abnormal differentiation trajectories in these cell types. CONCLUSION: Our study highlights the importance of PCLAF in the pathogenesis of AD, indicating it as a potential target for future therapeutic strategies aimed at alleviating the disease by addressing genetic and metabolic disruptions.