Integration of bulk and single-cell transcriptomics with spatial analysis identifies candidate hub genes and inferred regulatory programs in atopic dermatitis.

Background: Atopic dermatitis (AD) is a common chronic inflammatory disorder of the skin. Despite the availability of targeted biologics like dupilumab, heterogeneous treatment responses persist, highlighting the need for reliable biomarkers and a deeper understanding of cell-type-associated molecular programs within the tissue microenvironment. Methods: This exploratory study integrated public microarray, single-cell RNA sequencing, and spatial transcriptomic datasets. GSE130588, comprising 124 lesional AD skin samples, was used for weighted gene co-expression network analysis (WGCNA). GSE59294 comprised 40 skin biopsies from 18 patients, including 16 pre-treatment lesional, 12 post-treatment lesional, 7 pre-treatment non-lesional, and 5 post-treatment non-lesional samples, and was used for differential expression analysis. The single-cell query cohort comprised 66,846 cells from 25 samples, with downstream analyses restricted to 19 blister-derived samples. Regulon activity was computationally inferred. GSE197023 comprised 20 spatial transcriptomic samples, including 7 lesional AD, 7 non-lesional AD, and 6 healthy-control samples, and was analyzed using cell-population deconvolution. CCR7 immunoreactivity was assessed by immunohistochemistry in independent healthy-control and lesional AD skin samples ( Results: WGCNA identified disease-severity-associated gene modules enriched in immune and cell-cycle-related processes. Nominal- Conclusion: These cross-modal analyses linked candidate molecular signals todendritic-cell- and T- cell-associated patterns in AD. These findings are exploratory and hypothesis-generating and do not establish validated biomarkers, causal regulatory mechanisms, or predictors of treatment response. Independent cohort replication and functional and cell-type-specific validation are required.