Atopic Dermatitis (AD) or Eczema makes the skin red, itchy, and sensitive. It has no known cure and can trouble both children and adults. AD occurs due to a complex interaction of various changes, including epidermal barrier impairment, environmental exposure, and T lymphocyte invasion. Gene-level knowledge of cells and tissues is vital for determining treatment. This study explores the role of epidermal responses.
The researchers conducted cell and tissue gene expression analysis using transcriptomics. This study aims to understand keratinocyte transcriptomic programs. The researchers analyzed the epidermal cells with AD during swelling and treatment response stages. They filtered the whole skin transcriptome signals are in silico. The gene expressions of bulk skin with 7 datasets and 406 samples underwent analysis. Unsupervised clustering is the primary tool to identify keratinocyte phenotypes. Machine learning tools helped identify 19 cutaneous cell signatures of purified populations on publicly available data sets.
The study identified 3 keratinocyte transcript programs, KC1, KC2, and KC17, which were unique to immune signaling from disease-related T helper cells. They were cross-validated across various skin inflammations and disease stages. The immunosuppressant drug ciclosporin lowered the KC17 response without changing KC2, whereas the dupilumab antibody therapy reversed KC2 expression.
The genome analysis reveals keratinocyte programming effects on skin inflammation. It concludes that keratinocyte response during treatment is therapy-dependent. Further research is essential to resolve such epidermal abnormalities.