Mapping immune landscapes and immunopathomechanisms of inflammatory skin diseases through single-cell multi-omic analysis 2334083

Abstract

Abstract Introduction Inflammatory skin diseases are a heterogeneous group of chronic disorders characterized by persistent or recurrent inflammation with complex and multifactorial immunopathomechanisms. These diseases share overlapping clinical features and some overlapping molecular mechanisms, despite heterogeneity and complex pathophysiology, although their pathophysiology remains only partially understood. To guide treatment strategies and predict therapeutic response, characterization of shared or disease-specific pathomechanism is needed. In addition, how the clinical and immunological phenotypes of these diseases differ by ancestry remains underexplored. Methods Here, we newly generated a single-cell atlas of inflammatory skin diseases, atopic dermatitis (AD), generalized pustular psoriasis (GPP), plaque psoriasis (PSO), hidradenitis suppurativa (HS) and vitiligo (VIT). Our dataset includes single-cell RNA sequencing of 77 tissue biopsies and 85 peripheral blood mononuclear cell (PBMC) samples from Korean patients, and single-cell resolution spatial transcriptomics on 35 tissue biopsies, with a custom add-on panel to refine immune signatures. Results The tissue dataset enabled cross-disease stratification at the sample level, where heterogeneity in keratinocytes shaped by disease-specific cytokine milieus is a major determining factor in disease stratification. In addition, we identified some disease-specific features, especially in immune cell subsets including resident memory T cells and macrophage subsets, which we show can inform potential druggable targets. In parallel, we have profiled PBMCs using mass cytometry for protein level validation of immune phenotypes. Conclusion These integrated approaches will enable discovery of unique molecular signatures of inflammatory skin diseases, offering insights for the development of treatment strategies and predictive biomarkers for inflammatory skin diseases with overlapping phenotypes. Funding Source Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : RS-2024-00403375) Topic Categories Computational and Systems Immunology (COMP)