huSA: a comprehensive database for multi-dimensional resolution of bulk, single cell and spatial transcription profiles in skin diseases.

BACKGROUNDSkin diseases are among the most prevalent conditions worldwide, posing significant threats to human health by causing physical discomfort, psychological distress, and reduced quality of life. With the rapid advancement of high-throughput technologies, a substantial number of transcriptomic datasets, including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq, have been generated in the field of dermatology over the past decade. However, the lack of effective integration and standardized analysis pipelines limits the full utilization of these valuable resources in skin disease research.

OBJECTIVESTo address this gap, we aimed to construct a comprehensive, integrative, and user-friendly atlas that enables systematic exploration of skin transcriptomic data across multiple diseases and modalities.

METHODSWe developed the Human Skin Atlas (huSA) ('https://humanskinatlas.com/index.html'), a publicly accessible database that incorporates data from 17 skin diseases and 63 independent datasets, including 1 434 scRNA-seq, 63 spatial transcriptomics, and 1 502 bulk RNA-seq samples. The database provides standardized cell-type annotations, differential gene expression analysis, cell-cell interaction mapping, pathway and metabolic module enrichment, transcription factor regulatory inference, and differentiation state assessment for scRNA-seq data. Data from identical skin diseases were further integrated to enhance biological signal detection. For visualization, we embedded the 'cell × gene' and 'Cirrocumulus' platforms, offering interactive and customizable gene expression visualizations at both single-cell and spatial levels with user-defined parameters.

RESULTSThe huSA enables both individual dataset analysis and cross-dataset integration, providing robust, consistent, and scalable insights into skin disease biology. Demonstration analyses confirmed that results derived from either single datasets or aggregated multi-dataset integrations exhibited high reliability and biological relevance. The platform successfully supports diverse research needs, including cell-type-specific expression profiling, regulatory network construction, and spatial transcriptomic exploration.

CONCLUSIONSThe Human Skin Atlas (huSA) represents a state-of-the-art integrative resource for the skin research community. By offering multiscale analyses and interactive visualization tools, the huSA accelerates the discovery of molecular mechanisms underlying skin diseases and facilitates translational research efforts aimed at improving skin health.