Keloid disease is a significant clinical problem, especially in black populations, with an estimated incidence of 4–16%. Keloids are fibroproliferative dermal tumors developing as a result of deregulated wound healing. The dynamic nature of keloids is illustrated by clinical regression in the center, while the margin remains active growing into the surrounding healthy skin. Therefore, the gene expression profiles of fibroblasts from different sites of the keloids were characterized using Affymetrix microarrays covering the whole human genome. This study revealed 105 genes that were differentially regulated (79 genes were up‐regulated and 26 down‐regulated) in a unique gene expression profile in different sites of keloids where progression or regression of the process was in progress. The apoptosis inhibitor AVEN was found to be up‐regulated at the active margin of keloids, while apoptosis‐inducing genes such as ADAM12 and genes inducing extracellular matrix (ECM) degradation such as matrix metalloproteinase‐19 were up‐regulated in the regressing keloid center. We identified genes previously not described in the development of keloids. Activating proapoptotic genes or inhibiting ECM‐inducing genes as INHBA or monocyte chemoattractant protein‐1 might be possible target genes for new treatment strategies for keloid disease.

Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

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