RNA-seq and flow-cytometry of conventional, scalp, and palmoplantar psoriasis reveal shared and distinct molecular pathways

Sci Rep. 2018 Jul 27;8(1):11368. doi: 10.1038/s41598-018-29472-w.


It has long been recognized that anatomic location is an important feature for defining distinct subtypes of plaque psoriasis. However, little is known about the molecular differences between scalp, palmoplantar, and conventional plaque psoriasis. To investigate the molecular heterogeneity of these psoriasis subtypes, we performed RNA-seq and flow cytometry on skin samples from individuals with scalp, palmoplantar, and conventional plaque psoriasis, along with samples from healthy control patients. We performed differential expression analysis and network analysis using weighted gene coexpression network analysis (WGCNA). Our analysis revealed a core set of 763 differentially expressed genes common to all sub-types of psoriasis. In contrast, we identified 605, 632, and 262 genes uniquely differentially expressed in conventional, scalp, and palmoplantar psoriasis, respectively. WGCNA and pathway analysis revealed biological processes for the core genes as well as subtype-specific genes. Flow cytometry analysis revealed a shared increase in the percentage of CD4+ T regulatory cells in all psoriasis subtypes relative to controls, whereas distinct psoriasis subtypes displayed differences in IL-17A, IFN-gamma, and IL-22 production. This work reveals the molecular heterogeneity of plaque psoriasis and identifies subtype-specific signaling pathways that will aid in the development of therapy that is appropriate for each subtype of plaque psoriasis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • CD4-Positive T-Lymphocytes / metabolism
  • CD8-Positive T-Lymphocytes / metabolism
  • Cluster Analysis
  • Cytokines / metabolism
  • Female
  • Flow Cytometry*
  • Gene Expression Profiling
  • Gene Regulatory Networks
  • Humans
  • Male
  • Middle Aged
  • Principal Component Analysis
  • Psoriasis / genetics*
  • Scalp / pathology*
  • Sequence Analysis, RNA*
  • Signal Transduction / genetics*
  • Transcriptome / genetics


  • Cytokines