Research Paper Volume 16, Issue 3 pp 2194—2231

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies

Ying Xiong1, , Sidi Li2, , Yunmeng Bai3, , Ting Chen1, , Wenwen Sun1, , Lijie Chen1, , Jia Yu1, , Liwei Sun1, , Chijun Li1, , Jiajian Wang4,5,6,7,8,9, , Bo Wu1, ,

  • 1 Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
  • 2 Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China
  • 3 Department of Nephrology, Shenzhen key Laboratory of Kidney Diseases, Shenzhen People’s Hospital, The First Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
  • 4 Department of Pediatrics, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China
  • 5 Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China
  • 6 Clinical Laboratory Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen and Longgang District People’s Hospital of Shenzhen, Shenzhen 518172, China
  • 7 Center for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
  • 8 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
  • 9 Shenzhen Key Laboratory of Metabolic Health, Shenzhen 518055, China

Received: August 21, 2023       Accepted: December 13, 2023       Published: January 29, 2024
How to Cite

Copyright: © 2024 Xiong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.


Names of genes/proteins/cells
APP: Amyloid Beta Precursor Protein; CD44: Cell Surface Glycoprotein CD44; CD46: Membrane Cofactor Protein, Trophoblast-Lymphocyte Cross-Reactive Antigen; CD74: CD74 Antigen, Invariant Polypeptide of Major Histocompatibility Complex, Class II Antigen-Associated; CD99: Cell Surface Antigen 12E7; CDH: Cadherin 1-mediated signaling pathway, mainly involved in cell adhesion, which in turn interferes with cell invasion and metastasis; CDH1: Cadherin 1; COL4A2: Collagen Type IV Alpha 2 Chain; COL6A1: Collagen Type VI Alpha 1 Chain; COLLAGEN: Collagen; CXCL13: C-X-C Motif Chemokine Ligand 13; CTSRs: Cell type-specific regulons; DCs: Myeloid dendritic cells; DESMOSOME: Desmosome, regulons of Cellular Signaling and Adhesion; DSC3: Desmocollin 3; DSG1: Desmoglein 1; EGF: Epidermal Growth Factor; EGFR: Epidermal Growth Factor Receptor; FN1: Fibronectin 1; HBEGF: Heparin Binding EGF Like Growth Factor; ITGA2: Integrin Subunit Alpha 2; ITGAV: Integrin Subunit Alpha V; ITGB1: Integrin Subunit Beta 1; ITGB5: Integrin Subunit Beta 5; IFN-α: Interferon Alpha 1; IL-12: Interleukin 12; IL-23: Interleukin 23; IRF: Interferon-regulated transcription factor family; IRF3: Interferon Regulatory Factor 3; JAG1: Jagged Canonical Notch Ligand 1; JAG2: Jagged Canonical Notch Ligand 2; KLF5: KLF Transcription Factor 5; LAMC1: Laminin Subunit Gamma 1; LAMININ: LAMC1-mediated signaling pathways which are implicated in a variety of biological processes including cell adhesion, differentiation, migration, signaling, neuronal growth and metastasis; MIF: Macrophage Migration Inhibitory Factor; MPZ: Myelin Protein Zero; NFIB: Nuclear Factor I B; NFIX: Nuclear Factor I X; NOTCH: Notch signaling pathways are involved in cell differentiation, apoptosis, proliferation and cell boundary formation, and are important influences on cell morphogenesis; NOTCH2: Neurogenic locus notch homolog protein 2; pDCs: Plasmacytoid dendritic cells; PDGF: Platelet Derived Growth Factor; PERIOSTIN: POSTN, Periostin; RXRA: Retinoid X Receptor Alpha; STAT1: Signal Transducer and Activator of Transcription 1; Tc17: Tc17, the subtype of CD8 T cells; THBS: Thrombospondin 1 pathway, which is involved in platelet cytoplasmic Ca2+ elevation and protein metabolism in response; TLR7: Toll-like receptors 7; TLR8: Toll-like receptors 8; VSMC: Vascular smooth muscle cell; ZFP36L1: ZFP36 Ring Finger Protein Like 1; ZFX: Zinc Finger Protein X-Linked;
Analysis techniques and their associated specialized terminology
AGC: Automatic gain control; BSgenome.Hsapiens.UCSC.hg38: Biostrings objects storing whole genome sequences for Homo sapiens (Human) as supplied by UCSC (hg38, based on GRCh38.p13); Cellchat: An R package that can analyse cell-to-cell communication at the single-cell transcriptome level; CellPhoneDB: A software package, currently available in R and Python, that can use spatial information to define possible interacting cell pairs that share/coexist in a microenvironment; COGs: Clusters of Orthologous Groups; CV: Compensation voltage; DDA: Data-dependent acquisition; DTT: DL-Dithiothreitol; DMINDA 2.0: DNA motif recognition and analysis Version 2 is a finding and identification tool for DNA motifs; EASY-nLC 1200: The Thermo Scientific TM EASY-nLCTM 1200 System is a fully integrated system designed for rapid and reliable proteomics detection; eggNOG: A publicly accessible database including orthology linkages, gene evolutionary histories, and functional annotations; FDA: U.S. Food and Drug Administration; FDR: False discovery rates; FFPE: Formalin fixed paraffin-embedded; GEO: Gene Expression Omnibus database; GO: Gene Ontology; HCD: Higher-energy collisional dissociation; HOCOMOCO: HOmo sapiens COmprehensive MOdel Collection presents the Homo sapiens comprehensive model collection featuring meticulously hand-curated transcription factor binding site models; IAM: Iodoacetamide; IRIS3: An integrative cell-type-specific regulon inference server derived from single-cell RNA-Seq; iTALK: An R toolbox for describing and illustrating intercellular communication; KEGG: Kyoto Encyclopedia of Genes and Genomes; KOG: Clusters of Orthologous Groups in Eukaryotes; MEME: A set of programs for discovering motifs in proteins, DNA, and RNA; MS: Mass spectrometry; NicheNet: A method for predicting ligand-target associations by combining expression data from interacting cells with existing knowledge of signaling and gene regulatory networks; PCA: Principal component analysis; PsA: Psoriatic arthritis; PSM: Peptide-spectrum matches; QUBIC2: or QUalitative BIClustering algorithm Version 2, is an algorithm for clustering and identifying functional gene modules based on gene expression; RAS: Regulon activity score; RSS: Regulon specificity score; SDC: Sodium dodecyl sulfate; SingleCellSignalR: SingleCellSignalR is an R package for inferring inter-cell interactions, which performs the basic operations of identifying subgroups of cell taxa and cell types, and clustering the data; TEAB: Triethylammonium bicarbonate buffer; TOMTOM: An algorithm for discovering motifs and their similarities it is a part of the MEME software family; WoLF PSORT: Advanced protein subcellular localization prediction tool.