Cell signaling communication within papillary craniopharyngioma revealed by an integrated analysis of single-cell RNA sequencing and bulk RNA sequencing

Abstract

Objective: This study aims to elucidate the primary signaling communication among papillary craniopharyngioma (PCP) tumor cells.

Methods: Five samples of PCP were utilized for single-cell RNA sequencing. The most relevant ligand and receptor interactions among different cells were calculated using the CellChat package in R software. Bulk RNA sequencing of 11 tumor samples and five normal controls was used to investigate the pair interactions detected by single-cell RNA sequencing.

Results: Fibroblasts were not found in ACP, whereas they were detected in PCP. InferCNV revealed high CNV scores for the clusters of epithelial cells and fibroblasts using immune cells as a reference. Epithelial Mesenchymal Transition, Interferon Gamma Response, p53 Pathway, and Estrogen Response Early are pathways commonly shared by fibroblasts and epithelial cells, ranking high in priority. The Wnt signaling pathway and PI3K-Akt signaling pathway play a crucial role in facilitating communication between epithelial cells and fibroblasts. Neutrophils were recognized as the main receivers of incoming signals, with ANXA1-FPR1 and MIF-(CD74 + CXCR2) being identified as the primary signals transmitted from fibroblasts to neutrophils.

Conclusion: Through analyzing the communication of essential signaling pathways, ligands, and receptors among epithelial cells, fibroblasts, and neutrophils in PCP tumor tissues, we have identified certain molecules with promising prognostic and therapeutic potential.

Keywords: Communication; Fibroblasts; Papillary craniopharyngioma; Single-cell RNA sequencing.

Fig. 1

Cell types of fibroblasts revealed by scRNA-seq. A UMAP plots of fibroblasts in PCP tissues based on scRNA-seq data. B Heatmap showing the top 10 marker gene expression signatures. C Violin plots displaying the expression levels of distinct marker genes for five primary fibroblast subclusters

Fig. 2

InferCNV analysis results of epithelial cells and fibroblasts. A The heatmap displays the chromosomal mapping of epithelial cells’ single-cell large-scale copy number variations inferred through scRNA-seq. B CNV score of epithelial cells. C The heatmap shows the chromosomal mapping of single-cell large-scale CNVs in fibroblasts inferred through scRNA-seq. D CNV score of fibroblasts

Fig. 3

Enrichment analysis result of epithelial cells and fibroblasts. Functional enrichment of epithelial cells (A) and fibroblasts (B) using HALLMARK and Wiki Pathways databases. C AUCell scoring of the activity of the p53 pathway in different cell clusters of epithelial cells showed significantly lower activity in cell cluster 4. D The activity of the p53 pathway was relatively low in the fibroblast cell clusters

Fig. 4

Signaling communication between epithelial cells and fibroblasts through outgoing patterns. A Epithelial cells played a significant role in producing output signals among different cell subtypes. B, C Based on Cophenetic and Silhouette metrics, cell patterns were categorized into four groups using Nonnegative Matrix Factorization (NMF) method. D Communication interactions between epithelial cells and fibroblasts uncovered by analyzing Pattern 1 within the communication patterns. E Enrichment analysis result using the fibroblast subpopulation of Pattern 3 as the receptor and the epithelial cell subpopulation of Pattern 1 as the ligand

Fig. 5

Important ligand-receptor pairs between epithelial cells and fibroblasts. A Ligand-receptor pairs between epithelial cells and fibroblasts that were involved in the signaling pathway. B The Wnt signaling pathway involves epithelial cells acting as ligands. C WNT5A-MCAM pair emerged as the most significant regulatory factor in the pathway. D The detailed communication pattern of the WNT5A-MCAM pair in subclusters of epithelial cells and fibroblasts

Fig. 6

Pseudotime analysis of fibroblast subpopulations and highly variable genes. A Pseudotime ridge plot of fibroblast subpopulations.Pseudotime plot of highly variable genes in the Fibroblast_1 (B), Fibroblast_2 (C), Fibroblast_3 (D), Fibroblast_4 (E), and Fibroblast_5 (G) subpopulation. F Pseudotime plot of highly variable genes in the Pseudotime analysis of the WNT pathway in fibroblasts

Fig. 7

Enrichment analysis of ligand-receptor pairs between epithelial cells and fibroblasts, and SCENIC analysis of fibroblast subpopulations. A, B Enrichr analysis of ligand-receptor pairs in epithelial cells and fibroblasts. C SCENIC analysis of fibroblast subpopulations

Fig. 8

Incoming patterns of fibroblasts acting as ligands in the signaling communication with neutrophils. A Incoming patterns in PCP samples revealed by scRNA-seq. B, C Cell types and signaling pathways were classified into five distinct patterns through the analysis of the Cophenetic and Silhouette indices of incoming signaling. D Enrichr analysis result using Neutrophils as the receptor and other cells as ligands. E Enrichment analysis result using fibroblasts as the ligand and Neutrophils as the receptor

Fig. 9

Important ligand-receptor pairs between fibroblasts and neutrophils. A The transmission probability between fibroblasts and neutrophils. B The ANNEXIN signaling network with neutrophils as receptors. C ANXA1-FPR1 pair emerged as the most significant regulatory factor in the pathway. Enrichr analysis on all ligand-receptor interactions between fibroblasts (D) and neutrophils (E).

Fig. 10

Correlation of ligand-receptor genes in the Wnt pathway in PCP samples using bulk RNA sequencing. A Heatmap of Wnt pathway gene expressing pattern of genes identified by both scRNA-seq and bulk RNA sequencing. B–D Correlation analysis showed strong correlation between the ligand-receptor pairs of Wnt pathway