Understanding of mouse and human bladder at single-cell resolution: integrated analysis of trajectory and cell-cell interactive networks based on multiple scRNA-seq datasets

Abstract

Objectives: To elaborately decipher the mouse and human bladders at single-cell levels.

Materials and methods: We collected more than 50,000 cells from multiple datasets and created, up to date, the largest integrated bladder datasets. Pseudotime trajectory of urothelium and interstitial cells, as well as dynamic cell-cell interactions, was investigated. Biological activity scores and different roles of signaling pathways between certain cell clusters were also identified.

Results: The glucose score was significantly high in most urothelial cells, while the score of H3 acetylation was roughly equally distributed across all cell types. Several genes via a pseudotime pattern in mouse (Car3, Dkk2, Tnc, etc.) and human (FBLN1, S100A10, etc.) were discovered. S100A6, TMSB4X, and typical uroplakin genes seemed as shared pseudotime genes for urothelial cells in both human and mouse datasets. In combinational mouse (n = 16,688) and human (n = 22,080) bladders, we verified 1,330 and 1,449 interactive ligand-receptor pairs, respectively. The distinct incoming and outgoing signaling was significantly associated with specific cell types. Collagen was the strongest signal from fibroblasts to urothelial basal cells in mouse, while laminin pathway for urothelial basal cells to smooth muscle cells (SMCs) in human. Fibronectin 1 pathway was intensely sent by myofibroblasts, received by urothelial cells, and almost exclusively mediated by SMCs in mouse bladder. Interestingly, the cell cluster of SMCs 2 was the dominant sender and mediator for Notch signaling in the human bladder, while SMCs 1 was not. The expression of integrin superfamily (the most common communicative pairs) was depicted, and their co-expression patterns were located in certain cell types (eg, Itgb1 and Itgb4 in mouse and human basal cells).

Conclusions: This study provides a complete interpretation of the normal bladder at single-cell levels, offering an in-depth resource and foundation for future research.

Keywords: bladder; cell-cell communication; scRNA-seq; trajectory analysis.