Dataset Information

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.

SUBMITTER: Shi B

PROVIDER: S-EPMC8780900 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSingle-cell RNA sequencing (scRNA-seq) has quickly become one of the most dominant techniques in modern transcriptome assessment. In particular, 10X Genomics' Chromium system, with its high throughput approach, turn key and thorough user guide made this cutting-edge technique accessible to many laboratories using diverse animal models. However, standard pre-processing, including the alignment and cell filtering pipelines might not be ideal for every organism or tissue. Here we applied an alternative strategy, based on the pseudoaligner kallisto, on twenty-two publicly available single cell sequencing datasets from a wide range of tissues of eight organisms and compared the results with the standard 10X Genomics' Cell Ranger pipeline.ResultsIn most of the tested samples, kallisto produced higher sequencing read alignment rates and total gene detection rates in comparison to Cell Ranger. Although datasets processed with Cell Ranger had higher cell counts, outside of human and mouse datasets, these additional cells were routinely of low quality, containing low gene detection rates. Thorough downstream analysis of one kallisto processed dataset, obtained from the zebrafish pineal gland, revealed clearer clustering, allowing the identification of an additional photoreceptor cell type that previously went undetected. The finding of the new cluster suggests that the photoreceptive pineal gland is essentially a bi-chromatic tissue containing both green and red cone-like photoreceptors and implies that the alignment and pre-processing pipeline can affect the discovery of biologically-relevant cell types.ConclusionWhile Cell Ranger favors higher cell numbers, using kallisto results in datasets with higher median gene detection per cell. We could demonstrate that cell type identification was not hampered by the lower cell count, but in fact improved as a result of the high gene detection rate and the more stringent filtering. Depending on the acquired dataset, it can be beneficial to favor high quality cells and accept a lower cell count, leading to an improved classification of cell types.