Single‐cell transcriptomes of mouse bladder urothelium uncover novel cell type markers and urothelial differentiation characteristics

Li, Yan; Liu, Yaxiao; Gao, Zhengdong; Zhang, Lekai; Chen, Lipeng; Wu, Zonglong; Liu, Qinggang; Wang, Shuai; Zhou, Nan; Chai, Toby C.; Shi, Benkang

doi:10.1111/cpr.13007

Cited by 13 publications

(18 citation statements)

References 61 publications

(132 reference statements)

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“… 40 Intriguingly, trajectory analysis of urothelial cells would often be disrupted by a large number of ribosomal genes which also developed pseudotime properties in some datasets. This observation was also seen in a previous study, about 90% of top pseudotime genes were located in ribosomes, 9 yet the remaining genes (Gstm1, Tmsb4x, S100a6, and Malat1) were still aligned with our study. This phenomenon might imply that the urothelial cells may have been more heavily damaged during the sample preparation because of their exposure as the outermost layer or their intolerance and sensitivity to cell digestive agents (multiple enzymes).…”

Section: Discussionsupporting

confidence: 92%

“…Yu et al 8 have reported an admirable study sketching a single‐cell transcriptomic map of bladder in both mouse and human, but this research is entirely lacking any cell‐cell interaction analysis. Li et al 9 have demonstrated a novel cell type labeled by Plxna4 using scRNA‐seq in the mouse bladder, but this study is only focusing on the urothelial layer and without cell communicative network analysis or validation of any human samples. Studies have suggested that cross talk between cells profoundly impacts the development and the regeneration of the respiratory system.…”

Section: Introductionmentioning

confidence: 99%

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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

Shi

Chen

et al. 2021

Cell Proliferation

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

Shi

Chen

et al. 2021

Cell Proliferation

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“…Recently, female C57BL/6 mice urothelial cells were analyzed and classified into eight clusters dependent on expression of cell-specific markers. A novel urothelial cell type expressing Plxna4 was discovered in the mouse bladder ( Li et al., 2021 ) which is conserved in humans and may play a role in host immune response ( Wen et al., 2010 ).…”

Section: Animal Models For Studying Utimentioning

confidence: 99%

Recurrent Urinary Tract Infection: A Mystery in Search of Better Model Systems

Murray

Flores

Williams

et al. 2021

Front. Cell. Infect. Microbiol.

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Urinary tract infections (UTIs) are among the most common infectious diseases worldwide but are significantly understudied. Uropathogenic E. coli (UPEC) accounts for a significant proportion of UTI, but a large number of other species can infect the urinary tract, each of which will have unique host-pathogen interactions with the bladder environment. Given the substantial economic burden of UTI and its increasing antibiotic resistance, there is an urgent need to better understand UTI pathophysiology – especially its tendency to relapse and recur. Most models developed to date use murine infection; few human-relevant models exist. Of these, the majority of in vitro UTI models have utilized cells in static culture, but UTI needs to be studied in the context of the unique aspects of the bladder’s biophysical environment (e.g., tissue architecture, urine, fluid flow, and stretch). In this review, we summarize the complexities of recurrent UTI, critically assess current infection models and discuss potential improvements. More advanced human cell-based in vitro models have the potential to enable a better understanding of the etiology of UTI disease and to provide a complementary platform alongside animals for drug screening and the search for better treatments.

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“…Louvain clustering was performed on the gene features of scRNA-seq data collected from human lymphocytes, mouse bladder, mouse kidney, hydra, or planaria (Figure 1, S1) [Blondel 2008, Zheng 2017, Li 2021, Park 2018, Siebert 2019, Fincher 2018. Louvain clustering was performed on each sample independently, and across these samples we observed three general expression patterns: 1) A group of genes that are nearly ubiquitously expressed across all cells.…”

Section: Characterizing Patterns Of Gene Expression Variation Across Tissues and Multicellular Organismsmentioning

confidence: 99%

“…Using these approaches, it has been demonstrated that the HVG method only marginally improves performance compared to a randomized feature selection control ]. Yet, the HVG method remains extremely popular [Luecken 2019, Park 2018, Fincher 2018, Gerber 2018, Siebert 2019, Xi 2020, Diaz 2020, Collin 2021, Fawkner-Corbett 2021, Li 2021]. In addition to uncertainty about the ability of various methods to identify an informative set of biologically varying genes, the HVG, NBDisp, and Townes method all entail arbitrary decisions about the number of features to use in the downstream clustering procedure.…”

Section: Introductionmentioning

confidence: 99%

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

Sparta

Hamilton

Aragones

et al. 2021

Preprint

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Single-cell RNA sequencing (scRNA-seq) aims to characterize how variation in gene expression is distributed across cells in tissues and organisms. Yet, effective comprehension of these extremely high-dimensional datasets remains a critical barrier to progress in biological research. In standard analyses of scRNA-seq data, feature selection steps aim to reduce the dimensionality of the data by focusing on a subset of genes that are the most biologically variable across a set of cells. Ideally, these features provide the genes that are the most informative for partitioning groups of transcriptionally distinct cells, each representing a different cell type or identity. In this work, we propose a simple feature selection model where a binomial sampling process for each mRNA species produces a null model of technical variation. To compare our model to existing methods, we use scRNA-seq data where cell identities have been established a priori for each cell, and characterize whether different feature sets retain biologically varying genes, distort neighborhood structures, and allow popular clustering algorithms to partition groups of cells into their established classes. We find that our model of biological variation, which we term “Differentially Distributed Genes” or DDGs, outperforms existing methods, and enables dimensionality reduction without loss of critical structure within the data set.

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Single‐cell transcriptomes of mouse bladder urothelium uncover novel cell type markers and urothelial differentiation characteristics

Cited by 13 publications

References 61 publications

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

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

Recurrent Urinary Tract Infection: A Mystery in Search of Better Model Systems

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

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