Single-cell RNA sequencing reveals the cellular and molecular characteristics of high-grade and metastatic bladder cancer

Yue Zheng; Xin Wang; Xiaofeng Yang; Nianzeng Xing

doi:10.1007/s13402-023-00820-x

. 2023 May 11;46(5):1415–1427. doi: 10.1007/s13402-023-00820-x

Single-cell RNA sequencing reveals the cellular and molecular characteristics of high-grade and metastatic bladder cancer

Yue Zheng ¹, Xin Wang ², Xiaofeng Yang ^2,^✉, Nianzeng Xing ^3,⁴

PMCID: PMC12974668 PMID: 37170046

Abstract

Purpose

Metastatic bladder cancer (BC) has the highest somatic mutation frequency and recurrence rate of all tumors. However, the cellular and molecular characteristics of BC remain unclear.

Methods

We performed single-cell RNA sequencing (scRNA-seq) on the samples of paracancerous normal tissue (PNT), primary tumor (PT) and lymph node metastasis (LNM). The proportions and gene expression profiles of different cell types in the tumor microenvironment (TME) were investigated.

Results

In total, 50,158 cells were classified into six populations. Malignant cells of PT and LNM exhibited large mutant DNA fragments, while the cell phenotypes and gene expression profiles differed during differentiation. Metastasis was associated with a poorer prognosis than PT. Tumor-associated stromal cells and inhibitory immune cells were the main cell populations in PT and LNM. Cell-cell communication analysis revealed the roles of signaling pathways of inflammatory cancer-associated fibroblast (iCAF) and tumor-associated macrophage (TAM) in exhaustion of T cells. In addition, iCAF may recruit TAM to promote formation of the TME earlier than the differentiation of tumor cells.

Conclusion

This study through scRNA-seq enhanced our understanding of new features about the cellular and molecular similarities and differences of high-grade and metastatic bladder cancer, which might provide potential therapeutic targets in future treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13402-023-00820-x.

Keywords: Single-cell RNA sequencing, Bladder cancer, Metastasis, Tumor microenvironment, Cancer associated fibroblast, Tumor associated macrophage

Introduction

Bladder cancer (BC) is the tenth most common tumor and the ninth leading cause of cancer-related death worldwide [1]. In addition, BC has the third highest somatic mutation rate, after non-small cell lung cancer and melanoma [2], and the highest mutation frequency and overall mutation load of chromatin-regulatory genes of all tumors [3, 4]. The complex and variable intratumoral heterogeneity of BC has been correlated to therapeutic resistance and poor prognosis of patients with advanced and metastatic disease [5, 6].

Radical cystectomy and pelvic lymph node dissection are the main treatment in high-risk non-muscle invasive BC (NMIBC) and muscle-invasive non-metastatic BC (MIBC) [7]. However, doctors usually cannot confirm whether there exist metastatic lymph nodes during the surgery. When doing transurethral resection of the bladder (TURB), doctors usually cannot distinguish the boundary between nonmalignant tissues and tumor related tissues either, thus it is easily to left some residual tumor tissues and cause a high risk of recurrence [8].

Therefore, the detection of genetic material in BC is increasingly becoming the key to understand the formation mechanism, evolution process and to find more therapeutic options. The next generation sequencing has provided us a lot of mutant gene sets of BC [2, 3]. However, it exists many limitations in studying the integrity and accuracy of tumor cells [9]. The subsequent single-cell sequencing (SCS) has largely made up for this defect.

At present, single-cell RNA sequencing (scRNA-seq) is one of the most widely used SCS in cancer. Many studies have involved the evolution process of cancer stem cells, to the development of primary cancer lesions, and then to the diffusion into other tissues [10, 11]. It enables us not only to determine which cells belong to cancer cells and which cells provide help for the proliferation of tumor cells, but also to identify some rare cell subsets, which may provide us a more detailed understanding of tumor. Therefore, scRNA-seq is very suitable for studying complex and variable metastatic tumor tissues and tumor microenvironment (TME).

Here, we used scRNA-seq to detect the cell populations and gene expressions among paracancerous normal tissues (PNT), primary tumors (PT) and lymph node metastasis (LNM) of high-grade and metastatic BC. Trying to explain the relations and differences of the tumor tissues and to extend our knowledge about this disease. So as to provide potential therapeutic targets to improve the accuracy during surgery. In addition, as far as we know, this is the first time we used scRNA-seq to study the high-grade and metastatic BC.

Materials and methods

Study approval and patient consent

The study protocol was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (Taiyuan City, China) (K093) and conducted in accordance with the ethical principles for medical research involving human subjects described in the Declaration of Helsinki. Written informed consent was obtained from all subjects for inclusion of specimens in this study.

Human samples

All samples were obtained from the First Hospital of Shanxi Medical University, Taiyuan City, Shanxi Province, China. Nine samples were collected from the three patients with radical cystectomy and pelvic lymph node dissection. The samples were taken from the Paracancerous normal tissues (PNT), Primary tumors (PT) and Lymph node metastasis (LNM). 24 paired LNM, PNT and PT of paraffin specimens for immunohistochemistry.

Single-cell suspension preparation

BC specimens were immediately cut into small pieces and digested for 1 h at 37 °C with trypsin (Generay Biotech Co., Ltd., Shanghai, China), collagenase II (Invitrogen–Gibco, Carlsbad, CA, USA), and DNase (Wuhan Servicebio Technology Co., Ltd., Wuhan, China). Free cells were resuspended in calcium- and magnesium-free phosphate-buffered saline containing 0.04% bovine serum albumin and filtered through a 40-µm mesh for subsequent quantification of the proportion of living cells.

Single-cell RNA sequencing processing

Magnetic beads with barcodes, unique molecular indexes (UMI), primers and enzymes were put into the cell suspension. These beads were combined with different mRNA that came from different cells and then became single-cell gel beads with different barcodes. After that using 10×Genomics Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 to detect the barcodes of these beads, they were reversely transcribed to cDNA, and a library was then constructed by PCR amplification. Next, Illumina Nova 6000 PE150 platform was used to sequence the library.

Sequencing data analysis

Cell Ranger software (version 5.0.0) (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) was used to analyze the preliminary quality control information. By demultiplexing the barcodes library, the raw reads were projected to construct a transcriptomic map by STAR aligner [12], and the reads were down-sampled as required to generate normalized aggregate data across samples, producing a matrix of gene counts versus cells. To obtain quality control statistics such as high-quality cell number, gene median, sequencing saturation, Seurat (version 3.1.1) [13] was used to remove low quality cells and multiplet captures based on the criteria of gene numbers less than 200, UMI less than 1000 and log10GenesPerUMI less than 0.7. Then use DoubletFinder (version 2.0.2) [14] to remove doublets. Finally, NormalizeData function based on the default factor was used to normalized the gene expression of every single cell from the library in a general number. Use FindVariableGenes [15], RunPCA, FindClusters and RunUMAP functions to select the top variable genes, and then gather them into principal component and apply dimensionality reduction. Finally, a 2-dimensional Uniform Manifold Approximation and Projection (UMAP) algorithm was used to visualize the cell clusters. Though these analyses, we classified 16 clusters. FindAllMarkers function and SingleR [16] software package is used to find top markers of every cluster, and these clusters were identified into different annotations. Our clusters were defined as 6 clusters. Finally, FindMarkers function was used to detect differentially expressed genes (DEGs), and P value < 0.05 and |log2foldchange| > 0.58 were the thresholds of high expression.

Inference of CNV from scRNA-seq data

Infer CNV (v1.0.4) (https://github.com/broadinstitute/inferCNV) [17] was used to detect the CNV value (--cutoff 0.1). The principle of this analysis is to detect the copy number variation (CNV) of genes on cell chromosomes, such as insertion and deletion of DNA fragments, or duplication and breakage of the whole chromosome and then to visualize the expression density of these mutant genes. We appointed immune cells as the reference to distinguish malignant and nonmalignant cells. All expressive genes were sorted by the position of the chromosomes, then 101 genes were put as a unit to assess the average.

Cell trajectory analysis

The importCDS function of Monocle2(v2.9.0) [18] was used to turn the Seurat object to CellDataSet object. Then, differentialGeneTest function was used to select the genes of cells in order (ordering gene, qval < 0.01), and reduceDimension function was used to conduct dimension reduction and clustering. Finally, orderCells function was used to simulate the trajectory of cell differentiation.

GSVA analysis

Download the gene set from KEGG database(https://www.kegg.jp/) by the GSEABase package, and gene set variation analysis (GSVA) package (v1.30.0) [19] was used to assess the pathway activity score of every single cell. Then compared the activity differences among the three group were compared.

CellChat analysis

CellChat (v 1.1.3) package [20] was used to analyse the receptor-ligand interactions of every single cell. The expression matrix was imported into the createCellChat function to construct the cellchat objects. IdentifyOverExpressedGenes、identifyOverExpressedInteractions and projectData functions were used to preliminary analyze the information, and computeCommunProb、filterCommunication and computeCommunProbPathway function were used to count the numbers of receptor-ligand pairs. Finally aggregateNet function was used to combine the interaction network.

The cancer genome atlas database

The overall survival of the specific genes in PNT and LNM of bladder cancer patients was evaluated by the Kaplan-Meier method utilizing the KM plotter database (kmplot.com/analysis/).

Immunohistochemical analysis

The paraffin tissue sections are collected from the First Hospital of Shanxi Medical University. Immunohistochemistry through En vision method, the repair method through EDTA (8.0). The antibodies we have used in our experiment are as follows: Anti-IDO1 Antibody (rabbit, 1:300, Boster, PB9603); LYVE1 Antibody (rabbit, 1:200, Affinity, AF4202); SLITRK6 Antibody (rabbit, 1:200, Affinity, DF14624); Anti-LMO3 Polyclonal Antibody (rabbit, 1:300, Solarbio, K009846P); Secondary antibodies: Polymeric antibody rabbit IgG HRP (rabbit, PV-8000, boster, SV0002).

Statistical analysis

All statistical analyses and graph generation were performed in R (version 3.6.1) and GraphPad Prism (version 9.4.0).

Results

scRNA-seq revealed the whole cell atlas of BC

We sequenced nine samples from three patients, which were taken from PNT, PNT and LNM of each patient, all patients did radical cystectomy and pelvic lymph node dissection (Table 1). After scRNA-seq, 50,158 cells of the nine samples were reduced dimension and clustered for 16 cell populations (Fig. 1A, B). According to the expression of classical markers on the cell surface, we identified these 16 groups as six cell types, including B cells (CD79A), T cells (CD3D/E/G), endothelial cells (PECAM1), epithelial cells (EPCAM), fibroblasts (COL1A1) and macrophages (LYZ) (Fig. 1C).

Table 1.

The information of 9 samples from 3 patients

Gender	Age	Clinical T stage	Gathologic diagnosis	Tumor location and size	Sample (PT)	Sample (PNT)	Sample (LNM)
Male	57	T3	MIBC	Left wall and anterior inferior wall of the bladder (6.8 × 4.5 cm)	Left wall tumor	Normal mucosa	Left iliac vascular region lymph node
Male	66	T2	MIBC	Left wall of the bladder (3.8 × 1.0 cm)	Left wall tumor	Normal mucosa	Left iliac vascular region lymph node
Male	77	T2	MIBC	Top and right wall of the bladder (4.0 × 3.6 cm)	Right wall tumor	Normal mucosa	Right pelvic lymph node

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Fig. 1 — Identification of cell types from BC, and distributions among the three groups. **(A)** A brief workflow of sample preparation and scRNA-seq analysis. **(B-F)** The t-SNE map, bar graph and heatmap show the 6 types cell clusters, and they are in different proportion among the three groups

Malignant epithelial cells of PT and LNM showed a high level of CNV

Compared to normal clusters, clusters 1 ~ 5 showed high levels of copy number variation (CNV), these clusters exist in PT and LNM parts (cluster 6,9,14) (Fig. 2). Hence epithelial cells in PT and LNM are the main malignant cells.

Many classical chromosome mutations of BC were included (Fig. 2A), such as, the amplification of chromosome 7 (C7) and C17 exists in most urothelial carcinomas, which are important markers that reflect the malignancy and drug-sensitivity of BC [21, 22]; the upregulation of SOX4 located at C6p22 promotes the metastasis of BC [23]; the amplification of C1q23.3 has been proved to be common in high-grade and metastatic BC [24, 25]; And MiR-27a located at C19 has been proved to be an inhibitor of BC, to which C19 deletion of may be related [26].

Subpopulations and gene expression profiles of malignant epithelial cells of PT and LNM

The malignant epithelial cells were divided for 12 subpopulations, cluster 2/6/9 were in PNT, others in PT and LNM (Fig. 3I-K). The cell trajectory [27] analysis on epithelial cells showed the malignant epithelial cells separated to two brunches to PT and LNM (Fig. 3A-C). Which suggested that PT and LNM shared the same tumor cell lineage at an early stage, then evolved into different tumor cell phenotypes in the late stage.

Then, we analyzed the gene expressions during differentiation (Fig. 3D). The different states of gene expressions were integrated into four modules. The functions of highly expressed genes in module 1 were related to normal cell differentiation, organelle formation, oxidative respiration, lipid metabolism, autophagy, regulating the polarity of smooth muscle, and maintaining tissue smoothness (Fig. 3E; Supplementary Fig. 1A). Genes in module 2 were related to cell cycle, DNA damage repair, mismatch repair, excision repair, and activating protein AP-1 (activating protein-1) signal pathway that promotes cell proliferation (Fig. 3F; Supplementary Fig. 1B). Genes in module 3/4 were related to tumor signaling pathways, inflammatory response, hypoxia, mismatch repair, cell growth, development and metabolism, cell adhesion, apoptosis, extracellular matrix formation, and maintaining cell polarity (Fig. 3G, H; Supplementary Fig. 1C, D). Therefore, there are many mutations of mismatch repair systems and signaling pathways related to cell proliferation in cancer tissues.

As the same with different cell types, the feature of genes expressions is specific too (Fig. 3L). Through TCGA database of some high expressed genes, compared with PT (Supplementary Fig. 2A-F), we found genes in LNM were related to poorer prognosis and lower survival rate (Supplementary Fig. 2G-L). For example, people with the specific marker of LNM (LMO3) showed less survival month than with PT (SLITRK6) (Fig. 3O, R). The stain images showed SLITRK6 were taken higher positive number in PT, and LMO3 were higher in LNM (Fig. 3M, N, P, Q).

Cancer-associated fibroblast (CAF) is the main cell population in TME

In tumor microenvironment (TME), fibroblasts and immune cells are the main cell populations (Fig. 3S-W). CAF is considered an important factor in cancer metastasis and drug resistance [28], it abundant in both PNT and PT. Then we subdivided it into inflammatory CAF (iCAF), Myo-CAF and Myofibroblast subtypes (Fig. 3T).

By analyzing the highly expressed genes of iCAF and Myo-CAF, we found that the genes of Myo-CAF (WNT5A and ENPP2) and iCAF (SFRP4) regulate the Wnt signaling pathway, which is a common signaling pathway in most cancers [29] (Fig. 3W). NRG1 was highly expressed in both iCAF and Myo-CAF. Fusion is a common cause of this mutation, which promotes tumor progression by mediating ERBB2/3 signaling pathway [30, 31]. ERBB2/3 signaling pathway is one of the most common pathways in BC, especially in high grade BC [2].

Tumor-associated macrophage (TAM) is abundant in TME

Myeloid cells/macrophages were subdivided into M1 macrophage (cluster6), M2 macrophage (cluster1/2/4/7/8), and Dendritic cell (DC) (cluster3 ~ 5). (Fig. 4A-E). M2 macrophages were accounted for a large proportion in each group (Fig. 4D). In contrast to M1 macrophage, which inhibits cell growth and promotes cell killing function, M2 macrophage mainly promotes growth and tissue repair [32]. M2 macrophage is a kind of TAM, which promotes the proliferation of tumor cells and the inhibitory immune cells in TME [33, 34].

The high expressed genes of M2 and DC showed low poor prognosis (Fig. 3K, H; Supplementary Fig. 2M-P). Among them, LYVE1 high expressed in M2 macrophage was proved to promote tumors process and therapeutic resistance by regulating CSF1 [35], which is mainly secreted by CAF [36]. DC with highly expressed IDO1 has been proved to inhibit the immune cells [37]. The stain images showed LYVE1 and IDO1 were taken high positive number in PT (Fig. 4G, J), and they were high expressed in tumor cells and the interstitial cells close to tumors in TME (Fig. 3F, I). That suggest the iCAF and TAM may have a close relationship with tumor cells.

The inhibitory immune system including T regulatory cell (Treg), CD8 + exhausted T cells (Tex), and CD8 + resident memory T cells (Trm)

CD4 + T cell

T lymphocytes were divided as CD4 + naive T cells (CD4naive)、CD4 + helper T cells (Th)、CD4 + regulatory T cells (Treg), and CD4 + cytotoxic T cells (CD4cyto) (Fig. 5F). Treg is a kind of T cell that negatively regulates the natural killer cells and cytotoxic T cells. It largely exists in PT and LNM (Fig. 5H). T regulatory cell (Treg), CD8 + exhausted T cells (Tex), and CD8 + resident memory T cells (Trm) in the TME inhibit immune responses.

Another thing worth to notice is that there were two clusters with high expression of CD4cyto (Fig. 5F): one was cluster 6, the main part of PNT; another small part was cluster 3 that mainly contained CD4naive or Th, and this part is highly expressed in PT. The CD4cyto may differentiate into a new non-cytotoxic phenotype in PT, with functions similar to CD4naive or Th.

In order to confirm the existence of two different CD4cyto phenotypes, we compared the gene expressions of CD4cyto in benign and malignant tissues and found genetic differences between them (Fig. 5G). Next, to further understand the functions and signaling pathways of these different genes, we performed gene set variation analysis (GSVA) [30] on CD4cyto in the three groups (Fig. 5I, J).

It was found that the three groups had the same functions and pathways in some aspects, such as promoting inflammation and killing cells, but they are not as strong as those in PNT. As for the specificity, it is mainly about inflammation, cell killing and energy metabolism, and promotion of smooth muscle proliferation in PNT. On the contrary, PT and LNM are dominated by a positive regulation of energy metabolism, activation of pluripotent stem cells, and promotion of carcinogenesis of basal cells. (Fig. 5I, J).

CD8 + T cell

Next, we divided CD8 + T cells to CD8 + memory T cells (Tm), CD8 + exhausted T cells (Tex), CD8 + naive T cells (CD8naive), CD8 + cytotoxic T cells (CD8cyto) and CD8 + resident memory T cells (Trm). (Fig. 5B-D).

Tex is largely occupied in tumor tissues, which is characterized by the absence of the effector function of normal CD8 + T cells, instead of growth and metabolism, and highly expressed inhibitory receptors [38]. Trm is a kind of effector T cells [39], but we found it highly expressed CXCL13, CTLA4 and HAVCR2 in Trm (Fig. 5E), which not only shared the same markers as Tex but retained a certain degree of effector function. In TME, Trm might turn into a specific type of Tex. A study on ovarian cancer also found a Trm expressing similar genes as Tex and believed that this Trm was in fact Tex [40].

The iCAF-TAM-Treg-Tex axis promoted formation of malignant cells and the TME

In order to understand the mechanisms that these specific cell populations were induced in TME, we then used CellChat to analyze the cell-to-cell communication in all subpopulations. A huge and complicated communication network showed a total of 84 signaling pathways, with strongest expression at Tex (Fig. 6A-C). Therefore, either activating or inhibiting signals, Tex is the ultimate transmission target for most signaling pathways.

Fig. 6 — CellChat reveals the communications between different cell clusters through the analysis of ligand-receptor interactions. **(A)** The whole cell-cell interactions between different cell subtypes of our samples. **(B)** The strength of cell-cell interactions. **(C)** MHC I pathway shows all cell subpopulations send signals to Tex, and the ligand-receptor pairs. **(D)** iCAF through THY1 pathway to regulate TAM, and the ligand-receptor pairs. **(E)** iCAF through CSF pathway to regulate TAM, and the ligand-receptor pairs. **(F)** iCAF through COMPLEMNET pathway to regulate TAM, and the ligand-receptor pairs. **(G)** iCAF through ANGPTL pathway to regulate TAM, and the ligand-receptor pairs. **(H)** The signal pathway of Tregs to other T cell subtypes

We found that CAF was related to all cell subsets and has the most cellular communication relationship (Supplementary Fig. 3A-O). It was not only closely related to carcinogenesis of epithelial cells, but also fully involved in the regulation of immune cells. Among them, iCAF is the main cell type to send signal pathways to immune system (Supplementary Fig. 4F, C, I), like CXCL12-CXCR4 axis, which produced anti-apoptotic signals, promoting EMT [41] and also recruit TAM to TME [26]. Then the TAM sent inhibitory signals to the immune cells (Fig. 7).

Fig. 7 — The signal pathway net shows the iCAF as a signal centre to TAM, then the latter continue to regulate the T lymphocyte

Other pathways from iCAF to TAM were include: THY1, CSF, COMPLEMNET and ANGPTL (Fig. 6D-G). The main receptors were also integrin family members (ITGAM/AX, ITGB2, ITGA5 and ITGB1). Among them, ITGB2 was expressed not only in TAM, but also in Treg, CD4Tcyto, naive T cell and Tex. The signal from CAF to ITGB2 has been proved to promote tumor cell proliferation [42]. Meanwhile, this also showed that the non-cytotoxic subtype of ITGB2-expressing CD4Tcyto mentioned above in TME might receive signals from CAF. Another receptor strongly expressed in the signaling pathway was CSF1R. After receiving the iCAF signals, these TAMs proliferated and aggregated into TME [36].

Then, we analyzed the signaling pathways involved in T cells. Previous analysis of signaling pathways have covered most of the T lymphocytes regulation in TME. Because of Treg was mainly responsible for inhibiting T lymphocytes, we analyzed the signaling pathway of Treg on T cells separately.

We found that Treg signaled via HLA-A/B/C/E, part of the MHC I pathway, to CD8Tcyto and Tex (Fig. 6H). In TME, malignant cell and TAM inhibited the activity of natural killer cells and cytotoxic immune cells by releasing HLA-A/B/C/E and continued to recruit M2 macrophages to tumor cells, closing the loop of an inhibitory signaling pathway [43].

Discussion

In this study, we described the cellular and molecular landscape from PNT, PT and LNM three tissue groups of BC, including high-grade and metastatic tumors. This atlas showed the different distributions of cell types and gene expressions among the three groups, and the characteristics of tumor cells and TME. To find valuable therapeutic targets that may provide predictive information of more accurate treatment.

For tumor cells, we found the malignant epithelial cells of PT and LNM exist many DNA mutations. While these two also showed different phenotypes and gene expressions during differentiation. For example, compared with SLITRK6 high expressed in PT, LMO3 is abundant in LNM, which is further confirmed related to high-grade tumors and poor prognosis.

For cells in TME, TAM takes the mainly proportion in PT, LNM and PNT three groups. From the stained picture, we found part of the interstitial cells near tumor cells showed the same color with tumors (Fig. 4F, I). That may provide an idea, which is those stained interstitial cells may recruit the TAMs into TME. It also suggests a possibility that although the position of our PNT samples were nonmalignant tissues by eyes, but in fact, there already existed the TAMs that could promote the formation of TME. That means this TME may produce earlier than the tumor cells, and it may prepare to provide a suitable environment for tumor cells growth. Except for tumor cells, T cells with inhibitory receptors are the main characteristic of PT and LNM too. Whether CD4cyto and CD8cyto, they all become exhausted phenotype in TME.

Cell-cell communication analysis showed, the iCAF is the main regulator to active the formation of inhibitory T cells. That is, iCAF not only directly sent signals to T cell, but also activate TAM to send message to downstream Treg, then latter stimulate effective CD8 + T cells to express the inhibitory receptors and become Tex (Fig. 7). Through this analysis, which also confirmed that it has a high possibility the stained interstitial cells near tumors in result 5 is iCAF, which exist a very close relationship with the recruitment of TAMs.

In conclusion, these results indicate a large and complicated cell populations and their functions of high-grade and metastatic BC. Revealing many new information that previous studies may not find. Meanwhile, this study could bring the possibility that providing more opportunities for targeting therapies.

Electronic supplementary material

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Supplementary Material 5^{(426.5KB, jpg)}

Supplementary Material 6^{(323.5KB, jpg)}

Acknowledgements

We acknowledge the OE Biotech Co. Ltd (Shanghai, China) for providing single-cell RNA sequencing technology. And the support Funding: including the Central Guidance on Local Science and Technology Development Fund of Shanxi Province (No. YDZJSX2021C010); Nature Science Foundation of Shanxi Province (No.202103021224412); Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (No.20210005); Shanxi Provincial Basic Applied Research Project (No.20210302124611).

Author contributions

Material preparation, data collection and analysis were performed by Yue Zheng. The first draft of the manuscript was written by Yue Zheng and Xin Wang. Yue Zheng was the major contributor in designing and writing this manuscript. Xin Wang was responsible for writing the experimental part. Funding acquisition, reviewing and editing were performed by Xiaofeng Yang. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding support

Nature Science Foundation of Shanxi Province (No.202,103,021,224,412). The Central Guidance on Local Science and Technology Development Fund of Shanxi Province (No.YDZJSX2021C010). Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (No.20,210,005). Shanxi Provincial Basic Applied Research Project (No.20,210,302,124,611).

Data availability

All data generated or analyzed during this study are included in this published article.

Declarations

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

The research involving human samples have been performed in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (K093). Informed consent was obtained from the patients.

Consent for publication

Not applicable.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(523.3KB, jpg)}

Supplementary Material 2^{(530.9KB, jpg)}

Supplementary Material 3^{(350.5KB, jpg)}

Supplementary Material 4^{(335.3KB, jpg)}

Supplementary Material 5^{(426.5KB, jpg)}

Supplementary Material 6^{(323.5KB, jpg)}

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

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[CR27] 27.L. Haghverdi, M. Büttner, F.A. Wolf, F. Buettner, F.J. Theis, Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13(10), 845–848 (2016) [DOI] [PubMed] [Google Scholar]

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PERMALINK

Single-cell RNA sequencing reveals the cellular and molecular characteristics of high-grade and metastatic bladder cancer

Yue Zheng

Xin Wang

Xiaofeng Yang

Nianzeng Xing

Abstract

Purpose

Methods

Results

Conclusion

Supplementary Information

Introduction

Materials and methods

Study approval and patient consent

Human samples

Single-cell suspension preparation

Single-cell RNA sequencing processing

Sequencing data analysis

Inference of CNV from scRNA-seq data

Cell trajectory analysis

GSVA analysis

CellChat analysis

The cancer genome atlas database

Immunohistochemical analysis

Statistical analysis

Results

scRNA-seq revealed the whole cell atlas of BC

Table 1.

Fig. 1.

Malignant epithelial cells of PT and LNM showed a high level of CNV

Fig. 2.

Subpopulations and gene expression profiles of malignant epithelial cells of PT and LNM

Fig. 3.

Cancer-associated fibroblast (CAF) is the main cell population in TME

Tumor-associated macrophage (TAM) is abundant in TME

Fig. 4.

The inhibitory immune system including T regulatory cell (Treg), CD8 + exhausted T cells (Tex), and CD8 + resident memory T cells (Trm)

CD4 + T cell

Fig. 5.

CD8 + T cell

The iCAF-TAM-Treg-Tex axis promoted formation of malignant cells and the TME

Fig. 6.

Fig. 7.

Discussion

Electronic supplementary material

Acknowledgements

Author contributions

Funding support

Data availability

Declarations

Competing interests

Ethics approval and consent to participate

Consent for publication

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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