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. 2021 Apr 2:24:682-694.
doi: 10.1016/j.omtn.2021.03.017. eCollection 2021 Jun 4.

Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and transcriptional activities of ECs in CC

Affiliations

Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and transcriptional activities of ECs in CC

Chunbo Li et al. Mol Ther Nucleic Acids. .

Abstract

Cervical cancer (CC) is the fourth leading cause of deaths in gynecological malignancies. Although the etiology of CC has been extensively investigated, the exact pathogenesis of CC remains incomplete. Recently, single-cell technologies demonstrated advantages in exploring intra-tumoral diversification among various tumor cells. However, single-cell transcriptome analysis (single-cell RNA sequencing [scRNA-seq]) of CC cells and microenvironment has not been conducted. In this study, a total of 20,938 cells from CC and adjacent normal tissues were examined by scRNA-seq. We identified four tumor cell subpopulations in tumor cells, which had specific signature genes with different biological functions and presented different prognoses. Among them, we identified a subset of cancer stem cells (CSCs) that was related to the developmental hierarchy of tumor progression. Then, we compared the expressive differences between tumor-derived endothelial cells (TECs) and normal ECs (NECs) and revealed higher expression of several metabolism-related genes in TECs. Then, we explored the potential biological function of ECs in vascularization and found several marker genes, which played a prior role in connections between cancer cells and ECs. Our findings provide valuable resources for deciphering the intra-tumoral heterogeneity of CC and uncover the developmental procedure of ECs, which paves the way for CC therapy.

Keywords: cervical cancer; endothelial cell; single-cell RNA sequencing; tumor heterogeneity.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Identification of CC cell populations (A) An overview schematic of the cell populations with the CC and adjacent normal samples. (B) MRI showing the location and volume of CC. (C) The t-distributed stochastic neighbor embedding (t-SNE) plot demonstrating main cell types in CC. (D) Heatmap shows expression levels of specific markers in each cell cluster. (E) Expression levels of representative well-known markers across the cell types in CC. Color key from gray to red indicates relative expression levels from low to high. The expression level was normalized by the LogNormalize method in Seurat.
Figure 2
Figure 2
Heterogeneity of tumor cells in CC (A) t-SNE representation of 4 clusters generated from all tumor cells. (B) The cell number and percentage of assigned cell types. (C) Heatmap shows expression levels of specific markers of cell type in 4 clusters. (D) Expression levels of representative well-known markers across the 4 clusters (0, 1, 3, and 8) in cancer cells. (E) Heatmap shows the representative gene ontology and pathway terms enriched in each subgroup. Color key from blue to red indicates Z score of −log10(p value). (F) Heatmap shows the expression patterns of representative cancer markers across the 216 CC samples in the TCGA CESC cohort. (G) Kaplan-Meier survival analysis of tumor samples grouped in (F). Statistical significance was determined by log-rank test.
Figure 3
Figure 3
Expression patterns of cervical cancer stem cells with tumor progression (A) Violin plots display the expression of representative stem-related markers across four cancer cell clusters identified in CC. (B) ALDH1A1 and SOX2 expression stained in CC normal and cancer samples derived from THPA database. (C) Violin plots display the expression of representative stem-related markers in six subclusters of cancer cells in cluster 8. (D) Heatmap shows representative stem-related signaling pathways in six subclusters of cancer cells in cluster 8. (E) Pseudo-time trajectory of cancer cells in cluster 8 with gene expression profiles inferred by Monocle 2. Each point corresponds to a single cell. (F) The differentially expressed genes (rows) along the pseudo-time (columns) were clustered hierarchically into six profiles.
Figure 4
Figure 4
The difference between NECs and TECs in CC (A) Volcano plot of differentially expressed genes (DEGs) of NECs between tumor and normal samples. Symbols of top 10 upregulated and downregulated genes were annotated, respectively. (B) Gene Ontology analysis of upregulated DEGs. (C) Gene Ontology analysis of downregulated DEGs. (D) Violin plots show the smoothened expression distribution of selected genes involved in angiogenesis between NECs and TECs. (E) Violin plots show the smoothened expression distribution of selected genes involved in immune activation between NECs and TECs. (F) GSEA shows the differences in pathway activities between NECs and TECs.
Figure 5
Figure 5
Heterogeneity of ECs and its developmental trajectory (A) t-SNE plot shows seven subgroups of the EC cluster (cluster 4). (B) Violin plots show the expression of representative arterial and venous markers across seven subgroups. (C) Violin plots show the expression of representative capillary markers across seven subgroups of ECs. (D) Pseudo-time trajectory of ECs with gene expression profiles inferred by Monocle 2. Each point corresponds to a single cell. (E) The DEGs (rows) along the pseudo-time (columns) were clustered hierarchically into six profiles. (F–H) Gene Ontology analysis of each EC gene set.
Figure 6
Figure 6
The expression levels of ABC transport proteins and cell-cell communications between cancer/immune cells and ECs (A) t-SNE plot shows the expression levels of ABC transport proteins in all cell clusters. (B) Boxplots show the expression levels of ABC transport proteins between tumor and normal samples. (C) Violin plots show higher expression levels of ABC transport proteins in EC cluster. (D) The connections between tumor cells and ECs. (E) Cell-cell connections show the specific legend-receptor pairs between tumor cells and ECs in tumor. (F) Cell-cell connections show the specific legend-receptor pairs between epithelial cells and ECs in the adjacent normal sample. (G) Cell-cell connections show the specific legend-receptor pairs between immune cells and ECs in the tumor sample. (H) Cell-cell connections show the specific legend-receptor pairs between immune cells and ECs in the normal sample.

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