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. 2023 Nov 10:11:1250215.
doi: 10.3389/fcell.2023.1250215. eCollection 2023.

Single-cell RNA sequencing reveals cancer stem-like cells and dynamics in tumor microenvironment during cholangiocarcinoma progression

Jihye L Golino #  1 , Jing Bian #  2 , Xin Wang  1 , Jianyang Fu  1 , Xiao Bin Zhu  1 , Julie Yeo  1 , Michael Kelly  3 , Freddy E Escorcia  4   5 , Maggie Cam  2 , Changqing Xie  1   5
Affiliations

Single-cell RNA sequencing reveals cancer stem-like cells and dynamics in tumor microenvironment during cholangiocarcinoma progression

Jihye L Golino et al. Front Cell Dev Biol. .

Abstract

Cholangiocarcinoma is a malignancy of the bile ducts that is driven by activities of cancer stem-like cells and characterized by a heterogeneous tumor microenvironment. To better understand the transcriptional profiles of cancer stem-like cells and dynamics in the tumor microenvironment during the progression of cholangiocarcinoma, we performed single-cell RNA analysis on cells collected from three different timepoints of tumorigenesis in a YAP/AKT mouse model. Bulk RNA sequencing data from TCGA (The Cancer Genome Atlas program) and ICGC cohorts were used to verify and support the finding. In vitro and in vivo experiments were performed to assess the stemness of cancer stem-like cells. We identified Tm4sf1high malignant cells as cancer stem-like cells. Across timepoints of cholangiocarcinoma formation in YAP/AKT mice, we found dynamic change in cancer stem-like cell/stromal/immune cell composition. Nevertheless, the dynamic interaction among cancer stem-like cells, immune cells, and stromal cells at different timepoints was elaborated. Collectively, these data serve as a useful resource for better understanding cancer stem-like cell and malignant cell heterogeneity, stromal cell remodeling, and immune cell reprogramming. It also sheds new light on transcriptomic dynamics during cholangiocarcinoma progression at single-cell resolution.

Keywords: Tm4sf1; cancer stem-like cells; cholangiocarcinoma; single-cell RNA sequence; tumoral heterogeneity.

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Conflict of interest statement

Author MK was employed by the company Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Single-cell analysis of liver and cholangiocarcinoma from YAP/AKT mice. (A) A schematic diagram highlighting the workflow including isolation and sequencing of single cells for this study. Single cells were prepared from the liver tissues/tumors of YAP/AKT CCA mice at control (baseline) and different tumor progression timepoints, including week 5 (W05) and week 8 (W08). The transcriptome of single cells was sequenced using the 10x Chromium system. (B) The UMAP plot of 47,806 single cells to visualize cell-type clusters based on the expression of known markers (left panel) and isolated cells at indicated tumor progression stages, including control, week 5, and week 8. (C) Cell counts and markers used to annotate the known cell types, including epithelial cells, hepatocytes, immune cells, endothelial cells, and fibroblasts, from the liver tissue/tumors from YAP/AKT CCA mice. (D) The individual gene UMAP plots showing the expression levels and distribution of representative markers of known cell types, which distinctly separates epithelial cells, hepatocytes, immune cells, endothelial cells, and fibroblasts from the liver tissue/tumors from YAP/AKT CCA mice.
FIGURE 2
FIGURE 2
Dynamic intratumoral malignant cell heterogeneity in the CCA of YAP/AKT mice. (A) The PCA plot of 379 malignant epithelial cells from YAP/AKT mice colored by cluster (left panel) and timepoint (right panel). (B) The proportion of malignant cell subclusters in different timepoints of tumor progression. (C) Function enrichment analysis for differentially expressed genes between subclusters of malignant cells in (A). (D) Heatmap of expression profile of signature genes in indicated modules identified by WGCNA (weighted gene co-expression network analysis). A summary list of genes associated with the corresponding WGCNA module is shown in Supplementary Table S2. (E) The corresponding Kaplan–Meier overall survival curves of patients were grouped by the gene signatures in WGCNA modules (D) based on the ICGC cohort.
FIGURE 3
FIGURE 3
Characterization of the CSC cluster in CCA malignant cells of YAP/AKT mice. (A) Pseudotime trajectory analysis of the three subclusters of malignant cells annotated by subclusters of malignant cells. (B) The trend of Sox9 expression along the pseudotime trajectory of the three subclusters of malignant cells. (C) A heatmap of regulon scores from the SCENIC (single-cell regulatory network inference and clustering) analysis. Rows, individual regulons. Columns, cells organized according to re-clustering of malignant cells. (D) Violin-plot showing the relative MKi67 expression between subclusters of malignant cells. (E) Bar-plot showing the average GSVA stemness score for each malignant cell cluster.
FIGURE 4
FIGURE 4
TM4SF1 is a potential marker for the CSC cluster in CCA malignant cells. (A) Bar-plot showing the relative Tm4sf1 expression level between subclusters of malignant cells. (B) Association of TM4SF1 expression with the expression of CSC markers PROM1 (left panel) and SOX9 (right panel) in the CCA cohort. Scatterplots were generated using the Tumor IMmune Estimation Resource (TIMER) web tool (https://cistrome.shinyapps.io/timer/) to identify the expressions of PROM1 and SOX9 that are associated with TM4SF1 expression in the CCA cohort of the TCGA database. (C) Kaplan–Meier survival curves of the overall survival of the liver cancer cohort from the Human Protein Atlas datasets for TM4SF1 gene expression stratified by high (red) or low (green) expression levels. (D) tSNE showed TM4SF1 expression of human iCCA scRNA-seq data (GSE138709) and correlation with CytoTRACE score (R = 0.33). (E) Representative images of spheroids from HuCC-T1 and SNU1079 CCA cell lines. The bar graph shows the spheroid-forming capacity of TM4SF1+ cells determined by tumor spheroid assays (1,000 cells/well). *p < 0.05. The cell suspension was cultured in DMEM/F12 medium supplemented with 1X B27 supplement, hrEGF (20 ng/mL), and bFGF (10 ng/mL) for 7 days. The number of spheroids was counted. (F) Tumor-initiating capacity test from TM4SF1high HuCC-T1 cells. The upper panel shows tumor image from 1 × 103 TM4SF1high and TM4SF1low cells. The lower table shows the tumor formation numbers from three different diluted cell numbers.
FIGURE 5
FIGURE 5
Dynamic change of interaction of cancer stem-like cells with stromal cells during CCA development. (A) UMAP plot of immune cells was grouped into 10 cell subtypes and indicated by color (left panel) and timepoints (right panel). (B) Sankey plot showing dynamic changes in the proportion of stromal cells along with tumor progression in YAP/AKT CCA mice. (C) Scatterplot of incoming and outgoing interaction strengths of each cell population at the baseline (Ctrl) and change of strength between control and different timepoints (W05 and W08). Ctrl, control; W05, week 5; W08, week 8. (D) Circle plot displaying putative ligand–receptor interaction between different cell types, with the width of edges representing the numbers of the communication. The edge colors are consistent with the signal sender. A thicker edge line indicates a stronger signal. (E) Bubble plot of the communication probability of all the significant ligand–receptor pairs that contributed to interaction between malignant cells and various stromal cells. The dot color and size represent the communication probability and p-values. p-values were computed from the one-sided permutation test.
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