Single-cell transcriptome sequencing reveals spatial distribution of IL34+ cancer-associated fibroblasts in hepatocellular carcinoma tumor microenvironment

Abstract

We utilized scRNA-seq, a well-established technology, to uncover the gene expression characteristics of IL34⁺ CAFs within HCC. We analyzed the related mechanisms through in vitro and in vivo assays. To begin, we acquired scRNA-seq datasets about HCC, which enabled us to identify distinct cell subpopulations within HCC tissues. We conducted a differential analysis to pinpoint DEGs associated with normal fibroblasts (NFs) and CAFs. Subsequently, we isolated NFs and CAFs, followed by the sorting of IL34⁺ CAFs. These IL34⁺ CAFs were then co-cultured with T cells and HCC cells to investigate their potential role in Tregs infiltration, CD8⁺ T cell toxicity, and the biological processes of HCC cells. We validated our findings in vivo using a well-established mouse model. Our analysis of HCC tissues revealed the presence of seven primary cell subpopulations, with the most significant disparities observed within fibroblast subpopulations. Notably, high IL34 expression was linked to increased expression of receptor proteins and enhanced proliferative activity within CAFs, with specific expression in CAFs. Furthermore, we identified a substantial positive correlation between IL34 expression and the abundance of Tregs. Both in vitro and in vivo experiments demonstrated that IL34⁺ CAFs promoted Tregs infiltration while suppressing CD8⁺ T cell toxicity. Consequently, this promoted the growth and metastasis of HCC. In summary, our study affirms that IL34⁺ CAFs play a pivotal role in augmenting the proliferative activity of CAFs, facilitating Tregs infiltration, and inhibiting CD8⁺ T cell toxicity, ultimately fostering the growth and metastasis of HCC.

Fig. 1. Cell clustering and annotation of scRNA-seq data.

Note: A Differential expression analysis to identify highly variable genes. Red indicates the top 2000 highly variable genes, while black represents genes with low variability. The top 10 genes in the highly variable gene set are labeled. B Cell distribution in PC_1 and PC_2 after batch correction using Harmony. Each point represents a single cell. C Distribution of standard deviations for principal components (PCs). PCs with larger standard deviations are more important. D tSNE visualization of cell clustering, showing the aggregation and distribution of cells from both normal adjacent samples and HCC samples in two dimensions. Green represents normal adjacent samples, while deep blue represents HCC samples. E tSNE visualization of cell clustering, displaying the aggregation and distribution of cells from different sources. Each color represents a cluster. F Expression of known lineage-specific marker genes in different clusters. Darker blue indicates higher average expression levels, and larger circles represent more cells expressing the gene. G tSNE visualization of cell annotation based on clustering, with each color representing a cell subpopulation.

Fig. 2. Molecular characterization of fibroblasts in adjacent normal samples and HCC samples.

Note: A Expression patterns of fibroblast markers in different cell subgroups, where darker blue indicates higher average expression levels. B Proportions of different cell subgroups in each sample, represented by different colors. C P-values obtained from T-test analysis assessing differences in cell abundance between adjacent and HCC samples. HCC represents tumor samples, and NOR represents adjacent normal samples. D Communication network of cells in adjacent normal samples, where the thickness of the lines on the left denotes the number of pathways and the thickness of the lines on the right represents the interaction strength. E Communication network of cells in HCC samples, where the thickness of the lines on the left denotes the number of pathways and the thickness of the lines on the right represents the interaction strength. F Volcano plot showing differential gene expression between fibroblasts in adjacent normal and HCC samples. Red dots to the left of the dashed line represent genes with high expression in HCC samples, while dots to the right represent genes with low expression in HCC samples. G Bubble plot depicting GO and KEGG enrichment for highly expressed genes in HCC samples. The size of the circles represents the number of genes enriched in that pathway, and the color represents the relevance of enrichment for that pathway. H Bubble plot depicting GO and KEGG enrichment for lowly expressed genes in HCC samples. The size of the circles represents the number of genes enriched in that pathway, and the color represents the relevance of enrichment for that pathway.

Fig. 3. The effect of IL34 expression levels on the activity and proliferation capacity of CAFs.

Note: A Immunofluorescence detection of IL34, CSF1-R, and PTP-ζ receptor expression in CAFs and NFs. IL34, CSF1-R, and PTP-ζ are shown in green, and DAPI is shown in blue. Scale bar represents 75 µm. B Quantification of IL34 expression levels in tumor fibroblasts using RT-qPCR. C Quantification of CSF1-R expression levels in tumor fibroblasts using RT-qPCR. D Quantification of PTP-ζ expression levels in tumor fibroblasts using RT-qPCR. E Quantification of α-SMA expression levels in tumor fibroblasts treated with different concentrations of IL34 using RT-qPCR. F Quantification of Vimentin expression levels in tumor fibroblasts treated with different concentrations of IL34 using RT-qPCR. G Quantification of FAP expression levels in tumor fibroblasts treated with different concentrations of IL34 using RT-qPCR. H–J RT-qPCR analyses of α-SMA, FAP, and Vimentin expression levels in CAFs after IL34 silencing. K Western Blot analysis of α-SMA, FAP, and Vimentin protein expression levels in CAFs after IL34 silencing. L, M Plate colony assays to evaluate the proliferation capacity of CAFs after IL34 silencing. * represents a significant difference compared to the control group, NFs, or sh-NC group at P < 0.05. # represents a significant difference compared to the 25 ng/ml group at P < 0.05. & represents a significant difference compared to the 50 ng/ml group at P < 0.05. All cell experiments were repeated three times.

Fig. 4. The expression pattern and characterization of IL34 in CAFs.

Note: A Box plot of IL34 expression levels in adjacent normal samples and HCC samples from TCGA transcriptome data, including 50 adjacent normal tissue samples and 374 HCC tissue samples; B Expression pattern of IL34 in different cell subtypes, with deeper blue indicating higher expression levels; C Expression pattern of IL34 in NFs derived from adjacent normal samples and CAFs derived from HCC samples, with deeper blue indicating higher expression levels; D Volcano plot showing differentially expressed genes between IL34+ CAFs and IL34^- secretory CAFs, with blue representing genes significantly downregulated in IL34+ CAFs and red representing genes significantly upregulated in IL34+ CAFs; E Bar chart showing GO enrichment analysis results for downregulated genes in IL34+ CAFs, with blue, red, and green representing BP, CC, and MF, respectively; F Bar chart showing GO enrichment analysis results for upregulated genes in IL34+ CAFs, with blue, red, and green representing BP, CC, and MF, respectively; G Correlation between IL34 expression levels and Tregs content; H Immunofluorescence staining of CAFs and Tregs marker proteins in HCC tissue, with yellow indicating Foxp3, green indicating COL1A2, and blue indicating DAPI; I Corresponding phenotypic chart of CAFs and Tregs, with red representing Tregs and cyan representing CAFs.

Fig. 5. Influence of IL34⁺ CAFs on the Cytotoxicity of CD8⁺ T Cells.

Note: A, B Representative results and quantification of Foxp3 expression in T cells detected by flow cytometry; C, D Representative results and quantification of GzmB and Ki67 expression, cytotoxicity markers, in CD8⁺ T cells detected by flow cytometry. # denotes statistical significance compared to the IL34-CAFs group (P 0.05), & denotes statistical significance compared to the IL34+ CAFs group (P < 0.05); all cellular experiments were repeated three times.

Fig. 6. Impact of IL34 + CAFs on the proliferation, migration, and invasion abilities of HCC cells.

Note: A, B Scratch assays were performed to evaluate the migration ability of HCC cells among different treatment groups. C, D Transwell assays were conducted to assess the invasion ability of HCC cells in different treatment groups (scale bar=50 μm). E CCK-8 assays were used to measure the proliferation ability of HCC cells. * represents P < 0.05 compared to the NFs group, # represents P < 0.05 compared to the IL34-CAFs group. Each cellular experiment was repeated three times.

Fig. 7. In vitro experiments to validate the impact of IL34 + CAFs on tumor progression.

Note: A Immunohistochemistry was employed to examine the expression of Treg markers in HCC tissues, with a scale of 25 µm; B Statistical graph depicting the positive rate of immunohistochemistry; C, D Flow cytometry was conducted to detect the content of Tregs in HCC tissues; E, F Flow cytometry was utilized to evaluate the expression of the cytotoxic protein Granzyme B (GzmB) and Ki67 in CD8⁺ T cells of HCC tissues; G Comparison of tumor volume in mice from different treatment groups; H Statistical comparison of tumor weight in mice from different treatment groups; I, J H&E staining was conducted to examine lung metastasis in mice from each group (×100), with black arrows indicating areas of pathological tissue. * represents a significant difference compared to the IL34-NC+IgG group at a p-value of less than 0.05, # represents a significant difference compared to the IL34-OC+IgG group at a p-value of less than 0.05. Each group of mice experiments consisted of 8 mice.

Fig. 8

The molecular mechanism by which IL34 + CAFs promote the growth and metastasis of HCC.