Single-nucleus RNA sequencing and deep tissue proteomics reveal distinct tumour microenvironment in stage-I and II cervical cancer

Abstract

Background: Cervical cancer (CC) is the 3^rd most common cancer in women and the 4^th leading cause of deaths in gynaecological malignancies, yet the exact progression of CC is inconclusive, mainly due to the high complexity of the changing tumour microenvironment (TME) at different stages of tumorigenesis. Importantly, a detailed comparative single-nucleus transcriptomic analysis of tumour microenvironment (TME) of CC patients at different stages is lacking.

Methods: In this study, a total of 42,928 and 29,200 nuclei isolated from the tumour tissues of stage-I and II CC patients and subjected to single-nucleus RNA sequencing (snRNA-seq) analysis. The cell heterogeneity and functions were comparatively investigated using bioinformatic tools. In addition, label-free quantitative mass spectrometry based proteomic analysis was carried out. The proteome profiles of stage-I and II CC patients were compared, and an integrative analysis with the snRNA-seq was performed.

Results: Compared with the stage-I CC (CCI) patients, the immune response relevant signalling pathways were largely suppressed in various immune cells of the stage-II CC (CCII) patients, yet the signalling associated with cell and tissue development was enriched, as well as metabolism for energy production suggested by the upregulation of genes associated with mitochondria. This was consistent with the quantitative proteomic analysis that showed the dominance of proteins promoting cell growth and intercellular matrix development in the TME of CCII group. The interferon-α and γ responses appeared the most activated pathways in many cell populations of the CCI patients. Several collagens, such as COL12A1, COL5A1, COL4A1 and COL4A2, were found significantly upregulated in the CCII group, suggesting their roles in diagnosing CC progression. A novel transcript AC244205.1 was detected as the most upregulated gene in CCII patients, and its possible mechanistic role in CC may be investigated further.

Conclusions: Our study provides important resources for decoding the progression of CC and set the foundation for developing novel approaches for diagnosing CC and tackling the immunosuppressive TME.

Keywords: Cervical cancer; Collagen; Macrophage; Quantitative proteomics; Single-nucleus RNA sequencing; Tumour microenvironment.

Fig. 1

The snRNA-seq analysis of the tumour tissues of patients with stage-I and II cervical cancer. (A) Experimental design: the tumour tissues were collected from the patients at The First Affiliated Hospital/School of Clinical Medicine of Guangdong Pharmaceutical University. (B) t-Stochastic neighbour embedding (t-SNE) representation of aligned gene expression data in single nuclei extracted from the TME of CCI and CCII patients shows partition into 22 distinct clusters. (C) The proportions of the 22 cell clusters in the CCI and CCII groups. (D) The distributions of cell cycle phases in the t-SNE space. (E) Selected enriched genes used for biological identification of each cluster and the top 5 DEGs of each cluster (scale: log-transformed gene expression). MΦ represents macrophage; Treg cell, regulatory T cell; NK cell, natural killer cell; NPC, neural progenitor cell; GMPC, granulocyte-monocyte progenitor cell; pDC, plasmacytoid dendritic cell (see Table S2 for the full list of all marker genes detected)

Fig. 2

The MΦs of the CCI and CCII groups shows distinct levels of immune response. (A) The hierarchy diagram compares the expression and correlation of the top 50 DEGs between the CCI and CCII groups. (B) The 2D t-SNE graph compares the distribution of MΦs expressing (the normalised expression) the genes (FC > 1.2, P-value < 0.05) associated with the signalling of chemokines, cytokines and interleukins in the CCI and CCII groups. (C) The GSEA analysis of the Hallmark pathways enriched respectively by the DEGs of the CCI and CCII groups. The ranking of the genes significantly associated with the epithelial mesenchymal transition (D) and the IFN-α response pathway (E)

Fig. 3

The analysis of macrophage heterogeneity of the CCI and CCII groups. (A) The subtype analysis of the macrophages. Five subpopulations were identified, including resident-like (C0-Res), tumour-associated (C1-TAM), M2-like (C2-M2), MΦ/DC (C3-DC) and M1-like (C4-M1). The expression of top 5 DEGs was compared across different subtypes. (B) Comparison of the average expression of selected DEGs associated with macrophage function. (C) Pseudotime trajectory analysis of macrophage subtypes

Fig. 4

T cells were less activated in the CCII group patients. (A) The comparison of the proportions of CD8⁺, γδT and T_reg cells of entire T cell populations of the CCI and CCII groups. (B) The number of three T cell populations detected in the TME of individual patients. (C) The 2D-tSNE graphs comparatively display the distributions of different T cells, and the expression (in normalised value) of selected marker genes (incl. CD8A, CD4, PRF1, IFIT3, IFNGR1, IFNGR1, and CTLA4) in the CCI and CCII groups. (D) The hierarchical clustering of the top 100 DEGs between the three T cell types of the two groups (FC > 1.5 and P-value < 0.05)

Fig. 5

The B cells and DCs in the TME of the CCI group exhibiting enhanced MHC class-I pathway. (A) The proportions of B cells and DC cells detected in the CCI and CCII groups. (B) Comparison of the average expression of the genes associated with antigen presentation in the CD141⁺CLEC9A⁺ and pDC populations, with respect to their percentage of expression. (C) The top 25 biological processes enriched in CD141⁺CLEC9A⁺ and pDC populations. (D) Comparison of the normalised expression of selected genes associated with antigen presenting processes in B cells and DCs in the two groups. Student’s t-test was used to evaluate the significance (*: P < 0.05, **: P < 0.01, and ****: P < 0.0001). (E) The hierarchical clustering of the top 60 DEGs identified in the B cell populations between the two groups

Fig. 6

The function of NK cells was suppressed in the TME of CCII patients. (A) The hierarchical clustering of the top 50 DEGs of NK cells between the CCI and CCII groups. (B) The distribution of the NK cells expressing GZMA, GZMB, NKG7, and KLRC1 in a 2D-tSNE space. (C) The comparison of the average expression of the genes associated with chemokine and cytokine signalling with respect to their percentage of expression in the two groups. (D) The ranking of the DEGs supporting the enrichment of natural killer cell mediated cytotoxicity pathway in the CCI group, with respect to the CCII group. (E) The 2D-tSNE graphs show the distribution of NK cells of the two groups and the subtypes, as well as the proportions of each subtype in individual patient. (F) Pseudotime trajectory analysis of NK cells. (G) The seven states identified along the pseudotime and their composition of the subtypes of NK cells. (H) Comparison of the normalised expression of the marker genes in each state of the two group, including GZMA, GZMB, NKG7, KLRC1, CCL5 and CCL4I

Fig. 7

The tumour associated stem cells (TASCs) and epithelial cells were more immune response active in the TME of CCI patients. (A) The 2D-tSNE graphs show the expression density of selected marker genes of cancer stem cells (CSCs), mesenchymal stem cells (MSCs), adipose-derived stromal cells (ADSCs) and vascular stem cells (VSCs). (B) The heatmap hierarchically compares the top 10 DEGs of each stem cell-like population in the two groups. The GSEA of the top 6 biological processes enriched (P-value < 0.05) in the CSCs (C) and ADSCs (D) of CCI and CCII groups, respectively. The KEGG pathways enriched in the basal cells (E) and epithelial cells (F) of the CCI group. (G) The comparison of the average expression of keratin and collagen genes with respect to their percentage of expression in the two groups

Fig. 8

Quantitative proteomic comparison of the tumour tissues collected from the CCI (n = 3) and CCII (n = 3) patients. (A) Hierarchically clustering of DEP contents (in Z-score) identified from different patients. (B) Volcano graph displays the Log₂FC versus -Log₁₀P-value of the top 60 DEPs of the CCII relative to the CCI group. (C) The protein–protein interactions among the DEPs significantly upregulated in the CCII group. (D) The top 8 Hallmark pathways enriched in the two groups, predicted by the GSEA. (E) The expression profiles of the genes in different immune cells expressing the DEPs associated with INF-α response pathway identified in (D). (F) The correlation between the Log₂FC values of the top 20 DEPs in the CCII group relevant to immune processes (based on GO terms) and their gene expression in different immune cell populations of the two groups