Abstract Background Clear cell renal cell carcinoma (ccRCC) is the most common histologic type of RCC. However, the spatial and functional heterogeneity of immunosuppressive cells and the mechanisms by which their interactions promote immunosuppression in the ccRCC have not been thoroughly investigated. Methods To further investigate the cellular and regional heterogeneity of ccRCC, we analyzed single-cell and spatial transcriptome RNA sequencing data from four patients, which were obtained from samples from multiple regions, including the tumor core, tumor-normal interface, and distal normal tissue. On the basis, the findings were investigated in vitro using tissue and blood samples from 15 patients with ccRCC and validated in the broader samples on tissue microarrays. Results In this study, we revealed previously unreported subsets of both stromal and immune cells, as well as mapped their spatial location at finer resolution. In addition, we validated the clusters of tumor cells after removing batch effects according to six characterized gene sets, including epithelial-mesenchymal transition^high clusters, metastatic clusters and proximal tubule^high clusters. Importantly, we identified a special regulatory T (Treg) cell subpopulation that has the molecular characteristics of terminal effector Treg cells but expresses multiple cytokines, such as interleukin (IL)-1β and IL-18. This group of Treg cells has stronger immunosuppressive function and was associated with a worse prognosis in ccRCC cohorts. They were colocalized with MRC1^+FOLR2^+ tumor-associated macrophages (TAMs) at the tumor-normal interface to form a positive feedback loop, maintaining a synergistic procarcinogenic effect. In addition, we traced the origin of IL-1β^+ Treg cells and revealed that IL-18 can induce the expression of IL-1β in Treg cells via the ERK/NF-κB pathway. Conclusions We demonstrated a novel cancer-promoting Treg cell subset and its interactions with MRC1^+FOLR2^+TAMs, which provides new insight into Treg cell heterogeneity and potential therapeutic targets for ccRCC. Keywords: Tumor Microenvironment, T regulatory cell - Treg, Immunotherapy, Immunosuppression, Kidney Cancer __________________________________________________________________ WHAT IS ALREADY KNOWN ON THIS TOPIC * Some patients with clear cell renal cell carcinoma (ccRCC) have a positive response to immunotherapy, which can activate a patient’s immune system to attack tumor cells. However, most patients do not benefit from immunotherapy because of the heterogeneity of the tumor microenvironment (TME). * The interaction between immune cells is the key factor in the formation and consolidation of the immunosuppressive TME. WHAT THIS STUDY ADDS * We characterized the phenotypic heterogeneity and multicellular TME of ccRCC at a relatively fine resolution. * Interleukin (IL)-18 promotes the production of terminal effector regulatory T (Treg) cells with stronger immunosuppressive function via the ERK/NF-κB pathway. * Terminal effector Treg cells are linked to decreased survival, increased immune evasion and tumor growth via interactions with MRC1^+FOLR2^+ tumor-associated macrophages (TAMs). HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY * We have identified a novel cancer-promoting Treg cell subset and its interactions with MRC1^+FOLR2^+ TAMs. * This provides a potential strategy to inhibit the IL-18-IL-1β axis to optimize the immunotherapy of ccRCC. Introduction Clear cell renal cell carcinoma (ccRCC) is a common subtype of RCC, accounting for approximately 75% of all RCC cases and causing the majority of kidney cancer-related deaths.[47]1,[48]3 Therapies for ccRCC include nephrectomy, targeted therapy and immunotherapy. Some patients have a positive response to immunotherapy, especially immune checkpoint inhibitors, such as avelumab, nivolumab and ipilimumab, which can activate a patient’s immune system to attack tumor cells. However, most patients do not benefit from immunotherapy because of the heterogeneity of the tumor microenvironment (TME).[49]^4 Therefore, studying the TME of ccRCC is highly important for understanding the mechanism of disease development, optimizing treatment and promoting personalized treatment. Many immune cells recruited and regulated by cancer cells are present in ccRCC. Tumor-infiltrating immune cells and cancer cells interact with one another within the TME, and these interactions play crucial roles in the development and progression of ccRCC. In recent years, advances in technologies such as single-cell and spatial sequencing have allowed us to view the state of the TME macroscopically and microscopically. Moreover, genomic and single-cell transcriptomic sequencing of multiple tumor regions has achieved the in-depth characterization of the TME in ccRCC. For example, exhausted CD8^+ T cells are specifically located in different tumor regions; IL1B^+ macrophages are enriched at the tumor-normal interface and interact closely with RCC cells to promote epithelial-mesenchymal transition (EMT).[50]^5 These results highlight the phenotypic categorization of tumor cells and immune/stromal cells, as well as the intercellular communication between immune cells and stromal cells in the TME. However, owing to factors such as sample size and limited validation experiments, some essential cellular and molecular mechanisms are easily overlooked, especially the mechanisms by which immune cells and their interactions promote the formation of the immunosuppressive TME in ccRCC. The interaction between immune cells is the key factor in the formation and consolidation of immunosuppressive TME. In nasopharyngeal carcinoma, LAMP3^+ dendritic cells recruit peripheral blood regulatory T (Treg) cells into tumors through CCL17-CCR4 and CCL22-CCR4 and can also promote the exhaustion of CD8^+ T cells through programmed death-ligand 1 (PD-L1)-programmed cell death protein-1 and CD200-CD200R signals.[51]^6 In addition, in a variety of tumors, myeloid-derived suppressor cells (MDSCs) and M2 macrophages recruit infiltrating Treg cells by releasing CCL2 and increasing Treg cell activity via the secretion of transforming growth factor (TGF)-β.[52]^7 These results suggest that the potential crosstalk between multiple immune cells, especially Treg cells, M2 macrophages and MDSCs, may promote the formation of the immunosuppressive TME in ccRCC. Although previous studies have allowed us to understand the composition and heterogeneity of the ccRCC TME, the spatial and functional heterogeneity of immunosuppressive cells and the mechanisms by which their interactions promote immunosuppression in the TME have not been thoroughly investigated. In this study, we used a combined spatial and single-cell transcriptomic approach to analyze the TME in multiple regions of ccRCC tumors and compared the differences in cellular profiles. We analyzed the spatial and functional heterogeneity of stromal cells, tumor cells, and immune cells, focusing on analyzing the subtypes of Treg cells in different spatial regions and their interactions with other cells, with the goal of identifying potential targets or strategies for the immunotherapy of ccRCC. Results Global analysis of cell populations in ccRCC We presented in-depth cellular and molecular analyses of cell populations in RCC via the following complementary approaches: single-cell RNA sequencing (scRNA-seq) with spatial transcriptomics as discovery and spatial localization tools and multiparameter flow cytometry and multiplex immunohistochemistry (mIHC) for quantifying cell populations and their markers at the protein level. Our study included 15 patients (P1-P15) who underwent surgical resection and were diagnosed with ccRCC after histopathological analysis. We first collected tumor tissues from the first four patients (P1-P4) for scRNA-seq with spatial transcriptomics and then expanded the sample size to 15 patients for flow cytometry and histochemical staining for verification ([53]figure 1A). Patient clinical data, including sex, age, tumor size, grade, preoperative glomerular filtration rate, and ki67 and VEGFA expression, are shown in [54]online supplemental table S1. We detected a unilateral/bilateral decline in renal function consistent with tumor progression ([55]figure 1B, [56]online supplemental figure S1A and [57]online supplemental table S1). Tissue samples from the tumor core (TC), tumor-normal interface (IF) (including the tumor rim (TR) and adjacent normal kidney (AN)) and distal normal kidney (DN) were collected and analyzed ([58]figure 1A). To increase the single-cell resolution of immune cells, we first isolated all CD45^+ immune cells and then prepared scRNA-seq libraries from a nine-to-one mixture of CD45^+ immune cells and CD45^− cells for each sample, capturing transcriptomes for a total of 64,596 cells after stringent quality control ([59]online supplemental figure S1B). Data integration revealed 11 distinct clusters on the basis of the expression of typical marker genes, including T cells, natural killer (NK) cells, B cells, myeloid cells, mast cells, endothelial cells (ECs), fibroblasts and RCC cells ([60]figure 1C,D and [61]online supplemental figure S1C; [62]online supplemental table S2). Each cluster contained cells derived from all patients, suggesting that the cell types composing the ccRCC immune microenvironment are similar and lack major patient-specific batch effects ([63]figure 1E,F, [64]online supplemental figure S1D, E). RCC cells were identified in clusters specifically expressing CA9 ([65]figure 1D and [66]online supplemental figure S1C). Candidate markers for RCC cells, such as FABP6, FABP7 and ANGPTL4,[67]^8 were also validated in our scRNA-seq data, serving as a reference for future studies ([68]online supplemental figure S1F). According to our single-cell isolation results, T cells were the most abundant, especially CD4^+ T cells ([69]figure 1G and [70]online supplemental figure S1G). Flow cytometry analysis confirmed the increased relative proportions of CD3^+ cells and CD8^+ T cells in tumor regions, including the TC and TR regions, while CD4^+ T cells were evenly distributed in all regions ([71]figure 1H and [72]online supplemental figure S1H). The T-cell proportion quantified by scRNA-seq and flow cytometry verified the scRNA-seq results, serving as a cross-validation of these experimental approaches ([73]figure 1I). In addition to these changes in the T-cell compartment, we also observed a significant increase in the proportion of fibroblasts in the TR tissue compared with that in the TC and AN tissue ([74]figure 1F,G). Moreover, we found that mast cells characterized by TPSAB1-specific expression were enriched in tumor regions, which is consistent with previous reports[75]^9 ([76]figure 1F,G). The abundance and location of the stroma and immune cell populations in ccRCC were also revealed via spatial transcriptomics analysis, and the Pearson correlations of the single-cell and spatial transcriptomics data were consistent and reliable ([77]figure 1J,K and [78]online supplemental figure S1I). Figure 1. Global analysis of cell populations in ccRCC. (A) Study design and workflow of the ccRCC cohort. ‘‘n’’, sample number. (A, B) (including a and b), and (C) represent different regions for sampling; A: tumor core; B: tumor-normal interface; a: tumor rim, b: adjacent normal, C: distal normal kidney. (B) Heatmap illustrating the clinical features of the tumors sequenced. See [79]online supplemental table S1 for detailed information. (C) Overall UMAP plot of all the single cells in our study. scRNA-seq libraries from a nine-to-one mixture of CD45^+ immune cells and CD45^− cells for each sample. (D) Heatmap showing the top marker genes of 11 major cell types. All the marker genes are listed in [80]online supplemental table S2. (E) UMAP, (F) cell type, and (G) sampled region showing the tissue distributions of 11 major cell types. (H) Flow cytometry analysis of CD3^+ T cells in renal tumor and normal regions in all patients. ****p<0.0001, one-way analysis of variance; error bars, SD. (I) Comparison of CD4^+ and CD8^+ T-cell percentages as determined by flow cytometry (percentage of CD3^+ T cells) versus scRNA-seq (percentage of CD3^+ T cells) and fit with linear regression models. (J) Annotated spatial map of P7. Lines denote different regions. (K) Spatial transcriptomic feature plots showing the spatial distribution of each cell type in P7. AN, adjacent normal; ccRCC, clear cell renal cell carcinoma; DC, dendritic cell; DN, distal normal; EC, endothelial cell; NK, natural killer; PBMC, peripheral blood mononuclear cell; scRNA-seq, single-cell RNA sequencing; stRNA-seq, spatial transcriptome RNA sequencing; TC, tumor core; TMA, tissue microarray; TR, tumor rim; UMAP, uniform manifold approximation and projection. [81]Figure 1 [82]Open in a new tab Next, we performed subclustering analyses for major cell types and investigated their tissue distribution preferences. Subclustering of NK